Plot_results.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.383093Z",
     "start_time": "2024-05-13T09:38:19.984314Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import itertools\n",
    "import numpy as np\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "outputs": [],
   "source": [
    "def prepare(data):\n",
    "    \n",
    "    buildtimes = np.array([1.385, 0.696, 0.766, 0.689, 0.833])\n",
    "    \n",
    "    data['buldingtime'] = data['buldingtime'] + buildtimes.mean()\n",
    "    \n",
    "    data['import.start'] = pd.to_datetime(data['import.start'])\n",
    "    data['import.stop'] = pd.to_datetime(data['import.stop'])\n",
    "    data['created'] = pd.to_datetime(data['created'])\n",
    "    data['started'] = pd.to_datetime(data['started'])\n",
    "    data['finished'] = pd.to_datetime(data['finished'])\n",
    "    \n",
    "    data['starttime'] = (data['started'] - data['created']).dt.total_seconds() \n",
    "    data['dockertime'] = (data['finished'] - data['started']).dt.total_seconds() \n",
    "    data['importtime'] = (data['import.stop'] - data['import.start']).dt.total_seconds() \n",
    "    \n",
    "    end2end = pd.DataFrame({'end2end': (data['import.stop'] - data['created']).dt.total_seconds() +1 +data.buldingtime})\n",
    "    \n",
    "    started_temp = pd.DataFrame()\n",
    "    docker_temp = pd.DataFrame()\n",
    "    import_temp = pd.DataFrame()\n",
    "    total_temp = pd.DataFrame()\n",
    "    \n",
    "    for i in range(8,38,6):\n",
    "        started_temp.insert(range(8,38,6).index(i), \"\", (pd.to_datetime(data.iloc[:, i+1]) - pd.to_datetime(data.iloc[:, i])).dt.total_seconds() , True)\n",
    "        docker_temp.insert(range(8,38,6).index(i), \"\", (pd.to_datetime(data.iloc[:, i+2]) - pd.to_datetime(data.iloc[:, i+1])).dt.total_seconds() , True)\n",
    "        import_temp.insert(range(8,38,6).index(i), \"\", (pd.to_datetime(data.iloc[:, i+4]) - pd.to_datetime(data.iloc[:, i+3])).dt.total_seconds() , True)\n",
    "        total_temp.insert(range(8,38,6).index(i), \"\", (pd.to_datetime(data.iloc[:, i+4]) - pd.to_datetime(data.iloc[:, i+1])).dt.total_seconds() +1 , True)\n",
    "    \n",
    "    started_temp.columns = ['starttime{}'.format(i) for i in range(1, 6)]\n",
    "    docker_temp.columns = ['dockertime{}'.format(i) for i in range(1, 6)]\n",
    "    import_temp.columns = ['importtime{}'.format(i) for i in range(1, 6)]\n",
    "    total_temp.columns = ['end2end{}'.format(i) for i in range(1, 6)]\n",
    "    \n",
    "    data = pd.concat([data, end2end, started_temp, docker_temp, import_temp, total_temp], axis=1)\n",
    "    \n",
    "    return data"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.383256Z",
     "start_time": "2024-05-13T09:38:19.987768Z"
    }
   },
   "id": "b105810311362533"
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "outputs": [],
   "source": [
    "def display(column, scalar=1):\n",
    "    import pandas as pd\n",
    "\n",
    "    mean = scalar*column.mean()\n",
    "    median = scalar*column.median()\n",
    "    std = scalar*column.std()\n",
    "    min_max = [round(scalar*column.min(),2), round(scalar*column.max(),2)]\n",
    "    custom_desc = f\"{round(mean,2)} & {round(median,2)} & {round(std,2)} & {min_max}\"\n",
    "    print(custom_desc)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.418656Z",
     "start_time": "2024-05-13T09:38:19.990653Z"
    }
   },
   "id": "91dc1e6f022cb629"
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "outputs": [],
   "source": [
    "def extract_imports(imp):\n",
    "    imp = imp.split(\"'\")\n",
    "    l = \"\"\n",
    "    \n",
    "    for i in imp:\n",
    "        if i == \"(\" or i == \")\" or i == \",)\" or i == \",\" or i == \"()\" or i == \", \":\n",
    "            continue\n",
    "        else:\n",
    "            if l == \"\":\n",
    "                l = i\n",
    "            else:\n",
    "                l = l + \";\" + i\n",
    "    return l"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.418779Z",
     "start_time": "2024-05-13T09:38:19.993767Z"
    }
   },
   "id": "f769aa4008fe8dcf"
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/86/fyd29lqj0033vg6drb2b412m0000gn/T/ipykernel_56715/4029374880.py:4: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  if pd.isna(row[1]):\n",
      "/var/folders/86/fyd29lqj0033vg6drb2b412m0000gn/T/ipykernel_56715/4029374880.py:7: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  ins = set(row[1].split(\";\"))\n",
      "/var/folders/86/fyd29lqj0033vg6drb2b412m0000gn/T/ipykernel_56715/4029374880.py:8: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  all = set(row[2].split(\";\"))\n",
      "/var/folders/86/fyd29lqj0033vg6drb2b412m0000gn/T/ipykernel_56715/4029374880.py:9: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  if pd.isna(row[3]):\n",
      "/var/folders/86/fyd29lqj0033vg6drb2b412m0000gn/T/ipykernel_56715/4029374880.py:12: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  clu = set(row[3].split(\";\"))\n",
      "/var/folders/86/fyd29lqj0033vg6drb2b412m0000gn/T/ipykernel_56715/4029374880.py:13: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  install_libaries = pd.concat([install_libaries, pd.DataFrame([[row[0], len(ins), len(ins)-0, len(ins)-len(ins.intersection(all)), len(ins)-len(ins.intersection(clu))]])], ignore_index=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": "                                               imports  toInstall  \\\n0    argparse;copy;datetime;funcx;itertools;json;ma...          7   \n1    argparse;copy;funcx;itertools;json;matplotlib;...          6   \n2                                         argparse;sys          1   \n3                                                CLEAN          1   \n4                                             CLEAN;os          1   \n..                                                 ...        ...   \n202                                             timeit          0   \n203                                              torch          1   \n204                                  torch;torchvision          2   \n205                                          traceback          0   \n206                                         webbrowser          0   \n\n     vanilla_approach  all_top_10  cluster_top_10  \n0                   7           4               0  \n1                   6           4               0  \n2                   1           1               0  \n3                   1           1               0  \n4                   1           1               0  \n..                ...         ...             ...  \n202                 0           0               0  \n203                 1           0               0  \n204                 2           1               2  \n205                 0           0               0  \n206                 0           0               0  \n\n[207 rows x 5 columns]",
      "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>imports</th>\n      <th>toInstall</th>\n      <th>vanilla_approach</th>\n      <th>all_top_10</th>\n      <th>cluster_top_10</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>argparse;copy;datetime;funcx;itertools;json;ma...</td>\n      <td>7</td>\n      <td>7</td>\n      <td>4</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>argparse;copy;funcx;itertools;json;matplotlib;...</td>\n      <td>6</td>\n      <td>6</td>\n      <td>4</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>argparse;sys</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CLEAN</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CLEAN;os</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>202</th>\n      <td>timeit</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>203</th>\n      <td>torch</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>204</th>\n      <td>torch;torchvision</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>205</th>\n      <td>traceback</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>206</th>\n      <td>webbrowser</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>207 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "install = pd.read_csv(\"experiments/installations.csv\")\n",
    "install_libaries = pd.DataFrame()\n",
    "for index, row in install.iterrows():\n",
    "    if pd.isna(row[1]):\n",
    "        ins = set()\n",
    "    else: \n",
    "        ins = set(row[1].split(\";\"))\n",
    "    all = set(row[2].split(\";\"))\n",
    "    if pd.isna(row[3]):\n",
    "        clu = set()\n",
    "    else: \n",
    "        clu = set(row[3].split(\";\"))\n",
    "    install_libaries = pd.concat([install_libaries, pd.DataFrame([[row[0], len(ins), len(ins)-0, len(ins)-len(ins.intersection(all)), len(ins)-len(ins.intersection(clu))]])], ignore_index=True)\n",
    "install_libaries.columns = ['imports', 'toInstall', 'vanilla_approach', 'all_top_10', 'cluster_top_10']\n",
    "\n",
    "install_libaries"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.419176Z",
     "start_time": "2024-05-13T09:38:19.998259Z"
    }
   },
   "id": "861e765de22842b2"
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "outputs": [],
   "source": [
    "col = ['imports', 'buldingtime', 'installtime', 'end2end', \n",
    "       'starttime', 'dockertime', 'importtime', \n",
    "       'starttime1', 'starttime2', 'starttime3', 'starttime4', 'starttime5', \n",
    "       'dockertime1', 'dockertime2', 'dockertime3', 'dockertime4', 'dockertime5', \n",
    "       'importtime1', 'importtime2', 'importtime3', 'importtime4', 'importtime5', \n",
    "       'end2end1', 'end2end2', 'end2end3', 'end2end4', 'end2end5']"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.419245Z",
     "start_time": "2024-05-13T09:38:20.036946Z"
    }
   },
   "id": "8b12b92f7d9a3497"
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"data/every_package/run10.csv\")\n",
    "data = prepare(data)\n",
    "imp = data[col[0]]\n",
    "data = data[col[1:]]\n",
    "for i in range(9, 0, -1):\n",
    "    tmp = pd.read_csv(\"data/every_package/run\" + str(i) + \".csv\")\n",
    "    tmp = prepare(tmp)\n",
    "    tmp = tmp[col[1:]]\n",
    "    data = (data + tmp)\n",
    "data = data / 10\n",
    "data.insert(0, col[0], imp)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.419282Z",
     "start_time": "2024-05-13T09:38:20.039442Z"
    }
   },
   "id": "5463fa2f7a4dab52"
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "outputs": [
    {
     "data": {
      "text/plain": "        imports  buldingtime  installtime    end2end  starttime  dockertime  \\\n52          PIL      10.9538         6.36  14.244324   2.108298    1.193852   \n0      argparse       9.2838         5.31  12.507632   2.026819    1.210094   \n1         clean      10.9838         6.21  14.456444   2.264412    1.225619   \n2   collections       3.9738        -1.00   8.129689   2.838753    1.332040   \n3       colmena      19.2038        14.83  22.685633   2.292351    1.201289   \n..          ...          ...          ...        ...        ...         ...   \n87      urllib3       9.3338         5.57  13.074337   2.105103    1.662203   \n88         uuid      10.8538         7.12  14.093022   2.033960    1.218466   \n89     warnings       3.5238        -1.00   6.923177   2.180663    1.233780   \n90   webbrowser       3.5538        -1.00   7.236420   2.186071    1.513124   \n91         yaml       9.9738         5.69  13.483792   2.204105    1.326100   \n\n    importtime  starttime1  starttime2  starttime3  ...  importtime1  \\\n52    0.013953    0.667763    0.737138    0.742705  ...     0.001137   \n0     0.044723    0.643705    0.622289    0.749516  ...     0.010771   \n1     0.008079    0.806776    0.811559    0.876272  ...     0.000363   \n2     0.053543    0.755990    0.707457    0.751481  ...     0.017491   \n3     0.008861    0.774815    0.751169    0.748512  ...     0.000348   \n..         ...         ...         ...         ...  ...          ...   \n87    0.455932    0.718015    0.659613    0.754252  ...     0.079703   \n88    0.052394    0.635997    0.656979    0.617127  ...     0.011224   \n89    0.000008    0.768780    0.746880    0.726569  ...     0.000010   \n90    0.239609    0.743873    0.669989    0.748356  ...     0.119145   \n91    0.117158    0.761542    0.748223    0.695590  ...     0.025387   \n\n    importtime2  importtime3  importtime4  importtime5  end2end1  end2end2  \\\n52     0.000956     0.000969     0.000951     0.001033  1.014857  1.019006   \n0      0.011305     0.010256     0.010476     0.009689  1.024725  1.024697   \n1      0.000313     0.000283     0.000356     0.000386  1.013763  1.013838   \n2      0.017785     0.017393     0.017150     0.017925  1.046815  1.047690   \n3      0.000371     0.000343     0.000450     0.000418  1.015633  1.014370   \n..          ...          ...          ...          ...       ...       ...   \n87     0.079253     0.079546     0.079558     0.079631  1.092762  1.092503   \n88     0.011740     0.011830     0.012209     0.011898  1.026093  1.025645   \n89     0.000012     0.000008     0.000008     0.000007  1.029219  1.027995   \n90     0.124510     0.119990     0.123114     0.121590  1.148651  1.156002   \n91     0.027515     0.027888     0.027376     0.025443  1.041319  1.041094   \n\n    end2end3  end2end4  end2end5  \n52  1.015829  1.015196  1.015294  \n0   1.025290  1.025126  1.025162  \n1   1.015388  1.013718  1.013618  \n2   1.048248  1.047806  1.047264  \n3   1.014037  1.013939  1.013882  \n..       ...       ...       ...  \n87  1.092950  1.093041  1.092958  \n88  1.025738  1.025557  1.026064  \n89  1.029053  1.029911  1.028062  \n90  1.156349  1.153022  1.151060  \n91  1.041428  1.041193  1.040843  \n\n[92 rows x 27 columns]",
      "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>imports</th>\n      <th>buldingtime</th>\n      <th>installtime</th>\n      <th>end2end</th>\n      <th>starttime</th>\n      <th>dockertime</th>\n      <th>importtime</th>\n      <th>starttime1</th>\n      <th>starttime2</th>\n      <th>starttime3</th>\n      <th>...</th>\n      <th>importtime1</th>\n      <th>importtime2</th>\n      <th>importtime3</th>\n      <th>importtime4</th>\n      <th>importtime5</th>\n      <th>end2end1</th>\n      <th>end2end2</th>\n      <th>end2end3</th>\n      <th>end2end4</th>\n      <th>end2end5</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>52</th>\n      <td>PIL</td>\n      <td>10.9538</td>\n      <td>6.36</td>\n      <td>14.244324</td>\n      <td>2.108298</td>\n      <td>1.193852</td>\n      <td>0.013953</td>\n      <td>0.667763</td>\n      <td>0.737138</td>\n      <td>0.742705</td>\n      <td>...</td>\n      <td>0.001137</td>\n      <td>0.000956</td>\n      <td>0.000969</td>\n      <td>0.000951</td>\n      <td>0.001033</td>\n      <td>1.014857</td>\n      <td>1.019006</td>\n      <td>1.015829</td>\n      <td>1.015196</td>\n      <td>1.015294</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>argparse</td>\n      <td>9.2838</td>\n      <td>5.31</td>\n      <td>12.507632</td>\n      <td>2.026819</td>\n      <td>1.210094</td>\n      <td>0.044723</td>\n      <td>0.643705</td>\n      <td>0.622289</td>\n      <td>0.749516</td>\n      <td>...</td>\n      <td>0.010771</td>\n      <td>0.011305</td>\n      <td>0.010256</td>\n      <td>0.010476</td>\n      <td>0.009689</td>\n      <td>1.024725</td>\n      <td>1.024697</td>\n      <td>1.025290</td>\n      <td>1.025126</td>\n      <td>1.025162</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>clean</td>\n      <td>10.9838</td>\n      <td>6.21</td>\n      <td>14.456444</td>\n      <td>2.264412</td>\n      <td>1.225619</td>\n      <td>0.008079</td>\n      <td>0.806776</td>\n      <td>0.811559</td>\n      <td>0.876272</td>\n      <td>...</td>\n      <td>0.000363</td>\n      <td>0.000313</td>\n      <td>0.000283</td>\n      <td>0.000356</td>\n      <td>0.000386</td>\n      <td>1.013763</td>\n      <td>1.013838</td>\n      <td>1.015388</td>\n      <td>1.013718</td>\n      <td>1.013618</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>collections</td>\n      <td>3.9738</td>\n      <td>-1.00</td>\n      <td>8.129689</td>\n      <td>2.838753</td>\n      <td>1.332040</td>\n      <td>0.053543</td>\n      <td>0.755990</td>\n      <td>0.707457</td>\n      <td>0.751481</td>\n      <td>...</td>\n      <td>0.017491</td>\n      <td>0.017785</td>\n      <td>0.017393</td>\n      <td>0.017150</td>\n      <td>0.017925</td>\n      <td>1.046815</td>\n      <td>1.047690</td>\n      <td>1.048248</td>\n      <td>1.047806</td>\n      <td>1.047264</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>colmena</td>\n      <td>19.2038</td>\n      <td>14.83</td>\n      <td>22.685633</td>\n      <td>2.292351</td>\n      <td>1.201289</td>\n      <td>0.008861</td>\n      <td>0.774815</td>\n      <td>0.751169</td>\n      <td>0.748512</td>\n      <td>...</td>\n      <td>0.000348</td>\n      <td>0.000371</td>\n      <td>0.000343</td>\n      <td>0.000450</td>\n      <td>0.000418</td>\n      <td>1.015633</td>\n      <td>1.014370</td>\n      <td>1.014037</td>\n      <td>1.013939</td>\n      <td>1.013882</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>87</th>\n      <td>urllib3</td>\n      <td>9.3338</td>\n      <td>5.57</td>\n      <td>13.074337</td>\n      <td>2.105103</td>\n      <td>1.662203</td>\n      <td>0.455932</td>\n      <td>0.718015</td>\n      <td>0.659613</td>\n      <td>0.754252</td>\n      <td>...</td>\n      <td>0.079703</td>\n      <td>0.079253</td>\n      <td>0.079546</td>\n      <td>0.079558</td>\n      <td>0.079631</td>\n      <td>1.092762</td>\n      <td>1.092503</td>\n      <td>1.092950</td>\n      <td>1.093041</td>\n      <td>1.092958</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>uuid</td>\n      <td>10.8538</td>\n      <td>7.12</td>\n      <td>14.093022</td>\n      <td>2.033960</td>\n      <td>1.218466</td>\n      <td>0.052394</td>\n      <td>0.635997</td>\n      <td>0.656979</td>\n      <td>0.617127</td>\n      <td>...</td>\n      <td>0.011224</td>\n      <td>0.011740</td>\n      <td>0.011830</td>\n      <td>0.012209</td>\n      <td>0.011898</td>\n      <td>1.026093</td>\n      <td>1.025645</td>\n      <td>1.025738</td>\n      <td>1.025557</td>\n      <td>1.026064</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>warnings</td>\n      <td>3.5238</td>\n      <td>-1.00</td>\n      <td>6.923177</td>\n      <td>2.180663</td>\n      <td>1.233780</td>\n      <td>0.000008</td>\n      <td>0.768780</td>\n      <td>0.746880</td>\n      <td>0.726569</td>\n      <td>...</td>\n      <td>0.000010</td>\n      <td>0.000012</td>\n      <td>0.000008</td>\n      <td>0.000008</td>\n      <td>0.000007</td>\n      <td>1.029219</td>\n      <td>1.027995</td>\n      <td>1.029053</td>\n      <td>1.029911</td>\n      <td>1.028062</td>\n    </tr>\n    <tr>\n      <th>90</th>\n      <td>webbrowser</td>\n      <td>3.5538</td>\n      <td>-1.00</td>\n      <td>7.236420</td>\n      <td>2.186071</td>\n      <td>1.513124</td>\n      <td>0.239609</td>\n      <td>0.743873</td>\n      <td>0.669989</td>\n      <td>0.748356</td>\n      <td>...</td>\n      <td>0.119145</td>\n      <td>0.124510</td>\n      <td>0.119990</td>\n      <td>0.123114</td>\n      <td>0.121590</td>\n      <td>1.148651</td>\n      <td>1.156002</td>\n      <td>1.156349</td>\n      <td>1.153022</td>\n      <td>1.151060</td>\n    </tr>\n    <tr>\n      <th>91</th>\n      <td>yaml</td>\n      <td>9.9738</td>\n      <td>5.69</td>\n      <td>13.483792</td>\n      <td>2.204105</td>\n      <td>1.326100</td>\n      <td>0.117158</td>\n      <td>0.761542</td>\n      <td>0.748223</td>\n      <td>0.695590</td>\n      <td>...</td>\n      <td>0.025387</td>\n      <td>0.027515</td>\n      <td>0.027888</td>\n      <td>0.027376</td>\n      <td>0.025443</td>\n      <td>1.041319</td>\n      <td>1.041094</td>\n      <td>1.041428</td>\n      <td>1.041193</td>\n      <td>1.040843</td>\n    </tr>\n  </tbody>\n</table>\n<p>92 rows × 27 columns</p>\n</div>"
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values(by=['imports'], ascending=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.419576Z",
     "start_time": "2024-05-13T09:38:20.223129Z"
    }
   },
   "id": "cf030448cc5480c9"
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "outputs": [],
   "source": [
    "vanilla_approach = pd.read_csv(f\"new/vanilla_approach/run10.csv\")\n",
    "all_top_10 = pd.read_csv(f\"new/all_top_10/run10.csv\")\n",
    "cluster_top_10 = pd.read_csv(f\"new/cluster_top_10/run10.csv\")\n",
    "on_the_fly = pd.read_csv(f\"new/on_the_fly/run10.csv\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.419607Z",
     "start_time": "2024-05-13T09:38:20.230807Z"
    }
   },
   "id": "b7e2b77bc0726d30"
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "outputs": [],
   "source": [
    "for run in range(9, 0, -1):\n",
    "    vanilla_approach = pd.concat([vanilla_approach, pd.read_csv(f\"new/vanilla_approach/run{run}.csv\")])\n",
    "    all_top_10 = pd.concat([all_top_10, pd.read_csv(f\"new/all_top_10/run{run}.csv\")])\n",
    "    cluster_top_10 = pd.concat([cluster_top_10, pd.read_csv(f\"new/cluster_top_10/run{run}.csv\")])\n",
    "    on_the_fly = pd.concat([on_the_fly, pd.read_csv(f\"new/on_the_fly/run{run}.csv\")])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.424945Z",
     "start_time": "2024-05-13T09:38:20.253102Z"
    }
   },
   "id": "20565ce17b2897f5"
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "outputs": [],
   "source": [
    "vanilla_approach = prepare(vanilla_approach)\n",
    "all_top_10 = prepare(all_top_10)\n",
    "cluster_top_10 = prepare(cluster_top_10)\n",
    "on_the_fly = prepare(on_the_fly)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.636241Z",
     "start_time": "2024-05-13T09:38:20.404981Z"
    }
   },
   "id": "e7689597ae8c4d6d"
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "outputs": [],
   "source": [
    "vanilla_approach.loc[vanilla_approach['installtime'] == -1, 'installtime'] = 0\n",
    "all_top_10.loc[all_top_10['installtime'] == -1, 'installtime'] = 0\n",
    "cluster_top_10.loc[cluster_top_10['installtime'] == -1, 'installtime'] = 0\n",
    "on_the_fly.loc[on_the_fly['installtime'] == -1, 'installtime'] = 0"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.639293Z",
     "start_time": "2024-05-13T09:38:20.636875Z"
    }
   },
   "id": "f5e53eabfa7ff9a6"
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "outputs": [],
   "source": [
    "func_mapping = pd.read_csv('experiments/function_mapping.csv')\n",
    "func_mapping = func_mapping.drop(2)\n",
    "func_mapping['func4.imports'] = func_mapping[func_mapping['func4.imports'].str.len() >= 0]['func4.imports'].map(lambda x: extract_imports(x))\n",
    "func_mapping = func_mapping[func_mapping['func4.imports'] != \"\"]\n",
    "func_mapping.rename(columns={'func4.imports': 'imports'}, inplace=True)\n",
    "func_mapping.drop(columns=['Unnamed: 0'], inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.646417Z",
     "start_time": "2024-05-13T09:38:20.640054Z"
    }
   },
   "id": "6493930011605e84"
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "outputs": [
    {
     "data": {
      "text/plain": "   Unnamed: 0.1                         func4.name               imports  \\\n0             0  0-04-_check_slurm_job_statuses.py            subprocess   \n3             3              873-04-call_mtrack.py            mtrack;sys   \n4             4           43-04-make_corr_plots.py  json;os;xpcs_webplot   \n6             6           3-04-submit_slurm_job.py         os;subprocess   \n7             7         397-01-rejection_sample.py                random   \n\n            Cluster  \n0               Job  \n3      Data:_mtrack  \n4  Sci:_Instruments  \n6               Job  \n7           Trivial  ",
      "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: 0.1</th>\n      <th>func4.name</th>\n      <th>imports</th>\n      <th>Cluster</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0-04-_check_slurm_job_statuses.py</td>\n      <td>subprocess</td>\n      <td>Job</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>873-04-call_mtrack.py</td>\n      <td>mtrack;sys</td>\n      <td>Data:_mtrack</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>43-04-make_corr_plots.py</td>\n      <td>json;os;xpcs_webplot</td>\n      <td>Sci:_Instruments</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>6</td>\n      <td>3-04-submit_slurm_job.py</td>\n      <td>os;subprocess</td>\n      <td>Job</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>7</td>\n      <td>397-01-rejection_sample.py</td>\n      <td>random</td>\n      <td>Trivial</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "func_mapping.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.653049Z",
     "start_time": "2024-05-13T09:38:20.649735Z"
    }
   },
   "id": "adec8f19ecd82be1"
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "outputs": [],
   "source": [
    "counts = func_mapping.imports.value_counts()\n",
    "#counts = pd.DataFrame(counts)\n",
    "#counts.reset_index(inplace=True)\n",
    "#counts = counts._append({'imports': \"argparse;copy;datetime;funcx;itertools;json;matplotlib;numpy;os;pathlib;shutil;sys;threading;typing\", 'count': 1}, ignore_index=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.656509Z",
     "start_time": "2024-05-13T09:38:20.652321Z"
    }
   },
   "id": "eb87626280aee2"
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "outputs": [],
   "source": [
    "vanilla_approach_func = pd.merge(vanilla_approach, counts, on=\"imports\", how=\"left\")\n",
    "all_top_10_func = pd.merge(all_top_10, counts, on=\"imports\", how=\"left\")\n",
    "cluster_top_10_func = pd.merge(cluster_top_10, counts, on=\"imports\", how=\"left\")\n",
    "on_the_fly_func = pd.merge(on_the_fly, counts, on=\"imports\", how=\"left\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.686189Z",
     "start_time": "2024-05-13T09:38:20.655110Z"
    }
   },
   "id": "ccfd6cca09bbf621"
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "outputs": [],
   "source": [
    "vanilla_approach_func['count'] = vanilla_approach_func['count'].fillna(1)\n",
    "all_top_10_func['count'] = all_top_10_func['count'].fillna(1)\n",
    "cluster_top_10_func['count'] = cluster_top_10_func['count'].fillna(1)\n",
    "on_the_fly_func['count'] = on_the_fly_func['count'].fillna(1)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.689687Z",
     "start_time": "2024-05-13T09:38:20.664820Z"
    }
   },
   "id": "24c2fbab30acdda7"
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32.58 & 6.07 & 60.5 & [0.87, 259.47]\n",
      "5.21 & 3.87 & 7.11 & [0.87, 79.87]\n",
      "4.61 & 3.17 & 12.27 & [0.87, 220.47]\n",
      "0.89 & 0.87 & 0.03 & [0.87, 0.97]\n"
     ]
    }
   ],
   "source": [
    "display(pd.DataFrame({'values' : vanilla_approach_func.buldingtime.repeat(vanilla_approach_func['count'])})[\"values\"])\n",
    "display(pd.DataFrame({'values' : all_top_10_func.buldingtime.repeat(vanilla_approach_func['count'])})[\"values\"])\n",
    "display(pd.DataFrame({'values' : cluster_top_10_func.buldingtime.repeat(vanilla_approach_func['count'])})[\"values\"])\n",
    "display(pd.DataFrame({'values' : on_the_fly_func.buldingtime.repeat(vanilla_approach_func['count'])})[\"values\"])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.689825Z",
     "start_time": "2024-05-13T09:38:20.667929Z"
    }
   },
   "id": "394f53e72abf3368"
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "outputs": [],
   "source": [
    "#print(pd.DataFrame({'values' : vanilla_approach_func.buldingtime.repeat(vanilla_approach_func['count'])}).describe())\n",
    "#print(pd.DataFrame({'values' : all_top_10_func.buldingtime.repeat(vanilla_approach_func['count'])}).describe())\n",
    "#print(pd.DataFrame({'values' : cluster_top_10_func.buldingtime.repeat(vanilla_approach_func['count'])}).describe())\n",
    "#print(pd.DataFrame({'values' : on_the_fly_func.buldingtime.repeat(vanilla_approach_func['count'].iloc[0:207])}).describe())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.689923Z",
     "start_time": "2024-05-13T09:38:20.671812Z"
    }
   },
   "id": "bea3e5ee79bfe87c"
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "outputs": [],
   "source": [
    "#print(vanilla_approach['buldingtime'].describe())\n",
    "#print(all_top_10['buldingtime'].describe())\n",
    "#print(cluster_top_10['buldingtime'].describe())\n",
    "#print(on_the_fly_func['buldingtime'].describe())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.693927Z",
     "start_time": "2024-05-13T09:38:20.673817Z"
    }
   },
   "id": "eed7799a7790efe4"
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1460.000000\n",
      "mean        0.501588\n",
      "std         0.277609\n",
      "min         0.005342\n",
      "25%         0.253830\n",
      "50%         0.507780\n",
      "75%         0.706758\n",
      "max         0.982352\n",
      "Name: buldingtime, dtype: float64\n",
      "count    1478.000000\n",
      "mean        0.631175\n",
      "std         0.233329\n",
      "min         0.014668\n",
      "25%         0.435512\n",
      "50%         0.649668\n",
      "75%         0.812293\n",
      "max         0.986039\n",
      "Name: buldingtime, dtype: float64\n",
      "count    1490.000000\n",
      "mean        0.920605\n",
      "std         0.059780\n",
      "min         0.787092\n",
      "25%         0.865025\n",
      "50%         0.940888\n",
      "75%         0.971232\n",
      "max         0.996632\n",
      "Name: buldingtime, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "save_all_top_10 = (vanilla_approach_func['buldingtime'] - all_top_10_func['buldingtime'])/vanilla_approach_func['buldingtime']\n",
    "save_cluster_top_10 = (vanilla_approach_func['buldingtime'] - cluster_top_10_func['buldingtime'])/vanilla_approach_func['buldingtime']\n",
    "save_on_the_fly = (vanilla_approach_func['buldingtime'] - on_the_fly_func['buldingtime'])/vanilla_approach_func['buldingtime']\n",
    "\n",
    "print(save_all_top_10[save_all_top_10 > 0].describe())\n",
    "print(save_cluster_top_10[save_cluster_top_10 > 0].describe())\n",
    "print(save_on_the_fly[save_on_the_fly > 0].describe())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.708883Z",
     "start_time": "2024-05-13T09:38:20.676595Z"
    }
   },
   "id": "a55042fe987bc624"
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24.92 & 4.8 & 46.52 & [0.0, 197.2]\n",
      "3.87 & 3.0 & 4.47 & [0.0, 27.8]\n",
      "3.41 & 2.3 & 9.48 & [0.0, 179.2]\n",
      "6.21 & 2.67 & 11.04 & [0.0, 59.88]\n"
     ]
    }
   ],
   "source": [
    "display(pd.DataFrame({'values' : vanilla_approach_func.installtime.repeat(vanilla_approach_func['count'])})[\"values\"])\n",
    "display(pd.DataFrame({'values' : all_top_10_func.installtime.repeat(vanilla_approach_func['count'])})[\"values\"])\n",
    "display(pd.DataFrame({'values' : cluster_top_10_func.installtime.repeat(vanilla_approach_func['count'])})[\"values\"])\n",
    "display(pd.DataFrame({'values' : on_the_fly_func.installtime.repeat(vanilla_approach_func['count'])})[\"values\"])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.708993Z",
     "start_time": "2024-05-13T09:38:20.681621Z"
    }
   },
   "id": "3e8d7d683c3dac8"
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "outputs": [],
   "source": [
    "#print(pd.DataFrame({'values' : vanilla_approach_func.installtime.repeat(vanilla_approach_func['count'])}).describe())\n",
    "#print(pd.DataFrame({'values' : all_top_10_func.installtime.repeat(vanilla_approach_func['count'])}).describe())\n",
    "#print(pd.DataFrame({'values' : cluster_top_10_func.installtime.repeat(vanilla_approach_func['count'])}).describe())\n",
    "#print(pd.DataFrame({'values' : on_the_fly_func.installtime.repeat(vanilla_approach_func['count'])}).describe())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.710678Z",
     "start_time": "2024-05-13T09:38:20.685303Z"
    }
   },
   "id": "83299e2702408315"
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.09 & -0.07 & 0.15 & [-0.67, 0.27]\n",
      "-0.07 & -0.06 & 0.21 & [-0.61, 1.7]\n",
      "-0.06 & -0.04 & 0.13 & [-0.52, 0.42]\n"
     ]
    }
   ],
   "source": [
    "display(((all_top_10_func.starttime - vanilla_approach_func.starttime) / all_top_10_func.starttime).repeat(vanilla_approach_func['count']))\n",
    "display(((cluster_top_10_func.starttime - vanilla_approach_func.starttime) / all_top_10_func.starttime).repeat(vanilla_approach_func['count']))\n",
    "display(((on_the_fly_func.starttime - vanilla_approach_func.starttime) / all_top_10_func.starttime).repeat(vanilla_approach_func['count']))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.719096Z",
     "start_time": "2024-05-13T09:38:20.687786Z"
    }
   },
   "id": "6e147d8f369f9684"
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.31 & 0.31 & 0.03 & [0.22, 0.43]\n",
      "0.29 & 0.29 & 0.03 & [0.21, 0.39]\n",
      "0.29 & 0.29 & 0.05 & [0.22, 0.75]\n",
      "0.3 & 0.29 & 0.02 & [0.24, 0.41]\n"
     ]
    }
   ],
   "source": [
    "display(vanilla_approach_func.starttime.repeat(vanilla_approach_func['count']))\n",
    "display(all_top_10_func.starttime.repeat(vanilla_approach_func['count']))\n",
    "display(cluster_top_10_func.starttime.repeat(vanilla_approach_func['count']))\n",
    "display(on_the_fly_func.starttime.repeat(vanilla_approach_func['count']))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.721027Z",
     "start_time": "2024-05-13T09:38:20.691587Z"
    }
   },
   "id": "24e3577783c562a5"
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "outputs": [],
   "source": [
    "#print(pd.DataFrame({'values' : vanilla_approach_func.starttime.repeat(vanilla_approach_func['count'])}).describe())\n",
    "#print(pd.DataFrame({'values' : all_top_10_func.starttime.repeat(vanilla_approach_func['count'])}).describe())\n",
    "#print(pd.DataFrame({'values' : cluster_top_10_func.starttime.repeat(vanilla_approach_func['count'])}).describe())\n",
    "#print(pd.DataFrame({'values' : on_the_fly_func.starttime.repeat(vanilla_approach_func['count'].iloc[0:207])}).describe())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.732621Z",
     "start_time": "2024-05-13T09:38:20.695108Z"
    }
   },
   "id": "a3e4d9ad182be5d5"
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "outputs": [],
   "source": [
    "#print(vanilla_approach['starttime'].describe())\n",
    "#print(all_top_10['starttime'].describe())\n",
    "#print(cluster_top_10['starttime'].describe())\n",
    "#print(on_the_fly['starttime'].describe())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.732738Z",
     "start_time": "2024-05-13T09:38:20.697146Z"
    }
   },
   "id": "80f574bcf35a7db7"
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "247.2 & 247.1 & 23.58 & [172.61, 374.08]\n",
      "246.74 & 246.27 & 22.54 & [174.78, 369.92]\n",
      "248.15 & 247.49 & 23.87 & [163.28, 393.68]\n",
      "247.82 & 245.58 & 28.84 & [167.49, 383.57]\n"
     ]
    }
   ],
   "source": [
    "selected_columns = ['starttime1', 'starttime2', 'starttime3', 'starttime4', 'starttime5']\n",
    "tmp2 = np.tile(np.array(vanilla_approach_func['count'], dtype=np.int64), 5)\n",
    "tmp = pd.DataFrame({\"values\": vanilla_approach_func[selected_columns].values.flatten()})\n",
    "display(tmp['values'].repeat(tmp2), 1000)\n",
    "tmp = pd.DataFrame({\"values\": all_top_10_func[selected_columns].values.flatten()})\n",
    "display(tmp['values'].repeat(tmp2), 1000)\n",
    "tmp = pd.DataFrame({\"values\": cluster_top_10_func[selected_columns].values.flatten()})\n",
    "display(tmp['values'].repeat(tmp2), 1000)\n",
    "tmp = pd.DataFrame({\"values\": on_the_fly_func[selected_columns].values.flatten()})\n",
    "display(tmp['values'].repeat(tmp2), 1000)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.771487Z",
     "start_time": "2024-05-13T09:38:20.700342Z"
    }
   },
   "id": "2867ec324267f7f0"
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.69 & 0.01 & 13.09 & [-44.5, 81.02]\n",
      "1.29 & 0.12 & 13.64 & [-43.61, 89.65]\n",
      "1.22 & -0.33 & 15.67 & [-40.53, 86.49]\n"
     ]
    }
   ],
   "source": [
    "selected_columns = ['starttime1', 'starttime2', 'starttime3', 'starttime4', 'starttime5']\n",
    "tmp2 = np.tile(np.array(vanilla_approach_func['count'], dtype=np.int64), 5)\n",
    "tmp = pd.DataFrame({\"values\": vanilla_approach_func[selected_columns].values.flatten()})\n",
    "tmp3 = pd.DataFrame({\"values\": all_top_10_func[selected_columns].values.flatten()})\n",
    "tmp3.values = (tmp3.values - tmp.values) / tmp.values \n",
    "display(tmp3['values'].repeat(tmp2),100)\n",
    "tmp3 = pd.DataFrame({\"values\": cluster_top_10_func[selected_columns].values.flatten()})\n",
    "tmp3.values = (tmp3.values - tmp.values) / tmp.values \n",
    "display(tmp3['values'].repeat(tmp2), 100)\n",
    "tmp3 = pd.DataFrame({\"values\": on_the_fly_func[selected_columns].values.flatten()})\n",
    "tmp3.values = (tmp3.values - tmp.values) / tmp.values \n",
    "display(tmp3['values'].repeat(tmp2), 100)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.798828Z",
     "start_time": "2024-05-13T09:38:20.709500Z"
    }
   },
   "id": "626cfbeb33afcdb7"
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TtestResult(statistic=0.9311800866582932, pvalue=0.3517712775758818, df=20698.0)\n",
      "TtestResult(statistic=-1.0258886999380723, pvalue=0.3049560386469532, df=20698.0)\n",
      "TtestResult(statistic=-0.6651463747765552, pvalue=0.5059642866995582, df=20698.0)\n"
     ]
    }
   ],
   "source": [
    "print(stats.ttest_ind(vanilla_approach_func[selected_columns].values.flatten(), all_top_10_func[selected_columns].values.flatten()))\n",
    "print(stats.ttest_ind(vanilla_approach_func[selected_columns].values.flatten(), cluster_top_10_func[selected_columns].values.flatten()))\n",
    "print(stats.ttest_ind(vanilla_approach_func[selected_columns].values.flatten(), on_the_fly[selected_columns].values.flatten()))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.809238Z",
     "start_time": "2024-05-13T09:38:20.717155Z"
    }
   },
   "id": "b5a5ecda7226f37"
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "outputs": [],
   "source": [
    "#selected_columns = ['starttime1', 'starttime2', 'starttime3', 'starttime4', 'starttime5']\n",
    "#print(pd.DataFrame(vanilla_approach[selected_columns].values.flatten()).describe())\n",
    "#print(pd.DataFrame(all_top_10[selected_columns].values.flatten()).describe())\n",
    "#print(pd.DataFrame(cluster_top_10[selected_columns].values.flatten()).describe())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.809332Z",
     "start_time": "2024-05-13T09:38:20.722325Z"
    }
   },
   "id": "d7df953f4251b085"
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  0\n",
      "count  10350.000000\n",
      "mean      -0.027249\n",
      "std        0.059851\n",
      "min       -0.238135\n",
      "25%       -0.066973\n",
      "50%       -0.003854\n",
      "75%        0.004851\n",
      "max        0.197354\n",
      "                  0\n",
      "count  10350.000000\n",
      "mean      -0.026706\n",
      "std        0.061434\n",
      "min       -0.247379\n",
      "25%       -0.064403\n",
      "50%       -0.003467\n",
      "75%        0.004128\n",
      "max        0.276452\n",
      "                  0\n",
      "count  10350.000000\n",
      "mean       9.307487\n",
      "std       13.027966\n",
      "min       -0.112531\n",
      "25%        0.037611\n",
      "50%        4.815165\n",
      "75%        9.491269\n",
      "max       73.894004\n"
     ]
    }
   ],
   "source": [
    "slow_start_all_top_10 = (pd.DataFrame(all_top_10[selected_columns].values.flatten()) - pd.DataFrame(vanilla_approach[selected_columns].values.flatten())) / pd.DataFrame(vanilla_approach[selected_columns].values.flatten())\n",
    "slow_start_cluster_top_10 = (pd.DataFrame(cluster_top_10[selected_columns].values.flatten()) - pd.DataFrame(vanilla_approach[selected_columns].values.flatten())) / pd.DataFrame(vanilla_approach[selected_columns].values.flatten())\n",
    "slow_start_on_the_fly = (pd.DataFrame(on_the_fly[selected_columns].values.flatten()) - pd.DataFrame(vanilla_approach[selected_columns].values.flatten())) / pd.DataFrame(vanilla_approach[selected_columns].values.flatten())\n",
    "\n",
    "print(slow_start_all_top_10.describe())\n",
    "print(slow_start_cluster_top_10.describe())\n",
    "print(slow_start_on_the_fly.describe())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:39:07.650400Z",
     "start_time": "2024-05-13T09:39:07.635760Z"
    }
   },
   "id": "a0d21b8760cd75bd"
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34.89 & 7.53 & 61.93 & [2.18, 266.13]\n",
      "7.53 & 5.41 & 8.04 & [2.2, 86.77]\n",
      "6.88 & 4.76 & 12.81 & [2.18, 227.0]\n",
      "27.11 & 6.72 & 46.92 & [2.18, 219.0]\n"
     ]
    }
   ],
   "source": [
    "display(vanilla_approach_func.end2end.repeat(vanilla_approach_func['count']))\n",
    "display(all_top_10_func.end2end.repeat(vanilla_approach_func['count']))\n",
    "display(cluster_top_10_func.end2end.repeat(vanilla_approach_func['count']))\n",
    "display(on_the_fly_func.end2end.repeat(vanilla_approach_func['count']))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.814584Z",
     "start_time": "2024-05-13T09:38:20.732215Z"
    }
   },
   "id": "9970d08f2428621d"
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.48 & 1.2 & 0.68 & [1.01, 4.57]\n",
      "1.44 & 1.08 & 0.69 & [1.02, 4.37]\n",
      "1.44 & 1.1 & 0.69 & [1.01, 4.42]\n",
      "23.8 & 6.87 & 43.88 & [1.02, 219.3]\n"
     ]
    }
   ],
   "source": [
    "selected_columns = ['end2end1', 'end2end2', 'end2end3', 'end2end4', 'end2end5']\n",
    "tmp2 = np.tile(np.array(vanilla_approach_func['count'], dtype=np.int64), 5)\n",
    "tmp = pd.DataFrame({\"values\": vanilla_approach_func[selected_columns].values.flatten()})\n",
    "display(tmp['values'].repeat(tmp2))\n",
    "tmp = pd.DataFrame({\"values\": all_top_10_func[selected_columns].values.flatten()})\n",
    "display(tmp['values'].repeat(tmp2))\n",
    "tmp = pd.DataFrame({\"values\": cluster_top_10_func[selected_columns].values.flatten()})\n",
    "display(tmp['values'].repeat(tmp2))\n",
    "tmp = pd.DataFrame({\"values\": on_the_fly_func[selected_columns].values.flatten()})\n",
    "display(tmp['values'].repeat(tmp2))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.814812Z",
     "start_time": "2024-05-13T09:38:20.737334Z"
    }
   },
   "id": "fa3a396caa91faa9"
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             values\n",
      "count  60950.000000\n",
      "mean       0.460798\n",
      "std        0.683284\n",
      "min        0.000004\n",
      "25%        0.035532\n",
      "50%        0.183045\n",
      "75%        0.593505\n",
      "max        3.554009\n",
      "             values\n",
      "count  60950.000000\n",
      "mean       0.415841\n",
      "std        0.686280\n",
      "min        0.000004\n",
      "25%        0.014770\n",
      "50%        0.057012\n",
      "75%        0.581658\n",
      "max        3.349561\n",
      "             values\n",
      "count  60950.000000\n",
      "mean       0.421402\n",
      "std        0.690546\n",
      "min        0.000004\n",
      "25%        0.016395\n",
      "50%        0.077104\n",
      "75%        0.608022\n",
      "max        3.406486\n"
     ]
    }
   ],
   "source": [
    "selected_columns = ['importtime1', 'importtime2', 'importtime3', 'importtime4', 'importtime5']\n",
    "tmp2 = np.tile(np.array(vanilla_approach_func['count'], dtype=np.int64), 5)\n",
    "tmp = pd.DataFrame({\"values\": vanilla_approach_func[selected_columns].values.flatten()})\n",
    "print(pd.DataFrame({'values' : tmp['values'].repeat(tmp2)}).describe())\n",
    "tmp = pd.DataFrame({\"values\": all_top_10_func[selected_columns].values.flatten()})\n",
    "print(pd.DataFrame({'values' : tmp['values'].repeat(tmp2)}).describe())\n",
    "tmp = pd.DataFrame({\"values\": cluster_top_10_func[selected_columns].values.flatten()})\n",
    "print(pd.DataFrame({'values' : tmp['values'].repeat(tmp2)}).describe())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.815049Z",
     "start_time": "2024-05-13T09:38:20.746409Z"
    }
   },
   "id": "92bfbfe3d940e2e7"
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  0\n",
      "count  10350.000000\n",
      "mean       0.460580\n",
      "std        0.706681\n",
      "min        0.000004\n",
      "25%        0.035769\n",
      "50%        0.178937\n",
      "75%        0.515252\n",
      "max        3.554009\n",
      "                  0\n",
      "count  10350.000000\n",
      "mean       0.419829\n",
      "std        0.715521\n",
      "min        0.000004\n",
      "25%        0.014611\n",
      "50%        0.048442\n",
      "75%        0.496949\n",
      "max        3.349561\n",
      "                  0\n",
      "count  10350.000000\n",
      "mean       0.424262\n",
      "std        0.715730\n",
      "min        0.000004\n",
      "25%        0.016429\n",
      "50%        0.062429\n",
      "75%        0.504395\n",
      "max        3.406486\n"
     ]
    }
   ],
   "source": [
    "selected_columns = ['importtime1', 'importtime2', 'importtime3', 'importtime4', 'importtime5']\n",
    "print(pd.DataFrame(vanilla_approach[selected_columns].values.flatten()).describe())\n",
    "print(pd.DataFrame(all_top_10[selected_columns].values.flatten()).describe())\n",
    "print(pd.DataFrame(cluster_top_10[selected_columns].values.flatten()).describe())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.815349Z",
     "start_time": "2024-05-13T09:38:20.757024Z"
    }
   },
   "id": "97ec791ee1226365"
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             values\n",
      "count  12190.000000\n",
      "mean       0.944200\n",
      "std        1.493660\n",
      "min        0.000004\n",
      "25%        0.037687\n",
      "50%        0.206478\n",
      "75%        1.488200\n",
      "max        6.375797\n",
      "             values\n",
      "count  12190.000000\n",
      "mean       0.957254\n",
      "std        1.583239\n",
      "min        0.000004\n",
      "25%        0.021371\n",
      "50%        0.043915\n",
      "75%        1.437310\n",
      "max        6.562116\n",
      "             values\n",
      "count  12190.000000\n",
      "mean       0.914595\n",
      "std        1.522794\n",
      "min        0.000004\n",
      "25%        0.023293\n",
      "50%        0.067227\n",
      "75%        1.471975\n",
      "max        6.515993\n"
     ]
    }
   ],
   "source": [
    "print(pd.DataFrame({'values' : vanilla_approach_func.importtime.repeat(vanilla_approach_func['count'])}).describe())\n",
    "print(pd.DataFrame({'values' : all_top_10_func.importtime.repeat(vanilla_approach_func['count'])}).describe())\n",
    "print(pd.DataFrame({'values' : cluster_top_10_func.importtime.repeat(vanilla_approach_func['count'])}).describe())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.815437Z",
     "start_time": "2024-05-13T09:38:20.764048Z"
    }
   },
   "id": "3c508499d6472222"
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    2070.000000\n",
      "mean        0.875803\n",
      "std         1.373398\n",
      "min         0.000004\n",
      "25%         0.069330\n",
      "50%         0.257041\n",
      "75%         1.067550\n",
      "max         6.375797\n",
      "Name: importtime, dtype: float64\n",
      "count    2070.000000\n",
      "mean        0.864887\n",
      "std         1.436517\n",
      "min         0.000004\n",
      "25%         0.029079\n",
      "50%         0.100816\n",
      "75%         1.060296\n",
      "max         6.562116\n",
      "Name: importtime, dtype: float64\n",
      "count    2070.000000\n",
      "mean        0.845668\n",
      "std         1.402225\n",
      "min         0.000004\n",
      "25%         0.032106\n",
      "50%         0.119459\n",
      "75%         1.067447\n",
      "max         6.515993\n",
      "Name: importtime, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(vanilla_approach['importtime'].describe())\n",
    "print(all_top_10['importtime'].describe())\n",
    "print(cluster_top_10['importtime'].describe())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.822684Z",
     "start_time": "2024-05-13T09:38:20.770269Z"
    }
   },
   "id": "ab7bdf3ce2f499d2"
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             values\n",
      "count  60950.000000\n",
      "mean       1.480235\n",
      "std        0.681223\n",
      "min        1.013257\n",
      "25%        1.050882\n",
      "50%        1.201134\n",
      "75%        1.611863\n",
      "max        4.567813\n",
      "             values\n",
      "count  60950.000000\n",
      "mean       1.440212\n",
      "std        0.686282\n",
      "min        1.020899\n",
      "25%        1.038526\n",
      "50%        1.081928\n",
      "75%        1.604999\n",
      "max        4.372572\n",
      "             values\n",
      "count  60950.000000\n",
      "mean       1.441991\n",
      "std        0.688824\n",
      "min        1.014165\n",
      "25%        1.037694\n",
      "50%        1.104221\n",
      "75%        1.633173\n",
      "max        4.420920\n"
     ]
    }
   ],
   "source": [
    "selected_columns = ['end2end1', 'end2end2', 'end2end3', 'end2end4', 'end2end5']\n",
    "tmp2 = np.tile(np.array(vanilla_approach_func['count'], dtype=np.int64), 5)\n",
    "tmp = pd.DataFrame({\"values\": vanilla_approach_func[selected_columns].values.flatten()})\n",
    "print(pd.DataFrame({'values' : tmp['values'].repeat(tmp2)}).describe())\n",
    "tmp = pd.DataFrame({\"values\": all_top_10_func[selected_columns].values.flatten()})\n",
    "print(pd.DataFrame({'values' : tmp['values'].repeat(tmp2)}).describe())\n",
    "tmp = pd.DataFrame({\"values\": cluster_top_10_func[selected_columns].values.flatten()})\n",
    "print(pd.DataFrame({'values' : tmp['values'].repeat(tmp2)}).describe())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.822827Z",
     "start_time": "2024-05-13T09:38:20.775166Z"
    }
   },
   "id": "10014c188ebcfc38"
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "outputs": [],
   "source": [
    "toplot = pd.DataFrame({'values': pd.concat([vanilla_approach.buldingtime, all_top_10.buldingtime, cluster_top_10.buldingtime], ignore_index=True), 'what': ['vanilla_approach'] * len(vanilla_approach) + ['all_top_10'] * len(all_top_10) + ['cluster_top_10'] * len(cluster_top_10)})"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.822866Z",
     "start_time": "2024-05-13T09:38:20.785665Z"
    }
   },
   "id": "7d2088137db47043"
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": 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Wenq6tV94eLgjSwUAAIATqrbB6dy5cxo9erRmz56t0NBQR5cDJ3fhwgVJUq1ateTl5aW1a9dat91+++2qVauWcnNzrf0AAACA6znNcuQmk0kPP/ywvv32W2tbfn6+Jk+erIiICHXv3l3Lli2zbktKSlJubq7mzJmjYcOG6cUXX3RE2agmDh48KEnKzc3V5cuXbbZdvnxZubm5Nv0AAACA6znFGaf8/HyNHz9eR48etWmfPXu2fvjhB3300UdKT0/Xa6+9pqCgIPXp00czZsyo8HHNZnOF94Hq4fr/19euXbPZdv1zs9nMzwUAAIALKevffg4PTseOHdP48eNVWFho056bm6vk5GS9//77Cg4OVnBwsI4ePapVq1apT58+djn2gQMH7LIfOL+LFy+Wud/+/fsrtxgAAABUOw4PTrt27VK3bt00btw4mxvYHjp0SAUFBTYX6nfu3FmLFy+WxWKRu3vFZxl27NiRFdRcxNdff12mfkFBQdxIGQAAwIWYzeYynVBxeHAaOnRoie2ZmZmqV6+evL29rW0NGjRQfn6+srOzFRgYWOFje3h4EJxcxI4dO8rc74UXXqjkagAAgDMwm81KS0tTVlaWAgMDFRoayt+GKJXDg1Np8vLybEKTJOtzk8nkiJJQjZV1tTxW1QMAwDWkpqYqKSnJersSSWrcuLHi4uIUHR3twMrgrJxmVb0b+fj4FAtIRc99fX0dURKqsetveGuPfgAAoPpKTU1VfHy8WrdurYULF2rjxo1auHChWrdurfj4eKWmpjq6RDghpw1OjRo10uXLl1VQUGBty8zMlK+vr2677TYHVgYAAIDqymw2KykpSZGRkZo2bZp++uknvf/++/rpp580bdo0RUZGatGiRayyi2Kcdqpehw4d5Onpqf379ysiIkKStHfvXnXs2NEuC0MAAADA9aSlpSkjI0MhISHq27evTUBavHix7r33Xp07d05paWk2i5QBThuc/Pz81L9/f02fPl1vvPGGLly4oGXLlunNN990dGkAAACoprKysiRJW7ZsUb169fTAAw8oKChI6enp2rx5s7Zs2WLTDyjitMFJkiZNmqTp06fr6aeflr+/v1544QX17t3b0WUBAACgmiq65MPX11deXl5au3atddvtt98uX19f/fbbb1wagmKcKjgdPnzY5rmfn59mzZqlWbNmOagiAAAA1CQnTpyQJP3222/FFoXKzs62LkZ24sQJdenSpcrrg/PiYiEAAAC4jHPnzlkfl7aC8439AIngBAAAABdSWFho135wHU41VQ8AgKpgNpuVlpamrKwsBQYGKjQ0VB4eHo4uC0AV8PLysms/uA6CEwDApaSmpiopKUkZGRnWtsaNGysuLk7R0dEOrAxAVfjmm2/K3G/s2LGVXA2qE4ITAMBlpKamKj4+XnfffbcGDRpkXT1r165dio+PV0JCAuEJqOGuXLli135wHQQnuAQ3N7cyzVV2c3OrgmoAOILZbFZSUpLatm2rY8eOaefOndZtDRs2VNu2bbVo0SJFRUUxbQ+owX777Te79oPrIDjBJfj6+iovL69M/QDUTGlpacrIyLCZolckMzNTmZmZ1n7h4eFVXR6AKuLl5aX8/Pwy9QOux6p6cAm33367XfsBqH4uXrxo134AqqdatWrZtR9cB8EJLuHxxx+3az8A1U9Z78nCvVuAmq1+/fp27QfXQXCCSwgKCrJrPwDVz6effmrz3MPDQ76+vsWuZ7qxH4CapazT8pm+jxtxjRNcwtWrV+3aD0D1c+HCBZvnZrNZZrPZsB+AmoWperhVnHGCS5g/f771cUREhFq3bq0GDRqodevWioiIKLEfAACoebp37y5Jcnd3l7u77Z/C17cV9QOKcMYJLiEnJ0eSFBwcrDNnzlhX1bp48aLy8vLUoUMHHTx40NoPQM3m7u6umJgYtW/fXocOHdL27dtlsVgcXRaAKtC4cWNJksVikaenpzp16qT69evr0qVLOnDggAoKCmz6AUUITnAJPj4+unbtmk6ePKn169frhx9+UFZWlgIDAxUSEqJ+/fpZ+wGo+SwWi7Zt26Zt27Y5uhQAVSw0NFSNGzdWfn6+Ll++rH379tlsr1evnnx9fRUaGuqgCuGsmKoHl1C0Wl5ubq4mT54sb29vRUZGytvbW5MnT1Zubq5NPwA1z41TciraD0D15OHhobi4OGVnZ6tbt26Kjo5WeHi4oqOj1a1bN2VnZ2v06NHcCBvFuBUWFhY6uoiqZjabtX//foWFhfGmcBEmk0m9e/c27Pf555/L29u7CioCUNUeeeQRXblyxbBfnTp1tGHDhiqoCIAjpaamKikpyeam2E2aNNHo0aMVHR3twMpQ1cqaDZiqB5fg7e2twYMHa/Xq1aX2GTx4MKEJqMEef/xx/f3vfy9TPwA1X3R0tKKiopSWlmadvh8aGsqH6igVwQkuY9SoUZJUYngaPHiwdTuAmmnIkCE2wcnd3V1ubm4qLCy0WRhiyJAhjigPgAN4eHgoPDzc0WWgmiA4waWMGjVKzz77rNavX6/09HQFBQWpX79+nGkCXMCNZ55LWkWPM88AgNIQnOByvL299cQTTzi6DAAOUHRmec2aNbr+El83NzcNGjSIM88AgFIRnAAALoUzzwCAW0Fwgssxm81cCAq4OM48AwDKi+AEl1LS0qONGzdWXFwcS48CAACgVNzlDy4jNTVV8fHxat26tRYuXKiNGzdq4cKFat26teLj45WamuroEgEAAOCkCE5wCWazWUlJSYqMjFRCQoJMJpN27twpk8mkhIQERUZGatGiRTKbzY4uFQAAAE6IqXpwCWlpacrIyNAjjzyiYcOGFZuq9/DDD+vrr79WWloa93MAAABAMQQnuISsrCxJ0vvvvy8vLy+bbZcuXdLSpUtt+gEAAADXY6oeXEJAQID18bVr12y2Xf/8+n4AAABAEYITXILFYrFrPwAAALgWghNcwp49e+zaDwAAAK6F4ASXsH37drv2AwAAgGshOMElnD9/3q79AAAA4FpYVQ8uKSIiQuHh4dq3bx/T8wAAAGCI4ASXtGfPHgITAAAAyoypegAAAABggOAEl1CvXj279gMAAIBrITjBJbRt29au/QAAAOBaCE5wCa1atbJrPwAAALgWghNcgpeXl137AQAAwLUQnOASwsLCJEl16tQpcXtRe1E/AAAA4HosRw6XEBYWpoCAAGVnZ6tr167y9fXVlStXVKdOHf3222/atWuX6tWrR3ACAABAiQhOcAkeHh565ZVXFB8fr++//175+fnWbT4+PnJzc9O4cePk4eHhwCoBAADgrJiqB5cRHR2thISEYkuOBwYGKiEhQdHR0Q6qDAAAAM6OM05wKdHR0YqKilJaWpqysrIUGBio0NBQzjQBAADgpghOcDkeHh4KDw93dBkAAACoRpiqBwAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGPB1dAKqP9PR0Xb161dFloBT+/v4KCgpydBkAAAA1EsEJZZKdna2nnnpKFovF0aWgFO7u7kpJSVFAQICjSwEAAKhxCE4ok4CAAK1cubLGnHE6deqUEhMTNWXKFLVo0cLR5diFv78/oQkAAKCSEJxQZjVxGliLFi3Utm1bR5cBAAAAJ8fiEAAAAABggOAEAAAAAAYITgAAAABggOAEAAAAAAYITgAAAABggOAEAAAAAAYITgAAAABggOAEAAAAAAYITgAAAABggOAEAAAAAAY8HV0AAABVzWQyaf369UpPT1dQUJD69esnb29vR5cFAHBiBCcAgEtZvHixkpOTZTabbdqeeOIJjRo1yoGVAQCcGcEJAOAyFi9erNWrVysgIEBhYWHy8/NTXl6e9u/fr9WrV0sS4QkAUCKCEwDAJZhMJiUnJ8vHx0f//e9/tX37dus2d3d3+fj4KDk5Wc8++yzT9gAAxbA4BADAJaxfv15ms1n5+fmyWCw22ywWi/Lz82U2m7V+/XoHVQgAcGYEJwCASzhz5oxd+wEAXAvBCQDgEi5cuGDXfgAA12KXa5yysrJUr149ubm52WN3AADY3eXLl62Pu3TpojvuuEMmk0ne3t46ffq0du/eXawfAABFyh2czp8/r7feeksjR45U69at9dxzz2nv3r1q3LixFi1apPbt21dGnQAAVEhGRob18e7du61B6Wb9AAAoUu6petOnT1dWVpYCAgKUkpKiI0eOaPXq1erVq5dmzJhRGTUCAFBhnp5l+6ywrP0AAK6l3L8dvvnmG6WkpKhJkybasmWL7rvvPnXq1EmBgYF6+OGHK6NGAAAqrFmzZrp48WKZ+gEAcKNyn3Hy8fFRfn6+cnJy9O233yomJkbS76sQ1a1b1971AQBgF40aNbJrPwCAayn3Gaf7779fL7/8snx9fVW3bl3FxMRo48aNeuONN/TYY49VRo0AAFTYwYMH7doPAOBayh2cpk+frpUrV+rs2bMaNGiQfHx8ZDKZNGrUKD355JOVUSMAABXGcuQAgIood3Dy9PTUiBEjbNr69+9vp3IAAKgcJpPJrv0AAK6lTMFp2LBhZb5H0/LlyytUEAAAAAA4mzIFp27dulkfX758WWvWrNH999+vjh07ysvLSwcPHtTGjRuZqgcAcFoWi8Wu/QAArqVMwWns2LHWxyNGjNDkyZM1dOhQmz5dunTRmjVr7FsdAAAAADiBci9Hvn//fkVGRhZr79Spkw4fPmyXogAAAADAmZQ7ON1111167733lJ+fb227evWq5s2bp7CwMHvWBgAAAABOodyr6s2YMUMjR45UVFSUWrRoocLCQv38888KCgrSkiVLKqNGAAAAAHCocgenNm3aaNOmTfr66691/PhxSdKdd96pe+65R56e5d4dAABVwsvLS9euXStTPwAAbnRLScfb21sxMTGKiYmxczkAAFSOWrVqKScnp0z9AAC4UbmD008//aSZM2fqwIEDKigoKLb94MGDdikMAAB7at68eZmCU/PmzaugGgBAdVPu4DR58mTVqVNH7777rvz9/SujJgAA7C4vL8+u/QAArqXcwenEiRPasGGDWrRoURn1AABQKcpytqk8/QAArqXcy5F36NDBuigEAADVRe3ate3aDwDgWsp9xqlfv36aOnWqBgwYoBYtWhRbfah///72qg0AALt58MEH9d5770n6fZEjk8lk3Xb98wcffNAh9QEAnFu5g9PSpUvl6+urjRs3Ftvm5uZGcAIAOKXHH3/cGpyuD003Pn/88certC4AQPVQ7uC0bdu2yqgDAIBK5e3trcGDB2v16tWl9hk8eLC8vb2rsCoAQHVxS/dxunDhglatWqXjx4/LbDardevWeuKJJ9SyZUs7lwcAgP2MGjVKkrRmzRoVFhZa293d3fWnP/3Juh0AgBuVOzjt2bNHf/nLX9SuXTuFhYXJbDZr9+7dWrlypZYtW6bOnTtXRp0AANjFqFGj9Oyzz2r9+vVKT09XUFCQ+vXrx5kmAMBNlTs4vfXWW3rqqac0fvx4m/a3335bc+bMuekUCAAAnIG3t7eeeOIJR5cBAKhGyr0c+dGjRzVw4MBi7Y8//rgOHjxol6IAAKhMJpNJycnJevfdd5WcnFxssQgAAG5U7jNOTZs2VVpaWrHrmb7//ns1aNDAXnUBAFApFi9erLVr18pisVjbFi1axDVOAICbKndw+vOf/6z4+HidOHFCoaGhkn4PTStWrNArr7xi9wIBALCXxYsXlzil3GKxWNsJTwCAkpQ7OA0YMECStHLlSv3973+Xj4+PWrVqpcTERD300EN2LxAAAHswmUxas2bNTfusWbNGzz77LAtFAACKuaXlyB999FFFR0dbp+bt27dPwcHBdi2spjh//rxycnIcXQZucOrUKZt/4Vzq1q2rRo0aOboM1DApKSk2S5CXpLCwUCkpKRo8eHAVVQUAqC7KHZwOHjyoUaNGqW/fvpowYYIk6dVXX1VhYaGWLFmiO++80+5FVlfnz5/XU8OG65op39GloBSJiYmOLgEl8PL20coVywlPsKsvv/yyzP0ITgCAG5U7OL3++ut64IEHNG7cOGvb5s2b9cYbb+j111/XihUr7FpgdZaTk6Nrpnzlte4pi29dR5cDVAvuv+VIJ/6jnJwcghPs6uLFi9bHXl5eeuKJJxQbG6uNGzcqOTlZ165dK9YPAIAit3TGafbs2fLy8rK2ubu7a/jw4erXr59di6spLL51ZanNioMA4EjXr6K3fv161apVS5I0cuRIPfXUU4qNjS3WDwCAIuW+j1OTJk20c+fOYu3fffcdy5EDAJxWQUGB9fGTTz6pDRs26OLFi9qwYYOefPLJEvsBAFCk3GecRo0apSlTpmjfvn0KCQmRJB06dEiffPKJ4uPj7V4gAMC5pKen6+rVq44uo9yunylx+fJl/c///E+p/Y4cOVJVZdmVv7+/goKCHF0GANRI5Q5O/fr1U2BgoNauXat//OMf8vT0VIsWLfTBBx8oIiKiMmoEADiJ7OxsPfXUUzV6OtuFCxc0cuRIR5dxS9zd3ZWSkqKAgABHlwIANc4tLUfeo0cP9ejRw961AACcXEBAgFauXFktzziZTCaNHTvWsN+CBQuq7X2c/P39CU0AUEluKTh98skn+vDDD3X69Gn985//1IoVK9SgQYNq+wkdAKDsqvNUsKioKO3YseOm24umoQMAcL1yLw7xv//7v5o9e7YGDBhgXbo1ODhYH3zwgRYsWGD3AgEAsJfExERFRUWVuC0qKop7uwEASlXu4LRixQrNnDlTTz31lNzdf395v379NHv2bCUnJ9u9QAAA7CkxMVGbNm1STEyMJCkmJkabNm0iNAEAbqrcwSk9PV1t2rQp1t68eXNlZ2fboyYAACqVn5+fhg4dKkkaOnSo/Pz8HFwRAMDZlTs4derUSevWrbNpKyws1LJlyxQaGmqvugAAAIBKZTabtW/fPm3dulX79u2T2Wx2dElwYuVeHGLq1KkaOXKktm/fLpPJpISEBP3888/Ky8vT0qVLK6NGAAAAwK5SU1OVlJSkjIwMa1vjxo0VFxen6OhoB1YGZ1Xu4NS2bVv9+9//1oYNG3T8+HGZzWbdd999evTRR1W7du3KqBEAAACwm9TUVMXHxysyMlJ//etf1apVK508eVKrVq1SfHy8EhISCE8o5paWI/fx8dGAAQPk7u6uCxcuaO/evbpw4YJatWpl7/oAAAAAuzGbzUpKSlJkZKRmzpxpXewsODhYM2fO1NSpU7Vo0SJFRUXJw8PDwdXCmZT7Gqe9e/eqR48e2rVrly5cuKABAwZo2rRpeuSRR7Rp06bKqBEAAACwi7S0NGVkZOjJJ5+0hqYi7u7uevLJJ3Xu3DmlpaU5qEI4q3IHpzfffFOxsbHq1KmT1q5dKx8fH+3YsUMzZszQvHnzKqNGAAAAwC6ysrIkqdSZUkXtRf2AIuUOTkeOHNHTTz8tPz8/bdu2Tb1795a3t7e6du2q9PT0yqgRAAAAsIvAwEBJ0smTJ0vcXtRe1A8oUu7g1KBBAx07dkzHjh3TTz/9pHvvvVeS9PXXX6tJkyZ2LxAAAACwl9DQUDVu3FirVq2SxWKx2WaxWLRq1So1adKE2+ygmHIHpxEjRmjMmDEaOHCgOnbsqK5du2rx4sVKSEjQmDFjKqNGAAAAwC48PDwUFxennTt3aurUqfrxxx+Vm5urH3/8UVOnTtXOnTs1evRoFoZAMeVeVW/48OHq0qWLzp49q+7du0uS7r77bsXExKh9+/Z2LxAAAACwp+joaCUkJCgpKcnmg/8mTZqwFDlKdUvLkXfo0EEdOnSwPg8LC7NXPQAAAECli46OVlRUlNLS0pSVlaXAwECFhoZypgmluqXgBAAAAFR3Hh4eCg8Pd3QZqCbKfY0TAAAAALgaghMAAAAAGLjlqXqZmZkqKChQYWGhTXtQUFCFiwIAAAAAZ1Lu4PTVV19p2rRpOnfunE17YWGh3NzcdPDgQbsVBwAAAADOoNzBacaMGQoNDdWiRYvk7+9fGTUBAAAAgFMpd3DKyMjQ0qVL1bx588qoBwAAAACcTrmDU0REhPbu3UtwAgAAQLVmNpu5jxPKrNzBqUuXLkpISND27dvVokULeXl52WwfO3as3YoDAAAAKkNqaqqSkpKUkZFhbWvcuLHi4uIUHR3twMrgrModnHbs2KGQkBBdunRJly5dstnm5uZmt8IAAACAypCamqr4+PhiJwCysrIUHx+vhIQEwhOKKXdwWrFiRWXUAQAAAFQ6s9msuXPnqrCwUCaTyWZb0fN33nlHUVFRTNuDjTIFp3Xr1ik2Nlbe3t5at27dTfv279/fDmUBAAAA9rd//35lZ2fftM/ly5e1f/9+de7cuWqKQrVQpuA0b9489ezZU97e3po3b16p/dzc3AhOAAAAcFp79+4tcz+CE65XpuC0bdu2Eh8DAAAA1cnBgwetjz08PDRo0CDFxsZq48aNWrNmjcxmc7F+gHQL1zgBAAAA1dXZs2etjz/77DP5+vpKkkaOHKnhw4erT58+xfoBkuTu6AIAAACAqvLrr79aHyckJOjHH39Ubm6ufvzxRyUkJJTYD5A44wQAAIBySk9P19WrVx1dxi25fgnyPXv2aOfOnSVu8/Ly0pEjR6q0Nnvw9/dXUFCQo8uokQhOAAAAKLPs7Gw99dRTslgsji6lwq5du1bq8+zsbI0cObKqS6owd3d3paSkKCAgwNGl1Di3FJx++eUX/e///q9OnTql6dOnKzU1VS1btlRERIS96yuTLVu2aOvWrXrzzTcdcnwAAABXERAQoJUrV1bbM04mk0ljx4417LdgwQJ5e3tXQUX25e/vT2iqJOUOTrt379bIkSPVo0cPffnll8rPz9eJEyc0ffp0zZ07V717966MOks1a9YsffHFFwoLC6vS4wIAALiq6j4VLCoqSjt27Ljp9pCQkCqsCNVBuReHmDNnjsaPH6958+bJ0/P33DVhwgS9+uqrN73HU2UJDQ3V9OnTq/y4AAAAqJ4SExMVFRVV4raoqCglJiZWcUWoDsodnI4cOaKePXsWa7/vvvt0+vRpuxRVHg899JDc3Nyq/LgAAACovhITE7Vp0ybFxMRIkmJiYrRp0yZCE0pV7uDUtGlTHThwoFj79u3b1bRpU7sUBQAAAFQ2Pz8/DR06VJI0dOhQ+fn5ObgiOLNyX+P08ssva+LEiTpw4IDMZrPWrVunM2fO6LPPPtPs2bMro0YAAAAAcKhyn3F64IEHtGrVKl26dEl33nmntm7dKpPJpFWrVik2NrZCxZhMJj388MP69ttvrW35+fmaPHmyIiIi1L17dy1btqxCxwAAAACA8rql5cjbt29v97NL+fn5Gj9+vI4ePWrTPnv2bP3www/66KOPlJ6ertdee01BQUHq06ePtU+3bt3UrVu3ch/TbDZXuG5H7h+oycxmM+8hVKqiny9+1gDXxliAsv5/L3dwunDhgpYuXaoTJ07IZDIV2758+fLy7lLHjh3T+PHjVVhYaNOem5ur5ORkvf/++woODlZwcLCOHj2qVatW2QSnW1XStVr2dObMmUrdP1CTHTlyRLm5uY4uAzVY0RjNzxrg2hgLUFblDk7jxo1TZmamevfuLV9fX7sUsWvXLnXr1k3jxo2zuR/ToUOHVFBQoPDwcGtb586dtXjxYlksFrm7l3umoY2OHTvKw8OjQvu4mVq1alXavoGarm3btmrbtq2jy0ANVjRG87MGuDbGApjN5jKdUCl3cPrxxx+1evVqtW/f/pYKK0nRaiY3yszMVL169Wzu2tygQQPl5+crOztbgYGBFTquh4dHpQanytw3UNNV9vsTKPr54mcNcG2MBSircp+y6dSpU5XdrykvL88mNEmyPi9pmiAAAAAAVIZyn3FKTEzUkCFDtG3bNjVt2rTYzWfHjh1rt+J8fHyKBaSi5/aaJggAAAAARsodnN555x1dvnxZJ06c0NmzZ2223RiiKqpRo0a6fPmyCgoK5On5e6mZmZny9fXVbbfdZtdjAQAAAEBpyh2ctm7dqmXLlqlr166VUY+NDh06yNPTU/v371dERIQkae/everYsWOFF4YAAAAAgLIqd/oICgqSn59fZdRSjJ+fn/r376/p06crLS1NW7Zs0bJlyzR8+PAqOT4AAAAASLdwxunFF1/UxIkTNWLECDVr1sw6ha5Ily5d7FacJE2aNEnTp0/X008/LX9/f73wwgvq3bu3XY8BAAAAADdT7uD08ssvS5L++te/Ftvm5uamgwcPVqigw4cP2zz38/PTrFmzNGvWrArtFwAAAABuVbmD06FDhyqjDgAAAABwWmUKTunp6WrSpInc3NyUnp5+075BQUF2KQwAAAAAnEWZgtN9992nr776SvXr11evXr3k5uamwsJC6/ai5/aYqgcAAAAAzqZMwemNN96w3jdp69atlVoQAAAAADibMgWnyZMnKzo6WvXr11fTpk0ruyYAAAAAcCpluo/T9dPyAAAAAMDVlPkGuG5ubpVZBwAAAAA4rTIvRz5w4EC5uxvnLK6BKs49L9vRJQDVBu8XAADgjMocnJ555hnVqVOnMmupsfxOpjq6BAAOdP78eeXk5Di6DNzg1KlTNv/C+dStW1eNGjVydBkAIKmMwcnNzU19+/ZV/fr1K7ueGimvVbQsfgGOLgOoFtzzsmvUhw3nz5/XU8OG65op39GloBSJiYmOLgGl8PL20coVywlPAJxCmYITi0NUjMUvQJbaDRxdBgAHyMnJ0TVTvvJa95TFt66jywGqDfffcqQT/1FOTg7BCYBTKFNweuyxx+Tj41PZtQBAjWXxrcsHKAAAVGNlCk5vvvlmZdcBAAAAAE6rzMuRAwAAAICrIjgBAAAAgAGCEwAAAAAYIDgBAAAAgAGCEwAAAAAYIDgBAAAAgAGCEwAAAAAYIDgBAAAAgAGCEwAAAAAYIDgBAAAAgAGCEwAAAAAYIDgBAAAAgAGCEwAAAAAYIDgBAAAAgAGCEwAAAAAYIDgBAAAAgAGCEwAAAAAYIDgBAAAAgAGCEwAAAAAYIDgBAAAAgAGCEwAAAAAYIDgBAAAAgAGCEwAAAAAYIDgBAAAAgAGCEwAAAAAYIDgBAAAAgAFPRxcAAABQ050/f145OTmOLgMlOHXqlM2/cC5169ZVo0aNHF2GJIITAABApTp//ryeGjZc10z5ji4FN5GYmOjoElACL28frVyx3CnCE8EJAACgEuXk5OiaKV95rXvK4lvX0eUA1Yb7bznSif8oJyeH4AQAAOAqLL51ZandwNFlALhFLA4BAAAAAAYITgAAAABggOAEAAAAAAYITgAAAABggOAEAAAAAAYITgAAAABggOAEAAAAAAYITgAAAABggOAEAAAAAAYITgAAAABggOAEAAAAAAYITgAAAABggOAEAAAAAAY8HV0AALgC97xsR5cAVCu8ZwA4G4ITAFQBv5Opji4BAABUAMEJAKpAXqtoWfwCHF0GUG2452XzgQMAp0JwAoAqYPELkKV2A0eXAQAAbhGLQwAAAACAAYITAAAAABggOAEAAACAAYITAAAAABggOAEAAACAAYITAAAAABggOAEAAACAAe7jBAAAUAXc87IdXQJQrTjbe4bgBAAAUAX8TqY6ugQAFUBwAgAAqAJ5raJl8QtwdBlAteGel+1UHzgQnAAAAKqAxS9AltoNHF0GgFvE4hAAAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYIDgBAAAAAAGCE4AAAAAYMDT0QXcKovFoilTpujkyZOqXbu2Zs+erfr16zu6LAAAAAA1ULU947R582b5+vpq9erVGjhwoJYsWeLokgAAAADUUNU2OH333XeKioqSJPXo0UO7du1ycEUAAAAAaqpqG5yuXr0qf39/SVLt2rX166+/OrgiAAAAADVVtQ1O/v7+1rD066+/qk6dOg6uCAAAAEBNVW2DU1hYmHbs2CFJSk1NVXh4uIMrAgAAAFBTOU1wMplMevjhh/Xtt99a2/Lz8zV58mRFRESoe/fuWrZsmXVb7969lZeXp8GDB2v16tUaNWqUI8oGAAAA4AKcYjny/Px8jR8/XkePHrVpnz17tn744Qd99NFHSk9P12uvvaagoCD16dNHHh4eevPNNyt0XLPZXKHXO3r/QE1mNptrxHuoJnwNgCPVhLGgutcPOFpljwNl3bfDg9OxY8c0fvx4FRYW2rTn5uYqOTlZ77//voKDgxUcHKyjR49q1apV6tOnj12OfeDAAbvspzRnzpyp1P0DNdmRI0eUm5vr6DIqjHEAqJiaMBYwDgAV4yzjgMOD065du9StWzeNGzdOYWFh1vZDhw6poKDA5tqlzp07a/HixbJYLHJ3r/gsw44dO8rDw6PC+ylNrVq1Km3fQE3Xtm1btW3b1tFlVBjjAFAxNWEsYBwAKqayxwGz2VymEyoOD05Dhw4tsT0zM1P16tWTt7e3ta1BgwbKz89Xdna2AgMDK3xsDw+PSg1OlblvoKar7PdnVakJXwPgSDVhLKju9QOO5izjgNMsDnGjvLw8m9AkyfrcZDI5oiQAAAAALsppg5OPj0+xgFT03NfX1xElAQAAAHBRThucGjVqpMuXL6ugoMDalpmZKV9fX912220OrAwAAACAq3Ha4NShQwd5enpq//791ra9e/eqY8eOdlkYAgAAAADKymkTiJ+fn/r376/p06crLS1NW7Zs0bJlyzR8+HBHlwYAAADAxTh8Vb2bmTRpkqZPn66nn35a/v7+euGFF9S7d29HlwUAAADAxThVcDp8+LDNcz8/P82aNUuzZs1yUEX24f5bjqNLsAs3069yM19zdBkoRaGHlwq9azu6jAqrKe+XG9Wkr4uxwHnVlHFAqlnvmSI16WtiHHBejAOVx6mCU01Tt25deXn7SCf+4+hSgGrFy9tHdevWdXQZdsE4ANy6mjIWMA4At86ZxgG3wsLCQkcXUdXMZrP279+vsLCwSr+Z1vnz55WT41xp+VZlZmYqNzfX0WWgFLVq1VLDhg0dXYZd1K1bV40aNXJ0GXZTk8YBibHAmdWkcUCqWWMB4wCqCuNA+ZU1GxCcnOAuxAAAAAAco6zZwGlX1QMAAAAAZ0FwAgAAAAADBCcAAAAAMEBwAgAAAAADBCcAAAAAMEBwAgAAAAADBCcAAAAAMEBwAgAAAAADBCcAAAAAMEBwAgAAAAADBCcAAAAAMEBwAgAAAAADBCcAAAAAMEBwAgAAAAADBCcAAAAAMEBwAgAAAAADBCcAAAAAMODp6AIcobCwUJJkNpsdXAkAAAAARyrKBEUZoTQuGZwsFosk6cCBAw6uBAAAAIAzKMoIpXErNIpWNZDFYlFBQYHc3d3l5ubm6HIAAAAAOEhhYaEsFos8PT3l7l76lUwuGZwAAAAAoDxYHAIAAAAADBCcAAAAAMAAwQkAAAAADBCcAAAAAMAAwQkAAAAADBCcAAAAAMAAwQkAAAAADBCc4HATJ07UxIkTJUnz58/XsGHDJEkpKSnq1auXI0tzOsOGDdP8+fMdXQZQZa4fB7799lu1a9euzK/dtGmTLl26VFmllejUqVMKDQ0t1v7111/r4YcfVqdOnTR8+HD98ssvVVoX4AzK+x4uTWFhoVatWmWHihyz/9Ls2bNH9913X7H2Tz/9VPfff786deqkMWPGKCsrq8prw+8ITnC4KVOmaMqUKY4uA0ANcvbsWb388svKy8ursmOeO3dOzz//vPLz823a09PTNWbMGA0YMEAff/yxAgMDFRcXJ+4/D9ya3bt36/XXX6+2+y/J4cOH9dJLLxUbF9LS0jRlyhSNHTtWa9as0X//+19NmjSpSmvD/4/gBIerU6eO6tSp4+gyANQgVR1KtmzZogEDBsjb27vYtuTkZIWEhOjZZ5/VnXfeqTfffFNnz57Vrl27qrRGoKao7Pd3VY8fq1ev1uDBg1W/fv1i21auXKmHHnpI/fv3V/v27TV79mz95z//4ay1gxCcUGbjxo3Ta6+9ZtM2fvx4TZkyRXv37tWQIUPUqVMnhYWF6S9/+YsuXLgg6fepNsOGDdO8efPUrVs3RURE6M0337QOTNdP1buZrVu3qn///urYsaMiIiL0yiuv6Ndffy1T7VevXtWkSZMUGRmpkJAQ9enTR1u2bLFub9eunZKTk3X//fcrPDxc48ePt+47JSVFQ4YM0dtvv63w8HDFxMQoOTnZ+tphw4ZpxowZuu+++xQTE6OrV68qIyNDL730krp27apu3bpp5syZMplM1tckJyerT58+CgkJUbdu3ZSQkCCz2Wzd/ve//129evVSeHi4nnvuOZsB8vz58/rzn/+sjh076sEHH9TXX39dpu8B4MxuNobciqLpLvfdd59SUlIkSV988YUee+wxhYaGKjY2Vp9//rm1/7Bhw7RgwQJrDUOHDtXx48fLfLzt27frpZdeKvHs+ffff6+IiAjrcz8/PwUHB2v//v23+NUBzu/UqVN67rnnrL83ly9fbrP9zJkzateunc6cOWNtu366/rVr1zR16lR169ZN4eHhGjVqlM6fP68zZ85o+PDhkn7/3f3tt99K+j18FP3eHDZsmA4fPmzdb69evTRnzhx1795d/fv3v2kwKm3/KSkpeuihhxQaGqoBAwZo9+7dNvv/8MMP9cgjjygsLEwjR45UZmZmmb9XqampmjVrlkaMGFFs243jR5MmTRQUFKTvv/++zPuH/RCcUGZ9+/bVF198oWvXrkmSTCaTvvjiC9177716/vnnFRUVpU8//VQffPCBTp8+rffee8/62n379unkyZP6xz/+ob/+9a9avnx5uf7gP336tF566SUNHTpUmzZt0t/+9jd9/fXXWrt2bZlen5iYqJMnT2rZsmX69NNPFRERoSlTptiEmXfffVdTp07V8uXLdeTIEU2bNs267cCBAzp48KDWrFmjsWPHKiEhQV999ZV1e0pKiubMmaMFCxbI29tbTz/9tPLy8rRixQr97W9/0/bt2zV79mxJ0q5duzRz5ky98sor+te//qWEhAR9/PHH2rp1q6TfB/8FCxbo1Vdf1T//+U/Vrl1bL730kvVY69atU2xsrD777DOFhIRowoQJTPlBtXblyhXDMaS8ij7cSE5OVmxsrHbu3KkXXnhB/fr10/r16/XEE09o3Lhx+uGHH6yvWbJkiR588EGlpKSoUaNGGjlypM0YcTMzZ87U4MGDS9yWmZmp22+/3aatfv36ysjIuMWvDnBu+fn5evbZZ1W7dm2tXbtW06ZN0zvvvKPc3Nwy72PVqlXavXu3li1bpo8//li//vqr3njjDTVp0sR6re9XX32l8PBwbdu2TQsWLNBf//pX/fOf/1Tnzp01fPhw5eTkWPe3YcMGffDBB3rrrbfk5uZW6nFL2n9KSopmzJih559/XuvWrdM999yjkSNH6vz589bXzZ8/X3/+85+1Zs0a5eXl6YUXXijz15qUlKTevXuXuO3ChQuMH06E4IQyi46OlsVisX768tVXX8nX11cdO3ZUXFycxowZo+bNm6tz587q3bu3jh49an2t2WzWjBkz1Lp1a/Xr10/t27fXgQMHynxsi8WiqVOn6k9/+pOaNWum7t2765577rE5xs106dJFr7/+ujp06KCWLVvq2WefVXZ2ts2F43/5y18UExOjjh07asqUKdq0aZOuXLkiSXJzc9Ps2bPVtm1bPf744+rbt69NaIuJidEf//hHhYSE6Msvv9T58+c1Z84ctWvXTpGRkZo2bZr+8Y9/6Ndff1WtWrWUmJio3r17q1mzZurTp4/uuusu69eyZs0ajRgxQrGxsWrZsqWmTZumbt266bfffpMkPfjggxowYIDuuOMO/eUvf1FmZmaVXwAP2NNvv/1mOIaUV2BgoPVfX19frVq1Sg8++KBGjBihVq1a6ZlnnlHv3r21bNky62uio6M1YsQItWnTRjNmzFBWVpZ27NhR4a8vLy+v2BQ+b2/vMocyoLr56quvlJWVpTfeeEN33nmnevXqpalTp8rdvex/dp45c0Y+Pj5q2rSp2rRpo7feeksjR46Uh4eH6tatK0lq2LChvL29tXTpUj3//PO699571bJlS7388stq2rSpPvnkE+v+Hn30UbVr107t27e/6XFL2v+KFSs0bNgw9e/fX61bt9arr76qtm3bauXKldbXDRw4UP369VO7du30xhtvaN++fTpy5Eh5vm0l+u233xg/nIinowtA9eHt7a37779fn3/+ubp3767PP/9cDz74oBo1aqT+/fvrww8/1MGDB3Xs2DEdPnxYf/zjH62vrV+/vvz9/a3P/f39VVBQUOZjt2zZUt7e3lq0aJGOHj2qo0eP6tixY+rXr1+ZXt+/f39t2bJFa9eu1YkTJ/Tjjz9Kks30uOvrDQkJkdls1smTJyVJLVq0sJl7HBISotWrV1ufN23a1Pr4+PHjatmypXXgLdp3QUGBTp8+rZCQEPn6+mrevHnW79WpU6fUvXt3SdLJkycVHBxsfW2DBg1spkg2b97c+rjoe3rjxehAddKwYUPDMaSijh8/XuyMUHh4uP7v//7P+vz64/n7+6tVq1Y6fvy47r333god28fHp9gfOSaTSbfddluF9gs4q5MnT6pVq1Y2v/cHDhxo/eC1LAYNGqTPPvtM3bt3V9euXXX//fdrwIABJfY9fvy45syZo7lz51rb8vPz9fPPP1ufX/97uryOHz+uMWPG2LSFhYXZTOe9fvxo3ry5AgICdPz4cbVt2/aWjyuVPn74+flVaL+4NZxxQrnExsZq69atMplM2rZtm2JjY3X+/Hk9+uij+uabbxQcHKzJkyfrmWeesXldSRdMl2d62aFDh9S3b18dO3ZMERERSkxMVGxsbJlfP2HCBM2aNUu33XabhgwZoiVLlhTr4+XlZX1ssVgkyfrpmKen7WcMZrPZ5pMzHx+fEh9f37/o3y+//FIDBgzQxYsX1aNHD82bN89mwL3xWDfy8PAo1sZUPVRnZRlDKqqk96XFYrG+1yXj9/mtatSokS5evGjTdvHiRTVs2LDC+wackdHvMUklTpe7/gPVO++8U9u2bdOcOXPUsGFDzZ07V88++2yJv+/MZrMmT56sdevWWf/btGmT4uLirH1KGgPKqrTf64wfrofghHK55557ZDab9fe//12+vr6KiIjQ5s2bVbduXS1ZskRPP/20IiIi9Msvv9j1j/n169erS5cu+p//+R8NHTpUoaGhOnXqVJmOcfXqVX366ad655139OKLL+qBBx6wznu+/vUHDx60Pv7hhx/k5eWlVq1aSfr9ItfrF6L44YcfSv0UqVWrVvr555+VnZ1tbdu/f788PT11xx13KDk5WQMHDtTrr7+uJ554Qm3atNHp06ettbRo0UKHDh2yvvby5cu6++67bS6gBWqSyhhDbvyjrFWrVsUupt63b5/1PS7J5n135coVnT592i73nOnUqZP27t1rfZ6Xl6effvpJnTp1qvC+AWfUsmVLnTp1yuZ2ALNmzdLMmTOtz4s+rLz+d+v1v+fWrVunL774Qg899JBmzZqlpUuXau/evbp06VKJ7++MjAy1aNHC+t/ixYtveQGWsowf33//fanjx6lTp3TlypVKGT/OnTunc+fOMX44CMEJ5eLp6anevXtr8eLF6tOnj9zc3BQQEKD09HTt3LlTv/zyi9577z19/vnndp1/GxAQoMOHDystLU0nT57UW2+9pQMHDpTpGN7e3vLz89Pnn3+uM2fO6Msvv7Ten+H618+bN0+7du3S999/r5kzZ+qxxx5T7dq1JUm5ubmKj4/X8ePHtXbtWv3rX//S0KFDSzxeVFSUmjdvrgkTJujw4cP65ptvNGPGDD388MO67bbbFBAQoH379unw4cM6evSoJk6cqMzMTGstw4YN00cffaQtW7bo5MmTio+PV7NmzdSsWbOKfhsBp1QZY0jRNJZDhw7p119/1YgRI/Tvf/9bH330kX7++Wd9+OGH2rx5s4YMGWJ9zYYNG7Ru3TodP35cU6ZMUVBQkLp161bhr2/gwIH67rvv9N577+no0aOaNGmSmjVrZpd9A86oe/fuatCggaZNm6bjx49r69atWr16tcaPH2/t06BBAzVp0kQffPCBfvnlF6WkpGj79u3W7VeuXFFiYqJ1XNiwYYMaN26sevXqWd/fP/zwg/Lz8/XMM8/oo48+0rp163T69GnNmTNHmzZtUps2bW6p/hv3P2LECK1cuVLr1q3TyZMn9fbbb+vQoUN6/PHHra9Zvny5tm7dqkOHDmny5MmKiopSy5Ytb+n41xsyZIjWr1+v5ORkHTp0SBMmTFBMTIzNtH1UHa5xQrn17dtXa9asUd++fSVJDz30kHbv3q0XX3xRbm5u6tixo1577TXNnz/fbuFp2LBh+umnnzRixAj5+PioS5cuGjNmjD777DPD13p7e2vOnDmaNWuWVqxYoWbNmmn06NH629/+poMHD1oH1v79+2vixIn673//q759+9osK9ykSRM1bNhQjz/+uBo2bKg5c+aoc+fOJR7Pw8NDSUlJmjFjhv70pz+pdu3aeuSRR/TKK69IksaOHatJkyZp0KBB8vf3V8+ePTVkyBDrGa9+/frp/PnzSkhI0NWrV9W1a1fNmzevot9CwGlVxhgSGBioRx99VC+//LJeffVVjRgxQrNnz9b8+fM1Z84ctWrVSn/7298UGRlpfc0jjzyi1atXKz4+XhEREXr//ffLNOXISLNmzTR//ny98cYbWrhwocLDw7Vw4cKbruwFVGeenp5KSkrS66+/rscee0wNGjTQhAkTbK7LcXd3V2JiombMmKHY2FhFRkZq1KhRSk1NlSQ9+eSTysjI0P/7f/9POTk5CgkJ0aJFi+Th4aF27dopKipKgwcP1ty5cxUbG6uLFy9q3rx5unjxov7whz9o0aJFtxxcbrb/zMxMdejQQcuWLbMJZo899pjmzp2r9PR09ezZUwkJCRX6HhYJDw/X66+/rnnz5iknJ0dRUVGaMWOGXfaN8nMr5OIIQO3atdPy5ctL/AQ4JSVFCxYs0LZt2xxQGYCqMGzYMHXt2rVcSwgDgPT7fZzGjh1b6uIVqDmYqgcAAAAABpiqh2ovMTFRH3/8canbn3/+eY0aNaoKKwJQmdLS0vT000+Xuj0oKKhM03id7VgAKt+///1vTZw4sdTtnTt31tKlS6vdsVA1mKqHai8rK8t6o9qS1K1bVwEBAVVXEIBKZTKZdO7cuVK3e3p6VuieLY46FoDK9+uvvxZb3vt6vr6+atSoUbU7FqoGwQkAAAAADHCNEwAAAAAYIDgBAAAAgAGCEwAAAAAYIDgBAAAAgAGCEwAAAAAYIDgBAAAAgAGCEwAAAAAYIDgBAAAAgIH/D/9MWdCWmnoKAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x='what', y='values', data=toplot)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Time in seconds')\n",
    "plt.title('Build Time')\n",
    "plt.yscale('log')\n",
    "#plt.yticks([0.5, 1, 3, 5],[0.5, 1, 3, 5])\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.946035Z",
     "start_time": "2024-05-13T09:38:20.793544Z"
    }
   },
   "id": "3843bf05a219cfa1"
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "outputs": [],
   "source": [
    "toplot = pd.DataFrame({'values': pd.concat([vanilla_approach.installtime, all_top_10.installtime, cluster_top_10.installtime], ignore_index=True), 'what': ['vanilla_approach'] * len(vanilla_approach) + ['all_top_10'] * len(all_top_10) + ['cluster_top_10'] * len(cluster_top_10)})\n",
    "toplot = toplot[toplot['values']>-1]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:20.946128Z",
     "start_time": "2024-05-13T09:38:20.923019Z"
    }
   },
   "id": "b2976d7b61876cb4"
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x='what', y='values', data=toplot)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Time in seconds')\n",
    "plt.title('Install Time')\n",
    "plt.yscale('log')\n",
    "#plt.yticks([0.5, 1, 3, 5],[0.5, 1, 3, 5])\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:21.053423Z",
     "start_time": "2024-05-13T09:38:20.925366Z"
    }
   },
   "id": "4ad2fa6e476f597a"
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "outputs": [],
   "source": [
    "plot_build = pd.DataFrame({'values': pd.concat([vanilla_approach.buldingtime, all_top_10.buldingtime, cluster_top_10.buldingtime], ignore_index=True), 'what': ['Naïve'] * len(vanilla_approach) + ['All Top 10'] * len(all_top_10) + ['Cluster Top 10'] * len(cluster_top_10)})\n",
    "\n",
    "plot_install = pd.DataFrame({'values': pd.concat([vanilla_approach.installtime, all_top_10.installtime, cluster_top_10.installtime], ignore_index=True), 'what': ['Naïve'] * len(vanilla_approach) + ['All Top 10'] * len(all_top_10) + ['Cluster Top 10'] * len(cluster_top_10)})\n",
    "plot_install = plot_install[plot_install['values']>-1]\n",
    "\n",
    "\n",
    "toplot = pd.DataFrame(pd.concat([plot_build, plot_install], ignore_index=True))\n",
    "toplot['exp'] = ['Build Time'] * len(plot_build) + ['Install Time'] * len(plot_install) "
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:21.061885Z",
     "start_time": "2024-05-13T09:38:21.056551Z"
    }
   },
   "id": "c96db927f692dd04"
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "sns.boxplot(x='what', y='values', hue='exp', data=toplot)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Time in seconds')\n",
    "plt.yscale('log')\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1, 1), shadow=False, ncol=2)\n",
    "\n",
    "plt.savefig('figures/buildtime.pdf', format='pdf')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:21.394395Z",
     "start_time": "2024-05-13T09:38:21.061440Z"
    }
   },
   "id": "db3dfbcf51872e"
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "outputs": [],
   "source": [
    "#toplot = pd.DataFrame({'values': pd.concat([vanilla_approach.starttime, all_top_10.starttime, cluster_top_10.starttime], ignore_index=True), 'what': ['vanilla_approach'] * len(vanilla_approach) + ['all_top_10'] * len(all_top_10) + ['cluster_top_10'] * len(cluster_top_10)})\n",
    "toplot = pd.DataFrame({'values': pd.concat([vanilla_approach['starttime1'], vanilla_approach['starttime2'], vanilla_approach['starttime3'], vanilla_approach['starttime4'], vanilla_approach['starttime5'],\n",
    "               all_top_10['starttime1'], all_top_10['starttime2'], all_top_10['starttime3'], all_top_10['starttime4'], all_top_10['starttime5'],\n",
    "                cluster_top_10['starttime1'], cluster_top_10['starttime2'], cluster_top_10['starttime3'], cluster_top_10['starttime4'], cluster_top_10['starttime5'],\n",
    "               on_the_fly['starttime1'], on_the_fly['starttime2'], on_the_fly['starttime3'], on_the_fly['starttime4'], on_the_fly['starttime5']], ignore_index=True), \n",
    "            'what': ['Naïve'] * 5 * len(vanilla_approach) + ['All Top 10'] * 5 * len(all_top_10) + ['Cluster Top 10'] * 5 * len(cluster_top_10) + ['On-the-fly'] * 5 * len(cluster_top_10)})\n",
    "\n",
    "toplot['values'] = toplot['values'] * 1000"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:21.404507Z",
     "start_time": "2024-05-13T09:38:21.391649Z"
    }
   },
   "id": "271fd4500dfbdf99"
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "#plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x='what', y='values', data=toplot)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Time in ms')\n",
    "plt.yscale('log')\n",
    "plt.yticks([200, 300],[200, 300])\n",
    "\n",
    "plt.savefig('figures/start.pdf', format='pdf')\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:21.544493Z",
     "start_time": "2024-05-13T09:38:21.399812Z"
    }
   },
   "id": "d46b592e60d5cff0"
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "outputs": [],
   "source": [
    "toplot = pd.DataFrame({'values': pd.concat([vanilla_approach.importtime, all_top_10.importtime, cluster_top_10.importtime], ignore_index=True), 'what': ['vanilla_approach'] * len(vanilla_approach) + ['all_top_10'] * len(all_top_10) + ['cluster_top_10'] * len(cluster_top_10)})"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:21.548934Z",
     "start_time": "2024-05-13T09:38:21.544278Z"
    }
   },
   "id": "cf3ce189493273c1"
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x='what', y='values', data=toplot)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Time in seconds')\n",
    "plt.title('Import Time')\n",
    "plt.yscale('log')\n",
    "#plt.yticks([0.5, 1, 3, 5],[0.5, 1, 3, 5])\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:21.722208Z",
     "start_time": "2024-05-13T09:38:21.548781Z"
    }
   },
   "id": "60906fa63e1a0788"
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "outputs": [],
   "source": [
    "plot_start = pd.DataFrame({'values': pd.concat([vanilla_approach['starttime1'], vanilla_approach['starttime2'], vanilla_approach['starttime3'], vanilla_approach['starttime4'], vanilla_approach['starttime5'],\n",
    "               all_top_10['starttime1'], all_top_10['starttime2'], all_top_10['starttime3'], all_top_10['starttime4'], all_top_10['starttime5'],\n",
    "               cluster_top_10['starttime1'], cluster_top_10['starttime2'], cluster_top_10['starttime3'], cluster_top_10['starttime4'], cluster_top_10['starttime5']], ignore_index=True), \n",
    "            'what': ['Naïve'] * 5 * len(vanilla_approach) + ['All Top 10'] * 5 * len(all_top_10) + ['Cluster Top 10'] * 5 * len(cluster_top_10)})\n",
    "\n",
    "plot_import = pd.DataFrame({'values': pd.concat([vanilla_approach.importtime, all_top_10.importtime, cluster_top_10.importtime], ignore_index=True), 'what': ['Naïve'] * len(vanilla_approach) + ['All Top 10'] * len(all_top_10) + ['Cluster Top 10'] * len(cluster_top_10)})\n",
    "\n",
    "\n",
    "\n",
    "toplot = pd.DataFrame(pd.concat([plot_start, plot_import], ignore_index=True))\n",
    "toplot['exp'] = ['Start Time'] * len(plot_start) + ['Import Time'] * len(plot_import) "
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:21.729068Z",
     "start_time": "2024-05-13T09:38:21.722146Z"
    }
   },
   "id": "515808015a95ead2"
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "sns.boxplot(x='what', y='values', hue='exp', data=toplot)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Time in seconds')\n",
    "plt.yscale('log')\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1, 1), shadow=False, ncol=2)\n",
    "\n",
    "plt.savefig('figures/start_import.pdf', format='pdf')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:21.982675Z",
     "start_time": "2024-05-13T09:38:21.728467Z"
    }
   },
   "id": "3bb062261ca2360b"
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/86/fyd29lqj0033vg6drb2b412m0000gn/T/ipykernel_56715/1746028317.py:4: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  if pd.isna(row[1]):\n",
      "/var/folders/86/fyd29lqj0033vg6drb2b412m0000gn/T/ipykernel_56715/1746028317.py:7: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  ins = set(row[1].split(\";\"))\n",
      "/var/folders/86/fyd29lqj0033vg6drb2b412m0000gn/T/ipykernel_56715/1746028317.py:8: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  all = set(row[2].split(\";\"))\n",
      "/var/folders/86/fyd29lqj0033vg6drb2b412m0000gn/T/ipykernel_56715/1746028317.py:9: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  if pd.isna(row[3]):\n",
      "/var/folders/86/fyd29lqj0033vg6drb2b412m0000gn/T/ipykernel_56715/1746028317.py:12: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  clu = set(row[3].split(\";\"))\n"
     ]
    },
    {
     "data": {
      "text/plain": "   toInstall  AlreayInstalled_all  AlreayInstalled_cluster  Installed_all  \\\n0          7                    3                        7             10   \n1          6                    2                        6             10   \n2          1                    0                        1             10   \n3          1                    0                        1             10   \n4          1                    0                        1             10   \n\n   Installed_cluster  \n0                 10  \n1                 10  \n2                 10  \n3                 11  \n4                 11  ",
      "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>toInstall</th>\n      <th>AlreayInstalled_all</th>\n      <th>AlreayInstalled_cluster</th>\n      <th>Installed_all</th>\n      <th>Installed_cluster</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>7</td>\n      <td>3</td>\n      <td>7</td>\n      <td>10</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>6</td>\n      <td>2</td>\n      <td>6</td>\n      <td>10</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>10</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>10</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>10</td>\n      <td>11</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "install = pd.read_csv(\"experiments/installations.csv\")\n",
    "install_libaries = pd.DataFrame()\n",
    "for index, row in install.iterrows():\n",
    "    if pd.isna(row[1]):\n",
    "        ins = set()\n",
    "    else: \n",
    "        ins = set(row[1].split(\";\"))\n",
    "    all = set(row[2].split(\";\"))\n",
    "    if pd.isna(row[3]):\n",
    "        clu = set()\n",
    "    else: \n",
    "        clu = set(row[3].split(\";\"))\n",
    "    install_libaries = pd.concat([install_libaries, pd.DataFrame([[len(ins), len(ins.intersection(all)), len(ins.intersection(clu)), len(all), len(clu)]])], ignore_index=True)\n",
    "install_libaries.columns = ['toInstall', 'AlreayInstalled_all', 'AlreayInstalled_cluster', 'Installed_all', 'Installed_cluster']\n",
    "\n",
    "install_libaries.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:22.036331Z",
     "start_time": "2024-05-13T09:38:21.982245Z"
    }
   },
   "id": "a67357045bfa20fe"
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    149.000000\n",
      "mean       1.993289\n",
      "std        1.402202\n",
      "min        1.000000\n",
      "25%        1.000000\n",
      "50%        1.000000\n",
      "75%        2.000000\n",
      "max        7.000000\n",
      "Name: toInstall, dtype: float64\n",
      "count    149.000000\n",
      "mean       0.791946\n",
      "std        0.960540\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        1.000000\n",
      "75%        1.000000\n",
      "max        5.000000\n",
      "dtype: float64\n",
      "count    149.000000\n",
      "mean       0.067114\n",
      "std        0.300100\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        2.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "install_libaries = install_libaries[install_libaries.toInstall>0]\n",
    "print(install_libaries.toInstall.describe())\n",
    "print((install_libaries.toInstall - install_libaries.AlreayInstalled_all).describe())\n",
    "print((install_libaries.toInstall - install_libaries.AlreayInstalled_cluster).describe())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:22.036449Z",
     "start_time": "2024-05-13T09:38:22.015973Z"
    }
   },
   "id": "d5b85395dccaf92c"
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "outputs": [
    {
     "data": {
      "text/plain": "0.9463087248322147"
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = (install_libaries.toInstall - install_libaries.AlreayInstalled_cluster)\n",
    "len(tmp[tmp==0])/len(tmp)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:22.036538Z",
     "start_time": "2024-05-13T09:38:22.018684Z"
    }
   },
   "id": "84d75448244bc4de"
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    149.000000\n",
      "mean       8.006711\n",
      "std        1.402202\n",
      "min        3.000000\n",
      "25%        8.000000\n",
      "50%        9.000000\n",
      "75%        9.000000\n",
      "max        9.000000\n",
      "dtype: float64\n",
      "count    132.000000\n",
      "mean       6.931818\n",
      "std        2.801835\n",
      "min        1.000000\n",
      "25%        5.000000\n",
      "50%        8.000000\n",
      "75%        9.000000\n",
      "max       10.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print((install_libaries.Installed_all - install_libaries.toInstall).describe())\n",
    "tmp = install_libaries.Installed_cluster - install_libaries.toInstall\n",
    "print(tmp[tmp>0].describe())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:22.037148Z",
     "start_time": "2024-05-13T09:38:22.022384Z"
    }
   },
   "id": "b1904762687560a6"
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "outputs": [],
   "source": [
    "size_vanilla = pd.read_csv(\"data/vanilla_approach/size.csv\", header=None)\n",
    "size_cluster_top_10 = pd.read_csv(\"data/cluster_top_10/size.csv\", header=None)\n",
    "size_all_top_10 = pd.read_csv(\"data/all_top_10/size.csv\", header=None)\n",
    "size_onthefly = 1.01"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:22.037181Z",
     "start_time": "2024-05-13T09:38:22.024753Z"
    }
   },
   "id": "6ea1a9c1e371474d"
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "outputs": [],
   "source": [
    "size_vanilla[1] = [float(re.sub(r'GB$', '', size)) for size in size_vanilla[1]]\n",
    "size_cluster_top_10[1] = [float(re.sub(r'GB$', '', size)) for size in size_cluster_top_10[1]]\n",
    "size_all_top_10[1] = [float(re.sub(r'GB$', '', size)) for size in size_all_top_10[1]]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:22.037212Z",
     "start_time": "2024-05-13T09:38:22.030128Z"
    }
   },
   "id": "97805091430b3000"
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            values\n",
      "count  1219.000000\n",
      "mean      1.724643\n",
      "std       1.598838\n",
      "min       1.010000\n",
      "25%       1.010000\n",
      "50%       1.020000\n",
      "75%       1.250000\n",
      "max       7.330000\n",
      "            values\n",
      "count  1219.000000\n",
      "mean      8.466013\n",
      "std       0.092961\n",
      "min       8.450000\n",
      "25%       8.450000\n",
      "50%       8.450000\n",
      "75%       8.450000\n",
      "max       9.560000\n",
      "            values\n",
      "count  1219.000000\n",
      "mean      3.318958\n",
      "std       2.436412\n",
      "min       1.010000\n",
      "25%       1.250000\n",
      "50%       1.580000\n",
      "75%       6.370000\n",
      "max       7.330000\n"
     ]
    }
   ],
   "source": [
    "print(pd.DataFrame({'values' : size_vanilla[1].repeat(vanilla_approach_func['count'].iloc[0:207])}).describe())\n",
    "print(pd.DataFrame({'values' : size_all_top_10[1].repeat(vanilla_approach_func['count'].iloc[0:207])}).describe())\n",
    "print(pd.DataFrame({'values' : size_cluster_top_10[1].repeat(vanilla_approach_func['count'].iloc[0:207])}).describe())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:22.064826Z",
     "start_time": "2024-05-13T09:38:22.032811Z"
    }
   },
   "id": "689a1e548e403e8b"
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "outputs": [],
   "source": [
    "#func_mapping.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:22.064967Z",
     "start_time": "2024-05-13T09:38:22.038780Z"
    }
   },
   "id": "1193e24cb837950b"
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "outputs": [],
   "source": [
    "#task_mapping = pd.read_csv(\"funcx/task_function_mapping.csv\")\n",
    "#task_mapping.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:22.065006Z",
     "start_time": "2024-05-13T09:38:22.040760Z"
    }
   },
   "id": "203f358ec46b38f6"
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "outputs": [],
   "source": [
    "#funcs = pd.merge(func_mapping, task_mapping, on=\"func4.name\")\n",
    "#funcs.shape"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:22.065038Z",
     "start_time": "2024-05-13T09:38:22.042629Z"
    }
   },
   "id": "824ad9b1bef7e951"
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "outputs": [],
   "source": [
    "#counts = funcs.imports.value_counts()\n",
    "#counts = pd.DataFrame(counts)\n",
    "#counts.reset_index(inplace=True)\n",
    "#counts = counts._append({'imports': \"argparse;copy;datetime;funcx;itertools;json;matplotlib;numpy;os;pathlib;shutil;sys;threading;typing\", 'count': 1}, ignore_index=True)\n",
    "#counts.tail()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:22.065069Z",
     "start_time": "2024-05-13T09:38:22.044596Z"
    }
   },
   "id": "5146a10e98a9fc0"
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "outputs": [],
   "source": [
    "#install_libaries['imports'] = install.imports\n",
    "#install_libaries.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:22.065100Z",
     "start_time": "2024-05-13T09:38:22.046566Z"
    }
   },
   "id": "4fcb7ff37c069d24"
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "outputs": [],
   "source": [
    "#df = pd.merge(install_libaries[install_libaries.toInstall>0], counts, on=\"imports\")\n",
    "#df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:22.065129Z",
     "start_time": "2024-05-13T09:38:22.048490Z"
    }
   },
   "id": "e5051296da8a2121"
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "outputs": [],
   "source": [
    "#print(pd.DataFrame({'values' : df.toInstall.repeat(df['count'])}).describe())\n",
    "#print(pd.DataFrame({'values' : (df.toInstall - df.AlreayInstalled_all).repeat(df['count'])}).describe())\n",
    "#print(pd.DataFrame({'values' : (df.toInstall - df.AlreayInstalled_cluster).repeat(df['count'])}).describe())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:22.065161Z",
     "start_time": "2024-05-13T09:38:22.050046Z"
    }
   },
   "id": "ac6dbb89915f2b86"
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "outputs": [],
   "source": [
    "#install_libaries[install_libaries.toInstall>0].describe()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:22.065190Z",
     "start_time": "2024-05-13T09:38:22.051981Z"
    }
   },
   "id": "acca2a2e8ca3b2ce"
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "outputs": [],
   "source": [
    "max_start = max(itertools.chain.from_iterable([vanilla_approach[f'starttime{i+1}'] for i in range(5)]))\n",
    "\n",
    "plot_start = pd.DataFrame({'values': pd.concat([vanilla_approach['starttime1']/max_start, vanilla_approach['starttime2']/max_start, vanilla_approach['starttime3']/max_start, vanilla_approach['starttime4']/max_start, vanilla_approach['starttime5']/max_start, all_top_10['starttime1']/max_start, all_top_10['starttime2']/max_start, all_top_10['starttime3']/max_start, all_top_10['starttime4']/max_start, all_top_10['starttime5']/max_start,\n",
    "               cluster_top_10['starttime1']/max_start, cluster_top_10['starttime2']/max_start, cluster_top_10['starttime3']/max_start, cluster_top_10['starttime4']/max_start, cluster_top_10['starttime5']/max_start], ignore_index=True), \n",
    "            'what': ['vanilla_approach'] * 5 * len(vanilla_approach) + ['all_top_10'] * 5 * len(all_top_10) + ['cluster_top_10'] * 5 * len(cluster_top_10)})\n",
    "\n",
    "plot_build = pd.DataFrame({'values': pd.concat([vanilla_approach.buldingtime/max(vanilla_approach.buldingtime), all_top_10.buldingtime/max(vanilla_approach.buldingtime), cluster_top_10.buldingtime/max(vanilla_approach.buldingtime)], ignore_index=True), 'what': ['vanilla_approach'] * len(vanilla_approach) + ['all_top_10'] * len(all_top_10) + ['cluster_top_10'] * len(cluster_top_10)})\n",
    "\n",
    "plot_install = pd.DataFrame({'values': pd.concat([vanilla_approach.installtime/max(vanilla_approach.installtime), all_top_10.installtime/max(vanilla_approach.installtime), cluster_top_10.installtime/max(vanilla_approach.installtime)], ignore_index=True), 'what': ['vanilla_approach'] * len(vanilla_approach) + ['all_top_10'] * len(all_top_10) + ['cluster_top_10'] * len(cluster_top_10)})\n",
    "plot_install = plot_install[plot_install['values']>-1]\n",
    "\n",
    "plot_import = pd.DataFrame({'values': pd.concat(\n",
    "    [vanilla_approach.importtime/max(vanilla_approach.importtime), all_top_10.importtime/max(vanilla_approach.importtime), cluster_top_10.importtime/max(vanilla_approach.importtime)], ignore_index=True),\n",
    "                       'what': ['vanilla_approach'] * len(vanilla_approach) + ['all_top_10'] * len(all_top_10) + [\n",
    "                           'cluster_top_10'] * len(cluster_top_10)})\n",
    "\n",
    "toplot = pd.DataFrame(pd.concat([plot_build, plot_install, plot_start, plot_import], ignore_index=True))\n",
    "toplot['exp'] = ['Build'] * len(plot_build) + ['Install'] * len(plot_install) + ['Start'] * len(plot_start) + ['Import'] * len(plot_import)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:22.130078Z",
     "start_time": "2024-05-13T09:38:22.057438Z"
    }
   },
   "id": "f80d9ccc7c76ec1"
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='what', y='values', hue='exp', data=toplot)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Normalized Time')\n",
    "#plt.yscale('log')\n",
    "plt.ylim(0,0.2)\n",
    "plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.1), shadow=True, ncol=4)\n",
    "# Show the plot\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-13T09:38:22.236138Z",
     "start_time": "2024-05-13T09:38:22.067067Z"
    }
   },
   "id": "d3a55c8f965af870"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}