analyze-dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scale Features and Build Model\n",
    "### Scales Raw Features\n",
    "\n",
    "Import CSV of Aggregated Darshan Logs <br>\n",
    "Apply Log10 and Percent Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./raws.csv\",lineterminator='\\n',sep = ',' ,error_bad_lines=False)\n",
    "#df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(df.columns[0],axis = 1)\n",
    "df = df.drop(df.columns[0],axis = 1)\n",
    "f = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>posix_read_time</th>\n",
       "      <th>posix_write_time</th>\n",
       "      <th>posix_meta_time</th>\n",
       "      <th>posix_bytes_read</th>\n",
       "      <th>posix_bytes_read_100</th>\n",
       "      <th>posix_bytes_read_1K</th>\n",
       "      <th>posix_bytes_read_10K</th>\n",
       "      <th>posix_bytes_read_100K</th>\n",
       "      <th>posix_bytes_read_1lM</th>\n",
       "      <th>posix_bytes_read_4M</th>\n",
       "      <th>...</th>\n",
       "      <th>posix_m_align</th>\n",
       "      <th>lustre_number_of_files</th>\n",
       "      <th>lustre_mdts</th>\n",
       "      <th>lustre_osts</th>\n",
       "      <th>lustre_stripe_size</th>\n",
       "      <th>lustre_stripe_offset</th>\n",
       "      <th>lustre_stripe_width</th>\n",
       "      <th>lustre_number_of_osts</th>\n",
       "      <th>jobid</th>\n",
       "      <th>path</th>\n",
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       "      <td>9042391.0</td>\n",
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       "      <th>1</th>\n",
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       "      <td>2.390891e+10</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>13704.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1048576.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>9868242.0</td>\n",
       "      <td>../results/USER382862U_SpEC_id9868242_4-29-741...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>124.560730</td>\n",
       "      <td>42.051125</td>\n",
       "      <td>54.839272</td>\n",
       "      <td>5.019637e+10</td>\n",
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       "      <td>2515.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6704.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>360.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>9029683.0</td>\n",
       "      <td>../results/USER264205U_SpEC_id9029683_9-20-580...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25763.292969</td>\n",
       "      <td>582.297363</td>\n",
       "      <td>24.895737</td>\n",
       "      <td>5.488943e+12</td>\n",
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       "      <td>767.0</td>\n",
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       "      <td>2040.0</td>\n",
       "      <td>13683.0</td>\n",
       "      <td>626216.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2320.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>54.993103</td>\n",
       "      <td>1048576.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>9165166.0</td>\n",
       "      <td>../results/USER341996U_CCTM_v52_Linux2_x86_64i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>154.534821</td>\n",
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       "      <td>658.484985</td>\n",
       "      <td>2.293203e+10</td>\n",
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       "      <td>201.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>8269019.0</td>\n",
       "      <td>../results/USER141481U_SpEC_id8269019_3-3-4542...</td>\n",
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       "      <th>875282</th>\n",
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       "      <td>82.278084</td>\n",
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       "      <td>../results/USER303986U_testnodes_opt.x_id31428...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875286</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>875287 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        posix_read_time  posix_write_time  posix_meta_time  posix_bytes_read  \\\n",
       "0              0.000000          0.000000         0.000000      0.000000e+00   \n",
       "1            104.611641         10.024055        20.060841      2.390891e+10   \n",
       "2            124.560730         42.051125        54.839272      5.019637e+10   \n",
       "3          25763.292969        582.297363        24.895737      5.488943e+12   \n",
       "4            154.534821        681.548279       658.484985      2.293203e+10   \n",
       "...                 ...               ...              ...               ...   \n",
       "875282       138.354477         82.278084       194.485565      5.593977e+10   \n",
       "875283        54.443073        231.440857        25.271391      1.465277e+09   \n",
       "875284         0.000000          0.000000         0.000000      0.000000e+00   \n",
       "875285         0.000000          0.000000         0.000000      0.000000e+00   \n",
       "875286       227.063828        191.747269       172.671997      1.077412e+11   \n",
       "\n",
       "        posix_bytes_read_100  posix_bytes_read_1K  posix_bytes_read_10K  \\\n",
       "0                        0.0                  0.0                   0.0   \n",
       "1                   147688.0             141853.0               33215.0   \n",
       "2                   332059.0             684893.0              130143.0   \n",
       "3                    30785.0                767.0              508381.0   \n",
       "4                   588029.0            2926599.0              425127.0   \n",
       "...                      ...                  ...                   ...   \n",
       "875282              216146.0             535152.0               99814.0   \n",
       "875283                3099.0                  0.0              180662.0   \n",
       "875284                   0.0                  0.0                   0.0   \n",
       "875285                   0.0                  0.0                   0.0   \n",
       "875286              775359.0            1545229.0              305757.0   \n",
       "\n",
       "        posix_bytes_read_100K  posix_bytes_read_1lM  posix_bytes_read_4M  ...  \\\n",
       "0                         0.0                   0.0                  0.0  ...   \n",
       "1                    261267.0               51049.0                  0.0  ...   \n",
       "2                    919688.0                2515.0                  0.0  ...   \n",
       "3                      2040.0               13683.0             626216.0  ...   \n",
       "4                    336133.0                  98.0                  0.0  ...   \n",
       "...                       ...                   ...                  ...  ...   \n",
       "875282               889010.0               12485.0                  0.0  ...   \n",
       "875283                    0.0                   0.0                  0.0  ...   \n",
       "875284                    0.0                   0.0                  0.0  ...   \n",
       "875285                    0.0                   0.0                  0.0  ...   \n",
       "875286              1897233.0                1068.0                  0.0  ...   \n",
       "\n",
       "        posix_m_align  lustre_number_of_files  lustre_mdts  lustre_osts  \\\n",
       "0                 0.0                     4.0          1.0    36.000000   \n",
       "1             13704.0                   176.0          1.0   360.000000   \n",
       "2              6704.0                   224.0          1.0   360.000000   \n",
       "3              2320.0                   290.0          1.0    54.993103   \n",
       "4              6328.0                   201.0          1.0   360.000000   \n",
       "...               ...                     ...          ...          ...   \n",
       "875282         9920.0                   124.0          1.0   360.000000   \n",
       "875283         8704.0                  1088.0          1.0   360.000000   \n",
       "875284            0.0                     5.0          1.0    36.000000   \n",
       "875285            8.0                     2.0          1.0    36.000000   \n",
       "875286        15104.0                   128.0          1.0   360.000000   \n",
       "\n",
       "        lustre_stripe_size  lustre_stripe_offset  lustre_stripe_width  \\\n",
       "0                1048576.0                   0.0                  4.0   \n",
       "1                1048576.0                   0.0                176.0   \n",
       "2                1048576.0                   0.0                224.0   \n",
       "3                1048576.0                   0.0                290.0   \n",
       "4                1048576.0                   0.0                201.0   \n",
       "...                    ...                   ...                  ...   \n",
       "875282           1048576.0                   0.0                124.0   \n",
       "875283           1048576.0                   0.0               1088.0   \n",
       "875284           1048576.0                   0.0                  5.0   \n",
       "875285           1048576.0                   0.0                  2.0   \n",
       "875286           1048576.0                   0.0                128.0   \n",
       "\n",
       "        lustre_number_of_osts      jobid  \\\n",
       "0                         4.0  9042391.0   \n",
       "1                       147.0  9868242.0   \n",
       "2                       186.0  9029683.0   \n",
       "3                        52.0  9165166.0   \n",
       "4                       158.0  8269019.0   \n",
       "...                       ...        ...   \n",
       "875282                  110.0  7759072.0   \n",
       "875283                  359.0  7275819.0   \n",
       "875284                    5.0  8305062.0   \n",
       "875285                    1.0    31428.0   \n",
       "875286                  118.0  7525550.0   \n",
       "\n",
       "                                                     path  \n",
       "0       ../results/USER375904U_sss2_id9042391_9-26-414...  \n",
       "1       ../results/USER382862U_SpEC_id9868242_4-29-741...  \n",
       "2       ../results/USER264205U_SpEC_id9029683_9-20-580...  \n",
       "3       ../results/USER341996U_CCTM_v52_Linux2_x86_64i...  \n",
       "4       ../results/USER141481U_SpEC_id8269019_3-3-4542...  \n",
       "...                                                   ...  \n",
       "875282  ../results/USER336298U_SpEC_id7759072_10-30-62...  \n",
       "875283  ../results/USER331454U_pw.x_id7275819_8-2-4790...  \n",
       "875284  ../results/USER371284U_sss2_id8305062_3-14-211...  \n",
       "875285  ../results/USER303986U_testnodes_opt.x_id31428...  \n",
       "875286  ../results/USER382862U_SpEC_id7525550_9-24-380...  \n",
       "\n",
       "[875287 rows x 50 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['posix_read_time', 'posix_write_time', 'posix_meta_time',\n",
       "       'posix_bytes_read', 'posix_bytes_read_100', 'posix_bytes_read_1K',\n",
       "       'posix_bytes_read_10K', 'posix_bytes_read_100K', 'posix_bytes_read_1lM',\n",
       "       'posix_bytes_read_4M', 'posix_bytes_read_10M', 'posix_bytes_read_100M',\n",
       "       'posix_bytes_read_1G', 'posix_bytes_read_PLUS', 'posix_bytes_write',\n",
       "       'posix_bytes_write_100', 'posix_bytes_write_1K',\n",
       "       'posix_bytes_write_10K', 'posix_bytes_write_100K',\n",
       "       'posix_bytes_write_1M', 'posix_bytes_write_4M', 'posix_bytes_write_10M',\n",
       "       'posix_bytes_write_100M', 'posix_bytes_write_1G',\n",
       "       'posix_bytes_write_PLUS', 'posix_opens', 'posix_reads', 'posix_writes',\n",
       "       'posix_seeks', 'posix_stats', 'posix_mmaps', 'posix_fsyncs',\n",
       "       'posix_fdsyncs', 'posix_rename_sources', 'posix_rename_targets',\n",
       "       'posix_renamed_from', 'posix_renamed_mode', 'posix_number_of_files',\n",
       "       'nprocs', 'posix_f_align', 'posix_m_align', 'lustre_number_of_files',\n",
       "       'lustre_mdts', 'lustre_osts', 'lustre_stripe_size',\n",
       "       'lustre_stripe_offset', 'lustre_stripe_width', 'lustre_number_of_osts',\n",
       "       'jobid', 'path'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df = df.dropna(axis=0, how='any')\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#files\n",
    "f['p_files'] = df['posix_number_of_files']  + 1\n",
    "f['l_files'] = df['lustre_number_of_files']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0               0.0\n",
       "1          880136.0\n",
       "2         2379598.0\n",
       "3         8903411.0\n",
       "4         7846387.0\n",
       "            ...    \n",
       "875282    2234152.0\n",
       "875283     197651.0\n",
       "875284          0.0\n",
       "875285          0.0\n",
       "875286    6065006.0\n",
       "Name: p_accesses, Length: 875287, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#accesses\n",
    "df['p_accesses'] = df['posix_reads'] + df['posix_writes']\n",
    "f['p_accesses'] = df['p_accesses']\n",
    "\n",
    "f['p_accesses']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#bytes\n",
    "f['bytes'] = df['posix_bytes_read'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "f['opens'] = df['posix_opens']\n",
    "f['p_seeks'] = df['posix_seeks']\n",
    "f['p_stats'] = df['posix_stats']\n",
    "f['p_mode'] = df['posix_renamed_mode']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "f['l_n_osts'] = df['lustre_number_of_osts']\n",
    "f['l_stripe_w'] = df['lustre_stripe_width']\n",
    "f['l_mdts'] = df['lustre_mdts']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "f['p_nprocs'] = df['nprocs']\n",
    "f['p_falign'] = df['posix_f_align']\n",
    "f['p_malign'] = df['posix_m_align']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "f['p_reads'] = df['posix_reads'] \n",
    "f['p_writes'] = df['posix_writes'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "f['p_bytes_read_100'] = df['posix_bytes_read_100']\n",
    "f['p_bytes_read_1K'] = df['posix_bytes_read_1K']\n",
    "f['p_bytes_read_10K'] = df['posix_bytes_read_10K'] \n",
    "f['p_bytes_read_100K'] = df['posix_bytes_read_100K']\n",
    "f['p_bytes_read_1M'] = df['posix_bytes_read_1lM']\n",
    "f['p_bytes_read_4M'] = df['posix_bytes_read_4M']\n",
    "f['p_bytes_read_10M'] = df['posix_bytes_read_10M']\n",
    "f['p_bytes_read_100M'] = df['posix_bytes_read_100M']\n",
    "f['p_bytes_read_1G'] = df['posix_bytes_read_1G']\n",
    "f['p_bytes_read_PLUS'] = df['posix_bytes_read_PLUS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "f['p_bytes_write_100'] = df['posix_bytes_write_100']\n",
    "f['p_bytes_write_1K'] = df['posix_bytes_write_1K']\n",
    "f['p_bytes_write_10K'] = df['posix_bytes_write_10K']\n",
    "f['p_bytes_write_100K'] = df['posix_bytes_write_100K']\n",
    "f['p_bytes_write_1M'] = df['posix_bytes_write_1M']\n",
    "f['p_bytes_write_4M'] = df['posix_bytes_write_4M']\n",
    "f['p_bytes_write_10M'] = df['posix_bytes_write_10M']\n",
    "f['p_bytes_write_100M'] = df['posix_bytes_write_100M']\n",
    "f['p_bytes_write_1G'] = df['posix_bytes_write_1G']\n",
    "f['p_bytes_write_PLUS'] = df['posix_bytes_write_PLUS']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['time'] = df['posix_write_time'].astype('float') + df['posix_read_time'].astype('float') + df['posix_meta_time'].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['bytes'] = df['posix_bytes_read'].astype('float') + df['posix_bytes_write'].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "f['throughput'] = df['bytes'].astype('float') / df['time']\n",
    "\n",
    "f = f[f['throughput'] >0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "p_files               1.219280e+05\n",
       "l_files               1.219270e+05\n",
       "p_accesses            2.251942e+10\n",
       "bytes                 3.038456e+14\n",
       "opens                 5.531094e+08\n",
       "p_seeks               1.445220e+10\n",
       "p_stats               6.522921e+07\n",
       "p_mode                5.337293e+07\n",
       "l_n_osts              3.600000e+02\n",
       "l_stripe_w            7.438575e+06\n",
       "l_mdts                1.000000e+00\n",
       "p_nprocs              3.520000e+05\n",
       "p_falign              1.422540e+11\n",
       "p_malign              1.085312e+06\n",
       "p_reads               2.237846e+10\n",
       "p_writes              1.302770e+10\n",
       "p_bytes_read_100      5.221517e+08\n",
       "p_bytes_read_1K       2.074657e+10\n",
       "p_bytes_read_10K      1.536278e+09\n",
       "p_bytes_read_100K     1.515506e+08\n",
       "p_bytes_read_1M       4.044503e+08\n",
       "p_bytes_read_4M       6.561462e+07\n",
       "p_bytes_read_10M      2.083200e+06\n",
       "p_bytes_read_100M     2.872090e+05\n",
       "p_bytes_read_1G       1.792000e+06\n",
       "p_bytes_write_100     1.302770e+10\n",
       "p_bytes_write_1K      2.852127e+09\n",
       "p_bytes_write_10K     3.867477e+08\n",
       "p_bytes_write_100K    8.347452e+07\n",
       "p_bytes_write_1M      1.357245e+07\n",
       "p_bytes_write_4M      3.839488e+06\n",
       "p_bytes_write_10M     6.190660e+05\n",
       "p_bytes_write_100M    1.249280e+06\n",
       "p_bytes_write_1G      1.937500e+04\n",
       "throughput            2.344536e+09\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#delete columns with all zeros\n",
    "f = f.loc[:, (f != 0).any(axis=0)]\n",
    "\n",
    "#remove infinite values\n",
    "f = f.replace([np.inf, -np.inf], np.nan).dropna(axis=0)\n",
    "\n",
    "f.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p_files</th>\n",
       "      <th>l_files</th>\n",
       "      <th>p_accesses</th>\n",
       "      <th>bytes</th>\n",
       "      <th>opens</th>\n",
       "      <th>p_seeks</th>\n",
       "      <th>p_stats</th>\n",
       "      <th>p_mode</th>\n",
       "      <th>l_n_osts</th>\n",
       "      <th>l_stripe_w</th>\n",
       "      <th>...</th>\n",
       "      <th>p_bytes_read_1G</th>\n",
       "      <th>p_bytes_write_100</th>\n",
       "      <th>p_bytes_write_1K</th>\n",
       "      <th>p_bytes_write_10K</th>\n",
       "      <th>p_bytes_write_100K</th>\n",
       "      <th>p_bytes_write_1M</th>\n",
       "      <th>p_bytes_write_4M</th>\n",
       "      <th>p_bytes_write_10M</th>\n",
       "      <th>p_bytes_write_100M</th>\n",
       "      <th>p_bytes_write_1G</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>800.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>880136.0</td>\n",
       "      <td>2.390891e+10</td>\n",
       "      <td>8858.0</td>\n",
       "      <td>319241.0</td>\n",
       "      <td>34901.0</td>\n",
       "      <td>37230.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>202780.0</td>\n",
       "      <td>16078.0</td>\n",
       "      <td>21989.0</td>\n",
       "      <td>3156.0</td>\n",
       "      <td>1061.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>361.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>2379598.0</td>\n",
       "      <td>5.019637e+10</td>\n",
       "      <td>62398.0</td>\n",
       "      <td>1107764.0</td>\n",
       "      <td>270222.0</td>\n",
       "      <td>27594.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18852.0</td>\n",
       "      <td>264906.0</td>\n",
       "      <td>22246.0</td>\n",
       "      <td>4282.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>291.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>8903411.0</td>\n",
       "      <td>5.488943e+12</td>\n",
       "      <td>8711.0</td>\n",
       "      <td>2010273.0</td>\n",
       "      <td>28432.0</td>\n",
       "      <td>115194.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7302089.0</td>\n",
       "      <td>52045.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>25830.0</td>\n",
       "      <td>756.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>320.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>7846387.0</td>\n",
       "      <td>2.293203e+10</td>\n",
       "      <td>23158.0</td>\n",
       "      <td>6015926.0</td>\n",
       "      <td>400399.0</td>\n",
       "      <td>31536.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>489873.0</td>\n",
       "      <td>2821931.0</td>\n",
       "      <td>240372.0</td>\n",
       "      <td>17983.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>429.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>6647935.0</td>\n",
       "      <td>5.209185e+10</td>\n",
       "      <td>69261.0</td>\n",
       "      <td>4608438.0</td>\n",
       "      <td>410846.0</td>\n",
       "      <td>38982.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>255023.0</td>\n",
       "      <td>1927630.0</td>\n",
       "      <td>186048.0</td>\n",
       "      <td>11509.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875280</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>57344.0</td>\n",
       "      <td>3.006477e+10</td>\n",
       "      <td>1808.0</td>\n",
       "      <td>59128.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28672.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875281</th>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>102439.0</td>\n",
       "      <td>7.467916e+08</td>\n",
       "      <td>193.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>4818.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>10927.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875282</th>\n",
       "      <td>625.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>2234152.0</td>\n",
       "      <td>5.593977e+10</td>\n",
       "      <td>35112.0</td>\n",
       "      <td>1035457.0</td>\n",
       "      <td>159180.0</td>\n",
       "      <td>16644.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51807.0</td>\n",
       "      <td>397643.0</td>\n",
       "      <td>27690.0</td>\n",
       "      <td>3198.0</td>\n",
       "      <td>1207.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875283</th>\n",
       "      <td>1089.0</td>\n",
       "      <td>1088.0</td>\n",
       "      <td>197651.0</td>\n",
       "      <td>1.465277e+09</td>\n",
       "      <td>2112.0</td>\n",
       "      <td>12509.0</td>\n",
       "      <td>4198.0</td>\n",
       "      <td>473916.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>1088.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13824.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875286</th>\n",
       "      <td>583.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>6065006.0</td>\n",
       "      <td>1.077412e+11</td>\n",
       "      <td>119432.0</td>\n",
       "      <td>2750807.0</td>\n",
       "      <td>578508.0</td>\n",
       "      <td>17520.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>216962.0</td>\n",
       "      <td>1239000.0</td>\n",
       "      <td>77141.0</td>\n",
       "      <td>6985.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>671063 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        p_files  l_files  p_accesses         bytes     opens    p_seeks  \\\n",
       "1         800.0    176.0    880136.0  2.390891e+10    8858.0   319241.0   \n",
       "2         361.0    224.0   2379598.0  5.019637e+10   62398.0  1107764.0   \n",
       "3         291.0    290.0   8903411.0  5.488943e+12    8711.0  2010273.0   \n",
       "4         320.0    201.0   7846387.0  2.293203e+10   23158.0  6015926.0   \n",
       "6         429.0    190.0   6647935.0  5.209185e+10   69261.0  4608438.0   \n",
       "...         ...      ...         ...           ...       ...        ...   \n",
       "875280      2.0      1.0     57344.0  3.006477e+10    1808.0    59128.0   \n",
       "875281     14.0      4.0    102439.0  7.467916e+08     193.0       91.0   \n",
       "875282    625.0    124.0   2234152.0  5.593977e+10   35112.0  1035457.0   \n",
       "875283   1089.0   1088.0    197651.0  1.465277e+09    2112.0    12509.0   \n",
       "875286    583.0    128.0   6065006.0  1.077412e+11  119432.0  2750807.0   \n",
       "\n",
       "         p_stats    p_mode  l_n_osts  l_stripe_w  ...  p_bytes_read_1G  \\\n",
       "1        34901.0   37230.0     147.0       176.0  ...              0.0   \n",
       "2       270222.0   27594.0     186.0       224.0  ...              0.0   \n",
       "3        28432.0  115194.0      52.0       290.0  ...              0.0   \n",
       "4       400399.0   31536.0     158.0       201.0  ...              0.0   \n",
       "6       410846.0   38982.0     158.0       190.0  ...              0.0   \n",
       "...          ...       ...       ...         ...  ...              ...   \n",
       "875280      12.0       0.0       1.0         8.0  ...              0.0   \n",
       "875281     385.0    4818.0       4.0         4.0  ...              0.0   \n",
       "875282  159180.0   16644.0     110.0       124.0  ...              0.0   \n",
       "875283    4198.0  473916.0     359.0      1088.0  ...              0.0   \n",
       "875286  578508.0   17520.0     118.0       128.0  ...              0.0   \n",
       "\n",
       "        p_bytes_write_100  p_bytes_write_1K  p_bytes_write_10K  \\\n",
       "1                202780.0           16078.0            21989.0   \n",
       "2                 18852.0          264906.0            22246.0   \n",
       "3               7302089.0           52045.0               73.0   \n",
       "4                489873.0         2821931.0           240372.0   \n",
       "6                255023.0         1927630.0           186048.0   \n",
       "...                   ...               ...                ...   \n",
       "875280                0.0               0.0                0.0   \n",
       "875281               33.0              23.0            10927.0   \n",
       "875282            51807.0          397643.0            27690.0   \n",
       "875283               28.0               0.0               38.0   \n",
       "875286           216962.0         1239000.0            77141.0   \n",
       "\n",
       "        p_bytes_write_100K  p_bytes_write_1M  p_bytes_write_4M  \\\n",
       "1                   3156.0            1061.0               0.0   \n",
       "2                   4282.0              14.0               0.0   \n",
       "3                      9.0           25830.0             756.0   \n",
       "4                  17983.0             242.0               0.0   \n",
       "6                  11509.0             158.0               0.0   \n",
       "...                    ...               ...               ...   \n",
       "875280                 0.0           28672.0               0.0   \n",
       "875281                 0.0               0.0               0.0   \n",
       "875282              3198.0            1207.0               0.0   \n",
       "875283                 0.0           13824.0               0.0   \n",
       "875286              6985.0             272.0               0.0   \n",
       "\n",
       "        p_bytes_write_10M  p_bytes_write_100M  p_bytes_write_1G  \n",
       "1                     0.0                 0.0               0.0  \n",
       "2                     0.0                 0.0               0.0  \n",
       "3                     0.0                 0.0               0.0  \n",
       "4                     0.0                 0.0               0.0  \n",
       "6                     0.0                 0.0               0.0  \n",
       "...                   ...                 ...               ...  \n",
       "875280                0.0                 0.0               0.0  \n",
       "875281                0.0                 0.0               0.0  \n",
       "875282                0.0                 0.0               0.0  \n",
       "875283                0.0                 0.0               0.0  \n",
       "875286                0.0                 0.0               0.0  \n",
       "\n",
       "[671063 rows x 34 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = pd.DataFrame()\n",
    "t['throughput'] = f['throughput']\n",
    "f = f.drop(labels = 'throughput', axis = 1)\n",
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>throughput</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.803194e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.282342e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.083669e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.724841e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.581690e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875280</th>\n",
       "      <td>5.482767e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875281</th>\n",
       "      <td>2.136637e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875282</th>\n",
       "      <td>1.362498e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875283</th>\n",
       "      <td>1.942413e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875286</th>\n",
       "      <td>1.842942e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>671063 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          throughput\n",
       "1       1.803194e+08\n",
       "2       2.282342e+08\n",
       "3       2.083669e+08\n",
       "4       1.724841e+07\n",
       "6       4.581690e+07\n",
       "...              ...\n",
       "875280  5.482767e+08\n",
       "875281  2.136637e+07\n",
       "875282  1.362498e+08\n",
       "875283  1.942413e+07\n",
       "875286  1.842942e+08\n",
       "\n",
       "[671063 rows x 1 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "throughput    0.39201\n",
      "dtype: float64\n",
      "throughput    2.344536e+09\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(t.min())\n",
    "print(t.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.set_figwidth(16)\n",
    "fig.set_figheight(4)\n",
    "fig.suptitle('Distribution of Features', fontsize=20, fontweight='bold')\n",
    "\n",
    "f.drop(['bytes','p_falign'], axis = 1).boxplot(rot = 90)\n",
    "plt.show()\n",
    "\n",
    "plt.savefig('distribution-fliers.png')\n",
    "#no bytes or file alignment, bc throws off scaling. bytes in several other plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.set_figwidth(16)\n",
    "fig.set_figheight(4)\n",
    "fig.suptitle('Distribution of Features', fontsize=20, fontweight='bold')\n",
    "\n",
    "f.drop(['bytes','p_falign'], axis = 1).boxplot(rot = 90,showfliers= False)\n",
    "\n",
    "plt.show()\n",
    "plt.savefig('distribution-nofliers.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(t.throughput,  bins = 20)\n",
    "\n",
    "plt.suptitle('Throughput Histogram', fontsize=14, fontweight='bold')\n",
    "\n",
    "plt.ylabel('Number of Observations')\n",
    "plt.xlabel('Throughput Bytes/s')\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
 "nbformat": 4,
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}