01.06-create-faiths-pd-benchmarking-figure.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import numpy as np\n",
    "import os\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Patch\n",
    "from matplotlib.lines import Line2D\n",
    "%matplotlib inline\n",
    "\n",
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "data_dir = './data/'"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_dir = data_dir + 'large-public-subsets/results'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['merged-results.json']"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(results_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_files = glob.glob(os.path.join(results_dir, '*.json'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_files = [\n",
    "    ('merged-results.json', 'tuple()')\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_df(fp, q):\n",
    "    df = pd.DataFrame(json.load(open(os.path.join(results_dir, fp))))\n",
    "    df['sample_size'] = df['sample_size'].astype(int)\n",
    "    df = df.query(q)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([parse_df(fp, q) for fp, q in df_files])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['otu_size', 'sample_size', 'rep', 'seed', 'density', 'method',\n",
       "       'Running', 'Python time', 'Command being timed', 'User time (seconds)',\n",
       "       'System time (seconds)', 'Percent of CPU this job got',\n",
       "       'Elapsed (wall clock) time (h:mm:ss or m:ss)',\n",
       "       'Average shared text size (kbytes)',\n",
       "       'Average unshared data size (kbytes)', 'Average stack size (kbytes)',\n",
       "       'Average total size (kbytes)', 'Maximum resident set size (kbytes)',\n",
       "       'Average resident set size (kbytes)',\n",
       "       'Major (requiring I/O) page faults',\n",
       "       'Minor (reclaiming a frame) page faults', 'Voluntary context switches',\n",
       "       'Involuntary context switches', 'Swaps', 'File system inputs',\n",
       "       'File system outputs', 'Socket messages sent',\n",
       "       'Socket messages received', 'Signals delivered', 'Page size (bytes)',\n",
       "       'Exit status', 'Trace', 'File ', 'actua', 'count', '_vect', 'Memor',\n",
       "       'Comma'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Python time'] = df['Python time'].astype(float)\n",
    "df['Maximum resident set size (kbytes)'] = df['Maximum resident set size (kbytes)'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[100000]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(df['otu_size'].astype(int).unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Exit status'] = df['Exit status'].astype(float)\n",
    "df['sample_size'] = df['sample_size'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sub = df.loc[(df['otu_size'] == str(100000)) & (df['Exit status'] != 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>otu_size</th>\n",
       "      <th>rep</th>\n",
       "      <th>seed</th>\n",
       "      <th>density</th>\n",
       "      <th>Running</th>\n",
       "      <th>Python time</th>\n",
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       "      <th>User time (seconds)</th>\n",
       "      <th>System time (seconds)</th>\n",
       "      <th>Percent of CPU this job got</th>\n",
       "      <th>...</th>\n",
       "      <th>Signals delivered</th>\n",
       "      <th>Page size (bytes)</th>\n",
       "      <th>Exit status</th>\n",
       "      <th>Trace</th>\n",
       "      <th>File</th>\n",
       "      <th>actua</th>\n",
       "      <th>count</th>\n",
       "      <th>_vect</th>\n",
       "      <th>Memor</th>\n",
       "      <th>Comma</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample_size</th>\n",
       "      <th>method</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1250.0</th>\n",
       "      <th>skbio</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>stacked</th>\n",
       "      <td>10</td>\n",
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       "      <td>10</td>\n",
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       "      <td>10</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">2500.0</th>\n",
       "      <th>skbio</th>\n",
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       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
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       "      <td>10</td>\n",
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       "      <td>10</td>\n",
       "      <td>10</td>\n",
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       "      <td>10</td>\n",
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       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">5000.0</th>\n",
       "      <th>skbio</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
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       "      <td>0</td>\n",
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       "      <th>stacked</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
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       "      <td>10</td>\n",
       "      <td>10</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">10000.0</th>\n",
       "      <th>skbio</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stacked</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">20000.0</th>\n",
       "      <th>skbio</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>stacked</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>40000.0</th>\n",
       "      <th>stacked</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80000.0</th>\n",
       "      <th>stacked</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>12 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     otu_size  rep  seed  density  Running  Python time  \\\n",
       "sample_size method                                                        \n",
       "1250.0      skbio          10   10    10       10       10           10   \n",
       "            stacked        10   10    10       10       10           10   \n",
       "2500.0      skbio          10   10    10       10       10           10   \n",
       "            stacked        10   10    10       10       10           10   \n",
       "5000.0      skbio          10   10    10       10       10           10   \n",
       "            stacked        10   10    10       10       10           10   \n",
       "10000.0     skbio          10   10    10       10       10           10   \n",
       "            stacked        10   10    10       10       10           10   \n",
       "20000.0     skbio          10   10    10       10       10           10   \n",
       "            stacked        10   10    10       10       10           10   \n",
       "40000.0     stacked        10   10    10       10       10           10   \n",
       "80000.0     stacked        10   10    10       10       10           10   \n",
       "\n",
       "                     Command being timed  User time (seconds)  \\\n",
       "sample_size method                                              \n",
       "1250.0      skbio                     10                   10   \n",
       "            stacked                   10                   10   \n",
       "2500.0      skbio                     10                   10   \n",
       "            stacked                   10                   10   \n",
       "5000.0      skbio                     10                   10   \n",
       "            stacked                   10                   10   \n",
       "10000.0     skbio                     10                   10   \n",
       "            stacked                   10                   10   \n",
       "20000.0     skbio                     10                   10   \n",
       "            stacked                   10                   10   \n",
       "40000.0     stacked                   10                   10   \n",
       "80000.0     stacked                   10                   10   \n",
       "\n",
       "                     System time (seconds)  Percent of CPU this job got  ...  \\\n",
       "sample_size method                                                       ...   \n",
       "1250.0      skbio                       10                           10  ...   \n",
       "            stacked                     10                           10  ...   \n",
       "2500.0      skbio                       10                           10  ...   \n",
       "            stacked                     10                           10  ...   \n",
       "5000.0      skbio                       10                           10  ...   \n",
       "            stacked                     10                           10  ...   \n",
       "10000.0     skbio                       10                           10  ...   \n",
       "            stacked                     10                           10  ...   \n",
       "20000.0     skbio                       10                           10  ...   \n",
       "            stacked                     10                           10  ...   \n",
       "40000.0     stacked                     10                           10  ...   \n",
       "80000.0     stacked                     10                           10  ...   \n",
       "\n",
       "                     Signals delivered  Page size (bytes)  Exit status  Trace  \\\n",
       "sample_size method                                                              \n",
       "1250.0      skbio                   10                 10           10      0   \n",
       "            stacked                 10                 10           10      0   \n",
       "2500.0      skbio                   10                 10           10      0   \n",
       "            stacked                 10                 10           10      0   \n",
       "5000.0      skbio                   10                 10           10      0   \n",
       "            stacked                 10                 10           10      0   \n",
       "10000.0     skbio                   10                 10           10      0   \n",
       "            stacked                 10                 10           10      0   \n",
       "20000.0     skbio                   10                 10           10      0   \n",
       "            stacked                 10                 10           10      0   \n",
       "40000.0     stacked                 10                 10           10      0   \n",
       "80000.0     stacked                 10                 10           10      0   \n",
       "\n",
       "                     File   actua  count  _vect  Memor  Comma  \n",
       "sample_size method                                             \n",
       "1250.0      skbio        0      0      0      0      0      0  \n",
       "            stacked      0      0      0      0      0      0  \n",
       "2500.0      skbio        0      0      0      0      0      0  \n",
       "            stacked      0      0      0      0      0      0  \n",
       "5000.0      skbio        0      0      0      0      0      0  \n",
       "            stacked      0      0      0      0      0      0  \n",
       "10000.0     skbio        0      0      0      0      0      0  \n",
       "            stacked      0      0      0      0      0      0  \n",
       "20000.0     skbio        0      0      0      0      0      0  \n",
       "            stacked      0      0      0      0      0      0  \n",
       "40000.0     stacked      0      0      0      0      0      0  \n",
       "80000.0     stacked      0      0      0      0      0      0  \n",
       "\n",
       "[12 rows x 36 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sub.groupby(['sample_size', 'method']).agg('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "gby = df_sub.groupby(['sample_size', 'method']).agg('mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ratio(df):\n",
    "    return df.max() / df.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/garmstro/miniconda3/envs/faith-pd-bench/lib/python3.6/site-packages/ipykernel_launcher.py:2: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "gby_new = gby.copy().reset_index().groupby('sample_size').agg(ratio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/garmstro/miniconda3/envs/faith-pd-bench/lib/python3.6/site-packages/pandas/core/reshape/merge.py:617: UserWarning: merging between different levels can give an unintended result (2 levels on the left, 1 on the right)\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    }
   ],
   "source": [
    "gby_full = gby.unstack(level=1).join(gby_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "gby_full"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "with pd.ExcelWriter('speedup-table.xlsx') as writer:\n",
    "    gby_full.to_excel(writer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/garmstro/miniconda3/envs/faith-pd-bench/lib/python3.6/site-packages/ipykernel_launcher.py:41: MatplotlibDeprecationWarning: The 'basey' parameter of __init__() has been renamed 'base' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       1250.0\n",
      "1       1250.0\n",
      "2       2500.0\n",
      "3       5000.0\n",
      "4      20000.0\n",
      "        ...   \n",
      "133     1250.0\n",
      "134     2500.0\n",
      "135     5000.0\n",
      "136    10000.0\n",
      "137    20000.0\n",
      "Name: sample_size, Length: 120, dtype: float64\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 700x869.565 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('default')\n",
    "\n",
    "FONT_SIZE = 22\n",
    "paper_rc = {'lines.linewidth': 2, \n",
    "            'lines.markersize': 10}                  \n",
    "sns.set_context(\"paper\", rc = paper_rc)   \n",
    "plt.rcParams['xtick.labelsize'] = FONT_SIZE\n",
    "plt.rcParams['ytick.labelsize'] = FONT_SIZE\n",
    "font = {'weight' : 'bold',\n",
    "        'size'   : FONT_SIZE\n",
    "       }\n",
    "\n",
    "mpl.rc('font', **font)\n",
    "\n",
    "POINT_SIZE=3\n",
    "x_size = 7\n",
    "y_size = 2 / 1.61 * x_size\n",
    "fig, axs = plt.subplots(2, 1,\n",
    "                        figsize=(x_size, y_size),\n",
    "                        sharex=False)\n",
    "\n",
    "ax = sns.lineplot(\n",
    "    x='sample_size',\n",
    "    y='Maximum resident set size (kbytes)',\n",
    "    hue='method',\n",
    "    data=df_sub,\n",
    "    marker=\"o\",\n",
    "    err_style=\"bars\",\n",
    "    linewidth=POINT_SIZE,\n",
    "\n",
    "    ci=95,\n",
    "    hue_order=['skbio', 'stacked'],\n",
    "    ax=axs[1]\n",
    ")\n",
    "ax.legend().remove()\n",
    "ax.set_yscale('log', basey=2)\n",
    "ax.set_xscale('log')\n",
    "ax.set_xbound(1000, 100000)\n",
    "\n",
    "ax.set_xlabel('Number of samples', size=FONT_SIZE)\n",
    "ax.set_ylabel('Memory (kilobytes)', size=FONT_SIZE)\n",
    "\n",
    "plt.legend([],[], frameon=False)\n",
    "\n",
    "ax = sns.lineplot(\n",
    "    x='sample_size',\n",
    "    y='Python time',\n",
    "    hue='method',\n",
    "    data=df_sub,\n",
    "    marker=\"o\",\n",
    "    err_style=\"bars\",\n",
    "    ci=95,\n",
    "    linewidth=POINT_SIZE,\n",
    "    hue_order=['skbio', 'stacked'],\n",
    "    ax=axs[0]\n",
    ")\n",
    "\n",
    "print(df_sub['sample_size'])\n",
    "\n",
    "ax.legend().remove()\n",
    "# NOTE: cannot set aspect ratio\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "ax.set_xbound(1000, 100000)\n",
    "ax.set_xlabel('Number of samples', size=FONT_SIZE)\n",
    "ax.set_ylabel('Time (s)', size=FONT_SIZE)\n",
    "plt.legend([],[], frameon=False)\n",
    "plt.tight_layout()\n",
    "plt.savefig('21-02-17-figures/figure-01-benchmarking-results.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "palette = sns.color_palette()\n",
    "\n",
    "legend_elements = [\n",
    "    Patch(facecolor=palette[1], edgecolor='black',\n",
    "          linewidth=3,\n",
    "          label='Stacked Faith\\'s PD',\n",
    "          ),\n",
    "    Patch(facecolor=palette[0], edgecolor='black',\n",
    "          linewidth=3,\n",
    "          label='Reference Implementation'\n",
    "          ),\n",
    "]\n",
    "\n",
    "# Create the figure\n",
    "fig, ax = plt.subplots(figsize=(20, 2))\n",
    "legend = ax.legend(handles=legend_elements, loc='center',\n",
    "          fontsize=40, ncol=2, framealpha=0.0,\n",
    "         )\n",
    "plt.axis('off')\n",
    "plt.savefig('figures/figure-01-legend.png')\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "language": "python",
   "display_name": "Python 3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}