1-Benchmark-intro.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "sexual-encyclopedia",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acknowledged-pastor",
   "metadata": {},
   "source": [
    "# About\n",
    "\n",
    "This notebooks performs benchmarks on the \"standard solvers\".\n",
    "\n",
    "There are two standard solvers:\n",
    "\n",
    "* `scipy.linalg.sylvester`: Bartels-Stewart algorithm, uses `TRSYL` from LAPACK to solve the simplified eqation\n",
    "* `solve_sylvester_linear`: reformulate the equation as a linear system and solve with `numpy.linalg.solve`\n",
    "\n",
    "Note that the provided framework for benchmarking (via the function `benchmark_utils.benchmark`) **checks by default the validity every solution produced** to ensure consistency."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "attractive-prospect",
   "metadata": {},
   "source": [
    "## Reminder\n",
    "\n",
    "The `solve_sylvester_linear` uses the property that the Sylvester equation\n",
    "\n",
    "$$AX - XB = C$$\n",
    "\n",
    "can be formulated as\n",
    "\n",
    "$$\\mathcal A \\mathbf x = \\mathbf c \\; ,$$\n",
    "\n",
    "with $\\mathcal A := \\mathbb I_n \\otimes A - B^\\top \\otimes \\mathbb I_m, \\; \\mathbf x := \\text{vec}(X), \\; \\mathbf c := \\text{vec}(C)$. Refer to the report for details."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "convinced-morgan",
   "metadata": {},
   "source": [
    "# Benchmark standard solvers\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "going-blanket",
   "metadata": {},
   "source": [
    "## Howto\n",
    "\n",
    "We'll use `benchmark_utils.benchmark`, which provides an easy interface to vary parameters. Under the hood, it uses the function `benchmark_utils.multiple_runs` to which it passes a `solve_fun`. This solves the equation several times with random matrices of given dimensions, optionally checks the validity of the solution, and returns the computation times of each run. This will allow us to have a **mean** and **standard deviation** of the computational time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "turkish-correlation",
   "metadata": {},
   "outputs": [],
   "source": [
    "from benchmark_utils import benchmark, compute_nruns\n",
    "from utils import solve_sylvester_scipy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "drawn-fifty",
   "metadata": {},
   "source": [
    "For instance, let's use the built-in scipy solver of the Sylvester equation. We vary the size of the matrices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "specified-passport",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dim=(10, 10) 5 runs\n",
      "dim=(20, 20) 5 runs\n",
      "dim=(100, 100) 5 runs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'dim': (10, 10),\n",
       "  'time': [0.00036454200744628906,\n",
       "   0.00011444091796875,\n",
       "   0.00010395050048828125,\n",
       "   0.00010371208190917969,\n",
       "   0.00010204315185546875],\n",
       "  'test': 'test'},\n",
       " {'dim': (20, 20),\n",
       "  'time': [0.00031566619873046875,\n",
       "   0.0002524852752685547,\n",
       "   0.00024819374084472656,\n",
       "   0.0002422332763671875,\n",
       "   0.0002562999725341797],\n",
       "  'test': 'test'},\n",
       " {'dim': (100, 100),\n",
       "  'time': [0.023352861404418945,\n",
       "   0.0505373477935791,\n",
       "   0.02408456802368164,\n",
       "   0.021408557891845703,\n",
       "   0.023058652877807617],\n",
       "  'test': 'test'}]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = benchmark(\n",
    "    solve_sylvester_scipy, \n",
    "    vary_param=('dim', [(10, 10), (20, 20), (100, 100)]), \n",
    "    log_context={'test': 'test'}\n",
    ")\n",
    "r"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faced-honduras",
   "metadata": {},
   "source": [
    "## Note on `compute_nruns`\n",
    "\n",
    "This is used under the hood by `benchmark()` function if you provide `n_runs=(nruns_min, nruns_max)`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "competent-wholesale",
   "metadata": {},
   "source": [
    "Say we have:\n",
    "\n",
    "* smallest matrix size: 10x10\n",
    "* Biggest matrix size: 1000x1000\n",
    "\n",
    "And we want:\n",
    "\n",
    "* 100 runs for smallest\n",
    "* 2 runs for biggest\n",
    "\n",
    "Illustration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "precious-cooper",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = np.arange(1, 10000)\n",
    "y = list(map(lambda n: compute_nruns(10, 100, 1000, 2, n), x))\n",
    "plt.semilogx(x, y);\n",
    "plt.xlabel('matrix size $n$')\n",
    "plt.ylabel('number of runs');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prompt-rocket",
   "metadata": {},
   "source": [
    "## Benchmark scipy solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "structural-forestry",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "alleged-management",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(10, 10),\n",
       " (14, 14),\n",
       " (22, 22),\n",
       " (32, 32),\n",
       " (48, 48),\n",
       " (71, 71),\n",
       " (106, 106),\n",
       " (158, 158),\n",
       " (235, 235),\n",
       " (349, 349),\n",
       " (517, 517),\n",
       " (768, 768),\n",
       " (1140, 1140),\n",
       " (1692, 1692),\n",
       " (2511, 2511)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nruns_min, nruns_max = (2, 100)\n",
    "\n",
    "dimensions = [(n, n) for n in np.logspace(1, 3.40, dtype=int, num=15)]\n",
    "dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "seasonal-accountability",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dim=(10, 10) 100 runs\n",
      "dim=(14, 14) 94 runs\n",
      "dim=(22, 22) 86 runs\n",
      "dim=(32, 32) 79 runs\n",
      "dim=(48, 48) 72 runs\n",
      "dim=(71, 71) 65 runs\n",
      "dim=(106, 106) 58 runs\n",
      "dim=(158, 158) 51 runs\n",
      "dim=(235, 235) 44 runs\n",
      "dim=(349, 349) 36 runs\n",
      "dim=(517, 517) 30 runs\n",
      "dim=(768, 768) 23 runs\n",
      "dim=(1140, 1140) 16 runs\n",
      "dim=(1692, 1692) 9 runs\n",
      "dim=(2511, 2511) 2 runs\n"
     ]
    }
   ],
   "source": [
    "res_scipy = benchmark(\n",
    "    solve_sylvester_scipy,\n",
    "    vary_param=('dim', dimensions),\n",
    "    log_context={\n",
    "        'solve_fun': 'syl_scipy'\n",
    "    },\n",
    "    n_runs=(nruns_min, nruns_max)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "banned-spoke",
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_results(res_dic):\n",
    "    df = pd.DataFrame(res_dic)\n",
    "    df['time_mean'] = df.time.apply(np.mean)\n",
    "    df['time_std'] = df.time.apply(np.std)\n",
    "    df['m'] = df.dim.apply(lambda e: e[0])\n",
    "    df['n'] = df.dim.apply(lambda e: e[1])\n",
    "    df['time_lst'] = df.time.copy()\n",
    "    df.drop(columns='time', inplace=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "unusual-hudson",
   "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>dim</th>\n",
       "      <th>solve_fun</th>\n",
       "      <th>time_mean</th>\n",
       "      <th>time_std</th>\n",
       "      <th>m</th>\n",
       "      <th>n</th>\n",
       "      <th>time_lst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(10, 10)</td>\n",
       "      <td>syl_scipy</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>[0.0002503395080566406, 0.00011444091796875, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(14, 14)</td>\n",
       "      <td>syl_scipy</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>[0.00015854835510253906, 0.0001523494720458984...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(22, 22)</td>\n",
       "      <td>syl_scipy</td>\n",
       "      <td>0.000284</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>[0.0003142356872558594, 0.0002980232238769531,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(32, 32)</td>\n",
       "      <td>syl_scipy</td>\n",
       "      <td>0.000571</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>[0.0005908012390136719, 0.0005779266357421875,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(48, 48)</td>\n",
       "      <td>syl_scipy</td>\n",
       "      <td>0.001411</td>\n",
       "      <td>0.000082</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>[0.0013437271118164062, 0.0013554096221923828,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(71, 71)</td>\n",
       "      <td>syl_scipy</td>\n",
       "      <td>0.005439</td>\n",
       "      <td>0.006061</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>[0.00396728515625, 0.004088878631591797, 0.003...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(106, 106)</td>\n",
       "      <td>syl_scipy</td>\n",
       "      <td>0.028154</td>\n",
       "      <td>0.013119</td>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
       "      <td>[0.07665729522705078, 0.012686729431152344, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(158, 158)</td>\n",
       "      <td>syl_scipy</td>\n",
       "      <td>0.042520</td>\n",
       "      <td>0.016990</td>\n",
       "      <td>158</td>\n",
       "      <td>158</td>\n",
       "      <td>[0.06836128234863281, 0.06786561012268066, 0.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(235, 235)</td>\n",
       "      <td>syl_scipy</td>\n",
       "      <td>0.113755</td>\n",
       "      <td>0.025466</td>\n",
       "      <td>235</td>\n",
       "      <td>235</td>\n",
       "      <td>[0.08127927780151367, 0.08770227432250977, 0.1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(349, 349)</td>\n",
       "      <td>syl_scipy</td>\n",
       "      <td>0.196549</td>\n",
       "      <td>0.018025</td>\n",
       "      <td>349</td>\n",
       "      <td>349</td>\n",
       "      <td>[0.19087719917297363, 0.23731231689453125, 0.2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(517, 517)</td>\n",
       "      <td>syl_scipy</td>\n",
       "      <td>0.555931</td>\n",
       "      <td>0.066966</td>\n",
       "      <td>517</td>\n",
       "      <td>517</td>\n",
       "      <td>[0.6032826900482178, 0.546273946762085, 0.5354...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(768, 768)</td>\n",
       "      <td>syl_scipy</td>\n",
       "      <td>1.650280</td>\n",
       "      <td>0.074005</td>\n",
       "      <td>768</td>\n",
       "      <td>768</td>\n",
       "      <td>[1.6793005466461182, 1.562835454940796, 1.5596...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(1140, 1140)</td>\n",
       "      <td>syl_scipy</td>\n",
       "      <td>3.214704</td>\n",
       "      <td>0.107712</td>\n",
       "      <td>1140</td>\n",
       "      <td>1140</td>\n",
       "      <td>[3.3716964721679688, 3.1255195140838623, 3.180...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(1692, 1692)</td>\n",
       "      <td>syl_scipy</td>\n",
       "      <td>9.940027</td>\n",
       "      <td>0.183423</td>\n",
       "      <td>1692</td>\n",
       "      <td>1692</td>\n",
       "      <td>[9.921622276306152, 9.671865701675415, 9.77102...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(2511, 2511)</td>\n",
       "      <td>syl_scipy</td>\n",
       "      <td>37.319301</td>\n",
       "      <td>0.003058</td>\n",
       "      <td>2511</td>\n",
       "      <td>2511</td>\n",
       "      <td>[37.32235860824585, 37.316243171691895]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             dim  solve_fun  time_mean  time_std     m     n  \\\n",
       "0       (10, 10)  syl_scipy   0.000100  0.000017    10    10   \n",
       "1       (14, 14)  syl_scipy   0.000149  0.000008    14    14   \n",
       "2       (22, 22)  syl_scipy   0.000284  0.000009    22    22   \n",
       "3       (32, 32)  syl_scipy   0.000571  0.000011    32    32   \n",
       "4       (48, 48)  syl_scipy   0.001411  0.000082    48    48   \n",
       "5       (71, 71)  syl_scipy   0.005439  0.006061    71    71   \n",
       "6     (106, 106)  syl_scipy   0.028154  0.013119   106   106   \n",
       "7     (158, 158)  syl_scipy   0.042520  0.016990   158   158   \n",
       "8     (235, 235)  syl_scipy   0.113755  0.025466   235   235   \n",
       "9     (349, 349)  syl_scipy   0.196549  0.018025   349   349   \n",
       "10    (517, 517)  syl_scipy   0.555931  0.066966   517   517   \n",
       "11    (768, 768)  syl_scipy   1.650280  0.074005   768   768   \n",
       "12  (1140, 1140)  syl_scipy   3.214704  0.107712  1140  1140   \n",
       "13  (1692, 1692)  syl_scipy   9.940027  0.183423  1692  1692   \n",
       "14  (2511, 2511)  syl_scipy  37.319301  0.003058  2511  2511   \n",
       "\n",
       "                                             time_lst  \n",
       "0   [0.0002503395080566406, 0.00011444091796875, 0...  \n",
       "1   [0.00015854835510253906, 0.0001523494720458984...  \n",
       "2   [0.0003142356872558594, 0.0002980232238769531,...  \n",
       "3   [0.0005908012390136719, 0.0005779266357421875,...  \n",
       "4   [0.0013437271118164062, 0.0013554096221923828,...  \n",
       "5   [0.00396728515625, 0.004088878631591797, 0.003...  \n",
       "6   [0.07665729522705078, 0.012686729431152344, 0....  \n",
       "7   [0.06836128234863281, 0.06786561012268066, 0.0...  \n",
       "8   [0.08127927780151367, 0.08770227432250977, 0.1...  \n",
       "9   [0.19087719917297363, 0.23731231689453125, 0.2...  \n",
       "10  [0.6032826900482178, 0.546273946762085, 0.5354...  \n",
       "11  [1.6793005466461182, 1.562835454940796, 1.5596...  \n",
       "12  [3.3716964721679688, 3.1255195140838623, 3.180...  \n",
       "13  [9.921622276306152, 9.671865701675415, 9.77102...  \n",
       "14            [37.32235860824585, 37.316243171691895]  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = format_results(res_scipy)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "blocked-remainder",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x7f2022b17c40>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.loglog(df.n, df.time_mean, '-o')\n",
    "\n",
    "plt.fill_between(df.n, df.time_mean - df.time_std, df.time_mean + df.time_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "coral-andrews",
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import solve_sylvester_linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "detected-release",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(10, 10),\n",
       " (13, 13),\n",
       " (17, 17),\n",
       " (22, 22),\n",
       " (29, 29),\n",
       " (38, 38),\n",
       " (50, 50),\n",
       " (65, 65),\n",
       " (85, 85),\n",
       " (112, 112)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dimensions = [(n, n) for n in np.logspace(1, 2.05, dtype=int, num=10)]\n",
    "dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "clean-dialogue",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dim=(10, 10) 100 runs\n",
      "dim=(13, 13) 89 runs\n",
      "dim=(17, 17) 78 runs\n",
      "dim=(22, 22) 68 runs\n",
      "dim=(29, 29) 56 runs\n",
      "dim=(38, 38) 45 runs\n",
      "dim=(50, 50) 34 runs\n",
      "dim=(65, 65) 24 runs\n",
      "dim=(85, 85) 13 runs\n",
      "dim=(112, 112) 2 runs\n"
     ]
    }
   ],
   "source": [
    "res_linear = benchmark(\n",
    "    solve_sylvester_linear,\n",
    "    vary_param=('dim', dimensions),\n",
    "    log_context={\n",
    "        'solve_fun': 'syl_linear'\n",
    "    },\n",
    "    n_runs=(nruns_min, nruns_max)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "similar-cannon",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = format_results(res_linear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "criminal-parcel",
   "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>dim</th>\n",
       "      <th>solve_fun</th>\n",
       "      <th>time_mean</th>\n",
       "      <th>time_std</th>\n",
       "      <th>m</th>\n",
       "      <th>n</th>\n",
       "      <th>time_lst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(10, 10)</td>\n",
       "      <td>syl_linear</td>\n",
       "      <td>0.000916</td>\n",
       "      <td>0.003875</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>[0.0007412433624267578, 0.00021028518676757812...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(13, 13)</td>\n",
       "      <td>syl_linear</td>\n",
       "      <td>0.000466</td>\n",
       "      <td>0.000723</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>[0.0003974437713623047, 0.0003561973571777344,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(17, 17)</td>\n",
       "      <td>syl_linear</td>\n",
       "      <td>0.001493</td>\n",
       "      <td>0.005346</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>[0.0008883476257324219, 0.0008022785186767578,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(22, 22)</td>\n",
       "      <td>syl_linear</td>\n",
       "      <td>0.006331</td>\n",
       "      <td>0.011982</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>[0.002292633056640625, 0.002153635025024414, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(29, 29)</td>\n",
       "      <td>syl_linear</td>\n",
       "      <td>0.009533</td>\n",
       "      <td>0.007095</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>[0.008080005645751953, 0.016417503356933594, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(38, 38)</td>\n",
       "      <td>syl_linear</td>\n",
       "      <td>0.033849</td>\n",
       "      <td>0.004929</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>[0.0301513671875, 0.031366825103759766, 0.0323...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(50, 50)</td>\n",
       "      <td>syl_linear</td>\n",
       "      <td>0.139327</td>\n",
       "      <td>0.010877</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>[0.1330423355102539, 0.17487716674804688, 0.13...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(65, 65)</td>\n",
       "      <td>syl_linear</td>\n",
       "      <td>0.668639</td>\n",
       "      <td>0.343297</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>[0.48966121673583984, 0.6423828601837158, 0.56...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(85, 85)</td>\n",
       "      <td>syl_linear</td>\n",
       "      <td>2.066620</td>\n",
       "      <td>0.469928</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>[1.8087446689605713, 1.788835048675537, 2.1843...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(112, 112)</td>\n",
       "      <td>syl_linear</td>\n",
       "      <td>8.745095</td>\n",
       "      <td>0.026968</td>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "      <td>[8.71812629699707, 8.77206301689148]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          dim   solve_fun  time_mean  time_std    m    n  \\\n",
       "0    (10, 10)  syl_linear   0.000916  0.003875   10   10   \n",
       "1    (13, 13)  syl_linear   0.000466  0.000723   13   13   \n",
       "2    (17, 17)  syl_linear   0.001493  0.005346   17   17   \n",
       "3    (22, 22)  syl_linear   0.006331  0.011982   22   22   \n",
       "4    (29, 29)  syl_linear   0.009533  0.007095   29   29   \n",
       "5    (38, 38)  syl_linear   0.033849  0.004929   38   38   \n",
       "6    (50, 50)  syl_linear   0.139327  0.010877   50   50   \n",
       "7    (65, 65)  syl_linear   0.668639  0.343297   65   65   \n",
       "8    (85, 85)  syl_linear   2.066620  0.469928   85   85   \n",
       "9  (112, 112)  syl_linear   8.745095  0.026968  112  112   \n",
       "\n",
       "                                            time_lst  \n",
       "0  [0.0007412433624267578, 0.00021028518676757812...  \n",
       "1  [0.0003974437713623047, 0.0003561973571777344,...  \n",
       "2  [0.0008883476257324219, 0.0008022785186767578,...  \n",
       "3  [0.002292633056640625, 0.002153635025024414, 0...  \n",
       "4  [0.008080005645751953, 0.016417503356933594, 0...  \n",
       "5  [0.0301513671875, 0.031366825103759766, 0.0323...  \n",
       "6  [0.1330423355102539, 0.17487716674804688, 0.13...  \n",
       "7  [0.48966121673583984, 0.6423828601837158, 0.56...  \n",
       "8  [1.8087446689605713, 1.788835048675537, 2.1843...  \n",
       "9               [8.71812629699707, 8.77206301689148]  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "mounted-draft",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x7f201bf1f910>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.loglog(df.n, df.time_mean, '-o')\n",
    "plt.fill_between(df.n, df.time_mean - df.time_std, df.time_mean + df.time_std)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adapted-anaheim",
   "metadata": {},
   "source": [
    "## Save results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "assured-tampa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "understood-africa",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_dump = res_linear + res_scipy\n",
    "\n",
    "# Map np.int64 to int\n",
    "for v in res_dump:\n",
    "    v['dim'] = (int(v['dim'][0]), int(v['dim'][1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "pleasant-circus",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('res/benchmark_std.json', 'w') as f:\n",
    "    json.dump(res_dump, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incorporated-tourism",
   "metadata": {},
   "source": [
    "# Benchmark Bartel Stewart\n",
    "\n",
    "We want to measure the time required to compute the schur decomposition and the back mapping. We will use the scipy's solver. The function `utils.solve_bertel_stewart` handles the coordinate change and provides separate times for the shur decomp, solving triangular system and back mapping. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "continental-damage",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(10, 10),\n",
       " (14, 14),\n",
       " (22, 22),\n",
       " (32, 32),\n",
       " (48, 48),\n",
       " (71, 71),\n",
       " (106, 106),\n",
       " (158, 158),\n",
       " (235, 235),\n",
       " (349, 349),\n",
       " (517, 517),\n",
       " (768, 768),\n",
       " (1140, 1140),\n",
       " (1692, 1692),\n",
       " (2511, 2511)]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dimensions = [(n, n) for n in np.logspace(1, 3.40, dtype=int, num=15)]\n",
    "dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "renewable-label",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dim=(10, 10) 100 runs\n",
      "dim=(14, 14) 94 runs\n",
      "dim=(22, 22) 86 runs\n",
      "dim=(32, 32) 79 runs\n",
      "dim=(48, 48) 72 runs\n",
      "dim=(71, 71) 65 runs\n",
      "dim=(106, 106) 58 runs\n",
      "dim=(158, 158) 51 runs\n",
      "dim=(235, 235) 44 runs\n",
      "dim=(349, 349) 36 runs\n",
      "dim=(517, 517) 30 runs\n",
      "dim=(768, 768) 23 runs\n",
      "dim=(1140, 1140) 16 runs\n",
      "dim=(1692, 1692) 9 runs\n",
      "dim=(2511, 2511) 2 runs\n"
     ]
    }
   ],
   "source": [
    "res_bs_scipy = benchmark(\n",
    "    solve_sylvester_scipy,\n",
    "    vary_param=('dim', dimensions),\n",
    "    bartel_stewart=True,\n",
    "    log_context={\n",
    "        'solve_fun': 'syl_scipy',\n",
    "        'bartel_stewart': True\n",
    "    },\n",
    "    n_runs=(nruns_min, nruns_max)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "narrative-manhattan",
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_results_bs(res_dic):\n",
    "    df = pd.DataFrame(res_dic)\n",
    "    df.time = df.time.apply(np.array)\n",
    "    df['time_schur'] = df.time.apply(lambda lst: [tpl[0] for tpl in lst])\n",
    "    df['time_solve'] = df.time.apply(lambda lst: [tpl[1] for tpl in lst])\n",
    "    df['time_back'] = df.time.apply(lambda lst: [tpl[2] for tpl in lst])\n",
    "    df.drop(columns='time', inplace=True)\n",
    "\n",
    "    for e in df.columns:\n",
    "        if e.startswith('time_'):\n",
    "            df[f'{e}_mean'] = df[e].apply(np.mean)\n",
    "            df[f'{e}_std'] = df[e].apply(np.std)\n",
    "    \n",
    "    df['m'] = df.dim.apply(lambda e: e[0])\n",
    "    df['n'] = df.dim.apply(lambda e: e[1])\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "indonesian-chase",
   "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>dim</th>\n",
       "      <th>solve_fun</th>\n",
       "      <th>bartel_stewart</th>\n",
       "      <th>time_schur</th>\n",
       "      <th>time_solve</th>\n",
       "      <th>time_back</th>\n",
       "      <th>time_schur_mean</th>\n",
       "      <th>time_schur_std</th>\n",
       "      <th>time_solve_mean</th>\n",
       "      <th>time_solve_std</th>\n",
       "      <th>time_back_mean</th>\n",
       "      <th>time_back_std</th>\n",
       "      <th>m</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(10, 10)</td>\n",
       "      <td>syl_scipy</td>\n",
       "      <td>True</td>\n",
       "      <td>[0.0004780292510986328, 0.00011110305786132812...</td>\n",
       "      <td>[0.00015401840209960938, 0.0001032352447509765...</td>\n",
       "      <td>[1.6689300537109375e-05, 1.0251998901367188e-0...</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>2.848418e-06</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(14, 14)</td>\n",
       "      <td>syl_scipy</td>\n",
       "      <td>True</td>\n",
       "      <td>[0.00014829635620117188, 0.0001237392425537109...</td>\n",
       "      <td>[0.00012040138244628906, 0.0001208782196044921...</td>\n",
       "      <td>[1.1682510375976562e-05, 1.0967254638671875e-0...</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>2.492490e-07</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        dim  solve_fun  bartel_stewart  \\\n",
       "0  (10, 10)  syl_scipy            True   \n",
       "1  (14, 14)  syl_scipy            True   \n",
       "\n",
       "                                          time_schur  \\\n",
       "0  [0.0004780292510986328, 0.00011110305786132812...   \n",
       "1  [0.00014829635620117188, 0.0001237392425537109...   \n",
       "\n",
       "                                          time_solve  \\\n",
       "0  [0.00015401840209960938, 0.0001032352447509765...   \n",
       "1  [0.00012040138244628906, 0.0001208782196044921...   \n",
       "\n",
       "                                           time_back  time_schur_mean  \\\n",
       "0  [1.6689300537109375e-05, 1.0251998901367188e-0...         0.000089   \n",
       "1  [1.1682510375976562e-05, 1.0967254638671875e-0...         0.000129   \n",
       "\n",
       "   time_schur_std  time_solve_mean  time_solve_std  time_back_mean  \\\n",
       "0        0.000039         0.000079        0.000009        0.000010   \n",
       "1        0.000016         0.000110        0.000009        0.000011   \n",
       "\n",
       "   time_back_std   m   n  \n",
       "0   2.848418e-06  10  10  \n",
       "1   2.492490e-07  14  14  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = format_results_bs(res_bs_scipy)\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "committed-inspector",
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     "metadata": {},
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   ],
   "source": [
    "plt.loglog(df.n, df.time_solve_mean, '-o')\n",
    "plt.loglog(df.n, df.time_schur_mean, '-o')\n",
    "plt.loglog(df.n, df.time_back_mean, '-o')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "floppy-anchor",
   "metadata": {},
   "source": [
    "## Solve "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "clinical-protocol",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Map np.int64 to int\n",
    "for v in res_bs_scipy:\n",
    "    v['dim'] = (int(v['dim'][0]), int(v['dim'][1]))\n",
    "    \n",
    "with open('res/benchmark_bartel_stewart_scipy.json', 'w') as f:\n",
    "    json.dump(res_bs_scipy, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "composite-solomon",
   "metadata": {},
   "source": [
    "# Reload results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "loved-identity",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "with open('res/benchmark_bartel_stewart_scipy.json') as f:\n",
    "    res_bs_scipy = json.load(f)\n",
    "    \n",
    "    \n",
    "with open('res/benchmark_std.json') as f:\n",
    "    res_dump = json.load(f)\n",
    "    \n",
    "res_linear = [e for e in res_dump if e['solve_fun'] == 'syl_linear']\n",
    "res_scipy = [e for e in res_dump if e['solve_fun'] == 'syl_scipy']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "premium-hebrew",
   "metadata": {},
   "source": [
    "## Load matlab benchmark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "varying-aggregate",
   "metadata": {},
   "outputs": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n</th>\n",
       "      <th>time_mean</th>\n",
       "      <th>time_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31</td>\n",
       "      <td>0.004263</td>\n",
       "      <td>0.005665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>0.004294</td>\n",
       "      <td>0.001802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>63</td>\n",
       "      <td>0.004777</td>\n",
       "      <td>0.000208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89</td>\n",
       "      <td>0.011540</td>\n",
       "      <td>0.009081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>125</td>\n",
       "      <td>0.013048</td>\n",
       "      <td>0.000637</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     n  time_mean  time_std\n",
       "0   31   0.004263  0.005665\n",
       "1   44   0.004294  0.001802\n",
       "2   63   0.004777  0.000208\n",
       "3   89   0.011540  0.009081\n",
       "4  125   0.013048  0.000637"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_matlab_syl = pd.read_csv(\n",
    "    'matlab/benchmark_sylvester.csv',\n",
    "    names=['n', 'time_mean', 'time_std']\n",
    ")\n",
    "df_matlab_syl.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "filled-battle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>n</th>\n",
       "      <th>time_mean</th>\n",
       "      <th>time_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31</td>\n",
       "      <td>0.001078</td>\n",
       "      <td>0.000362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>0.000595</td>\n",
       "      <td>0.000143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>63</td>\n",
       "      <td>0.001092</td>\n",
       "      <td>0.000133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89</td>\n",
       "      <td>0.003844</td>\n",
       "      <td>0.002868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>125</td>\n",
       "      <td>0.004912</td>\n",
       "      <td>0.000219</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     n  time_mean  time_std\n",
       "0   31   0.001078  0.000362\n",
       "1   44   0.000595  0.000143\n",
       "2   63   0.001092  0.000133\n",
       "3   89   0.003844  0.002868\n",
       "4  125   0.004912  0.000219"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_matlab_schur = pd.read_csv(\n",
    "    'matlab/benchmark_schur.csv',\n",
    "    names=['n', 'time_mean', 'time_std']\n",
    ")\n",
    "df_matlab_schur.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "northern-dollar",
   "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>n</th>\n",
       "      <th>time_mean</th>\n",
       "      <th>time_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31</td>\n",
       "      <td>0.001391</td>\n",
       "      <td>0.002924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>0.000870</td>\n",
       "      <td>0.000469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>63</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>125</td>\n",
       "      <td>0.000066</td>\n",
       "      <td>0.000029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     n  time_mean  time_std\n",
       "0   31   0.001391  0.002924\n",
       "1   44   0.000870  0.000469\n",
       "2   63   0.000016  0.000004\n",
       "3   89   0.000033  0.000014\n",
       "4  125   0.000066  0.000029"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_matlab_matmul = pd.read_csv(\n",
    "    'matlab/benchmark_matmul.csv',\n",
    "    names=['n', 'time_mean', 'time_std']\n",
    ")\n",
    "df_matlab_matmul.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "expected-letter",
   "metadata": {},
   "source": [
    "# Plotting for the report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "warming-coverage",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/maousi/miniconda3/envs/ada/lib/python3.9/site-packages/ipypublish/scripts/nb_setup.py:226: MatplotlibDeprecationWarning: Support for setting the 'text.latex.preamble' or 'pgf.preamble' rcParam to a list of strings is deprecated since 3.3 and will be removed two minor releases later; set it to a single string instead.\n",
      "  mpl.rcParams[key] = val\n",
      "/home/maousi/miniconda3/envs/ada/lib/python3.9/site-packages/ipypublish/scripts/nb_setup.py:226: MatplotlibDeprecationWarning: \n",
      "The text.latex.preview rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "  mpl.rcParams[key] = val\n",
      "/home/maousi/miniconda3/envs/ada/lib/python3.9/site-packages/ipypublish/scripts/nb_setup.py:226: MatplotlibDeprecationWarning: \n",
      "The mathtext.fallback_to_cm rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "  mpl.rcParams[key] = val\n",
      "/home/maousi/miniconda3/envs/ada/lib/python3.9/site-packages/ipypublish/scripts/nb_setup.py:226: MatplotlibDeprecationWarning: Support for setting the 'mathtext.fallback_to_cm' rcParam is deprecated since 3.3 and will be removed two minor releases later; use 'mathtext.fallback : 'cm' instead.\n",
      "  mpl.rcParams[key] = val\n",
      "/home/maousi/miniconda3/envs/ada/lib/python3.9/site-packages/ipypublish/scripts/nb_setup.py:226: MatplotlibDeprecationWarning: \n",
      "The validate_bool_maybe_none function was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "  mpl.rcParams[key] = val\n",
      "/home/maousi/miniconda3/envs/ada/lib/python3.9/site-packages/ipypublish/scripts/nb_setup.py:226: MatplotlibDeprecationWarning: \n",
      "The savefig.jpeg_quality rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "  mpl.rcParams[key] = val\n",
      "/home/maousi/miniconda3/envs/ada/lib/python3.9/site-packages/ipypublish/scripts/nb_setup.py:226: MatplotlibDeprecationWarning: \n",
      "The animation.html_args rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "  mpl.rcParams[key] = val\n",
      "/home/maousi/miniconda3/envs/ada/lib/python3.9/site-packages/ipypublish/scripts/nb_setup.py:226: MatplotlibDeprecationWarning: \n",
      "The animation.avconv_args rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "  mpl.rcParams[key] = val\n"
     ]
    }
   ],
   "source": [
    "from ipypublish import nb_setup\n",
    "plt = nb_setup.setup_matplotlib()\n",
    "import seaborn as sns\n",
    "sns.set(style='whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "previous-armenia",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>dim</th>\n",
       "      <th>solve_fun</th>\n",
       "      <th>time_mean</th>\n",
       "      <th>time_std</th>\n",
       "      <th>m</th>\n",
       "      <th>n</th>\n",
       "      <th>time_lst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[10, 10]</td>\n",
       "      <td>syl_scipy</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>[0.0002503395080566406, 0.00011444091796875, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[14, 14]</td>\n",
       "      <td>syl_scipy</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>[0.00015854835510253906, 0.0001523494720458984...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        dim  solve_fun  time_mean  time_std   m   n  \\\n",
       "0  [10, 10]  syl_scipy   0.000100  0.000017  10  10   \n",
       "1  [14, 14]  syl_scipy   0.000149  0.000008  14  14   \n",
       "\n",
       "                                            time_lst  \n",
       "0  [0.0002503395080566406, 0.00011444091796875, 0...  \n",
       "1  [0.00015854835510253906, 0.0001523494720458984...  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_std_scipy = format_results(res_scipy)\n",
    "df_std_scipy.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "varied-acrylic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>dim</th>\n",
       "      <th>solve_fun</th>\n",
       "      <th>time_mean</th>\n",
       "      <th>time_std</th>\n",
       "      <th>m</th>\n",
       "      <th>n</th>\n",
       "      <th>time_lst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[10, 10]</td>\n",
       "      <td>syl_linear</td>\n",
       "      <td>0.000916</td>\n",
       "      <td>0.003875</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>[0.0007412433624267578, 0.00021028518676757812...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[13, 13]</td>\n",
       "      <td>syl_linear</td>\n",
       "      <td>0.000466</td>\n",
       "      <td>0.000723</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>[0.0003974437713623047, 0.0003561973571777344,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        dim   solve_fun  time_mean  time_std   m   n  \\\n",
       "0  [10, 10]  syl_linear   0.000916  0.003875  10  10   \n",
       "1  [13, 13]  syl_linear   0.000466  0.000723  13  13   \n",
       "\n",
       "                                            time_lst  \n",
       "0  [0.0007412433624267578, 0.00021028518676757812...  \n",
       "1  [0.0003974437713623047, 0.0003561973571777344,...  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_std_lin = format_results(res_linear)\n",
    "df_std_lin.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "everyday-story",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>dim</th>\n",
       "      <th>solve_fun</th>\n",
       "      <th>bartel_stewart</th>\n",
       "      <th>time_schur</th>\n",
       "      <th>time_solve</th>\n",
       "      <th>time_back</th>\n",
       "      <th>time_schur_mean</th>\n",
       "      <th>time_schur_std</th>\n",
       "      <th>time_solve_mean</th>\n",
       "      <th>time_solve_std</th>\n",
       "      <th>time_back_mean</th>\n",
       "      <th>time_back_std</th>\n",
       "      <th>m</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[10, 10]</td>\n",
       "      <td>syl_scipy</td>\n",
       "      <td>True</td>\n",
       "      <td>[0.0004780292510986328, 0.00011110305786132812...</td>\n",
       "      <td>[0.00015401840209960938, 0.0001032352447509765...</td>\n",
       "      <td>[1.6689300537109375e-05, 1.0251998901367188e-0...</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>2.848418e-06</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[14, 14]</td>\n",
       "      <td>syl_scipy</td>\n",
       "      <td>True</td>\n",
       "      <td>[0.00014829635620117188, 0.0001237392425537109...</td>\n",
       "      <td>[0.00012040138244628906, 0.0001208782196044921...</td>\n",
       "      <td>[1.1682510375976562e-05, 1.0967254638671875e-0...</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>2.492490e-07</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        dim  solve_fun  bartel_stewart  \\\n",
       "0  [10, 10]  syl_scipy            True   \n",
       "1  [14, 14]  syl_scipy            True   \n",
       "\n",
       "                                          time_schur  \\\n",
       "0  [0.0004780292510986328, 0.00011110305786132812...   \n",
       "1  [0.00014829635620117188, 0.0001237392425537109...   \n",
       "\n",
       "                                          time_solve  \\\n",
       "0  [0.00015401840209960938, 0.0001032352447509765...   \n",
       "1  [0.00012040138244628906, 0.0001208782196044921...   \n",
       "\n",
       "                                           time_back  time_schur_mean  \\\n",
       "0  [1.6689300537109375e-05, 1.0251998901367188e-0...         0.000089   \n",
       "1  [1.1682510375976562e-05, 1.0967254638671875e-0...         0.000129   \n",
       "\n",
       "   time_schur_std  time_solve_mean  time_solve_std  time_back_mean  \\\n",
       "0        0.000039         0.000079        0.000009        0.000010   \n",
       "1        0.000016         0.000110        0.000009        0.000011   \n",
       "\n",
       "   time_back_std   m   n  \n",
       "0   2.848418e-06  10  10  \n",
       "1   2.492490e-07  14  14  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bs_scipy = format_results_bs(res_bs_scipy)\n",
    "df_bs_scipy.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "expected-edwards",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_multiple_runs(df, x, y, ystd, label, color=None, ls='-', alpha=0.3):\n",
    "    plt.loglog(df[x], df[y], '-o', label=label, color=color, ls=ls)\n",
    "    plt.fill_between(df[x], df[y] - df[ystd], df[y] + df[ystd], alpha=alpha, color=color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "discrete-framing",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = sns.color_palette()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dress-chuck",
   "metadata": {},
   "source": [
    "## Figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "popular-memphis",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_power_law(xgrid, degree, shift=-2, ax=None):\n",
    "    \"\"\"Plot a straight line y = x ^ degree on a log-log scale.\"\"\"\n",
    "    if ax is None:\n",
    "        ax = plt.gca()\n",
    "        \n",
    "    y = xgrid ** degree * 10**shift\n",
    "    \n",
    "    plt.plot(xgrid, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "voluntary-distance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/pdf": 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       "<Figure size 1000x350 with 2 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(10, 3.5))\n",
    "\n",
    "# Plot solving time of solvers used for small systems\n",
    "plt.axes(ax[0])\n",
    "plot_multiple_runs(df_std_scipy, 'n', 'time_mean', 'time_std', 'scipy', color=cols[0])\n",
    "plot_multiple_runs(df_std_lin, 'n', 'time_mean', 'time_std', 'linear', color=cols[1])\n",
    "# Plot also matlab sylvester\n",
    "plot_multiple_runs(df_matlab_syl, 'n', 'time_mean', 'time_std', 'matlab', color=cols[2], ls='--', alpha=0.25)\n",
    "\n",
    "quad_grid = np.concatenate((df_std_lin.n, df_matlab_syl.n))\n",
    "quad_grid.sort()\n",
    "plt.loglog(quad_grid, quad_grid ** 2 * 10**(-6.5), '--k', label='quadratic')\n",
    "\n",
    "plt.legend()\n",
    "plt.axes(ax[1])\n",
    "\n",
    "# Plot time decomposition into the three phases of Bartel-Stewart\n",
    "plot_multiple_runs(df_bs_scipy, 'n', 'time_schur_mean', 'time_schur_std', label='schur', color=cols[3])\n",
    "plot_multiple_runs(df_bs_scipy, 'n', 'time_solve_mean', 'time_solve_std', label='solve', color=cols[4])\n",
    "plot_multiple_runs(df_bs_scipy, 'n', 'time_back_mean', 'time_back_std', label='back', color=cols[5])\n",
    "# Plot matlab for schur\n",
    "plot_multiple_runs(df_matlab_schur, 'n', 'time_mean', 'time_std', 'matlab schur', color=cols[6], ls='--', alpha=0.25)\n",
    "\n",
    "quad_grid = df_matlab_schur.n\n",
    "plt.loglog(quad_grid, quad_grid ** 2 * 10**(-7), '--k', label='quadratic')\n",
    "\n",
    "\n",
    "plt.legend()\n",
    "ax[0].set_xlabel('$n$'); ax[1].set_xlabel('$n$')\n",
    "ax[0].set_ylabel('Time [s]'); ax[1].set_ylabel('Time [s]');\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('res/benchmark_intro.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "simplified-baker",
   "metadata": {},
   "source": [
    "### Get the slopes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "further-storm",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "def get_slope(df, x: str, y: str, loglog=True, ignore_below: int = 100):\n",
    "    \"\"\"\n",
    "    Compute the slope of the regression line y = f(x), where x and y are the labels of the series in the dataframe.\n",
    "    Small matrix sizes often yield timing with huge variance, ignore_below handles this.\n",
    "    \n",
    "    :return: slope, intercept\n",
    "    \"\"\"\n",
    "    x = df[x]\n",
    "    y = df[y]\n",
    "    mask = (x > ignore_below)\n",
    "    x, y = x[mask], y[mask]\n",
    "    x, y = x.values.reshape(-1, 1), y.values.reshape(-1, 1)\n",
    "    if loglog:\n",
    "        x, y = np.log(x), np.log(y)\n",
    "        \n",
    "    model = LinearRegression().fit(x, y)\n",
    "    return model.coef_[0, 0], model.intercept_[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "incorporated-newman",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.000000000000001, -8.881784197001252e-16)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test\n",
    "x = np.linspace(0, 5, 10)\n",
    "test = pd.DataFrame([x, x**3]).T\n",
    "get_slope(test, 0, 1, ignore_below=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "urban-classics",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df              column               slope\n",
      "linear          time_mean            4.73\n",
      "scipy           time_mean            3.1\n",
      "bs_scipy        time_schur_mean      2.54\n",
      "bs_scipy        time_solve_mean      3.26\n",
      "bs_scipy        time_back_mean       2.66\n",
      "matlab_syl      time_mean            2.7\n",
      "matlab_schur    time_mean            2.75\n",
      "matlab_matmul   time_mean            2.66\n"
     ]
    }
   ],
   "source": [
    "dfs = {\n",
    "    'linear': df_std_lin,\n",
    "    'scipy': df_std_scipy,\n",
    "    'bs_scipy': df_bs_scipy,\n",
    "    'matlab_syl': df_matlab_syl,\n",
    "    'matlab_schur': df_matlab_schur,\n",
    "    'matlab_matmul': df_matlab_matmul\n",
    "}\n",
    "\n",
    "slopes = []\n",
    "print(f'{\"df\":<15} {\"column\":<20} slope')\n",
    "for name, df in dfs.items():\n",
    "    columns_meantime = [e for e in df.columns if e.endswith('_mean') and e.startswith('time')]\n",
    "    for col in columns_meantime:\n",
    "        ignore_below = 60 if name == 'linear' else 1000\n",
    "        slope, _ = get_slope(df, 'n', col, ignore_below=ignore_below)\n",
    "        print(f'{name:<15} {col:<20} {slope:.3}')\n",
    "        slopes.append({\n",
    "            'df': name,\n",
    "            'column': col,\n",
    "            'slope': slope\n",
    "        })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "headed-special",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df</th>\n",
       "      <th>column</th>\n",
       "      <th>slope</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>linear</td>\n",
       "      <td>time_mean</td>\n",
       "      <td>4.727516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>scipy</td>\n",
       "      <td>time_mean</td>\n",
       "      <td>3.104866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bs_scipy</td>\n",
       "      <td>time_schur_mean</td>\n",
       "      <td>2.537420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bs_scipy</td>\n",
       "      <td>time_solve_mean</td>\n",
       "      <td>3.255052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bs_scipy</td>\n",
       "      <td>time_back_mean</td>\n",
       "      <td>2.657267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>matlab_syl</td>\n",
       "      <td>time_mean</td>\n",
       "      <td>2.701849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>matlab_schur</td>\n",
       "      <td>time_mean</td>\n",
       "      <td>2.748671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>matlab_matmul</td>\n",
       "      <td>time_mean</td>\n",
       "      <td>2.656289</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "              df           column     slope\n",
       "0         linear        time_mean  4.727516\n",
       "1          scipy        time_mean  3.104866\n",
       "2       bs_scipy  time_schur_mean  2.537420\n",
       "3       bs_scipy  time_solve_mean  3.255052\n",
       "4       bs_scipy   time_back_mean  2.657267\n",
       "5     matlab_syl        time_mean  2.701849\n",
       "6   matlab_schur        time_mean  2.748671\n",
       "7  matlab_matmul        time_mean  2.656289"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(slopes)"
   ]
  }
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