plot_results.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.cluster.hierarchy import linkage, leaves_list\n",
    "from scipy.spatial.distance import squareform\n",
    "from matplotlib import colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# IEEE Access\n",
    "textwidth = 6.9907\n",
    "columnwidth = 3.35852\n",
    "\n",
    "figsizes = {\n",
    "    \"full_width_golden_ratio\": (textwidth, textwidth / 1.618),\n",
    "    \"full_width_small\": (textwidth, textwidth / 2 / 1.618),\n",
    "    \"half_width_golden_ratio\": (columnwidth, columnwidth / 1.618),\n",
    "    \"half_width_square\": (columnwidth, columnwidth),\n",
    "}\n",
    "\n",
    "# Define font for Latex PDF\n",
    "plt.rcParams.update(\n",
    "    {\n",
    "        \"text.usetex\": True,\n",
    "        \"text.latex.preamble\": r\"\\usepackage{amsmath}\",\n",
    "        \"font.family\": \"t1-times\",\n",
    "        \"font.size\": 10.0,\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot Synthetic results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_452764/159172196.py:4: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  \"random_model_pred\"]].apply(lambda x: hash(f\"{x[0]}-{x[1]}-{x[2]}-{x[3]}-{x[4]}\"), axis=1)\n"
     ]
    },
    {
     "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>value</th>\n",
       "      <th>confidence_low</th>\n",
       "      <th>confidence_high</th>\n",
       "      <th>n</th>\n",
       "      <th>k_reference</th>\n",
       "      <th>k_compare</th>\n",
       "      <th>trial</th>\n",
       "      <th>method_family</th>\n",
       "      <th>adjustment</th>\n",
       "      <th>random_model_true</th>\n",
       "      <th>random_model_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.500513</td>\n",
       "      <td>0.500513</td>\n",
       "      <td>0.500513</td>\n",
       "      <td>500</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>RI</td>\n",
       "      <td>NORMALIZED</td>\n",
       "      <td>PERM</td>\n",
       "      <td>PERM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.000696</td>\n",
       "      <td>-0.000696</td>\n",
       "      <td>-0.000696</td>\n",
       "      <td>500</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>RI</td>\n",
       "      <td>ADJUSTED</td>\n",
       "      <td>PERM</td>\n",
       "      <td>PERM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.148872</td>\n",
       "      <td>-0.148872</td>\n",
       "      <td>-0.148872</td>\n",
       "      <td>500</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>RI</td>\n",
       "      <td>STANDARDIZED</td>\n",
       "      <td>PERM</td>\n",
       "      <td>PERM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.005016</td>\n",
       "      <td>0.005016</td>\n",
       "      <td>0.005016</td>\n",
       "      <td>500</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>MI</td>\n",
       "      <td>NORMALIZED</td>\n",
       "      <td>PERM</td>\n",
       "      <td>PERM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.500770</td>\n",
       "      <td>0.500770</td>\n",
       "      <td>0.500770</td>\n",
       "      <td>500</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>RI</td>\n",
       "      <td>NORMALIZED</td>\n",
       "      <td>NUM</td>\n",
       "      <td>NUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179995</th>\n",
       "      <td>0.888280</td>\n",
       "      <td>0.856787</td>\n",
       "      <td>0.923058</td>\n",
       "      <td>500</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>999</td>\n",
       "      <td>MI</td>\n",
       "      <td>STANDARDIZED</td>\n",
       "      <td>NUM</td>\n",
       "      <td>NUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179996</th>\n",
       "      <td>0.001224</td>\n",
       "      <td>0.001013</td>\n",
       "      <td>0.001453</td>\n",
       "      <td>500</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>999</td>\n",
       "      <td>MI</td>\n",
       "      <td>ADJUSTED</td>\n",
       "      <td>NUM</td>\n",
       "      <td>NUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179997</th>\n",
       "      <td>1.737824</td>\n",
       "      <td>1.692250</td>\n",
       "      <td>1.783516</td>\n",
       "      <td>500</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>999</td>\n",
       "      <td>MI</td>\n",
       "      <td>STANDARDIZED</td>\n",
       "      <td>NUM</td>\n",
       "      <td>NUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179998</th>\n",
       "      <td>0.009879</td>\n",
       "      <td>0.009665</td>\n",
       "      <td>0.010111</td>\n",
       "      <td>500</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>999</td>\n",
       "      <td>MI</td>\n",
       "      <td>ADJUSTED</td>\n",
       "      <td>NUM</td>\n",
       "      <td>NUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179999</th>\n",
       "      <td>-0.724114</td>\n",
       "      <td>-0.753630</td>\n",
       "      <td>-0.693361</td>\n",
       "      <td>500</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>999</td>\n",
       "      <td>MI</td>\n",
       "      <td>STANDARDIZED</td>\n",
       "      <td>NUM</td>\n",
       "      <td>NUM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180000 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           value  confidence_low  confidence_high    n  k_reference  \\\n",
       "0       0.500513        0.500513         0.500513  500           10   \n",
       "1      -0.000696       -0.000696        -0.000696  500           10   \n",
       "2      -0.148872       -0.148872        -0.148872  500           10   \n",
       "3       0.005016        0.005016         0.005016  500           10   \n",
       "4       0.500770        0.500770         0.500770  500           10   \n",
       "...          ...             ...              ...  ...          ...   \n",
       "179995  0.888280        0.856787         0.923058  500           10   \n",
       "179996  0.001224        0.001013         0.001453  500           10   \n",
       "179997  1.737824        1.692250         1.783516  500           10   \n",
       "179998  0.009879        0.009665         0.010111  500           10   \n",
       "179999 -0.724114       -0.753630        -0.693361  500           10   \n",
       "\n",
       "        k_compare  trial method_family    adjustment random_model_true  \\\n",
       "0               2      0            RI    NORMALIZED              PERM   \n",
       "1               2      0            RI      ADJUSTED              PERM   \n",
       "2               2      0            RI  STANDARDIZED              PERM   \n",
       "3               2      0            MI    NORMALIZED              PERM   \n",
       "4               2      0            RI    NORMALIZED               NUM   \n",
       "...           ...    ...           ...           ...               ...   \n",
       "179995         14    999            MI  STANDARDIZED               NUM   \n",
       "179996         18    999            MI      ADJUSTED               NUM   \n",
       "179997         18    999            MI  STANDARDIZED               NUM   \n",
       "179998         22    999            MI      ADJUSTED               NUM   \n",
       "179999         22    999            MI  STANDARDIZED               NUM   \n",
       "\n",
       "       random_model_pred  \n",
       "0                   PERM  \n",
       "1                   PERM  \n",
       "2                   PERM  \n",
       "3                   PERM  \n",
       "4                    NUM  \n",
       "...                  ...  \n",
       "179995               NUM  \n",
       "179996               NUM  \n",
       "179997               NUM  \n",
       "179998               NUM  \n",
       "179999               NUM  \n",
       "\n",
       "[180000 rows x 11 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"./results/synthetic.csv\")\n",
    "\n",
    "df[\"experiment\"] = df[[\"k_compare\", \"method_family\", \"adjustment\", \"random_model_true\",\n",
    "                       \"random_model_pred\"]].apply(lambda x: hash(f\"{x[0]}-{x[1]}-{x[2]}-{x[3]}-{x[4]}\"), axis=1)\n",
    "df[\"trial\"] = df.groupby(\"experiment\").cumcount()\n",
    "del df[\"experiment\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df = pd.read_csv(\"./results/synthetic.csv\")\n",
    "\n",
    "n_trials = df[\"trial\"].nunique()\n",
    "\n",
    "# Assemble metric names for plotting\n",
    "df[\"one_sided\"] = (df[\"random_model_true\"] == \"PERM\") & (\n",
    "    df[\"random_model_true\"] != df[\"random_model_pred\"])\n",
    "df[\"metric_name\"] = df.apply(lambda x: f\"{'' if ((x['method_family'] == 'RI') and (x['adjustment'] == 'NORMALIZED')) or (\n",
    "    x['adjustment'] == 'NONE') else x['adjustment'][0]}{x['method_family']}$_\\\\text{{{x['random_model_pred'].lower()}}}{'^1' if x['one_sided'] else ''}$\", axis=1)\n",
    "\n",
    "# Filter only the highest value for each metric and number of clusters and trial\n",
    "df = df.sort_values(\"value\", ascending=False).drop_duplicates(\n",
    "    [\"metric_name\", \"trial\"], keep=\"first\"\n",
    ")\n",
    "\n",
    "metric_name_mapping = df[['method_family', 'adjustment', 'random_model_pred', 'one_sided', 'metric_name']].drop_duplicates().to_records(index=False)\n",
    "metric_name_mapping = {(m[0], m[1], m[2], m[3]): m[4] for m in metric_name_mapping}\n",
    "for method_family in df['method_family'].unique():\n",
    "    for adjustment in df['adjustment'].unique():\n",
    "        for one_sided in [True, False]:\n",
    "            if (method_family, adjustment, 'PERM', one_sided) not in metric_name_mapping:\n",
    "                metric_name_mapping[(method_family, adjustment, 'PERM', one_sided)] = metric_name_mapping[(method_family, adjustment, 'PERM', not one_sided)]\n",
    "\n",
    "data = df.groupby([\"metric_name\", \"k_compare\"]\n",
    "                  ).size().unstack().fillna(0).div(n_trials)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gridded Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "method_family = \"MI\"\n",
    "one_sided = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 699.07x432.058 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a grid of bar plots\n",
    "adjustment_types = [\"NORMALIZED\", \"ADJUSTED\", \"STANDARDIZED\"]\n",
    "random_models = [\"PERM\", \"NUM\", \"ALL\"]\n",
    "\n",
    "# Get the tab20c colormap\n",
    "tab20c = plt.get_cmap(\"tab20c\")\n",
    "\n",
    "fig, axes = plt.subplots(\n",
    "    figsize=figsizes[\"full_width_golden_ratio\"], ncols=3, nrows=3, sharex=True, sharey=False)\n",
    "\n",
    "for i, (axes_row, adjustment_type) in enumerate(zip(axes, adjustment_types)):\n",
    "    for j, (ax, random_model) in enumerate(zip(axes_row, random_models)):\n",
    "        metric_name = metric_name_mapping[(method_family, adjustment_type, random_model, one_sided)]\n",
    "        probabilities = data.loc[metric_name]\n",
    "        probabilities.plot.bar(\n",
    "            ax=ax, color=tab20c.colors[4*j+(2-i)], edgecolor=\"black\", linewidth=0.5)\n",
    "        if probabilities.max() > 0.5:\n",
    "            ax.set_ylim(0, 1)\n",
    "        else:\n",
    "            ax.set_ylim(0, 0.3)\n",
    "        # Plot horizontal line\n",
    "        ax.axhline(1 / len(probabilities), color=\"#7a7a7a\", linestyle=\"dashed\")\n",
    "\n",
    "pad = 5\n",
    "for ax, col in zip(axes[0], random_models):\n",
    "    ax.annotate(\n",
    "        col.lower(),\n",
    "        xy=(0.5, 1),\n",
    "        xytext=(0, pad),\n",
    "        xycoords=\"axes fraction\",\n",
    "        textcoords=\"offset points\",\n",
    "        size=\"large\",\n",
    "        ha=\"center\",\n",
    "        va=\"baseline\",\n",
    "    )\n",
    "\n",
    "for ax, row in zip(axes[:, 0], adjustment_types):\n",
    "    label = f\"{method_family}\"\n",
    "    if not ((row == \"NORMALIZED\") and (method_family == \"RI\")):\n",
    "        label = f\"{row[0]}{label}\"\n",
    "    if (row != \"NORMALIZED\") and one_sided:\n",
    "        label = label + \"$^1$\"\n",
    "    ax.set_ylabel(\n",
    "        label,\n",
    "    )\n",
    "\n",
    "ax = axes[1, 0]\n",
    "ax.annotate(\n",
    "    f\"Selection probability\",\n",
    "    xy=(0, 0.5),\n",
    "    xytext=(-ax.yaxis.labelpad - pad, 0),\n",
    "    xycoords=ax.yaxis.label,\n",
    "    textcoords=\"offset points\",\n",
    "    size=\"large\",\n",
    "    ha=\"right\",\n",
    "    va=\"center\",\n",
    "    rotation=90,\n",
    ")\n",
    "\n",
    "axes[2, 0].set_xlabel(None)\n",
    "axes[2, 1].set_xlabel(\"Selected number of clusters\")\n",
    "axes[2, 2].set_xlabel(None)\n",
    "\n",
    "fig.tight_layout(h_pad=-0.75)\n",
    "suffix = \"_one_sided\" if one_sided else \"\"\n",
    "fig.savefig(f\"./results/synthetic_{method_family}{suffix}.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One vs Two-sided"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 335.852x335.852 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(\n",
    "    2, 1, figsize=figsizes[\"half_width_square\"], sharey=True, sharex=True)\n",
    "\n",
    "\n",
    "# Color #00609c\n",
    "color_1 = \"#00609c\"\n",
    "\n",
    "# Color #00aeef\n",
    "color_2 = \"#00aeef\"\n",
    "\n",
    "for ax, color, metric_name in zip(axes, [color_1, color_2], [r\"SRI$_\\text{num}$\", r\"SRI$_\\text{num}^1$\"]):\n",
    "    probabilities = data.loc[metric_name]\n",
    "    probabilities.plot.bar(\n",
    "        ax=ax,\n",
    "        title=metric_name,\n",
    "        color=color,\n",
    "        edgecolor=\"black\",\n",
    "        linewidth=0.5,\n",
    "    )\n",
    "    ax.axhline(1 / len(probabilities), color=\"#7a7a7a\", linestyle=\"dashed\")\n",
    "    ax.set_ylabel(\"Selection probability\")\n",
    "    ax.set_xlabel(\"Selected number of clusters\")\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"results/synthetic_one_vs_two_sided.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot real results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "algorithm = \"AGGLOMERATIVE_CLUSTERING\"\n",
    "nsigma = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"10\" halign=\"left\">value</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">category</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metric_name</th>\n",
       "      <th>SMI$_\\text{all}$</th>\n",
       "      <th>SMI$_\\text{all}^1$</th>\n",
       "      <th>SMI$_\\text{num}$</th>\n",
       "      <th>SMI$_\\text{num}^1$</th>\n",
       "      <th>SMI$_\\text{perm}$</th>\n",
       "      <th>SRI$_\\text{all}$</th>\n",
       "      <th>SRI$_\\text{all}^1$</th>\n",
       "      <th>SRI$_\\text{num}$</th>\n",
       "      <th>SRI$_\\text{num}^1$</th>\n",
       "      <th>SRI$_\\text{perm}$</th>\n",
       "      <th>...</th>\n",
       "      <th>SMI$_\\text{all}^1$</th>\n",
       "      <th>SRI$_\\text{all}^1$</th>\n",
       "      <th>SRI$_\\text{num}$</th>\n",
       "      <th>SRI$_\\text{num}^1$</th>\n",
       "      <th>SRI$_\\text{all}$</th>\n",
       "      <th>SMI$_\\text{num}^1$</th>\n",
       "      <th>SMI$_\\text{all}$</th>\n",
       "      <th>SRI$_\\text{perm}$</th>\n",
       "      <th>SMI$_\\text{num}$</th>\n",
       "      <th>SMI$_\\text{perm}$</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alizadeh-2000-v1</th>\n",
       "      <td>-8.047028</td>\n",
       "      <td>-0.056849</td>\n",
       "      <td>10.741115</td>\n",
       "      <td>10.940811</td>\n",
       "      <td>10.778074</td>\n",
       "      <td>-12.384203</td>\n",
       "      <td>20.542693</td>\n",
       "      <td>10.803331</td>\n",
       "      <td>10.803331</td>\n",
       "      <td>10.790696</td>\n",
       "      <td>...</td>\n",
       "      <td>sig. $\\rightarrow$ insig.</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alizadeh-2000-v2</th>\n",
       "      <td>-6.542501</td>\n",
       "      <td>8.149059</td>\n",
       "      <td>31.401056</td>\n",
       "      <td>29.793395</td>\n",
       "      <td>30.582218</td>\n",
       "      <td>6.926347</td>\n",
       "      <td>95.954295</td>\n",
       "      <td>36.580792</td>\n",
       "      <td>43.790065</td>\n",
       "      <td>41.199148</td>\n",
       "      <td>...</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alizadeh-2000-v3</th>\n",
       "      <td>-5.335236</td>\n",
       "      <td>5.580363</td>\n",
       "      <td>22.872140</td>\n",
       "      <td>22.954725</td>\n",
       "      <td>23.171800</td>\n",
       "      <td>-8.886907</td>\n",
       "      <td>20.391458</td>\n",
       "      <td>17.581156</td>\n",
       "      <td>20.671998</td>\n",
       "      <td>25.046741</td>\n",
       "      <td>...</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armstrong-2002-v1</th>\n",
       "      <td>-11.819882</td>\n",
       "      <td>1.050537</td>\n",
       "      <td>23.108823</td>\n",
       "      <td>22.056448</td>\n",
       "      <td>22.412493</td>\n",
       "      <td>-36.953991</td>\n",
       "      <td>40.442147</td>\n",
       "      <td>12.105173</td>\n",
       "      <td>12.105173</td>\n",
       "      <td>12.092724</td>\n",
       "      <td>...</td>\n",
       "      <td>sig. $\\rightarrow$ insig.</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armstrong-2002-v2</th>\n",
       "      <td>-8.118987</td>\n",
       "      <td>5.415473</td>\n",
       "      <td>33.382242</td>\n",
       "      <td>33.845320</td>\n",
       "      <td>35.403753</td>\n",
       "      <td>-5.209109</td>\n",
       "      <td>50.673420</td>\n",
       "      <td>32.441782</td>\n",
       "      <td>34.084750</td>\n",
       "      <td>36.217783</td>\n",
       "      <td>...</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bhattacharjee-2001</th>\n",
       "      <td>-22.801583</td>\n",
       "      <td>1.591691</td>\n",
       "      <td>29.067148</td>\n",
       "      <td>28.204540</td>\n",
       "      <td>30.091475</td>\n",
       "      <td>-169.964956</td>\n",
       "      <td>97.173463</td>\n",
       "      <td>-24.898005</td>\n",
       "      <td>34.252008</td>\n",
       "      <td>30.727530</td>\n",
       "      <td>...</td>\n",
       "      <td>sig. $\\rightarrow$ insig.</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bittner-2000</th>\n",
       "      <td>-8.500528</td>\n",
       "      <td>-2.697391</td>\n",
       "      <td>-0.089699</td>\n",
       "      <td>-0.080493</td>\n",
       "      <td>-0.069886</td>\n",
       "      <td>-20.981778</td>\n",
       "      <td>-1.272655</td>\n",
       "      <td>-0.037716</td>\n",
       "      <td>-0.037716</td>\n",
       "      <td>-0.057084</td>\n",
       "      <td>...</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>insig. $\\rightarrow$ sig.</td>\n",
       "      <td>same</td>\n",
       "      <td>insig. $\\rightarrow$ sig.</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bredel-2005</th>\n",
       "      <td>-8.028681</td>\n",
       "      <td>0.504382</td>\n",
       "      <td>10.633686</td>\n",
       "      <td>10.406122</td>\n",
       "      <td>10.584589</td>\n",
       "      <td>-13.649338</td>\n",
       "      <td>27.769621</td>\n",
       "      <td>11.525334</td>\n",
       "      <td>15.334116</td>\n",
       "      <td>15.230404</td>\n",
       "      <td>...</td>\n",
       "      <td>sig. $\\rightarrow$ insig.</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chen-2002</th>\n",
       "      <td>-26.247870</td>\n",
       "      <td>-5.058589</td>\n",
       "      <td>-0.613379</td>\n",
       "      <td>-0.617263</td>\n",
       "      <td>-0.612760</td>\n",
       "      <td>-180.971675</td>\n",
       "      <td>11.128614</td>\n",
       "      <td>0.720974</td>\n",
       "      <td>0.720974</td>\n",
       "      <td>-0.996434</td>\n",
       "      <td>...</td>\n",
       "      <td>insig. $\\rightarrow$ sig.</td>\n",
       "      <td>insig. $\\rightarrow$ sig.</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>insig. $\\rightarrow$ sig.</td>\n",
       "      <td>same</td>\n",
       "      <td>insig. $\\rightarrow$ sig.</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chowdary-2006</th>\n",
       "      <td>-17.434276</td>\n",
       "      <td>-2.883345</td>\n",
       "      <td>7.086988</td>\n",
       "      <td>7.163330</td>\n",
       "      <td>6.266305</td>\n",
       "      <td>-77.902861</td>\n",
       "      <td>22.872121</td>\n",
       "      <td>6.285467</td>\n",
       "      <td>6.285467</td>\n",
       "      <td>7.530830</td>\n",
       "      <td>...</td>\n",
       "      <td>sig. $\\rightarrow$ insig.</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dyrskjot-2003</th>\n",
       "      <td>-6.534689</td>\n",
       "      <td>0.034023</td>\n",
       "      <td>8.884532</td>\n",
       "      <td>8.708112</td>\n",
       "      <td>10.497867</td>\n",
       "      <td>-14.256825</td>\n",
       "      <td>1.756332</td>\n",
       "      <td>4.587682</td>\n",
       "      <td>5.367524</td>\n",
       "      <td>7.529026</td>\n",
       "      <td>...</td>\n",
       "      <td>sig. $\\rightarrow$ insig.</td>\n",
       "      <td>sig. $\\rightarrow$ insig.</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Garber-2001</th>\n",
       "      <td>-12.339651</td>\n",
       "      <td>-5.665850</td>\n",
       "      <td>-0.122599</td>\n",
       "      <td>-0.313471</td>\n",
       "      <td>0.323424</td>\n",
       "      <td>-42.672581</td>\n",
       "      <td>-6.560096</td>\n",
       "      <td>-9.170791</td>\n",
       "      <td>-0.386445</td>\n",
       "      <td>3.685159</td>\n",
       "      <td>...</td>\n",
       "      <td>insig. $\\rightarrow$ sig.</td>\n",
       "      <td>opposite</td>\n",
       "      <td>opposite</td>\n",
       "      <td>sig. $\\rightarrow$ insig.</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>insig. $\\rightarrow$ sig.</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Golub-1999-v1</th>\n",
       "      <td>-10.131915</td>\n",
       "      <td>4.991562</td>\n",
       "      <td>43.125741</td>\n",
       "      <td>43.625798</td>\n",
       "      <td>41.176865</td>\n",
       "      <td>-2.819710</td>\n",
       "      <td>105.888898</td>\n",
       "      <td>39.796745</td>\n",
       "      <td>39.796745</td>\n",
       "      <td>39.714093</td>\n",
       "      <td>...</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>sig. $\\rightarrow$ insig.</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Golub-1999-v2</th>\n",
       "      <td>-7.752278</td>\n",
       "      <td>7.020583</td>\n",
       "      <td>35.043337</td>\n",
       "      <td>34.410706</td>\n",
       "      <td>34.464530</td>\n",
       "      <td>-1.064232</td>\n",
       "      <td>77.942839</td>\n",
       "      <td>35.825280</td>\n",
       "      <td>40.378610</td>\n",
       "      <td>40.930345</td>\n",
       "      <td>...</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>sig. $\\rightarrow$ insig.</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gordon-2002</th>\n",
       "      <td>-25.762982</td>\n",
       "      <td>-3.932149</td>\n",
       "      <td>12.070800</td>\n",
       "      <td>12.451221</td>\n",
       "      <td>12.618594</td>\n",
       "      <td>-180.065617</td>\n",
       "      <td>169.136318</td>\n",
       "      <td>3.055653</td>\n",
       "      <td>3.055653</td>\n",
       "      <td>-12.816386</td>\n",
       "      <td>...</td>\n",
       "      <td>opposite</td>\n",
       "      <td>opposite</td>\n",
       "      <td>opposite</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Khan-2001</th>\n",
       "      <td>-10.385312</td>\n",
       "      <td>1.735896</td>\n",
       "      <td>20.020452</td>\n",
       "      <td>19.790343</td>\n",
       "      <td>22.517910</td>\n",
       "      <td>-39.224801</td>\n",
       "      <td>-13.818749</td>\n",
       "      <td>4.359730</td>\n",
       "      <td>6.126311</td>\n",
       "      <td>19.549689</td>\n",
       "      <td>...</td>\n",
       "      <td>sig. $\\rightarrow$ insig.</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Laiho-2007</th>\n",
       "      <td>-8.249828</td>\n",
       "      <td>-2.355173</td>\n",
       "      <td>2.017078</td>\n",
       "      <td>1.813435</td>\n",
       "      <td>1.764101</td>\n",
       "      <td>-14.938764</td>\n",
       "      <td>21.362371</td>\n",
       "      <td>4.882401</td>\n",
       "      <td>4.882401</td>\n",
       "      <td>4.327799</td>\n",
       "      <td>...</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>insig. $\\rightarrow$ sig.</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lapointe-2004-v1</th>\n",
       "      <td>-11.713260</td>\n",
       "      <td>-2.750385</td>\n",
       "      <td>6.478402</td>\n",
       "      <td>6.327423</td>\n",
       "      <td>6.317094</td>\n",
       "      <td>-39.128784</td>\n",
       "      <td>0.657592</td>\n",
       "      <td>1.916159</td>\n",
       "      <td>4.948284</td>\n",
       "      <td>7.512667</td>\n",
       "      <td>...</td>\n",
       "      <td>sig. $\\rightarrow$ insig.</td>\n",
       "      <td>sig. $\\rightarrow$ insig.</td>\n",
       "      <td>sig. $\\rightarrow$ insig.</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lapointe-2004-v2</th>\n",
       "      <td>-17.094185</td>\n",
       "      <td>-5.340636</td>\n",
       "      <td>5.212568</td>\n",
       "      <td>5.036615</td>\n",
       "      <td>4.954668</td>\n",
       "      <td>-72.863619</td>\n",
       "      <td>-37.059073</td>\n",
       "      <td>-5.243384</td>\n",
       "      <td>-1.264266</td>\n",
       "      <td>4.937717</td>\n",
       "      <td>...</td>\n",
       "      <td>opposite</td>\n",
       "      <td>opposite</td>\n",
       "      <td>opposite</td>\n",
       "      <td>sig. $\\rightarrow$ insig.</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liang-2005</th>\n",
       "      <td>-6.491296</td>\n",
       "      <td>-0.091692</td>\n",
       "      <td>5.802837</td>\n",
       "      <td>5.925099</td>\n",
       "      <td>5.979170</td>\n",
       "      <td>-17.253356</td>\n",
       "      <td>13.070097</td>\n",
       "      <td>-0.155969</td>\n",
       "      <td>4.603180</td>\n",
       "      <td>4.642309</td>\n",
       "      <td>...</td>\n",
       "      <td>sig. $\\rightarrow$ insig.</td>\n",
       "      <td>same</td>\n",
       "      <td>sig. $\\rightarrow$ insig.</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nutt-2003-v1</th>\n",
       "      <td>-6.575029</td>\n",
       "      <td>0.394002</td>\n",
       "      <td>9.791296</td>\n",
       "      <td>9.744898</td>\n",
       "      <td>10.612386</td>\n",
       "      <td>-15.009886</td>\n",
       "      <td>-2.361018</td>\n",
       "      <td>5.569820</td>\n",
       "      <td>6.284944</td>\n",
       "      <td>12.741757</td>\n",
       "      <td>...</td>\n",
       "      <td>sig. $\\rightarrow$ insig.</td>\n",
       "      <td>sig. $\\rightarrow$ insig.</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nutt-2003-v2</th>\n",
       "      <td>-6.748144</td>\n",
       "      <td>-2.355521</td>\n",
       "      <td>0.089205</td>\n",
       "      <td>0.099035</td>\n",
       "      <td>0.011789</td>\n",
       "      <td>-13.682331</td>\n",
       "      <td>-1.586678</td>\n",
       "      <td>-0.308607</td>\n",
       "      <td>-0.308607</td>\n",
       "      <td>0.052484</td>\n",
       "      <td>...</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>insig. $\\rightarrow$ sig.</td>\n",
       "      <td>same</td>\n",
       "      <td>insig. $\\rightarrow$ sig.</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nutt-2003-v3</th>\n",
       "      <td>-3.416962</td>\n",
       "      <td>2.423147</td>\n",
       "      <td>11.969378</td>\n",
       "      <td>11.801304</td>\n",
       "      <td>11.595798</td>\n",
       "      <td>2.907610</td>\n",
       "      <td>21.987278</td>\n",
       "      <td>12.435287</td>\n",
       "      <td>12.435290</td>\n",
       "      <td>12.404263</td>\n",
       "      <td>...</td>\n",
       "      <td>sig. $\\rightarrow$ insig.</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>sig. $\\rightarrow$ insig.</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pomeroy-2002-v1</th>\n",
       "      <td>-7.955994</td>\n",
       "      <td>-2.986525</td>\n",
       "      <td>-0.499157</td>\n",
       "      <td>-0.497324</td>\n",
       "      <td>-0.498995</td>\n",
       "      <td>-18.209247</td>\n",
       "      <td>6.603529</td>\n",
       "      <td>-0.379980</td>\n",
       "      <td>-0.379980</td>\n",
       "      <td>-1.088384</td>\n",
       "      <td>...</td>\n",
       "      <td>same</td>\n",
       "      <td>insig. $\\rightarrow$ sig.</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>insig. $\\rightarrow$ sig.</td>\n",
       "      <td>same</td>\n",
       "      <td>insig. $\\rightarrow$ sig.</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pomeroy-2002-v2</th>\n",
       "      <td>-2.541306</td>\n",
       "      <td>3.447088</td>\n",
       "      <td>11.433476</td>\n",
       "      <td>11.815162</td>\n",
       "      <td>13.537443</td>\n",
       "      <td>-1.056572</td>\n",
       "      <td>9.682013</td>\n",
       "      <td>11.377889</td>\n",
       "      <td>13.100064</td>\n",
       "      <td>17.790671</td>\n",
       "      <td>...</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>sig. $\\rightarrow$ insig.</td>\n",
       "      <td>same</td>\n",
       "      <td>sig. $\\rightarrow$ insig.</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ramaswamy-2001</th>\n",
       "      <td>-13.901351</td>\n",
       "      <td>-1.841240</td>\n",
       "      <td>16.176535</td>\n",
       "      <td>17.124241</td>\n",
       "      <td>29.277253</td>\n",
       "      <td>-108.370116</td>\n",
       "      <td>-126.836082</td>\n",
       "      <td>-63.107415</td>\n",
       "      <td>-79.428278</td>\n",
       "      <td>25.113303</td>\n",
       "      <td>...</td>\n",
       "      <td>sig. $\\rightarrow$ insig.</td>\n",
       "      <td>opposite</td>\n",
       "      <td>opposite</td>\n",
       "      <td>opposite</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Risinger-2003</th>\n",
       "      <td>-8.036798</td>\n",
       "      <td>-3.654345</td>\n",
       "      <td>0.999508</td>\n",
       "      <td>1.098057</td>\n",
       "      <td>1.594077</td>\n",
       "      <td>-28.555887</td>\n",
       "      <td>-25.086813</td>\n",
       "      <td>-11.696208</td>\n",
       "      <td>-10.802740</td>\n",
       "      <td>-2.190671</td>\n",
       "      <td>...</td>\n",
       "      <td>insig. $\\rightarrow$ sig.</td>\n",
       "      <td>insig. $\\rightarrow$ sig.</td>\n",
       "      <td>insig. $\\rightarrow$ sig.</td>\n",
       "      <td>insig. $\\rightarrow$ sig.</td>\n",
       "      <td>insig. $\\rightarrow$ sig.</td>\n",
       "      <td>same</td>\n",
       "      <td>insig. $\\rightarrow$ sig.</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shipp-2002-v1</th>\n",
       "      <td>-14.357402</td>\n",
       "      <td>-3.918259</td>\n",
       "      <td>0.080767</td>\n",
       "      <td>0.085026</td>\n",
       "      <td>-0.053360</td>\n",
       "      <td>-43.670153</td>\n",
       "      <td>55.103997</td>\n",
       "      <td>10.611450</td>\n",
       "      <td>10.611450</td>\n",
       "      <td>-3.198880</td>\n",
       "      <td>...</td>\n",
       "      <td>insig. $\\rightarrow$ sig.</td>\n",
       "      <td>opposite</td>\n",
       "      <td>opposite</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>insig. $\\rightarrow$ sig.</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singh-2002</th>\n",
       "      <td>-17.423981</td>\n",
       "      <td>-3.277297</td>\n",
       "      <td>4.111547</td>\n",
       "      <td>4.164966</td>\n",
       "      <td>3.864183</td>\n",
       "      <td>-82.041801</td>\n",
       "      <td>2.527819</td>\n",
       "      <td>1.546597</td>\n",
       "      <td>1.546597</td>\n",
       "      <td>2.386477</td>\n",
       "      <td>...</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>insig. $\\rightarrow$ sig.</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Su-2001</th>\n",
       "      <td>-11.810326</td>\n",
       "      <td>4.753476</td>\n",
       "      <td>25.421435</td>\n",
       "      <td>27.581971</td>\n",
       "      <td>33.020767</td>\n",
       "      <td>-47.074279</td>\n",
       "      <td>-17.262260</td>\n",
       "      <td>4.522941</td>\n",
       "      <td>12.571753</td>\n",
       "      <td>48.746665</td>\n",
       "      <td>...</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tomlins-2006</th>\n",
       "      <td>-11.179727</td>\n",
       "      <td>2.077062</td>\n",
       "      <td>21.530153</td>\n",
       "      <td>21.319030</td>\n",
       "      <td>26.599344</td>\n",
       "      <td>-39.658160</td>\n",
       "      <td>-8.583774</td>\n",
       "      <td>8.785282</td>\n",
       "      <td>12.495782</td>\n",
       "      <td>27.683361</td>\n",
       "      <td>...</td>\n",
       "      <td>sig. $\\rightarrow$ insig.</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tomlins-2006-v2</th>\n",
       "      <td>-14.135197</td>\n",
       "      <td>-3.651593</td>\n",
       "      <td>7.987482</td>\n",
       "      <td>8.044620</td>\n",
       "      <td>8.113248</td>\n",
       "      <td>-53.045059</td>\n",
       "      <td>-30.252330</td>\n",
       "      <td>-1.029614</td>\n",
       "      <td>0.071389</td>\n",
       "      <td>5.787419</td>\n",
       "      <td>...</td>\n",
       "      <td>opposite</td>\n",
       "      <td>opposite</td>\n",
       "      <td>sig. $\\rightarrow$ insig.</td>\n",
       "      <td>sig. $\\rightarrow$ insig.</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West-2001</th>\n",
       "      <td>-10.111507</td>\n",
       "      <td>-2.723321</td>\n",
       "      <td>1.198586</td>\n",
       "      <td>1.209007</td>\n",
       "      <td>0.954182</td>\n",
       "      <td>-30.823890</td>\n",
       "      <td>-2.556604</td>\n",
       "      <td>-0.583212</td>\n",
       "      <td>-0.583212</td>\n",
       "      <td>0.008776</td>\n",
       "      <td>...</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>insig. $\\rightarrow$ sig.</td>\n",
       "      <td>same</td>\n",
       "      <td>insig. $\\rightarrow$ sig.</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yeoh-2002-v1</th>\n",
       "      <td>-28.603876</td>\n",
       "      <td>11.888964</td>\n",
       "      <td>123.625694</td>\n",
       "      <td>123.428108</td>\n",
       "      <td>123.774307</td>\n",
       "      <td>-9.007793</td>\n",
       "      <td>789.897564</td>\n",
       "      <td>158.415098</td>\n",
       "      <td>158.415098</td>\n",
       "      <td>155.033448</td>\n",
       "      <td>...</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yeoh-2002-v2</th>\n",
       "      <td>-27.144485</td>\n",
       "      <td>-2.691976</td>\n",
       "      <td>29.531602</td>\n",
       "      <td>28.161004</td>\n",
       "      <td>38.541806</td>\n",
       "      <td>-202.397278</td>\n",
       "      <td>-153.640997</td>\n",
       "      <td>-34.243250</td>\n",
       "      <td>-25.400488</td>\n",
       "      <td>37.325943</td>\n",
       "      <td>...</td>\n",
       "      <td>sig. $\\rightarrow$ insig.</td>\n",
       "      <td>opposite</td>\n",
       "      <td>opposite</td>\n",
       "      <td>opposite</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>opposite</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "      <td>same</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              value                                      \\\n",
       "metric_name        SMI$_\\text{all}$ SMI$_\\text{all}^1$ SMI$_\\text{num}$   \n",
       "dataset                                                                   \n",
       "Alizadeh-2000-v1          -8.047028          -0.056849        10.741115   \n",
       "Alizadeh-2000-v2          -6.542501           8.149059        31.401056   \n",
       "Alizadeh-2000-v3          -5.335236           5.580363        22.872140   \n",
       "Armstrong-2002-v1        -11.819882           1.050537        23.108823   \n",
       "Armstrong-2002-v2         -8.118987           5.415473        33.382242   \n",
       "Bhattacharjee-2001       -22.801583           1.591691        29.067148   \n",
       "Bittner-2000              -8.500528          -2.697391        -0.089699   \n",
       "Bredel-2005               -8.028681           0.504382        10.633686   \n",
       "Chen-2002                -26.247870          -5.058589        -0.613379   \n",
       "Chowdary-2006            -17.434276          -2.883345         7.086988   \n",
       "Dyrskjot-2003             -6.534689           0.034023         8.884532   \n",
       "Garber-2001              -12.339651          -5.665850        -0.122599   \n",
       "Golub-1999-v1            -10.131915           4.991562        43.125741   \n",
       "Golub-1999-v2             -7.752278           7.020583        35.043337   \n",
       "Gordon-2002              -25.762982          -3.932149        12.070800   \n",
       "Khan-2001                -10.385312           1.735896        20.020452   \n",
       "Laiho-2007                -8.249828          -2.355173         2.017078   \n",
       "Lapointe-2004-v1         -11.713260          -2.750385         6.478402   \n",
       "Lapointe-2004-v2         -17.094185          -5.340636         5.212568   \n",
       "Liang-2005                -6.491296          -0.091692         5.802837   \n",
       "Nutt-2003-v1              -6.575029           0.394002         9.791296   \n",
       "Nutt-2003-v2              -6.748144          -2.355521         0.089205   \n",
       "Nutt-2003-v3              -3.416962           2.423147        11.969378   \n",
       "Pomeroy-2002-v1           -7.955994          -2.986525        -0.499157   \n",
       "Pomeroy-2002-v2           -2.541306           3.447088        11.433476   \n",
       "Ramaswamy-2001           -13.901351          -1.841240        16.176535   \n",
       "Risinger-2003             -8.036798          -3.654345         0.999508   \n",
       "Shipp-2002-v1            -14.357402          -3.918259         0.080767   \n",
       "Singh-2002               -17.423981          -3.277297         4.111547   \n",
       "Su-2001                  -11.810326           4.753476        25.421435   \n",
       "Tomlins-2006             -11.179727           2.077062        21.530153   \n",
       "Tomlins-2006-v2          -14.135197          -3.651593         7.987482   \n",
       "West-2001                -10.111507          -2.723321         1.198586   \n",
       "Yeoh-2002-v1             -28.603876          11.888964       123.625694   \n",
       "Yeoh-2002-v2             -27.144485          -2.691976        29.531602   \n",
       "\n",
       "                                                                          \\\n",
       "metric_name        SMI$_\\text{num}^1$ SMI$_\\text{perm}$ SRI$_\\text{all}$   \n",
       "dataset                                                                    \n",
       "Alizadeh-2000-v1            10.940811         10.778074       -12.384203   \n",
       "Alizadeh-2000-v2            29.793395         30.582218         6.926347   \n",
       "Alizadeh-2000-v3            22.954725         23.171800        -8.886907   \n",
       "Armstrong-2002-v1           22.056448         22.412493       -36.953991   \n",
       "Armstrong-2002-v2           33.845320         35.403753        -5.209109   \n",
       "Bhattacharjee-2001          28.204540         30.091475      -169.964956   \n",
       "Bittner-2000                -0.080493         -0.069886       -20.981778   \n",
       "Bredel-2005                 10.406122         10.584589       -13.649338   \n",
       "Chen-2002                   -0.617263         -0.612760      -180.971675   \n",
       "Chowdary-2006                7.163330          6.266305       -77.902861   \n",
       "Dyrskjot-2003                8.708112         10.497867       -14.256825   \n",
       "Garber-2001                 -0.313471          0.323424       -42.672581   \n",
       "Golub-1999-v1               43.625798         41.176865        -2.819710   \n",
       "Golub-1999-v2               34.410706         34.464530        -1.064232   \n",
       "Gordon-2002                 12.451221         12.618594      -180.065617   \n",
       "Khan-2001                   19.790343         22.517910       -39.224801   \n",
       "Laiho-2007                   1.813435          1.764101       -14.938764   \n",
       "Lapointe-2004-v1             6.327423          6.317094       -39.128784   \n",
       "Lapointe-2004-v2             5.036615          4.954668       -72.863619   \n",
       "Liang-2005                   5.925099          5.979170       -17.253356   \n",
       "Nutt-2003-v1                 9.744898         10.612386       -15.009886   \n",
       "Nutt-2003-v2                 0.099035          0.011789       -13.682331   \n",
       "Nutt-2003-v3                11.801304         11.595798         2.907610   \n",
       "Pomeroy-2002-v1             -0.497324         -0.498995       -18.209247   \n",
       "Pomeroy-2002-v2             11.815162         13.537443        -1.056572   \n",
       "Ramaswamy-2001              17.124241         29.277253      -108.370116   \n",
       "Risinger-2003                1.098057          1.594077       -28.555887   \n",
       "Shipp-2002-v1                0.085026         -0.053360       -43.670153   \n",
       "Singh-2002                   4.164966          3.864183       -82.041801   \n",
       "Su-2001                     27.581971         33.020767       -47.074279   \n",
       "Tomlins-2006                21.319030         26.599344       -39.658160   \n",
       "Tomlins-2006-v2              8.044620          8.113248       -53.045059   \n",
       "West-2001                    1.209007          0.954182       -30.823890   \n",
       "Yeoh-2002-v1               123.428108        123.774307        -9.007793   \n",
       "Yeoh-2002-v2                28.161004         38.541806      -202.397278   \n",
       "\n",
       "                                                                           \\\n",
       "metric_name        SRI$_\\text{all}^1$ SRI$_\\text{num}$ SRI$_\\text{num}^1$   \n",
       "dataset                                                                     \n",
       "Alizadeh-2000-v1            20.542693        10.803331          10.803331   \n",
       "Alizadeh-2000-v2            95.954295        36.580792          43.790065   \n",
       "Alizadeh-2000-v3            20.391458        17.581156          20.671998   \n",
       "Armstrong-2002-v1           40.442147        12.105173          12.105173   \n",
       "Armstrong-2002-v2           50.673420        32.441782          34.084750   \n",
       "Bhattacharjee-2001          97.173463       -24.898005          34.252008   \n",
       "Bittner-2000                -1.272655        -0.037716          -0.037716   \n",
       "Bredel-2005                 27.769621        11.525334          15.334116   \n",
       "Chen-2002                   11.128614         0.720974           0.720974   \n",
       "Chowdary-2006               22.872121         6.285467           6.285467   \n",
       "Dyrskjot-2003                1.756332         4.587682           5.367524   \n",
       "Garber-2001                 -6.560096        -9.170791          -0.386445   \n",
       "Golub-1999-v1              105.888898        39.796745          39.796745   \n",
       "Golub-1999-v2               77.942839        35.825280          40.378610   \n",
       "Gordon-2002                169.136318         3.055653           3.055653   \n",
       "Khan-2001                  -13.818749         4.359730           6.126311   \n",
       "Laiho-2007                  21.362371         4.882401           4.882401   \n",
       "Lapointe-2004-v1             0.657592         1.916159           4.948284   \n",
       "Lapointe-2004-v2           -37.059073        -5.243384          -1.264266   \n",
       "Liang-2005                  13.070097        -0.155969           4.603180   \n",
       "Nutt-2003-v1                -2.361018         5.569820           6.284944   \n",
       "Nutt-2003-v2                -1.586678        -0.308607          -0.308607   \n",
       "Nutt-2003-v3                21.987278        12.435287          12.435290   \n",
       "Pomeroy-2002-v1              6.603529        -0.379980          -0.379980   \n",
       "Pomeroy-2002-v2              9.682013        11.377889          13.100064   \n",
       "Ramaswamy-2001            -126.836082       -63.107415         -79.428278   \n",
       "Risinger-2003              -25.086813       -11.696208         -10.802740   \n",
       "Shipp-2002-v1               55.103997        10.611450          10.611450   \n",
       "Singh-2002                   2.527819         1.546597           1.546597   \n",
       "Su-2001                    -17.262260         4.522941          12.571753   \n",
       "Tomlins-2006                -8.583774         8.785282          12.495782   \n",
       "Tomlins-2006-v2            -30.252330        -1.029614           0.071389   \n",
       "West-2001                   -2.556604        -0.583212          -0.583212   \n",
       "Yeoh-2002-v1               789.897564       158.415098         158.415098   \n",
       "Yeoh-2002-v2              -153.640997       -34.243250         -25.400488   \n",
       "\n",
       "                                      ...                   category  \\\n",
       "metric_name        SRI$_\\text{perm}$  ...         SMI$_\\text{all}^1$   \n",
       "dataset                               ...                              \n",
       "Alizadeh-2000-v1           10.790696  ...  sig. $\\rightarrow$ insig.   \n",
       "Alizadeh-2000-v2           41.199148  ...                       same   \n",
       "Alizadeh-2000-v3           25.046741  ...                       same   \n",
       "Armstrong-2002-v1          12.092724  ...  sig. $\\rightarrow$ insig.   \n",
       "Armstrong-2002-v2          36.217783  ...                       same   \n",
       "Bhattacharjee-2001         30.727530  ...  sig. $\\rightarrow$ insig.   \n",
       "Bittner-2000               -0.057084  ...                       same   \n",
       "Bredel-2005                15.230404  ...  sig. $\\rightarrow$ insig.   \n",
       "Chen-2002                  -0.996434  ...  insig. $\\rightarrow$ sig.   \n",
       "Chowdary-2006               7.530830  ...  sig. $\\rightarrow$ insig.   \n",
       "Dyrskjot-2003               7.529026  ...  sig. $\\rightarrow$ insig.   \n",
       "Garber-2001                 3.685159  ...  insig. $\\rightarrow$ sig.   \n",
       "Golub-1999-v1              39.714093  ...                       same   \n",
       "Golub-1999-v2              40.930345  ...                       same   \n",
       "Gordon-2002               -12.816386  ...                   opposite   \n",
       "Khan-2001                  19.549689  ...  sig. $\\rightarrow$ insig.   \n",
       "Laiho-2007                  4.327799  ...                       same   \n",
       "Lapointe-2004-v1            7.512667  ...  sig. $\\rightarrow$ insig.   \n",
       "Lapointe-2004-v2            4.937717  ...                   opposite   \n",
       "Liang-2005                  4.642309  ...  sig. $\\rightarrow$ insig.   \n",
       "Nutt-2003-v1               12.741757  ...  sig. $\\rightarrow$ insig.   \n",
       "Nutt-2003-v2                0.052484  ...                       same   \n",
       "Nutt-2003-v3               12.404263  ...  sig. $\\rightarrow$ insig.   \n",
       "Pomeroy-2002-v1            -1.088384  ...                       same   \n",
       "Pomeroy-2002-v2            17.790671  ...                       same   \n",
       "Ramaswamy-2001             25.113303  ...  sig. $\\rightarrow$ insig.   \n",
       "Risinger-2003              -2.190671  ...  insig. $\\rightarrow$ sig.   \n",
       "Shipp-2002-v1              -3.198880  ...  insig. $\\rightarrow$ sig.   \n",
       "Singh-2002                  2.386477  ...                   opposite   \n",
       "Su-2001                    48.746665  ...                       same   \n",
       "Tomlins-2006               27.683361  ...  sig. $\\rightarrow$ insig.   \n",
       "Tomlins-2006-v2             5.787419  ...                   opposite   \n",
       "West-2001                   0.008776  ...                       same   \n",
       "Yeoh-2002-v1              155.033448  ...                       same   \n",
       "Yeoh-2002-v2               37.325943  ...  sig. $\\rightarrow$ insig.   \n",
       "\n",
       "                                                                          \\\n",
       "metric_name                SRI$_\\text{all}^1$           SRI$_\\text{num}$   \n",
       "dataset                                                                    \n",
       "Alizadeh-2000-v1                         same                       same   \n",
       "Alizadeh-2000-v2                         same                       same   \n",
       "Alizadeh-2000-v3                         same                       same   \n",
       "Armstrong-2002-v1                        same                       same   \n",
       "Armstrong-2002-v2                        same                       same   \n",
       "Bhattacharjee-2001                       same                   opposite   \n",
       "Bittner-2000                             same                       same   \n",
       "Bredel-2005                              same                       same   \n",
       "Chen-2002           insig. $\\rightarrow$ sig.                       same   \n",
       "Chowdary-2006                            same                       same   \n",
       "Dyrskjot-2003       sig. $\\rightarrow$ insig.                       same   \n",
       "Garber-2001                          opposite                   opposite   \n",
       "Golub-1999-v1                            same                       same   \n",
       "Golub-1999-v2                            same                       same   \n",
       "Gordon-2002                          opposite                   opposite   \n",
       "Khan-2001                            opposite                       same   \n",
       "Laiho-2007                               same                       same   \n",
       "Lapointe-2004-v1    sig. $\\rightarrow$ insig.  sig. $\\rightarrow$ insig.   \n",
       "Lapointe-2004-v2                     opposite                   opposite   \n",
       "Liang-2005                               same  sig. $\\rightarrow$ insig.   \n",
       "Nutt-2003-v1        sig. $\\rightarrow$ insig.                       same   \n",
       "Nutt-2003-v2                             same                       same   \n",
       "Nutt-2003-v3                             same                       same   \n",
       "Pomeroy-2002-v1     insig. $\\rightarrow$ sig.                       same   \n",
       "Pomeroy-2002-v2                          same                       same   \n",
       "Ramaswamy-2001                       opposite                   opposite   \n",
       "Risinger-2003       insig. $\\rightarrow$ sig.  insig. $\\rightarrow$ sig.   \n",
       "Shipp-2002-v1                        opposite                   opposite   \n",
       "Singh-2002                               same                       same   \n",
       "Su-2001                              opposite                       same   \n",
       "Tomlins-2006                         opposite                       same   \n",
       "Tomlins-2006-v2                      opposite  sig. $\\rightarrow$ insig.   \n",
       "West-2001                                same                       same   \n",
       "Yeoh-2002-v1                             same                       same   \n",
       "Yeoh-2002-v2                         opposite                   opposite   \n",
       "\n",
       "                                                                          \\\n",
       "metric_name                SRI$_\\text{num}^1$           SRI$_\\text{all}$   \n",
       "dataset                                                                    \n",
       "Alizadeh-2000-v1                         same                   opposite   \n",
       "Alizadeh-2000-v2                         same                       same   \n",
       "Alizadeh-2000-v3                         same                   opposite   \n",
       "Armstrong-2002-v1                        same                   opposite   \n",
       "Armstrong-2002-v2                        same                   opposite   \n",
       "Bhattacharjee-2001                       same                   opposite   \n",
       "Bittner-2000                             same  insig. $\\rightarrow$ sig.   \n",
       "Bredel-2005                              same                   opposite   \n",
       "Chen-2002                                same  insig. $\\rightarrow$ sig.   \n",
       "Chowdary-2006                            same                   opposite   \n",
       "Dyrskjot-2003                            same                   opposite   \n",
       "Garber-2001         sig. $\\rightarrow$ insig.                   opposite   \n",
       "Golub-1999-v1                            same  sig. $\\rightarrow$ insig.   \n",
       "Golub-1999-v2                            same  sig. $\\rightarrow$ insig.   \n",
       "Gordon-2002                          opposite                       same   \n",
       "Khan-2001                                same                   opposite   \n",
       "Laiho-2007                               same                   opposite   \n",
       "Lapointe-2004-v1                         same                   opposite   \n",
       "Lapointe-2004-v2    sig. $\\rightarrow$ insig.                   opposite   \n",
       "Liang-2005                               same                   opposite   \n",
       "Nutt-2003-v1                             same                   opposite   \n",
       "Nutt-2003-v2                             same  insig. $\\rightarrow$ sig.   \n",
       "Nutt-2003-v3                             same  sig. $\\rightarrow$ insig.   \n",
       "Pomeroy-2002-v1                          same  insig. $\\rightarrow$ sig.   \n",
       "Pomeroy-2002-v2                          same  sig. $\\rightarrow$ insig.   \n",
       "Ramaswamy-2001                       opposite                   opposite   \n",
       "Risinger-2003       insig. $\\rightarrow$ sig.  insig. $\\rightarrow$ sig.   \n",
       "Shipp-2002-v1                        opposite                       same   \n",
       "Singh-2002                               same  insig. $\\rightarrow$ sig.   \n",
       "Su-2001                                  same                   opposite   \n",
       "Tomlins-2006                             same                   opposite   \n",
       "Tomlins-2006-v2     sig. $\\rightarrow$ insig.                   opposite   \n",
       "West-2001                                same  insig. $\\rightarrow$ sig.   \n",
       "Yeoh-2002-v1                             same                   opposite   \n",
       "Yeoh-2002-v2                         opposite                   opposite   \n",
       "\n",
       "                                                                  \\\n",
       "metric_name        SMI$_\\text{num}^1$           SMI$_\\text{all}$   \n",
       "dataset                                                            \n",
       "Alizadeh-2000-v1                 same                   opposite   \n",
       "Alizadeh-2000-v2                 same                   opposite   \n",
       "Alizadeh-2000-v3                 same                   opposite   \n",
       "Armstrong-2002-v1                same                   opposite   \n",
       "Armstrong-2002-v2                same                   opposite   \n",
       "Bhattacharjee-2001               same                   opposite   \n",
       "Bittner-2000                     same  insig. $\\rightarrow$ sig.   \n",
       "Bredel-2005                      same                   opposite   \n",
       "Chen-2002                        same  insig. $\\rightarrow$ sig.   \n",
       "Chowdary-2006                    same                   opposite   \n",
       "Dyrskjot-2003                    same                   opposite   \n",
       "Garber-2001                      same  insig. $\\rightarrow$ sig.   \n",
       "Golub-1999-v1                    same                   opposite   \n",
       "Golub-1999-v2                    same                   opposite   \n",
       "Gordon-2002                      same                   opposite   \n",
       "Khan-2001                        same                   opposite   \n",
       "Laiho-2007                       same  insig. $\\rightarrow$ sig.   \n",
       "Lapointe-2004-v1                 same                   opposite   \n",
       "Lapointe-2004-v2                 same                   opposite   \n",
       "Liang-2005                       same                   opposite   \n",
       "Nutt-2003-v1                     same                   opposite   \n",
       "Nutt-2003-v2                     same  insig. $\\rightarrow$ sig.   \n",
       "Nutt-2003-v3                     same                   opposite   \n",
       "Pomeroy-2002-v1                  same  insig. $\\rightarrow$ sig.   \n",
       "Pomeroy-2002-v2                  same  sig. $\\rightarrow$ insig.   \n",
       "Ramaswamy-2001                   same                   opposite   \n",
       "Risinger-2003                    same  insig. $\\rightarrow$ sig.   \n",
       "Shipp-2002-v1                    same  insig. $\\rightarrow$ sig.   \n",
       "Singh-2002                       same                   opposite   \n",
       "Su-2001                          same                   opposite   \n",
       "Tomlins-2006                     same                   opposite   \n",
       "Tomlins-2006-v2                  same                   opposite   \n",
       "West-2001                        same  insig. $\\rightarrow$ sig.   \n",
       "Yeoh-2002-v1                     same                   opposite   \n",
       "Yeoh-2002-v2                     same                   opposite   \n",
       "\n",
       "                                                                         \n",
       "metric_name        SRI$_\\text{perm}$ SMI$_\\text{num}$ SMI$_\\text{perm}$  \n",
       "dataset                                                                  \n",
       "Alizadeh-2000-v1                same             same              same  \n",
       "Alizadeh-2000-v2                same             same              same  \n",
       "Alizadeh-2000-v3                same             same              same  \n",
       "Armstrong-2002-v1               same             same              same  \n",
       "Armstrong-2002-v2               same             same              same  \n",
       "Bhattacharjee-2001              same             same              same  \n",
       "Bittner-2000                    same             same              same  \n",
       "Bredel-2005                     same             same              same  \n",
       "Chen-2002                       same             same              same  \n",
       "Chowdary-2006                   same             same              same  \n",
       "Dyrskjot-2003                   same             same              same  \n",
       "Garber-2001                     same             same              same  \n",
       "Golub-1999-v1                   same             same              same  \n",
       "Golub-1999-v2                   same             same              same  \n",
       "Gordon-2002                     same             same              same  \n",
       "Khan-2001                       same             same              same  \n",
       "Laiho-2007                      same             same              same  \n",
       "Lapointe-2004-v1                same             same              same  \n",
       "Lapointe-2004-v2                same             same              same  \n",
       "Liang-2005                      same             same              same  \n",
       "Nutt-2003-v1                    same             same              same  \n",
       "Nutt-2003-v2                    same             same              same  \n",
       "Nutt-2003-v3                    same             same              same  \n",
       "Pomeroy-2002-v1                 same             same              same  \n",
       "Pomeroy-2002-v2                 same             same              same  \n",
       "Ramaswamy-2001                  same             same              same  \n",
       "Risinger-2003                   same             same              same  \n",
       "Shipp-2002-v1                   same             same              same  \n",
       "Singh-2002                      same             same              same  \n",
       "Su-2001                         same             same              same  \n",
       "Tomlins-2006                    same             same              same  \n",
       "Tomlins-2006-v2                 same             same              same  \n",
       "West-2001                       same             same              same  \n",
       "Yeoh-2002-v1                    same             same              same  \n",
       "Yeoh-2002-v2                    same             same              same  \n",
       "\n",
       "[35 rows x 40 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"results/real.csv\")\n",
    "df = df[(df['algorithm'] == algorithm) & (df['adjustment'] == 'STANDARDIZED')]\n",
    "\n",
    "# Assemble metric names for plotting\n",
    "df[\"one_sided\"] = (df[\"random_model_true\"] == \"PERM\") & (\n",
    "    df[\"random_model_true\"] != df[\"random_model_pred\"])\n",
    "df[\"metric_name\"] = df.apply(lambda x: f\"{'' if ((x['method_family'] == 'RI') and (x['adjustment'] == 'NORMALIZED')) or (\n",
    "    x['adjustment'] == 'NONE') else x['adjustment'][0]}{x['method_family']}$_\\\\text{{{x['random_model_pred'].lower()}}}{'^1' if x['one_sided'] else ''}$\", axis=1)\n",
    "\n",
    "\n",
    "data = df.pivot(\n",
    "    index=\"dataset\",\n",
    "    columns=\"metric_name\",\n",
    "    values=[\"value\", \"confidence_low\", \"confidence_high\"],\n",
    ")\n",
    "\n",
    "\n",
    "def category_assignment(val, baseline, nsigma=3):\n",
    "    if not (abs(baseline) > nsigma) and (abs(val) > nsigma):\n",
    "        return r\"insig. $\\rightarrow$ sig.\"\n",
    "    elif (abs(baseline) > nsigma) and not (abs(val) > nsigma):\n",
    "        return r\"sig. $\\rightarrow$ insig.\"\n",
    "    elif (not (abs(baseline) > nsigma) and not (abs(val) > nsigma)) or (((val < 0) and (baseline < 0)) or ((val > 0) and (baseline > 0)) and ((abs(baseline) > nsigma) and (abs(val) > nsigma))):\n",
    "        return r\"same\"\n",
    "    else:\n",
    "        return r\"opposite\"\n",
    "    \n",
    "\n",
    "for metric_name in set(data.columns.levels[1]):\n",
    "    baseline_metric = metric_name.split(\"$\")[0] + r\"$_\\text{perm}$\"\n",
    "    data[(\"category\", metric_name)] = data[[(\"value\", metric_name), (\"value\", baseline_metric)]].apply(\n",
    "        lambda x: category_assignment(x[0], x[1], nsigma), raw=True, axis=1\n",
    "    )\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get tabular results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrrrrrrrr}\n",
      "\\toprule\n",
      " & SMI$_\\text{all}^1$ & SRI$_\\text{all}^1$ & SRI$_\\text{num}$ & SRI$_\\text{num}^1$ & SRI$_\\text{all}$ & SMI$_\\text{num}^1$ & SMI$_\\text{all}$ & SRI$_\\text{perm}$ & SMI$_\\text{num}$ & SMI$_\\text{perm}$ \\\\\n",
      "\\midrule\n",
      "sig. $\\rightarrow$ insig. & 14 & 3 & 3 & 3 & 4 & 0 & 1 & 0 & 0 & 0 \\\\\n",
      "same & 13 & 19 & 24 & 27 & 3 & 35 & 0 & 35 & 35 & 35 \\\\\n",
      "insig. $\\rightarrow$ sig. & 4 & 3 & 1 & 1 & 7 & 0 & 9 & 0 & 0 & 0 \\\\\n",
      "opposite & 4 & 10 & 7 & 4 & 21 & 0 & 25 & 0 & 0 & 0 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "for metric_name in data[\"category\"].columns:\n",
    "    results.append(data[\"category\"][metric_name].value_counts().rename(metric_name))\n",
    "results = pd.concat(results, axis=1).fillna(0).astype(int)\n",
    "print(results.to_latex())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot individual comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kai/mambaforge/envs/random_models/lib/python3.12/site-packages/matplotlib/cbook.py:1699: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
      "  return math.isfinite(val)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 335.852x335.852 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "method_1 = r\"SRI$_\\text{perm}$\"\n",
    "method_2 = r\"SRI$_\\text{num}^1$\"\n",
    "\n",
    "fig, ax = plt.subplots(figsize=figsizes[\"half_width_square\"])\n",
    "\n",
    "ax.axvline(0.0, color=\"black\")\n",
    "ax.axhline(0.0, color=\"black\")\n",
    "\n",
    "\n",
    "x_lim = [data[('value', method_1)].min(), data[('value', method_1)].max()]\n",
    "y_lim = [data[('value', method_2)].min(), data[('value', method_2)].max()]\n",
    "\n",
    "x_lim = [-7, 50]\n",
    "y_lim = [-20, 50]\n",
    "\n",
    "ax.fill_between(x_lim, -nsigma, nsigma, color=\"lightgrey\", alpha=1)\n",
    "ax.fill_between([-nsigma, nsigma], y_lim[0],\n",
    "                y_lim[1], color=\"lightgrey\", alpha=1)\n",
    "\n",
    "categories = sorted(data[(\"category\", method_2)].unique())\n",
    "\n",
    "\n",
    "for category, color in zip(categories, [\"tab:orange\", \"tab:pink\", \"tab:green\", \"tab:blue\"]):\n",
    "    df_selected = data[data[(\"category\", method_2)] == category].copy(deep=True)\n",
    "    if len(df_selected) == 0:\n",
    "        continue\n",
    "    # if category != \"same\":\n",
    "    #     print(df_selected[[(\"value\", method_1), (\"value\", method_2)]])\n",
    "\n",
    "    xerr = [\n",
    "        df_selected[\"value\"][method_1]\n",
    "        - df_selected[\"confidence_low\"][method_1],\n",
    "        df_selected[\"confidence_high\"][method_1]\n",
    "        - df_selected[\"value\"][method_1],\n",
    "    ]\n",
    "\n",
    "    yerr = [\n",
    "        df_selected[\"value\"][method_2]\n",
    "        - df_selected[\"confidence_low\"][method_2],\n",
    "        df_selected[\"confidence_high\"][method_2]\n",
    "        - df_selected[\"value\"][method_2],\n",
    "    ]\n",
    "\n",
    "    # ax.scatter(\n",
    "    #     df_selected[\"value\"][model_1_str],\n",
    "    #     df_selected[\"value\"][model_2_str],\n",
    "    #     color=color,\n",
    "    #     marker=marker,\n",
    "    #     s=size,\n",
    "    #     zorder=10,\n",
    "    # )\n",
    "    ax.errorbar(\n",
    "        df_selected[\"value\"][method_1],\n",
    "        df_selected[\"value\"][method_2],\n",
    "        xerr=xerr,\n",
    "        yerr=yerr,\n",
    "        fmt='x',\n",
    "        color=color,\n",
    "    )\n",
    "\n",
    "ax.set_aspect(\"equal\")\n",
    "x_min, x_max = ax.get_xlim()\n",
    "y_min, y_max = ax.get_ylim()\n",
    "xy_min, xy_max = min(x_min, y_min), max(x_max, y_max)\n",
    "ax.set_xlim([xy_min, xy_max])\n",
    "ax.set_ylim([xy_min, xy_max])\n",
    "\n",
    "ax.plot(\n",
    "    [xy_min, xy_max],\n",
    "    [xy_min, xy_max],\n",
    "    color=\"black\",\n",
    "    linestyle=\"dashed\",\n",
    ")\n",
    "ax.set_aspect(\"auto\")\n",
    "\n",
    "ax.set_xlim(x_lim)\n",
    "ax.set_ylim(y_lim)\n",
    "\n",
    "ax.set_xlabel(method_1)\n",
    "ax.set_ylabel(method_2)\n",
    "\n",
    "# Annotations for x-axis on the left and right outside the plot area\n",
    "ax.annotate(\n",
    "    \"Worse than\\nchance\",\n",
    "    xy=(0, 0),\n",
    "    xycoords=\"axes fraction\",\n",
    "    xytext=(10, -20),\n",
    "    textcoords=\"offset points\",\n",
    "    ha=\"center\",\n",
    "    va=\"top\",\n",
    ")\n",
    "ax.annotate(\n",
    "    \"Better than\\nchance\",\n",
    "    xy=(1, 0),\n",
    "    xycoords=\"axes fraction\",\n",
    "    xytext=(-10, -20),\n",
    "    textcoords=\"offset points\",\n",
    "    ha=\"center\",\n",
    "    va=\"top\",\n",
    ")\n",
    "\n",
    "# Annotations for y-axis on the top and bottom outside the plot area\n",
    "ax.annotate(\n",
    "    \"Better than\\nchance\",\n",
    "    xy=(0, 1),\n",
    "    xycoords=\"axes fraction\",\n",
    "    xytext=(-40, -30),\n",
    "    textcoords=\"offset points\",\n",
    "    ha=\"center\",\n",
    "    va=\"center\",\n",
    "    rotation=\"vertical\",\n",
    ")\n",
    "ax.annotate(\n",
    "    \"Worse than\\nchance\",\n",
    "    xy=(0, 0),\n",
    "    xycoords=\"axes fraction\",\n",
    "    xytext=(-40, 30),\n",
    "    textcoords=\"offset points\",\n",
    "    ha=\"center\",\n",
    "    va=\"center\",\n",
    "    rotation=\"vertical\",\n",
    ")\n",
    "\n",
    "# # Add labels I, II, III, IV to the quadrants\n",
    "# ax.text(0.98, 0.95, r\"\\textbf{I}\", ha=\"right\",\n",
    "#         va=\"center\", transform=ax.transAxes)\n",
    "# ax.text(0.02, 0.95, r\"\\textbf{II}\", ha=\"left\",\n",
    "#         va=\"center\", transform=ax.transAxes)\n",
    "# ax.text(0.02, 0.05, r\"\\textbf{III}\", ha=\"left\",\n",
    "#         va=\"center\", transform=ax.transAxes)\n",
    "# ax.text(0.98, 0.05, r\"\\textbf{IV}\", ha=\"right\",\n",
    "#         va=\"center\", transform=ax.transAxes)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(f\"./results/SRI_perm_vs_num1.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Spearman Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_sided = False\n",
    "\n",
    "df = pd.read_csv(\"results/real.csv\")\n",
    "df = df[df[\"adjustment\"].isin([\"ADJUSTED\", \"STANDARDIZED\"])]\n",
    "\n",
    "# Assemble metric names for plotting\n",
    "df[\"one_sided\"] = (df[\"random_model_true\"] == \"PERM\") & (\n",
    "    df[\"random_model_true\"] != df[\"random_model_pred\"])\n",
    "df[\"metric_name\"] = df.apply(lambda x: f\"{'' if ((x['method_family'] == 'RI') and (x['adjustment'] == 'NORMALIZED')) or (\n",
    "    x['adjustment'] == 'NONE') else x['adjustment'][0]}{x['method_family']}$_\\\\text{{{x['random_model_pred'].lower()}}}{'^1' if x['one_sided'] else ''}$\", axis=1)\n",
    "\n",
    "df = df[(df['one_sided'] == one_sided) | ((df['random_model_pred'] == \"PERM\") & (df['random_model_true'] == \"PERM\"))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>metric_name</th>\n",
       "      <th>AMI$_\\text{all}$</th>\n",
       "      <th>SMI$_\\text{all}$</th>\n",
       "      <th>ARI$_\\text{all}$</th>\n",
       "      <th>SRI$_\\text{all}$</th>\n",
       "      <th>ARI$_\\text{num}$</th>\n",
       "      <th>SRI$_\\text{num}$</th>\n",
       "      <th>ARI$_\\text{perm}$</th>\n",
       "      <th>SRI$_\\text{perm}$</th>\n",
       "      <th>SMI$_\\text{num}$</th>\n",
       "      <th>SMI$_\\text{perm}$</th>\n",
       "      <th>AMI$_\\text{num}$</th>\n",
       "      <th>AMI$_\\text{perm}$</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metric_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AMI$_\\text{all}$</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.799844</td>\n",
       "      <td>0.758471</td>\n",
       "      <td>0.711205</td>\n",
       "      <td>0.282921</td>\n",
       "      <td>0.170182</td>\n",
       "      <td>0.654019</td>\n",
       "      <td>0.528610</td>\n",
       "      <td>0.440597</td>\n",
       "      <td>0.471940</td>\n",
       "      <td>0.688019</td>\n",
       "      <td>0.677207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMI$_\\text{all}$</th>\n",
       "      <td>0.799844</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.846757</td>\n",
       "      <td>0.887464</td>\n",
       "      <td>0.311516</td>\n",
       "      <td>0.191201</td>\n",
       "      <td>0.329117</td>\n",
       "      <td>0.096642</td>\n",
       "      <td>0.017687</td>\n",
       "      <td>-0.007487</td>\n",
       "      <td>0.324061</td>\n",
       "      <td>0.276407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARI$_\\text{all}$</th>\n",
       "      <td>0.758471</td>\n",
       "      <td>0.846757</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.980183</td>\n",
       "      <td>0.594359</td>\n",
       "      <td>0.477002</td>\n",
       "      <td>0.564195</td>\n",
       "      <td>0.338303</td>\n",
       "      <td>0.288672</td>\n",
       "      <td>0.248632</td>\n",
       "      <td>0.513135</td>\n",
       "      <td>0.465955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRI$_\\text{all}$</th>\n",
       "      <td>0.711205</td>\n",
       "      <td>0.887464</td>\n",
       "      <td>0.980183</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.539557</td>\n",
       "      <td>0.420336</td>\n",
       "      <td>0.462011</td>\n",
       "      <td>0.221045</td>\n",
       "      <td>0.175498</td>\n",
       "      <td>0.129254</td>\n",
       "      <td>0.410308</td>\n",
       "      <td>0.360064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARI$_\\text{num}$</th>\n",
       "      <td>0.282921</td>\n",
       "      <td>0.311516</td>\n",
       "      <td>0.594359</td>\n",
       "      <td>0.539557</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.969417</td>\n",
       "      <td>0.518556</td>\n",
       "      <td>0.384774</td>\n",
       "      <td>0.488518</td>\n",
       "      <td>0.402129</td>\n",
       "      <td>0.450490</td>\n",
       "      <td>0.390333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRI$_\\text{num}$</th>\n",
       "      <td>0.170182</td>\n",
       "      <td>0.191201</td>\n",
       "      <td>0.477002</td>\n",
       "      <td>0.420336</td>\n",
       "      <td>0.969417</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.434428</td>\n",
       "      <td>0.337114</td>\n",
       "      <td>0.470547</td>\n",
       "      <td>0.386283</td>\n",
       "      <td>0.371505</td>\n",
       "      <td>0.315046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARI$_\\text{perm}$</th>\n",
       "      <td>0.654019</td>\n",
       "      <td>0.329117</td>\n",
       "      <td>0.564195</td>\n",
       "      <td>0.462011</td>\n",
       "      <td>0.518556</td>\n",
       "      <td>0.434428</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.924137</td>\n",
       "      <td>0.781918</td>\n",
       "      <td>0.780579</td>\n",
       "      <td>0.875699</td>\n",
       "      <td>0.896309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRI$_\\text{perm}$</th>\n",
       "      <td>0.528610</td>\n",
       "      <td>0.096642</td>\n",
       "      <td>0.338303</td>\n",
       "      <td>0.221045</td>\n",
       "      <td>0.384774</td>\n",
       "      <td>0.337114</td>\n",
       "      <td>0.924137</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.854767</td>\n",
       "      <td>0.884446</td>\n",
       "      <td>0.824316</td>\n",
       "      <td>0.868128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMI$_\\text{num}$</th>\n",
       "      <td>0.440597</td>\n",
       "      <td>0.017687</td>\n",
       "      <td>0.288672</td>\n",
       "      <td>0.175498</td>\n",
       "      <td>0.488518</td>\n",
       "      <td>0.470547</td>\n",
       "      <td>0.781918</td>\n",
       "      <td>0.854767</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.970730</td>\n",
       "      <td>0.917119</td>\n",
       "      <td>0.909983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMI$_\\text{perm}$</th>\n",
       "      <td>0.471940</td>\n",
       "      <td>-0.007487</td>\n",
       "      <td>0.248632</td>\n",
       "      <td>0.129254</td>\n",
       "      <td>0.402129</td>\n",
       "      <td>0.386283</td>\n",
       "      <td>0.780579</td>\n",
       "      <td>0.884446</td>\n",
       "      <td>0.970730</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.891579</td>\n",
       "      <td>0.921226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMI$_\\text{num}$</th>\n",
       "      <td>0.688019</td>\n",
       "      <td>0.324061</td>\n",
       "      <td>0.513135</td>\n",
       "      <td>0.410308</td>\n",
       "      <td>0.450490</td>\n",
       "      <td>0.371505</td>\n",
       "      <td>0.875699</td>\n",
       "      <td>0.824316</td>\n",
       "      <td>0.917119</td>\n",
       "      <td>0.891579</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.967099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMI$_\\text{perm}$</th>\n",
       "      <td>0.677207</td>\n",
       "      <td>0.276407</td>\n",
       "      <td>0.465955</td>\n",
       "      <td>0.360064</td>\n",
       "      <td>0.390333</td>\n",
       "      <td>0.315046</td>\n",
       "      <td>0.896309</td>\n",
       "      <td>0.868128</td>\n",
       "      <td>0.909983</td>\n",
       "      <td>0.921226</td>\n",
       "      <td>0.967099</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "metric_name        AMI$_\\text{all}$  SMI$_\\text{all}$  ARI$_\\text{all}$  \\\n",
       "metric_name                                                               \n",
       "AMI$_\\text{all}$           1.000000          0.799844          0.758471   \n",
       "SMI$_\\text{all}$           0.799844          1.000000          0.846757   \n",
       "ARI$_\\text{all}$           0.758471          0.846757          1.000000   \n",
       "SRI$_\\text{all}$           0.711205          0.887464          0.980183   \n",
       "ARI$_\\text{num}$           0.282921          0.311516          0.594359   \n",
       "SRI$_\\text{num}$           0.170182          0.191201          0.477002   \n",
       "ARI$_\\text{perm}$          0.654019          0.329117          0.564195   \n",
       "SRI$_\\text{perm}$          0.528610          0.096642          0.338303   \n",
       "SMI$_\\text{num}$           0.440597          0.017687          0.288672   \n",
       "SMI$_\\text{perm}$          0.471940         -0.007487          0.248632   \n",
       "AMI$_\\text{num}$           0.688019          0.324061          0.513135   \n",
       "AMI$_\\text{perm}$          0.677207          0.276407          0.465955   \n",
       "\n",
       "metric_name        SRI$_\\text{all}$  ARI$_\\text{num}$  SRI$_\\text{num}$  \\\n",
       "metric_name                                                               \n",
       "AMI$_\\text{all}$           0.711205          0.282921          0.170182   \n",
       "SMI$_\\text{all}$           0.887464          0.311516          0.191201   \n",
       "ARI$_\\text{all}$           0.980183          0.594359          0.477002   \n",
       "SRI$_\\text{all}$           1.000000          0.539557          0.420336   \n",
       "ARI$_\\text{num}$           0.539557          1.000000          0.969417   \n",
       "SRI$_\\text{num}$           0.420336          0.969417          1.000000   \n",
       "ARI$_\\text{perm}$          0.462011          0.518556          0.434428   \n",
       "SRI$_\\text{perm}$          0.221045          0.384774          0.337114   \n",
       "SMI$_\\text{num}$           0.175498          0.488518          0.470547   \n",
       "SMI$_\\text{perm}$          0.129254          0.402129          0.386283   \n",
       "AMI$_\\text{num}$           0.410308          0.450490          0.371505   \n",
       "AMI$_\\text{perm}$          0.360064          0.390333          0.315046   \n",
       "\n",
       "metric_name        ARI$_\\text{perm}$  SRI$_\\text{perm}$  SMI$_\\text{num}$  \\\n",
       "metric_name                                                                 \n",
       "AMI$_\\text{all}$            0.654019           0.528610          0.440597   \n",
       "SMI$_\\text{all}$            0.329117           0.096642          0.017687   \n",
       "ARI$_\\text{all}$            0.564195           0.338303          0.288672   \n",
       "SRI$_\\text{all}$            0.462011           0.221045          0.175498   \n",
       "ARI$_\\text{num}$            0.518556           0.384774          0.488518   \n",
       "SRI$_\\text{num}$            0.434428           0.337114          0.470547   \n",
       "ARI$_\\text{perm}$           1.000000           0.924137          0.781918   \n",
       "SRI$_\\text{perm}$           0.924137           1.000000          0.854767   \n",
       "SMI$_\\text{num}$            0.781918           0.854767          1.000000   \n",
       "SMI$_\\text{perm}$           0.780579           0.884446          0.970730   \n",
       "AMI$_\\text{num}$            0.875699           0.824316          0.917119   \n",
       "AMI$_\\text{perm}$           0.896309           0.868128          0.909983   \n",
       "\n",
       "metric_name        SMI$_\\text{perm}$  AMI$_\\text{num}$  AMI$_\\text{perm}$  \n",
       "metric_name                                                                \n",
       "AMI$_\\text{all}$            0.471940          0.688019           0.677207  \n",
       "SMI$_\\text{all}$           -0.007487          0.324061           0.276407  \n",
       "ARI$_\\text{all}$            0.248632          0.513135           0.465955  \n",
       "SRI$_\\text{all}$            0.129254          0.410308           0.360064  \n",
       "ARI$_\\text{num}$            0.402129          0.450490           0.390333  \n",
       "SRI$_\\text{num}$            0.386283          0.371505           0.315046  \n",
       "ARI$_\\text{perm}$           0.780579          0.875699           0.896309  \n",
       "SRI$_\\text{perm}$           0.884446          0.824316           0.868128  \n",
       "SMI$_\\text{num}$            0.970730          0.917119           0.909983  \n",
       "SMI$_\\text{perm}$           1.000000          0.891579           0.921226  \n",
       "AMI$_\\text{num}$            0.891579          1.000000           0.967099  \n",
       "AMI$_\\text{perm}$           0.921226          0.967099           1.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pivot = df.pivot(\n",
    "    columns=\"metric_name\", index=(\"dataset\", \"algorithm\"), values=\"value\"\n",
    ")\n",
    "correlations = df_pivot.corr(method=\"spearman\")\n",
    "\n",
    "# Convert the correlation matrix to a distance matrix\n",
    "distance_matrix = 1 - correlations\n",
    "\n",
    "# Perform hierarchical clustering\n",
    "linkage_matrix = linkage(squareform(distance_matrix), method=\"average\")\n",
    "\n",
    "# Get the order of rows according to the clustering\n",
    "leaf_order = leaves_list(linkage_matrix)\n",
    "\n",
    "# Reorder the correlation matrix according to the clustering result\n",
    "ordered_correlations = correlations.iloc[leaf_order, leaf_order]\n",
    "\n",
    "ordered_correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 335.852x335.852 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the correlation matrix\n",
    "fig, ax = plt.subplots(figsize=figsizes[\"half_width_square\"])\n",
    "ax = sns.heatmap(ordered_correlations, annot=False,\n",
    "                 cmap=\"RdBu\", vmin=-1.0, vmax=1.0)\n",
    "ax.set_xlabel(None)\n",
    "ax.set_ylabel(None)\n",
    "ax.set_aspect(\"equal\")\n",
    "\n",
    "# Use this if ordered_correlations is a DataFrame\n",
    "labels = ordered_correlations.columns\n",
    "# If ordered_correlations is not a DataFrame but a numpy array, you might set labels manually\n",
    "# labels = ['label1', 'label2', 'label3', ...]\n",
    "\n",
    "# Set x and y ticks\n",
    "tick_positions = np.arange(0.5, len(labels) + 0.5, 1)\n",
    "ax.set_xticks(tick_positions)\n",
    "ax.set_xticklabels(\n",
    "    labels, rotation=90\n",
    ")  # Rotate for better orientation if needed\n",
    "\n",
    "ax.set_yticks(tick_positions)\n",
    "ax.set_yticklabels(labels)  # Assuming you want labels on y-axis as well\n",
    "\n",
    "ax.tick_params(axis=\"x\", labelsize=8)  # Adjust x-axis label size\n",
    "ax.tick_params(axis=\"y\", labelsize=8)  # Adjust y-axis label size\n",
    "fig.tight_layout()\n",
    "\n",
    "# Find the colorbar attached to the axis\n",
    "cbar = ax.collections[0].colorbar\n",
    "\n",
    "# Adjust the colorbar size\n",
    "# Here, we're setting the fraction (relative size of the colorbar) and the pad (distance from the heatmap)\n",
    "cbar.ax.set_aspect(10)\n",
    "cbar.ax.set_position(\n",
    "    [ax.get_position().x1 + 0.04, ax.get_position().y0,\n",
    "     0.03, ax.get_position().height]\n",
    ")\n",
    "\n",
    "suffix = \"_one_sided\" if one_sided else \"\"\n",
    "\n",
    "plt.savefig(f\"./results/spearman_correlation_heatmap{suffix}.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Joint plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 699.07x317.759 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(\n",
    "    1, 2, figsize=(textwidth, textwidth / 2.2), gridspec_kw={\"width_ratios\": [1, 1.2]}\n",
    ")\n",
    "\n",
    "# Figure a)\n",
    "method_1 = r\"SRI$_\\text{perm}$\"\n",
    "method_2 = r\"SRI$_\\text{num}^1$\"\n",
    "\n",
    "ax1.axvline(0.0, color=\"black\", zorder = -1)\n",
    "ax1.axhline(0.0, color=\"black\", zorder = -1)\n",
    "\n",
    "\n",
    "x_lim = [data[(\"value\", method_1)].min(), data[(\"value\", method_1)].max()]\n",
    "y_lim = [data[(\"value\", method_2)].min(), data[(\"value\", method_2)].max()]\n",
    "\n",
    "x_lim = [-7, 50]\n",
    "y_lim = [-15, 50]\n",
    "\n",
    "ax1.fill_between(x_lim, -nsigma, nsigma, color=\"lightgrey\", alpha=1, zorder = -2)\n",
    "ax1.fill_between([-nsigma, nsigma], y_lim[0], y_lim[1], color=\"lightgrey\", alpha=1, zorder= -2)\n",
    "\n",
    "categories = sorted(data[(\"category\", method_2)].unique())\n",
    "\n",
    "\n",
    "for category, color in zip(\n",
    "    categories, [\"tab:orange\", \"tab:pink\", \"tab:green\", \"tab:blue\"]\n",
    "):\n",
    "    df_selected = data[data[(\"category\", method_2)] == category].copy(deep=True)\n",
    "    if len(df_selected) == 0:\n",
    "        continue\n",
    "    # if category != \"same\":\n",
    "    #     print(df_selected[[(\"value\", method_1), (\"value\", method_2)]])\n",
    "\n",
    "    xerr = [\n",
    "        df_selected[\"value\"][method_1] - df_selected[\"confidence_low\"][method_1],\n",
    "        df_selected[\"confidence_high\"][method_1] - df_selected[\"value\"][method_1],\n",
    "    ]\n",
    "\n",
    "    yerr = [\n",
    "        df_selected[\"value\"][method_2] - df_selected[\"confidence_low\"][method_2],\n",
    "        df_selected[\"confidence_high\"][method_2] - df_selected[\"value\"][method_2],\n",
    "    ]\n",
    "\n",
    "    ax1.scatter(\n",
    "        df_selected[\"value\"][method_1],\n",
    "        df_selected[\"value\"][method_2],\n",
    "        color=color,\n",
    "        marker=\"x\",\n",
    "        label=category,\n",
    "    )\n",
    "    # ax1.errorbar(\n",
    "    #     df_selected[\"value\"][method_1],\n",
    "    #     df_selected[\"value\"][method_2],\n",
    "    #     xerr=xerr,\n",
    "    #     yerr=yerr,\n",
    "    #     fmt=\"x\",\n",
    "    #     color=color,\n",
    "    #     label=category,\n",
    "    # )\n",
    "\n",
    "ax1.set_aspect(\"equal\")\n",
    "x_min, x_max = ax1.get_xlim()\n",
    "y_min, y_max = ax1.get_ylim()\n",
    "xy_min, xy_max = min(x_min, y_min), max(x_max, y_max)\n",
    "ax1.set_xlim([xy_min, xy_max])\n",
    "ax1.set_ylim([xy_min, xy_max])\n",
    "\n",
    "ax1.plot(\n",
    "    [xy_min, xy_max],\n",
    "    [xy_min, xy_max],\n",
    "    color=\"black\",\n",
    "    linestyle=\"dashed\",\n",
    "    zorder=-1\n",
    ")\n",
    "ax1.set_aspect(\"auto\")\n",
    "\n",
    "\n",
    "ax1.legend(loc=\"upper left\", fontsize=8,)\n",
    "\n",
    "ax1.set_xlim(x_lim)\n",
    "ax1.set_ylim(y_lim)\n",
    "\n",
    "ax1.set_xlabel(method_1)\n",
    "ax1.set_ylabel(method_2)\n",
    "\n",
    "# Annotations for x-axis on the left and right outside the plot area\n",
    "ax1.annotate(\n",
    "    \"Worse than\\nchance\",\n",
    "    xy=(0, 0),\n",
    "    xycoords=\"axes fraction\",\n",
    "    xytext=(10, -20),\n",
    "    textcoords=\"offset points\",\n",
    "    ha=\"center\",\n",
    "    va=\"top\",\n",
    ")\n",
    "ax1.annotate(\n",
    "    \"Better than\\nchance\",\n",
    "    xy=(1, 0),\n",
    "    xycoords=\"axes fraction\",\n",
    "    xytext=(-10, -20),\n",
    "    textcoords=\"offset points\",\n",
    "    ha=\"center\",\n",
    "    va=\"top\",\n",
    ")\n",
    "\n",
    "# Annotations for y-axis on the top and bottom outside the plot area\n",
    "ax1.annotate(\n",
    "    \"Better than\\nchance\",\n",
    "    xy=(0, 1),\n",
    "    xycoords=\"axes fraction\",\n",
    "    xytext=(-40, -25),\n",
    "    textcoords=\"offset points\",\n",
    "    ha=\"center\",\n",
    "    va=\"center\",\n",
    "    rotation=\"vertical\",\n",
    ")\n",
    "ax1.annotate(\n",
    "    \"Worse than\\nchance\",\n",
    "    xy=(0, 0),\n",
    "    xycoords=\"axes fraction\",\n",
    "    xytext=(-40, 20),\n",
    "    textcoords=\"offset points\",\n",
    "    ha=\"center\",\n",
    "    va=\"center\",\n",
    "    rotation=\"vertical\",\n",
    ")\n",
    "\n",
    "\n",
    "# # Add labels I, II, III, IV to the quadrants\n",
    "# ax1.text(0.98, 0.95, r\"\\textbf{I}\", ha=\"right\",\n",
    "#         va=\"center\", transform=ax1.transAxes)\n",
    "# ax1.text(0.02, 0.95, r\"\\textbf{II}\", ha=\"left\",\n",
    "#         va=\"center\", transform=ax1.transAxes)\n",
    "# ax1.text(0.02, 0.05, r\"\\textbf{III}\", ha=\"left\",\n",
    "#         va=\"center\", transform=ax1.transAxes)\n",
    "# ax1.text(0.98, 0.05, r\"\\textbf{IV}\", ha=\"right\",\n",
    "#         va=\"center\", transform=ax1.transAxes)\n",
    "\n",
    "fig.tight_layout()\n",
    "# Add the labels\n",
    "fig.text(0.02, 1.0, \"a)\", fontsize=12, weight=\"bold\", ha=\"left\", va=\"top\")\n",
    "fig.text(0.48, 1.0, \"b)\", fontsize=12, weight=\"bold\", ha=\"left\", va=\"top\")\n",
    "\n",
    "# Figure b)\n",
    "\n",
    "# Plot the correlation matrix\n",
    "ax2 = sns.heatmap(ordered_correlations, annot=False, cmap=\"RdBu\", vmin=-1.0, vmax=1.0)\n",
    "ax2.set_xlabel(None)\n",
    "ax2.set_ylabel(None)\n",
    "ax2.set_aspect(\"equal\")\n",
    "\n",
    "# Use this if ordered_correlations is a DataFrame\n",
    "labels = ordered_correlations.columns\n",
    "# If ordered_correlations is not a DataFrame but a numpy array, you might set labels manually\n",
    "# labels = ['label1', 'label2', 'label3', ...]\n",
    "\n",
    "# Set x and y ticks\n",
    "tick_positions = np.arange(0.5, len(labels) + 0.5, 1)\n",
    "ax2.set_xticks(tick_positions)\n",
    "ax2.set_xticklabels(labels, rotation=90)  # Rotate for better orientation if needed\n",
    "\n",
    "ax2.set_yticks(tick_positions)\n",
    "ax2.set_yticklabels(labels)  # Assuming you want labels on y-axis as well\n",
    "\n",
    "ax2.tick_params(axis=\"x\", labelsize=8)  # Adjust x-axis label size\n",
    "ax2.tick_params(axis=\"y\", labelsize=8)  # Adjust y-axis label size\n",
    "fig.tight_layout()\n",
    "\n",
    "# Find the colorbar attached to the axis\n",
    "cbar = ax2.collections[0].colorbar\n",
    "\n",
    "# Adjust the colorbar size\n",
    "# Here, we're setting the fraction (relative size of the colorbar) and the pad (distance from the heatmap)\n",
    "cbar.ax.set_aspect('auto')\n",
    "cbar.ax.set_position(\n",
    "    [\n",
    "        ax2.get_position().x1 + 0.04,\n",
    "        ax2.get_position().y0,\n",
    "        0.03,\n",
    "        ax2.get_position().height,\n",
    "    ]\n",
    ")\n",
    "\n",
    "fig.savefig(\"./results/mcpdesouto.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VQafTCYlEwuvFB277h3/4h5DnYtN7Wa1WSCSSsAljnU4nl9SWjWtVKpVUMzyBDE4Mom+4L+S2/CX5+KjnI87BkinEdfhstVqh0+miWjOdrvg7VbJK8nhzioOvfoGsKwTwFkT/21VfX4+WlhYYjUbY7XZUVFTwRKi9vR0SiQTd3d1Bw9fAbeycYuC52DIAAEKm96qvr0dDQwP0ej0nyKxder0eEomE0sklgFDrnnft2oWvfe1rWKtcO+O653QlKu9ztCgUCiiVSl5qoba2NvT09MTLhJiRDt7n6TzCyeItjjXkff6KQM/z5ccux+MPPo78Jfn4P//2f3Bm/MyMq1rmg5T0PldWVkKr1c56zXKoNdTxHDppNBpotVpuiGiz2WA0GiGTyRKS9UUgzEJWoe+/gBVEwN/58jm8gkkwufNnw3TZqSlzdfrhnyYscHnfb5jfoPAbhbjkh5ckTBCTgajGZfX19UGCGEnd50BB7Orq4hLPzjcWiyWonKZarUZjYyNUKhVUKlXc12VnLcjBwn9ZjeLvfiWILNkiIRbecSEcV+bNy4oW/yzcs9lGpC7scJkNyAZ8oTdXHL8CE/0TEC4SomxrGYZzhzNWEIEoe4oCgQD33nsvN39kt9thMBgiWp2yb98+biKeYRj09PTEJUtOYG2PQGcAG58Xb7IW5ABhRnFZxcJ5W+Inl8vhcDhmvY1IXUKlCXtN/xqefPJJyB7hz+mGWvecKUTVU9yxYwcYhkF/fz8OHDiAI0eO8AKyw7Ft2zbs378fBw4cQH9/P6xWa1yqrxmNxqACOv6JSVkkEknYWjPj4+Nwu928F0GkEuxwmV3Cd9sbt+HhXz2MsofKkCvhz9FoP9CG9UqnO1H1FHU6XdBQuLOzc8bjysvLUVdXh97eXggEAqxatSqiYfdcCDcvFq6gUThxb2pq4lKuE0SqEjiPWP7IVyFx5xWeh5Z/bOFyKfonnc0kouopVlVVYefOnaitrQUArn7zTMhkMhw7dgxlZWVcCMd8097ePitnTjix1Gq1cLlc3Ov48eMxspAg4svSgqVBacKW5C/BK9e/gsuWXMbrTfrPP2YKUYki68H1r98cyXyc0+mETCaD2+1Gf38/1q9fP6+pw0wmE2pqakJuE4vFQb1Cu90e1tual5eH4uJi3osgUo1nn30WqrtVQWnCsgRfSYH/MFuSL0FhbmG8zUwoUQ2fFQoFNmzYENGQ2Z8NGzbA4/EA8M1LdnZ2QqFQRGNCxLC1gQGfc6WpqQm1tbVQKpUhBXm+7QlkaGIIw1PDWFqwNGjb2ZGzGPGMoCC7IK42EenJs88+ix9v/zHKtpVBOCTEiqIV+MnlP8Fjf3oMfSN9vOEyK4wzVfJLR6ISxVjVb66qqsLRo0chEomiMWNGAofNDQ0NXBKDQGw2GxQKRVzj8oYmhvDABw/AMeZA6zWtPGE8M3IG975/LxZ4F2DLBVviZhORnvziF7/Ag48/CJlWBuESIS/k5tVvvcqr0eIvjJlIVKI4l/rNiUgdxqatAnwB5A0NDZDL5TAYDFz6qu7ubhgMhnm1I5DhqWE4xhw4OXwSm9/bzAnjmZEz2PzeZpwcPonFuYsx5h2Lq11EevHcc8/hgQceQFZ+FpYsXIKioqKQacLYZX2ZNlwOJOplftHUbw6XOuzLL7+MxoSEEqu6z/4CuLxwObav3Y7HPnmM+/yj5T+CNFea8LrP6UKmLfP75S9/ie9///sAfCu6Htr+EEamRqIufB8PUnKZ39GjR1FWVoYdO3Zw1fxKSkpmzKBdXV0dtGpk79690ZiQNiwtWIrWa1o5YdzU5atzs7xwOX511a9gPzZz/Od0hEoAC/i+IGwOQ3ZKwWKxoKqqCgaDAU6nE21tbdBqtVxwPtvDZo+nBLHJzcmTJ7F161YAQGNjI5qamiAQCFAcps54pg6XA4lrNT9KHRaapQVLsX0tPwZy+9rtIZ0vs6WtrQ12ux0KhYIXKC+VSqFUKlFdXc1NfcjlcigUCkgkEqhUKlRWVsJkMkGpVKKhoYETVP8EsY2NjVzyWSK5WL58Od5880089NBD2LFjB88HQIRnXqv57dmzh/fZ4XBQ6rAQnBk5g8c+eYzX9tgnj+FXV/1qzufWarXQaDRobm7m5lFZ9Ho9nE5nUGiS//RGKMeT2WyGWCzmfggpQWxyMTg4iIULfUPg6upqVFdXJ9ii1GJeq/m1trbCarXC4XBwa2kVCgUYhgE7lRnHzGVJSeCc4p51e7C8cDlODp/E997/HgYmB+Z0/lAJYPV6PQYGBlBfX8956P2XN/ovfwxcCglQgthkprW1FRdffDEOHz6caFNSllnNKZaVlaG1tTXian6hUoUFkslfqkBBZL3P/nOMzb3N2Fa2LeprWK3WoASwCoUCZrOZN+XBJshgRbO+vh4dHR1ckD6b95JNsUYJYpMPnU6He++9F4Dvx/AnP/lJgi1KUZgo6O3t5d47nU5m7969vLZwOJ1OZufOnYzL5WIYhmE6Ozu596mGy+ViAPDsHx0dZT777DNmdHR02mM9Hg8zMjLCnHWeZW773W3MesN6pre/lxkZGeFevf29zHrDeubmtpuZ9z9+nxkaGuJtn+nl8Xjm+xakJJH+H6UaOp2OAcAAYLZs2cJ4vd5EmxQ1ob5b8SQmjpZbb701IkdLe3s7+vv7uc/r1q1LSLquZKFIWIRnr3w2KHAb8Dlfnr/qeWy5YAutaCGmRa/Xc060H/3oR3j66afJqTIH5tXREohUKo17Itdkp0hYhCJhUchtSwqWYCB7bnOKRHqze/duThB/+MMfYteuXSSIc2ReHS2BHDhwAIODg7y2TPc8E0S0TE1NcREeP/jBD/DMM8+QIMaAeXW0BNLQ0IA1a9agvLwcYrEYFotlXrPkEEQ6k5OTgz/84Q946aWX8IMf/IAEMUbELJ9iJJmoy8rKYDaboVKpoFAosH///ohKGBBEqjNdfeW+4T4MTgyG3BaKzz77jDufWCzGD3/4Q54gzvZ8BJ+45lMEfMPwuro6bN26FWVlZdFcPq0YHJtCnzt0woc+9xiGJ73zct1kWEk0FxuSwf5ICVUwioUtM7rZtDkiIXvxxRdxaeWluPnVm2NyPiKYqERRoVCgrq6O4tPmyODYFOr+4xDueMmC0y6+MJ52jeG7rxzCTz9wzoswhnKWxZu52JAM9kdKYMEoVsj86y7bR+0Ynhye9jwvvfQS7rnnHgjyBBj0DM75fERoohLFcPkUidkxPDEF+/AEjjvGcOfLXwnjaZfv83HHGFzjXoxNxn7Vz3z/oAWWk421Dan0gxxYMOqud+/CobOHeIXoZ6qF8vLLL+Puu+8GwzDY/C+b8da335rT+YjwRCWKbD7F5uZmaLVaVFZW0vrKKCgtXoCX75RjZckCThgPHndxgriyZAG2X1UCaUF21NewWCwwGo0wmUxc6IbFYkF5eTlXj0aj0UCv10Oj0cBoNMas3nNgGdlAu1gbLBYLSkpKYDKZYDQaoVark8L+WBIojBvf2TgrQbzrrrvAMAzuu+8+PPvssziv6Lyoz0dMT9SOFoPBgDVr1oBhGOj1enKYRMl5Ir4wfvsFMyeIL268DIvmIIhA6Cw5crmc62mxqcXq6+tRXl4Ou92O+vr6Of9djY2NaG5uDiuM/jaEys7DLk1MlP3zQWlhaVDBqJnqK7/yyiucIH7ve9/DL37xC26EFs35iJmJKp8iAC6fIjF3zhMtQPMt38C3XzBzbc23fAPniRbAeWpu554uSw7gG4Y6nU44nU6YzeaI63A7nU40NTVNu09PTw80Gk3EGc1DZeSZL/sTQd9wX1DBKO0H2ml7dqdPnwbDMLj33nvx3HPPBXmZZ3s+YmYiFsWuri4YDAbU1NTgmmuuwdGjR9HQ0ICenh7U1tbiV7+ae5qrTOW0awyaN/6H16Z543/w4sbL5nxuNksO4IsTtdlsvPk4sVgMjUYDm80WFDMarmY2e9x0KcNMJhPKy8tn1WsLlZFnvuyPN/5OkBVFK9B0ZVNE9ZU1Gg0uu+wyVFdXBwliNOcjZiai4fPevXuhVCrR3d0NlUqFQ4cOob6+HiKRCNu2bcORI0e47BzE7PB3qqwsWYDf3F3BDaXvevUQ+kc8czo/myXHaDRyWXLYISdb6bCjoyNk5uyKioqwdbBnwmAwTCuI/jaw79n8jh0dHWhra4PT6UyY/bEkUMBevO7Faesrv/3227y43/Xr1/NKS8z2fMTsiKhGS21tLfR6PUQiEZxOJ+rr69HQ0MBb1VJTU8MrJ5ruxKJGS597DHe89JUgvnynHOeJFvCEcmlhNp64qgT/t+KSeavRUlFRweulGQyGpOlhRcJs7E9EjRY2TtE+ag/qwbECJ8mXoFXZircMb+GOO+7A2rVr0dHRgcLC4CJSszlfouutRENK1GhRKBRcGVKxWIzq6uqgZX5sNm0icgqFOZAUCgGAE0TgK+fLnS9bkC/wYEHu/C3f0mg06Ozs5ETEYrFAr9dHtJY9GUgF+xcKF6JV2YrhyeGgIa1/feXfGX+HO+64A16vF9/85jeRn58/p/OloiAmAxGJYklJCe9zqBixwH2ImVm4IAe7/+UyDE9MobSY32s5T7QAL91xGU4ctaIwd/6q8jU0NKC9vZ3rabGe3FQhVexfKFwYVqRKC0vxm9/8Bhs3boTX60VdXR2ef/75aXv6M52PiJ6IRNFqtfLqPrhcrpD7ELNn4YIcLFwQ+r+htHgBHPMoiIDvBy4ZRSRSUt1+AHjttdc4Qdy0aRNaW1upPG0CiejONzc3QywWIzs7G9nZ2VCpVNz77OxsZGVlRbSCgSAIPkajEd/5znc4QdTpdCSICSainmJ9ff204RcMw2DbtujriKQbXu/8JHEg5k4EfsW4cvHFF0MqleKmm24iQUwSIhLFhoYGztEy3T6ZjlAoRFZWFk6dOoXFixdDKBSGzHHn9XoxMTEx4/n8xXV8fHxWXxiGYegLFgDDMDh37hwEAgFyc3MTbQ4A4JJLLkFPTw9WrFhB/19JQkQhOUQw4cIGJiYmcPr0aYyMjIQ9lmEYTE5OzngNhmHQ1+eLNSstLZ1VEtHc3FxKOhoCgUCAFStWoKgodAmIeNDe3o4lS5bg6quvTpgNyUxKhOSkMmyeR6fTie7ubtTW1nKBvjabDUajETKZjPNazjU+TygU4vzzz8fU1BQ8ntCB12NjY/jb3/4247nGx8exefNmAL4v0mzi6pYtWxa3OLxUIjc3F9nZc1tPPhfa29vx7W9/G3l5eThw4AC+8Y1vJMwWIjRpL4pqtRqdnZ1QKpWw2+1Qq9Wcp1ytVsNs9q03ttlsqKuri3id7nSww7NwQ7TZDG2PHTvGvZ9t8DaJYnJhMBjw7W9/Gx6PBzU1Nbj44osTbRIRgrSfxDAYDLwlYGxPMDB7i0wmy+hyq8T8YjAYcPvtt8Pj8eDOO+/Enj17EtpjJcKT9qLIlkwAfA8m6xAymUxBCQgkEgksFkvI84yPj8PtdvNeBBEJRqORE8Q77rgDv/71r0kQk5i0F0XAt/RLo9GgurqaC/QNlyjAbreHbG9qaoJIJOJeK1eunC9ziTTiww8/xG233QaPx4ONGzfihRdeIEFMcjJCFOVyObRaLZdxZTrCiaVWq4XL5eJex48fnwdLiXRj7dq1uOmmm7Bx40a8+OKLJIgpQNo7WljEYjHUajWqq6vhcDggFouDeoV2uz2s9zkvLw95eXlxsJRIJ3Jzc9HW1oasrCwSxBQhrXuKJpOJl6iCTWRhs9l4c43+KBSKuNhGpC9vvPEGHnjgAS74PtFhQMTsSOueokQi4YmfxWKBWCwOmZDUZrNBoVCkVB5BIvl44403UFNTg6mpKcjlcnz3u99NtEnELElrUZTL5VyCXMCXoZmNSwR83miNRoPKykp0d3fHJEaRyFx++9vfcoJ4++234zvf+U6iTSKigJb5RclcliKNjo5GlGptZGQEa9euBQB88sknKCgoiPga5eXlYZOUErHnzTffhEqlwtTUFG677Ta8+uqryMlJ6z7HvJHoZX5pPadIEPHgrbfeglqtJkFME0gUCWIOnDlzBrfddhsmJydRW1tLgpgG0P8eQcyBpUuX4uWXX8abb76Jl156iQQxDaD/QYKIgqmpKU4A1Wo11Gp1gi0iYgUNnwlilvz+97/HpZdeSqua0hQSRYKYBW+//TZuvfVW/PWvf8UzzzyTaHOIeYBEkSAi5O2338Ytt9yCiYkJqFQqKtaWppAoEkQEvPPOO5wgbtiwAb/5zW+Sps4LEVtIFAliBt59911OEG+99Va89tprJIhpDIkiQUyD1+vFQw89hPHxcdxyyy14/fXXSRDTHBJFgpiGrKwsvPPOO9iyZQsJYoZAokgQIThz5gz3funSpdi1axeEQmECLSLiBYkiQQSwf/9+yGQyvPzyy4k2hUgAJIoE4cf+/ftx0003YWRkBG+++SYoiVTmQaJIEP9LR0cHbr75ZoyPj+Pmm2/G66+/DoFAkGiziDhDokgQ8JWuuOmmmzA2NoabbroJ7e3tNIeYoZAoEhmPyWTCjTfeiLGxMdx4440wGAwkiBkMiSKR8bz33nsYGxvDP/3TP5EgEpQ6jCCefPJJrF69GrfffjuVsSWop0hkJmazGWNjYwAAgUCA7373uySIBIAkFsWurq5Em0CkKe+99x6uvPJK/PM//zMnjATBkjTD5507d6KjowMCgQAMw8BsNmNgYCDRZhFpxn/913/hhhtuwOjoKBWoJ0KSNKIok8mwf/9+7vPBgwcTaA2Rjrz//vucIF5//fXYu3cvFixYkGiziCQjaYbPAoEAx44d4z47HI4EWkOkG++//z6+9a1vYWRkBNdddx327dsXUhAHJwbRN9wX8hx9w30YnBicb1OJBJM0PcUDBw6gsbERJSUlYBgGvb29NHwmYsIf//hHniC+8cYbYQVxs2kz7KN2vHjdiygtLOW29Q334a5374IkX4JWZSsWChfG808g4kjS9BQrKytx5MgRdHd3o6enBz09PYk2iUgThEIhcnJysH79+rCCCADDk8Owj9pxYugE7nr3Lq7HyAriiaETsI/aMTw5HE/ziTiTNKJYUlKCQ4cOcZ97e3tjcl6LxYKWlha0tLRArVbD6XRy22w2G1paWmA0GtHS0sLbRqQPl19+OT788EP89re/nXYOsbSwFC9e9yJWFK3ghPHQ2UOcIK4oWhHUgyTSj6QZPtfX10MsFnPeZ5fLhS+//HLO5zWZTGhsbAQAtLS0oKqqCmazGYCvXi/73mazoa6uDgaDYc7XJBLPhx9+iPz8fFRUVAAAvvnNb0Z0HCuMrBBufGcjAJAgZhBJ01M0GAzo6enhhs/+nuhosVgsaGpq4j6rVCpYLBbYbDbYbDbevjKZDCaTac7XJBLPhx9+iOuuuw5KpRKfffbZrI8vLSxF05VNvLamK5tIEDOEpBHFNWvW8D7HIiRHLpdj9+7d3Gd2eCyRSGAymSCRSHj7SyQSWCyWOV+XSBwfffQRrr/+egwPD0OhUKCsrGzW5+gb7oP2Ay2vTfuBNqxXmkgvkkYUJRIJKisroVAoIJFIeD28uaBSqbj3bW1tUCqVEIvFYecP7XZ7yPbx8XG43W7ei0guPvroI1x33XUYGhpCVVUV3nzzTeTn58/qHP5OlRVFK/Dq9a/y5hhJGNOfpBHFzs5Obuhst9tRX18f0/M7nU4YjcYZ5wzDiWVTUxNEIhH3WrlyZUztI+bGn/70J54gvvXWWygoKJjVOQIF8cXrXsRlSy4Lcr6QMKY3SSOKgcNnqVQa0/NrNBp0dHRALBYDAMRicVCv0G63c9sD0Wq1cLlc3Ov48eMxtY+Inj//+c+cIK5bty4qQQSAwtxCSPIlQU4Vf6+0JF+CwtzCWP8JRBKRNN5niUSC8vJyMAwDqVQKtVods3O3tLRAo9FAJpNxPUGlUgmdThe0r0KhCHmOvLw8yqKSpKxevRp///d/D4Zh8Lvf/S4qQQSAhcKFaFW2YnhyOMipwgpjYW4hBW6nOUkjip2dnbzeYqyy5BiNRsjlck4Q29vbufAff2w2GxQKRdieIpG8FBQU4K233uLez4WFwoVhRY+8z5lBQkTx0KFDuOyyy3htJpMJ27Ztg91uj9kyP5vNFtTjFIvF3HylwWCARqNBZWUluru7KUYxhfj4449hMpnw8MMPQyAQzFkMCYJlXkXRf4WKP21tbUGiKJPJ8Ic//IH7HIsVLTKZbNoSlTKZDM3NzQD4Xmoiufn4449x7bXXYnBwEMuWLcPdd9+daJOINGJeRfH111+Hy+WCSCTitYeKQWSX+bFi2dvbG1WMGZHefPLJJ1i/fj0GBwdx1VVXoba2NtEmEWnGvIqiVqtFT08PqqqqeO2dnZ1B+87XMj8iffjkk09w7bXXwu1246qrrsLvf/97FBaSJ5iILfMqiiKRKEgQAYRsMxgMPEdLrBJCEOnBgQMHOEH8x3/8RxJEYt6IS5xiJJ5kgUDA+0xDZ4JlYGAA69evh9vtxpVXXkmCSMwrcRHFjo4ODA4OBi2T83+1tbXFwxQiBZFKpWhubsZVV12Ft99+G0VFRYk2iUhjBMx07tkYkZWVFdQT9IdhGAgEAng8nvk2JWa43W6IRCK4XC4UFxfP6tjR0VFYrdYZ9xsZGcHatWsB+ObTZhN2Ul5ePut1v8kG+1yweDweKjaVAczluxUL4tJTrK+vx5EjR2C320O+jhw5grq6uniYQqQIPT09uPrqq3Hu3DmujQSRiAdxCd5Wq9XTzhGKRKKYLusjUoPBicGQS+rMZjOuU10Hx1kHtFot9uzZkyALiUwkLqIYytvsz8GDB8nbnGGEKxJlNpuxXrUe0vukWMGswL9u+tcEW0pkGnHLkuN2u7Fv3z4cPXo0aNuaNWuoPkqGEapIlMViwXXq6yD9nhTCJUIslS2FIC/8XDRBzAdxEcXe3l6sWrUKKpUK5eXlOHr0KNxuNzZv3ozVq1cjOzsb3d3d8TCFSBICi0R9+61v41ubvgXJvRIIlwixrHAZXr7+ZUrCQMSduIhic3MzDAYDvF4vuru70drayg2pN2zYgNbWVgrJyUD8hfHcxDks/sFiEkQi4cRlTrG8vJwTQblcjs7OTuj1+qDEskTmwRaJYqvmAUDzPzaTIBIJIy49xcAYRblcHiSI4TLqEOnLyMgIFYkiko64iOLAwABvRYvL5Qpa0ULlRTOLP//5z7iw4kLU7KuhIlFEUhG3OUWxWIySkhKUlJRApVJx79l2crRkDn/+859RfWs1Cu8uhMPrmLFI1ODEYFiBZLcTRKyI24oW/xUsDoeD935gYIASQGQIf/nLX1BVVYWB0wNY4F2AZQXLpi0SxTAMNps2h+w5stX3Nps2kzASMSMujpaGhoagRLOBULLQ9OfTTz/1CeLAACorK7Hv7n3IKciZtkhUYDwjK6D+5UgBX9xjPAtKhVuNA/jEmgpcTc909+/M8JkEWPQVcekpRuJlJk90ehBuqPvpp59CeYsS9mE7FAoF9u/fjxWLV4T1MpcWlmKhcGFQPONd796FQ2cPBdVnjqe3ml2NQ73X6Jjp/n3P9L0EWeYjLqK4e/duaLVaPP3003C73QCAffv2QaFQYPXq1dBqtTOcgUgFTg+dxj1/uCfkw/7ztp9DvFmMbzz6Dez7/b5ZVU0MFMaN72xMmCACoVfjAOD1Xu2jdgxPDsfVrlRhpvt3cvhkQu2LiyjKZDJIJBL8+Mc/RnFxMTo7O6FWq7F582b09PRAoVCQMKY4gxODeOC9B/CF44ugh/0v5/4C84VmCBf7lu7lFubO+vxsPKM/TVc2JSSeMRl7r6nETPdveeHyhNoXt2V+W7du5T43NzdDpVJh06ZNEIlE2LBhA2QyWTxMIeaJ4clhDE0MwcN4kC3I9vXo/nMj9tv244537uDad12zKyqxSLZ4xmTrvaYa092/Xyl/lVDb4pYQwh+TyYTq6mpe23RJaInkx/8h9zAeZCELfWN9ePCDBzlBfOX6V3Dp4ktnfW7/YWkyxTMmU+81FQl3/5YWLk2QRT7iIooOh4N7v3v3bgCAUqnk7UNZclIfVhiX5C2BF17etl1X7YqJIE4Xzxhvkq33mmqEu38Z4X1WqVRQKBSorKyERqOBwWDAqlWrAPhyKa5fv35WE++phndsClOu8ZDbBMNeYGLeK0LEnHBeZvsxO758Nbg07dM9T0clFoW5hZDkS4KGpYHxjIW58S1klay911RhuvuXaO9zXGq0sAQWuHe5XLDZbNznVArLibSOhHdsCv0v/P/wDE1icf2lyBHncTVaBMNeFHUMglkgwNC6hYCQP4WQrDVawiWI/eyzz7D+3vUQ3SXiTYdkC7LhYTxRz7clW0xgqN5rYOwkzS2GZ6b7d+zcMfz13r+md40WlsBVKyKRCGvWrOFe6Yh33APP0CQ89jGc0/8FU05fj5EVxOwhLwRjDASTqdNbDBVSEUoQH7v8MW6OkXW+RNOLYuMVQ8HGM8aTZO29pgoz3b9Ee5/j2lNMBBaLBXV1dTCbzbx2m80Go9EImUwGm82G+vr6WQ3hZ1NxbMo5jnP6v8BjH0O2ZAEK/3kVHO1fIHvIC09RFoaqF4IpDP59StaeIhD8a1+dW40X+l+AINsniM9e8yyuOf8a3n7ZgmxcWHIhfr3+1ym/2iPZeq+pxnT378vTX+LCZRcmrKeY1qLIil5FRQUC/8yKigpOKG02GzfXGSmzLcPoL4ws0wkikNyiCPCFkSULWfj5NT/HNedfE7RfkbAIz17zLM4rOm9e7SJSm4wocZooVCoV5HJ5ULv/PCbgCy6f79RlOeI8SGov4rWN/ENhWEFMBVwnXNh8wWZe28+u/hlPEIGvhkUvrH+BBJFIelL3GzkHTCYTJBIJr00ikcBisczbNaec47C3Hea1FXw07PM+pyCHDx/GupvXQftHfkjFzp6dIecMEzH3RxDRkJGiGC4m0m63hz1mfHw8KDFupATOKRbffTE8RVnIHvI5W1JNGA8fPoyqf65C0T1FyJZmY1nBMgpJIdKGjBTFcEwXQN7U1ASRSMS9Vq5cGdE5p1x8QVxcfylyVxZhqHphSgrjF198gaqbq1B4d6GvyFTBMrz8rZeTJqCaIOZKRoqiWCwO6hXa7fZpvc9arRYul4t7HT9+PKJrZeVlI7solxPEHHEeAIApzOKEkVkgAJOb/Mscv/zyS1xzzTU4/bfTEE4JcV7BeXj5Wy9TSAqRVsQlyWyyoVQqodPpgtoVCkXYY/Ly8pCXlzfra2UtyMGiuy+Bd9yDHBH/eFYYmVxBUOB2smG1WnH11Vfj1KlTuOSSS/DWnW8hX5Q/bYJYmkMkUpGMEUWn08n1BAMz8thsNigUinlbapi1IAdZC0Lf6lTxPkulUixfvhwlJSXo7OzEkiVLwu5LqziIVCatRdFkMqGjowOAb06wsrISKpUKAGAwGKDRaFBZWYnu7u5ZxShmImKxGPv378fExMS0gkgQqU5aB2/PJ3MJMGXXPs9EooO3jxw5gq6uLtTX18/pPAQxGxIdvJ3WPUUieqxWK6655hqcOHECQqEQ3/3udxNtEkHEhdSY0CLiCutUOXHiBL7+9a/j+uuvT7RJBBE3SBQJHjabjeshXnzxxejq6sLSpYnNhEwQ8YREkQDgy1py4K8HcPXVV+P48eO4+OKL8d5776G01JfnbrblOsMloQUQ1fkIIl6QKBIYnBhE3bt13CqUQEGcbR3jeNVFJuEl5gMSRQLDk8NwTbqQJcnChT+5EK+//TpPEGdbxzgedZGpID0xX5AoEigtLMVzVc+htKAUghIBtlm28erwlhb87/YIg7LjUReZCtIT8wWJYgZz9OhR3H777Tg1cAqP/ulRCAQClBaU8urwlhaUQiAQ4NE/PTqrXtd810WmgvTEfEFximnK0MQQXF5X2HT5A6cHcIPyBhw7dgxMEQPHegdOD5/Gknz+ahUv48XZkbMQQIDhyeFZrWdm6/pufGcj1xbLusisMLJCyF6HBJGYC9RTTENGPCN44IMHcOc7d+LTc5/ytvUN92Hjf26E2qDGSfdJrF69Gv/6yL9ClCdCtiAbZ0fP8vY/O3oW2YJsiPJEs856E4+6yFSQnog1JIppyKhnFAOjAzg1fAob39mIv5z7C4CvBLFvrA9ZS7NwYeOF+M+O/8TCRQvhHHfCw3hCns/DeOAcd85qfi5edZGpID0Ra0gU0xCpUIqnrniKq7d8xzt3oPNYJyeIjIeBIFuA5bLlKF7kW1s60xL42SyRD1XXdz6S0FJBemI+IFFMU74h/QZeuf4VThh/+F8/5Aniefnn4ZUbXkFpYSkKcwuxpHAJFi9YjGxBNu882YJsLF6wGEsKl0Q8fI5HXeR4CS+ReZAopjGXLr4Uu67axWsTZAtQml/KCSLgKza//YrtyMnOCRpCexgPcrJzsP2K7RE7WRYKF6JV2RrS2cEKY6uydU5JaKkgPTFfkPc5jekb7sPTPU8HtW9bu40nVn3Dfbi/636cHj7N9SxZsgXZOD18Gvd33T8rj+5C4cKwohcLJwgrvKEKqlP2b2IuUE8xTTkzcgYb/9MXG5gV8N/84PsPcs4XwNfrKhIWcYLoPz/nYTzIFmSjSFiUdL2uhcKFYQWWSqoS0UKimIYMTAxgU8cmbg7RCy9WFK3Az6/+Oc/5wgrj8OQw3BNuThAD5+c8jAfuCTetDiEyAhLFNMTd78apo6c4p0rpAt9wsuqCKp7zZesft6JvuA+FuYVYlL9o2vm5RfmLkq6nSBDzAc0pphl9fX24b9N9OD10Gqu3rsZy2XK8+k+vckJ36eJL8cr1r2DrH7dyQkfzcwTxFSSKc2R0dBS5ubmzOmZ8fHxebDlz5gzuueceHD9+HOeffz7+/aZ/h6RUAlGWCKOjo9x+q4tWQ3e1DoU5hcjx5GB0dBQ5yAnaj0WUJQI8CLmNIGJNop8zEsU50tvbi6KiokSbAQB46aWX8Le//Q3Lly/H7t27IWSEGDo9hCEMhdx/CEM4gzNxtpIgpmdoKPTzGi9IFFOUEc8IRj2jkAqlXNuWLVvg9Xpx07/cBPFSceKMI4gUhhwtKciIZwQ7vtyBJw4/gd7+Xni9XgBAbm4u6h+sh96lx44vd2DEM5JgSwki9SBRTEFGPaNwTbpwduIsth3chsd3PQ6v14uBiQE8cfgJnJ04C9ekC6MemgMkiNlCopgkjHhGMDAxEHJbTkkOr9cnFUpx36L74LV7kSXJwud/9znMp82cIC4RLsGjFz3KG1oTBBEZJIpJgP9wOFAYcyW5kGll+Nnxn3HCeO7cOTxY/yC+fPJLeAY8yJZm45m+Z0gQCSIGkCgmAf7DYX9htE/aUbatDMIlQgxODWLUM4r+/n7cc889OHr0KKRCKe7/2v28c91Xdl9YQRye8KJ/eCrktv7hKQxPeGP7hxFEChIzUTx69GisThU3bDYbWlpaYDQa0dLSAqfTmRA7pEIpHr3oUSwRLsHZibPYfng7uh3daDnWAuESISbOTqDxgkYwbgb3/OAeHOs7htLSUjyz+xkYh428c/2y95chh+HDE1482nUG2zrO4FyAMJ4bnsK2jjN4tOsMCSOR8UQtiocOHUJXVxf30mg0sbQrLqjVajQ2NkKlUkGlUqGuri4hdrDD4kcvehSLhYtxbuIcnrE9g3OT5zDRP4FjPz8GSa4EB/56ADl35GC1djWe0j2FPYN7uCHz9ou2Q5q9An3DYyGH4Sfdk3COetA3NAWtnzCeG/Z97huagmvMi9FJEkUis4kqTrGmpgZOpxNisZhrO3jwYKxsigs2m433WSaTwWQyxd0Odj7RNenCoxc9ipplNfjl0V9y2wW5Aiy/ezlOjp3E26K3ISwQQgABXhp6CfZJOzeHuAAlyD79AMYH7eg7/3k8cfgJbm7x3PAUWj7sR6EwC0sBThgf/Acpdn00gL6hKZQW5aCpeikWFVLoKpHZRPUNqK6uDupV7d27NyYGxQuTyQSJRMJrk0gksFgskMvlEZ9nZGQEWVnRz0LYJ+1wTjhxbvIcHv3ro5jy8oe2uSLfEsJdx3bB5XUBALKQhQJBAbJzs7H1/K3In8rHwMgwhsaAqQkx8Ld7Ibzw92DGGfzNPYjtHzhxZtiLpYVZeECxEM/2DKJvaApb/+BbzbK0MAuP/t9iFAomMDIyEfXfQhCxYGQksfG1UYlieXl5RG3JTLj5Q7vdHrJ9fHyct2bZ7XYDAKqqquZsS64kF2UPlcG+yHdtNrsNt12UywkiAIz2j6JrVxe8Y16853iPa89euAhLb28CSs7Dpx+tw3Xb7oX0hgeRW3IeJh2nYf6VFt8Z7Efe8otR+p2vks/+WbcFNzz6+Zz/DoJIB6ISRavVCp1Oh8rKSgC+okbt7e3o7u6OqXGJIJxYNjU1Yfv27fNyzcCiUP6CGMhE/wR6n+rFpH0yaJtnsB9nXtNi6e1NyC05jxO+SecZnHlNC89gP7IXLoL0hgd5x0lveJDbThCZTlSiqNPpoFQqeV/m2VR7SwbEYnFQr9But/PmSf3RarXYsmUL99ntdmPlypXo7Oycc0KIEc8Ifnb8Z7BP2OH0OLl2xstAkPWVQAogwPaK7ZC9I5v2fIcHJvGT9786z5LS87D7rbcAgBtKL8rPgodhMDTBACXnoWLrK3jsSjEWFWSHOStBxIehoaGYjMCiRcBEoWadnZ1BRh88eBBr1qyJmWHzjc1mg1qthtls5tpKSkrQ29sbVhj9cbvdEIlE+O///u+YZMk5MXoCzUea0T8xfW9NmivF9ou3h41F9Pcm+yPJz0J2lgDnhj1YXJgNMMC5EQ8WF2QDAuDcsAelRTnYQc4WIsEMDQ3hiiuugMvlQnFxcdyvH5WHIFAQu7q60NvbGxOD4oVMxu9t2Ww2KBSKiAQx1gxMDGDnkZ3on+iH1+nFpDN4aMztOzmAxz5/LGQsor8glhblYOf6pT7RA2Af9cIx6oEk/ytBLC3KQcv6UrRcW4rSohyIFmQhP5fi+YnMJuouwb59+7iwFoZh0NPTg1tvvTVmhsUDg8EAjUaDyspKdHd3w2AwxN0G/yQOi3IW4cRbJ5B3ex5vn0n7JBYvWQznlBMAQiZ76A8QxKbqpVhc6BO9xj/04dyIB1NewD7qq9Tnvw8A7KheivzcLBQKSRSJzCYqUdy2bRucTifsdjtkMhmcTicaGhpibdu8I5PJ0NzcDABQqVQJsSE/Ox+iXBEAX/D2Kc0pNJ9uhgceiHPEONt3FlPOKWyp2IJnTz7rG16H8MPk52ZBtCALAF/sWGF88N0+ThAB4MF/kHL7AKAhM0H8L1GH5NTV1aG3txcCgQCrVq1CV1dXrG3LCMbd47ji5BX4+yv/HlKhFPlL83GB6wK4p9zQnK/BjfffCO+YF8tvXI7HL3ocjx9+HMU5xZAI+TGWhcIsPLFuKUYnvSEFLjugA7jrowGeeBIE4SOqsZJMJsOxY8dQVlYGo9E48wFESBwOBzZt2gTNjzT4uONjAEBBdgEevvBhPH7R45DkSjDlmIJ31Lf0TiqU4vGLHsfDFz6MguyCoPMVCrOCBJGdZ2QdKTvXL0VpUU7Qcj+CIHxEJYpOpxMymQxutxv9/f1Yv349dDpdrG1La5xOJ+rq6vDFF19AKpXi61//OretILsgrHdZKpSGFMRQhJpn/PriBWiq5gtjuMw5BJGJRCWKGzZsgMfjQXFxMXbs2IHGxkbo9fqIjt25cydqa2sB+EJ72JUhmYTT6cSmTZtw+PBhSKVS/PrXvw7yhscCdp4x0KmyuDCHE0byOBMEn5h8G6qqquBwOGbcb9u2bRCLxVAqldxxiUjCkEjYHiIriC+88MK8CCLw1TzjjhBzh4sLfTGJT6xbSh5ngvAj6ln2Q4cO8VaE6HQ6tLW1TXtMZWUlNmzYgM7Ozmgvm9KMjIygvr4en3/++bwLIkuhMHyYDXmcCSKYuKYOYwO8BYKvYkq6u7tTLr7Rn7Kysoij7hmGwXXXXYf+/n7s3r17XgVxxYoVyMvLm3lHgkgyEj2lFtfUYWvWrIFCoYBUKkVHRwdMJhMXJ5iq5OfnIz8/P+L9d+3ahfvvvx/Dw8PzaBWQl5c3K7sIIlmYnAy/oiseRDWZFG3qsKqqKhgMBqxZswYMw0Cv12PdunXRmJAyOBwObNmyBWNjYwB8veTS0tIEW0UQRDgi6inu2bOH99nhcESVOszlcmHv3r146KGHUFxczHmfE7HoOx44HA4olUpYLBacPXsW//7v/55okwiCmIGIeoqtra2wWq1wOBycl1mhUIBhGC5lWCTJdtrb29Hf/1UWmHT2PjscDlRXV8NisWDRokXYtm1bok0iCCICIuopNjc3z5jfjA2zmQ6pVJqw4lDxxOl04tprr4XZbMaiRYvw3nvv4ZJLLkm0WQRBREBEPcVAQfQvZ8oOiUtKSmY8z4EDBzA4OMhrS4ds3f44nU5UV1ejp6cHixYtQldXFwkiQaQQUTla/Ie8IpEIGzZsiGgY3NDQgDVr1mD9+vWora3F6tWrUV1dHY0JSYtarUZPTw+kUik6OzvxzW9+M9EmEQQxCyIOyXG5XGhvb4dAIEBHR0fQdrPZjE2bNk17jrKyMpjNZrS3t8PpdGLHjh0oKyubvdVJzJNPPone3l7s27cPl156aaLNIQhilkQsiiKRCEqlEs3NzbBarUFi1tjYGPF5/OcVjx49ilWrVkVqRtKzdu1afP7558jJodUiBJGKzOqbW1ZWhtbW1pA1WiIlmuWByYzL5UJtbS2efPJJVFRUAAAJIkGkMFF9e6MVxGiXByYzt9xyC8xmM7788kvqIRJEGhDXb3C0ywOTGbPZDIlEAqPRSIJIEGlAXHNGRbs8MJkRi8UwmUy47LLLEm0KQRAxIK5dG6vVGtXywGTmrbfeSql61wRBTE9ce4o6nQ5lZWWzXh6YzPzd3/1dok0gCCKGRN1T3LlzJ3p6etDW1obOzk5UVlbOmNgh1HLBSJYHEgRBxIuoeorRlhUIFMSuri4u8SxBEEQyEFVPcS5lBfbt2webzQbAN3Tu6elJ6czbBEGkF1GJYrRlBbZt2wan0wm73Q6ZTAan04mGhoZoTCAIgpgXohLFaMsKlJeXo66uDr29vRAIBFi1ahW6urqiMYEgCGJeiGpO0b+sAADs3r07orICMpkMx44dQ1lZGYxGYzSXnjUWi4VbfuePzWZDS0sLjEYjWlpa4HQ642IPQRDJTVQ9xW3btuFrX/satFot1Go1bDYbamtrZxw+O51OyGQyOBwO9Pf3Y/369RCLxfNWp8VoNEImk8FisQRtU6vVMJvNAHwCWVdXB4PBMC92EASROszJ0fL000+joqICTU1N2L1794zHbdiwAR6PBwCwY8cOdHZ2QqFQRGNCRKhUqpDtrKOHRSaTpW1ZBIIgZkdUw2c2y3ZbWxtqa2sBABKJZNbnqaqq4mq+xBOTyRRkr0QiCdmjnBfG3cgZORtyU87IWWRNDsXHDoIggoiqp2i1WsEwDKxWKy677DL09vZGLG7JkDos3Pyhv12BjI+PY3x8nPscdcHuMRfy2m+HzHUavVc9h8mCpdym3JEzKHv/+5jKK8HRK59BnBccEQSBKEWxpqYGer0eZrMZLpcLOp0OixYtiui4ZE4dNp2zpampCdu3b5/7RcaHgJF+CIdPoez973PCyAqicPgUACBrcgRA0dyvRxDErIhKFJVKJbRaLZd9e8eOHREdF4vUYXq9HlarddprzLR0UCwWB/UK7XY7T6wD0Wq12LJlC/fZ7XZj5cqVkRntj2g5Jm7fh6xXbuSE8Xjlo1jZ/QSEw6cwUbgMvVc9h6mCJcDIyOzPTxDEnIhKFOvr64M8zV1dXTN6kWOROqy+vn5W+4dCqVRCp9MFtU/n9MnLy0NeXt6crw0ATPFy9F71HNczLP+vzQDACaL/kJogiPgSlSgKBALce++9KC8vh0wmw8DAAIxGY5Ao7tmzh/fZ4XAkLHWY/7BdJpPxttlsNigUiml7irFmsmApjlc+ygkiAByvfJQEkSASTFQz+Tt27ADDMOjv78eBAwdgtVpDOilaW1thtVrhcDg4R4xCoYhb6jCTyQSNRgPANyfoHzBuMBig0WhgNBqh0+niHqOYO3IGK7uf4LWt7H4CuSNn4moHQRB8BEwUqhSqcFWkbYEcPHgwJZO0ut1uiEQiuFyuGVOmBTJ25gg3pzhRuCzknOJkwVKMjIxg7dq1AIBPPvkEBQUFEV+jvLwc+fn5s7KLIJKBuXy3YkHUy/zcbjf27NmDPXv2wO12hxS/wDaXy4Wnn36aC2fp6upK+XIEs8Z1EsLXbuUJ4Oiib6L3qucwUbiMc76Ei2MkCGJ+iUoUe3t7sW7dOuzfvx/79+9HRUUFDh06NONx7e3t6O/v5z6vW7cu81aS5BUBBYuCnCqTBUs5YZzKK4E3N/JeIUEQsSMqR8vevXvR09PDa9NqtTMWb5JKpUEhORnHAhHGa17DsS/+xxd248dkwVLYrvolvLkF8OYWAZMUkkMQ8SaqniIbn+hPJGuYDxw4gMHBQV5bKhetipq84iBBZJkqWOITRIIgEkJUPcXAhAoAIior0NDQgDVr1qC8vBxisRgWiyVkvCBBEESiiHpFy7XXXsvlKYw0yWxZWRnMZjPa29vhdDqxY8eOkL1OgiCIRBHR8Pnpp5/mfV6zZg10Oh0Xb6jX6yPOiSgSiVBXV4etW7eSIBIEkXRE1FN86qmnIBaLoVAoOGdKWVlZxGueCYIgUoWIRLG+vh6bNm1Cb28v9u7dC4FAMK8ZswmCIBJFRKLI9gjLysq4Ia/L5cK+ffsAgASSIIi0IeospmazGfv378emTZugVCpx7733xtIugiCIhDArR8u+fftQW1uLrKwsVFdXw263Y/fu3fB6vXj++efn1VCCIIh4ELGjRaPRQCwWo6amBh0dHTMmekh32Dwa0ZQlGB0dxdDQzHVYRvySzA4NDcHr9UZ8DbfbjcnJyVnbRhCJhv1OzWcGremISBRlMhl2794Nu90Op9MJl8uFo0ePYtWqVfNsXvLCrsyJKvt2FGT6jxCReQwODkIkEsX9uhGlDguVAuzgwYPcKha5XJ5xAun1enHq1CksXLgQAoFg3q7Dlj04fvx4QtIopRp0v2ZHMt4vhmEwODiIZcuWISsr/sXbIuophuqllJSUwGQyoa2tDQcPHoRKpYp7Vb5EkpWVhRUrVsTtesXFxUnz0KYCdL9mR7Ldr0T0EFkikmG2rMChQ4eg1WqxevVqlJeXo729HbW1tThy5EhGCSJBEOlLRMNniUQCgUAAh8MBpVIJtVqNmpqahKp5ppDoLMSpBt2v2UH3K5iIHS1arRYbNmyYb3uIAPLy8vDYY4/FrJJgukP3a3bQ/Qomop7i3r17SRAJgsgIoipcRRAEka7E399NEASRxJAoEgRB+BFV5m0iPthsNhiNRshkMthsNtTX10MsFifarKTBYrFw1SC7u7uxe/du7v7QvZsejUYDrVZL9ysUDJG0yOVy7r3VamVUKlUCrUk+mpubee/97xfdu/CYzWYGAONwOLg2ul9fQcPnJCWwOJhMJsu8GtnTYLFY0NTUxH1WqVSwWCyw2Wx072bAZrNBJpPxPvuT6feLRDFJMZlMkEgkvDaJRAKLxZIgi5ILuVyO3bt3c5+dTicA3z2iexceo9EIlUrFa6P7xYdEMUlhv+SB2O32+BqSxPh/udva2qBUKiEWi+nehcHpdIacJ6T7xYdEMcUI9wBnMk6nE0ajEQaDYcb9Mpn29nYolcqI98/U+0WimKSIxeKgX2q73Z65HsFp0Gg06Ojo4O4N3btgTCYTampqQm6j+8WHRDFJCfeLrlAo4mxJctPS0gKNRgOZTAan0wmn00n3Lgzt7e3Q6/XQ6/Ww2WxoamqCxWKh+xUAxSkmKf7eQcDnIVQoFBn76x0Ko9EIuVzOCWJ7e3vI+Dq6d8E/sg0NDWhoaAh6zgC6X7T2OYmx2WzQ6XSorKxEd3c3L9g207HZbCgvL+e1icViOBwObjvdu2CcTif0ej00Gg3q6+vR0NAAuVxO98sPEkWCIAg/aE6RIAjCDxJFgiAIP0gUCYIg/CBRJAiC8INEkSAIwg8SRYIgCD8yRhSNRiMqKipQUlICvV4PwLf0iW1raWnh9tVoNEFtiYK1kbU53bHZbGhpaeFWXphMJu5vt1gsaGhogEAggEajQUtLC1paWtDQ0MBLf2UymXj7ZVoarMD7pNfr0dLSArVaDaPRmGjzZiThz3xi0znGF4PBwIjFYl4bm3AzEJ1OFy+zgvBP/skwDNPY2JhQe+KJf7JThvElj21sbOQ+OxyOoASpHR0djFgs5rVZrdag/TKJUPeJbevo6Ij4HJG0xYJkeuYzpqcI+FJNOZ1OXp44duG7/y8ou8wpEdhsNrS3t/PapFJpQmyJN4HJTgGgsbFxxr9fqVTC6XTyeoSB+QEzFf9VKWKxGHK5fMZsQkDo5zBUWyxItmc+o0QR8H2B2trauM9OpxM1NTW8NovFArlcngjz0NzcnJDrJgNsfZDAYVN9ff20x7E/con6P0sVnE4nbDYb1Gr1jPuGeg7n69lMtmc+4xJCqNVqNDc38/4j1Go1qqurQ+7PFkdiv7AqlYpL197Q0ACNRgMA0Ol06OzsRHt7O5egoLu7G83NzTCZTLBYLJDJZFxbKEwmE3p6erg0Tkqlkluwz/aEbDYbOjo6eL/2RqMRYrEYNpsNVquVO7/JZIJGo+EW/oc6NtnYvXs31Go1GhoaoFQqodFoQmZxMZlMEIvFsFgsGBgYgNVqDZncINPxfzY6OjrQ2dnJ+/EI9WyGeg5tNlvIZzPc8YHfDbPZHNK+pHzmEzJoTyDsvIrVamXMZjNjtVoZhmG4uRaHw8HNuVitVkapVPKOl8vl3PxHfX09U19fzzCMb76yubmZN1+j0+kYq9XKmyfT6XS8gkuBhJpLaW5u5hUSUiqVjNls5j6zfw9rk8Fg4J1vumOTEavVyuh0OkalUjEAeH9P4FyZ2Wzm/Z+E2y/TYP9+fzo6OhilUsk9K9M9m6Gew8C26Y4P/G5MR7I98xk3fGbnVYxGI6+Aj0qlgsFggMlk4nomOp0uaEgmk8m4+Q+xWMxlalGpVFCpVFCr1aioqEBLSwtqamqg0+m4uiH+5ThnS2VlJe9v8E8K6nA4uF9Fu93Om5uTSqXTHptMsJmeZTIZ6uvrYTAYoNPpeAWqApHL5VAoFFyvhAgPW66hoaEBAOb8bE53fOB3IxoS9cxnnCgCQG1tLW8OkW2LZhLZf8gmkUjgcDiwe/duDAwMcHM3crkcSqUSSqWS+7JHQqTp4JuamrjwoVQeQtpstqBiSTU1NTPeB7FYnHFhN9FSWVmJnp4e7nMkz2ao+8+2TXd8NM9iMjzzGSmKbDlM/14g65n299bV1tYGfdksFgsvrbv/L1BTUxNsNhvkcjmam5shFotDniPSL3Ak+7FzOo2Njdxc5myukWwE9vhMJtOMPY3y8nKup8CKamDPwGKxoLy8HHq9HkajkXedlpYWGI1G7ktmsVhQUVEBk8kEtVrNHWsymbhjjUYj75hUoaOjg3t+I302w7XNdHw0I5JkeOYzNp+iWq0O+lVsaGiATqfjtQVOJNfW1kIul3MTuhKJhHMGtLS0QCwWQyKRwG63QyKRQKVSwWQyoaOjg+vSs8OYUNhsNjQ3N6OiooILNamrqwPgc0LYbDZoNBpOeCUSCerq6rghEeAb1tTW1kImk017bLL1Ki0WC3p6eng/Tv6T6BaLBW1tbbDZbKisrIRSqeR+2NRqNSorKyEWiyGTyWAwGKDX69HY2Mj9n1VUVHAT/hqNhpektr6+nqsbrVKpUFFRgc7OTgC+nmhFRQV2794NuVyO8vJydHR0QCaTobq6Gh0dHXG8SzNjsVig0+m4v7+8vJxz/EkkEt4zHu7ZDHwO2aFqYFuo43t6eoK+G9ORbM98xooikXn4i6LJZOJ+FMvLyzlxZdPw++8beKz/+2QURWJuZFxIDkEAvmFkRUUFJBIJbDYb15ux2Wxcb5/ITDJyTpHIXNg5RcA3ZGbnK41GI/R6PSQSCTeMZvez2Wy8mDmbzcZNq/T09KTs/C0RGho+ExlD4JCYIEJBPcU5kPBsHkTEsL0/6tVFRyY96zSnOAfY+Cwi+ZHL5Vz5U2L2ZNKzTj3FOZIpGWwIIlOedRJFgiAIP2j4HCE2mw06nQ6VlZWw2+2oqanhAoznK5tHuGtGmnWHIKIh45/1iFNHZDAOh4ORyWRcxpXGxkYuG8h8ZfMId83ZZt0hiNlAzzrDUE8xAtgcieyvpVar5W2fKZsH++sZKpuH/zyN/7HhrtnU1MRlJmGJJusOQYSCnnUaPkdEYKKIcOuWQ9HU1ASpVMolp43FNdnMJCwzZaYmiEihZ50cLRHBZtXxZ76zeYS75lyy7hDETNCzTj3FiJDJZNDpdNBoNLxsIGzWFvYzmw9Qp9NBJpNxyQXY/0i1Ws1tm+nYcNcUi8Vobm4OaieIWEDPOi3zIwiC4EHDZ4IgCD9IFAmCIPwgUSQIgvCDRJEgCMIPEkWCIAg/SBQJgiD8IFEkCILwg0SRIAjCDxJFgiAIP0gUCYIg/CBRJAiC8INEkSAIwo//B/yYrpRGtc8DAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 335.852x335.852 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize=figsizes[\"half_width_square\"])\n",
    "\n",
    "# Figure a)\n",
    "method_1 = r\"SRI$_\\text{perm}$\"\n",
    "method_2 = r\"SRI$_\\text{num}^1$\"\n",
    "\n",
    "ax1.axvline(0.0, color=\"black\", zorder=-1)\n",
    "ax1.axhline(0.0, color=\"black\", zorder=-1)\n",
    "\n",
    "\n",
    "x_lim = [data[(\"value\", method_1)].min(), data[(\"value\", method_1)].max()]\n",
    "y_lim = [data[(\"value\", method_2)].min(), data[(\"value\", method_2)].max()]\n",
    "\n",
    "x_lim = [-7, 50]\n",
    "y_lim = [-15, 50]\n",
    "\n",
    "ax1.fill_between(x_lim, -nsigma, nsigma, color=\"lightgrey\", alpha=1, zorder=-2)\n",
    "ax1.fill_between(\n",
    "    [-nsigma, nsigma], y_lim[0], y_lim[1], color=\"lightgrey\", alpha=1, zorder=-2\n",
    ")\n",
    "\n",
    "categories = sorted(data[(\"category\", method_2)].unique())\n",
    "\n",
    "\n",
    "for category, color in zip(\n",
    "    categories, [\"tab:orange\", \"tab:pink\", \"tab:green\", \"tab:blue\"]\n",
    "):\n",
    "    df_selected = data[data[(\"category\", method_2)] == category].copy(deep=True)\n",
    "    if len(df_selected) == 0:\n",
    "        continue\n",
    "    # if category != \"same\":\n",
    "    #     print(df_selected[[(\"value\", method_1), (\"value\", method_2)]])\n",
    "\n",
    "    xerr = [\n",
    "        df_selected[\"value\"][method_1] - df_selected[\"confidence_low\"][method_1],\n",
    "        df_selected[\"confidence_high\"][method_1] - df_selected[\"value\"][method_1],\n",
    "    ]\n",
    "\n",
    "    yerr = [\n",
    "        df_selected[\"value\"][method_2] - df_selected[\"confidence_low\"][method_2],\n",
    "        df_selected[\"confidence_high\"][method_2] - df_selected[\"value\"][method_2],\n",
    "    ]\n",
    "\n",
    "    ax1.scatter(\n",
    "        df_selected[\"value\"][method_1],\n",
    "        df_selected[\"value\"][method_2],\n",
    "        color=color,\n",
    "        marker=\"x\",\n",
    "        label=category,\n",
    "    )\n",
    "    # ax1.errorbar(\n",
    "    #     df_selected[\"value\"][method_1],\n",
    "    #     df_selected[\"value\"][method_2],\n",
    "    #     xerr=xerr,\n",
    "    #     yerr=yerr,\n",
    "    #     fmt=\"x\",\n",
    "    #     color=color,\n",
    "    #     label=category,\n",
    "    # )\n",
    "\n",
    "ax1.set_aspect(\"equal\")\n",
    "x_min, x_max = ax1.get_xlim()\n",
    "y_min, y_max = ax1.get_ylim()\n",
    "xy_min, xy_max = min(x_min, y_min), max(x_max, y_max)\n",
    "ax1.set_xlim([xy_min, xy_max])\n",
    "ax1.set_ylim([xy_min, xy_max])\n",
    "\n",
    "ax1.plot(\n",
    "    [xy_min, xy_max], [xy_min, xy_max], color=\"black\", linestyle=\"dashed\", zorder=-1\n",
    ")\n",
    "ax1.set_aspect(\"auto\")\n",
    "\n",
    "\n",
    "ax1.legend(\n",
    "    loc=\"upper left\",\n",
    "    fontsize=8,\n",
    ")\n",
    "\n",
    "ax1.set_xlim(x_lim)\n",
    "ax1.set_ylim(y_lim)\n",
    "\n",
    "ax1.set_xlabel(method_1)\n",
    "ax1.set_ylabel(method_2)\n",
    "\n",
    "# Annotations for x-axis on the left and right outside the plot area\n",
    "ax1.annotate(\n",
    "    \"Worse than\\nchance\",\n",
    "    xy=(0, 0),\n",
    "    xycoords=\"axes fraction\",\n",
    "    xytext=(10, -20),\n",
    "    textcoords=\"offset points\",\n",
    "    ha=\"center\",\n",
    "    va=\"top\",\n",
    ")\n",
    "ax1.annotate(\n",
    "    \"Better than\\nchance\",\n",
    "    xy=(1, 0),\n",
    "    xycoords=\"axes fraction\",\n",
    "    xytext=(-10, -20),\n",
    "    textcoords=\"offset points\",\n",
    "    ha=\"center\",\n",
    "    va=\"top\",\n",
    ")\n",
    "\n",
    "# Annotations for y-axis on the top and bottom outside the plot area\n",
    "ax1.annotate(\n",
    "    \"Better than\\nchance\",\n",
    "    xy=(0, 1),\n",
    "    xycoords=\"axes fraction\",\n",
    "    xytext=(-40, -25),\n",
    "    textcoords=\"offset points\",\n",
    "    ha=\"center\",\n",
    "    va=\"center\",\n",
    "    rotation=\"vertical\",\n",
    ")\n",
    "ax1.annotate(\n",
    "    \"Worse than\\nchance\",\n",
    "    xy=(0, 0),\n",
    "    xycoords=\"axes fraction\",\n",
    "    xytext=(-40, 20),\n",
    "    textcoords=\"offset points\",\n",
    "    ha=\"center\",\n",
    "    va=\"center\",\n",
    "    rotation=\"vertical\",\n",
    ")\n",
    "\n",
    "\n",
    "# # Add labels I, II, III, IV to the quadrants\n",
    "# ax1.text(0.98, 0.95, r\"\\textbf{I}\", ha=\"right\",\n",
    "#         va=\"center\", transform=ax1.transAxes)\n",
    "# ax1.text(0.02, 0.95, r\"\\textbf{II}\", ha=\"left\",\n",
    "#         va=\"center\", transform=ax1.transAxes)\n",
    "# ax1.text(0.02, 0.05, r\"\\textbf{III}\", ha=\"left\",\n",
    "#         va=\"center\", transform=ax1.transAxes)\n",
    "# ax1.text(0.98, 0.05, r\"\\textbf{IV}\", ha=\"right\",\n",
    "#         va=\"center\", transform=ax1.transAxes)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"./results/mcpdesouto1.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultiIndex([(          'value',   'SMI$_\\text{all}$'),\n",
      "            (          'value', 'SMI$_\\text{all}^1$'),\n",
      "            (          'value',   'SMI$_\\text{num}$'),\n",
      "            (          'value', 'SMI$_\\text{num}^1$'),\n",
      "            (          'value',  'SMI$_\\text{perm}$'),\n",
      "            (          'value',   'SRI$_\\text{all}$'),\n",
      "            (          'value', 'SRI$_\\text{all}^1$'),\n",
      "            (          'value',   'SRI$_\\text{num}$'),\n",
      "            (          'value', 'SRI$_\\text{num}^1$'),\n",
      "            (          'value',  'SRI$_\\text{perm}$'),\n",
      "            ( 'confidence_low',   'SMI$_\\text{all}$'),\n",
      "            ( 'confidence_low', 'SMI$_\\text{all}^1$'),\n",
      "            ( 'confidence_low',   'SMI$_\\text{num}$'),\n",
      "            ( 'confidence_low', 'SMI$_\\text{num}^1$'),\n",
      "            ( 'confidence_low',  'SMI$_\\text{perm}$'),\n",
      "            ( 'confidence_low',   'SRI$_\\text{all}$'),\n",
      "            ( 'confidence_low', 'SRI$_\\text{all}^1$'),\n",
      "            ( 'confidence_low',   'SRI$_\\text{num}$'),\n",
      "            ( 'confidence_low', 'SRI$_\\text{num}^1$'),\n",
      "            ( 'confidence_low',  'SRI$_\\text{perm}$'),\n",
      "            ('confidence_high',   'SMI$_\\text{all}$'),\n",
      "            ('confidence_high', 'SMI$_\\text{all}^1$'),\n",
      "            ('confidence_high',   'SMI$_\\text{num}$'),\n",
      "            ('confidence_high', 'SMI$_\\text{num}^1$'),\n",
      "            ('confidence_high',  'SMI$_\\text{perm}$'),\n",
      "            ('confidence_high',   'SRI$_\\text{all}$'),\n",
      "            ('confidence_high', 'SRI$_\\text{all}^1$'),\n",
      "            ('confidence_high',   'SRI$_\\text{num}$'),\n",
      "            ('confidence_high', 'SRI$_\\text{num}^1$'),\n",
      "            ('confidence_high',  'SRI$_\\text{perm}$'),\n",
      "            (       'category', 'SMI$_\\text{all}^1$'),\n",
      "            (       'category', 'SRI$_\\text{all}^1$'),\n",
      "            (       'category',   'SRI$_\\text{num}$'),\n",
      "            (       'category', 'SRI$_\\text{num}^1$'),\n",
      "            (       'category',   'SRI$_\\text{all}$'),\n",
      "            (       'category', 'SMI$_\\text{num}^1$'),\n",
      "            (       'category',   'SMI$_\\text{all}$'),\n",
      "            (       'category',  'SRI$_\\text{perm}$'),\n",
      "            (       'category',   'SMI$_\\text{num}$'),\n",
      "            (       'category',  'SMI$_\\text{perm}$')],\n",
      "           names=[None, 'metric_name'])\n",
      "                             value                                      \\\n",
      "metric_name       SMI$_\\text{all}$ SMI$_\\text{all}^1$ SMI$_\\text{num}$   \n",
      "dataset                                                                  \n",
      "Alizadeh-2000-v1         -8.047028          -0.056849        10.741115   \n",
      "Alizadeh-2000-v2         -6.542501           8.149059        31.401056   \n",
      "Alizadeh-2000-v3         -5.335236           5.580363        22.872140   \n",
      "Armstrong-2002-v1       -11.819882           1.050537        23.108823   \n",
      "Armstrong-2002-v2        -8.118987           5.415473        33.382242   \n",
      "\n",
      "                                                                         \\\n",
      "metric_name       SMI$_\\text{num}^1$ SMI$_\\text{perm}$ SRI$_\\text{all}$   \n",
      "dataset                                                                   \n",
      "Alizadeh-2000-v1           10.940811         10.778074       -12.384203   \n",
      "Alizadeh-2000-v2           29.793395         30.582218         6.926347   \n",
      "Alizadeh-2000-v3           22.954725         23.171800        -8.886907   \n",
      "Armstrong-2002-v1          22.056448         22.412493       -36.953991   \n",
      "Armstrong-2002-v2          33.845320         35.403753        -5.209109   \n",
      "\n",
      "                                                                          \\\n",
      "metric_name       SRI$_\\text{all}^1$ SRI$_\\text{num}$ SRI$_\\text{num}^1$   \n",
      "dataset                                                                    \n",
      "Alizadeh-2000-v1           20.542693        10.803331          10.803331   \n",
      "Alizadeh-2000-v2           95.954295        36.580792          43.790065   \n",
      "Alizadeh-2000-v3           20.391458        17.581156          20.671998   \n",
      "Armstrong-2002-v1          40.442147        12.105173          12.105173   \n",
      "Armstrong-2002-v2          50.673420        32.441782          34.084750   \n",
      "\n",
      "                                     ...                   category  \\\n",
      "metric_name       SRI$_\\text{perm}$  ...         SMI$_\\text{all}^1$   \n",
      "dataset                              ...                              \n",
      "Alizadeh-2000-v1          10.790696  ...  sig. $\\rightarrow$ insig.   \n",
      "Alizadeh-2000-v2          41.199148  ...                       same   \n",
      "Alizadeh-2000-v3          25.046741  ...                       same   \n",
      "Armstrong-2002-v1         12.092724  ...  sig. $\\rightarrow$ insig.   \n",
      "Armstrong-2002-v2         36.217783  ...                       same   \n",
      "\n",
      "                                                                          \\\n",
      "metric_name       SRI$_\\text{all}^1$ SRI$_\\text{num}$ SRI$_\\text{num}^1$   \n",
      "dataset                                                                    \n",
      "Alizadeh-2000-v1                same             same               same   \n",
      "Alizadeh-2000-v2                same             same               same   \n",
      "Alizadeh-2000-v3                same             same               same   \n",
      "Armstrong-2002-v1               same             same               same   \n",
      "Armstrong-2002-v2               same             same               same   \n",
      "\n",
      "                                                                        \\\n",
      "metric_name       SRI$_\\text{all}$ SMI$_\\text{num}^1$ SMI$_\\text{all}$   \n",
      "dataset                                                                  \n",
      "Alizadeh-2000-v1          opposite               same         opposite   \n",
      "Alizadeh-2000-v2              same               same         opposite   \n",
      "Alizadeh-2000-v3          opposite               same         opposite   \n",
      "Armstrong-2002-v1         opposite               same         opposite   \n",
      "Armstrong-2002-v2         opposite               same         opposite   \n",
      "\n",
      "                                                                        \n",
      "metric_name       SRI$_\\text{perm}$ SMI$_\\text{num}$ SMI$_\\text{perm}$  \n",
      "dataset                                                                 \n",
      "Alizadeh-2000-v1               same             same              same  \n",
      "Alizadeh-2000-v2               same             same              same  \n",
      "Alizadeh-2000-v3               same             same              same  \n",
      "Armstrong-2002-v1              same             same              same  \n",
      "Armstrong-2002-v2              same             same              same  \n",
      "\n",
      "[5 rows x 40 columns]\n"
     ]
    }
   ],
   "source": [
    "print(data.columns)\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.read_csv(\"results/efficiency_experiment_combined.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"9\" halign=\"left\">mean</th>\n",
       "      <th colspan=\"9\" halign=\"left\">std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>method</th>\n",
       "      <th colspan=\"3\" halign=\"left\">SMI</th>\n",
       "      <th colspan=\"3\" halign=\"left\">SMI_MC</th>\n",
       "      <th colspan=\"3\" halign=\"left\">SRI</th>\n",
       "      <th colspan=\"3\" halign=\"left\">SMI</th>\n",
       "      <th colspan=\"3\" halign=\"left\">SMI_MC</th>\n",
       "      <th colspan=\"3\" halign=\"left\">SRI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random_model</th>\n",
       "      <th>ALL</th>\n",
       "      <th>NUM</th>\n",
       "      <th>PERM</th>\n",
       "      <th>ALL</th>\n",
       "      <th>NUM</th>\n",
       "      <th>PERM</th>\n",
       "      <th>ALL</th>\n",
       "      <th>NUM</th>\n",
       "      <th>PERM</th>\n",
       "      <th>ALL</th>\n",
       "      <th>NUM</th>\n",
       "      <th>PERM</th>\n",
       "      <th>ALL</th>\n",
       "      <th>NUM</th>\n",
       "      <th>PERM</th>\n",
       "      <th>ALL</th>\n",
       "      <th>NUM</th>\n",
       "      <th>PERM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.423105</td>\n",
       "      <td>0.831666</td>\n",
       "      <td>0.031781</td>\n",
       "      <td>0.327720</td>\n",
       "      <td>0.489016</td>\n",
       "      <td>0.307601</td>\n",
       "      <td>0.010944</td>\n",
       "      <td>0.001004</td>\n",
       "      <td>0.000881</td>\n",
       "      <td>0.010427</td>\n",
       "      <td>0.015978</td>\n",
       "      <td>0.007626</td>\n",
       "      <td>0.006694</td>\n",
       "      <td>0.008529</td>\n",
       "      <td>0.006774</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.000027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.048462</td>\n",
       "      <td>2.952126</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.032459</td>\n",
       "      <td>0.044218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.099016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.061130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10.041644</td>\n",
       "      <td>8.161240</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.107745</td>\n",
       "      <td>0.081994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>22.534986</td>\n",
       "      <td>19.425100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.286853</td>\n",
       "      <td>0.246450</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.248440</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.129748</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>46.259963</td>\n",
       "      <td>41.504172</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.612638</td>\n",
       "      <td>0.584325</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>88.981336</td>\n",
       "      <td>81.698942</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.938839</td>\n",
       "      <td>0.957799</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.583677</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.292889</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.498889</td>\n",
       "      <td>0.332274</td>\n",
       "      <td>0.847582</td>\n",
       "      <td>0.308247</td>\n",
       "      <td>0.013695</td>\n",
       "      <td>0.001114</td>\n",
       "      <td>0.000950</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.823483</td>\n",
       "      <td>0.007640</td>\n",
       "      <td>0.004932</td>\n",
       "      <td>0.004675</td>\n",
       "      <td>0.000194</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.000052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.128678</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.808641</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.878837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.833116</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.348914</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.542223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.340935</td>\n",
       "      <td>2.308747</td>\n",
       "      <td>0.319740</td>\n",
       "      <td>0.021907</td>\n",
       "      <td>0.001104</td>\n",
       "      <td>0.000988</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.007413</td>\n",
       "      <td>0.010961</td>\n",
       "      <td>0.009890</td>\n",
       "      <td>0.000351</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.000047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.379138</td>\n",
       "      <td>6.348964</td>\n",
       "      <td>0.347956</td>\n",
       "      <td>0.050559</td>\n",
       "      <td>0.001228</td>\n",
       "      <td>0.001107</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006854</td>\n",
       "      <td>0.030313</td>\n",
       "      <td>0.011041</td>\n",
       "      <td>0.000878</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.472333</td>\n",
       "      <td>19.521326</td>\n",
       "      <td>0.411447</td>\n",
       "      <td>0.114143</td>\n",
       "      <td>0.001444</td>\n",
       "      <td>0.001265</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.002343</td>\n",
       "      <td>0.061542</td>\n",
       "      <td>0.014339</td>\n",
       "      <td>0.002460</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>0.000055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3162</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.753444</td>\n",
       "      <td>61.597490</td>\n",
       "      <td>0.587108</td>\n",
       "      <td>0.299837</td>\n",
       "      <td>0.002174</td>\n",
       "      <td>0.001980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.002584</td>\n",
       "      <td>0.321801</td>\n",
       "      <td>0.002921</td>\n",
       "      <td>0.008171</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.000055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.663922</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.156979</td>\n",
       "      <td>0.829933</td>\n",
       "      <td>0.005722</td>\n",
       "      <td>0.005638</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.007186</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005647</td>\n",
       "      <td>0.023514</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>0.000137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   mean                                                       \\\n",
       "method              SMI                          SMI_MC                        \n",
       "random_model        ALL        NUM       PERM       ALL        NUM      PERM   \n",
       "n                                                                              \n",
       "10             1.423105   0.831666   0.031781  0.327720   0.489016  0.307601   \n",
       "12             4.048462   2.952126        NaN       NaN        NaN       NaN   \n",
       "13                  NaN        NaN   0.099016       NaN        NaN       NaN   \n",
       "14            10.041644   8.161240        NaN       NaN        NaN       NaN   \n",
       "16            22.534986  19.425100        NaN       NaN        NaN       NaN   \n",
       "17                  NaN        NaN   0.248440       NaN        NaN       NaN   \n",
       "18            46.259963  41.504172        NaN       NaN        NaN       NaN   \n",
       "20            88.981336  81.698942        NaN       NaN        NaN       NaN   \n",
       "23                  NaN        NaN   0.583677       NaN        NaN       NaN   \n",
       "31                  NaN        NaN   1.498889  0.332274   0.847582  0.308247   \n",
       "42                  NaN        NaN   4.128678       NaN        NaN       NaN   \n",
       "56                  NaN        NaN  11.878837       NaN        NaN       NaN   \n",
       "74                  NaN        NaN  29.348914       NaN        NaN       NaN   \n",
       "100                 NaN        NaN        NaN  0.340935   2.308747  0.319740   \n",
       "316                 NaN        NaN        NaN  0.379138   6.348964  0.347956   \n",
       "1000                NaN        NaN        NaN  0.472333  19.521326  0.411447   \n",
       "3162                NaN        NaN        NaN  0.753444  61.597490  0.587108   \n",
       "10000               NaN        NaN        NaN  1.663922        NaN  1.156979   \n",
       "\n",
       "                                                 std                       \\\n",
       "method             SRI                           SMI                        \n",
       "random_model       ALL       NUM      PERM       ALL       NUM       PERM   \n",
       "n                                                                           \n",
       "10            0.010944  0.001004  0.000881  0.010427  0.015978   0.007626   \n",
       "12                 NaN       NaN       NaN  0.032459  0.044218        NaN   \n",
       "13                 NaN       NaN       NaN       NaN       NaN   0.061130   \n",
       "14                 NaN       NaN       NaN  0.107745  0.081994        NaN   \n",
       "16                 NaN       NaN       NaN  0.286853  0.246450        NaN   \n",
       "17                 NaN       NaN       NaN       NaN       NaN   0.129748   \n",
       "18                 NaN       NaN       NaN  0.612638  0.584325        NaN   \n",
       "20                 NaN       NaN       NaN  0.938839  0.957799        NaN   \n",
       "23                 NaN       NaN       NaN       NaN       NaN   0.292889   \n",
       "31            0.013695  0.001114  0.000950       NaN       NaN   0.823483   \n",
       "42                 NaN       NaN       NaN       NaN       NaN   1.808641   \n",
       "56                 NaN       NaN       NaN       NaN       NaN   5.833116   \n",
       "74                 NaN       NaN       NaN       NaN       NaN  13.542223   \n",
       "100           0.021907  0.001104  0.000988       NaN       NaN        NaN   \n",
       "316           0.050559  0.001228  0.001107       NaN       NaN        NaN   \n",
       "1000          0.114143  0.001444  0.001265       NaN       NaN        NaN   \n",
       "3162          0.299837  0.002174  0.001980       NaN       NaN        NaN   \n",
       "10000         0.829933  0.005722  0.005638       NaN       NaN        NaN   \n",
       "\n",
       "                                                                          \n",
       "method          SMI_MC                           SRI                      \n",
       "random_model       ALL       NUM      PERM       ALL       NUM      PERM  \n",
       "n                                                                         \n",
       "10            0.006694  0.008529  0.006774  0.000133  0.000030  0.000027  \n",
       "12                 NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "13                 NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "14                 NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "16                 NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "17                 NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "18                 NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "20                 NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "23                 NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "31            0.007640  0.004932  0.004675  0.000194  0.000032  0.000052  \n",
       "42                 NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "56                 NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "74                 NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "100           0.007413  0.010961  0.009890  0.000351  0.000064  0.000047  \n",
       "316           0.006854  0.030313  0.011041  0.000878  0.000049  0.000044  \n",
       "1000          0.002343  0.061542  0.014339  0.002460  0.000087  0.000055  \n",
       "3162          0.002584  0.321801  0.002921  0.008171  0.000056  0.000055  \n",
       "10000         0.007186       NaN  0.005647  0.023514  0.000169  0.000137  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_df = df.groupby([\"n\", \"method\", \"random_model\"]).agg(\n",
    "    {\"runtime_seconds\": [\"mean\", \"std\", \"count\"]}\n",
    ")\n",
    "grouped_df.columns = [\"mean\", \"std\", \"count\"]\n",
    "grouped_df = grouped_df[grouped_df[\"count\"] == grouped_df[\"count\"].max()]\n",
    "grouped_df = grouped_df[['mean', 'std']].reset_index()\n",
    "grouped_df = grouped_df.pivot(index=\"n\", columns=[\"method\", \"random_model\"], values=[\"mean\", \"std\"])\n",
    "grouped_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 335.852x335.852 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize=figsizes[\"half_width_square\"])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "for random_model, color in zip([\"PERM\", \"NUM\", \"ALL\"], [\"tab:blue\", \"tab:orange\", \"tab:green\"]):\n",
    "    for method, ls, marker in zip([\"SMI\", \"SRI\", \"SMI_MC\"], [\"dashed\", \"dotted\", \"dashdot\"], [\"<\", \"^\", \">\"]):\n",
    "        series = grouped_df[\"mean\"][method][random_model].dropna()\n",
    "        err = grouped_df[\"std\"][method][random_model].dropna()\n",
    "\n",
    "        label = f\"{method}$\"\n",
    "        if method == \"SMI_MC\":\n",
    "            label = r\"SMI$^\\text{MC}\"\n",
    "        label += f\"_\\\\text{{{random_model.lower()}}}$\"\n",
    "\n",
    "        ax1.errorbar(\n",
    "            series.index,\n",
    "            series,\n",
    "            err,\n",
    "            marker=marker,\n",
    "            linestyle=ls,\n",
    "            color=color,\n",
    "            label=label,\n",
    "        )\n",
    "\n",
    "# Generate legend below the plot\n",
    "ax1.legend(\n",
    "    loc=\"upper center\",\n",
    "    bbox_to_anchor=(0.5, -0.2),\n",
    "    ncol=3,\n",
    "    fontsize=8,\n",
    "    frameon=False,\n",
    ")\n",
    "\n",
    "\n",
    "ax1.set_xlabel(\"$N$\")\n",
    "ax1.set_ylabel(\"Runtime (s)\")\n",
    "\n",
    "ax1.set_yscale(\"log\")\n",
    "ax1.set_xscale(\"log\")\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"results/efficiency_experiment.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "random_models",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}