diff --git "a/lm-watermarking-main/watermark_reliability_release/figure_notebooks/baseline_comparison.ipynb" "b/lm-watermarking-main/watermark_reliability_release/figure_notebooks/baseline_comparison.ipynb"
new file mode 100644--- /dev/null
+++ "b/lm-watermarking-main/watermark_reliability_release/figure_notebooks/baseline_comparison.ipynb"
@@ -0,0 +1,4063 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List, Tuple, Dict, Union, Optional, Any, Callable, Iterable, TypeVar\n",
+    "import os\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import rc\n",
+    "rc('font', **{'family': 'serif', 'serif': ['Computer Modern'], 'size': 13})\n",
+    "rc('lines', linewidth=5)\n",
+    "rc('text', usetex=True)\n",
+    "\n",
+    "import cmasher as cmr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "INPUT_DIR = OUTPUT_DIR = \"/cmlscratch/jkirchen/watermarking-root/lm-watermarking-dev/figure_notebooks\"\n",
+    "figure_dir = f\"{INPUT_DIR}/figures\"\n",
+    "\n",
+    "# save = False\n",
+    "save = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# enumerate data dir and load all csv files into a dataframe\n",
+    "def get_data_df(data_dir=None, data_path=None):\n",
+    "\n",
+    "    data_df = pd.DataFrame()\n",
+    "    if data_path is None:\n",
+    "        for file in sorted(os.listdir(data_dir)):\n",
+    "            if file.endswith(\".csv\"):\n",
+    "                file_path = os.path.join(data_dir, file)\n",
+    "                data = pd.read_csv(file_path)\n",
+    "                data_df = data_df.append(data)\n",
+    "    else:\n",
+    "        data_df = pd.read_csv(data_path)\n",
+    "\n",
+    "    return data_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def plot_grouped_series_data(data_df, x_col=\"idx_T\", y_col=\"avg\", y_err_df=None, y_err_col=None, y_err_join_idx=None, x_name=None,y_name=None, x_lim=None, y_lim=None, limit_x_series=None, limit_x_series_map=None, solid_alpha=0.95, dash_style=\":\", dash_color=\"gray\", dash_alpha=0.4, sortby=\"avg\", series_id_col=\"id\", series_readable_id_col=\"name\", series_readable_id_map=None, special_color_map=None, title=\"Figure Title\", legend_loc=\"upper left\", grid=True, fig_size=(10, 6), save_path=None, show=True):\n",
+    "\n",
+    "    # get unique series ids\n",
+    "    series_ids = data_df[series_id_col].unique()\n",
+    "    series_readable_ids = data_df[series_readable_id_col].unique()\n",
+    "\n",
+    "    # check that can make a 1-1 mapping, i.e. the human readble names are unique and map to 1 series id\n",
+    "    assert len(series_readable_ids) == len(series_ids), \"series_readable_ids is not a 1-1 mapping\"\n",
+    "    print(series_readable_ids)\n",
+    "\n",
+    "    # make human-readable series ids mapping\n",
+    "    series_readable_ids = data_df[[series_id_col, series_readable_id_col]].drop_duplicates().set_index(series_id_col).to_dict()[series_readable_id_col]\n",
+    "\n",
+    "    if isinstance(series_readable_id_map, dict):\n",
+    "        series_readable_ids = {k:series_readable_id_map[v] for k,v in series_readable_ids.items()}\n",
+    "        print(\"Used dict tom map series_readable_ids\")\n",
+    "    elif isinstance(series_readable_id_map, Callable):\n",
+    "        print(series_readable_ids.items())\n",
+    "        series_readable_ids = {k:series_readable_id_map(v) for k,v in series_readable_ids.items()}\n",
+    "        print(\"Used callable to map series_readable_ids\")\n",
+    "    print(series_readable_ids)\n",
+    "\n",
+    "\n",
+    "    # set up figure\n",
+    "    fig, ax = plt.subplots(figsize=fig_size)\n",
+    "\n",
+    "\n",
+    "    ####################################\n",
+    "    # # Custom sorting and droping stuff\n",
+    "    # # sort by average y_col value in the series\n",
+    "    if sortby == \"avg\":\n",
+    "        sorted_series_ids = data_df.groupby(series_id_col)[y_col].mean().sort_values(ascending=False).index\n",
+    "    elif sortby == \"mid\":\n",
+    "        # sort by the middle value in each y_col series\n",
+    "        sorted_series_ids = data_df.groupby(series_id_col)[y_col].apply(lambda x: x.iloc[len(x)//2]).sort_values(ascending=False).index\n",
+    "\n",
+    "    # trucate the series themselves if necessary\n",
+    "    if limit_x_series is not None: \n",
+    "        # only keep up to limit_x_series points\n",
+    "        data_df = data_df[data_df[x_col] <= limit_x_series]\n",
+    "\n",
+    "    \n",
+    "    # get a randomly dispersed colormap from cmr for these series\n",
+    "    # So that we can tell them apart\n",
+    "    cmap = cmr.take_cmap_colors(\"cmr.rainforest\", len(sorted_series_ids), cmap_range=(0.2, 0.8), return_fmt=\"hex\")\n",
+    "\n",
+    "    ####################################\n",
+    "    legend_skip_mask = []\n",
+    "    # plot each series\n",
+    "    for i, series_id in enumerate(sorted_series_ids):\n",
+    "        if special_color_map and series_readable_ids[series_id] in special_color_map.keys():\n",
+    "            color = special_color_map[series_readable_ids[series_id]]\n",
+    "        else:\n",
+    "            color = cmap[i]\n",
+    "\n",
+    "        series_df = data_df[data_df[series_id_col] == series_id]\n",
+    "        # ax.plot(series_df[x_col], series_df[y_col], label=series_readable_ids[series_id], color=COLORS[series_id], alpha=solid_alpha)\n",
+    "        # ax.plot(series_df[x_col], series_df[y_col], label=series_readable_ids[series_id])\n",
+    "\n",
+    "        # limit this specfic series if necessary using the limit map\n",
+    "        if limit_x_series_map is not None and series_readable_ids[series_id] in limit_x_series_map.keys():\n",
+    "            # only keep up to the limit in the map for this name\n",
+    "            series_df = series_df[series_df[x_col] <= limit_x_series_map[series_readable_ids[series_id]]]\n",
+    "\n",
+    "        # add std error bars if the col exists\n",
+    "        # ax.plot(series_df[x_col], series_df[y_col],  label=series_readable_ids[series_id], color=color, alpha=solid_alpha)\n",
+    "        if y_err_df is not None:\n",
+    "            #  plot the stderr col in the df matching it to the x_col in the series_df\n",
+    "            # get the error series that corresponds to the idx col values in the series_df\n",
+    "            y_err_subdf = y_err_df[y_err_df[series_id_col] == series_id]\n",
+    "            err_series = y_err_subdf[y_err_col]\n",
+    "            # err_series = y_err_subdf[y_err_subdf[y_err_join_idx].isin(y_err_subdf[y_err_join_idx])][y_err_col]\n",
+    "            ax.errorbar(series_df[x_col], series_df[y_col], yerr=err_series, label=series_readable_ids[series_id], color=color, alpha=0.1)\n",
+    "            \n",
+    "        ax.plot(series_df[x_col], series_df[y_col],  label=series_readable_ids[series_id], color=color, alpha=solid_alpha)\n",
+    "        legend_skip_mask.append(False)\n",
+    "\n",
+    "        # if the series was limited, plot the rest as a dashed line in gray\n",
+    "        if limit_x_series_map is not None and series_readable_ids[series_id] in limit_x_series_map.keys():\n",
+    "\n",
+    "            series_df = data_df[data_df[series_id_col] == series_id]\n",
+    "            # only keep after the limit in the map for this name\n",
+    "            series_df = series_df[series_df[x_col] >= limit_x_series_map[series_readable_ids[series_id]]]\n",
+    "\n",
+    "            # do the same err plotting but with dashed lines\n",
+    "            if y_err_df is not None:\n",
+    "                #  plot the stderr col in the df matching it to the x_col in the series_df\n",
+    "                # get the error series that corresponds to the idx col values in the series_df\n",
+    "                y_err_subdf = y_err_df[y_err_df[series_id_col] == series_id]\n",
+    "                err_series = y_err_subdf[y_err_col]\n",
+    "                # err_series = y_err_subdf[y_err_subdf[y_err_join_idx].isin(y_err_subdf[y_err_join_idx])][y_err_col]\n",
+    "                # ax.errorbar(series_df[x_col], series_df[y_col], yerr=err_series, label=series_readable_ids[series_id], color=dash_color, alpha=0.1, linestyle=dash_style)\n",
+    "                ax.errorbar(series_df[x_col], series_df[y_col], yerr=err_series, label=series_readable_ids[series_id], color=color, alpha=0.1)\n",
+    "\n",
+    "            ax.plot(series_df[x_col], series_df[y_col],  label=series_readable_ids[series_id], color=color, alpha=dash_alpha)\n",
+    "            legend_skip_mask.append(True)\n",
+    "\n",
+    "\n",
+    "    # set up legend, but exclude stderr if it's there\n",
+    "    handles, labels = ax.get_legend_handles_labels()\n",
+    "    print(handles)\n",
+    "    print(labels)\n",
+    "    if y_err_df is not None:\n",
+    "        # assume they're the first half\n",
+    "        handles = handles[:len(handles)//2]\n",
+    "        labels = labels[:len(labels)//2]\n",
+    "\n",
+    "    # if series were limited and dashing occured, we need to remove the duplicates where a dashed was also added\n",
+    "    # we can do this by removing every element with a dash as the linestyle, zipped with labels\n",
+    "    if limit_x_series_map:\n",
+    "        # handles, labels = zip(*[(h,l) for h,l in zip(handles, labels) if h.get_linestyle() != dash_style])\n",
+    "        # print([h.get_linestyle() for h in handles])\n",
+    "        # filter based on the legend_skip_mask\n",
+    "        handles, labels = zip(*[(h,l) for h,l,skip in zip(handles, labels, legend_skip_mask) if not skip])\n",
+    "    \n",
+    "    ax.legend(handles, labels,loc=legend_loc, fontsize=12)\n",
+    "    \n",
+    "    # set up title\n",
+    "    ax.set_title(title)\n",
+    "    # label axes\n",
+    "    ax.set_xlabel(x_name if x_name else x_col)\n",
+    "    ax.set_ylabel(y_name if y_name else y_col)\n",
+    "\n",
+    "    #set upper y_lim to 1\n",
+    "    ax.set_ylim(y_lim if y_lim else None)\n",
+    "    ax.set_xlim(x_lim if x_lim else None)\n",
+    "\n",
+    "    # turn on grid\n",
+    "    ax.grid(grid)\n",
+    "\n",
+    "    # tight\n",
+    "    fig.tight_layout()\n",
+    "    # save figure\n",
+    "    if save_path:\n",
+    "        # make if doesn't exist\n",
+    "        os.makedirs(os.path.dirname(save_path), exist_ok=True)\n",
+    "        if save_path.endswith(\".pdf\"):\n",
+    "            plt.savefig(save_path,format=\"pdf\")\n",
+    "        elif save_path.endswith(\".png\"):\n",
+    "            plt.savefig(save_path,format=\"png\")\n",
+    "    \n",
+    "    # show figure\n",
+    "    if show:\n",
+    "        plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def name_extractor(x):\n",
+    "    x = x.replace(\"no_wm_dipper_high_prefixes_eval\", \" Dipper\")\n",
+    "    x = x.replace(\"no_wm_dipper_high_eval\", \" Dipper\")\n",
+    "    x = x.replace(\"dipper_high_eval\", \" Dipper\")\n",
+    "    x = x.replace(\"no_wm_gpt_p4_prefixes_eval\", \" GPT\")\n",
+    "    x = x.replace(\"no_wm_gpt_p4_eval\", \" GPT\")\n",
+    "    x = x.replace(\"gpt_p4_eval\", \" GPT\")\n",
+    "    x = x.replace(\"prefixes\", \"\")\n",
+    "    x = x.replace(\"core\", \"\")\n",
+    "    x = x.replace(\"gen\", \"\")\n",
+    "    x = x.replace(\"eval\", \"\")\n",
+    "    x = x.replace(\"supp\", \"\")\n",
+    "    x = x.replace(\"simple_1\", \"\")\n",
+    "    x = x.replace(\"algorithm-3\", \"Algo3 \")\n",
+    "\n",
+    "    x = x.replace(\"50_200\", \"\")\n",
+    "    x = x.replace(\"100_600\", \"\")\n",
+    "    x = x.replace(\"200_1000\", \"\")\n",
+    "    \n",
+    "    x = x.replace(\"_\", \" \")\n",
+    "        \n",
+    "    x = x.replace(\"cp attack \", \" CP-\")\n",
+    "\n",
+    "    return x.strip()\n",
+    "\n",
+    "\n",
+    "color_map = {k:i for i,k in enumerate(['Dipper', 'GPT', 'CP-1-25%', 'CP-3-25%', 'CP-3-10%', 'CP-1-10%'])}\n",
+    "cmap = cmr.take_cmap_colors(\"cmr.rainforest\", len(color_map), cmap_range=(0.2, 0.8), return_fmt=\"hex\")\n",
+    "\n",
+    "color_map = {k:cmap[i] for k,i in color_map.items()}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>_defaultColorIndex</th>\n",
+       "      <th>fpr</th>\n",
+       "      <th>id</th>\n",
+       "      <th>name</th>\n",
+       "      <th>tpr</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>cxiy1v63</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>cxiy1v63</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "      <td>0.001923</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>cxiy1v63</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "      <td>0.286538</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>cxiy1v63</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "      <td>0.290385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>cxiy1v63</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "      <td>0.596154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1191</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.982</td>\n",
+       "      <td>llq7qgr4</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "      <td>0.998000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1192</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.982</td>\n",
+       "      <td>llq7qgr4</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1193</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.984</td>\n",
+       "      <td>llq7qgr4</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1194</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.992</td>\n",
+       "      <td>llq7qgr4</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1195</th>\n",
+       "      <td>7</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>llq7qgr4</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1196 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      _defaultColorIndex    fpr        id  \\\n",
+       "0                     10  0.000  cxiy1v63   \n",
+       "1                     10  0.000  cxiy1v63   \n",
+       "2                     10  0.000  cxiy1v63   \n",
+       "3                     10  0.000  cxiy1v63   \n",
+       "4                     10  0.000  cxiy1v63   \n",
+       "...                  ...    ...       ...   \n",
+       "1191                   7  0.982  llq7qgr4   \n",
+       "1192                   7  0.982  llq7qgr4   \n",
+       "1193                   7  0.984  llq7qgr4   \n",
+       "1194                   7  0.992  llq7qgr4   \n",
+       "1195                   7  1.000  llq7qgr4   \n",
+       "\n",
+       "                                                 name       tpr  \n",
+       "0             core_simple_1_200_1000_dipper_high_eval  0.000000  \n",
+       "1             core_simple_1_200_1000_dipper_high_eval  0.001923  \n",
+       "2             core_simple_1_200_1000_dipper_high_eval  0.286538  \n",
+       "3             core_simple_1_200_1000_dipper_high_eval  0.290385  \n",
+       "4             core_simple_1_200_1000_dipper_high_eval  0.596154  \n",
+       "...                                               ...       ...  \n",
+       "1191  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  0.998000  \n",
+       "1192  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  1.000000  \n",
+       "1193  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  1.000000  \n",
+       "1194  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  1.000000  \n",
+       "1195  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  1.000000  \n",
+       "\n",
+       "[1196 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rc('font', **{'family': 'serif', 'serif': ['Computer Modern'], 'size': 20})\n",
+    "\n",
+    "fig_name = \"baseline_attacked_z_score_ROC_T_1000\"\n",
+    "\n",
+    "data_dir = f\"{INPUT_DIR}/figure_data/{fig_name}\"\n",
+    "\n",
+    "df = get_data_df(data_dir)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['core_simple_1_200_1000_dipper_high_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_3-25%_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_1-25%_eval'\n",
+      " 'core_simple_1_200_1000_gpt_p4_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_1-10%_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_3-10%_eval']\n",
+      "dict_items([('cxiy1v63', 'core_simple_1_200_1000_dipper_high_eval'), ('jtkisfd7', 'core_simple_1_200_1000_gen_cp_attack_3-25%_eval'), ('rzrpza5t', 'core_simple_1_200_1000_gen_cp_attack_1-25%_eval'), ('g2cs5tvw', 'core_simple_1_200_1000_gpt_p4_eval'), ('62luh543', 'core_simple_1_200_1000_gen_cp_attack_1-10%_eval'), ('llq7qgr4', 'core_simple_1_200_1000_gen_cp_attack_3-10%_eval')])\n",
+      "Used callable to map series_readable_ids\n",
+      "{'cxiy1v63': 'Dipper', 'jtkisfd7': 'CP-3-25%', 'rzrpza5t': 'CP-1-25%', 'g2cs5tvw': 'GPT', '62luh543': 'CP-1-10%', 'llq7qgr4': 'CP-3-10%'}\n",
+      "[<matplotlib.lines.Line2D object at 0x7f87d1092b90>, <matplotlib.lines.Line2D object at 0x7f87d1092dd0>, <matplotlib.lines.Line2D object at 0x7f87d10924a0>, <matplotlib.lines.Line2D object at 0x7f87d10925f0>, <matplotlib.lines.Line2D object at 0x7f8807b2c820>, <matplotlib.lines.Line2D object at 0x7f8807b2c5b0>]\n",
+      "['Dipper', 'CP-1-25%', 'CP-3-25%', 'GPT', 'CP-1-10%', 'CP-3-10%']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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cB4/Hg8nJybxjnE4nWJbF+Pi4ZpksyyIWi8Hn84FlWV2Lp1dzjB7PRl+GVthcfUN669u/ufyYks2Vfmyu9kHa0Wq97fABvPXYEbzv5htwB3es4DkeIXrIUhbp9VewsvgMHIP/Exd++NeQZAlfXTkPMbeZv28NzmcBgz2mHoww3dhr0g42zWIGs9W1CjOkrBmrm11Y2bRidcOKS6lenL3I4tzrLF5f6oO07ZFKl7VEq1GWkU6n0dXVBTAMurb9GffaLDh1y2F87IM83nz9vjq+M1IPLbVcXTtJpVIYGhrCe/b4wGTzv+dIZhkv/qqITbsEmdEOsFLOvBVYV/rynn/2dVnx6K9/DNye0lOL2lkmk8EjjzyCe++9l7qIKyDLErLpJDLrF5FefwUbKy9gc+U8NtdehCRlkMnkkMlKyEkyvpF9Aauyds9LNWww47h5EAdMvbAzPTA3qcdEkoDkajcupXpxebkHl1M9uLzcg4XlbmQlEzYyZqxuWrG6aUUmVzxo/tx7bsHJmw7ibW8+jL3DfSXPSfdrfSwsLGDPnj1YWlrC4OBgU+pg+BZsp5FNMi6/f1UNrlp9wtl0F1Yu7dXceOGBz1B3Likrl13DysIPsLl2AZn115Bev4jMxuvIZjeQy+VnA5MBZLISIAMZOYfHshd3F1xlQALUQXPWKyew8eob8XjaitW0BetpC9bSFmxkGvvxtLZpQXK1G7kyIwgP7xsAO3RtH0mW8dLFFN5yyyH8xkfejjc59sJspt4iQgHWUMQTWVx0r0AeuDp5sMgz17WkHTu32Ht78A8f+48UXElRW4ORzuHll7+K5y9GsJbbQEaWsCnnsClnsZ7LIi3nkIaEtJwr6O7NQqq6CzibY5CTTFvBetOGpYX9WFrYi8uvXIelKwd2+c62vMmxt+pjX7m0jLGbD+H9d16v+TdktZhw6/X7YR/q2U0VSYehANtkuUwOFsaC5QM5vPrhFBjrtY8wrVCZTXdBylow1GPDe2+8HmaTCbcfPoj33Xx9XUbkktYjSTJeuriEzXQOspzD+mocF5e+gYurT+C1dBLLcqbudZBlICeZkJMYSFdv6Wd+cCcWLx3AylLhF0Qt77j9KP7Tz54suc/+kT4cP8TSF0tiSBRgDeJV12ZecC1mY2nrWcL53/1N+lAhBf73//0hgl+K48j+H+HGW56GdegyVpjN8gfWgCQzyEkMVkU7Vtd6kdnsxvrKIC6+xCG1UPhI446T1+ET42MFA4C6rGZwh4fVeZmEtCoKsAYgMcDm0TTKDW9Ir/cgu2lD4nd+g4JrB8rmJHz5n5/Fd5+4gEvJa7moreYMRnqXsJJ6EYfty/iI+wd4QUphE0C9Q2tOYpDNmSDLwOP/8l5cevlEwT7/9WN34E7+OvVns9mEffY+WOg5JWlzFGAN4EfvBmzdBUlb82TTXVhLDuPcb/8G7H3GmrpAakuWZQgvL+L0l+L46nfO4ej+QTAMgxdeXcSJvSncdGgBb9q3gpFhEUNDKVi617AhZ7GOLIRcCi8UrlhYgzpdTYcpb7VUlf9XrD3/n/F+/lZgWx6W44eG8I6T1+HQ3uYtF0ZIM1GAbbKMFVg+noX2onRb0us9WEsO44dTv4Y9/aWH/BNjWtvIIPr0q7jweqrkfuLyBv7szDwAoMuSQ19XDqbsS7jtxGtw3/UcUt1XcEVaxzJkLCsH1W62zFYgxVYwzQ+o+fsxYNBjs8BsZjBx+wze/KF7alcJQtoEBdgmu3IU6B3U6Mjb1gN869At+Opv/golimhR80+/io//wVexrpmxS4bVtoGB/hVct38Bh/ck8Us/swR2cBlm2yYkyFiTs7gireOcMr+lhiQZkK4ORlKC63a2rh1ztHM5mC1mdFt7cNzO4ye5X8GJkdHaVoqQNkEBtskWDjLo6ksXvK7E1y6LBY/c91F65tqixOUNeP+fr4LpWgB3/fPoZ5Po7llDf98qunvWYLKmYTJJeclExKv/UPqpQdUkiUF2dQivvXIES0tDYOQ+DHSzkLLdyGV6sGdwD97+pjfiF957Mu85aSaTwSNffwT33rOVEIHuSUJKowDbZBvDEoa6tJslDMPgF255J32QGVAuJ+H7T76C7//oZSytFK4QI0MGTCv43tM/wg1jT+CQ48ewmCQwjIxmDY61LN2EK5duQWrxBnSbB3HHLYfwgXffiAMj/bqOZxgGDJit/9I9SUhZFGCbzDRQfE5ib5cVv/0OTwNrQ4qR5RyyaRHZzcvYXLuMr5z9Hl65cg69/SsYGViDbN1AzrKBrHkTadMmNpg0JEbGqcPNrjkDk8mCD9z6AMaO/myzK0NIR6EA22TWocLuYcW93Bg9d20QWZaxsXwOK8ko0muvANhKDZjLrSK7cQWZ9AIg5yDJMn60fgXPDl4GBnFtoFGTqO1IhsHW+i7Ksmcm2Cy9uG74Vpw6+iG86WD16xUTQqpDAbbJzD05ADuShl/91Hz74RsbXp9Os7n2CpYvfQepy99BZuN19XUZQDYrIZPNQZQ2cUlaw+vSOl6TVosXVkd5HbKMCQxjwp7eI2B7DmO47ygGu/djsHs/hrr3Y7B7H4a698Nm6aOuXEKaiAJskzGmwuxNykfijfYjja1MB9hcfRGpy9/F+tIzWE89k7dtMbeB59KLEHNp5KStvLtL0ibStR66u0358KckpGZgNdtw49534uR1brxhz1thMdF6oIQYGQVYA6Pu4dqQpRxWkj/A4qv/hPWlp/O3AVjPZvHE5iWcy4gNXSScAWBiGHQzZtgYC3otfeizjWCw5xAG+q7DUD8Hm3UrNeaAbQSHh26GzULzoAlpFRRgSdvKZVJIvvwtLF+aQza9cO31nIxMNodsTsILUgpPZRewWa85MUWMde/D7QfehaGhN6Nn4A3oHngDzBQ8CWkrFGBJ28mml7C58s948jv/CyZGO3Cuy1l8LX2+5ufe3uVrY8zoM9nQx3Sjz9SDHqYPw917ccvhO7D/+M/BZKYuXkLaGQVY0lay6SWce+w+OPbkoPWEc0Fax3M5Ea9IKxWXnZ9GEJChpBBksHjBgQ8778GBoROw9x4B238MNssADTIipINRgCVtY3PtZZyP/RdksoWDkq5I63gml8Tr0lrZcrI5k5rIXg2i29II5jJdSL5+CAsXD2Px9aO4/sBNePBTThzZP1i7N0MIaXkUYEnL21x7BQsvhbB8+buQpPyEupelNTyTS+KStF62nEzOhGyusMUp5SxYvHQQ73jjXTh57A6M9DhgYrb+dPYO92Kgr9RSDYSQTkUBlrQkWZaxkpxH8sI/YHXpx9hMZ/OS1V+S1vBMbhGXZY3AKgNZiVGXXQOQt/SaePkALpy7BaspFrJkxdQv/DTucHLYZ6dBSIQQ/SjAkpYiSTJ+fO4ppF75a/QyzyOdyR/EtCpn8ET2smZCCFne6v7NSUzR6Th9G++Hg/0A3uvqw9tvO4pjB4fq8C4IIZ2AAixpCbIs4dJrj+Ob3/hz3HToCiwMsD3JZE6W8FxuEc/mFpHTCJ+SxGAza9oadMSoCQVhMjGwWs0Y6TuMn7rpM7hx/52NekuEkDZHAbbZaJBpSf9w9nEkfvwIbtr7FPYPreFmjeT5r0treDx7CSuy9sIJ2RyDbM4Ei9mEbtu1W95q6sYtB96N2w7dC27kFMwm+nMghNQOfaI0mVwiWZOlgzM5yVIWZyNfwL7Mt3D0DdpzWdNyDj/KXsELUkpze05i8kYEm66uE2c1deOtx8bxzhMfQb/NXpf6E0IIBdgm08pFDAAMGLA93Q2uTXM8ee4S/iX2Al56bUl97S2Hv40T7FOANX/fnCzhorSGl6QULkprmt3BOYlBJme6Okd1C8Mw6Lb24h3cz+Gdxz+CPttwvd4OIYQAoADbdKYiAbbLbAHb3f6jVv8+8gx+98+/lRcm2d4NTNzyVN5+kizjeUnEM9kkMlrJ96+ODM5JprwRwQDAyDaMHhrHB0bvQ18XBVZCSGNQgDWoQ317YGLas4v47GPn8c/z5xF/5jW8uK3Vqjh14lLBa/+eW8CzuUXN8nI5BhnpWou1x2aFJOVgNptx28Gfxk+96VMUWAkhDUcB1qBu2tMeS9Wtb2QQ/ffXIK5sILWyiT/84ndK7j96/HX84h3XlpFbkdN4PreE53Niwb7S1a5gi9kMqwWwmE1bz1lloFc6iP/4lv8Gbi9f67dECCG6UIA1qDuO3dDsKuzamW88jQcC/6JrXxMj44Njz+NnRgUAQEaW8MPsZbwopQqesmZzDCTZBLPZjN6e/MXqbeZe3OX4OK481YOj7Jtr8TYIIaQqFGANyGwy4Za9R5tdDU25nISnhcuIP/MaNtPXRvc+9uQr+P6TLwMAerutWNvQnjKjpd+WxiedP8Kbj15bUi6afV0zIX8mZ4LV0gWzuXB+0wn7KD506wPos+zBI089UsnbIoSQmqMAa0AMgOvtB5tdDSSX1vFvP7yAH7+wAFmWsZnO4W+//mTZ4yoJrgeGVuH7qSj2DGxspT+UM3hJWtYMrrIMmM3WguB6+6H34/bD98Ix8lYwDINMRv/5CSGkXijAGpCN6UW3pblrhT57/go++rmvQFzZrNs5bJYs7r83hlxfEt/LiLgibagLnxe2TxmYLLaC5d/u/8mvwN7bHs+rCSHthQKsAXUxzV2dRZZl/Jr/63UNrgAw/tZzuNJ3AT/O5I8O3hlcGZMFjCl/QuzhoZsxftt/o+BKCDEsCrAG1OzsiS+8KuKVS8t1PcdI/zrecss5RDKLJd8vY+oCY8ofyPTh0c/jhr3vaNtpTISQ9kABlhSY+75Qt7JvcezFzSfscHL/iCeRLBlcTWYbsCOIfuDNn8ON++6oW/0IIaRWKMCSPI89+TI+/7eP1bzcvh4r/v6Px3HdgSEsXPgyYueewcurhQOZFCZzN7DjeevxYR63H3pfzetGCCH1QAGWqM6/sohf+dw/VnTMHSevw9jNh9Sfu21mjN58CDce36Mm199u6eI/47Hn/hzfXX+laJmMuUsNrgxMODBwPW4//H6cOvpBmHc8iyWEEKOiAEtUP/Vrf1d2HwbA6c/9B9x2w3709egb6SzLMlYXH8fCS1/C9y4/ih9tXClevskMhjHjpv3vwluOunF0+M3otvTrfQuEEGIYFGA71LmXknjkO+ewtrk1Z/TR2Isa69IUeuj+e/ATt+lLgpFefw2pS99B6vL3kEg9jX9dexUZWSNRv4JhwDBdeMfxD+N9N/2GrnMQQohRdWyADYfDcLvdza6GNqZ+44hlWcYdH/0rJJfWKz721hv2w/V2rux+mc0kLp//31i69B2czyzhqc0FiLnyU35Mpi685Zgb77nxUxXXjRBCjKYjA6woivB4PAgEAhgfHwfLss2uUp4us7n8TlWQZRm3TwSQyZZoRZbwN3/4AVjMxafGyFIOi699HQsvnsFrmwv4/vprugIrsDXX9Q7Hr+CeG36tIJkEIYS0opaZSCiKInw+H3w+H2ZnZ+H1ehEOh6sqSxC2pqF4vV4MDw+DYZii/7afIxgMwuVyIRwOQxRFtaxwOAyPx4N4PL7r9wkAvV21H8gjyzI+/7ePVR1c4383WTK4ri89ixefmMLTz/05/mnpWfzTygu6gysYE27cfxfe88Zfp+BKCGkbLdGCFUURo6OjCIVC4Plry495vV7Mz8/D7/dXVJ4gCOA4DjzPw263a+6TTCYhCEJeN7IoiohEIohEInn7siyLs2fP5tVtN3qttQuwuZyEfxeu4Fvz53H6S9V9Afi3//VRdNuK3yria3N4+tnP4/HNS3g1s1pR2QxjBmPqwi/wf1xV3QghxKhaIsB6PB643e6CABYIBDA8PAyXywWn06m7vPn5eczNzYHjij9P9Pl8moE7EAggkUhAEATY7XaMjo5icnJS/5vRoVYt2KcTl+D9g69V9bxV8Zu/+HawA91Fty9d/Gd886k/wBMbl/UXyjBbgZWxwGLuxkdG/wRmU0vcioQQopvhP9UEQUAkEkEgENDcPj4+Dr/fX1GABVAyuMbjcTgcDs19GvHMdrcBNpPN4Tvxl/CrD359V+X4fvkd+KWfvq3o9o2VF/C9p2f0B1eGAcNYYLMO4NjwSbxx3ztxw953UD5hQkhbMnyAVQJrsYDocDgQDAYhiqLuwOf1esues1hAb4T+ruqT/b++sIoP/9cvVZxL+APvvhEnbzwAABge6MGpNx3CYF/xeqylnkMk9il8f614wggVw4BhrGB7D+GdJ34RY0d/FlZz8VYxIYS0A8MH2Hg8XjJwKoE3Go3qbsVW0zXcSDfuqbxF9+S5S/iTv/m+uuh5Je595xvwh7/6bt37n3/1G/jyD38bV7JlnrcyAGOyocvShzu5X8Id3C/BYmruMnyEENIohg+wyrPOYpTgq4wM3g2la1hPSzgejyMajWJsbKxmg5sU7z725or2f+7FBfynB/4RK2vpis/FAPidyTvL7ieuX8S5y9/FV576A0jSJvRkpTCZe3DHiV/CO098BH224YrrRgghrczwATaZTJZscSrBV5k2sxszMzMIhUIl94lEIkgmkxgbG8Pk5CTi8ThcLhd8Pl/JFvTm5iY2N69NW0mlUkX3PdQ7jEwmo7vefx95Giurla/dypgYzPzaXei1mYueb3nzCr5z/n8i9vI/ICelIUs6gjhjgslkw2/e8TX020YAoKL3s1vKuRp5zk5A17U+6LrWhxGup+EDbC0Cpx7hcLhkIAeudS1vHzXM8zwCgQAcDgdisVjR1uzMzAweeOABXXX5xj99A7YKkk18819fRTab1b3/m4734uheG2462gN56Tk88shzBfvIkPE6foAXmW9CQgYMkwWjo9kqg4FZ6sctuY/h0bO1X5WnEnNzc009f7ui61ofdF1ra21trdlVMH6A1WthYWFXx8/MzOD06dMl9ymWWpHjODidTng8HiQSCc19pqencf/996s/p1IpHD2qndP3Pe99D3qt+p5Vfvmff4zXxZdhsZT/VXZ1WfCnn7kHP3Fb+We8T7z6VUSf/sZWJhIpB8g6+oQZM249+F685433Y8C2p/z+dZLJZDA3NweXywVrDecUdzq6rvVB17U+dhsTasHwAZZlWV2t2JGRkarPIQgC4vH4rp6l8jyPSCSiJrHYyWazwWbTNzrYarHq+kP77hMX8EDgUV25i+//yNvwgbtuxAjbq6sO37/w/wEMIOfSQKkE/VcNdbH4wMlZ3LDvnbrKbwSrVd91JJWh61ofdF1rywjX0vABttQAJ2DrGS2AXc1NDQQCZbuHy3E4HAC2Bj/ttiy9vvad58p22r791iP4/U++C4f3DeouN5PbwOvLz0OW0pDlXNn97bY9+PW7vo4ua5/ucxBCSLszfC5ijuPUIKpFad3uJqhFIpGyx3u9XjWIGoEsy/iHb/245D4MgD/+zXsqCq4AEHv5K5ClLGSpdHBlAJza+zb8putbFFwJIWQHwwdYnudLdhEr03MqzeS0Xbm5tsDWPFtBEIrWRXm91lN2inn2fPFFyxU3O/aWTHOoRZJz+NfzfwNZLj5o6mT3PjgHb8B/OfXn+MDb/l8wTH1W/yGEkFZm+AA7MTEBAEVXqpmfn991cAXKd0U7nU7Islw0EM/Pz4Nl2YZ0D2dzEj706dLTiQDgD/7zXRWX/fry8xDXXy06qOmuviM4OcDhnaf+DHsPvKvi8gkhpFMYPsDyPA+n04kzZ85obg+Hw/D5fAWvK8vb7Vz5ZqdS3c/bTUxMIBgMam5TlqwrNwq5Vr589pmy+0QCH8Ebj1c+kvfyyvmiSSR6TBYctQzgyJt+B129hysumxBCOonhAywAhEIhhMPhglas1+vF1NSUZgs2GAxidnYWHo+nZNlKF3O5LmKlq3p2drbg+NHRUUxNTRWdxlNr4UjpADvQ24VDeweqKluSpaIDm/abe2EyWdHdf6KqsgkhpJMYfhQxsBX8YrEYfD4fWJbFyMgIEokEXC5X0aDmdDrBsizGx8dLlj02NgaWZXHq1Kmy9ZiamkIkEoHX60UymVQXGKjlWrDlpDM5PPn8pZL7PPCJd1Vd/sXlc0CRANtrsmDogP6cxYQQ0slaIsACW0G2khVueJ7H4uJizfZTOJ3OXT3z1aPUrNYfPvd62ePfdmv1y78JVx4r2oIdNnejf+StVZdNCCGdpGUCbCexaKRJlGUZz19YxG/9j7Mlj/2z6fdVPHJYsZ5J4dWlfy+6/VDXCHqHbq6qbEII6TQUYA2G0RhgtJnO4uTPaQ+w2umuU9U/Hz1/JQpJ1k6QPWDqwv69bwdjan52FEIIaQUtMcipk5h2ZCXM5STdwfWtb97dyN5nX/nHotNzDlh6Mbj3HbsqnxBCOgkFWAOymLZ+LbmchJ//7N/rPu6tb9pdgL209HTRbUd7j6DPPrqr8gkhpJNQgDWaHMBcTd7/te+cw1OJy7oP5W86WPVp02uvYD1dfE7wkUP3gmHodiGEEL3oE9Ng+qL96v//6xMXKjr29jceqOqcuewazsc+hWRuQ3sHBth/4O6qyiaEkE5FAdZgzNlr486uLOpfMDj6f+5Dl7XynMCynMNrz/4JzqeXkCvy/NVqsoHtrX7qDyGEdCIaRWwwew8Pqf///SdfLrnvJ8fH8P47rsfxQ6zarVyp1xL/C9969et4ZrN49/BIz6GqyyeEkE5FAdZgbjh5CADw+sJqyf2mP/oOfOSnbtvVuZ4//3/wpef+B8TcZsn99g/euKvzEEJIJ6IAazD7jg4DAKL//mrJ/UZvPlT1OWRZxl9/9xfw7OIPy+7LmLtw2H571ecihJBORQHWoJZXS7cq33C09PJ6xUiShNm5dyKVFsvuazLbYDJ14bZD76vqXIQQ0slokJMB5XISfj/4aMl9qhnQBAD//sJf6w6uDGPBz90+gwFb5cveEUJIp6MWrAF97V/Pldz+kZ+6tapyZVnG/Pm/LbsfY+rCrYfeh3tv+jT6bdW1lAkhpNNRgDWgfysz/9VU5YjelYUf4LWN0qvx7O89jF94axB7ac1XQgjZFQqwBrSwtF5y+w3HRiouU8pt4Pmn/xtWJO1k/gDAMGZ88l3/F1ZzdavxEEIIuYYCbAt6x+1HK9pflnJ49Zk/xqVs6cD9wHsfg4WCKyGE1AQNcmoxU7/8E9hn79O9vyzLeD0RxOri43g9Vzwz1E+c+Agslt5aVJEQQggowBpSOpMruu22G/ZXVNbChTCWLm4t0n4pqx1gGcaM4yOnKiqXEEJIaRRgDUaWZcw/XTrJhF6py9/FwotnAABpOaedzJ9hwJisuG54d1mhCCGE5KNnsAazvJStSTm57CouPf9FSLKMc2kRT2xeKtyJYWAy2WC19GKwe29NzkuIkeVyOWQyxQf6NUMmk4HFYsHGxgZyueK9V2SLxWKB2WxuifzoFGAN5ttfLT2NprfbqquchZfC2Mws4ezqBVzMauQ1ZgCTyQYwDLot+p/pEtKKZFnGxYsXIYpis6tSQJZlHDhwABcuXGiJoGEEZrMZ+/btw9DQkKGvGQVYA5HXLVi8kobNUjyI6kmRmF6/CPHVr+Pf1l/VDq4AmKvBFQBO2EerqzAhLUIJrvv27UNvb6+hPpQlScLKygr6+/thMtFTu1JkWUY2m0UqlcJrr72G9fV1HDx4sNnVKooCrEHIMiD/oPSaq3vYXpjN5f8AL5//a+SkDF7OrGhuZ0xdYJitcqzmHtx9/ScqrzAhLSKXy6nBdWSk8jnk9SZJEtLpNLq7uynA6jQwMACbzYYrV65g3759MJurSx1bb/TbNIplG7BR+vvOf//0PWWLWROfxsrCY1iVM8jIUuEOjAmMaetmtJq68XO3P4iRvsrm1RLSSpRnrr29NA2tnfT19UGWZcM9U9+uYS3YP/qjP8Lw8DA+9rGPNeqULUU+P1x2H/7G0l0huewaLp77s5L7mExdALa6he+96TdxcPCN+itJSAszUrcw2b1W+H02LMB+5jOfwdLSEh566CF8+tOfbtRpW8fF/pKbf+k/3AaTqfQNdUn4n8hsbI0WTsuFoxEZk0V97vrRtwRa4gYlhGgTBAF+vx/BYBAcx8Hr9QIAFhYWAAAOhwOTk5PNrGLHa+gz2KGhIbAs28hTtgStntydPjk+VnL78uXvIfX6t9SfX8os5+/AAAyz9es+PHgTBVdCWhzHcQgEAhAEARzHYWpqKm+71+uFx+NBKBRSX/P5fBAEIe81Uj9VPYN94okn8NBDD+GLX/wiUqmU7uNSqRT9YrXIpYPdg79+Nwb6bEW3ZzMpXHw+cO1nWcJz6cW8fRjGqrZeDw7duIvKEkJaQSAQgCiKCAaD6msulwsTExNNrFVnqbgF+4lPfCLvF/bxj38cc3NzuOuuu/L2e+KJJxCNRpFIJCAIAuLxOARBoC6LKjjfxpXcLr76dUjZayOGn0+L2JC2dREzpq3u4avefKD8YClCSOvzeDzw+Xzq567T6WxyjTpLRQH2s5/9LAKBAJxOJ3iehyiKmJubg9PphCAIOHbsGFKpFEZHRyEIAoCteUsKnufxhS98obbvoB3kirdgzSamZHIJWZaRuvSo+rMky3hqc+HaDsy1gU0AcGToFnCUd5h0sA8c+X3kSuT7bjQZW3/HDMOAAWC2mvHll3+3JmWPj4/D6/UiHo8DAO677z6IoohEIgEAiEQi8Pl8sNvt8Hq9SCaTEEURCwsL8Pv9uvdRzM7OguM4tdva7XYjHA7D5/OB53l4vV7Mzc0BQMGx7Uh3gF1aWkIwGEQkEsG73/3uvG3BYBBTU1M4c+YMeJ4Hy7K47777wLIsRkZGwLIsxsbGcPLkyZq/gbZwcaDopsH+4l3DALCx/Dwy2xZRP59JYXXbmq8M06V2DQPAndwv0/NX0tFymRwyaeMEWOBagK01ZcxLNBrF5OQk/H6/OhgK2GrRTk9Pw+PxIBAIgOO2est8Ph+8Xq/aoCq3D7DVWp6YmIDb7Qaw1R2tBFkAmJmZgd1ux8TEBM6cOVPz92pEugPsgw8+CL/fXxBcAWBychKRSAQPPfQQ3G43HnzwwZpWst3Jiz1Ft21sls5NnLr8aN7PL2a2PRPfNucVAPb2HceN+99VVR0JIa3Pbi/MBMeyLHieVwMnAExPT2N4eBg+nw8cx5XdBwDC4XDeGBuXy4VAIIBAIACWZSEIAnieBwD1v+1Od4CNx+OYmZkpun1ychKf+MQncO7cuZpUrKOUmKJT6lutLOWwfPnfrv0sy7iwbfQww+RnN3n78Z+HiaHcIoR0CiX38vbAqAfLsmBZFvF4vOix2/dJJpNgWRaRSETdvrCwoD4qrKYO7aBm03ROnTpFXcBVkDfNKNUx9MG7i4/4XVt6CrnMkvrzhWz+1JydAfbWQ++tqo6EkNYUjUYBAGNjpaf57ZYoiuA4Lm8QFQ2oqmGqxKGhIZw6RYNnKnap9Eo273/n9UW3rSRj6v/Lsown8wY3mfKevR4avBHdltLJLAgh7SUQCMDv91ecf0AURYiiWLIrd/s+PM/ntVa379PJdAfY7aOBiyn3kH5paQlf+tKX9J6yI8jP7Sm5/cYTxbcrWZsA4GJuDVey6+rPO1uv/JGfrrKGhLQXs9UMa5dx/lm2/bN2mWG21iZx/ezsLERRLEhAoSUej+cFw5mZGUxOTuZ165bax+l0YmxsDOFwOK/c7V3GnUh3F3EtRrgJgoD5+Xl88IMf3HVZncLWVepXdC0F1FMbV/I3bXvW2mtlMXrkZ2pcM0JaU62mwNSKJElIpVIYHBysaDUdQRAQCAQQiUTAcRxmZ2cBbD37FEURDodDnRIDbAVIJZOTz+fLmybD8zwikYg6GGlkZKRgGk25febm5uDz+ZBMJtXBVG63G5FIBH6/Xz2v1+vtmOexugPs/Pw8/vIv/xLDw8WT0s/PzxdtoSaTSczNzXXMha2F377vjpLb5avTcRZy63h1x7qv278Ovf34z8Nq7q519QghTcRxHPx+v+75pDzP5wXcnZTpNKWU20erLk6ns2Ofx+oOsKIols3CJMtyQRfB9m0Mw+jqriBbfnL0WMnt2bQIADh39b/5tkJsl7kXbzs2XtuKEUIIKauiUcQPPvhg1S1QpQVL9Du8b7Dk9lxmCbIsFyb2B6AE2Ldc9yH0WEuXQwjpXMpgpd3uQwrpDrA8z+Mzn/nMrk42NjaGhx9+eFdldIqhMhmcZCmHXGYZl3LrWJc0klEwAAMT3n785+tUQ0JIq4tEIpiZmdF8LlvJPkSb7gC7Pb1WtTiOq/t8rE6Ry6YAyPmZmxRXB6Qdt/MY6t7f2IoRQlqG0+lELBbb9T5Em+4Ae9999+36ZENDQ/jQhz5U1bGiKKqZpEZGRpBIJOByuXQ9mNcSDAYRCoXg9XrhdDrVkXHxeBxnzpzB9PS05hywWtejWtl0+e7hWw4UprUkhBDSGA1dcL1aoihidHQUoVAoL+h5vV7Mz89X1WUhiiIikUjBPC2WZXH27NmiwbXW9ahWLrOEhdxGXmJ/BXM1wN68/66CbYQQQhqj4gCbSqXg8/nUwORyufDggw9icLB+A2k8Hg/cbndB0AsEAhgeHobL5apqGHggEFDXq7Xb7RgdHS05Urpe9ahGNp3U7h4GAAa4jr0Ng937GlIXQgghhSoKsI8//jicTicWFxfV1xKJBB5++GGcPXsWt912W80rKAgCIpGIuiTSTuPj4/D7/VUFtvHxcd0pxOpZj2psrJzHxdxaka0Mbjlwd0PqQQghRFtFuYg9Hg+Gh4cRCoUQi8UQi8Xw8MMP49ixY5rL2NWCEtCKTQ9yOByIRCJ1H0JulHoAW3OKxYV5JHMbRfYw4WZalo4QQppKd4D9oz/6I3Ach+effx4f+tCHcPLkSZw8eRJutxuxWAxutxsPPfRQzSsYj8dLtjKVgKesGlEvRqkHAKTXLuDi6guQiuSHHuo5gOHew3WvByGEkOJ0B9hIJIJgMFh0eyAQwPz8fE0qtZ3yfLQYJehpreSgVzweRzAYRDweb2o99FpZmMeylNbeyJhw3N4ZixkTQoiR6X4Gm0wmcfz48ZL76Flxp1LJZLJk9igl6FXTNRuJRJBMJjE2NobJyUnE43G4XC74fL6CZ6m7rcfm5iY2NzfVn1OpIgOUAECWIcsyMpnCEcIAsLr0LKQil5phzOi12ose2wmU997J16AeWvW6ZjIZyLIMSZIgSVL5AxpM+dxU6litz372swC2pg8ODQ3BbrfD7Xbjs5/9LB588EEIgoDZ2VmcPn0aHMepAzoTiQSSySTGx8cbPt1wNyRJUj8nzebCFYiMcJ/qDrB6gmepFl616vVMUwmW20cN8zyPQCAAh8OBWCyWN1p4t/WYmZnBAw88UHY/SZKQlbNYX5fwyCOPaO5zuPdFLEubmttykgwhcR6PPK99bCeh1Jz10WrX1WKx4MCBA1hZWUE6XaTnxwCWl7XmtJf3wx/+EJ/61Kfwe7/3e3jXu96lvv7CCy/ggx/8IH74wx/it37rt7Bnzx7Mzs7i3LlzOHbsWEHyoJMnT+KZZ57Bpz71qZLne+GFF/Dtb38bX/nKV/DlL39ZVx3/9E//FABw/vx5AMDnP/95ddu3v/1t/NVf/RXe9a534fjx4/jWt74FnufxMz9TegWwdDqN9fV1PProo8hmC7PZra0VGwTaOLoDrJ7gWemivrW0sLBQfqdtin1TU9Y29Hg8SCQSNavH9PQ07r//fvXnVCqFo0ePFuxnMplgMVvQ02PDvffeq1nWK089hkcvrmtuM5vNeOvNd4E/rH1sJ8hkMpibm4PL5YLVam12ddpGq17XjY0NXLhwAf39/ejuNt6qUrIsY3l5GQMDA1UtC/rRj34UX/jCFwp63W699VZ88pOfxCc+8Ym8aZRmsxldXV0FUys//vGP47Of/Sx+53d+p+i54vE4otEoNjc31SX2ylFa0NvP43a78c1vfhPAVqB88skn8ZWvfAUcx+Ezn/kMPvKRj5Qtd2NjAz09Pbjzzjs1f6+VxoR6qOl6sHr2eeihh/DpT39a72nBsqyu1uPIyIjuMstR1j0UBEFt6e62HjabDTZb6fzCKoYBwzBFP8SyyGEhpx1gGTC4ft/bWuoDsF6sVitdhzpoteuay+XAMAxMJlNF6602itItrNSxEj6fDwBwzz33aG6/5557wHFcXrnM1c+XnedSliJNpVJFG0tjY2N5C6uXq68oinj88cfzyvz4xz+O0dFRvPDCC2rdYrFYxQ00k8mkfk5q3Y9GuEcbnsnpzJkzFQXYci3nZDIJoLatZ4fDAWDr25oSYJtRj2JeXHwCxXrsB217MNxDI4gJKebI784iY6RnsVfHXDAMAzAMrCYTXv59fct6hsPhsnPvlSBcjvJYrNafYdFoFIIgqI/clM/UTlidp6YLrsfjcXzxi18sul3JmlQJjuNKTn1RfkmVLKPn9XoRiUQq6gKuRz2qsbIwj4vZ4s8WjrNvrqqbiZBOkZEkpLO5ZldjGxmyrKzRwVTU7BEEQW0QFFMuAIuiiGAwiGg0irNnz+o/uQ4sy+YlJgKgZgHc/ln58MMPq42YRqedraeaL7hebgBEpR/+SndtMUrAriSDkvKNShRFzW9rSrDcPsipHvWoVDa9iIvnvlAiwQTA2Wm1IkJIadFoNG/apdPpxNSUvlbzbs3MzCAQCKifvRzHged5NeAmk0l4PB6EQqGG1KeeGrbgOrDVgq30m8nExARmZ2cRj8c1E/DPz89XHNTKLb80Pz8PlmXz3ms96lGJbCaFxGNbKxq9ll3V3olhcIjmwBLSMTiOK9sTt30siUKZmthoPp8PXq+3YPbGduPj4/B6vUUbQK2koQuuA6h4wXWe5+F0OnHmzBnNwBYOhzVbzcqycloJ+CcmJhAMBjVvMEEQEA6HC749VVuPaqVWrk3DyWxcgTD/cQDAq5mVoscwJivMpsL5YISQ9uR2u9UBR8VEIpGKg+no6Gje47zp6emyLdxyx4TDYTgcjoK6hMPhvFkd25P2aH3WthLdQ9ZqseB6teWEQiGEw+GCTEterxdTU1OaLcdgMIjZ2Vl4PJ6CbTzPQxRFzM7O5r0uCAJGR0cxNTWlOY2nmnpUa6j/2ojjhZe2vpRIsoz5jdc192dMVjCMGd2WgZrVgRBibEqPYLHHV6IoVpWfIBaLYXFxUf2np/u41DFK/ZTgKoqi+pjO4/HkBeZGjWdphIYuuF5tOSzLIhaLwefzgWVZXQudK4uoj4+Pa26fmppCJBKB1+tFMplUuyOKrQVbbT2q9eGfuhUAIEkZpC5/FwDwfFqEmCtMMMEwZjAmC4Z7DmGoZ39N60FIu7GaTMZaCVtjFHElQqEQ7rvvvoLVvJTBS/V4tqrMmtAjHo8jHo/D7XarQfXMmTOYnp4Gy7IIBAJ5wTQYDMLtdrd89zAAMLLO/IapVArJZFK9QAsLC1haWsLk5GRd14JtV6lUCkNDQzh8zx/AZL02SdpqssBmseIvP/cf8PbbjmJVfBIvP/kA0nIOX15+HhvSjtGPDAOTqRtggF859QU49rylwe/EeDKZDB555BHce++9hpgL1y5a9bpubGzg/PnzOHHihCETTUiSpCZt2M08XWU6zsjIiBqctnfHCoKAQCCgDm6anJxUg5xeShmRSATxeBxTU1Oa3b4KURRx4sQJzSk5SuhRvggoFhYWdI3VKfd7XVhYwJ49e7C0tNS0GKX7e9zx48extLQEt9sNr9eLkydP4sSJE/WsW0d74/E9AIDVxScAAE9tLhQGVwCMqQtggHee+EUKroR0sHJBieM4+P3+XU2BUcrQS2uajtY+jRrB3GgVdZT8xV/8Rc26iklp9qEeAMBq8nHIsoxz6cKblGHMYBgT7L1H4Lz+E42uIiGEkBJ090cwDEPBtUHe9443AAAym0mk117CkpQu0nrd6qZ7zxs/BYu5q6F1JIQQUpruAFsqg1MlXnjhhZqU0876eraCZXr9FQBF5r1eHRBhYsy4ef9djaweIYQQHXQH2FqN6KrVdJ+OIG0twXQ+s1Swibn6qztu5yk1IiGEGJDuAFur1WoqGd5NgE0phytFVs4BgH39pfOQEkIIaQ7dg5wEQcCLL764q67ihYWFipP9d7qXs8tFV85hYMJbrytMpEEIIaT5dAfYhYWFtsis0WpeyiwX3Xbrofdib//xxlWGEEKIbhVN0zl58mRVabcUiUSCBjlVIJ3bxCvZ4rmHbz34ngbWhhBCSCV0B9hy66HqtZsA3WkuLj2FXJH+YavJCm7kVINrRAghRK+GD3Kibmb9lldfKrrtSD8Hq9lWdDshhJDm0h1gazX6d2yMFgTXK7N5peg2W9dQA2tCCCGkUrq7iLWSNVfjL/7iL2pSTifIlgiwZnNvA2tCCDG6ncn+7XY73G43fD4f/H4/BEGA3+9HMBgEx3FqToJEIoFkMomJiYmarwrW6XQH2GQyieXlZQwM0HqjjSLl1opuM5l7GlgTQohRxeNxzeXqBEGAx+NBPB6H3+8Hx3EIBAIQBAEcxxUk2Hc4HBAEoWTifVEU8fDDW+tTJxIJCIKA06dPl01EpKy9nUgkAACBQEDdFolEEAgE4HK5wHEc5ubmcOrUqbYI9rq7iGVZplzEDXL9dcpc4xIrCVL2JkIIAI/HUxBcAeS1UvXwer1qK7gYn8+HsbExTE5Owu/3w263w+MpPRff5/NhamoKU1NTamB1uVzqdlEUEY/H4fV64fV64XA42iK4AhW0YEdHRxGNRjEyMoLx8XE4HA5wHIcPfvCD9axfR7pz9DiAkuGVEFKlt/7VFLIai2c0k7rgOgCLyYzHfnlW13FKQNwZXBVOp1P3wFKlFSqKYtEWqSAIiEQi4HkewFarV2nRalGC5/YyvV4vRkdH1ZY0AMRisbZYYH0n3QF2bm4u7+elpSVKe1gnxw5uDWBazqWL7mM20eo5hFQjK+WQyRknwMoAIMsAw6DSfqlwOFw0uCrKtUoVsVgMPM+XDHQ748D8/HzZ80ejUQiCoAZlJajWalyPkVWUaGK7oaEhDA3RSNZas3Zd67V/XWsVnav29h1vQG0IIUYmCAIcjtL5yMsFQFEUEQwGEY1GcfbsWd3nDofDEEURoVCo6D5aC65HIhEA+VM2H374YTVHwvz8/K4WhTeSqgMsqS9ZyuFyiQB7zH6ygbUhhLSTaDSKYDCo/ux0OksObtopGAxCFEW1VVqJmZkZBAIBtaXMcRx4nlcDbjKZhMfjKRm4WwUFWINaW3oKG3JWeyPD4MjQzY2tECHEcDiOU0fmFrP9WadCGahULeXYYDCIEydO4Pz587qeofp8Pni93rxz7wzS4+Pj8Hq9JZ8Ftwrdo4hJY6UufafoNjNjgdXc3cDaEEKMyO12q12uxZTbrmV0dBTDw8Pqv9nZWYiiCJ/Pl/fs1Ol0QhRFRCIRzWO2C4fDcDgcBYE9HA7n/awE1XZYeY1asAYk5TaxsvBY0e2MiX5thFTLYjI3uwoFdo4i1svv9yMcDiMSiWg+axVFsar877FYrOC1eDyO2dlZeL3evBHHwFZQ1DpGoQR5JbiKoohkMqlO80kkEgWDn9ohrS59UhvQxkoCUolF1hmGfm2EVEvvFJhGkSQJqVQKg4ODMJkq71QMhUKaiSaUwUuVPFsthed5TE1N5QW+M2fOgOf5kgOp4vE44vE43G43BEGAKIo4c+YMpqenwbIsAoFAXpnBYBBut7vlu4cBCrCGlN0sPv2JYcxAxYP5CSHtiud5xGIx+Hw+zM3NqakSAeQFV0EQEAgEEI1GEY1GwbKsGuT0mp6ezuv6FUWx5MhjURRx9913q93L2ykjhcfHx/PKXFhYaIsBTgAF2NZDGZwIIRrKTW3hOA5+v39XU2BYlq2oRaw1TWe3ZbYSGuRECCGE1AEFWEIIIaQOKMAakow1KYOcrJ2N2MQYbxQkIYSQfBRgDSibWcIrJbI42XuPNLA2hBBCqkEB1oAyG5fwamalyFYGjj1vaWh9CCGEVI4CrMFk0hI21y/i1WyRAMswuGHvOxpbKUIIIRWjAGswPX0WXFxOIC1Lmtutpm4cG6ZE/4QQYnQUYA3m1rcMYXHjYtHtx+08rGZbA2tECCGkGhRgDWafPQdZKrLQOsNguPdwYytECCGkKhRgDWZf92aJrZTFiRBCWgWlSjSY4/3ac18BqKttEELITkqu35GREQBbKQgnJyfh8/nU9IiCIMDv9yMYDILjOHi9XgBAIpFAMpnExMQE3G63uu/o6CicTqeajD8cDoNlWUxMTGBhYUFdJafUSjqdjAKsgcgyg+ODWaSK9BBThwMhZKd4PI777rsPp0+fzlu8XBAEeDyevHVVOY5DIBBQF2HfmQPY4XBAEARMTU1BFEVMT0/n7RMOh+F0OvNec7lcdXx3rY0+sQ2EYYAe0yIkFGvFUguWEJLv7rvvht/vzwuuAPJaqHp5vV61JZxMJgsWR9daecfj8VRW4Q5CLVgDYcDALF3GcpFBTgzDoMc61OBaEdJeRn8+iGxOexpcs+QtuG42IfZ3k2WO2OL1esFxXNH1WJ1OZ0ULrm9fSF0URV1L2XEcp3vfTkMB1kAGe9Iw517GUq5YHzGDvX3HGlonQtpNNichkzVQgJVlyLjaP1XhOItIJKI+My1m5zqspcRiMfA8D5Zly5arKLXYeqejAGsgd77xdQDAkqQxkphhAIbB3v4TDa4VIcSoBEFQBzUVoycAiqKIYDCIaDRacgF1UhkKsAYx2JPGr9zxPGTZgpRGFzFzdQWdPX3HG1wzQkg7ikajCAaD6s87By+R3WuZACuKImZmZgBsDUNPJBJwuVy6uzG0xONxBAIBJJNJxONxcBwHj8dT8GBfEQwGEQqF4PV64XQ6wbIsBEFAPB7HmTNnMD09XTDQQK/3vvk1AMC6nNVepo4xYcC2B93W/qrKJ4S0H57nkUgkCl6PRCKYm5tDOBxWByspU3UUY2NjRT/rSG20RIAVRRGjo6MIhUJ5Aczr9WJ+fr7gxtFD+eYWCATU1yKRCDweD/x+P2KxWMFDe1EUEYlE1LlfCpZlcfbs2aqDKwDcclgEAKzJWc3tDGMG23Oo6vIJIe3H6XQiHA5rvq50DYfD4ao+I8nutUSA9Xg8cLvdBQEsEAhgeHgYLperogftgiBAFMWC7hCn04mzZ89idHQUHo8Hc3NzBccGAgEkEgkIggC73Y7R0dGafAvs7coBAFalTOHGqwMf2J4Duz4PIZ3OYjbe7MSdo4j18vv9CIfDCIfDmr15NLq3uQwfYAVBQCQSyWtpbjc+Pg6/319RgA0EApientbcxvM8nE4nIpGIOhl75/nqecOuyoUBlmGsAIChbgqwhOyW3ikwjSJJElKpFAYHB2EyVR785+bm4PF4wLJs3uegKIp5SSZqQZm+Q/QxfIBVAuvOQKdwOBwIBoMVfVOLRCIIBoOIxWKa5fI8j0gkoj6XbSStFizDbP3RUQuWELITx3GIxWLw+XyYm5vDyMgIWJaFKIqYm5tDPB5X9xUEAYFAANFoFNFoFCzLYnp6uuxn5+zsrNpzF4lE4PP5cOrUqV2NgekEhg+w8Xi85C9fCYDRaFR3K9ZutyMejxvym1ixZ7AAtWAJIcUVe866/dEax3Hw+/0VP5NVHqcV60kk2gwfYJVnncUowbeSrpC5uTnN7t/t5wRQctBSPB5HNBrF2NjYrgY37aTVRazo6xqu2XkIIYTUl+EDbDKZLNlNqwTfSlujpcoMh8PgeV5zn0gkgmQyqQ5xj8fjcLlc8Pl8JVvQm5ub2Ny8lkAilUoV7JORJSzmNgpelwEwMpDL5ZDJFA/AZItyjeha1VarXtdMJgNZliFJEiTJQBmcrpKvTstT6kj0kSQJsiwjk8nAbDYXbDfCfWr4ANvobtzZ2VkAwOnTpwu2KQF3+6hhnucRCATgcDjUNGNaZmZm8MADD5Q897O5pObruVwWkBlEv/vv+BFe0vU+CDRHgZPda7XrarFYcODAAaysrCCdLrpUVdMtLy83uwotJZ1OY319HY8++iiy2cJHa2tra02oVT7DB1i9FhYWdl1GPB5X107UCpTFHugrybY9Ho/mpG8AmJ6exv3336/+nEqlcPToUfXnNdMazkmi5rFmswUHBm7Az75tvIJ307kymQzm5ubgcrlgtVqbXZ220arXdWNjAxcuXEB/fz+6u7ubXZ0CsixjeXkZAwMDtOZzBTY2NtDT04M777xT8/dai5iwW4YPsMpouHLK5ePUQ0kyUU26MGXkcbFnuzabDTabrejxl2yXryX83oEB8O7rJ1vqQ80IrFYrXbM6aLXrmsvlwDAMTCZTVdNg6k3pFlbqSPQxmUxgGKbo/WiEe9Twv81ySy0lk1vdqrudm+rxeOD1eqvOxelwOAAgb0h8JTZNGgn+rzrQz+Hm/XdVVS4hhJDmMHyA5ThODaJalNbtbuarKnO6SgVXr9erBtF6kJlii6wDP3Hdh6jriBBCWozhAyzP8yW7iJUpNdWuSRgMBjEyMqIZXLefNxqNqikWtSivVztlp1T4PNDf2GQXhBBCds/wAXZiYgJA8a7X+fn5qoNrOBzWzEkMXEvRqHA6nZBluWhX9Pz8PFiWrbol3W3NVXUcIYQQYzJ8gFVyA585c0Zzezgchs/nK3hdFEX4fL6ClW8U8XgcgiAU7RaORCJ5rdGJiYm8tRO3EwQB4XBYc2qPHtxeGp5PCCHtxvCjiAEgFAphdHQUExMTBcvVTU1NabZgg8EgZmdnEQwGsbi4mLdNEAR4PB44nU54vd6CY5PJJCKRSN5xyijh2dnZvKAsCAJGR0cxNTVVdV7Omw8tldxusQ5VVS4hpHMoDQ0lF7Hdbofb7VanHgqCAL/fj2AwCI7j1M++RCKBZDKJiYkJyi1cYy0RYFmWVZNZsyyra8F1ZUH08fHCuaMulwuCIBRtkQLag6ampqYQiUTg9XqRTCbVBQZ2uxZsT1cORYc4MQwYs/Hm7hFCjCEej+O+++4rWFVMaUjE43H4/X5wHIdAIKBOJdzZe+dwOEr26m0vNxKJIBQK6U46Uu6Y2dlZ9fFbscd2raglAiywFWQrSTTN83xBy1VRLBmEHtsXMq6VgZ40RFl7mg7DFKYAI4QQhcfjQSAQKPhcUlqpWr10WrxeL3w+X8ngpuRgF0Wx5OyOSo5RgquSIU9pxLTDwgItE2Db2d69V/BysY2M4R+TE9JSnvu3DwOSkQYVypBkGZcYBgADmMy44Sf+RteRSrdwsS/9TqdT98DL7S3IYoM5eZ4Hz/MIh8O6ytRzzMzMDM6fP59XZ5fLRQGW1IaNLf5NkIEJvfQMlpDakXKQS6xa1RSyDPnqZD2mgnz/4XC4bI+a1iBQLUou9d0m7amEMvVR65yRSKTmvYWNRgG2yWyWHLptaaDYF2qGQZ+NlqkjhBQSBKFsApxyQUoURQSDQUSjUZw9e7aW1Sur2DKjelPkGh0F2CbbO7hRcoDTLQda+xscIcR4otFo3iBPp9NpqIFFdrtd9zNeI6MA22Tvvvl1rMtbSy0VZnNicHz4ZKOrRAhpERzHlR20qbUAibKetVG1Q3AFWiDRRLvj9q5gqdgIYjDY03eswTUihLQKt9tdNJmOotx2LaOjoxgeHlb/Ketk1/qYYgOwRFHcVX55o6AWbJP1d28iUSTAgmGwf6B+CwwQ0pFM5ooGEtWfDBny1QU9tkYR6+X3+xEOh4sOCBJFseyKZFpisVhDjuE4DizLarayW32AE0ABtuletRRfFLi3axgDtr0NrA0h7U/vFJhGkSQJqVQKg4ODVa0HGwqFNBNNKIOX6vFstZou3GLHTE9PIxKJqF3W4XDY0N3XlaAA22SLppWi2w4O3EDL1BFCSuJ5Xs10Nzc3p6ZKBFCQ1jUQCCAajSIajYJlWUxPT1c0LUcpIxKJIB6Pw+fzweFwlAyI5Y6ZmppS09oCW4mA2mEOLAAwsiwXX4iU1E0qlcLQ0BA++3/eBPRvzdHZGUrfd9P9uOMNH2t85VpcJpPBI488gnvvvRdWq7XZ1WkbrXpdNzY2cP78eZw4cQLd3cZLO7rbFmynKvd7XVhYwJ49e7C0tITBwcEm1JAGORnazft+stlVIIQQUiUKsAZmNrVOK4EQQkg+CrCEEEJIHVCAJYQQQuqAAiwhhBBSBxRgCSGEkDqgAEsIIYTUAQVYQgghpA4owBJCCCF1QAGWEEIIqQPKRUwIIW3A5/MBgJqL2G63w+12w+fzwe/3QxAE+P1+BINBcBwHr9cLYCv3bzKZxMTEBNxudzPfQtuhAEsIIS0sHo9rrqYjCAI8Hg/i8Tj8fj84jkMgEFCXhtu5yo7D4YAgCCVX3xFFEQ8//DCArcAsCAJOnz5ddsEAQRAQiUQQCoUwNzdXsH12dlYtQxTFuqwA1AzURdxkm6Z08Y20kg4hpAyPx1MQXAHktVL18Hq9aiu4GJ/Ph7GxMUxOTsLv98Nut8Pj8ZQ8Jh6PIxKJQBRFzSXrlOA6OTmJyclJ8DxfUb2NjFqwRsUAZnN/s2tBSNv5/W/egZyUaXY18siyrC5NaTZZ8bv3fEfXcUpALLY4udPpLFjIvJjtLchiLVKlJcrzPICtVq/Soi2G53nwPI9wOKy5fWZmBufPn8+rs8vlaosl6yjAGhQDM8wm+vUQUms5KYOcbMAAqyxYKek/LhwOFw2uinKtUkUsFgPP8yW7e3d2787Pz5c9fymCIBQN6JFIZFdlGwF9ghvUoG0EvV1Dza4GIcTABEGAw+EouU+5ICWKIoLBIKLRKM6ePav73OFwGKIoIhQK6T5mJ0EQNF9nWRaiKFZdrlFQgDWotx79QLOrQAhpU9FoFMFgUP3Z6XRWNLAoGAxCFEW1q7jW7Ha75vPaVkMB1ogYBnfe8KvNrgUhxOA4jkMikSi5jzJqeDtloFK1lGODwSBOnDiB8+fPlx1JXIl2CK4AjSI2pOt6DsJkMje7GoQQg3O73YhEIiX3Kbddy+joKIaHh9V/s7OzEEURPp8vr+vW6XRCFEVEIhHNY8opNgBLFEXdg7OMjFqwBsTQ9x5C6sZsslY0kKgRZOSPItbL7/cjHA4XHRAkiiLsdnvF9YnFYgWvxeNxzM7Owuv15o04BraemWodUw7HcWBZVrOV3eoDnAAKsISQDqN3CkyjSJKEVCqFwcFBmEyVf7kOhUKaiSaUwUu1StrA8zympqbyAuGZM2fA87yuYFis23d6ehqRSETtdg6Hw7vqvjYSCrCEENLCeJ5HLBaDz+fD3NycmioRQF5wFQQBgUAA0WgU0WgULMtienq6omen09PTeV2/oiiWHXmsnDcSiSAej8Pn88HhcKhBdGpqCrOzs+qgq0Qi0RZzYAEKsIQQ0hb8fn/J7RzHwe/3l92vFJZlK24RK+ctpV1SI+5ED/sMqNvc3ewqEEII2SUKsAZ0ovdws6tACCFklyjAGtCxnkPNrgIhhJBdogBrQJYuttlVIIQQsksUYA1oYORtza4CIYSQXaIAa0C97JuaXQVCCCG7RAGWENIRZFludhVIDbXC75MCLCGkrVmtW6kH19bWmlwTUkurq6tgGEb9/RoRJZowGDPDgGHo10JIrZjNZrAsi0uXLgEAent71by/RiBJEtLpNDY2NqpKldhJZFlGNptFKpVCKpUCy7Iwm427MErLfJKLooiZmRkAwMjICBKJBFwuF9xud0PLrEc9tsvJMsxm434jI6QVHThwAADUIGsksixjfX0dPT09hgr8RmY2m3Hw4EEMDQ01uyoltUSAFUURo6OjCIVCeQv8er1ezM/PV5X6q5oy61GPnd4ydMuuyyCE5GMYBgcPHsS+ffuQyWSaXZ08mUwGjz76KO68805Dd3cahcVigdlsbokvIy0RYD0eD9xud15QA4BAIIDh4WG4XK6Klzaqpsx61GOnQ/vv3NXxhJDizGaz4boUzWYzstksuru7KcC2GcN3+AuCgEgkAq/Xq7l9fHy84pZjNWXWox6EEELal+EDrLJsUbHV7R0OByKRiLrwb73KrEc9CCGEtC/DB9h4PF5yvUIl4EWj0bqWWY96aDExxuq+IoQQUh3DB1hBEGC324tuV4KeIAh1LbMe9dByzD62q+MJIYQYg+EDbDKZLNlyVIJeJV2z1ZRZj3po2WMf3dXxhBBCjMHwo4jr8UyzmjJ3W4/NzU1sbm6qPy8tLW29vpZTX/ul638VycXdnYdsTXtYW1vDwsICjcqsIbqu9UHXtT6SySSA5qZUNHyA1WthYcEQZRY7ZmZmBg888EDB63/+0YT6/3+CXwfw6xWfkxBCiLaFhYWmJaQwfIBlWVZX63FkZKSuZe62HtPT07j//vvVn0VRxLFjx/DSSy8ZPhtJq0mlUjh69CguXLiAwcHBZlenbdB1rQ+6rvWxtLSE6667ruTYmXozfIAtd3GUboBSz0drUeZu62Gz2WCz2QpeHxoaoj+qOhkcHKRrWwd0XeuDrmt9NDO/s+EHOXEcpwYvLUqrstj81FqVWY96EEIIaV+GD7A8z5fsmlWmxVSSorCaMutRD0IIIe3L8AF2YmICwFaiBy3z8/MVB7Vqyqx1PWw2Gz73uc9pdhuT3aFrWx90XeuDrmt9GOG6MnILLAvvcrnA87xmrl+GYTA3N1cQ3JRl5Yol4K+mzGqOIYQQ0plaIsCWWiaOZVnNgDc7OwufzweWZbG4uFiTMqs5hhBCSGdqiQALbAU3JWDqWeg8Ho/j7rvvxvj4uJqof7dlVnsMIYQUEw6H6fOjTbVMgCWEkHYjiiKGh4cRCAQwPj5e0XRDYnwUYCugPNcFULPWazVl1qMezVSP9xOPxxEIBJBMJhGPx8FxHDweDyYnJzX3DwaDCIVC8Hq9cDqdYFkWgiAgHo/jzJkzmJ6eznss0ApqfV2rvUZ0vxYXj8cxOqov/3goFFLP0Y73q8Lr9cLj8ex6PIshPltlosvi4qLMcZwci8XyXp+cnJSnpqYaVmY96tFM9Xg/gUBADgQCea/Nzc3JLMvKHMfJi4uLBcf4/X4ZQME/lmUL6tYK6nFdq7lGdL+WFgqFZI7jZLfbLU9OTmr+c7vdMs/zece12/2aSCTkQCAg8zwvA5Dn5uZ2VZ5RPlspwOrkdDqLXmSWZau6Iaopsx71aKZav59EIiH7/X7NbbFYTAYgO53Ogm1+v18OBALy1NSU+mG3M0i3knrcJ9VcI7pfS5uampITiUTF+7TT/RoIBGS32y0HAgF5bm6uJgHWKJ+tFGB1SCQSMoCifwiTk5OaH9q1LrMe9WimeryfqakpzRaqwul0ap7T7/eXPK6V1Os+qfQa0f1aXrmWUSwW0wyc7XS/bqd8Cd5NgDXSZ6vhE00YgTIKuVgaRIfDgUgkUtGSdtWUWY96NFM93k8kEsGJEyeKLnyvPJcqljCkHRjlPjFKPWqlHu/H6/WWPWexcQNEm5E+WynA6hCPx0uO7lN+KdFotK5l1qMezVSP92O32yGKYst8aNeDUe4To9SjVurxfkrlLvf5fDS3vgpG+mw1/Go6RiAIQsnVdJRfTLFWU63KrEc9mqke72dubg6CIBT94FLKKjXCMh6PIxqNYmxsrCVHYjbiPtFzjeh+rV48HofD4dA1bafV79daM9JnK7VgdUgmkyVvdOUXU0mrqZoy61GPZqrX+ynVKgiHw+B5XnOfSCSCYDAIAGq3nMvlQiQSqej8zVbP+6SSa0T3a/VmZmbKdg23y/1aa0b6bKUWrA71+ACopsxW+SDSq9HvZ3Z2FgBw+vTpgm1KwN3+ocbzPAKBABwOB2KxWMu0Dup1XSu9RnS/ViccDpdd9rKd7tdaM9JnKwXYGlpYWDBEmfWoRzPV4v3E43H1mZbWB0+xieQcx8HpdMLj8SCRSOy6HkZS6XWt1zWi+zXfzMyM5pfA7Trxfq21Rny2UhexDizL6vqGMzIyUtcy61GPZmrk+/F4PPD7/Ziamqr4WJ7nIQhCyzwrbMZ9onWN6H6tnJKNaTetz1a7X2vNSJ+tFGB1KPXwG9jqvwdQsg+/FmXWox7N1Kj34/F44PV6qwquwNYQfaB1pvY04z7RukZ0v1YuEAiU7R4up9Xu11oz0mcrBVgdOI5TL7AW5ZtPJX8Y1ZRZj3o0UyPej8/nw6lTp0oGV6/Xq34otYN6XNdqrhHdr5WLRCJlj2+3+7XWjPTZSgFWB57nS3YfKF0xlSSnrqbMetSjmer9foLBIEZGRjSD6/bzRqNRCIJQtC7K660yaKQe17Waa0T3a+XKzccE2u9+rTUjfbZSgNVhYmICQPEul/n5+YovfDVl1qMezVTP9xMOhyGKomZwFQQhbyqD0+mELMtFP9jm5+fBsmzLtLTqcV2ruUZ0v1ZGKbdcd2W73a+1ZqjP1oqTK3aoUomgUSR35uLiojw1NVU0r2Y1ZVZzjJHV47rGYrGiCf9leSu5+Paco8XyvcrytRyloVCo1NswnFpf12qvEd2v5e9XhZLofnJysuR+7Xi/KvTmIm6Vz1YKsDpVs5SRsqQUy7I1K5OW/yp9XROJhMxxXMmlv7SO8/v9BUE5kUjILMvSdd22vdJrRPdr+euqCAQCMgBd16Xd7ldFKBTS9QWhVT5bacH1CoiiCJ/PB5ZldS3GG4/Hcffdd2N8fFxNJr3bMqs9xshqeV0dDkfZ6Qkcx2nOEYxEIgiFQkgmkxBFESzLtvTC1fW4X6u5RnS/lr+u2/c7ffq0rmvTLvdrOBxGIBAomFrEcRw4joPH4ynIatUqn60UYAkhhJA6oEFOhBBCSB1QgCWEEELqgAIsIYQQUgcUYAkhhJA6oABLCCGE1AEFWEIIIaQOKMASQgghdUABlhBCCKkDCrCEEEJIHVCAJYQQQuqAAiwhhBBSB5ZmV4AQsjuzs7MIBAJIJpPqa2NjY3n7JJPJgkUQFhcX1f8PBoMIBALqepgsy+aVoZRtt9vhcrk019ktV4aC53lMT0+XXVickJZX1Ro8hBDDmZqakgGUXAs3kUjIbrdbBiAvLi4WbFeWASu13qjT6dRc1ktvGYFAoOWXVSNED+oiJqRNOBwOACjZMuQ4DqFQCBzHaS7rx3EcgK2WarHj5+bmAAB33303RFGsuIzJyUmEQiHMzs7C6/UWrSshrY4CLCEdyOfz5XUpV8rv96trZ1bD6XTC6XQiGAyqXcqEtBsKsIR0IKfTWXZh+lKUVmokEqm6DGVh8N2UQYiRUYAlpANxHKfZvdtIyvlpsBNpVxRgCekA4XC4oMXqdrurLk8py+l0Vl2G0nIdHx+vugxCjIwCLCEdQOt5q9LNW42ZmRmwLAu/31/V8cFgEIIgYG5ujlqwpG3RPFhC2pwoiggEArtqbW7n9XohCALOnj1bUXAURRHRaBSBQAAAkEgkdhXkCTE6CrCEtBm/349QKATgWoKJSp+3BgIBzM/Pqz+Loqi2gk+dOpWXpKKYM2fO5HVLz8/PIx6Pw+v1aiaqIKTdUIAlpM34fD5MTk4CuNZq9Hg8FZXh8XjUMqo1MTGh+ZzX5/PB4XAgFotR9zBpa/QMlpA2xrIsnE4npqenm10Vld/vB8uyGB0dbXZVCKkrCrCEdACjPescGxuDIAiUZIK0NQqwhHQAp9NpqCCrdA1Ho9HmVoSQOqIAS0gHMNqzzpGREQBbI4kJaVcUYAkhDacE/N2kayTE6CjAEkJqSpnOU2oxAWWd2J3PYGdnZ+tXMUIajAIsIW1Cmeu6mxzDSouymq5bURQRj8fV5ezm5uYQj8c168PzPHiez5ujK4qi4bqyCdkNRpZludmVIIRUb3Z2Vk3qoAQrjuPUtV/1BK1gMIhAIKCWwbIsOI7DxMSE7qQQ4XA4LzmF4tSpU0XzHitZoTweD0RRpAQUpK1QgCWEEELqgLqICSGEkDqgAEsIIYTUAQVYQgghpA4owBJCCCF1QAGWEEIIqQMKsIQQQkgdUIAlhBBC6oACLCGEEFIHFGAJIYSQOqAASwghhNQBBVhCCCGkDijAEkIIIXVAAZYQQgipAwqwhBBCSB38/2EhDoyUDjI9AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_grouped_series_data(\n",
+    "    data_df=df,\n",
+    "    x_col=\"fpr\",\n",
+    "    y_col=\"tpr\",\n",
+    "    x_name=\"FPR\",\n",
+    "    y_name=\"TPR\",\n",
+    "    x_lim=(0,1),\n",
+    "    y_lim=(0,1),\n",
+    "    series_id_col=\"id\",\n",
+    "    series_readable_id_col=\"name\",\n",
+    "    series_readable_id_map=name_extractor,\n",
+    "    sortby=\"mid\",\n",
+    "    special_color_map=color_map,\n",
+    "    legend_loc=\"lower right\",\n",
+    "    title=f\"Z-Score\",\n",
+    "    fig_size=(5, 4),\n",
+    "    save_path=f\"{figure_dir}/{fig_name}.pdf\" if save else None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>_defaultColorIndex</th>\n",
+       "      <th>fpr</th>\n",
+       "      <th>id</th>\n",
+       "      <th>name</th>\n",
+       "      <th>tpr</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>cxiy1v63</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>cxiy1v63</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "      <td>0.001923</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>cxiy1v63</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "      <td>0.611538</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>cxiy1v63</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "      <td>0.615385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>cxiy1v63</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "      <td>0.657692</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>985</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.964</td>\n",
+       "      <td>llq7qgr4</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "      <td>0.998000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>986</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.968</td>\n",
+       "      <td>llq7qgr4</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "      <td>0.998000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>987</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.982</td>\n",
+       "      <td>llq7qgr4</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>988</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.988</td>\n",
+       "      <td>llq7qgr4</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>989</th>\n",
+       "      <td>7</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>llq7qgr4</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>990 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     _defaultColorIndex    fpr        id  \\\n",
+       "0                    10  0.000  cxiy1v63   \n",
+       "1                    10  0.000  cxiy1v63   \n",
+       "2                    10  0.000  cxiy1v63   \n",
+       "3                    10  0.000  cxiy1v63   \n",
+       "4                    10  0.000  cxiy1v63   \n",
+       "..                  ...    ...       ...   \n",
+       "985                   7  0.964  llq7qgr4   \n",
+       "986                   7  0.968  llq7qgr4   \n",
+       "987                   7  0.982  llq7qgr4   \n",
+       "988                   7  0.988  llq7qgr4   \n",
+       "989                   7  1.000  llq7qgr4   \n",
+       "\n",
+       "                                                name       tpr  \n",
+       "0            core_simple_1_200_1000_dipper_high_eval  0.000000  \n",
+       "1            core_simple_1_200_1000_dipper_high_eval  0.001923  \n",
+       "2            core_simple_1_200_1000_dipper_high_eval  0.611538  \n",
+       "3            core_simple_1_200_1000_dipper_high_eval  0.615385  \n",
+       "4            core_simple_1_200_1000_dipper_high_eval  0.657692  \n",
+       "..                                               ...       ...  \n",
+       "985  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  0.998000  \n",
+       "986  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  0.998000  \n",
+       "987  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  1.000000  \n",
+       "988  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  1.000000  \n",
+       "989  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  1.000000  \n",
+       "\n",
+       "[990 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig_name = \"baseline_attacked_winmax_ROC_T_1000\"\n",
+    "\n",
+    "data_dir = f\"{INPUT_DIR}/figure_data/{fig_name}\"\n",
+    "\n",
+    "df = get_data_df(data_dir)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['core_simple_1_200_1000_dipper_high_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_3-25%_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_1-25%_eval'\n",
+      " 'core_simple_1_200_1000_gpt_p4_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_1-10%_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_3-10%_eval']\n",
+      "dict_items([('cxiy1v63', 'core_simple_1_200_1000_dipper_high_eval'), ('jtkisfd7', 'core_simple_1_200_1000_gen_cp_attack_3-25%_eval'), ('rzrpza5t', 'core_simple_1_200_1000_gen_cp_attack_1-25%_eval'), ('g2cs5tvw', 'core_simple_1_200_1000_gpt_p4_eval'), ('62luh543', 'core_simple_1_200_1000_gen_cp_attack_1-10%_eval'), ('llq7qgr4', 'core_simple_1_200_1000_gen_cp_attack_3-10%_eval')])\n",
+      "Used callable to map series_readable_ids\n",
+      "{'cxiy1v63': 'Dipper', 'jtkisfd7': 'CP-3-25%', 'rzrpza5t': 'CP-1-25%', 'g2cs5tvw': 'GPT', '62luh543': 'CP-1-10%', 'llq7qgr4': 'CP-3-10%'}\n",
+      "[<matplotlib.lines.Line2D object at 0x7f87c9405780>, <matplotlib.lines.Line2D object at 0x7f87c94058d0>, <matplotlib.lines.Line2D object at 0x7f87c9405b70>, <matplotlib.lines.Line2D object at 0x7f87c9405e10>, <matplotlib.lines.Line2D object at 0x7f87c94060b0>, <matplotlib.lines.Line2D object at 0x7f87c9406350>]\n",
+      "['CP-3-25%', 'Dipper', 'CP-1-25%', 'CP-1-10%', 'CP-3-10%', 'GPT']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_grouped_series_data(\n",
+    "    data_df=df,\n",
+    "    x_col=\"fpr\",\n",
+    "    y_col=\"tpr\",\n",
+    "    x_name=\"FPR\",\n",
+    "    y_name=\"TPR\",\n",
+    "    x_lim=(0,1),\n",
+    "    y_lim=(0,1),\n",
+    "    series_id_col=\"id\",\n",
+    "    series_readable_id_col=\"name\",\n",
+    "    series_readable_id_map=name_extractor,\n",
+    "    special_color_map=color_map,\n",
+    "    sortby=\"mid\",\n",
+    "    legend_loc=\"lower right\",\n",
+    "    title=f\"WinMax\",\n",
+    "    fig_size=(5, 4),\n",
+    "    save_path=f\"{figure_dir}/{fig_name}.pdf\" if save else None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>_defaultColorIndex</th>\n",
+       "      <th>fpr</th>\n",
+       "      <th>id</th>\n",
+       "      <th>name</th>\n",
+       "      <th>tpr</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5ez7q3ys</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-10%_pre...</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5ez7q3ys</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-10%_pre...</td>\n",
+       "      <td>0.002000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.004000</td>\n",
+       "      <td>5ez7q3ys</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-10%_pre...</td>\n",
+       "      <td>0.002000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.004000</td>\n",
+       "      <td>5ez7q3ys</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-10%_pre...</td>\n",
+       "      <td>0.008000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.006000</td>\n",
+       "      <td>5ez7q3ys</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-10%_pre...</td>\n",
+       "      <td>0.008000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1724</th>\n",
+       "      <td>15</td>\n",
+       "      <td>0.965385</td>\n",
+       "      <td>plaqii3p</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...</td>\n",
+       "      <td>0.994231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1725</th>\n",
+       "      <td>15</td>\n",
+       "      <td>0.975000</td>\n",
+       "      <td>plaqii3p</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...</td>\n",
+       "      <td>0.994231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1726</th>\n",
+       "      <td>15</td>\n",
+       "      <td>0.975000</td>\n",
+       "      <td>plaqii3p</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...</td>\n",
+       "      <td>0.996154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1727</th>\n",
+       "      <td>15</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>plaqii3p</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...</td>\n",
+       "      <td>0.996154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1728</th>\n",
+       "      <td>15</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>plaqii3p</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1729 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      _defaultColorIndex       fpr        id  \\\n",
+       "0                      7  0.000000  5ez7q3ys   \n",
+       "1                      7  0.000000  5ez7q3ys   \n",
+       "2                      7  0.004000  5ez7q3ys   \n",
+       "3                      7  0.004000  5ez7q3ys   \n",
+       "4                      7  0.006000  5ez7q3ys   \n",
+       "...                  ...       ...       ...   \n",
+       "1724                  15  0.965385  plaqii3p   \n",
+       "1725                  15  0.975000  plaqii3p   \n",
+       "1726                  15  0.975000  plaqii3p   \n",
+       "1727                  15  1.000000  plaqii3p   \n",
+       "1728                  15  1.000000  plaqii3p   \n",
+       "\n",
+       "                                                   name       tpr  \n",
+       "0     core_simple_1_200_1000_gen_cp_attack_1-10%_pre...  0.000000  \n",
+       "1     core_simple_1_200_1000_gen_cp_attack_1-10%_pre...  0.002000  \n",
+       "2     core_simple_1_200_1000_gen_cp_attack_1-10%_pre...  0.002000  \n",
+       "3     core_simple_1_200_1000_gen_cp_attack_1-10%_pre...  0.008000  \n",
+       "4     core_simple_1_200_1000_gen_cp_attack_1-10%_pre...  0.008000  \n",
+       "...                                                 ...       ...  \n",
+       "1724  core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...  0.994231  \n",
+       "1725  core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...  0.994231  \n",
+       "1726  core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...  0.996154  \n",
+       "1727  core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...  0.996154  \n",
+       "1728  core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...  1.000000  \n",
+       "\n",
+       "[1729 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig_name = \"baseline_attacked_retrieval_ROC_T_1000\"\n",
+    "\n",
+    "data_dir = f\"{INPUT_DIR}/figure_data/{fig_name}\"\n",
+    "\n",
+    "df = get_data_df(data_dir)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['core_simple_1_200_1000_gen_cp_attack_1-10%_prefixes_eval_supp'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_1-25%_prefixes_eval_supp'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_3-10%_prefixes_eval_supp'\n",
+      " 'core_simple_1_200_1000_no_wm_dipper_high_prefixes_eval_supp'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_3-25%_prefixes_eval_supp'\n",
+      " 'core_simple_1_200_1000_no_wm_gpt_p4_prefixes_eval_supp']\n",
+      "dict_items([('5ez7q3ys', 'core_simple_1_200_1000_gen_cp_attack_1-10%_prefixes_eval_supp'), ('cdemf9mi', 'core_simple_1_200_1000_gen_cp_attack_1-25%_prefixes_eval_supp'), ('hfp5ofno', 'core_simple_1_200_1000_gen_cp_attack_3-10%_prefixes_eval_supp'), ('mgbk5ci5', 'core_simple_1_200_1000_no_wm_dipper_high_prefixes_eval_supp'), ('n9jnymb9', 'core_simple_1_200_1000_gen_cp_attack_3-25%_prefixes_eval_supp'), ('plaqii3p', 'core_simple_1_200_1000_no_wm_gpt_p4_prefixes_eval_supp')])\n",
+      "Used callable to map series_readable_ids\n",
+      "{'5ez7q3ys': 'CP-1-10%', 'cdemf9mi': 'CP-1-25%', 'hfp5ofno': 'CP-3-10%', 'mgbk5ci5': 'Dipper', 'n9jnymb9': 'CP-3-25%', 'plaqii3p': 'GPT'}\n",
+      "[<matplotlib.lines.Line2D object at 0x7f87c94c0c70>, <matplotlib.lines.Line2D object at 0x7f87c94c09d0>, <matplotlib.lines.Line2D object at 0x7f87c94c1030>, <matplotlib.lines.Line2D object at 0x7f87c94c12d0>, <matplotlib.lines.Line2D object at 0x7f87c94c1570>, <matplotlib.lines.Line2D object at 0x7f87c94c1810>]\n",
+      "['Dipper', 'GPT', 'CP-1-25%', 'CP-3-25%', 'CP-1-10%', 'CP-3-10%']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_grouped_series_data(\n",
+    "    data_df=df,\n",
+    "    x_col=\"fpr\",\n",
+    "    y_col=\"tpr\",\n",
+    "    x_name=\"FPR\",\n",
+    "    y_name=\"TPR\",\n",
+    "    x_lim=(0,1),\n",
+    "    y_lim=(0,1),\n",
+    "    series_id_col=\"id\",\n",
+    "    series_readable_id_col=\"name\",\n",
+    "    series_readable_id_map=name_extractor,\n",
+    "    special_color_map=color_map,\n",
+    "    sortby=\"mid\",\n",
+    "    legend_loc=\"lower right\",\n",
+    "    title=f\"Retrieval\",\n",
+    "    fig_size=(5, 4),\n",
+    "    save_path=f\"{figure_dir}/{fig_name}.pdf\" if save else None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>_defaultColorIndex</th>\n",
+       "      <th>fpr</th>\n",
+       "      <th>id</th>\n",
+       "      <th>name</th>\n",
+       "      <th>tpr</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>15</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>ab7zh7ta</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_dipper_high_eval_...</td>\n",
+       "      <td>0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>ab7zh7ta</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_dipper_high_eval_...</td>\n",
+       "      <td>0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>15</td>\n",
+       "      <td>0.030</td>\n",
+       "      <td>ab7zh7ta</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_dipper_high_eval_...</td>\n",
+       "      <td>0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>15</td>\n",
+       "      <td>0.030</td>\n",
+       "      <td>ab7zh7ta</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_dipper_high_eval_...</td>\n",
+       "      <td>0.002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>15</td>\n",
+       "      <td>0.090</td>\n",
+       "      <td>ab7zh7ta</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_dipper_high_eval_...</td>\n",
+       "      <td>0.002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1631</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.996</td>\n",
+       "      <td>yeewsgls</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_eval_supp</td>\n",
+       "      <td>0.966</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1632</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.996</td>\n",
+       "      <td>yeewsgls</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_eval_supp</td>\n",
+       "      <td>0.978</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1633</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.998</td>\n",
+       "      <td>yeewsgls</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_eval_supp</td>\n",
+       "      <td>0.978</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1634</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.998</td>\n",
+       "      <td>yeewsgls</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_eval_supp</td>\n",
+       "      <td>1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1635</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>yeewsgls</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_eval_supp</td>\n",
+       "      <td>1.000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1636 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      _defaultColorIndex    fpr        id  \\\n",
+       "0                     15  0.000  ab7zh7ta   \n",
+       "1                     15  0.002  ab7zh7ta   \n",
+       "2                     15  0.030  ab7zh7ta   \n",
+       "3                     15  0.030  ab7zh7ta   \n",
+       "4                     15  0.090  ab7zh7ta   \n",
+       "...                  ...    ...       ...   \n",
+       "1631                   0  0.996  yeewsgls   \n",
+       "1632                   0  0.996  yeewsgls   \n",
+       "1633                   0  0.998  yeewsgls   \n",
+       "1634                   0  0.998  yeewsgls   \n",
+       "1635                   0  1.000  yeewsgls   \n",
+       "\n",
+       "                                                   name    tpr  \n",
+       "0     core_simple_1_200_1000_no_wm_dipper_high_eval_...  0.000  \n",
+       "1     core_simple_1_200_1000_no_wm_dipper_high_eval_...  0.000  \n",
+       "2     core_simple_1_200_1000_no_wm_dipper_high_eval_...  0.000  \n",
+       "3     core_simple_1_200_1000_no_wm_dipper_high_eval_...  0.002  \n",
+       "4     core_simple_1_200_1000_no_wm_dipper_high_eval_...  0.002  \n",
+       "...                                                 ...    ...  \n",
+       "1631      core_simple_1_200_1000_no_wm_gpt_p4_eval_supp  0.966  \n",
+       "1632      core_simple_1_200_1000_no_wm_gpt_p4_eval_supp  0.978  \n",
+       "1633      core_simple_1_200_1000_no_wm_gpt_p4_eval_supp  0.978  \n",
+       "1634      core_simple_1_200_1000_no_wm_gpt_p4_eval_supp  1.000  \n",
+       "1635      core_simple_1_200_1000_no_wm_gpt_p4_eval_supp  1.000  \n",
+       "\n",
+       "[1636 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig_name = \"baseline_attacked_detectgpt_ROC_T_1000\"\n",
+    "\n",
+    "data_dir = f\"{INPUT_DIR}/figure_data/{fig_name}\"\n",
+    "\n",
+    "df = get_data_df(data_dir)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['core_simple_1_200_1000_no_wm_dipper_high_eval_supp'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_3-25%_eval_supp'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_1-25%_eval_supp'\n",
+      " 'core_simple_1_200_1000_no_wm_gpt_p4_eval_supp']\n",
+      "dict_items([('ab7zh7ta', 'core_simple_1_200_1000_no_wm_dipper_high_eval_supp'), ('dtrrqo1e', 'core_simple_1_200_1000_gen_cp_attack_3-25%_eval_supp'), ('miei424r', 'core_simple_1_200_1000_gen_cp_attack_1-25%_eval_supp'), ('yeewsgls', 'core_simple_1_200_1000_no_wm_gpt_p4_eval_supp')])\n",
+      "Used callable to map series_readable_ids\n",
+      "{'ab7zh7ta': 'Dipper', 'dtrrqo1e': 'CP-3-25%', 'miei424r': 'CP-1-25%', 'yeewsgls': 'GPT'}\n",
+      "[<matplotlib.lines.Line2D object at 0x7f87c936c0a0>, <matplotlib.lines.Line2D object at 0x7f87c936c250>, <matplotlib.lines.Line2D object at 0x7f87c936c4f0>, <matplotlib.lines.Line2D object at 0x7f87c936c790>]\n",
+      "['CP-1-25%', 'CP-3-25%', 'Dipper', 'GPT']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_grouped_series_data(\n",
+    "    data_df=df,\n",
+    "    x_col=\"fpr\",\n",
+    "    y_col=\"tpr\",\n",
+    "    x_name=\"FPR\",\n",
+    "    y_name=\"TPR\",\n",
+    "    x_lim=(0,1),\n",
+    "    y_lim=(0,1),\n",
+    "    series_id_col=\"id\",\n",
+    "    series_readable_id_col=\"name\",\n",
+    "    series_readable_id_map=name_extractor,\n",
+    "    special_color_map=color_map,\n",
+    "    sortby=\"mid\",\n",
+    "    legend_loc=\"lower right\",\n",
+    "    title=f\"DetectGPT\",\n",
+    "    fig_size=(5, 4),\n",
+    "    save_path=f\"{figure_dir}/{fig_name}.pdf\" if save else None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>_defaultColorIndex</th>\n",
+       "      <th>aucs</th>\n",
+       "      <th>id</th>\n",
+       "      <th>idx_T</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>15</td>\n",
+       "      <td>0.541998</td>\n",
+       "      <td>ab7zh7ta</td>\n",
+       "      <td>50</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_dipper_high_eval_...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15</td>\n",
+       "      <td>0.490884</td>\n",
+       "      <td>ab7zh7ta</td>\n",
+       "      <td>100</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_dipper_high_eval_...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>15</td>\n",
+       "      <td>0.456096</td>\n",
+       "      <td>ab7zh7ta</td>\n",
+       "      <td>200</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_dipper_high_eval_...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>15</td>\n",
+       "      <td>0.184254</td>\n",
+       "      <td>ab7zh7ta</td>\n",
+       "      <td>800</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_dipper_high_eval_...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>14</td>\n",
+       "      <td>0.524212</td>\n",
+       "      <td>dtrrqo1e</td>\n",
+       "      <td>50</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-25%_eva...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>14</td>\n",
+       "      <td>0.554044</td>\n",
+       "      <td>dtrrqo1e</td>\n",
+       "      <td>100</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-25%_eva...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>14</td>\n",
+       "      <td>0.544540</td>\n",
+       "      <td>dtrrqo1e</td>\n",
+       "      <td>200</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-25%_eva...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>14</td>\n",
+       "      <td>0.493348</td>\n",
+       "      <td>dtrrqo1e</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-25%_eva...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0.518452</td>\n",
+       "      <td>miei424r</td>\n",
+       "      <td>50</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-25%_eva...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0.637336</td>\n",
+       "      <td>miei424r</td>\n",
+       "      <td>100</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-25%_eva...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0.698908</td>\n",
+       "      <td>miei424r</td>\n",
+       "      <td>200</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-25%_eva...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0.618000</td>\n",
+       "      <td>miei424r</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-25%_eva...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.627297</td>\n",
+       "      <td>yeewsgls</td>\n",
+       "      <td>50</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_eval_supp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.662636</td>\n",
+       "      <td>yeewsgls</td>\n",
+       "      <td>100</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_eval_supp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.701192</td>\n",
+       "      <td>yeewsgls</td>\n",
+       "      <td>200</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_eval_supp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.138214</td>\n",
+       "      <td>yeewsgls</td>\n",
+       "      <td>300</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_eval_supp</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    _defaultColorIndex      aucs        id  idx_T  \\\n",
+       "0                   15  0.541998  ab7zh7ta     50   \n",
+       "1                   15  0.490884  ab7zh7ta    100   \n",
+       "2                   15  0.456096  ab7zh7ta    200   \n",
+       "3                   15  0.184254  ab7zh7ta    800   \n",
+       "4                   14  0.524212  dtrrqo1e     50   \n",
+       "5                   14  0.554044  dtrrqo1e    100   \n",
+       "6                   14  0.544540  dtrrqo1e    200   \n",
+       "7                   14  0.493348  dtrrqo1e   1000   \n",
+       "8                   13  0.518452  miei424r     50   \n",
+       "9                   13  0.637336  miei424r    100   \n",
+       "10                  13  0.698908  miei424r    200   \n",
+       "11                  13  0.618000  miei424r   1000   \n",
+       "12                   0  0.627297  yeewsgls     50   \n",
+       "13                   0  0.662636  yeewsgls    100   \n",
+       "14                   0  0.701192  yeewsgls    200   \n",
+       "15                   0  0.138214  yeewsgls    300   \n",
+       "\n",
+       "                                                 name  \n",
+       "0   core_simple_1_200_1000_no_wm_dipper_high_eval_...  \n",
+       "1   core_simple_1_200_1000_no_wm_dipper_high_eval_...  \n",
+       "2   core_simple_1_200_1000_no_wm_dipper_high_eval_...  \n",
+       "3   core_simple_1_200_1000_no_wm_dipper_high_eval_...  \n",
+       "4   core_simple_1_200_1000_gen_cp_attack_3-25%_eva...  \n",
+       "5   core_simple_1_200_1000_gen_cp_attack_3-25%_eva...  \n",
+       "6   core_simple_1_200_1000_gen_cp_attack_3-25%_eva...  \n",
+       "7   core_simple_1_200_1000_gen_cp_attack_3-25%_eva...  \n",
+       "8   core_simple_1_200_1000_gen_cp_attack_1-25%_eva...  \n",
+       "9   core_simple_1_200_1000_gen_cp_attack_1-25%_eva...  \n",
+       "10  core_simple_1_200_1000_gen_cp_attack_1-25%_eva...  \n",
+       "11  core_simple_1_200_1000_gen_cp_attack_1-25%_eva...  \n",
+       "12      core_simple_1_200_1000_no_wm_gpt_p4_eval_supp  \n",
+       "13      core_simple_1_200_1000_no_wm_gpt_p4_eval_supp  \n",
+       "14      core_simple_1_200_1000_no_wm_gpt_p4_eval_supp  \n",
+       "15      core_simple_1_200_1000_no_wm_gpt_p4_eval_supp  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig_name = \"baseline_attacked_detectgpt_AUC_at_T_1000\"\n",
+    "\n",
+    "data_dir = f\"{INPUT_DIR}/figure_data/{fig_name}\"\n",
+    "\n",
+    "df = get_data_df(data_dir)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['core_simple_1_200_1000_no_wm_dipper_high_eval_supp'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_3-25%_eval_supp'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_1-25%_eval_supp'\n",
+      " 'core_simple_1_200_1000_no_wm_gpt_p4_eval_supp']\n",
+      "dict_items([('ab7zh7ta', 'core_simple_1_200_1000_no_wm_dipper_high_eval_supp'), ('dtrrqo1e', 'core_simple_1_200_1000_gen_cp_attack_3-25%_eval_supp'), ('miei424r', 'core_simple_1_200_1000_gen_cp_attack_1-25%_eval_supp'), ('yeewsgls', 'core_simple_1_200_1000_no_wm_gpt_p4_eval_supp')])\n",
+      "Used callable to map series_readable_ids\n",
+      "{'ab7zh7ta': 'Dipper', 'dtrrqo1e': 'CP-3-25%', 'miei424r': 'CP-1-25%', 'yeewsgls': 'GPT'}\n",
+      "[<matplotlib.lines.Line2D object at 0x7f87c91f2530>, <matplotlib.lines.Line2D object at 0x7f87c91f2800>, <matplotlib.lines.Line2D object at 0x7f87c91f0f40>, <matplotlib.lines.Line2D object at 0x7f87c91f2b90>]\n",
+      "['GPT', 'CP-1-25%', 'CP-3-25%', 'Dipper']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_grouped_series_data(\n",
+    "    data_df=df,\n",
+    "    x_col=\"idx_T\",\n",
+    "    y_col=\"aucs\",\n",
+    "    x_name=\"Tokens (T)\",\n",
+    "    y_name=\"ROC-AUC\",\n",
+    "    x_lim=(50,1000),\n",
+    "    y_lim=(0,1),\n",
+    "    series_id_col=\"id\",\n",
+    "    series_readable_id_col=\"name\",\n",
+    "    series_readable_id_map=name_extractor,\n",
+    "    special_color_map=color_map,\n",
+    "    sortby=\"mid\",\n",
+    "    legend_loc=\"lower right\",\n",
+    "    title=f\"Detection Rate of DetectGPT after Attack\\nas function of Tokens Observed\",\n",
+    "    save_path=f\"{figure_dir}/{fig_name}.pdf\" if save else None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>_defaultColorIndex</th>\n",
+       "      <th>aucs</th>\n",
+       "      <th>id</th>\n",
+       "      <th>idx_T</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.493266</td>\n",
+       "      <td>5ez7q3ys</td>\n",
+       "      <td>50</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-10%_pre...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.506631</td>\n",
+       "      <td>5ez7q3ys</td>\n",
+       "      <td>100</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-10%_pre...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.517226</td>\n",
+       "      <td>5ez7q3ys</td>\n",
+       "      <td>150</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-10%_pre...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.525092</td>\n",
+       "      <td>5ez7q3ys</td>\n",
+       "      <td>200</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-10%_pre...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.554276</td>\n",
+       "      <td>5ez7q3ys</td>\n",
+       "      <td>250</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-10%_pre...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115</th>\n",
+       "      <td>15</td>\n",
+       "      <td>0.941667</td>\n",
+       "      <td>plaqii3p</td>\n",
+       "      <td>800</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>15</td>\n",
+       "      <td>0.941644</td>\n",
+       "      <td>plaqii3p</td>\n",
+       "      <td>850</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117</th>\n",
+       "      <td>15</td>\n",
+       "      <td>0.941617</td>\n",
+       "      <td>plaqii3p</td>\n",
+       "      <td>900</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>118</th>\n",
+       "      <td>15</td>\n",
+       "      <td>0.941627</td>\n",
+       "      <td>plaqii3p</td>\n",
+       "      <td>950</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>15</td>\n",
+       "      <td>0.941659</td>\n",
+       "      <td>plaqii3p</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>120 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     _defaultColorIndex      aucs        id  idx_T  \\\n",
+       "0                     7  0.493266  5ez7q3ys     50   \n",
+       "1                     7  0.506631  5ez7q3ys    100   \n",
+       "2                     7  0.517226  5ez7q3ys    150   \n",
+       "3                     7  0.525092  5ez7q3ys    200   \n",
+       "4                     7  0.554276  5ez7q3ys    250   \n",
+       "..                  ...       ...       ...    ...   \n",
+       "115                  15  0.941667  plaqii3p    800   \n",
+       "116                  15  0.941644  plaqii3p    850   \n",
+       "117                  15  0.941617  plaqii3p    900   \n",
+       "118                  15  0.941627  plaqii3p    950   \n",
+       "119                  15  0.941659  plaqii3p   1000   \n",
+       "\n",
+       "                                                  name  \n",
+       "0    core_simple_1_200_1000_gen_cp_attack_1-10%_pre...  \n",
+       "1    core_simple_1_200_1000_gen_cp_attack_1-10%_pre...  \n",
+       "2    core_simple_1_200_1000_gen_cp_attack_1-10%_pre...  \n",
+       "3    core_simple_1_200_1000_gen_cp_attack_1-10%_pre...  \n",
+       "4    core_simple_1_200_1000_gen_cp_attack_1-10%_pre...  \n",
+       "..                                                 ...  \n",
+       "115  core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...  \n",
+       "116  core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...  \n",
+       "117  core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...  \n",
+       "118  core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...  \n",
+       "119  core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...  \n",
+       "\n",
+       "[120 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig_name = \"baseline_attacked_retrieval_AUC_at_T_1000\"\n",
+    "\n",
+    "data_dir = f\"{INPUT_DIR}/figure_data/{fig_name}\"\n",
+    "\n",
+    "df = get_data_df(data_dir)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['core_simple_1_200_1000_gen_cp_attack_1-10%_prefixes_eval_supp'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_1-25%_prefixes_eval_supp'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_3-10%_prefixes_eval_supp'\n",
+      " 'core_simple_1_200_1000_no_wm_dipper_high_prefixes_eval_supp'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_3-25%_prefixes_eval_supp'\n",
+      " 'core_simple_1_200_1000_no_wm_gpt_p4_prefixes_eval_supp']\n",
+      "dict_items([('5ez7q3ys', 'core_simple_1_200_1000_gen_cp_attack_1-10%_prefixes_eval_supp'), ('cdemf9mi', 'core_simple_1_200_1000_gen_cp_attack_1-25%_prefixes_eval_supp'), ('hfp5ofno', 'core_simple_1_200_1000_gen_cp_attack_3-10%_prefixes_eval_supp'), ('mgbk5ci5', 'core_simple_1_200_1000_no_wm_dipper_high_prefixes_eval_supp'), ('n9jnymb9', 'core_simple_1_200_1000_gen_cp_attack_3-25%_prefixes_eval_supp'), ('plaqii3p', 'core_simple_1_200_1000_no_wm_gpt_p4_prefixes_eval_supp')])\n",
+      "Used callable to map series_readable_ids\n",
+      "{'5ez7q3ys': 'CP-1-10%', 'cdemf9mi': 'CP-1-25%', 'hfp5ofno': 'CP-3-10%', 'mgbk5ci5': 'Dipper', 'n9jnymb9': 'CP-3-25%', 'plaqii3p': 'GPT'}\n",
+      "[<matplotlib.lines.Line2D object at 0x7f87c92a1540>, <matplotlib.lines.Line2D object at 0x7f87c92a14b0>, <matplotlib.lines.Line2D object at 0x7f87c92a1660>, <matplotlib.lines.Line2D object at 0x7f87c92a1360>, <matplotlib.lines.Line2D object at 0x7f87c92a1c30>, <matplotlib.lines.Line2D object at 0x7f87c92a1ed0>, <matplotlib.lines.Line2D object at 0x7f87c92a2170>, <matplotlib.lines.Line2D object at 0x7f87c92a2440>]\n",
+      "['Dipper', 'Dipper', 'GPT', 'GPT', 'CP-3-25%', 'CP-1-25%', 'CP-1-10%', 'CP-3-10%']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_grouped_series_data(\n",
+    "    data_df=df,\n",
+    "    x_col=\"idx_T\",\n",
+    "    y_col=\"aucs\",\n",
+    "    x_name=\"Tokens (T)\",\n",
+    "    y_name=\"ROC-AUC\",\n",
+    "    x_lim=(50,1000),\n",
+    "    y_lim=(0,1),\n",
+    "    limit_x_series_map={\"GPT\": 300, \"Dipper\": 800},\n",
+    "    series_id_col=\"id\",\n",
+    "    series_readable_id_col=\"name\",\n",
+    "    series_readable_id_map=name_extractor,\n",
+    "    special_color_map=color_map,\n",
+    "    sortby=\"mid\",\n",
+    "    legend_loc=\"lower right\",\n",
+    "    title=f\"Detection Rate of Retrieval Detection after Attack\\nas function of Tokens Observed\",\n",
+    "    save_path=f\"{figure_dir}/{fig_name}.pdf\" if save else None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>_defaultColorIndex</th>\n",
+       "      <th>aucs</th>\n",
+       "      <th>id</th>\n",
+       "      <th>idx_T</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0.530634</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>0</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0.565264</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>1</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0.581183</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>2</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0.594298</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>3</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0.604494</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>4</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5986</th>\n",
+       "      <td>14</td>\n",
+       "      <td>0.874677</td>\n",
+       "      <td>321cm0qh</td>\n",
+       "      <td>995</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5987</th>\n",
+       "      <td>14</td>\n",
+       "      <td>0.873093</td>\n",
+       "      <td>321cm0qh</td>\n",
+       "      <td>996</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5988</th>\n",
+       "      <td>14</td>\n",
+       "      <td>0.870343</td>\n",
+       "      <td>321cm0qh</td>\n",
+       "      <td>997</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5989</th>\n",
+       "      <td>14</td>\n",
+       "      <td>0.843053</td>\n",
+       "      <td>321cm0qh</td>\n",
+       "      <td>998</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5990</th>\n",
+       "      <td>14</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>321cm0qh</td>\n",
+       "      <td>999</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5991 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      _defaultColorIndex      aucs        id  idx_T  \\\n",
+       "0                     13  0.530634  sebf8zdh      0   \n",
+       "1                     13  0.565264  sebf8zdh      1   \n",
+       "2                     13  0.581183  sebf8zdh      2   \n",
+       "3                     13  0.594298  sebf8zdh      3   \n",
+       "4                     13  0.604494  sebf8zdh      4   \n",
+       "...                  ...       ...       ...    ...   \n",
+       "5986                  14  0.874677  321cm0qh    995   \n",
+       "5987                  14  0.873093  321cm0qh    996   \n",
+       "5988                  14  0.870343  321cm0qh    997   \n",
+       "5989                  14  0.843053  321cm0qh    998   \n",
+       "5990                  14  0.750000  321cm0qh    999   \n",
+       "\n",
+       "                                                 name  \n",
+       "0             core_simple_1_200_1000_dipper_high_eval  \n",
+       "1             core_simple_1_200_1000_dipper_high_eval  \n",
+       "2             core_simple_1_200_1000_dipper_high_eval  \n",
+       "3             core_simple_1_200_1000_dipper_high_eval  \n",
+       "4             core_simple_1_200_1000_dipper_high_eval  \n",
+       "...                                               ...  \n",
+       "5986  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  \n",
+       "5987  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  \n",
+       "5988  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  \n",
+       "5989  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  \n",
+       "5990  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  \n",
+       "\n",
+       "[5991 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig_name = \"baseline_attacked_z_score_AUC_at_T_1000\"\n",
+    "\n",
+    "data_dir = f\"{INPUT_DIR}/figure_data/{fig_name}\"\n",
+    "\n",
+    "df = get_data_df(data_dir)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['core_simple_1_200_1000_dipper_high_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_3-25%_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_1-25%_eval'\n",
+      " 'core_simple_1_200_1000_gpt_p4_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_1-10%_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_3-10%_eval']\n",
+      "dict_items([('sebf8zdh', 'core_simple_1_200_1000_dipper_high_eval'), ('qlczzu10', 'core_simple_1_200_1000_gen_cp_attack_3-25%_eval'), ('uoyt86q9', 'core_simple_1_200_1000_gen_cp_attack_1-25%_eval'), ('fai4a3qm', 'core_simple_1_200_1000_gpt_p4_eval'), ('tfo9p2tr', 'core_simple_1_200_1000_gen_cp_attack_1-10%_eval'), ('321cm0qh', 'core_simple_1_200_1000_gen_cp_attack_3-10%_eval')])\n",
+      "Used callable to map series_readable_ids\n",
+      "{'sebf8zdh': 'Dipper', 'qlczzu10': 'CP-3-25%', 'uoyt86q9': 'CP-1-25%', 'fai4a3qm': 'GPT', 'tfo9p2tr': 'CP-1-10%', '321cm0qh': 'CP-3-10%'}\n",
+      "[<matplotlib.lines.Line2D object at 0x7f87c915e4a0>, <matplotlib.lines.Line2D object at 0x7f87c915de70>, <matplotlib.lines.Line2D object at 0x7f87c915e350>, <matplotlib.lines.Line2D object at 0x7f87c915e320>, <matplotlib.lines.Line2D object at 0x7f87c915ee60>, <matplotlib.lines.Line2D object at 0x7f87c915f100>, <matplotlib.lines.Line2D object at 0x7f87c915f3d0>, <matplotlib.lines.Line2D object at 0x7f87c915f670>]\n",
+      "['GPT', 'GPT', 'Dipper', 'Dipper', 'CP-3-25%', 'CP-1-25%', 'CP-3-10%', 'CP-1-10%']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_grouped_series_data(\n",
+    "    data_df=df,\n",
+    "    x_col=\"idx_T\",\n",
+    "    y_col=\"aucs\",\n",
+    "    x_name=\"Tokens (T)\",\n",
+    "    y_name=\"ROC-AUC\",\n",
+    "    x_lim=(50,1000),\n",
+    "    y_lim=(0,1),\n",
+    "    limit_x_series=995,\n",
+    "    limit_x_series_map={\"GPT\": 300, \"Dipper\": 800},\n",
+    "    series_id_col=\"id\",\n",
+    "    series_readable_id_col=\"name\",\n",
+    "    series_readable_id_map=name_extractor,\n",
+    "    special_color_map=color_map,\n",
+    "    sortby=\"mid\",\n",
+    "    legend_loc=\"lower right\",\n",
+    "    title=f\"Detection Rate of Z-Score Watermark Detector after Attack\\nas function of Tokens Observed\",\n",
+    "    save_path=f\"{figure_dir}/{fig_name}.pdf\" if save else None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>_defaultColorIndex</th>\n",
+       "      <th>aucs</th>\n",
+       "      <th>id</th>\n",
+       "      <th>idx_T</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0.493455</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>0</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0.530634</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>1</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0.563727</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>2</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0.576089</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>3</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0.587200</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>4</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5986</th>\n",
+       "      <td>14</td>\n",
+       "      <td>0.966755</td>\n",
+       "      <td>321cm0qh</td>\n",
+       "      <td>995</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5987</th>\n",
+       "      <td>14</td>\n",
+       "      <td>0.966618</td>\n",
+       "      <td>321cm0qh</td>\n",
+       "      <td>996</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5988</th>\n",
+       "      <td>14</td>\n",
+       "      <td>0.969299</td>\n",
+       "      <td>321cm0qh</td>\n",
+       "      <td>997</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5989</th>\n",
+       "      <td>14</td>\n",
+       "      <td>0.968422</td>\n",
+       "      <td>321cm0qh</td>\n",
+       "      <td>998</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5990</th>\n",
+       "      <td>14</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>321cm0qh</td>\n",
+       "      <td>999</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5991 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      _defaultColorIndex      aucs        id  idx_T  \\\n",
+       "0                     13  0.493455  sebf8zdh      0   \n",
+       "1                     13  0.530634  sebf8zdh      1   \n",
+       "2                     13  0.563727  sebf8zdh      2   \n",
+       "3                     13  0.576089  sebf8zdh      3   \n",
+       "4                     13  0.587200  sebf8zdh      4   \n",
+       "...                  ...       ...       ...    ...   \n",
+       "5986                  14  0.966755  321cm0qh    995   \n",
+       "5987                  14  0.966618  321cm0qh    996   \n",
+       "5988                  14  0.969299  321cm0qh    997   \n",
+       "5989                  14  0.968422  321cm0qh    998   \n",
+       "5990                  14  1.000000  321cm0qh    999   \n",
+       "\n",
+       "                                                 name  \n",
+       "0             core_simple_1_200_1000_dipper_high_eval  \n",
+       "1             core_simple_1_200_1000_dipper_high_eval  \n",
+       "2             core_simple_1_200_1000_dipper_high_eval  \n",
+       "3             core_simple_1_200_1000_dipper_high_eval  \n",
+       "4             core_simple_1_200_1000_dipper_high_eval  \n",
+       "...                                               ...  \n",
+       "5986  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  \n",
+       "5987  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  \n",
+       "5988  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  \n",
+       "5989  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  \n",
+       "5990  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  \n",
+       "\n",
+       "[5991 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig_name = \"baseline_attacked_winmax_AUC_at_T_1000\"\n",
+    "\n",
+    "data_dir = f\"{INPUT_DIR}/figure_data/{fig_name}\"\n",
+    "\n",
+    "df = get_data_df(data_dir)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['core_simple_1_200_1000_dipper_high_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_3-25%_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_1-25%_eval'\n",
+      " 'core_simple_1_200_1000_gpt_p4_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_1-10%_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_3-10%_eval']\n",
+      "dict_items([('sebf8zdh', 'core_simple_1_200_1000_dipper_high_eval'), ('qlczzu10', 'core_simple_1_200_1000_gen_cp_attack_3-25%_eval'), ('uoyt86q9', 'core_simple_1_200_1000_gen_cp_attack_1-25%_eval'), ('fai4a3qm', 'core_simple_1_200_1000_gpt_p4_eval'), ('tfo9p2tr', 'core_simple_1_200_1000_gen_cp_attack_1-10%_eval'), ('321cm0qh', 'core_simple_1_200_1000_gen_cp_attack_3-10%_eval')])\n",
+      "Used callable to map series_readable_ids\n",
+      "{'sebf8zdh': 'Dipper', 'qlczzu10': 'CP-3-25%', 'uoyt86q9': 'CP-1-25%', 'fai4a3qm': 'GPT', 'tfo9p2tr': 'CP-1-10%', '321cm0qh': 'CP-3-10%'}\n",
+      "[<matplotlib.lines.Line2D object at 0x7f87c900b3d0>, <matplotlib.lines.Line2D object at 0x7f87c9009b10>, <matplotlib.lines.Line2D object at 0x7f87c900b850>, <matplotlib.lines.Line2D object at 0x7f87c900b400>, <matplotlib.lines.Line2D object at 0x7f87c900b910>, <matplotlib.lines.Line2D object at 0x7f87c903c040>, <matplotlib.lines.Line2D object at 0x7f87c903c310>, <matplotlib.lines.Line2D object at 0x7f87c903c5b0>]\n",
+      "['GPT', 'GPT', 'CP-3-25%', 'Dipper', 'Dipper', 'CP-3-10%', 'CP-1-25%', 'CP-1-10%']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_grouped_series_data(\n",
+    "    data_df=df,\n",
+    "    x_col=\"idx_T\",\n",
+    "    y_col=\"aucs\",\n",
+    "    x_name=\"Tokens (T)\",\n",
+    "    y_name=\"ROC-AUC\",\n",
+    "    x_lim=(50,1000),\n",
+    "    y_lim=(0,1),\n",
+    "    limit_x_series=995,\n",
+    "    limit_x_series_map={\"GPT\": 300, \"Dipper\": 800},\n",
+    "    series_id_col=\"id\",\n",
+    "    series_readable_id_col=\"name\",\n",
+    "    series_readable_id_map=name_extractor,\n",
+    "    special_color_map=color_map,\n",
+    "    sortby=\"mid\",\n",
+    "    legend_loc=\"lower right\",\n",
+    "    title=f\"Detection Rate of WinMax Watermark Detector after Attack\\nas function of Tokens Observed\",\n",
+    "    save_path=f\"{figure_dir}/{fig_name}.pdf\" if save else None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot score charts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>_defaultColorIndex</th>\n",
+       "      <th>id</th>\n",
+       "      <th>idx_T</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7</td>\n",
+       "      <td>5ez7q3ys</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.063819</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-10%_pre...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7</td>\n",
+       "      <td>5ez7q3ys</td>\n",
+       "      <td>100</td>\n",
+       "      <td>0.110270</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-10%_pre...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>5ez7q3ys</td>\n",
+       "      <td>150</td>\n",
+       "      <td>0.148034</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-10%_pre...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7</td>\n",
+       "      <td>5ez7q3ys</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0.181076</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-10%_pre...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7</td>\n",
+       "      <td>5ez7q3ys</td>\n",
+       "      <td>250</td>\n",
+       "      <td>0.207427</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-10%_pre...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115</th>\n",
+       "      <td>15</td>\n",
+       "      <td>plaqii3p</td>\n",
+       "      <td>800</td>\n",
+       "      <td>0.286822</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>15</td>\n",
+       "      <td>plaqii3p</td>\n",
+       "      <td>850</td>\n",
+       "      <td>0.286875</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117</th>\n",
+       "      <td>15</td>\n",
+       "      <td>plaqii3p</td>\n",
+       "      <td>900</td>\n",
+       "      <td>0.286925</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>118</th>\n",
+       "      <td>15</td>\n",
+       "      <td>plaqii3p</td>\n",
+       "      <td>950</td>\n",
+       "      <td>0.286947</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>15</td>\n",
+       "      <td>plaqii3p</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>0.286950</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>120 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     _defaultColorIndex        id  idx_T      mean  \\\n",
+       "0                     7  5ez7q3ys     50  0.063819   \n",
+       "1                     7  5ez7q3ys    100  0.110270   \n",
+       "2                     7  5ez7q3ys    150  0.148034   \n",
+       "3                     7  5ez7q3ys    200  0.181076   \n",
+       "4                     7  5ez7q3ys    250  0.207427   \n",
+       "..                  ...       ...    ...       ...   \n",
+       "115                  15  plaqii3p    800  0.286822   \n",
+       "116                  15  plaqii3p    850  0.286875   \n",
+       "117                  15  plaqii3p    900  0.286925   \n",
+       "118                  15  plaqii3p    950  0.286947   \n",
+       "119                  15  plaqii3p   1000  0.286950   \n",
+       "\n",
+       "                                                  name  \n",
+       "0    core_simple_1_200_1000_gen_cp_attack_1-10%_pre...  \n",
+       "1    core_simple_1_200_1000_gen_cp_attack_1-10%_pre...  \n",
+       "2    core_simple_1_200_1000_gen_cp_attack_1-10%_pre...  \n",
+       "3    core_simple_1_200_1000_gen_cp_attack_1-10%_pre...  \n",
+       "4    core_simple_1_200_1000_gen_cp_attack_1-10%_pre...  \n",
+       "..                                                 ...  \n",
+       "115  core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...  \n",
+       "116  core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...  \n",
+       "117  core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...  \n",
+       "118  core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...  \n",
+       "119  core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...  \n",
+       "\n",
+       "[120 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig_name = \"baseline_attacked_retrieval_scores_at_T_1000_base\"\n",
+    "\n",
+    "data_dir = f\"{INPUT_DIR}/figure_data/{fig_name}\"\n",
+    "\n",
+    "df = get_data_df(data_dir)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['core_simple_1_200_1000_gen_cp_attack_1-10%_prefixes_eval_supp'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_1-25%_prefixes_eval_supp'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_3-10%_prefixes_eval_supp'\n",
+      " 'core_simple_1_200_1000_no_wm_dipper_high_prefixes_eval_supp'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_3-25%_prefixes_eval_supp'\n",
+      " 'core_simple_1_200_1000_no_wm_gpt_p4_prefixes_eval_supp']\n",
+      "dict_items([('5ez7q3ys', 'core_simple_1_200_1000_gen_cp_attack_1-10%_prefixes_eval_supp'), ('cdemf9mi', 'core_simple_1_200_1000_gen_cp_attack_1-25%_prefixes_eval_supp'), ('hfp5ofno', 'core_simple_1_200_1000_gen_cp_attack_3-10%_prefixes_eval_supp'), ('mgbk5ci5', 'core_simple_1_200_1000_no_wm_dipper_high_prefixes_eval_supp'), ('n9jnymb9', 'core_simple_1_200_1000_gen_cp_attack_3-25%_prefixes_eval_supp'), ('plaqii3p', 'core_simple_1_200_1000_no_wm_gpt_p4_prefixes_eval_supp')])\n",
+      "Used callable to map series_readable_ids\n",
+      "{'5ez7q3ys': 'CP-1-10%', 'cdemf9mi': 'CP-1-25%', 'hfp5ofno': 'CP-3-10%', 'mgbk5ci5': 'Dipper', 'n9jnymb9': 'CP-3-25%', 'plaqii3p': 'GPT'}\n",
+      "[<matplotlib.lines.Line2D object at 0x7f87c90bf760>, <matplotlib.lines.Line2D object at 0x7f87c90bf5e0>, <matplotlib.lines.Line2D object at 0x7f87c90bfbb0>, <matplotlib.lines.Line2D object at 0x7f87c90bfe50>, <matplotlib.lines.Line2D object at 0x7f87c8498130>, <matplotlib.lines.Line2D object at 0x7f87c84983d0>, <matplotlib.lines.Line2D object at 0x7f87c84980d0>, <matplotlib.lines.Line2D object at 0x7f87c8498610>]\n",
+      "['Dipper', 'Dipper', 'CP-1-25%', 'CP-3-25%', 'CP-1-10%', 'CP-3-10%', 'GPT', 'GPT']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_grouped_series_data(\n",
+    "    data_df=df,\n",
+    "    x_col=\"idx_T\",\n",
+    "    y_col=\"mean\",\n",
+    "    x_name=\"Tokens (T)\",\n",
+    "    y_name=\"Retrieval Score\",\n",
+    "    x_lim=(50,1000),\n",
+    "    y_lim=(0,1),\n",
+    "    limit_x_series_map={\"GPT\": 300, \"Dipper\": 800},\n",
+    "    series_id_col=\"id\",\n",
+    "    series_readable_id_col=\"name\",\n",
+    "    series_readable_id_map=name_extractor,\n",
+    "    special_color_map=color_map,\n",
+    "    sortby=\"mid\",\n",
+    "    legend_loc=\"lower right\",\n",
+    "    title=f\"Retrieval Detection Score of Human Text\\nas function of Tokens Observed\",\n",
+    "    save_path=f\"{figure_dir}/{fig_name}.pdf\" if save else None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>_defaultColorIndex</th>\n",
+       "      <th>id</th>\n",
+       "      <th>idx_T</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7</td>\n",
+       "      <td>5ez7q3ys</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.056824</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-10%_pre...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7</td>\n",
+       "      <td>5ez7q3ys</td>\n",
+       "      <td>100</td>\n",
+       "      <td>0.104464</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-10%_pre...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>5ez7q3ys</td>\n",
+       "      <td>150</td>\n",
+       "      <td>0.146198</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-10%_pre...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7</td>\n",
+       "      <td>5ez7q3ys</td>\n",
+       "      <td>200</td>\n",
+       "      <td>0.182940</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-10%_pre...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7</td>\n",
+       "      <td>5ez7q3ys</td>\n",
+       "      <td>250</td>\n",
+       "      <td>0.216587</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-10%_pre...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115</th>\n",
+       "      <td>15</td>\n",
+       "      <td>plaqii3p</td>\n",
+       "      <td>800</td>\n",
+       "      <td>0.529383</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>15</td>\n",
+       "      <td>plaqii3p</td>\n",
+       "      <td>850</td>\n",
+       "      <td>0.529549</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117</th>\n",
+       "      <td>15</td>\n",
+       "      <td>plaqii3p</td>\n",
+       "      <td>900</td>\n",
+       "      <td>0.529740</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>118</th>\n",
+       "      <td>15</td>\n",
+       "      <td>plaqii3p</td>\n",
+       "      <td>950</td>\n",
+       "      <td>0.529876</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>15</td>\n",
+       "      <td>plaqii3p</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>0.529974</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>120 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     _defaultColorIndex        id  idx_T      mean  \\\n",
+       "0                     7  5ez7q3ys     50  0.056824   \n",
+       "1                     7  5ez7q3ys    100  0.104464   \n",
+       "2                     7  5ez7q3ys    150  0.146198   \n",
+       "3                     7  5ez7q3ys    200  0.182940   \n",
+       "4                     7  5ez7q3ys    250  0.216587   \n",
+       "..                  ...       ...    ...       ...   \n",
+       "115                  15  plaqii3p    800  0.529383   \n",
+       "116                  15  plaqii3p    850  0.529549   \n",
+       "117                  15  plaqii3p    900  0.529740   \n",
+       "118                  15  plaqii3p    950  0.529876   \n",
+       "119                  15  plaqii3p   1000  0.529974   \n",
+       "\n",
+       "                                                  name  \n",
+       "0    core_simple_1_200_1000_gen_cp_attack_1-10%_pre...  \n",
+       "1    core_simple_1_200_1000_gen_cp_attack_1-10%_pre...  \n",
+       "2    core_simple_1_200_1000_gen_cp_attack_1-10%_pre...  \n",
+       "3    core_simple_1_200_1000_gen_cp_attack_1-10%_pre...  \n",
+       "4    core_simple_1_200_1000_gen_cp_attack_1-10%_pre...  \n",
+       "..                                                 ...  \n",
+       "115  core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...  \n",
+       "116  core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...  \n",
+       "117  core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...  \n",
+       "118  core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...  \n",
+       "119  core_simple_1_200_1000_no_wm_gpt_p4_prefixes_e...  \n",
+       "\n",
+       "[120 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig_name = \"baseline_attacked_retrieval_scores_at_T_1000_attacked\"\n",
+    "\n",
+    "data_dir = f\"{INPUT_DIR}/figure_data/{fig_name}\"\n",
+    "\n",
+    "df = get_data_df(data_dir)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['core_simple_1_200_1000_gen_cp_attack_1-10%_prefixes_eval_supp'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_1-25%_prefixes_eval_supp'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_3-10%_prefixes_eval_supp'\n",
+      " 'core_simple_1_200_1000_no_wm_dipper_high_prefixes_eval_supp'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_3-25%_prefixes_eval_supp'\n",
+      " 'core_simple_1_200_1000_no_wm_gpt_p4_prefixes_eval_supp']\n",
+      "dict_items([('5ez7q3ys', 'core_simple_1_200_1000_gen_cp_attack_1-10%_prefixes_eval_supp'), ('cdemf9mi', 'core_simple_1_200_1000_gen_cp_attack_1-25%_prefixes_eval_supp'), ('hfp5ofno', 'core_simple_1_200_1000_gen_cp_attack_3-10%_prefixes_eval_supp'), ('mgbk5ci5', 'core_simple_1_200_1000_no_wm_dipper_high_prefixes_eval_supp'), ('n9jnymb9', 'core_simple_1_200_1000_gen_cp_attack_3-25%_prefixes_eval_supp'), ('plaqii3p', 'core_simple_1_200_1000_no_wm_gpt_p4_prefixes_eval_supp')])\n",
+      "Used callable to map series_readable_ids\n",
+      "{'5ez7q3ys': 'CP-1-10%', 'cdemf9mi': 'CP-1-25%', 'hfp5ofno': 'CP-3-10%', 'mgbk5ci5': 'Dipper', 'n9jnymb9': 'CP-3-25%', 'plaqii3p': 'GPT'}\n",
+      "[<matplotlib.lines.Line2D object at 0x7f87c851beb0>, <matplotlib.lines.Line2D object at 0x7f87c85401f0>, <matplotlib.lines.Line2D object at 0x7f87c8540280>, <matplotlib.lines.Line2D object at 0x7f87c85405e0>, <matplotlib.lines.Line2D object at 0x7f87c8540430>, <matplotlib.lines.Line2D object at 0x7f87c8540910>, <matplotlib.lines.Line2D object at 0x7f87c8540bb0>, <matplotlib.lines.Line2D object at 0x7f87c8540e50>]\n",
+      "['Dipper', 'Dipper', 'GPT', 'GPT', 'CP-1-25%', 'CP-3-25%', 'CP-1-10%', 'CP-3-10%']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_grouped_series_data(\n",
+    "    data_df=df,\n",
+    "    x_col=\"idx_T\",\n",
+    "    y_col=\"mean\",\n",
+    "    x_name=\"Tokens (T)\",\n",
+    "    y_name=\"Retrieval Score\",\n",
+    "    x_lim=(50,1000),\n",
+    "    y_lim=(0,1),\n",
+    "    limit_x_series_map={\"GPT\": 300, \"Dipper\": 800},\n",
+    "    series_id_col=\"id\",\n",
+    "    series_readable_id_col=\"name\",\n",
+    "    series_readable_id_map=name_extractor,\n",
+    "    special_color_map=color_map,\n",
+    "    sortby=\"mid\",\n",
+    "    legend_loc=\"lower right\",\n",
+    "    title=f\"Retrieval Detection Score of Attacked Text\\nas function of Tokens Observed\",\n",
+    "    save_path=f\"{figure_dir}/{fig_name}.pdf\" if save else None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>_defaultColorIndex</th>\n",
+       "      <th>id</th>\n",
+       "      <th>idx_T</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>15</td>\n",
+       "      <td>ab7zh7ta</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.808905</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_dipper_high_eval_...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15</td>\n",
+       "      <td>ab7zh7ta</td>\n",
+       "      <td>100</td>\n",
+       "      <td>1.066200</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_dipper_high_eval_...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>15</td>\n",
+       "      <td>ab7zh7ta</td>\n",
+       "      <td>200</td>\n",
+       "      <td>1.431048</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_dipper_high_eval_...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>15</td>\n",
+       "      <td>ab7zh7ta</td>\n",
+       "      <td>800</td>\n",
+       "      <td>3.144001</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_dipper_high_eval_...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>14</td>\n",
+       "      <td>dtrrqo1e</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.805541</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-25%_eva...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>14</td>\n",
+       "      <td>dtrrqo1e</td>\n",
+       "      <td>100</td>\n",
+       "      <td>1.072253</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-25%_eva...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>14</td>\n",
+       "      <td>dtrrqo1e</td>\n",
+       "      <td>200</td>\n",
+       "      <td>1.444406</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-25%_eva...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>14</td>\n",
+       "      <td>dtrrqo1e</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>3.148789</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-25%_eva...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>13</td>\n",
+       "      <td>miei424r</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.810479</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-25%_eva...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>13</td>\n",
+       "      <td>miei424r</td>\n",
+       "      <td>100</td>\n",
+       "      <td>1.060504</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-25%_eva...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>13</td>\n",
+       "      <td>miei424r</td>\n",
+       "      <td>200</td>\n",
+       "      <td>1.437336</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-25%_eva...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>13</td>\n",
+       "      <td>miei424r</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>3.144430</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-25%_eva...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>0</td>\n",
+       "      <td>yeewsgls</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.809836</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_eval_supp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>0</td>\n",
+       "      <td>yeewsgls</td>\n",
+       "      <td>100</td>\n",
+       "      <td>1.064740</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_eval_supp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>0</td>\n",
+       "      <td>yeewsgls</td>\n",
+       "      <td>200</td>\n",
+       "      <td>1.410445</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_eval_supp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>0</td>\n",
+       "      <td>yeewsgls</td>\n",
+       "      <td>300</td>\n",
+       "      <td>3.130786</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_eval_supp</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    _defaultColorIndex        id  idx_T      mean  \\\n",
+       "0                   15  ab7zh7ta     50  0.808905   \n",
+       "1                   15  ab7zh7ta    100  1.066200   \n",
+       "2                   15  ab7zh7ta    200  1.431048   \n",
+       "3                   15  ab7zh7ta    800  3.144001   \n",
+       "4                   14  dtrrqo1e     50  0.805541   \n",
+       "5                   14  dtrrqo1e    100  1.072253   \n",
+       "6                   14  dtrrqo1e    200  1.444406   \n",
+       "7                   14  dtrrqo1e   1000  3.148789   \n",
+       "8                   13  miei424r     50  0.810479   \n",
+       "9                   13  miei424r    100  1.060504   \n",
+       "10                  13  miei424r    200  1.437336   \n",
+       "11                  13  miei424r   1000  3.144430   \n",
+       "12                   0  yeewsgls     50  0.809836   \n",
+       "13                   0  yeewsgls    100  1.064740   \n",
+       "14                   0  yeewsgls    200  1.410445   \n",
+       "15                   0  yeewsgls    300  3.130786   \n",
+       "\n",
+       "                                                 name  \n",
+       "0   core_simple_1_200_1000_no_wm_dipper_high_eval_...  \n",
+       "1   core_simple_1_200_1000_no_wm_dipper_high_eval_...  \n",
+       "2   core_simple_1_200_1000_no_wm_dipper_high_eval_...  \n",
+       "3   core_simple_1_200_1000_no_wm_dipper_high_eval_...  \n",
+       "4   core_simple_1_200_1000_gen_cp_attack_3-25%_eva...  \n",
+       "5   core_simple_1_200_1000_gen_cp_attack_3-25%_eva...  \n",
+       "6   core_simple_1_200_1000_gen_cp_attack_3-25%_eva...  \n",
+       "7   core_simple_1_200_1000_gen_cp_attack_3-25%_eva...  \n",
+       "8   core_simple_1_200_1000_gen_cp_attack_1-25%_eva...  \n",
+       "9   core_simple_1_200_1000_gen_cp_attack_1-25%_eva...  \n",
+       "10  core_simple_1_200_1000_gen_cp_attack_1-25%_eva...  \n",
+       "11  core_simple_1_200_1000_gen_cp_attack_1-25%_eva...  \n",
+       "12      core_simple_1_200_1000_no_wm_gpt_p4_eval_supp  \n",
+       "13      core_simple_1_200_1000_no_wm_gpt_p4_eval_supp  \n",
+       "14      core_simple_1_200_1000_no_wm_gpt_p4_eval_supp  \n",
+       "15      core_simple_1_200_1000_no_wm_gpt_p4_eval_supp  "
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig_name = \"baseline_attacked_detectgpt_scores_at_T_1000_base\"\n",
+    "\n",
+    "data_dir = f\"{INPUT_DIR}/figure_data/{fig_name}\"\n",
+    "\n",
+    "df = get_data_df(data_dir)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['core_simple_1_200_1000_no_wm_dipper_high_eval_supp'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_3-25%_eval_supp'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_1-25%_eval_supp'\n",
+      " 'core_simple_1_200_1000_no_wm_gpt_p4_eval_supp']\n",
+      "dict_items([('ab7zh7ta', 'core_simple_1_200_1000_no_wm_dipper_high_eval_supp'), ('dtrrqo1e', 'core_simple_1_200_1000_gen_cp_attack_3-25%_eval_supp'), ('miei424r', 'core_simple_1_200_1000_gen_cp_attack_1-25%_eval_supp'), ('yeewsgls', 'core_simple_1_200_1000_no_wm_gpt_p4_eval_supp')])\n",
+      "Used callable to map series_readable_ids\n",
+      "{'ab7zh7ta': 'Dipper', 'dtrrqo1e': 'CP-3-25%', 'miei424r': 'CP-1-25%', 'yeewsgls': 'GPT'}\n",
+      "[<matplotlib.lines.Line2D object at 0x7f87c83f4340>, <matplotlib.lines.Line2D object at 0x7f87c83f4580>, <matplotlib.lines.Line2D object at 0x7f87c83f48b0>, <matplotlib.lines.Line2D object at 0x7f87c83f4490>]\n",
+      "['CP-3-25%', 'CP-1-25%', 'Dipper', 'GPT']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_grouped_series_data(\n",
+    "    data_df=df,\n",
+    "    x_col=\"idx_T\",\n",
+    "    y_col=\"mean\",\n",
+    "    x_name=\"Tokens (T)\",\n",
+    "    y_name=\"DetectGPT Score\",\n",
+    "    x_lim=(50,1000),\n",
+    "    y_lim=(0,4),\n",
+    "    series_id_col=\"id\",\n",
+    "    series_readable_id_col=\"name\",\n",
+    "    series_readable_id_map=name_extractor,\n",
+    "    special_color_map=color_map,\n",
+    "    sortby=\"mid\",\n",
+    "    legend_loc=\"lower right\",\n",
+    "    title=f\"DetectGPT Score of Human Text as function of Tokens Observed\",\n",
+    "    save_path=f\"{figure_dir}/{fig_name}.pdf\" if save else None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>_defaultColorIndex</th>\n",
+       "      <th>id</th>\n",
+       "      <th>idx_T</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>15</td>\n",
+       "      <td>ab7zh7ta</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.865802</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_dipper_high_eval_...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15</td>\n",
+       "      <td>ab7zh7ta</td>\n",
+       "      <td>100</td>\n",
+       "      <td>1.050293</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_dipper_high_eval_...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>15</td>\n",
+       "      <td>ab7zh7ta</td>\n",
+       "      <td>200</td>\n",
+       "      <td>1.362028</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_dipper_high_eval_...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>15</td>\n",
+       "      <td>ab7zh7ta</td>\n",
+       "      <td>800</td>\n",
+       "      <td>2.352753</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_dipper_high_eval_...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>14</td>\n",
+       "      <td>dtrrqo1e</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.813760</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-25%_eva...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>14</td>\n",
+       "      <td>dtrrqo1e</td>\n",
+       "      <td>100</td>\n",
+       "      <td>1.138531</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-25%_eva...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>14</td>\n",
+       "      <td>dtrrqo1e</td>\n",
+       "      <td>200</td>\n",
+       "      <td>1.513916</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-25%_eva...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>14</td>\n",
+       "      <td>dtrrqo1e</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>3.131219</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-25%_eva...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>13</td>\n",
+       "      <td>miei424r</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.815771</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-25%_eva...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>13</td>\n",
+       "      <td>miei424r</td>\n",
+       "      <td>100</td>\n",
+       "      <td>1.217385</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-25%_eva...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>13</td>\n",
+       "      <td>miei424r</td>\n",
+       "      <td>200</td>\n",
+       "      <td>1.711331</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-25%_eva...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>13</td>\n",
+       "      <td>miei424r</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>3.459239</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_1-25%_eva...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>0</td>\n",
+       "      <td>yeewsgls</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.966074</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_eval_supp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>0</td>\n",
+       "      <td>yeewsgls</td>\n",
+       "      <td>100</td>\n",
+       "      <td>1.248599</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_eval_supp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>0</td>\n",
+       "      <td>yeewsgls</td>\n",
+       "      <td>200</td>\n",
+       "      <td>1.668495</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_eval_supp</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>0</td>\n",
+       "      <td>yeewsgls</td>\n",
+       "      <td>300</td>\n",
+       "      <td>2.182142</td>\n",
+       "      <td>core_simple_1_200_1000_no_wm_gpt_p4_eval_supp</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    _defaultColorIndex        id  idx_T      mean  \\\n",
+       "0                   15  ab7zh7ta     50  0.865802   \n",
+       "1                   15  ab7zh7ta    100  1.050293   \n",
+       "2                   15  ab7zh7ta    200  1.362028   \n",
+       "3                   15  ab7zh7ta    800  2.352753   \n",
+       "4                   14  dtrrqo1e     50  0.813760   \n",
+       "5                   14  dtrrqo1e    100  1.138531   \n",
+       "6                   14  dtrrqo1e    200  1.513916   \n",
+       "7                   14  dtrrqo1e   1000  3.131219   \n",
+       "8                   13  miei424r     50  0.815771   \n",
+       "9                   13  miei424r    100  1.217385   \n",
+       "10                  13  miei424r    200  1.711331   \n",
+       "11                  13  miei424r   1000  3.459239   \n",
+       "12                   0  yeewsgls     50  0.966074   \n",
+       "13                   0  yeewsgls    100  1.248599   \n",
+       "14                   0  yeewsgls    200  1.668495   \n",
+       "15                   0  yeewsgls    300  2.182142   \n",
+       "\n",
+       "                                                 name  \n",
+       "0   core_simple_1_200_1000_no_wm_dipper_high_eval_...  \n",
+       "1   core_simple_1_200_1000_no_wm_dipper_high_eval_...  \n",
+       "2   core_simple_1_200_1000_no_wm_dipper_high_eval_...  \n",
+       "3   core_simple_1_200_1000_no_wm_dipper_high_eval_...  \n",
+       "4   core_simple_1_200_1000_gen_cp_attack_3-25%_eva...  \n",
+       "5   core_simple_1_200_1000_gen_cp_attack_3-25%_eva...  \n",
+       "6   core_simple_1_200_1000_gen_cp_attack_3-25%_eva...  \n",
+       "7   core_simple_1_200_1000_gen_cp_attack_3-25%_eva...  \n",
+       "8   core_simple_1_200_1000_gen_cp_attack_1-25%_eva...  \n",
+       "9   core_simple_1_200_1000_gen_cp_attack_1-25%_eva...  \n",
+       "10  core_simple_1_200_1000_gen_cp_attack_1-25%_eva...  \n",
+       "11  core_simple_1_200_1000_gen_cp_attack_1-25%_eva...  \n",
+       "12      core_simple_1_200_1000_no_wm_gpt_p4_eval_supp  \n",
+       "13      core_simple_1_200_1000_no_wm_gpt_p4_eval_supp  \n",
+       "14      core_simple_1_200_1000_no_wm_gpt_p4_eval_supp  \n",
+       "15      core_simple_1_200_1000_no_wm_gpt_p4_eval_supp  "
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig_name = \"baseline_attacked_detectgpt_scores_at_T_1000_attacked\"\n",
+    "\n",
+    "data_dir = f\"{INPUT_DIR}/figure_data/{fig_name}\"\n",
+    "\n",
+    "df = get_data_df(data_dir)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['core_simple_1_200_1000_no_wm_dipper_high_eval_supp'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_3-25%_eval_supp'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_1-25%_eval_supp'\n",
+      " 'core_simple_1_200_1000_no_wm_gpt_p4_eval_supp']\n",
+      "dict_items([('ab7zh7ta', 'core_simple_1_200_1000_no_wm_dipper_high_eval_supp'), ('dtrrqo1e', 'core_simple_1_200_1000_gen_cp_attack_3-25%_eval_supp'), ('miei424r', 'core_simple_1_200_1000_gen_cp_attack_1-25%_eval_supp'), ('yeewsgls', 'core_simple_1_200_1000_no_wm_gpt_p4_eval_supp')])\n",
+      "Used callable to map series_readable_ids\n",
+      "{'ab7zh7ta': 'Dipper', 'dtrrqo1e': 'CP-3-25%', 'miei424r': 'CP-1-25%', 'yeewsgls': 'GPT'}\n",
+      "[<matplotlib.lines.Line2D object at 0x7f87c829cb80>, <matplotlib.lines.Line2D object at 0x7f87c829ce20>, <matplotlib.lines.Line2D object at 0x7f87c829d0c0>, <matplotlib.lines.Line2D object at 0x7f87c829d360>]\n",
+      "['CP-1-25%', 'GPT', 'CP-3-25%', 'Dipper']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_grouped_series_data(\n",
+    "    data_df=df,\n",
+    "    x_col=\"idx_T\",\n",
+    "    y_col=\"mean\",\n",
+    "    x_name=\"Tokens (T)\",\n",
+    "    y_name=\"DetectGPT Score\",\n",
+    "    x_lim=(50,1000),\n",
+    "    y_lim=(0,4),\n",
+    "    series_id_col=\"id\",\n",
+    "    series_readable_id_col=\"name\",\n",
+    "    series_readable_id_map=name_extractor,\n",
+    "    special_color_map=color_map,\n",
+    "    sortby=\"mid\",\n",
+    "    legend_loc=\"lower right\",\n",
+    "    title=f\"DetectGPT Score of Attacked Text as function of Tokens Observed\",\n",
+    "    save_path=f\"{figure_dir}/{fig_name}.pdf\" if save else None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>_defaultColorIndex</th>\n",
+       "      <th>avg</th>\n",
+       "      <th>id</th>\n",
+       "      <th>idx_T</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0.017216</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>0</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0.001781</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>1</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13</td>\n",
+       "      <td>-0.020606</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>2</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>13</td>\n",
+       "      <td>-0.000840</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>3</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0.033988</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>4</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7002</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.240709</td>\n",
+       "      <td>i136o2qh</td>\n",
+       "      <td>996</td>\n",
+       "      <td>core_simple_1_200_1000_gen_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7003</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.240324</td>\n",
+       "      <td>i136o2qh</td>\n",
+       "      <td>997</td>\n",
+       "      <td>core_simple_1_200_1000_gen_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7004</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.242315</td>\n",
+       "      <td>i136o2qh</td>\n",
+       "      <td>998</td>\n",
+       "      <td>core_simple_1_200_1000_gen_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7005</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.311337</td>\n",
+       "      <td>i136o2qh</td>\n",
+       "      <td>999</td>\n",
+       "      <td>core_simple_1_200_1000_gen_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7006</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.602194</td>\n",
+       "      <td>i136o2qh</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>core_simple_1_200_1000_gen_eval</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7007 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      _defaultColorIndex       avg        id  idx_T  \\\n",
+       "0                     13  0.017216  sebf8zdh      0   \n",
+       "1                     13  0.001781  sebf8zdh      1   \n",
+       "2                     13 -0.020606  sebf8zdh      2   \n",
+       "3                     13 -0.000840  sebf8zdh      3   \n",
+       "4                     13  0.033988  sebf8zdh      4   \n",
+       "...                  ...       ...       ...    ...   \n",
+       "7002                  10  0.240709  i136o2qh    996   \n",
+       "7003                  10  0.240324  i136o2qh    997   \n",
+       "7004                  10  0.242315  i136o2qh    998   \n",
+       "7005                  10  0.311337  i136o2qh    999   \n",
+       "7006                  10  0.602194  i136o2qh   1000   \n",
+       "\n",
+       "                                         name  \n",
+       "0     core_simple_1_200_1000_dipper_high_eval  \n",
+       "1     core_simple_1_200_1000_dipper_high_eval  \n",
+       "2     core_simple_1_200_1000_dipper_high_eval  \n",
+       "3     core_simple_1_200_1000_dipper_high_eval  \n",
+       "4     core_simple_1_200_1000_dipper_high_eval  \n",
+       "...                                       ...  \n",
+       "7002          core_simple_1_200_1000_gen_eval  \n",
+       "7003          core_simple_1_200_1000_gen_eval  \n",
+       "7004          core_simple_1_200_1000_gen_eval  \n",
+       "7005          core_simple_1_200_1000_gen_eval  \n",
+       "7006          core_simple_1_200_1000_gen_eval  \n",
+       "\n",
+       "[7007 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig_name = \"baseline_attacked_z_scores_at_T_1000_base\"\n",
+    "\n",
+    "data_dir = f\"{INPUT_DIR}/figure_data/{fig_name}\"\n",
+    "\n",
+    "df = get_data_df(data_dir)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['core_simple_1_200_1000_dipper_high_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_3-25%_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_1-25%_eval'\n",
+      " 'core_simple_1_200_1000_gpt_p4_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_1-10%_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_3-10%_eval'\n",
+      " 'core_simple_1_200_1000_gen_eval']\n",
+      "dict_items([('sebf8zdh', 'core_simple_1_200_1000_dipper_high_eval'), ('qlczzu10', 'core_simple_1_200_1000_gen_cp_attack_3-25%_eval'), ('uoyt86q9', 'core_simple_1_200_1000_gen_cp_attack_1-25%_eval'), ('fai4a3qm', 'core_simple_1_200_1000_gpt_p4_eval'), ('tfo9p2tr', 'core_simple_1_200_1000_gen_cp_attack_1-10%_eval'), ('321cm0qh', 'core_simple_1_200_1000_gen_cp_attack_3-10%_eval'), ('i136o2qh', 'core_simple_1_200_1000_gen_eval')])\n",
+      "Used callable to map series_readable_ids\n",
+      "{'sebf8zdh': 'Dipper', 'qlczzu10': 'CP-3-25%', 'uoyt86q9': 'CP-1-25%', 'fai4a3qm': 'GPT', 'tfo9p2tr': 'CP-1-10%', '321cm0qh': 'CP-3-10%', 'i136o2qh': ''}\n",
+      "[<matplotlib.lines.Line2D object at 0x7f87c82bca60>, <matplotlib.lines.Line2D object at 0x7f87c82bea10>, <matplotlib.lines.Line2D object at 0x7f87c82bcdf0>, <matplotlib.lines.Line2D object at 0x7f87c82be6b0>, <matplotlib.lines.Line2D object at 0x7f87c82bc040>, <matplotlib.lines.Line2D object at 0x7f87c82be1a0>, <matplotlib.lines.Line2D object at 0x7f87c82bff40>, <matplotlib.lines.Line2D object at 0x7f87c82bf760>]\n",
+      "['CP-3-10%', 'GPT', 'GPT', 'CP-3-25%', 'Dipper', 'Dipper', 'CP-1-10%', 'CP-1-25%']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_grouped_series_data(\n",
+    "    data_df=df,\n",
+    "    x_col=\"idx_T\",\n",
+    "    y_col=\"avg\",\n",
+    "    x_name=\"Tokens (T)\",\n",
+    "    y_name=\"Z-Score\",\n",
+    "    x_lim=(50,1000),\n",
+    "    y_lim=(0,15),\n",
+    "    limit_x_series=995,\n",
+    "    limit_x_series_map={\"GPT\": 300, \"Dipper\": 800},\n",
+    "    series_id_col=\"id\",\n",
+    "    series_readable_id_col=\"name\",\n",
+    "    series_readable_id_map=name_extractor,\n",
+    "    special_color_map=color_map,\n",
+    "    sortby=\"mid\",\n",
+    "    legend_loc=\"lower right\",\n",
+    "    title=f\"Z-Score of Human Text as function of Tokens Observed\",\n",
+    "    save_path=f\"{figure_dir}/{fig_name}.pdf\" if save else None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>_defaultColorIndex</th>\n",
+       "      <th>avg</th>\n",
+       "      <th>id</th>\n",
+       "      <th>idx_T</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>13</td>\n",
+       "      <td>-0.013017</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>0</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0.118169</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>1</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0.234424</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>2</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0.325232</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>3</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0.416801</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>4</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6155</th>\n",
+       "      <td>14</td>\n",
+       "      <td>2.391800</td>\n",
+       "      <td>321cm0qh</td>\n",
+       "      <td>996</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6156</th>\n",
+       "      <td>14</td>\n",
+       "      <td>2.381190</td>\n",
+       "      <td>321cm0qh</td>\n",
+       "      <td>997</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6157</th>\n",
+       "      <td>14</td>\n",
+       "      <td>2.408835</td>\n",
+       "      <td>321cm0qh</td>\n",
+       "      <td>998</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6158</th>\n",
+       "      <td>14</td>\n",
+       "      <td>2.350119</td>\n",
+       "      <td>321cm0qh</td>\n",
+       "      <td>999</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6159</th>\n",
+       "      <td>14</td>\n",
+       "      <td>1.405119</td>\n",
+       "      <td>321cm0qh</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>6160 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      _defaultColorIndex       avg        id  idx_T  \\\n",
+       "0                     13 -0.013017  sebf8zdh      0   \n",
+       "1                     13  0.118169  sebf8zdh      1   \n",
+       "2                     13  0.234424  sebf8zdh      2   \n",
+       "3                     13  0.325232  sebf8zdh      3   \n",
+       "4                     13  0.416801  sebf8zdh      4   \n",
+       "...                  ...       ...       ...    ...   \n",
+       "6155                  14  2.391800  321cm0qh    996   \n",
+       "6156                  14  2.381190  321cm0qh    997   \n",
+       "6157                  14  2.408835  321cm0qh    998   \n",
+       "6158                  14  2.350119  321cm0qh    999   \n",
+       "6159                  14  1.405119  321cm0qh   1000   \n",
+       "\n",
+       "                                                 name  \n",
+       "0             core_simple_1_200_1000_dipper_high_eval  \n",
+       "1             core_simple_1_200_1000_dipper_high_eval  \n",
+       "2             core_simple_1_200_1000_dipper_high_eval  \n",
+       "3             core_simple_1_200_1000_dipper_high_eval  \n",
+       "4             core_simple_1_200_1000_dipper_high_eval  \n",
+       "...                                               ...  \n",
+       "6155  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  \n",
+       "6156  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  \n",
+       "6157  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  \n",
+       "6158  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  \n",
+       "6159  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  \n",
+       "\n",
+       "[6160 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig_name = \"baseline_attacked_z_scores_at_T_1000_attacked\"\n",
+    "\n",
+    "data_dir = f\"{INPUT_DIR}/figure_data/{fig_name}\"\n",
+    "\n",
+    "df = get_data_df(data_dir)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['core_simple_1_200_1000_dipper_high_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_3-25%_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_1-25%_eval'\n",
+      " 'core_simple_1_200_1000_gpt_p4_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_1-10%_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_3-10%_eval']\n",
+      "dict_items([('sebf8zdh', 'core_simple_1_200_1000_dipper_high_eval'), ('qlczzu10', 'core_simple_1_200_1000_gen_cp_attack_3-25%_eval'), ('uoyt86q9', 'core_simple_1_200_1000_gen_cp_attack_1-25%_eval'), ('fai4a3qm', 'core_simple_1_200_1000_gpt_p4_eval'), ('tfo9p2tr', 'core_simple_1_200_1000_gen_cp_attack_1-10%_eval'), ('321cm0qh', 'core_simple_1_200_1000_gen_cp_attack_3-10%_eval')])\n",
+      "Used callable to map series_readable_ids\n",
+      "{'sebf8zdh': 'Dipper', 'qlczzu10': 'CP-3-25%', 'uoyt86q9': 'CP-1-25%', 'fai4a3qm': 'GPT', 'tfo9p2tr': 'CP-1-10%', '321cm0qh': 'CP-3-10%'}\n",
+      "[<matplotlib.lines.Line2D object at 0x7f87c81e5fc0>, <matplotlib.lines.Line2D object at 0x7f87c81e5990>, <matplotlib.lines.Line2D object at 0x7f87c81e5e70>, <matplotlib.lines.Line2D object at 0x7f87c81e5e40>, <matplotlib.lines.Line2D object at 0x7f87c81e6860>, <matplotlib.lines.Line2D object at 0x7f87c81e6b00>, <matplotlib.lines.Line2D object at 0x7f87c81e6da0>, <matplotlib.lines.Line2D object at 0x7f87c81e7040>]\n",
+      "['GPT', 'GPT', 'Dipper', 'Dipper', 'CP-1-25%', 'CP-3-25%', 'CP-3-10%', 'CP-1-10%']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_grouped_series_data(\n",
+    "    data_df=df,\n",
+    "    x_col=\"idx_T\",\n",
+    "    y_col=\"avg\",\n",
+    "    x_name=\"Tokens (T)\",\n",
+    "    y_name=\"Z-Score\",\n",
+    "    x_lim=(50,1000),\n",
+    "    y_lim=(0,15),\n",
+    "    limit_x_series=995,\n",
+    "    limit_x_series_map={\"GPT\": 300, \"Dipper\": 800},\n",
+    "    series_id_col=\"id\",\n",
+    "    series_readable_id_col=\"name\",\n",
+    "    series_readable_id_map=name_extractor,\n",
+    "    special_color_map=color_map,\n",
+    "    sortby=\"mid\",\n",
+    "    legend_loc=\"lower right\",\n",
+    "    title=f\"Z-Score of Attacked Text as function of Tokens Observed\",\n",
+    "    save_path=f\"{figure_dir}/{fig_name}.pdf\" if save else None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>_defaultColorIndex</th>\n",
+       "      <th>avg</th>\n",
+       "      <th>id</th>\n",
+       "      <th>idx_T</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>0</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0.445924</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>1</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0.802980</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>2</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>13</td>\n",
+       "      <td>1.066178</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>3</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>13</td>\n",
+       "      <td>1.310162</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>4</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7002</th>\n",
+       "      <td>10</td>\n",
+       "      <td>3.921619</td>\n",
+       "      <td>i136o2qh</td>\n",
+       "      <td>996</td>\n",
+       "      <td>core_simple_1_200_1000_gen_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7003</th>\n",
+       "      <td>10</td>\n",
+       "      <td>3.921713</td>\n",
+       "      <td>i136o2qh</td>\n",
+       "      <td>997</td>\n",
+       "      <td>core_simple_1_200_1000_gen_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7004</th>\n",
+       "      <td>10</td>\n",
+       "      <td>3.921943</td>\n",
+       "      <td>i136o2qh</td>\n",
+       "      <td>998</td>\n",
+       "      <td>core_simple_1_200_1000_gen_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7005</th>\n",
+       "      <td>10</td>\n",
+       "      <td>3.906037</td>\n",
+       "      <td>i136o2qh</td>\n",
+       "      <td>999</td>\n",
+       "      <td>core_simple_1_200_1000_gen_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7006</th>\n",
+       "      <td>10</td>\n",
+       "      <td>3.944808</td>\n",
+       "      <td>i136o2qh</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>core_simple_1_200_1000_gen_eval</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7007 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      _defaultColorIndex       avg        id  idx_T  \\\n",
+       "0                     13  0.000000  sebf8zdh      0   \n",
+       "1                     13  0.445924  sebf8zdh      1   \n",
+       "2                     13  0.802980  sebf8zdh      2   \n",
+       "3                     13  1.066178  sebf8zdh      3   \n",
+       "4                     13  1.310162  sebf8zdh      4   \n",
+       "...                  ...       ...       ...    ...   \n",
+       "7002                  10  3.921619  i136o2qh    996   \n",
+       "7003                  10  3.921713  i136o2qh    997   \n",
+       "7004                  10  3.921943  i136o2qh    998   \n",
+       "7005                  10  3.906037  i136o2qh    999   \n",
+       "7006                  10  3.944808  i136o2qh   1000   \n",
+       "\n",
+       "                                         name  \n",
+       "0     core_simple_1_200_1000_dipper_high_eval  \n",
+       "1     core_simple_1_200_1000_dipper_high_eval  \n",
+       "2     core_simple_1_200_1000_dipper_high_eval  \n",
+       "3     core_simple_1_200_1000_dipper_high_eval  \n",
+       "4     core_simple_1_200_1000_dipper_high_eval  \n",
+       "...                                       ...  \n",
+       "7002          core_simple_1_200_1000_gen_eval  \n",
+       "7003          core_simple_1_200_1000_gen_eval  \n",
+       "7004          core_simple_1_200_1000_gen_eval  \n",
+       "7005          core_simple_1_200_1000_gen_eval  \n",
+       "7006          core_simple_1_200_1000_gen_eval  \n",
+       "\n",
+       "[7007 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig_name = \"baseline_attacked_winmax_scores_at_T_1000_base\"\n",
+    "\n",
+    "data_dir = f\"{INPUT_DIR}/figure_data/{fig_name}\"\n",
+    "\n",
+    "df = get_data_df(data_dir)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['core_simple_1_200_1000_dipper_high_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_3-25%_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_1-25%_eval'\n",
+      " 'core_simple_1_200_1000_gpt_p4_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_1-10%_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_3-10%_eval'\n",
+      " 'core_simple_1_200_1000_gen_eval']\n",
+      "dict_items([('sebf8zdh', 'core_simple_1_200_1000_dipper_high_eval'), ('qlczzu10', 'core_simple_1_200_1000_gen_cp_attack_3-25%_eval'), ('uoyt86q9', 'core_simple_1_200_1000_gen_cp_attack_1-25%_eval'), ('fai4a3qm', 'core_simple_1_200_1000_gpt_p4_eval'), ('tfo9p2tr', 'core_simple_1_200_1000_gen_cp_attack_1-10%_eval'), ('321cm0qh', 'core_simple_1_200_1000_gen_cp_attack_3-10%_eval'), ('i136o2qh', 'core_simple_1_200_1000_gen_eval')])\n",
+      "Used callable to map series_readable_ids\n",
+      "{'sebf8zdh': 'Dipper', 'qlczzu10': 'CP-3-25%', 'uoyt86q9': 'CP-1-25%', 'fai4a3qm': 'GPT', 'tfo9p2tr': 'CP-1-10%', '321cm0qh': 'CP-3-10%', 'i136o2qh': ''}\n",
+      "[<matplotlib.lines.Line2D object at 0x7f87c80c8580>, <matplotlib.lines.Line2D object at 0x7f87c80c82b0>, <matplotlib.lines.Line2D object at 0x7f87c80c8700>, <matplotlib.lines.Line2D object at 0x7f87c80c8d90>, <matplotlib.lines.Line2D object at 0x7f87c80c8c40>, <matplotlib.lines.Line2D object at 0x7f87c80c9180>, <matplotlib.lines.Line2D object at 0x7f87c80c9570>, <matplotlib.lines.Line2D object at 0x7f87c80c9810>]\n",
+      "['CP-3-10%', 'GPT', 'GPT', 'CP-3-25%', 'Dipper', 'Dipper', 'CP-1-10%', 'CP-1-25%']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_grouped_series_data(\n",
+    "    data_df=df,\n",
+    "    x_col=\"idx_T\",\n",
+    "    y_col=\"avg\",\n",
+    "    x_name=\"Tokens (T)\",\n",
+    "    y_name=\"WinMax Z-Score\",\n",
+    "    x_lim=(50,1000),\n",
+    "    y_lim=(0,15),\n",
+    "    limit_x_series=995,\n",
+    "    limit_x_series_map={\"GPT\": 300, \"Dipper\": 800},\n",
+    "    series_id_col=\"id\",\n",
+    "    series_readable_id_col=\"name\",\n",
+    "    series_readable_id_map=name_extractor,\n",
+    "    special_color_map=color_map,\n",
+    "    sortby=\"mid\",\n",
+    "    legend_loc=\"lower right\",\n",
+    "    title=f\"WinMax Z-Score of Human Text as function of Tokens Observed\",\n",
+    "    save_path=f\"{figure_dir}/{fig_name}.pdf\" if save else None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>_defaultColorIndex</th>\n",
+       "      <th>avg</th>\n",
+       "      <th>id</th>\n",
+       "      <th>idx_T</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>0</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0.423250</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>1</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0.915359</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>2</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>13</td>\n",
+       "      <td>1.297526</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>3</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>13</td>\n",
+       "      <td>1.571870</td>\n",
+       "      <td>sebf8zdh</td>\n",
+       "      <td>4</td>\n",
+       "      <td>core_simple_1_200_1000_dipper_high_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6155</th>\n",
+       "      <td>14</td>\n",
+       "      <td>5.878581</td>\n",
+       "      <td>321cm0qh</td>\n",
+       "      <td>996</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6156</th>\n",
+       "      <td>14</td>\n",
+       "      <td>5.870938</td>\n",
+       "      <td>321cm0qh</td>\n",
+       "      <td>997</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6157</th>\n",
+       "      <td>14</td>\n",
+       "      <td>5.901743</td>\n",
+       "      <td>321cm0qh</td>\n",
+       "      <td>998</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6158</th>\n",
+       "      <td>14</td>\n",
+       "      <td>5.604085</td>\n",
+       "      <td>321cm0qh</td>\n",
+       "      <td>999</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6159</th>\n",
+       "      <td>14</td>\n",
+       "      <td>5.366133</td>\n",
+       "      <td>321cm0qh</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>core_simple_1_200_1000_gen_cp_attack_3-10%_eval</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>6160 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      _defaultColorIndex       avg        id  idx_T  \\\n",
+       "0                     13  0.000000  sebf8zdh      0   \n",
+       "1                     13  0.423250  sebf8zdh      1   \n",
+       "2                     13  0.915359  sebf8zdh      2   \n",
+       "3                     13  1.297526  sebf8zdh      3   \n",
+       "4                     13  1.571870  sebf8zdh      4   \n",
+       "...                  ...       ...       ...    ...   \n",
+       "6155                  14  5.878581  321cm0qh    996   \n",
+       "6156                  14  5.870938  321cm0qh    997   \n",
+       "6157                  14  5.901743  321cm0qh    998   \n",
+       "6158                  14  5.604085  321cm0qh    999   \n",
+       "6159                  14  5.366133  321cm0qh   1000   \n",
+       "\n",
+       "                                                 name  \n",
+       "0             core_simple_1_200_1000_dipper_high_eval  \n",
+       "1             core_simple_1_200_1000_dipper_high_eval  \n",
+       "2             core_simple_1_200_1000_dipper_high_eval  \n",
+       "3             core_simple_1_200_1000_dipper_high_eval  \n",
+       "4             core_simple_1_200_1000_dipper_high_eval  \n",
+       "...                                               ...  \n",
+       "6155  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  \n",
+       "6156  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  \n",
+       "6157  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  \n",
+       "6158  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  \n",
+       "6159  core_simple_1_200_1000_gen_cp_attack_3-10%_eval  \n",
+       "\n",
+       "[6160 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig_name = \"baseline_attacked_winmax_scores_at_T_1000_attacked\"\n",
+    "\n",
+    "data_dir = f\"{INPUT_DIR}/figure_data/{fig_name}\"\n",
+    "\n",
+    "df = get_data_df(data_dir)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['core_simple_1_200_1000_dipper_high_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_3-25%_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_1-25%_eval'\n",
+      " 'core_simple_1_200_1000_gpt_p4_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_1-10%_eval'\n",
+      " 'core_simple_1_200_1000_gen_cp_attack_3-10%_eval']\n",
+      "dict_items([('sebf8zdh', 'core_simple_1_200_1000_dipper_high_eval'), ('qlczzu10', 'core_simple_1_200_1000_gen_cp_attack_3-25%_eval'), ('uoyt86q9', 'core_simple_1_200_1000_gen_cp_attack_1-25%_eval'), ('fai4a3qm', 'core_simple_1_200_1000_gpt_p4_eval'), ('tfo9p2tr', 'core_simple_1_200_1000_gen_cp_attack_1-10%_eval'), ('321cm0qh', 'core_simple_1_200_1000_gen_cp_attack_3-10%_eval')])\n",
+      "Used callable to map series_readable_ids\n",
+      "{'sebf8zdh': 'Dipper', 'qlczzu10': 'CP-3-25%', 'uoyt86q9': 'CP-1-25%', 'fai4a3qm': 'GPT', 'tfo9p2tr': 'CP-1-10%', '321cm0qh': 'CP-3-10%'}\n",
+      "[<matplotlib.lines.Line2D object at 0x7f87c816afb0>, <matplotlib.lines.Line2D object at 0x7f87c816a980>, <matplotlib.lines.Line2D object at 0x7f87c816b310>, <matplotlib.lines.Line2D object at 0x7f87c816b5b0>, <matplotlib.lines.Line2D object at 0x7f87c81694e0>, <matplotlib.lines.Line2D object at 0x7f87c816b670>, <matplotlib.lines.Line2D object at 0x7f87c816bd90>, <matplotlib.lines.Line2D object at 0x7f87c2f200a0>]\n",
+      "['GPT', 'GPT', 'CP-1-25%', 'CP-3-25%', 'Dipper', 'Dipper', 'CP-1-10%', 'CP-3-10%']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_grouped_series_data(\n",
+    "    data_df=df,\n",
+    "    x_col=\"idx_T\",\n",
+    "    y_col=\"avg\",\n",
+    "    x_name=\"Tokens (T)\",\n",
+    "    y_name=\"WinMax Z-Score\",\n",
+    "    x_lim=(50,1000),\n",
+    "    y_lim=(0,15),\n",
+    "    limit_x_series=995,\n",
+    "    limit_x_series_map={\"GPT\": 300, \"Dipper\": 800},\n",
+    "    series_id_col=\"id\",\n",
+    "    series_readable_id_col=\"name\",\n",
+    "    series_readable_id_map=name_extractor,\n",
+    "    special_color_map=color_map,\n",
+    "    sortby=\"mid\",\n",
+    "    legend_loc=\"lower right\",\n",
+    "    title=f\"WinMax Z-Score of Attacked Text as function of Tokens Observed\",\n",
+    "    save_path=f\"{figure_dir}/{fig_name}.pdf\" if save else None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "watermarking-dev",
+   "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": "3.10.9"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}