From 65e508b5f3f8fadbd872d6d2254b12d21b4e3e02 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bj=C3=B6rn=20Daase?= <bjoern@daase.net>
Date: Mon, 14 Mar 2022 11:42:17 +0100
Subject: [PATCH] Cleanup notebook

---
 .../visualizations-checkpoint.ipynb           |  314 -----
 notebooks/visualizations.ipynb                | 1062 +----------------
 2 files changed, 43 insertions(+), 1333 deletions(-)
 delete mode 100644 notebooks/.ipynb_checkpoints/visualizations-checkpoint.ipynb

diff --git a/notebooks/.ipynb_checkpoints/visualizations-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/visualizations-checkpoint.ipynb
deleted file mode 100644
index eedbb11..0000000
--- a/notebooks/.ipynb_checkpoints/visualizations-checkpoint.ipynb
+++ /dev/null
@@ -1,314 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "e0f4bd60-51d8-425b-a8e6-6e3d251d24c7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.ticker as tck\n",
-    "from pathlib import Path\n",
-    "\n",
-    "colors = [\"#E69F00\" ,\"#009E73\" ,\"#0072B2\" ,\"#999999\", \"#56B4E9\", \"#F0E442\", \"#CC79A7\", \"#D55E00\"]\n",
-    "markers = [\"v\", \"x\", \"o\", \"^\", \"s\", \"<\", \">\", \"8\"]\n",
-    "\n",
-    "plt.rcParams.update({\n",
-    "        'font.size': '18',\n",
-    "        'svg.fonttype': 'none'\n",
-    "})\n",
-    "\n",
-    "plt.rc('axes', axisbelow=True)\n",
-    "\n",
-    "%config Completer.use_jedi = False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "9364a4ce",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_line_chart(x,y, labels, xlabel=\"\", ylabel=\"\", name=\"result.png\", legend_location=\"best\"):\n",
-    "    linewidth = 2\n",
-    "    fig = plt.subplots(figsize =(6, 2.5))\n",
-    "\n",
-    "    plt.minorticks_on()\n",
-    "    \n",
-    "    plt.grid(color='lightgrey', linestyle='-', linewidth=1, which=\"minor\")\n",
-    "    plt.grid(color='grey', linestyle='-', linewidth=1, which=\"major\")\n",
-    "    \n",
-    "    for x_values, y_values, label, color in zip(x, y, labels, colors):\n",
-    "        plt.plot(x_values, y_values, label=label, linewidth=linewidth, color=color)\n",
-    "        \n",
-    "        \n",
-    "    plt.xlabel(xlabel)\n",
-    "    \n",
-    "    plt.ylabel(ylabel)\n",
-    "    \n",
-    "    handles, labels = plt.gca().get_legend_handles_labels()\n",
-    "    # sort both labels and handles by labels\n",
-    "    labels, handles = zip(*sorted(zip(labels, handles), key=lambda t: int(t[0]) if t[0].isnumeric() else 0))\n",
-    "    if legend_location == \"upper center\":\n",
-    "        ncol = 4\n",
-    "        height = (len(lables) / ncol + 1) * 0.2 + 1\n",
-    "        plt.legend(handles, labels, frameon=False, ncol=ncol, loc=\"upper center\", bbox_to_anchor=(0.5,height), fontsize=14 ,labelspacing=1, columnspacing=2)\n",
-    "    else:        \n",
-    "        plt.legend(handles, labels, frameon=False, loc=\"lower right\", bbox_to_anchor=(0.85,0), fontsize=10 ,labelspacing=1, columnspacing=2)\n",
-    "    \n",
-    "    plt.savefig(name, bbox_inches='tight')\n",
-    "    plt.show()\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "24ebb2a2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def add_labels(x,y, precision):\n",
-    "    overset = 0.1 * max(y)\n",
-    "    for i in range(len(x)):\n",
-    "        if precision == -1:\n",
-    "            plt.text(i + 0.25, y[i] + overset, round(y[i]), ha = 'center')\n",
-    "        else:\n",
-    "            plt.text(i + 0.25, y[i] + overset, round(y[i], precision), ha = 'center')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "0a0d1fa6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_bar_chart(x,y, xlabel=\"\", ylabel=\"\", name=\"result.png\", lables=True, lableprecision=-1, yscale=\"linear\"):\n",
-    "    barWidth = 0.25\n",
-    "    fig, ax = plt.subplots(figsize =(6, 2.5))\n",
-    "    \n",
-    "    plt.minorticks_on()\n",
-    "    \n",
-    "    plt.grid(color='lightgrey', linestyle='-', linewidth=1, which=\"minor\", axis=\"y\")\n",
-    "    plt.grid(color='grey', linestyle='-', linewidth=1, which=\"major\", axis=\"y\")\n",
-    "    \n",
-    "    ax.set_yscale(yscale)\n",
-    "    \n",
-    "    y = [val for _,val in sorted(zip(x,y))]\n",
-    "    x.sort()\n",
-    "    \n",
-    "    x = [str(int(i)) if not pd.isna(i) else \"Never\" for i in x]\n",
-    "\n",
-    "    \n",
-    "    num_elements_in_x = len(x)\n",
-    "\n",
-    "    # Set position of bar on X axis\\n\",\n",
-    "    br1 = list(map(lambda x: x + barWidth, np.arange(num_elements_in_x)))\n",
-    "\n",
-    "    # Make the plot\\n\",\n",
-    "    plt.bar(br1, y, color=colors, width=barWidth,edgecolor ='black')\n",
-    "    \n",
-    "    if lables:\n",
-    "        add_labels(br1, y, lableprecision)\n",
-    "    \n",
-    "    # Adding Xticks\\n\",\n",
-    "    plt.xlabel(xlabel)\n",
-    "    plt.xticks([r + barWidth for r in range(num_elements_in_x)], x, minor=False)\n",
-    "    plt.gca().xaxis.set_minor_locator(tck.NullLocator())\n",
-    "    \n",
-    "    plt.ylabel(ylabel)\n",
-    "\n",
-    "    ylim_max = max(y) * 1.3\n",
-    "    plt.gca().set_ylim(None, ylim_max)\n",
-    "\n",
-    "    plt.savefig(name, bbox_inches='tight')\n",
-    "    plt.show()\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "b9948c35-5b85-4367-b0eb-b1e0ecf60303",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def combine_column_and_file_name(column, file):\n",
-    "    return f'{file}_{column}'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "171b5bc5-0a01-40f3-bcc3-c13cc4ee8cd1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_data(columns: list[str]):\n",
-    "    base_dir = Path('../experiment')\n",
-    "    csv_files = base_dir.glob('*.csv')\n",
-    "    \n",
-    "    dfs = {file.name : pd.read_csv(file) for file in csv_files}\n",
-    "    \n",
-    "    res_df = pd.DataFrame()\n",
-    "    for file_name, df in dfs.items():\n",
-    "        for column in columns:\n",
-    "            res_df[combine_column_and_file_name(file_name, column)] = df[column]\n",
-    "    return res_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1dee77f6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = get_data([\"validate.DIV2K.psnr_scale_2\", \"Epoch\"])\n",
-    "filtered_x = data.filter(regex=(\"Epoch_rfdn.*_600.csv\")).head(300).transpose()\n",
-    "filtered_y = data.filter(regex=(\"validate\\.DIV2K.psnr_scale_2.*_600.csv\")).head(300).transpose()\n",
-    "lables = [\"RFDN\", \"RFDN + RepVgg\"]\n",
-    "make_line_chart(filtered_x.values, filtered_y.values, lables, \"Epoch\", \"DIV2K PSNR\", \"epoch_psnr_scale.svg\", legend_location=\"upper center\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cc6f6ef7-fc8c-46fc-a157-d36379cbafd6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = get_data([\"validate.mean_forward_pass_time\", \"config.epochs_before_pruning\"])\n",
-    "filtered_y = data.filter(regex=(\"validate\\.mean_forward_pass_time_rfdn_advanced_600_epochs_before_pruning.*\")).mean()\n",
-    "filtered_x = data.filter(regex=(\"config\\.epochs_before_pruning_rfdn_advanced_600_epochs_before_pruning.*\")).head(1)\n",
-    "make_bar_chart(filtered_x.values[0], filtered_y.values * 1000, \"Epochs Before Pruning\", \"Time [ms]\", \"epochs_before_pruning_average_validation_time.svg\", True, lableprecision=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9282843f",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "data = get_data([\"num_parameters\", \"config.epochs_before_pruning\"])\n",
-    "filtered_y = data.filter(regex=(\"num_parameters_rfdn_advanced_600_epochs_before_pruning.*\")).min()\n",
-    "filtered_x = data.filter(regex=(\"config\\.epochs_before_pruning_rfdn_advanced_600_epochs_before_pruning.*\")).head(1)\n",
-    "make_bar_chart(filtered_x.values[0], filtered_y.values / 1000, \"Epochs Before Pruning\", \"Parameters [\\# in 1000]\", \"epochs_before_pruning_final_number_of_parameters.svg\", True, lableprecision=-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "510af0bf",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "data = get_data([\"num_parameters\", \"Epoch\"])\n",
-    "filtered_x = data.filter(regex=(\"Epoch_rfdn_advanced_600_epochs_before_pruning.*\")).head(600).transpose()\n",
-    "filtered_y = data.filter(regex=(\"num_parameters_rfdn_advanced_600_epochs_before_pruning.*\")).head(600).transpose()\n",
-    "lables = [140, 180, 19, 250, 48, 98, \"Never\"]\n",
-    "make_line_chart(filtered_x.values, filtered_y.values / 1000, lables, \"Epoch\", \"Parameters [\\# in 1000]\", \"epochs_before_pruning_number_of_parameters.svg\", legend_location=\"upper center\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "79334f28",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = get_data([\"validate.DIV2K.psnr_scale_2\", \"Epoch\"])\n",
-    "filtered_x = data.filter(regex=(\"Epoch_rfdn_advanced_600_epochs_before_pruning.*\")).head(600).transpose()\n",
-    "filtered_y = data.filter(regex=(\"validate\\.DIV2K.psnr_scale_2_rfdn_advanced_600_epochs_before_pruning.*\")).head(600).transpose()\n",
-    "lables = [140, 180, 19, 250, 48, 98, \"Never\"]\n",
-    "make_line_chart(filtered_x.values, filtered_y.values, lables, \"Epoch\", \"DIV2K PSNR\", \"epochs_before_pruning_div2k_psnr.svg\", legend_location=\"upper center\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e41bb476",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = get_data([\"test.BSD100.psnr_scale_2\", \"config.batch_size_test\"])\n",
-    "filtered_y = data.filter(regex=(\"test\\.BSD100\\.psnr_scale_2_rfdn_advanced_batch_size_test.*\")).head(1)\n",
-    "filtered_x = data.filter(regex=(\"config\\.batch_size_test_rfdn_advanced_batch_size_test.*\")).head(1)\n",
-    "make_bar_chart(filtered_x.values[0], filtered_y.values[0], \"Batch Size\", \"BSD100 PSNR\", \"batch_size_test_BSD100_PSNR.svg\", True, lableprecision=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "8704d843",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x180 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data = get_data([\"validate.mean_forward_pass_time\", \"config.batch_size_test\"])\n",
-    "filtered_y = data.filter(regex=(\"validate\\.mean_forward_pass_time_rfdn_advanced_batch_size*\")).mean()\n",
-    "filtered_x = data.filter(regex=(\"config\\.batch_size_test_rfdn_advanced_batch_size_test.*\")).head(1)\n",
-    "make_bar_chart(filtered_x.values[0], filtered_y.values * 1000, \"Batch Size\", \"Time [ms]\", \"batch_size_pruning_average_validation_time.svg\", True, lableprecision=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b9b2a0a8",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d4af58cf",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.10.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/visualizations.ipynb b/notebooks/visualizations.ipynb
index 92edb02..c34105b 100644
--- a/notebooks/visualizations.ipynb
+++ b/notebooks/visualizations.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "e0f4bd60-51d8-425b-a8e6-6e3d251d24c7",
    "metadata": {},
    "outputs": [],
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "9364a4ce",
    "metadata": {},
    "outputs": [],
@@ -68,7 +68,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "24ebb2a2",
    "metadata": {},
    "outputs": [],
@@ -84,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "0a0d1fa6",
    "metadata": {},
    "outputs": [],
@@ -132,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "b9948c35-5b85-4367-b0eb-b1e0ecf60303",
    "metadata": {},
    "outputs": [],
@@ -143,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "171b5bc5-0a01-40f3-bcc3-c13cc4ee8cd1",
    "metadata": {},
    "outputs": [],
@@ -164,616 +164,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "1dee77f6",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>validate.BSD100.psnr_scale_2_rfdn_advanced_600_epochs_no_batchnorm_pruning_512.csv</th>\n",
-       "      <th>Epoch_rfdn_advanced_600_epochs_no_batchnorm_pruning_512.csv</th>\n",
-       "      <th>validate.BSD100.psnr_scale_2_rfdn_advanced_600_epochs_no_batchnorm_pruning_256.csv</th>\n",
-       "      <th>Epoch_rfdn_advanced_600_epochs_no_batchnorm_pruning_256.csv</th>\n",
-       "      <th>validate.BSD100.psnr_scale_2_rfdn_advanced_600_epochs_no_batchnorm_pruning_128.csv</th>\n",
-       "      <th>Epoch_rfdn_advanced_600_epochs_no_batchnorm_pruning_128.csv</th>\n",
-       "      <th>validate.BSD100.psnr_scale_2_rfdn_advanced_600_epochs_no_batchnorm_pruning_64.csv</th>\n",
-       "      <th>Epoch_rfdn_advanced_600_epochs_no_batchnorm_pruning_64.csv</th>\n",
-       "      <th>validate.BSD100.psnr_scale_2_rfdn_advanced_600_epochs_no_batchnorm_pruning_32.csv</th>\n",
-       "      <th>Epoch_rfdn_advanced_600_epochs_no_batchnorm_pruning_32.csv</th>\n",
-       "      <th>...</th>\n",
-       "      <th>validate.BSD100.psnr_scale_2_rfdn_advanced_batchsize_2.csv</th>\n",
-       "      <th>Epoch_rfdn_advanced_batchsize_2.csv</th>\n",
-       "      <th>validate.BSD100.psnr_scale_2_rfdn_advanced_batchsize_32.csv</th>\n",
-       "      <th>Epoch_rfdn_advanced_batchsize_32.csv</th>\n",
-       "      <th>validate.BSD100.psnr_scale_2_rfdn_advanced_batchsize_4.csv</th>\n",
-       "      <th>Epoch_rfdn_advanced_batchsize_4.csv</th>\n",
-       "      <th>validate.BSD100.psnr_scale_2_rfdn_advanced_batchsize_64.csv</th>\n",
-       "      <th>Epoch_rfdn_advanced_batchsize_64.csv</th>\n",
-       "      <th>validate.BSD100.psnr_scale_2_rfdn_advanced_batchsize_8.csv</th>\n",
-       "      <th>Epoch_rfdn_advanced_batchsize_8.csv</th>\n",
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-       "      <th>0</th>\n",
-       "      <td>24.915600</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>24.920155</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>24.971610</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>24.918652</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>24.964062</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>28.712829</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>23.354594</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>28.634357</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>21.361633</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>28.043738</td>\n",
-       "      <td>1.0</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>27.640627</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>27.511779</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>27.613771</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>27.624159</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>27.562637</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>29.030615</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>25.195679</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>28.972371</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>23.402738</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>28.767008</td>\n",
-       "      <td>2.0</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>28.499629</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>28.511696</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>28.510866</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>28.507692</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>28.462854</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>29.193649</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>27.348171</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>29.098409</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>24.330820</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>28.914816</td>\n",
-       "      <td>3.0</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>28.820583</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>28.811745</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>28.833357</td>\n",
-       "      <td>4.0</td>\n",
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-       "      <td>4.0</td>\n",
-       "      <td>28.812940</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>29.598712</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>28.368428</td>\n",
-       "      <td>4.0</td>\n",
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-       "      <td>4.0</td>\n",
-       "      <td>25.281856</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>29.040610</td>\n",
-       "      <td>4.0</td>\n",
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-       "      <th>4</th>\n",
-       "      <td>28.951146</td>\n",
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-       "      <td>28.949492</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>28.984157</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>29.018170</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>29.029472</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>29.757564</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>28.678149</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>29.692334</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>26.328736</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>29.145673</td>\n",
-       "      <td>5.0</td>\n",
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-       "      <td>...</td>\n",
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-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>596.0</td>\n",
-       "      <td>30.410838</td>\n",
-       "      <td>596.0</td>\n",
-       "      <td>30.330705</td>\n",
-       "      <td>596.0</td>\n",
-       "      <td>30.342925</td>\n",
-       "      <td>596.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>596</th>\n",
-       "      <td>30.461159</td>\n",
-       "      <td>597.0</td>\n",
-       "      <td>30.428478</td>\n",
-       "      <td>597.0</td>\n",
-       "      <td>30.413903</td>\n",
-       "      <td>597.0</td>\n",
-       "      <td>30.335380</td>\n",
-       "      <td>597.0</td>\n",
-       "      <td>30.337302</td>\n",
-       "      <td>597.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>597</th>\n",
-       "      <td>30.466817</td>\n",
-       "      <td>598.0</td>\n",
-       "      <td>30.427417</td>\n",
-       "      <td>598.0</td>\n",
-       "      <td>30.415242</td>\n",
-       "      <td>598.0</td>\n",
-       "      <td>30.334592</td>\n",
-       "      <td>598.0</td>\n",
-       "      <td>30.333258</td>\n",
-       "      <td>598.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>598</th>\n",
-       "      <td>30.466124</td>\n",
-       "      <td>599.0</td>\n",
-       "      <td>30.428972</td>\n",
-       "      <td>599.0</td>\n",
-       "      <td>30.414175</td>\n",
-       "      <td>599.0</td>\n",
-       "      <td>30.327805</td>\n",
-       "      <td>599.0</td>\n",
-       "      <td>30.339802</td>\n",
-       "      <td>599.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>599</th>\n",
-       "      <td>30.469207</td>\n",
-       "      <td>600.0</td>\n",
-       "      <td>30.422152</td>\n",
-       "      <td>600.0</td>\n",
-       "      <td>30.419014</td>\n",
-       "      <td>600.0</td>\n",
-       "      <td>30.335648</td>\n",
-       "      <td>600.0</td>\n",
-       "      <td>30.340721</td>\n",
-       "      <td>600.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>600 rows × 48 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     validate.BSD100.psnr_scale_2_rfdn_advanced_600_epochs_no_batchnorm_pruning_512.csv  \\\n",
-       "0                                            24.915600                                    \n",
-       "1                                            27.640627                                    \n",
-       "2                                            28.499629                                    \n",
-       "3                                            28.820583                                    \n",
-       "4                                            28.951146                                    \n",
-       "..                                                 ...                                    \n",
-       "595                                          30.463720                                    \n",
-       "596                                          30.461159                                    \n",
-       "597                                          30.466817                                    \n",
-       "598                                          30.466124                                    \n",
-       "599                                          30.469207                                    \n",
-       "\n",
-       "     Epoch_rfdn_advanced_600_epochs_no_batchnorm_pruning_512.csv  \\\n",
-       "0                                                  1.0             \n",
-       "1                                                  2.0             \n",
-       "2                                                  3.0             \n",
-       "3                                                  4.0             \n",
-       "4                                                  5.0             \n",
-       "..                                                 ...             \n",
-       "595                                              596.0             \n",
-       "596                                              597.0             \n",
-       "597                                              598.0             \n",
-       "598                                              599.0             \n",
-       "599                                              600.0             \n",
-       "\n",
-       "     validate.BSD100.psnr_scale_2_rfdn_advanced_600_epochs_no_batchnorm_pruning_256.csv  \\\n",
-       "0                                            24.920155                                    \n",
-       "1                                            27.511779                                    \n",
-       "2                                            28.511696                                    \n",
-       "3                                            28.811745                                    \n",
-       "4                                            28.949492                                    \n",
-       "..                                                 ...                                    \n",
-       "595                                          30.425443                                    \n",
-       "596                                          30.428478                                    \n",
-       "597                                          30.427417                                    \n",
-       "598                                          30.428972                                    \n",
-       "599                                          30.422152                                    \n",
-       "\n",
-       "     Epoch_rfdn_advanced_600_epochs_no_batchnorm_pruning_256.csv  \\\n",
-       "0                                                  1.0             \n",
-       "1                                                  2.0             \n",
-       "2                                                  3.0             \n",
-       "3                                                  4.0             \n",
-       "4                                                  5.0             \n",
-       "..                                                 ...             \n",
-       "595                                              596.0             \n",
-       "596                                              597.0             \n",
-       "597                                              598.0             \n",
-       "598                                              599.0             \n",
-       "599                                              600.0             \n",
-       "\n",
-       "     validate.BSD100.psnr_scale_2_rfdn_advanced_600_epochs_no_batchnorm_pruning_128.csv  \\\n",
-       "0                                            24.971610                                    \n",
-       "1                                            27.613771                                    \n",
-       "2                                            28.510866                                    \n",
-       "3                                            28.833357                                    \n",
-       "4                                            28.984157                                    \n",
-       "..                                                 ...                                    \n",
-       "595                                          30.410838                                    \n",
-       "596                                          30.413903                                    \n",
-       "597                                          30.415242                                    \n",
-       "598                                          30.414175                                    \n",
-       "599                                          30.419014                                    \n",
-       "\n",
-       "     Epoch_rfdn_advanced_600_epochs_no_batchnorm_pruning_128.csv  \\\n",
-       "0                                                  1.0             \n",
-       "1                                                  2.0             \n",
-       "2                                                  3.0             \n",
-       "3                                                  4.0             \n",
-       "4                                                  5.0             \n",
-       "..                                                 ...             \n",
-       "595                                              596.0             \n",
-       "596                                              597.0             \n",
-       "597                                              598.0             \n",
-       "598                                              599.0             \n",
-       "599                                              600.0             \n",
-       "\n",
-       "     validate.BSD100.psnr_scale_2_rfdn_advanced_600_epochs_no_batchnorm_pruning_64.csv  \\\n",
-       "0                                            24.918652                                   \n",
-       "1                                            27.624159                                   \n",
-       "2                                            28.507692                                   \n",
-       "3                                            28.812188                                   \n",
-       "4                                            29.018170                                   \n",
-       "..                                                 ...                                   \n",
-       "595                                          30.330705                                   \n",
-       "596                                          30.335380                                   \n",
-       "597                                          30.334592                                   \n",
-       "598                                          30.327805                                   \n",
-       "599                                          30.335648                                   \n",
-       "\n",
-       "     Epoch_rfdn_advanced_600_epochs_no_batchnorm_pruning_64.csv  \\\n",
-       "0                                                  1.0            \n",
-       "1                                                  2.0            \n",
-       "2                                                  3.0            \n",
-       "3                                                  4.0            \n",
-       "4                                                  5.0            \n",
-       "..                                                 ...            \n",
-       "595                                              596.0            \n",
-       "596                                              597.0            \n",
-       "597                                              598.0            \n",
-       "598                                              599.0            \n",
-       "599                                              600.0            \n",
-       "\n",
-       "     validate.BSD100.psnr_scale_2_rfdn_advanced_600_epochs_no_batchnorm_pruning_32.csv  \\\n",
-       "0                                            24.964062                                   \n",
-       "1                                            27.562637                                   \n",
-       "2                                            28.462854                                   \n",
-       "3                                            28.812940                                   \n",
-       "4                                            29.029472                                   \n",
-       "..                                                 ...                                   \n",
-       "595                                          30.342925                                   \n",
-       "596                                          30.337302                                   \n",
-       "597                                          30.333258                                   \n",
-       "598                                          30.339802                                   \n",
-       "599                                          30.340721                                   \n",
-       "\n",
-       "     Epoch_rfdn_advanced_600_epochs_no_batchnorm_pruning_32.csv  ...  \\\n",
-       "0                                                  1.0           ...   \n",
-       "1                                                  2.0           ...   \n",
-       "2                                                  3.0           ...   \n",
-       "3                                                  4.0           ...   \n",
-       "4                                                  5.0           ...   \n",
-       "..                                                 ...           ...   \n",
-       "595                                              596.0           ...   \n",
-       "596                                              597.0           ...   \n",
-       "597                                              598.0           ...   \n",
-       "598                                              599.0           ...   \n",
-       "599                                              600.0           ...   \n",
-       "\n",
-       "     validate.BSD100.psnr_scale_2_rfdn_advanced_batchsize_2.csv  \\\n",
-       "0                                            28.712829            \n",
-       "1                                            29.030615            \n",
-       "2                                            29.193649            \n",
-       "3                                            29.598712            \n",
-       "4                                            29.757564            \n",
-       "..                                                 ...            \n",
-       "595                                                NaN            \n",
-       "596                                                NaN            \n",
-       "597                                                NaN            \n",
-       "598                                                NaN            \n",
-       "599                                                NaN            \n",
-       "\n",
-       "     Epoch_rfdn_advanced_batchsize_2.csv  \\\n",
-       "0                                    1.0   \n",
-       "1                                    2.0   \n",
-       "2                                    3.0   \n",
-       "3                                    4.0   \n",
-       "4                                    5.0   \n",
-       "..                                   ...   \n",
-       "595                                  NaN   \n",
-       "596                                  NaN   \n",
-       "597                                  NaN   \n",
-       "598                                  NaN   \n",
-       "599                                  NaN   \n",
-       "\n",
-       "     validate.BSD100.psnr_scale_2_rfdn_advanced_batchsize_32.csv  \\\n",
-       "0                                            23.354594             \n",
-       "1                                            25.195679             \n",
-       "2                                            27.348171             \n",
-       "3                                            28.368428             \n",
-       "4                                            28.678149             \n",
-       "..                                                 ...             \n",
-       "595                                                NaN             \n",
-       "596                                                NaN             \n",
-       "597                                                NaN             \n",
-       "598                                                NaN             \n",
-       "599                                                NaN             \n",
-       "\n",
-       "     Epoch_rfdn_advanced_batchsize_32.csv  \\\n",
-       "0                                     1.0   \n",
-       "1                                     2.0   \n",
-       "2                                     3.0   \n",
-       "3                                     4.0   \n",
-       "4                                     5.0   \n",
-       "..                                    ...   \n",
-       "595                                   NaN   \n",
-       "596                                   NaN   \n",
-       "597                                   NaN   \n",
-       "598                                   NaN   \n",
-       "599                                   NaN   \n",
-       "\n",
-       "     validate.BSD100.psnr_scale_2_rfdn_advanced_batchsize_4.csv  \\\n",
-       "0                                            28.634357            \n",
-       "1                                            28.972371            \n",
-       "2                                            29.098409            \n",
-       "3                                            29.468512            \n",
-       "4                                            29.692334            \n",
-       "..                                                 ...            \n",
-       "595                                                NaN            \n",
-       "596                                                NaN            \n",
-       "597                                                NaN            \n",
-       "598                                                NaN            \n",
-       "599                                                NaN            \n",
-       "\n",
-       "     Epoch_rfdn_advanced_batchsize_4.csv  \\\n",
-       "0                                    1.0   \n",
-       "1                                    2.0   \n",
-       "2                                    3.0   \n",
-       "3                                    4.0   \n",
-       "4                                    5.0   \n",
-       "..                                   ...   \n",
-       "595                                  NaN   \n",
-       "596                                  NaN   \n",
-       "597                                  NaN   \n",
-       "598                                  NaN   \n",
-       "599                                  NaN   \n",
-       "\n",
-       "     validate.BSD100.psnr_scale_2_rfdn_advanced_batchsize_64.csv  \\\n",
-       "0                                            21.361633             \n",
-       "1                                            23.402738             \n",
-       "2                                            24.330820             \n",
-       "3                                            25.281856             \n",
-       "4                                            26.328736             \n",
-       "..                                                 ...             \n",
-       "595                                                NaN             \n",
-       "596                                                NaN             \n",
-       "597                                                NaN             \n",
-       "598                                                NaN             \n",
-       "599                                                NaN             \n",
-       "\n",
-       "     Epoch_rfdn_advanced_batchsize_64.csv  \\\n",
-       "0                                     1.0   \n",
-       "1                                     2.0   \n",
-       "2                                     3.0   \n",
-       "3                                     4.0   \n",
-       "4                                     5.0   \n",
-       "..                                    ...   \n",
-       "595                                   NaN   \n",
-       "596                                   NaN   \n",
-       "597                                   NaN   \n",
-       "598                                   NaN   \n",
-       "599                                   NaN   \n",
-       "\n",
-       "     validate.BSD100.psnr_scale_2_rfdn_advanced_batchsize_8.csv  \\\n",
-       "0                                            28.043738            \n",
-       "1                                            28.767008            \n",
-       "2                                            28.914816            \n",
-       "3                                            29.040610            \n",
-       "4                                            29.145673            \n",
-       "..                                                 ...            \n",
-       "595                                                NaN            \n",
-       "596                                                NaN            \n",
-       "597                                                NaN            \n",
-       "598                                                NaN            \n",
-       "599                                                NaN            \n",
-       "\n",
-       "     Epoch_rfdn_advanced_batchsize_8.csv  \n",
-       "0                                    1.0  \n",
-       "1                                    2.0  \n",
-       "2                                    3.0  \n",
-       "3                                    4.0  \n",
-       "4                                    5.0  \n",
-       "..                                   ...  \n",
-       "595                                  NaN  \n",
-       "596                                  NaN  \n",
-       "597                                  NaN  \n",
-       "598                                  NaN  \n",
-       "599                                  NaN  \n",
-       "\n",
-       "[600 rows x 48 columns]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "not enough values to unpack (expected 2, got 0)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m/home/bjoern/HPI/Master/Machine_Intellgence_with_Deep_Learning/Efficient-Image-Super-Resolution/notebooks/visualizations.ipynb Cell 7'\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/bjoern/HPI/Master/Machine_Intellgence_with_Deep_Learning/Efficient-Image-Super-Resolution/notebooks/visualizations.ipynb#ch0000006?line=3'>4</a>\u001b[0m filtered_y \u001b[39m=\u001b[39m data\u001b[39m.\u001b[39mfilter(regex\u001b[39m=\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mvalidate\u001b[39m\u001b[39m\\\u001b[39m\u001b[39m.BSD100.psnr_scale_2.*_600.csv\u001b[39m\u001b[39m\"\u001b[39m))\u001b[39m.\u001b[39mhead(\u001b[39m300\u001b[39m)\u001b[39m.\u001b[39mtranspose()\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/bjoern/HPI/Master/Machine_Intellgence_with_Deep_Learning/Efficient-Image-Super-Resolution/notebooks/visualizations.ipynb#ch0000006?line=4'>5</a>\u001b[0m lables \u001b[39m=\u001b[39m [\u001b[39m\"\u001b[39m\u001b[39mRFDN\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mRFDN + RepVgg - Batch Norm\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mRFDN + RepVgg\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/bjoern/HPI/Master/Machine_Intellgence_with_Deep_Learning/Efficient-Image-Super-Resolution/notebooks/visualizations.ipynb#ch0000006?line=5'>6</a>\u001b[0m make_line_chart(filtered_x\u001b[39m.\u001b[39;49mvalues, filtered_y\u001b[39m.\u001b[39;49mvalues, lables, \u001b[39m\"\u001b[39;49m\u001b[39mEpoch\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39mBSD100 PSNR\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39mepoch_psnr_scale.svg\u001b[39;49m\u001b[39m\"\u001b[39;49m, legend_location\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mupper center\u001b[39;49m\u001b[39m\"\u001b[39;49m, ncol\u001b[39m=\u001b[39;49m\u001b[39m2\u001b[39;49m, columnspacing\u001b[39m=\u001b[39;49m\u001b[39m-\u001b[39;49m\u001b[39m10\u001b[39;49m)\n",
-      "\u001b[1;32m/home/bjoern/HPI/Master/Machine_Intellgence_with_Deep_Learning/Efficient-Image-Super-Resolution/notebooks/visualizations.ipynb Cell 2'\u001b[0m in \u001b[0;36mmake_line_chart\u001b[0;34m(x, y, labels, xlabel, ylabel, name, legend_location, ncol, columnspacing)\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/bjoern/HPI/Master/Machine_Intellgence_with_Deep_Learning/Efficient-Image-Super-Resolution/notebooks/visualizations.ipynb#ch0000001?line=17'>18</a>\u001b[0m handles, labels \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39mgca()\u001b[39m.\u001b[39mget_legend_handles_labels()\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/bjoern/HPI/Master/Machine_Intellgence_with_Deep_Learning/Efficient-Image-Super-Resolution/notebooks/visualizations.ipynb#ch0000001?line=18'>19</a>\u001b[0m \u001b[39m# sort both labels and handles by labels\u001b[39;00m\n\u001b[0;32m---> <a href='vscode-notebook-cell:/home/bjoern/HPI/Master/Machine_Intellgence_with_Deep_Learning/Efficient-Image-Super-Resolution/notebooks/visualizations.ipynb#ch0000001?line=19'>20</a>\u001b[0m labels, handles \u001b[39m=\u001b[39m \u001b[39mzip\u001b[39m(\u001b[39m*\u001b[39m\u001b[39msorted\u001b[39m(\u001b[39mzip\u001b[39m(labels, handles), key\u001b[39m=\u001b[39m\u001b[39mlambda\u001b[39;00m t: \u001b[39mint\u001b[39m(t[\u001b[39m0\u001b[39m]) \u001b[39mif\u001b[39;00m t[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39misnumeric() \u001b[39melse\u001b[39;00m math\u001b[39m.\u001b[39minf))\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/bjoern/HPI/Master/Machine_Intellgence_with_Deep_Learning/Efficient-Image-Super-Resolution/notebooks/visualizations.ipynb#ch0000001?line=20'>21</a>\u001b[0m \u001b[39mif\u001b[39;00m legend_location \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mupper center\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/bjoern/HPI/Master/Machine_Intellgence_with_Deep_Learning/Efficient-Image-Super-Resolution/notebooks/visualizations.ipynb#ch0000001?line=21'>22</a>\u001b[0m     ncol \u001b[39m=\u001b[39m ncol\n",
-      "\u001b[0;31mValueError\u001b[0m: not enough values to unpack (expected 2, got 0)"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x180 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "data = get_data([\"validate.BSD100.psnr_scale_2\", \"Epoch\"])\n",
     "filtered_x = data.filter(regex=(\"Epoch_rfdn.*_600.csv\")).head(300).transpose()\n",
@@ -784,23 +178,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "cc6f6ef7-fc8c-46fc-a157-d36379cbafd6",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x180 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "data = get_data([\"validate.mean_forward_pass_time\", \"config.pruning_interval\"])\n",
     "filtered_y = data.filter(regex=(\"validate\\.mean_forward_pass_time_rfdn_advanced_600_epochs_no_batchnorm_pruning_([0-9]*|none).csv\")).mean()\n",
@@ -810,61 +191,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "9282843f",
    "metadata": {
     "scrolled": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[512. 256. 128.  64.  32.  16.   8.  nan]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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n8+677/Lpp59y4YUXMnz4cK91Zs2aBeD33qIKFSpw/fXXe5W78r01bNjQ53JVfARqjJ4GlmFnds2XMWYiNn+dUkr51blzZ3766SfmzJnDwYMHiYmJoVGjRjzyyCOMHj2a2NjYPPWPHz/O/PnzqV+/PldddVWUos5fUv1Etu/yzu7tqUG9+mzbuaMIIip5NB1QPjQdkFIqGCLCyvvmB6zXZlKfQqXZKSxNB6SUUkrlQxsjpZRSUaeNkVJKqajTxkipUuzYsWOkpKQgItx55515lo0fPx4R8fl44oknvLa1b98+hgwZQmJiIhUrVqRBgwb87W9/48iRI0W0N6o009x0SpViwUwwN3XqVBISEvKUXXDBBXl+/+2337jwwgvZvXs3gwcPJjU1lfXr1zNjxgyWL1/OV1995Te5qVLB0MbIB83arUqDn376iWnTpjFq1CieeOIJr8zVv/32GwDNmjWjXr16edbNycnJU3fy5Mls27aNyZMn061bNwAuvvhiEhMTueeee7jnnnsYPHhwEexVyVdSvlOKOk4d2p0PHdqtSqqcnBzatWvH2WefzfTp00lJSfGa7XT8+PFMmDCBLVu2ULNmTeLi4ihf3vf/p+eddx4bN24kIyMDkdP3tp86dYoqVapQt25dNm/eHPH9Kq50aLfX64Q8tDukMyOxn8KuQCOgFt4ZF4wx5h+hbFMpFX6uCebeeOONgHVbtmzJH3/8QUxMDO3atWPs2LFcc801eepkZWURGxubpyECKFeuHJUrV+Z///sfBw4c8OruUypYQTdGItIIeBNoiv+0PwbQxkipKNqyZQsPPfQQ48aNIzk5OTcljqcaNWowaNAgOnToQHx8PBs2bGDatGlce+21vPjii9x66625dZs3b86GDRtYs2YNrVq1yi1fs2YNhw8fBmD79u3aGKkCC+XM6GmgIXAP8AkQMHmqUqro3XHHHaSkpDB69Oh8640cOdKr7Pbbbyc1NZVRo0Zx/fXX505ON3LkSN5880169+7NtGnTSE1N5ccff2TkyJFUqFCB7Oxsjh07FvZ9SU6qy7btewLWS2pwNlu37Q7766uiE0pjdAkwzRjjPeZTKVUsFHaCuVq1ajFkyBDGjx/P119/zZVXXglAx44dWbBgASNGjODaa68FICYmhgEDBtC8eXMWL15MtWrVwrovANu27+HIzssC1qtR/5Owv7YqWqE0RieALZEKRClVOOGaYC45ORnAa0j4DTfcQFpaGuvWreOPP/6gSZMm1K5dm3bt2lG+fHnOPffciOyXKhtCuen1A+DiSAWilCqccE0wt3HjRgCfk9zFxMTQqlUrOnbsSO3atdm7dy+rV6+mU6dOep+RKpRQzoxGA8tFZAzwtDHmRIRiUkoVQCgTzJ08eZKMjAyqV6+ep+6OHTuYMWMGtWrVokOHDvm+3qlTpxgxYgQ5OTk88MADYd0XVfaE0hh9BVQBHgP+KSK7gRyPOsYY0zCUAETE32D2DGPMGR51mwCTgU5ARezkfw8ZY7w6jEWkHPA3YDCQDOwHXgfGGWN0VlpV6oQywdyRI0dISUmhR48eNGvWLHc03cyZM0lPT2f+/PlUrlw5dxvp6em0a9eOnj17kpKSwtGjR5k/fz6rVq3ikUceoUuXLkWyj6r0CqUx2o4duh0JXwDPe5Rlu/8iIg2Br4GT2AbxKDAQ+EBErjHGfOSx/lRgBLAYmAI0c34/X0S6GmNOhX0vlCohKleuTK9evfjmm2948803SU9PJyEhga5du3L33XfTrl27PPUrVqxIy5YtefXVV9mzZw9xcXG0bduW999/v9hOeKdKlqAbI2NM5wjG8T9jzLwAdSYBNYALjDFrAETkZeBH4BkRaWqcW4ZFpDkwHFhkjOnl2oCIbAGeAm4EXg33TihVHCUnJ3vdTV+pUqWA147cVaxYkQULFoQ7NKVyFZus3SJSUUTO8LOsCnAd8JmrIQIwxqQDM4HGQFu3Vfpgb8yd5rGpF4BjwM1hC1wppVShFZfG6HpsI/GHiPwmIk+LiPuV1ZZAJeC/PtZd4Ty7N0ZtgVPAt+4VjTGZwBqPukoppaLMbzed06V1CmhqjMkWkf8Fsb2QBzBgG4yFwCagGtANuBPoJCIdnLOfuk7dXT7Wd5W5px2uCxwwxmT5qd9BRCrqiECllCoe8rtmtA07YMHV2RyRAQzGmAs9il4WkbXAI9jRcI8ArhsYfDUumc6z+00OcX7qetb3aoxEZBAwCKB69epMmDAh0C4opYqBkvK3qnH6ViynkBCRCkA6sMoY00FEegH/AYYaY2Z41P0TdhDDJGPM/U7ZOqC2Mcbrrj0ReR24AagU6MxIp5BQKrpEJOh0QNGemkGnkMjzOiFPIVFcrhnlYYzJBnYDrhTArgyI9XxUd5W5d+HtBhJEpJKf+ge0i04ppYqPYtkYiUgsUB/Y5xStw3a7XeSjenvn2f0U5jvsvuW5WcLZbiuPukoppaLMb2MkIstEJOTbqkWkqrPueUHUreVn0T+w17OWQO4Q7iVAZ/ftOkPBBwAbyTty7jXs9a2RHtsdiL1W9EpQO6OUUqpI5Hdm1BU4uwDbrOis66+hcfegiPxXRB4VkSEicpeIfALcBXyDnUPJ5T5s1oVlInKviAzFZm6oBww3bh2cxph1wDNAmogsEpEBIjIFeBL4HL3hVYVgw4YN/PWvf6VZs2ZUr16duLg4mjZtyujRo9mzx3uunQ0bNtCjRw/i4+OpUqUKHTt25JNPfE9xcOrUKaZOnUrTpk2JjY0lMTGRMWPGkJER2YxV9RokIyIBH/UaJEc0DqVcAmVgGCQiXUPcZiWCH3X3GfAnoB+28crBnuU8ADzp3BcEgDFmk4hcDPwTuJfTuemu9pEKCOxZ0VbsyLhrgQPYxm2cpgJSodi5cyd79uyhZ8+e1K9fn/Lly7Nu3Tqef/55FixYwJo1a6hduzYAmzdvpkOHDpQvX567776b6tWr88ILL3DVVVfx3nvv0bVr3j+nUaNG8dRTT9GzZ0/GjBnDzz//zFNPPcXq1av56KOPKFcuMj3pu3dso/c7+wLWe/3P3pm7lYqEQI3Rpc4jIowxbwFvhVD/Z+AvQdbNweakm1Kw6JSyLr/8ci6//HKv8ksvvZTevXsze/Zs7r77bgDuu+8+jhw5wqpVq3Kn577lllto3rw5w4YN45dffkFEAPjxxx95+umnSUtL44033sjdbkpKCiNGjGDBggXcdNNNkd9BpYoBv/92GWPKFfKhUy+qUi0pKQmAw4cPA5CRkcHbb79N586dcxsigDPOOIMBAwbw66+/8t133+WWz58/H2OM1/TfAwcOJC4ujnnzAqVrVKr0KJaj6ZQqjjIzMzlw4AA7d+5k2bJlDB48GIBu3boBsHbtWrKysrjoIu9Bn+3b20Gf7o3Rd999R7ly5bwyZMfGxtKqVas8dZUq7bQxUipIM2fO5MwzzyQxMZGrrrqKI0eOMG/ePDp27AjA7t32drh69bxvh3OVuaYAd9VPSEigUiXv2+Hq1avHgQMHOHFCb4dTZUMo8xkpVab16NGDpk2bkp6ezurVq3n77bfZv39/7vJjx44B+GxcYmNj89Rx/eyrrmf9ihUrhm0flCqutDFSKkj169enfv36gG2YevXqRdu2bTl+/Dj33XcfcXE2PWJWlndaxMxMOzDUVcf182+//ebztXzVV6o00246pQqoZcuWnH/++Tz77LMA1K1rk8u7d8W5uMrcu/Dq1q3LgQMHfDZeu3btIiEhQc+KVJmhZ0Y+iEh3oHtiYiLr16+PdjiqGDt8+DAHDx5k/fr1xMTEULFiRT766CNuvPHGPPXeesvewRAfH5/7mUpKSuLUqVMsWLCACy64ILduVlYWq1evpnXr1sXi81ccYgiGxhleRR2nNkY+GGOWAEvatGkzMDU1NdrhqCjbu3cvderU8Sr/9NNP2bRpE507d8b1ObnuuutYtGgROTk5nHeezVyVnp7OkiVLaNSoEX369Mm9z2j48OHMnDmTt99+m379+uVu9+mnn+b48eMMGTKE4vD5Kw4xBEPjDK+ijjPoxkhEzgXONca871Z2IfAgUBOYY4x5PvwhKhVdd9xxB3v27OGyyy4jKSmJzMxMVq1axYIFC6hatSpTppy+r3rSpEl8/PHHXHnllYwaNYpq1arxwgsvsGvXLpYuXZrbEAG0aNGCYcOGMX36dNLS0ujWrVtuBoZOnTrpDa+qTAnlzGgyttF5H0BEEoD3gDOA48AMEfnNGPNmuINUKpr69OnDnDlzmDt3Lvv370dESEpKYvDgwfz973+nQYMGuXXPPfdcvvrqK+69917++c9/cuLECVq3bs3777/vlQoIYNq0aSQnJ/P888+zdOlSEhISGD58OA8//HDEUgEpVRyF0hi1AdzPfPpgpwlvBfyKzTP3N+DN8ISmVPHQu3dvevfuHXT9Zs2a5V4jCiQmJoYxY8YwZsyYgoanVKkQyr9eZ3J6kjuAq4GvjDHrnYnqFmCTniqllFIhCaUxygBqAIhIDHAJsNxt+XHsmZJSQZs0aRI33HAD55xzDiJCcnJyvvUXLlxIhw4dqFKlClWrVqVjx468++67Puump6fz6KOP0qJFC6pWrUpCQgIdOnRg9uzZUZ36WSnlLZTG6EegrzMh3kDstaIP3ZYnAft9raiUP/fffz+ffPIJDRs2JD4+Pt+6kydPpnfv3mRmZvLwww8zYcIEMjIy+POf/8wrr+SdL/HUqVNcc801jB07lrZt2zJlyhQefPBBcnJyuO2227j33nsjuVtKqRCFcs3ocex0D65bxldjJ7dzuRI7v5BSQdu8eTPnnHMOYIeSpqen+6y3b98+xo0bR2pqKt988w0VKlQA7PDo1q1bM3z4cLp37061avbk/JtvvuHLL79k5MiRTJ06NXc7Q4cOpWnTpjz33HNMnjw5wnunlApW0GdGxpilwGXANGACcKVrdlXnbGknMDv8IRYvoXQrBZpF85FHHsmtG+psoqUlTldDFMjXX3/NiRMn+Otf/5rbEAFUqFCBm266icOHD+cZNPD7778Dp7MiuFSsWJGEhASqVKkScqxKqcgJ6aZXY8xy8l4ncpUfBNLCFVRxdv/991OzZk1at27NkSNH8q07d+5cn+Xjx49n8+bNdO/ePbcslNlES1OcwXKlzPGVq81VtmLFCvr27QtAu3btqFGjBo899hjJyclceOGFHD9+nNmzZ7Nq1Sr+/e9/hz1GpVTBaQaGEAXbrQRw8803e5Xt3LmTLVu20KZNG1q2bJlbHspsoqUpzmA1b94cgE8++YQRI0bkWfbpp58CsGPHjtyy+Ph43n77bQYMGJBnWHbVqlV544036NGjR9hjVEoVXEiNkYh0AIYBjYBagHhUMcaYhmGKrVgKtlvJn5deeolTp04xYMCAoOp7ziYarJISZ7BatGjBFVdcwVtvvcXdd9/NbbfdBsDs2bN57733gLzTM4CdYTU1NZXrrruODh06cOjQIZ555hluuukm3nrrLa644oqIxKqUCl0o6YAGAv8GTgAbgO2RCqq0Msbw0ksvERcXR58+fXzWyczMJD09nczMTH766Sfuuece4PRsomU5ztdee40BAwbwxBNP8PjjjwOQnJzMM888w8CBA3MHLwCsW7eODh06MHXqVIYMGZJb3qdPH1JTUxk4cCCbN28mJiYmYvEqpYIXypnR/cAa4CpjzIHIhFM8BJu1Oysri+zs7KCz265YsYItW7bwl7/8he3bfbflr776KpMmTcr9vV69ekyaNClPtudQlaY4J0yYwPDhw9m2bRtxcXE0adKEL7/8EoBatWrlrjtu3DgyMzNJTU312l779u2ZP38+y5YtIzExsUCx+nPFFVewd+/egPXq1KnDhx9+GLBecaBZpsNL4/QtlMboLODx0t4QQfBZuytVqkR2dnbQ2W1do9Luuusuv+sMHTqUyy67LM9sorGxsYXKoFta43R5/nmbpapfv36567q67Bo3buw1oKJ69eqA7cps0qRJgeP1Ze/evTz33HMB6w0ePFizN4eZxhlexTZrN/AzkP9dicqvw4cPs3jxYpo2bcoll1zit16g2UQ1zrxWrlzJzJkz6dSpU554//SnP7Fs2TKvARVHjhzhrbfeIj4+noYNS/XlTaVKlFAao0eAp0VktjHGeypLla958+aRlZVF//79Q1rPfTbRoviSL+o4586dy7Zt2wDYv38/J06cYOLEiYAdFOEaqg0wduxYNm7cSLt27ahevTrff/89L774IvXq1fManj5y5Ehefvll7r33XtatW8fFF1/MoUOHeOGFF9izZw/PPPMM5cvrYFKlioug/xqNMYtEJA74SUTeBLYCOd7VzD/CF17pMWvWLCpUqMAtt9wS8rrHjx/n0KFDEYjKW1HHOWvWLD7//PM8ZWPHjgWgU6dOeRqj888/n48++ohly5Zx7NgxGjRowIgRI7jvvvuoUaNGnm0kJSXx7bff8vDDD/Pxxx+zYMECKleuTKtWrZgyZQppaWXitjilSoxQRtM1Bh4GqgJ9/VQzgDZGHlauXMkPP/xAWlqa3xtC85tNdP369XTu3DnCUUYnzs8++yzoumlpaSE1Ig0bNmTOnDkhxaOUio5Q+imeBWpj5yz6AojMDSXFXCjdSi6zZs0CyPeenVBmEy1NcSqlFITWGLUHnjDGPB2pYEqCULqVwHZdzZ8/n/r163PVVVf53W4os4mWpjiVUgpCa4x+R6eICKlbCaBy5coBc8NB6LOJBlJS4lRKKQhtPqPXKSPJUJVSShWtUM6MngPmOCPpngK24D2aDmOMpglSSikVklAaox+xo+XaAN3zqafJvpRSSoUklMboYWxjpJRSSoVVKDe9jo9gHEoppcowzYfiQ7BZu5UqjJLy2dI4w0vj9C3kxkhEYoCm2KSpXqPxnKnJS7Rgs3arolOnfgP27doRsN5Z9RLZu7NkjKEpKZ8tjTO8NE7fQp3p9R7gXqBaPtV0AIMKu327dsDotwPXe/K6IohGKRVuQd9nJCIDgEnYCfYexE45Pg14HDgErARuD3uESimlSr1QbnodAqwwxnQBnnfKlhpj7gVaAsnoWVGupMQ6iEjAR1Kid9LRolQnsX5QcdZJrB/VOJVSpVso3XTNsGdEcHqId3kAY8weEXkem0T1xfCFV3Jt37mP3S8Frlf3tn2RDyYf+3bughfHBK53uyZAVUpFTihnRjlAhvOz67mm2/KtQKMwxKSUUqqMCaUx2g6kABhjsoAdQEe35W2x146UUkqpkITSTbccuBZwzSm9EBgpIpWxjdrNaBedUkqpAgilMfoX8IOIVDbGHAceAhoD/Zzly7DDvpVSSqmQhJIOaAOwwe33DOA6EakO5Bhj0iMQn1JKqTIgqGtGInKGiLwoIjd4LjPGHNWGSCmlVGEE1Rg5jc2N5J95QSmllCqQUEbT/YS9sVUppZQKq1Aao8eAO0SkcaSCUUopVTaFMpquKfbeonUi8g6wETjmUccYY/4RruCipainkNCU8uGlcYaXxhleGqdvoTRG491+7umnjgFKfGNU1FNIaEr58NI4w0vjDC+N07dQGqOUiEWhlFKqTAvlPqNtkQxEKaVU2RXKAAallFIqIkKd6bU80AO4EN/TjhtjTP/whKaUUqqsCLoxEpGawKdAKnaWV+M84/azAbQxUkopFZJQuukmYod3DwAaYhufq7CT7s0HvgNqhTtApZRSpV8ojdG1wMvGmJeA352yHGPMBmPMzcBxYFK4A1RKKVX6hdIY1cGe/QCcdJ5j3Za/CVwXhpjCQkTKicgoEflFRDJFZIeITBGRKtGOTSmlVF6hNEaHANcX+R9ANpDotjwbO6ihuJgKPInNqTccOxngCGCJiOgoQqWUKkZCGU33K/AnAGPMKRFZDdwqIrOBGOAW4H9hj7AARKQ5tgFaZIzp5Va+BXgKm4H81SiFp5RSykMoZwjLgOtFpJLz+5PYId6HgN+ANtizkeKgD3aAxTSP8hew+fRuLuqAlFJK+RfKmdGjwBPGmCwAY8zrInIS+8WeA/zHGPNaBGIsiLbAKeBb90JjTKaIrHGWK6WUKibEGBO4ksiZwDnAAWPM5ohHVUgisg6obYw5y8ey14EbgErGmBM+lg8CBjm/NsFtqvUwSAAOhHF7kaJxhpfGGV4aZ3hFIs4kY8yZoayQ75mRc6H/Wey9ReKU/RfoaYzZX9Aoi0AckOVnWaZbHa/GyBjzPPB8JIISkZXGmDaR2HY4aZzhpXGGl8YZXsUlzkDXjO7EniXsBRYB64AOwHMRjquwjgGV/CyLdaujlFKqGAh0zegW4GegvTHmDwAReQE7iq6GMeZIhOMrqN3An0Skkusal5t62O5Gr7MipZRS0RHozKgJMNvVEDmexg7lLs7Tj3+H3bd27oUiEgu0AlZGISaIUPdfBGic4aVxhpfGGV7FIs58BzCIyCmgrzHmFbeyBOxQ7suNMZ9GPsTQiUgL4Adgscd9RsOx9xn1NcbMi1Z8Siml8gpmaLdna+X6XTwrFhfGmHUi8gxwp4gsAt7FJnQdAXyO3vCqlFLFSjCNUTcRqeP2exy2QbpBRFp51DXGmOJy4+tIYCt2AMa12KGLTwPjjDGnoheWUkopT8F004XCGGNiCheSUkqpsibQAIYuIT4ui1ikJZSInCEi94vIOhH5Q0QOiMjXInKriBR5V6eI3CciC0XkfyJiRGRrEOtcKyIfichhETkmIr+KyPQIxthERF4RkZ9F5Kjzmr+IyJMicrZbPRGRm0VkgYhscuptF5G3ReTCSMXnI96aIvKEE0OmiOwXkU9FpGM+6wx1jr9xrsOGK5ag3t+CHLtwfZZFpLGIPCwiK5xj9YeIrBGRBzyz6ovIeLfj5Pm4y8/2Q34/8onV32une9RrJyJPichXIpLu1Lk1DPvf2e01B+QT4zuh7ltxk283nTHm86IKpDQSe9Pwe9h7s+ZguwnjsLnzXsJex7qniMN6FJtP8HugRqDKIvIQMB74AHgIe39WA6BlxCKE+sDZwGJgJ3bKkhbYLtcbRaSVMeY37L1kc4E1wAJgi7PeEOC/InJLpAeqiEgS8BlwBjALm1C4Ovb41POzTl3s3F/pznrhFOz7G9KxC/Nn+XZgGPA28Ao2438X7ASevUWkvTHmuMc6o/DOErDKc8MFeT+C8AXeI86yPX7vht2nX7CDpzrks72C7D/ABBF5xc+yks8Yo48IPYCLsNfXpnqUV8RmOD8ShZjOcft5PbA1n7pdnfjHRvtYOvHc4MRzt/N7eaCTj3pnYb+49gHlIhzTF8AO4OwQ1lkMrMY2BgZIKOr3N9RjF87PMjapcnUf5ROd17jTrWy8U5YcqfcjwPYM9vaWQPXOAqo4P1/vrHdrGPa/s1P2nfN8n58Y3wnXZyhSD6Bqfst1Xp/IquY873YvNPaG2wNARlEHZIwJZZqP+7HD+CdBbjdNND8z25zneABjzEnj4+zdGLMPO2qytvOICBG5FLgEeMwYs0dEKohIXIB1emInoRyMTTAcVsG+vwU4dmH7LBtjVhpjjvpY5Eq0nOprPRGpJiJ+e3MK8n4ES0Qqiojfs1hjzD5jTFDHoID7/zr2TPAeEakVRLxtRGSx05WaJSIbnG7A8m51XhORE766icV2lRsRmeZR/n8i8qXTtXhMRL4Rket9rG9EZLaIXO7UTweW5BezNkaR9S1wBLhbRG4QkQbOmzwJuAD7X1+x5PRdXwp8A/QXkV3YSRXTnesMXkloIxBDrIgkiEh9EbmS02mo3g1i9frY3INHIhUftmsGYLuILAGOAxlir6l5TVMiItWA6cBzxphvPZcXI76OXVF8lus7z/t8LFsLHAUynetU1/ioE9L7EYLrsd3Tf4jIbyLytIhUL8T2/Mlv/w22G7Q68EB+GxGRbsBX2MQEU7C3tPwXeBiY71Z1DlABO7+bp1vc6ri2OxHbpfsHMBa4F3tcForIMB/baIOdAfxbbDfrKz7qnBbtU7fS/gA6YjN/G7fH70CPYhBbft045zmx/oZNLjse6ImdxyoHO4NuXITju9PjuG0B/hrEet2c+i9HOL7FbsfoK+Cv2OsB653y2zzqzwD24HTRALMJczddsO9vQY5dJD/L2Kwu/8VeP2niVj4S+09IP+wZ5d+BXdgpYm4tzPsRZFzfAHcBPbBf0Aucba0FzvCzTr7ddCHuf2dnW3c5vy9z/h6T3OrkdtNhc2/uBZYD5T1eY5RTt7Pba+4BvvWoJ9heiLVuZa2ddR/1EfubzuegqkdMBuga9DGIxB+BPvK8UecDbwCPY7/M+2MvLh8DrohybPk1Rpe4faAGeCwb75TfEeH46mOvW/UAJmCvs4wMsE4j4CB24MOZEY7vI+c4bAYqupXHA4edP/RyTlkH5wu0j1u92RSjxijQsYvkZxk7IMLnNREfdWs5x/Ywbg1CKO9HIWO933mdB/wsL0hj5HP/8W6Mznc+Ry+71XFvjLo7v9+GnRrC/dEEjwYFeMIpa+pW1sUpG+NWNsV53SY+tnu7U/9Kj5jWhHRcI/FHoI/cN6QFtqtgiEd5HPai71YgJorx5dcYXeB8oHKwcz+5L0txlr1WxPG2xE4N4vMLy4lrO/YaRosiiGeJcxwm+lg2x1nWDHuR/0dgmUed2RSTxijQsYvkZxn4h3McngthnYd8fAEG9X6E4bhWcD6HX/tZHlJjlN/+49EYOWWvOH+XLZ3f3Ruju8l75urrMcvjffVsoF7CjmCt41b2bhDb7etW3wCvh3JcQ5npVYVuFPa0eaF7oTHmmIgsxXZDJWP/kytudjrPh4135vM9znN8EcaDMWatiKwGhuIMqnARkWTgU+yQ3suNMeuKICTXMdrrY5n7MRoGNAXGiMi5bnWqOs8pIlLNhDa4JGyCPHYR+SyLyHjgQewX4JAQVt3qPLtffA/2/SgUY0y2iOz2eO0CKeD+P4ht8CYDntfOXPd7/R07bN+X3EEoxqZOWwPcLCIPAJWBXth/nNyPo2AbmGvwP/DmR4/fQ5qmRxujyHLd1+ArK0V5j+dixRizT0S2A4kiEmeMcf9guS60/haF0CoDNd0LnHtLPsVe3O1qjFldRLF8i/0Cqe9jmfsxSsIOFnovn+1kEP57jgIK4diF/bPs3MP2EPAytivYhLB6I+fZ/WJ/sO9HoYjN/l8fWFHI7RRo/40xW0RkBvA3EenisXij85xhjPkoyFDmAFOx3XNnY/9JmuNRZyNwNbDdGPNzkNsNTWFPWfWR7+n3VNzui3Err4H97+QQHhcZizi+QPcZue57GOVRPsUpvylCcdXxU94F+1/Zx25lSdiBDUeAtkV8/OKxF253kvfaxdnYG1p/dX4/D/ufrOfjU0737/eIwvsb9LEL92cZGOds72X8XMfBNm7VfZQnYq9tHQAqh/p+hBBjLT/lj/s6Fm7LA3bTBbP/Tr3OeHTTOeUJ2NGF35K3m64KtoHeBtT0sb3KeNzvgx3Cn43tNv7A+TzEetRp67zOYnx0xwK1PX43BHF/lvujWP5XXopMw47A+afYaS2+wv5XPxD7BzLMGHOyKAMSkb7YLyGAM4GKIvKg8/s2Y8xct+qPYU/ZnxCRxtg7yy/BjlL6hNP3RYTbDLFpfz7B/lHFYq9h3YgdVjrG2Zeq2C/0ZOwF4CYi0sRjWx8ae+9M2BljDotNSfMcsEJEXsReH7rDeb7TqfcD9tjlISJ/dn5cYozxzC5QIMG+vwU4dtMI02fZGQY8AXuN6iPgJsmbTWifMeZD7JniFhF5EzvJ52HsBfQBzrI+xi0bQbDvRwgeFJH22OO03XnNbth/ir7BHjfXPiUBfZ1fmzvP3UXEdUY21xizLcT998sYc0BEHsdeb3IvzxCRW7Aj3DY4x2AT9p+GpkAadvDJZ27r/CYi72Eb0VjsNaVMj+1+55zJTQDWiMhC7D8hZ2P/Nrthj3HBhdJy6SP0B9AQe8q7E/vfx+/YYZdpUYrnM/xfgPzMR/0E7JDk3dh7T/4HPILHf05hjrE3sBR7J30m9sL5L9g//gZu9ZLz2RfXo3MRHNM0bJdNBraxXAZcHMR6swnzAIZg39+CHLtwfZbd9jvfOLEpi2YC67ANUTb22s9/gHbhfj98bOcv2DOFXc7nMAN7HeZ+z88/p89gAh7LYPffY7t3+YgvDvt3mXtm5LYsFZjnxH4Ce7b0Nfb+IF9nTL3cXt/vscLOgPAB9kw4C/s3+h4eI2spwJlRvlm7lVJKqaKgGRiUUkpFnTZGSimlok4bI6WUUlGnjZFSSqmo08ZIKaVU1GljpJRSKuq0MVJKKRV12hgpVQgiMt6Z1TI52rGUZSKyVUQ+i3YcquC0MVLFloh0dr7o3R/pIrJKRP4mIr6SdpZJzpfx+kKs38ppWJPDGJZSQdPGSJUE87F5v27B5uKKw+ZKmxHFmFwmYpNPbot2IIXUCptBOjm6YaiyShOlqpLge2PMPNcvTvr8n4EBIjLW+EmEKiJVjTF/RDIwY5ODFmmy25JGbBbQKsaY9GjHooovPTNSJY4x5nfgv9gJv86B09cMROR8EflARI4Ca51lfq/r+LrW4NSdLSIXicjnIpIhIgdEZKaInOFR12vbbmVNRORREdkpIlki8oOIdPMRQ5yIPCkie0TkmIisEJHLnRgKnDzSOR5bRaSuiMwXkcPOvnzgZGHPjRc7uRvAp25dorPd6lQSkftF5EcRyRSRIyKyRETO93hNV9fqrSIyTER+wiYZvUtEXhOREyLiNSmdc6yMiExzKxsqIstEZJez3h4RmaddiaWTnhmpEsf5T9s1Y6r71AsNsNNOLATeoHCT1bUC3sF+Sb+KzZ7cHzgFDApyG3OwmaafwKbXHwm8KSKNjTFb3eotxKbgfxM7pUAKdt6YLYWI36UKNrP2Cmy26RTgb8BbIpJqjMkBFmGnAhgEPIo96wRn1lYRqQC8D3QA5gLTsZPxDQS+EpFLjTErPV53JFALeAE78+oO7Nw7vbFTgUz3qH+L8+w+qdtdTtxPYbNEp2Knj7hMRFoYYw6GfjhUsRWu1PX60Ee4H5xOnz8OO5XFmUBL7BecAf7rVnerUzbAx3bGO8uSfSzbisfUGU7dU0B7j/Kl2MbljPy27Vb2DtjM+E65a4KySW5l3ZyyFzxey1VugjxWW4H1HmWf4XtCvL875Ve5ld2Knyk3sFOO56nvlFfDzsnzmVuZ6z07hPeEazHYKSC+9SgX7DW3tR7lVXzEcrmfffJ6H/VRsh7aTadKggnAfuyU0T8AtwNvAz086h3idHdTYf3XGOM5rfQn2N6E5CC38S/jfFOCnaAMO79OI7c63Z3nJ91XNMa8y+kzlMI4hT2zcPeJ89yI4NyMnU9qlYgkuB7Ys70PgUtEpLLHOi8bY/JM8W3sWdgrQFsRaeq2qDP2rHaOR/0MABEpJyLVndf8ATvD6YVBxq5KCO2mUyXB89iuLIOd4OxXY8whH/U2O1944fA/H2WubqFahdjGIY/1U7ANxiYfdTcAzYJ8LX92G49ZOwl9P5phRwzuz6dOArYrzuVXP/XmYGfqvQXbbYjzs6uhyiUil2HPii/EzkDqLj6YwFXJoY2RKgk2GmM+CqLeMT/l+Q0C8Pc3kF+jJvksC2Yb4uPnSM1yGY79EOxsq6PzqePZUPl8L4wx60RkDXCziDyAbeR6AcuMMXtzX1CkLXaG1k3AvdjrZ8exx2kBOviq1NHGSJUFrrOomthrCwCISCz2wr2vs5KisgX7xdoI7265JkUYR36N4Ubs9bpPjDGnwvBac4CpQBfs8a+KRxcdcBP2GtM1xpjcgRwiUgU9KyqV9L8LVRa4uoy6epSPIvp/A0uc51Huhc4Q8MJ20YXCdQ9QTR/LXgbq4OfMSETOCvG1XsXem3WL8zgKvOVRx3VG53n2dj/Rf89UBOiZkSoLPsJegH9YRGphz0YuAdqTd2h4NLwLfAAMdC7Qu4Z2D8LeJ9WyiOL4Dnvt6gERicdem9tijPkG+BdwBfC4cx3nE+B37KCDy7H3EXUJ9oWMMb+JyHvA9dhrQbN8XNdajG2g3xWR54ETTgwtif57piJA/8NQpZ4zqOEv2KHOw4F/YkeCdcJ+6UaNM9quF/YL/2JgCnAp0BPbPXa8iOLYjh2lWBmbZmk+cIezLBu4Fnt/0pnY0Y1Tgf/DDtKYVICXnIO9ByoGe+blGc9X2OOSgU0BNR57LKL+nqnIELeRp0qpYkRE1gEVjDFNA1ZWqoTTMyOloszHPTqIyLXYjAMfFn1EShU9PTNSKspEZBJwPvAp9mJ+K2yX2e9AK2PMzuhFp1TR0MZIqShzRs7dC/wJm/PtEHaQwFhjTDSHnStVZLQxUkopFXV6zUgppVTUaWOklFIq6rQxUkopFXXaGCmllIo6bYyUUkpF3f8DguXAp5mDcX4AAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 432x180 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "data = get_data([\"num_parameters\", \"config.pruning_interval\"])\n",
     "filtered_y = data.filter(regex=(\"num_parameters_rfdn_advanced_600_epochs_no_batchnorm_pruning_([0-9]*|none).csv\")).min()\n",
     "filtered_x = data.filter(regex=(\"config\\.pruning_interval_rfdn_advanced_600_epochs_no_batchnorm_pruning_([0-9]*|none).csv\")).head(1)\n",
-    "print(filtered_x.values[0])\n",
     "make_bar_chart(filtered_x.values[0], filtered_y.values / 1000, \"Pruning Interval\", \"Params [in 1000]\", \"pruning_interval_final_number_of_parameters.svg\", True, lableprecision=-1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": null,
    "id": "510af0bf",
    "metadata": {
     "scrolled": false
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x180 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "data = get_data([\"num_parameters\", \"Epoch\"])\n",
     "filtered_x = data.filter(regex=(\"Epoch_rfdn_advanced_600_epochs_no_batchnorm_pruning_([0-9]*|none).csv\")).head(600).transpose()\n",
@@ -875,23 +222,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "79334f28",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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5Pg7KAWVlzIj4PN3SRrvnUe63oAiOxsi9nOCpFEyRFL+ftJ3yZC0SSaJ+nsyrtyGxStzkFCRWhUSS+KktqJcJgkmsCgBxYvjZnfjpejSfCoJJcgqIi/pZqtu2sW15RRAYdhEkUormW8FNAopEStDMTsLYxR7nMzeBuEk027B7C5ESNN8OQA1Q/zz4OZCCM0jhNrx2cBKAgJ8BJwrqgdcW/F3YtiMRcGLgZ4PlKJwch603/QXNQqQaNBes75aGaQjSeW3BR/LSQRoU3BJwyyEXZt9NBtvWfLC+nw62JRHwmqElDU78QSTyINE68JqCz4ZC6/NLyTcHeXBLwGsNJtw1OLEgjURB86+ABPvwmreCgJPcgZ/egZuEHfkgf04MVEHcYP9Nj6zFLZmMWzERJ1FOfucGorUzcUqqyG5ejlsxEc1laH/5YZJzjiS3fS1+po1I1VRy29eQa9hItHZmcBxj1az+Sxu5bauQWBKvpR7NZwFIzj0at2ISfqqJXP0aJF5KpKwOP5cmu/Vl8PPEZxxCYtZhZLe+TPvyB5BIjMTsI/DadqKZdkBZkJmMn/kcTry0P/9qQ2ZIA5aIiHb8fDNmGLTnsyS8Nhr8CCu3PMeOpnU827idA2pmcfI+x7OmeRvffPwGVqVSfGP/Q3mu3ePZzS+Q8vJML62mJdXA0qzLtrxDjBwNJHFrXX73j6vJOQ6R8OubcqPMa11Mba6ddjdKRH1eLq2jNJ9lv7Z6fIKzc7sbZU77TpoSFaSdCM8kKqjIZzi2YR3v27yER2pmsy5ZzbZ4GXXZNg4Qh4SfZ9VLTzIz0oKfDU7g+ebwhOsuxkmEJ0UNJicBOEE6PxNMEg3ee20g0Z3BvEhwQm73dqKZYJn6gK9EKlvxUoCfQqKApHESkG8CwpO3xIJXzaZB0ojjgPgg4LW3BwHJAXGCE7PmC957QV4lDuCgGX+EvxlDI72m6/m57QPYWGPwkm/oKdFzPS3cQ3bTSwXvntn1V6Yt2IEA6Y6ZbewhteqJvbfX6X3q5UdIvfzIrveaS9P+0n/2SLMvQZCMT963z/keSkMSsCSob54NfB3Ybyi2aYqXqnLf6q24mmJncxv7T53Jsi2N7JRGnPwyntuylJpkNcvb2nilYT3tRIiTZ37coS4Cs+sO4Kltq3i41WPf5322+DG2eRFQjxaioEpdro3GSJKI+hzetJErk8vIP3IXZV6GBS1bqa+ZzfnPPsvMVAPHNqxnY6KSv9e4TM60c2DrNs5t2si0dBOHNG2mwsvsyruP4KC0RmKU5Tv/S/ff/PYd3S5L9XEbXmvf9+c1dz0/37T7b80Hr377nvM65u+2Z9DRTq8dSbQgmWb2Xg8ROqpy0YlzcSJx1MuG+82S27F+r/y2RaqorptIvnEzfroFJ1mBenk0245bVku0Zgbq5YhUTSXftBknUQ6OS3r1k2g+i8SSaDY4whIrofTA15Fv2ER63bNdHp/EnEW4ZXW0vXgnALVv+gLZ+tW0v/QfvNagDEv2PRG3YiJeewOR0lqckir89kac0mo0lwYR3NIa4tMXIAiZLSvJ7VhHU1saefFve+yvdMFB5JuWUrbwPNyyAxE3ino53NIavLYG8s1biVZPBxFSq5+k9fl/UrLvicSnHsDOe35OxZHvpPK4c4nWzSbftBVxHFa/8DhTKmJs/8e3Sc49mtKDTqFt6T1Eq6cTm7If6uWITZwb7mMnbmkNiEN86v5s+8tXaHzgaiqOfjcVR72btqV3E6mcTGL24Ug0gZ9uRfMZ/n7zH1k4SsEK+hiwROQwYD6wE3hAVXMFy94DXEoQqFqGI5Nm5KRzHlHXwXWE7a0ZHlq/hpjr8HJ9C3esXc7mVmVypITnNrTSduMSotEMLS01u9YXN4vj+PjZGCpO2P5R8LO1LAOzUlTmd5JxIjia5LU7VjGvbTurS2q5dsK+nLby37x2xyrenmllfbKKV0prAXjfhmepy7YNT08hcUCDZj3cCGX5LBKJEa2dhcSSiOOSXr9414m3g5bVkaiaAr6HJMrIbX0FicSCk0J7I9EJc9BsipQkKI1neGHrOl6J1nD0vKOZhYe40WBDvkd6/WIyG5aQmLMoOBE1bCT1ymMAVBz5ThKzDiM2eT6R6ul4zVvJ7XgVp6QSJ1GOE0uSb9jEhlfXMmPOvuBGyNWvId9Sj9e6k+ScRTiJMtqW3kNyn6OReAnixnBiSbLbXqH0oFPJ79xAavUTtC1/gGjNDGrf8L+gSnb7WiJVU4hNnIvX1sArL73AvAMOJjZhNqlVTxCtm436HpkNS3DL63CiCaK1M2l48HfEpxxA+aGnk2+uJ1IxAYnE9jr0mS0r8Vp3ktu2CidZQbR2Jj+45lYuvfRSvNadtCy+jfIj3gpA6/N3ULbg9bhlNXttpyvtrzxGfOqBuCWVaD7Hjrsup2T+CXhtO2l69A80LvoY+86dQ6xuFn66lXU/egPxqQcw6d0/GNDXyFcfCWvfOd8j6ri8+vxzHHzRH9n68O8p3bEGOfytZLdcj7NyGWUHL6L0gE/1ul31/aDGC0x6z//tsSw+JawjZKuoXbCA2tdftGtZ7es/3ad8Tznvl9Sc8ikSMw8GoGLRW7tMt+bO5X3a3nCRnlrwRCQJ/A04tWD2q8ApQAb4E3AM0Az8AviJqu4cttyOAYsWLdKnn356QOt+4xvf4NJLLx3iHO1tyZIlLFiwoOtl23ZSk4wzubSEf7+yliseW05Da4q51Rk+fdLJHP2TJ+m286gqldpCk1NBld9MmyTJEeHE3DNc0nolKUmwn7eWNe405ngbu9zEVqeGSf7IfUXaE2VMWvgG0mueIrd9XXBCTVSQq18NgMSSPFN+Muf8+J/47U2A4iQryTdtwS2twYkldm0ru30dXst2ErMPx29r6PNJE4IyOeigg3jXP7/DX+obueE15/DefQ7bK536HuK4e8zzcxmcaLzP++mu7IdK4T7achlEhIQbIZXPEQtfN7UHVb2mXIqouIgI9elWMl6euBsh6UZpyqVpyKTw1MdTn6zv0ZxN44jw77vv5rRTX09bPktDNoWqUhmWRc738dXHC89dirI93UZbPktrPsvGtib2q5yAKw559cn6efK+T3W8hNZcBkcEVxxcEXY0NrJdcogIEXFoy2fJeHkcEfLqk/byVMeS+Krk1afEjbKurQFHBEGojiVpzWeIORG2pVvJ+R5xN7JrGyVujNb87hq8IGhYR60ixQMLpnHwkZcMebkAqJfBzzXjRMrx2tbjtW/EiVag+HhhpxlVH3EiqJ/HT2/DT23BiVXhp4M2UPUziFsCgJfaxPrVL3HoR9YPqBOHiDyjqosG8xl7C1iXETTzPQ3cD8wDzgLuA2YA04AfEQSqpi43Mk50DM00Y8aMD99xxx2jnZ1e+aqs3LyBDbkEqxp2sG3HVrY31/Jca4KGvLtX+ojmqfGbUCAvEU7OPsn8/HpmeZtodkop0TSnZB8flrxqSS3k00h2z4Z3PeRdkCgHLw+ZFlAPnXNMcPGmdj5sWYo0rEfnHIv/+BVEVu9u7vHPuxnq5oEbJZX3yfvg5lPkJErKc4h4KZY1OSSjDs/XZ3DD/7+mjE8yIrTmlMaMR8IV2nJKe84n5gppT3EFSqIO65tz5H3FCf95Iw5ERHAc2NbmURp1SHs+W9o8Yq5QFhW2pjOkY4189cRK3j15/rAcz6Z8hjYvR4kbIe17bMm2kfLyZNWjOZ8lox4AqbzPurY2VDzac0rWV3wF3xcymqM+147vC2lyCIrjR8l4Slveo8KrJOsJ2zMZNJIBdUAdRB3Uc0HDA+p4oC5kExDJBtfE1AE3BxPXwXB2XlPAdwl6Q0iwTy8KXgRyiSCBFwHRIJ0f/lCLZsNlUcjFg3VVgnx3bEv8YL1cPFgvkoPmuuA1lgqWZ0rAzQfbzkdxIz6er4ijaLoUYim+dsgzvOug9/fx8/g42a246VU42U1kcy00tK8j7UPGa6c130qLl2ezV0qrJyS9VrZ7FaSIknd92vwEO3LVtPtx4m6WMjI0ZKtolyjtRMl5MTIaBcdHfRfHd2jxE6T9BKXkyHtRyjy44fSFTEzW9rs4Fi5cOOiA1VuT4DuAh4GTVYOWahH5OnAZsB44WFVXDyYDxUJVbwNuW7Ro0YcH+gt2uGpYvq8s3tLA4vXreHr1Ctq2t3BMXGnMu9RGPRYCJKOQbOZYbykP7nCo9Fv4VPsfme5vo1HKqdKha81NzFkUNHGte47E7CNI7nMU0bpZ/OzRV2D7Rt4ReY4pb7gIqZlBzRFnIZGg9rD1z19m550/BmDyuT9n05ST9/jF2JzO0Z712NaapT3n4c45icWbmrhn5XYmLLwWSh/mYy9+grvnf4bfvzAJ329m8aZuLuqMMEHxcj5lotBeTbNU9qkm5KvPxtYWVuxoZGXLNlbvbGVDSyue59Cez9LuZ9jQ3E5zOk9Lq0M6pwUBQ3HyMfx8nEQkC75L0o/S4gt5FfBjQN9qbp3t/W1R4qKUuT4iEHcUF3BFKXV9mvIube3C1HieqohPS9bBj5RwyOxkWNsJajeV0QRl0RiPPvo4Bx1yNH4+QkwTbGlJs7U5S4Qo+ZxLJgcRFxqbs5T6PhHAiURpb0+TzcMkB5o8n3KBjMJ016fZhRLXJ+85ZBWWtceoz0WYjBIReFWl4DpdUBcqBeaIz1RRtquwSh0qRUmiVDpB15ta1ycjStqFlMaoykSZGsmRjysZL8bUqJJM5GjNu6QQ4gLTI200i7CpaRLTI1VIJAueS448DRu3sr1hNS81N7Nqp09KM0yOZPHEYa0KFV6c2VLLpvxU1mYOZl4sh/gOkyIeaVEmx3LEfIek47M2VUJ5JEd1Mk08FyFSkiKbieNm4yRiGVb4Mbam4yQUWiqbcSubieSjlGdjVKTjbM9FcV1hoitoLkqtm2f2PgezT231gL43g9VbwNoHuKojWIVuIghYP/xvCVZjUXs2j6cev33wPl5dupkpUZ+aqM+hAHGPstb1JGPV1G5+jtodzyHqkUxt5YRcC2d32laPwUqEaN2cXU1obZJgwTceZdV9N+A8+BOqTvwg2a0vM/Ed3yE592jalt5Lct8TaUll8PNZMjmPWCxGU1MTTVtWcH0+w8FnH8XBh0zda1eT3/sjIid8hE2P3cKf4qfx/LNbWPHgo6ysbyXv+9S3BpdOI6KowsRYnmnxPClfKHN9aiJVfGX+NXhOlMntG5iZyHHEFGFSLE9NxCcei5DL5fj99gm82BxhTk0JUVeQHes5+ejDibkOL21rQRUOmVLOrPIIfrqFEgfirk8u7xFzHbx8jlRrM5F0E2WVVYgTIZ9N4+Xz4ZQj4rrk8zm8XBbUR30fH4f2SCl+LsNLzUGg8NI5nJhLW9bjmXU7+NfyNTy1s4UdLSncVIpovhRVj5qIw4REnFrNUxmLIW3bmZiDyeJT7bq8EnGpiSo1UUVcHwefUt+h1MvT6jhkE1EcX1EcfBFWNWRZ2axUu9DqgeMocyNKBUplBKqBrYkY5epTkfNoibn40TgiDpXZHJPa2tlamqXUV8R3ESDiKxFPUFwiPmQi4ItDxIf2Usi6UJZ1iOch6wpsmcDEeodcKks+laOhrIxUzifqZTnVPwy5P0drFBJ+E9m4kos7lLbncCWL7zvUpNuJeoXN15k9vk+egNsRgYJKJemkEE8F4QgnC/E9O9ao64HngOsj3t4tEX2Wl2AK/omAwm0Ju6qWa6ezau3De60eI8Ih1HBIX/aVSna7aG4vq+4xEm17EjZP6nV3dZF2gm/IyOstYCWA+k7zOjp4vjz02TG9eWr9Nn795EM461o5qjxDBXBQ0MRMVcNSJm19hPLmV0jkem6hdZKVRKqnIk6E+IyFEE2SbtxGxZxDKdnvJEr3Pxlxd389Pvix8/lc+7VcWv0xfl82ky0zToNzTiNVXU1bWxtLVuzEefkuRITNT91IPr9XlzMOQLhkltDant5j/uPrGrj8gVU8sqaBTc1p4nIAR9QuZUZZhLmuzwnzS2l3yvBxSLtJ9m16kaTXvqsZL+vE8MVFgeZkLVlNIChpN0lLtIpGyZOOp3GTyvTkej7UVMpHX38K7TuaaF67hfuX/YfJk9vRTDOnE2G1N41XG6eQ3eRRIWni8XbcZJoKzVHW4tGqUUqbfCK+EN2yiVI8/LhHNB8hn1C25ifQ7pVRlcmRzOVJRaM4bp7SXI6SdB6VEo7a2MTipX/Fa999wiwD3hlOeUeI+LBH/+Tmgm59u06AweuxPZY27O65F5zUj4CgiQ52nwU6koSz52UL+jGm83QOCLOaoz3uMe7t3mhZds/AkMwDmfyuLTpAbcue3ws8qMuF36NOi3avFRA3j6qzu1mPgmBVIJHauw1S3Dzqh02aHUGqIFjFanbi5yPkW8rCZkGIVTfg5yNIJE+uITx5Ox5OLEuktB1E8dpKcGI5IhUtaC6Cei5+NoYTzxCv3UlqyyRyDdXEaneQay1DM3Fw80Sn1iOlGaJeHlHB811SksBpTEA2glPTQrrcp7QZtD2OV53GlTy5bbWoq1DVTmXpdry2EjKZErJJJevHcRwP2mNEK5vxIoLbEMdtiuOXZ9B8FNJR/NYkXsInX5YjUdVAhDxpEmSjDpFYis1MYlq7R8Xw3Ovcq8F0a/eGLBemR6rKzS+u4w8PPcOZ0R0cA1CmzFr3d6ZuugfHz5NPVBBNdx+k3CkH8XzNKcT3OZZTD5tNfPJ+uKVV3aavr6+noaGBVCpFQ0MDtQcezZ35g3hLaQW33HJLl+tknDieE6HdrSGVKKc1Uo4iqDg0JCeSiLaTyUU5vCnI5+X3ruC+F15hYnUJ06NxLt5XSMSFx91DiXtCZXQntaVbOazmESq3Q1kuQ3u+jPsjC5m2o41JbhYvGqNq6kri9QkibhZpTvLSxIkk03lqmzNEmmNUzn0Zr7WE9IopAGRp4LkntgZVAGA/DoQV0PGr8XDgcDZ3+nQROv5dKnouLsrI0Pnk3hWvh27zEb/jjt7wmo/4RMpbcaI5vFQC9VyceBY3niG7sxr1IrjJILD6+UhwgqxIEyWP11aCRPJEylpR38VrLcHPRSmZsREnmcZrKyHXXE5y+ibceAYvnSCzo4YNsSlMr16B2xgnUtqOm0yBo2S2TCRdX0fp7PXEqhpxEsFn9dqTRMrbiJS1kt46kfrEBMoTOyn3G8i3lCNunmh1E24iTXrrRMTxiZS240SzpDZOBVGceJZ47Q7yqRJyTRW70mabqpDaJsqmbCUSSaGei1vSTn3rFKhtxYk6ZP0kbdlycmurWTjvQUBwS9rJUkeudSLp9ROI1+4gMjHKSw1R4pEUlW4jWYnSmJ7Atg3zaSmNkI0Js6KbOK7mPpxojo3Zg2jzpzEh+iwrtx1DvHoGqfSr7Ff2LM80Hs3aHZX46QR+tbAjU8aksiqiToL55SspdxvZ0FhDsnQu0ehE6pI+ExI5fvXwK0yf00Zzupo17ceQlTh1LRuo8TZRVjuX2rIK5k+sxHHyxJ0IvgrV1dUs3tBIsqycOm3huyvBEZ+cxnAFZs7Ictj0KmoSLsQhGnFpy/rUJl0qYkJFzEEEXBFirnDR315k68xJxPPt1Ga2EfdSVJQkqSmLUxZzaXbL2a9SmF2VoCQRJ5PJcM/Vv+Ht75jZ63d7uPQlYJ0nIscUvA+vVvJJETmrU1pV1b71owREZD+CTh2HA1OBKMG1sTuA/1PVzV2k/wHwGiAGPAtcqqr39WOflcC3gbcBtcAqgh6OvxmLNz3f9MRd/H75BCqrapk5aR6ydR1T6p9gzpq/IAX3u3QEq5rXf5rtpXM54LQPsP3v3yS7fS3TPvIHdja10PjXv1KDS8nco/faz4YNG1i5ciX19fU0NjWRcktoiE+gIT6BvFNDfvZJxMq3s7k5wvwtSwCorJlMqnoWWyVCXWI5c1o30Zit4XaOoSbfwkmNLzKvdglVk17FrS8ltWo6nhflkeS+/OfbN3GCl+d1tTuJ7BQy2+t25eVYHtudMcdDnDloPkqeoNBfTytQ8BNv2cF7hIgZr+xe5AE7t+39eYNt+4j4qBdBHR9iiqR3/7JOTtuEOkK2vo5oXTvZpFBRso7M1gk4ieBXcqapktT2iTCtAa0Sav1NwYm2tI1YZRNuaTuZ+jo0JpRM8BDZgu+5uPEMTjSPH89T/9xhaFkaZ1ojU8rXkW8tRT0XLz2ZkqkuXnoCsfIYkeR8GtM+icxvduWxNV/BlrZF5LK1RKqTNDZHOLjmpl3LFQfXnYS4E3AjM0Ecsm0371q+LTWVnFdLPNpCWfkCSkuPwGv4JBD0qAJAgjuXHacSN34YufY9Ox21eJMQdzYV8ToctwZJ30li4jKq9jzYuNH5ONFDWbNtM7np7bji05yZiPjVzDv8cCiZTsQtJx6t5Xe//h4f/9g5OJHpgCBOBSJB2fi+z1cf2cyObS7nHVjFQbVRqhIFTW4HQS77NvzsYiLxQ3GjYcPYAbuTfPrFbbuTVyoHTypl30VRJpa4VMQcymNCPnMSImUcFD9oV9q5c4LXp7acwFeeaWLRpBgnLUhwcF2M0mgw7NXuHnTTgbD33oF7Xq+s0QdpfaWWUnZQ0Xw/X6m7kuqTfkd82pt77IG3b13QnOJrGb96pZ6sDx89uJzXTE+wbOlSFuw3vdt1O5u34zlqExOY1L6RE447hnnzDiaRSPS4zmifIvsSsF4fTp2d1cU8BfocsAhKdApB1/kNQB5YCFwIvEdEDlXVbQAiMhd4NEzzQ6AJ+DBwl4i8UVXv6W1nIhID7gYOA34OvAS8EfgVMIng2tyY4KvPW37/TypL96e0tpwZ2x7ikLU3s3DdX3E635gJzPzCPZTudyISibF9yRLcZDkT3/1Dlu7IUb9TeWIj1CenU+ntvl7V3t7OkiVLWPnyy2zPKvP2WcWa6kN5rvRkIpLlFPdhjq95klqvianOFjL1dbQ6JbgVZ9C6bD3O2nac+HNUHLScxsULwZ/AFOAA7i/I2b40seeNhkfSURN08TZNCKrqHb2u/PDE4/hA8F79va8lRCqaidfupG3NbEj4VB9YBb5Dw+JGABKTWqk5dBZbH2jAS/tU7j+V6sPnsKzhd8yLrmJLrJo7217P4VWtrPj3Si767NcByLTcTtPqO4iXn0rZzPfhuDWAs+skct+y73PECbeR9hLc1/Rl9puwkHk15ZRHBREh1fhLsm1/DT5d7ECiyTcSiS/ipRWNzFmwkO/880Y+dsg1rGufw51Lj+YFv5UfnnIOMyqDXleqPpmW6xCngljpWxHZ8xaDuhLlmw/PwaWBk+YcxfHTS5lW0HS7ZMkS3NhyvNwKkpWfIpI4Gsfd83rD7c/XUV62glj+Nbzu6P/Z69i2p/+HXOpeAJLVXyaafN0e+Xj81QOpK/8zUvI+5tQtoMKp2+Mkm88eS1v9RUQSRxEvfStu7ACQ2K6AU+01ctU/HyIuPp8/5wxcd+/ybWgqIRI/eK/5AI7j8I3jpuIpJCJdn9yjsbkQ6/4KznET4NF6OL2qgfOO73qsg2iimx86wJGT41x9ah3lsT3Lp6/dvfedmOCDjyoZP8bKBT+g9pT7iE08rk/rAjgifOXoKpqzylGTB9Zx5vD992HZsmUcc8wxw34rxFDpLWDNGc6dq+q9wL2d54vIg8DNwAcIghPA94Aq4AhVXRymux5YCvxSRPbvQw3pQ8CRwEWq+vNw3lUicgtwiYhco6rrBvWhhsBPH32WRzYkKak9Gs/P8a5HL+CAjbt/1ZYdegaR8gl4bTupecNnidXN2jWemKryajrKTY/sZGXj7utISbeNxKQ5xBqDSmt9fT3/vut25u73DCcct4VkQ4Tml/Zl5uRHWLi1len1beSbKwjuF4dNe+Rw/a6rB34mQeOzh3b9QcLmLDfhUDY7xo5NTUSa47jJdqqPWEy0uon2bSdRUpNiwhHvxYl6bPzP93ETPnWHncXKtdVMbE6Q2d7CpBP3JxWP89AzP+OAiiXU1G2hrPZSYsndlX9Vn+Ts88g3VTD5+I8TLVnApOM9/GyeSEnwT/3bOz7ExPgW/Lap/Oi1U4g4wuN//caubcTLz2DiIWd0WzaN7rn8Y3MFr+bewCXH7rPX8mjJa8m2/Z1Y2dtIVHyk4ATWEaTLueaxDxFpzfDL+GpeedclJCK7rwWJOCQqzu92/64jfOrIw1CFCSVddwoorfs+aAZxyrtc/obD38z27duZPXt2l8vj5e/DcScRK3vbXsEOoMyZwbw5/6/bPEZi+1Ex9TaC34d7m1pXxZfe83qi0SiOM7DbwKOu0PMVtJ5deHgdZ7X7tKzf1nvibnQOVv1x1uxabp12HUfHX6b2wI/0K1h12L+m6+PbV0cddRTz5s1j8uTJg9rOSOoxYI3iybtjv9UAIlIKvBm4vyNYAahqq4hcDXyTIBA92ct2zwbagas6zf8pQRPhu9kdIEfFFY8t5cntE4kkYJ/N93DOQ+cA4DtRNtQcRsn84znwo5fvtd765jw/W9zEqy0ewWELgtWM5BrePe165pcuJ715Mo+VHMbm5TE23/k8+9dXU5qexs51x6Be+FVYCpOBfKcrNU48gp8JtpmcsYEJJzzG1jVvJfN4hNKZJUw99Ujy2RdYe+M2ohU++5y7kJJJh5De1kqsqgQ3HmXZo1dwTOnDRMpbWJl6HUcfeMVev0in/8+liFOCSBSRJUwq+PUbB7JTPsqLAmdNK9lrXRGHuoUfxfe2EkkGzThOxMWJ7D6xL5pUymNbZvLd46uJOP2/Cei0faby2ObzeefUrn/VRmIHUjH19m5P1tPnTeDp+57kWbeZCw87do9g1Vd1yZ57r4nEgoEBu1FeXk55edfBDMCNzsCtvKDf+dorDz2IxwdWKxgqyYjDrAqHJaOVATfB5VU3BHmZ+8tRyUIsFiuqYAVjZLR2EUkQdJJKAAcSXKeC4FoWwMEE56vH9l6bjrtZewxYErRpHA48q6qd+xw9SdCd6ciB5H+oLNlSz+Obkzhuno/ccxoTmlfuWrb9NV/hmgkf463zSvZYZ2fa48617Ty4fhNlkUZcmYGnEQ4sf563TbuDmYkXaH91Gqv/+AE0H2UmsDm6Gs0FTVBtq7uuRNccmaFs+lFsfWg1s99+FGWzJnDfkh+xf8uLlEzfyNKGizjujDNJHdlEclIF4jioTqbya+twIjN3NSElJ+2+1tSQP5qm2GM8vORIznzdp7tsPnHcnrsfvX1+z6NER5Mn9Lj844dWcV7ODy5MD0Bp1OGUmd13I4aeT9bJWJxfJILfY1fP7lOnZTMe6e4+a9GqhaOYkeLS20gXb+vvBlX1r/3OhMgnCa4pdVgLfFVV/xgufzvwF+DjqvrrTuseSNAs+D1V7XaMExGpJeiSf7OqvruL5duAV1R1r7q5iFxIcF2NysrKIy6++OL+fcA+aPJdXprzBiomzOStT3yCg9f9BYBtiVlsKZnPY/MvZMus11C7ZTGTNgRxO5OoouXgE3jLjL9xZHUw764lxxDZ3sICqmhdOQ83kSHfsvev6dyEdqL1u4PfBqeZQ17zLOm1s2mZ9gp3PDVjr3W8SZOZf1iWTUvzpF/t/w25D2cm8VR+Mi2ew9cr+j5K9XjyYkmOW+qC30tfW1+GO6xDPZixqsxt4OzpV+Cpy+/Wf220szMiLrvssmEfmim88t19kvB11w3iqtrvWpuITAf2J6hlHUbQ/Hedqv40XP4+4HrgAlX9f53W3Yegp98VqvqZHvYxg6AH4u9V9bwulq8HdqrqoT3ldbjGEvzyrb/llJmrePKJ6bzl8aDfSs3rP8Pkc34CwJ1r27lmaSunzUpy/kFlPL45w6otN3HmpOtRhZYV82lZMZ+yeatoePpw8q1le2w/vmAG2eWr0XwU55DV3Dr5PP53qkvT8k1Mee1BRMsS5DPPkW27i2TVJ7q9/tHRC2ogY9Zd9fg6LvzzC8F2fnxmr+lHYlw8GJkxHjs+y5UrHucjjwY/RvT8Hw3bfobTSOxjLIy7Odz7yO18Hic5BTc5cVj3M5QGUy5DMZZgb8Gl+6u/u0UJeusNuDlNVTcQ9BIEuDXsBPGUiCRV9XsE152g63FkOvphtnexrFBP2+jYTm/bGBa3LnmZLx15MztvXsqUxVuB4FEGHcEKIOoIgk/OV25duZkDIl/kzEmb8DIx6h98K60rg2tOqQ1BZ+TEZJ/M9uBRHLPfeQy5edPQZb8gU1/H3yuP5+3zJ1I5IUbl/Cm79hGJH0YkvveArIUG8+TS8xZNZ31DircsKK5286H0njmHcsPqZzlnn8NHOytmlEVrrEm4v3rrdHFdT8tF5J3AdwgGxV0BfHkoMqWqL4jIc8DHCXoHdnRSm9ZF8o55XQ8PvlsDwSOI9tqGiMQJ7sl6YEAZHgTPV5p23kzDA8tJhcEqOf94pn969/NzVD1mOtdzxcK/c+PmL3FA9BqSK6qpb55J68sLgwfzFXDKcuz/kfcA4GfzRMuTNGV8fp0+h3mTV/DMphP4RO1g+lgNTDzi8q037j/i+x1LKmIJ7n/jx0c7G8YUpQF1uhCRkwk6RiwCNgMfAX7XaczBwUoSPI0b4EWC+0K7GoGmo19zj+10quqLyLPAYSISV9XCoQiOIhjnZWBtfYNw3aO3cQZ/YfuTQUyOTZnFnK/uHltM/RTtO7/JRHkSXPjAlG+y4W9nsn3r7jG/EhMq2OfcE1jxm7/gpUqIHDERNx4EpI7XuCs813QUzzUdxexIK+4AesgZY8xo6teNBCKyUETuILh3aj/ga8B8Ve08QG5ft9dl25CIvBZYQNgDUFVbgduAk0XkkIJ0ZQT3Vr1MQQ9BEYmKyP4i0nkMkRuBEsIOFAU+Q9AP/GZGWKz9SRr+tgIAtzbJpAu+tGuZqk/7zm+RzwQfzc+71D94PJmCYAUw48zDSU6oYN75hzPvgtnItL0rorGCTnFVg7t9wxhjRkVfnzg8E/gWcA7Bif0K4NtD8LDGX4vIFILna60juI50BPAegicYfLYg7ZeB/wH+LSI/IXho5IcJmvhO73TT8DSCUSweAE4umH8VwXW5y0VkdpjmTcBbw89T8Gjc4ffcq5s5YsNteDtTOLUlTPzEImKTg2CjvtK66Tp8eQIo59UH3klmSRB1VJT9Pvg6Nt27BATK9wkCWNn0QwF4dcned5c4Bdeeast77hpujDFjUY8BS0Sqga8SXEuKATcQdDcfqhuKbwTeD7yPYKR7JQhcvyUYS3B9R0JVfUVEjge+D3yJ3WMJvqEvwzKF28iKyCkEYwm+l91jCX4KGPG79xY/9kuOfuhFACrOOAMnsR00i3o+y397K+0bkkx76xQcvYDMkq271kvtn6R87iT2m9v7owC6UltZ1nsiY4wZY3qrYa0mGJz6aYIg8TyAiHT7bPD+1LpU9Wb60Qynqi8Bb+lDurV08yxTVW0EPhlOo+qgnfeDrzTO2p/Zh59Mru0voDnqn3qF9g3BSN5+2+nUP9W6x3q6cHC9i5LdjL9mjDFjWW8Bq2PYgUVAX2ox2odtGuDfyzYwc/0yfCB53Dm0r0/Q8MIJTDk1x+b7dt9U2/B8FdmGJtzqUlLJLaTjDlVT9374YX/MrrAiMsYUn97OXD12azcDt/ile5m+qQFchwUnXsQL37kDOIDMNo986+7LcemtwaCp5SceyPd2BKNH/98AhxX6wQnVrGvJc2Ct9bowxhSfHke6MLuJyJnAmTNmzPjwHXfc0Wv63jT8+Qpq115NZsZM5JDr8J7bssfyxNRtpDeFd8AnI6RPP4CfbQ4eaP256fUku3qcqjHGjFELFy4c9pEuTEhVbwNuW7Ro0YcHOvxJx7AmXt5j2Zags0UqeSTx57fukc5JpKg6sIIt4e3SlbMmss+B+8PmHQAsOvjAHkecGC/D84zHoZmG23gpl/+GoZmKcT8jVS7dGXDACsfwew9BF/JlwP9T1VTPaxmAhx9+lrpMMKZe0v0ffH/P2lLJ9E1Ey06kY4CPRF051QmX02YlqU06gxoeyRhjilVv3dovAC4C3qiqmwrmnwr8leAmXCHobPERETkuvMnX9GDL03cxQVP4kWn4zcED8srmRmhdFTxvKjF5O9HSGXQErPiEYJzADy7o/hlGxhgz3vU20sUZQL5TsBKC+6RKCMb5ezNwLcHIFEP/3I1xqLb5JQD8+AEAuCUxKvdL7FqenJLEje9+5lKizgKVMcb0FrAOAe7uNO84YDbBYzq+qqq3q+oFwH+As4Y8h+NQVWswoIYfnQVA5b5TiJTsruwmJ8/cNQYgQKJuz6f/GmPMf6PeAtYEgpuHCx1P0ATY+YbfOwhGbTe9iO8KWLMBKN9nIn5ud1f1RNUBe6SPlI3u48SNMWYs6K3TRZ5gCKRCHc+96vy4+h10/6wpE8o11+NmtqASQ8OAVTK1Bo3UAztITN6KEzmGkqnVlM+bRNmMOutkYYwx9B6w1hI0Af4CQERc4ETgZVVt6JS24xH0pgcbng8eueVH54IEhz9eU4oSZfb5f8CNZ3AiH0PEYd8PvnY0s2qMMWNKb02CtwDvEJFPisiBBAPPTiDoIdjZUcCIjnZejNa/EDzrSqO7HwHiJmKothEpSSGuj4iNRGGMMZ31VsP6GXAeweNEIOjC/irw48JEIlIJnA5cPtQZHG9028rg1d1zpPVI/Eic6DyiiRNGI1vGGDPm9To0k4iUEzzwcB7BoziuDkc8L0xzDMGzsn6tqsuGJ6uja6iGZmr7f+dQvuMFUvM+BJyGO7+GyIKJQ5ZPY4wZi4ZiaCZU1aZ+TEcccYQO1GWXXaaLPzpNl56H3v/z/1Xf9we8rZ68+OKLw7Ld8bgP1aBchttIfZbxUi4jUSaq4+d4jdR+BlMuwNM6yPNvr0Mzhc++Op+ghlUP/EnHaS1qJLi5ZgAaS6ut958xxvRDb0MzTQceB6aw+4GIXxSRN6vqXcOduXFHfSTXBkCudloviY0xxhTqrZfgpQTB6pfAmcBngXaCzhimn2J+GsFHpZSquumjnR1jjCkqvTUJngLcrKoXhe//KSKNwNUiso+qdh4Fw/Qgmg+aA9WtYO5MGxTEGGP6o7ca1lTg/k7z/kPQPDi457T/F4pJ0ByoTjnTJ88Y5dwYY0xx6S1gRYGWTvM63tvdrf1UFssEf0RLiEbs2ZnGGNMfvQUsCAa67c98041EJOy34lqwMsaY/urLmfP7IvLlgvcuQbC6WiRs49pNVfWQIcvdOJOQIMar25ffCcYYYwr1ONKFiKylnzUpVZ0zyDyNSUMx0sXG27/DjJf+RLbuWCLnXzm0GTTGmDHMRroospEu7vz223XpeeiznzthwNvoi/Fy976NdDE29zPWR1Toj/FyvEZqP6M90oW1TY2giO8BoI7bS0pjjDGdWcAaQa5awDLGmIGygDWC3LCG5TvWS9AYY/rLAtYIcjpqWGIByxhj+ssC1ghywhqW51qToDHG9JcFrBHkWqcLY4wZMAtYI8jRPACeXcMyxph+69eZU0RKCAa9LSF4zMgmVW0fjoyNR9LRJGjXsIwxpt/68sThJPBp4Fxgf3Y/yBFARWQF8AfgCgtePesIWL5dwzLGmH7rbWimOoLHiRwErCZ4+vBGIA0kgGnA0cBcYBlwsqpuH+Y8j4qhGJopf+35xOqfZuMB5zHljM8PbQaNMWYMG/ahmYCrCB4nckYv6c4M01012KE3xvo0mKGZFl98mC49D73zBxcMeBt9MV6Gm7Ghmcbmfsb6EED9MV6O10jtZ6wPzXQm8GNVvb2XoHcbcHmY3nRDtKNbe3SUc2KMMcWnt4BVAWzo47Y2hOlNN3Zfw7JOF8YY01+9BazlwHtFRHpKFC5/L7BiqDI2LvlBt3bfsRqWMcb0V28B66fAa4FHReRsEdlHROIAIhIP358DPAq8BvhJf3YuIvuKyDdF5HERqReRFhFZLCJfEZHSTmm1l+krfdjfyT2s32Oz51DYVcOKxIZ7V8YYM+702DalqteLSC3wLeD3HfM7VbgESAGfV9Xr+7n/DwKfAP4B/BHIEQTIbwPvEpFjVDUVpn1fN9u4jKCX4m392O+VwEOd5vW16XPg1A9eIvFh35Uxxow3vV5MUdWfiMj1wFuBRXS6cRh4Cvi7Dqw7+1+A76lqU8G834jIy8BXgAuAX4T5+EPnlUVkOjCHoPfJC/3Y72NdbW/YhU2CRK2GZYwx/dWnq/+qugO4OpyGjKo+3c2imwgC1oJeNnE+QbNmv/MVNjl6qpru77oDFvYSlKhdwzLGmP4aq2MJTg9ft3aXIOzocT5BTe/Gfm7/CqAVSInIShH5dG8dS4ZE2CToRqyXoDHG9FefzpzhGIJvAo4kGN2io0lwI/A0cIeqtg1FhkTEBb4O5IEbekj6OoLmwGtVtbmPm88RXC+7g6A5cypBs+NPgUMJAuDwCUcVcew+LGOM6bceh2YCEJH3Az8CathzHMFCDcBnVfXaQWdI5OfAJ4FLVPV7PaS7EXgPcKKqPjyI/TkEAey07rYlIhcCFwJUVlYecfHFFw9oX2/f8Cuc7DbuXXAxO9srB5plY4wpOpdddtmwD830DsAHniAY/HYekCQIXMnw/fuAJwEPeMdght0g6I2owG97SVdNMJ7hS4Md6iPc3mvC/X63t7SDGZppyQUTdOl56B23/mbA2+iL8TLcjA3NNDb3M9aHAOqP8XK8Rmo/oz00U29Ngl8E7gdOUQ0vwOyWAl4BXhGRPwL3Al8i6PnXbyJyGfBV4Brgo70kPxeIA78byL66sDZ8rRui7XUjqM2KNQkaY0y/9dbp4iDghi6C1R7C5TcABw4kEyJyKXApcD3woTAa9+QCgutR/b3vqzvzw9duO3kMiV2dLuzxIsYY01+9BaxWgk4WfTEtTN8vIvJ1gpt/fw+c31twFJFFwCHAbaq6rZs0URHZX0Rmdppf20XaeLh/6N/NxwPQ0enC7sMyxpj+6q1J8E7gsyLylKp2+xAoETkduBi4tT87F5FPAN8A1gP3AGd36l2+VVXv7rTaBeFrT/deTQNeAh4ATi6Yf6eIbAKeYXcvwXMJalg/V9Un+5P/fuuoYdngt8YY0299uYZ1AnCbiKxi9wMcMwTXkKYBxxAMjbSe4BpWfxwZvs4Eruti+QPAroAVPv34vQTDKN3Vz31BcH3tLOBTQBXQBjwHXKqq/b2XawCCGpYbsWtYxhjTX72NJbhZRI4AvkwQKM7tItlG4MfA91V1Z392rqofAD7Qj/QpgkDTW7q1dNEFX1V/APygr/sbehawjDFmoPoylmAD8AXgCyIymU5jCarqluHN4jiyq9OFXcMyxpj+6tfFlDA4WYAasKCGFbHBb40xpt96HenCBETkTODMGTNmfPiOO7rtf9LzNn50FKIp1p59CzOn7Tu0GTTGmDFs4cKFwzvSRX8mgudarR6q7Y3VaVAjXbw/rkvPQ5cuf3bA2+iL8XL3vo10MTb3M9ZHVOiP8XK8Rmo/oz3SxVCO1l4FzBrC7Y1DQW02GkmMcj6MMab4jNXHi4xPYaeLeMyeOGyMMf3VY6cLEfFGKiP/HYIaVixuNSxjjOmv3noJKsGIEM/3YVvzAOtJ0I2gCbejhlUyupkxxpgi1FvAeplgeKQze9uQiHwF+OaQ5GocyudzyK4aljUJGmNMf/V2DesZ4LCRyMh4l8/nAFCEeMxGujDGmP7qLWA9BOzsPOp5N55n6B73Me7ksu3hX4Lr2ONFjDGmv3oMWKr6W1Wdo6rre9uQqt6uqucPXdbGl2xmd8DqNCK9McaYPrBu7SMkk80Ef4gdcmOMGQgbmqmPBjs0U/22tUy67kxUYujnnhn6DBpjzBg2YkMzAZXAJcAjQD3B87DqgYcJnoFVMdghN4plGujQTMuXPaFLz0OXvD8xoPX7Y7wMN2NDM43N/Yz1IYD6Y7wcr5Haz2gPzdTraO0icjBwBzCF4BlTLcA2oAI4Lpw+LiJvUNVlg4qe41jey4QP6LImQWOMGYgez54ikgBuASYA3wXmqGqlqs5Q1UpgTjh/EvBXEbEbjLqRy2aDP6zDhTHGDEhvP/ffA8wFzlbVr6nqusKFqrpOVb9K8CTifcP0pgteeB+W1bCMMWZgejt7vhl4UlVv6SmRqv4ZeBJ4y1BlbLzJ5a2GZYwxg9FbwDoE+Hcft/XvML3pQr4jYGEByxhjBqK3gDUB6PWm4dD6ML3pgt/RJGj3YRljzID0dvYsBdp7SdMhFaY3XfDz+fAvq2EZY8xA9Baw7Ow6RDyvo9OFHVJjjBmIXu/DAj4rIn3p/TdtsJkZz3wveBamWpOgMcYMSI9DM4mI38/tqaqOy6HIBzs004rFd3DA3V/Ej06Ez9w79Bk0xpgxbMSGZrJp8EMz3fnXn+nS89AXPzR5QOv3x3gZbsaGZhqb+xnrQwD1x3g5XiO1n9Eemsnap0ZIR5OgXcMyxpiBGVTAEpGpInKkiFQNUX7GLd8LewnajcPGGDMgvY0leKiI/K+ITOg0v05E/gW8CjwObBWRrw9jPoufdtSwrFJrjDED0dvZ86PAF4GdneZfDZwGrAH+BjQAl4rIWUOdwfFC82HAshqWMcYMSG8B61jgH6q7qgeIyCyCMQafBw5S1XcAC4GNwIeHK6PFz5oEjTFmMHoLWFOB5Z3mvS58/ZWqZgBUtR74A3D40GZv/FC/4w4BC1jGGDMQvQWsMqCx07yjAAX+02n+KqBmaLI1DnlhwLIbh40xZkB6O3tuAOZ1mncc0Kiqr3SaHwFahypj441oELDUaljGGDMgvY10cSNwErBIVTeLyLHAI8CfVfXdndJeAbxOVRcOZ4ZHy2BHulj50DXs//jleKVzkY/fOuT5M8aYsWzYR7ogeL5VDmgieEBjG0HvgeO6SLsKuHqwdzKP9WmgI13c9tvP69Lz0Bc+Pn9A6/fHeLl730a6GJv7GesjKvTHeDleI7WfMT3Shao+D7yV4FlXCwm6sb9bVR8tTCcipwETgX8NKnqOY9bpwhhjBqfX0dpV9Xbg9l7S3AWUD1WmxiNHOzpdWMAyxpiBsC5rI6Wj04X1EjTGmAHp9ewpIjNE5NMi8jERmVgw7wYR2SIibSLygIicOPzZLWJqTYLGGDMYvY0luD/wInA58EvgBRHZF7gfeA8QB3zgROBuETmiPzsXkX1F5Jsi8riI1ItIi4gsFpGviEhpp7SXiYh2M32uH/usFJGfi8hGEUmLyNIwGA9rJBFfOzIwnLsxxphxq7drWF8AYsBngM3At4FbgBLgGFV9EkBETg3nfwl4Zz/2/0HgE8A/gD8S9Eh8bbifd4nIMaqa6rTOxcD2TvOe6cvORCQG3A0cBvwceAl4I/ArYBJwWT/y3j8do1tZk6AxxgxIbwHrNcBVqvpzABFpA/4JfKEjWAGo6t0icjVwdj/3/xfge6raVDDvNyLyMvAV4ALgF53WuVVV1/ZzPx0+BBwJXNTxmYCrROQW4BIRuUZV1w1w2z2SXfe7WQ3LGGMGoi9jCb5Q8P7F8HVZF2mXALX92bmqPt0pWHW4KXxd0NV6IlIhIr32cOzC2UA7cFWn+T8FosC7O68wVCSsYak1CRpjzID0FrDiQGGTXMff6S7SZvqwvb6aHr5u7WLZCwQ3MqdF5FEReWNfNigiDsHgvM+pauf8P0lwLe7IAea3dx01LGsSNMaYAeltaCYfOFdVbwjf1wL1wCmqel+ntOcA16uqO6gMibjAw8AiYIGqrgjnfwY4AHiU4Plb+xFcW5sCfFBVr+1lu7UE175u1k7DSoXLtwGvqOpxXSy7ELgQoLKy8oiLL76435/roJoW5i9fTn5iNbfvNTyjMcaMb5dddtmwD83kEzw25H/D6auAB/y6YF7H9EfAG+zQGwSdIRT4ch/S1hJ0BmkAynpJOyPc7vXdLF8PLO5tnwMdmum3t1ylt117lV5z/W8HtH5/jJfhZmxoprG5n7E+BFB/jJfjNVL7Ge2hmfpyHehs9u5M8ZHu4l8fttctEfkW8EngSlX9Xm/pVXWHiPyGoHffccC/e0jeHr7Gu1meKEgz5A7cZxL/fuIxZk6dMFy7MMaYca23gPXaEckFwX1WBDW4a4CP9mPVteFrXS/pGgiuwU3rYt9xgtraA/3Yb7+ccOiZ3Pv3Z/nQR/rfnGiMMaaXgKWqw3YCLyQilwKXAtcDHwqrj301P3ztqoPGLqrqi8izwGEiEtfwacmhowg6jDzdj/0aY4wZQaPeZU1Evk7QpPd74HzVXWMYFaaJiEhlF/NnAB8DdhB0xuiYHxWR/UVkZqdVbiS46fnCTvM/Q/DYlJsH/kmMMcYMp4HcyzRkROQTwDcIOjzcA5zdaYSkrap6N1AGrBGRWwlGp+joJfihcNl7dc8RMaaF6R4ATi6YfxVwPnC5iMwO07yJ4BEq31bVNUP7CY0xxgyVUQ1Y7L7vaSZwXRfLHyAYSilFMPTT0cBZBEFqO0GQ+6EWjLrRE1XNisgpBEM/vZfgutUq4FMEYyUaY4wZo3q8D8vsTUTqgYEO31TH3uMgDodKgpurbR99MxLlMlKfZbyUi/2vjM39DKZcZqnqoLpJW8AaQSLytA72xrm+7edKVe18nc720f1+hr1cRvCzjItysf+VsbmfkSqX7ox6pwszLG6zfYw5I/VZrFz6Zzwdr/FULl2yGtYIGu1fJ6ZrVi5jj5XJ2DTa5WI1rJF15WhnwHTJymXssTIZm0a1XKyGZYwxpihYDcsYY0xRsIBljDGmKFjAMsYYUxQsYA0zEXFE5GIRWS4iaRF5VUR+LCKlo5238UJE9hWRb4rI4yJSLyItIrJYRL7S1XEWkf1E5FYRaRCRNhF5SERe1822rfyGiIiUiMgaEVER+UUXy61cRoiI1IjIj0TklfD41YvIf0TkxE7pxlSZjPbQTP8NfgJcBPwN+DHBU5MvIhg1/pSuBvs1/fZB4BPAPwgeJJojeDTOt4F3icgxHWNNishcgoGS88APCUYG+DBwl4i8UVXv6bRtK7+h8026eQyQlcvIEZFZwP0EQ9z9DlhJMErGwRQ8fmlMlslgnwBpU49POT6I4KnNt3Sa/ymCh12ePdp5HA8TsAio7GL+t8Pj/MmCeTcTPDX70IJ5ZQTDba0g7Dlr5TfkZXR4eOL73/DY/aLTciuXkSuLh4BXgSm9pBtzZWJNgsPrvYAAP+00/yqCpxufO9IZGo9U9WlV7WoMtZvC1wUAYdPEm4H7VXVxwfqtwNXAvuwekBms/IaEiLgEx+xO4K9dLLdyGSEichJwAsGg4ZvDRzGVdJFuTJaJBazhdSTBr449RpNX1TSwmD0L3Ay96eFrx8M9DwbiwGNdpH08fC0sEyu/oXExsD/wyW6WW7mMnDeFr+tF5DaCJ2G0ichKESkMKmOyTCxgDa+pwHbd8+nGHTYCdSISG+E8/VcIf9V/naAZ6oZw9tTwdWMXq3TMm1Ywz8pvkERkDsEz776pqmu7SWblMnL2C1+vAmqA9wMXAFng9yJyfrh8TJaJdboYXiVAVwUIkC5Ikx2Z7PxX+SlwDHCJqq4I53U0fXRVJulOaTr+tvIbnF8Da4DLe0hj5TJyysPXFuC1qpoFEJG/AauB74rIdYzRMrEa1vBqJ6hWdyVRkMYMIRH5FkHz05Wq+r2CRR3Huqsy6ao8rPwGIWxiej3wUVXN9ZDUymXkdDyZ/caOYAWgqg0EvWwnE9TCxmSZWMAaXpsIqsJdFeQ0giq0/QocQiJyGfBV4Brgo50Wbwpfp7G3jnmFTSBWfgMUHrPLgTuALSIyT0TmAbPCJJXhvCqsXEbShvB1SxfLNoev1YzRMrGANbyeIjjGRxXOFJEEcCjw9CjkadwSkUuBS4HrgQ9p2K+2wIsEzRbHdrH6MeFrYZlY+Q1cEpgAnA68XDDdHy4/N3z/IaxcRlJHp4jpXSzrmLeNsVomo31PwHiegIX0fG/CuaOdx/EyEXSwUIJg5fSQ7s8E95YcUjCv496Slex5b4mV38DLIwq8o4vpY+Gx+1f4fl8rlxEtl2qgmaCmVVYwfwrQCqwsmDfmymTUD+B4n4CfhwX2V4Jfkz8mGInh/p5OrDb16xh/IjzG64DzCH69F06nFqSdB+wk6Or+JeDjwHMEvQlPs/Ib9rKaTdc3Dlu5jFwZXBgeuyUEN3J/KfzfyQKvH8tlMuoHb7xPgAt8luDO8AxBu+/lhb9ubBr0Mb42/Efpbrq/U/oDgL8DjQQXgh8GTrHyG5Gy6jJgWbmMeDm8jeB+qjaCHoP/Bo4f62ViD3A0xhhTFKzThTHGmKJgAcsYY0xRsIBljDGmKFjAMsYYUxQsYBljjCkKFrCMMcYUBQtYxhhjioIFLGPMHkTkfhFZO9r5MKYzC1jGjAAROVlEtIcpP9p5NGasswc4GjOybiR45EZn/khnxJhiYwHLmJH1rKr+YbQzYUwxsiZBY8YQEZkdNhFeJiLvFZEXRCQtIuvDeXv9yBSRg0XkbyKyI0y7TES+ICJuF2kni8jPRGS1iGREZJuI3C0ip3aRdqqI3CgiDSLSJiJ3ici+w/XZjemN1bCMGVklIlLXxfysqjYXvD8T+AzwS4Knw76Z4OGUs4DzOxKJyCLgAYLHOHSkPRP4AXAIcE5B2tnAI8AkgueGPQ2UEjyQ7xTg7oL9lwIPEozofQkwB/g08HcRWaCq3kA+vDGDYaO1GzMCRORk4D89JPmnqp4RBpU1BNe0jlTVZ8P1heA5Q2cBx6rq4+H8R4CjgcNV9YWCtDcB7yR4FMS94fw7gDcCb1DVuzrlz1FVP/z7fuA1wBdV9YcFaT4P/LCr9Y0ZCdYkaMzIuhI4tYvpK53S3d0RrAA0+GXZETzeCiAiE4HjgH90BKuCtN/tlLYGeANwZ1fBpiNYFfCBn3Wad1/4Or/XT2nMMLAmQWNG1suqek8f0r3Uxbxl4es+4euc8HVpN2n9grTzACF4YmxfbFLVdKd5O8LX2j5uw5ghZTUsY8amvrTVSz+215G2r9cAerpG1Z/9GjNkLGAZMzYd2MO81Z1eD+oi7f4E/98daV4mCFaHDVUGjRlpFrCMGZtOFZHDO96EHSm+EL69FUBVtwGPAmeKyIJOab8cvv1bmHYn8C/gjSJySuedhesYM6bZNSxjRtbhInJuN8tuLfj7eeA+EfklsBl4C0HX89+r6mMF6T5N0K39oTDtFuAM4DTgho4egqFPEgS4f4nIdcAzQJKgl+Fa4IuD+2jGDC8LWMaMrPeGU1fmAx1jCv4DWEFQU9oP2AZ8K5x2UdWnReQ44BvAxwnun1pNEHx+3CntmvC+ra8BbwLOAxoIguOVg/1gxgw3uw/LmDGk4D6sb6jqZaObG2PGFruGZYwxpihYwDLGGFMULGAZY4wpCnYNyxhjTFGwGpYxxpiiYAHLGGNMUbCAZYwxpihYwDLGGFMULGAZY4wpCv8fluAGGe1F6g8AAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 432x180 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "data = get_data([\"validate.BSD100.psnr_scale_2\", \"Epoch\"])\n",
     "filtered_x = data.filter(regex=(\"Epoch_rfdn_advanced_600_epochs_no_batchnorm_pruning_([0-9]*|none).csv\")).head(600).transpose()\n",
@@ -902,23 +236,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "5a5eb199",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x180 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "data = get_data([\"validate.Urban100.psnr_scale_2\", \"Epoch\"])\n",
     "filtered_x = data.filter(regex=(\"Epoch_rfdn_advanced_600_epochs_no_batchnorm_pruning_([0-9]*|none).csv\")).head(600).transpose()\n",
@@ -932,20 +253,7 @@
    "execution_count": null,
    "id": "e41bb476",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x180 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "data = get_data([\"test.BSD100.psnr_scale_2\", \"config.batch_size_test\"])\n",
     "filtered_y = data.filter(regex=(\"test\\.BSD100\\.psnr_scale_2_rfdn_advanced_batch_size_test.*\")).head(1)\n",
@@ -955,23 +263,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "id": "8704d843",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x180 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "data = get_data([\"train.time\", \"config.batch_size\"])\n",
     "filtered_y = data.filter(regex=(\"train\\.time_rfdn_advanced_batchsize*\")).mean()\n",
@@ -981,23 +276,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": null,
    "id": "88a7d51d",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x180 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "data = get_data([\"validate.Urban100.psnr_scale_2\", \"Epoch\"])\n",
     "filtered_x = data.filter(regex=(\"Epoch_rfdn_advanced_600_epochs_no_batchnorm_pruning_[0-9]+_(cawr_lr|lr)_.*.csv\")).head(600).transpose()\n",
@@ -1008,303 +290,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": null,
    "id": "98256934",
    "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>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "      <th>4</th>\n",
-       "      <th>5</th>\n",
-       "      <th>6</th>\n",
-       "      <th>7</th>\n",
-       "      <th>8</th>\n",
-       "      <th>9</th>\n",
-       "      <th>...</th>\n",
-       "      <th>90</th>\n",
-       "      <th>91</th>\n",
-       "      <th>92</th>\n",
-       "      <th>93</th>\n",
-       "      <th>94</th>\n",
-       "      <th>95</th>\n",
-       "      <th>96</th>\n",
-       "      <th>97</th>\n",
-       "      <th>98</th>\n",
-       "      <th>99</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Epoch_rfdn_advanced_600_epochs_no_batchnorm_pruning_8_cawr_lr_3_5.csv</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>91.0</td>\n",
-       "      <td>92.0</td>\n",
-       "      <td>93.0</td>\n",
-       "      <td>94.0</td>\n",
-       "      <td>95.0</td>\n",
-       "      <td>96.0</td>\n",
-       "      <td>97.0</td>\n",
-       "      <td>98.0</td>\n",
-       "      <td>99.0</td>\n",
-       "      <td>100.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Epoch_rfdn_advanced_600_epochs_no_batchnorm_pruning_8_lr_3_5.csv</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>91.0</td>\n",
-       "      <td>92.0</td>\n",
-       "      <td>93.0</td>\n",
-       "      <td>94.0</td>\n",
-       "      <td>95.0</td>\n",
-       "      <td>96.0</td>\n",
-       "      <td>97.0</td>\n",
-       "      <td>98.0</td>\n",
-       "      <td>99.0</td>\n",
-       "      <td>100.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2 rows × 100 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                     0    1    2    3    4   \\\n",
-       "Epoch_rfdn_advanced_600_epochs_no_batchnorm_pru...  1.0  2.0  3.0  4.0  5.0   \n",
-       "Epoch_rfdn_advanced_600_epochs_no_batchnorm_pru...  1.0  2.0  3.0  4.0  5.0   \n",
-       "\n",
-       "                                                     5    6    7    8     9   \\\n",
-       "Epoch_rfdn_advanced_600_epochs_no_batchnorm_pru...  6.0  7.0  8.0  9.0  10.0   \n",
-       "Epoch_rfdn_advanced_600_epochs_no_batchnorm_pru...  6.0  7.0  8.0  9.0  10.0   \n",
-       "\n",
-       "                                                    ...    90    91    92  \\\n",
-       "Epoch_rfdn_advanced_600_epochs_no_batchnorm_pru...  ...  91.0  92.0  93.0   \n",
-       "Epoch_rfdn_advanced_600_epochs_no_batchnorm_pru...  ...  91.0  92.0  93.0   \n",
-       "\n",
-       "                                                      93    94    95    96  \\\n",
-       "Epoch_rfdn_advanced_600_epochs_no_batchnorm_pru...  94.0  95.0  96.0  97.0   \n",
-       "Epoch_rfdn_advanced_600_epochs_no_batchnorm_pru...  94.0  95.0  96.0  97.0   \n",
-       "\n",
-       "                                                      97    98     99  \n",
-       "Epoch_rfdn_advanced_600_epochs_no_batchnorm_pru...  98.0  99.0  100.0  \n",
-       "Epoch_rfdn_advanced_600_epochs_no_batchnorm_pru...  98.0  99.0  100.0  \n",
-       "\n",
-       "[2 rows x 100 columns]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x180 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "data = get_data([\"lr\", \"Epoch\"])\n",
     "filtered_x = data.filter(items=[\"Epoch_rfdn_advanced_600_epochs_no_batchnorm_pruning_8_cawr_lr_3_5.csv\", \"Epoch_rfdn_advanced_600_epochs_no_batchnorm_pruning_8_lr_3_5.csv\"]).head(100).transpose()\n",
     "filtered_y = data.filter(items=[\"lr_rfdn_advanced_600_epochs_no_batchnorm_pruning_8_cawr_lr_3_5.csv\", \"lr_rfdn_advanced_600_epochs_no_batchnorm_pruning_8_lr_3_5.csv\"]).head(100).transpose()\n",
-    "display(filtered_x)\n",
-    "lables = [\"CAWR\", \"Default\"]\n",
+    "lables = [\"CAWR\", \"Multistep\"]\n",
     "make_line_chart(filtered_x.values, filtered_y.values, lables, \"Epoch\", \"Learning Rate\", \"epoch_lr_cawr.svg\", legend_location=\"upper center\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": null,
    "id": "fcd550d8",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "num_parameters_rfdn_advanced_batchnorm_final_2x_09.csv    737\n",
-       "num_parameters_rfdn_advanced_final_2x_09.csv              734\n",
-       "num_parameters_rfdn_advanced_final_3x_09.csv              742\n",
-       "num_parameters_rfdn_advanced_pruning_final_2x_09.csv      173\n",
-       "num_parameters_rfdn_advanced_pruning_final_3x_09.csv      181\n",
-       "num_parameters_rfdn_final_2x_09.csv                       684\n",
-       "dtype: int64"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "test.BSD100.psnr_scale_2_rfdn_advanced_batchnorm_final_2x_09.csv    30.80\n",
-       "test.BSD100.psnr_scale_2_rfdn_advanced_final_2x_09.csv              30.60\n",
-       "test.BSD100.psnr_scale_3_rfdn_advanced_final_3x_09.csv              26.59\n",
-       "test.BSD100.psnr_scale_2_rfdn_advanced_pruning_final_2x_09.csv      30.28\n",
-       "test.BSD100.psnr_scale_3_rfdn_advanced_pruning_final_3x_09.csv      26.47\n",
-       "test.BSD100.psnr_scale_2_rfdn_final_2x_09.csv                       30.81\n",
-       "dtype: float64"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "test.Urban100.psnr_scale_2_rfdn_advanced_batchnorm_final_2x_09.csv    29.90\n",
-       "test.Urban100.psnr_scale_2_rfdn_advanced_final_2x_09.csv              29.46\n",
-       "test.Urban100.psnr_scale_3_rfdn_advanced_final_3x_09.csv              24.68\n",
-       "test.Urban100.psnr_scale_2_rfdn_advanced_pruning_final_2x_09.csv      28.22\n",
-       "test.Urban100.psnr_scale_3_rfdn_advanced_pruning_final_3x_09.csv      24.26\n",
-       "test.Urban100.psnr_scale_2_rfdn_final_2x_09.csv                       29.89\n",
-       "dtype: float64"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "test.Set5.psnr_scale_2_rfdn_advanced_batchnorm_final_2x_09.csv    35.73\n",
-       "test.Set5.psnr_scale_2_rfdn_advanced_final_2x_09.csv              35.42\n",
-       "test.Set5.psnr_scale_3_rfdn_advanced_final_3x_09.csv              31.90\n",
-       "test.Set5.psnr_scale_2_rfdn_advanced_pruning_final_2x_09.csv      35.04\n",
-       "test.Set5.psnr_scale_3_rfdn_advanced_pruning_final_3x_09.csv      31.40\n",
-       "test.Set5.psnr_scale_2_rfdn_final_2x_09.csv                       35.72\n",
-       "dtype: float64"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Series([], dtype: float64)"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "test.BSD100.ssim_scale_2_rfdn_advanced_batchnorm_final_2x_09.csv    0.9010\n",
-       "test.BSD100.ssim_scale_2_rfdn_advanced_final_2x_09.csv              0.8983\n",
-       "test.BSD100.ssim_scale_3_rfdn_advanced_final_3x_09.csv              0.7864\n",
-       "test.BSD100.ssim_scale_2_rfdn_advanced_pruning_final_2x_09.csv      0.8939\n",
-       "test.BSD100.ssim_scale_3_rfdn_advanced_pruning_final_3x_09.csv      0.7820\n",
-       "test.BSD100.ssim_scale_2_rfdn_final_2x_09.csv                       0.9013\n",
-       "dtype: float64"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "test.Urban100.ssim_scale_2_rfdn_advanced_batchnorm_final_2x_09.csv    0.9310\n",
-       "test.Urban100.ssim_scale_2_rfdn_advanced_final_2x_09.csv              0.9258\n",
-       "test.Urban100.ssim_scale_3_rfdn_advanced_final_3x_09.csv              0.8187\n",
-       "test.Urban100.ssim_scale_2_rfdn_advanced_pruning_final_2x_09.csv      0.9105\n",
-       "test.Urban100.ssim_scale_3_rfdn_advanced_pruning_final_3x_09.csv      0.8061\n",
-       "test.Urban100.ssim_scale_2_rfdn_final_2x_09.csv                       0.9314\n",
-       "dtype: float64"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "test.Set5.ssim_scale_2_rfdn_advanced_batchnorm_final_2x_09.csv    0.9500\n",
-       "test.Set5.ssim_scale_2_rfdn_advanced_final_2x_09.csv              0.9483\n",
-       "test.Set5.ssim_scale_3_rfdn_advanced_final_3x_09.csv              0.9091\n",
-       "test.Set5.ssim_scale_2_rfdn_advanced_pruning_final_2x_09.csv      0.9466\n",
-       "test.Set5.ssim_scale_3_rfdn_advanced_pruning_final_3x_09.csv      0.9038\n",
-       "test.Set5.ssim_scale_2_rfdn_final_2x_09.csv                       0.9501\n",
-       "dtype: float64"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Series([], dtype: float64)"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "data = get_data([\"num_parameters\", \"test.BSD100.psnr_scale_2\", \"test.BSD100.psnr_scale_3\", \"test.BSD100.psnr_scale_4\", \"test.Urban100.psnr_scale_2\", \"test.Urban100.psnr_scale_3\", \"test.Urban100.psnr_scale_4\", \"test.Set5.psnr_scale_2\", \"test.Set5.psnr_scale_3\", \"test.Set5.psnr_scale_4\", \"test.Set14.psnr_scale_2\", \"test.Set14.psnr_scale_3\", \"test.Set14.psnr_scale_4\", \"test.BSD100.ssim_scale_2\", \"test.BSD100.ssim_scale_3\", \"test.BSD100.ssim_scale_4\", \"test.Urban100.ssim_scale_2\", \"test.Urban100.ssim_scale_3\", \"test.Urban100.ssim_scale_4\", \"test.Set5.ssim_scale_2\", \"test.Set5.ssim_scale_3\", \"test.Set5.ssim_scale_4\", \"test.Set14.ssim_scale_2\", \"test.Set14.ssim_scale_3\", \"test.Set14.ssim_scale_4\"])\n",
-    "num_parameters = data.filter(regex=(\"num_parameters_.*\")).min().transpose()\n",
+    "data = get_data([\"num_parameters\", \"test.mean_forward_pass_time\", \"validate.BSD100.psnr_scale_2\", \"validate.BSD100.psnr_scale_3\", \"validate.BSD100.psnr_scale_4\", \"validate.Urban100.psnr_scale_2\", \"validate.Urban100.psnr_scale_3\", \"validate.Urban100.psnr_scale_4\", \"validate.Set5.psnr_scale_2\", \"validate.Set5.psnr_scale_3\", \"validate.Set5.psnr_scale_4\", \"validate.Set14.psnr_scale_2\", \"validate.Set14.psnr_scale_3\", \"validate.Set14.psnr_scale_4\", \"validate.BSD100.ssim_scale_2\", \"validate.BSD100.ssim_scale_3\", \"validate.BSD100.ssim_scale_4\", \"validate.Urban100.ssim_scale_2\", \"validate.Urban100.ssim_scale_3\", \"validate.Urban100.ssim_scale_4\", \"validate.Set5.ssim_scale_2\", \"validate.Set5.ssim_scale_3\", \"validate.Set5.ssim_scale_4\", \"validate.Set14.ssim_scale_2\", \"validate.Set14.ssim_scale_3\", \"validate.Set14.ssim_scale_4\"])\n",
     "\n",
-    "psnrBSD100 = data.filter(regex=(\"test\\.BSD100\\.psnr_scale_.*\")).max().transpose()\n",
-    "psnrUrban100 = data.filter(regex=(\"test\\.Urban100\\.psnr_scale_.*\")).max().transpose()\n",
-    "psnrSet5 = data.filter(regex=(\"test\\.Set5\\.psnr_scale_.*\")).max().transpose()\n",
-    "psnrSet14 = data.filter(regex=(\"test\\.Set14\\.psnr_scale_.*\")).max().transpose()\n",
+    "num_parameters = data.filter(regex=(\"num_parameters_.*last.*\")).min().transpose()\n",
     "\n",
-    "ssimBSD100 = data.filter(regex=(\"test\\.BSD100\\.ssim_scale_.*\")).max().transpose()\n",
-    "ssimUrban100 = data.filter(regex=(\"test\\.Urban100\\.ssim_scale_.*\")).max().transpose()\n",
-    "ssimSet5 = data.filter(regex=(\"test\\.Set5\\.ssim_scale_.*\")).max().transpose()\n",
-    "ssimSet14 = data.filter(regex=(\"test\\.Set14\\.ssim_scale_.*\")).max().transpose()\n",
+    "inference_time = data.filter(regex=(\"test\\.mean_forward_pass_time.*last.*\")).min().transpose()\n",
+    "\n",
+    "psnrBSD100 = data.filter(regex=(\"validate\\.BSD100\\.psnr_scale_.*last.*\")).max().transpose()\n",
+    "psnrUrban100 = data.filter(regex=(\"validate\\.Urban100\\.psnr_scale_.*last.*\")).max().transpose()\n",
+    "psnrSet5 = data.filter(regex=(\"validate\\.Set5\\.psnr_scale_.*last.*\")).max().transpose()\n",
+    "psnrSet14 = data.filter(regex=(\"validate\\.Set14\\.psnr_scale_.*last.*\")).max().transpose()\n",
+    "\n",
+    "ssimBSD100 = data.filter(regex=(\"validate\\.BSD100\\.ssim_scale_.*last.*\")).max().transpose()\n",
+    "ssimUrban100 = data.filter(regex=(\"validate\\.Urban100\\.ssim_scale_.*last.*\")).max().transpose()\n",
+    "ssimSet5 = data.filter(regex=(\"validate\\.Set5\\.ssim_scale_.*last.*\")).max().transpose()\n",
+    "ssimSet14 = data.filter(regex=(\"validate\\.Set14\\.ssim_scale_.*last.*\")).max().transpose()\n",
     "\n",
     "display((num_parameters / 1000).round().astype(int))\n",
     "\n",
+    "display(inference_time)\n",
+    "\n",
     "display(psnrBSD100.round(2))\n",
     "display(psnrUrban100.round(2))\n",
     "display(psnrSet5.round(2))\n",