Compare Predictions Boxplot and MAE for various Lengths.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %matplotlib notebook "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in /ssd_scratch/users/sriram.devata/torrna/lib/python3.8/site-packages (0.13.2)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /ssd_scratch/users/sriram.devata/torrna/lib/python3.8/site-packages (from seaborn) (1.22.4)\n",
      "Requirement already satisfied: pandas>=1.2 in /ssd_scratch/users/sriram.devata/torrna/lib/python3.8/site-packages (from seaborn) (2.0.3)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /ssd_scratch/users/sriram.devata/torrna/lib/python3.8/site-packages (from seaborn) (3.7.5)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /ssd_scratch/users/sriram.devata/torrna/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.1.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /ssd_scratch/users/sriram.devata/torrna/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /ssd_scratch/users/sriram.devata/torrna/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.51.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /ssd_scratch/users/sriram.devata/torrna/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n",
      "Requirement already satisfied: packaging>=20.0 in /ssd_scratch/users/sriram.devata/torrna/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /ssd_scratch/users/sriram.devata/torrna/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.3.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /ssd_scratch/users/sriram.devata/torrna/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.1.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /ssd_scratch/users/sriram.devata/torrna/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)\n",
      "Requirement already satisfied: importlib-resources>=3.2.0 in /ssd_scratch/users/sriram.devata/torrna/lib/python3.8/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (6.4.0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /ssd_scratch/users/sriram.devata/torrna/lib/python3.8/site-packages (from pandas>=1.2->seaborn) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.1 in /ssd_scratch/users/sriram.devata/torrna/lib/python3.8/site-packages (from pandas>=1.2->seaborn) (2024.1)\n",
      "Requirement already satisfied: zipp>=3.1.0 in /ssd_scratch/users/sriram.devata/torrna/lib/python3.8/site-packages (from importlib-resources>=3.2.0->matplotlib!=3.6.1,>=3.4->seaborn) (3.18.1)\n",
      "Requirement already satisfied: six>=1.5 in /home2/sriram.devata/.local/lib/python3.8/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.15.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import math\n",
    "from tqdm import tqdm\n",
    "import wandb\n",
    "import pickle\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(f\"/ssd_scratch/users/sriram.devata/model_mae_errors_compare_predictions.pkl\", \"rb\") as input_file:\n",
    "    model_maes, all_model_maes, model_angle_errors_to_export = pickle.load(input_file)\n",
    "with open(f\"/ssd_scratch/users/sriram.devata/spot_rna_mae_errors_compare_predictions.pkl\", \"rb\") as input_file:\n",
    "    spot_maes, all_spot_maes, spot_angle_errors_to_export = pickle.load(input_file)\n",
    "with open(f\"/ssd_scratch/users/sriram.devata/random_baseline_mae_errors_compare_predictions.pkl\", \"rb\") as input_file:\n",
    "    random_maes, all_random_maes, random_angle_errors_to_export = pickle.load(input_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8182\n",
      "70\n"
     ]
    }
   ],
   "source": [
    "print(len(all_model_maes[0]))\n",
    "print(len(model_angle_errors_to_export[0][0]))\n",
    "\n",
    "all_dihedral_angle_names = [\"alpha\" ,\"beta\" ,\"gamma\" ,\"delta\" ,\"chi\" ,\"epsilon\" ,\"zeta\" ,\"eta\" ,\"theta\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dihedral angle: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8182/8182 [00:07<00:00, 1033.18it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dihedral angle: 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8255/8255 [00:10<00:00, 813.09it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dihedral angle: 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8265/8265 [00:12<00:00, 653.25it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dihedral angle: 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8265/8265 [00:15<00:00, 545.86it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dihedral angle: 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8266/8266 [00:17<00:00, 482.28it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dihedral angle: 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8010/8010 [00:18<00:00, 432.09it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dihedral angle: 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8010/8010 [00:20<00:00, 382.35it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dihedral angle: 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7935/7935 [00:22<00:00, 351.17it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dihedral angle: 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8004/8004 [00:24<00:00, 321.71it/s]\n"
     ]
    }
   ],
   "source": [
    "all_dihedral_angle_names = [\"alpha ($\\\\alpha$)\" ,\"beta ($\\\\beta$)\" ,\"gamma ($\\\\gamma$)\" ,\"delta ($\\\\delta$)\" ,\"chi ($\\\\chi$)\" ,\"epsilon ($\\\\epsilon$)\" ,\"zeta ($\\\\zeta$)\" ,\"eta ($\\\\eta$)\" ,\"theta ($\\\\theta$)\"]\n",
    "\n",
    "\n",
    "green_diamond = dict(markerfacecolor='g', marker='D')\n",
    "data = pd.DataFrame(columns=[\"Dihedral angle\", \"TorRNA\", \"SPOT-RNA-1D\", \"Random Baseline\"])\n",
    "\n",
    "df_loc = 0\n",
    "for each_dihedral_angle_idx in range(len(all_model_maes)):\n",
    "    print(f\"Dihedral angle: {each_dihedral_angle_idx}\")\n",
    "#     data.append(all_model_maes[each_dihedral_angle_idx])\n",
    "#     data.append(all_spot_maes[each_dihedral_angle_idx])\n",
    "#     data.append(all_random_maes[each_dihedral_angle_idx])\n",
    "        \n",
    "    for each_error_idx in tqdm(range(len(all_model_maes[each_dihedral_angle_idx]))):\n",
    "        data.loc[df_loc] = [all_dihedral_angle_names[each_dihedral_angle_idx],\n",
    "                          all_model_maes[each_dihedral_angle_idx][each_error_idx],\n",
    "                         all_spot_maes[each_dihedral_angle_idx][each_error_idx],\n",
    "                         all_random_maes[each_dihedral_angle_idx][each_error_idx]]\n",
    "        df_loc += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,1, figsize=(15, 10))\n",
    "plt.rcParams.update({'font.size': 20})\n",
    "matplotlib.rc('xtick', labelsize=16) \n",
    "matplotlib.rc('ytick', labelsize=16)\n",
    "\n",
    "\n",
    "dd = pd.melt(data,id_vars=['Dihedral angle'],value_vars=[\"TorRNA\", \"SPOT-RNA-1D\", \"Random Baseline\"],var_name='Prediction Method')\n",
    "sns.boxplot(x='Dihedral angle',y='value',data=dd,hue='Prediction Method',\n",
    "           notch=True, fliersize=0)\n",
    "\n",
    "axs.set_ylabel(\"MAE of the predictions (°)\")\n",
    "# plt.title(\"Distribution of the MAEs of the predicted (pseudo)torsion angles\")\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "plt.legend(handles, labels, bbox_to_anchor=(1.01, 1.01), loc='upper right')\n",
    "plt.savefig('images/boxplot_maes.svg', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(16, 25, 32), (25, 33, 32), (33, 44, 31), (44, 59, 34), (59, 92, 31), (92, 148, 12)]\n",
      "\n",
      "Bucket length range: 16-25\n",
      "alpha ($\\alpha$): 43.832, beta ($\\beta$): 22.240, gamma ($\\gamma$): 39.559, delta ($\\delta$): 16.163, chi ($\\chi$): 20.779, epsilon ($\\epsilon$): 22.719, zeta ($\\zeta$): 31.272, eta ($\\eta$): 25.724, theta ($\\theta$): 28.684, \n",
      "Bucket length range: 25-33\n",
      "alpha ($\\alpha$): 42.350, beta ($\\beta$): 22.032, gamma ($\\gamma$): 38.628, delta ($\\delta$): 16.847, chi ($\\chi$): 23.163, epsilon ($\\epsilon$): 20.719, zeta ($\\zeta$): 35.001, eta ($\\eta$): 25.984, theta ($\\theta$): 32.743, \n",
      "Bucket length range: 33-44\n",
      "alpha ($\\alpha$): 42.068, beta ($\\beta$): 20.531, gamma ($\\gamma$): 38.783, delta ($\\delta$): 17.047, chi ($\\chi$): 24.513, epsilon ($\\epsilon$): 20.014, zeta ($\\zeta$): 28.719, eta ($\\eta$): 25.812, theta ($\\theta$): 29.996, \n",
      "Bucket length range: 44-59\n",
      "alpha ($\\alpha$): 42.954, beta ($\\beta$): 20.846, gamma ($\\gamma$): 35.914, delta ($\\delta$): 14.269, chi ($\\chi$): 19.134, epsilon ($\\epsilon$): 18.625, zeta ($\\zeta$): 30.388, eta ($\\eta$): 25.778, theta ($\\theta$): 29.846, \n",
      "Bucket length range: 59-92\n",
      "alpha ($\\alpha$): 40.634, beta ($\\beta$): 19.611, gamma ($\\gamma$): 33.958, delta ($\\delta$): 13.700, chi ($\\chi$): 17.969, epsilon ($\\epsilon$): 17.762, zeta ($\\zeta$): 25.580, eta ($\\eta$): 25.498, theta ($\\theta$): 27.158, \n",
      "Bucket length range: 92-148\n",
      "alpha ($\\alpha$): 43.758, beta ($\\beta$): 20.965, gamma ($\\gamma$): 37.258, delta ($\\delta$): 11.379, chi ($\\chi$): 19.703, epsilon ($\\epsilon$): 19.600, zeta ($\\zeta$): 29.036, eta ($\\eta$): 24.603, theta ($\\theta$): 28.068, [[43.832224263532034, 22.240429610946524, 39.55857998645303, 16.16330214399909, 20.77868355994746, 22.719157413477244, 31.272178147671788, 25.723763211344735, 28.683891659462315], [42.35048101593548, 22.032363812360746, 38.628405176986895, 16.846650897407983, 23.163385895185325, 20.719131893874266, 35.0005152383297, 25.9840532163919, 32.742640119811774], [42.06820346660236, 20.531193167291523, 38.78252426309381, 17.04742745300541, 24.512963883804563, 20.014356183804782, 28.71911731087411, 25.812252444639928, 29.996072892706724], [42.95447330796913, 20.84623603167139, 35.91365826202659, 14.268724231600597, 19.134098644594733, 18.62529024973976, 30.38811018373663, 25.778189743781493, 29.84604429959443], [40.63422253580985, 19.61099698597968, 33.95758145184696, 13.70046451300886, 17.969367602674776, 17.761590496221334, 25.580122078577457, 25.497763618296915, 27.157804393204984], [43.7575119263771, 20.9650987628451, 37.25769983735102, 11.378889141136616, 19.70271094978051, 19.600432628695867, 29.035720554102216, 24.603310717114457, 28.068352310321746]]\n"
     ]
    }
   ],
   "source": [
    "buckets_freq = 30\n",
    "\n",
    "all_rna_lengths = list()\n",
    "for each_rna_idx in range(len(model_angle_errors_to_export[0])): # For the first angle\n",
    "    all_rna_lengths.append(len(model_angle_errors_to_export[0][each_rna_idx]))\n",
    "all_rna_lengths = sorted(all_rna_lengths)\n",
    "    \n",
    "all_buckets = list()\n",
    "cur_length = all_rna_lengths[0]\n",
    "prev_length = cur_length\n",
    "bucket_start_length = cur_length\n",
    "cur_bucket_size = 0\n",
    "for each_rna_length in all_rna_lengths:\n",
    "    if prev_length != each_rna_length:\n",
    "        prev_length = cur_length\n",
    "        if cur_bucket_size > buckets_freq:\n",
    "            all_buckets.append((bucket_start_length, prev_length, cur_bucket_size))\n",
    "            bucket_start_length = cur_length\n",
    "            cur_bucket_size = 0\n",
    "        \n",
    "    cur_length = each_rna_length\n",
    "    cur_bucket_size += 1\n",
    "all_buckets.append((bucket_start_length, cur_length, cur_bucket_size))\n",
    "\n",
    "print(all_buckets)\n",
    "all_buckets_maes = [[list() for _ in all_dihedral_angle_names] for _ in all_buckets]\n",
    "\n",
    "for bucket_idx, (each_bucket_start, each_bucket_end, _) in enumerate(all_buckets):\n",
    "    print(f\"\\nBucket length range: {each_bucket_start}-{each_bucket_end}\")\n",
    "    for each_angle_idx in range(len(all_dihedral_angle_names)):\n",
    "            total_errors = 0\n",
    "            total_num_errors = 0\n",
    "\n",
    "            for each_rna_idx in range(len(model_angle_errors_to_export[each_angle_idx])):\n",
    "                len_rna = len(model_angle_errors_to_export[each_angle_idx][each_rna_idx])\n",
    "                if len_rna >= each_bucket_start and len_rna <= each_bucket_end:\n",
    "                    total_errors += sum(model_angle_errors_to_export[each_angle_idx][each_rna_idx])\n",
    "                    total_num_errors += len_rna\n",
    "            # mae = sum(model_angle_errors_to_export[each_angle_idx][:])/len(model_angle_errors_to_export[each_angle_idx][:])\n",
    "\n",
    "            mae = total_errors/total_num_errors\n",
    "            all_buckets_maes[bucket_idx][each_angle_idx] = mae\n",
    "            print(f\"{all_dihedral_angle_names[each_angle_idx]}: {mae:.3f}\", end=', ')\n",
    "\n",
    "print(all_buckets_maes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bucket</th>\n",
       "      <th>Angle</th>\n",
       "      <th>MAE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16-25\\n(32)</td>\n",
       "      <td>alpha ($\\alpha$)</td>\n",
       "      <td>43.832224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25-33\\n(32)</td>\n",
       "      <td>alpha ($\\alpha$)</td>\n",
       "      <td>42.350481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33-44\\n(31)</td>\n",
       "      <td>alpha ($\\alpha$)</td>\n",
       "      <td>42.068203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44-59\\n(34)</td>\n",
       "      <td>alpha ($\\alpha$)</td>\n",
       "      <td>42.954473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59-92\\n(31)</td>\n",
       "      <td>alpha ($\\alpha$)</td>\n",
       "      <td>40.634223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>92-148\\n(12)</td>\n",
       "      <td>alpha ($\\alpha$)</td>\n",
       "      <td>43.757512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16-25\\n(32)</td>\n",
       "      <td>beta ($\\beta$)</td>\n",
       "      <td>22.240430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>25-33\\n(32)</td>\n",
       "      <td>beta ($\\beta$)</td>\n",
       "      <td>22.032364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>33-44\\n(31)</td>\n",
       "      <td>beta ($\\beta$)</td>\n",
       "      <td>20.531193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>44-59\\n(34)</td>\n",
       "      <td>beta ($\\beta$)</td>\n",
       "      <td>20.846236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>59-92\\n(31)</td>\n",
       "      <td>beta ($\\beta$)</td>\n",
       "      <td>19.610997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>92-148\\n(12)</td>\n",
       "      <td>beta ($\\beta$)</td>\n",
       "      <td>20.965099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16-25\\n(32)</td>\n",
       "      <td>gamma ($\\gamma$)</td>\n",
       "      <td>39.558580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>25-33\\n(32)</td>\n",
       "      <td>gamma ($\\gamma$)</td>\n",
       "      <td>38.628405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>33-44\\n(31)</td>\n",
       "      <td>gamma ($\\gamma$)</td>\n",
       "      <td>38.782524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>44-59\\n(34)</td>\n",
       "      <td>gamma ($\\gamma$)</td>\n",
       "      <td>35.913658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>59-92\\n(31)</td>\n",
       "      <td>gamma ($\\gamma$)</td>\n",
       "      <td>33.957581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>92-148\\n(12)</td>\n",
       "      <td>gamma ($\\gamma$)</td>\n",
       "      <td>37.257700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>16-25\\n(32)</td>\n",
       "      <td>delta ($\\delta$)</td>\n",
       "      <td>16.163302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>25-33\\n(32)</td>\n",
       "      <td>delta ($\\delta$)</td>\n",
       "      <td>16.846651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>33-44\\n(31)</td>\n",
       "      <td>delta ($\\delta$)</td>\n",
       "      <td>17.047427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>44-59\\n(34)</td>\n",
       "      <td>delta ($\\delta$)</td>\n",
       "      <td>14.268724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>59-92\\n(31)</td>\n",
       "      <td>delta ($\\delta$)</td>\n",
       "      <td>13.700465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>92-148\\n(12)</td>\n",
       "      <td>delta ($\\delta$)</td>\n",
       "      <td>11.378889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>16-25\\n(32)</td>\n",
       "      <td>chi ($\\chi$)</td>\n",
       "      <td>20.778684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25-33\\n(32)</td>\n",
       "      <td>chi ($\\chi$)</td>\n",
       "      <td>23.163386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>33-44\\n(31)</td>\n",
       "      <td>chi ($\\chi$)</td>\n",
       "      <td>24.512964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>44-59\\n(34)</td>\n",
       "      <td>chi ($\\chi$)</td>\n",
       "      <td>19.134099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>59-92\\n(31)</td>\n",
       "      <td>chi ($\\chi$)</td>\n",
       "      <td>17.969368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>92-148\\n(12)</td>\n",
       "      <td>chi ($\\chi$)</td>\n",
       "      <td>19.702711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>16-25\\n(32)</td>\n",
       "      <td>epsilon ($\\epsilon$)</td>\n",
       "      <td>22.719157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>25-33\\n(32)</td>\n",
       "      <td>epsilon ($\\epsilon$)</td>\n",
       "      <td>20.719132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33-44\\n(31)</td>\n",
       "      <td>epsilon ($\\epsilon$)</td>\n",
       "      <td>20.014356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>44-59\\n(34)</td>\n",
       "      <td>epsilon ($\\epsilon$)</td>\n",
       "      <td>18.625290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>59-92\\n(31)</td>\n",
       "      <td>epsilon ($\\epsilon$)</td>\n",
       "      <td>17.761590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>92-148\\n(12)</td>\n",
       "      <td>epsilon ($\\epsilon$)</td>\n",
       "      <td>19.600433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>16-25\\n(32)</td>\n",
       "      <td>zeta ($\\zeta$)</td>\n",
       "      <td>31.272178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>25-33\\n(32)</td>\n",
       "      <td>zeta ($\\zeta$)</td>\n",
       "      <td>35.000515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>33-44\\n(31)</td>\n",
       "      <td>zeta ($\\zeta$)</td>\n",
       "      <td>28.719117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>44-59\\n(34)</td>\n",
       "      <td>zeta ($\\zeta$)</td>\n",
       "      <td>30.388110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>59-92\\n(31)</td>\n",
       "      <td>zeta ($\\zeta$)</td>\n",
       "      <td>25.580122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>92-148\\n(12)</td>\n",
       "      <td>zeta ($\\zeta$)</td>\n",
       "      <td>29.035721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>16-25\\n(32)</td>\n",
       "      <td>eta ($\\eta$)</td>\n",
       "      <td>25.723763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>25-33\\n(32)</td>\n",
       "      <td>eta ($\\eta$)</td>\n",
       "      <td>25.984053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>33-44\\n(31)</td>\n",
       "      <td>eta ($\\eta$)</td>\n",
       "      <td>25.812252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>44-59\\n(34)</td>\n",
       "      <td>eta ($\\eta$)</td>\n",
       "      <td>25.778190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>59-92\\n(31)</td>\n",
       "      <td>eta ($\\eta$)</td>\n",
       "      <td>25.497764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>92-148\\n(12)</td>\n",
       "      <td>eta ($\\eta$)</td>\n",
       "      <td>24.603311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>16-25\\n(32)</td>\n",
       "      <td>theta ($\\theta$)</td>\n",
       "      <td>28.683892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>25-33\\n(32)</td>\n",
       "      <td>theta ($\\theta$)</td>\n",
       "      <td>32.742640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>33-44\\n(31)</td>\n",
       "      <td>theta ($\\theta$)</td>\n",
       "      <td>29.996073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>44-59\\n(34)</td>\n",
       "      <td>theta ($\\theta$)</td>\n",
       "      <td>29.846044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>59-92\\n(31)</td>\n",
       "      <td>theta ($\\theta$)</td>\n",
       "      <td>27.157804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>92-148\\n(12)</td>\n",
       "      <td>theta ($\\theta$)</td>\n",
       "      <td>28.068352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Bucket                 Angle        MAE\n",
       "0    16-25\\n(32)      alpha ($\\alpha$)  43.832224\n",
       "1    25-33\\n(32)      alpha ($\\alpha$)  42.350481\n",
       "2    33-44\\n(31)      alpha ($\\alpha$)  42.068203\n",
       "3    44-59\\n(34)      alpha ($\\alpha$)  42.954473\n",
       "4    59-92\\n(31)      alpha ($\\alpha$)  40.634223\n",
       "5   92-148\\n(12)      alpha ($\\alpha$)  43.757512\n",
       "6    16-25\\n(32)        beta ($\\beta$)  22.240430\n",
       "7    25-33\\n(32)        beta ($\\beta$)  22.032364\n",
       "8    33-44\\n(31)        beta ($\\beta$)  20.531193\n",
       "9    44-59\\n(34)        beta ($\\beta$)  20.846236\n",
       "10   59-92\\n(31)        beta ($\\beta$)  19.610997\n",
       "11  92-148\\n(12)        beta ($\\beta$)  20.965099\n",
       "12   16-25\\n(32)      gamma ($\\gamma$)  39.558580\n",
       "13   25-33\\n(32)      gamma ($\\gamma$)  38.628405\n",
       "14   33-44\\n(31)      gamma ($\\gamma$)  38.782524\n",
       "15   44-59\\n(34)      gamma ($\\gamma$)  35.913658\n",
       "16   59-92\\n(31)      gamma ($\\gamma$)  33.957581\n",
       "17  92-148\\n(12)      gamma ($\\gamma$)  37.257700\n",
       "18   16-25\\n(32)      delta ($\\delta$)  16.163302\n",
       "19   25-33\\n(32)      delta ($\\delta$)  16.846651\n",
       "20   33-44\\n(31)      delta ($\\delta$)  17.047427\n",
       "21   44-59\\n(34)      delta ($\\delta$)  14.268724\n",
       "22   59-92\\n(31)      delta ($\\delta$)  13.700465\n",
       "23  92-148\\n(12)      delta ($\\delta$)  11.378889\n",
       "24   16-25\\n(32)          chi ($\\chi$)  20.778684\n",
       "25   25-33\\n(32)          chi ($\\chi$)  23.163386\n",
       "26   33-44\\n(31)          chi ($\\chi$)  24.512964\n",
       "27   44-59\\n(34)          chi ($\\chi$)  19.134099\n",
       "28   59-92\\n(31)          chi ($\\chi$)  17.969368\n",
       "29  92-148\\n(12)          chi ($\\chi$)  19.702711\n",
       "30   16-25\\n(32)  epsilon ($\\epsilon$)  22.719157\n",
       "31   25-33\\n(32)  epsilon ($\\epsilon$)  20.719132\n",
       "32   33-44\\n(31)  epsilon ($\\epsilon$)  20.014356\n",
       "33   44-59\\n(34)  epsilon ($\\epsilon$)  18.625290\n",
       "34   59-92\\n(31)  epsilon ($\\epsilon$)  17.761590\n",
       "35  92-148\\n(12)  epsilon ($\\epsilon$)  19.600433\n",
       "36   16-25\\n(32)        zeta ($\\zeta$)  31.272178\n",
       "37   25-33\\n(32)        zeta ($\\zeta$)  35.000515\n",
       "38   33-44\\n(31)        zeta ($\\zeta$)  28.719117\n",
       "39   44-59\\n(34)        zeta ($\\zeta$)  30.388110\n",
       "40   59-92\\n(31)        zeta ($\\zeta$)  25.580122\n",
       "41  92-148\\n(12)        zeta ($\\zeta$)  29.035721\n",
       "42   16-25\\n(32)          eta ($\\eta$)  25.723763\n",
       "43   25-33\\n(32)          eta ($\\eta$)  25.984053\n",
       "44   33-44\\n(31)          eta ($\\eta$)  25.812252\n",
       "45   44-59\\n(34)          eta ($\\eta$)  25.778190\n",
       "46   59-92\\n(31)          eta ($\\eta$)  25.497764\n",
       "47  92-148\\n(12)          eta ($\\eta$)  24.603311\n",
       "48   16-25\\n(32)      theta ($\\theta$)  28.683892\n",
       "49   25-33\\n(32)      theta ($\\theta$)  32.742640\n",
       "50   33-44\\n(31)      theta ($\\theta$)  29.996073\n",
       "51   44-59\\n(34)      theta ($\\theta$)  29.846044\n",
       "52   59-92\\n(31)      theta ($\\theta$)  27.157804\n",
       "53  92-148\\n(12)      theta ($\\theta$)  28.068352"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_dihedral_angle_names = [\"alpha ($\\\\alpha$)\" ,\"beta ($\\\\beta$)\" ,\"gamma ($\\\\gamma$)\" ,\"delta ($\\\\delta$)\" ,\"chi ($\\\\chi$)\" ,\"epsilon ($\\\\epsilon$)\" ,\"zeta ($\\\\zeta$)\" ,\"eta ($\\\\eta$)\" ,\"theta ($\\\\theta$)\"]\n",
    "\n",
    "data = pd.DataFrame(columns=[\"Bucket\", \"Angle\", \"MAE\"])\n",
    "\n",
    "df_loc = 0\n",
    "for each_dihedral_angle_idx in range(len(all_model_maes)):\n",
    "    for each_bucket_idx, each_bucket in enumerate(all_buckets):        \n",
    "        data.loc[df_loc] = [f\"{each_bucket[0]}-{each_bucket[1]}\\n({each_bucket[2]})\", all_dihedral_angle_names[each_dihedral_angle_idx], all_buckets_maes[each_bucket_idx][each_dihedral_angle_idx]]\n",
    "        df_loc += 1\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CQk5LP8CTyjymSvtYrdZyf55dEXl5eZoyZYri4uL0r3/9y+9+xhhdc801Kigo0Ouvvy6rlZcqqLwVK1Zo1qxZuuqqqzR27Fi/+vzwww964IEHNHjwYN10003VHKFnpX/2PWLECJdzFotFw4cPlyR9/PHHpzUu1C8bNmyQMUbBwcEaNGiQy/lu3bqpYcOGys3N1RdffOH3uBkZGfrxxx8luX+Mh4WFacCAAZIq9hgPDw9Xhw4dJJ05rwlxZij9mdqrVy/Fxsa6nC99HK5evVqHDx/2a8yafP9R0ddLoaGhfo3boEGDQIQHAB6x808dcOTIEW3ZskU2m039+/fX7t279eGHH2rv3r2KjIzUeeedp/Hjx1e4bpUvP/30k/NzT/V1s7Oz9eSTT2rv3r0KCgpS27ZtdcEFFzhfIAKBUvp49PRY3Lhxo+69916lpaUpJiZGPXv21EUXXaSoqKiAxzJ//nwtX75coaGhGjVqlN/91q9frw8++EAWi0UXXXRRwONC/ZOZmakZM2ZIki6++GK/+uTn5+v++++XJI0cOVJhYWGVuvZDDz2k77//Xu+9954aNWqk7du3+9XvxRdf1LJly/T444+rU6dObDqFSsvKytI111yjxo0b65///KdffYqLizVlyhRZLBa99tpr5fYcqKojR47o0Ucfde690bFjR40bN86l/m3Z+CUpISHB7fnSZME333wTsBhRfyxfvlxbt25VZmamEhIS1LdvX40dO9bll5alj8PY2FiPia4GDRro2LFj+uabb/S73/3Or+uX3UQ3kI/xwsJC5/NGRff/QN3m789UY4w2btzo1y8vfb3/qE4Vfb00YsQI2e12LV68WEuWLCn3C6Bjx445a0ffcMMN1Rk2AJCIrgu+/fZbSSVPqq+++qruvPNOFRYWlmtz1llnac6cOerWrVvArvvkk09KKtk8p3Xr1m7bHD9+XPfdd1+5Y3fccYf+8Ic/6KWXXlJkZGTA4kH9dfjwYb355puSpEsvvdRtm3nz5mnevHnljsXExOj555/XVVddVaXr33zzzcrJyVFWVpZ+/PFHbd26VVFRUXrjjTc8zg1Jevjhh7Vv3z7l5uZqz549Wr9+vYKDg/X888/r/PPPr1JMqJ8WLVqk9957Tw6Hw7lZYWZmpsaMGaO//e1vbvts3LhRzz//vIwxOnbsmNavX6/jx4+rT58+eu211yoVx5o1a/TMM89o/Pjx+v3vf+93v127dukvf/mLevXqdVr/wgd101133aU9e/Zozpw5iouL86vPU089pa+//lrPPvus2rZtG9B4duzYoenTp5c7Zrfbddttt+nvf/+78y8RSjVq1EgHDx7U7t273Y5XevzYsWPKzs5WREREQONF3fb222+7HEtMTNTrr79eblPNRo0aSZKOHj2qrKwsl9fuDodDP//8syRpz549fl8/Pj5eNptNxcXF2r17tzp16uTSpvQxXpFxX3vtNR0/flxhYWG64IIL/O6Huq/0sezrZ6rk32MuJyfH+VfJnt5/VJfKvF7q0KGDZs2apdtuu00jR45Unz59dNZZZ+nEiRNKSUlRTEyMZs+ercsvv7yaowdQ79VoYRD4xVc9w/fff99IMna73Ugyl112mdm6datzU7TzzjvPSDKJiYnm+PHjAYnpjTfecNagSk5Odjn/yy+/mOuuu84sXLjQ7N+/3+Tk5JjvvvvOzJw507mpw6hRo4zD4QhIPKi/CgsLzfDhw40k07VrV5Ofn1/u/Ntvv20ef/xxs2nTJpOWlmbS0tJMSkqKufDCC501cN95550qxRATE+McS5Jp2LCh+eSTT3z2K90UpPQjMjLSvPLKKy6boQD+evbZZ8s9piSZyZMnm8OHD3vsM2fOHJc+I0aMMLt27apUDKUbw8XFxZlffvnFedxX7cPi4mKTlJRkgoKCzLfffus8XrrnADWiURELFy50u0mnt9dUW7duNcHBwaZ///7lNqKtao3ojRs3mj/96U9m5cqV5tChQyY7O9t8++235s9//rMJCgoyksx1113n0q90s+qePXu6vF5KS0szcXFxzjlbdq4B3jzzzDPmueeeM9u2bTMZGRnmyJEjZtGiRaZ///7OjczK1oLOyMhwbqT87LPPuoxX+p6g9LV9RQwcONBIMuPHj3c59+OPPzrnR3BwsF/jffvttyYyMtJIMo8//niFYkHdt3TpUiPJWCwWs3HjRpfzpc8P/j5+Sts3bdo0IO+x/a0RXdXXSwsXLjSNGjVyee13+eWXm02bNlX5PgDAFxLRtYCvRHTpxguSzPnnn+9yPiMjwzRu3NhIMo8++miV41myZIkJDg42kszTTz9d4f5ff/21sdlsRpKZM2dOleNB/Va6SWFCQoL54YcfKtT3tttucyaOT01gV0ZqaqpZuXKlGT16tDMB4k9SOT093axbt85MnjzZSDLDhg2r8IY/QFkFBQVm586d5umnnzZxcXEmPj7erFy50mufoqIis3fvXvPqq6+aZs2ambCwMPPRRx9V+Nql8+qtt94qd9zXG6ynn37a7c7zJKJRUSdPnjTNmzc3DRs2NMeOHSt3ztNrqsLCQnPuueea0NBQs2PHjnLnqpqI9uaTTz5xvoY7NQGwbds2ExoaaiSZCRMmOBcZrFmzxvTq1cuZpJPk9ZdNgD8cDocZP368kWS6d+9e7txDDz1kJJnQ0FDz3HPPmV9++cUcOXLEzJ4920RGRjofi2PGjKnQNRcvXuzcCPGGG24wP/74o0lPTzeLFi0ybdu2dY4bGhrqc6z9+/ebVq1aGUnm4osvZrEL3Bo0aJCRZJo3b24+++wzc/LkSbNr1y5z5513GovF4nzMPfnkk17HefTRR52PzZSUlIDE5m8iuiqvlx544AHnXF2/fr3Jysoyu3fvNjNnzjTBwcEmLCzMLFy4MCD3AwCekIiuBXwloufNm+d8I/Lmm2+6bXPnnXcaSWbQoEHOY7NnzzZTpkxx+fj+++89xpKcnOxc0exuh2t/TZgwwUgy11xzTaXHAG6//XYjycTFxbld2eBLamqq85ciq1atch6fM2eO27nhbvW/Ow6Hw4wbN85IMrNmzapQTDfffLORZO68884K9QM8+eqrr4zFYjEtWrQwOTk5fvXZs2ePCQ8PN5GRkebQoUPO476eN5YvX24sFovbHdm9vcHasWOHCQ0NNd27dzcFBQUusZCIRkVMnTrVSDIffPCByzlPr6lmzJhhJJm//e1vLn28JaIr81rqVD169PC4WGD+/PkmNjbWZeVafHy8mTVrlnN1XyB+mQps3rzZ+Rjbt2+f83hRUZG58cYbXR6Hpe8tbrjhBudf4JTy97XUq6++6vyFS9mPFi1amCeffNK54tSbQ4cOmfbt2xtJZvTo0SYvLy+w3xjUGUeOHDEDBgxw+1j+05/+ZHr37m0kmVdeecXjGKWJ4JCQEPPll1+6bVOZ5wZ/EtFVeb30zjvvGEmmW7duprCw0OX8E088YSSZNm3a8NeZAKoVNaLrgLPOOsvt5+7aHDp0yHksJSVFb731lkvbqVOnqmPHji7H16xZo7Fjxyo7O1sPPPCAcwOsyujUqZPmzp2rAwcOVHoM1G933nmnnn/+ecXGxmrRokXOXasrIj4+Xo0aNdKhQ4fKPRY3b97sdm4MGTJESUlJPse1WCyaOnWq5s+frzlz5ujWW2/1O6arr75aL774oubMmaN//OMffvcDPDnvvPN0zjnn6LvvvtOGDRs0cOBAn31at26toUOHav78+Vq8eLGuvPJKSb6fN+bOnStjjPbt26chQ4aUa3Py5ElJ0sGDB53n/vnPf6pHjx768ssvlZeXp+zsbI0cObJcv7y8PEklm8iV9rv33nvL1TAFypozZ47sdrtefPFFvfjii+XO7dixQ1JJHdklS5aoSZMm+u9//6s5c+ZIKtlP4IsvvijXp3QDqPnz5zsfgytWrJBU8ddS7nTq1EmbN292+5po7Nix2rNnjz7++GN9++23Kioq0jnnnKMrrrhCGzdulCS1b98+4BtSo34qW6f5wIEDatGihSTJZrPppZde0s0336zPPvtM+/btU2RkpIYMGaJx48bpj3/8oySpa9euzv7+vpa69tprNW7cOH388cf6/vvvZbFY1LNnT/3ud7/Te++95zLuqY4ePaphw4bpxx9/1IgRIzR37lyXDReBUo0aNVJycrKWLFmiZcuWKTU1VY0bN9b48ePVu3dvNW3aVJLnx9ysWbN05513Kjg4WJ988onH1yKBeG5wpyqvl0r305k0aZLLngSSNHnyZN13333as2ePdu/erbPPPrvScQKANySi64D27dsrKipKmZmZOn78uNs2pcfLbjDy5ptvOp+QfPnqq680ZswYZWZm6v7779djjz1WpZhTU1MlSVFRUVUaB/XTPffco2eeeUYxMTFatGiRevfuXalxiouLlZ6eLqn8Y3HGjBlV+kWLJOemUUePHj0t/QBvKvO4ctfH3+eNbdu2eTyXl5enlStXSvotOV1q586d2rlzp8e+pf2mTp3qMwbUb0VFRc7Hizt79+7V3r171apVq3LHU1JSPPY5fPiwDh8+XO5YRV5LeeLrNVFsbKymTZvmcjw5OVmSXJIRQGWVPhYl94/Hrl27uiTojDFavXq1pPKPxYq8lmrSpInbX9r7eowfO3ZMw4YN0/fff6/hw4frs88+U2hoqF/XRP1lsVg0cuRIl8fVrl27dOjQISUkJOjcc8916ffCCy/o9ttvdyahx40b5/EagXhu8KYyr5f27dsnSYqOjnbbJyYmxvl5WlpaAKIEAPesNR0Aqs5ut2vChAmSpCVLlrhts3jxYklS3759Kzz+unXrNHr0aGcS+q9//WulY5Wk7OxszZs3r9LxoH6799579dRTTykmJkaLFy9Wnz59Kj3WZ599ppycHFkslkonsz1ZunSppJJfFJ2OfoAnx48f15YtWyT5/7jKz893JuQq8lj85z//KVNS9svlY/ny5ZKktm3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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,1, figsize=(15, 10))\n",
    "\n",
    "data_wide = data.pivot(index=\"Bucket\", columns=\"Angle\", values=\"MAE\")\n",
    "data_wide.head()\n",
    "sns.lineplot(data=data_wide, markers=True, dashes=True, linewidth=5, markersize=15)\n",
    "\n",
    "axs.set_ylabel(\"MAE of the predictions (°)\")\n",
    "axs.set_xlabel(\"Range of Length Bin\")#\\n(Frequency of each length bin)\")\n",
    "\n",
    "# plt.title(\"MAEs of the (pseudo)torsion angles for various RNA sequence lengths\")\n",
    "plt.legend(bbox_to_anchor=(1.25, 1.02), loc='upper right')\n",
    "plt.tight_layout()\n",
    "plt.savefig('images/various_length_bins.svg', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "torrna",
   "language": "python",
   "name": "torrna"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}