ArtStudies/M2/Clustering In Practice/sequence.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67779b5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sequenzo import SequenceData, get_turbulence, get_within_sequence_entropy\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ce720425",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[>] SequenceData initialized successfully! Here's a summary:\n",
      "[>] Number of sequences: 712\n",
      "[>] Number of time points: 72\n",
      "[>] Min/Max sequence length: 72 / 72\n",
      "[>] States: ['FE', 'HE', 'employment', 'joblessness', 'school', 'training']\n",
      "[>] Labels: ['FE', 'HE', 'employment', 'joblessness', 'school', 'training']\n",
      "[>] Weights: Not provided\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(\"./data/data_irlandais.csv\")\n",
    "time_columns = data.columns[data.columns.str.match(r\"^[A-Za-z]{3}\\.\\d{2}$\")]\n",
    "states = [\"joblessness\", \"FE\", \"training\", \"school\"]\n",
    "\n",
    "states = sorted(data[time_columns].melt(value_name=\"state\")[\"state\"].unique())\n",
    "sequence = SequenceData(\n",
    "    data=data,\n",
    "    time=list(time_columns),\n",
    "    states=states,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "35de38ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[!] One or more missing values were found after calculating the number of distinct subsequences. They have been replaced with a large number of 1e15 to ensure the calculation continues.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "turbulence = get_turbulence(sequence, norm=True)\n",
    "\n",
    "plt.figure(figsize=(10, 4))\n",
    "sns.histplot(turbulence, bins=20, color=\"blue\", label=\"Turbulence\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d0d89a7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sKiqSkSNHmop+VOwDAAAA4HNBqn379rJq1SozZmnKlCkmKGm5c70vlN3vf/97uXTpkhk/pT1PDzzwgCl3HhQU5NjnvffeM+GpS5cuplpfv379zL2nAAAAAMDngpR69NFHzVQa7ZXSkKVTabRC3/Lly8vpCAEAAADAg8ZIAQAAAIA3IkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAbwpSkydPFj8/P6epSZMmju1Xr16VESNGSK1ataRatWrSr18/yc7OdvoeGRkZ0qtXLwkJCZGIiAgZP368XLt2zQ2vBgAAAEBF4e/uA7jnnntky5YtjmV///89pLFjx8q6detk5cqVEhYWJiNHjpS+ffvKzp07zfbr16+bEBUVFSW7du2Ss2fPyuDBg6VKlSoybdo0t7weAAAAAL7P7UFKg5MGoRvl5eXJokWLZPny5dK5c2ezbvHixRIfHy+7d++Wjh07yqZNm+TIkSMmiEVGRkqrVq1k6tSp8uKLL5reroCAgJs+Z0FBgZns8vPzy/EVAgAAAPA1bh8jdfz4calbt67ccccdMmDAAHOpnkpNTZWioiLp2rWrY1+97C82NlZSUlLMss6bN29uQpRdQkKCCUZpaWmlPuf06dNND5d9iomJKdfXCAAAAMC3uDVIdejQQZYsWSIbNmyQBQsWyMmTJ+UXv/iFXLhwQbKyskyPUnh4uNNjNDTpNqXzkiHKvt2+rTRJSUmmx8s+ZWZmlsvrAwAAAOCb3HppX48ePRxft2jRwgSruLg4ef/99yU4OLjcnjcwMNBMAAAAAOCVl/aVpL1Pd999t5w4ccKMmyosLJTc3FynfbRqn31Mlc5vrOJnX77ZuCsAAAAA8LkgdfHiRfnyyy8lOjpa2rZta6rvJScnO7YfO3bMjKHq1KmTWdb5oUOHJCcnx7HP5s2bJTQ0VJo2beqW1wAAAADA97n10r7//M//lN69e5vL+c6cOSOTJk2SypUry5NPPmmKQAwbNkzGjRsnNWvWNOFo1KhRJjxpxT7VrVs3E5gGDRokM2bMMOOiJkyYYO49xaV7AAAAAHwySH3zzTcmNH333XdSp04deeCBB0xpc/1azZo1SypVqmRuxKvlyrUi3/z58x2P19C1du1aSUxMNAGratWqMmTIEJkyZYobXxUAAAAAX+fWILVixYpbbg8KCpJ58+aZqTTam7V+/fpyODoAAAAA8IIxUgAAAADgDQhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAAXBGk7rjjDvnuu+9+sD43N9dsAwAAAABfdltB6tSpU3L9+vUfrC8oKJDTp0+XxXEBAAAAgMfyt7LzmjVrHF9v3LhRwsLCHMsarJKTk6VBgwZle4QAAAAA4M09Un369DGTn5+fDBkyxLGsU//+/WXz5s3yxhtv3NaBvP766+b7jhkzxrHu6tWrMmLECKlVq5ZUq1ZN+vXrJ9nZ2U6Py8jIkF69eklISIhERETI+PHj5dq1a7d1DAAAAABQ5j1SxcXFZt6wYUPZt2+f1K5dW8qCfq//+q//khYtWjitHzt2rKxbt05Wrlxper9Gjhwpffv2lZ07dzp6wTRERUVFya5du+Ts2bMyePBgqVKlikybNq1Mjg0AAAAAymSM1MmTJ8ssRF28eFEGDBgg77zzjtSoUcOxPi8vTxYtWiQzZ86Uzp07S9u2bWXx4sUmMO3evdvss2nTJjly5IgsW7ZMWrVqJT169JCpU6fKvHnzpLCwsNTn1LFc+fn5ThMAAAAAlEuPVEk6HkqnnJwcR0+V3bvvvvuTv49euqe9Sl27dpVXX33VsT41NVWKiorMersmTZpIbGyspKSkSMeOHc28efPmEhkZ6dgnISFBEhMTJS0tTVq3bn3T55w+fbq88sorFl8xAAAAAPyMIKUhZMqUKdKuXTuJjo42Y5tux4oVK+TAgQPm0r4bZWVlSUBAgISHhzut19Ck2+z7lAxR9u32baVJSkqScePGOZa1RyomJua2XgMAAACAiue2gtTChQtlyZIlMmjQoNt+4szMTBk9erQpUBEUFCSuFBgYaCYAAAAAcNkYKR1/dN9998nPoZfu6WWBbdq0EX9/fzPt2LFD5syZY77WniV9Hr3Jb0latU+LSyid31jFz75s3wcAAAAAPCJIPfPMM7J8+fKf9cRdunSRQ4cOycGDBx2TXiqohSfsX2v1PR2HZXfs2DFT7rxTp05mWef6PTSQ2WkPV2hoqDRt2vRnHR8AAAAAlOmlfXp/p7ffflu2bNliSpZr4ClJK+39mOrVq0uzZs2c1lWtWtXcM8q+ftiwYWYsU82aNU04GjVqlAlPWmhCdevWzQQmvcRwxowZZlzUhAkTTAELLt0DAAAA4FFB6vPPPzflxtXhw4edtt1u4YmbmTVrllSqVMnciFdLlmtFvvnz5zu2V65cWdauXWuq9GnA0iCmNwrWQhgAAAAA4FFBatu2bWV/JCKyfft2p2UtQqH3hNKpNHFxcbJ+/fpyOR4AAAAAKLMxUgAAAABQkd1Wj9TDDz98y0v4tm7d+nOOCQAAAAB8L0jZx0fZFRUVmUp7Ol5KxygBAAAAgC/zv90iEDczefJkuXjx4s89JgAAAACoOGOkBg4cKO+++25ZfksAAAAA8O0glZKSYirtAQAAAIAvu61L+/r27eu0bLPZ5OzZs7J//36ZOHFiWR0bAAAAAPhOkAoLC3Na1pvmNm7c2NwIt1u3bmV1bAAAAADgO0Fq8eLFZX8kAAAAAODLQcouNTVV0tPTzdf33HOPtG7duqyOCwAAAAB8K0jl5ORI//79Zfv27RIeHm7W5ebmmhv1rlixQurUqVPWxwkAAAAA3l21b9SoUXLhwgVJS0uT8+fPm0lvxpufny8vvPBC2R8lAAAAAHh7j9SGDRtky5YtEh8f71jXtGlTmTdvHsUmAAAAAPi82+qRKi4ulipVqvxgva7TbQAAAADgy24rSHXu3FlGjx4tZ86ccaw7ffq0jB07Vrp06VKWxwcAAAAAvhGk3nrrLTMeqkGDBnLnnXeaqWHDhmbd3Llzy/4oAQAAAMDbx0jFxMTIgQMHzDipo0ePmnU6Xqpr165lfXwAAAAA4N09Ulu3bjVFJbTnyc/PTx555BFTwU+n9u3bm3tJ/fOf/yy/owUAAAAAbwtSs2fPluHDh0toaOgPtoWFhclvf/tbmTlzZlkeHwAAAAB4d5D67LPPpHv37qVu19LnqampZXFcAAAAAOAbQSo7O/umZc/t/P395dtvvy2L4wIAAAAA3whS9erVk8OHD5e6/fPPP5fo6OiyOC4AAAAA8I0g1bNnT5k4caJcvXr1B9uuXLkikyZNkkcffbQsjw8AAAAAPI6l8ucTJkyQDz/8UO6++24ZOXKkNG7c2KzXEujz5s2T69evyx//+MfyOlYAAAAA8L4gFRkZKbt27ZLExERJSkoSm81m1msp9ISEBBOmdB8AAAAA8GWWb8gbFxcn69evl++//15OnDhhwlSjRo2kRo0a5XOEAAAAAODtQcpOg5PehBcAAAAAKhpLxSYAAAAAAAQpAAAAALCMIAUAAAAA3hSkFixYIC1atJDQ0FAzderUST755BPHdr1f1YgRI6RWrVpSrVo16devn2RnZzt9j4yMDOnVq5eEhIRIRESEjB8/Xq5du+aGVwMAAACgonBrkKpfv768/vrrkpqaKvv375fOnTvLY489JmlpaWb72LFj5eOPP5aVK1fKjh075MyZM9K3b1/H4/W+VRqiCgsLTVn2pUuXypIlS+Tll19246sCAAAA4Otuu2pfWejdu7fT8muvvWZ6qXbv3m1C1qJFi2T58uUmYKnFixdLfHy82d6xY0fZtGmTHDlyRLZs2WLuX9WqVSuZOnWqvPjiizJ58mQJCAhw0ysDAAAA4Ms8ZoyU9i6tWLFCLl26ZC7x016qoqIi6dq1q2OfJk2aSGxsrKSkpJhlnTdv3tzpJsB6Y+D8/HxHr9bNFBQUmH1KTgAAAADgNUHq0KFDZvxTYGCgPPfcc7Jq1Spp2rSpZGVlmR6l8PBwp/01NOk2pfOSIcq+3b6tNNOnT5ewsDDHFBMTUy6vDQAAAIBvcnuQaty4sRw8eFD27NkjiYmJMmTIEHO5XnlKSkqSvLw8x5SZmVmuzwcAAADAt7h1jJTSXqe77rrLfN22bVvZt2+fvPnmm/Kb3/zGFJHIzc116pXSqn1RUVHma53v3bvX6fvZq/rZ97kZ7f3SCQAAAAC8skfqRsXFxWYMk4aqKlWqSHJysmPbsWPHTLlzHUOldK6XBubk5Dj22bx5symlrpcHAgAAAIDP9UjpJXY9evQwBSQuXLhgKvRt375dNm7caMYuDRs2TMaNGyc1a9Y04WjUqFEmPGnFPtWtWzcTmAYNGiQzZsww46ImTJhg7j1FjxMAAAAAnwxS2pM0ePBgOXv2rAlOenNeDVGPPPKI2T5r1iypVKmSuRGv9lJpRb758+c7Hl+5cmVZu3atGVulAatq1apmjNWUKVPc+KoAAAAA+Dq3Bim9T9StBAUFybx588xUmri4OFm/fn05HB0AAAAAeMkYKQAAAADwdAQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAABvClLTp0+X9u3bS/Xq1SUiIkL69Okjx44dc9rn6tWrMmLECKlVq5ZUq1ZN+vXrJ9nZ2U77ZGRkSK9evSQkJMR8n/Hjx8u1a9dc/GoAAAAAVBRuDVI7duwwIWn37t2yefNmKSoqkm7dusmlS5cc+4wdO1Y+/vhjWblypdn/zJkz0rdvX8f269evmxBVWFgou3btkqVLl8qSJUvk5ZdfdtOrAgAAAODr/N355Bs2bHBa1gCkPUqpqanyH//xH5KXlyeLFi2S5cuXS+fOnc0+ixcvlvj4eBO+OnbsKJs2bZIjR47Ili1bJDIyUlq1aiVTp06VF198USZPniwBAQFuenUAAAAAfJVHjZHS4KRq1qxp5hqotJeqa9eujn2aNGkisbGxkpKSYpZ13rx5cxOi7BISEiQ/P1/S0tJu+jwFBQVme8kJAAAAALwuSBUXF8uYMWPk/vvvl2bNmpl1WVlZpkcpPDzcaV8NTbrNvk/JEGXfbt9W2tissLAwxxQTE1NOrwoAAACAL/KYIKVjpQ4fPiwrVqwo9+dKSkoyvV/2KTMzs9yfEwAAAIDvcOsYKbuRI0fK2rVr5dNPP5X69es71kdFRZkiErm5uU69Ulq1T7fZ99m7d6/T97NX9bPvc6PAwEAzAQAAAIDX9UjZbDYTolatWiVbt26Vhg0bOm1v27atVKlSRZKTkx3rtDy6ljvv1KmTWdb5oUOHJCcnx7GPVgAMDQ2Vpk2buvDVAAAAAKgo/N19OZ9W5Pvoo4/MvaTsY5p03FJwcLCZDxs2TMaNG2cKUGg4GjVqlAlPWrFPabl0DUyDBg2SGTNmmO8xYcIE873pdQIAAADgc0FqwYIFZv7QQw85rdcS50OHDjVfz5o1SypVqmRuxKvV9rQi3/z58x37Vq5c2VwWmJiYaAJW1apVZciQITJlyhQXvxoAAAAAFYW/uy/t+zFBQUEyb948M5UmLi5O1q9fX8ZHBwAAAAAeXrUPAAAAALwFQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwJuC1Keffiq9e/eWunXrip+fn6xevdppu81mk5dfflmio6MlODhYunbtKsePH3fa5/z58zJgwAAJDQ2V8PBwGTZsmFy8eNHFrwQAAABAReLWIHXp0iVp2bKlzJs376bbZ8yYIXPmzJGFCxfKnj17pGrVqpKQkCBXr1517KMhKi0tTTZv3ixr16414ezZZ5914asAAAAAUNH4u/PJe/ToYaab0d6o2bNny4QJE+Sxxx4z6/72t79JZGSk6bnq37+/pKeny4YNG2Tfvn3Srl07s8/cuXOlZ8+e8pe//MX0dAEAAABAhRkjdfLkScnKyjKX89mFhYVJhw4dJCUlxSzrXC/ns4copftXqlTJ9GCVpqCgQPLz850mAAAAAPD6IKUhSmkPVEm6bN+m84iICKft/v7+UrNmTcc+NzN9+nQTyuxTTExMubwGAAAAAL7JY4NUeUpKSpK8vDzHlJmZ6e5DAgAAAOBFPDZIRUVFmXl2drbTel22b9N5Tk6O0/Zr166ZSn72fW4mMDDQVPkrOQEAAACA1wephg0bmjCUnJzsWKdjmXTsU6dOncyyznNzcyU1NdWxz9atW6W4uNiMpQIAAAAAn6vap/d7OnHihFOBiYMHD5oxTrGxsTJmzBh59dVXpVGjRiZYTZw40VTi69Onj9k/Pj5eunfvLsOHDzcl0ouKimTkyJGmoh8V+wAAAAD4ZJDav3+/PPzww47lcePGmfmQIUNkyZIl8vvf/97ca0rvC6U9Tw888IApdx4UFOR4zHvvvWfCU5cuXUy1vn79+pl7TwEAAACATwaphx56yNwvqjR+fn4yZcoUM5VGe6+WL19eTkcIAAAAAF40RgoAAAAAPBVBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACzyt/oAuEZ6xrcecwzp6eniCWrXri2xsbHiKTIyMuTcuXPiCTzp3HBePP+8KM7NzRUUFEhgYKB4Ak9qI09rJ086N5yX0nFuPP+88DPv5yFIeZizZ8+a+cBp74unGDhwoHiCwKAg+ccHH0h0dLRHtNOvH39crl65Ip7AU84N58U7zovi3JTGT0Rs4gk8pY08sZ085dxwXkrHufGO8+JJP/OCQ0LkaHq6V4UpP5vN5hlnz43y8/MlLCxM8vLyJDQ01K3H8t5775ng0uuJgdK40R1uPZa9e/fJvzZ/Iv/xf16Qdvc/5NZj+erwflm9YJp4ml+9MEUaNm7m1mPwxHPDefHc86I4NzeXvneHfLL0Ten12z9K4xZt3XosnthGntJOnnhuOC+l49x47nnxpJ952Rlfynt/Gi+pqanSpk0b8ZZsQI+Uh6oVESH14+LcegzHjn9p5mGR9aV+o3vc/gZTnvBmL/nDp3qtKM5NCZwXzz8vinNz6/NSq26cxxyLJ7SRp7aTJ5wbzkvpODfec1484WeetyJIwat4ypvd/sPHk3jCueG8eM95UZwbz+cJbeSp7eQJ54bzUjrOjfecF9w+qvYBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAAKmqQmjdvnjRo0ECCgoKkQ4cOsnfvXncfEgAAAAAf5RNB6n/+539k3LhxMmnSJDlw4IC0bNlSEhISJCcnx92HBgAAAMAH+YsPmDlzpgwfPlyeeuops7xw4UJZt26dvPvuu/LSSy/9YP+CggIz2eXl5Zl5fn6+uNvly5fN/MSJr6Sw8H+P0R0yM0//v/mxQ7I3KNCtx3LqyGdmfuJwqhReveLWY/G04+FYOBZvPh6OxfOPxdOOh2Px/GPxtOPhWDz/WM5nZZr5xYsXPeLzuP0YbDbbLffzs/3YHh6usLBQQkJC5IMPPpA+ffo41g8ZMkRyc3Plo48++sFjJk+eLK+88oqLjxQAAACAt8jMzJT69ev7bo/UuXPn5Pr16xIZGem0XpePHj1608ckJSWZSwHtiouL5fz581KrVi3x8/MTdyfgmJgY03ChoaFuPZaKjrbwHLSF56AtPAdt4TloC89BW3iWfC9tD+1nunDhgtStW/eW+3l9kLodgYGBZiopPDxcPIn+Z/Om/3C+jLbwHLSF56AtPAdt4TloC89BW3iWUC9sj7CwMN8vNlG7dm2pXLmyZGdnO63X5aioKLcdFwAAAADf5fVBKiAgQNq2bSvJyclOl+rpcqdOndx6bAAAAAB8k09c2qfjnbS4RLt27eTee++V2bNny6VLlxxV/LyJXnKoZdxvvPQQrkdbeA7awnPQFp6DtvActIXnoC08S6CPt4fXV+2ze+utt+TPf/6zZGVlSatWrWTOnDnmxrwAAAAAUNZ8JkgBAAAAgKt4/RgpAAAAAHA1ghQAAAAAWESQAgAAAACLCFIAAAAAYBFByoPMmzdPGjRoIEFBQabi4N69e919SD7n008/ld69e0vdunXFz89PVq9e7bRda6+8/PLLEh0dLcHBwdK1a1c5fvy40z7nz5+XAQMGmDt0h4eHy7Bhw+TixYsufiXeb/r06dK+fXupXr26RERESJ8+feTYsWNO+1y9elVGjBghtWrVkmrVqkm/fv1+cPPtjIwM6dWrl4SEhJjvM378eLl27ZqLX413W7BggbRo0cJx53m9B98nn3zi2E47uM/rr79uflaNGTPGsY72cI3Jkyebc19yatKkiWM77eBap0+floEDB5rzrb+fmzdvLvv373ds5/e36+hn1RvfG35+fub9UOHeG1q1D+63YsUKW0BAgO3dd9+1paWl2YYPH24LDw+3ZWdnu/vQfMr69ettf/zjH20ffvihVqu0rVq1ymn766+/bgsLC7OtXr3a9tlnn9l++ctf2ho2bGi7cuWKY5/u3bvbWrZsadu9e7ftn//8p+2uu+6yPfnkk254Nd4tISHBtnjxYtvhw4dtBw8etPXs2dMWGxtru3jxomOf5557zhYTE2NLTk627d+/39axY0fbfffd59h+7do1W7NmzWxdu3a1/fvf/zbtW7t2bVtSUpKbXpV3WrNmjW3dunW2L774wnbs2DHbH/7wB1uVKlVM2yjawT327t1ra9Cgga1Fixa20aNHO9bTHq4xadIk2z333GM7e/asY/r2228d22kH1zl//rwtLi7ONnToUNuePXtsX331lW3jxo22EydOOPbh97fr5OTkOL0vNm/ebD5Tbdu2rcK9NwhSHuLee++1jRgxwrF8/fp1W926dW3Tp09363H5shuDVHFxsS0qKsr25z//2bEuNzfXFhgYaPv73/9ulo8cOWIet2/fPsc+n3zyic3Pz892+vRpF78C3/vBrOd2x44djnOvH+ZXrlzp2Cc9Pd3sk5KSYpb1h2+lSpVsWVlZjn0WLFhgCw0NtRUUFLjhVfiOGjVq2P7617/SDm5y4cIFW6NGjcwHlAcffNARpGgP1wYp/dB9M7SDa7344ou2Bx54oNTt/P52r9GjR9vuvPNO0w4V7b3BpX0eoLCwUFJTU003tF2lSpXMckpKiluPrSI5efKkuaFzyXYICwszl1na20HnejlAu3btHPvo/tpee/bscctx+4q8vDwzr1mzppnre6KoqMipPfSymtjYWKf20Ms7IiMjHfskJCRIfn6+pKWlufw1+ILr16/LihUr5NKlS+YSP9rBPfSyGL3speR5V7SHa+mlYXop+B133GEuCdPLkRTt4Fpr1qwxv3cff/xxcxlY69at5Z133nFs5/e3ez/DLlu2TJ5++mlzeV9Fe28QpDzAuXPnzIeXkv+hlC7rDwa4hv1c36oddK4/xEvy9/c3H/5pq9tXXFxsxoDcf//90qxZM7NOz2dAQID5xXer9rhZe9m34ac7dOiQuZY9MDBQnnvuOVm1apU0bdqUdnADDbIHDhww4whvRHu4jn4IX7JkiWzYsMGMI9QP67/4xS/kwoULtIOLffXVV6YNGjVqJBs3bpTExER54YUXZOnSpWY7v7/dZ/Xq1ZKbmytDhw41yxXtveHv7gMAAP3r++HDh+Vf//qXuw+lwmrcuLEcPHjQ9Ax+8MEHMmTIENmxY4e7D6vCyczMlNGjR8vmzZtN4SG4T48ePRxfazEWDVZxcXHy/vvvm2IGcO0f27Qnadq0aWZZe6T0d8bChQvNzyq4z6JFi8x7RXtuKyJ6pDxA7dq1pXLlyj+oaKLLUVFRbjuuisZ+rm/VDjrPyclx2q5VZrQSEG11e0aOHClr166Vbdu2Sf369R3r9XzqJQP6l65btcfN2su+DT+d/gXxrrvukrZt25qekJYtW8qbb75JO7iYXhajP2PatGlj/lqukwbaOXPmmK/1r7a0h3voX9jvvvtuOXHiBO8LF9NKfNpDXlJ8fLzjUkt+f7vH119/LVu2bJFnnnnGsa6ivTcIUh7yAUY/vCQnJzv99UWXdYwCXKNhw4bmDVyyHfR6Xb122t4OOtcfDvphx27r1q2mvfSvlfjptN6Hhii9hEzPoZ7/kvQ9UaVKFaf20PLo+ouzZHvoJWklfznqX/K1tO2Nv3Rhjf6fLigooB1crEuXLuZcau+gfdK/xOv4HPvXtId7aJnsL7/80nyo533hWnrZ9423x/jiiy9MD6Hi97d7LF682FwuqeM57Srce8Pd1S7wv+XPtbrMkiVLTGWZZ5991pQ/L1nRBGVTCUtLbeqk//1nzpxpvv76668d5VP1vH/00Ue2zz//3PbYY4/dtHxq69atTQnWf/3rX6ayFuVTrUtMTDSlardv3+5URvXy5cuOfbSEqpZE37p1qymh2qlTJzPdWEK1W7dupoT6hg0bbHXq1PHKEqru9NJLL5lqiSdPnjT/73VZK1lt2rTJbKcd3Ktk1T5Fe7jG7373O/PzSd8XO3fuNKWatUSzVhhVtINrbwXg7+9ve+2112zHjx+3vffee7aQkBDbsmXLHPvw+9u1tLp0bGysqah4o4r03iBIeZC5c+ea/3h6Pykth673OUDZ0nscaIC6cRoyZIjZrqU7J06caIuMjDTBtkuXLua+OiV999135gdvtWrVTKnOp556ygQ0WHOzdtBJ7y1lp78An3/+eVOKW39p/upXvzJhq6RTp07ZevToYQsODjYfcvTDT1FRkRtekfd6+umnzT1a9GeP/jLT//f2EKVoB88KUrSHa/zmN7+xRUdHm/dFvXr1zHLJ+xbRDq718ccfmw/f+ru5SZMmtrfffttpO7+/XUvv4yUiPzjHFe294af/uLtXDAAAAAC8CWOkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAseb/ApIlph9oW54oAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "entropy = get_within_sequence_entropy(sequence, norm=True)\n",
    "\n",
    "plt.figure(figsize=(10, 4))\n",
    "sns.histplot(entropy, bins=20, color=\"blue\", label=\"Entropy\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b26e659",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.13.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}