burst_sequences.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_data = pd.read_csv('data/cells_no_repeats.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
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       "      <th>sequence_0</th>\n",
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      "text/plain": [
       "           sequence_0          sequence_1          sequence_2  count  \\\n",
       "0  864691135348297687  864691136418647703  864691135474761792      1   \n",
       "1  864691136388590711  864691136388590711  864691136388590711      3   \n",
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       "\n",
       "   diversity  \n",
       "0          3  \n",
       "1          1  \n",
       "2          2  \n",
       "3          3  \n",
       "4          3  "
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      "text/plain": [
       "           sequence_0          sequence_1          sequence_2  \\\n",
       "0  864691135761545526  864691135761545526  864691136812041331   \n",
       "1  864691136486788370  864691135866412566  864691136422832687   \n",
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       "4  864691136722356078  864691135509712324  864691135472023602   \n",
       "\n",
       "           sequence_3  count  diversity  \n",
       "0  864691136812041331      1          2  \n",
       "1  864691136056701144      1          4  \n",
       "2  864691136011228579      1          3  \n",
       "3  864691135575128350      1          4  \n",
       "4  864691136816759662      1          4  "
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       "      <td>1</td>\n",
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      "text/plain": [
       "           sequence_0          sequence_1          sequence_2  \\\n",
       "0  864691135420483058  864691135463697733  864691135415448506   \n",
       "1  864691135776654432  864691135776654432  864691136973670940   \n",
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       "4  864691135488499770  864691135462245533  864691135501549378   \n",
       "\n",
       "           sequence_3          sequence_4  count  diversity  \n",
       "0  864691136740412764  864691135684083895      1          5  \n",
       "1  864691135307087430  864691135307087430      1          3  \n",
       "2  864691135660521712  864691135660521712      1          2  \n",
       "3  864691136740406620  864691135730118585      1          5  \n",
       "4  864691134988717434  864691135855775022      1          5  "
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      "text/plain": [
       "           sequence_0          sequence_1          sequence_2  \\\n",
       "0  864691135739717012  864691136388590711  864691135415448506   \n",
       "1  864691135739717012  864691136388590711  864691135415448506   \n",
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       "3  864691136041046102  864691136422832687  864691135526099547   \n",
       "4  864691134917384458  864691135338086118  864691136723314173   \n",
       "\n",
       "           sequence_3          sequence_4          sequence_5  count  \\\n",
       "0  864691136740412764  864691135684083895  864691135463697733      1   \n",
       "1  864691136388590711  864691136388590711  864691135420483058      1   \n",
       "2  864691136740406620  864691135730118585  864691135730537145      1   \n",
       "3  864691136422832687  864691136486788370  864691135866412566      1   \n",
       "4  864691135937724981  864691135012855446  864691136134450827      1   \n",
       "\n",
       "   diversity  \n",
       "0          6  \n",
       "1          4  \n",
       "2          6  \n",
       "3          5  \n",
       "4          6  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sequence_counts_k3 = pd.read_csv(f'data/sequence_counts/unfiltered/split_seq/sequence_counts_k3.csv', index_col=0)\n",
    "sequence_counts_k4 = pd.read_csv(f'data/sequence_counts/unfiltered/split_seq/sequence_counts_k4.csv', index_col=0)\n",
    "sequence_counts_k5 = pd.read_csv(f'data/sequence_counts/unfiltered/split_seq/sequence_counts_k5.csv', index_col=0)\n",
    "sequence_counts_k6 = pd.read_csv(f'data/sequence_counts/unfiltered/split_seq/sequence_counts_k6.csv', index_col=0)\n",
    "sequence_counts = [sequence_counts_k3, sequence_counts_k4, sequence_counts_k5, sequence_counts_k6]\n",
    "display(sequence_counts_k3.head())\n",
    "display(sequence_counts_k4.head())\n",
    "display(sequence_counts_k5.head())\n",
    "display(sequence_counts_k6.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the top ten most frequent sequences from each value of k\n",
    "t = 20\n",
    "top_ten_k3 = sequence_counts_k3.sort_values(by='count', ascending=False).head(t)\n",
    "top_ten_k4 = sequence_counts_k4.sort_values(by='count', ascending=False).head(t)\n",
    "top_ten_k5 = sequence_counts_k5.sort_values(by='count', ascending=False).head(t)\n",
    "top_ten_k6 = sequence_counts_k6.sort_values(by='count', ascending=False).head(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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zc3MsWLAgbrzxxrj22msjQp4HAHR9WX8ibM+ePfHNb34z/uVf/iXy8vKy0ufixYsjPz8/s40YMSIr/QIAcGHKy8vj/fffj+XLl3+mfuR5AEBHynohrKamJvbv3x9f/OIXo1evXtGrV6/YuHFjPP3009GrV68oKCiIY8eORUNDQ4vz6uvro7CwsNU+Fy5cGI2NjZltz5492Q4bAIBzNH/+/Hjttddi/fr1MXz48Mz+wsJCeR4A0KVl/U8jb7nllnjvvfda7Lvrrrti7Nix8cADD8SIESOid+/esW7dupg1a1ZERGzfvj12794dJSUlrfaZm5sbubm52Q4VAIDzkCRJ3HvvvbFy5crYsGFDFBcXtzg+YcIEeR4A0KVlvRDWv3//zDoRJ/Xr1y8GDx6c2T9v3ryoqKiIQYMGxYABA+Lee++NkpIS3yQEANCFlZeXx7Jly+KVV16J/v37Z9b9ys/Pj759+0Z+fr48DwDo0tplsfyzefLJJ+Oiiy6KWbNmRVNTU5SWlsYzzzzTGaEAAHCOqqqqIiJi0qRJLfYvWbIk5s6dGxHyPACga+uQQtiGDRtavM7Ly4vKysqorKzsiMsDAJAFSZKctY08DwDoyrK+WD4AAAAAdEUKYQAAAACkgkIYAAAAAKmgEAYAAABAKiiEAQAAAJAKCmEAAAAApIJCGAAAAACpoBAGAAAAQCoohAEAAACQCgphAAAAAKSCQhgAAAAAqaAQBgAAAEAqKIQBAAAAkAoKYQAAAACkgkIYAAAAAKmgEAYAAABAKvTq7ABoP6MffD1rfX342LSs9QUAAADQGTwRBgAAAEAqKIQBAAAAkAoKYQAAAACkgkIYAAAAAKmgEAYAAABAKiiEAQAAAJAKCmEAAAAApIJCGAAAAACp0KuzA6D7Gv3g61nr68PHpmWtLwAAAIDWeCIMAAAAgFRQCAMAAAAgFRTCAAAAAEgFa4QBAAAtZHMt2IjT14Nt7/4BoC2eCAMAAAAgFRTCAAAAAEgFhTAAAAAAUiHrhbCqqqoYN25cDBgwIAYMGBAlJSXxb//2b5njR48ejfLy8hg8eHBceumlMWvWrKivr892GAAAtIO33norpk+fHkVFRZGTkxOrVq1qcXzu3LmRk5PTYps6dWrnBAsAcIqsF8KGDx8ejz32WNTU1MSWLVti8uTJMWPGjPjggw8iIuK+++6LV199NVasWBEbN26MvXv3xm233ZbtMAAAaAdHjhyJ8ePHR2VlZZttpk6dGvv27ctsL774YgdGCADQtqx/a+T06dNbvP6Hf/iHqKqqik2bNsXw4cPjueeei2XLlsXkyZMjImLJkiVx9dVXx6ZNm+JLX/pStsMBACCLysrKoqys7IxtcnNzo7CwsIMiAgA4d+26RtiJEydi+fLlceTIkSgpKYmampo4fvx4TJkyJdNm7NixMXLkyKiurm7PUAAA6CAbNmyIoUOHxpgxY+Kee+6JAwcOdHZIAAAR0Q5PhEVEvPfee1FSUhJHjx6NSy+9NFauXBmf//znY9u2bdGnT58YOHBgi/YFBQVRV1fXZn9NTU3R1NSUeX3w4MH2CBsAgM9o6tSpcdttt0VxcXHs2rUr/v7v/z7Kysqiuro6Lr744tPay/MAgI7ULoWwMWPGxLZt26KxsTH+9V//NebMmRMbN2684P4WL14cjzzySBYjBACgPcyePTvz7+uuuy7GjRsXV1xxRWzYsCFuueWW09rL82gvox98PWt9ffjYtA7vH4D20S5/GtmnT5/43Oc+FxMmTIjFixfH+PHj45/+6Z+isLAwjh07Fg0NDS3a19fXn3EdiYULF0ZjY2Nm27NnT3uEDQBAll1++eUxZMiQ2LlzZ6vH5XkAQEdqlyfCTtXc3BxNTU0xYcKE6N27d6xbty5mzZoVERHbt2+P3bt3R0lJSZvn5+bmRm5ubkeECgBAFn388cdx4MCBGDZsWKvH5XkAQEfKeiFs4cKFUVZWFiNHjoxDhw7FsmXLYsOGDbFmzZrIz8+PefPmRUVFRQwaNCgGDBgQ9957b5SUlPjGSACAbuDw4cMtnu6qra2Nbdu2xaBBg2LQoEHxyCOPxKxZs6KwsDB27doV999/f3zuc5+L0tLSTowaAOB3sl4I279/f3z961+Pffv2RX5+fowbNy7WrFkTf/qnfxoREU8++WRcdNFFMWvWrGhqaorS0tJ45plnsh0GPUA2112IsPYCAGTDli1b4uabb868rqioiIiIOXPmRFVVVfz617+On/zkJ9HQ0BBFRUVx6623xve+9z1PfQEAXULWC2HPPffcGY/n5eVFZWVlVFZWZvvSAAC0s0mTJkWSJG0eX7NmTQdGAwBwftplsXwAAAAA6GoUwgAAAABIhQ751kgAAADOnfVyAdqHJ8IAAAAASAWFMAAAAABSQSEMAAAAgFRQCAMAAAAgFRTCAAAAAEgFhTAAAAAAUkEhDAAAAIBUUAgDAAAAIBV6dXYA0JlGP/h61vr68LFpPa5/AAB6pmzmkRFySaD78EQYAAAAAKmgEAYAAABAKiiEAQAAAJAKCmEAAAAApIJCGAAAAACpoBAGAAAAQCoohAEAAACQCgphAAAAAKRCr84OAOi6Rj/4elb7+/CxaVntDwCAriubuWRreWR79w/0TJ4IAwAAACAVFMIAAAAASAWFMAAAAABSwRphQKeytgMAAAAdxRNhAAAAAKSCQhgAAAAAqaAQBgAAAEAqKIQBAAAAkAoKYQAAAACkgkIYAAAAAKmgEAYAAABAKiiEAQAAAJAKvbLd4eLFi+Pll1+O//zP/4y+ffvGDTfcED/4wQ9izJgxmTZHjx6Nb33rW7F8+fJoamqK0tLSeOaZZ6KgoCDb4QApN/rB17PW14ePTWvX/lu7Rnv3n+1rtNY/0LO89dZb8cQTT0RNTU3s27cvVq5cGTNnzswcT5IkFi1aFP/8z/8cDQ0NceONN0ZVVVVceeWVnRc0AMD/yfoTYRs3bozy8vLYtGlTrF27No4fPx633nprHDlyJNPmvvvui1dffTVWrFgRGzdujL1798Ztt92W7VAAAMiyI0eOxPjx46OysrLV448//ng8/fTT8eyzz8bmzZujX79+UVpaGkePHu3gSAEATpf1J8JWr17d4vXSpUtj6NChUVNTE3/8x38cjY2N8dxzz8WyZcti8uTJERGxZMmSuPrqq2PTpk3xpS99KdshAQCQJWVlZVFWVtbqsSRJ4qmnnorvfOc7MWPGjIiIeOGFF6KgoCBWrVoVs2fP7shQAQBO0+5rhDU2NkZExKBBgyIioqamJo4fPx5TpkzJtBk7dmyMHDkyqqur2zscAADaSW1tbdTV1bXI8/Lz82PixInyPACgS8j6E2Gf1tzcHAsWLIgbb7wxrr322oiIqKuriz59+sTAgQNbtC0oKIi6urpW+2lqaoqmpqbM64MHD7ZbzAAAXJiTudyp677K8wCArqJdC2Hl5eXx/vvvx9tvv/2Z+lm8eHE88sgjWYoKAICuQp4HdFW+FKln9d/WNUifdvvTyPnz58drr70W69evj+HDh2f2FxYWxrFjx6KhoaFF+/r6+igsLGy1r4ULF0ZjY2Nm27NnT3uFDQDABTqZy9XX17fYL88DALqKrBfCkiSJ+fPnx8qVK+PNN9+M4uLiFscnTJgQvXv3jnXr1mX2bd++PXbv3h0lJSWt9pmbmxsDBgxosQEA0LUUFxdHYWFhizzv4MGDsXnzZnkeANAlZP1PI8vLy2PZsmXxyiuvRP/+/TPrQeTn50ffvn0jPz8/5s2bFxUVFTFo0KAYMGBA3HvvvVFSUuIbIwEAurjDhw/Hzp07M69ra2tj27ZtMWjQoBg5cmQsWLAgvv/978eVV14ZxcXF8dBDD0VRUVHMnDmz84IGAPg/WS+EVVVVRUTEpEmTWuxfsmRJzJ07NyIinnzyybjoooti1qxZ0dTUFKWlpfHMM89kOxQAuoD2XjsC6FhbtmyJm2++OfO6oqIiIiLmzJkTS5cujfvvvz+OHDkSd999dzQ0NMRNN90Uq1evjry8vM4KGQAgI+uFsCRJztomLy8vKisro7KyMtuXBwCgHU2aNOmM+V5OTk48+uij8eijj3ZgVAAA56bdFssHAAAAgK5EIQwAAACAVMj6n0YCAAAAdAfWs00fT4QBAAAAkAoKYQAAAACkgkIYAAAAAKmgEAYAAABAKlgsH4BuLZsLnEacvshpe/ef7WtYpBUAANrmiTAAAAAAUkEhDAAAAIBUUAgDAAAAIBWsEQYAPZw1yAAAOkd752Hdqf+2rtHRPBEGAAAAQCoohAEAAACQCgphAAAAAKSCQhgAAAAAqaAQBgAAAEAqKIQBAAAAkAoKYQAAAACkgkIYAAAAAKnQq7MDAAC6t9EPvp7V/j58bFpW+wMAgJM8EQYAAABAKiiEAQAAAJAKCmEAAAAApIJCGAAAAACpoBAGAAAAQCoohAEAAACQCgphAAAAAKSCQhgAAAAAqaAQBgAAAEAqKIQBAAAAkAoKYQAAAACkgkIYAAAAAKmQ9ULYW2+9FdOnT4+ioqLIycmJVatWtTieJEk8/PDDMWzYsOjbt29MmTIlduzYke0wAADoBN/97ncjJyenxTZ27NjODgsAICLaoRB25MiRGD9+fFRWVrZ6/PHHH4+nn346nn322di8eXP069cvSktL4+jRo9kOBQCATnDNNdfEvn37Mtvbb7/d2SEBAERERK9sd1hWVhZlZWWtHkuSJJ566qn4zne+EzNmzIiIiBdeeCEKCgpi1apVMXv27GyHAwBAB+vVq1cUFhZ2dhgAAKfp0DXCamtro66uLqZMmZLZl5+fHxMnTozq6uo2z2tqaoqDBw+22AAA6Jp27NgRRUVFcfnll8edd94Zu3fvbrOtPA8A6EgdWgirq6uLiIiCgoIW+wsKCjLHWrN48eLIz8/PbCNGjGjXOAEAuDATJ06MpUuXxurVq6Oqqipqa2vjy1/+chw6dKjV9vI8AKAjdYtvjVy4cGE0NjZmtj179nR2SAAAtKKsrCy++tWvxrhx46K0tDR+/vOfR0NDQ7z00kuttpfnAQAdKetrhJ3JybUi6uvrY9iwYZn99fX18YUvfKHN83JzcyM3N7e9wwMAIMsGDhwYV111VezcubPV4/I8AKAjdegTYcXFxVFYWBjr1q3L7Dt48GBs3rw5SkpKOjIUAAA6wOHDh2PXrl0tfgkKANBZsv5E2OHDh1v8xq+2tja2bdsWgwYNipEjR8aCBQvi+9//flx55ZVRXFwcDz30UBQVFcXMmTOzHQoAAB3s29/+dkyfPj1GjRoVe/fujUWLFsXFF18cd9xxR2eHBgCQ/ULYli1b4uabb868rqioiIiIOXPmxNKlS+P++++PI0eOxN133x0NDQ1x0003xerVqyMvLy/boQAA0ME+/vjjuOOOO+LAgQNx2WWXxU033RSbNm2Kyy67rLNDAwDIfiFs0qRJkSRJm8dzcnLi0UcfjUcffTTblwYAoJMtX768s0MAAGhTt/jWSAAAAAD4rBTCAAAAAEgFhTAAAAAAUkEhDAAAAIBUUAgDAAAAIBUUwgAAAABIBYUwAAAAAFJBIQwAAACAVFAIAwAAACAVFMIAAAAASAWFMAAAAABSQSEMAAAAgFRQCAMAAAAgFRTCAAAAAEgFhTAAAAAAUkEhDAAAAIBUUAgDAAAAIBUUwgAAAABIBYUwAAAAAFJBIQwAAACAVFAIAwAAACAVFMIAAAAASAWFMAAAAABSQSEMAAAAgFRQCAMAAAAgFRTCAAAAAEgFhTAAAAAAUkEhDAAAAIBUUAgDAAAAIBUUwgAAAABIBYUwAAAAAFJBIQwAAACAVFAIAwAAACAVOrUQVllZGaNHj468vLyYOHFivPPOO50ZDgAAWSLPAwC6ok4rhP3sZz+LioqKWLRoUWzdujXGjx8fpaWlsX///s4KCQCALJDnAQBdVacVwn74wx/GN77xjbjrrrvi85//fDz77LNxySWXxPPPP99ZIQEAkAXyPACgq+rVGRc9duxY1NTUxMKFCzP7LrroopgyZUpUV1ef1r6pqSmampoyrxsbGyMi4uDBg+0WY3PTJ1ntr7VYs3kN/Xf+NfTf+dfo7v23dg1z0LH9t3YNc9Cx/bd1jWz3nSRJu10j7eR52b9Gd++/tWuYg47tv7VrmIOO7b+1a5iDju2/I66h/wu7Rrb7Pmuel3SC//qv/0oiIvnlL3/ZYv/f/d3fJddff/1p7RctWpREhM1ms9lsNltWtj179nRU2pM68jybzWaz2WyduZ0tz+uUJ8LO18KFC6OioiLzurm5Of73f/83Bg8eHDk5OW2ed/DgwRgxYkTs2bMnBgwY0BGhdqq0jTfCmNMw5rSNN8KY0zDmtI03ouuMOUmSOHToUBQVFXVaDLQkzzs3aRtvhDGnYcxpG2+EMadhzGkbb0TXGfO55nmdUggbMmRIXHzxxVFfX99if319fRQWFp7WPjc3N3Jzc1vsGzhw4Dlfb8CAAan5AYxI33gjjDkN0jbeCGNOg7SNN6JrjDk/P79Tr9/TyfPaV9rGG2HMaZC28UYYcxqkbbwRXWPM55Lndcpi+X369IkJEybEunXrMvuam5tj3bp1UVJS0hkhAQCQBfI8AKAr67Q/jayoqIg5c+bEH/7hH8b1118fTz31VBw5ciTuuuuuzgoJAIAskOcBAF1VpxXCbr/99vjv//7vePjhh6Ouri6+8IUvxOrVq6OgoCBr18jNzY1Fixad9rh9T5W28UYYcxqkbbwRxpwGaRtvRDrHnGbyvOxL23gjjDkN0jbeCGNOg7SNN6L7jTknSXx/OAAAAAA9X6esEQYAAAAAHU0hDAAAAIBUUAgDAAAAIBUUwgAAAABIhW5fCKusrIzRo0dHXl5eTJw4Md55550ztl+xYkWMHTs28vLy4rrrrouf//znHRTpZ7N48eL4oz/6o+jfv38MHTo0Zs6cGdu3bz/jOUuXLo2cnJwWW15eXgdF/Nl997vfPS3+sWPHnvGc7jq/J40ePfq0Mefk5ER5eXmr7bvbHL/11lsxffr0KCoqipycnFi1alWL40mSxMMPPxzDhg2Lvn37xpQpU2LHjh1n7fd87wMd6UxjPn78eDzwwANx3XXXRb9+/aKoqCi+/vWvx969e8/Y54W8NzrS2eZ57ty5p8U/derUs/bbVef5bONt7T2dk5MTTzzxRJt9dvU5PpfPpKNHj0Z5eXkMHjw4Lr300pg1a1bU19efsd8LvQfQc8nz2tbdcoBTyfN6Xp4Xkb5cT57X8/O8iPTlemnI87p1IexnP/tZVFRUxKJFi2Lr1q0xfvz4KC0tjf3797fa/pe//GXccccdMW/evHj33Xdj5syZMXPmzHj//fc7OPLzt3HjxigvL49NmzbF2rVr4/jx43HrrbfGkSNHznjegAEDYt++fZnto48+6qCIs+Oaa65pEf/bb7/dZtvuPL8n/epXv2ox3rVr10ZExFe/+tU2z+lOc3zkyJEYP358VFZWtnr88ccfj6effjqeffbZ2Lx5c/Tr1y9KS0vj6NGjbfZ5vveBjnamMX/yySexdevWeOihh2Lr1q3x8ssvx/bt2+MrX/nKWfs9n/dGRzvbPEdETJ06tUX8L7744hn77MrzfLbxfnqc+/bti+effz5ycnJi1qxZZ+y3K8/xuXwm3XffffHqq6/GihUrYuPGjbF379647bbbztjvhdwD6LnkefK8T+vO83tST8/zItKX68nzWteT8ryI9OV6qcjzkm7s+uuvT8rLyzOvT5w4kRQVFSWLFy9utf3Xvva1ZNq0aS32TZw4Mfnrv/7rdo2zPezfvz+JiGTjxo1ttlmyZEmSn5/fcUFl2aJFi5Lx48efc/ueNL8nffOb30yuuOKKpLm5udXj3XmOIyJZuXJl5nVzc3NSWFiYPPHEE5l9DQ0NSW5ubvLiiy+22c/53gc606ljbs0777yTRETy0UcftdnmfN8bnam1Mc+ZMyeZMWPGefXTXeb5XOZ4xowZyeTJk8/YpjvNcZKc/pnU0NCQ9O7dO1mxYkWmzW9+85skIpLq6upW+7jQewA9lzxPnvdpPWl+T+rJeV6SpC/Xk+f9Tk/O85IknbleT8zzuu0TYceOHYuampqYMmVKZt9FF10UU6ZMierq6lbPqa6ubtE+IqK0tLTN9l1ZY2NjREQMGjTojO0OHz4co0aNihEjRsSMGTPigw8+6IjwsmbHjh1RVFQUl19+edx5552xe/fuNtv2pPmN+N3P+E9/+tP4y7/8y8jJyWmzXXef45Nqa2ujrq6uxRzm5+fHxIkT25zDC7kPdHWNjY2Rk5MTAwcOPGO783lvdEUbNmyIoUOHxpgxY+Kee+6JAwcOtNm2J81zfX19vP766zFv3ryztu1Oc3zqZ1JNTU0cP368xZyNHTs2Ro4c2eacXcg9gJ5LnifPO1VPmt+I9OV5EXK9CHlea3raHPfEXK8n5nndthD2P//zP3HixIkoKChosb+goCDq6upaPaeuru682ndVzc3NsWDBgrjxxhvj2muvbbPdmDFj4vnnn49XXnklfvrTn0Zzc3PccMMN8fHHH3dgtBdu4sSJsXTp0li9enVUVVVFbW1tfPnLX45Dhw612r6nzO9Jq1atioaGhpg7d26bbbr7HH/ayXk6nzm8kPtAV3b06NF44IEH4o477ogBAwa02e583xtdzdSpU+OFF16IdevWxQ9+8IPYuHFjlJWVxYkTJ1pt35Pm+Sc/+Un079//rI+Od6c5bu0zqa6uLvr06XNaon+2z+iTbc71HHoueZ4871Q9ZX5PSlueFyHXk+f1/Dwvouflej01z+vV4VfkMysvL4/333//rH9DXFJSEiUlJZnXN9xwQ1x99dXx4x//OL73ve+1d5ifWVlZWebf48aNi4kTJ8aoUaPipZdeOqcKe3f33HPPRVlZWRQVFbXZprvPMf/f8ePH42tf+1okSRJVVVVnbNvd3xuzZ8/O/Pu6666LcePGxRVXXBEbNmyIW265pRMja3/PP/983HnnnWdd7Lg7zfG5fiYB50ae1/Xuc+1Bnpcu8rx05HkRPS/X66l5Xrd9ImzIkCFx8cUXn/bNBPX19VFYWNjqOYWFhefVviuaP39+vPbaa7F+/foYPnz4eZ3bu3fv+IM/+IPYuXNnO0XXvgYOHBhXXXVVm/H3hPk96aOPPoo33ngj/uqv/uq8zuvOc3xyns5nDi/kPtAVnUyOPvroo1i7du0Zf0vYmrO9N7q6yy+/PIYMGdJm/D1lnv/93/89tm/fft7v64iuO8dtfSYVFhbGsWPHoqGhoUX7s31Gn2xzrufQc8nz5Hmn6gnze1Ia87yI9OZ68rx05HkRPS/X68l5XrcthPXp0ycmTJgQ69aty+xrbm6OdevWtfjNyaeVlJS0aB8RsXbt2jbbdyVJksT8+fNj5cqV8eabb0ZxcfF593HixIl47733YtiwYe0QYfs7fPhw7Nq1q834u/P8nmrJkiUxdOjQmDZt2nmd153nuLi4OAoLC1vM4cGDB2Pz5s1tzuGF3Ae6mpPJ0Y4dO+KNN96IwYMHn3cfZ3tvdHUff/xxHDhwoM34e8I8R/zut/8TJkyI8ePHn/e5XW2Oz/aZNGHChOjdu3eLOdu+fXvs3r27zTm7kHsAPZc8T553qu48v6dKY54Xkc5cT56XnjwvoufkeqnI8zp8ef4sWr58eZKbm5ssXbo0+Y//+I/k7rvvTgYOHJjU1dUlSZIkf/EXf5E8+OCDmfa/+MUvkl69eiX/+I//mPzmN79JFi1alPTu3Tt57733OmsI5+yee+5J8vPzkw0bNiT79u3LbJ988kmmzanjfeSRR5I1a9Yku3btSmpqapLZs2cneXl5yQcffNAZQzhv3/rWt5INGzYktbW1yS9+8YtkypQpyZAhQ5L9+/cnSdKz5vfTTpw4kYwcOTJ54IEHTjvW3ef40KFDybvvvpu8++67SUQkP/zhD5N333038805jz32WDJw4MDklVdeSX79618nM2bMSIqLi5Pf/va3mT4mT56c/OhHP8q8Ptt9oLOdaczHjh1LvvKVryTDhw9Ptm3b1uK93dTUlOnj1DGf7b3R2c405kOHDiXf/va3k+rq6qS2tjZ54403ki9+8YvJlVdemRw9ejTTR3ea57P9XCdJkjQ2NiaXXHJJUlVV1Wof3W2Oz+Uz6W/+5m+SkSNHJm+++WayZcuWpKSkJCkpKWnRz5gxY5KXX3458/pc7gGkhzxPntdT5vfTenKelyTpy/XkeT0/z0uS9OV6acjzunUhLEmS5Ec/+lEycuTIpE+fPsn111+fbNq0KXPsT/7kT5I5c+a0aP/SSy8lV111VdKnT5/kmmuuSV5//fUOjvjCRESr25IlSzJtTh3vggULMv83BQUFyZ/92Z8lW7du7fjgL9Dtt9+eDBs2LOnTp0/y+7//+8ntt9+e7Ny5M3O8J83vp61ZsyaJiGT79u2nHevuc7x+/fpWf45Pjqm5uTl56KGHkoKCgiQ3Nze55ZZbTvt/GDVqVLJo0aIW+850H+hsZxpzbW1tm+/t9evXZ/o4dcxne290tjON+ZNPPkluvfXW5LLLLkt69+6djBo1KvnGN75xWqLTneb5bD/XSZIkP/7xj5O+ffsmDQ0NrfbR3eb4XD6Tfvvb3yZ/+7d/m/ze7/1ecskllyR//ud/nuzbt++0fj59zrncA0gXed6STJvungOcSp7X8/K8JElfrifP6/l5XpKkL9dLQ56X838BAgAAAECP1m3XCAMAAACA86EQBgAAAEAqKIQBAAAAkAoKYQAAAACkgkIYAAAAAKmgEAYAAABAKiiEAQAAAJAKCmEAAAAApIJCGAAAAACpoBAGAAAAQCoohAEAAACQCgphAAAAAKTC/wPlAiLxtnk1UgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the top ten most frequent sequences from each value of k\n",
    "fig, axs = plt.subplots(2, 2, figsize=(15, 10))\n",
    "axs[0, 0].bar(np.arange(1, t+1), top_ten_k3['count'])\n",
    "axs[0, 0].set_title('Top 10 Sequences for k=3')\n",
    "axs[0, 1].bar(np.arange(1, t+1), top_ten_k4['count'])\n",
    "axs[0, 1].set_title('Top 10 Sequences for k=4')\n",
    "axs[1, 0].bar(np.arange(1, t+1), top_ten_k5['count'])\n",
    "axs[1, 0].set_title('Top 10 Sequences for k=5')\n",
    "axs[1, 1].bar(np.arange(1, t+1), top_ten_k6['count'])\n",
    "axs[1, 1].set_title('Top 10 Sequences for k=6')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dictionary mapping each cell id to its cell type\n",
    "id_to_ct_dict = {}\n",
    "for i, row in cell_data.iterrows():\n",
    "    id_to_ct_dict[row['pt_root_id']] = row['cell_type']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each highly repeating sequence, here's what I want to do:\n",
    "\n",
    "1. Determine its cell type\n",
    "2. Determine the post-synaptic cell type for each appearance\n",
    "3. Determine if there is any overlap between the appearances\n",
    "4. Record centroid of each appearance (for plotting)\n",
    "\n",
    "Benchmark for highly repeating sequence: more than 5 appearances\n",
    "\n",
    "Start with doing this for k=5,6 since k=3,4 may not be as interesting (can add in k=3,4 later)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify k\n",
    "k = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>synapse_id_0</th>\n",
       "      <th>synapse_id_1</th>\n",
       "      <th>synapse_id_2</th>\n",
       "      <th>synapse_id_3</th>\n",
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       "      <th>density_score</th>\n",
       "      <th>post_pt_root_id</th>\n",
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       "      <th>sequence_rank_3</th>\n",
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       "      <td>0.413845</td>\n",
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       "      <td>675225</td>\n",
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       "      <td>0.409908</td>\n",
       "      <td>864691134884742906</td>\n",
       "      <td>6P-IT</td>\n",
       "      <td>417.8064</td>\n",
       "      <td>574.3232</td>\n",
       "      <td>903.032</td>\n",
       "      <td>864691135404012526</td>\n",
       "      <td>864691135660521712</td>\n",
       "      <td>864691135660521712</td>\n",
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       "      <td>6P-IT</td>\n",
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       "      <td>864691134988717434</td>\n",
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      "text/plain": [
       "            synapse_id_0  synapse_id_1  synapse_id_2  synapse_id_3  \\\n",
       "cluster_id                                                           \n",
       "0                 923889        912217        906864        900422   \n",
       "1                 677928        680389        677164        678984   \n",
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       "4                 708116        673286        684750        713306   \n",
       "\n",
       "            synapse_id_4  density_score     post_pt_root_id post_cell_type  \\\n",
       "cluster_id                                                                   \n",
       "0                 941001       0.413845  864691134884741370             4P   \n",
       "1                 681563       0.456460  864691134884742906          6P-IT   \n",
       "2                 679270       0.409908  864691134884742906          6P-IT   \n",
       "3                 709257       0.418260  864691134884742906          6P-IT   \n",
       "4                 690728       0.446153  864691134884742906          6P-IT   \n",
       "\n",
       "            centroid_x  centroid_y  centroid_z     sequence_rank_0  \\\n",
       "cluster_id                                                           \n",
       "0             573.3296    689.8856    1049.288  864691135420483058   \n",
       "1             500.4736   1020.7520    1002.048  864691135776654432   \n",
       "2             417.8064    574.3232     903.032  864691135404012526   \n",
       "3             488.3336    877.0656     937.072  864691136378754261   \n",
       "4             586.6272    744.3880     882.984  864691135488499770   \n",
       "\n",
       "               sequence_rank_1     sequence_rank_2     sequence_rank_3  \\\n",
       "cluster_id                                                               \n",
       "0           864691135463697733  864691135415448506  864691136740412764   \n",
       "1           864691135776654432  864691136973670940  864691135307087430   \n",
       "2           864691135660521712  864691135660521712  864691135660521712   \n",
       "3           864691136903065778  864691135772039931  864691136740406620   \n",
       "4           864691135462245533  864691135501549378  864691134988717434   \n",
       "\n",
       "               sequence_rank_4  \n",
       "cluster_id                      \n",
       "0           864691135684083895  \n",
       "1           864691135307087430  \n",
       "2           864691135660521712  \n",
       "3           864691135730118585  \n",
       "4           864691135855775022  "
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       "               sequence_rank_0     sequence_rank_1     sequence_rank_2  \\\n",
       "cluster_id                                                               \n",
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       "\n",
       "               sequence_rank_3     sequence_rank_4  \n",
       "cluster_id                                          \n",
       "0           864691136740412764  864691135684083895  \n",
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       "2           864691135660521712  864691135660521712  \n",
       "3           864691136740406620  864691135730118585  \n",
       "4           864691134988717434  864691135855775022  "
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      "text/plain": [
       "            synapse_id_0  synapse_id_1  synapse_id_2  synapse_id_3  \\\n",
       "cluster_id                                                           \n",
       "0                 923889        912217        906864        900422   \n",
       "1                 677928        680389        677164        678984   \n",
       "2                 657643        695427        680348        675225   \n",
       "3                 674327        704519        662648        676483   \n",
       "4                 708116        673286        684750        713306   \n",
       "\n",
       "            synapse_id_4  \n",
       "cluster_id                \n",
       "0                 941001  \n",
       "1                 681563  \n",
       "2                 679270  \n",
       "3                 709257  \n",
       "4                 690728  "
      ]
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   ],
   "source": [
    "clusters_w_seqs = pd.read_csv(f'data/synapse_clusters/with_sequences/clusters_k{k}.csv', index_col=0)\n",
    "\n",
    "seq_keys = [f'sequence_rank_{i}' for i in range(k)]\n",
    "all_sequences = clusters_w_seqs[seq_keys]\n",
    "\n",
    "syn_keys = [f'synapse_id_{i}' for i in range(k)]\n",
    "all_synapses = clusters_w_seqs[syn_keys]\n",
    "\n",
    "display(clusters_w_seqs.head())\n",
    "display(all_sequences.head())\n",
    "display(all_synapses.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(71, 7)\n"
     ]
    },
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      "text/plain": [
       "               sequence_0          sequence_1          sequence_2  \\\n",
       "433    864691135233408857  864691135233408857  864691135233408857   \n",
       "530    864691135848267614  864691135848267614  864691135848267614   \n",
       "16798  864691136811841139  864691136811841139  864691136811841139   \n",
       "3092   864691135213044608  864691135213044608  864691135213044608   \n",
       "364    864691137197327937  864691137197327937  864691137197327937   \n",
       "\n",
       "               sequence_3          sequence_4  count  diversity  \n",
       "433    864691135233408857  864691135233408857     76          1  \n",
       "530    864691135848267614  864691135848267614     47          1  \n",
       "16798  864691136811841139  864691136811841139     35          1  \n",
       "3092   864691135213044608  864691135213044608     33          1  \n",
       "364    864691137197327937  864691137197327937     29          1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Narrow our dataframe downt to highly repeating sequences\n",
    "burst_sequences = sequence_counts[k-3].loc[(sequence_counts[k-3]['diversity'] == 1)].loc[(sequence_counts[k-3]['count'] > 5)]\n",
    "burst_sequences = burst_sequences.sort_values(by='count', ascending=False)\n",
    "print(burst_sequences.shape)\n",
    "display(burst_sequences.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_cluster_ids(all_sequences, sequence):\n",
    "    clusters = all_sequences.loc[all_sequences.apply(lambda x: (tuple(sequence) == tuple(x)) or (tuple(sequence) == tuple(x[::-1])), axis=1)]\n",
    "    return clusters.index.values.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyze_burst(clusters_df, sequences_df, sequence, k):\n",
    "    pre_cell_type = id_to_ct_dict[sequence[0]]\n",
    "    cluster_ids = get_cluster_ids(sequences_df, sequence)\n",
    "    post_cell_types = []\n",
    "    post_cell_ids = []\n",
    "\n",
    "    for cluster_id in cluster_ids:\n",
    "        cluster = clusters_df.loc[cluster_id]\n",
    "        post_cell_types.append(cluster['post_cell_type'])\n",
    "        post_cell_ids.append(cluster['post_pt_root_id'])\n",
    "\n",
    "    return pre_cell_type, list(post_cell_types), list(post_cell_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Appearances: 76\n",
      "Pre cell type: 4P\n",
      "Post cell types: ['23P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '23P', '4P', '23P', '23P', '23P', '4P', '4P', '23P', '23P', '23P', '23P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '23P', '23P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '23P', '4P', '4P', '4P', '4P', '23P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '23P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P']\n",
      "Number of unique post-cells: 53\n",
      "\n",
      "Appearances: 47\n",
      "Pre cell type: 23P\n",
      "Post cell types: ['4P', '23P', '23P', '5P-ET', '4P', '4P', '23P', '4P', '5P-IT', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '4P', '23P', '23P', '23P', '4P', '23P', '23P', '4P', '23P', '23P', '23P', '4P', '23P', '4P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '5P-IT', '23P', '23P', '23P']\n",
      "Number of unique post-cells: 37\n",
      "\n",
      "Appearances: 35\n",
      "Pre cell type: 6P-IT\n",
      "Post cell types: ['4P', '4P', '5P-IT', '5P-IT', '5P-IT', '5P-IT', '5P-ET', '5P-ET', '5P-IT', '5P-IT', '4P', '5P-IT', '5P-IT', '5P-IT', '4P', '4P', '5P-IT', '5P-IT', '4P', '4P', '4P', '4P', '4P', '5P-ET', '5P-IT', '5P-IT', '5P-IT', '5P-IT', '5P-IT', '5P-IT', '5P-IT', '5P-IT', '5P-ET', '5P-ET', '5P-IT']\n",
      "Number of unique post-cells: 22\n",
      "\n",
      "Appearances: 33\n",
      "Pre cell type: 6P-IT\n",
      "Post cell types: ['6P-CT', '6P-IT', '6P-IT', '6P-IT', '6P-IT', '6P-IT', '6P-IT', '6P-IT', '6P-IT', '6P-IT', '6P-IT', '6P-IT', '5P-IT', '5P-IT', '6P-IT', '6P-IT', '6P-IT', '6P-IT', '6P-IT', '5P-IT', '6P-IT', '6P-IT', '6P-IT', '6P-IT', '6P-IT', '6P-IT', '6P-IT', '6P-IT', '6P-IT', '6P-IT', '6P-IT', '6P-IT', '6P-IT']\n",
      "Number of unique post-cells: 25\n",
      "\n",
      "Appearances: 29\n",
      "Pre cell type: 23P\n",
      "Post cell types: ['23P', '4P', '23P', '23P', '23P', '23P', '4P', '23P', '4P', '23P', '23P', '23P', '23P', '4P', '4P', '4P', '4P', '4P', '23P', '23P', '23P', '23P', '23P', '4P', '4P', '23P', '23P', '23P', '23P']\n",
      "Number of unique post-cells: 25\n",
      "\n",
      "Appearances: 26\n",
      "Pre cell type: 6P-CT\n",
      "Post cell types: ['6P-IT', '6P-CT', '6P-IT', '6P-IT', '5P-IT', '6P-IT', '6P-IT', '6P-IT', '5P-IT', '5P-IT', '6P-CT', '6P-IT', '6P-CT', '6P-IT', '6P-IT', '6P-CT', '5P-ET', '6P-CT', '6P-CT', '6P-CT', '6P-IT', '6P-CT', '6P-CT', '6P-IT', '6P-CT', '6P-IT']\n",
      "Number of unique post-cells: 22\n",
      "\n",
      "Appearances: 26\n",
      "Pre cell type: 6P-IT\n",
      "Post cell types: ['4P', '4P', '6P-IT', '6P-CT', '6P-IT', '6P-IT', '6P-IT', '6P-IT', '6P-IT', '5P-IT', '6P-CT', '6P-CT', '6P-CT', '6P-IT', '6P-IT', '4P', '4P', '4P', '6P-IT', '6P-CT', '6P-IT', '4P', '4P', '6P-IT', '5P-IT', '6P-CT']\n",
      "Number of unique post-cells: 23\n",
      "\n",
      "Appearances: 26\n",
      "Pre cell type: 23P\n",
      "Post cell types: ['23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '5P-ET', '4P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '5P-ET', '23P', '23P', '23P', '23P', '23P']\n",
      "Number of unique post-cells: 21\n",
      "\n",
      "Appearances: 24\n",
      "Pre cell type: 23P\n",
      "Post cell types: ['23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P']\n",
      "Number of unique post-cells: 18\n",
      "\n",
      "Appearances: 23\n",
      "Pre cell type: 23P\n",
      "Post cell types: ['5P-IT', '4P', '5P-IT', '23P', '23P', '23P', '23P', '4P', '4P', '4P', '4P', '4P', '5P-IT', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '23P', '4P', '4P']\n",
      "Number of unique post-cells: 15\n",
      "\n",
      "Appearances: 21\n",
      "Pre cell type: 4P\n",
      "Post cell types: ['23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P']\n",
      "Number of unique post-cells: 15\n",
      "\n",
      "Appearances: 21\n",
      "Pre cell type: 23P\n",
      "Post cell types: ['23P', '4P', '23P', '23P', '4P', '4P', '4P', '4P', '4P', '23P', '23P', '4P', '23P', '23P', '4P', '4P', '23P', '23P', '23P', '4P', '4P']\n",
      "Number of unique post-cells: 19\n",
      "\n",
      "Appearances: 21\n",
      "Pre cell type: 5P-IT\n",
      "Post cell types: ['4P', '4P', '4P', '5P-ET', '5P-IT', '4P', '4P', '5P-IT', '5P-IT', '5P-IT', '4P', '4P', '4P', '5P-IT', '4P', '5P-ET', '4P', '4P', '4P', '5P-IT', '5P-IT']\n",
      "Number of unique post-cells: 18\n",
      "\n",
      "Appearances: 21\n",
      "Pre cell type: 5P-IT\n",
      "Post cell types: ['5P-ET', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '4P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P']\n",
      "Number of unique post-cells: 15\n",
      "\n",
      "Appearances: 19\n",
      "Pre cell type: 6P-CT\n",
      "Post cell types: ['23P', '5P-IT', '4P', '4P', '23P', '23P', '23P', '5P-IT', '23P', '5P-IT', '23P', '4P', '23P', '4P', '4P', '5P-ET', '23P', '4P', '4P']\n",
      "Number of unique post-cells: 18\n",
      "\n",
      "Appearances: 17\n",
      "Pre cell type: 4P\n",
      "Post cell types: ['23P', '4P', '23P', '4P', '23P', '23P', '23P', '23P', '23P', '4P', '23P', '6P-IT', '6P-IT', '23P', '23P', '23P', '23P']\n",
      "Number of unique post-cells: 14\n",
      "\n",
      "Appearances: 17\n",
      "Pre cell type: 5P-IT\n",
      "Post cell types: ['5P-IT', '5P-IT', '5P-IT', '5P-IT', '5P-IT', '5P-IT', '5P-IT', '5P-ET', '4P', '5P-ET', '5P-ET', '5P-ET', '5P-IT', '5P-ET', '5P-IT', '5P-IT', '5P-IT']\n",
      "Number of unique post-cells: 12\n",
      "\n",
      "Appearances: 17\n",
      "Pre cell type: 23P\n",
      "Post cell types: ['23P', '4P', '23P', '4P', '4P', '23P', '23P', '23P', '23P', '23P', '4P', '23P', '4P', '4P', '23P', '23P', '23P']\n",
      "Number of unique post-cells: 14\n",
      "\n",
      "Appearances: 16\n",
      "Pre cell type: 6P-IT\n",
      "Post cell types: ['4P', '23P', '23P', '23P', '4P', '23P', '4P', '4P', '4P', '4P', '4P', '4P', '23P', '23P', '23P', '4P']\n",
      "Number of unique post-cells: 12\n",
      "\n",
      "Appearances: 14\n",
      "Pre cell type: 4P\n",
      "Post cell types: ['5P-IT', '5P-IT', '5P-IT', '5P-IT', '4P', '5P-IT', '5P-IT', '4P', '5P-IT', '5P-IT', '5P-IT', '4P', '23P', '23P']\n",
      "Number of unique post-cells: 13\n",
      "\n",
      "Appearances: 13\n",
      "Pre cell type: 4P\n",
      "Post cell types: ['23P', '23P', '23P', '4P', '4P', '4P', '4P', '23P', '23P', '23P', '23P', '23P', '23P']\n",
      "Number of unique post-cells: 5\n",
      "\n",
      "Appearances: 13\n",
      "Pre cell type: 4P\n",
      "Post cell types: ['5P-IT', '5P-IT', '5P-IT', '4P', '4P', '23P', '23P', '23P', '23P', '5P-IT', '5P-IT', '5P-IT', '5P-IT']\n",
      "Number of unique post-cells: 6\n",
      "\n",
      "Appearances: 13\n",
      "Pre cell type: 6P-IT\n",
      "Post cell types: ['4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P']\n",
      "Number of unique post-cells: 11\n",
      "\n",
      "Appearances: 13\n",
      "Pre cell type: 23P\n",
      "Post cell types: ['23P', '23P', '23P', '4P', '4P', '4P', '23P', '23P', '23P', '23P', '4P', '23P', '23P']\n",
      "Number of unique post-cells: 11\n",
      "\n",
      "Appearances: 13\n",
      "Pre cell type: 5P-IT\n",
      "Post cell types: ['23P', '5P-IT', '5P-IT', '5P-IT', '5P-IT', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '5P-IT']\n",
      "Number of unique post-cells: 10\n",
      "\n",
      "Appearances: 12\n",
      "Pre cell type: 5P-IT\n",
      "Post cell types: ['5P-ET', '5P-ET', '5P-ET', '5P-ET', '5P-ET', '5P-IT', '5P-ET', '4P', '4P', '5P-ET', '4P', '23P']\n",
      "Number of unique post-cells: 8\n",
      "\n",
      "Appearances: 12\n",
      "Pre cell type: 23P\n",
      "Post cell types: ['4P', '23P', '23P', '23P', '4P', '23P', '23P', '4P', '5P-ET', '23P', '23P', '5P-IT']\n",
      "Number of unique post-cells: 11\n",
      "\n",
      "Appearances: 12\n",
      "Pre cell type: 5P-IT\n",
      "Post cell types: ['5P-ET', '5P-ET', '5P-ET', '5P-ET', '5P-ET', '5P-ET', '5P-ET', '5P-ET', '5P-ET', '5P-ET', '5P-ET', '5P-ET']\n",
      "Number of unique post-cells: 8\n",
      "\n",
      "Appearances: 11\n",
      "Pre cell type: 4P\n",
      "Post cell types: ['4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P']\n",
      "Number of unique post-cells: 10\n",
      "\n",
      "Appearances: 11\n",
      "Pre cell type: 5P-IT\n",
      "Post cell types: ['4P', '4P', '4P', '23P', '23P', '23P', '23P', '23P', '4P', '23P', '23P']\n",
      "Number of unique post-cells: 9\n",
      "\n",
      "Appearances: 11\n",
      "Pre cell type: 23P\n",
      "Post cell types: ['23P', '23P', '23P', '23P', '4P', '23P', '23P', '23P', '23P', '23P', '23P']\n",
      "Number of unique post-cells: 11\n",
      "\n",
      "Appearances: 11\n",
      "Pre cell type: 4P\n",
      "Post cell types: ['23P', '5P-IT', '23P', '5P-ET', '4P', '4P', '23P', '23P', '4P', '4P', '5P-IT']\n",
      "Number of unique post-cells: 10\n",
      "\n",
      "Appearances: 11\n",
      "Pre cell type: 4P\n",
      "Post cell types: ['4P', '5P-IT', '5P-IT', '23P', '4P', '5P-IT', '5P-IT', '4P', '23P', '5P-IT', '4P']\n",
      "Number of unique post-cells: 10\n",
      "\n",
      "Appearances: 11\n",
      "Pre cell type: 6P-IT\n",
      "Post cell types: ['6P-IT', '5P-IT', '5P-IT', '5P-IT', '4P', '4P', '4P', '4P', '4P', '4P', '4P']\n",
      "Number of unique post-cells: 6\n",
      "\n",
      "Appearances: 11\n",
      "Pre cell type: 5P-IT\n",
      "Post cell types: ['4P', '4P', '4P', '4P', '4P', '4P', '5P-IT', '5P-IT', '4P', '4P', '4P']\n",
      "Number of unique post-cells: 10\n",
      "\n",
      "Appearances: 11\n",
      "Pre cell type: 23P\n",
      "Post cell types: ['23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P', '23P']\n",
      "Number of unique post-cells: 10\n",
      "\n",
      "Appearances: 11\n",
      "Pre cell type: 23P\n",
      "Post cell types: ['4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '23P', '4P']\n",
      "Number of unique post-cells: 9\n",
      "\n",
      "Appearances: 10\n",
      "Pre cell type: 4P\n",
      "Post cell types: ['4P', '4P', '4P', '5P-IT', '5P-IT', '5P-IT', '4P', '4P', '4P', '4P']\n",
      "Number of unique post-cells: 8\n",
      "\n",
      "Appearances: 10\n",
      "Pre cell type: 4P\n",
      "Post cell types: ['23P', '23P', '23P', '23P', '4P', '5P-IT', '4P', '4P', '23P', '23P']\n",
      "Number of unique post-cells: 7\n",
      "\n",
      "Appearances: 10\n",
      "Pre cell type: 6P-IT\n",
      "Post cell types: ['5P-ET', '5P-ET', '5P-ET', '5P-ET', '5P-IT', '6P-CT', '5P-ET', '5P-ET', '5P-ET', '5P-ET']\n",
      "Number of unique post-cells: 7\n",
      "\n",
      "Appearances: 10\n",
      "Pre cell type: 4P\n",
      "Post cell types: ['4P', '23P', '4P', '23P', '23P', '23P', '4P', '4P', '23P', '4P']\n",
      "Number of unique post-cells: 9\n",
      "\n",
      "Appearances: 10\n",
      "Pre cell type: 4P\n",
      "Post cell types: ['23P', '23P', '23P', '5P-ET', '23P', '5P-IT', '23P', '23P', '5P-IT', '23P']\n",
      "Number of unique post-cells: 9\n",
      "\n",
      "Appearances: 9\n",
      "Pre cell type: 5P-IT\n",
      "Post cell types: ['5P-IT', '4P', '4P', '4P', '4P', '4P', '4P', '4P', '4P']\n",
      "Number of unique post-cells: 6\n",
      "\n",
      "Appearances: 9\n",
      "Pre cell type: 5P-ET\n",
      "Post cell types: ['5P-ET', '5P-ET', '5P-ET', '5P-ET', '5P-IT', '5P-IT', '5P-ET', '5P-ET', '5P-ET']\n",
      "Number of unique post-cells: 6\n",
      "\n",
      "Appearances: 8\n",
      "Pre cell type: 6P-IT\n",
      "Post cell types: ['6P-IT', '6P-IT', '6P-IT', '6P-IT', '6P-IT', '6P-IT', '6P-IT', '6P-IT']\n",
      "Number of unique post-cells: 7\n",
      "\n",
      "Appearances: 8\n",
      "Pre cell type: 23P\n",
      "Post cell types: ['23P', '23P', '23P', '23P', '23P', '23P', '4P', '4P']\n",
      "Number of unique post-cells: 4\n",
      "\n",
      "Appearances: 8\n",
      "Pre cell type: 4P\n",
      "Post cell types: ['4P', '5P-IT', '23P', '4P', '4P', '4P', '23P', '5P-IT']\n",
      "Number of unique post-cells: 6\n",
      "\n",
      "Appearances: 8\n",
      "Pre cell type: 23P\n",
      "Post cell types: ['5P-IT', '5P-ET', '5P-IT', '5P-IT', '5P-ET', '5P-ET', '5P-ET', '4P']\n",
      "Number of unique post-cells: 6\n",
      "\n",
      "Appearances: 7\n",
      "Pre cell type: 5P-IT\n",
      "Post cell types: ['5P-ET', '5P-ET', '5P-ET', '5P-ET', '5P-ET', '5P-ET', '5P-ET']\n",
      "Number of unique post-cells: 4\n",
      "\n",
      "Appearances: 7\n",
      "Pre cell type: 5P-IT\n",
      "Post cell types: ['5P-IT', '5P-IT', '5P-IT', '5P-IT', '5P-IT', '5P-IT', '5P-ET']\n",
      "Number of unique post-cells: 6\n",
      "\n",
      "Appearances: 7\n",
      "Pre cell type: 5P-IT\n",
      "Post cell types: ['5P-ET', '5P-ET', '5P-ET', '5P-ET', '5P-ET', '5P-IT', '5P-ET']\n",
      "Number of unique post-cells: 5\n",
      "\n",
      "Appearances: 7\n",
      "Pre cell type: 4P\n",
      "Post cell types: ['23P', '4P', '4P', '4P', '4P', '4P', '5P-IT']\n",
      "Number of unique post-cells: 5\n",
      "\n",
      "Appearances: 7\n",
      "Pre cell type: 4P\n",
      "Post cell types: ['23P', '23P', '23P', '23P', '23P', '23P', '23P']\n",
      "Number of unique post-cells: 6\n",
      "\n",
      "Appearances: 7\n",
      "Pre cell type: 6P-CT\n",
      "Post cell types: ['5P-ET', '5P-ET', '5P-IT', '5P-ET', '5P-IT', '5P-ET', '5P-ET']\n",
      "Number of unique post-cells: 7\n",
      "\n",
      "Appearances: 7\n",
      "Pre cell type: 6P-CT\n",
      "Post cell types: ['5P-NP', '5P-IT', '5P-IT', '6P-CT', '5P-NP', '5P-NP', '5P-NP']\n",
      "Number of unique post-cells: 5\n",
      "\n",
      "Appearances: 7\n",
      "Pre cell type: 23P\n",
      "Post cell types: ['23P', '23P', '4P', '23P', '23P', '23P', '4P']\n",
      "Number of unique post-cells: 5\n",
      "\n",
      "Appearances: 7\n",
      "Pre cell type: 6P-IT\n",
      "Post cell types: ['6P-IT', '6P-CT', '6P-IT', '6P-IT', '6P-IT', '6P-IT', '6P-IT']\n",
      "Number of unique post-cells: 7\n",
      "\n",
      "Appearances: 7\n",
      "Pre cell type: 5P-ET\n",
      "Post cell types: ['6P-CT', '6P-IT', '6P-IT', '6P-CT', '6P-CT', '6P-CT', '5P-IT']\n",
      "Number of unique post-cells: 5\n",
      "\n",
      "Appearances: 6\n",
      "Pre cell type: 4P\n",
      "Post cell types: ['4P', '4P', '4P', '23P', '23P', '23P']\n",
      "Number of unique post-cells: 6\n",
      "\n",
      "Appearances: 6\n",
      "Pre cell type: 5P-IT\n",
      "Post cell types: ['23P', '23P', '23P', '23P', '4P', '23P']\n",
      "Number of unique post-cells: 5\n",
      "\n",
      "Appearances: 6\n",
      "Pre cell type: 5P-IT\n",
      "Post cell types: ['5P-IT', '4P', '5P-IT', '5P-IT', '5P-IT', '6P-IT']\n",
      "Number of unique post-cells: 4\n",
      "\n",
      "Appearances: 6\n",
      "Pre cell type: 5P-IT\n",
      "Post cell types: ['5P-ET', '5P-IT', '5P-IT', '5P-IT', '5P-IT', '5P-IT']\n",
      "Number of unique post-cells: 6\n",
      "\n",
      "Appearances: 6\n",
      "Pre cell type: 23P\n",
      "Post cell types: ['23P', '23P', '23P', '23P', '23P', '23P']\n",
      "Number of unique post-cells: 6\n",
      "\n",
      "Appearances: 6\n",
      "Pre cell type: 5P-IT\n",
      "Post cell types: ['23P', '23P', '23P', '23P', '23P', '23P']\n",
      "Number of unique post-cells: 3\n",
      "\n",
      "Appearances: 6\n",
      "Pre cell type: 23P\n",
      "Post cell types: ['4P', '23P', '23P', '23P', '23P', '23P']\n",
      "Number of unique post-cells: 5\n",
      "\n",
      "Appearances: 6\n",
      "Pre cell type: 4P\n",
      "Post cell types: ['23P', '23P', '4P', '23P', '23P', '23P']\n",
      "Number of unique post-cells: 6\n",
      "\n",
      "Appearances: 6\n",
      "Pre cell type: 4P\n",
      "Post cell types: ['4P', '4P', '4P', '4P', '4P', '23P']\n",
      "Number of unique post-cells: 5\n",
      "\n",
      "Appearances: 6\n",
      "Pre cell type: 6P-CT\n",
      "Post cell types: ['5P-ET', '5P-ET', '5P-ET', '5P-IT', '5P-IT', '6P-IT']\n",
      "Number of unique post-cells: 5\n",
      "\n",
      "Appearances: 6\n",
      "Pre cell type: 6P-CT\n",
      "Post cell types: ['5P-IT', '5P-IT', '5P-IT', '5P-ET', '5P-IT', '5P-IT']\n",
      "Number of unique post-cells: 4\n",
      "\n",
      "Appearances: 6\n",
      "Pre cell type: 5P-ET\n",
      "Post cell types: ['6P-CT', '6P-IT', '6P-IT', '6P-IT', '6P-IT', '6P-CT']\n",
      "Number of unique post-cells: 6\n",
      "\n",
      "Appearances: 6\n",
      "Pre cell type: 4P\n",
      "Post cell types: ['23P', '23P', '23P', '23P', '4P', '4P']\n",
      "Number of unique post-cells: 3\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pre_cell_types = []\n",
    "all_post_cell_types = []\n",
    "all_post_cell_ids = []\n",
    "seq_keys = [f'sequence_{i}' for i in range(k)]\n",
    "for i, row in burst_sequences.iterrows():\n",
    "    sequence = row[seq_keys].values\n",
    "\n",
    "    pre_cell_type, post_cell_types, post_cell_ids = analyze_burst(clusters_w_seqs, all_sequences, sequence, k)\n",
    "    pre_cell_types.append(pre_cell_type)\n",
    "    all_post_cell_types.extend(post_cell_types)\n",
    "    all_post_cell_ids.extend(post_cell_ids)\n",
    "\n",
    "    print(f'Appearances: {row[\"count\"]}')\n",
    "    print(f'Pre cell type: {pre_cell_type}')\n",
    "    print(f'Post cell types: {post_cell_types}')\n",
    "    print(f'Number of unique post-cells: {len(set(post_cell_ids))}')\n",
    "    print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cell_type\n",
       "23P      173.08\n",
       "4P       129.69\n",
       "5P-ET     14.29\n",
       "5P-IT     85.76\n",
       "5P-NP      4.37\n",
       "6P-CT     54.14\n",
       "6P-IT    100.76\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cells_by_type = cell_data.groupby('cell_type').size() / 100\n",
    "display(cells_by_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distribution of post cell types\n",
    "fig, ax = plt.subplots(figsize=(15, 10))\n",
    "cell_types = list(cells_by_type.index)\n",
    "cell_type_counts = [all_post_cell_types.count(cell_type) for cell_type in cell_types]\n",
    "ax.bar(cell_types, cell_type_counts)\n",
    "ax.bar(cell_types, cells_by_type, alpha=0.5)\n",
    "ax.set_xlabel('Cell Type')\n",
    "ax.set_ylabel('Count')\n",
    "ax.set_title(f'Distribution of Post Cell Types in \"Burst Sequence\" (k={k})')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distribution of pre cell types\n",
    "fig, ax = plt.subplots(figsize=(15, 10))\n",
    "cell_types = list(cells_by_type.index)\n",
    "cell_type_counts = [pre_cell_types.count(cell_type) for cell_type in cell_types]\n",
    "ax.bar(cell_types, cell_type_counts)\n",
    "ax.bar(cell_types, cells_by_type, alpha=0.5)\n",
    "ax.set_xlabel('Pre Cell Type')\n",
    "ax.set_ylabel('Count')\n",
    "ax.set_title(f'Distribution of Pre Cell Types in \"Burst Sequence\" (k={k})')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "microns",
   "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": "3.8.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}