A_plot_logos.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook used to plot the LOGOS of a set of CDR3 sequences"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logos plots allows us to better understand the type of sequences present in set. In particular, we can observe which set contains similar protein at similar location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import all the necessary modules and functions\n",
    "from automate import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CDR3</th>\n",
       "      <th>CDR3_al</th>\n",
       "      <th>tokenized_smiles</th>\n",
       "      <th>TCR BioIdentity</th>\n",
       "      <th>TCR Nucleotide Sequence</th>\n",
       "      <th>Experiment</th>\n",
       "      <th>ORF Coverage</th>\n",
       "      <th>Amino Acids</th>\n",
       "      <th>Start Index in Genome</th>\n",
       "      <th>End Index in Genome</th>\n",
       "      <th>v_gene</th>\n",
       "      <th>j_gene</th>\n",
       "      <th>Amino Acids 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CASSAQGTGDRGYTF</td>\n",
       "      <td>CASSA-----QGTGDRGYTF</td>\n",
       "      <td>[12, 16, 34, 34, 31, 31, 31, 31, 31, 27, 13]</td>\n",
       "      <td>CASSAQGTGDRGYTF+TCRBV27-01+TCRBJ01-02</td>\n",
       "      <td>GAGTCGCCCAGCCCCAACCAGACCTCTCTGTACTTCTGTGCCAGCA...</td>\n",
       "      <td>eAV93</td>\n",
       "      <td>ORF1ab,surface glycoprotein</td>\n",
       "      <td>ADAGFIKQY,AELEGIQY,LADAGFIKQY,TLADAGFIK</td>\n",
       "      <td>533</td>\n",
       "      <td>24073</td>\n",
       "      <td>TCRBV27-01</td>\n",
       "      <td>TCRBJ01-02</td>\n",
       "      <td>ADAGFIKQY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CASSLVATGNTGELFF</td>\n",
       "      <td>CASSL----VATGNTGELFF</td>\n",
       "      <td>[12, 16, 34, 34, 31, 31, 31, 31, 23, 27, 27, 13]</td>\n",
       "      <td>CASSLVATGNTGELFF+TCRBV07-09+TCRBJ02-02</td>\n",
       "      <td>CGCACAGAGCAGGGGGACTCGGCCATGTATCTCTGTGCCAGCAGCT...</td>\n",
       "      <td>eOX56</td>\n",
       "      <td>ORF1ab,surface glycoprotein</td>\n",
       "      <td>ADAGFIKQY,AELEGIQY,LADAGFIKQY,TLADAGFIK</td>\n",
       "      <td>533</td>\n",
       "      <td>24073</td>\n",
       "      <td>TCRBV07-09</td>\n",
       "      <td>TCRBJ02-02</td>\n",
       "      <td>ADAGFIKQY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CALKVGADTQYF</td>\n",
       "      <td>CALKV--------GADTQYF</td>\n",
       "      <td>[12, 16, 31, 31, 31, 31, 31, 31, 31, 31, 27, 13]</td>\n",
       "      <td>CALKVGADTQYF+TCRBV30-01+TCRBJ02-03</td>\n",
       "      <td>CTGAGTTCTAAGAAGCTCCTTCTCAGTGACTCTGGCTTCTATCTCT...</td>\n",
       "      <td>eQD124</td>\n",
       "      <td>ORF1ab,surface glycoprotein</td>\n",
       "      <td>ADAGFIKQY,AELEGIQY,LADAGFIKQY,TLADAGFIK</td>\n",
       "      <td>533</td>\n",
       "      <td>24073</td>\n",
       "      <td>TCRBV30-01</td>\n",
       "      <td>TCRBJ02-03</td>\n",
       "      <td>ADAGFIKQY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CASSLWASGRGGTGELFF</td>\n",
       "      <td>CASSLW--ASGRGGTGELFF</td>\n",
       "      <td>[12, 16, 34, 34, 31, 31, 34, 27, 27, 13]</td>\n",
       "      <td>CASSLWASGRGGTGELFF+TCRBV27-01+TCRBJ02-02</td>\n",
       "      <td>AGCCCCAACCAGACCTCTCTGTACTTCTGTGCCAGCAGTTTATGGG...</td>\n",
       "      <td>eAV93</td>\n",
       "      <td>ORF1ab,surface glycoprotein</td>\n",
       "      <td>ADAGFIKQY,AELEGIQY,LADAGFIKQY,TLADAGFIK</td>\n",
       "      <td>533</td>\n",
       "      <td>24073</td>\n",
       "      <td>TCRBV27-01</td>\n",
       "      <td>TCRBJ02-02</td>\n",
       "      <td>ADAGFIKQY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CASSLLGWEQLDEQFF</td>\n",
       "      <td>CASSL----LGWEQLDEQFF</td>\n",
       "      <td>[12, 16, 34, 34, 31, 31, 31, 31, 27, 27, 13]</td>\n",
       "      <td>CASSLLGWEQLDEQFF+TCRBV27-01+TCRBJ02-01</td>\n",
       "      <td>TCGCCCAGCCCCAACCAGACCTCTCTGTACTTCTGTGCCAGCAGTT...</td>\n",
       "      <td>eMR16</td>\n",
       "      <td>ORF1ab,surface glycoprotein</td>\n",
       "      <td>ADAGFIKQY,AELEGIQY,LADAGFIKQY,TLADAGFIK</td>\n",
       "      <td>533</td>\n",
       "      <td>24073</td>\n",
       "      <td>TCRBV27-01</td>\n",
       "      <td>TCRBJ02-01</td>\n",
       "      <td>ADAGFIKQY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236340</th>\n",
       "      <td>CASSSGPQETQYF</td>\n",
       "      <td>CASSS-------GPQETQYF</td>\n",
       "      <td>[12, 16, 34, 34, 34, 31, 31, 31, 31, 31, 31, 3...</td>\n",
       "      <td>CASSSGPQETQYF+TCRBV07-09+TCRBJ02-05</td>\n",
       "      <td>GAGATCCAGCGCACAGAGCAGGGGGACTCGGCCATGTATCTCTGTG...</td>\n",
       "      <td>eAV88</td>\n",
       "      <td>ORF10</td>\n",
       "      <td>AQVDVVNFNL,NYIAQVDVV</td>\n",
       "      <td>29630</td>\n",
       "      <td>29668</td>\n",
       "      <td>TCRBV07-09</td>\n",
       "      <td>TCRBJ02-05</td>\n",
       "      <td>AQVDVVNFNL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236341</th>\n",
       "      <td>CASSKRDSQETQYF</td>\n",
       "      <td>CASSK------RDSQETQYF</td>\n",
       "      <td>[12, 16, 34, 34, 31, 31, 31, 31, 31, 31, 34, 2...</td>\n",
       "      <td>CASSKRDSQETQYF+TCRBV07-09+TCRBJ02-05</td>\n",
       "      <td>ATCCAGCGCACAGAGCAGGGGGACTCGGCCATGTATCTCTGTGCCA...</td>\n",
       "      <td>eDH105</td>\n",
       "      <td>ORF10</td>\n",
       "      <td>AQVDVVNFNL,NYIAQVDVV</td>\n",
       "      <td>29630</td>\n",
       "      <td>29668</td>\n",
       "      <td>TCRBV07-09</td>\n",
       "      <td>TCRBJ02-05</td>\n",
       "      <td>AQVDVVNFNL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236342</th>\n",
       "      <td>CASSQLPGQGKVQYF</td>\n",
       "      <td>CASSQ-----LPGQGKVQYF</td>\n",
       "      <td>[12, 16, 34, 34, 31, 31, 31, 31, 31, 45, 27, 13]</td>\n",
       "      <td>CASSQLPGQGKVQYF+TCRBV04-01+TCRBJ02-07</td>\n",
       "      <td>CACGCCCTGCAGCCAGAAGACTCAGCCCTGTATCTCTGCGCCAGCA...</td>\n",
       "      <td>eAV93</td>\n",
       "      <td>ORF10</td>\n",
       "      <td>AQVDVVNFNL,NYIAQVDVV</td>\n",
       "      <td>29630</td>\n",
       "      <td>29668</td>\n",
       "      <td>TCRBV04-01</td>\n",
       "      <td>TCRBJ02-07</td>\n",
       "      <td>AQVDVVNFNL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236343</th>\n",
       "      <td>CASSPGDNEQFF</td>\n",
       "      <td>CASSP--------GDNEQFF</td>\n",
       "      <td>[12, 16, 34, 34, 45, 31, 31, 31, 31, 31, 31, 3...</td>\n",
       "      <td>CASSPGDNEQFF+TCRBV09-01+TCRBJ02-01</td>\n",
       "      <td>CTAAACCTGAGCTCTCTGGAGCTGGGGGACTCAGCTTTGTATTTCT...</td>\n",
       "      <td>eEE224</td>\n",
       "      <td>ORF10</td>\n",
       "      <td>AQVDVVNFNL,NYIAQVDVV</td>\n",
       "      <td>29630</td>\n",
       "      <td>29668</td>\n",
       "      <td>TCRBV09-01</td>\n",
       "      <td>TCRBJ02-01</td>\n",
       "      <td>AQVDVVNFNL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236344</th>\n",
       "      <td>CASSVGDPDTQYF</td>\n",
       "      <td>CASSV-------GDPDTQYF</td>\n",
       "      <td>[12, 16, 34, 34, 31, 31, 31, 31, 31, 31, 31, 4...</td>\n",
       "      <td>CASSVGDPDTQYF+TCRBV09-01+TCRBJ02-03</td>\n",
       "      <td>AACCTGAGCTCTCTGGAGCTGGGGGACTCAGCTTTGTATTTCTGTG...</td>\n",
       "      <td>eAV88</td>\n",
       "      <td>ORF10</td>\n",
       "      <td>AQVDVVNFNL,NYIAQVDVV</td>\n",
       "      <td>29630</td>\n",
       "      <td>29668</td>\n",
       "      <td>TCRBV09-01</td>\n",
       "      <td>TCRBJ02-03</td>\n",
       "      <td>AQVDVVNFNL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>236345 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      CDR3               CDR3_al  \\\n",
       "0          CASSAQGTGDRGYTF  CASSA-----QGTGDRGYTF   \n",
       "1         CASSLVATGNTGELFF  CASSL----VATGNTGELFF   \n",
       "2             CALKVGADTQYF  CALKV--------GADTQYF   \n",
       "3       CASSLWASGRGGTGELFF  CASSLW--ASGRGGTGELFF   \n",
       "4         CASSLLGWEQLDEQFF  CASSL----LGWEQLDEQFF   \n",
       "...                    ...                   ...   \n",
       "236340       CASSSGPQETQYF  CASSS-------GPQETQYF   \n",
       "236341      CASSKRDSQETQYF  CASSK------RDSQETQYF   \n",
       "236342     CASSQLPGQGKVQYF  CASSQ-----LPGQGKVQYF   \n",
       "236343        CASSPGDNEQFF  CASSP--------GDNEQFF   \n",
       "236344       CASSVGDPDTQYF  CASSV-------GDPDTQYF   \n",
       "\n",
       "                                         tokenized_smiles  \\\n",
       "0            [12, 16, 34, 34, 31, 31, 31, 31, 31, 27, 13]   \n",
       "1        [12, 16, 34, 34, 31, 31, 31, 31, 23, 27, 27, 13]   \n",
       "2        [12, 16, 31, 31, 31, 31, 31, 31, 31, 31, 27, 13]   \n",
       "3                [12, 16, 34, 34, 31, 31, 34, 27, 27, 13]   \n",
       "4            [12, 16, 34, 34, 31, 31, 31, 31, 27, 27, 13]   \n",
       "...                                                   ...   \n",
       "236340  [12, 16, 34, 34, 34, 31, 31, 31, 31, 31, 31, 3...   \n",
       "236341  [12, 16, 34, 34, 31, 31, 31, 31, 31, 31, 34, 2...   \n",
       "236342   [12, 16, 34, 34, 31, 31, 31, 31, 31, 45, 27, 13]   \n",
       "236343  [12, 16, 34, 34, 45, 31, 31, 31, 31, 31, 31, 3...   \n",
       "236344  [12, 16, 34, 34, 31, 31, 31, 31, 31, 31, 31, 4...   \n",
       "\n",
       "                                 TCR BioIdentity  \\\n",
       "0          CASSAQGTGDRGYTF+TCRBV27-01+TCRBJ01-02   \n",
       "1         CASSLVATGNTGELFF+TCRBV07-09+TCRBJ02-02   \n",
       "2             CALKVGADTQYF+TCRBV30-01+TCRBJ02-03   \n",
       "3       CASSLWASGRGGTGELFF+TCRBV27-01+TCRBJ02-02   \n",
       "4         CASSLLGWEQLDEQFF+TCRBV27-01+TCRBJ02-01   \n",
       "...                                          ...   \n",
       "236340       CASSSGPQETQYF+TCRBV07-09+TCRBJ02-05   \n",
       "236341      CASSKRDSQETQYF+TCRBV07-09+TCRBJ02-05   \n",
       "236342     CASSQLPGQGKVQYF+TCRBV04-01+TCRBJ02-07   \n",
       "236343        CASSPGDNEQFF+TCRBV09-01+TCRBJ02-01   \n",
       "236344       CASSVGDPDTQYF+TCRBV09-01+TCRBJ02-03   \n",
       "\n",
       "                                  TCR Nucleotide Sequence Experiment  \\\n",
       "0       GAGTCGCCCAGCCCCAACCAGACCTCTCTGTACTTCTGTGCCAGCA...      eAV93   \n",
       "1       CGCACAGAGCAGGGGGACTCGGCCATGTATCTCTGTGCCAGCAGCT...      eOX56   \n",
       "2       CTGAGTTCTAAGAAGCTCCTTCTCAGTGACTCTGGCTTCTATCTCT...     eQD124   \n",
       "3       AGCCCCAACCAGACCTCTCTGTACTTCTGTGCCAGCAGTTTATGGG...      eAV93   \n",
       "4       TCGCCCAGCCCCAACCAGACCTCTCTGTACTTCTGTGCCAGCAGTT...      eMR16   \n",
       "...                                                   ...        ...   \n",
       "236340  GAGATCCAGCGCACAGAGCAGGGGGACTCGGCCATGTATCTCTGTG...      eAV88   \n",
       "236341  ATCCAGCGCACAGAGCAGGGGGACTCGGCCATGTATCTCTGTGCCA...     eDH105   \n",
       "236342  CACGCCCTGCAGCCAGAAGACTCAGCCCTGTATCTCTGCGCCAGCA...      eAV93   \n",
       "236343  CTAAACCTGAGCTCTCTGGAGCTGGGGGACTCAGCTTTGTATTTCT...     eEE224   \n",
       "236344  AACCTGAGCTCTCTGGAGCTGGGGGACTCAGCTTTGTATTTCTGTG...      eAV88   \n",
       "\n",
       "                       ORF Coverage                              Amino Acids  \\\n",
       "0       ORF1ab,surface glycoprotein  ADAGFIKQY,AELEGIQY,LADAGFIKQY,TLADAGFIK   \n",
       "1       ORF1ab,surface glycoprotein  ADAGFIKQY,AELEGIQY,LADAGFIKQY,TLADAGFIK   \n",
       "2       ORF1ab,surface glycoprotein  ADAGFIKQY,AELEGIQY,LADAGFIKQY,TLADAGFIK   \n",
       "3       ORF1ab,surface glycoprotein  ADAGFIKQY,AELEGIQY,LADAGFIKQY,TLADAGFIK   \n",
       "4       ORF1ab,surface glycoprotein  ADAGFIKQY,AELEGIQY,LADAGFIKQY,TLADAGFIK   \n",
       "...                             ...                                      ...   \n",
       "236340                        ORF10                     AQVDVVNFNL,NYIAQVDVV   \n",
       "236341                        ORF10                     AQVDVVNFNL,NYIAQVDVV   \n",
       "236342                        ORF10                     AQVDVVNFNL,NYIAQVDVV   \n",
       "236343                        ORF10                     AQVDVVNFNL,NYIAQVDVV   \n",
       "236344                        ORF10                     AQVDVVNFNL,NYIAQVDVV   \n",
       "\n",
       "        Start Index in Genome  End Index in Genome      v_gene      j_gene  \\\n",
       "0                         533                24073  TCRBV27-01  TCRBJ01-02   \n",
       "1                         533                24073  TCRBV07-09  TCRBJ02-02   \n",
       "2                         533                24073  TCRBV30-01  TCRBJ02-03   \n",
       "3                         533                24073  TCRBV27-01  TCRBJ02-02   \n",
       "4                         533                24073  TCRBV27-01  TCRBJ02-01   \n",
       "...                       ...                  ...         ...         ...   \n",
       "236340                  29630                29668  TCRBV07-09  TCRBJ02-05   \n",
       "236341                  29630                29668  TCRBV07-09  TCRBJ02-05   \n",
       "236342                  29630                29668  TCRBV04-01  TCRBJ02-07   \n",
       "236343                  29630                29668  TCRBV09-01  TCRBJ02-01   \n",
       "236344                  29630                29668  TCRBV09-01  TCRBJ02-03   \n",
       "\n",
       "       Amino Acids 1  \n",
       "0          ADAGFIKQY  \n",
       "1          ADAGFIKQY  \n",
       "2          ADAGFIKQY  \n",
       "3          ADAGFIKQY  \n",
       "4          ADAGFIKQY  \n",
       "...              ...  \n",
       "236340    AQVDVVNFNL  \n",
       "236341    AQVDVVNFNL  \n",
       "236342    AQVDVVNFNL  \n",
       "236343    AQVDVVNFNL  \n",
       "236344    AQVDVVNFNL  \n",
       "\n",
       "[236345 rows x 13 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data\n",
    "df = pd.read_csv('peptide-detail-ci_filtered_aligned_merged.csv')\n",
    "df[\"Amino Acids 1\"] = df[\"Amino Acids\"].apply(lambda x: x.split(\",\")[0])\n",
    "df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First method used to plot the LOGOS:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This method is greatly inspired on the work done previously by Dr. Barbara Bravi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "./PGM/source\\numba_utilities.py:1124: NumbaPerformanceWarning: \u001b[1m\u001b[1mnp.dot() is faster on contiguous arrays, called on (array(float32, 2d, F), array(float32, 2d, A))\u001b[0m\u001b[0m\n",
      "  dmean_v_dw = np.dot(s1.T, V)\n",
      "./PGM/source\\numba_utilities.py:1125: NumbaPerformanceWarning: \u001b[1m\u001b[1mnp.dot() is faster on contiguous arrays, called on (array(float32, 2d, F), array(float32, 2d, A))\u001b[0m\u001b[0m\n",
      "  dvar_e_dw = np.dot(s2.T, V)\n",
      "./PGM/source\\numba_utilities.py:1126: NumbaPerformanceWarning: \u001b[1m\u001b[1mnp.dot() is faster on contiguous arrays, called on (array(float32, 2d, F), array(float32, 2d, A))\u001b[0m\u001b[0m\n",
      "  tmp3 = np.dot(s3.T, V)\n",
      "./PGM/source\\numba_utilities.py:961: NumbaPerformanceWarning: \u001b[1m\u001b[1mnp.dot() is faster on contiguous arrays, called on (array(float32, 1d, A), array(float32, 2d, A))\u001b[0m\u001b[0m\n",
      "  mean_V = np.dot(weights, V) / sum_weights\n"
     ]
    }
   ],
   "source": [
    "rootf = \".\" ## place here the folder where you save all the folders of the git ##\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.family'] = ['Garuda']\n",
    "mpl.rcParams['font.serif'] = ['Garuda-Oblique']\n",
    "\n",
    "curr_float = np.float32\n",
    "curr_int = np.int16\n",
    "\n",
    "def convert_number(seqs): # convert to numbers already aligned seqs\n",
    "    aa = ['A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V',  'W', 'Y','-']\n",
    "    aadict = {aa[k]: k for k in range(len(aa))}\n",
    "    msa_num = np.array(list(map(lambda x: [aadict[y] for y in x], seqs[0:])), dtype=curr_int, order=\"c\")\n",
    "    \n",
    "    return msa_num\n",
    "\n",
    "def convert_letter(seqs_n): # convert to numbers already aligned seqs\n",
    "    aa = ['A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V',  'W', 'Y','-']\n",
    "    aadictinv = {k: aa[k] for k in range(len(aa))} \n",
    "    seqs=[]\n",
    "    if type(seqs_n[0]) == curr_int:\n",
    "        seqs.append(''.join([aadictinv[e] for e in seqs_n]))\n",
    "    else:\n",
    "        for t in range(len(seqs_n)):\n",
    "            seqs.append(''.join([aadictinv[e] for e in seqs_n[t]]))\n",
    "    return seqs\n",
    "\n",
    "def uniqueIndexes(l):\n",
    "    seen = set()\n",
    "    res = []\n",
    "    for i, n in enumerate(l):\n",
    "        if n not in seen:\n",
    "            res.append(i)\n",
    "            seen.add(n)\n",
    "    return res\n",
    "\n",
    "#%matplotlib inline\n",
    "import sys, os, pickle\n",
    "sys.path.append(rootf + '/PGM/source/')\n",
    "sys.path.append(rootf + '/PGM/utilities/')\n",
    "sys.path.append(rootf + '/Align_utils/')\n",
    "import rbm,utilities\n",
    "import Proteins_utils, RBM_utils, utilities, sequence_logo, plots_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CDR3</th>\n",
       "      <th>CDR3_al</th>\n",
       "      <th>tokenized_smiles</th>\n",
       "      <th>TCR BioIdentity</th>\n",
       "      <th>TCR Nucleotide Sequence</th>\n",
       "      <th>Experiment</th>\n",
       "      <th>ORF Coverage</th>\n",
       "      <th>Amino Acids</th>\n",
       "      <th>Start Index in Genome</th>\n",
       "      <th>End Index in Genome</th>\n",
       "      <th>v_gene</th>\n",
       "      <th>j_gene</th>\n",
       "      <th>Amino Acids 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CASSAQGTGDRGYTF</td>\n",
       "      <td>CASSA-----QGTGDRGYTF</td>\n",
       "      <td>[12, 16, 34, 34, 31, 31, 31, 31, 31, 27, 13]</td>\n",
       "      <td>CASSAQGTGDRGYTF+TCRBV27-01+TCRBJ01-02</td>\n",
       "      <td>GAGTCGCCCAGCCCCAACCAGACCTCTCTGTACTTCTGTGCCAGCA...</td>\n",
       "      <td>eAV93</td>\n",
       "      <td>ORF1ab,surface glycoprotein</td>\n",
       "      <td>ADAGFIKQY,AELEGIQY,LADAGFIKQY,TLADAGFIK</td>\n",
       "      <td>533</td>\n",
       "      <td>24073</td>\n",
       "      <td>TCRBV27-01</td>\n",
       "      <td>TCRBJ01-02</td>\n",
       "      <td>ADAGFIKQY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CASSLVATGNTGELFF</td>\n",
       "      <td>CASSL----VATGNTGELFF</td>\n",
       "      <td>[12, 16, 34, 34, 31, 31, 31, 31, 23, 27, 27, 13]</td>\n",
       "      <td>CASSLVATGNTGELFF+TCRBV07-09+TCRBJ02-02</td>\n",
       "      <td>CGCACAGAGCAGGGGGACTCGGCCATGTATCTCTGTGCCAGCAGCT...</td>\n",
       "      <td>eOX56</td>\n",
       "      <td>ORF1ab,surface glycoprotein</td>\n",
       "      <td>ADAGFIKQY,AELEGIQY,LADAGFIKQY,TLADAGFIK</td>\n",
       "      <td>533</td>\n",
       "      <td>24073</td>\n",
       "      <td>TCRBV07-09</td>\n",
       "      <td>TCRBJ02-02</td>\n",
       "      <td>ADAGFIKQY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CALKVGADTQYF</td>\n",
       "      <td>CALKV--------GADTQYF</td>\n",
       "      <td>[12, 16, 31, 31, 31, 31, 31, 31, 31, 31, 27, 13]</td>\n",
       "      <td>CALKVGADTQYF+TCRBV30-01+TCRBJ02-03</td>\n",
       "      <td>CTGAGTTCTAAGAAGCTCCTTCTCAGTGACTCTGGCTTCTATCTCT...</td>\n",
       "      <td>eQD124</td>\n",
       "      <td>ORF1ab,surface glycoprotein</td>\n",
       "      <td>ADAGFIKQY,AELEGIQY,LADAGFIKQY,TLADAGFIK</td>\n",
       "      <td>533</td>\n",
       "      <td>24073</td>\n",
       "      <td>TCRBV30-01</td>\n",
       "      <td>TCRBJ02-03</td>\n",
       "      <td>ADAGFIKQY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CASSLWASGRGGTGELFF</td>\n",
       "      <td>CASSLW--ASGRGGTGELFF</td>\n",
       "      <td>[12, 16, 34, 34, 31, 31, 34, 27, 27, 13]</td>\n",
       "      <td>CASSLWASGRGGTGELFF+TCRBV27-01+TCRBJ02-02</td>\n",
       "      <td>AGCCCCAACCAGACCTCTCTGTACTTCTGTGCCAGCAGTTTATGGG...</td>\n",
       "      <td>eAV93</td>\n",
       "      <td>ORF1ab,surface glycoprotein</td>\n",
       "      <td>ADAGFIKQY,AELEGIQY,LADAGFIKQY,TLADAGFIK</td>\n",
       "      <td>533</td>\n",
       "      <td>24073</td>\n",
       "      <td>TCRBV27-01</td>\n",
       "      <td>TCRBJ02-02</td>\n",
       "      <td>ADAGFIKQY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CASSLLGWEQLDEQFF</td>\n",
       "      <td>CASSL----LGWEQLDEQFF</td>\n",
       "      <td>[12, 16, 34, 34, 31, 31, 31, 31, 27, 27, 13]</td>\n",
       "      <td>CASSLLGWEQLDEQFF+TCRBV27-01+TCRBJ02-01</td>\n",
       "      <td>TCGCCCAGCCCCAACCAGACCTCTCTGTACTTCTGTGCCAGCAGTT...</td>\n",
       "      <td>eMR16</td>\n",
       "      <td>ORF1ab,surface glycoprotein</td>\n",
       "      <td>ADAGFIKQY,AELEGIQY,LADAGFIKQY,TLADAGFIK</td>\n",
       "      <td>533</td>\n",
       "      <td>24073</td>\n",
       "      <td>TCRBV27-01</td>\n",
       "      <td>TCRBJ02-01</td>\n",
       "      <td>ADAGFIKQY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>CSASQGTYEQYF</td>\n",
       "      <td>CSASQ--------GTYEQYF</td>\n",
       "      <td>[12, 16, 34, 34, 31, 31, 31, 31, 31, 31, 31, 3...</td>\n",
       "      <td>CSASQGTYEQYF+TCRBV20-01+TCRBJ02-07</td>\n",
       "      <td>CTGACAGTGACCAGTGCCCATCCTGAAGACAGCAGCTTCTACATCT...</td>\n",
       "      <td>eEE240</td>\n",
       "      <td>ORF1ab</td>\n",
       "      <td>TMADLVYAL,YTMADLVYA</td>\n",
       "      <td>13805</td>\n",
       "      <td>13834</td>\n",
       "      <td>TCRBV20-01</td>\n",
       "      <td>TCRBJ02-07</td>\n",
       "      <td>TMADLVYAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>CSVEDPQVTNEQFF</td>\n",
       "      <td>CSVED------PQVTNEQFF</td>\n",
       "      <td>[12, 16, 34, 31, 31, 31, 31, 31, 31, 45, 23, 2...</td>\n",
       "      <td>CSVEDPQVTNEQFF+TCRBV29-01+TCRBJ02-01</td>\n",
       "      <td>GTGAGCAACATGAGCCCTGAAGACAGCAGCATATATCTCTGCAGCG...</td>\n",
       "      <td>eEE243</td>\n",
       "      <td>ORF1ab</td>\n",
       "      <td>TMADLVYAL,YTMADLVYA</td>\n",
       "      <td>13805</td>\n",
       "      <td>13834</td>\n",
       "      <td>TCRBV29-01</td>\n",
       "      <td>TCRBJ02-01</td>\n",
       "      <td>TMADLVYAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>CASSTSGKGEKLFF</td>\n",
       "      <td>CASST------SGKGEKLFF</td>\n",
       "      <td>[12, 16, 34, 34, 31, 31, 31, 31, 31, 31, 34, 2...</td>\n",
       "      <td>CASSTSGKGEKLFF+TCRBV18-01+TCRBJ01-04</td>\n",
       "      <td>ATCCAGCAGGTAGTGCGAGGAGATTCGGCAGCTTATTTCTGTGCCA...</td>\n",
       "      <td>eAV93</td>\n",
       "      <td>ORF1ab</td>\n",
       "      <td>TMADLVYAL,YTMADLVYA</td>\n",
       "      <td>13805</td>\n",
       "      <td>13834</td>\n",
       "      <td>TCRBV18-01</td>\n",
       "      <td>TCRBJ01-04</td>\n",
       "      <td>TMADLVYAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>CSASLGLYEQYF</td>\n",
       "      <td>CSASL--------GLYEQYF</td>\n",
       "      <td>[12, 16, 34, 34, 31, 31, 31, 31, 31, 31, 31, 3...</td>\n",
       "      <td>CSASLGLYEQYF+TCRBV20-X+TCRBJ02-07</td>\n",
       "      <td>CTGACAGTGACCAGTGCCCATCCTGAAGACAGCAGCTTCTACATCT...</td>\n",
       "      <td>eAV88</td>\n",
       "      <td>ORF1ab</td>\n",
       "      <td>TMADLVYAL,YTMADLVYA</td>\n",
       "      <td>13805</td>\n",
       "      <td>13834</td>\n",
       "      <td>TCRBV20-X</td>\n",
       "      <td>TCRBJ02-07</td>\n",
       "      <td>TMADLVYAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>CSASLGLYEQYF</td>\n",
       "      <td>CSASL--------GLYEQYF</td>\n",
       "      <td>[12, 16, 34, 34, 31, 31, 31, 31, 31, 31, 31, 3...</td>\n",
       "      <td>CSASLGLYEQYF+TCRBV20-01+TCRBJ02-07</td>\n",
       "      <td>CTGACAGTGACCAGTGCCCATCCTGAAGACAGCAGCTTCTACATCT...</td>\n",
       "      <td>eEE240</td>\n",
       "      <td>ORF1ab</td>\n",
       "      <td>TMADLVYAL,YTMADLVYA</td>\n",
       "      <td>13805</td>\n",
       "      <td>13834</td>\n",
       "      <td>TCRBV20-01</td>\n",
       "      <td>TCRBJ02-07</td>\n",
       "      <td>TMADLVYAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     CDR3               CDR3_al  \\\n",
       "0         CASSAQGTGDRGYTF  CASSA-----QGTGDRGYTF   \n",
       "1        CASSLVATGNTGELFF  CASSL----VATGNTGELFF   \n",
       "2            CALKVGADTQYF  CALKV--------GADTQYF   \n",
       "3      CASSLWASGRGGTGELFF  CASSLW--ASGRGGTGELFF   \n",
       "4        CASSLLGWEQLDEQFF  CASSL----LGWEQLDEQFF   \n",
       "...                   ...                   ...   \n",
       "99995        CSASQGTYEQYF  CSASQ--------GTYEQYF   \n",
       "99996      CSVEDPQVTNEQFF  CSVED------PQVTNEQFF   \n",
       "99997      CASSTSGKGEKLFF  CASST------SGKGEKLFF   \n",
       "99998        CSASLGLYEQYF  CSASL--------GLYEQYF   \n",
       "99999        CSASLGLYEQYF  CSASL--------GLYEQYF   \n",
       "\n",
       "                                        tokenized_smiles  \\\n",
       "0           [12, 16, 34, 34, 31, 31, 31, 31, 31, 27, 13]   \n",
       "1       [12, 16, 34, 34, 31, 31, 31, 31, 23, 27, 27, 13]   \n",
       "2       [12, 16, 31, 31, 31, 31, 31, 31, 31, 31, 27, 13]   \n",
       "3               [12, 16, 34, 34, 31, 31, 34, 27, 27, 13]   \n",
       "4           [12, 16, 34, 34, 31, 31, 31, 31, 27, 27, 13]   \n",
       "...                                                  ...   \n",
       "99995  [12, 16, 34, 34, 31, 31, 31, 31, 31, 31, 31, 3...   \n",
       "99996  [12, 16, 34, 31, 31, 31, 31, 31, 31, 45, 23, 2...   \n",
       "99997  [12, 16, 34, 34, 31, 31, 31, 31, 31, 31, 34, 2...   \n",
       "99998  [12, 16, 34, 34, 31, 31, 31, 31, 31, 31, 31, 3...   \n",
       "99999  [12, 16, 34, 34, 31, 31, 31, 31, 31, 31, 31, 3...   \n",
       "\n",
       "                                TCR BioIdentity  \\\n",
       "0         CASSAQGTGDRGYTF+TCRBV27-01+TCRBJ01-02   \n",
       "1        CASSLVATGNTGELFF+TCRBV07-09+TCRBJ02-02   \n",
       "2            CALKVGADTQYF+TCRBV30-01+TCRBJ02-03   \n",
       "3      CASSLWASGRGGTGELFF+TCRBV27-01+TCRBJ02-02   \n",
       "4        CASSLLGWEQLDEQFF+TCRBV27-01+TCRBJ02-01   \n",
       "...                                         ...   \n",
       "99995        CSASQGTYEQYF+TCRBV20-01+TCRBJ02-07   \n",
       "99996      CSVEDPQVTNEQFF+TCRBV29-01+TCRBJ02-01   \n",
       "99997      CASSTSGKGEKLFF+TCRBV18-01+TCRBJ01-04   \n",
       "99998         CSASLGLYEQYF+TCRBV20-X+TCRBJ02-07   \n",
       "99999        CSASLGLYEQYF+TCRBV20-01+TCRBJ02-07   \n",
       "\n",
       "                                 TCR Nucleotide Sequence Experiment  \\\n",
       "0      GAGTCGCCCAGCCCCAACCAGACCTCTCTGTACTTCTGTGCCAGCA...      eAV93   \n",
       "1      CGCACAGAGCAGGGGGACTCGGCCATGTATCTCTGTGCCAGCAGCT...      eOX56   \n",
       "2      CTGAGTTCTAAGAAGCTCCTTCTCAGTGACTCTGGCTTCTATCTCT...     eQD124   \n",
       "3      AGCCCCAACCAGACCTCTCTGTACTTCTGTGCCAGCAGTTTATGGG...      eAV93   \n",
       "4      TCGCCCAGCCCCAACCAGACCTCTCTGTACTTCTGTGCCAGCAGTT...      eMR16   \n",
       "...                                                  ...        ...   \n",
       "99995  CTGACAGTGACCAGTGCCCATCCTGAAGACAGCAGCTTCTACATCT...     eEE240   \n",
       "99996  GTGAGCAACATGAGCCCTGAAGACAGCAGCATATATCTCTGCAGCG...     eEE243   \n",
       "99997  ATCCAGCAGGTAGTGCGAGGAGATTCGGCAGCTTATTTCTGTGCCA...      eAV93   \n",
       "99998  CTGACAGTGACCAGTGCCCATCCTGAAGACAGCAGCTTCTACATCT...      eAV88   \n",
       "99999  CTGACAGTGACCAGTGCCCATCCTGAAGACAGCAGCTTCTACATCT...     eEE240   \n",
       "\n",
       "                      ORF Coverage                              Amino Acids  \\\n",
       "0      ORF1ab,surface glycoprotein  ADAGFIKQY,AELEGIQY,LADAGFIKQY,TLADAGFIK   \n",
       "1      ORF1ab,surface glycoprotein  ADAGFIKQY,AELEGIQY,LADAGFIKQY,TLADAGFIK   \n",
       "2      ORF1ab,surface glycoprotein  ADAGFIKQY,AELEGIQY,LADAGFIKQY,TLADAGFIK   \n",
       "3      ORF1ab,surface glycoprotein  ADAGFIKQY,AELEGIQY,LADAGFIKQY,TLADAGFIK   \n",
       "4      ORF1ab,surface glycoprotein  ADAGFIKQY,AELEGIQY,LADAGFIKQY,TLADAGFIK   \n",
       "...                            ...                                      ...   \n",
       "99995                       ORF1ab                      TMADLVYAL,YTMADLVYA   \n",
       "99996                       ORF1ab                      TMADLVYAL,YTMADLVYA   \n",
       "99997                       ORF1ab                      TMADLVYAL,YTMADLVYA   \n",
       "99998                       ORF1ab                      TMADLVYAL,YTMADLVYA   \n",
       "99999                       ORF1ab                      TMADLVYAL,YTMADLVYA   \n",
       "\n",
       "       Start Index in Genome  End Index in Genome      v_gene      j_gene  \\\n",
       "0                        533                24073  TCRBV27-01  TCRBJ01-02   \n",
       "1                        533                24073  TCRBV07-09  TCRBJ02-02   \n",
       "2                        533                24073  TCRBV30-01  TCRBJ02-03   \n",
       "3                        533                24073  TCRBV27-01  TCRBJ02-02   \n",
       "4                        533                24073  TCRBV27-01  TCRBJ02-01   \n",
       "...                      ...                  ...         ...         ...   \n",
       "99995                  13805                13834  TCRBV20-01  TCRBJ02-07   \n",
       "99996                  13805                13834  TCRBV29-01  TCRBJ02-01   \n",
       "99997                  13805                13834  TCRBV18-01  TCRBJ01-04   \n",
       "99998                  13805                13834   TCRBV20-X  TCRBJ02-07   \n",
       "99999                  13805                13834  TCRBV20-01  TCRBJ02-07   \n",
       "\n",
       "      Amino Acids 1  \n",
       "0         ADAGFIKQY  \n",
       "1         ADAGFIKQY  \n",
       "2         ADAGFIKQY  \n",
       "3         ADAGFIKQY  \n",
       "4         ADAGFIKQY  \n",
       "...             ...  \n",
       "99995     TMADLVYAL  \n",
       "99996     TMADLVYAL  \n",
       "99997     TMADLVYAL  \n",
       "99998     TMADLVYAL  \n",
       "99999     TMADLVYAL  \n",
       "\n",
       "[100000 rows x 13 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select sample of data\n",
    "data = df[:100000]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 396x129.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Visualize logo of clones with top response score ##\n",
    "ll_rel = data[\"CDR3_al\"]\n",
    "val_seqs = list((data['CDR3_al'].values))\n",
    "val_seqs_n = convert_number(val_seqs)\n",
    "s2=16\n",
    "ntop = 50\n",
    "\n",
    "pwm_top = utilities.average(val_seqs_n, c=21)\n",
    "fig = sequence_logo.Sequence_logo(pwm_top, figsize=(5.5,1.8), ylabel = 'bits', ticks_every=5, ticks_labels_size=s2-2,title_size=s2-2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Second method used to plot the LOGOS:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This methods uses an external package called **pyrepseq** to plot nicely the LOGOS of the CDR3 sequences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pyrepseq as rs\n",
    "import pyrepseq.plotting as rsp\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x144 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random \n",
    "N = 3\n",
    "idx = random.sample(range(1, 100), N)\n",
    "fig, axes = plt.subplots(ncols=N, nrows=3, sharey=True, figsize=(15, 2),\n",
    "                         gridspec_kw=dict(width_ratios=(1, 3, 1),\n",
    "                                          wspace=0.1, hspace=0.5)\n",
    "                         )\n",
    "for row, i in enumerate(idx):\n",
    "    rsp.seqlogos_vj(data.iloc[i:i+1], \"CDR3_al\", 'v_gene', 'j_gene', axes=axes[row, :3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x144 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=3, nrows=1, sharey=True, figsize=(20, 2),\n",
    "                         gridspec_kw=dict(width_ratios=(1, 3, 1),\n",
    "                                          wspace=0.1, hspace=0.5)\n",
    "                         )\n",
    "rsp.seqlogos_vj(data, \"CDR3_al\",'v_gene','j_gene',axes=axes[:3])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As this method is much **easier** to leverage, we will mainly use it to plot and analyse our sequences."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "m4r",
   "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": "3.9.16 (main, Jan 11 2023, 10:02:19) \n[Clang 14.0.6 ]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "527563dfeefebc70ac19b49ae61825616aae9a571fceec61e8d81d36990e36c5"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}