11_VAE_vampire_latent.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of the latent space distribution of our Variational AutoEncoder model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We analyse the latent space distribution of our model by computing the embeddings and generating new sequences. One important strength of the VAE model is its continuous and latent space distribution and therefore, its good generative ability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-10 09:58:18.988592: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "# Import all necessary modules\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import pickle\n",
    "from sonnia.processing import Processing\n",
    "from tqdm.notebook import tqdm\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.cluster import DBSCAN\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Input\n",
    "from tensorflow.keras.layers import Dense, Reshape, Activation\n",
    "from tensorflow.keras.layers import ReLU\n",
    "from tensorflow.keras.layers import Lambda\n",
    "from tensorflow.keras.layers import BatchNormalization\n",
    "from tensorflow.keras.utils import plot_model\n",
    "from tensorflow.keras.layers import Concatenate\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "from keras import backend as K\n",
    "import keras\n",
    "from keras import losses\n",
    "from tensorflow.python.framework.ops import disable_eager_execution\n",
    "from vampire_custom_keras import BetaWarmup, EmbedViaMatrix\n",
    "from keras import regularizers\n",
    "from keras.layers.core import Dropout\n",
    "\n",
    "disable_eager_execution()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\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",
       "  </tbody>\n",
       "</table>\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",
       "                                   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",
       "                            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",
       "                             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",
       "                  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",
       "   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",
       "  Amino Acids 1  \n",
       "0     ADAGFIKQY  \n",
       "1     ADAGFIKQY  \n",
       "2     ADAGFIKQY  \n",
       "3     ADAGFIKQY  \n",
       "4     ADAGFIKQY  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load dataset\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.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keep balanced sample of the dataset\n",
    "to_keep = [\"FFSNVTWFH\", \"AFLLFLVLI\", \"LSPRWYFYY\"]\n",
    "df = df[df[\"Amino Acids 1\"].isin(to_keep)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/m4r/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "43f9e6d360ca4c2db94d3e35f8adec25",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Encode sequences and labels \n",
    "from numpy import array\n",
    "from numpy import argmax\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "aa = [\"A\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"K\", \"L\", \"M\", \"N\", \"P\", \"Q\",\"R\", \"S\", \"T\", \"V\", \"W\", \"Y\", \"-\"]\n",
    "label_encoder = LabelEncoder()\n",
    "y_encoder =LabelEncoder()\n",
    "y_encoder = y_encoder.fit(df['Amino Acids'].unique())\n",
    "onehot_encoder = OneHotEncoder(sparse=False)\n",
    "integer_encoded = label_encoder.fit_transform(aa)\n",
    "integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)\n",
    "onehot_encoded = onehot_encoder.fit(integer_encoded)\n",
    "\n",
    "one_hot_status = True\n",
    "col_one_hot = []\n",
    "col_integer = []\n",
    "y_labels = []\n",
    "for k in tqdm(df.index):\n",
    "    integer_encoded = label_encoder.transform(list(df.loc[k,\"CDR3_al\"]))\n",
    "    col_integer.append(integer_encoded)\n",
    "    integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)\n",
    "    if one_hot_status:\n",
    "        onehot_encoded = onehot_encoder.transform(integer_encoded)\n",
    "        col_one_hot.append(onehot_encoded)\n",
    "    y_label = y_encoder.transform([df.loc[k,\"Amino Acids\"]])\n",
    "    y_labels.append(y_label)\n",
    "    \n",
    "y_labels = [int(y) for y in y_labels]\n",
    "if one_hot_status:\n",
    "    df[\"CDR3_al_one_hot\"] = col_one_hot\n",
    "df[\"CDR3_al_integer\"] = col_integer\n",
    "df[\"label\"] = y_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_cdr3_seqs(cdr3_seqs, labelencoder, onehotencoder, onehot=True):\n",
    "    \"\"\"Encode CDR3 sequences to one-hot encoding.\"\"\"\n",
    "    encoded_seqs = []\n",
    "    for cdr3 in cdr3_seqs:\n",
    "        integer_encoded = labelencoder.transform(list(cdr3))\n",
    "        integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)\n",
    "        if onehot:\n",
    "            onehot_encoded = onehotencoder.transform(integer_encoded)\n",
    "            encoded_seqs.append(onehot_encoded)\n",
    "        else:\n",
    "            encoded_seqs.append(integer_encoded)\n",
    "    return encoded_seqs\n",
    "\n",
    "col_one_hot = encode_cdr3_seqs(df[\"CDR3_al\"], label_encoder, onehot_encoder, onehot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/m4r/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8d759c725339495ca2a7fc3c2b48dc07",
       "version_major": 2,
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      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/m4r/lib/python3.9/site-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ba71c98a10714192bc43bd33ea9316c2",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Encode v-genes and j-genes\n",
    "v_labels = []\n",
    "v_encoder = LabelEncoder()\n",
    "v_encoder_one_hot = OneHotEncoder(sparse=False)\n",
    "v_encoder = v_encoder.fit(df['v_gene'].unique())\n",
    "v_encoder_one_hot = v_encoder_one_hot.fit(v_encoder.transform(df['v_gene'].unique()).reshape(-1,1))\n",
    "for k in tqdm(df.index):\n",
    "    v_label = v_encoder.transform([df.loc[k,\"v_gene\"]])\n",
    "    v_label = v_encoder_one_hot.transform(v_label.reshape(-1,1))\n",
    "    v_labels.append(v_label)\n",
    "df[\"v_gene_one_hot\"] = v_labels\n",
    "\n",
    "j_labels = []\n",
    "j_encoder = LabelEncoder()\n",
    "j_encoder_one_hot = OneHotEncoder(sparse=False)\n",
    "j_encoder = j_encoder.fit(df['j_gene'].unique())\n",
    "j_encoder_one_hot = j_encoder_one_hot.fit(j_encoder.transform(df['j_gene'].unique()).reshape(-1,1))\n",
    "for k in tqdm(df.index):\n",
    "    j_label = j_encoder.transform([df.loc[k,\"j_gene\"]])\n",
    "    j_label = j_encoder_one_hot.transform(j_label.reshape(-1,1))\n",
    "    j_labels.append(j_label)\n",
    "df[\"j_gene_one_hot\"] = j_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13 51\n"
     ]
    }
   ],
   "source": [
    "len_j_gene = len(j_encoder.classes_)\n",
    "len_v_gene = len(v_encoder.classes_)\n",
    "print(len_j_gene, len_v_gene)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/rl/d4jq2g217cv1hy_ssxxj4k0r0000gn/T/ipykernel_3245/2658324633.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data[\"v_gene_one_hot\"]  = data[\"v_gene_one_hot\"].apply(lambda x: x.reshape(len_v_gene,))\n",
      "/var/folders/rl/d4jq2g217cv1hy_ssxxj4k0r0000gn/T/ipykernel_3245/2658324633.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data[\"j_gene_one_hot\"]  = data[\"j_gene_one_hot\"].apply(lambda x: x.reshape(len_j_gene,))\n"
     ]
    }
   ],
   "source": [
    "# Last pre-processing steps\n",
    "df = df.sample(frac=1).reset_index(drop=True)\n",
    "data = df[['CDR3_al_one_hot', 'v_gene_one_hot', 'j_gene_one_hot']]\n",
    "data[\"v_gene_one_hot\"]  = data[\"v_gene_one_hot\"].apply(lambda x: x.reshape(len_v_gene,))\n",
    "data[\"j_gene_one_hot\"]  = data[\"j_gene_one_hot\"].apply(lambda x: x.reshape(len_j_gene,))\n",
    "data = [np.stack(col.values) for _, col in data.items()]\n",
    "X_train = data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define functions to build the model architecture\n",
    "def build(params):\n",
    "\n",
    "    beta = K.variable(params['beta'])\n",
    "\n",
    "    def sampling(args):\n",
    "        \"\"\"\n",
    "        This function draws a sample from the multivariate normal defined by\n",
    "        the latent variables.\n",
    "        \"\"\"\n",
    "        z_mean, z_log_var = args\n",
    "        epsilon = K.random_normal(shape=(params['batch_size'], params['latent_dim']), mean=0.0, stddev=1.0)\n",
    "        print(epsilon.shape)\n",
    "        # Reparameterization trick!\n",
    "        return (z_mean + K.exp(z_log_var / 2) * epsilon)\n",
    "\n",
    "    def vae_cdr3_loss(io_encoder, io_decoder):\n",
    "        \"\"\"\n",
    "        The loss function is the sum of the cross-entropy and KL divergence. KL\n",
    "        gets a weight of beta.\n",
    "        \"\"\"\n",
    "        # Here we multiply by the number of sites, so that we have a\n",
    "        # total loss across the sites rather than a mean loss.\n",
    "        xent_loss = params['max_cdr3_len'] * K.mean(losses.categorical_crossentropy(io_encoder, io_decoder))\n",
    "        kl_loss = -0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)\n",
    "        kl_loss *= beta\n",
    "        return (xent_loss + kl_loss)\n",
    "\n",
    "    # Input:\n",
    "    cdr3_input_shape = (params['max_cdr3_len'], params['n_aas'])\n",
    "    cdr3_input = Input(shape=cdr3_input_shape, name='cdr3_input')\n",
    "    v_gene_input = Input(shape=(params['n_v_genes'], ), name='v_gene_input')\n",
    "    j_gene_input = Input(shape=(params['n_j_genes'], ), name='j_gene_input')\n",
    "\n",
    "    # Encoding layers:\n",
    "    cdr3_embedding = EmbedViaMatrix(params['aa_embedding_dim'], name='cdr3_embedding')(cdr3_input)\n",
    "    cdr3_embedding_flat = Reshape([params['aa_embedding_dim'] * params['max_cdr3_len']],\n",
    "                                  name='cdr3_embedding_flat')(cdr3_embedding)\n",
    "    v_gene_embedding = Dense(params['v_gene_embedding_dim'], name='v_gene_embedding')(v_gene_input)\n",
    "    j_gene_embedding = Dense(params['j_gene_embedding_dim'], name='j_gene_embedding')(j_gene_input)\n",
    "    merged_embedding = keras.layers.concatenate([cdr3_embedding_flat, v_gene_embedding, j_gene_embedding],\n",
    "                                                name='merged_embedding')\n",
    "    encoder_dense_1 = Dense(params['dense_nodes'], activation='elu', kernel_regularizer=regularizers.l2(0.001), name='encoder_dense_1')(merged_embedding)\n",
    "    encoder_dense_2 = Dense(params['dense_nodes'], activation='elu', kernel_regularizer=regularizers.l2(0.001), name='encoder_dense_2')(encoder_dense_1)\n",
    "    encoder_dense_3 = Dense(params['dense_nodes'], activation='elu', kernel_regularizer=regularizers.l2(0.001), name='encoder_dense_1')(encoder_dense_2)\n",
    "    encoder_dense_4 = Dense(params['dense_nodes'], activation='elu', kernel_regularizer=regularizers.l2(0.001), name='encoder_dense_2')(encoder_dense_3)\n",
    "    # Latent layers:\n",
    "    z_mean = Dense(params['latent_dim'], name='z_mean')(encoder_dense_2)\n",
    "    z_log_var = Dense(params['latent_dim'], name='z_log_var')(encoder_dense_2)\n",
    "\n",
    "    # Decoding layers:\n",
    "    z_l = Lambda(sampling, output_shape=(params['latent_dim'], ), name='z')\n",
    "    decoder_dense_1_l = Dense(params['dense_nodes'], activation='elu', name='decoder_dense_1')\n",
    "    decoder_dense_2_l = Dense(params['dense_nodes'], activation='elu', name='decoder_dense_2')\n",
    "    cdr3_post_dense_flat_l = Dense(np.array(cdr3_input_shape).prod(), activation='linear', name='cdr3_post_dense_flat')\n",
    "    cdr3_post_dense_reshape_l = Reshape(cdr3_input_shape, name='cdr3_post_dense')\n",
    "    cdr3_output_l = Activation(activation='softmax', name='cdr3_output')\n",
    "    v_gene_output_l = Dense(params['n_v_genes'], activation='softmax', name='v_gene_output')\n",
    "    j_gene_output_l = Dense(params['n_j_genes'], activation='softmax', name='j_gene_output')\n",
    "\n",
    "    post_decoder = decoder_dense_2_l(decoder_dense_1_l(z_l([z_mean, z_log_var])))\n",
    "    cdr3_output = cdr3_output_l(cdr3_post_dense_reshape_l(cdr3_post_dense_flat_l(post_decoder)))\n",
    "    v_gene_output = v_gene_output_l(post_decoder)\n",
    "    j_gene_output = j_gene_output_l(post_decoder)\n",
    "\n",
    "    # Define the decoder components separately so we can have it as its own model.\n",
    "    z_mean_input = Input(shape=(params['latent_dim'], ))\n",
    "    decoder_post_decoder = decoder_dense_2_l(decoder_dense_1_l(z_mean_input))\n",
    "    decoder_cdr3_output = cdr3_output_l(cdr3_post_dense_reshape_l(cdr3_post_dense_flat_l(decoder_post_decoder)))\n",
    "    decoder_v_gene_output = v_gene_output_l(decoder_post_decoder)\n",
    "    decoder_j_gene_output = j_gene_output_l(decoder_post_decoder)\n",
    "\n",
    "    encoder = Model([cdr3_input, v_gene_input, j_gene_input], [z_mean, z_log_var])\n",
    "    decoder = Model(z_mean_input, [decoder_cdr3_output, decoder_v_gene_output, decoder_j_gene_output])\n",
    "    vae = Model([cdr3_input, v_gene_input, j_gene_input], [cdr3_output, v_gene_output, j_gene_output])\n",
    "    vae.compile(\n",
    "        optimizer=\"adam\",\n",
    "        loss={\n",
    "            'cdr3_output': vae_cdr3_loss,\n",
    "            'v_gene_output': keras.losses.categorical_crossentropy,\n",
    "            'j_gene_output': keras.losses.categorical_crossentropy,\n",
    "        },\n",
    "        loss_weights={\n",
    "            # Keep the cdr3_output weight to be 1. The weights are relative\n",
    "            # anyhow, and buried inside the vae_cdr3_loss is a beta weight that\n",
    "            # determines how much weight the KL loss has. If we keep this\n",
    "            # weight as 1 then we can interpret beta in a straightforward way.\n",
    "            \"cdr3_output\": 1,\n",
    "            \"j_gene_output\": 0.1305,\n",
    "            \"v_gene_output\": 0.8138\n",
    "        })\n",
    "\n",
    "    callbacks = [BetaWarmup(beta, params['beta'], params['warmup_period'])]\n",
    "\n",
    "    return {'encoder': encoder, 'decoder': decoder, 'vae': vae, 'callbacks': callbacks}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set parameters for training\n",
    "params = {\n",
    "            \"latent_dim\": 2,\n",
    "            \"dense_nodes\": 75,\n",
    "            \"aa_embedding_dim\": 21,\n",
    "            \"v_gene_embedding_dim\": len_v_gene,\n",
    "            \"j_gene_embedding_dim\": len_j_gene,\n",
    "            \"beta\": 0.75,\n",
    "            \"max_cdr3_len\": 20,\n",
    "            \"n_aas\": 21,\n",
    "            \"n_v_genes\": len_v_gene,\n",
    "            \"n_j_genes\": len_j_gene,\n",
    "            \"stopping_monitor\": \"val_loss\",\n",
    "            \"batch_size\": 50,\n",
    "            \"pretrains\": 2,\n",
    "            \"warmup_period\": 3,\n",
    "            \"epochs\": 4,\n",
    "            \"patience\": 20,\n",
    "            \"n_inputs\" : 20,\n",
    "            \"v_inputs\" : 54,\n",
    "            \"j_inputs\" : 13\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 2)\n"
     ]
    }
   ],
   "source": [
    "# Build model\n",
    "res = build(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#keep only first 47,000 rows\n",
    "X_train_2 = X_train.copy()\n",
    "for i in range(len(X_train)):\n",
    "    X_train_2[i] = X_train[i][:47000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 37600 samples, validate on 9400 samples\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-10 10:00:41.971570: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-06-10 10:00:42.000372: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:354] MLIR V1 optimization pass is not enabled\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "37400/37600 [============================>.] - ETA: 0s - loss: 16.8503 - cdr3_output_loss: 15.0938 - v_gene_output_loss: 1.7552 - j_gene_output_loss: 1.1344"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/m4r/lib/python3.9/site-packages/keras/engine/training_v1.py:2332: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  updates = self.state_updates\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37600/37600 [==============================] - 2s 55us/sample - loss: 16.8303 - cdr3_output_loss: 15.0752 - v_gene_output_loss: 1.7538 - j_gene_output_loss: 1.1334 - val_loss: 13.6596 - val_cdr3_output_loss: 12.1740 - val_v_gene_output_loss: 1.4660 - val_j_gene_output_loss: 1.0013\n",
      "Epoch 2/50\n",
      "37600/37600 [==============================] - 2s 47us/sample - loss: 10.7856 - cdr3_output_loss: 9.3952 - v_gene_output_loss: 1.3601 - j_gene_output_loss: 0.9090 - val_loss: 10.1145 - val_cdr3_output_loss: 8.7848 - val_v_gene_output_loss: 1.2991 - val_j_gene_output_loss: 0.8513\n",
      "Epoch 3/50\n",
      "37600/37600 [==============================] - 2s 46us/sample - loss: 11.3206 - cdr3_output_loss: 9.9921 - v_gene_output_loss: 1.3282 - j_gene_output_loss: 0.8468 - val_loss: 10.8947 - val_cdr3_output_loss: 9.5934 - val_v_gene_output_loss: 1.3115 - val_j_gene_output_loss: 0.7921\n",
      "Epoch 4/50\n",
      "37600/37600 [==============================] - 2s 47us/sample - loss: 11.8291 - cdr3_output_loss: 10.5101 - v_gene_output_loss: 1.3395 - j_gene_output_loss: 0.7890 - val_loss: 11.5205 - val_cdr3_output_loss: 10.2104 - val_v_gene_output_loss: 1.3374 - val_j_gene_output_loss: 0.7514\n",
      "Epoch 5/50\n",
      "37600/37600 [==============================] - 2s 47us/sample - loss: 12.3862 - cdr3_output_loss: 11.0742 - v_gene_output_loss: 1.3444 - j_gene_output_loss: 0.7530 - val_loss: 12.2442 - val_cdr3_output_loss: 10.9346 - val_v_gene_output_loss: 1.3472 - val_j_gene_output_loss: 0.7309\n",
      "Epoch 6/50\n",
      "37600/37600 [==============================] - 2s 47us/sample - loss: 12.2274 - cdr3_output_loss: 10.9329 - v_gene_output_loss: 1.3336 - j_gene_output_loss: 0.7096 - val_loss: 12.0522 - val_cdr3_output_loss: 10.7611 - val_v_gene_output_loss: 1.3317 - val_j_gene_output_loss: 0.7043\n",
      "Epoch 7/50\n",
      "37600/37600 [==============================] - 2s 46us/sample - loss: 12.1147 - cdr3_output_loss: 10.8343 - v_gene_output_loss: 1.3237 - j_gene_output_loss: 0.6714 - val_loss: 11.9859 - val_cdr3_output_loss: 10.7058 - val_v_gene_output_loss: 1.3245 - val_j_gene_output_loss: 0.6711\n",
      "Epoch 8/50\n",
      "37600/37600 [==============================] - 2s 47us/sample - loss: 12.0042 - cdr3_output_loss: 10.7357 - v_gene_output_loss: 1.3156 - j_gene_output_loss: 0.6368 - val_loss: 11.8232 - val_cdr3_output_loss: 10.5583 - val_v_gene_output_loss: 1.3138 - val_j_gene_output_loss: 0.6220\n",
      "Epoch 9/50\n",
      "37600/37600 [==============================] - 2s 47us/sample - loss: 11.9046 - cdr3_output_loss: 10.6475 - v_gene_output_loss: 1.3078 - j_gene_output_loss: 0.5994 - val_loss: 11.7445 - val_cdr3_output_loss: 10.5001 - val_v_gene_output_loss: 1.2980 - val_j_gene_output_loss: 0.5645\n",
      "Epoch 10/50\n",
      "37600/37600 [==============================] - 2s 46us/sample - loss: 11.8007 - cdr3_output_loss: 10.5544 - v_gene_output_loss: 1.3000 - j_gene_output_loss: 0.5652 - val_loss: 11.7006 - val_cdr3_output_loss: 10.4464 - val_v_gene_output_loss: 1.3116 - val_j_gene_output_loss: 0.5483\n",
      "Epoch 11/50\n",
      "37600/37600 [==============================] - 2s 46us/sample - loss: 11.7069 - cdr3_output_loss: 10.4699 - v_gene_output_loss: 1.2931 - j_gene_output_loss: 0.5301 - val_loss: 11.5582 - val_cdr3_output_loss: 10.3355 - val_v_gene_output_loss: 1.2781 - val_j_gene_output_loss: 0.5172\n",
      "Epoch 12/50\n",
      "37600/37600 [==============================] - 2s 46us/sample - loss: 11.6563 - cdr3_output_loss: 10.4286 - v_gene_output_loss: 1.2851 - j_gene_output_loss: 0.5106 - val_loss: 11.5722 - val_cdr3_output_loss: 10.3417 - val_v_gene_output_loss: 1.2899 - val_j_gene_output_loss: 0.4957\n",
      "Epoch 13/50\n",
      "37600/37600 [==============================] - 2s 47us/sample - loss: 11.5894 - cdr3_output_loss: 10.3683 - v_gene_output_loss: 1.2807 - j_gene_output_loss: 0.4863 - val_loss: 11.4457 - val_cdr3_output_loss: 10.2214 - val_v_gene_output_loss: 1.2850 - val_j_gene_output_loss: 0.4857\n",
      "Epoch 14/50\n",
      "37600/37600 [==============================] - 2s 49us/sample - loss: 11.5277 - cdr3_output_loss: 10.3135 - v_gene_output_loss: 1.2765 - j_gene_output_loss: 0.4681 - val_loss: 11.4423 - val_cdr3_output_loss: 10.2232 - val_v_gene_output_loss: 1.2831 - val_j_gene_output_loss: 0.4748\n",
      "Epoch 15/50\n",
      "37600/37600 [==============================] - 2s 49us/sample - loss: 11.4954 - cdr3_output_loss: 10.2876 - v_gene_output_loss: 1.2713 - j_gene_output_loss: 0.4582 - val_loss: 11.4171 - val_cdr3_output_loss: 10.2230 - val_v_gene_output_loss: 1.2573 - val_j_gene_output_loss: 0.4408\n",
      "Epoch 16/50\n",
      "37600/37600 [==============================] - 2s 49us/sample - loss: 11.4295 - cdr3_output_loss: 10.2275 - v_gene_output_loss: 1.2658 - j_gene_output_loss: 0.4451 - val_loss: 11.3246 - val_cdr3_output_loss: 10.1180 - val_v_gene_output_loss: 1.2687 - val_j_gene_output_loss: 0.4661\n",
      "Epoch 17/50\n",
      "37600/37600 [==============================] - 2s 47us/sample - loss: 11.4177 - cdr3_output_loss: 10.2184 - v_gene_output_loss: 1.2642 - j_gene_output_loss: 0.4358 - val_loss: 11.2579 - val_cdr3_output_loss: 10.0631 - val_v_gene_output_loss: 1.2609 - val_j_gene_output_loss: 0.4219\n",
      "Epoch 18/50\n",
      "37600/37600 [==============================] - 2s 49us/sample - loss: 11.3644 - cdr3_output_loss: 10.1701 - v_gene_output_loss: 1.2610 - j_gene_output_loss: 0.4225 - val_loss: 11.2444 - val_cdr3_output_loss: 10.0480 - val_v_gene_output_loss: 1.2612 - val_j_gene_output_loss: 0.4371\n",
      "Epoch 19/50\n",
      "37600/37600 [==============================] - 2s 48us/sample - loss: 11.3348 - cdr3_output_loss: 10.1430 - v_gene_output_loss: 1.2592 - j_gene_output_loss: 0.4136 - val_loss: 11.3145 - val_cdr3_output_loss: 10.1160 - val_v_gene_output_loss: 1.2633 - val_j_gene_output_loss: 0.4360\n",
      "Epoch 20/50\n",
      "37600/37600 [==============================] - 2s 47us/sample - loss: 11.3123 - cdr3_output_loss: 10.1248 - v_gene_output_loss: 1.2547 - j_gene_output_loss: 0.4093 - val_loss: 11.1855 - val_cdr3_output_loss: 10.0005 - val_v_gene_output_loss: 1.2525 - val_j_gene_output_loss: 0.4057\n",
      "Epoch 21/50\n",
      "37600/37600 [==============================] - 2s 48us/sample - loss: 11.2827 - cdr3_output_loss: 10.0964 - v_gene_output_loss: 1.2550 - j_gene_output_loss: 0.3999 - val_loss: 11.1688 - val_cdr3_output_loss: 9.9709 - val_v_gene_output_loss: 1.2667 - val_j_gene_output_loss: 0.4180\n",
      "Epoch 22/50\n",
      "37600/37600 [==============================] - 2s 48us/sample - loss: 11.2569 - cdr3_output_loss: 10.0757 - v_gene_output_loss: 1.2504 - j_gene_output_loss: 0.3948 - val_loss: 11.1966 - val_cdr3_output_loss: 10.0112 - val_v_gene_output_loss: 1.2578 - val_j_gene_output_loss: 0.3802\n",
      "Epoch 23/50\n",
      "37600/37600 [==============================] - 2s 50us/sample - loss: 11.2310 - cdr3_output_loss: 10.0492 - v_gene_output_loss: 1.2528 - j_gene_output_loss: 0.3845 - val_loss: 11.1207 - val_cdr3_output_loss: 9.9317 - val_v_gene_output_loss: 1.2597 - val_j_gene_output_loss: 0.3971\n",
      "Epoch 24/50\n",
      "37600/37600 [==============================] - 2s 47us/sample - loss: 11.2112 - cdr3_output_loss: 10.0323 - v_gene_output_loss: 1.2491 - j_gene_output_loss: 0.3823 - val_loss: 11.1267 - val_cdr3_output_loss: 9.9527 - val_v_gene_output_loss: 1.2413 - val_j_gene_output_loss: 0.3945\n",
      "Epoch 25/50\n",
      "37600/37600 [==============================] - 2s 48us/sample - loss: 11.1901 - cdr3_output_loss: 10.0133 - v_gene_output_loss: 1.2481 - j_gene_output_loss: 0.3773 - val_loss: 11.0914 - val_cdr3_output_loss: 9.9240 - val_v_gene_output_loss: 1.2376 - val_j_gene_output_loss: 0.3679\n",
      "Epoch 26/50\n",
      "37600/37600 [==============================] - 2s 47us/sample - loss: 11.1595 - cdr3_output_loss: 9.9874 - v_gene_output_loss: 1.2440 - j_gene_output_loss: 0.3689 - val_loss: 11.0877 - val_cdr3_output_loss: 9.9067 - val_v_gene_output_loss: 1.2523 - val_j_gene_output_loss: 0.3885\n",
      "Epoch 27/50\n",
      "37600/37600 [==============================] - 2s 48us/sample - loss: 11.1466 - cdr3_output_loss: 9.9766 - v_gene_output_loss: 1.2420 - j_gene_output_loss: 0.3665 - val_loss: 11.1256 - val_cdr3_output_loss: 9.9253 - val_v_gene_output_loss: 1.2744 - val_j_gene_output_loss: 0.3906\n",
      "Epoch 28/50\n",
      "37600/37600 [==============================] - 2s 47us/sample - loss: 11.1493 - cdr3_output_loss: 9.9775 - v_gene_output_loss: 1.2438 - j_gene_output_loss: 0.3641 - val_loss: 11.1251 - val_cdr3_output_loss: 9.9488 - val_v_gene_output_loss: 1.2459 - val_j_gene_output_loss: 0.3882\n",
      "Epoch 29/50\n",
      "37600/37600 [==============================] - 2s 47us/sample - loss: 11.1016 - cdr3_output_loss: 9.9358 - v_gene_output_loss: 1.2381 - j_gene_output_loss: 0.3588 - val_loss: 11.0654 - val_cdr3_output_loss: 9.9016 - val_v_gene_output_loss: 1.2343 - val_j_gene_output_loss: 0.3686\n",
      "Epoch 30/50\n",
      "37600/37600 [==============================] - 2s 47us/sample - loss: 11.0934 - cdr3_output_loss: 9.9291 - v_gene_output_loss: 1.2372 - j_gene_output_loss: 0.3551 - val_loss: 11.0425 - val_cdr3_output_loss: 9.8655 - val_v_gene_output_loss: 1.2510 - val_j_gene_output_loss: 0.3662\n",
      "Epoch 31/50\n",
      "37600/37600 [==============================] - 2s 47us/sample - loss: 11.0727 - cdr3_output_loss: 9.9088 - v_gene_output_loss: 1.2376 - j_gene_output_loss: 0.3531 - val_loss: 10.9726 - val_cdr3_output_loss: 9.8137 - val_v_gene_output_loss: 1.2325 - val_j_gene_output_loss: 0.3474\n",
      "Epoch 32/50\n",
      "37600/37600 [==============================] - 2s 47us/sample - loss: 11.0788 - cdr3_output_loss: 9.9175 - v_gene_output_loss: 1.2362 - j_gene_output_loss: 0.3489 - val_loss: 11.0405 - val_cdr3_output_loss: 9.8616 - val_v_gene_output_loss: 1.2560 - val_j_gene_output_loss: 0.3592\n",
      "Epoch 33/50\n",
      "37600/37600 [==============================] - 2s 48us/sample - loss: 11.0599 - cdr3_output_loss: 9.8985 - v_gene_output_loss: 1.2360 - j_gene_output_loss: 0.3459 - val_loss: 11.0278 - val_cdr3_output_loss: 9.8673 - val_v_gene_output_loss: 1.2322 - val_j_gene_output_loss: 0.3566\n",
      "Epoch 34/50\n",
      "37600/37600 [==============================] - 2s 47us/sample - loss: 11.0508 - cdr3_output_loss: 9.8917 - v_gene_output_loss: 1.2348 - j_gene_output_loss: 0.3402 - val_loss: 10.9688 - val_cdr3_output_loss: 9.8040 - val_v_gene_output_loss: 1.2395 - val_j_gene_output_loss: 0.3576\n",
      "Epoch 35/50\n",
      "37600/37600 [==============================] - 2s 48us/sample - loss: 11.0465 - cdr3_output_loss: 9.8870 - v_gene_output_loss: 1.2344 - j_gene_output_loss: 0.3445 - val_loss: 10.9950 - val_cdr3_output_loss: 9.8408 - val_v_gene_output_loss: 1.2287 - val_j_gene_output_loss: 0.3395\n",
      "Epoch 36/50\n",
      "37600/37600 [==============================] - 2s 47us/sample - loss: 11.0256 - cdr3_output_loss: 9.8691 - v_gene_output_loss: 1.2323 - j_gene_output_loss: 0.3380 - val_loss: 10.9950 - val_cdr3_output_loss: 9.8376 - val_v_gene_output_loss: 1.2293 - val_j_gene_output_loss: 0.3593\n",
      "Epoch 37/50\n",
      "37600/37600 [==============================] - 2s 47us/sample - loss: 11.0297 - cdr3_output_loss: 9.8744 - v_gene_output_loss: 1.2308 - j_gene_output_loss: 0.3352 - val_loss: 10.9535 - val_cdr3_output_loss: 9.7947 - val_v_gene_output_loss: 1.2355 - val_j_gene_output_loss: 0.3359\n",
      "Epoch 38/50\n",
      "37600/37600 [==============================] - 2s 48us/sample - loss: 11.0190 - cdr3_output_loss: 9.8675 - v_gene_output_loss: 1.2264 - j_gene_output_loss: 0.3348 - val_loss: 10.8860 - val_cdr3_output_loss: 9.7285 - val_v_gene_output_loss: 1.2332 - val_j_gene_output_loss: 0.3343\n",
      "Epoch 39/50\n",
      "37600/37600 [==============================] - 2s 47us/sample - loss: 11.0185 - cdr3_output_loss: 9.8646 - v_gene_output_loss: 1.2282 - j_gene_output_loss: 0.3357 - val_loss: 10.9073 - val_cdr3_output_loss: 9.7568 - val_v_gene_output_loss: 1.2263 - val_j_gene_output_loss: 0.3257\n",
      "Epoch 40/50\n",
      "37600/37600 [==============================] - 2s 47us/sample - loss: 10.9821 - cdr3_output_loss: 9.8316 - v_gene_output_loss: 1.2266 - j_gene_output_loss: 0.3298 - val_loss: 10.9382 - val_cdr3_output_loss: 9.7794 - val_v_gene_output_loss: 1.2364 - val_j_gene_output_loss: 0.3295\n",
      "Epoch 41/50\n",
      "37600/37600 [==============================] - 2s 48us/sample - loss: 10.9962 - cdr3_output_loss: 9.8443 - v_gene_output_loss: 1.2280 - j_gene_output_loss: 0.3327 - val_loss: 10.9522 - val_cdr3_output_loss: 9.8029 - val_v_gene_output_loss: 1.2255 - val_j_gene_output_loss: 0.3306\n",
      "Epoch 42/50\n",
      "37600/37600 [==============================] - 2s 48us/sample - loss: 10.9805 - cdr3_output_loss: 9.8297 - v_gene_output_loss: 1.2273 - j_gene_output_loss: 0.3301 - val_loss: 10.8544 - val_cdr3_output_loss: 9.7025 - val_v_gene_output_loss: 1.2312 - val_j_gene_output_loss: 0.3196\n",
      "Epoch 43/50\n",
      "37600/37600 [==============================] - 2s 47us/sample - loss: 10.9532 - cdr3_output_loss: 9.8041 - v_gene_output_loss: 1.2270 - j_gene_output_loss: 0.3269 - val_loss: 10.9181 - val_cdr3_output_loss: 9.7549 - val_v_gene_output_loss: 1.2423 - val_j_gene_output_loss: 0.3351\n",
      "Epoch 44/50\n",
      "37600/37600 [==============================] - 2s 48us/sample - loss: 10.9518 - cdr3_output_loss: 9.8050 - v_gene_output_loss: 1.2235 - j_gene_output_loss: 0.3245 - val_loss: 10.9340 - val_cdr3_output_loss: 9.7819 - val_v_gene_output_loss: 1.2312 - val_j_gene_output_loss: 0.3188\n",
      "Epoch 45/50\n",
      "37600/37600 [==============================] - 2s 47us/sample - loss: 10.9661 - cdr3_output_loss: 9.8214 - v_gene_output_loss: 1.2213 - j_gene_output_loss: 0.3210 - val_loss: 10.9404 - val_cdr3_output_loss: 9.7935 - val_v_gene_output_loss: 1.2216 - val_j_gene_output_loss: 0.3386\n",
      "Epoch 46/50\n",
      "37600/37600 [==============================] - 2s 47us/sample - loss: 10.9339 - cdr3_output_loss: 9.7903 - v_gene_output_loss: 1.2216 - j_gene_output_loss: 0.3205 - val_loss: 10.9043 - val_cdr3_output_loss: 9.7623 - val_v_gene_output_loss: 1.2196 - val_j_gene_output_loss: 0.3255\n",
      "Epoch 47/50\n",
      "37600/37600 [==============================] - 2s 48us/sample - loss: 10.9255 - cdr3_output_loss: 9.7826 - v_gene_output_loss: 1.2211 - j_gene_output_loss: 0.3177 - val_loss: 10.9438 - val_cdr3_output_loss: 9.7918 - val_v_gene_output_loss: 1.2299 - val_j_gene_output_loss: 0.3328\n",
      "Epoch 48/50\n",
      "37600/37600 [==============================] - 2s 49us/sample - loss: 10.9294 - cdr3_output_loss: 9.7864 - v_gene_output_loss: 1.2206 - j_gene_output_loss: 0.3200 - val_loss: 10.8586 - val_cdr3_output_loss: 9.7161 - val_v_gene_output_loss: 1.2211 - val_j_gene_output_loss: 0.3170\n",
      "Epoch 49/50\n",
      "37600/37600 [==============================] - 2s 48us/sample - loss: 10.9286 - cdr3_output_loss: 9.7866 - v_gene_output_loss: 1.2205 - j_gene_output_loss: 0.3177 - val_loss: 10.9150 - val_cdr3_output_loss: 9.7734 - val_v_gene_output_loss: 1.2198 - val_j_gene_output_loss: 0.3184\n",
      "Epoch 50/50\n",
      "37600/37600 [==============================] - 2s 47us/sample - loss: 10.9233 - cdr3_output_loss: 9.7806 - v_gene_output_loss: 1.2208 - j_gene_output_loss: 0.3194 - val_loss: 10.8941 - val_cdr3_output_loss: 9.7503 - val_v_gene_output_loss: 1.2225 - val_j_gene_output_loss: 0.3184\n"
     ]
    }
   ],
   "source": [
    "# Training VAE model\n",
    "history = res[\"vae\"].fit(X_train_2, X_train_2, epochs=50, batch_size=50, verbose=1, validation_split=0.2, callbacks=res[\"callbacks\"]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the history loss of the model with 4 subfigures\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.plot(history.history['cdr3_output_loss'][5:], label='Training cdr3 loss', color='blue')\n",
    "plt.plot(history.history['val_cdr3_output_loss'][5:], label='Validation cdr3 loss', color='red')\n",
    "plt.title('VAE model cdr3 loss')\n",
    "plt.legend()\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.plot(history.history['v_gene_output_loss'][5:], label='Training v_gene loss', color='blue')\n",
    "plt.plot(history.history['val_v_gene_output_loss'][5:], label='Validation v_gene loss', color='red')\n",
    "plt.title('VAE model v_gene loss')\n",
    "plt.legend()\n",
    "plt.subplot(2, 2, 3)\n",
    "plt.plot(history.history['j_gene_output_loss'][5:], label='Training j_gene loss', color='blue')\n",
    "plt.plot(history.history['val_j_gene_output_loss'][5:], label='Validation j_gene loss', color='red')\n",
    "plt.title('VAE model j_gene loss')\n",
    "plt.legend()\n",
    "plt.subplot(2, 2, 4)\n",
    "plt.plot(history.history['loss'][5:], label='Training loss', color='blue')\n",
    "plt.plot(history.history['val_loss'][5:], label='Validation loss', color='red')\n",
    "plt.title('VAE model loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspect weights (latent nodes features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fix v_gene and j_gene\n",
    "v_gene_input = np.zeros((1, 51))\n",
    "j_gene_input = np.zeros((1, 13))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/m4r/lib/python3.9/site-packages/keras/engine/training_v1.py:2356: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  updates=self.state_updates,\n"
     ]
    }
   ],
   "source": [
    "# Inspect weights interpretation\n",
    "encoder = res[\"encoder\"]\n",
    "decoder = res[\"decoder\"]\n",
    "\n",
    "store_dict = {}\n",
    "\n",
    "for letter in aa:\n",
    "    base_seq = \"-\" * 20\n",
    "    for pos in range(20):\n",
    "        test_seq_str = base_seq[:pos] + letter + base_seq[pos+1:]\n",
    "        test_seq = encode_cdr3_seqs([test_seq_str], label_encoder, onehot_encoder)\n",
    "        model_input = [test_seq, v_gene_input, j_gene_input]\n",
    "        model_input[0] = np.array(model_input[0])\n",
    "        model_input[1] = model_input[1].reshape(1, 51)\n",
    "        model_input[2] = model_input[2].reshape(1, 13)\n",
    "        z_mean, z_log_var = encoder.predict(model_input)\n",
    "        store_dict[test_seq_str] = z_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " '-A------------------': array([[ 0.01172497, -0.24340558]], dtype=float32),\n",
       " '--A-----------------': array([[-0.21529084, -0.15792784]], dtype=float32),\n",
       " '---A----------------': array([[-0.22978802, -0.20205748]], dtype=float32),\n",
       " '----A---------------': array([[-0.1884261, -0.154924 ]], dtype=float32),\n",
       " '-----A--------------': array([[-0.43059573,  0.0720991 ]], dtype=float32),\n",
       " '------A-------------': array([[-0.1003366 ,  0.17050874]], dtype=float32),\n",
       " '-------A------------': array([[-0.19179009,  0.1644978 ]], dtype=float32),\n",
       " '--------A-----------': array([[-0.44626042,  0.15044186]], dtype=float32),\n",
       " '---------A----------': array([[-6.5214932e-05,  1.4733434e-02]], dtype=float32),\n",
       " '----------A---------': array([[ 1.2928284, -0.7001935]], dtype=float32),\n",
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       " '--------------A-----': array([[0.342794  , 0.22721916]], dtype=float32),\n",
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       " '--------------M-----': array([[-0.25773954, -0.18479744]], dtype=float32),\n",
       " '---------------M----': array([[-0.25461674, -0.21134603]], dtype=float32),\n",
       " '----------------M---': array([[-0.3960585, -0.2520071]], dtype=float32),\n",
       " '-----------------M--': array([[-0.17831606, -0.1555047 ]], dtype=float32),\n",
       " '------------------M-': array([[-0.29112744, -0.18464264]], dtype=float32),\n",
       " '-------------------M': array([[-0.13519366, -0.14563343]], dtype=float32),\n",
       " 'N-------------------': array([[-0.04580109, -0.07907997]], dtype=float32),\n",
       " '-N------------------': array([[-0.15484928, -0.12704656]], dtype=float32),\n",
       " '--N-----------------': array([[-0.12742421, -0.10908867]], dtype=float32),\n",
       " '---N----------------': array([[-0.25582144, -0.16911963]], dtype=float32),\n",
       " '----N---------------': array([[-0.04507205, -0.139849  ]], dtype=float32),\n",
       " '-----N--------------': array([[-0.3794501,  0.042486 ]], dtype=float32),\n",
       " '------N-------------': array([[-0.10138425,  0.17308006]], dtype=float32),\n",
       " '-------N------------': array([[-0.14533727,  0.13783509]], dtype=float32),\n",
       " '--------N-----------': array([[-0.2940046,  0.1159974]], dtype=float32),\n",
       " '---------N----------': array([[-0.01175686,  0.02109974]], dtype=float32),\n",
       " '----------N---------': array([[ 1.1434973, -0.5435556]], dtype=float32),\n",
       " '-----------N--------': array([[-0.09212389, -0.44219804]], dtype=float32),\n",
       " '------------N-------': array([[ 0.08290336, -0.06867356]], dtype=float32),\n",
       " '-------------N------': array([[-0.07096225, -0.17303228]], dtype=float32),\n",
       " '--------------N-----': array([[ 0.14139897, -0.1865997 ]], dtype=float32),\n",
       " '---------------N----': array([[ 0.21945721, -0.34881595]], dtype=float32),\n",
       " '----------------N---': array([[-0.30483398, -0.20023662]], dtype=float32),\n",
       " '-----------------N--': array([[-0.22376578, -0.15923584]], dtype=float32),\n",
       " '------------------N-': array([[-0.0936662 , -0.18032262]], dtype=float32),\n",
       " '-------------------N': array([[-0.1698661 , -0.15446892]], dtype=float32),\n",
       " 'P-------------------': array([[-0.0574934 , -0.07833244]], dtype=float32),\n",
       " '-P------------------': array([[-0.15604654, -0.10621541]], dtype=float32),\n",
       " '--P-----------------': array([[-0.10160206, -0.12107835]], dtype=float32),\n",
       " '---P----------------': array([[-0.2752205 , -0.16170096]], dtype=float32),\n",
       " '----P---------------': array([[-0.20470035, -0.23917276]], dtype=float32),\n",
       " '-----P--------------': array([[-0.36314386,  0.09229672]], dtype=float32),\n",
       " '------P-------------': array([[-0.16090491,  0.21090275]], dtype=float32),\n",
       " '-------P------------': array([[-0.08883283,  0.16549009]], dtype=float32),\n",
       " '--------P-----------': array([[-0.33794403,  0.15679777]], dtype=float32),\n",
       " '---------P----------': array([[-0.08749943, -0.00930531]], dtype=float32),\n",
       " '----------P---------': array([[ 1.2450614, -0.5119501]], dtype=float32),\n",
       " '-----------P--------': array([[-0.14017871, -0.48353696]], dtype=float32),\n",
       " '------------P-------': array([[-0.12738992, -0.17068177]], dtype=float32),\n",
       " '-------------P------': array([[ 0.02233037, -0.3447135 ]], dtype=float32),\n",
       " '--------------P-----': array([[-0.084563  , -0.30925092]], dtype=float32),\n",
       " '---------------P----': array([[-0.15726109, -0.21300617]], dtype=float32),\n",
       " '----------------P---': array([[-0.48694944, -0.2611416 ]], dtype=float32),\n",
       " '-----------------P--': array([[-0.2968592, -0.1902667]], dtype=float32),\n",
       " '------------------P-': array([[-0.16591772, -0.1706008 ]], dtype=float32),\n",
       " '-------------------P': array([[-0.16027081, -0.15753812]], dtype=float32),\n",
       " 'Q-------------------': array([[-0.05397404, -0.07819267]], dtype=float32),\n",
       " '-Q------------------': array([[-0.21381538, -0.13696891]], dtype=float32),\n",
       " '--Q-----------------': array([[-0.10894841, -0.14159556]], dtype=float32),\n",
       " '---Q----------------': array([[-0.18991646, -0.13634501]], dtype=float32),\n",
       " '----Q---------------': array([[ 0.03642507, -0.17068774]], dtype=float32),\n",
       " '-----Q--------------': array([[-0.3386322 ,  0.06719527]], dtype=float32),\n",
       " '------Q-------------': array([[-0.09380768,  0.17812902]], dtype=float32),\n",
       " '-------Q------------': array([[-0.18682256,  0.16828263]], dtype=float32),\n",
       " '--------Q-----------': array([[-0.31163874,  0.11847774]], dtype=float32),\n",
       " '---------Q----------': array([[-0.04673387,  0.01790253]], dtype=float32),\n",
       " '----------Q---------': array([[ 1.3132192, -0.4444344]], dtype=float32),\n",
       " '-----------Q--------': array([[-0.09823234, -0.47308862]], dtype=float32),\n",
       " '------------Q-------': array([[-0.03446115, -0.15056872]], dtype=float32),\n",
       " '-------------Q------': array([[-0.09974144, -0.11814046]], dtype=float32),\n",
       " '--------------Q-----': array([[-0.15296316, -0.15843016]], dtype=float32),\n",
       " '---------------Q----': array([[-0.33199266, -0.15150177]], dtype=float32),\n",
       " '----------------Q---': array([[-0.22734989, -0.18632978]], dtype=float32),\n",
       " '-----------------Q--': array([[-0.10392714, -0.15951192]], dtype=float32),\n",
       " '------------------Q-': array([[-0.06008397, -0.17429364]], dtype=float32),\n",
       " '-------------------Q': array([[-0.1783053 , -0.15748212]], dtype=float32),\n",
       " 'R-------------------': array([[-0.05073311, -0.07815847]], dtype=float32),\n",
       " '-R------------------': array([[-0.20226501, -0.18298298]], dtype=float32),\n",
       " '--R-----------------': array([[-0.14897071, -0.19413742]], dtype=float32),\n",
       " '---R----------------': array([[-0.21603143, -0.22396836]], dtype=float32),\n",
       " '----R---------------': array([[-0.11311452, -0.14285305]], dtype=float32),\n",
       " '-----R--------------': array([[-0.41122884,  0.06606754]], dtype=float32),\n",
       " '------R-------------': array([[-0.128788 ,  0.1629639]], dtype=float32),\n",
       " '-------R------------': array([[-0.27359584,  0.16416031]], dtype=float32),\n",
       " '--------R-----------': array([[-0.44781145,  0.12050668]], dtype=float32),\n",
       " '---------R----------': array([[-0.06376537,  0.0163655 ]], dtype=float32),\n",
       " '----------R---------': array([[ 1.3543681, -0.4727576]], dtype=float32),\n",
       " '-----------R--------': array([[-0.115012  , -0.49046808]], dtype=float32),\n",
       " '------------R-------': array([[-0.33971623,  0.63734215]], dtype=float32),\n",
       " '-------------R------': array([[-0.01933668,  0.02800658]], dtype=float32),\n",
       " '--------------R-----': array([[-0.12306842, -0.04579676]], dtype=float32),\n",
       " '---------------R----': array([[-0.16280387, -0.17838395]], dtype=float32),\n",
       " '----------------R---': array([[-0.24674772, -0.15690729]], dtype=float32),\n",
       " '-----------------R--': array([[-0.08642124, -0.1605851 ]], dtype=float32),\n",
       " '------------------R-': array([[-0.04958651, -0.15693817]], dtype=float32),\n",
       " '-------------------R': array([[-0.17957285, -0.15211982]], dtype=float32),\n",
       " 'S-------------------': array([[-0.05765171, -0.07803422]], dtype=float32),\n",
       " '-S------------------': array([[-0.40722835, -0.12953363]], dtype=float32),\n",
       " '--S-----------------': array([[-0.01447933, -0.16881165]], dtype=float32),\n",
       " '---S----------------': array([[-0.08510793, -0.08936053]], dtype=float32),\n",
       " '----S---------------': array([[-0.04422886, -0.1377818 ]], dtype=float32),\n",
       " '-----S--------------': array([[-0.33233207,  0.0901712 ]], dtype=float32),\n",
       " '------S-------------': array([[-0.1043586,  0.2005536]], dtype=float32),\n",
       " '-------S------------': array([[-0.21621239,  0.1994242 ]], dtype=float32),\n",
       " '--------S-----------': array([[-0.30690476,  0.12905636]], dtype=float32),\n",
       " '---------S----------': array([[-0.07954982,  0.03169575]], dtype=float32),\n",
       " '----------S---------': array([[ 1.7147073, -0.3145935]], dtype=float32),\n",
       " '-----------S--------': array([[-0.05653422, -0.48933873]], dtype=float32),\n",
       " '------------S-------': array([[-0.18741736, -0.08170713]], dtype=float32),\n",
       " '-------------S------': array([[-1.1384469 ,  0.18902668]], dtype=float32),\n",
       " '--------------S-----': array([[-0.32173276, -0.05231037]], dtype=float32),\n",
       " '---------------S----': array([[-0.17867921, -0.11661211]], dtype=float32),\n",
       " '----------------S---': array([[-0.07303792, -0.1449287 ]], dtype=float32),\n",
       " '-----------------S--': array([[-0.15464605, -0.12080255]], dtype=float32),\n",
       " '------------------S-': array([[-0.03512858, -0.15563259]], dtype=float32),\n",
       " '-------------------S': array([[-0.15590377, -0.13741902]], dtype=float32),\n",
       " 'T-------------------': array([[-0.05718409, -0.07971014]], dtype=float32),\n",
       " '-T------------------': array([[-0.16238543, -0.15588209]], dtype=float32),\n",
       " '--T-----------------': array([[-0.0957343 , -0.20341098]], dtype=float32),\n",
       " '---T----------------': array([[-0.20938046, -0.13379359]], dtype=float32),\n",
       " '----T---------------': array([[-0.23267137, -0.16369778]], dtype=float32),\n",
       " '-----T--------------': array([[-0.44271535,  0.09205089]], dtype=float32),\n",
       " '------T-------------': array([[-0.09624695,  0.21520683]], dtype=float32),\n",
       " '-------T------------': array([[-0.22684456,  0.14673382]], dtype=float32),\n",
       " '--------T-----------': array([[-0.44088307,  0.12057831]], dtype=float32),\n",
       " '---------T----------': array([[0.02500604, 0.02221636]], dtype=float32),\n",
       " '----------T---------': array([[ 1.2059106, -0.5432118]], dtype=float32),\n",
       " '-----------T--------': array([[-0.07261824, -0.48680303]], dtype=float32),\n",
       " '------------T-------': array([[0.41581774, 0.05593256]], dtype=float32),\n",
       " '-------------T------': array([[-0.17117882, -0.1465767 ]], dtype=float32),\n",
       " '--------------T-----': array([[-0.24474783, -0.12368371]], dtype=float32),\n",
       " '---------------T----': array([[ 0.05965392, -0.27622736]], dtype=float32),\n",
       " '----------------T---': array([[-0.621296 , -0.2937727]], dtype=float32),\n",
       " '-----------------T--': array([[-0.0870903, -0.1666891]], dtype=float32),\n",
       " '------------------T-': array([[-0.5095183 , -0.19830352]], dtype=float32),\n",
       " '-------------------T': array([[-0.10220091, -0.14986083]], dtype=float32),\n",
       " 'V-------------------': array([[-0.044003  , -0.07974432]], dtype=float32),\n",
       " '-V------------------': array([[-0.15733105, -0.17646098]], dtype=float32),\n",
       " '--V-----------------': array([[-0.30035514, -0.18637365]], dtype=float32),\n",
       " '---V----------------': array([[-0.23145351, -0.19796929]], dtype=float32),\n",
       " '----V---------------': array([[-0.2931157 , -0.16985065]], dtype=float32),\n",
       " '-----V--------------': array([[-0.3918729 ,  0.06213843]], dtype=float32),\n",
       " '------V-------------': array([[-0.14466912,  0.19350064]], dtype=float32),\n",
       " '-------V------------': array([[-0.17828579,  0.1714707 ]], dtype=float32),\n",
       " '--------V-----------': array([[-0.40366304,  0.13673455]], dtype=float32),\n",
       " '---------V----------': array([[-0.04376118,  0.00334687]], dtype=float32),\n",
       " '----------V---------': array([[ 1.1705012 , -0.49425587]], dtype=float32),\n",
       " '-----------V--------': array([[-0.13512015, -0.49896818]], dtype=float32),\n",
       " '------------V-------': array([[-0.29534194,  0.03832822]], dtype=float32),\n",
       " '-------------V------': array([[ 0.13814624, -0.09044971]], dtype=float32),\n",
       " '--------------V-----': array([[ 0.02271898, -0.11108537]], dtype=float32),\n",
       " '---------------V----': array([[-0.4265151 , -0.18119532]], dtype=float32),\n",
       " '----------------V---': array([[-0.48029613, -0.21265379]], dtype=float32),\n",
       " '-----------------V--': array([[-0.09248888, -0.17036152]], dtype=float32),\n",
       " '------------------V-': array([[-0.15952396, -0.1633954 ]], dtype=float32),\n",
       " '-------------------V': array([[-0.13974366, -0.15441954]], dtype=float32),\n",
       " 'W-------------------': array([[-0.05126078, -0.07886703]], dtype=float32),\n",
       " '-W------------------': array([[-0.16266532, -0.13884464]], dtype=float32),\n",
       " '--W-----------------': array([[-0.1198969 , -0.14191577]], dtype=float32),\n",
       " '---W----------------': array([[-0.24099351, -0.16257825]], dtype=float32),\n",
       " '----W---------------': array([[-0.03839225, -0.10148884]], dtype=float32),\n",
       " '-----W--------------': array([[-0.393508  ,  0.06230711]], dtype=float32),\n",
       " '------W-------------': array([[-0.09899774,  0.1965676 ]], dtype=float32),\n",
       " '-------W------------': array([[-0.19718765,  0.17709976]], dtype=float32),\n",
       " '--------W-----------': array([[-0.27810955,  0.12752184]], dtype=float32),\n",
       " '---------W----------': array([[0.04106741, 0.02133811]], dtype=float32),\n",
       " '----------W---------': array([[ 1.2673678, -0.5095371]], dtype=float32),\n",
       " '-----------W--------': array([[-0.1226719 , -0.48729295]], dtype=float32),\n",
       " '------------W-------': array([[ 0.04171095, -0.15772215]], dtype=float32),\n",
       " '-------------W------': array([[-0.04364281, -0.13232338]], dtype=float32),\n",
       " '--------------W-----': array([[-0.10277855, -0.16406107]], dtype=float32),\n",
       " '---------------W----': array([[-0.33145532, -0.20718572]], dtype=float32),\n",
       " '----------------W---': array([[-0.26900467, -0.19829565]], dtype=float32),\n",
       " '-----------------W--': array([[-0.14380664, -0.15317258]], dtype=float32),\n",
       " '------------------W-': array([[-0.04129308, -0.1866208 ]], dtype=float32),\n",
       " '-------------------W': array([[-0.17319217, -0.15178841]], dtype=float32),\n",
       " 'Y-------------------': array([[-0.0590599, -0.0775191]], dtype=float32),\n",
       " '-Y------------------': array([[-0.10447815, -0.09884668]], dtype=float32),\n",
       " '--Y-----------------': array([[-0.1353363 , -0.07828682]], dtype=float32),\n",
       " '---Y----------------': array([[-0.14154193, -0.10320539]], dtype=float32),\n",
       " '----Y---------------': array([[ 0.01647747, -0.16842473]], dtype=float32),\n",
       " '-----Y--------------': array([[-0.27540606,  0.03127255]], dtype=float32),\n",
       " '------Y-------------': array([[-0.1071094 ,  0.10973867]], dtype=float32),\n",
       " '-------Y------------': array([[-0.14483404,  0.08453477]], dtype=float32),\n",
       " '--------Y-----------': array([[-0.23567331,  0.0707414 ]], dtype=float32),\n",
       " '---------Y----------': array([[-0.1341989 , -0.00604889]], dtype=float32),\n",
       " '----------Y---------': array([[ 1.0738486 , -0.46954632]], dtype=float32),\n",
       " '-----------Y--------': array([[ 0.68190825, -0.23942465]], dtype=float32),\n",
       " '------------Y-------': array([[-0.02730372, -0.20399764]], dtype=float32),\n",
       " '-------------Y------': array([[-0.01541746, -0.22033942]], dtype=float32),\n",
       " '--------------Y-----': array([[-0.04311619, -0.3673871 ]], dtype=float32),\n",
       " '---------------Y----': array([[-0.00828922,  0.09154797]], dtype=float32),\n",
       " '----------------Y---': array([[-0.20477946, -0.15481022]], dtype=float32),\n",
       " '-----------------Y--': array([[-0.40526563, -0.14828786]], dtype=float32),\n",
       " '------------------Y-': array([[-0.42775938, -0.09735993]], dtype=float32),\n",
       " '-------------------Y': array([[-0.05078617, -0.13277644]], dtype=float32),\n",
       " '--------------------': array([[-0.06485234, -0.07732648]], dtype=float32)}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_dict\n",
    "# unnatural sequences -> exisitng sequence and change each letter to another letter (for different base sequences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.04251851"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_dict['A-------------------'][0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Position: 0\n",
      "A [[-0.04251851 -0.07995893]]\n",
      "C [[-0.05980705 -0.07783613]]\n",
      "D [[-0.06043563 -0.07696941]]\n",
      "E [[-0.05647404 -0.07794988]]\n",
      "F [[-0.05390972 -0.07862596]]\n",
      "G [[-0.05007878 -0.07828657]]\n",
      "H [[-0.05413431 -0.07864583]]\n",
      "I [[-0.05152199 -0.07889771]]\n",
      "K [[-0.04374792 -0.07776256]]\n",
      "L [[-0.05379548 -0.07921298]]\n",
      "M [[-0.05744398 -0.07913647]]\n",
      "N [[-0.04580109 -0.07907997]]\n",
      "P [[-0.0574934  -0.07833244]]\n",
      "Q [[-0.05397404 -0.07819267]]\n",
      "R [[-0.05073311 -0.07815847]]\n",
      "S [[-0.05765171 -0.07803422]]\n",
      "T [[-0.05718409 -0.07971014]]\n",
      "V [[-0.044003   -0.07974432]]\n",
      "W [[-0.05126078 -0.07886703]]\n",
      "Y [[-0.0590599 -0.0775191]]\n",
      "- [[-0.06485234 -0.07732648]]\n",
      "Variance of means latent 1: 3.3947057e-05\n",
      "Variance of means latent 2: 6.2804145e-07\n",
      "Position: 1\n",
      "A [[ 0.01172497 -0.24340558]]\n",
      "C [[-0.17607526 -0.11132638]]\n",
      "D [[-0.14887737 -0.13296196]]\n",
      "E [[-0.23616223 -0.13872844]]\n",
      "F [[-0.2125647  -0.13892019]]\n",
      "G [[-0.13901393 -0.1630784 ]]\n",
      "H [[-0.14807215 -0.12202063]]\n",
      "I [[-0.19213851 -0.14406025]]\n",
      "K [[-0.15071839 -0.17147776]]\n",
      "L [[-0.19279636 -0.13015912]]\n",
      "M [[-0.17281015 -0.1328298 ]]\n",
      "N [[-0.15484928 -0.12704656]]\n",
      "P [[-0.15604654 -0.10621541]]\n",
      "Q [[-0.21381538 -0.13696891]]\n",
      "R [[-0.20226501 -0.18298298]]\n",
      "S [[-0.40722835 -0.12953363]]\n",
      "T [[-0.16238543 -0.15588209]]\n",
      "V [[-0.15733105 -0.17646098]]\n",
      "W [[-0.16266532 -0.13884464]]\n",
      "Y [[-0.10447815 -0.09884668]]\n",
      "- [[-0.06485234 -0.07732648]]\n",
      "Variance of means latent 1: 0.0056084194\n",
      "Variance of means latent 2: 0.0011448258\n",
      "Position: 2\n",
      "A [[-0.21529084 -0.15792784]]\n",
      "C [[-0.07176857 -0.11863409]]\n",
      "D [[-0.15246077 -0.15268502]]\n",
      "E [[-0.05164617 -0.13779648]]\n",
      "F [[-0.10387027 -0.13760456]]\n",
      "G [[-0.20769879 -0.15496776]]\n",
      "H [[-0.07378179 -0.10837401]]\n",
      "I [[-0.15170015 -0.15743336]]\n",
      "K [[-0.1224972  -0.14388154]]\n",
      "L [[-0.09952588 -0.14662962]]\n",
      "M [[-0.09031113 -0.16442367]]\n",
      "N [[-0.12742421 -0.10908867]]\n",
      "P [[-0.10160206 -0.12107835]]\n",
      "Q [[-0.10894841 -0.14159556]]\n",
      "R [[-0.14897071 -0.19413742]]\n",
      "S [[-0.01447933 -0.16881165]]\n",
      "T [[-0.0957343  -0.20341098]]\n",
      "V [[-0.30035514 -0.18637365]]\n",
      "W [[-0.1198969  -0.14191577]]\n",
      "Y [[-0.1353363  -0.07828682]]\n",
      "- [[-0.06485234 -0.07732648]]\n",
      "Variance of means latent 1: 0.0037307292\n",
      "Variance of means latent 2: 0.001044594\n",
      "Position: 3\n",
      "A [[-0.22978802 -0.20205748]]\n",
      "C [[-0.11898789 -0.10604282]]\n",
      "D [[-0.08590178 -0.22038108]]\n",
      "E [[-0.19243184 -0.1356013 ]]\n",
      "F [[-0.17613524 -0.12627786]]\n",
      "G [[-0.25945508 -0.21803111]]\n",
      "H [[-0.2643239  -0.15894216]]\n",
      "I [[-0.22661269 -0.16973594]]\n",
      "K [[-0.19685766 -0.22230047]]\n",
      "L [[-0.20623824 -0.15111455]]\n",
      "M [[-0.22356293 -0.13302359]]\n",
      "N [[-0.25582144 -0.16911963]]\n",
      "P [[-0.2752205  -0.16170096]]\n",
      "Q [[-0.18991646 -0.13634501]]\n",
      "R [[-0.21603143 -0.22396836]]\n",
      "S [[-0.08510793 -0.08936053]]\n",
      "T [[-0.20938046 -0.13379359]]\n",
      "V [[-0.23145351 -0.19796929]]\n",
      "W [[-0.24099351 -0.16257825]]\n",
      "Y [[-0.14154193 -0.10320539]]\n",
      "- [[-0.06485234 -0.07732648]]\n",
      "Variance of means latent 1: 0.0036325469\n",
      "Variance of means latent 2: 0.0019026345\n",
      "Position: 4\n",
      "A [[-0.1884261 -0.154924 ]]\n",
      "C [[-0.04464408 -0.11078724]]\n",
      "D [[-0.15326104 -0.20410493]]\n",
      "E [[-0.00497128 -0.151586  ]]\n",
      "F [[ 0.02263506 -0.12338167]]\n",
      "G [[-0.39494267 -0.178222  ]]\n",
      "H [[ 0.05855196 -0.15613699]]\n",
      "I [[-0.4828932   0.11058024]]\n",
      "K [[-0.1496364  -0.14809954]]\n",
      "L [[ 0.38054907 -0.04792117]]\n",
      "M [[-0.04899031 -0.14394629]]\n",
      "N [[-0.04507205 -0.139849  ]]\n",
      "P [[-0.20470035 -0.23917276]]\n",
      "Q [[ 0.03642507 -0.17068774]]\n",
      "R [[-0.11311452 -0.14285305]]\n",
      "S [[-0.04422886 -0.1377818 ]]\n",
      "T [[-0.23267137 -0.16369778]]\n",
      "V [[-0.2931157  -0.16985065]]\n",
      "W [[-0.03839225 -0.10148884]]\n",
      "Y [[ 0.01647747 -0.16842473]]\n",
      "- [[-0.06485234 -0.07732648]]\n",
      "Variance of means latent 1: 0.030538246\n",
      "Variance of means latent 2: 0.004592362\n",
      "Position: 5\n",
      "A [[-0.43059573  0.0720991 ]]\n",
      "C [[-0.21048132 -0.00500231]]\n",
      "D [[-0.4920703   0.01311832]]\n",
      "E [[-0.34500146  0.06244389]]\n",
      "F [[-0.3340936   0.06002156]]\n",
      "G [[-0.40813142  0.10957306]]\n",
      "H [[-0.38985303  0.06956858]]\n",
      "I [[-0.3769843   0.06173239]]\n",
      "K [[-0.33528066  0.05916287]]\n",
      "L [[-0.40983883  0.07334336]]\n",
      "M [[-0.39612776  0.07311378]]\n",
      "N [[-0.3794501  0.042486 ]]\n",
      "P [[-0.36314386  0.09229672]]\n",
      "Q [[-0.3386322   0.06719527]]\n",
      "R [[-0.41122884  0.06606754]]\n",
      "S [[-0.33233207  0.0901712 ]]\n",
      "T [[-0.44271535  0.09205089]]\n",
      "V [[-0.3918729   0.06213843]]\n",
      "W [[-0.393508    0.06230711]]\n",
      "Y [[-0.27540606  0.03127255]]\n",
      "- [[-0.06485234 -0.07732648]]\n",
      "Variance of means latent 1: 0.0076660924\n",
      "Variance of means latent 2: 0.0015345961\n",
      "Position: 6\n",
      "A [[-0.1003366   0.17050874]]\n",
      "C [[-0.08225974  0.04136746]]\n",
      "D [[-0.02285225  0.19748792]]\n",
      "E [[-0.08471514  0.18035457]]\n",
      "F [[-0.10294029  0.15861368]]\n",
      "G [[-0.19006918  0.2398552 ]]\n",
      "H [[-0.10733679  0.19891208]]\n",
      "I [[-0.14227492  0.17625597]]\n",
      "K [[-0.0984235  0.1906766]]\n",
      "L [[-0.08943458  0.20486587]]\n",
      "M [[-0.08238046  0.2120879 ]]\n",
      "N [[-0.10138425  0.17308006]]\n",
      "P [[-0.16090491  0.21090275]]\n",
      "Q [[-0.09380768  0.17812902]]\n",
      "R [[-0.128788   0.1629639]]\n",
      "S [[-0.1043586  0.2005536]]\n",
      "T [[-0.09624695  0.21520683]]\n",
      "V [[-0.14466912  0.19350064]]\n",
      "W [[-0.09899774  0.1965676 ]]\n",
      "Y [[-0.1071094   0.10973867]]\n",
      "- [[-0.06485234 -0.07732648]]\n",
      "Variance of means latent 1: 0.0011554367\n",
      "Variance of means latent 2: 0.0046420437\n",
      "Position: 7\n",
      "A [[-0.19179009  0.1644978 ]]\n",
      "C [[-0.12840223  0.03795327]]\n",
      "D [[-0.17385423  0.19927385]]\n",
      "E [[-0.18071444  0.16332662]]\n",
      "F [[-0.18683684  0.14857417]]\n",
      "G [[-0.11874795  0.18547946]]\n",
      "H [[-0.1743767   0.16480923]]\n",
      "I [[-0.23863162  0.15156236]]\n",
      "K [[-0.21406353  0.19099274]]\n",
      "L [[-0.20584235  0.19094115]]\n",
      "M [[-0.20466447  0.17802745]]\n",
      "N [[-0.14533727  0.13783509]]\n",
      "P [[-0.08883283  0.16549009]]\n",
      "Q [[-0.18682256  0.16828263]]\n",
      "R [[-0.27359584  0.16416031]]\n",
      "S [[-0.21621239  0.1994242 ]]\n",
      "T [[-0.22684456  0.14673382]]\n",
      "V [[-0.17828579  0.1714707 ]]\n",
      "W [[-0.19718765  0.17709976]]\n",
      "Y [[-0.14483404  0.08453477]]\n",
      "- [[-0.06485234 -0.07732648]]\n",
      "Variance of means latent 1: 0.0023376157\n",
      "Variance of means latent 2: 0.003879272\n",
      "Position: 8\n",
      "A [[-0.44626042  0.15044186]]\n",
      "C [[-0.1881073   0.01390171]]\n",
      "D [[-0.335838    0.17427954]]\n",
      "E [[-0.2966681   0.12108182]]\n",
      "F [[-0.28113234  0.09940505]]\n",
      "G [[-0.45602128  0.18363261]]\n",
      "H [[-0.28760487  0.12689725]]\n",
      "I [[-0.3503705   0.11294145]]\n",
      "K [[-0.3302903   0.15207842]]\n",
      "L [[-0.23708704  0.11556043]]\n",
      "M [[-0.29410785  0.1238923 ]]\n",
      "N [[-0.2940046  0.1159974]]\n",
      "P [[-0.33794403  0.15679777]]\n",
      "Q [[-0.31163874  0.11847774]]\n",
      "R [[-0.44781145  0.12050668]]\n",
      "S [[-0.30690476  0.12905636]]\n",
      "T [[-0.44088307  0.12057831]]\n",
      "V [[-0.40366304  0.13673455]]\n",
      "W [[-0.27810955  0.12752184]]\n",
      "Y [[-0.23567331  0.0707414 ]]\n",
      "- [[-0.06485234 -0.07732648]]\n",
      "Variance of means latent 1: 0.008404333\n",
      "Variance of means latent 2: 0.0030074131\n",
      "Position: 9\n",
      "A [[-6.5214932e-05  1.4733434e-02]]\n",
      "C [[-0.06541329 -0.0228258 ]]\n",
      "D [[0.15416104 0.02473988]]\n",
      "E [[-0.02616363  0.02665271]]\n",
      "F [[-0.04170989  0.01494395]]\n",
      "G [[-0.02261649 -0.00329313]]\n",
      "H [[0.01365177 0.02687848]]\n",
      "I [[-0.02458816  0.01828016]]\n",
      "K [[0.00288388 0.02020743]]\n",
      "L [[0.03096739 0.01229733]]\n",
      "M [[0.03845963 0.01928592]]\n",
      "N [[-0.01175686  0.02109974]]\n",
      "P [[-0.08749943 -0.00930531]]\n",
      "Q [[-0.04673387  0.01790253]]\n",
      "R [[-0.06376537  0.0163655 ]]\n",
      "S [[-0.07954982  0.03169575]]\n",
      "T [[0.02500604 0.02221636]]\n",
      "V [[-0.04376118  0.00334687]]\n",
      "W [[0.04106741 0.02133811]]\n",
      "Y [[-0.1341989  -0.00604889]]\n",
      "- [[-0.06485234 -0.07732648]]\n",
      "Variance of means latent 1: 0.003484026\n",
      "Variance of means latent 2: 0.00055268826\n",
      "Position: 10\n",
      "A [[ 1.2928284 -0.7001935]]\n",
      "C [[ 0.65277797 -0.19132367]]\n",
      "D [[ 0.9041041 -0.6903023]]\n",
      "E [[ 1.3184909  -0.41940078]]\n",
      "F [[ 1.2762467  -0.40555567]]\n",
      "G [[ 1.1560986  -0.53894055]]\n",
      "H [[ 1.2511331  -0.54821247]]\n",
      "I [[ 1.1796168  -0.48725697]]\n",
      "K [[ 1.5361683  -0.49401173]]\n",
      "L [[ 1.5329715  -0.48131347]]\n",
      "M [[ 1.3090218  -0.47967562]]\n",
      "N [[ 1.1434973 -0.5435556]]\n",
      "P [[ 1.2450614 -0.5119501]]\n",
      "Q [[ 1.3132192 -0.4444344]]\n",
      "R [[ 1.3543681 -0.4727576]]\n",
      "S [[ 1.7147073 -0.3145935]]\n",
      "T [[ 1.2059106 -0.5432118]]\n",
      "V [[ 1.1705012  -0.49425587]]\n",
      "W [[ 1.2673678 -0.5095371]]\n",
      "Y [[ 1.0738486  -0.46954632]]\n",
      "- [[-0.06485234 -0.07732648]]\n",
      "Variance of means latent 1: 0.12275203\n",
      "Variance of means latent 2: 0.018638898\n",
      "Position: 11\n",
      "A [[-0.02773002 -0.53937405]]\n",
      "C [[-0.07052349 -0.32514906]]\n",
      "D [[-0.12932791 -0.4928333 ]]\n",
      "E [[-0.0511758  -0.46320984]]\n",
      "F [[-0.07688637 -0.4544826 ]]\n",
      "G [[-0.09103686 -0.54483783]]\n",
      "H [[-0.11285079 -0.46085742]]\n",
      "I [[-0.06261162 -0.4713491 ]]\n",
      "K [[-0.06318416 -0.50791585]]\n",
      "L [[-0.10188445 -0.49292147]]\n",
      "M [[-0.11767055 -0.48593575]]\n",
      "N [[-0.09212389 -0.44219804]]\n",
      "P [[-0.14017871 -0.48353696]]\n",
      "Q [[-0.09823234 -0.47308862]]\n",
      "R [[-0.115012   -0.49046808]]\n",
      "S [[-0.05653422 -0.48933873]]\n",
      "T [[-0.07261824 -0.48680303]]\n",
      "V [[-0.13512015 -0.49896818]]\n",
      "W [[-0.1226719  -0.48729295]]\n",
      "Y [[ 0.68190825 -0.23942465]]\n",
      "- [[-0.06485234 -0.07732648]]\n",
      "Variance of means latent 1: 0.027923238\n",
      "Variance of means latent 2: 0.011183252\n",
      "Position: 12\n",
      "A [[0.5667089 0.4477082]]\n",
      "C [[-0.04255873 -0.07659084]]\n",
      "D [[-0.14131308 -0.15133697]]\n",
      "E [[ 0.10556872 -0.09318072]]\n",
      "F [[-0.00591265 -0.08529879]]\n",
      "G [[0.30236715 0.01526801]]\n",
      "H [[ 0.15626779 -0.08838902]]\n",
      "I [[-0.09023309  0.13981786]]\n",
      "K [[0.01679809 0.03747587]]\n",
      "L [[ 0.05098086 -0.17816198]]\n",
      "M [[ 0.01116817 -0.2229586 ]]\n",
      "N [[ 0.08290336 -0.06867356]]\n",
      "P [[-0.12738992 -0.17068177]]\n",
      "Q [[-0.03446115 -0.15056872]]\n",
      "R [[-0.33971623  0.63734215]]\n",
      "S [[-0.18741736 -0.08170713]]\n",
      "T [[0.41581774 0.05593256]]\n",
      "V [[-0.29534194  0.03832822]]\n",
      "W [[ 0.04171095 -0.15772215]]\n",
      "Y [[-0.02730372 -0.20399764]]\n",
      "- [[-0.06485234 -0.07732648]]\n",
      "Variance of means latent 1: 0.04358914\n",
      "Variance of means latent 2: 0.04252814\n",
      "Position: 13\n",
      "A [[0.80285627 0.05865835]]\n",
      "C [[-0.15741725 -0.04572988]]\n",
      "D [[-0.15491483 -0.08154094]]\n",
      "E [[-0.09576327  0.02027949]]\n",
      "F [[-0.15653384 -0.10602317]]\n",
      "G [[0.98914325 0.32860428]]\n",
      "H [[-0.06680585 -0.20117301]]\n",
      "I [[-0.04612121 -0.10175876]]\n",
      "K [[ 0.20329104 -0.07536705]]\n",
      "L [[-0.07378796 -0.12373854]]\n",
      "M [[-0.12801386 -0.18549755]]\n",
      "N [[-0.07096225 -0.17303228]]\n",
      "P [[ 0.02233037 -0.3447135 ]]\n",
      "Q [[-0.09974144 -0.11814046]]\n",
      "R [[-0.01933668  0.02800658]]\n",
      "S [[-1.1384469   0.18902668]]\n",
      "T [[-0.17117882 -0.1465767 ]]\n",
      "V [[ 0.13814624 -0.09044971]]\n",
      "W [[-0.04364281 -0.13232338]]\n",
      "Y [[-0.01541746 -0.22033942]]\n",
      "- [[-0.06485234 -0.07732648]]\n",
      "Variance of means latent 1: 0.14933799\n",
      "Variance of means latent 2: 0.019867066\n",
      "Position: 14\n",
      "A [[0.342794   0.22721916]]\n",
      "C [[-0.11416383 -0.1007222 ]]\n",
      "D [[-0.10360329 -0.1680682 ]]\n",
      "E [[-0.10174429 -0.13394547]]\n",
      "F [[-0.11366071 -0.12905888]]\n",
      "G [[ 0.01777411 -0.16077101]]\n",
      "H [[-0.03733226 -0.23583302]]\n",
      "I [[-0.11880641 -0.15303084]]\n",
      "K [[ 0.00300153 -0.13410254]]\n",
      "L [[-0.15399453 -0.16376197]]\n",
      "M [[-0.25773954 -0.18479744]]\n",
      "N [[ 0.14139897 -0.1865997 ]]\n",
      "P [[-0.084563   -0.30925092]]\n",
      "Q [[-0.15296316 -0.15843016]]\n",
      "R [[-0.12306842 -0.04579676]]\n",
      "S [[-0.32173276 -0.05231037]]\n",
      "T [[-0.24474783 -0.12368371]]\n",
      "V [[ 0.02271898 -0.11108537]]\n",
      "W [[-0.10277855 -0.16406107]]\n",
      "Y [[-0.04311619 -0.3673871 ]]\n",
      "- [[-0.06485234 -0.07732648]]\n",
      "Variance of means latent 1: 0.018721655\n",
      "Variance of means latent 2: 0.012131461\n",
      "Position: 15\n",
      "A [[-0.04728626 -0.21779421]]\n",
      "C [[-0.10961649 -0.11133681]]\n",
      "D [[-0.800977   -0.21844903]]\n",
      "E [[-0.07396445 -0.16106248]]\n",
      "F [[-0.1367987  -0.15378946]]\n",
      "G [[-0.2321123  -0.16411811]]\n",
      "H [[ 0.15821178 -0.2921707 ]]\n",
      "I [[-0.05244611 -0.20341519]]\n",
      "K [[-0.22950247 -0.11594596]]\n",
      "L [[-0.12294205 -0.14938308]]\n",
      "M [[-0.25461674 -0.21134603]]\n",
      "N [[ 0.21945721 -0.34881595]]\n",
      "P [[-0.15726109 -0.21300617]]\n",
      "Q [[-0.33199266 -0.15150177]]\n",
      "R [[-0.16280387 -0.17838395]]\n",
      "S [[-0.17867921 -0.11661211]]\n",
      "T [[ 0.05965392 -0.27622736]]\n",
      "V [[-0.4265151  -0.18119532]]\n",
      "W [[-0.33145532 -0.20718572]]\n",
      "Y [[-0.00828922  0.09154797]]\n",
      "- [[-0.06485234 -0.07732648]]\n",
      "Variance of means latent 1: 0.043886963\n",
      "Variance of means latent 2: 0.0074314107\n",
      "Position: 16\n",
      "A [[-0.22014971 -0.16690043]]\n",
      "C [[-0.12341377 -0.12409352]]\n",
      "D [[-0.1087317  -0.13861147]]\n",
      "E [[ 0.05963045 -0.12817514]]\n",
      "F [[-0.24530517 -0.1823099 ]]\n",
      "G [[-0.4422965  -0.17532793]]\n",
      "H [[-0.29764742 -0.22266352]]\n",
      "I [[-0.38289678 -0.19361436]]\n",
      "K [[ 0.09114283 -0.08518127]]\n",
      "L [[-0.24941519 -0.18670696]]\n",
      "M [[-0.3960585 -0.2520071]]\n",
      "N [[-0.30483398 -0.20023662]]\n",
      "P [[-0.48694944 -0.2611416 ]]\n",
      "Q [[-0.22734989 -0.18632978]]\n",
      "R [[-0.24674772 -0.15690729]]\n",
      "S [[-0.07303792 -0.1449287 ]]\n",
      "T [[-0.621296  -0.2937727]]\n",
      "V [[-0.48029613 -0.21265379]]\n",
      "W [[-0.26900467 -0.19829565]]\n",
      "Y [[-0.20477946 -0.15481022]]\n",
      "- [[-0.06485234 -0.07732648]]\n",
      "Variance of means latent 1: 0.030755563\n",
      "Variance of means latent 2: 0.0027713755\n",
      "Position: 17\n",
      "A [[ 0.12760665 -0.2126492 ]]\n",
      "C [[-0.11659931 -0.11124705]]\n",
      "D [[-0.14147721 -0.1511791 ]]\n",
      "E [[-0.18605933 -0.13901362]]\n",
      "F [[-0.11823742 -0.1452074 ]]\n",
      "G [[-0.11658876 -0.16336736]]\n",
      "H [[-0.24215913 -0.15939921]]\n",
      "I [[-0.14026403 -0.14152694]]\n",
      "K [[-0.20900585 -0.18721   ]]\n",
      "L [[-0.1813013  -0.13484792]]\n",
      "M [[-0.17831606 -0.1555047 ]]\n",
      "N [[-0.22376578 -0.15923584]]\n",
      "P [[-0.2968592 -0.1902667]]\n",
      "Q [[-0.10392714 -0.15951192]]\n",
      "R [[-0.08642124 -0.1605851 ]]\n",
      "S [[-0.15464605 -0.12080255]]\n",
      "T [[-0.0870903 -0.1666891]]\n",
      "V [[-0.09248888 -0.17036152]]\n",
      "W [[-0.14380664 -0.15317258]]\n",
      "Y [[-0.40526563 -0.14828786]]\n",
      "- [[-0.06485234 -0.07732648]]\n",
      "Variance of means latent 1: 0.009925424\n",
      "Variance of means latent 2: 0.0007689283\n",
      "Position: 18\n",
      "A [[-0.15659198 -0.16042224]]\n",
      "C [[-0.08329017 -0.12211013]]\n",
      "D [[ 0.0145153  -0.14024338]]\n",
      "E [[-0.11866912 -0.14700525]]\n",
      "F [[-0.05551945 -0.17203417]]\n",
      "G [[-0.1473468  -0.14770013]]\n",
      "H [[-0.03416346 -0.20311436]]\n",
      "I [[-0.18549109 -0.15864834]]\n",
      "K [[-0.16971737 -0.1010184 ]]\n",
      "L [[-0.02475276 -0.18413025]]\n",
      "M [[-0.29112744 -0.18464264]]\n",
      "N [[-0.0936662  -0.18032262]]\n",
      "P [[-0.16591772 -0.1706008 ]]\n",
      "Q [[-0.06008397 -0.17429364]]\n",
      "R [[-0.04958651 -0.15693817]]\n",
      "S [[-0.03512858 -0.15563259]]\n",
      "T [[-0.5095183  -0.19830352]]\n",
      "V [[-0.15952396 -0.1633954 ]]\n",
      "W [[-0.04129308 -0.1866208 ]]\n",
      "Y [[-0.42775938 -0.09735993]]\n",
      "- [[-0.06485234 -0.07732648]]\n",
      "Variance of means latent 1: 0.016594157\n",
      "Variance of means latent 2: 0.0010587433\n",
      "Position: 19\n",
      "A [[-0.14726715 -0.14102137]]\n",
      "C [[-0.11554118 -0.11287354]]\n",
      "D [[-0.1689043 -0.1397139]]\n",
      "E [[-0.14682117 -0.13164747]]\n",
      "F [[-0.17411113 -0.15033317]]\n",
      "G [[-0.13080262 -0.13663247]]\n",
      "H [[-0.2089655  -0.15883803]]\n",
      "I [[-0.13199937 -0.14448708]]\n",
      "K [[-0.12745607 -0.13245632]]\n",
      "L [[-0.19543895 -0.15702611]]\n",
      "M [[-0.13519366 -0.14563343]]\n",
      "N [[-0.1698661  -0.15446892]]\n",
      "P [[-0.16027081 -0.15753812]]\n",
      "Q [[-0.1783053  -0.15748212]]\n",
      "R [[-0.17957285 -0.15211982]]\n",
      "S [[-0.15590377 -0.13741902]]\n",
      "T [[-0.10220091 -0.14986083]]\n",
      "V [[-0.13974366 -0.15441954]]\n",
      "W [[-0.17319217 -0.15178841]]\n",
      "Y [[-0.05078617 -0.13277644]]\n",
      "- [[-0.06485234 -0.07732648]]\n",
      "Variance of means latent 1: 0.0014727286\n",
      "Variance of means latent 2: 0.00033416803\n"
     ]
    }
   ],
   "source": [
    "# Get most influential letter for each position\n",
    "vars_pos = {}\n",
    "for pos in range(20):\n",
    "    print(\"Position:\", pos)\n",
    "    pos_list_1 = []\n",
    "    pos_list_2 = []\n",
    "    for letter in aa:\n",
    "        print(letter, store_dict[\"-\" * pos + letter + \"-\" * (19 - pos)])\n",
    "        pos_list_1.append(store_dict[\"-\" * pos + letter + \"-\" * (19 - pos)][0][0])\n",
    "        pos_list_2.append(store_dict[\"-\" * pos + letter + \"-\" * (19 - pos)][0][1])\n",
    "\n",
    "    print(\"Variance of means latent 1:\", np.var(pos_list_1))\n",
    "    print(\"Variance of means latent 2:\", np.var(pos_list_2))\n",
    "    vars_pos[pos] = [np.var(pos_list_1), np.var(pos_list_2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UHwBQynBs9uzZatKkiUJDQxUVFaU1a9YUu29KSopuu+02tWjRQpUqVdKoUaMK7fPmm2+qd+/eql69uqpXr65+/fpp48aNpSkNAAAAFeS778x1dra0cqW9tcA3VUQ4FhAgdexotplaCQD+yeVwbOHChRo1apTGjRunxMRE9e7dWwMHDlRycnKR+2dkZKh27doaN26cOnToUOQ+sbGxuvXWW7Vq1SrFxcWpYcOG6t+/vw4cOOBqeQAAAKggznBMkpYvt68O+KYjR6StW812eYZjkhQdba4JxwDAPzksy7JceUC3bt3UuXNnzZkzJ/e+Vq1aaciQIZo6deo5H9u3b1917NhRs2bNOud+2dnZql69ul599VUNGzasRHWlp6crPDxcaWlpqlatWokeAwAAgNL57Tepbt28240bS7t3Sw6HbSXBx3z6qXTDDVKrVnlN+cvLu+9Kw4ZJvXpJ55gUAwDwIq7kRC6NHDtz5owSEhLUv3//Avf3799f69evd73SYpw6dUqZmZmqUaOG254TAAAA7rNhg7lu2lQKCpL27pV27bK1JPiYiphS6eRsyr95M035AcAfuRSOHT58WNnZ2YqIiChwf0REhFJTU91W1BNPPKH69eurX79+xe6TkZGh9PT0AhcAAABUDOeUyssvN6NtJGnZMvvqge+JjTXXFRGO0ZQfAPxbqRryO84aL29ZVqH7SmvatGn68MMPtWjRIoWGhha739SpUxUeHp57adCggVteHwAAAOfnDMcuvVRyTiqg7xjc5Y8/pC1bzHZFhGM05QcA/+ZSOFarVi0FBAQUGiV26NChQqPJSuPFF1/UlClTtHz5crVv3/6c+44dO1ZpaWm5l/3795f59QEAAHB+2dmSc2HxSy+VBgww26tWSWfO2FcXfMfatZJlSZdcItWrVzGv6ZxaSTgGAP7HpXAsODhYUVFRiomJKXB/TEyMevToUaZCpk+frsmTJ+vrr79WtHO5mHMICQlRtWrVClwAAABQ/n78UTp5Uqpa1TRL79BBql1bOnFCiouzuzr4gorsN+bEipUA4L9cnlY5ZswYvfXWW5o/f762bdum0aNHKzk5WSNHjpRkRnSdvcJkUlKSkpKSdOLECf3+++9KSkrST/mWnJk2bZrGjx+v+fPnq3HjxkpNTVVqaqpOnDhRxsMDAACAuzmnVHbtaqajVaqUN7WSvmNwBzvCMefIscREmvIDgL8JdPUBQ4cO1ZEjRzRp0iSlpKSobdu2Wrp0qRo1aiRJSklJUXJycoHHdOrUKXc7ISFBH3zwgRo1aqS9e/dKkmbPnq0zZ87oL3/5S4HHTZgwQRMnTnS1RAAAAJSj/P3GnPr3l95/3/QdmzLFnrrgG9LSzKqRUsWGY86m/CdPmqb8rVtX3GsDAOzlsCzLsrsId0hPT1d4eLjS0tKYYgkAAFCOWreWtm2TPv9cuvZac19KihQZKTkc0m+/mWmWQGksXSpdc43UtKn0yy8V+9q9eknr1knvvCPdeWfFvjYAwL1cyYlKtVolAAAA/NOxYyYYk6Ru3fLur1dPat/eNFFfscKW0uAj7JhS6URTfgDwT4RjAAAAKDHnKpXNmhUeHebsO7Z8ecXWBN/iDMf69q341yYcAwD/RDgGAACAEiuq35jTgAHmevlyM4IMcNWJE1J8vNm2c+QYTfkBwL8QjgEAAKDEzhWO9eolhYVJBw9KP/5YsXXBN6xfb0KpRo3MpaK1bClVrmya8u/YUfGvDwCwB+EYAAAASsSyzh2OhYbmjfZZtqzi6oLviI0113aMGpOkgACpUyezzdRKAPAfhGMAAAAokZ07pT/+MCFY+/ZF70PfMZSFnc34nZxTK53TOwEAvo9wDAAAACXiHDUWFSUFBxe9j7Pv2LffSn/+WTF1wTecOiVt2mS27WjG70RTfgDwP4RjAAAAKJFzTal0atVKql9fOn1aWrOmYuqCb4iLkzIzpYsukpo0sa8OmvIDgP8hHAMAAECJlCQcczjyRo/RdwyuyD+l0uGwrw6a8gOA/yEcAwAAwHmdPCl9/73ZPlc4JtF3DKVjdzN+J5ryA4D/IRwDAADAeSUkmClm9eubaW/n0q+fGfnzww/SgQMVUx+8259/Shs2mG27wzGJvmMA4G8IxwAAAHBeJZlS6VSzphQdbbZjYsqvJviODRukM2ekevWk5s3troZwDAD8DeEYAAAAzsuVcEyi7xhc4yn9xpyc4djmzTTlBwB/QDgGAACAc7Iss5KgVPJwzNl3LCZGyskpn7rgO/KHY56ApvwA4F8IxwAAAHBO+/dLqalSYKDUuXPJHnPppVLVqtKRI1JiYvnWB++WkZEXvnpKOBYQIHXsaLaZWgkAvo9wDAAAAOfknFLZoYMZTVMSQUHSFVeYbaZW4lw2bpROn5bq1DEjtjwFfccAwH8QjgEAAOCcXO035uScWrl8uXvrgW9xTqm87DLP6Dfm5FxUgnAMAHwf4RgAAADOyRmOde/u2uOcTfnXrZOOH3dvTfAdznCsb19byyjEOXIsMZGm/ADg6wjHAAAAUKyMDLNin+T6yLFmzaSmTaWsLCk21u2lwQdkZkrr15ttT+k35uRsyn/iBE35AcDXEY4BAACgWFu2mICsVi0TdLnKOXqMvmMoSny8dOqUVLOm1Lq13dUURFN+APAfhGMAAAAoVv5+Y6XpB0XfMZyLc0ThZZdJlTzwnQlN+QHAP3jgnyAAAAB4itI243e64gozAmfnTmnPHvfVBd/g7DfmaVMqnQjHAMA/EI4BAACgWHFx5rq04Vi1anmN/Bk9hvyyssxiDZLnNeN3cq5YmZgo5eTYWwsAoPwQjgEAAKBIqanS3r1mOmWXLqV/HvqOoSibN5tm99WrS+3a2V1N0WjKDwD+gXAMAAAARdqwwVy3aWNGgJWWs+/YN9+Y0UKAlDelsndvz+w3JtGUHwD8hYf+GQIAAIDdytpvzCkqSqpRQ0pPlzZuLHtd8A2e3m/Mydl3LD7e3joAAOWHcAwAAABFclc4FhAg9etntplaCUnKzpbWrDHb3hKOMXIMAHwX4RgAAAAKycqSNm0y22UNx6S8qZU05YckJSWZkYTVquVNW/RUznCMpvwA4LsIxwAAAFDIjz9KJ0+a8KJVq7I/nzMc27hR+uOPsj8fvFv+fmMBAfbWcj4tW0phYTTlBwBfRjgGAACAQpxTKrt2dU+z9AYNTMiWk2Ma88O/eUu/MUkKDJQ6dTLbTK0EAN9EOAYAAIBC3NVvLL8BA8w1fcf8W06O9/Qbc6LvGAD4NsIxAAAAFFIe4Vj+vmOW5b7nhXf5/nsztfaCC6TOne2upmRYsRIAfFupwrHZs2erSZMmCg0NVVRUlNY4P/opQkpKim677Ta1aNFClSpV0qhRo4rc75NPPlHr1q0VEhKi1q1ba/HixaUpDQAAAGX0xx/Szz+b7W7d3Pe8ffpIwcFScrK0fbv7nhfexTmlsmdPM2XRG9CUHwB8m8vh2MKFCzVq1CiNGzdOiYmJ6t27twYOHKjk5OQi98/IyFDt2rU1btw4dejQoch94uLiNHToUN15553asmWL7rzzTv31r3/Vhg0bXC0PAAAAZbRxo7m++GKpVi33PW/lyqYBu8Sqlf7MGY717WtrGS6hKT8A+DaXw7GZM2dqxIgRuvfee9WqVSvNmjVLDRo00Jw5c4rcv3Hjxnr55Zc1bNgwhYeHF7nPrFmzdNVVV2ns2LFq2bKlxo4dqyuvvFKzZs1ytTwAAACUUXlMqXSi75h/y8mRvv3WbHtLvzHJjHDr2NFs03cMAHyPS+HYmTNnlJCQoP7OhhH/079/f61fv77URcTFxRV6zgEDBpzzOTMyMpSenl7gAgAAgLIrz3DMecoXGytlZLj/+eHZfvpJOnLEjCKMjra7Gtc46yUcAwDf41I4dvjwYWVnZysiIqLA/REREUpNTS11EampqS4/59SpUxUeHp57adCgQalfHwAAAEZOjuTsbFEe4Vj79lJEhHTqlLRunfufH57NOaWyRw8pKMjeWlzFipUA4LtK1ZDf4XAUuG1ZVqH7yvs5x44dq7S0tNzL/v37y/T6AAAAkHbuNA35Q0NNkOVuDkfBVSvhX2JjzbU3Tal0coZjmzfTlB8AfI1L4VitWrUUEBBQaETXoUOHCo38ckXdunVdfs6QkBBVq1atwAUAAABl45xSGR1dfiN7nH3HCMf8i2Xl9Rvzpmb8TjTlBwDf5VI4FhwcrKioKMXExBS4PyYmRj169Ch1Ed27dy/0nMuXLy/TcwIAAMB15dlvzKlfP3OdmCj99lv5vQ48y88/S4cOmVGJXbrYXY3raMoPAL7L5WmVY8aM0VtvvaX58+dr27ZtGj16tJKTkzVy5EhJZrrjsGHDCjwmKSlJSUlJOnHihH7//XclJSXpp59+yv36ww8/rOXLl+uFF17Qzz//rBdeeEErVqzQqFGjynZ0AAAAcElFhGMREXkhw4oV5fc68CzOfmPdu0shIfbWUlr0HQMA3xTo6gOGDh2qI0eOaNKkSUpJSVHbtm21dOlSNWrUSJKUkpKi5OTkAo/p1KlT7nZCQoI++OADNWrUSHv37pUk9ejRQx999JHGjx+vp556Ss2aNdPChQvVrVu3MhwaAAAAXHHypPT992a7PMMxyUytTEqSli2Tbr+9fF8LnsEZjnljvzEnwjEA8E0Oy7Isu4twh/T0dIWHhystLY3+YwAAAKWwerXpBXXRRVJ5r3W0cqV05ZVmFFlKimnUD99lWVJkpJSaKq1a5Z09xyTphx+kdu2kqlWlY8ekSqVa3gwAUBFcyYn4dQ4AAABJFTOl0qlnT6lyZdNzzDlaDb5r504TjAUHS948OcTZlP/4cXNMAADfQDgGAAAASRUbjoWE5I0eYtVK3+ecUnnppSZc8lb5m/LHx9taCgDAjQjHAAAAIMuq2HBMMn3HJNN3DL7NF/qNOdF3DAB8D+EYAAAAlJxspr0FBkqdO1fMa/bvb67XrJFOnaqY10TFsyzCMQCAZyMcAwAAQO6osY4dK27aW4sWUsOG0pkzeeEJfM+ePdKvv0pBQVL37nZXU3bOcCwxUcrJsbcWAIB7EI4BAACgwqdUSmaFSufoMfqO+a7YWHPdpYtZhMHbtWpFU34A8DWEYwAAALAlHJPoO+YPnKMCnQsweLv8TfmZWgkAvoFwDAAAwM9lZEibN5vtig7HrrxSqlRJ2rZN2r+/Yl8bFcOX+o050XcMAHwL4RgAAICfS0oyfb9q1ZKaNq3Y165eXera1WzHxFTsa6P87dtnLgEBUo8edlfjPs5wLD7e3joAAO5BOAYAAODn8k+pdDgq/vWdfceYWul7nKPGoqOlCy6wtxZ3oik/APgWwjEAAAA/Z1e/MSdnOLZihZSdbU8NKB/OZvy+NKVSoik/APgawjEAAAA/Z3c41q2bVK2adPQoPZx8jS/2G5Noyg8AvoZwDAAAwI+lpkp795rplF262FNDYKBpzC9Jy5fbUwPc79dfpd27zYILvXrZXY370ZQfAHwH4RgAAIAf27DBXLdpY0Zv2WXAAHNN3zHf4Rw11rmzvf+2ygvhGAD4DsIxAAAAP2b3lEonZ9+xuDgpPd3eWuAevjql0skZjm3eTFN+APB2hGMAAAB+zFPCsSZNpObNTUP+lSvtrQXu4evhGE35AcB3EI4BAAD4qawsadMms213OCbljR6j75j3S0mRduwwvex697a7mvIRGCh16GC2mVoJAN6NcAwAAMBP/fijdPKk6QfVqpXd1dB3zJc4R4117ChdeKGdlZQv+o4BgG8gHAMAAPBTzimVXbuaFQXt1revGY2ze7f0yy92V4Oy8PUplU7R0eaacAwAvJsHnAYBAADADp7Sb8ypalWpZ0+zzdRK7+Yv4RhN+QHANxCOAQAA+ClPC8ekvL5jTK30XocOSdu2mW1f7TfmlL8p/65ddlcDACgtwjEAAAA/9Mcf0s8/m+1u3eytJT9n37GVK6XMTHtrQek4R421ayfVrGlvLeUtf1P++Hh7awEAlB7hGAAAgB/auNFcN28u1aplby35depkApXjx/NGtsG7OMOxvn1tLaPC0JQfALwf4RgAAIAfiosz1540pVIyCwNcdZXZpu+Yd/KXfmNOhGMA4P0IxwAAAPyQJ/Ybc3JOraTvmPc5fFj64Qezfdll9tZSUZwrVtKUHwC8F+EYAACAn8nJkTZsMNueGI45R47Fx0tHjthbC1yzZo25bt1aql3b3loqCk35AcD7EY4BAAD4mR07pGPHzBv6du3srqaw+vWltm0ly5JWrLC7GrjC36ZUSgWb8jO1EgC8E+EYAACAn3FOqYyOloKC7K2lOP37m2v6jnmX2Fhz7U/hmETfMQDwdoRjAAAAfsaT+4055e87Zln21oKS+eMP6fvvzba/hmPx8fbWAQAoHcIxAAAAP+MN4Vjv3lJoqHTggLRtm93VoCTWrDFBZosWUt26dldTsZzhGE35AcA7EY4BAAD4kRMnpK1bzbYnh2NhYXmrHbJqpXfwx35jTq1bmzCXpvwA4J0IxwAAAPxIfLwZ2dKggRQZaXc150bfMe/iz+FYYKDUsaPZpu8YAHifUoVjs2fPVpMmTRQaGqqoqCitca7ZXIzVq1crKipKoaGhatq0qebOnVton1mzZqlFixYKCwtTgwYNNHr0aJ0+fbo05QEAAKAY3jCl0snZd2z1aonTQs+WliYlJpptfwzHJJryA4A3czkcW7hwoUaNGqVx48YpMTFRvXv31sCBA5WcnFzk/nv27NGgQYPUu3dvJSYm6sknn9RDDz2kTz75JHef999/X0888YQmTJigbdu2ad68eVq4cKHGjh1b+iMDAABAId4UjrVpY0a3/fmntHat3dXgXNauNSMSL75Yql/f7mrsQTgGAN7L5XBs5syZGjFihO699161atVKs2bNUoMGDTRnzpwi9587d64aNmyoWbNmqVWrVrr33nt1zz336MUXX8zdJy4uTj179tRtt92mxo0bq3///rr11lsVz3IvAAAAbmNZ3hWOORxMrfQW/jyl0il/OEZTfgDwLi6FY2fOnFFCQoL6O89S/qd///5av359kY+Ji4srtP+AAQMUHx+vzMxMSVKvXr2UkJCgjRs3SpJ2796tpUuX6pprrim2loyMDKWnpxe4AAAAoHj79km//SYFBUmdOtldTck4TyNpyu/ZCMdoyg8A3sylcOzw4cPKzs5WREREgfsjIiKUmppa5GNSU1OL3D8rK0uHDx+WJN1yyy2aPHmyevXqpaCgIDVr1kyXX365nnjiiWJrmTp1qsLDw3MvDRo0cOVQAAAA/I5z1FjHjmY1SG/Qr58ZQfb991JKit3VoCjHj+dNJfTncCwwUOrQwWwztRIAvEupGvI7HI4Cty3LKnTf+fbPf39sbKyee+45zZ49W5s3b9aiRYv0xRdfaPLkycU+59ixY5WWlpZ72b9/f2kOBQAAwG9405RKp9q1pc6dzXZMjL21oGjr10vZ2VLjxlLDhnZXY6/oaHNNOAYA3iXQlZ1r1aqlgICAQqPEDh06VGh0mFPdunWL3D8wMFA1a9aUJD311FO68847de+990qS2rVrp5MnT+rvf/+7xo0bp0qVCmd4ISEhCgkJcaV8AAAAv+aN4ZhkplYmJJi+Y8OG2V0NzhYba679edSYE035AcA7uTRyLDg4WFFRUYo562O7mJgY9ejRo8jHdO/evdD+y5cvV3R0tIKCgiRJp06dKhSABQQEyLKs3FFmAAAAKL2MDCkx0Wx7Wzg2YIC5Xr6cRueeyNlvrG9fW8vwCM5wbPNm/q0CgDdxeVrlmDFj9NZbb2n+/Pnatm2bRo8ereTkZI0cOVKSme44LN9HeiNHjtS+ffs0ZswYbdu2TfPnz9e8efP06KOP5u4zePBgzZkzRx999JH27NmjmJgYPfXUU7ruuusUEBDghsMEAADwb4mJ0pkzZppikyZ2V+Oa7t2lCy6Qfv9d2rLF7mqQ38mT0qZNZpuRY3lN+dPTpV9+sbsaAEBJuTStUpKGDh2qI0eOaNKkSUpJSVHbtm21dOlSNWrUSJKUkpKi5OTk3P2bNGmipUuXavTo0XrttdcUGRmpV155RTfddFPuPuPHj5fD4dD48eN14MAB1a5dW4MHD9Zzzz3nhkMEAABA/imV52gV65GCg6XLL5c+/9ysWuktK236g7g4KStLatDA9Bzzd86m/Bs2SPHxUvPmdlcEACgJh+Uj8xbT09MVHh6utLQ0VatWze5yAAAAPMott0gLF0rPPSc9+aTd1bju1Velf/7ThGQrV9pdDZyeekp69lnpjjukd9+1uxrP8MAD0uzZ0iOPSC++aHc1AOC/XMmJSrVaJQAAALyLtzbjd3L2HVu71kzlg2dw9htjSmUemvIDgPchHAMAAPBxKSnSvn1mOmWXLnZXUzoXX2ym7WVm5q2OCHv9+aeZPijRjD+/6GhzTVN+APAehGMAAAA+zhlgtG0rVa1qby2l5XAUXLUS9vvuO7PIQ2Sk1KyZ3dV4DpryA4D3IRwDAADwcd4+pdKpf39zvWyZvXXAyD+l0tsWeShPzqb8ElMrAcBbEI4BAAD4OF8Jx664QgoIkLZvN9NEYS/6jRXP2XcsPt7eOgAAJUM4BgAA4MOysqRNm8y2t4djF14odetmtplaaa/Tp/NCV8KxwmjKDwDehXAMAADAh/3wg3TqlFStmtSypd3VlJ1zaiXhmL02bjQBWUSE1KKF3dV4Hmc4RlN+APAOhGMAAAA+zDm6p1s3qZIPnPk5m/KvWGFGxcEe9Bs7N5ryA4B38YFTJAAAABTHV/qNOUVHm+mVx47Rz8lO9Bs7t6AgmvIDgDchHAMAAPBhvhaOBQZK/fqZbVattMeZM9L69WabcKx49B0DAO9BOAYAAOCjjh41KztKeY3sfQF9x+wVHy/9+adUq5aZPoiiEY4BgPcgHAMAAPBRGzea6+bNpZo17a3FnZzh2IYNZnolKpZzSuVll9Fv7Fzyh2M05QcAz0Y4BgAA4KN8bUqlU6NGZoXE7Gxp5Uq7q/E/sbHmum9fO6vwfK1bSyEhNOUHAG9AOAYAAOCjfDUck/JWrWRqZcXKzJTWrTPb9Bs7N5ryA4D3IBwDAADwQTk5Ztqh5JvhmHNq5bJlkmXZW4s/2bxZOnlSqlFDatvW7mo8X3S0uSYcAwDPRjgGAADgg3bsMP24wsKkdu3srsb9+vY1I3P27pV27bK7Gv/h7DfWu7dUiXcS50VTfgDwDvxJAwAA8EHOKZXR0SZE8jVVqki9epntZcvsrcWfOMMxplSWjDMc27yZEY4A4MkIxwAAAHyQL/cbc3JOraTvWMXIypLWrDHbNOMvGWdT/rQ0mvIDgCcjHAMAAPBB/hCOOZvyr1olnTljby3+IClJOn5cCg+X2re3uxrvkL8pf3y8vbUAAIpHOAYAAOBjTpyQtm41274cjnXoINWubY43Ls7uanxf/n5jAQH21uJN6DsGAJ6PcAwAAMDHxMeb1SobNJAiI+2upvxUqlRw1UqUL/qNlQ4rVgKA5yMcAwAA8DH+MKXSib5jFSM7O6/fGOGYa2jKDwCej3AMAADAx/hTOHbVVeZ682bp99/trcWXbd0qHTsmVa0qdepkdzXehab8AOD5CMcAAAB8iGX5VzhWr55pDm9Z0ooVdlfju2JjzXWvXlJgoK2leJ38TfmZWgkAnolwDAAAwIfs3Sv99pt5Q+4vI3ycq1YytbL80G+sbJxTK1mxEgA8E+EYAACAD3GOGuvYUQoLs7WUCpO/7xg9ndwvJ0f69luzTThWOqxYCQCejXAMAADAh/jTlEqnXr1MEHjwoPTjj3ZX43t+/FE6elSqUiUv5IFraMoPAJ6NcAwAAMCH+GM4FhqaN6Jp2TJ7a/FFzimVPXqY6bpwXZs2NOUHAE9GOAYAAOAjTp+WEhPNdvfu9tZS0eg7Vn6czfj79rWzCu9GU34A8GyEYwAAAD4iMVHKzJTq1JEaN7a7morl7Dv27bfSn3/aW4svsSz6jbkLfccAwHMRjgEAAPiI/FMqHQ57a6lorVpJ9eub0XNr1thdje/Ytk36/XfT061LF7ur8W6EYwDguUoVjs2ePVtNmjRRaGiooqKitOY8ZyCrV69WVFSUQkND1bRpU82dO7fQPseOHdMDDzygevXqKTQ0VK1atdLSpUtLUx4AAIBf8sd+Y04OR97USvqOuY+z31j37lJwsL21eLv84RhN+QHAs7gcji1cuFCjRo3SuHHjlJiYqN69e2vgwIFKTk4ucv89e/Zo0KBB6t27txITE/Xkk0/qoYce0ieffJK7z5kzZ3TVVVdp7969+u9//6vt27frzTffVP369Ut/ZAAAAH7Gn8MxKW9qJX3H3McZjjGlsuxoyg8AnsthWa59btGtWzd17txZc+bMyb2vVatWGjJkiKZOnVpo/8cff1xLlizRtm3bcu8bOXKktmzZori4OEnS3LlzNX36dP38888KKuUSOOnp6QoPD1daWpqqVatWqucAAADwVgcPmmmFlSpJx45JVavaXVHFO3JEql3bjMr59Vfz/UDpWZYUGSmlppqm/ARkZdetm7Rxo/TRR9LQoXZXAwC+zZWcyKWRY2fOnFFCQoL6Oz+W+5/+/ftr/fr1RT4mLi6u0P4DBgxQfHy8MjMzJUlLlixR9+7d9cADDygiIkJt27bVlClTlJ2d7Up5AAAAfmvDBnPdtq1/BmOSVLOmFB1ttmNi7K3FF+zYYYKxkBAT6qDs6DsGAJ7JpXDs8OHDys7OVkRERIH7IyIilJqaWuRjUlNTi9w/KytLhw8fliTt3r1b//3vf5Wdna2lS5dq/PjxmjFjhp577rlia8nIyFB6enqBCwAAgL/y9ymVTvQdcx/nlMpLL5VCQ+2txVcQjgGAZypVQ37HWcsfWZZV6L7z7Z///pycHNWpU0dvvPGGoqKidMstt2jcuHEFpm6eberUqQoPD8+9NGjQoDSHAgAA4BMIxwznhIWYGCknx95avB39xtyPpvwA4JlcCsdq1aqlgICAQqPEDh06VGh0mFPdunWL3D8wMFA1a9aUJNWrV0+XXHKJAgICcvdp1aqVUlNTdebMmSKfd+zYsUpLS8u97N+/35VDAQAA8BlZWdKmTWbb38OxSy8100qPHJESE+2uxntZFuFYeaApPwB4JpfCseDgYEVFRSnmrCYOMTEx6tGjR5GP6d69e6H9ly9frujo6Nzm+z179tSuXbuUk+/jvR07dqhevXoKLmbN6JCQEFWrVq3ABQAAwB9t3Sr9+acUHi61aGF3NfYKCpKuuMJsM7Wy9Hbvlg4cMN9Pfw9c3SkoSGrf3mwztRIAPIfL0yrHjBmjt956S/Pnz9e2bds0evRoJScna+TIkZLMiK5hw4bl7j9y5Ejt27dPY8aM0bZt2zR//nzNmzdPjz76aO4+9913n44cOaKHH35YO3bs0JdffqkpU6bogQcecMMhAgAA+DbnlMpu3cxqlf7O2Xds+XJ76/BmsbHmuls3qXJlW0vxOfQdAwDPE+jqA4YOHaojR45o0qRJSklJUdu2bbV06VI1atRIkpSSkqLk5OTc/Zs0aaKlS5dq9OjReu211xQZGalXXnlFN910U+4+DRo00PLlyzV69Gi1b99e9evX18MPP6zHH3/cDYcIAADg2+g3VpCz79i6ddLx4/67emdZMKWy/DhXVCUcAwDP4bAs32gFmZ6ervDwcKWlpTHFEgAA+JUWLaQdO6SlS6WBA+2uxjNcfLHp6bRkiTR4sN3VeJ9GjaTkZDP67qqr7K7GtyQlSZ06SRdeKB09Kp1jXTMAQBm4khMx8B4AAMCLHTligjFJ6trV3lo8iXP0GH3HXLd3rwnGAgOlYtoKowycTfmPHTO93QAA9iMcAwAA8GIbN5rrSy6R/rcQOJQXjtF3zHXOKZXR0VKVKvbW4otoyg8AnodwDAAAwIvRb6xoV1whBQRIO3dKe/bYXY13cYZjffvaWoZPczblj4+3tw4AgEE4BgAA4MUIx4pWrZrUvbvZZvSYa5wrVdKMv/ywYiUAeBbCMQAAAC+VkyNt2GC2CccKGzDAXNN3rOT27zcj7QICpJ497a7GdzlXrNy8WfKN5dEAwLsRjgEAAHip7dultDQpLExq187uajyPs+/YN99IWVn21uItnFMqO3eWqla1txZfRlN+APAshGMAAABeyjmlsksXs7IgCoqKkmrUkNLT80bY4dyc4RhTKssXTfkBwLMQjgEAAHgp+o2dW0CA1K+f2abvWMkQjlUc+o4BgOcgHAMAAPBShGPn5+w7Rjh2fgcPmtU9K1WSevWyuxrfx4qVAOA5CMcAAAC80PHj0g8/mO1u3eytxZM5+45t3Cj98Ye9tXg656ixjh2lCy+0sxL/4AzHaMoPAPYjHAMAAPBC8fFmtcqGDaXISLur8VwXXSS1bm2+V998Y3c1no0plRWLpvwA4DkIxwAAALwQUypLzjl6bNkye+vwdIRjFSs4mKb8AOApCMcAAAC8EOFYyeXvO8b0taL99pv088+SwyH17m13Nf6DpvwA4BkIxwAAALyMZRGOueKyy8woneRkaft2u6vxTN9+a67bt5dq1LC3Fn9COAYAnoFwDAAAwMvs3SsdOiQFBUmdOtldjeerXDlvNBSrVhYtNtZcM6WyYuUPxxjVCAD2IRwDAADwMs5RY506SaGh9tbiLZxTK+k7VjT6jdmjTRszqpGm/ABgL8IxAAAAL8OUStc5m/LHxkoZGbaW4nEOH5Z+/NFsX3aZvbX4G5ryA4BnIBwDAADwMoRjrmvfXoqIkE6dktats7saz+LsN9amjVSrlr21+KPoaHNNOAYA9iEcAwAA8CKnT0uJiWabcKzkHI680WP0HSuIKZX2oik/ANiPcAwAAMCLJCZKmZlSnTpS48Z2V+NdnH3HCMcKcjbj79vXzir8lzMc27yZpvwAYBfCMQAAAC+Sf0qlw2FvLd7mqqvMdWKi9Ntv9tbiKY4elbZuNdv0G7OHsyn/H39Ie/bYXQ0A+CfCMQAAAC9Cv7HSq1PHrPApSStW2FuLp1izxoxWatnS9GRDxaMpPwDYj3AMAADAixCOlY2z79iyZfbW4SnoN+YZnFMr4+PtrQMA/BXhGAAAgJc4eFBKTpYqVcpb4Q6uyd93jP5OhGOeghUrAcBehGMAAABeYsMGc922rVS1qr21eKsePaTKlU3Pse+/t7saex07JiUlmW3CMXvRlB8A7EU4BgAA4CWYUll2ISHS5ZebbX9ftXLtWiknR2reXIqMtLsa/0ZTfgCwF+EYAACAlyAccw/6jhlMqfQcNOUv6ORJKTPT7ioA+BPCMQAAAC+QmSlt2mS2CcfKxhmOrVkjnTplby12sSxp1SqzTTjmGZxTK/09HFu7VrroInOZOdN//48CqFiEYwAAAF5g61bpzz+l8HCpRQu7q/FuLVpIDRtKZ87kjZ7yJz/8YKaWJiRIDgfhmKdgxUrpm2/MohnHjkmHDkmPPCI1bSq99JL5/QcA5YVwDAAAwAs4p1R262ZWq0TpORx5o8f8qe9YeroJGzp2NKFgWJj0739LDRrYXRkkmvIvXSpdc40ZKXb11dKbb0qNG5vFM8aMMSHZyy8TkgEoH5xaAQAAeAH6jbnXgAHm2h/6jlmW9MEHUsuWZppadrZ0443Stm3SAw/YXR2c2rb136b8ixdLQ4ZIGRnS9ddLn34q3XuvtGOHCckaNZJSU6VRo6RmzaRXXpFOn7a5aAA+hXAMAADACxCOudeVV5oReNu2Sfv3211N+fnxRzOF8vbbpZQU6eKLpa++kj75xAQO8Bz+2pT/ww+lm282fRWHDpX+7//MqrKSFBSUF5K9/rqZDp2SIj38sAnJXn2VkAyAe5QqHJs9e7aaNGmi0NBQRUVFac2aNefcf/Xq1YqKilJoaKiaNm2quXPnFrvvRx99JIfDoSFDhpSmNAAAAJ9z5Ii0c6fZ7trV3lp8RfXqed/LmBh7aykPzimUHTrkTaF87jnTb+zqq+2uDsXxt6b8CxaY4DY7Wxo+XHr/fROInS04WPr7383vwblzzVTggwelf/7TBL6zZ5tRZwBQWi6HYwsXLtSoUaM0btw4JSYmqnfv3ho4cKCSk5OL3H/Pnj0aNGiQevfurcTERD355JN66KGH9MknnxTad9++fXr00UfVu3dv148EAADAR23YYK4vuUSqWdPeWnyJs++YL02tLGoK5Q03mBFyTz6ZNyIHnsmfwrHZs6V77jH/Zv/xD2n+fCkg4NyPCQ42++7caR5/0UXSgQNmenDz5iY4IyQDUBouh2MzZ87UiBEjdO+996pVq1aaNWuWGjRooDlz5hS5/9y5c9WwYUPNmjVLrVq10r333qt77rlHL774YoH9srOzdfvtt+uZZ55R06ZNS3c0AAAAPsg5pbJ7d3vr8DXOvmMrVpgQydsVN4Vy0SKmUHqL/OGYLzflnzkzr9/dww9Lc+a4ttBISIh0333Srl1mamX9+mZ69H33mZDs9dfNarQAUFIuhWNnzpxRQkKC+js/Zvuf/v37a/369UU+Ji4urtD+AwYMUHx8vDIzM3PvmzRpkmrXrq0RI0aUqJaMjAylp6cXuAAAAPgi+o2Vj65dpfBw6ehR7x6pU9QUymefZQqlN/KHpvzPPWf+vUrS2LHSSy+ZFWRLIyTEhGy7dpmVVyMjTUg2cqQZafvmm4RkAErGpXDs8OHDys7OVkRERIH7IyIilJqaWuRjUlNTi9w/KytLhw8fliStW7dO8+bN05tvvlniWqZOnarw8PDcSwPWoAYAAD4oJydvWiXhmHsFBprG/JK0fLm9tZSGZZlm5kVNoRw3jimU3siXm/JbljR+vLlI0qRJJigrbTCWX2io9OCD0i+/SC+/LNWtK+3bZ/qUtWghvfWWafgPAMUpVUN+x1m/wSzLKnTf+fZ33n/8+HHdcccdevPNN1WrVq0S1zB27FilpaXlXvb78jJDAADAb/38sxkZVLmyGVUC9/LWvmPOKZS33cYUSl/ji33HLEt69FEThknS9OnSU0+5JxjLLzRUeughafduMyItIkLau1f6299MSDZ/PiEZgKK5FI7VqlVLAQEBhUaJHTp0qNDoMKe6desWuX9gYKBq1qypX375RXv37tXgwYMVGBiowMBAvfPOO1qyZIkCAwP1yy+/FPm8ISEhqlatWoELAACAr3FOqezSxYx0gns5w7G4OBNCerrjx03I0LEjUyh9la+FYzk5ZurjzJnm9r//bf4Nl6ewMGnUKBOSzZxpQrI9e6QRI8xIy7fflrKyyrcGAN7FpXAsODhYUVFRijlrveuYmBj16NGjyMd079690P7Lly9XdHS0goKC1LJlS23dulVJSUm5l+uuu06XX365kpKSmC4JAAD8Gv3GyleTJqaBd3a2tHKl3dUUzzmFskULacYM88aeKZS+yZea8mdnS/feaxruOxxmeuODD1bc61euLI0ebUKyF1+U6tQx23ffbUKy//yHkAyA4fK0yjFjxuitt97S/PnztW3bNo0ePVrJyckaOXKkJDPdcdiwYbn7jxw5Uvv27dOYMWO0bds2zZ8/X/PmzdOj//u4IDQ0VG3bti1wufDCC1W1alW1bdtWwcHBbjpUAAAA70M4Vv6co8c8te/Yjz9KV1xRcArl0qVMofRV+Zvy791rdzWll5kp3XmntGCBFBAgvfuuGbllh8qVzSIAu3ebKZ21a5v+ZHfdJbVubWojJAP8m8vh2NChQzVr1ixNmjRJHTt21LfffqulS5eq0f/+MqekpCg5OTl3/yZNmmjp0qWKjY1Vx44dNXnyZL3yyiu66aab3HcUAAAAPuj4cTNdTpK6dbO3Fl82YIC59rS+Y/mnUMbG5k2h3LpVGjjQ7upQXoKDpXbtzHZ8vL21lNaZM9LQoWa0Y2CgtHChdPvtdlclVali/k/t3i298IJUq5a0c6c0bJjUpo303ntmtBsA/+OwLG8frGukp6crPDxcaWlp9B8DAAA+YeVKs5pio0bePYLE0x0/LtWoYUaO7NolNWtmbz2WJX30kXkTf/Cgue+GG0yDcUaK+Yd//EN64w3p8cel55+3uxrXnD4t3XSTGd0YHCx98ol07bV2V1W0EyekV181Uy6PHDH3tWghPf20CfcCAuytD0DZuJITlWq1SgAAAJQ/plRWjKpVpZ49zbbdUyvzT6E8eJAplP4qOtpce1tT/pMnTRC2dKkZ6fjFF54bjEnSBRdITzxhmvVPmWJC8u3bzSi3tm3NyDdGkgH+gXAMAADAQxGOVRxn3zG7plYyhRL5eWNT/vR0s2LqN9+Y0Omrr6SrrrK7qpKpWlUaO9aEZM8+K1WvLv38swmp27c300JzcuyuEkB5IhwDAADwQJZFOFaRnH3HVq40jcQrinMKZcuWeatQDhki/fSTWYUyNLTiaoHn8Lam/H/8YYKwtWul8HApJkbq08fuqlxXrZr5f7dnjzRpknThheb/4i23mJDs//6PkAzwVYRjAAAAHmjPHun3380b5E6d7K7G93XqZJpzHz+eF0qWtx9/ND3lbr3VTKFs1sxMR1u8WGrcuGJqgGfK35Tf06dW/v67mQq8caNUs6YJmL090A8Pl556ygSTzzxjbv/4o/TXv0odOkj//S8hGeBrCMcAAAA8kDOg6dRJCgmxtxZ/UKlS3hSw8u47dvy49NhjZgrlqlVmdNjkyWZlUqZQwin/1EpPlZIi9e0rJSVJdeqYKcGdO9tclBuFh5vm/Hv3ShMmmJFlP/wg3Xyz+d28aBEhGeArCMcAAAA8EFMqK1559x3LP4XyxRfzplBu2yaNH88UShTkDMfi4+2tozj795upkz/9JNWvL337rZkO6osuvFCaONGEZE8/bUKy7783q3J27mxGe3pLbzgARSMcAwAA8ECEYxXPGY7Fx0tHjrj3uX/6qfAUyi+/ZAolipd/xUpPC1727JEuu0zaudOsovrtt1KLFnZXVf6qVzfTLPfsMYF21arSli3SjTeakOyzzzzvZwWgZAjHAAAAPMyff0qJiWabcKziREaakS+WJa1Y4Z7ndE6h7NCh8BTKQYPc8xrwTZ7alH/HDql3b1PTxRebYKxpU7urqlg1apj/x3v2SE8+aVbnTEoyI0Gjo6XPPyckA7wN4RgAAICHSUw0U+4iIsyoDFQc56qVZe07VtQUyuuvZwolSs4Tm/L/8IMZMXbggNSqlQnGGja0uyr71KwpPfecCQrHjpWqVJE2b5auu07q0kX64gtCMsBbEI4BAAB4mPxTKh0Oe2vxN/n7jpX2TW1xUyg//ZQplHCNJzXlT0w0zfd/+82MhIyNlerVs7sqz1CzpjRlignJHn/chGQJCdLgwVK3bmYVWkIywLMRjgEAAHgY+o3Zp3dvM6rrwAEzyssVRU2hnDSJKZQoPU8JxzZskK64wvTi69JFWrnSrE6JgmrVkp5/3ky3fOwxqXJladMm6ZprzO/zr74iJAM8FeEYAACAhyEcs09YmJk2JpV81criplD+9JP01FNMoUTp5V+x0q5Q5dtvpX79pGPHpJ49TT++GjXsqcVb1K4tTZtmQrJHHzW/VzZuNCF59+5lG5kKoHwQjgHA/1iW9Mor5lP+M2fsrgaAvzpwQNq/X6pUKW+1OlQs59TKkvQdO9cUyiZNyrVM+IG2baWgIPua8q9YIV19tXTihBk59vXXUrVqFV+Ht6pTR5o+3YRkY8aYkGzDBvM9vf126dQpuysE4EQ4BgAywdjjj0sPPyxNmGA+2UtLs7sqAP5owwZz3a6dWQENFc/ZlH/1aun06aL3YQolKkJIiNS+vdmu6KmVX34pXXutWT134EDTXJ7fSaUTESHNmCHt3i2NHi0FBkoffij16GGCMwD2IxwDAEkTJ5pP9iTzqd4330i9epnRGwBQkZhSab82baTISBMKrF1b8GuWJS1cyBRKVBw7+o4tWiTdcIOUkSENGSItXmzOj1A2detKM2ea88zataUtW8wI4ZgYuysDQDgGwO9NmWI+7Zekl1+W1q0zJy8//GD6QmzZYm99APwL4Zj9HI6ip1b+9JPpvXTLLWYKZdOmZjQNUyhRnio6HPvgA+mvf5UyM82/9Y8/NiPY4D6XXWZ+nl26SEePmmmW06fThwywE+EYAL82Y4Y0bpzZnjZNeughqVMn8+a0dWvT+6d3bz7RA1AxMjNN422JcMxuznBs2TIzhfJf/zJTKFeuzJtC+eOPZhU6oDzlD8fKOzyZP1+64w4pO1u66y7pvfdMzzO4X4MGZrGDu++WcnLM75hbbpFOnrS7MsA/EY4B8FuvvWZWEJKkyZNN7xinRo3MVJq+fc2bokGDpAULbCkTgB/ZutVM5bvwQumSS+yuxr9ddZUZQfb992YK5fTpZgrlddcxhRIVy9mU/+hRad++8nud116TRowwAdzIkdK8eVJAQPm9HszvkHnzzPc+MNCM0uveXfrlF7srA/wP4RgAv/Tmm9KDD5rtceOk8eML71O9ulmV6bbbzBuie+4xvckY8g6gvDinVHbrZlarhH1q1ZI6dzbb+adQfvYZUyhRsUJCzAIdUt7IUnebMSPvvGjUKGn2bH4HVRSHQ7r/frOwR0SE+ZCkSxczahVAxeFXHgC/88470j/+YbYfecSMGitOSIj07rvSk0+a2888Y0KyzMzyrxOA/6HfmGd5/HETij3zDFMoYa/oaHNdHn3Hnn02byT9k0+ahvEOh/tfB+fWq5f5+XbrJv3xh1kh9Pnn+VAWqCiEYwD8ysKFpreDZZlPSKdPP/8JYKVK0nPPSa+/bqYXvP22eYOUnl4hJQPwI4RjnuXmm830pqefZgol7FUeTfkty4yef+opc3vyZHO+QzBmn/r1pdWr86a3jh1rFkc4ccLuygDfRzgGwG8sXizdfrtpevq3v5mVKV05Afz736UlS6QqVUyD/t69TcN+AHCHI0eknTvNdteu9tYCwLO4uym/ZZnR81OmmNsvvlh0iwlUvJAQ0/5j7lzTa+6//zUfmOzaZXdlgG8jHAPgF778Uho61Ky+NGyYOeEoTS+NQYPMJ3oREaZJ86WXmt4QAFBWGzaY6xYtpBo17K0FgGdxZ1P+nBzT4+qll8ztV181QRk8h8NhWoDExkp165pp3V26SF99ZXdlgO8iHAPg85Yvl266yfQJu+UWs0x5WZrMRkWZqU8tW0q//mp6RHzzjfvqBeCfmFIJoDj5m/KXZWpldraZsjd3rglg5s2THnjAPTXC/Xr0MD/v7t2lY8dMW48pU+hDBpQHwjEAPi02VhoyRMrIkG64wTTjd8ey5I0bS+vXS5ddZnqPXX21eW4AKC3CMQDnUta+Y5mZ0h13mN6pAQHSe++ZRYbg2SIjzUqWf/97Xp+4v/xFOn7c7soA30I4BsBnrV8vXXut9Oef5pO2jz4yUxLcpXp1MyrtllukrCxp+HDTzJZP8wC4Kicnb1ol4RiAojjDsfh41x+bkWHaSzjPhRYulG67zb31ofyEhJiFoV5/3fz8Fi0yfyucfSoBlB3hGACftGmTWQL75EnpqqtMM9PgYPe/TkiI9P770uOPm9tPP22a/Wdmuv+1APiun382o1ArVza9hQDgbNHR5trVpvx//mlGzy9ebM5bFi827Sbgff7+d9P7tl496aefTB+yL7+0uyrANxCOAfA5SUlS//7mjWafPtKnn0qhoeX3epUqSc8/L82ebbbnzZMGD2a4O4CSc06p7NJFCgy0txYAnqk0TflPnjSj6L/6SgoLk774woymh/fq3t0EpD17Smlp5pxz8mQzAhlA6RGOAfApP/wg9etnmpb26GFOAitXrpjXvu8+6bPPzOstW2b6kR08WDGvDcC7xcWZa6ZUAiiOq03509OlAQOklSulCy6Qvv7anCPB+9WrZ36u991nRhE+/bQZDZiebndlgPciHAPgM7ZvNyd9R46Y0RdLl5qTwYp07bVmEYA6dcwItksvNctvA8C50IwfQEmUtCn/0aPmnGjdOik8XIqJMR/awXcEB5tZC2+9ZbY//VTq1s2cDwNwHeEYAJ/wyy/SFVdIv/0mdexoRm6Fh9tTS5cu5o1uixbS/v1m2PuqVfbUAsDzpafnhejdutlbCwDPVpJw7PffzTnRpk1SzZpmhBHBu+8aMUL69lupfn3Tv7JrV2nJErurArxPqcKx2bNnq0mTJgoNDVVUVJTWrFlzzv1Xr16tqKgohYaGqmnTppo7d26Br7/55pvq3bu3qlevrurVq6tfv37auHFjaUoD4If27TMngQcPSm3amE9Hq1e3t6YmTcyntb16mX4QAwaYJdMB4GybNplpMY0amakyAFCc/CtWFtWUPyVF6ttX2rJFiogwo9k7d67ICmGHbt3Mv4levcwHLtdfLz3zDH3IAFe4HI4tXLhQo0aN0rhx45SYmKjevXtr4MCBSk5OLnL/PXv2aNCgQerdu7cSExP15JNP6qGHHtInn3ySu09sbKxuvfVWrVq1SnFxcWrYsKH69++vAwcOlP7IAPiFX381wVhyshmp9c03Uq1adldl1Kxpgrq//tWsXnnnndJzz7m2whQA38eUSgAl1a5d8U359+83Uyd/+smMIlq9mtVv/UnduuY8+MEHze2JE80qpWlptpYFeA2HZbn2Nq1bt27q3Lmz5syZk3tfq1atNGTIEE2dOrXQ/o8//riWLFmibdu25d43cuRIbdmyRXHO7rNnyc7OVvXq1fXqq69q2LBhJaorPT1d4eHhSktLU7Vq1Vw5JABeKjXVrEa5Y4fUtGnekHJPk5MjPf649OKL5vbf/mZ6RLAiHQDJrDT2xRfSSy9Jo0bZXQ0ATxcVJW3eLP33v6YJuyTt3i1deaW0d6/UuLEJSZo2tbNK2Ontt6WRI6WMDPPh8aefSi1b2l0VUPFcyYlcGjl25swZJSQkqH///gXu79+/v9avX1/kY+Li4grtP2DAAMXHxyszM7PIx5w6dUqZmZmqUaOGK+UB8CO//25OAnfskBo2NP00PDEYk6RKlaTp06VXXzXbb74pXXeddOKE3ZUBsJtlMXIMgGvO7ju2fbsZMbZ3r3TxxebDQoIx/3bXXdKaNdJFF5l/H127moAMQPFcCscOHz6s7OxsRUREFLg/IiJCqampRT4mNTW1yP2zsrJ0+PDhIh/zxBNPqH79+up3jrWGMzIylJ6eXuACwD8cPSpddVXetIGVK02vHk/3wAPSokVSWJj01Vdm1FtKit1VAbDT7t3S4cNmpbFOneyuBoA3yB+O/fCDOZ84cEBq3doEYw0a2FsfPEOXLubfyGWXScePmymWTz9NHzKgOKVqyO9wOArctiyr0H3n27+o+yVp2rRp+vDDD7Vo0SKFhoYW+5xTp05VeHh47qUBfwUAv+Bsbu9sNPvNN1KzZnZXVXLXX29Wrqxd20yJ6N7dhHwA/JNz1FinTlJIiL21APAOznAsLs403//tN6lDB9N8n0U9kF+dOtKKFdJDD5nbkyebc9Fjx2wtC/BILoVjtWrVUkBAQKFRYocOHSo0Osypbt26Re4fGBiomjVrFrj/xRdf1JQpU7R8+XK1b9/+nLWMHTtWaWlpuZf9+/e7cigAvNDx49LAgWY1nlq1TDDWooXdVbmuWzdzQtu8uWmm27OnaZoLwP8wpRKAq5xN+Y8fl44cMSOEnB+8AWcLCpJefln6z3+k0FDT47JrVz6cBc7mUjgWHBysqKgoxcTEFLg/JiZGPXr0KPIx3bt3L7T/8uXLFR0draCgoNz7pk+frsmTJ+vrr79WdHT0eWsJCQlRtWrVClwA+K5Tp6RrrzWhUvXq5lOwNm3srqr0mjWT1q+XevQwn9717y998IHdVQGoaIRjAFwVEiJ17Gi2e/Uy50TVq9taErzAsGHS2rWmV+/OnebD2kWL7K4K8BwuT6scM2aM3nrrLc2fP1/btm3T6NGjlZycrJEjR0oyI7ryrzA5cuRI7du3T2PGjNG2bds0f/58zZs3T48++mjuPtOmTdP48eM1f/58NW7cWKmpqUpNTdUJulUDkHT6tBkC/u23UrVq0rJlZvqAt6tVy5zQ3nSTdOaMdPvt0vPPmwbdAHzfn39KSUlmm3AMgCvmzJGmTpW+/tqcGwElERVlZmBcfrlZGOqmm6Tx46XsbLsrA+znsCzX34bNnj1b06ZNU0pKitq2bauXXnpJl112mSTprrvu0t69exUbG5u7/+rVqzV69Gj9+OOPioyM1OOPP54bpklS48aNtW/fvkKvM2HCBE2cOLFENbmyRCcA75GRId14o7R0qVSlirR8uRlt5UtycqRHH5VeesncHjlS+ve/pcBAe+sCUL7WrTOjPurWlQ4elM7RvhUAALfJypL+9a+8c89Bg6T335cuvNDWsgC3cyUnKlU45okIxwDfk5kp/fWvZunp/Cs8+qpXXpFGjTIjx665RvroI+mCC+yuCkB5mTHDBONDhkiLF9tdDQDA37z/vnTvvWaWxsUXm79FbdvaXRXgPq7kRKVarRIAyltWlnTHHSYYCwmRlizx7WBMMisJffKJaZb65ZdmBaqz1jMB4EPoNwYAsNPtt5seuI0aSbt2mb9H//2v3VV5FsuSfvjBzOq48UapQQPphhtM3zb4FsIxAB4nO1u65x7p44/NCjuLFkn9+tldVcW44Qaz4lStWlJCgtS9u/Tzz3ZXBaA8EI4BAOzWqZPpQ3blldLJk9LNN0tjx/pvHzLLknbskF5/XRo61LQ+aNfOfIi9eLH066/mw/s2bczU1PR0uyuGuzCtEoBHycmR/vEP6a23pIAA8+nVkCF2V1Xxdu2SBg4019Wrmz/C/2vtCMAH/Pqr+fS5UiVzYl2lit0VAQD8WVaWCcVefNHcHjDArKReo4a9dVWEvXullSvNB9SrVkkHDhT8eliY6RF6+eVmpdh//9u0e5GkOnXM4hh33WX+psOz0HOMcAzwSpYl/fOf0muvmT8uH3xgPrHxV7//Ll13nRldEhws/ec/0i232F0VAHf45BPpL38xJ9mJiXZXAwCA8eGH0ogRZkXlpk3NB7Tt2tldlXsdOJAXhK1cacKx/IKDzeyNyy+XrrhC6trVtHnJb+lSafRoM8pMMiuBvvKK7y0c5u1cyYlYCw2AR7As05j6tdfMim1vv+3fwZgk1a4tffON6b22eLF0663S/v3m+8SqdoB3Y0olAMAT3Xqr1Lq1mbmxe7f5O7VggVkky1sdOiTFxuaNDnMGWk6BgVKXLiYIu/xyE3CFhZ37OQcNMm1fXn1VeuYZ0w6lZ0/pttukF16QLrqo3A4H5YSRYwBsZ1nSuHFmSLIkvfmmWTkHRna2NGaM+TRKku6/32wHBNhbF4DS691bWrvWfBAwfLjd1QAAUNCRIyYoi4kxt//1L2nKFO84/zx6VFq9Om902A8/FPx6pUpS5855I8N69SrbCvG//SaNHy/Nm2fe11SubKaoPvLI+UM2lC+mVRKOAV5l8mTp6afN9quvSg88YG89nmrWLBOSWZY0eLAZ9k6fIsD7ZGZK1apJp0+bBTdatLC7IgAACsvKMh9gT5tmbl91lfTRR57Xhyw9XVqzJm+aZFKSOV/Or337vJFhl10mXXih++tISJAeflhat87cbtzY9HC78UZmfdiFcIxwDPAa06ZJjz9utmfMMOEPivfJJ2aa5enTZvj3559LERF2VwXAFQkJUnS0WWzj8GEa+AIAPNvHH0t33y2dOiU1aWLafXToYF89p06ZAMo5TTI+vvDqmq1amSDs8sulvn3NSvAVwbKkhQulxx4zi+9I5vVfftkEdKhYhGOEY4BXePlladQosz1lihl+jPNbv9406j9yxJygfPUVI08Ab/Laa9KDD0pXX5232hUAAJ7s+++lG24wfcjCwqT58ytuoaiMDNOr0xmGffedGYWdX7NmedMk+/aV6tWrmNqKc/KkGQQwbZr5ULtSJenvfzczZioqqAPhGOEY4AXmzpXuu89sP/20aWSJktuxQxo40Jyg1KghffaZ6ZcAwPPdeaf03nvSxInShAl2VwMAQMkcPWoazi9bZm4/+qjpGRzo5mX+MjOlTZvypkmuX28CpvwaNMibJnn55VLDhu6twV327TP92j7+2Ny+8ELzvue++6SgIFtL8wuEY4RjgEebP98sES2ZPxbPP888/NI4dMj0Htu40Swv/e670s03210VgPNp3lzatUv6+mtpwAC7qwEAoOSys6WnnspbSOvKK800wpo1y/aciYl5I8PWrDEjr/KrWzdvZNjll0tNm3rX+4fVq00/si1bzO1WrUw/4f79bS3L5xGOEY4BHuuDD0zPLMsyfyBeesm7/rB5mlOnzCd4n31mbr/4ounbxvcU8EyHD0u1a5vto0dN3zEAALzNf/8r3XWXCbEaNzZ9yDp2LNljc3LMCpLOMGz1aiktreA+NWvmjQq7/HKpZUvvP7/NzjYrWo4bZ84HJNMqZcYM6eKL7a3NVxGOEY4BHum//zW9CbKzpZEjpdmzvf+PnCfIzjZB42uvmdv//KcJHb1hqW3A33z5pXTtteYkf9s2u6sBAKD0fvhBGjJE+uUX04fsrbfMh7ZnsyyzOvOqVXmXI0cK7hMeLvXpkzc6rG1b312w5o8/pEmTpFdfNSuCBgdLo0eb0KxqVbur8y2EY4RjgMdZskS66SbzB+Duu80fT1/9g2cHy5JmzjS9HyRzovL++1LlyraWBeAsTz0lPfus+bR9wQK7qwEAoGz++EO6/fa8BWZGjzZN6Pfty+sZtmqVlJpa8HFVqki9e+dNk+zUyf8+2N22zSxOtny5uV23rpmuOmwY75PchXCMcAzwKF9/LV1/vXTmjPk06Z13/O+PX0X5v/8zzb4zMqRu3aTPP8+bwgXAflddJa1YYRYl+cc/7K4GAICyy842C8w895y5Xa2alJ5ecJ/QUKlnz7xpkl260JBeMh9wf/mlCRV37TL3dekivfKKdOml9tbmCwjHCMcAj/HNN2YK0enTZuTYRx+5f0UbFLR2rQkjjx41y1ovXSpdcondVQHIzjY9xo4fl5KSpA4d7K4IAAD3WbRIGj5cOnHCBF+XXpo3TbJbNxOQoWgZGSYQmzzZnCdIpk/z889L9evbW5s3IxwjHAM8wpo10tVXm6bxgwebnmPBwXZX5R+2b5cGDpT27DENTZcskXr0sLsqwL/9+KPpoVKlinTsGB8UAAB8z2+/STt3Sp07096jNFJTTe+xBQvMqLIqVaQnnzQLbhEuus6VnIiZrADKxXffSYMGmWDs6qvNdD+CsYrTooUUFydFR5uGp1deKX3yid1VAYUdOWKm/06dKn34oem/kZ1td1Xl47vvzHWXLgRjAADfFBEh9epFMFZadeuaFS03bjQfbJ88acKyVq3MyDzfGNrkmTg1A+B2CQkmEDtxwgylXrRICgmxuyr/ExEhxcaaFUK/+EK6+WbTtH/UKLsrg7+yLLOi1bp1ZvrvunVFr9gYFia1a2ea83bsaC7t2plPT72ZMxyjhwgAADiX6GhzrvThh9K//iXt3Wta1FxxhTRrljkvgnsxrRKAW33/vQnEjh41nxp9/bX3v6H1dllZ0kMPSXPmmNuXXmr+sPbtaz6R4ueD8pKZKSUmFgzDfvut8H4tW5ogbN8+acsW8ynp2SpVMr3znGFZx47mMXXqlPNBuFG7dmbZ+08/NX0BAQAAzufkSdN7bPp005usUiVp5Ehp0iTTPgXFo+cY4Rhgi23bpD59pN9/N003ly83q9XAfpZl/qA+8UTB4diBgVLXriYoIyxDWaWlmdFRziBswwYztTq/4GDzaWivXmbVqh49pFq18r6ek2NWa0pKyrskJhZeAt6pXr2CYVnHjmYhCk9bAj09XbrwQvP/LyXFTJsAAAAoqT17pMcey2uVUr26CchGjqRdQ3EIx3g3DlS4nTtNMJaSYhpwfvONeSMIz7Jvn7RypZluuWqVtH9/wa8HBpp+SPnDsgsusKFQeIXk5LwgbN06M3L07LOKGjXMvyNnGBYdXbqGsqmpZlSZMyxLSpJ27Ci690aVKmYlyPzTMtu2tbeR7TffSP36SY0bm5NbAACA0li1Snr4YWnrVnO7TRvp5ZdNj2EURDhGOAZUqD17pMsuk3791UwbWrWKIb7ewLJM/4LY2LxLcnLBfQjL4JSdbU7CnFMk1641/+fP1qyZCcGcYVjLluU3iuvkSVOTMyxLSjIB3enThfcNCDDNbPNPy+zYseJ+Vz33nDR+vOkB+OGHFfOaAADAN2VlSW+9Zc4tjhwx9w0ZIr34ojkXg0E4RjjmsU6fNm9iNm40I4yqVzdvTGrUMBfnds2aLFXrLfbvN8HY3r3mTfDq1d7VAwgF5Q/LVq0qOiyLjs4Ly3r2JCzzVSdPmmmRzpFhcXHS8eMF9wkIMCNFnWFYjx5mmqOdsrLMiLKzp2UePlz0/g0aFJ6W2bix5HC4t67Bg83CGLNmmU97AQAAyuroUemZZ6TXXjMfZAYHS488Io0dK1Wtand19iMcIxzzCDk5Zqrdhg3msnGjmRKTmVmyx4eFFR+cnes+VkWsOAcPmqmUu3ZJF19sgrHISLurgjudPbJs376CXw8IKDiyjLDMe6WkFGycn5hoTrLyq1rVBGDOMKxrV+/oUWdZ5vdV/rAsKcmsnFmU8PDC0zJbtzYnnKV9/Tp1TED33XemJyMAAIC7/PSTWZE+JsbcrlfPNPG/4w7P68NakQjHCMdsceiQCcCcYdimTdKxY4X3q13bvDFo2tR8/ehRczlyJG/77DdkrqhSpejg7HyhWlBQ6V/THx06ZIKxn382oyy+/daMwIBv27vXhKDOsGzv3oJfDwgoPLKMT608T06OWUAjfxi2e3fh/Ro0yJse2auX6dsVEFDx9ZaX9PS8PmbOyw8/SGfOFN43KMj09Mg/yqxDBxOknc8vv5gPEIKDzWvyIQ4AAHA3y5I+/1waMybvA8Bu3Uw/Mn/9YI5wjHCs3P35p7R5c96IsA0bCr9JlszUyKgoM7qgWzdzadTo3NNVcnLM1J38YZlz+1z3/fGHeWxpVa1a8jDNeV29un+uDHLkiHT55abXz0UXmWCsSRO7q4Id8odlq1cXbjSePyzr08cELIRlFe/0aSk+Pq9X2Pr15ndmfg6H1L59XhjWs6fUsKE99drpzBkT+p89yqyoD3sk87vv7GmZF11U8O/c+++bT24vvdRMTwUAACgvGRmmjcOzz0onTpj7hg2Tpk71v1k+hGOEY26Vk2PeKOQfFfb990WP7mrVygRgzjCsXbuKG5GVkyOlpZ0/RDv7a8eOFb3aWUmFhxcdpoWEmDdHzotU8HZ5XSridaZPN28Y69Y1wVjz5mX+8cFH7NtXcGRZUWFZVFTBkWX8yna/w4fzVpBcu1ZKSCg8GqpyZfN72hmGXXppyUZB+SPLMv338odlSUmFpxk71ahRMCz74gtp4UIz3eGllyqqagAA4M9SUqQnn5TeftvcrlJFGjdOGj3af/p7E47xTqtMUlML9gnbtMlMAzlbRETeaLBu3czoEG98Y5WdXfT0zvMFbGlpdldur9q1TfjRurXdlcCT5Q/LVq8uPHXPGZb16WPCsl69CMtcZVmm759zeuTatdL27YX3q1u34CqSHTsynbys/vij4JTMpCTT8yMrq+j9P/pIGjq04uoDAADYuFF66CHz/l4y7Y1mzJCuv979CxB5GsIx3lmV2MmTZkRB/lFh+/cX3q9yZfMGNv+osAYNfP8/07lkZZlQrbgw7cwZ86a1qItU/Ncq8lLaOqpXlyZONP2HAFckJxccWXZ2WFapUsGRZYRlhZ05Y0YvOcOwdetMD8CztW5dMAxr2tS/f2dXlNOnTUCWf5TZli3mw6OkJDOyGAAAoCLl5EgffCA9/rhZpEiS+vUz0y/btLG1tHJFOMY7qSJlZ5sGzPlHhf3wQ+HpkQ6H+Q+Sv09Ymzb+2VsLQPlyhmXOwOzs1QOdYVn+kWXeOEK1KGfOmD4QJ06YDyryXxd13/Hjps/fhg0mgMkvONisGtqrl7l0704I40lycgpOfQcAALDDiROm99iMGaY3WUCAdN990jPPmLYQvoZwjHBMknTgQMGG+fHxeQ358ouMzAvBunY10yNpmA3ADvv3FxxZVlRY1rlzwZFl5RmWWZbrIda5vpZ/u7ipdyVRs2Ze0/yePU2A6C+9IwAAAFA2u3dLjz0mLVpkbteoIU2eLP397741KKbcw7HZs2dr+vTpSklJUZs2bTRr1iz17t272P1Xr16tMWPG6Mcff1RkZKT+9a9/aeTIkQX2+eSTT/TUU0/pl19+UbNmzfTcc8/phhtuKHFN/h6OnThhwq/8o8IOHCi8X5UqJvzK3yusfv2KrxcASuLXXwuGZbt2Ffx6pUqm6bkzLOvcWcrMdC3MOt99RS0+4k7BwdIFF5jfz/mvz76vShUzNbJXL6lFC0YhAQAAoGy++cYsGPTDD+Z227bSv/9tzqt9QbmGYwsXLtSdd96p2bNnq2fPnnr99df11ltv6aefflLDItZ837Nnj9q2bau//e1v+sc//qF169bp/vvv14cffqibbrpJkhQXF6fevXtr8uTJuuGGG7R48WI9/fTTWrt2rbp16+b2g/Z2WVnSjz8W7BP2009m2kZ+lSqZf9z5+4S1bm2GTgKANzpfWFaeQkKKD67Kch9N8QEAAGCXrCzp9delp54yiw1NnSo98YTdVblHuYZj3bp1U+fOnTVnzpzc+1q1aqUhQ4Zo6tSphfZ//PHHtWTJEm3bti33vpEjR2rLli2Ki4uTJA0dOlTp6en66quvcve5+uqrVb16dX344YclqstXwzHLMm8G848Ii4+XTp0qvG+DBgWDsM6dzZsvAPBVBw4UDMt27jTTC10NrEqyvy8NMQcAAADyO3LENOgfN8532nW4khO5dKp/5swZJSQk6ImzYsT+/ftr/fr1RT4mLi5O/fv3L3DfgAEDNG/ePGVmZiooKEhxcXEaPXp0oX1mzZpVbC0ZGRnKyMjIvZ2enu7KoXi8Tz6R3n3XBGKpqYW/XrWqab6cPwyrV6/i6wQAO9WvL912m7lIZgRtpUr21gQAAAB4m5o1Td8xf+VSOHb48GFlZ2crIiKiwP0RERFKLSrBkZSamlrk/llZWTp8+LDq1atX7D7FPackTZ06Vc8884wr5XuV7dulzz4z2wEBUvv2BVePbNGC6ZEAcDaCMQAAAACuKtUkEcdZXYAtyyp03/n2P/t+V59z7NixGjNmTO7t9PR0NWjQ4PzFe4lrrzVDGbt2NdMjK1e2uyIAAAAAAADf41I4VqtWLQUEBBQa0XXo0KFCI7+c6tatW+T+gYGBqlmz5jn3Ke45JSkkJEQhISGulO9V2rc3FwAAAAAAAJQflyagBAcHKyoqSjExMQXuj4mJUY8ePYp8TPfu3Qvtv3z5ckVHRyvof0t0FbdPcc8JAAAAAAAAuIPL0yrHjBmjO++8U9HR0erevbveeOMNJScna+TIkZLMdMcDBw7onXfekWRWpnz11Vc1ZswY/e1vf1NcXJzmzZtXYBXKhx9+WJdddpleeOEFXX/99frss8+0YsUKrV271k2HCQAAAAAAABTmcjg2dOhQHTlyRJMmTVJKSoratm2rpUuXqlGjRpKklJQUJScn5+7fpEkTLV26VKNHj9Zrr72myMhIvfLKK7rpppty9+nRo4c++ugjjR8/Xk899ZSaNWumhQsXqlu3bm44RAAAAAAAAKBoDsvZHd/LpaenKzw8XGlpaapWrZrd5QAAAAAAAMAmruRELHoPAAAAAAAAv0U4BgAAAAAAAL9FOAYAAAAAAAC/RTgGAAAAAAAAv0U4BgAAAAAAAL9FOAYAAAAAAAC/RTgGAAAAAAAAv0U4BgAAAAAAAL8VaHcB7mJZliQpPT3d5koAAAAAAABgJ2c+5MyLzsVnwrHjx49Lkho0aGBzJQAAAAAAAPAEx48fV3h4+Dn3cVglidC8QE5Ojg4ePKiqVavK4XDYXY5bpKenq0GDBtq/f7+qVatmdzlu5cvHJvn28fnysUkcnzfz5WOTOD5v5svHJvn28fnysUkcnzfz5WOTOD5v5svHJvn28fnisVmWpePHjysyMlKVKp27q5jPjByrVKmSLrroIrvLKBfVqlXzmX+cZ/PlY5N8+/h8+dgkjs+b+fKxSRyfN/PlY5N8+/h8+dgkjs+b+fKxSRyfN/PlY5N8+/h87djON2LMiYb8AAAAAAAA8FuEYwAAAAAAAPBbhGMeLCQkRBMmTFBISIjdpbidLx+b5NvH58vHJnF83syXj03i+LyZLx+b5NvH58vHJnF83syXj03i+LyZLx+b5NvH58vHVhI+05AfAAAAAAAAcBUjxwAAAAAAAOC3CMcAAAAAAADgtwjHAAAAAAAA4LcIxwAAAAAAAOC3CMc81OzZs9WkSROFhoYqKipKa9assbskt/j22281ePBgRUZGyuFw6NNPP7W7JLeZOnWqunTpoqpVq6pOnToaMmSItm/fbndZbjNnzhy1b99e1apVU7Vq1dS9e3d99dVXdpdVLqZOnSqHw6FRo0bZXYpbTJw4UQ6Ho8Clbt26dpflVgcOHNAdd9yhmjVrqnLlyurYsaMSEhLsLsstGjduXOjn53A49MADD9hdWpllZWVp/PjxatKkicLCwtS0aVNNmjRJOTk5dpfmNsePH9eoUaPUqFEjhYWFqUePHtq0aZPdZbnsfH+/LcvSxIkTFRkZqbCwMPXt21c//vijPcWWwvmOb9GiRRowYIBq1aolh8OhpKQkW+osrXMdX2Zmph5//HG1a9dOVapUUWRkpIYNG6aDBw/aV7ALzvezmzhxolq2bKkqVaqoevXq6tevnzZs2GBPsaXgyrnzP/7xDzkcDs2aNavC6iur8x3fXXfdVejv36WXXmpPsS4qyc9u27Ztuu666xQeHq6qVavq0ksvVXJycsUXWwrnO76izl0cDoemT59uT8EuON+xnThxQg8++KAuuugihYWFqVWrVpozZ449xZbC+Y7vt99+01133aXIyEhVrlxZV199tXbu3GlPsRWIcMwDLVy4UKNGjdK4ceOUmJio3r17a+DAgV7zi/JcTp48qQ4dOujVV1+1uxS3W716tR544AF99913iomJUVZWlvr376+TJ0/aXZpbXHTRRXr++ecVHx+v+Ph4XXHFFbr++uu96s1PSWzatElvvPGG2rdvb3cpbtWmTRulpKTkXrZu3Wp3SW7zxx9/qGfPngoKCtJXX32ln376STNmzNCFF15od2lusWnTpgI/u5iYGEnSzTffbHNlZffCCy9o7ty5evXVV7Vt2zZNmzZN06dP17///W+7S3Obe++9VzExMXr33Xe1detW9e/fX/369dOBAwfsLs0l5/v7PW3aNM2cOVOvvvqqNm3apLp16+qqq67S8ePHK7jS0jnf8Z08eVI9e/bU888/X8GVuce5ju/UqVPavHmznnrqKW3evFmLFi3Sjh07dN1119lQqevO97O75JJL9Oqrr2rr1q1au3atGjdurP79++v333+v4EpLp6Tnzp9++qk2bNigyMjICqrMPUpyfFdffXWBv4NLly6twApL73zH9ssvv6hXr15q2bKlYmNjtWXLFj311FMKDQ2t4EpL53zHl/9nlpKSovnz58vhcOimm26q4Epdd75jGz16tL7++mu999572rZtm0aPHq1//vOf+uyzzyq40tI51/FZlqUhQ4Zo9+7d+uyzz5SYmKhGjRqpX79+PvO+tlgWPE7Xrl2tkSNHFrivZcuW1hNPPGFTReVDkrV48WK7yyg3hw4dsiRZq1evtruUclO9enXrrbfesrsMtzl+/LjVvHlzKyYmxurTp4/18MMP212SW0yYMMHq0KGD3WWUm8cff9zq1auX3WVUmIcffthq1qyZlZOTY3cpZXbNNddY99xzT4H7brzxRuuOO+6wqSL3OnXqlBUQEGB98cUXBe7v0KGDNW7cOJuqKruz/37n5ORYdevWtZ5//vnc+06fPm2Fh4dbc+fOtaHCsjnX+cmePXssSVZiYmKF1uROJTn/2rhxoyXJ2rdvX8UU5SYlOba0tDRLkrVixYqKKcqNiju+X3/91apfv771ww8/WI0aNbJeeumlCq/NHYo6vuHDh1vXX3+9LfW4U1HHNnToUJ/5e1eS/3vXX3+9dcUVV1RMQW5U1LG1adPGmjRpUoH7OnfubI0fP74CK3OPs49v+/btliTrhx9+yL0vKyvLqlGjhvXmm2/aUGHFYeSYhzlz5owSEhLUv3//Avf3799f69evt6kqlEZaWpokqUaNGjZX4n7Z2dn66KOPdPLkSXXv3t3uctzmgQce0DXXXKN+/frZXYrb7dy5U5GRkWrSpIluueUW7d692+6S3GbJkiWKjo7WzTffrDp16qhTp05688037S6rXJw5c0bvvfee7rnnHjkcDrvLKbNevXrpm2++0Y4dOyRJW7Zs0dq1azVo0CCbK3OPrKwsZWdnFxoFEBYWprVr19pUlfvt2bNHqampBc5dQkJC1KdPH85dvFRaWpocDofPjMB1OnPmjN544w2Fh4erQ4cOdpfjFjk5Obrzzjv12GOPqU2bNnaXUy5iY2NVp04dXXLJJfrb3/6mQ4cO2V1SmeXk5OjLL7/UJZdcogEDBqhOnTrq1q2bT7Wcye+3337Tl19+qREjRthdilv06tVLS5Ys0YEDB2RZllatWqUdO3ZowIABdpdWZhkZGZJU4NwlICBAwcHBPnXuUhTCMQ9z+PBhZWdnKyIiosD9ERERSk1NtakquMqyLI0ZM0a9evVS27Zt7S7HbbZu3aoLLrhAISEhGjlypBYvXqzWrVvbXZZbfPTRR9q8ebOmTp1qdylu161bN73zzjtatmyZ3nzzTaWmpqpHjx46cuSI3aW5xe7duzVnzhw1b95cy5Yt08iRI/XQQw/pnXfesbs0t/v000917Ngx3XXXXXaX4haPP/64br31VrVs2VJBQUHq1KmTRo0apVtvvdXu0tyiatWq6t69uyZPnqyDBw8qOztb7733njZs2KCUlBS7y3Mb5/kJ5y6+4fTp03riiSd02223qVq1anaX4xZffPGFLrjgAoWGhuqll15STEyMatWqZXdZbvHCCy8oMDBQDz30kN2llIuBAwfq/fff18qVKzVjxgxt2rRJV1xxRe4beG916NAhnThxQs8//7yuvvpqLV++XDfccINuvPFGrV692u7y3O4///mPqlatqhtvvNHuUtzilVdeUevWrXXRRRcpODhYV199tWbPnq1evXrZXVqZtWzZUo0aNdLYsWP1xx9/6MyZM3r++eeVmprqU+cuRQm0uwAU7ewRAZZl+cQoAX/x4IMP6vvvv/e5dL1FixZKSkrSsWPH9Mknn2j48OFavXq11wdk+/fv18MPP6zly5d7TZ8HVwwcODB3u127durevbuaNWum//znPxozZoyNlblHTk6OoqOjNWXKFElSp06d9OOPP2rOnDkaNmyYzdW517x58zRw4ECv6ylTnIULF+q9997TBx98oDZt2igpKUmjRo1SZGSkhg8fbnd5bvHuu+/qnnvuUf369RUQEKDOnTvrtttu0+bNm+0uze04d/F+mZmZuuWWW5STk6PZs2fbXY7bXH755UpKStLhw4f15ptv6q9//as2bNigOnXq2F1amSQkJOjll1/W5s2bffb/2tChQ3O327Ztq+joaDVq1EhffvmlVwctzoVnrr/+eo0ePVqS1LFjR61fv15z585Vnz597CzP7ebPn6/bb7/dZ86zX3nlFX333XdasmSJGjVqpG+//Vb333+/6tWr5/UzUIKCgvTJJ59oxIgRqlGjhgICAtSvX78C7yd8FSPHPEytWrUUEBBQ6JPWQ4cOFfpEFp7pn//8p5YsWaJVq1bpoosusrsctwoODtbFF1+s6OhoTZ06VR06dNDLL79sd1lllpCQoEOHDikqKkqBgYEKDAzU6tWr9corrygwMFDZ2dl2l+hWVapUUbt27Xxm1Zl69eoVCmhbtWrlE4uY5Ldv3z6tWLFC9957r92luM1jjz2mJ554QrfccovatWunO++8U6NHj/apEZzNmjXT6tWrdeLECe3fv18bN25UZmammjRpYndpbuNc/ZZzF++WmZmpv/71r9qzZ49iYmJ8ZtSYZP7uXXzxxbr00ks1b948BQYGat68eXaXVWZr1qzRoUOH1LBhw9zzl3379umRRx5R48aN7S6vXNSrV0+NGjXy+nOYWrVqKTAw0C/OX9asWaPt27f7zPnLn3/+qSeffFIzZ87U4MGD1b59ez344IMaOnSoXnzxRbvLc4uoqKjcAREpKSn6+uuvdeTIEZ86dykK4ZiHCQ4OVlRUVO5qZE4xMTHq0aOHTVWhJCzL0oMPPqhFixZp5cqVPv/LQzLH7O3D2iXpyiuv1NatW5WUlJR7iY6O1u23366kpCQFBATYXaJbZWRkaNu2bapXr57dpbhFz549tX379gL37dixQ40aNbKpovKxYMEC1alTR9dcc43dpbjNqVOnVKlSwVORgICA3E/UfUmVKlVUr149/fHHH1q2bJmuv/56u0tymyZNmqhu3boFzl3OnDmj1atXc+7iJZzB2M6dO7VixQrVrFnT7pLKla+cv9x55536/vvvC5y/REZG6rHHHtOyZcvsLq9cHDlyRPv37/f6c5jg4GB16dLFL85f5s2bp6ioKJ/p85eZmanMzEy/OH8JDw9X7dq1tXPnTsXHx/vUuUtRmFbpgcaMGaM777xT0dHR6t69u9544w0lJydr5MiRdpdWZidOnNCuXbtyb+/Zs0dJSUmqUaOGGjZsaGNlZffAAw/ogw8+0GeffaaqVavmfoIeHh6usLAwm6sruyeffFIDBw5UgwYNdPz4cX300UeKjY3V119/bXdpZVa1atVCveGqVKmimjVr+kTPuEcffVSDBw9Ww4YNdejQIT377LNKT0/3mWlro0ePVo8ePTRlyhT99a9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      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the variance of the means for each position\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(list(vars_pos.keys()), [vars_pos[i][0] for i in vars_pos.keys()], label='Latent 1', color='blue')\n",
    "plt.title('Variance of means latent 1')\n",
    "plt.xticks(np.arange(0, 20, 1))\n",
    "plt.legend()\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(list(vars_pos.keys()), [vars_pos[i][1] for i in vars_pos.keys()], label='Latent 2', color='red')\n",
    "plt.title('Variance of means latent 2')\n",
    "plt.xticks(np.arange(0, 20, 1))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "# Map values to colors from blue to green as latent 1 increases\n",
    "minima = min([vars_pos[i][0] for i in vars_pos.keys()])\n",
    "maxima = max([vars_pos[i][0] for i in vars_pos.keys()])\n",
    "my_norm_lat1 = matplotlib.colors.Normalize(vmin=minima, vmax=maxima, clip=True)\n",
    "orig_cmap = plt.cm.BuGn\n",
    "colors = orig_cmap(np.linspace(0.5, 1, len([vars_pos[i][0] for i in vars_pos.keys()])))\n",
    "cmap_lat1 = matplotlib.colors.LinearSegmentedColormap.from_list(\"mycmap1\", colors)\n",
    "\n",
    "# Map values to colors from green to red as latent 2 increases\n",
    "minima = min([vars_pos[i][1] for i in vars_pos.keys()])\n",
    "maxima = max([vars_pos[i][1] for i in vars_pos.keys()])\n",
    "my_norm_lat2 = matplotlib.colors.Normalize(vmin=minima, vmax=maxima, clip=True)\n",
    "orig_cmap = plt.cm.BuGn\n",
    "colors = orig_cmap(np.linspace(0.5, 1, len([vars_pos[i][1] for i in vars_pos.keys()])))\n",
    "cmap_lat2 = matplotlib.colors.LinearSegmentedColormap.from_list(\"mycmap2\", colors)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Back to back bar chart in two figures with positive and negative values (symmetric around 0)\n",
    "sns.set(rc={'figure.figsize':(18,10)}, font_scale=3, style=\"white\", palette=\"bright\")\n",
    "plt.figure(figsize=(18, 10))\n",
    "plt.bar(list(vars_pos.keys()), [vars_pos[i][0] for i in vars_pos.keys()], color=cmap_lat1(my_norm_lat1([vars_pos[i][0] for i in vars_pos.keys()])))\n",
    "plt.bar(list(vars_pos.keys()), [-vars_pos[i][0] for i in vars_pos.keys()], color=cmap_lat1(my_norm_lat1([vars_pos[i][0] for i in vars_pos.keys()])))\n",
    "plt.title('Variance of the means in the first latent space for each position in the CDR3 sequence')\n",
    "plt.xticks(np.arange(0, 20, 1))\n",
    "plt.xlabel(\"Position\")\n",
    "plt.ylabel(\"Variance\")\n",
    "plt.savefig(\"./images/vae_var_position1.png\", dpi=300, bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(18,10)}, font_scale=3, style=\"white\", palette=\"bright\")\n",
    "plt.figure(figsize=(18, 10))\n",
    "plt.bar(list(vars_pos.keys()), [vars_pos[i][1] for i in vars_pos.keys()], color=cmap_lat2(my_norm_lat2([vars_pos[i][1] for i in vars_pos.keys()])))\n",
    "plt.bar(list(vars_pos.keys()), [-vars_pos[i][1] for i in vars_pos.keys()], color=cmap_lat2(my_norm_lat2([vars_pos[i][1] for i in vars_pos.keys()])))\n",
    "plt.title('Variance of the means in the second latent space for each position in the CDR3 sequence')\n",
    "plt.xticks(np.arange(0, 20, 1))\n",
    "plt.xlabel(\"Position\")\n",
    "plt.ylabel(\"Variance\")\n",
    "plt.savefig(\"./images/vae_var_position2.png\", dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Produce the same plot but turn by 90 degrees\n",
    "sns.set(rc={'figure.figsize':(18,10)}, font_scale=2, style=\"white\", palette=\"bright\")\n",
    "plt.figure(figsize=(18, 10))\n",
    "plt.barh(list(vars_pos.keys()), [vars_pos[i][0] for i in vars_pos.keys()], color=cmap_lat1(my_norm_lat1([vars_pos[i][0] for i in vars_pos.keys()])))\n",
    "plt.barh(list(vars_pos.keys()), [-vars_pos[i][0] for i in vars_pos.keys()], color=cmap_lat1(my_norm_lat1([vars_pos[i][0] for i in vars_pos.keys()])))\n",
    "plt.title('Variance of the means in the first latent space for each position in the CDR3 sequence')\n",
    "plt.yticks(np.arange(0, 20, 1))\n",
    "plt.ylabel(\"Position\")\n",
    "plt.xlabel(\"Variance\")\n",
    "plt.savefig(\"./images/vae_var_position1_reversed.png\", dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CASSA----RLAGGPNEQFF',\n",
       " 'CASSL-----GGNSNQPQHF',\n",
       " 'CASSI-------RSSYEQYF',\n",
       " 'CASSP-----GDFMGETQYF',\n",
       " 'CSVEG--------SSNEQFF',\n",
       " 'CASSL-----LAGGHNEQFF',\n",
       " 'CASSE------FFGTHEQYF',\n",
       " 'CSASS------ESSYNEQFF',\n",
       " 'CASSL-------AGAYEQYF',\n",
       " 'CASRQN--NADRVTNEKLFF',\n",
       " 'CASSI-----WTDDTGELFF',\n",
       " 'CASSL----RDRANTGELFF',\n",
       " 'CASSL------GGESTEAFF',\n",
       " 'CSGET---------SAEQFF',\n",
       " 'CSARS---------SYEQYF',\n",
       " 'CASSL-------SLSYEQYF',\n",
       " 'CASSL--------GGYEQYF',\n",
       " 'CASSLY---GPGSSYNEQFF',\n",
       " 'CASSL--------AGSEQYF',\n",
       " 'CASRG-----LASSTDTQYF',\n",
       " 'CASSL-------HPGSEQYF',\n",
       " 'CASSL-----GRASSYEQYF',\n",
       " 'CASSQD---PSGTANEKLFF',\n",
       " 'CASSL--------AGVGYTF',\n",
       " 'CASSL-------HPGVEQFF',\n",
       " 'CASSQ-------LAPDTQYF',\n",
       " 'CASSE------GTSGNEQFF',\n",
       " 'CASSP-----LVGGPAGYTF',\n",
       " 'CASL----------RLEQYF',\n",
       " 'CATRP-------IREETQYF',\n",
       " 'CASSL----LTSGRYNEQFF',\n",
       " 'CASSE------GQGNTEAFF',\n",
       " 'CATVT------GVLQETQYF',\n",
       " 'CASSFL---GTSGGIDIQYF',\n",
       " 'CASSG-------GWTTEAFF',\n",
       " 'CASSLG--FSNRGSYNEQFF',\n",
       " 'CASTTW--VAGVARPYEQYF',\n",
       " 'CASSL-------QAPHEQYF',\n",
       " 'CAGRQ----GQAFRVDEQYF',\n",
       " 'CASSI-------RSAYEQYF',\n",
       " 'CASSL--------GPLEQYF',\n",
       " 'CASSL-----GHPAGNTIYF',\n",
       " 'CASSA-------KGPGELFF',\n",
       " 'CASSL-----AGGLRQPQHF',\n",
       " 'CASSSL--ESDRASYNEQFF',\n",
       " 'CASSL----AMGPRYNEQFF',\n",
       " 'CASSQ----RGGTGGQPQHF',\n",
       " 'CASSQ-----LAGGPNEQFF',\n",
       " 'CSVIV-----GGGRGDTQYF',\n",
       " 'CASSP--------GGYEQYF',\n",
       " 'CASST-------RSSYEQYF',\n",
       " 'CASSF-------DRGYEQYF',\n",
       " 'CASSL------VGTSYGYTF',\n",
       " 'CASRG------QGGQETQYF',\n",
       " 'CASSS----ILTATYDGYTF',\n",
       " 'CASSS------GQGNTEAFF',\n",
       " 'CASSF-----SYRGNTEAFF',\n",
       " 'CASSQ---------NNEQFF',\n",
       " 'CASSI--------GSYGYTF',\n",
       " 'CASSP------VVGVREQFF',\n",
       " 'CASSF-----LGDGNTEAFF',\n",
       " 'CASSL-------YLADTQYF',\n",
       " 'CASGT-----SGRFYNEQFF',\n",
       " 'CASSI-------RASYEQYF',\n",
       " 'CASSS-------LPGETQYF',\n",
       " 'CASSF-----GTGVTYEQYF',\n",
       " 'CASSF----PLAGETYEQYF',\n",
       " 'CASSM-------RSAYEQYF',\n",
       " 'CSVHT------RGRDNEQFF',\n",
       " 'CASSF----TRGDTKNIQYF',\n",
       " 'CASSE------LENLYGYTF',\n",
       " 'CASSL-----LAGGLNEQFF',\n",
       " 'CASSI-------RSTGELFF',\n",
       " 'CASSG-------RSTGELFF',\n",
       " 'CASSP------DRGGSPLHF',\n",
       " 'CSASGQ--DRGFKGRETQYF',\n",
       " 'CASSE-----TVSSTDTQYF',\n",
       " 'CASSL-----GGGVLHEQYF',\n",
       " 'CASSN------SGAGEKLFF',\n",
       " 'CASSL-----GTPLNTEAFF',\n",
       " 'CASSL-------AGSYEQYF',\n",
       " 'CASSV-------RSSYEQYF',\n",
       " 'CASSG-----HPDSTDTQYF',\n",
       " 'CASSL-------DLTYEQYF',\n",
       " 'CASSI-------FYLQEQYF',\n",
       " 'CARSW-------GGAYEQYF',\n",
       " 'CASSL----SGLANTGELFF',\n",
       " 'CASSQ-----EYRGLYEQYF',\n",
       " 'CASSF------SGLSYEQYF',\n",
       " 'CASSS-----DASSYNEQFF',\n",
       " 'CASSS-------RSAYEQYF',\n",
       " 'CASSPP-SGGIGGSYNEQFF',\n",
       " 'CASSQ-------DPGYEQYF',\n",
       " 'CASSP------FEGTDTQYF',\n",
       " 'CASSLA---GLSISGNTIYF',\n",
       " 'CASSSP--FWAGGAYNEQFF',\n",
       " 'CASSD--------QGGEQFF',\n",
       " 'CASSL-------AGAGELFF',\n",
       " 'CASSI-------RSDEKLFF',\n",
       " 'CASSL----PQEGGDNEQFF',\n",
       " 'CASSL---------GGEQYF',\n",
       " 'CASSPL--TGGPSRKDTQYF',\n",
       " 'CASSL------VDSSYEQYF',\n",
       " 'CASSL-------SSGSPLHF',\n",
       " 'CASRG-----LTSGRYEQYF',\n",
       " 'CASSF----PAGPTTDTQYF',\n",
       " 'CASSF----GGAQVTYEQYF',\n",
       " 'CSASL-------GLAYEQYF',\n",
       " 'CASSL-------AGGETQYF',\n",
       " 'CASTP-----GQGPSTEQYF',\n",
       " 'CASSR-------DRGYEQYF',\n",
       " 'CASSP------WTGNSPLHF',\n",
       " 'CATSD-----GTGGTETQYF',\n",
       " 'CASSP-----PASGANVLTF',\n",
       " 'CATSTA--SGLAGLTDTQYF',\n",
       " 'CASSL-------VPNTEAFF',\n",
       " 'CASSL-----DANLAYGYTF',\n",
       " 'CASSE-----RTGGSTEAFF',\n",
       " 'CASSL--------AGVGAFF',\n",
       " 'CASSL--------AGVGEYF',\n",
       " 'CASSL-----SYRGNTEAFF',\n",
       " 'CASSL-------PDRGGYTF',\n",
       " 'CASSF-----QVLPYNEQFF',\n",
       " 'CASSG----EGQGRTGELFF',\n",
       " 'CASSL-----GTSEIDEQFF',\n",
       " 'CASST-------RSTDTQYF',\n",
       " 'CASSL-------SYTGELFF',\n",
       " 'CASSM-------RSTGELFF',\n",
       " 'CASSP-------QGNYGYTF',\n",
       " 'CASSLT--LNLDRDQETQYF',\n",
       " 'CASSLE---AGPSSYNEQFF',\n",
       " 'CASSHS---KDLLGTYEQYF',\n",
       " 'CASSL--------AGAEAFF',\n",
       " 'CASSL--------SGETQYF',\n",
       " 'CASSI-------TGGTEAFF',\n",
       " 'CASSL-----GTSSYNEQFF',\n",
       " 'CASST--------GAYGYTF',\n",
       " 'CASSV-----YRAVNTEAFF',\n",
       " 'CATSS----LSGGTTDTQYF',\n",
       " 'CASSL-------AGAYEQFF',\n",
       " 'CASSL-----AGGPYNEQFF',\n",
       " 'CASSQ-----DGRVSYEQYF',\n",
       " 'CASSV------GQGNQPQHF',\n",
       " 'CASPP-----RLHDYNEQFF',\n",
       " 'CASSL---------SREQYF',\n",
       " 'CATDR-------GSNSPLHF',\n",
       " 'CASSL------TGSLDGYTF',\n",
       " 'CASSP-------DRGYEQYF',\n",
       " 'CASSL-----LAGGYNEQFF',\n",
       " 'CASSP------PPSNYGYTF',\n",
       " 'CASSP-----LAGGPNEQFF',\n",
       " 'CASSS----GGGGLQETQYF',\n",
       " 'CASSQ--------GGYEQYF',\n",
       " 'CASSP-----GVYGNYGYTF',\n",
       " 'CASSF-------DLGTEAFF',\n",
       " 'CASSEF---LGTRALNEQFF',\n",
       " 'CASSW-----SGTGTYEQYF',\n",
       " 'CASSL------SSTLLPQHF',\n",
       " 'CASFD-------RGPYEQYF',\n",
       " 'CASSY--------SGSEQFF',\n",
       " 'CSVVG---------GSEQYF',\n",
       " 'CASSI--------VRNEQFF',\n",
       " 'CASSL-------GNSGELFF',\n",
       " 'CASSP-----LAGGYNEQFF',\n",
       " 'CASSL-------GGAYEQYF',\n",
       " 'CAWSL--------LIGTQYF',\n",
       " 'CSAGG--------STDTQYF',\n",
       " 'CASSL----ELGQGLHEQFF',\n",
       " 'CASSA----RVAAFQETQYF',\n",
       " 'CASST-------RAGYEQFF',\n",
       " 'CASSFL--LGPLSSYNEQFF',\n",
       " 'CASSL--------AGVELFF',\n",
       " 'CASST-------RSAYEQYF',\n",
       " 'CASSL-------DANYGYTF',\n",
       " 'CASSP-----QAGSYNEQFF',\n",
       " 'CASSL--------AGGEQYF',\n",
       " 'CAGDR------TYLRYGYTF',\n",
       " 'CASSV-----GGHVLETQYF',\n",
       " 'CASTSI---LTALANYGYTF',\n",
       " 'CASSL-----AGGGIEKLFF',\n",
       " 'CASSLF-GAGANSGANVLTF',\n",
       " 'CASSR-------GENQPQHF',\n",
       " 'CATRA-------DGSYEQYF',\n",
       " 'CASRP----LQGYVDEKLFF',\n",
       " 'CASSL-----SGGPYNEQFF',\n",
       " 'CASSG------NRDFYEQYF',\n",
       " 'CASSL--------GSTEAFF',\n",
       " 'CASSPSLKTGKHYSNQPQHF',\n",
       " 'CASSSD--QLSGVVRQPQHF',\n",
       " 'CASSI-------RSTDTQYF',\n",
       " 'CASSL--------AGVEAFF',\n",
       " 'CASSP----PPGRRNTEAFF',\n",
       " 'CASSL--------SNSPLHF',\n",
       " 'CASSL-------AGAREQYF',\n",
       " 'CASSY------QQGAQPQHF',\n",
       " 'CSAST--------ETYEQYF',\n",
       " 'CASSL----YAGGVHNEQFF',\n",
       " 'CASSH------IADSYEQYF',\n",
       " 'CAAGR--------EDTEAFF',\n",
       " 'CASSQD---WRVSNTGELFF',\n",
       " 'CASSQ------GPDPGGAFF',\n",
       " 'CASSQ-----LAGGGNEQFF',\n",
       " 'CASSL-----FATNTGELFF',\n",
       " 'CASSL-----ASGITDTQYF',\n",
       " 'CASSLY---RDRTTNEKLFF',\n",
       " 'CASSS-----GEVSYNEQFF',\n",
       " 'CASNN----PTGTSRYEQYF',\n",
       " 'CAASD---------YNEQFF',\n",
       " 'CASRA---------NTEAFF',\n",
       " 'CASSR-------MNSYEQYF',\n",
       " 'CART-----------YEQYF',\n",
       " 'CASSQ----DQSSGGNEQFF',\n",
       " 'CASSQ-----DLGQYYGYTF',\n",
       " 'CASSV----DRGRPYNEQFF',\n",
       " 'CASSL-----FSNLGQPQHF',\n",
       " 'CASNV------LGRIDEQFF',\n",
       " 'CASSL-------GVATEAFF',\n",
       " 'CASSR----WDRSTNEKLFF',\n",
       " 'CASSI-------YSNQPQHF',\n",
       " 'CASRE--------GGTEAFF',\n",
       " 'CASSL---------SNEQFF',\n",
       " 'CASSS----LTSGAYNEQFF',\n",
       " 'CASSL-------GMNTEAFF',\n",
       " 'CASSF-------KGTYGYTF',\n",
       " 'CASSQ-----LAGGNNEQFF',\n",
       " 'CASSG------VAGGYEQYF',\n",
       " 'CSVEG-----NQGAHTEAFF',\n",
       " 'CASSP--------TGVSLFF',\n",
       " 'CASSL-----AGGIYQPQHF',\n",
       " 'CAISE-----SPGGAGELFF',\n",
       " 'CASSR---------DKTGSF',\n",
       " 'CASSL------GRGFDEQFF',\n",
       " 'CASSQD---HGEPNTGELFF',\n",
       " 'CATGL--------STDTQYF',\n",
       " 'CASSS------TGGNTEAFF',\n",
       " 'CASSE-------SMGETQYF',\n",
       " 'CASSE-------WGDQPQHF',\n",
       " 'CASSY----YSTPALETQYF',\n",
       " 'CASSS----FGTGIVNEQFF',\n",
       " 'CASSQ-----ERRVNYEQYF',\n",
       " 'CALIG-----AASGTDTQYF',\n",
       " 'CASSG--------QRQFVFF',\n",
       " 'CASSL--------GGTEAFF',\n",
       " 'CASRG-----RTGTSYEQYF',\n",
       " 'CASSQ----GNHPVPYEQYF',\n",
       " 'CASSPS--TGAAEAKNIQYF',\n",
       " 'CASSL-------SGSLEQYF',\n",
       " 'CASST-----GRGSYNEQFF',\n",
       " 'CASSL-----SYRGNQPQHF',\n",
       " 'CASST------SGANQPQHF',\n",
       " 'CASSA-----DGLNTYEQYF',\n",
       " 'CASSS-------RSQETQYF',\n",
       " 'CASSY-----GTPPTYEQYF',\n",
       " 'CASSL------GGQSWGYTF',\n",
       " 'CASSL-------AGANEQFF',\n",
       " 'CASSL-----VQYSNQPQHF',\n",
       " 'CASSH-----GLAGEYEQYF',\n",
       " 'CASTV-----RGESYNEQFF',\n",
       " 'CSVLG--------GDTEAFF',\n",
       " 'CASSQ-------RSSAEQFF',\n",
       " 'CSVPG------GLGGGELFF',\n",
       " 'CASSE--------SGFGYTF',\n",
       " 'CASSI-------YSNTEAFF',\n",
       " 'CSVAT---------GQGWYF',\n",
       " 'CASSM--------GSYGYTF',\n",
       " 'CASSY--------SGVGYTF',\n",
       " 'CASSL---------GNEQFF',\n",
       " 'CASTG-------ESNQPQHF',\n",
       " 'CSASV----PTGVLSYEQYF',\n",
       " 'CASSQ-----DGQGTDTQYF',\n",
       " 'CASSR-----LAGGSNEQFF',\n",
       " 'CSASI--------QDNEQFF',\n",
       " 'CASSS-------RSSYEQYF',\n",
       " 'CSVEG-----RGGEAETQYF',\n",
       " 'CASSP------GGAGETQYF',\n",
       " 'CASSL-----AGQGSYEQYF',\n",
       " 'CASSG----PWGGRNTEAFF',\n",
       " 'CASSP------LPGASEAFF',\n",
       " 'CASTL----SVAASTDTQYF',\n",
       " 'CASSYY-MGQGSDNTGELFF',\n",
       " 'CASSL------SPTAYEQYF',\n",
       " 'CASNL-----LAGGQNEQFF',\n",
       " 'CASSE-----SILGYNEQFF',\n",
       " 'CASSE---------SDTQYF',\n",
       " 'CASSY-------SQGDTQFF',\n",
       " 'CASSH------GTDLYEQYF',\n",
       " 'CASSL----SIAGTGGEQYF',\n",
       " 'CASSP--------SSYEQYF',\n",
       " 'CASSL-----LAGGDNEQFF',\n",
       " 'CSASW--------GGTEAFF',\n",
       " 'CASRE---------GTEAFF',\n",
       " 'CASSQ-----LAGGYNEQFF',\n",
       " 'CASSPL--TGEPIAKNIQYF',\n",
       " 'CASSF------GGLNYGYTF',\n",
       " 'CASGG------QGTAGELFF',\n",
       " 'CASSFK--LGPLSSYNEQFF',\n",
       " 'CASSS-------DRGYEQYF',\n",
       " 'CASSSR-LLAGGPDTDTQYF',\n",
       " 'CASGV-----LGTGAYEQYF',\n",
       " 'CASSP-------YSNQPQHF',\n",
       " 'CASSS-----PPQGRTEAFF',\n",
       " 'CASSL-----AGGALYEQYF',\n",
       " 'CSQGR------MSSYNEQFF',\n",
       " 'CASRP----YAGGLYNEQFF',\n",
       " 'CASSW------EGGYNEQFF',\n",
       " 'CASSL-------AGGNEQFF',\n",
       " 'CSASL--------ESETQYF',\n",
       " 'CASSL--------GNTEAFF',\n",
       " 'CASSL------QGAHYGYTF',\n",
       " 'CSITG--------ANYEQYF',\n",
       " 'CASSL----AVGTSRHEQYF',\n",
       " 'CASNG------GRVTDTQYF',\n",
       " 'CSASK-----EGAEADTQYF',\n",
       " 'CASSY-----SLGQNTEAFF',\n",
       " 'CASSL-------TGGYEQYF',\n",
       " 'CASSS------PLRVYEQYF',\n",
       " 'CASSL----WASGAQTTQYF',\n",
       " 'CASTTP---LGLAGVHEQFF',\n",
       " 'CASSQ----DWASGSYEQYF',\n",
       " 'CASSQ-----EIGNNQPQHF',\n",
       " 'CASST----NRDRAKETQYF',\n",
       " 'CASSA-------DRNYGYTF',\n",
       " 'CASSVI--AGGREAKNIQYF',\n",
       " 'CASSP------SGREVEQFF',\n",
       " 'CSASV------RDGDNEQFF',\n",
       " 'CASSVF---PVLSTGNTIYF',\n",
       " 'CASSL----VPGPPYNEQFF',\n",
       " 'CSVEG------GSSYNEQFF',\n",
       " 'CASSE-----GLAGSYEQYF',\n",
       " 'CASSF----WGPTNTGELFF',\n",
       " 'CASSL-------GVNTEAFF',\n",
       " 'CASSD------PGQTTEAFF',\n",
       " 'CASSL--------AGVEQFF',\n",
       " 'CASSL-------GQGYEQYF',\n",
       " 'CASRG-----LAGGRYEQYF',\n",
       " 'CASSDTRGKSSRGADEKLFF',\n",
       " 'CASSL------GGAGYEQYF',\n",
       " 'CASSL--------AGVEQYF',\n",
       " 'CASSE-------LPNKPQHF',\n",
       " 'CASSP--------GGEKLFF',\n",
       " 'CASSA--------LTDEQYF',\n",
       " 'CASSL-------GGSYEQYF',\n",
       " 'CASSL------TGSGNEQFF',\n",
       " 'CASSP-------GGSYEQYF',\n",
       " 'CSASE--------ELETQYF',\n",
       " 'CASSL-------LPADEQYF',\n",
       " 'CASSY----SRDRGYYEQYF',\n",
       " 'CASSE-----GLAGVYEQYF',\n",
       " 'CASSQQ---LAGGDYNEQFF',\n",
       " 'CASSL-----LAGGGNEQFF',\n",
       " 'CASSL------VASTYEQYF',\n",
       " 'CASGA-------GPNQPQHF',\n",
       " 'CASTR--------SVYEQYF',\n",
       " 'CSAST------GLDQETQYF',\n",
       " 'CASSE-----KTGGSTEAFF',\n",
       " 'CASRG--------RSYEQYF',\n",
       " 'CSASS----GDLAGEETQYF',\n",
       " 'CASSY-----SYRGNTEAFF',\n",
       " 'CASSY------FAGQYGYTF',\n",
       " 'CASSI-------RAGYEQYF',\n",
       " 'CASSP-----AGVLYEKLFF',\n",
       " 'CASST--------GLYGYTF',\n",
       " 'CASSL-------GGSQPQHF',\n",
       " 'CATSD------LRSGDTQYF',\n",
       " 'CSASI--------MNTEAFF',\n",
       " 'CASSL---------GETQYF',\n",
       " 'CASSA------AGGQNEQYF',\n",
       " 'CASMNP---LAGAGTGELFF',\n",
       " 'CASSR-------GDTGELFF',\n",
       " 'CASSL-------GGSTEAFF',\n",
       " 'CASSL------QYSNQPQHF',\n",
       " 'CAGTS-----GGGPYNEQFF',\n",
       " 'CASSQ------DGQIWEQFF',\n",
       " 'CASST--------PGGEQYF',\n",
       " 'CASSL------VGGFTEAFF',\n",
       " 'CASST------GTATDTQYF',\n",
       " 'CAGGQ--------WNQPQHF',\n",
       " 'CASNE--------GDTEAFF',\n",
       " 'CASSQ-----DQRGGYEQYF',\n",
       " 'CASSPL---TPRVSKNIQYF',\n",
       " 'CASSPS---GLPPSQDTQYF',\n",
       " 'CATRT--------GNTEAFF',\n",
       " 'CASSD----PGQTSTDTQYF',\n",
       " 'CASSL-----VGLVNTEAFF',\n",
       " 'CASSP------PGGLETQYF',\n",
       " 'CASSY-----GTSFYNEQFF',\n",
       " 'CASSL-----SYRGNEKLFF',\n",
       " 'CASSE---------SHEQYF',\n",
       " 'CASSTY--SRTSGGTETQYF',\n",
       " 'CASSL--------AGIEQYF',\n",
       " 'CATSE------RRGNQPQHF',\n",
       " 'CASTL-----QASGANVLTF',\n",
       " 'CSVEE-------EAKNIQYF',\n",
       " 'CASSQ-----VGTGGYEQYF',\n",
       " 'CASSAQ--REGGSSTDTQYF',\n",
       " 'CASSLF---AGGVGYYGYTF',\n",
       " 'CASS----------LGEQYF',\n",
       " 'CASSL------VADTGELFF',\n",
       " 'CATRG-----KGLGVYEQYF',\n",
       " 'CSASF------ERGNNEQFF',\n",
       " 'CASSP--------ADTEAFF',\n",
       " 'CASSP----VRIGVYNEQFF',\n",
       " 'CASSF------SGGSYEQYV',\n",
       " 'CASSV-----GGGGRETQYF',\n",
       " 'CASSQ----SQQASTDTQYF',\n",
       " 'CASSL------RTDNSPLHF',\n",
       " 'CASSPL--SGVAQAKETQYF',\n",
       " 'CASSP-----TGGWTYEQYF',\n",
       " 'CASSI------DVSFYEQYF',\n",
       " 'CASSLE---GWAGDPNEQFF',\n",
       " 'CASSLY--LGGSGSYNEQFF',\n",
       " 'CAIMG-----AGTGPDTQYF',\n",
       " 'CASSS-----LAGGYNEQFF',\n",
       " 'CASSQ--------GAVELFF',\n",
       " 'CASSR-------RETNVLTF',\n",
       " 'CASSL-------LGNTEAFF',\n",
       " 'CASSL--------VGGEAFF',\n",
       " 'CASSI------DVWGNEQYF',\n",
       " 'CASSL----DFVEGNTEAFF',\n",
       " 'CASSPK-GTVVYHSNQPQHF',\n",
       " 'CASSPH--RDQGRRTDTQYF',\n",
       " 'CASSY-------GGEETQYF',\n",
       " 'CSVTD------SLSYNEQFF',\n",
       " 'CASSL-----AGVMNTEAFF',\n",
       " 'CASSR-----LAAYTDTQYF',\n",
       " 'CATSR-------DADQPQHF',\n",
       " 'CASSLP-VAETSGTYNEQFF',\n",
       " 'CASSQE---SVRTGGDEQYF',\n",
       " 'CATSD-------LGANEQFF',\n",
       " 'CASSL--------GYNEQFF',\n",
       " 'CAWSL------AGGPNEQFF',\n",
       " 'CASSL--------AGSGYTF',\n",
       " 'CASSP---------PPVQFF',\n",
       " 'CASSL------TRGSYEQYF',\n",
       " 'CASSL-------GSNQPQHF',\n",
       " 'CAWRV-------TTSYEQYF',\n",
       " 'CASTQ----TGTSNTGELFF',\n",
       " 'CASSL-------NGGGELFF',\n",
       " 'CASSL-----AGVAHGEQFF',\n",
       " 'CASSL--------EPGEQYF',\n",
       " 'CASSL-------AGGGEQYF',\n",
       " 'CASMD-----PGQGDTEAFF',\n",
       " 'CASRR--------GGTEAFF',\n",
       " 'CASER-------GSGQPQHF',\n",
       " 'CASSL------DSGGSEQFF',\n",
       " 'CASSY-----SSSGGAEQFF',\n",
       " 'CASRD------GQASLDYTF',\n",
       " 'CASSLH---LPEGSGNTIYF',\n",
       " 'CSVDG------GQAGTEAFF',\n",
       " 'CASTP------GTGYSPLHF',\n",
       " 'CASGM---------NQPQHF',\n",
       " 'CASSL-------RSSYEQYF',\n",
       " 'CASST-----LAGGYNEQFF',\n",
       " 'CASSS-----ILAGGNEQFF',\n",
       " 'CASSY-----WDRSSGELFF',\n",
       " 'CASSL-------GWNTEAFF',\n",
       " 'CASSL-----AGGSNQPQHF',\n",
       " 'CASSQ------YRADQPQHF',\n",
       " 'CASSP-----TGLLQETQYF',\n",
       " 'CASSL---------GIEQFF',\n",
       " 'CSVRA-----TSGWDNEQFF',\n",
       " 'CASSHS---GTSGSSYEQYF',\n",
       " 'CSASS--------VNTEAFF',\n",
       " 'CASSS-----LAGPYNEQFF',\n",
       " 'CASNV------QGGNQPQHF',\n",
       " 'CASRG------LTGAYEQYF',\n",
       " 'CASSL-------NRVYEQYF',\n",
       " 'CASVYQ---GQGSSYNEQFF',\n",
       " 'CASSY-------SSQAGYTF',\n",
       " 'CATSD----MRSSSYNEQFF',\n",
       " 'CASSQ-----LAGGHNEQFF',\n",
       " 'CASTH------WDRPYEQYF',\n",
       " 'CASSS-------TNTGELFF',\n",
       " 'CASSIG---LIVTSTDTQYF',\n",
       " 'CATSR--------EGQPQHF',\n",
       " 'CASSE--------PGNGYTF',\n",
       " 'CASSL------GLASAEQYF',\n",
       " 'CATSRM---WTVLGQETQYF',\n",
       " 'CASSPL---SSGQGSTEAFF',\n",
       " 'CAWR----------EAGLSF',\n",
       " 'CASSL----GVGEKLYDPFF',\n",
       " 'CASST-----GAGGNQPQHF',\n",
       " 'CASSH---------QAEQYF',\n",
       " 'CASSL------VGSSYEQYF',\n",
       " 'CASSL------TGGQETQYF',\n",
       " 'CASSAD---VTGGFTGELFF',\n",
       " 'CASSA----GLAGYTGELFF',\n",
       " 'CASSG-----QTGPLEKLFF',\n",
       " 'CASPIL--GQGVSGANVLTF',\n",
       " 'CASSL-------AGAQPQHF',\n",
       " 'CASSL-------EDRYEQYF',\n",
       " 'CASSF-----LAGGVNEQFF',\n",
       " 'CSVVE-------QGPYEQYF',\n",
       " 'CASEE----GGGALQETQYF',\n",
       " 'CASSWA---LQGRGRIGYTF',\n",
       " 'CASSF---------GNEQFF',\n",
       " 'CASSLD---FRGSVTYEQYF',\n",
       " 'CASSL-----GEKGADTQYF',\n",
       " 'CASSS------VGARNEQFF',\n",
       " 'CASIA----RGLSSYNEQFF',\n",
       " 'CASSE--------MGASAFF',\n",
       " 'CASSL--------AGLEQYF',\n",
       " 'CACVDR---GLMVDQDEQYF',\n",
       " 'CSVIA--------AAYEQYF',\n",
       " 'CASSL-----AGGSHTEAFF',\n",
       " 'CASSR-------PTGSGYTF',\n",
       " 'CASSI--------GGNEQFF',\n",
       " 'CASRP-----PSGPPYEQYF',\n",
       " 'CASSP---------GAQQFF',\n",
       " 'CASSQ-------DSGQPQHF',\n",
       " 'CASSF-----IGTVGETQYF',\n",
       " 'CASSS------TGWDEKLFF',\n",
       " 'CASSV-------RASYEQYF',\n",
       " 'CASSL--------AGNEQFF',\n",
       " 'CASSR-----LAGGVNEQFF',\n",
       " 'CASRD------RSMNTEAFF',\n",
       " 'CASSQ--------ESDEQYF',\n",
       " 'CASSLG---IGDGSNQPQHF',\n",
       " 'CSASE------ESSYNEQFF',\n",
       " 'CASSD------LESYNEQFF',\n",
       " 'CASSL----PRDRGTDTQYF',\n",
       " 'CASSE-------GQGYEQYF',\n",
       " 'CAISE-----GKQGTDTQYF',\n",
       " 'CASGG-----SGVSTGELFF',\n",
       " 'CASSS---------TNEQYF',\n",
       " 'CASRQ--------GAEGYTF',\n",
       " 'CATVV-----EGHLNTEAFF',\n",
       " 'CASSW--------TGNEQYF',\n",
       " 'CASTF----LPAGPQETQYF',\n",
       " 'CATN----------DYGYTF',\n",
       " 'CASSE-----NPISFYGYTF',\n",
       " 'CASNI------QGYGYEQYF',\n",
       " 'CSVA----------GLEAFF',\n",
       " 'CASSA------PGRDYGYTF',\n",
       " 'CASSQV-VSTVLLHTDTQYF',\n",
       " 'CASSQE--TTGGAGKDEQYF',\n",
       " 'CASSS-----SGTGPLEQYF',\n",
       " 'CASSQD---IGGGEGDTQYF',\n",
       " 'CATST-----IRGQGYEQYF',\n",
       " 'CASSF----DRGSPSYEQYF',\n",
       " 'CATSD-------LASDTQYF',\n",
       " 'CASSP----YPTGAQETQYF',\n",
       " 'CASSE------SAANYEQYF',\n",
       " 'CASSEV---GGASFGETQYF',\n",
       " 'CASSL-----AGVGYTEAFF',\n",
       " 'CASSQ-------DRGTEAFF',\n",
       " 'CASSPR---VGGAGTDTQYF',\n",
       " 'CASSL-------AGAQEQYF',\n",
       " 'CASSI------FGGSRELFF',\n",
       " 'CASSI-------FLWDTQYF',\n",
       " 'CASSP-------GGTYEQYF',\n",
       " 'CASSI-----EPLGTNEQFF',\n",
       " 'CASSQ----DWGLALNEQFF',\n",
       " 'CASSL-------VGGDTQYF',\n",
       " 'CASSAP---TSGSSEAEQFF',\n",
       " 'CASSL--------PLNGYTF',\n",
       " 'CASSL--------GGEKLFF',\n",
       " 'CASSA-------DRGYEQYF',\n",
       " 'CASSS----PTGIMNTEAFF',\n",
       " 'CASSI------AREGQPQHF',\n",
       " 'CASSY------SSGVETQYF',\n",
       " 'CSLMR-------QNHNEQFF',\n",
       " 'CASSQ------DPANYGYTF',\n",
       " 'CASSR-----AGGPLETQYF',\n",
       " 'CASST-------RSTGELFF',\n",
       " 'CAASQ-----VVDSNQPQHF',\n",
       " 'CASSL--------SYNEQFF',\n",
       " 'CASSLD---GAGDFTGELFF',\n",
       " 'CASRG-----RGSRVYEQYF',\n",
       " 'CASSI-------RAAGEQFF',\n",
       " 'CASSA-----GQSYNSPLHF',\n",
       " 'CATSR-----VNGKTDTQYF',\n",
       " 'CASSL--------GGETQYF',\n",
       " 'CASST-----STPLNTEAFF',\n",
       " 'CASSP----ELAGGQETQYF',\n",
       " 'CASSP-----GTSGIETQYF',\n",
       " 'CASSL-------WVGDTQYF',\n",
       " 'CASTP----RLAGIQETQYF',\n",
       " 'CASSM------GQNLNEQFF',\n",
       " 'CASRS-------IEYNEQFF',\n",
       " 'CASSL-------WGQETQYF',\n",
       " 'CASRG-----LAGDSYEQYF',\n",
       " 'CSASF------GGSADTQYF',\n",
       " 'CASSW--------GGLEDYF',\n",
       " 'CASDG------GGGANVLTF',\n",
       " 'CASSL-----AGGPLLEQYF',\n",
       " 'CASSQ------GHTSYEQYF',\n",
       " 'CASSD-------GTGNEQFF',\n",
       " 'CASSIV---GVGANDEKLFF',\n",
       " 'CATSD-------LTRDEQFF',\n",
       " 'CASSE-----IAYSGNTIYF',\n",
       " 'CSASG-------DYYHGYTF',\n",
       " 'CASSY------YDSGGEQFF',\n",
       " 'CASST------PNPYNEQFF',\n",
       " 'CASTD------PGQGYEQYF',\n",
       " 'CASSQ-----DPGFNQPQHF',\n",
       " 'CASSP----PTTSGGLEQYF',\n",
       " 'CASRG-----KRGDSYEQYF',\n",
       " 'CASSQD---FGLAGLREQYF',\n",
       " 'CASSE--------LSDEAFF',\n",
       " 'CASSV-------WTGVEQYF',\n",
       " 'CASSPL--VGLAGDKETQYF',\n",
       " 'CASSL----GTASGANVLTF',\n",
       " 'CATST-----LAGNTDTQYF',\n",
       " 'CASGA--------EDVEQFF',\n",
       " 'CASSR------DRGFPEAFF',\n",
       " 'CAIGP--------GDTEAFF',\n",
       " 'CASSP--------GFVEQYF',\n",
       " 'CASSW-------RLGEPQHF',\n",
       " 'CASSL---------ANEQYF',\n",
       " 'CASSI------YLWGNEQFF',\n",
       " 'CASSL------EAVGTEAFF',\n",
       " 'CSAGW--------ATTEAFF',\n",
       " 'CASSKL-AGGPREAKNIQYF',\n",
       " 'CASSP-------FGANVLTF',\n",
       " 'CSVES---------NTEAFF',\n",
       " 'CASKY------RGVSYEQYF',\n",
       " 'CASSL-----GFDSLGQQYF',\n",
       " 'CASSL------GSSYNEQFF',\n",
       " 'CASSD-----LPGPPGEQYF',\n",
       " 'CASSL----LGGPLYNEQFF',\n",
       " 'CATIG-------QERDGYTF',\n",
       " 'CSALD---------GNEAFF',\n",
       " 'CASST-------TGSGEQYF',\n",
       " 'CASSL-------RQGIEQYF',\n",
       " 'CASSL-----LAGGSETQYF',\n",
       " 'CASSQ-------DRGYEQYF',\n",
       " 'CASSL---------TNEQFF',\n",
       " 'CASRGI---TKPVDRSEAFF',\n",
       " 'CASSL-----VTGTNTEAFF',\n",
       " 'CASSP-------RGIQPQHF',\n",
       " 'CSVGH-------LNTGELFF',\n",
       " 'CASSR------RQPNTEAFF',\n",
       " 'CASSLG--LAGIANTGELFF',\n",
       " 'CASSH--------GGTEAFF',\n",
       " 'CASSL-------SVGTEAFF',\n",
       " 'CASST-------TGGDGYTF',\n",
       " 'CASSI-------RSAGEQFF',\n",
       " 'CASSQ-----ETIGDTEAFF',\n",
       " 'CASSH-----LAGGGNEQFF',\n",
       " 'CARSS------GGPFNEQFF',\n",
       " 'CASS----------YNEQFF',\n",
       " 'CASSI------AGPNTEAFF',\n",
       " 'CASSQ------EGRGWEQYF',\n",
       " 'CASSY------WAGDYEQYF',\n",
       " 'CASSQ-----GVQVYNEQFF',\n",
       " 'CASSL-----LAGGDNEQYF',\n",
       " 'CASSEG---FYRDSISPLHF',\n",
       " 'CASSA-----GQGMNTEAFF',\n",
       " 'CASSG--------DNLRGYF',\n",
       " 'CASSLA---GLSLAKNIQYF',\n",
       " 'CASSLA---GLAVAKNIQYF',\n",
       " 'CASSQ------EYGTGELFF',\n",
       " 'CASSST--SGGARSTDTQYF',\n",
       " 'CASSR-----LAGGYNEQFF',\n",
       " 'CASSE----GQLSTNEKLFF',\n",
       " 'CASST---------SQPQHF',\n",
       " 'CASSQ-----VWDGHSPLHF',\n",
       " 'CASSF--------VGAEQYF',\n",
       " 'CASSL----EGGWSSYEQYF',\n",
       " 'CASSQ--------GGGRQYF',\n",
       " 'CASSL-----SAASNQPQHF',\n",
       " 'CASSY---------GNTIYF',\n",
       " 'CASRG-----VRGASYEQYF',\n",
       " 'CASSE-------PSPGEQYF',\n",
       " 'CASSS-------GPSYEQYF',\n",
       " 'CASSL----LRGGQYNEQFF',\n",
       " 'CASSP------TTGGSTIYF',\n",
       " 'CATTG-----QGPFSYEQYF',\n",
       " 'CASSSP--DTGLYSNQPQHF',\n",
       " 'CASSPL--TGAPLPKNIQYF',\n",
       " 'CASSL----APAERANEQYF',\n",
       " 'CASTL-----AGGPYNEQFF',\n",
       " 'CATSR--------DSLEQYF',\n",
       " 'CASKG------LAGSYEQYF',\n",
       " 'CASSL--------GGNEQFF',\n",
       " 'CASAA-----LSGVQDTQYF',\n",
       " 'CASSW----EGNWKGYEQYF',\n",
       " 'CASSY-------SVEGGYTF',\n",
       " 'CASSY-------GGNTEAFF',\n",
       " 'CASTM-------PGRQSQYF',\n",
       " 'CASSY------IGTDYGYTF',\n",
       " 'CASSL--------MNTEAFF',\n",
       " 'CASSP-------DSHNEQFF',\n",
       " 'CSVGI------QDSYNEQFF',\n",
       " 'CASGH------DPPHNEQFF',\n",
       " 'CASSY-------GRRGEQYF',\n",
       " 'CASSL----AGRSDTGELFF',\n",
       " 'CASSL------AGAVTEAFF',\n",
       " 'CASTL-------AGAYEQYF',\n",
       " 'CASSQ--------LADEQFF',\n",
       " 'CATSD-----RVGDTDTQYF',\n",
       " 'CAGRE-------GSTDTQYF',\n",
       " 'CASSY-------STGNEQYF',\n",
       " 'CASSL------GAGNQPQHF',\n",
       " 'CATSDP---RLAGGRNEQFF',\n",
       " 'CASSN--------QGGEAFF',\n",
       " 'CASRG-----LAGETYEQYF',\n",
       " 'CASSL-----LAGGFNEQFF',\n",
       " 'CASSQ------GIMNTEAFF',\n",
       " 'CASSL-------TGGTEAFF',\n",
       " 'CASSVG---TGLGMNTEAFF',\n",
       " 'CASSLG---LAGGSTDTQYF',\n",
       " 'CASSP------TTGSYEQYF',\n",
       " 'CASSQ-------ETYYGYTF',\n",
       " 'CSASG--------QQETQYF',\n",
       " 'CASSW-------GTYNEQFF',\n",
       " 'CASSH----SPERSTDTQYF',\n",
       " 'CASG----------NNEQFF',\n",
       " 'CASSL-----GQGAADTQYF',\n",
       " 'CASSQD---SGTGVRGEQFF',\n",
       " 'CASSL-------TGNTEAFF',\n",
       " 'CASSL-------GSNYEQYF',\n",
       " 'CASSL----ARGHASYEQYF',\n",
       " 'CASSE-----FTSGGHEQFF',\n",
       " 'CASSQ-----GLSSSYEQYF',\n",
       " 'CASSFL---TGGPTRNEQFF',\n",
       " 'CASSL-------RQYNEQFF',\n",
       " 'CASSS--------DGNEQFF',\n",
       " 'CASST-------RAGVEQYF',\n",
       " 'CASSPR--PGLAPNTGELFF',\n",
       " 'CASSLG-SAGGGPSTDTQYF',\n",
       " 'CASSS-----GGSSYNEQFF',\n",
       " 'CASSS-----PQRGDTEAFF',\n",
       " 'CASSL----LVAGGWNEQFF',\n",
       " 'CASSQG---FWLAGGDTQYF',\n",
       " 'CASRLF---AGTGGNQPQHF',\n",
       " 'CASSLE--FLVGKNTDTQYF',\n",
       " 'CASSLT---GASSGANVLTF',\n",
       " 'CASST---------GTSEFF',\n",
       " 'CASSV--------RGNEQFF',\n",
       " 'CASSG--------LDYEQYF',\n",
       " 'CASSFP-WREASSSYNEQFF',\n",
       " 'CASSE----TGDGLFHEQYF',\n",
       " 'CASSP----TQRASSYEQYF',\n",
       " 'CASAN-----RDSPYNEQFF',\n",
       " 'CASEP------AGLYNEQFF',\n",
       " 'CASSL----LGGGADNEQFF',\n",
       " 'CASSQ-----DSGRGDEQFF',\n",
       " 'CASSLL---PAGTRNYEQYF',\n",
       " 'CASSL--------STDTQYF',\n",
       " 'CASSL----GPSTGAYEQYF',\n",
       " 'CASSP---------GLVKFF',\n",
       " 'CASSS-------RGGYEQYF',\n",
       " 'CASSD-----SYSGANVLTF',\n",
       " 'CASSL------DGLNQPQHF',\n",
       " 'CASSY-----SPGRNTEAFF',\n",
       " 'CASRG-----RDRDTYEQYF',\n",
       " 'CASSL-----SGTDSYEQYF',\n",
       " 'CASSL-------TSTDTQYF',\n",
       " 'CASSQ------TGYDTEAFF',\n",
       " 'CASSPR---GSGTYDYEQYF',\n",
       " 'CASIP----LTGDGNQPQHF',\n",
       " 'CSGTW---------GDTQYF',\n",
       " 'CSASA------SGGQETQYF',\n",
       " 'CASSSF---ATESSYNEQFF',\n",
       " 'CASSP-------GGAYEQYF',\n",
       " 'CASSL----LGLAGGGEQFF',\n",
       " 'CASSQ-------VGGTEAFF',\n",
       " 'CSASR------EGAQETQYF',\n",
       " 'CASSL-------DRGYEQYF',\n",
       " 'CASSS----SIQGPNEKLFF',\n",
       " 'CASSP--------DSYEQYF',\n",
       " 'CASSLTAQTLQGALSSPLHF',\n",
       " 'CASSQD--PGLALVNEKLFF',\n",
       " 'CASSQ-----DPGVSSGYTF',\n",
       " 'CASSF------LAGDNEQFF',\n",
       " 'CSASG--------TTHEQYF',\n",
       " 'CASSP-----DSSNTGELFF',\n",
       " 'CASSE-----GEGANQPQHF',\n",
       " 'CASSL-----SGTTYNEQFF',\n",
       " 'CASSLT---LQGAFYNEQFF',\n",
       " 'CASS-----------PDAFF',\n",
       " 'CASSP------GQGNYGYTF',\n",
       " 'CASGL-------ASTYEQYF',\n",
       " 'CASSDL---PSGGSNYGYTF',\n",
       " 'CASSF-----AKSRSNEQFF',\n",
       " 'CSASR------ERSQETQYF',\n",
       " 'CASSL-------RQGYEQYF',\n",
       " 'CATSD------LPGQDGYTF',\n",
       " 'CASSL-----VGGGRETQYF',\n",
       " 'CATTG---------GTEAFF',\n",
       " 'CASSV-------TANYGYTF',\n",
       " 'CASSS------TGGADTQYF',\n",
       " 'CASSPK---VIPPRGGELFF',\n",
       " 'CASSI------WAGVTEAFF',\n",
       " 'CSVED-----ASRSYNEQFF',\n",
       " 'CASSFL--LDPDSLSYEQYF',\n",
       " 'CASIM-------ATLNEQFF',\n",
       " 'CASSL-----GLAGGWEQFF',\n",
       " 'CASSL--------AGLEAFF',\n",
       " 'CSAKG------GRGPPEAFF',\n",
       " 'CATSR----VGTQTYNEQFF',\n",
       " 'CASSL-------IFSGELFF',\n",
       " 'CASSL-----YSGQGDTQYF',\n",
       " 'CASSQD---STGGLTDTQYF',\n",
       " 'CASSL-------DGNYGYTF',\n",
       " 'CASSI-----PGQGGQPQHF',\n",
       " 'CASSL-------IPAGGYTF',\n",
       " 'CASWG-----TSGPPYEQYF',\n",
       " 'CASSL-----ASTGSYEQYF',\n",
       " 'CASSP-----SSGGKNIQYF',\n",
       " 'CASSL--------MGPEQYF',\n",
       " 'CSVWG--------LGTEAFF',\n",
       " 'CASSPR---STGALTDTQYF',\n",
       " 'CASRL-------GSNQPQHF',\n",
       " 'CASSQ-----VFGRGHGYTF',\n",
       " 'CASSE-------SGVGGYTF',\n",
       " 'CASSV----STGSSNTEAFF',\n",
       " 'CASSP----VGQGPYQPQHF',\n",
       " 'CASRG----LAGGAGYEQYF',\n",
       " 'CASSAF---FGTPINQPQHF',\n",
       " 'CASSL--------AGSKLFF',\n",
       " 'CASSL--------AVDEQYF',\n",
       " 'CAWSV--------QGGEQYF',\n",
       " 'CASSPQ---SGGDYTDTQYF',\n",
       " 'CATSP-------NMNTEAFF',\n",
       " 'CASSP------PDSYYEQYF',\n",
       " 'CASSW------TSGTDTQYF',\n",
       " 'CASSG----VTGSMGNTQYF',\n",
       " 'CASSY----FGSLMNTEAFF',\n",
       " 'CASSA-------GQGKTQYF',\n",
       " 'CASSQ-----DSSSYNEQFF',\n",
       " 'CASSG-----GLTAYNEQFF',\n",
       " 'CASRPL---AGGLNTGELFF',\n",
       " 'CASSY----LAGSSYNEQFF',\n",
       " 'CASSF----LGAQGDHEQYF',\n",
       " 'CASSL-----AGGTYQPQHF',\n",
       " 'CSVEA------GSSYNEQFF',\n",
       " 'CASSRP---ESGGGRGELFF',\n",
       " 'CASSL---------SYEQYF',\n",
       " 'CASSR-------VLKSEAFF',\n",
       " 'CASSL-------GFPGELFF',\n",
       " 'CASTA-------YKGTEQYF',\n",
       " 'CASSL----GRWTPNTEAFF',\n",
       " 'CASSPA--DLAGGHSGELFF',\n",
       " 'CSPMG-----LDSYGNEQFF',\n",
       " 'CASSLP---GQGVWEETQYF',\n",
       " 'CATAD-------MVIDTQYF',\n",
       " 'CASRGM--TGGTYRKETQYF',\n",
       " 'CASSF------AGTTDGYTF',\n",
       " 'CASSQ-------GTSGRQFF',\n",
       " 'CASSP----PAGQAGNTIYF',\n",
       " 'CASHV------RGSTGELFF',\n",
       " 'CASSE-----GVAANYGYTF',\n",
       " 'CASSL-------AGGYEQYF',\n",
       " 'CASSL-------RDVAEAFF',\n",
       " 'CASSP----LTSGINYGYTF',\n",
       " 'CASSY---------TETQYF',\n",
       " 'CSAND------EGGPHEQFF',\n",
       " 'CASSS--------DSPEQFF',\n",
       " 'CASSS--------NTGELFF',\n",
       " 'CSVEV------SPRGYEQYF',\n",
       " 'CSGSI--------TGYEQFF',\n",
       " 'CASSL--------DSYEQYF',\n",
       " 'CASSE-----ERGVLETQYF',\n",
       " 'CASVP-------TGGETQYF',\n",
       " 'CASSF----PAGAVQETQYF',\n",
       " 'CAVPG------QENTGELFF',\n",
       " 'CASSS------GQGDSPLHF',\n",
       " 'CASSQ----EGIAESYEQYF',\n",
       " 'CASSS------YTGQETQYF',\n",
       " 'CASSG-----PGRLNTEAFF',\n",
       " 'CASSSL--TGEQTWRYEQYF',\n",
       " 'CASSL--------AGQRAFF',\n",
       " 'CASSG-------AYTDTQYF',\n",
       " 'CASSL-------DLTGELFF',\n",
       " 'CASSE----LPGVSNQPQHF',\n",
       " 'CASSS---------SYEQYF',\n",
       " 'CASR-----------FEQFF',\n",
       " 'CASSL-------AGAAPQFF',\n",
       " 'CASSL------VLRKETQYF',\n",
       " 'CASRT-----PAYSTDTQYF',\n",
       " 'CASSL------QQYTGELFF',\n",
       " 'CASSV-------GHTDEQYF',\n",
       " 'CASSP------WGLGETQYF',\n",
       " 'CASSR------PSEGSEQYF',\n",
       " 'CASRS-----GTGGTGELFF',\n",
       " 'CASSS-----TTRVSYEQYF',\n",
       " 'CASSI------TGVDYEQYF',\n",
       " 'CSAL----------YEKLFF',\n",
       " 'CASSV------GTVLYEQYF',\n",
       " 'CASSL-----AGGPSSEQFF',\n",
       " 'CASSY-------WEGTEAFF',\n",
       " 'CASSL--------AGTEAFF',\n",
       " 'CASSP-----GQGVSYEQYF',\n",
       " 'CASSI----LDLTTNTEAFF',\n",
       " 'CASSL------VETNYGYTF',\n",
       " 'CASGSS--MTGRVGQETQYF',\n",
       " 'CASSP------SGPSYEQYF',\n",
       " 'CASSK------GDSYNEQFF',\n",
       " 'CASSP-----DSSSGNTIYF',\n",
       " 'CASSS-----SSDSYNEQFF',\n",
       " 'CASSP-----SGQGAFEQYF',\n",
       " 'CASSS-------SRTQPQHF',\n",
       " 'CASSV-------AGAYEQYF',\n",
       " 'CASSS----LTSGGFNEQFF',\n",
       " 'CASSI-----VGGREETQYF',\n",
       " 'CASST---------GYEQYF',\n",
       " 'CASSV----ELAGITDTQYF',\n",
       " 'CASSF-------AGAGELFF',\n",
       " 'CSVTT--------SSYEQYF',\n",
       " 'CASSP------RDRDTEAFF',\n",
       " 'CASSL-------GGSYGYTF',\n",
       " 'CASSP-----WLGSYNEQFF',\n",
       " 'CASSI-------VSRDTQYF',\n",
       " 'CASSP------YQGDHEQFF',\n",
       " 'CASSQ-------DNYNEQFF',\n",
       " 'CASSE------LQTTDTQYF',\n",
       " 'CASSY------QGVNTEAFF',\n",
       " 'CASSL---------GDEQYF',\n",
       " 'CASSY------GGGSYEQYF',\n",
       " 'CASSG------PTGEEEQFF',\n",
       " 'CAISS---------GNTIYF',\n",
       " 'CSASE--------GGETQYF',\n",
       " 'CASQT---------GVGYTF',\n",
       " 'CSASD--------IGETQYF',\n",
       " 'CASSQ-----ETSGRETQYF',\n",
       " 'CASSL--------ENTEAFF',\n",
       " 'CASSPR--ESARTHNYGYTF',\n",
       " 'CATSS------PESTDTQYF',\n",
       " 'CATSRV-PQPQGMLRDTQYF',\n",
       " 'CASSQ----GPTPTSYEQYF',\n",
       " 'CASSV------GTGGYEQYF',\n",
       " 'CSASR------EVAYNEQFF',\n",
       " 'CSVGT--------VNTEAFF',\n",
       " 'CASSF----LTGLAKNIQYF',\n",
       " 'CASIPG---PSGPEYGELFF',\n",
       " 'CSVEG-------TGGYGYTF',\n",
       " 'CASSP-----TGTAGETQYF',\n",
       " 'CASSL-----IASSTGELFF',\n",
       " 'CSASW------GQGGNEQFF',\n",
       " 'CASSL--------AGAGYTF',\n",
       " 'CASSI-------RSSFEQYF',\n",
       " 'CASSG-----VRDGLQTQYF',\n",
       " 'CASSS------RDSTDTQYF',\n",
       " 'CATSDY---GTGSGANVLTF',\n",
       " 'CSASE--------GAETQYF',\n",
       " 'CASSI--------GVYGYTF',\n",
       " 'CASRG---------LGEQFF',\n",
       " 'CSASQ------WGAVYEQYF',\n",
       " 'CASSL-----VGLGGDTQYF',\n",
       " 'CATSD-----LRTPNTEAFF',\n",
       " 'CASSL----GQGNRNTEAFF',\n",
       " 'CSASGG--LALSITHNEQFF',\n",
       " 'CASSLT---GGRYGYNEQFF',\n",
       " 'CASSG------LVGGREQYF',\n",
       " 'CASSL------PSYRDGYTF',\n",
       " 'CASSF----SLAGLQETQYF',\n",
       " 'CASSL-----GADSRETQYF',\n",
       " 'CASSE------SYPTLKAFF',\n",
       " 'CASRDA--PGTSGSSNEQFF',\n",
       " 'CASSS-----DRGPDYGYTF',\n",
       " 'CASSY------QGQGMSQHF',\n",
       " 'CSASY-----ELAGNNEQFF',\n",
       " 'CASSSD--GGTPTAKNIQYF',\n",
       " 'CAWNK--------DLNEQFF',\n",
       " 'CASSL-----GTDSYNEQFF',\n",
       " 'CASSL-------QGGETQYF',\n",
       " 'CATSG------PRDAYEQYF',\n",
       " 'CASSL----PFRDSNYEQYF',\n",
       " 'CSAST-----TSGGQETQYF',\n",
       " 'CSVR----------TETQYF',\n",
       " 'CSASP---------YYEQYF',\n",
       " 'CASKT--------GGYEQYF',\n",
       " 'CASSD-----GQSRTETQYF',\n",
       " 'CASSP------GQTAYEQYF',\n",
       " 'CASSS------QGGGETQYF',\n",
       " 'CASSP-----LAGAYNEQFF',\n",
       " 'CASSV----VLAGSSYEQYF',\n",
       " 'CSVNS-------GSRYEQYF',\n",
       " 'CASSP------LRSYNEQFF',\n",
       " 'CASSL-------SGLNEQFF',\n",
       " 'CASSL-----KIGGVETQYF',\n",
       " 'CASSY-----GGGNYNEQFF',\n",
       " 'CASSP------LGQVYEQYF',\n",
       " 'CASSL--------AGNGYTF',\n",
       " 'CASSV------ASVTDTQYF',\n",
       " 'CASSLF-GLAGSPSTDTQYF',\n",
       " 'CASSL-----AGGPLQPQHF',\n",
       " 'CASSL-----AGASNQPQHF',\n",
       " 'CASSY-----SGSSYNEQFF',\n",
       " 'CASSL-----GDARNTEAFF',\n",
       " 'CASSS------LGSLDEQFF',\n",
       " 'CASSL----VRQGVNNEQFF',\n",
       " 'CASSI-------RSGYEQYF',\n",
       " 'CASRG-----LAGGHYEQYF',\n",
       " 'CASSL------AGGFTEAFF',\n",
       " 'CASSE-----VYSITGELFF',\n",
       " 'CASSPR---ETGTGYTEAFF',\n",
       " 'CASSS----LTSGGYNEQFF',\n",
       " 'CSASV------TDRGHEQYF',\n",
       " 'CASSL-----KQGRQETQYF',\n",
       " 'CAWSV--------QAAEQYF',\n",
       " 'CASST--------GSYGYTF',\n",
       " 'CASSS----PGEEFQNEQFF',\n",
       " 'CASAP------PAYQETQYF',\n",
       " 'CASSP----PSGGPYNEQFF',\n",
       " 'CATSR--------DLVTQYF',\n",
       " 'CASSR------QVSAETQYF',\n",
       " ...]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df[\"CDR3_al\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CASSI-------RSSYEQYF    13224\n",
       "CASSL-------AGAYEQYF     7735\n",
       "CASSI-------RSAYEQYF     1260\n",
       "CASSL-----SYRGNTEAFF      361\n",
       "CASSL--------AGVGYTF      289\n",
       "                        ...  \n",
       "CASSE-----LPSGTDTQYF        1\n",
       "CASSSIIGQGGTGGPHEQYF        1\n",
       "CASSP----IAGALYNEQFF        1\n",
       "CAISE-----FRQGRNEQFF        1\n",
       "CASSLT--GTEDQGYYEQYF        1\n",
       "Name: CDR3_al, Length: 15115, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print full value counts of CDR3s\n",
    "df[\"CDR3_al\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Position: 0\n",
      "Highest abs value letter for latent 1:  -\n",
      "Highest abs value letter for latent 2:  A\n",
      "Position: 1\n",
      "Highest abs value letter for latent 1:  S\n",
      "Highest abs value letter for latent 2:  A\n",
      "Position: 2\n",
      "Highest abs value letter for latent 1:  V\n",
      "Highest abs value letter for latent 2:  T\n",
      "Position: 3\n",
      "Highest abs value letter for latent 1:  P\n",
      "Highest abs value letter for latent 2:  R\n",
      "Position: 4\n",
      "Highest abs value letter for latent 1:  I\n",
      "Highest abs value letter for latent 2:  P\n",
      "Position: 5\n",
      "Highest abs value letter for latent 1:  D\n",
      "Highest abs value letter for latent 2:  G\n",
      "Position: 6\n",
      "Highest abs value letter for latent 1:  G\n",
      "Highest abs value letter for latent 2:  G\n",
      "Position: 7\n",
      "Highest abs value letter for latent 1:  R\n",
      "Highest abs value letter for latent 2:  S\n",
      "Position: 8\n",
      "Highest abs value letter for latent 1:  G\n",
      "Highest abs value letter for latent 2:  G\n",
      "Position: 9\n",
      "Highest abs value letter for latent 1:  D\n",
      "Highest abs value letter for latent 2:  -\n",
      "Position: 10\n",
      "Highest abs value letter for latent 1:  S\n",
      "Highest abs value letter for latent 2:  A\n",
      "Position: 11\n",
      "Highest abs value letter for latent 1:  Y\n",
      "Highest abs value letter for latent 2:  G\n",
      "Position: 12\n",
      "Highest abs value letter for latent 1:  A\n",
      "Highest abs value letter for latent 2:  R\n",
      "Position: 13\n",
      "Highest abs value letter for latent 1:  S\n",
      "Highest abs value letter for latent 2:  P\n",
      "Position: 14\n",
      "Highest abs value letter for latent 1:  A\n",
      "Highest abs value letter for latent 2:  Y\n",
      "Position: 15\n",
      "Highest abs value letter for latent 1:  D\n",
      "Highest abs value letter for latent 2:  N\n",
      "Position: 16\n",
      "Highest abs value letter for latent 1:  T\n",
      "Highest abs value letter for latent 2:  T\n",
      "Position: 17\n",
      "Highest abs value letter for latent 1:  Y\n",
      "Highest abs value letter for latent 2:  A\n",
      "Position: 18\n",
      "Highest abs value letter for latent 1:  T\n",
      "Highest abs value letter for latent 2:  H\n",
      "Position: 19\n",
      "Highest abs value letter for latent 1:  H\n",
      "Highest abs value letter for latent 2:  H\n"
     ]
    }
   ],
   "source": [
    "# Get most influential letter for each position\n",
    "for pos in range(20):\n",
    "    print(\"Position:\", pos)\n",
    "    pos_list_1 = []\n",
    "    pos_list_2 = []\n",
    "    for letter in aa:\n",
    "        pos_list_1.append(store_dict[\"-\" * pos + letter + \"-\" * (19 - pos)][0][0])\n",
    "        pos_list_2.append(store_dict[\"-\" * pos + letter + \"-\" * (19 - pos)][0][1])\n",
    "    print(\"Highest abs value letter for latent 1: \", aa[np.argmax(np.abs(pos_list_1))])\n",
    "    print(\"Highest abs value letter for latent 2: \", aa[np.argmax(np.abs(pos_list_2))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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