exploratory_analysis_rqn_2020-11-15.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "id": "7OLMsR3gviP7"
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# my custom packages\n",
    "import packages.vaccine.helper as hp  \n",
    "import packages.vaccine.vaccine as vc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concept Introduction\n",
    "For my own use.\n",
    "### What is an MHC class?\n",
    "MHC is a set of closely related genes that code for cell surface proteins essential for the adaptive immune system \\[[1](https://en.wikipedia.org/wiki/Major_histocompatibility_complex)\\]. \n",
    "\n",
    "This project focuses on two major MHC classes which code for antigen-presenting proteins: Class I and Class II. Class I interact with killer T-cells. Class II interact with T-lymphocytes. Each class has a different set of HLA alleles associated with it.\n",
    "\n",
    "\n",
    "### What are HLA alleles?\n",
    "HLA proteins are group of related proteins encoded by MHC gene complex in humans. Each allele can bind with different antigen (peptide) sequences and display them at the cell surface. We care about the quality of the binding. If a peptide binds well (i.e. with high affinity) to an HLA allele, we can consider that peptide as an epitope. This means the peptide (and therefore, the foreign invader) can be detected by the immune system.\n",
    "\n",
    "\n",
    "There are a vast number of HLA alleles. The most common alleles for each MHC class are listed as a [reference](https://help.iedb.org/hc/en-us/articles/114094151851) set. This project will focus on finding peptides which bind well to the alleles in these sets.\n",
    "\n",
    "### What is a pIC50 score?\n",
    "We need a way to measure how well an HLA allele binds to a certain peptide (higher is better).\n",
    "\n",
    "### What is the coefficient of determination $R^2$?\n",
    "\"It provides a measure of how well observed outcomes are replicated by the model, based on the proportion of total variation of outcomes explained by the model\" \\[[2](https://en.wikipedia.org/wiki/Coefficient_of_determination)\\]. It's a common scoring method for regression.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7wDEOIaDvg_S"
   },
   "source": [
    "# **Part 1.1 MHC I**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0FhoM6LyyXmz"
   },
   "source": [
    "# Part 1.1.1: Work\n",
    "\n",
    "**TODO:**\n",
    "- **Create and train a regression model for predicting pIC50 values for the data in `MHCI_Binding_Data.csv`.**\n",
    "- **Create a table where each row corresponds to one of the MHC I binding affinity models above, and the final column is the coefficient of determination (i.e. $R^2$) of the model using 5-fold cross validation.**\n",
    "\n",
    "**NOTES:**\n",
    "- **You only need to make predictions for the 27 alleles in `hla_ref_set.class_i.csv`**\n",
    "- **There are several ways to approach this - please refer to the writeup for ideas/suggestions**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SGFj7y3Cw0x-"
   },
   "source": [
    "### 1.1.1. Read in MHC class 1 allele reference set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "id": "56lCB5a3vxob"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PATH_TO_REF_SET = 'data/hla_ref_set.class_i.csv'\n",
    "ref_set_class_i = pd.read_csv(PATH_TO_REF_SET,header=None)\n",
    "\n",
    "# create a list of alleles\n",
    "class_i_list = ref_set_class_i[0].tolist()\n",
    "len(class_i_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.1. Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "id": "u5s9dwi6vUXw"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(157325, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>HLA Allele</th>\n",
       "      <th>peptide_length</th>\n",
       "      <th>sequence</th>\n",
       "      <th>IC50 (nM concentration)</th>\n",
       "      <th>pIC50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>human</td>\n",
       "      <td>HLA-A*01:01</td>\n",
       "      <td>8</td>\n",
       "      <td>ASFCGSPY</td>\n",
       "      <td>51.400000</td>\n",
       "      <td>7.289037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>human</td>\n",
       "      <td>HLA-A*01:01</td>\n",
       "      <td>8</td>\n",
       "      <td>LTDFGLSK</td>\n",
       "      <td>739.385479</td>\n",
       "      <td>6.131129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  species   HLA Allele  peptide_length  sequence  IC50 (nM concentration)  \\\n",
       "0   human  HLA-A*01:01               8  ASFCGSPY                51.400000   \n",
       "1   human  HLA-A*01:01               8  LTDFGLSK               739.385479   \n",
       "\n",
       "      pIC50  \n",
       "0  7.289037  \n",
       "1  6.131129  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read in data\n",
    "PATH_TO_MHC1_DATA = 'data/MHCI_Binding_Data.csv'\n",
    "MHC1_df = pd.read_csv(PATH_TO_MHC1_DATA) \n",
    "print(MHC1_df.shape)\n",
    "MHC1_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "119\n"
     ]
    }
   ],
   "source": [
    "# check whether there are other alleles present we don't care about (we care about 27)\n",
    "print(len(MHC1_df['HLA Allele'].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(111742, 6)\n"
     ]
    }
   ],
   "source": [
    "# filter out records which are not for the 27 listed alleles\n",
    "MHC1_filtered_df = MHC1_df[MHC1_df['HLA Allele'].isin(class_i_list)]\n",
    "print(MHC1_filtered_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HLA-A*02:01    12160\n",
       "HLA-A*03:01     7358\n",
       "HLA-A*11:01     6399\n",
       "HLA-A*02:03     6303\n",
       "HLA-A*31:01     5713\n",
       "HLA-A*02:06     5628\n",
       "HLA-A*68:02     5545\n",
       "HLA-A*01:01     4792\n",
       "HLA-B*07:02     4642\n",
       "HLA-A*26:01     4535\n",
       "HLA-B*15:01     4448\n",
       "HLA-A*68:01     3795\n",
       "HLA-A*33:01     3597\n",
       "HLA-B*08:01     3538\n",
       "HLA-B*40:01     3443\n",
       "HLA-B*58:01     3350\n",
       "HLA-A*24:02     3280\n",
       "HLA-B*35:01     3211\n",
       "HLA-A*30:01     2925\n",
       "HLA-B*57:01     2903\n",
       "HLA-B*51:01     2901\n",
       "HLA-A*23:01     2641\n",
       "HLA-B*44:02     2318\n",
       "HLA-A*30:02     2057\n",
       "HLA-B*53:01     1720\n",
       "HLA-B*44:03     1404\n",
       "HLA-A*32:01     1136\n",
       "Name: HLA Allele, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check how much data we have per allele\n",
    "MHC1_filtered_df['HLA Allele'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27\n"
     ]
    }
   ],
   "source": [
    "# determine if any of the 27 alleles are not represented (there aren't)\n",
    "print(len(MHC1_filtered_df['HLA Allele'].value_counts()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8      1854\n",
       "9     83083\n",
       "10    24300\n",
       "11     1668\n",
       "12      140\n",
       "13      268\n",
       "14      168\n",
       "15      206\n",
       "17       27\n",
       "18       27\n",
       "30        1\n",
       "Name: peptide_length, dtype: int64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check how much data we have per sequence length\n",
    "MHC1_filtered_df['peptide_length'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "# check whether we have any duplicate rows for a given sequence (there aren't)\n",
    "MHC1_grouped_df = MHC1_filtered_df.groupby(by=['HLA Allele','sequence']).count()\n",
    "MHC1_grouped_df = MHC1_grouped_df[MHC1_grouped_df['species'] > 1]\n",
    "print(MHC1_grouped_df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27\n",
      "\n",
      "9    83083\n",
      "Name: peptide_length, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "HLA-A*02:01    9051\n",
       "HLA-A*03:01    5488\n",
       "HLA-A*11:01    4544\n",
       "HLA-A*02:03    4428\n",
       "HLA-B*15:01    4101\n",
       "HLA-A*31:01    3945\n",
       "HLA-A*01:01    3902\n",
       "HLA-B*07:02    3868\n",
       "HLA-A*26:01    3766\n",
       "HLA-A*02:06    3733\n",
       "HLA-A*68:02    3672\n",
       "HLA-B*08:01    3027\n",
       "HLA-B*58:01    2984\n",
       "HLA-B*40:01    2824\n",
       "HLA-A*30:01    2565\n",
       "HLA-B*57:01    2529\n",
       "HLA-B*35:01    2514\n",
       "HLA-A*24:02    2395\n",
       "HLA-B*51:01    2239\n",
       "HLA-A*68:01    2036\n",
       "HLA-A*33:01    1929\n",
       "HLA-A*23:01    1915\n",
       "HLA-B*44:02    1682\n",
       "HLA-A*30:02    1330\n",
       "HLA-B*53:01    1052\n",
       "HLA-A*32:01     831\n",
       "HLA-B*44:03     733\n",
       "Name: HLA Allele, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check whether there is data for each allele for sequences of only length 9\n",
    "MHC1_length_9_df = MHC1_filtered_df[MHC1_filtered_df['peptide_length']==9]\n",
    "print(len(MHC1_length_9_df['HLA Allele'].unique()))\n",
    "print()\n",
    "\n",
    "# confirm how much data we have per sequence length\n",
    "print(MHC1_length_9_df['peptide_length'].value_counts().sort_index())\n",
    "\n",
    "# check how much data we have per allele\n",
    "MHC1_length_9_df['HLA Allele'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.1. Data Prep\n",
    "Most of the data is concentrated with sequences of length 9 and 10. Let's use only sequences of length 9 to build the regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(83083, 6)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read in data\n",
    "PATH_TO_MHC1_DATA = 'data/MHCI_Binding_Data.csv'\n",
    "MHC1_df = pd.read_csv(PATH_TO_MHC1_DATA) \n",
    "\n",
    "# filter out records with sequence length not equal to 9\n",
    "MHC1_df = MHC1_df[MHC1_df['peptide_length']==9]\n",
    "\n",
    "# filter out records which are not for the 27 listed alleles\n",
    "MHC1_df = MHC1_df[MHC1_df['HLA Allele'].isin(class_i_list)]\n",
    "\n",
    "MHC1_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>species</th>\n",
       "      <th>HLA Allele</th>\n",
       "      <th>peptide_length</th>\n",
       "      <th>sequence</th>\n",
       "      <th>IC50 (nM concentration)</th>\n",
       "      <th>pIC50</th>\n",
       "      <th>seq_standard</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>human</td>\n",
       "      <td>HLA-A*01:01</td>\n",
       "      <td>9</td>\n",
       "      <td>CSDAFYPFY</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>CSDAFYPFY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>human</td>\n",
       "      <td>HLA-A*01:01</td>\n",
       "      <td>9</td>\n",
       "      <td>CSDETTLYY</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>CSDETTLYY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   species   HLA Allele  peptide_length   sequence  IC50 (nM concentration)  \\\n",
       "69   human  HLA-A*01:01               9  CSDAFYPFY                      1.0   \n",
       "70   human  HLA-A*01:01               9  CSDETTLYY                      1.0   \n",
       "\n",
       "    pIC50 seq_standard  \n",
       "69    9.0    CSDAFYPFY  \n",
       "70    9.0    CSDETTLYY  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add a column with the standard 9-length sequence for every record\n",
    "MHC1_df.loc[:, ('seq_standard')]=MHC1_df.apply(lambda x: hp.get_n_length_seq(x['sequence'],9),axis=1)\n",
    "MHC1_df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  1.1.1. Encode the sequence into feature columns\n",
    "- Split 9 sequence into one column per letter.\n",
    "- Use ASCII number for each letter to convert to numerical data to use for regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>species</th>\n",
       "      <th>HLA Allele</th>\n",
       "      <th>peptide_length</th>\n",
       "      <th>sequence</th>\n",
       "      <th>IC50 (nM concentration)</th>\n",
       "      <th>pIC50</th>\n",
       "      <th>seq_standard</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
       "      <th>s7</th>\n",
       "      <th>s8</th>\n",
       "      <th>s9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>human</td>\n",
       "      <td>HLA-A*01:01</td>\n",
       "      <td>9</td>\n",
       "      <td>CSDAFYPFY</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>CSDAFYPFY</td>\n",
       "      <td>67</td>\n",
       "      <td>83</td>\n",
       "      <td>68</td>\n",
       "      <td>65</td>\n",
       "      <td>70</td>\n",
       "      <td>89</td>\n",
       "      <td>80</td>\n",
       "      <td>70</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   species   HLA Allele  peptide_length   sequence  IC50 (nM concentration)  \\\n",
       "69   human  HLA-A*01:01               9  CSDAFYPFY                      1.0   \n",
       "\n",
       "    pIC50 seq_standard  s1  s2  s3  s4  s5  s6  s7  s8  s9  \n",
       "69    9.0    CSDAFYPFY  67  83  68  65  70  89  80  70  89  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# encode letter as ASCII number\n",
    "col_list = ['s1','s2','s3','s4','s5','s6','s7','s8','s9']\n",
    "MHC1_df[col_list]=MHC1_df.seq_standard.apply(lambda x: pd.Series([ord(char) for char in str(x)]))\n",
    "MHC1_df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.1. Data Visualization\n",
    "To help see what type of regression model best fits the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 27 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hp.plot_separate_alleles(MHC1_df,class_i_list)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.1. Train Regression Models\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 19.2 s, sys: 117 ms, total: 19.3 s\n",
      "Wall time: 19.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# for each of the 27 alleles, learn a separate regression model\n",
    "regression_scores_mhc1 = []\n",
    "for allele in class_i_list:\n",
    "    m,s = hp.score_regression_model(MHC1_df,allele)\n",
    "    result = [allele,round(s,5),round(m,5)] # std_dev before mean\n",
    "    regression_scores_mhc1.append(result)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.1. Output Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(27, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Allele</th>\n",
       "      <th>Std_dev</th>\n",
       "      <th>R_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HLA-A*01:01</td>\n",
       "      <td>0.02086</td>\n",
       "      <td>0.35360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HLA-A*02:01</td>\n",
       "      <td>0.01320</td>\n",
       "      <td>0.27606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HLA-A*02:03</td>\n",
       "      <td>0.01531</td>\n",
       "      <td>0.31211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HLA-A*02:06</td>\n",
       "      <td>0.03010</td>\n",
       "      <td>0.17000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HLA-A*03:01</td>\n",
       "      <td>0.01229</td>\n",
       "      <td>0.33590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>HLA-A*11:01</td>\n",
       "      <td>0.02106</td>\n",
       "      <td>0.37462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>HLA-A*23:01</td>\n",
       "      <td>0.02401</td>\n",
       "      <td>0.29890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>HLA-A*24:02</td>\n",
       "      <td>0.04354</td>\n",
       "      <td>0.28921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>HLA-A*26:01</td>\n",
       "      <td>0.01598</td>\n",
       "      <td>0.18308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>HLA-A*30:01</td>\n",
       "      <td>0.03722</td>\n",
       "      <td>0.15079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HLA-A*30:02</td>\n",
       "      <td>0.09771</td>\n",
       "      <td>0.33859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>HLA-A*31:01</td>\n",
       "      <td>0.01835</td>\n",
       "      <td>0.33852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HLA-A*32:01</td>\n",
       "      <td>0.03819</td>\n",
       "      <td>0.18953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HLA-A*33:01</td>\n",
       "      <td>0.01792</td>\n",
       "      <td>0.36979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HLA-A*68:01</td>\n",
       "      <td>0.03748</td>\n",
       "      <td>0.23150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>HLA-A*68:02</td>\n",
       "      <td>0.03387</td>\n",
       "      <td>0.14563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>HLA-B*07:02</td>\n",
       "      <td>0.03024</td>\n",
       "      <td>0.40548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>HLA-B*08:01</td>\n",
       "      <td>0.02379</td>\n",
       "      <td>0.14862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>HLA-B*15:01</td>\n",
       "      <td>0.03586</td>\n",
       "      <td>0.18927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>HLA-B*35:01</td>\n",
       "      <td>0.03508</td>\n",
       "      <td>0.18460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>HLA-B*40:01</td>\n",
       "      <td>0.01969</td>\n",
       "      <td>0.55201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>HLA-B*44:02</td>\n",
       "      <td>0.03662</td>\n",
       "      <td>0.40879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>HLA-B*44:03</td>\n",
       "      <td>0.05368</td>\n",
       "      <td>0.50702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>HLA-B*51:01</td>\n",
       "      <td>0.01917</td>\n",
       "      <td>0.18812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>HLA-B*53:01</td>\n",
       "      <td>0.04178</td>\n",
       "      <td>0.21006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>HLA-B*57:01</td>\n",
       "      <td>0.05960</td>\n",
       "      <td>0.43066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>HLA-B*58:01</td>\n",
       "      <td>0.05605</td>\n",
       "      <td>0.35509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Allele  Std_dev      R_2\n",
       "0   HLA-A*01:01  0.02086  0.35360\n",
       "1   HLA-A*02:01  0.01320  0.27606\n",
       "2   HLA-A*02:03  0.01531  0.31211\n",
       "3   HLA-A*02:06  0.03010  0.17000\n",
       "4   HLA-A*03:01  0.01229  0.33590\n",
       "5   HLA-A*11:01  0.02106  0.37462\n",
       "6   HLA-A*23:01  0.02401  0.29890\n",
       "7   HLA-A*24:02  0.04354  0.28921\n",
       "8   HLA-A*26:01  0.01598  0.18308\n",
       "9   HLA-A*30:01  0.03722  0.15079\n",
       "10  HLA-A*30:02  0.09771  0.33859\n",
       "11  HLA-A*31:01  0.01835  0.33852\n",
       "12  HLA-A*32:01  0.03819  0.18953\n",
       "13  HLA-A*33:01  0.01792  0.36979\n",
       "14  HLA-A*68:01  0.03748  0.23150\n",
       "15  HLA-A*68:02  0.03387  0.14563\n",
       "16  HLA-B*07:02  0.03024  0.40548\n",
       "17  HLA-B*08:01  0.02379  0.14862\n",
       "18  HLA-B*15:01  0.03586  0.18927\n",
       "19  HLA-B*35:01  0.03508  0.18460\n",
       "20  HLA-B*40:01  0.01969  0.55201\n",
       "21  HLA-B*44:02  0.03662  0.40879\n",
       "22  HLA-B*44:03  0.05368  0.50702\n",
       "23  HLA-B*51:01  0.01917  0.18812\n",
       "24  HLA-B*53:01  0.04178  0.21006\n",
       "25  HLA-B*57:01  0.05960  0.43066\n",
       "26  HLA-B*58:01  0.05605  0.35509"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# output results\n",
    "mhc1_regression_scores_df = pd.DataFrame(data=regression_scores_mhc1,columns = ['Allele','Std_dev','R_2'])\n",
    "print(mhc1_regression_scores_df.shape)\n",
    "mhc1_regression_scores_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5v8eLVsFyan0"
   },
   "source": [
    "## Part 1.1.1: Answer\n",
    "\n",
    "**Briefly describe your approach for creating the regression model. Include the table as the output of the cell above or embed it as an image in this cell.**\n",
    "\n",
    "#### Data\n",
    "*First, I filtered out records from the MHC I binding data which were not for the alleles listed in the reference set. Additionally, I noticed that the vast majority of data for MHC I centered around a peptide length of 9, so I chose to limit my model to predicting pIC50 values for peptides of length 9. This allowed me to filter out records from the MHC I binding data which were not of length 9.*\n",
    "\n",
    "*From there, I encoded the peptide sequence by splitting each amino acid letter into its own column, the calculating the ASCII value for each letter. This results in a feature set of numerical values for the regression model.*\n",
    "\n",
    "\n",
    "#### Regression Model\n",
    "*I decided to create a different model for each allele in the reference set, rather than include the allele in the feature data. This ensures that the line of best fit found by the model is not influenced by the allele itself. By creating separate models, I prevent any relationship between alleles from manipulating the fit.*\n",
    "\n",
    "*I then attempted to visualize the features with PCA. This helped inform me that the data isn't really linear in nature. I chose to use a Random Forest regression for this reason, rather than use linear regression.*\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9QGviJUGzcPa"
   },
   "source": [
    "# Part 1.1.2: Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TODO:**\n",
    "- **Create and train a regression model for predicting pIC50 values for the data in `MHCII_Binding_Data.csv`.**\n",
    "- **Create a table where each row corresponds to one of the MHC II binding affinity models above, and the final column is the Coefficient of determination (ie. $R^2$) of the model using 5-fold cross validation.**\n",
    "\n",
    "*NOTES:*\n",
    "- **You only need to make predictions for the 27 alleles in `hla_ref_set.class_i.csv`**\n",
    "- **There are several ways to approach this - please refer to the writeup for ideas/suggestions**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SGFj7y3Cw0x-"
   },
   "source": [
    "### 1.1.2. Read in MHC class 2 allele reference set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "id": "56lCB5a3vxob"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PATH_TO_REF_SET = 'data/hla_ref_set.class_ii.csv'\n",
    "ref_set_class_ii = pd.read_csv(PATH_TO_REF_SET,header=None)\n",
    "\n",
    "# create a list of alleles\n",
    "class_ii_list = ref_set_class_ii[0].tolist()\n",
    "len(class_ii_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.2 Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "id": "u5s9dwi6vUXw"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(44914, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>mhc</th>\n",
       "      <th>peptide_length</th>\n",
       "      <th>sequence</th>\n",
       "      <th>IC50 (nM concentration)</th>\n",
       "      <th>pIC50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>human</td>\n",
       "      <td>HLA-DPA1*01/DPB1*0401</td>\n",
       "      <td>15</td>\n",
       "      <td>AKKYFAATQFEPLAA</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>human</td>\n",
       "      <td>HLA-DPA1*01/DPB1*0401</td>\n",
       "      <td>15</td>\n",
       "      <td>AWVDSGAQLGELYYA</td>\n",
       "      <td>8389.261745</td>\n",
       "      <td>5.076276</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  species                    mhc  peptide_length         sequence  \\\n",
       "0   human  HLA-DPA1*01/DPB1*0401              15  AKKYFAATQFEPLAA   \n",
       "1   human  HLA-DPA1*01/DPB1*0401              15  AWVDSGAQLGELYYA   \n",
       "\n",
       "   IC50 (nM concentration)     pIC50  \n",
       "0                 1.000000  9.000000  \n",
       "1              8389.261745  5.076276  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read in data\n",
    "PATH_TO_MHC2_DATA = 'data/MHCII_Binding_Data.csv'\n",
    "MHC2_df = pd.read_csv(PATH_TO_MHC2_DATA) \n",
    "print(MHC2_df.shape)\n",
    "MHC2_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rename allele column to match MHC1 data\n",
    "MHC2_df = MHC2_df.rename(columns={\"mhc\": \"HLA Allele\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26\n"
     ]
    }
   ],
   "source": [
    "# check whether there are other alleles present we don't care about (there aren't)\n",
    "print(len(MHC2_df['HLA Allele'].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(43000, 6)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "HLA-DRB1*0101              6427\n",
       "HLA-DRB1*1101              1794\n",
       "HLA-DRB1*0401              1769\n",
       "HLA-DRB1*1501              1769\n",
       "HLA-DRB5*0101              1769\n",
       "HLA-DRB1*0701              1745\n",
       "HLA-DQA1*0101/DQB1*0501    1739\n",
       "HLA-DQA1*0301/DQB1*0302    1719\n",
       "HLA-DRB1*0301              1715\n",
       "HLA-DQA1*0401/DQB1*0402    1701\n",
       "HLA-DQA1*0501/DQB1*0301    1689\n",
       "HLA-DQA1*0501/DQB1*0201    1658\n",
       "HLA-DQA1*0102/DQB1*0602    1629\n",
       "HLA-DRB1*0405              1582\n",
       "HLA-DRB1*1302              1580\n",
       "HLA-DRB4*0101              1521\n",
       "HLA-DRB1*0802              1520\n",
       "HLA-DRB1*0901              1520\n",
       "HLA-DRB3*0101              1501\n",
       "HLA-DPA1*0201/DPB1*0501    1410\n",
       "HLA-DPA1*0301/DPB1*0402    1407\n",
       "HLA-DPA1*0103/DPB1*0201    1404\n",
       "HLA-DPA1*0201/DPB1*0101    1399\n",
       "HLA-DPA1*0103/DPB1*0401    1033\n",
       "Name: HLA Allele, dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter out records which are not for the 27 listed alleles\n",
    "MHC2_filtered_df = MHC2_df[MHC2_df['HLA Allele'].isin(class_ii_list)]\n",
    "print(MHC2_filtered_df.shape)\n",
    "\n",
    "# check how much data we have per allele\n",
    "MHC2_filtered_df['HLA Allele'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['HLA-DRB1*1201', 'HLA-DRB3*0202', 'HLA-DPA1*0201/DPB1*1401']"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# determine if any of the 27 alleles are not represented\n",
    "print(len(MHC2_filtered_df['HLA Allele'].value_counts()))\n",
    "\n",
    "# determine which are missing\n",
    "included = list(MHC2_filtered_df['HLA Allele'].value_counts().index)\n",
    "missing = [each for each in class_ii_list if each not in included]\n",
    "missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9        17\n",
       "10       89\n",
       "11      116\n",
       "12       78\n",
       "13      425\n",
       "14      352\n",
       "15    40106\n",
       "16     1092\n",
       "17      348\n",
       "18      106\n",
       "19      121\n",
       "20      120\n",
       "21       18\n",
       "23        4\n",
       "37        8\n",
       "Name: peptide_length, dtype: int64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check how much data we have per sequence length\n",
    "MHC2_filtered_df['peptide_length'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "# check whether we have any duplicate rows for a given sequence\n",
    "MHC2_grouped_df = MHC2_filtered_df.groupby(by=['HLA Allele','sequence']).count()\n",
    "MHC2_grouped_df = MHC2_grouped_df[MHC2_grouped_df['species'] > 1]\n",
    "print(MHC2_grouped_df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\n",
      "\n",
      "15    40106\n",
      "Name: peptide_length, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "HLA-DRB1*0101              6340\n",
       "HLA-DRB1*1101              1701\n",
       "HLA-DRB1*0401              1699\n",
       "HLA-DRB1*1501              1684\n",
       "HLA-DRB5*0101              1684\n",
       "HLA-DRB1*0701              1675\n",
       "HLA-DRB1*0301              1658\n",
       "HLA-DQA1*0401/DQB1*0402    1579\n",
       "HLA-DQA1*0501/DQB1*0301    1573\n",
       "HLA-DQA1*0102/DQB1*0602    1515\n",
       "HLA-DRB1*0405              1512\n",
       "HLA-DRB1*1302              1511\n",
       "HLA-DRB4*0101              1498\n",
       "HLA-DRB1*0901              1498\n",
       "HLA-DRB1*0802              1498\n",
       "HLA-DRB3*0101              1463\n",
       "HLA-DQA1*0101/DQB1*0501    1306\n",
       "HLA-DPA1*0301/DPB1*0402    1293\n",
       "HLA-DPA1*0103/DPB1*0201    1292\n",
       "HLA-DQA1*0501/DQB1*0201    1290\n",
       "HLA-DPA1*0201/DPB1*0501    1287\n",
       "HLA-DQA1*0301/DQB1*0302    1286\n",
       "HLA-DPA1*0201/DPB1*0101    1285\n",
       "HLA-DPA1*0103/DPB1*0401     979\n",
       "Name: HLA Allele, dtype: int64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check whether there is data for each allele for sequences of only length 15\n",
    "MHC2_length_15_df = MHC2_filtered_df[MHC2_filtered_df['peptide_length']==15]\n",
    "print(len(MHC2_length_15_df['HLA Allele'].unique()))\n",
    "print()\n",
    "\n",
    "# check how much data we have per sequence length\n",
    "print(MHC2_length_15_df['peptide_length'].value_counts().sort_index())\n",
    "\n",
    "# check how much data we have per allele\n",
    "MHC2_length_15_df['HLA Allele'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.2 Data Prep\n",
    "Most of the data is concentrated with sequences of length 15. Let's use only sequences of length 15 to build the regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40106, 6)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read in data\n",
    "PATH_TO_MHC2_DATA = 'data/MHCII_Binding_Data.csv'\n",
    "MHC2_df = pd.read_csv(PATH_TO_MHC2_DATA) \n",
    "\n",
    "# rename allele column to match MHC1 data\n",
    "MHC2_df = MHC2_df.rename(columns={\"mhc\": \"HLA Allele\"})\n",
    "\n",
    "# filter out records with sequence length not equal to 15\n",
    "MHC2_df = MHC2_df[MHC2_df['peptide_length']==15]\n",
    "\n",
    "# filter out records which are not for the 27 listed alleles\n",
    "MHC2_df = MHC2_df[MHC2_df['HLA Allele'].isin(class_ii_list)]\n",
    "\n",
    "MHC2_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>HLA Allele</th>\n",
       "      <th>peptide_length</th>\n",
       "      <th>sequence</th>\n",
       "      <th>IC50 (nM concentration)</th>\n",
       "      <th>pIC50</th>\n",
       "      <th>seq_standard</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>human</td>\n",
       "      <td>HLA-DPA1*0103/DPB1*0201</td>\n",
       "      <td>15</td>\n",
       "      <td>EKKYFAATCFEPLAA</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>EKKYFAATCFEPLAA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1338</th>\n",
       "      <td>human</td>\n",
       "      <td>HLA-DPA1*0103/DPB1*0201</td>\n",
       "      <td>15</td>\n",
       "      <td>EKKYLAATQFEPLAA</td>\n",
       "      <td>1.186663</td>\n",
       "      <td>8.925673</td>\n",
       "      <td>EKKYLAATQFEPLAA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     species               HLA Allele  peptide_length         sequence  \\\n",
       "1337   human  HLA-DPA1*0103/DPB1*0201              15  EKKYFAATCFEPLAA   \n",
       "1338   human  HLA-DPA1*0103/DPB1*0201              15  EKKYLAATQFEPLAA   \n",
       "\n",
       "      IC50 (nM concentration)     pIC50     seq_standard  \n",
       "1337                 1.000000  9.000000  EKKYFAATCFEPLAA  \n",
       "1338                 1.186663  8.925673  EKKYLAATQFEPLAA  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add a column with the 15-length sequence for every record\n",
    "MHC2_df.loc[:, ('seq_standard')]=MHC2_df.apply(lambda x: hp.get_n_length_seq(x['sequence'],15),axis=1)\n",
    "MHC2_df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.2. Encode the sequence into feature columns\n",
    "- Split 15 sequence into one column per letter.\n",
    "- Use ASCII number for each letter to convert to numerical data to use for regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>HLA Allele</th>\n",
       "      <th>peptide_length</th>\n",
       "      <th>sequence</th>\n",
       "      <th>IC50 (nM concentration)</th>\n",
       "      <th>pIC50</th>\n",
       "      <th>seq_standard</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>...</th>\n",
       "      <th>s6</th>\n",
       "      <th>s7</th>\n",
       "      <th>s8</th>\n",
       "      <th>s9</th>\n",
       "      <th>s10</th>\n",
       "      <th>s11</th>\n",
       "      <th>s12</th>\n",
       "      <th>s13</th>\n",
       "      <th>s14</th>\n",
       "      <th>s15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>human</td>\n",
       "      <td>HLA-DPA1*0103/DPB1*0201</td>\n",
       "      <td>15</td>\n",
       "      <td>EKKYFAATCFEPLAA</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>EKKYFAATCFEPLAA</td>\n",
       "      <td>69</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>84</td>\n",
       "      <td>67</td>\n",
       "      <td>70</td>\n",
       "      <td>69</td>\n",
       "      <td>80</td>\n",
       "      <td>76</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1338</th>\n",
       "      <td>human</td>\n",
       "      <td>HLA-DPA1*0103/DPB1*0201</td>\n",
       "      <td>15</td>\n",
       "      <td>EKKYLAATQFEPLAA</td>\n",
       "      <td>1.186663</td>\n",
       "      <td>8.925673</td>\n",
       "      <td>EKKYLAATQFEPLAA</td>\n",
       "      <td>69</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>84</td>\n",
       "      <td>81</td>\n",
       "      <td>70</td>\n",
       "      <td>69</td>\n",
       "      <td>80</td>\n",
       "      <td>76</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     species               HLA Allele  peptide_length         sequence  \\\n",
       "1337   human  HLA-DPA1*0103/DPB1*0201              15  EKKYFAATCFEPLAA   \n",
       "1338   human  HLA-DPA1*0103/DPB1*0201              15  EKKYLAATQFEPLAA   \n",
       "\n",
       "      IC50 (nM concentration)     pIC50     seq_standard  s1  s2  s3  ...  s6  \\\n",
       "1337                 1.000000  9.000000  EKKYFAATCFEPLAA  69  75  75  ...  65   \n",
       "1338                 1.186663  8.925673  EKKYLAATQFEPLAA  69  75  75  ...  65   \n",
       "\n",
       "      s7  s8  s9  s10  s11  s12  s13  s14  s15  \n",
       "1337  65  84  67   70   69   80   76   65   65  \n",
       "1338  65  84  81   70   69   80   76   65   65  \n",
       "\n",
       "[2 rows x 22 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# encode letter as ASCII number\n",
    "col_list = ['s1','s2','s3','s4','s5','s6','s7','s8','s9','s10','s11','s12','s13','s14','s15']\n",
    "MHC2_df[col_list]=MHC2_df.seq_standard.apply(lambda x: pd.Series([ord(char) for char in str(x)]))\n",
    "MHC2_df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Visualization\n",
    "To help see what type of regression model best fits the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 27 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hp.plot_separate_alleles(MHC2_df, class_ii_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 15.4 s, sys: 64.4 ms, total: 15.5 s\n",
      "Wall time: 15.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# for each of the 27 alleles, learn a separate regression model\n",
    "regression_scores_mhc2 = []\n",
    "for allele in class_ii_list:\n",
    "    m,s = hp.score_regression_model(MHC2_df,allele)\n",
    "    result = [allele,round(s,5),round(m,5)] # std_dev before mean\n",
    "    regression_scores_mhc2.append(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(27, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Allele</th>\n",
       "      <th>Std_dev</th>\n",
       "      <th>R_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HLA-DRB1*0101</td>\n",
       "      <td>0.00681</td>\n",
       "      <td>0.03898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HLA-DRB1*0301</td>\n",
       "      <td>0.01134</td>\n",
       "      <td>0.05103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HLA-DRB1*0401</td>\n",
       "      <td>0.00723</td>\n",
       "      <td>0.04783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HLA-DRB1*0405</td>\n",
       "      <td>0.01512</td>\n",
       "      <td>0.05965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HLA-DRB1*0701</td>\n",
       "      <td>0.00835</td>\n",
       "      <td>0.06770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>HLA-DRB1*0802</td>\n",
       "      <td>0.00459</td>\n",
       "      <td>0.03301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>HLA-DRB1*0901</td>\n",
       "      <td>0.01660</td>\n",
       "      <td>0.01775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>HLA-DRB1*1101</td>\n",
       "      <td>0.01132</td>\n",
       "      <td>0.07161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>HLA-DRB1*1201</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>HLA-DRB1*1302</td>\n",
       "      <td>0.01044</td>\n",
       "      <td>0.04899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HLA-DRB1*1501</td>\n",
       "      <td>0.00731</td>\n",
       "      <td>0.05998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>HLA-DRB3*0101</td>\n",
       "      <td>0.00436</td>\n",
       "      <td>0.01177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HLA-DRB3*0202</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HLA-DRB4*0101</td>\n",
       "      <td>0.02139</td>\n",
       "      <td>0.08917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HLA-DRB5*0101</td>\n",
       "      <td>0.00539</td>\n",
       "      <td>0.04597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>HLA-DQA1*0501/DQB1*0201</td>\n",
       "      <td>0.04435</td>\n",
       "      <td>0.30175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>HLA-DQA1*0501/DQB1*0301</td>\n",
       "      <td>0.05656</td>\n",
       "      <td>0.31591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>HLA-DQA1*0301/DQB1*0302</td>\n",
       "      <td>0.01996</td>\n",
       "      <td>0.17956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>HLA-DQA1*0401/DQB1*0402</td>\n",
       "      <td>0.04000</td>\n",
       "      <td>0.39487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>HLA-DQA1*0101/DQB1*0501</td>\n",
       "      <td>0.04744</td>\n",
       "      <td>0.27361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>HLA-DQA1*0102/DQB1*0602</td>\n",
       "      <td>0.03282</td>\n",
       "      <td>0.24995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>HLA-DPA1*0201/DPB1*0101</td>\n",
       "      <td>0.04406</td>\n",
       "      <td>0.54587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>HLA-DPA1*0103/DPB1*0201</td>\n",
       "      <td>0.05688</td>\n",
       "      <td>0.55443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>HLA-DPA1*0103/DPB1*0401</td>\n",
       "      <td>0.04022</td>\n",
       "      <td>0.14371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>HLA-DPA1*0301/DPB1*0402</td>\n",
       "      <td>0.03519</td>\n",
       "      <td>0.54148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>HLA-DPA1*0201/DPB1*0501</td>\n",
       "      <td>0.03278</td>\n",
       "      <td>0.51146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>HLA-DPA1*0201/DPB1*1401</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Allele  Std_dev      R_2\n",
       "0             HLA-DRB1*0101  0.00681  0.03898\n",
       "1             HLA-DRB1*0301  0.01134  0.05103\n",
       "2             HLA-DRB1*0401  0.00723  0.04783\n",
       "3             HLA-DRB1*0405  0.01512  0.05965\n",
       "4             HLA-DRB1*0701  0.00835  0.06770\n",
       "5             HLA-DRB1*0802  0.00459  0.03301\n",
       "6             HLA-DRB1*0901  0.01660  0.01775\n",
       "7             HLA-DRB1*1101  0.01132  0.07161\n",
       "8             HLA-DRB1*1201  0.00000  0.00000\n",
       "9             HLA-DRB1*1302  0.01044  0.04899\n",
       "10            HLA-DRB1*1501  0.00731  0.05998\n",
       "11            HLA-DRB3*0101  0.00436  0.01177\n",
       "12            HLA-DRB3*0202  0.00000  0.00000\n",
       "13            HLA-DRB4*0101  0.02139  0.08917\n",
       "14            HLA-DRB5*0101  0.00539  0.04597\n",
       "15  HLA-DQA1*0501/DQB1*0201  0.04435  0.30175\n",
       "16  HLA-DQA1*0501/DQB1*0301  0.05656  0.31591\n",
       "17  HLA-DQA1*0301/DQB1*0302  0.01996  0.17956\n",
       "18  HLA-DQA1*0401/DQB1*0402  0.04000  0.39487\n",
       "19  HLA-DQA1*0101/DQB1*0501  0.04744  0.27361\n",
       "20  HLA-DQA1*0102/DQB1*0602  0.03282  0.24995\n",
       "21  HLA-DPA1*0201/DPB1*0101  0.04406  0.54587\n",
       "22  HLA-DPA1*0103/DPB1*0201  0.05688  0.55443\n",
       "23  HLA-DPA1*0103/DPB1*0401  0.04022  0.14371\n",
       "24  HLA-DPA1*0301/DPB1*0402  0.03519  0.54148\n",
       "25  HLA-DPA1*0201/DPB1*0501  0.03278  0.51146\n",
       "26  HLA-DPA1*0201/DPB1*1401  0.00000  0.00000"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mhc2_regression_scores_df = pd.DataFrame(data=regression_scores_mhc2,columns = ['Allele','Std_dev','R_2'])\n",
    "print(mhc2_regression_scores_df.shape)\n",
    "mhc2_regression_scores_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jsZ01KB4z5F1"
   },
   "source": [
    "# Part 1.1.2: Answer\n",
    "\n",
    "**Briefly describe your approach for creating the regression model. Include the table as the output of the cell above or embed it as an image in this cell.**\n",
    "\n",
    "#### Data\n",
    "*First, I filtered out records from the MHC II binding data which were not for the alleles listed in the reference set. Additionally, I noticed that the vast majority of data for MHC II centered around a peptide length of 15, so I chose to limit my model to predicting pIC50 values for peptides of length 15. This allowed me to filter out records from the MHC II binding data which were not of length 15.*\n",
    "\n",
    "*From there, I encoded the peptide sequence by splitting each amino acid letter into its own column, the calculating the ASCII value for each letter. This results in a feature set of numerical values for the regression model.*\n",
    "\n",
    "\n",
    "#### Regression Model\n",
    "*I decided to create a different model for each allele in the reference set, rather than include the allele in the feature data. This ensures that the line of best fit found by the model is not influenced by the allele itself. By creating separate models, I prevent any relationship between alleles from manipulating the fit.*\n",
    "\n",
    "*I then attempted to visualize the features with PCA. This helped inform me that the data isn't really linear in nature. I chose to use a Random Forest regression for this reason, rather than use linear regression.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1.2 Binding Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Db2o_wyqz-as"
   },
   "source": [
    "# Part 1.2: Work \n",
    "\n",
    "**TODO:** \n",
    "- **Split protein sequence from `SARS-CoV2-Spike.fasta` into overlapping k-mers for your choice of k.**\n",
    "\n",
    "**TIP: The `.fasta` files usually start with some header describing the meta information. The code below skips the header and joins the lines of the sequence to obtain the full protein sequence.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2. Read spike sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "id": "kzXgM_DV6aQm",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1273"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# build character array of full sequence\n",
    "PATH_TO_FASTA = 'data/SARS-CoV2-Spike.fasta' \n",
    "spike_sequence = hp.get_spike_sequence(PATH_TO_FASTA)\n",
    "spike_array = hp.get_spike_array(spike_sequence)\n",
    "assert len(spike_sequence) == len(spike_array)\n",
    "len(spike_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2. Split spike sequence into k-mers\n",
    "My method is to use a generator to return k-mers one at a time, rather than need to store all k-mers in a data structure. The generator terminates when there all k-mers have been generated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GOAqN8JC0HSI"
   },
   "source": [
    "## Part 1.2: Answer\n",
    "\n",
    "**What was the value of k you used to create the overlapping k-mers?**\n",
    "\n",
    "*For MHC I data, I used a value of k=9. For MHC II data, I used a value of k=15.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SGFj7y3Cw0x-"
   },
   "source": [
    "# Part 1.3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aFOsp1636vJC"
   },
   "source": [
    "## Part 1.3: Work\n",
    "\n",
    "**TODO:** \n",
    "- **Apply the models you created in part 1.1 to the k-mers you created in part 1.2.**\n",
    "- **Store the returned labeled (MHC, peptide) pairs where the label is the predicted pIC50 value.**\n",
    "- **Determine the min, max, mean, median, and std. dev of the predicted pIC50 values for each MHC class.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3. Define algorithm for estimating pIC50 for Spike Protein k-mers\n",
    "Generate a set of labeled (MHC,peptide) pairs, where the label is the predicted pIC50 value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['C', 'V', 'A', 'D', 'Y', 'S', 'V', 'L', 'Y'], dtype='<U1')"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq = hp.get_spike_array('CVADYSVLY')\n",
    "seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test predict_pIC50_values_for_allele()\n",
      "(5, 16)\n"
     ]
    }
   ],
   "source": [
    "print('test predict_pIC50_values_for_allele()')\n",
    "\n",
    "test_df = MHC1_df[MHC1_df['seq_standard'] == 'CVADYSVLY']\n",
    "print(test_df.shape)\n",
    "\n",
    "# train model for this allele\n",
    "test_model = hp.train_regresson_model(MHC1_df,'HLA-A*01:01')\n",
    "\n",
    "test_results = hp.predict_pIC50_values_for_allele(test_df, test_model, 'HLA-A*01:01', spike_array, 9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('HLA-A*01:01', 'CVADYSVLY', 360, 368, 7.874116247000001)\n"
     ]
    }
   ],
   "source": [
    "for each in test_results:\n",
    "    if round(each[4],2) == 7.87:\n",
    "        print(each)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test predict_pIC50_values()\n",
      "['N' 'C' 'V' 'A' 'D' 'Y' 'S' 'V' 'L' 'Y']\n",
      "['HLA-A*01:01', 'HLA-A*02:01']\n",
      "..\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "print('test predict_pIC50_values()')\n",
    "\n",
    "# test for 2 k-mers to ensure the correct number of results are found\n",
    "test_array = spike_array[359:369]\n",
    "print(test_array)\n",
    "\n",
    "test_alleles = class_i_list[:2]\n",
    "print(test_alleles)\n",
    "\n",
    "test_results = hp.predict_pIC50_values(test_alleles,MHC1_df,test_array,9)\n",
    "print(len(test_results))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('HLA-A*01:01', 'NCVADYSVL', 0, 8, 4.691983176635914),\n",
       " ('HLA-A*01:01', 'CVADYSVLY', 1, 9, 7.874116247000001),\n",
       " ('HLA-A*02:01', 'NCVADYSVL', 0, 8, 5.292583412663456),\n",
       " ('HLA-A*02:01', 'CVADYSVLY', 1, 9, 5.3770656045923655)]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...........................\n",
      "48\n"
     ]
    }
   ],
   "source": [
    "# test for MHC II to ensure the correct number of results are found\n",
    "# 3 alleles currently have no data so there should be 48 results\n",
    "\n",
    "test_array = spike_array[:16]\n",
    "test_results = hp.predict_pIC50_values(class_ii_list,MHC2_df,test_array,15)\n",
    "print(len(test_results))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3. Apply pIC50 Algorithm to MHC I  data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...........................\n",
      "CPU times: user 9min 21s, sys: 3.28 s, total: 9min 24s\n",
      "Wall time: 9min 27s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# 9 min for RandomForest with max_depth=2, random_state=0\n",
    "results_mhc1 = hp.predict_pIC50_values(class_i_list,MHC1_df,spike_array,9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# validate result\n",
    "\n",
    "# There are 1265 9-mers over sequence of 1273 letters\n",
    "expected = 27 * 1265\n",
    "actual = len(results_mhc1)\n",
    "assert expected == actual,actual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('HLA-A*01:01', 'CVADYSVLY', 360, 368, 7.874116247000001)\n",
      "('HLA-A*02:01', 'CVADYSVLY', 360, 368, 5.3770656045923655)\n",
      "('HLA-A*02:03', 'CVADYSVLY', 360, 368, 5.220575851750908)\n",
      "('HLA-A*02:06', 'CVADYSVLY', 360, 368, 5.087162087083698)\n",
      "('HLA-A*03:01', 'CVADYSVLY', 360, 368, 5.340607655234088)\n",
      "('HLA-A*11:01', 'CVADYSVLY', 360, 368, 5.475690080494777)\n",
      "('HLA-A*23:01', 'CVADYSVLY', 360, 368, 4.245378088)\n",
      "('HLA-A*24:02', 'CVADYSVLY', 360, 368, 4.11021548)\n",
      "('HLA-A*26:01', 'CVADYSVLY', 360, 368, 9.0)\n",
      "('HLA-A*30:01', 'CVADYSVLY', 360, 368, 5.025737213120219)\n",
      "('HLA-A*30:02', 'CVADYSVLY', 360, 368, 7.415541367)\n",
      "('HLA-A*31:01', 'CVADYSVLY', 360, 368, 4.893925228428852)\n",
      "('HLA-A*32:01', 'CVADYSVLY', 360, 368, 5.326367967120417)\n",
      "('HLA-A*33:01', 'CVADYSVLY', 360, 368, 4.621358255429664)\n",
      "('HLA-A*68:01', 'CVADYSVLY', 360, 368, 5.525344377018525)\n",
      "('HLA-A*68:02', 'CVADYSVLY', 360, 368, 5.2114482598819105)\n",
      "('HLA-B*07:02', 'CVADYSVLY', 360, 368, 4.892958847735635)\n",
      "('HLA-B*08:01', 'CVADYSVLY', 360, 368, 4.732034954724736)\n",
      "('HLA-B*15:01', 'CVADYSVLY', 360, 368, 5.977661255746697)\n",
      "('HLA-B*35:01', 'CVADYSVLY', 360, 368, 6.472212725139966)\n",
      "('HLA-B*40:01', 'CVADYSVLY', 360, 368, 4.796096701654636)\n",
      "('HLA-B*44:02', 'CVADYSVLY', 360, 368, 4.753590074599853)\n",
      "('HLA-B*44:03', 'CVADYSVLY', 360, 368, 4.579668060924763)\n",
      "('HLA-B*51:01', 'CVADYSVLY', 360, 368, 4.7130343035260225)\n",
      "('HLA-B*53:01', 'CVADYSVLY', 360, 368, 5.437747336221915)\n",
      "('HLA-B*57:01', 'CVADYSVLY', 360, 368, 5.102809342801464)\n",
      "('HLA-B*58:01', 'CVADYSVLY', 360, 368, 5.505511150238541)\n"
     ]
    }
   ],
   "source": [
    "# check whether results are reasonable\n",
    "for each in results_mhc1:\n",
    "    if each[1] == 'CVADYSVLY':\n",
    "        print(each)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3. Apply pIC50 Algorithm to MHC II  data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...........................\n",
      "CPU times: user 6min 14s, sys: 2.27 s, total: 6min 16s\n",
      "Wall time: 6min 18s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# 6 min for RandomForest with max_depth=2, random_state=0\n",
    "results_mhc2 = hp.predict_pIC50_values(class_ii_list,MHC2_df,spike_array,15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# validate result\n",
    "\n",
    "# There are 1259 15-mers over sequence of 1273 letters\n",
    "# 3 alleles have no data\n",
    "expected = 24 * 1259\n",
    "actual = len(results_mhc2)\n",
    "assert expected == actual,actual"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3. Calculate metrics for table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MHC Class</th>\n",
       "      <th>Min</th>\n",
       "      <th>Max</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Median</th>\n",
       "      <th>Std Dev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I</td>\n",
       "      <td>3.970037</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.190039</td>\n",
       "      <td>5.076415</td>\n",
       "      <td>0.521639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>II</td>\n",
       "      <td>4.955908</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.837599</td>\n",
       "      <td>5.798518</td>\n",
       "      <td>0.460943</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  MHC Class       Min  Max      Mean    Median   Std Dev\n",
       "0         I  3.970037  9.0  5.190039  5.076415  0.521639\n",
       "1        II  4.955908  9.0  5.837599  5.798518  0.460943"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate metrics for MHC I\n",
    "mch1_metrics = hp.calculate_metrics('I',results_mhc1)\n",
    "\n",
    "# calculate metrics for MHC II\n",
    "mch2_metrics = hp.calculate_metrics('II',results_mhc2)\n",
    "\n",
    "# format results into a table\n",
    "data_1_3 = [mch1_metrics,mch2_metrics]\n",
    "df_1_3 = pd.DataFrame(data=data_1_3,columns = ['MHC Class','Min','Max','Mean','Median','Std Dev'])\n",
    "df_1_3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P1G9VKcH6x7H"
   },
   "source": [
    "## Part 1.3: Answer\n",
    "\n",
    "**If you would like to manually fill in the table provided in the write-up, please replace the empty table below with a screenshot of your filled in table. Alternatively, you could display the table as the output of the above cell as long as it contains all of the relevant information.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SGFj7y3Cw0x-"
   },
   "source": [
    "# Part 1.4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rOYd4bHF8z9S"
   },
   "source": [
    "## Part 1.4: Work\n",
    "**TODO:**\n",
    "- **Devise and implement an algorithm for selecting SARS-CoV-2 peptides from the set you created in part 1.3 that maximizes the ‘allele coverage’. Please refer to the write-up for definitions and constraints.**\n",
    "- **Use your algorithm to compute the values required for the tables in the cell below.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 Min Selection Algorithm Part 1 - Build Coverage DataFrame\n",
    "- Take the predictions made for all k-mers in the spike sequence\n",
    "- Limit predictions to peptides with pIC50 value greater than p_min\n",
    "- Create a DataFrame which lists \n",
    "  - peptide k-mer from spike sequence\n",
    "  - start position in the spike sequence\n",
    "  - end position in the spike sequence\n",
    "  - for each allele in the reference set\n",
    "    - a flag which denotes if the allele is covered by this peptide \n",
    "    - (1 if covered, 0 otherwise)\n",
    "  - count column which sums all allele flags for this peptide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test get_list_above_p_min()\n",
      "34155\n",
      "\n",
      "4\n",
      "15\n",
      "313\n",
      "2335\n",
      "19696\n",
      "34154\n",
      "34155\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('HLA-A*02:03', 'VVFLHVTYV', 1059, 1067, 9.0),\n",
       " ('HLA-A*02:03', 'FIAGLIAIV', 1219, 1227, 9.0),\n",
       " ('HLA-A*26:01', 'CVADYSVLY', 360, 368, 9.0),\n",
       " ('HLA-A*68:02', 'FIAGLIAIV', 1219, 1227, 9.0)]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('test get_list_above_p_min()')\n",
    "print(len(results_mhc1))\n",
    "print()\n",
    "print(len(vc.get_list_above_p_min(results_mhc1,9.0)))\n",
    "print(len(vc.get_list_above_p_min(results_mhc1,8.0)))\n",
    "print(len(vc.get_list_above_p_min(results_mhc1,7.0)))\n",
    "print(len(vc.get_list_above_p_min(results_mhc1,6.0)))\n",
    "print(len(vc.get_list_above_p_min(results_mhc1,5.0)))\n",
    "print(len(vc.get_list_above_p_min(results_mhc1,4.0)))\n",
    "print(len(vc.get_list_above_p_min(results_mhc1,3.0)))\n",
    "\n",
    "vc.get_list_above_p_min(results_mhc1,9.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test get_peptide_coverage_dict()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'VVFLHVTYV_1059_1067': {'HLA-A*02:03'},\n",
       " 'FIAGLIAIV_1219_1227': {'HLA-A*02:03', 'HLA-A*68:02'},\n",
       " 'CVADYSVLY_360_368': {'HLA-A*26:01'}}"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('test get_peptide_coverage_dict()')\n",
    "test_above = vc.get_list_above_p_min(results_mhc1,9.0)\n",
    "test_d = vc.get_peptide_coverage_dict(test_above)\n",
    "test_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test build_row_df()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>peptide</th>\n",
       "      <td>FIAGLIAIV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start</th>\n",
       "      <td>1219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>end</th>\n",
       "      <td>1227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*01:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*02:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*02:03</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*02:06</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*03:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*11:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*23:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*24:02</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*26:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*30:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*30:02</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*31:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*32:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*33:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*68:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*68:02</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*07:02</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*08:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*15:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*35:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*40:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*44:02</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*44:03</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*51:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*53:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*57:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*58:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     0\n",
       "peptide      FIAGLIAIV\n",
       "start             1219\n",
       "end               1227\n",
       "HLA-A*01:01          0\n",
       "HLA-A*02:01          0\n",
       "HLA-A*02:03          1\n",
       "HLA-A*02:06          0\n",
       "HLA-A*03:01          0\n",
       "HLA-A*11:01          0\n",
       "HLA-A*23:01          0\n",
       "HLA-A*24:02          0\n",
       "HLA-A*26:01          0\n",
       "HLA-A*30:01          0\n",
       "HLA-A*30:02          0\n",
       "HLA-A*31:01          0\n",
       "HLA-A*32:01          0\n",
       "HLA-A*33:01          0\n",
       "HLA-A*68:01          0\n",
       "HLA-A*68:02          1\n",
       "HLA-B*07:02          0\n",
       "HLA-B*08:01          0\n",
       "HLA-B*15:01          0\n",
       "HLA-B*35:01          0\n",
       "HLA-B*40:01          0\n",
       "HLA-B*44:02          0\n",
       "HLA-B*44:03          0\n",
       "HLA-B*51:01          0\n",
       "HLA-B*53:01          0\n",
       "HLA-B*57:01          0\n",
       "HLA-B*58:01          0\n",
       "count                2"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('test build_row_df()')\n",
    "test_row = vc.build_row_df(test_d,'FIAGLIAIV_1219_1227',class_i_list)\n",
    "test_row.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test build_peptide_coverage_df()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>peptide</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>HLA-A*01:01</th>\n",
       "      <th>HLA-A*02:01</th>\n",
       "      <th>HLA-A*02:03</th>\n",
       "      <th>HLA-A*02:06</th>\n",
       "      <th>HLA-A*03:01</th>\n",
       "      <th>HLA-A*11:01</th>\n",
       "      <th>HLA-A*23:01</th>\n",
       "      <th>...</th>\n",
       "      <th>HLA-B*15:01</th>\n",
       "      <th>HLA-B*35:01</th>\n",
       "      <th>HLA-B*40:01</th>\n",
       "      <th>HLA-B*44:02</th>\n",
       "      <th>HLA-B*44:03</th>\n",
       "      <th>HLA-B*51:01</th>\n",
       "      <th>HLA-B*53:01</th>\n",
       "      <th>HLA-B*57:01</th>\n",
       "      <th>HLA-B*58:01</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VVFLHVTYV</td>\n",
       "      <td>1059</td>\n",
       "      <td>1067</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FIAGLIAIV</td>\n",
       "      <td>1219</td>\n",
       "      <td>1227</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CVADYSVLY</td>\n",
       "      <td>360</td>\n",
       "      <td>368</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     peptide  start   end  HLA-A*01:01  HLA-A*02:01  HLA-A*02:03  HLA-A*02:06  \\\n",
       "0  VVFLHVTYV   1059  1067            0            0            1            0   \n",
       "0  FIAGLIAIV   1219  1227            0            0            1            0   \n",
       "0  CVADYSVLY    360   368            0            0            0            0   \n",
       "\n",
       "   HLA-A*03:01  HLA-A*11:01  HLA-A*23:01  ...  HLA-B*15:01  HLA-B*35:01  \\\n",
       "0            0            0            0  ...            0            0   \n",
       "0            0            0            0  ...            0            0   \n",
       "0            0            0            0  ...            0            0   \n",
       "\n",
       "   HLA-B*40:01  HLA-B*44:02  HLA-B*44:03  HLA-B*51:01  HLA-B*53:01  \\\n",
       "0            0            0            0            0            0   \n",
       "0            0            0            0            0            0   \n",
       "0            0            0            0            0            0   \n",
       "\n",
       "   HLA-B*57:01  HLA-B*58:01  count  \n",
       "0            0            0      1  \n",
       "0            0            0      2  \n",
       "0            0            0      1  \n",
       "\n",
       "[3 rows x 31 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('test build_peptide_coverage_df()')\n",
    "test_coverage_df = vc.build_peptide_coverage_df(test_d, class_i_list)\n",
    "test_coverage_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test get_peptide_coverage_df()\n",
      "0\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>peptide</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>HLA-A*01:01</th>\n",
       "      <th>HLA-A*02:01</th>\n",
       "      <th>HLA-A*02:03</th>\n",
       "      <th>HLA-A*02:06</th>\n",
       "      <th>HLA-A*03:01</th>\n",
       "      <th>HLA-A*11:01</th>\n",
       "      <th>HLA-A*23:01</th>\n",
       "      <th>...</th>\n",
       "      <th>HLA-B*15:01</th>\n",
       "      <th>HLA-B*35:01</th>\n",
       "      <th>HLA-B*40:01</th>\n",
       "      <th>HLA-B*44:02</th>\n",
       "      <th>HLA-B*44:03</th>\n",
       "      <th>HLA-B*51:01</th>\n",
       "      <th>HLA-B*53:01</th>\n",
       "      <th>HLA-B*57:01</th>\n",
       "      <th>HLA-B*58:01</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VVFLHVTYV</td>\n",
       "      <td>1059</td>\n",
       "      <td>1067</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FIAGLIAIV</td>\n",
       "      <td>1219</td>\n",
       "      <td>1227</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CVADYSVLY</td>\n",
       "      <td>360</td>\n",
       "      <td>368</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     peptide  start   end  HLA-A*01:01  HLA-A*02:01  HLA-A*02:03  HLA-A*02:06  \\\n",
       "0  VVFLHVTYV   1059  1067            0            0            1            0   \n",
       "0  FIAGLIAIV   1219  1227            0            0            1            0   \n",
       "0  CVADYSVLY    360   368            0            0            0            0   \n",
       "\n",
       "   HLA-A*03:01  HLA-A*11:01  HLA-A*23:01  ...  HLA-B*15:01  HLA-B*35:01  \\\n",
       "0            0            0            0  ...            0            0   \n",
       "0            0            0            0  ...            0            0   \n",
       "0            0            0            0  ...            0            0   \n",
       "\n",
       "   HLA-B*40:01  HLA-B*44:02  HLA-B*44:03  HLA-B*51:01  HLA-B*53:01  \\\n",
       "0            0            0            0            0            0   \n",
       "0            0            0            0            0            0   \n",
       "0            0            0            0            0            0   \n",
       "\n",
       "   HLA-B*57:01  HLA-B*58:01  count  \n",
       "0            0            0      1  \n",
       "0            0            0      2  \n",
       "0            0            0      1  \n",
       "\n",
       "[3 rows x 31 columns]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('test get_peptide_coverage_df()')\n",
    "\n",
    "test_coverage_df = vc.get_peptide_coverage_df(results_mhc1,10.0,class_i_list)\n",
    "print(len(test_coverage_df))\n",
    "\n",
    "test_coverage_df = vc.get_peptide_coverage_df(results_mhc1,9.0,class_i_list)\n",
    "test_coverage_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4. Check coverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27\n",
      "25\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "# check coverage of alleles mhc 1\n",
    "\n",
    "for p_min in [3.0,6.0,9.0]:\n",
    "    mhc1_coverage_df = vc.get_peptide_coverage_df(results_mhc1,p_min,class_i_list)\n",
    "    a = pd.DataFrame(mhc1_coverage_df.drop(['peptide','start','end','count'],axis=1).sum(axis=0).sort_values(),columns=['total']).reset_index()\n",
    "    a.rename(columns={'index':'HLA Allele'})\n",
    "    print(a[a['total'] > 0].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\n",
      "21\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "# check coverage of alleles mhc 2\n",
    "\n",
    "for p_min in [3.0,6.0,9.0]:\n",
    "    df_coverage_mhc2 = vc.get_peptide_coverage_df(results_mhc2,p_min,class_ii_list)\n",
    "    a = pd.DataFrame(df_coverage_mhc2.drop(['peptide','start','end','count'],axis=1).sum(axis=0).sort_values(),columns=['total']).reset_index()\n",
    "    a.rename(columns={'index':'HLA Allele'})\n",
    "    print(a[a['total'] > 0].shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 Min Selection Algorithm Part 2 - Select Peptides for Minimum Coverage\n",
    "Choose peptides which cover as many alleles as possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test get_allele_least_coverage()\n"
     ]
    },
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>peptide</th>\n",
       "      <td>VVFLHVTYV</td>\n",
       "      <td>FIAGLIAIV</td>\n",
       "      <td>CVADYSVLY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start</th>\n",
       "      <td>1059</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*01:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*02:01</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*02:03</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*02:06</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*03:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*11:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*23:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*24:02</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*26:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*30:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*30:02</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*31:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*32:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*33:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*68:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*68:02</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*07:02</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*08:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*15:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*35:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*40:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*44:02</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*44:03</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*51:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*53:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*57:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*58:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     0          0          0\n",
       "peptide      VVFLHVTYV  FIAGLIAIV  CVADYSVLY\n",
       "start             1059       1219        360\n",
       "end               1067       1227        368\n",
       "HLA-A*01:01          0          0          0\n",
       "HLA-A*02:01          0          0          0\n",
       "HLA-A*02:03          1          1          0\n",
       "HLA-A*02:06          0          0          0\n",
       "HLA-A*03:01          0          0          0\n",
       "HLA-A*11:01          0          0          0\n",
       "HLA-A*23:01          0          0          0\n",
       "HLA-A*24:02          0          0          0\n",
       "HLA-A*26:01          0          0          1\n",
       "HLA-A*30:01          0          0          0\n",
       "HLA-A*30:02          0          0          0\n",
       "HLA-A*31:01          0          0          0\n",
       "HLA-A*32:01          0          0          0\n",
       "HLA-A*33:01          0          0          0\n",
       "HLA-A*68:01          0          0          0\n",
       "HLA-A*68:02          0          1          0\n",
       "HLA-B*07:02          0          0          0\n",
       "HLA-B*08:01          0          0          0\n",
       "HLA-B*15:01          0          0          0\n",
       "HLA-B*35:01          0          0          0\n",
       "HLA-B*40:01          0          0          0\n",
       "HLA-B*44:02          0          0          0\n",
       "HLA-B*44:03          0          0          0\n",
       "HLA-B*51:01          0          0          0\n",
       "HLA-B*53:01          0          0          0\n",
       "HLA-B*57:01          0          0          0\n",
       "HLA-B*58:01          0          0          0\n",
       "count                1          2          1"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('test get_allele_least_coverage()')\n",
    "test_coverage_df.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('HLA-A*68:02', 1)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vc.get_allele_least_coverage(test_coverage_df.head(),['HLA-A*02:06','HLA-A*02:03','HLA-A*68:02'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test find_first_peptide_cover()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'FIAGLIAIV'"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('test find_first_peptide_cover()')\n",
    "test_result = vc.find_first_peptide_cover(test_coverage_df,'HLA-A*68:02')\n",
    "test_result['peptide'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test update_selected_peptides()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>peptide</th>\n",
       "      <td>FIAGLIAIV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start</th>\n",
       "      <td>1219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>end</th>\n",
       "      <td>1227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*01:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*02:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*02:03</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*02:06</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*03:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*11:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*23:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*24:02</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*26:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*30:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*30:02</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*31:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*32:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*33:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*68:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*68:02</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*07:02</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*08:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*15:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*35:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*40:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*44:02</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*44:03</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*51:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*53:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*57:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*58:01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     0\n",
       "peptide      FIAGLIAIV\n",
       "start             1219\n",
       "end               1227\n",
       "HLA-A*01:01          0\n",
       "HLA-A*02:01          0\n",
       "HLA-A*02:03          1\n",
       "HLA-A*02:06          0\n",
       "HLA-A*03:01          0\n",
       "HLA-A*11:01          0\n",
       "HLA-A*23:01          0\n",
       "HLA-A*24:02          0\n",
       "HLA-A*26:01          0\n",
       "HLA-A*30:01          0\n",
       "HLA-A*30:02          0\n",
       "HLA-A*31:01          0\n",
       "HLA-A*32:01          0\n",
       "HLA-A*33:01          0\n",
       "HLA-A*68:01          0\n",
       "HLA-A*68:02          1\n",
       "HLA-B*07:02          0\n",
       "HLA-B*08:01          0\n",
       "HLA-B*15:01          0\n",
       "HLA-B*35:01          0\n",
       "HLA-B*40:01          0\n",
       "HLA-B*44:02          0\n",
       "HLA-B*44:03          0\n",
       "HLA-B*51:01          0\n",
       "HLA-B*53:01          0\n",
       "HLA-B*57:01          0\n",
       "HLA-B*58:01          0\n",
       "count                2"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('test update_selected_peptides()')\n",
    "\n",
    "test_selected_df = test_coverage_df[test_coverage_df['peptide']=='FIAGLIAIV']\n",
    "test_selected_df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'FIAGLIAIV'"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select the peptide with the highest coverage count\n",
    "test_selected_peptide = test_selected_df.head(1)\n",
    "test_peptide_dict = test_selected_peptide.to_dict()\n",
    "test_peptide_dict['peptide'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['FIAGLIAIV']"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_selected = []\n",
    "vc.update_selected_peptides(test_peptide_dict,test_selected)\n",
    "test_selected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test update_uncovered_alleles()\n",
      "HLA-A*01:01, HLA-A*02:01, HLA-A*02:03, HLA-A*02:06, HLA-A*03:01, HLA-A*11:01, HLA-A*23:01, HLA-A*24:02, HLA-A*26:01, HLA-A*30:01, HLA-A*30:02, HLA-A*31:01, HLA-A*32:01, HLA-A*33:01, HLA-A*68:01, HLA-A*68:02, HLA-B*07:02, HLA-B*08:01, HLA-B*15:01, HLA-B*35:01, HLA-B*40:01, HLA-B*44:02, HLA-B*44:03, HLA-B*51:01, HLA-B*53:01, HLA-B*57:01, HLA-B*58:01\n",
      "\n",
      "uncovered 27\n",
      "covered 2\n",
      "uncovered 25\n",
      "HLA-A*01:01, HLA-A*02:01, HLA-A*02:06, HLA-A*03:01, HLA-A*11:01, HLA-A*23:01, HLA-A*24:02, HLA-A*26:01, HLA-A*30:01, HLA-A*30:02, HLA-A*31:01, HLA-A*32:01, HLA-A*33:01, HLA-A*68:01, HLA-B*07:02, HLA-B*08:01, HLA-B*15:01, HLA-B*35:01, HLA-B*40:01, HLA-B*44:02, HLA-B*44:03, HLA-B*51:01, HLA-B*53:01, HLA-B*57:01, HLA-B*58:01\n"
     ]
    }
   ],
   "source": [
    "print('test update_uncovered_alleles()')\n",
    "test_uncovered = class_i_list.copy()\n",
    "print(*test_uncovered,sep=', ')\n",
    "print()\n",
    "test_num_covered = 0\n",
    "print('uncovered',len(test_uncovered))\n",
    "\n",
    "test_num_covered = vc.update_uncovered_alleles(test_peptide_dict,test_uncovered,test_num_covered)\n",
    "print('covered',test_num_covered)\n",
    "print('uncovered',len(test_uncovered))\n",
    "print(*test_uncovered,sep=', ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test get_nonoverlapping_peptides()\n",
      "(1265, 31)\n",
      "(1248, 31)\n"
     ]
    }
   ],
   "source": [
    "print('test get_nonoverlapping_peptides()')\n",
    "test_coverage_df = vc.get_peptide_coverage_df(results_mhc1,3.0,class_i_list)\n",
    "test_coverage_df\n",
    "\n",
    "print(test_coverage_df.shape)\n",
    "r1,r2,retained = vc.get_nonoverlapping_peptides(test_peptide_dict,test_coverage_df)\n",
    "print(retained.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>peptide</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>HLA-A*01:01</th>\n",
       "      <th>HLA-A*02:01</th>\n",
       "      <th>HLA-A*02:03</th>\n",
       "      <th>HLA-A*02:06</th>\n",
       "      <th>HLA-A*03:01</th>\n",
       "      <th>HLA-A*11:01</th>\n",
       "      <th>HLA-A*23:01</th>\n",
       "      <th>...</th>\n",
       "      <th>HLA-B*15:01</th>\n",
       "      <th>HLA-B*35:01</th>\n",
       "      <th>HLA-B*40:01</th>\n",
       "      <th>HLA-B*44:02</th>\n",
       "      <th>HLA-B*44:03</th>\n",
       "      <th>HLA-B*51:01</th>\n",
       "      <th>HLA-B*53:01</th>\n",
       "      <th>HLA-B*57:01</th>\n",
       "      <th>HLA-B*58:01</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FIAGLIAIV</td>\n",
       "      <td>1219</td>\n",
       "      <td>1227</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IAGLIAIVM</td>\n",
       "      <td>1220</td>\n",
       "      <td>1228</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AGLIAIVMV</td>\n",
       "      <td>1221</td>\n",
       "      <td>1229</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GLIAIVMVT</td>\n",
       "      <td>1222</td>\n",
       "      <td>1230</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LIAIVMVTI</td>\n",
       "      <td>1223</td>\n",
       "      <td>1231</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IAIVMVTIM</td>\n",
       "      <td>1224</td>\n",
       "      <td>1232</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AIVMVTIML</td>\n",
       "      <td>1225</td>\n",
       "      <td>1233</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IVMVTIMLC</td>\n",
       "      <td>1226</td>\n",
       "      <td>1234</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VMVTIMLCC</td>\n",
       "      <td>1227</td>\n",
       "      <td>1235</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     peptide  start   end  HLA-A*01:01  HLA-A*02:01  HLA-A*02:03  HLA-A*02:06  \\\n",
       "0  FIAGLIAIV   1219  1227            1            1            1            1   \n",
       "0  IAGLIAIVM   1220  1228            1            1            1            1   \n",
       "0  AGLIAIVMV   1221  1229            1            1            1            1   \n",
       "0  GLIAIVMVT   1222  1230            1            1            1            1   \n",
       "0  LIAIVMVTI   1223  1231            1            1            1            1   \n",
       "0  IAIVMVTIM   1224  1232            1            1            1            1   \n",
       "0  AIVMVTIML   1225  1233            1            1            1            1   \n",
       "0  IVMVTIMLC   1226  1234            1            1            1            1   \n",
       "0  VMVTIMLCC   1227  1235            1            1            1            1   \n",
       "\n",
       "   HLA-A*03:01  HLA-A*11:01  HLA-A*23:01  ...  HLA-B*15:01  HLA-B*35:01  \\\n",
       "0            1            1            1  ...            1            1   \n",
       "0            1            1            1  ...            1            1   \n",
       "0            1            1            1  ...            1            1   \n",
       "0            1            1            1  ...            1            1   \n",
       "0            1            1            1  ...            1            1   \n",
       "0            1            1            1  ...            1            1   \n",
       "0            1            1            1  ...            1            1   \n",
       "0            1            1            1  ...            1            1   \n",
       "0            1            1            1  ...            1            1   \n",
       "\n",
       "   HLA-B*40:01  HLA-B*44:02  HLA-B*44:03  HLA-B*51:01  HLA-B*53:01  \\\n",
       "0            1            1            1            1            1   \n",
       "0            1            1            1            1            1   \n",
       "0            1            1            1            1            1   \n",
       "0            1            1            1            1            1   \n",
       "0            1            1            1            1            1   \n",
       "0            1            1            1            1            1   \n",
       "0            1            1            1            1            1   \n",
       "0            1            1            1            1            1   \n",
       "0            1            1            1            1            1   \n",
       "\n",
       "   HLA-B*57:01  HLA-B*58:01  count  \n",
       "0            1            1     27  \n",
       "0            1            1     27  \n",
       "0            1            1     27  \n",
       "0            1            1     27  \n",
       "0            1            1     27  \n",
       "0            1            1     27  \n",
       "0            1            1     27  \n",
       "0            1            1     27  \n",
       "0            1            1     27  \n",
       "\n",
       "[9 rows x 31 columns]"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r1.sort_values(by='start')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>peptide</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>HLA-A*01:01</th>\n",
       "      <th>HLA-A*02:01</th>\n",
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       "      <th>HLA-A*02:06</th>\n",
       "      <th>HLA-A*03:01</th>\n",
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       "      <th>HLA-A*23:01</th>\n",
       "      <th>...</th>\n",
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       "      <th>HLA-B*44:02</th>\n",
       "      <th>HLA-B*44:03</th>\n",
       "      <th>HLA-B*51:01</th>\n",
       "      <th>HLA-B*53:01</th>\n",
       "      <th>HLA-B*57:01</th>\n",
       "      <th>HLA-B*58:01</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WPWYIWLGF</td>\n",
       "      <td>1211</td>\n",
       "      <td>1219</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PWYIWLGFI</td>\n",
       "      <td>1212</td>\n",
       "      <td>1220</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>0</th>\n",
       "      <td>WYIWLGFIA</td>\n",
       "      <td>1213</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>YIWLGFIAG</td>\n",
       "      <td>1214</td>\n",
       "      <td>1222</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>...</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IWLGFIAGL</td>\n",
       "      <td>1215</td>\n",
       "      <td>1223</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>0</th>\n",
       "      <td>WLGFIAGLI</td>\n",
       "      <td>1216</td>\n",
       "      <td>1224</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>0</th>\n",
       "      <td>LGFIAGLIA</td>\n",
       "      <td>1217</td>\n",
       "      <td>1225</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>27</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GFIAGLIAI</td>\n",
       "      <td>1218</td>\n",
       "      <td>1226</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FIAGLIAIV</td>\n",
       "      <td>1219</td>\n",
       "      <td>1227</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>27</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     peptide  start   end  HLA-A*01:01  HLA-A*02:01  HLA-A*02:03  HLA-A*02:06  \\\n",
       "0  WPWYIWLGF   1211  1219            1            1            1            1   \n",
       "0  PWYIWLGFI   1212  1220            1            1            1            1   \n",
       "0  WYIWLGFIA   1213  1221            1            1            1            1   \n",
       "0  YIWLGFIAG   1214  1222            1            1            1            1   \n",
       "0  IWLGFIAGL   1215  1223            1            1            1            1   \n",
       "0  WLGFIAGLI   1216  1224            1            1            1            1   \n",
       "0  LGFIAGLIA   1217  1225            1            1            1            1   \n",
       "0  GFIAGLIAI   1218  1226            1            1            1            1   \n",
       "0  FIAGLIAIV   1219  1227            1            1            1            1   \n",
       "\n",
       "   HLA-A*03:01  HLA-A*11:01  HLA-A*23:01  ...  HLA-B*15:01  HLA-B*35:01  \\\n",
       "0            1            1            1  ...            1            1   \n",
       "0            1            1            1  ...            1            1   \n",
       "0            1            1            1  ...            1            1   \n",
       "0            1            1            1  ...            1            1   \n",
       "0            1            1            1  ...            1            1   \n",
       "0            1            1            1  ...            1            1   \n",
       "0            1            1            1  ...            1            1   \n",
       "0            1            1            1  ...            1            1   \n",
       "0            1            1            1  ...            1            1   \n",
       "\n",
       "   HLA-B*40:01  HLA-B*44:02  HLA-B*44:03  HLA-B*51:01  HLA-B*53:01  \\\n",
       "0            1            1            1            1            1   \n",
       "0            1            1            1            1            1   \n",
       "0            1            1            1            1            1   \n",
       "0            1            1            1            1            1   \n",
       "0            1            1            1            1            1   \n",
       "0            1            1            1            1            1   \n",
       "0            1            1            1            1            1   \n",
       "0            1            1            1            1            1   \n",
       "0            1            1            1            1            1   \n",
       "\n",
       "   HLA-B*57:01  HLA-B*58:01  count  \n",
       "0            1            1     27  \n",
       "0            1            1     27  \n",
       "0            1            1     27  \n",
       "0            1            1     27  \n",
       "0            1            1     27  \n",
       "0            1            1     27  \n",
       "0            1            1     27  \n",
       "0            1            1     27  \n",
       "0            1            1     27  \n",
       "\n",
       "[9 rows x 31 columns]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2.sort_values(by='start')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(class_i_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test select_peptides()\n",
      "(3, 31)\n",
      "3\n",
      "['FIAGLIAIV', 'CVADYSVLY']\n"
     ]
    }
   ],
   "source": [
    "print('test select_peptides()')\n",
    "\n",
    "test_coverage_df = vc.get_peptide_coverage_df(results_mhc1,9.0,class_i_list)\n",
    "print(test_coverage_df.shape)\n",
    "\n",
    "test_num_covered,test_selected = vc.select_peptides(test_coverage_df,class_i_list,10)\n",
    "print(test_num_covered)\n",
    "print(test_selected)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
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       "      <th>HLA-B*15:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>HLA-B*35:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>HLA-B*40:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>HLA-B*44:02</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>HLA-B*44:03</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>HLA-B*51:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>HLA-B*53:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*57:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>HLA-B*58:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     0          0          0\n",
       "peptide      VVFLHVTYV  FIAGLIAIV  CVADYSVLY\n",
       "start             1059       1219        360\n",
       "end               1067       1227        368\n",
       "HLA-A*01:01          0          0          0\n",
       "HLA-A*02:01          0          0          0\n",
       "HLA-A*02:03          1          1          0\n",
       "HLA-A*02:06          0          0          0\n",
       "HLA-A*03:01          0          0          0\n",
       "HLA-A*11:01          0          0          0\n",
       "HLA-A*23:01          0          0          0\n",
       "HLA-A*24:02          0          0          0\n",
       "HLA-A*26:01          0          0          1\n",
       "HLA-A*30:01          0          0          0\n",
       "HLA-A*30:02          0          0          0\n",
       "HLA-A*31:01          0          0          0\n",
       "HLA-A*32:01          0          0          0\n",
       "HLA-A*33:01          0          0          0\n",
       "HLA-A*68:01          0          0          0\n",
       "HLA-A*68:02          0          1          0\n",
       "HLA-B*07:02          0          0          0\n",
       "HLA-B*08:01          0          0          0\n",
       "HLA-B*15:01          0          0          0\n",
       "HLA-B*35:01          0          0          0\n",
       "HLA-B*40:01          0          0          0\n",
       "HLA-B*44:02          0          0          0\n",
       "HLA-B*44:03          0          0          0\n",
       "HLA-B*51:01          0          0          0\n",
       "HLA-B*53:01          0          0          0\n",
       "HLA-B*57:01          0          0          0\n",
       "HLA-B*58:01          0          0          0\n",
       "count                1          2          1"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_coverage_df.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4. Apply Min Selection Algorithm to MHC I predictions\n",
    "For all required design limits and p_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "design_limits = [10000,10,20]\n",
    "p_mins = [3.0,6.0,9.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 19.7 s, sys: 132 ms, total: 19.9 s\n",
      "Wall time: 19.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# MHC I\n",
    "designs_mhc1, coverages_mhc1 = vc.design_coverage(class_i_list, results_mhc1, design_limits, p_mins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 3.0, 27, ['MFVFLVLLP'])\n",
      "(10000, 6.0, 25, ['CVADYSVLY', 'QELGKYEQY', 'YYHKNNKSW', 'MTSCCSCLK', 'QPRTFLLKY', 'VVFLHVTYV'])\n",
      "(10000, 9.0, 3, ['FIAGLIAIV', 'CVADYSVLY'])\n",
      "(10, 3.0, 27, ['MFVFLVLLP'])\n",
      "(10, 6.0, 25, ['CVADYSVLY', 'QELGKYEQY', 'YYHKNNKSW', 'MTSCCSCLK', 'QPRTFLLKY', 'VVFLHVTYV'])\n",
      "(10, 9.0, 3, ['FIAGLIAIV', 'CVADYSVLY'])\n",
      "(20, 3.0, 27, ['MFVFLVLLP'])\n",
      "(20, 6.0, 25, ['CVADYSVLY', 'QELGKYEQY', 'YYHKNNKSW', 'MTSCCSCLK', 'QPRTFLLKY', 'VVFLHVTYV'])\n",
      "(20, 9.0, 3, ['FIAGLIAIV', 'CVADYSVLY'])\n"
     ]
    }
   ],
   "source": [
    "for design in designs_mhc1:\n",
    "    print(design)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
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       "      <th>HLA-A*33:01</th>\n",
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       "      <th>HLA-B*40:01</th>\n",
       "      <td>1</td>\n",
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       "      <th>HLA-B*44:02</th>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "                     0\n",
       "peptide      MFVFLVLLP\n",
       "start                0\n",
       "end                  8\n",
       "HLA-A*01:01          1\n",
       "HLA-A*02:01          1\n",
       "HLA-A*02:03          1\n",
       "HLA-A*02:06          1\n",
       "HLA-A*03:01          1\n",
       "HLA-A*11:01          1\n",
       "HLA-A*23:01          1\n",
       "HLA-A*24:02          1\n",
       "HLA-A*26:01          1\n",
       "HLA-A*30:01          1\n",
       "HLA-A*30:02          1\n",
       "HLA-A*31:01          1\n",
       "HLA-A*32:01          1\n",
       "HLA-A*33:01          1\n",
       "HLA-A*68:01          1\n",
       "HLA-A*68:02          1\n",
       "HLA-B*07:02          1\n",
       "HLA-B*08:01          1\n",
       "HLA-B*15:01          1\n",
       "HLA-B*35:01          1\n",
       "HLA-B*40:01          1\n",
       "HLA-B*44:02          1\n",
       "HLA-B*44:03          1\n",
       "HLA-B*51:01          1\n",
       "HLA-B*53:01          1\n",
       "HLA-B*57:01          1\n",
       "HLA-B*58:01          1\n",
       "count               27"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check results\n",
    "coverage_10_3_mhc1 = coverages_mhc1[3]\n",
    "coverage_10_3_mhc1[coverage_10_3_mhc1['peptide'] == 'MFVFLVLLP'].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>HLA-B*15:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*35:01</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*40:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*44:02</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*44:03</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*51:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*53:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*57:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*58:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     0          0          0          0          0          0\n",
       "peptide      CVADYSVLY  VVFLHVTYV  MTSCCSCLK  YYHKNNKSW  QPRTFLLKY  QELGKYEQY\n",
       "start              360       1059       1236        143        270       1200\n",
       "end                368       1067       1244        151        278       1208\n",
       "HLA-A*01:01          1          0          0          0          0          0\n",
       "HLA-A*02:01          0          1          0          0          0          0\n",
       "HLA-A*02:03          0          1          0          0          0          0\n",
       "HLA-A*02:06          0          1          0          0          0          0\n",
       "HLA-A*03:01          0          0          1          0          0          0\n",
       "HLA-A*11:01          0          0          1          0          0          0\n",
       "HLA-A*23:01          0          0          0          1          0          0\n",
       "HLA-A*24:02          0          0          0          1          0          0\n",
       "HLA-A*26:01          1          0          0          0          0          0\n",
       "HLA-A*30:01          0          1          1          1          0          0\n",
       "HLA-A*30:02          1          0          0          0          1          1\n",
       "HLA-A*31:01          0          0          1          0          0          0\n",
       "HLA-A*32:01          0          1          0          0          0          1\n",
       "HLA-A*33:01          0          0          1          0          0          0\n",
       "HLA-A*68:01          0          0          1          0          0          0\n",
       "HLA-A*68:02          0          1          1          0          0          0\n",
       "HLA-B*07:02          0          0          0          0          1          0\n",
       "HLA-B*08:01          0          0          0          0          0          0\n",
       "HLA-B*15:01          0          0          0          0          1          1\n",
       "HLA-B*35:01          1          0          0          0          1          1\n",
       "HLA-B*40:01          0          0          0          0          0          1\n",
       "HLA-B*44:02          0          0          0          0          0          1\n",
       "HLA-B*44:03          0          0          0          0          0          1\n",
       "HLA-B*51:01          0          0          0          0          0          0\n",
       "HLA-B*53:01          0          0          0          0          1          0\n",
       "HLA-B*57:01          0          0          0          1          0          0\n",
       "HLA-B*58:01          0          0          0          1          0          0\n",
       "count                4          6          7          5          5          7"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check results\n",
    "coverage_10_6_mhc1 = coverages_mhc1[4]\n",
    "coverage_10_6_mhc1[coverage_10_6_mhc1['peptide'].isin(['CVADYSVLY', 'QELGKYEQY', 'YYHKNNKSW', 'MTSCCSCLK', 'QPRTFLLKY', 'VVFLHVTYV'])].T\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check if missing alleles have coverage at that p_min\n",
    "missing_coverage = []\n",
    "for each in results_mhc1:\n",
    "    if each[0] in ['HLA-B*08:01','HLA-B*51:01'] and each[4] > 6.0:\n",
    "        missing_coverage.append(each)\n",
    "        \n",
    "len(missing_coverage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>peptide</th>\n",
       "      <td>FIAGLIAIV</td>\n",
       "      <td>CVADYSVLY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start</th>\n",
       "      <td>1219</td>\n",
       "      <td>360</td>\n",
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       "      <th>end</th>\n",
       "      <td>1227</td>\n",
       "      <td>368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*01:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*02:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*02:03</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*02:06</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*03:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*11:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*23:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*24:02</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*26:01</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*30:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*30:02</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*31:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*32:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*33:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*68:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*68:02</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*07:02</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*08:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*15:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*35:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*40:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*44:02</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*44:03</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*51:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*53:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*57:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*58:01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     0          0\n",
       "peptide      FIAGLIAIV  CVADYSVLY\n",
       "start             1219        360\n",
       "end               1227        368\n",
       "HLA-A*01:01          0          0\n",
       "HLA-A*02:01          0          0\n",
       "HLA-A*02:03          1          0\n",
       "HLA-A*02:06          0          0\n",
       "HLA-A*03:01          0          0\n",
       "HLA-A*11:01          0          0\n",
       "HLA-A*23:01          0          0\n",
       "HLA-A*24:02          0          0\n",
       "HLA-A*26:01          0          1\n",
       "HLA-A*30:01          0          0\n",
       "HLA-A*30:02          0          0\n",
       "HLA-A*31:01          0          0\n",
       "HLA-A*32:01          0          0\n",
       "HLA-A*33:01          0          0\n",
       "HLA-A*68:01          0          0\n",
       "HLA-A*68:02          1          0\n",
       "HLA-B*07:02          0          0\n",
       "HLA-B*08:01          0          0\n",
       "HLA-B*15:01          0          0\n",
       "HLA-B*35:01          0          0\n",
       "HLA-B*40:01          0          0\n",
       "HLA-B*44:02          0          0\n",
       "HLA-B*44:03          0          0\n",
       "HLA-B*51:01          0          0\n",
       "HLA-B*53:01          0          0\n",
       "HLA-B*57:01          0          0\n",
       "HLA-B*58:01          0          0\n",
       "count                2          1"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check results\n",
    "coverage_10_9_mhc1 = coverages_mhc1[5]\n",
    "coverage_10_9_mhc1[coverage_10_9_mhc1['peptide'].isin(['FIAGLIAIV', 'CVADYSVLY'])].T\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4. Apply Min Selection Algorithm to MHC II predictions\n",
    "For all required design limits and p_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 22.3 s, sys: 153 ms, total: 22.5 s\n",
      "Wall time: 22.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# MHC II\n",
    "designs_mhc2, coverages_mhc2 = vc.design_coverage(class_ii_list, results_mhc2, design_limits, p_mins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 3.0, 24, ['MFVFLVLLPLVSSQC'])\n",
      "(10000, 6.0, 21, ['EMIAQYTSALLAGTI', 'WRVYSTGSNVFQTRA', 'QQLIRAAEIRASANL', 'ASVYAWNRKRISNCV'])\n",
      "(10000, 9.0, 1, ['IDRLITGRLQSLQTY'])\n",
      "(10, 3.0, 24, ['MFVFLVLLPLVSSQC'])\n",
      "(10, 6.0, 21, ['EMIAQYTSALLAGTI', 'WRVYSTGSNVFQTRA', 'QQLIRAAEIRASANL', 'ASVYAWNRKRISNCV'])\n",
      "(10, 9.0, 1, ['IDRLITGRLQSLQTY'])\n",
      "(20, 3.0, 24, ['MFVFLVLLPLVSSQC'])\n",
      "(20, 6.0, 21, ['EMIAQYTSALLAGTI', 'WRVYSTGSNVFQTRA', 'QQLIRAAEIRASANL', 'ASVYAWNRKRISNCV'])\n",
      "(20, 9.0, 1, ['IDRLITGRLQSLQTY'])\n"
     ]
    }
   ],
   "source": [
    "for design in designs_mhc2:\n",
    "    print(design)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4. Design Algorithm to Improve Coverage Fairness\n",
    "The min selection algorithm doesn't need 10 or 20 peptides to provides maximum coverage for an allele set. The minimum cover doesn't necessary result in equal distribution in allele coverage. \n",
    "\n",
    "With the remaining space in each design, I can add peptides to make the allele coverage more equal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test get_design_coverage()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>peptide</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>HLA-A*01:01</th>\n",
       "      <th>HLA-A*02:01</th>\n",
       "      <th>HLA-A*02:03</th>\n",
       "      <th>HLA-A*02:06</th>\n",
       "      <th>HLA-A*03:01</th>\n",
       "      <th>HLA-A*11:01</th>\n",
       "      <th>HLA-A*23:01</th>\n",
       "      <th>...</th>\n",
       "      <th>HLA-B*15:01</th>\n",
       "      <th>HLA-B*35:01</th>\n",
       "      <th>HLA-B*40:01</th>\n",
       "      <th>HLA-B*44:02</th>\n",
       "      <th>HLA-B*44:03</th>\n",
       "      <th>HLA-B*51:01</th>\n",
       "      <th>HLA-B*53:01</th>\n",
       "      <th>HLA-B*57:01</th>\n",
       "      <th>HLA-B*58:01</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FIAGLIAIV</td>\n",
       "      <td>1219</td>\n",
       "      <td>1227</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CVADYSVLY</td>\n",
       "      <td>360</td>\n",
       "      <td>368</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     peptide  start   end  HLA-A*01:01  HLA-A*02:01  HLA-A*02:03  HLA-A*02:06  \\\n",
       "0  FIAGLIAIV   1219  1227            0            0            1            0   \n",
       "0  CVADYSVLY    360   368            0            0            0            0   \n",
       "\n",
       "   HLA-A*03:01  HLA-A*11:01  HLA-A*23:01  ...  HLA-B*15:01  HLA-B*35:01  \\\n",
       "0            0            0            0  ...            0            0   \n",
       "0            0            0            0  ...            0            0   \n",
       "\n",
       "   HLA-B*40:01  HLA-B*44:02  HLA-B*44:03  HLA-B*51:01  HLA-B*53:01  \\\n",
       "0            0            0            0            0            0   \n",
       "0            0            0            0            0            0   \n",
       "\n",
       "   HLA-B*57:01  HLA-B*58:01  count  \n",
       "0            0            0      2  \n",
       "0            0            0      1  \n",
       "\n",
       "[2 rows x 31 columns]"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('test get_design_coverage()')\n",
    "\n",
    "test_coverage_df = coverages_mhc1[5]\n",
    "test_design = designs_mhc1[5][3]\n",
    "\n",
    "vc.get_design_coverage(test_design, test_coverage_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test calculate_allele_distribution()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HLA Allele</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HLA-A*01:01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HLA-A*02:01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HLA-A*02:03</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HLA-A*02:06</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HLA-A*03:01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>HLA-A*11:01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>HLA-A*23:01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>HLA-A*24:02</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>HLA-A*26:01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>HLA-A*30:01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HLA-A*30:02</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>HLA-A*31:01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HLA-A*32:01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HLA-A*33:01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HLA-A*68:01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>HLA-A*68:02</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>HLA-B*07:02</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>HLA-B*08:01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>HLA-B*15:01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>HLA-B*35:01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>HLA-B*40:01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>HLA-B*44:02</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>HLA-B*44:03</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>HLA-B*51:01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>HLA-B*53:01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>HLA-B*57:01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>HLA-B*58:01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     HLA Allele  Count\n",
       "0   HLA-A*01:01      0\n",
       "1   HLA-A*02:01      0\n",
       "2   HLA-A*02:03      1\n",
       "3   HLA-A*02:06      0\n",
       "4   HLA-A*03:01      0\n",
       "5   HLA-A*11:01      0\n",
       "6   HLA-A*23:01      0\n",
       "7   HLA-A*24:02      0\n",
       "8   HLA-A*26:01      1\n",
       "9   HLA-A*30:01      0\n",
       "10  HLA-A*30:02      0\n",
       "11  HLA-A*31:01      0\n",
       "12  HLA-A*32:01      0\n",
       "13  HLA-A*33:01      0\n",
       "14  HLA-A*68:01      0\n",
       "15  HLA-A*68:02      1\n",
       "16  HLA-B*07:02      0\n",
       "17  HLA-B*08:01      0\n",
       "18  HLA-B*15:01      0\n",
       "19  HLA-B*35:01      0\n",
       "20  HLA-B*40:01      0\n",
       "21  HLA-B*44:02      0\n",
       "22  HLA-B*44:03      0\n",
       "23  HLA-B*51:01      0\n",
       "24  HLA-B*53:01      0\n",
       "25  HLA-B*57:01      0\n",
       "26  HLA-B*58:01      0"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('test calculate_allele_distribution()')\n",
    "\n",
    "test_dist_df = vc.get_allele_distribution_df(test_design,test_coverage_df)\n",
    "test_dist_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Count', ylabel='HLA Allele'>"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PWoAFMNMkRj46sFv0X+IdtE2KqmR0INkiTxsCl6czos+X+NyFODrQNIVS5qikVwDbRsT/l7QZsEFE/Lry1o0gNkdNXRmoOSrpn8hOx49Lm9YmW1axV73KBLCicl0EsKdI+oKkf0/C1r5p+9aSFkm6PklcRWvH7pREszskfTq33/dJujnVvULSs3t9L8aMAmWGKvsAC4A/QnbFgez0vCrKRgd2KvfXkj4GrC/p3WRBPZDNj9wfES8gi//7Udp+PNnQ46VkQ5Z/LjjW58hugNs2Pean7dcDO0fE9sC36BA9aExdGKSZXKbj+FO6WhAAktaf9lG7U1YAW6NcRFxKNhfxXmDTiPhEKvtOslv0iYiVEfFA2t4zOlDSFsBGEfHz9D18BfibtK9FuUnRKQtgxlTNoKMpy3Qc35R0BrCxpEOBH5CtetaLygSwonKSXge8niy8+EFJ/5C7f+Sjkq6TdJ6kzdO2E4CD0uTsxcDf544xkZ5uSXZJtkWRAPYuCgQwY2aSKszknh1HRJxKdhp+PvBC4CMR8ZkS+65SACsq94N06faPEXEmWQcym+xM4KcRsSPZwkynpvL7A2dFxFxgL+CrkmYB5EKDyghgBwE7A6d0amwvc9SYKqnCTC7lcUTE5WQ3dFXN/sDmkg5Mr58laVvgEOCNqS3zisq1lPG8ACbpQeBRVl/qPY/s7ID0c34q+3NJ6wJzgPtzbVrG5CFIuwD2WrI5lN0jomMXHhFfAL4A2VWVkt+FMQOhCjO58IxD0nJJD3d4LJf08JSPWHy8UtGB3cp12m+al/gusEfa9Brg5vT8rvQaSf8NWBf4bVv93wDLJb0sXU35H2R34iLppWR38C6IiHxnY0xtqMJMLjzjiIgqr5zA1KMDi0SxoohByC4nf1XSJ8k6hr9N298P/Iuko8mGH4e0tHFlMYDzUrkjgLOA9cjmMVpzGacAGwDnpSu0d0XEgqIPbMxMMWgzeSoC2BxgQwtgU8MCmKkrVQtgT6GEAGaMGV0qE8DGxBx9VbrE+6Skt/b6Tnrh6EDTFOoogDXJHL2L7IrP2WU+WDe8dqxpElUKYFOlSebo0ohYAqxsr9cPjg40TaNKAWzczNFun8vRgWakKJXHERGXR8QHI+IDSQYrw9iYo71wdKAZNQo9DknL6fwHIjKvaqMO702Xxpmjg6Al6BzTtjyCA31MXZlJAWwSKhkd2K0cHQSw1Hm0zNEr6WyOntXNHE227MuAq8jM0TJDtb5wdKBpEt2U86d3ewzg2FONDuw3YhAyD+UESUuA/042qUr6eaikG8jiAyeZo7n6R5CZrXcAvyKZo5J2SW1/G3CGpHwkYd84OtA0hUJzVNKvyYYqHcf4EfHcKhs2qtgcNXVlIOZoRDwnIp6bfrY/enYaNRPAvi/pBkk3Sfq8smUdkXS4Vq9B+xNJ2xUcaygCmDFNoa+Uc0nPk/RhZSuoVUUVAth+EbED8GJgs1QX4OyIeEmacF0InFZwrMoFMLA5appDmXtVtpB0lKRfADeRTaj2+qOeDgMXwCKiFQMwm+xem2jbDrA+Ha4iDUMAA5ujpll0mxw9VNKVZHr2HODdwG8i4sSI+GWJfddCAMvVuZTsMutyMqGttf09kn5Fdsbx3tz2ifR02gJYL2yOmqbR7Yzjs8BawAERcXz6n7Uf8akuAhgAEfF6YAtgHeDVue2fjYjnkV15OT63fV56Om0BzOaoGTW6RQc+i2wu4LSkaH+TbE2VKhm4AJbfeUSskHQRsDdrRiGeSzaX0c60BbBe0YE2R03T6HZV5YGI+FxEvIpMkvoDcL+kWyT930E3RBVFB0raIM1TtBaY3gu4Nb3eNlf0jcDt7fW7RQcOCi86bZpG2bDiZWT3d5ya/nA7/pH2ybCiA9cHLpK0DtnQ60qgtcbrkcrChp8AHgIOblVSiehASbuktmwCvFnSiRHxom4fugibo6ZJlIoONIPDApipKwMRwIwxpojKOo6GmKOlFo0eljlqAcw0hamYo8ePkDladtFoRwcak2Mq5uhajI45uih6LBo9DHPUAphpGmNvjuaYtGj0IM1RC2Bm1Bh7cxQ6Lxrt6EBjiunWcTyL7I/yNEm3SfoowzFHD5G0FLgI2EHStpI+ljtzKSzXMkW7maOp/N6tbVq9aPSC6Lxo9NCiAy2AmabQLTrwAbJJwc9Jmkv2v/z9km4BLoiIfxxkQ1RRdKCkDciWrPxNzhz9t/Rea9Ho+VGwaLSjA41Zk7Ip58si4tSI2IlsYnDFAI49rOjAljm6BLiBbJ6jZY7mF42eSPexAI4ONKYbUzJHJd0VEVtX0J6Rx+aoqSvDMEc7TRi2N6IJAlgpgWtYApgxTWGqHUeVN7gMUwArK3A5OtCYHN0WZHpf0Vtk8wJV0RK7zpa0ZUQUKZRrlIuISyU9Rpa18ZGIOBm6CmBLASQVClx5ASy9bglgl5SpX5YLJ+7h2LYFmRbMG2jQmDEDo9sZx4YFjw2AT5XYd9MEsPZjTKSnjg40po1ul2NPnOa+H8tJVEg6hEyy6ka72PVFOiePF5X7QURcLumEiDizNRcBmQCmbInHr5MJYF3XwB10dCDZUIett15zTrlljq7Imestc9RXWEwd6TZU+XTRewAR8d5u70+RmYwOLMLRgca00W2ocm3usaDt9bWDbohmIDqwDI4ONKYDEdHzAVxfplxbnUfaXh8CnJ6enwD8nux/89bjBOCktjrbAze3bStVLvfe5sDVwBKyu3s/A8xO7+2Sjv1H4EHgply9idzznYEbyeSv01ntvxTWL3rstNNOUcQDy1fExF0PxQPLVxSWMaYqgGui5N93KQFM0nURsWP57sgUYQHM1JVhCGDGmDGmWx7HckkPS3oY2L71vLW9144bYo6uI+kbyQi9StI2BccqMkdLRQ+WxQKYaQrd1lXZMCI2So/ZuecbRsRGFbZpmObou4CHIuL5wCeAkwuOVWSOlo0e7ImjA02TqONQZWjRgWSXZb+cnn8LeE3e/YCe0YGLokf0YBksgJmmUWXH0QRzdEvgboCIeJJstbpNU/mJXJky5uik6MG29jo60IwUVXYcTYgOLLRCow9zVB2iBycVdnSgGTHqNlQZdnTgMmArWCWHPQ34XVubupqj6h092BMLYKZplFo7dhhoBqIDyTqRg4GfA28FruzQ2RRGB6pE9GBZHB1omsRMnnHUITrwi8Cmku4A3gd8qFVJJaID6RI9OBUcHWiaghedHjI2R01dqYU5OiYCmKMDzVhSt8lRaJYA5uhAM5bUseNokgA2kLVjweaoaRYWwAYngE0Zm6OmaVgAW5O+BbBe2Bw1o0ZtPI7EsKMDWwLYsqkKYGUIRweaEaM2cxyamejAlgAGXQQwHB1ozCRm8ozjeElH5V6fSWex61wmG6FFAlh7uRYtAWwdYC3gSiYLYF9NAtjvyHU+kiZyw5UjgLOA9cjkr1Vrx6a2bAK8WdKJEfGibh+6CJujpklYABsyFsBMXamFAGaMGV1G3hyV9FRJ/yrp1mSOntS2n/2Uxf/dJKmjyGVz1IwCgxQM63jGUYU5empE/AXwUmA3SW8ASFdsjgN2S3MTR9GZoZijxlTFoAXDOnYcAzVHI+LRiFiU3v8TcB2rL68eCnw2Ih5K769xa/ywzFFjqqIKwXBszNH0/sbAm4Er0qYXAC+Q9FNJiyXNz5WdSE+nbY72EsCMqZIqBMOxMUeTw3EO8OmIuDNtnk029Ngj7ePM1LkM1BztFR1oTJVUIRjWbahSZXTgF4DbI+KTuW3LgAsj4omI+DVwG1lHQluZaZmjxswkVQiGtVHOVVF0YHr//5Dp5O9ue+s7ZJ3QWZLmkA1d7swX6BYdaExTGLRgOPLRgZLmknU62wHXpTOXVgdyKdlcyM3AIuCDEfFgqjeR203H6EBJu6S2vw04Q9JNpT65MTPAIKMpbY4OGZujpq7UwhxtigCWyr1VUkjq+KV1EcAGunasMU2hbpOjMEQBDEDShmTex1VdjlX52rHg6EDTHOrYcQxTAINsQnUhsKLTQXoIYItiAGvHgqMDTbMYawFM2YJKW0XE9zocYyI9nfbasb1wdKBpGmMrgEmaRZZs/v5OB+hHAFOPtWN7maOODjRNozYeR6LK6MB2AWxDsuUSfpjmOp9JFvizICLylz3Krh27exSsHevoQDNq1GaOQxVFB6Z9twSwo1rbIuIPETEnt4/FZAtHT7pW2i06UKvXjl3Q6Qa5sjg60DSNkY8OzAlgt5IJYACnp6FMISoRHcjktWMB7oqIBd32W4SjA02TsAA2ZCyAmbpSCwHMGDO6jLU5KunwZIROSPqJpO0KjjWU6EALYKYp1PGMY5jm6NkR8ZI0l7EQOK3gWJVHB1oAM02ijh3H0MzRWL0YNWTrr6wx4dPDHB1IdKAFMNM0xtocTdveI+lXZGcc781tn0hPK48OtABmmsbYmqOt7RHx2Yh4HnAscHxu+7xW1Q7HH2h0oAUw0zTqNlQZdnRgnnNJQ5A2Ko8OtABmmkZtlHPNQHRgXlMnU9pvb687rOhAC2CmSYx7dOCR6RLtBPA+Vq9cPyPRgYOMdjOmSmyODhmbo6au1MIcrYsAln5+TNLdHdpUSuAalgBmTFOo2+QoVCOAfRfYtcM+ygpcQ1k71uaoaQq1mRzN0RK7zpa0ZUQUKZRrlIuISyU9BlwOfCQiTgaIiMUA6URhFRGxNG0vFLjyAlh63RLALilTvywXTtzDsecvYe1Zs3hi5UoW7rs9C+b1pYsYMzTGSgDrh0EKYL2wOWqaxtgIYP0ySAHM5qgZNeo2VKkyOnCqTFsAc3SgGTVqMzmqCqMDp0O36MBBYXPUNI6IqOQBPNL2+hCyyD6AE4Dfk/1v3nqcAJzUVmd74Oa2baXKtb2/MB1jZetYafsu6fUfgQeBm3J1JnLPdwZuJJO/Tme1/1JYv+ix0047RREPLF8RE3c9FA8sX1FYxpiqAK6Jkn/fFsCGjAUwU1dqIYAZY0aXcTFHi8xPRwcaMwXqeMZRhTlaZH46OtCYKVDHjmOg0YHqHv3n6EBjpsA4mKNdzU85OtCYvhkHc7Sr+RmODjSmb+o2VKkiOrCs+enoQGNKUhvlXBVFB0aX6D85OtCYKTHy0YGJjtF/ODrQmClhc3TI2Bw1daUW5mhDBLD3SbpZ0hJJV0h6dsGxOtZP7+2X9nGTpIEkgRlTd+o2OQrDFcCuB3aOiO2Bb5Fdku1Ex/rKbvk/DtgtIl6UO96UsDlqmkIdO45hCmCLIuLRtL/FTL56AnQXwIBDgc9GxENpf/dP9UPbHDVNYuwFsBzvYvWkaVkB7AXACyT9VNJiSfOZAjZHTdMYewEMQNJBZJkbp6wqUE4Am002dNkjteFMZQtbT8LmqBk1auNxJAYeHZgulxYKXJJeS+aI7B4Rnf6L71Z/GbA4Ip4Afi3pNrKO5Or8DsLRgWbEqM0chyqKDowu0X+SXgqcASwomp/oVh/4DrBn2tccsqHLnf1+dpujpmnM5BnH8ZKOyr0+k85i17lMNkKLBLD2cnmOAM4C1iObx2jNZZwCbACcl66w3hURCyCb48gNV4rqX0p2Jedm4M/AByPiwYI2dMXmqGkSFsCGjAUwU1dqIYAZY0YXm6PTMEdVMnrQmDowSMGwjmccjTFHKR89aMyMMmjBsI4dR2PM0TLRg8bMNFUIhjZHVzMVc7QwerCtvV0FMGOqpArB0OYo0zJHC6MHJxXuER1oTJVUIRjWbagy9OjAnDm6YArmaJ6i6EFjZpQqBMPaKOeamejAljk6v5s52qV+z+hBY+rAoAXDcY8OzJujE5IualVQiehAukQPGlM3BhlNaXN0yNgcNXWlFubomAhgA1071pimULfJUWiWADawtWPB0YGmOdSx42iSADaQtWPB0YGmWVgAW82UBLBB4OhA0zQsgDE9AawMvcxRRweaplEbjyPRtOjAUjg60IwatZnjUDOjAweCowNN03B04DSiAyXtktqyCfBmSSdGtjBT3zg60DQJC2BDxgKYqSu1EMCMMaPLuJujpczPYZmjFsBMU6jjGccwzdGy5mfl5qgFMNMk6thxDNMc7Wl+DsMctQBmmobN0eK2TKSn0zZHLYCZUcPmaAGDNEd7RQdaADNNo25DlaFHB5Zg2uZoLyyAmaZRG+VcMxAdWIbp1i+LBTDTJMY6OlDSLunYbwPOkHRTq4JKRAd2qz8VBhntZkyV2BwdMjZHTV2xOWqMqRR3HMaYvnHHYYzpG3ccxpi+ccdhjOkbdxzGmL5xx2GM6Rt7HENG0m+B/+hSZA7wwJCa0w9uV380sV3Pjog1b6bqgDuOmiHpmrISzjBxu/pj1NvloYoxpm/ccRhj+sYdR/34wkw3oAC3qz9Gul2e4zDG9I3POIwxfeOOY0hImi/ptrTEwoc6vK+09MIdkpZI2rFs3YrbdWBqzxJJP5O0Q+69pWnZiAlJA80KKNGuPST9IZdr+5GydStu1wdzbbpR0p8lPT29V+X39SVJ90u6seD9wf5+RYQfFT+AtcgCgJ4LPAW4AdiurcxeZAFBAl4GXFW2bsXtejmwSXr+hla70uulwJwZ+r72AL43lbpVtqut/JuBK6v+vtK+XwXsCNxY8P5Af798xjEcdgXuiIg7I+JPZAHLe7eV2Rv4SmQsBjZOSzOUqVtZuyLiZxHxUHq5mMn5q1Uxnc88o99XG/sD5wzo2F2JiB8Dv+tSZKC/X+44hsOWwN25152WWCgqU6Zule3K8y5WL9gNWdr7ZZKulXTYgNrUT7v+StINki6R1Frsuxbfl6Snki3cdX5uc1XfVxkG+vtVm7DiEafMEgtFZaa9PEMXSu9b0p5kHccrcpt3i4h7JT0DuFzSrel/vmG06zoyRfoRSXsB3yFbZa8W3xfZMOWnEZE/C6jq+yrDQH+/fMYxHJYBW+Ved1pioahMmbpVtgtJ25OFNe8dEQ+2tkfEvenn/WQB0rsOq10R8XBEPJKeXwysLWlOmbpVtivHO2gbplT4fZVhsL9fVUzU+LHGxNRs4E7gOayegHpRW5k3Mnny6hdl61bcrq3J0t1f3rZ9fWDD3POfAfOH2K5nstpD2pVsHV/N9PeVyj2NbL5h/WF8X7ljbEPx5OhAf788VBkCEfGkpCPJ1rVdC/hSRNwk6fD0/ueBi8lmvu8AHgX+tlvdIbbrI8CmwD9LAngyspukNgcuSNtmA2dHxPeH2K63AkdIehJ4jGz9nQBm+vsC2Ae4LCL+mKte2fcFIOkcsitNc9KSHf8ErJ1r10B/v2yOGmP6xnMcxpi+ccdhjOkbdxzGmL5xx2GM6Rt3HMaYvnHHYWqDpGdKOlfSryTdLOliSS8Y4P73kPTyQe1vnHHHYWqBMsHhAuCHEfG8iNgO+Ecy/2FQ7EF2t6+ZJu44TF3YE3giJ1ERERPATySdkrItfinp7bDq7OF7rbKSTpd0SHq+VNKJkq5Ldf5C0jbA4cDRKQ/jlUP8bCOHzVFTF14MXNth+1uAecAOZGuCXC2pzI1hD0TEjpL+J/CBiHi3pM8Dj0TEqYNq9LjiMw5Td14BnBMRf46I+4AfAbuUqPft9PNasns4zABxx2Hqwk3ATh22d7rtG+BJJv/+rtv2/uPp55/xmfXAccdh6sKVwDqSDm1tkLQL8BDwdklrSdqMLCLvF2TLaG4naR1JTwNeU+IYy4ENB9/08cM9sakFERGS9gE+mQJzV5BldB4FbEB2u3cAx0TEfwJI+iawBLgduL7EYb4LfEvS3sDfR8S/DfpzjAu+O9YY0zceqhhj+sYdhzGmb9xxGGP6xh2HMaZv3HEYY/rGHYcxpm/ccRhj+sYdhzGmb/4LWkJAe+/Z8x4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_dist_df.plot.scatter(x='Count', y='HLA Allele',figsize=(3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "design_10_6_mhc1= designs_mhc1[4][3]\n",
    "coverage_10_6_mhc1 = coverages_mhc1[4]\n",
    "\n",
    "dist_10_6_mhc1 = vc.get_allele_distribution_df(design_10_6_mhc1,coverage_10_6_mhc1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Count', ylabel='HLA Allele'>"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dist_10_6_mhc1.plot.scatter(x='Count', y='HLA Allele',figsize=(3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test select_allele_with_min_coverage()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'HLA-A*01:01'"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('test select_allele_with_min_coverage()')\n",
    "\n",
    "dist_10_6_mhc1 = vc.get_allele_distribution_df(design_10_6_mhc1,coverage_10_6_mhc1)\n",
    "test_allele = vc.select_allele_with_min_coverage(dist_10_6_mhc1,['HLA-A*01:01'])\n",
    "test_allele"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test get_available_coverage()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>peptide</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>HLA-A*01:01</th>\n",
       "      <th>HLA-A*02:01</th>\n",
       "      <th>HLA-A*02:03</th>\n",
       "      <th>HLA-A*02:06</th>\n",
       "      <th>HLA-A*03:01</th>\n",
       "      <th>HLA-A*11:01</th>\n",
       "      <th>HLA-A*23:01</th>\n",
       "      <th>...</th>\n",
       "      <th>HLA-B*15:01</th>\n",
       "      <th>HLA-B*35:01</th>\n",
       "      <th>HLA-B*40:01</th>\n",
       "      <th>HLA-B*44:02</th>\n",
       "      <th>HLA-B*44:03</th>\n",
       "      <th>HLA-B*51:01</th>\n",
       "      <th>HLA-B*53:01</th>\n",
       "      <th>HLA-B*57:01</th>\n",
       "      <th>HLA-B*58:01</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VVFLHVTYV</td>\n",
       "      <td>1059</td>\n",
       "      <td>1067</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     peptide  start   end  HLA-A*01:01  HLA-A*02:01  HLA-A*02:03  HLA-A*02:06  \\\n",
       "0  VVFLHVTYV   1059  1067            0            0            1            0   \n",
       "\n",
       "   HLA-A*03:01  HLA-A*11:01  HLA-A*23:01  ...  HLA-B*15:01  HLA-B*35:01  \\\n",
       "0            0            0            0  ...            0            0   \n",
       "\n",
       "   HLA-B*40:01  HLA-B*44:02  HLA-B*44:03  HLA-B*51:01  HLA-B*53:01  \\\n",
       "0            0            0            0            0            0   \n",
       "\n",
       "   HLA-B*57:01  HLA-B*58:01  count  \n",
       "0            0            0      1  \n",
       "\n",
       "[1 rows x 31 columns]"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('test get_available_coverage()')\n",
    "\n",
    "coverage_10_9_mhc1 = coverages_mhc1[5]\n",
    "test_coverage_df = vc.get_available_coverage(['FIAGLIAIV', 'CVADYSVLY'], coverage_10_9_mhc1)\n",
    "test_coverage_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test update_allele_distribution()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HLA Allele</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HLA-A*02:03</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    HLA Allele  Count\n",
       "2  HLA-A*02:03      1"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('test update_allele_distribution()')\n",
    "\n",
    "test_selected_peptide = vc.find_first_peptide_cover(test_coverage_df,'HLA-A*02:03')\n",
    "dist_10_6_mhc1[dist_10_6_mhc1['HLA Allele'] == 'HLA-A*02:03']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HLA Allele</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HLA-A*02:03</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    HLA Allele  Count\n",
       "2  HLA-A*02:03      2"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vc.update_allele_distribution(dist_10_6_mhc1, test_selected_peptide)\n",
    "dist_10_6_mhc1[dist_10_6_mhc1['HLA Allele'] == 'HLA-A*02:03']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>peptide</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>HLA-A*01:01</th>\n",
       "      <th>HLA-A*02:01</th>\n",
       "      <th>HLA-A*02:03</th>\n",
       "      <th>HLA-A*02:06</th>\n",
       "      <th>HLA-A*03:01</th>\n",
       "      <th>HLA-A*11:01</th>\n",
       "      <th>HLA-A*23:01</th>\n",
       "      <th>...</th>\n",
       "      <th>HLA-B*15:01</th>\n",
       "      <th>HLA-B*35:01</th>\n",
       "      <th>HLA-B*40:01</th>\n",
       "      <th>HLA-B*44:02</th>\n",
       "      <th>HLA-B*44:03</th>\n",
       "      <th>HLA-B*51:01</th>\n",
       "      <th>HLA-B*53:01</th>\n",
       "      <th>HLA-B*57:01</th>\n",
       "      <th>HLA-B*58:01</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VVFLHVTYV</td>\n",
       "      <td>1059</td>\n",
       "      <td>1067</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FIAGLIAIV</td>\n",
       "      <td>1219</td>\n",
       "      <td>1227</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CVADYSVLY</td>\n",
       "      <td>360</td>\n",
       "      <td>368</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     peptide  start   end  HLA-A*01:01  HLA-A*02:01  HLA-A*02:03  HLA-A*02:06  \\\n",
       "0  VVFLHVTYV   1059  1067            0            0            1            0   \n",
       "0  FIAGLIAIV   1219  1227            0            0            1            0   \n",
       "0  CVADYSVLY    360   368            0            0            0            0   \n",
       "\n",
       "   HLA-A*03:01  HLA-A*11:01  HLA-A*23:01  ...  HLA-B*15:01  HLA-B*35:01  \\\n",
       "0            0            0            0  ...            0            0   \n",
       "0            0            0            0  ...            0            0   \n",
       "0            0            0            0  ...            0            0   \n",
       "\n",
       "   HLA-B*40:01  HLA-B*44:02  HLA-B*44:03  HLA-B*51:01  HLA-B*53:01  \\\n",
       "0            0            0            0            0            0   \n",
       "0            0            0            0            0            0   \n",
       "0            0            0            0            0            0   \n",
       "\n",
       "   HLA-B*57:01  HLA-B*58:01  count  \n",
       "0            0            0      1  \n",
       "0            0            0      2  \n",
       "0            0            0      1  \n",
       "\n",
       "[3 rows x 31 columns]"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coverages_mhc1[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test increase_design_fairness()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['FIAGLIAIV', 'CVADYSVLY', 'VVFLHVTYV']"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('test increase_design_fairness()')\n",
    "\n",
    "test_design = ['FIAGLIAIV', 'CVADYSVLY']\n",
    "test_coverage = coverages_mhc1[5]\n",
    "test_design_limit = 10\n",
    "\n",
    "vc.increase_design_fairness(test_design, test_coverage, test_design_limit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before 10 9.0 3 ['FIAGLIAIV', 'CVADYSVLY']\n",
      "after  10 9.0 3 ['FIAGLIAIV', 'CVADYSVLY', 'VVFLHVTYV']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Count', ylabel='HLA Allele'>"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_designs_mhc1_after = vc.execute_design_supplement('I',[designs_mhc1[5]], [coverages_mhc1[5]])\n",
    "\n",
    "# check if coverage improved\n",
    "test_design = designs_mhc1[5][3]\n",
    "test_dist_10_9_mhc1 = vc.get_allele_distribution_df(test_design,coverages_mhc1[5])\n",
    "test_dist_10_9_mhc1.plot.scatter(x='Count', y='HLA Allele',figsize=(3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Count', ylabel='HLA Allele'>"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check if coverage improved\n",
    "test_design = test_designs_mhc1_after[0][3]\n",
    "test_dist_10_9_mhc1 = vc.get_allele_distribution_df(test_design,coverages_mhc1[5])\n",
    "test_dist_10_9_mhc1.plot.scatter(x='Count', y='HLA Allele',figsize=(3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before 10 6.0 25 ['CVADYSVLY', 'QELGKYEQY', 'YYHKNNKSW', 'MTSCCSCLK', 'QPRTFLLKY', 'VVFLHVTYV']\n",
      "after  10 6.0 25 ['CVADYSVLY', 'QELGKYEQY', 'YYHKNNKSW', 'MTSCCSCLK', 'QPRTFLLKY', 'VVFLHVTYV', 'GWTAGAAAY', 'QLTPTWRVY', 'VYYPDKVFR', 'YEQYIKWPW']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Count', ylabel='HLA Allele'>"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_designs_mhc1_after = vc.execute_design_supplement('I',[designs_mhc1[4]], [coverages_mhc1[4]])\n",
    "\n",
    "# check if coverage improved\n",
    "test_design = designs_mhc1[4][3]\n",
    "test_dist_10_6_mhc1 = vc.get_allele_distribution_df(test_design,coverages_mhc1[4])\n",
    "test_dist_10_6_mhc1.plot.scatter(x='Count', y='HLA Allele',figsize=(3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Count', ylabel='HLA Allele'>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_design = test_designs_mhc1_after[0][3]\n",
    "test_dist_10_6_mhc1 = vc.get_allele_distribution_df(test_design,coverages_mhc1[4])\n",
    "test_dist_10_6_mhc1.plot.scatter(x='Count', y='HLA Allele',figsize=(3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before 10 3.0 27 ['MFVFLVLLP']\n",
      "after  10 3.0 27 ['MFVFLVLLP', 'FVFLVLLPL', 'VSSQCVNLT', 'TRTQLPPAY', 'TNSFTRGVY', 'YPDKVFRSS', 'VLHSTQDLF', 'LPFFSNVTW', 'FHAIHVSGT', 'NGTKRFDNP']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Count', ylabel='HLA Allele'>"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_designs_mhc1_after = vc.execute_design_supplement('I',[designs_mhc1[3]], [coverages_mhc1[3]])\n",
    "\n",
    "# check if coverage improved\n",
    "test_design = designs_mhc1[3][3]\n",
    "test_dist_10_3_mhc1 = vc.get_allele_distribution_df(test_design,coverages_mhc1[3])\n",
    "test_dist_10_3_mhc1.plot.scatter(x='Count', y='HLA Allele',figsize=(3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Count', ylabel='HLA Allele'>"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6sdrgSApnIaM1ViWdBTwPfKtVQ0M2q5eQzXzh7XKm18Yq2RF5D/DtkjaNVP9Y4N3A0SPFMQb1ELKZL9wMrKp78YgjxRu+FbjPzJaMtr6kaaSB1APM7JlKXzSohZDN/LDOxyO2izfMfICmAVVVjEckTYi1KTA7nyl9tcL3Dmoi4hF9UMlYlfRGYBcz+/+StgImmtnvu966dZAwVgOvdM1YlfQ/SafsZ+ZVG5CmphypXtdks7JybWSzF0m6VNJvsxx2eF6/o6Q5ku7OwljZXLx7Z6ntAUlfKmz345IW5Lq3Snr5SMclCNY1qlzOHAZMB/4C6c4F6RS+W1SNR2xV7u2SPgtsIunDpNAhSOMpS81sV1LE4U/y+rNJlyevI13W/L+SfX2F9HDeLvk1La+/G5hqZnsA36VFvGLQPcJY9UGVTuSv+a6DAUjapLtNqiybrVXOzG4mjV18FNjSzD6fy36QFCuAma0ys2V5/YjxiJK2BTYzs1/m4/AvwD/kbc0pDKiGbNZDIh7RD1U6ke9IugR4qaTjgR+RZosbia7JZmXlJL0NeAcpuPkJSR8rPM/yGUl3SbpW0jZ53QzgmDyweyPwT4V9DOXF7Um3eRuUyWYfokQ2C+oljFVfjNiJmNkFpFP164BXA582sy9X2HY3ZbOycj/Kt4P/YmaXkTqT9UlnCL8ws71Ik1hdkMsfCVxhZpOBg4FvSJoAUAhAqiKbHQNMBc5v1dgwVusljFVfVPJEzGw26WGzbnMksI2ko/P77STtAhwHvCu3ZUpZuYa2XpTNJD0BPMOa28fXks4ayD+n5bK/lLQRMAlYWmjTEoZfpjTLZm8ljbkcYGYt/xSa2aXApZDuzlQ8FkEJYaz6ovRMRNJySU+1eC2X9FTdDVHFeMR25VptN49j/AA4MK96C7AgLz+U3yPpb4GNgD801X8MWC7p9fmuzH8lPTGMpNeRnjSebmbFjifoImGs+qL0TMTMunkHBsYej1gmpZXFKEK6Rf0NSV8gdRL/La//BPA1SaeSLlGOa6jrSlGHU3K5k4ArgI1J4x6NsY/zgYnAtfmu70NmNr3sCwf1EcaqH8Yim00CNg3ZbGyEbBZ4pZey2YuoIJsFQTAYdE02GxBjdf982/h5Se8d6ZgE9RKymQ88ymbjyVh9iHTn6KoqXyyoj5DN/NBN2WysjCdjdbGZzQNWNdcLukfIZr7opmw2aMZqu+8VslmNhGzmi0p5ImY228w+ZWafzOJZFQbGWB2JiEesl5DNfFHqiUhaTutfFpEcrs1afNYp485YDXpPQzY7rWnKiHBF+kM/ZbNhqGI8YrtytJDNckfSMFZvo7WxekU7YzVbuq8HfkUyVqtczgVdJGQzP7TT3rdo96ph32ONRxxtjCIkz2WGpHnAfyENyJJ/Hi/pHlJE4jBjtVD/JJJR+wDwINlYlbRPbvv7gEskFWMXgy4T8Yg+KDVWJf2edDnTckzAzF7ZzYatq4SxGnildmPVzF5hZq/MP5tfI3YgzmSzf5N0j6T5kr6qNDUmkk7Umjl9fy5pt5J9hWwWBCWMKu1d0s6SzlKaea5bdEM2O8LM9gR2B7bKdQGuMrPX5sHamcCFJfsK2cwhYaz6oMqzM9tKOkXSr4H5pMHYkX7BO6F22czMGtEF65Oe/bGm9QCb0OJuVMhmPglj1Q/tBlaPl3QbSRGfBHwYeMzMzjGz31TYtgvZrFDnZtKt2+Ukea6x/iOSHiSdiXy0sH4oL3YsmwX1EsaqL9qdiVwMrAccZWZn57+4o5GsvMhmAJjZO4BtgQ2BNxfWX2xmO5Pu4JxdWD8lL3Ysm4WxWi9hrPqiXTzidqSxgwuzJv4d0pwz3aR22ay4cTNbIWkWcChrxz1eQxr7aKZj2SziEesljFVftLs7s8zMvmJm+5OErD8DSyUtlPR/6m6IuhSPKGliHtdoTN59MHBffr9Loei7gEXN9dvFIwb9IeIRfVE1qHkJ6XmTC/Ivcctf2FHSq3jETYBZkjYkXZ7dBjTmzD1ZKWh5JfAkcGyjkirEI0raJ7dlc+AQSeeY2WvafemgHsJY9UOleMSgPkI2C7xSu2wWBEFQha51IuPEWK00IXcYqz4J2cwHYzFWz16HjNWqE3KHseqMkM38MBZjdT3WHWN1jo0wIXcYq/4I2cwXA2+sFhg2IXedxmrIZvUSspkvBt5YhdYTckc8ol9CNvNFu05kO9Iv6IWS7pf0GXpjrB4naTEwC9hT0i6SPls4oykt1zBU2xmrufyhjXVaMyH3dGs9IXfEIzojZDNftItHXEYaUPyKpMmkv/5LJS0Erjezf66zIepSPKKkiaRpPx8rGKs/y581JuSeZiUTckc8ok9CNvND1bT3JWZ2gZntTRpUXFHDvnsVj9gwVucB95DGRRrGanFC7qH8XA0Q8YjjgYhH9MGYjFVJD5nZjl1ozzpPGKuBV3ptrLYabBxeYHzIZpVksZDNgqCcsXYi3XzgppeyWVVZLGQzh4Sx6oN2k1d9vOwj0jhCt2hIZFdJ2t7MylTEtcqZ2c2SniVlhXzazM6DtrLZYgBJpbJYUTbL7xuy2U1V6gfd4YahRzi9afKq6VMicK4ftDsT2bTkNRH4YoVtjzfZrHkfQ3kx4hGdEcaqL9rd4j2nw20/WxC2kHQcSehqR7NEdjmtE9jLyv3IzGZLmmFmlzXGLiDJZkrTZH6LJJu1nVO47nhE0uUQO+4Y49Gd0jBWVxSeNmgYq3Gnpve0u5z5UtlnAGb20Xafj5F+xiOWEfGIzghj1RftLmfuLLymN72/s+6GqA/xiFWIeER/hLHqDDMb8QXcXaVcU52nm94fB1yUl2cAfyL9lW+8ZgDnNtXZA1jQtK5SucJn2wC3A/NITyF/GVg/f7ZP3vdfgCeA+YV6Q4XlqcC9JNHsItb4NaX1y1577723BfWwbPkKG3roSVu2fEW/m7JOANxho/w9N7Nqspmku8xsr+pdU1BGyGaBV3otmwVBEADt80SWS3pK0lPAHo3lxvqRNjxOjNUNJX07m6i/krRTyb7KjNVK8YpBdwjZzAft5p3Z1Mw2y6/1C8ubmtlmXWxTL43VDwFPmtmrgM8D55Xsq8xYrRqvGNRMxCP6wePlTM/iEUm3eq/My98F3lJ0S2DEeMQ5NkK8YlA/IZv5opudyHgwVrcHHgYws+dJs/xtmcsPFcpUMVaHxSs2tTfiEWsk4hF90c1OZDzEI5baqDYKY1Ut4hWHFY54xFoJ2cwX3i5neh2PuATYAVaLaC8B/tjUprbGqkaOVwxqJmQzX1Sai7cXqA/xiKQO5Vjgl8B7gdtadDyl8YiqEK8YdIeIR/RDP89EPMQjXg5sKekB4OPAGY1KqhCPSJt4xaD7RDyiD2JC7x4TxmrgFXfG6oDIZhGPGAw83gZWYXzJZhGP2EfCWPWBx05kPMlmMRdvnwhj1Q8hm9UnmwU9IoxVX4Rstjajls1GIozVeglj1RduPJFMr+MRG7LZkrHKZlWwiEeslTBWfeFmTET9iUdsyGbQRjYj4hFdEcaqL/p5JnK2pFMK7y+jtUR2DcNN1DLZrLlcg4ZstiGwHnAbw2Wzb2TZ7I8UOiJJQ4VLmpOAK4CNSaLZ6rl4c1s2Bw6RdI6Zvabdlw7qIYxVP4Rs1mNCNgu84k42C4JgMFjnjVVJL5b0r5Luy8bquU3bOUIp4nC+pJbSWBirPgnZzAcez0S6YaxeYGZ/A7wO2E/SOwHynZ8zgf3yWMYptCaMVWeEbOYHj51IrcaqmT1jZnPy538F7mLNLdvjgYvN7Mn8+VqP84ex6o+QzXwxMMZq/vylwCHArXnVrsCukn4haa6kaYWyQ3mxY2M1ZLN6CdnMFwNjrGZH5GrgS2b2u7x6fdLlyYF5G5fljqZWYzXiEeslZDNfeLuc6WY84qXAIjP7QmHdEuAGM1tpZr8H7id1KjSV6chYDeolZDNfuNHe1aV4xPz5/yYp7R9u+uj7pA7pCkmTSJc3vysWaBePGPSPkM38sM7HI0qaTOqAdgPuymc0jc7kZtLYyQJgDvApM3si1xsqbKZlPKKkfXLb3wdcIml+pW8e1ELEI/ogjNUeE8Zq4BV3xup4kc1yufdKMkktD2Ab2Szm4g0GHm8Dq9BD2QxA0qYkr+RXbfYVc/E6JIxVH3jsRHopm0EajJ0JrGi1kxFkszkWc/H2hTBW/TDQspnS5FM7mNkPW+xjKC92PBdvUC9hrPpiYGUzSRNICe+faLWD0chmGmEu3jBW6yWMVV+48UQy3YxHbJbNNiVNIfHjPE76MlJ40XQzK94+qToX7wFWMhdvxCPWSxirvnAzJqIuxSPmbTdks1Ma68zsz2Y2qbCNuaRJuYfdf20Xj6g1c/FOb/XwXtAdwlj1xTofj1iQze4jyWYAF+XLnVJUIR6R4XPxAjxkZtPbbTeohzBW/RCyWY8J2SzwijvZLAiCwWCgjVVJJ2YTdUjSzyXtVrKviEd0SMhmPvB4JtJLY/UqM3ttHvuYCVxYsq+IR3RGyGZ+8NiJ9MxYtTUTfUOan2atAaIRjNWIR+wDIZv5YqCN1bzuI5IeJJ2JfLSwfigvRjyiM0I288XAGquN9WZ2sZntDJwOnF1YP6VRtcX+Ix6xj4Rs5gtvlzO9jkcscg35MqWJiEd0RshmvnCjvasP8YhFVZ6k1S9qrhvxiD4J2cwPgx6PeHK+7TsEfBw4tlBvqLCZiEd0SMQj+iCM1R4TxmrgFXfGqhfZLP/8rKSHW7SpkiwWslkQlONtYBW6I5v9ANi3xTaqymIhmzkkjFUfuBlYLdCQyK6StL2ZlamIa5Uzs5slPQvMBj5tZucBmNlcgHwCsRozW5zXl8piRdksv2/IZjdVqR90hxuGHuH06+axwYQJrFy1ipmH78H0KaPSd4KaGCjZbDTUKZsF9RLGqi8GRjYbLXXKZmGs1ksYq77wdjnTzXjEsdKxbBbxiPUSxqov3AysqovxiJ3QLh4x6A9hrDrDzLryAp5uen8cKZYQYAbwJ9Jf+cZrBnBuU509gAVN6yqVa/p8Zt7Hqsa+8vp98vu/AE8A8wt1hgrLU4F7SaLZRazxa0rrl7323ntvC+ph2fIVNvTQk7Zs+Yp+N2WdALjDxvC7HrJZjwnZLPCKO9ksCILBYFCM1TLjNOIRxzEhm/nA45lIN4zVMuM04hHHKRGP6AePnUit8YhqH28Y8YjjkJDNfDEIxmpb41QRjzjuCNnMF4NgrLY1Ti3iEccdIZv5wtvlTDfiEasapxGPOE4I2cwXbrR3dSke0drEGyriEcctEY/oh3U+HjHTMt6QiEcc10Q8og/CWO0xYawGXnFnrI4T2ezjkhZImifpVkkvL9lXy/r5syPyNuZLCl8kGDi8DaxCb2Wzu4GpZrYH8F3Sbd5WtKyvFFNwJrCfmb2msL+gB4Sx6gOPnUgvZbM5ZvZM3t5cht+FAdrLZsDxwMVm9mTe3tKxf+1gNISx6oeBl80KfIg1A65VZbNdgV0l/ULSXEnTCLpOGKu+GHjZDEDSMaTMkPNXF6gmm61Purw5MLfhMqVJw4cRxmq9hLHqCzeeSKb2eMR8C7ZUFpP0VpKDcoCZtfpT1q7+EmCuma0Efi/pflKncntxAxbxiLUSxqov3IyJqEvxiNYm3lDS64BLgOll4xnt6gPfBw7K25pEurz5XccHI2hLGKu+6OeZyNmSTim8v4zWEtk1DDdRy2Sz5nJFTgKuADYmjXs0xj7OByYC1+a7tg+Z2XRIYyKFS5qy+jeT7ggtAF4APmVmT5S0IaiRMFb9ELJZjwnZLPCKO9ksCILBIIzVDoxVVYxXDLpDyGY+8HgmMm6MVarHKwY1E7KZHzx2IuPGWK0SrxjUT8hmvghjdQ1jMVZL4xWb2huyWY2EbOaLMFbpyFgtjVccVjjiEWslZDNfeLuc6Xk8YsFYnT4GY7VIWbxiUDMhm/nCjfau/sQjNozVae2M1Tb1R4xXDLpDyGZ+GPR4xKKxOiRpVqOCKsQj0iZeMeg+EY/ogzBWe0wYq4FX3BmrAyKbxVy8wcDjbWAVxpdsFnPx9pEwVn3gsRMZT7JZzMXbJ8JY9UPIZmsYk2wW9J4wVn0RshmdyWZVCGO1XsJY9YUbTyQz3uIRKxHxiPUSxqov3IyJaHzGIwZ9IIxVX0Q8YgfxiJL2yW3ZHDhE0jmWJrEKukwYq34I2azHhGwWeMWdbBYEwWAw6MZqJeM0jFWfhGzmA49nIr00Vqsap2GsOiNkMz947ER6aayOaJyGseqPkM18EcZqeVuG8mLHxmrIZvUSspkvwlgtoU5jNeIR6yVkM194u5zpeTxiBTo2VoN6CdnMF260d/UhHrEKndYPukPIZn4Y6HhESfvkfb8PuETS/EYFVYhHbFc/6D4Rj+iDMFZ7TBirgVfCWA2CoC9EJxIEQUdEJxIEQUdEJxIEQUdEJxIEQUdEJxIEQUdEJxIEQUeEJ9JjJP0B+Pc2RSYBy3rUnNEQ7Rod47FdLzezUT/cFZ2IMyTdMRbhp9tEu0bHILUrLmeCIOiI6ESCIOiI6ET8cWm/G1BCtGt0DEy7YkwkCIKOiDORIAg6IjqRHiHpY5LulTS/aea/xucHSvpzIZf204XPpkm6P09ZcUaP2/WpQpvulfSCpC3yZ4vzVBpDkjrON5D0dUlLJd1bWLeFpNmSFuWfm5fUbXmMqtbvRrsk7SBpjqSF+fh+rPDZDEmPFI7twb1qVy7X8t9uTMfLzOLV5RewO3Av8GJSmtyPgF2ayhwI/LBF3fVIQUivBF4E3APs1qt2NZU/BLit8H4xMKnG47Q/sBdwb2HdTOCMvHwGcN5ojlGV+l1s17bAXnl5U+C3hXbNAD7Zj+PV7t9uLMcrzkR6w98Cc83sGTN7HvgJcFjFuvsCD5jZ78zsr6Rg6kP71K4jgatr2vdamNlPgT82rT4UuDIvX0merqOJdseoSv2utMvMHjOzu/LycmAho5wpoBvtGoFR149OpDfcC+wvaUtJLwYOBnZoUe7vJd0j6SZJjYnBtwceLpQZ9ZQVNbSL/Pk0UhRlAwNukXSnpBNqalMz25jZY5B+KYGtW5Rpd4yq1O9Wu1YjaSfgdaSc3gYnS5qXL0tGfZnVYbvK/u1GfbzcBDWvy5jZQknnAbOBp0mn2883FbuLpB0/na+Pv0+aba/jKSs6bFeDQ4BfmFnxL99+ZvaopK2B2ZLuy38de03XjlEdSJpI6nxPMbOn8uqvkILFLf/8HPDBHjartn+7OBPpEWZ2uZntZWb7k05BFzV9/pSZPZ2XbwQ2kDSJ9Fe1eHZQ65QVI7WrwAdoupQxs0fzz6Wk8Ox962pXgceVZiFszEa4tEWZdseoSv1utQtJG5A6kG+Z2fca683scTN7wcxWAV+jvmNXqV1t/u1GfbyiE+kRucdH0o7Ae2j6hZT0Mmn1ROH7kv5tngBuB3aR9ApJLyL9Ms/qVbvyZy8BDgBuKKzbRNKmjWXg7aTLo7qZBRybl48ttqFAu2NUpX5X2pX/PS8HFprZhU2fbVt4exj1Hbsq7Wr3bzf649XJ6HC8RjWS/jNgAemS4S153YnAiXn5ZGB+/nwu8IZC3YNJI/sPAmf1sl35/XHANU31Xpnr3JPb3XG7SB3YY8BK0tnFh4AtgVtJZ0i3AlvkstsBN450jMrq96JdwBtJlyvzgKH8Ojh/9g3gN/mzWcC2PWxX6b/dWI5XGKtBEHREXM4EQdAR0YkEQdAR0YkEQdAR0YkEQdAR0YkEQdAR0YkEbsiuzDWSHpS0QNKNknatcfsHSnpDXdsLEtGJBC7IYtb1wI/NbGcz2w34Z2CbGndzIBCdSM1EJxJ44SBgpZl9tbHCzIaAn0s6XynL5DeS3g+rzyp+2Cgr6SJJx+XlxZLOkXRXrvM3+QG4E4FTc4bGm3r43dZp4gG8wAu7A3e2WP8eYAqwJ2nOlNslVXlQbJmZ7SXpv5NyOz4s6avA02Z2QV2NDuJMJPDPG4GrLT2s9jgp82SfCvUaD7vdCezUpbYFRCcS+GE+sHeL9a0e84cUWVD8/7tR0+fP5Z8vEGfcXSU6kcALtwEbSjq+sULSPsCTwPslrSdpK1Ik4K9JU5HuJmnD/JTxWyrsYzkppjCokeihAxeYmUk6DPiCUtDyClIO6CnARNITpwacZmb/ASDpO6SnYBcBd1fYzQ+A70o6FPgnM/tZ3d9jEImneIMg6Ii4nAmCoCOiEwmCoCOiEwmCoCOiEwmCoCOiEwmCoCOiEwmCoCOiEwmCoCOiEwmCoCP+E483LqsNTlHiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_design = test_designs_mhc1_after[0][3]\n",
    "test_dist_10_3_mhc1 = vc.get_allele_distribution_df(test_design,coverages_mhc1[3])\n",
    "test_dist_10_3_mhc1.plot.scatter(x='Count', y='HLA Allele',figsize=(3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>peptide</th>\n",
       "      <td>MFVFLVLLP</td>\n",
       "      <td>FVFLVLLPL</td>\n",
       "      <td>VSSQCVNLT</td>\n",
       "      <td>TRTQLPPAY</td>\n",
       "      <td>TNSFTRGVY</td>\n",
       "      <td>YPDKVFRSS</td>\n",
       "      <td>VLHSTQDLF</td>\n",
       "      <td>LPFFSNVTW</td>\n",
       "      <td>FHAIHVSGT</td>\n",
       "      <td>NGTKRFDNP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>28</td>\n",
       "      <td>37</td>\n",
       "      <td>46</td>\n",
       "      <td>55</td>\n",
       "      <td>64</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>end</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>27</td>\n",
       "      <td>36</td>\n",
       "      <td>45</td>\n",
       "      <td>54</td>\n",
       "      <td>63</td>\n",
       "      <td>72</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*01:01</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*02:01</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*02:03</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*02:06</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*03:01</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*11:01</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*23:01</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*24:02</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*26:01</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*30:01</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*30:02</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*31:01</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*32:01</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*33:01</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*68:01</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-A*68:02</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*07:02</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*08:01</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*15:01</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*35:01</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*40:01</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*44:02</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*44:03</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*51:01</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*53:01</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*57:01</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLA-B*58:01</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     0          0          0          0          0          0  \\\n",
       "peptide      MFVFLVLLP  FVFLVLLPL  VSSQCVNLT  TRTQLPPAY  TNSFTRGVY  YPDKVFRSS   \n",
       "start                0          1         10         19         28         37   \n",
       "end                  8          9         18         27         36         45   \n",
       "HLA-A*01:01          1          1          1          1          1          1   \n",
       "HLA-A*02:01          1          1          1          1          1          1   \n",
       "HLA-A*02:03          1          1          1          1          1          1   \n",
       "HLA-A*02:06          1          1          1          1          1          1   \n",
       "HLA-A*03:01          1          1          1          1          1          1   \n",
       "HLA-A*11:01          1          1          1          1          1          1   \n",
       "HLA-A*23:01          1          1          1          1          1          1   \n",
       "HLA-A*24:02          1          1          1          1          1          1   \n",
       "HLA-A*26:01          1          1          1          1          1          1   \n",
       "HLA-A*30:01          1          1          1          1          1          1   \n",
       "HLA-A*30:02          1          1          1          1          1          1   \n",
       "HLA-A*31:01          1          1          1          1          1          1   \n",
       "HLA-A*32:01          1          1          1          1          1          1   \n",
       "HLA-A*33:01          1          1          1          1          1          1   \n",
       "HLA-A*68:01          1          1          1          1          1          1   \n",
       "HLA-A*68:02          1          1          1          1          1          1   \n",
       "HLA-B*07:02          1          1          1          1          1          1   \n",
       "HLA-B*08:01          1          1          1          1          1          1   \n",
       "HLA-B*15:01          1          1          1          1          1          1   \n",
       "HLA-B*35:01          1          1          1          1          1          1   \n",
       "HLA-B*40:01          1          1          1          1          1          1   \n",
       "HLA-B*44:02          1          1          1          1          1          1   \n",
       "HLA-B*44:03          1          1          1          1          1          1   \n",
       "HLA-B*51:01          1          1          1          1          1          1   \n",
       "HLA-B*53:01          1          1          1          1          1          1   \n",
       "HLA-B*57:01          1          1          1          1          1          1   \n",
       "HLA-B*58:01          1          1          1          1          1          1   \n",
       "count               27         27         27         27         27         27   \n",
       "\n",
       "                     0          0          0          0  \n",
       "peptide      VLHSTQDLF  LPFFSNVTW  FHAIHVSGT  NGTKRFDNP  \n",
       "start               46         55         64         73  \n",
       "end                 54         63         72         81  \n",
       "HLA-A*01:01          1          1          1          1  \n",
       "HLA-A*02:01          1          1          1          1  \n",
       "HLA-A*02:03          1          1          1          1  \n",
       "HLA-A*02:06          1          1          1          1  \n",
       "HLA-A*03:01          1          1          1          1  \n",
       "HLA-A*11:01          1          1          1          1  \n",
       "HLA-A*23:01          1          1          1          1  \n",
       "HLA-A*24:02          1          1          1          1  \n",
       "HLA-A*26:01          1          1          1          1  \n",
       "HLA-A*30:01          1          1          1          1  \n",
       "HLA-A*30:02          1          1          1          1  \n",
       "HLA-A*31:01          1          1          1          1  \n",
       "HLA-A*32:01          1          1          1          1  \n",
       "HLA-A*33:01          1          1          1          1  \n",
       "HLA-A*68:01          1          1          1          1  \n",
       "HLA-A*68:02          1          1          1          1  \n",
       "HLA-B*07:02          1          1          1          1  \n",
       "HLA-B*08:01          1          1          1          1  \n",
       "HLA-B*15:01          1          1          1          1  \n",
       "HLA-B*35:01          1          1          1          1  \n",
       "HLA-B*40:01          1          1          1          1  \n",
       "HLA-B*44:02          1          1          1          1  \n",
       "HLA-B*44:03          1          1          1          1  \n",
       "HLA-B*51:01          1          1          1          1  \n",
       "HLA-B*53:01          1          1          1          1  \n",
       "HLA-B*57:01          1          1          1          1  \n",
       "HLA-B*58:01          1          1          1          1  \n",
       "count               27         27         27         27  "
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check results\n",
    "coverage_10_3_mhc1 = coverages_mhc1[3]\n",
    "coverage_10_3_mhc1[coverage_10_3_mhc1['peptide'].isin(['MFVFLVLLP', 'FVFLVLLPL', 'VSSQCVNLT', 'TRTQLPPAY', 'TNSFTRGVY', 'YPDKVFRSS', 'VLHSTQDLF', 'LPFFSNVTW', 'FHAIHVSGT', 'NGTKRFDNP'])].T\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4. Execute Design Supplement algorithm on MHC I Designs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before 10 3.0 27 ['MFVFLVLLP']\n",
      "after  10 3.0 27 ['MFVFLVLLP', 'FVFLVLLPL', 'VSSQCVNLT', 'TRTQLPPAY', 'TNSFTRGVY', 'YPDKVFRSS', 'VLHSTQDLF', 'LPFFSNVTW', 'FHAIHVSGT', 'NGTKRFDNP']\n",
      "before 10 6.0 25 ['CVADYSVLY', 'QELGKYEQY', 'YYHKNNKSW', 'MTSCCSCLK', 'QPRTFLLKY', 'VVFLHVTYV']\n",
      "after  10 6.0 25 ['CVADYSVLY', 'QELGKYEQY', 'YYHKNNKSW', 'MTSCCSCLK', 'QPRTFLLKY', 'VVFLHVTYV', 'GWTAGAAAY', 'QLTPTWRVY', 'VYYPDKVFR', 'YEQYIKWPW']\n",
      "before 10 9.0 3 ['FIAGLIAIV', 'CVADYSVLY']\n",
      "after  10 9.0 3 ['FIAGLIAIV', 'CVADYSVLY', 'VVFLHVTYV']\n",
      "before 20 3.0 27 ['MFVFLVLLP']\n",
      "after  20 3.0 27 ['MFVFLVLLP', 'FVFLVLLPL', 'VSSQCVNLT', 'TRTQLPPAY', 'TNSFTRGVY', 'YPDKVFRSS', 'VLHSTQDLF', 'LPFFSNVTW', 'FHAIHVSGT', 'NGTKRFDNP', 'VLPFNDGVY', 'FASTEKSNI', 'IRGWIFGTT', 'LDSKTQSLL', 'IVNNATNVV', 'IKVCEFQFC', 'NDPFLGVYY', 'HKNNKSWME', 'SEFRVYSSA', 'NNCTFEYVS']\n",
      "before 20 6.0 25 ['CVADYSVLY', 'QELGKYEQY', 'YYHKNNKSW', 'MTSCCSCLK', 'QPRTFLLKY', 'VVFLHVTYV']\n",
      "after  20 6.0 25 ['CVADYSVLY', 'QELGKYEQY', 'YYHKNNKSW', 'MTSCCSCLK', 'QPRTFLLKY', 'VVFLHVTYV', 'GWTAGAAAY', 'QLTPTWRVY', 'VYYPDKVFR', 'YEQYIKWPW', 'AYRFNGIGV', 'VTYVPAQEK', 'LPIGINITR', 'VLKGVKLHY', 'NYLYRLFRK', 'VEAEVQIDR', 'LPPAYTNSF', 'LPFFSNVTW', 'SVYAWNRKR', 'LEGKQGNFK']\n",
      "before 20 9.0 3 ['FIAGLIAIV', 'CVADYSVLY']\n",
      "after  20 9.0 3 ['FIAGLIAIV', 'CVADYSVLY', 'VVFLHVTYV']\n"
     ]
    }
   ],
   "source": [
    "# only for designs with a design limit\n",
    "designs_mhc1_after = vc.execute_design_supplement('I',designs_mhc1[3:], coverages_mhc1[3:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4. Execute Design Supplement algorithm on MHC II Designs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before 10 3.0 24 ['MFVFLVLLPLVSSQC']\n",
      "after  10 3.0 24 ['MFVFLVLLPLVSSQC', 'FVFLVLLPLVSSQCV', 'NLTTRTQLPPAYTNS', 'FTRGVYYPDKVFRSS', 'VLHSTQDLFLPFFSN', 'VTWFHAIHVSGTNGT', 'KRFDNPVLPFNDGVY', 'FASTEKSNIIRGWIF', 'GTTLDSKTQSLLIVN', 'NATNVVIKVCEFQFC']\n",
      "before 10 6.0 21 ['EMIAQYTSALLAGTI', 'WRVYSTGSNVFQTRA', 'QQLIRAAEIRASANL', 'ASVYAWNRKRISNCV']\n",
      "after  10 6.0 21 ['EMIAQYTSALLAGTI', 'WRVYSTGSNVFQTRA', 'QQLIRAAEIRASANL', 'ASVYAWNRKRISNCV', 'LLQYGSFCTQLNRAL', 'YQPYRVVVLSFELLH', 'WLGFIAGLIAIVMVT', 'GAAAYYVGYLQPRTF', 'QSIIAYTMSLGAENS', 'AVLYQDVNCTEVPVA']\n",
      "before 10 9.0 1 ['IDRLITGRLQSLQTY']\n",
      "after  10 9.0 1 ['IDRLITGRLQSLQTY', 'TGRLQSLQTYVTQQL']\n",
      "before 20 3.0 24 ['MFVFLVLLPLVSSQC']\n",
      "after  20 3.0 24 ['MFVFLVLLPLVSSQC', 'FVFLVLLPLVSSQCV', 'NLTTRTQLPPAYTNS', 'FTRGVYYPDKVFRSS', 'VLHSTQDLFLPFFSN', 'VTWFHAIHVSGTNGT', 'KRFDNPVLPFNDGVY', 'FASTEKSNIIRGWIF', 'GTTLDSKTQSLLIVN', 'NATNVVIKVCEFQFC', 'NDPFLGVYYHKNNKS', 'WMESEFRVYSSANNC', 'TFEYVSQPFLMDLEG', 'KQGNFKNLREFVFKN', 'IDGYFKIYSKHTPIN', 'LVRDLPQGFSALEPL', 'VDLPIGINITRFQTL', 'LALHRSYLTPGDSSS', 'GWTAGAAAYYVGYLQ', 'PRTFLLKYNENGTIT']\n",
      "before 20 6.0 21 ['EMIAQYTSALLAGTI', 'WRVYSTGSNVFQTRA', 'QQLIRAAEIRASANL', 'ASVYAWNRKRISNCV']\n",
      "after  20 6.0 21 ['EMIAQYTSALLAGTI', 'WRVYSTGSNVFQTRA', 'QQLIRAAEIRASANL', 'ASVYAWNRKRISNCV', 'LLQYGSFCTQLNRAL', 'YQPYRVVVLSFELLH', 'WLGFIAGLIAIVMVT', 'GAAAYYVGYLQPRTF', 'QSIIAYTMSLGAENS', 'AVLYQDVNCTEVPVA', 'GGNYNYLYRLFRKSN', 'CTMYICGDSTECSNL', 'LDKYFKNHTSPDVDL', 'FKIYSKHTPINLVRD', 'CASYQTQTNSPRRAR', 'FNCYFPLQSYGFQPT', 'QMAYRFNGIGVTQNV', 'SVLYNSASFSTFKCY', 'RNFYEPQIITTDNTF', 'HVTYVPAQEKNFTTA']\n",
      "before 20 9.0 1 ['IDRLITGRLQSLQTY']\n",
      "after  20 9.0 1 ['IDRLITGRLQSLQTY', 'TGRLQSLQTYVTQQL']\n"
     ]
    }
   ],
   "source": [
    "# only for designs with a design limit\n",
    "designs_mhc2_after = vc.execute_design_supplement('II',designs_mhc2[3:], coverages_mhc2[3:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['HLA-B*08:01', 'HLA-B*51:01']"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_designs_mhc1 = designs_mhc1_after + designs_mhc1[:3]\n",
    "combined_design_10_6_mhc1 = combined_designs_mhc1[1][3]\n",
    "\n",
    "coverage_10_6_mhc1 = coverages_mhc1[4]\n",
    "vc.get_uncovered_alleles(combined_design_10_6_mhc1, coverage_10_6_mhc1, class_i_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 3.0, 27, ['MFVFLVLLP', 'FVFLVLLPL', 'VSSQCVNLT', 'TRTQLPPAY', 'TNSFTRGVY', 'YPDKVFRSS', 'VLHSTQDLF', 'LPFFSNVTW', 'FHAIHVSGT', 'NGTKRFDNP'])\n",
      "(10, 6.0, 25, ['CVADYSVLY', 'QELGKYEQY', 'YYHKNNKSW', 'MTSCCSCLK', 'QPRTFLLKY', 'VVFLHVTYV', 'GWTAGAAAY', 'QLTPTWRVY', 'VYYPDKVFR', 'YEQYIKWPW'])\n",
      "(10, 9.0, 3, ['FIAGLIAIV', 'CVADYSVLY', 'VVFLHVTYV'])\n",
      "(20, 3.0, 27, ['MFVFLVLLP', 'FVFLVLLPL', 'VSSQCVNLT', 'TRTQLPPAY', 'TNSFTRGVY', 'YPDKVFRSS', 'VLHSTQDLF', 'LPFFSNVTW', 'FHAIHVSGT', 'NGTKRFDNP', 'VLPFNDGVY', 'FASTEKSNI', 'IRGWIFGTT', 'LDSKTQSLL', 'IVNNATNVV', 'IKVCEFQFC', 'NDPFLGVYY', 'HKNNKSWME', 'SEFRVYSSA', 'NNCTFEYVS'])\n",
      "(20, 6.0, 25, ['CVADYSVLY', 'QELGKYEQY', 'YYHKNNKSW', 'MTSCCSCLK', 'QPRTFLLKY', 'VVFLHVTYV', 'GWTAGAAAY', 'QLTPTWRVY', 'VYYPDKVFR', 'YEQYIKWPW', 'AYRFNGIGV', 'VTYVPAQEK', 'LPIGINITR', 'VLKGVKLHY', 'NYLYRLFRK', 'VEAEVQIDR', 'LPPAYTNSF', 'LPFFSNVTW', 'SVYAWNRKR', 'LEGKQGNFK'])\n",
      "(20, 9.0, 3, ['FIAGLIAIV', 'CVADYSVLY', 'VVFLHVTYV'])\n",
      "(10000, 3.0, 27, ['MFVFLVLLP'])\n",
      "(10000, 6.0, 25, ['CVADYSVLY', 'QELGKYEQY', 'YYHKNNKSW', 'MTSCCSCLK', 'QPRTFLLKY', 'VVFLHVTYV'])\n",
      "(10000, 9.0, 3, ['FIAGLIAIV', 'CVADYSVLY'])\n"
     ]
    }
   ],
   "source": [
    "for each in combined_designs_mhc1:\n",
    "    print(each)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4. Format MHC 1 Results into table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Design Limit</th>\n",
       "      <th>p_min</th>\n",
       "      <th>Actual Design Size</th>\n",
       "      <th>Coverage Fraction</th>\n",
       "      <th>Uncovered Alleles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>INF</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>INF</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.925926</td>\n",
       "      <td>[HLA-B*08:01, HLA-B*51:01]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>INF</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>[HLA-A*01:01, HLA-A*02:01, HLA-A*02:06, HLA-A*03:01, HLA-A*11:01, HLA-A*23:01, HLA-A*24:02, HLA-A*30:01, HLA-A*30:02, HLA-A*31:01, HLA-A*32:01, HLA-A*33:01, HLA-A*68:01, HLA-B*07:02, HLA-B*08:01, HLA-B*15:01, HLA-B*35:01, HLA-B*40:01, HLA-B*44:02, HLA-B*44:03, HLA-B*51:01, HLA-B*53:01, HLA-B*57:01, HLA-B*58:01]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.925926</td>\n",
       "      <td>[HLA-B*08:01, HLA-B*51:01]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>[HLA-A*01:01, HLA-A*02:01, HLA-A*02:06, HLA-A*03:01, HLA-A*11:01, HLA-A*23:01, HLA-A*24:02, HLA-A*30:01, HLA-A*30:02, HLA-A*31:01, HLA-A*32:01, HLA-A*33:01, HLA-A*68:01, HLA-B*07:02, HLA-B*08:01, HLA-B*15:01, HLA-B*35:01, HLA-B*40:01, HLA-B*44:02, HLA-B*44:03, HLA-B*51:01, HLA-B*53:01, HLA-B*57:01, HLA-B*58:01]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.925926</td>\n",
       "      <td>[HLA-B*08:01, HLA-B*51:01]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>[HLA-A*01:01, HLA-A*02:01, HLA-A*02:06, HLA-A*03:01, HLA-A*11:01, HLA-A*23:01, HLA-A*24:02, HLA-A*30:01, HLA-A*30:02, HLA-A*31:01, HLA-A*32:01, HLA-A*33:01, HLA-A*68:01, HLA-B*07:02, HLA-B*08:01, HLA-B*15:01, HLA-B*35:01, HLA-B*40:01, HLA-B*44:02, HLA-B*44:03, HLA-B*51:01, HLA-B*53:01, HLA-B*57:01, HLA-B*58:01]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Design Limit  p_min  Actual Design Size  Coverage Fraction  \\\n",
       "0          INF    3.0                   1           1.000000   \n",
       "1          INF    6.0                   6           0.925926   \n",
       "2          INF    9.0                   2           0.111111   \n",
       "3           10    3.0                  10           1.000000   \n",
       "4           10    6.0                  10           0.925926   \n",
       "5           10    9.0                   3           0.111111   \n",
       "6           20    3.0                  20           1.000000   \n",
       "7           20    6.0                  20           0.925926   \n",
       "8           20    9.0                   3           0.111111   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                          Uncovered Alleles  \n",
       "0                                                                                                                                                                                                                                                                                                                        []  \n",
       "1                                                                                                                                                                                                                                                                                                [HLA-B*08:01, HLA-B*51:01]  \n",
       "2  [HLA-A*01:01, HLA-A*02:01, HLA-A*02:06, HLA-A*03:01, HLA-A*11:01, HLA-A*23:01, HLA-A*24:02, HLA-A*30:01, HLA-A*30:02, HLA-A*31:01, HLA-A*32:01, HLA-A*33:01, HLA-A*68:01, HLA-B*07:02, HLA-B*08:01, HLA-B*15:01, HLA-B*35:01, HLA-B*40:01, HLA-B*44:02, HLA-B*44:03, HLA-B*51:01, HLA-B*53:01, HLA-B*57:01, HLA-B*58:01]  \n",
       "3                                                                                                                                                                                                                                                                                                                        []  \n",
       "4                                                                                                                                                                                                                                                                                                [HLA-B*08:01, HLA-B*51:01]  \n",
       "5  [HLA-A*01:01, HLA-A*02:01, HLA-A*02:06, HLA-A*03:01, HLA-A*11:01, HLA-A*23:01, HLA-A*24:02, HLA-A*30:01, HLA-A*30:02, HLA-A*31:01, HLA-A*32:01, HLA-A*33:01, HLA-A*68:01, HLA-B*07:02, HLA-B*08:01, HLA-B*15:01, HLA-B*35:01, HLA-B*40:01, HLA-B*44:02, HLA-B*44:03, HLA-B*51:01, HLA-B*53:01, HLA-B*57:01, HLA-B*58:01]  \n",
       "6                                                                                                                                                                                                                                                                                                                        []  \n",
       "7                                                                                                                                                                                                                                                                                                [HLA-B*08:01, HLA-B*51:01]  \n",
       "8  [HLA-A*01:01, HLA-A*02:01, HLA-A*02:06, HLA-A*03:01, HLA-A*11:01, HLA-A*23:01, HLA-A*24:02, HLA-A*30:01, HLA-A*30:02, HLA-A*31:01, HLA-A*32:01, HLA-A*33:01, HLA-A*68:01, HLA-B*07:02, HLA-B*08:01, HLA-B*15:01, HLA-B*35:01, HLA-B*40:01, HLA-B*44:02, HLA-B*44:03, HLA-B*51:01, HLA-B*53:01, HLA-B*57:01, HLA-B*58:01]  "
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_formatted_mhc1 = []\n",
    "combined_designs_mhc1 = designs_mhc1[:3] + designs_mhc1_after\n",
    "for i,design in enumerate(combined_designs_mhc1):\n",
    "    # unpack tuple\n",
    "    design_limit, p_min, n, selected = design\n",
    "    \n",
    "    # calculate coverage percent\n",
    "    coverage = float(n) / len(class_i_list)\n",
    "    \n",
    "    if design_limit == 10000:\n",
    "        design_limit = 'INF'\n",
    "        \n",
    "    uncovered_alleles = vc.get_uncovered_alleles(selected,coverages_mhc1[i],class_i_list)\n",
    "        \n",
    "    new_result = [design_limit,p_min,len(selected),coverage,uncovered_alleles]\n",
    "    results_formatted_mhc1.append(new_result)\n",
    "\n",
    "col_list = ['Design Limit','p_min','Actual Design Size','Coverage Fraction','Uncovered Alleles']\n",
    "pd.set_option('display.max_colwidth', None)\n",
    "pd.DataFrame(results_formatted_mhc1,columns=col_list)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4. Format MHC 2 Results into table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Design Limit</th>\n",
       "      <th>p_min</th>\n",
       "      <th>Actual Design Size</th>\n",
       "      <th>Coverage Fraction</th>\n",
       "      <th>Uncovered Alleles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>INF</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>[HLA-DRB1*1201, HLA-DRB3*0202, HLA-DPA1*0201/DPB1*1401]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>INF</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>[HLA-DRB1*0301, HLA-DRB1*0802, HLA-DRB1*1201, HLA-DRB3*0202, HLA-DQA1*0301/DQB1*0302, HLA-DPA1*0201/DPB1*1401]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>INF</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>[HLA-DRB1*0301, HLA-DRB1*0401, HLA-DRB1*0405, HLA-DRB1*0701, HLA-DRB1*0802, HLA-DRB1*0901, HLA-DRB1*1101, HLA-DRB1*1201, HLA-DRB1*1302, HLA-DRB1*1501, HLA-DRB3*0101, HLA-DRB3*0202, HLA-DRB4*0101, HLA-DRB5*0101, HLA-DQA1*0501/DQB1*0201, HLA-DQA1*0501/DQB1*0301, HLA-DQA1*0301/DQB1*0302, HLA-DQA1*0401/DQB1*0402, HLA-DQA1*0101/DQB1*0501, HLA-DQA1*0102/DQB1*0602, HLA-DPA1*0201/DPB1*0101, HLA-DPA1*0103/DPB1*0201, HLA-DPA1*0103/DPB1*0401, HLA-DPA1*0301/DPB1*0402, HLA-DPA1*0201/DPB1*0501, HLA-DPA1*0201/DPB1*1401]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>[HLA-DRB1*1201, HLA-DRB3*0202, HLA-DPA1*0201/DPB1*1401]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>[HLA-DRB1*0301, HLA-DRB1*0802, HLA-DRB1*1201, HLA-DRB3*0202, HLA-DQA1*0301/DQB1*0302, HLA-DPA1*0201/DPB1*1401]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>[HLA-DRB1*0301, HLA-DRB1*0401, HLA-DRB1*0405, HLA-DRB1*0701, HLA-DRB1*0802, HLA-DRB1*0901, HLA-DRB1*1101, HLA-DRB1*1201, HLA-DRB1*1302, HLA-DRB1*1501, HLA-DRB3*0101, HLA-DRB3*0202, HLA-DRB4*0101, HLA-DRB5*0101, HLA-DQA1*0501/DQB1*0201, HLA-DQA1*0501/DQB1*0301, HLA-DQA1*0301/DQB1*0302, HLA-DQA1*0401/DQB1*0402, HLA-DQA1*0101/DQB1*0501, HLA-DQA1*0102/DQB1*0602, HLA-DPA1*0201/DPB1*0101, HLA-DPA1*0103/DPB1*0201, HLA-DPA1*0103/DPB1*0401, HLA-DPA1*0301/DPB1*0402, HLA-DPA1*0201/DPB1*0501, HLA-DPA1*0201/DPB1*1401]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>[HLA-DRB1*1201, HLA-DRB3*0202, HLA-DPA1*0201/DPB1*1401]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>[HLA-DRB1*0301, HLA-DRB1*0802, HLA-DRB1*1201, HLA-DRB3*0202, HLA-DQA1*0301/DQB1*0302, HLA-DPA1*0201/DPB1*1401]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>[HLA-DRB1*0301, HLA-DRB1*0401, HLA-DRB1*0405, HLA-DRB1*0701, HLA-DRB1*0802, HLA-DRB1*0901, HLA-DRB1*1101, HLA-DRB1*1201, HLA-DRB1*1302, HLA-DRB1*1501, HLA-DRB3*0101, HLA-DRB3*0202, HLA-DRB4*0101, HLA-DRB5*0101, HLA-DQA1*0501/DQB1*0201, HLA-DQA1*0501/DQB1*0301, HLA-DQA1*0301/DQB1*0302, HLA-DQA1*0401/DQB1*0402, HLA-DQA1*0101/DQB1*0501, HLA-DQA1*0102/DQB1*0602, HLA-DPA1*0201/DPB1*0101, HLA-DPA1*0103/DPB1*0201, HLA-DPA1*0103/DPB1*0401, HLA-DPA1*0301/DPB1*0402, HLA-DPA1*0201/DPB1*0501, HLA-DPA1*0201/DPB1*1401]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Design Limit  p_min  Actual Design Size  Coverage Fraction  \\\n",
       "0          INF    3.0                   1           0.888889   \n",
       "1          INF    6.0                   4           0.777778   \n",
       "2          INF    9.0                   1           0.037037   \n",
       "3           10    3.0                  10           0.888889   \n",
       "4           10    6.0                  10           0.777778   \n",
       "5           10    9.0                   2           0.037037   \n",
       "6           20    3.0                  20           0.888889   \n",
       "7           20    6.0                  20           0.777778   \n",
       "8           20    9.0                   2           0.037037   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                Uncovered Alleles  \n",
       "0                                                                                                                                                                                                                                                                                                                                                                                                                                                                         [HLA-DRB1*1201, HLA-DRB3*0202, HLA-DPA1*0201/DPB1*1401]  \n",
       "1                                                                                                                                                                                                                                                                                                                                                                                                                  [HLA-DRB1*0301, HLA-DRB1*0802, HLA-DRB1*1201, HLA-DRB3*0202, HLA-DQA1*0301/DQB1*0302, HLA-DPA1*0201/DPB1*1401]  \n",
       "2  [HLA-DRB1*0301, HLA-DRB1*0401, HLA-DRB1*0405, HLA-DRB1*0701, HLA-DRB1*0802, HLA-DRB1*0901, HLA-DRB1*1101, HLA-DRB1*1201, HLA-DRB1*1302, HLA-DRB1*1501, HLA-DRB3*0101, HLA-DRB3*0202, HLA-DRB4*0101, HLA-DRB5*0101, HLA-DQA1*0501/DQB1*0201, HLA-DQA1*0501/DQB1*0301, HLA-DQA1*0301/DQB1*0302, HLA-DQA1*0401/DQB1*0402, HLA-DQA1*0101/DQB1*0501, HLA-DQA1*0102/DQB1*0602, HLA-DPA1*0201/DPB1*0101, HLA-DPA1*0103/DPB1*0201, HLA-DPA1*0103/DPB1*0401, HLA-DPA1*0301/DPB1*0402, HLA-DPA1*0201/DPB1*0501, HLA-DPA1*0201/DPB1*1401]  \n",
       "3                                                                                                                                                                                                                                                                                                                                                                                                                                                                         [HLA-DRB1*1201, HLA-DRB3*0202, HLA-DPA1*0201/DPB1*1401]  \n",
       "4                                                                                                                                                                                                                                                                                                                                                                                                                  [HLA-DRB1*0301, HLA-DRB1*0802, HLA-DRB1*1201, HLA-DRB3*0202, HLA-DQA1*0301/DQB1*0302, HLA-DPA1*0201/DPB1*1401]  \n",
       "5  [HLA-DRB1*0301, HLA-DRB1*0401, HLA-DRB1*0405, HLA-DRB1*0701, HLA-DRB1*0802, HLA-DRB1*0901, HLA-DRB1*1101, HLA-DRB1*1201, HLA-DRB1*1302, HLA-DRB1*1501, HLA-DRB3*0101, HLA-DRB3*0202, HLA-DRB4*0101, HLA-DRB5*0101, HLA-DQA1*0501/DQB1*0201, HLA-DQA1*0501/DQB1*0301, HLA-DQA1*0301/DQB1*0302, HLA-DQA1*0401/DQB1*0402, HLA-DQA1*0101/DQB1*0501, HLA-DQA1*0102/DQB1*0602, HLA-DPA1*0201/DPB1*0101, HLA-DPA1*0103/DPB1*0201, HLA-DPA1*0103/DPB1*0401, HLA-DPA1*0301/DPB1*0402, HLA-DPA1*0201/DPB1*0501, HLA-DPA1*0201/DPB1*1401]  \n",
       "6                                                                                                                                                                                                                                                                                                                                                                                                                                                                         [HLA-DRB1*1201, HLA-DRB3*0202, HLA-DPA1*0201/DPB1*1401]  \n",
       "7                                                                                                                                                                                                                                                                                                                                                                                                                  [HLA-DRB1*0301, HLA-DRB1*0802, HLA-DRB1*1201, HLA-DRB3*0202, HLA-DQA1*0301/DQB1*0302, HLA-DPA1*0201/DPB1*1401]  \n",
       "8  [HLA-DRB1*0301, HLA-DRB1*0401, HLA-DRB1*0405, HLA-DRB1*0701, HLA-DRB1*0802, HLA-DRB1*0901, HLA-DRB1*1101, HLA-DRB1*1201, HLA-DRB1*1302, HLA-DRB1*1501, HLA-DRB3*0101, HLA-DRB3*0202, HLA-DRB4*0101, HLA-DRB5*0101, HLA-DQA1*0501/DQB1*0201, HLA-DQA1*0501/DQB1*0301, HLA-DQA1*0301/DQB1*0302, HLA-DQA1*0401/DQB1*0402, HLA-DQA1*0101/DQB1*0501, HLA-DQA1*0102/DQB1*0602, HLA-DPA1*0201/DPB1*0101, HLA-DPA1*0103/DPB1*0201, HLA-DPA1*0103/DPB1*0401, HLA-DPA1*0301/DPB1*0402, HLA-DPA1*0201/DPB1*0501, HLA-DPA1*0201/DPB1*1401]  "
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_formatted_mhc2 = []\n",
    "combined_designs_mhc2 = designs_mhc2[:3] + designs_mhc2_after\n",
    "for i,design in enumerate(combined_designs_mhc2):\n",
    "    # unpack tuple\n",
    "    design_limit, p_min, n, selected = design\n",
    "    \n",
    "    # calculate coverage percent\n",
    "    coverage = float(n) / len(class_ii_list)\n",
    "    \n",
    "    if design_limit == 10000:\n",
    "        design_limit = 'INF'\n",
    "        \n",
    "    uncovered_alleles = vc.get_uncovered_alleles(selected,coverages_mhc2[i],class_ii_list)\n",
    "        \n",
    "    new_result = [design_limit,p_min,len(selected),coverage,uncovered_alleles]\n",
    "    results_formatted_mhc2.append(new_result)\n",
    "\n",
    "col_list = ['Design Limit','p_min','Actual Design Size','Coverage Fraction','Uncovered Alleles']\n",
    "pd.set_option('display.max_colwidth', None)\n",
    "pd.DataFrame(results_formatted_mhc2,columns=col_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "inHXdhtF9dhC"
   },
   "source": [
    "## Part 1.4: Answer\n",
    "\n",
    "**Briefly describe the algorithm you devised. If you would like to manually fill in the tables provided in the write-up, please replace the empty tables below with a screenshot of your filled in table. Alternatively, you could display the tables as the output of above cells as long as they contain all of the relevant information.**\n",
    "\n",
    "\n",
    "### Coverage DataFrame\n",
    "Build a data structure which summarizes the allele coverage for each valid peptide k-mer in the spike sequence. Valid k-mer's have a pIC50 greater than or equal to p_min.\n",
    "\n",
    "Steps:\n",
    "- Take the predictions made for all k-mers in the spike sequence.\n",
    "- Limit predictions to peptides with pIC50 value greater than p_min.\n",
    "- Create a DataFrame which lists:\n",
    "  - peptide k-mer from spike sequence\n",
    "  - start position in the spike sequence\n",
    "  - end position in the spike sequence\n",
    "  - for each allele in the reference set\n",
    "    - a flag which denotes if the allele is covered by this peptide \n",
    "    - 1 if covered, 0 otherwise\n",
    "  - count column which sums all allele flags for this peptide\n",
    "\n",
    "\n",
    "### Minimum Coverage\n",
    "Using the coverage DataFrame and design limit, generate a list of peptides which cover as many alleles as possible at least once.\n",
    "\n",
    "#### Select an Allele to Cover\n",
    "- Create a list of unselected alleles. \n",
    "  - The list contains all alleles in the reference set.\n",
    "- Select an allele with the minimum nonzero coverage count.\n",
    "  - Calculate the coverage count for each allele in the reference set using the coverage DataFrame. (Count is how many of the available peptide k-mers bind with that allele at the given p_min.)\n",
    "  - I start with the allele with the least coverage to ensure that allele is covered in the selection process. \n",
    "  - If an allele has a coverage count of zero, this means that no peptide k-mers which are currently available for selection can cover that allele, and it no longer makes sense to continue searching for matches for that allele.\n",
    "\n",
    "#### Select a Peptide\n",
    "- Find a peptide k-mer which covers that allele. Add that peptide to the list of selected peptides.\n",
    "  - The target allele was the minimum nonzero covered, so we are guaranteed to find a match.\n",
    "\n",
    "#### Update Coverage DataFrame and Uncovered Alleles\n",
    "- Remove any overlapping peptide k-mers from the list of available k-mers. \n",
    "  - This ensures that each peptide selected represents a different part of the spike sequence.\n",
    "- For each allele which the peptide covers, remove those alleles from the list of unselected alleles.\n",
    "  - This includes every allele the peptide covers, not just the target allele from the search.\n",
    "  - These alleles are now covered by at least one peptide and can be removed from consider.\n",
    "\n",
    "#### Repeat Selection Process\n",
    "- While the following criteria have not been met, continue the selection process:\n",
    "  - we have not reached the design limit for number of peptides selected\n",
    "  - we have uncovered alleles left in the list\n",
    "  - we have available peptides\n",
    "\n",
    "- Choose a remaining allele with the minimum coverage\n",
    "- Attempt to find a peptide to cover it.\n",
    "- If a peptide match is not found, remove the allele from list of uncovered and repeat the process.\n",
    "- If a peptide match is found, add that peptide to the design, remove overlapping available peptides, and remove all alleles from the uncovered list which that peptide covers.\n",
    "\n",
    "### Improve Coverage Fairness\n",
    "Now that we have a list of peptides which cover as many alleles as possible, we want to use any remaining space in the design to improve the coverage quality. Currently, some alleles are covered by many of the peptides in the design and some are covered by few (or one). For the least-covered alleles, the goal is to select additional peptides to cover those alleles another time and \"balance\" out the design as much as possible.\n",
    "\n",
    "#### Select an Allele to Cover\n",
    "- Determine the allele distribution of the current design.\n",
    "- Choose an allele which has the minimum nonzero coverage.\n",
    "\n",
    "#### Select a Peptide\n",
    "- From the list of peptides which are not currently in the design, attempt to select one which covers the target allele.\n",
    "- If a peptide k-mer match is not found, move to the next allele with least nonzero coverage.\n",
    "- If a peptide k-mer match is found, add it to the design, update the allele distribution of the design, and remove any overlapping peptides from the list of available peptide k-mers.\n",
    "\n",
    "#### Repeat Selection Process\n",
    "- Continue the process until any of the following criteria is met:\n",
    "  - we have reached the design limit\n",
    "  - we have no alleles left to target\n",
    "  - we have no available peptides left to select from\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xdwPdZ0TALwh"
   },
   "source": [
    "## Part 1.5: Work\n",
    "\n",
    "**TODO:**\n",
    "- **Determine criteria for selecting 20 peptides for your vaccine. You do not need to have an equal number of peptides ‘covering’ MHC I and MHC II alleles.**\n",
    "- **Apply this criteria to obtain the required information for table in the cell below.** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5. Visualize the results of 10 and 20 length designs with p_min 6 or more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_design(design,coverage_df):\n",
    "    '''\n",
    "    Plots the coverage count for each allele\n",
    "    in the design.\n",
    "    '''\n",
    "\n",
    "    dist_df = vc.get_allele_distribution_df(design,coverage_df)\n",
    "    dist_df.plot.scatter(x='Count', y='HLA Allele',figsize=(3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 3.0, 27, ['MFVFLVLLP'])\n",
      "(10000, 6.0, 25, ['CVADYSVLY', 'QELGKYEQY', 'YYHKNNKSW', 'MTSCCSCLK', 'QPRTFLLKY', 'VVFLHVTYV'])\n",
      "(10000, 9.0, 3, ['FIAGLIAIV', 'CVADYSVLY'])\n",
      "(10, 3.0, 27, ['MFVFLVLLP'])\n",
      "(10, 6.0, 25, ['CVADYSVLY', 'QELGKYEQY', 'YYHKNNKSW', 'MTSCCSCLK', 'QPRTFLLKY', 'VVFLHVTYV'])\n",
      "(10, 9.0, 3, ['FIAGLIAIV', 'CVADYSVLY'])\n",
      "(20, 3.0, 27, ['MFVFLVLLP'])\n",
      "(20, 6.0, 25, ['CVADYSVLY', 'QELGKYEQY', 'YYHKNNKSW', 'MTSCCSCLK', 'QPRTFLLKY', 'VVFLHVTYV'])\n",
      "(20, 9.0, 3, ['FIAGLIAIV', 'CVADYSVLY'])\n",
      "\n",
      "(10, 3.0, 27, ['MFVFLVLLP', 'FVFLVLLPL', 'VSSQCVNLT', 'TRTQLPPAY', 'TNSFTRGVY', 'YPDKVFRSS', 'VLHSTQDLF', 'LPFFSNVTW', 'FHAIHVSGT', 'NGTKRFDNP'])\n",
      "(10, 6.0, 25, ['CVADYSVLY', 'QELGKYEQY', 'YYHKNNKSW', 'MTSCCSCLK', 'QPRTFLLKY', 'VVFLHVTYV', 'GWTAGAAAY', 'QLTPTWRVY', 'VYYPDKVFR', 'YEQYIKWPW'])\n",
      "(10, 9.0, 3, ['FIAGLIAIV', 'CVADYSVLY', 'VVFLHVTYV'])\n",
      "(20, 3.0, 27, ['MFVFLVLLP', 'FVFLVLLPL', 'VSSQCVNLT', 'TRTQLPPAY', 'TNSFTRGVY', 'YPDKVFRSS', 'VLHSTQDLF', 'LPFFSNVTW', 'FHAIHVSGT', 'NGTKRFDNP', 'VLPFNDGVY', 'FASTEKSNI', 'IRGWIFGTT', 'LDSKTQSLL', 'IVNNATNVV', 'IKVCEFQFC', 'NDPFLGVYY', 'HKNNKSWME', 'SEFRVYSSA', 'NNCTFEYVS'])\n",
      "(20, 6.0, 25, ['CVADYSVLY', 'QELGKYEQY', 'YYHKNNKSW', 'MTSCCSCLK', 'QPRTFLLKY', 'VVFLHVTYV', 'GWTAGAAAY', 'QLTPTWRVY', 'VYYPDKVFR', 'YEQYIKWPW', 'AYRFNGIGV', 'VTYVPAQEK', 'LPIGINITR', 'VLKGVKLHY', 'NYLYRLFRK', 'VEAEVQIDR', 'LPPAYTNSF', 'LPFFSNVTW', 'SVYAWNRKR', 'LEGKQGNFK'])\n",
      "(20, 9.0, 3, ['FIAGLIAIV', 'CVADYSVLY', 'VVFLHVTYV'])\n"
     ]
    }
   ],
   "source": [
    "for design in designs_mhc1:\n",
    "    print(design)\n",
    "\n",
    "print()\n",
    "    \n",
    "for design in designs_mhc1_after:\n",
    "\n",
    "    print(design)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5. Visualize MHC I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 31)\n",
      "(958, 31)\n",
      "(958, 31)\n"
     ]
    }
   ],
   "source": [
    "# prep variables\n",
    "design_10_9_mhc1 = designs_mhc1_after[2][3]\n",
    "coverage_10_9_mhc1 = coverages_mhc1[5]\n",
    "print(coverage_10_9_mhc1.shape)\n",
    "\n",
    "design_10_6_mhc1 = designs_mhc1_after[1][3]\n",
    "coverage_10_6_mhc1 = coverages_mhc1[4]\n",
    "print(coverage_10_6_mhc1.shape)\n",
    "\n",
    "design_20_6_mhc1 = designs_mhc1_after[4][3]\n",
    "coverage_20_6_mhc1 = coverages_mhc1[7]\n",
    "print(coverage_20_6_mhc1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wqZqxehQTF20IXJmufL5Y43dX4lg9kzO1DE5JfwlsGxH/T9KmwOyI+HXPazeC2OA0uTJjg1PSv1Bcah+bVq1DMSVgp+16JmVVlWsjZT1L0pmSfpEkqn3T+q0kLZJ0UxKrquY63TnJX3dK+lxpvx+UtDRte5WkF3Y6L8YMK3W6IW8FFgC/h+JOAcWld6+oG6vXqtxfS/oksIGk91KE20Ax3vFgRGxHEY33w7T+OIpuxSspuiP/t+JYX6B4yGzb9Jqf1t8E7BIROwDfokUsn+kdNl77S53G4g9plD8AJG3Q2yrVlrLWKBcRl1OMLXwA2CQiPp3KvpvicXciYmVEPJTWd4zVk7Q58OyI+Gk6D18B/ibta1FpYHPaUpaZOo4h7D91GotvSjoD2EjSocD3KWbv6kTPpKyqcpJeD7yBIsD3YUn/VHpe4xOSbpR0gaTN0rrjgYPSAOulwD+WjjGRFreguH3aoErKeg8VUpbpLjZeB0PHxiIiTqG4xL4QeCnw8Yj4fI1991LKqir3/XSb9fcRcRZFo7E2xV/8n0TEThSTDZ2Syu8PnBMRc4G9ga9KmgVQCtqpI2UdBOwCnNyqsp0MTjM1bLwOhlqeRURcSfHQVK/ZH9hM0oHp8wskbQscArwp1WVeVbmGrl2WsiQ9DDzB6tuyF1BcBZDe56eyP5W0HjAHeLBUp2VM7l40S1mvoxgT2SMiWv5pi4gzgTOhuBtS81yYCmy8DobKKwtJyyU91uK1XNJj3a6IasbqtSvXar9pnOG7wJ5p1WuBpWn57vQZSf8NWA/4TdP29wPLJb0q3QX5O4onXJH0SoonYxdERLmBMT3ExutgqLyyiIhe3vGA6cfqVclbVfF7UNz6/aqkz1A0Bn+f1n8I+FdJR1F0LQ5pKNsqIvLmpXKHA+cA61OMSzTGJk4GZgMXpLupd0fEgqofbLqHjdf+Mx0paw6woaWs6WEpy+RKL6SsZ1FDyjLGjBY9k7LGxOB8dbod+4ykt3c6J52wZGRyJkcpa5gMzrsp7tScW+eHtcOSkcmdXkpZ02WYDM67ImIxsLJ5u6lgycgMA72UssbN4Gz3uxyrZ4aeWnkWEXFlRHwkIj6cBK06jI3B2QnH6plRoNKzkLSc1v8oROE6PbvFdzNl6AzObtCQjI5umgrA7oDJiUFKWZNQzVi9duVoIWWlBqNhcF5Na4PznHYGZ7JWXwVcS2Fw1umGTQlLRiZ32unez2336sKxpxurN9X4PSg8keMlLQb+lmJglPR+qKSbKaL1Jhmcpe0PpzBM7wR+STI4Je2a6v4O4AxJ5bi+KeNYPZMzlQanpF9TdENa9tkj4sW9rNioYoPT5Mq0Dc6IeFFEvDi9N786NhSZSVn/LulmSUskfVHFlIRIOkyr50z9saTtK47VFynLmJyZUrq3pG0kfUzFTGC9ohdS1n4RsSPwcmDTtC3AuRHxijRouhA4teJYPZeywAanyZs6z4ZsLulIST8DllAMinb6hzwTui5lRUTjkfq1KZ5tiab1ABvQ4u5PP6QssMFp8qfdAOehkq6mUKPnAO8F7o+IEyLi5zX2nYWUVdrmcopbosspJLPG+vdL+iXFlcUHSusn0uKMpaxO2OA0w0C7K4vTgbWAAyLiuPQXdCoyUi5SFgAR8QZgc2Bd4DWl9adHxDYUd0yOK62flxZnLGXZ4DSjQLtYvRdQ9O1PTXr0NynmDOklXZeyyjuPiBWSLgH2Yc2YwPMpxiaambGU1SlWzwanGQba3Q15KCK+EBGvphCXHgUelHSrpP/d7YqoR7F6kmancYfGJMh7A7elz9uWir4JuKN5+3axet3CMXFmGKgb2LuM4nmKU9I/1pb/MKdIv2L1NgAukbQuRbfqaqAxJ+kRKgJ3nwYeAQ5ubKQasXqSdk112Rh4i6QTIuJl7X50FTY4Te7UitUz3cNSlsmVaUtZxhhTpmeNxZAYnLUmNu6XwWkpy+TMdAzO40bI4Kw7sbFj9czYMx2Dcy1Gx+BcFB0mNu6HwWkpywwDY29wlpg0sXE3DU5LWWYUGHuDE1pPbOxYPWMm066xeAHFP8RTJd0u6RP0x+A8RNJdwCXAjpK2lfTJ0hVKZbmGsdnO4Ezl92ms0+qJjRdE64mN+xarZynL5Ey7WL2HKAb2viBpLsVf8wcl3QpcFBH/3M2KqEexepJmU0y3eH/J4PyP9F1jYuP5UTGxsWP1jCmom+69LCJOiYidKQb3VnTh2P2K1WsYnIuBmynGLRoGZ3li44n03AjgWD1jmpmWwSnp7ojYqgf1GXlscJpc6ZXB2WrQr/nAwyBl1ZKq+iVlGZMz020sevlAST+lrLpSlWP1zNjTbpKhD1Z9RdHP7xUN2epcSVtERJXKuEa5iLhc0pMUWRUfj4iToK2UdReApEqpqixlpc8NKeuyOtvX5eKJezmmaZKhBfO6GshlzIxod2WxYcVrNvDZGvseNimr+RgTadGxesbQ/tbpCTPc95MlsQlJh1CIT+1olq3OpnXidlW570fElZKOj4izGmMLUEhZKqYn/DqFlNV2ztZux+pRdGPYaqs1x4UbBueKkjXeMDh9Z8TkQrtuyOeqvgOIiA+0+36aDDJWrwrH6hlD+27IDaXXgqbPN3S7IhpArF4dHKtnTCIiOr6Am+qUa9rm8abPhwCnpeXjgd9R/NVuvI4HTmzaZgdgadO6WuVK320GXAcspnhq9vPA2um7XdOxfw88DCwpbTdRWt4FuIVCyDqN1X5K5fZVr5133jmqeGj5ipi4+5F4aPmKyjLG9Arg+mjz/24tKUvSjRGxU/0myFRhKcvkSq+kLGPMmNEuz2K5pMckPQbs0FhurO+04yExONeV9I1kZl4raeuKY1UZnLVi+epiKcvkTLt5QzaMiGen19ql5Q0j4tk9rFM/Dc73AI9ExEuATwMnVRyryuCsG8vXEcfqmdzJsRvSt1g9iluoX07L3wJeW3YzoGOs3qLoEMtXB0tZZhjoZWMxDAbnFsA9ABHxDMWsa5uk8hOlMnUMzkmxfE31dayeGXp62VgMQ6xepZ0ZUzA41SKWb1Jhx+qZESC3bki/Y/WWAVvCKmHrOcBvm+rU1uBU51i+jljKMsNArblO+4EGEKtH0XAcDPwUeDtwdYsGpjJWTzVi+eriWD2TO4O8ssghVu9sYBNJdwIfBD7a2Eg1YvVoE8s3HRyrZ3LGEyP3GRucJlcGZnCOiZTlWD0zNuQ2wAnDJWU5Vs+MDTk2FsMkZXVlrlOwwWnyx1JW96SsaWOD0wwDlrLWZMpSVidscJpRIBvPItHvWL2GlLVsulJWHcKxemYEyGbMQoOJ1WtIWdBGysKxesYM9MriOElHlj6fRWvZ6nwmm5lVUlZzuQYNKWtdYC3gaiZLWV9NUtZvKTU4kiZKXZHDgXOA9SmErFVznaa6bAy8RdIJEfGydj+6ChucJncsZfUZS1kmVwYmZRljRouRNzgl/Ymkf5N0WzI4T2zaz34qovGWSGopV9ngzBNLbP0lxyuLXhicp0TEnwKvBHaX9EaAdKflWGD3NNZwJK3pi8Fp6mOJrf/k2Fh01eCMiCciYlH6/g/Ajay+FXoocHpEPJK+X+Mx834ZnKY+ltgGw9gYnOn7jYC3AFelVdsB20n6iaRrJM0vlZ1IizM2ODtJWWZqWGIbDGNjcCbH4jzgcxHxq7R6bYpuxZ5pH2elBqWrBmenWD0zNSyxDYbcuiG9jNU7E7gjIj5TWrcMuDgino6IXwO3UzQeNJWZkcFpuosltsGQje6tHsXqpe//F4XK/d6mr75D0fCcI2kORbfkV+UC7WL1zOCwxNZ/Rj5WT9JcioZme+DGdIXSaDQupxjbWAosAj4SEQ+n7SZKu2kZqydp11T3dwBnSFpS65ebruAYwv5ig7PP2OA0uTIwg3NYpKxU7u2SQlLLE9VGyurqXKfG5ExuA5zQRykLQNKGFF7GtW2O1fO5TsFGosmbHBuLfkpZUAyKLgRWtDpIBylrUXRhrlOwkWjyZ6ylLBWTBG0ZEd9rcYyJtDjjuU47YSPRDANjK2VJmkWR6P2hVgeYipSlDnOddjI4bSSaYSAbzyLRy1i9ZilrQ4qpAX6QxiufTxGSsyAiyrcr6s51ukdUzHXqWD0zCmQzZqEexeqlfTekrCMb6yLi0YiYU9rHNRSTG0+6r9kuVk+r5zpd0OohtLrYSDTDwMjH6pWkrNsopCyA01I3pRLViNVj8lynAHdHxIJ2+63CRqLJHUtZfcZSlsmVgUlZxpjRYqwNTkmHJTNzQtKPJW1fcay+xOpZyjI5k+OVRT8NznMj4hVpbGIhcGrFsXoeq2cpy+ROjo1F3wzOWD1hMhTzi6wxgNPB4OxKrJ6lLDMMjLXBmda9X9IvKa4sPlBaP5EWex6rZynLDANja3A21kfE6RGxDXAMcFxp/bzGpi2O39VYPUtZZhjIrRvS71i9MueTuhdN9DxWz1KWGQay0b01gFi9siJOoZPf0bxtv2L1LGWZ3Bn3WL0j0u3UCeCDrJ5RfSCxeo6JMzljg7PP2OA0uTIwgzMXKSu9f1LSPS3qVEuq6peUZUzO5DbACb2Rsr4L7NZiH3Wlqr7MdWqD0+RMNgOcJRqy1bmStoiIKpVxjXIRcbmkJ4ErgY9HxEkAEXENQLogWEVE3JXWV0pVZSkrfW5IWZfV2b4uF0/cyzEXLmadWbN4euVKFu67AwvmTUnnMKanjJWUNRW6KWV1wganGQbGRsqaKt2UsmxwmlEgt25IL2P1psuMpSzH6plRIJsBTvUwVm8mtIvV6xY2OM1QEBE9eQGPN30+hCLODuB44HcUf7Ubr+OBE5u22QFY2rSuVrmm7xemY6xsHCut3zV9/j3wMLCktM1EaXkX4BYKIes0VvspldtXvXbeeeeo4qHlK2Li7kfioeUrKssY0yuA66PN/7uWsvqMpSyTKwOTsowxo8W4GJxVBqZj9YypSY5XFr0wOKsMTMfqGVOTHBuLrsbqqX0snmP1jKnJOBicbQ1MOVbPmFqMg8HZ1sAMx+oZU4vcuiG9iNWra2A6Vs+YNmSje6tHsXrRJhZPjtUzpjYjH6uXaBmLh2P1jKmNDc4+Y4PT5MrADM4hkbI+KGmppMWSrpL0wopjtdw+fbdf2scSSV1JzDImR3Ib4IT+Slk3AbtExA7Atyhun7ai5fYqHp8/Ftg9Il5WOt60sMFpcibHxqKfUtaiiHgi7e8aJt/1ANpLWcChwOkR8Uja34PT/dE2OE3ujL2UVeI9rB74rCtlbQdsJ+knkq6RNJ9pYIPTDANjL2UBSDqIIrPi5FUF6klZa1N0S/ZMdThLxeTLk7DBaUaBbDyLRNdj9dKtzUqpStLrKByOPSKi1Z/ydtsvA66JiKeBX0u6naLxuK68g3CsnhkBshmzUI9i9aJNLJ6kVwJnAAuqxhvabQ98B9gr7WsORbfkV6320w4bnGYYGOSVxXGSjix9PovWstX5TDYzq6Ss5nJlDgfOAdanGJdojE2cDMwGLkh3Q++OiAVQjFmUuiJV219OcQdmKfBH4CMR8XBFHdpig9PkjqWsPmMpy+TKwKQsY8xoYYNzBganasbyGZMDM5X+cryyGBqDk/qxfMYMlG5Ifzk2FkNjcNaJ5TNm0HRL+rPBuZrpGJyVsXxN9W0rZRnTS7ol/dngZEYGZ2Us36TCHWL1jOkl3ZL+cuuG9D1Wr2RwLpiGwVmmKpbPmIHSLekvG91bg4nVaxic89sZnG227xjLZ0wOdEP6G/dYvbLBOSHpksYGqhGrR5tYPmNyY6axjTY4+4wNTpMrAzM4x0TK6upcp8bkTG4DnDBcUlbX5joFx+qZvMmxsRgmKasrc52CY/VM/ljKWs20pKxu4Fg9MwxYymJmUlYdOhmcjtUzw0A2nkVi2GL1auFYPTMKZDNmoeGM1esKjtUzw4Bj9WYQqydp11SXjYG3SDohismGpoxj9UzuWMrqM5ayTK4MTMoyxowW425w1jIw+2VwWsoyOZPjlUU/Dc66BmbPDU5LWSZ3cmws+mlwdjQw+2FwWsoyw4ANzuq6TKTFGRuclrLMKGCDs4JuGpydYvUsZZlhILduSN9j9WowY4OzE5ayzDCQje6tAcTq1WGm29fFUpbJnbGO1ZO0azr2O4AzJC1pbKAasXrttp8OM409M6aX2ODsMzY4Ta7Y4DTGdAU3FsaYWrixMMbUwo2FMaYWbiyMMbVwY2GMqYUbC2NMLexZ9BlJvwH+s02ROcBDfarOVHC9psYw1uuFEbHmw0sJNxaZIen6dmLMoHC9psYo1svdEGNMLdxYGGNq4cYiP84cdAUqcL2mxsjVy2MWxpha+MrCGFMLNxYDQNJ8SbenqQU+2uJ7pSkH7pS0WNJOmdRrT0mPlrJV60QldqNeX5L0oKRbKr4f1PnqVK9Bna8tJS2SdKukJSmLtrnM1M9ZRPjVxxewFkWAzouBZwE3A9s3ldmbImBHwKuAazOp157A9wZwzl4N7ATcUvF9389XzXoN6nxtDuyUljcEftGN/8d8ZdF/dgPujIhfRcQfKAKH92kqsw/wlSi4BtgoTUkw6HoNhIj4EfDbNkUGcb7q1GsgRMT9EXFjWl4O3MqaifRTPmduLPrPFsA9pc+tphaoU2YQ9QL4c0k3S7pM0rQmge4BgzhfdRno+ZK0NfBKivzYMlM+Z9kE9o4RdaYWmPH0A9OgzjFvpFCCH5e0N/AdihnaBs0gzlcdBnq+JM2myKc9MiIea/66xSZtz5mvLPrPMmDL0udWUwvUKdP3ekXEYxHxeFq+FFhH0pwe16sOgzhfHRnk+ZK0DkVD8fWI+HaLIlM+Z24s+s91wLaSXiTpWRRTGFzSVOYS4O/SiPWrgEcj4v5B10vS86VVk0LvRvH/z8M9rlcdBnG+OjKo85WOeTZwa0ScWlFsyufM3ZA+ExHPSDqCYk7WtYAvRcQSSYel778IXEoxWn0n8ATw95nU6+3A4ZKeAZ6kmNOl55f7ks6juLMwJ0298C/AOqV69f181azXQM4XsDvwt8DPS1Na/DOwValuUz5nNjiNMbVwN8QYUws3FsaYWrixMMbUwo2FMaYWbiyMMbVwY2GyIXkJ50v6paSlki6VtF0X97+npL/o1v7GDTcWJguSSHQR8IOI2CYitqdwAzbr4mH2BNxYTBM3FiYX9gKeTsIQABExAfxY0smSbpH0c0nvhFVXCd9rlJV0mqRD0vJdkk6QdGPa5k/TA1WHAUelbIm/6uNvGwlscJpceDlwQ4v1bwPmATtSzHlxnaQf1djfQxGxk6T/Dnw4It4r6YvA4xFxSrcqPU74ysLkzl8C50XEHyPiAeCHwK41tms8PHUDsHWP6jZWuLEwubAE2LnF+laPUgM8w+T/f9dr+v6p9P5HfAXdFdxYmFy4GlhX0qGNFZJ2BR4B3ilpLUmbUkTZ/YxiCsjtJa0r6TnAa2scYzlFzJyZBm5xTRZEREh6K/AZFWHBK4C7gCOB2RSZoAEcHRH/BSDpm8Bi4A7gphqH+S7wLUn7AP8YEf/R7d8xyvipU2NMLdwNMcbUwo2FMaYWbiyMMbVwY2GMqYUbC2NMLdxYGGNq4cbCGFMLNxbGmFr8fzN+ld75aj9fAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bOd8rySg8F72csjTdzGyxmMzVxGTNTEkfBp4EvtKpoalG4bno5ZQltaHHsM1MokPxRuCrOW0qOv5Y4HXA0UXRe6nhopdTlmQmMzW4KLyiKLtXAneY2brJHi9pIWHy8iAz+32pL5oYLno5ZRj5KLxuUXaRt9I2iamSUXiERYV2BK6LVzyfK/G9k6Ou8WzO8ChlZkr6S2BvM/v/knYFZprZLwfeuhHEzUwnVXoyMyX9T8Ll9alx17aEZfqKjhuYcJVXrotw9RRJF0j6WRSkjoj795S0XNKtUZrKW3v0gCh23S3p05l63ydpdTz2eknPLvq9OE4dKTP0eAOwCPgdhDsChMvtQVE2Cq9Tub+W9DFgB0nvIATTQJjfeMjM9iHE2X0v7j+NMJR4MWEI8v9yzvVZwgNje8fXwrj/VmC+me0HfI0OUXpOd9wKrQdlOoo/xNl8A5C0w2CbVFq4mlDOzK4hzCW8B9jFzD4Ry76N8Mg6ZrbZzNbH/YVReJJ2B3Yysx/F38M/AX8T61qemcSsnXBVNR7/Vx/KdBSXSTofeJqk44HvEFbVKmJgwlVeOUmvAl5NCNt9WNJ7M89ffFTSLZIul7Rb3LcUOCZOpn4L+PvMOcbi5mzCLdIWecLV28kRrpyJuBVaLwo7CjM7m3BZfQXwfOAjZvaZEnUPUrjKK/edeCv1d2Z2IaHDmE74S/9DM3sJYSGgs2P5xcBFZjYHOAz4kqRpAJmQnDLC1THAfOCsTo1N1cysErdC60Upj8LMriM8ADVoFgO7STo6vn+WpL2B44DXxrbMyyvXUrCzwpWkh4Hfs/XW6+WEv/7Enwtj2R9J2g6YBTyUadM6xg8p2oWrVxLmQA4ys45/Ds3sAuACCHc9Sv4uRhq3QutF7hWFpA2SHu3w2iDp0X43RCWj8LqV61RvnFf4BnBw3PUKYHXcvie+R9KfAdsBv2o7/gFgg6SXxbsd/5XwpCqSXkx4wnWRmWU7F6cAt0LrRe4VhZkN8s4GTD0KL0/MyovMg3B790uSPknoCP5b3P9+4B8lnUQYThzX0rAVYu3mxXInABcB2xPmIVpzEWcBM4HL4x3Te8xsUd4XdsbjVmh9mIpwNQvY0YWrqeHClZMq/RaunkIJ4cpxnNFhYMJVQ8zMl8dbrk9KelPR78SZiAtX9SBF4apOZuY9hDsyF5f5Ys54XLiqD4MUrqZKnczMtWa2EtjcfpzTHReu6sUghaummZndvpcLV224cFUvSuVRmNl1ZvZBM/tAlK/K0Bgzs4hUo/CqxIWrepHrUUjaQOf/ECJ4TDt1+KxXamdmOlOjJVyd3LZUgLsUaVKlcDUOlYzC61aODsJV7CxaZuYNdDYzL+pmZkYb9WXAjwlmZpmhl1OAC1f1oZvC/fRurz6ce6pReJONzIPggSyVtBL4W8IkKPHn8ZJuI8ThjTMzM8efQDBH7wZ+TjQzJR0Y2/5m4HxJ2Yg9pwQew1cPcs1MSb8kDD06jtHN7LmDbNio4mamkypTMjPN7Dlm9tz4s/1V2EkkJlz9q6TbJK2S9DmFZQKR9C5tXcP0B5L2zTmXC1dOo5lUCrekvSR9WGGFrkExCOHqSDPbH3ghsGs8FuBiM3tRnCBdBpyTcy4XrgaEm5n1oMyzHrtLWiLpJ8AqwgRo0X/iXui7cGVmrcfipxOeVbG2/QA70OEujwtXg8PNzPrQbTLzeEk3EHTnWcA7gAfM7HQz+2mJupMQrjLHXEO47bmBIJC19r9b0s8JVxTvyewfi5s9C1fORNzMrBfdrijOA7YBjjKz0+JfzsmIRqkIVwCY2auB3YEZwKGZ/eeZ2V6EOyOnZfbPi5s9C1duZk7Ezcx60S0K71mEsfw5UXm+jLCmxyDpu3CVrdzMNkq6GjicidF+lxLmItrpWbjyKLyJuJlZL7rd9VhvZp81s5cTpKTfAg9JWiPp//S7IRpQFJ6kmXGeobUg8WHAHfH93pmirwXuaj++WxSeM3U8Cq9elA3XXUd4PuLs+B+143/KSTKsKLwdgKslzSAMpW4AWmuEnqgQjrsJeAQ4tnWQSkThSTowtmVn4PWSTjezF3T70s5W3MysD6Wi8Jz+4cKVkypTEq4cx3FaDKyjqImZWWqRYTcznX5RV8FsKmbmaSNkZpZdZNjNTKdn6iyYTcXM3IbRMTOXW8Eiw25mOv2g7oJZ483MDOMWGe6nmenClVN3wazxZiZ0XmTYo/CcflJ3waxbR/Eswn/CcyTdKemjDMfMPE7SWuBqYH9Je0v6WObKJLdcy8TsZmbG8oe39mnrIsOLrPMiwx6F5/RM3QWzblF46wmTeJ+VNIfwV/whSWuAK83sH/rZEA0oCk/STMISiA9kzMx/i5+1FhleaDmLDHsUntMv6iyYlU3hXmdmZ5vZAYSJvI19OPewovBaZuZK4DbCPEXLzMwuMjwWnwMBPArPGQx1jf6bkpkp6R4z23MA7Rl53Mx0UmUQZmanCb72k9ZBuColTLlw5TSdqXYUg3xAZJjCVVlhyoWrAVFXU7FpdFsA6H15HxHG9YOiJVJdLGm2meXpaxPKmdk1kh4nZE18xMzOhK7C1VoASbnCVFa4iu9bwtW3yxzv5HPV2H2c0rYA0KJ5Hh6WIt2uKHbMec0EPlWi7roJV+3nGIubHoU3AOpuKjaNbrdHT++x7scz0hKSjiNITd1oF6k+T+dk7Lxy3zGz6yQtNbMLW3MJEIQrhSUDv0IQrrquodrvKDzC0IU99/Q5YNhqKm7M2O8tU7FudwSaQLehx6fzPgMws/d0+3yKVBmFl4dH4Q2AupuKTaPb0OPmzGtR2/ub+90QVRCFVwaPwhsMdTcVG4eZFb6AW8uUazvmsbb3xwHnxu2lwG8If61br6XAGW3H7AesbttXqlzms92AG4GVhKdfPwNMj58dGM/9O+BhYFXmuLHM9nzgdoJsdS5b/ZPc4/NeBxxwgDlbWb9ho43d84it37Cx6qY0HuAmy/l3W0q4knSLmb2kfPfj5OHClZMqgxCuHMdpEN3yKDZIelTSo8B+re3W/qKKa2JmzpD01Whc/ljS3Jxz5ZmZpaL0HCcFepHbuq3rsaOZ7RRf0zPbO5rZTj21uDvDNDPfDjxiZs8DPgGcmXOuPDOzbJSe41RKrzF8KQ49hhaFR7hN+sW4/TXgFVn3Agqj8JZbQZSe41RNP+S2QXYUdTAzZwP3ApjZk4TV0HaJ5ccyZcqYmeOi9Nra61F4TmX0I4ZvkB1FHaLwcq1Lm4SZqQ5ReuMKexSeUyH9kNtSG3oMOwpvHbAHbJGxngr8uq1NXc1MFUfpOU6l9ENuK7X26DBQBVF4hE7jWOBHwJuAGzp0LrlReCoRpec4KdBrDF+VVxQpROF9HthF0t3A+4APtQ5SiSg8ukTpOU5q9BLD54sUDxk3M51UqcTMbIhw5VF4TiNIbTIT6iVceRSeUxsGYmZWSJ2EK1971KkFKZuZTROuHCdJUjczGyNcFeFmplMl/TAzk/EoIsOOwmsJV+umKlyVwTwKz6mQkTIzVU0UXku4gi7CFR6F59SYupuZp0laknl/IZ1FqksZb1zmCVft5Vq0hKsZwDbADYwXrr4Uhatfk+lsJI1lhh8nABcB2xNkqy1rj8a27Ay8XtLpZvaCbl/acaqgVzPThash48KVkyqVCFeO44wOI29mSvoTSf8i6Y5oZp7RVs+RCnF2qyR1FKdG3cz09T+dIlK8ohiEmXm2mf0p8GJggaTXAMQ7KqcCC+LcwhI6M7JmZq8ijtMMUuwo+mpmmtnvzWx5/PwPwC1svd15PHCemT0SP5/wqPgom5m+/qdTlsaYmfHzpwGvB66Pu/YB9pH0Q0krJC3MlB2Lmz2bmakKV/0QcZxm0BgzMzoUlwCfNrNfxN3TCUOJg2MdF8bOpK9mZqpReL7+p1OW1IYeg4zCuwC4y8w+mdm3DrjKzDaZ2S+BOwkdB21lejIzU8XX/3TKkozCrQFF4cXP/zdBz35H20f/TOh0LpI0izAU+UW2QLcovFGgVxHHaQYjH4UnaQ6hk9kXuCVembQ6jGsIcxmrgeXAB83s4XjcWKaajlF4kg6MbX8zcL6kVaW+eWL0EpHmNAM3M4eMm5lOqlRiZtZFuIrl3iTJJHX8JXURrnztUacRpDaZCUMUrgAk7UjwLn7c5Vy+9uiI4Tbq5EixoximcAVhAnQZsLHTSQqEq+Xma4/WDrdRJ0+jhSuFBXz2MLNvdjjHWNzsee1RJx3cRp0ajRWuJE0jJG+/v9MJJiNcqWDt0VTNzCbiNurUSMajiAwyCq9duNqREN//3Tg3+UxCwM0iM8velii79uhBlrP2qEfhpYPbqFMjmTkKDSgKL9bdEq6WtPaZ2W/NbFamjhWEhYbH3bvsFoWnrWuPLur0QJmTHm6jTo2Rj8LLCFd3EIQrgHPj0CQXlYjCY/zaowD3mNmibvU61eM26uRx4WrIuHDlpEolwpXjOKNDo81MSe+KxuWYpB9I2jfnXCMdhddEXLiaHCleUQzTzLzYzF4U5yKWAefknGtko/CaiAtXkyfFjmJoZqZtXbwYwvofEyZsCszMWkfhNREXrqZGo83MuO/dkn5OuKJ4T2b/WNwc2Si8JuLC1dRorJnZ2m9m55nZXsApwGmZ/fNah3Y4/0hE4TURF66mRmpDj2FH4WW5lDikaGNko/CaiAtXUyMZhVsVROFltW+CIn5X+7GjHoXXRFy4mjxNj8I7Md4yHQPex9aVzRsVhddEPP5vcriZOWTczHRSpRIzMxXhKv78mKR7O7SplDDlwpXTdFKbzITBCFffAF7aoY6ywtRIC1duKTpFJDOZmaElUl0sabaZ5WlzE8qZ2TWSHgeuAz5iZmcCmNkKgHghsAUzWxv35wpTWeEqvm8JV98uc3zqXDV2H6dcsZJtp01j0+bNLDtiPxbNm5Qm4jSARglXk6GfwlWquKXolKUxwtVk6adwlaqZ6ZaiU5bUhh6DjMKbKj0LV6lG4bml6JQlmclMDTAKrxe6ReHVHbcUndKY2UBewGNt748jRNABLAV+Q/hr3XotBc5oO2Y/YHXbvlLl2j5fFs+xuXWuuP/A+P53wMPAqswxY5nt+cDtBNnqXLb6J7nH570OOOAAS431Gzba2D2P2PoNG6tuilMhwE2W8+/Whash48KVkyqVCFeO44wOTTEz88xKj8LDhSunmBSvKAZhZuaZlY2PwvNYOKcMKXYUfY3CU/cou0ZH4blw5ZSlCWZmV7NSDY7Cc+HKKUsTzMyuZqU1OArPhSunLKkNPQYRhVfWrGxcFJ4LV05ZklG4NaAoPOsSZSePwvNYOKcUIx+FF+kYZYdH4QEeC+cU42bmkHEz00mVSszMmghX75O0WtJKSddLenbOuToeHz87MtaxSlJtfQrH6UZqk5kwXOHqVmC+me0HfI1wi7QTHY9XeAT+VGCBmb0gc75a4WamU0SKHcUwhavlZvb7WN8Kxt/dALoLV8DxwHlm9kis76Gpf+1qcDPTKUPjhasMb2frJGdZ4WofYB9JP5S0QtJCaoSbmU5ZGi9cAUg6hpA5cdaWAuWEq+mEocjBsQ0XKiyEPA43M526k4xHEel7FF68fZkrTEl6JcHROMjMOv0p7Xb8OmCFmW0CfinpTkLHcWO2AvMoPKfmJDNHoQFF4VmXKDtJLwbOBxblzS90Ox74Z+CQWNcswlDkF53qSRE3M52yVHlFcZqkJZn3F9JZpLqU8cZlnnDVXi7LCcBFwPaEeYjWXMRZwEzg8njH8x4zWwRhjiIz/Mg7/hrCnZbVwB+BD5rZwzltSBI3M50yuHA1ZFy4clKlEuHKcZzRwc3MHsxMlYzSc/Jx2asepHhFURszk/JRek4HXPaqDyl2FLUxM8tE6TmdcdmrXriZuZWpmJm5UXpt7U1SuKoSl73qhZuZ9GRm5kbpjSucaBRelbjsVS9SG3oMPQovY2YumoKZmSUvSs/pgMte9SIZhVvVROG1zMyF3czMLscXRuk5+bjsVR+aHoWXNTPHJF3dOkAlovDoEqXnlMNj+OqBm5lDxs1MJ1UqMTMbIlyNxNqjjlNEapOZUC/hqvZrj1aNm5n1IMWOok7CVa3XHq0aNzPrgwtXW5mScOVMDTcz64ULV/QmXJXBzcyJuJlZL5LxKCJ1i8IrRapReFXiZma9SGaOQvWMwnOmiJuZ9cKj8HqIwpN0YGzLzsDrJZ1uYSEgpwRuZtYHF66GjAtXTqpUIlw5jjM6NN3MLGVWupk5OFy4qgcpXlEM08wsa1a6mTkAXLiqDyl2FMM0MwvNSjczB4MLV/XCzcz8tozFzZ7NTBeuJuLCVb1wMzOHfpqZHoU3EReu6kVqQ4+hR+GVoGcz05mIC1f1IhmFWxVE4ZWh1+OdfFy4qg+NjsKTdGA895uB8yWtah2gElF43Y53yuFRePXAzcwh42amkypuZjqO0xPeUTiOU4h3FI7jFOIdheM4hXhH4ThOId5ROI5TiHcUjuMU4h7FkJH0K+DfuxSZBawfUnNSoWnfOdXv+2wz6/gwkncUiSHppjzpZVRp2neu4/f1oYfjOIV4R+E4TiHeUaTHBVU3oAKa9p1r9319jsJxnEL8isJxnEK8o0gESQsl3RmXBPhQ1e0ZNJL2kLRc0hpJq2K+aSOQtI2kWyV9s+q2lMU7igSQtA1wHvAaYF9gsaR9q23VwHkSeL+Z/RnwMuDdDfjOLd4LrKm6EZPBO4o0eClwt5n9wsz+QAgMPrziNg0UM3vAzG6J2xsI/3EmlW5eRyTNAV5LSEyrDd5RpMFs4N7M+0kvCVBnJM0FXkzIJB11PgmcTM3WgvGOIg16XhKgrkiaScg8XWJmj1bdnkEi6XXAQ2Z2c9VtmSzeUaTBOmCPzPtGLAkgaVtCJ/EVM/t61e0ZAguARXGZiUuBQyV9udomlcM9igSQNB34GfAK4D7gRuAoMxvZVO+40PMXgV+b2ZKKmzN0JB0MfMDMXldxU0rhVxQJYGZPAicS1k1dA1w2yp1EZAHwt4S/qq2lJw+rulFOZ/yKwnGcQvyKwnGcQryjcBynEO8oHMcpxDsKx3EK8Y7CcZxCvKNwkkHSMyVdKunnklZL+pakffpY/8GS/qJf9TUJ7yicJIgC1pXAd81sLzPbF/gHYLc+nuZgwDuKKeAdhZMKhwCbzOxzrR1mNgb8QNJZkm6X9FNJb4EtVwdb8hwknSvpuLi9VtLpkm6Jx/xpfPDsXcBJUe76qyF+t9ozveoGOE7khUCnh6XeCMwD9iesh3GjpO+XqG+9mb1E0n8nqNLvkPQ54DEzO7tfjW4KfkXhpM5fApeY2R/N7EHge8CBJY5rPWR2MzB3QG1rDN5ROKmwCjigw/5Oj+BDSMjK/vvdru3zJ+LPP+JXzj3jHYWTCjcAMyQd39oh6UDgEeAtMWdyV+DlwE8IyzLuK2mGpKcSnrwtYgOwY/+bPvp4T+skgZmZpDcAn4zhwhuBtcASYCZwGyHM52Qz+w8ASZcBK4G7gFtLnOYbwNckHQ78vZn9W7+/x6jiT486jlOIDz0cxynEOwrHcQrxjsJxnEK8o3AcpxDvKBzHKcQ7CsdxCvGOwnGcQryjcBynkP8EdDwnNhx3bxcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize\n",
    "visualize_design(design_10_9_mhc1,coverage_10_9_mhc1)\n",
    "visualize_design(design_10_6_mhc1,coverage_10_6_mhc1)\n",
    "visualize_design(design_20_6_mhc1,coverage_20_6_mhc1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5. Visualize MHC II"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 31)\n",
      "(1259, 31)\n",
      "(1259, 31)\n"
     ]
    }
   ],
   "source": [
    "# prep variables\n",
    "design_10_9_mhc2 = designs_mhc2_after[2][3]\n",
    "coverage_10_9_mhc2 = coverages_mhc2[5]\n",
    "print(coverage_10_9_mhc2.shape)\n",
    "\n",
    "design_10_6_mhc2 = designs_mhc2_after[1][3]\n",
    "coverage_10_6_mhc2 = coverages_mhc2[4]\n",
    "print(coverage_10_6_mhc2.shape)\n",
    "\n",
    "design_20_6_mhc2 = designs_mhc2_after[4][3]\n",
    "coverage_20_6_mhc2 = coverages_mhc2[7]\n",
    "print(coverage_20_6_mhc2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize\n",
    "visualize_design(design_10_9_mhc2,coverage_10_9_mhc2)\n",
    "visualize_design(design_10_6_mhc2,coverage_10_6_mhc2)\n",
    "visualize_design(design_20_6_mhc2,coverage_20_6_mhc2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 6.0, 25, ['CVADYSVLY', 'QELGKYEQY', 'YYHKNNKSW', 'MTSCCSCLK', 'QPRTFLLKY', 'VVFLHVTYV'])\n",
      "(10, 9.0, 3, ['FIAGLIAIV', 'CVADYSVLY'])\n",
      "(20, 6.0, 25, ['CVADYSVLY', 'QELGKYEQY', 'YYHKNNKSW', 'MTSCCSCLK', 'QPRTFLLKY', 'VVFLHVTYV'])\n",
      "(20, 9.0, 3, ['FIAGLIAIV', 'CVADYSVLY'])\n",
      "\n",
      "(10, 6.0, 21, ['EMIAQYTSALLAGTI', 'WRVYSTGSNVFQTRA', 'QQLIRAAEIRASANL', 'ASVYAWNRKRISNCV'])\n",
      "(10, 9.0, 1, ['IDRLITGRLQSLQTY'])\n",
      "(20, 6.0, 21, ['EMIAQYTSALLAGTI', 'WRVYSTGSNVFQTRA', 'QQLIRAAEIRASANL', 'ASVYAWNRKRISNCV'])\n",
      "(20, 9.0, 1, ['IDRLITGRLQSLQTY'])\n"
     ]
    }
   ],
   "source": [
    "for design in designs_mhc1:\n",
    "    if design[0] <= 20 and design[1] >= 6.0:\n",
    "        print(design)\n",
    "    \n",
    "print()\n",
    "\n",
    "for design in designs_mhc2:\n",
    "    if design[0] <= 20 and design[1] >= 6.0:\n",
    "        print(design)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5. Select best 10 from MHC I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 9.0, 3, ['FIAGLIAIV', 'CVADYSVLY', 'VVFLHVTYV'])\n",
      "\n",
      "(10, 6.0, 25, ['CVADYSVLY', 'QELGKYEQY', 'YYHKNNKSW', 'MTSCCSCLK', 'QPRTFLLKY', 'VVFLHVTYV'])\n",
      "\n",
      "(20, 6.0, 25, ['CVADYSVLY', 'QELGKYEQY', 'YYHKNNKSW', 'MTSCCSCLK', 'QPRTFLLKY', 'VVFLHVTYV', 'GWTAGAAAY', 'QLTPTWRVY', 'VYYPDKVFR', 'YEQYIKWPW', 'AYRFNGIGV', 'VTYVPAQEK', 'LPIGINITR', 'VLKGVKLHY', 'NYLYRLFRK', 'VEAEVQIDR', 'LPPAYTNSF', 'LPFFSNVTW', 'SVYAWNRKR', 'LEGKQGNFK'])\n",
      "\n",
      "['CVADYSVLY', 'QELGKYEQY', 'VVFLHVTYV', 'FIAGLIAIV', 'YYHKNNKSW', 'QPRTFLLKY', 'MTSCCSCLK', 'GWTAGAAAY', 'VYYPDKVFR', 'QLTPTWRVY']\n"
     ]
    }
   ],
   "source": [
    "# MHC I\n",
    "supple_10_9 = designs_mhc1_after[2][3]\n",
    "orig_10_6 = designs_mhc1[4][3]\n",
    "supple_20_6 = designs_mhc1_after[4][3]\n",
    "coverage_supple_20_6 = coverages_mhc1[7]\n",
    "\n",
    "print(designs_mhc1_after[2])\n",
    "print()\n",
    "print(designs_mhc1[4])\n",
    "print()\n",
    "print(designs_mhc1_after[4])\n",
    "print()\n",
    "\n",
    "best_10_mhc1 = vc.select_best_10(supple_10_9, orig_10_6, supple_20_6, coverage_supple_20_6)\n",
    "print(best_10_mhc1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5. Select best 10 from MHC II"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 9.0, 1, ['IDRLITGRLQSLQTY', 'TGRLQSLQTYVTQQL'])\n",
      "\n",
      "(10, 6.0, 21, ['EMIAQYTSALLAGTI', 'WRVYSTGSNVFQTRA', 'QQLIRAAEIRASANL', 'ASVYAWNRKRISNCV'])\n",
      "\n",
      "(20, 6.0, 21, ['EMIAQYTSALLAGTI', 'WRVYSTGSNVFQTRA', 'QQLIRAAEIRASANL', 'ASVYAWNRKRISNCV', 'LLQYGSFCTQLNRAL', 'YQPYRVVVLSFELLH', 'WLGFIAGLIAIVMVT', 'GAAAYYVGYLQPRTF', 'QSIIAYTMSLGAENS', 'AVLYQDVNCTEVPVA', 'GGNYNYLYRLFRKSN', 'CTMYICGDSTECSNL', 'LDKYFKNHTSPDVDL', 'FKIYSKHTPINLVRD', 'CASYQTQTNSPRRAR', 'FNCYFPLQSYGFQPT', 'QMAYRFNGIGVTQNV', 'SVLYNSASFSTFKCY', 'RNFYEPQIITTDNTF', 'HVTYVPAQEKNFTTA'])\n",
      "\n",
      "['WRVYSTGSNVFQTRA', 'QQLIRAAEIRASANL', 'TGRLQSLQTYVTQQL', 'ASVYAWNRKRISNCV', 'IDRLITGRLQSLQTY', 'EMIAQYTSALLAGTI', 'YQPYRVVVLSFELLH', 'AVLYQDVNCTEVPVA', 'CTMYICGDSTECSNL', 'FKIYSKHTPINLVRD']\n"
     ]
    }
   ],
   "source": [
    "# MHC II\n",
    "supple_10_9 = designs_mhc2_after[2][3]\n",
    "orig_10_6 = designs_mhc2[4][3]\n",
    "supple_20_6 = designs_mhc2_after[4][3]\n",
    "coverage_supple_20_6 = coverages_mhc2[7]\n",
    "\n",
    "print(designs_mhc2_after[2])\n",
    "print()\n",
    "print(designs_mhc2[4])\n",
    "print()\n",
    "print(designs_mhc2_after[4])\n",
    "print()\n",
    "\n",
    "best_10_mhc2 = vc.select_best_10(supple_10_9, orig_10_6, supple_20_6, coverage_supple_20_6)\n",
    "print(best_10_mhc2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5. Format for table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_1_5_list(start_num, mhc_class, chosen, predictions):\n",
    "    '''\n",
    "    Gathers data from predictions list for each \n",
    "    chosen peptide and structures the data for display.\n",
    "    \n",
    "    Args:\n",
    "        start_num: Integer representing the ID of the first element in the formatted list.\n",
    "        mhc_class: Name of the MHC class\n",
    "        chosen: List of selected peptides\n",
    "        predictions: List of predicted peptide binding affinities \n",
    "        in which each element in the list is a Tuple of the form:\n",
    "        (allele, peptide, start, end, pIC50).\n",
    "        \n",
    "    Returns:\n",
    "        List of structured elements in which each \n",
    "        element in the list is a Tuple of the form:\n",
    "        (curr_num, peptide, allele, mhc_class, pIC50).\n",
    "    '''\n",
    "    temp = []\n",
    "\n",
    "    for allele, peptide, start, end, pIC50 in predictions:\n",
    "        if peptide in chosen:\n",
    "            # add tuple to temporary list\n",
    "            temp.append((peptide, allele, mhc_class, pIC50))\n",
    "\n",
    "    # sort temp by peptide\n",
    "    temp = sorted(temp, key=lambda x: x[0])\n",
    "\n",
    "    # set number for each peptide\n",
    "    curr_num = start_num\n",
    "    temp2 = []\n",
    "    visited = []\n",
    "\n",
    "    for peptide, allele, mhc_class, pIC50 in temp:\n",
    "        if peptide not in visited:\n",
    "            visited.append(peptide)\n",
    "            curr_num += 1\n",
    "\n",
    "        temp2.append((curr_num, peptide, allele, mhc_class, pIC50))\n",
    "\n",
    "    return temp2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(270, 5)\n"
     ]
    }
   ],
   "source": [
    "# MHC I\n",
    "build_list_mhc1 = build_1_5_list(0,'I',best_10_mhc1, results_mhc1)\n",
    "col_list = ['Peptide','Sequence','Target Allele','MHC Class','Predicted pIC50']\n",
    "build_list_mhc1_df = pd.DataFrame(build_list_mhc1, columns=col_list)\n",
    "print(build_list_mhc1_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(240, 5)\n"
     ]
    }
   ],
   "source": [
    "# MHC II\n",
    "build_list_mhc2 = build_1_5_list(10,'II',best_10_mhc2, results_mhc2)\n",
    "col_list = ['Peptide','Sequence','Target Allele','MHC Class','Predicted pIC50']\n",
    "build_list_mhc2_df = pd.DataFrame(build_list_mhc2, columns=col_list)\n",
    "print(build_list_mhc2_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(510, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Peptide</th>\n",
       "      <th>Sequence</th>\n",
       "      <th>Target Allele</th>\n",
       "      <th>MHC Class</th>\n",
       "      <th>Predicted pIC50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>CVADYSVLY</td>\n",
       "      <td>HLA-A*01:01</td>\n",
       "      <td>I</td>\n",
       "      <td>7.874116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>CVADYSVLY</td>\n",
       "      <td>HLA-A*02:01</td>\n",
       "      <td>I</td>\n",
       "      <td>5.377066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>CVADYSVLY</td>\n",
       "      <td>HLA-A*02:03</td>\n",
       "      <td>I</td>\n",
       "      <td>5.220576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>CVADYSVLY</td>\n",
       "      <td>HLA-A*02:06</td>\n",
       "      <td>I</td>\n",
       "      <td>5.087162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>CVADYSVLY</td>\n",
       "      <td>HLA-A*03:01</td>\n",
       "      <td>I</td>\n",
       "      <td>5.340608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Peptide   Sequence Target Allele MHC Class  Predicted pIC50\n",
       "0        1  CVADYSVLY   HLA-A*01:01         I         7.874116\n",
       "1        1  CVADYSVLY   HLA-A*02:01         I         5.377066\n",
       "2        1  CVADYSVLY   HLA-A*02:03         I         5.220576\n",
       "3        1  CVADYSVLY   HLA-A*02:06         I         5.087162\n",
       "4        1  CVADYSVLY   HLA-A*03:01         I         5.340608"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine\n",
    "build_list_df = pd.concat([build_list_mhc1_df,build_list_mhc2_df])\n",
    "print(build_list_df.shape)\n",
    "build_list_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5. Write to file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write to file\n",
    "build_list_df.to_csv(r'data/build_list.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5. Calculate coverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(510, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Peptide</th>\n",
       "      <th>Sequence</th>\n",
       "      <th>Target Allele</th>\n",
       "      <th>MHC Class</th>\n",
       "      <th>Predicted pIC50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>CVADYSVLY</td>\n",
       "      <td>HLA-A*01:01</td>\n",
       "      <td>I</td>\n",
       "      <td>7.874116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>CVADYSVLY</td>\n",
       "      <td>HLA-A*02:01</td>\n",
       "      <td>I</td>\n",
       "      <td>5.377066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>CVADYSVLY</td>\n",
       "      <td>HLA-A*02:03</td>\n",
       "      <td>I</td>\n",
       "      <td>5.220576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>CVADYSVLY</td>\n",
       "      <td>HLA-A*02:06</td>\n",
       "      <td>I</td>\n",
       "      <td>5.087162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>CVADYSVLY</td>\n",
       "      <td>HLA-A*03:01</td>\n",
       "      <td>I</td>\n",
       "      <td>5.340608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Peptide   Sequence Target Allele MHC Class  Predicted pIC50\n",
       "0        1  CVADYSVLY   HLA-A*01:01         I         7.874116\n",
       "1        1  CVADYSVLY   HLA-A*02:01         I         5.377066\n",
       "2        1  CVADYSVLY   HLA-A*02:03         I         5.220576\n",
       "3        1  CVADYSVLY   HLA-A*02:06         I         5.087162\n",
       "4        1  CVADYSVLY   HLA-A*03:01         I         5.340608"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read in file\n",
    "build_list_df = pd.read_csv(r'data/build_list.csv')\n",
    "print(build_list_df.shape)\n",
    "build_list_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MHC Class  Target Allele          \n",
       "I          HLA-A*01:01                 2\n",
       "           HLA-A*02:01                 3\n",
       "           HLA-A*02:03                 3\n",
       "           HLA-A*02:06                 2\n",
       "           HLA-A*03:01                 1\n",
       "           HLA-A*11:01                 1\n",
       "           HLA-A*23:01                 3\n",
       "           HLA-A*24:02                 3\n",
       "           HLA-A*26:01                 1\n",
       "           HLA-A*30:01                 5\n",
       "           HLA-A*30:02                 5\n",
       "           HLA-A*31:01                 2\n",
       "           HLA-A*32:01                 4\n",
       "           HLA-A*33:01                 2\n",
       "           HLA-A*68:01                 2\n",
       "           HLA-A*68:02                 3\n",
       "           HLA-B*07:02                 1\n",
       "           HLA-B*15:01                 4\n",
       "           HLA-B*35:01                 5\n",
       "           HLA-B*40:01                 1\n",
       "           HLA-B*44:02                 1\n",
       "           HLA-B*44:03                 1\n",
       "           HLA-B*53:01                 1\n",
       "           HLA-B*57:01                 1\n",
       "           HLA-B*58:01                 1\n",
       "II         HLA-DPA1*0103/DPB1*0201     7\n",
       "           HLA-DPA1*0103/DPB1*0401     7\n",
       "           HLA-DPA1*0201/DPB1*0101     7\n",
       "           HLA-DPA1*0201/DPB1*0501     6\n",
       "           HLA-DPA1*0301/DPB1*0402     6\n",
       "           HLA-DQA1*0101/DQB1*0501     4\n",
       "           HLA-DQA1*0102/DQB1*0602     1\n",
       "           HLA-DQA1*0401/DQB1*0402     1\n",
       "           HLA-DQA1*0501/DQB1*0201     5\n",
       "           HLA-DQA1*0501/DQB1*0301     9\n",
       "           HLA-DRB1*0101              10\n",
       "           HLA-DRB1*0401              10\n",
       "           HLA-DRB1*0405              10\n",
       "           HLA-DRB1*0701               9\n",
       "           HLA-DRB1*0901              10\n",
       "           HLA-DRB1*1101              10\n",
       "           HLA-DRB1*1302               3\n",
       "           HLA-DRB1*1501               9\n",
       "           HLA-DRB3*0101               1\n",
       "           HLA-DRB4*0101               6\n",
       "           HLA-DRB5*0101               8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate total coverage\n",
    "class_i_and_ii_list = class_i_list + class_ii_list\n",
    "\n",
    "# filter list to only consider pIC50 >= 6.0\n",
    "build_list_filtered_df=build_list_df[build_list_df['Predicted pIC50'] >= 6.0]\n",
    "build_list_filtered_df\n",
    "\n",
    "build_list_filtered_df[['MHC Class','Target Allele']].value_counts().sort_index()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UrzAwkPFAPac"
   },
   "source": [
    "## Part 1.5: Answer\n",
    "\n",
    "**Explain the criteria you used to select peptides. What is the expected coverage of your design?**\n",
    "\n",
    "#### 10 peptides for MHC I and 10 peptides for MHC II\n",
    "To provide the most even coverage across MHC I and MHC II, I chose to include 10 peptides from each of the MHC classes. To ensure adequate binding, I limited peptide choices to those with pIC50 <= 6.0.\n",
    "\n",
    "#### Peptides with the strongest binding affinity\n",
    "A pIC50 value of 9.0 is the highest possible binding affinity found in the MHC binding data we were provided. I wanted my design to include as many of these peptides as possible so that the vaccine would bind as well as possible to the MHC receptors. Even with the supplemented design to increase allele coverage, my designs only had a limited number of peptide k-mers which actually had a pIC50 = 9.0. I could include all of them without exceeding the limit of 10 peptides for a given MHC class.\n",
    "\n",
    "#### Peptides which provided minimum allele coverage\n",
    "In addition to strong binding affinity, I wanted a vaccine design to include as wide of allele coverage as possible. The minimum coverage designs with p_min = 6.0 covered almost as many alleles as with p_min=3.0, but had much stronger binding affinity, so I chose to limit my selection to p_min=6.0 or greater.\n",
    "\n",
    "The minimum coverage designs from 1.4 with p_min=6.0 represent a design in which all alleles are covered at least once. Design limits of 10 and 20 both resulted in the same minimum coverage set of peptides. I found that I could include all alleles from these minimum coverage designs without exceeding the limit of 10 peptides for a given MHC class.\n",
    "\n",
    "#### Peptides which provided the most coverage per peptide.\n",
    "With any remaining space in the 10-slot space for each MHC class, I selected peptides from the supplemented design with design_limit=20 and p_min=6.0 which covered the most number of peptides. This allowed me to select peptides which increased coverage for the most alleles possible.\n",
    "\n",
    "#### Summary\n",
    "I chose 10 peptides using this criteria for each MHC class. Together, these represent the 20 peptides from the spike protein I would select for the vaccine.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "Class|HLA Allele|Count\n",
    "---|---|---\n",
    "I|HLA-A*01:01|2\n",
    "I|HLA-A*02:01|3\n",
    "I|HLA-A*02:03|3\n",
    "I|HLA-A*02:06|2\n",
    "I|HLA-A*03:01|1\n",
    "I|HLA-A*11:01|1\n",
    "I|HLA-A*23:01|3\n",
    "I|HLA-A*24:02|3\n",
    "I|HLA-A*26:01|1\n",
    "I|HLA-A*30:01|5\n",
    "I|HLA-A*30:02|5\n",
    "I|HLA-A*31:01|2\n",
    "I|HLA-A*32:01|4\n",
    "I|HLA-A*33:01|2\n",
    "I|HLA-A*68:01|2\n",
    "I|HLA-A*68:02|3\n",
    "I|HLA-B*07:02|1\n",
    "I|HLA-B*15:01|4\n",
    "I|HLA-B*35:01|5\n",
    "I|HLA-B*40:01|1\n",
    "I|HLA-B*44:02|1\n",
    "I|HLA-B*44:03|1\n",
    "I|HLA-B*53:01|1\n",
    "I|HLA-B*57:01|1\n",
    "I|HLA-B*58:01|1\n",
    "I|HLA-B*51:01|0\n",
    "I|HLA-B*08:01|0\n",
    "II|HLA-DPA1*0103/DPB1*0201|7\n",
    "II|HLA-DPA1*0103/DPB1*0401|7\n",
    "II|HLA-DPA1*0201/DPB1*0101|7\n",
    "II|HLA-DPA1*0201/DPB1*0501|6\n",
    "II|HLA-DPA1*0301/DPB1*0402|6\n",
    "II|HLA-DQA1*0101/DQB1*0501|4\n",
    "II|HLA-DQA1*0102/DQB1*0602|1\n",
    "II|HLA-DQA1*0401/DQB1*0402|1\n",
    "II|HLA-DQA1*0501/DQB1*0201|5\n",
    "II|HLA-DQA1*0501/DQB1*0301|9\n",
    "II|HLA-DRB1*0101|10\n",
    "II|HLA-DRB1*0401|10\n",
    "II|HLA-DRB1*0405|10\n",
    "II|HLA-DRB1*0701|9\n",
    "II|HLA-DRB1*0901|10\n",
    "II|HLA-DRB1*1101|10\n",
    "II|HLA-DRB1*1302|3\n",
    "II|HLA-DRB1*1501|9\n",
    "II|HLA-DRB3*0101|1\n",
    "II|HLA-DRB4*0101|6\n",
    "II|HLA-DRB5*0101|8\n",
    "II|HLA-DPA1*0201/DPB1*1401|0\n",
    "II|HLA-DQA1*0301/DQB1*0302|0\n",
    "II|HLA-DRB1*0301|0\n",
    "II|HLA-DRB1*0802|0\n",
    "II|HLA-DRB1*1201|0\n",
    "II|HLA-DRB3*0202|0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.5. Peptide Table "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# pd.set_option('display.max_rows', None)\n",
    "# build_list_filtered_df"
   ]
  }
 ],
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