03_combined_result_from_10_fragments.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ce17a9b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<frozen importlib._bootstrap>:228: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Pandarallel will run on 48 workers.\n",
      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import shutil\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from multiprocessing import Pool, cpu_count\n",
    "from functools import partial\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_context(\"paper\", font_scale=1.5)\n",
    "sns.set_style('white')\n",
    "\n",
    "from functions import *\n",
    "\n",
    "from pandarallel import pandarallel\n",
    "\n",
    "pandarallel.initialize(progress_bar=False)\n",
    "\n",
    "with SequenceFile(\"data/uniprot-9606.fasta\", digital=True, alphabet=alphabet) as seq_file:\n",
    "    sequences = list(seq_file)\n",
    "    \n",
    "\n",
    "uniprot = fasta_reader('data/uniprot-9606.fasta')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9558739f",
   "metadata": {},
   "outputs": [],
   "source": [
    "aa_known = 'ACDEFGHIKLMNPQRSTVWY'\n",
    "prob_range = [0.9, 0.8, 0.6, 0.4, 0.2, 0.3, 0.1, 0.09, 0.08, 0.07, 0.06, 0.05]\n",
    "repeats = 10\n",
    "\n",
    "\n",
    "lengths = [100, 50, 25, 15, 10, 5]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4e231710",
   "metadata": {},
   "outputs": [],
   "source": [
    "def combine_scores(query, all_dfs):\n",
    "    try:\n",
    "        temp_df = pd.concat([i[i.Query == query] for i in all_dfs]).reset_index(drop=True)\n",
    "        summed = temp_df.groupby('Accession')['Score'].sum().sort_values(ascending=False)\n",
    "        return summed.index[0]\n",
    "    except Exception as exp:\n",
    "        if temp_df.size == 0:\n",
    "            return 'No hits for this accession (not found)'\n",
    "        else:\n",
    "            return str(exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c3775f40",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stats_accuracy(prob, length, repeats=10):\n",
    "    \n",
    "    all_dfs = []\n",
    "    for r in range(repeats):\n",
    "\n",
    "        fname = 'results/scan_results/' + f'{r}_frag_length_{length}_{aa_known}_max_prob_{prob}_del.pkl.gz'\n",
    "        test_df = pd.read_pickle(fname)\n",
    "        all_dfs.append(test_df)\n",
    "    \n",
    "    results0 = pd.DataFrame({'Query': uniprot.Accession})\n",
    "    results0['Accession'] = results0['Query'].parallel_apply(lambda x: combine_scores(x, all_dfs))\n",
    "    \n",
    "    # accuracy\n",
    "    acc = results0[results0['Query'] == results0['Accession']].shape[0]/results0.shape[0]\n",
    "    del results0, all_dfs\n",
    "    return acc\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "707e720f",
   "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>known aa</th>\n",
       "      <th>max_prob</th>\n",
       "      <th>total queries</th>\n",
       "      <th>hits</th>\n",
       "      <th>identified</th>\n",
       "      <th>fragment#</th>\n",
       "      <th>identified_frac</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACDEFGHIKLMNPQRSTVWY</td>\n",
       "      <td>0.9</td>\n",
       "      <td>20181</td>\n",
       "      <td>20178</td>\n",
       "      <td>19446</td>\n",
       "      <td>0</td>\n",
       "      <td>0.96358</td>\n",
       "      <td>100 AA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACDEFGHIKLMNPQRSTVWY</td>\n",
       "      <td>0.8</td>\n",
       "      <td>20181</td>\n",
       "      <td>20179</td>\n",
       "      <td>19436</td>\n",
       "      <td>0</td>\n",
       "      <td>0.963084</td>\n",
       "      <td>100 AA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               known aa  max_prob total queries   hits identified fragment#  \\\n",
       "0  ACDEFGHIKLMNPQRSTVWY       0.9         20181  20178      19446         0   \n",
       "1  ACDEFGHIKLMNPQRSTVWY       0.8         20181  20179      19436         0   \n",
       "\n",
       "  identified_frac    Type  \n",
       "0         0.96358  100 AA  \n",
       "1        0.963084  100 AA  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pd.read_pickle('results/all_fragments_all_AA_no_indels.pkl.gz').head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "70792a42",
   "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>known aa</th>\n",
       "      <th>max_prob</th>\n",
       "      <th>identified_frac</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [known aa, max_prob, identified_frac, Type]\n",
       "Index: []"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_results = pd.DataFrame(columns=['known aa', 'max_prob', 'identified_frac', 'Type'])\n",
    "all_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "84ea76cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_queries = uniprot.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3065048",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f6003fff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "72\r"
     ]
    }
   ],
   "source": [
    "#### This takes a LOT of time!\n",
    "aa_known = 'ACDEFGHIKLMNPQRSTVWY'\n",
    "prob_range = [0.9, 0.8, 0.6, 0.4, 0.3, 0.2, 0.1, 0.09, 0.08, 0.07, 0.06, 0.05]\n",
    "repeats = 10\n",
    "\n",
    "\n",
    "lengths = [100, 50, 25, 15, 10, 5]\n",
    "\n",
    "counter = 0\n",
    "for prob in prob_range:\n",
    "    for length in lengths:\n",
    "        all_results.loc[counter] = [aa_known, prob, stats_accuracy(prob, length), f'{length} AA', ]\n",
    "        counter += 1\n",
    "        print(counter, end='\\r')\n",
    "#1:18:50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "83fb823b",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_results.to_pickle('results/frag_combined_all_aa_no_indels.pkl.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "727469c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_results = pd.read_pickle('results/frag_combined_all_aa_no_indels.pkl.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d3e2c42e",
   "metadata": {},
   "outputs": [],
   "source": [
    "frag_full_all_aa = pd.read_pickle('results/frag_and_full_len_all_aa_no_indels_.pkl.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "75a6638b",
   "metadata": {},
   "outputs": [],
   "source": [
    "full_len = frag_full_all_aa[frag_full_all_aa['Type'] == 'Full length']\n",
    "all_results1 = pd.concat([full_len[all_results.columns], all_results, ]).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ef15e05f",
   "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></th>\n",
       "      <th>identified_frac</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <th>max_prob</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10 AA</th>\n",
       "      <th>0.2</th>\n",
       "      <td>0.051291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100 AA</th>\n",
       "      <th>0.2</th>\n",
       "      <td>0.960359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15 AA</th>\n",
       "      <th>0.2</th>\n",
       "      <td>0.703880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25 AA</th>\n",
       "      <th>0.2</th>\n",
       "      <td>0.940880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 AA</th>\n",
       "      <th>0.2</th>\n",
       "      <td>0.000213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50 AA</th>\n",
       "      <th>0.2</th>\n",
       "      <td>0.953090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Full length</th>\n",
       "      <th>0.2</th>\n",
       "      <td>0.983995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      identified_frac\n",
       "Type        max_prob                 \n",
       "10 AA       0.2              0.051291\n",
       "100 AA      0.2              0.960359\n",
       "15 AA       0.2              0.703880\n",
       "25 AA       0.2              0.940880\n",
       "5 AA        0.2              0.000213\n",
       "50 AA       0.2              0.953090\n",
       "Full length 0.2              0.983995"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupedby = pd.DataFrame(frag_full_all_aa.groupby(['Type', 'max_prob', ])['identified_frac'].mean())\n",
    "groupedby.query('max_prob == 0.2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "f01f4e71",
   "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>known aa</th>\n",
       "      <th>max_prob</th>\n",
       "      <th>identified_frac</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ACDEFGHIKLMNPQRSTVWY</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.968485</td>\n",
       "      <td>10 AA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ACDEFGHIKLMNPQRSTVWY</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.969377</td>\n",
       "      <td>10 AA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ACDEFGHIKLMNPQRSTVWY</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.968237</td>\n",
       "      <td>10 AA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>ACDEFGHIKLMNPQRSTVWY</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.966206</td>\n",
       "      <td>10 AA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>ACDEFGHIKLMNPQRSTVWY</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.223081</td>\n",
       "      <td>10 AA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>ACDEFGHIKLMNPQRSTVWY</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10 AA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>ACDEFGHIKLMNPQRSTVWY</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10 AA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>ACDEFGHIKLMNPQRSTVWY</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10 AA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>ACDEFGHIKLMNPQRSTVWY</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10 AA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>ACDEFGHIKLMNPQRSTVWY</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10 AA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>ACDEFGHIKLMNPQRSTVWY</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10 AA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                known aa  max_prob identified_frac   Type\n",
       "15  ACDEFGHIKLMNPQRSTVWY      0.90        0.968485  10 AA\n",
       "21  ACDEFGHIKLMNPQRSTVWY      0.80        0.969377  10 AA\n",
       "27  ACDEFGHIKLMNPQRSTVWY      0.60        0.968237  10 AA\n",
       "33  ACDEFGHIKLMNPQRSTVWY      0.40        0.966206  10 AA\n",
       "39  ACDEFGHIKLMNPQRSTVWY      0.20        0.223081  10 AA\n",
       "45  ACDEFGHIKLMNPQRSTVWY      0.10             0.0  10 AA\n",
       "51  ACDEFGHIKLMNPQRSTVWY      0.09             0.0  10 AA\n",
       "57  ACDEFGHIKLMNPQRSTVWY      0.08             0.0  10 AA\n",
       "63  ACDEFGHIKLMNPQRSTVWY      0.07             0.0  10 AA\n",
       "69  ACDEFGHIKLMNPQRSTVWY      0.06             0.0  10 AA\n",
       "75  ACDEFGHIKLMNPQRSTVWY      0.05             0.0  10 AA"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_results1[all_results1['Type'] == '10 AA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fedc4b4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/bikash/anaconda3/lib/python3.9/site-packages/matplotlib/axes/_base.py:2283: UserWarning: Warning: converting a masked element to nan.\n",
      "  xys = np.asarray(xys)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "posts = [0.9, 0.8, 0.6, 0.4, 0.3, 0.2]\n",
    "plt.figure(figsize=(8, 4))\n",
    "# ax = sns.pointplot(data=frag_full_all_aa[frag_full_all_aa['max_prob'].apply(lambda x: x in posts)], \\\n",
    "#                      x='max_prob', y='identified_frac', hue='Type', \\\n",
    "#                   palette=\"Set1\", markers=list('o^sP*XD'), scale=1.2)\n",
    "ax = sns.lineplot(data=frag_full_all_aa[frag_full_all_aa['max_prob'].apply(lambda x: x in posts)], \\\n",
    "                     x='max_prob', y='identified_frac', hue='Type', \\\n",
    "                   marker='D', markersize=6, lw=2.5, err_style='bars', legend=None, \\\n",
    "                 )\n",
    "ax = sns.lineplot(data=all_results1[all_results1['max_prob'].apply(lambda x: x in posts)], \\\n",
    "                     x='max_prob', y='identified_frac', hue='Type', \\\n",
    "                   marker='o', markersize=6, lw=2.5, linestyle=':')\n",
    "ax.set_xlabel(r'${p_{max}}$')\n",
    "ax.set_ylabel('Accuracy');\n",
    "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1));\n",
    "plt.savefig('figs/fig2_with_combined_result.pdf', bbox_inches='tight');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d1002ecc",
   "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>known aa</th>\n",
       "      <th>max_prob</th>\n",
       "      <th>identified_frac</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ACDEFGHIKLMNPQRSTVWY</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.968485</td>\n",
       "      <td>10 AA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ACDEFGHIKLMNPQRSTVWY</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.969377</td>\n",
       "      <td>10 AA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ACDEFGHIKLMNPQRSTVWY</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.968237</td>\n",
       "      <td>10 AA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>ACDEFGHIKLMNPQRSTVWY</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.966206</td>\n",
       "      <td>10 AA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>ACDEFGHIKLMNPQRSTVWY</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.74803</td>\n",
       "      <td>10 AA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>ACDEFGHIKLMNPQRSTVWY</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.223081</td>\n",
       "      <td>10 AA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                known aa  max_prob identified_frac   Type\n",
       "16  ACDEFGHIKLMNPQRSTVWY       0.9        0.968485  10 AA\n",
       "22  ACDEFGHIKLMNPQRSTVWY       0.8        0.969377  10 AA\n",
       "28  ACDEFGHIKLMNPQRSTVWY       0.6        0.968237  10 AA\n",
       "34  ACDEFGHIKLMNPQRSTVWY       0.4        0.966206  10 AA\n",
       "40  ACDEFGHIKLMNPQRSTVWY       0.3         0.74803  10 AA\n",
       "46  ACDEFGHIKLMNPQRSTVWY       0.2        0.223081  10 AA"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tt = all_results1[all_results1['max_prob'].apply(lambda x: x in posts)]\n",
    "tt[tt['Type'] == '10 AA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7e279899",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2618502551905257"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tt = frag_full_all_aa[frag_full_all_aa['max_prob'].apply(lambda x: x in posts)]\n",
    "tt[(tt['Type'] == '10 AA') & (tt['max_prob'] == 0.3)]['identified_frac'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "66ccf765",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.486"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.262 - 0.748"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "991540e0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc499904",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "005103bd",
   "metadata": {},
   "source": [
    "#####  100 fragments (5AA) for pmax = 0.9 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "id": "9cc83d83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r-- 1 bikash goldmans  5.3K Feb 12 02:40 999_frag_length_5_ACDEFGHIKLMNPQRSTVWY_max_prob_0.9_del.pkl.gz\r\n",
      "-rw-r--r-- 1 bikash goldmans  4.6K Feb 12 02:30 998_frag_length_5_ACDEFGHIKLMNPQRSTVWY_max_prob_0.9_del.pkl.gz\r\n",
      "-rw-r--r-- 1 bikash goldmans  5.1K Feb 12 02:20 997_frag_length_5_ACDEFGHIKLMNPQRSTVWY_max_prob_0.9_del.pkl.gz\r\n",
      "-rw-r--r-- 1 bikash goldmans  5.1K Feb 12 02:11 996_frag_length_5_ACDEFGHIKLMNPQRSTVWY_max_prob_0.9_del.pkl.gz\r\n",
      "-rw-r--r-- 1 bikash goldmans  5.2K Feb 12 02:01 995_frag_length_5_ACDEFGHIKLMNPQRSTVWY_max_prob_0.9_del.pkl.gz\r\n",
      "-rw-r--r-- 1 bikash goldmans  5.3K Feb 12 01:51 994_frag_length_5_ACDEFGHIKLMNPQRSTVWY_max_prob_0.9_del.pkl.gz\r\n",
      "-rw-r--r-- 1 bikash goldmans  4.9K Feb 12 01:41 993_frag_length_5_ACDEFGHIKLMNPQRSTVWY_max_prob_0.9_del.pkl.gz\r\n",
      "-rw-r--r-- 1 bikash goldmans  4.1K Feb 12 01:32 992_frag_length_5_ACDEFGHIKLMNPQRSTVWY_max_prob_0.9_del.pkl.gz\r\n",
      "-rw-r--r-- 1 bikash goldmans  4.9K Feb 12 01:22 991_frag_length_5_ACDEFGHIKLMNPQRSTVWY_max_prob_0.9_del.pkl.gz\r\n",
      "-rw-r--r-- 1 bikash goldmans  4.9K Feb 12 01:12 990_frag_length_5_ACDEFGHIKLMNPQRSTVWY_max_prob_0.9_del.pkl.gz\r\n",
      "grep: write error: Broken pipe\r\n",
      "ls: write error: Broken pipe\r\n"
     ]
    }
   ],
   "source": [
    "! ls -lht results/scan_results | grep frag_length_5_ACDEFGHIKLMNPQRSTVWY_max_prob_0.9_del.pkl.gz | head -n 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "4fdeec2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.777777777777778"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1000*4/60/24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "93d27ad0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "110\r"
     ]
    }
   ],
   "source": [
    "# Uncomment and run this first time\n",
    "length = 5\n",
    "prob = 0.9\n",
    "accs = []\n",
    "fragments = []\n",
    "\n",
    "for r in range(10, 120, 10):\n",
    "    try:\n",
    "        accs.append(stats_accuracy(prob, length, r))\n",
    "        fragments.append(r)\n",
    "        print(r, end='\\r')\n",
    "    except Exception:\n",
    "        break\n",
    "\n",
    "fiveaa_frag = pd.DataFrame({'Fragments (5AA length)': fragments, 'Accuracy': accs})\n",
    "fiveaa_frag.to_pickle('results/scan_results/fiveaa_fragments_combined_results.pkl.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "169fa6c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fiveaa_frag = pd.DataFrame({'Fragments (5AA length)': fragments, 'Accuracy': accs})\n",
    "# fiveaa_frag.to_pickle('results/scan_results/fiveaa_fragments_combined_results.pkl.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44aa4b73",
   "metadata": {},
   "outputs": [],
   "source": [
    "fiveaa_frag = pd.read_pickle('results/scan_results/fiveaa_fragments_combined_results.pkl.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "c7083332",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9, 5)"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prob, length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "id": "fa6b96d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "990\r"
     ]
    }
   ],
   "source": [
    "for r in range(10, 1000, 10):\n",
    "    try:\n",
    "        if r not in fiveaa_frag['Fragments (5AA length)'].values:\n",
    "            print(r, end='\\r')\n",
    "            fiveaa_frag.loc[len(fiveaa_frag)] = [r, stats_accuracy(prob, length, r)]\n",
    "            \n",
    "    except Exception:\n",
    "        break\n",
    "\n",
    "fiveaa_frag.to_pickle('results/scan_results/fiveaa_fragments_combined_results.pkl.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "36411145",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 110 in fiveaa_frag['Fragments (5AA length)'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "id": "5ed69555",
   "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>Fragments (5AA length)</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30.0</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.0</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>950.0</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>960.0</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>970.0</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>980.0</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>990.0</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Fragments (5AA length)  Accuracy\n",
       "0                     10.0      0.02\n",
       "1                     20.0      0.03\n",
       "2                     30.0      0.03\n",
       "3                     40.0      0.04\n",
       "4                     50.0      0.04\n",
       "..                     ...       ...\n",
       "94                   950.0      0.10\n",
       "95                   960.0      0.10\n",
       "96                   970.0      0.10\n",
       "97                   980.0      0.10\n",
       "98                   990.0      0.10\n",
       "\n",
       "[99 rows x 2 columns]"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fiveaa_frag.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2397192f",
   "metadata": {},
   "outputs": [],
   "source": [
    "fiveaa_frag = pd.read_pickle('results/scan_results/fiveaa_fragments_combined_results.pkl.gz') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c075288f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.lineplot(data=fiveaa_frag, x='Fragments (5AA length)', y='Accuracy', marker='o', \\\n",
    "                 markersize=6, lw=2.5,)\n",
    "ax.set_ylim(0, 1);\n",
    "ax.set_xlabel('Number of 5AA fragments')\n",
    "ax.set_ylabel('Accuracy');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afef1c1a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d246c779",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.lineplot(data=fiveaa_frag, x='Fragments (5AA length)', y='Accuracy', marker='o', \\\n",
    "                 markersize=6, lw=2.5, )\n",
    "# ax.set_ylim(0, 1);\n",
    "# ax.set_xlabel('Number of 5AA fragments')\n",
    "# ax.set_ylabel('Accuracy');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0a650a8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.figure(figsize=(15, 4))\n",
    "ax = sns.lineplot(data=fiveaa_frag[0:], x='Fragments (5AA length)', y='Accuracy', marker='o', \\\n",
    "                 markersize=0, lw=2.5, )\n",
    "ax.set_xscale('log');\n",
    "# ax.set_ylim(0, 1);\n",
    "# ax.set_xlabel('Number of 5AA fragments')\n",
    "# ax.set_ylabel('Accuracy');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16a061ba",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "c625ab77",
   "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>known aa</th>\n",
       "      <th>max_prob</th>\n",
       "      <th>identified_frac</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ACDEFGHIKLMNPQRSTVWY</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.015856</td>\n",
       "      <td>5 AA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               known aa  max_prob  identified_frac  Type\n",
       "5  ACDEFGHIKLMNPQRSTVWY       0.9         0.015856  5 AA"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_results[(all_results['Type'] == '5 AA') & (all_results['max_prob'] == 0.9)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "0e0ba0ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.9063446014127146"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(0.061939-0.015856)/0.015856"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e45ca88",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab44d0c6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "132244b6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}