{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d612bda6-046b-4a28-a9fa-ae5b433d1f73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 5198121 lines from .txt files.\n"
     ]
    }
   ],
   "source": [
    "# load ensembl pool\n",
    "\n",
    "\n",
    "\n",
    "import os\n",
    "\n",
    "# Path to the directory containing .txt files\n",
    "directory = 'speciesrepo'\n",
    "\n",
    "# List to hold all lines from all files\n",
    "ensemblPoolAnnotated = []\n",
    "\n",
    "# Loop through all files in the directory\n",
    "for filename in os.listdir(directory):\n",
    "    if filename.endswith('.txt'):\n",
    "        filepath = os.path.join(directory, filename)\n",
    "        with open(filepath, 'r', encoding='utf-8') as file:\n",
    "            lines = file.readlines()\n",
    "            ensemblPoolAnnotated.extend([line.strip() for line in lines])  # Strip newline characters\n",
    "\n",
    "# Now `all_lines` contains all lines from all .txt files\n",
    "print(f\"Loaded {len(ensemblPoolAnnotated)} lines from .txt files.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ca1f3a1f-c5a8-4a6a-9c97-62e4b3314214",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 103608 lines.\n"
     ]
    }
   ],
   "source": [
    "cleavageProject = []\n",
    "# Define the path to the file\n",
    "file_path = 'stabilitySeqPrediction/decoded_seqs_train_ALL.txt'\n",
    "\n",
    "# Read all lines into a list\n",
    "with open(file_path, 'r', encoding='utf-8') as f:\n",
    "    cleavageProject = [line.strip().strip('A') for line in f]\n",
    "\n",
    "# Now 'lines' contains all lines in the file, without newline characters\n",
    "print(f\"Loaded {len(cleavageProject)} lines.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "99100042-66c7-45e2-89e1-6f2b98cc7552",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 91101 transcripts.\n"
     ]
    }
   ],
   "source": [
    "from Bio import SeqIO\n",
    "\n",
    "# Define the path to your FASTA file\n",
    "fasta_file = './3utrvirus/TILE_mpra.fa'\n",
    "\n",
    "# Load each transcript sequence into a list\n",
    "utrvirus = [str(record.seq) for record in SeqIO.parse(fasta_file, \"fasta\")]\n",
    "\n",
    "print(f\"Loaded {len(utrvirus)} transcripts.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "159a34f2-041e-4c71-b3b5-3f663eeadd73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 378253 transcripts.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Define the path to your FASTA file\n",
    "fasta_file = './virus/RiboV1.4_Contigs.fasta'\n",
    "\n",
    "# Load each transcript sequence into a list\n",
    "virus = [str(record.seq) for record in SeqIO.parse(fasta_file, \"fasta\")]\n",
    "\n",
    "print(f\"Loaded {len(virus)} transcripts.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7151e220-3073-45a9-974e-e93f522bc8be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4615"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(virus[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6f7554c1-65e7-4175-b8e9-9bcee459b3ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "seqPoolList = ensemblPoolAnnotated+cleavageProject+utrvirus+virus\n",
    "seqPool = set()\n",
    "for i in seqPoolList:\n",
    "    seqPool.add(i)\n",
    "seqPool = list(seqPool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "364e95ce-33e2-46e9-9bef-9c22dd3b6c9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5610734"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(seqPool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "32725fcc-cd82-42c9-bf44-73d46ccf3902",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(G|i|(C|u3u|(G(G(A(U(U(UAGC(U(C(A|i||u3u|(G(U(G(G(UAGAGCG)C)C)AGA)C)U)G)A)A)G)A)U)C)U)G)C)CA\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "import RNA\n",
    "\n",
    "def annotate_decorated_sequence(decorated_seq, structure):\n",
    "    # Step 1: Extract base positions (ignore |...| decorations)\n",
    "    base_positions = []\n",
    "    pure_seq = ''\n",
    "    i = 0\n",
    "    while i < len(decorated_seq):\n",
    "        if decorated_seq[i] == '|':\n",
    "            j = decorated_seq.find('|', i+1)\n",
    "            i = j + 1\n",
    "        else:\n",
    "            base_positions.append(i)\n",
    "            pure_seq += decorated_seq[i]\n",
    "            i += 1\n",
    "\n",
    "    # Step 2: Pairing positions using structure\n",
    "    stack = []\n",
    "    pairs = {}\n",
    "    for idx, s in enumerate(structure):\n",
    "        if s == '(':\n",
    "            stack.append(idx)\n",
    "        elif s == ')':\n",
    "            j = stack.pop()\n",
    "            pairs[j] = idx\n",
    "            pairs[idx] = j\n",
    "\n",
    "    # Step 3: Insert parentheses into the decorated sequence\n",
    "    annotated = []\n",
    "    for i, char in enumerate(decorated_seq):\n",
    "        if i in base_positions:\n",
    "            base_idx = base_positions.index(i)\n",
    "            if base_idx in pairs and base_idx < pairs[base_idx]:\n",
    "                annotated.append('(')\n",
    "            annotated.append(char)\n",
    "            if base_idx in pairs and base_idx > pairs[base_idx]:\n",
    "                annotated.append(')')\n",
    "        else:\n",
    "            annotated.append(char)\n",
    "\n",
    "    return ''.join(annotated)\n",
    "\n",
    "# Example usage\n",
    "decorated = \"G|i|C|u3u|GGAUUUAGCUCA|i||u3u|GUGGUAGAGCGCCAGACUGAAGAUCUGCCA\"\n",
    "structure, energy = RNA.fold(decorated.replace('|u5u|','').replace('|u3u|','').replace('|t|','').replace('|s|','').replace('|i|',''))\n",
    "annotated = annotate_decorated_sequence(decorated, structure)\n",
    "print(annotated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a1f91f6d-6d62-4904-adae-7f1449d41ecb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dotbrackets = {}\n",
    "import json\n",
    "with open('dots.json') as tmpinputCollector:\n",
    "    dotbrackets = json.load(tmpinputCollector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7a47cf29-d8bc-4ef2-a935-fa9dea3ee41a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 22%|██▏       | 1219235/5610734 [00:11<00:39, 110127.49it/s] \n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1019222/3468054446.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mseq\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseqPool\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mseq\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdotbrackets\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0mdotbrackets\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mseq\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menergy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRNA\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'|u5u|'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'|u3u|'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'|t|'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'|s|'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'|i|'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.local/lib/python3.11/site-packages/RNA/RNA.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m   9339\u001b[0m     \u001b[0;32mand\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9340\u001b[0m     \u001b[0mstructure\u001b[0m \u001b[0mRNA\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfold_compound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0minstead\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9341\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9342\u001b[0m     \"\"\"\n\u001b[0;32m-> 9343\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_RNA\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "seqPool.sort(key=len)\n",
    "\n",
    "\n",
    "for seq in tqdm(seqPool):\n",
    "    if seq in dotbrackets: continue\n",
    "    dotbrackets[seq], energy = RNA.fold(seq.replace('|u5u|','').replace('|u3u|','').replace('|t|','').replace('|s|','').replace('|i|',''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bbb53ac6-18c7-4219-8e80-c084a1429d80",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "with open(f\"dots.json\", \"w\") as outfile:\n",
    "    json.dump(dotbrackets, outfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "8ccebba6-04bd-42dd-868f-bd109bf9e29b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('AAGTACCGGCGTGGCGGCGCCTCAGCCCGGCCTGGGCGAGCCCTGGGTGCTCCGCCGGGCAGCTCACGGCGCCCCGTATGGCCTGGGGATCCTAAGAGGCCCTGTGACCCCCCTCGCCTGGTCTCCCTCTCACCCCTGGAGGGTTGCCGCAGCTCCGGGGCCCCCGGGCAGGAAGGGCGCACTGGTCGTCCCGGGAGAGGG|i|GTCTGAGCAGAGGGCGGGGTGCAGGCGGAATGGCCCTCGTGCCCTATGAGGAGACCACGGAATTTGGGTTGCAGAAATTCCACAAGCCTCTTGCAACTTTTTCCTTTGCAAACCACACGATCCAGATCCGGCAGGACTGGAGACACCTGGGAGTCGCAGCGGTGGTTTGGGATGCG|i|GCCATCGTTCTTTCCACATACCTGGAGATGGGAGCTGTGGAGCTCAGGGGCCGCTCTGCCGTGGAGCTGGGTGCTGGCACGGGGCTGGTGGGCATAGTGGCTGCCCTGCTGG|i|CTGCTGCTTTCCAAAATTCGACAGGGAGCATGGGCAGGGGTGGTGGTTGAAAGTCTG',\n",
       " '....(((((((((.(((((((.(((((((((..(((((.(((((.((.(((((((.((((.(((...))))))).(.((((((...(.((((((((..((((((((((.((((((((.((((..(((((.((....)))))))..))))..))((((((((..((((((.(......))).))))..)))))))).)))))).....((((((((.((((((((((.((..(((((((((((...))))))).).))).(((((((.........))))))).....)).))))).))))).))))))))...(((.....(((((.(((....))))))))......)))..))))))).)))..)))))))).))))))))((((((((((......)))))...))))).))))))))).)))))))))).)))..)).))))))))))).)))...))))))((((.((((.(((((((((((.(.((((.(((((........)).))).))))).)))).))))))).))))...)))).')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(dotbrackets.items())[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "de5abb36-a0ad-47ce-a893-434dccfd6126",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "546"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len('....(((((((((.(((((((.(((((((((..(((((.(((((.((.(((((((.((((.(((...))))))).(.((((((...(.((((((((..((((((((((.((((((((.((((..(((((.((....)))))))..))))..))((((((((..((((((.(......))).))))..)))))))).)))))).....((((((((.((((((((((.((..(((((((((((...))))))).).))).(((((((.........))))))).....)).))))).))))).))))))))...(((.....(((((.(((....))))))))......)))..))))))).)))..)))))))).))))))))((((((((((......)))))...))))).))))))))).)))))))))).)))..)).))))))))))).)))...))))))((((.((((.(((((((((((.(.((((.(((((........)).))).))))).)))).))))))).))))...)))).')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c29934bc-eff5-4c3a-a303-d3000fde57e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "dotBracketsSeqTotal = []\n",
    "unBracketedSeqTotal = []\n",
    "for stuff in seqPool:\n",
    "    if stuff in dotbrackets: dotBracketsSeqTotal.append(stuff)\n",
    "    else: unBracketedSeqTotal.append(stuff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8df87404-5254-42af-be6e-943d793411f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "random.shuffle(dotBracketsSeqTotal)\n",
    "\n",
    "# Split into 5 as evenly as possible\n",
    "def split_into_chunks(lst, n_chunks):\n",
    "    k, m = divmod(len(lst), n_chunks)\n",
    "    return [lst[i * k + min(i, m):(i + 1) * k + min(i + 1, m)] for i in range(n_chunks)]\n",
    "\n",
    "splits = split_into_chunks(dotBracketsSeqTotal, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "fde528d7-f154-4274-8ef3-f57e671f18f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ATGAAAATTATCAAACTAGTGAGGTTAATTGTGACTAAACCACAAAACACACACACTGTACGTGTGTGGATCGACCTGGAGCAAAGAAAAACAAGGGTTTACCCTTGTTTTCATCAAATTCACCAAATTGACAAGGAAAAATCGATGTTTATCAGTTGCATGTAGTAAAATTTCGATCATCTACCCATACCCGGTGAAGTG|i|GTGTGAAGGAAGAAGGTTCAAATTCTAAGAAATTGGAAGGATCACAAGACCGAG|i|GTAAATCCTCGACTTAGGCGTATGGACGGCTTGGAACGTGCACACTTGATCTACGCCCGCCTCACGCTTCCGTCACAAATTATTTTGATATGGGTTAAGTTTTGGTCATATGTATTTAAGCTTATGTAGTATGTCGATATCCTTGTCGTTGGGTAAAAACTTAGTAGTTGAAGTCATGTCGTGTCCAATGTAAACTTTGTCCGTCTTGGGTTTTGTCAAAGATGTTAAATGTCGAAGTTTGTTTGTGTAAAACAGGTTGTTAATAGTTTTAAATGAATAGGTCATGCCGAAATT'"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "splits[4][5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e26022b-1bc5-425b-b1c9-fa5732705da5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c7d44143-3197-4d16-8740-582334831015",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 243847/243847 [06:40<00:00, 608.91it/s]\n"
     ]
    }
   ],
   "source": [
    "# we would like to construct 5 categories: \n",
    "# 1. dot-bracket annotated sequences only for global understanding (split1)\n",
    "# 2. sequences to dot bracket prediction for dynamics understanding (split2)\n",
    "# 3. dot-bracket annotated sequences to dot bracket for in context fidelity(split3)\n",
    "# 4. dot-bracket prediction to sequence for model to be creative at designing its own sequence(split4)\n",
    "# 5. annotated sequences only(unBracketedSeqTotal)\n",
    "# 6. sequences to inline dot bracket prediction(split5)\n",
    "\n",
    "split1 = []\n",
    "split2 = []\n",
    "split3 = []\n",
    "split4 = []\n",
    "split6 = []\n",
    "\n",
    "for seq in tqdm(splits[0]):\n",
    "    dots = dotbrackets[seq]\n",
    "    fullSequence = annotate_decorated_sequence(seq,dots)\n",
    "    split1.append(fullSequence)\n",
    "\n",
    "for seq in splits[1]:\n",
    "    dots = dotbrackets[seq]\n",
    "    dottedSeq = annotate_decorated_sequence(seq,dots)\n",
    "    fullSequence = f'{seq}~$predict_dot_bracket_sequence\\n{dottedSeq}'\n",
    "    split2.append(fullSequence)\n",
    "\n",
    "for seq in splits[2]:\n",
    "    dots = dotbrackets[seq]\n",
    "    dottedSeq = annotate_decorated_sequence(seq,dots)\n",
    "    fullSequence = f'{dottedSeq}~$get_pure_dot_bracket_representation\\n{dots}'\n",
    "    split3.append(fullSequence)\n",
    "\n",
    "for seq in splits[3]:\n",
    "    dots = dotbrackets[seq]\n",
    "    dottedSeq = annotate_decorated_sequence(seq,dots)\n",
    "    fullSequence = f'{dots}~$design_sequence_from_given_dots\\n{dottedSeq}'\n",
    "    split4.append(fullSequence)\n",
    "\n",
    "for seq in splits[4]:\n",
    "    dots = dotbrackets[seq]\n",
    "    dottedSeq = annotate_decorated_sequence(seq,dots)\n",
    "    fullSequence = f'{seq}~$predict_pure_dot_bracket\\n{dots}'\n",
    "    split6.append(fullSequence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3897cfca-918b-4259-a746-9047641bbb2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'(G(GTAA(G(C(G(G(G(G(C(G(C(G(CC(G(G(G(G(C(T(GC(G(CC(G(C(T(G(CCCCCTCCCG)C)G)G)C)TCCT(G(G(C(C(GGCCGC)G)G)C)C)G)C)TTC)A)G)C)C)T)C)TG)C)G)C)AG(G(G(C(G(G(C(C(G(G(G(G(GCA(G(GATCCCGTGC)C)AAC)C)C)C)C)G)AG)C)C)AG)C)T)(G(G(GA(C(AAAGCT)G)AC)C)C)TG)C)C)T)C)G)C)CTC)C)(A(G|i|(A(C(T(A(T(C(A(T(C(T(C(T(A(C(T(AATACATAAAATAATAACAAAGTAATTCTTCAACT)G)G)T)G)G)A)G)CAACA(G(A(A(G(CAACG)|i|T)T)T)C)CACA)T)G)CA)T)A)CG)T)C)T)TAC'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "split1[666]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d2dde075-b492-421e-a562-633cdc43439a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "# we would like to construct 5 categories: \n",
    "# 1. dot-bracket annotated sequences only for global understanding (split1)\n",
    "# 2. sequences to dot bracket prediction for dynamics understanding (split2)\n",
    "# 3. dot-bracket annotated sequences to dot bracket for in context fidelity(split3)\n",
    "# 4. dot-bracket prediction to sequence for model to be creative at designing its own sequence(split4)\n",
    "# 5. annotated sequences only(unBracketedSeqTotal)\n",
    "# 6. sequences to inline dot bracket prediction(split6)\n",
    "\n",
    "\n",
    "with open(f\"split1.json\", \"w\") as outfile:\n",
    "    json.dump(split1, outfile)\n",
    "\n",
    "with open(f\"split2.json\", \"w\") as outfile:\n",
    "    json.dump(split2, outfile)\n",
    "\n",
    "with open(f\"split3.json\", \"w\") as outfile:\n",
    "    json.dump(split3, outfile)\n",
    "\n",
    "with open(f\"split4.json\", \"w\") as outfile:\n",
    "    json.dump(split4, outfile)\n",
    "\n",
    "with open(f\"split5.json\", \"w\") as outfile:\n",
    "    json.dump(unBracketedSeqTotal, outfile)\n",
    "\n",
    "with open(f\"split6.json\", \"w\") as outfile:\n",
    "    json.dump(split6, outfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9fc206d3-cb67-4137-9525-39d86d888392",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5771083"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(seqPoolList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "91dfd9d1-289b-4d5f-828f-59a2150b46df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'((((((((...((((((((.....)))..)))))))))))))..'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d8bec72-16a2-494e-bcbd-d35819eb7bb9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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