Feature Extraction Program from the generated files.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa17ac5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "os.chdir(\"/home/t326h379/Prot_T5/Prot_T5_csv\")\n",
    "\n",
    "Header_name = [\"Amino_Acid\"]\n",
    "\n",
    "col_of_feature = [i for i in range(1,1025)]\n",
    "\n",
    "Header_name = Header_name + col_of_feature\n",
    "\n",
    "def open_file(label_i, protein_id, csv_file_name, Position_individual,Sequences_individual):  \n",
    "\n",
    "    \n",
    "    df = pd.read_csv(csv_file_name, header=None)\n",
    "    \n",
    "    df.columns = Header_name\n",
    "\n",
    "    \n",
    "        \n",
    "\n",
    "    with open(csv_file_name) as outfile:\n",
    "        feature_vector = []\n",
    "        AA = []\n",
    "        lines = outfile.readlines()\n",
    "        for line in lines:\n",
    "            list = line.split(\",\")\n",
    "            AA.append(list[0])\n",
    "           \n",
    "        AA = ''.join(AA)\n",
    "        Position_individual = int(Position_individual)\n",
    "        \n",
    "        \n",
    "        index = Position_individual-1\n",
    "        \n",
    "        if (AA[index] == \"N\"):\n",
    "        \n",
    "\n",
    "            matrix = df.iloc[index]\n",
    "            grabbed_matrix =  np.array(matrix)\n",
    "\n",
    "\n",
    "            flatten_value = grabbed_matrix.ravel()\n",
    "\n",
    "            feature_vector = flatten_value.tolist()\n",
    "\n",
    "\n",
    "            listToStr = ','.join([str(elem) for elem in feature_vector])\n",
    "            \n",
    "\n",
    "\n",
    "\n",
    "            with open('Feature_Extraction_August_12_Subash_123_DeepNGlyPred_data.txt','a+') as outfile:\n",
    "                actual_index_when_positioned_from_one = Position_individual\n",
    "                \n",
    "                outfile.write(str(label_i))\n",
    "                outfile.write(\",\")\n",
    "                outfile.write(str(protein_id))\n",
    "                outfile.write(\",\")\n",
    "                outfile.write(str(actual_index_when_positioned_from_one))\n",
    "                outfile.write(\",\")\n",
    "                outfile.write(Sequences_individual)\n",
    "                outfile.write(\",\")\n",
    "\n",
    "                outfile.write(listToStr)\n",
    "                outfile.write(\"\\n\")\n",
    "\n",
    "PID = []\n",
    "Position = []\n",
    "Sequences = []\n",
    "label = []\n",
    "with open(\"N_linked_glycosylation_protein_training_file_without_independnet_testing_protein.txt\") as hoverboard:\n",
    "    for line in hoverboard:\n",
    "        x = line.split()\n",
    "        PID.append(x[1])\n",
    "        Position.append(x[2])\n",
    "        Sequences.append(x[3])\n",
    "        label.append(x[0])\n",
    "        \n",
    "for i in range(len(Sequences)):\n",
    "    \n",
    "    print(i,\"\\t\",PID[i],\"\\t\",Position[i])\n",
    "    protein_id = PID[i]\n",
    "    label_i = label[i]\n",
    "    csv_file_name = PID[i]+\"_Prot_Trans_.csv\"\n",
    "    Position_individual = Position[i]\n",
    "    Sequences_individual = Sequences[i].strip(\"\\n\").strip(\"-\")\n",
    "    open_file(label_i, protein_id, csv_file_name, Position_individual,Sequences_individual)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0bf666d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.4-TensorFlow-2.3.1 [jupyter_python]",
   "language": "python",
   "name": "sys_python_3.7.4-tensorflow-2.3.1"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}