show_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['123', '234', '345', '456', '567']\n"
     ]
    }
   ],
   "source": [
    "str = '1234567'\n",
    "index = 0\n",
    "a = []\n",
    "while index + 3 <= len(str):\n",
    "    a.append(str[index:index+3])\n",
    "    index+=1\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Bio import SeqIO\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HMINKKSLLQNLLSKCKTTFQQSFTNANITLKDEKWLKNVRTAYFVCDHDGSVELAYLPNVLPKELVEEFTEKFESIQTGRKKDTGYSGILDNSMPFNYVTADLSQELGQYLSEIVNPQINYYISKLLTCVSSRTINYLVSLNDSYYALNNCLYPSTAFNSLKPSNDGHRIRKPHKDNLDITPSSLFYFGNFQNTEGYLELTDKNCKVFVQPGDVLFFKGNEYKHVVANITSGWRIGLVYFAHKGSKTKPYYEDTQKNSLKIHKETK\n"
     ]
    }
   ],
   "source": [
    "for record in SeqIO.parse(\"PDNA-TEST_sequence.fasta\", \"fasta\"):\n",
    "    print(record.seq)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0': 134995, '1': 9549}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_label = {}\n",
    "for record in SeqIO.parse(\"PDNA-543_label.fasta\", \"fasta\"):\n",
    "   # print(record.seq)\n",
    "    for c in record.seq:\n",
    "        count_label[c]=count_label.setdefault(c, 0)+1\n",
    "        \n",
    "count_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID: 1DFM_A\n",
      "Name: 1DFM_A\n",
      "Description: 1DFM_A\n",
      "Number of features: 0\n",
      "Seq('KIDITDYNHADEILNPQLWKEIEETLLKPLHVKASDQASKVGSLIFDPVGTNQY...VTY', SingleLetterAlphabet())\n"
     ]
    }
   ],
   "source": [
    "for record in SeqIO.parse(\"metaDBsite_PDNA316_sequence.fasta\", \"fasta\"):\n",
    "    print(record)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID: 1DFM_A\n",
      "Name: 1DFM_A\n",
      "Description: 1DFM_A\n",
      "Number of features: 0\n",
      "Seq('000000000000000000000000000000001001110000000001100100...000', SingleLetterAlphabet())\n"
     ]
    }
   ],
   "source": [
    "for record in SeqIO.parse(\"metaDBsite_PDNA316_label.fasta\", \"fasta\"):\n",
    "    print(record)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "count = {}\n",
    "for record in SeqIO.parse(\"PDNA-543_sequence.fasta\", \"fasta\"):\n",
    "    #print(record.seq)\n",
    "    for c in record.seq:\n",
    "        count[c]=count.setdefault(c, 0)+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': 10485,\n",
       " 'V': 9365,\n",
       " 'K': 10413,\n",
       " 'I': 8543,\n",
       " 'S': 8619,\n",
       " 'Q': 5922,\n",
       " 'Y': 4897,\n",
       " 'C': 1929,\n",
       " 'R': 9278,\n",
       " 'T': 7094,\n",
       " 'L': 14638,\n",
       " 'N': 5920,\n",
       " 'F': 5878,\n",
       " 'D': 7807,\n",
       " 'E': 10479,\n",
       " 'G': 8782,\n",
       " 'M': 2907,\n",
       " 'P': 6336,\n",
       " 'W': 1861,\n",
       " 'H': 3391}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_items([('A', 10485), ('V', 9365), ('K', 10413), ('I', 8543), ('S', 8619), ('Q', 5922), ('Y', 4897), ('C', 1929), ('R', 9278), ('T', 7094), ('L', 14638), ('N', 5920), ('F', 5878), ('D', 7807), ('E', 10479), ('G', 8782), ('M', 2907), ('P', 6336), ('W', 1861), ('H', 3391)])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count.items()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "x = []\n",
    "name = []\n",
    "for key, val in count.items():\n",
    "    x.append(val)\n",
    "    name.append(key)\n",
    "#plt.plot(x)\n",
    "y_pos = np.arange(len(name))\n",
    "plt.bar(name, x, align='center', alpha=0.5)\n",
    "plt.ylabel('No of Appearance')\n",
    "plt.xlabel('Character')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def _sequence_to_embeb(sequence):\n",
    "    emb = []\n",
    "    fill_num = 0.05\n",
    "    no_of_cell = 20\n",
    "    slide = 10\n",
    "    scan = 6\n",
    "    # m = 6, n = 10\n",
    "    for i in range(5):\n",
    "        row = [fill_num]*no_of_cell\n",
    "        emb.append(row)\n",
    "\n",
    "    for letter in sequence:\n",
    "        row = _gen_one_hot(letter)\n",
    "        emb.append(row)\n",
    "\n",
    "    for i in range(5):\n",
    "        row = [fill_num]*no_of_cell\n",
    "        emb.append(row)\n",
    "\n",
    "    emb = np.array(emb)\n",
    "\n",
    "    return emb\n",
    "\n",
    "def _gen_one_hot(letter):\n",
    "    no_of_cell = 20\n",
    "    row = [0]*no_of_cell\n",
    "    if letter == 'A':\n",
    "        row[0] = 1\n",
    "    elif letter == 'V':\n",
    "        row[1] = 1\n",
    "    elif letter == 'K':\n",
    "        row[2] = 1\n",
    "    elif letter == 'I':\n",
    "        row[3] = 1\n",
    "    elif letter == 'S':\n",
    "        row[4] = 1\n",
    "    elif letter == 'Q':\n",
    "        row[5] = 1\n",
    "    elif letter == 'Y':\n",
    "        row[6] = 1\n",
    "    elif letter == 'C':\n",
    "        row[7] = 1\n",
    "    elif letter == 'R':\n",
    "        row[8] = 1\n",
    "    elif letter == 'T':\n",
    "        row[9] = 1\n",
    "    elif letter == 'L':\n",
    "        row[10] = 1\n",
    "    elif letter == 'N':\n",
    "        row[11] = 1\n",
    "    elif letter == 'F':\n",
    "        row[12] = 1\n",
    "    elif letter == 'D':\n",
    "        row[13] = 1\n",
    "    elif letter == 'E':\n",
    "        row[14] = 1\n",
    "    elif letter == 'G':\n",
    "        row[15] = 1\n",
    "    elif letter == 'M':\n",
    "        row[16] = 1\n",
    "    elif letter == 'P':\n",
    "        row[17] = 1\n",
    "    elif letter == 'W':\n",
    "        row[18] = 1\n",
    "    elif letter == 'H':\n",
    "        row[19] = 1\n",
    "\n",
    "    return row\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "str = 'AWH'\n",
    "a = _sequence_to_embeb(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05,\n",
       "       0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}