{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2015-07-05 17:14:17--  ftp://ftp.vectorbase.org/public_data/organism_data/agambiae/Genome/agambiae.CHROMOSOMES-PEST.AgamP3.fa.gz\n",
      "           => ‘gambiae.fa.gz’\n",
      "Resolving ftp.vectorbase.org (ftp.vectorbase.org)... 129.74.255.228\n",
      "Connecting to ftp.vectorbase.org (ftp.vectorbase.org)|129.74.255.228|:21... connected.\n",
      "Logging in as anonymous ... Logged in!\n",
      "==> SYST ... done.    ==> PWD ... done.\n",
      "==> TYPE I ... done.  ==> CWD (1) /public_data/organism_data/agambiae/Genome ... done.\n",
      "==> SIZE agambiae.CHROMOSOMES-PEST.AgamP3.fa.gz ... 81591806\n",
      "==> PASV ... done.    ==> RETR agambiae.CHROMOSOMES-PEST.AgamP3.fa.gz ... done.\n",
      "Length: 81591806 (78M) (unauthoritative)\n",
      "\n",
      "agambiae.CHROMOSOME 100%[=====================>]  77.81M   583KB/s   in 3m 54s \n",
      "\n",
      "2015-07-05 17:18:12 (341 KB/s) - ‘gambiae.fa.gz’ saved [81591806]\n",
      "\n",
      "--2015-07-05 17:18:12--  https://www.vectorbase.org/download/anopheles-atroparvus-ebroscaffoldsaatre1fagz\n",
      "Resolving www.vectorbase.org (www.vectorbase.org)... 129.74.255.228\n",
      "Connecting to www.vectorbase.org (www.vectorbase.org)|129.74.255.228|:443... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://www.vectorbase.org/sites/default/files/ftp/downloads/Anopheles-atroparvus-EBRO_SCAFFOLDS_AatrE1.fa.gz [following]\n",
      "--2015-07-05 17:18:14--  https://www.vectorbase.org/sites/default/files/ftp/downloads/Anopheles-atroparvus-EBRO_SCAFFOLDS_AatrE1.fa.gz\n",
      "Reusing existing connection to www.vectorbase.org:443.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 60941709 (58M) [application/x-gzip]\n",
      "Saving to: ‘atroparvus.fa.gz’\n",
      "\n",
      "atroparvus.fa.gz    100%[=====================>]  58.12M   282KB/s   in 2m 54s \n",
      "\n",
      "2015-07-05 17:21:09 (341 KB/s) - ‘atroparvus.fa.gz’ saved [60941709/60941709]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!rm -f atroparvus.fa.gz gambiae.fa.gz 2>/dev/null\n",
    "!wget ftp://ftp.vectorbase.org/public_data/organism_data/agambiae/Genome/agambiae.CHROMOSOMES-PEST.AgamP3.fa.gz -O gambiae.fa.gz\n",
    "!wget https://www.vectorbase.org/download/anopheles-atroparvus-ebroscaffoldsaatre1fagz -O atroparvus.fa.gz\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "import gzip\n",
    "from Bio import SeqIO, SeqUtils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gambiae_name = 'gambiae.fa.gz'\n",
    "atroparvus_name = 'atroparvus.fa.gz'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chromosome:AgamP3:2L:1:49364325:1 chromosome 2L\n",
      "chromosome:AgamP3:2R:1:61545105:1 chromosome 2R\n",
      "chromosome:AgamP3:3L:1:41963435:1 chromosome 3L\n",
      "chromosome:AgamP3:3R:1:53200684:1 chromosome 3R\n",
      "chromosome:AgamP3:UNKN:1:42389979:1 chromosome UNKN\n",
      "chromosome:AgamP3:X:1:24393108:1 chromosome X\n",
      "chromosome:AgamP3:Y_unplaced:1:237045:1 chromosome Y_unplaced\n"
     ]
    }
   ],
   "source": [
    "recs = SeqIO.parse(gzip.open(gambiae_name), 'fasta')\n",
    "for rec in recs:\n",
    "    print(rec.description)\n",
    "#Do not do this with atroparvus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "recs = SeqIO.parse(gzip.open(gambiae_name), 'fasta')\n",
    "chrom_Ns = {}\n",
    "chrom_sizes = {}\n",
    "for rec in recs:\n",
    "    chrom = rec.description.split(':')[2]\n",
    "    if chrom in ['UNKN', 'Y_unplaced']:\n",
    "        continue\n",
    "    chrom_Ns[chrom] = []\n",
    "    on_N = False\n",
    "    curr_size = 0\n",
    "    for pos, nuc in enumerate(rec.seq):\n",
    "        if nuc in ['N', 'n']:\n",
    "            curr_size += 1\n",
    "            on_N = True\n",
    "        else:\n",
    "            if on_N:\n",
    "                chrom_Ns[chrom].append(curr_size)\n",
    "                curr_size = 0\n",
    "            on_N = False\n",
    "    if on_N:\n",
    "        chrom_Ns[chrom].append(curr_size)\n",
    "    chrom_sizes[chrom] = len(rec.seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2L (49364325): %Ns (1.7), num Ns: 957, max N: 28884\n",
      "3R (53200684): %Ns (1.8), num Ns: 1128, max N: 24292\n",
      "X (24393108): %Ns (4.1), num Ns: 1287, max N: 21132\n",
      "2R (61545105): %Ns (2.3), num Ns: 1658, max N: 36427\n",
      "3L (41963435): %Ns (2.9), num Ns: 1272, max N: 31063\n"
     ]
    }
   ],
   "source": [
    "for chrom, Ns in chrom_Ns.items():\n",
    "    size = chrom_sizes[chrom]\n",
    "    print('%s (%s): %%Ns (%.1f), num Ns: %d, max N: %d' % (chrom, size, 100 * sum(Ns) / size, len(Ns), max(Ns)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Atroparvus super-contigs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "#SeqUtils\n",
    "recs = SeqIO.parse(gzip.open(atroparvus_name), 'fasta')\n",
    "sizes = []\n",
    "size_N = []\n",
    "for rec in recs:\n",
    "    size = len(rec.seq)\n",
    "    sizes.append(size)\n",
    "    count_N = 0\n",
    "    for nuc in rec.seq:\n",
    "        if nuc in ['n', 'N']:\n",
    "            count_N += 1\n",
    "    size_N.append((size, count_N / size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1371, 8304.0, 163596.00656455141, 20238125, 1004, 1563.0, 56612.0)\n"
     ]
    }
   ],
   "source": [
    "print(len(sizes), np.median(sizes), np.mean(sizes), max(sizes), min(sizes),\n",
    "      np.percentile(sizes, 10), np.percentile(sizes, 90))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f67d4b1b910>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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k1jC4laTm2gTcUG7fAhzqUj+SNB/9flCU1BwGt5LUUKXn9RiqIcZXAseyPJDt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f67d7714b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "small_split = 4800\n",
    "large_split = 540000\n",
    "fig, axs = plt.subplots(1, 3, figsize=(16, 9), squeeze=False)\n",
    "xs, ys = zip(*[(x, 100 * y) for x, y in size_N if x <= small_split])\n",
    "axs[0, 0].plot(xs, ys, '.')\n",
    "axs[0, 0].set_ylim(-0.1, 3.5)\n",
    "xs, ys = zip(*[(x, 100 * y) for x, y in size_N if x > small_split and x <= large_split])\n",
    "axs[0, 1].plot(xs, ys, '.')\n",
    "axs[0, 1].set_xlim(small_split, large_split)\n",
    "xs, ys = zip(*[(x, 100 * y) for x, y in size_N if x > large_split])\n",
    "axs[0, 2].plot(xs, ys, '.')\n",
    "axs[0, 0].set_ylabel('Fraction of Ns', fontsize=12)\n",
    "axs[0, 1].set_xlabel('Contig size', fontsize=12)\n",
    "fig.suptitle('Fraction of Ns per contig size', fontsize=26)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}