Isotopes

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab --no-import-all inline\n",
    "from pyteomics import mass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def isotope_table(peptide, element='C', mass_data=mass.nist_mass):\n",
    "    composition = mass.Composition(peptide)\n",
    "    count = composition[element]\n",
    "    composition -= {element: count}\n",
    "    \n",
    "    # modified from the source of most_probable_isotopic_composition()\n",
    "    first, second = sorted([i for i in mass_data[element] if i],\n",
    "                key=lambda x: -mass_data[element][x][1])[:2]\n",
    "    out = []\n",
    "    for i in range(count+1):\n",
    "        c = composition + {mass._make_isotope_string(element, first): count-i,\n",
    "                           mass._make_isotope_string(element, second): i}\n",
    "        out.append((mass.isotopic_composition_abundance(c), c))\n",
    "    return out\n",
    "\n",
    "def mass_table(peptide, element='C', mass_data=mass.nist_mass):\n",
    "    return [(mass.calculate_mass(c), abundance) for abundance, c in\n",
    "            isotope_table(peptide, element, mass_data)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f7cb61d9358>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7cb62860b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table = np.array(mass_table('PEPTIDE'))\n",
    "pylab.scatter(table[:,0], table[:,1])"
   ]
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
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 },
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