levitsky/XIC.ipynb

## XIC.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from pyteomics import mzml\n",
    "import pylab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = 'my.mzML'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "mz = 400.\n",
    "tol = 0.05\n",
    "left, right = mz-tol, mz+tol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "rt, intens = [], []\n",
    "with mzml.MzML(f) as reader:\n",
    "    for scan in reader:\n",
    "        if scan['ms level'] == 1:\n",
    "            i = scan['m/z array'].searchsorted(left)\n",
    "            j = scan['m/z array'].searchsorted(right)\n",
    "            rt.append(scan['scanList']['scan'][0]['scan start time'])\n",
    "            # integrate, since the scans are in profile mode\n",
    "            intens.append(scan['intensity array'][i:j].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8b2f98aa90>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aO4+xff/h2OPIV6HrMDX9H50xkNMH9vzwulT9MUNPY1D/vjkv72jHMXa05b++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\nIlKllABERKrU/weiLSVG4vtDIAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(rt, intens, label='m/z = {:.3f}'.format(mz))\n",
    "pylab.xlabel('RT, ' + rt[0].unit_info)\n",
    "pylab.legend()"
   ]
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {},
	"outputs": [],
	"source": [
	"%matplotlib inline\n",
	"from pyteomics import mzml\n",
	"import pylab"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 6,
	"metadata": {},
	"outputs": [],
	"source": [
	"f = 'my.mzML'"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 4,
	"metadata": {},
	"outputs": [],
	"source": [
	"mz = 400.\n",
	"tol = 0.05\n",
	"left, right = mz-tol, mz+tol"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 16,
	"metadata": {},
	"outputs": [],
	"source": [
	"rt, intens = [], []\n",
	"with mzml.MzML(f) as reader:\n",
	" for scan in reader:\n",
	" if scan['ms level'] == 1:\n",
	" i = scan['m/z array'].searchsorted(left)\n",
	" j = scan['m/z array'].searchsorted(right)\n",
	" rt.append(scan['scanList']['scan'][0]['scan start time'])\n",
	" # integrate, since the scans are in profile mode\n",
	" intens.append(scan['intensity array'][i:j].sum())"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 25,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"<matplotlib.legend.Legend at 0x7f8b2f98aa90>"
	]
	},
	"execution_count": 25,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
	"image/png": 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aO4+xff/h2OPIV6HrMDX9H50xkNMH9vzwulT9MUNPY1D/vjkv72jHMXa05b++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\nIlKllABERKrU/weiLSVG4vtDIAAAAABJRU5ErkJggg==\n",
	"text/plain": [
	"<Figure size 432x288 with 1 Axes>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"pylab.plot(rt, intens, label='m/z = {:.3f}'.format(mz))\n",
	"pylab.xlabel('RT, ' + rt[0].unit_info)\n",
	"pylab.legend()"
	]
	}
	],
	"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": "3.6.6"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}