index.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[check pytraj website for more code examples](http://amber-md.github.io/pytraj/)\n",
    "\n",
    "[visualize trajectory online](membrane/sim.ipynb)\n",
    "<img src=ngl.png href=3pqr.ipynb></img>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) \n",
    "# try it your self\n",
    "# make sure to click 'Cell', then 'Run All' to initilize pytraj.\n",
    "# enter the command or modify below code and then Shift-Enter to run.\n",
    "\n",
    "import pytraj as pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lysozyme.mdcrd  tz2.nc          tz2.ortho.parm7\r\n",
      "lysozyme.prmtop tz2.ortho.nc    tz2.parm7\r\n"
     ]
    }
   ],
   "source": [
    "!ls ./data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "traj = pt.iterload('lysozyme.mdcrd', 'lysozyme.prmtop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pytraj.TrajectoryIterator, 200 frames: \n",
      "Size: 0.008762 (GB)\n",
      "<Topology: 1960 atoms, 129 residues, 1 mols, non-PBC>\n",
      "           \n"
     ]
    }
   ],
   "source": [
    "print(traj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 14.21385988,  14.29338811,  14.36196037, ...,  14.17920482,\n",
       "        14.22669114,  14.21764279])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt.radgyr(traj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# supported limited progress bar\n",
    "\n",
    "data = pt.compute('''\n",
    "molsurf @CA\n",
    "multidihedral resrange 3-10\n",
    "vector :2 :9\n",
    "distance :3 :7\n",
    "''', traj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(24, 25), (40, 41), (60, 61), (67, 68), (91, 92), (101, 102), (116, 117), (135, 136), (145, 146), (155, 156)]\n"
     ]
    }
   ],
   "source": [
    "# calulate lysozyme S2 order paramter\n",
    "\n",
    "\n",
    "# select H (backbone) indices. Need to provide corresponding Topology and mask\n",
    "h = pt.select_atoms(traj.top, '@H')\n",
    "n = h - 1\n",
    "# create atom pairs\n",
    "nh = list(zip(n, h))\n",
    "print(nh[:10])\n",
    "\n",
    "# calulate S2\n",
    "S2 = pt.NH_order_parameters(traj, nh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10a0910f0>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cfXV+rxsxwu56ymE4Zi9+GmN37rTtit13wiv0qmYrOVl8qapuABPvP/0p8zbb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\nB4fQWd4YK0WnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109e3c710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "from matplotlib import pyplot as plt\n",
    "plt.plot(S2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pytraj as pt\n",
    "import nglview as nv\n",
    "\n",
    "traj = pt.load(nv.datafiles.TRR, top=nv.datafiles.PDB)\n",
    "view = nv.show_pytraj(traj)\n",
    "view"
   ]
  }
 ],
 "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}