{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a1588ef9",
   "metadata": {},
   "source": [
    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/cbc-univie/mdy-newanalysis-package/blob/master/docs/notebooks/rdf.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5515278d",
   "metadata": {},
   "source": [
    "# Radial distribution function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fb8776a",
   "metadata": {},
   "source": [
    "We calculate the radial distribution function (and other gfunctions)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ff3dba1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import sys\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "import glob"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55784533",
   "metadata": {},
   "source": [
    "Some configurations if we are in a google colab environment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a81ccca5",
   "metadata": {},
   "outputs": [],
   "source": [
    "IN_COLAB = 'google.colab' in sys.modules\n",
    "HAS_NEWANALYSIS = 'newanalysis' in sys.modules\n",
    "if IN_COLAB:\n",
    "    if not'MDAnalysis' in sys.modules:\n",
    "        !pip install MDAnalysis\n",
    "    import os\n",
    "    if not os.path.exists(\"/content/mdy-newanalysis-package/\"):\n",
    "        !git clone https://github.com/cbc-univie/mdy-newanalysis-package.git\n",
    "    !pwd\n",
    "    if not HAS_NEWANALYSIS:\n",
    "        %cd /content/mdy-newanalysis-package/newanalysis_source/\n",
    "        !pip install .\n",
    "    %cd /content/mdy-newanalysis-package/docs/notebooks/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95da3a41",
   "metadata": {},
   "source": [
    "Next we import MDAnalysis and functions needed from newanalysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3c2f8203",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import MDAnalysis\n",
    "except ModuleNotFoundError as e:\n",
    "    if not IN_COLAB:\n",
    "        !conda install --yes --prefix {sys.prefix} MDAnalysis\n",
    "        import MDAnalysis\n",
    "    else:\n",
    "        print(\"Probably the cell above had a problem.\")\n",
    "        raise e\n",
    "try:\n",
    "    from newanalysis.gfunction import RDF\n",
    "    from newanalysis.helpers import dipByResidue\n",
    "    from newanalysis.functions import atomsPerResidue, residueFirstAtom\n",
    "except ModuleNotFoundError as e:\n",
    "    if not IN_COLAB:\n",
    "        print(\"Install newanalysis fist.\")\n",
    "        print(\"Go to the newanalysis_source folder and run:\")\n",
    "        print(\"conda activate newanalysis-dev\")\n",
    "        print(\"pip install .\")\n",
    "    else:\n",
    "        print(\"Probably the cell above had a problem.\")\n",
    "    raise e"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f8c1f63",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9a4e344",
   "metadata": {},
   "source": [
    "Next we create our MDAnalysis Universe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d664f2b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "base='../../test_cases/data/1mq_swm4_equilibrium/'\n",
    "psf=base+'mqs0_swm4_1000.psf'\n",
    "#Check PSF:\n",
    "if np.array_equal(MDAnalysis.Universe(psf).atoms.masses, MDAnalysis.Universe(psf).atoms.masses.astype(bool)):\n",
    "    print(\"Used wrong PSF format (masses unreadable!)\")\n",
    "    sys.exit()\n",
    "\n",
    "u=MDAnalysis.Universe(psf,base+\"1mq_swm4.dcd\")\n",
    "\n",
    "skip=20\n",
    "#dt=round(u.trajectory.dt,4)\n",
    "\n",
    "#n = int(u.trajectory.n_frames/skip)\n",
    "#if u.trajectory.n_frames%skip != 0:\n",
    "#    n+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe28f64e",
   "metadata": {},
   "source": [
    "Now we do the selections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "998f92e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "sel_solute=u.select_atoms(\"resname MQS0\")\n",
    "sel=u.select_atoms(\"resname SWM4\")\n",
    "\n",
    "nsolute=1\n",
    "nwat = sel.n_residues"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "791aeff3",
   "metadata": {},
   "source": [
    "## Use the RDF class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5ef3bf49",
   "metadata": {},
   "outputs": [],
   "source": [
    "histo_min=0.0\n",
    "histo_dx=0.05\n",
    "histo_invdx=1.0/histo_dx\n",
    "histo_max=20.0\n",
    "boxl = np.float64(round(u.coord.dimensions[0],4))\n",
    "\n",
    "sol_wat = RDF([\"all\"],histo_min,histo_max,histo_dx,nsolute,nwat,nsolute,nwat,boxl)\n",
    "#could also specify a certain one:\n",
    "sol_wat_000 = RDF([\"000\"],histo_min,histo_max,histo_dx,nsolute,nwat,nsolute,nwat,boxl) #rdf\n",
    "#include the picture with the different functions!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9d5ef3c",
   "metadata": {},
   "source": [
    "some more needed variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c908acd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "charge_solute=sel_solute.charges\n",
    "charge_wat=sel.charges\n",
    "\n",
    "apr_solute = atomsPerResidue(sel_solute)\n",
    "rfa_solute = residueFirstAtom(sel_solute)\n",
    "apr_wat = atomsPerResidue(sel)\n",
    "rfa_wat = residueFirstAtom(sel)\n",
    "\n",
    "    \n",
    "mass_solute=sel_solute.masses\n",
    "mass_wat=sel.masses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5d1b632",
   "metadata": {},
   "source": [
    "Loop throguh the trajectory and get the center of masses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d78f9648",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[1AFrame 0 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 20 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 40 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 60 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 80 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 100 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 120 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 140 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 160 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 180 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 200 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 220 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 240 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 260 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 280 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 300 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 320 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 340 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 360 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 380 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 400 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 420 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 440 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 460 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 480 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 500 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 520 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 540 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 560 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 580 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 600 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 620 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 640 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 660 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 680 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 700 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 720 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 740 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 760 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 780 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 800 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 820 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 840 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 860 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 880 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 900 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 920 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 940 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 960 of 1000 \tElapsed time: 0.00 hours\n",
      "\u001b[1AFrame 980 of 1000 \tElapsed time: 0.00 hours\n",
      "Passed time:  0.09380483627319336\n"
     ]
    }
   ],
   "source": [
    "start=time.time()\n",
    "print(\"\")\n",
    "\n",
    "for ts in u.trajectory[::skip]:\n",
    "    print(\"\\033[1AFrame %d of %d\" % (ts.frame,u.trajectory.n_frames), \"\\tElapsed time: %.2f hours\" % ((time.time()-start)/3600))\n",
    "    \n",
    "    # efficiently calculate center-of-mass coordinates and dipole moments\n",
    "    coor_wat = np.ascontiguousarray(sel.positions,dtype='double')\n",
    "    coor_solute = np.ascontiguousarray(sel_solute.positions,dtype='double')\n",
    "    com_wat=sel.center_of_mass(compound='residues')\n",
    "    com_solute=sel_solute.center_of_mass(compound='residues')\n",
    "    dip_wat=dipByResidue(coor_wat,charge_wat,mass_wat,nwat,apr_wat,rfa_wat,com_wat)\n",
    "    dip_solute=dipByResidue(coor_solute,charge_solute,mass_solute,nsolute,apr_solute,rfa_solute,com_solute)\n",
    "\n",
    "    #Alternatively:\n",
    "#    com_wat  = centerOfMassByResidue(sel,coor=coor_wat,masses=mass_wat,apr=apr_wat,rfa=rfa_wat)\n",
    "#    com_solute  = centerOfMassByResidue(sel_solute,coor=coor_solute,masses=mass_solute,apr=apr_solute,rfa=rfa_solute)\n",
    "#    dip_wat= dipoleMomentByResidue(sel,coor=coor_wat,charges=charge_wat,masses=mass_wat,com=com_wat,apr=apr_wat,rfa=rfa_wat)\n",
    "#    dip_solute=dipoleMomentByResidue(sel_solute,coor=coor_solute,charges=charge_solute,masses=mass_solute,com=com_solute,apr=apr_solute,rfa=rfa_solute)\n",
    "\n",
    "    sol_wat.calcFrame(com_solute,com_wat,dip_solute,dip_wat)\n",
    "\n",
    "print(\"Passed time: \",time.time()-start)\n",
    "\n",
    "Path(\"output\").mkdir(parents=True, exist_ok=True)\n",
    "sol_wat.write(\"output/rdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51b054b7",
   "metadata": {},
   "source": [
    "## Plot the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0b89b9e7",
   "metadata": {
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x,y = np.genfromtxt(\"output/rdf_g000.dat\", unpack=True)\n",
    "\n",
    "plt.title(\"Radial distribution function\")\n",
    "plt.plot(x,y, label=\"MQS0 - water\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66930374",
   "metadata": {},
   "source": [
    "**Cleanup**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7a3880e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "for file in glob.glob(\"output/rdf*\"):\n",
    "    Path(file).unlink() #remove files"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "newanalysis-dev",
   "language": "python",
   "name": "newanalysis-dev"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}