{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to train a GAP model from scratch\n",
    "\n",
    "## steps\n",
    "    1. generate a small dataset of water structures \n",
    "        - use CP2K if you have access to it\n",
    "        - otherwise: use any simple potential implemented in ASE, just for trying this out I have used EMT here\n",
    "    1. generate e0 values: isolated atoms in the dataset\n",
    "    1. separate a training and a validation dataset\n",
    "    1. **train the model**\n",
    "    1. look at the outcome of the model\n",
    "    \n",
    "## here we will fit twice, to see the difference between a 2b-only and a 2b+3b fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-08T08:48:40.136302Z",
     "start_time": "2018-10-08T08:48:40.130282Z"
    }
   },
   "outputs": [],
   "source": [
    "# general imports \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from copy import deepcopy as cp\n",
    "\n",
    "# ase imports\n",
    "import ase.io\n",
    "from ase import Atoms, Atom\n",
    "from ase import units\n",
    "from ase.build import molecule\n",
    "# for MD\n",
    "from ase.md.langevin import Langevin\n",
    "from ase.io.trajectory import Trajectory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T17:07:43.520727Z",
     "start_time": "2018-10-07T17:07:43.460810Z"
    }
   },
   "outputs": [],
   "source": [
    "# helper functions\n",
    "def make_water(density, super_cell=[3, 3, 3]):\n",
    "    \"\"\" Geenrates a supercell of water molecules with a desired density.\n",
    "        Density in g/cm^3!!!\"\"\"\n",
    "    h2o = molecule('H2O')\n",
    "    a = np.cbrt((sum(h2o.get_masses()) * units.m ** 3 * 1E-6 ) / (density * units.mol))\n",
    "    h2o.set_cell((a, a, a))\n",
    "    h2o.set_pbc((True, True, True))\n",
    "    #return cp(h2o.repeat(super_cell))\n",
    "    return h2o.repeat(super_cell)\n",
    "\n",
    "def rms_dict(x_ref, x_pred):\n",
    "    \"\"\" Takes two datasets of the same shape and returns a dictionary containing RMS error data\"\"\"\n",
    "\n",
    "    x_ref = np.array(x_ref)\n",
    "    x_pred = np.array(x_pred)\n",
    "\n",
    "    if np.shape(x_pred) != np.shape(x_ref):\n",
    "        raise ValueError('WARNING: not matching shapes in rms')\n",
    "\n",
    "    error_2 = (x_ref - x_pred) ** 2\n",
    "\n",
    "    average = np.sqrt(np.average(error_2))\n",
    "    std_ = np.sqrt(np.var(error_2))\n",
    "\n",
    "    return {'rmse': average, 'std': std_}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## generating data only with ASE, using the EMT calculator\n",
    "\n",
    "This is only for the demonstration of how to do it, this run is will be done very fast. There is no practical use of the data beyond learning the use teach_sparse, quip, etc. with it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T17:10:50.964994Z",
     "start_time": "2018-10-07T17:07:43.523997Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Energy per atom: Epot = 0.885eV  Ekin = 0.000eV (T=  0K)  Etot = 0.885eV\n",
      "Energy per atom: Epot = 0.820eV  Ekin = 0.053eV (T=413K)  Etot = 0.874eV\n",
      "Energy per atom: Epot = 0.660eV  Ekin = 0.208eV (T=1611K)  Etot = 0.868eV\n",
      "Energy per atom: Epot = 0.632eV  Ekin = 0.224eV (T=1736K)  Etot = 0.857eV\n",
      "Energy per atom: Epot = 0.869eV  Ekin = 0.005eV (T= 39K)  Etot = 0.874eV\n",
      "Energy per atom: Epot = 0.781eV  Ekin = 0.088eV (T=682K)  Etot = 0.869eV\n",
      "Energy per atom: Epot = 0.756eV  Ekin = 0.117eV (T=903K)  Etot = 0.872eV\n",
      "Energy per atom: Epot = 0.706eV  Ekin = 0.164eV (T=1270K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.703eV  Ekin = 0.158eV (T=1223K)  Etot = 0.861eV\n",
      "Energy per atom: Epot = 0.867eV  Ekin = 0.006eV (T= 47K)  Etot = 0.873eV\n",
      "Energy per atom: Epot = 0.704eV  Ekin = 0.160eV (T=1241K)  Etot = 0.864eV\n",
      "Energy per atom: Epot = 0.679eV  Ekin = 0.190eV (T=1468K)  Etot = 0.868eV\n",
      "Energy per atom: Epot = 0.822eV  Ekin = 0.051eV (T=391K)  Etot = 0.872eV\n",
      "Energy per atom: Epot = 0.782eV  Ekin = 0.088eV (T=677K)  Etot = 0.869eV\n",
      "Energy per atom: Epot = 0.775eV  Ekin = 0.096eV (T=739K)  Etot = 0.871eV\n",
      "Energy per atom: Epot = 0.751eV  Ekin = 0.119eV (T=920K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.752eV  Ekin = 0.117eV (T=904K)  Etot = 0.869eV\n",
      "Energy per atom: Epot = 0.764eV  Ekin = 0.107eV (T=825K)  Etot = 0.871eV\n",
      "Energy per atom: Epot = 0.752eV  Ekin = 0.120eV (T=927K)  Etot = 0.871eV\n",
      "Energy per atom: Epot = 0.747eV  Ekin = 0.123eV (T=953K)  Etot = 0.871eV\n",
      "Energy per atom: Epot = 0.724eV  Ekin = 0.145eV (T=1119K)  Etot = 0.868eV\n",
      "Energy per atom: Epot = 0.703eV  Ekin = 0.166eV (T=1281K)  Etot = 0.869eV\n",
      "Energy per atom: Epot = 0.738eV  Ekin = 0.132eV (T=1022K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.703eV  Ekin = 0.165eV (T=1280K)  Etot = 0.868eV\n",
      "Energy per atom: Epot = 0.697eV  Ekin = 0.172eV (T=1331K)  Etot = 0.869eV\n",
      "Energy per atom: Epot = 0.731eV  Ekin = 0.140eV (T=1083K)  Etot = 0.871eV\n",
      "Energy per atom: Epot = 0.716eV  Ekin = 0.155eV (T=1199K)  Etot = 0.871eV\n",
      "Energy per atom: Epot = 0.706eV  Ekin = 0.163eV (T=1257K)  Etot = 0.869eV\n",
      "Energy per atom: Epot = 0.721eV  Ekin = 0.149eV (T=1151K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.685eV  Ekin = 0.184eV (T=1424K)  Etot = 0.869eV\n",
      "Energy per atom: Epot = 0.706eV  Ekin = 0.164eV (T=1271K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.721eV  Ekin = 0.150eV (T=1161K)  Etot = 0.871eV\n",
      "Energy per atom: Epot = 0.690eV  Ekin = 0.180eV (T=1396K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.663eV  Ekin = 0.207eV (T=1601K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.623eV  Ekin = 0.247eV (T=1907K)  Etot = 0.869eV\n",
      "Energy per atom: Epot = 0.609eV  Ekin = 0.261eV (T=2022K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.576eV  Ekin = 0.294eV (T=2271K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.563eV  Ekin = 0.306eV (T=2370K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.571eV  Ekin = 0.298eV (T=2307K)  Etot = 0.869eV\n",
      "Energy per atom: Epot = 0.605eV  Ekin = 0.265eV (T=2050K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.566eV  Ekin = 0.302eV (T=2338K)  Etot = 0.869eV\n",
      "Energy per atom: Epot = 0.579eV  Ekin = 0.290eV (T=2242K)  Etot = 0.868eV\n",
      "Energy per atom: Epot = 0.580eV  Ekin = 0.289eV (T=2234K)  Etot = 0.868eV\n",
      "Energy per atom: Epot = 0.557eV  Ekin = 0.311eV (T=2406K)  Etot = 0.868eV\n",
      "Energy per atom: Epot = 0.605eV  Ekin = 0.264eV (T=2046K)  Etot = 0.870eV\n",
      "Energy per atom: Epot = 0.541eV  Ekin = 0.327eV (T=2530K)  Etot = 0.868eV\n",
      "Energy per atom: Epot = 0.521eV  Ekin = 0.347eV (T=2686K)  Etot = 0.868eV\n",
      "Energy per atom: Epot = 0.535eV  Ekin = 0.339eV (T=2622K)  Etot = 0.874eV\n",
      "Energy per atom: Epot = 0.540eV  Ekin = 0.333eV (T=2574K)  Etot = 0.873eV\n",
      "Energy per atom: Epot = 0.597eV  Ekin = 0.278eV (T=2151K)  Etot = 0.875eV\n",
      "Energy per atom: Epot = 0.534eV  Ekin = 0.340eV (T=2631K)  Etot = 0.874eV\n",
      "Energy per atom: Epot = 0.490eV  Ekin = 0.384eV (T=2968K)  Etot = 0.874eV\n",
      "Energy per atom: Epot = 0.496eV  Ekin = 0.378eV (T=2928K)  Etot = 0.875eV\n",
      "Energy per atom: Epot = 0.495eV  Ekin = 0.384eV (T=2970K)  Etot = 0.879eV\n",
      "Energy per atom: Epot = 0.456eV  Ekin = 0.424eV (T=3277K)  Etot = 0.880eV\n",
      "Energy per atom: Epot = 0.454eV  Ekin = 0.430eV (T=3325K)  Etot = 0.883eV\n",
      "Energy per atom: Epot = 0.527eV  Ekin = 0.358eV (T=2767K)  Etot = 0.884eV\n",
      "Energy per atom: Epot = 0.493eV  Ekin = 0.391eV (T=3027K)  Etot = 0.885eV\n",
      "Energy per atom: Epot = 0.496eV  Ekin = 0.389eV (T=3011K)  Etot = 0.885eV\n",
      "Energy per atom: Epot = 0.500eV  Ekin = 0.384eV (T=2971K)  Etot = 0.884eV\n",
      "Energy per atom: Epot = 0.508eV  Ekin = 0.374eV (T=2895K)  Etot = 0.882eV\n",
      "Energy per atom: Epot = 0.470eV  Ekin = 0.411eV (T=3176K)  Etot = 0.881eV\n",
      "Energy per atom: Epot = 0.430eV  Ekin = 0.451eV (T=3489K)  Etot = 0.881eV\n",
      "Energy per atom: Epot = 0.404eV  Ekin = 0.478eV (T=3696K)  Etot = 0.882eV\n",
      "Energy per atom: Epot = 0.410eV  Ekin = 0.471eV (T=3645K)  Etot = 0.881eV\n",
      "Energy per atom: Epot = 0.473eV  Ekin = 0.408eV (T=3160K)  Etot = 0.881eV\n",
      "Energy per atom: Epot = 0.429eV  Ekin = 0.451eV (T=3490K)  Etot = 0.880eV\n",
      "Energy per atom: Epot = 0.439eV  Ekin = 0.442eV (T=3423K)  Etot = 0.881eV\n",
      "Energy per atom: Epot = 0.465eV  Ekin = 0.416eV (T=3216K)  Etot = 0.881eV\n",
      "Energy per atom: Epot = 0.448eV  Ekin = 0.432eV (T=3346K)  Etot = 0.880eV\n",
      "Energy per atom: Epot = 0.465eV  Ekin = 0.417eV (T=3222K)  Etot = 0.882eV\n",
      "Energy per atom: Epot = 0.457eV  Ekin = 0.423eV (T=3274K)  Etot = 0.880eV\n",
      "Energy per atom: Epot = 0.425eV  Ekin = 0.456eV (T=3528K)  Etot = 0.881eV\n",
      "Energy per atom: Epot = 0.415eV  Ekin = 0.465eV (T=3598K)  Etot = 0.880eV\n",
      "Energy per atom: Epot = 0.458eV  Ekin = 0.427eV (T=3301K)  Etot = 0.884eV\n",
      "Energy per atom: Epot = 0.444eV  Ekin = 0.442eV (T=3423K)  Etot = 0.886eV\n",
      "Energy per atom: Epot = 0.386eV  Ekin = 0.499eV (T=3858K)  Etot = 0.885eV\n",
      "Energy per atom: Epot = 0.386eV  Ekin = 0.499eV (T=3864K)  Etot = 0.885eV\n",
      "Energy per atom: Epot = 0.421eV  Ekin = 0.466eV (T=3609K)  Etot = 0.887eV\n",
      "Energy per atom: Epot = 0.390eV  Ekin = 0.498eV (T=3854K)  Etot = 0.888eV\n",
      "Energy per atom: Epot = 0.404eV  Ekin = 0.488eV (T=3777K)  Etot = 0.892eV\n",
      "Energy per atom: Epot = 0.411eV  Ekin = 0.480eV (T=3714K)  Etot = 0.891eV\n",
      "Energy per atom: Epot = 0.429eV  Ekin = 0.460eV (T=3558K)  Etot = 0.889eV\n",
      "Energy per atom: Epot = 0.391eV  Ekin = 0.497eV (T=3848K)  Etot = 0.888eV\n",
      "Energy per atom: Epot = 0.379eV  Ekin = 0.507eV (T=3924K)  Etot = 0.886eV\n",
      "Energy per atom: Epot = 0.429eV  Ekin = 0.460eV (T=3555K)  Etot = 0.889eV\n",
      "Energy per atom: Epot = 0.431eV  Ekin = 0.458eV (T=3545K)  Etot = 0.889eV\n",
      "Energy per atom: Epot = 0.440eV  Ekin = 0.447eV (T=3458K)  Etot = 0.887eV\n",
      "Energy per atom: Epot = 0.417eV  Ekin = 0.467eV (T=3610K)  Etot = 0.884eV\n",
      "Energy per atom: Epot = 0.445eV  Ekin = 0.442eV (T=3422K)  Etot = 0.888eV\n",
      "Energy per atom: Epot = 0.437eV  Ekin = 0.447eV (T=3461K)  Etot = 0.884eV\n",
      "Energy per atom: Epot = 0.439eV  Ekin = 0.447eV (T=3460K)  Etot = 0.886eV\n",
      "Energy per atom: Epot = 0.424eV  Ekin = 0.464eV (T=3587K)  Etot = 0.888eV\n",
      "Energy per atom: Epot = 0.394eV  Ekin = 0.493eV (T=3817K)  Etot = 0.887eV\n",
      "Energy per atom: Epot = 0.370eV  Ekin = 0.515eV (T=3984K)  Etot = 0.885eV\n",
      "Energy per atom: Epot = 0.373eV  Ekin = 0.511eV (T=3951K)  Etot = 0.883eV\n",
      "Energy per atom: Epot = 0.402eV  Ekin = 0.485eV (T=3748K)  Etot = 0.886eV\n",
      "Energy per atom: Epot = 0.380eV  Ekin = 0.506eV (T=3912K)  Etot = 0.886eV\n",
      "Energy per atom: Epot = 0.404eV  Ekin = 0.483eV (T=3736K)  Etot = 0.887eV\n",
      "Energy per atom: Epot = 0.443eV  Ekin = 0.444eV (T=3436K)  Etot = 0.887eV\n",
      "Energy per atom: Epot = 0.419eV  Ekin = 0.469eV (T=3626K)  Etot = 0.888eV\n",
      "Energy per atom: Epot = 0.440eV  Ekin = 0.451eV (T=3489K)  Etot = 0.891eV\n",
      "Energy per atom: Epot = 0.434eV  Ekin = 0.453eV (T=3505K)  Etot = 0.887eV\n",
      "Energy per atom: Epot = 0.423eV  Ekin = 0.468eV (T=3619K)  Etot = 0.891eV\n",
      "Energy per atom: Epot = 0.369eV  Ekin = 0.523eV (T=4047K)  Etot = 0.892eV\n",
      "Energy per atom: Epot = 0.413eV  Ekin = 0.478eV (T=3701K)  Etot = 0.891eV\n",
      "Energy per atom: Epot = 0.359eV  Ekin = 0.531eV (T=4108K)  Etot = 0.890eV\n",
      "Energy per atom: Epot = 0.401eV  Ekin = 0.491eV (T=3799K)  Etot = 0.892eV\n",
      "Energy per atom: Epot = 0.414eV  Ekin = 0.477eV (T=3688K)  Etot = 0.890eV\n",
      "Energy per atom: Epot = 0.376eV  Ekin = 0.513eV (T=3969K)  Etot = 0.889eV\n",
      "Energy per atom: Epot = 0.368eV  Ekin = 0.522eV (T=4035K)  Etot = 0.890eV\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Energy per atom: Epot = 0.370eV  Ekin = 0.521eV (T=4029K)  Etot = 0.891eV\n",
      "Energy per atom: Epot = 0.379eV  Ekin = 0.512eV (T=3959K)  Etot = 0.891eV\n",
      "Energy per atom: Epot = 0.393eV  Ekin = 0.502eV (T=3881K)  Etot = 0.894eV\n",
      "Energy per atom: Epot = 0.373eV  Ekin = 0.515eV (T=3984K)  Etot = 0.888eV\n",
      "Energy per atom: Epot = 0.367eV  Ekin = 0.530eV (T=4100K)  Etot = 0.897eV\n",
      "Energy per atom: Epot = 0.380eV  Ekin = 0.520eV (T=4026K)  Etot = 0.900eV\n",
      "Energy per atom: Epot = 0.384eV  Ekin = 0.523eV (T=4048K)  Etot = 0.908eV\n",
      "Energy per atom: Epot = 0.417eV  Ekin = 0.485eV (T=3751K)  Etot = 0.902eV\n",
      "Energy per atom: Epot = 0.402eV  Ekin = 0.518eV (T=4005K)  Etot = 0.919eV\n",
      "Energy per atom: Epot = 0.417eV  Ekin = 0.558eV (T=4313K)  Etot = 0.974eV\n"
     ]
    }
   ],
   "source": [
    "# Running MD with ASE's EMT\n",
    "\n",
    "from ase.calculators.emt import EMT\n",
    "calc = EMT()\n",
    "\n",
    "T = 150  # Kelvin\n",
    "\n",
    "# Set up a grid of water\n",
    "water = make_water(1.0, [3, 3, 3])\n",
    "water.set_calculator(calc)\n",
    "\n",
    "# We want to run MD using the Langevin algorithm\n",
    "# with a time step of 1 fs, the temperature T and the friction\n",
    "# coefficient to 0.002 atomic units.\n",
    "dyn = Langevin(water, 1 * units.fs, T * units.kB, 0.0002)\n",
    "\n",
    "def printenergy(a=water):  # store a reference to atoms in the definition.\n",
    "    \"\"\"Function to print the potential, kinetic and total energy.\"\"\"\n",
    "    epot = a.get_potential_energy() / len(a)\n",
    "    ekin = a.get_kinetic_energy() / len(a)\n",
    "    print('Energy per atom: Epot = %.3feV  Ekin = %.3feV (T=%3.0fK)  '\n",
    "          'Etot = %.3feV' % (epot, ekin, ekin / (1.5 * units.kB), epot + ekin))\n",
    "\n",
    "dyn.attach(printenergy, interval=5)\n",
    "\n",
    "# We also want to save the positions of all atoms after every 5th time step.\n",
    "traj = Trajectory('dyn_emt.traj', 'w', water)\n",
    "dyn.attach(traj.write, interval=5)\n",
    "\n",
    "# Now run the dynamics\n",
    "printenergy(water)\n",
    "dyn.run(600)   # CHANGE THIS IF YOU WANT LONGER/SHORTER RUN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T17:10:51.483185Z",
     "start_time": "2018-10-07T17:10:50.968420Z"
    }
   },
   "outputs": [],
   "source": [
    "# wrap and save traj in .xyz --- the .traj is a non human readable database file, xyz is much better\n",
    "out_traj = ase.io.read('dyn_emt.traj', ':')\n",
    "for at in out_traj:\n",
    "    at.wrap()\n",
    "    if 'momenta' in at.arrays: del at.arrays['momenta']\n",
    "ase.io.write('dyn_emt.xyz', out_traj, 'xyz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## get e0 for H and O - energies of the isolated atoms\n",
    "\n",
    "This is the energy of the isolated atom, will be in the teach_sparse string in the following format: `e0={H:energy:O:energy}`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T17:10:51.495057Z",
     "start_time": "2018-10-07T17:10:51.486315Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e0_H: 3.21\n",
      "e0_O: 4.6\n"
     ]
    }
   ],
   "source": [
    "isolated_H = Atoms('H', calculator=EMT())\n",
    "isolated_O = Atoms('O', calculator=EMT())\n",
    "\n",
    "print('e0_H:',isolated_H.get_potential_energy())\n",
    "print('e0_O:',isolated_O.get_potential_energy())\n",
    "\n",
    "# this made the e0 string be the following: e0={H:3.21:O:4.6}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T13:10:08.501028Z",
     "start_time": "2018-10-07T13:10:08.496414Z"
    }
   },
   "source": [
    "## separate the dataset into a training and a validation set\n",
    "\n",
    "As we have 120 frames from the 600fs MD, I will do it 60,60 with taking even and odd frames for the two"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T17:10:51.802364Z",
     "start_time": "2018-10-07T17:10:51.497953Z"
    }
   },
   "outputs": [],
   "source": [
    "ase.io.write('train.xyz', out_traj[0::2])  \n",
    "ase.io.write('validate.xyz', out_traj[1::2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T17:10:51.813416Z",
     "start_time": "2018-10-07T17:10:51.805556Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['numbers', 'positions'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_traj[0].arrays.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T12:48:02.357449Z",
     "start_time": "2018-10-07T12:48:02.347940Z"
    }
   },
   "source": [
    "## train our GAP model from the command line\n",
    "\n",
    "Will use a fit of 2b only, using the desciptor distance_2b.\n",
    "\n",
    "Let's understand how this works. The bash command takes named arguments separated by spaces.\n",
    "\n",
    "- `teach_sparse` the command which actually does the fit  \n",
    "- `e0={H:3.21:O:4.6}` the energies of the isolated atoms\n",
    "- `energy_parameter_name=energy force_parameter_name=forces` names of the parameters\n",
    "- `do_copy_at_file=F sparse_separate_file=T` just needed, don't want to copy the training data and using separate files for the xml makes it faster\n",
    "- `gp_file=GAP.xml` filename of the potential parameters, I have always used this name, because I had separate directories for the different trainings potentials\n",
    "- `at_file=train.xyz` training file\n",
    "- `default_sigma={0.008 0.04 0 0}` sigma values to be used for energies, forces, stresses, hessians in order; this represents the accuracy of the data and the relative weight of them in the fit (more accurate --> more significant in the fit)\n",
    "- `gap={...}` the potential to be fit, separated by ':'\n",
    "\n",
    "**distance_2b**\n",
    "- `cutoff=4.0` radial, practically the highest distance the descriptor takes into account \n",
    "- `covariance_type=ard_se` use gausses in the fit\n",
    "- `delta=0.5` what relative portion of the things shall be determined by this potential\n",
    "- `theta_uniform=1.0` width of the gaussians\n",
    "- `sparse_method=uniform` use uniform bins to choose the sparse points\n",
    "- `add_species=T ` take the species into account, so it will generate more GAPs automatically (see the output)\n",
    "- `n_sparse=10` number of sparse points\n",
    "\n",
    "\n",
    "## notice, that the script is running in parallel, using all 8 cores of the current machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T17:10:55.183809Z",
     "start_time": "2018-10-07T17:10:51.816438Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "libAtoms::Hello World: 07/10/2018   18:10:52\n",
      "libAtoms::Hello World: git version  https://github.com/libAtoms/QUIP.git,531330f-dirty\n",
      "libAtoms::Hello World: QUIP_ARCH    linux_x86_64_gfortran_openmp\n",
      "libAtoms::Hello World: compiled on  Jul  2 2018 at 21:44:13\n",
      "libAtoms::Hello World: OpenMP parallelisation with 8 threads\n",
      "WARNING: libAtoms::Hello World: environment variable OMP_STACKSIZE not set explicitly. The default value - system and compiler dependent - may be too small for some applications.\n",
      "libAtoms::Hello World: Random Seed = 65452599\n",
      "libAtoms::Hello World: global verbosity = 0\n",
      "\n",
      "Calls to system_timer will do nothing by default\n",
      "\n",
      "\n",
      "================================ Input parameters ==============================\n",
      "\n",
      "at_file = train.xyz\n",
      "gap = \"distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform add_species=T n_sparse=10\"\n",
      "e0 = H:3.21:O:4.6\n",
      "e0_offset = 0.0\n",
      "do_e0_avg = T\n",
      "default_sigma = \"0.008 0.04 0 0\"\n",
      "sparse_jitter = 1.0e-10\n",
      "hessian_delta = 1.0e-2\n",
      "core_param_file = quip_params.xml\n",
      "core_ip_args =\n",
      "energy_parameter_name = energy\n",
      "force_parameter_name = forces\n",
      "virial_parameter_name = virial\n",
      "hessian_parameter_name = hessian\n",
      "config_type_parameter_name = config_type\n",
      "sigma_parameter_name = sigma\n",
      "config_type_sigma =\n",
      "sigma_per_atom = T\n",
      "do_copy_at_file = F\n",
      "sparse_separate_file = T\n",
      "sparse_use_actual_gpcov = F\n",
      "gp_file = GAP.xml\n",
      "verbosity = NORMAL\n",
      "rnd_seed = -1\n",
      "do_ip_timing = F\n",
      "template_file = template.xyz\n",
      "\n",
      "========================================  ======================================\n",
      "\n",
      "\n",
      "============== Gaussian Approximation Potentials - Database fitting ============\n",
      "\n",
      "\n",
      "Initial parsing of command line arguments finished.\n",
      "Found 1 GAPs.\n",
      "Descriptors have been parsed\n",
      "XYZ file read\n",
      "Old GAP: {distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform add_species=T n_sparse=10}\n",
      "New GAP: {distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform               n_sparse=10 Z1=8 Z2=8}\n",
      "New GAP: {distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform               n_sparse=10 Z1=8 Z2=1}\n",
      "New GAP: {distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform               n_sparse=10 Z1=1 Z2=1}\n",
      "Multispecies support added where requested\n",
      "Number of target energies (property name: energy) found: 60\n",
      "Number of target forces (property name: forces) found: 14580\n",
      "Number of target virials (property name: virial) found: 0\n",
      "Number of target Hessian eigenvalues (property name: hessian) found: 0\n",
      "Cartesian coordinates transformed to descriptors\n",
      "Started sparse covariance matrix calculation of coordinate 1\n",
      "\n",
      "Finished sparse covariance matrix calculation of coordinate 1\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate1_sparse  done in .91605700000000012 cpu secs, .11785597354173660 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate1         done in .91605700000000012 cpu secs, .11797238700091839 wall clock secs.\n",
      "Started sparse covariance matrix calculation of coordinate 2\n",
      "\n",
      "Finished sparse covariance matrix calculation of coordinate 2\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate2_sparse  done in 3.7722349999999998 cpu secs, .47368377633392811 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate2         done in 3.7722349999999998 cpu secs, .47379869595170021 wall clock secs.\n",
      "Started sparse covariance matrix calculation of coordinate 3\n",
      "\n",
      "Finished sparse covariance matrix calculation of coordinate 3\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate3_sparse  done in 3.5482220000000000 cpu secs, .44678380154073238 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate3         done in 3.5522219999999995 cpu secs, .44689681194722652 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_LinearAlgebra       done in .56005000000000749E-001 cpu secs, .13479800894856453E-001 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_FunctionValues      done in .00000000000000000E+000 cpu secs, .14016032218933105E-003 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse                     done in 8.3005189999999995 cpu secs, 1.0586479119956493 wall clock secs.\n",
      "TIMER: GP sparsify                                        done in 9.8286140000000000 cpu secs, 1.4589964970946312 wall clock secs.\n",
      "\n",
      "libAtoms::Finalise: 07/10/2018   18:10:55\n",
      "libAtoms::Finalise: Bye-Bye!\n"
     ]
    }
   ],
   "source": [
    "! teach_sparse e0={H:3.21:O:4.6} energy_parameter_name=energy force_parameter_name=forces do_copy_at_file=F sparse_separate_file=T gp_file=GAP.xml at_file=train.xyz default_sigma={0.008 0.04 0 0} gap={distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform add_species=T n_sparse=10}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T12:56:59.802090Z",
     "start_time": "2018-10-07T12:56:58.506422Z"
    }
   },
   "source": [
    "## use the potential with QUIP on trani.xyz and validate.xyz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T17:11:02.087146Z",
     "start_time": "2018-10-07T17:10:55.187005Z"
    }
   },
   "outputs": [],
   "source": [
    "# calculate train.xyz\n",
    "\n",
    "! quip E=T F=T atoms_filename=train.xyz param_filename=GAP.xml | grep AT | sed 's/AT//' >> quip_train.xyz\n",
    "! quip E=T F=T atoms_filename=validate.xyz param_filename=GAP.xml | grep AT | sed 's/AT//' >> quip_validate.xyz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T12:58:09.146524Z",
     "start_time": "2018-10-07T12:58:09.136959Z"
    }
   },
   "source": [
    "## make simple plots of the energies and forces on the EMT and GAP datas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-08T08:48:46.462404Z",
     "start_time": "2018-10-08T08:48:46.441786Z"
    }
   },
   "outputs": [],
   "source": [
    "def energy_plot(in_file, out_file, ax, title='Plot of energy'):\n",
    "    \"\"\" Plots the distribution of energy per atom on the output vs the input\"\"\"\n",
    "    # read files\n",
    "    in_atoms = ase.io.read(in_file, ':')\n",
    "    out_atoms = ase.io.read(out_file, ':')\n",
    "    # list energies\n",
    "    ener_in = [at.get_potential_energy() / len(at.get_chemical_symbols()) for at in in_atoms]\n",
    "    ener_out = [at.get_potential_energy() / len(at.get_chemical_symbols()) for at in out_atoms]\n",
    "    # scatter plot of the data\n",
    "    ax.scatter(ener_in, ener_out)\n",
    "    # get the appropriate limits for the plot\n",
    "    for_limits = np.array(ener_in +ener_out)   \n",
    "    elim = (for_limits.min() - 0.05, for_limits.max() + 0.05)\n",
    "    ax.set_xlim(elim)\n",
    "    ax.set_ylim(elim)\n",
    "    # add line of slope 1 for refrence\n",
    "    ax.plot(elim, elim, c='k')\n",
    "    # set labels\n",
    "    ax.set_ylabel('energy by GAP / eV')\n",
    "    ax.set_xlabel('energy by EMT / eV')\n",
    "    #set title\n",
    "    ax.set_title(title)\n",
    "    # add text about RMSE\n",
    "    _rms = rms_dict(ener_in, ener_out)\n",
    "    rmse_text = 'RMSE:\\n' + str(np.round(_rms['rmse'], 3)) + ' +- ' + str(np.round(_rms['std'], 3)) + 'eV/atom'\n",
    "    ax.text(0.9, 0.1, rmse_text, transform=ax.transAxes, fontsize='large', horizontalalignment='right', \n",
    "            verticalalignment='bottom')\n",
    "    \n",
    "def force_plot(in_file, out_file, ax, symbol='HO', title='Plot of force'):\n",
    "    \"\"\" Plots the distribution of firce components per atom on the output vs the input \n",
    "        only plots for the given atom type(s)\"\"\"\n",
    "    \n",
    "    in_atoms = ase.io.read(in_file, ':')\n",
    "    out_atoms = ase.io.read(out_file, ':')\n",
    "    \n",
    "    # extract data for only one species\n",
    "    in_force, out_force = [], []\n",
    "    for at_in, at_out in zip(in_atoms, out_atoms):\n",
    "        # get the symbols\n",
    "        sym_all = at_in.get_chemical_symbols()\n",
    "        # add force for each atom\n",
    "        for j, sym in enumerate(sym_all):\n",
    "            if sym in symbol:\n",
    "                in_force.append(at_in.get_forces()[j])\n",
    "                #out_force.append(at_out.get_forces()[j]) \\  \n",
    "                out_force.append(at_out.arrays['force'][j]) # because QUIP and ASE use different names\n",
    "    # convert to np arrays, much easier to work with\n",
    "    #in_force = np.array(in_force)\n",
    "    #out_force = np.array(out_force)\n",
    "    # scatter plot of the data\n",
    "    ax.scatter(in_force, out_force)\n",
    "    # get the appropriate limits for the plot\n",
    "    for_limits = np.array(in_force + out_force)   \n",
    "    flim = (for_limits.min() - 1, for_limits.max() + 1)\n",
    "    ax.set_xlim(flim)\n",
    "    ax.set_ylim(flim)\n",
    "    # add line of \n",
    "    ax.plot(flim, flim, c='k')\n",
    "    # set labels\n",
    "    ax.set_ylabel('force by GAP / (eV/Å)')\n",
    "    ax.set_xlabel('force by EMT / (eV/Å)')\n",
    "    #set title\n",
    "    ax.set_title(title)\n",
    "    # add text about RMSE\n",
    "    _rms = rms_dict(in_force, out_force)\n",
    "    rmse_text = 'RMSE:\\n' + str(np.round(_rms['rmse'], 3)) + ' +- ' + str(np.round(_rms['std'], 3)) + 'eV/Å'\n",
    "    ax.text(0.9, 0.1, rmse_text, transform=ax.transAxes, fontsize='large', horizontalalignment='right', \n",
    "            verticalalignment='bottom')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-08T08:53:58.018401Z",
     "start_time": "2018-10-08T08:53:50.892329Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax_list = plt.subplots(nrows=3, ncols=2, gridspec_kw={'hspace': 0.3})\n",
    "fig.set_size_inches(15, 20)\n",
    "ax_list = ax_list.flat[:]\n",
    "\n",
    "energy_plot('train.xyz', 'quip_train.xyz', ax_list[0], 'Energy on training data')\n",
    "energy_plot('validate.xyz', 'quip_validate.xyz', ax_list[1], 'Energy on validation data')\n",
    "force_plot('train.xyz', 'quip_train.xyz', ax_list[2], 'H', 'Force on training data - H')\n",
    "force_plot('train.xyz', 'quip_train.xyz', ax_list[3], 'O', 'Force on training data - O')\n",
    "force_plot('validate.xyz', 'quip_validate.xyz', ax_list[4], 'H', 'Force on validation data - H')\n",
    "force_plot('validate.xyz', 'quip_validate.xyz', ax_list[5], 'O',  'Force on validation data - O')\n",
    "\n",
    "# if you wanted to have the same limits on the firce plots\n",
    "#for ax in ax_list[2:]:\n",
    "#    flim = (-20, 20)\n",
    "#    ax.set_xlim(flim)\n",
    "#    ax.set_ylim(flim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T12:48:02.357449Z",
     "start_time": "2018-10-07T12:48:02.347940Z"
    }
   },
   "source": [
    "## train our GAP_3b model from the command line\n",
    "\n",
    "Let's add three ody terms to the fit, which will hopefully improve it. We will be using the desciprtors distance_2b and angle_3b.\n",
    "\n",
    "**angle_3b**\n",
    "- `theta_fac=0.5` this takes the input data and determines the width from that; useful here, because the dimensions of the descriptor are different\n",
    "- `n_sparse=50` higher dimensional space, more sparse points\n",
    "\n",
    "## both training and quip takes significantly more time than the last one!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T17:12:12.409600Z",
     "start_time": "2018-10-07T17:11:09.080521Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "libAtoms::Hello World: 07/10/2018   18:11:09\n",
      "libAtoms::Hello World: git version  https://github.com/libAtoms/QUIP.git,531330f-dirty\n",
      "libAtoms::Hello World: QUIP_ARCH    linux_x86_64_gfortran_openmp\n",
      "libAtoms::Hello World: compiled on  Jul  2 2018 at 21:44:13\n",
      "libAtoms::Hello World: OpenMP parallelisation with 8 threads\n",
      "WARNING: libAtoms::Hello World: environment variable OMP_STACKSIZE not set explicitly. The default value - system and compiler dependent - may be too small for some applications.\n",
      "libAtoms::Hello World: Random Seed = 65469770\n",
      "libAtoms::Hello World: global verbosity = 0\n",
      "\n",
      "Calls to system_timer will do nothing by default\n",
      "\n",
      "\n",
      "================================ Input parameters ==============================\n",
      "\n",
      "at_file = train.xyz\n",
      "gap = \"distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform add_species=T n_sparse=10 : angle_3b cutoff=3.5 covariance_type=ard_se delta=0.5 theta_fac=0.5 add_species=T n_sparse=30 sparse_method=uniform\"\n",
      "e0 = H:3.21:O:4.6\n",
      "e0_offset = 0.0\n",
      "do_e0_avg = T\n",
      "default_sigma = \"0.008 0.04 0 0\"\n",
      "sparse_jitter = 1.0e-10\n",
      "hessian_delta = 1.0e-2\n",
      "core_param_file = quip_params.xml\n",
      "core_ip_args =\n",
      "energy_parameter_name = energy\n",
      "force_parameter_name = forces\n",
      "virial_parameter_name = virial\n",
      "hessian_parameter_name = hessian\n",
      "config_type_parameter_name = config_type\n",
      "sigma_parameter_name = sigma\n",
      "config_type_sigma =\n",
      "sigma_per_atom = T\n",
      "do_copy_at_file = F\n",
      "sparse_separate_file = T\n",
      "sparse_use_actual_gpcov = F\n",
      "gp_file = GAP_3b.xml\n",
      "verbosity = NORMAL\n",
      "rnd_seed = -1\n",
      "do_ip_timing = F\n",
      "template_file = template.xyz\n",
      "\n",
      "========================================  ======================================\n",
      "\n",
      "\n",
      "============== Gaussian Approximation Potentials - Database fitting ============\n",
      "\n",
      "\n",
      "Initial parsing of command line arguments finished.\n",
      "Found 2 GAPs.\n",
      "Descriptors have been parsed\n",
      "XYZ file read\n",
      "Old GAP: {distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform add_species=T n_sparse=10}\n",
      "New GAP: {distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform               n_sparse=10 Z1=8 Z2=8}\n",
      "New GAP: {distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform               n_sparse=10 Z1=8 Z2=1}\n",
      "New GAP: {distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform               n_sparse=10 Z1=1 Z2=1}\n",
      "Old GAP: { angle_3b cutoff=3.5 covariance_type=ard_se delta=0.5 theta_fac=0.5 add_species=T n_sparse=30 sparse_method=uniform}\n",
      "New GAP: { angle_3b cutoff=3.5 covariance_type=ard_se delta=0.5 theta_fac=0.5               n_sparse=30 sparse_method=uniform Z=8 Z1=8 Z2=8}\n",
      "New GAP: { angle_3b cutoff=3.5 covariance_type=ard_se delta=0.5 theta_fac=0.5               n_sparse=30 sparse_method=uniform Z=8 Z1=8 Z2=1}\n",
      "New GAP: { angle_3b cutoff=3.5 covariance_type=ard_se delta=0.5 theta_fac=0.5               n_sparse=30 sparse_method=uniform Z=8 Z1=1 Z2=1}\n",
      "New GAP: { angle_3b cutoff=3.5 covariance_type=ard_se delta=0.5 theta_fac=0.5               n_sparse=30 sparse_method=uniform Z=1 Z1=8 Z2=8}\n",
      "New GAP: { angle_3b cutoff=3.5 covariance_type=ard_se delta=0.5 theta_fac=0.5               n_sparse=30 sparse_method=uniform Z=1 Z1=8 Z2=1}\n",
      "New GAP: { angle_3b cutoff=3.5 covariance_type=ard_se delta=0.5 theta_fac=0.5               n_sparse=30 sparse_method=uniform Z=1 Z1=1 Z2=1}\n",
      "Multispecies support added where requested\n",
      "Number of target energies (property name: energy) found: 60\n",
      "Number of target forces (property name: forces) found: 14580\n",
      "Number of target virials (property name: virial) found: 0\n",
      "Number of target Hessian eigenvalues (property name: hessian) found: 0\n",
      "Cartesian coordinates transformed to descriptors\n",
      "Started sparse covariance matrix calculation of coordinate 1\n",
      "\n",
      "Finished sparse covariance matrix calculation of coordinate 1\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate1_sparse  done in .89205500000000093 cpu secs, .11343244090676308 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate1         done in .89205500000000093 cpu secs, .11355240270495415 wall clock secs.\n",
      "Started sparse covariance matrix calculation of coordinate 2\n",
      "\n",
      "Finished sparse covariance matrix calculation of coordinate 2\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate2_sparse  done in 3.6442280000000018 cpu secs, .45859701186418533 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate2         done in 3.6442280000000018 cpu secs, .45871092379093170 wall clock secs.\n",
      "Started sparse covariance matrix calculation of coordinate 3\n",
      "\n",
      "Finished sparse covariance matrix calculation of coordinate 3\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate3_sparse  done in 3.4482159999999986 cpu secs, .43381043337285519 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate3         done in 3.4482159999999986 cpu secs, .43392318300902843 wall clock secs.\n",
      "Started sparse covariance matrix calculation of coordinate 4\n",
      "\n",
      "Finished sparse covariance matrix calculation of coordinate 4\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate4_sparse  done in 9.9606220000000008 cpu secs, 1.2572825513780117 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate4         done in 9.9606220000000008 cpu secs, 1.2574077267199755 wall clock secs.\n",
      "Started sparse covariance matrix calculation of coordinate 5\n",
      "\n",
      "Finished sparse covariance matrix calculation of coordinate 5\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate5_sparse  done in 50.367147000000003 cpu secs, 6.3618728630244732 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate5         done in 50.367147000000003 cpu secs, 6.3619935847818851 wall clock secs.\n",
      "Started sparse covariance matrix calculation of coordinate 6\n",
      "\n",
      "Finished sparse covariance matrix calculation of coordinate 6\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate6_sparse  done in 55.067442000000000 cpu secs, 6.9305843152105808 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate6         done in 55.067442000000000 cpu secs, 6.9306991323828697 wall clock secs.\n",
      "Started sparse covariance matrix calculation of coordinate 7\n",
      "\n",
      "Finished sparse covariance matrix calculation of coordinate 7\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate7_sparse  done in 25.993623999999983 cpu secs, 3.2758836857974529 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate7         done in 25.993623999999983 cpu secs, 3.2760034948587418 wall clock secs.\n",
      "Started sparse covariance matrix calculation of coordinate 8\n",
      "\n",
      "Finished sparse covariance matrix calculation of coordinate 8\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate8_sparse  done in 112.66704200000001 cpu secs, 14.186459138989449 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate8         done in 112.67104200000003 cpu secs, 14.186579804867506 wall clock secs.\n",
      "Started sparse covariance matrix calculation of coordinate 9\n",
      "\n",
      "Finished sparse covariance matrix calculation of coordinate 9\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate9_sparse  done in 103.17444799999998 cpu secs, 12.990166073665023 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_Coordinate9         done in 103.17444799999998 cpu secs, 12.990285659208894 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_LinearAlgebra       done in .44402700000000550 cpu secs, .12523609958589077 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse_FunctionValues      done in .00000000000000000E+000 cpu secs, .13986043632030487E-003 wall clock secs.\n",
      "TIMER: gpFull_covarianceMatrix_sparse                     done in 365.71485500000000 cpu secs, 46.178485954180360 wall clock secs.\n",
      "TIMER: GP sparsify                                        done in 368.54703300000000 cpu secs, 47.065343659371138 wall clock secs.\n",
      "\n",
      "libAtoms::Finalise: 07/10/2018   18:12:12\n",
      "libAtoms::Finalise: Bye-Bye!\n"
     ]
    }
   ],
   "source": [
    "! teach_sparse e0={H:3.21:O:4.6} energy_parameter_name=energy force_parameter_name=forces do_copy_at_file=F sparse_separate_file=T gp_file=GAP_3b.xml at_file=train.xyz default_sigma={0.008 0.04 0 0} gap={distance_2b cutoff=4.0 covariance_type=ard_se delta=0.5 theta_uniform=1.0 sparse_method=uniform add_species=T n_sparse=10 : angle_3b cutoff=3.5 covariance_type=ard_se delta=0.5 theta_fac=0.5 add_species=T n_sparse=30 sparse_method=uniform}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T12:56:59.802090Z",
     "start_time": "2018-10-07T12:56:58.506422Z"
    }
   },
   "source": [
    "## use the potential with QUIP on trani.xyz and validate.xyz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-07T17:12:47.633785Z",
     "start_time": "2018-10-07T17:12:12.412800Z"
    }
   },
   "outputs": [],
   "source": [
    "# calculate train.xyz\n",
    "\n",
    "! quip E=T F=T atoms_filename=train.xyz param_filename=GAP_3b.xml | grep AT | sed 's/AT//' >> quip_3b_train.xyz\n",
    "! quip E=T F=T atoms_filename=validate.xyz param_filename=GAP_3b.xml | grep AT | sed 's/AT//' >> quip_3b_validate.xyz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## look at the outputs - clear improvement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-08T08:54:43.555206Z",
     "start_time": "2018-10-08T08:54:36.324200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax_list = plt.subplots(nrows=3, ncols=2, gridspec_kw={'hspace': 0.3})\n",
    "fig.set_size_inches(15, 20)\n",
    "ax_list = ax_list.flat[:]\n",
    "\n",
    "energy_plot('train.xyz', 'quip_3b_train.xyz', ax_list[0], 'Energy on training data')\n",
    "energy_plot('validate.xyz', 'quip_3b_validate.xyz', ax_list[1], 'Energy on validation data')\n",
    "force_plot('train.xyz', 'quip_3b_train.xyz', ax_list[2], 'H', 'Force on training data - H')\n",
    "force_plot('train.xyz', 'quip_3b_train.xyz', ax_list[3], 'O', 'Force on training data - O')\n",
    "force_plot('validate.xyz', 'quip_3b_validate.xyz', ax_list[4], 'H', 'Force on validation data - H')\n",
    "force_plot('validate.xyz', 'quip_3b_validate.xyz', ax_list[5], 'O',  'Force on validation data - O')\n",
    "\n",
    "# if you wanted to have the same limits on the firce plots\n",
    "#for ax in ax_list[2:]:\n",
    "#    flim = (-20, 20)\n",
    "#    ax.set_xlim(flim)\n",
    "#    ax.set_ylim(flim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}