{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pymatgen.matproj.rest import MPRester\n",
    "from pymatgen.phasediagram.pdmaker import PhaseDiagram\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating the phase diagram\n",
    "\n",
    "To generate a phase diagram, we obtain entries from the Materials Project and call the PhaseDiagram class in pymatgen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#This initializes the REST adaptor. You may need to put your own API key in as an arg.\n",
    "a = MPRester()\n",
    "\n",
    "#Entries are the basic unit for thermodynamic and other analyses in pymatgen.\n",
    "#This gets all entries belonging to the Ca-C-O system.\n",
    "entries = a.get_entries_in_chemsys(['Ca', 'C', 'O'])\n",
    "\n",
    "#With entries, you can do many sophisticated analyses, like creating phase diagrams.\n",
    "pd = PhaseDiagram(entries)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the phase diagram\n",
    "\n",
    "To plot a phase diagram, we send our phase diagram object into the PDPlotter class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jPLnpU8RfKfiIuMDSpUv55sK1BJvNRmxsrMfvr9FlrvyrVi1o3FijOSKuoHt8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\nOPFhjDHGWJHBiQ9jjDHGioz/A1WyZ4Tw1wpmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10abd2290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pymatgen.phasediagram.plotter import PDPlotter\n",
    "#Let's show all phases, including unstable ones\n",
    "plotter = PDPlotter(pd, show_unstable=True)\n",
    "plotter.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating energy above hull and other phase equilibria properties\n",
    "\n",
    "To perform more sophisticated analyses, use the PDAnalyzer object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pymatgen.phasediagram.pdanalyzer import PDAnalyzer\n",
    "a = PDAnalyzer(pd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Materials ID Composition     Ehull                           Decomposition\n",
      "0         mp-21          Ca  0.013583                                1.00 Ca2\n",
      "1      mp-10683          Ca  0.396161                                1.00 Ca2\n",
      "2        mp-166          Ca  0.013330                                1.00 Ca2\n",
      "3        mp-132          Ca  0.000000                                1.00 Ca2\n",
      "4         mp-45          Ca  0.000508                                1.00 Ca2\n",
      "5        mp-169           C  0.004413                                1.00 C16\n",
      "6     mp-569416           C  0.028801                                1.00 C16\n",
      "7         mp-66           C  0.135631                                1.00 C16\n",
      "8         mp-47           C  0.160479                                1.00 C16\n",
      "9         mp-48           C  0.002697                                1.00 C16\n",
      "10    mp-611426           C  0.145308                                1.00 C16\n",
      "11    mp-616440           C  0.140564                                1.00 C16\n",
      "12    mp-611448           C  0.142026                                1.00 C16\n",
      "13    mp-569567           C  0.174406                                1.00 C16\n",
      "14    mp-569517           C  0.145186                                1.00 C16\n",
      "15        mp-24           C  0.832340                                1.00 C16\n",
      "16    mp-569304           C  0.029940                                1.00 C16\n",
      "17    mp-579909           C  0.944071                                1.00 C16\n",
      "18    mp-624889           C  1.255021                                1.00 C16\n",
      "19    mp-568410           C  0.506378                                1.00 C16\n",
      "20    mp-570002           C  0.762539                                1.00 C16\n",
      "21    mp-630227           C  0.393490                                1.00 C16\n",
      "22    mp-667273           C  0.388760                                1.00 C16\n",
      "23    mp-680372           C  0.407085                                1.00 C16\n",
      "24    mp-683919           C  0.348592                                1.00 C16\n",
      "25    mp-937760           C  0.003670                                1.00 C16\n",
      "26    mp-568806           C  0.006175                                1.00 C16\n",
      "27    mp-568286           C  0.000000                                1.00 C16\n",
      "28    mp-568028           C  0.528945                                1.00 C16\n",
      "29    mp-606949           C  0.003575                                1.00 C16\n",
      "..          ...         ...       ...                                     ...\n",
      "33    mp-611836          O2  0.013366                                 1.00 O8\n",
      "34    mp-560602          O2  0.414823                                 1.00 O8\n",
      "35    mp-607540          O2  0.112355                                 1.00 O8\n",
      "36    mp-570697        CaC2  0.762125                     0.67 C16 + 0.33 Ca2\n",
      "37      mp-2482        CaC2  0.022313                     0.67 C16 + 0.33 Ca2\n",
      "38       mp-917        CaC2  0.014315                     0.67 C16 + 0.33 Ca2\n",
      "39      mp-1575        CaC2  0.015081                     0.67 C16 + 0.33 Ca2\n",
      "40    mp-684668        CaC2  0.068324                     0.67 C16 + 0.33 Ca2\n",
      "41    mp-642822        CaC2  0.024576                     0.67 C16 + 0.33 Ca2\n",
      "42    mp-545512         CaO  0.065085                             1.00 Ca1 O1\n",
      "43      mp-2605         CaO  0.000000                             1.00 Ca1 O1\n",
      "44    mp-634859        CaO2  0.007227                   0.67 Ca1 O1 + 0.33 O8\n",
      "45     mp-20066         CO2  0.000000                              1.00 C4 O8\n",
      "46    mp-556660          CO  0.692016                   0.25 C16 + 0.75 C4 O8\n",
      "47     mp-11875          CO  0.668938                   0.25 C16 + 0.75 C4 O8\n",
      "48    mp-561848        C3O2  0.513353                   0.40 C16 + 0.60 C4 O8\n",
      "49    mp-556034         CO2  0.028775                              1.00 C4 O8\n",
      "50     mp-11725         CO2  0.025395                              1.00 C4 O8\n",
      "51    mp-644607         CO2  0.028281                              1.00 C4 O8\n",
      "52      mp-3205       CaCO3  0.169799                          1.00 Ca2 C2 O6\n",
      "53      mp-4626       CaCO3  0.024281                          1.00 Ca2 C2 O6\n",
      "54    mp-558543    Ca(CO2)2  0.046302  0.07 C16 + 0.71 Ca2 C2 O6 + 0.21 C4 O8\n",
      "55      mp-3953       CaCO3  0.000000                          1.00 Ca2 C2 O6\n",
      "56    mp-561412       CaCO3  0.007359                          1.00 Ca2 C2 O6\n",
      "57    mp-560265       CaCO3  0.008073                          1.00 Ca2 C2 O6\n",
      "58    mp-556235       CaCO3  0.002015                          1.00 Ca2 C2 O6\n",
      "59    mp-553939       CaCO3  0.013584                          1.00 Ca2 C2 O6\n",
      "60    mp-641635       CaCO3  1.368847                          1.00 Ca2 C2 O6\n",
      "61    mp-548403       CaCO3  0.312190                          1.00 Ca2 C2 O6\n",
      "62    mp-696740       CaCO3  2.017768                          1.00 Ca2 C2 O6\n",
      "\n",
      "[63 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "import collections\n",
    "\n",
    "data = collections.defaultdict(list)\n",
    "for e in entries:\n",
    "    decomp, ehull = a.get_decomp_and_e_above_hull(e)\n",
    "    data[\"Materials ID\"].append(e.entry_id)\n",
    "    data[\"Composition\"].append(e.composition.reduced_formula)\n",
    "    data[\"Ehull\"].append(ehull)    \n",
    "    data[\"Decomposition\"].append(\" + \".join([\"%.2f %s\" % (v, k.composition.formula) for k, v in decomp.items()]))\n",
    "\n",
    "from pandas import DataFrame\n",
    "df = DataFrame(data, columns=[\"Materials ID\", \"Composition\", \"Ehull\", \"Decomposition\"])\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}