{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# useful to autoreload the module without restarting the kernel\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mppi import Parsers as P\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial of the PwParser class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial describes the usage of the class PwParser of mppi used to extract information for the XML output file data-file-schema produced\n",
    "by pw."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parse of a output file of Silicon "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Follow the tutorial for the QeCalculator to produce the xml file used by the PwParser.__\n",
    "\n",
    "The class is initialized by specifying the name of the xml file including its relative path "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse file : QeCalculator_test/ecut_50.save/data-file-schema.xml\n"
     ]
    }
   ],
   "source": [
    "results = P.PwParser('QeCalculator_test/ecut_50.save/data-file-schema.xml')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the object is initialized several attributes are set, for instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hartree atomic units'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "{'Si': ['2.808600000000000e1', 'Si.pbe-mt_fhi.UPF']}\n",
      "[['Si', [-1.2875, 1.2875, 1.2875]], ['Si', [1.2875, -1.2875, -1.2875]]]\n",
      "4\n",
      "32\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "print(results.natoms)\n",
    "print(results.atomic_species)\n",
    "print(results.atomic_positions)\n",
    "print(results.nbands)\n",
    "print(results.nkpoints)\n",
    "print(results.spin_degen)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can extract some lattice-related properties. For instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.3\n",
      "[-5.15  0.    5.15] [0.   5.15 5.15] [-5.15  5.15  0.  ]\n",
      "273.1817500000001\n"
     ]
    }
   ],
   "source": [
    "print(results.alat) # the lattice parameter in a.u.\n",
    "lat = results.lattice # the array with the lattice vectors\n",
    "a1,a2,a3 = lat\n",
    "print(a1,a2,a3)\n",
    "print(results.eval_lattice_volume()) # lattice volume in a.u."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check that, since the lattice is fcc the volume is $a_{lat}^3/4$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "273.1817500000001"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.alat**3/4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we need the lattice vectors in units of the lattice constant the `get_lattice` method can be used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.5,  0. ,  0.5],\n",
       "       [ 0. ,  0.5,  0.5],\n",
       "       [-0.5,  0.5,  0. ]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.get_lattice(rescale=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also compute the vectors of the reciprocal lattice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1. -1.  1.] [1. 1. 1.] [-1.  1. -1.]\n"
     ]
    }
   ],
   "source": [
    "rec_lat = results.get_reciprocal_lattice(rescale=True)\n",
    "b1,b2,b3 = rec_lat\n",
    "print(b1,b2,b3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the tools of the `ParsersUtils` module to compute the lattice volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.9999999999999987"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P.ParsersUtils.eval_lattice_volume(rec_lat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9080043357303277"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(2*np.pi)**3/results.eval_lattice_volume()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We check that if rescale=False, the volume of the reciprocal lattice is $(2\\pi)^3/vol_{lat}$. Instead, if the option rescale = True\n",
    "is chosen the volume of the reciprocal lattice is 4, as expected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can access to the lattice symmetries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of symmetries 48\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  1.],\n",
       "       [-1., -1., -1.],\n",
       "       [ 1.,  0.,  0.]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "syms = results.syms\n",
    "print('number of symmetries',len(syms))\n",
    "syms[13]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can print the ks energies and weight for each kpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0.] [-0.21239769  0.2248015   0.2248019   0.2248019 ] [0.00925926]\n",
      "[-0.16666667  0.16666667 -0.16666667] [-0.19923027  0.13749243  0.20843608  0.2084364 ] [0.05555556]\n",
      "[-0.33333333  0.33333333 -0.33333333] [-0.16224325  0.02984914  0.18835302  0.18835333] [0.05555556]\n",
      "[ 0.5 -0.5  0.5] [-0.12761605 -0.0295912   0.1810951   0.18109542] [0.02777778]\n",
      "[0.         0.33333333 0.        ] [-0.19474764  0.14929523  0.18151447  0.18151464] [0.05555556]\n",
      "[-0.16666667  0.5        -0.16666667] [-0.16503928  0.06221564  0.15212893  0.16133873] [0.11111111]\n",
      "[ 0.66666667 -0.33333333  0.66666667] [-0.12174836 -0.01658335  0.12355478  0.16017609] [0.11111111]\n",
      "[ 0.5        -0.16666667  0.5       ] [-0.1357136   0.00516689  0.11382729  0.17756576] [0.11111111]\n",
      "[3.33333333e-01 2.77555756e-17 3.33333333e-01] [-0.17779262  0.08837439  0.13721986  0.20417699] [0.05555556]\n",
      "[0.         0.66666667 0.        ] [-0.14298339  0.04081044  0.13688804  0.13688817] [0.05555556]\n",
      "[ 0.83333333 -0.16666667  0.83333333] [-0.10062076 -0.01579602  0.0928995   0.13189133] [0.11111111]\n",
      "[ 6.66666667e-01 -5.55111512e-17  6.66666667e-01] [-0.09359008 -0.02315423  0.06458065  0.14797813] [0.05555556]\n",
      "[ 0. -1.  0.] [-0.06137332 -0.06137309  0.12102867  0.12102879] [0.02777778]\n",
      "[ 0.66666667 -0.33333333  1.        ] [-0.12899013  0.01785089  0.09513332  0.14145473] [0.11111111]\n",
      "[ 0.5        -0.16666667  0.83333333] [-0.09323708 -0.02570101  0.07904937  0.12455705] [0.11111111]\n",
      "[-0.33333333 -1.          0.        ] [-0.05709944 -0.05709919  0.09247379  0.09247395] [0.11111111]\n",
      "[-0.16666667  0.16666667  0.16666667] [-0.19923026  0.13749218  0.20843636  0.20843636] [0.01851852]\n",
      "[-0.33333333  0.33333333  0.33333333] [-0.16224319  0.02984888  0.18835326  0.18835326] [0.01851852]\n",
      "[ 0.5 -0.5 -0.5] [-0.12761587 -0.02959158  0.18109533  0.18109533] [0.00925926]\n",
      "[ 0.16666667 -0.16666667  0.5       ] [-0.16503929  0.06221573  0.15212865  0.16133889] [0.05555556]\n",
      "[-0.16666667  0.16666667  0.5       ] [-0.16503926  0.06221543  0.15212897  0.16133886] [0.05555556]\n",
      "[-0.66666667  0.66666667 -0.33333333] [-0.12174836 -0.01658332  0.12355456  0.16017627] [0.05555556]\n",
      "[ 0.66666667 -0.66666667 -0.33333333] [-0.12174821 -0.01658369  0.12355481  0.16017623] [0.05555556]\n",
      "[-0.5         0.5        -0.16666667] [-0.13571357  0.00516682  0.11382715  0.17756596] [0.05555556]\n",
      "[ 0.5        -0.5        -0.16666667] [-0.1357135   0.00516657  0.11382734  0.17756593] [0.05555556]\n",
      "[-0.33333333  0.33333333  0.        ] [-0.1777926   0.08837415  0.13721989  0.20417721] [0.05555556]\n",
      "[-0.83333333  0.83333333 -0.16666667] [-0.10062077 -0.01579597  0.09289929  0.13189146] [0.05555556]\n",
      "[ 0.83333333 -0.83333333 -0.16666667] [-0.10062068 -0.01579618  0.09289944  0.13189145] [0.05555556]\n",
      "[-0.66666667  0.66666667  0.        ] [-0.09358994 -0.02315446  0.06458057  0.14797828] [0.05555556]\n",
      "[-0.66666667  1.         -0.33333333] [-0.12899018  0.0178511   0.09513332  0.14145455] [0.11111111]\n",
      "[0.5        0.83333333 0.16666667] [-0.09323712 -0.02570097  0.07904955  0.12455686] [0.05555556]\n",
      "[ 0.5        -0.83333333 -0.16666667] [-0.09323691 -0.02570134  0.07904948  0.12455708] [0.05555556]\n"
     ]
    }
   ],
   "source": [
    "for k,e,w in zip(results.kpoints,results.evals,results.weights):\n",
    "    print(k,e,w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can convert direct lattice quantities, like the atomic position, in crystal coordinates.\n",
    "For instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.125, -0.125, -0.125])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atom1 = np.array([0.125,-0.125,-0.125]) # in units of alat\n",
    "P.ParsersUtils.convert_to_crystal(results.get_lattice(rescale=True),atom1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The k points can be expressed in crystal coordinates, _i.e._ in terms of the basis vector of the reciprocal lattice.\n",
    "\n",
    "For instance we compute the first 10 k points in these coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([0., 0., 0.]),\n",
       " array([0.        , 0.        , 0.16666667]),\n",
       " array([0.        , 0.        , 0.33333333]),\n",
       " array([ 0. ,  0. , -0.5]),\n",
       " array([0.        , 0.16666667, 0.16666667]),\n",
       " array([0.        , 0.16666667, 0.33333333]),\n",
       " array([ 0.        ,  0.16666667, -0.5       ]),\n",
       " array([ 0.        ,  0.16666667, -0.33333333]),\n",
       " array([ 0.        ,  0.16666667, -0.16666667]),\n",
       " array([0.        , 0.33333333, 0.33333333])]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rec_lat = results.get_reciprocal_lattice(rescale=True)\n",
    "kpoints_crystal = []\n",
    "for k in results.kpoints[:10]:\n",
    "    kpoints_crystal.append(P.ParsersUtils.convert_to_crystal(rec_lat,k))\n",
    "kpoints_crystal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check in the log of QuantumESPRESSO that these are the k points in crystal coordinates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also perform the inverse procedure, for instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0.]\n",
      "[-0.16666667  0.16666667 -0.16666667]\n",
      "[-0.33333333  0.33333333 -0.33333333]\n",
      "[ 0.5 -0.5  0.5]\n",
      "[0.         0.33333333 0.        ]\n",
      "[-0.16666667  0.5        -0.16666667]\n",
      "[ 0.66666667 -0.33333333  0.66666667]\n",
      "[ 0.5        -0.16666667  0.5       ]\n",
      "[0.33333333 0.         0.33333333]\n",
      "[0.         0.66666667 0.        ]\n"
     ]
    }
   ],
   "source": [
    "for k in kpoints_crystal:\n",
    "    print(P.ParsersUtils.convert_to_cartesian(rec_lat,k))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are also several get methods, for instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.117171271888534"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.get_fermi()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The method get_evals  return an array with the ks energies for each kpoint. The energies are expressed in eV and the \n",
    "energy of VBM is used as reference. A gap, both direct or indirect, can be set. In this case the energies of the empty ks states is\n",
    "shifted. This procedure __does not__ update the occupation levels of the empty states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are no empty bands. No energy shift has been applied\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-1.18968067e+01, -1.08435998e-05, -6.32798614e-09,  0.00000000e+00])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.get_evals(set_gap=1.2)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are no empty states. Gap cannot be computed.\n"
     ]
    }
   ],
   "source": [
    "results.get_gap()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We test the PwParser using a nscf output file, in this case empty bands are present and the gap can be computed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse file : QeCalculator_test/bands_8.save/data-file-schema.xml\n"
     ]
    }
   ],
   "source": [
    "result_nscf = P.PwParser('QeCalculator_test/bands_8.save/data-file-schema.xml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_nscf.occupations[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Indirect gap system\n",
      "===================\n",
      "Gap : 0.7360305350920111 eV\n",
      "Direct gap : 2.5651597869383274 eV\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'gap': 0.7360305350920111,\n",
       " 'direct_gap': 2.5651597869383274,\n",
       " 'position_cbm': 12,\n",
       " 'positon_vbm': 0}"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_nscf.get_gap()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Apply a scissor of 0.43484021306167264 eV\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-1.18968473e+01, -3.77496830e-06, -2.75945489e-10,  0.00000000e+00,\n",
       "        3.00000000e+00,  3.00000000e+00,  3.00000201e+00,  3.58149154e+00])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_nscf.get_evals(set_direct_gap=3.0)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we see that in this case the energy of the 5-th states has been shift to 3 eV to implement the set_direct_gap requirement. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also possible to add an explicit scissor to shift the empty bands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Apply a scissor of 1.0 eV\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-1.18968473e+01, -3.77496830e-06, -2.75945489e-10,  0.00000000e+00,\n",
       "        3.56515979e+00,  3.56515979e+00,  3.56516180e+00,  4.14665133e+00])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_nscf.get_evals(set_scissor=1.)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We compute the transition energies from the full to empty bands. Here we show the transition\n",
    "for the first k point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14.89684725, 14.89684725, 14.89684927, 15.47833879,  3.00000377,\n",
       "        3.00000377,  3.00000579,  3.58149531,  3.        ,  3.        ,\n",
       "        3.00000201,  3.58149154,  3.        ,  3.        ,  3.00000201,\n",
       "        3.58149154])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_nscf.get_transitions(set_direct_gap=3.0)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parse of a Graphene output file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Run the Analysis_BandStructure notebook to produce the xml file used by the PwParser.__\n",
    "\n",
    "We parse a graphene output file to test if systems without a gap are correctly parsed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse file : Pw_bands/graphene_nscf.save/data-file-schema.xml\n"
     ]
    }
   ],
   "source": [
    "results_gra = P.PwParser('Pw_bands/graphene_nscf.save/data-file-schema.xml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'smearing'"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_gra.occupations_kind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "       4.22181265e-29, 6.89778699e-37, 1.76721673e-41, 1.28104982e-57])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_gra.occupations[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1. , 1. , 1. , 0.5, 0.5, 0. , 0. , 0. ])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_gra.occupations[18] #the position of K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Direct gap system\n",
      "=================\n",
      "Gap : 2.715685454290906e-10 eV\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'gap': 2.715685454290906e-10,\n",
       " 'direct_gap': 2.715685454290906e-10,\n",
       " 'position_cbm': 18,\n",
       " 'positon_vbm': 18}"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_gra.get_gap()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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