{
 "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 InputFiles as I, Calculators as C, Utilities as U, Parsers as P\n",
    "from mppi.Calculators import Tools\n",
    "import matplotlib.pyplot as plt\n",
    "## use ipyml for interactive plots\n",
    "%matplotlib inline  \n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "omp = 1\n",
    "mpi = 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial of the YamboParser class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial describes the usage of the YamboParser class. The parser contains instances \n",
    "of several classes, namely : \n",
    "* the YamboOutputParser that deals with the o- file(s) produced by a yambo computation\n",
    "* the YamboDipolesParser that parse the dipoles database created by Yambo\n",
    "* the YamboDftParser that extract lattice and electronic information (ad the dft level) from the\n",
    "  ns.db1 database written by Yambo in the SAVE folder\n",
    "\n",
    "The class is designed to deal with the output of the run method of YamboCalculator.\n",
    "\n",
    "Here we present a first example by performing a gw calculation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialize a Yambo calculator with scheduler direct\n"
     ]
    }
   ],
   "source": [
    "rr = C.RunRules(mpi=mpi,omp_num_threads=omp)\n",
    "code = C.YamboCalculator(rr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_dir = 'QeCalculator_test/outdir_nscf/bands_8.save'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SAVE folder YamboCalculator_test/SAVE already present. No operations performed.\n"
     ]
    }
   ],
   "source": [
    "run_dir = 'YamboCalculator_test'\n",
    "Tools.init_yambo_dir(yambo_dir=run_dir,input_dir=input_dir,overwrite_if_found=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "inp = I.YamboInput(args='yambo -d -k hartee -g n -p p -V qp',folder=run_dir)\n",
    "inp.set_kRange(1,2)\n",
    "#inp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skip the run of qp_test1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'output': {'qp': 'YamboCalculator_test/qp_test1/o-qp_test1.qp'},\n",
       " 'report': 'YamboCalculator_test/qp_test1/r-qp_test1_HF_and_locXC_gw0_dyson_em1d_ppa_el_el_corr',\n",
       " 'dft': 'YamboCalculator_test/SAVE/ns.db1',\n",
       " 'QP': 'YamboCalculator_test/qp_test1/ndb.QP',\n",
       " 'HF_and_locXC': 'YamboCalculator_test/qp_test1/ndb.HF_and_locXC',\n",
       " 'dipoles': 'YamboCalculator_test/qp_test1/ndb.dipoles',\n",
       " 'pp': 'YamboCalculator_test/qp_test1/ndb.pp'}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = code.run(input = inp, run_dir = run_dir, name='qp_test1')\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The calculator contain the references to the o- files and database with the output data.\n",
    "The parser extract the data from the files, as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m \u001b[0mP\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mYamboParser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextendOut\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m     \n",
       "Class that perform the parsing starting from the results :py:class:`dict` built\n",
       "by the :class:`YamboCalculator` class. In the actual implementation of the class the\n",
       "parser is able to deal with the o- files, the dipoles database, the ``ndb.RT_G_PAR``\n",
       "and the ``ns.db1`` database written in the SAVE folder.\n",
       "\n",
       "Args:\n",
       "    results (:py:class:`dict`): The dictionary of the results built by the\n",
       "        :class:`YamboCalculator` class\n",
       "    verbose (:py:class:`boolean`) : Determine the amount of information provided on terminal\n",
       "    extendOut (:py:class:`boolean`) : Determine which dictionary is used as reference for the\n",
       "                    names of the variables in the :class:`YamboOutputParser`\n",
       "\n",
       "Attributes:\n",
       "    data (:class:`YamboOutputParser`) : contains the instance of the :class:`YamboOutputParser`\n",
       "        class that manage the parsing of the ``o-* files``\n",
       "    dipoles (:class:`YamboDipolesParser`) : contains the instance of the :class:`YamboDipolesParser`\n",
       "        class that manages the parsing of the ``dipoles`` database\n",
       "    dft (:class:`YamboDftParser`) : contains the instance of the :class:`YamboDftParser` that\n",
       "        manages the parsing of the ``ns.db1`` database\n",
       "    qp (:class:`YamboQPParser`) : contains the instance of the :class:`YamboQPParser` that\n",
       "        manages the parsing of the ``ndb.QP`` database\n",
       "    RTCarriers (:class:`YamboRTCarriersParser`) : contains the instance of the\n",
       "        :class:`YamboRTCarriersParser` that manages the parsing of the `ndb.RT_carriers`\n",
       "        database\n",
       "    RTGreen (:class:`YamboRTGlesserParser`) : contains the instance of the\n",
       "        :class:`YamboRTGlesserParser` that manages the parsing of the `ndb.RT_G_PAR`\n",
       "        database\n",
       "\u001b[0;31mInit docstring:\u001b[0m Initialize the data member of the class.\n",
       "\u001b[0;31mFile:\u001b[0m           ~/Applications/MPPI/mppi/Parsers/YamboParser.py\n",
       "\u001b[0;31mType:\u001b[0m           type\n",
       "\u001b[0;31mSubclasses:\u001b[0m     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "P.YamboParser?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the extendOut option has to chosen in agreement with the one the input, otherwise\n",
    "the parser atribute the name of the variables of the o- files in an erroneous way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse file YamboCalculator_test/qp_test1/o-qp_test1.qp\n",
      "Parse file : YamboCalculator_test/SAVE/ns.db1\n",
      "Parse file : YamboCalculator_test/qp_test1/ndb.QP\n",
      "Parse file : YamboCalculator_test/qp_test1/ndb.dipoles\n",
      "Spin dipoles not found in the ndb.dipoles\n"
     ]
    }
   ],
   "source": [
    "data = P.YamboParser(results,extendOut=False,verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some information on the parsed data can be obtained using the get_info method of the class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "YamboOutputParser variables structure\n",
      "suffix qp with dict_keys(['kpoint', 'band', 'E0', 'EmE0', 'sce0'])\n",
      " \n",
      "YamboDipolesParser variables structure\n",
      "dip_ir shape (32, 4, 4, 3, 2)\n",
      "dip_p shape (32, 4, 4, 3, 2)\n",
      "dip_v shape (32, 4, 4, 3, 2)\n",
      "dip_spin shape (1,)\n",
      " \n",
      "YamboDftParser variables structure\n",
      "number of k points 32\n",
      "number of bands 8\n",
      "spin degeneration 1\n",
      " \n",
      "YamboQPParser variables structure\n",
      "QP_table shape (16, 3)\n",
      "QP_kpts shape (32, 3)\n",
      "QP_E shape (16, 2)\n",
      "QP_Eo shape (16,)\n",
      "QP_Z shape (16, 2)\n",
      " \n"
     ]
    }
   ],
   "source": [
    "data.get_info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data are encapsulated in the attributes of the class, for instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'qp': {'kpoint': array([1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2., 2., 2., 2., 2., 2.]),\n",
       "  'band': array([1., 2., 3., 4., 5., 6., 7., 8., 1., 2., 3., 4., 5., 6., 7., 8.]),\n",
       "  'E0': array([-1.189685e+01, -1.216440e-06,  0.000000e+00,  0.000000e+00,\n",
       "          2.565152e+00,  2.565152e+00,  2.565152e+00,  3.146653e+00,\n",
       "         -1.153855e+01, -2.375836e+00, -4.453420e-01, -4.453410e-01,\n",
       "          2.294498e+00,  3.205499e+00,  3.205499e+00,  5.284825e+00]),\n",
       "  'EmE0': array([-1.564291,  0.722413,  0.741914,  0.741673,  1.86755 ,  1.867259,\n",
       "          1.829724,  2.395583, -1.427122,  0.369035,  0.648066,  0.670303,\n",
       "          1.853613,  1.981288,  1.955341,  2.673125]),\n",
       "  'sce0': array([ 4.57619 ,  2.008809,  2.031459,  2.031144, -2.003587, -2.003874,\n",
       "         -2.046769, -1.926959,  4.600586,  2.457894,  2.028683,  2.054817,\n",
       "         -1.999774, -2.067204, -2.096922, -1.932993])}}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mppi.Parsers.YamboDftParser.YamboDftParser at 0x7f4707cc38e0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mppi.Parsers.YamboDipolesParser.YamboDipolesParser at 0x7f4707cc3250>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dipoles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In what follows we describe the features of the various classes using dedicated examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A YamboParser object can be also instanciated directly using the name of the folders that contain the\n",
    "o-* files and the databases, for instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "outputPath = 'YamboCalculator_test/qp_test1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse file YamboCalculator_test/qp_test1/o-qp_test1.qp\n",
      "Parse file : YamboCalculator_test/SAVE/ns.db1\n",
      "Parse file : YamboCalculator_test/qp_test1/ndb.QP\n",
      "Parse file : YamboCalculator_test/qp_test1/ndb.dipoles\n",
      "Spin dipoles not found in the ndb.dipoles\n"
     ]
    }
   ],
   "source": [
    "data2 = P.YamboParser.from_path(run_dir=run_dir,outputPath=outputPath,verbose=True,extendOut=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "YamboOutputParser variables structure\n",
      "suffix qp with dict_keys(['kpoint', 'band', 'E0', 'EmE0', 'sce0'])\n",
      " \n",
      "YamboDipolesParser variables structure\n",
      "dip_ir shape (32, 4, 4, 3, 2)\n",
      "dip_p shape (32, 4, 4, 3, 2)\n",
      "dip_v shape (32, 4, 4, 3, 2)\n",
      "dip_spin shape (1,)\n",
      " \n",
      "YamboDftParser variables structure\n",
      "number of k points 32\n",
      "number of bands 8\n",
      "spin degeneration 1\n",
      " \n",
      "YamboQPParser variables structure\n",
      "QP_table shape (16, 3)\n",
      "QP_kpts shape (32, 3)\n",
      "QP_E shape (16, 2)\n",
      "QP_Eo shape (16,)\n",
      "QP_Z shape (16, 2)\n",
      " \n"
     ]
    }
   ],
   "source": [
    "data2.get_info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of the YamboOutputParser class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The class is designed to deal with the list of o- files produced by Yambo\n",
    "We present some example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "qp_output = {'qp' : 'YamboParser_test/qp_results/o-qp_test1.qp'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "qp_output_extendOut = {'qp' : 'YamboCalculator_test/qp_test_ExtendOut/o-qp_test_ExtendOut.qp'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "rt_output = {'carriers':'YamboParser_test/rt_results/o-NETime_1000-dephase_0.01-freq_1.55-pol_circular.carriers',\n",
    "             'spin_magnetization':'YamboParser_test/rt_results/o-NETime_1000-dephase_0.01-freq_1.55-pol_circular.spin_magnetization',\n",
    "             'external_field':'YamboParser_test/rt_results/o-NETime_1000-dephase_0.01-freq_1.55-pol_circular.external_field',\n",
    "             'polarization':'YamboParser_test/rt_results/o-NETime_1000-dephase_0.01-freq_1.55-pol_circular.polarization'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the list of files is passed to the parser the extendOut option used to run the computation has to be provided,\n",
    "otherwise the parser could perform a wrong assignement of the names of the variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m \u001b[0mP\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mYamboOutputParser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextendOut\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m     \n",
       "Class that performs the parsing of a Yambo o- file(s). The class ineriths from :py:class:`dict`\n",
       "and the instance of the class is a dictionary with the data. The keys correspond to the extension\n",
       "of the parsed files.\n",
       "\n",
       "Args:\n",
       "    output (:py:class:`list`): Dictionary with the structure of the output of :py:meth:`get_output_files`\n",
       "        of the `YamboCalculator` module.\n",
       "    verbose (:py:class:`boolean`) : Determine the amount of information provided on terminal\n",
       "    extendOut (:py:class:`boolean`) : Determine which dictionary is used as reference for the\n",
       "                    names of the variables\n",
       "\u001b[0;31mInit docstring:\u001b[0m Initialize the data member of the class.\n",
       "\u001b[0;31mFile:\u001b[0m           ~/Applications/MPPI/mppi/Parsers/YamboOutputParser.py\n",
       "\u001b[0;31mType:\u001b[0m           type\n",
       "\u001b[0;31mSubclasses:\u001b[0m     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "P.YamboOutputParser?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse file YamboParser_test/qp_results/o-qp_test1.qp\n"
     ]
    }
   ],
   "source": [
    "results = P.YamboOutputParser(output=qp_output,extendOut=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "results is a dictionary with the structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'qp': {'kpoint': array([1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2., 2., 2., 2., 2., 2.]),\n",
       "  'band': array([1., 2., 3., 4., 5., 6., 7., 8., 1., 2., 3., 4., 5., 6., 7., 8.]),\n",
       "  'E0': array([-1.190291e+01, -1.176000e-05, -1.176000e-05,  0.000000e+00,\n",
       "          2.551225e+00,  2.551230e+00,  2.551230e+00,  3.152071e+00,\n",
       "         -1.110872e+01, -3.914389e+00, -7.552550e-01, -7.552460e-01,\n",
       "          1.962407e+00,  3.490390e+00,  3.490396e+00,  6.757975e+00]),\n",
       "  'EmE0': array([-1.50691 ,  0.7861  ,  0.7914  ,  0.7771  ,  1.884047,  1.907533,\n",
       "          1.906476,  2.349819, -1.21027 ,  0.216875,  0.676644,  0.653277,\n",
       "          1.799721,  2.065016,  2.084261,  2.726886]),\n",
       "  'sce0': array([ 4.48108 ,  2.194   ,  2.201   ,  2.184061, -2.137494, -2.110904,\n",
       "         -2.112035, -2.203245,  4.52994 ,  2.934759,  2.263484,  2.236098,\n",
       "         -2.186759, -2.20382 , -2.182057, -2.267519])}}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For several usages it can be useful to access to the results with the object.attribute sintax. \n",
    "In this case it is possible to use the AttributeDict class the perform this conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj = U.AttributeDict(**results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and we can access as"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.190291e+01, -1.176000e-05, -1.176000e-05,  0.000000e+00,\n",
       "        2.551225e+00,  2.551230e+00,  2.551230e+00,  3.152071e+00,\n",
       "       -1.110872e+01, -3.914389e+00, -7.552550e-01, -7.552460e-01,\n",
       "        1.962407e+00,  3.490390e+00,  3.490396e+00,  6.757975e+00])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.qp.E0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test of a parsing with extendOut = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse file YamboCalculator_test/qp_test_ExtendOut/o-qp_test_ExtendOut.qp\n"
     ]
    }
   ],
   "source": [
    "results = P.YamboOutputParser(output=qp_output_extendOut,extendOut=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now all the variables are available, for instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.724502, 0.846966, 0.847311, 0.847307, 0.839802, 0.839785,\n",
       "       0.839137, 0.853679, 0.719236, 0.823056, 0.842047, 0.842398,\n",
       "       0.844161, 0.839568, 0.839091, 0.850125])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results['qp']['z_Re']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see a further example by parsing the typical files of a real-time computation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse file YamboParser_test/rt_results/o-NETime_1000-dephase_0.01-freq_1.55-pol_circular.carriers\n",
      "Parse file YamboParser_test/rt_results/o-NETime_1000-dephase_0.01-freq_1.55-pol_circular.spin_magnetization\n",
      "Parse file YamboParser_test/rt_results/o-NETime_1000-dephase_0.01-freq_1.55-pol_circular.external_field\n",
      "Parse file YamboParser_test/rt_results/o-NETime_1000-dephase_0.01-freq_1.55-pol_circular.polarization\n"
     ]
    }
   ],
   "source": [
    "results = P.YamboOutputParser(rt_output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case we have several files, so the dictionary has more than one key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['carriers', 'spin_magnetization', 'external_field', 'polarization'])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can easily perform some plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9cad0ef5b0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "time = results['carriers']['time']\n",
    "elec = results['carriers']['dne']\n",
    "field_int = results['external_field']['Intensity']\n",
    "pol = results['polarization']['Pol_x']\n",
    "\n",
    "ratio = max(field_int)/max(elec)\n",
    "\n",
    "plt.plot(time,ratio*elec,label='dne')\n",
    "plt.plot(time,field_int,label='field_int')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.plot(time,pol,label='Pol_x')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again we can access to the results using the attribute syntax. For instance if we are intersted to the spin_magnetization\n",
    "part of the resutls we can set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "spin_results = U.AttributeDict(**results['spin_magnetization'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9cacfaedf0>]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(spin_results.time,spin_results.Mc_z)\n",
    "plt.plot(spin_results.time,spin_results.Mv_z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  YamboOutputParser for a ypp computation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We test the functionality of the Parser to deal with the output of a ypp -s b computation for plotting the bands structure along a path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "bands_output = {'bands_interpolated':'YamboCalculator_test/bands_test1/o-bands_test1.bands_interpolated'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse file YamboCalculator_test/bands_test1/o-bands_test1.bands_interpolated\n"
     ]
    }
   ],
   "source": [
    "results = P.YamboOutputParser(bands_output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.04593836, 0.09187671, 0.13781506, 0.18375342,\n",
       "       0.22969177, 0.27563013, 0.32156848, 0.36750684, 0.41344519,\n",
       "       0.45938355, 0.5053219 , 0.55126025, 0.59719861, 0.64313696,\n",
       "       0.68907532, 0.73501367, 0.78095203, 0.82689038, 0.87282874,\n",
       "       0.91876709, 0.96470544, 1.0106438 , 1.0565822 , 1.1017687 ,\n",
       "       1.1469552 , 1.19214171, 1.23732823, 1.28251474, 1.32770126,\n",
       "       1.37288778, 1.4180743 , 1.46326081, 1.50844733, 1.55363385,\n",
       "       1.59882037, 1.64400689, 1.6891934 , 1.73437992, 1.77956644,\n",
       "       1.82475296, 1.86993948, 1.91512599, 1.96031251, 2.005499  ,\n",
       "       2.05068555, 2.09587206, 2.14105858, 2.1862451 , 2.2314316 ,\n",
       "       2.2766181 , 2.3235426 , 2.3704671 , 2.41739152, 2.46431598,\n",
       "       2.51124044, 2.5581649 , 2.60508936, 2.65201382, 2.69893828,\n",
       "       2.74586274, 2.79278721, 2.83971167, 2.88663613, 2.93210418,\n",
       "       2.97757224, 3.0230403 , 3.06850835, 3.11397641, 3.15944447,\n",
       "       3.20491252, 3.25038058, 3.29584864, 3.34131669, 3.38678475,\n",
       "       3.43225281, 3.47772086, 3.52318892, 3.56865697, 3.61412503,\n",
       "       3.65959309, 3.70506114, 3.7505292 , 3.79599726, 3.84146531,\n",
       "       3.88693337, 3.93240143, 3.97786948, 4.02333754, 4.0688056 ,\n",
       "       4.11427365, 4.1597417 , 4.2052098 , 4.2506778 ])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results['bands_interpolated']['col0'] # kpath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.35540660e-06, -1.35540660e-06, -2.37583629e+00, -2.37583629e+00,\n",
       "       -2.37583629e+00, -2.37583629e+00, -5.30497690e+00, -5.30497690e+00,\n",
       "       -5.30497690e+00, -5.30497690e+00, -4.42422173e+00, -4.42422173e+00,\n",
       "       -2.05464056e+00, -2.05464056e+00, -5.30497690e+00, -5.30497690e+00,\n",
       "       -5.30497690e+00, -5.30497690e+00, -2.37583629e+00, -2.37583629e+00,\n",
       "       -2.37583629e+00, -2.37583629e+00, -1.35540660e-06, -1.35540660e-06,\n",
       "       -1.35540660e-06, -1.35540660e-06, -2.05464056e+00, -2.05464056e+00,\n",
       "       -2.05464056e+00, -2.05464056e+00, -5.00667257e+00, -5.00667257e+00,\n",
       "       -5.00667257e+00, -5.00667257e+00, -5.00667257e+00, -7.78724511e+00,\n",
       "       -7.78724511e+00, -7.78724511e+00, -7.78724511e+00, -5.00667257e+00,\n",
       "       -5.00667257e+00, -5.00667257e+00, -5.00667257e+00, -5.00667257e+00,\n",
       "       -1.35540660e-06, -2.05464056e+00, -2.05464056e+00, -2.05464056e+00,\n",
       "       -1.35540660e-06, -1.35540660e-06, -1.35540660e-06, -1.35540660e-06,\n",
       "       -1.35540660e-06, -2.05464056e+00, -2.05464056e+00, -2.05464056e+00,\n",
       "       -2.05464056e+00, -5.00667257e+00, -5.00667257e+00, -5.00667257e+00,\n",
       "       -5.00667257e+00, -7.78724511e+00, -7.78724511e+00, -7.78724511e+00,\n",
       "       -7.78724511e+00, -7.78724511e+00, -7.67094267e+00, -7.67094267e+00,\n",
       "       -6.74725642e+00, -5.63143757e+00, -5.63143757e+00, -6.74725642e+00,\n",
       "       -6.74725642e+00, -6.74725642e+00, -6.74725642e+00, -6.74725567e+00,\n",
       "       -7.67094267e+00, -7.67094267e+00, -6.74725567e+00, -7.67094267e+00,\n",
       "       -7.67094267e+00, -6.54702153e+00, -5.63143688e+00, -5.63143688e+00,\n",
       "       -5.63143688e+00, -5.63143688e+00, -3.71240251e+00, -4.42422154e+00,\n",
       "       -2.05464056e+00, -2.05464056e+00, -2.05464056e+00, -1.35540660e-06,\n",
       "       -1.35540660e-06, -1.35540660e-06])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results['bands_interpolated']['col1'] # energies of the first band included in the computation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The parser cannot determine the names of the columns because the number of colums depend on the\n",
    "number of bands considered. The meaning of the columns can be identified by knowing the input used to \n",
    "perform the post processing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = results['bands_interpolated']\n",
    "kpath = r['col0']\n",
    "band3 = r['col1']\n",
    "band4 = r['col2']\n",
    "band5 = r['col3']\n",
    "band6 = r['col4']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9cacf895e0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OIYPBGclAU9fQEtl0nG2cua6e4CQfeTox5BoGq5X53/0uoZERer46e4E+365dCKs1b4mGmbDIzwauBy4QQuyY+Cn4ClbOzeejjI/j2fpq3LGe115n6N5fUHHNNTjPOXvKa8JkYv53voMMhej63OcJ9vUR6u8n1N9P2OVCKkrCa/K88grS48mJW0XDvnadupHV3h5Z92w/is+Xs3VlkukWuXXlSjCb8WzbNuN3DBw5QqC9PSO+TmEyYVuzWt2PiXNttRrb+SI8MIhxWsSKxmSfdLTQw8hrzU0gpVohMQm00MLZXCvqay3qf0wmLAujh4Sa6usJ9fQiAwE1Jj6NGPLJ2Fatovbj/8rYU08x8uCDUf923jfewLZq1Yy2eLki7YQgKeWLQGFm1sSg5MwzETYbxz71qSm1HaIRHhnB3NiodiyJgqW5mYbbP0/3F7/EwXPPm/qiwYChtBRDiQMR5zKF3W6Ew4Hj9NOT+l3SQbv9P3Rp/NrZptpalj63Ja0NpHygZfkZJzbyDBYLtpUrGb7vPobvuy/qMVq9lHRxbNjA4M/v4cBbzo07VjgcLNvybF4SiEJDQ5iqogu5ubkZDAa1xGyUrE4NS6QKYkdS5SJmiw2fjPZlYlkws4doZJ0N9YxNGFKQXgz5dKpvugn3c8/T8+Wv0MNXoo5JpGBdtjghMjtTwWCz0fDFL+DZtj3+YKOBquuuw+icPWOt/MorMVZVR8KfkBLF5yfsGkUZdaF4PAmty3HqqRis1oTGZgLH6acz77vfiWsNeltbGX3oYQJHOrAunv0DV4iE+vsxlpdjmFRTZt43vo5ne/S/vaGkBEeGOsJXffCDqjsizp1Z4OhRhu65F9/u3ZScdVZG5k6G0OAA1lmqPBosFszz5xM8ejRS6TAa5hRDEP1tbRjKyzFWVs46RhP5WGJvqqtXy05MuGAy4VrREEYjC//7R7j+8pfo9cSFgdIL35qx+ZJlzgo5QMVVV1Fx1VUZOZcQIqkGr4WCMBgiHVliYdu9m9GHHsa/b29RCvn022zrsmVJ1RhPFVNlJZXXXBN3XGh4WBXyPXtzLuRSSsKDQ7O6VkB1bYR6ejDFqM1trKjAUFrK8AMPJpXR6nn1VawtLTH3hbRIlZhCPpEUpOUIpJMMFA1jeTmVV1+d0XNmijkt5DqJY126FIxGfPv2FV0LMy2rs5AxVVZiamzMS0y/Mj6O9Ptnda0AlF9+OZam5phuNSEE5e96F+MvvJDUBrrBbqfs7W+POcbStJDSiy6MafVqUUZajkAmXSuFji7kOglhsFqxLl6Ef+++fC8laUL9/ZG2YIWMbeVKfHtzL+Ra04bp6fmTKb/88oTu3Bq+cDt84faMrU1DmEws+M//jDlmikVuNKaV1VlsnBBlbHVyg3XFSnz7ikvIpZSE+wcK3iIHNToicLgt5yUTQkNqr87pBbOKDVNNDRgME1Uca+ZUY/G585vqpI11xXJC3d1qpbwiITwyggwGIxErhYx11UpQlKS70qRLKAGLvBgQZnOknvpccquALuQ6SWBbqVZ18yXZNSWfhDPQYCBX2FapRUN9b+bWvRI+QSxyOO5e0YVcR2cWrCtWABSVn3x6MlAhY54/D0NZWc43PCMFs6pmD/8rFo4LeeH/vTOJLuQ6CWOqrcVYVYVv3958LyVhiknIhRB52fAMDw1iLC/PW1ZiJjFrQl4Ef+9Mogu5TsKoQrOiSC3y4rjVtq1aiX/ffmS0pJMsERoYLIo9hETQLPJMx5AXOrqQ6ySFdfkK/AcPRprxFjqh/n6EwxEzK7eQsK5ahfT5IvVHcoFWMOtEwDQRS677yHV0YmBduQLp96fc0ivXhPr7ozZLKFTyseEZq2BWseHYuBHrihXYclAGupDQhVwnKSKRK3uLw08eKpIYcg3r4kUIiyWnfvLQ0BCm6uL5souFpbmZxY/+saj+5plAF3KdpLAuXgwmU9H4yYshPX8ywmzGumwZ/hxFriiBAIrLhan6xHCtzFV0IddJCmGxYF28uGgiV4pNyEFNDPK9uScnvT7DgxNNl6tPDNfKXEUXcp2ksa5cgX9f4ScFKR4Pyvh40Qm5bdUqwiMjkbZl2SQ0qCYDmXQhL2r0olk6SWNbsRLXY//H8O//FzGpxndcDAJDSQnG0lIMzlKEKX6DChkKo7jHCI+NqTXTlcSt1PBEj8ViFHKAoV/fl1DrMIPNqjYvcTox2GyQRJtA3y61DZ0u5MWNLuQ6SWM/eSMAPV/9an4XkiDFVj/dtmIFhpIShu69N2dzxqozrlP46EKukzSOjRtZ9uLfUHzJNdlFCaO43YTH3Cjj7oSSXoTRiKHEibHUicHpBENybeYMNmtRhR+C2qFo6ZZnCbvGEhgtkV4vYbcbxe1Oqa+qqaoKsy7kRY0u5DopUWziWGwYy8owlpXlexk6RYK+2amjo6NT5OhCrqOjo1Pk6EKuo6OjU+ToQq6jo6NT5OhCrqOjo1Pk6EKuo6OjU+ToQq6jo6NT5OhCrqOjo1PkZETIhRCXCiH2CSEOCiE+l4lz6ujo6OgkRtpCLoQwAj8CLgNWA+8VQqxO97w6Ojo6OomRiRT904CDUsrDAEKI3wNXAG9m4NxT+M7vbuZN147IY6uw8O9X3k9ddVOmpypKevrb+cofrsMvA3HHVhor+N6NT2IwJle7pJj5yq+u4ewV7+LiM96X0HglHOa2X1zGcHgk6bnOn385N7zjS0kfp1P8/OjhT/PqwAuzvv6+9f+Pi898b0bnzISQzweOTnrcCZw+fZAQ4mbgZoCmptSE1+UfZMDgjTw+YvHyu2e/wieu/kVK5zvR+N2Wr/AP6yjNcXTcY1DYbvKy/8g2Vi6e8ac6IQn43TzCHoZ39Scs5Ac6tvOMuZtaoeBQEr957TMpBLr+wA3oQj4XeWroKfrNCtXh6O+Zcd9oxufMWdEsKeVPgZ8CbNq0KaXWJ3fe8HDk/63HRvni42/h6dA2Pq4oCMPc3reVisLTY6+xNCj4xhUvsqpx9oJLv/jT97h74JccOPrynBHyvqF2APoZSfiY/R0vAXB9w03c+PbbEj7ugz8+k3azK5nl6ZwgSEWh2yQ5I9jIf978TM7mzYT6HQMWTnq8YOK5rFJVYsE+soYjJti285fZnq7g2brj5xw1gXVkPVUlsZs9zG84DYAjA7tzsbSCYGCkA4BeQ3y3k4Z2fbTrlSjlhjr6TAY87r6kjtMpfnr7duIzCCrMC3I6byaE/FVgmRBikRDCArwHeCwD541JpcPC6yNX4FQUHmz9VbanK3gefPM3lIUVXh99JxUOc8yxzfPWY1cUjrk7crS6/DM0qtoW/UkIbJe7A7ui0DxvfVJzVViXANDW+ffkFqlT9BzsfAWAypLlOZ03bSGXUoaAjwFPAXuAB6SUWTf17BYj0ljO6eFGngkNMjx0KNtTFiyDA/v5a2iI05T5GM1lWOO0UKsptVEfNNATGszRCvPPiLsn8v8jx15K6Jju0CD1QQM1pbak5qqsUIX/QNf2pI7TKX4OdKut82prTs7pvBlxLEspn5BSLpdSLpFSfj0T50yEqhILJZarCQrBo//4Zq6mLTj++NI3CQmBzXwVlY74PTQrHBbKAna68MYde6Iw4h2I/L+tZ0dCx3ThpSxgpyKBazqZuvrTMUjJoYHCb1Ctk1mOuNqxKZKGupNyOm9R7xBWOCz0ypPZKC081PcKUlHyvaSco4RDPNS/jVOklR5lA5Ulsd0qAEaDwBquoscIAX8i7cSKH5dvKPL/Q4P74o4P+MfoMYI1XIXRkHgzY4D6ymrqQ3DE05X0OnWKm05/Pw1BQU1Zcndx6VLUrd4qHWaGPUFuWHkpt3c8xp0PvI0Ka3nMY8wGM+89707KK1pys8gUGRlu43fPf5GQDMUcN+wfpdMIH2t+Gz/bHUzIIgcwMx9FHONo58ssWXJRJpZc0LgCo5ilpCIsaXfH34s/2vkyihCYmZ/0XDVOK5UBC0eFHrky1+iUHqoCdmqc1pzOW9xCXmJhT5eLi874ND9pe4yHfJ3g64x5jCIE8rkvcMu7fpujVabG/c/dzv+4WjHI+JGaLYrgwjNu4zuvvkZTlSOh85ssK4CttPe+NieEfCzkpiwsqQ6YOSrix/G2974GgMmyLOm5apxWrIEyDtkHCIcCGE3JuWZ0ipOAf4xuI9QFKql25vZvXtxC7jAz7Algs1fy+E27Ejrm/b88hWeGW7kly2tLl6eHd7PJYOUXN2xL+Jjh8QCVcSJWNMzODaDcR/vg3hRXWFyMhT04wgJboJSDtiFknNwD7bqYnclvWlU7LYQC9fgNg3T3vMaCBWekvG6d3PNG/xssLl9MqaU0qeM6u7aiCEE41IjDkltpLWofeaXDwqg3SFhJPL/o4vpT2W9QaG9/PosrS4/DbX/loFFyUUPiyTqhsILLF0p4Y66ifD6VIYUj7qPxB58AjMkAdsWIDNTgNhgYHIq9EXnEfZTKkEJFefKuFbPRQEBZDEBb19aU1quTH/xhPzf8+QZ+/eavkz62rVs1usKGJZleVlyKXsgVCS5vMOFjLtzwYQCe2XlvtpaVNk9PJDhduDHx+4aRiWsQLxlIo8ZpoTZoot0/nPT6ihEXQSxhM26/KsxHjr0Sc3y7f5iaoImaFG+Rg2Y1BLF9DiVdnQj0efoIKSEODh9M+ljtLk5acxuxAsUu5BMRGsOexLP1Gho3sl4x85fBN7K1rLR5emgnG6WFuvq1CR8zMnEN4iUDaVQ7rTgCJbQr/pTWWGy4hIIpbGEgqPq82/t2xhzfrvgpCZRQneKmlc3ZRGlYoc11JKXjdfJDn0dNFmt3tSd97BH3UarCCqVl8zK8qvgUtZBrboRhT+IWOcDFdaewxxDm6NHEEkNyyZEjf2OfQeGiuk1JHaddg0SjVqpLLBCoZtgoGB09sTM8paLgMghE2Aa2lZil5Mjo4VnHj452MGwUEKhSr1MK1JbaaQiaaPMNxB+sUzD0e/oB6HB1EFbCSR3b7h+mNmiiuiS3EStQ5EJepQn5eOIWOcCFGz4EwDNv/Dzja0qXp3feA8CFG25O6rihiWuQqGul2mnFM+Fm6OiM7WYodjyePkJCIMMO5lWW0RgStHtmT9PXrocnMD9li7zGacUZdNCm+FI6Xic/9Hp6AQgoAbrGk8sDaJd+7P6SnEesQJELeWXEIk9OyOfPP401ipGn+1/LxrLS4umBHaxXTDTOOyWp45J1rdQ4LQwEWgBo79uR1FzFxsjEHYeilFJbZqUyaKU9NHsilHY9BvyLUvaR1zgtSF81g0aBa3RubCifCPRN+oJvH21P+LjR0Q6GDQIC1Sl/+adDUQt5xYSPfCRJ1wrARTUb2WUI0dWVeHhftunsfJk3DWEuqk1OxCEF14rTSpd/GUYpaR9JfmOnmBgZU3MLFFlBTYkFc6CcowaFUDC6tdw+chCDlHT5l6VlkXv8agW89s5/pLZwnZzT7+mPhB0m4yc/0qm6aT3++Sl/+adDUceRl1pNmAwiaYsc4KKTPsgPtmzju3/9JCvKWmKONQjBOzZ9gnnzkvNba3R0vMifd/wEJU5yz15XGwAXbviXpOcY9gSwmAw4LIl1/CmxGDGYHDSE4ch4d9LzFROjY+rvFxZVVDut7PfVEaroo7tnOwsXnj1j/JHxbhrD0GZyUJLg9ZxOjdNKf2Ax8Hfael9n/Zp/TudX0MkRvZ5ellcuZ//wfo4ksVHd3qsGT/QHFufFR17UQi6EoMJhSUnIm5rO4XRsPKOM8sxI/AiWrhe+xFff82TS87S3P88Nz36UQWNi9TrOwJ5SAomWDCREYvMIIahxWqkL22gPZr5jSSExMnG7rIgqqp0WRv0LgVbaurZFFfL24Ch1YRtjTmvC13M6NaVWuvxLKZWSthRC2XTyQ5+nj3W16wiGg0m5VtpHDmKUkm7/krz4yItayGEiu3M8edcKwM+ufwVFiV3LBODj953Fdl9P3HHTOXr0JT747EeRAv54zt20NJ8X9xiDIbU/ybAn8TorGtVOC06lgr30oIRDGIxF/3aIilb5MGSop7rESrd/OfAkRwZntpVVwiE6CLNJqUnrA1njtBDCyvywoM1zYt/xnChIKen39lNnr8NsMPNy98sJH9s+3s28sOBNbLqQp0JlSWoWOYAwGDAa4l/0Tc4mXvAcYMDTT42jdtZxXu8wft8IAEMjbdy65RP4BdxzzrezXs9kxBNIXshLLOCuw2vv5VDbM9RWr4g53uGowWJNLm25EBj1qUlPirmBGqeF4XA98xSFA6OHGBlumzK2f3AfXoOAUF3KoYdApGhSg3RwOOiaMU80bLYKbPbKlOfUSQ9XwIU/7KfOUUe5tZzHDj2GJ+jBYT5ev2h0tAMZJSyxLThKg7SxRxyPpsslxS/kDjNtA+NZnWNT/SnQdoDtbX/hkjXXRh3TP7CPt//flaoITOAUkp+fcQcrlr09q+sDNfxwRUNyIlvttNI1uBDYxbv//m9xx9eHJX/5wI6is9xHA2OUKAoWs2Ni89LAQsXMHwK9/OGxd0Y/xreQxvrUfZ02sxGn1UQFNbxi7OAts8wzGaci+cuVT1FalnxZAJ300UIP60rqMAp1b6Td1c7q6tUA/ObPH+XbvS9EP9gIF4arqHRYMBlzH0NSXJ/IKFQ6LLzmGcnqHCubN2M/dD/bjj4/q5A/3forvAbBx0tX47BXA3DmyitZvOitWV2bxognmHQDhGqnhceHz+FbG2E8ELsu+a6hPfyJAfr6dtHQuDGdpeackaCb0rDAZjZGbnvPq/047zRHj1gqsZTyuQPn8IE16VlWNU4LYfOH+EL9XwnL2Mkl7a52fu87yqGO59mw9n1pzauTGloyUJ297njkyuhxIX+s9xWWhCRXzzt3xrEGYeCVns1p3cWlQ/ELeYmFEU8AKWXKG1PxMDesY4Pfz/ahPbOOeab7JZYEgnzo8l+BObdF5RVFMuINJn1LV1NixRc2cMHZd1Jmix1//vL2n/Cn1v+ivWtr8Ql5yEOJYsBmNVBqNWExGnCZz+LWy26KOn7MF8T39F+oSTP6oMZppcffwHsu/c+4Y48c+Ru/f+5W2nvf0IU8T2gx5HWOOmodtQhEJATxqOsoe/Bzm3Mx117231GP/8OPX6LamavVTqWo48hBda0EwxK3P/6mZcpYS9mEgwPBEUYmfOCTGfAOsN0/wEXGipyLOMCYL0RYkQknA2lo1umgO/4eQ3OjGnrZMTBzg7DQGVUC2BUTVrMRIQTVTguD7tlrzGjXI91NqxqnlYEY80xm3rxTMEnJkZG523s230wWcqvRyjznvEjkyrN7HwDgwqbZ77AHxv15SQaCE0DINXdCKklByXBKuVqW9LW+mdmgz7Y/jSLgwiTro2QKbbM3+agV9U0XS9Q06utPwqpIjqRQTCjfjMgQZsWMbaIpdbXTwmCMsg6D4/6JcWla5KWWhIXcbHawQDHQ7kk+OkonM/R5+qiwVmAxqp+jlvKWiEX+9JGnWeUPsGDx7EELg+4ANXlyrRS9kFelmKafLOsaTsWiSLZ1zSy09czBR2kOBlnekht/+HS03z3ROisamj9vIAGL3GA0sRAjHd7+5BeYZ0aFxBi2YTOrb/fqEmvMLy/teqTr76wusTLsCRIMJ9ZLtsXkjFk6QCe79Hn6qHPURR4vKltEu6udnvEe3vAc463+MNSujHpsIKQw6g3qFnmqaKVsh5IsnJUsloaTWO/3s71ramzpiG+ErUNvctG4B9GUn04ww0nWWdHQQuQ0CzQezSYnR0Lu5BaXZ0JBH2MGgQzbsJuPW+Sxvrw010q6fRdrStXjE31vtjjq6RBhwqHsvpd1otPn7aN2UnhxS1kL3pCX3+/9PQAXlS2DWbpKaZ/BfMSQwwkg5LlyrVC/hlN8fvaOHWFsUoTHlqNbCCO5UJRBWWN21zALWkJUsq4VzYJPxEcO0OSo56hBKSqhcU3UWQkFS7BNCHmN08rguB85S8kEzVpP9g5nOrUTH+r+scS+KFvKFxMQgu6ewivmNhfo8/RR76iPPG4pbwHgf/f9nsWBIIsXnjPrsZoLLR/p+XACCHmuXCtUNLMpbEBBsmNStcCnjzzN/LBkdeOp2Z0/BhEfeZLCYzEZKLOZEvKRAzSXLSIkBF3d25NeY77QKh8GQ85JrhULvqCCJxA9JHBwPECZzYTFlN7H4/gdT2LvzebadQAc6S6cQm5zhaASZNA7OMW10jJRg8kdHOfCcQ8sPG3W4wcid3G6RZ4SZXYzQiTfXCJpDAbWVyzDJGFb7zaklLgCLl7qeomLxsYQCxPvr5lpRjxBjAZBmS35aNIap5WBJIWmo6d4hHx0TK0p7Q+XRyzyeHciA25/2m4VOC7kA4la5BM1dvT2cLln0DuIRFJrP+5aqXPU4TCpWZ0XeTywYPZgBs0YysT7JhWKXsiNBkG53Zx0c4lUcNSvY00wxL2t97L+1+s5+3dnE5IhLvTE/rbONkOeABX2xAtmTSZeKN5kmuepdx1HBmePpy80RsbVKBBPuBLrJNcKqOFi0Rh0BzLi69R85IlGrlRXLadUkXp7uDyghR5Odq0IIVhUvoj5mNUKqTHKJ2QqZDVVij4hCFT3StZdKwANa/n8zvt44YJPga0CgOrDf2N9uB8a1mV//lkY8QSSdqtoVJdYOTyQ2AZmTc0qHIqkw1U8reG0yofucBW2CVeJ9mEbmsUiHxoPsKimJO25SyxGbGZDwkIuDAaaMXNEbw+XcybHkE/mC6ffjrzv3YjFF8c8fmDcj8VkwGnNj6SeEEJe4TBnf7MToH4tawIB1lScBCvfpj736kMwbyMYk4sYySTD40Eqk4xY0ahyWni1PbEvQWEw0ISRI0UUgjjqHQLAFaqJuFaq40TrDI77OaUl/eJVQgiqS6wJhXdqtFgq2OYvnut7oqAJee20onjrDCXg6ocFsffAtBjybGWXxyMt14oQ4rtCiL1CiJ1CiD8IISoytK6kqHRYsh5+CEDdakBAb6v6OOiD7jfy6lYBdbMz2TorGjUlFoY8AcJK7KYXGk3mMjrC2S1SlklG/COYpMStHPeRx4qfDyuSofHMJXbUlCae3QnQ4lxAj1Hg8ehWeS7p8/RhMpioslVNfaHzVfXfOJ/xQXf+sjohfR/508BaKeV6YD/w+fSXlDxavZWsY3VC1SLY/2d49R544bugBAtCyFMtnVnttCJl4lE/zY5GjhkkwaAnpflyzUjARZkCYIhErWiVCaNtdo54Aigy/axOjdo4MevTaa5cBsDRzsRrYeukT7+3n1p7LQYxTRKPbgVL6ayJQBqD45nZV0mVtIRcSvkXKaVW5ORlYEH6S0qeSoc5+1ErGs1nwbHt8KdPwd/uArMDFuYnEQjUYvjDnmCkf2myJFNvBaC5YjFhITh27NWU5ss1rpCHMqne7moWOWhp+jMtZS1UMFMfymTqrQAsajgZgDY9ljyn9Hp6Z7hVCIfgwF+g6QwwxG75N+gO5C2GHDLrI78J+N/ZXhRC3AzcDNDU1JTBadWkIG8wjC8YnvJhzQqX/ydc8OXjjy0lYHXi9oe44K7n6J/0ob31/CX82yWxv8nTxRMIEwgpSScDadROWJ6X/OAF4rn3VtSX8q23roOjf6Kj9zVaWuJ3PMo3I2EfZRNvc63WCqi/96M7unjsja4p47UcoUyFkdWVWukf87Po83+KO7a+1MaTH1XDWI8MH8jI/JliyDfEoHeQZRN3DLNxePQwB1JY+4baDdSX1Mccs29oH81lzdhMmS9M1+fpY2nF0qlP7n0cRo/CZd8G4NEdx7jtgTcIR0kkk1KtrZMv4gq5EOIZoCHKS1+QUj46MeYLQAj47WznkVL+FPgpwKZNmxJzyCaIFhc87AnQWG7P5KlnYjBA6cw33LFhL31jft6+rpEltSU8uL2TnZ3Z74UZqbOSopCf3FzJ7W9bidsXu3rk9o5h/n5wkPqJwmBHBvemNF+uGZFBaoX6ntBcKwCfu2wlL+yPvqlYYjVxSnNmOvW857QmDAaBEmcPYm/PGH95sxdXyEFDWNLu7szI/Jnih6/9kEcPPsr3zv8eFzRdEHXMP479g3999l8JKMm7OS9supDvb/7+rK+7Ai7e8/h7uG3TbVy3+rqkzx+Pfk8/Z807a+qTL/8PVLbA8ksBeOPoKAaD4NZzl8w4XgjBVafkxSEBJCDkUsoLY70uhLgBeAfwVjlbznOW0SI2hseD2RfyWdA2W993ehNnL63htY6R7JbWnUCL1km2zoqG2Wjg5ihvzOnc91I7fz84iNHWRKkiOTJ2NKX5cs2oDNMsVAtu8t3appYqNrVUzXZYxphXYeeTFy6PO+7pN3tVIfeGaDE6aA+MZH1tydAx1kFIhrjtuduiirkm4ovKF/G1s7+GJYEWihrf3PpNjsZ5P3W5uwjJUFKd7RPFE/TgDrqnJANxbDscfRku/VbErTLiCVDrtPKpi2O3RMwHablWhBCXAp8BzpNS5m3363i9lfzVABmZVkq21Gai1+XL+ryppucnS5ld/aJwB8I0Y6ajCGKdpaIwYgC7UGPCrebCzX/TsnJdviDNthqe8HQgFQUxS5GmXNPt7uYt89/CiH+E2567jW+f+21ObVBD8nb27+RTz32KReWL+NnFP6PSltzdzKLyRTzZ9mTc+QF6xpMr8+sP+/HE2ZjXvkSmxJC//GN1k3PD8Y5gQ55ApEhfoZGuj/y/ACvw9ET85MtSyo+kvaok0VwrQ3kUcm2zVftDO62mnFjk2p1Aqj7yRCmNCE2IJks5O/yDWZ0vE3h9QwSEwCLUti1Z3z9JA+2L0uUNsqismTHfUQaH9lNTk909lkRQpEKPp4eLWy7mX9b9Cx9++sPc9vxtU8asqFyRkogDNJY04gq4GA+OU2KOnojVPd495d9E6Bzr5NonrmXIN5TwOgBwdcPuR+C0m8FWFnl92BPM+ucsVdIScinl0vijso/mVjg65KUvjhVsNhqyYr1Ob+7gtJni+p3jnnM8ELeWdeewd2Le7FoKpROt4MZ8QZodjTwZHMDvG8VqK8/qvOkwOqLehpuFusbJm52FRkTIfUFaatZA34u0d75cEEI+6B0kpIRoLGmk1FLKTy/6KU+1P4U/rG7sm41mLm6+mHJrau8FTUB7xntYUhHdzadZ4okKuSIVvvT3L+EP+/nsqZ+dGVY4DYfZwca6iRaGr/4clLAq5JMY8QRornIkNH+uOSEyOysdFkwGwbf/vJdv/zn+JtyvbjqN85bXxh2XDMPjAexmY8TqK7WacAdCKIrEYEg+22vLvj5u/EViIX5mo1pvJptoFvmYL0RT5VKkq5X3/u5czCT+u5UazHzvXQ9TXp581NLoaAef/uNVuJLYSPNLBYwgRDkGoV6nQiXiWvGGaF68Cd78CV987XuUv/6DmMcJ4MMrr2PzmZ/O2to08dQE12lxcuXyKzN2/kZnY2Se2YRcW0M8y13jt3t+y7bebXztrK/xT8v+KfHFKGHY/gtY8TY1Z2QSw+OBrBtMqXJCCLnZaOCXN57GkaHYGYcef5ivP7GHI4PjQIaF3DM1Td5pMyEleILhlOovtA+ov8sX374KuyW2JdlSXYLJmF1fqtac2eUNctHa67m48zl8IvE7jrBU+Dse/vjiv/OBt/8s6fn/+OKdvIyXsw0OjHGsq8ksMZiQ9s3YzIa8pU8nQonFhEGodzzzGjfxz7aFdPuH4x63JzTGD/f8mvNP/1TW/OkRIXdmp96+9gURy9qe/Fosyx3UEMj/eO0/OG/Bebxr6buSW8xIB3gGYfklU54OhRVcvlDKGdTZ5oQQcoBzltVwDjUxx/hDqpCPpenyiMbwtMJVTuvE5qAvlJKQa2u84ayWrIt0Iky2yKtrlvO96/6W9Dne/8tTeLD3Za4PhzAYE78mSjjEg70vsdFg4cc3vJL0vF/84y5s5sLuhWkwCEptZlw+9dp88Z+fSOi4P/z1M3y580lefeNeTtv4L1lZm+bWiPiQM0yNvQajMEY2NKPRPd5NS5naQzOW5R5SQnzhb1/AZrLx1bO+mvyX9+DBiUVNjTQa9WrNWwrTIs+/QuQQq8mI1WTA5c18FuiwJzBlI8QZEb7U5nJ5gzgsxoIQcVAtRiFS/30Arm6+jCNGeGXHz5M6buuOezhiVI9PBV9QiVQ+LGRKbaak35uXnfV5KhTJ71p/mZ1FoYqo0+yk1FKalfObDCbqHHWzRqQEw0H6Pf0RH3aXuyvqOIC/H/s7rYOtfO60z1Fjj23YRWVgv/rvNCHPVXRYqhT+uzvDlNnNuNIQo9kY8QSnxHKXTljhYylGrrh8wYg7oxAwGASlVhOuNO5mLj7z36hQJA+++ZukjnvgzfuoUCQXn/lvKc2bk4zfDFBmS/69abNX8u6yFTwbHqG7KzsNP7rd3TSURMsJzByNJY2zulZ6Pb1IJOtq12EUxpghiPuHVSHevHBzagsZOKDWHS+pnvL0cCRfQxfygqDMlp4YzcbQeGBKj0fNFZFq5MqYL0SZvbA8X6UpCM1krLZy/ql0Gc+GR+jTKkjGoa+3lWfDI7yrdFnKETK+oBJpKlHIlNlNuLzJv1/++Uy1Vt0DL30z00sCVIs8W24VjYaShlmFXHt+gXMBdY66mL70Q6OHaChpiLsZOisDB2ZY40CkcU2qGdTZZs4JeanNnHHXiroREpzyba25VlKNJXf5gpGQv0Kh1GZKe3/hqtNuIywEj7z8rYTGP/LytwgLwdWn3RZ/8Cz4Q+Ep6fmFSioWOcC8eZvYbCznYdde/L7Ml4XoGe/JupA3ljTS6+klrMzsozrZRx/Lcgc4PHKYJeXxM5VnZWA/1MysJ5NuBnW2Kfx3d4ZRXSuZtchHvUGkhKrJUSvW9CxylzeUUg/ObFKWgS/BpqZzOAs7Dw3uIBSMHfMfCvp4aHAHZ2KnqWn2Dubx8AXDBR1DrlFmT/36vnfNBxg2CP78j8xa5d6Ql2H/cNYiVjTmOecRUkIM+mYmmmnC3VDSQKOzcVbXSlgJc3j0cMyIlph4R2C8D6pnCnmh+8gLSylyQJnNROdwZqsJHM/qnORamYhaSdVHPuYLsrg2/XZjmaTMbqJrJP2yA9csu5JPHvgNl9y3iVjyGgb6jILPL313WvP5ggo1zsJ/q5fZUjcyTtvwLyzd8V98/cjj/Ojex5M6tlKYuPfqP1PinOkHnyyi2UQ7f5e7a0a7te7xbqpsVdhMNhpLGnlq/CnCShjjtNKyXe4u/GF/6kI+S8QKqJ9xs1FQEicUOF8U/rs7w6iulcxa5FqdlcmulRKr+gdP2SL3hSJ+9kKh1GZmzD+W9nnOO/UT3NS9lUF/fDdAlbWM8077ZFrzFc1mp10t6xBWJMYkk8iEwcDtJ3+SR9+8P6njRkMenpNjvPbmA7zltI/PeL3Hnd3QQ43J2Z3TmeyjbyxpJCRDDHgHZpS9PTR6CIDF5YtTW8QsESug+sgrHPlr5RaPwlKKHFBmN2U8amUoykaIyWjAbjbi9ic/l5QSl7ewolYgMz5yAJPZxv+78uEMrCgxfKEiEXLb8dyD8hR8saduuIlTN9yU1DEedx9nPXQBr3e+GFXIp2d1ZotYSUE97h4WlatZlprl3j3ePUPID46oFnXKFvnAATCYoLJ5xktqeHFhfR4nM/d85DYzgZCCLzhzUyVVZtsIcaYofL6gQkiRkfobhUKZzcyYL0SeqhWnjC+oFMVmZ+mkCoi5wuGsY7k08YbrcNTXu8e7MQjDzO45GcZpcVJqLp0h5FJKusePhz/GstwPjxymzlGXerz7wH6oWhy1kfpIARfMgjkp5MczFDNFpLnDtI2QUqspJR+59kEuPNeKibAi8QQy9yWYC3zBMNYi2eyE41mEuWJjyQJ2SV/UPqzd493U2msxG7JvVDQ4Z4YgugIuPCHPFNeKtq7pHBo9lGbESvTQQ5iZ8FdozD0hn1RlLlMMeQJYjAYc0zZCSlOsgKhFLhSea0WrgJj98ryZxB9Uisq1kkuLHGBD/Sa8BsH+Q3+e8VouQg81GktmRqRMr/OiWe7TszsVqdA22pa6WyUcgqHDUUMPYaKWUoHWIoe5KORZEKORcTWrc/pGiNOWWk1yLXKh0Fwr+bj1T5ewIgmEi8O1oiWAZXozPh4bl18BwI62p2e8lotkII1oMeJa/ZXJa2hwNswQ/C53F96QN3UhHzkCSjBq6KGUkhFPoGCzOmEOCnlEjDJ4+zrsCcxwq8BEc4lULPICda1oXyzp1FvJNf6Q6gbSLfLZaWjcSH1YsmNw95TnFamoFnmWY8gj6yhpYNQ/OqWjT7Twx2iCf2hEjVhJfaNz9ogVtz9ESJH6ZmchkQ3XyrAnEDXjy2k1p2aRF6xr5XiXoGLBF1QbcxRD0azJXYJyzUZLFa8HpnbSGfINEVSCObXIYepGZs94DxaDhSpb1ZRxM4Q87dDDA+q/NTN75QyPa5UPdYu8YMiGa2W2FlBquF7yH8qxiGulwCzyLNzNZBstOqkYLPJSq1phMh9flBuq19BrFPR0vx55LppbI5vMc85T550k0lrEyuQOP5Nbw2kcGjlErb025S5FDOyHklq1YNY0pnf/KkTmnJBnw7UyMq0WuYbWtzPZcD3tbqHQLPJsfAlmm2IScoNB4LSm9uWfLhsWXQTAjgOPRZ7LVVanRrSIlGg++miW+6GRQyyuSNEah7gRK4C+2VlIOCxGjAaRMdeKlHJGdyANp82EIkk6XM/lDWExGrAWmDugGKNWIq6VItjsBK2eTe6v7/Ill2JXJK/3HG8vmO3OQNOJNJiIYpFPZnJrOFB9+YdHD7O0Io0WwoMHZo1YGSnwErYwB4VcCEFZhjIUQb0NDisy6m1XpHBWkn7yMV+QMrup4NKBbWYDJoMoqs1O38RmZzGUsYWJ5hJ5uL5ms4N1wsaO8c7Icz3jPZSYSyg1Z6ehxHSmN5gIKmpDielfJNMt957xHrwhb+r+8fFBtb1blIgVKA7XSmE5YXNEJkvZanWKZ/ORg2rB1pclfk61zkrh3cYJIfImNKkSca0UQUIQpFcBMV02lC/hntHdeNx9OJx1EbfGDIOi4xW4/xoIT6zTaIKrfwVLUmzmMInJG5l9nj4kcoZrZXpruBmp+YoCHS/B7kdg75/A54o9qVQmTjybayWIEGS9wXk6zEkhV+utZMYij+U/K02xJrlaZ6Uw/zRldnORuVY0H3lx3HyW2cwcG/HmZe4N888m7HqTzzxyBeUmO6+HRlhdEcVK3f9nCLjh9I+oj1+9B/Y9mREhbxAW/tbzKl+4/wKGpfo+a7BWTRljMpioMzn5y+776N37R9oV9Xoteepr4HerDZTH+8Bkh+UXQ/nC+BNbnLDo3KgvDY8HKLebky5klksKUy2yjFozJDNWj+Y/i+5aOV4EKRlU10phfvtnqnBWrjjuIy8Wi9zEnu78WOQnr76Gtbt/wUHphpAbuwyzebh35sBj26B+DVzy9YnHr8GxzLSZO2+oizdCfraH+wHJilCQVa5pNcql5JKRQZ62CrYLtazyZkVQobjAXgGLz4fll8DyS8HqTHtNhZ6eD3NUyEttJtoHMlOTfCiGa+W4jzy5D6bLF6Kh3Jb+4rJAqTV/t/6pUExRK5B6l6BMUOJs4Hc3Hg8/5MnPwmu/Vl0oWiEpRYFjr8P6a46PW3AKvPITCPnBZE19AVLytmP7eNuSt8K7fwJBL3xzIXTvhLVXHh83dJjb+rq47R0/gE03pj7fLPxuawdnL6mhqdoBzOzHW4gUx/1mhsnkhyXWRkhpigW6CrGErUaZvTgtcnuxCLldTSJTlAKoMNl0BgQ90LPz+HMD+yEwBgs2HX9u/iYIB6AnsT6sszJ0GMb71XkBzHaYtxE6Xp46TnvcdGZ680XBEwjx+Ud28eMXDkWeKwaLfG4KeQb9vCOeIEaDiJpOn3rUSqiAXSuZc0vlguLzkZuQMvXOUhll4YSgThbSY9vUf+efcvw57f/pulc6XlL/nSzQTWdA12swuS1gx0tq4s4sm5Pp0D/mB+Dlw8fdOXPGIhdC3CaEkEKImkycL9uUThSzCoWVtM815AlQYTdjiLIRUmJN3iIPhBS8wTCl1sL0eqlRKwUgMgniK6JaK1Bg9WzKGqGy5bjAAnRuA2v51FC98gXgrD8u8qkSTaCbzlCt/a5JLp+Ol9UvGUPmv5w1IT/cP06fS/3ymBMWuRBiIXAx0JH+cnJDpBNLBqye2bI6ASwmNaknmXm0D3AhW+RaO7JiQHOtFFpy1WwcL4NQIF+WTWeqwqllJx/bDvM3ThVRIVT3Sme6Qv7KTIFeeLr679GJu4LxATV5R3O/ZJgBtz/y/5fbhvAFw3gC4ahF8QqJTLy7vw98BiiOTzaTrZ70PyzD49GzOjWSjfIo1DorGmUphlTmC38wjNVkKLjkqtnIVwXEWVl4uuq3HjoMAQ/07p7qVtGYfzIMHQLP0MzXEiEi0KdPfb6kRrXQNfdOxD+eHSHXLHKTQfDy4cFZu38VGmkJuRDiCuCYlPKNBMbeLITYJoTY1t/fn860aaP5szPRiWU4Tp1izYJNlEgJW2thvnEiQlMkkSvF0nhZI58VEKOi+as7XobuN0CGVet7OtrmZ9drqc0TawOz6Qz1dS3Rx2hVN0GzQP+YH4OAs5fW8PLhwaLI6oQEhFwI8YwQojXKzxXA7cCXE5lISvlTKeUmKeWm2trs9v+LRyatnmFPYErT5emoNckTn0e7pS5c10rmW+Vlk2Lp16lx/L1ZINe3Zrnqt+546fhmZjSLfN5GQEBnihuesQR64RngG4GBfaqgzz85vTDHGPS7A1SVWDl7aTWH+8fZ3zsGFL5FHvf+XUp5YbTnhRDrgEXAGxO3rQuA14QQp0kpZ3ZGLSA0t0W6YqQVzKqIURVNq4CYKMd95AXqWimkzbgE8IWKzSIvsFLBBoMqpB0vq9mc5QuhtH7mOFu5KvqpRq7EEmjNjXJoi3pXcNbHUpsjAfrH/NQ4LZy+qBqAp3arUlb0FvlsSCl3SSnrpJQtUsoWoBM4udBFHDLnHvAEwgRCSmyLPEkf+fHuQIVpARSfRR4umjorcDxktWB85KAK6eABOPx8dGtcY8EmNXIlybLNBDyqQM/m965aDCV18MqP1XZsWYgf1+h3+6kttbJmXhlOq4kte1U38Akr5MVMpm5fE/GflSZpkUdcKwVaa6W00Dbj4lBsrhWT0YDTaiqcqBU4LpzeodhCPv9ktYrgcHty5+96LbZAC6GK/MgR9fHC05I7fxIMjPmpdVoxGQ2c2lKJdyIPodBdKxl7h09Y5gOZOl82cWaouYTWAirWHznZBswuXxCDgBJLoQp58VnkxVLCVqMsxc5SWWPeBtV/DVMzOqejbYIm617R4tRjCbQm8nWro3bxyQRSyohFDnDGYtW94rAYC949V5hqkWWMkU4smbHIY8WYavNIKRMKgRvzhXBaTVETjAqB40JeQEITA19IKejyo9Eos+ev3kpUTFbV2j66FRpPmn1c/Row2eC5b8LOBxI/f88uqF0VW6C1sMSFp88+Jk1cvhCBkDJDyAvdrQJzVMhBtXpS+bC81jHMM2+qFeEO9buB2J1DnDYTYUXiCyrYLfG/1V3ewq18CGA1GbGaDIUTVREHfzCMrTQ7EQ7ZotRWYK4VgNM/rIqopWT2MUYznHGLuinpjlI1cTacdXDKDbHHNJwE666Bjdcnft4k0WLIa5zq+0Xzkxe6WwXmspCnWMD/m0/s4dX2YcxG1WKeX2FnXsXslQoj7dH8wcSE3Fe4BbM0iqneSrHFkYO6h9Pj8sUfmEvW/JP6E48Lv6r+ZBqjCa78WebPOwktq1OzyE1GA+8+eT4WY+HvscxZIU+lrnZYkbQec3HDWS189Z1rEptHK5zlC1GXQMcstTtQYf9ZMtmYI9sU22YnqEbG/r6xfC9jzqFZ5LWT7uC+dsXafC0nKYrrHZ5BUille6jfjTcYZv2C8oSPSbYCYqG7ViCzrfKyTbHFkcOE26/QXCtzgOmulWJi7gp5ChtKOztHAZITcttxizwRxnyhgnetZLJ5dbYpSteKXXVdFURN8jnEgNuPySCoKHBDKhqFfQ+fRVJxrezqHKHEYmRRTeLtozSLPNH60i5fsOBdK6U2E11Z6CsZDAbp7OzE58ucf/g/L6unzBZkz549GTtntjmlSsFpMTAeKMwm3CcqalantWAjxmJR2IqRRcom3AOJhgUC7Dw2ypr55Uk1YS1NwiJXFInbX7hNJTRUt1TmLfLOzk5KS0tpaWnJSLVCRZEEu0ZpKLNRV1aYrfOmI6XE3tXLv54+NrFfUtjvhROJfrefmtLCDzWMxhx2rZhQJIwHwgmND4YV3uxysX5+4m4VmGSRJ+DGGfOHkLJwszo1SrOUsOLz+aiurs5YyVllIlW8WErYgrrWiopKmiuKJzLoRGHArWZ1FiNzVsgjYYEJflj2947hDymsX1iR1DzOJOp3RwpmFbgVVmoz4wsqBDPQYWk6mRRdzcNcbHfKJqMBgdA3PHNM/5h/SsRKMTFnhfx44azEPiy7tI3OJC1yq8mIxWRIyEd+vIRtYVvkZUWSpq9Z5IYissiBiI+2WCKDTgQURTLgDhRlxArMZSG3J1dlbuexUUptJpqrHUnPVWo1JeQjdxWRRQ6FLzRaEb5Edby9vZ21azMfN3z++eezbZvaBu3SSy/lpJNOYs2aNXzkIx8hHJ7p2jNOLLig0vRPcEa8QcKKLFqLvLBNvyySrGtlV+co6xeUp3Trn2jhLM3CLfQNLm0D97p7XkmqF2aFw8Ivbzw1pd8vrEjaB8YJJRGSJwvQIn/ggQcoKytDSslVV13Fgw8+yHve854pY7TN9G88sZcfbTkY83wGIfjcZSt566ooNcJ1EiZaMlAxMWeFPJkmt/5QmL09Lj54zuKU5nImapF7C7uphMZpi6q4+pQFeIKJbRQD9Lv8bG0fom1gnPULKuKOv+P/dvNmlyvyWJESbyCM0SAStrBVBBaTAQGsnlfGVy6PnZEbCoW49tpree2111izZg2//vWvueuuu/i///s/vF4vZ511Fj/5yU8QQnD++edz+umns2XLFkZGRrjnnnt4y1vegtfr5cYbb+SNN95g5cqVeL3HQzXLysoi8wQCgaiGgdEgKLWZOH1xVdzf7undvbywv18X8jQp5mQgmMtCbk+8rva+njGCYZlUItBknFZTYj7yInGtVDgsfPfqGFXworC1bYhrfvJSyht4mpvEbDJEXA/ZYN++fdxzzz2cffbZ3HTTTfz3f/83H/vYx/jyl9WOhtdffz2PP/44l19+OaAK8tatW3niiSe44447eOaZZ/if//kfHA4He/bsYefOnZx88slT5rjkkkvYunUrl112GVddddWMNQghKLeb+dH71sdd7znffjYjvWfnOv1uNXdBt8iLjGTqamsZneuS3OicPFfXSPwkF20tzgIPP0yFZPckplvOo54AR4Y8LKsrTaj4WKosXLiQs88+G4DrrruOH/7whyxatIjvfOc7eDwehoaGWLNmTUTI3/3udwNwyimn0N7eDsALL7zAxz/+cQDWr1/P+vVTBfmpp57C5/Nx7bXX8uyzz3LRRRelvN5ye3Zi+ucaA2NqSepiFfI5u9kZKceagDWzs3OEqhILCyrtKc2lWuTx53F5gzgsRsxFUG0tWbSa4Klaj+EJkzyZZKxUmO7qEEJw66238tBDD7Fr1y4+9KEPTck8tVrVD77RaCQUSlxQbTYbV1xxBY8++mha6y23m3WLPAP0u/1YTIZIkbtiozhXnSHK7GZ2do7y8PbOmONeaRti3fzUNjpBtbCHx4Nx59l1bLTg3Sqpkm6f1LCSGyHv6OjgpZde4swzz+T+++/nnHPO4R//+Ac1NTW43W4eeuihqO6QyZx77rncf//9XHDBBbS2trJz504A3G43Y2NjNDY2EgqF+NOf/sRb3vKWtNZbbjdzoM+d1jl0JmLIndaiSh6bzJwW8qYqBy8dHuSlw4Nxx/7zqQvTmsftD3Hbg2/EHXtqS3baWOUbh8WI0SBSDqkLKyDIfnLPihUr+NGPfsRNN93E6tWrueWWWxgeHmbt2rU0NDRw6qmnxj3HLbfcwo033siqVatYtWoVp5yi9rkcHx/nne98J36/H0VR2Lx5Mx/5yEfSWq9ukWeGAXfxJgPBHBfy33zw9MhudSyEIGW3CsCH3rKYy9Y2JtRcvK6seN9MsRBCpFWeNSwlBoPIqsXU0tLC3r17Zzx/5513cuedd854/rnnnov8v6amJuIjt9vt/P73v486x6uvvpqRtWroQp4Z+sf8LKhMPkekUJjTQm63GGlKIcEnWYQQLKwq3jdJpkinF2VYkVl3qxQjZXYzgZBSlOV6C4n+MT8bm4r3bvjE21XTKVjSsR7Disxq2GGxUpbmJrIOhMIKQ55AUbtWdCHXyRllaXQW0i3y6GjRQIVeLqGQGRoPICXUOouzhC3McdeKTm4ps5tSbiqsKBJzkfXezAXphnWmgi8Y5qndPfhDavVLs1FwyZoGHJbCk5O/HxzgWJwmKD2jxZ0MBLqQ6+SQtCxyqbtWopEPIX98ZzefnhaB9bUrQrz/zJacrSERhscDXHfPKwkFGQgBi2sT7/xVaOhCrpMz0vaRG3Uhn04+hHxfjwuLycBfP3UeQsDbf/gi+3rGcjZ/ouzuciEl/Md7NnBKc+yNTIfFRFVJ8bpW9HtVnZxRZjfjn4iwSAZFSpQcWOS5KGOr8c53vjMjc+VDyA/1j7O4poSFVQ4WVDpYUlvCof7CS0ra3aWW1jh3WS0LKh0xf4pZxEEXcp0ckmpDCi2rsxib4kbjkUcewenMzG28dk1zKeQH+9wsrTu+/qV1Tg72jeds/kRp7XIxv8JOZZGLdCKk7VoRQvwr8FEgDPxJSvmZtFelc0IyueJk3I2lJz8HPbsAMErJ4kAYq9kAhjRsj4Z1cNm3Yg7Jdhlbt9vN3XffzU9/+lOuueaa1H+XCUxGA06rKWdC7guGOTrs4d0nz488t7TOyQPbOhn1BCl3FE6Jid3HRlkzryzfy8gJaVnkQojNwBXASVLKNcBdGVmVzglJWYqhctpeVS7s8X379nHrrbeyZ88eysrKImVsX331VVpbW/F6vTz++OOR8VoZ2x/84AfccccdAFPK2N5xxx1s3749Mv5LX/oSt912Gw5H5hLE0smYTZa2gXGkhCW1Uy1ygIP9heMnd/tDtA2OszbFiqXFRroW+S3At6SUfgApZV/6S9I5UdEKZyVkPU6ynD2+IG0D4yypdWLKcnW6bJax3bFjB4cOHeL73/9+ZGwmKMthmv7BiQJdk10rmqgf6hvnlOb4zTBywZ5udaNTt8gTYznwFiHEK0KI54UQs1YUEkLcLITYJoTY1t/fn+a0OsVIeaQmeWo+8lwkBGWzjO1LL73Etm3baGlp4ZxzzmH//v2cf/75aa+53J56WGeyHOxzYxCwqKYk8tyCSgcWk4GDBbTh2XpM3eicKxZ5XCEXQjwjhGiN8nMFqkVfBZwB/BvwgJilqpGU8qdSyk1Syk21tbUZ/SV0ioNUS9nmUsi1MrZApIwtMKWMbTy0MrbAlDK2t9xyC11dXbS3t/Piiy+yfPnyKYW3UiWXhbMO9rtZWOWYUtfFaBAsrimJWOuFwO4uFzVOK3VFnOSTDHHvU6WUF872mhDiFuARqXa53SqEUIAaQDe5dWaQTHu9yUSaSuQgISibZWyzRS6F/FCfe4p/XGNJnTNiBRcCrRMbncVaXzxZ0nU4/hHYDGwRQiwHLMBAuovSOTGxmY1YTIakRSesSIRItuly8uSijO3kuVpbW9Nar0auhDysSA4PjHPu8pl31EtrnTy5q7sgqjD6gmEO9rl566q6vK4jl6TrI78XWCyEaAV+D3xgwjrX0YmKmqafvI/cKLJbi7yYKbeb8QbDBCZqn2SLzmEPgZDC0igW+dI6J4pUo1ryzf7eMUKKZO28ueEfhzQtcillALguQ2vRmQOU2U3Ju1b0yocx0WK3Xb4gNc7s+YQ1H/iSupIZr0VCEPvcrGrMb6RI6zEXAGvmkJDrmZ06OSWVCAtdyGOTVFhnGmhp+EtrS2e8tqimBCEoiFT93V2jlNpMLKxKvatXsaELuU5OKbOZkw4/VGRuIlaKlVzVWznY56bGaY2avWkzG1lY6SiIyJXWLtec2ugEXch1ckxZqhb53PlMJk2uugSpNVZmulU01Jor+RXyUFhhb7drTvnHQRdynRyjppMnL+QnSsGsbJCLLkFSSg71j0cNPdRYWufk8MB4JO4/HxzqH8cfUuZMIpCGLuQ6OaV8ogFzosFNUkq1qUQOhDwXZWzPP/98VqxYwYYNG9iwYQN9felXtciFa2XAHWDUG5ySmj+dJbUlBEIKx4Zjd+TJJnt71I3OfG+45hq9sYROTimzmwmGJb6ggt0SP95YSlXMTyQf+W9/+1s2bdqUsfNFhNyTPSGPVmNlOpOLZzVVZ64oWDIcmigh0FKTn/nzhS7kOjllcoRFLCH/9tZvs3doL1KCJxDCYjJgNqZ3A7myaiWfPe2zMcdku4xtNrCYDNjNxqTDOpNBq6MS07UyEc1ysM/NBSvrs7aWWBzqH6epyoHVlN+kpFyjC7lOTimLFM4K0lBuizteThSxzVUEwr59+7jnnns4++yzuemmmyJlbL/85S8DcP311/P4449Hqh9qZWyfeOIJ7rjjDp555pkpZWx37tzJySefPGWOG2+8EaPRyJVXXskXv/jFjPxu2c7uPNTnxmEx0hjjb1buMFPjtHL30/v56QuHkzr/jWcv4qObl876eiCk8P57X+Fjm5dxzrKaWcdNb3oxV9CFXCenJFo4S7Ocx/0hDvW7WVRTQqkt+00LslnGFlS3yvz58xkbG+PKK6/kvvvu4/3vf3/a6y6zZ7e5xJvdLlY0lMb90vnSO1axtW0oqXO/eHCA/3ujK6aQ7+8d4+XDQyyt655VyMOKpG1gnPNXzL2ifLqQ6+SU8iQLZ+Wy8iHMXsZ227ZtLFy4kK9+9aspl7EFmD9f7axTWlrK+973PrZu3ZoRIc+mRa4okj1dLq7YOC/u2Cs2zOeKDfPjjpvMd5/ay4+fPxyzTotWkGt3l2vW8xwd8hAIKyyZgxa5HrWik1OOdwlKLCkol5UPIbtlbEOhEAMDak25YDDI448/nrEoGVXIs9Ml6OiwhzF/KGux2WvmlRNWJPt6Zu8wpAn4nm7XrOGNhxLw45+o6Ba5Tk5JtllwrhsvZ7OMrd/v55JLLiEYDBIOh7nwwgv50Ic+lJF1l9nN7OnOTqs1TUSzVbtE6+Kzu8vFSQsrZlmDapH7ggqH+90sq59ZJiASWaMLuY5Odkm2b2cuXSu5KGM7uX9nJsmma6X12Cgmg2B5Q3YEsqnKQanNFBHr6YQVyZ7uMc5eWs3fDw6yu8sVVcgP9c9eQuBER3et6OQUs9GAw5J4qJyiSAxCYJhDdTNSodxuxu0PEQpnvpTt7i4XS+ucWQvpE0KwurFsVv9328A43mCYd540D4vJMKvgH+xzs6R29hICJzK6kOvknGRqkuuVDxND20QeS7IgWSLs7nJlvSTsmnnl7O2J7v/WhHv9ggpWNpRGFXythMBcDD0EXch18kAyoXJhKXO20VnMZCtNv8/lY8DtZ+387Ka8r5lXFvF/T2d3lwuLycDSOidr5qmW+/QSD1oJgbm40Qm6kOvkAbWUbeI+cr1gVnyyVZM82xudGmsmvihao7hNdneNsrKhFLPRwJp55Yx6g3ROq+cSqZWuW+Q6OrlBK5yVCGFFYtKFPC7aBl+mhVyL317VOHNzMZMsrXViNRnYfWyq20RKOeHaUYV+coTLZCKhh7qQ6+jkBrUmeeJx5LpFHp9suVZ2d7loqXZkPavWZDRE9X8fG/Ey4gmyeuKOYGVDGQYBb06z3A9qJQTK4pd9OBHRhVwn55TZEu/bmcvNzlyUsQ0EAtx8880sX76clStX8vDDD2dkjqwJefdoznpfrp5Xzu6u0Sn+7+OuHdUSt1uMLKl1RrHIx1lcWzJnv/R1IdfJOVqXICVOAwIpJYpyYm12fv3rX6euro79+/fz5ptvct5552XkvMmWPkiEUW+Qo0NeVs/LTW3vNfPKcPlCU/zfu7tcGASsaiibMm6GkPe55+xGJ+gJQTp5oNxuRpEwHgjNesve841v4NuzF4M/xJjJgD/NErYA1lUrabj99phjsl3G9t57740kHRkMBmpqZq/klww2sxGLyZBRi/zNCbHMVbcdbZ7dXS4WVjkm1jDKklrnlJLHa+eX88cdXQy4/dQ4rXgCIY6NeHlP7cKcrLMQ0S1ynZwTqYAYJ+ZZu8POpUG+b98+br31Vvbs2UNZWVmkjO2rr75Ka2srXq+Xxx9/PDJeK2P7gx/8gDvuuANgShnbO+64I5LNOTIyAsCXvvQlTj75ZK6++mp6e3sztvbyFPqhxkKL316TI4t8ZUMpRoOYkvDTesw1Y/7V0zY8D/ePA3N3oxN0i1wnD0RqknuDzK+wRx3TcPvteANhDvSNUVvloNxhycnaslnGNhQK0dnZyVlnncXdd9/N3Xffzac//Wnuu+++jKw902n6u7tc1JdZqXFaM3bOWNjMRpbUlkQEetDtp8flm+GjX9OoWe6jnLe8ds6HHoIu5Dp5INGY50jlwxxuYGWzjG11dTUOhyMi/ldffTX33HNPxtZeZku9JrmUkoe2d/Kdp/ZFzhEMK2xeUZex9SXC2nnlPPL6MZZ/8cnIpud0i7zcYWZhlZ27ntrHD545ENkQb85Te7lCQBdynZyTaOGsXNcih+NlbM8888xIGdt//OMfU8rYXnXVVTHPoZWxveCCC6aUsRVCcPnll/Pcc89xwQUX8Ne//pXVq1dnbO3ldjP9bn/Sx3UOe7j9D628sL+fTc2VbGqpirz2jvWNGVtfIty6eQn15baIW63UZuK0RVUzxt35rnW8dGgw8nh5ffZqwRQDupDr5BwtwuLLj+7mu0/tizz/hbPLME6qSa1Z5LkMKctmGVuAb3/721x//fV88pOfpLa2ll/84hcZW3u53cyLBwe46O7nkzquc9iLEPDvV6zh2tOb8xrCt7SulM9eujLuuPOW13Le8rnXCWg2xPSaBblg06ZNUour1Zl7KIrka4+/Sd+Yb8rz71thYuGiqe2+TEYD88ptOevZWSjs2bOHVatWJXXMPw4N8NuXOyJ9ThOl3G7m1vOXRiJFdAoXIcR2KeWm6c+nZZELITYAPwZsQAi4VUq5NZ1z6pz4GAyCr75zzYzn9+zZQ3P13CxDmgnOWlLDWUsyE86oU1ykG374HeAOKeUG4MsTj3V0dHR0cki6Qi4BbUu5HOhK83w6c5x8uPoKDf0a6CRLupudnwSeEkLchfqlcNZsA4UQNwM3AzQ1NaU5rc6JiM1mY3BwkOrq6jnnE9eQUjI4OIjNNjeLP+mkRtzNTiHEM0BDlJe+ALwVeF5K+bAQ4hrgZinlhfEm1Tc7daIRDAbp7OycEqc9F7HZbCxYsACzee71ntSJzWybnWlFrQghRoEKKaUUqgk1KqWMm8+rC7mOjo5O8swm5On6yLsArXzbBcCBNM+no6Ojo5Mk6frIPwT8hxDCBPiY8IHr6Ojo6OSOtIRcSvkicErcgTo6Ojo6WSMvmZ1CiH7gSIqH1wADGVzOiYZ+fWZHvzax0a9PbArh+jRLKWfUJsiLkKeDEGJbNGe/jop+fWZHvzax0a9PbAr5+uiNJXR0dHSKHF3IdXR0dIqcYhTyn+Z7AQWOfn1mR782sdGvT2wK9voUnY9cR0dHR2cqxWiR6+jo6OhMQhdyHR0dnSKnqIRcCHGpEGKfEOKgEOJz+V5PISGEuFcI0SeEaM33WgoNIcRCIcQWIcSbQojdQohP5HtNhYQQwiaE2CqEeGPi+tyR7zUVGkIIoxDidSHE4/leSzSKRsiFEEbgR8BlwGrgvUKIzHWuLX5+CVya70UUKCHgNinlauAM4KP6e2cKfuACKeVJwAbgUiHEGfldUsHxCWBPvhcxG0Uj5MBpwEEp5WEpZQD4PXBFntdUMEgpXwCG8r2OQkRK2S2lfG3i/2OoH8j5+V1V4SBV3BMPzRM/ehTEBEKIBcDbgZ/ney2zUUxCPh84OulxJ/qHUSdJhBAtwEbglTwvpaCYcB3sAPqAp6WU+vU5zg+AzwBKntcxK8Uk5Do6aSGEcAIPA5+UUrryvZ5CQkoZnui9uwA4TQixNs9LKgiEEO8A+qSU2/O9llgUk5AfAxZOerxg4jkdnbgIIcyoIv5bKeUj+V5PoSKlHAG2oO+3aJwNvFMI0Y7qzr1ACPGb/C5pJsUk5K8Cy4QQi4QQFuA9wGN5XpNOETDRveoeYI+U8u58r6fQEELUCiEqJv5vBy4C9uZ1UQWClPLzUsoFUsoWVM15Vkp5XZ6XNYOiEXIpZQj4GPAU6mbVA1LK3fldVeEghPgd8BKwQgjRKYT4YL7XVECcDVyPak3tmPh5W74XVUA0AluEEDtRDaanpZQFGWanEx09RV9HR0enyCkai1xHR0dHJzq6kOvo6OgUObqQ6+jo6BQ5upDr6OjoFDm6kOvo6OgUObqQ6+jo6BQ5upDr6OjoFDn/HzTXGcHjOHMgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(kpath,band3,label='band3')\n",
    "plt.plot(kpath,band4,label='band4')\n",
    "plt.plot(kpath,band5,label='band5')\n",
    "plt.plot(kpath,band6,label='band6')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of the YamboDipolesParser class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To analyze the YamboDipolesParser we build the dipoles using Yambo. We start from a nscf\n",
    "computation on GaAs on a regular grid with 12 bands (8 full and 4 empties, since the bands\n",
    "are spin splitted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "source_dir = 'Pw_bands/gaas_nscf_so.save'\n",
    "save_dir = Tools.make_p2y(source_dir,overwrite_if_found=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create folder YamboParser_test/DipolesParser\n",
      "Executing command: cd Pw_bands/gaas_nscf_so.save; p2y\n",
      "Create a symlink of /home/marco/Applications/MPPI/sphinx_source/tutorials/Pw_bands/gaas_nscf_so.save/SAVE in YamboParser_test/DipolesParser\n",
      "Executing command: cd YamboParser_test/DipolesParser;OMP_NUM_THREADS=1 mpirun -np 1 yambo\n"
     ]
    }
   ],
   "source": [
    "run_dir = 'YamboParser_test/DipolesParser'\n",
    "Tools.init_yambo_run_dir(save_dir,run_dir,overwrite_if_found=False,make_link=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'args': 'yambo -dipoles -V all',\n",
       " 'folder': 'YamboParser_test/DipolesParser',\n",
       " 'filename': 'yambo.in',\n",
       " 'arguments': ['DipBandsALL'],\n",
       " 'variables': {'StdoHash': [40.0, ''],\n",
       "  'Nelectro': [8.0, ''],\n",
       "  'ElecTemp': [0.0, 'eV'],\n",
       "  'BoseTemp': [-1.0, 'eV'],\n",
       "  'OccTresh': [1e-05, ''],\n",
       "  'NLogCPUs': [0.0, ''],\n",
       "  'MEM_tresh': [51200.0, 'Kb'],\n",
       "  'DIP_Threads': [0.0, ''],\n",
       "  'DipoleEtresh': [1e-05, 'eV'],\n",
       "  'DBsIOoff': 'none',\n",
       "  'DBsFRAGpm': 'none',\n",
       "  'PAR_def_mode': 'balanced',\n",
       "  'DipComputed': 'R P V Spin',\n",
       "  'DipBands': [[1, 12], '']}}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp = I.YamboInput(args='yambo -dipoles -V all',folder=run_dir)\n",
    "inp['variables']['DipComputed'] = 'R P V Spin'\n",
    "inp['arguments'].append(\"DipBandsALL\")\n",
    "inp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the options ``DipBandsALL`` enables the computation of the dipoles elements among all the bands, and not\n",
    "only in for the full-empty couples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "run command: mpirun -np 1 yambo -F dipoles.in -J dipoles -C dipoles\n",
      "computation dipoles is running...\n",
      "computation dipoles ended\n",
      "\n",
      "        There are no o-* files.\n",
      "        Maybe you have performed a ypp computation or wait_end_run and/or\n",
      "        the dry_run option are active.\n",
      "        Otherwise a possible error has occured during the computation\n",
      "        \n",
      "Run performed in 06s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'output': {},\n",
       " 'report': 'YamboParser_test/DipolesParser/dipoles/r-dipoles_dipoles',\n",
       " 'dft': 'YamboParser_test/DipolesParser/SAVE/ns.db1',\n",
       " 'dipoles': 'YamboParser_test/DipolesParser/dipoles/ndb.dipoles'}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "code.run(input=inp,run_dir=run_dir,name='dipoles',mpi=1,skip=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once that the computation is over, and the database is built by Yambo we parse it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "dipole_file = os.path.join(run_dir,'dipoles/ndb.dipoles')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse file : YamboParser_test/DipolesParser/dipoles/ndb.dipoles\n"
     ]
    }
   ],
   "source": [
    "dipoles = P.YamboDipolesParser(dipole_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "YamboDipolesParser variables structure\n",
      "dip_ir shape (16, 12, 12, 3, 2)\n",
      "dip_p shape (16, 12, 12, 3, 2)\n",
      "dip_v shape (16, 12, 12, 3, 2)\n",
      "dip_spin shape (16, 12, 12, 3, 2)\n"
     ]
    }
   ],
   "source": [
    "dipoles.get_info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We consider the r dipole (x component) and look for the values of the valence->conduction\n",
    "elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 0.00000000e+00+0.00000000e+00j -7.32989130e+00-1.04414460e+00j\n",
      "   -2.67618113e-04-2.73337086e-02j -8.31398256e-01-8.24659863e-01j\n",
      "    1.54225410e-02+5.53155198e-01j -8.47292442e-03-3.52913430e-02j\n",
      "    3.94122962e-02+4.51266933e-01j -3.50684921e-02-4.23464784e-02j\n",
      "   -3.27529949e-03+3.62501186e-03j  3.12675546e-02-8.78397798e-02j\n",
      "   -6.91983853e-03-5.34757557e-03j  5.07940800e-03-9.23038442e-02j]\n",
      "  [-7.32989130e+00+1.04414460e+00j  0.00000000e+00+0.00000000e+00j\n",
      "    8.01318921e-01+8.54408462e-01j -3.56489315e-02-9.21653706e-03j\n",
      "   -9.18567469e-03-1.86620892e-02j  5.51941446e-01+1.59714051e-03j\n",
      "   -8.44179368e-03-1.80563835e-02j -4.53646536e-01+2.62720464e-02j\n",
      "   -3.27381201e-02-8.40906364e-02j  2.53831680e-03-5.56781058e-03j\n",
      "    2.48079709e-02-9.19902074e-02j  5.51535117e-03+1.76623507e-03j]\n",
      "  [-2.67618113e-04+2.73337086e-02j  8.01318921e-01-8.54408462e-01j\n",
      "    0.00000000e+00+0.00000000e+00j  1.89030428e+00+4.66548965e-01j\n",
      "    5.79517535e-02+7.19914584e-02j  2.97413490e-01+5.37567659e-01j\n",
      "    3.39576600e-02+1.71525456e-02j  1.13901180e+00-2.80942908e-02j\n",
      "   -1.35458846e-01+8.48610027e-02j -5.79681904e-03-1.26648203e-02j\n",
      "   -8.71556302e-02-1.00831534e-01j -5.21431367e-03+1.90947218e-02j]\n",
      "  [-8.31398256e-01+8.24659863e-01j -3.56489315e-02+9.21653706e-03j\n",
      "    1.89030428e+00-4.66548965e-01j  0.00000000e+00+0.00000000e+00j\n",
      "    5.45636211e-01-3.35822521e-01j  1.14411805e-02-7.20224684e-02j\n",
      "    2.58112611e-02+1.15460258e+00j -1.03327761e-02+2.34740261e-03j\n",
      "   -9.08485260e-03+1.39249252e-02j  1.57530187e-01+1.64761717e-02j\n",
      "   -9.18993867e-03-6.50085470e-03j  1.11691402e-01+7.31907914e-02j]\n",
      "  [ 1.54225410e-02-5.53155198e-01j -9.18567469e-03+1.86620892e-02j\n",
      "    5.79517535e-02-7.19914584e-02j  5.45636211e-01+3.35822521e-01j\n",
      "    0.00000000e+00+0.00000000e+00j  2.11427743e-01-9.56134044e-01j\n",
      "    1.62831255e+00-7.55540099e-01j  6.61581637e-02+1.10979076e-01j\n",
      "   -9.25205611e-02+4.45597370e-02j  1.38243035e-01+2.87229424e-01j\n",
      "    4.08191950e-02-2.36504566e-04j  2.30604964e-01+2.95389137e-01j]\n",
      "  [-8.47292442e-03+3.52913430e-02j  5.51941446e-01-1.59714051e-03j\n",
      "    2.97413490e-01-5.37567659e-01j  1.14411805e-02+7.20224684e-02j\n",
      "    2.11427743e-01+9.56134044e-01j  0.00000000e+00+0.00000000e+00j\n",
      "   -5.85802578e-02-1.12608495e-01j -1.63776512e+00+7.00740450e-01j\n",
      "   -1.39602536e-01+3.03461916e-01j -5.13598540e-02+6.83745581e-02j\n",
      "   -3.35693039e-01+1.78584191e-01j -7.62539820e-03-1.13538619e-02j]\n",
      "  [ 3.94122962e-02-4.51266933e-01j -8.44179368e-03+1.80563835e-02j\n",
      "    3.39576600e-02-1.71525456e-02j  2.58112611e-02-1.15460258e+00j\n",
      "    1.62831255e+00+7.55540099e-01j -5.85802578e-02+1.12608495e-01j\n",
      "    0.00000000e+00+0.00000000e+00j -2.28599993e+01-4.96581405e+01j\n",
      "   -1.40120452e-03-3.13814570e-02j  3.07801000e+00-1.74927227e+00j\n",
      "   -8.07164003e-03-1.42328602e-02j -8.70404117e-01+5.57876659e-01j]\n",
      "  [-3.50684921e-02+4.23464784e-02j -4.53646536e-01-2.62720464e-02j\n",
      "    1.13901180e+00+2.80942908e-02j -1.03327761e-02-2.34740261e-03j\n",
      "    6.61581637e-02-1.10979076e-01j -1.63776512e+00-7.00740450e-01j\n",
      "   -2.28599993e+01+4.96581405e+01j  0.00000000e+00+0.00000000e+00j\n",
      "   -3.38338401e+00-1.12316434e+00j -8.75460474e-03-8.31172632e-04j\n",
      "    4.99613685e-01+9.46940754e-01j  8.01958247e-03-4.09287053e-02j]\n",
      "  [-3.27529949e-03-3.62501186e-03j -3.27381201e-02+8.40906364e-02j\n",
      "   -1.35458846e-01-8.48610027e-02j -9.08485260e-03-1.39249252e-02j\n",
      "   -9.25205611e-02-4.45597370e-02j -1.39602536e-01-3.03461916e-01j\n",
      "   -1.40120452e-03+3.13814570e-02j -3.38338401e+00+1.12316434e+00j\n",
      "    0.00000000e+00+0.00000000e+00j -1.38856218e+00-5.58888916e+00j\n",
      "   -2.31086113e+00+1.17762304e+00j -1.41328812e-01+1.49001606e-01j]\n",
      "  [ 3.12675546e-02+8.78397798e-02j  2.53831680e-03+5.56781058e-03j\n",
      "   -5.79681904e-03+1.26648203e-02j  1.57530187e-01-1.64761717e-02j\n",
      "    1.38243035e-01-2.87229424e-01j -5.13598540e-02-6.83745581e-02j\n",
      "    3.07801000e+00+1.74927227e+00j -8.75460474e-03+8.31172632e-04j\n",
      "   -1.38856218e+00+5.58888916e+00j  0.00000000e+00+0.00000000e+00j\n",
      "    2.52960030e-02-1.81659493e-01j -7.61363207e-01+2.53330306e+00j]\n",
      "  [-6.91983853e-03+5.34757557e-03j  2.48079709e-02+9.19902074e-02j\n",
      "   -8.71556302e-02+1.00831534e-01j -9.18993867e-03+6.50085470e-03j\n",
      "    4.08191950e-02+2.36504566e-04j -3.35693039e-01-1.78584191e-01j\n",
      "   -8.07164003e-03+1.42328602e-02j  4.99613685e-01-9.46940754e-01j\n",
      "   -2.31086113e+00-1.17762304e+00j  2.52960030e-02+1.81659493e-01j\n",
      "    0.00000000e+00+0.00000000e+00j -4.97362491e+00+1.64461368e+01j]\n",
      "  [ 5.07940800e-03+9.23038442e-02j  5.51535117e-03-1.76623507e-03j\n",
      "   -5.21431367e-03-1.90947218e-02j  1.11691402e-01-7.31907914e-02j\n",
      "    2.30604964e-01-2.95389137e-01j -7.62539820e-03+1.13538619e-02j\n",
      "   -8.70404117e-01-5.57876659e-01j  8.01958247e-03+4.09287053e-02j\n",
      "   -1.41328812e-01-1.49001606e-01j -7.61363207e-01-2.53330306e+00j\n",
      "   -4.97362491e+00-1.64461368e+01j  0.00000000e+00+0.00000000e+00j]]]\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "k = 0\n",
    "vb = 7\n",
    "cb = 8\n",
    "print(dipoles.r_dipole(k,vb,cb,0))\n",
    "print(dipoles.r_dipole(k,cb,vb,0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we observe that inverting the order of the bands changes sign to the imaginary part of the matrix\n",
    "element, as expected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Analysis of the YamboDftParser class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This database collects information about the lattice and electronic properties\n",
    "of the system.\n",
    "\n",
    "The information codified in this database are usually equivalent to the ones in the QuantumESPRESSO\n",
    "data-file-schema.xml and the YamboDftParser shares many common functions with the PwParser.\n",
    "\n",
    "To perform a comparison we consider the example of bulk WSe2, that has a exaghonal symmetry (ibrav=4 in pw input) with\n",
    "layered structure in the $z$ direction. We consider a 12x12x3 $k$-sampling with 100 bands and we compare the `data-file-schema.xml` and\n",
    "the `ns.db1` database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "xml = 'YamboParser_test/dftParsers_results/WSe2_12x12x3_100bands/data-file-schema.xml'\n",
    "nsdb = 'YamboParser_test/dftParsers_results/WSe2_12x12x3_100bands/ns.db1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse file : YamboParser_test/dftParsers_results/WSe2_12x12x3_100bands/data-file-schema.xml\n",
      "Parse file : YamboParser_test/dftParsers_results/WSe2_12x12x3_100bands/ns.db1\n"
     ]
    }
   ],
   "source": [
    "pw_data = P.PwParser(xml)\n",
    "yambo_data = P.YamboDftParser(nsdb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 6.198302   5.3678865 24.483292 ]\n",
      "[[ 6.198302   0.         0.       ]\n",
      " [-3.099151   5.3678865  0.       ]\n",
      " [ 0.         0.        24.483292 ]]\n",
      "100\n",
      "52\n",
      "1\n",
      "12\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 1.0000001,  0.       ,  0.       ],\n",
       "       [ 0.       , -1.       ,  0.       ],\n",
       "       [ 0.       ,  0.       , -1.0000001]], dtype=float32)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(yambo_data.alat)\n",
    "print(yambo_data.lattice)\n",
    "print(yambo_data.nbands)\n",
    "print(yambo_data.nbands_full)\n",
    "print(yambo_data.spin_degen)\n",
    "print(len(yambo_data.syms))\n",
    "yambo_data.syms[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.198301719262\n",
      "[[ 6.19830172  0.          0.        ]\n",
      " [-3.09915086  5.36788675  0.        ]\n",
      " [ 0.          0.         24.48329179]]\n"
     ]
    }
   ],
   "source": [
    "print(pw_data.alat)\n",
    "print(pw_data.lattice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Note that Yambo uses a vector alat variable. For this reason if we compare the expression of the lattice vector expressed in units of alat and the \n",
    "ones of the reciprocal axis vector expressed in units of 2$\\pi$/alat they have different components. This can be confirmed by looking in the log and\n",
    "in r_setup file in the folder `YamboParser_test/dftParsers_results/WSe2_12x12x3_100bands`.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.       ,  0.       ,  0.       ],\n",
       "       [-0.5      ,  0.8660254,  0.       ],\n",
       "       [ 0.       ,  0.       ,  3.95     ]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pw_data.get_lattice(rescale=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.       ,  0.       ,  0.       ],\n",
       "       [-0.5      ,  0.8660254,  0.       ],\n",
       "       [ 0.       ,  0.       ,  3.9499998]], dtype=float32)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yambo_data.get_lattice(rescale=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the get_lattice method of yambo_data rescale the lattice vectors w.r.t the first component of the vector alat parameter of yambo, so the result is equivalent to the one of the pw_data class.\n",
    "\n",
    "Due to the same approach also the reciprocal lattice are consistent in the two classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  0.57735027, -0.        ],\n",
       "       [ 0.        ,  1.15470054,  0.        ],\n",
       "       [ 0.        , -0.        ,  0.25316456]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pw_data.get_reciprocal_lattice(rescale=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  0.5773503 , -0.        ],\n",
       "       [ 0.        ,  1.1547006 ,  0.        ],\n",
       "       [ 0.        , -0.        ,  0.25316456]], dtype=float32)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yambo_data.get_reciprocal_lattice(rescale=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also compare the expressions of the k-points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  0.        ,  0.        ],\n",
       "       [ 0.        ,  0.        ,  0.08438819],\n",
       "       [ 0.        ,  0.09622504,  0.        ],\n",
       "       [ 0.        ,  0.09622504,  0.08438819],\n",
       "       [ 0.        ,  0.19245009,  0.        ],\n",
       "       [ 0.        ,  0.19245009,  0.08438819],\n",
       "       [ 0.        ,  0.28867513,  0.        ],\n",
       "       [ 0.        ,  0.28867513,  0.08438819],\n",
       "       [ 0.        ,  0.38490018,  0.        ],\n",
       "       [ 0.        ,  0.38490018,  0.08438819],\n",
       "       [ 0.        ,  0.48112522,  0.        ],\n",
       "       [ 0.        ,  0.48112522,  0.08438819],\n",
       "       [ 0.        , -0.57735027,  0.        ],\n",
       "       [ 0.        , -0.57735027,  0.08438819],\n",
       "       [ 0.08333333,  0.14433757,  0.        ],\n",
       "       [ 0.08333333,  0.14433757,  0.08438819],\n",
       "       [ 0.08333333,  0.24056261,  0.        ],\n",
       "       [ 0.08333333,  0.24056261,  0.08438819],\n",
       "       [ 0.08333333,  0.33678766,  0.        ],\n",
       "       [ 0.08333333,  0.33678766,  0.08438819]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pw_data.kpoints[0:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  0.        ,  0.        ],\n",
       "       [ 0.        ,  0.        ,  0.33333334],\n",
       "       [ 0.        ,  0.08333333,  0.        ],\n",
       "       [ 0.        ,  0.08333333,  0.33333334],\n",
       "       [ 0.        ,  0.16666666,  0.        ],\n",
       "       [ 0.        ,  0.16666666,  0.33333334],\n",
       "       [ 0.        ,  0.24999999,  0.        ],\n",
       "       [ 0.        ,  0.24999999,  0.33333334],\n",
       "       [ 0.        ,  0.3333333 ,  0.        ],\n",
       "       [ 0.        ,  0.3333333 ,  0.33333334],\n",
       "       [ 0.        ,  0.41666666,  0.        ],\n",
       "       [ 0.        ,  0.41666666,  0.33333334],\n",
       "       [ 0.        , -0.49999997,  0.        ],\n",
       "       [ 0.        , -0.49999997,  0.33333334],\n",
       "       [ 0.08333334,  0.12499999,  0.        ],\n",
       "       [ 0.08333334,  0.12499999,  0.33333334],\n",
       "       [ 0.08333334,  0.20833333,  0.        ],\n",
       "       [ 0.08333334,  0.20833333,  0.33333334],\n",
       "       [ 0.08333334,  0.29166666,  0.        ],\n",
       "       [ 0.08333334,  0.29166666,  0.33333334]], dtype=float32)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yambo_data.kpoints[0:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which are different because they represent the cart. coord. in units 2pi/alat and again yambo uses a vector alat."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  0.        ,  0.        ],\n",
       "       [ 0.        ,  0.        ,  0.08438819],\n",
       "       [ 0.        ,  0.09622505,  0.        ],\n",
       "       [ 0.        ,  0.09622505,  0.08438819],\n",
       "       [ 0.        ,  0.19245009,  0.        ],\n",
       "       [ 0.        ,  0.19245009,  0.08438819],\n",
       "       [ 0.        ,  0.28867513,  0.        ],\n",
       "       [ 0.        ,  0.28867513,  0.08438819],\n",
       "       [ 0.        ,  0.38490018,  0.        ],\n",
       "       [ 0.        ,  0.38490018,  0.08438819],\n",
       "       [ 0.        ,  0.48112527,  0.        ],\n",
       "       [ 0.        ,  0.48112527,  0.08438819],\n",
       "       [ 0.        , -0.57735026,  0.        ],\n",
       "       [ 0.        , -0.57735026,  0.08438819],\n",
       "       [ 0.08333334,  0.14433756,  0.        ],\n",
       "       [ 0.08333334,  0.14433756,  0.08438819],\n",
       "       [ 0.08333334,  0.24056263,  0.        ],\n",
       "       [ 0.08333334,  0.24056263,  0.08438819],\n",
       "       [ 0.08333334,  0.33678767,  0.        ],\n",
       "       [ 0.08333334,  0.33678767,  0.08438819]], dtype=float32)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yambo_data.get_kpoints(use_scalar_alat=True)[0:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this way the components are equal to the ones of the pw_data class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The class contain also methods to compute the lattice volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "814.6027"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yambo_data.eval_lattice_volume(rescale=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "814.6027389471978"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pw_data.eval_lattice_volume(rescale=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.29232928"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yambo_data.eval_reciprocal_lattice_volume(rescale=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2923292502226171"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pw_data.eval_reciprocal_lattice_volume(rescale=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, we show the kpoints distribution after a Fixsymm procedure. We use the same WSe2 calculation but we load the ndb\n",
    "obtained after the Fixsymm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse file : YamboParser_test/rt_results/ns.db1\n"
     ]
    }
   ],
   "source": [
    "dft_ndb = 'YamboParser_test/rt_results/ns.db1'\n",
    "fixsymm_data = P.YamboDftParser(dft_ndb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "kpoints = fixsymm_data.get_kpoints(use_scalar_alat=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in cartesian coordinates\n",
    "Gamma = np.array([0.,0.,0.])\n",
    "K = np.array([1/3.,1/np.sqrt(3.),0])\n",
    "M = np.array([0.,1/np.sqrt(3.),0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "222"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numk = kpoints.shape[0]\n",
    "numk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb098acc700>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "\n",
    "for ind in range(numk): # limit to 50 for the non expanded grid\n",
    "    if kpoints[ind,2] == 0.: # project on the xy plane\n",
    "        plt.scatter(kpoints[ind,0],kpoints[ind,1])\n",
    "        plt.text(kpoints[ind,0]-0.01,kpoints[ind,1]+0.02,ind)\n",
    "IBZ = [Gamma[0:2],K[0:2], M[0:2],Gamma[0:2]]\n",
    "plt.plot(*np.column_stack(IBZ),color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(9, 9))\n",
    "ax = fig.add_subplot(projection='3d')\n",
    "ax.view_init(10, 35)\n",
    "for ind in range(numk):\n",
    "    ax.scatter3D(kpoints[ind,0],kpoints[ind,1],kpoints[ind,2])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of the YamboQPParser class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This database collects information about the quasi-particle correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse file : YamboCalculator_test/qp_test1/ndb.QP\n",
      "[8, 32, 16, 0, 0, 24]\n"
     ]
    }
   ],
   "source": [
    "qp_ndb = 'YamboCalculator_test/qp_test1/ndb.QP'\n",
    "qp_data = P.YamboQPParser(qp_ndb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from netCDF4 import Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = Dataset(qp_ndb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(data=[ 8., 32., 16.,  0.,  0., 24.],\n",
       "             mask=False,\n",
       "       fill_value=1e+20,\n",
       "            dtype=float32)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d['PARS'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'netCDF4._netCDF4.Dataset'>\n",
       "root group (NETCDF4 data model, file format HDF5):\n",
       "    dimensions(sizes): D_0000000001(1), D_0000000003(3), D_0000000002(2), D_0000000100(100), D_0000000006(6), QP_desc_size(24), QP_string_len(100), D_0000000016(16), D_0000000032(32)\n",
       "    variables(dimensions): float32 QP_DB_kind(D_0000000001), float32 HEAD_VERSION(D_0000000003), float32 HEAD_REVISION(D_0000000001), float32 SERIAL_NUMBER(D_0000000001), float32 SPIN_VARS(D_0000000002), float32 HEAD_D_LATT(D_0000000003), |S1 CUTOFF(D_0000000001, D_0000000100), float32 TEMPERATURES(D_0000000002), |S1 G_energies_xc_KIND(D_0000000001, D_0000000100), |S1 G_wavefunctions_xc_KIND(D_0000000001, D_0000000100), |S1 Xp_energies_xc_KIND(D_0000000001, D_0000000100), |S1 Xp_wavefunctions_xc_KIND(D_0000000001, D_0000000100), float32 PARS(D_0000000006), int32 QP_N_DESCRIPTORS(D_0000000001), int32 QP_DESCRIPTORS_SIZES(QP_desc_size), |S1 QP_DESCRIPTORS_NAMES(QP_desc_size, QP_string_len), |S1 QP_DESCRIPTORS_KINDS(QP_desc_size, QP_string_len), |S1 QP_DESCRIPTORS_TERMS(QP_desc_size, QP_string_len), |S1 QP_GW_solver(D_0000000001, D_0000000100), |S1 QP_GW_approximation(D_0000000001, D_0000000100), float32 QP_PPA_imaginary_Energy(D_0000000001), int32 QP_GW_SC_iterations(D_0000000001), int32 QP_dS_dw_steps(D_0000000001), float32 QP_dS_dw_step(D_0000000001), int32 QP_X_Gs(D_0000000001), int32 QP_X_bands(D_0000000002), float32 QP_X_poles(D_0000000001), float32 QP_X_e_h_E_range(D_0000000002), |S1 QP_X_Hxc_Kernel(D_0000000001, D_0000000100), int8 QP_X_BZ_energy_Double_Grid(D_0000000001), int32 QP_Sc_G_bands(D_0000000002), float32 QP_Sc_G_damping(D_0000000001), int8 QP_Sc_bands_terminator(D_0000000001), int32 QP_Sx_RL_components(D_0000000001), |S1 QP_EMPTY_STR_Nr18(D_0000000001, D_0000000100), int32 QP_QP_@_state_1_K_range(D_0000000002), int32 QP_QP_@_state_1_b_range(D_0000000002), |S1 QP_GF_energies_kind(D_0000000001, D_0000000100), |S1 QP_GF_WFs_kind(D_0000000001, D_0000000100), |S1 QP_Xs_energies_kind(D_0000000001, D_0000000100), |S1 QP_Xs_WFs_kind(D_0000000001, D_0000000100), float32 QP_table(D_0000000003, D_0000000016), float32 QP_kpts(D_0000000003, D_0000000032), float32 QP_E(D_0000000016, D_0000000002), float32 QP_Eo(D_0000000016), float32 QP_Z(D_0000000016, D_0000000002)\n",
       "    groups: "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "......"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Analysis of the YamboRTCarriersParser class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This database collects information about the time, energy and $k$-points distribution of the \n",
    "real-time carriers.\n",
    "\n",
    "We consider the test case of a RT simulation performed on bulk WSe2 on a 12x12x3 k-sampling (with a Fixsymm procedure due to \n",
    "the linear external field in the $x$ direction). The ns.db1 associated built by the Fixsymm is included in the folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "rt_ndb = 'YamboParser_test/rt_results/ndb.RT_carriers'\n",
    "dft_ndb = 'YamboParser_test/rt_results/ns.db1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse file : YamboParser_test/rt_results/ndb.RT_carriers\n",
      "Parse file : YamboParser_test/rt_results/ns.db1\n"
     ]
    }
   ],
   "source": [
    "rt_data = P.YamboRTCarriersParser(rt_ndb)\n",
    "dft_data = P.YamboDftParser(dft_ndb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "YamboRTCarriersParser variables structure\n",
      "Bands used and number of k-points [ 49  54 222]\n",
      "kpoints shape (222, 3)\n",
      "E_bare shape (1332,)\n",
      "f_bare shape (1332,)\n",
      "delta_E shape (16, 1332)\n",
      "delta_f (16, 1332)\n"
     ]
    }
   ],
   "source": [
    "rt_data.get_info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = rt_data.build_f_bare_dos(eta=0.05,dE=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "222"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(d.dos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d.plot(plt,legend=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}