text: # coding: utf-8
  # Copyright (c) Pymatgen Development Team.
  # Distributed under the terms of the MIT License
  
  
  import re
  import numpy as np
  import warnings
  import collections
  import os
  from monty.io import zopen
  from monty.serialization import loadfn
  from monty.json import MSONable
  import fnmatch
  import itertools
  import warnings
  import spglib
  from collections import defaultdict
  from collections import OrderedDict
  from pymatgen.electronic_structure.core import Spin, Orbital
  from pymatgen.io.vasp.outputs import Vasprun
  from pymatgen.electronic_structure.dos import Dos, LobsterCompleteDos
  from pymatgen.core.structure import Structure
  from pymatgen.electronic_structure.bandstructure import Kpoint
  from pymatgen.io.vasp.inputs import Kpoints
  from pymatgen.electronic_structure.bandstructure import LobsterBandStructureSymmLine
  from pymatgen.core.structure import Structure
  from pymatgen.io.vasp.inputs import Incar, Kpoints, Potcar
  from pymatgen.symmetry.bandstructure import HighSymmKpath
  
  """
  Module for reading Lobster output files. For more information
  on LOBSTER see www.cohp.de.
  """
  
  __author__ = "Janine George, Marco Esters"
  __copyright__ = "Copyright 2017, The Materials Project"
  __version__ = "0.2"
  __maintainer__ = "Janine George, Marco Esters "
  __email__ = "esters@uoregon.edu, janine.george@uclouvain.be"
  __date__ = "Dec 13, 2017"
  
  MODULE_DIR = os.path.dirname(os.path.abspath(__file__))
  
  
  class Cohpcar:
      """
      Class to read COHPCAR/COOPCAR files generated by LOBSTER.
  
      Args:
          are_coops: Determines if the file is a list of COHPs or COOPs.
            Default is False for COHPs.
  
          filename: Name of the COHPCAR file. If it is None, the default
            file name will be chosen, depending on the value of are_coops.
  
  
      .. attribute: cohp_data
  
           Dict that contains the COHP data of the form:
             {bond: {"COHP": {Spin.up: cohps, Spin.down:cohps},
                     "ICOHP": {Spin.up: icohps, Spin.down: icohps},
                     "length": bond length,
                     "sites": sites corresponding to the bond}
           Also contains an entry for the average, which does not have
           a "length" key.
  
      .. attribute: efermi
  
           The Fermi energy in eV.
  
      .. attribute: energies
  
           Sequence of energies in eV. Note that LOBSTER shifts the energies
           so that the Fermi energy is at zero.
  
      .. attribute: is_spin_polarized
  
           Boolean to indicate if the calculation is spin polarized.
  
      .. attribute: orb_res_cohp
  
          orb_cohp[label] = {bond_data["orb_label"]: {"COHP": {Spin.up: cohps, Spin.down:cohps},
                                                       "ICOHP": {Spin.up: icohps, Spin.down: icohps},
                                                       "orbitals": orbitals,
                                                       "length": bond lengths,
                                                       "sites": sites corresponding to the bond}}
  
      """
  
      def __init__(self, are_coops=False, filename=None):
          self.are_coops = are_coops
          if filename is None:
              filename = "COOPCAR.lobster" if are_coops \
                  else "COHPCAR.lobster"
  
          with zopen(filename, "rt") as f:
              contents = f.read().split("\n")
  
          # The parameters line is the second line in a COHPCAR file. It
          # contains all parameters that are needed to map the file.
          parameters = contents[1].split()
          # Subtract 1 to skip the average
          num_bonds = int(parameters[0]) - 1
          self.efermi = float(parameters[-1])
          if int(parameters[1]) == 2:
              spins = [Spin.up, Spin.down]
              self.is_spin_polarized = True
          else:
              spins = [Spin.up]
              self.is_spin_polarized = False
  
          # The COHP data start in row num_bonds + 3
          data = np.array([np.array(row.split(), dtype=float)
                           for row in contents[num_bonds + 3:]]).transpose()
          self.energies = data[0]
  
          cohp_data = {"average": {"COHP": {spin: data[1 + 2 * s * (num_bonds + 1)]
                                            for s, spin in enumerate(spins)},
                                   "ICOHP": {spin: data[2 + 2 * s * (num_bonds + 1)]
                                             for s, spin in enumerate(spins)}}}
          orb_cohp = {}
  
          # present for Lobster versions older than Lobster 2.2.0
          veryold = False
          # the labeling had to be changed: there are more than one COHP for each atom combination
          # this is done to make the labeling consistent with ICOHPLIST.lobster
          bondnumber = 0
          for bond in range(num_bonds):
              bond_data = self._get_bond_data(contents[3 + bond])
  
              label = str(bondnumber)
  
              orbs = bond_data["orbitals"]
              cohp = {spin: data[2 * (bond + s * (num_bonds + 1)) + 3]
                      for s, spin in enumerate(spins)}
  
              icohp = {spin: data[2 * (bond + s * (num_bonds + 1)) + 4]
                       for s, spin in enumerate(spins)}
              if orbs is None:
                  bondnumber = bondnumber + 1
                  label = str(bondnumber)
                  cohp_data[label] = {"COHP": cohp, "ICOHP": icohp,
                                      "length": bond_data["length"],
                                      "sites": bond_data["sites"]}
  
              elif label in orb_cohp:
                  orb_cohp[label].update(
                      {bond_data["orb_label"]: {"COHP": cohp, "ICOHP": icohp, "orbitals": orbs,
                                                "length": bond_data["length"], "sites": bond_data["sites"]}})
              else:
                  # present for Lobster versions older than Lobster 2.2.0
                  if bondnumber == 0:
                      veryold = True
                  if veryold:
                      bondnumber += 1
                      label = str(bondnumber)
  
                  orb_cohp[label] = {bond_data["orb_label"]: {"COHP": cohp,
                                                              "ICOHP": icohp,
                                                              "orbitals": orbs,
                                                              "length": bond_data["length"],
                                                              "sites": bond_data["sites"]}}
  
          # present for lobster older than 2.2.0
          if veryold:
              for bond in orb_cohp:
                  cohp_data[bond] = {"COHP": None, "ICOHP": None,
                                     "length": bond_data["length"],
                                     "sites": bond_data["sites"]}
  
          self.orb_res_cohp = orb_cohp if orb_cohp else None
          self.cohp_data = cohp_data
  
      @staticmethod
      def _get_bond_data(line):
          """
          Subroutine to extract bond label, site indices, and length from
          a LOBSTER header line. The site indices are zero-based, so they
          can be easily used with a Structure object.
  
          Example header line: No.4:Fe1->Fe9(2.4524893531900283)
          Example header line for orbtial-resolved COHP:
              No.1:Fe1[3p_x]->Fe2[3d_x^2-y^2](2.456180552772262)
  
          Args:
              line: line in the COHPCAR header describing the bond.
  
          Returns:
              Dict with the bond label, the bond length, a tuple of the site
              indices, a tuple containing the orbitals (if orbital-resolved),
              and a label for the orbitals (if orbital-resolved).
          """
  
          orb_labs = ["s", "p_y", "p_z", "p_x", "d_xy", "d_yz", "d_z^2",
                      "d_xz", "d_x^2-y^2", "f_y(3x^2-y^2)", "f_xyz",
                      "f_yz^2", "f_z^3", "f_xz^2", "f_z(x^2-y^2)", "f_x(x^2-3y^2)"]
  
          line = line.rsplit("(", 1)
          # bondnumber = line[0].replace("->", ":").replace(".", ":").split(':')[1]
          length = float(line[-1][:-1])
  
          sites = line[0].replace("->", ":").split(":")[1:3]
          site_indices = tuple(int(re.split(r"\D+", site)[1]) - 1
                               for site in sites)
  
          # species = tuple(re.split(r"\d+", site)[0] for site in sites)
          if "[" in sites[0]:
              orbs = [re.findall(r"\[(.*)\]", site)[0] for site in sites]
              orbitals = [tuple((int(orb[0]), Orbital(orb_labs.index(orb[1:]))))
                          for orb in orbs]
              orb_label = "%d%s-%d%s" % (orbitals[0][0], orbitals[0][1].name,
                                         orbitals[1][0], orbitals[1][1].name)
  
          else:
              orbitals = None
              orb_label = None
  
          # a label based on the species alone is not feasible, there can be more than one bond for each atom combination
          # label = "%s" % (bondnumber)
  
          bond_data = {"length": length, "sites": site_indices,
                       "orbitals": orbitals, "orb_label": orb_label}
          return bond_data
  
  
  class Icohplist:
      """
      Class to read ICOHPLIST/ICOOPLIST files generated by LOBSTER.
  
      Args:
          are_coops: Determines if the file is a list of ICOHPs or ICOOPs.
            Defaults to False for ICOHPs.
  
          filename: Name of the ICOHPLIST file. If it is None, the default
            file name will be chosen, depending on the value of are_coops.
  
  
      .. attribute: are_coops
           Boolean to indicate if the populations are COOPs or COHPs.
  
      .. attribute: is_spin_polarized
           Boolean to indicate if the calculation is spin polarized.
  
      .. attribute: Icohplist
          Dict containing the listfile data of the form:
             {bond: "length": bond length,
                    "number_of_bonds": number of bonds
                    "icohp": {Spin.up: ICOHP(Ef) spin up, Spin.down: ...}}
  
      .. attribute: IcohpCollection
          IcohpCollection Object
  
      """
  
      def __init__(self, are_coops=False, filename=None):
  
          self.are_coops = are_coops
          if filename is None:
              filename = "ICOOPLIST.lobster" if are_coops \
                  else "ICOHPLIST.lobster"
  
          # LOBSTER list files have an extra trailing blank line
          # and we don't need the header.
          with zopen(filename) as f:
              data = f.read().split("\n")[1:-1]
          if len(data) == 0:
              raise IOError("ICOHPLIST file contains no data.")
  
          # Which Lobster version?
          if len(data[0].split()) == 8:
              version = '3.1.1'
          elif len(data[0].split()) == 6:
              version = '2.2.1'
              warnings.warn('Please consider using the new Lobster version. See www.cohp.de.')
          else:
              raise ValueError
  
          # If the calculation is spin polarized, the line in the middle
          # of the file will be another header line.
          if "distance" in data[len(data) // 2]:
              num_bonds = len(data) // 2
              if num_bonds == 0:
                  raise IOError("ICOHPLIST file contains no data.")
              self.is_spin_polarized = True
          else:
              num_bonds = len(data)
              self.is_spin_polarized = False
  
          list_labels = []
          list_atom1 = []
          list_atom2 = []
          list_length = []
          list_translation = []
          list_num = []
          list_icohp = []
          for bond in range(num_bonds):
              line = data[bond].split()
              icohp = {}
              if version == '2.2.1':
                  label = "%s" % (line[0])
                  atom1 = str(line[1])
                  atom2 = str(line[2])
                  length = float(line[3])
                  icohp[Spin.up] = float(line[4])
                  num = int(line[5])
                  translation = [0, 0, 0]
                  if self.is_spin_polarized:
                      icohp[Spin.down] = float(data[bond + num_bonds + 1].split()[4])
  
              elif version == '3.1.1':
                  label = "%s" % (line[0])
                  atom1 = str(line[1])
                  atom2 = str(line[2])
                  length = float(line[3])
                  translation = [int(line[4]), int(line[5]), int(line[6])]
                  icohp[Spin.up] = float(line[7])
                  num = int(1)
  
                  if self.is_spin_polarized:
                      icohp[Spin.down] = float(data[bond + num_bonds + 1].split()[7])
  
              list_labels.append(label)
              list_atom1.append(atom1)
              list_atom2.append(atom2)
              list_length.append(length)
              list_translation.append(translation)
              list_num.append(num)
              list_icohp.append(icohp)
  
          # to avoid circular dependencies
          from pymatgen.electronic_structure.cohp import IcohpCollection
          self._icohpcollection = IcohpCollection(are_coops=are_coops, list_labels=list_labels, list_atom1=list_atom1,
                                                  list_atom2=list_atom2, list_length=list_length,
                                                  list_translation=list_translation, list_num=list_num,
                                                  list_icohp=list_icohp, is_spin_polarized=self.is_spin_polarized)
  
      @property
      def icohplist(self):
          """
          Returns: icohplist compatible with older version of this class
          """
          icohplist_new = {}
          for key, value in self._icohpcollection._icohplist.items():
              icohplist_new[key] = {"length": value._length, "number_of_bonds": value._num,
                                    "icohp": value._icohp, "translation": value._translation}
          return icohplist_new
  
      @property
      def icohpcollection(self):
          """
          Returns: IcohpCollection object
          """
          return self._icohpcollection
  
  
  class Doscar:
      """
      Class to deal with Lobster's projected DOS and local projected DOS.
      The beforehand quantum-chemical calculation was performed with VASP
  
      Args:
          doscar: DOSCAR filename, typically "DOSCAR.lobster"
          structure_file: for vasp, this is typically "POSCAR"
          dftprogram: so far only "vasp" is implemented
  
      .. attribute:: completedos
  
          LobsterCompleteDos Object
  
      .. attribute:: pdos
          List of Dict including numpy arrays with pdos. Access as pdos[atomindex]['orbitalstring']['Spin.up/Spin.down']
  
      .. attribute:: tdos
          Dos Object of the total density of states
  
      .. attribute:: energies
          numpy array of the energies at which the DOS was calculated (in eV, relative to Efermi)
  
      .. attribute:: tdensities
          tdensities[Spin.up]: numpy array of the total density of states for the Spin.up contribution at each of the
              energies
          tdensities[Spin.down]: numpy array of the total density of states for the Spin.down contribution at each of the
              energies
  
          if is_spin_polarized=False:
          tdensities[Spin.up]: numpy array of the total density of states
  
      .. attribute:: is_spin_polarized
          Boolean. Tells if the system is spin polarized
  
  
      """
  
      def __init__(self, doscar="DOSCAR.lobster", structure_file="POSCAR", dftprogram="Vasp"):
  
          self._doscar = doscar
          if dftprogram == "Vasp":
              self._final_structure = Structure.from_file(structure_file)
  
          self._parse_doscar()
  
      def _parse_doscar(self):
          doscar = self._doscar
  
          tdensities = {}
          f = open(doscar)
          natoms = int(f.readline().split()[0])
          efermi = float([f.readline() for nn in range(4)][3].split()[17])
          dos = []
          orbitals = []
          for atom in range(natoms + 1):
              line = f.readline()
              ndos = int(line.split()[2])
              orbitals.append(line.split(';')[-1].split())
              line = f.readline().split()
              cdos = np.zeros((ndos, len(line)))
              cdos[0] = np.array(line)
              for nd in range(1, ndos):
                  line = f.readline().split()
                  cdos[nd] = np.array(line)
              dos.append(cdos)
          f.close()
          doshere = np.array(dos[0])
          if len(doshere[0, :]) == 5:
              self._is_spin_polarized = True
          elif len(doshere[0, :]) == 3:
              self._is_spin_polarized = False
          else:
              raise ValueError("There is something wrong with the DOSCAR. Can't extract spin polarization.")
          energies = doshere[:, 0]
          if not self._is_spin_polarized:
              tdensities[Spin.up] = doshere[:, 1]
              pdoss = []
              spin = Spin.up
              for atom in range(natoms):
                  pdos = defaultdict(dict)
                  data = dos[atom + 1]
                  _, ncol = data.shape
                  orbnumber = 0
                  for j in range(1, ncol):
                      orb = orbitals[atom + 1][orbnumber]
                      pdos[orb][spin] = data[:, j]
                      orbnumber = orbnumber + 1
                  pdoss.append(pdos)
          else:
              tdensities[Spin.up] = doshere[:, 1]
              tdensities[Spin.down] = doshere[:, 2]
              pdoss = []
              for atom in range(natoms):
                  pdos = defaultdict(dict)
                  data = dos[atom + 1]
                  _, ncol = data.shape
                  orbnumber = 0
                  for j in range(1, ncol):
                      if j % 2 == 0:
                          spin = Spin.down
                      else:
                          spin = Spin.up
                      orb = orbitals[atom + 1][orbnumber]
                      pdos[orb][spin] = data[:, j]
                      if j % 2 == 0:
                          orbnumber = orbnumber + 1
                  pdoss.append(pdos)
          self._efermi = efermi
          self._pdos = pdoss
          self._tdos = Dos(efermi, energies, tdensities)
          self._energies = energies
          self._tdensities = tdensities
          final_struct = self._final_structure
  
          pdossneu = {final_struct[i]: pdos for i, pdos in enumerate(self._pdos)}
  
          self._completedos = LobsterCompleteDos(final_struct, self._tdos, pdossneu)
  
      @property
      def completedos(self):
          return self._completedos
  
      @property
      def pdos(self):
          return self._pdos
  
      @property
      def tdos(self):
          return self._tdos
  
      @property
      def energies(self):
          return self._energies
  
      @property
      def tdensities(self):
          return self._tdensities
  
      @property
      def is_spin_polarized(self):
          return self._is_spin_polarized
  
  
  class Charge:
      """Class to read CHARGE files generated by LOBSTER
          Args:
              filename: filename for the CHARGE file, typically "CHARGE.lobster"
  
          .. attribute: atomlist
              List of atoms in CHARGE.lobster
          .. attribute: types
              List of types of atoms in CHARGE.lobster
          .. attribute: Mulliken
              List of Mulliken charges of atoms in CHARGE.lobster
          .. attribute: Loewdin
              List of Loewdin charges of atoms in CHARGE.Loewdin
          .. attribute: num_atoms
              Number of atoms in CHARGE.lobster
  
      """
  
      def __init__(self, filename="CHARGE.lobster"):
          with zopen(filename) as f:
              data = f.read().split("\n")[3:-3]
          if len(data) == 0:
              raise IOError("CHARGES file contains no data.")
  
          self.num_atoms = len(data)
          self.atomlist = []
          self.types = []
          self.Mulliken = []
          self.Loewdin = []
          for atom in range(0, self.num_atoms):
              line = data[atom].split()
              self.atomlist.append(line[1] + line[0])
              self.types.append(line[1])
              self.Mulliken.append(float(line[2]))
              self.Loewdin.append(float(line[3]))
  
      def get_structure_with_charges(self, structure_filename):
          """
          get a Structure with Mulliken and Loewdin charges as site properties
          Args:
              structure_filename: filename of POSCAR
          Returns:
              Structure Object with Mulliken and Loewdin charges as site properties
          """
  
          struct = Structure.from_file(structure_filename)
          Mulliken = self.Mulliken
          Loewdin = self.Loewdin
          site_properties = {"Mulliken Charges": Mulliken, "Loewdin Charges": Loewdin}
          new_struct = struct.copy(site_properties=site_properties)
          return new_struct
  
  
  class Lobsterout:
      """
      Class to read in the lobsterout and evaluate the spilling, save the basis, save warnings, save infos
      Args:
        filename: filename of lobsterout
  
        .. attribute: basis_functions
          list of basis functions that were used in lobster run as strings
  
        .. attribute: basis_type
           list of basis type that were used in lobster run as strings
  
        .. attribute: chargespilling
           list of charge spilling (first entry: result for spin 1, second entry: result for spin 2 or not present)
  
        .. attribute: dftprogram
           string representing the dft program used for the calculation of the wave function
  
        .. attribute: elements
           list of strings of elements that were present in lobster calculation
  
        .. attribute: has_CHARGE
           Boolean, indicates that CHARGE.lobster is present
  
        .. attribute: has_COHPCAR
          Boolean, indicates that COHPCAR.lobster and ICOHPLIST.lobster are present
  
        .. attribute: has_COOPCAR
          Boolean, indicates that COOPCAR.lobster and ICOOPLIST.lobster are present
  
        .. attribute: has_DOSCAR
          Boolean, indicates that DOSCAR.lobster is present
  
        .. attribute: has_Projection
          Boolean, indcates that projectionData.lobster is present
  
        .. attribute: has_bandoverlaps
          Boolean, indcates that bandOverlaps.lobster is present
  
        .. attribute: has_density_of_energies
          Boolean, indicates that DensityOfEnergy.lobster is present
  
        .. attribute: has_fatbands
          Boolean, indicates that fatband calculation was performed
  
        .. attribute: has_grosspopulation
          Boolean, indicates that GROSSPOP.lobster is present
  
        .. attribute: info_lines
          string with additional infos on the run
  
        .. attribute: info_orthonormalization
          string with infos on orthonormalization
  
        .. attribute: is_restart_from_projection
          Boolean that indicates that calculation was restartet from existing projection file
  
        .. attribute: lobster_version
          string that indicates Lobster version
  
        .. attribute: number_of_spins
          Integer indicating the number of spins
  
        .. attribute: number_of_threads
         integer that indicates how many threads were used
  
        .. attribute: timing
          dict with infos on timing
  
        .. attribute: totalspilling
          list of values indicating the total spilling for spin channel 1 (and spin channel 2)
  
        .. attribute: warninglines
          string with all warnings
  
  
      """
  
      def __init__(self, filename="lobsterout"):
  
          warnings.warn("Make sure the lobsterout is read in correctly. This is a brand new class.")
          # read in file
          with zopen(filename) as f:
              data = f.read().split("\n")  # [3:-3]
          if len(data) == 0:
              raise IOError("lobsterout does not contain any data")
  
          LobsterDict = {}
          # check if Lobster starts from a projection
          self.is_restart_from_projection = self._starts_from_projection(data=data)
  
          self.lobster_version = self._get_lobster_version(data=data)
  
          self.number_of_threads = int(self._get_threads(data=data))
          self.dftprogram = self._get_dft_program(data=data)
  
          self.number_of_spins = self._get_number_of_spins(data=data)
          chargespilling, totalspilling = self._get_spillings(data=data, number_of_spins=self.number_of_spins)
          self.chargespilling = chargespilling
          self.totalspilling = totalspilling
  
          elements, basistype, basisfunctions = self._get_elements_basistype_basisfunctions(data=data)
          self.elements = elements
          self.basis_type = basistype
          self.basis_functions = basisfunctions
  
          wall_time, user_time, sys_time = self._get_timing(data=data)
          timing = {}
          timing['walltime'] = wall_time
          timing['usertime'] = user_time
          timing['sys_time'] = sys_time
          self.timing = timing
  
          warninglines = self._get_all_warning_lines(data=data)
          self.warninglines = warninglines
  
          orthowarning = self._get_warning_orthonormalization(data=data)
          self.info_orthonormalization = orthowarning
          # print(orthowarning)
  
          infos = self._get_all_info_lines(data=data)
          self.info_lines = infos
  
          self.has_DOSCAR = self._has_DOSCAR(data=data)
          self.has_COHPCAR = self._has_COOPCAR(data=data)
          self.has_COOPCAR = self._has_COHPCAR(data=data)
          self.has_CHARGE = self._has_CHARGE(data=data)
          self.has_Projection = self._has_projection(data=data)
          self.has_bandoverlaps = self._has_bandoverlaps(data=data)
          self.has_fatbands = self._has_fatband(data=data)
          self.has_grosspopulation = self._has_grosspopulation(data=data)
          self.has_density_of_energies = self._has_density_of_energies(data=data)
  
      def get_doc(self):
          """
  
          Returns: LobsterDict with all the information stored in lobsterout
          """
  
          LobsterDict = {}
          # check if Lobster starts from a projection
          LobsterDict['restart_from_projection'] = self.is_restart_from_projection
          LobsterDict['lobster_version'] = self.lobster_version
          LobsterDict['threads'] = self.number_of_threads
          LobsterDict['Dftprogram'] = self.dftprogram
  
          number_of_spins = self.number_of_spins
          LobsterDict['chargespilling'] = self.chargespilling
          LobsterDict['totalspilling'] = self.totalspilling
  
          LobsterDict['elements'] = self.elements
          LobsterDict['basistype'] = self.basis_type
          LobsterDict['basisfunctions'] = self.basis_functions
  
          LobsterDict['timing'] = self.timing
  
          LobsterDict['warnings'] = self.warninglines
  
          LobsterDict['orthonormalization'] = self.info_orthonormalization
  
          LobsterDict['infos'] = self.info_lines
  
          LobsterDict['hasDOSCAR'] = self.has_DOSCAR
          LobsterDict['hasCOHPCAR'] = self.has_COHPCAR
          LobsterDict['hasCOOPCAR'] = self.has_COOPCAR
          LobsterDict['hasCHARGE'] = self.has_CHARGE
          LobsterDict['hasProjection'] = self.has_Projection
          LobsterDict['hasbandoverlaps'] = self.has_bandoverlaps
          LobsterDict['hasfatband'] = self.has_fatbands
          LobsterDict['hasGrossPopuliation'] = self.has_grosspopulation
          LobsterDict['hasDensityOfEnergies'] = self.has_density_of_energies
  
          return LobsterDict
  
      def _get_lobster_version(self, data):
          for row in data:
              splitrow = row.split()
              if len(splitrow) > 1:
                  if splitrow[0] == "LOBSTER":
                      return splitrow[1]
  
      def _has_bandoverlaps(self, data):
          if 'WARNING: I dumped the band overlap matrices to the file bandOverlaps.lobster.' in data:
              return True
          else:
              return False
  
      def _starts_from_projection(self, data):
          if 'loading projection from projectionData.lobster...' in data:
              return True
          else:
              return False
  
      def _has_DOSCAR(self, data):
          if 'writing DOSCAR.lobster...' in data and 'SKIPPING writing DOSCAR.lobster...' not in data:
              return True
          else:
              return False
  
      def _has_COOPCAR(self, data):
          if 'writing COOPCAR.lobster and ICOOPLIST.lobster...' in data and \
                  'SKIPPING writing COOPCAR.lobster and ICOOPLIST.lobster...' not in data:
              return True
          else:
              return False
  
      def _has_COHPCAR(self, data):
          if 'writing COHPCAR.lobster and ICOHPLIST.lobster...' in data and \
                  'SKIPPING writing COHPCAR.lobster and ICOHPLIST.lobster...' not in data:
              return True
          else:
              return False
  
      def _has_CHARGE(self, data):
          # weitere optionen testen -> auch hier kann uebersprungen werden
          if 'SKIPPING writing CHARGE.lobster...' not in data:
              return True
          else:
              return False
  
      def _has_grosspopulation(self, data):
          if 'writing CHARGE.lobster and GROSSPOP.lobster...' in data:
              return True
          else:
              return False
  
      def _has_projection(self, data):
          if 'saving projection to projectionData.lobster...' in data:
              return True
          else:
              return False
  
      def _has_fatband(self, data):
          for row in data:
              splitrow = row.split()
              if len(splitrow) > 1:
                  if splitrow[1] == 'FatBand':
                      return True
          return False
  
      def _has_density_of_energies(self, data):
          if "writing DensityOfEnergy.lobster..." in data:
              return True
          else:
              return False
  
      def _get_dft_program(self, data):
          for row in data:
              splitrow = row.split()
              if len(splitrow) > 4:
                  if splitrow[3] == "program...":
                      return splitrow[4]
  
      def _get_number_of_spins(self, data):
          if "spillings for spin channel 2" in data:
              # print('two spin channels')
              return 2
          else:
              return 1
  
      def _get_threads(self, data):
          for row in data:
              splitrow = row.split()
              if len(splitrow) > 11:
                  if (splitrow[11]) == "threads":
                      return splitrow[10]
  
      def _get_spillings(self, data, number_of_spins):
          charge_spilling = []
          total_spilling = []
  
          for row in data:
              splitrow = row.split()
  
              if len(splitrow) > 2:
                  if splitrow[2] == 'spilling:':
                      if splitrow[1] == 'charge':
                          charge_spilling.append(np.float(splitrow[3].replace('%', '')) / 100.0)
                      if splitrow[1] == 'total':
                          total_spilling.append(np.float(splitrow[3].replace('%', '')) / 100.0)
  
              if len(charge_spilling) == number_of_spins and len(total_spilling) == number_of_spins:
                  break
  
          return charge_spilling, total_spilling
  
      def _get_elements_basistype_basisfunctions(self, data):
          begin = False
          end = False
          elements = []
          basistype = []
          basisfunctions = []
          for row in data:
  
              if begin and not end:
                  # print(row)
                  splitrow = row.split()
                  if splitrow[0] not in ['INFO:', 'WARNING:', 'setting', 'calculating', 'post-processing', 'saving',
                                         'spillings', 'writing']:
  
                      elements.append(splitrow[0])
                      basistype.append(splitrow[1].replace('(', '').replace(')', ''))
                      # last sign is a ''
                      basisfunctions.append(splitrow[2:])
                  else:
                      end = True
              if "setting up local basis functions..." in row:
                  begin = True
                  # print(row)
          return elements, basistype, basisfunctions
  
      def _get_timing(self, data):
          # will give back wall, user and sys time
          begin = False
          # end=False
          # time=[]
  
          for row in data:
              splitrow = row.split()
              if 'finished' in splitrow:
                  begin = True
              if begin:
                  if 'wall' in splitrow:
                      wall_time = (splitrow[2:10])
                  if 'user' in splitrow:
                      user_time = (splitrow[0:8])
                  if 'sys' in splitrow:
                      sys_time = (splitrow[0:8])
  
          wall_time_dict = {"h": wall_time[0], "min": wall_time[2], "s": wall_time[4], "ms": wall_time[6]}
          user_time_dict = {"h": user_time[0], "min": user_time[2], "s": user_time[4], "ms": user_time[6]}
          sys_time_dict = {"h": sys_time[0], "min": sys_time[2], "s": sys_time[4], "ms": sys_time[6]}
  
          return wall_time_dict, user_time_dict, sys_time_dict
  
      def _get_warning_orthonormalization(self, data):
          orthowarning = []
          for row in data:
              splitrow = row.split()
              if 'orthonormalized' in splitrow:
                  orthowarning.append(" ".join(splitrow[1:]))
          return orthowarning
  
      def _get_all_warning_lines(self, data):
          warnings = []
          for row in data:
              splitrow = row.split()
              if len(splitrow) > 0:
                  if splitrow[0] == 'WARNING:':
                      warnings.append(" ".join(splitrow[1:]))
  
          return warnings
          # print(warnings)
  
      def _get_all_info_lines(self, data):
          infos = []
          for row in data:
              splitrow = row.split()
              if len(splitrow) > 0:
                  if splitrow[0] == 'INFO:':
                      infos.append(" ".join(splitrow[1:]))
  
          return infos
  
  
  class Fatband:
      """
      reads in FATBAND_x_y.lobster files
      Args:
          filenames (list or string): can be a list of file names or a path to a folder folder from which all "FATBAND_*"
              files will be read
          vasprun: corresponding vasprun file
          Kpointsfile: KPOINTS file for bandstructure calculation, typically "KPOINTS"
  
      .. attribute: efermi
  
          efermi that was read in from vasprun.xml
  
      .. attribute: eigenvals
          {Spin.up:[][],Spin.down:[][]}, the first index of the array
              [][] refers to the band and the second to the index of the
              kpoint. The kpoints are ordered according to the order of the
              kpoints array. If the band structure is not spin polarized, we
              only store one data set under Spin.up.
  
      .. attribute: is_spinpolarized
  
          Boolean that tells you whether this was a spin-polarized calculation
  
      .. attribute: kpoints_array
  
          list of kpoint as numpy arrays, in frac_coords of the given lattice by default
  
      .. attribute: label_dict
  
           (dict) of {} this link a kpoint (in frac coords or cartesian coordinates depending on the coords).
  
      .. attribute: lattice
  
          lattice object of reciprocal lattice as read in from vasprun.xml
  
      .. attribute: nbands
  
          number of bands used in the calculation
  
      .. attribute: p_eigenvals
  
          dict of orbital projections as {spin: array of dict}.
          The indices of the array are [band_index, kpoint_index].
          The dict is then built the following way:
          {"string of element": "string of orbital as read in from FATBAND file"}
          If the band structure is not spin polarized, we only store one data set under Spin.up.
  
      .. attribute: structure
  
          structure read in from vasprun.xml
      """
  
      def __init__(self, filenames=".", vasprun='vasprun.xml', Kpointsfile='KPOINTS'):
  
          warnings.warn('Make sure all relevant FATBAND files were generated and read in!')
          warnings.warn('Use Lobster 3.2.0 or newer for fatband calculations!')
  
          VASPRUN = Vasprun(filename=vasprun, ionic_step_skip=None,
                            ionic_step_offset=0, parse_dos=True,
                            parse_eigen=False, parse_projected_eigen=False,
                            parse_potcar_file=False, occu_tol=1e-8,
                            exception_on_bad_xml=True)
          self.structure = VASPRUN.final_structure
          self.lattice = self.structure.lattice.reciprocal_lattice
          self.efermi = VASPRUN.efermi
          kpoints_object = Kpoints.from_file(Kpointsfile)
  
          atomtype = []
          atomnames = []
          orbital_names = []
  
          if not isinstance(filenames, list) or filenames is None:
              filenames_new = []
              if filenames is None:
                  filenames = '.'
              for file in os.listdir(filenames):
                  if fnmatch.fnmatch(file, 'FATBAND_*.lobster'):
                      filenames_new.append(os.path.join(filenames, file))
              filenames = filenames_new
          if len(filenames) == 0:
              raise ValueError("No FATBAND files in folder or given")
          for ifilename, filename in enumerate(filenames):
              with zopen(filename, "rt") as f:
                  contents = f.read().split("\n")
  
              # TODO: could be replaced for future versions of Lobster, get atomname from filename
              atomnames.append(os.path.split(filename)[1].split('_')[1].capitalize())
              parameters = contents[0].split()
              atomtype.append(re.split(r"[0-9]+", parameters[3])[0].capitalize())
              orbital_names.append(parameters[4])
  
          # get atomtype orbital dict
          atom_orbital_dict = {}
          for iatom, atom in enumerate(atomnames):
              if atom not in atom_orbital_dict:
                  atom_orbital_dict[atom] = []
              atom_orbital_dict[atom].append(orbital_names[iatom])
          # test if there are the same orbitals twice or if two different formats were used or if all necessary orbitals
          # are there
          for key, items in atom_orbital_dict.items():
              if len(set(items)) != len(items):
                  raise (ValueError("The are two FATBAND files for the same atom and orbital. The program will stop."))
              split = []
              for item in items:
                  split.append(item.split("_")[0])
              for orb, number in collections.Counter(split).items():
                  if number != 1 and number != 3 and number != 5 and number != 7:
                      raise ValueError(
                          "Make sure all relevant orbitals were generated and that no duplicates (2p and 2p_x) are "
                          "present")
  
          kpoints_array = []
          for ifilename, filename in enumerate(filenames):
              with zopen(filename, "rt") as f:
                  contents = f.read().split("\n")
  
              if ifilename == 0:
                  self.nbands = int(parameters[6])
                  self.number_kpts = kpoints_object.num_kpts - int(contents[1].split()[2]) + 1
  
              if len(contents[1:]) == self.nbands + 2:
                  self.is_spinpolarized = False
              elif len(contents[1:]) == self.nbands * 2 + 2:
                  self.is_spinpolarized = True
              else:
                  linenumbers = []
                  for iline, line in enumerate(contents[1:self.nbands * 2 + 4]):
                      # print(line)
                      # if line in ['\n', '\r\n']:
                      #    linenumbers.append(iline)
                      if line.split()[0] == '#':
                          linenumbers.append(iline)
  
                  if ifilename == 0:
                      if len(linenumbers) == 2:
                          self.is_spinpolarized = True
                      else:
                          self.is_spinpolarized = False
  
              if ifilename == 0:
                  eigenvals = {}
                  eigenvals[Spin.up] = [[collections.defaultdict(float)
                                         for i in range(self.number_kpts)]
                                        for j in range(self.nbands)]
                  if self.is_spinpolarized:
                      eigenvals[Spin.down] = [[collections.defaultdict(float)
                                               for i in range(self.number_kpts)]
                                              for j in range(self.nbands)]
  
                  p_eigenvals = {}
                  p_eigenvals[Spin.up] = [
                      [{str(e): {str(orb): collections.defaultdict(float) for orb in atom_orbital_dict[e]}
                        for e in atomnames}
                       for i in range(self.number_kpts)]
                      for j in range(self.nbands)]
  
                  if self.is_spinpolarized:
                      p_eigenvals[Spin.down] = [
                          [{str(e): {str(orb): collections.defaultdict(float) for orb in atom_orbital_dict[e]}
                            for e in atomnames}
                           for i in range(self.number_kpts)]
                          for j in range(self.nbands)]
  
              ikpoint = -1
              for iline, line in enumerate(contents[1:-1]):
                  if line.split()[0] == '#':
                      Kpointnumber = int(line.split()[2])
                      KPOINT = np.array([float(line.split()[4]), float(line.split()[5]), float(line.split()[6])])
                      if ifilename == 0:
                          kpoints_array.append(KPOINT)
  
                      linenumber = 0
                      iband = 0
                      ikpoint += 1
                  if linenumber == self.nbands:
                      iband = 0
                  if line.split()[0] != '#':
  
                      if linenumber < self.nbands:
                          if ifilename == 0:
                              eigenvals[Spin.up][iband][ikpoint] = float(line.split()[1]) + self.efermi
  
                          p_eigenvals[Spin.up][iband][ikpoint][atomnames[ifilename]][orbital_names[ifilename]] = float(
                              line.split()[2])
                      if linenumber >= self.nbands and self.is_spinpolarized:
                          # print(line.split())
                          # print(iband)
                          if ifilename == 0:
                              eigenvals[Spin.down][iband][ikpoint] = float(line.split()[1]) + self.efermi
                          p_eigenvals[Spin.down][iband][ikpoint][atomnames[ifilename]][
                              orbital_names[ifilename]] = float(line.split()[2])
  
                      linenumber += 1
                      iband += 1
  
          self.kpoints_array = kpoints_array
          self.eigenvals = eigenvals
          self.p_eigenvals = p_eigenvals
  
          label_dict = {}
          for ilabel, label in enumerate(kpoints_object.labels[-self.number_kpts:], start=0):
  
              if label is not None:
                  label_dict[label] = kpoints_array[ilabel]
  
          self.label_dict = label_dict
  
      def get_bandstructure(self):
          """
          returns a LobsterBandStructureSymmLine object which can be plotted with a normal BSPlotter
          """
  
          return LobsterBandStructureSymmLine(kpoints=self.kpoints_array, eigenvals=self.eigenvals, lattice=self.lattice,
                                              efermi=self.efermi, labels_dict=self.label_dict,
                                              structure=self.structure,
                                              projections=self.p_eigenvals)
  
  
  class Lobsterin(dict, MSONable):
      """
      This class can handle and generate lobsterin files
      Furthermore, it can also modify INCAR files for lobster, generate KPOINT files for fatband calculations in Lobster,
      and generate the standard primitive cells in a POSCAR file that are needed for the fatband calculations.
      There are also several standard lobsterin files that can be easily generated.
      Args:
          settingsdict: dict to initialize Lobsterin
  
      """
  
      # all keywords known to this class so far
      # reminder: lobster is not case sensitive
      AVAILABLEKEYWORDS = ['COHPstartEnergy', 'COHPendEnergy', 'basisSet', 'cohpGenerator',
                           'gaussianSmearingWidth', 'saveProjectionToFile', 'basisfunctions', 'skipdos',
                           'skipcohp', 'skipcoop', 'skipPopulationAnalysis', 'skipGrossPopulation',
                           'userecommendedbasisfunctions', 'loadProjectionFromFile', 'forceEnergyRange',
                           'DensityOfEnergy', 'BWDF', 'BWDFCOHP', 'skipProjection', 'createFatband',
                           'writeBasisFunctions', 'writeMatricesToFile', 'realspaceHamiltonian',
                           'realspaceOverlap', 'printPAWRealSpaceWavefunction', 'printLCAORealSpaceWavefunction',
                           'noFFTforVisualization', 'RMSp', 'onlyReadVasprun.xml', 'noMemoryMappedFiles',
                           'skipPAWOrthonormalityTest', 'doNotIgnoreExcessiveBands', 'doNotUseAbsoluteSpilling',
                           'skipReOrthonormalization', 'forceV1HMatrix', 'useOriginalTetrahedronMethod',
                           'useDecimalPlaces', 'kSpaceCOHP']
  
      # keyword + one float can be used in file
      FLOATKEYWORDS = ['COHPstartEnergy', 'COHPendEnergy', 'gaussianSmearingWidth', 'useDecimalPlaces']
      # one of these keywords +endstring can be used in file
      STRINGKEYWORDS = ['basisSet', 'cohpGenerator', 'realspaceHamiltonian', 'realspaceOverlap',
                        'printPAWRealSpaceWavefunction', 'printLCAORealSpaceWavefunction', 'kSpaceCOHP']
      # the keyword alone will turn on or off a function
      BOOLEANKEYWORDS = ['saveProjectionToFile', 'skipdos', 'skipcohp', 'skipcoop', 'loadProjectionFromFile',
                         'forceEnergyRange', 'DensityOfEnergy', 'BWDF', 'BWDFCOHP', 'skipPopulationAnalysis',
                         'skipGrossPopulation', 'userecommendedbasisfunctions', 'skipProjection',
                         'writeBasisFunctions', 'writeMatricesToFile', 'noFFTforVisualization', 'RMSp',
                         'onlyReadVasprun.xml', 'noMemoryMappedFiles', 'skipPAWOrthonormalityTest',
                         'doNotIgnoreExcessiveBands', 'doNotUseAbsoluteSpilling', 'skipReOrthonormalization',
                         'forceV1HMatrix', 'useOriginalTetrahedronMethod']
      # several of these keywords + ending can be used in a lobsterin file:
      LISTKEYWORDS = ['basisfunctions', 'cohpbetween', 'createFatband']
  
      def __init__(self, settingsdict: dict):
          super().__init__()
          # check for duplicates
          listkey = [key.lower() for key in settingsdict.keys()]
          if len(listkey) != len(list(set(listkey))):
              raise IOError("There are duplicates for the keywords! The program will stop here.")
          self.update(settingsdict)
  
      def __setitem__(self, key, val):
          """
          Add parameter-val pair to Lobsterin.  Warns if parameter is not in list of
          valid lobsterintags. Also cleans the parameter and val by stripping
          leading and trailing white spaces. Similar to INCAR class.
          """
          # due to the missing case sensitivity of lobster, the following code is neccessary
          found = False
          for key_here in self.keys():
              if key.strip().lower() == key_here.lower():
                  new_key = key_here
                  found = True
          if not found:
              new_key = key
          if new_key.lower() not in [element.lower() for element in Lobsterin.AVAILABLEKEYWORDS]:
              raise (ValueError("Key is currently not available"))
  
          super().__setitem__(new_key, val.strip() if isinstance(val, str) else val)
  
      def __getitem__(self, item):
          """
          implements getitem from dict to avoid problems with cases
          """
          found = False
          for key_here in self.keys():
              if item.strip().lower() == key_here.lower():
                  new_key = key_here
                  found = True
          if not found:
              new_key = item
  
          val = dict.__getitem__(self, new_key)
          return val
  
      def diff(self, other):
          """
          Diff function for lobsterin. Compares two lobsterin and indicates which parameters are the same.
          Similar to the diff in INCAR.
          Args:
              other (Lobsterin): Lobsterin object to compare to
          Returns:
              dict with differences and similarities
          """
          similar_param = {}
          different_param = {}
          key_list_others = [element.lower() for element in other.keys()]
  
          for k1, v1 in self.items():
              k1lower = k1.lower()
              if k1lower not in key_list_others:
                  different_param[k1.upper()] = {"lobsterin1": v1, "lobsterin2": None}
              else:
                  for key_here in other.keys():
                      if k1.lower() == key_here.lower():
                          new_key = key_here
  
                  if isinstance(v1, str):
                      if v1.strip().lower() != other[new_key].strip().lower():
  
                          different_param[k1.upper()] = {"lobsterin1": v1, "lobsterin2": other[new_key]}
                      else:
                          similar_param[k1.upper()] = v1
                  elif isinstance(v1, list):
                      new_set1 = set([element.strip().lower() for element in v1])
                      new_set2 = set([element.strip().lower() for element in other[new_key]])
                      if new_set1 != new_set2:
                          different_param[k1.upper()] = {"lobsterin1": v1, "lobsterin2": other[new_key]}
                  else:
                      if v1 != other[new_key]:
                          different_param[k1.upper()] = {"lobsterin1": v1, "lobsterin2": other[new_key]}
                      else:
                          similar_param[k1.upper()] = v1
  
          for k2, v2 in other.items():
              if k2.upper() not in similar_param and k2.upper() not in different_param:
                  for key_here in self.keys():
                      if k2.lower() == key_here.lower():
                          new_key = key_here
                      else:
                          new_key = k2
                  if new_key not in self:
                      different_param[k2.upper()] = {"lobsterin1": None, "lobsterin2": v2}
          return {"Same": similar_param, "Different": different_param}
  
      def _get_nbands(self, structure: Structure):
          """
          get number of nbands
          """
          if self.get("basisfunctions") is None:
              raise IOError("No basis functions are provided. The program cannot calculate nbands.")
          else:
              basis_functions = []
              for string_basis in self.get("basisfunctions"):
                  # string_basis.lstrip()
                  string_basis_raw = string_basis.strip().split(" ")
                  while "" in string_basis_raw:
                      string_basis_raw.remove("")
                  for i in range(0, int(structure.composition.element_composition[string_basis_raw[0]])):
                      basis_functions.extend(string_basis_raw[1:])
  
          no_basis_functions = 0
          for basis in basis_functions:
              if "s" in basis:
                  no_basis_functions = no_basis_functions + 1
              elif "p" in basis:
                  no_basis_functions = no_basis_functions + 3
              elif "d" in basis:
                  no_basis_functions = no_basis_functions + 5
              elif "f" in basis:
                  no_basis_functions = no_basis_functions + 7
  
          return int(no_basis_functions)
  
      def write_lobsterin(self, path="lobsterin", overwritedict=None):
          """
          writes a lobsterin file
          Args:
              path (str): filename of the lobsterin file that will be written
              overwritedict (dict): dict that can be used to overwrite lobsterin, e.g. {"skipdos": True}
          """
  
          # will overwrite previous entries
          # has to search first if entry is already in Lobsterindict (due to case insensitivity)
          if overwritedict is not None:
              for key, entry in overwritedict.items():
                  found = False
                  for key2 in self.keys():
                      if key.lower() == key2.lower():
                          self.get[key2] = entry
                          found = True
                  if not found:
                      self.get[key] = entry
  
          filename = path
          with open(filename, 'w') as f:
              for key in Lobsterin.AVAILABLEKEYWORDS:
                  if key.lower() in [element.lower() for element in self.keys()]:
                      if key.lower() in [element.lower() for element in Lobsterin.FLOATKEYWORDS]:
                          f.write(key + ' ' + str(self.get(key)) + '\n')
                      elif key.lower() in [element.lower() for element in Lobsterin.BOOLEANKEYWORDS]:
                          # checks if entry is True or False
                          for key_here in self.keys():
                              if key.lower() == key_here.lower():
                                  new_key = key_here
                          if self.get(new_key):
                              f.write(key + '\n')
                      elif key.lower() in [element.lower() for element in Lobsterin.STRINGKEYWORDS]:
                          f.write(key + ' ' + str(self.get(key) + '\n'))
                      elif key.lower() in [element.lower() for element in Lobsterin.LISTKEYWORDS]:
                          for entry in self.get(key):
                              f.write(key + ' ' + str(entry) + '\n')
  
      def as_dict(self):
          """
          get dict
          """
          d = dict(self)
          d["@module"] = self.__class__.__module__
          d["@class"] = self.__class__.__name__
          return d
  
      @classmethod
      def from_dict(cls, d):
          """
          initalize from dict
          """
          return Lobsterin({k: v for k, v in d.items() if k not in ["@module",
                                                                    "@class"]})
  
      def write_INCAR(self, incar_input="INCAR", incar_output="INCAR.lobster", poscar_input="POSCAR",
                      further_settings=None):
          """
          Will only make the run static, insert nbands, make ISYM=-1, set LWAVE=True and write a new INCAR.
          You have to check for the rest.
          Args:
              incar_input (str): path to input INCAR
              incar_output (str): path to output INCAR
              poscar_input (str) path to input POSCAR
              further_settings (dict): A dict can be used to include further settings, e.g. {"ISMEAR":-5}
          """
          # reads old incar from file, this one will be modified
          incar = Incar.from_file(incar_input)
          warnings.warn("Please check your incar_input before using it. This method only changes three settings!")
          incar["ISYM"] = -1
          incar["NSW"] = 0
          incar["LWAVE"] = True
          # get nbands from _get_nbands (use basis set that is inserted)
          incar["NBANDS"] = self._get_nbands(Structure.from_file(poscar_input))
          if further_settings is not None:
              for key, item in further_settings.items():
                  incar[key] = further_settings[key]
          # print it to file
          incar.write_file(incar_output)
  
      @staticmethod
      def _get_basis(structure: Structure, potcar_symbols: list):
          """
          will get the basis from given potcar_symbols (e.g., ["Fe_pv","Si"]
          #include this in lobsterin class
          Args:
              structure (Structure): Structure object
              potcar_symbols: list of potcar symbols
          Returns:
              returns basis
          """
          Potcar_names = [name for name in potcar_symbols]
  
          AtomTypes_Potcar = [name.split('_')[0] for name in Potcar_names]
  
          AtomTypes = structure.symbol_set
  
          if set(AtomTypes) != set(AtomTypes_Potcar):
              raise IOError("Your POSCAR does not correspond to your POTCAR!")
          BASIS = loadfn(os.path.join(MODULE_DIR, "BASIS_PBE_54.yaml"))['BASIS']
  
          basis_functions = []
          list_forin = []
          for itype, type in enumerate(Potcar_names):
              if type not in BASIS:
                  raise ValueError("You have to provide the basis for" + str(
                      type) + "manually. We don't have any information on this POTCAR.")
              basis_functions.append(BASIS[type].split())
              tojoin = str(AtomTypes_Potcar[itype]) + " "
              tojoin2 = "".join(str(str(e) + " ") for e in BASIS[type].split())
              list_forin.append(str(tojoin + tojoin2))
          return list_forin
  
      @staticmethod
      def write_POSCAR_with_standard_primitive(POSCAR_input="POSCAR", POSCAR_output="POSCAR.lobster", symprec=0.01):
          """
          writes a POSCAR with the standard primitive cell. This is needed to arrive at the correct kpath
          Args:
              POSCAR_input (str): filename of input POSCAR
              POSCAR_output (str): filename of output POSCAR
              symprec (float): precision to find symmetry
          """
          structure = Structure.from_file(POSCAR_input)
          kpath = HighSymmKpath(structure, symprec=symprec)
          new_structure = kpath.prim
          new_structure.to(fmt='POSCAR', filename=POSCAR_output)
  
      @staticmethod
      def write_KPOINTS(POSCAR_input="POSCAR", KPOINTS_output="KPOINTS.lobster", reciprocal_density=100, from_grid=False,
                        input_grid=[5, 5, 5], line_mode=True,
                        kpoints_line_density=20, symprec=0.01):
          """
          writes a KPOINT file for lobster (no symmetry considered!, ISYM=-1)
          Args:
              POSCAR_input (str): path to POSCAR
              KPOINTS_output (str): path to output KPOINTS
              reciprocal_density (int): Grid density
              from_grid (bool): If True KPOINTS will be generated with the help of a grid given in input_grid. Otherwise,
                  they will be generated from the reciprocal_density
              input_grid (list): grid to generate the KPOINTS file
              line_mode (bool): If True, band structure will be generated
              kpoints_line_density (int): density of the lines in the band structure
              symprec (float): precision to determine symmetry
  
          """
          structure = Structure.from_file(POSCAR_input)
          # should this really be static? -> make it similar to INCAR?
          if not from_grid:
              kpointgrid = Kpoints.automatic_density_by_vol(structure, reciprocal_density).kpts
              mesh = kpointgrid[0]
          else:
              mesh = input_grid
  
          # The following code is taken from: SpacegroupAnalyzer
          # we need to switch off symmetry here
          latt = structure.lattice.matrix
          positions = structure.frac_coords
          unique_species = []
          zs = []
          magmoms = []
  
          for species, g in itertools.groupby(structure,
                                              key=lambda s: s.species):
              if species in unique_species:
                  ind = unique_species.index(species)
                  zs.extend([ind + 1] * len(tuple(g)))
              else:
                  unique_species.append(species)
                  zs.extend([len(unique_species)] * len(tuple(g)))
  
          for site in structure:
              if hasattr(site, 'magmom'):
                  magmoms.append(site.magmom)
              elif site.is_ordered and hasattr(site.specie, 'spin'):
                  magmoms.append(site.specie.spin)
              else:
                  magmoms.append(0)
  
          # For now, we are setting magmom to zero. (Taken from INCAR class)
          cell = latt, positions, zs, magmoms
          mapping, grid = spglib.get_ir_reciprocal_mesh(mesh, cell, is_shift=[0, 0, 0])
  
          # get the kpoints for the grid
          kpts = []
          weights = []
          all_labels = []
          for i, (ir_gp_id, gp) in enumerate(zip(mapping, grid)):
              # print("%3d ->%3d %s" % (i, ir_gp_id, gp.astype(float) / mesh))
              kpts.append(gp.astype(float) / mesh)
              weights.append(1)
              all_labels.append("")
  
          # line mode
          if line_mode:
              kpath = HighSymmKpath(structure, symprec=symprec)
              if not np.allclose(kpath.prim.lattice.matrix, structure.lattice.matrix):
                  raise ValueError(
                      "You are not using the standard primitive cell. The k-path is not correct. Please generate a "
                      "standard primitive cell first.")
  
              frac_k_points, labels = kpath.get_kpoints(
                  line_density=kpoints_line_density,
                  coords_are_cartesian=False)
  
              for k in range(len(frac_k_points)):
                  kpts.append(frac_k_points[k])
                  weights.append(0.0)
                  all_labels.append(labels[k])
  
          comment = (
              "ISYM=-1, grid: " + str(mesh) if not line_mode else "ISYM=-1, grid: " + str(mesh) + " plus kpoint path")
  
          KpointObject = Kpoints(comment=comment,
                                 style=Kpoints.supported_modes.Reciprocal,
                                 num_kpts=len(kpts), kpts=kpts, kpts_weights=weights,
                                 labels=all_labels)
  
          KpointObject.write_file(filename=KPOINTS_output)
  
      @classmethod
      def from_file(cls, lobsterin: str):
          """
          Args:
              lobsterin (str): path to lobsterin
  
          Returns:
              Lobsterin object
          """
          with zopen(lobsterin) as f:
              data = f.read().split("\n")
          if len(data) == 0:
              raise IOError("lobsterin file contains no data.")
          Lobsterindict = {}
  
          for datum in data:
              # will remove all commments to avoid complications
              raw_datum = datum.split('!')[0]
              raw_datum = raw_datum.split('//')[0]
              raw_datum = raw_datum.split('#')[0]
              raw_datum = raw_datum.split(' ')
              while "" in raw_datum:
                  raw_datum.remove("")
              if len(raw_datum) > 1:
                  # check which type of keyword this is, handle accordingly
                  if raw_datum[0].lower() not in [datum2.lower() for datum2 in Lobsterin.LISTKEYWORDS]:
                      if raw_datum[0].lower() not in [datum2.lower() for datum2 in Lobsterin.FLOATKEYWORDS]:
                          if raw_datum[0].lower() not in Lobsterindict:
                              Lobsterindict[raw_datum[0].lower()] = " ".join(raw_datum[1:])
                          else:
                              raise ValueError("Same keyword " + str(raw_datum[0].lower()) + "twice!")
                      else:
                          if raw_datum[0].lower() not in Lobsterindict:
                              Lobsterindict[raw_datum[0].lower()] = float(raw_datum[1])
                          else:
                              raise ValueError("Same keyword " + str(raw_datum[0].lower()) + "twice!")
                  else:
                      if raw_datum[0].lower() not in Lobsterindict:
                          Lobsterindict[raw_datum[0].lower()] = [" ".join(raw_datum[1:])]
                      else:
                          Lobsterindict[raw_datum[0].lower()].append(" ".join(raw_datum[1:]))
              elif len(raw_datum) > 0:
                  Lobsterindict[raw_datum[0].lower()] = True
  
          return cls(Lobsterindict)
  
      @staticmethod
      def _get_potcar_symbols(POTCAR_input: str) -> list:
          """
          will return the name of the species in the POTCAR
          Args:
           POTCAR_input(str): string to potcar file
          Returns:
              list of the names of the species in string format
          """
          potcar = Potcar.from_file(POTCAR_input)
          for pot in potcar:
              if pot.potential_type != "PAW":
                  raise IOError("Lobster only works with PAW! Use different POTCARs")
  
          if potcar.functional != "PBE":
              raise IOError("We only have BASIS options for PBE so far")
  
          Potcar_names = [name["symbol"] for name in potcar.spec]
          return Potcar_names
  
      @classmethod
      def standard_calculations_from_vasp_files(cls, POSCAR_input="POSCAR", INCAR_input="INCAR", POTCAR_input=None,
                                                dict_for_basis=None,
                                                option='standard'):
          """
          will generate Lobsterin with standard settings
  
          Args:
              POSCAR_input(str): path to POSCAR
              INCAR_input(str): path to INCAR
              POTCAR_input (str): path to POTCAR
              dict_for_basis (dict): can be provided: it should look the following:
                  dict_for_basis={"Fe":'3p 3d 4s 4f', "C": '2s 2p'} and will overwrite all settings from POTCAR_input
  
              option (str): 'standard' will start a normal lobster run where COHPs, COOPs, DOS, CHARGE etc. will be
                  calculated
                  'standard_from_projection' will start a normal lobster run from a projection
                  'standard_with_fatband' will do a fatband calculation, run over all orbitals
                  'onlyprojection' will only do a projection
                  'onlydos' will only calculate a projected dos
                  'onlycohp' will only calculate cohp
                  'onlycoop' will only calculate coop
                  'onlycohpcoop' will only calculate cohp and coop
  
          Returns:
              Lobsterin Object with standard settings
          """
          warnings.warn(
              "Always check and test the provided basis functions. The spilling of your Lobster calculation might help")
          # warn that fatband calc cannot be done with tetrahedron method at the moment
          if option not in ['standard', 'standard_from_projection', 'standard_with_fatband', 'onlyprojection', 'onlydos',
                            'onlycohp', 'onlycoop', 'onlycohpcoop']:
              raise ValueError("The option is not valid!")
  
          Lobsterindict = {}
          # this basis set covers most elements
          Lobsterindict['basisSet'] = 'pbeVaspFit2015'
          # energies around e-fermi
          Lobsterindict['COHPstartEnergy'] = -15.0
          Lobsterindict['COHPendEnergy'] = 5.0
  
          if option in ['standard', 'onlycohp', 'onlycoop', 'onlycohpcoop', 'standard_with_fatband']:
              # every interaction with a distance of 6.0 is checked
              Lobsterindict['cohpGenerator'] = "from 0.1 to 6.0 orbitalwise"
              # the projection is saved
              Lobsterindict['saveProjectionToFile'] = True
  
          if option == 'standard_from_projection':
              Lobsterindict['cohpGenerator'] = "from 0.1 to 6.0 orbitalwise"
              Lobsterindict['loadProjectionFromFile'] = True
  
          if option == 'onlycohp':
              Lobsterindict['skipdos'] = True
              Lobsterindict['skipcoop'] = True
              Lobsterindict['skipPopulationAnalysis'] = True
              Lobsterindict['skipGrossPopulation'] = True
  
          if option == 'onlycoop':
              Lobsterindict['skipdos'] = True
              Lobsterindict['skipcohp'] = True
              Lobsterindict['skipPopulationAnalysis'] = True
              Lobsterindict['skipGrossPopulation'] = True
  
          if option == 'onlycohpcoop':
              Lobsterindict['skipdos'] = True
              Lobsterindict['skipPopulationAnalysis'] = True
              Lobsterindict['skipGrossPopulation'] = True
  
          if option == 'onlydos':
              Lobsterindict['skipcohp'] = True
              Lobsterindict['skipcoop'] = True
              Lobsterindict['skipPopulationAnalysis'] = True
              Lobsterindict['skipGrossPopulation'] = True
  
          if option == 'onlyprojection':
              Lobsterindict['skipdos'] = True
              Lobsterindict['skipcohp'] = True
              Lobsterindict['skipcoop'] = True
              Lobsterindict['skipPopulationAnalysis'] = True
              Lobsterindict['skipGrossPopulation'] = True
              Lobsterindict['saveProjectionToFile'] = True
  
          incar = Incar.from_file(INCAR_input)
          if incar["ISMEAR"] == 0:
              Lobsterindict['gaussianSmearingWidth'] = incar["SIGMA"]
          if incar["ISMEAR"] != 0 and option == "standard_with_fatband":
              raise ValueError("ISMEAR has to be 0 for a fatband calculation with Lobster")
          if dict_for_basis is not None:
              # dict_for_basis={"Fe":'3p 3d 4s 4f', "C": '2s 2p'}
              # will just insert this basis and not check with poscar
              basis = [key + ' ' + value for key, value in dict_for_basis.items()]
          else:
              # get basis from POTCAR
              potcar_names = Lobsterin._get_potcar_symbols(POTCAR_input=POTCAR_input)
  
              basis = Lobsterin._get_basis(structure=Structure.from_file(POSCAR_input),
                                           potcar_symbols=potcar_names)
          Lobsterindict["basisfunctions"] = basis
          if option == 'standard_with_fatband':
              Lobsterindict['createFatband'] = basis
  
          return cls(Lobsterindict)
  
  
  class Bandoverlaps:
      """
      Class to read in bandOverlaps.lobster files. These files are not created during every Lobster run.
      Args:
          filename: filename of the "bandOverlaps.lobster" file
  
      .. attribute: bandoverlapsdict is a dict of the following form:
          {spin:{"kpoint as string": {"maxDeviation": float that describes the max deviation, "matrix": 2D
          array of the size number of bands times number of bands including the overlap matrices with } }}
  
      .. attribute: maxDeviation is a list of floats describing the maximal Deviation for each problematic kpoint
  
      """
  
      def __init__(self, filename="bandOverlaps.lobster"):
  
          with zopen(filename, "rt") as f:
              contents = f.read().split("\n")
  
          self._read(contents)
  
      def _read(self, contents: list):
          """
          will read in all contents of the file
          Args:
           contents: list of strings
          """
          self.bandoverlapsdict = {}
          self.maxDeviation = []
          # This has to be done like this because there can be different numbers of problematic k-points per spin
          for line in contents:
              if "Overlap Matrix (abs) of the orthonormalized projected bands for spin 0" in line:
                  spin = Spin.up
              elif "Overlap Matrix (abs) of the orthonormalized projected bands for spin 1" in line:
                  spin = Spin.down
              elif "k-point" in line:
                  kpoint = line.split(" ")
                  kpoint_array = []
                  for kpointel in kpoint:
                      if kpointel not in ["at", "k-point", ""]:
                          kpoint_array.append(str(kpointel))
  
              elif "maxDeviation" in line:
                  if spin not in self.bandoverlapsdict:
                      self.bandoverlapsdict[spin] = {}
                  if not " ".join(kpoint_array) in self.bandoverlapsdict[spin]:
                      self.bandoverlapsdict[spin][" ".join(kpoint_array)] = {}
                  maxdev = line.split(" ")[2]
                  self.bandoverlapsdict[spin][" ".join(kpoint_array)]["maxDeviation"] = float(maxdev)
                  self.maxDeviation.append(float(maxdev))
                  self.bandoverlapsdict[spin][" ".join(kpoint_array)]["matrix"] = []
  
              else:
                  overlaps = []
                  for el in (line.split(" ")):
                      if el not in [""]:
                          overlaps.append(float(el))
                  self.bandoverlapsdict[spin][" ".join(kpoint_array)]["matrix"].append(overlaps)
  
      def has_good_quality_maxDeviation(self, limit_maxDeviation=0.1) -> bool:
          """
          will check if the maxDeviation from the ideal bandoverlap is smaller or equal to limit_maxDeviation
          Args:
           limit_maxDeviation: limit of the maxDeviation
          Returns:
               Boolean that will give you information about the quality of the projection
          """
  
          for deviation in self.maxDeviation:
              if deviation > limit_maxDeviation:
                  return False
          return True
  
      def has_good_quality_check_occupied_bands(self, number_occ_bands_spin_up, number_occ_bands_spin_down=None,
                                                spin_polarized=False, limit_deviation=0.1) -> bool:
          """
          will check if the deviation from the ideal bandoverlap of all occupied bands is smaller or equal to
          limit_deviation
  
          Args:
          number_occ_bands_spin_up (int): number of occupied bands of spin up
          number_occ_bands_spin_down (int): number of occupied bands of spin down
          spin_polarized (bool):  If True, then it was a spin polarized calculation
          limit_deviation (float): limit of the maxDeviation
          Returns:
               Boolean that will give you information about the quality of the projection
          """
  
          for matrix in self.bandoverlapsdict[Spin.up].values():
              for iband1, band1 in enumerate(matrix["matrix"]):
                  for iband2, band2 in enumerate(band1):
                      if iband1 < number_occ_bands_spin_up and iband2 < number_occ_bands_spin_up:
                          if iband1 == iband2:
                              if abs(band2 - 1.0) > limit_deviation:
                                  return False
                          else:
                              if band2 > limit_deviation:
                                  return False
  
          if spin_polarized:
              for matrix in self.bandoverlapsdict[Spin.down].values():
                  for iband1, band1 in enumerate(matrix["matrix"]):
                      for iband2, band2 in enumerate(band1):
                          if iband1 < number_occ_bands_spin_down and iband2 < number_occ_bands_spin_down:
                              if iband1 == iband2:
                                  if abs(band2 - 1.0) > limit_deviation:
                                      return False
                              else:
                                  if band2 > limit_deviation:
                                      return False
  
          return True
  
  
  class Grosspop:
      """
      Class to read in GROSSPOP.lobster files.
      Args:
          filename: filename of the "GROSSPOP.lobster" file
  
      .. attribute: list_dict_grosspop
         which is a list of dicts including all information about the grosspopulations, one sample dict looks like this:
          {'element': 'O', 'Mulliken GP': {'2s': '1.80', '2p_y': '1.83', '2p_z': '1.79', '2p_x': '1.75', 'total': '7.18'},
           'Loewdin GP': {'2s': '1.60', '2p_y': '1.82', '2p_z': '1.77', '2p_x': '1.73', 'total': '6.92'}}
          The 0. entry of the list refers to the first atom in GROSSPOP.lobster and so on.
      """
  
      def __init__(self, filename="GROSSPOP.lobster"):
  
          # opens file
          with zopen(filename, "rt") as f:
              contents = f.read().split("\n")
  
          self.list_dict_grosspop = []
          # transfers content of file to list of dict
          for line in contents[3:]:
              cleanline = [i for i in line.split(" ") if not i == '']
              if len(cleanline) == 5:
                  smalldict = {}
                  smalldict["element"] = cleanline[1]
                  smalldict["Mulliken GP"] = {}
                  smalldict["Loewdin GP"] = {}
                  smalldict["Mulliken GP"][cleanline[2]] = float(cleanline[3])
                  smalldict["Loewdin GP"][cleanline[2]] = float(cleanline[4])
              elif len(cleanline) > 0:
                  smalldict["Mulliken GP"][cleanline[0]] = float(cleanline[1])
                  smalldict["Loewdin GP"][cleanline[0]] = float(cleanline[2])
                  if 'total' in cleanline[0]:
                      self.list_dict_grosspop.append(smalldict)
  