Source code for

"""Reads Quantum ESPRESSO files.

Read multiple structures and results from pw.x output files. Read
structures from pw.x input files.

Built for PWSCF v.5.3.0 but should work with earlier and later versions.
Can deal with most major functionality, with the notable exception of ibrav,
for which we only support ibrav == 0 and force CELL_PARAMETERS to be provided

Units are converted using CODATA 2006, as used internally by Quantum

import operator as op
import re
import warnings
from collections import defaultdict
from copy import deepcopy
from pathlib import Path

import numpy as np

from ase.atoms import Atoms
from ase.calculators.calculator import kpts2ndarray, kpts2sizeandoffsets
from ase.calculators.singlepoint import (SinglePointDFTCalculator,
from ase.constraints import FixAtoms, FixCartesian
from import chemical_symbols
from ase.dft.kpoints import kpoint_convert
from import pw_keys
from import Namelist
from ase.units import create_units
from ase.utils import deprecated, reader, writer

# Quantum ESPRESSO uses CODATA 2006 internally
units = create_units('2006')

# Section identifiers
_PW_START = 'Program PWSCF'
_PW_END = 'End of self-consistent calculation'
_PW_MAGMOM = 'Magnetic moment per site'
_PW_FORCE = 'Forces acting on atoms'
_PW_TOTEN = '!    total energy'
_PW_STRESS = 'total   stress'
_PW_FERMI = 'the Fermi energy is'
_PW_HIGHEST_OCCUPIED = 'highest occupied level'
_PW_HIGHEST_OCCUPIED_LOWEST_FREE = 'highest occupied, lowest unoccupied level'
_PW_KPTS = 'number of k points='
_PW_BANDSTRUCTURE = 'End of band structure calculation'
_PW_DIPOLE = "Debye"
_PW_DIPOLE_DIRECTION = "Computed dipole along edir"

# ibrav error message
ibrav_error_message = (
    'ASE does not support ibrav != 0. Note that with ibrav '
    '== 0, Quantum ESPRESSO will still detect the symmetries '
    'of your system because the CELL_PARAMETERS are defined '
    'to a high level of precision.')

[docs]@reader def read_espresso_out(fileobj, index=slice(None), results_required=True): """Reads Quantum ESPRESSO output files. The atomistic configurations as well as results (energy, force, stress, magnetic moments) of the calculation are read for all configurations within the output file. Will probably raise errors for broken or incomplete files. Parameters ---------- fileobj : file|str A file like object or filename index : slice The index of configurations to extract. results_required : bool If True, atomistic configurations that do not have any associated results will not be included. This prevents double printed configurations and incomplete calculations from being returned as the final configuration with no results data. Yields ------ structure : Atoms The next structure from the index slice. The Atoms has a SinglePointCalculator attached with any results parsed from the file. """ # work with a copy in memory for faster random access pwo_lines = fileobj.readlines() # TODO: index -1 special case? # Index all the interesting points indexes = { _PW_START: [], _PW_END: [], _PW_CELL: [], _PW_POS: [], _PW_MAGMOM: [], _PW_FORCE: [], _PW_TOTEN: [], _PW_STRESS: [], _PW_FERMI: [], _PW_HIGHEST_OCCUPIED: [], _PW_HIGHEST_OCCUPIED_LOWEST_FREE: [], _PW_KPTS: [], _PW_BANDS: [], _PW_BANDSTRUCTURE: [], _PW_DIPOLE: [], _PW_DIPOLE_DIRECTION: [], } for idx, line in enumerate(pwo_lines): for identifier in indexes: if identifier in line: indexes[identifier].append(idx) # Configurations are either at the start, or defined in ATOMIC_POSITIONS # in a subsequent step. Can deal with concatenated output files. all_config_indexes = sorted(indexes[_PW_START] + indexes[_PW_POS]) # Slice only requested indexes # setting results_required argument stops configuration-only # structures from being returned. This ensures the [-1] structure # is one that has results. Two cases: # - SCF of last configuration is not converged, job terminated # abnormally. # - 'relax' and 'vc-relax' re-prints the final configuration but # only 'vc-relax' recalculates. if results_required: results_indexes = sorted(indexes[_PW_TOTEN] + indexes[_PW_FORCE] + indexes[_PW_STRESS] + indexes[_PW_MAGMOM] + indexes[_PW_BANDS] + indexes[_PW_BANDSTRUCTURE]) # Prune to only configurations with results data before the next # configuration results_config_indexes = [] for config_index, config_index_next in zip( all_config_indexes, all_config_indexes[1:] + [len(pwo_lines)]): if any(config_index < results_index < config_index_next for results_index in results_indexes): results_config_indexes.append(config_index) # slice from the subset image_indexes = results_config_indexes[index] else: image_indexes = all_config_indexes[index] # Extract initialisation information each time PWSCF starts # to add to subsequent configurations. Use None so slices know # when to fill in the blanks. pwscf_start_info = {idx: None for idx in indexes[_PW_START]} for image_index in image_indexes: # Find the nearest calculation start to parse info. Needed in, # for example, relaxation where cell is only printed at the # start. if image_index in indexes[_PW_START]: prev_start_index = image_index else: # The greatest start index before this structure prev_start_index = [idx for idx in indexes[_PW_START] if idx < image_index][-1] # add structure to reference if not there if pwscf_start_info[prev_start_index] is None: pwscf_start_info[prev_start_index] = parse_pwo_start( pwo_lines, prev_start_index) # Get the bounds for information for this structure. Any associated # values will be between the image_index and the following one, # EXCEPT for cell, which will be 4 lines before if it exists. for next_index in all_config_indexes: if next_index > image_index: break else: # right to the end of the file next_index = len(pwo_lines) # Get the structure # Use this for any missing data prev_structure = pwscf_start_info[prev_start_index]['atoms'] if image_index in indexes[_PW_START]: structure = prev_structure.copy() # parsed from start info else: if _PW_CELL in pwo_lines[image_index - 5]: # CELL_PARAMETERS would be just before positions if present cell, cell_alat = get_cell_parameters( pwo_lines[image_index - 5:image_index]) else: cell = prev_structure.cell cell_alat = pwscf_start_info[prev_start_index]['alat'] # give at least enough lines to parse the positions # should be same format as input card n_atoms = len(prev_structure) positions_card = get_atomic_positions( pwo_lines[image_index:image_index + n_atoms + 1], n_atoms=n_atoms, cell=cell, alat=cell_alat) # convert to Atoms object symbols = [label_to_symbol(position[0]) for position in positions_card] positions = [position[1] for position in positions_card] structure = Atoms(symbols=symbols, positions=positions, cell=cell, pbc=True) # Extract calculation results # Energy energy = None for energy_index in indexes[_PW_TOTEN]: if image_index < energy_index < next_index: energy = float( pwo_lines[energy_index].split()[-2]) * units['Ry'] # Forces forces = None for force_index in indexes[_PW_FORCE]: if image_index < force_index < next_index: # Before QE 5.3 'negative rho' added 2 lines before forces # Use exact lines to stop before 'non-local' forces # in high verbosity if not pwo_lines[force_index + 2].strip(): force_index += 4 else: force_index += 2 # assume contiguous forces = [ [float(x) for x in force_line.split()[-3:]] for force_line in pwo_lines[force_index:force_index + len(structure)]] forces = np.array(forces) * units['Ry'] / units['Bohr'] # Stress stress = None for stress_index in indexes[_PW_STRESS]: if image_index < stress_index < next_index: sxx, sxy, sxz = pwo_lines[stress_index + 1].split()[:3] _, syy, syz = pwo_lines[stress_index + 2].split()[:3] _, _, szz = pwo_lines[stress_index + 3].split()[:3] stress = np.array([sxx, syy, szz, syz, sxz, sxy], dtype=float) # sign convention is opposite of ase stress *= -1 * units['Ry'] / (units['Bohr'] ** 3) # Magmoms magmoms = None for magmoms_index in indexes[_PW_MAGMOM]: if image_index < magmoms_index < next_index: magmoms = [ float(mag_line.split()[-1]) for mag_line in pwo_lines[magmoms_index + 1: magmoms_index + 1 + len(structure)]] # Dipole moment dipole = None if indexes[_PW_DIPOLE]: for dipole_index in indexes[_PW_DIPOLE]: if image_index < dipole_index < next_index: _dipole = float(pwo_lines[dipole_index].split()[-2]) for dipole_index in indexes[_PW_DIPOLE_DIRECTION]: if image_index < dipole_index < next_index: _direction = pwo_lines[dipole_index].strip() prefix = 'Computed dipole along edir(' _direction = _direction[len(prefix):] _direction = int(_direction[0]) dipole = np.eye(3)[_direction - 1] * _dipole * units['Debye'] # Fermi level / highest occupied level efermi = None for fermi_index in indexes[_PW_FERMI]: if image_index < fermi_index < next_index: efermi = float(pwo_lines[fermi_index].split()[-2]) if efermi is None: for ho_index in indexes[_PW_HIGHEST_OCCUPIED]: if image_index < ho_index < next_index: efermi = float(pwo_lines[ho_index].split()[-1]) if efermi is None: for holf_index in indexes[_PW_HIGHEST_OCCUPIED_LOWEST_FREE]: if image_index < holf_index < next_index: efermi = float(pwo_lines[holf_index].split()[-2]) # K-points ibzkpts = None weights = None kpoints_warning = "Number of k-points >= 100: " + \ "set verbosity='high' to print them." for kpts_index in indexes[_PW_KPTS]: nkpts = int(re.findall(r'\b\d+\b', pwo_lines[kpts_index])[0]) kpts_index += 2 if pwo_lines[kpts_index].strip() == kpoints_warning: continue # QE prints the k-points in units of 2*pi/alat # with alat defined as the length of the first # cell vector cell = structure.get_cell() alat = np.linalg.norm(cell[0]) ibzkpts = [] weights = [] for i in range(nkpts): L = pwo_lines[kpts_index + i].split() weights.append(float(L[-1])) coord = np.array([L[-6], L[-5], L[-4].strip('),')], dtype=float) coord *= 2 * np.pi / alat coord = kpoint_convert(cell, ckpts_kv=coord) ibzkpts.append(coord) ibzkpts = np.array(ibzkpts) weights = np.array(weights) # Bands kpts = None kpoints_warning = "Number of k-points >= 100: " + \ "set verbosity='high' to print the bands." for bands_index in indexes[_PW_BANDS] + indexes[_PW_BANDSTRUCTURE]: if image_index < bands_index < next_index: bands_index += 1 # skip over the lines with DFT+U occupation matrices if 'enter write_ns' in pwo_lines[bands_index]: while 'exit write_ns' not in pwo_lines[bands_index]: bands_index += 1 bands_index += 1 if pwo_lines[bands_index].strip() == kpoints_warning: continue assert ibzkpts is not None spin, bands, eigenvalues = 0, [], [[], []] while True: L = pwo_lines[bands_index].replace('-', ' -').split() if len(L) == 0: if len(bands) > 0: eigenvalues[spin].append(bands) bands = [] elif L == ['occupation', 'numbers']: # Skip the lines with the occupation numbers bands_index += len(eigenvalues[spin][0]) // 8 + 1 elif L[0] == 'k' and L[1].startswith('='): pass elif 'SPIN' in L: if 'DOWN' in L: spin += 1 else: try: bands.extend(map(float, L)) except ValueError: break bands_index += 1 if spin == 1: assert len(eigenvalues[0]) == len(eigenvalues[1]) assert len(eigenvalues[0]) == len(ibzkpts), \ (np.shape(eigenvalues), len(ibzkpts)) kpts = [] for s in range(spin + 1): for w, k, e in zip(weights, ibzkpts, eigenvalues[s]): kpt = SinglePointKPoint(w, s, k, eps_n=e) kpts.append(kpt) # Put everything together # # In PW the forces are consistent with the "total energy"; that's why # its value must be assigned to free_energy. # PW doesn't compute the extrapolation of the energy to 0K smearing # the closer thing to this is again the total energy that contains # the correct (i.e. variational) form of the band energy is # Eband = \int e N(e) de for e<Ef , where N(e) is the DOS # This differs by the term (-TS) from the sum of KS eigenvalues: # Eks = \sum wg(n,k) et(n,k) # which is non variational. When a Fermi-Dirac function is used # for a given T, the variational energy is REALLY the free energy F, # and F = E - TS , with E = non variational energy. # calc = SinglePointDFTCalculator(structure, energy=energy, free_energy=energy, forces=forces, stress=stress, magmoms=magmoms, efermi=efermi, ibzkpts=ibzkpts, dipole=dipole) calc.kpts = kpts structure.calc = calc yield structure
def parse_pwo_start(lines, index=0): """Parse Quantum ESPRESSO calculation info from lines, starting from index. Return a dictionary containing extracted information. - `celldm(1)`: lattice parameters (alat) - `cell`: unit cell in Angstrom - `symbols`: element symbols for the structure - `positions`: cartesian coordinates of atoms in Angstrom - `atoms`: an `ase.Atoms` object constructed from the extracted data Parameters ---------- lines : list[str] Contents of PWSCF output file. index : int Line number to begin parsing. Only first calculation will be read. Returns ------- info : dict Dictionary of calculation parameters, including `celldm(1)`, `cell`, `symbols`, `positions`, `atoms`. Raises ------ KeyError If interdependent values cannot be found (especially celldm(1)) an error will be raised as other quantities cannot then be calculated (e.g. cell and positions). """ # TODO: extend with extra DFT info? info = {} for idx, line in enumerate(lines[index:], start=index): if 'celldm(1)' in line: # celldm(1) has more digits than alat!! info['celldm(1)'] = float(line.split()[1]) * units['Bohr'] info['alat'] = info['celldm(1)'] elif 'number of atoms/cell' in line: info['nat'] = int(line.split()[-1]) elif 'number of atomic types' in line: info['ntyp'] = int(line.split()[-1]) elif 'crystal axes:' in line: info['cell'] = info['celldm(1)'] * np.array([ [float(x) for x in lines[idx + 1].split()[3:6]], [float(x) for x in lines[idx + 2].split()[3:6]], [float(x) for x in lines[idx + 3].split()[3:6]]]) elif 'positions (alat units)' in line: info['symbols'], info['positions'] = [], [] for at_line in lines[idx + 1:idx + 1 + info['nat']]: sym, x, y, z = parse_position_line(at_line) info['symbols'].append(label_to_symbol(sym)) info['positions'].append([x * info['celldm(1)'], y * info['celldm(1)'], z * info['celldm(1)']]) # This should be the end of interesting info. # Break here to avoid dealing with large lists of kpoints. # Will need to be extended for DFTCalculator info. break # Make atoms for convenience info['atoms'] = Atoms(symbols=info['symbols'], positions=info['positions'], cell=info['cell'], pbc=True) return info def parse_position_line(line): """Parse a single line from a pw.x output file. The line must contain information about the atomic symbol and the position, e.g. 995 Sb tau( 995) = ( 1.4212023 0.7037863 0.1242640 ) Parameters ---------- line : str Line to be parsed. Returns ------- sym : str Atomic symbol. x : float x-position. y : float y-position. z : float z-position. """ pat = re.compile(r'\s*\d+\s*(\S+)\s*tau\(\s*\d+\)\s*=' r'\s*\(\s*(\S+)\s+(\S+)\s+(\S+)\s*\)') match = pat.match(line) assert match is not None sym, x, y, z =, 2, 3, 4) return sym, float(x), float(y), float(z)
[docs]@reader def read_espresso_in(fileobj): """Parse a Quantum ESPRESSO input files, '.in', '.pwi'. ESPRESSO inputs are generally a fortran-namelist format with custom blocks of data. The namelist is parsed as a dict and an atoms object is constructed from the included information. Parameters ---------- fileobj : file | str A file-like object that supports line iteration with the contents of the input file, or a filename. Returns ------- atoms : Atoms Structure defined in the input file. Raises ------ KeyError Raised for missing keys that are required to process the file """ # parse namelist section and extract remaining lines data, card_lines = read_fortran_namelist(fileobj) # get the cell if ibrav=0 if 'system' not in data: raise KeyError('Required section &SYSTEM not found.') elif 'ibrav' not in data['system']: raise KeyError('ibrav is required in &SYSTEM') elif data['system']['ibrav'] == 0: # celldm(1) is in Bohr, A is in angstrom. celldm(1) will be # used even if A is also specified. if 'celldm(1)' in data['system']: alat = data['system']['celldm(1)'] * units['Bohr'] elif 'A' in data['system']: alat = data['system']['A'] else: alat = None cell, _ = get_cell_parameters(card_lines, alat=alat) else: raise ValueError(ibrav_error_message) # species_info holds some info for each element species_card = get_atomic_species( card_lines, n_species=data['system']['ntyp']) species_info = {} for ispec, (label, weight, pseudo) in enumerate(species_card): symbol = label_to_symbol(label) # starting_magnetization is in fractions of valence electrons magnet_key = f"starting_magnetization({ispec + 1})" magmom = data["system"].get(magnet_key, 0.0) species_info[symbol] = {"weight": weight, "pseudo": pseudo, "magmom": magmom} positions_card = get_atomic_positions( card_lines, n_atoms=data['system']['nat'], cell=cell, alat=alat) symbols = [label_to_symbol(position[0]) for position in positions_card] positions = [position[1] for position in positions_card] constraint_flags = [position[2] for position in positions_card] magmoms = [species_info[symbol]["magmom"] for symbol in symbols] # TODO: put more info into the atoms object # e.g magmom, forces. atoms = Atoms(symbols=symbols, positions=positions, cell=cell, pbc=True, magmoms=magmoms) atoms.set_constraint(convert_constraint_flags(constraint_flags)) return atoms
def get_atomic_positions(lines, n_atoms, cell=None, alat=None): """Parse atom positions from ATOMIC_POSITIONS card. Parameters ---------- lines : list[str] A list of lines containing the ATOMIC_POSITIONS card. n_atoms : int Expected number of atoms. Only this many lines will be parsed. cell : np.array Unit cell of the crystal. Only used with crystal coordinates. alat : float Lattice parameter for atomic coordinates. Only used for alat case. Returns ------- positions : list[(str, (float, float, float), (int, int, int))] A list of the ordered atomic positions in the format: label, (x, y, z), (if_x, if_y, if_z) Force multipliers are set to None if not present. Raises ------ ValueError Any problems parsing the data result in ValueError """ positions = None # no blanks or comment lines, can the consume n_atoms lines for positions trimmed_lines = (line for line in lines if line.strip() and line[0] != '#') for line in trimmed_lines: if line.strip().startswith('ATOMIC_POSITIONS'): if positions is not None: raise ValueError('Multiple ATOMIC_POSITIONS specified') # Priority and behaviour tested with QE 5.3 if 'crystal_sg' in line.lower(): raise NotImplementedError('CRYSTAL_SG not implemented') elif 'crystal' in line.lower(): cell = cell elif 'bohr' in line.lower(): cell = np.identity(3) * units['Bohr'] elif 'angstrom' in line.lower(): cell = np.identity(3) # elif 'alat' in line.lower(): # cell = np.identity(3) * alat else: if alat is None: raise ValueError('Set lattice parameter in &SYSTEM for ' 'alat coordinates') # Always the default, will be DEPRECATED as mandatory # in future cell = np.identity(3) * alat positions = [] for _ in range(n_atoms): split_line = next(trimmed_lines).split() # These can be fractions and other expressions position =[1]), infix_float(split_line[2]), infix_float(split_line[3])), cell) if len(split_line) > 4: force_mult = tuple(int(split_line[i]) for i in (4, 5, 6)) else: force_mult = None positions.append((split_line[0], position, force_mult)) return positions def get_atomic_species(lines, n_species): """Parse atomic species from ATOMIC_SPECIES card. Parameters ---------- lines : list[str] A list of lines containing the ATOMIC_POSITIONS card. n_species : int Expected number of atom types. Only this many lines will be parsed. Returns ------- species : list[(str, float, str)] Raises ------ ValueError Any problems parsing the data result in ValueError """ species = None # no blanks or comment lines, can the consume n_atoms lines for positions trimmed_lines = (line.strip() for line in lines if line.strip() and not line.startswith('#')) for line in trimmed_lines: if line.startswith('ATOMIC_SPECIES'): if species is not None: raise ValueError('Multiple ATOMIC_SPECIES specified') species = [] for _dummy in range(n_species): label_weight_pseudo = next(trimmed_lines).split() species.append((label_weight_pseudo[0], float(label_weight_pseudo[1]), label_weight_pseudo[2])) return species def get_cell_parameters(lines, alat=None): """Parse unit cell from CELL_PARAMETERS card. Parameters ---------- lines : list[str] A list with lines containing the CELL_PARAMETERS card. alat : float | None Unit of lattice vectors in Angstrom. Only used if the card is given in units of alat. alat must be None if CELL_PARAMETERS card is in Bohr or Angstrom. For output files, alat will be parsed from the card header and used in preference to this value. Returns ------- cell : np.array | None Cell parameters as a 3x3 array in Angstrom. If no cell is found None will be returned instead. cell_alat : float | None If a value for alat is given in the card header, this is also returned, otherwise this will be None. Raises ------ ValueError If CELL_PARAMETERS are given in units of bohr or angstrom and alat is not """ cell = None cell_alat = None # no blanks or comment lines, can take three lines for cell trimmed_lines = (line for line in lines if line.strip() and line[0] != '#') for line in trimmed_lines: if line.strip().startswith('CELL_PARAMETERS'): if cell is not None: # multiple definitions raise ValueError('CELL_PARAMETERS specified multiple times') # Priority and behaviour tested with QE 5.3 if 'bohr' in line.lower(): if alat is not None: raise ValueError('Lattice parameters given in ' '&SYSTEM celldm/A and CELL_PARAMETERS ' 'bohr') cell_units = units['Bohr'] elif 'angstrom' in line.lower(): if alat is not None: raise ValueError('Lattice parameters given in ' '&SYSTEM celldm/A and CELL_PARAMETERS ' 'angstrom') cell_units = 1.0 elif 'alat' in line.lower(): # Output file has (alat = value) (in Bohrs) if '=' in line: alat = float(line.strip(') \n').split()[-1]) * units['Bohr'] cell_alat = alat elif alat is None: raise ValueError('Lattice parameters must be set in ' '&SYSTEM for alat units') cell_units = alat elif alat is None: # may be DEPRECATED in future cell_units = units['Bohr'] else: # may be DEPRECATED in future cell_units = alat # Grab the parameters; blank lines have been removed cell = [[ffloat(x) for x in next(trimmed_lines).split()[:3]], [ffloat(x) for x in next(trimmed_lines).split()[:3]], [ffloat(x) for x in next(trimmed_lines).split()[:3]]] cell = np.array(cell) * cell_units return cell, cell_alat def convert_constraint_flags(constraint_flags): """Convert Quantum ESPRESSO constraint flags to ASE Constraint objects. Parameters ---------- constraint_flags : list[tuple[int, int, int]] List of constraint flags (0: fixed, 1: moved) for all the atoms. If the flag is None, there are no constraints on the atom. Returns ------- constraints : list[FixAtoms | FixCartesian] List of ASE Constraint objects. """ constraints = [] for i, constraint in enumerate(constraint_flags): if constraint is None: continue # mask: False (0): moved, True (1): fixed mask = ~np.asarray(constraint, bool) constraints.append(FixCartesian(i, mask)) return canonicalize_constraints(constraints) def canonicalize_constraints(constraints): """Canonicalize ASE FixCartesian constraints. If the given FixCartesian constraints share the same `mask`, they can be merged into one. Further, if `mask == (True, True, True)`, they can be converted as `FixAtoms`. This method "canonicalizes" FixCartesian objects in such a way. Parameters ---------- constraints : List[FixCartesian] List of ASE FixCartesian constraints. Returns ------- constrants_canonicalized : List[FixAtoms | FixCartesian] List of ASE Constraint objects. """ # indices_for_masks = defaultdict(list) for constraint in constraints: key = tuple((constraint.mask).tolist()) indices_for_masks[key].extend(constraint.index.tolist()) constraints_canonicalized = [] for mask, indices in indices_for_masks.items(): if mask == (False, False, False): # no directions are fixed continue if mask == (True, True, True): # all three directions are fixed constraints_canonicalized.append(FixAtoms(indices)) else: constraints_canonicalized.append(FixCartesian(indices, mask)) return constraints_canonicalized def str_to_value(string): """Attempt to convert string into int, float (including fortran double), or bool, in that order, otherwise return the string. Valid (case-insensitive) bool values are: '.true.', '.t.', 'true' and 't' (or false equivalents). Parameters ---------- string : str Test to parse for a datatype Returns ------- value : any Parsed string as the most appropriate datatype of int, float, bool or string. """ # Just an integer try: return int(string) except ValueError: pass # Standard float try: return float(string) except ValueError: pass # Fortran double try: return ffloat(string) except ValueError: pass # possible bool, else just the raw string if string.lower() in ('.true.', '.t.', 'true', 't'): return True elif string.lower() in ('.false.', '.f.', 'false', 'f'): return False else: return string.strip("'")
[docs]def read_fortran_namelist(fileobj): """Takes a fortran-namelist formatted file and returns nested dictionaries of sections and key-value data, followed by a list of lines of text that do not fit the specifications. Behaviour is taken from Quantum ESPRESSO 5.3. Parses fairly convoluted files the same way that QE should, but may not get all the MANDATORY rules and edge cases for very non-standard files Ignores anything after '!' in a namelist, split pairs on ',' to include multiple key=values on a line, read values on section start and end lines, section terminating character, '/', can appear anywhere on a line. All of these are ignored if the value is in 'quotes'. Parameters ---------- fileobj : file An open file-like object. Returns ------- data : dict[str, dict] Dictionary for each section in the namelist with key = value pairs of data. additional_cards : list[str] Any lines not used to create the data, assumed to belong to 'cards' in the input file. """ data = {} card_lines = [] in_namelist = False section = 'none' # can't be in a section without changing this for line in fileobj: # leading and trailing whitespace never needed line = line.strip() if line.startswith('&'): # inside a namelist section = line.split()[0][1:].lower() # case insensitive if section in data: # Repeated sections are completely ignored. # (Note that repeated keys overwrite within a section) section = "_ignored" data[section] = {} in_namelist = True if not in_namelist and line: # Stripped line is Truthy, so safe to index first character if line[0] not in ('!', '#'): card_lines.append(line) if in_namelist: # parse k, v from line: key = [] value = None in_quotes = False for character in line: if character == ',' and value is not None and not in_quotes: # finished value: data[section][''.join(key).strip()] = str_to_value( ''.join(value).strip()) key = [] value = None elif character == '=' and value is None and not in_quotes: # start writing value value = [] elif character == "'": # only found in value anyway in_quotes = not in_quotes value.append("'") elif character == '!' and not in_quotes: break elif character == '/' and not in_quotes: in_namelist = False break elif value is not None: value.append(character) else: key.append(character) if value is not None: data[section][''.join(key).strip()] = str_to_value( ''.join(value).strip()) return Namelist(data), card_lines
def ffloat(string): """Parse float from fortran compatible float definitions. In fortran exponents can be defined with 'd' or 'q' to symbolise double or quad precision numbers. Double precision numbers are converted to python floats and quad precision values are interpreted as numpy longdouble values (platform specific precision). Parameters ---------- string : str A string containing a number in fortran real format Returns ------- value : float | np.longdouble Parsed value of the string. Raises ------ ValueError Unable to parse a float value. """ if 'q' in string.lower(): return np.longdouble(string.lower().replace('q', 'e')) else: return float(string.lower().replace('d', 'e')) def label_to_symbol(label): """Convert a valid espresso ATOMIC_SPECIES label to a chemical symbol. Parameters ---------- label : str chemical symbol X (1 or 2 characters, case-insensitive) or chemical symbol plus a number or a letter, as in "Xn" (e.g. Fe1) or "X_*" or "X-*" (e.g. C1, C_h; max total length cannot exceed 3 characters). Returns ------- symbol : str The best matching species from ase.utils.chemical_symbols Raises ------ KeyError Couldn't find an appropriate species. Notes ----- It's impossible to tell whether e.g. He is helium or hydrogen labelled 'e'. """ # possibly a two character species # ase Atoms need proper case of chemical symbols. if len(label) >= 2: test_symbol = label[0].upper() + label[1].lower() if test_symbol in chemical_symbols: return test_symbol # finally try with one character test_symbol = label[0].upper() if test_symbol in chemical_symbols: return test_symbol else: raise KeyError('Could not parse species from label {}.' ''.format(label)) def infix_float(text): """Parse simple infix maths into a float for compatibility with Quantum ESPRESSO ATOMIC_POSITIONS cards. Note: this works with the example, and most simple expressions, but the capabilities of the two parsers are not identical. Will also parse a normal float value properly, but slowly. >>> infix_float('1/2*3^(-1/2)') 0.28867513459481287 Parameters ---------- text : str An arithmetic expression using +, -, *, / and ^, including brackets. Returns ------- value : float Result of the mathematical expression. """ def middle_brackets(full_text): """Extract text from innermost brackets.""" start, end = 0, len(full_text) for (idx, char) in enumerate(full_text): if char == '(': start = idx if char == ')': end = idx + 1 break return full_text[start:end] def eval_no_bracket_expr(full_text): """Calculate value of a mathematical expression, no brackets.""" exprs = [('+', op.add), ('*', op.mul), ('/', op.truediv), ('^', op.pow)] full_text = full_text.lstrip('(').rstrip(')') try: return float(full_text) except ValueError: for symbol, func in exprs: if symbol in full_text: left, right = full_text.split(symbol, 1) # single split return func(eval_no_bracket_expr(left), eval_no_bracket_expr(right)) while '(' in text: middle = middle_brackets(text) text = text.replace(middle, f'{eval_no_bracket_expr(middle)}') return float(eval_no_bracket_expr(text)) # Number of valence electrons in the pseudopotentials recommended by # These are just used as a fallback for # calculating initial magetization values which are given as a fraction # of valence electrons. SSSP_VALENCE = [ 0, 1.0, 2.0, 3.0, 4.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 13.0, 14.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 12.0, 13.0, 14.0, 15.0, 6.0, 7.0, 18.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 36.0, 27.0, 14.0, 15.0, 30.0, 15.0, 32.0, 19.0, 12.0, 13.0, 14.0, 15.0, 16.0, 18.0] def kspacing_to_grid(atoms, spacing, calculated_spacing=None): """ Calculate the kpoint mesh that is equivalent to the given spacing in reciprocal space (units Angstrom^-1). The number of kpoints is each dimension is rounded up (compatible with CASTEP). Parameters ---------- atoms: ase.Atoms A structure that can have get_reciprocal_cell called on it. spacing: float Minimum K-Point spacing in $A^{-1}$. calculated_spacing : list If a three item list (or similar mutable sequence) is given the members will be replaced with the actual calculated spacing in $A^{-1}$. Returns ------- kpoint_grid : [int, int, int] MP grid specification to give the required spacing. """ # No factor of 2pi in ase, everything in A^-1 # reciprocal dimensions r_x, r_y, r_z = np.linalg.norm(atoms.cell.reciprocal(), axis=1) kpoint_grid = [int(r_x / spacing) + 1, int(r_y / spacing) + 1, int(r_z / spacing) + 1] for i, _ in enumerate(kpoint_grid): if not atoms.pbc[i]: kpoint_grid[i] = 1 if calculated_spacing is not None: calculated_spacing[:] = [r_x / kpoint_grid[0], r_y / kpoint_grid[1], r_z / kpoint_grid[2]] return kpoint_grid def format_atom_position(atom, crystal_coordinates, mask='', tidx=None): """Format one line of atomic positions in Quantum ESPRESSO ATOMIC_POSITIONS card. >>> for atom in make_supercell(bulk('Li', 'bcc'), np.ones(3)-np.eye(3)): >>> format_atom_position(atom, True) Li 0.0000000000 0.0000000000 0.0000000000 Li 0.5000000000 0.5000000000 0.5000000000 Parameters ---------- atom : Atom A structure that has symbol and [position | (a, b, c)]. crystal_coordinates: bool Whether the atomic positions should be written to the QE input file in absolute (False, default) or relative (crystal) coordinates (True). mask, optional : str String of ndim=3 0 or 1 for constraining atomic positions. tidx, optional : int Magnetic type index. Returns ------- atom_line : str Input line for atom position """ if crystal_coordinates: coords = [atom.a, atom.b, atom.c] else: coords = atom.position line_fmt = '{atom.symbol}' inps = dict(atom=atom) if tidx is not None: line_fmt += '{tidx}' inps["tidx"] = tidx line_fmt += ' {coords[0]:.10f} {coords[1]:.10f} {coords[2]:.10f} ' inps["coords"] = coords line_fmt += ' ' + mask + '\n' astr = line_fmt.format(**inps) return astr
[docs]@writer def write_espresso_in(fd, atoms, input_data=None, pseudopotentials=None, kspacing=None, kpts=None, koffset=(0, 0, 0), crystal_coordinates=False, additional_cards=None, **kwargs): """ Create an input file for pw.x. Use set_initial_magnetic_moments to turn on spin, if nspin is set to 2 with no magnetic moments, they will all be set to 0.0. Magnetic moments will be converted to the QE units (fraction of valence electrons) using any pseudopotential files found, or a best guess for the number of valence electrons. Units are not converted for any other input data, so use Quantum ESPRESSO units (Usually Ry or atomic units). Keys with a dimension (e.g. Hubbard_U(1)) will be incorporated as-is so the `i` should be made to match the output. Implemented features: - Conversion of :class:`ase.constraints.FixAtoms` and :class:`ase.constraints.FixCartesian`. - ``starting_magnetization`` derived from the ``magmoms`` and pseudopotentials (searches default paths for pseudo files.) - Automatic assignment of options to their correct sections. Not implemented: - Non-zero values of ibrav - Lists of k-points - Other constraints - Hubbard parameters - Validation of the argument types for input - Validation of required options Parameters ---------- fd: file | str A file to which the input is written. atoms: Atoms A single atomistic configuration to write to ``fd``. input_data: dict A flat or nested dictionary with input parameters for pw.x pseudopotentials: dict A filename for each atomic species, e.g. {'O': 'O.pbe-rrkjus.UPF', 'H': 'H.pbe-rrkjus.UPF'}. A dummy name will be used if none are given. kspacing: float Generate a grid of k-points with this as the minimum distance, in A^-1 between them in reciprocal space. If set to None, kpts will be used instead. kpts: (int, int, int), dict or np.ndarray If ``kpts`` is a tuple (or list) of 3 integers, it is interpreted as the dimensions of a Monkhorst-Pack grid. If ``kpts`` is set to ``None``, only the Γ-point will be included and QE will use routines optimized for Γ-point-only calculations. Compared to Γ-point-only calculations without this optimization (i.e. with ``kpts=(1, 1, 1)``), the memory and CPU requirements are typically reduced by half. If kpts is a dict, it will either be interpreted as a path in the Brillouin zone (*) if it contains the 'path' keyword, otherwise it is converted to a Monkhorst-Pack grid (**). If ``kpts`` is a NumPy array, the raw k-points will be passed to Quantum Espresso as given in the array (in crystal coordinates). Must be of shape (n_kpts, 4). The fourth column contains the k-point weights. (*) see ase.dft.kpoints.bandpath (**) see ase.calculators.calculator.kpts2sizeandoffsets koffset: (int, int, int) Offset of kpoints in each direction. Must be 0 (no offset) or 1 (half grid offset). Setting to True is equivalent to (1, 1, 1). crystal_coordinates: bool Whether the atomic positions should be written to the QE input file in absolute (False, default) or relative (crystal) coordinates (True). """ # Convert to a namelist to make working with parameters much easier # Note that the name ``input_data`` is chosen to prevent clash with # ``parameters`` in Calculator objects input_parameters = Namelist(input_data) input_parameters.to_nested('pw', **kwargs) # Convert ase constraints to QE constraints # Nx3 array of force multipliers matches what QE uses # Do this early so it is available when constructing the atoms card moved = np.ones((len(atoms), 3), dtype=bool) for constraint in atoms.constraints: if isinstance(constraint, FixAtoms): moved[constraint.index] = False elif isinstance(constraint, FixCartesian): moved[constraint.index] = ~constraint.mask else: warnings.warn(f'Ignored unknown constraint {constraint}') masks = [] for atom in atoms: # only inclued mask if something is fixed if not all(moved[atom.index]): mask = ' {:d} {:d} {:d}'.format(*moved[atom.index]) else: mask = '' masks.append(mask) # Species info holds the information on the pseudopotential and # associated for each element if pseudopotentials is None: pseudopotentials = {} species_info = {} for species in set(atoms.get_chemical_symbols()): # Look in all possible locations for the pseudos and try to figure # out the number of valence electrons pseudo = pseudopotentials[species] species_info[species] = {'pseudo': pseudo} # Convert atoms into species. # Each different magnetic moment needs to be a separate type even with # the same pseudopotential (e.g. an up and a down for AFM). # if any magmom are > 0 or nspin == 2 then use species labels. # Rememeber: magnetisation uses 1 based indexes atomic_species = {} atomic_species_str = [] atomic_positions_str = [] nspin = input_parameters['system'].get('nspin', 1) # 1 is the default noncolin = input_parameters['system'].get('noncolin', False) rescale_magmom_fac = kwargs.get('rescale_magmom_fac', 1.0) if any(atoms.get_initial_magnetic_moments()): if nspin == 1 and not noncolin: # Force spin on input_parameters['system']['nspin'] = 2 nspin = 2 if nspin == 2 or noncolin: # Magnetic calculation on for atom, mask, magmom in zip( atoms, masks, atoms.get_initial_magnetic_moments()): if (atom.symbol, magmom) not in atomic_species: # for qe version 7.2 or older magmon must be rescale by # about a factor 10 to assume sensible values # since qe-v7.3 magmom values will be provided unscaled fspin = float(magmom) / rescale_magmom_fac # Index in the atomic species list sidx = len(atomic_species) + 1 # Index for that atom type; no index for first one tidx = sum(atom.symbol == x[0] for x in atomic_species) or ' ' atomic_species[(atom.symbol, magmom)] = (sidx, tidx) # Add magnetization to the input file mag_str = f"starting_magnetization({sidx})" input_parameters['system'][mag_str] = fspin species_pseudo = species_info[atom.symbol]['pseudo'] atomic_species_str.append( f"{atom.symbol}{tidx} {atom.mass} {species_pseudo}\n") # lookup tidx to append to name sidx, tidx = atomic_species[(atom.symbol, magmom)] # construct line for atomic positions atomic_positions_str.append( format_atom_position( atom, crystal_coordinates, mask=mask, tidx=tidx) ) else: # Do nothing about magnetisation for atom, mask in zip(atoms, masks): if atom.symbol not in atomic_species: atomic_species[atom.symbol] = True # just a placeholder species_pseudo = species_info[atom.symbol]['pseudo'] atomic_species_str.append( f"{atom.symbol} {atom.mass} {species_pseudo}\n") # construct line for atomic positions atomic_positions_str.append( format_atom_position(atom, crystal_coordinates, mask=mask) ) # Add computed parameters # different magnetisms means different types input_parameters['system']['ntyp'] = len(atomic_species) input_parameters['system']['nat'] = len(atoms) # Use cell as given or fit to a specific ibrav if 'ibrav' in input_parameters['system']: ibrav = input_parameters['system']['ibrav'] if ibrav != 0: raise ValueError(ibrav_error_message) else: # Just use standard cell block input_parameters['system']['ibrav'] = 0 # Construct input file into this pwi = input_parameters.to_string(list_form=True) # Pseudopotentials pwi.append('ATOMIC_SPECIES\n') pwi.extend(atomic_species_str) pwi.append('\n') # KPOINTS - add a MP grid as required if kspacing is not None: kgrid = kspacing_to_grid(atoms, kspacing) elif kpts is not None: if isinstance(kpts, dict) and 'path' not in kpts: kgrid, shift = kpts2sizeandoffsets(atoms=atoms, **kpts) koffset = [] for i, x in enumerate(shift): assert x == 0 or abs(x * kgrid[i] - 0.5) < 1e-14 koffset.append(0 if x == 0 else 1) else: kgrid = kpts else: kgrid = "gamma" # True and False work here and will get converted by ':d' format if isinstance(koffset, int): koffset = (koffset, ) * 3 # BandPath object or bandpath-as-dictionary: if isinstance(kgrid, dict) or hasattr(kgrid, 'kpts'): pwi.append('K_POINTS crystal_b\n') assert hasattr(kgrid, 'path') or 'path' in kgrid kgrid = kpts2ndarray(kgrid, atoms=atoms) pwi.append(f'{len(kgrid)}\n') for k in kgrid: pwi.append(f"{k[0]:.14f} {k[1]:.14f} {k[2]:.14f} 0\n") pwi.append('\n') elif isinstance(kgrid, str) and (kgrid == "gamma"): pwi.append('K_POINTS gamma\n') pwi.append('\n') elif isinstance(kgrid, np.ndarray): if np.shape(kgrid)[1] != 4: raise ValueError('Only Nx4 kgrids are supported right now.') pwi.append('K_POINTS crystal\n') pwi.append(f'{len(kgrid)}\n') for k in kgrid: pwi.append(f"{k[0]:.14f} {k[1]:.14f} {k[2]:.14f} {k[3]:.14f}\n") pwi.append('\n') else: pwi.append('K_POINTS automatic\n') pwi.append(f"{kgrid[0]} {kgrid[1]} {kgrid[2]} " f" {koffset[0]:d} {koffset[1]:d} {koffset[2]:d}\n") pwi.append('\n') # CELL block, if required if input_parameters['SYSTEM']['ibrav'] == 0: pwi.append('CELL_PARAMETERS angstrom\n') pwi.append('{cell[0][0]:.14f} {cell[0][1]:.14f} {cell[0][2]:.14f}\n' '{cell[1][0]:.14f} {cell[1][1]:.14f} {cell[1][2]:.14f}\n' '{cell[2][0]:.14f} {cell[2][1]:.14f} {cell[2][2]:.14f}\n' ''.format(cell=atoms.cell)) pwi.append('\n') # Positions - already constructed, but must appear after namelist if crystal_coordinates: pwi.append('ATOMIC_POSITIONS crystal\n') else: pwi.append('ATOMIC_POSITIONS angstrom\n') pwi.extend(atomic_positions_str) pwi.append('\n') # DONE! fd.write(''.join(pwi)) if additional_cards: if isinstance(additional_cards, list): additional_cards = "\n".join(additional_cards) additional_cards += "\n" fd.write(additional_cards)
[docs]def write_espresso_ph( fd, input_data=None, qpts=None, nat_todo_indices=None, **kwargs) -> None: """ Function that write the input file for a ph.x calculation. Normal namelist cards are passed in the input_data dictionary. Which can be either nested or flat, ASE style. The q-points are passed in the qpts list. If qplot is set to True then qpts is expected to be a list of list|tuple of length 4. Where the first three elements are the coordinates of the q-point in units of 2pi/alat and the last element is the weight of the q-point. if qplot is set to False then qpts is expected to be a simple list of length 4 (single q-point). Finally if ldisp is set to True, the above is discarded and the q-points are read from the nq1, nq2, nq3 cards in the input_data dictionary. Additionally, a nat_todo_indices kwargs (list[int]) can be specified in the kwargs. It will be used if nat_todo is set to True in the input_data dictionary. Globally, this function follows the convention set in the ph.x documentation ( Parameters ---------- fd The file descriptor of the input file. kwargs kwargs dictionary possibly containing the following keys: - input_data: dict - qpts: list[list[float]] | list[tuple[float]] | list[float] - nat_todo_indices: list[int] Returns ------- None """ input_data = Namelist(input_data) input_data.to_nested('ph', **kwargs) input_ph = input_data["inputph"] inp_nat_todo = input_ph.get("nat_todo", 0) qpts = qpts or (0, 0, 0) pwi = input_data.to_string() fd.write(pwi) qplot = input_ph.get("qplot", False) ldisp = input_ph.get("ldisp", False) if qplot: fd.write(f"{len(qpts)}\n") for qpt in qpts: fd.write( f"{qpt[0]:0.8f} {qpt[1]:0.8f} {qpt[2]:0.8f} {qpt[3]:1d}\n" ) elif not (qplot or ldisp): fd.write(f"{qpts[0]:0.8f} {qpts[1]:0.8f} {qpts[2]:0.8f}\n") if inp_nat_todo: tmp = [str(i) for i in nat_todo_indices] fd.write(" ".join(tmp)) fd.write("\n")
[docs]def read_espresso_ph(fileobj): """ Function that reads the output of a ph.x calculation. It returns a dictionary where each q-point number is a key and the value is a dictionary with the following keys if available: - qpoints: The q-point in cartesian coordinates. - kpoints: The k-points in cartesian coordinates. - dieltensor: The dielectric tensor. - borneffcharge: The effective Born charges. - borneffcharge_dfpt: The effective Born charges from DFPT. - polarizability: The polarizability tensor. - modes: The phonon modes. - eqpoints: The symmetrically equivalent q-points. - freqs: The phonon frequencies. - mode_symmetries: The symmetries of the modes. - atoms: The atoms object. Some notes: - For some reason, the cell is not defined to high level of precision in ph.x outputs. Be careful when using the atoms object retrieved from this function. - This function can be called on incomplete calculations i.e. if the calculation couldn't diagonalize the dynamical matrix for some q-points, the results for the other q-points will still be returned. Parameters ---------- fileobj The file descriptor of the output file. Returns ------- dict The results dictionnary as described above. """ freg = re.compile(r"-?(?:0|[1-9]\d*)(?:\.\d+)?(?:[eE][+\-]?\d+)?") QPOINTS = r"(?i)^\s*Calculation\s*of\s*q" NKPTS = r"(?i)^\s*number\s*of\s*k\s*points\s*" DIEL = r"(?i)^\s*Dielectric\s*constant\s*in\s*cartesian\s*axis\s*$" BORN = r"(?i)^\s*Effective\s*charges\s*\(d\s*Force\s*/\s*dE\)" POLA = r"(?i)^\s*Polarizability\s*(a.u.)\^3" REPR = r"(?i)^\s*There\s*are\s*\d+\s*irreducible\s*representations\s*$" EQPOINTS = r"(?i)^\s*Number\s*of\s*q\s*in\s*the\s*star\s*=\s*" DIAG = r"(?i)^\s*Diagonalizing\s*the\s*dynamical\s*matrix\s*$" MODE_SYM = r"(?i)^\s*Mode\s*symmetry,\s*" BORN_DFPT = r"(?i)^\s*Effective\s*charges\s*\(d\s*P\s*/\s*du\)" POSITIONS = r"(?i)^\s*site\s*n\..*\(alat\s*units\)" ALAT = r"(?i)^\s*celldm\(1\)=" CELL = ( r"^\s*crystal\s*axes:\s*\(cart.\s*coord.\s*in\s*units\s*of\s*alat\)" ) ELECTRON_PHONON = r"(?i)^\s*electron-phonon\s*interaction\s*...\s*$" output = { QPOINTS: [], NKPTS: [], DIEL: [], BORN: [], BORN_DFPT: [], POLA: [], REPR: [], EQPOINTS: [], DIAG: [], MODE_SYM: [], POSITIONS: [], ALAT: [], CELL: [], ELECTRON_PHONON: [], } names = { QPOINTS: "qpoints", NKPTS: "kpoints", DIEL: "dieltensor", BORN: "borneffcharge", BORN_DFPT: "borneffcharge_dfpt", POLA: "polarizability", REPR: "representations", EQPOINTS: "eqpoints", DIAG: "freqs", MODE_SYM: "mode_symmetries", POSITIONS: "positions", ALAT: "alat", CELL: "cell", ELECTRON_PHONON: "ep_data", } unique = { QPOINTS: True, NKPTS: False, DIEL: True, BORN: True, BORN_DFPT: True, POLA: True, REPR: True, EQPOINTS: True, DIAG: True, MODE_SYM: True, POSITIONS: True, ALAT: True, CELL: True, ELECTRON_PHONON: True, } results = {} fdo_lines = [i for i in if i] n_lines = len(fdo_lines) for idx, line in enumerate(fdo_lines): for key in output: if bool(re.match(key, line)): output[key].append(idx) output = {key: np.array(value) for key, value in output.items()} def _read_qpoints(idx): match = re.findall(freg, fdo_lines[idx]) return tuple(round(float(x), 7) for x in match) def _read_kpoints(idx): n_kpts = int(re.findall(freg, fdo_lines[idx])[0]) kpts = [] for line in fdo_lines[idx + 2: idx + 2 + n_kpts]: if bool("^\s*k\(.*wk", line)): kpts.append([round(float(x), 7) for x in re.findall(freg, line)[1:]]) return np.array(kpts) def _read_repr(idx): n_repr, curr, n = int(re.findall(freg, fdo_lines[idx])[0]), 0, 0 representations = {} while idx + n < n_lines: if"^\s*Representation.*modes", fdo_lines[idx + n]): curr = int(re.findall(freg, fdo_lines[idx + n])[0]) representations[curr] = {"done": False, "modes": []} if"Calculated\s*using\s*symmetry", fdo_lines[idx + n]) \ or"-\s*Done\s*$", fdo_lines[idx + n]): representations[curr]["done"] = True if"(?i)^\s*(mode\s*#\s*\d\s*)+", fdo_lines[idx + n]): representations[curr]["modes"] = _read_modes(idx + n) if curr == n_repr: break n += 1 return representations def _read_modes(idx): n = 1 n_modes = len(re.findall(r"mode", fdo_lines[idx])) modes = [] while not modes or bool(re.match(r"^\s*\(", fdo_lines[idx + n])): tmp = re.findall(freg, fdo_lines[idx + n]) modes.append([round(float(x), 5) for x in tmp]) n += 1 return np.hsplit(np.array(modes), n_modes) def _read_eqpoints(idx): n_star = int(re.findall(freg, fdo_lines[idx])[0]) return np.loadtxt( fdo_lines[idx + 2: idx + 2 + n_star], usecols=(1, 2, 3) ).reshape(-1, 3) def _read_freqs(idx): n = 0 freqs = [] stop = 0 while not freqs or stop < 2: if bool("^\s*freq", fdo_lines[idx + n])): tmp = re.findall(freg, fdo_lines[idx + n])[1] freqs.append(float(tmp)) if bool("\*{5,}", fdo_lines[idx + n])): stop += 1 n += 1 return np.array(freqs) def _read_sym(idx): n = 1 sym = {} while bool(re.match(r"^\s*freq", fdo_lines[idx + n])): r = re.findall("\\d+", fdo_lines[idx + n]) r = tuple(range(int(r[0]), int(r[1]) + 1)) sym[r] = fdo_lines[idx + n].split("-->")[1].strip() sym[r] = re.sub(r"\s+", " ", sym[r]) n += 1 return sym def _read_epsil(idx): epsil = np.zeros((3, 3)) for n in range(1, 4): tmp = re.findall(freg, fdo_lines[idx + n]) epsil[n - 1] = [round(float(x), 9) for x in tmp] return epsil def _read_born(idx): n = 1 born = [] while idx + n < n_lines: if"^\s*atom\s*\d\s*\S", fdo_lines[idx + n]): pass elif"^\s*E\*?(x|y|z)\s*\(", fdo_lines[idx + n]): tmp = re.findall(freg, fdo_lines[idx + n]) born.append([round(float(x), 5) for x in tmp]) else: break n += 1 born = np.array(born) return np.vsplit(born, len(born) // 3) def _read_born_dfpt(idx): n = 1 born = [] while idx + n < n_lines: if"^\s*atom\s*\d\s*\S", fdo_lines[idx + n]): pass elif"^\s*P(x|y|z)\s*\(", fdo_lines[idx + n]): tmp = re.findall(freg, fdo_lines[idx + n]) born.append([round(float(x), 5) for x in tmp]) else: break n += 1 born = np.array(born) return np.vsplit(born, len(born) // 3) def _read_pola(idx): pola = np.zeros((3, 3)) for n in range(1, 4): tmp = re.findall(freg, fdo_lines[idx + n])[:3] pola[n - 1] = [round(float(x), 2) for x in tmp] return pola def _read_positions(idx): positions = [] symbols = [] n = 1 while re.findall(r"^\s*\d+", fdo_lines[idx + n]): symbols.append(fdo_lines[idx + n].split()[1]) positions.append( [round(float(x), 5) for x in re.findall(freg, fdo_lines[idx + n])[-3:]] ) n += 1 atoms = Atoms(positions=positions, symbols=symbols) atoms.pbc = True return atoms def _read_alat(idx): return round(float(re.findall(freg, fdo_lines[idx])[1]), 5) def _read_cell(idx): cell = [] n = 1 while re.findall(r"^\s*a\(\d\)", fdo_lines[idx + n]): cell.append([round(float(x), 4) for x in re.findall(freg, fdo_lines[idx + n])[-3:]]) n += 1 return np.array(cell) def _read_electron_phonon(idx): results = {} broad_re = ( r"^\s*Gaussian\s*Broadening:\s+([\d.]+)\s+Ry, ngauss=\s+\d+" ) dos_re = ( r"^\s*DOS\s*=\s*([\d.]+)\s*states/" r"spin/Ry/Unit\s*Cell\s*at\s*Ef=\s+([\d.]+)\s+eV" ) lg_re = ( r"^\s*lambda\(\s+(\d+)\)=\s+([\d.]+)\s+gamma=\s+([\d.]+)\s+GHz" ) end_re = r"^\s*Number\s*of\s*q\s*in\s*the\s*star\s*=\s+(\d+)$" lambdas = [] gammas = [] current = None n = 1 while idx + n < n_lines: line = fdo_lines[idx + n] broad_match = re.match(broad_re, line) dos_match = re.match(dos_re, line) lg_match = re.match(lg_re, line) end_match = re.match(end_re, line) if broad_match: if lambdas: results[current]["lambdas"] = lambdas results[current]["gammas"] = gammas lambdas = [] gammas = [] current = float(broad_match[1]) results[current] = {} elif dos_match: results[current]["dos"] = float(dos_match[1]) results[current]["fermi"] = float(dos_match[2]) elif lg_match: lambdas.append(float(lg_match[2])) gammas.append(float(lg_match[3])) if end_match: results[current]["lambdas"] = lambdas results[current]["gammas"] = gammas break n += 1 return results properties = { NKPTS: _read_kpoints, DIEL: _read_epsil, BORN: _read_born, BORN_DFPT: _read_born_dfpt, POLA: _read_pola, REPR: _read_repr, EQPOINTS: _read_eqpoints, DIAG: _read_freqs, MODE_SYM: _read_sym, POSITIONS: _read_positions, ALAT: _read_alat, CELL: _read_cell, ELECTRON_PHONON: _read_electron_phonon, } iblocks = np.append(output[QPOINTS], n_lines) for qnum, (past, future) in enumerate(zip(iblocks[:-1], iblocks[1:])): qpoint = _read_qpoints(past) results[qnum + 1] = curr_result = {"qpoint": qpoint} for prop in properties: p = (past < output[prop]) & (output[prop] < future) selected = output[prop][p] if len(selected) == 0: continue if unique[prop]: idx = output[prop][p][-1] curr_result[names[prop]] = properties[prop](idx) else: tmp = {k + 1: 0 for k in range(len(selected))} for k, idx in enumerate(selected): tmp[k + 1] = properties[prop](idx) curr_result[names[prop]] = tmp alat = curr_result.pop("alat", 1.0) atoms = curr_result.pop("positions", None) cell = curr_result.pop("cell", np.eye(3)) if atoms: atoms.positions *= alat * units["Bohr"] atoms.cell = cell * alat * units["Bohr"] atoms.wrap() curr_result["atoms"] = atoms return results
[docs]def write_fortran_namelist( fd, input_data=None, binary=None, additional_cards=None, **kwargs) -> None: """ Function which writes input for simple espresso binaries. List of supported binaries are in the file. Non-exhaustive list (to complete) Note: "EOF" is appended at the end of the file. ( Additional fields are written 'as is' in the input file. It is expected to be a string or a list of strings. Parameters ---------- fd The file descriptor of the input file. input_data: dict A flat or nested dictionary with input parameters for the binary.x binary: str Name of the binary additional_cards: str | list[str] Additional fields to be written at the end of the input file, after the namelist. It is expected to be a string or a list of strings. Returns ------- None """ input_data = Namelist(input_data) if binary: input_data.to_nested(binary, **kwargs) pwi = input_data.to_string() fd.write(pwi) if additional_cards: if isinstance(additional_cards, list): additional_cards = "\n".join(additional_cards) additional_cards += "\n" fd.write(additional_cards) fd.write("EOF")
@deprecated('Please use the class', DeprecationWarning) def construct_namelist(parameters=None, keys=None, warn=False, **kwargs): """ Construct an ordered Namelist containing all the parameters given (as a dictionary or kwargs). Keys will be inserted into their appropriate section in the namelist and the dictionary may contain flat and nested structures. Any kwargs that match input keys will be incorporated into their correct section. All matches are case-insensitive, and returned Namelist object is a case-insensitive dict. If a key is not known to ase, but in a section within `parameters`, it will be assumed that it was put there on purpose and included in the output namelist. Anything not in a section will be ignored (set `warn` to True to see ignored keys). Keys with a dimension (e.g. Hubbard_U(1)) will be incorporated as-is so the `i` should be made to match the output. The priority of the keys is: kwargs[key] > parameters[key] > parameters[section][key] Only the highest priority item will be included. .. deprecated:: 3.23.0 Please use :class:`` instead. Parameters ---------- parameters: dict Flat or nested set of input parameters. keys: Namelist | dict Namelist to use as a template for the output. warn: bool Enable warnings for unused keys. Returns ------- input_namelist: Namelist pw.x compatible namelist of input parameters. """ if keys is None: keys = deepcopy(pw_keys) # Convert everything to Namelist early to make case-insensitive if parameters is None: parameters = Namelist() else: # Maximum one level of nested dict # Don't modify in place parameters_namelist = Namelist() for key, value in parameters.items(): if isinstance(value, dict): parameters_namelist[key] = Namelist(value) else: parameters_namelist[key] = value parameters = parameters_namelist # Just a dict kwargs = Namelist(kwargs) # Final parameter set input_namelist = Namelist() # Collect for section in keys: sec_list = Namelist() for key in keys[section]: # Check all three separately and pop them all so that # we can check for missing values later value = None if key in parameters.get(section, {}): value = parameters[section].pop(key) if key in parameters: value = parameters.pop(key) if key in kwargs: value = kwargs.pop(key) if value is not None: sec_list[key] = value # Check if there is a key(i) version (no extra parsing) for arg_key in list(parameters.get(section, {})): if arg_key.split('(')[0].strip().lower() == key.lower(): sec_list[arg_key] = parameters[section].pop(arg_key) cp_parameters = parameters.copy() for arg_key in cp_parameters: if arg_key.split('(')[0].strip().lower() == key.lower(): sec_list[arg_key] = parameters.pop(arg_key) cp_kwargs = kwargs.copy() for arg_key in cp_kwargs: if arg_key.split('(')[0].strip().lower() == key.lower(): sec_list[arg_key] = kwargs.pop(arg_key) # Add to output input_namelist[section] = sec_list unused_keys = list(kwargs) # pass anything else already in a section for key, value in parameters.items(): if key in keys and isinstance(value, dict): input_namelist[key].update(value) elif isinstance(value, dict): unused_keys.extend(list(value)) else: unused_keys.append(key) if warn and unused_keys: warnings.warn('Unused keys: {}'.format(', '.join(unused_keys))) return input_namelist @deprecated('Please use the .to_string() method of Namelist instead.', DeprecationWarning) def namelist_to_string(input_parameters): """Format a Namelist object as a string for writing to a file. Assume sections are ordered (taken care of in namelist construction) and that repr converts to a QE readable representation (except bools) .. deprecated:: 3.23.0 Please use the :meth:`` method instead. Parameters ---------- input_parameters : Namelist | dict Expecting a nested dictionary of sections and key-value data. Returns ------- pwi : List[str] Input line for the namelist """ pwi = [] for section in input_parameters: pwi.append(f'&{section.upper()}\n') for key, value in input_parameters[section].items(): if value is True: pwi.append(f' {key:16} = .true.\n') elif value is False: pwi.append(f' {key:16} = .false.\n') elif isinstance(value, Path): pwi.append(f' {key:16} = "{value}"\n') else: # repr format to get quotes around strings pwi.append(f' {key:16} = {value!r}\n') pwi.append('/\n') # terminate section pwi.append('\n') return pwi