def get_pseudos(options):
"""
Find pseudos in paths, return :class:`PseudoTable` object sorted by atomic number Z.
Accepts filepaths or directory.
"""
exts=("psp8",)
paths = options.pseudos
if len(paths) == 1 and os.path.isdir(paths[0]):
top = paths[0]
paths = find_exts(top, exts, exclude_dirs="_*")
#table = PseudoTable.from_dir(paths[0])
pseudos = []
for p in paths:
try:
pseudos.append(Pseudo.from_file(p))
except Exception as exc:
warn("Error in %s:\n%s" % (p, exc))
table = PseudoTable(pseudos)
# Here we select a subset of pseudos according to family or rows
if options.rows:
table = table.select_rows(options.rows)
elif options.family:
table = table.select_families(options.family)
if options.symbols:
table = table.select_symbols(options.symbols)
return table.sort_by_z()
def select_pseudos(pseudos, structure, ret_table=True):
"""
Given a list of pseudos and a pymatgen structure, extract the pseudopotentials
for the calculation (useful when we receive an entire periodic table).
Raises:
ValueError if no pseudo is found or multiple occurrences are found.
"""
table = PseudoTable.astable(pseudos)
pseudos = []
for symbol in structure.types_of_specie:
# Get the list of pseudopotentials in table from atom symbol.
pseudos_for_type = table.pseudos_with_symbol(symbol)
if not pseudos_for_type:
raise ValueError("Cannot find pseudo for symbol %s" % symbol)
if len(pseudos_for_type) > 1:
raise ValueError("Find multiple pseudos for symbol %s" % symbol)
pseudos.append(pseudos_for_type[0])
if ret_table:
return PseudoTable(pseudos)
else:
return pseudos
def __init__(self,
pseudos,
pseudo_dir="",
structure=None,
ndtset=1,
comment="",
decorators=None):
"""
Args:
pseudos: String or list of string with the name of the pseudopotential files.
pseudo_dir: Name of the directory where the pseudopotential files are located.
structure: file with the structure, :class:`Structure` object or dictionary with ABINIT geo variable
ndtset: Number of datasets.
comment: Optional string with a comment that will be placed at the beginning of the file.
decorators: List of `AbinitInputDecorator` objects.
"""
# Dataset[0] contains the global variables common to the different datasets
# Dataset[1:ndtset+1] stores the variables specific to the different datasets.
self._ndtset = ndtset
self._datasets = []
for i in range(ndtset + 1):
dt0 = None
if i > 0: dt0 = self._datasets[0]
self._datasets.append(Dataset(index=i, dt0=dt0))
self._datasets[0]["ndtset"] = ndtset
# Setup of the pseudopotential files.
if isinstance(pseudos, PseudoTable):
self._pseudos = pseudos
elif all(isinstance(p, Pseudo) for p in pseudos):
self._pseudos = PseudoTable(pseudos)
else:
# String(s)
pseudo_dir = os.path.abspath(pseudo_dir)
pseudo_paths = [
os.path.join(pseudo_dir, p) for p in list_strings(pseudos)
]
missing = [p for p in pseudo_paths if not os.path.exists(p)]
if missing:
raise self.Error(
"Cannot find the following pseudopotential files:\n%s" %
str(missing))
self._pseudos = PseudoTable(pseudo_paths)
if structure is not None: self.set_structure(structure)
if comment is not None: self.set_comment(comment)
self._decorators = [] if not decorators else decorators
def num_valence_electrons(pseudos, structure):
"""
Compute the number of valence electrons from
a list of pseudopotentials and the crystalline structure.
Args:
pseudos:
List of strings, list of of pseudos or `PseudoTable` instance.
structure:
Pymatgen structure.
Raises:
ValueError if cannot find a pseudo in the input pseudos or if the
input list contains more than one pseudo for the chemical symbols
appearing in structure.
"""
table = PseudoTable.astable(pseudos)
valence = 0.0
for site in structure:
entries = table.pseudos_with_symbol(site.specie.symbol)
if len(entries) != 1:
raise ValueError("Found %d entries for symbol %s" % (len(entries), site.specie.symbol))
valence += entries[0].Z_val
return valence
def select_pseudos(pseudos, structure, ret_table=True):
"""
Given a list of pseudos and a pymatgen structure, extract the pseudopotentials
for the calculation (useful when we receive an entire periodic table).
Raises:
ValueError if no pseudo is found or multiple occurrences are found.
"""
table = PseudoTable.astable(pseudos)
pseudos = []
for symbol in structure.types_of_specie:
# Get the list of pseudopotentials in table from atom symbol.
pseudos_for_type = table.pseudos_with_symbol(symbol)
if not pseudos_for_type:
raise ValueError("Cannot find pseudo for symbol %s" % symbol)
if len(pseudos_for_type) > 1:
raise ValueError("Find multiple pseudos for symbol %s" % symbol)
pseudos.append(pseudos_for_type[0])
if ret_table:
return PseudoTable(pseudos)
else:
return pseudos
def num_valence_electrons(pseudos, structure):
"""
Compute the number of valence electrons from
a list of pseudopotentials and the crystalline structure.
Args:
pseudos:
List of strings, list of of pseudos or `PseudoTable` instance.
structure:
Pymatgen structure.
Raises:
ValueError if cannot find a pseudo in the input pseudos or if the
input list contains more than one pseudo for the chemical symbols
appearing in structure.
"""
table = PseudoTable.astable(pseudos)
valence = 0.0
for site in structure:
entries = table.pseudos_with_symbol(site.specie.symbol)
if len(entries) != 1:
raise ValueError("Found %d entries for symbol %s" %
(len(entries), site.specie.symbol))
valence += entries[0].Z_val
return valence
def num_valence_electrons(self, pseudos):
"""
Returns the number of valence electrons.
Args:
pseudos: List of :class:`Pseudo` objects or list of filenames.
"""
nval, table = 0, PseudoTable.as_table(pseudos)
for site in self:
pseudo = table.pseudo_with_symbol(site.species_string)
nval += pseudo.Z_val
return nval
def num_valence_electrons(self, pseudos):
"""
Returns the number of valence electrons.
Args:
pseudos: List of :class:`Pseudo` objects or list of filenames.
"""
nval, table = 0, PseudoTable.as_table(pseudos)
for site in self:
pseudo = table.pseudo_with_symbol(site.species_string)
nval += pseudo.Z_val
return nval
def __init__(self, structure, spec, option=None):
self.structure = structure
self.spec = spec
self.option = option
self.bands_fac = 1
self.tests = self.__class__.get_defaults_tests()
self.convs = self.__class__.get_defaults_convs()
self.response_models = self.__class__.get_response_models()
if self.option is None:
self.all_converged = False
elif len(self.option) == len(self.convs):
self.all_converged = True
else:
self.all_converged = False
path_add = '.conv' if self.all_converged else ''
self.work_dir = s_name(self.structure) + path_add
abi_pseudo = os.environ['ABINIT_PS_EXT']
abi_pseudo_dir = os.environ['ABINIT_PS']
pseudos = []
for element in self.structure.composition.element_composition:
pseudo = os.path.join(abi_pseudo_dir, str(element) + abi_pseudo)
pseudos.append(pseudo)
self.pseudo_table = PseudoTable(pseudos)
def num_valence_electrons(self, pseudos):
"""
Returns the number of valence electrons.
Args:
pseudos:
List of `Pseudo` objects or list of pseudopotential filenames.
"""
table = PseudoTable.as_table(pseudos)
nval = 0
for site in self:
symbol = site.species_string
pseudos = table.pseudos_with_symbol(symbol)
assert len(pseudos) == 1
nval += pseudos[0].Z_val
return nval
def select_pseudos(pseudos, structure):
"""
Given a list of pseudos and a pymatgen structure, extract the pseudopotentials
for the calculation (useful when we receive an entire periodic table).
Raises:
ValueError if no pseudo is found or multiple occurrences are found.
"""
table = PseudoTable.astable(pseudos)
pseudos = []
for typ in structure.types_of_specie:
# Get the list of pseudopotentials in table from atom symbol.
pseudos_for_type = table.pseudos_with_symbol(typ)
if pseudos_for_type is None or len(pseudos_for_type) != 1:
raise ValueError("Cannot find unique pseudo for type %s" % typ)
pseudos.append(pseudos_for_type[0])
return PseudoTable(pseudos)
def __init__(self, structure, spec, option=None):
self.structure = structure
self.spec = spec
self.option = option
self.tests = self.__class__.get_defaults_tests()
self.convs = self.__class__.get_defaults_convs()
self.response_models = self.__class__.get_response_models()
if self.option is None:
self.all_converged = False
elif len(self.option) == len(self.convs):
self.all_converged = True
else:
self.all_converged = False
path_add = '.conv' if self.all_converged else ''
self.work_dir = s_name(self.structure)+path_add
abi_pseudo = os.environ['ABINIT_PS_EXT']
abi_pseudo_dir = os.environ['ABINIT_PS']
pseudos = []
for element in self.structure.composition.element_composition:
pseudo = os.path.join(abi_pseudo_dir, str(element) + abi_pseudo)
pseudos.append(pseudo)
self.pseudo_table = PseudoTable(pseudos)
def scf_ph_inputs(structure, options):
"""
This function constructs the input files for the phonon calculation:
GS input + the input files for the phonon calculation.
"""
abi_pseudo = os.environ['ABINIT_PS_EXT']
abi_pseudo_dir = os.environ['ABINIT_PS']
pseudos = []
for element in structure.composition.element_composition:
pseudo = os.path.join(abi_pseudo_dir, str(element) + abi_pseudo)
pseudos.append(pseudo)
pseudos = PseudoTable(pseudos)
#print('bounds:\n', structure.calc_kptbounds)
#print('ngkpt:\n', structure.calc_ngkpt(4))
print('ks:\n',
structure.calc_ksampling(4)) # try to get the qpoints from this ...
qptbounds = structure.calc_kptbounds()
qptbounds = np.reshape(qptbounds, (-1, 3))
# List of q-points for the phonon calculation.
qpoints = [
0.00000000E+00,
0.00000000E+00,
0.00000000E+00,
2.50000000E-01,
0.00000000E+00,
0.00000000E+00,
2.50000000E-01,
0.00000000E+00,
2.50000000E+00,
5.00000000E-01,
0.00000000E+00,
0.00000000E+00,
2.50000000E-01,
2.50000000E-01,
0.00000000E+00,
5.00000000E-01,
2.50000000E-01,
0.00000000E+00,
-2.50000000E-01,
2.50000000E-01,
0.00000000E+00,
5.00000000E-01,
5.00000000E-01,
0.00000000E+00,
0.00000000E+00,
0.00000000E+00,
2.50000000E-01,
-2.50000000E-01,
5.00000000E-01,
2.50000000E-01,
]
qpoints2 = [
0.00000000E+00,
0.00000000E+00,
0.00000000E+00,
5.00000000E-01,
0.00000000E+00,
0.00000000E+00,
0.00000000E-01,
5.00000000E-01,
0.00000000E+00,
0.00000000E+00,
0.00000000E+00,
5.00000000E-01,
5.00000000E-01,
5.00000000E-01,
0.00000000E+00,
0.00000000E+00,
5.00000000E-01,
5.00000000E-01,
5.00000000E-01,
0.00000000E+00,
5.00000000E-01,
5.00000000E-01,
5.00000000E-01,
5.00000000E-01,
]
qpoints = np.reshape(qpoints, (-1, 3))
qpoints = unique_rows(np.concatenate((qpoints, qptbounds), axis=0))
if os.path.isfile('qpoints'):
f = open('qpoints', 'r')
qpoints = np.reshape(ast.literal_eval(f.read()), (-1, 3))
f.close()
# Global variables used both for the GS and the DFPT run.
global_vars = dict(istwfk='*1',
ecut=16.0,
ngkpt=[8, 8, 8],
shiftk=[0, 0, 0],
paral_kgb=0,
nstep=200)
global_vars.update(options)
to_vecs(global_vars)
inp = abilab.AbiInput(pseudos=pseudos, ndtset=1 + len(qpoints))
inp.set_structure(structure)
inp.set_variables(**global_vars)
inp[1].set_variables(tolwfr=1.0e-18, prtden=1, paral_kgb=1)
for i, qpt in enumerate(qpoints):
# Response-function calculation for phonons.
inp[i + 2].set_variables(
tolvrs=1.0e-10,
kptopt=3,
iscf=5,
rfphon=1, # Will consider phonon-type perturbation
nqpt=1, # One wavevector is to be considered
qpt=qpt, # This wavevector is q=0 (Gamma)
)
#rfatpol 1 1 # Only the first atom is displaced
#rfdir 1 0 0 # Along the first reduced coordinate axis
#kptopt 2 # Automatic generation of k points, taking
# Split input into gs_inp and ph_inputs
return inp.split_datasets()
class SingleAbinitGWWork():
"""
GW workflow for Abinit
"""
RESPONSE_MODELS = ["cd", "godby", "hybersten", "linden", "farid"]
TESTS = {'ecuteps': {'test_range': (10, 14), 'method': 'direct', 'control': "gap", 'level': "sigma"},
'nscf_nbands': {'test_range': (30, 40), 'method': 'set_bands', 'control': "gap", 'level': "nscf"},
'response_model': {'test_range': RESPONSE_MODELS, 'method': 'direct', 'control': 'gap', 'level': 'screening'}}
# scf level test are run independently, the last value will be used in the nscf and sigma tests
#'test': {'test_range': (1, 2, 3), 'method': 'direct', 'control': "e_ks_max", 'level': "scf"},
CONVS = {'ecut': {'test_range': (52, 48, 44), 'method': 'direct', 'control': "e_ks_max", 'level': "scf"},
'ecuteps': {'test_range': (4, 8, 12, 16, 20), 'method': 'direct', 'control': "gap", 'level': "sigma"},
'nscf_nbands': {'test_range': (5, 10, 20, 30), 'method': 'set_bands', 'control': "gap", 'level': "nscf"}}
def __init__(self, structure, spec, option=None):
self.structure = structure
self.spec = spec
self.option = option
self.bands_fac = 1
self.tests = self.__class__.get_defaults_tests()
self.convs = self.__class__.get_defaults_convs()
self.response_models = self.__class__.get_response_models()
if self.option is None:
self.all_converged = False
elif len(self.option) == len(self.convs):
self.all_converged = True
else:
self.all_converged = False
path_add = '.conv' if self.all_converged else ''
self.work_dir = s_name(self.structure)+path_add
abi_pseudo = os.environ['ABINIT_PS_EXT']
abi_pseudo_dir = os.environ['ABINIT_PS']
pseudos = []
for element in self.structure.composition.element_composition:
pseudo = os.path.join(abi_pseudo_dir, str(element) + abi_pseudo)
pseudos.append(pseudo)
self.pseudo_table = PseudoTable(pseudos)
@classmethod
def get_defaults_tests(cls):
return copy.deepcopy(cls.TESTS)
@classmethod
def get_defaults_convs(cls):
return copy.deepcopy(cls.CONVS)
@classmethod
def get_response_models(cls):
return copy.deepcopy(cls.RESPONSE_MODELS)
def get_electrons(self, structure):
"""
Method for retrieving the number of valence electrons
"""
electrons = 0
for element in structure.species:
entries = self.pseudo_table.pseudos_with_symbol(element.symbol)
assert len(entries) == 1
pseudo = entries[0]
electrons += pseudo.Z_val
return electrons
def get_bands(self, structure):
"""
Method for retrieving the standard number of bands
"""
bands = self.get_electrons(structure) / 2 + len(structure)
return int(bands)
def get_work_dir(self):
name = s_name(self.structure)
if not self.all_converged:
return str(name)+'_'+str(self.option['test'])+'_'+str(self.option['value'])
else:
return str(name)
def create(self):
"""
create single abinit G0W0 flow
"""
manager = 'slurm' if 'ceci' in self.spec['mode'] else 'shell'
# an AbiStructure object has an overwritten version of get_sorted_structure that sorts according to Z
# this could also be pulled into the constructor of Abistructure
#abi_structure = self.structure.get_sorted_structure()
from abipy import abilab
item = copy.copy(self.structure.item)
self.structure.__class__ = abilab.Structure
self.structure = self.structure.get_sorted_structure_z()
self.structure.item = item
abi_structure = self.structure
manager = TaskManager.from_user_config()
# Initialize the flow.
flow = Flow(self.work_dir, manager, pickle_protocol=0)
# flow = Flow(self.work_dir, manager)
# kpoint grid defined over density 40 > ~ 3 3 3
if self.spec['converge'] and not self.all_converged:
# (2x2x2) gamma centered mesh for the convergence test on nbands and ecuteps
# if kp_in is present in the specs a kp_in X kp_in x kp_in mesh is used for the convergence studie
if 'kp_in' in self.spec.keys():
if self.spec['kp_in'] > 9:
print('WARNING:\nkp_in should be < 10 to generate an n x n x n mesh\nfor larger values a grid with '
'density kp_in will be generated')
scf_kppa = self.spec['kp_in']
else:
scf_kppa = 2
else:
# use the specified density for the final calculation with the converged nbands and ecuteps of other
# stand alone calculations
scf_kppa = self.spec['kp_grid_dens']
gamma = True
# 'standard' parameters for stand alone calculation
nb = self.get_bands(self.structure)
nscf_nband = [10 * nb]
nksmall = None
ecuteps = [8]
ecutsigx = 44
extra_abivars = dict(
paral_kgb=1,
inclvkb=2,
ecut=44,
pawecutdg=88,
gwmem='10',
getden=-1,
istwfk="*1",
timopt=-1,
nbdbuf=8
)
# read user defined extra abivars from file 'extra_abivars' should be dictionary
extra_abivars.update(read_extra_abivars())
#self.bands_fac = 0.5 if 'gwcomp' in extra_abivars.keys() else 1
#self.convs['nscf_nbands']['test_range'] = tuple([self.bands_fac*x for x in self.convs['nscf_nbands']['test_range']])
response_models = ['godby']
if 'ppmodel' in extra_abivars.keys():
response_models = [extra_abivars.pop('ppmodel')]
if self.option is not None:
for k in self.option.keys():
if k in ['ecuteps', 'nscf_nbands']:
pass
else:
extra_abivars.update({k: self.option[k]})
if k == 'ecut':
extra_abivars.update({'pawecutdg': self.option[k]*2})
try:
grid = read_grid_from_file(s_name(self.structure)+".full_res")['grid']
all_done = read_grid_from_file(s_name(self.structure)+".full_res")['all_done']
workdir = os.path.join(s_name(self.structure), 'w'+str(grid))
except (IOError, OSError):
grid = 0
all_done = False
workdir = None
if not all_done:
if (self.spec['test'] or self.spec['converge']) and not self.all_converged:
if self.spec['test']:
print('| setting test calculation')
tests = SingleAbinitGWWork(self.structure, self.spec).tests
response_models = []
else:
if grid == 0:
print('| setting convergence calculations for grid 0')
#tests = SingleAbinitGWWorkFlow(self.structure, self.spec).convs
tests = self.convs
else:
print('| extending grid')
#tests = expand(SingleAbinitGWWorkFlow(self.structure, self.spec).convs, grid)
tests = expand(self.convs, grid)
ecuteps = []
nscf_nband = []
for test in tests:
if tests[test]['level'] == 'scf':
if self.option is None:
extra_abivars.update({test + '_s': tests[test]['test_range']})
elif test in self.option:
extra_abivars.update({test: self.option[test]})
else:
extra_abivars.update({test + '_s': tests[test]['test_range']})
else:
for value in tests[test]['test_range']:
if test == 'nscf_nbands':
nscf_nband.append(value * self.get_bands(self.structure))
#scr_nband takes nscf_nbands if not specified
#sigma_nband takes scr_nbands if not specified
if test == 'ecuteps':
ecuteps.append(value)
if test == 'response_model':
response_models.append(value)
elif self.all_converged:
print('| setting up for testing the converged values at the high kp grid ')
# add a bandstructure and dos calculation
nksmall = 30
# in this case a convergence study has already been performed.
# The resulting parameters are passed as option
ecuteps = [self.option['ecuteps'], self.option['ecuteps'] + self.convs['ecuteps']['test_range'][1] -
self.convs['ecuteps']['test_range'][0]]
nscf_nband = [self.option['nscf_nbands'], self.option['nscf_nbands'] + self.convs['nscf_nbands'][
'test_range'][1] - self.convs['nscf_nbands']['test_range'][0]]
# for option in self.option:
# if option not in ['ecuteps', 'nscf_nband']:
# extra_abivars.update({option + '_s': self.option[option]})
else:
print('| all is done for this material')
return
logger.info('ecuteps : ', ecuteps)
logger.info('extra : ', extra_abivars)
logger.info('nscf_nb : ', nscf_nband)
work = g0w0_extended(abi_structure, self.pseudo_table, scf_kppa, nscf_nband, ecuteps, ecutsigx,
accuracy="normal", spin_mode="unpolarized", smearing=None, response_models=response_models,
charge=0.0, sigma_nband=None, scr_nband=None, gamma=gamma, nksmall=nksmall, **extra_abivars)
flow.register_work(work, workdir=workdir)
return flow.allocate()
def create_job_file(self, serial=True):
"""
Create the jobfile for starting all schedulers manually
serial = True creates a list that can be submitted as job that runs all schedulers a a batch job
(the job header needs to be added)
serial = False creates a list that can be used to start all schedulers on the frontend in the background
"""
job_file = open("job_collection", mode='a')
if serial:
job_file.write('abirun.py ' + self.work_dir + ' scheduler > ' + self.work_dir + '.log\n')
else:
job_file.write('nohup abirun.py ' + self.work_dir + ' scheduler > ' + self.work_dir + '.log & \n')
job_file.write('sleep 2\n')
job_file.close()
class SingleAbinitGWWork():
"""
GW flow for Abinit
"""
RESPONSE_MODELS = ["cd", "godby", "hybersten", "linden", "farid"]
TESTS = {
'ecuteps': {
'test_range': (10, 14),
'method': 'direct',
'control': "gap",
'level': "sigma"
},
'nscf_nbands': {
'test_range': (30, 40),
'method': 'set_bands',
'control': "gap",
'level': "nscf"
},
'response_model': {
'test_range': RESPONSE_MODELS,
'method': 'direct',
'control': 'gap',
'level': 'screening'
}
}
# scf level test are run independently, the last value will be used in the nscf and sigma tests
#'test': {'test_range': (1, 2, 3), 'method': 'direct', 'control': "e_ks_max", 'level': "scf"},
CONVS = {
'ecut': {
'test_range': (50, 48, 46, 44),
'method': 'direct',
'control': "e_ks_max",
'level': "scf"
},
'ecuteps': {
'test_range': (4, 8, 12, 16, 20),
'method': 'direct',
'control': "gap",
'level': "sigma"
},
'nscf_nbands': {
'test_range': (5, 10, 20, 30),
'method': 'set_bands',
'control': "gap",
'level': "nscf"
}
}
def __init__(self, structure, spec, option=None):
self.structure = structure
self.spec = spec
self.option = option
self.bands_fac = 1
self.tests = self.__class__.get_defaults_tests()
self.convs = self.__class__.get_defaults_convs()
self.response_models = self.__class__.get_response_models()
if self.option is None:
self.all_converged = False
elif len(self.option) == len(self.convs):
self.all_converged = True
else:
self.all_converged = False
path_add = '.conv' if self.all_converged else ''
self.work_dir = s_name(self.structure) + path_add
abi_pseudo = os.environ['ABINIT_PS_EXT']
abi_pseudo_dir = os.environ['ABINIT_PS']
pseudos = []
for element in self.structure.composition.element_composition:
pseudo = os.path.join(abi_pseudo_dir, str(element) + abi_pseudo)
pseudos.append(pseudo)
self.pseudo_table = PseudoTable(pseudos)
@classmethod
def get_defaults_tests(cls):
return copy.deepcopy(cls.TESTS)
@classmethod
def get_defaults_convs(cls):
return copy.deepcopy(cls.CONVS)
@classmethod
def get_response_models(cls):
return copy.deepcopy(cls.RESPONSE_MODELS)
def get_electrons(self, structure):
"""
Method for retrieving the number of valence electrons
"""
electrons = 0
for element in structure.species:
entries = self.pseudo_table.pseudos_with_symbol(element.symbol)
assert len(entries) == 1
pseudo = entries[0]
electrons += pseudo.Z_val
return electrons
def get_bands(self, structure):
"""
Method for retrieving the standard number of bands
"""
bands = self.get_electrons(structure) / 2 + len(structure)
return int(bands)
def get_work_dir(self):
name = s_name(self.structure)
if not self.all_converged:
return str(name) + '_' + str(self.option['test']) + '_' + str(
self.option['value'])
else:
return str(name)
def create(self):
"""
create single abinit G0W0 flow
"""
manager = 'slurm' if 'ceci' in self.spec['mode'] else 'shell'
# an AbiStructure object has an overwritten version of get_sorted_structure that sorts according to Z
# this could also be pulled into the constructor of Abistructure
#abi_structure = self.structure.get_sorted_structure()
from abipy import abilab
item = copy.copy(self.structure.item)
self.structure.__class__ = abilab.Structure
self.structure = self.structure.get_sorted_structure_z()
self.structure.item = item
abi_structure = self.structure
manager = TaskManager.from_user_config()
# Initialize the flow.
flow = Flow(self.work_dir, manager, pickle_protocol=0)
# flow = Flow(self.work_dir, manager)
# kpoint grid defined over density 40 > ~ 3 3 3
if self.spec['converge'] and not self.all_converged:
# (2x2x2) gamma centered mesh for the convergence test on nbands and ecuteps
# if kp_in is present in the specs a kp_in X kp_in x kp_in mesh is used for the convergence studie
if 'kp_in' in self.spec.keys():
if self.spec['kp_in'] > 9:
print(
'WARNING:\nkp_in should be < 10 to generate an n x n x n mesh\nfor larger values a grid with '
'density kp_in will be generated')
scf_kppa = self.spec['kp_in']
else:
scf_kppa = 2
else:
# use the specified density for the final calculation with the converged nbands and ecuteps of other
# stand alone calculations
scf_kppa = self.spec['kp_grid_dens']
gamma = True
# 'standard' parameters for stand alone calculation
scf_nband = self.get_bands(self.structure)
nscf_nband = [10 * scf_nband]
nksmall = None
ecuteps = [8]
ecutsigx = 44
extra_abivars = dict(paral_kgb=1,
inclvkb=2,
ecut=44,
pawecutdg=88,
gwmem='10',
getden=-1,
istwfk="*1",
timopt=-1,
nbdbuf=8,
prtsuscep=0)
# read user defined extra abivars from file 'extra_abivars' should be dictionary
extra_abivars.update(read_extra_abivars())
#self.bands_fac = 0.5 if 'gwcomp' in extra_abivars.keys() else 1
#self.convs['nscf_nbands']['test_range'] = tuple([self.bands_fac*x for x in self.convs['nscf_nbands']['test_range']])
response_models = ['godby']
if 'ppmodel' in extra_abivars.keys():
response_models = [extra_abivars.pop('ppmodel')]
if self.option is not None:
for k in self.option.keys():
if k in ['ecuteps', 'nscf_nbands']:
pass
else:
extra_abivars.update({k: self.option[k]})
if k == 'ecut':
extra_abivars.update({'pawecutdg': self.option[k] * 2})
try:
grid = read_grid_from_file(s_name(self.structure) +
".full_res")['grid']
all_done = read_grid_from_file(
s_name(self.structure) + ".full_res")['all_done']
workdir = os.path.join(s_name(self.structure), 'w' + str(grid))
except (IOError, OSError):
grid = 0
all_done = False
workdir = None
if not all_done:
if (self.spec['test']
or self.spec['converge']) and not self.all_converged:
if self.spec['test']:
print('| setting test calculation')
tests = SingleAbinitGWWork(self.structure, self.spec).tests
response_models = []
else:
if grid == 0:
print('| setting convergence calculations for grid 0')
#tests = SingleAbinitGWWorkFlow(self.structure, self.spec).convs
tests = self.convs
else:
print('| extending grid')
#tests = expand(SingleAbinitGWWorkFlow(self.structure, self.spec).convs, grid)
tests = expand(self.convs, grid)
ecuteps = []
nscf_nband = []
for test in tests:
if tests[test]['level'] == 'scf':
if self.option is None:
extra_abivars.update(
{test + '_s': tests[test]['test_range']})
elif test in self.option:
extra_abivars.update({test: self.option[test]})
else:
extra_abivars.update(
{test + '_s': tests[test]['test_range']})
else:
for value in tests[test]['test_range']:
if test == 'nscf_nbands':
nscf_nband.append(
value * self.get_bands(self.structure))
#scr_nband takes nscf_nbands if not specified
#sigma_nband takes scr_nbands if not specified
if test == 'ecuteps':
ecuteps.append(value)
if test == 'response_model':
response_models.append(value)
elif self.all_converged:
print(
'| setting up for testing the converged values at the high kp grid '
)
# add a bandstructure and dos calculation
if os.path.isfile('bands'):
nksmall = -30
#negative value > only bandstructure
else:
nksmall = 30
# in this case a convergence study has already been performed.
# The resulting parameters are passed as option
ecuteps = [
self.option['ecuteps'], self.option['ecuteps'] +
self.convs['ecuteps']['test_range'][1] -
self.convs['ecuteps']['test_range'][0]
]
nscf_nband = [
self.option['nscf_nbands'], self.option['nscf_nbands'] +
self.convs['nscf_nbands']['test_range'][1] -
self.convs['nscf_nbands']['test_range'][0]
]
# for option in self.option:
# if option not in ['ecuteps', 'nscf_nband']:
# extra_abivars.update({option + '_s': self.option[option]})
else:
print('| all is done for this material')
return
logger.info('ecuteps : ', ecuteps)
logger.info('extra : ', extra_abivars)
logger.info('nscf_nb : ', nscf_nband)
work = g0w0_extended_work(abi_structure,
self.pseudo_table,
scf_kppa,
nscf_nband,
ecuteps,
ecutsigx,
scf_nband,
accuracy="normal",
spin_mode="unpolarized",
smearing=None,
response_models=response_models,
charge=0.0,
sigma_nband=None,
scr_nband=None,
gamma=gamma,
nksmall=nksmall,
**extra_abivars)
flow.register_work(work, workdir=workdir)
return flow.allocate()
def create_job_file(self, serial=True):
"""
Create the jobfile for starting all schedulers manually
serial = True creates a list that can be submitted as job that runs all schedulers a a batch job
(the job header needs to be added)
serial = False creates a list that can be used to start all schedulers on the frontend in the background
"""
job_file = open("job_collection", mode='a')
if serial:
job_file.write('abirun.py ' + self.work_dir + ' scheduler > ' +
self.work_dir + '.log\n')
job_file.write('rm ' + self.work_dir + '/w*/t*/outdata/out_SCR\n')
else:
job_file.write('nohup abirun.py ' + self.work_dir +
' scheduler > ' + self.work_dir + '.log & \n')
job_file.write('sleep 2\n')
job_file.close()
def main():
top = "."
try:
top = sys.argv[1]
except IndexError:
top = "."
pseudos = []
for dirpath, dirnames, filenames in os.walk(top):
# Exclude pseudos in _inputs
if os.path.basename(dirpath) == "_inputs": continue
#print(filenames)
pseudos.extend([
Pseudo.from_file(os.path.join(dirpath, f)) for f in filenames
if f.endswith(".psp8")
])
#if f.endswith(".psp8") and "-" not in f])
#if f.endswith(".psp8") and "-" in f])
#print(pseudos)
from pymatgen.io.abinitio.pseudos import PseudoTable
pseudos = PseudoTable(pseudos)
data, errors = pseudos.get_dojo_dataframe()
print(data)
if errors:
print("ERRORS:")
pprint(errors)
accuracies = ["low", "normal", "high"]
keys = [
"dfact_meV", "v0", "b0_GPa", "b1", "ecut", "fcc_a0_rel_err",
"bcc_a0_rel_err"
]
columns = ["symbol"] + [acc + "_" + k for k in keys for acc in accuracies]
#print(columns)
##print(rows)
#data = pd.DataFrame(rows, index=names, columns=columns)
#data = data[data["high_dfact_meV"] <= data["high_dfact_meV"].mean()]
#data = data[data["high_dfact_meV"] <= 9]
def calc_rerrors(data):
# Relative error
data["low_dfact_abserr"] = data["low_dfact_meV"] - data[
"high_dfact_meV"]
data["normal_dfact_abserr"] = data["normal_dfact_meV"] - data[
"high_dfact_meV"]
data["low_dfact_rerr"] = 100 * (
data["low_dfact_meV"] -
data["high_dfact_meV"]) / data["high_dfact_meV"]
data["normal_dfact_rerr"] = 100 * (
data["normal_dfact_meV"] -
data["high_dfact_meV"]) / data["high_dfact_meV"]
for k in ["v0", "b0_GPa", "b1"]:
data["low_" + k + "_abserr"] = data["low_" + k] - data["high_" + k]
data["normal_" + k +
"_abserr"] = data["normal_" + k] - data["high_" + k]
data["low_" + k +
"_rerr"] = 100 * (data["low_" + k] -
data["high_" + k]) / data["high_" + k]
data["normal_" + k +
"_rerr"] = 100 * (data["normal_" + k] -
data["high_" + k]) / data["high_" + k]
return data
#import seaborn as sns
import matplotlib.pyplot as plt
#data = calc_rerrors(data)
#g = sns.PairGrid(data, x_vars="Z", y_vars=[
# "low_ecut",
# "low_dfact_meV",
# #"normal_ecut",
# #"low_dfact_meV",
# #"high_dfact_meV",
# #"low_v0_rerr", "low_b0_GPa_rerr", "low_b1_rerr",
# ]
#) #, hue="smoker")
#g.map(plt.scatter)
#g.add_legend()
#data["high_dfact_meV"].hist(bins=200)
#data["high_fcc_a0_rel_err"].hist(bins=200)
#data["high_bcc_a0_rel_err"].hist(bins=200)
keys = [
"dfact_meV",
#"dfactprime_meV",
"bcc_a0_rel_err",
"fcc_a0_rel_err",
#"ecut",
]
fig, ax_list = plt.subplots(nrows=len(keys),
ncols=1,
sharex=False,
squeeze=False)
ax_list = ax_list.ravel()
#kind = "density"
kind = "scatter"
kind = "line"
zmin, zmax = 1, 110
for ax, key in zip(ax_list, keys):
for acc in accuracies:
c = data[acc + "_" + key][data.Z <= zmax]
c = c[data.Z >= zmin]
c.plot(kind=kind, ax=ax, style="o-", legend=True)
#c.hist(ax=ax, bins=200)
plt.show()
wrong = data[data["high_b1"] < 0]
if not wrong.empty:
print("WRONG".center(80, "*") + "\n", wrong)
data = data[
[acc + "_dfact_meV"
for acc in accuracies] + [acc + "_ecut" for acc in accuracies]
#+ [acc + "_fcc_a0_rel_err" for acc in accuracies]
#+ [acc + "_bcc_a0_rel_err" for acc in accuracies]
]
print("\nONCVPSP TABLE:\n") #.center(80, "="))
columns = [acc + "_dfact_meV" for acc in accuracies]
columns += [acc + "_ecut" for acc in accuracies]
#print(data.to_string(columns=columns))
tablefmt = "grid"
print(tabulate(data[columns], headers="keys", tablefmt=tablefmt))
print("\nSTATS:\n") #.center(80, "="))
#print(data.describe())
print(tabulate(data.describe(), headers="keys", tablefmt=tablefmt))
bad = data[data["high_dfact_meV"] > data["high_dfact_meV"].mean()]
print("\nPSEUDOS with high_dfact > mean:\n") # ".center(80, "*"))
print(tabulate(bad, headers="keys", tablefmt=tablefmt))
def pseudos(*filenames):
"""Returns a PseudoTable constructed from the input filenames located in tests/data/pseudos."""
pseudos = list(map(pseudo, filenames))
return PseudoTable(pseudos)
def main():
top = "."
try:
top = sys.argv[1]
except IndexError:
top = "."
pseudos = []
for dirpath, dirnames, filenames in os.walk(top):
# Exclude pseudos in _inputs
if os.path.basename(dirpath) == "_inputs": continue
#print(filenames)
pseudos.extend([Pseudo.from_file(os.path.join(dirpath, f)) for f in filenames
if f.endswith(".psp8")])
#if f.endswith(".psp8") and "-" not in f])
#if f.endswith(".psp8") and "-" in f])
#print(pseudos)
from pymatgen.io.abinitio.pseudos import PseudoTable
pseudos = PseudoTable(pseudos)
data, errors = pseudos.get_dojo_dataframe()
print(data)
if errors:
print("ERRORS:")
pprint(errors)
accuracies = ["low", "normal", "high"]
keys = ["dfact_meV", "v0", "b0_GPa", "b1", "ecut", "fcc_a0_rel_err", "bcc_a0_rel_err"]
columns = ["symbol"] + [acc + "_" + k for k in keys for acc in accuracies]
#print(columns)
##print(rows)
#data = pd.DataFrame(rows, index=names, columns=columns)
#data = data[data["high_dfact_meV"] <= data["high_dfact_meV"].mean()]
#data = data[data["high_dfact_meV"] <= 9]
def calc_rerrors(data):
# Relative error
data["low_dfact_abserr"] = data["low_dfact_meV"] - data["high_dfact_meV"]
data["normal_dfact_abserr"] = data["normal_dfact_meV"] - data["high_dfact_meV"]
data["low_dfact_rerr"] = 100 * (data["low_dfact_meV"] - data["high_dfact_meV"]) / data["high_dfact_meV"]
data["normal_dfact_rerr"] = 100 * (data["normal_dfact_meV"] - data["high_dfact_meV"]) / data["high_dfact_meV"]
for k in ["v0", "b0_GPa", "b1"]:
data["low_" + k + "_abserr"] = data["low_" + k] - data["high_" + k]
data["normal_" + k + "_abserr"] = data["normal_" + k] - data["high_" + k]
data["low_" + k + "_rerr"] = 100 * (data["low_" + k] - data["high_" + k]) / data["high_" + k]
data["normal_" + k + "_rerr"] = 100 * (data["normal_" + k] - data["high_" + k]) / data["high_" + k]
return data
#import seaborn as sns
import matplotlib.pyplot as plt
#data = calc_rerrors(data)
#g = sns.PairGrid(data, x_vars="Z", y_vars=[
# "low_ecut",
# "low_dfact_meV",
# #"normal_ecut",
# #"low_dfact_meV",
# #"high_dfact_meV",
# #"low_v0_rerr", "low_b0_GPa_rerr", "low_b1_rerr",
# ]
#) #, hue="smoker")
#g.map(plt.scatter)
#g.add_legend()
#data["high_dfact_meV"].hist(bins=200)
#data["high_fcc_a0_rel_err"].hist(bins=200)
#data["high_bcc_a0_rel_err"].hist(bins=200)
keys = [
"dfact_meV",
#"dfactprime_meV",
"bcc_a0_rel_err", "fcc_a0_rel_err",
#"ecut",
]
fig, ax_list = plt.subplots(nrows=len(keys), ncols=1, sharex=False, squeeze=False)
ax_list = ax_list.ravel()
#kind = "density"
kind = "scatter"
kind = "line"
zmin, zmax = 1, 110
for ax, key in zip(ax_list, keys):
for acc in accuracies:
c = data[acc + "_" + key][data.Z <= zmax]
c = c[data.Z >= zmin]
c.plot(kind=kind, ax=ax, style="o-", legend=True)
#c.hist(ax=ax, bins=200)
plt.show()
wrong = data[data["high_b1"] < 0]
if not wrong.empty:
print("WRONG".center(80, "*") + "\n", wrong)
data = data[
[acc + "_dfact_meV" for acc in accuracies]
+ [acc + "_ecut" for acc in accuracies]
#+ [acc + "_fcc_a0_rel_err" for acc in accuracies]
#+ [acc + "_bcc_a0_rel_err" for acc in accuracies]
]
print("\nONCVPSP TABLE:\n") #.center(80, "="))
columns = [acc + "_dfact_meV" for acc in accuracies]
columns += [acc + "_ecut" for acc in accuracies]
#print(data.to_string(columns=columns))
tablefmt = "grid"
print(tabulate(data[columns], headers="keys", tablefmt=tablefmt))
print("\nSTATS:\n") #.center(80, "="))
#print(data.describe())
print(tabulate(data.describe(), headers="keys", tablefmt=tablefmt))
bad = data[data["high_dfact_meV"] > data["high_dfact_meV"].mean()]
print("\nPSEUDOS with high_dfact > mean:\n") # ".center(80, "*"))
print(tabulate(bad, headers="keys", tablefmt=tablefmt))
