def test_fixes(self):
flow = Flow(workdir=test_dir,
manager=TaskManager.from_file(
os.path.join(test_dir, "taskmanager.yml")))
inp = {}
flow.register_task(input=inp)
flow.allocate()
def __init__(self, code='vasp', system_id='mp-149', kpar=False, **kwargs):
"""
system_id is to be a mp-id to take a base structure from the mp-database
kwarg can be used to personalize all lists
code specifies the name of the code to be tested, it is assumed that an executable with this name is
present in the current work dir
to generate the job scripts a taskmanager.yml file is needed in the current work dir
#todo add example
list of paralelizaions
"""
self.code = code
self.subject = os.path.join(os.getcwd(), code)
self.manager = TaskManager.from_user_config()
self.script_list = []
self.system_id = system_id
self.kpar = kpar
self.kpden = None
mp_key = os.environ['MP_KEY']
with MPRester(mp_key) as mp_database:
self.structure = mp_database.get_structure_by_material_id(system_id, final=True)
self.name = str(self.structure.composition.reduced_formula) + "_" + str(self.system_id)
self.np_list = [1, 4, 9, 16, 25, 36, 64, 100, 144]
#self.np_list = [1, 8, 27, 64, 125, 216, 343]
self.sizes = [1, 2, 3]
self.parameter_lists = None
if self.code == 'vasp':
self.parameter_lists = {'NPAR': [0, 0.5, 1]}
self.reset_bar()
self.inpset = BenchVaspInputSet()
self.inpset.set_input()
def __init__(self, workdir, name=None, flows=None, manager=None, timelimit=None):
"""
Args:
workdir: Working directory
name: Name assigned to the `BatchLauncher`.
flows: List of `Flow` objects.
manager: :class:`TaskManager` object responsible for the submission of the jobs.
If manager is None, the object is initialized from the yaml file
located either in the working directory or in the user configuration dir.
timelimit: Time limit (int with seconds or string with time given with
the slurm convention: "days-hours:minutes:seconds".
If timelimit is None, the default value specified in the `batch_adapter` is taken.
"""
self.workdir = os.path.abspath(workdir)
if not os.path.exists(self.workdir):
os.makedirs(self.workdir)
else:
pass
# raise RuntimeError("Directory %s already exists. Use BatchLauncher.pickle_load()" % self.workdir)
self.name = os.path.basename(self.workdir) if name is None else name
self.script_file = File(os.path.join(self.workdir, "run.sh"))
self.qerr_file = File(os.path.join(self.workdir, "queue.qerr"))
self.qout_file = File(os.path.join(self.workdir, "queue.qout"))
self.log_file = File(os.path.join(self.workdir, "run.log"))
self.batch_pidfile = File(os.path.join(self.workdir, "batch.pid"))
from .tasks import TaskManager
manager = TaskManager.as_manager(manager)
# Extract the qadapater to be used for the batch script.
try:
self.qadapter = qad = manager.batch_adapter
except AttributeError:
raise RuntimeError("Your manager.yml file does not define an entry for the batch_adapter")
if qad is None:
raise RuntimeError("Your manager.yml file does not define an entry for the batch_adapter")
# Set mpi_procs to 1 just to be on the safe side
# Then allow the user to change the timelimit via __init__
qad.set_mpi_procs(1)
if timelimit is not None:
self.set_timelimit(timelimit)
# FIXME: Remove me!
self.set_timelimit(36000)
# Initialize list of flows.
if flows is None:
flows = []
if not isinstance(flows, (list, tuple)):
flows = [flows]
self.flows = flows
def load_flow(inp, runpath):
pickle_file = os.path.join(runpath,'__AbinitFlow__.pickle')
try:
flow = pickle.Unpickler(open(pickle_file)).load()
except IOError as e:
manager = TaskManager.from_user_config()
flow = PhononFlow(runpath, manager, inp, ngqpt=[2,2,2], do_nscf = False)
flow.ph_tolvrs = 1.e-2
flow.build_tasks()
flow = flow.allocate()
return flow
def __init__(self, workdir=None, manager=None, trials=ALL_TRIALS, accuracies=ALL_ACCURACIES):
"""
Args:
workdir: Working directory.
manager: :class:`TaskManager` object that will handle the sumbmission of the jobs.
"""
self.workdir = os.path.abspath(workdir) if workdir is not None else os.path.join(os.getcwd(), "DOJO")
self.manager = TaskManager.from_user_config() if manager is None else manager
self.trials, self.accuracies = trials, accuracies
# List of pseudos analyzed by the Dojo and corresponding flows.
self.pseudos, self.flows = [], []
def calc_phbands_and_dos(self, ngqpt=None, ndivsm=20, nqsmall=10, asr=2, chneut=1, dipdip=1, dos_method="tetra",
workdir=None, manager=None, verbose=0, plot=True, ret_task=False):
"""
Execute anaddb to compute the phonon band structure and the phonon DOS
Args:
ngqpt: Number of divisions for the q-mesh in the DDB file. Auto-detected if None (default)
asr, chneut, dipdp: Anaddb input variable. See official documentation.
workdir: Working directory. If None, a temporary directory is created.
manager: :class:`TaskManager` object. If None, the object is initialized from the configuration file
verbose: verbosity level. Set it to a value > 0 to get more information
"""
if ngqpt is None: ngqpt = self.guessed_ngqpt
inp = AnaddbInput.phbands_and_dos(
self.structure, ngqpt=ngqpt, ndivsm=ndivsm, nqsmall=nqsmall,
q1shft=(0,0,0), qptbounds=None, asr=asr, chneut=chneut, dipdip=dipdip, dos_method=dos_method)
if manager is None: manager = TaskManager.from_user_config()
if workdir is None: workdir = tempfile.mkdtemp()
if verbose:
print("workdir:", workdir)
print("ANADDB INPUT:\n", inp)
task = AnaddbTask(inp, self.filepath, workdir=workdir, manager=manager.to_shell_manager(mpi_procs=1))
if ret_task:
return task
# Run the task here.
task.start_and_wait(autoparal=False)
report = task.get_event_report()
if not report.run_completed:
raise TaskException(task=task, report=report)
with task.open_phbst() as phbst_ncfile, task.open_phdos() as phdos_ncfile:
phbands, phdos = phbst_ncfile.phbands, phdos_ncfile.phdos
if plot:
phbands.plot_with_phdos(phdos, title="Phonon bands and DOS of %s" % self.structure.formula)
return phbands, phdos
def calc_phmodes_at_qpoint(self, qpoint=None, asr=2, chneut=1, dipdip=1,
workdir=None, manager=None, verbose=0, ret_task=False):
"""
Execute anaddb to compute phonon modes at the given q-point.
Args:
qpoint: Reduced coordinates of the qpoint where phonon modes are computed
asr, chneut, dipdp: Anaddb input variable. See official documentation.
workdir: Working directory. If None, a temporary directory is created.
manager: :class:`TaskManager` object. If None, the object is initialized from the configuration file
verbose: verbosity level. Set it to a value > 0 to get more information
"""
if qpoint is None:
qpoint = self.qpoints[0]
if len(self.qpoints) != 1:
raise ValueError("%s contains %s qpoints and the choice is ambiguous.\n"
"Please specify the qpoint in calc_phmodes_at_qpoint" % (self, len(self.qpoints)))
inp = AnaddbInput.modes_at_qpoint(self.structure, qpoint, asr=asr, chneut=chneut, dipdip=dipdip)
if manager is None: manager = TaskManager.from_user_config()
if workdir is None: workdir = tempfile.mkdtemp()
if verbose:
print("workdir:", workdir)
print("ANADDB INPUT:\n", inp)
task = AnaddbTask(inp, self.filepath, workdir=workdir, manager=manager.to_shell_manager(mpi_procs=1))
if ret_task:
return task
# Run the task here
task.start_and_wait(autoparal=False)
report = task.get_event_report()
if not report.run_completed:
raise TaskException(task=task, report=report)
with task.open_phbst() as ncfile:
return ncfile.phbands
def __init__(self, code='vasp', system_id='mp-149', kpar=False, **kwargs):
"""
system_id is to be a mp-id to take a base structure from the mp-database
kwarg can be used to personalize all lists
code specifies the name of the code to be tested, it is assumed that an executable with this name is
present in the current work dir
to generate the job scripts a taskmanager.yml file is needed in the current work dir
#todo add example
list of paralelizaions
"""
self.code = code
self.subject = os.path.join(os.getcwd(), code)
self.manager = TaskManager.from_user_config()
self.script_list = []
self.system_id = system_id
self.kpar = kpar
self.kpden = None
mp_key = os.environ['MP_KEY']
with MPRester(mp_key) as mp_database:
self.structure = mp_database.get_structure_by_material_id(
system_id, final=True)
self.name = str(
self.structure.composition.reduced_formula) + "_" + str(
self.system_id)
self.np_list = [1, 4, 9, 16, 25, 36, 64, 100, 144]
#self.np_list = [1, 8, 27, 64, 125, 216, 343]
self.sizes = [1, 2, 3]
self.parameter_lists = None
if self.code == 'vasp':
self.parameter_lists = {'NPAR': [0, 0.5, 1]}
self.reset_bar()
self.inpset = BenchVaspInputSet()
self.inpset.set_input()
def _setup_faketask(name, manager=None, workdir="."):
tmp_dir = os.path.join(workdir, "__" + str(name) + "_run__")
if manager is None:
manager = TaskManager.from_user_config()
manager = manager.to_shell_manager()
return Work(workdir=tmp_dir, manager=manager)
from pymatgen.io.abinitio.workflows import Workflow
from pymatgen.io.abinitio.tasks import TaskManager, ScfTask, NscfTask
from pymatgen.io.abinitio.flows import AbinitFlow
from pymatgen.symmetry.bandstructure import HighSymmKpath
from abipy.htc.input import AbiInput
from abipy.core.constants import Energy
from myscripts.pseudos import get_psp
from myscripts.structure import HalfHeusler
scratchdir = '/p/lscratchd/damewood'
basename = os.path.dirname(os.path.abspath(__file__)).split(os.path.sep)[-1]
workdir = os.path.join(scratchdir,basename)
logging.basicConfig()
manager = TaskManager.from_user_config()
acell_opt = {
'N': {
'alpha': 4.961,
'beta' : 4.912,
'gamma': 5.139,
},
'P': {
'alpha': 5.600,
'beta' : 5.717,
'gamma': 5.715,
},
'Si': {
'alpha': 5.629,
'beta' : 5.778,
def _runem_all(self):
"""
This function checks the status of all tasks,
tries to fix tasks that went unconverged, abicritical, or queuecritical
and tries to run all the tasks that can be submitted.+
"""
excs = []
flow = self.flow
# Allow to change the manager at run-time
if self.use_dynamic_manager:
from pymatgen.io.abinitio.tasks import TaskManager
new_manager = TaskManager.from_user_config()
for work in flow:
work.set_manager(new_manager)
nqjobs = 0
if self.contact_resource_manager:
# This call is expensive and therefore it's optional
nqjobs = flow.get_njobs_in_queue()
if nqjobs is None:
nqjobs = 0
if flow.manager.has_queue: logger.warning('Cannot get njobs_inqueue')
if nqjobs >= self.max_njobs_inqueue:
logger.info("Too many jobs in the queue, returning")
return
if self.max_nlaunches == -1:
max_nlaunch = self.max_njobs_inqueue - nqjobs
else:
max_nlaunch = min(self.max_njobs_inqueue - nqjobs, self.max_nlaunches)
# check status and print it.
flow.check_status(show=False)
# fix problems
# Try to restart the unconverged tasks
# todo donot fire here but prepare for fireing in rapidfire
for task in self.flow.unconverged_tasks:
try:
logger.info("Flow will try restart task %s" % task)
fired = task.restart()
if fired:
self.nlaunch += 1
max_nlaunch -= 1
if max_nlaunch == 0:
logger.info("Restart: too many jobs in the queue, returning")
flow.pickle_dump()
return
except task.RestartError:
excs.append(straceback())
# move here from withing rapid fire ...
# fix only prepares for restarting, and sets to ready
nfixed = flow.fix_abi_critical()
if nfixed: print("Fixed %d AbiCritical errors" % nfixed)
# Temporarily disable by MG because I don't know if fix_critical works after the
# introduction of the new qadapters
if False:
nfixed = flow.fix_queue_critical()
if nfixed: print("Fixed %d QueueCritical errors" % nfixed)
# update database
flow.pickle_dump()
# Submit the tasks that are ready.
try:
nlaunch = PyLauncher(flow).rapidfire(max_nlaunch=max_nlaunch, sleep_time=10)
self.nlaunch += nlaunch
if nlaunch:
print("[%s] Number of launches: %d" % (time.asctime(), nlaunch))
except Exception:
excs.append(straceback())
flow.show_status()
if excs:
logger.critical("*** Scheduler exceptions:\n *** %s" % "\n".join(excs))
self.exceptions.extend(excs)
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 _runem_all(self):
"""
This function checks the status of all tasks,
tries to fix tasks that went unconverged, abicritical, or queuecritical
and tries to run all the tasks that can be submitted.+
"""
excs = []
flow = self.flow
# Allow to change the manager at run-time
if self.use_dynamic_manager:
from pymatgen.io.abinitio.tasks import TaskManager
new_manager = TaskManager.from_user_config()
for work in flow:
work.set_manager(new_manager)
nqjobs = flow.get_njobs_in_queue()
if nqjobs is None:
nqjobs = 0
print('Cannot get njobs_inqueue')
if nqjobs >= self.max_njobs_inqueue:
print("Too many jobs in the queue, returning")
return
if self.max_nlaunch == -1:
max_nlaunch = self.max_njobs_inqueue - nqjobs
else:
max_nlaunch = min(self.max_njobs_inqueue - nqjobs, self.max_nlaunch)
# check status
flow.check_status()
flow.show_status()
# fix problems
# Try to restart the unconverged tasks
# todo donot fire here but prepare for fireing in rapidfire
for task in self.flow.unconverged_tasks:
try:
logger.info("AbinitFlow will try restart task %s" % task)
fired = task.restart()
if fired:
self.nlaunch += 1
max_nlaunch -= 1
if max_nlaunch == 0:
print("Restart: too many jobs in the queue, returning")
flow.pickle_dump()
return
except Exception:
excs.append(straceback())
# move here from withing rapid fire ...
# fix only prepares for restarting, and sets to ready
flow.fix_critical()
# update database
flow.pickle_dump()
#if self.num_restarts == self.max_num_restarts:
# info_msg = "Reached maximum number of restarts. Cannot restart anymore Returning"
# logger.info(info_msg)
# self.history.append(info_msg)
# return 1
# Submit the tasks that are ready.
try:
nlaunch = PyLauncher(flow).rapidfire(max_nlaunch=max_nlaunch, sleep_time=10)
self.nlaunch += nlaunch
if nlaunch:
print("[%s] Number of launches: %d" % (time.asctime(), nlaunch))
except Exception:
excs.append(straceback())
flow.show_status()
if excs:
logger.critical("*** Scheduler exceptions:\n *** %s" % "\n".join(excs))
self.exceptions.extend(excs)
def _runem_all(self):
"""
This function checks the status of all tasks,
tries to fix tasks that went unconverged, abicritical, or queuecritical
and tries to run all the tasks that can be submitted.+
"""
excs = []
flow = self.flow
# Allow to change the manager at run-time
if self.use_dynamic_manager:
from pymatgen.io.abinitio.tasks import TaskManager
new_manager = TaskManager.from_user_config()
for work in flow:
work.set_manager(new_manager)
nqjobs = flow.get_njobs_in_queue()
if nqjobs is None:
nqjobs = 0
print('Cannot get njobs_inqueue')
if nqjobs >= self.max_njobs_inqueue:
print("Too many jobs in the queue, returning")
return
if self.max_nlaunch == -1:
max_nlaunch = self.max_njobs_inqueue - nqjobs
else:
max_nlaunch = min(self.max_njobs_inqueue - nqjobs,
self.max_nlaunch)
# check status
flow.check_status()
flow.show_status()
# fix problems
# Try to restart the unconverged tasks
# todo donot fire here but prepare for fireing in rapidfire
for task in self.flow.unconverged_tasks:
try:
logger.info("Flow will try restart task %s" % task)
fired = task.restart()
if fired:
self.nlaunch += 1
max_nlaunch -= 1
if max_nlaunch == 0:
print("Restart: too many jobs in the queue, returning")
flow.pickle_dump()
return
except Exception:
excs.append(straceback())
# move here from withing rapid fire ...
# fix only prepares for restarting, and sets to ready
flow.fix_critical()
# update database
flow.pickle_dump()
#if self.num_restarts == self.max_num_restarts:
# info_msg = "Reached maximum number of restarts. Cannot restart anymore Returning"
# logger.info(info_msg)
# self.history.append(info_msg)
# return 1
# Submit the tasks that are ready.
try:
nlaunch = PyLauncher(flow).rapidfire(max_nlaunch=max_nlaunch,
sleep_time=10)
self.nlaunch += nlaunch
if nlaunch:
print("[%s] Number of launches: %d" %
(time.asctime(), nlaunch))
except Exception:
excs.append(straceback())
flow.show_status()
if excs:
logger.critical("*** Scheduler exceptions:\n *** %s" %
"\n".join(excs))
self.exceptions.extend(excs)
def _runem_all(self):
"""
This function checks the status of all tasks,
tries to fix tasks that went unconverged, abicritical, or queuecritical
and tries to run all the tasks that can be submitted.+
"""
excs = []
flow = self.flow
# Allow to change the manager at run-time
if self.use_dynamic_manager:
from pymatgen.io.abinitio.tasks import TaskManager
new_manager = TaskManager.from_user_config()
for work in flow:
work.set_manager(new_manager)
nqjobs = 0
if self.contact_resource_manager:
# This call is expensive and therefore it's optional
nqjobs = flow.get_njobs_in_queue()
if nqjobs is None:
nqjobs = 0
if flow.manager.has_queue:
logger.warning("Cannot get njobs_inqueue")
if nqjobs >= self.max_njobs_inqueue:
print("Too many jobs in the queue: %s, returning" % nqjobs)
return
if self.max_nlaunches == -1:
max_nlaunch = self.max_njobs_inqueue - nqjobs
else:
max_nlaunch = min(self.max_njobs_inqueue - nqjobs, self.max_nlaunches)
# check status.
flow.check_status(show=False)
# This check is not perfect, we should make a list of tasks to sumbit
# and select only the subset so that we don't exceeed mac_ncores_used
# Many sections of this code should be rewritten.
# if self.max_ncores_used is not None and flow.ncores_used > self.max_ncores_used:
if self.max_ncores_used is not None and flow.ncores_allocated > self.max_ncores_used:
print("Cannot exceed max_ncores_used %s" % self.max_ncores_used)
logger.info("Cannot exceed max_ncores_used %s" % self.max_ncores_used)
return
# Try to restart the unconverged tasks
# TODO: do not fire here but prepare for fireing in rapidfire
for task in self.flow.unconverged_tasks:
try:
logger.info("Flow will try restart task %s" % task)
fired = task.restart()
if fired:
self.nlaunch += 1
max_nlaunch -= 1
if max_nlaunch == 0:
logger.info("Restart: too many jobs in the queue, returning")
flow.pickle_dump()
return
except task.RestartError:
excs.append(straceback())
# Temporarily disable by MG because I don't know if fix_critical works after the
# introduction of the new qadapters
# reenabled by MsS disable things that do not work at low level
# fix only prepares for restarting, and sets to ready
if self.fix_qcritical:
nfixed = flow.fix_queue_critical()
if nfixed:
print("Fixed %d QCritical error(s)" % nfixed)
nfixed = flow.fix_abicritical()
if nfixed:
print("Fixed %d AbiCritical error(s)" % nfixed)
# update database
flow.pickle_dump()
# Submit the tasks that are ready.
try:
nlaunch = PyLauncher(flow).rapidfire(max_nlaunch=max_nlaunch, sleep_time=10)
self.nlaunch += nlaunch
if nlaunch:
print("[%s] Number of launches: %d" % (time.asctime(), nlaunch))
except Exception:
excs.append(straceback())
# check status.
# flow.check_status(show=True)
flow.show_status()
if excs:
logger.critical("*** Scheduler exceptions:\n *** %s" % "\n".join(excs))
self.exceptions.extend(excs)
def test_fixes(self):
flow = Flow(workdir=test_dir, manager=TaskManager.from_file(os.path.join(test_dir, "taskmanager.yml")))
inp = {}
flow.register_task(input=inp)
flow.allocate()
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()