def snl_to_wf_elastic(snl, parameters):
# parameters["user_vasp_settings"] specifies user defined incar/kpoints parameters
fws = []
connections = defaultdict(list)
parameters = parameters if parameters else {}
snl_priority = parameters.get('priority', 1)
priority = snl_priority * 2 # once we start a job, keep going!
f = Composition(snl.structure.composition.reduced_formula).alphabetical_formula
# add the SNL to the SNL DB and figure out duplicate group
tasks = [AddSNLTask()]
spec = {'task_type': 'Add to SNL database', 'snl': snl.as_dict(),
'_queueadapter': QA_DB, '_priority': snl_priority}
if 'snlgroup_id' in parameters and isinstance(snl, MPStructureNL):
spec['force_mpsnl'] = snl.as_dict()
spec['force_snlgroup_id'] = parameters['snlgroup_id']
del spec['snl']
fws.append(Firework(tasks, spec,
name=get_slug(f + '--' + spec['task_type']), fw_id=0))
connections[0] = [1]
parameters["exact_structure"] = True
# run GGA structure optimization for force convergence
spec = snl_to_wf._snl_to_spec(snl, parameters=parameters)
user_vasp_settings = parameters.get("user_vasp_settings")
spec = update_spec_force_convergence(spec, user_vasp_settings)
spec['run_tags'].append("origin")
spec['_priority'] = priority
spec['_queueadapter'] = QA_VASP
del spec['_dupefinder']
spec['task_type'] = "Vasp force convergence optimize structure (2x)"
tasks = [VaspWriterTask(), get_custodian_task(spec)]
fws.append(Firework(tasks, spec,
name=get_slug(f + '--' + spec['task_type']), fw_id=1))
# insert into DB - GGA structure optimization
spec = {'task_type': 'VASP db insertion', '_priority': priority,
'_allow_fizzled_parents': True, '_queueadapter': QA_DB,
'clean_task_doc':True, 'elastic_constant':"force_convergence"}
fws.append(Firework([VaspToDBTask()], spec,
name=get_slug(f + '--' + spec['task_type']), fw_id=2))
connections[1] = [2]
spec = {'task_type': 'Setup Deformed Struct Task', '_priority': priority,
'_queueadapter': QA_CONTROL}
fws.append(Firework([SetupDeformedStructTask()], spec,
name=get_slug(f + '--' + spec['task_type']),fw_id=3))
connections[2] = [3]
wf_meta = get_meta_from_structure(snl.structure)
wf_meta['run_version'] = 'May 2013 (1)'
if '_materialsproject' in snl.data and 'submission_id' in snl.data['_materialsproject']:
wf_meta['submission_id'] = snl.data['_materialsproject']['submission_id']
return Workflow(fws, connections, name=Composition(
snl.structure.composition.reduced_formula).alphabetical_formula, metadata=wf_meta)
def snl_to_wf_phonon(snl, parameters):
# parameters["user_vasp_settings"] specifies user defined incar/kpoints parameters
fws = []
connections = defaultdict(list)
parameters = parameters if parameters else {}
snl_priority = parameters.get('priority', 1)
priority = snl_priority * 2 # once we start a job, keep going!
f = Composition(snl.structure.composition.reduced_formula).alphabetical_formula
# add the SNL to the SNL DB and figure out duplicate group
tasks = [AddSNLTask()]
spec = {'task_type': 'Add to SNL database', 'snl': snl.as_dict(),
'_queueadapter': QA_DB, '_priority': snl_priority}
if 'snlgroup_id' in parameters and isinstance(snl, MPStructureNL):
spec['force_mpsnl'] = snl.as_dict()
spec['force_snlgroup_id'] = parameters['snlgroup_id']
del spec['snl']
fws.append(Firework(tasks, spec,
name=get_slug(f + '--' + spec['task_type']), fw_id=0))
connections[0] = [1]
parameters["exact_structure"] = True
# run GGA structure optimization for force convergence
spec = snl_to_wf._snl_to_spec(snl, parameters=parameters)
user_vasp_settings = parameters.get("user_vasp_settings")
spec = update_spec_force_convergence(spec, user_vasp_settings)
spec['run_tags'].append("origin")
spec['_priority'] = priority
spec['_queueadapter'] = QA_VASP
del spec['_dupefinder']
spec['task_type'] = "Vasp force convergence optimize structure (2x)"
tasks = [VaspWriterTask(), get_custodian_task(spec)]
fws.append(Firework(tasks, spec,
name=get_slug(f + '--' + spec['task_type']), fw_id=1))
# insert into DB - GGA structure optimization
spec = {'task_type': 'VASP db insertion', '_priority': priority,
'_allow_fizzled_parents': True, '_queueadapter': QA_DB,
'clean_task_doc':True, 'elastic_constant':"force_convergence"}
fws.append(Firework([VaspToDBTask()], spec,
name=get_slug(f + '--' + spec['task_type']), fw_id=2))
connections[1] = [2]
spec = {'task_type': 'Setup Deformed Struct Task', '_priority': priority,
'_queueadapter': QA_CONTROL}
fws.append(Firework([SetupDeformedStructTask()], spec,
name=get_slug(f + '--' + spec['task_type']),fw_id=3))
connections[2] = [3]
wf_meta = get_meta_from_structure(snl.structure)
wf_meta['run_version'] = 'May 2013 (1)'
if '_materialsproject' in snl.data and 'submission_id' in snl.data['_materialsproject']:
wf_meta['submission_id'] = snl.data['_materialsproject']['submission_id']
return Workflow(fws, connections, name=Composition(
snl.structure.composition.reduced_formula).alphabetical_formula, metadata=wf_meta)
def run_task(self, fw_spec):
# Read structure from previous relaxation
relaxed_struct = fw_spec['output']['crystal']
# Generate deformed structures
d_struct_set = DeformedStructureSet(relaxed_struct, ns=0.06)
wf=[]
for i, d_struct in enumerate(d_struct_set.def_structs):
fws=[]
connections={}
f = Composition(d_struct.formula).alphabetical_formula
snl = StructureNL(d_struct, 'Joseph Montoya <*****@*****.**>',
projects=["Elasticity"])
tasks = [AddSNLTask()]
snl_priority = fw_spec.get('priority', 1)
spec = {'task_type': 'Add Deformed Struct to SNL database',
'snl': snl.as_dict(),
'_queueadapter': QA_DB,
'_priority': snl_priority}
if 'snlgroup_id' in fw_spec and isinstance(snl, MPStructureNL):
spec['force_mpsnl'] = snl.as_dict()
spec['force_snlgroup_id'] = fw_spec['snlgroup_id']
del spec['snl']
fws.append(Firework(tasks, spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=-1000+i*10))
connections[-1000+i*10] = [-999+i*10]
spec = snl_to_wf._snl_to_spec(snl,
parameters={'exact_structure':True})
spec = update_spec_force_convergence(spec)
spec['deformation_matrix'] = d_struct_set.deformations[i].tolist()
spec['original_task_id'] = fw_spec["task_id"]
spec['_priority'] = fw_spec['_priority']*2
#Turn off dupefinder for deformed structure
del spec['_dupefinder']
spec['task_type'] = "Optimize deformed structure"
fws.append(Firework([VaspWriterTask(), SetupElastConstTask(),
get_custodian_task(spec)],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=-999+i*10))
priority = fw_spec['_priority']*3
spec = {'task_type': 'VASP db insertion',
'_priority': priority,
'_allow_fizzled_parents': True,
'_queueadapter': QA_DB,
'elastic_constant':"deformed_structure",
'clean_task_doc':True,
'deformation_matrix':d_struct_set.deformations[i].tolist(),
'original_task_id':fw_spec["task_id"]}
fws.append(Firework([VaspToDBTask()],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=-998+i*10))
connections[-999+i*10] = [-998+i*10]
wf.append(Workflow(fws, connections))
return FWAction(additions=wf)
def run_task(self, fw_spec):
# Read structure from previous relaxation
relaxed_struct = fw_spec['output']['crystal']
# Generate deformed structures
d_struct_set = DeformedStructureSet(relaxed_struct, ns=0.06)
wf=[]
for i, d_struct in enumerate(d_struct_set.def_structs):
fws=[]
connections={}
f = Composition(d_struct.formula).alphabetical_formula
snl = StructureNL(d_struct, 'Joseph Montoya <*****@*****.**>',
projects=["Elasticity"])
tasks = [AddSNLTask()]
snl_priority = fw_spec.get('priority', 1)
spec = {'task_type': 'Add Deformed Struct to SNL database',
'snl': snl.as_dict(),
'_queueadapter': QA_DB,
'_priority': snl_priority}
if 'snlgroup_id' in fw_spec and isinstance(snl, MPStructureNL):
spec['force_mpsnl'] = snl.as_dict()
spec['force_snlgroup_id'] = fw_spec['snlgroup_id']
del spec['snl']
fws.append(Firework(tasks, spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=-1000+i*10))
connections[-1000+i*10] = [-999+i*10]
spec = snl_to_wf._snl_to_spec(snl,
parameters={'exact_structure':True})
spec = update_spec_force_convergence(spec)
spec['deformation_matrix'] = d_struct_set.deformations[i].tolist()
spec['original_task_id'] = fw_spec["task_id"]
spec['_priority'] = fw_spec['_priority']*2
#Turn off dupefinder for deformed structure
del spec['_dupefinder']
spec['task_type'] = "Optimize deformed structure"
fws.append(Firework([VaspWriterTask(), SetupElastConstTask(),
get_custodian_task(spec)],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=-999+i*10))
priority = fw_spec['_priority']*3
spec = {'task_type': 'VASP db insertion',
'_priority': priority,
'_allow_fizzled_parents': True,
'_queueadapter': QA_DB,
'elastic_constant':"deformed_structure",
'clean_task_doc':True,
'deformation_matrix':d_struct_set.deformations[i].tolist(),
'original_task_id':fw_spec["task_id"]}
fws.append(Firework([VaspToDBTask(), AddElasticDataToDBTask()], spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=-998+i*10))
connections[-999+i*10] = [-998+i*10]
wf.append(Workflow(fws, connections))
return FWAction(additions=wf)
def append_wf(fw_id, parent_fw_id=None):
wf = lpdb.workflows.find_one({'nodes':fw_id}, {'parent_links':1,'links':1,'name':1})
try:
if parent_fw_id is None:
parent_fw_id = wf['parent_links'][str(fw_id)][-1]
# non-defused AddEStructureTask v2 already in children?
for child_fw_id in wf['links'][str(parent_fw_id)]:
if child_fw_id == parent_fw_id: continue
child_fw = lpdb.fireworks.find_one({'fw_id': child_fw_id}, {'spec.task_type':1, 'state':1})
if child_fw['spec']['task_type'] == 'Controller: add Electronic Structure v2':
if child_fw['state'] == 'DEFUSED':
lpdb.reignite_fw(child_fw_id)
print 'AddEStructureTask v2', child_fw_id , 'reignited for', fw_id
elif child_fw['state'] == 'FIZZLED':
lpdb.rerun_fw(child_fw_id)
print 'AddEStructureTask v2', child_fw_id , 'marked for rerun for', fw_id
elif child_fw['state'] == 'COMPLETED':
print 'AddEStructureTask v2 already successfully run for', fw_id
sec_child_fw_id = wf['links'][str(child_fw_id)][0]
sec_child_fw = lpdb.fireworks.find_one({'fw_id': sec_child_fw_id}, {'spec.task_type':1, 'state':1})
if sec_child_fw['state'] == 'FIZZLED':
lpdb.rerun_fw(sec_child_fw_id)
print 'FIZZLED -> marked for rerun:', sec_child_fw_id, sec_child_fw['spec']['task_type']
else:
print 'AddEStructureTask v2 added but neither DEFUSED, FIZZLED, or COMPLETED for', fw_id
return
f = lpdb.get_wf_summary_dict(fw_id)['name'].replace(' ', '_')
name = get_slug(f + '--' + spec['task_type'])
fw = Firework([AddEStructureTask()], spec, name=name)
lpdb.append_wf(Workflow([fw]), [parent_fw_id])
print name, 'added for', fw_id
except ValueError:
raise ValueError('could not append controller task to wf', wf['name'])
def append_wf(fw_id, parent_fw_id=None):
wf = lpdb.workflows.find_one({'nodes': fw_id}, {
'parent_links': 1,
'links': 1,
'name': 1
})
try:
if parent_fw_id is None:
parent_fw_id = wf['parent_links'][str(fw_id)][-1]
# non-defused AddEStructureTask v2 already in children?
for child_fw_id in wf['links'][str(parent_fw_id)]:
if child_fw_id == parent_fw_id: continue
child_fw = lpdb.fireworks.find_one({'fw_id': child_fw_id}, {
'spec.task_type': 1,
'state': 1
})
if child_fw['spec'][
'task_type'] == 'Controller: add Electronic Structure v2':
if child_fw['state'] == 'DEFUSED':
lpdb.reignite_fw(child_fw_id)
print 'AddEStructureTask v2', child_fw_id, 'reignited for', fw_id
elif child_fw['state'] == 'FIZZLED':
lpdb.rerun_fw(child_fw_id)
print 'AddEStructureTask v2', child_fw_id, 'marked for rerun for', fw_id
elif child_fw['state'] == 'COMPLETED':
print 'AddEStructureTask v2 already successfully run for', fw_id
sec_child_fw_id = wf['links'][str(child_fw_id)][0]
sec_child_fw = lpdb.fireworks.find_one(
{'fw_id': sec_child_fw_id}, {
'spec.task_type': 1,
'state': 1
})
if sec_child_fw['state'] == 'FIZZLED':
lpdb.rerun_fw(sec_child_fw_id)
print 'FIZZLED -> marked for rerun:', sec_child_fw_id, sec_child_fw[
'spec']['task_type']
else:
print 'AddEStructureTask v2 added but neither DEFUSED, FIZZLED, or COMPLETED for', fw_id
return
f = lpdb.get_wf_summary_dict(fw_id)['name'].replace(' ', '_')
name = get_slug(f + '--' + spec['task_type'])
fw = Firework([AddEStructureTask()], spec, name=name)
lpdb.append_wf(Workflow([fw]), [parent_fw_id])
print name, 'added for', fw_id
except ValueError:
raise ValueError('could not append controller task to wf', wf['name'])
def task_dict_to_wf(task_dict, launchpad):
fw_id = launchpad.get_new_fw_id()
l_id = launchpad.get_new_launch_id()
spec = {'task_type': task_dict['task_type'], 'run_tags': task_dict['run_tags'],
'vaspinputset_name': None, 'vasp': None, 'mpsnl': task_dict['snl'],
'snlgroup_id': task_dict['snlgroup_id']}
tasks = [DummyLegacyTask()]
launch_dir = task_dict['dir_name_full']
stored_data = {'error_list': []}
update_spec = {'prev_vasp_dir': task_dict['dir_name'],
'prev_task_type': spec['task_type'],
'mpsnl': spec['mpsnl'], 'snlgroup_id': spec['snlgroup_id'],
'run_tags': spec['run_tags']}
fwaction = FWAction(stored_data=stored_data, update_spec=update_spec)
if task_dict['completed_at']:
complete_date = datetime.datetime.strptime(task_dict['completed_at'], "%Y-%m-%d %H:%M:%S")
state_history = [{"created_on": complete_date, 'state': 'COMPLETED'}]
else:
state_history = []
launches = [Launch('COMPLETED', launch_dir, fworker=None, host=None, ip=None, action=fwaction,
state_history=state_history, launch_id=l_id, fw_id=fw_id)]
f = Composition(task_dict['pretty_formula']).alphabetical_formula
fw = Firework(tasks, spec, name=get_slug(f + '--' + spec['task_type']), launches=launches, state='COMPLETED', created_on=None,
fw_id=fw_id)
wf_meta = get_meta_from_structure(Structure.from_dict(task_dict['snl']))
wf_meta['run_version'] = 'preproduction (0)'
wf = Workflow.from_FireWork(fw, name=f, metadata=wf_meta)
launchpad.add_wf(wf, reassign_all=False)
launchpad._upsert_launch(launches[0])
print 'ADDED', fw_id
# return fw_id
return fw_id
def task_dict_to_wf(task_dict, launchpad):
fw_id = launchpad.get_new_fw_id()
l_id = launchpad.get_new_launch_id()
spec = {'task_type': task_dict['task_type'], 'run_tags': task_dict['run_tags'],
'vaspinputset_name': None, 'vasp': None, 'mpsnl': task_dict['snl'],
'snlgroup_id': task_dict['snlgroup_id']}
tasks = [DummyLegacyTask()]
launch_dir = task_dict['dir_name_full']
stored_data = {'error_list': []}
update_spec = {'prev_vasp_dir': task_dict['dir_name'],
'prev_task_type': spec['task_type'],
'mpsnl': spec['mpsnl'], 'snlgroup_id': spec['snlgroup_id'],
'run_tags': spec['run_tags']}
fwaction = FWAction(stored_data=stored_data, update_spec=update_spec)
if task_dict['completed_at']:
complete_date = datetime.datetime.strptime(task_dict['completed_at'], "%Y-%m-%d %H:%M:%S")
state_history = [{"created_on": complete_date, 'state': 'COMPLETED'}]
else:
state_history = []
launches = [Launch('COMPLETED', launch_dir, fworker=None, host=None, ip=None, action=fwaction,
state_history=state_history, launch_id=l_id, fw_id=fw_id)]
f = Composition.from_formula(task_dict['pretty_formula']).alphabetical_formula
fw = FireWork(tasks, spec, name=get_slug(f + '--' + spec['task_type']), launches=launches, state='COMPLETED', created_on=None,
fw_id=fw_id)
wf_meta = get_meta_from_structure(Structure.from_dict(task_dict['snl']))
wf_meta['run_version'] = 'preproduction (0)'
wf = Workflow.from_FireWork(fw, name=f, metadata=wf_meta)
launchpad.add_wf(wf, reassign_all=False)
launchpad._upsert_launch(launches[0])
print 'ADDED', fw_id
# return fw_id
return fw_id
def add_namefile(original_wf, use_slug=True):
"""
Every FireWork begins by writing an empty file with the name
"FW--<fw.name>". This makes it easy to figure out what jobs are in what
launcher directories, e.g. "ls -l launch*/FW--*" from within a "block" dir.
Args:
original_wf (Workflow)
use_slug (bool): whether to replace whitespace-type chars with a slug
"""
wf_dict = original_wf.to_dict()
for idx, fw in enumerate(wf_dict["fws"]):
fname = "FW--{}".format(fw["name"])
if use_slug:
fname = get_slug(fname)
wf_dict["fws"][idx]["spec"]["_tasks"].insert(0, FileWriteTask(
files_to_write=[{"filename": fname, "contents": ""}]).to_dict())
return Workflow.from_dict(wf_dict)
def add_namefile(original_wf, use_slug=True):
"""
Every FireWork begins by writing an empty file with the name
"FW--<fw.name>". This makes it easy to figure out what jobs are in what
launcher directories, e.g. "ls -l launch*/FW--*" from within a "block" dir.
Args:
original_wf (Workflow)
use_slug (bool): whether to replace whitespace-type chars with a slug
Returns:
Workflow
"""
for idx, fw in enumerate(original_wf.fws):
fname = "FW--{}".format(fw.name)
if use_slug:
fname = get_slug(fname)
original_wf.fws[idx].tasks.insert(0, FileWriteTask(
files_to_write=[{"filename": fname, "contents": ""}]))
return original_wf
def run_task(self, fw_spec):
# import here to prevent import errors in bigger MPCollab
# get the band structure and nelect from files
"""
prev_dir = get_loc(fw_spec['prev_vasp_dir'])
vasprun_loc = zpath(os.path.join(prev_dir, 'vasprun.xml'))
kpoints_loc = zpath(os.path.join(prev_dir, 'KPOINTS'))
vr = Vasprun(vasprun_loc)
bs = vr.get_band_structure(kpoints_filename=kpoints_loc)
"""
filename = get_slug(
'JOB--' + fw_spec['mpsnl'].structure.composition.reduced_formula + '--'
+ fw_spec['task_type'])
with open(filename, 'w+') as f:
f.write('')
# get the band structure and nelect from DB
block_part = get_block_part(fw_spec['prev_vasp_dir'])
db_dir = os.environ['DB_LOC']
assert isinstance(db_dir, object)
db_path = os.path.join(db_dir, 'tasks_db.json')
with open(db_path) as f:
creds = json.load(f)
connection = MongoClient(creds['host'], creds['port'])
tdb = connection[creds['database']]
tdb.authenticate(creds['admin_user'], creds['admin_password'])
props = {"calculations": 1, "task_id": 1, "state": 1, "pseudo_potential": 1, "run_type": 1, "is_hubbard": 1, "hubbards": 1, "unit_cell_formula": 1}
m_task = tdb.tasks.find_one({"dir_name": block_part}, props)
if not m_task:
time.sleep(60) # only thing to think of is wait for DB insertion(?)
m_task = tdb.tasks.find_one({"dir_name": block_part}, props)
if not m_task:
raise ValueError("Could not find task with dir_name: {}".format(block_part))
if m_task['state'] != 'successful':
raise ValueError("Cannot run Boltztrap; parent job unsuccessful")
nelect = m_task['calculations'][0]['input']['parameters']['NELECT']
bs_id = m_task['calculations'][0]['band_structure_fs_id']
print bs_id, type(bs_id)
fs = gridfs.GridFS(tdb, 'band_structure_fs')
bs_dict = json.loads(fs.get(bs_id).read())
bs_dict['structure'] = m_task['calculations'][0]['output']['crystal']
bs = BandStructure.from_dict(bs_dict)
print 'Band Structure found:', bool(bs)
print nelect
# run Boltztrap
runner = BoltztrapRunner(bs, nelect)
dir = runner.run(path_dir=os.getcwd())
# put the data in the database
bta = BoltztrapAnalyzer.from_files(dir)
# 8/21/15 - Anubhav removed fs_id (also see line further below, ted['boltztrap_full_fs_id'] ...)
# 8/21/15 - this is to save space in MongoDB, as well as non-use of full Boltztrap output (vs rerun)
"""
data = bta.as_dict()
data.update(get_meta_from_structure(bs._structure))
data['snlgroup_id'] = fw_spec['snlgroup_id']
data['run_tags'] = fw_spec['run_tags']
data['snl'] = fw_spec['mpsnl']
data['dir_name_full'] = dir
data['dir_name'] = get_block_part(dir)
data['task_id'] = m_task['task_id']
del data['hall'] # remove because it is too large and not useful
fs = gridfs.GridFS(tdb, "boltztrap_full_fs")
btid = fs.put(json.dumps(jsanitize(data)))
"""
# now for the "sanitized" data
ted = bta.as_dict()
del ted['seebeck']
del ted['hall']
del ted['kappa']
del ted['cond']
# ted['boltztrap_full_fs_id'] = btid
ted['snlgroup_id'] = fw_spec['snlgroup_id']
ted['run_tags'] = fw_spec['run_tags']
ted['snl'] = fw_spec['mpsnl'].as_dict()
ted['dir_name_full'] = dir
ted['dir_name'] = get_block_part(dir)
ted['task_id'] = m_task['task_id']
ted['pf_doping'] = bta.get_power_factor(output='tensor', relaxation_time=self.TAU)
ted['zt_doping'] = bta.get_zt(output='tensor', relaxation_time=self.TAU, kl=self.KAPPAL)
ted['pf_eigs'] = self.get_eigs(ted, 'pf_doping')
ted['pf_best'] = self.get_extreme(ted, 'pf_eigs')
ted['pf_best_dope18'] = self.get_extreme(ted, 'pf_eigs', max_didx=3)
ted['pf_best_dope19'] = self.get_extreme(ted, 'pf_eigs', max_didx=4)
ted['zt_eigs'] = self.get_eigs(ted, 'zt_doping')
ted['zt_best'] = self.get_extreme(ted, 'zt_eigs')
ted['zt_best_dope18'] = self.get_extreme(ted, 'zt_eigs', max_didx=3)
ted['zt_best_dope19'] = self.get_extreme(ted, 'zt_eigs', max_didx=4)
ted['seebeck_eigs'] = self.get_eigs(ted, 'seebeck_doping')
ted['seebeck_best'] = self.get_extreme(ted, 'seebeck_eigs')
ted['seebeck_best_dope18'] = self.get_extreme(ted, 'seebeck_eigs', max_didx=3)
ted['seebeck_best_dope19'] = self.get_extreme(ted, 'seebeck_eigs', max_didx=4)
ted['cond_eigs'] = self.get_eigs(ted, 'cond_doping')
ted['cond_best'] = self.get_extreme(ted, 'cond_eigs')
ted['cond_best_dope18'] = self.get_extreme(ted, 'cond_eigs', max_didx=3)
ted['cond_best_dope19'] = self.get_extreme(ted, 'cond_eigs', max_didx=4)
ted['kappa_eigs'] = self.get_eigs(ted, 'kappa_doping')
ted['kappa_best'] = self.get_extreme(ted, 'kappa_eigs', maximize=False)
ted['kappa_best_dope18'] = self.get_extreme(ted, 'kappa_eigs', maximize=False, max_didx=3)
ted['kappa_best_dope19'] = self.get_extreme(ted, 'kappa_eigs', maximize=False, max_didx=4)
try:
from mpcollab.thermoelectrics.boltztrap_TE import BoltzSPB
bzspb = BoltzSPB(ted)
maxpf_p = bzspb.get_maximum_power_factor('p', temperature=0, tau=1E-14, ZT=False, kappal=0.5,\
otherprops=('get_seebeck_mu_eig', 'get_conductivity_mu_eig', \
'get_thermal_conductivity_mu_eig', 'get_average_eff_mass_tensor_mu'))
maxpf_n = bzspb.get_maximum_power_factor('n', temperature=0, tau=1E-14, ZT=False, kappal=0.5,\
otherprops=('get_seebeck_mu_eig', 'get_conductivity_mu_eig', \
'get_thermal_conductivity_mu_eig', 'get_average_eff_mass_tensor_mu'))
maxzt_p = bzspb.get_maximum_power_factor('p', temperature=0, tau=1E-14, ZT=True, kappal=0.5, otherprops=('get_seebeck_mu_eig', 'get_conductivity_mu_eig', \
'get_thermal_conductivity_mu_eig', 'get_average_eff_mass_tensor_mu'))
maxzt_n = bzspb.get_maximum_power_factor('n', temperature=0, tau=1E-14, ZT=True, kappal=0.5, otherprops=('get_seebeck_mu_eig', 'get_conductivity_mu_eig', \
'get_thermal_conductivity_mu_eig', 'get_average_eff_mass_tensor_mu'))
ted['zt_best_finemesh'] = {'p': maxzt_p, 'n': maxzt_n}
ted['pf_best_finemesh'] = {'p': maxpf_p, 'n': maxpf_n}
except:
import traceback
traceback.print_exc()
print 'COULD NOT GET FINE MESH DATA'
# add is_compatible
mpc = MaterialsProjectCompatibility("Advanced")
try:
func = m_task["pseudo_potential"]["functional"]
labels = m_task["pseudo_potential"]["labels"]
symbols = ["{} {}".format(func, label) for label in labels]
parameters = {"run_type": m_task["run_type"],
"is_hubbard": m_task["is_hubbard"],
"hubbards": m_task["hubbards"],
"potcar_symbols": symbols}
entry = ComputedEntry(Composition(m_task["unit_cell_formula"]),
0.0, 0.0, parameters=parameters,
entry_id=m_task["task_id"])
ted["is_compatible"] = bool(mpc.process_entry(entry))
except:
traceback.print_exc()
print 'ERROR in getting compatibility, task_id: {}'.format(m_task["task_id"])
ted["is_compatible"] = None
tdb.boltztrap.insert(jsanitize(ted))
update_spec = {'prev_vasp_dir': fw_spec['prev_vasp_dir'],
'boltztrap_dir': os.getcwd(),
'prev_task_type': fw_spec['task_type'],
'mpsnl': fw_spec['mpsnl'].as_dict(),
'snlgroup_id': fw_spec['snlgroup_id'],
'run_tags': fw_spec['run_tags'], 'parameters': fw_spec.get('parameters')}
return FWAction(update_spec=update_spec)
def structure_to_wf(structure):
"""
This method starts with a Structure object and creates a Workflow object
The workflow has two steps - a structure relaxation and a static run
:param structure:
:return:
"""
fws = [] # list of FireWorks to run
connections = defaultdict(list) # dependencies between FireWorks
# generate VASP input objects for 1st VASP run - this is put in the FW spec
mpvis = MPGGAVaspInputSet(user_incar_settings={'NPAR': 2})
incar = mpvis.get_incar(structure)
poscar = mpvis.get_poscar(structure)
kpoints = mpvis.get_kpoints(structure)
potcar = mpvis.get_potcar(structure)
# serialize the VASP input objects to the FW spec
spec = {}
spec['vasp'] = {}
spec['vasp']['incar'] = incar.as_dict()
spec['vasp']['poscar'] = poscar.as_dict()
spec['vasp']['kpoints'] = kpoints.as_dict()
spec['vasp']['potcar'] = potcar.as_dict()
spec['vaspinputset_name'] = mpvis.__class__.__name__
spec['task_type'] = 'GGA optimize structure (2x) example'
# set up the custodian that we want to run
jobs = VaspJob.double_relaxation_run('', gzipped=False)
for j in jobs: # turn off auto npar, it doesn't work for >1 node
j.auto_npar = False
handlers = [VaspErrorHandler(), FrozenJobErrorHandler(), MeshSymmetryErrorHandler(),
NonConvergingErrorHandler()]
c_params = {'jobs': [j.as_dict() for j in jobs], 'handlers': [h.as_dict() for h in handlers], 'max_errors': 5}
custodiantask = VaspCustodianTaskEx(c_params)
# 1st Firework - run GGA optimize structure
# VaspWriterTask - write input files (INCAR, POSCAR, KPOINTS, POSCAR) based on spec
# CustodianTaskEx - run VASP within a custodian
tasks = [VaspWriterTask(), custodiantask]
fws.append(Firework(tasks, spec, name=get_name(structure, spec['task_type']), fw_id=1))
# 2nd Firework - insert previous run into DB
spec = {'task_type': 'VASP db insertion example'}
fws.append(
Firework([VaspToDBTaskEx()], spec, name=get_name(structure, spec['task_type']), fw_id=2))
connections[1] = [2]
# 3rd Firework - static run.
# VaspCopyTask - copy output from previous run to this directory
# SetupStaticRunTask - override old parameters for static run
# CustodianTaskEx - run VASP within a custodian
spec = {'task_type': 'GGA static example'}
copytask = VaspCopyTask({'use_CONTCAR': True, 'skip_CHGCAR': True})
setuptask = SetupStaticRunTask()
custodiantask = VaspCustodianTaskEx({'jobs': [VaspJob('', auto_npar=False).as_dict()], 'handlers': [h.as_dict() for h in handlers], 'max_errors': 5})
fws.append(Firework([copytask, setuptask, custodiantask], spec, name=get_name(structure, spec['task_type']), fw_id=3))
connections[2] = [3]
# 4th Firework - insert previous run into DB
spec = {'task_type': 'VASP db insertion example'}
fws.append(
Firework([VaspToDBTaskEx()], spec, name=get_name(structure, spec['task_type']), fw_id=4))
connections[3] = [4]
return Workflow(fws, connections, name=get_slug(structure.formula))
def launch_rocket_to_queue(launchpad, fworker, qadapter, launcher_dir='.', reserve=False,
strm_lvl='INFO', create_launcher_dir=False, fill_mode=False,
fw_id=None):
"""
Submit a single job to the queue.
Args:
launchpad (LaunchPad)
fworker (FWorker)
qadapter (QueueAdapterBase)
launcher_dir (str): The directory where to submit the job
reserve (bool): Whether to queue in reservation mode
strm_lvl (str): level at which to stream log messages
create_launcher_dir (bool): Whether to create a subfolder launcher+timestamp, if needed
fill_mode (bool): whether to submit jobs even when there is nothing to run
(only in non-reservation mode)
fw_id (int): specific fw_id to reserve (reservation mode only)
"""
fworker = fworker if fworker else FWorker()
launcher_dir = os.path.abspath(launcher_dir)
l_logger = get_fw_logger('queue.launcher', l_dir=launchpad.logdir, stream_level=strm_lvl)
l_logger.debug('getting queue adapter')
qadapter = load_object(qadapter.to_dict()) # make a defensive copy, mainly for reservation mode
fw, launch_id = None, None # only needed in reservation mode
if not os.path.exists(launcher_dir):
raise ValueError('Desired launch directory {} does not exist!'.format(launcher_dir))
if '--offline' in qadapter['rocket_launch'] and not reserve:
raise ValueError("Must use reservation mode (-r option) of qlaunch "
"when using offline option of rlaunch!!")
if reserve and 'singleshot' not in qadapter.get('rocket_launch', ''):
raise ValueError('Reservation mode of queue launcher only works for singleshot Rocket Launcher!')
if fill_mode and reserve:
raise ValueError("Fill_mode cannot be used in conjunction with reserve mode!")
if fw_id and not reserve:
raise ValueError("qlaunch for specific fireworks may only be used in reservation mode.")
if fill_mode or launchpad.run_exists(fworker):
launch_id = None
try:
if reserve:
if fw_id:
l_logger.debug('finding a FW to reserve...')
fw, launch_id = launchpad.reserve_fw(fworker, launcher_dir, fw_id=fw_id)
if not fw:
l_logger.info('No jobs exist in the LaunchPad for submission to queue!')
return False
l_logger.info('reserved FW with fw_id: {}'.format(fw.fw_id))
# update qadapter job_name based on FW name
job_name = get_slug(fw.name)[0:QUEUE_JOBNAME_MAXLEN]
qadapter.update({'job_name': job_name})
if '_queueadapter' in fw.spec:
l_logger.debug('updating queue params using Firework spec..')
qadapter.update(fw.spec['_queueadapter'])
# reservation mode includes --fw_id in rocket launch
qadapter['rocket_launch'] += ' --fw_id {}'.format(fw.fw_id)
# update launcher_dir if _launch_dir is selected in reserved fw
if '_launch_dir' in fw.spec:
fw_launch_dir = os.path.expandvars(fw.spec['_launch_dir'])
if not os.path.isabs(fw_launch_dir):
fw_launch_dir = os.path.join(launcher_dir, fw_launch_dir)
launcher_dir = fw_launch_dir
makedirs_p(launcher_dir)
launchpad.change_launch_dir(launch_id, launcher_dir)
elif create_launcher_dir:
# create launcher_dir
launcher_dir = create_datestamp_dir(launcher_dir, l_logger, prefix='launcher_')
launchpad.change_launch_dir(launch_id, launcher_dir)
elif create_launcher_dir:
# create launcher_dir
launcher_dir = create_datestamp_dir(launcher_dir, l_logger, prefix='launcher_')
# move to the launch directory
l_logger.info('moving to launch_dir {}'.format(launcher_dir))
with cd(launcher_dir):
if '--offline' in qadapter['rocket_launch']:
setup_offline_job(launchpad, fw, launch_id)
l_logger.debug('writing queue script')
with open(SUBMIT_SCRIPT_NAME, 'w') as f:
queue_script = qadapter.get_script_str(launcher_dir)
f.write(queue_script)
l_logger.info('submitting queue script')
reservation_id = qadapter.submit_to_queue(SUBMIT_SCRIPT_NAME)
if not reservation_id:
raise RuntimeError('queue script could not be submitted, check queue '
'script/queue adapter/queue server status!')
elif reserve:
launchpad.set_reservation_id(launch_id, reservation_id)
return reservation_id
except:
log_exception(l_logger, 'Error writing/submitting queue script!')
if reserve and launch_id is not None:
try:
l_logger.info('Un-reserving FW with fw_id, launch_id: {}, {}'.format(
fw.fw_id, launch_id))
launchpad.cancel_reservation(launch_id)
launchpad.forget_offline(launch_id)
except:
log_exception(l_logger, 'Error unreserving FW with fw_id {}'.format(fw.fw_id))
return False
else:
l_logger.info('No jobs exist in the LaunchPad for submission to queue!')
return None # note: this is a hack (rather than False) to indicate a soft failure to rapidfire()
def run_task(self, fw_spec):
print "sleeping 10s for Mongo"
time.sleep(10)
print "done sleeping"
print "the gap is {}, the cutoff is {}".format(fw_spec["analysis"]["bandgap"], self.gap_cutoff)
if fw_spec["analysis"]["bandgap"] >= self.gap_cutoff:
static_dens = 90
uniform_dens = 1000
line_dens = 20
else:
static_dens = 450
uniform_dens = 1500
line_dens = 30
if fw_spec["analysis"]["bandgap"] <= self.metal_cutoff:
user_incar_settings = {"ISMEAR": 1, "SIGMA": 0.2}
else:
user_incar_settings = {}
print "Adding more runs..."
type_name = "GGA+U" if "GGA+U" in fw_spec["prev_task_type"] else "GGA"
snl = StructureNL.from_dict(fw_spec["mpsnl"])
f = Composition(snl.structure.composition.reduced_formula).alphabetical_formula
fws = []
connections = {}
priority = fw_spec["_priority"]
trackers = [
Tracker("FW_job.out"),
Tracker("FW_job.error"),
Tracker("vasp.out"),
Tracker("OUTCAR"),
Tracker("OSZICAR"),
]
trackers_db = [Tracker("FW_job.out"), Tracker("FW_job.error")]
# run GGA static
spec = fw_spec # pass all the items from the current spec to the new
spec.update(
{
"task_type": "{} static v2".format(type_name),
"_queueadapter": QA_VASP_SMALL,
"_dupefinder": DupeFinderVasp().to_dict(),
"_priority": priority,
"_trackers": trackers,
}
)
fws.append(
Firework(
[
VaspCopyTask({"use_CONTCAR": True, "skip_CHGCAR": True}),
SetupStaticRunTask({"kpoints_density": static_dens, "user_incar_settings": user_incar_settings}),
get_custodian_task(spec),
],
spec,
name=get_slug(f + "--" + spec["task_type"]),
fw_id=-10,
)
)
# insert into DB - GGA static
spec = {
"task_type": "VASP db insertion",
"_queueadapter": QA_DB,
"_allow_fizzled_parents": True,
"_priority": priority * 2,
"_dupefinder": DupeFinderDB().to_dict(),
"_trackers": trackers_db,
}
fws.append(Firework([VaspToDBTask()], spec, name=get_slug(f + "--" + spec["task_type"]), fw_id=-9))
connections[-10] = -9
# run GGA Uniform
spec = {
"task_type": "{} Uniform v2".format(type_name),
"_queueadapter": QA_VASP,
"_dupefinder": DupeFinderVasp().to_dict(),
"_priority": priority,
"_trackers": trackers,
}
fws.append(
Firework(
[
VaspCopyTask({"use_CONTCAR": False}),
SetupNonSCFTask({"mode": "uniform", "kpoints_density": uniform_dens}),
get_custodian_task(spec),
],
spec,
name=get_slug(f + "--" + spec["task_type"]),
fw_id=-8,
)
)
connections[-9] = -8
# insert into DB - GGA Uniform
spec = {
"task_type": "VASP db insertion",
"_queueadapter": QA_DB,
"_allow_fizzled_parents": True,
"_priority": priority * 2,
"_dupefinder": DupeFinderDB().to_dict(),
"_trackers": trackers_db,
}
fws.append(
Firework(
[VaspToDBTask({"parse_uniform": True})], spec, name=get_slug(f + "--" + spec["task_type"]), fw_id=-7
)
)
connections[-8] = -7
# run GGA Band structure
spec = {
"task_type": "{} band structure v2".format(type_name),
"_queueadapter": QA_VASP,
"_dupefinder": DupeFinderVasp().to_dict(),
"_priority": priority,
"_trackers": trackers,
}
fws.append(
Firework(
[
VaspCopyTask({"use_CONTCAR": False}),
SetupNonSCFTask({"mode": "line", "kpoints_line_density": line_dens}),
get_custodian_task(spec),
],
spec,
name=get_slug(f + "--" + spec["task_type"]),
fw_id=-6,
)
)
connections[-7] = [-6]
# insert into DB - GGA Band structure
spec = {
"task_type": "VASP db insertion",
"_queueadapter": QA_DB,
"_allow_fizzled_parents": True,
"_priority": priority * 2,
"_dupefinder": DupeFinderDB().to_dict(),
"_trackers": trackers_db,
}
fws.append(Firework([VaspToDBTask({})], spec, name=get_slug(f + "--" + spec["task_type"]), fw_id=-5))
connections[-6] = -5
if fw_spec.get("parameters") and fw_spec["parameters"].get("boltztrap"):
# run Boltztrap
from mpworks.firetasks.boltztrap_tasks import BoltztrapRunTask
spec = {
"task_type": "{} Boltztrap".format(type_name),
"_queueadapter": QA_DB,
"_dupefinder": DupeFinderDB().to_dict(),
"_priority": priority,
}
fws.append(Firework([BoltztrapRunTask()], spec, name=get_slug(f + "--" + spec["task_type"]), fw_id=-4))
connections[-7].append(-4)
wf = Workflow(fws, connections)
print "Done adding more runs..."
return FWAction(additions=wf)
def snl_to_wf(snl, parameters=None):
fws = []
connections = defaultdict(list)
parameters = parameters if parameters else {}
snl_priority = parameters.get('priority', 1)
priority = snl_priority * 2 # once we start a job, keep going!
f = Composition(snl.structure.composition.reduced_formula).alphabetical_formula
snl_spec = {}
if 'snlgroup_id' in parameters:
if 'mpsnl' in parameters:
snl_spec['mpsnl'] = parameters['mpsnl']
elif isinstance(snl, MPStructureNL):
snl_spec['mpsnl'] = snl.as_dict()
else:
raise ValueError("improper use of force SNL")
snl_spec['snlgroup_id'] = parameters['snlgroup_id']
else:
# add the SNL to the SNL DB and figure out duplicate group
tasks = [AddSNLTask()]
spec = {'task_type': 'Add to SNL database', 'snl': snl.as_dict(), '_queueadapter': QA_DB, '_priority': snl_priority}
fws.append(Firework(tasks, spec, name=get_slug(f + '--' + spec['task_type']), fw_id=0))
connections[0] = [1]
trackers = [Tracker('FW_job.out'), Tracker('FW_job.error'), Tracker('vasp.out'), Tracker('OUTCAR'), Tracker('OSZICAR'), Tracker('OUTCAR.relax1'), Tracker('OUTCAR.relax2')]
trackers_db = [Tracker('FW_job.out'), Tracker('FW_job.error')]
# run GGA structure optimization
spec = _snl_to_spec(snl, enforce_gga=True, parameters=parameters)
spec.update(snl_spec)
spec['_priority'] = priority
spec['_queueadapter'] = QA_VASP
spec['_trackers'] = trackers
tasks = [VaspWriterTask(), get_custodian_task(spec)]
fws.append(Firework(tasks, spec, name=get_slug(f + '--' + spec['task_type']), fw_id=1))
# insert into DB - GGA structure optimization
spec = {'task_type': 'VASP db insertion', '_priority': priority*2,
'_allow_fizzled_parents': True, '_queueadapter': QA_DB, "_dupefinder": DupeFinderDB().to_dict(), '_trackers': trackers_db}
fws.append(
Firework([VaspToDBTask()], spec, name=get_slug(f + '--' + spec['task_type']), fw_id=2))
connections[1] = [2]
# determine if GGA+U FW is needed
incar = MPVaspInputSet().get_incar(snl.structure).as_dict()
ggau_compound = ('LDAU' in incar and incar['LDAU'])
if not parameters.get('skip_bandstructure', False) and (not ggau_compound or parameters.get('force_gga_bandstructure', False)):
spec = {'task_type': 'Controller: add Electronic Structure v2', '_priority': priority,
'_queueadapter': QA_CONTROL}
fws.append(
Firework([AddEStructureTask()], spec, name=get_slug(f + '--' + spec['task_type']),
fw_id=3))
connections[2] = [3]
if ggau_compound:
spec = _snl_to_spec(snl, enforce_gga=False, parameters=parameters)
del spec['vasp'] # we are stealing all VASP params and such from previous run
spec['_priority'] = priority
spec['_queueadapter'] = QA_VASP
spec['_trackers'] = trackers
fws.append(Firework(
[VaspCopyTask(), SetupGGAUTask(),
get_custodian_task(spec)], spec, name=get_slug(f + '--' + spec['task_type']),
fw_id=10))
connections[2].append(10)
spec = {'task_type': 'VASP db insertion', '_queueadapter': QA_DB,
'_allow_fizzled_parents': True, '_priority': priority, "_dupefinder": DupeFinderDB().to_dict(), '_trackers': trackers_db}
fws.append(
Firework([VaspToDBTask()], spec, name=get_slug(f + '--' + spec['task_type']), fw_id=11))
connections[10] = [11]
if not parameters.get('skip_bandstructure', False):
spec = {'task_type': 'Controller: add Electronic Structure v2', '_priority': priority,
'_queueadapter': QA_CONTROL}
fws.append(
Firework([AddEStructureTask()], spec, name=get_slug(f + '--' + spec['task_type']),
fw_id=12))
connections[11] = [12]
wf_meta = get_meta_from_structure(snl.structure)
wf_meta['run_version'] = 'May 2013 (1)' # not maintained
if '_materialsproject' in snl.data and 'submission_id' in snl.data['_materialsproject']:
wf_meta['submission_id'] = snl.data['_materialsproject']['submission_id']
return Workflow(fws, connections, name=Composition(
snl.structure.composition.reduced_formula).alphabetical_formula, metadata=wf_meta)
def get_name(structure, task_type):
return get_slug(structure.formula + '--' + task_type)
def run_task(self, fw_spec):
# import here to prevent import errors in bigger MPCollab
# get the band structure and nelect from files
"""
prev_dir = get_loc(fw_spec['prev_vasp_dir'])
vasprun_loc = zpath(os.path.join(prev_dir, 'vasprun.xml'))
kpoints_loc = zpath(os.path.join(prev_dir, 'KPOINTS'))
vr = Vasprun(vasprun_loc)
bs = vr.get_band_structure(kpoints_filename=kpoints_loc)
"""
filename = get_slug(
'JOB--' + fw_spec['mpsnl'].structure.composition.reduced_formula +
'--' + fw_spec['task_type'])
with open(filename, 'w+') as f:
f.write('')
# get the band structure and nelect from DB
block_part = get_block_part(fw_spec['prev_vasp_dir'])
db_dir = os.environ['DB_LOC']
assert isinstance(db_dir, object)
db_path = os.path.join(db_dir, 'tasks_db.json')
with open(db_path) as f:
creds = json.load(f)
connection = MongoClient(creds['host'], creds['port'])
tdb = connection[creds['database']]
tdb.authenticate(creds['admin_user'], creds['admin_password'])
props = {
"calculations": 1,
"task_id": 1,
"state": 1,
"pseudo_potential": 1,
"run_type": 1,
"is_hubbard": 1,
"hubbards": 1,
"unit_cell_formula": 1
}
m_task = tdb.tasks.find_one({"dir_name": block_part}, props)
if not m_task:
time.sleep(
60) # only thing to think of is wait for DB insertion(?)
m_task = tdb.tasks.find_one({"dir_name": block_part}, props)
if not m_task:
raise ValueError(
"Could not find task with dir_name: {}".format(block_part))
if m_task['state'] != 'successful':
raise ValueError(
"Cannot run Boltztrap; parent job unsuccessful")
nelect = m_task['calculations'][0]['input']['parameters']['NELECT']
bs_id = m_task['calculations'][0]['band_structure_fs_id']
print bs_id, type(bs_id)
fs = gridfs.GridFS(tdb, 'band_structure_fs')
bs_dict = json.loads(fs.get(bs_id).read())
bs_dict['structure'] = m_task['calculations'][0]['output'][
'crystal']
bs = BandStructure.from_dict(bs_dict)
print("find previous run with block_part {}".format(block_part))
print 'Band Structure found:', bool(bs)
print(bs.as_dict())
print("nelect: {}".format(nelect))
# run Boltztrap
doping = []
for d in [1e16, 1e17, 1e18, 1e19, 1e20]:
doping.extend([1 * d, 2.5 * d, 5 * d, 7.5 * d])
doping.append(1e21)
runner = BoltztrapRunner(bs, nelect, doping=doping)
dir = runner.run(path_dir=os.getcwd())
# put the data in the database
bta = BoltztrapAnalyzer.from_files(dir)
# 8/21/15 - Anubhav removed fs_id (also see line further below, ted['boltztrap_full_fs_id'] ...)
# 8/21/15 - this is to save space in MongoDB, as well as non-use of full Boltztrap output (vs rerun)
"""
data = bta.as_dict()
data.update(get_meta_from_structure(bs._structure))
data['snlgroup_id'] = fw_spec['snlgroup_id']
data['run_tags'] = fw_spec['run_tags']
data['snl'] = fw_spec['mpsnl']
data['dir_name_full'] = dir
data['dir_name'] = get_block_part(dir)
data['task_id'] = m_task['task_id']
del data['hall'] # remove because it is too large and not useful
fs = gridfs.GridFS(tdb, "boltztrap_full_fs")
btid = fs.put(json.dumps(jsanitize(data)))
"""
# now for the "sanitized" data
ted = bta.as_dict()
del ted['seebeck']
del ted['hall']
del ted['kappa']
del ted['cond']
# ted['boltztrap_full_fs_id'] = btid
ted['snlgroup_id'] = fw_spec['snlgroup_id']
ted['run_tags'] = fw_spec['run_tags']
ted['snl'] = fw_spec['mpsnl'].as_dict()
ted['dir_name_full'] = dir
ted['dir_name'] = get_block_part(dir)
ted['task_id'] = m_task['task_id']
ted['pf_doping'] = bta.get_power_factor(output='tensor',
relaxation_time=self.TAU)
ted['zt_doping'] = bta.get_zt(output='tensor',
relaxation_time=self.TAU,
kl=self.KAPPAL)
ted['pf_eigs'] = self.get_eigs(ted, 'pf_doping')
ted['pf_best'] = self.get_extreme(ted, 'pf_eigs')
ted['pf_best_dope18'] = self.get_extreme(ted,
'pf_eigs',
max_didx=3)
ted['pf_best_dope19'] = self.get_extreme(ted,
'pf_eigs',
max_didx=4)
ted['zt_eigs'] = self.get_eigs(ted, 'zt_doping')
ted['zt_best'] = self.get_extreme(ted, 'zt_eigs')
ted['zt_best_dope18'] = self.get_extreme(ted,
'zt_eigs',
max_didx=3)
ted['zt_best_dope19'] = self.get_extreme(ted,
'zt_eigs',
max_didx=4)
ted['seebeck_eigs'] = self.get_eigs(ted, 'seebeck_doping')
ted['seebeck_best'] = self.get_extreme(ted, 'seebeck_eigs')
ted['seebeck_best_dope18'] = self.get_extreme(ted,
'seebeck_eigs',
max_didx=3)
ted['seebeck_best_dope19'] = self.get_extreme(ted,
'seebeck_eigs',
max_didx=4)
ted['cond_eigs'] = self.get_eigs(ted, 'cond_doping')
ted['cond_best'] = self.get_extreme(ted, 'cond_eigs')
ted['cond_best_dope18'] = self.get_extreme(ted,
'cond_eigs',
max_didx=3)
ted['cond_best_dope19'] = self.get_extreme(ted,
'cond_eigs',
max_didx=4)
ted['kappa_eigs'] = self.get_eigs(ted, 'kappa_doping')
ted['kappa_best'] = self.get_extreme(ted,
'kappa_eigs',
maximize=False)
ted['kappa_best_dope18'] = self.get_extreme(ted,
'kappa_eigs',
maximize=False,
max_didx=3)
ted['kappa_best_dope19'] = self.get_extreme(ted,
'kappa_eigs',
maximize=False,
max_didx=4)
try:
from mpcollab.thermoelectrics.boltztrap_TE import BoltzSPB
bzspb = BoltzSPB(ted)
maxpf_p = bzspb.get_maximum_power_factor(
'p',
temperature=0,
tau=1E-14,
ZT=False,
kappal=0.5,
otherprops=('get_seebeck_mu_eig',
'get_conductivity_mu_eig',
'get_thermal_conductivity_mu_eig',
'get_average_eff_mass_tensor_mu'))
maxpf_n = bzspb.get_maximum_power_factor(
'n',
temperature=0,
tau=1E-14,
ZT=False,
kappal=0.5,
otherprops=('get_seebeck_mu_eig',
'get_conductivity_mu_eig',
'get_thermal_conductivity_mu_eig',
'get_average_eff_mass_tensor_mu'))
maxzt_p = bzspb.get_maximum_power_factor(
'p',
temperature=0,
tau=1E-14,
ZT=True,
kappal=0.5,
otherprops=('get_seebeck_mu_eig',
'get_conductivity_mu_eig',
'get_thermal_conductivity_mu_eig',
'get_average_eff_mass_tensor_mu'))
maxzt_n = bzspb.get_maximum_power_factor(
'n',
temperature=0,
tau=1E-14,
ZT=True,
kappal=0.5,
otherprops=('get_seebeck_mu_eig',
'get_conductivity_mu_eig',
'get_thermal_conductivity_mu_eig',
'get_average_eff_mass_tensor_mu'))
ted['zt_best_finemesh'] = {'p': maxzt_p, 'n': maxzt_n}
ted['pf_best_finemesh'] = {'p': maxpf_p, 'n': maxpf_n}
except:
import traceback
traceback.print_exc()
print 'COULD NOT GET FINE MESH DATA'
# add is_compatible
mpc = MaterialsProjectCompatibility("Advanced")
try:
func = m_task["pseudo_potential"]["functional"]
labels = m_task["pseudo_potential"]["labels"]
symbols = ["{} {}".format(func, label) for label in labels]
parameters = {
"run_type": m_task["run_type"],
"is_hubbard": m_task["is_hubbard"],
"hubbards": m_task["hubbards"],
"potcar_symbols": symbols
}
entry = ComputedEntry(Composition(m_task["unit_cell_formula"]),
0.0,
0.0,
parameters=parameters,
entry_id=m_task["task_id"])
ted["is_compatible"] = bool(mpc.process_entry(entry))
except:
traceback.print_exc()
print 'ERROR in getting compatibility, task_id: {}'.format(
m_task["task_id"])
ted["is_compatible"] = None
tdb.boltztrap.insert(jsanitize(ted))
update_spec = {
'prev_vasp_dir': fw_spec['prev_vasp_dir'],
'boltztrap_dir': os.getcwd(),
'prev_task_type': fw_spec['task_type'],
'mpsnl': fw_spec['mpsnl'].as_dict(),
'snlgroup_id': fw_spec['snlgroup_id'],
'run_tags': fw_spec['run_tags'],
'parameters': fw_spec.get('parameters')
}
return FWAction(update_spec=update_spec)
def launch_rocket_to_queue(launchpad,
fworker,
qadapter,
launcher_dir='.',
reserve=False,
strm_lvl='INFO',
create_launcher_dir=False,
fill_mode=False,
fw_id=None):
"""
Submit a single job to the queue.
Args:
launchpad (LaunchPad)
fworker (FWorker)
qadapter (QueueAdapterBase)
launcher_dir (str): The directory where to submit the job
reserve (bool): Whether to queue in reservation mode
strm_lvl (str): level at which to stream log messages
create_launcher_dir (bool): Whether to create a subfolder launcher+timestamp, if needed
fill_mode (bool): whether to submit jobs even when there is nothing to run
(only in non-reservation mode)
fw_id (int): specific fw_id to reserve (reservation mode only)
"""
fworker = fworker if fworker else FWorker()
launcher_dir = os.path.abspath(launcher_dir)
l_logger = get_fw_logger('queue.launcher',
l_dir=launchpad.logdir,
stream_level=strm_lvl)
l_logger.debug('getting queue adapter')
qadapter = load_object(qadapter.to_dict(
)) # make a defensive copy, mainly for reservation mode
fw, launch_id = None, None # only needed in reservation mode
if not os.path.exists(launcher_dir):
raise ValueError(
'Desired launch directory {} does not exist!'.format(launcher_dir))
if '--offline' in qadapter['rocket_launch'] and not reserve:
raise ValueError("Must use reservation mode (-r option) of qlaunch "
"when using offline option of rlaunch!!")
if reserve and 'singleshot' not in qadapter.get('rocket_launch', ''):
raise ValueError(
'Reservation mode of queue launcher only works for singleshot Rocket Launcher!'
)
if fill_mode and reserve:
raise ValueError(
"Fill_mode cannot be used in conjunction with reserve mode!")
if fw_id and not reserve:
raise ValueError(
"qlaunch for specific fireworks may only be used in reservation mode."
)
if fill_mode or launchpad.run_exists(fworker):
launch_id = None
try:
if reserve:
if fw_id:
l_logger.debug('finding a FW to reserve...')
fw, launch_id = launchpad.reserve_fw(fworker,
launcher_dir,
fw_id=fw_id)
if not fw:
l_logger.info(
'No jobs exist in the LaunchPad for submission to queue!'
)
return False
l_logger.info('reserved FW with fw_id: {}'.format(fw.fw_id))
# update qadapter job_name based on FW name
job_name = get_slug(fw.name)[0:QUEUE_JOBNAME_MAXLEN]
qadapter.update({'job_name': job_name})
if '_queueadapter' in fw.spec:
l_logger.debug(
'updating queue params using Firework spec..')
qadapter.update(fw.spec['_queueadapter'])
# reservation mode includes --fw_id in rocket launch
qadapter['rocket_launch'] += ' --fw_id {}'.format(fw.fw_id)
# update launcher_dir if _launch_dir is selected in reserved fw
if '_launch_dir' in fw.spec:
fw_launch_dir = os.path.expandvars(fw.spec['_launch_dir'])
if not os.path.isabs(fw_launch_dir):
fw_launch_dir = os.path.join(launcher_dir,
fw_launch_dir)
launcher_dir = fw_launch_dir
makedirs_p(launcher_dir)
launchpad.change_launch_dir(launch_id, launcher_dir)
elif create_launcher_dir:
# create launcher_dir
launcher_dir = create_datestamp_dir(launcher_dir,
l_logger,
prefix='launcher_')
launchpad.change_launch_dir(launch_id, launcher_dir)
elif create_launcher_dir:
# create launcher_dir
launcher_dir = create_datestamp_dir(launcher_dir,
l_logger,
prefix='launcher_')
# move to the launch directory
l_logger.info('moving to launch_dir {}'.format(launcher_dir))
with cd(launcher_dir):
if '--offline' in qadapter['rocket_launch']:
setup_offline_job(launchpad, fw, launch_id)
l_logger.debug('writing queue script')
with open(SUBMIT_SCRIPT_NAME, 'w') as f:
queue_script = qadapter.get_script_str(launcher_dir)
f.write(queue_script)
l_logger.info('submitting queue script')
reservation_id = qadapter.submit_to_queue(SUBMIT_SCRIPT_NAME)
if not reservation_id:
raise RuntimeError(
'queue script could not be submitted, check queue '
'script/queue adapter/queue server status!')
elif reserve:
launchpad.set_reservation_id(launch_id, reservation_id)
return reservation_id
except:
log_exception(l_logger, 'Error writing/submitting queue script!')
if reserve and launch_id is not None:
try:
l_logger.info(
'Un-reserving FW with fw_id, launch_id: {}, {}'.format(
fw.fw_id, launch_id))
launchpad.cancel_reservation(launch_id)
launchpad.forget_offline(launch_id)
except:
log_exception(
l_logger,
'Error unreserving FW with fw_id {}'.format(fw.fw_id))
return False
else:
l_logger.info(
'No jobs exist in the LaunchPad for submission to queue!')
return None # note: this is a hack (rather than False) to indicate a soft failure to rapidfire()
def run_task(self, fw_spec):
if '_fizzled_parents' in fw_spec and not 'prev_vasp_dir' in fw_spec:
prev_dir = get_loc(fw_spec['_fizzled_parents'][0]['launches'][0]['launch_dir'])
update_spec = {} # add this later when creating new FW
fizzled_parent = True
parse_dos = False
else:
prev_dir = get_loc(fw_spec['prev_vasp_dir'])
update_spec = {'prev_vasp_dir': prev_dir,
'prev_task_type': fw_spec['prev_task_type'],
'run_tags': fw_spec['run_tags'], 'parameters': fw_spec.get('parameters')}
fizzled_parent = False
parse_dos = 'Uniform' in fw_spec['prev_task_type']
if 'run_tags' in fw_spec:
self.additional_fields['run_tags'] = fw_spec['run_tags']
else:
self.additional_fields['run_tags'] = fw_spec['_fizzled_parents'][0]['spec']['run_tags']
if MOVE_TO_GARDEN_DEV:
prev_dir = move_to_garden(prev_dir, prod=False)
elif MOVE_TO_GARDEN_PROD:
prev_dir = move_to_garden(prev_dir, prod=True)
# get the directory containing the db file
db_dir = os.environ['DB_LOC']
db_path = os.path.join(db_dir, 'tasks_db.json')
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger('MPVaspDrone')
logger.setLevel(logging.INFO)
sh = logging.StreamHandler(stream=sys.stdout)
sh.setLevel(getattr(logging, 'INFO'))
logger.addHandler(sh)
with open(db_path) as f:
db_creds = json.load(f)
drone = MPVaspDrone(
host=db_creds['host'], port=db_creds['port'],
database=db_creds['database'], user=db_creds['admin_user'],
password=db_creds['admin_password'],
collection=db_creds['collection'], parse_dos=parse_dos,
additional_fields=self.additional_fields,
update_duplicates=self.update_duplicates)
t_id, d = drone.assimilate(prev_dir, launches_coll=LaunchPad.auto_load().launches)
mpsnl = d['snl_final'] if 'snl_final' in d else d['snl']
snlgroup_id = d['snlgroup_id_final'] if 'snlgroup_id_final' in d else d['snlgroup_id']
update_spec.update({'mpsnl': mpsnl, 'snlgroup_id': snlgroup_id})
print 'ENTERED task id:', t_id
stored_data = {'task_id': t_id}
if d['state'] == 'successful':
update_spec['analysis'] = d['analysis']
update_spec['output'] = d['output']
return FWAction(stored_data=stored_data, update_spec=update_spec)
# not successful - first test to see if UnconvergedHandler is needed
if not fizzled_parent:
unconverged_tag = 'unconverged_handler--{}'.format(fw_spec['prev_task_type'])
output_dir = last_relax(os.path.join(prev_dir, 'vasprun.xml'))
ueh = UnconvergedErrorHandler(output_filename=output_dir)
if ueh.check() and unconverged_tag not in fw_spec['run_tags']:
print 'Unconverged run! Creating dynamic FW...'
spec = {'prev_vasp_dir': prev_dir,
'prev_task_type': fw_spec['task_type'],
'mpsnl': mpsnl, 'snlgroup_id': snlgroup_id,
'task_type': fw_spec['prev_task_type'],
'run_tags': list(fw_spec['run_tags']),
'parameters': fw_spec.get('parameters'),
'_dupefinder': DupeFinderVasp().to_dict(),
'_priority': fw_spec['_priority']}
snl = StructureNL.from_dict(spec['mpsnl'])
spec['run_tags'].append(unconverged_tag)
spec['_queueadapter'] = QA_VASP
fws = []
connections = {}
f = Composition.from_formula(
snl.structure.composition.reduced_formula).alphabetical_formula
fws.append(FireWork(
[VaspCopyTask({'files': ['INCAR', 'KPOINTS', 'POSCAR', 'POTCAR', 'CONTCAR'],
'use_CONTCAR': False}), SetupUnconvergedHandlerTask(),
get_custodian_task(spec)], spec, name=get_slug(f + '--' + spec['task_type']),
fw_id=-2))
spec = {'task_type': 'VASP db insertion', '_allow_fizzled_parents': True,
'_priority': fw_spec['_priority'], '_queueadapter': QA_DB,
'run_tags': list(fw_spec['run_tags'])}
spec['run_tags'].append(unconverged_tag)
fws.append(
FireWork([VaspToDBTask()], spec, name=get_slug(f + '--' + spec['task_type']),
fw_id=-1))
connections[-2] = -1
wf = Workflow(fws, connections)
return FWAction(detours=wf)
# not successful and not due to convergence problem - FIZZLE
raise ValueError("DB insertion successful, but don't know how to fix this FireWork! Can't continue with workflow...")
def snl_to_wf(snl, parameters=None):
fws = []
connections = {}
parameters = parameters if parameters else {}
snl_priority = parameters.get('priority', 1)
priority = snl_priority * 2 # once we start a job, keep going!
f = Composition.from_formula(
snl.structure.composition.reduced_formula).alphabetical_formula
# add the SNL to the SNL DB and figure out duplicate group
tasks = [AddSNLTask()]
spec = {
'task_type': 'Add to SNL database',
'snl': snl.to_dict,
'_queueadapter': QA_DB,
'_priority': snl_priority
}
if 'snlgroup_id' in parameters and isinstance(snl, MPStructureNL):
spec['force_mpsnl'] = snl.to_dict
spec['force_snlgroup_id'] = parameters['snlgroup_id']
del spec['snl']
fws.append(
FireWork(tasks,
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=0))
connections[0] = [1]
# run GGA structure optimization
spec = _snl_to_spec(snl, enforce_gga=True)
spec['_priority'] = priority
spec['_queueadapter'] = QA_VASP
tasks = [VaspWriterTask(), get_custodian_task(spec)]
fws.append(
FireWork(tasks,
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=1))
# insert into DB - GGA structure optimization
spec = {
'task_type': 'VASP db insertion',
'_priority': priority,
'_allow_fizzled_parents': True,
'_queueadapter': QA_DB
}
fws.append(
FireWork([VaspToDBTask()],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=2))
connections[1] = [2]
if not parameters.get('skip_bandstructure', False):
spec = {
'task_type': 'Controller: add Electronic Structure v2',
'_priority': priority,
'_queueadapter': QA_CONTROL
}
fws.append(
FireWork([AddEStructureTask()],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=3))
connections[2] = [3]
# determine if GGA+U FW is needed
incar = MPVaspInputSet().get_incar(snl.structure).to_dict
if 'LDAU' in incar and incar['LDAU']:
spec = _snl_to_spec(snl, enforce_gga=False)
del spec[
'vasp'] # we are stealing all VASP params and such from previous run
spec['_priority'] = priority
spec['_queueadapter'] = QA_VASP
fws.append(
FireWork(
[VaspCopyTask(),
SetupGGAUTask(),
get_custodian_task(spec)],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=10))
connections[2].append(10)
spec = {
'task_type': 'VASP db insertion',
'_queueadapter': QA_DB,
'_allow_fizzled_parents': True,
'_priority': priority
}
fws.append(
FireWork([VaspToDBTask()],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=11))
connections[10] = [11]
if not parameters.get('skip_bandstructure', False):
spec = {
'task_type': 'Controller: add Electronic Structure v2',
'_priority': priority,
'_queueadapter': QA_CONTROL
}
fws.append(
FireWork([AddEStructureTask()],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=12))
connections[11] = [12]
wf_meta = get_meta_from_structure(snl.structure)
wf_meta['run_version'] = 'May 2013 (1)'
if '_materialsproject' in snl.data and 'submission_id' in snl.data[
'_materialsproject']:
wf_meta['submission_id'] = snl.data['_materialsproject'][
'submission_id']
return Workflow(
fws,
connections,
name=Composition.from_formula(
snl.structure.composition.reduced_formula).alphabetical_formula,
metadata=wf_meta)
def snl_to_wf(snl, parameters=None):
fws = []
connections = defaultdict(list)
parameters = parameters if parameters else {}
snl_priority = parameters.get('priority', 1)
priority = snl_priority * 2 # once we start a job, keep going!
f = Composition(
snl.structure.composition.reduced_formula).alphabetical_formula
snl_spec = {}
if 'snlgroup_id' in parameters:
if 'mpsnl' in parameters:
snl_spec['mpsnl'] = parameters['mpsnl']
elif isinstance(snl, MPStructureNL):
snl_spec['mpsnl'] = snl.as_dict()
else:
raise ValueError("improper use of force SNL")
snl_spec['snlgroup_id'] = parameters['snlgroup_id']
else:
# add the SNL to the SNL DB and figure out duplicate group
tasks = [AddSNLTask()]
spec = {
'task_type': 'Add to SNL database',
'snl': snl.as_dict(),
'_queueadapter': QA_DB,
'_priority': snl_priority
}
fws.append(
Firework(tasks,
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=0))
connections[0] = [1]
trackers = [
Tracker('FW_job.out'),
Tracker('FW_job.error'),
Tracker('vasp.out'),
Tracker('OUTCAR'),
Tracker('OSZICAR'),
Tracker('OUTCAR.relax1'),
Tracker('OUTCAR.relax2')
]
trackers_db = [Tracker('FW_job.out'), Tracker('FW_job.error')]
# run GGA structure optimization
spec = _snl_to_spec(snl, enforce_gga=True, parameters=parameters)
spec.update(snl_spec)
spec['_priority'] = priority
spec['_queueadapter'] = QA_VASP
spec['_trackers'] = trackers
tasks = [VaspWriterTask(), get_custodian_task(spec)]
fws.append(
Firework(tasks,
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=1))
# insert into DB - GGA structure optimization
spec = {
'task_type': 'VASP db insertion',
'_priority': priority * 2,
'_allow_fizzled_parents': True,
'_queueadapter': QA_DB,
"_dupefinder": DupeFinderDB().to_dict(),
'_trackers': trackers_db
}
fws.append(
Firework([VaspToDBTask()],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=2))
connections[1] = [2]
# determine if GGA+U FW is needed
incar = MPVaspInputSet().get_incar(snl.structure).as_dict()
ggau_compound = ('LDAU' in incar and incar['LDAU'])
if not parameters.get('skip_bandstructure', False) and (
not ggau_compound
or parameters.get('force_gga_bandstructure', False)):
spec = {
'task_type': 'Controller: add Electronic Structure v2',
'_priority': priority,
'_queueadapter': QA_CONTROL
}
fws.append(
Firework([AddEStructureTask()],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=3))
connections[2] = [3]
if ggau_compound:
spec = _snl_to_spec(snl, enforce_gga=False, parameters=parameters)
del spec[
'vasp'] # we are stealing all VASP params and such from previous run
spec['_priority'] = priority
spec['_queueadapter'] = QA_VASP
spec['_trackers'] = trackers
fws.append(
Firework(
[VaspCopyTask(),
SetupGGAUTask(),
get_custodian_task(spec)],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=10))
connections[2].append(10)
spec = {
'task_type': 'VASP db insertion',
'_queueadapter': QA_DB,
'_allow_fizzled_parents': True,
'_priority': priority,
"_dupefinder": DupeFinderDB().to_dict(),
'_trackers': trackers_db
}
fws.append(
Firework([VaspToDBTask()],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=11))
connections[10] = [11]
if not parameters.get('skip_bandstructure', False):
spec = {
'task_type': 'Controller: add Electronic Structure v2',
'_priority': priority,
'_queueadapter': QA_CONTROL
}
fws.append(
Firework([AddEStructureTask()],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=12))
connections[11] = [12]
wf_meta = get_meta_from_structure(snl.structure)
wf_meta['run_version'] = 'May 2013 (1)'
if '_materialsproject' in snl.data and 'submission_id' in snl.data[
'_materialsproject']:
wf_meta['submission_id'] = snl.data['_materialsproject'][
'submission_id']
return Workflow(
fws,
connections,
name=Composition(
snl.structure.composition.reduced_formula).alphabetical_formula,
metadata=wf_meta)
def equilibrium_constant_fws(mission, solvent, solvent_method, use_vdw_surface,
qm_method, reaction_id,
dupefinder=None, priority=1, parent_fwid=None,
additional_user_tags=None,
depend_on_parent=False):
energy_method, sol_qm_method, geom_method = qm_method.split("//")
if '||' in energy_method:
sp_qm_method, bsse_qm_method = energy_method.split("||")
qm_method = "//".join([sp_qm_method, sol_qm_method, geom_method])
else:
bsse_qm_method = energy_method
coll = get_reactions_collection()
reaction_doc = coll.find_one(filter={"reaction_id": reaction_id})
reactant_snls = [StructureNL.from_dict(s) for s in
reaction_doc["reactant_snls"]]
product_snls = [StructureNL.from_dict(s) for s in
reaction_doc["product_snls"]]
reactant_nicknames = reaction_doc['reactant_nicknames']
product_nicknames = reaction_doc['product_nicknames']
reactant_charges = reaction_doc['reactant_charges']
product_charges = reaction_doc['product_charges']
reactant_spin_multiplicities = reaction_doc['reactant_spin_multiplicities']
product_spin_multiplicities = reaction_doc['product_spin_multiplicities']
reactant_fragments = reaction_doc['reactant_fragments']
product_fragments = reaction_doc['product_fragments']
fwid_base = 1
if parent_fwid:
if not (isinstance(parent_fwid, int) or isinstance(parent_fwid, list)):
raise ValueError("Parent FireWork ID must be integer or list")
parent_fwid = parent_fwid if isinstance(parent_fwid, list) \
else [parent_fwid]
fwid_base = max(parent_fwid) + 1
current_fwid = fwid_base
fws = []
links_dict = dict()
for snl, nick_name, charge, spin, fragments in \
zip(reactant_snls + product_snls,
reactant_nicknames + product_nicknames,
reactant_charges + product_charges,
reactant_spin_multiplicities + product_spin_multiplicities,
reactant_fragments + product_fragments):
mol = snl.structure
mol.set_charge_and_spin(charge, spin)
snl_tasks = [AddEGSNLTask()]
snl_spec = {'task_type': 'Add to SNL database',
'snl': snl.as_dict(),
'_priority': priority}
priority *= 2 # once we start a job, keep going!
snl_fw = Firework(snl_tasks, snl_spec,
name=get_slug(nick_name + ' -- Add to SNL database'),
fw_id=current_fwid)
fws.append(snl_fw)
sp_fws, sp_links_dict = single_point_energy_fws(
mol, name=nick_name, mission=mission, solvent=solvent,
solvent_method=solvent_method,
use_vdW_surface=use_vdw_surface, qm_method=qm_method,
pop_method=None, dupefinder=dupefinder,
priority=priority, parent_fwid=snl_fw.fw_id,
additional_user_tags=additional_user_tags,
depend_on_parent_fw=True, large=True)
fws.extend(sp_fws)
sp_children = set()
sp_parents = set()
for k, v2 in sp_links_dict.items():
v1 = links_dict.get(k, [])
links_dict[k] = list((set(v1) if isinstance(v1, list) else {v1}) |
(set(v2) if isinstance(v2, list) else {v2}))
if isinstance(k, list):
sp_parents |= set(k)
else:
sp_parents.add(k)
if isinstance(v2, list):
sp_children |= set(v2)
else:
sp_children.add(v2)
sp_last_fwids = list(sp_children - sp_parents)
bsse_fws, bsse_links_dict = counterpoise_correction_generation_fw(
molname=nick_name, charge=charge, spin_multiplicity=spin,
qm_method=bsse_qm_method, fragments=fragments,
mission=mission, priority=priority, parent_fwid=sp_last_fwids,
additional_user_tags=additional_user_tags, large=True)
fws.extend(bsse_fws)
for k, v2 in bsse_links_dict.items():
v1 = links_dict.get(k, [])
links_dict[k] = list((set(v1) if isinstance(v1, list) else {v1}) |
(set(v2) if isinstance(v2, list) else {v2}))
current_fwid = max([fw.fw_id for fw in bsse_fws]) + 1
if depend_on_parent:
all_fwids = [fw.fw_id for fw in fws]
for p_fwid in parent_fwid:
links_dict[p_fwid] = all_fwids
return fws, links_dict
def snl_to_wf(snl, parameters=None):
fws = []
connections = {}
parameters = parameters if parameters else {}
snl_priority = parameters.get('priority', 1)
priority = snl_priority * 2 # once we start a job, keep going!
f = Composition.from_formula(snl.structure.composition.reduced_formula).alphabetical_formula
# add the SNL to the SNL DB and figure out duplicate group
tasks = [AddSNLTask()]
spec = {'task_type': 'Add to SNL database', 'snl': snl.to_dict, '_queueadapter': QA_DB, '_priority': snl_priority}
if 'snlgroup_id' in parameters and isinstance(snl, MPStructureNL):
spec['force_mpsnl'] = snl.to_dict
spec['force_snlgroup_id'] = parameters['snlgroup_id']
del spec['snl']
fws.append(FireWork(tasks, spec, name=get_slug(f + '--' + spec['task_type']), fw_id=0))
connections[0] = [1]
# run GGA structure optimization
spec = _snl_to_spec(snl, enforce_gga=True)
spec['_priority'] = priority
spec['_queueadapter'] = QA_VASP
tasks = [VaspWriterTask(), get_custodian_task(spec)]
fws.append(FireWork(tasks, spec, name=get_slug(f + '--' + spec['task_type']), fw_id=1))
# insert into DB - GGA structure optimization
spec = {'task_type': 'VASP db insertion', '_priority': priority,
'_allow_fizzled_parents': True, '_queueadapter': QA_DB}
fws.append(
FireWork([VaspToDBTask()], spec, name=get_slug(f + '--' + spec['task_type']), fw_id=2))
connections[1] = [2]
if not parameters.get('skip_bandstructure', False):
spec = {'task_type': 'Controller: add Electronic Structure v2', '_priority': priority,
'_queueadapter': QA_CONTROL}
fws.append(
FireWork([AddEStructureTask()], spec, name=get_slug(f + '--' + spec['task_type']),
fw_id=3))
connections[2] = [3]
# determine if GGA+U FW is needed
incar = MPVaspInputSet().get_incar(snl.structure).to_dict
if 'LDAU' in incar and incar['LDAU']:
spec = _snl_to_spec(snl, enforce_gga=False)
del spec['vasp'] # we are stealing all VASP params and such from previous run
spec['_priority'] = priority
spec['_queueadapter'] = QA_VASP
fws.append(FireWork(
[VaspCopyTask(), SetupGGAUTask(),
get_custodian_task(spec)], spec, name=get_slug(f + '--' + spec['task_type']),
fw_id=10))
connections[2].append(10)
spec = {'task_type': 'VASP db insertion', '_queueadapter': QA_DB,
'_allow_fizzled_parents': True, '_priority': priority}
fws.append(
FireWork([VaspToDBTask()], spec, name=get_slug(f + '--' + spec['task_type']), fw_id=11))
connections[10] = [11]
if not parameters.get('skip_bandstructure', False):
spec = {'task_type': 'Controller: add Electronic Structure v2', '_priority': priority,
'_queueadapter': QA_CONTROL}
fws.append(
FireWork([AddEStructureTask()], spec, name=get_slug(f + '--' + spec['task_type']),
fw_id=12))
connections[11] = [12]
wf_meta = get_meta_from_structure(snl.structure)
wf_meta['run_version'] = 'May 2013 (1)'
if '_materialsproject' in snl.data and 'submission_id' in snl.data['_materialsproject']:
wf_meta['submission_id'] = snl.data['_materialsproject']['submission_id']
return Workflow(fws, connections, name=Composition.from_formula(
snl.structure.composition.reduced_formula).alphabetical_formula, metadata=wf_meta)
def launch_rocket_to_queue(launchpad,
fworker,
qadapter,
launcher_dir='.',
reserve=False,
strm_lvl='INFO'):
"""
Submit a single job to the queue.
:param launchpad: (LaunchPad)
:param fworker: (FWorker)
:param qadapter: (QueueAdapterBase)
:param launcher_dir: (str) The directory where to submit the job
:param reserve: (bool) Whether to queue in reservation mode
:param strm_lvl: (str) level at which to stream log messages
"""
fworker = fworker if fworker else FWorker()
launcher_dir = os.path.abspath(launcher_dir)
l_logger = get_fw_logger('queue.launcher',
l_dir=launchpad.logdir,
stream_level=strm_lvl)
# get the queue adapter
l_logger.debug('getting queue adapter')
qadapter = load_object(qadapter.to_dict(
)) # make a defensive copy, mainly for reservation mode
# make sure launch_dir exists:
if not os.path.exists(launcher_dir):
raise ValueError(
'Desired launch directory {} does not exist!'.format(launcher_dir))
if launchpad.run_exists(fworker):
try:
# move to the launch directory
l_logger.info('moving to launch_dir {}'.format(launcher_dir))
os.chdir(launcher_dir)
oldlaunch_dir = None
if '--offline' in qadapter['rocket_launch'] and not reserve:
raise ValueError(
"Must use reservation mode (-r option) of qlaunch when using offline mode (--offline option) of rlaunch!!"
)
elif reserve:
l_logger.debug('finding a FW to reserve...')
fw, launch_id = launchpad._reserve_fw(fworker, launcher_dir)
if not fw:
l_logger.info(
'No jobs exist in the LaunchPad for submission to queue!'
)
return False
l_logger.info('reserved FW with fw_id: {}'.format(fw.fw_id))
# set job name to the FW name
job_name = get_slug(fw.name)
job_name = job_name[0:20] if len(job_name) > 20 else job_name
qadapter.update({'job_name':
job_name}) # set the job name to FW name
if '_queueadapter' in fw.spec:
l_logger.debug(
'updating queue params using FireWork spec..')
qadapter.update(fw.spec['_queueadapter'])
# update the exe to include the FW_id
if 'singleshot' not in qadapter.get('rocket_launch', ''):
raise ValueError(
'Reservation mode of queue launcher only works for singleshot Rocket Launcher!'
)
qadapter['rocket_launch'] += ' --fw_id {}'.format(fw.fw_id)
if '--offline' in qadapter['rocket_launch']:
# handle _launch_dir parameter early...
if '_launch_dir' in fw.spec:
os.chdir(fw.spec['_launch_dir'])
oldlaunch_dir = launcher_dir
launcher_dir = os.path.abspath(os.getcwd())
launchpad._change_launch_dir(launch_id, launcher_dir)
# write FW.json
fw.to_file("FW.json")
# write Launchid
with open('FW_offline.json', 'w') as f:
f.write('{"launch_id":%s}' % launch_id)
launchpad.add_offline_run(launch_id, fw.fw_id, fw.name)
# write and submit the queue script using the queue adapter
l_logger.debug('writing queue script')
with open(SUBMIT_SCRIPT_NAME, 'w') as f:
queue_script = qadapter.get_script_str(launcher_dir)
f.write(queue_script)
l_logger.info('submitting queue script')
reservation_id = qadapter.submit_to_queue(SUBMIT_SCRIPT_NAME)
if not reservation_id:
raise RuntimeError(
'queue script could not be submitted, check queue adapter and queue server status!'
)
elif reserve:
launchpad.set_reservation_id(launch_id, reservation_id)
return reservation_id
except:
log_exception(l_logger, 'Error writing/submitting queue script!')
return False
finally:
if oldlaunch_dir:
os.chdir(
oldlaunch_dir
) # this only matters in --offline mode with _launch_dir!
else:
l_logger.info(
'No jobs exist in the LaunchPad for submission to queue!')
return False
def launch_rocket_to_queue(launchpad, fworker, qadapter, launcher_dir='.', reserve=False, strm_lvl='INFO'):
"""
Submit a single job to the queue.
:param launchpad: (LaunchPad)
:param fworker: (FWorker)
:param qadapter: (QueueAdapterBase)
:param launcher_dir: (str) The directory where to submit the job
:param reserve: (bool) Whether to queue in reservation mode
:param strm_lvl: (str) level at which to stream log messages
"""
fworker = fworker if fworker else FWorker()
launcher_dir = os.path.abspath(launcher_dir)
l_logger = get_fw_logger('queue.launcher', l_dir=launchpad.logdir, stream_level=strm_lvl)
l_logger.debug('getting queue adapter')
qadapter = load_object(qadapter.to_dict()) # make a defensive copy, mainly for reservation mode
fw, launch_id = None, None # only needed in reservation mode
oldlaunch_dir = None # only needed in --offline mode with _launch_dir option
if not os.path.exists(launcher_dir):
raise ValueError('Desired launch directory {} does not exist!'.format(launcher_dir))
if '--offline' in qadapter['rocket_launch'] and not reserve:
raise ValueError("Must use reservation mode (-r option) of qlaunch when using offline option of rlaunch!!")
if reserve and 'singleshot' not in qadapter.get('rocket_launch', ''):
raise ValueError('Reservation mode of queue launcher only works for singleshot Rocket Launcher!')
if launchpad.run_exists(fworker):
try:
# move to the launch directory
l_logger.info('moving to launch_dir {}'.format(launcher_dir))
with cd(launcher_dir):
if reserve:
l_logger.debug('finding a FW to reserve...')
fw, launch_id = launchpad.reserve_fw(fworker, launcher_dir)
if not fw:
l_logger.info('No jobs exist in the LaunchPad for submission to queue!')
return False
l_logger.info('reserved FW with fw_id: {}'.format(fw.fw_id))
# update qadapter job_name based on FW name
job_name = get_slug(fw.name)[0:20]
qadapter.update({'job_name': job_name})
if '_queueadapter' in fw.spec:
l_logger.debug('updating queue params using Firework spec..')
qadapter.update(fw.spec['_queueadapter'])
# reservation mode includes --fw_id in rocket launch
qadapter['rocket_launch'] += ' --fw_id {}'.format(fw.fw_id)
if '--offline' in qadapter['rocket_launch']:
# handle _launch_dir parameter now b/c we can't call
# launchpad.change_launch_dir() later on in offline mode
if '_launch_dir' in fw.spec:
os.chdir(fw.spec['_launch_dir'])
oldlaunch_dir = launcher_dir
launcher_dir = os.path.abspath(os.getcwd())
launchpad.change_launch_dir(launch_id, launcher_dir)
setup_offline_job(launchpad, fw, launch_id)
l_logger.debug('writing queue script')
with open(SUBMIT_SCRIPT_NAME, 'w') as f:
queue_script = qadapter.get_script_str(launcher_dir)
f.write(queue_script)
l_logger.info('submitting queue script')
reservation_id = qadapter.submit_to_queue(SUBMIT_SCRIPT_NAME)
if not reservation_id:
if reserve:
l_logger.info('Un-reserving FW with fw_id, launch_id: {}, {}'.format(fw.fw_id, launch_id))
launchpad.cancel_reservation(launch_id)
raise RuntimeError('queue script could not be submitted, check queue script/queue adapter/queue server status!')
elif reserve:
launchpad.set_reservation_id(launch_id, reservation_id)
return reservation_id
except:
log_exception(l_logger, 'Error writing/submitting queue script!')
return False
finally:
if oldlaunch_dir:
os.chdir(oldlaunch_dir) # this only matters in --offline mode with _launch_dir!
else:
l_logger.info('No jobs exist in the LaunchPad for submission to queue!')
return False
def launch_rocket_to_queue(launchpad,
fworker,
qadapter,
launcher_dir='.',
reserve=False,
strm_lvl='INFO',
create_launcher_dir=False):
"""
Submit a single job to the queue.
:param launchpad: (LaunchPad)
:param fworker: (FWorker)
:param qadapter: (QueueAdapterBase)
:param launcher_dir: (str) The directory where to submit the job
:param reserve: (bool) Whether to queue in reservation mode
:param strm_lvl: (str) level at which to stream log messages
:param create_launcher_dir: (bool) Whether to create a subfolder launcher+timestamp, if needed
"""
fworker = fworker if fworker else FWorker()
launcher_dir = os.path.abspath(launcher_dir)
l_logger = get_fw_logger('queue.launcher',
l_dir=launchpad.logdir,
stream_level=strm_lvl)
l_logger.debug('getting queue adapter')
qadapter = load_object(qadapter.to_dict(
)) # make a defensive copy, mainly for reservation mode
fw, launch_id = None, None # only needed in reservation mode
if not os.path.exists(launcher_dir):
raise ValueError(
'Desired launch directory {} does not exist!'.format(launcher_dir))
if '--offline' in qadapter['rocket_launch'] and not reserve:
raise ValueError(
"Must use reservation mode (-r option) of qlaunch when using offline option of rlaunch!!"
)
if reserve and 'singleshot' not in qadapter.get('rocket_launch', ''):
raise ValueError(
'Reservation mode of queue launcher only works for singleshot Rocket Launcher!'
)
if launchpad.run_exists(fworker):
try:
if reserve:
l_logger.debug('finding a FW to reserve...')
fw, launch_id = launchpad.reserve_fw(fworker, launcher_dir)
if not fw:
l_logger.info(
'No jobs exist in the LaunchPad for submission to queue!'
)
return False
l_logger.info('reserved FW with fw_id: {}'.format(fw.fw_id))
# update qadapter job_name based on FW name
job_name = get_slug(fw.name)[0:QUEUE_JOBNAME_MAXLEN]
qadapter.update({'job_name': job_name})
if '_queueadapter' in fw.spec:
l_logger.debug(
'updating queue params using Firework spec..')
qadapter.update(fw.spec['_queueadapter'])
# reservation mode includes --fw_id in rocket launch
qadapter['rocket_launch'] += ' --fw_id {}'.format(fw.fw_id)
# update launcher_dir if _launch_dir is selected in reserved fw
if '_launch_dir' in fw.spec:
fw_launch_dir = os.path.expandvars(fw.spec['_launch_dir'])
if not os.path.isabs(fw_launch_dir):
fw_launch_dir = os.path.join(launcher_dir,
fw_launch_dir)
launcher_dir = fw_launch_dir
try:
os.makedirs(launcher_dir)
except OSError as exception:
if exception.errno != errno.EEXIST:
raise
launchpad.change_launch_dir(launch_id, launcher_dir)
elif create_launcher_dir:
# create launcher_dir
launcher_dir = create_datestamp_dir(launcher_dir,
l_logger,
prefix='launcher_')
launchpad.change_launch_dir(launch_id, launcher_dir)
elif create_launcher_dir:
# create launcher_dir
launcher_dir = create_datestamp_dir(launcher_dir,
l_logger,
prefix='launcher_')
# move to the launch directory
l_logger.info('moving to launch_dir {}'.format(launcher_dir))
with cd(launcher_dir):
if '--offline' in qadapter['rocket_launch']:
setup_offline_job(launchpad, fw, launch_id)
l_logger.debug('writing queue script')
with open(SUBMIT_SCRIPT_NAME, 'w') as f:
queue_script = qadapter.get_script_str(launcher_dir)
f.write(queue_script)
l_logger.info('submitting queue script')
reservation_id = qadapter.submit_to_queue(SUBMIT_SCRIPT_NAME)
if not reservation_id:
if reserve:
l_logger.info(
'Un-reserving FW with fw_id, launch_id: {}, {}'.
format(fw.fw_id, launch_id))
launchpad.cancel_reservation(launch_id)
raise RuntimeError(
'queue script could not be submitted, check queue script/queue adapter/queue server status!'
)
elif reserve:
launchpad.set_reservation_id(launch_id, reservation_id)
return reservation_id
except:
log_exception(l_logger, 'Error writing/submitting queue script!')
return False
else:
l_logger.info(
'No jobs exist in the LaunchPad for submission to queue!')
return False
def run_task(self, fw_spec):
print 'sleeping 10s for Mongo'
time.sleep(10)
print 'done sleeping'
print 'the gap is {}, the cutoff is {}'.format(fw_spec['analysis']['bandgap'], self.gap_cutoff)
if fw_spec['analysis']['bandgap'] >= self.gap_cutoff:
static_dens = 90
uniform_dens = 1000
line_dens = 20
else:
static_dens = 450
uniform_dens = 1500
line_dens = 30
if fw_spec['analysis']['bandgap'] <= self.metal_cutoff:
user_incar_settings = {"ISMEAR": 1, "SIGMA": 0.2}
else:
user_incar_settings = {}
print 'Adding more runs...'
type_name = 'GGA+U' if 'GGA+U' in fw_spec['prev_task_type'] else 'GGA'
snl = StructureNL.from_dict(fw_spec['mpsnl'])
f = Composition(snl.structure.composition.reduced_formula).alphabetical_formula
fws = []
connections = {}
priority = fw_spec['_priority']
trackers = [Tracker('FW_job.out'), Tracker('FW_job.error'), Tracker('vasp.out'), Tracker('OUTCAR'), Tracker('OSZICAR')]
trackers_db = [Tracker('FW_job.out'), Tracker('FW_job.error')]
# run GGA static
spec = fw_spec # pass all the items from the current spec to the new
spec.update({'task_type': '{} static v2'.format(type_name), '_queueadapter': QA_VASP_SMALL,
'_dupefinder': DupeFinderVasp().to_dict(), '_priority': priority, '_trackers': trackers})
fws.append(
Firework(
[VaspCopyTask({'use_CONTCAR': True, 'skip_CHGCAR': True}), SetupStaticRunTask({"kpoints_density": static_dens, 'user_incar_settings': user_incar_settings}),
get_custodian_task(spec)], spec, name=get_slug(f+'--'+spec['task_type']), fw_id=-10))
# insert into DB - GGA static
spec = {'task_type': 'VASP db insertion', '_queueadapter': QA_DB,
'_allow_fizzled_parents': True, '_priority': priority*2, "_dupefinder": DupeFinderDB().to_dict(), '_trackers': trackers_db}
fws.append(
Firework([VaspToDBTask()], spec, name=get_slug(f+'--'+spec['task_type']), fw_id=-9))
connections[-10] = -9
# run GGA Uniform
spec = {'task_type': '{} Uniform v2'.format(type_name), '_queueadapter': QA_VASP,
'_dupefinder': DupeFinderVasp().to_dict(), '_priority': priority, '_trackers': trackers}
fws.append(Firework(
[VaspCopyTask({'use_CONTCAR': False}), SetupNonSCFTask({'mode': 'uniform', "kpoints_density": uniform_dens}),
get_custodian_task(spec)], spec, name=get_slug(f+'--'+spec['task_type']), fw_id=-8))
connections[-9] = -8
# insert into DB - GGA Uniform
spec = {'task_type': 'VASP db insertion', '_queueadapter': QA_DB,
'_allow_fizzled_parents': True, '_priority': priority*2, "_dupefinder": DupeFinderDB().to_dict(), '_trackers': trackers_db}
fws.append(
Firework([VaspToDBTask({'parse_uniform': True})], spec, name=get_slug(f+'--'+spec['task_type']),
fw_id=-7))
connections[-8] = -7
# run GGA Band structure
spec = {'task_type': '{} band structure v2'.format(type_name), '_queueadapter': QA_VASP,
'_dupefinder': DupeFinderVasp().to_dict(), '_priority': priority, '_trackers': trackers}
fws.append(Firework([VaspCopyTask({'use_CONTCAR': False}), SetupNonSCFTask({'mode': 'line', "kpoints_line_density": line_dens}),
get_custodian_task(spec)], spec, name=get_slug(f+'--'+spec['task_type']),
fw_id=-6))
connections[-7] = [-6]
# insert into DB - GGA Band structure
spec = {'task_type': 'VASP db insertion', '_queueadapter': QA_DB,
'_allow_fizzled_parents': True, '_priority': priority*2, "_dupefinder": DupeFinderDB().to_dict(), '_trackers': trackers_db}
fws.append(Firework([VaspToDBTask({})], spec, name=get_slug(f+'--'+spec['task_type']), fw_id=-5))
connections[-6] = -5
if fw_spec.get('parameters') and fw_spec['parameters'].get('boltztrap'):
# run Boltztrap
spec = {'task_type': '{} Boltztrap'.format(type_name), '_queueadapter': QA_DB,
'_dupefinder': DupeFinderDB().to_dict(), '_priority': priority}
fws.append(Firework(
[BoltztrapRunTask()], spec, name=get_slug(f+'--'+spec['task_type']), fw_id=-4))
connections[-7].append(-4)
wf = Workflow(fws, connections)
print 'Done adding more runs...'
return FWAction(additions=wf)
def run_task(self, fw_spec):
if '_fizzled_parents' in fw_spec and not 'prev_vasp_dir' in fw_spec:
prev_dir = get_loc(
fw_spec['_fizzled_parents'][0]['launches'][0]['launch_dir'])
update_spec = {} # add this later when creating new FW
fizzled_parent = True
parse_dos = False
else:
prev_dir = get_loc(fw_spec['prev_vasp_dir'])
update_spec = {
'prev_vasp_dir': prev_dir,
'prev_task_type': fw_spec['prev_task_type'],
'run_tags': fw_spec['run_tags'],
'parameters': fw_spec.get('parameters')
}
fizzled_parent = False
parse_dos = 'Uniform' in fw_spec['prev_task_type']
if 'run_tags' in fw_spec:
self.additional_fields['run_tags'] = fw_spec['run_tags']
else:
self.additional_fields['run_tags'] = fw_spec['_fizzled_parents'][
0]['spec']['run_tags']
if MOVE_TO_GARDEN_DEV:
prev_dir = move_to_garden(prev_dir, prod=False)
elif MOVE_TO_GARDEN_PROD:
prev_dir = move_to_garden(prev_dir, prod=True)
# get the directory containing the db file
db_dir = os.environ['DB_LOC']
db_path = os.path.join(db_dir, 'tasks_db.json')
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger('MPVaspDrone')
logger.setLevel(logging.INFO)
sh = logging.StreamHandler(stream=sys.stdout)
sh.setLevel(getattr(logging, 'INFO'))
logger.addHandler(sh)
with open(db_path) as f:
db_creds = json.load(f)
drone = MPVaspDrone(host=db_creds['host'],
port=db_creds['port'],
database=db_creds['database'],
user=db_creds['admin_user'],
password=db_creds['admin_password'],
collection=db_creds['collection'],
parse_dos=parse_dos,
additional_fields=self.additional_fields,
update_duplicates=self.update_duplicates)
t_id, d = drone.assimilate(
prev_dir, launches_coll=LaunchPad.auto_load().launches)
mpsnl = d['snl_final'] if 'snl_final' in d else d['snl']
snlgroup_id = d['snlgroup_id_final'] if 'snlgroup_id_final' in d else d[
'snlgroup_id']
update_spec.update({'mpsnl': mpsnl, 'snlgroup_id': snlgroup_id})
print 'ENTERED task id:', t_id
stored_data = {'task_id': t_id}
if d['state'] == 'successful':
update_spec['analysis'] = d['analysis']
update_spec['output'] = d['output']
update_spec['vasp'] = {
'incar': d['calculations'][-1]['input']['incar'],
'kpoints': d['calculations'][-1]['input']['kpoints']
}
update_spec["task_id"] = t_id
return FWAction(stored_data=stored_data, update_spec=update_spec)
# not successful - first test to see if UnconvergedHandler is needed
if not fizzled_parent:
unconverged_tag = 'unconverged_handler--{}'.format(
fw_spec['prev_task_type'])
output_dir = last_relax(os.path.join(prev_dir, 'vasprun.xml'))
ueh = UnconvergedErrorHandler(output_filename=output_dir)
# TODO: make this a little more flexible
if ueh.check() and unconverged_tag not in fw_spec['run_tags']:
print 'Unconverged run! Creating dynamic FW...'
spec = {
'prev_vasp_dir': prev_dir,
'prev_task_type': fw_spec['task_type'],
'mpsnl': mpsnl,
'snlgroup_id': snlgroup_id,
'task_type': fw_spec['prev_task_type'],
'run_tags': list(fw_spec['run_tags']),
'parameters': fw_spec.get('parameters'),
'_dupefinder': DupeFinderVasp().to_dict(),
'_priority': fw_spec['_priority']
}
# Pass elastic tensor spec
if 'deformation_matrix' in fw_spec.keys():
spec['deformation_matrix'] = fw_spec['deformation_matrix']
spec['original_task_id'] = fw_spec['original_task_id']
snl = StructureNL.from_dict(spec['mpsnl'])
spec['run_tags'].append(unconverged_tag)
spec['_queueadapter'] = QA_VASP
fws = []
connections = {}
f = Composition(snl.structure.composition.reduced_formula
).alphabetical_formula
fws.append(
Firework([
VaspCopyTask({
'files': [
'INCAR', 'KPOINTS', 'POSCAR', 'POTCAR',
'CONTCAR'
],
'use_CONTCAR':
False
}),
SetupUnconvergedHandlerTask(),
get_custodian_task(spec)
],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=-2))
spec = {
'task_type': 'VASP db insertion',
'_allow_fizzled_parents': True,
'_priority': fw_spec['_priority'],
'_queueadapter': QA_DB,
'run_tags': list(fw_spec['run_tags'])
}
if 'deformation_matrix' in fw_spec.keys():
spec['deformation_matrix'] = fw_spec['deformation_matrix']
spec['original_task_id'] = fw_spec['original_task_id']
spec['run_tags'].append(unconverged_tag)
fws.append(
Firework([VaspToDBTask()],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=-1))
connections[-2] = -1
wf = Workflow(fws, connections)
return FWAction(detours=wf)
# not successful and not due to convergence problem - FIZZLE
raise ValueError("DB insertion successful, but don't know how to \
fix this Firework! Can't continue with workflow...")
def structure_to_wf(structure):
"""
This method starts with a Structure object and creates a Workflow object
The workflow has two steps - a structure relaxation and a static run
:param structure:
:return:
"""
fws = [] # list of FireWorks to run
connections = defaultdict(list) # dependencies between FireWorks
# generate VASP input objects for 1st VASP run - this is put in the FW spec
mpvis = MPGGAVaspInputSet(user_incar_settings={'NPAR': 2})
incar = mpvis.get_incar(structure)
poscar = mpvis.get_poscar(structure)
kpoints = mpvis.get_kpoints(structure)
potcar = mpvis.get_potcar(structure)
# serialize the VASP input objects to the FW spec
spec = {}
spec['vasp'] = {}
spec['vasp']['incar'] = incar.as_dict()
spec['vasp']['poscar'] = poscar.as_dict()
spec['vasp']['kpoints'] = kpoints.as_dict()
spec['vasp']['potcar'] = potcar.as_dict()
spec['vaspinputset_name'] = mpvis.__class__.__name__
spec['task_type'] = 'GGA optimize structure (2x) example'
# set up the custodian that we want to run
jobs = VaspJob.double_relaxation_run('')
for j in jobs: # turn off auto npar, it doesn't work for >1 node
j.auto_npar = False
handlers = [
VaspErrorHandler(),
FrozenJobErrorHandler(),
MeshSymmetryErrorHandler(),
NonConvergingErrorHandler()
]
c_params = {
'jobs': [j.as_dict() for j in jobs],
'handlers': [h.as_dict() for h in handlers],
'max_errors': 5
}
custodiantask = VaspCustodianTaskEx(c_params)
# 1st Firework - run GGA optimize structure
# VaspWriterTask - write input files (INCAR, POSCAR, KPOINTS, POSCAR) based on spec
# CustodianTaskEx - run VASP within a custodian
tasks = [VaspWriterTask(), custodiantask]
fws.append(
Firework(tasks,
spec,
name=get_name(structure, spec['task_type']),
fw_id=1))
# 2nd Firework - insert previous run into DB
spec = {'task_type': 'VASP db insertion example'}
fws.append(
Firework([VaspToDBTaskEx()],
spec,
name=get_name(structure, spec['task_type']),
fw_id=2))
connections[1] = [2]
# 3rd Firework - static run.
# VaspCopyTask - copy output from previous run to this directory
# SetupStaticRunTask - override old parameters for static run
# CustodianTaskEx - run VASP within a custodian
spec = {'task_type': 'GGA static example'}
copytask = VaspCopyTask({'use_CONTCAR': True, 'skip_CHGCAR': True})
setuptask = SetupStaticRunTask()
custodiantask = VaspCustodianTaskEx({
'jobs': [VaspJob('', auto_npar=False).as_dict()],
'handlers': [h.as_dict() for h in handlers],
'max_errors':
5
})
fws.append(
Firework([copytask, setuptask, custodiantask],
spec,
name=get_name(structure, spec['task_type']),
fw_id=3))
connections[2] = [3]
# 4th Firework - insert previous run into DB
spec = {'task_type': 'VASP db insertion example'}
fws.append(
Firework([VaspToDBTaskEx()],
spec,
name=get_name(structure, spec['task_type']),
fw_id=4))
connections[3] = [4]
return Workflow(fws, connections, name=get_slug(structure.formula))
def _write_formula_file(self, fw_spec):
filename = get_slug(
'JOB--' + fw_spec['mpsnl'].structure.composition.reduced_formula + '--'
+ fw_spec['task_type'])
with open(filename, 'w+') as f:
f.write('')
def run_task(self, fw_spec):
print "sleeping 10s for Mongo"
time.sleep(10)
print "done sleeping"
print "the gap is {}, the cutoff is {}".format(fw_spec["analysis"]["bandgap"], self.gap_cutoff)
if fw_spec["analysis"]["bandgap"] >= self.gap_cutoff:
print "Adding more runs..."
type_name = "GGA+U" if "GGA+U" in fw_spec["prev_task_type"] else "GGA"
snl = StructureNL.from_dict(fw_spec["mpsnl"])
f = Composition(snl.structure.composition.reduced_formula).alphabetical_formula
fws = []
connections = {}
priority = fw_spec["_priority"]
# run GGA static
spec = fw_spec # pass all the items from the current spec to the new
# one
spec.update(
{
"task_type": "{} static".format(type_name),
"_queueadapter": QA_VASP,
"_dupefinder": DupeFinderVasp().to_dict(),
"_priority": priority,
}
)
fws.append(
Firework(
[VaspCopyTask({"use_CONTCAR": True}), SetupStaticRunTask(), get_custodian_task(spec)],
spec,
name=get_slug(f + "--" + spec["task_type"]),
fw_id=-10,
)
)
# insert into DB - GGA static
spec = {
"task_type": "VASP db insertion",
"_queueadapter": QA_DB,
"_allow_fizzled_parents": True,
"_priority": priority,
"_dupefinder": DupeFinderDB().to_dict(),
}
fws.append(Firework([VaspToDBTask()], spec, name=get_slug(f + "--" + spec["task_type"]), fw_id=-9))
connections[-10] = -9
# run GGA Uniform
spec = {
"task_type": "{} Uniform".format(type_name),
"_queueadapter": QA_VASP,
"_dupefinder": DupeFinderVasp().to_dict(),
"_priority": priority,
}
fws.append(
Firework(
[
VaspCopyTask({"use_CONTCAR": False}),
SetupNonSCFTask({"mode": "uniform"}),
get_custodian_task(spec),
],
spec,
name=get_slug(f + "--" + spec["task_type"]),
fw_id=-8,
)
)
connections[-9] = -8
# insert into DB - GGA Uniform
spec = {
"task_type": "VASP db insertion",
"_queueadapter": QA_DB,
"_allow_fizzled_parents": True,
"_priority": priority,
"_dupefinder": DupeFinderDB().to_dict(),
}
fws.append(
Firework(
[VaspToDBTask({"parse_uniform": True})], spec, name=get_slug(f + "--" + spec["task_type"]), fw_id=-7
)
)
connections[-8] = -7
# run GGA Band structure
spec = {
"task_type": "{} band structure".format(type_name),
"_queueadapter": QA_VASP,
"_dupefinder": DupeFinderVasp().to_dict(),
"_priority": priority,
}
fws.append(
Firework(
[VaspCopyTask({"use_CONTCAR": False}), SetupNonSCFTask({"mode": "line"}), get_custodian_task(spec)],
spec,
name=get_slug(f + "--" + spec["task_type"]),
fw_id=-6,
)
)
connections[-7] = -6
# insert into DB - GGA Band structure
spec = {
"task_type": "VASP db insertion",
"_queueadapter": QA_DB,
"_allow_fizzled_parents": True,
"_priority": priority,
"_dupefinder": DupeFinderDB().to_dict(),
}
fws.append(Firework([VaspToDBTask({})], spec, name=get_slug(f + "--" + spec["task_type"]), fw_id=-5))
connections[-6] = -5
wf = Workflow(fws, connections)
print "Done adding more runs..."
return FWAction(additions=wf)
return FWAction()
def snl_to_eg_wf(snl, parameters=None):
fws_all = []
parameters = parameters if parameters else {}
snl_priority = parameters.get('priority', 1)
mission = parameters.get('mission', 'Electron Genome Production')
priority = snl_priority * 2 # once we start a job, keep going!
f = Composition(snl.structure.composition.reduced_formula). \
alphabetical_formula
molname = parameters.get("nick_name", f)
if "reaction_id" not in parameters:
# add the SNL to the SNL DB and figure out duplicate group
tasks = [AddEGSNLTask()]
spec = {
'task_type': 'Add to SNL database',
'snl': snl.as_dict(),
'_priority': snl_priority
}
if 'snlgroup_id' in parameters and isinstance(snl, EGStructureNL):
spec['force_egsnl'] = snl.as_dict()
spec['force_snlgroup_id'] = parameters['snlgroup_id']
del spec['snl']
fws_all.append(
Firework(tasks,
spec,
name=get_slug(molname + ' -- Add to SNL database'),
fw_id=1))
default_solvents = [
'diglym', 'acetonitrile', 'dmso', 'thf', 'dimethylamine',
'dimethoxyethane', 'dimethylaniline', 'tetraglyme'
]
workflow_type = parameters.get('workflow', 'ipea')
ref_charge = parameters.get('ref_charge', 0)
spin_multiplicities = parameters.get('spin_multiplicities', (2, 1, 2))
user_tags = {"initial_charge": ref_charge}
if 'derivation_name' in parameters:
user_tags['derivation_name'] = parameters['derivation_name']
solvent_method = parameters.get("solvent_method", "ief-pcm")
use_vdw_surface = parameters.get("use_vdW_surface", False)
qm_method = parameters.get("qm_method", None)
population_method = parameters.get("population_method", None)
check_large = parameters.get("check_large", True)
if workflow_type == 'ipea':
solvent = parameters.get('solvent', "water")
fws_tasks, connections = multistep_ipea_fws(
mol=snl.structure,
name=molname,
mission=mission,
solvent=solvent,
solvent_method=solvent_method,
use_vdW_surface=use_vdw_surface,
ref_charge=ref_charge,
spin_multiplicities=spin_multiplicities,
dupefinder=DupeFinderEG(),
priority=priority,
parent_fwid=1,
additional_user_tags=user_tags,
qm_method=qm_method,
check_large=check_large)
elif workflow_type == 'multiple solvent ipea':
solvents = parameters.get('solvents', default_solvents)
fws_tasks, connections = multi_solvent_ipea_fws(
mol=snl.structure,
name=molname,
mission=mission,
solvents=solvents,
solvent_method=solvent_method,
use_vdW_surface=use_vdw_surface,
ref_charge=ref_charge,
spin_multiplicities=spin_multiplicities,
dupefinder=DupeFinderEG(),
priority=priority,
parent_fwid=1,
additional_user_tags=user_tags,
qm_method=qm_method)
elif workflow_type == 'solvation energy':
solvents = parameters.get('solvents', default_solvents)
fws_tasks, connections = solvation_energy_fws(
mol=snl.structure,
name=molname,
mission=mission,
solvents=solvents,
solvent_method=solvent_method,
use_vdW_surface=use_vdw_surface,
dupefinder=DupeFinderEG(),
priority=priority,
parent_fwid=1,
additional_user_tags=user_tags,
qm_method=qm_method)
elif workflow_type == "single point energy":
solvent = parameters.get('solvent', "water")
fws_tasks, connections = single_point_energy_fws(
mol=snl.structure,
name=molname,
mission=mission,
solvent=solvent,
solvent_method=solvent_method,
use_vdW_surface=use_vdw_surface,
qm_method=qm_method,
pop_method=population_method,
dupefinder=DupeFinderEG(),
priority=priority,
parent_fwid=1,
additional_user_tags=user_tags)
elif workflow_type == "md relax":
high_temperature = parameters.get("high_temperature", 323.15)
low_temperature = parameters.get("low_temperature", 273.15)
md_steps = parameters.get("md_steps", 500)
time_step = parameters.get("time_step", 1.0)
md_runs = parameters.get("md_runs", 3)
normal_basis = parameters.get("normal_basis", "6-31G*")
diffuse_basis = parameters.get("diffuse_basis", "6-31+G*")
charge_threshold = parameters.get("charge_threshold", -0.5)
fws_tasks, connections = md_relax_fws(
mol=snl.structure,
name=molname,
mission=mission,
qm_method=qm_method,
high_temperature=high_temperature,
low_temperature=low_temperature,
md_steps=md_steps,
time_step=time_step,
md_runs=md_runs,
normal_basis=normal_basis,
diffuse_basis=diffuse_basis,
charge_threshold=charge_threshold,
dupefinder=DupeFinderEG(),
priority=priority,
parent_fwid=1,
additional_user_tags=user_tags)
elif workflow_type == "equilibrium constant":
solvent = parameters.get('solvent', "water")
reaction_id = parameters.get('reaction_id')
fws_tasks, connections = equilibrium_constant_fws(
mission=mission,
solvent=solvent,
solvent_method=solvent_method,
use_vdw_surface=use_vdw_surface,
qm_method=qm_method,
reaction_id=reaction_id,
dupefinder=DupeFinderEG(),
priority=priority,
parent_fwid=None,
additional_user_tags=user_tags,
depend_on_parent=False)
elif workflow_type == "bsse":
charge = snl.structure.charge
spin_multiplicity = snl.structure.spin_multiplicity
fragments = parameters.get("fragments", None)
fws_tasks, connections = counterpoise_correction_generation_fw(
molname=molname,
charge=charge,
spin_multiplicity=spin_multiplicity,
qm_method=qm_method,
fragments=fragments,
mission=mission,
priority=priority,
parent_fwid=1,
additional_user_tags=user_tags)
else:
raise ValueError(
'Workflow "{}" is not supported yet'.format(workflow_type))
fws_all.extend(fws_tasks)
wf_meta = get_meta_from_structure(snl.structure)
wf_meta['run_version'] = 'Jan 27, 2014'
if '_electrolytegenome' in snl.data and \
'submission_id' in snl.data['_electrolytegenome']:
wf_meta['submission_id'] = snl.data['_electrolytegenome'][
'submission_id']
return Workflow(fws_all, connections, name=molname, metadata=wf_meta)
def main():
import argparse
parser = argparse.ArgumentParser(
description="Run A QChem Job for a QChem Input File")
parser.add_argument(
"-d",
"--directory",
dest="directory",
type=str,
required=True,
help=
"the directory contains all the QChem jobs to be pretended to run again"
)
parser.add_argument("-p",
"--priority",
dest="priority",
type=int,
default=100,
help="the FireWorks priority")
parser.add_argument("-b",
"--batch_size",
dest="batch_size",
type=int,
default=100,
help="the number of FireWorks in a Workflow")
options = parser.parse_args()
fw_priority = options.priority
batch_size = options.batch_size
lp = LaunchPad.auto_load()
src_dir = os.path.abspath(options.directory)
src_dir_sub_dirs = glob.glob(os.path.join(src_dir, "*"))
num_dirs = len(src_dir_sub_dirs)
current_fwid = 1
links_dict = dict()
fws_all = []
num_fw_in_current_batch = 0
batch_num = 1
for i, sd in enumerate(src_dir_sub_dirs):
if not os.path.isdir(sd):
continue
fw_json_filename = os.path.join(sd, "FW.json")
if not (os.path.exists(fw_json_filename)
or os.path.exists(fw_json_filename + ".gz")):
continue
with zopen(zpath(fw_json_filename), 'rt') as f:
fw_dict = json.load(f)
print("{percent:4.2%} completed, processing directory {d:s}, "
"molecule name {molname:s}," \
" mission {mission:s}".format(percent=i / float(num_dirs), d=sd,
molname=
fw_dict['spec']['user_tags'][
'molname'],
mission=
fw_dict['spec']['user_tags'][
'mission']))
molname = fw_dict['spec']['user_tags']['molname']
egsnl_tasks = [AddEGSNLTask()]
if 'mol' in fw_dict:
mol = Molecule.from_dict(fw_dict['spec']['mol'])
else:
mol = Molecule.from_dict(
fw_dict['spec']['qcinp']['jobs'][0]['molecule'])
snl = StructureNL(mol, "Xiaohui Qu <*****@*****.**>",
"Electrolyte Genome")
egsnl_task_spec = {
'task_type': 'Add to SNL database',
'snl': snl.as_dict(),
'_category': 'Parse Previous QChem Job',
'_priority': fw_priority
}
snl_fw_id = current_fwid
current_fwid += 1
fws_all.append(
Firework(
egsnl_tasks,
egsnl_task_spec,
name=get_slug(molname +
' -- Add to SNL database For fake QChem Task'),
fw_id=snl_fw_id))
fake_qchem_tasks = [FakeRunQChemTask()]
src_qchem_dir = sd
fake_qchem_spec = {
'_priority': fw_priority * 2,
'src_qchem_dir': src_qchem_dir,
'_category': 'Parse Previous QChem Job',
'run_tags': fw_dict['spec']['run_tags'],
'implicit_solvent': fw_dict['spec']['implicit_solvent'],
'task_type': fw_dict['spec']['task_type'],
'charge': fw_dict['spec']['charge'],
'spin_multiplicity': fw_dict['spec']['spin_multiplicity'],
'num_atoms': fw_dict['spec']['num_atoms'],
'user_tags': fw_dict['spec']['user_tags'],
'mol': mol.as_dict(),
'inchi': fw_dict['spec']['inchi'],
'_dupefinder': fw_dict['spec']['_dupefinder'],
'qcinp': fw_dict['spec']['qcinp'],
'qm_method': fw_dict['spec']['qm_method'],
'inchi_root': fw_dict['spec']['inchi_root']
}
for k in ['mixed_basis', 'mixed_aux_basis']:
if k in fw_dict['spec']:
fake_qchem_spec[k] = fw_dict['spec'][k]
fake_qchem_fw_id = current_fwid
current_fwid += 1
fws_all.append(
Firework(fake_qchem_tasks,
fake_qchem_spec,
name='Fake' + fw_dict['name'],
fw_id=fake_qchem_fw_id))
links_dict[snl_fw_id] = fake_qchem_fw_id
num_fw_in_current_batch += 1
if num_fw_in_current_batch >= 100:
wf = Workflow(fws_all, links_dict,
"Read Previous QChem Jobs Id-{}".format(batch_num))
lp.add_wf(wf)
batch_num += 1
links_dict = dict()
fws_all = []
num_fw_in_current_batch = 0
if num_fw_in_current_batch > 0:
wf = Workflow(fws_all, links_dict, "Read Previous QChem Jobs")
lp.add_wf(wf)
def _write_formula_file(self, fw_spec):
filename = get_slug('JOB--' +
fw_spec['mpsnl']['reduced_cell_formula_abc'] +
'--' + fw_spec['task_type'])
with open(filename, 'w+') as f:
f.write('')
def get_name(structure, task_type):
return get_slug(structure.formula + '--' + task_type)
def run_task(self, fw_spec):
print 'sleeping 10s for Mongo'
time.sleep(10)
print 'done sleeping'
print 'the gap is {}, the cutoff is {}'.format(
fw_spec['analysis']['bandgap'], self.gap_cutoff)
if fw_spec['analysis']['bandgap'] >= self.gap_cutoff:
print 'Adding more runs...'
type_name = 'GGA+U' if 'GGA+U' in fw_spec[
'prev_task_type'] else 'GGA'
snl = StructureNL.from_dict(fw_spec['mpsnl'])
f = Composition(
snl.structure.composition.reduced_formula).alphabetical_formula
fws = []
connections = {}
priority = fw_spec['_priority']
# run GGA static
spec = fw_spec # pass all the items from the current spec to the new
# one
spec.update({
'task_type': '{} static'.format(type_name),
'_queueadapter': QA_VASP,
'_dupefinder': DupeFinderVasp().to_dict(),
'_priority': priority
})
fws.append(
Firework([
VaspCopyTask({'use_CONTCAR': True}),
SetupStaticRunTask(),
get_custodian_task(spec)
],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=-10))
# insert into DB - GGA static
spec = {
'task_type': 'VASP db insertion',
'_queueadapter': QA_DB,
'_allow_fizzled_parents': True,
'_priority': priority,
"_dupefinder": DupeFinderDB().to_dict()
}
fws.append(
Firework([VaspToDBTask()],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=-9))
connections[-10] = -9
# run GGA Uniform
spec = {
'task_type': '{} Uniform'.format(type_name),
'_queueadapter': QA_VASP,
'_dupefinder': DupeFinderVasp().to_dict(),
'_priority': priority
}
fws.append(
Firework([
VaspCopyTask({'use_CONTCAR': False}),
SetupNonSCFTask({'mode': 'uniform'}),
get_custodian_task(spec)
],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=-8))
connections[-9] = -8
# insert into DB - GGA Uniform
spec = {
'task_type': 'VASP db insertion',
'_queueadapter': QA_DB,
'_allow_fizzled_parents': True,
'_priority': priority,
"_dupefinder": DupeFinderDB().to_dict()
}
fws.append(
Firework([VaspToDBTask({'parse_uniform': True})],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=-7))
connections[-8] = -7
# run GGA Band structure
spec = {
'task_type': '{} band structure'.format(type_name),
'_queueadapter': QA_VASP,
'_dupefinder': DupeFinderVasp().to_dict(),
'_priority': priority
}
fws.append(
Firework([
VaspCopyTask({'use_CONTCAR': False}),
SetupNonSCFTask({'mode': 'line'}),
get_custodian_task(spec)
],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=-6))
connections[-7] = -6
# insert into DB - GGA Band structure
spec = {
'task_type': 'VASP db insertion',
'_queueadapter': QA_DB,
'_allow_fizzled_parents': True,
'_priority': priority,
"_dupefinder": DupeFinderDB().to_dict()
}
fws.append(
Firework([VaspToDBTask({})],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=-5))
connections[-6] = -5
wf = Workflow(fws, connections)
print 'Done adding more runs...'
return FWAction(additions=wf)
return FWAction()
def _write_formula_file(self, fw_spec):
filename = get_slug(
'JOB--' + fw_spec['mpsnl']['reduced_cell_formula_abc'] + '--'
+ fw_spec['task_type'])
with open(filename, 'w+') as f:
f.write('')
def run_task(self, fw_spec):
print 'sleeping 10s for Mongo'
time.sleep(10)
print 'done sleeping'
print 'the gap is {}, the cutoff is {}'.format(
fw_spec['analysis']['bandgap'], self.gap_cutoff)
if fw_spec['analysis']['bandgap'] >= self.gap_cutoff:
static_dens = 90
uniform_dens = 1000
line_dens = 20
else:
static_dens = 450
uniform_dens = 1500
line_dens = 30
if fw_spec['analysis']['bandgap'] <= self.metal_cutoff:
user_incar_settings = {"ISMEAR": 1, "SIGMA": 0.2}
else:
user_incar_settings = {}
print 'Adding more runs...'
type_name = 'GGA+U' if 'GGA+U' in fw_spec['prev_task_type'] else 'GGA'
snl = StructureNL.from_dict(fw_spec['mpsnl'])
f = Composition(
snl.structure.composition.reduced_formula).alphabetical_formula
fws = []
connections = {}
priority = fw_spec['_priority']
trackers = [
Tracker('FW_job.out'),
Tracker('FW_job.error'),
Tracker('vasp.out'),
Tracker('OUTCAR'),
Tracker('OSZICAR')
]
trackers_db = [Tracker('FW_job.out'), Tracker('FW_job.error')]
# run GGA static
spec = fw_spec # pass all the items from the current spec to the new
spec.update({
'task_type': '{} static v2'.format(type_name),
'_queueadapter': QA_VASP_SMALL,
'_dupefinder': DupeFinderVasp().to_dict(),
'_priority': priority,
'_trackers': trackers
})
fws.append(
Firework([
VaspCopyTask({
'use_CONTCAR': True,
'skip_CHGCAR': True
}),
SetupStaticRunTask({
"kpoints_density": static_dens,
'user_incar_settings': user_incar_settings
}),
get_custodian_task(spec)
],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=-10))
# insert into DB - GGA static
spec = {
'task_type': 'VASP db insertion',
'_queueadapter': QA_DB,
'_allow_fizzled_parents': True,
'_priority': priority * 2,
"_dupefinder": DupeFinderDB().to_dict(),
'_trackers': trackers_db
}
fws.append(
Firework([VaspToDBTask()],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=-9))
connections[-10] = -9
# run GGA Uniform
spec = {
'task_type': '{} Uniform v2'.format(type_name),
'_queueadapter': QA_VASP,
'_dupefinder': DupeFinderVasp().to_dict(),
'_priority': priority,
'_trackers': trackers
}
fws.append(
Firework([
VaspCopyTask({'use_CONTCAR': False}),
SetupNonSCFTask({
'mode': 'uniform',
"kpoints_density": uniform_dens
}),
get_custodian_task(spec)
],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=-8))
connections[-9] = -8
# insert into DB - GGA Uniform
spec = {
'task_type': 'VASP db insertion',
'_queueadapter': QA_DB,
'_allow_fizzled_parents': True,
'_priority': priority * 2,
"_dupefinder": DupeFinderDB().to_dict(),
'_trackers': trackers_db
}
fws.append(
Firework([VaspToDBTask({'parse_uniform': True})],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=-7))
connections[-8] = -7
# run GGA Band structure
spec = {
'task_type': '{} band structure v2'.format(type_name),
'_queueadapter': QA_VASP,
'_dupefinder': DupeFinderVasp().to_dict(),
'_priority': priority,
'_trackers': trackers
}
fws.append(
Firework([
VaspCopyTask({'use_CONTCAR': False}),
SetupNonSCFTask({
'mode': 'line',
"kpoints_line_density": line_dens
}),
get_custodian_task(spec)
],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=-6))
connections[-7] = [-6]
# insert into DB - GGA Band structure
spec = {
'task_type': 'VASP db insertion',
'_queueadapter': QA_DB,
'_allow_fizzled_parents': True,
'_priority': priority * 2,
"_dupefinder": DupeFinderDB().to_dict(),
'_trackers': trackers_db
}
fws.append(
Firework([VaspToDBTask({})],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=-5))
connections[-6] = -5
if fw_spec.get('parameters') and fw_spec['parameters'].get(
'boltztrap'):
# run Boltztrap
from mpworks.firetasks.boltztrap_tasks import BoltztrapRunTask
spec = {
'task_type': '{} Boltztrap'.format(type_name),
'_queueadapter': QA_DB,
'_dupefinder': DupeFinderDB().to_dict(),
'_priority': priority
}
fws.append(
Firework([BoltztrapRunTask()],
spec,
name=get_slug(f + '--' + spec['task_type']),
fw_id=-4))
connections[-7].append(-4)
wf = Workflow(fws, connections)
print 'Done adding more runs...'
return FWAction(additions=wf)
def task_dict_to_wf(task_dict, launchpad):
fw_id = launchpad.get_new_fw_id()
l_id = launchpad.get_new_launch_id()
spec = {
"task_type": task_dict["task_type"],
"run_tags": task_dict["run_tags"],
"vaspinputset_name": None,
"vasp": None,
"mpsnl": task_dict["snl"],
"snlgroup_id": task_dict["snlgroup_id"],
}
tasks = [DummyLegacyTask()]
launch_dir = task_dict["dir_name_full"]
stored_data = {"error_list": []}
update_spec = {
"prev_vasp_dir": task_dict["dir_name"],
"prev_task_type": spec["task_type"],
"mpsnl": spec["mpsnl"],
"snlgroup_id": spec["snlgroup_id"],
"run_tags": spec["run_tags"],
}
fwaction = FWAction(stored_data=stored_data, update_spec=update_spec)
if task_dict["completed_at"]:
complete_date = datetime.datetime.strptime(task_dict["completed_at"], "%Y-%m-%d %H:%M:%S")
state_history = [{"created_on": complete_date, "state": "COMPLETED"}]
else:
state_history = []
launches = [
Launch(
"COMPLETED",
launch_dir,
fworker=None,
host=None,
ip=None,
action=fwaction,
state_history=state_history,
launch_id=l_id,
fw_id=fw_id,
)
]
f = Composition(task_dict["pretty_formula"]).alphabetical_formula
fw = Firework(
tasks,
spec,
name=get_slug(f + "--" + spec["task_type"]),
launches=launches,
state="COMPLETED",
created_on=None,
fw_id=fw_id,
)
wf_meta = get_meta_from_structure(Structure.from_dict(task_dict["snl"]))
wf_meta["run_version"] = "preproduction (0)"
wf = Workflow.from_FireWork(fw, name=f, metadata=wf_meta)
launchpad.add_wf(wf, reassign_all=False)
launchpad._upsert_launch(launches[0])
print "ADDED", fw_id
# return fw_id
return fw_id
def run_task(self, fw_spec):
print 'sleeping 10s for Mongo'
time.sleep(10)
print 'done sleeping'
print 'the gap is {}, the cutoff is {}'.format(fw_spec['analysis']['bandgap'], self.gap_cutoff)
if fw_spec['analysis']['bandgap'] >= self.gap_cutoff:
print 'Adding more runs...'
type_name = 'GGA+U' if 'GGA+U' in fw_spec['prev_task_type'] else 'GGA'
snl = StructureNL.from_dict(fw_spec['mpsnl'])
f = Composition.from_formula(snl.structure.composition.reduced_formula).alphabetical_formula
fws = []
connections = {}
priority = fw_spec['_priority']
# run GGA static
spec = fw_spec # pass all the items from the current spec to the new
# one
spec.update({'task_type': '{} static'.format(type_name), '_queueadapter': QA_VASP,
'_dupefinder': DupeFinderVasp().to_dict(), '_priority': priority})
fws.append(
FireWork(
[VaspCopyTask({'use_CONTCAR': True}), SetupStaticRunTask(),
get_custodian_task(spec)], spec, name=get_slug(f+'--'+spec['task_type']), fw_id=-10))
# insert into DB - GGA static
spec = {'task_type': 'VASP db insertion', '_queueadapter': QA_DB,
'_allow_fizzled_parents': True, '_priority': priority}
fws.append(
FireWork([VaspToDBTask()], spec, name=get_slug(f+'--'+spec['task_type']), fw_id=-9))
connections[-10] = -9
# run GGA Uniform
spec = {'task_type': '{} Uniform'.format(type_name), '_queueadapter': QA_VASP,
'_dupefinder': DupeFinderVasp().to_dict(), '_priority': priority}
fws.append(FireWork(
[VaspCopyTask({'use_CONTCAR': False}), SetupNonSCFTask({'mode': 'uniform'}),
get_custodian_task(spec)], spec, name=get_slug(f+'--'+spec['task_type']), fw_id=-8))
connections[-9] = -8
# insert into DB - GGA Uniform
spec = {'task_type': 'VASP db insertion', '_queueadapter': QA_DB,
'_allow_fizzled_parents': True, '_priority': priority}
fws.append(
FireWork([VaspToDBTask({'parse_uniform': True})], spec, name=get_slug(f+'--'+spec['task_type']),
fw_id=-7))
connections[-8] = -7
# run GGA Band structure
spec = {'task_type': '{} band structure'.format(type_name), '_queueadapter': QA_VASP,
'_dupefinder': DupeFinderVasp().to_dict(), '_priority': priority}
fws.append(FireWork([VaspCopyTask({'use_CONTCAR': False}), SetupNonSCFTask({'mode': 'line'}),
get_custodian_task(spec)], spec, name=get_slug(f+'--'+spec['task_type']),
fw_id=-6))
connections[-7] = -6
# insert into DB - GGA Band structure
spec = {'task_type': 'VASP db insertion', '_queueadapter': QA_DB,
'_allow_fizzled_parents': True, '_priority': priority}
fws.append(FireWork([VaspToDBTask({})], spec, name=get_slug(f+'--'+spec['task_type']), fw_id=-5))
connections[-6] = -5
wf = Workflow(fws, connections)
print 'Done adding more runs...'
return FWAction(additions=wf)
return FWAction()
