def __init__(self, calc):
"""
Init
"""
from aiida.common import aiidalogger
self._logger = aiidalogger.getChild('parser').getChild(
self.__class__.__name__)
self._calc = calc
def __init__(self, *args, **kwargs): # pylint: disable=unused-argument
"""
__init__ method of the Transport base class.
"""
from aiida.common import aiidalogger
self._logger = aiidalogger.getChild('transport').getChild(
self.__class__.__name__)
self._logger_extra = None
self._is_open = False
self._enters = 0
self._safe_open_interval = DEFAULT_TRANSPORT_INTERVAL
def __init__(self, calc_parser_cls=None, **kwargs): # pylint: disable=unused-argument
super(BaseFileParser, self).__init__()
self._logger = aiidalogger.getChild(self.__class__.__name__)
self._vasp_parser = calc_parser_cls
self.settings = None
if calc_parser_cls is not None:
calc_parser_cls.get_quantity.append(self.get_quantity)
self.settings = calc_parser_cls.settings
self._parsable_items = {}
self._parsed_data = {}
self._data_obj = None
def __init__(self, calculation):
"""Initialize the instance of YamboParser"""
from aiida.common import aiidalogger
self._logger = aiidalogger.getChild('parser').getChild(
self.__class__.__name__)
# check for valid input
if not isinstance(calculation, YamboCalculation):
raise OutputParsingError(
"Input calculation must be a YamboCalculation")
self._calc = calculation
self._eels_array_linkname = 'array_eels'
self._eps_array_linkname = 'array_eps'
self._alpha_array_linkname = 'array_alpha'
self._qp_array_linkname = 'array_qp'
self._ndb_linkname = 'array_ndb'
self._ndb_QP_linkname = 'array_ndb_QP'
self._ndb_HF_linkname = 'array_ndb_HFlocXC'
self._lifetime_bands_linkname = 'bands_lifetime'
self._quasiparticle_bands_linkname = 'bands_quasiparticle'
self._parameter_linkname = 'output_parameters'
super(YamboParser, self).__init__(calculation)
from aiida.common import aiidalogger
from aiida.orm.workflow import Workflow
from aiida.orm import Code, Computer
from aiida.orm import CalculationFactory, DataFactory
__copyright__ = u"Copyright (c), This file is part of the AiiDA platform. For further information please visit http://www.aiida.net/. All rights reserved."
__license__ = "MIT license, see LICENSE.txt file."
__version__ = "0.7.0"
__authors__ = "The AiiDA team."
UpfData = DataFactory('upf')
ParameterData = DataFactory('parameter')
KpointsData = DataFactory('array.kpoints')
StructureData = DataFactory('structure')
logger = aiidalogger.getChild('WorkflowXTiO3')
## ===============================================
## WorkflowXTiO3_EOS
## ===============================================
class WorkflowXTiO3_EOS(Workflow):
def __init__(self, **kwargs):
super(WorkflowXTiO3_EOS, self).__init__(**kwargs)
## ===============================================
## Structure generators
## ===============================================
def get_structure(self, alat=4, x_material='Ba'):
# -*- coding: utf-8 -*-
###########################################################################
# Copyright (c), The AiiDA team. All rights reserved. #
# This file is part of the AiiDA code. #
# #
# The code is hosted on GitHub at https://github.com/aiidateam/aiida_core #
# For further information on the license, see the LICENSE.txt file #
# For further information please visit http://www.aiida.net #
###########################################################################
from aiida.common import aiidalogger
from aiida.common.datastructures import wf_states, wf_exit_call, wf_default_call
logger = aiidalogger.getChild('workflowmanager')
def execute_steps():
"""
This method loops on the RUNNING workflows and handled the execution of the
steps until each workflow reaches an end (or gets stopped for errors).
In the loop for each RUNNING workflow the method loops also in each of its
RUNNING steps, testing if all the calculation and subworkflows attached to
the step are FINISHED. In this case the step is set as FINISHED and the
workflow is advanced to the step's next method present in the db with
``advance_workflow``, otherwise if any step's JobCalculation is found in
NEW state the method will submit. If none of the previous conditions apply
the step is flagged as ERROR and cannot proceed anymore, blocking the future
execution of the step and, connected, the workflow.
Finally, for each workflow the method tests if there are INITIALIZED steps
"""AiiDA - Workflow to get bandstructure of a material using VASP"""
from aiida.common import aiidalogger
from aiida.orm.workflow import Workflow
from aiida.orm import DataFactory
from aiida_vasp.calcs.maker import VaspMaker
LOGGER = aiidalogger.getChild('Bandstructure')
PARAMETER_CLS = DataFactory('parameter')
class Bandstructure(Workflow):
"""
AiiDA workflow to get bandstructure of a material using VASP.
Necessary steps are:
* selfconsistent run with few kpoints
* nonselfconsistent run with more kpoints and CHGCAR from
selfconsistent run
"""
def get_calc_maker(self):
"""Return an VaspMaker instance."""
from aiida.orm import Code
params = self.get_parameters()
maker = VaspMaker(structure=params['structure'])
maker.add_parameters(icharg=0,
istart=0,
lorbit=11,
lsorbit=True,
sigma=0.05,
ismear=0,
from aiida.orm.workflow import Workflow
from aiida.orm import Code, Computer
from aiida.orm import CalculationFactory, DataFactory
from aiida.orm.utils import load_node
__copyright__ = u"Copyright (c), This file is part of the AiiDA platform. For further information please visit http://www.aiida.net/. All rights reserved."
__license__ = "MIT license, see LICENSE.txt file."
__version__ = "0.7.0"
__authors__ = "The AiiDA team."
UpfData = DataFactory('upf')
ParameterData = DataFactory('parameter')
KpointsData = DataFactory('array.kpoints')
StructureData = DataFactory('structure')
logger = aiidalogger.getChild('WorkflowStressTensor')
## ===============================================
## Workflow_PW_Stress_Tensor_MLagrange
## ===============================================
class Workflow_PW_stress_tensor_MLagrange(Workflow):
def __init__(self, **kwargs):
super(Workflow_PW_stress_tensor_MLagrange, self).__init__(**kwargs)
## ===============================================
## Structure generators
## ===============================================
def get_MLagrange_distorted_structure(self, structure_id, M_MLagrange_eps):
# For further information on the license, see the LICENSE.txt file #
# For further information please visit http://www.aiida.net #
###########################################################################
from __future__ import division
import aiida.common
from aiida.common import aiidalogger
from aiida.orm.workflow import Workflow
from aiida.orm import Code, Computer
from aiida.orm import CalculationFactory, DataFactory
UpfData = DataFactory('upf')
ParameterData = DataFactory('parameter')
KpointsData = DataFactory('array.kpoints')
StructureData = DataFactory('structure')
logger = aiidalogger.getChild('WorkflowXTiO3')
## ===============================================
## WorkflowXTiO3_EOS
## ===============================================
class WorkflowXTiO3_EOS(Workflow):
def __init__(self, **kwargs):
super(WorkflowXTiO3_EOS, self).__init__(**kwargs)
## ===============================================
## Structure generators
## ===============================================
# -*- coding: utf-8 -*-
import aiida.common
from aiida.common import aiidalogger
from aiida.orm.workflow import Workflow
from aiida.orm import Code, Computer
__copyright__ = u"Copyright (c), This file is part of the AiiDA platform. For further information please visit http://www.aiida.net/. All rights reserved."
__license__ = "MIT license, see LICENSE.txt file."
__version__ = "0.7.1"
__authors__ = "The AiiDA team."
logger = aiidalogger.getChild('WorkflowDemo')
class WorkflowDemo(Workflow):
def __init__(self, **kwargs):
super(WorkflowDemo, self).__init__(**kwargs)
def generate_calc(self):
from aiida.orm import Code, Computer, CalculationFactory
from aiida.common.datastructures import calc_states
CustomCalc = CalculationFactory('simpleplugins.templatereplacer')
computer = Computer.get("localhost")
calc = CustomCalc(computer=computer, withmpi=True)
calc.set_resources({"num_machines": 1, "num_mpiprocs_per_machine": 1})
calc.store()
calc._set_state(calc_states.FINISHED)
from aiida.orm.workflow import Workflow
from aiida.orm import Code, Computer
from aiida.orm import CalculationFactory, DataFactory
from aiida.orm.utils import load_node
__copyright__ = u"Copyright (c), This file is part of the AiiDA platform. For further information please visit http://www.aiida.net/. All rights reserved."
__license__ = "MIT license, see LICENSE.txt file."
__version__ = "0.7.0"
__authors__ = "The AiiDA team."
LapwbasisData = DataFactory('lapwbasis')
ParameterData = DataFactory('parameter')
KpointsData = DataFactory('array.kpoints')
StructureData = DataFactory('structure')
logger = aiidalogger.getChild('WorkflowEOS')
## ===============================================
## Workflow_LAPW_EOS
## ===============================================
class Workflow_LAPW_EOS(Workflow):
def __init__(self, **kwargs):
super(Workflow_LAPW_EOS, self).__init__(**kwargs)
## ===============================================
## Structure generators
## ===============================================
def get_structure(self, structure_id, scale=1.0):
from aiida.workflows.user.epfl_theos.quantumespresso.pw import PwWorkflow, PwrestartWorkflow
from aiida.workflows.user.epfl_theos.quantumespresso.helpers import get_pw_wfs_with_parameters
from aiida.common.example_helpers import test_and_get_code
__copyright__ = u"Copyright (c), 2015, ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE (Theory and Simulation of Materials (THEOS) and National Centre for Computational Design and Discovery of Novel Materials (NCCR MARVEL)), Switzerland and ROBERT BOSCH LLC, USA. All rights reserved."
__license__ = "MIT license, see LICENSE.txt file"
__version__ = "0.4.1"
__contributors__ = "Gianluca Prandini"
UpfData = DataFactory('upf')
ParameterData = DataFactory('parameter')
KpointsData = DataFactory('array.kpoints')
StructureData = DataFactory('structure')
PwCalculation = CalculationFactory('quantumespresso.pw')
SimpleCalculation = CalculationFactory('quantumespresso.simple')
logger = aiidalogger.getChild('ColourWorkflow')
## ===============================================
## ColourWorkflow
## ===============================================
class ColourWorkflow(Workflow):
"""
Workflow to compute the IPA dielectric function of a structure.
Inputs:
{ 'pw_' + any parameter to be used in the pw workflow for the self-consistent DFT calculation
'nscf_' + any parameter to be used in the pwrestart workflow for the nscf calculation
'simple_' + any parameter to be used in the simple.x calculation
"""AiiDA - VASP workflow to get tight binding model of a material using VASP and wannier90"""
from aiida.common import aiidalogger
from aiida.common.exceptions import NotExistent
from aiida.orm import DataFactory, Group, CalculationFactory, Code
from aiida.orm.workflow import Workflow
from aiida_vasp.calcs.maker import VaspMaker
LOGGER = aiidalogger.getChild('Tbmodel')
PARAMETER_CLS = DataFactory('parameter')
class TbmodelWorkflow(Workflow):
"""
AiiDA workflow to get tight binding model of a material using VASP and wannier90.
Necessary steps are:
* selfconsistent run with few kpoints
* nonselfconsistent run with lwannier90
* lwannier90 run with projections block
* wannier.x run with hr_plot = True
"""
def __init__(self, **kwargs):
super(TbmodelWorkflow, self).__init__(**kwargs)
self.group = None
try:
self.group = Group.get_from_string('tbmodel')
except NotExistent:
self.group = Group(name='tbmodel')
self.group.store()
from aiida.orm.workflow import Workflow
from aiida.orm import Code, Computer
from aiida.orm import CalculationFactory, DataFactory
from aiida.orm.utils import load_node
__copyright__ = u"Copyright (c), This file is part of the AiiDA platform. For further information please visit http://www.aiida.net/. All rights reserved."
__license__ = "MIT license, see LICENSE.txt file."
__version__ = "0.7.0"
__authors__ = "The AiiDA team."
UpfData = DataFactory('upf')
ParameterData = DataFactory('parameter')
KpointsData = DataFactory('array.kpoints')
StructureData = DataFactory('structure')
logger = aiidalogger.getChild('WorkflowElasticConstants')
## ===============================================
## Workflow_PW_Elastic_Constants_Lagrange
## ===============================================
class Workflow_PW_elastic_constants_Lagrange(Workflow):
def __init__(self, **kwargs):
super(Workflow_PW_elastic_constants_Lagrange, self).__init__(**kwargs)
## ===============================================
## Structure generators
## ===============================================
def get_Lagrange_distorted_structure(self, structure_id, M_Lagrange_eps):
def parse_from_calc(self, manual=True, custom_instruct=None):
"""
Parses the datafolder, stores results.
"""
from aiida.common.exceptions import InvalidOperation
from aiida.common import aiidalogger
from aiida.utils.logger import get_dblogger_extra
parserlogger = aiidalogger.getChild('vaspparser')
logger_extra = get_dblogger_extra(self._calc)
# suppose at the start that the job is successful
successful = True
parser_warnings = {} # for logging non-critical events
# check that calculation is in the right state
if not manual:
state = self._calc.get_state()
if state != calc_states.PARSING:
raise InvalidOperation("Calculation not in {} state".format(
calc_states.PARSING))
# get parser instructions
# TODO: output parser should NOT interpret the input !!!
try:
instruct = self._calc.get_inputs_dict().pop(
self._calc.get_linkname('settings'))
instruct = instruct.get_dict()
instruct = instruct[u'PARSER_INSTRUCTIONS']
########## Abel Modification to test custom parsers
if custom_instruct is not None:
instruct = custom_instruct
##########
# check if structure, data, and error parsers are specified
# if not append defaults
itypes = [i['type'] for i in instruct]
# structure
if not 'structure' in itypes:
instruct.append({
'instr': 'default_structure_parser',
'type': 'structure',
'params': {}
})
parser_warnings.setdefault(
'Structure parser instruction not found!',
'default_structure_parser loaded.')
# error
if not 'error' in itypes:
instruct.append({
'instr': 'default_error_parser',
'type': 'error',
'params': {}
})
parser_warnings.setdefault(
'Error parser instruction not found!',
'default_error_parser loaded.')
# output
if not 'data' in itypes:
instruct.append({
'instr': 'default_vasprun_parser',
'type': 'data',
'params': {}
})
parser_warnings.setdefault(
'Data parser instruction not found!',
'default_data_parser_parser loaded.')
except:
parser_warnings.setdefault('Parser instructions not found',
'Default instructions were loaded.')
# don't crash, load default instructions instead
instruct = [
# output
{
'instr': 'default_vasprun_parser',
'type': 'data',
'params': {}
},
# error
{
'instr': 'default_error_parser',
'type': 'error',
'params': {}
},
# structure
{
'instr': 'default_structure_parser',
'type': 'structure',
'params': {}
}
]
# select the folder object
out_folder = self._calc.get_retrieved_node()
# check what is inside the folder
list_of_files = out_folder.get_folder_list()
# === check if mandatory files exist ===
# default output file should exist
if not self._calc._default_output in list_of_files:
successful = False
parserlogger.error("Standard output file ({}) not found".format(
self._calc._default_output),
extra=logger_extra)
return successful, ()
# output structure file should exist
if not self._calc._output_structure in list_of_files:
successful = False
parserlogger.error("Output structure file ({}) not found".format(
self._calc._output_structure),
extra=logger_extra)
return successful, ()
# stderr file should exist
if not self._calc._SCHED_ERROR_FILE in list_of_files:
successful = False
parserlogger.error("STDERR file ({}) not found".format(
self._calc._SCHED_ERROR_FILE),
extra=logger_extra)
return successful, ()
instr_node_list = []
errors_node_list = []
# === execute instructions ===
# print instruct
for instr in instruct:
# create an executable instruction
try:
# load instruction
itype = instr['type'].lower()
iname = instr['instr']
iparams = instr['params']
ifull_name = "{}.{}".format(itype, iname)
# append parameters
if itype == 'error':
iparams.setdefault('SCHED_ERROR_FILE',
self._calc._SCHED_ERROR_FILE)
elif itype == 'structure':
iparams.setdefault('OUTPUT_STRUCTURE',
self._calc._output_structure)
# instantiate
instr = ParserInstructionFactory(ifull_name)
instr_exe = instr(out_folder,
params=iparams if iparams else None)
except ValueError:
parser_warnings.setdefault(
'{}_instruction'.format(instr),
'Invalid parser instruction - could not be instantiated!')
instr_exe = None
# execute
if instr_exe:
try:
for item in instr_exe.execute(): # store the results
instr_node_list.append(item)
except Exception as e:
print instr, e
# parser_warnings['output'].setdefault( Modified by Abel
parser_warnings.setdefault(
'output', {
'{}_instruction'.format(instr),
'Failed to execute. Errors: {}'.format(e)
})
# add all parser warnings to the error list
parser_warnings = ParameterData(dict=parser_warnings)
errors_node_list.append(('parser_warnings', parser_warnings))
# === save the outputs ===
new_nodes_list = []
# save the errors/warrnings
for item in errors_node_list:
new_nodes_list.append(item)
# save vasp data
if instr_node_list:
for item in instr_node_list:
new_nodes_list.append(item)
return successful, new_nodes_list
# ~ import aiida.common
from aiida.common import aiidalogger
from aiida.common.exceptions import NotExistent
from aiida.orm.workflow import Workflow
from aiida.orm import DataFactory, Group, CalculationFactory, Code
from aiida.orm.calculation.job.vasp.maker import VaspMaker
logger = aiidalogger.getChild('Tbmodel')
ParameterData = DataFactory('parameter')
class TbmodelWorkflow(Workflow):
'''
AiiDA workflow to get tight binding model of a material using VASP and
wannier90.
Necessary steps are:
* selfconsistent run with few kpoints
* nonselfconsistent run with lwannier90
* lwannier90 run with projections block
* wannier.x run with hr_plot = True
'''
def __init__(self, **kwargs):
super(TbmodelWorkflow, self).__init__(**kwargs)
self.group = None
try:
self.group = Group.get_from_string('tbmodel')
except NotExistent:
self.group = Group(name='tbmodel')
self.group.store()
import os
from aiida.orm.workflow import Workflow
from aiida.workflows.user.hpc_workflow.hpc import HpcWorkflow
from aiida.orm import DataFactory
from aiida.common import aiidalogger
StructureData = DataFactory('structure')
UpfData = DataFactory('upf')
ParameterData = DataFactory('parameter')
KpointsData = DataFactory('array.kpoints')
pseudo_family = 'ssp_eff_pbe'
logger = aiidalogger.getChild('BenchWorkflow')
class BenchWorkflow(Workflow):
def __init__(self, **kwargs):
super(BenchWorkflow, self).__init__(**kwargs)
self.times = []
## ===============================================
## Wf steps
## ===============================================
@Workflow.step
def start(self):
work_params = self.get_parameters()
self.append_to_report("HPC params are: {}".format(
work_params['hpc_params_0'].get_attrs()))
self.next(self.launch_hpcworkflow)
@Workflow.step
def launch_hpcworkflow(self):
"""AiiDA - VASP workflow to run a VASP scf calculation"""
from aiida.common import aiidalogger
from aiida.orm import Workflow
from .helper import WorkflowHelper
LOGGER = aiidalogger.getChild('ScfWorkflow')
class ScfWorkflow(Workflow):
"""
AiiDA workflow to run a VASP scf calculation
the resulting WAVECAR and CHGCAR can then be reused in further workflows.
"""
Helper = WorkflowHelper
def __init__(self, **kwargs):
self.helper = self.Helper(parent=self)
super(ScfWorkflow, self).__init__(**kwargs)
def get_calc_maker(self):
maker = self.helper._get_calc_maker('vasp.scf') # pylint: disable=protected-access
maker.add_parameters(icharg=0, istart=0)
return maker
@Workflow.step
# pylint: disable=protected-access
def start(self):
"""Submit the calculation"""
params = self.get_parameters()
the routines make reference to the suitable plugins for all
plugin-specific operations.
"""
import os
from aiida.common import aiidalogger
from aiida.common import exceptions
from aiida.common.datastructures import calc_states
from aiida.common.folders import SandboxFolder
from aiida.common.links import LinkType
from aiida.common.log import get_dblogger_extra
from aiida.orm import load_node, DataFactory
from aiida.orm.data.folder import FolderData
from aiida.scheduler.datastructures import JOB_STATES
execlogger = aiidalogger.getChild('execmanager')
def submit_calculation(calculation, transport):
"""
Submit a calculation
:param calculation: the instance of JobCalculation to submit.
:param transport: an already opened transport to use to submit the calculation.
"""
from aiida.orm import Code
from aiida.common.exceptions import InputValidationError
from aiida.orm.data.remote import RemoteData
computer = calculation.get_computer()
def parse_from_calc(self):
"""
Parses the datafolder, stores results.
This parser for this code ...
"""
from aiida.common.exceptions import InvalidOperation
from aiida.common import aiidalogger
from aiida.utils.logger import get_dblogger_extra
parserlogger = aiidalogger.getChild('yamboparser')
logger_extra = get_dblogger_extra(self._calc)
# suppose at the start that the job is unsuccessful, unless proven otherwise
successful = False
# check whether the yambo calc was an initialisation (p2y)
try:
settings_dict = self._calc.inp.settings.get_dict()
settings_dict = _uppercase_dict(settings_dict,
dict_name='settings')
except AttributeError:
settings_dict = {}
initialise = settings_dict.pop('INITIALISE', None)
# select the folder object
out_folder = self._calc.get_retrieved_node()
# check what is inside the folder
list_of_files = out_folder.get_folder_list()
try:
input_params = self._calc.inp.parameters.get_dict()
except AttributeError:
if not initialise:
raise ParsingError("Input parameters not found!")
else:
input_params = {}
# retrieve the cell: if parent_calc is a YamboCalculation we must find the original PwCalculation
# going back through the graph tree.
parent_calc = self._calc.inp.parent_calc_folder.inp.remote_folder
cell = {}
if isinstance(parent_calc, YamboCalculation):
has_found_cell = False
while (not has_found_cell):
try:
cell = parent_calc.inp.structure.cell
has_found_cell = True
except AttributeError:
parent_calc = parent_calc.inp.parent_calc_folder.inp.remote_folder
elif isinstance(parent_calc, PwCalculation):
cell = self._calc.inp.parent_calc_folder.inp.remote_folder.inp.structure.cell
output_params = {'warnings': [], 'errors': [], 'yambo_wrote': False}
new_nodes_list = []
ndbqp = {}
ndbhf = {}
try:
results = YamboFolder(out_folder.get_abs_path())
except Exception, e:
success = False
raise ParsingError("Unexpected behavior of YamboFolder: %s" % e)
wf_default_call, calc_states)
from aiida.common.utils import str_timedelta
from aiida.common import aiidalogger
from aiida.orm.implementation.calculation import JobCalculation
from aiida.backends.utils import get_automatic_user
from aiida.utils import timezone
from aiida.utils.logger import get_dblogger_extra
__copyright__ = u"Copyright (c), This file is part of the AiiDA platform. For further information please visit http://www.aiida.net/. All rights reserved."
__license__ = "MIT license, see LICENSE.txt file."
__version__ = "0.7.1"
__authors__ = "The AiiDA team."
logger = aiidalogger.getChild('Workflow')
class WorkflowKillError(AiidaException):
"""
An exception raised when a workflow failed to be killed.
The error_message_list attribute contains the error messages from
all the subworkflows.
"""
def __init__(self, *args, **kwargs):
# Call the base class constructor with the parameters it needs
super(WorkflowKillError, self).__init__(*args)
self.error_message_list = kwargs.pop('error_message_list', [])
if kwargs:
raise ValueError("Unknown parameters passed to WorkflowKillError")
def parse_from_calc(self):
"""
Parses the datafolder, stores results.
This parser for this simple code does simply store in the DB a node
representing the file of forces in real space
"""
from aiida.common.exceptions import InvalidOperation
from aiida.common import aiidalogger
from aiida.djsite.utils import get_dblogger_extra
import ase, ase.io
parserlogger = aiidalogger.getChild('aseparser')
logger_extra = get_dblogger_extra(self._calc)
# suppose at the start that the job is successful
successful = True
# check that calculation is in the right state
state = self._calc.get_state()
if state != calc_states.PARSING:
raise InvalidOperation("Calculation not in {} state".format(
calc_states.PARSING))
# select the folder object
out_folder = self._calc.get_retrieved_node()
# check what is inside the folder
list_of_files = out_folder.get_folder_list()
# at least the stdout should exist
if not self._calc._OUTPUT_FILE_NAME in list_of_files:
successful = False
parserlogger.error("Standard output not found", extra=logger_extra)
return successful, ()
# output structure
has_out_atoms = True if self._calc._output_aseatoms in list_of_files else False
if has_out_atoms:
out_atoms = ase.io.read(
out_folder.get_abs_path(self._calc._output_aseatoms))
out_structure = StructureData().set_ase(out_atoms)
# load the results dictionary
json_outfile = out_folder.get_abs_path(self._calc._OUTPUT_FILE_NAME)
with open(json_outfile, 'r') as f:
json_params = json.load(f)
# extract arrays from json_params
dictionary_array = {}
for k, v in list(json_params.iteritems()):
if isinstance(v, (list, tuple)):
dictionary_array[k] = json_params.pop(k)
# look at warnings
warnings = []
with open(out_folder.get_abs_path(self._calc._SCHED_ERROR_FILE)) as f:
errors = f.read()
if errors:
warnings = [errors]
json_params['warnings'] = warnings
# save the outputs
new_nodes_list = []
# save the arrays
if dictionary_array:
array_data = ArrayData()
for k, v in dictionary_array.iteritems():
array_data.set_array(k, numpy.array(v))
new_nodes_list.append((self._outarray_name, array_data))
# save the parameters
if json_params:
parameter_data = ParameterData(dict=json_params)
new_nodes_list.append((self._outdict_name, parameter_data))
if has_out_atoms:
structure_data = StructureData()
new_nodes_list.append((self._outstruc_name, structure_data))
return successful, new_nodes_list
the data structure to be filled for job submission (JobTemplate), and
the data structure that is returned when querying for jobs in the scheduler
(JobInfo).
"""
from __future__ import division
from aiida.common.extendeddicts import (
DefaultFieldsAttributeDict, Enumerate)
from aiida.common import aiidalogger
__copyright__ = u"Copyright (c), This file is part of the AiiDA platform. For further information please visit http://www.aiida.net/. All rights reserved."
__license__ = "MIT license, see LICENSE.txt file."
__version__ = "0.7.1"
__authors__ = "The AiiDA team."
scheduler_logger = aiidalogger.getChild('scheduler')
class JobState(Enumerate):
pass
# This is the list of possible job states
# Note on names: Jobs are the entities on a
# scheduler; Calcs are the calculations in
# the AiiDA database (whose list of possible
# statuses is defined in aida.common.datastructures
# with the calc_states Enumerate).
# NOTE: for the moment, I don't define FAILED
# (I put everything in DONE)
job_states = JobState((
'UNDETERMINED',
# -*- coding: utf-8 -*-
import aiida.common
from aiida.common import aiidalogger
from aiida.orm.workflow import Workflow
from aiida.orm import Code, Computer
__copyright__ = u"Copyright (c), This file is part of the AiiDA platform. For further information please visit http://www.aiida.net/. All rights reserved."
__license__ = "MIT license, see LICENSE.txt file."
__version__ = "0.7.0"
__authors__ = "The AiiDA team."
logger = aiidalogger.getChild('WorkflowDemo')
class WorkflowDemo(Workflow):
def __init__(self, **kwargs):
super(WorkflowDemo, self).__init__(**kwargs)
def generate_calc(self):
from aiida.orm import Code, Computer, CalculationFactory
from aiida.common.datastructures import calc_states
CustomCalc = CalculationFactory('simpleplugins.templatereplacer')
computer = Computer.get("localhost")
calc = CustomCalc(computer=computer, withmpi=True)
calc.set_resources({"num_machines": 1, "num_mpiprocs_per_machine": 1})
calc.store()
calc._set_state(calc_states.FINISHED)
from aiida.orm.workflow import Workflow
from aiida.orm import Code, Computer
from aiida.orm import CalculationFactory, DataFactory
from aiida.orm.utils import load_node
__copyright__ = u"Copyright (c), This file is part of the AiiDA platform. For further information please visit http://www.aiida.net/. All rights reserved."
__license__ = "MIT license, see LICENSE.txt file."
__version__ = "0.7.0"
__authors__ = "The AiiDA team."
UpfData = DataFactory('upf')
ParameterData = DataFactory('parameter')
KpointsData = DataFactory('array.kpoints')
StructureData = DataFactory('structure')
logger = aiidalogger.getChild('WorkflowStressTensor')
## ===============================================
## Workflow_PW_Stress_Tensor_Lagrange
## ===============================================
class Workflow_PW_stress_tensor_Lagrange(Workflow):
def __init__(self, **kwargs):
super(Workflow_PW_stress_tensor_Lagrange, self).__init__(**kwargs)
## ===============================================
## Structure generators
## ===============================================
# For further information please visit http://www.aiida.net #
###########################################################################
"""
This module defines the main data structures used by the Scheduler.
In particular, there is the definition of possible job states (job_states),
the data structure to be filled for job submission (JobTemplate), and
the data structure that is returned when querying for jobs in the scheduler
(JobInfo).
"""
from __future__ import division
from aiida.common.extendeddicts import (DefaultFieldsAttributeDict, Enumerate)
from aiida.common import aiidalogger
SCHEDULER_LOGGER = aiidalogger.getChild('scheduler')
class JobState(Enumerate):
pass
# This is the list of possible job states
# Note on names: Jobs are the entities on a
# scheduler; Calcs are the calculations in
# the AiiDA database (whose list of possible
# statuses is defined in aida.common.datastructures
# with the calc_states Enumerate).
# NOTE: for the moment, I don't define FAILED
# (I put everything in DONE)
JOB_STATES = JobState((
