def _inner(file_path, input_settings=None):
# Create a test computer
computer = localhost
process_type = 'aiida.calculations:{}'.format('vasp.vasp')
node = CalcJobNode(computer=computer, process_type=process_type)
node.set_attribute('input_filename', 'INCAR')
node.set_attribute('output_filename', 'OUTCAR')
#node.set_attribute('error_filename', 'aiida.err')
node.set_attribute('scheduler_stderr', '_scheduler-stderr.txt')
node.set_attribute('scheduler_stdout', '_scheduler-stdout.txt')
node.set_option('resources', {
'num_machines': 1,
'num_mpiprocs_per_machine': 1
})
node.set_option('max_wallclock_seconds', 1800)
if input_settings is None:
input_settings = {}
settings = Dict(dict=input_settings)
node.add_incoming(settings,
link_type=LinkType.INPUT_CALC,
link_label='settings')
settings.store()
node.store()
# Create a `FolderData` that will represent the `retrieved` folder. Store the test
# output fixture in there and link it.
retrieved = FolderData()
retrieved.put_object_from_tree(file_path)
retrieved.add_incoming(node,
link_type=LinkType.CREATE,
link_label='retrieved')
retrieved.store()
return node
def _fixture_calc_job_node(entry_point_name, computer, test_name, attributes=None):
"""Fixture to generate a mock `CalcJobNode` for testing parsers.
:param entry_point_name: entry point name of the calculation class
:param computer: a `Computer` instance
:param test_name: relative path of directory with test output files in the `fixtures/{entry_point_name}` folder
:param attributes: any optional attributes to set on the node
:return: `CalcJobNode` instance with an attached `FolderData` as the `retrieved` node
"""
from aiida.common.links import LinkType
from aiida.orm import CalcJobNode, FolderData
from aiida.plugins.entry_point import format_entry_point_string
entry_point = format_entry_point_string('aiida.calculations', entry_point_name)
node = CalcJobNode(computer=computer, process_type=entry_point)
node.set_attribute('input_filename', 'aiida.in')
node.set_attribute('output_filename', 'aiida.out')
node.set_attribute('error_filename', 'aiida.err')
node.set_option('resources', {'num_machines': 1, 'num_mpiprocs_per_machine': 1})
node.set_option('max_wallclock_seconds', 1800)
if attributes:
node.set_attribute_many(attributes)
node.store()
basepath = os.path.dirname(os.path.abspath(__file__))
filepath = os.path.join(basepath, 'parsers', 'fixtures', entry_point_name[len('codtools.'):], test_name)
retrieved = FolderData()
retrieved.put_object_from_tree(filepath)
retrieved.add_incoming(node, link_type=LinkType.CREATE, link_label='retrieved')
retrieved.store()
return node
def retrieve_calculation(calculation, transport, retrieved_temporary_folder):
"""Retrieve all the files of a completed job calculation using the given transport.
If the job defined anything in the `retrieve_temporary_list`, those entries will be stored in the
`retrieved_temporary_folder`. The caller is responsible for creating and destroying this folder.
:param calculation: the instance of CalcJobNode to update.
:param transport: an already opened transport to use for the retrieval.
:param retrieved_temporary_folder: the absolute path to a directory in which to store the files
listed, if any, in the `retrieved_temporary_folder` of the jobs CalcInfo
"""
logger_extra = get_dblogger_extra(calculation)
workdir = calculation.get_remote_workdir()
execlogger.debug('Retrieving calc {}'.format(calculation.pk),
extra=logger_extra)
execlogger.debug('[retrieval of calc {}] chdir {}'.format(
calculation.pk, workdir),
extra=logger_extra)
# If the calculation already has a `retrieved` folder, simply return. The retrieval was apparently already completed
# before, which can happen if the daemon is restarted and it shuts down after retrieving but before getting the
# chance to perform the state transition. Upon reloading this calculation, it will re-attempt the retrieval.
link_label = calculation.link_label_retrieved
if calculation.get_outgoing(FolderData,
link_label_filter=link_label).first():
execlogger.warning(
'CalcJobNode<{}> already has a `{}` output folder: skipping retrieval'
.format(calculation.pk, link_label))
return
# Create the FolderData node into which to store the files that are to be retrieved
retrieved_files = FolderData()
with transport:
transport.chdir(workdir)
# First, retrieve the files of folderdata
retrieve_list = calculation.get_retrieve_list()
retrieve_temporary_list = calculation.get_retrieve_temporary_list()
retrieve_singlefile_list = calculation.get_retrieve_singlefile_list()
with SandboxFolder() as folder:
retrieve_files_from_list(calculation, transport, folder.abspath,
retrieve_list)
# Here I retrieved everything; now I store them inside the calculation
retrieved_files.put_object_from_tree(folder.abspath)
# Second, retrieve the singlefiles, if any files were specified in the 'retrieve_temporary_list' key
if retrieve_singlefile_list:
with SandboxFolder() as folder:
_retrieve_singlefiles(calculation, transport, folder,
retrieve_singlefile_list, logger_extra)
# Retrieve the temporary files in the retrieved_temporary_folder if any files were
# specified in the 'retrieve_temporary_list' key
if retrieve_temporary_list:
retrieve_files_from_list(calculation, transport,
retrieved_temporary_folder,
retrieve_temporary_list)
# Log the files that were retrieved in the temporary folder
for filename in os.listdir(retrieved_temporary_folder):
execlogger.debug(
"[retrieval of calc {}] Retrieved temporary file or folder '{}'"
.format(calculation.pk, filename),
extra=logger_extra)
# Store everything
execlogger.debug('[retrieval of calc {}] '
'Storing retrieved_files={}'.format(
calculation.pk, retrieved_files.pk),
extra=logger_extra)
retrieved_files.store()
# Make sure that attaching the `retrieved` folder with a link is the last thing we do. This gives the biggest chance
# of making this method idempotent. That is to say, if a runner gets interrupted during this action, it will simply
# retry the retrieval, unless we got here and managed to link it up, in which case we move to the next task.
retrieved_files.add_incoming(calculation,
link_type=LinkType.CREATE,
link_label=calculation.link_label_retrieved)
def _generate_calc_job_node(
entry_point_name,
results_folder,
inputs=None,
computer=None,
outputs=None,
outfile_override=None,
):
"""
Generate a CalcJob node with fake retrieved node in the
tests/data
"""
calc_class = CalculationFactory(entry_point_name)
entry_point = format_entry_point_string('aiida.calculations',
entry_point_name)
builder = calc_class.get_builder()
if not computer:
computer = db_test_app.localhost
node = CalcJobNode(computer=computer, process_type=entry_point)
# Monkypatch the inputs
if inputs is not None:
inputs = AttributeDict(inputs)
node.__dict__['inputs'] = inputs
# Add direct inputs, pseudos are omitted
for k, v in inputs.items():
if isinstance(v, Node):
if not v.is_stored:
v.store()
node.add_incoming(v,
link_type=LinkType.INPUT_CALC,
link_label=k)
options = builder.metadata.options
options.update(inputs.metadata.options)
node.set_attribute('input_filename', options.input_filename)
node.set_attribute('seedname', options.seedname)
node.set_attribute('output_filename', options.output_filename)
node.set_attribute('error_filename', 'aiida.err')
node.set_option('resources', {
'num_machines': 1,
'num_mpiprocs_per_machine': 1
})
node.set_option('max_wallclock_seconds', 1800)
node.store()
filepath = this_folder.parent / 'data' / results_folder
retrieved = FolderData()
retrieved.put_object_from_tree(str(filepath.resolve()))
# Apply overriding output files
if outfile_override is not None:
for key, content in outfile_override.items():
if content is None:
retrieved.delete_object(key)
continue
buf = BytesIO(content.encode())
retrieved.put_object_from_filelike(buf, key)
retrieved.add_incoming(node,
link_type=LinkType.CREATE,
link_label='retrieved')
retrieved.store()
if outputs is not None:
for label, out_node in outputs.items():
out_node.add_incoming(node,
link_type=LinkType.CREATE,
link_label=label)
if not out_node.is_stored:
out_node.store()
return node