def read_test(self, outfolder):
import os
import importlib
import json
from aiida.orm import JobCalculation
from aiida.orm.utils import load_node
from aiida.orm.importexport import import_data
imported = import_data(outfolder,
ignore_unknown_nodes=True,
silent=True)
calc = None
for _, pk in imported['Node']['new']:
c = load_node(pk)
if issubclass(c.__class__, JobCalculation):
calc = c
break
retrieved = calc.out.retrieved
retrieve_temporary_list = calc.get_attr('retrieve_temporary_list',
None)
if retrieve_temporary_list:
from aiida.orm.data.folder import FolderData
retrieved_temporary_folder = FolderData()
retrieved_temporary_folder.replace_with_folder(
retrieved.get_abs_path('.'), overwrite=True)
else:
retrieved_temporary_folder = None
try:
with open(os.path.join(outfolder, '_aiida_checks.json')) as f:
tests = json.load(f)
except IOError:
raise ValueError("This test does not provide a check file!")
except ValueError:
raise ValueError(
"This test does provide a check file, but it cannot "
"be JSON-decoded!")
mod_path = 'aiida.backends.tests.parser_tests.{}'.format(
os.path.split(outfolder)[1])
skip_test = False
try:
m = importlib.import_module(mod_path)
skip_test = m.skip_condition()
except Exception:
pass
if skip_test:
raise SkipTestException
return calc, tests, retrieved_temporary_folder
def recollect_atomic_calculations(**kwargs):
"""
Collect dynamical matrix files into a single folder, putting a different number at the end of
each final dynamical matrix file, obtained from the input link, which corresponds to its place
in the list of q-points originally generated by distribute_qpoints.
:param kwargs: keys are the string representation of the hubbard atom index and the value is the
corresponding retrieved folder object.
:return: FolderData object containing the perturbation files of the computed HpBaseWorkChain
"""
import os
import errno
output_folder_sub = HpCalculation._OUTPUT_SUBFOLDER
output_folder_raw = HpCalculation._FOLDER_RAW
output_prefix = HpCalculation()._PREFIX
# Initialize the merged folder, by creating the subdirectory for the perturbation files
merged_folder = FolderData()
folder_path = os.path.normpath(merged_folder.get_abs_path('.'))
output_path = os.path.join(folder_path, output_folder_raw)
try:
os.makedirs(output_path)
except OSError as error:
if error.errno == errno.EEXIST and os.path.isdir(output_path):
pass
else:
raise
for atomic_site_index, retrieved_folder in kwargs.iteritems():
filepath = os.path.join(
output_folder_raw,
'{}.chi.pert_{}.dat'.format(output_prefix, atomic_site_index))
filepath_src = retrieved_folder.get_abs_path(filepath)
filepath_dst = filepath
merged_folder.add_path(filepath_src, filepath_dst)
# TODO: currently the Hp code requires the .save folder that is written by the original
# PwCalculation, for the final post-processing matrix collection step. It doesn't really need all
# the information contained in that folder, and requiring it means, copying it from remote to a
# local folder and then reuploading it to remote folder. This is unnecessarily heavy
retrieved_folder = kwargs.values()[0]
dirpath = os.path.join(output_folder_sub, output_prefix + '.save')
dirpath_src = retrieved_folder.get_abs_path(dirpath)
dirpath_dst = dirpath
merged_folder.add_path(dirpath_src, dirpath_dst)
retrieved_folder = kwargs.values()[0]
filepath = os.path.join(output_folder_sub, output_prefix + '.occup')
filepath_src = retrieved_folder.get_abs_path(filepath)
filepath_dst = filepath
merged_folder.add_path(filepath_src, filepath_dst)
return merged_folder
def get_retrieved(self, parameters):
"""Set up a fake 'retrieved' dict.
As well as an output folder.
"""
import tempfile
import shutil
from aiida.orm.data.folder import FolderData
tmp_dir = tempfile.mkdtemp()
test_files = ['HKUST-1.cssr', 'HKUST-1.sa', 'HKUST-1.volpo']
output_files = parameters.output_files
for ftest, fout in list(zip(test_files, output_files)):
shutil.copyfile(
os.path.join(zt.TEST_DIR, ftest), os.path.join(tmp_dir, fout))
res = FolderData()
res.replace_with_folder(tmp_dir)
shutil.rmtree(tmp_dir)
retrieved = {'retrieved': res}
return retrieved
def test_cif_structure_roundtrip(self):
from aiida.tools.dbexporters.tcod import export_cif, export_values
from aiida.orm import Code
from aiida.orm import JobCalculation
from aiida.orm.data.cif import CifData
from aiida.orm.data.parameter import ParameterData
from aiida.orm.data.upf import UpfData
from aiida.orm.data.folder import FolderData
from aiida.common.folders import SandboxFolder
from aiida.common.datastructures import calc_states
import tempfile
with tempfile.NamedTemporaryFile() as f:
f.write('''
data_test
_cell_length_a 10
_cell_length_b 10
_cell_length_c 10
_cell_angle_alpha 90
_cell_angle_beta 90
_cell_angle_gamma 90
loop_
_atom_site_label
_atom_site_fract_x
_atom_site_fract_y
_atom_site_fract_z
C 0 0 0
O 0.5 0.5 0.5
''')
f.flush()
a = CifData(file=f.name)
c = a._get_aiida_structure()
c.store()
pd = ParameterData()
code = Code(local_executable='test.sh')
with tempfile.NamedTemporaryFile() as f:
f.write("#/bin/bash\n\necho test run\n")
f.flush()
code.add_path(f.name, 'test.sh')
code.store()
calc = JobCalculation(computer=self.computer)
calc.set_resources({'num_machines': 1, 'num_mpiprocs_per_machine': 1})
calc.add_link_from(code, "code")
calc.set_environment_variables({
'PATH': '/dev/null',
'USER': '******'
})
with tempfile.NamedTemporaryFile(prefix="Fe") as f:
f.write("<UPF version=\"2.0.1\">\nelement=\"Fe\"\n")
f.flush()
upf = UpfData(file=f.name)
upf.store()
calc.add_link_from(upf, "upf")
with tempfile.NamedTemporaryFile() as f:
f.write("data_test")
f.flush()
cif = CifData(file=f.name)
cif.store()
calc.add_link_from(cif, "cif")
calc.store()
calc._set_state(calc_states.SUBMITTING)
with SandboxFolder() as f:
calc._store_raw_input_folder(f.abspath)
fd = FolderData()
with open(
fd._get_folder_pathsubfolder.get_abs_path(
calc._SCHED_OUTPUT_FILE), 'w') as f:
f.write("standard output")
f.flush()
with open(
fd._get_folder_pathsubfolder.get_abs_path(
calc._SCHED_ERROR_FILE), 'w') as f:
f.write("standard error")
f.flush()
fd.store()
fd.add_link_from(calc, calc._get_linkname_retrieved(), LinkType.CREATE)
pd.add_link_from(calc, "calc", LinkType.CREATE)
pd.store()
with self.assertRaises(ValueError):
export_cif(c, parameters=pd)
c.add_link_from(calc, "calc", LinkType.CREATE)
export_cif(c, parameters=pd)
values = export_values(c, parameters=pd)
values = values['0']
self.assertEquals(values['_tcod_computation_environment'],
['PATH=/dev/null\nUSER=unknown'])
self.assertEquals(values['_tcod_computation_command'],
['cd 1; ./_aiidasubmit.sh'])
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 JobCalculation 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)
execlogger.debug("Retrieving calc {}".format(calculation.pk),
extra=logger_extra)
workdir = calculation._get_remote_workdir()
execlogger.debug("[retrieval of calc {}] chdir {}".format(
calculation.pk, workdir),
extra=logger_extra)
# Create the FolderData node to attach everything to
retrieved_files = FolderData()
retrieved_files.add_link_from(calculation,
label=calculation._get_linkname_retrieved(),
link_type=LinkType.CREATE)
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.replace_with_folder(folder.abspath, overwrite=True)
# Second, retrieve the singlefiles
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.dbnode.pk),
extra=logger_extra)
retrieved_files.store()
def test_complex_graph_import_export(self):
"""
This test checks that a small and bit complex graph can be correctly
exported and imported.
It will create the graph, store it to the database, export it to a file
and import it. In the end it will check if the initial nodes are present
at the imported graph.
"""
import tempfile
import shutil
import os
from aiida.orm.calculation.job import JobCalculation
from aiida.orm.data.folder import FolderData
from aiida.orm.data.parameter import ParameterData
from aiida.orm.data.remote import RemoteData
from aiida.common.links import LinkType
from aiida.orm.importexport import export, import_data
from aiida.orm.utils import load_node
from aiida.common.exceptions import NotExistent
temp_folder = tempfile.mkdtemp()
try:
calc1 = JobCalculation()
calc1.set_computer(self.computer)
calc1.set_resources({
"num_machines": 1,
"num_mpiprocs_per_machine": 1
})
calc1.label = "calc1"
calc1.store()
calc1._set_state(u'RETRIEVING')
pd1 = ParameterData()
pd1.label = "pd1"
pd1.store()
pd2 = ParameterData()
pd2.label = "pd2"
pd2.store()
rd1 = RemoteData()
rd1.label = "rd1"
rd1.set_remote_path("/x/y.py")
rd1.set_computer(self.computer)
rd1.store()
rd1.add_link_from(calc1, link_type=LinkType.CREATE)
calc2 = JobCalculation()
calc2.set_computer(self.computer)
calc2.set_resources({
"num_machines": 1,
"num_mpiprocs_per_machine": 1
})
calc2.label = "calc2"
calc2.store()
calc2.add_link_from(pd1, link_type=LinkType.INPUT)
calc2.add_link_from(pd2, link_type=LinkType.INPUT)
calc2.add_link_from(rd1, link_type=LinkType.INPUT)
calc2._set_state(u'SUBMITTING')
fd1 = FolderData()
fd1.label = "fd1"
fd1.store()
fd1.add_link_from(calc2, link_type=LinkType.CREATE)
node_uuids_labels = {
calc1.uuid: calc1.label,
pd1.uuid: pd1.label,
pd2.uuid: pd2.label,
rd1.uuid: rd1.label,
calc2.uuid: calc2.label,
fd1.uuid: fd1.label
}
filename = os.path.join(temp_folder, "export.tar.gz")
export([fd1.dbnode], outfile=filename, silent=True)
self.clean_db()
import_data(filename, silent=True, ignore_unknown_nodes=True)
for uuid, label in node_uuids_labels.iteritems():
try:
load_node(uuid)
except NotExistent:
self.fail("Node with UUID {} and label {} was not "
"found.".format(uuid, label))
finally:
# Deleting the created temporary folder
shutil.rmtree(temp_folder, ignore_errors=True)
def retrieve_computed_for_authinfo(authinfo):
from aiida.orm import JobCalculation
from aiida.common.folders import SandboxFolder
from aiida.orm.data.folder import FolderData
from aiida.utils.logger import get_dblogger_extra
from aiida.orm import DataFactory
import os
if not authinfo.enabled:
return
calcs_to_retrieve = list(
JobCalculation._get_all_with_state(state=calc_states.COMPUTED,
computer=authinfo.dbcomputer,
user=authinfo.aiidauser))
retrieved = []
# I avoid to open an ssh connection if there are no
# calcs with state not COMPUTED
if len(calcs_to_retrieve):
# Open connection
with authinfo.get_transport() as t:
for calc in calcs_to_retrieve:
logger_extra = get_dblogger_extra(calc)
t._set_logger_extra(logger_extra)
try:
calc._set_state(calc_states.RETRIEVING)
except ModificationNotAllowed:
# Someone else has already started to retrieve it,
# just log and continue
execlogger.debug("Attempting to retrieve more than once "
"calculation {}: skipping!".format(
calc.pk),
extra=logger_extra)
continue # with the next calculation to retrieve
try:
execlogger.debug("Retrieving calc {}".format(calc.pk),
extra=logger_extra)
workdir = calc._get_remote_workdir()
retrieve_list = calc._get_retrieve_list()
retrieve_singlefile_list = calc._get_retrieve_singlefile_list(
)
execlogger.debug("[retrieval of calc {}] "
"chdir {}".format(calc.pk, workdir),
extra=logger_extra)
t.chdir(workdir)
retrieved_files = FolderData()
retrieved_files.add_link_from(
calc,
label=calc._get_linkname_retrieved(),
link_type=LinkType.CREATE)
# First, retrieve the files of folderdata
with SandboxFolder() as folder:
for item in retrieve_list:
# I have two possibilities:
# * item is a string
# * or is a list
# then I have other two possibilities:
# * there are file patterns
# * or not
# First decide the name of the files
if isinstance(item, list):
tmp_rname, tmp_lname, depth = item
# if there are more than one file I do something differently
if t.has_magic(tmp_rname):
remote_names = t.glob(tmp_rname)
local_names = []
for rem in remote_names:
to_append = rem.split(
os.path.sep
)[-depth:] if depth > 0 else []
local_names.append(
os.path.sep.join([tmp_lname] +
to_append))
else:
remote_names = [tmp_rname]
to_append = remote_names.split(
os.path.sep
)[-depth:] if depth > 0 else []
local_names = [
os.path.sep.join([tmp_lname] +
to_append)
]
if depth > 1: # create directories in the folder, if needed
for this_local_file in local_names:
new_folder = os.path.join(
folder.abspath,
os.path.split(this_local_file)[0])
if not os.path.exists(new_folder):
os.makedirs(new_folder)
else: # it is a string
if t.has_magic(item):
remote_names = t.glob(item)
local_names = [
os.path.split(rem)[1]
for rem in remote_names
]
else:
remote_names = [item]
local_names = [os.path.split(item)[1]]
for rem, loc in zip(remote_names, local_names):
execlogger.debug(
"[retrieval of calc {}] "
"Trying to retrieve remote item '{}'".
format(calc.pk, rem),
extra=logger_extra)
t.get(rem,
os.path.join(folder.abspath, loc),
ignore_nonexisting=True)
# Here I retrieved everything;
# now I store them inside the calculation
retrieved_files.replace_with_folder(folder.abspath,
overwrite=True)
# Second, retrieve the singlefiles
with SandboxFolder() as folder:
singlefile_list = []
for (linkname, subclassname,
filename) in retrieve_singlefile_list:
execlogger.debug(
"[retrieval of calc {}] Trying "
"to retrieve remote singlefile '{}'".format(
calc.pk, filename),
extra=logger_extra)
localfilename = os.path.join(
folder.abspath,
os.path.split(filename)[1])
t.get(filename,
localfilename,
ignore_nonexisting=True)
singlefile_list.append(
(linkname, subclassname, localfilename))
# ignore files that have not been retrieved
singlefile_list = [
i for i in singlefile_list if os.path.exists(i[2])
]
# after retrieving from the cluster, I create the objects
singlefiles = []
for (linkname, subclassname,
filename) in singlefile_list:
SinglefileSubclass = DataFactory(subclassname)
singlefile = SinglefileSubclass()
singlefile.set_file(filename)
singlefile.add_link_from(calc,
label=linkname,
link_type=LinkType.CREATE)
singlefiles.append(singlefile)
# Finally, store
execlogger.debug("[retrieval of calc {}] "
"Storing retrieved_files={}".format(
calc.pk, retrieved_files.dbnode.pk),
extra=logger_extra)
retrieved_files.store()
for fil in singlefiles:
execlogger.debug(
"[retrieval of calc {}] "
"Storing retrieved_singlefile={}".format(
calc.pk, fil.dbnode.pk),
extra=logger_extra)
fil.store()
# If I was the one retrieving, I should also be the only
# one parsing! I do not check
calc._set_state(calc_states.PARSING)
Parser = calc.get_parserclass()
# If no parser is set, the calculation is successful
successful = True
if Parser is not None:
# TODO: parse here
parser = Parser(calc)
successful, new_nodes_tuple = parser.parse_from_calc()
for label, n in new_nodes_tuple:
n.add_link_from(calc,
label=label,
link_type=LinkType.CREATE)
n.store()
if successful:
try:
calc._set_state(calc_states.FINISHED)
except ModificationNotAllowed:
# I should have been the only one to set it, but
# in order to avoid unuseful error messages, I
# just ignore
pass
else:
try:
calc._set_state(calc_states.FAILED)
except ModificationNotAllowed:
# I should have been the only one to set it, but
# in order to avoid unuseful error messages, I
# just ignore
pass
execlogger.error(
"[parsing of calc {}] "
"The parser returned an error, but it should have "
"created an output node with some partial results "
"and warnings. Check there for more information on "
"the problem".format(calc.pk),
extra=logger_extra)
retrieved.append(calc)
except Exception:
import traceback
tb = traceback.format_exc()
newextradict = logger_extra.copy()
newextradict['full_traceback'] = tb
if calc.get_state() == calc_states.PARSING:
execlogger.error("Error parsing calc {}. "
"Traceback: {}".format(calc.pk, tb),
extra=newextradict)
# TODO: add a 'comment' to the calculation
try:
calc._set_state(calc_states.PARSINGFAILED)
except ModificationNotAllowed:
pass
else:
execlogger.error("Error retrieving calc {}. "
"Traceback: {}".format(calc.pk, tb),
extra=newextradict)
try:
calc._set_state(calc_states.RETRIEVALFAILED)
except ModificationNotAllowed:
pass
raise
return retrieved
def retrieve_computed_for_authinfo(authinfo):
from aiida.orm import JobCalculation
from aiida.common.folders import SandboxFolder
from aiida.orm.data.folder import FolderData
from aiida.common.log import get_dblogger_extra
from aiida.orm import DataFactory
from aiida.backends.utils import QueryFactory
import os
if not authinfo.enabled:
return
qmanager = QueryFactory()()
# I create a unique set of pairs (computer, aiidauser)
calcs_to_retrieve = qmanager.query_jobcalculations_by_computer_user_state(
state=calc_states.COMPUTED,
computer=authinfo.dbcomputer,
user=authinfo.aiidauser)
retrieved = []
# I avoid to open an ssh connection if there are no
# calcs with state not COMPUTED
if len(calcs_to_retrieve):
# Open connection
with authinfo.get_transport() as t:
for calc in calcs_to_retrieve:
logger_extra = get_dblogger_extra(calc)
t._set_logger_extra(logger_extra)
try:
calc._set_state(calc_states.RETRIEVING)
except ModificationNotAllowed:
# Someone else has already started to retrieve it,
# just log and continue
execlogger.debug("Attempting to retrieve more than once "
"calculation {}: skipping!".format(
calc.pk),
extra=logger_extra)
continue # with the next calculation to retrieve
try:
execlogger.debug("Retrieving calc {}".format(calc.pk),
extra=logger_extra)
workdir = calc._get_remote_workdir()
retrieve_list = calc._get_retrieve_list()
retrieve_temporary_list = calc._get_retrieve_temporary_list(
)
retrieve_singlefile_list = calc._get_retrieve_singlefile_list(
)
execlogger.debug("[retrieval of calc {}] "
"chdir {}".format(calc.pk, workdir),
extra=logger_extra)
t.chdir(workdir)
retrieved_files = FolderData()
retrieved_files.add_link_from(
calc,
label=calc._get_linkname_retrieved(),
link_type=LinkType.CREATE)
# First, retrieve the files of folderdata
with SandboxFolder() as folder:
retrieve_files_from_list(calc, t, folder,
retrieve_list)
# Here I retrieved everything; now I store them inside the calculation
retrieved_files.replace_with_folder(folder.abspath,
overwrite=True)
# Second, retrieve the singlefiles
with SandboxFolder() as folder:
singlefile_list = []
for (linkname, subclassname,
filename) in retrieve_singlefile_list:
execlogger.debug(
"[retrieval of calc {}] Trying "
"to retrieve remote singlefile '{}'".format(
calc.pk, filename),
extra=logger_extra)
localfilename = os.path.join(
folder.abspath,
os.path.split(filename)[1])
t.get(filename,
localfilename,
ignore_nonexisting=True)
singlefile_list.append(
(linkname, subclassname, localfilename))
# ignore files that have not been retrieved
singlefile_list = [
i for i in singlefile_list if os.path.exists(i[2])
]
# after retrieving from the cluster, I create the objects
singlefiles = []
for (linkname, subclassname,
filename) in singlefile_list:
SinglefileSubclass = DataFactory(subclassname)
singlefile = SinglefileSubclass()
singlefile.set_file(filename)
singlefile.add_link_from(calc,
label=linkname,
link_type=LinkType.CREATE)
singlefiles.append(singlefile)
# Retrieve the temporary files in a separate temporary folder if any files were
# specified in the 'retrieve_temporary_list' key
if retrieve_temporary_list:
retrieved_temporary_folder = FolderData()
with SandboxFolder() as folder:
retrieve_files_from_list(calc, t, folder,
retrieve_temporary_list)
retrieved_temporary_folder.replace_with_folder(
folder.abspath, overwrite=True)
# Log the files that were retrieved in the temporary folder
for entry in retrieved_temporary_folder.get_folder_list(
):
execlogger.debug(
"[retrieval of calc {}] Retrieved temporary file or folder '{}'"
.format(calc.pk, entry),
extra=logger_extra)
else:
retrieved_temporary_folder = None
# Finally, store the retrieved_files node. The retrieved_temporary_folder node
# is explicitly not stored, but will just be passed to the parser.parse_from calc call
execlogger.debug(
"[retrieval of calc {}] Storing retrieved_files={}".
format(calc.pk, retrieved_files.dbnode.pk),
extra=logger_extra)
retrieved_files.store()
for fil in singlefiles:
execlogger.debug(
"[retrieval of calc {}] Storing retrieved_singlefile={}"
.format(calc.pk, fil.dbnode.pk),
extra=logger_extra)
fil.store()
# If I was the one retrieving, I should also be the only one parsing! I do not check
calc._set_state(calc_states.PARSING)
Parser = calc.get_parserclass()
# If no parser is set, the calculation is successful
successful = True
if Parser is not None:
parser = Parser(calc)
successful, new_nodes_tuple = parser.parse_from_calc(
retrieved_temporary_folder)
for label, n in new_nodes_tuple:
n.add_link_from(calc,
label=label,
link_type=LinkType.CREATE)
n.store()
if successful:
try:
calc._set_state(calc_states.FINISHED)
except ModificationNotAllowed:
# I should have been the only one to set it, but
# in order to avoid unuseful error messages, I
# just ignore
pass
else:
try:
calc._set_state(calc_states.FAILED)
except ModificationNotAllowed:
# I should have been the only one to set it, but
# in order to avoid unuseful error messages, I
# just ignore
pass
execlogger.error(
"[parsing of calc {}] "
"The parser returned an error, but it should have "
"created an output node with some partial results "
"and warnings. Check there for more information on "
"the problem".format(calc.pk),
extra=logger_extra)
retrieved.append(calc)
except Exception:
import traceback
tb = traceback.format_exc()
newextradict = logger_extra.copy()
newextradict['full_traceback'] = tb
if calc.get_state() == calc_states.PARSING:
execlogger.error("Error parsing calc {}. "
"Traceback: {}".format(calc.pk, tb),
extra=newextradict)
# TODO: add a 'comment' to the calculation
try:
calc._set_state(calc_states.PARSINGFAILED)
except ModificationNotAllowed:
pass
else:
execlogger.error("Error retrieving calc {}. "
"Traceback: {}".format(calc.pk, tb),
extra=newextradict)
try:
calc._set_state(calc_states.RETRIEVALFAILED)
except ModificationNotAllowed:
pass
raise
return retrieved
def migrate_as_main(work_dir,
input_rel_path,
output_rel_path,
resources=None,
input_links=None):
""" migrate existing CRYSTAL17 calculation as a WorkCalculation,
which imitates a ``crystal17.main`` calculation
:param work_dir: the absolute path to the directory to holding the files
:param input_rel_path: relative path (from work_dir) to .d12 file
:param output_rel_path: relative path (from work_dir) to .out file
:param resources: a dict of of job resource parameters (not yet implemented)
:param input_links: a dict of existing nodes to link inputs to (allowed keys: 'structure', 'settings', 'parameters')
Example of input_links={'structure': {"cif_file": CifNode}},
will create a link (via a workcalculation) from the CifNode to the input StructureData
:raise IOError: if the work_dir or files do not exist
:raises aiida.common.exceptions.ParsingError: if the input parsing fails
:raises aiida.parsers.exceptions.OutputParsingError: if the output parsing fails
:return: the calculation node
:rtype: aiida.orm.WorkCalculation
"""
from aiida.orm.data.folder import FolderData
from aiida_crystal17.calculations.cry_main import CryMainCalculation
from aiida_crystal17.parsers.cry_basic import CryBasicParser
calc = CryMainCalculation()
parser_cls = CryBasicParser
# TODO optionally use transport to remote work directory
if not os.path.exists(work_dir):
raise IOError("work_dir doesn't exist: {}".format(work_dir))
input_path = os.path.join(work_dir, input_rel_path)
if not os.path.exists(input_path):
raise IOError("input_path doesn't exist: {}".format(input_path))
output_path = os.path.join(work_dir, output_rel_path)
if not os.path.exists(output_path):
raise IOError("output_path doesn't exist: {}".format(output_path))
if resources:
raise NotImplementedError("saving resources to ImmigrantCalculation")
# resources = {} if resources is None else resources
inputs = create_inputs(input_path, output_path)
psuccess, output_nodes = parse_mainout(output_path,
parser_class=parser_cls.__name__,
init_struct=inputs['structure'],
init_settings=inputs['settings'])
outparams = output_nodes.pop("parameters")
perrors = outparams.get_attr("errors") + outparams.get_attr(
"parser_warnings")
if perrors or not psuccess:
raise ParsingError(
"the parser failed, raising the following errors:\n{}".format(
"\n\t".join(perrors)))
folder = FolderData()
folder.add_path(input_path, calc._DEFAULT_INPUT_FILE) # pylint: disable=protected-access
folder.add_path(output_path, calc._DEFAULT_OUTPUT_FILE) # pylint: disable=protected-access
# create links from existing nodes to inputs
input_links = {} if not input_links else input_links
for key, nodes_dict in input_links.items():
_run_dummy_workchain(
nodes_dict,
{key: inputs[key]},
)
# assign linknames
inputs_dict = {
calc.get_linkname("parameters"): inputs['parameters'],
calc.get_linkname("structure"): inputs['structure'],
calc.get_linkname("settings"): inputs['settings']
}
for el, basis in inputs["basis"].items():
inputs_dict[calc.get_linkname_basisset(el)] = basis
outputs_dict = {parser_cls.get_linkname_outparams(): outparams}
if "settings" in output_nodes:
outputs_dict[parser_cls.get_linkname_outsettings()] = output_nodes.pop(
"settings")
if "structure" in output_nodes:
outputs_dict[parser_cls.get_linkname_outstructure(
)] = output_nodes.pop("structure")
if output_nodes:
raise ParsingError("unknown key(s) in output_nodes: {}".format(
list(output_nodes.keys())))
outputs_dict["retrieved"] = folder
calcnode = _run_dummy_workchain(inputs_dict, outputs_dict,
CryMainImmigrant)
calcnode.label = "CryMainImmigrant"
calcnode.description = "an immigrated CRYSTAL17 calculation into the {} format".format(
calc.__class__)
return calcnode
