def setUpClass(cls):
cls.execution_path = os.path.dirname(os.path.abspath(__file__))
cls.project = Project(os.path.join(cls.execution_path, "lammps"))
structure = Atoms(
symbols="Fe2",
positions=np.outer(np.arange(2), np.ones(3)),
cell=2 * np.eye(3),
)
cls.job = Lammps(
project=ProjectHDFio(project=cls.project, file_name="lammps"),
job_name="lammps",
)
cls.job.server.run_mode.interactive = True
cls.job.structure = structure
cls.minimize_job = Lammps(
project=ProjectHDFio(project=cls.project, file_name="lammps"),
job_name="minimize_lammps",
)
cls.minimize_control_job = Lammps(
project=ProjectHDFio(project=cls.project, file_name="lammps"),
job_name="minimize_control_lammps",
)
def setUp(self) -> None:
super().setUp()
self.job = Lammps(
project=ProjectHDFio(project=self.project, file_name="lammps"),
job_name="lammps",
)
self.ref = Lammps(
project=ProjectHDFio(project=self.project, file_name="ref"),
job_name="ref",
)
# Creating jobs this way puts them at the right spot, but decouples them from our self.project instance.
# I still don't understand what's happening as deeply as I'd like (at all!) but I've been fighting with it too
# long, so for now I will just force the issue by redefining the project attribute(s). -Liam Huber
self.project = self.job.project
self.ref_project = self.ref.project
def create_job(self, job_type, job_name, delete_existing_job=False):
"""
Create one of the following jobs:
- 'StructureContainer’:
- ‘StructurePipeline’:
- ‘AtomisticExampleJob’: example job just generating random number
- ‘ExampleJob’: example job just generating random number
- ‘Lammps’:
- ‘KMC’:
- ‘Sphinx’:
- ‘Vasp’:
- ‘GenericMaster’:
- ‘SerialMaster’: series of jobs run in serial
- ‘AtomisticSerialMaster’:
- ‘ParallelMaster’: series of jobs run in parallel
- ‘KmcMaster’:
- ‘ThermoLambdaMaster’:
- ‘RandomSeedMaster’:
- ‘MeamFit’:
- ‘Murnaghan’:
- ‘MinimizeMurnaghan’:
- ‘ElasticMatrix’:
- ‘ConvergenceVolume’:
- ‘ConvergenceEncutParallel’:
- ‘ConvergenceKpointParallel’:
- ’PhonopyMaster’:
- ‘DefectFormationEnergy’:
- ‘LammpsASE’:
- ‘PipelineMaster’:
- ’TransformationPath’:
- ‘ThermoIntEamQh’:
- ‘ThermoIntDftEam’:
- ‘ScriptJob’: Python script or jupyter notebook job container
- ‘ListMaster': list of jobs
Args:
job_type (str): job type can be ['StructureContainer’, ‘StructurePipeline’, ‘AtomisticExampleJob’,
‘ExampleJob’, ‘Lammps’, ‘KMC’, ‘Sphinx’, ‘Vasp’, ‘GenericMaster’,
‘SerialMaster’, ‘AtomisticSerialMaster’, ‘ParallelMaster’, ‘KmcMaster’,
‘ThermoLambdaMaster’, ‘RandomSeedMaster’, ‘MeamFit’, ‘Murnaghan’,
‘MinimizeMurnaghan’, ‘ElasticMatrix’, ‘ConvergenceVolume’,
‘ConvergenceEncutParallel’, ‘ConvergenceKpointParallel’, ’PhonopyMaster’,
‘DefectFormationEnergy’, ‘LammpsASE’, ‘PipelineMaster’,
’TransformationPath’, ‘ThermoIntEamQh’, ‘ThermoIntDftEam’, ‘ScriptJob’,
‘ListMaster']
job_name (str): name of the job
Returns:
GenericJob: job object depending on the job_type selected
"""
job = JobType(
job_type,
project=ProjectHDFio(project=self.copy(), file_name=job_name),
job_name=job_name,
job_class_dict=self.job_type.job_class_dict,
delete_existing_job=delete_existing_job,
)
if self.user is not None:
job.user = self.user
return job
def setUpClass(cls):
state.update({
'resource_paths':
os.path.join(os.path.dirname(os.path.abspath(__file__)),
"../static")
})
cls.execution_path = os.path.dirname(os.path.abspath(__file__))
cls.project = Project(os.path.join(cls.execution_path, "test_vasp"))
cls.job = cls.project.create_job("Vasp", "trial")
cls.job_spin = cls.project.create_job("Vasp", "spin")
cls.job_spin.structure = CrystalStructure("Fe",
BravaisBasis="bcc",
a=2.83)
cls.job_spin.structure = cls.job_spin.structure.repeat(2)
cls.job_spin.structure[2] = "Se"
cls.job_spin.structure[3] = "O"
cls.job_metadyn = cls.project.create_job("VaspMetadyn",
"trial_metadyn")
cls.job_complete = Vasp(
project=ProjectHDFio(project=cls.project,
file_name="vasp_complete"),
job_name="vasp_complete",
)
poscar_file = posixpath.join(
cls.execution_path,
"../static/vasp_test_files/full_job_sample/POSCAR")
cls.job_complete.structure = read_atoms(poscar_file,
species_from_potcar=True)
poscar_file = posixpath.join(
cls.execution_path,
"../static/vasp_test_files/poscar_samples/POSCAR_metadyn")
cls.job_metadyn.structure = read_atoms(poscar_file)
def setUpClass(cls):
cls.execution_path = os.path.dirname(os.path.abspath(__file__))
cls.project = Project(os.path.join(cls.execution_path, "lammps"))
cls.job = Lammps(
project=ProjectHDFio(project=cls.project, file_name="lammps"),
job_name="lammps",
)
cls.job_water = Lammps(
project=ProjectHDFio(project=cls.project,
file_name="lammps_water"),
job_name="lammps_water",
)
cls.job_water_dump = Lammps(
project=ProjectHDFio(project=cls.project,
file_name="lammps_water_dump"),
job_name="lammps_water_dump",
)
cls.job_dump = Lammps(
project=ProjectHDFio(project=cls.project,
file_name="lammps_dump_static"),
job_name="lammps_dump_static",
)
cls.job_vcsgc_input = Lammps(
project=ProjectHDFio(project=cls.project,
file_name="lammps_vcsgc_input"),
job_name="lammps_vcsgc_input",
)
cls.minimize_job = Lammps(
project=ProjectHDFio(project=cls.project, file_name="lammps"),
job_name="minimize_lammps",
)
cls.minimize_control_job = Lammps(
project=ProjectHDFio(project=cls.project, file_name="lammps"),
job_name="minimize_control_lammps",
)
cls.job_read_restart = Lammps(
project=ProjectHDFio(project=cls.project, file_name="lammps"),
job_name="read_restart",
)
cls.job_average = Lammps(
project=ProjectHDFio(project=cls.project, file_name="lammps"),
job_name="average",
)
cls.job_fail = Lammps(
project=ProjectHDFio(project=cls.project, file_name="lammps"),
job_name="fail",
)
def setUpClass(cls):
cls.execution_path = os.path.dirname(os.path.abspath(__file__))
cls.project = Project(os.path.join(cls.execution_path, "gpaw"))
atoms = Atoms("Fe1", positions=np.zeros((1, 3)), cell=np.eye(3))
job = Gpaw(
project=ProjectHDFio(project=cls.project, file_name="gpaw"),
job_name="gpaw",
)
job.structure = atoms
job.encut = 300
job.set_kpoints([5, 5, 5])
job.to_hdf()
cls.job = Gpaw(
project=ProjectHDFio(project=cls.project, file_name="gpaw"),
job_name="gpaw",
)
cls.job.from_hdf()
def test_serialization(self):
self.job.to_hdf()
loaded = Gpaw(
project=ProjectHDFio(project=self.project, file_name="gpaw"),
job_name="gpaw",
)
loaded.from_hdf()
self.assertEqual(self.job.encut, loaded.encut)
def setUpClass(cls):
cls.execution_path = os.path.dirname(os.path.abspath(__file__))
cls.project = Project(os.path.join(cls.execution_path, "test_quickff"))
cls.job = cls.project.create_job("QuickFF", "trial")
cls.job_complete = QuickFF(
project=ProjectHDFio(project=cls.project, file_name="quickff_complete"),
job_name="quickff_complete",
)
def setUp(self) -> None:
self.job = ToyAtomisticJob(
project=ProjectHDFio(project=self.project, file_name="test_job"),
job_name="test_job",
)
self.job.structure = CrystalStructure(element="Al",
bravais_basis="fcc",
lattice_constants=4).repeat(4)
self.job.run()
def setUpClass(cls):
cls.execution_path = os.path.dirname(os.path.abspath(__file__))
cls.project = Project(os.path.join(cls.execution_path, "test_job"))
cls.job = AtomisticGenericJob(
project=ProjectHDFio(project=cls.project, file_name="test_job"),
job_name="test_job",
)
cls.job.structure = CrystalStructure(element="Al",
bravais_basis="fcc",
lattice_constants=4).repeat(4)
def setUp(self):
if self.project.load('trial') is not None:
self.project.remove_job('trial')
self.job = self.project.create_job("QuickFF", 'trial')
if self.project.load('quickff_complete') is not None:
self.project.remove_job("quickff_complete")
self.job_complete = QuickFF(
project=ProjectHDFio(project=self.project, file_name="quickff_complete"),
job_name="quickff_complete",
)
def setUp(self):
if self.project.load('trial') is not None:
self.project.remove_job("trial")
self.job = self.project.create_job("Yaff", "trial")
if self.project.load('yaff_complete') is not None:
self.project.remove_job("yaff_complete")
self.job_complete = Yaff(
project=ProjectHDFio(project=self.project,
file_name="yaff_complete"),
job_name="yaff_complete",
)
def setUpClass(cls):
state.update({
"resource_paths":
os.path.join(os.path.dirname(os.path.abspath(__file__)),
"../static/calphy_test_files")
})
cls.execution_path = os.path.dirname(os.path.abspath(__file__))
# cls.file_location = os.path.dirname(os.path.abspath(__file__))
cls.project = Project(os.path.join(cls.execution_path, "test_calphy"))
cls.job = Calphy(
project=ProjectHDFio(project=cls.project, file_name="test_calphy"),
job_name="test_calphy",
)
filepath = os.path.join(os.path.dirname(os.path.abspath(__file__)),
"../static/")
cls.output_project = Project(os.path.join(filepath, "test_files"))
def load_file(fp, filetype=None, project=None):
"""
Load the file and return an appropriate object containing the data.
Args:
fp (str / file): path to the file or file object to be displayed.
filetype (str/None): File extension, if given this overwrites the assumption based on the filename.
project (pyiron-Project/None): Project calling this function, provided to all objects referring to such.
Supported file types are:
'.h5', '.hdf'
'.json'
'.txt'
'.csv'
'.ipynb'
Image extensions supported by PIL
Returns:
:class:`FileHDFio`: pointing to the file of filetype = '.h5'
dict: containing data from file of filetype = '.json'
list: of all lines from file for filetype = '.txt'
:class:`pandas.DataFrame`: containing data from file of filetype = '.csv'
"""
def _load_txt(file):
if isinstance(file, str):
with open(file, encoding="utf8") as f:
return f.readlines()
else:
return file.readlines()
def _load_ipynb(file):
return OwnNotebookNode(nbformat.read(file, as_version=4))
def _load_json(file):
if isinstance(file, str):
with open(file) as f:
return json.load(f)
else:
return json.load(file)
def _load_csv(file):
return pandas.read_csv(file)
def _load_img(file):
return Image.open(file)
def _load_default(file):
try:
return _load_txt(file)
except Exception as e:
raise IOError("File could not be loaded.") from e
def _resolve_filetype(file, _filetype):
if _filetype is None and isinstance(file, str):
_, _filetype = os.path.splitext(file)
elif _filetype is None and hasattr(file, "name"):
_, _filetype = os.path.splitext(file.name)
elif _filetype is None:
return None
elif _filetype[0] != ".":
_filetype = "." + _filetype
return _filetype.lower()
filetype = _resolve_filetype(fp, filetype)
if filetype in [".h5", ".hdf"] and isinstance(fp, str):
if project is None:
return FileHDFio(file_name=fp)
else:
return ProjectHDFio(file_name=fp, project=project)
elif filetype in [".json"]:
return _load_json(fp)
elif filetype in [".txt"]:
return _load_txt(fp)
elif filetype in [".csv"]:
return _load_csv(fp)
elif _has_imported["nbformat"] and filetype in [".ipynb"]:
return _load_ipynb(fp)
elif _has_imported["PIL"] and filetype in Image.registered_extensions():
return _load_img(fp)
else:
return _load_default(fp)
def load_file(filename, filetype=None, project=None):
"""
Load the file and return an appropriate object containing the data.
Args:
filename (str): path to the file to be displayed.
filetype (str/None): File extension, if given this overwrites the assumption based on the filename.
project (pyiron-Project/None): Project calling this function, provided to all objects referring to such.
Supported file types are:
'.h5', '.hdf'
'.json'
'.txt'
'.csv'
'.ipynb'
Image extensions supported by PIL
Returns:
:class:`FileHDFio`: pointing to the file of filetype = '.h5'
dict: containing data from file of filetype = '.json'
list: of all lines from file for filetype = '.txt'
:class:`pandas.DataFrame`: containing data from file of filetype = '.csv'
"""
def _load_txt(file):
with open(file, encoding='utf8') as f:
return f.readlines()
def _load_ipynb(file):
return OwnNotebookNode(nbformat.read(file, as_version=4))
def _load_json(file):
with open(file) as f:
return json.load(f)
def _load_csv(file):
return pandas.read_csv(file)
def _load_img(file):
return Image.open(file)
def _load_default(file):
try:
return _load_txt(file)
except Exception as e:
raise IOError("File could not be loaded.") from e
if filetype is None:
_, filetype = os.path.splitext(filename)
elif filetype[0] != '.':
filetype = '.' + filetype
filetype = filetype.lower()
if filetype in ['.h5', '.hdf']:
if project is None:
return FileHDFio(file_name=filename)
else:
return ProjectHDFio(file_name=filename, project=project)
elif filetype in ['.json']:
return _load_json(filename)
elif filetype in ['.txt']:
return _load_txt(filename)
elif filetype in ['.csv']:
return _load_csv(filename)
elif _has_imported['nbformat'] and filetype in ['.ipynb']:
return _load_ipynb(filename)
elif _has_imported['PIL'] and filetype in Image.registered_extensions():
return _load_img(filename)
else:
return _load_default(filename)