def setUp(self):
self.current_dir = os.path.dirname(os.path.abspath(__file__)).replace(
'\\', '/')
self.empty_hdf5 = FileHDFio(file_name=self.current_dir +
'/filehdfio_empty.h5')
self.full_hdf5 = FileHDFio(file_name=self.current_dir +
'/filehdfio_full.h5')
self.es_hdf5 = FileHDFio(file_name=self.current_dir +
"/../../static/dft/es_hdf.h5")
def setUpClass(cls):
cls.current_dir = os.path.dirname(os.path.abspath(__file__)).replace('\\', '/')
cls.empty_hdf5 = FileHDFio(file_name=cls.current_dir + '/filehdfio_empty.h5')
cls.full_hdf5 = FileHDFio(file_name=cls.current_dir + '/filehdfio_full.h5')
cls.es_hdf5 = FileHDFio(file_name=cls.current_dir + "/../../static/dft/es_hdf.h5")
with cls.full_hdf5.open('content') as hdf:
hdf['array'] = np.array([1, 2, 3, 4, 5, 6])
hdf['array_3d'] = np.array([[1, 2, 3], [4, 5, 6]])
hdf['traj'] = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9]]])
hdf['dict'] = {'key_1': 1, 'key_2': 'hallo'}
hdf['dict_numpy'] = {'key_1': 1, 'key_2': np.array([1, 2, 3, 4, 5, 6])}
def test_to_hdf(self):
if sys.version_info[0] >= 3:
filename = os.path.join(
os.path.dirname(os.path.abspath(__file__)),
"../../static/dft/test_es_hdf.h5",
)
abs_filename = os.path.abspath(filename)
hdf_obj = FileHDFio(abs_filename)
es_obj_old = self.es_list[1]
es_obj_old.to_hdf(hdf_obj, group_name="written_es")
es_obj_new = ElectronicStructure()
es_obj_new.from_hdf(hdf=hdf_obj, group_name="written_es")
self.assertTrue(
np.array_equal(
hdf_obj["written_es/dos/grand_dos_matrix"],
es_obj_old.grand_dos_matrix,
))
self.assertEqual(es_obj_old.efermi, es_obj_new.efermi)
self.assertEqual(es_obj_old.is_metal, es_obj_new.is_metal)
self.assertEqual(es_obj_old.vbm, es_obj_new.vbm)
self.assertEqual(es_obj_old.cbm, es_obj_new.cbm)
self.assertTrue(
np.array_equal(es_obj_new.grand_dos_matrix,
es_obj_old.grand_dos_matrix))
self.assertTrue(
np.array_equal(es_obj_new.resolved_densities,
es_obj_old.resolved_densities))
def store_custom_output_dict(output_dict):
folder = Path(".").cwd().parts[-1]
hdf_file = Path(".").cwd().parents[1] / folder
hdf_file = str(hdf_file) + ".h5"
hdf = FileHDFio(hdf_file)
hdf[folder].create_group("output")
for k, v in output_dict.items():
hdf[folder + "/output"][k] = v
def store_custom_output_dict(output_dict):
folder = Path('.').cwd().parts[-1]
hdf_file = Path('.').cwd().parents[1] / folder
hdf_file = str(hdf_file) + '.h5'
hdf = FileHDFio(hdf_file)
hdf[folder].create_group('output')
for k, v in output_dict.items():
hdf[folder + '/output'][k] = v
def setUpClass(cls):
cls.current_dir = os.path.dirname(os.path.abspath(__file__)).replace(
"\\", "/")
cls.empty_hdf5 = FileHDFio(file_name=cls.current_dir +
"/filehdfio_empty.h5")
cls.full_hdf5 = FileHDFio(file_name=cls.current_dir +
"/filehdfio_full.h5")
cls.es_hdf5 = FileHDFio(file_name=cls.current_dir +
"/../../static/dft/es_hdf.h5")
with cls.full_hdf5.open("content") as hdf:
hdf["array"] = np.array([1, 2, 3, 4, 5, 6])
hdf["array_3d"] = np.array([[1, 2, 3], [4, 5, 6]])
hdf["traj"] = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9]]])
hdf["dict"] = {"key_1": 1, "key_2": "hallo"}
hdf["dict_numpy"] = {
"key_1": 1,
"key_2": np.array([1, 2, 3, 4, 5, 6])
}
def get_custom_dict():
folder = Path('.').cwd().parts[-1]
hdf_file = Path('.').cwd().parents[1]/folder
hdf_file = str(hdf_file)+'.h5'
if Path(hdf_file).exists():
hdf = FileHDFio(hdf_file)
custom_dict = GenericParameters()
for k, v in zip(hdf[folder+'/input/custom_dict/data_dict']['Parameter'],
hdf[folder+'/input/custom_dict/data_dict']['Value']):
custom_dict[k] = v
return custom_dict
else:
print(hdf_file, 'not found')
return None
def test_from_hdf(self):
if sys.version_info[0] >= 3:
filename = os.path.join(os.path.dirname(os.path.abspath(__file__)),
"../../static/atomistics/test_hdf")
abs_filename = os.path.abspath(filename)
hdf_obj = FileHDFio(abs_filename)
pos, cell = generate_fcc_lattice()
basis_store = Atoms(symbols='Al', positions=pos, cell=cell)
basis_store.set_repeat([2, 2, 2])
basis_store.to_hdf(hdf_obj, "simple_structure")
basis = Atoms().from_hdf(hdf_obj, group_name="simple_structure")
self.assertEqual(len(basis), 8)
self.assertEqual(basis.get_majority_species()[1], "Al")
self.assertEqual(basis.get_spacegroup()['Number'], 225)
def test_to_hdf(self):
if sys.version_info[0] >= 3:
filename = os.path.join(os.path.dirname(os.path.abspath(__file__)),
"../../static/atomistics/test_hdf")
abs_filename = os.path.abspath(filename)
hdf_obj = FileHDFio(abs_filename)
pos, cell = generate_fcc_lattice()
basis = Atoms(symbols='Al', positions=pos, cell=cell)
basis.set_repeat([2, 2, 2])
basis.to_hdf(hdf_obj, "test_structure")
self.assertTrue(
np.array_equal(hdf_obj["test_structure/positions"],
basis.positions))
basis_new = Atoms().from_hdf(hdf_obj, "test_structure")
self.assertEqual(basis, basis_new)
def get_custom_dict():
folder = Path(".").cwd().parts[-1]
hdf_file = Path(".").cwd().parents[1] / folder
hdf_file = str(hdf_file) + ".h5"
if Path(hdf_file).exists():
hdf = FileHDFio(hdf_file)
custom_dict = GenericParameters()
for k, v in zip(
hdf[folder + "/input/custom_dict/data_dict"]["Parameter"],
hdf[folder + "/input/custom_dict/data_dict"]["Value"],
):
custom_dict[k] = v
return custom_dict
else:
print(hdf_file, "not found")
return None
def test_from_hdf(self):
filename = os.path.join(os.path.dirname(os.path.abspath(__file__)),
"../../static/dft/es_hdf.h5")
abs_filename = os.path.abspath(filename)
hdf_obj = FileHDFio(abs_filename)
es_obj_old = ElectronicStructure()
es_obj_old.from_hdf_old(hdf_obj, "es_old")
es_obj_new = ElectronicStructure()
es_obj_new.from_hdf(hdf=hdf_obj, group_name="es_new")
self.assertEqual(es_obj_old.efermi, es_obj_new.efermi)
self.assertEqual(es_obj_old.is_metal, es_obj_new.is_metal)
self.assertEqual(es_obj_old.vbm, es_obj_new.vbm)
self.assertEqual(es_obj_old.cbm, es_obj_new.cbm)
self.assertTrue(
np.array_equal(es_obj_new.grand_dos_matrix,
es_obj_old.grand_dos_matrix))
def create_table(self, enforce_update=False, level=3, file=None, job_status_list=None):
skip_table_update = False
filter_funct = self.filter_function
if job_status_list is None:
job_status_list = ["finished"]
if self._is_file():
if file is None:
file = FileHDFio(
file_name=self._project.path + self.name + ".h5", h5_path="/"
)
temp_user_function_dict, temp_system_function_dict = self._get_data_from_hdf5(
hdf=file
)
job_stored_ids = self._get_job_ids()
job_update_lst = [
self._project.inspect(job_id)
for job_id in self._project.get_job_ids()
if job_id not in job_stored_ids
]
job_update_lst = [
job
for job in job_update_lst
if job.status in job_status_list and filter_funct(job)
]
keys_update_user_lst = [
key
for key in self.add._user_function_dict.keys()
if key not in temp_user_function_dict.keys()
]
keys_update_system_lst = [
k
for k, v in self.add._system_function_dict.items()
if v and not temp_system_function_dict[k]
]
if (
len(job_update_lst) == 0
and len(keys_update_user_lst) == 0
and keys_update_system_lst == 0
and not enforce_update
):
skip_table_update = True
else:
job_update_lst = [
self._project.inspect(job_id) for job_id in self._project.get_job_ids()
]
job_update_lst = [
job
for job in job_update_lst
if job.status in job_status_list and filter_funct(job)
]
keys_update_user_lst, keys_update_system_lst = [], []
if not skip_table_update and len(job_update_lst) != 0:
df_new_ids = self._iterate_over_job_lst(
job_lst=job_update_lst, function_lst=self.add._function_lst, level=level
)
else:
df_new_ids = pandas.DataFrame({})
if not skip_table_update and (
len(keys_update_user_lst) != 0 or len(keys_update_system_lst) != 0
):
job_update_lst = [
self._project.inspect(job_id) for job_id in self._get_job_ids()
]
job_update_lst = [
job
for job in job_update_lst
if job is not None and job.status in job_status_list and filter_funct(job)
]
function_lst = [
v
for k, v in self.add._user_function_dict.items()
if k in keys_update_system_lst
] + [
funct
for funct in self.add._system_function_lst
if funct.__name__ in keys_update_system_lst
]
df_new_keys = self._iterate_over_job_lst(
job_lst=job_update_lst, function_lst=function_lst, level=level
)
else:
df_new_keys = pandas.DataFrame({})
if len(self._df) > 0 and len(df_new_keys) > 0:
self._df = pandas.concat(
[self._df, df_new_keys], axis=1, sort=False
).reset_index(drop=True)
if len(self._df) > 0 and len(df_new_ids) > 0:
self._df = pandas.concat([self._df, df_new_ids], sort=False).reset_index(
drop=True
)
elif len(df_new_ids) > 0:
self._df = df_new_ids
def from_hdf(self):
file = FileHDFio(file_name=self._project.path + self.name + ".h5", h5_path="/")
self.add._from_hdf(file)
def from_hdf(self):
file = FileHDFio(file_name=self._project.path + self.name + '.h5',
h5_path='/')
self.add._from_hdf(file)