def test_dataverse_init_invalid(self):
"""Test Dataverse.init() with invalid data."""
pdv = Dataverse()
# invalid data
for data in test_config["invalid_set_types"]:
with pytest.raises(AssertionError):
pdv.set(data)
def test_dataverse_is_valid_valid(self, import_dataverse_min_dict):
"""Test Dataverse.is_valid() with valid data.
Parameters
----------
import_dataverse_min_dict : dict
Fixture, which returns a flat dataset dict() coming from
`tests/data/dataverse_min.json`.
"""
data = import_dataverse_min_dict
dv = Dataverse()
dv.set(data)
assert dv.is_valid()
def test_dataverse_dict_format_wrong(self, import_dataverse_min_dict):
"""Test Dataverse.dict() with non-valid format.
Parameters
----------
import_dataverse_min_dict : dict
Fixture, which returns a flat dataset dict() coming from
`tests/data/dataverse_min.json`.
"""
data = import_dataverse_min_dict
dv = Dataverse()
dv.set(data)
assert not dv.dict('wrong')
def test_dataverse_json_dv_up_valid_not(self, import_dataverse_min_dict):
"""Test Dataverse.json() with format=`dv_up` and non-valid data.
Parameters
----------
import_dataverse_min_dict : dict
Fixture, which returns a flat dataset dict() coming from
`tests/data/dataverse_min.json`.
"""
data = import_dataverse_min_dict
dv = Dataverse()
dv.set(data)
dv.name = None
assert not dv.json()
def test_dataverse_json_dv_up_valid(self, import_dataverse_min_dict):
"""Test Dataverse.json() with format=`dv_up` and valid data.
Parameters
----------
import_dataverse_min_dict : dict
Fixture, which returns a flat dataset dict() coming from
`tests/data/dataverse_min.json`.
"""
data = import_dataverse_min_dict
dv = Dataverse()
dv.set(data)
assert dv.json()
assert isinstance(dv.json(), str)
def test_dataverse_json_format_wrong_valid(self,
import_dataverse_min_dict):
"""Test Dataverse.json() with non-valid format and valid data.
Parameters
----------
import_dataverse_min_dict : dict
Fixture, which returns a flat dataset dict() coming from
`tests/data/dataverse_min.json`.
"""
data = import_dataverse_min_dict
dv = Dataverse()
dv.set(data)
dv.datasets = [Dataset()]
dv.dataverses = [Dataverse()]
dv.pid = 'doi:10.11587/EVMUHP'
data = dv.json('wrong')
assert not data
def test_dataverse_json_all_valid(self, import_dataverse_min_dict):
"""Test Dataverse.json() with format=`all` and valid data.
Parameters
----------
import_dataverse_min_dict : dict
Fixture, which returns a flat dataset dict() coming from
`tests/data/dataverse_min.json`.
"""
data = import_dataverse_min_dict
dv = Dataverse()
dv.set(data)
dv.datasets = [Dataset()]
dv.dataverses = [Dataverse()]
dv.pid = 'doi:10.11587/EVMUHP'
data = dv.json('all')
assert data
assert isinstance(data, str)
def test_dataverse_set_dv_up(self, import_dataverse_min_dict):
"""Test Dataverse.set() with format=`dv_up`.
Parameters
----------
import_dataverse_min_dict : dict
Fixture, which returns a flat dataset dict() coming from
`tests/data/dataverse_min.json`.
"""
data = import_dataverse_min_dict
dv = Dataverse()
dv.set(data)
assert isinstance(dv.datasets, list)
assert not dv.datasets
assert isinstance(dv.dataverses, list)
assert not dv.dataverses
assert not dv.pid
assert dv.alias == 'test-pyDataverse'
assert dv.name == 'Test pyDataverse'
assert len(dv.dataverseContacts) == 1
assert dv.dataverseContacts[0]['contactEmail'] == '*****@*****.**'
def test_dataverse_dict_all_valid(self, import_dataverse_min_dict):
"""Test Dataverse.dict() with format=`all` and valid data.
Parameters
----------
import_dataverse_min_dict : dict
Fixture, which returns a flat dataset dict() coming from
`tests/data/dataverse_min.json`.
"""
data = import_dataverse_min_dict
dv = Dataverse()
dv.set(data)
dv.datasets = [Dataset()]
dv.dataverses = [Dataverse()]
dv.pid = 'doi:10.11587/EVMUHP'
data = dv.dict('all')
assert data
assert isinstance(data, dict)
assert data['alias'] == 'test-pyDataverse'
assert data['name'] == 'Test pyDataverse'
assert data['dataverseContacts'][0]['contactEmail'] == '*****@*****.**'
assert data['pid'] == 'doi:10.11587/EVMUHP'
def create_testdata(config_file: str, force: bool) -> None:
"""Create testdata defined in a config file.
Creates a pre-defined set of testdata on your
instance. By default, the function uses the
AUSSDA test data repository, which is so far not
publicly available. If `PRODUCTION` is `true`,
this function will not execute, as long as you
not add `--force` to the function call. This is
to protect from unwanted changes on a production
instance.
"""
# Init
if config.PRODUCTION and not force:
print(
"Create testdata on a PRODUCTION instance not allowed. Use --force to force it."
)
sys.exit()
pid_idx = []
users = read_json(config.USER_FILENAME)
workflow = read_json(os.path.join(ROOT_DIR, config_file))
# Dataverses
for dv_conf in workflow["dataverses"]:
dv_alias = None
if "create" in dv_conf:
api = NativeApi(
config.BASE_URL,
users[dv_conf["create"]["user-handle"]]["api-token"])
dv = Dataverse()
dv_filename = os.path.join(ROOT_DIR,
dv_conf["create"]["metadata-filename"])
dv.from_json(read_file(dv_filename))
if "update" in dv_conf["create"]:
for key, val in dv_conf["create"]["update"].items():
kwargs = {key: val}
dv.set(kwargs)
dv_alias = dv.get()["alias"]
resp = api.create_dataverse(dv_conf["create"]["parent"], dv.json())
if "publish" in dv_conf:
api = NativeApi(
config.BASE_URL,
users[dv_conf["publish"]["user-handle"]]["api-token"])
if not dv_alias and "alias" in dv_conf["publish"]:
dv_alias = dv_conf["publish"]["alias"]
resp = api.publish_dataverse(dv_alias)
# Datasets
for ds_conf in workflow["datasets"]:
pid = None
if "create" in ds_conf:
api = NativeApi(
config.BASE_URL,
users[ds_conf["create"]["user-handle"]]["api-token"])
ds = Dataset()
ds_filename = os.path.join(ROOT_DIR,
ds_conf["create"]["metadata-filename"])
ds.from_json(read_file(ds_filename))
if "update" in ds_conf["create"]:
for key, val in ds_conf["create"]["update"].items():
kwargs = {key: val}
ds.set(kwargs)
resp = api.create_dataset(dv_alias, ds.json())
pid = resp.json()["data"]["persistentId"]
pid_idx.append(pid)
if "publish" in ds_conf:
if not pid:
print("ERROR: PID missing!")
sys.exit()
api = NativeApi(
config.BASE_URL,
users[ds_conf["publish"]["user-handle"]]["api-token"])
resp = api.publish_dataset(pid, release_type="major")
# Datafiles
for dataset_id, ds_datafiles in workflow["datafiles"].items():
if int(dataset_id) == workflow["datasets"][int(dataset_id)]["id"]:
pid = pid_idx[int(dataset_id)]
else:
print("ERROR: Dataset ID not matching.")
sys.exit()
for df_conf in ds_datafiles:
if "upload" in df_conf:
api = NativeApi(
config.BASE_URL,
users[df_conf["upload"]["user-handle"]]["api-token"],
)
metadata = read_json(df_conf["upload"]["metadata-filename"])
df = Datafile()
df.set(metadata)
if "update" in df_conf["upload"]:
for key, val in df_conf["upload"]["update"].items():
kwargs = {key: val}
df.set(kwargs)
df.set({"pid": pid})
filename = df_conf["upload"]["filename"]
resp = api.upload_datafile(pid, filename, df.json())
if filename[-4:] == ".sav" or filename[-4:] == ".dta":
sleep(30)
else:
sleep(3)
if "publish-dataset" in df_conf:
api = NativeApi(
config.BASE_URL,
users[df_conf["publish-dataset"]["user-handle"]]["api-token"],
)
if df_conf["publish-dataset"]:
resp = api.publish_dataset(pid, release_type="major")