def test_get_parameters(mocker):
mocker.patch.dict("braincube_connector.parameters._default_parameters",
{"page_size": 8})
mocker.patch.dict("braincube_connector.instances.instances",
{"parameter_set": {}})
assert parameters.get_parameter("Nonexistent") is None
assert parameters.get_parameter("page_size") == 8
parameters.set_parameter({"page_size": 5})
assert parameters.get_parameter("page_size") == 5
assert instances.instances["parameter_set"]["page_size"] == 5
def _extract_format_data(raw_dataset: Dict[str, Any]) -> Dict[int, Any]:
"""Extract the requested data from the json.
The function extracts the data keys and types and convert the columns
using the types.
Args:
raw_dataset: An unformated dataset received from braindata.
Returns:
A formated dictionary {column_key: formated column data}
"""
formatted_dataset = {}
for col in raw_dataset["datadefs"]:
col_id = int(col["id"].split("/d")[1])
if col["type"] == "DATETIME" and parameters.get_parameter("parse_date"):
formatted_dataset[col_id] = _to_datetime(col[DATACOL])
elif col["type"] == "NUMERIC":
try:
formatted_dataset[col_id] = list(map(int, col[DATACOL]))
except ValueError:
formatted_dataset[col_id] = list(map(float, col[DATACOL]))
else:
formatted_dataset[col_id] = col[DATACOL]
return formatted_dataset
def test_reset(mocker):
mocker.patch.dict("braincube_connector.parameters._default_parameters",
{"page_size": 8})
mocker.patch.dict("braincube_connector.instances.instances",
{"parameter_set": {}})
parameters.set_parameter({"page_size": 5})
parameters.reset_parameter()
assert parameters.get_parameter("page_size") == 8
assert instances.instances["parameter_set"] == {}
def get_parameter_key(cls, key) -> str:
"""Get baseEntity_name_key parameter.and name_bcid parameter.
Args:
key: "name" to get the name key or "bcid" to get the bcid key
Returns:
The name key of the baseEntity or The bcid key of the baseEntity]
"""
parameter_key = parameters.get_parameter("{0}_{1}_key".format(cls.__name__, key))
return (
parameter_key if parameter_key else cls.__base__.get_parameter_key(key) # type: ignore
)
def create_collection_from_path(
cls,
request_path: str,
entity_path: str,
caller,
page: int = -1,
page_size: int = -1,
braincube_name: str = constants.EMPTY_STRING,
**kwargs,
) -> List[Any]:
"""Create many memory_base from a request path.
Args:
request_path: Webservice path to request.
entity_path: Path of the entity on the webservice.
caller: class used to call this method, useful to indicate which is the parent entity.
page: Index of page to return, all pages are return if page=-1
page_size: Number of memory_base per page.
braincube_name: name of the braincube linked to this entity,
useful if you use a placeholder in your config.
**kwargs: Additional keyword argument to pass to an object initialization.
Returns:
The list of created created entity.
"""
if page_size == -1:
page_size = parameters.get_parameter("page_size")
offset = 0 if page < 0 else page * page_size
entity_list: List[Any] = []
while True:
json_data = client.request_ws(
"{path}?offset={offset}&size={size}".format(
path=request_path.format(webservice="braincube"), offset=offset, size=page_size,
),
braincube_name=braincube_name,
)
new_entities = [
cls.create_from_json(elmt, entity_path, caller, **kwargs)
for elmt in json_data["items"]
]
entity_list += new_entities
if not new_entities or page > -1:
break
else:
offset += page_size
return entity_list