def __call__(
self,
chunk_size: int = None,
name: str = None,
unit: str = None,
is_string: bool = None,
is_step: bool = None,
asset_ids: List[int] = None,
root_asset_ids: List[int] = None,
metadata: Dict[str, Any] = None,
external_id_prefix: str = None,
created_time: Dict[str, Any] = None,
last_updated_time: Dict[str, Any] = None,
limit: int = None,
include_metadata=True,
) -> Generator[Union[TimeSeries, TimeSeriesList], None, None]:
"""Iterate over time series
Fetches time series as they are iterated over, so you keep a limited number of objects in memory.
Args:
chunk_size (int, optional): Number of time series to return in each chunk. Defaults to yielding one time series a time.
name (str): Name of the time series. Often referred to as tag.
unit (str): Unit of the time series.
is_string (bool): Whether the time series is an string time series.
is_step (bool): Whether the time series is a step (piecewise constant) time series.
asset_ids (List[int], optional): List time series related to these assets.
root_asset_ids (List[int], optional): List time series related to assets under these root assets.
metadata (Dict[str, Any]): Custom, application specific metadata. String key -> String value
created_time (Dict[str, Any]): Range between two timestamps
last_updated_time (Dict[str, Any]): Range between two timestamps
external_id_prefix (str): Filter by this (case-sensitive) prefix for the external ID.
limit (int, optional): Maximum number of time series to return. Defaults to return all items.
include_metadata (bool, optional): Ignored. Only present in parameter list for backward compatibility.
Yields:
Union[TimeSeries, TimeSeriesList]: yields TimeSeries one by one if chunk is not specified, else TimeSeriesList objects.
"""
filter = TimeSeriesFilter(
name=name,
unit=unit,
is_step=is_step,
is_string=is_string,
asset_ids=asset_ids,
root_asset_ids=root_asset_ids,
metadata=metadata,
created_time=created_time,
last_updated_time=last_updated_time,
external_id_prefix=external_id_prefix,
).dump(camel_case=True)
return self._list_generator(method="POST", chunk_size=chunk_size, filter=filter, limit=limit)
def test_search_with_filter(self, mock_ts_response):
res = TS_API.search(name="n",
description="d",
query="q",
filter=TimeSeriesFilter(unit="bla"))
assert mock_ts_response.calls[0].response.json()["items"] == res.dump(
camel_case=True)
req_body = jsgz_load(mock_ts_response.calls[0].request.body)
assert "bla" == req_body["filter"]["unit"]
assert {
"name": "n",
"description": "d",
"query": "q"
} == req_body["search"]
def test_search(self, mock_ts_response):
res = TS_API.search(filter=TimeSeriesFilter(is_string=True))
assert mock_ts_response.calls[0].response.json()["items"] == res.dump(
camel_case=True)
assert {
"search": {
"name": None,
"description": None,
"query": None
},
"filter": {
"isString": True
},
"limit": None,
} == jsgz_load(mock_ts_response.calls[0].request.body)
def test_search(self):
res = COGNITE_CLIENT.time_series.search(
name="test__timestamp_multiplied",
filter=TimeSeriesFilter(created_time={"min": 0}))
assert len(res) > 0
def test_aggregate(self, new_ts):
res = COGNITE_CLIENT.time_series.aggregate(filter=TimeSeriesFilter(
name="any"))
assert res[0].count > 0
def list(
self,
name: str = None,
unit: str = None,
is_string: bool = None,
is_step: bool = None,
asset_ids: List[int] = None,
asset_external_ids: List[str] = None,
root_asset_ids: List[int] = None,
asset_subtree_ids: List[int] = None,
asset_subtree_external_ids: List[str] = None,
data_set_ids: List[int] = None,
data_set_external_ids: List[str] = None,
metadata: Dict[str, Any] = None,
external_id_prefix: str = None,
created_time: Dict[str, Any] = None,
last_updated_time: Dict[str, Any] = None,
partitions: int = None,
limit: int = 25,
include_metadata=True,
) -> TimeSeriesList:
"""`List over time series <https://docs.cognite.com/api/v1/#operation/listTimeSeries>`_
Fetches time series as they are iterated over, so you keep a limited number of objects in memory.
Args:
name (str): Name of the time series. Often referred to as tag.
unit (str): Unit of the time series.
is_string (bool): Whether the time series is an string time series.
is_step (bool): Whether the time series is a step (piecewise constant) time series.
asset_ids (List[int], optional): List time series related to these assets.
asset_external_ids (List[str], optional): List time series related to these assets.
root_asset_ids (List[int], optional): List time series related to assets under these root assets.
asset_subtree_ids (List[int]): List of asset subtrees ids to filter on.
asset_subtree_external_ids (List[str]): List of asset subtrees external ids to filter on.
data_set_ids (List[int]): Return only assets in the specified data sets with these ids.
data_set_external_ids (List[str]): Return only assets in the specified data sets with these external ids.
metadata (Dict[str, Any]): Custom, application specific metadata. String key -> String value
created_time (Union[Dict[str, int], TimestampRange]): Range between two timestamps. Possible keys are `min` and `max`, with values given as time stamps in ms.
last_updated_time (Union[Dict[str, int], TimestampRange]): Range between two timestamps. Possible keys are `min` and `max`, with values given as time stamps in ms.
external_id_prefix (str): Filter by this (case-sensitive) prefix for the external ID.
limit (int, optional): Maximum number of time series to return. Defaults to 25. Set to -1, float("inf") or None to return all items.
partitions (int): Retrieve time series in parallel using this number of workers. Also requires `limit=None` to be passed.
include_metadata (bool, optional): Ignored. Only present in parameter list for backward compatibility.
Returns:
TimeSeriesList: The requested time series.
Examples:
List time series::
>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> res = c.time_series.list(limit=5)
Iterate over time series::
>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> for ts in c.time_series:
... ts # do something with the time_series
Iterate over chunks of time series to reduce memory load::
>>> from cognite.client import CogniteClient
>>> c = CogniteClient()
>>> for ts_list in c.time_series(chunk_size=2500):
... ts_list # do something with the time_series
"""
if asset_subtree_ids or asset_subtree_external_ids:
asset_subtree_ids = self._process_ids(asset_subtree_ids,
asset_subtree_external_ids,
wrap_ids=True)
if data_set_ids or data_set_external_ids:
data_set_ids = self._process_ids(data_set_ids,
data_set_external_ids,
wrap_ids=True)
filter = TimeSeriesFilter(
name=name,
unit=unit,
is_step=is_step,
is_string=is_string,
asset_ids=asset_ids,
asset_external_ids=asset_external_ids,
root_asset_ids=root_asset_ids,
asset_subtree_ids=asset_subtree_ids,
metadata=metadata,
data_set_ids=data_set_ids,
created_time=created_time,
last_updated_time=last_updated_time,
external_id_prefix=external_id_prefix,
).dump(camel_case=True)
return self._list(method="POST",
filter=filter,
limit=limit,
partitions=partitions)