def add_to_upload_queue(
self,
rows: Union[
Dict[int, List[Union[int, float, str]]],
List[Tuple[int, Union[int, float, str]]],
List[Dict[str, Any]],
SequenceData,
],
column_external_ids: Optional[List[str]] = None,
id: int = None,
external_id: str = None,
) -> None:
"""
Add sequence rows to upload queue. Mirrors implementation of SequenceApi.insert. Inserted rows will be
cached until uploaded
Args:
rows: The rows to be inserted. Can either be a list of tuples, a list of ["rownumber": ..., "values": ...]
objects, a dictionary of rowNumber: data, or a SequenceData object.
column_external_ids: List of external id for the columns of the sequence
id: Sequence internal ID
Use if external_id is None
external_id: Sequence external ID
Us if id is None
"""
if len(rows) == 0:
pass
either_id = EitherId(id=id, external_id=external_id)
if isinstance(rows, SequenceData):
# Already in desired format
pass
elif isinstance(rows, dict):
rows = [{"rowNumber": row_number, "values": values} for row_number, values in rows.items()]
rows = SequenceData(id=id, external_id=id, rows=rows, columns=column_external_ids)
elif isinstance(rows, list):
if isinstance(rows[0], tuple) or isinstance(rows[0], list):
rows = [{"rowNumber": row_number, "values": values} for row_number, values in rows]
rows = SequenceData(id=id, external_id=id, rows=rows, columns=column_external_ids)
else:
raise TypeError("Unsupported type for sequence rows: {}".format(type(rows)))
with self.lock:
seq = self.upload_queue.get(either_id)
if seq is not None:
# Update sequence
seq.values = [*seq.values, *rows.values]
seq.row_numbers = [*seq.values, *rows.row_numbers]
self.upload_queue[either_id] = seq
else:
self.upload_queue[either_id] = rows
def retrieve(
self,
start: int,
end: int,
column_ids: Optional[List[int]] = None,
column_external_ids: Optional[List[str]] = None,
external_id: str = None,
id: int = None,
limit: int = None,
) -> SequenceData:
"""Retrieve data from a sequence
Args:
start (int): Row number to start from (inclusive).
end (int): Upper limit on the row number (exclusive).
column_ids (Optional[List[int]]): List of ids for the columns of the sequence.
If 'None' is passed to both column_ids and columns_external_ids, all columns will be retrieved.
column_external_ids (Optional[List[str]]): List of external id for the columns of the sequence.
id (int): Id of sequence.
external_id (str): External id of sequence.
limit (int): Maximum number of rows to return.
Returns:
List of sequence data
Examples:
>>> from cognite.client.experimental import CogniteClient
>>> c = CogniteClient()
>>> res = c.sequences.data.retrieve(id=0, start=0, end=None)
>>> tuples = [(r,v) for r,v in res.items()] # You can use this iterator in for loops and list comprehensions,
>>> single_value = res[23] # ... get the values at a single row number,
>>> col = res.get_column(external_id='columnExtId') # ... get the array of values for a specific column,
>>> df = res.to_pandas() # ... or convert the result to a dataframe
"""
utils._auxiliary.assert_exactly_one_of_id_or_external_id(
id, external_id)
post_obj = self._process_ids(id, external_id, wrap_ids=True)[0]
post_obj.update(
self._process_columns(column_ids=column_ids,
column_external_ids=column_external_ids))
post_obj.update({
"inclusiveFrom": start,
"exclusiveTo": end,
"limit": limit
})
seqdata = []
column_response = self._fetch_data(post_obj,
lambda data: seqdata.extend(data))
return SequenceData(id=id,
external_id=external_id,
rows=seqdata,
columns=column_response)
def _fetch_sequence(post_obj):
post_obj.update(
self._process_columns(column_external_ids=column_external_ids))
post_obj.update({"start": start, "end": end, "limit": limit})
seqdata = []
columns = []
for data, columns in self._fetch_data(post_obj):
seqdata.extend(data)
return SequenceData(id=post_obj.get("id"),
external_id=post_obj.get("externalId"),
rows=seqdata,
columns=columns)