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float_series.py
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float_series.py
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#
# Copyright (c) 2022, Neptune Labs Sp. z o.o.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from typing import (
Iterable,
Optional,
Union,
)
from neptune.new.attributes.series.fetchable_series import FetchableSeries
from neptune.new.attributes.series.series import Series
from neptune.new.internal.backends.api_model import FloatSeriesValues
from neptune.new.internal.operation import (
ClearFloatLog,
ConfigFloatSeries,
LogFloats,
Operation,
)
from neptune.new.internal.utils import verify_type
from neptune.new.internal.utils.logger import logger
from neptune.new.types.series.float_series import FloatSeries as FloatSeriesVal
Val = FloatSeriesVal
Data = Union[float, int]
LogOperation = LogFloats
class FloatSeries(
Series[Val, Data, LogOperation], FetchableSeries[FloatSeriesValues], max_batch_size=100, operation_cls=LogOperation
):
def configure(
self,
min: Optional[Union[float, int]] = None,
max: Optional[Union[float, int]] = None,
unit: Optional[str] = None,
wait: bool = False,
) -> None:
verify_type("min", min, (float, int))
verify_type("max", max, (float, int))
verify_type("unit", unit, str)
with self._container.lock():
self._enqueue_operation(ConfigFloatSeries(self._path, min, max, unit), wait)
def _get_clear_operation(self) -> Operation:
return ClearFloatLog(self._path)
def _get_config_operation_from_value(self, value: Val) -> Optional[Operation]:
return ConfigFloatSeries(self._path, value.min, value.max, value.unit)
def _data_to_value(self, values: Iterable, **kwargs) -> Val:
if kwargs:
logger.warning("Warning: unexpected arguments (%s) in FloatSeries", kwargs)
return FloatSeriesVal(values)
def _is_value_type(self, value) -> bool:
return isinstance(value, FloatSeriesVal)
def fetch_last(self) -> float:
val = self._backend.get_float_series_attribute(self._container_id, self._container_type, self._path)
return val.last
def _fetch_values_from_backend(self, offset, limit) -> FloatSeriesValues:
return self._backend.get_float_series_values(
self._container_id, self._container_type, self._path, offset, limit
)