perun.processing¶

Processing Module.

Attributes¶

log

Functions¶

`processEnergyData`(→ Tuple[Any, Any])	Calculate total energy and average power from an energy or power time series.
`processSensorData`(→ perun.data_model.data.DataNode)	Calculate metrics based on raw values.
`processDataNode`(→ perun.data_model.data.DataNode)	Recursively calculate metrics on the dataNode tree.
`processRegionsWithSensorData`(→ None)	Complete region information using sensor data found on the data node (in place op).
`addRunAndRuntimeInfoToRegion`(→ None)	Process run and runtime stats in region objects (in place operation).
`getInterpolatedValues`(→ Tuple[numpy.ndarray, ...)	Extract a time range out of a time series, and interpolate the values at the edges.

Module Contents¶

perun.processing.log[source]¶

perun.processing.processEnergyData(raw_data: perun.data_model.data.RawData, start: perun.data_model.measurement_type.Number | None = None, end: perun.data_model.measurement_type.Number | None = None) → Tuple[Any, Any][source]¶

Calculate total energy and average power from an energy or power time series.

Using the start and end parameters the results can be limited to certain areas of the application run.

Parameters:

raw_data (RawData) – Raw Data from sensor
start (Optional[Number], optional) – Start time of region, by default None
end (Optional[Number], optional) – End time of region, by default None

Returns:

Tuple with total energy in joules and avg power in watts.

Return type:

_type_

perun.processing.processSensorData(sensorData: perun.data_model.data.DataNode) → perun.data_model.data.DataNode[source]¶

Calculate metrics based on raw values.

Parameters:: sensorData (DataNode) – DataNode with raw sensor data.
Returns:: DataNode with computed metrics.
Return type:: DataNode

perun.processing.processDataNode(dataNode: perun.data_model.data.DataNode, perunConfig: configparser.ConfigParser, force_process: bool = False) → perun.data_model.data.DataNode[source]¶

Recursively calculate metrics on the dataNode tree.

Parameters:

dataNode (DataNode) – Root data node tree.
perunConfig (ConfigParser) – Perun configuration
force_process (bool, optional) – Force recomputation of child node metrics, by default False

Returns:

Data node with computed metrics.

Return type:

DataNode

perun.processing.processRegionsWithSensorData(regions: List[perun.data_model.data.Region], dataNode: perun.data_model.data.DataNode) → None[source]¶

Complete region information using sensor data found on the data node (in place op).

Parameters:

regions (List[Region]) – List of regions that use the same data node.
dataNode (DataNode) – Data node with sensor data.

perun.processing.addRunAndRuntimeInfoToRegion(region: perun.data_model.data.Region) → None[source]¶

Process run and runtime stats in region objects (in place operation).

Parameters:: region (Region) – Region object

perun.processing.getInterpolatedValues(t: numpy.ndarray, x: numpy.ndarray, start: perun.data_model.measurement_type.Number, end: perun.data_model.measurement_type.Number) → Tuple[numpy.ndarray, numpy.ndarray][source]¶

Extract a time range out of a time series, and interpolate the values at the edges.

Parameters:

t (np.ndarray) – Original time steps
x (np.ndarray) – Original values
start (Number) – Start of the roi
end (Number) – End of the roi

Returns:

Tuple with the new time steps and values.

Return type:

Tuple[np.ndarray, np.ndarray]