perun.processing

Processing Module.

Module Contents

Functions

processEnergyData(→ Tuple[Any, Any])	Calculate energy and power from an accumulated energy vector. (SEE RAPL).
processPowerData(→ Tuple[Any, Any])	Calculate energy and power from 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(regions, dataNode)	Complete region information using sensor data found on the data node (in place op).
addRunAndRuntimeInfoToRegion(region)	Process run and runtime stats in region objects (in place operation).
getInterpolatedValues(→ Tuple[numpy.ndarray, ...)	Filter timeseries with a start and end limit, and interpolate the values at the edges.

Attributes

log

perun.processing.log

perun.processing.processEnergyData(raw_data: perun.data_model.data.RawData, start: numpy.number | None = None, end: numpy.number | None = None) → Tuple[Any, Any]

Calculate energy and power from an accumulated energy vector. (SEE RAPL).

Using the start and end parameters does the calculation within the selected time range.

Parameters

raw_dataRawData

Raw Data from sensor

startOptional[np.number], optional

Start time of region, by default None

endOptional[np.number], optional

End time of region, by default None

Returns

_type_

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

perun.processing.processPowerData(raw_data: perun.data_model.data.RawData, start: numpy.number | None = None, end: numpy.number | None = None) → Tuple[Any, Any]

Calculate energy and power from power time series.

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

Parameters

raw_dataRawData

Raw Data from sensor

startOptional[np.number], optional

Start time of region, by default None

endOptional[np.number], optional

End time of region, by default None

Returns

_type_

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

perun.processing.processSensorData(sensorData: perun.data_model.data.DataNode) → perun.data_model.data.DataNode

Calculate metrics based on raw values.

Parameters

sensorDataDataNode

DataNode with raw sensor data.

Returns

DataNode

DataNode with computed metrics.

perun.processing.processDataNode(dataNode: perun.data_model.data.DataNode, perunConfig: configparser.ConfigParser, force_process=False) → perun.data_model.data.DataNode

Recursively calculate metrics on the dataNode tree.

Parameters

dataNodeDataNode

Root data node tree.

perunConfig: ConfigParser

Perun configuration

force_processbool, optional

Force recomputation of child node metrics, by default False

Returns

DataNode

Data node with computed metrics.

perun.processing.processRegionsWithSensorData(regions: List[perun.data_model.data.Region], dataNode: perun.data_model.data.DataNode)

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

Parameters

regionsList[Region]

List of regions that use the same data node.

dataNodeDataNode

Data node with sensor data.

perun.processing.addRunAndRuntimeInfoToRegion(region: perun.data_model.data.Region)

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

Parameters

regionRegion

Region object

perun.processing.getInterpolatedValues(t: numpy.ndarray, x: numpy.ndarray, start: numpy.number, end: numpy.number) → Tuple[numpy.ndarray, numpy.ndarray]

Filter timeseries with a start and end limit, and interpolate the values at the edges.

Parameters

tnp.ndarray

Original time steps

xnp.ndarray

Original values

startnp.number

Start of the region of interest

endnp.number

End of the roi

Returns

np.ndarray

ROI values