palantir.workflow.Observable

class palantir.workflow.Observable(parent, key, label)

A class for any element to monitor. A workflow can be the monitoring of several observables

__init__(parent, key, label)

Methods

`__init__`(parent, key, label)
`buildRelativeTrace`(f, outputNature, ...[, ...])
`buildRolledTrace`(f, outputNature, ...)
`buildTraceFromFilterSet`(FullData, ...[, ...])
`delimitTimeChunk`(timeChunk)	Fits the timeChunk time interval to the observable's time interval
`doFiltering`(mjd, y, timeChunk)
`doReporting`(secondsBetweenOutputs, outputNature)
`getAllanDeviation`(key, method)
`getMean`(key)
`getMjd`([key])
`getParentWorkflow`()
`getPathToFilters`()
`getRelativeUptime`(startMjd, endMjd)
`getReport`(key)
`getReports`(startMjd, endMjd)
`getSecondsBetweenOutputs`()
`getTimeRangeSeconds`()
`getTitleDates`()	Gets the human-readable date interval
`getTrace`([key])
`getUncertainty`(key)
`getUptime`(key)
`getWebState`([quality0, quality1, forceState])	Get a single webState from a set of data (stages)
`getXlabel`()	Gets a default time x-axis label for basic plots
`hasBeenProcessed`()
`hasEnoughPoints`()
`isDeterministic`([context])
`isOperational`()	Based on the mesurands involved in the observable, define if it should be operational or not.
`loadDefaultFilters`(start, end)
`loadDefaultTimeChunk`()
`plotAllTemplates`([save, baseSavePath, suffix])	Plots template pages for each filtering stage
`plotDevAdditionalData`([method, stageKey])	Plot additional data to the deviation plot
`plotDeviation`([stageKey, method, linestyle, ...])
`plotDistribution`([stageKey, orientation, nbins])
`plotDistributionAdditionalData`(stageKey)	Plot additional data to the distribution plot
`plotFilters`([stages])	Parameters stages [int], optional List of filtering methods IDs to plot. The default is [].
`plotPhase`([stageKey])	Plots template pages for each filtering stage
`plotQuickview`([stages, plotRange])
`plotTemplatePage`(stageKey[, save, savePath, ...])
`plotTrace`([stageKey])
`plotTraceAdditionalData`(stageKey)	Plot additional data to the trace plot
`processChunkedData`(FullData, mesurandModel, ...)
`processFullData`(FullData, mesurandModel[, ...])	Parameters filterSets: list[dict]
`processMeasurementsUptime`(...)
`setTimeChunk`(timeChunk)
`timeIntervalIsLargeEnough`()