lsst.analysis.tools

Contributing

lsst.analysis.tools is developed at https://github.com/lsst/analysis_tools. You can find Jira issues for this module under the analysis_tools component.

Python API reference

lsst.analysis.tools Package

Classes

AnalysisAction(*args, **kw)	Base class interface for the various actions used in analysis tools.
AnalysisBaseConfig(*args, **kw)	Base class for all configs used to define an AnalysisPipelineTask
AnalysisBaseConnections(*[, config])	Base class for Connections used for AnalysisTools PipelineTasks.
AnalysisPipelineTask(*[, config, log, ...])	Base class for PipelineTasks intended to run AnalysisTools.
AnalysisTool(*args, **kw)	A tool which which calculates a single type of analysis on input data, though it may return more than one result.
BaseMetricAction(*args, **kw)
BasePrep(*args, **kw)
BaseProcess(*args, **kw)
BaseProduce(*args, **kw)
JointAction(*args, **kw)	A JointAction is an AnalysisAction that is a composite of a PlotAction and a MetricAction
JointResults(plot, metric)
KeyedDataAction(*args, **kw)	A KeyedDataAction is an AnalysisAction that returns KeyedData when called.
MetricAction(*args, **kw)	A MetricAction is an AnalysisAction that returns a Measurement or a Mapping of str to Measurement when called.
MetricMeasurementBundle(*args, **kwargs)	A specialized dict for storing outputs from multiple AnalysisMetric actions.
PlotAction(*args, **kw)	A PlotAction is an AnalysisAction that returns a PlotType or a Mapping of str to PlotType when called.
PlotTypes	alias of Figure
Scalar()	This is an interface only class, and is intended to abstract around all the various types of numbers used in Python.
ScalarAction(*args, **kw)	A ScalarAction is an AnalysisAction that returns a Scalar when called.
ScalarType	alias of type[Scalar]
VectorAction(*args, **kw)	A VectorAction is an AnalysisAction that returns a Vector when called.

Variables

KeyedResults
MetricResultType
PlotResultType
nansigmaMad(x[, axis, center, scale, nan_policy])	Compute the median absolute deviation of the data along the given axis.
sigmaMad(x[, axis, center, scale, nan_policy])	Compute the median absolute deviation of the data along the given axis.