MakeMetricTableTask¶

class lsst.analysis.tools.tasks.MakeMetricTableTask(*, config: PipelineTaskConfig | None = None, log: logging.Logger | LsstLogAdapter | None = None, initInputs: dict[str, Any] | None = None, **kwargs: Any)¶

Bases: AnalysisPipelineTask

Turn metric bundles which are per tract into a summary metric table.

TO DO: DM-44485 make sure this works for visit level data as well.

Attributes Summary

`canMultiprocess`
`warnings_all`

Methods Summary

`collectInputNames`()	Get the names of the inputs.
`emptyMetadata`()	Empty (clear) the metadata for this Task and all sub-Tasks.
`getFullMetadata`()	Get metadata for all tasks.
`getFullName`()	Get the task name as a hierarchical name including parent task names.
`getName`()	Get the name of the task.
`getTaskDict`()	Get a dictionary of all tasks as a shallow copy.
`getTractCorners`(skymap, tract)	Calculate the corners of a tract, given skymap.
`loadData`(handle[, names])	Load the minimal set of keyed data from the input dataset.
`makeField`(doc)	Make a `lsst.pex.config.ConfigurableField` for this task.
`makeSubtask`(name, **keyArgs)	Create a subtask as a new instance as the `name` attribute of this task.
`parsePlotInfo`(inputs, dataId[, connectionName])	Parse the inputs and dataId to get the information needed to to add to the figure.
`run`(tracts, metricBundles, skymap)	Take the metric bundles and expand them out, add the tract corner information and then make a table of the information.
`runQuantum`(butlerQC, inputRefs, outputRefs)	Take a metric bundle for all the tracts and seperate it into different metrics then make it into a table.
`timer`(name[, logLevel])	Context manager to log performance data for an arbitrary block of code.

Attributes Documentation

canMultiprocess: ClassVar[bool] = True¶

warnings_all = ('divide by zero encountered in divide', 'invalid value encountered in arcsin', 'invalid value encountered in cos', 'invalid value encountered in divide', 'invalid value encountered in log10', 'invalid value encountered in scalar divide', 'invalid value encountered in sin', 'invalid value encountered in sqrt', 'invalid value encountered in true_divide', 'Mean of empty slice')¶

Methods Documentation

collectInputNames() → Iterable[str]¶

Get the names of the inputs.

If using the default loadData method this will gather the names of the keys to be loaded from an input dataset.

Returns:

inputsIterable of str: The names of the keys in the KeyedData object to extract.

emptyMetadata() → None¶: Empty (clear) the metadata for this Task and all sub-Tasks.

getFullMetadata() → TaskMetadata¶

Get metadata for all tasks.

Returns:

metadataTaskMetadata: The keys are the full task name. Values are metadata for the top-level task and all subtasks, sub-subtasks, etc.

Notes

The returned metadata includes timing information (if @timer.timeMethod is used) and any metadata set by the task. The name of each item consists of the full task name with . replaced by :, followed by . and the name of the item, e.g.:

topLevelTaskName:subtaskName:subsubtaskName.itemName

using : in the full task name disambiguates the rare situation that a task has a subtask and a metadata item with the same name.

getFullName() → str¶

Get the task name as a hierarchical name including parent task names.

Returns:

fullNamestr

The full name consists of the name of the parent task and each subtask separated by periods. For example:

The full name of top-level task “top” is simply “top”.
The full name of subtask “sub” of top-level task “top” is “top.sub”.
The full name of subtask “sub2” of subtask “sub” of top-level task “top” is “top.sub.sub2”.

getName() → str¶

Get the name of the task.

Returns:

taskNamestr: Name of the task.

See also

getFullName: Get the full name of the task.

getTaskDict() → dict[str, weakref.ReferenceType[lsst.pipe.base.task.Task]]¶

Get a dictionary of all tasks as a shallow copy.

Returns:

taskDictdict: Dictionary containing full task name: task object for the top-level task and all subtasks, sub-subtasks, etc.

getTractCorners(skymap, tract)¶

Calculate the corners of a tract, given skymap.

Parameters:

skymaplsst.skymap
tractint

Returns:

cornerslist of tuples of float

Notes

Corners are returned in degrees and wrapped in ra.

loadData(handle: DeferredDatasetHandle, names: Iterable[str] | None = None) → KeyedData¶

Load the minimal set of keyed data from the input dataset.

Parameters:

handleDeferredDatasetHandle: Handle to load the dataset with only the specified columns.
namesIterable of str: The names of keys to extract from the dataset. If names is None then the collectInputNames method is called to generate the names. For most purposes these are the names of columns to load from a catalog or data frame.

Returns:

result: KeyedData: The dataset with only the specified keys loaded.

classmethod makeField(doc: str) → ConfigurableField¶

Make a lsst.pex.config.ConfigurableField for this task.

Parameters:

docstr: Help text for the field.

Returns:

configurableFieldlsst.pex.config.ConfigurableField: A ConfigurableField for this task.

Examples

Provides a convenient way to specify this task is a subtask of another task.

Here is an example of use:

class OtherTaskConfig(lsst.pex.config.Config):
    aSubtask = ATaskClass.makeField("brief description of task")

makeSubtask(name: str, **keyArgs: Any) → None¶

Create a subtask as a new instance as the name attribute of this task.

Parameters:

namestr

Brief name of the subtask.

**keyArgs

Extra keyword arguments used to construct the task. The following arguments are automatically provided and cannot be overridden:

config.
parentTask.

Notes

The subtask must be defined by Task.config.name, an instance of ConfigurableField or RegistryField.

parsePlotInfo(inputs: Mapping[str, Any] | None, dataId: DataCoordinate | None, connectionName: str = 'data') → Mapping[str, str]¶

Parse the inputs and dataId to get the information needed to to add to the figure.

Parameters:

inputs: `dict`: The inputs to the task
dataCoordinate: `lsst.daf.butler.DataCoordinate`: The dataId that the task is being run on.
connectionName: `str`, optional: Name of the input connection to use for determining table name.

Returns:

plotInfodict

run(tracts, metricBundles, skymap)¶

Take the metric bundles and expand them out, add the tract corner information and then make a table of the information.

Parameters:

tractslist: A list of the tracts that the metricBundles cover
metricBundleslist of: lsst.analysis.tools.interfaces._metricMeasurementBundle.MetricMeasurementBundle
skymaplsst.skymap

Returns:

tMetricspipe.base.Struct containing astropy.table.Table

runQuantum(butlerQC, inputRefs, outputRefs)¶

Take a metric bundle for all the tracts and seperate it into different metrics then make it into a table.

Parameters:

butlerQClsst.pipe.base.QuantumContext
inputRefslsst.pipe.base.InputQuantizedConnection
outputRefslsst.pipe.base.OutputQuantizedConnection

timer(name: str, logLevel: int = 10) → Iterator[None]¶

Context manager to log performance data for an arbitrary block of code.

Parameters:

namestr: Name of code being timed; data will be logged using item name: Start and End.
logLevelint: A logging level constant.

Navigation

MakeMetricTableTask¶