PatchSummaryTask

class lsst.faro.summary.PatchSummaryTask(config, *args, **kwargs)

Bases: lsst.faro.base.CatalogSummaryBaseTask

Attributes Summary

canMultiprocess

Methods Summary

adaptArgsAndRun(inputData, inputDataIds, …) A wrapper around run used by MetricsControllerTask.
addStandardMetadata(measurement, outputDataId) Add data ID-specific metadata required for all metrics.
areInputDatasetsScalar(config) Return input dataset multiplicity.
emptyMetadata() Empty (clear) the metadata for this Task and all sub-Tasks.
getAllSchemaCatalogs() Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.
getFullMetadata() Get metadata for all tasks.
getFullName() Get the task name as a hierarchical name including parent task names.
getInputDatasetTypes(config) Return input dataset types for this task.
getName() Get the name of the task.
getResourceConfig() Return resource configuration for this task.
getSchemaCatalogs() Get the schemas generated by this task.
getTaskDict() Get a dictionary of all tasks as a shallow copy.
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.
run(measurements) Run the MetricTask on in-memory data.
runQuantum(butlerQC, inputRefs, outputRefs) Do Butler I/O to provide in-memory objects for run.
timer(name[, logLevel]) Context manager to log performance data for an arbitrary block of code.

Attributes Documentation

canMultiprocess = True

Methods Documentation

adaptArgsAndRun(inputData, inputDataIds, outputDataId)

A wrapper around run used by MetricsControllerTask.

Task developers should not need to call or override this method.

Parameters:
inputData : dict from str to any

Dictionary whose keys are the names of input parameters and values are Python-domain data objects (or lists of objects) retrieved from data butler. Input objects may be None to represent missing data.

inputDataIds : dict from str to list of dataId

Dictionary whose keys are the names of input parameters and values are data IDs (or lists of data IDs) that the task consumes for corresponding dataset type. Data IDs are guaranteed to match data objects in inputData.

outputDataId : dict from str to dataId

Dictionary containing a single key, "measurement", which maps to a single data ID for the measurement. The data ID must have the same granularity as the metric.

Returns:
struct : lsst.pipe.base.Struct

A Struct containing at least the following component:

  • measurement: the value of the metric, computed from inputData (lsst.verify.Measurement or None). The measurement is guaranteed to contain not only the value of the metric, but also any mandatory supplementary information.
Raises:
lsst.verify.tasks.MetricComputationError

Raised if an algorithmic or system error prevents calculation of the metric. Examples include corrupted input data or unavoidable exceptions raised by analysis code. The MetricComputationError should be chained to a more specific exception describing the root cause.

Not having enough data for a metric to be applicable is not an error, and should not trigger this exception.

Notes

This implementation calls run on the contents of inputData, followed by calling addStandardMetadata on the result before returning it.

Examples

Consider a metric that characterizes PSF variations across the entire field of view, given processed images. Then, if run has the signature run(images):

inputData = {'images': [image1, image2, ...]}
inputDataIds = {'images': [{'visit': 42, 'ccd': 1},
                           {'visit': 42, 'ccd': 2},
                           ...]}
outputDataId = {'measurement': {'visit': 42}}
result = task.adaptArgsAndRun(
    inputData, inputDataIds, outputDataId)
addStandardMetadata(measurement, outputDataId)

Add data ID-specific metadata required for all metrics.

This method currently does not add any metadata, but may do so in the future.

Parameters:
measurement : lsst.verify.Measurement

The Measurement that the metadata are added to.

outputDataId : dataId

The data ID to which the measurement applies, at the appropriate level of granularity.

Notes

This method should not be overridden by subclasses.

This method is not responsible for shared metadata like the execution environment (which should be added by this MetricTask’s caller), nor for metadata specific to a particular metric (which should be added when the metric is calculated).

Warning

This method’s signature will change whenever additional data needs to be provided. This is a deliberate restriction to ensure that all subclasses pass in the new data as well.

classmethod areInputDatasetsScalar(config)

Return input dataset multiplicity.

Parameters:
config : cls.ConfigClass

Configuration for this task.

Returns:
datasets : Dict [str, bool]

Dictionary where the key is the name of the input dataset (must match a parameter to run) and the value is True if run takes only one object and False if it takes a list.

Notes

The default implementation extracts a PipelineTaskConnections object from config.

emptyMetadata()

Empty (clear) the metadata for this Task and all sub-Tasks.

getAllSchemaCatalogs()

Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.

Returns:
schemacatalogs : dict

Keys are butler dataset type, values are a empty catalog (an instance of the appropriate lsst.afw.table Catalog type) for all tasks in the hierarchy, from the top-level task down through all subtasks.

Notes

This method may be called on any task in the hierarchy; it will return the same answer, regardless.

The default implementation should always suffice. If your subtask uses schemas the override Task.getSchemaCatalogs, not this method.

getFullMetadata()

Get metadata for all tasks.

Returns:
metadata : lsst.daf.base.PropertySet

The PropertySet 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()

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

Returns:
fullName : str

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”.
classmethod getInputDatasetTypes(config)

Return input dataset types for this task.

Parameters:
config : cls.ConfigClass

Configuration for this task.

Returns:
datasets : dict from str to str

Dictionary where the key is the name of the input dataset (must match a parameter to run) and the value is the name of its Butler dataset type.

Notes

The default implementation extracts a PipelineTaskConnections object from config.

getName()

Get the name of the task.

Returns:
taskName : str

Name of the task.

See also

getFullName
getResourceConfig()

Return resource configuration for this task.

Returns:
Object of type ResourceConfig or None if resource
configuration is not defined for this task.
getSchemaCatalogs()

Get the schemas generated by this task.

Returns:
schemaCatalogs : dict

Keys are butler dataset type, values are an empty catalog (an instance of the appropriate lsst.afw.table Catalog type) for this task.

See also

Task.getAllSchemaCatalogs

Notes

Warning

Subclasses that use schemas must override this method. The default implementation returns an empty dict.

This method may be called at any time after the Task is constructed, which means that all task schemas should be computed at construction time, not when data is actually processed. This reflects the philosophy that the schema should not depend on the data.

Returning catalogs rather than just schemas allows us to save e.g. slots for SourceCatalog as well.

getTaskDict()

Get a dictionary of all tasks as a shallow copy.

Returns:
taskDict : dict

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

classmethod makeField(doc)

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

Parameters:
doc : str

Help text for the field.

Returns:
configurableField : lsst.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, **keyArgs)

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

Parameters:
name : str

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.

run(measurements)

Run the MetricTask on in-memory data.

Parameters:
**kwargs

Keyword arguments matching the inputs given in the class config; see lsst.pipe.base.PipelineTask.run for more details.

Returns:
struct : lsst.pipe.base.Struct

A Struct containing at least the following component:

  • measurement: the value of the metric (lsst.verify.Measurement or None). This method is not responsible for adding mandatory metadata (e.g., the data ID); this is handled by the caller.
Raises:
lsst.verify.tasks.MetricComputationError

Raised if an algorithmic or system error prevents calculation of the metric. Examples include corrupted input data or unavoidable exceptions raised by analysis code. The MetricComputationError should be chained to a more specific exception describing the root cause.

Not having enough data for a metric to be applicable is not an error, and should not trigger this exception.

Notes

All input data must be treated as optional. This maximizes the MetricTask’s usefulness for incomplete pipeline runs or runs with optional processing steps. If a metric cannot be calculated because the necessary inputs are missing, the MetricTask must return None in place of the measurement.

runQuantum(butlerQC, inputRefs, outputRefs)

Do Butler I/O to provide in-memory objects for run.

This specialization of runQuantum performs error-handling specific to MetricTasks. Most or all of this functionality may be moved to activators in the future.

timer(name, logLevel=10)

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

Parameters:
name : str

Name of code being timed; data will be logged using item name: Start and End.

logLevel

A logging level constant.

See also

timer.logInfo

Examples

Creating a timer context:

with self.timer("someCodeToTime"):
    pass  # code to time