PpdbMetricTask¶

class lsst.verify.tasks.PpdbMetricTask(**kwargs)¶

Bases: lsst.verify.gen2tasks.MetricTask

A base class for tasks that compute metrics from a prompt products database.

Parameters:	**kwargs Constructor parameters are the same as for `lsst.pipe.base.PipelineTask`.

Notes

This class should be customized by overriding makeMeasurement and getOutputMetricName. You should not need to override run or adaptArgsAndRun.

Methods Summary

`adaptArgsAndRun`(inputData, inputDataIds, …)	Compute a measurement from a database.
`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.
`getOutputMetricName`(config)	Identify the metric calculated by this `MetricTask`.
`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.
`makeMeasurement`(dbHandle, outputDataId)	Compute the metric from database data.
`makeSubtask`(name, **keyArgs)	Create a subtask as a new instance as the `name` attribute of this task.
`run`(dbInfo)	Compute a measurement from a database.
`timer`(name[, logLevel])	Context manager to log performance data for an arbitrary block of code.

Methods Documentation

adaptArgsAndRun(inputData, inputDataIds, outputDataId)¶

Compute a measurement from a database.

Parameters:	inputData : `dict` [`str`, any] Dictionary with one key: `"dbInfo"` The dataset (of the type indicated by `getInputDatasetTypes`) from which to load the database. inputDataIds : `dict` [`str`, data ID] Dictionary with one key: `"dbInfo"` The data ID of the input data. Since there can only be one prompt products database per dataset, the value must be an empty data ID. outputDataId : `dict` [`str`, data ID] Dictionary with one key: `"measurement"` The data ID for the measurement, at the appropriate level of granularity for the metric.
Returns:	result : `lsst.pipe.base.Struct` Result struct with component: `measurement` the value of the metric computed over the portion of the dataset that matches `outputDataId` (`lsst.verify.Measurement` or `None`)
Raises:	lsst.verify.tasks.MetricComputationError Raised if an algorithmic or system error prevents calculation of the metric.

Notes

This implementation calls dbLoader to acquire a database handle, then passes it and the value of outputDataId to makeMeasurement. The result of makeMeasurement is returned to the caller.

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 must be called by any subclass that overrides adaptArgsAndRun, but should be ignored otherwise. It 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

Task.getAllSchemaCatalogs

Notes

Warning

Subclasses that use schemas must override this method. The default implemenation 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("a brief description of what this task does")

makeMeasurement(dbHandle, outputDataId)¶

Compute the metric from database data.

Parameters:	dbHandle : `lsst.dax.ppdb.Ppdb` A database instance. outputDataId : any data ID type The subset of the database to which this measurement applies. May be empty to represent the entire dataset.
Returns:	measurement : `lsst.verify.Measurement` or `None` The measurement corresponding to the input data.
Raises:	MetricComputationError Raised if an algorithmic or system error prevents calculation of the metric. See `adaptArgsAndRun` for expected behavior.

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 pex_config ConfigurableField or RegistryField.

run(dbInfo)¶

Compute a measurement from a database.

Parameters:	dbInfo The dataset (of the type indicated by `getInputDatasetTypes`) from which to load the database.
Returns:	result : `lsst.pipe.base.Struct` Result struct with component: `measurement` the value of the metric computed over the entire database (`lsst.verify.Measurement` or `None`)
Raises:	MetricComputationError Raised if an algorithmic or system error prevents calculation of the metric.

Notes

This method is provided purely for compatibility with frameworks that don’t support adaptArgsAndRun. The latter method should be considered the primary entry point for this task, as it lets callers define metrics that apply to only a subset of the data.

timer(name, logLevel=10000)¶

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 `lsst.log` level constant.

Navigation

PpdbMetricTask¶