Inheritance diagram for lsst.pipe.base.pipelineTask.PipelineTask:
Public Member Functions | |
| def | __init__ (self, config=None, log=None, initInputs=None, kwargs) |
| def | run (self, kwargs) |
| def | runQuantum |
| def | getResourceConfig (self) |
| def | emptyMetadata (self) |
| def | getSchemaCatalogs (self) |
| def | getAllSchemaCatalogs (self) |
| def | getFullMetadata (self) |
| def | getFullName (self) |
| def | getName (self) |
| def | getTaskDict (self) |
| def | makeSubtask (self, name, keyArgs) |
| def | timer (self, name, logLevel=Log.DEBUG) |
| def | makeField (cls, doc) |
| def | __reduce__ (self) |
Public Attributes | |
| metadata | |
| log | |
| config | |
Static Public Attributes | |
| bool | canMultiprocess = True |
Detailed Description
Base class for all pipeline tasks.
This is an abstract base class for PipelineTasks which represents an
algorithm executed by framework(s) on data which comes from data butler,
resulting data is also stored in a data butler.
PipelineTask inherits from a `pipe.base.Task` and uses the same
configuration mechanism based on `pex.config`. `PipelineTask` classes also
have a `PipelineTaskConnections` class associated with their config which
defines all of the IO a `PipelineTask` will need to do. PipelineTask
sub-class typically implements `run()` method which receives Python-domain
data objects and returns `pipe.base.Struct` object with resulting data.
`run()` method is not supposed to perform any I/O, it operates entirely on
in-memory objects. `runQuantum()` is the method (can be re-implemented in
sub-class) where all necessary I/O is performed, it reads all input data
from data butler into memory, calls `run()` method with that data, examines
returned `Struct` object and saves some or all of that data back to data
butler. `runQuantum()` method receives a `ButlerQuantumContext` instance to
facilitate I/O, a `InputQuantizedConnection` instance which defines all
input `lsst.daf.butler.DatasetRef`, and a `OutputQuantizedConnection`
instance which defines all the output `lsst.daf.butler.DatasetRef` for a
single invocation of PipelineTask.
Subclasses must be constructable with exactly the arguments taken by the
PipelineTask base class constructor, but may support other signatures as
well.
Attributes
----------
canMultiprocess : bool, True by default (class attribute)
This class attribute is checked by execution framework, sub-classes
can set it to ``False`` in case task does not support multiprocessing.
Parameters
----------
config : `pex.config.Config`, optional
Configuration for this task (an instance of ``self.ConfigClass``,
which is a task-specific subclass of `PipelineTaskConfig`).
If not specified then it defaults to `self.ConfigClass()`.
log : `lsst.log.Log`, optional
Logger instance whose name is used as a log name prefix, or ``None``
for no prefix.
initInputs : `dict`, optional
A dictionary of objects needed to construct this PipelineTask, with
keys matching the keys of the dictionary returned by
`getInitInputDatasetTypes` and values equivalent to what would be
obtained by calling `Butler.get` with those DatasetTypes and no data
IDs. While it is optional for the base class, subclasses are
permitted to require this argument.
Definition at line 32 of file pipelineTask.py.
Constructor & Destructor Documentation
◆ __init__()
| def lsst.pipe.base.pipelineTask.PipelineTask.__init__ | ( | self, | |
config = None, |
|||
log = None, |
|||
initInputs = None, |
|||
| kwargs | |||
| ) |
Definition at line 85 of file pipelineTask.py.
Member Function Documentation
◆ __reduce__()
|
inherited |
◆ emptyMetadata()
|
inherited |
◆ getAllSchemaCatalogs()
|
inherited |
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()
|
inherited |
Get metadata for all tasks.
Returns
-------
metadata : `lsst.daf.base.PropertySet`
The `~lsst.daf.base.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()
|
inherited |
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".
◆ getName()
|
inherited |
◆ getResourceConfig()
| def lsst.pipe.base.pipelineTask.PipelineTask.getResourceConfig | ( | self | ) |
Return resource configuration for this task. Returns ------- Object of type `~config.ResourceConfig` or ``None`` if resource configuration is not defined for this task.
Definition at line 148 of file pipelineTask.py.
◆ getSchemaCatalogs()
|
inherited |
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.
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.
See also
--------
Task.getAllSchemaCatalogs
◆ getTaskDict()
|
inherited |
◆ makeField()
|
inherited |
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")
◆ makeSubtask()
|
inherited |
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()
| def lsst.pipe.base.pipelineTask.PipelineTask.run | ( | self, | |
| kwargs | |||
| ) |
Run task algorithm on in-memory data.
This method should be implemented in a subclass. This method will
receive keyword arguments whose names will be the same as names of
connection fields describing input dataset types. Argument values will
be data objects retrieved from data butler. If a dataset type is
configured with ``multiple`` field set to ``True`` then the argument
value will be a list of objects, otherwise it will be a single object.
If the task needs to know its input or output DataIds then it has to
override `runQuantum` method instead.
This method should return a `Struct` whose attributes share the same
name as the connection fields describing output dataset types.
Returns
-------
struct : `Struct`
Struct with attribute names corresponding to output connection
fields
Examples
--------
Typical implementation of this method may look like::
def run(self, input, calib):
# "input", "calib", and "output" are the names of the config fields
# Assuming that input/calib datasets are `scalar` they are simple objects,
# do something with inputs and calibs, produce output image.
image = self.makeImage(input, calib)
# If output dataset is `scalar` then return object, not list
return Struct(output=image)
Definition at line 88 of file pipelineTask.py.
◆ runQuantum()
| def lsst.pipe.base.pipelineTask.PipelineTask.runQuantum | ( | self, | |
| butlerQC | |||
| ) |
Definition at line 127 of file pipelineTask.py.
◆ timer()
|
inherited |
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.
Examples
--------
Creating a timer context::
with self.timer("someCodeToTime"):
pass # code to time
See also
--------
timer.logInfo
Member Data Documentation
◆ canMultiprocess
|
static |
Definition at line 83 of file pipelineTask.py.
◆ config
◆ log
◆ metadata
The documentation for this class was generated from the following file:
- python/lsst/pipe/base/pipelineTask.py
