Tasks, as implemented by lsst.pipe.base, are documented with the task topic type. This page describes how to write task topic pages for pipelines.lsst.io.

Starter template¶

Create a new task topic from Slack.[1] Open a direct message with @sqrbot-jr and type:

create file


Then select Science Pipelines documentation > Task topic.

 [1] The task topic file template is maintained in the lsst/templates repository.

For an example task named lsst.example.ExampleCmdLineTask, the rendered template looks like this:

.. lsst-task-topic:: lsst.example.ExampleCmdLineTask

##################
##################

.. Summary paragraph (a few sentences)
.. The aim is to say what the task is for

ExampleCmdLineTask [active tense verb] ...
.. If the task consumes or creates datasets, name those datasets here.
.. If there are many datasets, name the ones that people use more frequently.
ExampleCmdLineTask is available as a :ref:command-line task <pipe-tasks-command-line-tasks>, :command:exampleCmdLineTask.py.

Processing summary
==================

.. If the task does not break work down into multiple steps, don't use a list.
.. Instead, summarize the computation itself in a paragraph or two.

ExampleCmdLineTask runs this sequence of operations:

#. Runs this thing. (FIXME)

#. Processes processes that intermediate result. (FIXME)

#. Stores those results in this last step. (FIXME)

============================================

.. code-block:: text

exampleCmdLineTask.py REPOPATH [@file [@file2 ...]] [--output OUTPUTREPO | --rerun RERUN] [--id] [other options]

Key arguments:

:option:REPOPATH
The input Butler repository's URI or file path.

Key options:

:option:--id:
The data IDs to process.

.. seealso::

See :ref:command-line-task-argument-reference for details and additional options.

Python API summary
==================

Butler datasets
===============

When run as the exampleCmdLineTask.py command-line task, or directly through the ~lsst.example.ExampleCmdLineTask.runDataRef method, ExampleCmdLineTask obtains datasets from the input Butler data repository and persists outputs to the output Butler data repository.
Note that configurations for ExampleCmdLineTask, and its subtasks, affect what datasets are persisted and what their content is.

Input datasets
--------------

fixmeDatasetName
Brief description of the dataset.

Output datasets
---------------

fixmeOutputDatasetName
Brief description of this output dataset.

=====================

Configuration fields
====================

Examples
========

.. Add a brief example here.
.. If there are multiple examples
.. (such as one from a command-line context and another that uses the Python API)
.. you can separate each example into a different subsection for clarity.

Debugging
=========

.. If the task provides debug variables document them here using a definition list.


The next sections describe the key components of task topics.

File name and location¶

Task topic files are located in the tasks/ subdirectory of the module documentation directory within a package. The page itself is named after the fully-qualified name of the task class with a .rst extension.

For example, suppose a task class is lsst.pipe.tasks.processCcd.ProcessCcdTask. Its task topic page is located in the pipe_tasks repository like this:

.
└── doc
├── index.rst


Preamble¶

The lsst-task-topic directive at the top of the page declares that the page is a canonical reference for the specified task class. For example:

.. lsst-task-topic:: lsst.pipe.tasks.processCcd.ProcessCcdTask


Through this directive, other pages can reference this page using the lsst-task role:

:lsst-task:lsst.pipe.tasks.processCcd.ProcessCcdTask


In addition, other pages can use the lsst-tasks and lsst-cmdlinetasks directives to automatically list task pages that are marked by an lsst-task-topic directive. See the Module homepage topic type for an example of this strategy.

Title¶

The title (top-level header) of the task topic is the class’s name (without the module). No special code formatting is applied to the title.

Caution

If there are two tasks of the same class name, the additional tasks should have their module name in parentheses after the class name. For example: RegisterTask (lsst.pipe.tasks.ingest).

Context paragraph¶

Directly below the title, write a paragraph or two (though not many) that describe what the task is for. The aim of this content is to help a reader navigate the documentation and understand whether this task is relevant to what they are trying to understand.

Consider including the following information in the context paragraphs:

• The names of important datasets that are created by the task.
• Whether the task is a command-line task or not (and if so, the name of the executable).

This is a succinct context paragraph for ProcessCcdTask:

ProcessCcdTask provides a preliminary astrometric and photometric calibration for a single frame (a raw dataset), yielding a calexp dataset.
ProcessCcdTask is available as a command-line task <pipe-tasks-command-line-tasks>, processCcd.py.


Processing summary section¶

The “Processing summary” section outlines the algorithm that the task implements. Like the context paragraph above it, the “Processing summary” should be brief and highly scannable. The reader should be able to quickly grasp what the task does through this section. For algorithmic or usage details, refer the reader to the “In depth” section.

In most cases you can express the algorithm as an enumerated list. Introduce the list with a sentence like this:

ProcessCcdTask runs this sequence of operations:


If a step is implemented by a subtask, refer to the subtask by its configuration name and with the default target in parentheses:

#. Removes instrumental signature from the raw dataset by calling the
:lsst-config-field:~lsst.pipe.tasks.processCcd.ProcessCcdConfig.isr subtask
(default: :lsst-task:~lsst.ip.isr.isrTask.IsrTask).


If an important configuration field (besides a retargetable subtask) controls the flow of a task, you should point out that configuration field as well.

• Note the use of the active, present-tense verb that describes what the task does.

• Use the lsst-config-field role to link to documentation for the configuration field.

The argument of the lsst-config-field role is the fully-qualified name of the configuration field, as a member of the Config class (not as a member of the task class).

• Use the lsst-task role to refer to other task topic pages.

Command-line interface section¶

If the task is a command-line task, include this “Command-line interface” section in the task topic. This section briefly reminds users what the command-line interface for a task looks like and primarily refers the reader to centralized documentation for command-line task usage in the lsst.pipe.base docs. To implement this section, copy from the template or example shown above.

Caution

The “Command-line interface” component of the task topic isn’t fully developed yet. As the Science Pipelines migrate towards PipelineTask we will introduce a rigorous system for documenting command-line activator usage. This means that while you can put some effort into this section now, don’t too much effort into this content.

Python API summary section¶

The “Python API summary” section provides a bridge to the API reference for task classes, which are written as numpydoc docstrings (as are all Python APIs).

This section is automatically generated with the lsst-task-api-summary directive. The directive’s argument is the task’s fully-qualified name. For example:

.. lsst-task-api-summary:: lsst.pipe.tasks.processCcd.ProcessCcdTask


Butler datasets section¶

The “Butler datasets” section lists the datasets that are consumed and generated by this task. Input datasets are listed in a subsection called “Input datasets,” while output datasets are listed in “Output datasets.”

Each dataset is represented by a definition list item. The dataset name is the “term” and is formatted as a code literal (wrapped in double backticks). The definition is free-form content that briefly describes the dataset.

An example of an input dataset description:

raw
Raw dataset from a camera, as ingested into the input Butler data repository.
Unpersisted by the :lsst-config-field:~lsst.pipe.tasks.processCcd.ProcessCcdConfig.isr subtask.


An example of an output dataset description:

calexp
The calibrated exposure.
Persisted by the :lsst-config-field:~lsst.pipe.tasks.processCcd.ProcessCcdConfig.calibrate subtask.

The default subtask (:lsst-task:~lsst.pipe.tasks.calibrate.CalibrateTask) adds the following metadata:

MAGZERO_RMS
The RMS (standard deviation) of MAGZERO, measured by the :lsst-config-field:~lsst.pipe.tasks.calibrate.CalibrateTask.photoCal subtask.
MAGZERO_NOBJ: Number of stars used to estimate MAGZERO.
This is ngood reported by the :lsst-config-field:~lsst.pipe.tasks.calibrate.CalibrateTask.photoCal subtask.
COLORTERM1
Always 0.0.
COLORTERM2
Always 0.0.
COLORTERM3
Always 0.0.


Caution

The “Butler datasets” component of the task topic isn’t fully developed yet. As the Science Pipelines migrates towards PipelineTask, we will introduce a rigorous system for documenting datasets and their relationship to tasks. This means that while you can put some effort into this section now, don’t put too much effort into this content.

The “Retargetable subtasks” section describes the configuration fields associated with subtasks or subtask-like objects. Specifically, this section lists all ConfigurableField or RegistryField types.

This section should only include an lsst-task-config-subtasks directive. The directive’s argument is the task’s fully-qualified name. For example:

.. lsst-task-config-subtasks:: lsst.pipe.tasks.processCcd.ProcessCcdTask


Configuration fields section¶

The “Configuration fields” section describes the task’s configuration fields that aren’t ConfigurableField or RegistryField types.

This section should only include a lsst-task-config-fields directive. The argument of the directive is the task’s fully-qualified name. For example:

.. lsst-task-config-fields:: lsst.pipe.tasks.processCcd.ProcessCcdTask


In depth section¶

You can include an “In depth” section in the task topic to go into greater depth about the algorithms that the task implements. The discussion can touch on both the scientific aspects of the task as well as concrete details like configuration fields and subtasks. This section can be as long as it needs to be and can organized into subsections.

The “In depth” section is located after “Configuration fields” but before “Examples.” If this type of content is not present, leave this section out. It can always be added later.

Examples section¶

In this section, provide examples that show how the task can be used. Ideally, the examples should be runnable by a user either on the command-line or Python REPL, as appropriate.

Caution

How DM includes examples in user documentation is still being developed. The new system will facilitate testing, dataset delivery, and integration with Jupyter.

In the meantime, you can include examples in plain reStructuredText on a best-effort basis with the expectation that they will be reimplemented later. Use the code-block directive to include code samples, and command-line prompts and outputs.

Debugging section¶

You can port the debugging section from existing task documentation into reStructuredText in the “Debugging” section. Document individual fields in the debug info dictionary with a reStructuredText definition list.