Pipelines
It is important to note that a Pipeline is also a Step, so everything that applies to a Step in the For Users chapter also applies to Pipelines.
Configuring a Pipeline
This section describes how to set parameters on the individual steps in a pipeline. To change the order of steps in a pipeline, one must write a Pipeline subclass in Python. That is described in the Pipelines section of the developer documentation.
Just as with Steps, Pipelines can by configured either by a parameter file or directly from Python.
From a parameter file
A Pipeline parameter file follows the same format as a Step parameter file: ASDF Parameter Files
Here is an example pipeline parameter file for the Image2Pipeline
class:
#ASDF 1.0.0
#ASDF_STANDARD 1.5.0
%YAML 1.1
%TAG ! tag:stsci.edu:asdf/
--- !core/asdf-1.1.0
asdf_library: !core/software-1.0.0 {author: Space Telescope Science Institute, homepage: 'http://github.com/spacetelescope/asdf',
name: asdf, version: 2.7.3}
class: jwst.pipeline.Image2Pipeline
name: Image2Pipeline
parameters:
save_bsub: false
steps:
- class: jwst.flatfield.flat_field_step.FlatFieldStep
name: flat_field
parameters:
skip = True
- class: jwst.resample.resample_step.ResampleStep
name: resample
parameters:
pixel_scale_ratio: 1.0
pixfrac: 1.0
Just like a Step
, it must have name
and class
values.
Here the class
must refer to a subclass of stpipe.Pipeline
.
Following name
and class
is the steps
section. Under
this section is a subsection for each step in the pipeline. The easiest
way to get started on a parameter file is to call Step.export_config
and
then edit the file that is created. This will generate an ASDF config file
that includes every available parameter, which can then be trimmed to the
parameters that require customization.
For each Step’s section, the parameters for that step may either be specified inline, or specified by referencing an external parameter file just for that step. For example, a pipeline parameter file that contains:
steps:
- class: jwst.resample.resample_step.ResampleStep
name: resample
parameters:
pixel_scale_ratio: 1.0
pixfrac: 1.0
is equivalent to:
steps:
- class: jwst.resample.resample_step.ResampleStep
name: resample
parameters:
config_file = myresample.asdf
with the file myresample.asdf.
in the same directory:
class: jwst.resample.resample_step.ResampleStep
name: resample
parameters:
pixel_scale_ratio: 1.0
pixfrac: 1.0
If both a config_file
and additional parameters are specified, the
config_file
is loaded, and then the local parameters override
them.
Any optional parameters for each Step may be omitted, in which case defaults will be used.
From Python
A pipeline may be configured from Python by passing a nested dictionary of parameters to the Pipeline’s constructor. Each key is the name of a step, and the value is another dictionary containing parameters for that step. For example, the following is the equivalent of the parameter file above:
from stpipe.pipeline import Image2Pipeline
steps = {
'resample': {'pixel_scale_ratio': 1.0, 'pixfrac': 1.0}
}
pipe = Image2Pipeline(steps=steps)
Running a Pipeline
From the commandline
The same strun
script used to run Steps from the commandline can
also run Pipelines.
The only wrinkle is that any parameters overridden from the
commandline use dot notation to specify the parameter name. For
example, to override the pixfrac
value on the resample
step in the example above, one can do:
> strun stpipe.pipeline.Image2Pipeline --steps.resample.pixfrac=2.0
From Python
Once the pipeline has been configured (as above), just call the instance to run it.
pipe()
Caching details
The results of a Step are cached using Python pickles. This allows virtually most of the standard Python data types to be cached. In addition, any FITS models that are the result of a step are saved as standalone FITS files to make them more easily used by external tools. The filenames are based on the name of the substep within the pipeline.
Hooks
Each Step in a pipeline can also have pre- and post-hooks associated. Hooks themselves are Step instances, but there are some conveniences provided to make them easier to specify in a parameter file.
Pre-hooks are run right before the Step. The inputs to the pre-hook are the same as the inputs to their parent Step. Post-hooks are run right after the Step. The inputs to the post-hook are the return value(s) from the parent Step. The return values are always passed as a list. If the return value from the parent Step is a single item, a list of this single item is passed to the post hooks. This allows the post hooks to modify the return results, if necessary.
Hooks are specified using the pre_hooks
and post_hooks
parameters
associated with each step. More than one pre- or post-hook may be assigned, and
they are run in the order they are given. There can also be pre_hooks
and
post_hooks
on the Pipeline as a whole (since a Pipeline is also a Step).
Each of these parameters is a list of strings, where each entry is one of:
An external commandline application. The arguments can be accessed using {0}, {1} etc. (See
stpipe.subproc.SystemCall
).A dot-separated path to a Python Step class.
A dot-separated path to a Python function.
For example, here’s a post_hook
that will display a FITS file in
the ds9
FITS viewer the flat_field
step has done flat field
correction on it:
steps:
- class: jwst.resample.resample_step.ResampleStep
name: resample
parameters:
post_hooks = "ds9 {0}",