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. To figure out what parameters are available, use the stspec script (just as with a regular step):

> stspec stpipe.test.test_pipeline.TestPipeline
science_filename = input_file()  # The input science filename
flat_filename = input_file()     # The input flat filename
skip = bool(default=False)   # Skip this step
output_filename = output_file()  # The output filename
[steps]
[[combine]]
config_file = string(default=None)
skip = bool(default=False)   # Skip this step
[[flat_field]]
threshold = float(default=0.0)# The threshold below which to remove
multiplier = float(default=1.0)# Multiply by this number
skip = bool(default=False)   # Skip this step
config_file = string(default=None)

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}",