Notebook development workflow#
This document is a description of the JWST Data Analysis Tools (JDAT) approach to “Notebook-Driven Development”. The procedures here outline the process for getting a notebook through successive development stages to become something that can be “live” on the spacetelescope notebooks repository.
These notebooks can have many varied science cases, but follow a relatively standard workflow:
These stages and the process for moving from one to the other are described below.
The procedure to submit the notebook via a Pull Request is described at the GitHub section of this documentation. This is repeated for each of the 5 stages.
Note that there is much more information on writing Jupyter notebooks at the STScI notebook style guide, and similar guidance for Python code at the STScI Python style guide. These guidelines are in place to make review steps easier.
Please note that all JDAT related code should be written Python 3; Python 2 is not supported.
The primary purpose of this stage is to record a scientific workflow, but without including actual code. This stage is generally done primarily by a scientist. Reasonably often, notebooks can skip this stage if they are simpler or if the underlying tools are already well-enough developed to be immediately implemented.
To begin a notebook at this stage, the notebook author should start with either the notebook template from the notebook style guide or a blank Jupyter notebook. They then write out their workflow in words. Where possible, they put example code of the sort they would like to see, even if it is not implemented yet.
For example, an author might write this in such a notebook:
In [ ]: spectral_line = find_line(jwst_miri_spectrum) # `spectral_line` should be a list of line centers and names of lines indexed by spaxel, # found using a derivative-based line-finder.
even if the
find_line function doesn’t yet exist anywhere.
The top-level header of the notebook (i.e., the title) should have “Draft: ” at the start to make it clear this is a draft notebook. The filename should not have draft in it, however, as the filename will generally remain the same throughout the later stages.
Once they have the draft ready, the author should create a pull request with the draft notebook’s content (see instructions at the end of this document).
The primary purpose of this stage is to get a functioning notebook to record a workflow. This stage is also typically done by a scientist (although with developers available to ask questions). It is also frequently the first step of development. That is, if the workflow is already reasonable to implement with existing tools, the draft notebook is not necessary.
In this stage the notebook should actually execute from beginning to end, but it is fine to be “rough around the edges”. E.g., the notebook might have several cells that say things like:
In [ ]: spec = Spectrum(np.linspace(a, b, 1000)*u.angstrom, some_complex_function(...))
the scientist might think this is too complicated, and so to communicate their desire for an improved
workflow, they create a “Developer Note”. A developer note should be a part of the notebook itself and should be a
single markdown cell (not code cell - code examples in a dev note can be done as literal markdown blocks - i.e.
surrounded by ``` for blocks or ` for inline code). That cell should begin with the text
E.g., a markdown cell might be added below the above cell in a notebook, which would say:
*Developer Note:* Creating the spectrum above is a bit complicated, and it would improve the workflow if there was a single simple function that just did `spec = simulate_jwst_spectrum(a, b)`.
thereby providing guidance for where specific development would simplify the workflow.
If a notebook is freshly created in this form, the author can follow the “Procedure to submit a notebook as a Pull Request” (found at the end of this document), skipping the Draft Stage step.
If the notebook was already created in the Draft Stage step and the “Procedure to submit a notebook as a Pull Request” has already been followed, the author should just create a new branch to modify the existing code and then create a new Pull Request with the changes once they are ready.
In either case, the title (but not filename) of the notebook should begin with “Baseline:” to indicate the notebook is in the Baseline Stage.
Once the Pull Request has been created, the notebook will automatically be built in the repository so that reviewers can view it. Reviewers can then comment on the notebook in Github. At this stage the bar is still relatively low for review - primarily things like ensuring the notebook does run from beginning-to-end and that data files or the like were not accidentally committed to the repository.
Finally, there are three important technicalities for notebooks that become relevant at this stage (and continue for future stages):
1. The output cells of a notebook should always be cleared before a git commit is made. Notebook outputs can sometimes be quite large (in the megabytes for plots or the like), and git is intended for source code, not data. Clearing the outputs also ensures the notebook can be run from beginning to end and therefore be reproduced by others.
2. Any data files required for a notebook need to be accessible by others who may be reviewing or testing the notebook.
The STScI guidelines on data storage for notebooks
should be followed here. The specific addition for the JWST Notebooks is that notebook data should be
DMD_Managed_Data/JWST/jwst-data_analysis_tools Box folder (or subfolders thereof).
If you do not have access to this box folder already, ask a Project Scientist and they should be able to get you added.
Note that if a baseline notebook is using data that should not yet be public, the easiest choice is probably central store,
but in that case it is critical that the notebook state prominently that it must be run inside the STScI network.
3. A notebook should state clearly what version of various dependencies were used to generate the notebook.
These versions should be placed in a
requirements file in the same directory as the notebook itself. An example of this file
is in the``example_notebook`` folder.
That will ensure reviewers/testers can be sure that if they encounter problems, it is not due to software version mis-matches.
The notebook will undergo a scientific and a technical review, which might also yield additional developer notes. It will then be merged into the repository once the review comments have been addressed. This concludes the Baseline Stage.
Along and after the Draft and Baseline stages, there is potential for considerable development to be necessary. A baseline notebook may contain a large number of areas where more development is desired in data analysis tools, or it may only require a few minor adjustments (or none at all!). This stage is therefore the most flexible and dependent on developer resources, etc. In general the intent is for developers to be able to re-use bits of code from the notebook as tests for development, while occasionally (if necessary) asking the notebook author for guidance to ensure the implementation actually meets the notebook’s needs. There is not a formal process for this step, but it is intended that the JDAT planning process (currently on Jira) keeps track of specific steps needed before a given notebook can proceed on to the next stage.
Once a baseline notebook has been completed, the next stage is to build the baseline into a notebook that uses the DAT’s or associated community-developed software as consistently as possible. This is typically done via a developer reviewing a baseline notebook and working with the scientist to develop additional DAT code, particularly focused on resolving the “developer notes”. It is at the discretion of the notebook author and developer together which of them actually modifies the notebook and sources the Pull Request, but it is likely both will be involved to some degree. An example approach is for the developer to take the baseline notebook, mark it up with comments like (using the example from above):
In [ ]: spec = Spectrum(np.linspace(a, b, 1000)*u.angstrom, some_complex_function(...))
Creating the spectrum above is a bit complicated, and it would improve the workflow if there was a single simple function that just did
spec = simulate_jwst_spectrum(a, b)
*Development:* This has now been implemented as JWSTSimulator.make_spectrum(a, b, anotherparameterthatturnsouttobeimportant). Can you try that and ensure it works here?
and then create a git commit with these comments. The original author would then address the comments in a follow-on commit. There might be multiple pull requests of this sort as the notebook driven development continues. But once all developer notes have been addressed, the developer and author can declare the notebook ready to be called “Advanced”.
Once the notebook authors (original author and developer/reviewer) have agreed it is ready, one of them follows the Pull Request workflow as described above, but with the notebook title now changed to be just the title itself (no “Draft:” or Baseline:”). The Pull Request is then reviewed by one of the project scientists, and merged when everyone is satisfied with the notebook.
Revision Based on Community Feedback#
Of course, science does not stand still! As time passes some of the completed notebooks may have enhancements or changes necessary. In general these follow the standard Pull Request workflow and can be submitted by anyone once the notebook is public (both in and out of STScI). While the repo maintainers manage this process, the notebook authors may be called in from time to time to provide opinions or perspectives on any proposed changes.