Guidelines for User Tutorials#


Guidelines for anyone creating or updating user-facing tutorials for the Rubin Observatory, such as demonstrations of how to use the Rubin Science Platform (RSP) or how to analyze Legacy Survey of Space and Time (LSST) data. Principles, conventions, templates, formats, and styles for code and text are provided. All Rubin staff and the broader science community should use these guidelines if contributing to the set of user-facing tutorials maintained by the Rubin Community Science team (CST).


All tutorials should be guided by the following principles, and adhere to the Rubin Developers Guide.

Inclusive: Follow the best practices described under Accessibility considerations. Offensive or exclusionary language is never permitted (e.g., violent or ableist terms). Ensure jargon and acronyms are defined, regardless of target audience. See the Rubin Observatory Communications Code of Conduct for additional guidance.

Level-appropriate: Identify and teach to a target audience (e.g., beginner, intermediate, or advanced).

Skill-focused: Teach one to a few skills, techniques, or RSP functionality, and/or provide a demonstration of a scientific analysis with LSST data.

Consumable: The tutorial should take about 30 minutes to complete (not including any provided “Exercises for the learner”).

Well-referenced: Include citations, references, and external links (e.g., code package documentation). Reference any precursor (or advanced) tutorials that users can consider as prerequisite (or as follow-up).

Properly-credited: Appropriate acknowledgments should be provided to credit individuals whose work was used. This sets a precedent of prioritizing credits in an openly collaborative environment. Cite other scientists or papers within the text of the tutorial where appropriate.

Clearly-written: Write in short, clear, instructional statements. Describe the actions a user should take and the expected results. Instructional text should be written in the imperative mood, as is commonly adopted for technical writing. Find further guidelines in Rubin’s User documentation style guide.

How to contribute#

Anyone is welcome to create a tutorial and then work with the Rubin Community Science team (CST) to have it ingested and made available alongside the rest of the tutorials. CST members will help with the review process and GitHub workflows. Contact any co-author of this document to get started.

Jupyter Notebooks#


Use the template Jupyter notebook in the cst-dev GitHub repository, which is part of the rubin-dp0 GitHub Organization. The template contains an example of the header and the mandatory first section, which are described in Section structure.

Use of PEP8 and flake8#

PEP8 is the style guide for Python code that comprises the standard library of the distribution, and flake8 is a tool to ensure compliance with these standards.

Use flake8 to ensure notebook code conforms to PEP 8 – Style Guide for Python Code, with a few exceptions.

Notebook tutorial developers must install the following packages locally in their home directory:

pip install --user flake8-nb
pip install --user pycodestyle_magic

It is known that the most up-to-date version of flake8 has some issues. If errors are encountered such as AttributeError: '_io.StringIO' object has no attribute 'buffer', force-downgrade flake8 from version 4.0.1 to 3.9.2 with pip install flake8==3.9.2.

Create the flake8 config file: These instructions use emacs, but it doesn’t matter so long as the end result is a correctly-named file with the right contents. Start in the home directory and execute the following.

touch .config/flake8
emacs .config/flake8

Then copy-paste the following into the opened config file.

max-line-length = 99
ignore = E133, E226, E228, E266, N802, N803, N806, N812, N813, N815, N816, W503

Use x-s then x-c to save and exit emacs.

While developing a notebook have the following “magic” commands as the first code cell:

%load_ext pycodestyle_magic
import logging

Whenever a cell is executed, it will use flake8 to check for adherence to the PEP8 coding style guide, and report violations which can be fixed immediately. When the notebook is ready to be merged, the cell with the magic commands must be removed.

When the notebook is complete execute the following from the command line in the notebook’s directory:

flake8-nb notebook_name.ipynb

This will do a final check of any violations with PEP8. This will catch things that can be missed line-by-line, such as packages that are imported but never used.

Markdown cells#

Monospace font: Any references to variables used in code cells or any code commands should be in monospaced font.

Warnings: Use of indented text should be limited to warnings, e.g., > **Warning:** the following cell produces a warning which is ok to ignore because....

Section structure#

First markdown cell: Set the title using heading level 1 (single #). Display the Rubin Observatory logo at upper left. To the right of the logo list the contact author, date last verified, LSST Science Pipelines version, container size, and targeted learning level, in that order.

Second to seventh markdown cells: A very brief description, a list of core skills, a list of the LSST data products, a list of the python packages used by the notebook, the credits and acknowledgements, and information about where users should go to get support, in that order. It is ok to limit the lists to include only the main data products and packages that the tutorial is focused on teaching. It is ok to omit basic support packages (e.g., os, glob, numpy, matplotlib). The contents of cells two through five are used to generate the table of notebook metadata in the file for the repository. It is a stretch goal to be able to auto-generate the table by scraping these notebook metadata.

The first section should be named “Introduction” using heading level 2: ## 1. Introduction. Provide a brief narrative about this notebook, e.g., “This notebook will teach the user…”. Cite or link to any external information or documentation, and cross-reference to other notebooks.

The first subsection should always be ### 1.1. Import packages. It should have a markdown cell that provides explanations and/or links to external package documentation, as appropriate. All package imports must be done in the first code cell.

The second subsection should always be ### 1.2. Define functions and parameters. Globally defined utility functions, classes, plotting defaults, or constants should be here. Instantiations of the TAP or butler services should also be done here.

Single-use functions or classes can be defined immediately before they are used, for pedagogical purposes; see the guidelines for functions and classes in the Code cells section below. It is ok to have sub-subsections, such as #### 1.2.1. Define global cosmological parameter values or #### 1.2.2. Define a function to make an image cutout.

Additional sections must be numbered to enable referencing in support requests, e.g., “I’m having trouble with the second code cell in Section 2.3.” Use descriptive section titles, e.g., 2.2 Create a color-magnitude diagram instead of 2.2 Plot, so that the auto-generated table of contents is more useful. Do not use title case for section headings; use sentence case. (This Is Title Case. This is sentence case.)

Exercises for the learner: It is very common, but not mandatory, to end all notebook tutorials with a section called Exercises for the learner with suggestions of how the user can make changes to the tutorial test options and examples, or guide them on the next step forward on their own.

Code cells#

All python code in Jupyter Notebooks should adhere to the Code Style Guidelines in the Rubin Developer’s Guide. Follow the guidelines above for the Use of PEP8 and flake8.

Comments: Avoid using comments within a code cell as documentation (i.e., with #). Markdown cells are the preferred way to provide descriptive text.

Functions and classes#

Functions and classes should be named following the Naming Conventions defined in the Rubin Developer’s Guide.

Globally defined functions or classes which are used more than once in a notebook should be defined in Section 1.2, but single-use functions or classes can be defined immediately before they are used.

Hiding long functions. Functions or classes that are particularly long blocks of code (e.g., >20 lines) can be hidden by going to the “View” menu item and choosing “Collapse Selected Code”, or by clicking on the blue bar that appears to the left of a selected cell. Hidden cells should be described in the preceding markdown cell with text like “the following hidden cell contains code that defines the make_cmd_plot function”. The first hidden cell in a notebook should include instructions for displaying the cell, such as “to see the contents of the hidden cell, select View from the menu bar and then Expand Selected Code or click on the vertical next to the cell or on the three dots that denote that the cell is hidden”.

It is a stretch goal to create a package of commonly-used functions in order to avoid users encountering long blocks of code, and help keep notebooks readable.

TAP queries#

TAP queries should always be run as asynchronous as this is the best practice and a good habit for users.

As the execution of TAP queries can be time-variable, the notebook’s narrative text should not include any estimates for how long the query should take, to avoid confusing or concerning the user. The html files of executed versions of the notebooks (see Converted notebooks) will show the execution time, should the user require an estimate.

Clearing memory#

To reduce the memory footprint of a notebook, remove figures once they’re no longer needed. See the remove_figure function defined in the DP0 notebook 03_Image_Display_and_Manipulation.ipynb in the tutorial-notebooks repository. This is only necessary in notebooks that demonstrate data visualization with large datasets. Better ways to clear the memory are under consideration (see Stretch goals).

Assert statements#

Where essential, or where a very specific value is expected, the assert command can be used to demonstrate to users that a condition is true. For example, assert statements can be used to confirm that service objects like TAP are not None or null before moving on and using that instance, or to check that values meet expectations (e.g., total rows returned from a query). Do not use assert statements when, e.g., querying dynamic (prompt) datasets, which could return different results and cause the assert statement to fail. Consider more pedagogical alternatives when possible (e.g., printing schema columns would also fail if the TAP service was not instantiated).

Known warnings#

If a code cell produces a warning which is known and it should be ignored, the preferred method is to add a markdown cell before the code cell which produces the warning, to tell the user it is acceptable to ignore. It is not preferred to use, e.g., warnings.simplefilter("ignore", category=UserWarning), because ignoring categories of warnings can allow real issues to go unnoticed.



Results from a Table Access Protocol (TAP) service search are best displayed as an astropy table using .to_table(), or as a pandas dataframe using .to_table().to_pandas().

Do not use the .to_table().show_in_notebook() method. This can cause issues in the RSP JupyterLab environment that cause the notebook to hang indefinitely.


Size: Plots should be large enough such that the details in the data are easily discerned, but small enough to fit within a small browser window (e.g., a laptop screen). Typically, a statement such as fig = plt.figure(figsize=(6, 4)) is sufficient (or (6, 6) for square plots).

Labels: Axes labels with units are mandatory. A legend must be included if multiple types of data are co-plotted. A descriptive title is encouraged but not mandatory.

Style: In general, the default matplotlib style is sufficient and should be adopted for plot attributes such as line thickness, tick labels, fontsize, and so on. However, the default matplotlib color palette is not sufficient, and the recommendations under Accessibility considerations should be used to create colorblind-friendly plots.

Error bars: Error bars should be included wherever possible, and especially in cases where analyses such as line fitting is being performed on the data in the plot, to help the user understand data quality.

Captions: A markdown cell underneath the figure should provide a figure number and a caption that explains the main attributes of the plot. This caption should serve as alt-text (as described under Accessibility considerations) and also as a way for the user to confirm the plot appears as expected.

Image display#

Image orientation: When using a World Coordinate System (WCS), display East left, North up. If only using pixels, (0,0) should be the lower-left, which is the default for awfDisplay.

When using other plotting packages, transformations might be needed in order to match the afwDisplay default. See the LSST Science Pipelines documentation about Image Indexing.

Since use of “extent” is necessary for displaying a WCS overlay for deepCoadds, use it all the time:

deepCoadd = butler.get('deepCoadd', dataId=dataId)
deepCoadd_bbox = butler.get('deepCoadd_calexp.bbox', dataId=dataId)
deepCoadd_wcs = butler.get('deepCoadd_calexp.wcs', dataId=dataId)
deepCoadd_WCSfMd = WCS(deepCoadd_wcs.getFitsMetadata())
deepCoadd_extent = (deepCoadd_bbox.beginX, deepCoadd_bbox.endX, deepCoadd_bbox.beginY, deepCoadd_bbox.endY)
plt.imshow(deepCoadd.image.array, cmap='gray', vmin=0, vmax=2, extent=deepCoadd_extent, origin='lower')

Documentation-based tutorials#

Tutorials for the Portal and API Aspects, or tutorials written as scripts that can be copy-pasted into the command line interface in the Notebook Aspect, are written in reStructuredText (RST) format and are kept within the data release documentation at DP0.2 Tutorials and DP0.3 Tutorials.

Header and section structure#

All tutorials should have a descriptive title. At the top of the page, the tutorial should list the contact authors, the date last verified to run, and the targeted learning level, before providing a brief narrative introduction.

The rest of the tutorial should be divided into sequentially numbered steps. Steps should be short (one to a few sentences) and provide a single action item for the user.

It is very common, but not mandatory, to end all tutorials with a section called “Exercises for the learner” with suggestions of how the user can make changes to the tutorial test options and examples, or guide them on the next step forward on their own.

Code blocks#

Ensure that all code and any Astronomical Data Query Language (ADQL) statements are put into code boxes in RST so that users may copy-paste whenever possible. In RST, this is done as in the following example.

.. code-block:: SQL

  SELECT e, q, incl
  FROM dp03_catalogs_10yr.MPCORB
  WHERE ssObjectId > 9000000000000000000


Use screenshots to demonstrate the steps of the tutorial, to show the user what to do, and to show the expected results for comparison. Augment screenshots with indicators (e.g., arrows or circles) to guide the users attention as needed.

Data visualization should Colorblind-friendly plots.

Caption and alt-text: All figures should have a caption and an Alternative-text (alt-text) statement. The motivation and guidance for writing alt-text is provided under Accessibility considerations.

To add a caption and alt-text to an image in rST, use the :alt: command as in the following example.

.. figure:: /_static/figure_filename.png
  :name: name_of_figure
  :alt: Descriptive text of image (use tab to indent second line of text)

  The caption goes here, indented the same way, but with an empty line between code and caption text.

Accessibility considerations#

The following set of best practices to be implemented for Rubin tutorials is a work in progress.

Vision-impaired astronomers#

Colorblind-friendly plots#

The most common form of colorblindness is being unable to differentiate red and green. Guidelines for colorblind-friendly plots includes the following.

  • Do not use red and green together.

  • Use color combinations that are high contrast.

  • Do not use color alone, but with different symbol and line styles.

In Jupyter Notebooks, in order to be accessible to those with Color Vision Deficiency (CVD or colorblind), plots color tables with matplotlib should be either a greyscale, a preceptually uniform sequential colormap like viridis or cividis, or tableau-colorblind10.

The tableau-colorblind10 color table can be loaded with the following python code.

import matplotlib.pyplot as plt'tableau-colorblind10')

For the LSST filter set ugrizy, adopt the same colors as Dark Energy Survey (DES), which were chosen to be colorblind-friendly. The following python code can be used to create a dictionary that assigns colors by filter name.

plot_filter_colors = {'u': '#56b4e9', 'g': '#008060', 'r': '#ff4000', 'i': '#850000', 'z': '#6600cc', 'y': '#000000'}

Alternative-text (alt-text)#

Alt-text is added to figures, images, and graphics in the documentation and documentation-based tutorials (but not notebooks, where figures are typically dynamically generated) to ensure that visually impaired individuals, who use screen readers, are given sufficient information to understand what is displayed.

In general, alt-text descriptions can be written as either a literal description of the figure or image, or a more creative description. In CST tutorials, most figures display screen shots of the RSP portal interface and should describe the information in a practical way versus a creative way.

Guidelines for writing alt-text:

  • Be brief, if possible. Write in short, succinct sentences.

  • Spell out acronyms (e.g. Right Ascension versus RA).

  • Avoid jargon or undefined terms.

  • Symbols and equations should be expressed in words (e.g. use “equals” rather than “=”).

  • Write for the text to be read aloud. Written visual cues (e.g. quotation marks or dashes) are not necessary.

  • Pictures should be described in terms of what the listener needs to know (e.g., a large galaxy in the center).

  • For RSP screenshots, state which interface is being shown and describe the actions the user should take and the expected results, or the main functionality of the interface (as appropriate).

  • Where possible, use consistent terms such as the JupyterLab User Interface Naming Conventions.

  • Limit the use of visual cues, such as colors or shapes, or visual-centric language (e.g., “as you can see”).

  • If color is a useful attribute to distinguish items in a figure, then describe the attribute rather than the color (e.g. a blue star versus a red star could be described as a hotter star and a cooler star).

  • For plots, include type of plot (e.g., bar, scatter), titles and labels, and a general explanation of the data and what it means.

Converted notebooks#

For offline viewing, create html versions of executed notebooks and not pdf versions, as the latter are typically less compatible with screen readers.

At this time it is not necessary to use, e.g., nbconvert, but a customized application might be considered in the future (and is included under Stretch goals).

Neurodivergent astronomers#

Use fonts that work well for people with dyslexia, such as sans serif, monospaced, and roman font types such as Helvetica, Courier, Arial, Verdana and CMU (Computer Modern Unicode), OpenDyslexic.

Italic fonts decrease readability and should be used sparingly.

Avoid text crowding and long paragraphs. Use short sentences and, where possible, arrange text in shorter paragraphs.


A few useful resources for accessibility include:

Stretch goals#

Work is on-going in these areas, and in time they will become part of the guidelines above.

Notebook metadata: Embed notebook metadata (e.g., version, skills, packages) in a way that can be scraped and used to auto-generate the file or a Table of Contents, to enable users to browse notebook contents.

Accessibility: Continue to improve tutorials’ accessibility to people with disabilities by finding and implementing, e.g., screen reader compatibility software, data sonification packages, customized use of nbconvert, additional policies for supporting neurodivergent users, and other jupyter notebook accessibility techniques.

Translations: At minimum, translate any undergraduate-level tutorials into Spanish. Additionally, improve tutorials’ accessibility to non-English speakers by finding and implementing automatic translation and localization software.

Clearing memory: Develop a best practice for how to keep notebook memory usage in check, in addition to deleting figures. E.g., whether or not the del command is sufficient for this.

Package of commonly-used functions: Create recipes for common user activities. These could be, e.g., ADQL searches for the portal, code snippets for the command line, or python modules that can be imported. When these are used in the advanced notebooks, also demonstrate use of the inspect.getsource functionality for users to display function code.