Updated MIRI LRS spectral extraction notebook#330
Updated MIRI LRS spectral extraction notebook#330ianyuwong wants to merge 11 commits intospacetelescope:mainfrom
Conversation
- Updated documentation and explanatory text - Added smoothed background example to Example 3 - New Example 5 on optimal spectral extraction
|
Check out this pull request on See visual diffs & provide feedback on Jupyter Notebooks. Powered by ReviewNB |
|
hi, i'll review this in the next week thank you! |
cshanahan1
left a comment
There was a problem hiding this comment.
This looks great and I was able to run it with no issues. I opened up a PR to your branch with some minor fixes, mainly typos.
The CI is failing here (at least in part) because it is detecting a change in the original notebook and trying to run it, but it doesn't exist anymore. To fix this, you can start a new branch from main, then do 'git mv miri_lrs_advanced_extraction_part1.ipynb miri_lrs_advanced_extraction.ipynb' so git sees it as a rename and not a delete and add. Then, you can re-apply the other changes from this branch and force push back to it.
notebook edits
|
I created a new branch and new PR #332 |
|
closing, replaced with #332 |
This notebook checklist has been made available to us by the the Notebooks For All team.
Its purpose is to serve as a guide for both the notebook author and the technical reviewer highlighting critical aspects to consider when striving to develop an accessible and effective notebook.
The First Cell
<h1>or# in markdown).1., 2.,etc. in Markdown).The Rest of the Cells
#in Markdown) used in the notebook.Text
Code
Images
All images (jpg, png, svgs) have an image description. This could be
altproperty)altattribute with no value)Any text present in images exists in a text form outside of the image (this can be alt text, captions, or surrounding text.)
Visualizations
All visualizations have an image description. Review the previous section, Images, for more information on how to add it.
Visualization descriptions include
All visualizations and their parts have enough color contrast (color contrast checker) to be legible. Remember that transparent colors have lower contrast than their opaque versions.
All visualizations convey information with more visual cues than color coding. Use text labels, patterns, or icons alongside color to achieve this.
All visualizations have an additional way for notebook readers to access the information. Linking to the original data, including a table of the data in the same notebook, or sonifying the plot are all options.