Episodes modified using Matplotlib Object Oriented Style by GParolini · Pull Request #321 · datacarpentry/image-processing

GParolini · 2024-03-03T12:37:13Z

If this pull request addresses an open issue on the repository, please add 'Closes #NN' below, where NN is the issue number.
Closes #319 [Matplotlib is currently used in a hybrid way (explicit vs. implicit interfaces)]

Please briefly summarise the changes made in the pull request, and the reason(s) for making these changes.
The episodes are now using Matplotlib Object Oriented Style. In a few places, I needed also to change the explanatory text when the text referred to displaying figures with Matplotlib.
In Episode 3, I simplified the two lines of code
fig, ax = plt.subplots()
fig, ax = plt.subplots(ncols=2)
removing the first line.

If any relevant discussions have taken place elsewhere, please provide links to these.

Details

For more guidance on how to contribute changes to a Carpentries project, please review the Contributing Guide and Code of Conduct.

Please keep in mind that lesson Maintainers are volunteers and it may be some time before they can respond to your contribution. Although not all contributions can be incorporated into the lesson materials, we appreciate your time and effort to improve the curriculum. If you have any questions about the lesson maintenance process or would like to volunteer your time as a contribution reviewer, please contact The Carpentries Team at team@carpentries.org.

github-actions · 2024-03-03T12:37:27Z

Thank you!

Thank you for your pull request 😃

🤖 This automated message can help you check the rendered files in your submission for clarity. If you have any questions, please feel free to open an issue in {sandpaper}.

If you have files that automatically render output (e.g. R Markdown), then you should check for the following:

🎯 correct output
🖼️ correct figures
❓ new warnings
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Rendered Changes

🔍 Inspect the changes: https://github.com/datacarpentry/image-processing/compare/md-outputs..md-outputs-PR-321

The following changes were observed in the rendered markdown documents:

 02-image-basics.md         | 20 +++++------
 03-skimage-images.md       | 39 ++++++++++-----------
 04-drawing.md              | 20 +++++------
 05-creating-histograms.md  | 87 +++++++++++++++++++++++-----------------------
 06-blurring.md             | 14 +++-----
 07-thresholding.md         | 62 ++++++++++++++++-----------------
 08-connected-components.md | 26 +++++++-------
 09-challenges.md           | 22 ++++++------
 md5sum.txt                 | 16 ++++-----
 9 files changed, 151 insertions(+), 155 deletions(-)

What does this mean?

If you have source files that require output and figures to be generated (e.g. R Markdown), then it is important to make sure the generated figures and output are reproducible.

This output provides a way for you to inspect the output in a diff-friendly manner so that it's easy to see the changes that occur due to new software versions or randomisation.

⏱️ Updated at 2024-03-12 16:33:49 +0000

mkcor

Thank you so much for this work, @GParolini! Look, @uschille, it turns out we could find someone to tackle this good first issue. 😃

episodes/03-skimage-images.md

episodes/05-creating-histograms.md

uschille

Great work @GParolini, thank you so much! The suggestions I have are beyond the scope of the PR and more about improving the current lesson text. Happy to merge this PR and look into text revisions separately.

episodes/02-image-basics.md

uschille · 2024-03-11T22:01:35Z

episodes/05-creating-histograms.md

 - We can create histograms of images with the `np.histogram` function.
 - We can separate the RGB channels of an image using slicing operations.
- We can display histograms using the `matplotlib pyplot` `figure()`, `title()`, `xlabel()`, `ylabel()`, `xlim()`, `plot()`, and `show()` functions.
+- We can display histograms using the `matplotlib pyplot` `subplots()`, `set_title()`, `set_xlabel()`, `set_ylabel()`, `set_xlim()`, `plot()`, and `show()` functions.


This sentence is a bit condensend and probably a relict from earlier revisions. I would suggest the following version:

Suggested change

- We can display histograms using the `matplotlib pyplot` `subplots()`, `set_title()`, `set_xlabel()`, `set_ylabel()`, `set_xlim()`, `plot()`, and `show()` functions.

- We can display histograms using `plt.plot()` with the `bin_edges` and `histogram` values returned by `np.histogram()`.

(using the idiomatic plt for matplotlib.pyplot similar to how np and iio are used in previous bullet points)

Perhaps move the point on RGB channels to the bottom of the list and rephrase?

We can separate the RGB channels of an image using slicing operations and create and plot histograms for each channel separately.

We could add a separate bullet point for set_xlabel() etc., but perhaps those are already used before this episode?

I agree. Shifting the sentence on the RGB channels in the last position brings in sequence the explanations on creating and displaying histograms.
Perhaps before the RGB statement a sentence like the following can be added to explain the use of set label and limit options:

The artists "X axis label" and "Y axis label" can be added and customised using set_xlabel(), set_ylabel(), set_xlim(), set_ylim().

👍 I like this sentence. I would just suggest to avoid the term "artist" because I'm not sure learners will be familiar with the Matplotlib terminology and it may cause unnecessary cognitive load. So perhaps:

The plot can be customised using set_xlabel(), set_ylabel(), set_xlim(), set_ylim(), and set_title().

@GParolini, would you like to add the newly ordered and edited bullet list to your pull request?

I also noticed I made a mistake in my previous suggestion by not using object-oriented syntax:

Suggested change

- We can display histograms using the `matplotlib pyplot` `subplots()`, `set_title()`, `set_xlabel()`, `set_ylabel()`, `set_xlim()`, `plot()`, and `show()` functions.

- We can display histograms using `ax.plot()` with the `bin_edges` and `histogram` values returned by `np.histogram()`.

Ok. I committed the changes to the branch.

Fix typo/grammar Co-authored-by: Marianne Corvellec <marianne.corvellec@ens-lyon.org>

Co-authored-by: Marianne Corvellec <marianne.corvellec@ens-lyon.org>

tobyhodges · 2024-03-12T13:37:21Z

Thanks so much for contributing these improvements, @GParolini 🙌 and of course @mkcor and @uschille for reviewing

mkcor

Great! 🎉

uschille

Fantastic, @GParolini. I have added one minor edit and will go ahead and merge this PR.

episodes/05-creating-histograms.md

Auto-generated via {sandpaper} Source : ff9e06a Branch : main Author : Ulf Schiller <uschille@users.noreply.github.com> Time : 2024-03-12 16:37:53 +0000 Message : Merge pull request #321 from GParolini/oo-matplotlib Episodes modified using Matplotlib Object Oriented Style

uschille · 2024-03-12T16:39:00Z

Great work @GParolini, and great teamwork @mkcor and @tobyhodges!

The rebuild of the webpages also fixed some rendering issues related to carpentries/sandpaper#470 - thanks to the contributors over there!

Auto-generated via {sandpaper} Source : 7205fd1 Branch : md-outputs Author : GitHub Actions <actions@github.com> Time : 2024-03-12 16:38:40 +0000 Message : markdown source builds Auto-generated via {sandpaper} Source : ff9e06a Branch : main Author : Ulf Schiller <uschille@users.noreply.github.com> Time : 2024-03-12 16:37:53 +0000 Message : Merge pull request #321 from GParolini/oo-matplotlib Episodes modified using Matplotlib Object Oriented Style

Auto-generated via {sandpaper} Source : ff9e06a Branch : main Author : Ulf Schiller <uschille@users.noreply.github.com> Time : 2024-03-12 16:37:53 +0000 Message : Merge pull request datacarpentry#321 from GParolini/oo-matplotlib Episodes modified using Matplotlib Object Oriented Style

Auto-generated via {sandpaper} Source : 6170e57 Branch : md-outputs Author : GitHub Actions <actions@github.com> Time : 2024-03-12 16:49:44 +0000 Message : markdown source builds Auto-generated via {sandpaper} Source : ff9e06a Branch : main Author : Ulf Schiller <uschille@users.noreply.github.com> Time : 2024-03-12 16:37:53 +0000 Message : Merge pull request datacarpentry#321 from GParolini/oo-matplotlib Episodes modified using Matplotlib Object Oriented Style

Auto-generated via `{sandpaper}` Source : 6170e57 Branch : md-outputs Author : GitHub Actions <actions@github.com> Time : 2024-03-12 16:49:44 +0000 Message : markdown source builds Auto-generated via {sandpaper} Source : ff9e06a Branch : main Author : Ulf Schiller <uschille@users.noreply.github.com> Time : 2024-03-12 16:37:53 +0000 Message : Merge pull request datacarpentry#321 from GParolini/oo-matplotlib Episodes modified using Matplotlib Object Oriented Style

episodes modified with OO-matplotlib

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mkcor approved these changes Mar 11, 2024

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uschille and others added 2 commits March 11, 2024 18:07

Update episodes/03-skimage-images.md

d292c47

Fix typo/grammar Co-authored-by: Marianne Corvellec <marianne.corvellec@ens-lyon.org>

Update episodes/03-skimage-images.md

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Co-authored-by: Marianne Corvellec <marianne.corvellec@ens-lyon.org>

uschille approved these changes Mar 11, 2024

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github-actions bot pushed a commit that referenced this pull request Mar 11, 2024

differences for PR #321

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uschille added 2 commits March 12, 2024 10:12

Update episodes/05-creating-histograms.md

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Update episodes/02-image-basics.md

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differences for PR #321

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mkcor approved these changes Mar 12, 2024

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final list in episode 5 changed

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differences for PR #321

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uschille merged commit ff9e06a into datacarpentry:main Mar 12, 2024

mkcor mentioned this pull request Mar 12, 2024

Prefix functions used for customising plots. #322

Merged

	- We can display histograms using the `matplotlib pyplot` `subplots()`, `set_title()`, `set_xlabel()`, `set_ylabel()`, `set_xlim()`, `plot()`, and `show()` functions.
	- We can display histograms using `plt.plot()` with the `bin_edges` and `histogram` values returned by `np.histogram()`.

	- We can display histograms using the `matplotlib pyplot` `subplots()`, `set_title()`, `set_xlabel()`, `set_ylabel()`, `set_xlim()`, `plot()`, and `show()` functions.
	- We can display histograms using `ax.plot()` with the `bin_edges` and `histogram` values returned by `np.histogram()`.

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