Episodes modified using Matplotlib Object Oriented Style

[GParolini]

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.

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[github-actions]

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
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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. 😃

[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.

[uschille]

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?

[GParolini]

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().

[uschille]

👍 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?

[uschille]

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()`.

[GParolini]

Ok. I committed the changes to the branch.

[tobyhodges]

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.

[uschille]

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!