Add support for multiple hatches, edgecolors and linewidths in histograms #28073

Impaler343 · 2024-04-13T17:59:17Z

PR summary

Closes #26718 Distributes keyword args passed to each Patch using a cycler. Probably not the best way to do this?

PR checklist

"closes #0000" is in the body of the PR description to link the related issue
new and changed code is tested
[N/A] Plotting related features are demonstrated in an example
[N/A] New Features and API Changes are noted with a directive and release note
[N/A] Documentation complies with general and docstring guidelines

jklymak · 2024-04-13T19:58:54Z

I'd suggest showing what this does with an example either in the GitHub pr description, or ideally in the gallery

Impaler343 · 2024-04-19T02:59:52Z

I'm not really sure if I need to add a new test or just modify an exisiting one(test_hist_stacked_bar) in test_axes.py

oscargus · 2024-04-25T10:02:27Z

I guess there are two things here:

Add/modify an example in the gallery to illustrate how it is used. (Will be easier for the reviewers to get an idea of how it is used etc.)
Add a test to get the code coverage up and make sure no one breaks it later. Maybe there is some old example that one can "hi-jack", but otherwise create a new test with all the bells and whistles turned on.

Impaler343 · 2024-05-13T05:19:20Z

pinging @story645 for review. The failing tests are unrelated

story645

also needs updated documentation that the patch properties are now vectorized (@timhoffm any concerns here?)

lib/matplotlib/axes/_axes.py

galleries/examples/statistics/histogram_multihist.py

timhoffm · 2024-05-19T20:25:31Z

also needs updated documentation that the patch properties are now vectorized (@timhoffm any concerns here?)

What should we be concerned about? We already have vectorized label and color. It's only fair to vectorize these inside hist() as well. This should be documented with **kwargs (can't immediately suggest, because that part has not been touched in the PR). Something like:

        **kwargs
            `~matplotlib.patches.Patch` properties. The following properties additionally
            accept lists of property values, one element for each dataset:
            *edgecolors*, *linewidths*, *linestyles*, *hatches*.

This should also get a what's new entry.

story645 · 2024-05-19T22:32:01Z

What should we be concerned about? We already have vectorized label and color.

My bias is vectorize everything so I don't have concerns, but in the past for some vectorization discussions there have been concerns about the tradeoffs. But if there isn't opposition, awesome!

timhoffm · 2024-05-19T22:47:12Z

I don't see any drawbacks for hist()

Impaler343 · 2024-05-25T19:12:07Z

So we are planning to vectorize all parameters of Patches? Like joinstyle, capstyle etc.

galleries/examples/statistics/histogram_multihist.py

story645 · 2024-05-26T15:09:35Z

So we are planning to vectorize all parameters of Patches? Like joinstyle, capstyle etc

Not at this time w/ the current architecture, especially because nobody has asked for those.

Impaler343 · 2024-05-29T08:40:26Z

Codecov is acting fishy, it passed once and failed again after squashing. Anything else to add/change?

lib/matplotlib/axes/_axes.py

lib/matplotlib/tests/test_axes.py

lib/matplotlib/axes/_axes.py

doc/users/next_whats_new/histogram_vectorized_parameters.rst

galleries/examples/statistics/histogram_multihist.py

doc/users/next_whats_new/histogram_vectorized_parameters.rst

lib/matplotlib/axes/_axes.py

Impaler343 · 2024-06-05T10:00:23Z

As of the last commit, this behavior is consistent for all histtypes unless mentioned otherwise:

If edgecolor, facecolor and color are mentioned, color is completely ignored.
If edgecolor and color are mentioned, color is applied to only the faces and edgecolor to the edges.
If facecolor and color are mentioned, color is applied to only the edges and facecolor to the faces.
If facecolor and edgecolor are mentioned, edgecolor is applied to edges, and facecolor is applied to faces.
If only color is mentioned, the face is colored for all filled histograms, and only the edge is colored for unfilled histograms with the values in color.
If only facecolor is mentioned, the face is colored for all filled histograms with the value in facecolor, and the edges are colored with the default color cycle for unfilled histograms.
If only edgecolor is mentioned, the face is colored by the default color cycle for all filled histograms, and the edge is colored with the values in edgecolor for all histograms.
I feel this is the required behavior, but I am unsure if the color setting for the kwargs can be written more concisely.
Let me know if I should start writing tests based on this structure.

story645 · 2024-06-06T16:02:03Z

@Impaler343 I started trying to flow chart that and then realized it's probably clearer as a table. Let me know if this jives with what you're saying:

hist type	color	facecolor	edgecolor	patch face color	patch edge color
all	set	set	set	facecolor	edgecolor
all	set	None	set	color	edgecolor
all	set	set	None	facecolor	color
all	None	set	set	facecolor	edgecolor
bar, barstacked, stepfilled	set	None	None	color	'none'
step	set	None	None	'none'	color
bar, barstacked, stepfilled	None	set	None	color	'none'
step	None	set	None	'none'	default color cycle
bar, barstacked, stepfilled	None	None	set	default color cycle	'none'
step	None	None	set	'none'	edgecolor
bar, barstacked, stepfilled	None	None	None
step	None	None	None

Impaler343 · 2024-06-06T16:52:32Z

hist type	color	facecolor	edgecolor	patch face color	patch edge color
all	set	set	set	facecolor	edgecolor
all	set	None	set	color	edgecolor
all	set	set	None	facecolor	color
all	None	set	set	facecolor	edgecolor
bar, barstacked, stepfilled	set	None	None	color	'none'
step	set	None	None	'none'	color
bar, barstacked, stepfilled	None	set	None	*facecolor*	'none'
step	None	set	None	'none'	default color cycle
bar, barstacked, stepfilled	None	None	set	default color cycle	*edgecolor*
step	None	None	set	'none'	edgecolor
bar, barstacked, stepfilled	None	None	None	default color cycle	'none'
step	None	None	None	'none'	default color cycle

Have bolded the corrected ones

Impaler343 · 2024-06-07T10:41:23Z

I'm unable to fix CircleCI errors for docs. Could someone help me out?

story645 · 2024-06-07T17:08:00Z

Hi, so the error is in your what's new:

/home/circleci/project/doc/users/next_whats_new/histogram_vectorized_parameters.rst:47: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/circleci/project/doc/users/next_whats_new/histogram_vectorized_parameters.rst:51: ERROR: Unexpected indentation.
/home/circleci/project/doc/users/next_whats_new/histogram_vectorized_parameters.rst:64: WARNING: Explicit markup ends without a blank line; unexpected unindent.
/home/circleci/project/doc/users/next_whats_new/histogram_vectorized_parameters.rst:68: ERROR: Unexpected indentation.

error: https://app.circleci.com/pipelines/github/matplotlib/matplotlib/31532/workflows/2533c013-7833-4d0c-ae72-dead7c7fbc76/jobs/83481?invite=true#step-113-207133_109
rendering: https://output.circle-artifacts.com/output/job/2c962d26-1c66-49dd-a987-e5bf25c8fcbe/artifacts/0/doc/build/html/users/next_whats_new/histogram_vectorized_parameters.html

doc/users/next_whats_new/histogram_vectorized_parameters.rst

lib/matplotlib/axes/_axes.py

lib/matplotlib/tests/test_axes.py

lib/matplotlib/axes/_axes.py

Impaler343 · 2024-06-08T08:57:50Z

Ok it turns out the vectorization of colors is somewhat incomplete:

When we pass None, calls the color cycle, which is fine.
When we pass a list of valid colors, it uses them, which is also fine.
When we pass a list of Nones or even if one value in the list is None, it throws an error saying invalid RBGA argument. What can we do in this situation? Should we say that we use the colour cycle even if one value in the list is None and raise a warning? Or call the color cycle's next color whenever a None is encountered in a list? Or simply assume that the user will not pass a list like color= [None, "green", None] and ignore this check?

story645 · 2024-06-09T13:49:55Z

or simply assume that the user will not pass a list like color= [None, "green", None]

I think this input is ambiguous cause it can mean either:

opt those two lines into the color cycle such that it maps to ['C0', "green", 'C1']
set both lines to the same default ['C0', "green", 'C0']

So I think it's ok to error out (unless/until someone comes around expecting 1 or 2) but we should maybe special case and have a better error message, like if not all(colors) (if any are NaN) "Ambiguous color specification: colors in the list may not be None"

Or something like that.

Impaler343 · 2024-06-11T12:18:58Z

The added lines have all been covered, codecov seems to still fail

story645 · 2024-06-17T14:14:57Z

Now, to test the table we need to write 10 individual tests, so that we dont pass all of the parameters

Can parameterize this as a kwargs arg dict [{'color': 'blue' }, {'edgecolor': 'blue', 'facecolor':orange}, ...]

Impaler343 · 2024-06-17T15:54:57Z

Now, to test the table we need to write 10 individual tests, so that we dont pass all of the parameters

Can parameterize this as a kwargs arg dict [{'color': 'blue' }, {'edgecolor': 'blue', 'facecolor':orange}, ...]

Oh yes! I completely missed this method

lib/matplotlib/tests/test_axes.py

Impaler343 · 2024-06-25T05:55:51Z

hist type	color	facecolor	edgecolor	patch face color	patch edge color
bar, barstacked, stepfilled	set	set	set	facecolor	edgecolor
step	set	set	set	'none'	edgecolor
bar, barstacked, stepfilled	set	None	set	color	edgecolor
step	set	None	set	'none'	edgecolor
bar, barstacked, stepfilled	set	set	None	facecolor	color
step	set	set	None	'none'	color
bar, barstacked, stepfilled	None	set	set	facecolor	edgecolor
step	None	set	set	'none'	edgecolor
bar, barstacked, stepfilled	set	None	None	color	rcParam(black)
step	set	None	None	'none'	color
bar, barstacked, stepfilled	None	set	None	facecolor	rcParam(black)
step	None	set	None	'none'	default color cycle(blue)
bar, barstacked, stepfilled	None	None	set	default color cycle(blue)	edgecolor
step	None	None	set	'none'	edgecolor
bar, barstacked, stepfilled	None	None	None	default color cycle(blue)	rcParam(black)
step	None	None	None	'none'	default color cycle(blue)

Updated table by expanding the first few cases and correcting:

whenever edgecolor is not set, it defaults to its rcParam in the stepfilled case.
facecolor is none in the case of step

lib/matplotlib/tests/test_axes.py

story645

please don't special case none. Here you end up only checking the alpha channel. Otherwise, I think this hits the balance of everything tested but way fewer images tests?

lib/matplotlib/tests/test_axes.py

story645 · 2024-06-27T17:46:17Z

PR cleanliness test is failing on the added/removed test images, that can get fixed by a squash rebase
https://matplotlib.org/devdocs/devel/development_workflow.html#manage-commit-history

Specify extensions for test Added modified baseline images Modified test for histogram with single parameters Fixed test Add modified baseline images Made changes concise according to suggestion Made a more detailed gallery example Fix Docs Added whats new note, documentation for vectorization, doc fix Added new test, and reverted changes in old test Added baseline images Modified test to pass codecov, added plot in whats new entry Fix test Added baseline images Altered whats new entry, docs and gallery example Resolved edgecolor and facecolor setting Minor fix Fix docs Modified files to include facecolor and added test Removed figsize from test Add multiple baseline image names Fixed test? Fixed test? Removed parametrize usage Add baseline images Add baseline image Fix docs Fix docs Deleted baseline images, changed test Fix test Fix test Handled None array passing to color Handled passing None list Modified nested patch condition Minor Fix Grammar nits Modified test, edited None handling in sequence so that it errors out

Separated tests Modified test Modified test to pass by using halved zorders

Edited expected facecolors for step Changed blue to C0

galleries/examples/statistics/histogram_multihist.py

lib/matplotlib/axes/_axes.py

story645 · 2024-06-28T05:40:00Z

lib/matplotlib/tests/test_axes.py

@@ -4614,6 +4672,10 @@ def test_hist_emptydata():
    ax.hist([[], range(10), range(10)], histtype="step")


+def test_hist_none_patch():
+    plt.hist([1, 2], label=["First", "Second"])


Suggested change

plt.hist([1, 2], label=["First", "Second"])

plt.hist([ ], label=["First", "Second"])

None patch is the empty list/array case

And what you probably want to test is

assert len(patches) == 0

This too works, basically the None condition is hit when we don't have enough datasets to cover each label. This is the doing of itertools.zip_longest

Should i still change it up? As the previous one seems clearer

Sorry, I thought that zip_longest would copy the patch to both labels. I think there needs to be some sort of assert that you're test is doing what you think it is - maybe then checking that len(patches) != len(labels)

What is len(lbs) here?

The number of labels that have been used in the plot

Sorry, I meant what were the actual numbers/does this test pass.

But yes if this test passes and coverage improves (add a comment that this test is to cover the if not patch case), then I'll let this concern go.

But also assert len(patches) < len(labels) too I think?

Yes the tests pass, and yes we can add the length(patches) < len(labels) one as well, does the same comparison.
The actual numbers are 1 < 2

story645 · 2024-07-01T19:11:16Z

Cause I think this maybe got lost, I was suggesting that this construction makes the skip case clearer:

        for patch, lbl in itertools.zip_longest(patches, labels):
            
            if not patch: continue 

            p = patch[0]
            kwargs.update({
                'hatch': next(hatches),
                'linewidth': next(linewidths),
                'linestyle': next(linestyles),
                'edgecolor': next(edgecolors),
                'facecolor': next(facecolors),
            })
            p._internal_update(kwargs)
            if lbl is not None:
                p.set_label(lbl)
            for p in patch[1:]:
                p._internal_update(kwargs)
                p.set_label('_nolegend_')

story645

Thanks for all your patience and extra thanks for tracing through and figuring out and testing our histogram color semantics/precedence 😅

I think the plot tests are now at a state where it's easy to see which parameter failed but there's also now only six image tests.

I'm pretty sure that the color semantics test doesn't try to draw anything but there might be a "build only artist tree" function that I'm missing.

timhoffm · 2024-07-08T13:47:22Z

lib/matplotlib/tests/test_axes.py

+def test_hist_none_patch():
+    # To cover None patches when excess labels are provided
+    labels = ["First", "Second"]
+    patches = [[1, 2]]
+    fig, ax = plt.subplots()
+    ax.hist(patches, label=labels)
+    _, lbls = ax.get_legend_handles_labels()
+    assert (len(lbls) < len(labels) and len(patches) < len(labels))


Can you please elaborate what this tests? What are None patches? Does this express what you want to test?

Suggested change

def test_hist_none_patch():

# To cover None patches when excess labels are provided

labels = ["First", "Second"]

patches = [[1, 2]]

fig, ax = plt.subplots()

ax.hist(patches, label=labels)

_, lbls = ax.get_legend_handles_labels()

assert (len(lbls) < len(labels) and len(patches) < len(labels))

def test_hist_unused_labels():

# When a list with one dataset and N elements is provided and N labels, ensure

# hat the first label is used for the dataset and all other labels are ignored

fig, ax = plt.subplots()

ax.hist([[1, 2, 3]], label=["values", "unused", "also unused"])

_, labels = ax.get_legend_handles_labels()

assert labels == ["values"]

None patches are the ones generated when the number of labels specified are more than the number of patches(Filled in by zip_longest). The suggested test also covers the line and has clearer labels, ill change it up

lib/matplotlib/tests/test_axes.py

timhoffm · 2024-07-08T14:14:07Z

lib/matplotlib/tests/test_axes.py

+        for i, (xi, value) in enumerate(zip(x, values)):
+            axr.hist(xi, bins=bins, histtype=histtype, **{kw: value},
+                     zorder=(len(x)-i)/2)


I believe the index/zorder acrobatics is only to get the draw order. It's simpler to do this via

Suggested change

for i, (xi, value) in enumerate(zip(x, values)):

axr.hist(xi, bins=bins, histtype=histtype, **{kw: value},

zorder=(len(x)-i)/2)

for x, value in reversed(zip(xs, values)):

axr.hist(x, bins=bins, histtype=histtype, **{kw: value})

Yes, it now occurred to me that I had tried this earlier - gives completely different plots
Vectorized:

Superimposed:

did you fix the typo:

for xi, value in reversed(zip(x, values)): axr.hist(xi, bins=bins, histtype=histtype, **{kw: value})

for x, value in reversed(list(zip(xs, values))): axr.hist(x, bins=bins, histtype=histtype, **{kw: value})

yes, I tried this one

doc/users/next_whats_new/histogram_vectorized_parameters.rst

galleries/examples/statistics/histogram_multihist.py

timhoffm · 2024-07-08T14:27:01Z

galleries/examples/statistics/histogram_multihist.py

+# * linestyles
+#
+#
+# Edge-Colors


I'm not convinced that there is much to learn by having individual examples for every parameter. This feels lengthy without providing much additional insight. Maybe the whats new example or 2 individually crafted examples (one simple and one using multiple values simultaneously (e.g. edgecolor, facecolor, hatch) are better?

But I won't block over this. However, if you keep this, please use a consistent section naming, preferably as the parameter is written.

what's most important to me is that the example only set one parameter rather than trying to set multiple. But also the reason I encouraged this approach was b/c https://matplotlib.org/devdocs/gallery/pie_and_polar_charts/pie_features.html#sphx-glr-gallery-pie-and-polar-charts-pie-features-py was in response to folks not knowing that they could set all these parameters. Basically, I wanted something that would pop up in a search.

My point is that these styling parameters all work conceptually similar. Repeating the same pattern for every parameter feels a bit lengthy and boring to me. I think the pie parameters are somewhat different in that they have different effects and one cannot guess what one is doing based on the example of another.

But it's not worth a longer discussion. If you feel they are all valuable, I won't block over that.

story645 · 2024-07-09T18:37:31Z

#28533 reminded me that this should get a versionadded directive

https://matplotlib.org/devdocs/devel/api_changes.html#announce-new-and-deprecated-api

story645 · 2024-07-10T14:57:24Z

lib/matplotlib/axes/_axes.py

+            datasets in *x*:
+            *edgecolors*, *facecolors*, *linewidths*, *linestyles*, *hatches*.
+
+            .. versionadded:: 3.10


Sorry wgough

Suggested change

.. versionadded:: 3.10

.. versionadded:: 3.10

List input to hatch parameters

Sorry forgot to add, here especially you should briefly describe what's added. My sentence here is super clunky English, so more here for example than something you should stick with.

I didnt get you....

github-actions bot added the Documentation: examples files in galleries/examples label Apr 14, 2024

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Impaler343 added 2 commits June 27, 2024 21:42

Add support for multiple hatches, edgecolor and linewidth in histograms

1a6ec0f

Add hatch, linewidth and edgecolor parameters to multihist example

0e52daa

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Reduced to 13 tests in total

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Added color semantics test and modified vectorized parameters test

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Edited expected facecolors for step Changed blue to C0

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	plt.hist([1, 2], label=["First", "Second"])
	plt.hist([ ], label=["First", "Second"])

Add support for multiple hatches, edgecolors and linewidths in histograms #28073

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Add support for multiple hatches, edgecolors and linewidths in histograms #28073

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