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Added test_hist2d#27418

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rupink wants to merge 2 commits intomatplotlib:mainfrom
rupink:test_hist2d
Open

Added test_hist2d#27418
rupink wants to merge 2 commits intomatplotlib:mainfrom
rupink:test_hist2d

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@rupink rupink commented Dec 1, 2023

PR summary

I have added a test for Axes.hist2d in lib/matplotlib/tests/test_datetime.py that tests with randomly created data if the data correctly plots. It is similar to the Axes.hist method of testing, as it uses similar generated data and methods.

This addresses the Axes.hist2d task from #26864.

Below is a picture of the generated plots.
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Comment on lines +365 to +367
values1 = np.random.randint(1, 10, 30)
values2 = np.random.randint(1, 10, 30)
values3 = np.random.randint(1, 10, 30)
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If you want to use randomness, you'll need to set a seed (we use 19680801 for tests).

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Makes sense

Comment on lines +375 to +380
hist2d1 = ax1.hist2d(
x_values,
values1,
bins=10,
cmap='Blues'
)
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This fits in one line, AFAICT.

Comment on lines +383 to +397
hist2d2 = ax2.hist2d(
x_values,
values2,
bins=10,
cmap='Greens'
)
ax2.set_title('Axes.hist2d - Data 2')

hist2d3 = ax3.hist2d(
x_values,
values3,
bins=10,
cmap='Reds'
)
ax3.set_title('Axes.hist2d - Data 3')
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I don't see what the difference is between the first plot and these two?

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To possibly clarify a bit: it would make more sense to use dates for the y-values as well. So one idea is to keep one of the subplots, swap x and y values for the next (dates on y-axis) and let the third have dates for both x and y-values.

@QuLogic QuLogic mentioned this pull request Dec 2, 2023
64 tasks
values2 = np.random.randint(1, 10, 30)
values3 = np.random.randint(1, 10, 30)

x_values = mpl.dates.date2num([start_date + i * time_delta for i in range(30)])
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date2num means you are passing numbers into hist2d. We want to test actual dates.

x_values = mpl.dates.date2num([start_date + i * time_delta for i in range(30)])

# Using Axes.hist2d
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, constrained_layout=True)
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Suggested change
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, constrained_layout=True)
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, layout='constrained')

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5 participants