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Remove old normalising code from plt.hist #9121

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Merged
merged 4 commits into from
Oct 19, 2017
Merged

Remove old normalising code from plt.hist #9121

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096ce51
Remove old numpy histogram note
dstansby Aug 29, 2017
02cfe85
Clear up variable naming
dstansby Aug 29, 2017
d79d6bb
Remove un-used code
dstansby Aug 29, 2017
2dde48a
Add test for density histogram:
dstansby Aug 30, 2017
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Add test for density histogram:
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@dstansby
dstansby committed Aug 30, 2017
commit 2dde48aed02e63fff1477673735fc3dce388c3bd
Binary file added lib/matplotlib/tests/baseline_images/test_axes/hist_density.png
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8 changes: 8 additions & 0 deletions lib/matplotlib/tests/test_axes.py
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Expand Up @@ -1464,6 +1464,14 @@ def test_hist_step_filled():
assert all([p.get_facecolor() == p.get_edgecolor() for p in patches])


@image_comparison(baseline_images=['hist_density'], extensions=['png'])
def test_hist_density():
np.random.seed(19680801)
data = np.random.standard_normal(2000)
fig, ax = plt.subplots()
ax.hist(data, density=True)
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Does this actually need an image test, or can we just test that np.histogram properly applies the density kwarg?

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I don't think there was ever any image test that used the 'density' or 'norm' kwargs, so I thought it would be a good idea to add one.

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I think we are trying to keep image tests to things where the actual image appearance might change if code changes. So sure if there is no hist image test there should be one. If such a test does exist then this just tests the value of the bars, and I think you can do that w/o an image comparison. But I’m not passionate about it or anything.

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Beating a dead horse, but:

np.random.seed(19680801)
data = np.random.standard_normal(2000)
fig, ax = plt.subplots()
n, bins, patches = ax.hist(data, density=True)
assert np.isclose(np.sum(n * np.diff(bins)), 1.0)



@image_comparison(baseline_images=['hist_step_log_bottom'],
remove_text=True, extensions=['png'])
def test_hist_step_log_bottom():
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