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Axes.hist: use bottom for minimum if log and histtype='step...' #4608

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Merged
merged 2 commits into from
Jul 10, 2015
Merged

Axes.hist: use bottom for minimum if log and histtype='step...' #4608

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54ec514
Axes.hist: use bottom for minimum if > 0
duncanmmacleod Jul 9, 2015
72f72eb
test_hist_log_bottom: new test to check #4606
duncanmmacleod Jul 10, 2015
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4 changes: 3 additions & 1 deletion lib/matplotlib/axes/_axes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5909,7 +5909,9 @@ def hist(self, x, bins=10, range=None, normed=False, weights=None,
logbase = self.yaxis._scale.base

# Setting a minimum of 0 results in problems for log plots
if normed or weights is not None:
if np.min(bottom) > 0:
minimum = np.min(bottom)
elif normed or weights is not None:
# For normed data, set to log base * minimum data value
# (gives 1 full tick-label unit for the lowest filled bin)
ndata = np.array(n)
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Binary file added lib/matplotlib/tests/baseline_images/test_axes/hist_step_log_bottom.png
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24 changes: 24 additions & 0 deletions lib/matplotlib/tests/test_axes.py
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Expand Up @@ -1082,6 +1082,30 @@ def test_hist_steplog():
plt.hist(data_big, 100, histtype='stepfilled', log=True, orientation='horizontal')


@image_comparison(baseline_images=['hist_step_log_bottom'],
remove_text=True, extensions=['png'])
def test_hist_step_log_bottom():
# check that bottom doesn't get overwritten by the 'minimum' on a
# log scale histogram (https://github.com/matplotlib/matplotlib/pull/4608)
np.random.seed(0)
data = np.random.standard_normal(2000)
fig = plt.figure()
ax = fig.add_subplot(111)
# normal hist (should clip minimum to 1/base)
ax.hist(data, bins=10, log=True, histtype='stepfilled',
alpha=0.5, color='b')
# manual bottom < 1/base (previously buggy, see #4608)
ax.hist(data, bins=10, log=True, histtype='stepfilled',
alpha=0.5, color='g', bottom=1e-2)
# manual bottom > 1/base
ax.hist(data, bins=10, log=True, histtype='stepfilled',
alpha=0.5, color='r', bottom=0.5)
# array bottom with some less than 1/base (should clip to 1/base)
ax.hist(data, bins=10, log=True, histtype='stepfilled',
alpha=0.5, color='y', bottom=np.arange(10))
ax.set_ylim(9e-3, 1e3)


def contour_dat():
x = np.linspace(-3, 5, 150)
y = np.linspace(-3, 5, 120)
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