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Backport PR #9121 on branch v2.1.x #9563

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Merged
merged 1 commit into from
Oct 26, 2017
Merged

Backport PR #9121 on branch v2.1.x #9563

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40 changes: 16 additions & 24 deletions lib/matplotlib/axes/_axes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6105,13 +6105,6 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,
--------
hist2d : 2D histograms

Notes
-----
Until numpy release 1.5, the underlying numpy histogram function was
incorrect with ``normed=True`` if bin sizes were unequal. MPL
inherited that error. It is now corrected within MPL when using
earlier numpy versions.

"""
# Avoid shadowing the builtin.
bin_range = range
Expand Down Expand Up @@ -6204,38 +6197,37 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,
else:
hist_kwargs = dict(range=bin_range)

n = []
# List to store all the top coordinates of the histograms
tops = []
mlast = None
# Loop through datasets
for i in xrange(nx):
# this will automatically overwrite bins,
# so that each histogram uses the same bins
m, bins = np.histogram(x[i], bins, weights=w[i], **hist_kwargs)
m = m.astype(float) # causes problems later if it's an int
if mlast is None:
mlast = np.zeros(len(bins)-1, m.dtype)
if density and not stacked:
db = np.diff(bins)
m = (m.astype(float) / db) / m.sum()
if stacked:
if mlast is None:
mlast = np.zeros(len(bins)-1, m.dtype)
m += mlast
mlast[:] = m
n.append(m)
tops.append(m)

# If a stacked density plot, normalize so the area of all the stacked
# histograms together is 1
if stacked and density:
db = np.diff(bins)
for m in n:
m[:] = (m.astype(float) / db) / n[-1].sum()
for m in tops:
m[:] = (m.astype(float) / db) / tops[-1].sum()
if cumulative:
slc = slice(None)
if cbook.is_numlike(cumulative) and cumulative < 0:
slc = slice(None, None, -1)

if density:
n = [(m * np.diff(bins))[slc].cumsum()[slc] for m in n]
tops = [(m * np.diff(bins))[slc].cumsum()[slc] for m in tops]
else:
n = [m[slc].cumsum()[slc] for m in n]
tops = [m[slc].cumsum()[slc] for m in tops]

patches = []

Expand All @@ -6253,7 +6245,7 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,

if rwidth is not None:
dr = np.clip(rwidth, 0, 1)
elif (len(n) > 1 and
elif (len(tops) > 1 and
((not stacked) or rcParams['_internal.classic_mode'])):
dr = 0.8
else:
Expand All @@ -6279,7 +6271,7 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,
_barfunc = self.bar
bottom_kwarg = 'bottom'

for m, c in zip(n, color):
for m, c in zip(tops, color):
if bottom is None:
bottom = np.zeros(len(m))
if stacked:
Expand Down Expand Up @@ -6323,7 +6315,7 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,
# For data that is normed to form a probability density,
# set to minimum data value / logbase
# (gives 1 full tick-label unit for the lowest filled bin)
ndata = np.array(n)
ndata = np.array(tops)
minimum = (np.min(ndata[ndata > 0])) / logbase
else:
# For non-normed (density = False) data,
Expand All @@ -6346,7 +6338,7 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,
fill = (histtype == 'stepfilled')

xvals, yvals = [], []
for m in n:
for m in tops:
if stacked:
# starting point for drawing polygon
y[0] = y[1]
Expand Down Expand Up @@ -6409,9 +6401,9 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,
p.set_label('_nolegend_')

if nx == 1:
return n[0], bins, cbook.silent_list('Patch', patches[0])
return tops[0], bins, cbook.silent_list('Patch', patches[0])
else:
return n, bins, cbook.silent_list('Lists of Patches', patches)
return tops, bins, cbook.silent_list('Lists of Patches', patches)

@_preprocess_data(replace_names=["x", "y", "weights"], label_namer=None)
def hist2d(self, x, y, bins=10, range=None, normed=False, weights=None,
Expand Down
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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Original file line number Diff line number Diff line change
Expand Up @@ -1493,6 +1493,14 @@ 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)


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