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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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Clear up variable naming
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@dstansby
dstansby committed Aug 29, 2017
commit 02cfe85fe7cccc6a9694f5455657e76eb0ca02a5
28 changes: 16 additions & 12 deletions lib/matplotlib/axes/_axes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6080,8 +6080,10 @@ 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
Expand All @@ -6097,21 +6099,23 @@ def hist(self, x, bins=None, range=None, density=None, weights=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 @@ -6129,7 +6133,7 @@ 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 @@ -6155,7 +6159,7 @@ 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 @@ -6199,7 +6203,7 @@ 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 @@ -6222,7 +6226,7 @@ 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 @@ -6285,9 +6289,9 @@ 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