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Histogram compatibility with numpy 7364 #7856

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ca99315
Replace hist normed arg with density arg in a backward-compatible way.
chelseatroy Jan 17, 2017
9034fc7
Update documentation to reflect addition of density arg to hist
chelseatroy Jan 17, 2017
932e4c8
Adjust documentation and test formatting
chelseatroy Jan 17, 2017
a09dd87
Raise if density and normed are both set in hist
chelseatroy Jan 18, 2017
a49cd96
Update documentation to match pep8 guidelines
chelseatroy Jun 27, 2017
408f236
Merge branch 'master' into histogram_compatibility_with_numpy_7364
chelseatroy Jun 27, 2017
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101 changes: 79 additions & 22 deletions lib/matplotlib/axes/_axes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5810,10 +5810,10 @@ def table(self, **kwargs):
#### Data analysis

@_preprocess_data(replace_names=["x", 'weights'], label_namer="x")
def hist(self, x, bins=None, range=None, normed=False, weights=None,
def hist(self, x, bins=None, range=None, density=None, weights=None,
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Changing the order is probably not a good idea, for backwards compatibility's sake, even though people really shouldn't be expecting order-only arguments to work this far in.

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Hi @QuLogic!

My understanding is that if a user is expecting order-only arguments to work this far in, then their argument will just take the name density instead of normed now. And since the name density is meant, in this change, to actually replace normed, the code will still be backward compatible. If the developer specifically named it normed, then the fact that it's named allows it to work even though normed is at the end now.

Please let me know if this is inaccurate.

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I think this is safe to do. If people are using positional it just works, if they are using keyword it still just works. By putting this here we make it clearer which the preferred name is.

cumulative=False, bottom=None, histtype='bar', align='mid',
orientation='vertical', rwidth=None, log=False,
color=None, label=None, stacked=False,
color=None, label=None, stacked=False, normed=None,
**kwargs):
"""
Plot a histogram.
Expand Down Expand Up @@ -5858,7 +5858,10 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,

Default is ``None``

normed : boolean, optional
normed, density : boolean, optional
Either the 'normed' or the 'density' arg can be set to
accomplish this behavior:

If `True`, the first element of the return tuple will
be the counts normalized to form a probability density, i.e.,
the area (or integral) under the histogram will sum to 1.
Expand All @@ -5867,24 +5870,31 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,
of observations. If `stacked` is also `True`, the sum of the
histograms is normalized to 1.

Default is ``False``
Default is ``None`` for both 'normed' and 'density.' If either is
set, then that value will be used. If neither are set, then the args
will be treated as 'False.'

If both are set to different things, the hist function raises an
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I suggest 'if both are set, raise' even if they are the same.

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Change made and pushed.

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The docs are out of sync with the code.

error.

weights : (n, ) array_like or None, optional
An array of weights, of the same shape as `x`. Each value in `x`
only contributes its associated weight towards the bin count
(instead of 1). If `normed` is True, the weights are normalized,
so that the integral of the density over the range remains 1.
(instead of 1). If `normed` or 'density' is True,
the weights are normalized, so that the integral of the density
over the range remains 1.

Default is ``None``

cumulative : boolean, optional
If `True`, then a histogram is computed where each bin gives the
counts in that bin plus all bins for smaller values. The last bin
gives the total number of datapoints. If `normed` is also `True`
then the histogram is normalized such that the last bin equals 1.
If `cumulative` evaluates to less than 0 (e.g., -1), the direction
of accumulation is reversed. In this case, if `normed` is also
`True`, then the histogram is normalized such that the first bin
gives the total number of datapoints. If `normed` or 'density'
is also `True` then the histogram is normalized such that the last
bin equals 1. If `cumulative` evaluates to less than 0 (e.g., -1),
the direction of accumulation is reversed. In this case, if
`normed` and/or 'density' is also `True`, then the histogram is
normalized such that the first bin
equals 1.

Default is ``False``
Expand Down Expand Up @@ -5967,12 +5977,13 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,
Returns
-------
n : array or list of arrays
The values of the histogram bins. See **normed** and **weights**
for a description of the possible semantics. If input **x** is an
array, then this is an array of length **nbins**. If input is a
sequence arrays ``[data1, data2,..]``, then this is a list of
arrays with the values of the histograms for each of the arrays
in the same order.
The values of the histogram bins. See **normed or density**
and **weights** for a description of the possible semantics.
If input **x** is an array, then this is an array of length
**nbins**. If input is a sequence arrays
``[data1, data2,..]``, then this is a list of arrays with
the values of the histograms for each of the arrays in the
same order.

bins : array
The edges of the bins. Length nbins + 1 (nbins left edges and right
Expand All @@ -5999,6 +6010,51 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,
earlier numpy versions.

"""

# Sets the density variable, if necessary, to its predecessor, 'normed.'
if density is not None and normed is not None:
raise ValueError('The density and normed arguments represent the '
'same concept. Please set only one of them.')
elif normed is not None and density is None:
density = normed
elif normed is None and density is None:
density = False

def _normalize_input(inp, ename='input'):
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I am confused by this? Did this come from the merge?

"""Normalize 1 or 2d input into list of np.ndarray or
a single 2D np.ndarray.

Parameters
----------
inp : iterable
ename : str, optional
Name to use in ValueError if `inp` can not be normalized

"""
if (isinstance(x, np.ndarray) or
not iterable(cbook.safe_first_element(inp))):
# TODO: support masked arrays;
inp = np.asarray(inp)
if inp.ndim == 2:
# 2-D input with columns as datasets; switch to rows
inp = inp.T
elif inp.ndim == 1:
# new view, single row
inp = inp.reshape(1, inp.shape[0])
else:
raise ValueError(
"{ename} must be 1D or 2D".format(ename=ename))
if inp.shape[1] < inp.shape[0]:
warnings.warn(
'2D hist input should be nsamples x nvariables;\n '
'this looks transposed '
'(shape is %d x %d)' % inp.shape[::-1])
else:
# multiple hist with data of different length
inp = [np.asarray(xi) for xi in inp]

return inp

# Avoid shadowing the builtin.
bin_range = range
del range
Expand Down Expand Up @@ -6093,7 +6149,7 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,
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 normed and not stacked:
if density and not stacked:
db = np.diff(bins)
m = (m.astype(float) / db) / m.sum()
if stacked:
Expand All @@ -6103,7 +6159,7 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,
mlast[:] = m
n.append(m)

if stacked and normed:
if stacked and density:
db = np.diff(bins)
for m in n:
m[:] = (m.astype(float) / db) / n[-1].sum()
Expand All @@ -6112,7 +6168,7 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,
if cbook.is_numlike(cumulative) and cumulative < 0:
slc = slice(None, None, -1)

if normed:
if density:
n = [(m * np.diff(bins))[slc].cumsum()[slc] for m in n]
else:
n = [m[slc].cumsum()[slc] for m in n]
Expand Down Expand Up @@ -6199,8 +6255,9 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,
# Setting a minimum of 0 results in problems for log plots
if np.min(bottom) > 0:
minimum = np.min(bottom)
elif normed or weights is not None:
# For normed data, set to minimum data value / logbase
elif density or weights is not 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)
minimum = (np.min(ndata[ndata > 0])) / logbase
Expand Down
11 changes: 11 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 @@ -2775,6 +2775,17 @@ def test_hist_stacked_normed():
ax.hist((d1, d2), stacked=True, normed=True)


def test_hist_stacked_density():
# make some data
d1 = np.linspace(1, 3, 20)
d2 = np.linspace(0, 10, 50)
fig, ax = plt.subplots()
ax.hist((d1, d2), stacked=True, density=True)

with pytest.raises(ValueError):
ax.hist((d1, d2), stacked=True, density=True, normed=False)


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