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Add 'density' kwarg to histogram#8993

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Merged
dstansby merged 4 commits intomatplotlib:masterfromdstansby:hist-kwarg-fix
Aug 7, 2017
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118 changes: 66 additions & 52 deletionslib/matplotlib/axes/_axes.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -91,7 +91,6 @@ def _plot_args_replacer(args, data):
# The axes module contains all the wrappers to plotting functions.
# All the other methods should go in the _AxesBase class.


class Axes(_AxesBase):
"""
The :class:`Axes` contains most of the figure elements:
Expand DownExpand Up@@ -5852,10 +5851,10 @@ 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,
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 All@@ -5879,55 +5878,60 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,
arrays which are not required to be of the same length

bins : integer or array_like or 'auto', optional
If an integer is given, `bins + 1` bin edges are returned,
If an integer is given, ``bins + 1`` bin edges are returned,
consistently with :func:`numpy.histogram` for numpy version >=
1.3.

Unequally spaced bins are supported if`bins` is a sequence.
Unequally spaced bins are supported if*bins* is a sequence.

If Numpy 1.11 is installed, may also be ``'auto'``.

Default is taken from the rcParam ``hist.bins``.

range : tuple or None, optional
The lower and upper range of the bins. Lower and upper outliers
are ignored. If not provided,`range` is (x.min(), x.max()). Range
has no effect if`bins` is a sequence.
are ignored. If not provided,*range* is``(x.min(), x.max())``.
Rangehas no effect if*bins* is a sequence.

If`bins` is a sequence or`range` is specified, autoscaling
If*bins* is a sequence or*range* is specified, autoscaling
is based on the specified bin range instead of the
range of x.

Default is ``None``

normed : boolean, optional
If `True`, the first element of the return tuple will
density : boolean, optional
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.
This is achieved dividing the count by the number of observations
times the bin width and*not* dividing by the total number
of observations. If`stacked` is also `True`, the sum of the
histograms is normalized to 1.
This is achievedbydividing the count by the number of
observationstimes the bin width and not dividing by the total
numberof observations. If*stacked* is also ``True``, the sum of
thehistograms 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 *density* and *normed* are set an error is raised.

weights : (n, ) array_like or None, optional
An array of weights, of the same shape as`x`. Each value in`x`
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
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`isalso
`True`, then the histogram is normalized such that the first bin
equals 1.
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 accumulationisreversed.
In this case, if *normed* and/or *density* is also ``True``, then
the histogram is normalized such that the first binequals 1.

Default is ``False``

Expand DownExpand Up@@ -5973,22 +5977,23 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,

rwidth : scalar or None, optional
The relative width of the bars as a fraction of the bin width. If
`None`, automatically compute the width.
``None``, automatically compute the width.

Ignored if`histtype` is 'step' or 'stepfilled'.
Ignored if*histtype* is 'step' or 'stepfilled'.

Default is ``None``

log : boolean, optional
If `True`, the histogram axis will be set to a log scale. If `log`
is `True` and `x` is a 1D array, empty bins will be filtered out
and only the non-empty (`n`, `bins`, `patches`) will be returned.
If ``True``, the histogram axis will be set to a log scale. If
*log* is ``True`` and *x* is a 1D array, empty bins will be
filtered out and only the non-empty ``(n, bins, patches)``
will be returned.

Default is ``False``

color : color or array_like of colors or None, optional
Color spec or sequence of color specs, one per dataset. Default
(`None`) uses the standard line color sequence.
(``None``) uses the standard line color sequence.

Default is ``None``

Expand All@@ -6000,21 +6005,22 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,
default is ``None``

stacked : boolean, optional
If `True`, multiple data are stacked on top of each other If
`False` multiple data are aranged side by side if histtype is
If ``True``, multiple data are stacked on top of each other If
``False`` multiple data are aranged side by side if histtype is
'bar' or on top of each other if histtype is 'step'

Default is ``False``

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@@ -6036,8 +6042,8 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,
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
incorrect with ``normed=True`` if bin sizes were unequal. MPL
inherited that error. It is now corrected within MPL when using
earlier numpy versions.

"""
Expand DownExpand Up@@ -6069,6 +6075,12 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,
if histtype == 'barstacked' and not stacked:
stacked = True

if density is not None and normed is not None:
raise ValueError("kwargs 'density' and 'normed' cannot be used "
"simultaneously. "
"Please only use 'density', since 'normed'"
"will be deprecated.")

# process the unit information
self._process_unit_info(xdata=x, kwargs=kwargs)
x = self.convert_xunits(x)
Expand DownExpand Up@@ -6120,11 +6132,11 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,
xmin = min(xmin, xi.min())
xmax = max(xmax, xi.max())
bin_range = (xmin, xmax)

# hist_kwargs = dict(range=range, normed=bool(normed))
# We will handle the normed kwarg within mpl until we
# get to the point of requiring numpy >= 1.5.
hist_kwargs = dict(range=bin_range)
density = bool(density) or bool(normed)
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why do you need to cast it to bool?

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Ifdensity/normed isNone thenbool(None) evaluates toFalse.

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We don't strictlyneed the cast to bool, but it also does not hurt.

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but doesn't if None also evaluate to false when it's not an equality operation?

if density and not stacked:
hist_kwargs = dict(range=bin_range, density=density)
else:
hist_kwargs = dict(range=bin_range)

n = []
mlast = None
Expand All@@ -6135,7 +6147,7 @@ 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)
ifnormed and not stacked:
ifdensity and not stacked:
db = np.diff(bins)
m = (m.astype(float) / db) / m.sum()
if stacked:
Expand All@@ -6145,7 +6157,7 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,
mlast[:] = m
n.append(m)

if stacked andnormed:
if stacked anddensity:
db = np.diff(bins)
for m in n:
m[:] = (m.astype(float) / db) / n[-1].sum()
Expand All@@ -6154,7 +6166,7 @@ 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)

ifnormed:
ifdensity:
n = [(m * np.diff(bins))[slc].cumsum()[slc] for m in n]
else:
n = [m[slc].cumsum()[slc] for m in n]
Expand DownExpand Up@@ -6241,13 +6253,15 @@ 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
else:
# For non-normed data, set the min to 1 / log base,
# For non-normed (density = False) data,
# set the min to 1 / log base,
# again so that there is 1 full tick-label unit
# for the lowest bin
minimum = 1.0 / logbase
Expand Down
21 changes: 21 additions & 0 deletionslib/matplotlib/tests/test_axes.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -2776,6 +2776,27 @@ def test_hist_stacked_normed():
ax.hist((d1, d2), stacked=True, normed=True)


@image_comparison(baseline_images=['hist_stacked_normed'], extensions=['png'])
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)


@pytest.mark.parametrize('normed', [False, True])
@pytest.mark.parametrize('density', [False, True])
def test_hist_normed_density(normed, density):
# Normed and density should not be used simultaneously
d1 = np.linspace(1, 3, 20)
d2 = np.linspace(0, 10, 50)
fig, ax = plt.subplots()
# test that kwargs normed and density cannot be set both.
with pytest.raises(Exception):
ax.hist((d1, d2), stacked=True, normed=normed, density=density)


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