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FIX: if bins input to hist is str, treat like no bins #8638

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Merged
merged 4 commits into from
Apr 2, 2019
Merged

FIX: if bins input to hist is str, treat like no bins #8638

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0c8e9d8
FIX: if bins input to hist is str, treat like no bins
tacaswell May 18, 2017
5892d62
TST: add test of correctly getting the limits for multi-histogram
tacaswell Feb 24, 2019
239be7b
MNT: copy logic from numpy
tacaswell Feb 25, 2019
c6f0554
FIX: restore (and test) handling of nan in hist data
tacaswell Mar 31, 2019
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65 changes: 53 additions & 12 deletions lib/matplotlib/axes/_axes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,6 +36,30 @@
from matplotlib.axes._base import _AxesBase, _process_plot_format
from matplotlib.axes._secondary_axes import SecondaryAxis

try:
from numpy.lib.histograms import histogram_bin_edges
except ImportError:
# this function is new in np 1.15
def histogram_bin_edges(arr, bins, range=None, weights=None):
# this in True for 1D arrays, and False for None and str
if np.ndim(bins) == 1:
return bins

if isinstance(bins, str):
# rather than backporting the internals, just do the full
# computation. If this is too slow for users, they can
# update numpy, or pick a manual number of bins
return np.histogram(arr, bins, range, weights)[1]
else:
if bins is None:
# hard-code numpy's default
bins = 10
if range is None:
range = np.min(arr), np.max(arr)

return np.linspace(*range, bins + 1)


_log = logging.getLogger(__name__)


Expand Down Expand Up @@ -6611,9 +6635,6 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,
if bin_range is not None:
bin_range = self.convert_xunits(bin_range)

# Check whether bins or range are given explicitly.
binsgiven = np.iterable(bins) or bin_range is not None

# We need to do to 'weights' what was done to 'x'
if weights is not None:
w = cbook._reshape_2D(weights, 'weights')
Expand All @@ -6638,22 +6659,42 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,
"sets and %d colors were provided" % (nx, len(color)))
raise ValueError(error_message)

# If bins are not specified either explicitly or via range,
# we need to figure out the range required for all datasets,
# and supply that to np.histogram.
if not binsgiven and not input_empty:
hist_kwargs = dict()

# if the bin_range is not given, compute without nan numpy
# does not do this for us when guessing the range (but will
# happily ignore nans when computing the histogram).
if bin_range is None:
xmin = np.inf
xmax = -np.inf
for xi in x:
if len(xi) > 0:
if len(xi):
# python's min/max ignore nan,
# np.minnan returns nan for all nan input
xmin = min(xmin, np.nanmin(xi))
xmax = max(xmax, np.nanmax(xi))
bin_range = (xmin, xmax)
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You either need to keep this code for the case when bins is an integer, or you need to add a case for integral bins in the histogram_bin_edges backport

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ah, good catch on that, I fell down a different rabbit hole (see my comment at the issue level)...

# make sure we have seen at least one non-nan and finite
# value before we reset the bin range
if not np.isnan([xmin, xmax]).any() and not (xmin > xmax):
bin_range = (xmin, xmax)

# If bins are not specified either explicitly or via range,
# we need to figure out the range required for all datasets,
# and supply that to np.histogram.
if not input_empty and len(x) > 1:
if weights is not None:
_w = np.concatenate(w)
else:
_w = None

bins = histogram_bin_edges(np.concatenate(x),
bins, bin_range, _w)
else:
hist_kwargs['range'] = bin_range

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

# List to store all the top coordinates of the histograms
tops = []
Expand Down
20 changes: 20 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 @@ -6346,3 +6346,23 @@ def test_datetime_masked():
ax.plot(x, m)
# these are the default viewlim
assert ax.get_xlim() == (730120.0, 733773.0)


def test_hist_auto_bins():
_, bins, _ = plt.hist([[1, 2, 3], [3, 4, 5, 6]], bins='auto')
assert bins[0] <= 1
assert bins[-1] >= 6


def test_hist_nan_data():
fig, (ax1, ax2) = plt.subplots(2)

data = [1, 2, 3]
nan_data = data + [np.nan]

bins, edges, _ = ax1.hist(data)
with np.errstate(invalid='ignore'):
nanbins, nanedges, _ = ax2.hist(nan_data)

assert np.allclose(bins, nanbins)
assert np.allclose(edges, nanedges)