8000 [WIP] Masking invalid x and/or weights in hist (#6483) by TrishGillett · Pull Request #7133 · matplotlib/matplotlib · GitHub
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[WIP] Masking invalid x and/or weights in hist (#6483) #7133

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[WIP] Masking invalid x and/or weights in hist (#6483) #7133

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1d9783b
Make _normalize_input always output a list of arrays.
TrishGillett Jul 16, 2016
b16144f
Tidying up the data prep work in hist.
TrishGillett Jul 16, 2016
bf8d2cc
Mask invalid entries in x and weights before plotting histograms.
TrishGillett Jul 16, 2016
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Make _normalize_input always output a list of arrays.
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@TrishGillett
TrishGillett committed Sep 18, 2016
commit 1d9783b0a522267156e6683a7353c06771b4ca3c
31 changes: 18 additions & 13 deletions lib/matplotlib/axes/_axes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5871,7 +5871,7 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,
Parameters
----------
x : (n,) array or sequence of (n,) arrays
Input values, this takes either a single array or a sequency of
Input values, this takes either a single array or a sequence of
arrays which are not required to be of the same length

bins : integer or array_like or 'auto', optional
Expand Down Expand Up @@ -6042,8 +6042,8 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,

"""
def _normalize_input(inp, ename='input'):
"""Normalize 1 or 2d input into list of np.ndarray or
a single 2D np.ndarray.
"""Normalize 1 or 2d input into a list of one or more np.ndarrays
which have potentially different lengths.

Parameters
----------
Expand All @@ -6054,25 +6054,30 @@ def _normalize_input(inp, ename='input'):
"""
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

if inp.shape[1] < inp.shape[0]:
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I am 👎 on this warning, I think it is assuming too much about how users will be using this method.

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I now see that this is just moving this warning around. I would be in favor of removing it, but if it is already in the code base, mostly ignore my previous comment.

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@TrishGillett TrishGillett Sep 19, 2016 •
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This is actually just migrated up from where it was before (old line 6068). I don't care for it either but left it alone thinking it probably got debated when it was added. I'm more than happy to remove.

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Gotcha. I'm good either way.

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Absent a compelling reason I err on the side of not removing things (which may not be a fully defend able position, but it is a safe conservative one)

Chasing this back through git blame, the warning came into the code base in
214976f in 2008.

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I am also in favor of removing it (I am biased as I am in a field where nsamples << nvariables )

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Oh, wow, that I can see being a problem.
Maybe throw an error (or raise a warning) if x.shape[0] == 1?

warnings.warn(
'2D hist input should be nsamples x nvariables;\n '
'this looks transposed '
'(shape is %d x %d)' % inp.shape[::-1])

# Change to a list of arrays
inp = [inp[i, :] for i in range(inp.shape[0])]

elif inp.ndim == 1:
# new view, single row
inp = inp.reshape(1, inp.shape[0])
# Change to a list with a single array
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]
# Change to a list of arrays
inp = [np.asarray(arr) for arr in inp]

return inp

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