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Simplify parsing of errorbar input. #13124

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Merged
merged 1 commit into from
Jan 17, 2019
+20 −33
Merged

Simplify parsing of errorbar input. #13124

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53 changes: 20 additions & 33 deletions lib/matplotlib/axes/_axes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3108,41 +3108,28 @@ def xywhere(xs, ys, mask):
return xs, ys

def extract_err(err, data):
'''private function to compute error bars

Parameters
----------
err : iterable
xerr or yerr from errorbar
data : iterable
x or y from errorbar
'''
try:
"""
Private function to parse *err* and subtract/add it to *data*.

Both *err* and *data* are already iterables at this point.
"""
try: # Asymmetric error: pair of 1D iterables.
a, b = err
iter(a)
iter(b)
except (TypeError, ValueError):
pass
else:
if np.iterable(a) and np.iterable(b):
# using list comps rather than arrays to preserve units
low = [thisx - thiserr for thisx, thiserr
in cbook.safezip(data, a)]
high = [thisx + thiserr for thisx, thiserr
in cbook.safezip(data, b)]
return low, high
# Check if xerr is scalar or symmetric. Asymmetric is handled
# above. This prevents Nx2 arrays from accidentally
# being accepted, when the user meant the 2xN transpose.
# special case for empty lists
if len(err) > 1:
fe = cbook.safe_first_element(err)
if len(err) != len(data) or np.size(fe) > 1:
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the check that len(err) == len(data) is taken care of by safezip, so can be dropped.

raise ValueError("err must be [ scalar | N, Nx1 "
"or 2xN array-like ]")
# using list comps rather than arrays to preserve units
low = [thisx - thiserr for thisx, thiserr
in cbook.safezip(data, err)]
high = [thisx + thiserr for thisx, thiserr
in cbook.safezip(data, err)]
a = b = err # Symmetric error: 1D iterable.
# This could just be `np.ndim(a) > 1 and np.ndim(b) > 1`, except
# for the (undocumented, but tested) support for (n, 1) arrays.
a_sh = np.shape(a)
b_sh = np.shape(b)
if (len(a_sh) > 2 or (len(a_sh) == 2 and a_sh[1] != 1)
or len(b_sh) > 2 or (len(b_sh) == 2 and b_sh[1] != 1)):
raise ValueError(
"err must be a scalar or a 1D or (2, n) array-like")
# Using list comprehensions rather than arrays to preserve units.
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Would it be worth it to special-case if v and e are arrays to speed up the calculation? Probably not.

low = [v - e for v, e in cbook.safezip(data, a)]
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What units has this been tested with? This is basically the same issue as bar in #12903 so we should make sure unit handling is the same. In particular if v is a datetime, and e a timedelta, I assume this works? What about for pint units? And are we sure this works w/ pandas? Not that I think #12903 checked all those boxes, but I think we should be thinking about how to uniformly handle this case and bar. ie. we now have _convert_dx, and maybe errorbar should use it as well.

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@anntzer anntzer Jan 16, 2019 •
edited
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Do you want to first make a PR letting errorbar use _convert_dx? I can rebase this one onto yours after it's merged.

However it's not actually clear to me it's the "same" issue as #12903; in bar() you need to be able to support deunitized widths (because the default, 0.8, is deunitized...) whereas here we can just always assume that the error has the same unit (or a compatible one) as the value.

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I agree they are different and maybe using the same helper doesn't make sense. (I actually think defaulting width=0.8 in bar is ambiguous and a mistake, but...).

Tests with datetime-like obects would be helpful. Not sure categoricals need to be tested (when would the error be a +/- f? )

And we should decide if we should add pint to the tests.

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@anntzer anntzer Jan 17, 2019 •
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tbh I don't particularly care about adding tests for units for this PR specifically.

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actually I guess thats fair since you didn't change the algorithm...

high = [v + e for v, e in cbook.safezip(data, b)]
return low, high

if xerr is not None:
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