From a2595e095617787b3695992893e0dcedb5e1babf Mon Sep 17 00:00:00 2001 From: Antony Lee Date: Mon, 7 Jan 2019 00:15:58 +0100 Subject: [PATCH] Simplify parsing of errorbar input. --- lib/matplotlib/axes/_axes.py | 53 ++++++++++++++---------------------- 1 file changed, 20 insertions(+), 33 deletions(-) diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py index fd724f55876c..782af5b47cae 100644 --- a/lib/matplotlib/axes/_axes.py +++ b/lib/matplotlib/axes/_axes.py @@ -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: - 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. + low = [v - e for v, e in cbook.safezip(data, a)] + high = [v + e for v, e in cbook.safezip(data, b)] return low, high if xerr is not None: pFad - Phonifier reborn

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