Jan 17, 2019 · Jan 6, 2019 · Jan 6, 2019 · Jan 7, 2019 · Jan 16, 2019 · Jan 16, 2019
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
            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:
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:
Copy link ContributorAuthor anntzerJan 6, 2019 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. 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.
Copy link Member timhoffmJan 7, 2019 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. 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)]
Copy link Member jklymakJan 16, 2019 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. 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. Copy link ContributorAuthor anntzerJan 16, 2019• edited Loading Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. 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. Copy link Member jklymakJan 17, 2019 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. 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. Copy link ContributorAuthor anntzerJan 17, 2019• edited Loading Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. tbh I don't particularly care about adding tests for units for this PR specifically. Copy link Member jklymakJan 17, 2019 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. 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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