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Fix autoscaling to exclude inifinite data limits when possible. #18276

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Merged
merged 4 commits into from
Aug 18, 2020
Merged

Fix autoscaling to exclude inifinite data limits when possible. #18276

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8896d90
Fix autoscaling to exclude inifinite data limits when possible. Fix #…
l-johnston Aug 17, 2020
1718f34
Improve finite data limit algorithm efficiency
l-johnston Aug 17, 2020
3aecf00
Reduce the data limit logic some more
l-johnston Aug 17, 2020
ee086d6
Improve minimum_minpos efficiency
l-johnston Aug 18, 2020
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36 changes: 15 additions & 21 deletions lib/matplotlib/axes/_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2539,25 +2539,20 @@ def handle_single_axis(scale, autoscaleon, shared_axes, interval,
return # nothing to do...

shared = shared_axes.get_siblings(self)
dl = [ax.dataLim for ax in shared]
# ignore non-finite data limits if good limits exist
finite_dl = [d for d in dl if np.isfinite(d).all()]
if len(finite_dl):
# if finite limits exist for at least one axis (and the
# other is infinite), restore the finite limits
x_finite = [d for d in dl
if (np.isfinite(d.intervalx).all() and
(d not in finite_dl))]
y_finite = [d for d in dl
if (np.isfinite(d.intervaly).all() and
(d not in finite_dl))]

dl = finite_dl
dl.extend(x_finite)
dl.extend(y_finite)

bb = mtransforms.BboxBase.union(dl)
x0, x1 = getattr(bb, interval)
# Base autoscaling on finite data limits when there is at least one
# finite data limit among all the shared_axes and intervals.
# Also, find the minimum minpos for use in the margin calculation.
x_values = []
minimum_minpos = np.inf
for ax in shared:
x_values.extend(getattr(ax.dataLim, interval))
minimum_minpos = min(minimum_minpos,
getattr(ax.dataLim, minpos))
x_values = np.extract(np.isfinite(x_values), x_values)
if x_values.size >= 1:
x0, x1 = (x_values.min(), x_values.max())
else:
x0, x1 = (-np.inf, np.inf)
# If x0 and x1 are non finite, use the locator to figure out
# default limits.
locator = axis.get_major_locator()
Expand All @@ -2578,10 +2573,9 @@ def handle_single_axis(scale, autoscaleon, shared_axes, interval,

# Add the margin in figure space and then transform back, to handle
# non-linear scales.
minpos = getattr(bb, minpos)
transform = axis.get_transform()
inverse_trans = transform.inverted()
x0, x1 = axis._scale.limit_range_for_scale(x0, x1, minpos)
x0, x1 = axis._scale.limit_range_for_scale(x0, x1, minimum_minpos)
x0t, x1t = transform.transform([x0, x1])
delta = (x1t - x0t) * margin
if not np.isfinite(delta):
Expand Down
9 changes: 9 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 @@ -6480,3 +6480,12 @@ def test_relative_ticklabel_sizes(size):
for name, axis in zip(['x', 'y'], [ax.xaxis, ax.yaxis]):
for tick in axis.get_major_ticks():
assert tick.label1.get_size() == axis._get_tick_label_size(name)


def test_multiplot_autoscale():
fig = plt.figure()
ax1, ax2 = fig.subplots(2, 1, sharex='all')
ax1.scatter([1, 2, 3, 4], [2, 3, 2, 3])
ax2.axhspan(-5, 5)
xlim = ax1.get_xlim()
assert np.allclose(xlim, [0.5, 4.5])