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Merge pull request #9576 from matplotlib/auto-backport-of-pr-9477
Backport PR #9477 on branch v2.1.x
2 parents d5f0b56 + 668ff58 commit 187dd92

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‎doc/api/api_changes.rst

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@@ -10,9 +10,24 @@ out what caused the breakage and how to fix it by updating your code.
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For new features that were added to Matplotlib, please see
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:ref:`whats-new`.
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API Changes in 2.1.1
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====================
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Default behavior of log scales reverted to clip <= 0 values
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-----------------------------------------------------------
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The change it 2.1.0 to mask in logscale by default had more disruptive
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changes than anticipated and has been reverted, however the clipping is now
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done in a way that fixes the issues that motivated changing the default behavior
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to ``'mask'``.
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As a side effect of this change, error bars which go negative now work as expected
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on log scales.
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API Changes in 2.1.0
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====================
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Default behavior of log scales changed to mask <= 0 values
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----------------------------------------------------------
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‎lib/matplotlib/axes/_axes.py

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@@ -1556,11 +1556,9 @@ def loglog(self, *args, **kwargs):
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dx = {'basex': kwargs.pop('basex', 10),
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'subsx': kwargs.pop('subsx', None),
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'nonposx': kwargs.pop('nonposx', 'mask'),
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}
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dy = {'basey': kwargs.pop('basey', 10),
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'subsy': kwargs.pop('subsy', None),
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'nonposy': kwargs.pop('nonposy', 'mask'),
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}
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self.set_xscale('log', **dx)
@@ -2851,11 +2849,6 @@ def errorbar(self, x, y, yerr=None, xerr=None,
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Valid kwargs for the marker properties are
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%(Line2D)s
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Notes
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-----
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Error bars with negative values will not be shown when plotted on a
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logarithmic axis.
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"""
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kwargs = cbook.normalize_kwargs(kwargs, _alias_map)
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# anything that comes in as 'None', drop so the default thing

‎lib/matplotlib/axes/_base.py

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@@ -2970,11 +2970,6 @@ def set_xscale(self, value, **kwargs):
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matplotlib.scale.LogisticTransform : logit transform
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"""
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# If the scale is being set to log, mask nonposx to prevent headaches
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# around zero
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if value.lower() == 'log' and 'nonposx' not in kwargs:
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kwargs['nonposx'] = 'mask'
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g = self.get_shared_x_axes()
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for ax in g.get_siblings(self):
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ax.xaxis._set_scale(value, **kwargs)
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matplotlib.scale.LogisticTransform : logit transform
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"""
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# If the scale is being set to log, mask nonposy to prevent headaches
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# around zero
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if value.lower() == 'log' and 'nonposy' not in kwargs:
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kwargs['nonposy'] = 'mask'
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g = self.get_shared_y_axes()
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for ax in g.get_siblings(self):
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ax.yaxis._set_scale(value, **kwargs)

‎lib/matplotlib/scale.py

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@@ -92,15 +92,24 @@ class LogTransformBase(Transform):
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def __init__(self, nonpos):
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Transform.__init__(self)
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if nonpos == 'mask':
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self._fill_value = np.nan
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else:
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self._fill_value = 1e-300
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self._clip = {"clip": True, "mask": False}[nonpos]
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def transform_non_affine(self, a):
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with np.errstate(invalid="ignore"):
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a = np.where(a <= 0, self._fill_value, a)
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return np.divide(np.log(a, out=a), np.log(self.base), out=a)
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with np.errstate(divide="ignore", invalid="ignore"):
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out = np.log(a)
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out /= np.log(self.base)
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if self._clip:
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# SVG spec says that conforming viewers must support values up
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# to 3.4e38 (C float); however experiments suggest that Inkscape
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# (which uses cairo for rendering) runs into cairo's 24-bit limit
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# (which is apparently shared by Agg).
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# Ghostscript (used for pdf rendering appears to overflow even
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# earlier, with the max value around 2 ** 15 for the tests to pass.
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# On the other hand, in practice, we want to clip beyond
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# np.log10(np.nextafter(0, 1)) ~ -323
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# so 1000 seems safe.
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out[a <= 0] = -1000
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return out
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class InvertedLogTransformBase(Transform):
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if axis.axis_name == 'x':
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base = kwargs.pop('basex', 10.0)
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subs = kwargs.pop('subsx', None)
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nonpos = kwargs.pop('nonposx', 'mask')
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nonpos = kwargs.pop('nonposx', 'clip')
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else:
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base = kwargs.pop('basey', 10.0)
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subs = kwargs.pop('subsy', None)
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nonpos = kwargs.pop('nonposy', 'mask')
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nonpos = kwargs.pop('nonposy', 'clip')
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if len(kwargs):
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raise ValueError(("provided too many kwargs, can only pass "
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"{'basex', 'subsx', nonposx'} or "
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"{'basey', 'subsy', nonposy'}. You passed ") +
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"{!r}".format(kwargs))
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if nonpos not in ['mask', 'clip']:
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raise ValueError("nonposx, nonposy kwarg must be 'mask' or 'clip'")
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def __init__(self, nonpos):
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Transform.__init__(self)
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if nonpos == 'mask':
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self._fill_value = np.nan
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else:
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self._fill_value = 1e-300
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self._nonpos = nonpos
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self._clip = {"clip": True, "mask": False}[nonpos]
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def transform_non_affine(self, a):
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"""logit transform (base 10), masked or clipped"""
443-
with np.errstate(invalid="ignore"):
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a = np.select(
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[a <= 0, a >= 1], [self._fill_value, 1 - self._fill_value], a)
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return np.log10(a / (1 - a))
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with np.errstate(divide="ignore", invalid="ignore"):
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out = np.log10(a / (1 - a))
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if self._clip: # See LogTransform for choice of clip value.
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out[a <= 0] = -1000
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out[1 <= a] = 1000
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return out
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def inverted(self):
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return LogisticTransform(self._nonpos)

‎lib/matplotlib/tests/baseline_images/test_axes/log_scales.png

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‎lib/matplotlib/tests/baseline_images/test_axes/log_scales.svg

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‎lib/matplotlib/tests/baseline_images/test_path/semi_log_with_zero.png

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‎lib/matplotlib/tests/baseline_images/test_scale/logscale_mask.png

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‎lib/matplotlib/tests/baseline_images/test_scale/logscale_nonpos_values.png

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‎lib/matplotlib/tests/test_path.py

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@@ -87,13 +87,15 @@ def test_path_clipping():
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xy, facecolor='none', edgecolor='red', closed=True))
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@image_comparison(baseline_images=['semi_log_with_zero'], extensions=['png'])
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@image_comparison(baseline_images=['semi_log_with_zero'], extensions=['png'],
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style='mpl20')
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def test_log_transform_with_zero():
9293
x = np.arange(-10, 10)
9394
y = (1.0 - 1.0/(x**2+1))**20
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fig, ax = plt.subplots()
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ax.semilogy(x, y, "-o", lw=15)
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ax.semilogy(x, y, "-o", lw=15, markeredgecolor='k')
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ax.set_ylim(1e-7, 1)
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ax.grid(True)
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‎lib/matplotlib/tests/test_scale.py

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import matplotlib.pyplot as plt
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import numpy as np
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import io
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import pytest
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@image_comparison(baseline_images=['log_scales'], remove_text=True)
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ax.plot(np.exp(-xs**2))
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fig.canvas.draw()
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ax.set(yscale="log")
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def test_extra_kwargs_raise():
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fig, ax = plt.subplots()
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with pytest.raises(ValueError):
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ax.set_yscale('log', nonpos='mask')
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@image_comparison(baseline_images=['logscale_nonpos_values'], remove_text=True,
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extensions=['png'], style='mpl20')
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def test_logscale_nonpos_values():
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np.random.seed(19680801)
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xs = np.random.normal(size=int(1e3))
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fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
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ax1.hist(xs, range=(-5, 5), bins=10)
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ax1.set_yscale('log')
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ax2.hist(xs, range=(-5, 5), bins=10)
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ax2.set_yscale('log', nonposy='mask')
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xdata = np.arange(0, 10, 0.01)
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ydata = np.exp(-xdata)
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edata = 0.2*(10-xdata)*np.cos(5*xdata)*np.exp(-xdata)
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ax3.fill_between(xdata, ydata - edata, ydata + edata)
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ax3.set_yscale('log')
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x = np.logspace(-1, 1)
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y = x ** 3
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yerr = x**2
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ax4.errorbar(x, y, yerr=yerr)
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ax4.set_yscale('log')
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ax4.set_xscale('log')

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