matplotlib · dstansby · Oct 26, 2017 · Oct 25, 2017
diff --git a/doc/api/api_changes.rst b/doc/api/api_changes.rst
@@ -10,9 +10,24 @@ out what caused the breakage and how to fix it by updating your code.
 For new features that were added to Matplotlib, please see
 :ref:`whats-new`.
 
+API Changes in 2.1.1
+====================
+
+Default behavior of log scales reverted to clip <= 0 values
+-----------------------------------------------------------
+
+The change it 2.1.0 to mask in logscale by default had more disruptive
+changes than anticipated and has been reverted, however the clipping is now
+done in a way that fixes the issues that motivated changing the default behavior
+to ``'mask'``.
+
+As a side effect of this change, error bars which go negative now work as expected
+on log scales.
+
 API Changes in 2.1.0
 ====================
 
+
 Default behavior of log scales changed to mask <= 0 values
 ----------------------------------------------------------
 

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

diff --git a/lib/matplotlib/axes/_base.py b/lib/matplotlib/axes/_base.py
@@ -2970,11 +2970,6 @@ def set_xscale(self, value, **kwargs):
 
         matplotlib.scale.LogisticTransform : logit transform
         """
-        # If the scale is being set to log, mask nonposx to prevent headaches
-        # around zero
-        if value.lower() == 'log' and 'nonposx' not in kwargs:
-            kwargs['nonposx'] = 'mask'
-
         g = self.get_shared_x_axes()
         for ax in g.get_siblings(self):
             ax.xaxis._set_scale(value, **kwargs)
@@ -3292,11 +3287,6 @@ def set_yscale(self, value, **kwargs):
 
         matplotlib.scale.LogisticTransform : logit transform
         """
-        # If the scale is being set to log, mask nonposy to prevent headaches
-        # around zero
-        if value.lower() == 'log' and 'nonposy' not in kwargs:
-            kwargs['nonposy'] = 'mask'
-
         g = self.get_shared_y_axes()
         for ax in g.get_siblings(self):
             ax.yaxis._set_scale(value, **kwargs)

diff --git a/lib/matplotlib/scale.py b/lib/matplotlib/scale.py
@@ -92,15 +92,24 @@ class LogTransformBase(Transform):
 
     def __init__(self, nonpos):
         Transform.__init__(self)
-        if nonpos == 'mask':
-            self._fill_value = np.nan
-        else:
-            self._fill_value = 1e-300
+        self._clip = {"clip": True, "mask": False}[nonpos]
 
     def transform_non_affine(self, a):
-        with np.errstate(invalid="ignore"):
-            a = np.where(a <= 0, self._fill_value, a)
-        return np.divide(np.log(a, out=a), np.log(self.base), out=a)
+        with np.errstate(divide="ignore", invalid="ignore"):
+            out = np.log(a)
+        out /= np.log(self.base)
+        if self._clip:
+            # SVG spec says that conforming viewers must support values up
+            # to 3.4e38 (C float); however experiments suggest that Inkscape
+            # (which uses cairo for rendering) runs into cairo's 24-bit limit
+            # (which is apparently shared by Agg).
+            # Ghostscript (used for pdf rendering appears to overflow even
+            # earlier, with the max value around 2 ** 15 for the tests to pass.
+            # On the other hand, in practice, we want to clip beyond
+            #     np.log10(np.nextafter(0, 1)) ~ -323
+            # so 1000 seems safe.
+            out[a <= 0] = -1000
+        return out
 
 
 class InvertedLogTransformBase(Transform):
@@ -220,11 +229,17 @@ def __init__(self, axis, **kwargs):
         if axis.axis_name == 'x':
             base = kwargs.pop('basex', 10.0)
             subs = kwargs.pop('subsx', None)
-            nonpos = kwargs.pop('nonposx', 'mask')
+            nonpos = kwargs.pop('nonposx', 'clip')
         else:
             base = kwargs.pop('basey', 10.0)
             subs = kwargs.pop('subsy', None)
-            nonpos = kwargs.pop('nonposy', 'mask')
+            nonpos = kwargs.pop('nonposy', 'clip')
+
+        if len(kwargs):
+            raise ValueError(("provided too many kwargs, can only pass "
+                              "{'basex', 'subsx', nonposx'} or "
+                              "{'basey', 'subsy', nonposy'}.  You passed ") +
+                             "{!r}".format(kwargs))
 
         if nonpos not in ['mask', 'clip']:
             raise ValueError("nonposx, nonposy kwarg must be 'mask' or 'clip'")
@@ -432,18 +447,17 @@ class LogitTransform(Transform):
 
     def __init__(self, nonpos):
         Transform.__init__(self)
-        if nonpos == 'mask':
-            self._fill_value = np.nan
-        else:
-            self._fill_value = 1e-300
         self._nonpos = nonpos
+        self._clip = {"clip": True, "mask": False}[nonpos]
 
     def transform_non_affine(self, a):
         """logit transform (base 10), masked or clipped"""
-        with np.errstate(invalid="ignore"):
-            a = np.select(
-                [a <= 0, a >= 1], [self._fill_value, 1 - self._fill_value], a)
-        return np.log10(a / (1 - a))
+        with np.errstate(divide="ignore", invalid="ignore"):
+            out = np.log10(a / (1 - a))
+        if self._clip:  # See LogTransform for choice of clip value.
+            out[a <= 0] = -1000
+            out[1 <= a] = 1000
+        return out
 
     def inverted(self):
         return LogisticTransform(self._nonpos)

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

diff --git a/lib/matplotlib/tests/test_scale.py b/lib/matplotlib/tests/test_scale.py
@@ -4,6 +4,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import io
+import pytest
 
 
 @image_comparison(baseline_images=['log_scales'], remove_text=True)
@@ -65,3 +66,36 @@ def test_logscale_mask():
     ax.plot(np.exp(-xs**2))
     fig.canvas.draw()
     ax.set(yscale="log")
+
+
+def test_extra_kwargs_raise():
+    fig, ax = plt.subplots()
+    with pytest.raises(ValueError):
+        ax.set_yscale('log', nonpos='mask')
+
+
+@image_comparison(baseline_images=['logscale_nonpos_values'], remove_text=True,
+                  extensions=['png'], style='mpl20')
+def test_logscale_nonpos_values():
+    np.random.seed(19680801)
+    xs = np.random.normal(size=int(1e3))
+    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
+    ax1.hist(xs, range=(-5, 5), bins=10)
+    ax1.set_yscale('log')
+    ax2.hist(xs, range=(-5, 5), bins=10)
+    ax2.set_yscale('log', nonposy='mask')
+
+    xdata = np.arange(0, 10, 0.01)
+    ydata = np.exp(-xdata)
+    edata = 0.2*(10-xdata)*np.cos(5*xdata)*np.exp(-xdata)
+
+    ax3.fill_between(xdata, ydata - edata, ydata + edata)
+    ax3.set_yscale('log')
+
+    x = np.logspace(-1, 1)
+    y = x ** 3
+    yerr = x**2
+    ax4.errorbar(x, y, yerr=yerr)
+
+    ax4.set_yscale('log')
+    ax4.set_xscale('log')