Oct 25, 2017 · Oct 19, 2017 · Oct 22, 2017 · Oct 22, 2017 · Oct 22, 2017 · Oct 22, 2017
diff --git a/doc/api/api_changes.rst b/doc/api/api_changes.rst
 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

        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)
            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

        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)

        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

    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):
        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'")

    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
            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
 import matplotlib.pyplot as plt
 import numpy as np
 import io
 import pytest


 @image_comparison(baseline_images=['log_scales'], remove_text=True)
    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')
Original file line number	Diff line number	Diff line change
Expand Up		@@ -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
		----------------------------------------------------------

Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -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)
Expand DownExpand Up		@@ -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
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -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)
Expand DownExpand Up		@@ -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)
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -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):
Expand DownExpand Up		@@ -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))
Copy link ContributorAuthor anntzerOct 22, 2017 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. just format the entire string? no need to add, also no need for`!r` as`repr(dict) == format(dict, "")` Copy link Member tacaswellOct 23, 2017 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. If you try to format the whole string it blows up on a key error with "'basex', 'subsx', nonposx'". Copy link ContributorAuthor anntzerOct 23, 2017 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. "{" -> "{{". If you think it's not an improvement, then at least replace "{!r}.format(...)" by repr(...), no? seems a bit perverse otherwise :)

		if nonpos not in ['mask', 'clip']:
		raise ValueError("nonposx, nonposy kwarg must be 'mask' or 'clip'")
Expand DownExpand Up		@@ -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)
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -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/(x2+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)


Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -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)
Expand DownExpand Up		@@ -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(5xdata)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')