Nov 28, 2018 · Nov 22, 2018
diff --git a/doc/api/next_api_changes/2018-11-23-AL.rst b/doc/api/next_api_changes/2018-11-23-AL.rst
 Log-scaled axes avoid having zero or only one tick
 ``````````````````````````````````````````````````

 When the default `LogLocator` would generate no ticks for an axis (e.g., an
 axis with limits from 0.31 to 0.39) or only a single tick, it now instead falls
 back on the linear `AutoLocator` to pick reasonable tick positions.
diff --git a/lib/matplotlib/tests/test_ticker.py b/lib/matplotlib/tests/test_ticker.py
 import warnings

 import numpy as np
 from numpy.testing import assert_almost_equal
 from numpy.testing import assert_almost_equal, assert_array_equal
 import pytest

 import matplotlib
        test_value = np.array([0.5, 1., 2., 4., 8., 16., 32., 64., 128., 256.])
        assert_almost_equal(loc.tick_values(1, 100), test_value)

    def test_switch_to_autolocator(self):
        loc = mticker.LogLocator(subs="all")
        assert_array_equal(loc.tick_values(0.45, 0.55),
                           [0.44, 0.46, 0.48, 0.5, 0.52, 0.54, 0.56])

    def test_set_params(self):
        """
        Create log locator with default value, base=10.0, subs=[1.0],
diff --git a/lib/matplotlib/ticker.py b/lib/matplotlib/ticker.py
                    "log-scaled.")

        _log.debug('vmin %s vmax %s', vmin, vmax)
        vmin = math.log(vmin) / math.log(b)
        vmax = math.log(vmax) / math.log(b)

        if vmax < vmin:
            vmin, vmax = vmax, vmin
        log_vmin = math.log(vmin) / math.log(b)
        log_vmax = math.log(vmax) / math.log(b)

        numdec = math.floor(vmax) - math.ceil(vmin)
        numdec = math.floor(log_vmax) - math.ceil(log_vmin)

        if isinstance(self._subs, str):
            _first = 2.0 if self._subs == 'auto' else 1.0
            while numdec // stride + 1 > numticks:
                stride += 1

        # Does subs include anything other than 1?
        # Does subs include anything other than 1?  Essentially a hack to know
        # whether we're a major or a minor locator.
        have_subs = len(subs) > 1 or (len(subs) == 1 and subs[0] != 1.0)

        decades = np.arange(math.floor(vmin) - stride,
                            math.ceil(vmax) + 2 * stride, stride)
        decades = np.arange(math.floor(log_vmin) - stride,
                            math.ceil(log_vmax) + 2 * stride, stride)

        if hasattr(self, '_transform'):
            ticklocs = self._transform.inverted().transform(decades)
            if have_subs:
                if stride == 1:
                    ticklocs = np.ravel(np.outer(subs, ticklocs))
                else:
                    # no ticklocs if we have more than one decade
                    # between major ticks.
                    ticklocs = []
                    # No ticklocs if we have >1 decade between major ticks.
                    ticklocs = np.array([])
        else:
            if have_subs:
                ticklocs = []
                if stride == 1:
                    for decadeStart in b ** decades:
                        ticklocs.extend(subs * decadeStart)
                    ticklocs = np.concatenate(
                        [subs * decade_start for decade_start in b ** decades])
                else:
                    ticklocs = np.array([])
            else:
                ticklocs = b ** decades

        _log.debug('ticklocs %r', ticklocs)
        return self.raise_if_exceeds(np.asarray(ticklocs))
        if (len(subs) > 1
                and stride == 1
                and ((vmin <= ticklocs) & (ticklocs <= vmax)).sum() <= 1):
            # If we're a minor locator *that expects at least two ticks per
            # decade* and the major locator stride is 1 and there's no more
            # than one minor tick, switch to AutoLocator.
            return AutoLocator().tick_values(vmin, vmax)
        return self.raise_if_exceeds(ticklocs)

    def view_limits(self, vmin, vmax):
        'Try to choose the view limits intelligently'
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,6 @@
		Log-scaled axes avoid having zero or only one tick
		``````````````````````````````````````````````````

		When the default `LogLocator` would generate no ticks for an axis (e.g., an
		axis with limits from 0.31 to 0.39) or only a single tick, it now instead falls
		back on the linear `AutoLocator` to pick reasonable tick positions.
Original file line number	Diff line number	Diff line change
		@@ -1,7 +1,7 @@
		import warnings

		import numpy as np
		from numpy.testing import assert_almost_equal
		from numpy.testing import assert_almost_equal, assert_array_equal
		import pytest

		import matplotlib
Expand DownExpand Up		@@ -179,6 +179,11 @@ def test_basic(self):
		test_value = np.array([0.5, 1., 2., 4., 8., 16., 32., 64., 128., 256.])
		assert_almost_equal(loc.tick_values(1, 100), test_value)

		def test_switch_to_autolocator(self):
		loc = mticker.LogLocator(subs="all")
		assert_array_equal(loc.tick_values(0.45, 0.55),
		[0.44, 0.46, 0.48, 0.5, 0.52, 0.54, 0.56])

		def test_set_params(self):
		"""
		Create log locator with default value, base=10.0, subs=[1.0],
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -2165,13 +2165,13 @@ def tick_values(self, vmin, vmax):
		"log-scaled.")

		_log.debug('vmin %s vmax %s', vmin, vmax)
		vmin = math.log(vmin) / math.log(b)
		vmax = math.log(vmax) / math.log(b)

		if vmax < vmin:
		vmin, vmax = vmax, vmin
		log_vmin = math.log(vmin) / math.log(b)
		log_vmax = math.log(vmax) / math.log(b)

		numdec = math.floor(vmax) - math.ceil(vmin)
		numdec = math.floor(log_vmax) - math.ceil(log_vmin)

		if isinstance(self._subs, str):
		_first = 2.0 if self._subs == 'auto' else 1.0
Expand All		@@ -2195,32 +2195,40 @@ def tick_values(self, vmin, vmax):
		while numdec // stride + 1 > numticks:
		stride += 1

		# Does subs include anything other than 1?
		# Does subs include anything other than 1? Essentially a hack to know
		# whether we're a major or a minor locator.
		have_subs = len(subs) > 1 or (len(subs) == 1 and subs[0] != 1.0)

		decades = np.arange(math.floor(vmin) - stride,
		math.ceil(vmax) + 2 * stride, stride)
		decades = np.arange(math.floor(log_vmin) - stride,
		math.ceil(log_vmax) + 2 * stride, stride)

		if hasattr(self, '_transform'):
		ticklocs = self._transform.inverted().transform(decades)
		if have_subs:
		if stride == 1:
		ticklocs = np.ravel(np.outer(subs, ticklocs))
		else:
		# no ticklocs if we have more than one decade
		# between major ticks.
		ticklocs = []
		# No ticklocs if we have >1 decade between major ticks.
		ticklocs = np.array([])
		else:
		if have_subs:
		ticklocs = []
		if stride == 1:
		for decadeStart in b ** decades:
		ticklocs.extend(subs * decadeStart)
		ticklocs = np.concatenate(
		[subs * decade_start for decade_start in b ** decades])
		else:
		ticklocs = np.array([])
		else:
		ticklocs = b ** decades

		_log.debug('ticklocs %r', ticklocs)
		return self.raise_if_exceeds(np.asarray(ticklocs))
		if (len(subs) > 1
		and stride == 1
		and ((vmin <= ticklocs) & (ticklocs <= vmax)).sum() <= 1):
		# If we're a minor locator *that expects at least two ticks per
		# decade* and the major locator stride is 1 and there's no more
		# than one minor tick, switch to AutoLocator.
		return AutoLocator().tick_values(vmin, vmax)
		return self.raise_if_exceeds(ticklocs)

		def view_limits(self, vmin, vmax):
		'Try to choose the view limits intelligently'
Expand Down