BUG: fix minpos handling and other log ticker problems

efiring · efiring · commit e3de3bea24a1 · 2016-12-11T13:21:31.000-10:00
Closes #7595, #7493, #7587. Bbox._minpos is now initialized to [np.inf, np.inf] instead of [1e-7, 1e-7]. Old code with a colorbar in which the formatter is set to a LogFormatter and a SymlogNorm is used now works again (although the results are better in 2.0 if the colorbar is left to handle the formatter automatically). LogLocator now has its own nonsingular() method which provides a reasonable starting point for a log axis when no data have been plotted.
diff --git a/lib/matplotlib/scale.py b/lib/matplotlib/scale.py
@@ -264,6 +264,10 @@ def limit_range_for_scale(self, vmin, vmax, minpos):
         """
         Limit the domain to positive values.
         """
+        if not np.isfinite(minpos):
+            minpos = 1e-300  # This value should rarely if ever
+                             # end up with a visible effect.
+
         return (minpos if vmin <= 0 else vmin,
                 minpos if vmax <= 0 else vmax)
 
@@ -499,7 +503,10 @@ def limit_range_for_scale(self, vmin, vmax, minpos):
         """
         Limit the domain to values between 0 and 1 (excluded).
         """
-        return (minpos if vmin <= 0 else minpos,
+        if not np.isfinite(minpos):
+            minpos = 1e-7    # This value should rarely if ever
+                             # end up with a visible effect.
+        return (minpos if vmin <= 0 else vmin,
                 1 - minpos if vmax >= 1 else vmax)
 
 
diff --git a/lib/matplotlib/ticker.py b/lib/matplotlib/ticker.py
@@ -914,6 +914,16 @@ def set_locs(self, locs=None):
             except AttributeError:
                 pass
 
+        if vmin > vmax:
+            vmin, vmax = vmax, vmin
+
+        if linthresh is None and vmin <= 0:
+            # It's probably a colorbar with
+            # a format kwarg setting a LogFormatter in the manner
+            # that worked with 1.5.x, but that doesn't work now.
+            self._sublabels = set((1,))  # label powers of base
+            return
+
         if linthresh is not None:  # symlog
             # Only compute the number of decades in the logarithmic part of the
             # axis
@@ -951,7 +961,7 @@ def __call__(self, x, pos=None):
         vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander=0.05)
         d = abs(vmax - vmin)
         b = self._base
-        if x == 0.0:
+        if x == 0.0:  # Symlog
             return '0'
         sign = np.sign(x)
         x = abs(x)
@@ -2032,36 +2042,41 @@ def view_limits(self, vmin, vmax):
         'Try to choose the view limits intelligently'
         b = self._base
 
-        if vmax < vmin:
-            vmin, vmax = vmax, vmin
+        vmin, vmax = self.nonsingular(vmin, vmax)
 
         if self.axis.axes.name == 'polar':
             vmax = math.ceil(math.log(vmax) / math.log(b))
             vmin = b ** (vmax - self.numdecs)
-            return vmin, vmax
-
-        minpos = self.axis.get_minpos()
-
-        if minpos <= 0 or not np.isfinite(minpos):
-            raise ValueError(
-                "Data has no positive values, and therefore can not be "
-                "log-scaled.")
-
-        if vmin <= 0:
-            vmin = minpos
 
         if rcParams['axes.autolimit_mode'] == 'round_numbers':
             if not is_decade(vmin, self._base):
                 vmin = decade_down(vmin, self._base)
             if not is_decade(vmax, self._base):
                 vmax = decade_up(vmax, self._base)
 
-            if vmin == vmax:
-                vmin = decade_down(vmin, self._base)
-                vmax = decade_up(vmax, self._base)
+        return vmin, vmax
 
-        result = mtransforms.nonsingular(vmin, vmax)
-        return result
+    def nonsingular(self, vmin, vmax):
+        if not np.isfinite(vmin) or not np.isfinite(vmax):
+            return 1, 10  # initial range, no data plotted yet
+
+        if vmin > vmax:
+            vmin, vmax = vmax, vmin
+        if vmax <= 0:
+            warnings.warn(
+                "Data has no positive values, and therefore cannot be "
+                "log-scaled.")
+            return 1, 10
+
+        minpos = self.axis.get_minpos()
+        if not np.isfinite(minpos):
+            minpos = 1e-300  # This should never take effect.
+        if vmin <= 0:
+            vmin = minpos
+        if vmin == vmax:
+            vmin = decade_down(vmin, self._base)
+            vmax = decade_up(vmax, self._base)
+        return vmin, vmax
 
 
 class SymmetricalLogLocator(Locator):
@@ -2260,32 +2275,7 @@ def tick_values(self, vmin, vmax):
         if hasattr(self.axis, 'axes') and self.axis.axes.name == 'polar':
             raise NotImplementedError('Polar axis cannot be logit scaled yet')
 
-        # what to do if a window beyond ]0, 1[ is chosen
-        if vmin <= 0.0:
-            if self.axis is not None:
-                vmin = self.axis.get_minpos()
-
-            if (vmin <= 0.0) or (not np.isfinite(vmin)):
-                raise ValueError(
-                    "Data has no values in ]0, 1[ and therefore can not be "
-                    "logit-scaled.")
-
-        # NOTE: for vmax, we should query a property similar to get_minpos, but
-        # related to the maximal, less-than-one data point. Unfortunately,
-        # get_minpos is defined very deep in the BBox and updated with data,
-        # so for now we use the trick below.
-        if vmax >= 1.0:
-            if self.axis is not None:
-                vmax = 1 - self.axis.get_minpos()
-
-            if (vmax >= 1.0) or (not np.isfinite(vmax)):
-                raise ValueError(
-                    "Data has no values in ]0, 1[ and therefore can not be "
-                    "logit-scaled.")
-
-        if vmax < vmin:
-            vmin, vmax = vmax, vmin
-
+        vmin, vmax = self.nonsingular(vmin, vmax)
         vmin = np.log10(vmin / (1 - vmin))
         vmax = np.log10(vmax / (1 - vmax))
 
@@ -2320,6 +2310,36 @@ def tick_values(self, vmin, vmax):
 
         return self.raise_if_exceeds(np.array(ticklocs))
 
+    def nonsingular(self, vmin, vmax):
+        initial_range = (1e-7, 1 - 1e-7)
+        if not np.isfinite(vmin) or not np.isfinite(vmax):
+            return initial_range  # no data plotted yet
+
+        if vmin > vmax:
+            vmin, vmax = vmax, vmin
+
+        # what to do if a window beyond ]0, 1[ is chosen
+        if self.axis is not None:
+            minpos = self.axis.get_minpos()
+            if not np.isfinite(minpos):
+                return initial_range  # again, no data plotted
+        else:
+            minpos = 1e-7  # should not occur in normal use
+
+        # NOTE: for vmax, we should query a property similar to get_minpos, but
+        # related to the maximal, less-than-one data point. Unfortunately,
+        # Bbox._minpos is defined very deep in the BBox and updated with data,
+        # so for now we use 1 - minpos as a substitute.
+
+        if vmin <= 0:
+            vmin = minpos
+        if vmax >= 1:
+            vmax = 1 - minpos
+        if vmin == vmax:
+            return 0.1 * vmin, 1 - 0.1 * vmin
+
+        return vmin, vmax
+
 
 class AutoLocator(MaxNLocator):
     def __init__(self):
diff --git a/lib/matplotlib/transforms.py b/lib/matplotlib/transforms.py
@@ -792,7 +792,7 @@ def __init__(self, points, **kwargs):
             raise ValueError('Bbox points must be of the form '
                              '"[[x0, y0], [x1, y1]]".')
         self._points = points
-        self._minpos = np.array([0.0000001, 0.0000001])
+        self._minpos = np.array([np.inf, np.inf])
         self._ignore = True
         # it is helpful in some contexts to know if the bbox is a
         # default or has been mutated; we store the orig points to
