matplotlib
diff --git a/‎lib/matplotlib/ticker.py
Lines changed: 37 additions & 24 deletions b/‎lib/matplotlib/ticker.py
Lines changed: 37 additions & 24 deletions
@@ -2792,33 +2792,46 @@ def ideal_ticks(x):
         return MaxNLocator.tick_values(self, vmin, vmax)
 
     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
-
+        standard_minpos = 1e-7
+        initial_range = (standard_minpos, 1 - standard_minpos)
+        swap_vlims = False
         if vmin > vmax:
+            swap_vlims = True
             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
+        if not np.isfinite(vmin) or not np.isfinite(vmax):
+            vmin, vmax = initial_range  # Initial range, no data plotted yet.
+        elif vmax <= 0:
+            cbook._warn_external(
+                "Data has no positive values, and therefore cannot be "
+                "logit-scaled."
+            )
+            vmin, vmax = initial_range
+        elif vmin >= 1:
+            cbook._warn_external(
+                "Data has no values smaller than one, and therefore cannot "
+                "be logit-scaled."
+            )
+            vmin, vmax = initial_range
         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
-
+            minpos = (
+                self.axis.get_minpos()
+                if self.axis is not None
+                else standard_minpos
+            )
+            if not np.isfinite(minpos):
+                minpos = standard_minpos  # This should never take effect.
+            if vmin <= 0:
+                vmin = minpos
+            # 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 vmax >= 1:
+                vmax = 1 - minpos
+            if vmin == vmax:
+                vmin, vmax = 0.1 * vmin, 1 - 0.1 * vmin
+        if swap_vlims:
+            vmin, vmax = vmax, vmin
         return vmin, vmax