Implements a new version of symlog that is smooth around linthresh contributed by ssyr. Also includes a cleaner implementation of the symlog ticker. Closes matplotlib#396.

Author: mdboom

doc/users/whats_new.rst

@@ -181,7 +181,8 @@ Other improvements
 * :meth:`~matplotlib.pyplot.scatter` now accepts empty inputs.
 
 * The behavior for 'symlog' scale has been fixed, but this may result
-  in some minor changes to existing plots.
+  in some minor changes to existing plots.  This work was refined by
+  ssyr.
 
 * Peter Butterworth added named figure support to
   :func:`~matplotlib.pyplot.figure`.

examples/pylab_examples/symlog_demo.py

@@ -21,7 +21,7 @@
 subplot(313)
 plot(x, np.sin(x / 3.0))
 xscale('symlog')
-yscale('symlog')
+yscale('symlog', linthreshy=0.015)
 grid(True)
 ylabel('symlog both')
 

lib/matplotlib/scale.py

@@ -312,58 +312,56 @@ class SymmetricalLogScale(ScaleBase):
     name = 'symlog'
 
     class SymmetricalLogTransform(Transform):
+            input_dims = 1
+            output_dims = 1
+            is_separable = True
+
+            def __init__(self, base, linthresh):
+                Transform.__init__(self)
+                self.base = base
+                self.linthresh = linthresh
+                self._log_base = np.log(base)
+                self._linadjust = (np.log(linthresh) / self._log_base) / linthresh
+
+            def transform(self, a):
+                a = np.asarray(a)
+                sign = np.sign(a)
+                masked = ma.masked_inside(a, -self.linthresh, self.linthresh, copy=False)
+                log = sign * self.linthresh * (1 + ma.log(np.abs(masked) / self.linthresh))
+                if masked.mask.any():
+                    return np.asarray(ma.where(masked.mask,
+                                                a,
+                                                log))
+                else:
+                    return np.asarray(log)
+
+            def inverted(self):
+                return SymmetricalLogScale.InvertedSymmetricalLogTransform(self.base, self.linthresh)
+
+    class InvertedSymmetricalLogTransform(Transform):
         input_dims = 1
         output_dims = 1
         is_separable = True
 
         def __init__(self, base, linthresh):
             Transform.__init__(self)
             self.base = base
-            self.linthresh = abs(linthresh)
+            self.linthresh = linthresh
             self._log_base = np.log(base)
-            logb_linthresh = np.log(linthresh) / self._log_base
-            self._linadjust = 1.0 - logb_linthresh
-            self._linscale = 1.0 / linthresh
+            self._log_linthresh = np.log(linthresh) / self._log_base
+            self._linadjust = linthresh / (np.log(linthresh) / self._log_base)
 
         def transform(self, a):
             a = np.asarray(a)
             sign = np.sign(a)
             masked = ma.masked_inside(a, -self.linthresh, self.linthresh, copy=False)
+            exp = sign * self.linthresh * ma.exp(sign * masked / self.linthresh - 1)
             if masked.mask.any():
-                log = sign * (ma.log(np.abs(masked)) / self._log_base + self._linadjust)
-                return np.asarray(ma.where(masked.mask, a * self._linscale, log))
+                return np.asarray(ma.where(masked.mask,
+                                            a,
+                                            exp))
             else:
-                return sign * (np.log(np.abs(a)) / self._log_base + self._linadjust)
-
-        def inverted(self):
-            return SymmetricalLogScale.InvertedSymmetricalLogTransform(
-                self.base, self.linthresh)
-
-    class InvertedSymmetricalLogTransform(Transform):
-        input_dims = 1
-        output_dims = 1
-        is_separable = True
-
-        def __init__(self, base, linthresh):
-            Transform.__init__(self)
-            self.base = base
-            self.linthresh = linthresh
-            log_base = np.log(base)
-            logb_linthresh = np.log(linthresh) / log_base
-            self._linadjust = 1.0 - logb_linthresh
-
-        def transform(self, a):
-            a = np.asarray(a)
-            sign = np.sign(a)
-            masked = ma.masked_inside(a, -1.0, 1.0, copy=False)
-            result = np.where((a >= -1.0) & (a <= 1.0),
-                              a * self.linthresh,
-                              sign * np.power(self.base, np.abs(a - sign * self._linadjust)))
-            return result
-
-        def inverted(self):
-            return SymmetricalLogScale.SymmetricalLogTransform(
-                self.base, self.linthresh)
+                return np.asarray(exp)
 
     def __init__(self, axis, **kwargs):
         """