SymmetricalLogTransform and inverse return same type as input.

efiring · efiring · commit b218e7c6d972 · 2019-05-21T15:01:45.000-10:00
Their transform_non_affine methods have been updated following
the strategy used in LogTransform so that they work correctly
with masked arrays or plain ndarrays, but do not require any
explicit np.ma functions.
diff --git a/lib/matplotlib/scale.py b/lib/matplotlib/scale.py
@@ -485,18 +485,14 @@ def __init__(self, base, linthresh, linscale):
         self._log_base = np.log(base)
 
     def transform_non_affine(self, a):
-        sign = np.sign(a)
-        masked = ma.masked_inside(a,
-                                  -self.linthresh,
-                                  self.linthresh,
-                                  copy=True)
-        log = sign * self.linthresh * (
-            self._linscale_adj +
-            ma.log(np.abs(masked) / self.linthresh) / self._log_base)
-        if masked.mask.any():
-            return ma.where(masked.mask, a * self._linscale_adj, log)
-        else:
-            return log
+        abs_a = np.abs(a)
+        with np.errstate(divide="ignore", invalid="ignore"):
+            out = np.sign(a) * self.linthresh * (
+                self._linscale_adj +
+                np.log(abs_a / self.linthresh) / self._log_base)
+            inside = abs_a <= self.linthresh
+        out[inside] = a[inside] * self._linscale_adj
+        return out
 
     def inverted(self):
         return InvertedSymmetricalLogTransform(self.base, self.linthresh,
@@ -519,16 +515,13 @@ def __init__(self, base, linthresh, linscale):
         self._linscale_adj = (linscale / (1.0 - self.base ** -1))
 
     def transform_non_affine(self, a):
-        sign = np.sign(a)
-        masked = ma.masked_inside(a, -self.invlinthresh,
-                                  self.invlinthresh, copy=True)
-        exp = sign * self.linthresh * (
-            ma.power(self.base, (sign * (masked / self.linthresh))
-            - self._linscale_adj))
-        if masked.mask.any():
-            return ma.where(masked.mask, a / self._linscale_adj, exp)
-        else:
-            return exp
+        abs_a = np.abs(a)
+        with np.errstate(divide="ignore", invalid="ignore"):
+            out = np.sign(a) * self.linthresh * (
+                np.power(self.base, abs_a / self.linthresh - self._linscale_adj))
+            inside = abs_a <= self.invlinthresh
+        out[inside] = a[inside] / self._linscale_adj
+        return out
 
     def inverted(self):
         return SymmetricalLogTransform(self.base,
