Various fixes suggested by Eric

Author: Fabio Zanini

lib/matplotlib/scale.py

@@ -86,8 +86,8 @@ def get_transform(self):
 
 def _mask_non_positives(a):
     """
-    Return a Numpy masked array where all non-positive values are
-    replaced with NaNs.  If there are no non-positive values, the
+    Return a Numpy array where all non-positive values are
+    replaced with NaNs. If there are no non-positive values, the
     original array is returned.
     """
     mask = a <= 0.0
@@ -97,6 +97,7 @@ def _mask_non_positives(a):
 
 
 def _clip_non_positives(a):
+    a = np.array(a, float)
     a[a <= 0.0] = 1e-300
     return a
 
@@ -120,8 +121,6 @@ class Log10Transform(LogTransformBase):
 
     def transform_non_affine(self, a):
         a = self._handle_nonpos(a * 10.0)
-        if isinstance(a, ma.MaskedArray):
-            return ma.log10(a)
         return np.log10(a)
 
     def inverted(self):
@@ -147,8 +146,6 @@ class Log2Transform(LogTransformBase):
 
     def transform_non_affine(self, a):
         a = self._handle_nonpos(a * 2.0)
-        if isinstance(a, ma.MaskedArray):
-            return ma.log(a) / np.log(2)
         return np.log2(a)
 
     def inverted(self):
@@ -174,8 +171,6 @@ class NaturalLogTransform(LogTransformBase):
 
     def transform_non_affine(self, a):
         a = self._handle_nonpos(a * np.e)
-        if isinstance(a, ma.MaskedArray):
-            return ma.log(a)
         return np.log(a)
 
     def inverted(self):
@@ -212,8 +207,6 @@ def __init__(self, base, nonpos):
 
     def transform_non_affine(self, a):
         a = self._handle_nonpos(a * self.base)
-        if isinstance(a, ma.MaskedArray):
-            return ma.log(a) / np.log(self.base)
         return np.log(a) / np.log(self.base)
 
     def inverted(self):
@@ -480,18 +473,18 @@ def get_transform(self):
 
 def _mask_non_logit(a):
     """
-    Return a Numpy masked array where all values outside ]0, 1[ are
-    masked. If all values are inside ]0, 1[, the original array is
-    returned.
+    Return a Numpy array where all values outside ]0, 1[ are
+    replaced with NaNs. If all values are inside ]0, 1[, the original
+    array is returned.
     """
-    a = a.copy()
     mask = (a <= 0.0) | (a >= 1.0)
-    a[mask] = np.nan
+    if mask.any():
+        return np.where(mask, np.nan, a)
     return a
 
 
 def _clip_non_logit(a):
-    a = a.copy()
+    a = np.array(a, float)
     a[a <= 0.0] = 1e-300
     a[a >= 1.0] = 1 - 1e-300
     return a
@@ -514,8 +507,6 @@ def __init__(self, nonpos):
     def transform_non_affine(self, a):
         """logit transform (base 10), masked or clipped"""
         a = self._handle_nonpos(a)
-        if isinstance(a, ma.MaskedArray):
-            return ma.log10(1.0 * a / (1.0 - a))
         return np.log10(1.0 * a / (1.0 - a))
 
     def inverted(self):
@@ -574,6 +565,9 @@ def set_default_locators_and_formatters(self, axis):
         axis.set_minor_formatter(LogitFormatter())
 
     def limit_range_for_scale(self, vmin, vmax, minpos):
+        """
+        Limit the domain to values between 0 and 1 (excluded).
+        """
         return (vmin <= 0 and minpos or vmin,
                 vmax >= 1 and (1 - minpos) or vmax)
 

lib/matplotlib/tests/test_scale.py

@@ -19,7 +19,7 @@ def test_log_scales():
 def test_logit_scales():
     ax = plt.subplot(111, xscale='logit')
 
-    # Typical exctinction curve for logit
+    # Typical extinction curve for logit
     x = np.array([0.001, 0.003, 0.01, 0.03, 0.1, 0.2, 0.3, 0.4, 0.5,
                   0.6, 0.7, 0.8, 0.9, 0.97, 0.99, 0.997, 0.999])
     y = 1.0 / x

lib/matplotlib/ticker.py

@@ -812,14 +812,17 @@ class LogitFormatter(Formatter):
     def __call__(self, x, pos=None):
         s = ''
         if 0.01 <= x <= 0.99:
-            if x in [.01, 0.1, 0.5, 0.9, 0.99]:
-                s = '{:.2f}'.format(x)
+            s = '{:.2f}'.format(x)
         elif x < 0.01:
             if is_decade(x):
-                s = '$10^{%.0f}$' % np.log10(x)
-        elif x > 0.99:
+                s = '$10^{{{:.0f}}}$'.format(np.log10(x))
+            else:
+                s = '${:.5f}$'.format(x)
+        else:  # x > 0.99
             if is_decade(1-x):
-                s = '$1-10^{%.0f}$' % np.log10(1-x)
+                s = '$1-10^{{{:.0f}}}$'.format(np.log10(1-x))
+            else:
+                s = '$1-{:.5f}$'.format(1-x)
         return s
 
     def format_data_short(self, value):