matplotlib
diff --git a/‎CHANGELOG
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diff --git a/‎lib/matplotlib/colors.py
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+115Lines changed: 115 additions & 0 deletions b/‎lib/matplotlib/colors.py
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diff --git a/‎lib/matplotlib/tests/test_colors.py
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+44-1Lines changed: 44 additions & 1 deletion b/‎lib/matplotlib/tests/test_colors.py
Copy file name to clipboardExpand all lines: lib/matplotlib/tests/test_colors.py
+44-1Lines changed: 44 additions & 1 deletion
@@ -1,3 +1,7 @@
+2012-11-13 Add a symmetric log normalization class to colors.py.
+           Also added some tests for the normalization class.
+           Till Stensitzki
+
 2012-10-05 Add support for saving animations as animated GIFs. - JVDP
 
 2012-08-11 Fix path-closing bug in patches.Polygon, so that regardless
 
@@ -999,6 +999,121 @@ def autoscale_None(self, A):
             self.vmin = ma.min(A)
         if self.vmax is None:
             self.vmax = ma.max(A)
+            
+
+class SymLogNorm(Normalize):
+    """
+    The symmetrical logarithmic scale is logarithmic in both the
+    positive and negative directions from the origin.
+
+    Since the values close to zero tend toward infinity, there is a
+    need to have a range around zero that is linear.  The parameter
+    *linthresh* allows the user to specify the size of this range
+    (-*linthresh*, *linthresh*).
+    """
+    def __init__(self,  linthresh, linscale=1.0,
+                 vmin=None, vmax=None, clip=False):
+        """
+        *linthresh*:
+        The range within which the plot is linear (to
+        avoid having the plot go to infinity around zero).
+
+        *linscale*:
+        This allows the linear range (-*linthresh* to *linthresh*)
+        to be stretched relative to the logarithmic range.  Its
+        value is the number of decades to use for each half of the
+        linear range.  For example, when *linscale* == 1.0 (the
+        default), the space used for the positive and negative
+        halves of the linear range will be equal to one decade in
+        the logarithmic range. Defaults to 1.
+        """
+        Normalize.__init__(self, vmin, vmax, clip)
+        self.linthresh = linthresh
+        self._linscale_adj = (linscale / (1.0 - np.e ** -1))
+
+    def __call__(self, value, clip=None):
+        if clip is None:
+            clip = self.clip
+
+        result, is_scalar = self.process_value(value)
+        self.autoscale_None(result)
+        vmin, vmax = self.vmin, self.vmax
+        
+        if vmin > vmax:
+            raise ValueError("minvalue must be less than or equal to maxvalue")
+        elif vmin == vmax:
+            result.fill(0)
+        else:
+            if clip:
+                mask = ma.getmask(result)
+                result = ma.array(np.clip(result.filled(vmax), vmin, vmax),
+                                mask=mask)
+            # in-place equivalent of above can be much faster
+            resdat = self._transform(result.data)
+            resdat -= self._lower
+            resdat /= (self._upper - self._lower)
+
+        if is_scalar:
+            result = result[0]
+        return result
+    
+    def _transform(self, a):        
+        """
+        Inplace transformation.
+        """
+        masked = np.abs(a) > self.linthresh
+        sign = np.sign(a[masked])
+        log = (self._linscale_adj + np.log(np.abs(a[masked]) / self.linthresh))
+        log *= sign * self.linthresh
+        a[masked] = log
+        a[~masked] *= self._linscale_adj
+        return a
+        
+    def _inv_transform(self, a):
+        """
+        Inverse inplace Transformation.
+        """
+        masked = np.abs(a) > (self.linthresh * self._linscale_adj)
+        sign = np.sign(a[masked])
+        exp = np.exp(sign * a[masked] / self.linthresh - self._linscale_adj)
+        exp *= sign * self.linthresh
+        a[masked] = exp
+        a[~masked] /= self._linscale_adj
+        return a
+        
+    def _transform_vmin_vmax(self):
+        """
+        Calculates vmin and vmax in the transformed system.
+        """
+        vmin, vmax = self.vmin, self.vmax
+        arr = np.array([vmax, vmin])
+        self._upper, self._lower  = self._transform(arr)
+
+
+    def inverse(self, value):
+        if not self.scaled():
+            raise ValueError("Not invertible until scaled")
+        val = ma.asarray(value)
+        val = val * (self._upper - self._lower) + self._lower
+        return self._inv_transform(val)
+        
+    def autoscale(self, A):
+        """
+        Set *vmin*, *vmax* to min, max of *A*.
+        """        
+        self.vmin = ma.min(A)
+        self.vmax = ma.max(A)
+        self._transform_vmin_vmax()
+ 
+    def autoscale_None(self, A):
+        """ autoscale only None-valued vmin or vmax """
+        if self.vmin is not None and self.vmax is not None:
+            pass
+        if self.vmin is None:
+            self.vmin = ma.min(A)
+        if self.vmax is None:
+            self.vmax = ma.max(A)
+        self._transform_vmin_vmax()
 
 
 class BoundaryNorm(Normalize):
 
@@ -4,7 +4,7 @@
 
 from __future__ import print_function
 import numpy as np
-from numpy.testing.utils import assert_array_equal
+from numpy.testing.utils import assert_array_equal, assert_array_almost_equal
 import matplotlib.colors as mcolors
 import matplotlib.cm as cm
 
@@ -46,3 +46,46 @@ def test_LogNorm():
     ln = mcolors.LogNorm(clip=True, vmax=5)
     assert_array_equal(ln([1, 6]), [0, 1.0])
 
+def test_Normalize():
+    norm = mcolors.Normalize()
+    vals = np.arange(-10, 10, 1, dtype=np.float)
+    _inverse_tester(norm, vals)
+    _scalar_tester(norm, vals)
+    _mask_tester(norm, vals)
+
+
+def test_SymLogNorm():
+    """
+    Test SymLogNorm behavior
+    """
+    norm = mcolors.SymLogNorm(3, vmax=5, linscale=1.2)
+    vals = np.array([-30, -1, 2, 6], dtype=np.float)
+    normed_vals = norm(vals)
+    expected = [ 0., 0.53980074, 0.826991, 1.02758204]
+    assert_array_almost_equal(normed_vals, expected)
+    _inverse_tester(norm, vals)
+    _scalar_tester(norm, vals)
+    _mask_tester(norm, vals)
+
+
+def _inverse_tester(norm_instance, vals):
+    """
+    Checks if the inverse of the given normalization is working.
+    """
+    assert_array_almost_equal(norm_instance.inverse(norm_instance(vals)), vals)
+
+def _scalar_tester(norm_instance, vals):
+    """
+    Checks if scalars and arrays are handled the same way.
+    Tests only for float.
+    """
+    scalar_result = [norm_instance(float(v)) for v in vals]
+    assert_array_almost_equal(scalar_result, norm_instance(vals))
+
+def _mask_tester(norm_instance, vals):
+    """
+    Checks mask handling
+    """
+    masked_array = np.ma.array(vals)
+    masked_array[0] = np.ma.masked
+    assert_array_equal(masked_array.mask, norm_instance(masked_array).mask)