Fixes matplotlib#1246. Repeated drawing handling fixed by correcting the affine/non-affine transformation of a BlendedGenericTransform.

pelson · pelson · commit b930aa763394 · 2012-09-17T11:56:21.000+01:00
diff --git a/lib/matplotlib/__init__.py b/lib/matplotlib/__init__.py
@@ -1075,6 +1075,7 @@ def tk_window_focus():
     'matplotlib.tests.test_patches',
     'matplotlib.tests.test_pickle',
     'matplotlib.tests.test_rcparams',
+    'matplotlib.tests.test_scale',
     'matplotlib.tests.test_simplification',
     'matplotlib.tests.test_spines',
     'matplotlib.tests.test_text',
diff --git a/lib/matplotlib/scale.py b/lib/matplotlib/scale.py
@@ -1,16 +1,17 @@
 from __future__ import print_function
-import textwrap
+
 import numpy as np
 from numpy import ma
-MaskedArray = ma.MaskedArray
 
-from cbook import dedent
-from ticker import NullFormatter, ScalarFormatter, LogFormatterMathtext, Formatter
-from ticker import NullLocator, LogLocator, AutoLocator, SymmetricalLogLocator, FixedLocator
-from ticker import is_decade
-from transforms import Transform, IdentityTransform
+from matplotlib.cbook import dedent
+from matplotlib.ticker import (NullFormatter, ScalarFormatter, 
+                               LogFormatterMathtext)
+from matplotlib.ticker import (NullLocator, LogLocator, AutoLocator, 
+                               SymmetricalLogLocator)
+from matplotlib.transforms import Transform, IdentityTransform
 from matplotlib import docstring
 
+
 class ScaleBase(object):
     """
     The base class for all scales.
@@ -31,15 +32,15 @@ def get_transform(self):
         Return the :class:`~matplotlib.transforms.Transform` object
         associated with this scale.
         """
-        raise NotImplementedError
+        raise NotImplementedError()
 
     def set_default_locators_and_formatters(self, axis):
         """
         Set the :class:`~matplotlib.ticker.Locator` and
         :class:`~matplotlib.ticker.Formatter` objects on the given
         axis to match this scale.
         """
-        raise NotImplementedError
+        raise NotImplementedError()
 
     def limit_range_for_scale(self, vmin, vmax, minpos):
         """
@@ -51,6 +52,7 @@ def limit_range_for_scale(self, vmin, vmax, minpos):
         """
         return vmin, vmax
 
+
 class LinearScale(ScaleBase):
     """
     The default linear scale.
@@ -90,10 +92,12 @@ def _mask_non_positives(a):
         return ma.MaskedArray(a, mask=mask)
     return a
 
+
 def _clip_non_positives(a):
     a[a <= 0.0] = 1e-300
     return a
 
+
 class LogScale(ScaleBase):
     """
     A standard logarithmic scale.  Care is taken so non-positive
@@ -116,21 +120,20 @@ class LogTransformBase(Transform):
         output_dims = 1
         is_separable = True
         has_inverse = True
-
+        
         def __init__(self, nonpos):
             Transform.__init__(self)
             if nonpos == 'mask':
                 self._handle_nonpos = _mask_non_positives
             else:
                 self._handle_nonpos = _clip_non_positives
 
-
     class Log10Transform(LogTransformBase):
         base = 10.0
 
-        def transform(self, a):
+        def transform_non_affine(self, a):
             a = self._handle_nonpos(a * 10.0)
-            if isinstance(a, MaskedArray):
+            if isinstance(a, ma.MaskedArray):
                 return ma.log10(a)
             return np.log10(a)
 
@@ -141,9 +144,10 @@ class InvertedLog10Transform(Transform):
         input_dims = 1
         output_dims = 1
         is_separable = True
+        has_inverse = True
         base = 10.0
 
-        def transform(self, a):
+        def transform_non_affine(self, a):
             return ma.power(10.0, a) / 10.0
 
         def inverted(self):
@@ -152,9 +156,9 @@ def inverted(self):
     class Log2Transform(LogTransformBase):
         base = 2.0
 
-        def transform(self, a):
+        def transform_non_affine(self, a):
             a = self._handle_nonpos(a * 2.0)
-            if isinstance(a, MaskedArray):
+            if isinstance(a, ma.MaskedArray):
                 return ma.log(a) / np.log(2)
             return np.log2(a)
 
@@ -165,9 +169,10 @@ class InvertedLog2Transform(Transform):
         input_dims = 1
         output_dims = 1
         is_separable = True
+        has_inverse = True
         base = 2.0
 
-        def transform(self, a):
+        def transform_non_affine(self, a):
             return ma.power(2.0, a) / 2.0
 
         def inverted(self):
@@ -176,9 +181,9 @@ def inverted(self):
     class NaturalLogTransform(LogTransformBase):
         base = np.e
 
-        def transform(self, a):
+        def transform_non_affine(self, a):
             a = self._handle_nonpos(a * np.e)
-            if isinstance(a, MaskedArray):
+            if isinstance(a, ma.MaskedArray):
                 return ma.log(a)
             return np.log(a)
 
@@ -189,9 +194,10 @@ class InvertedNaturalLogTransform(Transform):
         input_dims = 1
         output_dims = 1
         is_separable = True
+        has_inverse = True
         base = np.e
 
-        def transform(self, a):
+        def transform_non_affine(self, a):
             return ma.power(np.e, a) / np.e
 
         def inverted(self):
@@ -201,7 +207,8 @@ class LogTransform(Transform):
         input_dims = 1
         output_dims = 1
         is_separable = True
-
+        has_inverse = True
+        
         def __init__(self, base, nonpos):
             Transform.__init__(self)
             self.base = base
@@ -210,9 +217,9 @@ def __init__(self, base, nonpos):
             else:
                 self._handle_nonpos = _clip_non_positives
 
-        def transform(self, a):
+        def transform_non_affine(self, a):
             a = self._handle_nonpos(a * self.base)
-            if isinstance(a, MaskedArray):
+            if isinstance(a, ma.MaskedArray):
                 return ma.log(a) / np.log(self.base)
             return np.log(a) / np.log(self.base)
 
@@ -223,18 +230,18 @@ class InvertedLogTransform(Transform):
         input_dims = 1
         output_dims = 1
         is_separable = True
-
+        has_inverse = True
+        
         def __init__(self, base):
             Transform.__init__(self)
             self.base = base
 
-        def transform(self, a):
+        def transform_non_affine(self, a):
             return ma.power(self.base, a) / self.base
 
         def inverted(self):
             return LogScale.LogTransform(self.base)
-
-
+                
     def __init__(self, axis, **kwargs):
         """
         *basex*/*basey*:
@@ -318,7 +325,7 @@ class SymmetricalLogTransform(Transform):
         output_dims = 1
         is_separable = True
         has_inverse = True
-
+        
         def __init__(self, base, linthresh, linscale):
             Transform.__init__(self)
             self.base = base
@@ -327,7 +334,7 @@ def __init__(self, base, linthresh, linscale):
             self._linscale_adj = (linscale / (1.0 - self.base ** -1))
             self._log_base = np.log(base)
 
-        def transform(self, a):
+        def transform_non_affine(self, a):
             sign = np.sign(a)
             masked = ma.masked_inside(a, -self.linthresh, self.linthresh, copy=False)
             log = sign * self.linthresh * (
@@ -346,7 +353,8 @@ class InvertedSymmetricalLogTransform(Transform):
         input_dims = 1
         output_dims = 1
         is_separable = True
-
+        has_inverse = True
+        
         def __init__(self, base, linthresh, linscale):
             Transform.__init__(self)
             symlog = SymmetricalLogScale.SymmetricalLogTransform(base, linthresh, linscale)
@@ -356,7 +364,7 @@ def __init__(self, base, linthresh, linscale):
             self.linscale = linscale
             self._linscale_adj = (linscale / (1.0 - self.base ** -1))
 
-        def transform(self, a):
+        def transform_non_affine(self, a):
             sign = np.sign(a)
             masked = ma.masked_inside(a, -self.invlinthresh, self.invlinthresh, copy=False)
             exp = sign * self.linthresh * (
@@ -436,17 +444,19 @@ def get_transform(self):
         return self._transform
 
 
-
 _scale_mapping = {
     'linear'            : LinearScale,
     'log'               : LogScale,
     'symlog'            : SymmetricalLogScale
     }
+
+
 def get_scale_names():
     names = _scale_mapping.keys()
     names.sort()
     return names
 
+
 def scale_factory(scale, axis, **kwargs):
     """
     Return a scale class by name.
@@ -464,6 +474,7 @@ def scale_factory(scale, axis, **kwargs):
 scale_factory.__doc__ = dedent(scale_factory.__doc__) % \
     {'names': " | ".join(get_scale_names())}
 
+
 def register_scale(scale_class):
     """
     Register a new kind of scale.
@@ -472,6 +483,7 @@ def register_scale(scale_class):
     """
     _scale_mapping[scale_class.name] = scale_class
 
+
 def get_scale_docs():
     """
     Helper function for generating docstrings related to scales.
@@ -488,6 +500,7 @@ def get_scale_docs():
         docs.append("")
     return "\n".join(docs)
 
+
 docstring.interpd.update(
     scale = ' | '.join([repr(x) for x in get_scale_names()]),
     scale_docs = get_scale_docs().strip(),
diff --git a/lib/matplotlib/tests/baseline_images/test_scale/log_scales.pdf b/lib/matplotlib/tests/baseline_images/test_scale/log_scales.pdf
diff --git a/lib/matplotlib/tests/baseline_images/test_scale/log_scales.svg b/lib/matplotlib/tests/baseline_images/test_scale/log_scales.svg
@@ -30,12 +30,13 @@ z
    </g>
    <g id="line2d_1">
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-M545.841 372
-L545.841 -1" style="fill:none;stroke:#0000ff;"/>
+M393.006 388.8
+L393.006 43.2" style="fill:none;stroke:#0000ff;"/>
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    <g id="line2d_2">
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+M315.491 66.9305
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    <g id="matplotlib.axis_1">
     <g id="xtick_1">
diff --git a/lib/matplotlib/tests/test_scale.py b/lib/matplotlib/tests/test_scale.py
@@ -5,9 +5,8 @@
 
 
 @image_comparison(baseline_images=['log_scales'])
-def test_complete():
+def test_log_scales():
     ax = plt.subplot(122, yscale='log', xscale='symlog')
     
     ax.axvline(24.1)
-    ax.axhline(24.1)
-    plt.show()
+    ax.axhline(24.1)
diff --git a/lib/matplotlib/transforms.py b/lib/matplotlib/transforms.py
@@ -1943,18 +1943,18 @@ def transform_non_affine(self, points):
         y = self._y
 
         if x == y and x.input_dims == 2:
-            return x.transform(points)
+            return x.transform_non_affine(points)
 
         if x.input_dims == 2:
-            x_points = x.transform(points)[:, 0:1]
+            x_points = x.transform_non_affine(points)[:, 0:1]
         else:
-            x_points = x.transform(points[:, 0])
+            x_points = x.transform_non_affine(points[:, 0])
             x_points = x_points.reshape((len(x_points), 1))
 
         if y.input_dims == 2:
-            y_points = y.transform(points)[:, 1:]
+            y_points = y.transform_non_affine(points)[:, 1:]
         else:
-            y_points = y.transform(points[:, 1])
+            y_points = y.transform_non_affine(points[:, 1])
             y_points = y_points.reshape((len(y_points), 1))
 
         if isinstance(x_points, MaskedArray) or isinstance(y_points, MaskedArray):
@@ -1969,19 +1969,16 @@ def inverted(self):
 
     def get_affine(self):
         if self._invalid or self._affine is None:
-            if self._x.is_affine and self._y.is_affine:
-                if self._x == self._y:
-                    self._affine = self._x.get_affine()
-                else:
-                    x_mtx = self._x.get_affine().get_matrix()
-                    y_mtx = self._y.get_affine().get_matrix()
-                    # This works because we already know the transforms are
-                    # separable, though normally one would want to set b and
-                    # c to zero.
-                    mtx = np.vstack((x_mtx[0], y_mtx[1], [0.0, 0.0, 1.0]))
-                    self._affine = Affine2D(mtx)
+            if self._x == self._y:
+                self._affine = self._x.get_affine()
             else:
-                self._affine = IdentityTransform()
+                x_mtx = self._x.get_affine().get_matrix()
+                y_mtx = self._y.get_affine().get_matrix()
+                # This works because we already know the transforms are
+                # separable, though normally one would want to set b and
+                # c to zero.
+                mtx = np.vstack((x_mtx[0], y_mtx[1], [0.0, 0.0, 1.0]))
+                self._affine = Affine2D(mtx)
             self._invalid = 0
         return self._affine
     get_affine.__doc__ = Transform.get_affine.__doc__
