Simplify units.Registry.get_converter.

anntzer · anntzer · commit a611314fb51a · 2019-02-27T18:39:30.000+01:00
The caching of the converter was disabled 10 years ago (be73ef4) and is unlikely to ever work as the correct converter for ndarrays depends on the type of the contents, not on the container, so just drop the cache-related code. When a masked array is passed, it's OK to just get the first underlying data, even if it is masked (as it should have the same type as the unmasked entries), for the purpose of getting the converter.
diff --git a/lib/matplotlib/units.py b/lib/matplotlib/units.py
@@ -55,9 +55,10 @@ class ConversionError(TypeError):
 
 class AxisInfo(object):
     """
-    Information to support default axis labeling, tick labeling, and
-    default limits. An instance of this class must be returned by
-    :meth:`ConversionInterface.axisinfo`.
+    Information to support default axis labeling, tick labeling, and limits.
+
+    An instance of this class must be returned by
+    `ConversionInterface.axisinfo`.
     """
     def __init__(self, majloc=None, minloc=None,
                  majfmt=None, minfmt=None, label=None,
@@ -96,8 +97,7 @@ class ConversionInterface(object):
     @staticmethod
     def axisinfo(unit, axis):
         """
-        Return an `~units.AxisInfo` instance for the axis with the
-        specified units.
+        Return an `~units.AxisInfo` for the axis with the specified units.
         """
         return None
 
@@ -112,19 +112,19 @@ def default_units(x, axis):
     def convert(obj, unit, axis):
         """
         Convert *obj* using *unit* for the specified *axis*.
-        If *obj* is a sequence, return the converted sequence.
-        The output must be a sequence of scalars that can be used by the numpy
-        array layer.
+
+        If *obj* is a sequence, return the converted sequence.  The output must
+        be a sequence of scalars that can be used by the numpy array layer.
         """
         return obj
 
     @staticmethod
     def is_numlike(x):
         """
-        The Matplotlib datalim, autoscaling, locators etc work with
-        scalars which are the units converted to floats given the
-        current unit.  The converter may be passed these floats, or
-        arrays of them, even when units are set.
+        The Matplotlib datalim, autoscaling, locators etc work with scalars
+        which are the units converted to floats given the current unit.  The
+        converter may be passed these floats, or arrays of them, even when
+        units are set.
         """
         if np.iterable(x):
             for thisx in x:
@@ -134,73 +134,28 @@ def is_numlike(x):
 
 
 class Registry(dict):
-    """
-    A register that maps types to conversion interfaces.
-    """
-    def __init__(self):
-        dict.__init__(self)
-        self._cached = {}
+    """Register types with conversion interface."""
 
     def get_converter(self, x):
-        """
-        Get the converter for data that has the same type as *x*. If no
-        converters are registered for *x*, returns ``None``.
-        """
-
-        if not len(self):
-            return None  # nothing registered
-        # DISABLED idx = id(x)
-        # DISABLED cached = self._cached.get(idx)
-        # DISABLED if cached is not None: return cached
-
-        converter = None
-        classx = getattr(x, '__class__', None)
-
-        if classx is not None:
-            converter = self.get(classx)
-
-        if converter is None and hasattr(x, "values"):
-            # this unpacks pandas series or dataframes...
-            x = x.values
-
-        # If x is an array, look inside the array for data with units
+        """Get the converter interface instance for *x*, or None."""
         if isinstance(x, np.ndarray):
-            # If there are no elements in x, infer the units from its dtype
-            if not x.size:
-                return self.get_converter(np.array([0], dtype=x.dtype))
-            xravel = x.ravel()
+            # In case x in a masked array, access the underlying data (only its
+            # type matters).  If x is a regular ndarray, getdata() just returns
+            # the array itself.
+            x = np.ma.getdata(x).ravel()
+        try:
+            return self[type(x)]
+        except KeyError:
             try:
-                # pass the first value of x that is not masked back to
-                # get_converter
-                if not np.all(xravel.mask):
-                    # Get first non-masked item
-                    converter = self.get_converter(
-                        xravel[np.argmin(xravel.mask)])
-                    return converter
-            except AttributeError:
-                # not a masked_array
-                # Make sure we don't recurse forever -- it's possible for
-                # ndarray subclasses to continue to return subclasses and
-                # not ever return a non-subclass for a single element.
-                next_item = xravel[0]
-                if (not isinstance(next_item, np.ndarray) or
-                        next_item.shape != x.shape):
-                    converter = self.get_converter(next_item)
-                return converter
-
-        # If we haven't found a converter yet, try to get the first element
-        if converter is None:
-            try:
-                thisx = cbook.safe_first_element(x)
+                first = safe_first_element(x)
             except (TypeError, StopIteration):
                 pass
             else:
-                if classx and classx != getattr(thisx, '__class__', None):
-                    converter = self.get_converter(thisx)
-                    return converter
-
-        # DISABLED self._cached[idx] = converter
-        return converter
+                # Avoid infinite recursion for pathological iterables for which
+                # indexing returns instances of the same iterable class.
+                if type(first) is not type(x):
+                    return self.get_converter(first)
+        return None
 
 
 registry = Registry()
