Added an unmodified copy of SCons to third_party.

scons-2.0.1/engine/SCons/Memoize.py

+#
+# Copyright (c) 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010 The SCons Foundation
+#
+# Permission is hereby granted, free of charge, to any person obtaining
+# a copy of this software and associated documentation files (the
+# "Software"), to deal in the Software without restriction, including
+# without limitation the rights to use, copy, modify, merge, publish,
+# distribute, sublicense, and/or sell copies of the Software, and to
+# permit persons to whom the Software is furnished to do so, subject to
+# the following conditions:
+#
+# The above copyright notice and this permission notice shall be included
+# in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY
+# KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+# LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+# OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+# WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+#
+
+__revision__ = "src/engine/SCons/Memoize.py 5134 2010/08/16 23:02:40 bdeegan"
+
+__doc__ = """Memoizer
+
+A metaclass implementation to count hits and misses of the computed
+values that various methods cache in memory.
+
+Use of this modules assumes that wrapped methods be coded to cache their
+values in a consistent way.  Here is an example of wrapping a method
+that returns a computed value, with no input parameters:
+
+    memoizer_counters = []                                      # Memoization
+
+    memoizer_counters.append(SCons.Memoize.CountValue('foo'))   # Memoization
+
+    def foo(self):
+
+        try:                                                    # Memoization
+            return self._memo['foo']                            # Memoization
+        except KeyError:                                        # Memoization
+            pass                                                # Memoization
+
+        result = self.compute_foo_value()
+
+        self._memo['foo'] = result                              # Memoization
+
+        return result
+
+Here is an example of wrapping a method that will return different values
+based on one or more input arguments:
+
+    def _bar_key(self, argument):                               # Memoization
+        return argument                                         # Memoization
+
+    memoizer_counters.append(SCons.Memoize.CountDict('bar', _bar_key)) # Memoization
+
+    def bar(self, argument):
+
+        memo_key = argument                                     # Memoization
+        try:                                                    # Memoization
+            memo_dict = self._memo['bar']                       # Memoization
+        except KeyError:                                        # Memoization
+            memo_dict = {}                                      # Memoization
+            self._memo['dict'] = memo_dict                      # Memoization
+        else:                                                   # Memoization
+            try:                                                # Memoization
+                return memo_dict[memo_key]                      # Memoization
+            except KeyError:                                    # Memoization
+                pass                                            # Memoization
+
+        result = self.compute_bar_value(argument)
+
+        memo_dict[memo_key] = result                            # Memoization
+
+        return result
+
+At one point we avoided replicating this sort of logic in all the methods
+by putting it right into this module, but we've moved away from that at
+present (see the "Historical Note," below.).
+
+Deciding what to cache is tricky, because different configurations
+can have radically different performance tradeoffs, and because the
+tradeoffs involved are often so non-obvious.  Consequently, deciding
+whether or not to cache a given method will likely be more of an art than
+a science, but should still be based on available data from this module.
+Here are some VERY GENERAL guidelines about deciding whether or not to
+cache return values from a method that's being called a lot:
+
+    --  The first question to ask is, "Can we change the calling code
+        so this method isn't called so often?"  Sometimes this can be
+        done by changing the algorithm.  Sometimes the *caller* should
+        be memoized, not the method you're looking at.
+
+    --  The memoized function should be timed with multiple configurations
+        to make sure it doesn't inadvertently slow down some other
+        configuration.
+
+    --  When memoizing values based on a dictionary key composed of
+        input arguments, you don't need to use all of the arguments
+        if some of them don't affect the return values.
+
+Historical Note:  The initial Memoizer implementation actually handled
+the caching of values for the wrapped methods, based on a set of generic
+algorithms for computing hashable values based on the method's arguments.
+This collected caching logic nicely, but had two drawbacks:
+
+    Running arguments through a generic key-conversion mechanism is slower
+    (and less flexible) than just coding these things directly.  Since the
+    methods that need memoized values are generally performance-critical,
+    slowing them down in order to collect the logic isn't the right
+    tradeoff.
+
+    Use of the memoizer really obscured what was being called, because
+    all the memoized methods were wrapped with re-used generic methods.
+    This made it more difficult, for example, to use the Python profiler
+    to figure out how to optimize the underlying methods.
+"""
+
+import types
+
+# A flag controlling whether or not we actually use memoization.
+use_memoizer = None
+
+CounterList = []
+
+class Counter(object):
+    """
+    Base class for counting memoization hits and misses.
+
+    We expect that the metaclass initialization will have filled in
+    the .name attribute that represents the name of the function
+    being counted.
+    """
+    def __init__(self, method_name):
+        """
+        """
+        self.method_name = method_name
+        self.hit = 0
+        self.miss = 0
+        CounterList.append(self)
+    def display(self):
+        fmt = "    %7d hits %7d misses    %s()"
+        print fmt % (self.hit, self.miss, self.name)
+    def __cmp__(self, other):
+        try:
+            return cmp(self.name, other.name)
+        except AttributeError:
+            return 0
+
+class CountValue(Counter):
+    """
+    A counter class for simple, atomic memoized values.
+
+    A CountValue object should be instantiated in a class for each of
+    the class's methods that memoizes its return value by simply storing
+    the return value in its _memo dictionary.
+
+    We expect that the metaclass initialization will fill in the
+    .underlying_method attribute with the method that we're wrapping.
+    We then call the underlying_method method after counting whether
+    its memoized value has already been set (a hit) or not (a miss).
+    """
+    def __call__(self, *args, **kw):
+        obj = args[0]
+        if self.method_name in obj._memo:
+            self.hit = self.hit + 1
+        else:
+            self.miss = self.miss + 1
+        return self.underlying_method(*args, **kw)
+
+class CountDict(Counter):
+    """
+    A counter class for memoized values stored in a dictionary, with
+    keys based on the method's input arguments.
+
+    A CountDict object is instantiated in a class for each of the
+    class's methods that memoizes its return value in a dictionary,
+    indexed by some key that can be computed from one or more of
+    its input arguments.
+
+    We expect that the metaclass initialization will fill in the
+    .underlying_method attribute with the method that we're wrapping.
+    We then call the underlying_method method after counting whether the
+    computed key value is already present in the memoization dictionary
+    (a hit) or not (a miss).
+    """
+    def __init__(self, method_name, keymaker):
+        """
+        """
+        Counter.__init__(self, method_name)
+        self.keymaker = keymaker
+    def __call__(self, *args, **kw):
+        obj = args[0]
+        try:
+            memo_dict = obj._memo[self.method_name]
+        except KeyError:
+            self.miss = self.miss + 1
+        else:
+            key = self.keymaker(*args, **kw)
+            if key in memo_dict:
+                self.hit = self.hit + 1
+            else:
+                self.miss = self.miss + 1
+        return self.underlying_method(*args, **kw)
+
+class Memoizer(object):
+    """Object which performs caching of method calls for its 'primary'
+    instance."""
+
+    def __init__(self):
+        pass
+
+def Dump(title=None):
+    if title:
+        print title
+    CounterList.sort()
+    for counter in CounterList:
+        counter.display()
+
+class Memoized_Metaclass(type):
+    def __init__(cls, name, bases, cls_dict):
+        super(Memoized_Metaclass, cls).__init__(name, bases, cls_dict)
+
+        for counter in cls_dict.get('memoizer_counters', []):
+            method_name = counter.method_name
+
+            counter.name = cls.__name__ + '.' + method_name
+            counter.underlying_method = cls_dict[method_name]
+
+            replacement_method = types.MethodType(counter, None, cls)
+            setattr(cls, method_name, replacement_method)
+
+def EnableMemoization():
+    global use_memoizer
+    use_memoizer = 1
+
+# Local Variables:
+# tab-width:4
+# indent-tabs-mode:nil
+# End:
+# vim: set expandtab tabstop=4 shiftwidth=4: