Package: mementos
A quick way to make Python classes automatically memoize (a.k.a. cache) their
instances based on the arguments with which they are instantiated (i.e. args to
their__init__).It’s a simple way to avoid repetitively creating expensive-to-create objects,
and to make sure objects that have a natural ‘identity’ are created only once.
If you want to be fancy,mementosimplements theMultitonsoftware pattern.UsageSay you have a classThingthat requires expensive computation to create, or
that should be created only once. Easy peasy:from mementos import mementos

class Thing(mementos):

    def __init__(self, name):
        self.name = name

    ...ThenThingobjects will be memoized:t1 = Thing("one")
t2 = Thing("one")
assert t1 is t2    # same instantiation args => same objectUnder the HoodWhen you define a classclass Thing(mementos), it looks like you’re
subclassing themementosclass. Not really.mementosis a metaclass,
not a superclass. The full expression is equivalent toclass Thing(with_metaclass(MementoMetaclass, object)), wherewith_metaclassandMementoMetaclassare also provided by themementosmodule.Metaclasses are not normal superclasses; instead they define how a class is
constructed. In effect, they define the mysterious__new__method that most
classes don’t bother defining. In this case,mementossays in effect, “hey,
look in the cache for this object before you create another one.”If you like, you can use the longer invocation with the fullwith_metaclassspec, but it’s not necessary unless you define your own memoizing functions.
More on that below.Python 2 vs. Python 3Python 2 and 3 have different forms for specifying metaclasses.
In Python 2:from mementos import MementoMetaclass

class Thing(object):

    __metaclass__ = MementoMetaclass  # now I'm memoized!

    ...Whereas Python 3 uses:class Thing3(object, metaclass=MementoMetaclass):

    ...mementossupports either of these. But Python 2 and Python 3 don’t
recognize each other’s syntax for metaclass specification, so straightforward
code for one won’t even compile for the other. Thewith_metaclass()function shown above is the way to go for cross-version compatibility. It’s
very similar to that found in thesixcross-version compatibility module.Careful with Call SignaturesMementoMetaclasscaches on call signature, which can vary greatly in Python,
even for logically identical calls. This is especially true if kwargs are used.
E.g.def func(a, b=2): passcan be calledfunc(1),func(1, 2),func(a=1),func(1, b=2), orfunc(a=2, b=2). All of these resolve to
the same logical call–and this is just for two parameters! If there is more
than one keyword, they can be arbitrarily ordered, creatingmanylogically
identical permutations.So if you instantiate an object once, then again with a logically identical call
but using a different calling structure/signature, the object won’t be created
and cached just once–it will be created and cached multiple times.:o1 = Thing("lovely")
o2 = Thing(name="lovely")
assert o1 is not o2     # because the call signature is differentThis may degrade performance, and can also create errors, if you’re counting onmementosto create just one object. So don’t do that. Use a consistent
calling style, and it won’t be a problem.In most cases, this isn’t an issue, because objects tend to be instantiated with
a limited number of parameters, and you can take care that you instantiate them
with parallel call signatures. Since this works 99% of the time and has a simple
implementation, it’s worth the price of this inelegance.Partial SignaturesIf you want only part of the initialization-time call signature (i.e. arguments
to__init__) to define an object’s identity/cache key, there are two
approaches. One is to useMementoMetaclassand design__init__without
superfluous attributes, then create one or more secondary methods to add/set
useful-but-not-essential data. E.g.:class OtherThing(with_metaclass(MementoMetaclass, object)):

    def __init__(self, name):
        self.name = name
        self.color = None   # unset for now
        self.weight = None

    def set(self, color=None, weight=None):
        self.color = color or self.color
        self.weight = weight or self.weight
        return self

ot1 = OtherThing("one").set(color='blue')
ot2 = OtherThing("one").set(weight='light')
assert ot1 is ot2
assert ot1.color == ot2.color == 'blue'
assert ot1.weight == ot2.weight == 'light'Or you can just define your own memoizing metaclass, using the factory function
described below.Visiting the FactoryThe first iteration ofmementosdefined a single metaclass. It’s since been
reimplemented as a parameterized meta-metaclass. Cool, huh? That basically means
that it defines a function,memento_factory()that, given a metaclass name
and a function defining how cache keys are constructed, returns a corresponding
metaclass.MementoMetaclassis the only metaclass that the module
pre-defines, but it’s easy to define your own memoizing metaclass.:from mementos import memento_factory, with_metaclass

IdTracker = memento_factory('IdTracker',
                            lambda cls, args, kwargs: (cls, id(args[0])) )

class MyTracker(with_metaclass(IdTracker, object)):
    ...

    # object identity is the object id of first argument to __init__
    # (and there must be one, else the args[0] reference => IndexError)The first argument tomemento_factory()is the name of the metaclass being
defined. The second is a callable (e.g. lambda expression or function object)
that takes three arguments: a class object, an argumentlist, and a keyword
argdict. Note that there is no*or**magic–args passed to the
key function have already been resolved into basic data structures.The callable must return a globally-unique, hashable key for an object. This key
will be stored in the_memento_cache, which is a simpledict.When various arguments are used as the cache key/object identity, you may use atuplethat includes the class and arguments you want to key off of. This can
also help debugging, should you need to examine the_memento_cachecache
directly. But in cases like theIdTrackerabove, it’s not mandatory that you
keep extra information around. The rawid(args[0])integer value would
suffice, as would a constructed string or other immutable, hashable value.In cases where arguments are very flexible, or involve flexible data types,
a high-powered hashing function such as that provided bySuperHashmight come in handy.
E.g.:from superhash import superhash

SuperHashMeta = memento_factory('SuperHashMeta',
                            lambda cls, args, kwargs: (cls, superhash(args)) )For the 1% edge-cases where multiple call variations must be
conclusively resolved to a unique canonical signature, that can be done on a
custom basis (based on the specific args). Or in Python 2.7 and 3.x, theinspectmodule’sgetcallargs()function can be used to create a generic
“call fingerprint” that can be used as a key. (See the tests for example code.)NotesSeeCHANGES.rstfor the extended Change Log.mementosis not to be confused withmemento, which does something completely
different.mementoswas originally derived froman ActiveState recipeby Valentino Volonghi. While the current implementation quite different and
the scope much broader, the availability of that recipe was what enabled
this module and the growing list of modules that depend on it. This is what
open source evolution is all about. Thank you, Valentino!It is safe to memoize multiple classes at the same time. They will all be
stored in the same cache, but their class is a part of the cache key, so the
values are distinct.This implementation isnotthread-safe, in and of itself. If you’re in a
multi-threaded environment, consider wrapping object instantiation in a
lock.Automated multi-version testing managed withpytest,pytest-cov,coverageandtox. Continuous integration testing
withTravis-CI.
Packaging linting withpyroma.The author,Jonathan Euniceor@jeunice on Twitterwelcomes
your comments and suggestions.InstallationTo install or upgrade to the latest version:pip install -U mementosYou may need to prefix these withsudoto authorize
installation. In environments without super-user privileges, you may want to
usepip’s--useroption, to install only for a single user, rather
than system-wide. Depending on your system configuration, you may also
need to use separatepip2andpip3programs to install for Python
2 and 3 respectively. As a fall-back for cases where the releationship betweenpipand the Python interpreter you want to run is unclear, you can
invokepipas a module under a specific Python executable:python3.6 -m pip install -U mementosTestingTo run the module tests, use one of these commands:tox                # normal run - speed optimized
tox -e py27        # run for a specific version only (e.g. py27, py34)
tox -c toxcov.ini  # run full coverage tests