Metadata-Version: 1.1
Name: lazy-python
Version: 0.2.1
Summary: Lazy evaluation for python 3
Home-page: https://github.com/llllllllll/lazy_python
Author: Joe Jevnik
Author-email: joejev@gmail.com
License: GPL-2
Description: ``lazy 0.2``
        ============
        
        |build status|
        
        I will write this later...
        
        What is lazy?
        -------------
        
        ``lazy`` is a module for making python `lazily
        evaluated <http://en.wikipedia.org/wiki/Lazy_evaluation>`__ (kinda).
        
        ``lazy`` runs under python 3.5 and 3.4.
        
        Why lazy?
        ---------
        
        Why not lazy?
        
        I think lazy computation is pretty cool, I also think python is pretty
        cool; combining them is double cool.
        
        How to lazy?
        ------------
        
        There are 3 means of using lazy code:
        
        1. ``lazy_function``
        2. ``run_lazy``
        3. IPython cell and line magics
        
        ``lazy_function``
        ^^^^^^^^^^^^^^^^^
        
        ``lazy_function`` takes a python function and returns a new function that is
        the lazy version. This can be used as a decorator.
        
        Example:
        
        .. code:: python
        
            @lazy_function
            def f(a, b):
                return a + b
        
        Calling ``f(1, 2)`` will return a ``thunk`` that will add 1 and 2 when it
        needs to be strict. Doing anything with the returned thunk will keep
        chaining on more computations until it must be strictly evaluated.
        
        Lazy functions allow for lexical closures also:
        
        .. code:: python
        
            @lazy_function
            def f(a):
                def g(b):
                    return a + b
                return g
        
        When we call ``f(1)`` we will get back a ``thunk`` like we would expect;
        however, this thunk is wrapping the function ``g``. Because ``g`` was created
        in a lazy context, it will also be a ``lazy_function`` implicitly. This means
        that ``type(f(1)(2))`` is ``thunk``; but, ``f(1)(2) == 3``.
        
        We can use strict to strictly evaluate parts of a lazy function, for example:
        
        .. code:: python
        
            >>> @lazy_function
            ... def no_strict():
            ...    print('test')
            ...
            >>> strict(no_strict())
        
        
        In this example, we never forced print, so we never saw the result of the call.
        Consider this function though:
        
        .. code:: python
        
            >>> @lazy_function
            ... def with_strict():
            ...    strict(print('test'))
            ...
            >>> strict(with_strict())
            test
            >>> result = with_strict()
            >>> strict(result)
            test
        
        Here we can see how strict works inside of a lazy function. ``strict`` causes
        the argument to be strictly evaluated, forcing the call to print. We can also
        see that just calling ``with_strict`` is not enough to evaluate the function,
        we need to force a dependency on the result.
        
        
        
        This is implemented at the bytecode level to frontload a large part of the cost
        of using the lazy machinery. There is very little overhead at function call
        time as most of the overhead was spent at function creation (definiton) time.
        
        ``run_lazy``
        ^^^^^^^^^^^^
        
        We can convert normal python into lazy python with the ``run_lazy`` function
        which takes a string, the 'name', globals, and locals. This is like ``exec`` or
        ``eval`` for lazy python. This will mutate the provided globals and locals so
        that we can access the lazily evaluated code.
        
        Example:
        
        .. code:: python
        
            >>> code = """
            print('not lazy')
            strict(print('lazy'))
            """
            >>> run_lazy(code)
            lazy
        
        
        This also uses the same bytecode manipulation as ``lazy_function`` so they will
        give the same results.
        
        
        IPython cell and line magics
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        If you have IPython installed, you may use the cell and line magic machinery to
        write and evaluate lazy code. For example:
        
        .. code:: python
        
           In [1]: from lazy import strict
        
           In [2]: %lazy 2 + 2  # line magic acts as an expression
           Out[2]: 4
        
           In [3]: type(_2)
           Out[3]: lazy._thunk.thunk
        
           In [4]: %%lazy  # cell magic is treated as a statement
              ...: print('lazy')
              ...: strict(print('strict'))
              ...:
           strict
        
        
        
        ``thunk``
        ~~~~~~~~~
        
        At its core, lazy is just a way of converting expressions into a tree
        of deferred computation objects called ``thunk``\ s. thunks wrap normal
        functions by not evaluating them until the value is needed. A ``thunk``
        wrapped function can accept ``thunk``\ s as arguments; this is how the
        tree is built. Some computations cannot be deferred because there is some state
        that is needed to construct the thunk, or the python standard defines the
        return of some method to be a specific type. These are refered to as 'strict
        points'. Examples of strict points are ``str`` and ``bool`` because the python
        standard says that these functions must return an instance of their own
        type. Most of these converters are strict; however, some other things are
        strict because it solves recursion issues in the interpreter, like accessing
        ``__class__`` on a thunk.
        
        
        Custom Strictness Properties
        ----------------------------
        
        ``strict`` is actually a type that cannot be put into a ``thunk``. For
        example:
        
        .. code:: python
        
            >>> type(thunk(strict, 2))
            int
        
        Notice that this is not a thunk, and has been strictly evaluated.
        
        To create custom strict objects, you can subclass ``strict``. This
        prevents the object from getting wrapped in thunks allowing you to
        create strict data structures.
        
        Objects may also define a ``__strict__`` method that defines how to
        strictly evalueate the object. For example, an object could be defined
        as:
        
        .. code:: python
        
            class StrictFive(object):
                def __strict__(self):
                    return 5
        
        This would make ``strict(StrictFive())`` return 5 instead of an instance
        of ``StrictFive``.
        
        ``undefined``
        ^^^^^^^^^^^^^
        
        ``undefined`` is a value that cannot be strictly evaluated. It is useful as a
        placeholder for computations.
        
        We can imagine ``undefined`` in python as:
        
        .. code:: python
        
           @thunk.fromexpr
           @Exception.__new__
           class undefined(Exception):
               def __strict__(self):
                   raise self
        
        This object will raise an instance of itself when it is evaluated.
        This is presented as an equivalent definition, though it is actually in c to
        make nicer stack traces.
        
        Known Issues
        ------------
        
        Currently, the following things are known to not work:
        
        Recursively defined ``thunk``\ s
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        A recursively defined ``thunk`` is a thunk that appears in its own graph twice.
        For example:
        
        .. code:: python
        
            >>> a = thunk(lambda: a)
            >>> strict(a)
        
        This will cause an infinite loop because in order to strictly evaluate ``a``,
        we will call the function which returns ``a`` which we will try to strictly
        evaluate.
        
        Status: Bug, might fix.
        
        This is basically correct, for example:
        
        .. code:: python
        
            >>> a = lambda: a()
            >>> a()
            ...
            RuntimeError: maximum recursion depth exceeded
        
        The difference in the thunk example is that we will drop into c code to preform
        the recursion so it will not terminate in a reasonable amount of time.
        
        The potential fix could be to try to detect these cycles and raise some
        Exception; however, this might be a very expensive check in the good case
        making ``thunk`` evaluation much slower.
        
        Gotchas
        -------
        
        I opened it up in the repl, everything is strict!
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        Because the python spec says the ``__repr__`` of an object must return a
        ``str``, a call to ``repr`` must strictly evaluate the contents so that
        we can see what it is. The repl will implicitly call ``repr`` on things
        to display them. We can see that this is a thunk by doing:
        
        .. code:: python
        
            >>> a = thunk(operator.add, 2, 3)
            >>> type(a)
            lazy.thunk.thunk
            >>> a
            5
        
        Again, because we need to compute something to represent it, the repl is
        a bad use case for this, and might make it appear at first like this is
        always strict.
        
        ``print`` didn't do anything!
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        Um, what did you think it would do?
        
        If we write:
        
        .. code:: python
        
            @lazy_function
            def f(a, b):
                print('printing the sum of %s and %s' % (a, b))
                return a + b
        
        Then there is no reason that the print call should be executed. No
        computation depends on the results, so it is casually skipped.
        
        The solution is to force a dependency:
        
        .. code:: python
        
            @lazy_function
            def f(a, b):
                strict(print('printing the sum of %s and %s' % (a, b)))
                return a + b
        
        ``strict`` is a function that is used to strictly evaluate things.
        Because the body of the function is interpreted as lazy python, the
        function call is converted into a ``thunk``, and therefore we can
        ``strict`` it.
        
        This is true for *any* side-effectful function call.
        
        x is being evaluated strictly when I think it should be lazy
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        There are some cases where things MUST be strict based on the python
        language spec. Because this is not really a new language, just an
        automated way of writing really inefficient python, python's rules must
        be followed.
        
        For example, ``__bool__``, ``__int__``, and other converters expect that
        the return type must be a the proper type. This counts as a place where
        strictness is needed1.
        
        This might not be the case though, instead, I might have missed
        something and you are correct, it should be lazy. If you think I missed
        something, open an issue and I will try to address it as soon as
        possible.
        
        Some stateful thing is broken
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        Sorry, you are using unmanaged state and lazy evaluation, you deserve
        this. ``thunks`` cache the normal form so that calling strict the second
        time will refer to the cached value. If this depended on some stateful
        function, then it will not work as intended.
        
        I tried to do x with a ``thunk`` and it broke!
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        The library is probably broken. This was written on a whim and I barely
        thought through the use cases.
        
        Please open an issue and I will try to get back to you as soon as
        possible.
        
        Notes
        ~~~~~
        
        1. The function call for the constructor will be made lazy in the
           ``LazyTransformer`` (like ``thunk(int, your_thunk)``), so while this
           is a place where strictness is needed, it can still be 'optimized'
           away.
        
        .. |build status| image:: https://travis-ci.org/llllllllll/lazy_python.svg?branch=master
           :target: https://travis-ci.org/llllllllll/lazy_python
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: GNU General Public License v2 (GPLv2)
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Operating System :: POSIX
Classifier: Topic :: Software Development :: Pre-processors
