Metadata-Version: 1.1
Name: unpythonic
Version: 0.14.0
Summary: Supercharge your Python with parts of Lisp and Haskell.
Home-page: https://github.com/Technologicat/unpythonic
Author: Juha Jeronen
Author-email: juha.m.jeronen@gmail.com
License: BSD
Description: We provide missing features for Python, mainly from the list processing
        tradition, but with some haskellisms mixed in. We place a special emphasis on
        **clear, pythonic syntax**.
        
        Optionally, we also provide extensions to the Python language as a set of
        syntactic macros that are designed to work together. Each macro adds an
        orthogonal piece of functionality that can (mostly) be mixed and matched
        with the others.
        
        Design considerations are simplicity, robustness, and minimal dependencies.
        Currently none required; MacroPy optional, to enable the syntactic macros.
        
        **Without macros**, our features include tail call optimization (TCO), TCO'd
        loops in FP style, call/ec, let & letrec, assign-once, multi-expression lambdas,
        dynamic assignment (a.k.a. *parameterize*, *special variables*), memoization
        (also for generators and iterables), currying, function composition,
        folds and scans (left and right), unfold, lazy partial unpacking of iterables,
        functional update for sequences, pythonic lispy linked lists (``cons``), and
        compact syntax for creating mathematical sequences that support infix math.
        
        Our curry modifies Python's reduction rules. It passes any extra arguments
        through on the right, and calls a callable return value on the remaining
        arguments, so that we can::
        
            mymap = lambda f: curry(foldr, composerc(cons, f), nil)
            myadd = lambda a, b: a + b
            assert curry(mymap, myadd, ll(1, 2, 3), ll(2, 4, 6)) == ll(3, 6, 9)
        
            with_n = lambda *args: (partial(f, n) for n, f in args)
            clip = lambda n1, n2: composel(*with_n((n1, drop), (n2, take)))
            assert tuple(curry(clip, 5, 10, range(20))) == tuple(range(5, 15))
        
        If MacroPy is installed, ``unpythonic.syntax`` becomes available. It provides
        macros that essentially extend the Python language, adding features that would
        be either complicated or impossible to provide (and/or use) otherwise.
        
        **With macros**, we add automatic currying, automatic tail-call optimization
        (TCO), call-by-need (lazy functions), continuations (``call/cc`` for Python),
        ``let-syntax`` (splice code at macro expansion time), lexically scoped
        ``let`` and ``do`` with lean syntax, implicit return statements, and
        easy-to-use multi-expression lambdas with local variables.
        
        The TCO macro has a fairly extensive expression analyzer, so things like
        ``and``, ``or``, ``a if p else b`` and any uses of the ``do[]`` and ``let[]``
        macros are accounted for when performing the tail-call transformation.
        
        The continuation system is based on a semi-automated partial conversion into
        continuation-passing style (CPS), with continuations represented as closures.
        It also automatically applies TCO, using the same machinery as the TCO macro.
        To keep the runtime overhead somewhat reasonable, the continuation is captured
        only where explicitly requested with ``call_cc[]``.
        
        Macro examples::
        
            # let, letseq (let*), letrec with no boilerplate
            a = let((x, 17),
                    (y, 23))[
                      (x, y)]
        
            # alternate haskelly syntax
            a = let[((x, 21),(y, 17), (z, 4)) in x + y + z]
            a = let[x + y + z, where((x, 21), (y, 17), (z, 4))]
        
            # cond: multi-branch "if" expression
            answer = lambda x: cond[x == 2, "two",
                                    x == 3, "three",
                                    "something else"]
            assert answer(42) == "something else"
        
            # do: imperative code in any expression position
            y = do[local[x << 17],
                   print(x),
                   x << 23,
                   x]
            assert y == 23
        
            # autocurry like Haskell
            with curry:
                def add3(a, b, c):
                    return a + b + c
                assert add3(1)(2)(3) == 6
                # actually partial application so these work, too
                assert add3(1, 2)(3) == 6
                assert add3(1)(2, 3) == 6
                assert add3(1, 2, 3) == 6
        
                mymap = lambda f: foldr(composerc(cons, f), nil)
                myadd = lambda a, b: a + b
                assert mymap(myadd, ll(1, 2, 3), ll(2, 4, 6)) == ll(3, 6, 9)
        
            # lazy functions (call-by-need) like Haskell
            with lazify:
                def f(a, b):
                    return a
                def g(a, b):
                    return f(2*a, 3*b)
                assert g(21, 1/0) == 42  # the 1/0 is never evaluated
        
            # automatic tail-call optimization (TCO) like Scheme, Racket
            with tco:
                assert letrec((evenp, lambda x: (x == 0) or oddp(x - 1)),
                              (oddp,  lambda x: (x != 0) and evenp(x - 1)))[
                                evenp(10000)] is True
        
            # lambdas with multiple expressions, local variables, and a name
            with multilambda, namedlambda:
                myadd = lambda x, y: [print("myadding", x, y),
                                      local[tmp << x + y],
                                      print("result is", tmp),
                                      tmp]
                assert myadd(2, 3) == 5
                assert myadd.__name__ == "myadd"
        
            # implicit "return" in tail position, like Lisps
            with autoreturn:
                def f():
                    print("hi")
                    "I'll just return this"
                assert f() == "I'll just return this"
        
                def g(x):
                    if x == 1:
                        "one"
                    elif x == 2:
                        "two"
                    else:
                        "something else"
                assert g(1) == "one"
                assert g(2) == "two"
                assert g(42) == "something else"
        
            # splice code at macro expansion time
            with let_syntax:
                with block(a) as twice:
                    a
                    a
                with block(x, y, z) as appendxyz:
                    lst += [x, y, z]
                lst = []
                twice(appendxyz(7, 8, 9))
                assert lst == [7, 8, 9]*2
        
            # lispy prefix syntax for function calls
            with prefix:
                (print, "hello world")
        
            # the LisThEll programming language
            with prefix, curry:
                mymap = lambda f: (foldr, (compose, cons, f), nil)
                double = lambda x: 2 * x
                (print, (mymap, double, (q, 1, 2, 3)))
                assert (mymap, double, (q, 1, 2, 3)) == ll(2, 4, 6)
        
            # the HasThon programming language
            with curry, lazify:
                def add2first(a, b, c):
                    return a + b
                assert add2first(2)(3)(1/0) == 5
        
                assert letrec[((c, 42),
                               (d, 1/0),
                               (e, 2*c)) in
                              add2first(c)(e)(d)] == 126
        
            # call/cc for Python
            with continuations:
                stack = []
                def amb(lst, cc):  # McCarthy's amb operator
                    if not lst:
                        return fail()
                    first, *rest = tuple(lst)
                    if rest:
                        ourcc = cc
                        stack.append(lambda: amb(rest, cc=ourcc))
                    return first
                def fail():
                    if stack:
                        f = stack.pop()
                        return f()
        
                def pythagorean_triples(maxn):
                    z = call_cc[amb(range(1, maxn+1))]
                    y = call_cc[amb(range(1, z+1))]
                    x = call_cc[amb(range(1, y+1))]
                    if x*x + y*y != z*z:
                        return fail()
                    return x, y, z
                x = pythagorean_triples(20)
                while x:
                    print(x)
                    x = fail()
        
            # if Python didn't already have generators, we could add them with call/cc:
            with continuations:
                @dlet((k, None))  # let-over-def decorator
                def g():
                    if k:
                        return k()
                    def my_yield(value, cc):
                        k << cc        # rebind the k in the @dlet env
                        cc = identity  # override current continuation
                        return value
                    # generator body
                    call_cc[my_yield(1)]
                    call_cc[my_yield(2)]
                    call_cc[my_yield(3)]
                out = []
                x = g()
                while x is not None:
                    out.append(x)
                    x = g()
                assert out == [1, 2, 3]
        
        For documentation and full examples, see the project's GitHub homepage,
        and the docstrings of the individual features. For even more examples,
        see the unit tests included in the source distribution.
        
Keywords: functional-programming,language-extension,syntactic-macros,tail-call-optimization,tco,continuations,currying,lazy-evaluation,dynamic-variable,macros,lisp,scheme,racket,haskell
Platform: Linux
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Provides: unpythonic
