Metadata-Version: 1.1
Name: ffilupa
Version: 1.0.0a2
Summary: cffi implement of lupa with lowlevel lua API
Home-page: https://github.com/TitanSnow/lupa/tree/ffi
Author: TitanSnow
Author-email: tttnns1024@gmail.com
License: MIT style
Description: ffilupa
        =======
        
        cffi_ implement of lupa_ with lowlevel lua API
        
        .. _cffi: https://bitbucket.org/cffi/cffi
        .. _lupa: https://github.com/scoder/lupa
        
        Major features
        --------------
        
        * complete lupa compatibility and features
        
        * much more easier to hack on, extend and *monkey patch* as it is written in Python using cffi, not Cython or C
        
        * expose all lua C APIs and lua state object, easy to do lowlevel operations
        
          .. code:: python
        
            >>> from ffilupa import LuaRuntime, lua
            >>> lua_runtime = LuaRuntime()
            >>> # get lua state
            >>> L = lua_runtime.lua_state
            >>> code = b'return "Hello" .. "World!"'
            >>> # loadbuffer
            >>> status = lua.lib.luaL_loadbuffer(L, code, len(code), b'<python>')
            >>> status == lua.lib.LUA_OK
            True
            >>> def insert_traceback():
            ...     """insert ``debug.traceback`` to the bottom of stack"""
            ...     lua.lib.lua_getglobal(L, b'debug')
            ...     lua.lib.lua_pushstring(L, b'traceback')
            ...     lua.lib.lua_gettable(L, -2)
            ...     lua.lib.lua_insert(L, 1)
            ...     lua.lib.lua_pop(L, 1)
            >>> insert_traceback()
            >>> # protected call
            >>> status = lua.lib.lua_pcall(L, 0, lua.lib.LUA_MULTRET, 1)
            >>> status == lua.lib.LUA_OK
            True
            >>> # get return value
            >>> lua.ffi.string(lua.lib.lua_tostring(L, -1))
            b'HelloWorld!'
        
        
        Lupa
        ====
        
        Lupa integrates the runtimes of Lua_ or LuaJIT2_ into CPython.
        It is a partial rewrite of LunaticPython_ in Cython_ with some
        additional features such as proper coroutine support.
        
        .. _Lua: http://lua.org/
        .. _LuaJIT2: http://luajit.org/
        .. _LunaticPython: http://labix.org/lunatic-python
        .. _Cython: http://cython.org
        
        For questions not answered here, please contact the `Lupa mailing list`_.
        
        .. _`Lupa mailing list`: http://www.freelists.org/list/lupa-dev
        
        .. contents:: :local:
        
        
        Major features
        --------------
        
        * separate Lua runtime states through a ``LuaRuntime`` class
        
        * Python coroutine wrapper for Lua coroutines
        
        * iteration support for Python objects in Lua and Lua objects in
          Python
        
        * proper encoding and decoding of strings (configurable per runtime,
          UTF-8 by default)
        
        * frees the GIL and supports threading in separate runtimes when
          calling into Lua
        
        * tested with Python 2.6/3.2 and later
        
        * written for LuaJIT2 (tested with LuaJIT 2.0.2), but also works
          with the normal Lua interpreter (5.1 and 5.2)
        
        * easy to hack on and extend as it is written in Cython, not C
        
        
        Why the name?
        -------------
        
        In Latin, "lupa" is a female wolf, as elegant and wild as it sounds.
        If you don't like this kind of straight forward allegory to an
        endangered species, you may also happily assume it's just an
        amalgamation of the phonetic sounds that start the words "Lua" and
        "Python", two from each to keep the balance.
        
        
        Why use it?
        -----------
        
        It complements Python very well.  Lua is a language as dynamic as
        Python, but LuaJIT compiles it to very fast machine code, sometimes
        faster than many statically compiled languages for computational code.
        The language runtime is very small and carefully designed for
        embedding.  The complete binary module of Lupa, including a statically
        linked LuaJIT2 runtime, only weighs some 700KB on a 64 bit machine.
        With standard Lua 5.1, it's less than 400KB.
        
        However, the Lua ecosystem lacks many of the batteries that Python
        readily includes, either directly in its standard library or as third
        party packages. This makes real-world Lua applications harder to write
        than equivalent Python applications. Lua is therefore not commonly
        used as primary language for large applications, but it makes for a
        fast, high-level and resource-friendly backup language inside of
        Python when raw speed is required and the edit-compile-run cycle of
        binary extension modules is too heavy and too static for agile
        development or hot-deployment.
        
        Lupa is a very fast and thin wrapper around Lua or LuaJIT.  It makes it
        easy to write dynamic Lua code that accompanies dynamic Python code by
        switching between the two languages at runtime, based on the tradeoff
        between simplicity and speed.
        
        
        Examples
        --------
        
        ..
              ## doctest helpers:
              >>> try: _ = sorted
              ... except NameError:
              ...     def sorted(seq):
              ...         l = list(seq)
              ...         l.sort()
              ...         return l
        
        .. code:: python
        
              >>> import lupa
              >>> from lupa import LuaRuntime
              >>> lua = LuaRuntime(unpack_returned_tuples=True)
        
              >>> lua.eval('1+1')
              2
        
              >>> lua_func = lua.eval('function(f, n) return f(n) end')
        
              >>> def py_add1(n): return n+1
              >>> lua_func(py_add1, 2)
              3
        
              >>> lua.eval('python.eval(" 2 ** 2 ")') == 4
              True
              >>> lua.eval('python.builtins.str(4)') == '4'
              True
        
        The function ``lua_type(obj)`` can be used to find out the type of a
        wrapped Lua object in Python code, as provided by Lua's ``type()``
        function:
        
        .. code:: python
        
              >>> lupa.lua_type(lua_func)
              'function'
              >>> lupa.lua_type(lua.eval('{}'))
              'table'
        
        To help in distinguishing between wrapped Lua objects and normal
        Python objects, it returns ``None`` for the latter:
        
        .. code:: python
        
              >>> lupa.lua_type(123) is None
              True
              >>> lupa.lua_type('abc') is None
              True
              >>> lupa.lua_type({}) is None
              True
        
        Note the flag ``unpack_returned_tuples=True`` that is passed to create
        the Lua runtime.  It is new in Lupa 0.21 and changes the behaviour of
        tuples that get returned by Python functions.  With this flag, they
        explode into separate Lua values:
        
        .. code:: python
        
              >>> lua.execute('a,b,c = python.eval("(1,2)")')
              >>> g = lua.globals()
              >>> g.a
              1
              >>> g.b
              2
              >>> g.c is None
              True
        
        When set to False, functions that return a tuple pass it through to the
        Lua code:
        
        .. code:: python
        
              >>> non_explode_lua = lupa.LuaRuntime(unpack_returned_tuples=False)
              >>> non_explode_lua.execute('a,b,c = python.eval("(1,2)")')
              >>> g = non_explode_lua.globals()
              >>> g.a
              (1, 2)
              >>> g.b is None
              True
              >>> g.c is None
              True
        
        Since the default behaviour (to not explode tuples) might change in a
        later version of Lupa, it is best to always pass this flag explicitly.
        
        
        Python objects in Lua
        ---------------------
        
        Python objects are either converted when passed into Lua (e.g.
        numbers and strings) or passed as wrapped object references.
        
        .. code:: python
        
              >>> wrapped_type = lua.globals().type     # Lua's own type() function
              >>> wrapped_type(1) == 'number'
              True
              >>> wrapped_type('abc') == 'string'
              True
        
        Wrapped Lua objects get unwrapped when they are passed back into Lua,
        and arbitrary Python objects get wrapped in different ways:
        
        .. code:: python
        
              >>> wrapped_type(wrapped_type) == 'function'  # unwrapped Lua function
              True
              >>> wrapped_type(len) == 'userdata'       # wrapped Python function
              True
              >>> wrapped_type([]) == 'userdata'        # wrapped Python object
              True
        
        Lua supports two main protocols on objects: calling and indexing.  It
        does not distinguish between attribute access and item access like
        Python does, so the Lua operations ``obj[x]`` and ``obj.x`` both map
        to indexing.  To decide which Python protocol to use for Lua wrapped
        objects, Lupa employs a simple heuristic.
        
        Pratically all Python objects allow attribute access, so if the object
        also has a ``__getitem__`` method, it is preferred when turning it
        into an indexable Lua object.  Otherwise, it becomes a simple object
        that uses attribute access for indexing from inside Lua.
        
        Obviously, this heuristic will fail to provide the required behaviour
        in many cases, e.g. when attribute access is required to an object
        that happens to support item access.  To be explicit about the
        protocol that should be used, Lupa provides the helper functions
        ``as_attrgetter()`` and ``as_itemgetter()`` that restrict the view on
        an object to a certain protocol, both from Python and from inside
        Lua:
        
        .. code:: python
        
              >>> lua_func = lua.eval('function(obj) return obj["get"] end')
              >>> d = {'get' : 'value'}
        
              >>> value = lua_func(d)
              >>> value == d['get'] == 'value'
              True
        
              >>> value = lua_func( lupa.as_itemgetter(d) )
              >>> value == d['get'] == 'value'
              True
        
              >>> dict_get = lua_func( lupa.as_attrgetter(d) )
              >>> dict_get == d.get
              True
              >>> dict_get('get') == d.get('get') == 'value'
              True
        
              >>> lua_func = lua.eval(
              ...     'function(obj) return python.as_attrgetter(obj)["get"] end')
              >>> dict_get = lua_func(d)
              >>> dict_get('get') == d.get('get') == 'value'
              True
        
        Note that unlike Lua function objects, callable Python objects support
        indexing in Lua:
        
        .. code:: python
        
              >>> def py_func(): pass
              >>> py_func.ATTR = 2
        
              >>> lua_func = lua.eval('function(obj) return obj.ATTR end')
              >>> lua_func(py_func)
              2
              >>> lua_func = lua.eval(
              ...     'function(obj) return python.as_attrgetter(obj).ATTR end')
              >>> lua_func(py_func)
              2
              >>> lua_func = lua.eval(
              ...     'function(obj) return python.as_attrgetter(obj)["ATTR"] end')
              >>> lua_func(py_func)
              2
        
        
        Iteration in Lua
        ----------------
        
        Iteration over Python objects from Lua's for-loop is fully supported.
        However, Python iterables need to be converted using one of the
        utility functions which are described here.  This is similar to the
        functions like ``pairs()`` in Lua.
        
        To iterate over a plain Python iterable, use the ``python.iter()``
        function.  For example, you can manually copy a Python list into a Lua
        table like this:
        
        .. code:: python
        
              >>> lua_copy = lua.eval('''
              ...     function(L)
              ...         local t, i = {}, 1
              ...         for item in python.iter(L) do
              ...             t[i] = item
              ...             i = i + 1
              ...         end
              ...         return t
              ...     end
              ... ''')
        
              >>> table = lua_copy([1,2,3,4])
              >>> len(table)
              4
              >>> table[1]   # Lua indexing
              1
        
        Python's ``enumerate()`` function is also supported, so the above
        could be simplified to:
        
        .. code:: python
        
              >>> lua_copy = lua.eval('''
              ...     function(L)
              ...         local t = {}
              ...         for index, item in python.enumerate(L) do
              ...             t[ index+1 ] = item
              ...         end
              ...         return t
              ...     end
              ... ''')
        
              >>> table = lua_copy([1,2,3,4])
              >>> len(table)
              4
              >>> table[1]   # Lua indexing
              1
        
        For iterators that return tuples, such as ``dict.iteritems()``, it is
        convenient to use the special ``python.iterex()`` function that
        automatically explodes the tuple items into separate Lua arguments:
        
        .. code:: python
        
              >>> lua_copy = lua.eval('''
              ...     function(d)
              ...         local t = {}
              ...         for key, value in python.iterex(d.items()) do
              ...             t[key] = value
              ...         end
              ...         return t
              ...     end
              ... ''')
        
              >>> d = dict(a=1, b=2, c=3)
              >>> table = lua_copy( lupa.as_attrgetter(d) )
              >>> table['b']
              2
        
        Note that accessing the ``d.items`` method from Lua requires passing
        the dict as ``attrgetter``.  Otherwise, attribute access in Lua would
        use the ``getitem`` protocol of Python dicts and look up ``d['items']``
        instead.
        
        
        None vs. nil
        ------------
        
        While ``None`` in Python and ``nil`` in Lua differ in their semantics, they
        usually just mean the same thing: no value.  Lupa therefore tries to map one
        directly to the other whenever possible:
        
        .. code:: python
        
              >>> lua.eval('nil') is None
              True
              >>> is_nil = lua.eval('function(x) return x == nil end')
              >>> is_nil(None)
              True
        
        The only place where this cannot work is during iteration, because Lua
        considers a ``nil`` value the termination marker of iterators.  Therefore,
        Lupa special cases ``None`` values here and replaces them by a constant
        ``python.none`` instead of returning ``nil``:
        
        .. code:: python
        
              >>> _ = lua.require("table")
              >>> func = lua.eval('''
              ...     function(items)
              ...         local t = {}
              ...         for value in python.iter(items) do
              ...             table.insert(t, value == python.none)
              ...         end
              ...         return t
              ...     end
              ... ''')
        
              >>> items = [1, None ,2]
              >>> list(func(items).values())
              [False, True, False]
        
        Lupa avoids this value escaping whenever it's obviously not necessary.
        Thus, when unpacking tuples during iteration, only the first value will
        be subject to ``python.none`` replacement, as Lua does not look at the
        other items for loop termination anymore.  And on ``enumerate()``
        iteration, the first value is known to be always a number and never None,
        so no replacement is needed.
        
        .. code:: python
        
              >>> func = lua.eval('''
              ...     function(items)
              ...         for a, b, c, d in python.iterex(items) do
              ...             return {a == python.none, a == nil,   -->  a == python.none
              ...                     b == python.none, b == nil,   -->  b == nil
              ...                     c == python.none, c == nil,   -->  c == nil
              ...                     d == python.none, d == nil}   -->  d == nil ...
              ...         end
              ...     end
              ... ''')
        
              >>> items = [(None, None, None, None)]
              >>> list(func(items).values())
              [True, False, False, True, False, True, False, True]
        
              >>> items = [(None, None)]   # note: no values for c/d => nil in Lua
              >>> list(func(items).values())
              [True, False, False, True, False, True, False, True]
        
        
        Note that this behaviour changed in Lupa 1.0.  Previously, the ``python.none``
        replacement was done in more places, which made it not always very predictable.
        
        
        Lua Tables
        ----------
        
        Lua tables mimic Python's mapping protocol.  For the special case of
        array tables, Lua automatically inserts integer indices as keys into
        the table.  Therefore, indexing starts from 1 as in Lua instead of 0
        as in Python.  For the same reason, negative indexing does not work.
        It is best to think of Lua tables as mappings rather than arrays, even
        for plain array tables.
        
        .. code:: python
        
              >>> table = lua.eval('{10,20,30,40}')
              >>> table[1]
              10
              >>> table[4]
              40
              >>> list(table)
              [1, 2, 3, 4]
              >>> list(table.values())
              [10, 20, 30, 40]
              >>> len(table)
              4
        
              >>> mapping = lua.eval('{ [1] = -1 }')
              >>> list(mapping)
              [1]
        
              >>> mapping = lua.eval('{ [20] = -20; [3] = -3 }')
              >>> mapping[20]
              -20
              >>> mapping[3]
              -3
              >>> sorted(mapping.values())
              [-20, -3]
              >>> sorted(mapping.items())
              [(3, -3), (20, -20)]
        
              >>> mapping[-3] = 3     # -3 used as key, not index!
              >>> mapping[-3]
              3
              >>> sorted(mapping)
              [-3, 3, 20]
              >>> sorted(mapping.items())
              [(-3, 3), (3, -3), (20, -20)]
        
        To simplify the table creation from Python, the ``LuaRuntime`` comes with
        a helper method that creates a Lua table from Python arguments:
        
        .. code:: python
        
              >>> t = lua.table(1, 2, 3, 4)
              >>> lupa.lua_type(t)
              'table'
              >>> list(t)
              [1, 2, 3, 4]
        
              >>> t = lua.table(1, 2, 3, 4, a=1, b=2)
              >>> t[3]
              3
              >>> t['b']
              2
        
        A second helper method, ``.table_from()``, is new in Lupa 1.1 and accepts
        any number of mappings and sequences/iterables as arguments.  It collects
        all values and key-value pairs and builds a single Lua table from them.
        Any keys that appear in multiple mappings get overwritten with their last
        value (going from left to right).
        
        .. code:: python
        
              >>> t = lua.table_from([1, 2, 3], {'a': 1, 'b': 2}, (4, 5), {'b': 42})
              >>> t['b']
              42
              >>> t[5]
              5
        
        A lookup of non-existing keys or indices returns None (actually ``nil``
        inside of Lua).  A lookup is therefore more similar to the ``.get()``
        method of Python dicts than to a mapping lookup in Python.
        
        .. code:: python
        
              >>> table[1000000] is None
              True
              >>> table['no such key'] is None
              True
              >>> mapping['no such key'] is None
              True
        
        Note that ``len()`` does the right thing for array tables but does not
        work on mappings:
        
        .. code:: python
        
              >>> len(table)
              4
              >>> len(mapping)
              0
        
        This is because ``len()`` is based on the ``#`` (length) operator in
        Lua and because of the way Lua defines the length of a table.
        Remember that unset table indices always return ``nil``, including
        indices outside of the table size.  Thus, Lua basically looks for an
        index that returns ``nil`` and returns the index before that.  This
        works well for array tables that do not contain ``nil`` values, gives
        barely predictable results for tables with 'holes' and does not work
        at all for mapping tables.  For tables with both sequential and
        mapping content, this ignores the mapping part completely.
        
        Note that it is best not to rely on the behaviour of len() for
        mappings.  It might change in a later version of Lupa.
        
        Similar to the table interface provided by Lua, Lupa also supports
        attribute access to table members:
        
        .. code:: python
        
              >>> table = lua.eval('{ a=1, b=2 }')
              >>> table.a, table.b
              (1, 2)
              >>> table.a == table['a']
              True
        
        This enables access to Lua 'methods' that are associated with a table,
        as used by the standard library modules:
        
        .. code:: python
        
              >>> string = lua.eval('string')    # get the 'string' library table
              >>> print( string.lower('A') )
              a
        
        
        Python Callables
        ----------------
        
        As discussed earlier, Lupa allows Lua scripts to call Python functions
        and methods:
        
        .. code:: python
        
              >>> def add_one(num):
              ...     return num + 1
              >>> lua_func = lua.eval('function(num, py_func) return py_func(num) end')
              >>> lua_func(48, add_one)
              49
        
              >>> class MyClass():
              ...     def my_method(self):
              ...         return 345
              >>> obj = MyClass()
              >>> lua_func = lua.eval('function(py_obj) return py_obj:my_method() end')
              >>> lua_func(obj)
              345
        
        Lua doesn't have a dedicated syntax for named arguments, so by default
        Python callables can only be called using positional arguments.
        
        A common pattern for implementing named arguments in Lua is passing them
        in a table as the first and only function argument.  See
        http://lua-users.org/wiki/NamedParameters for more details.  Lupa supports
        this pattern by providing two decorators: ``lupa.unpacks_lua_table``
        for Python functions and ``lupa.unpacks_lua_table_method`` for methods
        of Python objects.
        
        Python functions/methods wrapped in these decorators can be called from
        Lua code as ``func(foo, bar)``, ``func{foo=foo, bar=bar}``
        or ``func{foo, bar=bar}``.  Example:
        
        .. code:: python
        
              >>> @lupa.unpacks_lua_table
              ... def add(a, b):
              ...     return a + b
              >>> lua_func = lua.eval('function(a, b, py_func) return py_func{a=a, b=b} end')
              >>> lua_func(5, 6, add)
              11
              >>> lua_func = lua.eval('function(a, b, py_func) return py_func{a, b=b} end')
              >>> lua_func(5, 6, add)
              11
        
        If you do not control the function implementation, you can also just
        manually wrap a callable object when passing it into Lupa:
        
        .. code:: python
        
              >>> import operator
              >>> wrapped_py_add = lupa.unpacks_lua_table(operator.add)
        
              >>> lua_func = lua.eval('function(a, b, py_func) return py_func{a, b} end')
              >>> lua_func(5, 6, wrapped_py_add)
              11
        
        There are some limitations:
        
        1. Avoid using ``lupa.unpacks_lua_table`` and ``lupa.unpacks_lua_table_method``
           for functions where the first argument can be a Lua table.  In this case
           ``py_func{foo=bar}`` (which is the same as ``py_func({foo=bar})`` in Lua)
           becomes ambiguous: it could mean either "call ``py_func`` with a named
           ``foo`` argument" or "call ``py_func`` with a positional ``{foo=bar}``
           argument".
        
        2. One should be careful with passing ``nil`` values to callables wrapped in
           ``lupa.unpacks_lua_table`` or ``lupa.unpacks_lua_table_method`` decorators.
           Depending on the context, passing ``nil`` as a parameter can mean either
           "omit a parameter" or "pass None".  This even depends on the Lua version.
        
           It is possible to use ``python.none`` instead of ``nil`` to pass None values
           robustly.  Arguments with ``nil`` values are also fine when standard braces
           ``func(a, b, c)`` syntax is used.
        
        Because of these limitations lupa doesn't enable named arguments for all
        Python callables automatically.  Decorators allow to enable named arguments
        on a per-callable basis.
        
        
        Lua Coroutines
        --------------
        
        The next is an example of Lua coroutines.  A wrapped Lua coroutine
        behaves exactly like a Python coroutine.  It needs to get created at
        the beginning, either by using the ``.coroutine()`` method of a
        function or by creating it in Lua code.  Then, values can be sent into
        it using the ``.send()`` method or it can be iterated over.  Note that
        the ``.throw()`` method is not supported, though.
        
        .. code:: python
        
              >>> lua_code = '''\
              ...     function(N)
              ...         for i=0,N do
              ...             coroutine.yield( i%2 )
              ...         end
              ...     end
              ... '''
              >>> lua = LuaRuntime()
              >>> f = lua.eval(lua_code)
        
              >>> gen = f.coroutine(4)
              >>> list(enumerate(gen))
              [(0, 0), (1, 1), (2, 0), (3, 1), (4, 0)]
        
        An example where values are passed into the coroutine using its
        ``.send()`` method:
        
        .. code:: python
        
              >>> lua_code = '''\
              ...     function()
              ...         local t,i = {},0
              ...         local value = coroutine.yield()
              ...         while value do
              ...             t[i] = value
              ...             i = i + 1
              ...             value = coroutine.yield()
              ...         end
              ...         return t
              ...     end
              ... '''
              >>> f = lua.eval(lua_code)
        
              >>> co = f.coroutine()   # create coroutine
              >>> co.send(None)        # start coroutine (stops at first yield)
        
              >>> for i in range(3):
              ...     co.send(i*2)
        
              >>> mapping = co.send(None)   # loop termination signal
              >>> sorted(mapping.items())
              [(0, 0), (1, 2), (2, 4)]
        
        It also works to create coroutines in Lua and to pass them back into
        Python space:
        
        .. code:: python
        
              >>> lua_code = '''\
              ...   function f(N)
              ...         for i=0,N do
              ...             coroutine.yield( i%2 )
              ...         end
              ...   end ;
              ...   co1 = coroutine.create(f) ;
              ...   co2 = coroutine.create(f) ;
              ...
              ...   status, first_result = coroutine.resume(co2, 2) ;   -- starting!
              ...
              ...   return f, co1, co2, status, first_result
              ... '''
        
              >>> lua = LuaRuntime()
              >>> f, co, lua_gen, status, first_result = lua.execute(lua_code)
        
              >>> # a running coroutine:
        
              >>> status
              True
              >>> first_result
              0
              >>> list(lua_gen)
              [1, 0]
              >>> list(lua_gen)
              []
        
              >>> # an uninitialised coroutine:
        
              >>> gen = co(4)
              >>> list(enumerate(gen))
              [(0, 0), (1, 1), (2, 0), (3, 1), (4, 0)]
        
              >>> gen = co(2)
              >>> list(enumerate(gen))
              [(0, 0), (1, 1), (2, 0)]
        
              >>> # a plain function:
        
              >>> gen = f.coroutine(4)
              >>> list(enumerate(gen))
              [(0, 0), (1, 1), (2, 0), (3, 1), (4, 0)]
        
        
        Threading
        ---------
        
        The following example calculates a mandelbrot image in parallel
        threads and displays the result in PIL. It is based on a `benchmark
        implementation`_ for the `Computer Language Benchmarks Game`_.
        
        .. _`Computer Language Benchmarks Game`: http://shootout.alioth.debian.org/u64/benchmark.php?test=all&lang=luajit&lang2=python3
        .. _`benchmark implementation`: http://shootout.alioth.debian.org/u64/program.php?test=mandelbrot&lang=luajit&id=1
        
        .. code:: python
        
            lua_code = '''\
                function(N, i, total)
                    local char, unpack = string.char, unpack
                    local result = ""
                    local M, ba, bb, buf = 2/N, 2^(N%8+1)-1, 2^(8-N%8), {}
                    local start_line, end_line = N/total * (i-1), N/total * i - 1
                    for y=start_line,end_line do
                        local Ci, b, p = y*M-1, 1, 0
                        for x=0,N-1 do
                            local Cr = x*M-1.5
                            local Zr, Zi, Zrq, Ziq = Cr, Ci, Cr*Cr, Ci*Ci
                            b = b + b
                            for i=1,49 do
                                Zi = Zr*Zi*2 + Ci
                                Zr = Zrq-Ziq + Cr
                                Ziq = Zi*Zi
                                Zrq = Zr*Zr
                                if Zrq+Ziq > 4.0 then b = b + 1; break; end
                            end
                            if b >= 256 then p = p + 1; buf[p] = 511 - b; b = 1; end
                        end
                        if b ~= 1 then p = p + 1; buf[p] = (ba-b)*bb; end
                        result = result .. char(unpack(buf, 1, p))
                    end
                    return result
                end
            '''
        
            image_size = 1280   # == 1280 x 1280
            thread_count = 8
        
            from lupa import LuaRuntime
            lua_funcs = [ LuaRuntime(encoding=None).eval(lua_code)
                          for _ in range(thread_count) ]
        
            results = [None] * thread_count
            def mandelbrot(i, lua_func):
                results[i] = lua_func(image_size, i+1, thread_count)
        
            import threading
            threads = [ threading.Thread(target=mandelbrot, args=(i,lua_func))
                        for i, lua_func in enumerate(lua_funcs) ]
            for thread in threads:
                thread.start()
            for thread in threads:
                thread.join()
        
            result_buffer = b''.join(results)
        
            # use PIL to display the image
            import Image
            image = Image.fromstring('1', (image_size, image_size), result_buffer)
            image.show()
        
        Note how the example creates a separate ``LuaRuntime`` for each thread
        to enable parallel execution.  Each ``LuaRuntime`` is protected by a
        global lock that prevents concurrent access to it.  The low memory
        footprint of Lua makes it reasonable to use multiple runtimes, but
        this setup also means that values cannot easily be exchanged between
        threads inside of Lua.  They must either get copied through Python
        space (passing table references will not work, either) or use some Lua
        mechanism for explicit communication, such as a pipe or some kind of
        shared memory setup.
        
        
        Restricting Lua access to Python objects
        ----------------------------------------
        
        ..
                >>> try: unicode = unicode
                ... except NameError: unicode = str
        
        Lupa provides a simple mechanism to control access to Python objects.
        Each attribute access can be passed through a filter function as
        follows:
        
        .. code:: python
        
                >>> def filter_attribute_access(obj, attr_name, is_setting):
                ...     if isinstance(attr_name, unicode):
                ...         if not attr_name.startswith('_'):
                ...             return attr_name
                ...     raise AttributeError('access denied')
        
                >>> lua = lupa.LuaRuntime(
                ...           register_eval=False,
                ...           attribute_filter=filter_attribute_access)
                >>> func = lua.eval('function(x) return x.__class__ end')
                >>> func(lua)
                Traceback (most recent call last):
                 ...
                AttributeError: access denied
        
        The ``is_setting`` flag indicates whether the attribute is being read
        or set.
        
        Note that the attributes of Python functions provide access to the
        current ``globals()`` and therefore to the builtins etc.  If you want
        to safely restrict access to a known set of Python objects, it is best
        to work with a whitelist of safe attribute names.  One way to do that
        could be to use a well selected list of dedicated API objects that you
        provide to Lua code, and to only allow Python attribute access to the
        set of public attribute/method names of these objects.
        
        Since Lupa 1.0, you can alternatively provide dedicated getter and
        setter function implementations for a ``LuaRuntime``:
        
        .. code:: python
        
                >>> def getter(obj, attr_name):
                ...     if attr_name == 'yes':
                ...         return getattr(obj, attr_name)
                ...     raise AttributeError(
                ...         'not allowed to read attribute "%s"' % attr_name)
        
                >>> def setter(obj, attr_name, value):
                ...     if attr_name == 'put':
                ...         setattr(obj, attr_name, value)
                ...         return
                ...     raise AttributeError(
                ...         'not allowed to write attribute "%s"' % attr_name)
        
                >>> class X(object):
                ...     yes = 123
                ...     put = 'abc'
                ...     noway = 2.1
        
                >>> x = X()
        
                >>> lua = lupa.LuaRuntime(attribute_handlers=(getter, setter))
                >>> func = lua.eval('function(x) return x.yes end')
                >>> func(x)  # getting 'yes'
                123
                >>> func = lua.eval('function(x) x.put = "ABC"; end')
                >>> func(x)  # setting 'put'
                >>> print(x.put)
                ABC
                >>> func = lua.eval('function(x) x.noway = 42; end')
                >>> func(x)  # setting 'noway'
                Traceback (most recent call last):
                 ...
                AttributeError: not allowed to write attribute "noway"
        
        
        Importing Lua binary modules
        ----------------------------
        
        **This will usually work as is**, but here are the details, in case
        anything goes wrong for you.
        
        To use binary modules in Lua, you need to compile them against the
        header files of the LuaJIT sources that you used to build Lupa, but do
        not link them against the LuaJIT library.
        
        Furthermore, CPython needs to enable global symbol visibility for
        shared libraries before loading the Lupa module.  This can be done by
        calling ``sys.setdlopenflags(flag_values)``.  Importing the ``lupa``
        module will automatically try to set up the correct ``dlopen`` flags
        if it can find the platform specific ``DLFCN`` Python module that
        defines the necessary flag constants.  In that case, using binary
        modules in Lua should work out of the box.
        
        If this setup fails, however, you have to set the flags manually.
        When using the above configuration call, the argument ``flag_values``
        must represent the sum of your system's values for ``RTLD_NEW`` and
        ``RTLD_GLOBAL``.  If ``RTLD_NEW`` is 2 and ``RTLD_GLOBAL`` is 256, you
        need to call ``sys.setdlopenflags(258)``.
        
        Assuming that the Lua luaposix_ (``posix``) module is available, the
        following should work on a Linux system:
        
        .. code:: python
        
              >>> import sys
              >>> orig_dlflags = sys.getdlopenflags()
              >>> sys.setdlopenflags(258)
              >>> import lupa
              >>> sys.setdlopenflags(orig_dlflags)
        
              >>> lua = lupa.LuaRuntime()
              >>> posix_module = lua.require('posix')     # doctest: +SKIP
        
        .. _luaposix: http://git.alpinelinux.org/cgit/luaposix
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Other Scripting Engines
Classifier: Operating System :: OS Independent
Classifier: Topic :: Software Development
