Lupa
=====

Lupa integrates the LuaJIT2_ runtime into CPython.  It is a partial
rewrite of LunaticPython_ in Cython_ with some additional features
such as proper coroutine support.

.. _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::


Major features
---------------

* separate Lua runtime states through a ``LuaRuntime`` class

* Python coroutine wrapper for Lua coroutines

* 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

* supports Python 2.x and 3.x, potentially starting with Python 2.3
  (currently untested)

* written for LuaJIT2 (tested with LuaJIT 2.0.0-beta5), but reportedly
  works with the normal Lua interpreter (5.1+)

* easy to hack on and extend as it is written in Cython, not C


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 other compiled languages`_ for computational code.
The language runtime is extremely small and carefully designed for
embedding.  The complete binary module of Lupa, including a statically
linked LuaJIT2 runtime, is only some 500KB on a 64 bit machine.

.. _`faster than many other compiled languages`: http://shootout.alioth.debian.org/u64/performance.php?test=mandelbrot

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 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.

..
      >>> import sys
      >>> try:
      ...     orig_dlflags = sys.getdlopenflags()
      ...     sys.setdlopenflags(258)
      ...     import lupa
      ...     sys.setdlopenflags(orig_dlflags)
      ... except: pass


Examples
---------

..
      ## doctest helpers:
      >>> try: _ = sorted
      ... except NameError:
      ...     def sorted(seq):
      ...         l = list(seq)
      ...         l.sort()
      ...         return l

::

      >>> import lupa
      >>> from lupa import LuaRuntime
      >>> lua = LuaRuntime()

      >>> 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


Python objects in Lua
----------------------

Python objects are either converted when passed into Lua (e.g.
numbers and strings) or passed as wrapped object references.

::

      >>> lua_type = lua.globals().type   # Lua's type() function
      >>> lua_type(1) == 'number'
      True
      >>> lua_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::

      >>> lua_type(lua_type) == 'function'  # unwrapped Lua function
      True
      >>> lua_type(eval) == 'userdata'      # wrapped Python function
      True
      >>> lua_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::

      >>> lua_func = lua.eval('function(obj) return obj["get"] end')
      >>> d = {'get' : 'got'}

      >>> value = lua_func(d)
      >>> value == 'got'
      True

      >>> dict_get = lua_func( lupa.as_attrgetter(d) )
      >>> dict_get('get') == 'got'
      True

      >>> lua_func = lua.eval(
      ...     'function(obj) return python.as_attrgetter(obj)["get"] end')
      >>> dict_get = lua_func(d)
      >>> dict_get('get') == 'got'
      True

Note that unlike Lua function objects, callable Python objects are
indexable::

      >>> 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::

      >>> 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::

      >>> 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::

      >>> 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.


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.

::

      >>> 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)]

A lookup of nonexisting 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.

::

      >>> 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::

      >>> 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::

      >>> 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::

      >>> string = lua.eval('string')    # get the 'string' library table
      >>> print( string.lower('A') )
      a


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.

::

      >>> 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::

      >>> 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
      >>> list(mapping.items())
      [(0, 0), (1, 2), (2, 4)]

It also works to create coroutines in Lua and to pass them back into
Python space::

      >>> 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

::

        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.


Importing Lua binary modules
-----------------------------

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::

      >>> 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
