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.
For questions not answered here, please contact the Lupa mailing list.
- Major features
- Why the name?
- Why use it?
- Which Lua version?
- Examples
- Python objects in Lua
- Iteration in Lua
- None vs. nil
- Lua Tables
- Python Callables
- Lua Coroutines
- Threading
- Restricting Lua access to Python objects
- Restricting Lua Memory Usage
- Importing Lua binary modules
- Building with different Lua versions
- separate Lua runtime states through a
LuaRuntimeclass - 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 2.7/3.6 and later
- ships with Lua 5.1, 5.2, 5.3 and 5.4 as well as LuaJIT 2.0 and 2.1 on systems that support it.
- easy to hack on and extend as it is written in Cython, not C
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.
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 800KB on a 64 bit machine. With standard Lua 5.2, it's less than 600KB.
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.
The binary wheels include different Lua versions as well as LuaJIT, if supported.
By default, import lupa uses the latest Lua version, but you can choose
a specific one via import:
try:
import lupa.luajit21 as lupa
except ImportError:
try:
import lupa.lua54 as lupa
except ImportError:
try:
import lupa.lua53 as lupa
except ImportError:
import lupa
print(f"Using {lupa.LuaRuntime().lua_implementation} (compiled with {lupa.LUA_VERSION})")>>> from lupa.lua54 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'
TrueThe 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:
>>> 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:
>>> lupa.lua_type(123) is None
True
>>> lupa.lua_type('abc') is None
True
>>> lupa.lua_type({}) is None
TrueNote 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:
>>> lua.execute('a,b,c = python.eval("(1,2)")')
>>> g = lua.globals()
>>> g.a
1
>>> g.b
2
>>> g.c is None
TrueWhen set to False, functions that return a tuple pass it through to the Lua code:
>>> 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
TrueSince 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 are either converted when passed into Lua (e.g. numbers and strings) or passed as wrapped object references.
>>> wrapped_type = lua.globals().type # Lua's own type() function
>>> wrapped_type(1) == 'number'
True
>>> wrapped_type('abc') == 'string'
TrueWrapped Lua objects get unwrapped when they are passed back into Lua, and arbitrary Python objects get wrapped in different ways:
>>> wrapped_type(wrapped_type) == 'function' # unwrapped Lua function
True
>>> wrapped_type(len) == 'userdata' # wrapped Python function
True
>>> wrapped_type([]) == 'userdata' # wrapped Python object
TrueLua 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' : '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'
TrueNote that unlike Lua function objects, callable Python objects support indexing in Lua:
>>> 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)
2Iteration 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
1Python'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
1For 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']
2Note 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.
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:
>>> lua.eval('nil') is None
True
>>> is_nil = lua.eval('function(x) return x == nil end')
>>> is_nil(None)
TrueThe 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:
>>> _ = 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.
>>> 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 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)]To simplify the table creation from Python, the LuaRuntime comes with
a helper method that creates a Lua table from Python arguments:
>>> 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']
2A 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).
>>> t = lua.table_from([1, 2, 3], {'a': 1, 'b': 2}, (4, 5), {'b': 42})
>>> t['b']
42
>>> t[5]
5A 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.
>>> table[1000000] is None
True
>>> table['no such key'] is None
True
>>> mapping['no such key'] is None
TrueNote that len() does the right thing for array tables but does not
work on mappings:
>>> len(table)
4
>>> len(mapping)
0This 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']
TrueThis 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') )
aAs discussed earlier, Lupa allows Lua scripts to call Python functions and methods:
>>> 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)
345Lua 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:
>>> @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)
11If you do not control the function implementation, you can also just manually wrap a callable object when passing it into Lupa:
>>> 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)
11There are some limitations:
Avoid using
lupa.unpacks_lua_tableandlupa.unpacks_lua_table_methodfor functions where the first argument can be a Lua table. In this casepy_func{foo=bar}(which is the same aspy_func({foo=bar})in Lua) becomes ambiguous: it could mean either "callpy_funcwith a namedfooargument" or "callpy_funcwith a positional{foo=bar}argument".One should be careful with passing
nilvalues to callables wrapped inlupa.unpacks_lua_tableorlupa.unpacks_lua_table_methoddecorators. Depending on the context, passingnilas a parameter can mean either "omit a parameter" or "pass None". This even depends on the Lua version.It is possible to use
python.noneinstead ofnilto pass None values robustly. Arguments withnilvalues are also fine when standard bracesfunc(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.
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
>>> 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:
>>> 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)]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.
lua_code = '''\
function(N, i, total)
local char, unpack = string.char, table.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 Pillow to display the image
from PIL import Image
image = Image.frombytes('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.
Lupa provides a simple mechanism to control access to Python objects. Each attribute access can be passed through a filter function as follows:
>>> 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 deniedThe 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:
>>> 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"Lupa provides a simple mechanism to control the maximum memory
usage of the Lua Runtime since version 2.0.
By default Lupa does not interfere with Lua's memory allocation, to opt-in
you must set the max_memory when creating the LuaRuntime.
The LuaRuntime provides three methods for controlling and reading the
memory usage:
get_memory_used(total=False)to get the current memory usage of the LuaRuntime.get_max_memory(total=False)to get the current memory limit.0means there is no memory limitation.set_max_memory(max_memory, total=False)to change the memory limit. Values below or equal to 0 mean no limit.
There is always some memory used by the LuaRuntime itself (around ~20KiB,
depending on your lua version and other factors) which is excluded from all
calculations unless you specify total=True.
>>> from lupa import lua52
>>> lua = lua52.LuaRuntime(max_memory=0) # 0 for unlimited, default is None
>>> lua.get_memory_used() # memory used by your code
0
>>> total_lua_memory = lua.get_memory_used(total=True) # includes memory used by the runtime itself
>>> assert total_lua_memory > 0 # exact amount depends on your lua version and other factorsLua code hitting the memory limit will receive memory errors:
>>> lua.set_max_memory(100)
>>> lua.eval("string.rep('a', 1000)") # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
lupa.LuaMemoryError: not enough memoryLuaMemoryError inherits from LuaError and MemoryError.
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:
>>> import sys
>>> orig_dlflags = sys.getdlopenflags()
>>> sys.setdlopenflags(258)
>>> import lupa
>>> sys.setdlopenflags(orig_dlflags)
>>> lua = lupa.LuaRuntime()
>>> posix_module = lua.require('posix') # doctest: +SKIPThe build is configured to automatically search for an installed version of first LuaJIT and then Lua, and failing to find either, to use the bundled LuaJIT or Lua version.
If you wish to build Lupa with a specific version of Lua, you can configure the following options on setup:
| Option | Description |
|---|---|
--lua-lib <libfile> |
Lua library file path, e.g. --lua-lib /usr/local/lib/lualib.a |
--lua-includes <incdir> |
Lua include directory, e.g. --lua-includes /usr/local/include |
--use-bundle |
Use bundled LuaJIT or Lua instead of searching for an installed version. |
--no-bundle |
Don't use the bundled LuaJIT/Lua, search for an installed version of LuaJIT or Lua,
e.g. using pkg-config. |
--no-lua-jit |
Don't use or search for LuaJIT, only use or search Lua instead. |