cl4py 1.1.1

Common Lisp for Python

The library cl4py (pronounce as clappy) allows Python programs to call Common Lisp libraries.

Motivation

You are a Python programmer, but you want access to some of the powerful features of Lisp, for example to compile code at run time? Or you want to use some awesome Lisp libraries? In that case, cl4py is here to help you.

Tutorial

You can start any number of Lisp subprocesses within Python, like this:

>>> import cl4py
>>> lisp = cl4py.Lisp()

Of course, this requires you have some Lisp installed. If not, use something like apt install sbcl, pacman -S sbcl or brew install sbcl to correct this deficiency. Once you have a running Lisp process, you can execute Lisp code on it:

>>> lisp.eval("(+ 2 3)")
5

>>> add = lisp.eval("(function +)")
>>> add(1, 2, 3, 4)
10

>>> div = lisp.eval("(function /)")
>>> div(2, 4)
Fraction(1, 2)

Some Lisp data structures have no direct equivalent in Python, most notably, cons cells.

>>> lisp.eval("(cons 1 2)")
cl4py.Cons(1, 2)

>>> lst = lisp.eval("(cons 1 (cons 2 nil))")
cl4py.List(1, 2)
>>> lst.car
1
>>> lst.cdr
cl4py.List(2) # an abbreviation for cl4py.Cons(2, None)

# conversion works vice versa, too:
>>> lisp.eval(cl4py.List('+', 2, 9))
11

It soon becomes clumsy to look up individual Lisp functions by name. Instead, it is possible to convert entire Lisp packages to Python modules, like this:

>>> cl = lisp.find_package('CL')
>>> cl.oppd(5)
True

>>> cl.cons(5, None)
cl4py.List(5)

>>> cl.remove(5, [1, 5, 2, 7, 5, 9])
[1, 2, 3, 4]

# Higher-order functions work, too!
>>> cl.mapcar(cl.constantly(4), Quote(1, 2, 3))
cl4py.List(4, 4, 4)

For convenience, Python strings are not treated as Lisp strings, but inserted literally into the evaluated Lisp code. This means that in order to actually send a string to Lisp, it must be wrapped into a cl4py.String, like this:

>>> lisp.eval(cl4py.String("foo"))
cl4py.String("foo")

Related Projects

• burgled-batteries - A bridge between Python and Lisp. The goal is that Lisp programs can use Python libraries, which is in some sense the opposite of cl4py. Furthermore it relies on the less portable mechanism of FFI calls.

• CLAUDE - An earlier attempt to access Lisp libraries from Python. The key difference is that cl4py does not run Lisp directly in the host process. This makes cl4py more portable, but complicates the exchange of data.

• cl-python - A much heavier solution than cl4py — let’s simply implement Python in Lisp! An amazing project. However, cl-python cannot access foreign libraries, e.g., NumPy. And people are probably hesitant to migrate away from CPython.

• Hy - Python, but with Lisp syntax. This project is certainly a great way to get started with Lisp. It allows you to study the advantages of Lisp’s seemingly weird syntax, without leaving the comfortable Python ecosystem. Once you understand the advantages of Lisp, you will doubly appreciate cl4py for your projects.