4 library(janus): Call Python from Prolog
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      • SWI-Prolog Python interface
        • library(janus): Call Python from Prolog
          • py_version/0
          • py_call/1
          • py_call/2
          • py_call/3
          • py_iter/2
          • py_iter/3
          • py_setattr/3
          • py_is_object/1
          • py_is_dict/1
          • py_free/1
          • py_with_gil/1
          • py_gil_owner/1
          • py_func/3
          • py_func/4
          • py_dot/3
          • py_dot/4
          • values/3
          • keys/2
          • key/2
          • items/2
          • py_shell/0
          • py_pp/1
          • py_pp/2
          • py_pp/3
          • py_object_dir/2
          • py_object_dict/2
          • py_obj_dir/2
          • py_obj_dict/2
          • py_type/2
          • py_isinstance/2
          • py_module_exists/1
          • py_hasattr/2
          • py_import/2
          • py_module/2
          • py_initialize/3
          • py_lib_dirs/1
          • py_add_lib_dir/1
          • py_add_lib_dir/2
          • Handling Python errors in Prolog
          • Calling and data translation errors
          • Janus and virtual environments (venv)
Availability::- use_module(library(janus)).(can be autoloaded)
Source[nondet]py_iter(+Iterator, -Value)
[nondet]py_iter(+Iterator, -Value, +Options)
True when Value is returned by the Python Iterator. Python iterators may be used to implement non-deterministic foreign predicates. The implementation uses these steps:

  1. Evaluate Iterator as py_call/2 evaluates its first argument, except the Obj:Attr = Value construct is not accepted.
  2. Call __iter__ on the result to get the iterator itself.
  3. Get the __next__ function of the iterator.
  4. Loop over the return values of the next function. If the Python return value unifies with Value, succeed with a choicepoint. Abort on Python or unification exceptions.
  5. Re-satisfaction continues at (4).

The example below uses the built-in iterator range():

?- py_iter(range(1,3), X).
X = 1 ;
X = 2.

Note that the implementation performs a look ahead, i.e., after successful unification it callsā€˜next()` again. On failure the Prolog predicate succeeds deterministically. On success, the next candidate is stored.

Note that a Python generator is a Python iterator. Therefore, given the Python generator expression below, we can use py_iter(squares(1,5),X) to generate the squares on backtracking.

def squares(start, stop):
     for i in range(start, stop):
         yield i * i
Options is processed as with py_call/3.
Compatibility
PIP. The same remarks as for py_call/2 apply.
bug
Iterator may not depend on janus.query(), i.e., it is not possible to iterate over a Python iterator that under the hoods relies on a Prolog non-deterministic predicate.