cpython

>>> class Potato(object): ... def __getslice__(self, start, stop): ... print start, stop ... >>> sys.maxint 9223372036854775807 >>> x = sys.maxint + 69 >>> print x 9223372036854775876 >>> Potato()[123:x] 123 9223372036854775807 Why the call to getslice doesn't respect the stop I sent in, instead silently substituting 2^63 - 1? Does it mean that implementing __getslice__ for your own syntax will generally be unsafe with longs? I can do whatever I need with __getitem__ anyway, I'm just wondering why __getslice__ is apparently broken. Edit: Where is the code in CPython which truncates the slice?

I have a dict subclass like this: class MyDict(dict): def __getitem__(self, name): return globals()[name] This class can be used with eval and exec without issues: >>> eval('bytearray', MyDict()) <class 'bytearray'> >>> exec('print(bytearray)', MyDict()) <class 'bytearray'> But if I instantiate a function object with the types.FunctionType constructor, the function can't access any builtins: import types func = lambda: bytearray func_copy = types.FunctionType(func.__code__, MyDict(), func.__name__, func.__defaults__, func.__closure__) print(func_copy()) # Traceback (most recent call last): #

I'm very confused as to how exactly I can ensure thread-safety when calling Python code from a C (or C++) thread. The Python documentation seems to be saying that the usual idiom to do so is: PyGILState_STATE gstate; gstate = PyGILState_Ensure(); /* Perform Python actions here. */ result = CallSomeFunction(); /* evaluate result or handle exception */ /* Release the thread. No Python API allowed beyond this point. */ PyGILState_Release(gstate); And indeed, this stackoverflow answer seems to confirm as much. But a commenter (with a very high reputation) says otherwise. The commenter says you

Why is shallow-copying a list using a slice so much faster than using list builtin? In [1]: x = range(10) In [2]: timeit x_ = x[:] 10000000 loops, best of 3: 83.2 ns per loop In [3]: timeit x_ = list(x) 10000000 loops, best of 3: 147 ns per loop Usually when I see weird things like this, they're fixed in python3 - but this discrepancy is still there: In [1]: x = list(range(10)) In [2]: timeit x_ = x[:] 10000000 loops, best of 3: 100 ns per loop In [3]: timeit x_ = list(x) 10000000 loops, best of 3: 178 ns per loop The difference is in additional function call (just SLICE+0 vs CALL_FUNCTION 1

In my python debugger I have a way of remapping a string to a filename so that when you are stepping through an exec'd function inside the debugger you can list lines pygmentized, or view them along inside an editor like Emacs via realgud . So I'd like to be able to extract the string in an exec statement when CPython is stopped inside evaluating that. I already have a mechanism that can look back in the call frame to see if the caller was an EXEC_STMT and I can look back one instruction to see if the previous instruction was say DUP_TOP . So I'd be home free if I could just figure out a way

I have an instance of a class from SWIG-wrapped C++ library from which I would like to extract its reference, in order to be able to use it inside a Cython file, in which I am directly linking to the same C++ library by using a more lightweight self-made Cython wrapper of the same class. I know it would not be as easy as accessing some hidden attribute, but I imagine there might be some function within SWIG or CPython that could potentially do that if linked to from within Cython (some PyObject_*, perhaps?). Unfortunately, I don't know enough about SWIG or CPython internals to know how to do

I have a shared resource with high initialisation cost and thus I want to access it across the system (it's used for some instrumentation basically, so has to be light weight). So I created a module managing the setup and access to it. It does a lazy initialise of the resource and stores it in a module global variable. I then use functions of this module across the system to operate on the resource. - Now I am wondering whether (or how often) I will have to reinitialise the resource? - I know objects are garbage collected in CPython on (or better around) zero reference count, but is storing in

I'd like to write a performance-sensitive application in Python, so executing it under PyPy is a natural choice. However, a significant portion of my code depends on numpy, scipy, and scikit-learn. Would it be possible to embed a CPython instance within a running PyPy program in order to call array-oriented code? If not, what's the easiest way to make PyPy and CPython talk to each other? No, you can't embed CPython inside PyPy AFAIK. You can, however, use distributed/parallel execution systems to make PyPy talk to CPython. Both execnet and Pyro mention this precise PyPy <-> CPython use case.