I don't know about the best solution, but here's a solution:
from cpython.ref cimport PyObject, Py_XINCREF, Py_XDECREF
DEF SIZE = 32
cdef class TrieNode:
cdef PyObject *members[SIZE]
def __cinit__(self):
cdef object temp_object
for i in range(SIZE):
temp_object = int(i)
# increment its refcount so it's not gc'd.
# We hold a reference to the object and are responsible for
# decref-ing it in __dealloc__.
Py_XINCREF(<PyObject*>temp_object)
self.members[i] = <PyObject*>temp_object
def __init__(self):
# just to show that it works...
for i in range(SIZE):
print <object>self.members[i]
def __dealloc__(self):
# make sure we decref the members elements.
for i in range(SIZE):
Py_XDECREF(self.members[i])
self.members[i] = NULLHow about this?
class TrieNode():
def __init__(self, length = 32):
self.members = list()
self.length = length
for i in range(length):
self.members.append(None)
def set(self, idx, item):
if idx < self.length and idx >= 0:
self.members[idx] = item
else:
print "ERROR: Specified index out of range."
# Alternately, you could raise an IndexError.
def unset(self, idx):
if idx < self.length and idx >= 0:
self.members[idx] = None
else:
raise IndexError("Specified index out of range (0..%d)." % self.length)Python has a builtin array module supporting dynamic 1-dimensional arrays of primitive types. It is possible to access the underlying C array of a Python array from within Cython. At the same time they are ordinary Python objects which can be stored in lists and serialized between processes when using multiprocessing.,NB: the import brings the regular Python array object into the namespace while the cimport adds functions accessible from Cython.,A Python array is constructed with a type signature and sequence of initial values. For the possible type signatures, refer to the Python documentation for the array module.,To avoid having to use the array constructor from the Python module, it is possible to create a new array with the same type as a template, and preallocate a given number of elements. The array is initialized to zero when requested.
import cythonfrom cython.cimports.cpython
import array
import array
a = cython.declare(array.array, array.array('i', [1, 2, 3]))
ca = cython.declare(cython.int[: ], a)
print(ca[0])from cpython cimport array
import array
cdef array.array a = array.array('i', [1, 2, 3])
cdef int[: ] ca = a
print(ca[0])from cython.cimports.cpython
import array
import array
a = cython.declare(array.array, array.array('i', [1, 2, 3]))
ca = cython.declare(cython.int[: ], a)
@cython.cfunc
def overhead(a: cython.object) - > cython.int:
ca: cython.int[: ] = a
return ca[0]
@cython.cfunc
def no_overhead(ca: cython.int[: ]) - > cython.int:
return ca[0]
print(overhead(a)) # new memory view will be constructed, overhead
print(no_overhead(ca)) # ca is already a memory view, so no overheadfrom cpython cimport array
import array
cdef array.array a = array.array('i', [1, 2, 3])
cdef int[: ] ca = a
cdef int overhead(object a):
cdef int[: ] ca = a
return ca[0]
cdef int no_overhead(int[: ] ca):
return ca[0]
print(overhead(a)) # new memory view will be constructed, overhead
print(no_overhead(ca)) # ca is already a memory view, so no overheadfrom cython.cimports.cpython
import array
import array
a = cython.declare(array.array, array.array('i', [1, 2, 3]))
# access underlying pointer:
print(a.data.as_ints[0])
from cython.cimports.libc.string
import memset
memset(a.data.as_voidptr, 0, len(a) * cython.sizeof(cython.int))Some formats require a read-only bytes-like object, and set a pointer instead of a buffer structure. They work by checking that the object’s PyBufferProcs.bf_releasebuffer field is NULL, which disallows mutable objects such as bytearray.,In general, when a format sets a pointer to a buffer, the buffer is managed by the corresponding Python object, and the buffer shares the lifetime of this object. You won’t have to release any memory yourself. The only exceptions are es, es#, et and et#.,Like s*, but the Python object may also be None, in which case the buf member of the Py_buffer structure is set to NULL.,This format accepts any object which implements the read-write buffer interface. It fills a Py_buffer structure provided by the caller. The buffer may contain embedded null bytes. The caller have to call PyBuffer_Release() when it is done with the buffer.
status = converter(object, address);
static PyObject *
weakref_ref(PyObject * self, PyObject * args) {
PyObject * object;
PyObject * callback = NULL;
PyObject * result = NULL;
if (PyArg_UnpackTuple(args, "ref", 1, 2, & object, & callback)) {
result = PyWeakref_NewRef(object, callback);
}
return result;
}PyArg_ParseTuple(args, "O|O:ref", & object, & callback)Notes on generators Terminology Function analysis The generator structure Layout Allocation Compiling to native code The next() function ,Function calls to locally defined inner functions are supported as long as they can be fully inlined.,The following feature (literal_unroll()) is experimental and was added in version 0.47.,Using a function to limit the inlining depth of a recursive function
from numba
import jit
@jit
def add1(x):
return x + 1
@jit
def bar(fn, x):
return fn(x)
@jit
def foo(x):
return bar(add1, x)
# Passing add1 within numba compiled code.
print(foo(1))
# Passing add1 into bar from interpreted code
print(bar(add1, 1))try: ... except: ...
try: ... except Exception: ...
try:
do_work()
except Exception:
handle_error_case()
return error_code>>> @jit(nopython = True)
...def foo():
...
try:
...print('main block')
...except Exception:
...print('handler block')
...
else:
...print('else block')
...
finally:
...print('final block')
...
>>>
foo()
main block
else block
final blockhomogeneous_tuple = (1, 2, 3, 4)