I have a ctypes structure.
class S1 (ctypes.Structure):
_fields_ = [
(\'A\', ctypes.c_uint16 * 10),
(\'B\', ctypes.c_uint32),
(\'C\',
Probably something like this:
def getdict(struct):
return dict((field, getattr(struct, field)) for field, _ in struct._fields_)
>>> x = S1()
>>> getdict(x)
{'A': <__main__.c_ushort_Array_10 object at 0x100490680>, 'C': 0L, 'B': 0L}
As you can see, it works with numbers but it doesn't work as nicely with arrays -- you will have to take care of converting arrays to lists yourself. A more sophisticated version that tries to convert arrays is as follows:
def getdict(struct):
result = {}
for field, _ in struct._fields_:
value = getattr(struct, field)
# if the type is not a primitive and it evaluates to False ...
if (type(value) not in [int, long, float, bool]) and not bool(value):
# it's a null pointer
value = None
elif hasattr(value, "_length_") and hasattr(value, "_type_"):
# Probably an array
value = list(value)
elif hasattr(value, "_fields_"):
# Probably another struct
value = getdict(value)
result[field] = value
return result
If you have numpy
and want to be able to handle multidimensional C arrays, you should add import numpy as np
and change:
value = list(value)
to:
value = np.ctypeslib.as_array(value).tolist()
This will give you a nested list.