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.