我想将字符串列表发送给C函数：
from ctypes import c_double, c_void_p, Structure, cast, c_char_p, c_size_t, POINTER
import numpy as np
class FFIArray(Structure):
"""
Convert sequence of structs or types to C-compatible void array
"""
_fields_ = [("data", c_void_p), ("len", c_size_t)]
@classmethod
def from_param(cls, seq):
""" Allow implicit conversions """
return seq if isinstance(seq, cls) else cls(seq)
def __init__(self, seq, data_type):
array = np.ctypeslib.as_array((data_type * len(seq))(*seq))
self._buffer = array.data
self.data = cast(array.ctypes.data_as(POINTER(data_type)), c_void_p)
self.len = len(array)
class Coordinates(Structure):
_fields_ = [("lat", c_double), ("lon", c_double)]
def __str__(self):
return "Latitude: {}, Longitude: {}".format(self.lat, self.lon)
if __name__ == "__main__":
tup = Coordinates(0.0, 1.0)
coords = [tup, tup]
a = b"foo"
b = b"bar"
words = [a, b]
coord_array = FFIArray(coords, data_type=Coordinates)
print(coord_array)
word_array = FFIArray(words, c_char_p)
print(word_array)
这适用于例如c_double，但是当我尝试使用c_char_p并失败时，出现以下错误（在python 2.7.16和3.7.4以及NumPy 1.16.5、1.17.2上进行测试）：
Traceback (most recent call last):
File "/Users/sth/dev/test/venv3/lib/python3.7/site-packages/numpy/core/_internal.py", line 600, in _dtype_from_pep3118
dtype, align = __dtype_from_pep3118(stream, is_subdtype=False)
File "/Users/sth/dev/test/venv3/lib/python3.7/site-packages/numpy/core/_internal.py", line 677, in __dtype_from_pep3118
raise ValueError("Unknown PEP 3118 data type specifier %r" % stream.s)
ValueError: Unknown PEP 3118 data type specifier 'z'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "so_example.py", line 42, in <module>
word_array = FFIArray(words, c_char_p)
File "so_example.py", line 19, in __init__
array = np.ctypeslib.as_array((data_type * len(seq))(*seq))
File "/Users/sth/dev/test/venv3/lib/python3.7/site-packages/numpy/ctypeslib.py", line 523, in as_array
return array(obj, copy=False)
ValueError: '<z' is not a valid PEP 3118 buffer format string
有一个更好的方法吗？我也不想使用numpy，尽管它对于将数字类型和numpy数组的可迭代对象转换为其他地方的_FFIArray很有用。
参考方案
列出[Python 3.Docs]: ctypes - A foreign function library for Python。
我（尚未）深入了解NumPy的错误（到目前为止，我到达了_multiarray_umath（C）源，但我不知道如何调用_internal.py中的函数）。
同时，这是一个不使用NumPy的变体（在这种情况下是不需要的，但是您提到在其他部分使用了它，因此这可能只能解决部分问题）。
code03.py：
#!/usr/bin/env python3
import sys
import ctypes
import numpy as np
class FFIArray(ctypes.Structure):
"""
Convert sequence of structs or types to C-compatible void array
"""
_fields_ = [
("data", ctypes.c_void_p),
("len", ctypes.c_size_t)
]
@classmethod
def from_param(cls, seq, data_type):
""" Allow implicit conversions """
return seq if isinstance(seq, cls) else cls(seq, data_type)
def __init__(self, seq, data_type):
self.len = len(seq)
self._data_type = data_type
self._DataTypeArr = self._data_type * self.len
self.data = ctypes.cast(self._DataTypeArr(*seq), ctypes.c_void_p)
def __str__(self):
ret = super().__str__() # Python 3
#ret = super(FFIArray, self).__str__() # !!! Python 2 !!!
ret += "\nType: {0:s}\nLength: {1:d}\nElement Type: {2:}\nElements:\n".format(
self.__class__.__name__, self.len, self._data_type)
arr_data = self._DataTypeArr.from_address(self.data)
for idx, item in enumerate(arr_data):
ret += " {0:d}: {1:}\n".format(idx, item)
return ret
class Coordinates(ctypes.Structure):
_fields_ = [
("lat", ctypes.c_double),
("lon", ctypes.c_double)
]
def __str__(self):
return "Latitude: {0:.3f}, Longitude: {1:.3f}".format(self.lat, self.lon)
def main():
coord_list = [Coordinates(i+ 1, i * 2) for i in range(4)]
s0 = b"foo"
s1 = b"bar"
word_list = [s0, s1]
coord_array = FFIArray(coord_list, data_type=Coordinates)
print(coord_array)
word_array = FFIArray(word_list, ctypes.c_char_p)
print(word_array)
if __name__ == "__main__":
print("Python {0:s} {1:d}bit on {2:s}\n".format(" ".join(item.strip() for item in sys.version.split("\n")), 64 if sys.maxsize > 0x100000000 else 32, sys.platform))
print("NumPy: {0:s}\n".format(np.version.version))
main()
print("\nDone.")
笔记：
修复了FFIArray.from_param中的错误（缺少arg）
初始化程序的NumPy用法很尴尬：
从字节值创建一个ctypes数组
创建一个np数组（上一步结果之外）
创建一个ctypes指针（上一步结果之外）
对原始代码做了一些小的重构
输出：
[cfati@CFATI-5510-0:e:\Work\Dev\StackOverflow\q058049957]> "e:\Work\Dev\VEnvs\py_064_03.07.03_test0\Scripts\python.exe" code03.py
Python 3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 22:22:05) [MSC v.1916 64 bit (AMD64)] 64bit on win32
NumPy: 1.16.2
<__main__.FFIArray object at 0x0000019CFEB63648>
Type: FFIArray
Length: 4
Element Type: <class '__main__.Coordinates'>
Elements:
0: Latitude: 1.000, Longitude: 0.000
1: Latitude: 2.000, Longitude: 2.000
2: Latitude: 3.000, Longitude: 4.000
3: Latitude: 4.000, Longitude: 6.000
<__main__.FFIArray object at 0x0000019CFEB637C8>
Type: FFIArray
Length: 2
Element Type: <class 'ctypes.c_char_p'>
Elements:
0: b'foo'
1: b'bar'
Done.
@ EDIT0
PEP 3118定义了用于访问（共享）内存的标准。其中一部分是格式字符串说明符，用于在缓冲区内容和相关数据之间进行转换。这些列在[Python.Docs]: PEP 3118 - Additions to the struct string-syntax中，并从[Python 3.Docs]: struct - Format Characters进行扩展。 ctypes类型具有一个（!!! undocumented !!!）_type_属性，该属性（我认为）在执行从/到np的转换时使用：
>>> import ctypes
>>>
>>> data_types = list()
>>>
>>> for attr_name in dir(ctypes):
... attr = getattr(ctypes, attr_name, None)
... if isinstance(attr, (type,)) and issubclass(attr, (ctypes._SimpleCData,)):
... data_types.append((attr, attr_name))
...
>>> for data_type, data_type_name in data_types:
... print("{0:} ({1:}) - {2:}".format(data_type, data_type_name, getattr(data_type, "_type_", None)))
...
<class 'ctypes.HRESULT'> (HRESULT) - l
<class '_ctypes._SimpleCData'> (_SimpleCData) - None
<class 'ctypes.c_bool'> (c_bool) - ?
<class 'ctypes.c_byte'> (c_byte) - b
<class 'ctypes.c_char'> (c_char) - c
<class 'ctypes.c_char_p'> (c_char_p) - z
<class 'ctypes.c_double'> (c_double) - d
<class 'ctypes.c_float'> (c_float) - f
<class 'ctypes.c_long'> (c_int) - l
<class 'ctypes.c_short'> (c_int16) - h
<class 'ctypes.c_long'> (c_int32) - l
<class 'ctypes.c_longlong'> (c_int64) - q
<class 'ctypes.c_byte'> (c_int8) - b
<class 'ctypes.c_long'> (c_long) - l
<class 'ctypes.c_double'> (c_longdouble) - d
<class 'ctypes.c_longlong'> (c_longlong) - q
<class 'ctypes.c_short'> (c_short) - h
<class 'ctypes.c_ulonglong'> (c_size_t) - Q
<class 'ctypes.c_longlong'> (c_ssize_t) - q
<class 'ctypes.c_ubyte'> (c_ubyte) - B
<class 'ctypes.c_ulong'> (c_uint) - L
<class 'ctypes.c_ushort'> (c_uint16) - H
<class 'ctypes.c_ulong'> (c_uint32) - L
<class 'ctypes.c_ulonglong'> (c_uint64) - Q
<class 'ctypes.c_ubyte'> (c_uint8) - B
<class 'ctypes.c_ulong'> (c_ulong) - L
<class 'ctypes.c_ulonglong'> (c_ulonglong) - Q
<class 'ctypes.c_ushort'> (c_ushort) - H
<class 'ctypes.c_void_p'> (c_void_p) - P
<class 'ctypes.c_void_p'> (c_voidp) - P
<class 'ctypes.c_wchar'> (c_wchar) - u
<class 'ctypes.c_wchar_p'> (c_wchar_p) - Z
<class 'ctypes.py_object'> (py_object) - O
如上所示，找不到c_char_p和c_whar_p或与标准不匹配。乍一看，这似乎是ctypes错误，因为它不遵守标准，但是我不会急于在进一步调查之前主张这一事实（并可能会提交错误）（特别是因为该区域已经报告了错误） ：[Python.Bugs]: ctypes arrays have incorrect buffer information (PEP-3118)）。
下面是一个变体，它也处理np数组。
code04.py：
#!/usr/bin/env python3
import sys
import ctypes
import numpy as np
class FFIArray(ctypes.Structure):
"""
Convert sequence of structs or types to C-compatible void array
"""
_fields_ = [
("data", ctypes.c_void_p),
("len", ctypes.c_size_t)
]
_special_np_types_mapping = {
ctypes.c_char_p: "S",
ctypes.c_wchar_p: "U",
}
@classmethod
def from_param(cls, seq, data_type=ctypes.c_void_p):
""" Allow implicit conversions """
return seq if isinstance(seq, cls) else cls(seq, data_type=data_type)
def __init__(self, seq, data_type=ctypes.c_void_p):
self.len = len(seq)
self.__data_type = data_type # Used just to hold the value passed to the initializer
if isinstance(seq, np.ndarray):
arr = np.ctypeslib.as_ctypes(seq)
self._data_type = arr._type_ # !!! data_type is ignored in this case !!!
self._DataTypeArr = arr.__class__
self.data = ctypes.cast(arr, ctypes.c_void_p)
else:
self._data_type = data_type
self._DataTypeArr = self._data_type * self.len
self.data = ctypes.cast(self._DataTypeArr(*seq), ctypes.c_void_p)
def __str__(self):
strings = [super().__str__()] # Python 3
#strings = [super(FFIArray, self).__str__()] # !!! Python 2 (ugly) !!!
strings.append("Type: {0:s}\nElement Type: {1:}{2:}\nElements ({3:d}):".format(
self.__class__.__name__, self._data_type,
"" if self._data_type == self.__data_type else " ({0:})".format(self.__data_type),
self.len))
arr_data = self._DataTypeArr.from_address(self.data)
for idx, item in enumerate(arr_data):
strings.append(" {0:d}: {1:}".format(idx, item))
return "\n".join(strings) + "\n"
def to_np(self):
arr_data = self._DataTypeArr.from_address(self.data)
if self._data_type in self._special_np_types_mapping:
dtype = np.dtype(self._special_np_types_mapping[self._data_type] + str(max(len(item) for item in arr_data)))
np_arr = np.empty(self.len, dtype=dtype)
for idx, item in enumerate(arr_data):
np_arr[idx] = item
return np_arr
else:
return np.ctypeslib.as_array(arr_data)
class Coordinates(ctypes.Structure):
_fields_ = [
("lat", ctypes.c_double),
("lon", ctypes.c_double)
]
def __str__(self):
return "Latitude: {0:.3f}, Longitude: {1:.3f}".format(self.lat, self.lon)
def main():
coord_list = [Coordinates(i + 1, i * 2) for i in range(4)]
s0 = b"foo"
s1 = b"bar (beyond all recognition)" # To avoid having 2 equal strings
word_list = [s0, s1]
coord_array0 = FFIArray(coord_list, data_type=Coordinates)
print(coord_array0)
word_array0 = FFIArray(word_list, data_type=ctypes.c_char_p)
print(word_array0)
print("to_np: {0:}\n".format(word_array0.to_np()))
np_array_src = np.array([0, -3.141593, 2.718282, -0.577, 0.618])
float_array0 = FFIArray.from_param(np_array_src, data_type=None)
print(float_array0)
np_array_dst = float_array0.to_np()
print("to_np: {0:}".format(np_array_dst))
print("Equal np arrays: {0:}\n".format(all(np_array_src == np_array_dst)))
empty_array0 = FFIArray.from_param([])
print(empty_array0)
if __name__ == "__main__":
print("Python {0:s} {1:d}bit on {2:s}\n".format(" ".join(item.strip() for item in sys.version.split("\n")), 64 if sys.maxsize > 0x100000000 else 32, sys.platform))
print("NumPy: {0:s}\n".format(np.version.version))
main()
print("\nDone.")
输出：
[cfati@CFATI-5510-0:e:\Work\Dev\StackOverflow\q058049957]> "e:\Work\Dev\VEnvs\py_064_03.07.03_test0\Scripts\python.exe" code04.py
Python 3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 22:22:05) [MSC v.1916 64 bit (AMD64)] 64bit on win32
NumPy: 1.16.2
<__main__.FFIArray object at 0x000002484A2265C8>
Type: FFIArray
Element Type: <class '__main__.Coordinates'>
Elements (4):
0: Latitude: 1.000, Longitude: 0.000
1: Latitude: 2.000, Longitude: 2.000
2: Latitude: 3.000, Longitude: 4.000
3: Latitude: 4.000, Longitude: 6.000
<__main__.FFIArray object at 0x000002484A2267C8>
Type: FFIArray
Element Type: <class 'ctypes.c_char_p'>
Elements (2):
0: b'foo'
1: b'bar (beyond all recognition)'
to_np: [b'foo' b'bar (beyond all recognition)']
<__main__.FFIArray object at 0x000002484A2264C8>
Type: FFIArray
Element Type: <class 'ctypes.c_double'> (None)
Elements (5):
0: 0.0
1: -3.141593
2: 2.718282
3: -0.577
4: 0.618
to_np: [ 0. -3.141593 2.718282 -0.577 0.618 ]
Equal np arrays: True
<__main__.FFIArray object at 0x000002484A226848>
Type: FFIArray
Element Type: <class 'ctypes.c_void_p'>
Elements (0):
Done.
当然，这是可能性之一。另一个可能涉及（不推荐使用）[SciPy.Docs]: numpy.char.array用法，但是我不想使事情复杂化（没有明确的情况）。
@ EDIT1
在np数组转换中添加了FFIArray（我不是np专家，因此对于一个人来说可能看起来很麻烦）。字符串需要特殊处理，因此不会发布新的代码版本（因为更改不是很重要），而是在以前的版本上使用。
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