重大更新：修改为使用正确的代码读取预处理的数组文件（下面的函数using_preprocessed_file()），这极大地改变了结果。
为了确定 Python 中哪种方法更快（仅使用内置函数和标准库），我创建了一个脚本来对可用于执行此操作的不同技术进行基准测试（通过 timeit）。这有点偏长，所以为了避免分心，我只发布经过测试的代码和相关结果。 （如果对该方法有足够的兴趣，我会发布整个脚本。）
这里是比较的代码的 sn-ps：
@TESTCASE('Read and constuct piecemeal with struct')
def read_file_piecemeal():
structures = []
with open(test_filenames[0], 'rb') as inp:
size = fmt1.size
while True:
buffer = inp.read(size)
if len(buffer) != size: # EOF?
break
structures.append(fmt1.unpack(buffer))
return structures
@TESTCASE('Read all-at-once, then slice and struct')
def read_entire_file():
offset, unpack, size = 0, fmt1.unpack, fmt1.size
structures = []
with open(test_filenames[0], 'rb') as inp:
buffer = inp.read() # read entire file
while True:
chunk = buffer[offset: offset+size]
if len(chunk) != size: # EOF?
break
structures.append(unpack(chunk))
offset += size
return structures
@TESTCASE('Convert to array (@randomir part 1)')
def convert_to_array():
data = array.array('d')
record_size_in_bytes = 9*4 + 16*8 # 9 ints + 16 doubles (standard sizes)
with open(test_filenames[0], 'rb') as fin:
for record in iter(partial(fin.read, record_size_in_bytes), b''):
values = struct.unpack("<2i5d2idi3d2i3didi3d", record)
data.extend(values)
return data
@TESTCASE('Read array file (@randomir part 2)', setup='create_preprocessed_file')
def using_preprocessed_file():
data = array.array('d')
with open(test_filenames[1], 'rb') as fin:
n = os.fstat(fin.fileno()).st_size // 8
data.fromfile(fin, n)
return data
def create_preprocessed_file():
""" Save array created by convert_to_array() into a separate test file. """
test_filename = test_filenames[1]
if not os.path.isfile(test_filename): # doesn't already exist?
data = convert_to_array()
with open(test_filename, 'wb') as file:
data.tofile(file)
这是在我的系统上运行它们的结果：
Fastest to slowest execution speeds using Python 3.6.1
(10 executions, best of 3 repetitions)
Size of structure: 164
Number of structures in test file: 40,000
file size: 6,560,000 bytes
Read array file (@randomir part 2): 0.06430 secs, relative 1.00x ( 0.00% slower)
Read all-at-once, then slice and struct: 0.39634 secs, relative 6.16x ( 516.36% slower)
Read and constuct piecemeal with struct: 0.43283 secs, relative 6.73x ( 573.09% slower)
Convert to array (@randomir part 1): 1.38310 secs, relative 21.51x (2050.87% slower)
有趣的是，大多数 sn-ps 实际上在 Python 2 中更快...
Fastest to slowest execution speeds using Python 2.7.13
(10 executions, best of 3 repetitions)
Size of structure: 164
Number of structures in test file: 40,000
file size: 6,560,000 bytes
Read array file (@randomir part 2): 0.03586 secs, relative 1.00x ( 0.00% slower)
Read all-at-once, then slice and struct: 0.27871 secs, relative 7.77x ( 677.17% slower)
Read and constuct piecemeal with struct: 0.40804 secs, relative 11.38x (1037.81% slower)
Convert to array (@randomir part 1): 1.45830 secs, relative 40.66x (3966.41% slower)