在Python中，我如何读取二进制文件并循环遍历该文件的每个字节？
答案 0 :(得分：330)
Python 2.4及早期
f = open("myfile", "rb")
try:
byte = f.read(1)
while byte != "":
# Do stuff with byte.
byte = f.read(1)
finally:
f.close()
Python 2.5-2.7
with open("myfile", "rb") as f:
byte = f.read(1)
while byte != "":
# Do stuff with byte.
byte = f.read(1)
请注意，with语句在2.5以下的Python版本中不可用。要在v 2.5中使用它，您需要导入它：
from __future__ import with_statement
在2.6中，这不是必需的。
Python 3
在Python 3中，它有点不同。我们将不再以字节模式从流中获取原始字符，而是字节对象，因此我们需要更改条件：
with open("myfile", "rb") as f:
byte = f.read(1)
while byte != b"":
# Do stuff with byte.
byte = f.read(1)
或者正如benhoyt所说，跳过不相等并利用b""评估为假的事实。这使代码在2.6和3.x之间兼容，无需任何更改。如果从字节模式转换为文本或反向，它还可以避免更改条件。
with open("myfile", "rb") as f:
byte = f.read(1)
while byte:
# Do stuff with byte.
byte = f.read(1)
答案 1 :(得分：156)
此生成器从文件中生成字节，以块的形式读取文件：
def bytes_from_file(filename, chunksize=8192):
with open(filename, "rb") as f:
while True:
chunk = f.read(chunksize)
if chunk:
for b in chunk:
yield b
else:
break
# example:
for b in bytes_from_file('filename'):
do_stuff_with(b)
有关iterators和generators的信息，请参阅Python文档。
答案 2 :(得分：48)
如果文件不是太大，将其保存在内存中就会出现问题：
bytes_read = open("filename", "rb").read()
for b in bytes_read:
process_byte(b)
其中process_byte表示您要对传入的字节执行的某些操作。
如果您想一次处理一个块：
file = open("filename", "rb")
try:
bytes_read = file.read(CHUNKSIZE)
while bytes_read:
for b in bytes_read:
process_byte(b)
bytes_read = file.read(CHUNKSIZE)
finally:
file.close()
答案 3 :(得分：31)
要读取文件 - 一次一个字节（忽略缓冲） - 您可以使用two-argument iter(callable, sentinel) built-in function：
with open(filename, 'rb') as file:
for byte in iter(lambda: file.read(1), b''):
# Do stuff with byte
它调用file.read(1)，直到它不返回b''（空字节串）。对于大文件，内存不会无限增长。您可以将buffering=0传递给open()，以禁用缓冲 - 它保证每次迭代只读取一个字节（慢）。
with - 语句自动关闭文件 - 包括下面的代码引发异常的情况。
尽管默认情况下存在内部缓冲，但一次处理一个字节仍然效率低下。例如，这是blackhole.py实用程序，它会占用它所提供的所有内容：
#!/usr/bin/env python3
"""Discard all input. `cat > /dev/null` analog."""
import sys
from functools import partial
from collections import deque
chunksize = int(sys.argv[1]) if len(sys.argv) > 1 else (1 << 15)
deque(iter(partial(sys.stdin.detach().read, chunksize), b''), maxlen=0)
示例：
$ dd if=/dev/zero bs=1M count=1000 | python3 blackhole.py
在我的机器上chunksize == 32768时处理 ~1.5 GB / s ，在chunksize == 1时只处理 ~7.5 MB / s 。也就是说，一次读取一个字节要慢200倍。如果您可以重写您的处理以一次使用多个字节，并且 您需要性能，请将其考虑在内。
mmap允许您同时将文件视为bytearray和文件对象。如果您需要访问两个接口，它可以作为将整个文件加载到内存中的替代方法。特别是，您可以使用普通for循环在内存映射文件上一次迭代一个字节：
from mmap import ACCESS_READ, mmap
with open(filename, 'rb', 0) as f, mmap(f.fileno(), 0, access=ACCESS_READ) as s:
for byte in s: # length is equal to the current file size
# Do stuff with byte
mmap支持切片表示法。例如，mm[i:i+len]从位置len开始的文件中返回i个字节。 Python 3.2之前不支持上下文管理器协议;在这种情况下，您需要明确地调用mm.close()。使用mmap对每个字节进行迭代会占用比file.read(1)更多的内存，但mmap的速度要快一个数量级。
答案 4 :(得分：19)
总结chrispy，Skurmedel，Ben Hoyt和Peter Hansen的所有优点，这将是一次一个字节处理二进制文件的最佳解决方案：
with open("myfile", "rb") as f:
while True:
byte = f.read(1)
if not byte:
break
do_stuff_with(ord(byte))
对于python 2.6及以上版本，因为：
或使用J. F. Sebastians解决方案提高速度
from functools import partial
with open(filename, 'rb') as file:
for byte in iter(partial(file.read, 1), b''):
# Do stuff with byte
或者，如果你想将它作为代码表示的生成器函数：
def bytes_from_file(filename):
with open(filename, "rb") as f:
while True:
byte = f.read(1)
if not byte:
break
yield(ord(byte))
# example:
for b in bytes_from_file('filename'):
do_stuff_with(b)
答案 5 :(得分：16)
在Python中读取二进制文件并循环遍历每个字节
Python 3.5中的新功能是pathlib模块，它有一个专门用于读取文件作为字节的便捷方法，允许我们迭代字节。我认为这是一个体面的（如果快速和肮脏）答案：
import pathlib
for byte in pathlib.Path(path).read_bytes():
print(byte)
有趣的是，这是提及pathlib的唯一答案。
在Python 2中，你可能会这样做（正如Vinay Sajip所暗示的那样）：
with open(path, 'b') as file:
for byte in file.read():
print(byte)
如果文件可能太大而无法在内存中进行迭代，那么您可以使用带有iter签名的callable, sentinel函数（Python 2版本）以惯用方式对其进行分块：< / p>
with open(path, 'b') as file:
callable = lambda: file.read(1024)
sentinel = bytes() # or b''
for chunk in iter(callable, sentinel):
for byte in chunk:
print(byte)
（其他几个答案提到了这一点，但很少提供合理的读取大小。）
让我们创建一个执行此操作的函数，包括用于Python 3.5 +的标准库的惯用法：
from pathlib import Path
from functools import partial
from io import DEFAULT_BUFFER_SIZE
def file_byte_iterator(path):
"""given a path, return an iterator over the file
that lazily loads the file
"""
path = Path(path)
with path.open('rb') as file:
reader = partial(file.read1, DEFAULT_BUFFER_SIZE)
file_iterator = iter(reader, bytes())
for chunk in file_iterator:
for byte in chunk:
yield byte
请注意，我们使用file.read1。 file.read阻塞，直到获得所请求的所有字节或EOF。 file.read1允许我们避免阻止，因此可以更快地返回。没有其他答案也提到这一点。
让我们创建一个带有兆字节（实际上是mebibyte）的伪随机数据的文件：
import random
import pathlib
path = 'pseudorandom_bytes'
pathobj = pathlib.Path(path)
pathobj.write_bytes(
bytes(random.randint(0, 255) for _ in range(2**20)))
现在让我们迭代它并在内存中实现它：
>>> l = list(file_byte_iterator(path))
>>> len(l)
1048576
我们可以检查数据的任何部分，例如，最后100个和前100个字节：
>>> l[-100:]
[208, 5, 156, 186, 58, 107, 24, 12, 75, 15, 1, 252, 216, 183, 235, 6, 136, 50, 222, 218, 7, 65, 234, 129, 240, 195, 165, 215, 245, 201, 222, 95, 87, 71, 232, 235, 36, 224, 190, 185, 12, 40, 131, 54, 79, 93, 210, 6, 154, 184, 82, 222, 80, 141, 117, 110, 254, 82, 29, 166, 91, 42, 232, 72, 231, 235, 33, 180, 238, 29, 61, 250, 38, 86, 120, 38, 49, 141, 17, 190, 191, 107, 95, 223, 222, 162, 116, 153, 232, 85, 100, 97, 41, 61, 219, 233, 237, 55, 246, 181]
>>> l[:100]
[28, 172, 79, 126, 36, 99, 103, 191, 146, 225, 24, 48, 113, 187, 48, 185, 31, 142, 216, 187, 27, 146, 215, 61, 111, 218, 171, 4, 160, 250, 110, 51, 128, 106, 3, 10, 116, 123, 128, 31, 73, 152, 58, 49, 184, 223, 17, 176, 166, 195, 6, 35, 206, 206, 39, 231, 89, 249, 21, 112, 168, 4, 88, 169, 215, 132, 255, 168, 129, 127, 60, 252, 244, 160, 80, 155, 246, 147, 234, 227, 157, 137, 101, 84, 115, 103, 77, 44, 84, 134, 140, 77, 224, 176, 242, 254, 171, 115, 193, 29]
不要执行以下操作 - 这会拉出任意大小的块，直到它到达换行符 - 当块太小时太慢，并且可能太大：
with open(path, 'rb') as file:
for chunk in file: # text newline iteration - not for bytes
for byte in chunk:
yield byte
以上只适用于语义上人类可读的文本文件（如纯文本，代码，标记，降价等...基本上任何ascii，utf，latin等编码）。
答案 6 :(得分：5)
Python 3，一次读取所有文件：
with open("filename", "rb") as binary_file:
# Read the whole file at once
data = binary_file.read()
print(data)
您可以使用data变量迭代任意内容。
答案 7 :(得分：4)
如果要阅读大量二进制数据，可能需要考虑struct module。它被记录为转换“在C和Python类型之间”，但当然，字节是字节，并且这些是否被创建为C类型并不重要。例如，如果二进制数据包含两个2字节整数和一个4字节整数，则可以按如下方式读取它们（示例来自struct文档）：
>>> struct.unpack('hhl', b'\x00\x01\x00\x02\x00\x00\x00\x03')
(1, 2, 3)
您可能会发现这比显式循环文件内容更方便，更快捷或两者兼而有之。
答案 8 :(得分：3)
尝试上述所有方法并使用@Aaron Hall的答案后，在运行Windows 10、8 Gb RAM和32位Python 3.5的计算机上，我收到了约90 Mb文件的内存错误。一位同事建议我改用numpy，这样会产生奇迹。
到目前为止，读取整个二进制文件（我已经测试过）的最快方法是：
import numpy as np
file = "binary_file.bin"
data = np.fromfile(file, 'u1')
到目前为止，速度比任何其他方法都要快。希望它能对某人有所帮助！
答案 9 :(得分：2)
如果你正在寻找快速的东西，这是我一直在使用的方法多年来一直工作：
from array import array
with open( path, 'rb' ) as file:
data = array( 'B', file.read() ) # buffer the file
# evaluate it's data
for byte in data:
v = byte # int value
c = chr(byte)
如果你想迭代字符而不是整数，你可以简单地使用data = file.read()，它应该是py3中的bytes（）对象。
答案 10 :(得分：2)
对于大尺寸我认为使用生成器不会坏，这个答案是为了读取文件之类的东西，虽然@codeapp 有一个类似的答案，我认为删除内部循环会更有意义。
def read_chunk(file_object, chunk_size=125):
while True:
file = file_object.read(chunk_size)
if not file:
break
yield file
#sample use
buffer = io.BytesIO()
file = open('myfile', 'r')
for chunk in read_chunk(file):
buffer.write(chunk)
buffer.seek(0)
// save the file or do whatever you want here
您仍然可以将它用作普通列表，我认为这没有任何用处
file_list = list(read_chunk(file, chunk_size=10000))
for i in file_list:
# do something
并获取每个块的索引
for index, chunk in enumurate(read_chunk(file, chunk_size=10000)):
#use the index as a number index
# you can try and get the size of each chunk with this
length = len(chunk)
答案 11 :(得分：1)
此帖子本身并非对该问题的直接答案。相反，它是一个数据驱动的可扩展基准，可用于比较已发布到此问题的许多答案（以及在以后的更新版本，更现代的Python版本中使用新功能的变体），因此应该有助于确定哪个具有最佳性能。
在少数情况下，我已经修改了引用答案中的代码，以使其与基准框架兼容。
首先，这是当前最新版本的Python 2和3的结果。
Fastest to slowest execution speeds with 32-bit Python 2.7.16
numpy version 1.16.5
Test file size: 1,024 KiB
100 executions, best of 3 repetitions
1 Tcll (array.array) : 3.8943 secs, rel speed 1.00x, 0.00% slower (262.95 KiB/sec)
2 Vinay Sajip (read all into memory) : 4.1164 secs, rel speed 1.06x, 5.71% slower (248.76 KiB/sec)
3 codeape + iter + partial : 4.1616 secs, rel speed 1.07x, 6.87% slower (246.06 KiB/sec)
4 codeape : 4.1889 secs, rel speed 1.08x, 7.57% slower (244.46 KiB/sec)
5 Vinay Sajip (chunked) : 4.1977 secs, rel speed 1.08x, 7.79% slower (243.94 KiB/sec)
6 Aaron Hall (Py 2 version) : 4.2417 secs, rel speed 1.09x, 8.92% slower (241.41 KiB/sec)
7 gerrit (struct) : 4.2561 secs, rel speed 1.09x, 9.29% slower (240.59 KiB/sec)
8 Rick M. (numpy) : 8.1398 secs, rel speed 2.09x, 109.02% slower (125.80 KiB/sec)
9 Skurmedel : 31.3264 secs, rel speed 8.04x, 704.42% slower ( 32.69 KiB/sec)
Benchmark runtime (min:sec) - 03:26
Fastest to slowest execution speeds with 32-bit Python 3.8.0
numpy version 1.17.4
Test file size: 1,024 KiB
100 executions, best of 3 repetitions
1 Vinay Sajip + "yield from" + "walrus operator" : 3.5235 secs, rel speed 1.00x, 0.00% slower (290.62 KiB/sec)
2 Aaron Hall + "yield from" : 3.5284 secs, rel speed 1.00x, 0.14% slower (290.22 KiB/sec)
3 codeape + iter + partial + "yield from" : 3.5303 secs, rel speed 1.00x, 0.19% slower (290.06 KiB/sec)
4 Vinay Sajip + "yield from" : 3.5312 secs, rel speed 1.00x, 0.22% slower (289.99 KiB/sec)
5 codeape + "yield from" + "walrus operator" : 3.5370 secs, rel speed 1.00x, 0.38% slower (289.51 KiB/sec)
6 codeape + "yield from" : 3.5390 secs, rel speed 1.00x, 0.44% slower (289.35 KiB/sec)
7 jfs (mmap) : 4.0612 secs, rel speed 1.15x, 15.26% slower (252.14 KiB/sec)
8 Vinay Sajip (read all into memory) : 4.5948 secs, rel speed 1.30x, 30.40% slower (222.86 KiB/sec)
9 codeape + iter + partial : 4.5994 secs, rel speed 1.31x, 30.54% slower (222.64 KiB/sec)
10 codeape : 4.5995 secs, rel speed 1.31x, 30.54% slower (222.63 KiB/sec)
11 Vinay Sajip (chunked) : 4.6110 secs, rel speed 1.31x, 30.87% slower (222.08 KiB/sec)
12 Aaron Hall (Py 2 version) : 4.6292 secs, rel speed 1.31x, 31.38% slower (221.20 KiB/sec)
13 Tcll (array.array) : 4.8627 secs, rel speed 1.38x, 38.01% slower (210.58 KiB/sec)
14 gerrit (struct) : 5.0816 secs, rel speed 1.44x, 44.22% slower (201.51 KiB/sec)
15 Rick M. (numpy) + "yield from" : 11.8084 secs, rel speed 3.35x, 235.13% slower ( 86.72 KiB/sec)
16 Skurmedel : 11.8806 secs, rel speed 3.37x, 237.18% slower ( 86.19 KiB/sec)
17 Rick M. (numpy) : 13.3860 secs, rel speed 3.80x, 279.91% slower ( 76.50 KiB/sec)
Benchmark runtime (min:sec) - 04:47
我还用更大的10 MiB测试文件（运行了将近一个小时）运行了它，并获得了与上面显示的结果相当的性能结果。
以下是用于进行基准测试的代码：
from __future__ import print_function
import array
import atexit
from collections import deque, namedtuple
import io
from mmap import ACCESS_READ, mmap
import numpy as np
from operator import attrgetter
import os
import random
import struct
import sys
import tempfile
from textwrap import dedent
import time
import timeit
import traceback
try:
xrange
except NameError: # Python 3
xrange = range
class KiB(int):
""" KibiBytes - multiples of the byte units for quantities of information. """
def __new__(self, value=0):
return 1024*value
BIG_TEST_FILE = 1 # MiBs or 0 for a small file.
SML_TEST_FILE = KiB(64)
EXECUTIONS = 100 # Number of times each "algorithm" is executed per timing run.
TIMINGS = 3 # Number of timing runs.
CHUNK_SIZE = KiB(8)
if BIG_TEST_FILE:
FILE_SIZE = KiB(1024) * BIG_TEST_FILE
else:
FILE_SIZE = SML_TEST_FILE # For quicker testing.
# Common setup for all algorithms -- prefixed to each algorithm's setup.
COMMON_SETUP = dedent("""
# Make accessible in algorithms.
from __main__ import array, deque, get_buffer_size, mmap, np, struct
from __main__ import ACCESS_READ, CHUNK_SIZE, FILE_SIZE, TEMP_FILENAME
from functools import partial
try:
xrange
except NameError: # Python 3
xrange = range
""")
def get_buffer_size(path):
""" Determine optimal buffer size for reading files. """
st = os.stat(path)
try:
bufsize = st.st_blksize # Available on some Unix systems (like Linux)
except AttributeError:
bufsize = io.DEFAULT_BUFFER_SIZE
return bufsize
# Utility primarily for use when embedding additional algorithms into benchmark.
VERIFY_NUM_READ = """
# Verify generator reads correct number of bytes (assumes values are correct).
bytes_read = sum(1 for _ in file_byte_iterator(TEMP_FILENAME))
assert bytes_read == FILE_SIZE, \
'Wrong number of bytes generated: got {:,} instead of {:,}'.format(
bytes_read, FILE_SIZE)
"""
TIMING = namedtuple('TIMING', 'label, exec_time')
class Algorithm(namedtuple('CodeFragments', 'setup, test')):
# Default timeit "stmt" code fragment.
_TEST = """
#for b in file_byte_iterator(TEMP_FILENAME): # Loop over every byte.
# pass # Do stuff with byte...
deque(file_byte_iterator(TEMP_FILENAME), maxlen=0) # Data sink.
"""
# Must overload __new__ because (named)tuples are immutable.
def __new__(cls, setup, test=None):
""" Dedent (unindent) code fragment string arguments.
Args:
`setup` -- Code fragment that defines things used by `test` code.
In this case it should define a generator function named
`file_byte_iterator()` that will be passed that name of a test file
of binary data. This code is not timed.
`test` -- Code fragment that uses things defined in `setup` code.
Defaults to _TEST. This code that's timed.
"""
test = cls._TEST if test is None else test # Use default unless one is provided.
# Uncomment to replace all performance tests with one that verifies the correct
# number of bytes values are being generated by the file_byte_iterator function.
#test = VERIFY_NUM_READ
return tuple.__new__(cls, (dedent(setup), dedent(test)))
algorithms = {
'Aaron Hall (Py 2 version)': Algorithm("""
def file_byte_iterator(path):
with open(path, "rb") as file:
callable = partial(file.read, 1024)
sentinel = bytes() # or b''
for chunk in iter(callable, sentinel):
for byte in chunk:
yield byte
"""),
"codeape": Algorithm("""
def file_byte_iterator(filename, chunksize=CHUNK_SIZE):
with open(filename, "rb") as f:
while True:
chunk = f.read(chunksize)
if chunk:
for b in chunk:
yield b
else:
break
"""),
"codeape + iter + partial": Algorithm("""
def file_byte_iterator(filename, chunksize=CHUNK_SIZE):
with open(filename, "rb") as f:
for chunk in iter(partial(f.read, chunksize), b''):
for b in chunk:
yield b
"""),
"gerrit (struct)": Algorithm("""
def file_byte_iterator(filename):
with open(filename, "rb") as f:
fmt = '{}B'.format(FILE_SIZE) # Reads entire file at once.
for b in struct.unpack(fmt, f.read()):
yield b
"""),
'Rick M. (numpy)': Algorithm("""
def file_byte_iterator(filename):
for byte in np.fromfile(filename, 'u1'):
yield byte
"""),
"Skurmedel": Algorithm("""
def file_byte_iterator(filename):
with open(filename, "rb") as f:
byte = f.read(1)
while byte:
yield byte
byte = f.read(1)
"""),
"Tcll (array.array)": Algorithm("""
def file_byte_iterator(filename):
with open(filename, "rb") as f:
arr = array.array('B')
arr.fromfile(f, FILE_SIZE) # Reads entire file at once.
for b in arr:
yield b
"""),
"Vinay Sajip (read all into memory)": Algorithm("""
def file_byte_iterator(filename):
with open(filename, "rb") as f:
bytes_read = f.read() # Reads entire file at once.
for b in bytes_read:
yield b
"""),
"Vinay Sajip (chunked)": Algorithm("""
def file_byte_iterator(filename, chunksize=CHUNK_SIZE):
with open(filename, "rb") as f:
chunk = f.read(chunksize)
while chunk:
for b in chunk:
yield b
chunk = f.read(chunksize)
"""),
} # End algorithms
#
# Versions of algorithms that will only work in certain releases (or better) of Python.
#
if sys.version_info >= (3, 3):
algorithms.update({
'codeape + iter + partial + "yield from"': Algorithm("""
def file_byte_iterator(filename, chunksize=CHUNK_SIZE):
with open(filename, "rb") as f:
for chunk in iter(partial(f.read, chunksize), b''):
yield from chunk
"""),
'codeape + "yield from"': Algorithm("""
def file_byte_iterator(filename, chunksize=CHUNK_SIZE):
with open(filename, "rb") as f:
while True:
chunk = f.read(chunksize)
if chunk:
yield from chunk
else:
break
"""),
"jfs (mmap)": Algorithm("""
def file_byte_iterator(filename):
with open(filename, "rb") as f, \
mmap(f.fileno(), 0, access=ACCESS_READ) as s:
yield from s
"""),
'Rick M. (numpy) + "yield from"': Algorithm("""
def file_byte_iterator(filename):
# data = np.fromfile(filename, 'u1')
yield from np.fromfile(filename, 'u1')
"""),
'Vinay Sajip + "yield from"': Algorithm("""
def file_byte_iterator(filename, chunksize=CHUNK_SIZE):
with open(filename, "rb") as f:
chunk = f.read(chunksize)
while chunk:
yield from chunk # Added in Py 3.3
chunk = f.read(chunksize)
"""),
}) # End Python 3.3 update.
if sys.version_info >= (3, 5):
algorithms.update({
'Aaron Hall + "yield from"': Algorithm("""
from pathlib import Path
def file_byte_iterator(path):
''' Given a path, return an iterator over the file
that lazily loads the file.
'''
path = Path(path)
bufsize = get_buffer_size(path)
with path.open('rb') as file:
reader = partial(file.read1, bufsize)
for chunk in iter(reader, bytes()):
yield from chunk
"""),
}) # End Python 3.5 update.
if sys.version_info >= (3, 8, 0):
algorithms.update({
'Vinay Sajip + "yield from" + "walrus operator"': Algorithm("""
def file_byte_iterator(filename, chunksize=CHUNK_SIZE):
with open(filename, "rb") as f:
while chunk := f.read(chunksize):
yield from chunk # Added in Py 3.3
"""),
'codeape + "yield from" + "walrus operator"': Algorithm("""
def file_byte_iterator(filename, chunksize=CHUNK_SIZE):
with open(filename, "rb") as f:
while chunk := f.read(chunksize):
yield from chunk
"""),
}) # End Python 3.8.0 update.update.
#### Main ####
def main():
global TEMP_FILENAME
def cleanup():
""" Clean up after testing is completed. """
try:
os.remove(TEMP_FILENAME) # Delete the temporary file.
except Exception:
pass
atexit.register(cleanup)
# Create a named temporary binary file of pseudo-random bytes for testing.
fd, TEMP_FILENAME = tempfile.mkstemp('.bin')
with os.fdopen(fd, 'wb') as file:
os.write(fd, bytearray(random.randrange(256) for _ in range(FILE_SIZE)))
# Execute and time each algorithm, gather results.
start_time = time.time() # To determine how long testing itself takes.
timings = []
for label in algorithms:
try:
timing = TIMING(label,
min(timeit.repeat(algorithms[label].test,
setup=COMMON_SETUP + algorithms[label].setup,
repeat=TIMINGS, number=EXECUTIONS)))
except Exception as exc:
print('{} occurred timing the algorithm: "{}"\n {}'.format(
type(exc).__name__, label, exc))
traceback.print_exc(file=sys.stdout) # Redirect to stdout.
sys.exit(1)
timings.append(timing)
# Report results.
print('Fastest to slowest execution speeds with {}-bit Python {}.{}.{}'.format(
64 if sys.maxsize > 2**32 else 32, *sys.version_info[:3]))
print(' numpy version {}'.format(np.version.full_version))
print(' Test file size: {:,} KiB'.format(FILE_SIZE // KiB(1)))
print(' {:,d} executions, best of {:d} repetitions'.format(EXECUTIONS, TIMINGS))
print()
longest = max(len(timing.label) for timing in timings) # Len of longest identifier.
ranked = sorted(timings, key=attrgetter('exec_time')) # Sort so fastest is first.
fastest = ranked[0].exec_time
for rank, timing in enumerate(ranked, 1):
print('{:<2d} {:>{width}} : {:8.4f} secs, rel speed {:6.2f}x, {:6.2f}% slower '
'({:6.2f} KiB/sec)'.format(
rank,
timing.label, timing.exec_time, round(timing.exec_time/fastest, 2),
round((timing.exec_time/fastest - 1) * 100, 2),
(FILE_SIZE/timing.exec_time) / KiB(1), # per sec.
width=longest))
print()
mins, secs = divmod(time.time()-start_time, 60)
print('Benchmark runtime (min:sec) - {:02d}:{:02d}'.format(int(mins),
int(round(secs))))
main()
答案 12 :(得分：0)
这是一个使用Numpy fromfile寻址上述@Nirmal注释来读取网络字节序数据的示例：
dtheader= np.dtype([('Start Name','b', (4,)),
('Message Type', np.int32, (1,)),
('Instance', np.int32, (1,)),
('NumItems', np.int32, (1,)),
('Length', np.int32, (1,)),
('ComplexArray', np.int32, (1,))])
dtheader=dtheader.newbyteorder('>')
headerinfo = np.fromfile(iqfile, dtype=dtheader, count=1)
print(raw['Start Name'])
我希望这会有所帮助。问题在于fromfile无法识别EOF，并允许优雅地中断任意大小文件的循环。