这是一种相当节省时间和资源的方法，它可以并行读取所有文件的值并计算它们的平均值，但一次只能读取每个文件的一行——但它确实会暂时将整个第一个.dat文件读入内存为了确定每个文件中有多少行和列的数字。
你没有说你的“数字”是整数还是浮点数或什么，所以这会将它们读入浮点数（即使它们不是也可以工作）。无论如何，平均值被计算并作为浮点数输出。
更新
根据您的评论，我已经修改了我的原始答案，以计算sigma每行和每列中的值的总体标准偏差 ( )。它在计算它们的平均值之后立即执行此操作，因此不需要第二次重新读取所有数据。此外，为了响应评论中提出的建议，已添加上下文管理器以确保关闭所有输入文件。
请注意，标准偏差只打印出来，不会写入输出文件，但是对相同或单独的文件执行此操作应该很容易添加。
from contextlib import contextmanager
from itertools import izip
from glob import iglob
from math import sqrt
from sys import exit
@contextmanager
def multi_file_manager(files, mode='rt'):
files = [open(file, mode) for file in files]
yield files
for file in files:
file.close()
# generator function to read, convert, and yield each value from a text file
def read_values(file, datatype=float):
for line in file:
for value in (datatype(word) for word in line.split()):
yield value
# enumerate multiple egual length iterables simultaneously as (i, n0, n1, ...)
def multi_enumerate(*iterables, **kwds):
start = kwds.get('start', 0)
return ((n,)+t for n, t in enumerate(izip(*iterables), start))
DATA_FILE_PATTERN = 'data*.dat'
MIN_DATA_FILES = 2
with multi_file_manager(iglob(DATA_FILE_PATTERN)) as datfiles:
num_files = len(datfiles)
if num_files < MIN_DATA_FILES:
print('Less than {} .dat files were found to process, '
'terminating.'.format(MIN_DATA_FILES))
exit(1)
# determine number of rows and cols from first file
temp = [line.split() for line in datfiles[0]]
num_rows = len(temp)
num_cols = len(temp[0])
datfiles[0].seek(0) # rewind first file
del temp # no longer needed
print '{} .dat files found, each must have {} rows x {} cols\n'.format(
num_files, num_rows, num_cols)
means = []
std_devs = []
divisor = float(num_files-1) # Bessel's correction for sample standard dev
generators = [read_values(file) for file in datfiles]
for _ in xrange(num_rows): # main processing loop
for _ in xrange(num_cols):
# create a sequence of next cell values from each file
values = tuple(next(g) for g in generators)
mean = float(sum(values)) / num_files
means.append(mean)
means_diff_sq = ((value-mean)**2 for value in values)
std_dev = sqrt(sum(means_diff_sq) / divisor)
std_devs.append(std_dev)
print 'Average and (standard deviation) of values:'
with open('means.txt', 'wt') as averages:
for i, mean, std_dev in multi_enumerate(means, std_devs):
print '{:.2f} ({:.2f})'.format(mean, std_dev),
averages.write('{:.2f}'.format(mean)) # note std dev not written
if i % num_cols != num_cols-1: # not last column?
averages.write(' ') # delimiter between values on line
else:
print # newline
averages.write('\n')