我正在使用python读取大量数据并将它们拆分成各种文件。我正在寻找一种方法来加快我已有的代码。进来的数字是小端32位浮点数。我已经进行了几次测试。
首先测试8分钟完成:
f = open('filename','rb')
#file_out is a list of many open writing files 'wb'
while chunk:
for i in range(self.num_files):
chunk = f.read(4)
file_out[i].write(chunk)
这个速度可以接受,但是当我尝试添加一些操作时,事情会急剧减慢到56分钟:
file_old = [0,0,0,...,0]
f = open('filename','rb')
#file_out is a list of many open writing files 'wb'
while chunk:
for i in range(self.num_files):
chunk = f.read(4)
num_chunk = numpy.fromstring(chunk, dtype = numpy.float32)
file_out[i].write(num_chunk-file_old[i])
file_old[i] = num_chunk
我在缩短的样本上对上面的代码运行了cProfile。结果如下:
写= 3.457
Numpy fromstring = 2.274
读= 1.370
我怎样才能加快速度呢?
答案 0 :(得分:1)
我能够使用numpy.fromfile
发现一种更快捷的数据读取方式。我写了一个快速的小测试脚本,如下所示:
from os.path import join
import numpy
import struct
from time import time
def main():
#Set the path name and filename
folder = join("Tone_Tests","1khz_10ns_0907153323")
fn = join(folder,"Channel1.raw32")
#Test 1
start = time()
f = open(fn,'rb')
array = read_fromstring(f)
f.close()
print "Test fromString = ",time()-start
del array
#Test 2
start = time()
f = open(fn,'rb')
array = read_struct(f)
f.close()
print "Test fromStruct = ",time()-start
del array
#Test 3
start = time()
f = open(fn,'rb')
array = read_fromfile(f)
f.close()
print "Test fromfile = ",time()-start
del array
def read_fromstring(f):
#Use Numpy fromstring, read each 4 bytes, convert, store in list
data = []
chunk = f.read(4)
while chunk:
num_chunk = numpy.fromstring(chunk, dtype = 'float32')
data.append(num_chunk)
chunk = f.read(4)
return numpy.array(data)
def read_struct(f):
#Same as numpy froms string but using the struct.
data = []
chunk = f.read(4)
while chunk:
num_chunk = struct.unpack('<f',chunk)
data.append(num_chunk)
chunk = f.read(4)
return numpy.array(data)
def read_fromfile(f):
return numpy.fromfile(f, dtype = 'float32', count = -1)
终端的定时输出是:
Test fromString = 4.43499994278
Test fromStruct = 2.42199993134
Test fromfile = 0.00399994850159
使用python -m cProfile -s time filename.py > profile.txt
表示时间是:
ncalls tottime percall cumtime percall filename:lineno(function)
1 1.456 1.456 4.272 4.272 Read_Data_tester.py:42(read_fromstring)
1 1.162 1.162 2.369 2.369 Read_Data_tester.py:56(read_struct)
1 0.000 0.000 0.005 0.005 Read_Data_tester.py:70(read_fromfile)
答案 1 :(得分:-2)
我认为您可以使用线程(使用线程模块)。
这将使您使用主代码在parralel中运行函数,因此您可以通过文件的三分之一开始,另一半开始,另一半开始。因此,每个人只需要处理一个数据季度,因此 只需要一个时间。
(我说应该有开销所以不会那么快)