Python 代码加速通常是为了提高计算性能、减少运行时间。以下是一些常见的 Python 加速方法,适用于不同场景:
1、问题背景
def novo (infile, seqList, out) :
uDic = dict()
rDic = dict()
nmDic = dict()
with open(infile, 'r') as infile, open(seqList, 'r') as RADlist :
samples = [line.strip() for line in RADlist]
lines = [line.strip() for line in infile]
#Create dictionaires with all the samples
for i in samples:
uDic[i.replace(" ","")] = 0
rDic[i.replace(" ","")] = 0
nmDic[i.replace(" ","")] = 0
for k in lines:
l1 = k.split("\t")
l2 = l1[0].split(";")
l3 = l2[0].replace(">","")
if len(l1)<2:
continue
if l1[4] == "U":
for k in uDic.keys():
if k == l3:
uDic[k] += 1
if l1[4] == "R":
for j in rDic.keys():
if j == l3:
rDic[j] += 1
if l1[4] == "NM":
for h in nmDic.keys():
if h == l3:
nmDic[h] += 1
f = open(out, "w")
f.write("Sample"+"\t"+"R"+"\t"+"U"+"\t"+"NM"+"\t"+"TOTAL"+"\t"+"%R"+"\t"+"%U"+"\t"+"%NM"+"\n")
for i in samples:
U = int()
R = int()
NM = int ()
for k, j in uDic.items():
if k == i:
U = j
for o, p in rDic.items():
if o == i:
R = p
for y,u in nmDic.items():
if y == i:
NM = u
TOTAL = int(U + R + NM)
try:
f.write(i+"\t"+str(R)+"\t"+str(U)+"\t"+str(NM)+"\t"+str(TOTAL)+"\t"+str(float(R) / TOTAL)+"\t"+str(float(U) / TOTAL)+"\t"+str(float(NM) / TOTAL)+"\n")
except:
continue
f.close()
上面是一个 Python 代码,它从文本文件中读取字符串并将其搜索一个输入文件中,并将这些字符串在输出文件中出现的次数打印出来。问题是,该代码在处理大文件时速度很慢。
2、解决方案
方法一
一个提高代码速度的方法是使用迭代器来逐行读取文件,而不是一次性将整个文件读入内存。这可以节省大量的内存,并允许代码处理更大的文件。
from collections import Counter
import csv
# Count
counts = Counter()
with open(infile, 'r') as infile:
for line in infile:
l1 = line.strip().split("\t")
l2 = l1[0].split(";")
l3 = l2[0].replace(">","")
if len(l1)<2:
continue
counts[(l1[4], l3)] += 1
# Produce output
types = ['R', 'U', 'NM']
with open(seqList, 'r') as RADlist, open(out, 'w') as outfile:
f = csv.writer(outfile, delimiter='\t')
f.writerow(types + ['TOTAL'] + ['%' + t for t in types])
for sample in RADlist:
sample = sample.strip()
countrow = [counts((t, sample)) for t in types]
total = sum(countrow)
f.writerow([sample] + countrow + [total] + [c/total for c in countrow])
方法二
另一个提高代码速度的方法是使用并行处理。这可以利用多核 CPU 的优势,同时处理多个任务。
from concurrent.futures import ProcessPoolExecutor
from collections import Counter
# Count
def count_sample(sample, infile):
counts = Counter()
with open(infile, 'r') as infile:
for line in infile:
l1 = line.strip().split("\t")
l2 = l1[0].split(";")
l3 = l2[0].replace(">","")
if len(l1)<2:
continue
counts[(l1[4], l3)] += 1
return sample, counts
# Produce output
types = ['R', 'U', 'NM']
with ProcessPoolExecutor() as executor, open(seqList, 'r') as RADlist, open(out, 'w') as outfile:
f = csv.writer(outfile, delimiter='\t')
f.writerow(types + ['TOTAL'] + ['%' + t for t in types])
for sample, counts in executor.map(count_sample, RADlist, [infile] * len(RADlist)):
countrow = [counts[(t, sample)] for t in types]
total = sum(countrow)
f.writerow([sample] + countrow + [total] + [c/total for c in countrow])
通过这些方法,可以显著加快 Python 代码的执行速度。
原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。
如有侵权,请联系 cloudcommunity@tencent.com 删除。
原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。
如有侵权,请联系 cloudcommunity@tencent.com 删除。