Python程序设计#4作业

作业题目

基于#3作业获取的数据（No_Smoothing，非平滑数据），计算出LOWESS（局部加权回归，fraction取前后各5年的数据）结果，该结果可以与test.txt文件中的Lowess字段进行比较。

作业内容

程序源代码嵌入下方的code block中。

```python import aiohttp import asyncio import argparse import json import xml.etree.ElementTree as xmlet from matplotlib import colors import matplotlib.pyplot as pl import numpy as np import statsmodels.api as sm lowess = sm.nonparametric.lowess

async def main(host,port,start,end,fmt): url=f'http://{host}:{port}/{fmt}/start/{start}/end/{end}' async with aiohttp.ClientSession(trust_env=True) as session: async with session.get(url,verify_ssl=False) as response: print("Status:", response.status) print("Content-type:", response.headers['content-type']) text = await response.text() temper=list() if fmt=='csv': lines=text.split('\n') for line in lines: if line=="": continue words=line.split(',') temper.append({'year':words[0],'temperature':words[1],'lowess':words[2]})

        elif fmt=='xml':
            root=xmlet.fromstring(text)
            for child in root:
                temper.append({'year':child[0].text,'temperature':child[1].text,'lowess':child[2].text})
        elif fmt=='json':
            temper=json.loads(text)

        for words in temper:
            print('year:{0} temperature:{1} lowess:{2} '.format(words['year'],words['temperature'],words['lowess']))


        xList = list()
        yList = list()
        for item in temper:
            xList.append(item['year'])
            yList.append(item['temperature'])
        x=np.array(xList)
        y=np.array(yList)
        d = lowess(y,x,frac=10/len(temper))

        print(d[:,1])

        pl.clf()
        pl.grid()
        pl.xlim(1880,2020)
        pl.ylim(-0.5,1.5)
        pl.plot(x,y,'o',label="initial",color="silver")
        pl.plot(x,d[:,1],'k',label="lowess")
        pl.legend()
        pl.show()

loop = asyncio.get_event_loop()

loop.run_until_complete(main())

if name ==' main ': parser = argparse.ArgumentParser(description='world temperature') parser.add_argument('--start',dest='start',type=int,default=1880) parser.add_argument('--end',dest='end',type=int,default=2020) parser.add_argument('--fmt',dest='fmt',default='json') parser.add_argument('host') parser.add_argument('port') args=parser.parse_args() print(f'{args}')

asyncio.run(main(args.host,args.port,args.start,args.end,args.fmt))

```

代码说明

本次作业是在上一个作业之上调用lowess库拟合曲线，并利用了python的画图功能绘制最后的拟合图像。

主要增加的代码部分为：

```python xList = list() yList = list() for item in temper: xList.append(item['year']) yList.append(item['temperature']) x=np.array(xList) y=np.array(yList) d = lowess(y,x,frac=10/len(temper))

        print(d[:,1])

        pl.clf()
        pl.grid()
        pl.xlim(1880,2020)
        pl.ylim(-0.5,1.5)
        pl.plot(x,y,'o',label="initial",color="silver")
        pl.plot(x,d[:,1],'k',label="lowess")
        pl.legend()
        pl.show()

```

拟合lowess的结果如下,与题目所给lowess吻合，并且我们的精度更高。

-0.08144779 -0.12008878 -0.15881339 -0.19614487 -0.23326259 -0.25917118 -0.26827201 -0.26864019 -0.26318052 -0.25464983 -0.24974514 -0.25420199 -0.26251882 -0.25562321 -0.23511587 -0.21920332 -0.20210456 -0.17923063 -0.16369371 -0.16968201 -0.19561231 -0.22810348 -0.2539592 -0.27745884 -0.30570607 -0.3347918 -0.35633241 -0.37072506 -0.38825524 -0.40659592 -0.41041234 -0.38828298 -0.35011102 -0.32172921 -0.31019817 -0.30188395 -0.29379681 -0.29339035 -0.29791136 -0.29215244 -0.27664492 -0.26332536 -0.25326153 -0.24114275 -0.23064979 -0.2238896 -0.21905266 -0.21037642 -0.19771053 -0.19095205 -0.19169238 -0.18695124 -0.17747809 -0.16736763 -0.15620801 -0.13675994 -0.10493252 -0.06076713 -0.0117206 0.03228486 0.06544592 0.09127118 0.10638015 0.10034034 0.07448895 0.04064708 0.00480866 -0.03636271 -0.0704062 -0.0819276 -0.07521314 -0.0704123 -0.07140659 -0.07259443 -0.06517353 -0.05518938 -0.0483255 -0.03671756 -0.01276659 0.01333896 0.02551233 0.01328195 -0.00920192 -0.02644841 -0.0387297 -0.05215666 -0.05734057 -0.04807596 -0.03151384 -0.01742263 -0.00357373 0.00382378 0.00202471 -0.00316851 0.00534132 0.02275157 0.04304114 0.0753689 0.12261457 0.16570641 0.19780009 0.21300344 0.21616041 0.21129991 0.21107274 0.22279201 0.24297974 0.27118568 0.30626764 0.32931736 0.33134639 0.32704439 0.32826922 0.33199645 0.34077176 0.36725327 0.39990774 0.42235573 0.44362863 0.47176327 0.50126276 0.52336387 0.55017207 0.58510615 0.61173863 0.620659 0.62526428 0.63459115 0.64173617 0.64281338 0.64788152 0.66539588 0.69783501 0.7404656 0.78662114 0.83159711 0.87563015 0.91146532 0.94557196 0.97656371 1.00582661

画图，拟合图像如下