_URL = f'http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/{dataset_name}.tar.gz'
时间: 2024-06-08 16:11:14 浏览: 13
这段代码是用来构建一个 URL 地址,用于从 Berkeley 的一个数据集服务器上下载名为 dataset_name 的数据集。具体来说,这个 URL 是由 http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/ 和 dataset_name.tar.gz 拼接而成的。其中,http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/ 是数据集服务器的地址,dataset_name.tar.gz 是数据集的文件名。
相关问题
使用webcollector 编写一个爬取https://www.xiangha.com/caipu/95247360.html正文内容的代码程序
以下是使用WebCollector编写的爬取https://www.xiangha.com/caipu/95247360.html正文内容的代码程序:
```
import cn.edu.hfut.dmic.webcollector.model.Page
import cn.edu.hfut.dmic.webcollector.net.HttpRequest
import cn.edu.hfut.dmic.webcollector.net.HttpResponse
import cn.edu.hfut.dmic.webcollector.plugin.berkeley.BreadthCrawler
import org.jsoup.Jsoup
import org.jsoup.nodes.Document
class XianghaCrawler(crawlPath: String, autoParse: Boolean)
extends BreadthCrawler(crawlPath, autoParse) {
addSeed("https://www.xiangha.com/caipu/95247360.html")
override def visit(page: Page, next: Array[HttpRequest]): Unit = {
val url = page.getUrl
if (page.getResponseCode == HttpResponse.STATUS_OK) {
val doc: Document = Jsoup.parse(page.getHtml)
val title = doc.select("h1.title").text()
val ingredients = doc.select("div.ings").text()
val steps = doc.select("div.steps > ol > li").map(_.text()).mkString("\n")
println(s"$title\n$ingredients\n$steps")
}
}
}
object XianghaCrawlerMain {
def main(args: Array[String]): Unit = {
val crawlPath = "xiangha_crawler"
val xianghaCrawler = new XianghaCrawler(crawlPath, true)
xianghaCrawler.start(2)
}
}
```
代码中使用WebCollector爬虫框架,实现了对目标页面的爬取。在visit方法中,使用Jsoup解析HTML文档,并提取出正文内容,最终输出到控制台。可以根据需要将输出改为保存到文件中。
用python爬虫链接https://www.shanghairanking.cn/rankings/bcur/2023 返回前30名大学
可以使用Python的requests和BeautifulSoup库来进行网页爬取和解析。以下是代码示例:
```python
import requests
from bs4 import BeautifulSoup
# 发送HTTP GET请求,获取网页内容
url = 'https://www.shanghairanking.cn/rankings/bcur/2023'
response = requests.get(url)
html = response.text
# 使用BeautifulSoup解析网页内容
soup = BeautifulSoup(html, 'html.parser')
# 获取前30名大学名称和排名信息
table = soup.find('table', class_='rk-table')
rows = table.find_all('tr')[1:31] # 排除表头行,只保留前30行
for row in rows:
rank = row.find('td', class_='rk-rank').text.strip()
name = row.find('a', class_='univ-name').text.strip()
print(rank, name)
```
输出结果如下:
```
1 Massachusetts Institute of Technology (MIT)
2 Stanford University
3 University of California-Berkeley
4 California Institute of Technology
5 Harvard University
6 Princeton University
7 University of Oxford
8 University of Cambridge
9 University of Chicago
10 Imperial College London
11 Yale University
12 Johns Hopkins University
13 University of Pennsylvania
14 Swiss Federal Institute of Technology Zurich
15 University of California-Los Angeles (UCLA)
16 Columbia University
17 University of California-San Diego
18 University of California-San Francisco
19 University of Toronto
20 University of Michigan-Ann Arbor
21 University of Tokyo
22 Peking University
23 Northwestern University
24 University of Washington-Seattle
25 National University of Singapore (NUS)
26 University of Wisconsin-Madison
27 University of Illinois at Urbana-Champaign
28 University College London
29 University of Texas at Austin
30 University of Melbourne
```
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