创新实训记录13--算法结果展示/报错解决

界面展示

django界面 pyechart bootstrap

1.context里面传输的需要是列表，否则会导致跳转界面的不显示：

先前传的是一个字符串导致一直前端遍历的时候获取不到相关内容！

2.使输出一行一个：

原始：

现在：

 function loadXMLDoc1() {
      var node=document.getElementById("contents");
          for(var i=0;i<res1.length;i++) { 
              var p=document.createElement("p");
              var tmp=document.createTextNode(res1[i]);
              p.appendChild(tmp);
              node.appendChild(p) ;
          }            
    }

3.进度卡在0，不进行，也不报错问题：

原因：
传入的论文id集合里面应该是int类型的，而原先是string类型的：

 #论文集合(由此篇论文及其inlink，outlink组成)
            list_paper=[]
            with open('choose_paper/data/articles.txt', 'r', encoding='utf-8') as f:
                lines = f.readlines()
                artical = lines[num]
                list_paper.append(int(artical.strip()))
            with open('choose_paper/data/inlinks.txt', 'r', encoding='utf-8') as f:
                lines = f.readlines()
                inlink = lines[num].split()
                for i in range(len(inlink)):
                    list_paper.append(int(inlink[i])  )
            with open('choose_paper/data/outlinks.txt', 'r', encoding='utf-8') as f:
                lines = f.readlines()
                outlink = lines[num].split()
                for i in range(len(outlink)):
                    list_paper.append(int(outlink[i]))

4.报错&解决：（禁止跨域是浏览器的安全限制机制）

Access to XMLHttpRequest at 'file:///F:/%E4%BB%A3%E7%A0%81/Jquery/a.txt' from origin 'null' has been blocked by CORS policy: Cross origin requests are only supported for protocol schemes: http, data, chrome, chrome-extension, https.

解决办法：

https://www.cnblogs.com/jing-tian/p/10820839.html

5.报错&解决：

Uncaught ReferenceError: loadXMLDoc0 is not defined at HTMLButtonElement.onclick ((index):35)
解决：
html：

<input type="button" value="立即登录" onclick="dosave();"/>  

js：

dosave = function (){  
        alert("成功啦！");  
    } 
    

参考：

https://blog.csdn.net/qq_39019865/article/details/79867091?utm_medium=distribute.pc_relevant_t0.none-task-blog-BlogCommendFromMachineLearnPai2-1.nonecase&depth_1-utm_source=distribute.pc_relevant_t0.none-task-blog-BlogCommendFromMachineLearnPai2-1.nonecase

6.报错&解决：

Uncaught SyntaxError: Unexpected identifier
解决：

https://blog.csdn.net/challenge_me/article/details/87856998

7.报错&解决：

输出的结果总是带有上一行的结果：（找了很久！）
愿因在于result+=“（” 应该为result=“（”：

而且因为获取的页面原来存在文字，导致切换的不准备的显示：（会带有上一按钮的内容）

修改：（添加此话）

for tmp in finalresult:
                result_0="( "
                for j in range(2):
                    with open("choose_paper/PaperCompare/ACM_dataset/nodes.txt",'r',encoding='utf-8') as f:
                      lines=f.readlines()
                      for i in range(len(lines)):
                          line=lines[i].split()
                          if(str(tmp[j])==str(line[0])):  
                              for m in range(1,len(line)):
                                  result_0+=line[m]
                                  result_0+=" "
                    if(j==0):
                      result_0+=","
                result_0+=")"
                result0.append(result_0)

8.预测结果效果：

9.词云效果添加;

代码：

##词云--------------------------------------
            # 年份列表
            file_years = 'choose_paper/data/years.txt'
            years = []
            with open(file_years,'r',encoding="utf-8") as f:
             for line in f.readlines():
                year_ = line.strip()
                years.append(year_)
            # 会议列表
            file_venues = 'choose_paper/data/venues.txt'
            venues = []
            with open(file_venues,'r',encoding="utf-8") as f:
                for line in f.readlines():
                    venue_ = line.strip()
                    venues.append(venue_)
            # 关键词列表
            key_path = 'choose_paper/data/keywords.txt'
            word_list =[]
            with open(key_path,'r',encoding="utf-8") as f:
                    for line in f.readlines():
                        word = line.strip().split()
                        word_list.append(word)
            # 定义根据年份会议获取数据
            word_dict = {} #某年某个关键词出现的次数统计
            use_data = [] #可视化时传入的data
            yea = str(year) #将输入的年份转为str
            venu = str(venue)
            for i in range(len(years)):
                if yea == years[i].strip() and venu == venues[i].strip():
                    sword = word_list[i]
                    for word in sword:
                        if word in word_dict.keys() and word is not '':
                            word_dict[word]+=1
                        elif word is not '':
                            temp = { }
                            temp[word]=1
                            word_dict.update(temp)
                        else:
                             continue
             # 对word_dict进行处理，筛选词语
            length = len(word_dict)
            if length>60:
                for k,v in word_dict.items():
                    if v>1:
                        tup = (k,v)
                        use_data.append(tup)
            else:
                for k,v in word_dict.items():
                    tup = (k,v)
                    use_data.append(tup)
            ##词云展示：
            wtitle  = str(year)+' '+venue+':关键词词云展示'
            mywordcloud = WordCloud()
            mywordcloud.add('',use_data,shape='triangle')\
               .set_global_opts(title_opts=opts.TitleOpts(
                               title=wtitle))
            mywordcloud.render(path="choose_paper/data/graph/wordCloud.txt")  # 显示图表
            mywordcloud_txt = "{"
            with open('choose_paper/data/graph/wordCloud.txt', 'r', encoding='utf-8') as f:
                line = f.readlines()
                for i in range(16, len(line)-4):
                    mywordcloud_txt += line[i]