元素\nresponse.xpath('//ul/li/text()').extract() #网站的描述\nresponse.xpath('//ul/li/a/text()').extract() #网站的标题\nresponse.xpath('//ul/li/a/@href').extract() #网站的链接\n\n#拼接更多的.xpath()来进一步获取某个节点\nfor sel in response.xpath('//ul/li'):\n title = sel.xpath('a/text()').extract()\n link = sel.xpath('a/@href').extract()\n desc = sel.xpath('text()').extract()\n print title, link, desc\n \n#在spider中加入以下代码，再次爬取dmoz.org将会看到爬取到的网站信息被成功输出\nimport scrapy\n\nclass DmozSpider(scrapy.Spider):\n name = \"dmoz\"\n allowed_domains = [\"dmoz-odp.org\"]\n start_urls = [\n \"http://www.dmoz-odp.org/Computers/Programming/Languages/Python/Books/\",\n \"http://www.dmoz-odp.org/Computers/Programming/Languages/Python/Resources/\"\n ]\n\n def parse(self, response):\n for sel in response.xpath('//ul/li'):\n title = sel.xpath('a/text()').extract()\n link = sel.xpath('a/@href').extract()\n desc = sel.xpath('text()').extract()\n print title, link, desc\n\n \n#使用item\n# item对象是自定义的python字典。可以使用标准的字典语法获取到其每个字段的值。\n>>> item = DmozItem() #终端命令\n>>> item['title'] = 'Example title'\n>>> item['title']\n'Example title'\n\n# 为了将爬取的数据返回，最终的代码是：\nimport scrapy\n\nfrom tutorial.items import DmozItem\n\nclass DmozSpider(scrapy.Spider):\n name = \"dmoz\"\n allowed_domains = [\"dmoz-odp.org\"]\n start_urls = [\n \"http://www.dmoz-odp.org/Computers/Programming/Languages/Python/Books/\",\n \"http://www.dmoz-odp.org/Computers/Programming/Languages/Python/Resources/\"\n ]\n\n def parse(self, response):\n for sel in response.xpath('//ul/li'):\n item = DmozItem()\n item['title'] = sel.xpath('a/text()').extract()\n item['link'] = sel.xpath('a/@href').extract()\n item['desc'] = sel.xpath('text()').extract()\n yield item\n \n \n#追踪连接\n#接来下不仅仅满足与爬取books及resources页面，想要过去所有python directory的内容。\n# 实现这个功能的改进版spider：\nimport scrapy\n\nfrom tutorial.items import DmozItem\n\nclass DmozSpider(scrapy.Spider):\n name = \"dmoz\"\n allowed_domains = [\"dmoz-odp.org\"]\n start_urls = [\n \"http://www.dmoz-odp.org/Computers/Programming/Languages/Python/\",\n ]\n\n def parse(self, response): #加个这段代码反而没有爬到数据\n for href in response.css(\"ul.directory.dir-col > li > a::attr('href')\"):\n url = response.urljoin(response.url, href.extract())\n yield scrapy.Request(url, callback=self.parse_dir_contents)\n\n def parse_dir_contents(self, response):\n for sel in response.xpath('//ul/li'):\n item = DmozItem()\n item['title'] = sel.xpath('a/text()').extract()\n item['link'] = sel.xpath('a/@href').extract()\n item['desc'] = sel.xpath('text()').extract()\n yield item\n \n# 现在parse() 仅仅从页面中提取我们感兴趣的链接，使用 response.urljoin 方法构造一个绝对路径的URL(页面上的链接都是相对路径的)，\n# 产生(yield)一个请求， 该请求使用 parse_dir_contents() 方法作为回调函数, 用于最终产生我们想要的数据.。\n\n#保存爬到的数据\n# 最简单存储爬取的数据的方式是使用 Feed exports:\n# scrapy crawl dmoz -o items.json 终端命令\n# 该命令采用JSON格式对爬到的数据进行序列化，生成items.json文件\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n"},{"alpha_fraction":0.6107417941093445,"alphanum_fraction":0.6544976234436035,"avg_line_length":27.320213317871094,"blob_id":"64b604c83ef60a92e1c0d7d0f44ae3a6d2fd51e2","content_id":"71286966213ff6dff02abfc5f1f6f03f8a76ba9b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":31953,"license_type":"no_license","max_line_length":170,"num_lines":940,"path":"/Recommender/Recommender.py","repo_name":"zhangbc07/Project","src_encoding":"UTF-8","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\n\n\nimport pandas as pd\nimport re\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sqlalchemy import create_engine\nengine=create_engine('mysql+pymysql://root:Tsta@888@127.0.0.1:3306/data?charset=utf8')\nimport warnings\nwarnings.filterwarnings('ignore')\nsql=pd.read_sql('all_gzdata',engine,chunksize=10000)\n\n\n# In[ ]:\n\n\ncounts1=[i['fullURLId'].value_counts() for i in sql] # counts1此时是个列表\ncounts1=pd.concat(counts1).groupby(level_0).sum() # 由于sql分块的 上面counts1也是，concat纵向向链接\ncounts1=counts1.reset_index()\ncounts1.columns=['index','num']\ncounts1['type']=counts1['index'].str.extract('(\\d{3})',expand=True)\ncounts1.head()\ncounts1_=counts1[['type','num']].groupby('type').sum()\ncounts1_.sort_values(by='num',ascending=False,inplace=True)\ncounts1_['percentage']=(counts1_['num']/counts1_['num'].sum())*100\ncounts1_.head()\n\n\n# In[ ]:\n\n\na=counts1.set_index('type')\nb=counts1.groupby('type').sum()\nc=pd.merge(a,b,left_index=True,right_index=True)# 均以行索引为链接键 合并\nc['percentage']=(c['num_x']/c['num_y'])*100\nc.head()\nc.reset_index(inplace=True) # inplace是否改变自身，True则改变原数据\nd=c.sort_values(by=['type','percentage'],ascending=[True,False]).reset_index()\nd.head() \n\n\n# In[ ]:\n\n\nsql=pd.read_sql('all_gzdata',engine,chunksize=10000)\ndef count107(i):\n j=i[['fullURL']][i['fullURLId'].str.contains('107')].copy()\n j['type']=None\n j['type'][j['fullURL'].str.contains('info/.+?/')]='知识首页'\n j['type'][j['fullURL'].str.contains('info/.+?/.+?')]='知识列表页'\n j['type'][j['fullURL'].str.contains('/\\d+?_*\\d+?\\.html')]='知识内容页'\n return j['type'].value_counts()\ncounts2=[count107(i) for i in sql]\ncounts2=pd.concat(counts2).groupby(level_0).sum()\ncounts2_=pd.DataFrame(counts2).reset_index()\ncounts2_['percentage']=(counts2_['type']/counts2_['type'].sum())*100\ncounts2_.columns=['107类型','num','百分比']\ncounts2.head()\n\n\n# In[ ]:\n\n\ndef countquestion(i):\n j=i[['fullURLId']][i['fullURL'].str.contains('\\?')].copy()\n return j\nsql=pd.read_sql('all_gzdata',engine,chunksize=10000)\ncounts3=[countquestion(i)['fullURLId'].value_counts() for i in sql]\ncounts3=pd.concat(counts3).groupby(level_0).sum()\ncounts3\ncounts3_=pd.DataFrame(counts3)\ncounts3_['percentage']=(counts3_['fullURLId']/counts3_['fullURLId'].sum())*100\ncounts3_.reset_index(inplace=True)\ncounts3_.columns=['fullURLId','num','percentage']\ncounts3_.sort_values(by='percentage',ascending=False,inplace=True)\ncounts3_.reset_index(inplace=True)\nc3=(counts3_['num'].sum()/counts1['num'].sum())*100\nc3#统计?网页各类占比情况，探究?网页特征及在总样本占比\n\n\n# In[ ]:\n\n\ndef page199(i):\n j=i[['pageTitle']][i['fullURLId'].str.contains('199') & i['fullURL'].str.contains('\\?')]\n j['title']=1\n j['title'][j['pageTitle'].str.contains('法律快车-律师助手')]='法律快车-律师助手'\n j['title'][j['pageTitle'].str.contains('免费发布法律咨询')]='免费发布法律咨询'\n j['title'][j['pageTitle'].str.contains('咨询发布成功')]='咨询发布成功'\n j['title'][j['pageTitle'].str.contains('法律快搜')]='快搜'\n j['title'][j['title']==1]='其他类型'\n return j\nsql=pd.read_sql('all_gzdata',engine,chunksize=10000)\ncounts4=[page199(i) for i in sql]\ncounts4_=pd.concat(counts4)\nct4_result=counts4_['title'].value_counts()\nct4_result=pd.DataFrame(ct4_result)\nct4_result.reset_index(inplace=True)\nct4_result['percentage']=(ct4_result['title']/ct4_result['title'].sum())*100\nct4_result.columns=['title','num','percentage']\nct4_result.sort_values(by='num',ascending=False,inplace=True)\nct4_result#统计199，其他类网址细分类别特征，占比\n\n\n# In[ ]:\n\n\ndef wandering(i):\n j=i[['fullURLId','fullURL','pageTitle']][(i['fullURL'].str.contains('.html'))==False]\n return j\nsql=pd.read_sql('all_gzdata',engine,chunksize=10000)\ncounts5=[wandering(i) for i in sql]\ncounts5_=pd.concat(counts5)\n\nct5Id=counts5_['fullURLId'].value_counts()\nct5Id\nct5Id_=pd.DataFrame(ct5Id)\nct5Id_.reset_index(inplace=True)\nct5Id_['index']=ct5Id_['index'].str.extract('(\\d{3})',expand=True)\nct5Id_=pd.DataFrame(ct5Id_.groupby('index').sum())\nct5Id_['percentage']=(ct5Id_['fullURLId']/ct5Id_['fullURLId'].sum())*100\nct5Id_.reset_index(inplace=True)\nct5Id_.columns=['fullURLId','num','percentage']\nct5Id_.sort_values(by='num',ascending=False,inplace=True)\nct5Id_.reset_index(inplace=True)\n#分析闲逛网站的用户浏览特征，哪些吸引了这些闲逛用户\nct5Id\n\n\n# In[ ]:\n\n\nsql=pd.read_sql('all_gzdata',engine,chunksize=10000)\ncounts6=[i['realIP'].value_counts() for i in sql]\ncounts6_=pd.concat(counts6).groupby(level=0).sum()\ncounts6_=pd.DataFrame(counts6_)\ncounts6_[1]=1\nct6=counts6_.groupby('realIP').sum()\nct6.columns=['用户数'] # 点击次数情况，来表征用户，即点击n次的用户数为m\nct6.index.name='点击次数' # realIP 出现次数 \nct6['用户百分比']=ct6['用户数']/ct6['用户数'].sum()*100\nct6['记录百分比']= ct6['用户数']*ct6.index/counts6_['realIP'].sum()*100 # 点击n次记录数占总记录数的比值\nct6.sort_index(inplace=True)\nct6_=ct6.iloc[:7,:].T\nct6['用户数'].sum() #总点击次数\nct6_.insert(0,'总计',[ct6['用户数'].sum(),100,100])\nct6_['7次以上']=ct6_.iloc[:,0]-ct6_.iloc[:,1:].sum(axis=1)\nct6_\n\n\n# In[ ]:\n\n\nbeyond7=ct6.index[7:]\nbins=[7,100,1000,50000] #用于划分区间\nbeyond7_cut=pd.cut(beyond7,bins,right=True,labels=['8~100','101~1000','1000以上'])#right=True 即(7,100],(101,1000]\nbeyond7_cut_=pd.DataFrame(beyond7_cut.value_counts())\nbeyond7_cut_.index.name='点击次数'\nbeyond7_cut_.columns=['用户数']\nbeyond7_cut_.iloc[0,0]=ct6.loc[8:100,:]['用户数'].sum() #依次为点击在(7,100]用户数,iloc索引号比第几个少一\nbeyond7_cut_.iloc[1,0]=ct6.loc[101:1000,:]['用户数'].sum() #注意，这里使用iloc会有问题，因这里行索引并非0开始的连续整数，而是名称索引，非自然整数索引\nbeyond7_cut_.iloc[2,0]=ct6.loc[1001:,:]['用户数'].sum()\nbeyond7_cut_.sort_values(by='用户数',ascending=False,inplace=True)\nbeyond7_cut_.reset_index(drop=True,inplace=True)\nbeyond7_cut_ # 点击8次以上情况统计分析，点击分布情况\n\n\n# In[ ]:\n\n\nfor_once=counts6_[counts6_['realIP']==1] # 分析点击一次用户行为特征\ndel for_once[1] # 去除多余列\nfor_once.columns=['点击次数']\nfor_once.index.name='realIP' # IP 找到，以下开始以此为基准 链接数据，merge\n\nsql=pd.read_sql('all_gzdata',engine,chunksize=10000)\nfor_once_=[i[['fullURLId','fullURL','realIP']] for i in sql]\nfor_once_=pd.concat(for_once_)\nfor_once_merge=pd.merge(for_once,for_once_,right_on='realIP',left_index=True,how='left')\nfor_once_merge #浏览一次用户行为信息，浏览的网页，可分析网页类型\nfor_once_url=pd.DataFrame(for_once_merge['fullURL'].value_counts())\nfor_once_url.reset_index(inplace=True)\nfor_once_url.columns=['fullURL','num']\nfor_once_url['percentage']=for_once_url['num']/for_once_url['num'].sum()*100\nfor_once_url #浏览一次用户行为信息，浏览的网页，可分析网页类型:税法、婚姻、问题\n# 分析用户点击情况，点击频率特征，点一次就走 跳失率。。\n\n\n# In[ ]:\n\n\ndef url_click(i):\n j=i[['fullURL','fullURLId','realIP']][i['fullURL'].str.contains('\\.html')] # \\带不带都一样\n return j\n\nsql=pd.read_sql('select * from all_gzdata',engine,chunksize=10000)\ncounts8=[url_click(i) for i in sql]\ncounts8_=pd.concat(counts8)\nct8=counts8_['fullURL'].value_counts()\nct8=pd.DataFrame(ct8)\nct8.columns=['点击次数']\nct8.index.name='网址'\nct8.sort_values(by='点击次数',ascending=False).iloc[:20,:] #网址的点击率排行，用户关注度\n\nct8_=ct8.reset_index() #500和501一样结果，思路不一样，推第一个500\nclick_beyond_500=ct8_[(ct8_['网址'].str.contains('/\\d+?_*\\d+?\\.html')) & (ct8_['点击次数']>50)]\nclick_beyond_501=ct8_[ct8_['网址'].str.contains('/\\d+?_*\\d+?\\.html')][ct8_['点击次数']>50] # 会报警\n#网页点击情况分析，点击最多是哪些，关注度\nct8\n\n\n# In[ ]:\n\n\nfor i in sql: #逐块变换并去重\n d = i.copy()\n d['fullURL'] = d['fullURL'].str.replace('_\\d{0,2}.html', '.html') #将下划线后面部分去掉，规范为标准网址\n d = d.drop_duplicates() #删除重复记录\n d.to_sql('changed_gzdata', engine, index = False, if_exists = 'append') #保存\n\n\n# In[ ]:\n\n\ndef scanning_url(i):\n j=i.copy()\n j['fullURL']=j['fullURL'].str.replace('_\\d{0,2}.html','.html')\n return j\nsql=pd.read_sql('select * from all_gzdata',engine,chunksize=10000)\ncounts9=[scanning_url(i) for i in sql]\ncounts9_=pd.concat(counts9)\nct9=counts9_['fullURL'].value_counts() # 每个网页被点击总次数，多页合为一页\nct9_=counts9_[['realIP','fullURL']].groupby('fullURL').count() # 每个IP对所点击网页的点击次数，多页合为一页\nct9__=ct9_['realIP'].value_counts()\nct9__20=ct9__[:20]\nct9__20.plot()\n\n\n# In[ ]:\n\n\n#另一种分析视角，翻页行为统计分析\npattern=re.compile('http://(.*\\d+?)_\\w+_\\w+\\.html$|http://(.*\\d+?)_\\w+\\.html$|http://(.*\\w+?).html$',re.S) # re.S 字符串跨行匹配;# 获取网址中以http://与.html中间的主体部分,即去掉翻页的内容，即去掉尾部\"_d\"\n# 三个分别对应 主站点，\nc9=click_beyond_500.set_index('网址').sort_index().copy() #sort_index()保证了 同一主站翻页网页是按从第一页开始排列的，即首页、下一页，为下方计算下一页点击占上一页比率铺垫\nc9['websitemain']=np.nan # 统计主站点,即记录主站点，翻页的首页点击次数\nfor i in range(len(c9)):\n items=re.findall(pattern,c9.index[i])\n if len(items)==0:\n temp=np.nan\n else:\n for j in items[0]:\n if j!='':\n temp=j\n c9.iloc[i,1]=temp\nc9.head()\nc9_0=c9['websitemain'].value_counts()\nc9_0=pd.DataFrame(c9_0)\nc9_1=c9_0[c9_0['websitemain']>=2].copy() # 滤掉不存在翻页的网页\nc9_1.columns=['Times'] # 用于识别是一类网页，主网址出现次数\nc9_1.index.name='websitemain'\nc9_1['num']=np.arange(1,len(c9_1)+1)\nc9_2=pd.merge(c9,c9_1,left_on='websitemain',right_index=True,how='right') # 链接左列与右索引 且左列与右索引的保留二者的行，且已右边为基础（即右边全保留，在此基础上添加左边的二者有共通行的）\n# 当列与列做链接 索引会被忽略舍去，链接列与索引或索引与索引时索引会保留\nc9_2.sort_index(inplace=True)\n\nc9_2['per']=np.nan\ndef getper(x):\n print(x)\n for i in range(len(x)-1):\n x.iloc[i+1,-1]=x.iloc[i+1,0]/x.iloc[i,0] # 翻页与上一页点击率比值，保存在最后一列处；同类网页下一页与上一页点击率比\n return x\nresult=pd.DataFrame()\nfor i in range(1,c9_2['num'].max()+1): # 多少种翻页类，c9_2['num'].max()+1，从1开始\n k=getper(c9_2[c9_2['num']==i]) # 同类翻页网页下一页与上一页点击数比值\n result=pd.concat([result,k])\nc9_2['Times'].value_counts()\n\nflipPageResult=result[result['Times']<10]\nflipPageResult.head()\n# 用户翻页行为分析，网站停留情况，文章分页优化\n\n\n# In[ ]:\n\n\ndef countmidurl(i): # 删除数据规则之中间网页（带midques_）\n j=i[['fullURL','fullURLId','realIP']].copy()\n j['type']='非中间类型网页'\n j['type'][j['fullURL'].str.contains('midques_')]='中间类型网页'\n return j['type'].value_counts()\n\n\n# In[ ]:\n\n\nsql=pd.read_sql('select * from all_gzdata',engine,chunksize=10000)\ncounts10=[countmidurl(i) for i in sql]\ncounts10_=pd.concat(counts10).groupby(level=0).sum()\ncounts10_\n\n\n# In[ ]:\n\n\ndef count_no_html(i): # 删除数据规则之目录页（带html）\n j=i[['fullURL','pageTitle','fullURLId']].copy()\n j['type']='有html'\n j['type'][j['fullURL'].str.contains('\\.html')==False]=\"无html\"\n return j['type'].value_counts()\nsql=pd.read_sql('select * from all_gzdata',engine,chunksize=10000)\ncounts11=[count_no_html(i) for i in sql]\ncounts11_=pd.concat(counts11).groupby(level=0).sum()\ncounts11_\n\n\n# In[ ]:\n\n\ndef count_law_consult(i): # 数据删除规则之咨询、律师助手登录\n j=i[['fullURL','pageTitle','fullURLId']].copy()\n j['type']='其他'\n j['pageTitle'].fillna('空',inplace=True)\n j['type'][j['pageTitle'].str.contains('快车-律师助手')]='快车-律师助手'\n j['type'][j['pageTitle'].str.contains('咨询发布成功')]='咨询发布成功'\n j['type'][(j['pageTitle'].str.contains('免费发布法律咨询')) | (j['pageTitle'].str.contains('法律快搜'))]='快搜免费发布法律咨询'\n return j['type'].value_counts()\nsql=pd.read_sql('select * from all_gzdata',engine,chunksize=10000)\ncounts12=[count_law_consult(i) for i in sql]\ncounts12_=pd.concat(counts12).groupby(level=0).sum()\ncounts12_\n\n\n# In[ ]:\n\n\ndef count_law_ques(i): # 数据删除规则之去掉与分析网站无关的网页\n j=i[['fullURL']].copy()\n j['fullURL']=j['fullURL'].str.replace('\\?.*','')\n j['type']='主网址不包含关键字'\n j['type'][j['fullURL'].str.contains('lawtime')]='主网址包含关键字'\n return j\nsql=pd.read_sql('select * from all_gzdata',engine,chunksize=10000)\ncounts13=[count_law_ques(i) for i in sql]\ncounts13_=pd.concat(counts13)['type'].value_counts()\ncounts13_\n\n\n# In[ ]:\n\n\ndef count_duplicate(i): # 数据删除规则之去掉同一用户同一时间同一网页的重复数据（可能是记录错误）\n j=i[['fullURL','realIP','timestamp_format']].copy()\n return j\nsql=pd.read_sql('select * from all_gzdata',engine,chunksize=10000)\ncounts14=[count_duplicate(i) for i in sql]\ncounts14_=pd.concat(counts14)\nprint(len(counts14_[counts14_.duplicated()==True]),len(counts14_.drop_duplicates()))\n\nct14=counts14_.drop_duplicates()\n\n\n# In[ ]:\n\n\n# 开始对数据库数据进行清洗操作，构建模型数据\nsql=pd.read_sql('select * from all_gzdata',engine,chunksize=10000)\nfor i in sql: #只要.html结尾的 & 截取问号左边的值 & 只要包含主网址（lawtime)的&网址中间没有midques_的\n d = i[['realIP', 'fullURL','pageTitle','userID','timestamp_format']].copy() # 只要网址列\n d['fullURL'] = d['fullURL'].str.replace('\\?.*','') # 网址中问号后面的部分\n d = d[(d['fullURL'].str.contains('\\.html')) & (d['fullURL'].str.contains('lawtime')) & (d['fullURL'].str.contains('midques_') == False)] # 只要含有.html的网址\n # 保存到数据库中\n d.to_sql('cleaned_one', engine, index = False, if_exists = 'append')\n\n\n# In[ ]:\n\n\nsql=pd.read_sql('select * from cleaned_one',engine,chunksize=10000)\nfor i in sql: #删除 快车-律师助手 & 免费发布法律咨询 & 咨询发布成功 & 法律快搜）\n d = i[['realIP','fullURL','pageTitle','userID','timestamp_format']]# 只要网址列\n d['pageTitle'].fillna(u'空',inplace=True)\n d = d[(d['pageTitle'].str.contains(u'快车-律师助手') == False) & (d['pageTitle'].str.contains(u'咨询发布成功') == False) & \n (d['pageTitle'].str.contains(u'免费发布法律咨询') == False) & (d['pageTitle'].str.contains(u'法律快搜') == False)\n ].copy()\n # 保存到数据库中\n d.to_sql('cleaned_two', engine, index = False, if_exists = 'append')\n\n\n# In[ ]:\n\n\nsql=pd.read_sql('select * from cleaned_two',engine,chunksize=10000)\ndef dropduplicate(i): \n j = i[['realIP','fullURL','pageTitle','userID','timestamp_format']].copy()\n return j\n\n\n# In[ ]:\n\n\ncount15 = [dropduplicate(i) for i in sql]\ncount15 = pd.concat(count15)\nprint(len(count15)) # 2011695\ncount16 = count15.drop_duplicates(['fullURL','userID','timestamp_format']) # 一定要进行二次删除重复，因为不同的块中会有重复值\nprint(len(count16)) # 646915\ncount16.to_sql('cleaned_three', engine)\n#数据清洗及保存到库的操作，清洗完毕\n\n\n# In[ ]:\n\n\n# 查看各表的长度\nsql = pd.read_sql('all_gzdata', engine, chunksize = 10000)\nlens = 0\nfor i in sql:\n temp = len(i)\n lens = temp + lens\nprint(lens) #836877\n\n\n# In[ ]:\n\n\n# 查看cleaned_one表中的记录数\nsql1 = pd.read_sql('cleaned_one', engine, chunksize = 10000)\nlens1 = 0\nfor i in sql1:\n temp = len(i)\n lens1 = temp + lens1\nprint(lens1)#1341130\n\n\n# In[ ]:\n\n\n# 查看cleaned_two表中的记录数\nsql2 = pd.read_sql('cleaned_two', engine, chunksize = 10000)\nlens2 = 0\nfor i in sql2:\n temp = len(i)\n lens2 = temp + lens2\nprint(lens2)#2011695\n\n\n# In[ ]:\n\n\n# 查看cleaned_three表中的记录数\nsql3 = pd.read_sql('cleaned_three', engine, chunksize = 10000)\nlens3 = 0\nfor i in sql3:\n temp = len(i)\n lens3 = temp + lens3\nprint(lens3)\n\n\n# In[ ]:\n\n\nsql = pd.read_sql('cleaned_three', engine, chunksize = 10000)\nl0 = 0\nl1 = 0\nl2 = 0\nfor i in sql:\n d = i.copy()\n # 获取所有记录的个数\n l0 += len(d)\n # 获取类似于http://www.lawtime.cn***/2007020619634_2.html格式的记录的个数\n # 匹配1 易知，匹配1一定包含匹配2\n x1 = d[d['fullURL'].str.contains('_\\d{0,2}.html')]\n l1 += len(x1)\n # 匹配2\n # 获取类似于http://www.lawtime.cn***/29_1_p3.html格式的记录的个数\n x2 = d[d['fullURL'].str.contains('_\\d{0,2}_\\w{0,2}.html')]\n l2 += len(x2)\n# x1.to_sql('l1', engine, index=False, if_exists = 'append') # 保存\n# x2.to_sql('l2', engine, index=False, if_exists = 'append') # 保存\n \nprint(l0,l1,l2)\n\n\n# In[ ]:\n\n\n# 去掉翻页的网址\nsql = pd.read_sql('cleaned_three', engine, chunksize = 10000)\nl4 = 0\nfor i in sql: #初筛\n d = i.copy()\n # 注意！！！替换1和替换2的顺序不能颠倒，否则删除不完整\n # 替换1 将类似于http://www.lawtime.cn***/29_1_p3.html下划线后面部分\"_1_p3\"去掉，规范为标准网址 \n d['fullURL'] = d['fullURL'].str.replace('_\\d{0,2}_\\w{0,2}.html','.html')#这部分网址有 9260 个\n # 替换2 将类似于http://www.lawtime.cn***/2007020619634_2.html下划线后面部分\"_2\"去掉，规范为标准网址\n d['fullURL'] = d['fullURL'].str.replace('_\\d{0,2}.html','.html') #这部分网址有 55455-9260 = 46195 个\n d = d.drop_duplicates(['fullURL','userID']) # 删除重复记录(删除有相同网址和相同用户ID的)【不完整】因为不同的数据块中依然有重复数据\n l4 += len(d)\n d.to_sql('changed_1', engine, index=False, if_exists = 'append') # 保存\nprint(l4 )\n\n# In[ ]:\n\n\nsql = pd.read_sql('changed_1', engine, chunksize = 10000)\ndef dropduplicate(i): #二次筛选\n j = i[['realIP','fullURL','pageTitle','userID','timestamp_format']].copy()\n return j\ncounts1 = [dropduplicate(i) for i in sql]\ncounts1 = pd.concat(counts1)\nprint(len(counts1))# 1095216\n\n# In[ ]:\n\n\na = counts1.drop_duplicates(['fullURL','userID'])\nprint(len(a))# 528166\na.to_sql('changed_2', engine) # 保存\n\n\n# In[ ]:\n\n\n# 验证是否清洗干净，即changed_2已不存翻页网址\nsql = pd.read_sql('changed_2', engine, chunksize = 10000)\nl0 = 0\nl1 = 0\nl2 = 0\nfor i in sql:\n d = i.copy()\n # 获取所有记录的个数\n l0 += len(d)\n # 获取类似于http://www.lawtime.cn***/2007020619634_2.html格式的记录的个数\n # 匹配1 易知，匹配1一定包含匹配2\n x1 = d[d['fullURL'].str.contains('_\\d{0,2}.html')]\n l1 += len(x1)\n # 匹配2\n # 获取类似于http://www.lawtime.cn***/29_1_p3.html格式的记录的个数\n x2 = d[d['fullURL'].str.contains('_\\d{0,2}_\\w{0,2}.html')]\n l2 += len(x2)\nprint(l0,l1,l2)# 528166 0 0表示已经删除成功\n\n\n# In[ ]:\n\n\n# 手动添加咨询类和知识类网址的类别，type={'咨询类','知识类'}\nsql = pd.read_sql('changed_2', engine, chunksize = 10000)\ndef countzhishi(i):\n j = i[['fullURL']].copy()\n j['type'] = 'else'\n j['type'][j['fullURL'].str.contains('(info)|(faguizt)')] = 'zhishi'\n j['type'][j['fullURL'].str.contains('(ask)|(askzt)')] = 'zixun'\n return j\ncounts17 = [countzhishi(i) for i in sql]\ncounts17 = pd.concat(counts17)\ncounts17['type'].value_counts()\na = counts17['type'].value_counts()\nb = pd.DataFrame(a)\nb.columns = ['num']\nb.index.name = 'type'\nb['per'] = b['num']/b['num'].sum()*100\nb\n\n\n# In[ ]:\n\n\n# 接上；咨询类较单一，知识类有丰富的二级、三级分类，以下作分析\nc = counts17[counts17['type']=='zhishi']\nd = c[c['fullURL'].str.contains('info')]\nprint(len(d)) # 102140\nd['iszsk'] = 'else' # 结果显示是空 \nd['iszsk'][d['fullURL'].str.contains('info')] = 'infoelsezsk' # 102032\nd['iszsk'][d['fullURL'].str.contains('zhishiku')] = 'zsk' # 108\nd['iszsk'].value_counts() \n# 由结果可知，除了‘info'和’zhishifku'没有其他类型，且 【info类型（不包含zhishiku)：infoelsezsk】和【包含zhishiku：zsk】类型无相交的部分。\n# 因此分析知识类别下的二级类型时，需要分两部分考虑，求每部分的类别，再求并集，即为所有二级类型\n\n\n# In[ ]:\n\n\n# 对于http://www.lawtime.cn/info/jiaotong/jtsgcl/2011070996791.html类型的网址进行这样匹配,获取二级类别名称\"jiaotong\"\npattern = re.compile('/info/(.*?)/',re.S)\ne = d[d['iszsk'] == 'infoelsezsk']\nfor i in range(len(e)): #用上面已经处理的'iszsk'列分成两种类别的网址，分别使用正则表达式进行匹配,较慢\n e.iloc[i,2] = re.findall(pattern, e.iloc[i,0])[0]\nprint(e.head())\n\n# In[ ]:\n\n\n# 对于http://www.lawtime.cn/zhishiku/laodong/info/***.html类型的网址进行这样匹配,获取二级类别名称\"laodong\"\n# 由于还有一类是http://www.lawtime.cn/zhishiku/laodong/***.html，所以使用'zhishiku/(.*?)/'进行匹配\npattern1 = re.compile('zhishiku/(.*?)/',re.S)\nf = d[d['iszsk'] == 'zsk']\nfor i in range(len(f)):\n# print i \n f.iloc[i,2] = re.findall(pattern1, f.iloc[i,0])[0]\nprint(f.head())\n\ne.columns = ['fullURL', 'type1', 'type2']\nprint(e.head())\nf.columns = ['fullURL', 'type1', 'type2']\nprint(f.head())\n\n\n# In[ ]:\n\n\n# 将两类处理过二级类别的记录合并，求二级类别的交集\ng = pd.concat([e,f])\nh = g['type2'].value_counts()\n# 求两类网址中的二级类别数，由结果可知，两类网址的二级类别的集合的并集满足所需条件\nlen(e['type2'].value_counts()) # 66\nlen(f['type2'].value_counts()) # 31\nlen(g['type2'].value_counts()) # 69\nprint(h.head())\nprint(h.index) # 列出知识类别下的所有的二级类别\n\nfor i in range(len(h.index)): # 将二级类别存入到数据库中\n x=g[g['type2']==h.index[i]]\n x.to_sql('h.index', engine, if_exists='append')\n\n\n# In[ ]:\n\n\nq = e.copy()\nq['type3'] = np.nan\nresultype3 = pd.DataFrame([],columns=q.columns)\nfor i in range(len(h.index)): # 正则匹配出三级类别\n pattern2 = re.compile('/info/'+h.index[i]+'/(.*?)/',re.S)\n current = q[q['type2'] == h.index[i]]\n print(current.head())\n for j in range(len(current)):\n findresult = re.findall(pattern2, current.iloc[j,0])\n if findresult == []: # 若匹配结果是空，则将空值进行赋值给三级类别\n current.iloc[j,3] = np.nan\n else:\n current.iloc[j,3] = findresult[0]\n resultype3 = pd.concat([resultype3,current])# 将处理后的数据拼接,即网址做索引，三列为一级、二级、三级类别\nresultype3.set_index('fullURL',inplace=True)\nresultype3.head(10)\n# 统计婚姻类下面的三级类别的数目\nj = resultype3[resultype3['type2'] == 'hunyin']['type3'].value_counts()\nprint(len(j)) # 145\nj.head()\n\n\n# In[ ]:\n\n\nype3nums = resultype3.pivot_table(index = ['type2','type3'], aggfunc = 'count') #类别3排序\n# 方式2: Type3nums = resultype3.groupby([resultype3['type2'],resultype3['type3']]).count()\nr = Type3nums.reset_index().sort_values(by=['type2','type1'],ascending=[True,False])\nr.set_index(['type2','type3'],inplace = True)\n\nr.to_excel('2_2_3Type3nums.xlsx')\nr\n\n\n# In[ ]:\n\n\n# 读取数据库数据 ，属性规约，提取模型需要的数据（属性）；此处是只选择用户和用户访问的网页\nsql = pd.read_sql('changed_2', engine, chunksize = 10000)\nl1 = 0\nl2 = 0 \nfor i in sql:\n zixun = i[['userID','fullURL']][i['fullURL'].str.contains('(ask)|(askzt)')].copy()\n l1 = len(zixun) + l1\n hunyin = i[['userID','fullURL']][i['fullURL'].str.contains('hunyin')].copy() \n l2 = len(hunyin) + l2\n zixun.to_sql('zixunformodel', engine, index=False,if_exists = 'append')\n hunyin.to_sql('hunyinformodel', engine, index=False,if_exists = 'append')\nprint(l1,l2) # 393185 16982\n\n# In[ ]:\n\n\n# 方法二：\nm = counts17[counts17['type'] == 'zixun']\nn = counts17[counts17['fullURL'].str.contains('hunyin')]\np = m[m['fullURL'].str.contains('hunyin')]\np # 结果为空，可知，包含zixun的页面中不包含hunyin，两者没有交集\n#savetosql(m,'zixun')\n#savetosql(n,'hunyin')\nm.to_sql('zixun',engine)\nn.to_sql('hunyin',engine)\n\n\n# In[ ]:\n\n\n## 推荐，基于物品的协同过滤推荐、随机推荐、按流行度推荐\n\ndata=pd.read_sql('hunyinformodel',engine)\ndata.head()\ndef jaccard(a,b): # 杰卡德相似系数，对0-1矩阵，故需要先将数据转成0-1矩阵\n return 1.0*(a*b).sum()/(a+b-a*b).sum()\nclass recommender():\n sim=None\n def similarity(self,x,distance):\n y=np.ones((len(x),len(x)))\n for i in range(len(x)):\n for j in range(len(x)):\n y[i,j]=distance(x[i],x[j])\n return y\n def fit(self,x,distance=jaccard):\n self.sim=self.similarity(x,distance)\n return self.sim\n def recommend(self,a):\n return np.dot(self.sim,a)*(1-a)\nlen(data['fullURL'].value_counts())\nlen(data['realIP'].value_counts())\n# 网址数远小于用户数，即商品数小于使用的客户数，采用物品的协同过滤推荐\n\n\n# In[ ]:\n\n\nstart0=time.clock()\ndata.sort_values(by=['realIP','fullURL'],ascending=[True,True],inplace=True)\nrealIP=data['realIP'].value_counts().index\nrealIP=np.sort(realIP)\nfullURL=data['fullURL'].value_counts().index\nfullURl=np.sort(fullURL)\nd=pd.DataFrame([],index=realIP,columns=fullURL)\nfor i in range(len(data)):\n a=data.iloc[i,0]\n b=data.iloc[i,1]\n d.loc[a,b]=1\nd.fillna(0,inplace=True)\nend0=time.clock()\nusetime0=end0-start0\nprint('转化0-1矩阵耗时' + str(usetime0) +'s!')\n#d.to_csv()\n\n\n# In[ ]:\n\n\ndf=d.copy()\nsampler=np.random.permutation(len(df))\ndf = df.take(sample)\ntrain=df.iloc[:int(len(df)*0.9),:] \ntest=df.iloc[int(len(df)*0.9):,:] \n\n\n# In[ ]:\n\n\ndf=df.values()\ndf_train=df[:int(len(df)*0.9),:] # 9299\ndf_test=df[int(len(df)*0.9):,:] # 1034\ndf_train=df_tain.T\ndf_test=df_test.T\nprint(df_train.shape,df_test.shape) # (4339L, 9299L) (4339L, 1034L)\n\n\n# In[ ]:\n\n\nstart1=time.clock()\nr=recommender()\nsim=r.fit(df_train) # 计算相似度矩阵\nend1=time.clock()\na=pd.DataFrame(sim)\nusetime1=end1-start1\nprint('建立矩阵耗时'+ str(usetime1)+'s!')\nprint(a.shape)\n\n\n# In[ ]:\n\n\na.index=train.columns\n#a.columns=train.columns\na.columns=train.index\na.head(20)\n\n\n# In[ ]:\n\n\nstart2=time.clock()\nresult=r.recommend(df_test)\nend2=time.clock()\n\nresult1=pd.DataFrame(result)\nusetime2=end2-start2\n\nprint('测试集推荐函数耗时'+str(usetime2)+'s!')\nresult1.head()\nresult1.index=test.columns\nresult1.columns=test.index\nresult1\n\n\n# In[ ]:\n\n\ndef xietong_result(k,recomMatrix): # k表推荐个数，recomMatrix推荐矩阵表DataFrame\n recomMatrix.fillna(0.0,inplace=True)\n n=range(1,k+1)\n recommends=['xietong'+str(y) for y in n]\n currentemp=pd.DataFrame([],index=recomMatrix.columns,columns=recommends)\n for i in range(len(recomMatrix.columns)):\n temp = recomMatrix.sort_values(by = recomMatrix.columns[i], ascending = False)\n j = 0 \n while j < k:\n currentemp.iloc[i,j] = temp.index[j]\n if temp.iloc[j,i] == 0.0:\n currentemp.iloc[i,j:k] = np.nan\n break\n j += 1\n return currentemp\nstart3 = time.clock()\nxietong_result = xietong_result(3, result1)\nend3 = time.clock()\nprint('按照协同过滤推荐方法为用户推荐3个未浏览过的网址耗时为' + str(end3 - start3)+'s!') #29.4996622053s!\nxietong_result.head()\n\n\n# In[ ]:\n\n\n# test = df.iloc[int(len(df)*0.9):, :] # 所有测试数据df此时是矩阵，这样不能用\nrandata = 1 - df_test # df_test是用户浏览过的网页的矩阵形式，randata则表示是用户未浏览过的网页的矩阵形式\nrandmatrix = pd.DataFrame(randata, index = test.columns,columns=test.index)#这是用户未浏览过(待推荐）的网页的表格形式\ndef rand_recommd(K, recomMatrix):# \n import random # 注意：这个random是random模块，\n import numpy as np\n \n recomMatrix.fillna(0.0,inplace=True) # 此处必须先填充空值\n recommends = ['recommed'+str(y) for y in range(1,K+1)]\n currentemp = pd.DataFrame([],index = recomMatrix.columns, columns = recommends)\n \n for i in range(len(recomMatrix.columns)): #len(res.columns)1\n curentcol = recomMatrix.columns[i]\n temp = recomMatrix[curentcol][recomMatrix[curentcol]!=0] # 未曾浏览过\n # = temp.index[random.randint(0,len(temp))]\n if len(temp) == 0:\n currentemp.iloc[i,:] = np.nan\n elif len(temp) < K:\n r = temp.index.take(np.random.permutation(len(temp))) #注意：这个random是numpy模块的下属模块\n currentemp.iloc[i,:len(r)] = r\n else:\n r = random.sample(temp.index, K)\n currentemp.iloc[i,:] = r\n return currentemp\n\n\n# In[ ]:\n\n\nstart4 = time.clock()\nrandom_result = rand_recommd(3, randmatrix) # 调用随机推荐函数\nend4 = time.clock()\nprint('随机为用户推荐3个未浏览过的网址耗时为' + str(end4 - start4)+'s!') # 2.1900423292s!\n#保存的表名命名格式为“3_1_k此表功能名称”，是本小节生成的第5张表格，功能为random_result：显示随机推荐的结果\n#random_result.to_csv('random_result.csv')\nrandom_result # 结果中出现了全空的行，这是冷启动现象,浏览该网页仅此IP一个，其他IP不曾浏览无相似系数\n\n\n# In[ ]:\n\n\ndef popular_recommed(K, recomMatrix):\n recomMatrix.fillna(0.0,inplace=True)\n import numpy as np\n recommends = ['recommed'+str(y) for y in range(1,K+1)]\n currentemp = pd.DataFrame([],index = recomMatrix.columns, columns = recommends)\n \n for i in range(len(recomMatrix.columns)):\n curentcol = recomMatrix.columns[i]\n temp = recomMatrix[curentcol][recomMatrix[curentcol]!=0]\n if len(temp) == 0:\n currentemp.iloc[i,:] = np.nan\n elif len(temp) < K:\n r = temp.index #注意：这个random是numpy模块的下属模块\n currentemp.iloc[i,:len(r)] = r\n else:\n r = temp.index[:K]\n currentemp.iloc[i,:] = r\n \n return currentemp \n\n\n# In[ ]:\n\n\n# 确定用户未浏览的网页（可推荐的）的数据表格\nTEST = 1-df_test # df_test是用户浏览过的网页的矩阵形式，TEST则是未曾浏览过的\ntest2 = pd.DataFrame(TEST, index = test.columns, columns=test.index)\nprint(test2.head())\nprint(test2.shape )\n \n# 确定网页浏览热度排名：\nhotPopular = data['fullURL'].value_counts()\nhotPopular = pd.DataFrame(hotPopular)\nprint(hotPopular.head())\nprint(hotPopular.shape)\n \n# 按照流行度对可推荐的所有网址排序\ntest3 = test2.reset_index()\nlist_custom = list(hotPopular.index)\ntest3['index'] = test3['index'].astype('category')\ntest3['index'].cat.reorder_categories(list_custom, inplace=True)\ntest3.sort_values('index',inplace = True)\ntest3.set_index ('index', inplace = True)\nprint(test3.head())\nprint(test3.shape)\n \n# 按照流行度为用户推荐3个未浏览过的网址\nrecomMatrix = test3 #\nstart5 = time.clock()\npopular_result = popular_recommed(3, recomMatrix)\nend5 = time.clock()\nprint('按照流行度为用户推荐3个未浏览过的网址耗时为' + str(end5 - start5)+'s!')#7.70043007471s!\n \n#保存的表名命名格式为“3_1_k此表功能名称”，是本小节生成的第6张表格，功能为popular_result：显示按流行度推荐的结果\n#popular_result.to_csv('3_1_6popular_result.csv')\n \npopular_result\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6338797807693481,\n \"alphanum_fraction\": 0.6495446562767029,\n \"avg_line_length\": 16.024845123291016,\n \"blob_id\": \"e8e015af63da92b36eef02ae6d968eccdbaf8dcb\",\n \"content_id\": \"effcb9c5c88f832b490045c458cf46d43054f1c1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2745,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 161,\n \"path\": \"/Churn_warning/Churn_warning.py\",\n \"repo_name\": \"zhangbc07/Project\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python\\n# coding: utf-8\\n\\n# In[1]:\\n\\n\\nimport pandas as pd\\nimport numpy as np\\nimport warnings\\nwarnings.filterwarnings('ignore')\\n\\n\\n# In[2]:\\n\\n\\nchurn_df=pd.read_csv('D:/pydata/churn.csv')\\ncol_names=churn_df.columns.tolist()\\n\\n\\n# In[3]:\\n\\n\\nchurn_df.head()\\n\\n\\n# In[4]:\\n\\n\\ncol_names\\n\\n\\n# In[5]:\\n\\n\\nchurn_result=churn_df['Churn?']\\ny=np.where(churn_result=='True.',1,0)\\n\\n\\n# In[6]:\\n\\n\\nto_drop=['State','Area Code','Phone','Churn?']\\n\\n\\n# In[7]:\\n\\n\\nchurn_feat_space=churn_df.drop(to_drop,axis=1)\\n\\n\\n# In[8]:\\n\\n#'yes'/'no'has to be converted to boolean values\\nyes_no_cols=[\\\"Int'l Plan\\\",\\\"VMail Plan\\\"]\\nchurn_feat_space[yes_no_cols]=churn_feat_space[yes_no_cols]=='yes'\\n\\n\\n# In[9]:\\n\\n\\nfeatures=churn_feat_space.columns\\nx=churn_feat_space.as_matrix().astype(np.float)\\n\\n\\n# In[10]:\\n\\n\\nfrom sklearn.preprocessing import StandardScaler\\nscaler=StandardScaler()\\nx=scaler.fit_transform(x)\\n\\nprint (\\\"Feature space holds %d observations and %d features\\\" % x.shape)\\nprint (\\\"Unique target labels:\\\", np.unique(y))\\nprint (x[0])\\nprint (len(y[y == 0]))\\n\\n\\n# In[11]:\\n\\n\\nx\\n\\n\\n# In[12]:\\n\\n\\nfrom sklearn.model_selection import KFold\\n\\n\\n# In[13]:\\n\\n\\ndef run_cv(x,y,clf_class,**kwargs):\\n kf = KFold(n_splits=5,shuffle=True,random_state=1)\\n y_pred = y.copy()\\n\\n for train_index, test_index in kf.split(y):\\n x_train, x_test = x[train_index], x[test_index]\\n y_train = y[train_index]\\n clf = clf_class(**kwargs)\\n clf.fit(x_train,y_train)\\n y_pred[test_index] = clf.predict(x_test)\\n return y_pred\\n\\n\\n# In[14]:\\n\\n\\nfrom sklearn.svm import SVC\\nfrom sklearn.ensemble import RandomForestClassifier as RF\\nfrom sklearn.neighbors import KNeighborsClassifier as KNN\\n\\n\\n# In[15]:\\n\\n\\ndef accuracy(y_true,y_pred):\\n return np.mean(y_true == y_pred)\\n\\nprint (\\\"Support vector machines:\\\")\\nprint (\\\"%.3f\\\" % accuracy(y, run_cv(x,y,SVC)))\\nprint('Random forest:')\\nprint('%.3f' %accuracy(y,run_cv(x,y,RF)))\\nprint('K-nearest-neighbors:')\\nprint('%.3f' %accuracy(y,run_cv(x,y,KNN)))\\n\\n\\n# In[16]:\\n\\n\\ndef run_prob_cv(x,y,clf_class,**kwargs):\\n kf=KFold(n_splits=5,shuffle=True)\\n y_prob = np.zeros((len(y),2))\\n for train_index,test_index in kf.split(y):\\n x_train,x_test=x[train_index],x[test_index]\\n y_train=y[train_index]\\n clf=clf_class(**kwargs)\\n clf.fit(x_train,y_train)\\n y_prob[test_index]=clf.predict_proba(x_test)\\n return y_prob\\n\\n\\n# In[17]:\\n\\n\\npred_prob=run_prob_cv(x,y,RF,n_estimators=10)\\npred_churn=pred_prob[:,1]\\nis_churn=y==1\\ncounts=pd.value_counts(pred_churn)\\n\\ntrue_prob={}\\nfor prob in counts.index:\\n true_prob[prob]=np.mean(is_churn[pred_churn==prob])\\n true_prob=pd.Series(true_prob)\\n \\ncounts = pd.concat([counts,true_prob], axis=1).reset_index()\\ncounts.columns=['pred_prob','counts','true_prob']\\ncounts\\n\\n\\n# In[ ]:\\n\\n\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6245222687721252,\n \"alphanum_fraction\": 0.6667197346687317,\n \"avg_line_length\": 19.644737243652344,\n \"blob_id\": \"41cfa9b8ea2ca2de1a61b6361dfa8922cc68759f\",\n \"content_id\": \"0f579d81b1f23bd688379df8619f9311af71354c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 6518,\n \"license_type\": \"no_license\",\n \"max_line_length\": 152,\n \"num_lines\": 304,\n \"path\": \"/Mp_loan/Mp_loan.py\",\n \"repo_name\": \"zhangbc07/Project\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python\\n# coding: utf-8\\n#数据下载：https://mirror.shileizcc.com/Other/LoanStats3a.csv\\n# In[1]:\\n\\n\\nimport pandas as pd\\nimport warnings\\nwarnings.filterwarnings('ignore')\\nloans_2007=pd.read_csv('./LoanStats3a.csv',skiprows=1)\\n#清除不必要的列\\nhalf_count=len(loans_2007)/2\\nloans_2007=loans_2007.dropna(thresh=half_count,axis=1)\\nloans_2007=loans_2007.drop(['desc','url'],axis=1)\\nloans_2007.to_csv('./loans_2007.csv',index=False)\\n\\n\\n# In[2]:\\n\\n\\nloans_2007=pd.read_csv('./loans_2007.csv')\\n\\n\\n# In[3]:\\n\\n\\nloans_2007.head()\\n\\n\\n# In[4]:\\n\\n\\n#清除无用数据\\nloans_2007=loans_2007.drop(['id','member_id','funded_amnt','funded_amnt_inv','grade','sub_grade','emp_title','issue_d'],axis=1)\\nloans_2007 = loans_2007.drop(['zip_code', 'out_prncp', 'out_prncp_inv', 'total_pymnt','total_pymnt_inv', 'total_rec_prncp'], axis=1)\\nloans_2007 = loans_2007.drop(['total_rec_int', 'total_rec_late_fee','recoveries', 'collection_recovery_fee', 'last_pymnt_d', 'last_pymnt_amnt'], axis=1)\\n\\n\\n# In[5]:\\n\\n\\nprint(loans_2007.iloc[0])\\nprint(loans_2007.shape[1])\\n\\n\\n# In[6]:\\n\\n\\nprint(loans_2007['loan_status'].value_counts())\\n\\n\\n# In[7]:\\n\\n\\nloans_2007=loans_2007[(loans_2007['loan_status']=='Fully Paid') | (loans_2007['loan_status']=='Charged Off')]\\n\\nstatus_replace={\\n 'loan_status':{\\n 'Fully Paid':1,\\n 'Charged Off':0,\\n }\\n}\\nloans_2007=loans_2007.replace(status_replace)\\n\\n\\n# In[8]:\\n\\n\\nloans_2007.head()\\n\\n\\n# In[9]:\\n\\n\\n#去除空数据\\norig_columns=loans_2007.columns\\ndrop_columns=[]\\nfor col in orig_columns:\\n col_series=loans_2007[col].dropna().unique( )\\n if len(col_series)==1:\\n drop_columns.append(col)\\nloans_2007=loans_2007.drop(drop_columns,axis=1)\\n\\n\\n# In[10]:\\n\\n\\nloans_2007.to_csv('./filtered_loans_2007.csv',index=False)\\n\\n\\n# In[11]:\\n\\n\\n#统计缺失值\\nloans=pd.read_csv('./filtered_loans_2007.csv')\\nnull_counts=loans.isnull().sum()\\n\\n\\n# In[12]:\\n\\n\\nnull_counts\\n\\n\\n# In[13]:\\n\\n\\n#去掉缺失值较多的数据\\nloans=loans.drop('pub_rec_bankruptcies',axis=1)\\nloans=loans.dropna(axis=0)\\n\\n\\n# In[14]:\\n\\n\\nloans.dtypes.value_counts()\\n\\n\\n# In[15]:\\n\\n\\n#对object数据进行格式转换\\nmapping_dict={\\n 'emp_length':{\\n '10+ years':10,\\n '9 years':9,\\n '8 years':8,\\n '7 years':7,\\n '6 years':6,\\n '5 years':5,\\n '4 years':4,\\n '3 years':3,\\n '2 years':2,\\n '1 year':1,\\n 'n/a':0\\n }\\n}\\nloans=loans.drop(['last_credit_pull_d','earliest_cr_line','addr_state','title'],axis=1)\\nloans['int_rate']=loans['int_rate'].str.rstrip('%').astype('float')\\nloans['revol_util']=loans['revol_util'].str.rstrip('%').astype('float')\\nloans=loans.replace(mapping_dict)\\n\\n\\n# In[16]:\\n\\n\\ncat_columns=['home_ownership','verification_status','emp_length','purpose','term']\\ndummy_df=pd.get_dummies(loans[cat_columns])\\nloans=pd.concat([loans,dummy_df],axis=1)\\nloans=loans.drop(cat_columns,axis=1)\\nloans=loans.drop('pymnt_plan',axis=1)\\n\\nloans.to_csv('./cleaned_loans2007.csv',index=False)\\n\\n\\n# In[17]:\\n\\n\\nloans=pd.read_csv('./cleaned_loans2007.csv')\\n\\n\\n# In[18]:\\n\\n\\nloans.info()\\n\\n\\n# In[19]:\\n\\n\\nfrom sklearn.linear_model import LogisticRegression\\nlr=LogisticRegression()\\ncols = loans.columns\\ntrain_cols = cols.drop('loan_status')\\n#得到特征\\nfeatures = loans[train_cols]\\n#得到标签\\ntarget = loans['loan_status']\\nlr.fit(features, target)\\npredictions = lr.predict(features)\\n\\n\\n# In[20]:\\n\\n\\n#第一次测试——使用逻辑回归测试\\nfrom sklearn.linear_model import LogisticRegression\\nfrom sklearn.model_selection import cross_val_predict,KFold\\nlr = LogisticRegression()\\nkf = KFold()\\npredictions = cross_val_predict(lr, features, target, cv=kf)\\npredictions = pd.Series(predictions)\\n\\n#定义指标\\n#True positives \\ntp_filter = (predictions == 1) & (loans['loan_status']== 1) \\ntp = len(predictions[tp_filter])\\n#False positives\\nfp_filter = (predictions == 1) & (loans['loan_status']== 0) \\nfp = len(predictions[fp_filter])\\n#True negatives \\ntn_filter = (predictions == 0) & (loans['loan_status']== 0) \\ntn = len(predictions[tn_filter])\\n#False negatives \\nfn_filter = (predictions == 0) & (loans['loan_status'] == 1) \\nfn = len(predictions[fn_filter])\\n# Rates\\ntpr = tp / float((tp + fn))\\nfpr = fp / float((fp + tn))\\n\\nprint(tpr)\\nprint(fpr)\\n\\n\\n# In[22]:\\n\\n\\n#第二次测试——添加权重项，平衡数据的权重和\\nlr=LogisticRegression(class_weight='balanced')\\nkf=KFold()\\npredictions=cross_val_predict(lr,features,target,cv=kf)\\npredictions=pd.Series(predictions)\\n\\n#True positives \\ntp_filter = (predictions == 1) & (loans['loan_status']== 1) \\ntp = len(predictions[tp_filter])\\n#False positives\\nfp_filter = (predictions == 1) & (loans['loan_status']== 0) \\nfp = len(predictions[fp_filter])\\n#True negatives \\ntn_filter = (predictions == 0) & (loans['loan_status']== 0) \\ntn = len(predictions[tn_filter])\\n#False negatives \\nfn_filter = (predictions == 0) & (loans['loan_status'] == 1) \\nfn = len(predictions[fn_filter])\\n# Rates\\ntpr = tp / float((tp + fn))\\nfpr = fp / float((fp + tn))\\n\\nprint(tpr)\\nprint(fpr)\\n\\n\\n# In[23]:\\n\\n\\n#第三次测试——自定义权重项\\npenalty={\\n 0:5,\\n 1:1\\n}\\nlr=LogisticRegression(class_weight=penalty)\\nkf=KFold()\\npredictions=cross_val_predict(lr,features,target,cv=kf)\\npredictions=pd.Series(predictions)\\n\\n#True positives \\ntp_filter = (predictions == 1) & (loans['loan_status']== 1) \\ntp = len(predictions[tp_filter])\\n#False positives\\nfp_filter = (predictions == 1) & (loans['loan_status']== 0) \\nfp = len(predictions[fp_filter])\\n#True negatives \\ntn_filter = (predictions == 0) & (loans['loan_status']== 0) \\ntn = len(predictions[tn_filter])\\n#False negatives \\nfn_filter = (predictions == 0) & (loans['loan_status'] == 1) \\nfn = len(predictions[fn_filter])\\n# Rates\\ntpr = tp / float((tp + fn))\\nfpr = fp / float((fp + tn))\\n\\nprint(tpr)\\nprint(fpr)\\n\\n\\n# In[24]:\\n\\n\\n#第四次测试——随机森林\\nfrom sklearn.ensemble import RandomForestClassifier\\nrf=RandomForestClassifier(n_estimators=10,class_weight='balanced',random_state=1)\\nkf=KFold()\\npredictions=cross_val_predict(lr,features,target,cv=kf)\\npredictions=pd.Series(predictions)\\n\\n#True positives \\ntp_filter = (predictions == 1) & (loans['loan_status']== 1) \\ntp = len(predictions[tp_filter])\\n#False positives\\nfp_filter = (predictions == 1) & (loans['loan_status']== 0) \\nfp = len(predictions[fp_filter])\\n#True negatives \\ntn_filter = (predictions == 0) & (loans['loan_status']== 0) \\ntn = len(predictions[tn_filter])\\n#False negatives \\nfn_filter = (predictions == 0) & (loans['loan_status'] == 1) \\nfn = len(predictions[fn_filter])\\n# Rates\\ntpr = tp / float((tp + fn))\\nfpr = fp / float((fp + tn))\\n\\nprint(tpr)\\nprint(fpr)\\n\\n\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5996158719062805,\n \"alphanum_fraction\": 0.6531891226768494,\n \"avg_line_length\": 20.544662475585938,\n \"blob_id\": \"0bf57ec2b3e1fc90948d58b27fe5c9b4648fe229\",\n \"content_id\": \"409bf22b7d60903b7c8a15472d8fda6f35252fc7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 12705,\n \"license_type\": \"no_license\",\n \"max_line_length\": 103,\n \"num_lines\": 459,\n \"path\": \"/The_Big_Band/The_Big_Band.py\",\n \"repo_name\": \"zhangbc07/Project\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python\\n# coding: utf-8\\n\\n# In[1]:\\n\\n\\nimport pandas as pd\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\n\\n\\n# In[2]:\\n\\n\\n#配置乐队的夏天主题色\\npurple = (0.22,0.09,0.59) # 紫色\\nyellow = (0.99,0.89,0.27) # 黄色\\ngreen = (0.36,0.94,0.55) # 绿色\\nblue = (0.06,0.24,0.78) # 蓝色\\nred = (0.98,0.31,0.36) # 红色\\n\\n\\n# In[3]:\\n\\n\\ndata1=pd.read_excel('D:/pydata/乐队的夏天.xlsx','第一场')\\ndata2=pd.read_excel('D:/pydata/乐队的夏天.xlsx','第二场两两PK赛')\\ndata3=pd.read_excel('D:/pydata/乐队的夏天.xlsx','第三场累计积分赛')\\ndata4=pd.read_excel('D:/pydata/乐队的夏天.xlsx','第四场复活赛')\\ndata5=pd.read_excel('D:/pydata/乐队的夏天.xlsx','第五场9进7')\\ndata6=pd.read_excel('D:/pydata/乐队的夏天.xlsx','第六场总决赛')\\n\\n\\n# In[4]:\\n\\n\\ndata1.head()\\n\\n\\n# In[5]:\\n\\n\\ndata1[data1['总得分'].isnull()]\\n\\n\\n# In[6]:\\n\\n\\ndata1=data1.dropna(axis=0)\\n\\n\\n# In[7]:\\n\\n\\ndata1\\n\\n\\n# In[8]:\\n\\n\\ndata6.tail()\\n\\n\\n# In[9]:\\n\\n\\nfig,ax=plt.subplots(figsize=(12,12))\\ndata1['总得分'].value_counts(bins=5,sort=False).plot.bar(ax=ax)\\n\\nfor tick in ax.get_xticklabels():\\n tick.set_rotation(360)\\n \\nax.grid(False)\\n\\n\\n# In[10]:\\n\\n\\n#标准分函数定义\\ndef z_score_normalize(series):\\n mean=series.mean()\\n std_dv=series.std()\\n return series.apply(lambda x:(x-mean)/std_dv)\\n\\n\\n# In[11]:\\n\\n\\n#用循环的方式批量对每场比赛的得分做处理\\ncompetition=[data1,data2,data3,data4,data5,data6]\\n\\nfor period in competition:\\n period['超级乐迷得分_标准分']=z_score_normalize(period['超级乐迷得分'])\\n period['专业乐迷得分_标准分']=z_score_normalize(period['专业乐迷得分'])\\n period['大众乐迷得分_标准分']=z_score_normalize(period['大众乐迷得分'])\\n period['总得分_标准分']=z_score_normalize(period['总得分'])\\n\\n\\n# In[12]:\\n\\n\\ndata1.sort_values(by='总得分_标准分',ascending=False)\\n\\n\\n# In[13]:\\n\\n\\n#每场数据是分开的，先截取原来的数据拼在一起成为一个长表\\ndata1_score = data1[['场数','乐队','歌曲','排名','超级乐迷得分_标准分', '专业乐迷得分_标准分','大众乐迷得分_标准分','总得分_标准分']]\\ndata2_score = data2[['场数','乐队','歌曲','排名','超级乐迷得分_标准分','专业乐迷得分_标准分','大众乐迷得分_标准分','总得分_标准分']]\\ndata3_score = data3[['场数','乐队','歌曲','排名','超级乐迷得分_标准分','专业乐迷得分_标准分','大众乐迷得分_标准分','总得分_标准分']]\\ndata4_score = data4[['场数','乐队','歌曲','排名','超级乐迷得分_标准分','专业乐迷得分_标准分','大众乐迷得分_标准分','总得分_标准分']]\\ndata5_score = data5[['场数','乐队','歌曲','排名','超级乐迷得分_标准分','专业乐迷得分_标准分','大众乐迷得分_标准分','总得分_标准分']]\\ndata6_score = data6[['场数','乐队','歌曲','排名','超级乐迷得分_标准分','专业乐迷得分_标准分','大众乐迷得分_标准分','总得分_标准分']]\\n\\ntotal_score=pd.concat([data1_score,data2_score,data3_score,data4_score,data5_score,data6_score])\\n\\n\\n# In[14]:\\n\\n\\ntotal_score_mean=total_score.groupby(['乐队'])[['总得分_标准分']].mean().sort_values(by='总得分_标准分')\\n\\ntotal_score_mean.tail(7).sort_values(by='总得分_标准分',ascending=False)\\n\\n\\n# In[15]:\\n\\n\\n#中文显示\\nfrom pylab import *\\nmpl.rcParams['font.sans-serif'] = ['SimHei']\\nmpl.rcParams['axes.unicode_minus'] = False\\n\\n#对乐队总得分_标准分做可视化\\ny=np.arange(len(total_score_mean.index))\\nx=np.array(list(total_score_mean['总得分_标准分']))\\nfig,ax=plt.subplots(figsize=(12,12))\\ntotal_score_mean.plot.barh(ax=ax,alpha=0.7,title='27只乐队场均表现',color='g')\\nfor a,b in zip(x,y):\\n plt.text(a,b,'%.2f' % a,ha='center',va='center',fontsize=12)\\nax.grid(False)\\n\\n\\n# In[16]:\\n\\n\\ntotal_score[total_score['乐队']=='痛仰乐队']\\n#再见杰克和公路之歌我经常单曲循环，传唱度比较高，看来我的审美还是符合主流\\n\\n\\n# In[17]:\\n\\n\\ntotal_score[total_score['乐队']=='面孔乐队']\\n#张三的歌我确实觉得不如原唱\\n\\n\\n# In[18]:\\n\\n\\ntotal_score[total_score['乐队']=='刺猬']\\n#火车一度单曲循环一整天\\n\\n\\n# In[44]:\\n\\n\\ntotal_score[total_score['乐队']=='旅行团乐队']\\n#周末玩具实在是不好听\\n\\n\\n# In[19]:\\n\\n\\nsuper_score_mean = total_score.groupby(['乐队'])[['超级乐迷得分_标准分']].mean().sort_values(by = '超级乐迷得分_标准分')\\npro_score_mean = total_score.groupby(['乐队'])[['专业乐迷得分_标准分']].mean().sort_values(by = '专业乐迷得分_标准分')\\npublic_score_mean = total_score.groupby(['乐队'])[['大众乐迷得分_标准分']].mean().sort_values(by = '大众乐迷得分_标准分')\\n\\n\\n# In[20]:\\n\\n\\nfig,ax=plt.subplots(1,3,figsize=(16,6))\\n\\nsuper_score_mean.tail(5).plot.barh(ax=ax[0],color = '#dc2624',alpha=0.7,title='超级乐迷心中TOP5',grid=False)\\npro_score_mean.tail(5).plot.barh(ax=ax[1],color = '#2b4750',alpha=0.7,title='专业乐迷心中TOP5',grid=False)\\npublic_score_mean.tail(5).plot.barh(ax=ax[2],color = '#649E7D',alpha=0.7,title='大众乐迷心中TOP5',grid=False)\\n\\n\\n# In[21]:\\n\\n\\nsuper_score_mean_top5 = super_score_mean.tail(5)\\npro_score_mean_top5 = pro_score_mean.tail(5)\\npublic_score_mean_top5 = public_score_mean.tail(5)\\n\\nsuper5=set(super_score_mean_top5.index)\\npro5=set(pro_score_mean_top5.index)\\npub5=set(public_score_mean_top5.index)\\n\\ncross = super5&pro5&pub5\\nprint('同时在三个群体中位列心目前五的乐队是:\\\\n',cross)\\nprint('\\\\n')\\n\\nsuper_pro = super5 - pro5\\nprint('在超级乐迷心中前五，但不在专业乐迷心中的前五乐队是:\\\\n',super_pro)\\nprint('\\\\n')\\n\\nsuper_pub = super5 - pub5\\nprint('在超级乐迷心中前五，但不在大众乐迷心中的前五乐队是:\\\\n',super_pub)\\nprint('\\\\n')\\n\\npro_super = pro5 - super5\\nprint('在专业乐迷心中前五，但不在超级乐迷心中的前五乐队是:\\\\n',pro_super)\\nprint('\\\\n')\\n\\npro_pub = pro5 - pub5\\nprint('在专业乐迷心中前五，但不在大众乐迷心中的前五乐队是:\\\\n',pro_pub)\\nprint('\\\\n')\\n\\npub_super = pub5 - super5\\nprint('在大众乐迷心中前五，但不在超级乐迷心中的前五乐队是:\\\\n',pub_super)\\nprint('\\\\n')\\n\\npub_pro = pub5 - pro5\\nprint('在大众乐迷心中前五，但不在专业乐迷心中的前五乐队是:\\\\n',pub_pro)\\nprint('\\\\n')\\n\\n\\n# In[22]:\\n\\n\\nprint('第五场中表演的九支乐队分别是：')\\ndata5['乐队']\\n\\n\\n# In[ ]:\\n\\n\\ntop9_rank = total_score[total_score['乐队'].isin(list(data5['乐队']))].iloc[:,[0,1,3]]\\n\\ntop9_rank = top9_rank.sort_values(by='场数')\\n\\nfig = plt.figure(figsize=(16,10), dpi=100 )\\nax = fig.add_subplot(1,1,1)\\n\\norder = ['第一场','第二场','第二场复活赛','第三场上','第三场下','第四场','第五场','第六场上']\\n\\nax.plot(order,[0,0,0,0,0,0,0,0],c='w')\\n\\nhgxs = top9_rank[top9_rank['乐队']=='海龟先生'].iloc[[0,3,1,2,4]]\\nax.plot('场数','排名',data=hgxs,ls ='--',marker='o',markersize=30,label='海龟先生')\\n\\npnxl = top9_rank[top9_rank['乐队']=='盘尼西林'].iloc[[0,3,1,2,4,5]]\\nax.plot('场数','排名',data=pnxl,ls ='--',marker='o',markersize=30,label='盘尼西林')\\n\\nxkz = top9_rank[top9_rank['乐队']=='新裤子'].iloc[[0,3,1,2,4,5]]\\nax.plot('场数','排名',data=xkz,ls ='--',marker='o',markersize=30,label='新裤子')\\n\\ncw = top9_rank[top9_rank['乐队']=='刺猬'].iloc[[0,3,4,1,2,5,6]]\\nax.plot('场数','排名',data=cw,ls ='--',marker='o',markersize=30,label='刺猬')\\n\\nty = top9_rank[top9_rank['乐队']=='痛仰乐队'].iloc[[0,1,5,3,2,4]]\\nax.plot('场数','排名',data=ty,ls ='--',marker='o',markersize=30,label='痛仰乐队')\\n\\nclick = top9_rank[top9_rank['乐队']=='Click#15'].iloc[[0,1,2,6,3,4,5]]\\nax.plot('场数','排名',data=click,ls ='--',marker='o',markersize=30,label='Click#15')\\n\\njlzr = top9_rank[top9_rank['乐队']=='九连真人'].iloc[[0,3,1,2,4,5]]\\nax.plot('场数','排名',data=jlzr,ls ='--',marker='o',markersize=30,label='九连真人')\\n\\nmk = top9_rank[top9_rank['乐队']=='面孔乐队'].iloc[[0,3,4,1,2,5]]\\nax.plot('场数','排名',data=mk,ls ='--',marker='o',markersize=30,label='面孔乐队')\\n\\nlxt = top9_rank[top9_rank['乐队']=='旅行团乐队'].iloc[[0,3,1,2,4,5]]\\nax.plot('场数','排名',data=lxt,ls ='--',marker='o',markersize=30,label='旅行团乐队')\\n\\nax.set_xticks(np.arange(len(order)))\\nax.set_yticks(np.arange(1,15))\\nax.set_xticklabels(order)\\nax.invert_yaxis()\\nax.set_ylim(15,0)\\nax.grid(False)\\nplt.title('九只队伍排名升降',fontdict = {'fontsize':20})\\nax.legend(ncol=3)\\n\\n\\n# 歌曲分析\\n\\n# In[ ]:\\n\\n\\n#得票率=得票数/总票数，然后变成一个0-1之间的数字\\n\\n\\n# In[24]:\\n\\n\\n#定义归一化函数并应用\\ndef normalize(series,x_max):\\n return series.apply(lambda x: x/x_max)\\n\\n\\n# In[25]:\\n\\n\\nfor data in [data1,data2,data5,data6]:\\n data['超级乐迷_归一分'] = normalize(data['超级乐迷得分'],50)\\n\\ndata3['超级乐迷_归一分'] = normalize(data3['超级乐迷得分'],60)\\ndata4['超级乐迷_归一分'] = normalize(data4['超级乐迷得分'],40)\\n\\nfor data in [data1,data2,data3,data4,data5,data6]:\\n data['专业乐迷_归一分'] = normalize(data['专业乐迷得分'],40)\\n \\nfor data in [data1,data2]:\\n data['大众乐迷_归一分'] = normalize(data['大众乐迷得分'],100)\\nfor data in [data3,data4,data5,data6]:\\n data['大众乐迷_归一分'] = normalize(data['大众乐迷得分'],360)\\n\\n\\n# In[26]:\\n\\n\\ndata6\\n\\n\\n# In[27]:\\n\\n\\n#批量对总得分应用归一化函数\\nmatch_total = {'data1':190,'data2':190,'data3':460,'data4':440,'data5':450,'data6':450}\\ndata_list = [data1,data2,data3,data4,data5,data6]\\n\\nfor i,data in enumerate(data_list):\\n data['总得分_归一分'] = normalize(data['总得分'],match_total['data'+ str(i+1)])\\n\\n\\n# In[28]:\\n\\n\\ncol_index = [3,-4,-3,-2,-1]\\ntotal_nor_score = pd.DataFrame()\\nfor data in data_list:\\n total_nor_score = total_nor_score.append([data.iloc[:,col_index]])\\n\\n\\n# In[29]:\\n\\n\\ntotal_nor_score.sort_values(by = '总得分_归一分',ascending=False)\\n\\n\\n# In[30]:\\n\\n\\ntotal_nor_score.describe()\\n\\n\\n# In[31]:\\n\\n\\nfig,ax = plt.subplots(1,1,figsize = (8,4))\\ntotal_nor_score.boxplot(ax = ax,grid=False)\\nax.set(title = '各组评委打分分布')\\n\\n\\n# In[32]:\\n\\n\\ntop10_songs = total_nor_score.sort_values(by = '总得分_归一分').tail(10)\\ntop10_songs.plot.barh(x='歌曲',y='总得分_归一分',color=purple)\\n\\nprint('截止第六期，最受欢迎的10首歌分别是:\\\\n',list(top10_songs['歌曲']))\\n\\n\\n# In[33]:\\n\\n\\ntotal_nor_score.sort_values(by = '超级乐迷_归一分').tail(10).plot.barh(x='歌曲',y='超级乐迷_归一分',color=red)\\n\\n\\n# In[34]:\\n\\n\\ntotal_nor_score.sort_values(by = '专业乐迷_归一分').tail(10).plot.barh(x='歌曲',y='专业乐迷_归一分',color=yellow)\\n\\n\\n# In[35]:\\n\\n\\ntotal_nor_score.sort_values(by = '大众乐迷_归一分').tail(10).plot.barh(x='歌曲',y='大众乐迷_归一分',color=green)\\n\\n\\n# In[36]:\\n\\n\\ntotal_nor_score[total_nor_score['歌曲'] == '我愿意']\\n\\n\\n# In[37]:\\n\\n\\ntotal_nor_score_t = pd.merge(total_nor_score, total_score[['乐队','歌曲']], on='歌曲')\\ntotal_nor_score_t[total_nor_score_t['乐队'] == '海龟先生']\\n\\n\\n# In[38]:\\n\\n\\ntotal_nor_score_t[total_nor_score_t['乐队'] == '海龟先生'].mean()\\n\\n\\n# In[39]:\\n\\n\\ntotal_nor_score_t.iloc[[0,32,61,76]].mean()\\n\\n\\n# In[40]:\\n\\n\\ntotal_nor_score_t.groupby('乐队').mean().sort_values(by = '总得分_归一分',ascending = False).head(9)\\n\\n\\n# In[41]:\\n\\n\\ntotal_score_mean.sort_values(by = '总得分_标准分',ascending= False).head(9)\\n\\n\\n# In[42]:\\n\\n\\n# 这里面有些中途甚至很早就被淘汰了的乐队，如果把这些乐队排除，那么在最终的7只乐队里面，各组评委最喜欢谁呢？\\nimport warnings\\nwarnings.filterwarnings('ignore')\\nfinal_7 = ['新裤子','痛仰乐队','九连真人','Click#15','刺猬','盘尼西林','旅行团乐队']\\nfinal_7_super_score = super_score_mean.loc[final_7,].sort_values(by='超级乐迷得分_标准分')\\nfinal_7_pro_score = pro_score_mean.loc[final_7,].sort_values(by='专业乐迷得分_标准分')\\nfinal_7_public_score = public_score_mean.loc[final_7,].sort_values(by='大众乐迷得分_标准分')\\nfinal_7_total_score = total_score_mean.loc[final_7,].sort_values(by='总得分_标准分')\\n\\nfig,ax = plt.subplots(4,1,figsize = (10,20))\\n\\nfinal_7_total_score.plot.barh(ax=ax[0],color = yellow,alpha=0.7,title='我心中Hot5',grid=False)\\nfinal_7_super_score.plot.barh(ax=ax[1],color = '#dc2624',alpha=0.7,title='超级乐迷心中Hot5',grid=False)\\nfinal_7_pro_score.plot.barh(ax=ax[2],color = '#2b4750',alpha=0.7,title='专业乐迷心中Hot5',grid=False)\\nfinal_7_public_score.plot.barh(ax=ax[3],color = '#649E7D',alpha=0.7,title='大众乐迷心中Hot5',grid=False)\\n\\n\\n# In[43]:\\n\\n\\ndata6.iloc[:,:-9]\\n\\n\\n# In[ ]:\\n\\n\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5913457274436951,\n \"alphanum_fraction\": 0.6318603157997131,\n \"avg_line_length\": 21.707780838012695,\n \"blob_id\": \"4ffacdaf3dbfa5a40ae3bbef86ac7ca0c5e300a0\",\n \"content_id\": \"0e1b29d95308fee9cc8b78ef04c5867458da68ab\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 12915,\n \"license_type\": \"no_license\",\n \"max_line_length\": 135,\n \"num_lines\": 527,\n \"path\": \"/Work/fangtx_esf.py\",\n \"repo_name\": \"zhangbc07/Project\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python\\n# coding: utf-8\\n\\n# In[731]:\\n\\n\\nimport numpy as np\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nplt.style.use('ggplot')\\nfrom pyecharts.charts import Map,Pie,Bar\\nfrom wordcloud import WordCloud\\nimport re\\nimport seaborn as sns\\nfrom scipy import stats\\nfrom pylab import mpl\\nmpl.rcParams['font.sans-serif']=['SimHei']\\nplt.rc('figure',figsize=(20,20))\\nplt.rcParams['figure.dpi']=mpl.rcParams['axes.unicode_minus']=False\\nget_ipython().run_line_magic('matplotlib', 'inline')\\nimport warnings\\nwarnings.filterwarnings('ignore')\\npd.set_option('display.float_format', lambda x: '%.2f' % x)\\n\\n\\n# In[642]:\\n\\n\\ndata=pd.read_csv('./project/fangtx.csv',encoding='gb18030')\\n\\n\\n# 数据观察\\n\\n# In[643]:\\n\\n\\ndata.head()\\n# data.shape\\n# data.sample(10)\\n# data.info()\\n# data.isnull().sum()\\n\\n\\n# In[644]:\\n\\n\\ndata.describe()\\n\\n\\n# 数据预处理\\n\\n# In[646]:\\n\\n\\ndata['size'].unique()\\n\\n\\n# In[647]:\\n\\n\\nindex=data[data['size'].isin(['联排','双拼','叠加','32室2厅','0室0厅','独栋'])].index\\ndata=data.drop(index)#去除不合理数据和别墅、联排等类型\\n\\n\\n# In[648]:\\n\\n\\ndata.shape\\n\\n\\n# In[649]:\\n\\n\\ndata.isnull().sum()\\ndata.dropna(axis=0,inplace=True)\\n\\n\\n# In[650]:\\n\\n\\nsns.boxplot(data=data['price_sum'])\\n\\n\\n# In[651]:\\n\\n\\nsns.boxplot(data=data['area'])\\n\\n\\n# In[652]:\\n\\n\\ndata=data.drop(data[data['price']>200000].index)\\n\\n\\n# In[653]:\\n\\n\\ndata=data.drop(data[data['area']>300].index)\\n\\n\\n# In[657]:\\n\\n\\ndata['dire'].unique()\\ndata['buildtime'].unique()\\nindex=data[data['buildtime'].isin(['2021年建','未知','2020年建','2022年建','2024年建','2025年建'])].index\\ndata=data.drop(index)#去除预计建成时间\\ndata['floor'].unique()\\n\\n\\n# In[663]:\\n\\n\\ndata.to_csv('./data_clean.csv',index=False,encoding='gb18030')\\n\\n\\n# In[678]:\\n\\n\\ndata=pd.read_csv('./data_clean.csv',encoding='gb18030')\\ndata.loc[data.city=='深圳',:].to_csv('./sz_data_clean.csv',index=False,encoding='gb18030')\\ndata.loc[data.city=='广州',:].to_csv('./gz_data_clean.csv',index=False,encoding='gb18030')\\n\\n\\n# 数据分析与可视化\\n\\n# In[679]:\\n\\n\\n#广东省房价总体情况\\ng=data.groupby('city')\\nr=g['price'].agg(['mean','count'])\\nr=r.sort_values('mean',ascending=False)\\ndisplay(r)\\nr.plot(kind='bar')\\n\\n\\n# In[558]:\\n\\n\\n#广深两市房价远超其他城市，珠三角城市房价相对较高，粤东粤西边缘城市靠后\\n\\n\\n# In[605]:\\n\\n\\nimport jieba\\nimport jieba.analyse\\nrows=pd.read_csv('data_clean.csv', header=0,encoding='gb18030',dtype=str)\\nsegments = []\\nfor index, row in rows.iterrows():\\n content = row[1]#提出小区名字的数据\\n words = jieba.analyse.textrank(content, topK=50,withWeight=False,allowPOS=('ns', 'n', 'vn', 'v')) #TextRank 关键词抽取，只获取固定词性\\n for word in words:# 记录全局分词\\n segments.append({'word':word, 'count':1}) \\ndfSg = pd.DataFrame(segments)\\n# 词频统计\\ndfWord = dfSg.groupby('word')['count'].sum()\\ndfWord.sort_values(ascending=False)[:30]#取前30输出\\n\\n\\n# In[682]:\\n\\n\\n#房屋大小与面积关系\\ndef area_price_relation_sz(city):\\n data=pd.read_csv('./sz_data_clean.csv',encoding='gb18030')\\n g=sns.jointplot(x='area',\\n y='price',\\n data=data, \\n kind='reg' ,\\n stat_func=stats.pearsonr\\n )\\n g.fig.set_dpi(100)\\n g.ax_joint.set_xlabel('面积', fontweight='bold')\\n g.ax_joint.set_ylabel('价格', fontweight='bold')\\n return g\\n\\ndef area_price_relation_gz(city):\\n data=pd.read_csv('./gz_data_clean.csv',encoding='gb18030')\\n g=sns.jointplot(x='area',\\n y='price',\\n data=data, \\n kind='reg' ,\\n stat_func=stats.pearsonr\\n )\\n g.fig.set_dpi(100)\\n g.ax_joint.set_xlabel('面积', fontweight='bold')\\n g.ax_joint.set_ylabel('价格', fontweight='bold')\\n return g\\n\\n\\n# In[684]:\\n\\n\\narea_price_relation_sz('深圳')\\narea_price_relation_gz('广州')\\n\\n\\n# In[787]:\\n\\n\\n# 地铁线分布与地铁线距离关系\\ndef get_distance(city,data=data):\\n station=[]#站\\n distance=[]#距离\\n station_count=[]#地铁线房源分布数量\\n station_name=[]#地铁线\\n data1=data.loc[data.city==city,:]\\n data1=data1.reset_index(drop=True)#重置索引\\n for i in range(len(data1)):\\n s=re.findall('\\\\d+',data1.loc[i,'advantage'])#用正则表达式匹配advantage标签\\n if len(s)==2:\\n distance.append(s[1])#距离\\n station.append(s[0])#站线\\n data1.loc[i,'distance']=s[1]\\n data1.to_csv('{}_distance.csv'.format(city),index=False,encoding='gb18030')\\n# data.to_csv('./gz_distance.csv',index=False,encoding='gb18030')#重新保存数据，后续进行分析\\n count=list(set(station))#列表去掉重复值的方法\\n count.sort()#列表排序\\n for i in count:\\n station_count.append( station.count('{}'.format(i)) ) #统计各个站线房源分布数量\\n station_name.append('{}号线'.format(i)) #相应站线 \\n \\n plt.bar(station_name,station_count)\\n plt.title('地铁房源分布')\\n\\n\\n# In[788]:\\n\\n\\nget_distance('深圳')\\n\\n\\n# In[767]:\\n\\n\\nget_distance('广州')\\n\\n\\n# In[770]:\\n\\n\\ndef distance_price_relation(city_short):\\n data=pd.read_csv('{}_distance.csv'.format(city_short),encoding='gb18030')\\n g=sns.jointplot(x='distance',\\n y='price',\\n data=data.dropna(subset=['distance']),\\n kind='reg',\\n stat_func=stats.pearsonr\\n )\\n g.fig.set_dpi(100)\\n g.ax_joint.set_xlabel('最近地铁距离',fontweight='bold')\\n g.ax_joint.set_ylabel('价格',fontweight='bold')\\n return g\\n\\n\\n# In[771]:\\n\\n\\ndistance_price_relation('深圳')\\n\\n\\n# In[772]:\\n\\n\\ndistance_price_relation('广州')\\n\\n\\n# In[723]:\\n\\n\\nsize_count=data['size'].value_counts().values[:5]\\nsize_kind=data['size'].value_counts().index[:5]\\n# sns.barplot(size_kind,size_count)\\nplt.bar(size_kind,size_count)\\nplt.title('热门户型分布')\\n\\n\\n# In[732]:\\n\\n\\ntime=data[data.city==city].buildtime.value_counts().index.tolist()[:5]\\ncount=data[data.city==city].buildtime.value_counts().values.tolist()[:5]\\nlabels=list(time)\\nplt.pie(count,labels=labels,autopct = '%3.1f%%',startangle = 180,shadow=True,colors=['c','r','gray','g','y'])\\nplt.title('建造年份统计')\\n\\n\\n# In[733]:\\n\\n\\ndire=data.dire.value_counts().index.tolist()\\ncount=data.dire.value_counts().values.tolist()\\nlabels=list(dire)\\nplt.pie(count,labels=labels,autopct = '%3.1f%%',startangle = 180,shadow=True,colors = ['c', 'r', 'y', 'g', 'gray'])\\nplt.title('房屋朝向')\\n\\n\\n# In[741]:\\n\\n\\ndef distance_price_relation(city_short):\\n data=pd.read_csv('./sz_distance.csv',encoding='gb18030')\\n g=sns.jointplot(x='distance',\\n y='price',\\n data=data.dropna(subset=['distance']),\\n kind='reg',\\n stat_func=stats.pearsonr\\n )\\n g.fig.set_dpi(100)\\n g.ax_joint.set_xlabel('最近地铁距离',fontweight='bold')\\n g.ax_joint.set_ylabel('价格',fontweight='bold')\\n return g\\n\\n\\n# In[742]:\\n\\n\\ndistance_price_relation('深圳')\\n\\n\\n# 机器学习预测房价\\n\\n# In[833]:\\n\\n\\nsz_data=pd.read_csv('sz_distance.csv',encoding='gb18030')\\ngz_data=pd.read_csv('gz_distance.csv',encoding='gb18030')\\n\\n\\n# In[834]:\\n\\n\\ndef transform(data):\\n for i in range(len(data)):\\n words=list(jieba.cut(data.loc[i,'advantage']))\\n if '满二' in words:\\n data.loc[i,'exemption of business tax']=1\\n else:\\n data.loc[i,'exemption of business tax']=0\\n if '满五' in words:\\n data.loc[i,'exemption of double tax']=1\\n else:\\n data.loc[i,'exemption of double tax']=0\\n if '教育' in words:\\n data.loc[i,'quality education']=1\\n else:\\n data.loc[i,'quality education']=0\\n \\ntransform(sz_data)\\ntransform(gz_data)\\n\\n\\n# In[831]:\\n\\n\\nnew_data=pd.DataFrame()\\ndef datatrans(new_data,data,dire_sum=list(gz_data['dire'].unique())):\\n new_data['city']=data['city']\\n new_data['area']=data['area']\\n new_data['buildtime']=data['buildtime']\\n new_data['distance']=data['distance']\\n for i in range(len(data)):\\n s=re.findall('\\\\d+',data.loc[i,'size'])\\n new_data.loc[i,'room_num']=float(s[0])\\n new_data.loc[i,'hall_num']=float(s[1])\\n \\n if '低层' in data.loc[i,'floor']:\\n new_data.loc[i,'floor']=1\\n elif '中层' in data.loc[i,'floor']:\\n new_data.loc[i,'floor']=2\\n elif '高层' in data.loc[i,'floor']:\\n new_data.loc[i,'floor']=3\\n \\n dire=data.loc[i,'dire']\\n idx=dire_sum.index(dire)+1\\n new_data.loc[i,'dire']=idx\\n \\n new_data['exemption of business tax']=data['exemption of business tax']\\n new_data['exemption of double tax']=data['exemption of double tax']\\n new_data['quality education']=data['quality education']\\n\\ndatatrans(new_data,sz_data)\\nnew_data1=pd.DataFrame()\\ndatatrans(new_data1,gz_data)\\nnew_data1=pd.concat([new_data1,new_data],axis=0,ignore_index=True)\\n\\n\\n# In[838]:\\n\\n\\ngz_price = gz_data['price']\\nsz_price = sz_data['price']\\nprice = pd.concat([gz_price,sz_price],axis=0,ignore_index=True)\\nnew_data1 = new_data1.join(pd.get_dummies(new_data1.city))\\nnew_data1.drop('city',axis=1,inplace=True)\\nnew_data1.to_csv('./new_data7.20.csv',index=False,encoding='gb18030')#房价预测，回归\\n\\n\\n# In[836]:\\n\\n\\ndata=pd.read_csv('./new_data7.20.csv')\\ndata.head()\\n\\n\\n# In[839]:\\n\\n\\ndata=pd.read_csv('new_data7.20.csv')\\ndata['distance'].fillna(5000,inplace=True)\\ndata['buildtime'].fillna(data['buildtime'].mode()[0],inplace=True)\\nX = data\\ny=price\\n\\n#数据分割，随机采样25%作为测试样本，其余作为训练样本\\nfrom sklearn.model_selection import train_test_split\\nx_train, x_test, y_train, y_test = train_test_split(X, y, random_state=0, test_size=0.25)\\n\\n#数据标准化处理 归一化\\nfrom sklearn.preprocessing import StandardScaler\\nss_x = StandardScaler()\\nx_train = ss_x.fit_transform(x_train)\\nx_test = ss_x.transform(x_test)\\n\\n\\n# In[840]:\\n\\n\\nfrom sklearn.linear_model import LinearRegression\\nlr = LinearRegression() #初始化\\nlr.fit(x_train, y_train) #训练数据\\nlr_y_predict = lr.predict(x_test) #回归预测\\n#性能测评：使用R方得分指标对模型预测结果进行评价\\nfrom sklearn.metrics import r2_score\\nprint(\\\"LinearRegression模型的R方得分为：\\\", r2_score(y_test, lr_y_predict))\\n\\nplt.figure(figsize=(15, 5))\\nplt.plot(y_test.values[:100], \\\"-r\\\", label=\\\"真实值\\\")\\nplt.plot(lr_y_predict[:100], \\\"-g\\\", label=\\\"预测值\\\")\\nplt.legend()\\nplt.title(\\\"线性回归预测结果\\\")\\n\\n\\n# In[847]:\\n\\n\\nparam_grid = [\\n {\\n 'weights':['uniform'],\\n 'n_neighbors':[i for i in range(1,12)]\\n \\n },\\n {\\n 'weights':['distance'],\\n 'n_neighbors':[i for i in range(1,12)],\\n 'p':[i for i in range(1,6)]\\n }\\n]\\nfrom sklearn.neighbors import KNeighborsRegressor\\nknnrgr = KNeighborsRegressor()\\nfrom sklearn.model_selection import GridSearchCV\\ngrid_search = GridSearchCV(knnrgr,param_grid)\\ngrid_search.fit(x_train,y_train)\\n\\n\\n# In[848]:\\n\\n\\nprint(grid_search.best_params_)\\nprint(grid_search.best_score_)\\n\\n\\n# In[870]:\\n\\n\\nfrom sklearn.linear_model import Ridge,Lasso,ElasticNet,SGDRegressor,BayesianRidge\\nfrom sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor,ExtraTreesRegressor\\nfrom sklearn.svm import SVR\\nfrom xgboost import XGBRegressor\\nfrom sklearn.metrics import mean_squared_error\\nmodels = [Ridge(),Lasso(alpha=0.01,max_iter=10000),RandomForestRegressor(),\\nGradientBoostingRegressor(),SVR(),ElasticNet(alpha=0.001,max_iter=10000),\\nSGDRegressor(max_iter=1000,tol=1e-3),BayesianRidge(),ExtraTreesRegressor(),\\nXGBRegressor(max_depth=5, learning_rate=0.1, n_estimators=160, silent=False, objective='reg:gamma')]\\nnames = [ \\\"邻回归\\\", \\\"Lasso回归\\\", \\\"随机森林\\\", \\\"梯度提升树\\\", \\\"支持向量机\\\" , \\\"弹性网络\\\",\\\"梯度下降回归\\\",\\\"贝叶斯线性回归\\\",\\\"极端随机森林回归\\\",\\\"Xgboost回归\\\"]\\nfor name, model in zip(names, models):\\n model.fit(x_train,y_train)\\n predicted= model.predict(x_test)\\n print(\\\"{}: {:.6f}, {:.4f}\\\".format(name,model.score(x_test,y_test),mean_squared_error(y_test, predicted)))\\n\\n\\n# In[871]:\\n\\n\\nclass grid():\\n def __init__(self,model):\\n self.model = model\\n \\n def grid_get(self,X,y,param_grid):\\n grid_search = GridSearchCV(self.model,param_grid,cv=5,n_jobs=-1)\\n grid_search.fit(X,y)\\n print(grid_search.best_params_, grid_search.best_score_)\\n print(pd.DataFrame(grid_search.cv_results_)[['params','mean_test_score','std_test_score']])\\n\\n\\n# In[872]:\\n\\n\\ngrid(Lasso()).grid_get(x_train,y_train,{'alpha': [0.0004,0.0005,0.0007,0.0006,0.0009,0.0008],'max_iter':[10000]})\\n\\n\\n# In[873]:\\n\\n\\ngrid(Ridge()).grid_get(x_train,y_train,{'alpha':[35,40,45,50,55,60,65,70,80,90]})\\n\\n\\n# In[875]:\\n\\n\\ngrid(ElasticNet()).grid_get(x_train,y_train,{'alpha':[0.0005,0.0008,0.004,0.005],'l1_ratio':[0.08,0.1,0.3,0.5,0.7],'max_iter':[10000]})\\n\\n\\n# In[ ]:\\n\\n\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.648419976234436,\n \"alphanum_fraction\": 0.661752462387085,\n \"avg_line_length\": 26.85178565979004,\n \"blob_id\": \"8c543990cd7a111e748d1cd9e6ea0cc4770cf471\",\n \"content_id\": \"763b0c2b6de7abc7712c22b73f47bdc8b95944cb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 20505,\n \"license_type\": \"no_license\",\n \"max_line_length\": 148,\n \"num_lines\": 560,\n \"path\": \"/Crawler/Crawler.py\",\n \"repo_name\": \"zhangbc07/Project\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python\\n# coding: utf-8\\n\\n# requests是python的一个第三方库，比自带的urllib更加简单方便和人性化 \\n# 使用requests获取网页的源代码，最简单的情况下只需要两行代码 \\n# Get与Post方法使用 对于大多数直接在浏览器输入网址就能访问的网页，使用requests的GET方法就能获取到网页的源代码 \\n# 这里需要使用 .content 来显示网页的源代码。如果不加 .content, 则得到的只会是网页访问的状态码，类似于下面这样： <Response [200]>\\n\\n# In[3]:\\n\\n\\nimport requests\\nhtml = requests.get('https://shileizcc.com').content\\n\\n\\n# 对于不能直接在浏览器中输入网址访问的页面，就需要使用requests的POST方法来获取源代码 \\n# data 这个字典的内容和项数需要根据实际情况做修改\\n\\n# In[4]:\\n\\n\\nimport requests\\ndata = {'key1': 'value1',\\n 'key2': 'value2'}\\nhtml = requests.post('网址', data=data).content\\n\\n\\n# 还有一些网址，提交的内容需要是 json 格式，因此我们的 post 方法的参数可以做一些修改：\\n\\n# In[5]:\\n\\n\\nhtml = requests.post('https://shileizcc.com', json=data).content\\n\\n\\n# xpath是一种HTML和XML查询语言，能在HTML和XML的树状结构中寻找节点，在Python中安装lxml库来使用xpath技术 \\n# 使用xpath 语句：/html/body/div[@class=\\\"useful\\\"]/ul/li/text()\\n\\n# In[6]:\\n\\n\\nsource='''\\n<html>\\n <head>\\n <title>测试</title>\\n </head>\\n <body>\\n <div class=\\\"useful\\\">\\n <ul>\\n <li class=\\\"info\\\">我需要的信息1</li>\\n <li class=\\\"info\\\">我需要的信息2</li>\\n <li class=\\\"info\\\">我需要的信息3</li>\\n </ul>\\n </div>\\n <div class=\\\"useless\\\">\\n <ul>\\n <li class=\\\"info\\\">垃圾1</li>\\n <li class=\\\"info\\\">垃圾2</li>\\n </ul>\\n </div>\\n </body>\\n</html>\\n'''\\n\\n\\n# In[7]:\\n\\n\\nfrom lxml import html\\nselector=html.fromstring(source)\\ninfo=selector.xpath('//div[@class=\\\"useful\\\"]/ul/li/text()')\\nprint(info)\\n\\n\\n# 豆瓣爬虫\\n\\n# In[10]:\\n\\n\\nimport requests\\nimport lxml.html\\nimport csv\\n\\ndoubanUrl='https://movie.douban.com/top250?start{}&filter='\\n\\n\\n# In[11]:\\n\\n\\ndef getSource(url):\\n '''\\n 获取网页源代码\\n :param url:\\n :return:String\\n '''\\n head={'use-agent':'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/49.0.2623.108 Safari/537.36'}\\n content=requests.get(url,headers=head)\\n content.encoding='utf-8'#强制修改编码，防止windows下出现乱码\\n return content.content\\n\\ndef getEveryItem(source):\\n '''\\n 获取每一部电影的相关信息，movie_dict字典用于保存电影的信息\\n :param source:\\n :return:[movie1_dict,movie2_dict,movie3_dict,...]\\n '''\\n selector=lxml.html.document_fromstring(source)\\n movieItemList=selector.xpath('//div[@class=\\\"info\\\"]')#此处使用了先抓大再抓小的技巧\\n movieList=[]\\n \\n for eachMovie in movieList:\\n movieList={}\\n title-eachMovie.xpath('div[@class=\\\"hd\\\"]/a/span[@class=\\\"title\\\"]/text()')\\n print(title)\\n otherTitle=eachMovie.xpath('div[@class=\\\"hd\\\"]/a/span[@class=\\\"other\\\"]/text()')\\n link=eachMovie.xpath('div[@class=\\\"hd\\\"]/a/@href')[0]\\n directorAndActor=eachMovie.xpath('div[@class=\\\"bd\\\"]/p[@class='']/text()')\\n star=eachMovie.xpath('div[@class=\\\"bd\\\"]/div[@class=\\\"star\\\"]/span[@class=\\\"rating_num\\\"]/text()')[0]\\n quote=eachMovie.xpath('div[@class=\\\"bd\\\"]/p[@class=\\\"quote\\\"]/span/text()')\\n if quote:\\n quote=quote[0]\\n else:\\n quote=''\\n \\n movieDict['title'] = ''.join(title + otherTitle)\\n movieDict['url'] = link\\n movieDict['directorAndActor'] = ''.join(directorAndActor).replace(' ', '').replace('\\\\r', '').replace('\\\\n', '')\\n movieDict['star'] = star\\n movieDict['quote'] = quote\\n print(movieDict)\\n movieList.append(movieDict)\\n return movieList\\n\\ndef writeData(movieList):\\n with open('doubanMovie_example2.csv', 'w', encoding='UTF-8', newline='') as f:\\n writer = csv.DictWriter(f, fieldnames=['title', 'directorAndActor', 'star', 'quote', 'url'])\\n writer.writeheader()\\n for each in movieList:\\n print(each)\\n writer.writerow(each)\\n \\nif __name__ == '__main__':\\n movieList = []\\n for i in range(20):\\n pageLink = doubanUrl.format(i * 1)\\n print(pageLink)\\n source = getSource(pageLink)\\n movieList += getEveryItem(source)\\n print(movieList[:10])\\n movieList = sorted(movieList, key=lambda k: k['star'], reverse=True) #根据字典中的key=star这一项的value来排序,倒序。\\n writeData(movieList)\\n\\n\\n# 使用Beautiful Soup4 同样用来从HTML和XML中提取数据，某些方面比xpath易懂，但不如xpath简洁，速度也相较慢 \\n# 主要使用的是find和find_all方法 \\n# soup = BeautifulSoup(网页源代码, '解析器'),解释器可以填html.parser,也可以填lxml\\n\\n# In[14]:\\n\\n\\nsource='''\\n<html>\\n <head>\\n <title>测试</title>\\n </head>\\n <body>\\n <div class=\\\"useful\\\">\\n <ul>\\n <li class=\\\"info\\\">我需要的信息1</li>\\n <li class=\\\"info\\\">我需要的信息2</li>\\n <li class=\\\"info\\\">我需要的信息3</li>\\n </ul>\\n </div>\\n <div class=\\\"useless\\\">\\n <ul>\\n <li class=\\\"info\\\">垃圾1</li>\\n <li class=\\\"info\\\">垃圾2</li>\\n </ul>\\n </div>\\n </body>\\n</html>\\n'''\\n\\n\\n# In[15]:\\n\\n\\nfrom bs4 import BeautifulSoup\\nsoup=BeautifulSoup(source,'lxml')\\nuseful=soup.find('div',class_=\\\"useful\\\")\\ninfo=useful.find_all('li')\\nfor i in info:\\n print(i.string)\\n \\n#find_all与find的不同在于，find_all返回的是列表，如果没有找到，就会返回空列表；\\n#find返回的直接是一个BeautifulSoup Tag对象，如果有多个符合条件的BeautifulSoup Tag，则返回第一个，找不到则返回None。\\n#两者参数完全相同：\\n #find_all( name , attrs , recursive , text , **kwargs )\\n #name就是HTML的标签名，类似于body, div, ul, li之类。\\n #attrs参数的值是一个字典，字典的key是属性名，字典的value是属性值：>>> find_all(attrs={'class': 'useful'})\\n #recursive的值为True或者False，当它为False的时候，Beautiful Soup不会搜索子节点。\\n #text可以是一个字符串或者是正则表达式。用于搜索标签里面的文本信息：\\n #**kwargs表示key=value形式的参数。一般这里的key是属性，value是属性值。\\n #这个大多数情况下与标签配合使用，但是有时候如果属性值非常特殊，也可以单独使用：\\n #find_all('div', id='test')\\n #find_all(class_='iamstrange')\\n #需要注意的是，如果某个标签的属性是class的话，在参数里面需要在class后面加一个下划线，写成“class_”避免和Python的关键字class冲突：\\n #find_all('div', class_='useful')\\n\\n\\n# 使用多进程进行检测\\n\\n# In[16]:\\n\\n\\nfrom multiprocessing.dummy import Pool as ThreadPool\\nimport threading\\nimport requests\\nimport time\\n \\n \\ndef getsource(url):\\n global hasViewed #修改全局变量,需要使用global关键字\\n print(url)\\n html = requests.get(url)\\n hasViewed.append(url)\\n \\n \\ndef threadGetSource(url):\\n print(url)\\n x = threading.Thread(target=getsource, args=(url,)) #这里的args=(url,) 逗号是必须的,因为加了逗号才是只有一个元素的元组\\n x.start()\\n \\nurls = []\\nhasViewed = []\\n \\nfor i in range(1, 21):\\n newpage = 'http://tieba.baidu.com/p/3522395718?pn={}'.format(i)\\n urls.append(newpage)\\n \\n#==========单线程=====================================\\ntime1 = time.time()\\nhasViewed = []\\nfor i in urls:\\n getsource(i)\\nprint('单线程耗时：{}'.format(time.time() - time1))\\n \\n#============Python multiprocessing.dumpy多线程=======\\npool = ThreadPool(20)\\ntime3 = time.time()\\nhasViewed = []\\nresults = pool.map(getsource, urls)\\npool.close()\\npool.join()\\nprint('并行耗时：{}'.format(time.time() - time3))\\n \\n#======Python threading 多线程=======================\\ntime5 = time.time()\\nhasViewed = []\\n[threadGetSource(url) for url in urls]\\nwhile not len(hasViewed) == 20:\\n pass\\nprint('thread并行耗时: {}'.format(time.time() - time5))\\n\\n\\n# Ajax技术介绍 \\n# AJAX 是 Asynchronous JavaScript And XML 的首字母缩写，意为：异步JavaScript与XML。 \\n# 使用Ajax技术，可以在不刷新网页的情况下，更新网页数据。 \\n# 使用Ajax技术的网页，一般会使用HTML编写网页的框架，在打开网页的时候，首先加载的是这个框架。 \\n# 剩下的部分或者全部内容，将会在框架加载完成以后再通过JavaScript在后台加载。 \\n# JSON \\n# JSON的全称是JavaScript Object Notation，它是一种轻量级的数据交换格式。 \\n# 我们在网络之间传递数据的时候，绝大多数情况下都是传递的字符串。 \\n# 当我们需要把Python里面数据发送给网页或者其他的编程语言的时候，可以先将Python的数据转化为JSON格式的字符串，然后将字符串传递给其他的语言，其他语言再将JSON格式的字符串转化为它自己的数据格式。\\n\\n# In[ ]:\\n\\n\\n#初始化Selenium\\nfrom selenium import webdriver\\ndriver=webdriver.Chrome(r'D:\\\\pydata\\\\chromedriver.exe')\\n# driver.get(\\\"http://v.youku.com/v_show/id_XMTY2NTk5ODAwMA==.html?from=y1.3-idx-beta-1519-23042.223465.3-3\\\")\\n\\n\\n# 网页动态加载的内容经过加密后，我们是无法看懂密文的，可以用JavaScript解析，用Selenium来模拟浏览器解析JavaScript，再爬取被解析后的代码 \\n\\n# In[17]:\\n\\n\\n#等待信息出现\\nfrom selenium.webdriver.support.ui import WebDriverWait\\nfrom selenium.webdriver.common.by import By\\nfrom selenium.webdriver.support import expected_conditions as EC\\nWebDriverWait(driver,300).until(EC.presence_of_all_elements_located((By.CLASS_NAME,\\\"con\\\")))\\n#WebDriverWait会阻塞程序额运行，并每0.5秒检查一次网页源代码。看需要的内容是否出现，如果没有就继续等待。\\n# 在上面的代码中，设定了超时时间为300秒。在300秒内，如果有某个元素出现，那么就解除阻塞，继续运行后面的代码；\\n# 如果等待的内容始终不出现，那么就会抛出一个超时的Exception。\\nEC.presence_of_element_located((By.CLASS_NAME, \\\"con\\\"))\\n# 这里的EC其实就是 expected_conditions，也就是期望的条件。>>>期望的条件.元素出现\\n# 而这里的元素就是一个class=\\\"con\\\"的元素。\\n# 这里除了指定class以外，还可以指定很多其他的属性，例如：\\nBy.ID\\nBy.NAME\\nBy.XPATH\\n# 通过元组的形式传递给presence_of_element_located方法。\\n# 在网页中寻找我们需要的内容，可以使用类似与Beautiful Soup4 的语法：\\nelement = driver.find_element_by_id(\\\"passwd-id\\\") #如果有多个符合条件的，返回第一个\\nelement = driver.find_element_by_name(\\\"passwd\\\") #如果有多个符合条件的，返回第一个\\nelement_list = driver.find_elements_by_id(\\\"passwd-id\\\") #以列表形式返回所有的符合条件的element\\nelement_list = driver.find_elements_by_name(\\\"passwd\\\") #以列表形式返回所有的符合条件的element\\n# 也可以使用XPath：\\nelement = driver.find_element_by_xpath(\\\"//input[@id='passwd-id']\\\") #如果有多个符合条件的，返回第一个\\nelement = driver.find_element_by_xpath(\\\"//input[@id='passwd-id']\\\") #以列表形式返回所有的符合条件的element\\n# 但是有一点需要特别注意：这些名字都是find_element开头的，因此他们返回的都是element对象。这些方法他们的目的是寻找element，而不是提取里面的值。\\n\\n\\n# 模拟登陆知乎\\n\\n# In[ ]:\\n\\n\\nimport requests\\ndata={'User-Agent':'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.82 Safari/537.36'}\\nsource=requests.get('http://zhihu.com',headers=data).text\\n# print(source)\\n\\n# 使用selenium来进行模拟登陆。流程如下：\\n# 初始化chromedriver\\n# 打开知乎登录页面\\n# 找到用户名的输入框，输入用户名\\n# 找到密码输入框，输入用户名\\n# 按下回车键\\n\\nfrom selenium import webdriver\\nfrom selenium.webdriver.common.keys import Keys\\nimport time\\nuser_name='******@qq.com'\\npass_word='******'\\ndriver=webdriver.Chrome(r'D:\\\\pydata\\\\chromedriver.exe')#填写chromedriver路径\\ndriver.get(\\\"https://www.zhihu.com/#signin\\\")\\n\\nelem=driver.find_element_by_class_name('account')#寻找账号输入框\\nelem.clear()\\nelem.send_keys(user_name)#输入账号\\npassword=driver.find_element_by_class_name('password')#寻找密码输入框\\npassword.clear()\\npassword.send_keys(pass_word)#输入密码\\nelem.send_keys(Keys.RETURN)#模拟键盘回车键\\ntime.sleep(10)#可以直接sleep，也可以等待某个条件出现\\nprint(driver.page_source)\\ndriver.close()#在视频中，这一行会被注释掉，以方便观察结果\\n\\n\\n# 机锋网爬虫 \\n\\n# In[ ]:\\n\\n\\nlogin_url = 'http://my.gfan.com/login?'\\nemail = '******@163.com'\\npassword = '******'\\n \\ndata = {'gotoUrl': 'http://my.gfan.com/',\\n 'loginName': email,\\n 'password': password}\\n \\nheader = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.116 Safari/537.36',\\n 'Accept': 'text / html, application / xhtml + xml, application / xml;q = 0.9, image / webp, * / *;q = 0.8',\\n 'Accept-Encoding': 'gzip, deflate, sdch, br',\\n 'Accept-Language': 'zh-CN,zh;q=0.8,en;q=0.6'}\\n \\nsession = requests.Session()\\nhtml = session.get(login_url, headers=header).content.decode()\\n \\ncaptcha_url = 'http://my.gfan.com/captcha'\\n \\nwith open('captcha.png', 'wb') as f:\\n f.write(requests.get(captcha_url).content)\\n \\ncaptcha_solution = input('captcha code is:')\\ndata['authCode'] = captcha_solution\\n \\nresult = session.post(login_url, data=data, headers=header).content\\nprint(result.decode())\\n\\n# 手动输入验证码的一般流程如下： \\n# 随机提交数据，获取POST的数据内容和格式 \\n# 分析网页源代码，获取验证码地址 \\n# 下载验证码到本地 \\n# 打开验证码，人眼读取内容 \\n# 构造POST的数据，填入验证码 \\n# POST提交\\n\\n\\n# Scrapy \\n# 1、创建一个Scrapy项目 \\n# 2、定义提取的Item \\n# 3、编写爬取网站的 spider 并提取 Item \\n# 4、编写 Item Pipeline 来存储提取到的Item(即数据)\\n\\n# In[33]:\\n\\n\\n#创建项目\\n# scrapy startproject tutorial 是在本地环境上创建 cmd\\n#该命令会创建包含下面内容的tutorial目录：\\n# tutorial/\\n# scrapy.cfg 该项目的配置文件\\n# tutorial/ 该项目的python模块\\n# __init__.py 项目中的item文件\\n# items.py 项目中的item文件\\n# pipelines.py 项目中的pipelines文件\\n# settings.py 项目中的设置文件\\n# spiders/ 放置spider代码的目录\\n# __init__.py\\n# ...\\n\\n#定义item\\n#Item 是保存爬取到的数据的容器；其使用方法和python字典类似， 并且提供了额外保护机制来避免拼写错误导致的未定义字段错误。\\n# 编辑tutorial中的item.py文件。代码如下：\\nimport scrapy\\nclass DmozItem(scrapy.Item):\\n title = scrapy.Field()\\n link = scrapy.Field()\\n desc = scrapy.Field()\\n \\n#编写第一个爬虫（spider）\\n#spider是用户编写用于从单个网站（或者一些网站）爬取数据的类\\n# 其包含了一个用于下载的初始URL，如何跟进网页中的链接以及如何分析页面中的内容， 提取生成 item 的方法。\\nclass DmozSpider(scrapy.Spider): #创建文件dmoz_spider放在tutorial/spiders目录下面\\n name = \\\"dmoz\\\" #用于区别spider，必须唯一\\n allowed_domains = [\\\"dmoz-odp.org\\\"]\\n start_urls = [\\n \\\"http://www.dmoz-odp.org/Computers/Programming/Languages/Python/Books/\\\",\\n \\\"http://www.dmoz-odp.org/Computers/Programming/Languages/Python/Resources/\\\"\\n ] #包含了Spider在启动时进行爬取的url列表。\\n \\n def parse(self, response): #是spider的一个方法。 被调用时，每个初始URL完成下载后生成的 Response 对象将会作为唯一的参数传递给该函数。\\n filename = response.url.split(\\\"/\\\")[-2]\\n with open(filename, 'wb') as f:\\n f.write(response.body)\\n \\n#爬取\\n#进入项目的根目录，执行下列命令启动spider：\\n# scrapy crawl dmoz 在cmd下面运行 会在根目录下面创建url所对应内容的文件\\n\\n#提取item\\n#进入项目根目录，执行下列命令来启动shell：\\n# scrapy shell \\\"http://www.dmoz.org/Computers/Programming/Languages/Python/Books/\\\"\\n\\n\\n# 提取item \\n# 进入项目根目录，执行下列命令来启动shell： \\n# scrapy shell \\\"http://www.dmoz.org/Computers/Programming/Languages/Python/Books/\\\"\\n\\n\\n#提取数据\\nresponse.xpath('//ul/li') #选择该页面中网站列表里所有<li>元素\\nresponse.xpath('//ul/li/text()').extract() #网站的描述\\nresponse.xpath('//ul/li/a/text()').extract() #网站的标题\\nresponse.xpath('//ul/li/a/@href').extract() #网站的链接\\n\\n#拼接更多的.xpath()来进一步获取某个节点\\nfor sel in response.xpath('//ul/li'):\\n title = sel.xpath('a/text()').extract()\\n link = sel.xpath('a/@href').extract()\\n desc = sel.xpath('text()').extract()\\n print title, link, desc\\n \\n#在spider中加入以下代码，再次爬取dmoz.org将会看到爬取到的网站信息被成功输出\\nimport scrapy\\n\\nclass DmozSpider(scrapy.Spider):\\n name = \\\"dmoz\\\"\\n allowed_domains = [\\\"dmoz-odp.org\\\"]\\n start_urls = [\\n \\\"http://www.dmoz-odp.org/Computers/Programming/Languages/Python/Books/\\\",\\n \\\"http://www.dmoz-odp.org/Computers/Programming/Languages/Python/Resources/\\\"\\n ]\\n\\n def parse(self, response):\\n for sel in response.xpath('//ul/li'):\\n title = sel.xpath('a/text()').extract()\\n link = sel.xpath('a/@href').extract()\\n desc = sel.xpath('text()').extract()\\n print title, link, desc\\n\\n \\n#使用item\\n# item对象是自定义的python字典。可以使用标准的字典语法获取到其每个字段的值。\\n>>> item = DmozItem() #终端命令\\n>>> item['title'] = 'Example title'\\n>>> item['title']\\n'Example title'\\n\\n# 为了将爬取的数据返回，最终的代码是：\\nimport scrapy\\n\\nfrom tutorial.items import DmozItem\\n\\nclass DmozSpider(scrapy.Spider):\\n name = \\\"dmoz\\\"\\n allowed_domains = [\\\"dmoz-odp.org\\\"]\\n start_urls = [\\n \\\"http://www.dmoz-odp.org/Computers/Programming/Languages/Python/Books/\\\",\\n \\\"http://www.dmoz-odp.org/Computers/Programming/Languages/Python/Resources/\\\"\\n ]\\n\\n def parse(self, response):\\n for sel in response.xpath('//ul/li'):\\n item = DmozItem()\\n item['title'] = sel.xpath('a/text()').extract()\\n item['link'] = sel.xpath('a/@href').extract()\\n item['desc'] = sel.xpath('text()').extract()\\n yield item\\n \\n \\n#追踪连接\\n#接来下不仅仅满足与爬取books及resources页面，想要过去所有python directory的内容。\\n# 实现这个功能的改进版spider：\\nimport scrapy\\n\\nfrom tutorial.items import DmozItem\\n\\nclass DmozSpider(scrapy.Spider):\\n name = \\\"dmoz\\\"\\n allowed_domains = [\\\"dmoz-odp.org\\\"]\\n start_urls = [\\n \\\"http://www.dmoz-odp.org/Computers/Programming/Languages/Python/\\\",\\n ]\\n\\n def parse(self, response): #加个这段代码反而没有爬到数据\\n for href in response.css(\\\"ul.directory.dir-col > li > a::attr('href')\\\"):\\n url = response.urljoin(response.url, href.extract())\\n yield scrapy.Request(url, callback=self.parse_dir_contents)\\n\\n def parse_dir_contents(self, response):\\n for sel in response.xpath('//ul/li'):\\n item = DmozItem()\\n item['title'] = sel.xpath('a/text()').extract()\\n item['link'] = sel.xpath('a/@href').extract()\\n item['desc'] = sel.xpath('text()').extract()\\n yield item\\n \\n# 现在parse() 仅仅从页面中提取我们感兴趣的链接，使用 response.urljoin 方法构造一个绝对路径的URL(页面上的链接都是相对路径的)，\\n# 产生(yield)一个请求， 该请求使用 parse_dir_contents() 方法作为回调函数, 用于最终产生我们想要的数据.。\\n\\n#保存爬到的数据\\n# 最简单存储爬取的数据的方式是使用 Feed exports:\\n# scrapy crawl dmoz -o items.json 终端命令\\n# 该命令采用JSON格式对爬到的数据进行序列化，生成items.json文件\\n\\n\\n# In[ ]:\\n\\n\\n\\n\\n\\n# In[ ]:\\n\\n\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6107417941093445,\n \"alphanum_fraction\": 0.6544976234436035,\n \"avg_line_length\": 27.320213317871094,\n \"blob_id\": \"64b604c83ef60a92e1c0d7d0f44ae3a6d2fd51e2\",\n \"content_id\": \"71286966213ff6dff02abfc5f1f6f03f8a76ba9b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 31953,\n \"license_type\": \"no_license\",\n \"max_line_length\": 170,\n \"num_lines\": 940,\n \"path\": \"/Recommender/Recommender.py\",\n \"repo_name\": \"zhangbc07/Project\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python\\n# coding: utf-8\\n\\n# In[ ]:\\n\\n\\nimport pandas as pd\\nimport re\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\nfrom sqlalchemy import create_engine\\nengine=create_engine('mysql+pymysql://root:Tsta@888@127.0.0.1:3306/data?charset=utf8')\\nimport warnings\\nwarnings.filterwarnings('ignore')\\nsql=pd.read_sql('all_gzdata',engine,chunksize=10000)\\n\\n\\n# In[ ]:\\n\\n\\ncounts1=[i['fullURLId'].value_counts() for i in sql] # counts1此时是个列表\\ncounts1=pd.concat(counts1).groupby(level_0).sum() # 由于sql分块的 上面counts1也是，concat纵向向链接\\ncounts1=counts1.reset_index()\\ncounts1.columns=['index','num']\\ncounts1['type']=counts1['index'].str.extract('(\\\\d{3})',expand=True)\\ncounts1.head()\\ncounts1_=counts1[['type','num']].groupby('type').sum()\\ncounts1_.sort_values(by='num',ascending=False,inplace=True)\\ncounts1_['percentage']=(counts1_['num']/counts1_['num'].sum())*100\\ncounts1_.head()\\n\\n\\n# In[ ]:\\n\\n\\na=counts1.set_index('type')\\nb=counts1.groupby('type').sum()\\nc=pd.merge(a,b,left_index=True,right_index=True)# 均以行索引为链接键 合并\\nc['percentage']=(c['num_x']/c['num_y'])*100\\nc.head()\\nc.reset_index(inplace=True) # inplace是否改变自身，True则改变原数据\\nd=c.sort_values(by=['type','percentage'],ascending=[True,False]).reset_index()\\nd.head() \\n\\n\\n# In[ ]:\\n\\n\\nsql=pd.read_sql('all_gzdata',engine,chunksize=10000)\\ndef count107(i):\\n j=i[['fullURL']][i['fullURLId'].str.contains('107')].copy()\\n j['type']=None\\n j['type'][j['fullURL'].str.contains('info/.+?/')]='知识首页'\\n j['type'][j['fullURL'].str.contains('info/.+?/.+?')]='知识列表页'\\n j['type'][j['fullURL'].str.contains('/\\\\d+?_*\\\\d+?\\\\.html')]='知识内容页'\\n return j['type'].value_counts()\\ncounts2=[count107(i) for i in sql]\\ncounts2=pd.concat(counts2).groupby(level_0).sum()\\ncounts2_=pd.DataFrame(counts2).reset_index()\\ncounts2_['percentage']=(counts2_['type']/counts2_['type'].sum())*100\\ncounts2_.columns=['107类型','num','百分比']\\ncounts2.head()\\n\\n\\n# In[ ]:\\n\\n\\ndef countquestion(i):\\n j=i[['fullURLId']][i['fullURL'].str.contains('\\\\?')].copy()\\n return j\\nsql=pd.read_sql('all_gzdata',engine,chunksize=10000)\\ncounts3=[countquestion(i)['fullURLId'].value_counts() for i in sql]\\ncounts3=pd.concat(counts3).groupby(level_0).sum()\\ncounts3\\ncounts3_=pd.DataFrame(counts3)\\ncounts3_['percentage']=(counts3_['fullURLId']/counts3_['fullURLId'].sum())*100\\ncounts3_.reset_index(inplace=True)\\ncounts3_.columns=['fullURLId','num','percentage']\\ncounts3_.sort_values(by='percentage',ascending=False,inplace=True)\\ncounts3_.reset_index(inplace=True)\\nc3=(counts3_['num'].sum()/counts1['num'].sum())*100\\nc3#统计?网页各类占比情况，探究?网页特征及在总样本占比\\n\\n\\n# In[ ]:\\n\\n\\ndef page199(i):\\n j=i[['pageTitle']][i['fullURLId'].str.contains('199') & i['fullURL'].str.contains('\\\\?')]\\n j['title']=1\\n j['title'][j['pageTitle'].str.contains('法律快车-律师助手')]='法律快车-律师助手'\\n j['title'][j['pageTitle'].str.contains('免费发布法律咨询')]='免费发布法律咨询'\\n j['title'][j['pageTitle'].str.contains('咨询发布成功')]='咨询发布成功'\\n j['title'][j['pageTitle'].str.contains('法律快搜')]='快搜'\\n j['title'][j['title']==1]='其他类型'\\n return j\\nsql=pd.read_sql('all_gzdata',engine,chunksize=10000)\\ncounts4=[page199(i) for i in sql]\\ncounts4_=pd.concat(counts4)\\nct4_result=counts4_['title'].value_counts()\\nct4_result=pd.DataFrame(ct4_result)\\nct4_result.reset_index(inplace=True)\\nct4_result['percentage']=(ct4_result['title']/ct4_result['title'].sum())*100\\nct4_result.columns=['title','num','percentage']\\nct4_result.sort_values(by='num',ascending=False,inplace=True)\\nct4_result#统计199，其他类网址细分类别特征，占比\\n\\n\\n# In[ ]:\\n\\n\\ndef wandering(i):\\n j=i[['fullURLId','fullURL','pageTitle']][(i['fullURL'].str.contains('.html'))==False]\\n return j\\nsql=pd.read_sql('all_gzdata',engine,chunksize=10000)\\ncounts5=[wandering(i) for i in sql]\\ncounts5_=pd.concat(counts5)\\n\\nct5Id=counts5_['fullURLId'].value_counts()\\nct5Id\\nct5Id_=pd.DataFrame(ct5Id)\\nct5Id_.reset_index(inplace=True)\\nct5Id_['index']=ct5Id_['index'].str.extract('(\\\\d{3})',expand=True)\\nct5Id_=pd.DataFrame(ct5Id_.groupby('index').sum())\\nct5Id_['percentage']=(ct5Id_['fullURLId']/ct5Id_['fullURLId'].sum())*100\\nct5Id_.reset_index(inplace=True)\\nct5Id_.columns=['fullURLId','num','percentage']\\nct5Id_.sort_values(by='num',ascending=False,inplace=True)\\nct5Id_.reset_index(inplace=True)\\n#分析闲逛网站的用户浏览特征，哪些吸引了这些闲逛用户\\nct5Id\\n\\n\\n# In[ ]:\\n\\n\\nsql=pd.read_sql('all_gzdata',engine,chunksize=10000)\\ncounts6=[i['realIP'].value_counts() for i in sql]\\ncounts6_=pd.concat(counts6).groupby(level=0).sum()\\ncounts6_=pd.DataFrame(counts6_)\\ncounts6_[1]=1\\nct6=counts6_.groupby('realIP').sum()\\nct6.columns=['用户数'] # 点击次数情况，来表征用户，即点击n次的用户数为m\\nct6.index.name='点击次数' # realIP 出现次数 \\nct6['用户百分比']=ct6['用户数']/ct6['用户数'].sum()*100\\nct6['记录百分比']= ct6['用户数']*ct6.index/counts6_['realIP'].sum()*100 # 点击n次记录数占总记录数的比值\\nct6.sort_index(inplace=True)\\nct6_=ct6.iloc[:7,:].T\\nct6['用户数'].sum() #总点击次数\\nct6_.insert(0,'总计',[ct6['用户数'].sum(),100,100])\\nct6_['7次以上']=ct6_.iloc[:,0]-ct6_.iloc[:,1:].sum(axis=1)\\nct6_\\n\\n\\n# In[ ]:\\n\\n\\nbeyond7=ct6.index[7:]\\nbins=[7,100,1000,50000] #用于划分区间\\nbeyond7_cut=pd.cut(beyond7,bins,right=True,labels=['8~100','101~1000','1000以上'])#right=True 即(7,100],(101,1000]\\nbeyond7_cut_=pd.DataFrame(beyond7_cut.value_counts())\\nbeyond7_cut_.index.name='点击次数'\\nbeyond7_cut_.columns=['用户数']\\nbeyond7_cut_.iloc[0,0]=ct6.loc[8:100,:]['用户数'].sum() #依次为点击在(7,100]用户数,iloc索引号比第几个少一\\nbeyond7_cut_.iloc[1,0]=ct6.loc[101:1000,:]['用户数'].sum() #注意，这里使用iloc会有问题，因这里行索引并非0开始的连续整数，而是名称索引，非自然整数索引\\nbeyond7_cut_.iloc[2,0]=ct6.loc[1001:,:]['用户数'].sum()\\nbeyond7_cut_.sort_values(by='用户数',ascending=False,inplace=True)\\nbeyond7_cut_.reset_index(drop=True,inplace=True)\\nbeyond7_cut_ # 点击8次以上情况统计分析，点击分布情况\\n\\n\\n# In[ ]:\\n\\n\\nfor_once=counts6_[counts6_['realIP']==1] # 分析点击一次用户行为特征\\ndel for_once[1] # 去除多余列\\nfor_once.columns=['点击次数']\\nfor_once.index.name='realIP' # IP 找到，以下开始以此为基准 链接数据，merge\\n\\nsql=pd.read_sql('all_gzdata',engine,chunksize=10000)\\nfor_once_=[i[['fullURLId','fullURL','realIP']] for i in sql]\\nfor_once_=pd.concat(for_once_)\\nfor_once_merge=pd.merge(for_once,for_once_,right_on='realIP',left_index=True,how='left')\\nfor_once_merge #浏览一次用户行为信息，浏览的网页，可分析网页类型\\nfor_once_url=pd.DataFrame(for_once_merge['fullURL'].value_counts())\\nfor_once_url.reset_index(inplace=True)\\nfor_once_url.columns=['fullURL','num']\\nfor_once_url['percentage']=for_once_url['num']/for_once_url['num'].sum()*100\\nfor_once_url #浏览一次用户行为信息，浏览的网页，可分析网页类型:税法、婚姻、问题\\n# 分析用户点击情况，点击频率特征，点一次就走 跳失率。。\\n\\n\\n# In[ ]:\\n\\n\\ndef url_click(i):\\n j=i[['fullURL','fullURLId','realIP']][i['fullURL'].str.contains('\\\\.html')] # \\\\带不带都一样\\n return j\\n\\nsql=pd.read_sql('select * from all_gzdata',engine,chunksize=10000)\\ncounts8=[url_click(i) for i in sql]\\ncounts8_=pd.concat(counts8)\\nct8=counts8_['fullURL'].value_counts()\\nct8=pd.DataFrame(ct8)\\nct8.columns=['点击次数']\\nct8.index.name='网址'\\nct8.sort_values(by='点击次数',ascending=False).iloc[:20,:] #网址的点击率排行，用户关注度\\n\\nct8_=ct8.reset_index() #500和501一样结果，思路不一样，推第一个500\\nclick_beyond_500=ct8_[(ct8_['网址'].str.contains('/\\\\d+?_*\\\\d+?\\\\.html')) & (ct8_['点击次数']>50)]\\nclick_beyond_501=ct8_[ct8_['网址'].str.contains('/\\\\d+?_*\\\\d+?\\\\.html')][ct8_['点击次数']>50] # 会报警\\n#网页点击情况分析，点击最多是哪些，关注度\\nct8\\n\\n\\n# In[ ]:\\n\\n\\nfor i in sql: #逐块变换并去重\\n d = i.copy()\\n d['fullURL'] = d['fullURL'].str.replace('_\\\\d{0,2}.html', '.html') #将下划线后面部分去掉，规范为标准网址\\n d = d.drop_duplicates() #删除重复记录\\n d.to_sql('changed_gzdata', engine, index = False, if_exists = 'append') #保存\\n\\n\\n# In[ ]:\\n\\n\\ndef scanning_url(i):\\n j=i.copy()\\n j['fullURL']=j['fullURL'].str.replace('_\\\\d{0,2}.html','.html')\\n return j\\nsql=pd.read_sql('select * from all_gzdata',engine,chunksize=10000)\\ncounts9=[scanning_url(i) for i in sql]\\ncounts9_=pd.concat(counts9)\\nct9=counts9_['fullURL'].value_counts() # 每个网页被点击总次数，多页合为一页\\nct9_=counts9_[['realIP','fullURL']].groupby('fullURL').count() # 每个IP对所点击网页的点击次数，多页合为一页\\nct9__=ct9_['realIP'].value_counts()\\nct9__20=ct9__[:20]\\nct9__20.plot()\\n\\n\\n# In[ ]:\\n\\n\\n#另一种分析视角，翻页行为统计分析\\npattern=re.compile('http://(.*\\\\d+?)_\\\\w+_\\\\w+\\\\.html$|http://(.*\\\\d+?)_\\\\w+\\\\.html$|http://(.*\\\\w+?).html$',re.S) # re.S 字符串跨行匹配;# 获取网址中以http://与.html中间的主体部分,即去掉翻页的内容，即去掉尾部\\\"_d\\\"\\n# 三个分别对应 主站点，\\nc9=click_beyond_500.set_index('网址').sort_index().copy() #sort_index()保证了 同一主站翻页网页是按从第一页开始排列的，即首页、下一页，为下方计算下一页点击占上一页比率铺垫\\nc9['websitemain']=np.nan # 统计主站点,即记录主站点，翻页的首页点击次数\\nfor i in range(len(c9)):\\n items=re.findall(pattern,c9.index[i])\\n if len(items)==0:\\n temp=np.nan\\n else:\\n for j in items[0]:\\n if j!='':\\n temp=j\\n c9.iloc[i,1]=temp\\nc9.head()\\nc9_0=c9['websitemain'].value_counts()\\nc9_0=pd.DataFrame(c9_0)\\nc9_1=c9_0[c9_0['websitemain']>=2].copy() # 滤掉不存在翻页的网页\\nc9_1.columns=['Times'] # 用于识别是一类网页，主网址出现次数\\nc9_1.index.name='websitemain'\\nc9_1['num']=np.arange(1,len(c9_1)+1)\\nc9_2=pd.merge(c9,c9_1,left_on='websitemain',right_index=True,how='right') # 链接左列与右索引 且左列与右索引的保留二者的行，且已右边为基础（即右边全保留，在此基础上添加左边的二者有共通行的）\\n# 当列与列做链接 索引会被忽略舍去，链接列与索引或索引与索引时索引会保留\\nc9_2.sort_index(inplace=True)\\n\\nc9_2['per']=np.nan\\ndef getper(x):\\n print(x)\\n for i in range(len(x)-1):\\n x.iloc[i+1,-1]=x.iloc[i+1,0]/x.iloc[i,0] # 翻页与上一页点击率比值，保存在最后一列处；同类网页下一页与上一页点击率比\\n return x\\nresult=pd.DataFrame()\\nfor i in range(1,c9_2['num'].max()+1): # 多少种翻页类，c9_2['num'].max()+1，从1开始\\n k=getper(c9_2[c9_2['num']==i]) # 同类翻页网页下一页与上一页点击数比值\\n result=pd.concat([result,k])\\nc9_2['Times'].value_counts()\\n\\nflipPageResult=result[result['Times']<10]\\nflipPageResult.head()\\n# 用户翻页行为分析，网站停留情况，文章分页优化\\n\\n\\n# In[ ]:\\n\\n\\ndef countmidurl(i): # 删除数据规则之中间网页（带midques_）\\n j=i[['fullURL','fullURLId','realIP']].copy()\\n j['type']='非中间类型网页'\\n j['type'][j['fullURL'].str.contains('midques_')]='中间类型网页'\\n return j['type'].value_counts()\\n\\n\\n# In[ ]:\\n\\n\\nsql=pd.read_sql('select * from all_gzdata',engine,chunksize=10000)\\ncounts10=[countmidurl(i) for i in sql]\\ncounts10_=pd.concat(counts10).groupby(level=0).sum()\\ncounts10_\\n\\n\\n# In[ ]:\\n\\n\\ndef count_no_html(i): # 删除数据规则之目录页（带html）\\n j=i[['fullURL','pageTitle','fullURLId']].copy()\\n j['type']='有html'\\n j['type'][j['fullURL'].str.contains('\\\\.html')==False]=\\\"无html\\\"\\n return j['type'].value_counts()\\nsql=pd.read_sql('select * from all_gzdata',engine,chunksize=10000)\\ncounts11=[count_no_html(i) for i in sql]\\ncounts11_=pd.concat(counts11).groupby(level=0).sum()\\ncounts11_\\n\\n\\n# In[ ]:\\n\\n\\ndef count_law_consult(i): # 数据删除规则之咨询、律师助手登录\\n j=i[['fullURL','pageTitle','fullURLId']].copy()\\n j['type']='其他'\\n j['pageTitle'].fillna('空',inplace=True)\\n j['type'][j['pageTitle'].str.contains('快车-律师助手')]='快车-律师助手'\\n j['type'][j['pageTitle'].str.contains('咨询发布成功')]='咨询发布成功'\\n j['type'][(j['pageTitle'].str.contains('免费发布法律咨询')) | (j['pageTitle'].str.contains('法律快搜'))]='快搜免费发布法律咨询'\\n return j['type'].value_counts()\\nsql=pd.read_sql('select * from all_gzdata',engine,chunksize=10000)\\ncounts12=[count_law_consult(i) for i in sql]\\ncounts12_=pd.concat(counts12).groupby(level=0).sum()\\ncounts12_\\n\\n\\n# In[ ]:\\n\\n\\ndef count_law_ques(i): # 数据删除规则之去掉与分析网站无关的网页\\n j=i[['fullURL']].copy()\\n j['fullURL']=j['fullURL'].str.replace('\\\\?.*','')\\n j['type']='主网址不包含关键字'\\n j['type'][j['fullURL'].str.contains('lawtime')]='主网址包含关键字'\\n return j\\nsql=pd.read_sql('select * from all_gzdata',engine,chunksize=10000)\\ncounts13=[count_law_ques(i) for i in sql]\\ncounts13_=pd.concat(counts13)['type'].value_counts()\\ncounts13_\\n\\n\\n# In[ ]:\\n\\n\\ndef count_duplicate(i): # 数据删除规则之去掉同一用户同一时间同一网页的重复数据（可能是记录错误）\\n j=i[['fullURL','realIP','timestamp_format']].copy()\\n return j\\nsql=pd.read_sql('select * from all_gzdata',engine,chunksize=10000)\\ncounts14=[count_duplicate(i) for i in sql]\\ncounts14_=pd.concat(counts14)\\nprint(len(counts14_[counts14_.duplicated()==True]),len(counts14_.drop_duplicates()))\\n\\nct14=counts14_.drop_duplicates()\\n\\n\\n# In[ ]:\\n\\n\\n# 开始对数据库数据进行清洗操作，构建模型数据\\nsql=pd.read_sql('select * from all_gzdata',engine,chunksize=10000)\\nfor i in sql: #只要.html结尾的 & 截取问号左边的值 & 只要包含主网址（lawtime)的&网址中间没有midques_的\\n d = i[['realIP', 'fullURL','pageTitle','userID','timestamp_format']].copy() # 只要网址列\\n d['fullURL'] = d['fullURL'].str.replace('\\\\?.*','') # 网址中问号后面的部分\\n d = d[(d['fullURL'].str.contains('\\\\.html')) & (d['fullURL'].str.contains('lawtime')) & (d['fullURL'].str.contains('midques_') == False)] # 只要含有.html的网址\\n # 保存到数据库中\\n d.to_sql('cleaned_one', engine, index = False, if_exists = 'append')\\n\\n\\n# In[ ]:\\n\\n\\nsql=pd.read_sql('select * from cleaned_one',engine,chunksize=10000)\\nfor i in sql: #删除 快车-律师助手 & 免费发布法律咨询 & 咨询发布成功 & 法律快搜）\\n d = i[['realIP','fullURL','pageTitle','userID','timestamp_format']]# 只要网址列\\n d['pageTitle'].fillna(u'空',inplace=True)\\n d = d[(d['pageTitle'].str.contains(u'快车-律师助手') == False) & (d['pageTitle'].str.contains(u'咨询发布成功') == False) & \\n (d['pageTitle'].str.contains(u'免费发布法律咨询') == False) & (d['pageTitle'].str.contains(u'法律快搜') == False)\\n ].copy()\\n # 保存到数据库中\\n d.to_sql('cleaned_two', engine, index = False, if_exists = 'append')\\n\\n\\n# In[ ]:\\n\\n\\nsql=pd.read_sql('select * from cleaned_two',engine,chunksize=10000)\\ndef dropduplicate(i): \\n j = i[['realIP','fullURL','pageTitle','userID','timestamp_format']].copy()\\n return j\\n\\n\\n# In[ ]:\\n\\n\\ncount15 = [dropduplicate(i) for i in sql]\\ncount15 = pd.concat(count15)\\nprint(len(count15)) # 2011695\\ncount16 = count15.drop_duplicates(['fullURL','userID','timestamp_format']) # 一定要进行二次删除重复，因为不同的块中会有重复值\\nprint(len(count16)) # 646915\\ncount16.to_sql('cleaned_three', engine)\\n#数据清洗及保存到库的操作，清洗完毕\\n\\n\\n# In[ ]:\\n\\n\\n# 查看各表的长度\\nsql = pd.read_sql('all_gzdata', engine, chunksize = 10000)\\nlens = 0\\nfor i in sql:\\n temp = len(i)\\n lens = temp + lens\\nprint(lens) #836877\\n\\n\\n# In[ ]:\\n\\n\\n# 查看cleaned_one表中的记录数\\nsql1 = pd.read_sql('cleaned_one', engine, chunksize = 10000)\\nlens1 = 0\\nfor i in sql1:\\n temp = len(i)\\n lens1 = temp + lens1\\nprint(lens1)#1341130\\n\\n\\n# In[ ]:\\n\\n\\n# 查看cleaned_two表中的记录数\\nsql2 = pd.read_sql('cleaned_two', engine, chunksize = 10000)\\nlens2 = 0\\nfor i in sql2:\\n temp = len(i)\\n lens2 = temp + lens2\\nprint(lens2)#2011695\\n\\n\\n# In[ ]:\\n\\n\\n# 查看cleaned_three表中的记录数\\nsql3 = pd.read_sql('cleaned_three', engine, chunksize = 10000)\\nlens3 = 0\\nfor i in sql3:\\n temp = len(i)\\n lens3 = temp + lens3\\nprint(lens3)\\n\\n\\n# In[ ]:\\n\\n\\nsql = pd.read_sql('cleaned_three', engine, chunksize = 10000)\\nl0 = 0\\nl1 = 0\\nl2 = 0\\nfor i in sql:\\n d = i.copy()\\n # 获取所有记录的个数\\n l0 += len(d)\\n # 获取类似于http://www.lawtime.cn***/2007020619634_2.html格式的记录的个数\\n # 匹配1 易知，匹配1一定包含匹配2\\n x1 = d[d['fullURL'].str.contains('_\\\\d{0,2}.html')]\\n l1 += len(x1)\\n # 匹配2\\n # 获取类似于http://www.lawtime.cn***/29_1_p3.html格式的记录的个数\\n x2 = d[d['fullURL'].str.contains('_\\\\d{0,2}_\\\\w{0,2}.html')]\\n l2 += len(x2)\\n# x1.to_sql('l1', engine, index=False, if_exists = 'append') # 保存\\n# x2.to_sql('l2', engine, index=False, if_exists = 'append') # 保存\\n \\nprint(l0,l1,l2)\\n\\n\\n# In[ ]:\\n\\n\\n# 去掉翻页的网址\\nsql = pd.read_sql('cleaned_three', engine, chunksize = 10000)\\nl4 = 0\\nfor i in sql: #初筛\\n d = i.copy()\\n # 注意！！！替换1和替换2的顺序不能颠倒，否则删除不完整\\n # 替换1 将类似于http://www.lawtime.cn***/29_1_p3.html下划线后面部分\\\"_1_p3\\\"去掉，规范为标准网址 \\n d['fullURL'] = d['fullURL'].str.replace('_\\\\d{0,2}_\\\\w{0,2}.html','.html')#这部分网址有 9260 个\\n # 替换2 将类似于http://www.lawtime.cn***/2007020619634_2.html下划线后面部分\\\"_2\\\"去掉，规范为标准网址\\n d['fullURL'] = d['fullURL'].str.replace('_\\\\d{0,2}.html','.html') #这部分网址有 55455-9260 = 46195 个\\n d = d.drop_duplicates(['fullURL','userID']) # 删除重复记录(删除有相同网址和相同用户ID的)【不完整】因为不同的数据块中依然有重复数据\\n l4 += len(d)\\n d.to_sql('changed_1', engine, index=False, if_exists = 'append') # 保存\\nprint(l4 )\\n\\n# In[ ]:\\n\\n\\nsql = pd.read_sql('changed_1', engine, chunksize = 10000)\\ndef dropduplicate(i): #二次筛选\\n j = i[['realIP','fullURL','pageTitle','userID','timestamp_format']].copy()\\n return j\\ncounts1 = [dropduplicate(i) for i in sql]\\ncounts1 = pd.concat(counts1)\\nprint(len(counts1))# 1095216\\n\\n# In[ ]:\\n\\n\\na = counts1.drop_duplicates(['fullURL','userID'])\\nprint(len(a))# 528166\\na.to_sql('changed_2', engine) # 保存\\n\\n\\n# In[ ]:\\n\\n\\n# 验证是否清洗干净，即changed_2已不存翻页网址\\nsql = pd.read_sql('changed_2', engine, chunksize = 10000)\\nl0 = 0\\nl1 = 0\\nl2 = 0\\nfor i in sql:\\n d = i.copy()\\n # 获取所有记录的个数\\n l0 += len(d)\\n # 获取类似于http://www.lawtime.cn***/2007020619634_2.html格式的记录的个数\\n # 匹配1 易知，匹配1一定包含匹配2\\n x1 = d[d['fullURL'].str.contains('_\\\\d{0,2}.html')]\\n l1 += len(x1)\\n # 匹配2\\n # 获取类似于http://www.lawtime.cn***/29_1_p3.html格式的记录的个数\\n x2 = d[d['fullURL'].str.contains('_\\\\d{0,2}_\\\\w{0,2}.html')]\\n l2 += len(x2)\\nprint(l0,l1,l2)# 528166 0 0表示已经删除成功\\n\\n\\n# In[ ]:\\n\\n\\n# 手动添加咨询类和知识类网址的类别，type={'咨询类','知识类'}\\nsql = pd.read_sql('changed_2', engine, chunksize = 10000)\\ndef countzhishi(i):\\n j = i[['fullURL']].copy()\\n j['type'] = 'else'\\n j['type'][j['fullURL'].str.contains('(info)|(faguizt)')] = 'zhishi'\\n j['type'][j['fullURL'].str.contains('(ask)|(askzt)')] = 'zixun'\\n return j\\ncounts17 = [countzhishi(i) for i in sql]\\ncounts17 = pd.concat(counts17)\\ncounts17['type'].value_counts()\\na = counts17['type'].value_counts()\\nb = pd.DataFrame(a)\\nb.columns = ['num']\\nb.index.name = 'type'\\nb['per'] = b['num']/b['num'].sum()*100\\nb\\n\\n\\n# In[ ]:\\n\\n\\n# 接上；咨询类较单一，知识类有丰富的二级、三级分类，以下作分析\\nc = counts17[counts17['type']=='zhishi']\\nd = c[c['fullURL'].str.contains('info')]\\nprint(len(d)) # 102140\\nd['iszsk'] = 'else' # 结果显示是空 \\nd['iszsk'][d['fullURL'].str.contains('info')] = 'infoelsezsk' # 102032\\nd['iszsk'][d['fullURL'].str.contains('zhishiku')] = 'zsk' # 108\\nd['iszsk'].value_counts() \\n# 由结果可知，除了‘info'和’zhishifku'没有其他类型，且 【info类型（不包含zhishiku)：infoelsezsk】和【包含zhishiku：zsk】类型无相交的部分。\\n# 因此分析知识类别下的二级类型时，需要分两部分考虑，求每部分的类别，再求并集，即为所有二级类型\\n\\n\\n# In[ ]:\\n\\n\\n# 对于http://www.lawtime.cn/info/jiaotong/jtsgcl/2011070996791.html类型的网址进行这样匹配,获取二级类别名称\\\"jiaotong\\\"\\npattern = re.compile('/info/(.*?)/',re.S)\\ne = d[d['iszsk'] == 'infoelsezsk']\\nfor i in range(len(e)): #用上面已经处理的'iszsk'列分成两种类别的网址，分别使用正则表达式进行匹配,较慢\\n e.iloc[i,2] = re.findall(pattern, e.iloc[i,0])[0]\\nprint(e.head())\\n\\n# In[ ]:\\n\\n\\n# 对于http://www.lawtime.cn/zhishiku/laodong/info/***.html类型的网址进行这样匹配,获取二级类别名称\\\"laodong\\\"\\n# 由于还有一类是http://www.lawtime.cn/zhishiku/laodong/***.html，所以使用'zhishiku/(.*?)/'进行匹配\\npattern1 = re.compile('zhishiku/(.*?)/',re.S)\\nf = d[d['iszsk'] == 'zsk']\\nfor i in range(len(f)):\\n# print i \\n f.iloc[i,2] = re.findall(pattern1, f.iloc[i,0])[0]\\nprint(f.head())\\n\\ne.columns = ['fullURL', 'type1', 'type2']\\nprint(e.head())\\nf.columns = ['fullURL', 'type1', 'type2']\\nprint(f.head())\\n\\n\\n# In[ ]:\\n\\n\\n# 将两类处理过二级类别的记录合并，求二级类别的交集\\ng = pd.concat([e,f])\\nh = g['type2'].value_counts()\\n# 求两类网址中的二级类别数，由结果可知，两类网址的二级类别的集合的并集满足所需条件\\nlen(e['type2'].value_counts()) # 66\\nlen(f['type2'].value_counts()) # 31\\nlen(g['type2'].value_counts()) # 69\\nprint(h.head())\\nprint(h.index) # 列出知识类别下的所有的二级类别\\n\\nfor i in range(len(h.index)): # 将二级类别存入到数据库中\\n x=g[g['type2']==h.index[i]]\\n x.to_sql('h.index', engine, if_exists='append')\\n\\n\\n# In[ ]:\\n\\n\\nq = e.copy()\\nq['type3'] = np.nan\\nresultype3 = pd.DataFrame([],columns=q.columns)\\nfor i in range(len(h.index)): # 正则匹配出三级类别\\n pattern2 = re.compile('/info/'+h.index[i]+'/(.*?)/',re.S)\\n current = q[q['type2'] == h.index[i]]\\n print(current.head())\\n for j in range(len(current)):\\n findresult = re.findall(pattern2, current.iloc[j,0])\\n if findresult == []: # 若匹配结果是空，则将空值进行赋值给三级类别\\n current.iloc[j,3] = np.nan\\n else:\\n current.iloc[j,3] = findresult[0]\\n resultype3 = pd.concat([resultype3,current])# 将处理后的数据拼接,即网址做索引，三列为一级、二级、三级类别\\nresultype3.set_index('fullURL',inplace=True)\\nresultype3.head(10)\\n# 统计婚姻类下面的三级类别的数目\\nj = resultype3[resultype3['type2'] == 'hunyin']['type3'].value_counts()\\nprint(len(j)) # 145\\nj.head()\\n\\n\\n# In[ ]:\\n\\n\\nype3nums = resultype3.pivot_table(index = ['type2','type3'], aggfunc = 'count') #类别3排序\\n# 方式2: Type3nums = resultype3.groupby([resultype3['type2'],resultype3['type3']]).count()\\nr = Type3nums.reset_index().sort_values(by=['type2','type1'],ascending=[True,False])\\nr.set_index(['type2','type3'],inplace = True)\\n\\nr.to_excel('2_2_3Type3nums.xlsx')\\nr\\n\\n\\n# In[ ]:\\n\\n\\n# 读取数据库数据 ，属性规约，提取模型需要的数据（属性）；此处是只选择用户和用户访问的网页\\nsql = pd.read_sql('changed_2', engine, chunksize = 10000)\\nl1 = 0\\nl2 = 0 \\nfor i in sql:\\n zixun = i[['userID','fullURL']][i['fullURL'].str.contains('(ask)|(askzt)')].copy()\\n l1 = len(zixun) + l1\\n hunyin = i[['userID','fullURL']][i['fullURL'].str.contains('hunyin')].copy() \\n l2 = len(hunyin) + l2\\n zixun.to_sql('zixunformodel', engine, index=False,if_exists = 'append')\\n hunyin.to_sql('hunyinformodel', engine, index=False,if_exists = 'append')\\nprint(l1,l2) # 393185 16982\\n\\n# In[ ]:\\n\\n\\n# 方法二：\\nm = counts17[counts17['type'] == 'zixun']\\nn = counts17[counts17['fullURL'].str.contains('hunyin')]\\np = m[m['fullURL'].str.contains('hunyin')]\\np # 结果为空，可知，包含zixun的页面中不包含hunyin，两者没有交集\\n#savetosql(m,'zixun')\\n#savetosql(n,'hunyin')\\nm.to_sql('zixun',engine)\\nn.to_sql('hunyin',engine)\\n\\n\\n# In[ ]:\\n\\n\\n## 推荐，基于物品的协同过滤推荐、随机推荐、按流行度推荐\\n\\ndata=pd.read_sql('hunyinformodel',engine)\\ndata.head()\\ndef jaccard(a,b): # 杰卡德相似系数，对0-1矩阵，故需要先将数据转成0-1矩阵\\n return 1.0*(a*b).sum()/(a+b-a*b).sum()\\nclass recommender():\\n sim=None\\n def similarity(self,x,distance):\\n y=np.ones((len(x),len(x)))\\n for i in range(len(x)):\\n for j in range(len(x)):\\n y[i,j]=distance(x[i],x[j])\\n return y\\n def fit(self,x,distance=jaccard):\\n self.sim=self.similarity(x,distance)\\n return self.sim\\n def recommend(self,a):\\n return np.dot(self.sim,a)*(1-a)\\nlen(data['fullURL'].value_counts())\\nlen(data['realIP'].value_counts())\\n# 网址数远小于用户数，即商品数小于使用的客户数，采用物品的协同过滤推荐\\n\\n\\n# In[ ]:\\n\\n\\nstart0=time.clock()\\ndata.sort_values(by=['realIP','fullURL'],ascending=[True,True],inplace=True)\\nrealIP=data['realIP'].value_counts().index\\nrealIP=np.sort(realIP)\\nfullURL=data['fullURL'].value_counts().index\\nfullURl=np.sort(fullURL)\\nd=pd.DataFrame([],index=realIP,columns=fullURL)\\nfor i in range(len(data)):\\n a=data.iloc[i,0]\\n b=data.iloc[i,1]\\n d.loc[a,b]=1\\nd.fillna(0,inplace=True)\\nend0=time.clock()\\nusetime0=end0-start0\\nprint('转化0-1矩阵耗时' + str(usetime0) +'s!')\\n#d.to_csv()\\n\\n\\n# In[ ]:\\n\\n\\ndf=d.copy()\\nsampler=np.random.permutation(len(df))\\ndf = df.take(sample)\\ntrain=df.iloc[:int(len(df)*0.9),:] \\ntest=df.iloc[int(len(df)*0.9):,:] \\n\\n\\n# In[ ]:\\n\\n\\ndf=df.values()\\ndf_train=df[:int(len(df)*0.9),:] # 9299\\ndf_test=df[int(len(df)*0.9):,:] # 1034\\ndf_train=df_tain.T\\ndf_test=df_test.T\\nprint(df_train.shape,df_test.shape) # (4339L, 9299L) (4339L, 1034L)\\n\\n\\n# In[ ]:\\n\\n\\nstart1=time.clock()\\nr=recommender()\\nsim=r.fit(df_train) # 计算相似度矩阵\\nend1=time.clock()\\na=pd.DataFrame(sim)\\nusetime1=end1-start1\\nprint('建立矩阵耗时'+ str(usetime1)+'s!')\\nprint(a.shape)\\n\\n\\n# In[ ]:\\n\\n\\na.index=train.columns\\n#a.columns=train.columns\\na.columns=train.index\\na.head(20)\\n\\n\\n# In[ ]:\\n\\n\\nstart2=time.clock()\\nresult=r.recommend(df_test)\\nend2=time.clock()\\n\\nresult1=pd.DataFrame(result)\\nusetime2=end2-start2\\n\\nprint('测试集推荐函数耗时'+str(usetime2)+'s!')\\nresult1.head()\\nresult1.index=test.columns\\nresult1.columns=test.index\\nresult1\\n\\n\\n# In[ ]:\\n\\n\\ndef xietong_result(k,recomMatrix): # k表推荐个数，recomMatrix推荐矩阵表DataFrame\\n recomMatrix.fillna(0.0,inplace=True)\\n n=range(1,k+1)\\n recommends=['xietong'+str(y) for y in n]\\n currentemp=pd.DataFrame([],index=recomMatrix.columns,columns=recommends)\\n for i in range(len(recomMatrix.columns)):\\n temp = recomMatrix.sort_values(by = recomMatrix.columns[i], ascending = False)\\n j = 0 \\n while j < k:\\n currentemp.iloc[i,j] = temp.index[j]\\n if temp.iloc[j,i] == 0.0:\\n currentemp.iloc[i,j:k] = np.nan\\n break\\n j += 1\\n return currentemp\\nstart3 = time.clock()\\nxietong_result = xietong_result(3, result1)\\nend3 = time.clock()\\nprint('按照协同过滤推荐方法为用户推荐3个未浏览过的网址耗时为' + str(end3 - start3)+'s!') #29.4996622053s!\\nxietong_result.head()\\n\\n\\n# In[ ]:\\n\\n\\n# test = df.iloc[int(len(df)*0.9):, :] # 所有测试数据df此时是矩阵，这样不能用\\nrandata = 1 - df_test # df_test是用户浏览过的网页的矩阵形式，randata则表示是用户未浏览过的网页的矩阵形式\\nrandmatrix = pd.DataFrame(randata, index = test.columns,columns=test.index)#这是用户未浏览过(待推荐）的网页的表格形式\\ndef rand_recommd(K, recomMatrix):# \\n import random # 注意：这个random是random模块，\\n import numpy as np\\n \\n recomMatrix.fillna(0.0,inplace=True) # 此处必须先填充空值\\n recommends = ['recommed'+str(y) for y in range(1,K+1)]\\n currentemp = pd.DataFrame([],index = recomMatrix.columns, columns = recommends)\\n \\n for i in range(len(recomMatrix.columns)): #len(res.columns)1\\n curentcol = recomMatrix.columns[i]\\n temp = recomMatrix[curentcol][recomMatrix[curentcol]!=0] # 未曾浏览过\\n # = temp.index[random.randint(0,len(temp))]\\n if len(temp) == 0:\\n currentemp.iloc[i,:] = np.nan\\n elif len(temp) < K:\\n r = temp.index.take(np.random.permutation(len(temp))) #注意：这个random是numpy模块的下属模块\\n currentemp.iloc[i,:len(r)] = r\\n else:\\n r = random.sample(temp.index, K)\\n currentemp.iloc[i,:] = r\\n return currentemp\\n\\n\\n# In[ ]:\\n\\n\\nstart4 = time.clock()\\nrandom_result = rand_recommd(3, randmatrix) # 调用随机推荐函数\\nend4 = time.clock()\\nprint('随机为用户推荐3个未浏览过的网址耗时为' + str(end4 - start4)+'s!') # 2.1900423292s!\\n#保存的表名命名格式为“3_1_k此表功能名称”，是本小节生成的第5张表格，功能为random_result：显示随机推荐的结果\\n#random_result.to_csv('random_result.csv')\\nrandom_result # 结果中出现了全空的行，这是冷启动现象,浏览该网页仅此IP一个，其他IP不曾浏览无相似系数\\n\\n\\n# In[ ]:\\n\\n\\ndef popular_recommed(K, recomMatrix):\\n recomMatrix.fillna(0.0,inplace=True)\\n import numpy as np\\n recommends = ['recommed'+str(y) for y in range(1,K+1)]\\n currentemp = pd.DataFrame([],index = recomMatrix.columns, columns = recommends)\\n \\n for i in range(len(recomMatrix.columns)):\\n curentcol = recomMatrix.columns[i]\\n temp = recomMatrix[curentcol][recomMatrix[curentcol]!=0]\\n if len(temp) == 0:\\n currentemp.iloc[i,:] = np.nan\\n elif len(temp) < K:\\n r = temp.index #注意：这个random是numpy模块的下属模块\\n currentemp.iloc[i,:len(r)] = r\\n else:\\n r = temp.index[:K]\\n currentemp.iloc[i,:] = r\\n \\n return currentemp \\n\\n\\n# In[ ]:\\n\\n\\n# 确定用户未浏览的网页（可推荐的）的数据表格\\nTEST = 1-df_test # df_test是用户浏览过的网页的矩阵形式，TEST则是未曾浏览过的\\ntest2 = pd.DataFrame(TEST, index = test.columns, columns=test.index)\\nprint(test2.head())\\nprint(test2.shape )\\n \\n# 确定网页浏览热度排名：\\nhotPopular = data['fullURL'].value_counts()\\nhotPopular = pd.DataFrame(hotPopular)\\nprint(hotPopular.head())\\nprint(hotPopular.shape)\\n \\n# 按照流行度对可推荐的所有网址排序\\ntest3 = test2.reset_index()\\nlist_custom = list(hotPopular.index)\\ntest3['index'] = test3['index'].astype('category')\\ntest3['index'].cat.reorder_categories(list_custom, inplace=True)\\ntest3.sort_values('index',inplace = True)\\ntest3.set_index ('index', inplace = True)\\nprint(test3.head())\\nprint(test3.shape)\\n \\n# 按照流行度为用户推荐3个未浏览过的网址\\nrecomMatrix = test3 #\\nstart5 = time.clock()\\npopular_result = popular_recommed(3, recomMatrix)\\nend5 = time.clock()\\nprint('按照流行度为用户推荐3个未浏览过的网址耗时为' + str(end5 - start5)+'s!')#7.70043007471s!\\n \\n#保存的表名命名格式为“3_1_k此表功能名称”，是本小节生成的第6张表格，功能为popular_result：显示按流行度推荐的结果\\n#popular_result.to_csv('3_1_6popular_result.csv')\\n \\npopular_result\\n\\n\\n# In[ ]:\\n\\n\\n\\n\\n\\n# In[ ]:\\n\\n\\n\\n\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":6,"string":"6"}}},{"rowIdx":2979362,"cells":{"repo_name":{"kind":"string","value":"guiqiqi/DataAnalyser4Lab1"},"repo_url":{"kind":"string","value":"https://github.com/guiqiqi/DataAnalyser4Lab1"},"snapshot_id":{"kind":"string","value":"7a222ab18f32d650dc737911e20df60b91a5a69a"},"revision_id":{"kind":"string","value":"3dd7bc03cc42c1fabf8e4836fa3c75ca2d155a8b"},"directory_id":{"kind":"string","value":"6868c17c2d1d9357217591e1ddd9fbbe0cea5869"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-04-26T09:58:01.010413","string":"2020-04-26T09:58:01.010413"},"revision_date":{"kind":"timestamp","value":"2019-03-14T16:33:35","string":"2019-03-14T16:33:35"},"committer_date":{"kind":"timestamp","value":"2019-03-14T16:33:35","string":"2019-03-14T16:33:35"},"github_id":{"kind":"number","value":173473558,"string":"173,473,558"},"star_events_count":{"kind":"number","value":1,"string":"1"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6814516186714172,"alphanum_fraction":0.7163978219032288,"avg_line_length":20.257143020629883,"blob_id":"e3e9e1ccec64eb592b39bd6e86128bb62481a2bb","content_id":"f7c8758c799fde26a1beac8f4a6601dcd21d1f4c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":744,"license_type":"no_license","max_line_length":79,"num_lines":35,"path":"/README.md","repo_name":"guiqiqi/DataAnalyser4Lab1","src_encoding":"UTF-8","text":"# DataAnalyser4Lab1\nData analysis program for PE1\n\n## Operating environment\nPython3.x - https://www.python.org/ftp/python/3.7.2/python-3.7.2.exe\n\nWindows xp ++\n\n## Prepare using\n 1. Download and python 3.x from link above (if you have not installed before)\n 2. Using command tool(cmd) to run command: `pip install matplotlib`\n 3. Open `main.pyw` and follow the instructions\n\n## Data Format\nSave the data to a file in the following format:\n\n6.23 6.11 5.89 5.97 ...\n\n## Instructions:\nSelect/enter the path of the Datafile ->\n\nFill in the data 'Standard value' ->\n\nClick Generate ->\n\nGet table.txt bar.png and axis.png in current path.\n\n## Third party library\nmatplotlib\n\n## Author\nguiqiqi187@gmail.com\n\n## Result\nSee `bar.png`, `axis.png` and `table.txt`.\n"},{"alpha_fraction":0.6340557336807251,"alphanum_fraction":0.6482971906661987,"avg_line_length":26.839284896850586,"blob_id":"7b60b3481324c2f303b8b10fe58cc7a9e5953a20","content_id":"d7be92c6abb1c43dec6c938ffa6c6f30b0a18163","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1615,"license_type":"no_license","max_line_length":84,"num_lines":56,"path":"/ui.py","repo_name":"guiqiqi/DataAnalyser4Lab1","src_encoding":"UTF-8","text":"\"\"\"Draw a GUI using TK\"\"\"\r\n\r\nfrom consts import *\r\n\r\ndef _exit():\r\n\troot.destroy()\r\n\tsys.exit()\r\n\r\n_select = lambda : _PATH.set(askopenfilename())\r\nopen_fail = lambda: msgbox.showerror(\"Error\", \"Open file error!\")\r\ninvalid_value = lambda: msgbox.showerror(\"Error\", \"Invalid standard value!\")\r\nsuccess = lambda: msgbox.showinfo(\"Success\", \"Analysis completed! \\nNow exiting...\")\r\n\r\ndef notepad(file):\r\n\tprogram = \"notepad \" if os.name == \"nt\" else \"vi\"\r\n\tos.popen(program + file, \"r\")\r\n_help = lambda : notepad(HELP)\r\n\r\n_title = \"Data Analyser\"\r\n\r\n_resizableX = False\r\n_resizableY = False\r\n\r\nroot = Tk()\r\n\r\n_PATH = StringVar()\r\n_STANDARD = StringVar()\r\n\r\ndef init(function):\r\n\t\"\"\"Initalize UI\"\"\"\r\n\r\n\t_menubar = Menu(root)\r\n\t_menubar.add_command(label = \"Select Data File\", command = _select)\r\n\t_menubar.add_command(label = \"Help\", command = _help)\r\n\t_menubar.add_command(label = \"Exit\", command = _exit)\r\n\r\n\t_label_file = Label(root, text = \"Datafile location: \")\r\n\t_label_file.grid(row = 0, column = 0, pady = 10)\r\n\r\n\t_choose_entry = Entry(root, textvariable = _PATH)\r\n\t_choose_entry.grid(row = 0, column = 1, padx = 10)\r\n\r\n\t_label_standard = Label(root, text = \"Standard value: \")\r\n\t_label_standard.grid(row = 1, column = 0)\r\n\r\n\t_standard_entry = Entry(root, textvariable = _STANDARD)\r\n\t_standard_entry.grid(row = 1, column = 1)\r\n\r\n\t_run_btn = Button(root, text = \"Generate\",\r\n\t\tcommand = lambda: function(_PATH, _STANDARD))\r\n\t_run_btn.grid(row = 2, column = 0, columnspan = 2, pady = 10)\r\n\r\n\troot.resizable(_resizableX, _resizableY)\r\n\troot.config(menu = _menubar)\r\n\troot.geometry('330x120')\r\n\troot.title(_title)\r\n"},{"alpha_fraction":0.5765494108200073,"alphanum_fraction":0.5919597744941711,"avg_line_length":25.38532066345215,"blob_id":"681ae9e5ee93b781b12e108f95bc4850c5c7b96f","content_id":"84a3b34625e8b13d515daa9b172a168bc5f163cc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2985,"license_type":"no_license","max_line_length":61,"num_lines":109,"path":"/tools.py","repo_name":"guiqiqi/DataAnalyser4Lab1","src_encoding":"UTF-8","text":"\"\"\"Text I/O functions\"\"\"\r\n\r\nfrom consts import *\r\n\r\nclass Tools(object):\r\n\tdef drawtable(table):\r\n\t\twith open(TABLE, \"w\") as handler:\r\n\t\t\thandler.write(table)\r\n\r\n\tdef drawbar(axis, normal):\r\n\t\t# Prepare for datas\r\n\t\t_sum = sum(axis.values())\r\n\t\txtick = set()\r\n\t\tx, y = list(), list()\r\n\t\tfor splice, value in axis.items():\r\n\t\t\tgap = splice[1] - splice[0]\r\n\t\t\txtick.add(splice[0])\r\n\t\t\txtick.add(splice[1])\r\n\t\t\tx.append((splice[0] + splice[1]) / 2)\r\n\t\t\ty.append(value / gap / _sum)\r\n\t\txtick = list(xtick); xtick.sort()\r\n\t\tfigure, axes = plt.subplots(dpi = 360, facecolor = \"white\")\r\n\r\n\t\t# Draw the bar\r\n\t\taxes_bar = axes\r\n\t\taxes_bar.bar(x, y, width = gap,\r\n\t\t\tfacecolor = \"blue\", edgecolor = \"yellow\",\r\n\t\t\tlinestyle = '--', linewidth = 1, alpha = 0.6)\r\n\t\taxes_bar.set_xticks(xtick)\r\n\r\n\t\t# Add data annotations\r\n\t\tfor position, value in zip(x, y):\r\n\t\t\tplt.text(position, value + 0.02, \"%.2f\" % value,\r\n\t\t\t\tha = \"center\", va = \"bottom\", fontsize = 7)\r\n\r\n\t\t# Draw thee normal distribution\r\n\t\taxes_normal = axes.twinx()\r\n\t\taxes_normal.scatter(normal.keys(), normal.values(),\r\n\t\t\tcolor = \"red\", alpha = 0.5)\r\n\t\t# axes_normal.set_yticks([])\r\n\r\n\t\tplt.ylim(0, max(y) + 0.3)\r\n\t\t# plt.show()\r\n\t\tplt.savefig(BAR)\r\n\t\tplt.close(figure)\r\n\r\n\tdef drawline(axis):\r\n\t\t# Prepare for datas\r\n\t\tx, y = list(), [0] * (SEGMENT + 1)\r\n\t\tfor splice in axis.keys():\r\n\t\t\tx.extend(splice)\r\n\t\tx = list(set(x)); x.sort()\r\n\t\toffset = (max(x) - min(x)) / (SEGMENT * 2)\r\n\r\n\t\t# Draw the axis\r\n\t\tfigure = plt.figure(dpi = 360, facecolor = \"white\")\r\n\t\taxes = plt.subplot(111)\r\n\t\taxes.plot(x, y, color = \"black\", linewidth = 0)\r\n\t\taxes.spines['right'].set_color('none')\r\n\t\taxes.spines['top'].set_color('none')\r\n\t\taxes.spines['bottom'].set_position(('data',0))\r\n\t\taxes.spines['left'].set_position(('data',0))\r\n\t\taxes.set_xticks(x)\r\n\t\taxes.set_yticks([])\r\n\t\taxes.set_ylim(-0.5, 0.5)\r\n\t\tfor position, count in axis.items():\r\n\t\t\tx_position = position[0]\r\n\t\t\taxes.text(x_position + offset, 0.05, count, size = 10)\r\n\r\n\t\t# plt.show()\r\n\t\tplt.savefig(AXIS)\r\n\t\tplt.close(figure)\r\n\r\n\tdef spliter(string):\r\n\t\tstring = string.strip()\r\n\t\tstring = string.replace('\\n', ' ')\r\n\t\tsubstrings = string.split(' ')\r\n\t\tdatas = [float(item) for item in substrings]\r\n\t\treturn datas \r\n\r\n\tdef annotation(**annotations):\r\n\t\tresult = list()\r\n\t\tmodel = \"**{key} = {value}\"\r\n\t\tfor key, value in annotations.items():\r\n\t\t\tresult.append(model.format(\r\n\t\t\t\tkey = key, value = value))\r\n\t\treturn '\\n'.join(result)\r\n\r\n\tdef title():\r\n\t\ttitler = list()\r\n\t\ttitles = [\"No.\", \"t\", \"t-<t>\", \"(t-<t>)^2\"]\r\n\t\tdivider = '-' * WIDTH * len(titles)\r\n\t\tfor title in titles:\r\n\t\t\ttitler.append(title.center(WIDTH))\r\n\t\treturn ''.join(titler) + '\\n' + divider\r\n\r\n\tdef table(iterators):\r\n\t\t_flag = True\r\n\t\tresult = list()\r\n\t\twhile _flag:\r\n\t\t\tfor iterator in iterators:\r\n\t\t\t\ttry:\r\n\t\t\t\t\tdata = next(iterator)\r\n\t\t\t\texcept StopIteration as _error:\r\n\t\t\t\t\t_flag = False\r\n\t\t\t\t\tbreak\r\n\t\t\t\tresult.append(str(data).center(WIDTH))\r\n\t\t\tresult.append('\\n')\r\n\t\treturn ''.join(result).strip('\\n')\r\n"},{"alpha_fraction":0.7084078788757324,"alphanum_fraction":0.7280858755111694,"avg_line_length":23.409090042114258,"blob_id":"abfb8e679958d704f7e7a5e37adfcc0a4b5496c6","content_id":"e86795666b6e022bf973e75c02ee4d3d3255a366","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":559,"license_type":"no_license","max_line_length":52,"num_lines":22,"path":"/consts.py","repo_name":"guiqiqi/DataAnalyser4Lab1","src_encoding":"UTF-8","text":"\"\"\"Define consts\"\"\"\r\n\r\nimport math\r\nimport matplotlib.pyplot as plt\r\n\r\nfrom tkinter.filedialog import askopenfilename\r\nfrom tkinter import *\r\nimport tkinter.messagebox as msgbox\r\nfrom tkinter.ttk import *\r\n\r\nimport os\r\nimport sys\r\n\r\nACCURACY = 10000 # default accuracy is 10**-5\r\nWIDTH = 12 # default width for every table column\r\nSEGMENT = 7 # default segments for operator linspace\r\n\r\nAXIS = \"axis.png\" # default axis figure filename\r\nBAR = \"bar.png\" # default bar figure filename\r\nTABLE = \"table.txt\" # default table filename\r\n\r\nHELP = \"help\" # Help file\r\n"},{"alpha_fraction":0.6413708925247192,"alphanum_fraction":0.6413708925247192,"avg_line_length":16.522727966308594,"blob_id":"c0e7f0c037fd05a7616e3409d2a41dbe65bd6713","content_id":"2e728ed59066ed01adaa2609c73bd8fd19810a54","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":817,"license_type":"no_license","max_line_length":44,"num_lines":44,"path":"/main.pyw","repo_name":"guiqiqi/DataAnalyser4Lab1","src_encoding":"UTF-8","text":"\r\nfrom calculator import Calculate\r\nfrom tools import Tools\r\n\r\nimport ui\r\n\r\ndef run(path, standard):\r\n\ttry:\r\n\t\tpath = path.get()\r\n\t\twith open(path, \"r\") as handler:\r\n\t\t\tstring = handler.read()\r\n\texcept Exception as error:\r\n\t\tui.open_fail()\r\n\t\treturn\r\n\r\n\ttry:\r\n\t\tstandard = int(standard.get())\r\n\texcept ValueError as error:\r\n\t\tui.invalid_value()\r\n\t\treturn\r\n\r\n\tdatas = Tools.spliter(string)\r\n\tinstance = Calculate(standard, datas)\r\n\r\n\ttable = '\\n'.join([\r\n\t\tTools.title(),\r\n\t\tTools.table(instance.iterators()) + '\\n',\r\n\t\tTools.annotation(**instance.information())\r\n\t])\r\n\tTools.drawtable(table)\r\n\r\n\taxis = instance.axis()\r\n\tnormal = instance.normal()\r\n\r\n\tTools.drawline(axis)\r\n\r\n\tinstance.reset()\r\n\tTools.drawbar(axis, normal)\r\n\r\n\tui.success()\r\n\treturn\r\n\r\nif __name__ == \"__main__\":\r\n\tui.init(run)\r\n\tui.root.mainloop()\r\n"},{"alpha_fraction":0.6046424508094788,"alphanum_fraction":0.6091351509094238,"avg_line_length":21.429824829101562,"blob_id":"ecc208dc213e43ccdd7ab294ab8334d85c3d19fd","content_id":"cd59cf056ff1cca481e55105e72c6a2d8fbf4975","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2671,"license_type":"no_license","max_line_length":57,"num_lines":114,"path":"/calculator.py","repo_name":"guiqiqi/DataAnalyser4Lab1","src_encoding":"UTF-8","text":"\"\"\"Iterator and general calc functions support\"\"\"\r\n\r\nfrom consts import *\r\n\r\nclass Calculate(object):\r\n\t\"\"\"Calculate constant for datas\"\"\"\r\n\tdef __init__(self, standard, datas):\r\n\t\t\"\"\"init for iterators\"\"\"\r\n\t\tself.standard = standard\r\n\t\tself.datas = datas\r\n\t\tself._average = self.average()\r\n\r\n\tdef reset(self):\r\n\t\tnew_iterator = Calculate(self.standard, self.datas)\r\n\t\tself = new_iterator\r\n\r\n\tdef information(self):\r\n\t\treturn {\r\n\t\t\t\"max\": max(self.datas),\r\n\t\t\t\"min\": min(self.datas),\r\n\t\t\t\"range\": self.range(),\r\n\t\t\t\"average\": self.average(),\r\n\t\t\t\"sigma\": self.sigma()\r\n\t\t}\r\n\r\n\tdef index(self):\r\n\t\tfor index in range(1, len(self.datas) + 1):\r\n\t\t\tyield index\r\n\t\treturn\r\n\r\n\tdef data(self):\r\n\t\tfor item in self.datas:\r\n\t\t\tyield item\r\n\t\treturn\r\n\r\n\tdef difference(self):\r\n\t\tfor item in self.datas:\r\n\t\t\tyield self.round(item - self._average)\r\n\t\treturn\r\n\r\n\tdef variance(self):\r\n\t\tfor item in self.datas:\r\n\t\t\tyield self.round((item - self._average) ** 2)\r\n\t\treturn\r\n\r\n\t# def rho(self):\r\n\t# \tsigma = self.sigma()\r\n\t# \taverage = self.average()\r\n\t# \tfor data in self.datas:\r\n\t# \t\tyield self.round(self._rho(sigma, average, data))\r\n\t# \treturn\r\n\r\n\tdef iterators(self):\r\n\t\treturn [\r\n\t\t\tself.index(),\r\n\t\t\tself.data(),\r\n\t\t\tself.difference(), \r\n\t\t\tself.variance()\r\n\t\t]\r\n\r\n\tdef average(self):\r\n\t\treturn sum(self.datas)/len(self.datas)\r\n\r\n\tdef range(self):\r\n\t\treturn self.round(max(self.datas) - min(self.datas))\r\n\r\n\tdef sigma(self):\r\n\t\tsum_variance = sum(self.variance())\r\n\t\treturn math.sqrt(sum_variance / len(self.datas))\r\n\r\n\tdef linspace(self, segment = SEGMENT):\r\n\t\taxis = dict()\r\n\t\t_max, _min = max(self.datas), min(self.datas)\r\n\t\tlength = _max - _min\r\n\t\tsplice = length / segment\r\n\t\twhile _min < _max:\r\n\t\t\tstart, end = _min, self.round(_min + splice)\r\n\t\t\taxis[(start, end)] = list()\r\n\t\t\t_min = end\r\n\r\n\t\tfor data in self.datas:\r\n\t\t\tfor interval in axis.keys():\r\n\t\t\t\tstart, end = interval\r\n\t\t\t\tif start <= data <= end:\r\n\t\t\t\t\taxis[interval].append(data)\r\n\t\t\t\t\tbreak\r\n\t\t\tcontinue\r\n\r\n\t\treturn axis\r\n\r\n\tdef axis(self):\r\n\t\tline = self.linspace()\r\n\t\treturn {key: len(datas) for key, datas in line.items()}\r\n\r\n\tdef normal(self):\r\n\t\tline = self.linspace()\r\n\t\tnormal = dict()\r\n\t\tsigma, average = self.sigma(), self.average()\r\n\t\tfor key, datas in line.items():\r\n\t\t\tkey = (key[0] + key[1]) / 2\r\n\t\t\trho = self.rho(sigma, average, key)\r\n\t\t\tnormal[key] = self.round(rho)\r\n\t\treturn normal\r\n\r\n\t@staticmethod\r\n\tdef round(number, accuracy = ACCURACY):\r\n\t\treturn (round(number * accuracy) / accuracy)\r\n\r\n\t@staticmethod\r\n\tdef rho(sigma, average, data):\r\n\t\t_upi = 1 / (math.sqrt(2 * math.pi))\r\n\t\texponent = -1 * \\\r\n\t\t(data - average) ** 2 / (2 * sigma ** 2)\r\n\t\treturn (_upi / sigma) * (math.e ** exponent)\r\n"},{"alpha_fraction":0.36390531063079834,"alphanum_fraction":0.5872781276702881,"avg_line_length":22.14285659790039,"blob_id":"7745e4bf7d795849d4610cb2e839069c4e69b992","content_id":"fcbe4dfd83c0707c07dc7b18faf87dc38698b774","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":676,"license_type":"no_license","max_line_length":53,"num_lines":28,"path":"/test.py","repo_name":"guiqiqi/DataAnalyser4Lab1","src_encoding":"UTF-8","text":"\"\"\"Unittest\"\"\"\r\n\r\n# from consts import *\r\n# __import__('pdb').set_trace()\r\n# from calculator import Calculate\r\n# from tools import Tools\r\n\r\n# string = \\\r\n# \"\"\"\r\n# 6.33 6.00 5.78 6.04 6.08\r\n# 6.23 5.94 5.96 6.16 5.90\r\n# 6.10 6.19 6.31 6.10 6.26\r\n# 5.98 5.88 5.94 6.08 6.21\r\n# 6.02 6.06 6.10 5.93 6.19\r\n# 5.85 5.50 5.56 6.52 6.03\r\n# 5.96 6.26 6.15 5.90 6.23\r\n# 6.10 5.83 5.43 6.36 6.06\r\n# 6.90 6.44 6.26 6.15 6.24\r\n# 6.20 6.40 6.42 6.39 6.43\r\n# \"\"\"\r\n# standard = 6\r\n\r\n# datas = Tools.spliter(string)\r\n# instance = Calculate(standard, datas)\r\n\r\n# print (Tools.title())\r\n# print (Tools.table(instance.iterators()), end = '')\r\n# print (Tools.annotation(**instance.information()))\r\n"},{"alpha_fraction":0.5620093941688538,"alphanum_fraction":0.5792778730392456,"avg_line_length":15.216216087341309,"blob_id":"f470b751422b7230bad41b431b67f59d6839cac4","content_id":"c15a7b6b25c47f52e0c1b6342a43eb40ff0c7b0c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":637,"license_type":"no_license","max_line_length":47,"num_lines":37,"path":"/setup.py","repo_name":"guiqiqi/DataAnalyser4Lab1","src_encoding":"UTF-8","text":"\"\"\"Package source code to single exe file\"\"\"\r\n\r\nimport cx_Freeze\r\nimport sys\r\nimport traceback\r\n\r\nbase = None\r\n\r\nif sys.platform == \"win32\":\r\n\tbase = \"Win32GUI\"\r\n\r\nexecutables = [\r\n\tcx_Freeze.Executable(\r\n\t\t\"main.pyw\", base = base\r\n\t)\r\n]\r\n\r\ncx_Freeze.setup(\r\n\tname = \"DataAnalyser4Lab1\",\r\n\toptions = {\r\n\t\t# \"include_files\": [\"help\"],\r\n\t\t# \"optimize\": 2,\r\n\t\t\"build_exe\": {\r\n\t\t\t\"packages\": [\r\n\t\t\t\t\"matplotlib.pyplot\",\r\n\t\t\t\t\"tkinter.filedialog\",\r\n\t\t\t\t\"tkinter.ttk\",\r\n\t\t\t\t\"tkinter\",\r\n\t\t\t\t\"math\", \"os\"\r\n\t\t\t]\r\n\t\t}\r\n\t},\r\n\tversion = \"0.0.1\",\r\n\tdescription = \"Data analysis program for PE1\",\r\n\texecutables = executables,\r\n\tauthor = \"guiqiqi187@gmail.com\"\r\n)\r\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6814516186714172,\n \"alphanum_fraction\": 0.7163978219032288,\n \"avg_line_length\": 20.257143020629883,\n \"blob_id\": \"e3e9e1ccec64eb592b39bd6e86128bb62481a2bb\",\n \"content_id\": \"f7c8758c799fde26a1beac8f4a6601dcd21d1f4c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 744,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 35,\n \"path\": \"/README.md\",\n \"repo_name\": \"guiqiqi/DataAnalyser4Lab1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# DataAnalyser4Lab1\\nData analysis program for PE1\\n\\n## Operating environment\\nPython3.x - https://www.python.org/ftp/python/3.7.2/python-3.7.2.exe\\n\\nWindows xp ++\\n\\n## Prepare using\\n 1. Download and python 3.x from link above (if you have not installed before)\\n 2. Using command tool(cmd) to run command: `pip install matplotlib`\\n 3. Open `main.pyw` and follow the instructions\\n\\n## Data Format\\nSave the data to a file in the following format:\\n\\n6.23 6.11 5.89 5.97 ...\\n\\n## Instructions:\\nSelect/enter the path of the Datafile ->\\n\\nFill in the data 'Standard value' ->\\n\\nClick Generate ->\\n\\nGet table.txt bar.png and axis.png in current path.\\n\\n## Third party library\\nmatplotlib\\n\\n## Author\\nguiqiqi187@gmail.com\\n\\n## Result\\nSee `bar.png`, `axis.png` and `table.txt`.\\n\"\n },\n {\n \"alpha_fraction\": 0.6340557336807251,\n \"alphanum_fraction\": 0.6482971906661987,\n \"avg_line_length\": 26.839284896850586,\n \"blob_id\": \"7b60b3481324c2f303b8b10fe58cc7a9e5953a20\",\n \"content_id\": \"d7be92c6abb1c43dec6c938ffa6c6f30b0a18163\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1615,\n \"license_type\": \"no_license\",\n \"max_line_length\": 84,\n \"num_lines\": 56,\n \"path\": \"/ui.py\",\n \"repo_name\": \"guiqiqi/DataAnalyser4Lab1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Draw a GUI using TK\\\"\\\"\\\"\\r\\n\\r\\nfrom consts import *\\r\\n\\r\\ndef _exit():\\r\\n\\troot.destroy()\\r\\n\\tsys.exit()\\r\\n\\r\\n_select = lambda : _PATH.set(askopenfilename())\\r\\nopen_fail = lambda: msgbox.showerror(\\\"Error\\\", \\\"Open file error!\\\")\\r\\ninvalid_value = lambda: msgbox.showerror(\\\"Error\\\", \\\"Invalid standard value!\\\")\\r\\nsuccess = lambda: msgbox.showinfo(\\\"Success\\\", \\\"Analysis completed! \\\\nNow exiting...\\\")\\r\\n\\r\\ndef notepad(file):\\r\\n\\tprogram = \\\"notepad \\\" if os.name == \\\"nt\\\" else \\\"vi\\\"\\r\\n\\tos.popen(program + file, \\\"r\\\")\\r\\n_help = lambda : notepad(HELP)\\r\\n\\r\\n_title = \\\"Data Analyser\\\"\\r\\n\\r\\n_resizableX = False\\r\\n_resizableY = False\\r\\n\\r\\nroot = Tk()\\r\\n\\r\\n_PATH = StringVar()\\r\\n_STANDARD = StringVar()\\r\\n\\r\\ndef init(function):\\r\\n\\t\\\"\\\"\\\"Initalize UI\\\"\\\"\\\"\\r\\n\\r\\n\\t_menubar = Menu(root)\\r\\n\\t_menubar.add_command(label = \\\"Select Data File\\\", command = _select)\\r\\n\\t_menubar.add_command(label = \\\"Help\\\", command = _help)\\r\\n\\t_menubar.add_command(label = \\\"Exit\\\", command = _exit)\\r\\n\\r\\n\\t_label_file = Label(root, text = \\\"Datafile location: \\\")\\r\\n\\t_label_file.grid(row = 0, column = 0, pady = 10)\\r\\n\\r\\n\\t_choose_entry = Entry(root, textvariable = _PATH)\\r\\n\\t_choose_entry.grid(row = 0, column = 1, padx = 10)\\r\\n\\r\\n\\t_label_standard = Label(root, text = \\\"Standard value: \\\")\\r\\n\\t_label_standard.grid(row = 1, column = 0)\\r\\n\\r\\n\\t_standard_entry = Entry(root, textvariable = _STANDARD)\\r\\n\\t_standard_entry.grid(row = 1, column = 1)\\r\\n\\r\\n\\t_run_btn = Button(root, text = \\\"Generate\\\",\\r\\n\\t\\tcommand = lambda: function(_PATH, _STANDARD))\\r\\n\\t_run_btn.grid(row = 2, column = 0, columnspan = 2, pady = 10)\\r\\n\\r\\n\\troot.resizable(_resizableX, _resizableY)\\r\\n\\troot.config(menu = _menubar)\\r\\n\\troot.geometry('330x120')\\r\\n\\troot.title(_title)\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.5765494108200073,\n \"alphanum_fraction\": 0.5919597744941711,\n \"avg_line_length\": 25.38532066345215,\n \"blob_id\": \"681ae9e5ee93b781b12e108f95bc4850c5c7b96f\",\n \"content_id\": \"84a3b34625e8b13d515daa9b172a168bc5f163cc\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2985,\n \"license_type\": \"no_license\",\n \"max_line_length\": 61,\n \"num_lines\": 109,\n \"path\": \"/tools.py\",\n \"repo_name\": \"guiqiqi/DataAnalyser4Lab1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Text I/O functions\\\"\\\"\\\"\\r\\n\\r\\nfrom consts import *\\r\\n\\r\\nclass Tools(object):\\r\\n\\tdef drawtable(table):\\r\\n\\t\\twith open(TABLE, \\\"w\\\") as handler:\\r\\n\\t\\t\\thandler.write(table)\\r\\n\\r\\n\\tdef drawbar(axis, normal):\\r\\n\\t\\t# Prepare for datas\\r\\n\\t\\t_sum = sum(axis.values())\\r\\n\\t\\txtick = set()\\r\\n\\t\\tx, y = list(), list()\\r\\n\\t\\tfor splice, value in axis.items():\\r\\n\\t\\t\\tgap = splice[1] - splice[0]\\r\\n\\t\\t\\txtick.add(splice[0])\\r\\n\\t\\t\\txtick.add(splice[1])\\r\\n\\t\\t\\tx.append((splice[0] + splice[1]) / 2)\\r\\n\\t\\t\\ty.append(value / gap / _sum)\\r\\n\\t\\txtick = list(xtick); xtick.sort()\\r\\n\\t\\tfigure, axes = plt.subplots(dpi = 360, facecolor = \\\"white\\\")\\r\\n\\r\\n\\t\\t# Draw the bar\\r\\n\\t\\taxes_bar = axes\\r\\n\\t\\taxes_bar.bar(x, y, width = gap,\\r\\n\\t\\t\\tfacecolor = \\\"blue\\\", edgecolor = \\\"yellow\\\",\\r\\n\\t\\t\\tlinestyle = '--', linewidth = 1, alpha = 0.6)\\r\\n\\t\\taxes_bar.set_xticks(xtick)\\r\\n\\r\\n\\t\\t# Add data annotations\\r\\n\\t\\tfor position, value in zip(x, y):\\r\\n\\t\\t\\tplt.text(position, value + 0.02, \\\"%.2f\\\" % value,\\r\\n\\t\\t\\t\\tha = \\\"center\\\", va = \\\"bottom\\\", fontsize = 7)\\r\\n\\r\\n\\t\\t# Draw thee normal distribution\\r\\n\\t\\taxes_normal = axes.twinx()\\r\\n\\t\\taxes_normal.scatter(normal.keys(), normal.values(),\\r\\n\\t\\t\\tcolor = \\\"red\\\", alpha = 0.5)\\r\\n\\t\\t# axes_normal.set_yticks([])\\r\\n\\r\\n\\t\\tplt.ylim(0, max(y) + 0.3)\\r\\n\\t\\t# plt.show()\\r\\n\\t\\tplt.savefig(BAR)\\r\\n\\t\\tplt.close(figure)\\r\\n\\r\\n\\tdef drawline(axis):\\r\\n\\t\\t# Prepare for datas\\r\\n\\t\\tx, y = list(), [0] * (SEGMENT + 1)\\r\\n\\t\\tfor splice in axis.keys():\\r\\n\\t\\t\\tx.extend(splice)\\r\\n\\t\\tx = list(set(x)); x.sort()\\r\\n\\t\\toffset = (max(x) - min(x)) / (SEGMENT * 2)\\r\\n\\r\\n\\t\\t# Draw the axis\\r\\n\\t\\tfigure = plt.figure(dpi = 360, facecolor = \\\"white\\\")\\r\\n\\t\\taxes = plt.subplot(111)\\r\\n\\t\\taxes.plot(x, y, color = \\\"black\\\", linewidth = 0)\\r\\n\\t\\taxes.spines['right'].set_color('none')\\r\\n\\t\\taxes.spines['top'].set_color('none')\\r\\n\\t\\taxes.spines['bottom'].set_position(('data',0))\\r\\n\\t\\taxes.spines['left'].set_position(('data',0))\\r\\n\\t\\taxes.set_xticks(x)\\r\\n\\t\\taxes.set_yticks([])\\r\\n\\t\\taxes.set_ylim(-0.5, 0.5)\\r\\n\\t\\tfor position, count in axis.items():\\r\\n\\t\\t\\tx_position = position[0]\\r\\n\\t\\t\\taxes.text(x_position + offset, 0.05, count, size = 10)\\r\\n\\r\\n\\t\\t# plt.show()\\r\\n\\t\\tplt.savefig(AXIS)\\r\\n\\t\\tplt.close(figure)\\r\\n\\r\\n\\tdef spliter(string):\\r\\n\\t\\tstring = string.strip()\\r\\n\\t\\tstring = string.replace('\\\\n', ' ')\\r\\n\\t\\tsubstrings = string.split(' ')\\r\\n\\t\\tdatas = [float(item) for item in substrings]\\r\\n\\t\\treturn datas \\r\\n\\r\\n\\tdef annotation(**annotations):\\r\\n\\t\\tresult = list()\\r\\n\\t\\tmodel = \\\"**{key} = {value}\\\"\\r\\n\\t\\tfor key, value in annotations.items():\\r\\n\\t\\t\\tresult.append(model.format(\\r\\n\\t\\t\\t\\tkey = key, value = value))\\r\\n\\t\\treturn '\\\\n'.join(result)\\r\\n\\r\\n\\tdef title():\\r\\n\\t\\ttitler = list()\\r\\n\\t\\ttitles = [\\\"No.\\\", \\\"t\\\", \\\"t-<t>\\\", \\\"(t-<t>)^2\\\"]\\r\\n\\t\\tdivider = '-' * WIDTH * len(titles)\\r\\n\\t\\tfor title in titles:\\r\\n\\t\\t\\ttitler.append(title.center(WIDTH))\\r\\n\\t\\treturn ''.join(titler) + '\\\\n' + divider\\r\\n\\r\\n\\tdef table(iterators):\\r\\n\\t\\t_flag = True\\r\\n\\t\\tresult = list()\\r\\n\\t\\twhile _flag:\\r\\n\\t\\t\\tfor iterator in iterators:\\r\\n\\t\\t\\t\\ttry:\\r\\n\\t\\t\\t\\t\\tdata = next(iterator)\\r\\n\\t\\t\\t\\texcept StopIteration as _error:\\r\\n\\t\\t\\t\\t\\t_flag = False\\r\\n\\t\\t\\t\\t\\tbreak\\r\\n\\t\\t\\t\\tresult.append(str(data).center(WIDTH))\\r\\n\\t\\t\\tresult.append('\\\\n')\\r\\n\\t\\treturn ''.join(result).strip('\\\\n')\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.7084078788757324,\n \"alphanum_fraction\": 0.7280858755111694,\n \"avg_line_length\": 23.409090042114258,\n \"blob_id\": \"abfb8e679958d704f7e7a5e37adfcc0a4b5496c6\",\n \"content_id\": \"e86795666b6e022bf973e75c02ee4d3d3255a366\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 559,\n \"license_type\": \"no_license\",\n \"max_line_length\": 52,\n \"num_lines\": 22,\n \"path\": \"/consts.py\",\n \"repo_name\": \"guiqiqi/DataAnalyser4Lab1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Define consts\\\"\\\"\\\"\\r\\n\\r\\nimport math\\r\\nimport matplotlib.pyplot as plt\\r\\n\\r\\nfrom tkinter.filedialog import askopenfilename\\r\\nfrom tkinter import *\\r\\nimport tkinter.messagebox as msgbox\\r\\nfrom tkinter.ttk import *\\r\\n\\r\\nimport os\\r\\nimport sys\\r\\n\\r\\nACCURACY = 10000 # default accuracy is 10**-5\\r\\nWIDTH = 12 # default width for every table column\\r\\nSEGMENT = 7 # default segments for operator linspace\\r\\n\\r\\nAXIS = \\\"axis.png\\\" # default axis figure filename\\r\\nBAR = \\\"bar.png\\\" # default bar figure filename\\r\\nTABLE = \\\"table.txt\\\" # default table filename\\r\\n\\r\\nHELP = \\\"help\\\" # Help file\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.6413708925247192,\n \"alphanum_fraction\": 0.6413708925247192,\n \"avg_line_length\": 16.522727966308594,\n \"blob_id\": \"c0e7f0c037fd05a7616e3409d2a41dbe65bd6713\",\n \"content_id\": \"2e728ed59066ed01adaa2609c73bd8fd19810a54\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 817,\n \"license_type\": \"no_license\",\n \"max_line_length\": 44,\n \"num_lines\": 44,\n \"path\": \"/main.pyw\",\n \"repo_name\": \"guiqiqi/DataAnalyser4Lab1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\r\\nfrom calculator import Calculate\\r\\nfrom tools import Tools\\r\\n\\r\\nimport ui\\r\\n\\r\\ndef run(path, standard):\\r\\n\\ttry:\\r\\n\\t\\tpath = path.get()\\r\\n\\t\\twith open(path, \\\"r\\\") as handler:\\r\\n\\t\\t\\tstring = handler.read()\\r\\n\\texcept Exception as error:\\r\\n\\t\\tui.open_fail()\\r\\n\\t\\treturn\\r\\n\\r\\n\\ttry:\\r\\n\\t\\tstandard = int(standard.get())\\r\\n\\texcept ValueError as error:\\r\\n\\t\\tui.invalid_value()\\r\\n\\t\\treturn\\r\\n\\r\\n\\tdatas = Tools.spliter(string)\\r\\n\\tinstance = Calculate(standard, datas)\\r\\n\\r\\n\\ttable = '\\\\n'.join([\\r\\n\\t\\tTools.title(),\\r\\n\\t\\tTools.table(instance.iterators()) + '\\\\n',\\r\\n\\t\\tTools.annotation(**instance.information())\\r\\n\\t])\\r\\n\\tTools.drawtable(table)\\r\\n\\r\\n\\taxis = instance.axis()\\r\\n\\tnormal = instance.normal()\\r\\n\\r\\n\\tTools.drawline(axis)\\r\\n\\r\\n\\tinstance.reset()\\r\\n\\tTools.drawbar(axis, normal)\\r\\n\\r\\n\\tui.success()\\r\\n\\treturn\\r\\n\\r\\nif __name__ == \\\"__main__\\\":\\r\\n\\tui.init(run)\\r\\n\\tui.root.mainloop()\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.6046424508094788,\n \"alphanum_fraction\": 0.6091351509094238,\n \"avg_line_length\": 21.429824829101562,\n \"blob_id\": \"ecc208dc213e43ccdd7ab294ab8334d85c3d19fd\",\n \"content_id\": \"cd59cf056ff1cca481e55105e72c6a2d8fbf4975\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2671,\n \"license_type\": \"no_license\",\n \"max_line_length\": 57,\n \"num_lines\": 114,\n \"path\": \"/calculator.py\",\n \"repo_name\": \"guiqiqi/DataAnalyser4Lab1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Iterator and general calc functions support\\\"\\\"\\\"\\r\\n\\r\\nfrom consts import *\\r\\n\\r\\nclass Calculate(object):\\r\\n\\t\\\"\\\"\\\"Calculate constant for datas\\\"\\\"\\\"\\r\\n\\tdef __init__(self, standard, datas):\\r\\n\\t\\t\\\"\\\"\\\"init for iterators\\\"\\\"\\\"\\r\\n\\t\\tself.standard = standard\\r\\n\\t\\tself.datas = datas\\r\\n\\t\\tself._average = self.average()\\r\\n\\r\\n\\tdef reset(self):\\r\\n\\t\\tnew_iterator = Calculate(self.standard, self.datas)\\r\\n\\t\\tself = new_iterator\\r\\n\\r\\n\\tdef information(self):\\r\\n\\t\\treturn {\\r\\n\\t\\t\\t\\\"max\\\": max(self.datas),\\r\\n\\t\\t\\t\\\"min\\\": min(self.datas),\\r\\n\\t\\t\\t\\\"range\\\": self.range(),\\r\\n\\t\\t\\t\\\"average\\\": self.average(),\\r\\n\\t\\t\\t\\\"sigma\\\": self.sigma()\\r\\n\\t\\t}\\r\\n\\r\\n\\tdef index(self):\\r\\n\\t\\tfor index in range(1, len(self.datas) + 1):\\r\\n\\t\\t\\tyield index\\r\\n\\t\\treturn\\r\\n\\r\\n\\tdef data(self):\\r\\n\\t\\tfor item in self.datas:\\r\\n\\t\\t\\tyield item\\r\\n\\t\\treturn\\r\\n\\r\\n\\tdef difference(self):\\r\\n\\t\\tfor item in self.datas:\\r\\n\\t\\t\\tyield self.round(item - self._average)\\r\\n\\t\\treturn\\r\\n\\r\\n\\tdef variance(self):\\r\\n\\t\\tfor item in self.datas:\\r\\n\\t\\t\\tyield self.round((item - self._average) ** 2)\\r\\n\\t\\treturn\\r\\n\\r\\n\\t# def rho(self):\\r\\n\\t# \\tsigma = self.sigma()\\r\\n\\t# \\taverage = self.average()\\r\\n\\t# \\tfor data in self.datas:\\r\\n\\t# \\t\\tyield self.round(self._rho(sigma, average, data))\\r\\n\\t# \\treturn\\r\\n\\r\\n\\tdef iterators(self):\\r\\n\\t\\treturn [\\r\\n\\t\\t\\tself.index(),\\r\\n\\t\\t\\tself.data(),\\r\\n\\t\\t\\tself.difference(), \\r\\n\\t\\t\\tself.variance()\\r\\n\\t\\t]\\r\\n\\r\\n\\tdef average(self):\\r\\n\\t\\treturn sum(self.datas)/len(self.datas)\\r\\n\\r\\n\\tdef range(self):\\r\\n\\t\\treturn self.round(max(self.datas) - min(self.datas))\\r\\n\\r\\n\\tdef sigma(self):\\r\\n\\t\\tsum_variance = sum(self.variance())\\r\\n\\t\\treturn math.sqrt(sum_variance / len(self.datas))\\r\\n\\r\\n\\tdef linspace(self, segment = SEGMENT):\\r\\n\\t\\taxis = dict()\\r\\n\\t\\t_max, _min = max(self.datas), min(self.datas)\\r\\n\\t\\tlength = _max - _min\\r\\n\\t\\tsplice = length / segment\\r\\n\\t\\twhile _min < _max:\\r\\n\\t\\t\\tstart, end = _min, self.round(_min + splice)\\r\\n\\t\\t\\taxis[(start, end)] = list()\\r\\n\\t\\t\\t_min = end\\r\\n\\r\\n\\t\\tfor data in self.datas:\\r\\n\\t\\t\\tfor interval in axis.keys():\\r\\n\\t\\t\\t\\tstart, end = interval\\r\\n\\t\\t\\t\\tif start <= data <= end:\\r\\n\\t\\t\\t\\t\\taxis[interval].append(data)\\r\\n\\t\\t\\t\\t\\tbreak\\r\\n\\t\\t\\tcontinue\\r\\n\\r\\n\\t\\treturn axis\\r\\n\\r\\n\\tdef axis(self):\\r\\n\\t\\tline = self.linspace()\\r\\n\\t\\treturn {key: len(datas) for key, datas in line.items()}\\r\\n\\r\\n\\tdef normal(self):\\r\\n\\t\\tline = self.linspace()\\r\\n\\t\\tnormal = dict()\\r\\n\\t\\tsigma, average = self.sigma(), self.average()\\r\\n\\t\\tfor key, datas in line.items():\\r\\n\\t\\t\\tkey = (key[0] + key[1]) / 2\\r\\n\\t\\t\\trho = self.rho(sigma, average, key)\\r\\n\\t\\t\\tnormal[key] = self.round(rho)\\r\\n\\t\\treturn normal\\r\\n\\r\\n\\t@staticmethod\\r\\n\\tdef round(number, accuracy = ACCURACY):\\r\\n\\t\\treturn (round(number * accuracy) / accuracy)\\r\\n\\r\\n\\t@staticmethod\\r\\n\\tdef rho(sigma, average, data):\\r\\n\\t\\t_upi = 1 / (math.sqrt(2 * math.pi))\\r\\n\\t\\texponent = -1 * \\\\\\r\\n\\t\\t(data - average) ** 2 / (2 * sigma ** 2)\\r\\n\\t\\treturn (_upi / sigma) * (math.e ** exponent)\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.36390531063079834,\n \"alphanum_fraction\": 0.5872781276702881,\n \"avg_line_length\": 22.14285659790039,\n \"blob_id\": \"7745e4bf7d795849d4610cb2e839069c4e69b992\",\n \"content_id\": \"fcbe4dfd83c0707c07dc7b18faf87dc38698b774\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 676,\n \"license_type\": \"no_license\",\n \"max_line_length\": 53,\n \"num_lines\": 28,\n \"path\": \"/test.py\",\n \"repo_name\": \"guiqiqi/DataAnalyser4Lab1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Unittest\\\"\\\"\\\"\\r\\n\\r\\n# from consts import *\\r\\n# __import__('pdb').set_trace()\\r\\n# from calculator import Calculate\\r\\n# from tools import Tools\\r\\n\\r\\n# string = \\\\\\r\\n# \\\"\\\"\\\"\\r\\n# 6.33 6.00 5.78 6.04 6.08\\r\\n# 6.23 5.94 5.96 6.16 5.90\\r\\n# 6.10 6.19 6.31 6.10 6.26\\r\\n# 5.98 5.88 5.94 6.08 6.21\\r\\n# 6.02 6.06 6.10 5.93 6.19\\r\\n# 5.85 5.50 5.56 6.52 6.03\\r\\n# 5.96 6.26 6.15 5.90 6.23\\r\\n# 6.10 5.83 5.43 6.36 6.06\\r\\n# 6.90 6.44 6.26 6.15 6.24\\r\\n# 6.20 6.40 6.42 6.39 6.43\\r\\n# \\\"\\\"\\\"\\r\\n# standard = 6\\r\\n\\r\\n# datas = Tools.spliter(string)\\r\\n# instance = Calculate(standard, datas)\\r\\n\\r\\n# print (Tools.title())\\r\\n# print (Tools.table(instance.iterators()), end = '')\\r\\n# print (Tools.annotation(**instance.information()))\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.5620093941688538,\n \"alphanum_fraction\": 0.5792778730392456,\n \"avg_line_length\": 15.216216087341309,\n \"blob_id\": \"f470b751422b7230bad41b431b67f59d6839cac4\",\n \"content_id\": \"c15a7b6b25c47f52e0c1b6342a43eb40ff0c7b0c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 637,\n \"license_type\": \"no_license\",\n \"max_line_length\": 47,\n \"num_lines\": 37,\n \"path\": \"/setup.py\",\n \"repo_name\": \"guiqiqi/DataAnalyser4Lab1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Package source code to single exe file\\\"\\\"\\\"\\r\\n\\r\\nimport cx_Freeze\\r\\nimport sys\\r\\nimport traceback\\r\\n\\r\\nbase = None\\r\\n\\r\\nif sys.platform == \\\"win32\\\":\\r\\n\\tbase = \\\"Win32GUI\\\"\\r\\n\\r\\nexecutables = [\\r\\n\\tcx_Freeze.Executable(\\r\\n\\t\\t\\\"main.pyw\\\", base = base\\r\\n\\t)\\r\\n]\\r\\n\\r\\ncx_Freeze.setup(\\r\\n\\tname = \\\"DataAnalyser4Lab1\\\",\\r\\n\\toptions = {\\r\\n\\t\\t# \\\"include_files\\\": [\\\"help\\\"],\\r\\n\\t\\t# \\\"optimize\\\": 2,\\r\\n\\t\\t\\\"build_exe\\\": {\\r\\n\\t\\t\\t\\\"packages\\\": [\\r\\n\\t\\t\\t\\t\\\"matplotlib.pyplot\\\",\\r\\n\\t\\t\\t\\t\\\"tkinter.filedialog\\\",\\r\\n\\t\\t\\t\\t\\\"tkinter.ttk\\\",\\r\\n\\t\\t\\t\\t\\\"tkinter\\\",\\r\\n\\t\\t\\t\\t\\\"math\\\", \\\"os\\\"\\r\\n\\t\\t\\t]\\r\\n\\t\\t}\\r\\n\\t},\\r\\n\\tversion = \\\"0.0.1\\\",\\r\\n\\tdescription = \\\"Data analysis program for PE1\\\",\\r\\n\\texecutables = executables,\\r\\n\\tauthor = \\\"guiqiqi187@gmail.com\\\"\\r\\n)\\r\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":8,"string":"8"}}},{"rowIdx":2979363,"cells":{"repo_name":{"kind":"string","value":"sergiojx/pactools"},"repo_url":{"kind":"string","value":"https://github.com/sergiojx/pactools"},"snapshot_id":{"kind":"string","value":"9a29ea1247f36aa1cf6a6ed33821e879a27dd9c3"},"revision_id":{"kind":"string","value":"f1ed7095e3dbd663001facaf5646c59c74aaec38"},"directory_id":{"kind":"string","value":"2d9acb85206069f88dababdc2a4197c823f10970"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-08-04T02:26:18.166968","string":"2020-08-04T02:26:18.166968"},"revision_date":{"kind":"timestamp","value":"2019-07-01T21:44:53","string":"2019-07-01T21:44:53"},"committer_date":{"kind":"timestamp","value":"2019-07-01T21:44:53","string":"2019-07-01T21:44:53"},"github_id":{"kind":"null"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6072889566421509,"alphanum_fraction":0.6162142157554626,"avg_line_length":39.74242401123047,"blob_id":"291123c9d72cc7fb3943f913b332858924c1aa91","content_id":"c38b64025cd22953515b8adf8e8cb9c42f098b21","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2689,"license_type":"no_license","max_line_length":79,"num_lines":66,"path":"/examples/plot_mne_epoch_masking.py","repo_name":"sergiojx/pactools","src_encoding":"UTF-8","text":"\"\"\"\nInterface with MNE-python\n-------------------------\nThis example shows how to use this package with MNE-python.\n\nIt relies on the function ``raw_to_mask``, which takes as input a MNE.Raw\ninstance and an events array, and returns the corresponding input signals\nand masks for the ``Comodulogram.fit`` method.\n\nNote that this example is quite bad since it does not contain any\nphase-amplitude coupling. It is just an API example.\n\"\"\"\nimport mne\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom pactools import raw_to_mask, Comodulogram, MaskIterator\n\n###############################################################################\n# Load the dataset\ndata_path = mne.datasets.sample.data_path()\nraw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'\nevent_fname = data_path + ('/MEG/sample/sample_audvis_filt-0-40_raw-'\n 'eve.fif')\nraw = mne.io.read_raw_fif(raw_fname, preload=True)\nevents = mne.read_events(event_fname)\n\n###############################################################################\n# Here we define how to build the epochs: which channels will be selected, and\n# on which time window around each event.\n\n# select the time interval around the events\ntmin, tmax = -5, 15\n# select the channels (phase_channel, amplitude_channel)\nixs = (8, 10)\n\n###############################################################################\n# Then, we create the inputs with the function raw_to_mask, which creates the\n# input arrays and the mask arrays. These arrays are then given to a\n# comodulogram instance with the `fit` method, and the `plot` method draws the\n# results.\n\n# create the input array for Comodulogram.fit\n\nlow_sig, high_sig, mask = raw_to_mask(raw, ixs=ixs, events=events, tmin=tmin,\n tmax=tmax)\n\n###############################################################################\n# The mask is an iterable which goes over the _unique_ events in the event\n# array (if it is 3D). PAC is estimated where the `mask` is `False`.\n# Alternatively, we could also compute the `MaskIterator` object directly.\n# This is useful if you want to compute PAC on other kinds of time series,\n# for example source time courses.\n\nlow_sig, high_sig = raw[8, :][0], raw[10, :][0]\nmask = MaskIterator(events, tmin, tmax, raw.n_times, raw.info['sfreq'])\n\n# create the instance of Comodulogram\nestimator = Comodulogram(fs=raw.info['sfreq'],\n low_fq_range=np.linspace(1, 10, 20), low_fq_width=2.,\n method='tort', progress_bar=True)\n# compute the comodulogram\nestimator.fit(low_sig, high_sig, mask)\n# plot the results\nestimator.plot(tight_layout=False)\nplt.show()\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6072889566421509,\n \"alphanum_fraction\": 0.6162142157554626,\n \"avg_line_length\": 39.74242401123047,\n \"blob_id\": \"291123c9d72cc7fb3943f913b332858924c1aa91\",\n \"content_id\": \"c38b64025cd22953515b8adf8e8cb9c42f098b21\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2689,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 66,\n \"path\": \"/examples/plot_mne_epoch_masking.py\",\n \"repo_name\": \"sergiojx/pactools\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\nInterface with MNE-python\\n-------------------------\\nThis example shows how to use this package with MNE-python.\\n\\nIt relies on the function ``raw_to_mask``, which takes as input a MNE.Raw\\ninstance and an events array, and returns the corresponding input signals\\nand masks for the ``Comodulogram.fit`` method.\\n\\nNote that this example is quite bad since it does not contain any\\nphase-amplitude coupling. It is just an API example.\\n\\\"\\\"\\\"\\nimport mne\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\n\\nfrom pactools import raw_to_mask, Comodulogram, MaskIterator\\n\\n###############################################################################\\n# Load the dataset\\ndata_path = mne.datasets.sample.data_path()\\nraw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'\\nevent_fname = data_path + ('/MEG/sample/sample_audvis_filt-0-40_raw-'\\n 'eve.fif')\\nraw = mne.io.read_raw_fif(raw_fname, preload=True)\\nevents = mne.read_events(event_fname)\\n\\n###############################################################################\\n# Here we define how to build the epochs: which channels will be selected, and\\n# on which time window around each event.\\n\\n# select the time interval around the events\\ntmin, tmax = -5, 15\\n# select the channels (phase_channel, amplitude_channel)\\nixs = (8, 10)\\n\\n###############################################################################\\n# Then, we create the inputs with the function raw_to_mask, which creates the\\n# input arrays and the mask arrays. These arrays are then given to a\\n# comodulogram instance with the `fit` method, and the `plot` method draws the\\n# results.\\n\\n# create the input array for Comodulogram.fit\\n\\nlow_sig, high_sig, mask = raw_to_mask(raw, ixs=ixs, events=events, tmin=tmin,\\n tmax=tmax)\\n\\n###############################################################################\\n# The mask is an iterable which goes over the _unique_ events in the event\\n# array (if it is 3D). PAC is estimated where the `mask` is `False`.\\n# Alternatively, we could also compute the `MaskIterator` object directly.\\n# This is useful if you want to compute PAC on other kinds of time series,\\n# for example source time courses.\\n\\nlow_sig, high_sig = raw[8, :][0], raw[10, :][0]\\nmask = MaskIterator(events, tmin, tmax, raw.n_times, raw.info['sfreq'])\\n\\n# create the instance of Comodulogram\\nestimator = Comodulogram(fs=raw.info['sfreq'],\\n low_fq_range=np.linspace(1, 10, 20), low_fq_width=2.,\\n method='tort', progress_bar=True)\\n# compute the comodulogram\\nestimator.fit(low_sig, high_sig, mask)\\n# plot the results\\nestimator.plot(tight_layout=False)\\nplt.show()\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":2979364,"cells":{"repo_name":{"kind":"string","value":"Introduction-to-Programming-OSOWSKI/3-11-letter-position-AlexMartin23"},"repo_url":{"kind":"string","value":"https://github.com/Introduction-to-Programming-OSOWSKI/3-11-letter-position-AlexMartin23"},"snapshot_id":{"kind":"string","value":"8e11c83661d1634cb5b92522ea71ee2e77702cd7"},"revision_id":{"kind":"string","value":"f1372f68735168259a38700d5a32dbd5dc0eb71a"},"directory_id":{"kind":"string","value":"45462c0605f2b8025a61a8c399df1e2be40a73e1"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2023-03-06T10:56:18.846967","string":"2023-03-06T10:56:18.846967"},"revision_date":{"kind":"timestamp","value":"2021-02-04T18:33:28","string":"2021-02-04T18:33:28"},"committer_date":{"kind":"timestamp","value":"2021-02-04T18:33:28","string":"2021-02-04T18:33:28"},"github_id":{"kind":"number","value":335996968,"string":"335,996,968"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5175096988677979,"alphanum_fraction":0.533073902130127,"avg_line_length":28.375,"blob_id":"8278e152fc05b41e82c12fde80fbc6764717f969","content_id":"55ebb6ab97442374508ae1fdc6da4c15a601cea1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":257,"license_type":"no_license","max_line_length":43,"num_lines":8,"path":"/main.py","repo_name":"Introduction-to-Programming-OSOWSKI/3-11-letter-position-AlexMartin23","src_encoding":"UTF-8","text":"def letterPos(strWord, strLetter):\r\n srtTest = \"\" #initialize test variable \r\n intreturn = 0\r\n for i in range (0, len(strWord)):\r\n strTest = strWord[i]\r\n if strTest == strLetter:\r\n return i + 1\r\n return 0\r\n \r\n "}],"string":"[\n {\n \"alpha_fraction\": 0.5175096988677979,\n \"alphanum_fraction\": 0.533073902130127,\n \"avg_line_length\": 28.375,\n \"blob_id\": \"8278e152fc05b41e82c12fde80fbc6764717f969\",\n \"content_id\": \"55ebb6ab97442374508ae1fdc6da4c15a601cea1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 257,\n \"license_type\": \"no_license\",\n \"max_line_length\": 43,\n \"num_lines\": 8,\n \"path\": \"/main.py\",\n \"repo_name\": \"Introduction-to-Programming-OSOWSKI/3-11-letter-position-AlexMartin23\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def letterPos(strWord, strLetter):\\r\\n srtTest = \\\"\\\" #initialize test variable \\r\\n intreturn = 0\\r\\n for i in range (0, len(strWord)):\\r\\n strTest = strWord[i]\\r\\n if strTest == strLetter:\\r\\n return i + 1\\r\\n return 0\\r\\n \\r\\n \"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":2979365,"cells":{"repo_name":{"kind":"string","value":"kalladhamodar/remote_addree_repository"},"repo_url":{"kind":"string","value":"https://github.com/kalladhamodar/remote_addree_repository"},"snapshot_id":{"kind":"string","value":"170fe61017323a79b681d124bcbe5650d5a92751"},"revision_id":{"kind":"string","value":"44106a30f9b8667e1b3c2a9147c0eacf92124312"},"directory_id":{"kind":"string","value":"5d57f7415bbf6822d00c72735cdc9987b2a8230b"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-06-19T12:43:24.175659","string":"2020-06-19T12:43:24.175659"},"revision_date":{"kind":"timestamp","value":"2019-07-13T11:05:20","string":"2019-07-13T11:05:20"},"committer_date":{"kind":"timestamp","value":"2019-07-13T11:05:20","string":"2019-07-13T11:05:20"},"github_id":{"kind":"number","value":196712196,"string":"196,712,196"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.7634408473968506,"alphanum_fraction":0.7634408473968506,"avg_line_length":17.600000381469727,"blob_id":"9fb074395d984721ceb7f42401e07d7c4f15cc97","content_id":"060b9dbd4642447c2dad7b5a248b1d5b53658636","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":93,"license_type":"no_license","max_line_length":33,"num_lines":5,"path":"/address/addresspp/apps.py","repo_name":"kalladhamodar/remote_addree_repository","src_encoding":"UTF-8","text":"from django.apps import AppConfig\n\n\nclass AddressppConfig(AppConfig):\n name = 'addresspp'\n"},{"alpha_fraction":0.64462810754776,"alphanum_fraction":0.64462810754776,"avg_line_length":37.68000030517578,"blob_id":"f308147b245e96551fc6c6de9ccf010bbd1577b7","content_id":"8c5ce1f1111ede25162005bb8a901fc532d9ea58","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":968,"license_type":"no_license","max_line_length":80,"num_lines":25,"path":"/address/addresspp/views.py","repo_name":"kalladhamodar/remote_addree_repository","src_encoding":"UTF-8","text":"from django.shortcuts import render\nfrom .models import addressdata\nfrom .forms import addressform\nfrom django.http.response import HttpResponse\ndef addressview(request):\n if request.method=='POST':\n cform=addressform(request.POST)\n if cform.is_valid():\n name=request.POST.get('name','')\n mobileno=request.POST.get('mobileno','')\n city=request.POST.get('city','')\n address=request.POST.get('address','')\n a=addressdata(name=name,mobileno=mobileno,city=city,address=address)\n a.save()\n cform=addressform()\n return render(request,'addressform.html',{'kalla':cform})\n\n else:\n return HttpResponse(\"Invalid User Data\")\n else:\n cform=addressform()\n return render(request,'addressform.html',{'kalla':cform})\ndef fetchingdata(request):\n data=addressdata.objects.all()\n return render(request,'addressdata.html',{'Dhamodar':data})\n\n"},{"alpha_fraction":0.5369886755943298,"alphanum_fraction":0.5535247921943665,"avg_line_length":24,"blob_id":"58918958e274de62db5ff235ebc3e972ed5b59a8","content_id":"8f2634d3c970c40d0fb8cb078366586008238099","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"HTML","length_bytes":1149,"license_type":"no_license","max_line_length":102,"num_lines":46,"path":"/address/templates/addressdata.html","repo_name":"kalladhamodar/remote_addree_repository","src_encoding":"UTF-8","text":"<html>\n<head>\n <title>Displaying Data</title>\n <meta charset=\"utf-8\">\n <meta name=\"viewport\" content=\"width=device-width, initial-scale=1\">\n <link rel=\"stylesheet\" href=\"https://maxcdn.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css\">\n <script src=\"https://ajax.googleapis.com/ajax/libs/jquery/3.4.1/jquery.min.js\"></script>\n <script src=\"https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.7/umd/popper.min.js\"></script>\n <script src=\"https://m axcdn.bootstrapcdn.com/bootstrap/4.3.1/js/bootstrap.min.js\"></script>\n</head>\n<body>\n<h1 class=\"text-center\">Address Details</h1>\n{% if data %}\n <table class=\"table table-bordered table-center \">\n\n <thead>\n <tr>\n <th>Name</th>\n <th>Mobileno</th>\n <th>city</th>\n <th>Address</th>\n </tr>\n </thead>\n {% for i in data %}\n <tbody>\n <tr>\n <td>{{ i.name }}</td>\n <td>{{ i.mobileno }}</td>\n <td>{{ i.city }}</td>\n <td>{{ i.address }}</td>\n\n </tr>\n </tbody>\n\n {% endfor %}\n\n\n\n</table>\n\n {% else %}\n <h1>Data is not visible</h1>\n {% endif %}\n\n</body>\n</html>"},{"alpha_fraction":0.7260869741439819,"alphanum_fraction":0.7652173638343811,"avg_line_length":37.16666793823242,"blob_id":"eb81b42f2b58e72a89e09da8c2404cdbe71522e1","content_id":"54dc479ed7b5934507b9c31a1c235c2b2ee82f67","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":230,"license_type":"no_license","max_line_length":44,"num_lines":6,"path":"/address/addresspp/models.py","repo_name":"kalladhamodar/remote_addree_repository","src_encoding":"UTF-8","text":"from django.db import models\nclass addressdata(models.Model):\n name=models.CharField(max_length=100)\n mobileno=models.BigIntegerField()\n city=models.CharField(max_length=100)\n address=models.CharField(max_length=100)\n\n"},{"alpha_fraction":0.4614593982696533,"alphanum_fraction":0.4614593982696533,"avg_line_length":23.350000381469727,"blob_id":"cfc63b4f0324f396f40e685734dee807ad23b818","content_id":"7003909589b03a194fec9b7822207fdf222f34cc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":973,"license_type":"no_license","max_line_length":45,"num_lines":40,"path":"/address/addresspp/forms.py","repo_name":"kalladhamodar/remote_addree_repository","src_encoding":"UTF-8","text":"from django import forms\nclass addressform(forms.Form):\n name=forms.CharField(\n label='Enter your name',\n widget=forms.TextInput(\n attrs={\n 'placeholder':'your name',\n 'class':'form-control',\n\n }\n )\n )\n mobileno=forms.IntegerField(\n label='Enter your mobile no',\n widget=forms.NumberInput(\n attrs={\n 'placeholder':'your no',\n 'class':'form-control',\n }\n )\n )\n city=forms.CharField(\n label=\"Enter your city\",\n widget=forms.TextInput(\n attrs={\n 'placeholder':'your city',\n 'class':'form-control',\n }\n )\n )\n address=forms.CharField(\n label=\"Enter your address\",\n widget=forms.Textarea(\n attrs={\n 'placeholder':'your address',\n 'class':'form-control',\n\n }\n )\n )"}],"string":"[\n {\n \"alpha_fraction\": 0.7634408473968506,\n \"alphanum_fraction\": 0.7634408473968506,\n \"avg_line_length\": 17.600000381469727,\n \"blob_id\": \"9fb074395d984721ceb7f42401e07d7c4f15cc97\",\n \"content_id\": \"060b9dbd4642447c2dad7b5a248b1d5b53658636\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 93,\n \"license_type\": \"no_license\",\n \"max_line_length\": 33,\n \"num_lines\": 5,\n \"path\": \"/address/addresspp/apps.py\",\n \"repo_name\": \"kalladhamodar/remote_addree_repository\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.apps import AppConfig\\n\\n\\nclass AddressppConfig(AppConfig):\\n name = 'addresspp'\\n\"\n },\n {\n \"alpha_fraction\": 0.64462810754776,\n \"alphanum_fraction\": 0.64462810754776,\n \"avg_line_length\": 37.68000030517578,\n \"blob_id\": \"f308147b245e96551fc6c6de9ccf010bbd1577b7\",\n \"content_id\": \"8c5ce1f1111ede25162005bb8a901fc532d9ea58\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 968,\n \"license_type\": \"no_license\",\n \"max_line_length\": 80,\n \"num_lines\": 25,\n \"path\": \"/address/addresspp/views.py\",\n \"repo_name\": \"kalladhamodar/remote_addree_repository\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.shortcuts import render\\nfrom .models import addressdata\\nfrom .forms import addressform\\nfrom django.http.response import HttpResponse\\ndef addressview(request):\\n if request.method=='POST':\\n cform=addressform(request.POST)\\n if cform.is_valid():\\n name=request.POST.get('name','')\\n mobileno=request.POST.get('mobileno','')\\n city=request.POST.get('city','')\\n address=request.POST.get('address','')\\n a=addressdata(name=name,mobileno=mobileno,city=city,address=address)\\n a.save()\\n cform=addressform()\\n return render(request,'addressform.html',{'kalla':cform})\\n\\n else:\\n return HttpResponse(\\\"Invalid User Data\\\")\\n else:\\n cform=addressform()\\n return render(request,'addressform.html',{'kalla':cform})\\ndef fetchingdata(request):\\n data=addressdata.objects.all()\\n return render(request,'addressdata.html',{'Dhamodar':data})\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5369886755943298,\n \"alphanum_fraction\": 0.5535247921943665,\n \"avg_line_length\": 24,\n \"blob_id\": \"58918958e274de62db5ff235ebc3e972ed5b59a8\",\n \"content_id\": \"8f2634d3c970c40d0fb8cb078366586008238099\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"HTML\",\n \"length_bytes\": 1149,\n \"license_type\": \"no_license\",\n \"max_line_length\": 102,\n \"num_lines\": 46,\n \"path\": \"/address/templates/addressdata.html\",\n \"repo_name\": \"kalladhamodar/remote_addree_repository\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"<html>\\n<head>\\n <title>Displaying Data</title>\\n <meta charset=\\\"utf-8\\\">\\n <meta name=\\\"viewport\\\" content=\\\"width=device-width, initial-scale=1\\\">\\n <link rel=\\\"stylesheet\\\" href=\\\"https://maxcdn.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css\\\">\\n <script src=\\\"https://ajax.googleapis.com/ajax/libs/jquery/3.4.1/jquery.min.js\\\"></script>\\n <script src=\\\"https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.7/umd/popper.min.js\\\"></script>\\n <script src=\\\"https://m axcdn.bootstrapcdn.com/bootstrap/4.3.1/js/bootstrap.min.js\\\"></script>\\n</head>\\n<body>\\n<h1 class=\\\"text-center\\\">Address Details</h1>\\n{% if data %}\\n <table class=\\\"table table-bordered table-center \\\">\\n\\n <thead>\\n <tr>\\n <th>Name</th>\\n <th>Mobileno</th>\\n <th>city</th>\\n <th>Address</th>\\n </tr>\\n </thead>\\n {% for i in data %}\\n <tbody>\\n <tr>\\n <td>{{ i.name }}</td>\\n <td>{{ i.mobileno }}</td>\\n <td>{{ i.city }}</td>\\n <td>{{ i.address }}</td>\\n\\n </tr>\\n </tbody>\\n\\n {% endfor %}\\n\\n\\n\\n</table>\\n\\n {% else %}\\n <h1>Data is not visible</h1>\\n {% endif %}\\n\\n</body>\\n</html>\"\n },\n {\n \"alpha_fraction\": 0.7260869741439819,\n \"alphanum_fraction\": 0.7652173638343811,\n \"avg_line_length\": 37.16666793823242,\n \"blob_id\": \"eb81b42f2b58e72a89e09da8c2404cdbe71522e1\",\n \"content_id\": \"54dc479ed7b5934507b9c31a1c235c2b2ee82f67\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 230,\n \"license_type\": \"no_license\",\n \"max_line_length\": 44,\n \"num_lines\": 6,\n \"path\": \"/address/addresspp/models.py\",\n \"repo_name\": \"kalladhamodar/remote_addree_repository\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.db import models\\nclass addressdata(models.Model):\\n name=models.CharField(max_length=100)\\n mobileno=models.BigIntegerField()\\n city=models.CharField(max_length=100)\\n address=models.CharField(max_length=100)\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.4614593982696533,\n \"alphanum_fraction\": 0.4614593982696533,\n \"avg_line_length\": 23.350000381469727,\n \"blob_id\": \"cfc63b4f0324f396f40e685734dee807ad23b818\",\n \"content_id\": \"7003909589b03a194fec9b7822207fdf222f34cc\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 973,\n \"license_type\": \"no_license\",\n \"max_line_length\": 45,\n \"num_lines\": 40,\n \"path\": \"/address/addresspp/forms.py\",\n \"repo_name\": \"kalladhamodar/remote_addree_repository\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django import forms\\nclass addressform(forms.Form):\\n name=forms.CharField(\\n label='Enter your name',\\n widget=forms.TextInput(\\n attrs={\\n 'placeholder':'your name',\\n 'class':'form-control',\\n\\n }\\n )\\n )\\n mobileno=forms.IntegerField(\\n label='Enter your mobile no',\\n widget=forms.NumberInput(\\n attrs={\\n 'placeholder':'your no',\\n 'class':'form-control',\\n }\\n )\\n )\\n city=forms.CharField(\\n label=\\\"Enter your city\\\",\\n widget=forms.TextInput(\\n attrs={\\n 'placeholder':'your city',\\n 'class':'form-control',\\n }\\n )\\n )\\n address=forms.CharField(\\n label=\\\"Enter your address\\\",\\n widget=forms.Textarea(\\n attrs={\\n 'placeholder':'your address',\\n 'class':'form-control',\\n\\n }\\n )\\n )\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":5,"string":"5"}}},{"rowIdx":2979366,"cells":{"repo_name":{"kind":"string","value":"vinatsx/kattis"},"repo_url":{"kind":"string","value":"https://github.com/vinatsx/kattis"},"snapshot_id":{"kind":"string","value":"023d6b0bd97eb04278e65d6216abc948da1a23e5"},"revision_id":{"kind":"string","value":"322bb2d1c48c587bfcee71fab3e755fb98cab206"},"directory_id":{"kind":"string","value":"63320876161f0933041d3cf047b2f751931655b4"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2023-03-24T23:20:39.531263","string":"2023-03-24T23:20:39.531263"},"revision_date":{"kind":"timestamp","value":"2016-03-21T14:09:30","string":"2016-03-21T14:09:30"},"committer_date":{"kind":"timestamp","value":"2016-03-21T14:09:30","string":"2016-03-21T14:09:30"},"github_id":{"kind":"null"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.734375,"alphanum_fraction":0.75,"avg_line_length":41.66666793823242,"blob_id":"8675d4746800e57fe44a350cdd95ccab9623e8e4","content_id":"e40ec3dba8a5b101441c7f0b17daf66fcd2104e9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":128,"license_type":"no_license","max_line_length":51,"num_lines":3,"path":"/polymul1/README.txt","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"Author: Fabian Schilling (fabsch@kth)\nProblem: https://open.kattis.com/problems/polymul1\nExecution: python polymul1.py < samples/<sample.in>\n"},{"alpha_fraction":0.40164995193481445,"alphanum_fraction":0.4069880247116089,"avg_line_length":26.35398292541504,"blob_id":"06ec39f602da92d6b661d3a66fc7cd842b3080c8","content_id":"be1b514eab62166b1b0ae507fafac0b4b8413593","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":6182,"license_type":"no_license","max_line_length":85,"num_lines":226,"path":"/minspantree/Main.java","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"/**\n* @author Fabian Schilling (fabsch@kth.se)\n*/\n\nimport java.util.Comparator;\nimport java.util.Collections;\nimport java.util.Vector;\n\n\npublic class Main {\n\n public static IO io = new IO(System.in, System.out);\n\n public static void shortestpath1() {\n\n while (io.hasMoreTokens()) {\n\n int n = io.getInt(); // number of nodes in the graph\n if (n == 0) break;\n int m = io.getInt(); // number of edges\n int q = io.getInt(); // number of queries\n int s = io.getInt(); // index of the starting node\n\n Node[] nodes = new Node[n];\n\n for (int i = 0; i < n; i++) {\n nodes[i] = new Node(i);\n }\n\n for (int i = 0; i < m; i++) {\n int u = io.getInt();\n int v = io.getInt();\n int w = io.getInt();\n\n nodes[u].adjacencies.add(new Edge(nodes[v], w));\n\n }\n\n Library.dijkstra(nodes[s]);\n\n for (int i = 0; i < q; i++) {\n int query = io.getInt();\n\n int distance = nodes[query].distance;\n\n if (distance == Integer.MAX_VALUE) {\n io.println(\"Impossible\");\n } else {\n io.println(distance);\n }\n\n }\n\n io.println();\n }\n }\n\n public static void shortestpath2() {\n\n while (io.hasMoreTokens()) {\n\n int n = io.getInt(); // number of nodes in the graph\n if (n == 0) break;\n int m = io.getInt(); // number of edges\n int q = io.getInt(); // number of queries\n int s = io.getInt(); // index of the starting node\n\n Node[] nodes = new Node[n];\n\n for (int i = 0; i < n; i++) {\n nodes[i] = new Node(i);\n }\n\n for (int i = 0; i < m; i++) {\n int u = io.getInt();\n int v = io.getInt();\n int t0 = io.getInt();\n int P = io.getInt();\n int d = io.getInt();\n\n nodes[u].adjacencies.add(new Edge(nodes[v], d, t0, P));\n\n }\n\n Library.dijkstraTimetable(nodes[s]);\n\n for (int i = 0; i < q; i++) {\n int query = io.getInt();\n\n int distance = nodes[query].distance;\n\n if (distance == Integer.MAX_VALUE) {\n io.println(\"Impossible\");\n } else {\n io.println(distance);\n }\n\n }\n\n io.println();\n }\n\n }\n\n public static void shortestpath3() {\n\n while (io.hasMoreTokens()) {\n\n int n = io.getInt(); // number of nodes in the graph\n if (n == 0) break;\n int m = io.getInt(); // number of edges\n int q = io.getInt(); // number of queries\n int s = io.getInt(); // index of the starting node\n\n Node[] nodes = new Node[n];\n\n for (int i = 0; i < n; i++) {\n nodes[i] = new Node(i);\n }\n\n Edge[] edges = new Edge[m];\n\n for (int i = 0; i < m; i++) {\n int u = io.getInt();\n int v = io.getInt();\n int w = io.getInt();\n\n edges[i] = new Edge(nodes[u], nodes[v], w);\n\n }\n\n Library.bellmanFord(nodes, edges, nodes[s]);\n\n for (int i = 0; i < q; i++) {\n int query = io.getInt();\n\n int distance = nodes[query].distance;\n\n if (distance == Integer.MAX_VALUE) {\n io.println(\"Impossible\");\n } else if (distance == -Integer.MIN_VALUE) {\n io.println(\"-Infinity\");\n } else {\n io.println(distance);\n }\n\n }\n\n io.println();\n }\n }\n\n public static void minspantree() {\n\n while (io.hasMoreTokens()) {\n\n int n = io.getInt(); // number of nodes in the graph\n if (n == 0) break;\n int m = io.getInt(); // number of edges\n\n Node[] nodes = new Node[n];\n\n for (int i = 0; i < n; i++) {\n nodes[i] = new Node(i);\n }\n\n for (int i = 0; i < m; i++) {\n int u = io.getInt();\n int v = io.getInt();\n int w = io.getInt();\n\n if (u != v) {\n nodes[u].adjacencies.add(new Edge(nodes[v], w));\n nodes[v].adjacencies.add(new Edge(nodes[u], w)); // undirected\n }\n }\n\n Library.prim(nodes);\n\n Vector<Edge> edges = new Vector<>();\n long total = 0;\n\n for (Node node: nodes) {\n if (node.previous != null) {\n total += node.distance;\n if (node.previous.index < node.index) {\n edges.add(new Edge(node.previous, node, node.distance));\n } else {\n edges.add(new Edge(node, node.previous, node.distance));\n }\n }\n }\n\n if (edges.isEmpty() || edges.size() != n - 1) {\n io.println(\"Impossible\");\n } else {\n\n // lexicographic sort\n Collections.sort(edges, new Comparator<Edge>() {\n @Override\n public int compare(Edge o1, Edge o2) {\n if (o1.source.index != o2.source.index) {\n return Integer.compare(o1.source.index, o2.source.index);\n } else {\n return Integer.compare(o1.target.index, o2.target.index);\n }\n }\n });\n io.println(total);\n for (Edge e: edges) {\n io.println(e.source.index + \" \" + e.target.index);\n }\n }\n }\n\n }\n\n public static void main(String[] args) {\n\n //shortestpath1();\n //shortestpath2();\n //shortestpath3();\n minspantree();\n io.close();\n }\n}\n"},{"alpha_fraction":0.7394366264343262,"alphanum_fraction":0.7394366264343262,"avg_line_length":46.33333206176758,"blob_id":"cc2eea9e98ecc6dbfb0c09d0a8a0288d34acfb69","content_id":"6cad3abc00c9b88a5c990a1e8746488b00afe968","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":142,"license_type":"no_license","max_line_length":61,"num_lines":3,"path":"/allpairspath/README.txt","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"Author: Fabian Schilling (fabsch@kth)\nProblem: https://open.kattis.com/problems/allpairspath\nExecution: javac Main.java && java Main < samples/<sample.in>\n"},{"alpha_fraction":0.4532037377357483,"alphanum_fraction":0.45806336402893066,"avg_line_length":32.26347351074219,"blob_id":"b98087c1637357c7610e195137b2509269444468","content_id":"b9489e519196e55f90243d0353800bc0b84309a0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":5556,"license_type":"no_license","max_line_length":113,"num_lines":167,"path":"/eulerianpath/Main.java","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"/**\n* @author Fabian Schilling (fabsch@kth.se) and Hugo Sandelius (hugosa@kth.se)\n*/\n\nimport java.util.*;\n\npublic class Main {\n\n public static void main(String[] args) {\n Kattio io = new Kattio(System.in, System.out);\n while (true) {\n int n = io.getInt();\n\n if (n == 0) {\n break;\n }\n\n List<LinkedList<Integer>> neighbors = new ArrayList<>();\n for (int i = 0; i < n; i++) {\n neighbors.add(new LinkedList<Integer>());\n }\n\n int[] inEdges = new int[n];\n\n int m = io.getInt();\n for (int i = 0; i < m; i++) {\n int u = io.getInt();\n int v = io.getInt();\n inEdges[v]++;\n neighbors.get(u).add(v);\n }\n\n List<Integer> eulerianPath = findEulerianPath(neighbors, inEdges, m);\n\n\n if (eulerianPath == null) {\n io.println(\"Impossible\");\n } else {\n for (int i : eulerianPath) {\n io.print(i + \" \");\n }\n io.println();\n }\n }\n\n io.flush();\n }\n\n // returns a starting point, or -1 if no eulerian path possible\n public static int findStartingPoint(List<LinkedList<Integer>> neighbors, int[] inEdges) {\n int i = 0;\n int startingPoint = -2;\n for (LinkedList<Integer> neighborList : neighbors) {\n // if outEdges > inEdges, this should be our starting point\n if (neighborList.size() > inEdges[i]) {\n // but if outEdges > inEdges + 1, no eulerian path possible\n if (neighborList.size() > inEdges[i] + 1) {\n return -1;\n }\n // but if there is more than one vertex like this, no eulerian path possible\n if (startingPoint != -2) {\n return -1;\n } else {\n startingPoint = i;\n }\n }\n i++;\n }\n\n // if we didn't find a starting point, just start at an arbitrary point\n if (startingPoint == -2) {\n startingPoint = 0;\n }\n\n return startingPoint;\n }\n\n // returns an end point, or -1 if no eulerian path possible, or -2 if no specific end point\n public static int findEndPoint(List<LinkedList<Integer>> neighbors, int[] inEdges) {\n int i = 0;\n int endPoint = -2;\n for (LinkedList<Integer> neighborList : neighbors) {\n // if outEdges > inEdges, this should be our starting point\n if (neighborList.size() < inEdges[i]) {\n // but if outEdges > inEdges + 1, no eulerian path possible\n if (neighborList.size() < inEdges[i] - 1) {\n return -1;\n }\n // but if there is more than one vertex like this, no eulerian path possible\n if (endPoint != -2) {\n return -1;\n } else {\n endPoint = i;\n }\n }\n i++;\n }\n\n return endPoint;\n }\n\n /* Finds a eulerian path in the graph described by the adjacency lists in 'neighors'\n * 'inEdges' is an array, where inEdges[i] is an array of indexes of inEdges to node with index i\n * 'edges' is the total amount of edges\n * */\n public static List<Integer> findEulerianPath(List<LinkedList<Integer>> neighbors, int[] inEdges, int edges) {\n LinkedList<Integer> path = new LinkedList<>();\n int pathPos = 0;\n\n int index = findStartingPoint(neighbors, inEdges);\n int endPoint = findEndPoint(neighbors, inEdges);\n\n if (index == -1 || endPoint == -1) {\n return null;\n }\n\n boolean endAlreadyReached = false;\n\n int startPoint = index;\n while (true) {\n path.add(pathPos, index);\n\n if (!neighbors.get(index).isEmpty()) {\n // if we have neighbors, visit them\n index = neighbors.get(index).pollFirst();\n } else {\n /* if we have no unvisited neighbors, we need to have either\n ended up at our starting point or at the only possible\n end point for eulerian path. Otherwise, no eulerian\n path possible\n */\n if (index != startPoint && index != endPoint) {\n\n if (!endAlreadyReached) {\n endAlreadyReached = true;\n } else {\n return null;\n }\n }\n\n // try to find a node in the path with unvisited neighbors\n boolean foundNew = false;\n int i = 0;\n for (int vertex : path) {\n if (!neighbors.get(vertex).isEmpty()) {\n startPoint = vertex;\n pathPos = i;\n index = neighbors.get(vertex).pollFirst();\n foundNew = true;\n break;\n }\n i++;\n }\n\n // if we didn't find any, we're finished\n if (!foundNew) {\n if (edges > path.size() - 1) {\n // not all edges are in our path\n return null;\n }\n Collections.reverse(path);\n return path;\n }\n }\n }\n }\n}\n\n"},{"alpha_fraction":0.31608134508132935,"alphanum_fraction":0.32902032136917114,"avg_line_length":19.41509437561035,"blob_id":"7b0dafec0e9c399aee8f65aa3c95f1875e4bc2b0","content_id":"3c06ed99cd237ae410fb874cad31dda9bd830ee7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":1082,"license_type":"no_license","max_line_length":57,"num_lines":53,"path":"/cd/CD.java","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"/**\n* @author Fabian Schilling (fabsch@kth.se)\n*/\n\n\npublic class CD {\n\n static final int M = 1000000;\n\n static Kattio io = new Kattio(System.in, System.out);\n\n static int[] nlist = new int[M];\n static int[] mlist = new int[M];\n\n public static void main(String[] args) {\n\n while (true) {\n\n int n = io.getInt();\n int m = io.getInt();\n\n if (n == 0 && m == 0) {\n break;\n }\n\n for (int i = 0; i < n; i++) {\n nlist[i] = io.getInt();\n }\n\n for (int i = 0; i < m; i++) {\n mlist[i] = io.getInt();\n }\n\n int sell = 0;\n int ni = 0, mi = 0;\n\n while (ni < n && mi < m) {\n if (nlist[ni] > mlist[mi]) {\n mi++;\n } else if (nlist[ni] < mlist[mi]) {\n ni++;\n } else {\n sell++;\n ni++;\n mi++;\n }\n }\n\n io.println(sell);\n }\n io.close();\n }\n}\n"},{"alpha_fraction":0.3693790137767792,"alphanum_fraction":0.3704496920108795,"avg_line_length":22.578947067260742,"blob_id":"84a0ec4b1a8409a68b230a9d510415c5f1fcb599","content_id":"87bc14f9f3631367abb295bf6f32a70af507ee34","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":934,"license_type":"no_license","max_line_length":66,"num_lines":38,"path":"/rationalarithmetic/Main.java","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"/**\r\n* @author Fabian Schilling (fabsch@kth.se) and Hugo Sandelius (hugosa@kth.se)\r\n*/\r\n\r\npublic class Main {\r\n\r\n public static IO io = new IO(System.in, System.out);\r\n\r\n public static void main(String[] args) {\r\n\r\n int n = io.getInt();\r\n\r\n for (int i = 0; i < n; i++) {\r\n\r\n Rational x = new Rational(io.getLong(), io.getLong());\r\n String op = io.getWord();\r\n Rational y = new Rational(io.getLong(), io.getLong());\r\n\r\n switch (op) {\r\n case \"+\":\r\n io.println(x.add(y));\r\n break;\r\n case \"-\":\r\n io.println(x.subtract(y));\r\n break;\r\n case \"/\":\r\n io.println(x.divide(y));\r\n break;\r\n case \"*\":\r\n io.println(x.multiply(y));\r\n break;\r\n }\r\n\r\n }\r\n\r\n io.close();\r\n }\r\n}\r\n"},{"alpha_fraction":0.7396449446678162,"alphanum_fraction":0.7435897588729858,"avg_line_length":28.823530197143555,"blob_id":"7962fc4a2e660eed48a4297ba9efcf6bb1c31f37","content_id":"6c3913264666b7d55f3acef685b3661378582fcb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":507,"license_type":"no_license","max_line_length":244,"num_lines":17,"path":"/README.md","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"# Kattis\n\nSome of the problems I solved during the course \"Problem Solving and Programming under Pressure\" at KTH.\n\nThe directory names correspond to the problem ID on Kattis, i.e. https://open.kattis.com/problems/factovisors for problem \"factovisors\". Most problems are solved in Java due to time constraints on Kattis, the others are solved using Python 2.7.\n\n# Execution\n\nFor Java:\n```\njavac Main.java && java Main < samples/<sample.in>\n```\n\nFor Python:\n```\npython <problem>.py < samples/<sample.in>\n```\n"},{"alpha_fraction":0.4735177755355835,"alphanum_fraction":0.5098814368247986,"avg_line_length":22.88679313659668,"blob_id":"652cf6515df76b2931e72ef6ef06fa2a69da7529","content_id":"0f15c8d519978f6ed65b4d022dbc62acb00c85fc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1265,"license_type":"no_license","max_line_length":54,"num_lines":53,"path":"/help2/help2.py","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"#!/usr/bin/python\n\n__author__ = \"Fabian Schilling\"\n__email__ = \"fabsch@kth.se\"\n\nimport sys\n\ndef is_var(w):\n return w[0] == '<' and w[-1] == '>'\n\ndef replace(pat, word, substitute):\n for i in range(len(pat)):\n if pat[i] == word:\n pat[i] = substitute\n\ndef find(pat1, pat2):\n\n for i in range(len(pat1)):\n if is_var(pat1[i]) and not is_var(pat2[i]):\n replace(pat1, pat1[i], pat2[i])\n return find(pat1, pat2)\n if not is_var(pat1[i]) and is_var(pat2[i]):\n replace(pat2, pat2[i], pat1[i])\n return find(pat1, pat2)\n for i in range(len(pat1)):\n if is_var(pat1[i]) and is_var(pat2[i]):\n replace(pat1, pat1[i], \"xxx\")\n replace(pat2, pat2[i], \"xxx\")\n return find(pat1, pat2)\n if pat1 != pat2:\n print('-')\n return\n print(\" \".join(pat1))\n return\n\ndef solve(pat1, pat2):\n\n if len(pat1) != len(pat2):\n print('-')\n return\n\n find(pat1, pat2)\n\ndef main():\n input = sys.stdin.readlines()\n num_cases = int(input.pop(0))\n for i in range(num_cases):\n pat1 = input.pop(0).splitlines()[0].split(' ')\n pat2 = input.pop(0).splitlines()[0].split(' ')\n solve(pat1, pat2)\n\nif __name__ == \"__main__\":\n main()"},{"alpha_fraction":0.4612877070903778,"alphanum_fraction":0.4735126197338104,"avg_line_length":21.72222137451172,"blob_id":"6928995811cf1c19491e9c129bdd56c7c7c57e1a","content_id":"f11a3fbadbdf0d799de184007271404533f624a8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":1227,"license_type":"no_license","max_line_length":72,"num_lines":54,"path":"/turbo/Turbo.java","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"/**\n* @author Fabian Schilling (fabsch@kth.se)\n*/\n\nimport java.util.Arrays;\nimport java.util.HashMap;\n\n\npublic class Turbo {\n\n static Kattio io = new Kattio(System.in, System.out);\n\n static int LEN = 100001;\n\n static int n;\n static int[] nums = new int[LEN]; // mapping from index to number\n static int[] pos = new int[LEN]; // mapping from number to index\n\n static Fenwick shift = new Fenwick(LEN); // keeps track of the shift\n\n\n public static void solve() {\n\n int left = 1;\n int right = n;\n for (int i = 1; i <= n; i++) {\n if (i % 2 == 1) { // odd\n shift.update(pos[left], -1);\n io.println(shift.sum(1, pos[left]));\n left++; // move one step right\n } else { // even\n shift.update(pos[right], -1);\n io.println(shift.sum(pos[right], n));\n right--; // move one step left\n }\n }\n }\n\n public static void main(String[] args) {\n\n n = io.getInt();\n\n for (int i = 1; i <= n; i++) {\n int num = io.getInt();\n nums[i] = num;\n pos[num] = i;\n shift.update(i, 1);\n }\n\n solve();\n\n io.close();\n }\n}\n"},{"alpha_fraction":0.5426945090293884,"alphanum_fraction":0.5547122359275818,"avg_line_length":25.366666793823242,"blob_id":"0de163392e0de2af2bfef60aba6d8ebabc563936","content_id":"562ae738d2bd815e5be00c46104e92e639483195","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1581,"license_type":"no_license","max_line_length":87,"num_lines":60,"path":"/uxuhulvoting/uxuhulvoting.py","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"#!/usr/bin/python\n\n__author__ = \"Fabian Schilling\"\n__email__ = \"fabsch@kth.se\"\n\nimport sys\n\n\ndef solve(prefs, states, priest, state):\n\n if priest == len(prefs):\n return state # no more priests left, state is final\n elif states[priest][state] is not None:\n return states[priest][state] # state already computed\n\n best = None\n for i in range(3): # every priest has three possible stone flips\n vote = (state ^ (1 << i))\n result = solve(prefs, states, priest + 1, vote)\n\n # the lower the preference the better\n if best is None or prefs[priest][result] < prefs[priest][best]:\n best = result\n\n # save the best state for the current priest and return\n states[priest][state] = best\n return best\n\n\ndef main(args):\n\n data = sys.stdin.readlines()\n\n n = int(data.pop(0)) # number of voting rounds\n\n for i in range(n):\n\n m = int(data.pop(0)) # number of voting priests\n\n prefs = [] # preferences for each priest\n states = [[None for _ in range(8)] for _ in range(m)] # saved states\n\n for priest in range(m):\n prefs.append([int(x) - 1 for x in data.pop(0).split()]) # normalize to 0-7\n\n state = solve(prefs, states, 0, 0) # solve for priest 0 and state 0 (=NNN)\n\n # print final state (0 = N, 1 = Y)\n endstate = '{:03b}'.format(state)\n sol = []\n for c in endstate:\n if c == '0':\n sol.append('N')\n else:\n sol.append('Y')\n print(''.join(sol))\n\n\nif __name__ == '__main__':\n main(sys.argv)"},{"alpha_fraction":0.4229075014591217,"alphanum_fraction":0.4302496314048767,"avg_line_length":18.485713958740234,"blob_id":"ef8b60a9f25b9c01be3f55c4d11d322e909c8ccf","content_id":"3c26b3b2684f036529aa63943ed9f394eec1a907","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":681,"license_type":"no_license","max_line_length":56,"num_lines":35,"path":"/crypto/Main.java","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"/**\n* @author Fabian Schilling (fabsch@kth.se)\n*/\n\nimport java.awt.*;\nimport java.util.ArrayList;\n\npublic class Main {\n\n public static IO io = new IO(System.in, System.out);\n\n public static void main(String[] args) {\n\n long N = io.getLong();\n\n long maxZeros = 0;\n long minBase = 2;\n for (long base = N; base >= 2; base--) {\n long n = N;\n long zeros = 0;\n while (n % base == 0) {\n n /= base;\n zeros++;\n }\n if (zeros >= maxZeros) {\n maxZeros = zeros;\n minBase = base;\n }\n }\n\n io.println(minBase);\n\n io.close();\n }\n}"},{"alpha_fraction":0.43195003271102905,"alphanum_fraction":0.439535915851593,"avg_line_length":25.678571701049805,"blob_id":"a9d069bd81102fde546411247b8e699badedeb38","content_id":"9a1ddb3112667b952ec9f7ae33b46d89df8a27a7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":2241,"license_type":"no_license","max_line_length":74,"num_lines":84,"path":"/shortestpath2/Library.java","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"/**\n* @author Fabian Schilling (fabsch@kth.se)\n*/\n\nimport java.util.Comparator;\nimport java.util.PriorityQueue;\n\npublic class Library {\n\n public static void dijkstra(Node start) {\n start.distance = 0;\n\n Comparator<Node> comp = new Comparator<Node>() {\n @Override\n public int compare(Node o1, Node o2) {\n return Integer.compare(o1.distance, o2.distance);\n }\n };\n PriorityQueue<Node> queue = new PriorityQueue<>(comp);\n queue.add(start);\n\n while (!queue.isEmpty()) {\n\n Node u = queue.poll();\n\n for (Edge e: u.adjacencies) {\n\n Node v = e.target;\n int weight = e.weight;\n int distance = u.distance + weight;\n\n if (distance < v.distance) {\n\n queue.remove(v);\n\n v.distance = distance;\n v.previous = u;\n queue.add(v);\n }\n }\n }\n }\n\n public static void dijkstraTimetable(Node start) {\n\n start.distance = 0;\n\n Comparator<Node> comp = new Comparator<Node>() {\n @Override\n public int compare(Node o1, Node o2) {\n return Integer.compare(o1.distance, o2.distance);\n }\n };\n PriorityQueue<Node> queue = new PriorityQueue<>(comp);\n queue.add(start);\n\n while (!queue.isEmpty()) {\n\n Node u = queue.poll();\n\n for (Edge e: u.adjacencies) {\n\n int distance = e.t0 + e.weight;\n\n if (u.distance > e.t0) {\n if (e.P == 0) { // cannot be reached\n distance = Integer.MAX_VALUE;\n } else { // find the next possible time to traverse\n int steps = 1 + (((u.distance - e.t0) - 1) / e.P);\n distance = e.t0 + steps * e.P + e.weight;\n }\n }\n\n Node v = e.target;\n if (distance < v.distance) {\n queue.remove(v);\n v.distance = distance;\n v.previous = u;\n queue.add(v);\n }\n }\n }\n }\n}\n"},{"alpha_fraction":0.3834788501262665,"alphanum_fraction":0.39019477367401123,"avg_line_length":20.579710006713867,"blob_id":"5e8a63fe0f259d1ddf77e54c0141d2b0885ad2dc","content_id":"36a8fb20261e9788db56eb4f14253589df828255","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":1489,"license_type":"no_license","max_line_length":54,"num_lines":69,"path":"/fenwick/Fenwick.java","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"/**\n* @author Fabian Schilling (fabsch@kth.se)\n*/\n\npublic class Fenwick {\n\n private long[] tree;\n private int n;\n\n public Fenwick(int n) {\n this.tree = new long[n + 1];\n this.n = n;\n }\n\n public long query(int index) {\n index--;\n if (index == -1) {\n return 0;\n }\n long sum = tree[0];\n while (index > 0) {\n sum += tree[index];\n index -= (index & -index);\n }\n return sum;\n }\n\n public void update(int index, long delta) {\n if (index == 0) {\n tree[0] += delta;\n } else {\n while (index <= n) {\n tree[index] += delta;\n index += (index & -index);\n }\n }\n }\n\n public static void main(String[] args) {\n\n Kattio io = new Kattio(System.in, System.out);\n\n int n = io.getInt();\n int q = io.getInt();\n\n Fenwick fenwick = new Fenwick(n);\n\n for (int i = 0; i < q; i++) {\n char op = io.getWord().charAt(0);\n int index = io.getInt();\n\n switch (op) {\n case '+':\n long delta = io.getLong();\n fenwick.update(index, delta);\n break;\n case '?':\n io.println(fenwick.query(index));\n break;\n default:\n System.exit(-1);\n break;\n }\n }\n\n io.close();\n\n }\n}\n"},{"alpha_fraction":0.4948875308036804,"alphanum_fraction":0.5023857951164246,"avg_line_length":23.032787322998047,"blob_id":"b6406e1426328388d2e1ea8a0991d04ffb8120b5","content_id":"7a4c3487560fff408eee641263ac58d58fd69a29","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1467,"license_type":"no_license","max_line_length":94,"num_lines":61,"path":"/longincsubseq/longincsubseq.py","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"#!/usr/bin/python\n\n__author__ = \"Fabian Schilling\"\n__email__ = \"fabsch@kth.se\"\n\nimport sys\nimport math\n\ndef solve(seq, n):\n\n # pred[k]: stores index of predecessor of seq[k] lor lincsubseq ending at seq[k]\n pred = [None] * n\n # mem[k]: stores index k of smalles value seq[k] s.t. lincsubseq of len j ending at seq[k]\n mem = [None] * (n + 1)\n # length of the lincsubseq found so far\n l = 0\n\n for i in range(n):\n\n # binary search for largest j <= l such that seq[mem[j]] < seq[i]\n lo = 1\n hi = l\n while lo <= hi:\n mid = int(math.ceil((lo + hi) / 2))\n if seq[mem[mid]] < seq[i]:\n lo = mid + 1\n else:\n hi = mid - 1\n\n # after search, lo is 1 greater than len of longest prefix of seq[i]\n new_l = lo\n\n # the pred. of seq[i] is the last index of subseq. of length new_l - 1\n pred[i] = mem[new_l - 1]\n mem[new_l] = i\n\n # if we found longer subseq., update\n l = max(l, new_l)\n\n # reconstruct solution from pred & mem\n sol = [None] * l\n k = mem[l]\n for i in reversed(range(l)):\n sol[i] = k\n k = pred[k]\n\n return sol\n\ndef main():\n data = sys.stdin.readlines()\n while data:\n n = int(data.pop(0))\n seq = [int(x) for x in data.pop(0).split(' ')]\n sol = solve(seq, n)\n print(len(sol))\n print(' '.join([str(x) for x in sol]))\n\n\n\nif __name__ == '__main__':\n main()\n\n"},{"alpha_fraction":0.41633063554763794,"alphanum_fraction":0.42993950843811035,"avg_line_length":21.545454025268555,"blob_id":"7b57cb203dea326cf0487d3d3143b7299c9c5e3a","content_id":"840f592bd8b02e0ac88748f329c8937e16200419","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":1984,"license_type":"no_license","max_line_length":57,"num_lines":88,"path":"/unionfind/UnionFind.java","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"/**\n* @author Fabian Schilling (fabsch@kth.se)\n*/\n\npublic class UnionFind {\n\n static final int MAX = 1000000;\n\n static int n; // 1 <= n <= 1,000,000\n static int q; // 0 <= q <= 1,000,000\n\n\n static Kattio io = new Kattio(System.in, System.out);\n\n private int[] parents;\n private int[] height;\n\n public UnionFind(int nodes) {\n this.parents = new int[nodes];\n this.height = new int[nodes];\n\n for (int i = 0; i < nodes; i++) {\n parents[i] = i; // make self-connection\n }\n }\n\n public int find(int node) {\n if (parents[node] != node) {\n parents[node] = find(parents[node]);\n }\n return parents[node];\n }\n\n public boolean same(int a, int b) {\n return find(a) == find(b);\n }\n\n public void union(int a, int b) {\n int aRoot = find(a);\n int bRoot = find(b);\n\n if (aRoot == bRoot) {\n return;\n }\n\n if (height[aRoot] < height[bRoot]) {\n parents[aRoot] = bRoot;\n } else if (height[aRoot] > height[bRoot]) {\n parents[bRoot] = aRoot;\n } else {\n parents[bRoot] = aRoot; // doesn't matter\n height[aRoot] += 1;\n }\n }\n\n public static void main(String[] args) {\n\n n = io.getInt();\n q = io.getInt();\n\n UnionFind unionFind = new UnionFind(n);\n\n while (io.hasMoreTokens()) {\n\n char op = io.getWord().charAt(0);\n int a = io.getInt();\n int b = io.getInt();\n\n switch (op) {\n case '=':\n unionFind.union(a, b);\n break;\n case '?':\n if (unionFind.same(a, b)) {\n io.println(\"yes\");\n } else {\n io.println(\"no\");\n }\n break;\n default:\n System.exit(-1);\n }\n\n }\n\n io.close();\n }\n}\n"},{"alpha_fraction":0.4349234402179718,"alphanum_fraction":0.4411071836948395,"avg_line_length":32.62376403808594,"blob_id":"f02f9ec7e6e4b19115063d5a4d4f8214a392936c","content_id":"7c884d06cd85098a1843c4b9893aab82aaba8564","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":3396,"license_type":"no_license","max_line_length":110,"num_lines":101,"path":"/george/Main.java","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"/**\n* @author Fabian Schilling (fabsch@kth.se)\n*/\n\nimport java.util.Vector;\n\npublic class Main {\n\n static IO io = new IO(System.in, System.out);\n\n public static void main(String[] args) {\n\n int N = io.getInt(); // Number of intersections (nodes)\n int M = io.getInt(); // Number of streets (edges - undirected)\n\n int A = io.getInt() - 1; // Intersection (node) where Luka starts\n int B = io.getInt() - 1; // Intersection (node) Luka must get to\n int K = io.getInt(); // Difference in starting times (Luka starts at A exactly K minutes after George)\n int G = io.getInt(); // Number of intersections on George's route\n\n // Easier to model with adj matrix\n long[][] weights = new long[N][N];\n long[][] times = new long[N][N];\n\n // Fill weights with infinity\n for (int i = 0; i < N; i++) {\n for (int j = 0; j < N; j++) {\n weights[i][j] = Integer.MAX_VALUE;\n times[i][j] = Integer.MAX_VALUE;\n }\n }\n\n // Nodes that george is visiting\n int[] george = new int[G];\n for (int i = 0; i < G; i++) {\n george[i] = io.getInt() - 1;\n }\n\n // Fill in edge weights\n for (int i = 0; i < M; i++) {\n int a = io.getInt() - 1;\n int b = io.getInt() - 1;\n int w = io.getInt();\n weights[a][b] = w;\n weights[b][a] = w; // undirected\n }\n\n // Add cumulative time that george occupied\n int time = 0; // Time until edge is occupied\n for (int i = 0; i < G - 1; i++) {\n times[george[i]][george[i + 1]] = time;\n times[george[i + 1]][george[i]] = time; // undirected\n time += weights[george[i]][george[i + 1]]; // weights the same for george\n }\n\n // Temporary dijkstra variables\n long[] dist = new long[N];\n boolean[] visited = new boolean[N];\n for (int i = 0; i < N; i++) {\n dist[i] = Integer.MAX_VALUE;\n visited[i] = false;\n }\n\n // Dijkstra with constraints\n dist[A] = K;\n for (int i = 0; i < N; i++) {\n int min = -1;\n for (int j = 0; j < N; j++) {\n if (!visited[j] && (min == -1 || dist[j] < dist[min])) {\n min = j;\n }\n }\n visited[min] = true;\n\n for (int j = 0; j < N; j++) {\n // Check constraints before updating distances\n if (dist[min] < times[min][j]) { // regular dijkstra update\n if (dist[min] + weights[min][j] < dist[j]) {\n dist[j] = dist[min] + weights[min][j];\n }\n } else { // add time that george blocks\n long blocking;\n if (dist[min] > weights[min][j] + times[min][j]) {\n blocking = dist[min];\n } else {\n blocking = weights[min][j] + times[min][j];\n }\n blocking += weights[min][j]; // add regular weights always\n if (blocking < dist[j]) {\n dist[j] = blocking;\n }\n }\n }\n }\n\n // started at K so we have to subtract that\n io.println(dist[B] - dist[A]);\n\n io.close();\n }\n}\n"},{"alpha_fraction":0.7419354915618896,"alphanum_fraction":0.7419354915618896,"avg_line_length":40.33333206176758,"blob_id":"a7eb6235705af70820c653b0e4c198a9d66e894c","content_id":"fb6c89efb06ad158e7bee98b3004eff20ebf53d7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":124,"license_type":"no_license","max_line_length":49,"num_lines":3,"path":"/ignore/README.txt","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"Author: Fabian Schilling (fabsch@kth)\nProblem: https://open.kattis.com/problems/ignore\nExecution: python ignore.py < samples/<sample.in>\n"},{"alpha_fraction":0.5111111402511597,"alphanum_fraction":0.5377777814865112,"avg_line_length":14.066666603088379,"blob_id":"e182da6fab9f3a409de12955c2a662715c119081","content_id":"30e9a39ab5b94ebfdb1d79d6ecbd8a754a69b5d3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":225,"license_type":"no_license","max_line_length":53,"num_lines":15,"path":"/bestcompression/bestcompression.py","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"#!/usr/bin/python\n\n__author__ = \"Fabian Schilling\"\n__email__ = \"fabsch@kth.se\"\n\nimport sys\n\ninput = map(int, sys.stdin.readlines()[0].split(\" \"))\nN = input.pop(0)\nb = input.pop(0)\n\nif (2 ** (b + 1)) - 1 >= N:\n\tprint(\"yes\")\nelse:\n\tprint(\"no\")"},{"alpha_fraction":0.33557048439979553,"alphanum_fraction":0.35282838344573975,"avg_line_length":23.255813598632812,"blob_id":"817e7c8b38b29bf35096273113bafc4a89d97179","content_id":"3cfecf71e4e380af376d6e87880309011886ad04","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":1043,"license_type":"no_license","max_line_length":76,"num_lines":43,"path":"/convexhull/Hull.java","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"/**\n* @author Fabian Schilling (fabsch@kth.se)\n*/\n\nimport java.util.Arrays;\n\npublic class Hull {\n\n public double cross(Point o, Point a, Point b) {\n return (a.x - o.x) * (b.y - o.y) - (a.y - o.y) * (b.x - o.x);\n }\n\n public Point[] convexHull(Point[] ps) {\n\n if (ps.length > 1) {\n int n = ps.length, k = 0;\n Point[] h = new Point[2 * n];\n\n Arrays.sort(ps);\n\n // Build lower hull\n for (int i = 0; i < n; i++) {\n while (k >= 2 && cross(h[k - 2], h[k - 1], ps[i]) <= 0) k--;\n h[k++] = ps[i];\n }\n\n // Build upper hull\n for (int i = n - 2, t = k + 1; i >= 0; i--) {\n while (k >= t && cross(h[k - 2], h[k - 1], ps[i]) <= 0) k--;\n h[k++] = ps[i];\n }\n\n if (k > 1) {\n h = Arrays.copyOfRange(h, 0, k - 1);\n }\n return h;\n } else if (ps.length <= 1) {\n return ps;\n } else {\n return null;\n }\n }\n}\n"},{"alpha_fraction":0.4364641010761261,"alphanum_fraction":0.46408841013908386,"avg_line_length":15.454545021057129,"blob_id":"0749706f42123c0d6e6afb2a7b85cca25de18837","content_id":"99b4047bb1f387ce51c4b8c540ae6e8477807e1c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":362,"license_type":"no_license","max_line_length":51,"num_lines":22,"path":"/beatspread/beatspread.py","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"#!/usr/bin/python\n\n__author__ = \"Fabian Schilling\"\n__email__ = \"fabsch@kth.se\"\n\nimport sys\n\ndata = sys.stdin.readlines()\n\nn = int(data.pop(0))\n\nfor _ in range(n):\n s, d = [int(x) for x in data.pop(0).split(' ')]\n\n sm = s + d\n\n if sm % 2 == 1 or s < d:\n print('impossible')\n else:\n s1 = sm / 2\n s2 = s - s1\n print(str(s1) + ' ' + str(s2))\n"},{"alpha_fraction":0.7647058963775635,"alphanum_fraction":0.7647058963775635,"avg_line_length":44.33333206176758,"blob_id":"9861c529b7a1a6f5a727fe5ca4e17e33b91e5145","content_id":"0c51a78fc2eb8fd5319cfc3e78dc5416b1743d92","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":136,"license_type":"no_license","max_line_length":55,"num_lines":3,"path":"/uxuhulvoting/README.txt","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"Author: Fabian Schilling (fabsch@kth)\nProblem: https://open.kattis.com/problems/uxuhulvoting\nExecution: python uxuhulvoting.py < samples/<sample.in>\n"},{"alpha_fraction":0.3788328468799591,"alphanum_fraction":0.3870755136013031,"avg_line_length":24.923076629638672,"blob_id":"0bc29c94fe2fe27f0fc30ca27422af2b45d86953","content_id":"195c9fd47f27002bceb6187b14b4d2cf2b81d3cb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":3033,"license_type":"no_license","max_line_length":62,"num_lines":117,"path":"/almostunionfind/AlmostUnionFind.java","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"/**\n* @author Fabian Schilling (fabsch@kth.se)\n*/\n\nimport java.util.*;\n\npublic class AlmostUnionFind {\n\n static Kattio io = new Kattio(System.in, System.out);\n static int n; // 1 <= n <= 100000\n static int m; // 1 <= n <= 100000\n\n private int[] root;\n private ArrayList<Integer>[] sets;\n\n @SuppressWarnings(\"unchecked\")\n public AlmostUnionFind(int nodes) {\n root = new int[nodes + 1];\n sets = new ArrayList[nodes + 1];\n\n for (int i = 1; i <= nodes; i++) {\n root[i] = i; // make self-connection\n sets[i] = new ArrayList<>();\n sets[i].add(i);\n }\n }\n\n public void union(int p, int q) {\n\n int x = root[p];\n int y = root[q];\n\n if (x == y) {\n return; // already in the same set\n }\n\n if (sets[x].size() >= sets[y].size()) {\n for (int i = 0; i < sets[y].size(); i++) {\n int n = (Integer) sets[y].get(i);\n sets[x].add(n);\n root[n] = x;\n }\n sets[y].clear();\n } else {\n for (int i = 0; i < sets[x].size(); i++) {\n int n = (Integer) sets[x].get(i);\n sets[y].add(n);\n root[n] = y;\n }\n sets[x].clear();\n }\n }\n\n public void move(int p, int q) {\n\n if (root[p] == root[q]) {\n return; // already int the same set\n }\n\n sets[root[p]].remove((Object) p);\n sets[root[q]].add(p);\n root[p] = root[q];\n\n }\n\n public int sum(int p) {\n int sum = 0;\n for (int i = 0; i < sets[root[p]].size(); i++) {\n sum += (Integer) sets[root[p]].get(i);\n }\n return sum;\n }\n\n public int count(int p) {\n return sets[root[p]].size();\n }\n\n public static void main(String[] args) {\n\n while (io.hasMoreTokens()) {\n\n n = io.getInt();\n m = io.getInt();\n\n AlmostUnionFind almost = new AlmostUnionFind(n);\n\n for (int i = 0; i < m; i++) {\n\n int op = io.getInt();\n\n switch (op) {\n case 1: // union p and q\n int p = io.getInt();\n int q = io.getInt();\n almost.union(p, q);\n break;\n case 2: // move p to set containing q\n int x = io.getInt();\n int y = io.getInt();\n almost.move(x, y);\n break;\n case 3: // numsum\n int z = io.getInt();\n int sum = almost.sum(z);\n int count = almost.count(z);\n System.out.println(count + \" \" + sum);\n //io.println(count + \" \" + sum);\n break;\n default:\n System.out.println(\"Shit...\");\n }\n }\n }\n\n io.close();\n }\n}\n"},{"alpha_fraction":0.7575757503509521,"alphanum_fraction":0.7575757503509521,"avg_line_length":43,"blob_id":"fe66fdc62f4e991f234119483d0fd340002c4913","content_id":"aa8139dbf25a1990c7f1cf91b1ff4c5fc8d18973","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":132,"license_type":"no_license","max_line_length":53,"num_lines":3,"path":"/beatspread/README.txt","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"Author: Fabian Schilling (fabsch@kth)\nProblem: https://open.kattis.com/problems/beatspread\nExecution: python beatspread.py < samples/<sample.in>\n"},{"alpha_fraction":0.5031545758247375,"alphanum_fraction":0.5205047130584717,"avg_line_length":19.467741012573242,"blob_id":"e1a060984e1380a2af8db91e29de99d03f8ce1d0","content_id":"0f3a9de44918cdbe91ae17973ecf5f9fa6dbaed0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1268,"license_type":"no_license","max_line_length":94,"num_lines":62,"path":"/deadfraction/deadfraction.py","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"#!/usr/bin/python\n\n__author__ = \"Fabian Schilling\"\n__email__ = \"fabsch@kth.se\"\n\nimport sys\nfrom fractions import gcd\n\n# solution inspired by Khan academy\n\n\ndef fraction(n):\n\n # these are the values we're looking for (min denominator!)\n bestnum = 0\n bestden = sys.maxint # high initial value!\n\n # try different fractions while minimizing the denominator\n for i in range(len(n)):\n\n\n a = int(n[i:])\n b = pow(10, len(n) - i) - 1\n c = 0 if i == 0 else int(n[:i])\n\n num = a + (b * c)\n den = b * pow(10, i)\n\n # try to simplify the fraction\n g = gcd(num, den)\n num /= g\n den /= g\n\n # minimize the denominator\n if den < bestden:\n bestnum = num\n bestden = den\n\n print(str(bestnum) + '/' + str(bestden))\n\ndef main():\n\n #data = open('/Users/fabianschilling/Downloads/deadfraction-sample-data/B.in').readlines()\n data = sys.stdin.readlines()\n #data = ['0.474612399...']\n\n for n in data:\n\n # strip away \"\\n\" so we can check break condition\n n = n.strip('\\n')\n\n if n == '0':\n break\n\n # strip \"0.\" in the beginning and \"...\" in the end\n n = n[2:]\n n = n[:-3]\n\n fraction(n)\n\nif __name__ == '__main__':\n main()"},{"alpha_fraction":0.7460317611694336,"alphanum_fraction":0.7460317611694336,"avg_line_length":41,"blob_id":"813a850f19a6da5681ea4ac54dc3088f41c4b543","content_id":"76e7c8eb149e85414a6136fbedf4abcd14c89edd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":126,"license_type":"no_license","max_line_length":50,"num_lines":3,"path":"/ljutnja/README.txt","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"Author: Fabian Schilling (fabsch@kth)\nProblem: https://open.kattis.com/problems/ljutnja\nExecution: python ljutnja.py < samples/<sample.in>\n"},{"alpha_fraction":0.7538461685180664,"alphanum_fraction":0.7538461685180664,"avg_line_length":42.33333206176758,"blob_id":"806bcb0cb20b952fbfe3e06348ac30a4f3edfdd4","content_id":"b88d83b1f9ebf7522a38ed8e9fa1cec687f04d20","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":130,"license_type":"no_license","max_line_length":52,"num_lines":3,"path":"/spiderman/README.txt","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"Author: Fabian Schilling (fabsch@kth)\nProblem: https://open.kattis.com/problems/spiderman\nExecution: python spiderman.py < samples/<sample.in>\n"},{"alpha_fraction":0.4746639132499695,"alphanum_fraction":0.48241984844207764,"avg_line_length":22.48101234436035,"blob_id":"6d5dc8d69c5550e179b72386e25f59a777aa3440","content_id":"29f54fbf074b5ef5bcf6a35ebfb761692b0b0f62","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":1934,"license_type":"no_license","max_line_length":80,"num_lines":79,"path":"/rationalarithmetic/Rational.java","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"/**\r\n* @author Fabian Schilling (fabsch@kth.se) and Hugo Sandelius (hugosa@kth.se)\r\n*/\r\n\r\npublic class Rational {\r\n\r\n private static final Rational ZERO = new Rational(0, 1);\r\n\r\n private long num;\r\n private long den;\r\n\r\n public Rational(long num, long den) {\r\n this.num = num;\r\n this.den = den;\r\n this.normalize();\r\n this.reduce();\r\n }\r\n\r\n private void reduce() {\r\n long g = gcd(this.num, this.den);\r\n this.num /= g;\r\n this.den /= g;\r\n }\r\n\r\n private void normalize() {\r\n if (this.den < 0) {\r\n this.num *= -1;\r\n this.den *= -1;\r\n }\r\n }\r\n\r\n public int compareTo(Rational r) {\r\n long lhs = this.num * r.den;\r\n long rhs = this.den * r.num;\r\n if (lhs < rhs) return -1;\r\n if (lhs > rhs) return +1;\r\n return 0;\r\n }\r\n\r\n public Rational add(Rational r) {\r\n long num = this.num * r.den + this.den * r.num;\r\n long den = this.den * r.den;\r\n return new Rational(num, den);\r\n }\r\n\r\n public Rational subtract(Rational r) {\r\n long num = this.num * r.den - this.den * r.num;\r\n long den = this.den * r.den;\r\n return new Rational(num, den);\r\n }\r\n\r\n public Rational multiply(Rational r) {\r\n long num = this.num * r.num;\r\n long den = this.den * r.den;\r\n return new Rational(num, den);\r\n }\r\n\r\n public Rational divide(Rational r) {\r\n long num = this.num * r.den;\r\n long den = this.den * r.num;\r\n return new Rational(num, den);\r\n }\r\n\r\n public String toString() {\r\n return this.num + \" / \" + this.den;\r\n }\r\n\r\n public boolean isPositive() {\r\n return (this.num > 0 && this.den > 0) || (this.num < 0 && this.den < 0);\r\n }\r\n\r\n public static long gcd(long m, long n) {\r\n if (m < 0) m = -m;\r\n if (n < 0) n = -n;\r\n if (n == 0) return m;\r\n else return gcd(n, m % n);\r\n }\r\n\r\n}\r\n"},{"alpha_fraction":0.5131264925003052,"alphanum_fraction":0.5202863812446594,"avg_line_length":25.744680404663086,"blob_id":"31033907db888f2b5d6fbd8efc77e392f32fa838","content_id":"626f729a9bc9af0797a09ac1c0af8f59944b5cc4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":2514,"license_type":"no_license","max_line_length":68,"num_lines":94,"path":"/setstack/SetStack.java","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"/**\n* @author Fabian Schilling (fabsch@kth.se)\n*/\n\nimport java.util.*;\n\npublic class SetStack {\n\n static Kattio io = new Kattio(System.in, System.out);\n\n Stack<HashSet<Object>> stack; // generic stack of sets\n HashMap<HashSet<Object>, Integer> map; // retain size of subsets\n\n public SetStack() {\n this.stack = new Stack<HashSet<Object>>();\n this.map = new HashMap<HashSet<Object>, Integer>();\n }\n\n public void push() {\n this.stack.push(new HashSet<Object>());\n }\n\n public void dup() {\n HashSet<Object> element = this.stack.pop();\n this.stack.push(element);\n this.stack.push(element);\n }\n\n public void union() {\n HashSet<Object> element1 = this.stack.pop();\n HashSet<Object> element2 = this.stack.pop();\n HashSet<Object> newSet = new HashSet<Object>();\n newSet.addAll(element1);\n newSet.addAll(element2);\n this.stack.push(newSet);\n }\n\n public void intersect() {\n HashSet<Object> element1 = this.stack.pop();\n HashSet<Object> element2 = this.stack.pop();\n HashSet<Object> newSet = new HashSet<Object>();\n newSet.addAll(element1);\n newSet.retainAll(element2);\n this.stack.push(newSet);\n }\n\n public void add() {\n HashSet<Object> element1 = this.stack.pop();\n HashSet<Object> element2 = this.stack.pop();\n HashSet<Object> newSet = new HashSet<Object>();\n if (map.containsKey(element1)) {\n newSet.add(map.get(element1));\n } else {\n Integer size = map.size();\n map.put(element1, size);\n newSet.add(size);\n }\n newSet.addAll(element2);\n this.stack.push(newSet);\n }\n\n public static void main(String[] args) {\n\n SetStack setstack = new SetStack();\n\n int t = io.getInt();\n\n for (int i = 0; i < t; i++) {\n\n int n = io.getInt();\n\n for (int j = 0; j < n; j++) {\n\n char op = io.getWord().charAt(0);\n\n switch (op) {\n case 'P': setstack.push(); break;\n case 'D': setstack.dup(); break;\n case 'U': setstack.union(); break;\n case 'I': setstack.intersect(); break;\n case 'A': setstack.add(); break;\n default: System.exit(-1);\n }\n\n io.println(setstack.stack.peek().size());\n }\n\n io.println(\"***\");\n }\n\n io.close();\n\n }\n}\n"},{"alpha_fraction":0.7647058963775635,"alphanum_fraction":0.7647058963775635,"avg_line_length":44.33333206176758,"blob_id":"31fd29e5717ffac276e36bef30a329023bcd7068","content_id":"8da77b9ddf84f22ca8bfff4dde40a22594ff2189","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":136,"license_type":"no_license","max_line_length":55,"num_lines":3,"path":"/deadfraction/README.txt","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"Author: Fabian Schilling (fabsch@kth)\nProblem: https://open.kattis.com/problems/deadfraction\nExecution: python deadfraction.py < samples/<sample.in>\n"},{"alpha_fraction":0.4460338056087494,"alphanum_fraction":0.4629388749599457,"avg_line_length":18.71794891357422,"blob_id":"c6e3bdf29ea644d04f126382f51d44f3dc3fd5d9","content_id":"056e6c79d3818a69e1e6a81eb61dcd7b1901fb7d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":769,"license_type":"no_license","max_line_length":60,"num_lines":39,"path":"/hidingplaces/Position.java","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"/**\n* @author Fabian Schilling (fabsch@kth.se)\n*/\n\n\npublic class Position {\n\n int x;\n int y;\n int jumps;\n\n public Position(int x, int y) {\n this.x = x;\n this.y = y;\n this.jumps = 0;\n }\n\n public Position(int x, int y, int jumps) {\n this.x = x;\n this.y = y;\n this.jumps = jumps;\n }\n\n public Position(String pos) {\n char[] xy = pos.toCharArray();\n this.x = (int) xy[0] - 97; // char to int\n this.y = 8 - ((int) xy[1] - 48); // char to int\n }\n\n public String chessNotation() {\n char x = (char) (this.x + 97);\n char y = (char) ((8 - this.y) + 48);\n return \"\" + x + y;\n }\n\n public String toString() {\n return this.chessNotation();// + \" @ \" + this.jumps;\n }\n}\n"},{"alpha_fraction":0.7681159377098083,"alphanum_fraction":0.7681159377098083,"avg_line_length":45,"blob_id":"5d360b41bd12bfe83a8d1f73cb33e0c2ec523629","content_id":"1ad9000f819a1d9d74d18584510fb9406fe599c4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":138,"license_type":"no_license","max_line_length":56,"num_lines":3,"path":"/longincsubseq/README.txt","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"Author: Fabian Schilling (fabsch@kth)\nProblem: https://open.kattis.com/problems/longincsubseq\nExecution: python longincsubseq.py < samples/<sample.in>\n"},{"alpha_fraction":0.3901035785675049,"alphanum_fraction":0.3981587886810303,"avg_line_length":22.80821990966797,"blob_id":"992679dc65dd71e9f989cb08327094d12b5b9a63","content_id":"3fa87a7c19f3d16740e10585f584b55cf4e95778","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":1738,"license_type":"no_license","max_line_length":80,"num_lines":73,"path":"/pebblesolitaire2/Main.java","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"/**\n* @author Fabian Schilling (fabsch@kth.se)\n*/\n\n// naive solution\n\nimport java.util.HashMap;\n\npublic class Main {\n\n public static int mincount;\n public static HashMap<String, Integer> map;\n\n public static int solve(char[] board, int count) {\n\n String b = new String(board);\n\n if (map.containsKey(b)) {\n return map.get(b);\n }\n\n char[] newBoard = board.clone();\n\n for (int i = 0; i < board.length - 2; i++) {\n if (board[i] == 'o' && board[i + 1] == 'o' && board[i + 2] == '-') {\n board[i] = '-';\n board[i + 1] = '-';\n board[i + 2] = 'o';\n map.put(b, solve(board, count - 1));\n board = newBoard.clone();\n }\n if (board[i] == '-' && board[i + 1] == 'o' && board[i + 2] == 'o') {\n board[i] = 'o';\n board[i + 1] = '-';\n board[i + 2] = '-';\n map.put(b, solve(board, count - 1));\n board = newBoard.clone();\n }\n }\n\n if (count < mincount) {\n mincount = count;\n }\n\n return mincount;\n }\n\n public static void main(String[] args) {\n\n IO io = new IO(System.in, System.out);\n\n int n = io.getInt();\n\n map = new HashMap<>();\n\n for (int i = 0; i < n; i++) {\n mincount = Integer.MAX_VALUE;\n char[] board = io.getWord().toCharArray();\n int count = 0;\n for (char c : board) {\n if (c == 'o') {\n count++;\n }\n }\n map.clear();\n //io.println(b);\n io.println(solve(board, count));\n\n }\n\n io.close();\n }\n}\n"},{"alpha_fraction":0.7681159377098083,"alphanum_fraction":0.7681159377098083,"avg_line_length":45,"blob_id":"f85f0d430815730800ee1603d31f86856125eae1","content_id":"6eb6f1644e8c21735c79400910b24692889d780f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":138,"license_type":"no_license","max_line_length":56,"num_lines":3,"path":"/perfectpowers/README.txt","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"Author: Fabian Schilling (fabsch@kth)\nProblem: https://open.kattis.com/problems/perfectpowers\nExecution: python perfectpowers.py < samples/<sample.in>\n"},{"alpha_fraction":0.7746478915214539,"alphanum_fraction":0.7746478915214539,"avg_line_length":46.33333206176758,"blob_id":"e9fbc461a1a69a4eca28c374904f8b06f4cbc488","content_id":"9a38507f2bb53d5a3da9af2edd23e925f7bd2a7e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":142,"license_type":"no_license","max_line_length":58,"num_lines":3,"path":"/bestcompression/README.txt","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"Author: Fabian Schilling (fabsch@kth)\nProblem: https://open.kattis.com/problems/bestcompression\nExecution: python bestcompression.py < samples/<sample.in>\n"},{"alpha_fraction":0.4071895480155945,"alphanum_fraction":0.4215686321258545,"avg_line_length":19.13157844543457,"blob_id":"a12d0cb509560a3d9a3d80d585de1818c29614e0","content_id":"b6ee77c1d20dc2ab8123f7024eb1951cae23526d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":1530,"license_type":"no_license","max_line_length":62,"num_lines":76,"path":"/howmanyzeros/Main.java","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"/**\n* @author Fabian Schilling (fabsch@kth.se)\n*/\n\n\npublic class Main {\n\n public static IO io = new IO(System.in, System.out);\n\n public static long countZerosUpTo(long n) {\n\n long zeros = 0;\n long digit, sliceLeft, sliceRight;\n\n for (int i = 1; ; i *= 10) {\n\n // Slice number and get middle digit\n digit = n / i;\n sliceRight = n % i;\n sliceLeft = digit / 10;\n digit = digit % 10;\n\n // Whole number processed\n if (sliceLeft == 0) {\n return zeros;\n }\n\n // Special case for digit zero\n if (digit == 0) {\n zeros += (sliceLeft - 1) * i + sliceRight + 1;\n } else {\n zeros += sliceLeft * i;\n }\n }\n }\n\n public static long countZeros(long n) {\n\n long zeros = 0;\n\n // Special case for zero\n if (n == 0) {\n return 1;\n }\n\n // Count zeros\n while (n > 0) {\n if (n % 10 == 0) {\n zeros++;\n }\n n /= 10;\n }\n\n return zeros;\n }\n\n public static void main(String[] args) {\n\n while (io.hasMoreTokens()) {\n\n long m = io.getLong();\n if (m < 0) break;\n long n = io.getLong();\n\n long zeros = countZeros(m);\n long lower = countZerosUpTo(m);\n long upper = countZerosUpTo(n);\n\n\n io.println(zeros + upper - lower);\n\n }\n\n io.close();\n }\n}\n"},{"alpha_fraction":0.4371603727340698,"alphanum_fraction":0.44399648904800415,"avg_line_length":28.261537551879883,"blob_id":"aa2ec246ef91e4865cacc69bc6a4ed8bcec4ec45","content_id":"3f6af098053cd227a280df69c33d361ff7c68daf","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":5705,"license_type":"no_license","max_line_length":120,"num_lines":195,"path":"/shortestpath3/Library.java","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"/**\n* @author Fabian Schilling (fabsch@kth.se)\n*/\n\n/**\n * Author: Fabian Schilling & Hugo Sandelius\n */\n\nimport java.util.*;\n\npublic class Library {\n\n // shortestpath1\n public static void dijkstra(Node start) {\n start.distance = 0;\n\n Comparator<Node> comp = (o1, o2) -> Integer.compare(o1.distance, o2.distance);\n PriorityQueue<Node> queue = new PriorityQueue<>(comp);\n queue.add(start);\n\n while (!queue.isEmpty()) {\n\n Node u = queue.poll();\n\n for (Edge e: u.adjacencies) {\n\n Node v = e.target;\n int weight = e.weight;\n int distance = u.distance + weight;\n\n if (distance < v.distance) {\n\n queue.remove(v);\n\n v.distance = distance;\n v.previous = u;\n queue.add(v);\n }\n }\n }\n }\n\n // shortestpath2\n public static void dijkstraTimetable(Node start) {\n\n start.distance = 0;\n\n Comparator<Node> comp = (o1, o2) -> Integer.compare(o1.distance, o2.distance);\n PriorityQueue<Node> queue = new PriorityQueue<>(comp);\n queue.add(start);\n\n while (!queue.isEmpty()) {\n\n Node u = queue.poll();\n\n for (Edge e: u.adjacencies) {\n\n int distance = e.t0 + e.weight;\n\n if (u.distance > e.t0) {\n if (e.P == 0) { // cannot be reached\n distance = Integer.MAX_VALUE;\n } else { // find the next possible time to traverse\n int steps = 1 + (((u.distance - e.t0) - 1) / e.P);\n distance = e.t0 + steps * e.P + e.weight;\n }\n }\n\n Node v = e.target;\n if (distance < v.distance) {\n queue.remove(v);\n v.distance = distance;\n v.previous = u;\n queue.add(v);\n }\n }\n }\n }\n\n // shortestpath3\n public static void bellmanFord(Node[] nodes, Edge[] edges, Node start) {\n\n start.distance = 0;\n\n for (int i = 1; i < nodes.length; i++) {\n for (Edge e: edges) {\n if (e.source.distance != Integer.MAX_VALUE && e.source.distance + e.weight < e.target.distance) {\n e.target.distance = e.source.distance + e.weight;\n e.target.previous = e.source;\n }\n }\n }\n\n //System.out.println(\"Detecting negative cycles...\");\n for (int i = 1; i < nodes.length; i++) {\n //System.out.println(\"Round \" + i);\n for (Edge e: edges) {\n if (e.source.distance == Integer.MIN_VALUE) {\n e.target.distance = Integer.MIN_VALUE;\n } else if (e.source.distance != Integer.MAX_VALUE && e.source.distance + e.weight < e.target.distance) {\n e.target.distance = Integer.MIN_VALUE;\n }\n }\n }\n }\n\n // construct path\n public static Vector<Node> constructPathTo(Node end) {\n Vector<Node> path = new Vector<>();\n Node prev = end;\n while (prev != null) {\n path.insertElementAt(prev, 0);\n prev = prev.previous;\n }\n return path;\n }\n\n public static void printPathTo(Node end) {\n Vector<Node> path = constructPathTo(end);\n System.err.print(\"Path: \");\n for (Node node: path) {\n System.err.print(node.index + \" \");\n }\n System.err.println();\n }\n\n // minspantree\n public static void prim(Node[] nodes) {\n\n // Order nodes by distance\n Comparator<Node> comp = (o1, o2) -> Integer.compare(o1.distance, o2.distance);\n PriorityQueue<Node> queue = new PriorityQueue<>(comp);\n\n // Add all nodes to queue\n for (Node node: nodes) {\n queue.add(node);\n }\n\n Node peek = queue.peek();\n peek.distance = 0;\n\n while (!queue.isEmpty()) {\n Node u = queue.poll();\n\n for (Edge edge: u.adjacencies) {\n\n Node v = edge.target;\n\n if (!v.visited && edge.weight < v.distance) {\n queue.remove(v);\n v.distance = edge.weight;\n v.previous = u;\n u.visited = true;\n queue.add(v);\n }\n }\n }\n }\n\n // allpairspath\n public static void floydWarshall(int[][] dist) {\n\n // Initialize self-connections as 0\n for (int i = 0; i < dist.length; i++) {\n dist[i][i] = 0;\n }\n\n // Run Floyd-Warshall\n for (int k = 0; k < dist.length; k++) {\n for (int i = 0; i < dist.length; i++) {\n for (int j = 0; j < dist.length; j++) {\n\n if (dist[i][k] == Integer.MAX_VALUE || dist[k][j] == Integer.MAX_VALUE) {\n continue; // dont even have to look at this\n }\n\n if (dist[i][j] > dist[i][k] + dist[k][j]) {\n dist[i][j] = dist[i][k] + dist[k][j];\n }\n }\n }\n }\n\n // Detect negative cycles\n for (int i = 0; i < dist.length; i++) {\n for (int j = 0; j < dist.length; j++) {\n for (int k = 0; dist[i][j] != Integer.MIN_VALUE && k < dist.length; k++) {\n if (dist[i][k] != Integer.MAX_VALUE && dist[k][j] != Integer.MAX_VALUE && dist[k][k] < 0) {\n dist[i][j] = Integer.MIN_VALUE;\n }\n }\n }\n }\n }\n}"},{"alpha_fraction":0.4995878040790558,"alphanum_fraction":0.5597691535949707,"avg_line_length":25.39130401611328,"blob_id":"f3bbfb19f57a8b7bee2b2354535c771c302521c6","content_id":"fdee65f7af8f3a05b0d364b7030862319e64b16b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1213,"license_type":"no_license","max_line_length":72,"num_lines":46,"path":"/polymul1/polymul1.py","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"#!/usr/bin/python\n\n__author__ = \"Fabian Schilling\"\n__email__ = \"fabsch@kth.se\"\n\nimport sys\n\ndef quadmul(pol1, pol2):\n result = [0]*(len(pol1)+len(pol2))\n for index1, coeff1 in enumerate(pol1):\n for index2, coeff2 in enumerate(pol2):\n result[index1+index2] += coeff1*coeff2\n return result\n\ndef karatsuba(pol1, pol2):\n if len(pol1) < 10 or len(pol2) < 10:\n return quadmul(pol1, pol2)\n m = max(len(pol1), len(pol2))\n m2 = m // 2\n low1 = pol1[:m2]\n high1 = pol1[m2:]\n low2 = pol2[:m2]\n high2 = pol2[m2:]\n\n z0 = karatsuba(low1, low2)\n z1 = karatsuba((low1+high1), (low2+high2))\n z2 = karatsuba(high1, high2)\n return (z2*10**(2*m2)) + ((z1-z2-z0)*10**(m2))+(z0)\n\nT = int(sys.stdin.readline().strip(\"\\n\"))\n\nfor _ in range(T):\n deg1 = int(sys.stdin.readline().strip(\"\\n\"))\n pol1 = [int(x) for x in sys.stdin.readline().strip(\"\\n\").split(\" \")]\n deg2 = int(sys.stdin.readline().strip(\"\\n\"))\n pol2 = [int(x) for x in sys.stdin.readline().strip(\"\\n\").split(\" \")]\n\n res = quadmul(pol1, pol2)\n \n deg = 0\n for index, coeff in enumerate(res):\n if coeff != 0:\n deg = index\n\n print deg\n print \" \".join([str(x) for x in res[:deg+1]])"},{"alpha_fraction":0.5787139534950256,"alphanum_fraction":0.5831485390663147,"avg_line_length":19.5,"blob_id":"7a7180dbe0c530d719c80647079980251868be1f","content_id":"9b2ac9f198cfd4dcd28e82c1c1bb55e7c239d81a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":451,"license_type":"no_license","max_line_length":91,"num_lines":22,"path":"/islandhopping/Edge.java","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"/**\n* @author Fabian Schilling (fabsch@kth.se)\n*/\n\n\npublic class Edge implements Comparable<Edge> {\n\n public Node from;\n public Node to;\n public double distance;\n\n public Edge(Node from, Node to) {\n this.from = from;\n this.to = to;\n this.distance = Math.sqrt(Math.pow(from.x - to.x, 2) + Math.pow(from.y - to.y, 2));\n }\n\n @Override\n public int compareTo(Edge o) {\n return Double.compare(distance, o.distance);\n }\n}\n"},{"alpha_fraction":0.44621938467025757,"alphanum_fraction":0.4781682789325714,"avg_line_length":23.102563858032227,"blob_id":"6fe54624596d7833bf8814d6081492bb54457261","content_id":"61a685438e33693665585c284586ce99c89d92b6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":939,"license_type":"no_license","max_line_length":54,"num_lines":39,"path":"/ignore/ignore.py","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"#!/usr/bin/python\n\n__author__ = \"Fabian Schilling\"\n__email__ = \"fabsch@kth.se\"\n\nimport sys\n\n# conver int to str under certain base\ndef int2str(num, base):\n (d, m) = divmod(num, base)\n if d > 0:\n return int2str(d, base) + str(m)\n return str(m)\n\n# convert from base 7 to (0, 1, 2, 5, 6, 8, 9) system\ndef convert_sys(num_str):\n ls = list(num_str)\n for i, dig in enumerate(ls):\n if dig == '3':\n ls[i] = '5'\n elif dig == '4':\n ls[i] = '6'\n elif dig == '5':\n ls[i] = '8'\n elif dig == '6':\n ls[i] = '9'\n return ''.join(ls)\n\nnums = [int(x) for x in sys.stdin.readlines()]\n\nfor num in nums:\n num_str = list(convert_sys(int2str(num, 7))[::-1])\n # turn upside down, i.e. 6 -> 9 and 9 -> 6\n for i, char in enumerate(num_str):\n if char == '6':\n num_str[i] = '9'\n elif char == '9':\n num_str[i] = '6'\n print(''.join(num_str))"},{"alpha_fraction":0.7213114500045776,"alphanum_fraction":0.7377049326896667,"avg_line_length":39.66666793823242,"blob_id":"546fd4a011686bba6b23da3977f8ddd9d24ba078","content_id":"1154482db1819b63dd84d03392f74ec0c692c1ab","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":122,"license_type":"no_license","max_line_length":48,"num_lines":3,"path":"/help2/README.txt","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"Author: Fabian Schilling (fabsch@kth)\nProblem: https://open.kattis.com/problems/help2\nExecution: python help2.py < samples/<sample.in>\n"},{"alpha_fraction":0.5630372762680054,"alphanum_fraction":0.5687679052352905,"avg_line_length":20.84375,"blob_id":"793566c48b5386d358e4b675db0284d7efe9b2f0","content_id":"cc7ba9beafa4a1cf88b80d49c3bd4497f1e54b46","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":698,"license_type":"no_license","max_line_length":61,"num_lines":32,"path":"/ljutnja/ljutnja.py","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"#!/usr/bin/python\n\n__author__ = \"Fabian Schilling\"\n__email__ = \"fabsch@kth.se\"\n\nimport sys\n\ndef solve(wishes, m, n):\n\n remainder = sum(wishes) - m # number of remaining candies\n\n anger = 0\n for i, wish in enumerate(wishes):\n kids = n - i # number of remaining kids\n candies = min(wish, remainder / kids)\n anger += (candies * candies)\n remainder -= candies\n #print(remainder)\n\n print(anger)\n\ndef main():\n input = sys.stdin.readlines()\n first = map(int, input.pop(0).split(' '))\n m = first[0] # number of candies\n n = first[1] # number of children\n wishes = map(int, input)\n wishes.sort()\n solve(wishes, m, n)\n\nif __name__ == '__main__':\n main()"},{"alpha_fraction":0.4575757682323456,"alphanum_fraction":0.4727272689342499,"avg_line_length":17.85714340209961,"blob_id":"c2782ae933d45673a281d0d3b56c963a53c64fe2","content_id":"0a2c0d3a67bfb7e2d502faa4538b5362827caf05","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Java","length_bytes":660,"license_type":"no_license","max_line_length":51,"num_lines":35,"path":"/turbo/Fenwick.java","repo_name":"vinatsx/kattis","src_encoding":"UTF-8","text":"/**\n* @author Fabian Schilling (fabsch@kth.se)\n*/\n\n\npublic class Fenwick {\n\n static final int MAX = 1000000;\n\n private long[] tree;\n private int n;\n\n public Fenwick(int n) {\n this.tree = new long[MAX];\n this.n = n;\n }\n\n public long query(int index) {\n long result = 0;\n for (int i = index; i > 0; i -= (i & -i)) {\n result += tree[i];\n }\n return result;\n }\n\n public void update(int index, long delta) {\n for (int i = index; i <= n; i += (i &-i)) {\n tree[i] += delta;\n }\n }\n\n public long sum(int left, int right) {\n return query(right) - query(left - 1);\n }\n}\n"}],"string":"[\n {\n \"alpha_fraction\": 0.734375,\n \"alphanum_fraction\": 0.75,\n \"avg_line_length\": 41.66666793823242,\n \"blob_id\": \"8675d4746800e57fe44a350cdd95ccab9623e8e4\",\n \"content_id\": \"e40ec3dba8a5b101441c7f0b17daf66fcd2104e9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 128,\n \"license_type\": \"no_license\",\n \"max_line_length\": 51,\n \"num_lines\": 3,\n \"path\": \"/polymul1/README.txt\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"Author: Fabian Schilling (fabsch@kth)\\nProblem: https://open.kattis.com/problems/polymul1\\nExecution: python polymul1.py < samples/<sample.in>\\n\"\n },\n {\n \"alpha_fraction\": 0.40164995193481445,\n \"alphanum_fraction\": 0.4069880247116089,\n \"avg_line_length\": 26.35398292541504,\n \"blob_id\": \"06ec39f602da92d6b661d3a66fc7cd842b3080c8\",\n \"content_id\": \"be1b514eab62166b1b0ae507fafac0b4b8413593\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 6182,\n \"license_type\": \"no_license\",\n \"max_line_length\": 85,\n \"num_lines\": 226,\n \"path\": \"/minspantree/Main.java\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/**\\n* @author Fabian Schilling (fabsch@kth.se)\\n*/\\n\\nimport java.util.Comparator;\\nimport java.util.Collections;\\nimport java.util.Vector;\\n\\n\\npublic class Main {\\n\\n public static IO io = new IO(System.in, System.out);\\n\\n public static void shortestpath1() {\\n\\n while (io.hasMoreTokens()) {\\n\\n int n = io.getInt(); // number of nodes in the graph\\n if (n == 0) break;\\n int m = io.getInt(); // number of edges\\n int q = io.getInt(); // number of queries\\n int s = io.getInt(); // index of the starting node\\n\\n Node[] nodes = new Node[n];\\n\\n for (int i = 0; i < n; i++) {\\n nodes[i] = new Node(i);\\n }\\n\\n for (int i = 0; i < m; i++) {\\n int u = io.getInt();\\n int v = io.getInt();\\n int w = io.getInt();\\n\\n nodes[u].adjacencies.add(new Edge(nodes[v], w));\\n\\n }\\n\\n Library.dijkstra(nodes[s]);\\n\\n for (int i = 0; i < q; i++) {\\n int query = io.getInt();\\n\\n int distance = nodes[query].distance;\\n\\n if (distance == Integer.MAX_VALUE) {\\n io.println(\\\"Impossible\\\");\\n } else {\\n io.println(distance);\\n }\\n\\n }\\n\\n io.println();\\n }\\n }\\n\\n public static void shortestpath2() {\\n\\n while (io.hasMoreTokens()) {\\n\\n int n = io.getInt(); // number of nodes in the graph\\n if (n == 0) break;\\n int m = io.getInt(); // number of edges\\n int q = io.getInt(); // number of queries\\n int s = io.getInt(); // index of the starting node\\n\\n Node[] nodes = new Node[n];\\n\\n for (int i = 0; i < n; i++) {\\n nodes[i] = new Node(i);\\n }\\n\\n for (int i = 0; i < m; i++) {\\n int u = io.getInt();\\n int v = io.getInt();\\n int t0 = io.getInt();\\n int P = io.getInt();\\n int d = io.getInt();\\n\\n nodes[u].adjacencies.add(new Edge(nodes[v], d, t0, P));\\n\\n }\\n\\n Library.dijkstraTimetable(nodes[s]);\\n\\n for (int i = 0; i < q; i++) {\\n int query = io.getInt();\\n\\n int distance = nodes[query].distance;\\n\\n if (distance == Integer.MAX_VALUE) {\\n io.println(\\\"Impossible\\\");\\n } else {\\n io.println(distance);\\n }\\n\\n }\\n\\n io.println();\\n }\\n\\n }\\n\\n public static void shortestpath3() {\\n\\n while (io.hasMoreTokens()) {\\n\\n int n = io.getInt(); // number of nodes in the graph\\n if (n == 0) break;\\n int m = io.getInt(); // number of edges\\n int q = io.getInt(); // number of queries\\n int s = io.getInt(); // index of the starting node\\n\\n Node[] nodes = new Node[n];\\n\\n for (int i = 0; i < n; i++) {\\n nodes[i] = new Node(i);\\n }\\n\\n Edge[] edges = new Edge[m];\\n\\n for (int i = 0; i < m; i++) {\\n int u = io.getInt();\\n int v = io.getInt();\\n int w = io.getInt();\\n\\n edges[i] = new Edge(nodes[u], nodes[v], w);\\n\\n }\\n\\n Library.bellmanFord(nodes, edges, nodes[s]);\\n\\n for (int i = 0; i < q; i++) {\\n int query = io.getInt();\\n\\n int distance = nodes[query].distance;\\n\\n if (distance == Integer.MAX_VALUE) {\\n io.println(\\\"Impossible\\\");\\n } else if (distance == -Integer.MIN_VALUE) {\\n io.println(\\\"-Infinity\\\");\\n } else {\\n io.println(distance);\\n }\\n\\n }\\n\\n io.println();\\n }\\n }\\n\\n public static void minspantree() {\\n\\n while (io.hasMoreTokens()) {\\n\\n int n = io.getInt(); // number of nodes in the graph\\n if (n == 0) break;\\n int m = io.getInt(); // number of edges\\n\\n Node[] nodes = new Node[n];\\n\\n for (int i = 0; i < n; i++) {\\n nodes[i] = new Node(i);\\n }\\n\\n for (int i = 0; i < m; i++) {\\n int u = io.getInt();\\n int v = io.getInt();\\n int w = io.getInt();\\n\\n if (u != v) {\\n nodes[u].adjacencies.add(new Edge(nodes[v], w));\\n nodes[v].adjacencies.add(new Edge(nodes[u], w)); // undirected\\n }\\n }\\n\\n Library.prim(nodes);\\n\\n Vector<Edge> edges = new Vector<>();\\n long total = 0;\\n\\n for (Node node: nodes) {\\n if (node.previous != null) {\\n total += node.distance;\\n if (node.previous.index < node.index) {\\n edges.add(new Edge(node.previous, node, node.distance));\\n } else {\\n edges.add(new Edge(node, node.previous, node.distance));\\n }\\n }\\n }\\n\\n if (edges.isEmpty() || edges.size() != n - 1) {\\n io.println(\\\"Impossible\\\");\\n } else {\\n\\n // lexicographic sort\\n Collections.sort(edges, new Comparator<Edge>() {\\n @Override\\n public int compare(Edge o1, Edge o2) {\\n if (o1.source.index != o2.source.index) {\\n return Integer.compare(o1.source.index, o2.source.index);\\n } else {\\n return Integer.compare(o1.target.index, o2.target.index);\\n }\\n }\\n });\\n io.println(total);\\n for (Edge e: edges) {\\n io.println(e.source.index + \\\" \\\" + e.target.index);\\n }\\n }\\n }\\n\\n }\\n\\n public static void main(String[] args) {\\n\\n //shortestpath1();\\n //shortestpath2();\\n //shortestpath3();\\n minspantree();\\n io.close();\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7394366264343262,\n \"alphanum_fraction\": 0.7394366264343262,\n \"avg_line_length\": 46.33333206176758,\n \"blob_id\": \"cc2eea9e98ecc6dbfb0c09d0a8a0288d34acfb69\",\n \"content_id\": \"6cad3abc00c9b88a5c990a1e8746488b00afe968\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 142,\n \"license_type\": \"no_license\",\n \"max_line_length\": 61,\n \"num_lines\": 3,\n \"path\": \"/allpairspath/README.txt\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"Author: Fabian Schilling (fabsch@kth)\\nProblem: https://open.kattis.com/problems/allpairspath\\nExecution: javac Main.java && java Main < samples/<sample.in>\\n\"\n },\n {\n \"alpha_fraction\": 0.4532037377357483,\n \"alphanum_fraction\": 0.45806336402893066,\n \"avg_line_length\": 32.26347351074219,\n \"blob_id\": \"b98087c1637357c7610e195137b2509269444468\",\n \"content_id\": \"b9489e519196e55f90243d0353800bc0b84309a0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 5556,\n \"license_type\": \"no_license\",\n \"max_line_length\": 113,\n \"num_lines\": 167,\n \"path\": \"/eulerianpath/Main.java\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/**\\n* @author Fabian Schilling (fabsch@kth.se) and Hugo Sandelius (hugosa@kth.se)\\n*/\\n\\nimport java.util.*;\\n\\npublic class Main {\\n\\n public static void main(String[] args) {\\n Kattio io = new Kattio(System.in, System.out);\\n while (true) {\\n int n = io.getInt();\\n\\n if (n == 0) {\\n break;\\n }\\n\\n List<LinkedList<Integer>> neighbors = new ArrayList<>();\\n for (int i = 0; i < n; i++) {\\n neighbors.add(new LinkedList<Integer>());\\n }\\n\\n int[] inEdges = new int[n];\\n\\n int m = io.getInt();\\n for (int i = 0; i < m; i++) {\\n int u = io.getInt();\\n int v = io.getInt();\\n inEdges[v]++;\\n neighbors.get(u).add(v);\\n }\\n\\n List<Integer> eulerianPath = findEulerianPath(neighbors, inEdges, m);\\n\\n\\n if (eulerianPath == null) {\\n io.println(\\\"Impossible\\\");\\n } else {\\n for (int i : eulerianPath) {\\n io.print(i + \\\" \\\");\\n }\\n io.println();\\n }\\n }\\n\\n io.flush();\\n }\\n\\n // returns a starting point, or -1 if no eulerian path possible\\n public static int findStartingPoint(List<LinkedList<Integer>> neighbors, int[] inEdges) {\\n int i = 0;\\n int startingPoint = -2;\\n for (LinkedList<Integer> neighborList : neighbors) {\\n // if outEdges > inEdges, this should be our starting point\\n if (neighborList.size() > inEdges[i]) {\\n // but if outEdges > inEdges + 1, no eulerian path possible\\n if (neighborList.size() > inEdges[i] + 1) {\\n return -1;\\n }\\n // but if there is more than one vertex like this, no eulerian path possible\\n if (startingPoint != -2) {\\n return -1;\\n } else {\\n startingPoint = i;\\n }\\n }\\n i++;\\n }\\n\\n // if we didn't find a starting point, just start at an arbitrary point\\n if (startingPoint == -2) {\\n startingPoint = 0;\\n }\\n\\n return startingPoint;\\n }\\n\\n // returns an end point, or -1 if no eulerian path possible, or -2 if no specific end point\\n public static int findEndPoint(List<LinkedList<Integer>> neighbors, int[] inEdges) {\\n int i = 0;\\n int endPoint = -2;\\n for (LinkedList<Integer> neighborList : neighbors) {\\n // if outEdges > inEdges, this should be our starting point\\n if (neighborList.size() < inEdges[i]) {\\n // but if outEdges > inEdges + 1, no eulerian path possible\\n if (neighborList.size() < inEdges[i] - 1) {\\n return -1;\\n }\\n // but if there is more than one vertex like this, no eulerian path possible\\n if (endPoint != -2) {\\n return -1;\\n } else {\\n endPoint = i;\\n }\\n }\\n i++;\\n }\\n\\n return endPoint;\\n }\\n\\n /* Finds a eulerian path in the graph described by the adjacency lists in 'neighors'\\n * 'inEdges' is an array, where inEdges[i] is an array of indexes of inEdges to node with index i\\n * 'edges' is the total amount of edges\\n * */\\n public static List<Integer> findEulerianPath(List<LinkedList<Integer>> neighbors, int[] inEdges, int edges) {\\n LinkedList<Integer> path = new LinkedList<>();\\n int pathPos = 0;\\n\\n int index = findStartingPoint(neighbors, inEdges);\\n int endPoint = findEndPoint(neighbors, inEdges);\\n\\n if (index == -1 || endPoint == -1) {\\n return null;\\n }\\n\\n boolean endAlreadyReached = false;\\n\\n int startPoint = index;\\n while (true) {\\n path.add(pathPos, index);\\n\\n if (!neighbors.get(index).isEmpty()) {\\n // if we have neighbors, visit them\\n index = neighbors.get(index).pollFirst();\\n } else {\\n /* if we have no unvisited neighbors, we need to have either\\n ended up at our starting point or at the only possible\\n end point for eulerian path. Otherwise, no eulerian\\n path possible\\n */\\n if (index != startPoint && index != endPoint) {\\n\\n if (!endAlreadyReached) {\\n endAlreadyReached = true;\\n } else {\\n return null;\\n }\\n }\\n\\n // try to find a node in the path with unvisited neighbors\\n boolean foundNew = false;\\n int i = 0;\\n for (int vertex : path) {\\n if (!neighbors.get(vertex).isEmpty()) {\\n startPoint = vertex;\\n pathPos = i;\\n index = neighbors.get(vertex).pollFirst();\\n foundNew = true;\\n break;\\n }\\n i++;\\n }\\n\\n // if we didn't find any, we're finished\\n if (!foundNew) {\\n if (edges > path.size() - 1) {\\n // not all edges are in our path\\n return null;\\n }\\n Collections.reverse(path);\\n return path;\\n }\\n }\\n }\\n }\\n}\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.31608134508132935,\n \"alphanum_fraction\": 0.32902032136917114,\n \"avg_line_length\": 19.41509437561035,\n \"blob_id\": \"7b0dafec0e9c399aee8f65aa3c95f1875e4bc2b0\",\n \"content_id\": \"3c06ed99cd237ae410fb874cad31dda9bd830ee7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 1082,\n \"license_type\": \"no_license\",\n \"max_line_length\": 57,\n \"num_lines\": 53,\n \"path\": \"/cd/CD.java\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/**\\n* @author Fabian Schilling (fabsch@kth.se)\\n*/\\n\\n\\npublic class CD {\\n\\n static final int M = 1000000;\\n\\n static Kattio io = new Kattio(System.in, System.out);\\n\\n static int[] nlist = new int[M];\\n static int[] mlist = new int[M];\\n\\n public static void main(String[] args) {\\n\\n while (true) {\\n\\n int n = io.getInt();\\n int m = io.getInt();\\n\\n if (n == 0 && m == 0) {\\n break;\\n }\\n\\n for (int i = 0; i < n; i++) {\\n nlist[i] = io.getInt();\\n }\\n\\n for (int i = 0; i < m; i++) {\\n mlist[i] = io.getInt();\\n }\\n\\n int sell = 0;\\n int ni = 0, mi = 0;\\n\\n while (ni < n && mi < m) {\\n if (nlist[ni] > mlist[mi]) {\\n mi++;\\n } else if (nlist[ni] < mlist[mi]) {\\n ni++;\\n } else {\\n sell++;\\n ni++;\\n mi++;\\n }\\n }\\n\\n io.println(sell);\\n }\\n io.close();\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.3693790137767792,\n \"alphanum_fraction\": 0.3704496920108795,\n \"avg_line_length\": 22.578947067260742,\n \"blob_id\": \"84a0ec4b1a8409a68b230a9d510415c5f1fcb599\",\n \"content_id\": \"87bc14f9f3631367abb295bf6f32a70af507ee34\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 934,\n \"license_type\": \"no_license\",\n \"max_line_length\": 66,\n \"num_lines\": 38,\n \"path\": \"/rationalarithmetic/Main.java\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/**\\r\\n* @author Fabian Schilling (fabsch@kth.se) and Hugo Sandelius (hugosa@kth.se)\\r\\n*/\\r\\n\\r\\npublic class Main {\\r\\n\\r\\n public static IO io = new IO(System.in, System.out);\\r\\n\\r\\n public static void main(String[] args) {\\r\\n\\r\\n int n = io.getInt();\\r\\n\\r\\n for (int i = 0; i < n; i++) {\\r\\n\\r\\n Rational x = new Rational(io.getLong(), io.getLong());\\r\\n String op = io.getWord();\\r\\n Rational y = new Rational(io.getLong(), io.getLong());\\r\\n\\r\\n switch (op) {\\r\\n case \\\"+\\\":\\r\\n io.println(x.add(y));\\r\\n break;\\r\\n case \\\"-\\\":\\r\\n io.println(x.subtract(y));\\r\\n break;\\r\\n case \\\"/\\\":\\r\\n io.println(x.divide(y));\\r\\n break;\\r\\n case \\\"*\\\":\\r\\n io.println(x.multiply(y));\\r\\n break;\\r\\n }\\r\\n\\r\\n }\\r\\n\\r\\n io.close();\\r\\n }\\r\\n}\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.7396449446678162,\n \"alphanum_fraction\": 0.7435897588729858,\n \"avg_line_length\": 28.823530197143555,\n \"blob_id\": \"7962fc4a2e660eed48a4297ba9efcf6bb1c31f37\",\n \"content_id\": \"6c3913264666b7d55f3acef685b3661378582fcb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 507,\n \"license_type\": \"no_license\",\n \"max_line_length\": 244,\n \"num_lines\": 17,\n \"path\": \"/README.md\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Kattis\\n\\nSome of the problems I solved during the course \\\"Problem Solving and Programming under Pressure\\\" at KTH.\\n\\nThe directory names correspond to the problem ID on Kattis, i.e. https://open.kattis.com/problems/factovisors for problem \\\"factovisors\\\". Most problems are solved in Java due to time constraints on Kattis, the others are solved using Python 2.7.\\n\\n# Execution\\n\\nFor Java:\\n```\\njavac Main.java && java Main < samples/<sample.in>\\n```\\n\\nFor Python:\\n```\\npython <problem>.py < samples/<sample.in>\\n```\\n\"\n },\n {\n \"alpha_fraction\": 0.4735177755355835,\n \"alphanum_fraction\": 0.5098814368247986,\n \"avg_line_length\": 22.88679313659668,\n \"blob_id\": \"652cf6515df76b2931e72ef6ef06fa2a69da7529\",\n \"content_id\": \"0f15c8d519978f6ed65b4d022dbc62acb00c85fc\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1265,\n \"license_type\": \"no_license\",\n \"max_line_length\": 54,\n \"num_lines\": 53,\n \"path\": \"/help2/help2.py\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/python\\n\\n__author__ = \\\"Fabian Schilling\\\"\\n__email__ = \\\"fabsch@kth.se\\\"\\n\\nimport sys\\n\\ndef is_var(w):\\n return w[0] == '<' and w[-1] == '>'\\n\\ndef replace(pat, word, substitute):\\n for i in range(len(pat)):\\n if pat[i] == word:\\n pat[i] = substitute\\n\\ndef find(pat1, pat2):\\n\\n for i in range(len(pat1)):\\n if is_var(pat1[i]) and not is_var(pat2[i]):\\n replace(pat1, pat1[i], pat2[i])\\n return find(pat1, pat2)\\n if not is_var(pat1[i]) and is_var(pat2[i]):\\n replace(pat2, pat2[i], pat1[i])\\n return find(pat1, pat2)\\n for i in range(len(pat1)):\\n if is_var(pat1[i]) and is_var(pat2[i]):\\n replace(pat1, pat1[i], \\\"xxx\\\")\\n replace(pat2, pat2[i], \\\"xxx\\\")\\n return find(pat1, pat2)\\n if pat1 != pat2:\\n print('-')\\n return\\n print(\\\" \\\".join(pat1))\\n return\\n\\ndef solve(pat1, pat2):\\n\\n if len(pat1) != len(pat2):\\n print('-')\\n return\\n\\n find(pat1, pat2)\\n\\ndef main():\\n input = sys.stdin.readlines()\\n num_cases = int(input.pop(0))\\n for i in range(num_cases):\\n pat1 = input.pop(0).splitlines()[0].split(' ')\\n pat2 = input.pop(0).splitlines()[0].split(' ')\\n solve(pat1, pat2)\\n\\nif __name__ == \\\"__main__\\\":\\n main()\"\n },\n {\n \"alpha_fraction\": 0.4612877070903778,\n \"alphanum_fraction\": 0.4735126197338104,\n \"avg_line_length\": 21.72222137451172,\n \"blob_id\": \"6928995811cf1c19491e9c129bdd56c7c7c57e1a\",\n \"content_id\": \"f11a3fbadbdf0d799de184007271404533f624a8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 1227,\n \"license_type\": \"no_license\",\n \"max_line_length\": 72,\n \"num_lines\": 54,\n \"path\": \"/turbo/Turbo.java\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/**\\n* @author Fabian Schilling (fabsch@kth.se)\\n*/\\n\\nimport java.util.Arrays;\\nimport java.util.HashMap;\\n\\n\\npublic class Turbo {\\n\\n static Kattio io = new Kattio(System.in, System.out);\\n\\n static int LEN = 100001;\\n\\n static int n;\\n static int[] nums = new int[LEN]; // mapping from index to number\\n static int[] pos = new int[LEN]; // mapping from number to index\\n\\n static Fenwick shift = new Fenwick(LEN); // keeps track of the shift\\n\\n\\n public static void solve() {\\n\\n int left = 1;\\n int right = n;\\n for (int i = 1; i <= n; i++) {\\n if (i % 2 == 1) { // odd\\n shift.update(pos[left], -1);\\n io.println(shift.sum(1, pos[left]));\\n left++; // move one step right\\n } else { // even\\n shift.update(pos[right], -1);\\n io.println(shift.sum(pos[right], n));\\n right--; // move one step left\\n }\\n }\\n }\\n\\n public static void main(String[] args) {\\n\\n n = io.getInt();\\n\\n for (int i = 1; i <= n; i++) {\\n int num = io.getInt();\\n nums[i] = num;\\n pos[num] = i;\\n shift.update(i, 1);\\n }\\n\\n solve();\\n\\n io.close();\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5426945090293884,\n \"alphanum_fraction\": 0.5547122359275818,\n \"avg_line_length\": 25.366666793823242,\n \"blob_id\": \"0de163392e0de2af2bfef60aba6d8ebabc563936\",\n \"content_id\": \"562ae738d2bd815e5be00c46104e92e639483195\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1581,\n \"license_type\": \"no_license\",\n \"max_line_length\": 87,\n \"num_lines\": 60,\n \"path\": \"/uxuhulvoting/uxuhulvoting.py\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/python\\n\\n__author__ = \\\"Fabian Schilling\\\"\\n__email__ = \\\"fabsch@kth.se\\\"\\n\\nimport sys\\n\\n\\ndef solve(prefs, states, priest, state):\\n\\n if priest == len(prefs):\\n return state # no more priests left, state is final\\n elif states[priest][state] is not None:\\n return states[priest][state] # state already computed\\n\\n best = None\\n for i in range(3): # every priest has three possible stone flips\\n vote = (state ^ (1 << i))\\n result = solve(prefs, states, priest + 1, vote)\\n\\n # the lower the preference the better\\n if best is None or prefs[priest][result] < prefs[priest][best]:\\n best = result\\n\\n # save the best state for the current priest and return\\n states[priest][state] = best\\n return best\\n\\n\\ndef main(args):\\n\\n data = sys.stdin.readlines()\\n\\n n = int(data.pop(0)) # number of voting rounds\\n\\n for i in range(n):\\n\\n m = int(data.pop(0)) # number of voting priests\\n\\n prefs = [] # preferences for each priest\\n states = [[None for _ in range(8)] for _ in range(m)] # saved states\\n\\n for priest in range(m):\\n prefs.append([int(x) - 1 for x in data.pop(0).split()]) # normalize to 0-7\\n\\n state = solve(prefs, states, 0, 0) # solve for priest 0 and state 0 (=NNN)\\n\\n # print final state (0 = N, 1 = Y)\\n endstate = '{:03b}'.format(state)\\n sol = []\\n for c in endstate:\\n if c == '0':\\n sol.append('N')\\n else:\\n sol.append('Y')\\n print(''.join(sol))\\n\\n\\nif __name__ == '__main__':\\n main(sys.argv)\"\n },\n {\n \"alpha_fraction\": 0.4229075014591217,\n \"alphanum_fraction\": 0.4302496314048767,\n \"avg_line_length\": 18.485713958740234,\n \"blob_id\": \"ef8b60a9f25b9c01be3f55c4d11d322e909c8ccf\",\n \"content_id\": \"3c26b3b2684f036529aa63943ed9f394eec1a907\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 681,\n \"license_type\": \"no_license\",\n \"max_line_length\": 56,\n \"num_lines\": 35,\n \"path\": \"/crypto/Main.java\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/**\\n* @author Fabian Schilling (fabsch@kth.se)\\n*/\\n\\nimport java.awt.*;\\nimport java.util.ArrayList;\\n\\npublic class Main {\\n\\n public static IO io = new IO(System.in, System.out);\\n\\n public static void main(String[] args) {\\n\\n long N = io.getLong();\\n\\n long maxZeros = 0;\\n long minBase = 2;\\n for (long base = N; base >= 2; base--) {\\n long n = N;\\n long zeros = 0;\\n while (n % base == 0) {\\n n /= base;\\n zeros++;\\n }\\n if (zeros >= maxZeros) {\\n maxZeros = zeros;\\n minBase = base;\\n }\\n }\\n\\n io.println(minBase);\\n\\n io.close();\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.43195003271102905,\n \"alphanum_fraction\": 0.439535915851593,\n \"avg_line_length\": 25.678571701049805,\n \"blob_id\": \"a9d069bd81102fde546411247b8e699badedeb38\",\n \"content_id\": \"9a1ddb3112667b952ec9f7ae33b46d89df8a27a7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 2241,\n \"license_type\": \"no_license\",\n \"max_line_length\": 74,\n \"num_lines\": 84,\n \"path\": \"/shortestpath2/Library.java\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/**\\n* @author Fabian Schilling (fabsch@kth.se)\\n*/\\n\\nimport java.util.Comparator;\\nimport java.util.PriorityQueue;\\n\\npublic class Library {\\n\\n public static void dijkstra(Node start) {\\n start.distance = 0;\\n\\n Comparator<Node> comp = new Comparator<Node>() {\\n @Override\\n public int compare(Node o1, Node o2) {\\n return Integer.compare(o1.distance, o2.distance);\\n }\\n };\\n PriorityQueue<Node> queue = new PriorityQueue<>(comp);\\n queue.add(start);\\n\\n while (!queue.isEmpty()) {\\n\\n Node u = queue.poll();\\n\\n for (Edge e: u.adjacencies) {\\n\\n Node v = e.target;\\n int weight = e.weight;\\n int distance = u.distance + weight;\\n\\n if (distance < v.distance) {\\n\\n queue.remove(v);\\n\\n v.distance = distance;\\n v.previous = u;\\n queue.add(v);\\n }\\n }\\n }\\n }\\n\\n public static void dijkstraTimetable(Node start) {\\n\\n start.distance = 0;\\n\\n Comparator<Node> comp = new Comparator<Node>() {\\n @Override\\n public int compare(Node o1, Node o2) {\\n return Integer.compare(o1.distance, o2.distance);\\n }\\n };\\n PriorityQueue<Node> queue = new PriorityQueue<>(comp);\\n queue.add(start);\\n\\n while (!queue.isEmpty()) {\\n\\n Node u = queue.poll();\\n\\n for (Edge e: u.adjacencies) {\\n\\n int distance = e.t0 + e.weight;\\n\\n if (u.distance > e.t0) {\\n if (e.P == 0) { // cannot be reached\\n distance = Integer.MAX_VALUE;\\n } else { // find the next possible time to traverse\\n int steps = 1 + (((u.distance - e.t0) - 1) / e.P);\\n distance = e.t0 + steps * e.P + e.weight;\\n }\\n }\\n\\n Node v = e.target;\\n if (distance < v.distance) {\\n queue.remove(v);\\n v.distance = distance;\\n v.previous = u;\\n queue.add(v);\\n }\\n }\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.3834788501262665,\n \"alphanum_fraction\": 0.39019477367401123,\n \"avg_line_length\": 20.579710006713867,\n \"blob_id\": \"5e8a63fe0f259d1ddf77e54c0141d2b0885ad2dc\",\n \"content_id\": \"36a8fb20261e9788db56eb4f14253589df828255\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 1489,\n \"license_type\": \"no_license\",\n \"max_line_length\": 54,\n \"num_lines\": 69,\n \"path\": \"/fenwick/Fenwick.java\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/**\\n* @author Fabian Schilling (fabsch@kth.se)\\n*/\\n\\npublic class Fenwick {\\n\\n private long[] tree;\\n private int n;\\n\\n public Fenwick(int n) {\\n this.tree = new long[n + 1];\\n this.n = n;\\n }\\n\\n public long query(int index) {\\n index--;\\n if (index == -1) {\\n return 0;\\n }\\n long sum = tree[0];\\n while (index > 0) {\\n sum += tree[index];\\n index -= (index & -index);\\n }\\n return sum;\\n }\\n\\n public void update(int index, long delta) {\\n if (index == 0) {\\n tree[0] += delta;\\n } else {\\n while (index <= n) {\\n tree[index] += delta;\\n index += (index & -index);\\n }\\n }\\n }\\n\\n public static void main(String[] args) {\\n\\n Kattio io = new Kattio(System.in, System.out);\\n\\n int n = io.getInt();\\n int q = io.getInt();\\n\\n Fenwick fenwick = new Fenwick(n);\\n\\n for (int i = 0; i < q; i++) {\\n char op = io.getWord().charAt(0);\\n int index = io.getInt();\\n\\n switch (op) {\\n case '+':\\n long delta = io.getLong();\\n fenwick.update(index, delta);\\n break;\\n case '?':\\n io.println(fenwick.query(index));\\n break;\\n default:\\n System.exit(-1);\\n break;\\n }\\n }\\n\\n io.close();\\n\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.4948875308036804,\n \"alphanum_fraction\": 0.5023857951164246,\n \"avg_line_length\": 23.032787322998047,\n \"blob_id\": \"b6406e1426328388d2e1ea8a0991d04ffb8120b5\",\n \"content_id\": \"7a4c3487560fff408eee641263ac58d58fd69a29\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1467,\n \"license_type\": \"no_license\",\n \"max_line_length\": 94,\n \"num_lines\": 61,\n \"path\": \"/longincsubseq/longincsubseq.py\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/python\\n\\n__author__ = \\\"Fabian Schilling\\\"\\n__email__ = \\\"fabsch@kth.se\\\"\\n\\nimport sys\\nimport math\\n\\ndef solve(seq, n):\\n\\n # pred[k]: stores index of predecessor of seq[k] lor lincsubseq ending at seq[k]\\n pred = [None] * n\\n # mem[k]: stores index k of smalles value seq[k] s.t. lincsubseq of len j ending at seq[k]\\n mem = [None] * (n + 1)\\n # length of the lincsubseq found so far\\n l = 0\\n\\n for i in range(n):\\n\\n # binary search for largest j <= l such that seq[mem[j]] < seq[i]\\n lo = 1\\n hi = l\\n while lo <= hi:\\n mid = int(math.ceil((lo + hi) / 2))\\n if seq[mem[mid]] < seq[i]:\\n lo = mid + 1\\n else:\\n hi = mid - 1\\n\\n # after search, lo is 1 greater than len of longest prefix of seq[i]\\n new_l = lo\\n\\n # the pred. of seq[i] is the last index of subseq. of length new_l - 1\\n pred[i] = mem[new_l - 1]\\n mem[new_l] = i\\n\\n # if we found longer subseq., update\\n l = max(l, new_l)\\n\\n # reconstruct solution from pred & mem\\n sol = [None] * l\\n k = mem[l]\\n for i in reversed(range(l)):\\n sol[i] = k\\n k = pred[k]\\n\\n return sol\\n\\ndef main():\\n data = sys.stdin.readlines()\\n while data:\\n n = int(data.pop(0))\\n seq = [int(x) for x in data.pop(0).split(' ')]\\n sol = solve(seq, n)\\n print(len(sol))\\n print(' '.join([str(x) for x in sol]))\\n\\n\\n\\nif __name__ == '__main__':\\n main()\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.41633063554763794,\n \"alphanum_fraction\": 0.42993950843811035,\n \"avg_line_length\": 21.545454025268555,\n \"blob_id\": \"7b57cb203dea326cf0487d3d3143b7299c9c5e3a\",\n \"content_id\": \"840f592bd8b02e0ac88748f329c8937e16200419\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 1984,\n \"license_type\": \"no_license\",\n \"max_line_length\": 57,\n \"num_lines\": 88,\n \"path\": \"/unionfind/UnionFind.java\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/**\\n* @author Fabian Schilling (fabsch@kth.se)\\n*/\\n\\npublic class UnionFind {\\n\\n static final int MAX = 1000000;\\n\\n static int n; // 1 <= n <= 1,000,000\\n static int q; // 0 <= q <= 1,000,000\\n\\n\\n static Kattio io = new Kattio(System.in, System.out);\\n\\n private int[] parents;\\n private int[] height;\\n\\n public UnionFind(int nodes) {\\n this.parents = new int[nodes];\\n this.height = new int[nodes];\\n\\n for (int i = 0; i < nodes; i++) {\\n parents[i] = i; // make self-connection\\n }\\n }\\n\\n public int find(int node) {\\n if (parents[node] != node) {\\n parents[node] = find(parents[node]);\\n }\\n return parents[node];\\n }\\n\\n public boolean same(int a, int b) {\\n return find(a) == find(b);\\n }\\n\\n public void union(int a, int b) {\\n int aRoot = find(a);\\n int bRoot = find(b);\\n\\n if (aRoot == bRoot) {\\n return;\\n }\\n\\n if (height[aRoot] < height[bRoot]) {\\n parents[aRoot] = bRoot;\\n } else if (height[aRoot] > height[bRoot]) {\\n parents[bRoot] = aRoot;\\n } else {\\n parents[bRoot] = aRoot; // doesn't matter\\n height[aRoot] += 1;\\n }\\n }\\n\\n public static void main(String[] args) {\\n\\n n = io.getInt();\\n q = io.getInt();\\n\\n UnionFind unionFind = new UnionFind(n);\\n\\n while (io.hasMoreTokens()) {\\n\\n char op = io.getWord().charAt(0);\\n int a = io.getInt();\\n int b = io.getInt();\\n\\n switch (op) {\\n case '=':\\n unionFind.union(a, b);\\n break;\\n case '?':\\n if (unionFind.same(a, b)) {\\n io.println(\\\"yes\\\");\\n } else {\\n io.println(\\\"no\\\");\\n }\\n break;\\n default:\\n System.exit(-1);\\n }\\n\\n }\\n\\n io.close();\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.4349234402179718,\n \"alphanum_fraction\": 0.4411071836948395,\n \"avg_line_length\": 32.62376403808594,\n \"blob_id\": \"f02f9ec7e6e4b19115063d5a4d4f8214a392936c\",\n \"content_id\": \"7c884d06cd85098a1843c4b9893aab82aaba8564\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 3396,\n \"license_type\": \"no_license\",\n \"max_line_length\": 110,\n \"num_lines\": 101,\n \"path\": \"/george/Main.java\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/**\\n* @author Fabian Schilling (fabsch@kth.se)\\n*/\\n\\nimport java.util.Vector;\\n\\npublic class Main {\\n\\n static IO io = new IO(System.in, System.out);\\n\\n public static void main(String[] args) {\\n\\n int N = io.getInt(); // Number of intersections (nodes)\\n int M = io.getInt(); // Number of streets (edges - undirected)\\n\\n int A = io.getInt() - 1; // Intersection (node) where Luka starts\\n int B = io.getInt() - 1; // Intersection (node) Luka must get to\\n int K = io.getInt(); // Difference in starting times (Luka starts at A exactly K minutes after George)\\n int G = io.getInt(); // Number of intersections on George's route\\n\\n // Easier to model with adj matrix\\n long[][] weights = new long[N][N];\\n long[][] times = new long[N][N];\\n\\n // Fill weights with infinity\\n for (int i = 0; i < N; i++) {\\n for (int j = 0; j < N; j++) {\\n weights[i][j] = Integer.MAX_VALUE;\\n times[i][j] = Integer.MAX_VALUE;\\n }\\n }\\n\\n // Nodes that george is visiting\\n int[] george = new int[G];\\n for (int i = 0; i < G; i++) {\\n george[i] = io.getInt() - 1;\\n }\\n\\n // Fill in edge weights\\n for (int i = 0; i < M; i++) {\\n int a = io.getInt() - 1;\\n int b = io.getInt() - 1;\\n int w = io.getInt();\\n weights[a][b] = w;\\n weights[b][a] = w; // undirected\\n }\\n\\n // Add cumulative time that george occupied\\n int time = 0; // Time until edge is occupied\\n for (int i = 0; i < G - 1; i++) {\\n times[george[i]][george[i + 1]] = time;\\n times[george[i + 1]][george[i]] = time; // undirected\\n time += weights[george[i]][george[i + 1]]; // weights the same for george\\n }\\n\\n // Temporary dijkstra variables\\n long[] dist = new long[N];\\n boolean[] visited = new boolean[N];\\n for (int i = 0; i < N; i++) {\\n dist[i] = Integer.MAX_VALUE;\\n visited[i] = false;\\n }\\n\\n // Dijkstra with constraints\\n dist[A] = K;\\n for (int i = 0; i < N; i++) {\\n int min = -1;\\n for (int j = 0; j < N; j++) {\\n if (!visited[j] && (min == -1 || dist[j] < dist[min])) {\\n min = j;\\n }\\n }\\n visited[min] = true;\\n\\n for (int j = 0; j < N; j++) {\\n // Check constraints before updating distances\\n if (dist[min] < times[min][j]) { // regular dijkstra update\\n if (dist[min] + weights[min][j] < dist[j]) {\\n dist[j] = dist[min] + weights[min][j];\\n }\\n } else { // add time that george blocks\\n long blocking;\\n if (dist[min] > weights[min][j] + times[min][j]) {\\n blocking = dist[min];\\n } else {\\n blocking = weights[min][j] + times[min][j];\\n }\\n blocking += weights[min][j]; // add regular weights always\\n if (blocking < dist[j]) {\\n dist[j] = blocking;\\n }\\n }\\n }\\n }\\n\\n // started at K so we have to subtract that\\n io.println(dist[B] - dist[A]);\\n\\n io.close();\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7419354915618896,\n \"alphanum_fraction\": 0.7419354915618896,\n \"avg_line_length\": 40.33333206176758,\n \"blob_id\": \"a7eb6235705af70820c653b0e4c198a9d66e894c\",\n \"content_id\": \"fb6c89efb06ad158e7bee98b3004eff20ebf53d7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 124,\n \"license_type\": \"no_license\",\n \"max_line_length\": 49,\n \"num_lines\": 3,\n \"path\": \"/ignore/README.txt\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"Author: Fabian Schilling (fabsch@kth)\\nProblem: https://open.kattis.com/problems/ignore\\nExecution: python ignore.py < samples/<sample.in>\\n\"\n },\n {\n \"alpha_fraction\": 0.5111111402511597,\n \"alphanum_fraction\": 0.5377777814865112,\n \"avg_line_length\": 14.066666603088379,\n \"blob_id\": \"e182da6fab9f3a409de12955c2a662715c119081\",\n \"content_id\": \"30e9a39ab5b94ebfdb1d79d6ecbd8a754a69b5d3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 225,\n \"license_type\": \"no_license\",\n \"max_line_length\": 53,\n \"num_lines\": 15,\n \"path\": \"/bestcompression/bestcompression.py\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/python\\n\\n__author__ = \\\"Fabian Schilling\\\"\\n__email__ = \\\"fabsch@kth.se\\\"\\n\\nimport sys\\n\\ninput = map(int, sys.stdin.readlines()[0].split(\\\" \\\"))\\nN = input.pop(0)\\nb = input.pop(0)\\n\\nif (2 ** (b + 1)) - 1 >= N:\\n\\tprint(\\\"yes\\\")\\nelse:\\n\\tprint(\\\"no\\\")\"\n },\n {\n \"alpha_fraction\": 0.33557048439979553,\n \"alphanum_fraction\": 0.35282838344573975,\n \"avg_line_length\": 23.255813598632812,\n \"blob_id\": \"817e7c8b38b29bf35096273113bafc4a89d97179\",\n \"content_id\": \"3cfecf71e4e380af376d6e87880309011886ad04\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 1043,\n \"license_type\": \"no_license\",\n \"max_line_length\": 76,\n \"num_lines\": 43,\n \"path\": \"/convexhull/Hull.java\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/**\\n* @author Fabian Schilling (fabsch@kth.se)\\n*/\\n\\nimport java.util.Arrays;\\n\\npublic class Hull {\\n\\n public double cross(Point o, Point a, Point b) {\\n return (a.x - o.x) * (b.y - o.y) - (a.y - o.y) * (b.x - o.x);\\n }\\n\\n public Point[] convexHull(Point[] ps) {\\n\\n if (ps.length > 1) {\\n int n = ps.length, k = 0;\\n Point[] h = new Point[2 * n];\\n\\n Arrays.sort(ps);\\n\\n // Build lower hull\\n for (int i = 0; i < n; i++) {\\n while (k >= 2 && cross(h[k - 2], h[k - 1], ps[i]) <= 0) k--;\\n h[k++] = ps[i];\\n }\\n\\n // Build upper hull\\n for (int i = n - 2, t = k + 1; i >= 0; i--) {\\n while (k >= t && cross(h[k - 2], h[k - 1], ps[i]) <= 0) k--;\\n h[k++] = ps[i];\\n }\\n\\n if (k > 1) {\\n h = Arrays.copyOfRange(h, 0, k - 1);\\n }\\n return h;\\n } else if (ps.length <= 1) {\\n return ps;\\n } else {\\n return null;\\n }\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.4364641010761261,\n \"alphanum_fraction\": 0.46408841013908386,\n \"avg_line_length\": 15.454545021057129,\n \"blob_id\": \"0749706f42123c0d6e6afb2a7b85cca25de18837\",\n \"content_id\": \"99b4047bb1f387ce51c4b8c540ae6e8477807e1c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 362,\n \"license_type\": \"no_license\",\n \"max_line_length\": 51,\n \"num_lines\": 22,\n \"path\": \"/beatspread/beatspread.py\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/python\\n\\n__author__ = \\\"Fabian Schilling\\\"\\n__email__ = \\\"fabsch@kth.se\\\"\\n\\nimport sys\\n\\ndata = sys.stdin.readlines()\\n\\nn = int(data.pop(0))\\n\\nfor _ in range(n):\\n s, d = [int(x) for x in data.pop(0).split(' ')]\\n\\n sm = s + d\\n\\n if sm % 2 == 1 or s < d:\\n print('impossible')\\n else:\\n s1 = sm / 2\\n s2 = s - s1\\n print(str(s1) + ' ' + str(s2))\\n\"\n },\n {\n \"alpha_fraction\": 0.7647058963775635,\n \"alphanum_fraction\": 0.7647058963775635,\n \"avg_line_length\": 44.33333206176758,\n \"blob_id\": \"9861c529b7a1a6f5a727fe5ca4e17e33b91e5145\",\n \"content_id\": \"0c51a78fc2eb8fd5319cfc3e78dc5416b1743d92\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 136,\n \"license_type\": \"no_license\",\n \"max_line_length\": 55,\n \"num_lines\": 3,\n \"path\": \"/uxuhulvoting/README.txt\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"Author: Fabian Schilling (fabsch@kth)\\nProblem: https://open.kattis.com/problems/uxuhulvoting\\nExecution: python uxuhulvoting.py < samples/<sample.in>\\n\"\n },\n {\n \"alpha_fraction\": 0.3788328468799591,\n \"alphanum_fraction\": 0.3870755136013031,\n \"avg_line_length\": 24.923076629638672,\n \"blob_id\": \"0bc29c94fe2fe27f0fc30ca27422af2b45d86953\",\n \"content_id\": \"195c9fd47f27002bceb6187b14b4d2cf2b81d3cb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 3033,\n \"license_type\": \"no_license\",\n \"max_line_length\": 62,\n \"num_lines\": 117,\n \"path\": \"/almostunionfind/AlmostUnionFind.java\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/**\\n* @author Fabian Schilling (fabsch@kth.se)\\n*/\\n\\nimport java.util.*;\\n\\npublic class AlmostUnionFind {\\n\\n static Kattio io = new Kattio(System.in, System.out);\\n static int n; // 1 <= n <= 100000\\n static int m; // 1 <= n <= 100000\\n\\n private int[] root;\\n private ArrayList<Integer>[] sets;\\n\\n @SuppressWarnings(\\\"unchecked\\\")\\n public AlmostUnionFind(int nodes) {\\n root = new int[nodes + 1];\\n sets = new ArrayList[nodes + 1];\\n\\n for (int i = 1; i <= nodes; i++) {\\n root[i] = i; // make self-connection\\n sets[i] = new ArrayList<>();\\n sets[i].add(i);\\n }\\n }\\n\\n public void union(int p, int q) {\\n\\n int x = root[p];\\n int y = root[q];\\n\\n if (x == y) {\\n return; // already in the same set\\n }\\n\\n if (sets[x].size() >= sets[y].size()) {\\n for (int i = 0; i < sets[y].size(); i++) {\\n int n = (Integer) sets[y].get(i);\\n sets[x].add(n);\\n root[n] = x;\\n }\\n sets[y].clear();\\n } else {\\n for (int i = 0; i < sets[x].size(); i++) {\\n int n = (Integer) sets[x].get(i);\\n sets[y].add(n);\\n root[n] = y;\\n }\\n sets[x].clear();\\n }\\n }\\n\\n public void move(int p, int q) {\\n\\n if (root[p] == root[q]) {\\n return; // already int the same set\\n }\\n\\n sets[root[p]].remove((Object) p);\\n sets[root[q]].add(p);\\n root[p] = root[q];\\n\\n }\\n\\n public int sum(int p) {\\n int sum = 0;\\n for (int i = 0; i < sets[root[p]].size(); i++) {\\n sum += (Integer) sets[root[p]].get(i);\\n }\\n return sum;\\n }\\n\\n public int count(int p) {\\n return sets[root[p]].size();\\n }\\n\\n public static void main(String[] args) {\\n\\n while (io.hasMoreTokens()) {\\n\\n n = io.getInt();\\n m = io.getInt();\\n\\n AlmostUnionFind almost = new AlmostUnionFind(n);\\n\\n for (int i = 0; i < m; i++) {\\n\\n int op = io.getInt();\\n\\n switch (op) {\\n case 1: // union p and q\\n int p = io.getInt();\\n int q = io.getInt();\\n almost.union(p, q);\\n break;\\n case 2: // move p to set containing q\\n int x = io.getInt();\\n int y = io.getInt();\\n almost.move(x, y);\\n break;\\n case 3: // numsum\\n int z = io.getInt();\\n int sum = almost.sum(z);\\n int count = almost.count(z);\\n System.out.println(count + \\\" \\\" + sum);\\n //io.println(count + \\\" \\\" + sum);\\n break;\\n default:\\n System.out.println(\\\"Shit...\\\");\\n }\\n }\\n }\\n\\n io.close();\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7575757503509521,\n \"alphanum_fraction\": 0.7575757503509521,\n \"avg_line_length\": 43,\n \"blob_id\": \"fe66fdc62f4e991f234119483d0fd340002c4913\",\n \"content_id\": \"aa8139dbf25a1990c7f1cf91b1ff4c5fc8d18973\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 132,\n \"license_type\": \"no_license\",\n \"max_line_length\": 53,\n \"num_lines\": 3,\n \"path\": \"/beatspread/README.txt\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"Author: Fabian Schilling (fabsch@kth)\\nProblem: https://open.kattis.com/problems/beatspread\\nExecution: python beatspread.py < samples/<sample.in>\\n\"\n },\n {\n \"alpha_fraction\": 0.5031545758247375,\n \"alphanum_fraction\": 0.5205047130584717,\n \"avg_line_length\": 19.467741012573242,\n \"blob_id\": \"e1a060984e1380a2af8db91e29de99d03f8ce1d0\",\n \"content_id\": \"0f3a9de44918cdbe91ae17973ecf5f9fa6dbaed0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1268,\n \"license_type\": \"no_license\",\n \"max_line_length\": 94,\n \"num_lines\": 62,\n \"path\": \"/deadfraction/deadfraction.py\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/python\\n\\n__author__ = \\\"Fabian Schilling\\\"\\n__email__ = \\\"fabsch@kth.se\\\"\\n\\nimport sys\\nfrom fractions import gcd\\n\\n# solution inspired by Khan academy\\n\\n\\ndef fraction(n):\\n\\n # these are the values we're looking for (min denominator!)\\n bestnum = 0\\n bestden = sys.maxint # high initial value!\\n\\n # try different fractions while minimizing the denominator\\n for i in range(len(n)):\\n\\n\\n a = int(n[i:])\\n b = pow(10, len(n) - i) - 1\\n c = 0 if i == 0 else int(n[:i])\\n\\n num = a + (b * c)\\n den = b * pow(10, i)\\n\\n # try to simplify the fraction\\n g = gcd(num, den)\\n num /= g\\n den /= g\\n\\n # minimize the denominator\\n if den < bestden:\\n bestnum = num\\n bestden = den\\n\\n print(str(bestnum) + '/' + str(bestden))\\n\\ndef main():\\n\\n #data = open('/Users/fabianschilling/Downloads/deadfraction-sample-data/B.in').readlines()\\n data = sys.stdin.readlines()\\n #data = ['0.474612399...']\\n\\n for n in data:\\n\\n # strip away \\\"\\\\n\\\" so we can check break condition\\n n = n.strip('\\\\n')\\n\\n if n == '0':\\n break\\n\\n # strip \\\"0.\\\" in the beginning and \\\"...\\\" in the end\\n n = n[2:]\\n n = n[:-3]\\n\\n fraction(n)\\n\\nif __name__ == '__main__':\\n main()\"\n },\n {\n \"alpha_fraction\": 0.7460317611694336,\n \"alphanum_fraction\": 0.7460317611694336,\n \"avg_line_length\": 41,\n \"blob_id\": \"813a850f19a6da5681ea4ac54dc3088f41c4b543\",\n \"content_id\": \"76e7c8eb149e85414a6136fbedf4abcd14c89edd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 126,\n \"license_type\": \"no_license\",\n \"max_line_length\": 50,\n \"num_lines\": 3,\n \"path\": \"/ljutnja/README.txt\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"Author: Fabian Schilling (fabsch@kth)\\nProblem: https://open.kattis.com/problems/ljutnja\\nExecution: python ljutnja.py < samples/<sample.in>\\n\"\n },\n {\n \"alpha_fraction\": 0.7538461685180664,\n \"alphanum_fraction\": 0.7538461685180664,\n \"avg_line_length\": 42.33333206176758,\n \"blob_id\": \"806bcb0cb20b952fbfe3e06348ac30a4f3edfdd4\",\n \"content_id\": \"b88d83b1f9ebf7522a38ed8e9fa1cec687f04d20\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 130,\n \"license_type\": \"no_license\",\n \"max_line_length\": 52,\n \"num_lines\": 3,\n \"path\": \"/spiderman/README.txt\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"Author: Fabian Schilling (fabsch@kth)\\nProblem: https://open.kattis.com/problems/spiderman\\nExecution: python spiderman.py < samples/<sample.in>\\n\"\n },\n {\n \"alpha_fraction\": 0.4746639132499695,\n \"alphanum_fraction\": 0.48241984844207764,\n \"avg_line_length\": 22.48101234436035,\n \"blob_id\": \"6d5dc8d69c5550e179b72386e25f59a777aa3440\",\n \"content_id\": \"29f54fbf074b5ef5bcf6a35ebfb761692b0b0f62\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 1934,\n \"license_type\": \"no_license\",\n \"max_line_length\": 80,\n \"num_lines\": 79,\n \"path\": \"/rationalarithmetic/Rational.java\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/**\\r\\n* @author Fabian Schilling (fabsch@kth.se) and Hugo Sandelius (hugosa@kth.se)\\r\\n*/\\r\\n\\r\\npublic class Rational {\\r\\n\\r\\n private static final Rational ZERO = new Rational(0, 1);\\r\\n\\r\\n private long num;\\r\\n private long den;\\r\\n\\r\\n public Rational(long num, long den) {\\r\\n this.num = num;\\r\\n this.den = den;\\r\\n this.normalize();\\r\\n this.reduce();\\r\\n }\\r\\n\\r\\n private void reduce() {\\r\\n long g = gcd(this.num, this.den);\\r\\n this.num /= g;\\r\\n this.den /= g;\\r\\n }\\r\\n\\r\\n private void normalize() {\\r\\n if (this.den < 0) {\\r\\n this.num *= -1;\\r\\n this.den *= -1;\\r\\n }\\r\\n }\\r\\n\\r\\n public int compareTo(Rational r) {\\r\\n long lhs = this.num * r.den;\\r\\n long rhs = this.den * r.num;\\r\\n if (lhs < rhs) return -1;\\r\\n if (lhs > rhs) return +1;\\r\\n return 0;\\r\\n }\\r\\n\\r\\n public Rational add(Rational r) {\\r\\n long num = this.num * r.den + this.den * r.num;\\r\\n long den = this.den * r.den;\\r\\n return new Rational(num, den);\\r\\n }\\r\\n\\r\\n public Rational subtract(Rational r) {\\r\\n long num = this.num * r.den - this.den * r.num;\\r\\n long den = this.den * r.den;\\r\\n return new Rational(num, den);\\r\\n }\\r\\n\\r\\n public Rational multiply(Rational r) {\\r\\n long num = this.num * r.num;\\r\\n long den = this.den * r.den;\\r\\n return new Rational(num, den);\\r\\n }\\r\\n\\r\\n public Rational divide(Rational r) {\\r\\n long num = this.num * r.den;\\r\\n long den = this.den * r.num;\\r\\n return new Rational(num, den);\\r\\n }\\r\\n\\r\\n public String toString() {\\r\\n return this.num + \\\" / \\\" + this.den;\\r\\n }\\r\\n\\r\\n public boolean isPositive() {\\r\\n return (this.num > 0 && this.den > 0) || (this.num < 0 && this.den < 0);\\r\\n }\\r\\n\\r\\n public static long gcd(long m, long n) {\\r\\n if (m < 0) m = -m;\\r\\n if (n < 0) n = -n;\\r\\n if (n == 0) return m;\\r\\n else return gcd(n, m % n);\\r\\n }\\r\\n\\r\\n}\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.5131264925003052,\n \"alphanum_fraction\": 0.5202863812446594,\n \"avg_line_length\": 25.744680404663086,\n \"blob_id\": \"31033907db888f2b5d6fbd8efc77e392f32fa838\",\n \"content_id\": \"626f729a9bc9af0797a09ac1c0af8f59944b5cc4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 2514,\n \"license_type\": \"no_license\",\n \"max_line_length\": 68,\n \"num_lines\": 94,\n \"path\": \"/setstack/SetStack.java\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/**\\n* @author Fabian Schilling (fabsch@kth.se)\\n*/\\n\\nimport java.util.*;\\n\\npublic class SetStack {\\n\\n static Kattio io = new Kattio(System.in, System.out);\\n\\n Stack<HashSet<Object>> stack; // generic stack of sets\\n HashMap<HashSet<Object>, Integer> map; // retain size of subsets\\n\\n public SetStack() {\\n this.stack = new Stack<HashSet<Object>>();\\n this.map = new HashMap<HashSet<Object>, Integer>();\\n }\\n\\n public void push() {\\n this.stack.push(new HashSet<Object>());\\n }\\n\\n public void dup() {\\n HashSet<Object> element = this.stack.pop();\\n this.stack.push(element);\\n this.stack.push(element);\\n }\\n\\n public void union() {\\n HashSet<Object> element1 = this.stack.pop();\\n HashSet<Object> element2 = this.stack.pop();\\n HashSet<Object> newSet = new HashSet<Object>();\\n newSet.addAll(element1);\\n newSet.addAll(element2);\\n this.stack.push(newSet);\\n }\\n\\n public void intersect() {\\n HashSet<Object> element1 = this.stack.pop();\\n HashSet<Object> element2 = this.stack.pop();\\n HashSet<Object> newSet = new HashSet<Object>();\\n newSet.addAll(element1);\\n newSet.retainAll(element2);\\n this.stack.push(newSet);\\n }\\n\\n public void add() {\\n HashSet<Object> element1 = this.stack.pop();\\n HashSet<Object> element2 = this.stack.pop();\\n HashSet<Object> newSet = new HashSet<Object>();\\n if (map.containsKey(element1)) {\\n newSet.add(map.get(element1));\\n } else {\\n Integer size = map.size();\\n map.put(element1, size);\\n newSet.add(size);\\n }\\n newSet.addAll(element2);\\n this.stack.push(newSet);\\n }\\n\\n public static void main(String[] args) {\\n\\n SetStack setstack = new SetStack();\\n\\n int t = io.getInt();\\n\\n for (int i = 0; i < t; i++) {\\n\\n int n = io.getInt();\\n\\n for (int j = 0; j < n; j++) {\\n\\n char op = io.getWord().charAt(0);\\n\\n switch (op) {\\n case 'P': setstack.push(); break;\\n case 'D': setstack.dup(); break;\\n case 'U': setstack.union(); break;\\n case 'I': setstack.intersect(); break;\\n case 'A': setstack.add(); break;\\n default: System.exit(-1);\\n }\\n\\n io.println(setstack.stack.peek().size());\\n }\\n\\n io.println(\\\"***\\\");\\n }\\n\\n io.close();\\n\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7647058963775635,\n \"alphanum_fraction\": 0.7647058963775635,\n \"avg_line_length\": 44.33333206176758,\n \"blob_id\": \"31fd29e5717ffac276e36bef30a329023bcd7068\",\n \"content_id\": \"8da77b9ddf84f22ca8bfff4dde40a22594ff2189\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 136,\n \"license_type\": \"no_license\",\n \"max_line_length\": 55,\n \"num_lines\": 3,\n \"path\": \"/deadfraction/README.txt\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"Author: Fabian Schilling (fabsch@kth)\\nProblem: https://open.kattis.com/problems/deadfraction\\nExecution: python deadfraction.py < samples/<sample.in>\\n\"\n },\n {\n \"alpha_fraction\": 0.4460338056087494,\n \"alphanum_fraction\": 0.4629388749599457,\n \"avg_line_length\": 18.71794891357422,\n \"blob_id\": \"c6e3bdf29ea644d04f126382f51d44f3dc3fd5d9\",\n \"content_id\": \"056e6c79d3818a69e1e6a81eb61dcd7b1901fb7d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 769,\n \"license_type\": \"no_license\",\n \"max_line_length\": 60,\n \"num_lines\": 39,\n \"path\": \"/hidingplaces/Position.java\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/**\\n* @author Fabian Schilling (fabsch@kth.se)\\n*/\\n\\n\\npublic class Position {\\n\\n int x;\\n int y;\\n int jumps;\\n\\n public Position(int x, int y) {\\n this.x = x;\\n this.y = y;\\n this.jumps = 0;\\n }\\n\\n public Position(int x, int y, int jumps) {\\n this.x = x;\\n this.y = y;\\n this.jumps = jumps;\\n }\\n\\n public Position(String pos) {\\n char[] xy = pos.toCharArray();\\n this.x = (int) xy[0] - 97; // char to int\\n this.y = 8 - ((int) xy[1] - 48); // char to int\\n }\\n\\n public String chessNotation() {\\n char x = (char) (this.x + 97);\\n char y = (char) ((8 - this.y) + 48);\\n return \\\"\\\" + x + y;\\n }\\n\\n public String toString() {\\n return this.chessNotation();// + \\\" @ \\\" + this.jumps;\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7681159377098083,\n \"alphanum_fraction\": 0.7681159377098083,\n \"avg_line_length\": 45,\n \"blob_id\": \"5d360b41bd12bfe83a8d1f73cb33e0c2ec523629\",\n \"content_id\": \"1ad9000f819a1d9d74d18584510fb9406fe599c4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 138,\n \"license_type\": \"no_license\",\n \"max_line_length\": 56,\n \"num_lines\": 3,\n \"path\": \"/longincsubseq/README.txt\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"Author: Fabian Schilling (fabsch@kth)\\nProblem: https://open.kattis.com/problems/longincsubseq\\nExecution: python longincsubseq.py < samples/<sample.in>\\n\"\n },\n {\n \"alpha_fraction\": 0.3901035785675049,\n \"alphanum_fraction\": 0.3981587886810303,\n \"avg_line_length\": 22.80821990966797,\n \"blob_id\": \"992679dc65dd71e9f989cb08327094d12b5b9a63\",\n \"content_id\": \"3fa87a7c19f3d16740e10585f584b55cf4e95778\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 1738,\n \"license_type\": \"no_license\",\n \"max_line_length\": 80,\n \"num_lines\": 73,\n \"path\": \"/pebblesolitaire2/Main.java\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/**\\n* @author Fabian Schilling (fabsch@kth.se)\\n*/\\n\\n// naive solution\\n\\nimport java.util.HashMap;\\n\\npublic class Main {\\n\\n public static int mincount;\\n public static HashMap<String, Integer> map;\\n\\n public static int solve(char[] board, int count) {\\n\\n String b = new String(board);\\n\\n if (map.containsKey(b)) {\\n return map.get(b);\\n }\\n\\n char[] newBoard = board.clone();\\n\\n for (int i = 0; i < board.length - 2; i++) {\\n if (board[i] == 'o' && board[i + 1] == 'o' && board[i + 2] == '-') {\\n board[i] = '-';\\n board[i + 1] = '-';\\n board[i + 2] = 'o';\\n map.put(b, solve(board, count - 1));\\n board = newBoard.clone();\\n }\\n if (board[i] == '-' && board[i + 1] == 'o' && board[i + 2] == 'o') {\\n board[i] = 'o';\\n board[i + 1] = '-';\\n board[i + 2] = '-';\\n map.put(b, solve(board, count - 1));\\n board = newBoard.clone();\\n }\\n }\\n\\n if (count < mincount) {\\n mincount = count;\\n }\\n\\n return mincount;\\n }\\n\\n public static void main(String[] args) {\\n\\n IO io = new IO(System.in, System.out);\\n\\n int n = io.getInt();\\n\\n map = new HashMap<>();\\n\\n for (int i = 0; i < n; i++) {\\n mincount = Integer.MAX_VALUE;\\n char[] board = io.getWord().toCharArray();\\n int count = 0;\\n for (char c : board) {\\n if (c == 'o') {\\n count++;\\n }\\n }\\n map.clear();\\n //io.println(b);\\n io.println(solve(board, count));\\n\\n }\\n\\n io.close();\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7681159377098083,\n \"alphanum_fraction\": 0.7681159377098083,\n \"avg_line_length\": 45,\n \"blob_id\": \"f85f0d430815730800ee1603d31f86856125eae1\",\n \"content_id\": \"6eb6f1644e8c21735c79400910b24692889d780f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 138,\n \"license_type\": \"no_license\",\n \"max_line_length\": 56,\n \"num_lines\": 3,\n \"path\": \"/perfectpowers/README.txt\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"Author: Fabian Schilling (fabsch@kth)\\nProblem: https://open.kattis.com/problems/perfectpowers\\nExecution: python perfectpowers.py < samples/<sample.in>\\n\"\n },\n {\n \"alpha_fraction\": 0.7746478915214539,\n \"alphanum_fraction\": 0.7746478915214539,\n \"avg_line_length\": 46.33333206176758,\n \"blob_id\": \"e9fbc461a1a69a4eca28c374904f8b06f4cbc488\",\n \"content_id\": \"9a38507f2bb53d5a3da9af2edd23e925f7bd2a7e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 142,\n \"license_type\": \"no_license\",\n \"max_line_length\": 58,\n \"num_lines\": 3,\n \"path\": \"/bestcompression/README.txt\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"Author: Fabian Schilling (fabsch@kth)\\nProblem: https://open.kattis.com/problems/bestcompression\\nExecution: python bestcompression.py < samples/<sample.in>\\n\"\n },\n {\n \"alpha_fraction\": 0.4071895480155945,\n \"alphanum_fraction\": 0.4215686321258545,\n \"avg_line_length\": 19.13157844543457,\n \"blob_id\": \"a12d0cb509560a3d9a3d80d585de1818c29614e0\",\n \"content_id\": \"b6ee77c1d20dc2ab8123f7024eb1951cae23526d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 1530,\n \"license_type\": \"no_license\",\n \"max_line_length\": 62,\n \"num_lines\": 76,\n \"path\": \"/howmanyzeros/Main.java\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/**\\n* @author Fabian Schilling (fabsch@kth.se)\\n*/\\n\\n\\npublic class Main {\\n\\n public static IO io = new IO(System.in, System.out);\\n\\n public static long countZerosUpTo(long n) {\\n\\n long zeros = 0;\\n long digit, sliceLeft, sliceRight;\\n\\n for (int i = 1; ; i *= 10) {\\n\\n // Slice number and get middle digit\\n digit = n / i;\\n sliceRight = n % i;\\n sliceLeft = digit / 10;\\n digit = digit % 10;\\n\\n // Whole number processed\\n if (sliceLeft == 0) {\\n return zeros;\\n }\\n\\n // Special case for digit zero\\n if (digit == 0) {\\n zeros += (sliceLeft - 1) * i + sliceRight + 1;\\n } else {\\n zeros += sliceLeft * i;\\n }\\n }\\n }\\n\\n public static long countZeros(long n) {\\n\\n long zeros = 0;\\n\\n // Special case for zero\\n if (n == 0) {\\n return 1;\\n }\\n\\n // Count zeros\\n while (n > 0) {\\n if (n % 10 == 0) {\\n zeros++;\\n }\\n n /= 10;\\n }\\n\\n return zeros;\\n }\\n\\n public static void main(String[] args) {\\n\\n while (io.hasMoreTokens()) {\\n\\n long m = io.getLong();\\n if (m < 0) break;\\n long n = io.getLong();\\n\\n long zeros = countZeros(m);\\n long lower = countZerosUpTo(m);\\n long upper = countZerosUpTo(n);\\n\\n\\n io.println(zeros + upper - lower);\\n\\n }\\n\\n io.close();\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.4371603727340698,\n \"alphanum_fraction\": 0.44399648904800415,\n \"avg_line_length\": 28.261537551879883,\n \"blob_id\": \"aa2ec246ef91e4865cacc69bc6a4ed8bcec4ec45\",\n \"content_id\": \"3f6af098053cd227a280df69c33d361ff7c68daf\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 5705,\n \"license_type\": \"no_license\",\n \"max_line_length\": 120,\n \"num_lines\": 195,\n \"path\": \"/shortestpath3/Library.java\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/**\\n* @author Fabian Schilling (fabsch@kth.se)\\n*/\\n\\n/**\\n * Author: Fabian Schilling & Hugo Sandelius\\n */\\n\\nimport java.util.*;\\n\\npublic class Library {\\n\\n // shortestpath1\\n public static void dijkstra(Node start) {\\n start.distance = 0;\\n\\n Comparator<Node> comp = (o1, o2) -> Integer.compare(o1.distance, o2.distance);\\n PriorityQueue<Node> queue = new PriorityQueue<>(comp);\\n queue.add(start);\\n\\n while (!queue.isEmpty()) {\\n\\n Node u = queue.poll();\\n\\n for (Edge e: u.adjacencies) {\\n\\n Node v = e.target;\\n int weight = e.weight;\\n int distance = u.distance + weight;\\n\\n if (distance < v.distance) {\\n\\n queue.remove(v);\\n\\n v.distance = distance;\\n v.previous = u;\\n queue.add(v);\\n }\\n }\\n }\\n }\\n\\n // shortestpath2\\n public static void dijkstraTimetable(Node start) {\\n\\n start.distance = 0;\\n\\n Comparator<Node> comp = (o1, o2) -> Integer.compare(o1.distance, o2.distance);\\n PriorityQueue<Node> queue = new PriorityQueue<>(comp);\\n queue.add(start);\\n\\n while (!queue.isEmpty()) {\\n\\n Node u = queue.poll();\\n\\n for (Edge e: u.adjacencies) {\\n\\n int distance = e.t0 + e.weight;\\n\\n if (u.distance > e.t0) {\\n if (e.P == 0) { // cannot be reached\\n distance = Integer.MAX_VALUE;\\n } else { // find the next possible time to traverse\\n int steps = 1 + (((u.distance - e.t0) - 1) / e.P);\\n distance = e.t0 + steps * e.P + e.weight;\\n }\\n }\\n\\n Node v = e.target;\\n if (distance < v.distance) {\\n queue.remove(v);\\n v.distance = distance;\\n v.previous = u;\\n queue.add(v);\\n }\\n }\\n }\\n }\\n\\n // shortestpath3\\n public static void bellmanFord(Node[] nodes, Edge[] edges, Node start) {\\n\\n start.distance = 0;\\n\\n for (int i = 1; i < nodes.length; i++) {\\n for (Edge e: edges) {\\n if (e.source.distance != Integer.MAX_VALUE && e.source.distance + e.weight < e.target.distance) {\\n e.target.distance = e.source.distance + e.weight;\\n e.target.previous = e.source;\\n }\\n }\\n }\\n\\n //System.out.println(\\\"Detecting negative cycles...\\\");\\n for (int i = 1; i < nodes.length; i++) {\\n //System.out.println(\\\"Round \\\" + i);\\n for (Edge e: edges) {\\n if (e.source.distance == Integer.MIN_VALUE) {\\n e.target.distance = Integer.MIN_VALUE;\\n } else if (e.source.distance != Integer.MAX_VALUE && e.source.distance + e.weight < e.target.distance) {\\n e.target.distance = Integer.MIN_VALUE;\\n }\\n }\\n }\\n }\\n\\n // construct path\\n public static Vector<Node> constructPathTo(Node end) {\\n Vector<Node> path = new Vector<>();\\n Node prev = end;\\n while (prev != null) {\\n path.insertElementAt(prev, 0);\\n prev = prev.previous;\\n }\\n return path;\\n }\\n\\n public static void printPathTo(Node end) {\\n Vector<Node> path = constructPathTo(end);\\n System.err.print(\\\"Path: \\\");\\n for (Node node: path) {\\n System.err.print(node.index + \\\" \\\");\\n }\\n System.err.println();\\n }\\n\\n // minspantree\\n public static void prim(Node[] nodes) {\\n\\n // Order nodes by distance\\n Comparator<Node> comp = (o1, o2) -> Integer.compare(o1.distance, o2.distance);\\n PriorityQueue<Node> queue = new PriorityQueue<>(comp);\\n\\n // Add all nodes to queue\\n for (Node node: nodes) {\\n queue.add(node);\\n }\\n\\n Node peek = queue.peek();\\n peek.distance = 0;\\n\\n while (!queue.isEmpty()) {\\n Node u = queue.poll();\\n\\n for (Edge edge: u.adjacencies) {\\n\\n Node v = edge.target;\\n\\n if (!v.visited && edge.weight < v.distance) {\\n queue.remove(v);\\n v.distance = edge.weight;\\n v.previous = u;\\n u.visited = true;\\n queue.add(v);\\n }\\n }\\n }\\n }\\n\\n // allpairspath\\n public static void floydWarshall(int[][] dist) {\\n\\n // Initialize self-connections as 0\\n for (int i = 0; i < dist.length; i++) {\\n dist[i][i] = 0;\\n }\\n\\n // Run Floyd-Warshall\\n for (int k = 0; k < dist.length; k++) {\\n for (int i = 0; i < dist.length; i++) {\\n for (int j = 0; j < dist.length; j++) {\\n\\n if (dist[i][k] == Integer.MAX_VALUE || dist[k][j] == Integer.MAX_VALUE) {\\n continue; // dont even have to look at this\\n }\\n\\n if (dist[i][j] > dist[i][k] + dist[k][j]) {\\n dist[i][j] = dist[i][k] + dist[k][j];\\n }\\n }\\n }\\n }\\n\\n // Detect negative cycles\\n for (int i = 0; i < dist.length; i++) {\\n for (int j = 0; j < dist.length; j++) {\\n for (int k = 0; dist[i][j] != Integer.MIN_VALUE && k < dist.length; k++) {\\n if (dist[i][k] != Integer.MAX_VALUE && dist[k][j] != Integer.MAX_VALUE && dist[k][k] < 0) {\\n dist[i][j] = Integer.MIN_VALUE;\\n }\\n }\\n }\\n }\\n }\\n}\"\n },\n {\n \"alpha_fraction\": 0.4995878040790558,\n \"alphanum_fraction\": 0.5597691535949707,\n \"avg_line_length\": 25.39130401611328,\n \"blob_id\": \"f3bbfb19f57a8b7bee2b2354535c771c302521c6\",\n \"content_id\": \"fdee65f7af8f3a05b0d364b7030862319e64b16b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1213,\n \"license_type\": \"no_license\",\n \"max_line_length\": 72,\n \"num_lines\": 46,\n \"path\": \"/polymul1/polymul1.py\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/python\\n\\n__author__ = \\\"Fabian Schilling\\\"\\n__email__ = \\\"fabsch@kth.se\\\"\\n\\nimport sys\\n\\ndef quadmul(pol1, pol2):\\n result = [0]*(len(pol1)+len(pol2))\\n for index1, coeff1 in enumerate(pol1):\\n for index2, coeff2 in enumerate(pol2):\\n result[index1+index2] += coeff1*coeff2\\n return result\\n\\ndef karatsuba(pol1, pol2):\\n if len(pol1) < 10 or len(pol2) < 10:\\n return quadmul(pol1, pol2)\\n m = max(len(pol1), len(pol2))\\n m2 = m // 2\\n low1 = pol1[:m2]\\n high1 = pol1[m2:]\\n low2 = pol2[:m2]\\n high2 = pol2[m2:]\\n\\n z0 = karatsuba(low1, low2)\\n z1 = karatsuba((low1+high1), (low2+high2))\\n z2 = karatsuba(high1, high2)\\n return (z2*10**(2*m2)) + ((z1-z2-z0)*10**(m2))+(z0)\\n\\nT = int(sys.stdin.readline().strip(\\\"\\\\n\\\"))\\n\\nfor _ in range(T):\\n deg1 = int(sys.stdin.readline().strip(\\\"\\\\n\\\"))\\n pol1 = [int(x) for x in sys.stdin.readline().strip(\\\"\\\\n\\\").split(\\\" \\\")]\\n deg2 = int(sys.stdin.readline().strip(\\\"\\\\n\\\"))\\n pol2 = [int(x) for x in sys.stdin.readline().strip(\\\"\\\\n\\\").split(\\\" \\\")]\\n\\n res = quadmul(pol1, pol2)\\n \\n deg = 0\\n for index, coeff in enumerate(res):\\n if coeff != 0:\\n deg = index\\n\\n print deg\\n print \\\" \\\".join([str(x) for x in res[:deg+1]])\"\n },\n {\n \"alpha_fraction\": 0.5787139534950256,\n \"alphanum_fraction\": 0.5831485390663147,\n \"avg_line_length\": 19.5,\n \"blob_id\": \"7a7180dbe0c530d719c80647079980251868be1f\",\n \"content_id\": \"9b2ac9f198cfd4dcd28e82c1c1bb55e7c239d81a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 451,\n \"license_type\": \"no_license\",\n \"max_line_length\": 91,\n \"num_lines\": 22,\n \"path\": \"/islandhopping/Edge.java\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/**\\n* @author Fabian Schilling (fabsch@kth.se)\\n*/\\n\\n\\npublic class Edge implements Comparable<Edge> {\\n\\n public Node from;\\n public Node to;\\n public double distance;\\n\\n public Edge(Node from, Node to) {\\n this.from = from;\\n this.to = to;\\n this.distance = Math.sqrt(Math.pow(from.x - to.x, 2) + Math.pow(from.y - to.y, 2));\\n }\\n\\n @Override\\n public int compareTo(Edge o) {\\n return Double.compare(distance, o.distance);\\n }\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.44621938467025757,\n \"alphanum_fraction\": 0.4781682789325714,\n \"avg_line_length\": 23.102563858032227,\n \"blob_id\": \"6fe54624596d7833bf8814d6081492bb54457261\",\n \"content_id\": \"61a685438e33693665585c284586ce99c89d92b6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 939,\n \"license_type\": \"no_license\",\n \"max_line_length\": 54,\n \"num_lines\": 39,\n \"path\": \"/ignore/ignore.py\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/python\\n\\n__author__ = \\\"Fabian Schilling\\\"\\n__email__ = \\\"fabsch@kth.se\\\"\\n\\nimport sys\\n\\n# conver int to str under certain base\\ndef int2str(num, base):\\n (d, m) = divmod(num, base)\\n if d > 0:\\n return int2str(d, base) + str(m)\\n return str(m)\\n\\n# convert from base 7 to (0, 1, 2, 5, 6, 8, 9) system\\ndef convert_sys(num_str):\\n ls = list(num_str)\\n for i, dig in enumerate(ls):\\n if dig == '3':\\n ls[i] = '5'\\n elif dig == '4':\\n ls[i] = '6'\\n elif dig == '5':\\n ls[i] = '8'\\n elif dig == '6':\\n ls[i] = '9'\\n return ''.join(ls)\\n\\nnums = [int(x) for x in sys.stdin.readlines()]\\n\\nfor num in nums:\\n num_str = list(convert_sys(int2str(num, 7))[::-1])\\n # turn upside down, i.e. 6 -> 9 and 9 -> 6\\n for i, char in enumerate(num_str):\\n if char == '6':\\n num_str[i] = '9'\\n elif char == '9':\\n num_str[i] = '6'\\n print(''.join(num_str))\"\n },\n {\n \"alpha_fraction\": 0.7213114500045776,\n \"alphanum_fraction\": 0.7377049326896667,\n \"avg_line_length\": 39.66666793823242,\n \"blob_id\": \"546fd4a011686bba6b23da3977f8ddd9d24ba078\",\n \"content_id\": \"1154482db1819b63dd84d03392f74ec0c692c1ab\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 122,\n \"license_type\": \"no_license\",\n \"max_line_length\": 48,\n \"num_lines\": 3,\n \"path\": \"/help2/README.txt\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"Author: Fabian Schilling (fabsch@kth)\\nProblem: https://open.kattis.com/problems/help2\\nExecution: python help2.py < samples/<sample.in>\\n\"\n },\n {\n \"alpha_fraction\": 0.5630372762680054,\n \"alphanum_fraction\": 0.5687679052352905,\n \"avg_line_length\": 20.84375,\n \"blob_id\": \"793566c48b5386d358e4b675db0284d7efe9b2f0\",\n \"content_id\": \"cc7ba9beafa4a1cf88b80d49c3bd4497f1e54b46\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 698,\n \"license_type\": \"no_license\",\n \"max_line_length\": 61,\n \"num_lines\": 32,\n \"path\": \"/ljutnja/ljutnja.py\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/python\\n\\n__author__ = \\\"Fabian Schilling\\\"\\n__email__ = \\\"fabsch@kth.se\\\"\\n\\nimport sys\\n\\ndef solve(wishes, m, n):\\n\\n remainder = sum(wishes) - m # number of remaining candies\\n\\n anger = 0\\n for i, wish in enumerate(wishes):\\n kids = n - i # number of remaining kids\\n candies = min(wish, remainder / kids)\\n anger += (candies * candies)\\n remainder -= candies\\n #print(remainder)\\n\\n print(anger)\\n\\ndef main():\\n input = sys.stdin.readlines()\\n first = map(int, input.pop(0).split(' '))\\n m = first[0] # number of candies\\n n = first[1] # number of children\\n wishes = map(int, input)\\n wishes.sort()\\n solve(wishes, m, n)\\n\\nif __name__ == '__main__':\\n main()\"\n },\n {\n \"alpha_fraction\": 0.4575757682323456,\n \"alphanum_fraction\": 0.4727272689342499,\n \"avg_line_length\": 17.85714340209961,\n \"blob_id\": \"c2782ae933d45673a281d0d3b56c963a53c64fe2\",\n \"content_id\": \"0a2c0d3a67bfb7e2d502faa4538b5362827caf05\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Java\",\n \"length_bytes\": 660,\n \"license_type\": \"no_license\",\n \"max_line_length\": 51,\n \"num_lines\": 35,\n \"path\": \"/turbo/Fenwick.java\",\n \"repo_name\": \"vinatsx/kattis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/**\\n* @author Fabian Schilling (fabsch@kth.se)\\n*/\\n\\n\\npublic class Fenwick {\\n\\n static final int MAX = 1000000;\\n\\n private long[] tree;\\n private int n;\\n\\n public Fenwick(int n) {\\n this.tree = new long[MAX];\\n this.n = n;\\n }\\n\\n public long query(int index) {\\n long result = 0;\\n for (int i = index; i > 0; i -= (i & -i)) {\\n result += tree[i];\\n }\\n return result;\\n }\\n\\n public void update(int index, long delta) {\\n for (int i = index; i <= n; i += (i &-i)) {\\n tree[i] += delta;\\n }\\n }\\n\\n public long sum(int left, int right) {\\n return query(right) - query(left - 1);\\n }\\n}\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":42,"string":"42"}}},{"rowIdx":2979367,"cells":{"repo_name":{"kind":"string","value":"bijp/spj_08"},"repo_url":{"kind":"string","value":"https://github.com/bijp/spj_08"},"snapshot_id":{"kind":"string","value":"976617c4a07a800abdd19055f4df15b5fb3b31e0"},"revision_id":{"kind":"string","value":"778943d2a2cc061bff8c7ebd3cd05a37b7975d71"},"directory_id":{"kind":"string","value":"ba10320760e17cd79fe6b09f95b4cb706603ee3c"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2022-12-09T20:28:11.492676","string":"2022-12-09T20:28:11.492676"},"revision_date":{"kind":"timestamp","value":"2020-08-07T07:40:56","string":"2020-08-07T07:40:56"},"committer_date":{"kind":"timestamp","value":"2020-08-07T07:40:56","string":"2020-08-07T07:40:56"},"github_id":{"kind":"number","value":285766198,"string":"285,766,198"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6160950064659119,"alphanum_fraction":0.6187335252761841,"avg_line_length":19.486486434936523,"blob_id":"9f3317fab674dfab231ed88cd237aa833321f439","content_id":"4b3341b6e07bf090918fb583196f82d0d45b453b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":790,"license_type":"no_license","max_line_length":83,"num_lines":37,"path":"/spj_08/spj/pylib/MobileOpLibAdmin.py","repo_name":"bijp/spj_08","src_encoding":"UTF-8","text":"\nfrom spj.pylib.MobileOpLibShare import Mobile_SHARE\n\n\nclass MobileOpLibAdmin:\n ROBOT_LIBRARY_SCOPE = 'GLOBAL'\n\n def vcode_login(self,vcode):\n\n code = 'new UiSelector().text(\"请输入vcode\")'\n ele = Mobile_SHARE.wd.find_element_by_android_uiautomator(code)\n ele.send_keys(vcode)\n\n\n code = 'new UiSelector().text(\"登录\")'\n ele = Mobile_SHARE.wd.find_element_by_android_uiautomator(code)\n ele.click()\n\n eles = Mobile_SHARE.wd.find_elements_by_id('android:id/alertTitle')\n # 找到报错窗口，登录失败\n if eles:\n errInfo = Mobile_SHARE.wd.find_element_by_id('android:id/message').text\n print(errInfo)\n return False,errInfo\n\n return True,''\n\n\n\n\n\n\n\n\n\n\n\ngit@github.com:bijp/spj_08.git"},{"alpha_fraction":0.5333333611488342,"alphanum_fraction":0.6666666865348816,"avg_line_length":6.5,"blob_id":"b5b8331ebb0c2de5bfc6c810355d13e005229208","content_id":"033f07f6135a4b3eb9b2c1c984ae6bee51d8f7c2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":15,"license_type":"no_license","max_line_length":8,"num_lines":2,"path":"/README.md","repo_name":"bijp/spj_08","src_encoding":"UTF-8","text":"# spj_08\ndsada\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6160950064659119,\n \"alphanum_fraction\": 0.6187335252761841,\n \"avg_line_length\": 19.486486434936523,\n \"blob_id\": \"9f3317fab674dfab231ed88cd237aa833321f439\",\n \"content_id\": \"4b3341b6e07bf090918fb583196f82d0d45b453b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 790,\n \"license_type\": \"no_license\",\n \"max_line_length\": 83,\n \"num_lines\": 37,\n \"path\": \"/spj_08/spj/pylib/MobileOpLibAdmin.py\",\n \"repo_name\": \"bijp/spj_08\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\nfrom spj.pylib.MobileOpLibShare import Mobile_SHARE\\n\\n\\nclass MobileOpLibAdmin:\\n ROBOT_LIBRARY_SCOPE = 'GLOBAL'\\n\\n def vcode_login(self,vcode):\\n\\n code = 'new UiSelector().text(\\\"请输入vcode\\\")'\\n ele = Mobile_SHARE.wd.find_element_by_android_uiautomator(code)\\n ele.send_keys(vcode)\\n\\n\\n code = 'new UiSelector().text(\\\"登录\\\")'\\n ele = Mobile_SHARE.wd.find_element_by_android_uiautomator(code)\\n ele.click()\\n\\n eles = Mobile_SHARE.wd.find_elements_by_id('android:id/alertTitle')\\n # 找到报错窗口，登录失败\\n if eles:\\n errInfo = Mobile_SHARE.wd.find_element_by_id('android:id/message').text\\n print(errInfo)\\n return False,errInfo\\n\\n return True,''\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\ngit@github.com:bijp/spj_08.git\"\n },\n {\n \"alpha_fraction\": 0.5333333611488342,\n \"alphanum_fraction\": 0.6666666865348816,\n \"avg_line_length\": 6.5,\n \"blob_id\": \"b5b8331ebb0c2de5bfc6c810355d13e005229208\",\n \"content_id\": \"033f07f6135a4b3eb9b2c1c984ae6bee51d8f7c2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 15,\n \"license_type\": \"no_license\",\n \"max_line_length\": 8,\n \"num_lines\": 2,\n \"path\": \"/README.md\",\n \"repo_name\": \"bijp/spj_08\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# spj_08\\ndsada\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2979368,"cells":{"repo_name":{"kind":"string","value":"HongxuanWang/DE-ETL-Game"},"repo_url":{"kind":"string","value":"https://github.com/HongxuanWang/DE-ETL-Game"},"snapshot_id":{"kind":"string","value":"3cc33ad19b0a872c329035b55dd199cfb278b35e"},"revision_id":{"kind":"string","value":"bb7acd9fdeff047618dabe68f34b4d762aebc4a7"},"directory_id":{"kind":"string","value":"641b33526e36f5fb3fc4959477bca740114a7abf"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2022-11-10T17:56:21.637969","string":"2022-11-10T17:56:21.637969"},"revision_date":{"kind":"timestamp","value":"2020-06-17T17:38:24","string":"2020-06-17T17:38:24"},"committer_date":{"kind":"timestamp","value":"2020-06-17T17:38:24","string":"2020-06-17T17:38:24"},"github_id":{"kind":"number","value":265392095,"string":"265,392,095"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5776081681251526,"alphanum_fraction":0.5788804292678833,"avg_line_length":35.738319396972656,"blob_id":"cd8f2c2805b065d2bc41bc77f7c1835b8b20ed42","content_id":"afcd53bc03c8643d7841da8c6da24fd63eedf355","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3930,"license_type":"no_license","max_line_length":118,"num_lines":107,"path":"/etl.py","repo_name":"HongxuanWang/DE-ETL-Game","src_encoding":"UTF-8","text":"# import necessary libraries\nimport pandas as pd \nfrom pyspark.sql import SparkSession, SQLContext\nfrom pyspark.context import SparkContext\nfrom pyspark.sql.functions import *\nfrom pyspark.sql.types import *\nimport os\n# create sparksession\nspark = SparkSession \\\n .builder \\\n .appName(\"PysparkGame\") \\\n .getOrCreate()\n\n#Build SQL context object\nsqlContext = SQLContext(spark)\n\ndef process_data(file_path): \n files = os.listdir(file_path)\n for f in files:\n\n df = spark.read.json('{}/'.format(file_path)+f)\n\n # add source file name\n regex_str = \"[\\/]([^\\/]+[\\/][^\\/]+)$\"\n df = df.withColumn(\"sourcefile\", regexp_extract(input_file_name(),regex_str,1))\n\n # covert all string fields to lowercse\n fields = df.schema.fields\n stringFields = filter(lambda f: isinstance(f.dataType, StringType), fields)\n nonStringFields = map(lambda f: col(f.name), filter(lambda f: not isinstance(f.dataType, StringType), fields))\n stringFieldsTransformed = map(lambda f: lower(col(f.name)).alias(f.name), stringFields) \n allFields = [*stringFieldsTransformed, *nonStringFields]\n df = df.select(allFields)\n\n\n df.createOrReplaceTempView('df')\n print('1')\n\n\n if spark.sql(\"select count(*) as count from df \\\n where event_type not in ('player_death','pickup_items', 'game_econ', 'login')\")\\\n .toPandas()['count'][0]>0:\n spark.sql(\"\\\n select * from df \\\n where event_type \\\n not in ('player_death','pickup_items', 'game_econ', 'login') \")\\\n .write.format('json').mode('append').save('./malformed.log/malformed_unidentified_events.json')\n\n if \"_corrupt_record\" in df.columns:\n spark.sql(\"\\\n select * from df \\\n where _corrupt_record like '{%'\")\\\n .write.format('json').mode('append').save('./malformed.log/malformed_broken.json')\n\n if \"_corrupt_record\" in df.columns:\n spark.sql(\"\\\n select _corrupt_record, sourcefile from df\\\n where _corrupt_record not like '{%' \")\\\n .write.format('json').mode('append').save('./error.log/error.json')\n\n #export valid records in each event type table\n if not os.path.isdir('./Result'):\n os.mkdir('./Result')\n\n\n def save_to_csv(event_type, sql_code, source_file_name ):\n spark.sql(sql_code)\\\n .write.csv('./Result/'+event_type +'_tb_'+source_file_name, header = 'True', mode = 'overwrite')\n\n login_sql = \"\\\n select userid, time,sourcefile from df \\\n where event_type = 'login' \"\n save_to_csv(\"login\", login_sql, f)\n\n\n\n pick_up_items_sql = \"\\\n select userid, time, item_id, item_sku, gold_earned,sourcefile from df \\\n where event_type = 'pickup_items'\"\n save_to_csv(\"pick_up_items\", pick_up_items_sql, f)\n\n\n game_econ_sql = \"\\\n select userid, time, item_sku, gold_spent,sourcefile from df \\\n where event_type = 'game_econ' \"\n save_to_csv(\"game_econ\", game_econ_sql, f)\n print('2')\n\n\n dmg=df.select('userid', 'time', 'killer', 'dmg_table',\"sourcefile\")\\\n .where(df['event_type']== 'player_death')\\\n .withColumn(\"dmg_table\", explode('dmg_table'))\\\n .select('userid', 'time', 'killer', lower(col(\"dmg_table.npc\")).alias(\"npc\"), \"dmg_table.dmg\", \"sourcefile\")\n dmg.createOrReplaceTempView('dmg')\n player_death_sql = \"\\\n select userid, time,killer,npc,sum(dmg) as total_dmg, sourcefile from dmg\\\n group by userid, time,killer, npc,sourcefile\"\n save_to_csv(\"player_death\", player_death_sql,f)\n print('Success.')\n\n\ndef main(file_path = 'Data'):\n process_data(file_path)\n\n\nif __name__ == \"__main__\":\n main()"},{"alpha_fraction":0.779891312122345,"alphanum_fraction":0.779891312122345,"avg_line_length":44.96875,"blob_id":"f028af7375f1996439ebd192a586ebd77ce9bbce","content_id":"a5bd042dbaeda7eb45a8e8827bc73f20bddf415e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":1472,"license_type":"no_license","max_line_length":300,"num_lines":32,"path":"/README.md","repo_name":"HongxuanWang/DE-ETL-Game","src_encoding":"UTF-8","text":"# Take-Home-Assignment-Perfect-World-Entertainment\n\n## Introduction\nThree test files in JSON lines format have been provided and contain meta data of users event activity log. In this submission, data has been processed with PySpark and turned into CSV files, then loaded into a MySQL database with event tables. A report is generated for loaded data with sql queries.\n\n\n## Table design\nEvent tables are designed to optimize queries for report. Time-series data with relevant inforamtion for each event type has been logged to a different event type table.\n\n## Exploratory\n- etl-Spark.ipynb : exploring provided files and demsontrating data pipeline\n- loadtomysql.ipynb: load csv to MySQL database\n- report.ipynb: reports from loaded data using sql and python \n\n\n## Python scripts\n- etl.py: Read JSON logs and JSON metadata and turned them into CSV.\n\n### Steps:\nHave all provided files in `Data` folder.\n`python etl.py`\nProcess data from `Data` folder with etl pipeline that will create valid rows in CSV files in `Result` folder, malformed records in `malformed.log` and `error.log`. \n\nRun `loadtomysql.ipynb`.\nRead csv from `Result` folder and insert data into each event table. This can be automated to py file as well. \n\nRun `report.ipnb`.\nLoop through all three files source and query result into report.\n\nIf given more time:\n- Valid rows can be directly inserted to database with python scripts. \n- In real world application, data can be streamed with the py file. \n"}],"string":"[\n {\n \"alpha_fraction\": 0.5776081681251526,\n \"alphanum_fraction\": 0.5788804292678833,\n \"avg_line_length\": 35.738319396972656,\n \"blob_id\": \"cd8f2c2805b065d2bc41bc77f7c1835b8b20ed42\",\n \"content_id\": \"afcd53bc03c8643d7841da8c6da24fd63eedf355\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3930,\n \"license_type\": \"no_license\",\n \"max_line_length\": 118,\n \"num_lines\": 107,\n \"path\": \"/etl.py\",\n \"repo_name\": \"HongxuanWang/DE-ETL-Game\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# import necessary libraries\\nimport pandas as pd \\nfrom pyspark.sql import SparkSession, SQLContext\\nfrom pyspark.context import SparkContext\\nfrom pyspark.sql.functions import *\\nfrom pyspark.sql.types import *\\nimport os\\n# create sparksession\\nspark = SparkSession \\\\\\n .builder \\\\\\n .appName(\\\"PysparkGame\\\") \\\\\\n .getOrCreate()\\n\\n#Build SQL context object\\nsqlContext = SQLContext(spark)\\n\\ndef process_data(file_path): \\n files = os.listdir(file_path)\\n for f in files:\\n\\n df = spark.read.json('{}/'.format(file_path)+f)\\n\\n # add source file name\\n regex_str = \\\"[\\\\/]([^\\\\/]+[\\\\/][^\\\\/]+)$\\\"\\n df = df.withColumn(\\\"sourcefile\\\", regexp_extract(input_file_name(),regex_str,1))\\n\\n # covert all string fields to lowercse\\n fields = df.schema.fields\\n stringFields = filter(lambda f: isinstance(f.dataType, StringType), fields)\\n nonStringFields = map(lambda f: col(f.name), filter(lambda f: not isinstance(f.dataType, StringType), fields))\\n stringFieldsTransformed = map(lambda f: lower(col(f.name)).alias(f.name), stringFields) \\n allFields = [*stringFieldsTransformed, *nonStringFields]\\n df = df.select(allFields)\\n\\n\\n df.createOrReplaceTempView('df')\\n print('1')\\n\\n\\n if spark.sql(\\\"select count(*) as count from df \\\\\\n where event_type not in ('player_death','pickup_items', 'game_econ', 'login')\\\")\\\\\\n .toPandas()['count'][0]>0:\\n spark.sql(\\\"\\\\\\n select * from df \\\\\\n where event_type \\\\\\n not in ('player_death','pickup_items', 'game_econ', 'login') \\\")\\\\\\n .write.format('json').mode('append').save('./malformed.log/malformed_unidentified_events.json')\\n\\n if \\\"_corrupt_record\\\" in df.columns:\\n spark.sql(\\\"\\\\\\n select * from df \\\\\\n where _corrupt_record like '{%'\\\")\\\\\\n .write.format('json').mode('append').save('./malformed.log/malformed_broken.json')\\n\\n if \\\"_corrupt_record\\\" in df.columns:\\n spark.sql(\\\"\\\\\\n select _corrupt_record, sourcefile from df\\\\\\n where _corrupt_record not like '{%' \\\")\\\\\\n .write.format('json').mode('append').save('./error.log/error.json')\\n\\n #export valid records in each event type table\\n if not os.path.isdir('./Result'):\\n os.mkdir('./Result')\\n\\n\\n def save_to_csv(event_type, sql_code, source_file_name ):\\n spark.sql(sql_code)\\\\\\n .write.csv('./Result/'+event_type +'_tb_'+source_file_name, header = 'True', mode = 'overwrite')\\n\\n login_sql = \\\"\\\\\\n select userid, time,sourcefile from df \\\\\\n where event_type = 'login' \\\"\\n save_to_csv(\\\"login\\\", login_sql, f)\\n\\n\\n\\n pick_up_items_sql = \\\"\\\\\\n select userid, time, item_id, item_sku, gold_earned,sourcefile from df \\\\\\n where event_type = 'pickup_items'\\\"\\n save_to_csv(\\\"pick_up_items\\\", pick_up_items_sql, f)\\n\\n\\n game_econ_sql = \\\"\\\\\\n select userid, time, item_sku, gold_spent,sourcefile from df \\\\\\n where event_type = 'game_econ' \\\"\\n save_to_csv(\\\"game_econ\\\", game_econ_sql, f)\\n print('2')\\n\\n\\n dmg=df.select('userid', 'time', 'killer', 'dmg_table',\\\"sourcefile\\\")\\\\\\n .where(df['event_type']== 'player_death')\\\\\\n .withColumn(\\\"dmg_table\\\", explode('dmg_table'))\\\\\\n .select('userid', 'time', 'killer', lower(col(\\\"dmg_table.npc\\\")).alias(\\\"npc\\\"), \\\"dmg_table.dmg\\\", \\\"sourcefile\\\")\\n dmg.createOrReplaceTempView('dmg')\\n player_death_sql = \\\"\\\\\\n select userid, time,killer,npc,sum(dmg) as total_dmg, sourcefile from dmg\\\\\\n group by userid, time,killer, npc,sourcefile\\\"\\n save_to_csv(\\\"player_death\\\", player_death_sql,f)\\n print('Success.')\\n\\n\\ndef main(file_path = 'Data'):\\n process_data(file_path)\\n\\n\\nif __name__ == \\\"__main__\\\":\\n main()\"\n },\n {\n \"alpha_fraction\": 0.779891312122345,\n \"alphanum_fraction\": 0.779891312122345,\n \"avg_line_length\": 44.96875,\n \"blob_id\": \"f028af7375f1996439ebd192a586ebd77ce9bbce\",\n \"content_id\": \"a5bd042dbaeda7eb45a8e8827bc73f20bddf415e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 1472,\n \"license_type\": \"no_license\",\n \"max_line_length\": 300,\n \"num_lines\": 32,\n \"path\": \"/README.md\",\n \"repo_name\": \"HongxuanWang/DE-ETL-Game\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Take-Home-Assignment-Perfect-World-Entertainment\\n\\n## Introduction\\nThree test files in JSON lines format have been provided and contain meta data of users event activity log. In this submission, data has been processed with PySpark and turned into CSV files, then loaded into a MySQL database with event tables. A report is generated for loaded data with sql queries.\\n\\n\\n## Table design\\nEvent tables are designed to optimize queries for report. Time-series data with relevant inforamtion for each event type has been logged to a different event type table.\\n\\n## Exploratory\\n- etl-Spark.ipynb : exploring provided files and demsontrating data pipeline\\n- loadtomysql.ipynb: load csv to MySQL database\\n- report.ipynb: reports from loaded data using sql and python \\n\\n\\n## Python scripts\\n- etl.py: Read JSON logs and JSON metadata and turned them into CSV.\\n\\n### Steps:\\nHave all provided files in `Data` folder.\\n`python etl.py`\\nProcess data from `Data` folder with etl pipeline that will create valid rows in CSV files in `Result` folder, malformed records in `malformed.log` and `error.log`. \\n\\nRun `loadtomysql.ipynb`.\\nRead csv from `Result` folder and insert data into each event table. This can be automated to py file as well. \\n\\nRun `report.ipnb`.\\nLoop through all three files source and query result into report.\\n\\nIf given more time:\\n- Valid rows can be directly inserted to database with python scripts. \\n- In real world application, data can be streamed with the py file. \\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2979369,"cells":{"repo_name":{"kind":"string","value":"RenHao116/WURacing-tests"},"repo_url":{"kind":"string","value":"https://github.com/RenHao116/WURacing-tests"},"snapshot_id":{"kind":"string","value":"cb5e64516cecd58f67e6869624db81c3f08b3634"},"revision_id":{"kind":"string","value":"9558624f71ab04e9dbf86d92f83e5e262c9d2b5f"},"directory_id":{"kind":"string","value":"4be944a746ceea62bff4d600a0423a15efb18e78"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-07-26T11:58:54.559790","string":"2021-07-26T11:58:54.559790"},"revision_date":{"kind":"timestamp","value":"2017-11-04T22:16:41","string":"2017-11-04T22:16:41"},"committer_date":{"kind":"timestamp","value":"2017-11-04T22:16:41","string":"2017-11-04T22:16:41"},"github_id":{"kind":"number","value":109534253,"string":"109,534,253"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.584725558757782,"alphanum_fraction":0.6252983212471008,"avg_line_length":27,"blob_id":"a1f3c4892c994863e29ccb06194a7dba08415204","content_id":"8d9e1e817d438b245c6ffaaa5a0144c93d4a1849","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":419,"license_type":"no_license","max_line_length":72,"num_lines":15,"path":"/WashU Racing/testDB.py","repo_name":"RenHao116/WURacing-tests","src_encoding":"UTF-8","text":"import redis\nimport serial\nr = redis.StrictRedis(host='localhost' , port=6379, db=0)\nser = serial.Serial('/dev/ttyACM0', 9600)\nfor x in range(1,10) :\n #r.zadd('data',float(ser.read_until(' ')),str(ser.read_until('\\n')))\n r.zadd('data',x , ser.readline())\n\n\nf = open('testingdata.text','w')\n#f.write(str(r.zrange('data',0,-1)))\nfor value in r.zrange('data',0,-1):\n #f.write(key+\" \")\n f.write(value)\nf.close()"},{"alpha_fraction":0.6116788387298584,"alphanum_fraction":0.645255446434021,"avg_line_length":20.21875,"blob_id":"16bbca084fb4982fee32f057e64675c6113df7d7","content_id":"9ffbe4ced5ea2c3927702437f3275edba5af8d6b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":685,"license_type":"no_license","max_line_length":53,"num_lines":32,"path":"/WashU Racing/sketch_oct07b/sketch_oct07b.ino","repo_name":"RenHao116/WURacing-tests","src_encoding":"UTF-8","text":"#include <Wire.h>\n#include <SD.h>\n#include <SPI.h>\n#include <SoftwareSerial.h>\n#include \"Canbus.h\" // don't forget to include these\n#include \"defaults.h\"\n#include \"global.h\"\n#include \"mcp2515.h\"\n#include \"mcp2515_defs.h\"\n#include \"stdlib.h\"\n#include \"PacketSender.h\"\nvoid setup() {\n // put your setup code here, to run once:\n Serial. begin(9600);\n if (Canbus.init(CANSPEED_500)) {\n Serial.println(\"CAN Init ok\");\n }\n else {\n Serial.println(\"Couldn't Init CAN\");\n }\n}\n\nvoid loop() {\n // put your main code here, to run repeatedly:\n tCAN message;\n if(mcp2515_check_message()){\n if(mcp2515_get_message(&message)){\n Serial.println(message.id);\n }\n }\n\n}\n \n"},{"alpha_fraction":0.8085106611251831,"alphanum_fraction":0.8085106611251831,"avg_line_length":22.5,"blob_id":"0f4f5d44d3c29b61cab726c81415a0000a60ccce","content_id":"6052ea6cb6a842b5f89d8a60c3c3b40706aacb5b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":47,"license_type":"no_license","max_line_length":29,"num_lines":2,"path":"/README.md","repo_name":"RenHao116/WURacing-tests","src_encoding":"UTF-8","text":"# WURacing-tests\nSome scripts used by WURacing\n"}],"string":"[\n {\n \"alpha_fraction\": 0.584725558757782,\n \"alphanum_fraction\": 0.6252983212471008,\n \"avg_line_length\": 27,\n \"blob_id\": \"a1f3c4892c994863e29ccb06194a7dba08415204\",\n \"content_id\": \"8d9e1e817d438b245c6ffaaa5a0144c93d4a1849\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 419,\n \"license_type\": \"no_license\",\n \"max_line_length\": 72,\n \"num_lines\": 15,\n \"path\": \"/WashU Racing/testDB.py\",\n \"repo_name\": \"RenHao116/WURacing-tests\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import redis\\nimport serial\\nr = redis.StrictRedis(host='localhost' , port=6379, db=0)\\nser = serial.Serial('/dev/ttyACM0', 9600)\\nfor x in range(1,10) :\\n #r.zadd('data',float(ser.read_until(' ')),str(ser.read_until('\\\\n')))\\n r.zadd('data',x , ser.readline())\\n\\n\\nf = open('testingdata.text','w')\\n#f.write(str(r.zrange('data',0,-1)))\\nfor value in r.zrange('data',0,-1):\\n #f.write(key+\\\" \\\")\\n f.write(value)\\nf.close()\"\n },\n {\n \"alpha_fraction\": 0.6116788387298584,\n \"alphanum_fraction\": 0.645255446434021,\n \"avg_line_length\": 20.21875,\n \"blob_id\": \"16bbca084fb4982fee32f057e64675c6113df7d7\",\n \"content_id\": \"9ffbe4ced5ea2c3927702437f3275edba5af8d6b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 685,\n \"license_type\": \"no_license\",\n \"max_line_length\": 53,\n \"num_lines\": 32,\n \"path\": \"/WashU Racing/sketch_oct07b/sketch_oct07b.ino\",\n \"repo_name\": \"RenHao116/WURacing-tests\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#include <Wire.h>\\n#include <SD.h>\\n#include <SPI.h>\\n#include <SoftwareSerial.h>\\n#include \\\"Canbus.h\\\" // don't forget to include these\\n#include \\\"defaults.h\\\"\\n#include \\\"global.h\\\"\\n#include \\\"mcp2515.h\\\"\\n#include \\\"mcp2515_defs.h\\\"\\n#include \\\"stdlib.h\\\"\\n#include \\\"PacketSender.h\\\"\\nvoid setup() {\\n // put your setup code here, to run once:\\n Serial. begin(9600);\\n if (Canbus.init(CANSPEED_500)) {\\n Serial.println(\\\"CAN Init ok\\\");\\n }\\n else {\\n Serial.println(\\\"Couldn't Init CAN\\\");\\n }\\n}\\n\\nvoid loop() {\\n // put your main code here, to run repeatedly:\\n tCAN message;\\n if(mcp2515_check_message()){\\n if(mcp2515_get_message(&message)){\\n Serial.println(message.id);\\n }\\n }\\n\\n}\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.8085106611251831,\n \"alphanum_fraction\": 0.8085106611251831,\n \"avg_line_length\": 22.5,\n \"blob_id\": \"0f4f5d44d3c29b61cab726c81415a0000a60ccce\",\n \"content_id\": \"6052ea6cb6a842b5f89d8a60c3c3b40706aacb5b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 47,\n \"license_type\": \"no_license\",\n \"max_line_length\": 29,\n \"num_lines\": 2,\n \"path\": \"/README.md\",\n \"repo_name\": \"RenHao116/WURacing-tests\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# WURacing-tests\\nSome scripts used by WURacing\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":3,"string":"3"}}},{"rowIdx":2979370,"cells":{"repo_name":{"kind":"string","value":"kamal0072/Python-Code"},"repo_url":{"kind":"string","value":"https://github.com/kamal0072/Python-Code"},"snapshot_id":{"kind":"string","value":"d59b7885eeb1c97c8c14a6292b5319e4d659d1c1"},"revision_id":{"kind":"string","value":"4c567f2bf68a2572993e347fa658362b9875b11a"},"directory_id":{"kind":"string","value":"3d6f5340a8c3c0300a01e30b0889ab12d70d5ec1"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2023-06-05T03:18:00.701389","string":"2023-06-05T03:18:00.701389"},"revision_date":{"kind":"timestamp","value":"2021-06-17T20:01:43","string":"2021-06-17T20:01:43"},"committer_date":{"kind":"timestamp","value":"2021-06-17T20:01:43","string":"2021-06-17T20:01:43"},"github_id":{"kind":"number","value":377071121,"string":"377,071,121"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5199453830718994,"alphanum_fraction":0.5549180507659912,"avg_line_length":18.0625,"blob_id":"b61f88e2bd06b6e6bc6740264c8ba1152312d0bf","content_id":"b02cb046129a357578ecfceec5c801e6cf0f31e8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3660,"license_type":"no_license","max_line_length":62,"num_lines":192,"path":"/firstcode.py","repo_name":"kamal0072/Python-Code","src_encoding":"UTF-8","text":"# first_name=\"john\"\n# last_name=\"khan\"\n# roll=105\n# a=10\n# b=a\n# a=12\n# print(id(a))\n# print(id(a))\n# print(id(b))\n# list1=['kama',12,'khan',23.15]\n# print(\"The For Loop Is going to apply From here....\")\n# for x in list1:\n# print(x)\n# if x==12:\n# break\n# else:\n# print(\"This is outside of For Loop\")\n# print(\"End Of for Loop\")\n# print(type(list1))\n# print(type(first_name))\n# print(type(roll))\n\n# a=b=c=d=10,30,20,30\n# print(a,b,c,d)\n# print(type(a))\n# print(a)\n\n#type of varialbles\n#local Variables\n\n# def school():\n# x=10\n# return x #this is local scope/variable\n# print(x)\n# var=school()\n# print(\"The Valuse of variables is:\",var)\n# print(x) #trying to access local variable so that got err\n\n\n\n# name=\"peter\" #This is Global variable\n# def school(name):\n# # global name\n# print(name)\n# x=10\n# return x #this is local scope/variable\n# print(\"The Global Variable Is:\",name)\n# var=school(\"name\")\n# print(\"The Valuse of variables is:\",var)\n# print(name) #trying to global local variable so that got err\n# del name\n# print(name)\n\n# name=\"peter\"\n# def show():\n# # global name\n# name=\"bablu\"\n# # global name\n# #local Variable\n# print(\"The Global Variable Inside Function:\",name)\n# show()\n# print(\"The Global Variable Outside Function:\",name)\n\n# a=20\n# b=\"Kamal\"\n# c=23.50000544545\n# d=-10+10j\n\n# print(\"d is a complex number\") \n# print(\"a\",\"=\",type(a),\n# \"b\",\"=\",type(b),\n# \"c\",\"=\",type(c),\n# \"d\",\"=\",type(d),\n# isinstance(d,complex)\n# )\n# print(\n# \"Id of a=\",id(a),\n# \"Id Of b=\",id(b),\n# \"Id Of c=\",id(c),\n# \"Id Of d=\",id(d))\n# print(\"\")\n\n# a=0\n# try:\n# b=1/a\n# raise Exception(\"Can not do it\")\n# except:\n# print(\"All Is Not Ok\")\n# finally:\n# print('All Okey')\n\n# import calendar as cal \n# print(cal.month_name[5]) \n\n# x = 0b10100 #Binary Literals \n# y = 100 #Decimal Literal \n# z = 0o215 #Octal Literal \n# u = 0o12 #Hexadecimal Literal \n \n# #Float Literal \n# float_1 = 100.5 \n# float_2 = 1.5e5 \n \n# #Complex Literal \n# a = 5+3.14j \n \n# print(x, y, z, u) \n# print(float_1, float_2) \n# print(a, a.imag, a.real) \n\n\n# conditional statements\n\n# x=int(input(\"Enter Your Age:\"))\n# y=int(input(\"Enter Your Age:\"))\n# z=int(input(\"Enter Your Age:\"))\n# # name=input(\"enter your name=\")\n# if x>y and x>z:\n# print(\" x is oldest\")\n# if y>x and y>z:\n# print(\"Y Is oldest \")\n# if z>x and z>y:\n# print(\"z is oldest\")\n\n# a=int(input(\"Enter Your age of a=\"))\n# b=int(input(\"Enter Your age of b=\"))\n# c=int(input(\"Enter Your age of c=\"))\n\n# if a>b or b>c:\n# print(\"a is right\") \n\n# x=int(input(\"Enter The value of x:\"))\n# if x%2==0:\n# print(\"So x is even\")\n# else:\n# print(\"then x is not even\")\n\n# x=int(input(\"Enter x:\"))\n# y=int(input(\"Enter y:\"))\n\n# if x>y:\n# print(\"X is right\")\n# elif x>=y:\n# print(\"x is may be right\")\n# else:\n# print(\"x must be wronmg\")\n\n\n\n\n\n\n#looping starts here\n# name=[45,8,8,5]\n# list1=[55,45,23.4]\n# for x in name:\n# print(x)\n# print(\"-------\")\n# for y in list1:\n# print(y)\n\n# z=name+list1\n# print(z)\n# for val in z:\n# print(val)\n\n# x=[45,4.4,23,45]\n# for mul in x:\n# res=mul+10\n# print(res)\n\n# list = [10,30,23,43,65,12] \n# sum = 0 \n# for i in list: \n# sum = sum+i \n# print(\"The sum is:\",sum) \n\n# lis1=['kk','hj','jha']\n\n# for x in range(len(lis1)):\n# print(list[x])\n\n# list = ['Peter','Joseph','Ricky','Devansh'] \n# for i in range(len(lis1)): \n# print(\"Hello\",list[i]) \n\n\n# rows=int(input(\"Enter Number of rows:\"))\n# for i in range(0,rows+1):\n# for j in range(i):\n# print(\"*\",end='')\n# print(\"\")\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5199453830718994,\n \"alphanum_fraction\": 0.5549180507659912,\n \"avg_line_length\": 18.0625,\n \"blob_id\": \"b61f88e2bd06b6e6bc6740264c8ba1152312d0bf\",\n \"content_id\": \"b02cb046129a357578ecfceec5c801e6cf0f31e8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3660,\n \"license_type\": \"no_license\",\n \"max_line_length\": 62,\n \"num_lines\": 192,\n \"path\": \"/firstcode.py\",\n \"repo_name\": \"kamal0072/Python-Code\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# first_name=\\\"john\\\"\\n# last_name=\\\"khan\\\"\\n# roll=105\\n# a=10\\n# b=a\\n# a=12\\n# print(id(a))\\n# print(id(a))\\n# print(id(b))\\n# list1=['kama',12,'khan',23.15]\\n# print(\\\"The For Loop Is going to apply From here....\\\")\\n# for x in list1:\\n# print(x)\\n# if x==12:\\n# break\\n# else:\\n# print(\\\"This is outside of For Loop\\\")\\n# print(\\\"End Of for Loop\\\")\\n# print(type(list1))\\n# print(type(first_name))\\n# print(type(roll))\\n\\n# a=b=c=d=10,30,20,30\\n# print(a,b,c,d)\\n# print(type(a))\\n# print(a)\\n\\n#type of varialbles\\n#local Variables\\n\\n# def school():\\n# x=10\\n# return x #this is local scope/variable\\n# print(x)\\n# var=school()\\n# print(\\\"The Valuse of variables is:\\\",var)\\n# print(x) #trying to access local variable so that got err\\n\\n\\n\\n# name=\\\"peter\\\" #This is Global variable\\n# def school(name):\\n# # global name\\n# print(name)\\n# x=10\\n# return x #this is local scope/variable\\n# print(\\\"The Global Variable Is:\\\",name)\\n# var=school(\\\"name\\\")\\n# print(\\\"The Valuse of variables is:\\\",var)\\n# print(name) #trying to global local variable so that got err\\n# del name\\n# print(name)\\n\\n# name=\\\"peter\\\"\\n# def show():\\n# # global name\\n# name=\\\"bablu\\\"\\n# # global name\\n# #local Variable\\n# print(\\\"The Global Variable Inside Function:\\\",name)\\n# show()\\n# print(\\\"The Global Variable Outside Function:\\\",name)\\n\\n# a=20\\n# b=\\\"Kamal\\\"\\n# c=23.50000544545\\n# d=-10+10j\\n\\n# print(\\\"d is a complex number\\\") \\n# print(\\\"a\\\",\\\"=\\\",type(a),\\n# \\\"b\\\",\\\"=\\\",type(b),\\n# \\\"c\\\",\\\"=\\\",type(c),\\n# \\\"d\\\",\\\"=\\\",type(d),\\n# isinstance(d,complex)\\n# )\\n# print(\\n# \\\"Id of a=\\\",id(a),\\n# \\\"Id Of b=\\\",id(b),\\n# \\\"Id Of c=\\\",id(c),\\n# \\\"Id Of d=\\\",id(d))\\n# print(\\\"\\\")\\n\\n# a=0\\n# try:\\n# b=1/a\\n# raise Exception(\\\"Can not do it\\\")\\n# except:\\n# print(\\\"All Is Not Ok\\\")\\n# finally:\\n# print('All Okey')\\n\\n# import calendar as cal \\n# print(cal.month_name[5]) \\n\\n# x = 0b10100 #Binary Literals \\n# y = 100 #Decimal Literal \\n# z = 0o215 #Octal Literal \\n# u = 0o12 #Hexadecimal Literal \\n \\n# #Float Literal \\n# float_1 = 100.5 \\n# float_2 = 1.5e5 \\n \\n# #Complex Literal \\n# a = 5+3.14j \\n \\n# print(x, y, z, u) \\n# print(float_1, float_2) \\n# print(a, a.imag, a.real) \\n\\n\\n# conditional statements\\n\\n# x=int(input(\\\"Enter Your Age:\\\"))\\n# y=int(input(\\\"Enter Your Age:\\\"))\\n# z=int(input(\\\"Enter Your Age:\\\"))\\n# # name=input(\\\"enter your name=\\\")\\n# if x>y and x>z:\\n# print(\\\" x is oldest\\\")\\n# if y>x and y>z:\\n# print(\\\"Y Is oldest \\\")\\n# if z>x and z>y:\\n# print(\\\"z is oldest\\\")\\n\\n# a=int(input(\\\"Enter Your age of a=\\\"))\\n# b=int(input(\\\"Enter Your age of b=\\\"))\\n# c=int(input(\\\"Enter Your age of c=\\\"))\\n\\n# if a>b or b>c:\\n# print(\\\"a is right\\\") \\n\\n# x=int(input(\\\"Enter The value of x:\\\"))\\n# if x%2==0:\\n# print(\\\"So x is even\\\")\\n# else:\\n# print(\\\"then x is not even\\\")\\n\\n# x=int(input(\\\"Enter x:\\\"))\\n# y=int(input(\\\"Enter y:\\\"))\\n\\n# if x>y:\\n# print(\\\"X is right\\\")\\n# elif x>=y:\\n# print(\\\"x is may be right\\\")\\n# else:\\n# print(\\\"x must be wronmg\\\")\\n\\n\\n\\n\\n\\n\\n#looping starts here\\n# name=[45,8,8,5]\\n# list1=[55,45,23.4]\\n# for x in name:\\n# print(x)\\n# print(\\\"-------\\\")\\n# for y in list1:\\n# print(y)\\n\\n# z=name+list1\\n# print(z)\\n# for val in z:\\n# print(val)\\n\\n# x=[45,4.4,23,45]\\n# for mul in x:\\n# res=mul+10\\n# print(res)\\n\\n# list = [10,30,23,43,65,12] \\n# sum = 0 \\n# for i in list: \\n# sum = sum+i \\n# print(\\\"The sum is:\\\",sum) \\n\\n# lis1=['kk','hj','jha']\\n\\n# for x in range(len(lis1)):\\n# print(list[x])\\n\\n# list = ['Peter','Joseph','Ricky','Devansh'] \\n# for i in range(len(lis1)): \\n# print(\\\"Hello\\\",list[i]) \\n\\n\\n# rows=int(input(\\\"Enter Number of rows:\\\"))\\n# for i in range(0,rows+1):\\n# for j in range(i):\\n# print(\\\"*\\\",end='')\\n# print(\\\"\\\")\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":2979371,"cells":{"repo_name":{"kind":"string","value":"heavensward-argentlux/ninjavan-cash-mgt"},"repo_url":{"kind":"string","value":"https://github.com/heavensward-argentlux/ninjavan-cash-mgt"},"snapshot_id":{"kind":"string","value":"d2b5cd1b1eeabf828e00939201605a460960872c"},"revision_id":{"kind":"string","value":"b892f43c8a25ad87eb80cfdbdc7335c21bf4d7f2"},"directory_id":{"kind":"string","value":"efb72b246f3d04336233f11e1f71411ec3341e5f"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"timestamp","value":"2023-03-24T14:42:29.454415","string":"2023-03-24T14:42:29.454415"},"revision_date":{"kind":"timestamp","value":"2021-10-18T03:25:52","string":"2021-10-18T03:25:52"},"committer_date":{"kind":"timestamp","value":"2021-10-18T03:25:52","string":"2021-10-18T03:25:52"},"github_id":{"kind":"number","value":335513457,"string":"335,513,457"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6009615659713745,"alphanum_fraction":0.6177884340286255,"avg_line_length":33.70833206176758,"blob_id":"9628e9e35e7da00959040f7b82ba4c148764d9a9","content_id":"a363f6aae350069bd609e66f0da0d0bce72baf0d","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":832,"license_type":"permissive","max_line_length":73,"num_lines":24,"path":"/authorization/main.py","repo_name":"heavensward-argentlux/ninjavan-cash-mgt","src_encoding":"UTF-8","text":"def cors_enabled_function(request):\n # For more information about CORS and CORS preflight requests, see\n # https://developer.mozilla.org/en-US/docs/Glossary/Preflight_request\n # for more information.\n\n # Set CORS headers for the preflight request\n if request.method == 'OPTIONS':\n # Allows GET requests from any origin with the Content-Type\n # header and caches preflight response for an 3600s\n headers = {\n 'Access-Control-Allow-Origin': '*',\n 'Access-Control-Allow-Methods': '*',\n 'Access-Control-Allow-Headers': '*',\n 'Access-Control-Max-Age': '3600'\n }\n\n return ('', 204, headers)\n\n # Set CORS headers for the main request\n headers = {\n 'Access-Control-Allow-Origin': '*'\n }\n\n return ('Cors enabled!', 200, headers)"},{"alpha_fraction":0.6613463163375854,"alphanum_fraction":0.6710617542266846,"avg_line_length":30.326086044311523,"blob_id":"8b476890af79116475a1f92e8ed3c9e16b2a4f53","content_id":"46f01d9196bf4f6623da0297f6d89afaaafe7a07","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1441,"license_type":"permissive","max_line_length":100,"num_lines":46,"path":"/api-gateway/yaml-composer.py","repo_name":"heavensward-argentlux/ninjavan-cash-mgt","src_encoding":"UTF-8","text":"import yaml\n\ndef compose(out_file, base_file, paths_files):\n\tbase = load_base(base_file)\n\tbase['paths'] = {}\n\tfor file in paths_files:\n\t\twith open(file, 'r') as stream:\n\t\t\ttry:\n\t\t\t\tpaths = yaml.safe_load(stream)['paths'] # {'/policies' : {...}, '/policies/{policyId}' : {...} }\n\t\t\t\tfor path in paths.keys():\n\t\t\t\t\tbase['paths'][path] = paths[path]\t\t\t# assign above ^ to {'paths' : {...} }\n\t\t\texcept yaml.YAMLError as e: print(e)\n\t\tstream.close()\n\tstatus = write_yaml(out_file, base)\n\treturn status, base\n\ndef load_base(base_file):\n\twith open(base_file, 'r') as stream:\n\t\ttry: base = yaml.safe_load(stream)\n\t\texcept yaml.YAMLError as e: print(e)\n\tstream.close()\n\treturn base\n\ndef write_yaml(out_file, data):\n\twith open(out_file, 'w') as stream:\n\t\ttry: yaml.safe_dump(data, stream)\n\t\texcept yaml.YAMLError as e: print(e)\n\tstream.close()\n\treturn 'OK'\n\nif __name__ == \"__main__\":\n\tbase_file = 'gateway-v3-base.yaml'\n\tpath_files = [\n\t\t'paths/policy/gateway-v3-policies.yaml',\n\t\t'paths/policy/gateway-v3-expenses.yaml',\n\t\t'paths/policy/gateway-v3-approved.yaml',\n\t\t'paths/transaction/gateway-v3-transactions.yaml',\n\t\t'paths/transaction/gateway-v3-histories.yaml',\n\t\t'paths/transaction/gateway-v3-images.yaml',\n\t\t'paths/user/gateway-v3-locations.yaml',\n\t\t'paths/user/gateway-v3-roles.yaml',\n\t\t'paths/user/gateway-v3-users.yaml',\n\t\t'paths/gateway-v3-analytics.yaml'\n\t]\n\tstatus, _ = compose(\"gateway-v3.13.yaml\", base_file, path_files)\n\tprint(status)\n"},{"alpha_fraction":0.8169013857841492,"alphanum_fraction":0.8169013857841492,"avg_line_length":22.66666603088379,"blob_id":"fe356edb7f3cf8cdf617d7087493f29a5b575777","content_id":"0e255715529d2bf94df6e794c0a0dfd7b318e79d","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":71,"license_type":"permissive","max_line_length":49,"num_lines":3,"path":"/README.md","repo_name":"heavensward-argentlux/ninjavan-cash-mgt","src_encoding":"UTF-8","text":"# ninjavan-cash-mgt\n\nNinjaVan's Petty Cash Management Capstone Project\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6009615659713745,\n \"alphanum_fraction\": 0.6177884340286255,\n \"avg_line_length\": 33.70833206176758,\n \"blob_id\": \"9628e9e35e7da00959040f7b82ba4c148764d9a9\",\n \"content_id\": \"a363f6aae350069bd609e66f0da0d0bce72baf0d\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 832,\n \"license_type\": \"permissive\",\n \"max_line_length\": 73,\n \"num_lines\": 24,\n \"path\": \"/authorization/main.py\",\n \"repo_name\": \"heavensward-argentlux/ninjavan-cash-mgt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def cors_enabled_function(request):\\n # For more information about CORS and CORS preflight requests, see\\n # https://developer.mozilla.org/en-US/docs/Glossary/Preflight_request\\n # for more information.\\n\\n # Set CORS headers for the preflight request\\n if request.method == 'OPTIONS':\\n # Allows GET requests from any origin with the Content-Type\\n # header and caches preflight response for an 3600s\\n headers = {\\n 'Access-Control-Allow-Origin': '*',\\n 'Access-Control-Allow-Methods': '*',\\n 'Access-Control-Allow-Headers': '*',\\n 'Access-Control-Max-Age': '3600'\\n }\\n\\n return ('', 204, headers)\\n\\n # Set CORS headers for the main request\\n headers = {\\n 'Access-Control-Allow-Origin': '*'\\n }\\n\\n return ('Cors enabled!', 200, headers)\"\n },\n {\n \"alpha_fraction\": 0.6613463163375854,\n \"alphanum_fraction\": 0.6710617542266846,\n \"avg_line_length\": 30.326086044311523,\n \"blob_id\": \"8b476890af79116475a1f92e8ed3c9e16b2a4f53\",\n \"content_id\": \"46f01d9196bf4f6623da0297f6d89afaaafe7a07\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1441,\n \"license_type\": \"permissive\",\n \"max_line_length\": 100,\n \"num_lines\": 46,\n \"path\": \"/api-gateway/yaml-composer.py\",\n \"repo_name\": \"heavensward-argentlux/ninjavan-cash-mgt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import yaml\\n\\ndef compose(out_file, base_file, paths_files):\\n\\tbase = load_base(base_file)\\n\\tbase['paths'] = {}\\n\\tfor file in paths_files:\\n\\t\\twith open(file, 'r') as stream:\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tpaths = yaml.safe_load(stream)['paths'] # {'/policies' : {...}, '/policies/{policyId}' : {...} }\\n\\t\\t\\t\\tfor path in paths.keys():\\n\\t\\t\\t\\t\\tbase['paths'][path] = paths[path]\\t\\t\\t# assign above ^ to {'paths' : {...} }\\n\\t\\t\\texcept yaml.YAMLError as e: print(e)\\n\\t\\tstream.close()\\n\\tstatus = write_yaml(out_file, base)\\n\\treturn status, base\\n\\ndef load_base(base_file):\\n\\twith open(base_file, 'r') as stream:\\n\\t\\ttry: base = yaml.safe_load(stream)\\n\\t\\texcept yaml.YAMLError as e: print(e)\\n\\tstream.close()\\n\\treturn base\\n\\ndef write_yaml(out_file, data):\\n\\twith open(out_file, 'w') as stream:\\n\\t\\ttry: yaml.safe_dump(data, stream)\\n\\t\\texcept yaml.YAMLError as e: print(e)\\n\\tstream.close()\\n\\treturn 'OK'\\n\\nif __name__ == \\\"__main__\\\":\\n\\tbase_file = 'gateway-v3-base.yaml'\\n\\tpath_files = [\\n\\t\\t'paths/policy/gateway-v3-policies.yaml',\\n\\t\\t'paths/policy/gateway-v3-expenses.yaml',\\n\\t\\t'paths/policy/gateway-v3-approved.yaml',\\n\\t\\t'paths/transaction/gateway-v3-transactions.yaml',\\n\\t\\t'paths/transaction/gateway-v3-histories.yaml',\\n\\t\\t'paths/transaction/gateway-v3-images.yaml',\\n\\t\\t'paths/user/gateway-v3-locations.yaml',\\n\\t\\t'paths/user/gateway-v3-roles.yaml',\\n\\t\\t'paths/user/gateway-v3-users.yaml',\\n\\t\\t'paths/gateway-v3-analytics.yaml'\\n\\t]\\n\\tstatus, _ = compose(\\\"gateway-v3.13.yaml\\\", base_file, path_files)\\n\\tprint(status)\\n\"\n },\n {\n \"alpha_fraction\": 0.8169013857841492,\n \"alphanum_fraction\": 0.8169013857841492,\n \"avg_line_length\": 22.66666603088379,\n \"blob_id\": \"fe356edb7f3cf8cdf617d7087493f29a5b575777\",\n \"content_id\": \"0e255715529d2bf94df6e794c0a0dfd7b318e79d\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 71,\n \"license_type\": \"permissive\",\n \"max_line_length\": 49,\n \"num_lines\": 3,\n \"path\": \"/README.md\",\n \"repo_name\": \"heavensward-argentlux/ninjavan-cash-mgt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# ninjavan-cash-mgt\\n\\nNinjaVan's Petty Cash Management Capstone Project\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":3,"string":"3"}}},{"rowIdx":2979372,"cells":{"repo_name":{"kind":"string","value":"LouiseMcMahon/UKCaveGIS"},"repo_url":{"kind":"string","value":"https://github.com/LouiseMcMahon/UKCaveGIS"},"snapshot_id":{"kind":"string","value":"4dcdd1b352fcaba143008c5948cd0fa39e797536"},"revision_id":{"kind":"string","value":"b943aa8ce15674267d83e19f9a152a9b73f2d910"},"directory_id":{"kind":"string","value":"a8e3b7196bb67cdbee128ba556b44a0e1dd0a228"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-08-02T00:12:39.762179","string":"2020-08-02T00:12:39.762179"},"revision_date":{"kind":"timestamp","value":"2020-01-14T20:02:00","string":"2020-01-14T20:02:00"},"committer_date":{"kind":"timestamp","value":"2020-01-14T20:02:00","string":"2020-01-14T20:02:00"},"github_id":{"kind":"number","value":211171974,"string":"211,171,974"},"star_events_count":{"kind":"number","value":3,"string":"3"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"string","value":"MIT"},"gha_created_at":{"kind":"timestamp","value":"2019-09-26T20:02:43","string":"2019-09-26T20:02:43"},"gha_updated_at":{"kind":"timestamp","value":"2020-01-13T22:57:45","string":"2020-01-13T22:57:45"},"gha_pushed_at":{"kind":"timestamp","value":"2020-01-14T20:02:01","string":"2020-01-14T20:02:01"},"gha_language":{"kind":"string","value":"Python"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5776138305664062,"alphanum_fraction":0.5932061672210693,"avg_line_length":32.72300338745117,"blob_id":"a3b101439e194a09ccac3792850c4ba469a92008","content_id":"5eb6505b5716f7525054cf21ee42511e5b8ecde3","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":7183,"license_type":"permissive","max_line_length":125,"num_lines":213,"path":"/app/ukcavegis/pipelines.py","repo_name":"LouiseMcMahon/UKCaveGIS","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n\n# Define your item pipelines here\n#\n# Don't forget to add your pipeline to the ITEM_PIPELINES setting\n# See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html\nfrom scrapy.exporters import JsonItemExporter\nfrom scrapy.exceptions import DropItem\nfrom OSGridConverter import grid2latlong\nfrom OSGridConverter import latlong2grid\nimport simplekml\nimport re\nimport gpxpy\nimport gpxpy.gpx\n\n\n#Ensures all required fields are present and sets any empty or missing ones to None\nclass FieldCheck(object):\n def process_item(self, item, spider):\n if 'name' not in item.keys():\n raise DropItem('No name%s' % item)\n\n if 'registry' not in item.keys():\n raise DropItem('No registry set%s' % item)\n\n #2 letters + 10 numbers = 12 char min for NGR\n if 'ngr' not in item.keys() or not isinstance(item['ngr'], str) or len(item['ngr']) < 12:\n item['ngr'] = None\n\n if 'wgS84' not in item.keys():\n item['wgS84'] = None\n\n if item['wgS84'] is None and item['ngr'] is None :\n raise DropItem('wgS84 and ngr not set %s' % item)\n\n if 'length' not in item.keys():\n item['length'] = None\n\n if 'depth' not in item.keys():\n item['depth'] = None\n\n if 'altitude' not in item.keys():\n item['altitude'] = None\n\n if 'tags' not in item.keys():\n item['tags'] = None\n\n return item\n\n# Converts fields to more usefull types\nclass TypeConversion(object):\n def process_item(self, item, spider):\n if isinstance(item['wgS84'], str) and ',' in item['wgS84']:\n item['wgS84'] = item['wgS84'].split(',')\n item['wgS84'][0] = float(item['wgS84'][0])\n item['wgS84'][1] = float(item['wgS84'][1])\n\n if not isinstance(item['wgS84'], list) or len(item['wgS84']) != 2:\n item['wgS84'] = None\n\n if item['wgS84'] is None and item['ngr'] is None:\n raise DropItem('wgS84 and ngr not set %s' % item)\n\n if isinstance(item['tags'], str):\n item['tags'] = item['tags'].split(',')\n\n item['altitude'] = self.convert_string_to_int(item['altitude'])\n item['depth'] = self.convert_string_to_int(item['depth'])\n item['length'] = self.convert_string_to_int(item['length'])\n\n return item\n\n def convert_string_to_int(self, string):\n try:\n string = re.sub('[^0-9]','', string)\n string = int(string)\n except:\n return None\n\n return string\n\n# If ngr or wgS84 is missing this will attempt to generate one from the other\n# The item will not reach this pipeline if both are missing\nclass GeoDataCheck(object):\n def process_item(self, item, spider):\n if item['ngr'] is not None:\n item['ngr'] = self.pad_ngr(item['ngr'])\n\n if item['wgS84'] is None and item['ngr'] is None :\n raise DropItem('wgS84 and ngr not set %s' % item)\n\n if item['wgS84'] is None:\n latlong = grid2latlong(item['ngr'], tag='WGS84')\n item['wgS84'] = [\n latlong.latitude,\n latlong.longitude\n ]\n\n if item['ngr'] is None:\n item['ngr'] = str(latlong2grid(item['wgS84'][0], item['wgS84'][1], tag='WGS84'))\n\n return item\n\n # Returns a NGR formatted with a space after the map code and a space between the two sets of numbers\n def pad_ngr(self, ngr):\n ngr = ngr.replace(' ', '')\n\n map_code = ngr[0:2]\n numbers = ngr[2::]\n\n if len(numbers) % 2 != 0:\n return None\n\n midpoint = int((len(numbers)/2) )\n easting = numbers[0:midpoint]\n northing = numbers[midpoint::]\n\n return map_code + ' ' + easting + ' ' + northing\n\n# Generates a KML file for each region in the app/data folder\nclass KMLPipeline(object):\n documents = {}\n unsaved_count = {}\n\n def open_spider(self, spider):\n kml = simplekml.Kml()\n kml.document.name = spider.registry + ' caves and mines'\n self.documents[spider.registry] = kml\n self.unsaved_count[spider.registry] = 0\n\n def close_spider(self, spider):\n self.documents[spider.registry].save('data/' + spider.registry + '.kml')\n\n def process_item(self, item, spider):\n item_cordinates = None\n if item['altitude'] is not None and item['altitude'] > 0:\n item_cordinates = [(item['wgS84'][0], item['wgS84'][1], str(item['altitude']))]\n else:\n item_cordinates = [(item['wgS84'][0], item['wgS84'][1])]\n\n self.documents[spider.registry].newpoint(name=item['name'], coords=item_cordinates)\n\n self.unsaved_count[spider.registry] += 1\n if(self.unsaved_count[spider.registry] > 10):\n self.unsaved_count[spider.registry] = 0\n self.documents[spider.registry].save('data/' + spider.registry + '.kml')\n\n return item\n\n# Generates a GPX file for each region in the app/data folder\nclass GPXPipeline(object):\n documents = {}\n unsaved_count = {}\n\n def open_spider(self, spider):\n gpx = gpxpy.gpx.GPX()\n gpx.name = spider.registry + ' caves and mines'\n gpx.link = 'https://github.com/LouiseMcMahon/UKCaveGIS'\n gpx.link_text = 'UK Cave GIS'\n gpx.author_name = 'Louise McMahon'\n gpx.author_email = 'louise@louisemcmahon.co.uk'\n gpx.creator = 'UK Cave GIS by Louise McMahon'\n self.documents[spider.registry] = gpx\n self.unsaved_count[spider.registry] = 0\n\n def close_spider(self, spider):\n self.save_file(spider.registry)\n\n def process_item(self, item, spider):\n waypoint = gpxpy.gpx.GPXWaypoint(\n latitude=item['wgS84'][0],\n longitude=item['wgS84'][1],\n name=item['name']\n )\n\n if item['altitude'] is not None and item['altitude'] > 0:\n waypoint.elevation = item['altitude']\n\n self.documents[spider.registry].waypoints.append(waypoint)\n\n self.unsaved_count[spider.registry] += 1\n if(self.unsaved_count[spider.registry] > 10):\n self.unsaved_count[spider.registry] = 0\n self.save_file(spider.registry)\n\n return item\n\n def save_file(self, registry):\n xml = self.documents[registry].to_xml()\n f= open('data/' + registry + '.gpx', 'w+')\n f.seek(0)\n f.truncate()\n f.write(xml)\n f.close()\n\n# Generates a JSON file for each region in the app/data folder\nclass JsonPipeline(object):\n files = {}\n exporters = {}\n\n def open_spider(self, spider):\n self.files[spider.registry] = open('data/' + spider.registry + '.json', 'wb')\n self.exporters[spider.registry] = JsonItemExporter(self.files[spider.registry], encoding='utf-8', ensure_ascii=False)\n self.exporters[spider.registry].start_exporting()\n\n def close_spider(self, spider):\n self.exporters[spider.registry].finish_exporting()\n self.files[spider.registry].close()\n\n def process_item(self, item, spider):\n exported_item = {k: v for k, v in item.items() if v is not None}\n self.exporters[spider.registry].export_item(exported_item)\n return item\n"},{"alpha_fraction":0.6069204211235046,"alphanum_fraction":0.6235294342041016,"avg_line_length":40.28571319580078,"blob_id":"9358368c2e73ff603f386146afefe317cfa3c3f9","content_id":"c4e73483653386bf4daef10602360a705af7d67c","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1445,"license_type":"permissive","max_line_length":102,"num_lines":35,"path":"/app/ukcavegis/spiders/mattvoyseyregistry.py","repo_name":"LouiseMcMahon/UKCaveGIS","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\nimport scrapy\nimport logging\nfrom ukcavegis.items import Entry\nfrom ukcavegis.utils import extract_with_css, extract_with_xpath\n\nclass MattVoyseyRegistry(scrapy.Spider):\n name = None\n allowed_domains = [None]\n start_urls = [None]\n registry = None\n\n def parse(self, response):\n # follow links to cave pages\n for href in response.css('.rowhover tr')[1:].css('a::attr(href)'):\n yield response.follow(href, self.parse_cave)\n\n # follow pagination links\n for href in response.xpath('/html/body/div/div/p[1]').css('a::attr(href)'):\n yield response.follow(href, self.parse)\n\n def parse_cave(self, response):\n entry = Entry()\n\n entry['name'] = extract_with_css(response, 'h1::text')\n entry['ngr'] = extract_with_xpath(response, '/html/body/div[2]/table/tr[1]/td[2]/text()')\n entry['wgS84'] = extract_with_xpath(response, '/html/body/div[2]/table/tr[2]/td[2]/text()')\n entry['length'] = extract_with_xpath(response, '/html/body/div[2]/table/tr[3]/td[2]/text()')\n entry['depth'] = extract_with_xpath(response, '/html/body/div[2]/table/tr[4]/td[2]/text()')\n entry['altitude'] = extract_with_xpath(response, '/html/body/div[2]/table/tr[5]/td[2]/text()')\n entry['tags'] = extract_with_xpath(response, '/html/body/div[2]/table/tr[6]/td[2]/text()')\n entry['registry'] = self.registry\n\n\n yield entry\n"},{"alpha_fraction":0.6486486196517944,"alphanum_fraction":0.6486486196517944,"avg_line_length":27.461538314819336,"blob_id":"7a99c9de3344715c5ccaf61d8f44e5e6fa769ce9","content_id":"1d1ae6ba521db59980f6c34ac50d80424bda0c5c","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":370,"license_type":"permissive","max_line_length":56,"num_lines":13,"path":"/app/ukcavegis/utils.py","repo_name":"LouiseMcMahon/UKCaveGIS","src_encoding":"UTF-8","text":"def extract_with_css(response, query):\n return_value = response.css(query).extract_first()\n if (return_value):\n return return_value.strip()\n else:\n return None\n\ndef extract_with_xpath(response, query):\n return_value = response.xpath(query).extract_first()\n if(return_value):\n return return_value.strip()\n else:\n return None\n"},{"alpha_fraction":0.6818181872367859,"alphanum_fraction":0.6897233128547668,"avg_line_length":19.239999771118164,"blob_id":"504eb663011caa31f30a4cccf0cc90f6274c6c73","content_id":"4ab86ff2ab5e0b47aa0bcea4ee1724da1e9ffab7","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Dockerfile","length_bytes":506,"license_type":"permissive","max_line_length":50,"num_lines":25,"path":"/Dockerfile","repo_name":"LouiseMcMahon/UKCaveGIS","src_encoding":"UTF-8","text":"FROM python:3.7\n\n# os setup\nRUN apt-get update && apt-get -y install \\\n python-lxml \\\n build-essential \\\n libssl-dev \\\n libffi-dev \\\n python-dev \\\n libxml2-dev \\\n libxslt1-dev \\\n && rm -rf /var/lib/apt/lists/*\nRUN mkdir -p /usr/src/app\nWORKDIR /usr/src/app\n\n# install requirements\nCOPY requirements.txt /usr/src/app/\nRUN pip install --no-cache-dir -r requirements.txt\n\n# move codebase over\n# COPY ukcavegis /usr/src/app/ukcavegis\n COPY app /usr/src/app\n\n# run the spider\nCMD [\"python\", \"start.py\"]\n"},{"alpha_fraction":0.7152317762374878,"alphanum_fraction":0.7185430526733398,"avg_line_length":32.55555725097656,"blob_id":"5f6b1a3dc30b33ef5633cfe5e2e85594ab7e5f7d","content_id":"e4d82ea904fa2bc1060221045deaa410396a472f","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":302,"license_type":"permissive","max_line_length":67,"num_lines":9,"path":"/app/ukcavegis/spiders/scotlandregistry.py","repo_name":"LouiseMcMahon/UKCaveGIS","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n\nfrom ukcavegis.spiders.mattvoyseyregistry import MattVoyseyRegistry\n\nclass ScotlandRegistry(MattVoyseyRegistry):\n name = 'scotlandregistry'\n allowed_domains = ['registry.gsg.org.uk']\n start_urls = ['http://registry.gsg.org.uk/sr/browse.php']\n registry = 'Scotland'\n"},{"alpha_fraction":0.7198697328567505,"alphanum_fraction":0.723127007484436,"avg_line_length":33.11111068725586,"blob_id":"7c060d8881c64e514ebbee138187d70b66e4b1bc","content_id":"5d987874b2c29cc4bba3c2e272f922d6ac164d86","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":307,"license_type":"permissive","max_line_length":71,"num_lines":9,"path":"/app/ukcavegis/spiders/dcaregistry.py","repo_name":"LouiseMcMahon/UKCaveGIS","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n\nfrom ukcavegis.spiders.mattvoyseyregistry import MattVoyseyRegistry\n\nclass DcaRegistry(MattVoyseyRegistry):\n name = 'dcaregistry'\n allowed_domains = ['registry.thedca.org.uk']\n start_urls = ['https://registry.thedca.org.uk/registry/browse.php']\n registry = 'Derbyshire'\n"},{"alpha_fraction":0.7660377621650696,"alphanum_fraction":0.7660377621650696,"avg_line_length":43.16666793823242,"blob_id":"467a1922cb30d4d202a6a39d9927a5f3a7116f3b","content_id":"070c0dcf5d83a7a599cf6dceb57d7415aa653b06","detected_licenses":["MIT"],"is_generated":false,"is_vendor":true,"language":"Markdown","length_bytes":265,"license_type":"permissive","max_line_length":84,"num_lines":6,"path":"/.github/CONTRIBUTING.md","repo_name":"LouiseMcMahon/UKCaveGIS","src_encoding":"UTF-8","text":"## Contributing\n\nPull requests are welcome but we ask a few things:\n* Leave the code in a better state than when you started.\n* If you make changes requiring documentation add it.\n* If you wish to add a large change or feature make a issue first and check with us.\n"},{"alpha_fraction":0.7098976373672485,"alphanum_fraction":0.7133105993270874,"avg_line_length":31.55555534362793,"blob_id":"3ec45891f7cc1625cbe56a031e4448ab81bc2db3","content_id":"9fb080202bdd2d4a8a8c2a733caac807bd49c966","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":293,"license_type":"permissive","max_line_length":67,"num_lines":9,"path":"/app/ukcavegis/spiders/ducregistry.py","repo_name":"LouiseMcMahon/UKCaveGIS","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n\nfrom ukcavegis.spiders.mattvoyseyregistry import MattVoyseyRegistry\n\nclass DucRegistry(MattVoyseyRegistry):\n name = 'ducregistry'\n allowed_domains = ['dcuc.org.uk']\n start_urls = ['https://dcuc.org.uk/registry/r/browse.php']\n registry = 'DevonAndCornwall'\n"},{"alpha_fraction":0.7074829936027527,"alphanum_fraction":0.7108843326568604,"avg_line_length":31.66666603088379,"blob_id":"23cc6623e208fcbb22ec4e4d0aec127bb6c26c9e","content_id":"17360e94a5ddaee7dade76546c3a02488629ea9a","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":294,"license_type":"permissive","max_line_length":67,"num_lines":9,"path":"/app/ukcavegis/spiders/mendipregistry.py","repo_name":"LouiseMcMahon/UKCaveGIS","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n\nfrom ukcavegis.spiders.mattvoyseyregistry import MattVoyseyRegistry\n\nclass MendipRegistry(MattVoyseyRegistry):\n name = 'mendipregistry'\n allowed_domains = ['www.mcra.org.uk']\n start_urls = ['http://www.mcra.org.uk/registry/browse.php']\n registry = 'Mendip'\n"},{"alpha_fraction":0.7284345030784607,"alphanum_fraction":0.7316293716430664,"avg_line_length":33.77777862548828,"blob_id":"a21f0032162e73238f598b47b2eac3ce53e9b5ce","content_id":"36c4a886d0233d1eb4febfd6f098349724a930d8","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":313,"license_type":"permissive","max_line_length":67,"num_lines":9,"path":"/app/ukcavegis/spiders/fodccagregistry.py","repo_name":"LouiseMcMahon/UKCaveGIS","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n\nfrom ukcavegis.spiders.mattvoyseyregistry import MattVoyseyRegistry\n\nclass FodccagRegistry(MattVoyseyRegistry):\n name = 'fodccagregistry'\n allowed_domains = ['fodccag.org.uk']\n start_urls = ['http://www.fodccag.org.uk/registry/browse.php']\n registry = 'GloucestershireAndWye'\n"},{"alpha_fraction":0.8974359035491943,"alphanum_fraction":0.8974359035491943,"avg_line_length":8.75,"blob_id":"b0e4eaee62457b34b1598463efc13eb98461161e","content_id":"29bafe623146e6cb3ae89427f8f5a1a60da4d2ab","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":39,"license_type":"permissive","max_line_length":15,"num_lines":4,"path":"/requirements.txt","repo_name":"LouiseMcMahon/UKCaveGIS","src_encoding":"UTF-8","text":"scrapy\nsimplekml\nOSGridConverter\ngpxpy\n"},{"alpha_fraction":0.8651315569877625,"alphanum_fraction":0.8651315569877625,"avg_line_length":42.42856979370117,"blob_id":"06596c41572f534a75f498d6a10d2dc0ce46717f","content_id":"ceb6674daf7b77b3eaccf8c38f033d85df7284ca","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":608,"license_type":"permissive","max_line_length":132,"num_lines":14,"path":"/app/start.py","repo_name":"LouiseMcMahon/UKCaveGIS","src_encoding":"UTF-8","text":"import scrapy\nfrom scrapy.crawler import CrawlerProcess\nfrom ukcavegis.spiders import dcaregistry, mendipregistry, scotlandregistry, cnccregistry, cccregistry, fodccagregistry, ducregistry\nfrom scrapy.utils.project import get_project_settings\n\nprocess = CrawlerProcess(get_project_settings())\nprocess.crawl(dcaregistry.DcaRegistry)\nprocess.crawl(mendipregistry.MendipRegistry)\nprocess.crawl(scotlandregistry.ScotlandRegistry)\nprocess.crawl(cnccregistry.CnccRegistry)\nprocess.crawl(ducregistry.DucRegistry)\nprocess.crawl(fodccagregistry.FodccagRegistry)\nprocess.crawl(cccregistry.CccRegistry)\nprocess.start()\n"},{"alpha_fraction":0.6440678238868713,"alphanum_fraction":0.6513317227363586,"avg_line_length":21.94444465637207,"blob_id":"203b07e33858435e63ad34442fceb9eb8bbdd2e5","content_id":"7c50310103d9d21f6cd2d0a33bc28e5df4936023","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":413,"license_type":"permissive","max_line_length":52,"num_lines":18,"path":"/app/ukcavegis/items.py","repo_name":"LouiseMcMahon/UKCaveGIS","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n\n# Define here the models for your scraped items\n#\n# See documentation in:\n# https://doc.scrapy.org/en/latest/topics/items.html\n\nimport scrapy\n\nclass Entry(scrapy.Item):\n tags = scrapy.Field()\n name = scrapy.Field()\n altitude = scrapy.Field()\n ngr = scrapy.Field()\n depth = scrapy.Field()\n length = scrapy.Field()\n registry = scrapy.Field()\n wgS84 = scrapy.Field()\n"},{"alpha_fraction":0.5878220200538635,"alphanum_fraction":0.5925058722496033,"avg_line_length":31.024999618530273,"blob_id":"21f1bae9a3b1c8748f2ecf8a097a81700f2f0eb0","content_id":"e59e083d745e9b1b39c66d57ecc1e053ae7c1346","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1281,"license_type":"permissive","max_line_length":83,"num_lines":40,"path":"/app/ukcavegis/spiders/cnccregistry.py","repo_name":"LouiseMcMahon/UKCaveGIS","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\nimport scrapy\nimport logging\nfrom ukcavegis.utils import extract_with_css, extract_with_xpath\nfrom ukcavegis.items import Entry\n\nclass CnccRegistry(scrapy.Spider):\n name = 'cnccregistry'\n allowed_domains = ['cncc.org.uk']\n start_urls = ['https://cncc.org.uk/caving/caves/postback.php']\n registry = 'Yorkshire'\n\n def parse(self, response):\n # follow links to cave pages\n for link in response.xpath('//a'):\n if '/cave/' in link.attrib['href']:\n yield response.follow(link, self.parse_cave)\n\n # follow pagination links\n for href in response.xpath('/html/body/div/div/p[1]').css('a::attr(href)'):\n yield response.follow(href, self.parse)\n\n def parse_cave(self, response):\n entry = Entry()\n\n entry['name'] = extract_with_css(response, 'h1::text')\n entry['registry'] = self.registry\n\n for p in response.xpath('//p/text()').getall():\n if 'NGR:' in p:\n entry['ngr'] = self.split_colon_value(p)\n\n if 'Lat/long:' in p:\n entry['wgS84'] = self.split_colon_value(p)\n\n yield entry\n\n def split_colon_value(self, value):\n return_value = value.split(':')\n return return_value[1].strip()\n"},{"alpha_fraction":0.6267908215522766,"alphanum_fraction":0.637535810470581,"avg_line_length":44.032257080078125,"blob_id":"fc724530953484386b854aab67c346844bafe3e3","content_id":"a3d5011ee130d5756e3d7a22063862b90299620a","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1396,"license_type":"permissive","max_line_length":141,"num_lines":31,"path":"/app/ukcavegis/spiders/cccregistry.py","repo_name":"LouiseMcMahon/UKCaveGIS","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\nimport scrapy\nimport logging\nfrom ukcavegis.utils import extract_with_css, extract_with_xpath\nfrom ukcavegis.items import Entry\n\nclass CccRegistry(scrapy.Spider):\n name = 'cccregistry'\n allowed_domains = ['cambriancavingcouncil.org.uk']\n start_urls = ['http://www.cambriancavingcouncil.org.uk/registry/ccr_registry.php?reg=All+Wales+and+the+Marches&nam=']\n registry = 'WalesAndMarches'\n\n def parse(self, response):\n # follow links to cave pages\n for link in response.xpath('/html/body/table//a'):\n yield response.follow(link, self.parse_cave)\n\n def parse_cave(self, response):\n entry = Entry()\n\n entry['name'] = extract_with_css(response, '.Heading1::text')\n entry['ngr'] = extract_with_xpath(response, '/html/body/table/tr[1]/td[2]/text()[3]').replace('NGR: ','').replace('\\xa0',' ').strip()\n entry['length'] = extract_with_xpath(response, '/html/body/table/tr[1]/td[1]/text()[2]').replace('L:','').strip()\n entry['depth'] = extract_with_xpath(response, '/html/body/table/tr[1]/td[1]/text()[3]').replace('V:','').strip()\n entry['altitude'] = extract_with_xpath(response, '/html/body/table/tr[1]/td[2]/text()[4]').replace('m.asl', '').strip()\n entry['registry'] = self.registry\n\n yield entry\n\n def split_colon_value(self, value):\n return_value = value.split(':')\n"},{"alpha_fraction":0.7598353028297424,"alphanum_fraction":0.7634949684143066,"avg_line_length":53.650001525878906,"blob_id":"889a6367fc26307fbe888c03266b5273a266167a","content_id":"352cf58eba8fd4f1f0501da59912b9cba4fcf1e1","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":2186,"license_type":"permissive","max_line_length":380,"num_lines":40,"path":"/README.md","repo_name":"LouiseMcMahon/UKCaveGIS","src_encoding":"UTF-8","text":"# UK Caves GIS\nThe aim of this project is to provide GIS files for each caving region which contain the entrances of every known cave programtically.\n\nThe aim is to provide:\n\n- [x] KML\n- [x] JSON\n- [x] GPX\n\nFor the following registrys:\n- [x] [Derbyshire](https://registry.thedca.org.uk/])\n- [x] [North Yorkshire](https://cncc.org.uk/caving/caves/index.php?keyword=&sort=last_updated%20DESC)\n- [x] [Wales and related areas](http://www.cambriancavingcouncil.org.uk/registry/ccr_registry.php?reg=All+Wales+and+the+Marches&nam=)\n- [x] [Gloucestershire and Wye Valley](http://www.fodccag.org.uk/registry/registrysearch.php)\n- [X] [Devon and Cornwall](https://dcuc.org.uk/registry/r/registrysearch.php)\n- [x] [Mendip](http://www.mcra.org.uk/registry/)\n- [x] [Scotland](http://registry.gsg.org.uk/sr/registrysearch.php)\n- [ ] [Ireland](http://www.ubss.org.uk/search_irishcaves.php)\n\n# Download\nTo download the output from this repository go to the releases page and download the file for the region you are interested in.\n\n## Accuracy\nAll efforts have been made to ensure accuracy of the output files but not every registry entry is acurate to begin with. Not every registry entry lists WGS84 or NGR which means it has to be derived from the other if availaible which can introduce an error. If you chose to use this data for navigation its not my fault if you end up wandering around on leck fell lost in the mist!\n\n# Developing\nPull requests welcome but please read the contribution guidelines first [here](.github/contributing.md)\n\nThe tool is written in python 3.4 using [scrapy](https://scrapy.org/) the output is released by commits to master via travis CI\nDocker is used manage the python enviroment.\n\n## Running\n1. First build the docker image `docker-compose build`\n1. Then run the spiders with `docker-compose up`\n\nTo run scrapy commands run `docker-compose run scrapy scrapy <command>` such as docker-compose run scrapy scrapy crawl <registry>` to crawl a single registry.\n\nTo have access to the containers shell run `docker-compose run scrapy /bin/bash`\n\nYou may need to reset the owner and permissions of files created by docker/scrapy using `sudo chmod` & `sudo chown` on linux.\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5776138305664062,\n \"alphanum_fraction\": 0.5932061672210693,\n \"avg_line_length\": 32.72300338745117,\n \"blob_id\": \"a3b101439e194a09ccac3792850c4ba469a92008\",\n \"content_id\": \"5eb6505b5716f7525054cf21ee42511e5b8ecde3\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 7183,\n \"license_type\": \"permissive\",\n \"max_line_length\": 125,\n \"num_lines\": 213,\n \"path\": \"/app/ukcavegis/pipelines.py\",\n \"repo_name\": \"LouiseMcMahon/UKCaveGIS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n\\n# Define your item pipelines here\\n#\\n# Don't forget to add your pipeline to the ITEM_PIPELINES setting\\n# See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html\\nfrom scrapy.exporters import JsonItemExporter\\nfrom scrapy.exceptions import DropItem\\nfrom OSGridConverter import grid2latlong\\nfrom OSGridConverter import latlong2grid\\nimport simplekml\\nimport re\\nimport gpxpy\\nimport gpxpy.gpx\\n\\n\\n#Ensures all required fields are present and sets any empty or missing ones to None\\nclass FieldCheck(object):\\n def process_item(self, item, spider):\\n if 'name' not in item.keys():\\n raise DropItem('No name%s' % item)\\n\\n if 'registry' not in item.keys():\\n raise DropItem('No registry set%s' % item)\\n\\n #2 letters + 10 numbers = 12 char min for NGR\\n if 'ngr' not in item.keys() or not isinstance(item['ngr'], str) or len(item['ngr']) < 12:\\n item['ngr'] = None\\n\\n if 'wgS84' not in item.keys():\\n item['wgS84'] = None\\n\\n if item['wgS84'] is None and item['ngr'] is None :\\n raise DropItem('wgS84 and ngr not set %s' % item)\\n\\n if 'length' not in item.keys():\\n item['length'] = None\\n\\n if 'depth' not in item.keys():\\n item['depth'] = None\\n\\n if 'altitude' not in item.keys():\\n item['altitude'] = None\\n\\n if 'tags' not in item.keys():\\n item['tags'] = None\\n\\n return item\\n\\n# Converts fields to more usefull types\\nclass TypeConversion(object):\\n def process_item(self, item, spider):\\n if isinstance(item['wgS84'], str) and ',' in item['wgS84']:\\n item['wgS84'] = item['wgS84'].split(',')\\n item['wgS84'][0] = float(item['wgS84'][0])\\n item['wgS84'][1] = float(item['wgS84'][1])\\n\\n if not isinstance(item['wgS84'], list) or len(item['wgS84']) != 2:\\n item['wgS84'] = None\\n\\n if item['wgS84'] is None and item['ngr'] is None:\\n raise DropItem('wgS84 and ngr not set %s' % item)\\n\\n if isinstance(item['tags'], str):\\n item['tags'] = item['tags'].split(',')\\n\\n item['altitude'] = self.convert_string_to_int(item['altitude'])\\n item['depth'] = self.convert_string_to_int(item['depth'])\\n item['length'] = self.convert_string_to_int(item['length'])\\n\\n return item\\n\\n def convert_string_to_int(self, string):\\n try:\\n string = re.sub('[^0-9]','', string)\\n string = int(string)\\n except:\\n return None\\n\\n return string\\n\\n# If ngr or wgS84 is missing this will attempt to generate one from the other\\n# The item will not reach this pipeline if both are missing\\nclass GeoDataCheck(object):\\n def process_item(self, item, spider):\\n if item['ngr'] is not None:\\n item['ngr'] = self.pad_ngr(item['ngr'])\\n\\n if item['wgS84'] is None and item['ngr'] is None :\\n raise DropItem('wgS84 and ngr not set %s' % item)\\n\\n if item['wgS84'] is None:\\n latlong = grid2latlong(item['ngr'], tag='WGS84')\\n item['wgS84'] = [\\n latlong.latitude,\\n latlong.longitude\\n ]\\n\\n if item['ngr'] is None:\\n item['ngr'] = str(latlong2grid(item['wgS84'][0], item['wgS84'][1], tag='WGS84'))\\n\\n return item\\n\\n # Returns a NGR formatted with a space after the map code and a space between the two sets of numbers\\n def pad_ngr(self, ngr):\\n ngr = ngr.replace(' ', '')\\n\\n map_code = ngr[0:2]\\n numbers = ngr[2::]\\n\\n if len(numbers) % 2 != 0:\\n return None\\n\\n midpoint = int((len(numbers)/2) )\\n easting = numbers[0:midpoint]\\n northing = numbers[midpoint::]\\n\\n return map_code + ' ' + easting + ' ' + northing\\n\\n# Generates a KML file for each region in the app/data folder\\nclass KMLPipeline(object):\\n documents = {}\\n unsaved_count = {}\\n\\n def open_spider(self, spider):\\n kml = simplekml.Kml()\\n kml.document.name = spider.registry + ' caves and mines'\\n self.documents[spider.registry] = kml\\n self.unsaved_count[spider.registry] = 0\\n\\n def close_spider(self, spider):\\n self.documents[spider.registry].save('data/' + spider.registry + '.kml')\\n\\n def process_item(self, item, spider):\\n item_cordinates = None\\n if item['altitude'] is not None and item['altitude'] > 0:\\n item_cordinates = [(item['wgS84'][0], item['wgS84'][1], str(item['altitude']))]\\n else:\\n item_cordinates = [(item['wgS84'][0], item['wgS84'][1])]\\n\\n self.documents[spider.registry].newpoint(name=item['name'], coords=item_cordinates)\\n\\n self.unsaved_count[spider.registry] += 1\\n if(self.unsaved_count[spider.registry] > 10):\\n self.unsaved_count[spider.registry] = 0\\n self.documents[spider.registry].save('data/' + spider.registry + '.kml')\\n\\n return item\\n\\n# Generates a GPX file for each region in the app/data folder\\nclass GPXPipeline(object):\\n documents = {}\\n unsaved_count = {}\\n\\n def open_spider(self, spider):\\n gpx = gpxpy.gpx.GPX()\\n gpx.name = spider.registry + ' caves and mines'\\n gpx.link = 'https://github.com/LouiseMcMahon/UKCaveGIS'\\n gpx.link_text = 'UK Cave GIS'\\n gpx.author_name = 'Louise McMahon'\\n gpx.author_email = 'louise@louisemcmahon.co.uk'\\n gpx.creator = 'UK Cave GIS by Louise McMahon'\\n self.documents[spider.registry] = gpx\\n self.unsaved_count[spider.registry] = 0\\n\\n def close_spider(self, spider):\\n self.save_file(spider.registry)\\n\\n def process_item(self, item, spider):\\n waypoint = gpxpy.gpx.GPXWaypoint(\\n latitude=item['wgS84'][0],\\n longitude=item['wgS84'][1],\\n name=item['name']\\n )\\n\\n if item['altitude'] is not None and item['altitude'] > 0:\\n waypoint.elevation = item['altitude']\\n\\n self.documents[spider.registry].waypoints.append(waypoint)\\n\\n self.unsaved_count[spider.registry] += 1\\n if(self.unsaved_count[spider.registry] > 10):\\n self.unsaved_count[spider.registry] = 0\\n self.save_file(spider.registry)\\n\\n return item\\n\\n def save_file(self, registry):\\n xml = self.documents[registry].to_xml()\\n f= open('data/' + registry + '.gpx', 'w+')\\n f.seek(0)\\n f.truncate()\\n f.write(xml)\\n f.close()\\n\\n# Generates a JSON file for each region in the app/data folder\\nclass JsonPipeline(object):\\n files = {}\\n exporters = {}\\n\\n def open_spider(self, spider):\\n self.files[spider.registry] = open('data/' + spider.registry + '.json', 'wb')\\n self.exporters[spider.registry] = JsonItemExporter(self.files[spider.registry], encoding='utf-8', ensure_ascii=False)\\n self.exporters[spider.registry].start_exporting()\\n\\n def close_spider(self, spider):\\n self.exporters[spider.registry].finish_exporting()\\n self.files[spider.registry].close()\\n\\n def process_item(self, item, spider):\\n exported_item = {k: v for k, v in item.items() if v is not None}\\n self.exporters[spider.registry].export_item(exported_item)\\n return item\\n\"\n },\n {\n \"alpha_fraction\": 0.6069204211235046,\n \"alphanum_fraction\": 0.6235294342041016,\n \"avg_line_length\": 40.28571319580078,\n \"blob_id\": \"9358368c2e73ff603f386146afefe317cfa3c3f9\",\n \"content_id\": \"c4e73483653386bf4daef10602360a705af7d67c\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1445,\n \"license_type\": \"permissive\",\n \"max_line_length\": 102,\n \"num_lines\": 35,\n \"path\": \"/app/ukcavegis/spiders/mattvoyseyregistry.py\",\n \"repo_name\": \"LouiseMcMahon/UKCaveGIS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\nimport scrapy\\nimport logging\\nfrom ukcavegis.items import Entry\\nfrom ukcavegis.utils import extract_with_css, extract_with_xpath\\n\\nclass MattVoyseyRegistry(scrapy.Spider):\\n name = None\\n allowed_domains = [None]\\n start_urls = [None]\\n registry = None\\n\\n def parse(self, response):\\n # follow links to cave pages\\n for href in response.css('.rowhover tr')[1:].css('a::attr(href)'):\\n yield response.follow(href, self.parse_cave)\\n\\n # follow pagination links\\n for href in response.xpath('/html/body/div/div/p[1]').css('a::attr(href)'):\\n yield response.follow(href, self.parse)\\n\\n def parse_cave(self, response):\\n entry = Entry()\\n\\n entry['name'] = extract_with_css(response, 'h1::text')\\n entry['ngr'] = extract_with_xpath(response, '/html/body/div[2]/table/tr[1]/td[2]/text()')\\n entry['wgS84'] = extract_with_xpath(response, '/html/body/div[2]/table/tr[2]/td[2]/text()')\\n entry['length'] = extract_with_xpath(response, '/html/body/div[2]/table/tr[3]/td[2]/text()')\\n entry['depth'] = extract_with_xpath(response, '/html/body/div[2]/table/tr[4]/td[2]/text()')\\n entry['altitude'] = extract_with_xpath(response, '/html/body/div[2]/table/tr[5]/td[2]/text()')\\n entry['tags'] = extract_with_xpath(response, '/html/body/div[2]/table/tr[6]/td[2]/text()')\\n entry['registry'] = self.registry\\n\\n\\n yield entry\\n\"\n },\n {\n \"alpha_fraction\": 0.6486486196517944,\n \"alphanum_fraction\": 0.6486486196517944,\n \"avg_line_length\": 27.461538314819336,\n \"blob_id\": \"7a99c9de3344715c5ccaf61d8f44e5e6fa769ce9\",\n \"content_id\": \"1d1ae6ba521db59980f6c34ac50d80424bda0c5c\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 370,\n \"license_type\": \"permissive\",\n \"max_line_length\": 56,\n \"num_lines\": 13,\n \"path\": \"/app/ukcavegis/utils.py\",\n \"repo_name\": \"LouiseMcMahon/UKCaveGIS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def extract_with_css(response, query):\\n return_value = response.css(query).extract_first()\\n if (return_value):\\n return return_value.strip()\\n else:\\n return None\\n\\ndef extract_with_xpath(response, query):\\n return_value = response.xpath(query).extract_first()\\n if(return_value):\\n return return_value.strip()\\n else:\\n return None\\n\"\n },\n {\n \"alpha_fraction\": 0.6818181872367859,\n \"alphanum_fraction\": 0.6897233128547668,\n \"avg_line_length\": 19.239999771118164,\n \"blob_id\": \"504eb663011caa31f30a4cccf0cc90f6274c6c73\",\n \"content_id\": \"4ab86ff2ab5e0b47aa0bcea4ee1724da1e9ffab7\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Dockerfile\",\n \"length_bytes\": 506,\n \"license_type\": \"permissive\",\n \"max_line_length\": 50,\n \"num_lines\": 25,\n \"path\": \"/Dockerfile\",\n \"repo_name\": \"LouiseMcMahon/UKCaveGIS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"FROM python:3.7\\n\\n# os setup\\nRUN apt-get update && apt-get -y install \\\\\\n python-lxml \\\\\\n build-essential \\\\\\n libssl-dev \\\\\\n libffi-dev \\\\\\n python-dev \\\\\\n libxml2-dev \\\\\\n libxslt1-dev \\\\\\n && rm -rf /var/lib/apt/lists/*\\nRUN mkdir -p /usr/src/app\\nWORKDIR /usr/src/app\\n\\n# install requirements\\nCOPY requirements.txt /usr/src/app/\\nRUN pip install --no-cache-dir -r requirements.txt\\n\\n# move codebase over\\n# COPY ukcavegis /usr/src/app/ukcavegis\\n COPY app /usr/src/app\\n\\n# run the spider\\nCMD [\\\"python\\\", \\\"start.py\\\"]\\n\"\n },\n {\n \"alpha_fraction\": 0.7152317762374878,\n \"alphanum_fraction\": 0.7185430526733398,\n \"avg_line_length\": 32.55555725097656,\n \"blob_id\": \"5f6b1a3dc30b33ef5633cfe5e2e85594ab7e5f7d\",\n \"content_id\": \"e4d82ea904fa2bc1060221045deaa410396a472f\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 302,\n \"license_type\": \"permissive\",\n \"max_line_length\": 67,\n \"num_lines\": 9,\n \"path\": \"/app/ukcavegis/spiders/scotlandregistry.py\",\n \"repo_name\": \"LouiseMcMahon/UKCaveGIS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n\\nfrom ukcavegis.spiders.mattvoyseyregistry import MattVoyseyRegistry\\n\\nclass ScotlandRegistry(MattVoyseyRegistry):\\n name = 'scotlandregistry'\\n allowed_domains = ['registry.gsg.org.uk']\\n start_urls = ['http://registry.gsg.org.uk/sr/browse.php']\\n registry = 'Scotland'\\n\"\n },\n {\n \"alpha_fraction\": 0.7198697328567505,\n \"alphanum_fraction\": 0.723127007484436,\n \"avg_line_length\": 33.11111068725586,\n \"blob_id\": \"7c060d8881c64e514ebbee138187d70b66e4b1bc\",\n \"content_id\": \"5d987874b2c29cc4bba3c2e272f922d6ac164d86\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 307,\n \"license_type\": \"permissive\",\n \"max_line_length\": 71,\n \"num_lines\": 9,\n \"path\": \"/app/ukcavegis/spiders/dcaregistry.py\",\n \"repo_name\": \"LouiseMcMahon/UKCaveGIS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n\\nfrom ukcavegis.spiders.mattvoyseyregistry import MattVoyseyRegistry\\n\\nclass DcaRegistry(MattVoyseyRegistry):\\n name = 'dcaregistry'\\n allowed_domains = ['registry.thedca.org.uk']\\n start_urls = ['https://registry.thedca.org.uk/registry/browse.php']\\n registry = 'Derbyshire'\\n\"\n },\n {\n \"alpha_fraction\": 0.7660377621650696,\n \"alphanum_fraction\": 0.7660377621650696,\n \"avg_line_length\": 43.16666793823242,\n \"blob_id\": \"467a1922cb30d4d202a6a39d9927a5f3a7116f3b\",\n \"content_id\": \"070c0dcf5d83a7a599cf6dceb57d7415aa653b06\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": true,\n \"language\": \"Markdown\",\n \"length_bytes\": 265,\n \"license_type\": \"permissive\",\n \"max_line_length\": 84,\n \"num_lines\": 6,\n \"path\": \"/.github/CONTRIBUTING.md\",\n \"repo_name\": \"LouiseMcMahon/UKCaveGIS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"## Contributing\\n\\nPull requests are welcome but we ask a few things:\\n* Leave the code in a better state than when you started.\\n* If you make changes requiring documentation add it.\\n* If you wish to add a large change or feature make a issue first and check with us.\\n\"\n },\n {\n \"alpha_fraction\": 0.7098976373672485,\n \"alphanum_fraction\": 0.7133105993270874,\n \"avg_line_length\": 31.55555534362793,\n \"blob_id\": \"3ec45891f7cc1625cbe56a031e4448ab81bc2db3\",\n \"content_id\": \"9fb080202bdd2d4a8a8c2a733caac807bd49c966\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 293,\n \"license_type\": \"permissive\",\n \"max_line_length\": 67,\n \"num_lines\": 9,\n \"path\": \"/app/ukcavegis/spiders/ducregistry.py\",\n \"repo_name\": \"LouiseMcMahon/UKCaveGIS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n\\nfrom ukcavegis.spiders.mattvoyseyregistry import MattVoyseyRegistry\\n\\nclass DucRegistry(MattVoyseyRegistry):\\n name = 'ducregistry'\\n allowed_domains = ['dcuc.org.uk']\\n start_urls = ['https://dcuc.org.uk/registry/r/browse.php']\\n registry = 'DevonAndCornwall'\\n\"\n },\n {\n \"alpha_fraction\": 0.7074829936027527,\n \"alphanum_fraction\": 0.7108843326568604,\n \"avg_line_length\": 31.66666603088379,\n \"blob_id\": \"23cc6623e208fcbb22ec4e4d0aec127bb6c26c9e\",\n \"content_id\": \"17360e94a5ddaee7dade76546c3a02488629ea9a\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 294,\n \"license_type\": \"permissive\",\n \"max_line_length\": 67,\n \"num_lines\": 9,\n \"path\": \"/app/ukcavegis/spiders/mendipregistry.py\",\n \"repo_name\": \"LouiseMcMahon/UKCaveGIS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n\\nfrom ukcavegis.spiders.mattvoyseyregistry import MattVoyseyRegistry\\n\\nclass MendipRegistry(MattVoyseyRegistry):\\n name = 'mendipregistry'\\n allowed_domains = ['www.mcra.org.uk']\\n start_urls = ['http://www.mcra.org.uk/registry/browse.php']\\n registry = 'Mendip'\\n\"\n },\n {\n \"alpha_fraction\": 0.7284345030784607,\n \"alphanum_fraction\": 0.7316293716430664,\n \"avg_line_length\": 33.77777862548828,\n \"blob_id\": \"a21f0032162e73238f598b47b2eac3ce53e9b5ce\",\n \"content_id\": \"36c4a886d0233d1eb4febfd6f098349724a930d8\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 313,\n \"license_type\": \"permissive\",\n \"max_line_length\": 67,\n \"num_lines\": 9,\n \"path\": \"/app/ukcavegis/spiders/fodccagregistry.py\",\n \"repo_name\": \"LouiseMcMahon/UKCaveGIS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n\\nfrom ukcavegis.spiders.mattvoyseyregistry import MattVoyseyRegistry\\n\\nclass FodccagRegistry(MattVoyseyRegistry):\\n name = 'fodccagregistry'\\n allowed_domains = ['fodccag.org.uk']\\n start_urls = ['http://www.fodccag.org.uk/registry/browse.php']\\n registry = 'GloucestershireAndWye'\\n\"\n },\n {\n \"alpha_fraction\": 0.8974359035491943,\n \"alphanum_fraction\": 0.8974359035491943,\n \"avg_line_length\": 8.75,\n \"blob_id\": \"b0e4eaee62457b34b1598463efc13eb98461161e\",\n \"content_id\": \"29bafe623146e6cb3ae89427f8f5a1a60da4d2ab\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 39,\n \"license_type\": \"permissive\",\n \"max_line_length\": 15,\n \"num_lines\": 4,\n \"path\": \"/requirements.txt\",\n \"repo_name\": \"LouiseMcMahon/UKCaveGIS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"scrapy\\nsimplekml\\nOSGridConverter\\ngpxpy\\n\"\n },\n {\n \"alpha_fraction\": 0.8651315569877625,\n \"alphanum_fraction\": 0.8651315569877625,\n \"avg_line_length\": 42.42856979370117,\n \"blob_id\": \"06596c41572f534a75f498d6a10d2dc0ce46717f\",\n \"content_id\": \"ceb6674daf7b77b3eaccf8c38f033d85df7284ca\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 608,\n \"license_type\": \"permissive\",\n \"max_line_length\": 132,\n \"num_lines\": 14,\n \"path\": \"/app/start.py\",\n \"repo_name\": \"LouiseMcMahon/UKCaveGIS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import scrapy\\nfrom scrapy.crawler import CrawlerProcess\\nfrom ukcavegis.spiders import dcaregistry, mendipregistry, scotlandregistry, cnccregistry, cccregistry, fodccagregistry, ducregistry\\nfrom scrapy.utils.project import get_project_settings\\n\\nprocess = CrawlerProcess(get_project_settings())\\nprocess.crawl(dcaregistry.DcaRegistry)\\nprocess.crawl(mendipregistry.MendipRegistry)\\nprocess.crawl(scotlandregistry.ScotlandRegistry)\\nprocess.crawl(cnccregistry.CnccRegistry)\\nprocess.crawl(ducregistry.DucRegistry)\\nprocess.crawl(fodccagregistry.FodccagRegistry)\\nprocess.crawl(cccregistry.CccRegistry)\\nprocess.start()\\n\"\n },\n {\n \"alpha_fraction\": 0.6440678238868713,\n \"alphanum_fraction\": 0.6513317227363586,\n \"avg_line_length\": 21.94444465637207,\n \"blob_id\": \"203b07e33858435e63ad34442fceb9eb8bbdd2e5\",\n \"content_id\": \"7c50310103d9d21f6cd2d0a33bc28e5df4936023\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 413,\n \"license_type\": \"permissive\",\n \"max_line_length\": 52,\n \"num_lines\": 18,\n \"path\": \"/app/ukcavegis/items.py\",\n \"repo_name\": \"LouiseMcMahon/UKCaveGIS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n\\n# Define here the models for your scraped items\\n#\\n# See documentation in:\\n# https://doc.scrapy.org/en/latest/topics/items.html\\n\\nimport scrapy\\n\\nclass Entry(scrapy.Item):\\n tags = scrapy.Field()\\n name = scrapy.Field()\\n altitude = scrapy.Field()\\n ngr = scrapy.Field()\\n depth = scrapy.Field()\\n length = scrapy.Field()\\n registry = scrapy.Field()\\n wgS84 = scrapy.Field()\\n\"\n },\n {\n \"alpha_fraction\": 0.5878220200538635,\n \"alphanum_fraction\": 0.5925058722496033,\n \"avg_line_length\": 31.024999618530273,\n \"blob_id\": \"21f1bae9a3b1c8748f2ecf8a097a81700f2f0eb0\",\n \"content_id\": \"e59e083d745e9b1b39c66d57ecc1e053ae7c1346\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1281,\n \"license_type\": \"permissive\",\n \"max_line_length\": 83,\n \"num_lines\": 40,\n \"path\": \"/app/ukcavegis/spiders/cnccregistry.py\",\n \"repo_name\": \"LouiseMcMahon/UKCaveGIS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\nimport scrapy\\nimport logging\\nfrom ukcavegis.utils import extract_with_css, extract_with_xpath\\nfrom ukcavegis.items import Entry\\n\\nclass CnccRegistry(scrapy.Spider):\\n name = 'cnccregistry'\\n allowed_domains = ['cncc.org.uk']\\n start_urls = ['https://cncc.org.uk/caving/caves/postback.php']\\n registry = 'Yorkshire'\\n\\n def parse(self, response):\\n # follow links to cave pages\\n for link in response.xpath('//a'):\\n if '/cave/' in link.attrib['href']:\\n yield response.follow(link, self.parse_cave)\\n\\n # follow pagination links\\n for href in response.xpath('/html/body/div/div/p[1]').css('a::attr(href)'):\\n yield response.follow(href, self.parse)\\n\\n def parse_cave(self, response):\\n entry = Entry()\\n\\n entry['name'] = extract_with_css(response, 'h1::text')\\n entry['registry'] = self.registry\\n\\n for p in response.xpath('//p/text()').getall():\\n if 'NGR:' in p:\\n entry['ngr'] = self.split_colon_value(p)\\n\\n if 'Lat/long:' in p:\\n entry['wgS84'] = self.split_colon_value(p)\\n\\n yield entry\\n\\n def split_colon_value(self, value):\\n return_value = value.split(':')\\n return return_value[1].strip()\\n\"\n },\n {\n \"alpha_fraction\": 0.6267908215522766,\n \"alphanum_fraction\": 0.637535810470581,\n \"avg_line_length\": 44.032257080078125,\n \"blob_id\": \"fc724530953484386b854aab67c346844bafe3e3\",\n \"content_id\": \"a3d5011ee130d5756e3d7a22063862b90299620a\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1396,\n \"license_type\": \"permissive\",\n \"max_line_length\": 141,\n \"num_lines\": 31,\n \"path\": \"/app/ukcavegis/spiders/cccregistry.py\",\n \"repo_name\": \"LouiseMcMahon/UKCaveGIS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\nimport scrapy\\nimport logging\\nfrom ukcavegis.utils import extract_with_css, extract_with_xpath\\nfrom ukcavegis.items import Entry\\n\\nclass CccRegistry(scrapy.Spider):\\n name = 'cccregistry'\\n allowed_domains = ['cambriancavingcouncil.org.uk']\\n start_urls = ['http://www.cambriancavingcouncil.org.uk/registry/ccr_registry.php?reg=All+Wales+and+the+Marches&nam=']\\n registry = 'WalesAndMarches'\\n\\n def parse(self, response):\\n # follow links to cave pages\\n for link in response.xpath('/html/body/table//a'):\\n yield response.follow(link, self.parse_cave)\\n\\n def parse_cave(self, response):\\n entry = Entry()\\n\\n entry['name'] = extract_with_css(response, '.Heading1::text')\\n entry['ngr'] = extract_with_xpath(response, '/html/body/table/tr[1]/td[2]/text()[3]').replace('NGR: ','').replace('\\\\xa0',' ').strip()\\n entry['length'] = extract_with_xpath(response, '/html/body/table/tr[1]/td[1]/text()[2]').replace('L:','').strip()\\n entry['depth'] = extract_with_xpath(response, '/html/body/table/tr[1]/td[1]/text()[3]').replace('V:','').strip()\\n entry['altitude'] = extract_with_xpath(response, '/html/body/table/tr[1]/td[2]/text()[4]').replace('m.asl', '').strip()\\n entry['registry'] = self.registry\\n\\n yield entry\\n\\n def split_colon_value(self, value):\\n return_value = value.split(':')\\n\"\n },\n {\n \"alpha_fraction\": 0.7598353028297424,\n \"alphanum_fraction\": 0.7634949684143066,\n \"avg_line_length\": 53.650001525878906,\n \"blob_id\": \"889a6367fc26307fbe888c03266b5273a266167a\",\n \"content_id\": \"352cf58eba8fd4f1f0501da59912b9cba4fcf1e1\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 2186,\n \"license_type\": \"permissive\",\n \"max_line_length\": 380,\n \"num_lines\": 40,\n \"path\": \"/README.md\",\n \"repo_name\": \"LouiseMcMahon/UKCaveGIS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# UK Caves GIS\\nThe aim of this project is to provide GIS files for each caving region which contain the entrances of every known cave programtically.\\n\\nThe aim is to provide:\\n\\n- [x] KML\\n- [x] JSON\\n- [x] GPX\\n\\nFor the following registrys:\\n- [x] [Derbyshire](https://registry.thedca.org.uk/])\\n- [x] [North Yorkshire](https://cncc.org.uk/caving/caves/index.php?keyword=&sort=last_updated%20DESC)\\n- [x] [Wales and related areas](http://www.cambriancavingcouncil.org.uk/registry/ccr_registry.php?reg=All+Wales+and+the+Marches&nam=)\\n- [x] [Gloucestershire and Wye Valley](http://www.fodccag.org.uk/registry/registrysearch.php)\\n- [X] [Devon and Cornwall](https://dcuc.org.uk/registry/r/registrysearch.php)\\n- [x] [Mendip](http://www.mcra.org.uk/registry/)\\n- [x] [Scotland](http://registry.gsg.org.uk/sr/registrysearch.php)\\n- [ ] [Ireland](http://www.ubss.org.uk/search_irishcaves.php)\\n\\n# Download\\nTo download the output from this repository go to the releases page and download the file for the region you are interested in.\\n\\n## Accuracy\\nAll efforts have been made to ensure accuracy of the output files but not every registry entry is acurate to begin with. Not every registry entry lists WGS84 or NGR which means it has to be derived from the other if availaible which can introduce an error. If you chose to use this data for navigation its not my fault if you end up wandering around on leck fell lost in the mist!\\n\\n# Developing\\nPull requests welcome but please read the contribution guidelines first [here](.github/contributing.md)\\n\\nThe tool is written in python 3.4 using [scrapy](https://scrapy.org/) the output is released by commits to master via travis CI\\nDocker is used manage the python enviroment.\\n\\n## Running\\n1. First build the docker image `docker-compose build`\\n1. Then run the spiders with `docker-compose up`\\n\\nTo run scrapy commands run `docker-compose run scrapy scrapy <command>` such as docker-compose run scrapy scrapy crawl <registry>` to crawl a single registry.\\n\\nTo have access to the containers shell run `docker-compose run scrapy /bin/bash`\\n\\nYou may need to reset the owner and permissions of files created by docker/scrapy using `sudo chmod` & `sudo chown` on linux.\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":16,"string":"16"}}},{"rowIdx":2979373,"cells":{"repo_name":{"kind":"string","value":"foy4748/university_fees"},"repo_url":{"kind":"string","value":"https://github.com/foy4748/university_fees"},"snapshot_id":{"kind":"string","value":"4872edf56a8eb166ca6197ff129fff47a3cf2b32"},"revision_id":{"kind":"string","value":"4c5ab6e8dc94996320f6f85745f03c505722bafe"},"directory_id":{"kind":"string","value":"d6f5d896a9a3d677b101087d816b795c386f33df"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"timestamp","value":"2023-07-13T02:48:46.260613","string":"2023-07-13T02:48:46.260613"},"revision_date":{"kind":"timestamp","value":"2021-08-21T18:54:32","string":"2021-08-21T18:54:32"},"committer_date":{"kind":"timestamp","value":"2021-08-21T18:54:32","string":"2021-08-21T18:54:32"},"github_id":{"kind":"number","value":398825034,"string":"398,825,034"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5382612943649292,"alphanum_fraction":0.5419052839279175,"avg_line_length":26.056337356567383,"blob_id":"beac3b24d12820a14b3db1743b47cdf3ef683e7a","content_id":"86a66eaa11ed806d3673b3598cd052ab216fc9ab","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1921,"license_type":"no_license","max_line_length":64,"num_lines":71,"path":"/analysis.py","repo_name":"foy4748/university_fees","src_encoding":"UTF-8","text":"from openpyxl import load_workbook as lw\nimport unidecode\n\n#Native libraries\nimport re\nimport csv\nimport os\n#################\n\n#Reading the Workbook\nwb = lw('US-News Ranking.xlsx', data_only= True)\n\n#Getting sheet names\nsheets = wb.sheetnames\n\n#Getting rid of previously stored data\nfiles = os.listdir(\"./\")\nif \"compiled.csv\" in files:\n os.remove(\"compiled.csv\")\n\n#Finds which column contains tuition fee\ndef tuitionColumn(sheetname, wb):\n w_sh = wb[sheetname]\n letters = [\"A\", \"B\", \"C\", \"D\",\"E\",\"F\",\"G\", \"H\",\"I\",\"J\"]\n results = []\n for j in range(1, len(letters)):\n cell = w_sh.cell(row=1,column=j).value\n if cell == None:\n continue\n matched = re.search(r\"^tuition\", cell, re.IGNORECASE)\n if matched != None:\n col = j\n break\n else:\n col = None\n continue\n return col\n\n#Read through University names, Department and Tuition fee\ndef readSheet(sheetname, wb,tC): #tC = tuition column\n if tC == None:\n return\n i = 2\n w_sh = wb[sheetname]\n with open('compiled.csv','a+',newline='') as f:\n cursor = csv.writer(f)\n while w_sh.cell(row=i, column=tC).value != None:\n university = w_sh.cell(row=i, column=2).value\n university = unidecode.unidecode(university)\n tuition_fee = w_sh.cell(row=i, column=tC).value\n\n if type(tuition_fee) is float:\n tf = tuition_fee\n else:\n try:\n tf = float(re.sub(r'[\\D]', \"\", tuition_fee))\n except:\n tf = ''\n cursor.writerow([university,sheetname,tf])\n i = i + 1\n\n#Main\nif __name__ == '__main__':\n lst = []\n for sheet in sheets:\n tc = tuitionColumn(sheet, wb)\n if tc == None:\n continue\n lst.append((sheet, tc))\n for i in lst:\n readSheet(i[0], wb, i[1])\n"},{"alpha_fraction":0.5047438144683838,"alphanum_fraction":0.5161290168762207,"avg_line_length":26.63157844543457,"blob_id":"ed59852951a1d6e8efa29c2bafcb881e8f20ea23","content_id":"f4281d940c2356b9678a0135a9d7e7184f0bb454","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":527,"license_type":"no_license","max_line_length":61,"num_lines":19,"path":"/structuring.py","repo_name":"foy4748/university_fees","src_encoding":"UTF-8","text":"import csv\nimport json\n\nwith open('./compiled.csv', newline='') as f:\n reader = csv.reader(f)\n storage = dict()\n for row in reader:\n if row[2] == '':\n continue\n university = row[0].split()\n uni = ' '.join(university)\n if uni not in storage:\n storage[uni] = {'dept':[row[1]], 'fee':[row[2]] }\n else:\n storage[uni]['dept'].append(row[1])\n storage[uni]['fee'].append(row[2])\n\nwith open('compiled.json', 'w') as f:\n json.dump(storage, f)\n\n\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5382612943649292,\n \"alphanum_fraction\": 0.5419052839279175,\n \"avg_line_length\": 26.056337356567383,\n \"blob_id\": \"beac3b24d12820a14b3db1743b47cdf3ef683e7a\",\n \"content_id\": \"86a66eaa11ed806d3673b3598cd052ab216fc9ab\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1921,\n \"license_type\": \"no_license\",\n \"max_line_length\": 64,\n \"num_lines\": 71,\n \"path\": \"/analysis.py\",\n \"repo_name\": \"foy4748/university_fees\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from openpyxl import load_workbook as lw\\nimport unidecode\\n\\n#Native libraries\\nimport re\\nimport csv\\nimport os\\n#################\\n\\n#Reading the Workbook\\nwb = lw('US-News Ranking.xlsx', data_only= True)\\n\\n#Getting sheet names\\nsheets = wb.sheetnames\\n\\n#Getting rid of previously stored data\\nfiles = os.listdir(\\\"./\\\")\\nif \\\"compiled.csv\\\" in files:\\n os.remove(\\\"compiled.csv\\\")\\n\\n#Finds which column contains tuition fee\\ndef tuitionColumn(sheetname, wb):\\n w_sh = wb[sheetname]\\n letters = [\\\"A\\\", \\\"B\\\", \\\"C\\\", \\\"D\\\",\\\"E\\\",\\\"F\\\",\\\"G\\\", \\\"H\\\",\\\"I\\\",\\\"J\\\"]\\n results = []\\n for j in range(1, len(letters)):\\n cell = w_sh.cell(row=1,column=j).value\\n if cell == None:\\n continue\\n matched = re.search(r\\\"^tuition\\\", cell, re.IGNORECASE)\\n if matched != None:\\n col = j\\n break\\n else:\\n col = None\\n continue\\n return col\\n\\n#Read through University names, Department and Tuition fee\\ndef readSheet(sheetname, wb,tC): #tC = tuition column\\n if tC == None:\\n return\\n i = 2\\n w_sh = wb[sheetname]\\n with open('compiled.csv','a+',newline='') as f:\\n cursor = csv.writer(f)\\n while w_sh.cell(row=i, column=tC).value != None:\\n university = w_sh.cell(row=i, column=2).value\\n university = unidecode.unidecode(university)\\n tuition_fee = w_sh.cell(row=i, column=tC).value\\n\\n if type(tuition_fee) is float:\\n tf = tuition_fee\\n else:\\n try:\\n tf = float(re.sub(r'[\\\\D]', \\\"\\\", tuition_fee))\\n except:\\n tf = ''\\n cursor.writerow([university,sheetname,tf])\\n i = i + 1\\n\\n#Main\\nif __name__ == '__main__':\\n lst = []\\n for sheet in sheets:\\n tc = tuitionColumn(sheet, wb)\\n if tc == None:\\n continue\\n lst.append((sheet, tc))\\n for i in lst:\\n readSheet(i[0], wb, i[1])\\n\"\n },\n {\n \"alpha_fraction\": 0.5047438144683838,\n \"alphanum_fraction\": 0.5161290168762207,\n \"avg_line_length\": 26.63157844543457,\n \"blob_id\": \"ed59852951a1d6e8efa29c2bafcb881e8f20ea23\",\n \"content_id\": \"f4281d940c2356b9678a0135a9d7e7184f0bb454\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 527,\n \"license_type\": \"no_license\",\n \"max_line_length\": 61,\n \"num_lines\": 19,\n \"path\": \"/structuring.py\",\n \"repo_name\": \"foy4748/university_fees\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import csv\\nimport json\\n\\nwith open('./compiled.csv', newline='') as f:\\n reader = csv.reader(f)\\n storage = dict()\\n for row in reader:\\n if row[2] == '':\\n continue\\n university = row[0].split()\\n uni = ' '.join(university)\\n if uni not in storage:\\n storage[uni] = {'dept':[row[1]], 'fee':[row[2]] }\\n else:\\n storage[uni]['dept'].append(row[1])\\n storage[uni]['fee'].append(row[2])\\n\\nwith open('compiled.json', 'w') as f:\\n json.dump(storage, f)\\n\\n\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2979374,"cells":{"repo_name":{"kind":"string","value":"ryantylerkane/Movie-Reviews"},"repo_url":{"kind":"string","value":"https://github.com/ryantylerkane/Movie-Reviews"},"snapshot_id":{"kind":"string","value":"9e6cdd4329bd4b62549e15a52abf2963a9aaea9d"},"revision_id":{"kind":"string","value":"bb8a863e18b8707e85ecb4b539f76fc7b56c6246"},"directory_id":{"kind":"string","value":"5af1169cad32f7a23341e172317856dbf3605d53"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2022-11-24T21:26:24.606234","string":"2022-11-24T21:26:24.606234"},"revision_date":{"kind":"timestamp","value":"2020-07-30T04:45:01","string":"2020-07-30T04:45:01"},"committer_date":{"kind":"timestamp","value":"2020-07-30T04:45:01","string":"2020-07-30T04:45:01"},"github_id":{"kind":"number","value":277402726,"string":"277,402,726"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6739332675933838,"alphanum_fraction":0.6783198118209839,"avg_line_length":55.869232177734375,"blob_id":"82e8455e5ab937c42140fa2b0981e138d5e2a945","content_id":"ec79599ac9928f7c9d82b7655da67044cbd1c0bb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":7523,"license_type":"no_license","max_line_length":228,"num_lines":130,"path":"/NB.py","repo_name":"ryantylerkane/Movie-Reviews","src_encoding":"UTF-8","text":"#NB.py\r\n#This program takes four input arguments in the following order:\r\n#A training file, a test file, a file where the parameters of the resulting model\r\n#will be saved, and the output file hwere predictions made by the classifier on the\r\n#test data will be written.\r\n#The last line in the output file will list the overall accuracy of the classifier\r\n#on the test data.\r\n\r\n#The program implements Naive Bayes utilizing the training and test data compiled by pre-processing.py. The\r\n#classifier uses bag-of-words features with Add-one smoothing.\r\n\r\nimport sys #Needed to obtain input parameters from the command line.\r\nimport math #Needed to perform probability calculations in logspace.\r\n\r\n#sys.argv[1] = training file\r\n#sys.argv[2] = test file\r\n#sys.argv[3] = parameters output file\r\n#sys.argv[4] = predictions (output) file\r\n\r\ndef countVocabulary(): #Function extracts each word from the vocabulary file and places them into the array. The indices of these values will be used to determine where the counts for each word are placed in the feature vectors.\r\n inputHandler = open(\"imdb.vocab\", 'r', encoding='unicode_escape') #Open the vocab file located in the same directory as this program.\r\n vocab = 0\r\n for term in inputHandler:\r\n vocab += 1\r\n inputHandler.close()\r\n return vocab\r\n\r\ndef collectCounts(classDicts, classes): #Function contructs construct feature probabilities and prior probabilities using the provided training file.\r\n \r\n inputHandler = open(sys.argv[1], 'r', encoding='unicode_escape') #Open the training file to read its contents.\r\n \r\n for line in inputHandler:\r\n counts = line.split() #Split the line on whitespace.\r\n if counts[0] not in classDicts.keys():\r\n classDicts[counts[0]] = {}\r\n classes[counts[0]] = 0\r\n currentClass = counts[0]\r\n classes[currentClass] += 1 #Increase the document count for this class.\r\n\r\n for c in counts[1:]:\r\n word, num = c.split('~') #Separate the word and its count from each other.\r\n if word not in classDicts[currentClass].keys():\r\n classDicts[currentClass][word] = int(num)\r\n else:\r\n classDicts[currentClass][word] += int(num) \r\n inputHandler.close()\r\n\r\ndef writeTraining(outputHandler, classDicts, classDenominators):\r\n outputHandler.write(\"Probabilities of Context Features Include Add One Smoothing: \\n\")\r\n for key in classDicts.keys():\r\n for k in classDicts[key].keys():\r\n outputHandler.write(\"c(\" + k + \"|\" + key+\")=\" + str(classDicts[key][k])+\",\")\r\n probWithAddOne = (classDicts[key][k]+1)/(classDenominators[key])\r\n classDicts[key][k] = probWithAddOne\r\n outputHandler.write(\"p(\" + k + \"|\" + key+\")=\" + str(probWithAddOne)+\"\\n\")\r\n outputHandler.write(\"\\n\"+\"All values not seen in training will receive a probability of: \" + str(1/classDenominators[key])+\"\\n\\n\")\r\n\r\n\r\ndef predictClass(classProbs, outputPredictions, actualClass, numPredictions, numRightLabels):\r\n outputPredictions.write(\"max(\")\r\n for key in classProbs.keys():\r\n outputPredictions.write(\"p\" + \"(\" + key + \"| d\" + str(numPredictions)+ \") = \" + str(classProbs[key])+\" \")\r\n outputPredictions.write(\") = \")\r\n\r\n maxClass = max(classProbs, key = classProbs.get)\r\n outputPredictions.write(maxClass + \" || Provided label: \" + actualClass)\r\n \r\n if maxClass==actualClass:\r\n numRightLabels +=1 #Increase the number of correct guesses.\r\n \r\n return numRightLabels\r\n \r\ndef processTest(classDicts, classes, vocab):\r\n\r\n inputHandler = open(sys.argv[2], 'r', encoding='unicode_escape') #Open the testing file to read its contents.\r\n outputParameter = open(sys.argv[3], 'w') #Open the parameters output file.\r\n outputPredictions = open(sys.argv[4], 'w') #Open the predictions output file.\r\n outputPredictions.write(\"ALL PROVIDED VALUES ARE IN POSITIVE LOG SPACE: \\n\")\r\n numRightLabels = 0\r\n numPredictions = 0\r\n classDenominators = {} #Calculate the denominator for each class so it doesn't have to be done for every context feature calculation.\r\n\r\n #Calculate the prior probability values prior to performing calculations.\r\n numDocs = sum(classes.values())\r\n outputParameter.write(\"PRIOR PROBABILITIES: \")\r\n for key in classes.keys(): #For each class count.\r\n classes[key] = classes[key]/numDocs #Calculate the prior probability for a given class.\r\n classDenominators[key] = sum(classDicts[key].values()) + vocab #Calculate the denominators that will be needed for Add one smoothing.\r\n outputParameter.write(\"p(\" + key+\") = \" + str(classes[key]) + \" \")\r\n outputParameter.write(\"\\n\\n\")\r\n writeTraining(outputParameter, classDicts, classDenominators) #Write model parameters to the file.\r\n \r\n for line in inputHandler:\r\n #We must perfrom the probability calculations for a given training document in log space to avoid underflow errors (which will occur with the provided test documents if done in linear space).\r\n #We would ideally use negative log as it allows us to represent the probabilities as positive values, but doing so would represent smaller probabilities as larger numbers, making it more\r\n #difficult to find the maximum value. As such, we will use positive log space.\r\n classProbs = {} #Will hold the total probabilities calculated for a given class.\r\n numPredictions +=1 #Increase the total number of files viewed.\r\n for key in classes.keys():\r\n classProbs[key] = math.log10(classes[key]) #Set the probability equal to the prior probability in order to begin building the product.\r\n \r\n counts = line.split() #Split the line on whitespace.\r\n actualClass = counts[0] #Obtain the class that the string that the document is associated with.\r\n \r\n for c in counts[1:]:\r\n word, num = c.split('~') #Separate the word and its count from each other.\r\n #We do not have to worry about a word not being found in the vocabulary, as all non-vocabulary words were removed during pre-processing.\r\n for key in classes.keys():\r\n if word not in classDicts[key].keys(): #If the word was not seen in the classes training.\r\n probWithAddOne = 1/(classDenominators[key]) #Make the zero count equal to one due to Add-one smoothing.\r\n \r\n else:\r\n probWithAddOne = classDicts[key][word] #One is already added to the probabilities in all training words (see writeTraining function). \r\n\r\n totalProb = math.log10(probWithAddOne) * int(num)\r\n classProbs[key] += totalProb #Keep running total of probability for that class.\r\n \r\n numRightLabels = predictClass(classProbs, outputPredictions, actualClass, numPredictions, numRightLabels)\r\n outputPredictions.write(\"\\n\")\r\n\r\n outputPredictions.write(\"Number of predictions made: \" + str(numPredictions) + \" Percent Accuracy: \" + str(numRightLabels/numPredictions * 100) + \"%\") \r\n outputParameter.close()\r\n outputPredictions.close()\r\n inputHandler.close()\r\n \r\nclassDicts = {} #Will allow us to access a class's word count by providing the class's name.\r\nclasses = {} #Will allow us to check the document count of a class by referencing its name.\r\nvocab = countVocabulary()\r\ncollectCounts(classDicts, classes)\r\nprocessTest(classDicts, classes, vocab)\r\n"},{"alpha_fraction":0.7856217622756958,"alphanum_fraction":0.7966321110725403,"avg_line_length":116.76923370361328,"blob_id":"5d45c59ccff5c4fa237c119835d560e2c922ad30","content_id":"985b7ca1b629ccbdd4304590234d66f0aa687523","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":1544,"license_type":"no_license","max_line_length":232,"num_lines":13,"path":"/README.txt","repo_name":"ryantylerkane/Movie-Reviews","src_encoding":"UTF-8","text":"This project implements a Naive Bayes classifier that is trained using 25,000 positive and negative reviews gathered from imdb.com. After training, the program will output the probability assigned to each lexicon during the process.\r\nThe classifier is then tested on an additional 25,000 test files which creates one output file comparing the prediction from the model to the actual label (positive or negative) provided in the test data.\r\n\r\npre-processing.py:\r\n1. The vocabulary file must be named \"imdb.vocab\" and must appear in the same directory as the program file.\r\n2. The program requires that the input directory contains \"pos\" and \"neg\" sub-folders, each containing their respective test/train documents.\r\n3. The output file will take the name of the last directory in the provided input path. This should be \"test\" or \"train\" if the same directory structure for the raw data is used.\r\n4. Unicode encoding was required to be used for the input files due to the usage of some Unicode characters in the test/training reviews.\r\n\r\nNB.py:\r\n1. The prior probabilities and context features will be outputted in linear space, as there was no problem with underflow when ran on the testing/training corpus.\r\n2. The probabilities found in the prediction files will be outputted in log space. Negative log space was avoided so that the \"max\" function could easily be used to determine the predicted class.\r\n3. Unicode encoding was required to be used for the input files due to the usage of some Unicode characters in the test/training reviews.\r\n"},{"alpha_fraction":0.634036123752594,"alphanum_fraction":0.6373220086097717,"avg_line_length":77.39130401611328,"blob_id":"0a52c1d8140aa595d221e35cdf59709432c17a8c","content_id":"7c3bd1822c2180273c751cb85c6bef2c07970ada","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":7304,"license_type":"no_license","max_line_length":437,"num_lines":92,"path":"/pre-processing.py","repo_name":"ryantylerkane/Movie-Reviews","src_encoding":"UTF-8","text":"#pre-process.py\r\n#This program takes a training or test directory as input, performs pre-processing steps,\r\n#and saves the contents of the files in a vector format to be used in NB.py\r\n\r\n#The pre-processing steps will separate punctuation from words, make all characters\r\n#and lowercase each character. Punctuation and words (if any) that do not appear in the vocabulary\r\n#file will not be included in the output vector.\r\n\r\nimport sys #Needed to obtain training/test directory from the command line input parameters.\r\nimport os #Needed navigate to provided directory and read all of its files.\r\nimport re #Needed to identify punctuation during preprocessing.\r\n\r\ndef loadVocabulary(): #Function extracts each word from the vocabulary file and places them into the array. The indices of these values will be used to determine where the counts for each word are placed in the feature vectors.\r\n inputHandler = open(\"imdb.vocab\", 'r', encoding='unicode_escape') #Open the vocab file located in the same directory as this program.\r\n vocabArray = {}\r\n for term in inputHandler:\r\n newTerm = term.replace(\"\\n\",\"\")\r\n vocabArray[newTerm] = 0 #Append the term to the end of the array. This will allow the array to hold each word in the same order as the file.\r\n inputHandler.close()\r\n return vocabArray\r\n\r\ndef processVectorBOW(termString, vocabulary): #Function will check if a term belongs to the vocabulary and update the corresponding value in the document vector if necessary.\r\n if termString in vocabulary.keys(): #Update the count of that documents feature vector.\r\n vocabulary[termString] += 1 #Increase the count of that term in the document by one.\r\n #If the token is not in the vocabulary, it can be ignored.\r\n\r\ndef writeVector(vocabulary, classType, outputHandler): #Function records the class and feature vector that was obtained through preprocessing to an output file.\r\n outputHandler.write(classType + \" \")\r\n for key in vocabulary.keys():\r\n if vocabulary[key] >0:\r\n outputHandler.write(key + \"~\" + str(vocabulary[key]) + \" \") #Write each element of the feature vector with a space between each index. Use ~ to separate word from frequency as \"=\" is a character found in some words in vocabulary.\r\n vocabulary[key]=0\r\n outputHandler.write(\"\\n\") #Skip to the next line once the feature vector has been completed.\r\n \r\ndef processDirectory(vocabulary): \r\n fileCount=0 #Integer that will allow us to count number of files processed (for debugging purposes).\r\n\r\n directory = sys.argv[1] #Extract the directory from the command line argument.\r\n\r\n print(\"Directory inputted: \" + directory+'\\n')\r\n\r\n folders = directory.split('\\\\') #Split each sub-directory into its own word so we can extract the class name and determine whether we received a test or training directory. \r\n #classType = folders[-1] #Use if we are providing just a negative or just a positive training/test directory.\r\n classes = [\"pos\", \"neg\"] #Both test and training directories will have positive and negative sub-directories.\r\n folderType = folders[-1] #Test or training folder.\r\n\r\n outputFileName = folderType +\".txt\" #Create an output file where the name indicates whether it is a training or test directory.\r\n outputHandler = open(outputFileName, 'w') #Open output file for writing.\r\n for posOrNeg in classes:\r\n for file in os.listdir(sys.argv[1]+\"\\\\\"+posOrNeg): #For each file in the test or training directory. \r\n fileCount +=1 #Increment the count for debugging purposes.\r\n try:\r\n inputHandler = open(directory+\"\\\\\"+ posOrNeg+\"\\\\\"+file,'r', encoding='unicode_escape') #Open input file for reading. Need to use unicode character encoding to avoid errors used with default encoding.\r\n docVector = [0] * len(vocabulary) #Create a new features vector for the document that was just opened.\r\n for line in inputHandler: #For each line in the training/test file.\r\n lowerLine = line.lower() #Convert uppercase letters to lowercase.\r\n tokens = lowerLine.split() #Split on whitespace.\r\n for token in tokens:\r\n #Attempt to capture series of symbols such as emoticons that were deemed significant enough to appear in the vocabulary. These are primarily continuous sequences of symbols, but there are a few that contained letters and numbers which are encompossed in a separate list. \r\n if re.match(r\"^\\W+$\", token) or token in {\"8)\", \":o)\", \"=o)\",\"8(\",\"=p\",\"=d\",\":d\",\":p\",\":-d\",\":-p\"}: #If the token is strictly symbols, leave it as is. This will allow us to match emoticons found in the vocabulary without splitting the symbols. Note that this leaves us the possibility of not breaking up strings like \"...\" and \"--\" between words. These are not part of the vocabulary and will be discarded anyway.\r\n processVectorBOW(token, vocabulary)\r\n else: #Token is either a word containg only letters or a mixture of words and letters.\r\n #Note that the vocabulary file contains words with \"'\" and \"'\" in the middle of their strings. Normally we would want to split words on these characters even if they are in the middle of the string.\r\n #However, since it was not done in the test file, it is best to leave them as is for easier matching to the vocabulary array.\r\n processedTokens=re.findall(r\"[\\w'-]+|[.,!?;:\\\"/&~<>()_]\", token)\r\n\r\n #Using this regex, strings where \"-\" or \"'\" are in the first or last position won't be split. We must check for these prior to writing to the features vector.\r\n for pToken in processedTokens:\r\n newTokens=[]\r\n if len(pToken) > 1 and (pToken[0] == \"'\" or pToken[0]==\"-\"):\r\n newTokens.append(pToken[:1])\r\n pToken = pToken[1:]\r\n if len(pToken) > 1 and (pToken[-1] == \"'\" or pToken[-1]==\"-\"): #Can't make this elif, as a string by have \"'\" in the beginning and the end.\r\n newTokens.append(pToken[:(len(pToken)-1)])\r\n pToken=pToken[(len(pToken)-1):]\r\n\r\n if len(newTokens) ==0: #No splitting was required on the string, it can be processed as is.\r\n processVectorBOW(pToken, vocabulary)\r\n else: #Strings were split, we have to process the new pieces.\r\n newTokens.append(pToken)\r\n for t in newTokens:\r\n processVectorBOW(t, vocabulary)\r\n writeVector(vocabulary, posOrNeg, outputHandler)\r\n inputHandler.close()\r\n \r\n except Exception as ex:\r\n raise ex\r\n print(\"Files unable to be read in directory: \" + directory)\r\n outputHandler.close()\r\n\r\nvocabulary = loadVocabulary() \r\nprocessDirectory(vocabulary)\r\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6739332675933838,\n \"alphanum_fraction\": 0.6783198118209839,\n \"avg_line_length\": 55.869232177734375,\n \"blob_id\": \"82e8455e5ab937c42140fa2b0981e138d5e2a945\",\n \"content_id\": \"ec79599ac9928f7c9d82b7655da67044cbd1c0bb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 7523,\n \"license_type\": \"no_license\",\n \"max_line_length\": 228,\n \"num_lines\": 130,\n \"path\": \"/NB.py\",\n \"repo_name\": \"ryantylerkane/Movie-Reviews\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#NB.py\\r\\n#This program takes four input arguments in the following order:\\r\\n#A training file, a test file, a file where the parameters of the resulting model\\r\\n#will be saved, and the output file hwere predictions made by the classifier on the\\r\\n#test data will be written.\\r\\n#The last line in the output file will list the overall accuracy of the classifier\\r\\n#on the test data.\\r\\n\\r\\n#The program implements Naive Bayes utilizing the training and test data compiled by pre-processing.py. The\\r\\n#classifier uses bag-of-words features with Add-one smoothing.\\r\\n\\r\\nimport sys #Needed to obtain input parameters from the command line.\\r\\nimport math #Needed to perform probability calculations in logspace.\\r\\n\\r\\n#sys.argv[1] = training file\\r\\n#sys.argv[2] = test file\\r\\n#sys.argv[3] = parameters output file\\r\\n#sys.argv[4] = predictions (output) file\\r\\n\\r\\ndef countVocabulary(): #Function extracts each word from the vocabulary file and places them into the array. The indices of these values will be used to determine where the counts for each word are placed in the feature vectors.\\r\\n inputHandler = open(\\\"imdb.vocab\\\", 'r', encoding='unicode_escape') #Open the vocab file located in the same directory as this program.\\r\\n vocab = 0\\r\\n for term in inputHandler:\\r\\n vocab += 1\\r\\n inputHandler.close()\\r\\n return vocab\\r\\n\\r\\ndef collectCounts(classDicts, classes): #Function contructs construct feature probabilities and prior probabilities using the provided training file.\\r\\n \\r\\n inputHandler = open(sys.argv[1], 'r', encoding='unicode_escape') #Open the training file to read its contents.\\r\\n \\r\\n for line in inputHandler:\\r\\n counts = line.split() #Split the line on whitespace.\\r\\n if counts[0] not in classDicts.keys():\\r\\n classDicts[counts[0]] = {}\\r\\n classes[counts[0]] = 0\\r\\n currentClass = counts[0]\\r\\n classes[currentClass] += 1 #Increase the document count for this class.\\r\\n\\r\\n for c in counts[1:]:\\r\\n word, num = c.split('~') #Separate the word and its count from each other.\\r\\n if word not in classDicts[currentClass].keys():\\r\\n classDicts[currentClass][word] = int(num)\\r\\n else:\\r\\n classDicts[currentClass][word] += int(num) \\r\\n inputHandler.close()\\r\\n\\r\\ndef writeTraining(outputHandler, classDicts, classDenominators):\\r\\n outputHandler.write(\\\"Probabilities of Context Features Include Add One Smoothing: \\\\n\\\")\\r\\n for key in classDicts.keys():\\r\\n for k in classDicts[key].keys():\\r\\n outputHandler.write(\\\"c(\\\" + k + \\\"|\\\" + key+\\\")=\\\" + str(classDicts[key][k])+\\\",\\\")\\r\\n probWithAddOne = (classDicts[key][k]+1)/(classDenominators[key])\\r\\n classDicts[key][k] = probWithAddOne\\r\\n outputHandler.write(\\\"p(\\\" + k + \\\"|\\\" + key+\\\")=\\\" + str(probWithAddOne)+\\\"\\\\n\\\")\\r\\n outputHandler.write(\\\"\\\\n\\\"+\\\"All values not seen in training will receive a probability of: \\\" + str(1/classDenominators[key])+\\\"\\\\n\\\\n\\\")\\r\\n\\r\\n\\r\\ndef predictClass(classProbs, outputPredictions, actualClass, numPredictions, numRightLabels):\\r\\n outputPredictions.write(\\\"max(\\\")\\r\\n for key in classProbs.keys():\\r\\n outputPredictions.write(\\\"p\\\" + \\\"(\\\" + key + \\\"| d\\\" + str(numPredictions)+ \\\") = \\\" + str(classProbs[key])+\\\" \\\")\\r\\n outputPredictions.write(\\\") = \\\")\\r\\n\\r\\n maxClass = max(classProbs, key = classProbs.get)\\r\\n outputPredictions.write(maxClass + \\\" || Provided label: \\\" + actualClass)\\r\\n \\r\\n if maxClass==actualClass:\\r\\n numRightLabels +=1 #Increase the number of correct guesses.\\r\\n \\r\\n return numRightLabels\\r\\n \\r\\ndef processTest(classDicts, classes, vocab):\\r\\n\\r\\n inputHandler = open(sys.argv[2], 'r', encoding='unicode_escape') #Open the testing file to read its contents.\\r\\n outputParameter = open(sys.argv[3], 'w') #Open the parameters output file.\\r\\n outputPredictions = open(sys.argv[4], 'w') #Open the predictions output file.\\r\\n outputPredictions.write(\\\"ALL PROVIDED VALUES ARE IN POSITIVE LOG SPACE: \\\\n\\\")\\r\\n numRightLabels = 0\\r\\n numPredictions = 0\\r\\n classDenominators = {} #Calculate the denominator for each class so it doesn't have to be done for every context feature calculation.\\r\\n\\r\\n #Calculate the prior probability values prior to performing calculations.\\r\\n numDocs = sum(classes.values())\\r\\n outputParameter.write(\\\"PRIOR PROBABILITIES: \\\")\\r\\n for key in classes.keys(): #For each class count.\\r\\n classes[key] = classes[key]/numDocs #Calculate the prior probability for a given class.\\r\\n classDenominators[key] = sum(classDicts[key].values()) + vocab #Calculate the denominators that will be needed for Add one smoothing.\\r\\n outputParameter.write(\\\"p(\\\" + key+\\\") = \\\" + str(classes[key]) + \\\" \\\")\\r\\n outputParameter.write(\\\"\\\\n\\\\n\\\")\\r\\n writeTraining(outputParameter, classDicts, classDenominators) #Write model parameters to the file.\\r\\n \\r\\n for line in inputHandler:\\r\\n #We must perfrom the probability calculations for a given training document in log space to avoid underflow errors (which will occur with the provided test documents if done in linear space).\\r\\n #We would ideally use negative log as it allows us to represent the probabilities as positive values, but doing so would represent smaller probabilities as larger numbers, making it more\\r\\n #difficult to find the maximum value. As such, we will use positive log space.\\r\\n classProbs = {} #Will hold the total probabilities calculated for a given class.\\r\\n numPredictions +=1 #Increase the total number of files viewed.\\r\\n for key in classes.keys():\\r\\n classProbs[key] = math.log10(classes[key]) #Set the probability equal to the prior probability in order to begin building the product.\\r\\n \\r\\n counts = line.split() #Split the line on whitespace.\\r\\n actualClass = counts[0] #Obtain the class that the string that the document is associated with.\\r\\n \\r\\n for c in counts[1:]:\\r\\n word, num = c.split('~') #Separate the word and its count from each other.\\r\\n #We do not have to worry about a word not being found in the vocabulary, as all non-vocabulary words were removed during pre-processing.\\r\\n for key in classes.keys():\\r\\n if word not in classDicts[key].keys(): #If the word was not seen in the classes training.\\r\\n probWithAddOne = 1/(classDenominators[key]) #Make the zero count equal to one due to Add-one smoothing.\\r\\n \\r\\n else:\\r\\n probWithAddOne = classDicts[key][word] #One is already added to the probabilities in all training words (see writeTraining function). \\r\\n\\r\\n totalProb = math.log10(probWithAddOne) * int(num)\\r\\n classProbs[key] += totalProb #Keep running total of probability for that class.\\r\\n \\r\\n numRightLabels = predictClass(classProbs, outputPredictions, actualClass, numPredictions, numRightLabels)\\r\\n outputPredictions.write(\\\"\\\\n\\\")\\r\\n\\r\\n outputPredictions.write(\\\"Number of predictions made: \\\" + str(numPredictions) + \\\" Percent Accuracy: \\\" + str(numRightLabels/numPredictions * 100) + \\\"%\\\") \\r\\n outputParameter.close()\\r\\n outputPredictions.close()\\r\\n inputHandler.close()\\r\\n \\r\\nclassDicts = {} #Will allow us to access a class's word count by providing the class's name.\\r\\nclasses = {} #Will allow us to check the document count of a class by referencing its name.\\r\\nvocab = countVocabulary()\\r\\ncollectCounts(classDicts, classes)\\r\\nprocessTest(classDicts, classes, vocab)\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.7856217622756958,\n \"alphanum_fraction\": 0.7966321110725403,\n \"avg_line_length\": 116.76923370361328,\n \"blob_id\": \"5d45c59ccff5c4fa237c119835d560e2c922ad30\",\n \"content_id\": \"985b7ca1b629ccbdd4304590234d66f0aa687523\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 1544,\n \"license_type\": \"no_license\",\n \"max_line_length\": 232,\n \"num_lines\": 13,\n \"path\": \"/README.txt\",\n \"repo_name\": \"ryantylerkane/Movie-Reviews\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"This project implements a Naive Bayes classifier that is trained using 25,000 positive and negative reviews gathered from imdb.com. After training, the program will output the probability assigned to each lexicon during the process.\\r\\nThe classifier is then tested on an additional 25,000 test files which creates one output file comparing the prediction from the model to the actual label (positive or negative) provided in the test data.\\r\\n\\r\\npre-processing.py:\\r\\n1. The vocabulary file must be named \\\"imdb.vocab\\\" and must appear in the same directory as the program file.\\r\\n2. The program requires that the input directory contains \\\"pos\\\" and \\\"neg\\\" sub-folders, each containing their respective test/train documents.\\r\\n3. The output file will take the name of the last directory in the provided input path. This should be \\\"test\\\" or \\\"train\\\" if the same directory structure for the raw data is used.\\r\\n4. Unicode encoding was required to be used for the input files due to the usage of some Unicode characters in the test/training reviews.\\r\\n\\r\\nNB.py:\\r\\n1. The prior probabilities and context features will be outputted in linear space, as there was no problem with underflow when ran on the testing/training corpus.\\r\\n2. The probabilities found in the prediction files will be outputted in log space. Negative log space was avoided so that the \\\"max\\\" function could easily be used to determine the predicted class.\\r\\n3. Unicode encoding was required to be used for the input files due to the usage of some Unicode characters in the test/training reviews.\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.634036123752594,\n \"alphanum_fraction\": 0.6373220086097717,\n \"avg_line_length\": 77.39130401611328,\n \"blob_id\": \"0a52c1d8140aa595d221e35cdf59709432c17a8c\",\n \"content_id\": \"7c3bd1822c2180273c751cb85c6bef2c07970ada\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 7304,\n \"license_type\": \"no_license\",\n \"max_line_length\": 437,\n \"num_lines\": 92,\n \"path\": \"/pre-processing.py\",\n \"repo_name\": \"ryantylerkane/Movie-Reviews\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#pre-process.py\\r\\n#This program takes a training or test directory as input, performs pre-processing steps,\\r\\n#and saves the contents of the files in a vector format to be used in NB.py\\r\\n\\r\\n#The pre-processing steps will separate punctuation from words, make all characters\\r\\n#and lowercase each character. Punctuation and words (if any) that do not appear in the vocabulary\\r\\n#file will not be included in the output vector.\\r\\n\\r\\nimport sys #Needed to obtain training/test directory from the command line input parameters.\\r\\nimport os #Needed navigate to provided directory and read all of its files.\\r\\nimport re #Needed to identify punctuation during preprocessing.\\r\\n\\r\\ndef loadVocabulary(): #Function extracts each word from the vocabulary file and places them into the array. The indices of these values will be used to determine where the counts for each word are placed in the feature vectors.\\r\\n inputHandler = open(\\\"imdb.vocab\\\", 'r', encoding='unicode_escape') #Open the vocab file located in the same directory as this program.\\r\\n vocabArray = {}\\r\\n for term in inputHandler:\\r\\n newTerm = term.replace(\\\"\\\\n\\\",\\\"\\\")\\r\\n vocabArray[newTerm] = 0 #Append the term to the end of the array. This will allow the array to hold each word in the same order as the file.\\r\\n inputHandler.close()\\r\\n return vocabArray\\r\\n\\r\\ndef processVectorBOW(termString, vocabulary): #Function will check if a term belongs to the vocabulary and update the corresponding value in the document vector if necessary.\\r\\n if termString in vocabulary.keys(): #Update the count of that documents feature vector.\\r\\n vocabulary[termString] += 1 #Increase the count of that term in the document by one.\\r\\n #If the token is not in the vocabulary, it can be ignored.\\r\\n\\r\\ndef writeVector(vocabulary, classType, outputHandler): #Function records the class and feature vector that was obtained through preprocessing to an output file.\\r\\n outputHandler.write(classType + \\\" \\\")\\r\\n for key in vocabulary.keys():\\r\\n if vocabulary[key] >0:\\r\\n outputHandler.write(key + \\\"~\\\" + str(vocabulary[key]) + \\\" \\\") #Write each element of the feature vector with a space between each index. Use ~ to separate word from frequency as \\\"=\\\" is a character found in some words in vocabulary.\\r\\n vocabulary[key]=0\\r\\n outputHandler.write(\\\"\\\\n\\\") #Skip to the next line once the feature vector has been completed.\\r\\n \\r\\ndef processDirectory(vocabulary): \\r\\n fileCount=0 #Integer that will allow us to count number of files processed (for debugging purposes).\\r\\n\\r\\n directory = sys.argv[1] #Extract the directory from the command line argument.\\r\\n\\r\\n print(\\\"Directory inputted: \\\" + directory+'\\\\n')\\r\\n\\r\\n folders = directory.split('\\\\\\\\') #Split each sub-directory into its own word so we can extract the class name and determine whether we received a test or training directory. \\r\\n #classType = folders[-1] #Use if we are providing just a negative or just a positive training/test directory.\\r\\n classes = [\\\"pos\\\", \\\"neg\\\"] #Both test and training directories will have positive and negative sub-directories.\\r\\n folderType = folders[-1] #Test or training folder.\\r\\n\\r\\n outputFileName = folderType +\\\".txt\\\" #Create an output file where the name indicates whether it is a training or test directory.\\r\\n outputHandler = open(outputFileName, 'w') #Open output file for writing.\\r\\n for posOrNeg in classes:\\r\\n for file in os.listdir(sys.argv[1]+\\\"\\\\\\\\\\\"+posOrNeg): #For each file in the test or training directory. \\r\\n fileCount +=1 #Increment the count for debugging purposes.\\r\\n try:\\r\\n inputHandler = open(directory+\\\"\\\\\\\\\\\"+ posOrNeg+\\\"\\\\\\\\\\\"+file,'r', encoding='unicode_escape') #Open input file for reading. Need to use unicode character encoding to avoid errors used with default encoding.\\r\\n docVector = [0] * len(vocabulary) #Create a new features vector for the document that was just opened.\\r\\n for line in inputHandler: #For each line in the training/test file.\\r\\n lowerLine = line.lower() #Convert uppercase letters to lowercase.\\r\\n tokens = lowerLine.split() #Split on whitespace.\\r\\n for token in tokens:\\r\\n #Attempt to capture series of symbols such as emoticons that were deemed significant enough to appear in the vocabulary. These are primarily continuous sequences of symbols, but there are a few that contained letters and numbers which are encompossed in a separate list. \\r\\n if re.match(r\\\"^\\\\W+$\\\", token) or token in {\\\"8)\\\", \\\":o)\\\", \\\"=o)\\\",\\\"8(\\\",\\\"=p\\\",\\\"=d\\\",\\\":d\\\",\\\":p\\\",\\\":-d\\\",\\\":-p\\\"}: #If the token is strictly symbols, leave it as is. This will allow us to match emoticons found in the vocabulary without splitting the symbols. Note that this leaves us the possibility of not breaking up strings like \\\"...\\\" and \\\"--\\\" between words. These are not part of the vocabulary and will be discarded anyway.\\r\\n processVectorBOW(token, vocabulary)\\r\\n else: #Token is either a word containg only letters or a mixture of words and letters.\\r\\n #Note that the vocabulary file contains words with \\\"'\\\" and \\\"'\\\" in the middle of their strings. Normally we would want to split words on these characters even if they are in the middle of the string.\\r\\n #However, since it was not done in the test file, it is best to leave them as is for easier matching to the vocabulary array.\\r\\n processedTokens=re.findall(r\\\"[\\\\w'-]+|[.,!?;:\\\\\\\"/&~<>()_]\\\", token)\\r\\n\\r\\n #Using this regex, strings where \\\"-\\\" or \\\"'\\\" are in the first or last position won't be split. We must check for these prior to writing to the features vector.\\r\\n for pToken in processedTokens:\\r\\n newTokens=[]\\r\\n if len(pToken) > 1 and (pToken[0] == \\\"'\\\" or pToken[0]==\\\"-\\\"):\\r\\n newTokens.append(pToken[:1])\\r\\n pToken = pToken[1:]\\r\\n if len(pToken) > 1 and (pToken[-1] == \\\"'\\\" or pToken[-1]==\\\"-\\\"): #Can't make this elif, as a string by have \\\"'\\\" in the beginning and the end.\\r\\n newTokens.append(pToken[:(len(pToken)-1)])\\r\\n pToken=pToken[(len(pToken)-1):]\\r\\n\\r\\n if len(newTokens) ==0: #No splitting was required on the string, it can be processed as is.\\r\\n processVectorBOW(pToken, vocabulary)\\r\\n else: #Strings were split, we have to process the new pieces.\\r\\n newTokens.append(pToken)\\r\\n for t in newTokens:\\r\\n processVectorBOW(t, vocabulary)\\r\\n writeVector(vocabulary, posOrNeg, outputHandler)\\r\\n inputHandler.close()\\r\\n \\r\\n except Exception as ex:\\r\\n raise ex\\r\\n print(\\\"Files unable to be read in directory: \\\" + directory)\\r\\n outputHandler.close()\\r\\n\\r\\nvocabulary = loadVocabulary() \\r\\nprocessDirectory(vocabulary)\\r\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":3,"string":"3"}}},{"rowIdx":2979375,"cells":{"repo_name":{"kind":"string","value":"ghevcoul/fractal_app"},"repo_url":{"kind":"string","value":"https://github.com/ghevcoul/fractal_app"},"snapshot_id":{"kind":"string","value":"540ff3422fc80df5ac0c8332359dddc56067a9c5"},"revision_id":{"kind":"string","value":"99c5833b90b668f8c91b3056583dd46eafd947f5"},"directory_id":{"kind":"string","value":"5c67255442702a93b2813a775fa014e4b87df0fc"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-01-01T04:04:54.382966","string":"2021-01-01T04:04:54.382966"},"revision_date":{"kind":"timestamp","value":"2016-05-04T19:51:30","string":"2016-05-04T19:51:30"},"committer_date":{"kind":"timestamp","value":"2016-05-04T19:51:30","string":"2016-05-04T19:51:30"},"github_id":{"kind":"number","value":58070237,"string":"58,070,237"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6301145553588867,"alphanum_fraction":0.6317512392997742,"avg_line_length":19.366666793823242,"blob_id":"7b254f386e9aa9b36d0d3e411e7efe68c530ea4c","content_id":"56de958fef53f0b366f36e7036e182ec545c8638","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":611,"license_type":"no_license","max_line_length":68,"num_lines":30,"path":"/app/__init__.py","repo_name":"ghevcoul/fractal_app","src_encoding":"UTF-8","text":"#!/usr/bin/env python3\n\nfrom flask import Flask, Markup, render_template, url_for\n\nfrom . import fractal_tree\n\napp = Flask(__name__)\n\n\n@app.route(\"/hello\")\ndef hello_world():\n \"\"\"\n Simple Hello, World to test if server is up.\n \"\"\"\n return \"Hello, World!\"\n\n@app.route(\"/\")\n@app.route(\"/tree\")\ndef display_tree():\n \"\"\"\n Make a random fractal tree and display it.\n \"\"\"\n tree = fractal_tree.build_tree(use_random=True)\n svg_string = fractal_tree.get_tree_xml(tree)\n\n return render_template(\"fractal.html\", image=Markup(svg_string))\n\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n"},{"alpha_fraction":0.7577639818191528,"alphanum_fraction":0.7577639818191528,"avg_line_length":22,"blob_id":"c2475bba30714f85d9af8f69780663d0af237366","content_id":"446df30f99fdb4b651a52a090cf2d7597029440d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":161,"license_type":"no_license","max_line_length":58,"num_lines":7,"path":"/README.md","repo_name":"ghevcoul/fractal_app","src_encoding":"UTF-8","text":"# Fractals!\n\nA simple Flask app for randomly generating fractal trees. \n\n### TO-DO:\n* Play with randomness parameters to make grasses\n* Speed up tree generation\n"},{"alpha_fraction":0.44680851697921753,"alphanum_fraction":0.6808510422706604,"avg_line_length":14.666666984558105,"blob_id":"a4483907c01abca5e9c8606bc26ce606b0c8afca","content_id":"f8823ea12a629db13645c695d21adec81c522cbb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":47,"license_type":"no_license","max_line_length":16,"num_lines":3,"path":"/requirements.txt","repo_name":"ghevcoul/fractal_app","src_encoding":"UTF-8","text":"svgwrite==1.1.6\nflask==0.10.1\nwerkzeug==0.11.8\n"},{"alpha_fraction":0.5163841843605042,"alphanum_fraction":0.5380790829658508,"avg_line_length":27.9150333404541,"blob_id":"6c3892d1c4756403dd5c34fb1896e23545a938f1","content_id":"3f1f41ce41c70edbf13ee688cac0152e8a94beb7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4425,"license_type":"no_license","max_line_length":85,"num_lines":153,"path":"/app/fractal_tree.py","repo_name":"ghevcoul/fractal_app","src_encoding":"UTF-8","text":"\"\"\"\nA recursive fractal tree builder.\n\"\"\"\nimport math\nimport random\n\nimport svgwrite\n\ndef build_tree(start=(0, 0), branch_len=150, angle=270, use_random=True):\n \"\"\"\n A recursive function to build a fractal tree.\n\n Input:\n start - (x, y) tuple giving the starting point of the tree\n branch_len - the length of this branch of the tree\n\n Output:\n tree - list of (x1, y1, x2, y2) defining the line segments of this tree\n \"\"\"\n params = {\n \"length\": (0.45, 0.825),\n \"angle\": (-65, 65),\n \"branches\": [2, 2, 2, 3, 3, 3, 4, 5]\n }\n\n if branch_len <= 3:\n return []\n else:\n tree = []\n x_end = start[0] + (branch_len * math.cos(math.radians(angle)))\n y_end = start[1] + (branch_len * math.sin(math.radians(angle)))\n tree.append((start[0], start[1], x_end, y_end))\n\n if use_random:\n for _ in range(random.choice(params[\"branches\"])):\n tree += build_tree(\n (x_end, y_end),\n branch_len * random.uniform(\n params[\"length\"][0],\n params[\"length\"][1]\n ),\n angle + random.randrange(params[\"angle\"][0], params[\"angle\"][1]),\n use_random=use_random\n )\n else:\n tree += build_tree(\n (x_end, y_end),\n branch_len * 0.61,\n angle - 45,\n use_random=use_random\n )\n tree += build_tree(\n (x_end, y_end),\n branch_len * 0.61,\n angle + 45,\n use_random=use_random\n )\n\n return tree\n\ndef tree_normalize(tree):\n \"\"\"\n Takes a list of line segments defining a tree and normalizes them by \n making all coordinates positive.\n \"\"\"\n # Find the minimum x and y values\n x_vals = [(i[0], i[2]) for i in tree]\n x_vals = [item for sublist in x_vals for item in sublist]\n y_vals = [(i[1], i[3]) for i in tree]\n y_vals = [item for sublist in y_vals for item in sublist]\n\n x_shift = abs(min(x_vals))\n y_shift = abs(min(y_vals))\n\n # Add the shift values to each point\n new_tree = []\n for line in tree:\n new_tree.append((\n line[0] + x_shift,\n line[1] + y_shift,\n line[2] + x_shift,\n line[3] + y_shift\n ))\n return new_tree\n\ndef find_branch_length(branch):\n \"\"\"\n Calculates the length of the given branch.\n \"\"\"\n return math.sqrt((branch[2] - branch[0])**2 + (branch[3] - branch[1])**2)\n\ndef get_max_vals(tree):\n \"\"\"\n Finds the max x and y values in the given tree and returns them.\n \"\"\"\n x_vals = [(i[0], i[2]) for i in tree]\n x_vals = [item for sublist in x_vals for item in sublist]\n y_vals = [(i[1], i[3]) for i in tree]\n y_vals = [item for sublist in y_vals for item in sublist]\n\n return max(x_vals), max(y_vals)\n\ndef generate_svg(tree):\n \"\"\"\n Takes a list of line segments defining a tree and builds the SVG for them.\n \"\"\"\n tree = tree_normalize(tree)\n\n dwg = svgwrite.Drawing(size=get_max_vals(tree))\n\n # Add each branch to the drawing\n for branch in tree:\n width = math.floor(find_branch_length(branch) / 8)\n if width < 1:\n width = 1\n color = svgwrite.rgb(139, 69, 19)\n if find_branch_length(branch) < 6:\n color = svgwrite.rgb(34, 139, 34)\n dwg.add(dwg.line(\n start=branch[:2],\n end=branch[2:],\n stroke=color,\n stroke_width=width,\n stroke_linecap=\"round\"\n ))\n # The linejoin parameter only works for shapes and polylines...\n #svgwrite.mixins.Presentation.stroke(dwg, linejoin=\"round\")\n\n return dwg\n\ndef write_tree_file(tree, filename):\n \"\"\"\n Takes a list of line segments defining a tree and writes them to an SVG file.\n \"\"\"\n svg = generate_svg(tree)\n # Save the drawing\n svg.saveas(filename)\n\ndef get_tree_xml(tree):\n \"\"\"\n Takes a list of line segments defining a tree and returns the XML\n for the SVG file as a string.\n \"\"\"\n svg = generate_svg(tree)\n return svg.tostring()\n\n\nif __name__ == \"__main__\":\n RANDOMIZED = False\n STARTLENGTH = 100\n TREE = build_tree((0, 0), STARTLENGTH, 270, use_random=RANDOMIZED)\n # write_tree(TREE, \"test.svg\")\n print(get_tree_xml(TREE))\n\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6301145553588867,\n \"alphanum_fraction\": 0.6317512392997742,\n \"avg_line_length\": 19.366666793823242,\n \"blob_id\": \"7b254f386e9aa9b36d0d3e411e7efe68c530ea4c\",\n \"content_id\": \"56de958fef53f0b366f36e7036e182ec545c8638\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 611,\n \"license_type\": \"no_license\",\n \"max_line_length\": 68,\n \"num_lines\": 30,\n \"path\": \"/app/__init__.py\",\n \"repo_name\": \"ghevcoul/fractal_app\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python3\\n\\nfrom flask import Flask, Markup, render_template, url_for\\n\\nfrom . import fractal_tree\\n\\napp = Flask(__name__)\\n\\n\\n@app.route(\\\"/hello\\\")\\ndef hello_world():\\n \\\"\\\"\\\"\\n Simple Hello, World to test if server is up.\\n \\\"\\\"\\\"\\n return \\\"Hello, World!\\\"\\n\\n@app.route(\\\"/\\\")\\n@app.route(\\\"/tree\\\")\\ndef display_tree():\\n \\\"\\\"\\\"\\n Make a random fractal tree and display it.\\n \\\"\\\"\\\"\\n tree = fractal_tree.build_tree(use_random=True)\\n svg_string = fractal_tree.get_tree_xml(tree)\\n\\n return render_template(\\\"fractal.html\\\", image=Markup(svg_string))\\n\\n\\nif __name__ == \\\"__main__\\\":\\n app.run(debug=True)\\n\"\n },\n {\n \"alpha_fraction\": 0.7577639818191528,\n \"alphanum_fraction\": 0.7577639818191528,\n \"avg_line_length\": 22,\n \"blob_id\": \"c2475bba30714f85d9af8f69780663d0af237366\",\n \"content_id\": \"446df30f99fdb4b651a52a090cf2d7597029440d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 161,\n \"license_type\": \"no_license\",\n \"max_line_length\": 58,\n \"num_lines\": 7,\n \"path\": \"/README.md\",\n \"repo_name\": \"ghevcoul/fractal_app\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Fractals!\\n\\nA simple Flask app for randomly generating fractal trees. \\n\\n### TO-DO:\\n* Play with randomness parameters to make grasses\\n* Speed up tree generation\\n\"\n },\n {\n \"alpha_fraction\": 0.44680851697921753,\n \"alphanum_fraction\": 0.6808510422706604,\n \"avg_line_length\": 14.666666984558105,\n \"blob_id\": \"a4483907c01abca5e9c8606bc26ce606b0c8afca\",\n \"content_id\": \"f8823ea12a629db13645c695d21adec81c522cbb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 47,\n \"license_type\": \"no_license\",\n \"max_line_length\": 16,\n \"num_lines\": 3,\n \"path\": \"/requirements.txt\",\n \"repo_name\": \"ghevcoul/fractal_app\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"svgwrite==1.1.6\\nflask==0.10.1\\nwerkzeug==0.11.8\\n\"\n },\n {\n \"alpha_fraction\": 0.5163841843605042,\n \"alphanum_fraction\": 0.5380790829658508,\n \"avg_line_length\": 27.9150333404541,\n \"blob_id\": \"6c3892d1c4756403dd5c34fb1896e23545a938f1\",\n \"content_id\": \"3f1f41ce41c70edbf13ee688cac0152e8a94beb7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4425,\n \"license_type\": \"no_license\",\n \"max_line_length\": 85,\n \"num_lines\": 153,\n \"path\": \"/app/fractal_tree.py\",\n \"repo_name\": \"ghevcoul/fractal_app\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\nA recursive fractal tree builder.\\n\\\"\\\"\\\"\\nimport math\\nimport random\\n\\nimport svgwrite\\n\\ndef build_tree(start=(0, 0), branch_len=150, angle=270, use_random=True):\\n \\\"\\\"\\\"\\n A recursive function to build a fractal tree.\\n\\n Input:\\n start - (x, y) tuple giving the starting point of the tree\\n branch_len - the length of this branch of the tree\\n\\n Output:\\n tree - list of (x1, y1, x2, y2) defining the line segments of this tree\\n \\\"\\\"\\\"\\n params = {\\n \\\"length\\\": (0.45, 0.825),\\n \\\"angle\\\": (-65, 65),\\n \\\"branches\\\": [2, 2, 2, 3, 3, 3, 4, 5]\\n }\\n\\n if branch_len <= 3:\\n return []\\n else:\\n tree = []\\n x_end = start[0] + (branch_len * math.cos(math.radians(angle)))\\n y_end = start[1] + (branch_len * math.sin(math.radians(angle)))\\n tree.append((start[0], start[1], x_end, y_end))\\n\\n if use_random:\\n for _ in range(random.choice(params[\\\"branches\\\"])):\\n tree += build_tree(\\n (x_end, y_end),\\n branch_len * random.uniform(\\n params[\\\"length\\\"][0],\\n params[\\\"length\\\"][1]\\n ),\\n angle + random.randrange(params[\\\"angle\\\"][0], params[\\\"angle\\\"][1]),\\n use_random=use_random\\n )\\n else:\\n tree += build_tree(\\n (x_end, y_end),\\n branch_len * 0.61,\\n angle - 45,\\n use_random=use_random\\n )\\n tree += build_tree(\\n (x_end, y_end),\\n branch_len * 0.61,\\n angle + 45,\\n use_random=use_random\\n )\\n\\n return tree\\n\\ndef tree_normalize(tree):\\n \\\"\\\"\\\"\\n Takes a list of line segments defining a tree and normalizes them by \\n making all coordinates positive.\\n \\\"\\\"\\\"\\n # Find the minimum x and y values\\n x_vals = [(i[0], i[2]) for i in tree]\\n x_vals = [item for sublist in x_vals for item in sublist]\\n y_vals = [(i[1], i[3]) for i in tree]\\n y_vals = [item for sublist in y_vals for item in sublist]\\n\\n x_shift = abs(min(x_vals))\\n y_shift = abs(min(y_vals))\\n\\n # Add the shift values to each point\\n new_tree = []\\n for line in tree:\\n new_tree.append((\\n line[0] + x_shift,\\n line[1] + y_shift,\\n line[2] + x_shift,\\n line[3] + y_shift\\n ))\\n return new_tree\\n\\ndef find_branch_length(branch):\\n \\\"\\\"\\\"\\n Calculates the length of the given branch.\\n \\\"\\\"\\\"\\n return math.sqrt((branch[2] - branch[0])**2 + (branch[3] - branch[1])**2)\\n\\ndef get_max_vals(tree):\\n \\\"\\\"\\\"\\n Finds the max x and y values in the given tree and returns them.\\n \\\"\\\"\\\"\\n x_vals = [(i[0], i[2]) for i in tree]\\n x_vals = [item for sublist in x_vals for item in sublist]\\n y_vals = [(i[1], i[3]) for i in tree]\\n y_vals = [item for sublist in y_vals for item in sublist]\\n\\n return max(x_vals), max(y_vals)\\n\\ndef generate_svg(tree):\\n \\\"\\\"\\\"\\n Takes a list of line segments defining a tree and builds the SVG for them.\\n \\\"\\\"\\\"\\n tree = tree_normalize(tree)\\n\\n dwg = svgwrite.Drawing(size=get_max_vals(tree))\\n\\n # Add each branch to the drawing\\n for branch in tree:\\n width = math.floor(find_branch_length(branch) / 8)\\n if width < 1:\\n width = 1\\n color = svgwrite.rgb(139, 69, 19)\\n if find_branch_length(branch) < 6:\\n color = svgwrite.rgb(34, 139, 34)\\n dwg.add(dwg.line(\\n start=branch[:2],\\n end=branch[2:],\\n stroke=color,\\n stroke_width=width,\\n stroke_linecap=\\\"round\\\"\\n ))\\n # The linejoin parameter only works for shapes and polylines...\\n #svgwrite.mixins.Presentation.stroke(dwg, linejoin=\\\"round\\\")\\n\\n return dwg\\n\\ndef write_tree_file(tree, filename):\\n \\\"\\\"\\\"\\n Takes a list of line segments defining a tree and writes them to an SVG file.\\n \\\"\\\"\\\"\\n svg = generate_svg(tree)\\n # Save the drawing\\n svg.saveas(filename)\\n\\ndef get_tree_xml(tree):\\n \\\"\\\"\\\"\\n Takes a list of line segments defining a tree and returns the XML\\n for the SVG file as a string.\\n \\\"\\\"\\\"\\n svg = generate_svg(tree)\\n return svg.tostring()\\n\\n\\nif __name__ == \\\"__main__\\\":\\n RANDOMIZED = False\\n STARTLENGTH = 100\\n TREE = build_tree((0, 0), STARTLENGTH, 270, use_random=RANDOMIZED)\\n # write_tree(TREE, \\\"test.svg\\\")\\n print(get_tree_xml(TREE))\\n\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":4,"string":"4"}}},{"rowIdx":2979376,"cells":{"repo_name":{"kind":"string","value":"naotohori/pca_imkl"},"repo_url":{"kind":"string","value":"https://github.com/naotohori/pca_imkl"},"snapshot_id":{"kind":"string","value":"7fae1e9d95bd1435c3e6edaf0bf62f686599e550"},"revision_id":{"kind":"string","value":"af9989a78391f72a36612b6ee3393739c8f2805d"},"directory_id":{"kind":"string","value":"f4fa4f1075769ae1b3b53cc462060b2e18cf01ea"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-01-25T04:02:54.846794","string":"2021-01-25T04:02:54.846794"},"revision_date":{"kind":"timestamp","value":"2017-03-22T19:46:52","string":"2017-03-22T19:46:52"},"committer_date":{"kind":"timestamp","value":"2017-03-22T19:46:52","string":"2017-03-22T19:46:52"},"github_id":{"kind":"number","value":30076123,"string":"30,076,123"},"star_events_count":{"kind":"number","value":1,"string":"1"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5234493017196655,"alphanum_fraction":0.5385779142379761,"avg_line_length":18.969696044921875,"blob_id":"b7cd78ed68fc9478a599be54fc99e53b517467b5","content_id":"5c10eed1a4e9d646d78090d680b0b51eff061326","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":661,"license_type":"no_license","max_line_length":89,"num_lines":33,"path":"/post_pca.py","repo_name":"naotohori/pca_imkl","src_encoding":"UTF-8","text":"#!/usr/bin/env python \n\nimport sys\n\nif len(sys.argv) != 4 :\n print ''\n print ' Usage : SCRIPT [input pca file] [# of eigen values] [prefix of output files]'\n print ''\n sys.exit(2)\n\nf_in = open(sys.argv[1], 'r')\nn_vec = int(sys.argv[2].strip())\nprefix = sys.argv[3]\n\nf_value = open(prefix+'.value', 'w')\nfs_vec = []\nfor i in xrange(n_vec) :\n fs_vec.append(open(prefix+'_%i.vec'%(i+1,) , 'w'))\n\ni = 0\nf_out = f_value\nfor line in f_in :\n if line.find('#eigenvector') != -1 :\n i += 1\n if i > n_vec :\n break\n f_out = fs_vec[i-1] \n f_out.write(line)\n\nf_in.close()\nf_value.close()\nfor f in fs_vec :\n f.close()\n\n\n"},{"alpha_fraction":0.6708428263664246,"alphanum_fraction":0.7277904152870178,"avg_line_length":37.173912048339844,"blob_id":"6d8faf29a0d4494873eddda42a76b14c9ae1640d","content_id":"e8c51ca5e0557c0ace072564c64e8652a13a2ed9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Makefile","length_bytes":878,"license_type":"no_license","max_line_length":290,"num_lines":23,"path":"/Makefile","repo_name":"naotohori/pca_imkl","src_encoding":"UTF-8","text":"FC = ifort\nOPT = -O3\nDLIB = -lmkl_intel_lp64 -lmkl_intel_thread -lmkl_core -liomp5 -lpthread -lmkl_lapack95_lp64\nMKLINCLUDE = ${MKLROOT}/include\n\nMKLPATH = ${MKLROOT}/lib/intel64\nMKLINCLUDELAPACK = ${MKLROOT}/include/intel64/lp64\n\nall : dcd2vcvmatrix.exe pca_imkl.exe dihxy2vcvmatrix.exe\n\ndcd2vcvmatrix.exe :\n\t$(FC) $(OPT) -o $@ dcd2vcvmatrix.F90\n\ndihxy2vcvmatrix.exe :\n\t$(FC) $(OPT) -o $@ dihxy2vcvmatrix.F90\n\npca_imkl.exe :\n\t$(FC) $(OPT) -o $@ pca_imkl.f90 -L$(MKLPATH) -I$(MKLINCLUDE) -I$(MKLINCLUDELAPACK) $(DLIB)\n\nclean :\n\trm -rf dcd2vcvmatrix.exe pca_imkl.exe dihxy2vcvmatrix.exe\n\n#ifort pca_imkl.f90 -o pca_imkl -O3 -L$MKLROOT/lib/em64t -I$MKLROOT/include -I$MKLROOT/include/em64t/lp64 -lmkl_lapack95_lp64 -Wl,--start-group $MKLROOT/lib/em64t/libmkl_intel_lp64.a $MKLROOT/lib/em64t/libmkl_intel_thread.a $MKLROOT/lib/em64t/libmkl_core.a -Wl,--end-group -liomp5 -lpthread\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5234493017196655,\n \"alphanum_fraction\": 0.5385779142379761,\n \"avg_line_length\": 18.969696044921875,\n \"blob_id\": \"b7cd78ed68fc9478a599be54fc99e53b517467b5\",\n \"content_id\": \"5c10eed1a4e9d646d78090d680b0b51eff061326\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 661,\n \"license_type\": \"no_license\",\n \"max_line_length\": 89,\n \"num_lines\": 33,\n \"path\": \"/post_pca.py\",\n \"repo_name\": \"naotohori/pca_imkl\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python \\n\\nimport sys\\n\\nif len(sys.argv) != 4 :\\n print ''\\n print ' Usage : SCRIPT [input pca file] [# of eigen values] [prefix of output files]'\\n print ''\\n sys.exit(2)\\n\\nf_in = open(sys.argv[1], 'r')\\nn_vec = int(sys.argv[2].strip())\\nprefix = sys.argv[3]\\n\\nf_value = open(prefix+'.value', 'w')\\nfs_vec = []\\nfor i in xrange(n_vec) :\\n fs_vec.append(open(prefix+'_%i.vec'%(i+1,) , 'w'))\\n\\ni = 0\\nf_out = f_value\\nfor line in f_in :\\n if line.find('#eigenvector') != -1 :\\n i += 1\\n if i > n_vec :\\n break\\n f_out = fs_vec[i-1] \\n f_out.write(line)\\n\\nf_in.close()\\nf_value.close()\\nfor f in fs_vec :\\n f.close()\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6708428263664246,\n \"alphanum_fraction\": 0.7277904152870178,\n \"avg_line_length\": 37.173912048339844,\n \"blob_id\": \"6d8faf29a0d4494873eddda42a76b14c9ae1640d\",\n \"content_id\": \"e8c51ca5e0557c0ace072564c64e8652a13a2ed9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Makefile\",\n \"length_bytes\": 878,\n \"license_type\": \"no_license\",\n \"max_line_length\": 290,\n \"num_lines\": 23,\n \"path\": \"/Makefile\",\n \"repo_name\": \"naotohori/pca_imkl\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"FC = ifort\\nOPT = -O3\\nDLIB = -lmkl_intel_lp64 -lmkl_intel_thread -lmkl_core -liomp5 -lpthread -lmkl_lapack95_lp64\\nMKLINCLUDE = ${MKLROOT}/include\\n\\nMKLPATH = ${MKLROOT}/lib/intel64\\nMKLINCLUDELAPACK = ${MKLROOT}/include/intel64/lp64\\n\\nall : dcd2vcvmatrix.exe pca_imkl.exe dihxy2vcvmatrix.exe\\n\\ndcd2vcvmatrix.exe :\\n\\t$(FC) $(OPT) -o $@ dcd2vcvmatrix.F90\\n\\ndihxy2vcvmatrix.exe :\\n\\t$(FC) $(OPT) -o $@ dihxy2vcvmatrix.F90\\n\\npca_imkl.exe :\\n\\t$(FC) $(OPT) -o $@ pca_imkl.f90 -L$(MKLPATH) -I$(MKLINCLUDE) -I$(MKLINCLUDELAPACK) $(DLIB)\\n\\nclean :\\n\\trm -rf dcd2vcvmatrix.exe pca_imkl.exe dihxy2vcvmatrix.exe\\n\\n#ifort pca_imkl.f90 -o pca_imkl -O3 -L$MKLROOT/lib/em64t -I$MKLROOT/include -I$MKLROOT/include/em64t/lp64 -lmkl_lapack95_lp64 -Wl,--start-group $MKLROOT/lib/em64t/libmkl_intel_lp64.a $MKLROOT/lib/em64t/libmkl_intel_thread.a $MKLROOT/lib/em64t/libmkl_core.a -Wl,--end-group -liomp5 -lpthread\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2979377,"cells":{"repo_name":{"kind":"string","value":"Sobhy92/Fyyur"},"repo_url":{"kind":"string","value":"https://github.com/Sobhy92/Fyyur"},"snapshot_id":{"kind":"string","value":"023a5593a58db8cbfbd5e691e8da8af637f154ca"},"revision_id":{"kind":"string","value":"6e3170a256105b3f98886ed551a062a573e86cb3"},"directory_id":{"kind":"string","value":"0a5c934b01baf6bf58639797498c4aae11931711"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"timestamp","value":"2023-01-19T00:00:58.781114","string":"2023-01-19T00:00:58.781114"},"revision_date":{"kind":"timestamp","value":"2020-11-27T11:24:34","string":"2020-11-27T11:24:34"},"committer_date":{"kind":"timestamp","value":"2020-11-27T11:24:34","string":"2020-11-27T11:24:34"},"github_id":{"kind":"number","value":313139843,"string":"313,139,843"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.611474335193634,"alphanum_fraction":0.6158006191253662,"avg_line_length":32.67715072631836,"blob_id":"a5cbf391a9a57158315fccb108ee7700ed627864","content_id":"8fa4c42384bdd35c15e1f15b2f4343b560e657c4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":20341,"license_type":"no_license","max_line_length":122,"num_lines":604,"path":"/app.py","repo_name":"Sobhy92/Fyyur","src_encoding":"UTF-8","text":"#----------------------------------------------------------------------------#\n# Imports\n#----------------------------------------------------------------------------#\n\nimport json\nimport dateutil.parser\nimport babel\nimport sys\nfrom flask import Flask, render_template, request, Response, flash, redirect, url_for,abort\nfrom flask_moment import Moment\nfrom flask_sqlalchemy import SQLAlchemy\nimport logging\nfrom logging import Formatter, FileHandler\nfrom flask_wtf import Form\nfrom sqlalchemy import func\nfrom forms import *\nfrom config import SQLALCHEMY_DATABASE_URI\nfrom flask_migrate import Migrate\n#----------------------------------------------------------------------------#\n# App Config.\n#----------------------------------------------------------------------------#\n\napp = Flask(__name__)\nmoment = Moment(app)\napp.config.from_object('config')\n# DONE: connect to a local postgresql database\n\napp.config['SQLALCHEMY_DATABASE_URI'] = SQLALCHEMY_DATABASE_URI\ndb = SQLAlchemy(app)\n#----------------------------------------------------------------------------#\n# Models.\n#----------------------------------------------------------------------------#\n#show is an association table\nclass Show(db.Model):\n __tablename__ = 'Show'\n id = db.Column(db.Integer, primary_key=True)\n artist_id = db.Column(db.Integer,db.ForeignKey('Artist.id'), nullable=False)\n venue_id = db.Column(db.Integer,db.ForeignKey('Venue.id'), nullable=False)\n start_time = db.Column(db.DateTime, nullable=False)\n venue = db.relationship(\"Venue\", back_populates=\"venue_relation\")\n artist = db.relationship(\"Artist\", back_populates=\"artist_relation\")\n\nclass Venue(db.Model):\n __tablename__ = 'Venue'\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.String)\n city = db.Column(db.String(120))\n state = db.Column(db.String(120))\n address = db.Column(db.String(120))\n phone = db.Column(db.String(120))\n image_link = db.Column(db.String(500))\n facebook_link = db.Column(db.String(120))\n venue_relation = db.relationship(\"Show\", back_populates=\"venue\")\n\n# DONE: implement any missing fields, as a database migration using Flask-Migrate\n\nclass Artist(db.Model):\n __tablename__ = 'Artist'\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.String)\n city = db.Column(db.String(120))\n state = db.Column(db.String(120))\n phone = db.Column(db.String(120))\n genres = db.Column(db.String(120))\n image_link = db.Column(db.String(500))\n facebook_link = db.Column(db.String(120))\n artist_relation = db.relationship(\"Show\", back_populates=\"artist\")\n\n\n\nmigrate = Migrate(app, db)\n# DONE: implement any missing fields, as a database migration using Flask-Migrate\n# DONE Implement Show and Artist models, and complete all model relationships and properties, as a database migration.\n\n#----------------------------------------------------------------------------#\n# Filters.\n#----------------------------------------------------------------------------#\n\ndef format_datetime(value, format='medium'):\n date = dateutil.parser.parse(value)\n if format == 'full':\n format=\"EEEE MMMM, d, y 'at' h:mma\"\n elif format == 'medium':\n format=\"EE MM, dd, y h:mma\"\n return babel.dates.format_datetime(date, format)\n\napp.jinja_env.filters['datetime'] = format_datetime\n\n#----------------------------------------------------------------------------#\n# Controllers.\n#----------------------------------------------------------------------------#\n\n@app.route('/')\ndef index():\n return render_template('pages/home.html')\n\n\n# Venues\n# ----------------------------------------------------------------\n\n@app.route('/venues')\ndef venues():\n\n # DONE: replace with real venues data.\n # # num_shows should be aggregated based on number of upcoming shows per venue.\n\n all_places = db.session.query(func.count(Venue.id), Venue.city, Venue.state).group_by(Venue.city, Venue.state).all()\n data = []\n\n for place in all_places:\n place_venues = db.session.query(Venue).filter_by(state=place.state).filter_by(city=place.city).all()\n my_venue = []\n for venue in place_venues:\n upcoming_num = db.session.query(Show).filter(Show.venue_id==venue.id).filter(Show.start_time>datetime.now()).count()\n my_venue.append({\n \"id\": venue.id,\n \"name\": venue.name,\n \"num_upcoming_shows\": upcoming_num\n })\n data.append({\n \"city\": place.city,\n \"state\": place.state,\n \"venues\": my_venue\n })\n\n return render_template('pages/venues.html', areas=data)\n\n@app.route('/venues/search', methods=['POST'])\ndef search_venues():\n # DONE: implement search on Venues with partial string search. Ensure it is case-insensitive.\n # seach for Hop should return \"The Musical Hop\".\n # search for \"Music\" should return \"The Musical Hop\" and \"Park Square Live Music & Coffee\"\n data = request.form\n search_writing = data['search_term']\n\n # make query to get data related the search input\n my_venues = db.session.query(Venue).filter(Venue.name.ilike('%{}%'.format(search_writing))).all()\n response = {}\n response['count'] = len(my_venues)\n response['data'] = my_venues\n return render_template('pages/search_venues.html', results=response, search_term=request.form.get('search_term', ''))\n\n@app.route('/venues/<int:venue_id>')\ndef show_venue(venue_id):\n # shows the venue page with the given venue_id\n # DONE: replace with real venue data from the venues table, using venue_id\n my_venue = db.session.query(Venue).filter(Venue.id == venue_id).one()\n if my_venue:\n #make join to get all shows and their related artists according to given venue_id\n\n all_shows = db.session.query(Show).join(Artist).filter(Show.venue_id == venue_id)\n past_shows = []\n upcoming_shows = []\n current_time = datetime.now()\n for show in all_shows:\n add_show = {\n \"artist_id\": show.artist_id,\n \"artist_name\": show.artist.name,\n \"artist_image_link\": show.artist.image_link,\n \"start_time\": show.start_time.strftime('%c')\n }\n\n #determine if show is past or coming\n if (show.start_time < current_time):\n past_shows.append(add_show)\n else:\n upcoming_shows.append(add_show)\n\n data = {\n \"id\": my_venue.id,\n \"name\": my_venue.name,\n \"address\": my_venue.address,\n \"city\": my_venue.city,\n \"state\": my_venue.state,\n \"phone\": my_venue.phone,\n \"facebook_link\": my_venue.facebook_link,\n \"image_link\": my_venue.image_link,\n \"past_shows\": past_shows,\n \"upcoming_shows\": upcoming_shows,\n \"past_shows_count\": len(past_shows),\n \"upcoming_shows_count\": len(upcoming_shows),\n }\n return render_template('pages/show_venue.html', venue=data)\n else:\n return render_template('errors/404.html')\n# Create Venue\n# ----------------------------------------------------------------\n\n@app.route('/venues/create', methods=['GET'])\ndef create_venue_form():\n form = VenueForm()\n return render_template('forms/new_venue.html', form=form)\n\n@app.route('/venues/create', methods=['POST'])\ndef create_venue_submission():\n data = request.form\n name = data['name']\n city = data['city']\n state = data['state']\n address = data['address']\n phone = data['phone']\n image_link = data['image_link']\n facebook_link = data['facebook_link']\n\n error = False\n try:\n new_venue = Venue(name=name,\n city=city,\n state=state,\n address=address,\n phone=phone,\n image_link=image_link,\n facebook_link=facebook_link,\n )\n db.session.add(new_venue)\n db.session.commit()\n except:\n error = True\n db.session.rollback()\n print(sys.exc_info())\n finally:\n db.session.close()\n if error:\n flash('An error occurred. Venue ' + name + ' could not be listed.')\n else:\n flash('Venue ' + request.form['name'] + ' was successfully listed!')\n # DONE: insert form data as a new Venue record in the db, instead\n # DONE: modify data to be the data object returned from db insertion\n\n # on successful db insert, flash success\n\n # DONE: on unsuccessful db insert, flash an error instead.\n # e.g., flash('An error occurred. Venue ' + data.name + ' could not be listed.')\n # see: http://flask.pocoo.org/docs/1.0/patterns/flashing/\n return render_template('pages/home.html')\n\n@app.route('/venues/<venue_id>', methods=['DELETE'])\ndef delete_venue(venue_id):\n # DONE: Complete this endpoint for taking a venue_id, and using\n # SQLAlchemy ORM to delete a record. Handle cases where the session commit could fail.\n error = False\n try:\n db.session.query(Venue).filter(Venue.id == venue_id).delete()\n db.session.commit()\n except:\n db.session.rollback()\n error = True\n print(sys.exc_info())\n finally:\n db.session.close()\n if error:\n flash('Error! record can not be deleted!')\n else:\n flash('Record was successfully deleted!')\n return None\n # BONUS CHALLENGE: Implement a button to delete a Venue on a Venue Page, have it so that\n # clicking that button delete it from the db then redirect the user to the homepage\n\n# Artists\n# ----------------------------------------------------------------\n@app.route('/artists')\ndef artists():\n\n # DONE: replace with real data returned from querying the database\n\n\n all_artists = db.session.query(Artist.id, Artist.name)\n data = []\n for artist in all_artists:\n data.append({\n \"id\": artist[0],\n \"name\": artist[1]\n })\n return render_template('pages/artists.html', artists=data)\n\n@app.route('/artists/search', methods=['POST'])\ndef search_artists():\n # DONE: implement search on artists with partial string search. Ensure it is case-insensitive.\n # seach for \"A\" should return \"Guns N Petals\", \"Matt Quevado\", and \"The Wild Sax Band\".\n # search for \"band\" should return \"The Wild Sax Band\".\n\n data = request.form\n search_writing = data['search_term']\n\n #get data from database according to given search value\n my_artists = db.session.query(Artist).filter(Artist.name.ilike('%{}%'.format(search_writing))).all()\n response = {}\n response['count'] = len(my_artists)\n response['data'] = my_artists\n return render_template('pages/search_artists.html', results=response, search_term=request.form.get('search_term', ''))\n\n@app.route('/artists/<int:artist_id>')\ndef show_artist(artist_id):\n # shows the venue page with the given venue_id\n # DONR: replace with real venue data from the venues table, using venue_id\n my_artist = db.session.query(Artist).filter(Artist.id == artist_id).one()\n if my_artist:\n\n # make join to get all shows and their related venues according to given artist_id\n all_shows = db.session.query(Show).join(Venue).filter(Show.artist_id == artist_id)\n past_shows = []\n upcoming_shows = []\n current_time = datetime.now()\n for show in all_shows:\n add_show = {\n \"venue_id\": show.artist_id,\n \"venue_name\": show.venue.name,\n \"venue_image_link\": show.venue.image_link,\n \"start_time\": show.start_time.strftime('%c')\n }\n\n #determine if show is past or coming\n if (show.start_time < current_time):\n past_shows.append(add_show)\n else:\n upcoming_shows.append(add_show)\n\n data = {\n \"id\": my_artist.id,\n \"name\": my_artist.name,\n \"city\": my_artist.city,\n \"state\": my_artist.state,\n \"genres\": my_artist.genres.replace('{', '').replace('}', '').split(','),\n \"phone\": my_artist.phone,\n \"facebook_link\": my_artist.facebook_link,\n \"image_link\": my_artist.image_link,\n \"past_shows\": past_shows,\n \"upcoming_shows\": upcoming_shows,\n \"past_shows_count\": len(past_shows),\n \"upcoming_shows_count\": len(upcoming_shows),\n }\n return render_template('pages/show_venue.html', venue=data)\n else:\n return render_template('errors/404.html')\n\n# Update\n# ----------------------------------------------------------------\n@app.route('/artists/<int:artist_id>/edit', methods=['GET'])\ndef edit_artist(artist_id):\n form = ArtistForm()\n\n #get record that needs to be changed from our database\n my_artist = db.session.query(Artist).filter(Artist.id == artist_id).one()\n if my_artist:\n form.name.data = my_artist.name\n form.city.data = my_artist.city\n form.state.data = my_artist.state\n form.phone.data = my_artist.phone\n form.genres.data = my_artist.genres\n form.facebook_link.data = my_artist.facebook_link\n form.image_link.data = my_artist.image_link\n return render_template('forms/edit_artist.html', form=form, artist=my_artist)\n else:\n return render_template('errors/404.html')\n # DONE: populate form with fields from artist with ID <artist_id>\n\n\n@app.route('/artists/<int:artist_id>/edit', methods=['POST'])\ndef edit_artist_submission(artist_id):\n # DONE: take values from the form submitted, and update existing\n # artist record with ID <artist_id> using the new attributes\n\n my_artist = db.session.query(Artist).filter(Artist.id == artist_id).one()\n data = request.form\n name = data['name']\n city = data['city']\n state = data['state']\n genres = data.getlist('genres')\n phone = data['phone']\n image_link = data['image_link']\n facebook_link = data['facebook_link']\n error = False\n\n try:\n #applying changes to the row\n my_artist.name = name,\n my_artist.city = city,\n my_artist.state = state,\n my_artist.phone = phone,\n my_artist.genres = genres,\n my_artist.image_link = image_link,\n my_artist.facebook_link = facebook_link,\n db.session.commit()\n except:\n error = True\n db.session.rollback()\n print(sys.exc_info())\n finally:\n db.session.close()\n if error:\n flash('An error occurred. Artist ' + name + ' could not be updated.')\n else:\n return redirect(url_for('show_artist', artist_id=artist_id))\n\n@app.route('/venues/<int:venue_id>/edit', methods=['GET'])\ndef edit_venue(venue_id):\n form = VenueForm()\n\n #get venue record that needs to be changed\n my_venue = db.session.query(Venue).filter(Venue.id == venue_id).one()\n if my_venue:\n form.name.data = my_venue.name\n form.city.data = my_venue.city\n form.state.data = my_venue.state\n form.phone.data = my_venue.phone\n form.address.data = my_venue.address\n form.facebook_link.data = my_venue.facebook_link\n form.image_link.data = my_venue.image_link\n return render_template('forms/edit_venue.html', form=form, venue=my_venue)\n else:\n return render_template('errors/404.html')\n # DONE: populate form with values from venue with ID <venue_id>\n\n@app.route('/venues/<int:venue_id>/edit', methods=['POST'])\ndef edit_venue_submission(venue_id):\n # DONE: take values from the form submitted, and update existing\n # venue record with ID <venue_id> using the new attributes\n\n my_venue = db.session.query(Venue).filter(Venue.id == venue_id).one()\n data = request.form\n name = data['name']\n city = data['city']\n state = data['state']\n address = data['address']\n phone = data['phone']\n image_link = data['image_link']\n facebook_link = data['facebook_link']\n error = False\n\n try:\n #updating the values to the defined Venue object\n my_venue.name = name,\n my_venue.city = city,\n my_venue.state = state,\n my_venue.address = address,\n my_venue.phone = phone,\n my_venue.image_link = image_link,\n my_venue.facebook_link = facebook_link,\n db.session.commit()\n except:\n error = True\n db.session.rollback()\n print(sys.exc_info())\n finally:\n db.session.close()\n\n if error:\n flash('An error occurred. Venue ' + name + ' could not be updated.')\n else:\n return redirect(url_for('show_venue', venue_id=venue_id))\n\n# Create Artist\n# ----------------------------------------------------------------\n\n@app.route('/artists/create', methods=['GET'])\ndef create_artist_form():\n form = ArtistForm()\n return render_template('forms/new_artist.html', form=form)\n\n@app.route('/artists/create', methods=['POST'])\ndef create_artist_submission():\n # called upon submitting the new artist listing form\n # DONE: insert form data as a new Venue record in the db, instead\n # DONE: modify data to be the data object returned from db insertion\n\n data = request.form\n name = data['name']\n city = data['city']\n state = data['state']\n phone = data['phone']\n genres = data.getlist('genres')\n image_link = data['image_link']\n facebook_link = data['facebook_link']\n\n error = False\n try:\n new_artist = Artist(name=name,\n city=city,\n state=state,\n phone=phone,\n genres=genres,\n image_link=image_link,\n facebook_link=facebook_link,\n )\n db.session.add(new_artist)\n db.session.commit()\n except:\n error = True\n db.session.rollback()\n print(sys.exc_info())\n finally:\n db.session.close()\n if error:\n flash('An error occurred. Artist ' + name + ' could not be listed.')\n else:\n flash('Artist ' + request.form['name'] + ' was successfully listed!')\n\n # on successful db insert, flash success\n\n # DONE: on unsuccessful db insert, flash an error instead.\n # e.g., flash('An error occurred. Artist ' + data.name + ' could not be listed.')\n return render_template('pages/home.html')\n\n\n# Shows\n# ----------------------------------------------------------------\n\n@app.route('/shows')\ndef shows():\n # displays list of shows at /shows\n # Done: replace with real venues data.\n # num_shows should be aggregated based on number of upcoming shows per venue.\n data = []\n\n #query all_shows rows in the Show table\n all_shows = db.session.query(Show.artist_id, Show.venue_id, Show.start_time).all()\n for show in all_shows:\n\n #get artist row and venue row related to every show\n artist = db.session.query(Artist.name, Artist.image_link).filter(Artist.id == show[0]).one()\n venue = db.session.query(Venue.name).filter(Venue.id == show[1]).one()\n data.append({\n \"venue_id\": show[1],\n \"venue_name\": venue[0],\n \"artist_id\": show[0],\n \"artist_name\": artist[0],\n \"artist_image_link\": artist[1],\n \"start_time\": show[2].strftime(\"%c\")\n })\n return render_template('pages/shows.html', shows=data)\n\n@app.route('/shows/create')\ndef create_shows():\n # renders form. do not touch.\n form = ShowForm()\n return render_template('forms/new_show.html', form=form)\n\n@app.route('/shows/create', methods=['POST'])\ndef create_show_submission():\n # called to create new shows in the db, upon submitting new show listing form\n # DONE: insert form data as a new Show record in the db, instead\n\n data = request.form\n artist_id = data['artist_id']\n venue_id = data['venue_id']\n start_time = data['start_time']\n error=False\n try:\n new_show = Show(artist_id=artist_id, venue_id=venue_id, start_time=start_time)\n db.session.add(new_show)\n db.session.commit()\n except:\n error = True\n db.session.rollback()\n print(sys.exc_info())\n finally:\n db.session.close()\n if error:\n flash('An error occurred. Show could not be listed.')\n else:\n flash('Show was successfully listed!')\n return render_template('pages/home.html')\n\n # on successful db insert, flash success\n\n # DONE: on unsuccessful db insert, flash an error instead.\n # e.g., flash('An error occurred. Show could not be listed.')\n # see: http://flask.pocoo.org/docs/1.0/patterns/flashing/\n\n\n\n@app.errorhandler(404)\ndef not_found_error(error):\n return render_template('errors/404.html'), 404\n\n@app.errorhandler(500)\ndef server_error(error):\n return render_template('errors/500.html'), 500\n\n\nif not app.debug:\n file_handler = FileHandler('error.log')\n file_handler.setFormatter(\n Formatter('%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]')\n )\n app.logger.setLevel(logging.INFO)\n file_handler.setLevel(logging.INFO)\n app.logger.addHandler(file_handler)\n app.logger.info('errors')\n\n#----------------------------------------------------------------------------#\n# Launch.\n#----------------------------------------------------------------------------#\n\n# Default port:\nif __name__ == '__main__':\n app.run()\n\n# Or specify port manually:\n'''\nif __name__ == '__main__':\n port = int(os.environ.get('PORT', 5000))\n app.run(host='0.0.0.0', port=port)\n'''\n"}],"string":"[\n {\n \"alpha_fraction\": 0.611474335193634,\n \"alphanum_fraction\": 0.6158006191253662,\n \"avg_line_length\": 32.67715072631836,\n \"blob_id\": \"a5cbf391a9a57158315fccb108ee7700ed627864\",\n \"content_id\": \"8fa4c42384bdd35c15e1f15b2f4343b560e657c4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 20341,\n \"license_type\": \"no_license\",\n \"max_line_length\": 122,\n \"num_lines\": 604,\n \"path\": \"/app.py\",\n \"repo_name\": \"Sobhy92/Fyyur\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#----------------------------------------------------------------------------#\\n# Imports\\n#----------------------------------------------------------------------------#\\n\\nimport json\\nimport dateutil.parser\\nimport babel\\nimport sys\\nfrom flask import Flask, render_template, request, Response, flash, redirect, url_for,abort\\nfrom flask_moment import Moment\\nfrom flask_sqlalchemy import SQLAlchemy\\nimport logging\\nfrom logging import Formatter, FileHandler\\nfrom flask_wtf import Form\\nfrom sqlalchemy import func\\nfrom forms import *\\nfrom config import SQLALCHEMY_DATABASE_URI\\nfrom flask_migrate import Migrate\\n#----------------------------------------------------------------------------#\\n# App Config.\\n#----------------------------------------------------------------------------#\\n\\napp = Flask(__name__)\\nmoment = Moment(app)\\napp.config.from_object('config')\\n# DONE: connect to a local postgresql database\\n\\napp.config['SQLALCHEMY_DATABASE_URI'] = SQLALCHEMY_DATABASE_URI\\ndb = SQLAlchemy(app)\\n#----------------------------------------------------------------------------#\\n# Models.\\n#----------------------------------------------------------------------------#\\n#show is an association table\\nclass Show(db.Model):\\n __tablename__ = 'Show'\\n id = db.Column(db.Integer, primary_key=True)\\n artist_id = db.Column(db.Integer,db.ForeignKey('Artist.id'), nullable=False)\\n venue_id = db.Column(db.Integer,db.ForeignKey('Venue.id'), nullable=False)\\n start_time = db.Column(db.DateTime, nullable=False)\\n venue = db.relationship(\\\"Venue\\\", back_populates=\\\"venue_relation\\\")\\n artist = db.relationship(\\\"Artist\\\", back_populates=\\\"artist_relation\\\")\\n\\nclass Venue(db.Model):\\n __tablename__ = 'Venue'\\n id = db.Column(db.Integer, primary_key=True)\\n name = db.Column(db.String)\\n city = db.Column(db.String(120))\\n state = db.Column(db.String(120))\\n address = db.Column(db.String(120))\\n phone = db.Column(db.String(120))\\n image_link = db.Column(db.String(500))\\n facebook_link = db.Column(db.String(120))\\n venue_relation = db.relationship(\\\"Show\\\", back_populates=\\\"venue\\\")\\n\\n# DONE: implement any missing fields, as a database migration using Flask-Migrate\\n\\nclass Artist(db.Model):\\n __tablename__ = 'Artist'\\n id = db.Column(db.Integer, primary_key=True)\\n name = db.Column(db.String)\\n city = db.Column(db.String(120))\\n state = db.Column(db.String(120))\\n phone = db.Column(db.String(120))\\n genres = db.Column(db.String(120))\\n image_link = db.Column(db.String(500))\\n facebook_link = db.Column(db.String(120))\\n artist_relation = db.relationship(\\\"Show\\\", back_populates=\\\"artist\\\")\\n\\n\\n\\nmigrate = Migrate(app, db)\\n# DONE: implement any missing fields, as a database migration using Flask-Migrate\\n# DONE Implement Show and Artist models, and complete all model relationships and properties, as a database migration.\\n\\n#----------------------------------------------------------------------------#\\n# Filters.\\n#----------------------------------------------------------------------------#\\n\\ndef format_datetime(value, format='medium'):\\n date = dateutil.parser.parse(value)\\n if format == 'full':\\n format=\\\"EEEE MMMM, d, y 'at' h:mma\\\"\\n elif format == 'medium':\\n format=\\\"EE MM, dd, y h:mma\\\"\\n return babel.dates.format_datetime(date, format)\\n\\napp.jinja_env.filters['datetime'] = format_datetime\\n\\n#----------------------------------------------------------------------------#\\n# Controllers.\\n#----------------------------------------------------------------------------#\\n\\n@app.route('/')\\ndef index():\\n return render_template('pages/home.html')\\n\\n\\n# Venues\\n# ----------------------------------------------------------------\\n\\n@app.route('/venues')\\ndef venues():\\n\\n # DONE: replace with real venues data.\\n # # num_shows should be aggregated based on number of upcoming shows per venue.\\n\\n all_places = db.session.query(func.count(Venue.id), Venue.city, Venue.state).group_by(Venue.city, Venue.state).all()\\n data = []\\n\\n for place in all_places:\\n place_venues = db.session.query(Venue).filter_by(state=place.state).filter_by(city=place.city).all()\\n my_venue = []\\n for venue in place_venues:\\n upcoming_num = db.session.query(Show).filter(Show.venue_id==venue.id).filter(Show.start_time>datetime.now()).count()\\n my_venue.append({\\n \\\"id\\\": venue.id,\\n \\\"name\\\": venue.name,\\n \\\"num_upcoming_shows\\\": upcoming_num\\n })\\n data.append({\\n \\\"city\\\": place.city,\\n \\\"state\\\": place.state,\\n \\\"venues\\\": my_venue\\n })\\n\\n return render_template('pages/venues.html', areas=data)\\n\\n@app.route('/venues/search', methods=['POST'])\\ndef search_venues():\\n # DONE: implement search on Venues with partial string search. Ensure it is case-insensitive.\\n # seach for Hop should return \\\"The Musical Hop\\\".\\n # search for \\\"Music\\\" should return \\\"The Musical Hop\\\" and \\\"Park Square Live Music & Coffee\\\"\\n data = request.form\\n search_writing = data['search_term']\\n\\n # make query to get data related the search input\\n my_venues = db.session.query(Venue).filter(Venue.name.ilike('%{}%'.format(search_writing))).all()\\n response = {}\\n response['count'] = len(my_venues)\\n response['data'] = my_venues\\n return render_template('pages/search_venues.html', results=response, search_term=request.form.get('search_term', ''))\\n\\n@app.route('/venues/<int:venue_id>')\\ndef show_venue(venue_id):\\n # shows the venue page with the given venue_id\\n # DONE: replace with real venue data from the venues table, using venue_id\\n my_venue = db.session.query(Venue).filter(Venue.id == venue_id).one()\\n if my_venue:\\n #make join to get all shows and their related artists according to given venue_id\\n\\n all_shows = db.session.query(Show).join(Artist).filter(Show.venue_id == venue_id)\\n past_shows = []\\n upcoming_shows = []\\n current_time = datetime.now()\\n for show in all_shows:\\n add_show = {\\n \\\"artist_id\\\": show.artist_id,\\n \\\"artist_name\\\": show.artist.name,\\n \\\"artist_image_link\\\": show.artist.image_link,\\n \\\"start_time\\\": show.start_time.strftime('%c')\\n }\\n\\n #determine if show is past or coming\\n if (show.start_time < current_time):\\n past_shows.append(add_show)\\n else:\\n upcoming_shows.append(add_show)\\n\\n data = {\\n \\\"id\\\": my_venue.id,\\n \\\"name\\\": my_venue.name,\\n \\\"address\\\": my_venue.address,\\n \\\"city\\\": my_venue.city,\\n \\\"state\\\": my_venue.state,\\n \\\"phone\\\": my_venue.phone,\\n \\\"facebook_link\\\": my_venue.facebook_link,\\n \\\"image_link\\\": my_venue.image_link,\\n \\\"past_shows\\\": past_shows,\\n \\\"upcoming_shows\\\": upcoming_shows,\\n \\\"past_shows_count\\\": len(past_shows),\\n \\\"upcoming_shows_count\\\": len(upcoming_shows),\\n }\\n return render_template('pages/show_venue.html', venue=data)\\n else:\\n return render_template('errors/404.html')\\n# Create Venue\\n# ----------------------------------------------------------------\\n\\n@app.route('/venues/create', methods=['GET'])\\ndef create_venue_form():\\n form = VenueForm()\\n return render_template('forms/new_venue.html', form=form)\\n\\n@app.route('/venues/create', methods=['POST'])\\ndef create_venue_submission():\\n data = request.form\\n name = data['name']\\n city = data['city']\\n state = data['state']\\n address = data['address']\\n phone = data['phone']\\n image_link = data['image_link']\\n facebook_link = data['facebook_link']\\n\\n error = False\\n try:\\n new_venue = Venue(name=name,\\n city=city,\\n state=state,\\n address=address,\\n phone=phone,\\n image_link=image_link,\\n facebook_link=facebook_link,\\n )\\n db.session.add(new_venue)\\n db.session.commit()\\n except:\\n error = True\\n db.session.rollback()\\n print(sys.exc_info())\\n finally:\\n db.session.close()\\n if error:\\n flash('An error occurred. Venue ' + name + ' could not be listed.')\\n else:\\n flash('Venue ' + request.form['name'] + ' was successfully listed!')\\n # DONE: insert form data as a new Venue record in the db, instead\\n # DONE: modify data to be the data object returned from db insertion\\n\\n # on successful db insert, flash success\\n\\n # DONE: on unsuccessful db insert, flash an error instead.\\n # e.g., flash('An error occurred. Venue ' + data.name + ' could not be listed.')\\n # see: http://flask.pocoo.org/docs/1.0/patterns/flashing/\\n return render_template('pages/home.html')\\n\\n@app.route('/venues/<venue_id>', methods=['DELETE'])\\ndef delete_venue(venue_id):\\n # DONE: Complete this endpoint for taking a venue_id, and using\\n # SQLAlchemy ORM to delete a record. Handle cases where the session commit could fail.\\n error = False\\n try:\\n db.session.query(Venue).filter(Venue.id == venue_id).delete()\\n db.session.commit()\\n except:\\n db.session.rollback()\\n error = True\\n print(sys.exc_info())\\n finally:\\n db.session.close()\\n if error:\\n flash('Error! record can not be deleted!')\\n else:\\n flash('Record was successfully deleted!')\\n return None\\n # BONUS CHALLENGE: Implement a button to delete a Venue on a Venue Page, have it so that\\n # clicking that button delete it from the db then redirect the user to the homepage\\n\\n# Artists\\n# ----------------------------------------------------------------\\n@app.route('/artists')\\ndef artists():\\n\\n # DONE: replace with real data returned from querying the database\\n\\n\\n all_artists = db.session.query(Artist.id, Artist.name)\\n data = []\\n for artist in all_artists:\\n data.append({\\n \\\"id\\\": artist[0],\\n \\\"name\\\": artist[1]\\n })\\n return render_template('pages/artists.html', artists=data)\\n\\n@app.route('/artists/search', methods=['POST'])\\ndef search_artists():\\n # DONE: implement search on artists with partial string search. Ensure it is case-insensitive.\\n # seach for \\\"A\\\" should return \\\"Guns N Petals\\\", \\\"Matt Quevado\\\", and \\\"The Wild Sax Band\\\".\\n # search for \\\"band\\\" should return \\\"The Wild Sax Band\\\".\\n\\n data = request.form\\n search_writing = data['search_term']\\n\\n #get data from database according to given search value\\n my_artists = db.session.query(Artist).filter(Artist.name.ilike('%{}%'.format(search_writing))).all()\\n response = {}\\n response['count'] = len(my_artists)\\n response['data'] = my_artists\\n return render_template('pages/search_artists.html', results=response, search_term=request.form.get('search_term', ''))\\n\\n@app.route('/artists/<int:artist_id>')\\ndef show_artist(artist_id):\\n # shows the venue page with the given venue_id\\n # DONR: replace with real venue data from the venues table, using venue_id\\n my_artist = db.session.query(Artist).filter(Artist.id == artist_id).one()\\n if my_artist:\\n\\n # make join to get all shows and their related venues according to given artist_id\\n all_shows = db.session.query(Show).join(Venue).filter(Show.artist_id == artist_id)\\n past_shows = []\\n upcoming_shows = []\\n current_time = datetime.now()\\n for show in all_shows:\\n add_show = {\\n \\\"venue_id\\\": show.artist_id,\\n \\\"venue_name\\\": show.venue.name,\\n \\\"venue_image_link\\\": show.venue.image_link,\\n \\\"start_time\\\": show.start_time.strftime('%c')\\n }\\n\\n #determine if show is past or coming\\n if (show.start_time < current_time):\\n past_shows.append(add_show)\\n else:\\n upcoming_shows.append(add_show)\\n\\n data = {\\n \\\"id\\\": my_artist.id,\\n \\\"name\\\": my_artist.name,\\n \\\"city\\\": my_artist.city,\\n \\\"state\\\": my_artist.state,\\n \\\"genres\\\": my_artist.genres.replace('{', '').replace('}', '').split(','),\\n \\\"phone\\\": my_artist.phone,\\n \\\"facebook_link\\\": my_artist.facebook_link,\\n \\\"image_link\\\": my_artist.image_link,\\n \\\"past_shows\\\": past_shows,\\n \\\"upcoming_shows\\\": upcoming_shows,\\n \\\"past_shows_count\\\": len(past_shows),\\n \\\"upcoming_shows_count\\\": len(upcoming_shows),\\n }\\n return render_template('pages/show_venue.html', venue=data)\\n else:\\n return render_template('errors/404.html')\\n\\n# Update\\n# ----------------------------------------------------------------\\n@app.route('/artists/<int:artist_id>/edit', methods=['GET'])\\ndef edit_artist(artist_id):\\n form = ArtistForm()\\n\\n #get record that needs to be changed from our database\\n my_artist = db.session.query(Artist).filter(Artist.id == artist_id).one()\\n if my_artist:\\n form.name.data = my_artist.name\\n form.city.data = my_artist.city\\n form.state.data = my_artist.state\\n form.phone.data = my_artist.phone\\n form.genres.data = my_artist.genres\\n form.facebook_link.data = my_artist.facebook_link\\n form.image_link.data = my_artist.image_link\\n return render_template('forms/edit_artist.html', form=form, artist=my_artist)\\n else:\\n return render_template('errors/404.html')\\n # DONE: populate form with fields from artist with ID <artist_id>\\n\\n\\n@app.route('/artists/<int:artist_id>/edit', methods=['POST'])\\ndef edit_artist_submission(artist_id):\\n # DONE: take values from the form submitted, and update existing\\n # artist record with ID <artist_id> using the new attributes\\n\\n my_artist = db.session.query(Artist).filter(Artist.id == artist_id).one()\\n data = request.form\\n name = data['name']\\n city = data['city']\\n state = data['state']\\n genres = data.getlist('genres')\\n phone = data['phone']\\n image_link = data['image_link']\\n facebook_link = data['facebook_link']\\n error = False\\n\\n try:\\n #applying changes to the row\\n my_artist.name = name,\\n my_artist.city = city,\\n my_artist.state = state,\\n my_artist.phone = phone,\\n my_artist.genres = genres,\\n my_artist.image_link = image_link,\\n my_artist.facebook_link = facebook_link,\\n db.session.commit()\\n except:\\n error = True\\n db.session.rollback()\\n print(sys.exc_info())\\n finally:\\n db.session.close()\\n if error:\\n flash('An error occurred. Artist ' + name + ' could not be updated.')\\n else:\\n return redirect(url_for('show_artist', artist_id=artist_id))\\n\\n@app.route('/venues/<int:venue_id>/edit', methods=['GET'])\\ndef edit_venue(venue_id):\\n form = VenueForm()\\n\\n #get venue record that needs to be changed\\n my_venue = db.session.query(Venue).filter(Venue.id == venue_id).one()\\n if my_venue:\\n form.name.data = my_venue.name\\n form.city.data = my_venue.city\\n form.state.data = my_venue.state\\n form.phone.data = my_venue.phone\\n form.address.data = my_venue.address\\n form.facebook_link.data = my_venue.facebook_link\\n form.image_link.data = my_venue.image_link\\n return render_template('forms/edit_venue.html', form=form, venue=my_venue)\\n else:\\n return render_template('errors/404.html')\\n # DONE: populate form with values from venue with ID <venue_id>\\n\\n@app.route('/venues/<int:venue_id>/edit', methods=['POST'])\\ndef edit_venue_submission(venue_id):\\n # DONE: take values from the form submitted, and update existing\\n # venue record with ID <venue_id> using the new attributes\\n\\n my_venue = db.session.query(Venue).filter(Venue.id == venue_id).one()\\n data = request.form\\n name = data['name']\\n city = data['city']\\n state = data['state']\\n address = data['address']\\n phone = data['phone']\\n image_link = data['image_link']\\n facebook_link = data['facebook_link']\\n error = False\\n\\n try:\\n #updating the values to the defined Venue object\\n my_venue.name = name,\\n my_venue.city = city,\\n my_venue.state = state,\\n my_venue.address = address,\\n my_venue.phone = phone,\\n my_venue.image_link = image_link,\\n my_venue.facebook_link = facebook_link,\\n db.session.commit()\\n except:\\n error = True\\n db.session.rollback()\\n print(sys.exc_info())\\n finally:\\n db.session.close()\\n\\n if error:\\n flash('An error occurred. Venue ' + name + ' could not be updated.')\\n else:\\n return redirect(url_for('show_venue', venue_id=venue_id))\\n\\n# Create Artist\\n# ----------------------------------------------------------------\\n\\n@app.route('/artists/create', methods=['GET'])\\ndef create_artist_form():\\n form = ArtistForm()\\n return render_template('forms/new_artist.html', form=form)\\n\\n@app.route('/artists/create', methods=['POST'])\\ndef create_artist_submission():\\n # called upon submitting the new artist listing form\\n # DONE: insert form data as a new Venue record in the db, instead\\n # DONE: modify data to be the data object returned from db insertion\\n\\n data = request.form\\n name = data['name']\\n city = data['city']\\n state = data['state']\\n phone = data['phone']\\n genres = data.getlist('genres')\\n image_link = data['image_link']\\n facebook_link = data['facebook_link']\\n\\n error = False\\n try:\\n new_artist = Artist(name=name,\\n city=city,\\n state=state,\\n phone=phone,\\n genres=genres,\\n image_link=image_link,\\n facebook_link=facebook_link,\\n )\\n db.session.add(new_artist)\\n db.session.commit()\\n except:\\n error = True\\n db.session.rollback()\\n print(sys.exc_info())\\n finally:\\n db.session.close()\\n if error:\\n flash('An error occurred. Artist ' + name + ' could not be listed.')\\n else:\\n flash('Artist ' + request.form['name'] + ' was successfully listed!')\\n\\n # on successful db insert, flash success\\n\\n # DONE: on unsuccessful db insert, flash an error instead.\\n # e.g., flash('An error occurred. Artist ' + data.name + ' could not be listed.')\\n return render_template('pages/home.html')\\n\\n\\n# Shows\\n# ----------------------------------------------------------------\\n\\n@app.route('/shows')\\ndef shows():\\n # displays list of shows at /shows\\n # Done: replace with real venues data.\\n # num_shows should be aggregated based on number of upcoming shows per venue.\\n data = []\\n\\n #query all_shows rows in the Show table\\n all_shows = db.session.query(Show.artist_id, Show.venue_id, Show.start_time).all()\\n for show in all_shows:\\n\\n #get artist row and venue row related to every show\\n artist = db.session.query(Artist.name, Artist.image_link).filter(Artist.id == show[0]).one()\\n venue = db.session.query(Venue.name).filter(Venue.id == show[1]).one()\\n data.append({\\n \\\"venue_id\\\": show[1],\\n \\\"venue_name\\\": venue[0],\\n \\\"artist_id\\\": show[0],\\n \\\"artist_name\\\": artist[0],\\n \\\"artist_image_link\\\": artist[1],\\n \\\"start_time\\\": show[2].strftime(\\\"%c\\\")\\n })\\n return render_template('pages/shows.html', shows=data)\\n\\n@app.route('/shows/create')\\ndef create_shows():\\n # renders form. do not touch.\\n form = ShowForm()\\n return render_template('forms/new_show.html', form=form)\\n\\n@app.route('/shows/create', methods=['POST'])\\ndef create_show_submission():\\n # called to create new shows in the db, upon submitting new show listing form\\n # DONE: insert form data as a new Show record in the db, instead\\n\\n data = request.form\\n artist_id = data['artist_id']\\n venue_id = data['venue_id']\\n start_time = data['start_time']\\n error=False\\n try:\\n new_show = Show(artist_id=artist_id, venue_id=venue_id, start_time=start_time)\\n db.session.add(new_show)\\n db.session.commit()\\n except:\\n error = True\\n db.session.rollback()\\n print(sys.exc_info())\\n finally:\\n db.session.close()\\n if error:\\n flash('An error occurred. Show could not be listed.')\\n else:\\n flash('Show was successfully listed!')\\n return render_template('pages/home.html')\\n\\n # on successful db insert, flash success\\n\\n # DONE: on unsuccessful db insert, flash an error instead.\\n # e.g., flash('An error occurred. Show could not be listed.')\\n # see: http://flask.pocoo.org/docs/1.0/patterns/flashing/\\n\\n\\n\\n@app.errorhandler(404)\\ndef not_found_error(error):\\n return render_template('errors/404.html'), 404\\n\\n@app.errorhandler(500)\\ndef server_error(error):\\n return render_template('errors/500.html'), 500\\n\\n\\nif not app.debug:\\n file_handler = FileHandler('error.log')\\n file_handler.setFormatter(\\n Formatter('%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]')\\n )\\n app.logger.setLevel(logging.INFO)\\n file_handler.setLevel(logging.INFO)\\n app.logger.addHandler(file_handler)\\n app.logger.info('errors')\\n\\n#----------------------------------------------------------------------------#\\n# Launch.\\n#----------------------------------------------------------------------------#\\n\\n# Default port:\\nif __name__ == '__main__':\\n app.run()\\n\\n# Or specify port manually:\\n'''\\nif __name__ == '__main__':\\n port = int(os.environ.get('PORT', 5000))\\n app.run(host='0.0.0.0', port=port)\\n'''\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":2979378,"cells":{"repo_name":{"kind":"string","value":"Telexine/RPI-IOT-HomeAppliance"},"repo_url":{"kind":"string","value":"https://github.com/Telexine/RPI-IOT-HomeAppliance"},"snapshot_id":{"kind":"string","value":"fed9b367e09acd45941c0bc0442ca95bd9d9fed8"},"revision_id":{"kind":"string","value":"540f1b1aa277112c873f90d7136e1938a510873e"},"directory_id":{"kind":"string","value":"3d163eb8d3fde5202e80b24d2f794d6079309e84"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-01-20T07:41:53.475772","string":"2021-01-20T07:41:53.475772"},"revision_date":{"kind":"timestamp","value":"2019-01-12T06:57:58","string":"2019-01-12T06:57:58"},"committer_date":{"kind":"timestamp","value":"2019-01-12T06:57:58","string":"2019-01-12T06:57:58"},"github_id":{"kind":"number","value":101553217,"string":"101,553,217"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":1,"string":"1"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"timestamp","value":"2017-08-27T13:13:14","string":"2017-08-27T13:13:14"},"gha_updated_at":{"kind":"timestamp","value":"2017-09-01T07:38:22","string":"2017-09-01T07:38:22"},"gha_pushed_at":{"kind":"timestamp","value":"2017-12-20T13:19:02","string":"2017-12-20T13:19:02"},"gha_language":{"kind":"string","value":"CSS"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6869972944259644,"alphanum_fraction":0.7319034934043884,"avg_line_length":22.3125,"blob_id":"90b939ec725b2b5c33de8cf131cdf570df8ab1fa","content_id":"6b749bd49d8dee6910300f6cc3d2766af7d4de84","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":1492,"license_type":"permissive","max_line_length":162,"num_lines":64,"path":"/README.md","repo_name":"Telexine/RPI-IOT-HomeAppliance","src_encoding":"UTF-8","text":"\n\n# RRPI-IOT-HomeAppliance\n\nRPI-IOT-HomeAppliance\n \n* [Slide](https://github.com/Telexine/RPI-IOT-HomeAppliance/blob/master/_IOT%20Home%20appliance%20with%20Cordova%20and%20Firebase%20%20.pptx) - Slide Presentation\n\n* [Experiment](https://github.com/Telexine/RPI-IOT-HomeAppliance/blob/master/IOT%20SMART%20HOME%20explain%20to%20experiment.docx) - Experiment (Thai)\n\n## Hardware\n\n* [Raspberry Pi 2 model B with Rasbian OS](https://www.raspberrypi.org/products/raspberry-pi-2-model-b/) - Mainboard\n* [5V RELAY 8-CH 10A 250VAC GTTH-1015](https://gravitechthai.com/product_detail.php?d=859/) - For Controling Electricity\n\n## Wiring\n \n### Folder\n```\nRPI - put it in Raspberry\nClient - Put in your PC and run \n npm update\n npm install\n \n == For android emulator \n - make sure you have android studio\n \n cordova platform add android\n cordova run android\n \n == *** For Web Browser \n \n cordova run browser\n \n == For IOS emulator \n - make sure you have Xcode (MacOS)\n \n cordova platform add IOS\n cordova run IOS\n \n```\n### Prerequisite\n\n```\n## Raspberry pi IMAGE\n[Minibian] (https://minibianpi.wordpress.com/)\n\n```\n### Installing in Raspberry\n\n```\n## Install Python\napt-get install python3\napt-get install python3-pip\n\n## Install pip\npip3 install pyrebase\n```\n\n\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details\n"},{"alpha_fraction":0.5409961938858032,"alphanum_fraction":0.5739463567733765,"avg_line_length":24.58823585510254,"blob_id":"eb3a6fb73a5d6c7e3f06116b170b687904a8b56d","content_id":"0494b76207879f36daedeb98e27bbd859a6143c4","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1305,"license_type":"permissive","max_line_length":93,"num_lines":51,"path":"/RPI/app.py","repo_name":"Telexine/RPI-IOT-HomeAppliance","src_encoding":"UTF-8","text":"#!/usr/bin/env python3\n\nimport RPi.GPIO as GPIO\nimport datetime as t\nfrom time import sleep\nfrom firebase import firebase\n\nGPIO.setmode(GPIO.BCM)\n\n#INIT\ndevicesID = {\n 's1' :{'pin': 17},\n 's2' :{'pin': 18},\n 's3' :{'pin': 27},\n 's4' :{'pin': 22},\n 's5' :{'pin': 23},\n 's6' :{'pin': 24},\n 's7' :{'pin': 10},\n 's8' :{'pin': 9}\n}\n\nfor i in devicesID:\n GPIO.setup(devicesID[i]['pin'], GPIO.OUT)\n#END INIT\n\ndef compareState(devices,getState):\n if GPIO.input(devicesID[devices]['pin']) == getState:\n\t\n if getState == True:\n GPIO.output(devicesID[devices]['pin'], GPIO.LOW)\n\t print \"%s : DEVICE %s Turn ON \" % (t.datetime.now(),devices)\n else:\n GPIO.output(devicesID[devices]['pin'], GPIO.HIGH)\n\t print \"%s : DEVICE %s Turn OF \" % (t.datetime.now(),devices)\n\n\nfirebase = firebase.FirebaseApplication('https://rpi-iot-homeappliance.firebaseio.com', None)\nwhile True:\n \n s1 = firebase.get('/devicesID','1/state')\n s2 = firebase.get('/devicesID','2/state')\n s3 = firebase.get('/devicesID','3/state')\n s4 = firebase.get('/devicesID','4/state') \n compareState('s1',s1)\n compareState('s2',s2)\n compareState('s3',s3)\n compareState('s4',s3)\n sleep(0.01)\n\n\nGPIO.cleanup()\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6869972944259644,\n \"alphanum_fraction\": 0.7319034934043884,\n \"avg_line_length\": 22.3125,\n \"blob_id\": \"90b939ec725b2b5c33de8cf131cdf570df8ab1fa\",\n \"content_id\": \"6b749bd49d8dee6910300f6cc3d2766af7d4de84\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 1492,\n \"license_type\": \"permissive\",\n \"max_line_length\": 162,\n \"num_lines\": 64,\n \"path\": \"/README.md\",\n \"repo_name\": \"Telexine/RPI-IOT-HomeAppliance\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n\\n# RRPI-IOT-HomeAppliance\\n\\nRPI-IOT-HomeAppliance\\n \\n* [Slide](https://github.com/Telexine/RPI-IOT-HomeAppliance/blob/master/_IOT%20Home%20appliance%20with%20Cordova%20and%20Firebase%20%20.pptx) - Slide Presentation\\n\\n* [Experiment](https://github.com/Telexine/RPI-IOT-HomeAppliance/blob/master/IOT%20SMART%20HOME%20explain%20to%20experiment.docx) - Experiment (Thai)\\n\\n## Hardware\\n\\n* [Raspberry Pi 2 model B with Rasbian OS](https://www.raspberrypi.org/products/raspberry-pi-2-model-b/) - Mainboard\\n* [5V RELAY 8-CH 10A 250VAC GTTH-1015](https://gravitechthai.com/product_detail.php?d=859/) - For Controling Electricity\\n\\n## Wiring\\n \\n### Folder\\n```\\nRPI - put it in Raspberry\\nClient - Put in your PC and run \\n npm update\\n npm install\\n \\n == For android emulator \\n - make sure you have android studio\\n \\n cordova platform add android\\n cordova run android\\n \\n == *** For Web Browser \\n \\n cordova run browser\\n \\n == For IOS emulator \\n - make sure you have Xcode (MacOS)\\n \\n cordova platform add IOS\\n cordova run IOS\\n \\n```\\n### Prerequisite\\n\\n```\\n## Raspberry pi IMAGE\\n[Minibian] (https://minibianpi.wordpress.com/)\\n\\n```\\n### Installing in Raspberry\\n\\n```\\n## Install Python\\napt-get install python3\\napt-get install python3-pip\\n\\n## Install pip\\npip3 install pyrebase\\n```\\n\\n\\n\\n## License\\n\\nThis project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details\\n\"\n },\n {\n \"alpha_fraction\": 0.5409961938858032,\n \"alphanum_fraction\": 0.5739463567733765,\n \"avg_line_length\": 24.58823585510254,\n \"blob_id\": \"eb3a6fb73a5d6c7e3f06116b170b687904a8b56d\",\n \"content_id\": \"0494b76207879f36daedeb98e27bbd859a6143c4\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1305,\n \"license_type\": \"permissive\",\n \"max_line_length\": 93,\n \"num_lines\": 51,\n \"path\": \"/RPI/app.py\",\n \"repo_name\": \"Telexine/RPI-IOT-HomeAppliance\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python3\\n\\nimport RPi.GPIO as GPIO\\nimport datetime as t\\nfrom time import sleep\\nfrom firebase import firebase\\n\\nGPIO.setmode(GPIO.BCM)\\n\\n#INIT\\ndevicesID = {\\n 's1' :{'pin': 17},\\n 's2' :{'pin': 18},\\n 's3' :{'pin': 27},\\n 's4' :{'pin': 22},\\n 's5' :{'pin': 23},\\n 's6' :{'pin': 24},\\n 's7' :{'pin': 10},\\n 's8' :{'pin': 9}\\n}\\n\\nfor i in devicesID:\\n GPIO.setup(devicesID[i]['pin'], GPIO.OUT)\\n#END INIT\\n\\ndef compareState(devices,getState):\\n if GPIO.input(devicesID[devices]['pin']) == getState:\\n\\t\\n if getState == True:\\n GPIO.output(devicesID[devices]['pin'], GPIO.LOW)\\n\\t print \\\"%s : DEVICE %s Turn ON \\\" % (t.datetime.now(),devices)\\n else:\\n GPIO.output(devicesID[devices]['pin'], GPIO.HIGH)\\n\\t print \\\"%s : DEVICE %s Turn OF \\\" % (t.datetime.now(),devices)\\n\\n\\nfirebase = firebase.FirebaseApplication('https://rpi-iot-homeappliance.firebaseio.com', None)\\nwhile True:\\n \\n s1 = firebase.get('/devicesID','1/state')\\n s2 = firebase.get('/devicesID','2/state')\\n s3 = firebase.get('/devicesID','3/state')\\n s4 = firebase.get('/devicesID','4/state') \\n compareState('s1',s1)\\n compareState('s2',s2)\\n compareState('s3',s3)\\n compareState('s4',s3)\\n sleep(0.01)\\n\\n\\nGPIO.cleanup()\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2979379,"cells":{"repo_name":{"kind":"string","value":"januka36/dataviz-flights"},"repo_url":{"kind":"string","value":"https://github.com/januka36/dataviz-flights"},"snapshot_id":{"kind":"string","value":"a7bf4c5c0fd2e46e169dc890c660af4246c1e1be"},"revision_id":{"kind":"string","value":"502e6e5065a6626896125d7086a98415d6934e5f"},"directory_id":{"kind":"string","value":"b62157d35074c959d8cbfa5c3201ab5664174601"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"timestamp","value":"2023-04-17T18:49:15.567257","string":"2023-04-17T18:49:15.567257"},"revision_date":{"kind":"timestamp","value":"2021-05-09T22:46:13","string":"2021-05-09T22:46:13"},"committer_date":{"kind":"timestamp","value":"2021-05-09T22:46:13","string":"2021-05-09T22:46:13"},"github_id":{"kind":"number","value":365846136,"string":"365,846,136"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6718232035636902,"alphanum_fraction":0.6872928142547607,"avg_line_length":31.518518447875977,"blob_id":"135a13b9524f6c3e90a833752ec2e31b72a438c4","content_id":"60ef03b408bde7c5d4e5914bf026f631644f50bf","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2715,"license_type":"no_license","max_line_length":101,"num_lines":81,"path":"/flights.py","repo_name":"januka36/dataviz-flights","src_encoding":"UTF-8","text":"import numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sb\r\n\r\n%matplotlib inline\r\n\r\nflights = pd.read_csv('C:\\Users\\Januka Fernando\\Desktop\\Data Visualization Project\\Notebook\\flights_data.csv')\r\nprint(flights.shape)\r\nflights.head()\r\n\r\n######Bar Graph########\r\n\r\n#Normal plotting of the 'Source' Column\r\nsb.countplot(data = flights, x= 'Source')\r\nplt.ylabel('Number of flights',fontsize=12)\r\nplt.xlabel('Source',fontsize=12)\r\n\r\n#Rotating x label 30 degrees\r\nbase_color = sb.color_palette()[0]\r\nsb.countplot(data = flights, x = 'Source', color = base_color)\r\nplt.xticks(rotation = 30)\r\n\r\n#Arranging x labels in decreasing order\r\nbase_color = sb.color_palette()[1]\r\ngen_order = flights['Source'].value_counts().index\r\nsb.countplot(data = flights, x = 'Source', color = base_color, order = gen_order)\r\n\r\n#Plotting 'Airline' column with 90 degree rotation of x labels\r\nbase_color = sb.color_palette()[2]\r\nsb.countplot(data = flights, x = 'Airline', color = base_color)\r\nplt.xticks(rotation = 90);\r\n\r\n#Plotting 'Airline' column as y axis\r\nbase_color = sb.color_palette()[2]\r\nsb.countplot(data = flights, y = 'Airline', color = base_color)\r\n\r\n#Plotting columns with no value_counts\r\nna_counts = flights.isna().sum()\r\nbase_color = sb.color_palette()[0]\r\nsb.barplot(na_counts.index.values, na_counts, color = base_color)\r\nplt.xticks(rotation = 90)\r\nplt.ylabel('Number of missing values', fontsize = 12)\r\n\r\n\r\n#######Pie Chart########\r\n\r\n#Pie chart of the 'Destinantion' column\r\nsorted_counts = flights['Destination'].value_counts()\r\nplt.pie(sorted_counts, labels = sorted_counts.index, startangle = 90,\r\n counterclock = False);\r\nplt.axis('square')\r\nplt.title('Flight Destination\\'s')\r\n\r\n#Dough-nut Pie chart of the 'Destinantion' column\r\nsorted_counts = flights['Destination'].value_counts()\r\nplt.pie(sorted_counts, labels = sorted_counts.index, startangle = 90,\r\n counterclock = False, wedgeprops = {'width' : 0.4});\r\nplt.axis('square');\r\n\r\n\r\n#######Histograms######\r\n\r\n#Histogram (default) of 'Duration' column\r\nplt.hist(data = flights, x = 'Duration(minutes)')\r\n\r\n#Histogram of 'Prize' column with user defined bin number\r\nplt.hist(data = flights, x = 'Price', bins = 20)\r\n\r\n#Histogram of 'Prize' column with user defined bin size\r\nbins = np.arange(0, flights['Price'].max()+1, 1200)\r\nplt.hist(data = flights, x = 'Price', bins = bins)\r\nplt.show()\r\n\r\n#Histogram of 'price' column with and without kde\r\nsb.distplot(flights['Price']);\r\nsb.distplot(flights['Price'], kde=False);\r\n\r\n#Histogram of 'price' column with hist_kws attribute\r\nbin_edges = np.arange(0, flights['Price'].max()+1, 1200)\r\nsb.distplot(flights['Price'], bins = bin_edges, kde = False, hist_kws = {'alpha' : 1});\r\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6718232035636902,\n \"alphanum_fraction\": 0.6872928142547607,\n \"avg_line_length\": 31.518518447875977,\n \"blob_id\": \"135a13b9524f6c3e90a833752ec2e31b72a438c4\",\n \"content_id\": \"60ef03b408bde7c5d4e5914bf026f631644f50bf\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2715,\n \"license_type\": \"no_license\",\n \"max_line_length\": 101,\n \"num_lines\": 81,\n \"path\": \"/flights.py\",\n \"repo_name\": \"januka36/dataviz-flights\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import numpy as np\\r\\nimport pandas as pd\\r\\nimport matplotlib.pyplot as plt\\r\\nimport seaborn as sb\\r\\n\\r\\n%matplotlib inline\\r\\n\\r\\nflights = pd.read_csv('C:\\\\Users\\\\Januka Fernando\\\\Desktop\\\\Data Visualization Project\\\\Notebook\\\\flights_data.csv')\\r\\nprint(flights.shape)\\r\\nflights.head()\\r\\n\\r\\n######Bar Graph########\\r\\n\\r\\n#Normal plotting of the 'Source' Column\\r\\nsb.countplot(data = flights, x= 'Source')\\r\\nplt.ylabel('Number of flights',fontsize=12)\\r\\nplt.xlabel('Source',fontsize=12)\\r\\n\\r\\n#Rotating x label 30 degrees\\r\\nbase_color = sb.color_palette()[0]\\r\\nsb.countplot(data = flights, x = 'Source', color = base_color)\\r\\nplt.xticks(rotation = 30)\\r\\n\\r\\n#Arranging x labels in decreasing order\\r\\nbase_color = sb.color_palette()[1]\\r\\ngen_order = flights['Source'].value_counts().index\\r\\nsb.countplot(data = flights, x = 'Source', color = base_color, order = gen_order)\\r\\n\\r\\n#Plotting 'Airline' column with 90 degree rotation of x labels\\r\\nbase_color = sb.color_palette()[2]\\r\\nsb.countplot(data = flights, x = 'Airline', color = base_color)\\r\\nplt.xticks(rotation = 90);\\r\\n\\r\\n#Plotting 'Airline' column as y axis\\r\\nbase_color = sb.color_palette()[2]\\r\\nsb.countplot(data = flights, y = 'Airline', color = base_color)\\r\\n\\r\\n#Plotting columns with no value_counts\\r\\nna_counts = flights.isna().sum()\\r\\nbase_color = sb.color_palette()[0]\\r\\nsb.barplot(na_counts.index.values, na_counts, color = base_color)\\r\\nplt.xticks(rotation = 90)\\r\\nplt.ylabel('Number of missing values', fontsize = 12)\\r\\n\\r\\n\\r\\n#######Pie Chart########\\r\\n\\r\\n#Pie chart of the 'Destinantion' column\\r\\nsorted_counts = flights['Destination'].value_counts()\\r\\nplt.pie(sorted_counts, labels = sorted_counts.index, startangle = 90,\\r\\n counterclock = False);\\r\\nplt.axis('square')\\r\\nplt.title('Flight Destination\\\\'s')\\r\\n\\r\\n#Dough-nut Pie chart of the 'Destinantion' column\\r\\nsorted_counts = flights['Destination'].value_counts()\\r\\nplt.pie(sorted_counts, labels = sorted_counts.index, startangle = 90,\\r\\n counterclock = False, wedgeprops = {'width' : 0.4});\\r\\nplt.axis('square');\\r\\n\\r\\n\\r\\n#######Histograms######\\r\\n\\r\\n#Histogram (default) of 'Duration' column\\r\\nplt.hist(data = flights, x = 'Duration(minutes)')\\r\\n\\r\\n#Histogram of 'Prize' column with user defined bin number\\r\\nplt.hist(data = flights, x = 'Price', bins = 20)\\r\\n\\r\\n#Histogram of 'Prize' column with user defined bin size\\r\\nbins = np.arange(0, flights['Price'].max()+1, 1200)\\r\\nplt.hist(data = flights, x = 'Price', bins = bins)\\r\\nplt.show()\\r\\n\\r\\n#Histogram of 'price' column with and without kde\\r\\nsb.distplot(flights['Price']);\\r\\nsb.distplot(flights['Price'], kde=False);\\r\\n\\r\\n#Histogram of 'price' column with hist_kws attribute\\r\\nbin_edges = np.arange(0, flights['Price'].max()+1, 1200)\\r\\nsb.distplot(flights['Price'], bins = bin_edges, kde = False, hist_kws = {'alpha' : 1});\\r\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":2979380,"cells":{"repo_name":{"kind":"string","value":"sirius0411/swagger-android-codegen"},"repo_url":{"kind":"string","value":"https://github.com/sirius0411/swagger-android-codegen"},"snapshot_id":{"kind":"string","value":"edce1cb62f9958636117dced885781cc0056f9d6"},"revision_id":{"kind":"string","value":"4718d18ac66c504929fae5dbe2f2c0cce31c3ba2"},"directory_id":{"kind":"string","value":"1114c8827d65f4feccbc86de4288839d5bc23546"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-01-10T03:21:59.302523","string":"2021-01-10T03:21:59.302523"},"revision_date":{"kind":"timestamp","value":"2015-11-16T07:59:48","string":"2015-11-16T07:59:48"},"committer_date":{"kind":"timestamp","value":"2015-11-16T07:59:48","string":"2015-11-16T07:59:48"},"github_id":{"kind":"number","value":46255806,"string":"46,255,806"},"star_events_count":{"kind":"number","value":3,"string":"3"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6731391549110413,"alphanum_fraction":0.6731391549110413,"avg_line_length":17.235294342041016,"blob_id":"97b29ed35d363db928c36d771c179bd7070c8428","content_id":"09bf1757c4950fbbbbe855de2b7b956514bf25a2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":311,"license_type":"no_license","max_line_length":44,"num_lines":17,"path":"/swagger_codegen.py","repo_name":"sirius0411/swagger-android-codegen","src_encoding":"UTF-8","text":"\"\"\"\nswagger codegen entrance\n\"\"\"\n\nimport shell\nimport swagger_parser\nimport code_generator\nimport util\n\ndef main():\n config = shell.getConfig()\n swagger = swagger_parser.parse(config)\n code_generator.generate(config, swagger)\n print('generate complete')\n \nif __name__ == '__main__':\n main()"},{"alpha_fraction":0.5528219938278198,"alphanum_fraction":0.5571635365486145,"avg_line_length":36.03571319580078,"blob_id":"94d69bffd8f9f82e6aafbe4e0b04682a74f3fc6d","content_id":"9143d3f9dfc9a5801e8439a0211d202a1de6eb5a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2075,"license_type":"no_license","max_line_length":136,"num_lines":56,"path":"/shell.py","repo_name":"sirius0411/swagger-android-codegen","src_encoding":"UTF-8","text":"import sys\nimport getopt\n\n__version__ = '0.0.1'\n\ndef printVersion():\n print('swagger codegen version %s' % __version__)\n\ndef printHelp():\n print('usage: python swagger_codegen.py [option] -j jsonPath -l language [-i | -t template | -o output]')\n print('Options and arguments:')\n print('-j jsonPath : the source swagger json path (url or file path) (also --json=jsonPath)')\n print('-l language : destination language, support values[\"android\", \"rxandroid\"] (also --language=language)')\n print('-v : print the version info (also --version)')\n print('-h : print the help info (also --help)')\n print('-i : input missing field (e.g.: mooc swagger don\\'t have host info) (also --input)')\n print('-t template : template directory that will be used (also --template==)')\n print('-o output : output directory that the code will be placed (also --output==)')\n\ndef getConfig():\n try:\n opts, args = getopt.getopt(sys.argv[1:], 'hvj:il:t:o:', ['help', 'version', 'json=', 'input', 'language', 'template', 'output'])\n except getopt.GetoptError as e:\n print(e)\n exit(1)\n\n config = {}\n\n for opt, val in opts:\n if opt in ('-v', '--version'): #version\n printVersion()\n exit(0)\n elif opt in ('-h', '--help'): #help\n printHelp()\n exit(0)\n elif opt in ('-j', '--json'): #input json path(required)\n config['json'] = val\n elif opt in ('-i', '--input'): #input missing field\n config['input'] = True\n elif opt in ('-l', '--language'): #generate language(required)\n config['language'] = val\n elif opt in ('-t', '--template'): #template dir\n config['template'] = val\n elif opt in ('-o', '--output'): #output dir\n config['output'] = val\n\n if not 'json' in config.keys():\n print('-j missing')\n printHelp()\n exit(1)\n if not 'language' in config.keys():\n print('-l missing')\n printHelp()\n exit(1)\n\n return config"},{"alpha_fraction":0.6138098835945129,"alphanum_fraction":0.6151977777481079,"avg_line_length":26.98058319091797,"blob_id":"cb8dd4f2220eaa7a6b9d0305bd60202a060a9a8a","content_id":"e9f81e0212af219d43c4fe8df2fa602f22433a72","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2884,"license_type":"no_license","max_line_length":121,"num_lines":103,"path":"/swagger_parser.py","repo_name":"sirius0411/swagger-android-codegen","src_encoding":"UTF-8","text":"\"\"\"\nswagger parser\nparse swagger json to a tuple\n\"\"\"\n\nimport json\nimport util\nimport models as beans\nfrom urllib import request\nfrom urllib import parse as urlparse\n\ndef parse(config):\n \"\"\"\n parse the given json to a Swagger object\n \"\"\"\n\n path = config['json']\n jsonStr = _getJson(path)\n swaggerObj = json.loads(jsonStr)\n\n #parse info\n infoDict = swaggerObj.get('info')\n info = beans.Info()\n if infoDict:\n info.title = infoDict.get('title')\n info.version = infoDict.get('version')\n info.description = infoDict.get('description')\n\n #parse url\n schemes = swaggerObj.get('schemes') #required\n host = swaggerObj.get('host') #required\n basePath = swaggerObj.get('basePath') #optional\n\n #parse model\n models = []\n definitions = swaggerObj.get('definitions')\n if definitions:\n for key in definitions:\n models.append(beans.Model(key, definitions.get(key)))\n\n #parse parameters\n parameters = []\n parametersArray = swaggerObj.get('parameters')\n if parametersArray:\n for key in parametersArray:\n parameters.append(beans.Parameter(key, parametersArray.get(key)))\n\n #parse paths\n paths = []\n pathsArray = swaggerObj.get('paths')\n if pathsArray:\n for key in pathsArray:\n paths.append(beans.Path(key, pathsArray.get(key)))\n\n #parse tags\n tags = swaggerObj.get('tags')\n\n #responses\n responses = []\n responseArray = swaggerObj.get('responses')\n if responseArray:\n for key in responseArray:\n responses.append(beans.Response(key, responseArray.get(key)))\n\n inputMissingField = config.get('input', False)\n if inputMissingField:\n if not schemes:\n schemes = []\n schemes.append(input(\"'schemes' is missing, please input:\"))\n if not host:\n host = input(\"'host' is missing, please input:\")\n else:\n if util.isUrl(path):\n pathParseTuple = urlparse.urlparse(path)\n if not schemes:\n schemes = []\n schemes.append(pathParseTuple[0])\n if not host:\n host = pathParseTuple[1]\n if not schemes or not host:\n print('Missing field(s). Auto complete failed. Try using -i or --input to fill missing field(s) by yourself')\n exit(1)\n\n swagger = beans.Swagger()\n swagger.info = info\n swagger.schemes = schemes\n swagger.host = host\n swagger.basePath = basePath\n swagger.models = models\n swagger.parameters = parameters\n swagger.paths = paths\n swagger.tags = tags\n swagger.responses = responses\n return swagger\n\ndef _getJson(path):\n if util.isUrl(path):\n with request.urlopen(path) as r:\n data = r.read()\n return data.decode('utf-8')\n else:\n with open(path) as f:\n return f.read()\n"},{"alpha_fraction":0.5211859941482544,"alphanum_fraction":0.5221996903419495,"avg_line_length":35.33701705932617,"blob_id":"a61b355496c0fb547ae6c31533435f83a40391a5","content_id":"990da8470f775ddf3c0a46de513570503c1f6358","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":19734,"license_type":"no_license","max_line_length":113,"num_lines":543,"path":"/generators/rxandroid_codegen.py","repo_name":"sirius0411/swagger-android-codegen","src_encoding":"UTF-8","text":"\"\"\"\nrxandroid codegen for code_generator.py\nworks with RxJava & RxAndroid\ngenerate a standalone gradle module\n\npacakge com.guokr.android\nstructure:\n----src/main--java/com/guokr/android--model/\n | | |-api/\n | | |-NetManager.java\n | |-AndroidManifest.xml\n |-build.gradle\n\nissues:\n# model\n-- format is ignored(all 'number' 'integer' will be parsed as 'int'), format info will be placed in descriptions\n-- enum won't be parsed at a java Enum(marked in description instead)\n# api\n-- api method name may confilct\n-- only the last tag in 'tags' will be parsed\n-- many swagger tags are ignored\n\"\"\"\n\nimport pystache\nimport os\nimport util\nimport re\n\nmodulePackage = 'com.guokr.android'\nmodelPackage = modulePackage + '.model'\napiPackage = modulePackage + '.api'\nrootDir = ''\nsrcMainDir = ''\ncodeRootDir = ''\nmodelDir = ''\napiDir = ''\n\ndef generate(swagger, templateDir, outputDir):\n '''\n render the templates by pystache and save the results to the given path\n '''\n global rootDir, srcMainDir, codeRootDir, modelDir, apiDir\n rootDir = formatPath(outputDir)\n srcMainDir = formatPath(rootDir + '/src/main')\n codeRootDir = formatPath(srcMainDir + '/java/' + modulePackage.replace('.', '/'))\n modelDir = formatPath(codeRootDir + '/model')\n apiDir = formatPath(codeRootDir + '/api')\n try:\n os.makedirs(modelDir)\n os.makedirs(apiDir)\n except:\n pass\n\n renderBuildGradle(swagger, formatPath(templateDir + '/build.gradle.mustache'))\n renderProguard(swagger, formatPath(templateDir + '/proguard-rules.pro.mustache'))\n renderAndroidManifest(swagger, formatPath(templateDir + '/AndroidManifest.xml.mustache'))\n renderModel(swagger, formatPath(templateDir + '/model.mustache'))\n renderApi(swagger, formatPath(templateDir + '/api.mustache'))\n renderNetManager(swagger, formatPath(templateDir + '/NetManager.mustache'))\n\ndef renderBuildGradle(swagger, templatePath):\n if not isTemplatePathValid(templatePath):\n return\n with open(templatePath) as templateFile:\n templateData = templateFile.read()\n #nothing needs to render. simply save it\n savePath = rootDir + '/build.gradle'\n saveTo(savePath, templateData)\n\ndef renderProguard(swagger, templatePath):\n if not isTemplatePathValid(templatePath):\n return\n with open(templatePath) as templateFile:\n templateData = templateFile.read()\n #nothing needs to render. simply save it\n savePath = rootDir + '/proguard-rules.pro'\n saveTo(savePath, templateData)\n\ndef renderAndroidManifest(swagger, templatePath):\n if not isTemplatePathValid(templatePath):\n return\n with open(templatePath) as templateFile:\n templateData = templateFile.read()\n context = {}\n context['modulePackage'] = modulePackage\n result = pystache.render(templateData, context)\n savePath = srcMainDir + '/AndroidManifest.xml'\n saveTo(savePath, result)\n\ndef renderModel(swagger, templatePath):\n if not isTemplatePathValid(templatePath):\n return\n with open(templatePath) as templateFile:\n templateData = templateFile.read()\n '''\n context sample\n {\n 'package': 'xxx',\n 'imports': [{'import':'xxx'}, {'import':'xxx'}, ...],\n 'classname': 'xxx',\n 'fields': [\n {\n 'baseName': 'xxx',\n 'dataType': 'xxx',\n 'name': 'xxx',\n 'description': 'xxx',\n 'getter': 'xxx',\n 'setter': 'xxx',\n 'format': 'xxx',\n 'enum': [xxx, xxx, xxx]\n 'isEnum': true|false\n }, ...\n ]\n }\n '''\n for model in swagger.models:\n context = {}\n context['package'] = modelPackage\n context['classname'] = toClassName(model.name)\n fields = []\n imports = set()\n # parse model\n for modelField in model.properties:\n field = {}\n field['baseName'] = modelField.name\n datatype = None\n if modelField.type:\n datatype = toDataType(modelField.type)\n elif modelField.ref: # no type key, should have '$ref'\n datatype = refToClassName(modelField.ref, True)\n field['datatype'] = datatype\n field['name'] = toFieldName(modelField.name)\n if modelField.description:\n field['description'] = modelField.description\n if modelField.format:\n if not field.get('description'):\n # format will be rendered only if there's description\n field['description'] = 'accepted format'\n field['format'] = modelField.format\n if modelField.enum:\n if not field.get('description'):\n # enum will be rendered only if there's description\n field['description'] = 'accepted value'\n field['enum'] = modelField.enum\n field['isEnum'] = 'true'\n field['getter'] = toGetter(field['name'])\n field['setter'] = toSetter(field['name'])\n fields.append(field)\n # get the import set for 'imports'\n if modelField.type == 'array':\n imports.add('array')\n # when type == array, the property should have a key 'items'\n if modelField.itemref:\n # item type defined in '$ref'\n imports.add(modelField.itemref)\n field['datatype'] = field['datatype'] % refToClassName(modelField.itemref, True)\n elif modelField.itemtype:\n field['datatype'] = field['datatype'] % toDataType(modelField.itemtype, True)\n if modelField.itemenum:\n if not field.get('description'):\n # enum will be rendered only if there's description\n field['description'] = 'accepted item value'\n field['enum'] = modelField.itemenum\n field['isEnum'] = 'true'\n if modelField.ref:\n imports.add(modelField.ref)\n\n context['fields'] = fields\n context['imports'] = toImports(imports)\n result = pystache.render(templateData, context)\n savePath = modelDir + '/' + context['classname'] + '.java'\n saveTo(savePath, result)\n\ndef renderApi(swagger, templatePath):\n if not isTemplatePathValid(templatePath):\n return\n with open(templatePath) as f:\n template = f.read()\n '''\n api context\n {\n 'package': 'xxx',\n 'modelPackage': 'xxx',\n 'imports': [{'import': 'xxx'}, ...]\n 'classname': 'xxx', // seems that each class is created according to the 'tags''s last item\n 'operations': [\n {\n 'summary': 'xxx',\n 'apiDescription': 'xxx',\n 'methodName': 'xxx',\n 'httpMethod': 'xxx',\n 'path': 'xxx',\n 'responseType': 'xxx',\n 'hasForm': true/false，\n 'parameters':[\n {\n 'paramIn': 'xxx',\n 'inKey': 'xxx',\n 'paramName': 'xxx',\n 'paramDescription': 'xxx',\n 'paramType': 'xxx',\n 'isEnum': 'xxx',\n 'enumValues': [...],\n 'hasMore': true/false\n }\n ]\n }\n ]\n }\n '''\n # get tags list first, the list item claims the api class name\n contexts = {}\n tags = set()\n if swagger.tags:\n for tag in swagger.tags:\n tags.add(tag.get('name'))\n for path in swagger.paths:\n for operation in path.operations:\n if operation.tags:\n tags.add(operation.tags[-1])\n else:\n # this operation has no tags, generate one by the path. add the tag to the operation's 'tags'\n tag = getTagByPath(path.path)\n tags.add(tag)\n operation.tags = [tag]\n # now we have all tags. create blank render context for each one\n for tag in tags:\n context = {}\n context['package'] = apiPackage\n context['modelPackage'] = modelPackage\n context['classname'] = toApiClassName(tag)\n context['operations'] = []\n contexts[tag] = context\n # fill operations\n for path in swagger.paths:\n for operation in path.operations:\n # tags has been filled above, won't be null\n tag = operation.tags[-1]\n context = contexts[tag]\n optContext = {}\n optContext['summary'] = operation.summary\n optContext['apiDescription'] = operation.description\n\n pathVar = path.path\n pathVar = re.sub(r'^/*(.*)', r'\\1', pathVar)\n pathVar = re.sub(r'([^/]*)/*$', r'\\1', pathVar)\n optContext['path'] = pathVar\n\n optContext['httpMethod'] = toFieldName(operation.method, True)\n optContext['methodName'] = toApiMethodName(operation.method, path.path)\n\n responseType = None\n for responseItem in operation.responses:\n if responseItem.code.startswith('2'):\n # try to parse response type\n if not responseItem.schema:\n responseType = 'Void'\n else:\n isArray = responseItem.schema.get('type')\n if isArray:\n items = responseItem.schema.get('items')\n if items:\n itemRef = items.get('$ref')\n if itemRef:\n responseType = toDataType('array') % refToClassName(itemRef, True)\n else:\n ref = responseItem.schema.get('$ref')\n responseType = refToClassName(ref, True)\n optContext['responseType'] = responseType\n\n parameters = []\n for parameter in operation.parameters:\n paramSrc = None\n if parameter.ref:\n # parse the reference\n type, name = util.parseSwaggerRef(parameter.ref)\n for p in swagger.parameters:\n if p.key == name:\n paramSrc = p\n else:\n paramSrc = parameter\n pContext = {}\n # if paramter 'in' is 'formData', 'paramIn' value in Retrofit is 'Field'\n if paramSrc.location == 'formData':\n pContext['paramIn'] = 'Field'\n optContext['hasForm'] = True\n else:\n pContext['paramIn'] = util.camelize(paramSrc.location)\n # in retrofit annotations, @Body and @QueryMap(impossible in this parser) don't need a value\n if str(paramSrc.location).lower() != 'body':\n pContext['inKey'] = paramSrc.name\n pContext['paramName'] = toFieldName(paramSrc.name)\n pContext['paramDescription'] = paramSrc.description\n pType = None\n # now simply consider the schema is a json object, not an array\n if paramSrc.schema:\n ref = paramSrc.schema.get(\"$ref\")\n if ref:\n pType = refToClassName(ref, True)\n else:\n pType = toDataType(paramSrc.type)\n if paramSrc.type == 'array':\n if paramSrc.itemRef:\n pType = pType % refToClassName(paramSrc.itemRef, True)\n elif paramSrc.itemType:\n pType = pType % toDataType(paramSrc.itemType, True)\n if paramSrc.itemEnum:\n pContext['isEnum'] = 'true'\n pContext['enumValues'] = paramSrc.itemEnum\n pContext['paramType'] = pType\n pContext['hasMore'] = True\n parameters.append(pContext)\n if len(parameters) > 0:\n parameters[-1]['hasMore'] = False\n optContext['parameters'] = parameters\n context['operations'].append(optContext)\n # now we have contexts. render each one\n for key in contexts:\n context = contexts.get(key)\n result = pystache.render(template, context)\n savePath = apiDir + '/' + context['classname'] + '.java'\n saveTo(savePath, result)\n\ndef renderNetManager(swagger, templatePath):\n if not isTemplatePathValid(templatePath):\n return\n with open(templatePath) as f:\n template = f.read()\n '''\n context sample\n {\n 'package': 'xxx',\n 'host': 'xxx',\n 'basePath': 'xxx',\n 'protocol': 'xxx'\n }\n '''\n context = {}\n context['package'] = modulePackage\n context['host'] = swagger.host\n context['basePath'] = swagger.basePath\n context['protocol'] = str(swagger.schemes[0]).lower()\n result = pystache.render(template, context)\n savePath = codeRootDir + '/NetManager.java'\n saveTo(savePath, result)\n\ndef saveTo(path, content):\n '''\n save content to path in utf-8\n '''\n #pass\n print('rendering %s' % path)\n with open(path, mode='w', encoding='utf-8') as f:\n f.write(content)\n\ndef isTemplatePathValid(path):\n if not os.path.exists(path):\n print('Could not find template file \"%s\", skip this' % path)\n return False\n if not os.path.isfile(path):\n print('The path \"%s\" is not a valid file, skip this' % path)\n return False\n return True\n\nreservedWords = (\"abstract\", \"continue\", \"for\", \"new\", \"switch\", \"assert\",\n \"default\", \"if\", \"package\", \"synchronized\", \"boolean\", \"do\", \"goto\", \"private\",\n \"this\", \"break\", \"double\", \"implements\", \"protected\", \"throw\", \"byte\", \"else\",\n \"import\", \"public\", \"throws\", \"case\", \"enum\", \"instanceof\", \"return\", \"transient\",\n \"catch\", \"extends\", \"int\", \"short\", \"try\", \"char\", \"final\", \"interface\", \"static\",\n \"void\", \"class\", \"finally\", \"long\", \"strictfp\", \"volatile\", \"const\", \"float\",\n \"native\", \"super\", \"while\")\n\ndef toClassName(name):\n '''\n format class name to match java rules\n '''\n # model name cannot use reserved keyword, e.g. return\n if name in reservedWords:\n raise RuntimeError('%s (reserved word) cannot be used as a model name' % name)\n\n # camelize the model name\n # phone_number => PhoneNumber\n return util.camelize(name)\n\ndef toApiClassName(name):\n '''\n format to api class name\n e.g.: /person/{id} => PersonPersonId\n '''\n return toClassName(name) + 'Api'\n\ntypeMap = {\n 'string': 'String',\n 'integer': 'int',\n 'boolean': 'boolean',\n 'array': 'List<%s>',\n 'number': 'int'\n }\n\ntypeMapClass = {\n 'string': 'String',\n 'integer': 'Integer',\n 'boolean': 'Boolean',\n 'array': 'List<%s>',\n 'number': 'Integer'\n }\n\ndef toDataType(type, castToClass = False):\n '''\n convert swagger type to java type\n '''\n result = None\n if castToClass:\n result = typeMapClass.get(type)\n else:\n result = typeMap.get(type)\n if not result:\n print('unknown data type %s' % type)\n return result\n\ndef toImports(typeSet):\n '''\n convert the set of classes to import to an array that used for mustache rendering\n '''\n result = []\n for type in typeSet:\n item = {}\n if type == 'array':\n item['import'] = 'java.util.List'\n else:\n item['import'] = refToClassName(type)\n result.append(item)\n return result\n\ndef refToClassName(ref, needSimple=False):\n '''\n convert a ref string to java class name\n '''\n try:\n refType, refName = util.parseSwaggerRef(ref)\n except RuntimeError:\n print('\"%s\" is not a swagger reference' % ref)\n return ref\n prefix = ''\n if refType == 'definitions': # models\n prefix = modelPackage\n location = refName.split('/')\n result = prefix\n if len(location) > 0:\n location[-1] = util.camelize(location[-1])\n for l in location:\n result += '.' + l\n\n # simple class name\n if needSimple:\n result = result.split('.')[-1]\n\n return result\n\ndef toFieldName(name, isStatic=False, isApiName = False):\n '''\n format field name to match java rules\n '''\n # replace - with _ e.g. created-at => created_at\n name = name.replace('-', '_')\n\n # if it's all uppper case, do nothing\n if util.isAllUpperCase(name):\n return name\n\n # camelize (lower first character) the variable name\n # pet_id => petId\n name = util.camelize(name, True)\n\n if isStatic:\n # uppercase all characters with _ divider\n # pet_id => PET_ID\n name = re.sub(r'([A-Z])', r'_\\1', name).upper()\n\n if isApiName:\n name = re.sub(r'{([^}]*)}', r'_\\1', name)\n\n # for reserved word or word starting with number, append _\n if name in reservedWords or name[0].isdigit():\n name = escapeReservedWord(name)\n\n return name\n\ndef toDefaultValue(value):\n '''\n convert swagger value to java type\n '''\n result = str(value)\n # number, do nothing\n if result.isdigit():\n return result\n\n # boolean\n if result.lower() == 'true' or result.lower() == 'false':\n return result.lower()\n\n return result\n\ndef toGetter(name):\n '''\n format getter name to match java rules\n '''\n return 'get' + util.camelize(name)\n\ndef toSetter(name):\n '''\n format setter name to match java rules\n '''\n return 'set' + util.camelize(name)\n\ndef escapeReservedWord(word):\n return '_' + word\n\ndef formatPath(path):\n path = os.path.normcase(path)\n path = os.path.normpath(path)\n return path\n\ndef getTagByPath(path):\n '''\n when path doesn't have a tag. generate one by the path root\n e.g. /product/info => tag = Product\n '''\n path = re.sub(r'/{1,}', '', path, 1)\n if '/' in path:\n parts = path.split('/')\n path = parts[0]\n return util.camelize(path)\n\ndef toApiMethodName(httpMethod, path):\n '''\n use the api http method + path to generate a java api method name\n e.g. GET /user/favorite/{favorite_id}/tag => getUserFavoriteTag\n '''\n result = util.camelize(path)\n result = re.sub(r'{([^}]*)}', '', result)\n #parts = result.split('{')\n return str(httpMethod).lower() + result"},{"alpha_fraction":0.5004173517227173,"alphanum_fraction":0.5183639526367188,"avg_line_length":27.200000762939453,"blob_id":"9a517e4edfb7d485ab6f3e6d47bb9e1cdabb1da9","content_id":"66e040aabf83d5e077433e35280f31ecc8b4d329","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2398,"license_type":"no_license","max_line_length":124,"num_lines":85,"path":"/util.py","repo_name":"sirius0411/swagger-android-codegen","src_encoding":"UTF-8","text":"import re\nimport os\n\nurlRegex = re.compile(\n r'^(?:http|ftp)s?://' # http:// or https://\n r'(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\\.)+(?:[A-Z]{2,6}\\.?|[A-Z0-9-]{2,}\\.?)|' #domain...\n r'localhost|' #localhost...\n r'\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3})' # ...or ip\n r'(?::\\d+)?' # optional port\n r'(?:/?|[/?]\\S+)$', re.IGNORECASE)\n\ndef isUrl(string):\n '''\n return if the given string is an url\n '''\n return urlRegex.match(string)\n\ndef camelize(word, lowercaseFirstLetter=False):\n '''\n translate from swagger-codegen DefaultCodegen.java#camelize()\n '''\n # Replace all slashes with dots (package separator)\n word = word.replace('/', '.')\n\n # case out dots\n parts = re.split('\\\\.', word)\n f = []\n for z in parts:\n if len(z) > 0:\n f.append(z[0].upper())\n f.append(z[1:])\n word = ''.join(f)\n\n '''\n // why again? skip this segment. i have no idea what's this means\n m = p.matcher(word);\n while (m.find()) {\n word = m.replaceFirst(\"\" + Character.toUpperCase(m.group(1).charAt(0)) + m.group(1).substring(1)/*.toUpperCase()*/);\n m = p.matcher(word);\n }\n '''\n\n # Uppercase the class name\n '''\n // seems that we don't need this segment\n p = Pattern.compile(\"(\\\\.?)(\\\\w)([^\\\\.]*)$\");\n m = p.matcher(word);\n if (m.find()) {\n String rep = m.group(1) + m.group(2).toUpperCase() + m.group(3);\n rep = rep.replaceAll(\"\\\\$\", \"\\\\\\\\\\\\$\");\n word = m.replaceAll(rep);\n }\n '''\n\n # Replace two underscores with $ to support inner classes.\n word = re.sub('(__)(.)', lambda matched : '$' + matched.group(2).upper(), word)\n\n # Remove all underscores\n parts = re.split('_', word)\n f = []\n for z in parts:\n if len(z) > 0:\n f.append(z[0].upper())\n f.append(z[1:])\n word = ''.join(f)\n\n if lowercaseFirstLetter:\n word = word[0].lower() + word[1:]\n \n return word\n\nupperReg = re.compile('^[A-Z_]*$')\ndef isAllUpperCase(text):\n return upperReg.match(text)\n\ndef parseSwaggerRef(ref):\n '''\n parse swagger ref '#/{type}/{name}'\n return a tuple (type, name)\n '''\n # try to split to 3 parts\n parts = ref.split('/', 2)\n if not parts or len(parts) != 3:\n raise RuntimeError('\"%s\" is not a legal swagger reference' % ref)\n return parts[1], parts[2]"},{"alpha_fraction":0.6981292366981506,"alphanum_fraction":0.7002550959587097,"avg_line_length":26.9761905670166,"blob_id":"f3dcd6408f6c27afbe9d4fc8fa2205ba1cd391e9","content_id":"4e2d0fa5e57a3f36767395eb0253ce961864a630","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":2352,"license_type":"no_license","max_line_length":115,"num_lines":84,"path":"/README.md","repo_name":"sirius0411/swagger-android-codegen","src_encoding":"UTF-8","text":"# swagger-android-codegen\ngenerate a swagger RESTful client on Android based on Retrofit and RxJava\n\nThis project is modified from the client generator part in our group's swagger workflow.\n\n**still in development**\n\n\n---\n##Known Issues\n---\n\n- **format** is ignored(all 'number' 'integer' will be parsed as 'int'), format info will be placed in descriptions\n- **enum** won't be parsed as a **Enum**, accept values are in description\n- api method name may confilct\n- many keys are ignored\n\n---\n##Requirements\n---\n\n- Python 3.0+\n\t\t\n\t\tMy version is Python 3.4.3\n\t\t\n- latest pystache (https://github.com/defunkt/pystache)\n\n pip install pystache\n\t\n---\n##Use it\n---\n\n\t\tpython swagger_codegen.py [option] -j jsonPath -l language [ -t template | -o output]\n\t\t\n###Options and arguments:\n- -j jsonPath : the source swagger json path (url or file path) (also --json=jsonPath)\n- -l language : destination language, **ONLY SUPPORT** rxandroid yet (also --language=language)\n- -t template : template directory that will be used (also --template==)\n- -o output : output directory that the code will be placed (also --output==)\n- -v : print the version info (also --version)\n- -h : print the help info (also --help)\n\n###Rx part\n\nThis tool have a default generator for RxJava/RxAndroid.\n\nUse\n\n\t\tpython .\\swagger_codegen.py -j ... -l rxandroid\n\t\t\nto generate an Android Studio module.\n\nImport the generated module ('rxandroid_api' by default).\n\n\t\t# in Android Studio\n\t\tFile -> New -> Import Module -> add the rxandroid_api module's path\n\t\t\nNow your project may look like this:\n\n\t\tYourProject----rxandroid_api\n\t\t\t\t\t |\n\t\t\t\t\t |-app\n\t\t\t\t\t |\n\t\t\t\t\t |-other module\n\t\t\t\t\t \nIn your app module build.gradle, add the code\n\n \tcompile project(':rxandroid_api')\n\t\t\n\t\t\nNow you can use the generated APIs easily in your module by using codes like below\n\n\t\t// get api instance by class\n\t\t// same as Retrofit\n\t\tNetManager.getInstance().getApi(YourApi.class)\n\t\t\n\t\t// the NetManager will hold the api instance as cache, you should release the resources by the code below\n\t\tNetManager.getInstance().recycle()\n\t\t\n\t\t// use changeBaseUrl() to reset Retrofit.baseUrl() to change different flavors\n\t\tNetManager.getInstance().changeBaseUrl(newBaseUrl)\n\t\t\n\t\t// edit NetManager's debug constants and getAPIRootUrl() method to change the url path generator's behavior\n\n\n"},{"alpha_fraction":0.5846355557441711,"alphanum_fraction":0.5846355557441711,"avg_line_length":23.924623489379883,"blob_id":"751bbd07538915dc1d39766e2c2f2284b9b0ebad","content_id":"f68fa64280144a845ddc48d593e55b048e5d7efd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":9921,"license_type":"no_license","max_line_length":236,"num_lines":398,"path":"/models.py","repo_name":"sirius0411/swagger-android-codegen","src_encoding":"UTF-8","text":"class Swagger:\n\n @property\n def info(self):\n return self._info\n @info.setter\n def info(self, value):\n self._info = value\n\n @property\n def schemes(self):\n return self._schemes\n @schemes.setter\n def schemes(self, value):\n self._schemes = value\n\n @property\n def host(self):\n return self._host\n @host.setter\n def host(self, value):\n self._host = value\n\n @property\n def basePath(self):\n return self._basePath\n @basePath.setter\n def basePath(self, value):\n self._basePath = value\n\n @property\n def models(self):\n return self._models\n @models.setter\n def models(self, value):\n self._models = value\n\n @property\n def parameters(self):\n return self._parameters\n @parameters.setter\n def parameters(self, value):\n self._parameters = value\n\n @property\n def paths(self):\n return self._paths\n @paths.setter\n def paths(self, value):\n self._paths = value\n\n @property\n def tags(self):\n return self._tags\n @tags.setter\n def tags(self, value):\n self._tags = value\n\n @property\n def responses(self):\n return self._responses\n @responses.setter\n def responses(self, value):\n self._responses = value\n\n def __str__(self, **kwargs):\n return 'Swagger[info = %s, schemes = %s, host = %s, basePath = %s, models = %s]' % (self.info, self.schemes, self.host, self.basePath, self.models)\n def __repr__(self):\n return self.__str__()\n\n\nclass Property:\n def __init__(self, key, definition):\n self._name = key\n self._type = definition.get('type')\n self._format = definition.get('format')\n self._default = definition.get('default')\n self._description = definition.get('description')\n self._enum = definition.get('enum')\n self._items = definition.get('items')\n if self._items:\n self._itemref = self._items.get('$ref')\n self._itemtype = self._items.get('type')\n self._itemenum = self._items.get('enum')\n self._ref = definition.get('$ref')\n\n @property\n def name(self):\n return self._name\n @property\n def type(self):\n return self._type\n @property\n def format(self):\n return self._format\n @property\n def default(self):\n return self._default\n @property\n def description(self):\n return self._description\n @property\n def enum(self):\n return self._enum\n @property\n def items(self):\n return self._items\n @property\n def itemref(self):\n return self._itemref\n @property\n def itemtype(self):\n return self._itemtype\n @property\n def itemenum(self):\n return self._itemenum\n @property\n def ref(self):\n return self._ref\n\n def __str__(self, **kwargs):\n return 'Property[name = %s, type = %s, format = %s, default = %s, description = %s, enum = %s, items = %s, ref = %s]' % (self._name, self._type, self._format, self._default, self._description, self._enum, self._items, self._ref)\n def __repr__(self):\n return self.__str__()\n\nclass Model:\n def __init__(self, key, definition):\n self._name = key\n self._properties = []\n self._required = definition.get('required')\n properties = definition.get('properties')\n if properties:\n for key in properties:\n self._properties.append(Property(key, properties.get(key)))\n return\n\n @property\n def name(self):\n return self._name\n @property\n def required(self):\n return self._required\n @property\n def properties(self):\n return self._properties\n\n def __str__(self):\n return 'Model[name = %s, required = %s, properties = %s]' % (self._name, self._required, self._properties)\n def __repr__(self):\n return self.__str__()\n\nclass Info:\n \n @property\n def title(self):\n return self._title\n @title.setter\n def title(self, value):\n self._title = value\n\n @property\n def version(self):\n return self._version\n @version.setter\n def version(self, value):\n self._version = value\n\n @property\n def description(self):\n return self._description\n @description.setter\n def description(self, value):\n self._description = value\n\n def __str__(self, **kwargs):\n return 'Info[title = %s, version = %s, description = %s]' % (self.title, self.version, self.description)\n def __repr__(self):\n return self.__str__()\n\nclass Parameter:\n\n def __init__(self, key, definition):\n self.key = key\n self.ref = definition.get('$ref')\n self.required = definition.get('required')\n self.type = definition.get('type')\n self.name = definition.get('name')\n self.location = definition.get('in')\n self.description = definition.get('description')\n self.default = definition.get('default')\n self.enum = definition.get('enum')\n self.schema = definition.get('schema')\n items = definition.get('items')\n if items:\n self.itemEnum = items.get('enum')\n self.itemType = items.get('type')\n self.itemRef = items.get('$ref')\n return\n\n @property\n def ref(self):\n return self._ref\n @ref.setter\n def ref(self, value):\n self._ref = value\n\n @property\n def key(self):\n return self._key\n @key.setter\n def key(self, value):\n self._key = value\n\n @property\n def name(self):\n return self._name\n @name.setter\n def name(self, name):\n self._name = name\n\n # since 'in' is a reserved name in python, use 'location' instead.\n @property\n def location(self):\n return self._location\n @location.setter\n def location(self, value):\n self._location = value\n\n @property\n def description(self):\n return self._description\n @description.setter\n def description(self, value):\n self._description = value\n\n @property\n def required(self):\n return self._required\n @required.setter\n def required(self, value):\n self._required = value\n\n @property\n def type(self):\n return self._type\n @type.setter\n def type(self, value):\n self._type = value\n\n @property\n def default(self):\n return self._default\n @default.setter\n def default(self, value):\n self._default = value\n\n @property\n def itemType(self):\n return self._itemType\n @itemType.setter\n def itemType(self, value):\n self._itemType = value\n\n @property\n def enum(self):\n return self._enum\n @enum.setter\n def enum(self, value):\n self._enum = value\n\n @property\n def itemEnum(self):\n return self._itemEnum\n @itemEnum.setter\n def itemEnum(self, value):\n self._itemEnum = value\n\n @property\n def schema(self):\n return self._schema\n @schema.setter\n def schema(self, value):\n self._schema = value\n\n @property\n def itemRef(self):\n return self._itemRef\n @itemRef.setter\n def itemRef(self, value):\n self._itemRef = value\n\nclass Path:\n\n def __init__(self, key, definition):\n self.path = key\n self.operations = []\n for methodKey in definition:\n self.operations.append(Operation(methodKey, definition.get(methodKey)))\n\n @property\n def path(self):\n return self._path\n @path.setter\n def path(self, value):\n self._path = value\n\n @property\n def operations(self):\n return self._operations\n @operations.setter\n def operations(self, value):\n self._operations = value\n\nclass Operation:\n\n def __init__(self, key, definition):\n self.method = key\n self.summary = definition.get('summary')\n self.description = definition.get('description')\n self.tags = definition.get('tags')\n self.parameters = []\n parametersArray = definition.get('parameters')\n if parametersArray:\n for i in parametersArray:\n self.parameters.append(Parameter(None, i))\n self.responses = []\n for i in definition.get('responses'):\n self.responses.append(Response(i, definition.get('responses').get(i)))\n\n @property\n def method(self):\n return self._method\n @method.setter\n def method(self, value):\n self._method = value\n\n @property\n def summary(self):\n return self._summary\n @summary.setter\n def summary(self, value):\n self._summary = value\n\n @property\n def description(self):\n return self._description\n @description.setter\n def description(self, value):\n self._description = value\n\n @property\n def parameters(self):\n return self._parameters\n @parameters.setter\n def parameters(self, value):\n self._parameters = value\n\n @property\n def tags(self):\n return self._tags\n @tags.setter\n def tags(self, value):\n self._tags = value\n\n @property\n def responses(self):\n return self._responses\n @responses.setter\n def responses(self, value):\n self._responses = value\n\nclass Response:\n\n def __init__(self, code, definition):\n self.code = code\n self.description = definition.get('description')\n self.schema = definition.get('schema')\n \n @property\n def code(self):\n return self._code\n @code.setter\n def code(self, value):\n self._code = value\n\n @property\n def schema(self):\n return self._schema\n @schema.setter\n def schema(self, value):\n self._schema = value\n\n @property\n def description(self):\n return self._description\n @description.setter\n def description(self, value):\n self._description = value"},{"alpha_fraction":0.6330097317695618,"alphanum_fraction":0.6349514722824097,"avg_line_length":31.203125,"blob_id":"12e739576d0054dba52bc8f9204b1788a56d6663","content_id":"dfc9976d50b58d42a8ee9794ebb21b75b051fb09","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2062,"license_type":"no_license","max_line_length":111,"num_lines":64,"path":"/code_generator.py","repo_name":"sirius0411/swagger-android-codegen","src_encoding":"UTF-8","text":"\"\"\"\ncode generator factory\n\"\"\"\n\nimport importlib\nimport os\n\nprefix = 'generators.'\nsuffix = '_codegen'\n\ndef generate(config, swagger):\n '''\n generate the code by the given config and swagger object\n\n add {language}_codegen.py to ./generators to support the specific language\n each codegen.py must have a method <generate(swagger, templateDir, outputDir)>\n '''\n language = config.get('language')\n templateDir = config.get('template')\n outputDir = config.get('output')\n #fill with default value\n if not templateDir:\n templateDir = getDefaultTemplateDir(language)\n if not outputDir:\n outputDir = getDefaultOutputDir(language)\n \n #load specific generator by language\n try:\n generator = importlib.import_module(prefix + language + suffix)\n\n #path check\n if not os.path.exists(templateDir):\n print('The template directory \"%s\" is not exists. Use -t to offer a legal directory' % templateDir)\n exit(1)\n if not os.path.isdir(templateDir):\n print('The template directory \"%s\" is not a directory. Use -t to offer one' % templateDir)\n exit(1)\n if not os.path.exists(outputDir):\n print('The output directory \"%s\" is not exists. Create it.' % outputDir)\n os.makedirs(outputDir)\n else:\n if not os.path.isdir(outputDir):\n print('The output directory \"%s\" is not a directory. Use -o to offer one' % outputDir)\n exit(1)\n\n # begin\n generator.generate(swagger, templateDir, outputDir)\n except ImportError:\n print('unsupport language %s' % language)\n exit(1)\n\ndef getDefaultTemplateDir(language):\n '''\n return the default template dir by the given language\n '''\n path = \"./template/\" + str(language) + \"/\"\n return os.path.normcase(path)\n\ndef getDefaultOutputDir(language):\n '''\n return the output template dir by the given language\n '''\n path = \"./output/\" + str(language) + \"_api/\"\n return os.path.normcase(path)"}],"string":"[\n {\n \"alpha_fraction\": 0.6731391549110413,\n \"alphanum_fraction\": 0.6731391549110413,\n \"avg_line_length\": 17.235294342041016,\n \"blob_id\": \"97b29ed35d363db928c36d771c179bd7070c8428\",\n \"content_id\": \"09bf1757c4950fbbbbe855de2b7b956514bf25a2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 311,\n \"license_type\": \"no_license\",\n \"max_line_length\": 44,\n \"num_lines\": 17,\n \"path\": \"/swagger_codegen.py\",\n \"repo_name\": \"sirius0411/swagger-android-codegen\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\nswagger codegen entrance\\n\\\"\\\"\\\"\\n\\nimport shell\\nimport swagger_parser\\nimport code_generator\\nimport util\\n\\ndef main():\\n config = shell.getConfig()\\n swagger = swagger_parser.parse(config)\\n code_generator.generate(config, swagger)\\n print('generate complete')\\n \\nif __name__ == '__main__':\\n main()\"\n },\n {\n \"alpha_fraction\": 0.5528219938278198,\n \"alphanum_fraction\": 0.5571635365486145,\n \"avg_line_length\": 36.03571319580078,\n \"blob_id\": \"94d69bffd8f9f82e6aafbe4e0b04682a74f3fc6d\",\n \"content_id\": \"9143d3f9dfc9a5801e8439a0211d202a1de6eb5a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2075,\n \"license_type\": \"no_license\",\n \"max_line_length\": 136,\n \"num_lines\": 56,\n \"path\": \"/shell.py\",\n \"repo_name\": \"sirius0411/swagger-android-codegen\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nimport getopt\\n\\n__version__ = '0.0.1'\\n\\ndef printVersion():\\n print('swagger codegen version %s' % __version__)\\n\\ndef printHelp():\\n print('usage: python swagger_codegen.py [option] -j jsonPath -l language [-i | -t template | -o output]')\\n print('Options and arguments:')\\n print('-j jsonPath : the source swagger json path (url or file path) (also --json=jsonPath)')\\n print('-l language : destination language, support values[\\\"android\\\", \\\"rxandroid\\\"] (also --language=language)')\\n print('-v : print the version info (also --version)')\\n print('-h : print the help info (also --help)')\\n print('-i : input missing field (e.g.: mooc swagger don\\\\'t have host info) (also --input)')\\n print('-t template : template directory that will be used (also --template==)')\\n print('-o output : output directory that the code will be placed (also --output==)')\\n\\ndef getConfig():\\n try:\\n opts, args = getopt.getopt(sys.argv[1:], 'hvj:il:t:o:', ['help', 'version', 'json=', 'input', 'language', 'template', 'output'])\\n except getopt.GetoptError as e:\\n print(e)\\n exit(1)\\n\\n config = {}\\n\\n for opt, val in opts:\\n if opt in ('-v', '--version'): #version\\n printVersion()\\n exit(0)\\n elif opt in ('-h', '--help'): #help\\n printHelp()\\n exit(0)\\n elif opt in ('-j', '--json'): #input json path(required)\\n config['json'] = val\\n elif opt in ('-i', '--input'): #input missing field\\n config['input'] = True\\n elif opt in ('-l', '--language'): #generate language(required)\\n config['language'] = val\\n elif opt in ('-t', '--template'): #template dir\\n config['template'] = val\\n elif opt in ('-o', '--output'): #output dir\\n config['output'] = val\\n\\n if not 'json' in config.keys():\\n print('-j missing')\\n printHelp()\\n exit(1)\\n if not 'language' in config.keys():\\n print('-l missing')\\n printHelp()\\n exit(1)\\n\\n return config\"\n },\n {\n \"alpha_fraction\": 0.6138098835945129,\n \"alphanum_fraction\": 0.6151977777481079,\n \"avg_line_length\": 26.98058319091797,\n \"blob_id\": \"cb8dd4f2220eaa7a6b9d0305bd60202a060a9a8a\",\n \"content_id\": \"e9f81e0212af219d43c4fe8df2fa602f22433a72\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2884,\n \"license_type\": \"no_license\",\n \"max_line_length\": 121,\n \"num_lines\": 103,\n \"path\": \"/swagger_parser.py\",\n \"repo_name\": \"sirius0411/swagger-android-codegen\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\nswagger parser\\nparse swagger json to a tuple\\n\\\"\\\"\\\"\\n\\nimport json\\nimport util\\nimport models as beans\\nfrom urllib import request\\nfrom urllib import parse as urlparse\\n\\ndef parse(config):\\n \\\"\\\"\\\"\\n parse the given json to a Swagger object\\n \\\"\\\"\\\"\\n\\n path = config['json']\\n jsonStr = _getJson(path)\\n swaggerObj = json.loads(jsonStr)\\n\\n #parse info\\n infoDict = swaggerObj.get('info')\\n info = beans.Info()\\n if infoDict:\\n info.title = infoDict.get('title')\\n info.version = infoDict.get('version')\\n info.description = infoDict.get('description')\\n\\n #parse url\\n schemes = swaggerObj.get('schemes') #required\\n host = swaggerObj.get('host') #required\\n basePath = swaggerObj.get('basePath') #optional\\n\\n #parse model\\n models = []\\n definitions = swaggerObj.get('definitions')\\n if definitions:\\n for key in definitions:\\n models.append(beans.Model(key, definitions.get(key)))\\n\\n #parse parameters\\n parameters = []\\n parametersArray = swaggerObj.get('parameters')\\n if parametersArray:\\n for key in parametersArray:\\n parameters.append(beans.Parameter(key, parametersArray.get(key)))\\n\\n #parse paths\\n paths = []\\n pathsArray = swaggerObj.get('paths')\\n if pathsArray:\\n for key in pathsArray:\\n paths.append(beans.Path(key, pathsArray.get(key)))\\n\\n #parse tags\\n tags = swaggerObj.get('tags')\\n\\n #responses\\n responses = []\\n responseArray = swaggerObj.get('responses')\\n if responseArray:\\n for key in responseArray:\\n responses.append(beans.Response(key, responseArray.get(key)))\\n\\n inputMissingField = config.get('input', False)\\n if inputMissingField:\\n if not schemes:\\n schemes = []\\n schemes.append(input(\\\"'schemes' is missing, please input:\\\"))\\n if not host:\\n host = input(\\\"'host' is missing, please input:\\\")\\n else:\\n if util.isUrl(path):\\n pathParseTuple = urlparse.urlparse(path)\\n if not schemes:\\n schemes = []\\n schemes.append(pathParseTuple[0])\\n if not host:\\n host = pathParseTuple[1]\\n if not schemes or not host:\\n print('Missing field(s). Auto complete failed. Try using -i or --input to fill missing field(s) by yourself')\\n exit(1)\\n\\n swagger = beans.Swagger()\\n swagger.info = info\\n swagger.schemes = schemes\\n swagger.host = host\\n swagger.basePath = basePath\\n swagger.models = models\\n swagger.parameters = parameters\\n swagger.paths = paths\\n swagger.tags = tags\\n swagger.responses = responses\\n return swagger\\n\\ndef _getJson(path):\\n if util.isUrl(path):\\n with request.urlopen(path) as r:\\n data = r.read()\\n return data.decode('utf-8')\\n else:\\n with open(path) as f:\\n return f.read()\\n\"\n },\n {\n \"alpha_fraction\": 0.5211859941482544,\n \"alphanum_fraction\": 0.5221996903419495,\n \"avg_line_length\": 35.33701705932617,\n \"blob_id\": \"a61b355496c0fb547ae6c31533435f83a40391a5\",\n \"content_id\": \"990da8470f775ddf3c0a46de513570503c1f6358\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 19734,\n \"license_type\": \"no_license\",\n \"max_line_length\": 113,\n \"num_lines\": 543,\n \"path\": \"/generators/rxandroid_codegen.py\",\n \"repo_name\": \"sirius0411/swagger-android-codegen\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\nrxandroid codegen for code_generator.py\\nworks with RxJava & RxAndroid\\ngenerate a standalone gradle module\\n\\npacakge com.guokr.android\\nstructure:\\n----src/main--java/com/guokr/android--model/\\n | | |-api/\\n | | |-NetManager.java\\n | |-AndroidManifest.xml\\n |-build.gradle\\n\\nissues:\\n# model\\n-- format is ignored(all 'number' 'integer' will be parsed as 'int'), format info will be placed in descriptions\\n-- enum won't be parsed at a java Enum(marked in description instead)\\n# api\\n-- api method name may confilct\\n-- only the last tag in 'tags' will be parsed\\n-- many swagger tags are ignored\\n\\\"\\\"\\\"\\n\\nimport pystache\\nimport os\\nimport util\\nimport re\\n\\nmodulePackage = 'com.guokr.android'\\nmodelPackage = modulePackage + '.model'\\napiPackage = modulePackage + '.api'\\nrootDir = ''\\nsrcMainDir = ''\\ncodeRootDir = ''\\nmodelDir = ''\\napiDir = ''\\n\\ndef generate(swagger, templateDir, outputDir):\\n '''\\n render the templates by pystache and save the results to the given path\\n '''\\n global rootDir, srcMainDir, codeRootDir, modelDir, apiDir\\n rootDir = formatPath(outputDir)\\n srcMainDir = formatPath(rootDir + '/src/main')\\n codeRootDir = formatPath(srcMainDir + '/java/' + modulePackage.replace('.', '/'))\\n modelDir = formatPath(codeRootDir + '/model')\\n apiDir = formatPath(codeRootDir + '/api')\\n try:\\n os.makedirs(modelDir)\\n os.makedirs(apiDir)\\n except:\\n pass\\n\\n renderBuildGradle(swagger, formatPath(templateDir + '/build.gradle.mustache'))\\n renderProguard(swagger, formatPath(templateDir + '/proguard-rules.pro.mustache'))\\n renderAndroidManifest(swagger, formatPath(templateDir + '/AndroidManifest.xml.mustache'))\\n renderModel(swagger, formatPath(templateDir + '/model.mustache'))\\n renderApi(swagger, formatPath(templateDir + '/api.mustache'))\\n renderNetManager(swagger, formatPath(templateDir + '/NetManager.mustache'))\\n\\ndef renderBuildGradle(swagger, templatePath):\\n if not isTemplatePathValid(templatePath):\\n return\\n with open(templatePath) as templateFile:\\n templateData = templateFile.read()\\n #nothing needs to render. simply save it\\n savePath = rootDir + '/build.gradle'\\n saveTo(savePath, templateData)\\n\\ndef renderProguard(swagger, templatePath):\\n if not isTemplatePathValid(templatePath):\\n return\\n with open(templatePath) as templateFile:\\n templateData = templateFile.read()\\n #nothing needs to render. simply save it\\n savePath = rootDir + '/proguard-rules.pro'\\n saveTo(savePath, templateData)\\n\\ndef renderAndroidManifest(swagger, templatePath):\\n if not isTemplatePathValid(templatePath):\\n return\\n with open(templatePath) as templateFile:\\n templateData = templateFile.read()\\n context = {}\\n context['modulePackage'] = modulePackage\\n result = pystache.render(templateData, context)\\n savePath = srcMainDir + '/AndroidManifest.xml'\\n saveTo(savePath, result)\\n\\ndef renderModel(swagger, templatePath):\\n if not isTemplatePathValid(templatePath):\\n return\\n with open(templatePath) as templateFile:\\n templateData = templateFile.read()\\n '''\\n context sample\\n {\\n 'package': 'xxx',\\n 'imports': [{'import':'xxx'}, {'import':'xxx'}, ...],\\n 'classname': 'xxx',\\n 'fields': [\\n {\\n 'baseName': 'xxx',\\n 'dataType': 'xxx',\\n 'name': 'xxx',\\n 'description': 'xxx',\\n 'getter': 'xxx',\\n 'setter': 'xxx',\\n 'format': 'xxx',\\n 'enum': [xxx, xxx, xxx]\\n 'isEnum': true|false\\n }, ...\\n ]\\n }\\n '''\\n for model in swagger.models:\\n context = {}\\n context['package'] = modelPackage\\n context['classname'] = toClassName(model.name)\\n fields = []\\n imports = set()\\n # parse model\\n for modelField in model.properties:\\n field = {}\\n field['baseName'] = modelField.name\\n datatype = None\\n if modelField.type:\\n datatype = toDataType(modelField.type)\\n elif modelField.ref: # no type key, should have '$ref'\\n datatype = refToClassName(modelField.ref, True)\\n field['datatype'] = datatype\\n field['name'] = toFieldName(modelField.name)\\n if modelField.description:\\n field['description'] = modelField.description\\n if modelField.format:\\n if not field.get('description'):\\n # format will be rendered only if there's description\\n field['description'] = 'accepted format'\\n field['format'] = modelField.format\\n if modelField.enum:\\n if not field.get('description'):\\n # enum will be rendered only if there's description\\n field['description'] = 'accepted value'\\n field['enum'] = modelField.enum\\n field['isEnum'] = 'true'\\n field['getter'] = toGetter(field['name'])\\n field['setter'] = toSetter(field['name'])\\n fields.append(field)\\n # get the import set for 'imports'\\n if modelField.type == 'array':\\n imports.add('array')\\n # when type == array, the property should have a key 'items'\\n if modelField.itemref:\\n # item type defined in '$ref'\\n imports.add(modelField.itemref)\\n field['datatype'] = field['datatype'] % refToClassName(modelField.itemref, True)\\n elif modelField.itemtype:\\n field['datatype'] = field['datatype'] % toDataType(modelField.itemtype, True)\\n if modelField.itemenum:\\n if not field.get('description'):\\n # enum will be rendered only if there's description\\n field['description'] = 'accepted item value'\\n field['enum'] = modelField.itemenum\\n field['isEnum'] = 'true'\\n if modelField.ref:\\n imports.add(modelField.ref)\\n\\n context['fields'] = fields\\n context['imports'] = toImports(imports)\\n result = pystache.render(templateData, context)\\n savePath = modelDir + '/' + context['classname'] + '.java'\\n saveTo(savePath, result)\\n\\ndef renderApi(swagger, templatePath):\\n if not isTemplatePathValid(templatePath):\\n return\\n with open(templatePath) as f:\\n template = f.read()\\n '''\\n api context\\n {\\n 'package': 'xxx',\\n 'modelPackage': 'xxx',\\n 'imports': [{'import': 'xxx'}, ...]\\n 'classname': 'xxx', // seems that each class is created according to the 'tags''s last item\\n 'operations': [\\n {\\n 'summary': 'xxx',\\n 'apiDescription': 'xxx',\\n 'methodName': 'xxx',\\n 'httpMethod': 'xxx',\\n 'path': 'xxx',\\n 'responseType': 'xxx',\\n 'hasForm': true/false，\\n 'parameters':[\\n {\\n 'paramIn': 'xxx',\\n 'inKey': 'xxx',\\n 'paramName': 'xxx',\\n 'paramDescription': 'xxx',\\n 'paramType': 'xxx',\\n 'isEnum': 'xxx',\\n 'enumValues': [...],\\n 'hasMore': true/false\\n }\\n ]\\n }\\n ]\\n }\\n '''\\n # get tags list first, the list item claims the api class name\\n contexts = {}\\n tags = set()\\n if swagger.tags:\\n for tag in swagger.tags:\\n tags.add(tag.get('name'))\\n for path in swagger.paths:\\n for operation in path.operations:\\n if operation.tags:\\n tags.add(operation.tags[-1])\\n else:\\n # this operation has no tags, generate one by the path. add the tag to the operation's 'tags'\\n tag = getTagByPath(path.path)\\n tags.add(tag)\\n operation.tags = [tag]\\n # now we have all tags. create blank render context for each one\\n for tag in tags:\\n context = {}\\n context['package'] = apiPackage\\n context['modelPackage'] = modelPackage\\n context['classname'] = toApiClassName(tag)\\n context['operations'] = []\\n contexts[tag] = context\\n # fill operations\\n for path in swagger.paths:\\n for operation in path.operations:\\n # tags has been filled above, won't be null\\n tag = operation.tags[-1]\\n context = contexts[tag]\\n optContext = {}\\n optContext['summary'] = operation.summary\\n optContext['apiDescription'] = operation.description\\n\\n pathVar = path.path\\n pathVar = re.sub(r'^/*(.*)', r'\\\\1', pathVar)\\n pathVar = re.sub(r'([^/]*)/*$', r'\\\\1', pathVar)\\n optContext['path'] = pathVar\\n\\n optContext['httpMethod'] = toFieldName(operation.method, True)\\n optContext['methodName'] = toApiMethodName(operation.method, path.path)\\n\\n responseType = None\\n for responseItem in operation.responses:\\n if responseItem.code.startswith('2'):\\n # try to parse response type\\n if not responseItem.schema:\\n responseType = 'Void'\\n else:\\n isArray = responseItem.schema.get('type')\\n if isArray:\\n items = responseItem.schema.get('items')\\n if items:\\n itemRef = items.get('$ref')\\n if itemRef:\\n responseType = toDataType('array') % refToClassName(itemRef, True)\\n else:\\n ref = responseItem.schema.get('$ref')\\n responseType = refToClassName(ref, True)\\n optContext['responseType'] = responseType\\n\\n parameters = []\\n for parameter in operation.parameters:\\n paramSrc = None\\n if parameter.ref:\\n # parse the reference\\n type, name = util.parseSwaggerRef(parameter.ref)\\n for p in swagger.parameters:\\n if p.key == name:\\n paramSrc = p\\n else:\\n paramSrc = parameter\\n pContext = {}\\n # if paramter 'in' is 'formData', 'paramIn' value in Retrofit is 'Field'\\n if paramSrc.location == 'formData':\\n pContext['paramIn'] = 'Field'\\n optContext['hasForm'] = True\\n else:\\n pContext['paramIn'] = util.camelize(paramSrc.location)\\n # in retrofit annotations, @Body and @QueryMap(impossible in this parser) don't need a value\\n if str(paramSrc.location).lower() != 'body':\\n pContext['inKey'] = paramSrc.name\\n pContext['paramName'] = toFieldName(paramSrc.name)\\n pContext['paramDescription'] = paramSrc.description\\n pType = None\\n # now simply consider the schema is a json object, not an array\\n if paramSrc.schema:\\n ref = paramSrc.schema.get(\\\"$ref\\\")\\n if ref:\\n pType = refToClassName(ref, True)\\n else:\\n pType = toDataType(paramSrc.type)\\n if paramSrc.type == 'array':\\n if paramSrc.itemRef:\\n pType = pType % refToClassName(paramSrc.itemRef, True)\\n elif paramSrc.itemType:\\n pType = pType % toDataType(paramSrc.itemType, True)\\n if paramSrc.itemEnum:\\n pContext['isEnum'] = 'true'\\n pContext['enumValues'] = paramSrc.itemEnum\\n pContext['paramType'] = pType\\n pContext['hasMore'] = True\\n parameters.append(pContext)\\n if len(parameters) > 0:\\n parameters[-1]['hasMore'] = False\\n optContext['parameters'] = parameters\\n context['operations'].append(optContext)\\n # now we have contexts. render each one\\n for key in contexts:\\n context = contexts.get(key)\\n result = pystache.render(template, context)\\n savePath = apiDir + '/' + context['classname'] + '.java'\\n saveTo(savePath, result)\\n\\ndef renderNetManager(swagger, templatePath):\\n if not isTemplatePathValid(templatePath):\\n return\\n with open(templatePath) as f:\\n template = f.read()\\n '''\\n context sample\\n {\\n 'package': 'xxx',\\n 'host': 'xxx',\\n 'basePath': 'xxx',\\n 'protocol': 'xxx'\\n }\\n '''\\n context = {}\\n context['package'] = modulePackage\\n context['host'] = swagger.host\\n context['basePath'] = swagger.basePath\\n context['protocol'] = str(swagger.schemes[0]).lower()\\n result = pystache.render(template, context)\\n savePath = codeRootDir + '/NetManager.java'\\n saveTo(savePath, result)\\n\\ndef saveTo(path, content):\\n '''\\n save content to path in utf-8\\n '''\\n #pass\\n print('rendering %s' % path)\\n with open(path, mode='w', encoding='utf-8') as f:\\n f.write(content)\\n\\ndef isTemplatePathValid(path):\\n if not os.path.exists(path):\\n print('Could not find template file \\\"%s\\\", skip this' % path)\\n return False\\n if not os.path.isfile(path):\\n print('The path \\\"%s\\\" is not a valid file, skip this' % path)\\n return False\\n return True\\n\\nreservedWords = (\\\"abstract\\\", \\\"continue\\\", \\\"for\\\", \\\"new\\\", \\\"switch\\\", \\\"assert\\\",\\n \\\"default\\\", \\\"if\\\", \\\"package\\\", \\\"synchronized\\\", \\\"boolean\\\", \\\"do\\\", \\\"goto\\\", \\\"private\\\",\\n \\\"this\\\", \\\"break\\\", \\\"double\\\", \\\"implements\\\", \\\"protected\\\", \\\"throw\\\", \\\"byte\\\", \\\"else\\\",\\n \\\"import\\\", \\\"public\\\", \\\"throws\\\", \\\"case\\\", \\\"enum\\\", \\\"instanceof\\\", \\\"return\\\", \\\"transient\\\",\\n \\\"catch\\\", \\\"extends\\\", \\\"int\\\", \\\"short\\\", \\\"try\\\", \\\"char\\\", \\\"final\\\", \\\"interface\\\", \\\"static\\\",\\n \\\"void\\\", \\\"class\\\", \\\"finally\\\", \\\"long\\\", \\\"strictfp\\\", \\\"volatile\\\", \\\"const\\\", \\\"float\\\",\\n \\\"native\\\", \\\"super\\\", \\\"while\\\")\\n\\ndef toClassName(name):\\n '''\\n format class name to match java rules\\n '''\\n # model name cannot use reserved keyword, e.g. return\\n if name in reservedWords:\\n raise RuntimeError('%s (reserved word) cannot be used as a model name' % name)\\n\\n # camelize the model name\\n # phone_number => PhoneNumber\\n return util.camelize(name)\\n\\ndef toApiClassName(name):\\n '''\\n format to api class name\\n e.g.: /person/{id} => PersonPersonId\\n '''\\n return toClassName(name) + 'Api'\\n\\ntypeMap = {\\n 'string': 'String',\\n 'integer': 'int',\\n 'boolean': 'boolean',\\n 'array': 'List<%s>',\\n 'number': 'int'\\n }\\n\\ntypeMapClass = {\\n 'string': 'String',\\n 'integer': 'Integer',\\n 'boolean': 'Boolean',\\n 'array': 'List<%s>',\\n 'number': 'Integer'\\n }\\n\\ndef toDataType(type, castToClass = False):\\n '''\\n convert swagger type to java type\\n '''\\n result = None\\n if castToClass:\\n result = typeMapClass.get(type)\\n else:\\n result = typeMap.get(type)\\n if not result:\\n print('unknown data type %s' % type)\\n return result\\n\\ndef toImports(typeSet):\\n '''\\n convert the set of classes to import to an array that used for mustache rendering\\n '''\\n result = []\\n for type in typeSet:\\n item = {}\\n if type == 'array':\\n item['import'] = 'java.util.List'\\n else:\\n item['import'] = refToClassName(type)\\n result.append(item)\\n return result\\n\\ndef refToClassName(ref, needSimple=False):\\n '''\\n convert a ref string to java class name\\n '''\\n try:\\n refType, refName = util.parseSwaggerRef(ref)\\n except RuntimeError:\\n print('\\\"%s\\\" is not a swagger reference' % ref)\\n return ref\\n prefix = ''\\n if refType == 'definitions': # models\\n prefix = modelPackage\\n location = refName.split('/')\\n result = prefix\\n if len(location) > 0:\\n location[-1] = util.camelize(location[-1])\\n for l in location:\\n result += '.' + l\\n\\n # simple class name\\n if needSimple:\\n result = result.split('.')[-1]\\n\\n return result\\n\\ndef toFieldName(name, isStatic=False, isApiName = False):\\n '''\\n format field name to match java rules\\n '''\\n # replace - with _ e.g. created-at => created_at\\n name = name.replace('-', '_')\\n\\n # if it's all uppper case, do nothing\\n if util.isAllUpperCase(name):\\n return name\\n\\n # camelize (lower first character) the variable name\\n # pet_id => petId\\n name = util.camelize(name, True)\\n\\n if isStatic:\\n # uppercase all characters with _ divider\\n # pet_id => PET_ID\\n name = re.sub(r'([A-Z])', r'_\\\\1', name).upper()\\n\\n if isApiName:\\n name = re.sub(r'{([^}]*)}', r'_\\\\1', name)\\n\\n # for reserved word or word starting with number, append _\\n if name in reservedWords or name[0].isdigit():\\n name = escapeReservedWord(name)\\n\\n return name\\n\\ndef toDefaultValue(value):\\n '''\\n convert swagger value to java type\\n '''\\n result = str(value)\\n # number, do nothing\\n if result.isdigit():\\n return result\\n\\n # boolean\\n if result.lower() == 'true' or result.lower() == 'false':\\n return result.lower()\\n\\n return result\\n\\ndef toGetter(name):\\n '''\\n format getter name to match java rules\\n '''\\n return 'get' + util.camelize(name)\\n\\ndef toSetter(name):\\n '''\\n format setter name to match java rules\\n '''\\n return 'set' + util.camelize(name)\\n\\ndef escapeReservedWord(word):\\n return '_' + word\\n\\ndef formatPath(path):\\n path = os.path.normcase(path)\\n path = os.path.normpath(path)\\n return path\\n\\ndef getTagByPath(path):\\n '''\\n when path doesn't have a tag. generate one by the path root\\n e.g. /product/info => tag = Product\\n '''\\n path = re.sub(r'/{1,}', '', path, 1)\\n if '/' in path:\\n parts = path.split('/')\\n path = parts[0]\\n return util.camelize(path)\\n\\ndef toApiMethodName(httpMethod, path):\\n '''\\n use the api http method + path to generate a java api method name\\n e.g. GET /user/favorite/{favorite_id}/tag => getUserFavoriteTag\\n '''\\n result = util.camelize(path)\\n result = re.sub(r'{([^}]*)}', '', result)\\n #parts = result.split('{')\\n return str(httpMethod).lower() + result\"\n },\n {\n \"alpha_fraction\": 0.5004173517227173,\n \"alphanum_fraction\": 0.5183639526367188,\n \"avg_line_length\": 27.200000762939453,\n \"blob_id\": \"9a517e4edfb7d485ab6f3e6d47bb9e1cdabb1da9\",\n \"content_id\": \"66e040aabf83d5e077433e35280f31ecc8b4d329\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2398,\n \"license_type\": \"no_license\",\n \"max_line_length\": 124,\n \"num_lines\": 85,\n \"path\": \"/util.py\",\n \"repo_name\": \"sirius0411/swagger-android-codegen\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import re\\nimport os\\n\\nurlRegex = re.compile(\\n r'^(?:http|ftp)s?://' # http:// or https://\\n r'(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\\\\.)+(?:[A-Z]{2,6}\\\\.?|[A-Z0-9-]{2,}\\\\.?)|' #domain...\\n r'localhost|' #localhost...\\n r'\\\\d{1,3}\\\\.\\\\d{1,3}\\\\.\\\\d{1,3}\\\\.\\\\d{1,3})' # ...or ip\\n r'(?::\\\\d+)?' # optional port\\n r'(?:/?|[/?]\\\\S+)$', re.IGNORECASE)\\n\\ndef isUrl(string):\\n '''\\n return if the given string is an url\\n '''\\n return urlRegex.match(string)\\n\\ndef camelize(word, lowercaseFirstLetter=False):\\n '''\\n translate from swagger-codegen DefaultCodegen.java#camelize()\\n '''\\n # Replace all slashes with dots (package separator)\\n word = word.replace('/', '.')\\n\\n # case out dots\\n parts = re.split('\\\\\\\\.', word)\\n f = []\\n for z in parts:\\n if len(z) > 0:\\n f.append(z[0].upper())\\n f.append(z[1:])\\n word = ''.join(f)\\n\\n '''\\n // why again? skip this segment. i have no idea what's this means\\n m = p.matcher(word);\\n while (m.find()) {\\n word = m.replaceFirst(\\\"\\\" + Character.toUpperCase(m.group(1).charAt(0)) + m.group(1).substring(1)/*.toUpperCase()*/);\\n m = p.matcher(word);\\n }\\n '''\\n\\n # Uppercase the class name\\n '''\\n // seems that we don't need this segment\\n p = Pattern.compile(\\\"(\\\\\\\\.?)(\\\\\\\\w)([^\\\\\\\\.]*)$\\\");\\n m = p.matcher(word);\\n if (m.find()) {\\n String rep = m.group(1) + m.group(2).toUpperCase() + m.group(3);\\n rep = rep.replaceAll(\\\"\\\\\\\\$\\\", \\\"\\\\\\\\\\\\\\\\\\\\\\\\$\\\");\\n word = m.replaceAll(rep);\\n }\\n '''\\n\\n # Replace two underscores with $ to support inner classes.\\n word = re.sub('(__)(.)', lambda matched : '$' + matched.group(2).upper(), word)\\n\\n # Remove all underscores\\n parts = re.split('_', word)\\n f = []\\n for z in parts:\\n if len(z) > 0:\\n f.append(z[0].upper())\\n f.append(z[1:])\\n word = ''.join(f)\\n\\n if lowercaseFirstLetter:\\n word = word[0].lower() + word[1:]\\n \\n return word\\n\\nupperReg = re.compile('^[A-Z_]*$')\\ndef isAllUpperCase(text):\\n return upperReg.match(text)\\n\\ndef parseSwaggerRef(ref):\\n '''\\n parse swagger ref '#/{type}/{name}'\\n return a tuple (type, name)\\n '''\\n # try to split to 3 parts\\n parts = ref.split('/', 2)\\n if not parts or len(parts) != 3:\\n raise RuntimeError('\\\"%s\\\" is not a legal swagger reference' % ref)\\n return parts[1], parts[2]\"\n },\n {\n \"alpha_fraction\": 0.6981292366981506,\n \"alphanum_fraction\": 0.7002550959587097,\n \"avg_line_length\": 26.9761905670166,\n \"blob_id\": \"f3dcd6408f6c27afbe9d4fc8fa2205ba1cd391e9\",\n \"content_id\": \"4e2d0fa5e57a3f36767395eb0253ce961864a630\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 2352,\n \"license_type\": \"no_license\",\n \"max_line_length\": 115,\n \"num_lines\": 84,\n \"path\": \"/README.md\",\n \"repo_name\": \"sirius0411/swagger-android-codegen\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# swagger-android-codegen\\ngenerate a swagger RESTful client on Android based on Retrofit and RxJava\\n\\nThis project is modified from the client generator part in our group's swagger workflow.\\n\\n**still in development**\\n\\n\\n---\\n##Known Issues\\n---\\n\\n- **format** is ignored(all 'number' 'integer' will be parsed as 'int'), format info will be placed in descriptions\\n- **enum** won't be parsed as a **Enum**, accept values are in description\\n- api method name may confilct\\n- many keys are ignored\\n\\n---\\n##Requirements\\n---\\n\\n- Python 3.0+\\n\\t\\t\\n\\t\\tMy version is Python 3.4.3\\n\\t\\t\\n- latest pystache (https://github.com/defunkt/pystache)\\n\\n pip install pystache\\n\\t\\n---\\n##Use it\\n---\\n\\n\\t\\tpython swagger_codegen.py [option] -j jsonPath -l language [ -t template | -o output]\\n\\t\\t\\n###Options and arguments:\\n- -j jsonPath : the source swagger json path (url or file path) (also --json=jsonPath)\\n- -l language : destination language, **ONLY SUPPORT** rxandroid yet (also --language=language)\\n- -t template : template directory that will be used (also --template==)\\n- -o output : output directory that the code will be placed (also --output==)\\n- -v : print the version info (also --version)\\n- -h : print the help info (also --help)\\n\\n###Rx part\\n\\nThis tool have a default generator for RxJava/RxAndroid.\\n\\nUse\\n\\n\\t\\tpython .\\\\swagger_codegen.py -j ... -l rxandroid\\n\\t\\t\\nto generate an Android Studio module.\\n\\nImport the generated module ('rxandroid_api' by default).\\n\\n\\t\\t# in Android Studio\\n\\t\\tFile -> New -> Import Module -> add the rxandroid_api module's path\\n\\t\\t\\nNow your project may look like this:\\n\\n\\t\\tYourProject----rxandroid_api\\n\\t\\t\\t\\t\\t |\\n\\t\\t\\t\\t\\t |-app\\n\\t\\t\\t\\t\\t |\\n\\t\\t\\t\\t\\t |-other module\\n\\t\\t\\t\\t\\t \\nIn your app module build.gradle, add the code\\n\\n \\tcompile project(':rxandroid_api')\\n\\t\\t\\n\\t\\t\\nNow you can use the generated APIs easily in your module by using codes like below\\n\\n\\t\\t// get api instance by class\\n\\t\\t// same as Retrofit\\n\\t\\tNetManager.getInstance().getApi(YourApi.class)\\n\\t\\t\\n\\t\\t// the NetManager will hold the api instance as cache, you should release the resources by the code below\\n\\t\\tNetManager.getInstance().recycle()\\n\\t\\t\\n\\t\\t// use changeBaseUrl() to reset Retrofit.baseUrl() to change different flavors\\n\\t\\tNetManager.getInstance().changeBaseUrl(newBaseUrl)\\n\\t\\t\\n\\t\\t// edit NetManager's debug constants and getAPIRootUrl() method to change the url path generator's behavior\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5846355557441711,\n \"alphanum_fraction\": 0.5846355557441711,\n \"avg_line_length\": 23.924623489379883,\n \"blob_id\": \"751bbd07538915dc1d39766e2c2f2284b9b0ebad\",\n \"content_id\": \"f68fa64280144a845ddc48d593e55b048e5d7efd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 9921,\n \"license_type\": \"no_license\",\n \"max_line_length\": 236,\n \"num_lines\": 398,\n \"path\": \"/models.py\",\n \"repo_name\": \"sirius0411/swagger-android-codegen\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Swagger:\\n\\n @property\\n def info(self):\\n return self._info\\n @info.setter\\n def info(self, value):\\n self._info = value\\n\\n @property\\n def schemes(self):\\n return self._schemes\\n @schemes.setter\\n def schemes(self, value):\\n self._schemes = value\\n\\n @property\\n def host(self):\\n return self._host\\n @host.setter\\n def host(self, value):\\n self._host = value\\n\\n @property\\n def basePath(self):\\n return self._basePath\\n @basePath.setter\\n def basePath(self, value):\\n self._basePath = value\\n\\n @property\\n def models(self):\\n return self._models\\n @models.setter\\n def models(self, value):\\n self._models = value\\n\\n @property\\n def parameters(self):\\n return self._parameters\\n @parameters.setter\\n def parameters(self, value):\\n self._parameters = value\\n\\n @property\\n def paths(self):\\n return self._paths\\n @paths.setter\\n def paths(self, value):\\n self._paths = value\\n\\n @property\\n def tags(self):\\n return self._tags\\n @tags.setter\\n def tags(self, value):\\n self._tags = value\\n\\n @property\\n def responses(self):\\n return self._responses\\n @responses.setter\\n def responses(self, value):\\n self._responses = value\\n\\n def __str__(self, **kwargs):\\n return 'Swagger[info = %s, schemes = %s, host = %s, basePath = %s, models = %s]' % (self.info, self.schemes, self.host, self.basePath, self.models)\\n def __repr__(self):\\n return self.__str__()\\n\\n\\nclass Property:\\n def __init__(self, key, definition):\\n self._name = key\\n self._type = definition.get('type')\\n self._format = definition.get('format')\\n self._default = definition.get('default')\\n self._description = definition.get('description')\\n self._enum = definition.get('enum')\\n self._items = definition.get('items')\\n if self._items:\\n self._itemref = self._items.get('$ref')\\n self._itemtype = self._items.get('type')\\n self._itemenum = self._items.get('enum')\\n self._ref = definition.get('$ref')\\n\\n @property\\n def name(self):\\n return self._name\\n @property\\n def type(self):\\n return self._type\\n @property\\n def format(self):\\n return self._format\\n @property\\n def default(self):\\n return self._default\\n @property\\n def description(self):\\n return self._description\\n @property\\n def enum(self):\\n return self._enum\\n @property\\n def items(self):\\n return self._items\\n @property\\n def itemref(self):\\n return self._itemref\\n @property\\n def itemtype(self):\\n return self._itemtype\\n @property\\n def itemenum(self):\\n return self._itemenum\\n @property\\n def ref(self):\\n return self._ref\\n\\n def __str__(self, **kwargs):\\n return 'Property[name = %s, type = %s, format = %s, default = %s, description = %s, enum = %s, items = %s, ref = %s]' % (self._name, self._type, self._format, self._default, self._description, self._enum, self._items, self._ref)\\n def __repr__(self):\\n return self.__str__()\\n\\nclass Model:\\n def __init__(self, key, definition):\\n self._name = key\\n self._properties = []\\n self._required = definition.get('required')\\n properties = definition.get('properties')\\n if properties:\\n for key in properties:\\n self._properties.append(Property(key, properties.get(key)))\\n return\\n\\n @property\\n def name(self):\\n return self._name\\n @property\\n def required(self):\\n return self._required\\n @property\\n def properties(self):\\n return self._properties\\n\\n def __str__(self):\\n return 'Model[name = %s, required = %s, properties = %s]' % (self._name, self._required, self._properties)\\n def __repr__(self):\\n return self.__str__()\\n\\nclass Info:\\n \\n @property\\n def title(self):\\n return self._title\\n @title.setter\\n def title(self, value):\\n self._title = value\\n\\n @property\\n def version(self):\\n return self._version\\n @version.setter\\n def version(self, value):\\n self._version = value\\n\\n @property\\n def description(self):\\n return self._description\\n @description.setter\\n def description(self, value):\\n self._description = value\\n\\n def __str__(self, **kwargs):\\n return 'Info[title = %s, version = %s, description = %s]' % (self.title, self.version, self.description)\\n def __repr__(self):\\n return self.__str__()\\n\\nclass Parameter:\\n\\n def __init__(self, key, definition):\\n self.key = key\\n self.ref = definition.get('$ref')\\n self.required = definition.get('required')\\n self.type = definition.get('type')\\n self.name = definition.get('name')\\n self.location = definition.get('in')\\n self.description = definition.get('description')\\n self.default = definition.get('default')\\n self.enum = definition.get('enum')\\n self.schema = definition.get('schema')\\n items = definition.get('items')\\n if items:\\n self.itemEnum = items.get('enum')\\n self.itemType = items.get('type')\\n self.itemRef = items.get('$ref')\\n return\\n\\n @property\\n def ref(self):\\n return self._ref\\n @ref.setter\\n def ref(self, value):\\n self._ref = value\\n\\n @property\\n def key(self):\\n return self._key\\n @key.setter\\n def key(self, value):\\n self._key = value\\n\\n @property\\n def name(self):\\n return self._name\\n @name.setter\\n def name(self, name):\\n self._name = name\\n\\n # since 'in' is a reserved name in python, use 'location' instead.\\n @property\\n def location(self):\\n return self._location\\n @location.setter\\n def location(self, value):\\n self._location = value\\n\\n @property\\n def description(self):\\n return self._description\\n @description.setter\\n def description(self, value):\\n self._description = value\\n\\n @property\\n def required(self):\\n return self._required\\n @required.setter\\n def required(self, value):\\n self._required = value\\n\\n @property\\n def type(self):\\n return self._type\\n @type.setter\\n def type(self, value):\\n self._type = value\\n\\n @property\\n def default(self):\\n return self._default\\n @default.setter\\n def default(self, value):\\n self._default = value\\n\\n @property\\n def itemType(self):\\n return self._itemType\\n @itemType.setter\\n def itemType(self, value):\\n self._itemType = value\\n\\n @property\\n def enum(self):\\n return self._enum\\n @enum.setter\\n def enum(self, value):\\n self._enum = value\\n\\n @property\\n def itemEnum(self):\\n return self._itemEnum\\n @itemEnum.setter\\n def itemEnum(self, value):\\n self._itemEnum = value\\n\\n @property\\n def schema(self):\\n return self._schema\\n @schema.setter\\n def schema(self, value):\\n self._schema = value\\n\\n @property\\n def itemRef(self):\\n return self._itemRef\\n @itemRef.setter\\n def itemRef(self, value):\\n self._itemRef = value\\n\\nclass Path:\\n\\n def __init__(self, key, definition):\\n self.path = key\\n self.operations = []\\n for methodKey in definition:\\n self.operations.append(Operation(methodKey, definition.get(methodKey)))\\n\\n @property\\n def path(self):\\n return self._path\\n @path.setter\\n def path(self, value):\\n self._path = value\\n\\n @property\\n def operations(self):\\n return self._operations\\n @operations.setter\\n def operations(self, value):\\n self._operations = value\\n\\nclass Operation:\\n\\n def __init__(self, key, definition):\\n self.method = key\\n self.summary = definition.get('summary')\\n self.description = definition.get('description')\\n self.tags = definition.get('tags')\\n self.parameters = []\\n parametersArray = definition.get('parameters')\\n if parametersArray:\\n for i in parametersArray:\\n self.parameters.append(Parameter(None, i))\\n self.responses = []\\n for i in definition.get('responses'):\\n self.responses.append(Response(i, definition.get('responses').get(i)))\\n\\n @property\\n def method(self):\\n return self._method\\n @method.setter\\n def method(self, value):\\n self._method = value\\n\\n @property\\n def summary(self):\\n return self._summary\\n @summary.setter\\n def summary(self, value):\\n self._summary = value\\n\\n @property\\n def description(self):\\n return self._description\\n @description.setter\\n def description(self, value):\\n self._description = value\\n\\n @property\\n def parameters(self):\\n return self._parameters\\n @parameters.setter\\n def parameters(self, value):\\n self._parameters = value\\n\\n @property\\n def tags(self):\\n return self._tags\\n @tags.setter\\n def tags(self, value):\\n self._tags = value\\n\\n @property\\n def responses(self):\\n return self._responses\\n @responses.setter\\n def responses(self, value):\\n self._responses = value\\n\\nclass Response:\\n\\n def __init__(self, code, definition):\\n self.code = code\\n self.description = definition.get('description')\\n self.schema = definition.get('schema')\\n \\n @property\\n def code(self):\\n return self._code\\n @code.setter\\n def code(self, value):\\n self._code = value\\n\\n @property\\n def schema(self):\\n return self._schema\\n @schema.setter\\n def schema(self, value):\\n self._schema = value\\n\\n @property\\n def description(self):\\n return self._description\\n @description.setter\\n def description(self, value):\\n self._description = value\"\n },\n {\n \"alpha_fraction\": 0.6330097317695618,\n \"alphanum_fraction\": 0.6349514722824097,\n \"avg_line_length\": 31.203125,\n \"blob_id\": \"12e739576d0054dba52bc8f9204b1788a56d6663\",\n \"content_id\": \"dfc9976d50b58d42a8ee9794ebb21b75b051fb09\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2062,\n \"license_type\": \"no_license\",\n \"max_line_length\": 111,\n \"num_lines\": 64,\n \"path\": \"/code_generator.py\",\n \"repo_name\": \"sirius0411/swagger-android-codegen\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\ncode generator factory\\n\\\"\\\"\\\"\\n\\nimport importlib\\nimport os\\n\\nprefix = 'generators.'\\nsuffix = '_codegen'\\n\\ndef generate(config, swagger):\\n '''\\n generate the code by the given config and swagger object\\n\\n add {language}_codegen.py to ./generators to support the specific language\\n each codegen.py must have a method <generate(swagger, templateDir, outputDir)>\\n '''\\n language = config.get('language')\\n templateDir = config.get('template')\\n outputDir = config.get('output')\\n #fill with default value\\n if not templateDir:\\n templateDir = getDefaultTemplateDir(language)\\n if not outputDir:\\n outputDir = getDefaultOutputDir(language)\\n \\n #load specific generator by language\\n try:\\n generator = importlib.import_module(prefix + language + suffix)\\n\\n #path check\\n if not os.path.exists(templateDir):\\n print('The template directory \\\"%s\\\" is not exists. Use -t to offer a legal directory' % templateDir)\\n exit(1)\\n if not os.path.isdir(templateDir):\\n print('The template directory \\\"%s\\\" is not a directory. Use -t to offer one' % templateDir)\\n exit(1)\\n if not os.path.exists(outputDir):\\n print('The output directory \\\"%s\\\" is not exists. Create it.' % outputDir)\\n os.makedirs(outputDir)\\n else:\\n if not os.path.isdir(outputDir):\\n print('The output directory \\\"%s\\\" is not a directory. Use -o to offer one' % outputDir)\\n exit(1)\\n\\n # begin\\n generator.generate(swagger, templateDir, outputDir)\\n except ImportError:\\n print('unsupport language %s' % language)\\n exit(1)\\n\\ndef getDefaultTemplateDir(language):\\n '''\\n return the default template dir by the given language\\n '''\\n path = \\\"./template/\\\" + str(language) + \\\"/\\\"\\n return os.path.normcase(path)\\n\\ndef getDefaultOutputDir(language):\\n '''\\n return the output template dir by the given language\\n '''\\n path = \\\"./output/\\\" + str(language) + \\\"_api/\\\"\\n return os.path.normcase(path)\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":8,"string":"8"}}},{"rowIdx":2979381,"cells":{"repo_name":{"kind":"string","value":"AFriemann/mdc"},"repo_url":{"kind":"string","value":"https://github.com/AFriemann/mdc"},"snapshot_id":{"kind":"string","value":"516fbbce5ae2ec475485f732f90e537daffc99bd"},"revision_id":{"kind":"string","value":"147c3de43f3da4afe56c9bbda3edb8d3c152e7ab"},"directory_id":{"kind":"string","value":"2b30dfbbcc2ac90292a756ad56480a05f1582487"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-08-29T03:31:55.423313","string":"2021-08-29T03:31:55.423313"},"revision_date":{"kind":"timestamp","value":"2019-09-09T06:48:28","string":"2019-09-09T06:48:28"},"committer_date":{"kind":"timestamp","value":"2019-09-09T06:48:28","string":"2019-09-09T06:48:28"},"github_id":{"kind":"number","value":121800761,"string":"121,800,761"},"star_events_count":{"kind":"number","value":8,"string":"8"},"fork_events_count":{"kind":"number","value":11,"string":"11"},"gha_license_id":{"kind":"string","value":"BSD-3-Clause"},"gha_created_at":{"kind":"timestamp","value":"2018-02-16T21:06:46","string":"2018-02-16T21:06:46"},"gha_updated_at":{"kind":"timestamp","value":"2020-08-06T12:34:13","string":"2020-08-06T12:34:13"},"gha_pushed_at":{"kind":"timestamp","value":"2021-08-02T16:08:34","string":"2021-08-02T16:08:34"},"gha_language":{"kind":"string","value":"Python"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5918854475021362,"alphanum_fraction":0.5918854475021362,"avg_line_length":14.55555534362793,"blob_id":"6c0ed93668aac23a782e0de2f1a68f55f44b1ae7","content_id":"c3f8cb940583a8c2a745d9fe1446bae8f378e95e","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Makefile","length_bytes":419,"license_type":"permissive","max_line_length":55,"num_lines":27,"path":"/Makefile","repo_name":"AFriemann/mdc","src_encoding":"UTF-8","text":"all: test install\n\ntest:\n\ttox\n\ninstall:\n\tpip install --user --upgrade .\n\nclean:\n\t-rm *.xml\n\t-rm -rf .tox\n\t-rm -rf cover\n\t-rm -rf *.egg-info\n\t-find . -type f -iname \"*.pyc\" | xargs -r rm\n\t-find . -type d -iname \"__pycache__\" | xargs -r rm -rf\n\nhelp:\n\t@echo 'Usage:'\n\t@echo ' make [TARGET]'\n\t@echo\n\t@echo 'Targets:'\n\t@echo ' test'\n\t@echo ' install'\n\t@echo ' clean'\n\t@echo ' help'\n\n.PHONY: all clean install test help"},{"alpha_fraction":0.6094661951065063,"alphanum_fraction":0.6094661951065063,"avg_line_length":25.745664596557617,"blob_id":"fe38a982fbfa62308f44f571684ac9408bd74506","content_id":"3627e814bd76c1048a8b1ea547bc53850a50e67e","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4627,"license_type":"permissive","max_line_length":104,"num_lines":173,"path":"/mdc/decorators.py","repo_name":"AFriemann/mdc","src_encoding":"UTF-8","text":"from __future__ import unicode_literals\nfrom __future__ import print_function\nfrom __future__ import division\nfrom __future__ import absolute_import\nfrom future import standard_library\n\nstandard_library.install_aliases()\nimport sys\nimport inspect\nfrom functools import wraps\n\nfrom mdc.context import new_log_context\n\n\ndef has_argument(func, arg):\n\n \"\"\"\n Backwards-compatible method to check if a function has an actual argument (not an *args or **kwargs)\n of the name :param arg:\n \"\"\"\n\n try:\n return arg in inspect.signature(func).parameters\n except AttributeError:\n pass\n\n try:\n spec = inspect.getfullargspec(func)\n return arg in (spec.args + spec.kwonlyargs)\n except AttributeError:\n pass\n\n return arg in inspect.getargspec(func).args\n\n\ndef bind_argument(func, arg, args, kwargs):\n\n \"\"\"\n Backwards-compatible method to get the value of argument :param arg:\n when function :param func: is called as func(*args, **kwargs)\n \"\"\"\n\n if not has_argument(func, arg):\n raise ValueError(\"Function {} has no argument named {}\".format(func, arg))\n\n try:\n signature = inspect.signature(func)\n bound = signature.bind(*args, **kwargs)\n bound.apply_defaults()\n return bound.arguments[arg]\n except AttributeError:\n pass\n\n bound = inspect.getcallargs(func, *args, **kwargs)\n return bound[arg]\n\n\ndef wrap_function_or_generator(func, pass_context_as=None, context_dict=None):\n\n if pass_context_as is not None:\n if not has_argument(func, pass_context_as):\n raise ValueError(\n \"Function {} needs to have a {} argument to be able to recieve the context\".format(\n func, pass_context_as\n )\n )\n\n if context_dict is None:\n context_dict = {}\n\n if inspect.isgeneratorfunction(func):\n\n if pass_context_as is not None:\n raise NotImplementedError(\n \"Passing the context object to a generator is not supported\"\n )\n\n @wraps(func)\n def wrapper(*args, **kwargs):\n\n to_send = None\n generator = func(*args, **kwargs)\n\n while True:\n try:\n with new_log_context(**context_dict) as context:\n to_yield = generator.send(to_send)\n except StopIteration:\n return\n else:\n to_send = yield to_yield\n\n else:\n\n @wraps(func)\n def wrapper(*args, **kwargs):\n with new_log_context(**context_dict) as context:\n if pass_context_as is None:\n return func(*args, **kwargs)\n else:\n new_kwargs = {pass_context_as: context}\n new_kwargs.update(**kwargs)\n return func(*args, **new_kwargs)\n\n return wrapper\n\n\ndef log_value(**mdc_kwargs):\n\n \"\"\"Adds constant values to the logging context\"\"\"\n\n def decorator(func):\n return wrap_function_or_generator(func, context_dict=mdc_kwargs)\n\n return decorator\n\n\ndef log_value_and_pass_context(**mdc_kwargs):\n\n \"\"\"Adds constant values to the logging context and\n passes it as an argument to the function under the name 'ctx'\"\"\"\n\n def decorator(func):\n return wrap_function_or_generator(\n func, pass_context_as=\"ctx\", context_dict=mdc_kwargs\n )\n\n return decorator\n\n\nwith_mdc = log_value_and_pass_context\n\n\ndef pass_context(name):\n\n \"\"\"Passes a new context as an argument to the function with the name given\"\"\"\n\n def decorator(func):\n return wrap_function_or_generator(func, pass_context_as=name)\n\n return decorator\n\n\ndef log_argument(argument, destination=\"\", formatter=None):\n\n \"\"\"Adds the argument value passed to the decorated function to the logging context,\n formatted by formatter and under the name given by destination\"\"\"\n\n destination = destination or argument\n\n if formatter is None:\n\n def formatter(x):\n return x\n\n def decorator(func):\n\n # Raise early if the function does not have said argument\n if not has_argument(func, argument):\n raise ValueError(\"Function {} has no argument {}\".format(func, argument))\n\n @wraps(func)\n def wrapper(*args, **kwargs):\n\n raw_argument = bind_argument(func, argument, args, kwargs)\n\n return wrap_function_or_generator(\n func, context_dict={destination: formatter(raw_argument)}\n )(*args, **kwargs)\n\n return wrapper\n\n return decorator\n"},{"alpha_fraction":0.6498371362686157,"alphanum_fraction":0.6612377762794495,"avg_line_length":13.97560977935791,"blob_id":"f0dac6acbdba23502ed946dbf0c00e1744943a9d","content_id":"3d81bbc562bf9c5cf06d122a76e5b4fe325d386f","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"INI","length_bytes":614,"license_type":"permissive","max_line_length":76,"num_lines":41,"path":"/tox.ini","repo_name":"AFriemann/mdc","src_encoding":"UTF-8","text":"[tox]\nenvlist = py2, py3\n\n[testenv]\npassenv = *\ndeps =\n nose\n rednose\n spec\n flake8\ncommands =\n -flake8 mdc\n nosetests --with-doctest --with-specplugin {posargs}\n\n[testenv:watch]\ndeps =\n nose\n rednose\n nose-watch\n spec\ncommands = nosetests --with-watch --with-specplugin --with-doctest {posargs}\n\n[testenv:coverage]\ndeps =\n nose\n rednose\n coverage\ncommands = nosetests --quiet --with-coverage --cover-package mdc {posargs}\n\n[testenv:style]\ndeps =\n pylint\n flake8\ncommands =\n flake8 mdc\n -pylint mdc\n\n[testenv:repl]\nenvlist = py3\ndeps = ipython\ncommands = ipython\n"},{"alpha_fraction":0.5684038996696472,"alphanum_fraction":0.5985342264175415,"avg_line_length":22.834951400756836,"blob_id":"59cfe5bf59414268a1aed1f79297058688238af8","content_id":"73edb8108c785a104f92cc26e88a17e166898e36","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"reStructuredText","length_bytes":2456,"license_type":"permissive","max_line_length":249,"num_lines":103,"path":"/README.rst","repo_name":"AFriemann/mdc","src_encoding":"UTF-8","text":"MDC\n===\n\n.. image:: https://travis-ci.org/AFriemann/mdc.svg?branch=master\n :target: https://travis-ci.org/AFriemann/mdc\n\nThis is thought to be an easy to use, import and go, library for Mapped Diagnostic Context style logging.\n\nLogs should include all necessary fields collected by the python logging library.\nHelper methods are provided to add context fields where required.\n\n**NOTE** `1.2.0`: recent changes will result in changed json output if using the `MDCHandler` class:\n\n.. code:: python\n\n >>> with MDC(foobar=\"baz\"):\n ... logging.warning(\"test\") \n { \"message\": \"test\", \"foobar\": \"baz\" }\n\n\nthis is a temporary solution to avoid breaking existing APIs. Please excuse any issues this might cause. In the future output handling should be done on the client side to avoid any possible breakage. The MDCHandler class will eventually be removed.\n\nInstallation\n------------\n\nPyPi:\n\n.. code:: bash\n\n $ pip install --user mdc\n\nFrom source:\n\n.. code:: bash\n\n $ pip install --user .\n\nUsage\n-----\n\nAdd a handler to the root logger or set the base handler with *logging.basicConfig*:\n\n.. code:: python\n\n >>> import logging\n >>> from mdc import MDCHandler\n\n >>> # use the MDCHandler only\n >>> logging.basicConfig(level=logging.DEBUG, handlers=[MDCHandler()])\n\n >>> # the MDC contextmanager\n >>> with MDC(foo='bar'):\n ... logging.warning('foobar')\n { ..., \"mdc\": { \"foo\": \"bar\" }, \"extra\": {}, ... }\n\n >>> # the with_mdc decorator\n >>> @with_mdc(test='123')\n ... def foobar(ctx):\n ... logging.warning('some warning')\n\n >>> foobar()\n { ..., \"mdc\": { \"test\": \"123\" }, \"extra\": {}, ... }\n\n >>> # logging with extra fields\n >>> logging.error('some error', extra=dict(foo='bar'))\n { ..., \"mdc\": {}, \"extra\": { \"foo\": \"bar\" }, ... }\n\nBy default log messages will include the following fields:\n\n.. code:: json\n\n {\n \"message\": \"deleting context b8321b4f-19ff-4c98-b011-5a97178e7ad6\",\n \"logger\": \"mdc\",\n \"timestamp\": \"2018-03-07T21:39:12.010851\",\n \"level\": \"DEBUG\",\n \"mdc\": {\n \"foo\": \"bar\",\n \"index\": 76\n },\n \"extra\": {},\n \"python\": {\n \"module\": \"__init__\",\n \"function\": \"MDC\",\n \"path\": \"/home/user/git/mdc/mdc/__init__.py\",\n \"file\": \"__init__.py\",\n \"line\": 56,\n \"process\": {\n \"name\": \"MainProcess\",\n \"id\": 31678\n },\n \"thread\": {\n \"name\": \"MainThread\",\n \"id\": 140433289192768\n }\n }\n }\n\nRunning tests:\n\n.. code:: bash\n\n $ tox\n\n"},{"alpha_fraction":0.6977491974830627,"alphanum_fraction":0.6993569135665894,"avg_line_length":23.39215660095215,"blob_id":"fdf4690258d426e5b67f60171a6f2f334a05811d","content_id":"f1eb2c927568636a38a44367aebe2fadf41c48c6","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1244,"license_type":"permissive","max_line_length":90,"num_lines":51,"path":"/mdc/__init__.py","repo_name":"AFriemann/mdc","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n\"\"\"\n.. module: mdc\n.. moduleauthor:: Aljosha Friemann a.friemann@automate.wtf\n\"\"\"\nfrom __future__ import (absolute_import, division, print_function,\n unicode_literals)\n\nimport inspect\nimport logging\nimport sys\nfrom functools import wraps\n\nfrom future import standard_library\nfrom mdc.context import *\nfrom mdc.decorators import *\nfrom pythonjsonlogger import jsonlogger\n\nstandard_library.install_aliases()\n\n\nMDContext = new_log_context\nMDC = new_log_context\n\nLOGGER = logging.getLogger(__name__)\nLOGGER.setLevel(logging.ERROR)\n\n\ndef patch(old_factory):\n def record_factory(*args, **kwargs):\n record = old_factory(*args, **kwargs)\n\n for key, value in get_mdc_fields().items():\n setattr(record, key, value)\n\n return record\n\n return record_factory\n\n\ntry:\n logging.setLogRecordFactory(patch(logging.getLogRecordFactory()))\nexcept AttributeError:\n logging.LogRecord = patch(logging.LogRecord)\n\n\n# legacy handler to avoid breaking existing implementations, this will be removed with 2.x\nclass MDCHandler(logging.StreamHandler):\n def __init__(self, *args, **kwargs):\n logging.StreamHandler.__init__(self, *args, **kwargs)\n self.setFormatter(jsonlogger.JsonFormatter())\n"},{"alpha_fraction":0.5874326825141907,"alphanum_fraction":0.5895870923995972,"avg_line_length":22.20833396911621,"blob_id":"67424458437edf5cbd6be624b142bcf49ae0aab9","content_id":"f89662b758a663a071acc054e1cfc8cb695262aa","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2785,"license_type":"permissive","max_line_length":85,"num_lines":120,"path":"/tests/mdc_tests.py","repo_name":"AFriemann/mdc","src_encoding":"UTF-8","text":"import logging\n\nfrom mdc import with_mdc, MDC, log_value, new_log_context, log_argument, pass_context\n\n\ndef assert_context_has(**kwargs):\n record = logging.makeLogRecord({})\n for key, value in kwargs.items():\n assert hasattr(record, key)\n assert getattr(record, key) == value\n\n\ndef test_attaching_fields():\n assert logging._mdc is not None\n\n with MDC(foo=\"bar\") as context:\n assert context is not None\n assert context.foo == \"bar\"\n\n logging.debug(\"abc\")\n\n with MDC(bar=\"foo\") as context2:\n assert context2 is not None\n assert context2.bar == \"foo\"\n\n try:\n raise RuntimeError(\"test\")\n except Exception as e:\n logging.exception(e, extra=dict(foo=\"oink\"))\n\n\ndef test_mdc_decorator():\n @with_mdc(baz=\"bar\")\n def foobar(x, ctx=None):\n logging.debug(x)\n\n assert ctx.baz == \"bar\", ctx.baz\n\n foobar(1)\n\n\ndef test_generator():\n @log_value(inside=True)\n def my_generator():\n for i in range(5):\n assert_context_has(inside=True)\n yield i\n\n assert list(my_generator()) == list(range(5))\n\n with new_log_context(inside=False):\n for _ in my_generator():\n assert_context_has(inside=False)\n\n\ndef test_log_value():\n @log_value(foo=\"bar\")\n def my_function():\n assert_context_has(foo=\"bar\")\n\n assert not hasattr(logging.makeLogRecord({}), \"foo\")\n my_function()\n assert not hasattr(logging.makeLogRecord({}), \"foo\")\n\n\ndef test_nested_contexts():\n\n with new_log_context(food=\"spam\"):\n with new_log_context(food=\"ham\"):\n with new_log_context(food=\"eggs\"):\n assert_context_has(food=\"eggs\")\n assert_context_has(food=\"ham\")\n assert_context_has(food=\"spam\")\n\n\ndef test_log_argument():\n @log_argument(\"food\")\n def eat(food):\n assert_context_has(food=food)\n\n for food in [\"spam\", \"ham\", \"eggs\"]:\n eat(food)\n\n\ndef test_log_argument_with_destination():\n @log_argument(\"food\", \"abcd\")\n def eat(food):\n assert_context_has(abcd=food)\n\n for food in [\"spam\", \"ham\", \"eggs\"]:\n eat(food)\n\n\ndef test_log_argument_with_formatter():\n @log_argument(\"food\", formatter=str.upper)\n def eat(food):\n assert_context_has(food=str.upper(food))\n\n for food in [\"spam\", \"ham\", \"eggs\"]:\n eat(food)\n\n\ndef test_pass_context_in_args():\n @pass_context(\"ctx\")\n def eat(food, ctx):\n ctx.food = food\n assert_context_has(food=food)\n\n for food in [\"spam\", \"ham\", \"eggs\"]:\n eat(food)\n\n\ndef test_pass_context_in_kwargs():\n @pass_context(\"ctx\")\n def eat(food, ctx=None):\n ctx.food = food\n assert_context_has(food=food)\n\n for food in [\"spam\", \"ham\", \"eggs\"]:\n eat(food)\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5918854475021362,\n \"alphanum_fraction\": 0.5918854475021362,\n \"avg_line_length\": 14.55555534362793,\n \"blob_id\": \"6c0ed93668aac23a782e0de2f1a68f55f44b1ae7\",\n \"content_id\": \"c3f8cb940583a8c2a745d9fe1446bae8f378e95e\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Makefile\",\n \"length_bytes\": 419,\n \"license_type\": \"permissive\",\n \"max_line_length\": 55,\n \"num_lines\": 27,\n \"path\": \"/Makefile\",\n \"repo_name\": \"AFriemann/mdc\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"all: test install\\n\\ntest:\\n\\ttox\\n\\ninstall:\\n\\tpip install --user --upgrade .\\n\\nclean:\\n\\t-rm *.xml\\n\\t-rm -rf .tox\\n\\t-rm -rf cover\\n\\t-rm -rf *.egg-info\\n\\t-find . -type f -iname \\\"*.pyc\\\" | xargs -r rm\\n\\t-find . -type d -iname \\\"__pycache__\\\" | xargs -r rm -rf\\n\\nhelp:\\n\\t@echo 'Usage:'\\n\\t@echo ' make [TARGET]'\\n\\t@echo\\n\\t@echo 'Targets:'\\n\\t@echo ' test'\\n\\t@echo ' install'\\n\\t@echo ' clean'\\n\\t@echo ' help'\\n\\n.PHONY: all clean install test help\"\n },\n {\n \"alpha_fraction\": 0.6094661951065063,\n \"alphanum_fraction\": 0.6094661951065063,\n \"avg_line_length\": 25.745664596557617,\n \"blob_id\": \"fe38a982fbfa62308f44f571684ac9408bd74506\",\n \"content_id\": \"3627e814bd76c1048a8b1ea547bc53850a50e67e\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4627,\n \"license_type\": \"permissive\",\n \"max_line_length\": 104,\n \"num_lines\": 173,\n \"path\": \"/mdc/decorators.py\",\n \"repo_name\": \"AFriemann/mdc\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from __future__ import unicode_literals\\nfrom __future__ import print_function\\nfrom __future__ import division\\nfrom __future__ import absolute_import\\nfrom future import standard_library\\n\\nstandard_library.install_aliases()\\nimport sys\\nimport inspect\\nfrom functools import wraps\\n\\nfrom mdc.context import new_log_context\\n\\n\\ndef has_argument(func, arg):\\n\\n \\\"\\\"\\\"\\n Backwards-compatible method to check if a function has an actual argument (not an *args or **kwargs)\\n of the name :param arg:\\n \\\"\\\"\\\"\\n\\n try:\\n return arg in inspect.signature(func).parameters\\n except AttributeError:\\n pass\\n\\n try:\\n spec = inspect.getfullargspec(func)\\n return arg in (spec.args + spec.kwonlyargs)\\n except AttributeError:\\n pass\\n\\n return arg in inspect.getargspec(func).args\\n\\n\\ndef bind_argument(func, arg, args, kwargs):\\n\\n \\\"\\\"\\\"\\n Backwards-compatible method to get the value of argument :param arg:\\n when function :param func: is called as func(*args, **kwargs)\\n \\\"\\\"\\\"\\n\\n if not has_argument(func, arg):\\n raise ValueError(\\\"Function {} has no argument named {}\\\".format(func, arg))\\n\\n try:\\n signature = inspect.signature(func)\\n bound = signature.bind(*args, **kwargs)\\n bound.apply_defaults()\\n return bound.arguments[arg]\\n except AttributeError:\\n pass\\n\\n bound = inspect.getcallargs(func, *args, **kwargs)\\n return bound[arg]\\n\\n\\ndef wrap_function_or_generator(func, pass_context_as=None, context_dict=None):\\n\\n if pass_context_as is not None:\\n if not has_argument(func, pass_context_as):\\n raise ValueError(\\n \\\"Function {} needs to have a {} argument to be able to recieve the context\\\".format(\\n func, pass_context_as\\n )\\n )\\n\\n if context_dict is None:\\n context_dict = {}\\n\\n if inspect.isgeneratorfunction(func):\\n\\n if pass_context_as is not None:\\n raise NotImplementedError(\\n \\\"Passing the context object to a generator is not supported\\\"\\n )\\n\\n @wraps(func)\\n def wrapper(*args, **kwargs):\\n\\n to_send = None\\n generator = func(*args, **kwargs)\\n\\n while True:\\n try:\\n with new_log_context(**context_dict) as context:\\n to_yield = generator.send(to_send)\\n except StopIteration:\\n return\\n else:\\n to_send = yield to_yield\\n\\n else:\\n\\n @wraps(func)\\n def wrapper(*args, **kwargs):\\n with new_log_context(**context_dict) as context:\\n if pass_context_as is None:\\n return func(*args, **kwargs)\\n else:\\n new_kwargs = {pass_context_as: context}\\n new_kwargs.update(**kwargs)\\n return func(*args, **new_kwargs)\\n\\n return wrapper\\n\\n\\ndef log_value(**mdc_kwargs):\\n\\n \\\"\\\"\\\"Adds constant values to the logging context\\\"\\\"\\\"\\n\\n def decorator(func):\\n return wrap_function_or_generator(func, context_dict=mdc_kwargs)\\n\\n return decorator\\n\\n\\ndef log_value_and_pass_context(**mdc_kwargs):\\n\\n \\\"\\\"\\\"Adds constant values to the logging context and\\n passes it as an argument to the function under the name 'ctx'\\\"\\\"\\\"\\n\\n def decorator(func):\\n return wrap_function_or_generator(\\n func, pass_context_as=\\\"ctx\\\", context_dict=mdc_kwargs\\n )\\n\\n return decorator\\n\\n\\nwith_mdc = log_value_and_pass_context\\n\\n\\ndef pass_context(name):\\n\\n \\\"\\\"\\\"Passes a new context as an argument to the function with the name given\\\"\\\"\\\"\\n\\n def decorator(func):\\n return wrap_function_or_generator(func, pass_context_as=name)\\n\\n return decorator\\n\\n\\ndef log_argument(argument, destination=\\\"\\\", formatter=None):\\n\\n \\\"\\\"\\\"Adds the argument value passed to the decorated function to the logging context,\\n formatted by formatter and under the name given by destination\\\"\\\"\\\"\\n\\n destination = destination or argument\\n\\n if formatter is None:\\n\\n def formatter(x):\\n return x\\n\\n def decorator(func):\\n\\n # Raise early if the function does not have said argument\\n if not has_argument(func, argument):\\n raise ValueError(\\\"Function {} has no argument {}\\\".format(func, argument))\\n\\n @wraps(func)\\n def wrapper(*args, **kwargs):\\n\\n raw_argument = bind_argument(func, argument, args, kwargs)\\n\\n return wrap_function_or_generator(\\n func, context_dict={destination: formatter(raw_argument)}\\n )(*args, **kwargs)\\n\\n return wrapper\\n\\n return decorator\\n\"\n },\n {\n \"alpha_fraction\": 0.6498371362686157,\n \"alphanum_fraction\": 0.6612377762794495,\n \"avg_line_length\": 13.97560977935791,\n \"blob_id\": \"f0dac6acbdba23502ed946dbf0c00e1744943a9d\",\n \"content_id\": \"3d81bbc562bf9c5cf06d122a76e5b4fe325d386f\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"INI\",\n \"length_bytes\": 614,\n \"license_type\": \"permissive\",\n \"max_line_length\": 76,\n \"num_lines\": 41,\n \"path\": \"/tox.ini\",\n \"repo_name\": \"AFriemann/mdc\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"[tox]\\nenvlist = py2, py3\\n\\n[testenv]\\npassenv = *\\ndeps =\\n nose\\n rednose\\n spec\\n flake8\\ncommands =\\n -flake8 mdc\\n nosetests --with-doctest --with-specplugin {posargs}\\n\\n[testenv:watch]\\ndeps =\\n nose\\n rednose\\n nose-watch\\n spec\\ncommands = nosetests --with-watch --with-specplugin --with-doctest {posargs}\\n\\n[testenv:coverage]\\ndeps =\\n nose\\n rednose\\n coverage\\ncommands = nosetests --quiet --with-coverage --cover-package mdc {posargs}\\n\\n[testenv:style]\\ndeps =\\n pylint\\n flake8\\ncommands =\\n flake8 mdc\\n -pylint mdc\\n\\n[testenv:repl]\\nenvlist = py3\\ndeps = ipython\\ncommands = ipython\\n\"\n },\n {\n \"alpha_fraction\": 0.5684038996696472,\n \"alphanum_fraction\": 0.5985342264175415,\n \"avg_line_length\": 22.834951400756836,\n \"blob_id\": \"59cfe5bf59414268a1aed1f79297058688238af8\",\n \"content_id\": \"73edb8108c785a104f92cc26e88a17e166898e36\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"reStructuredText\",\n \"length_bytes\": 2456,\n \"license_type\": \"permissive\",\n \"max_line_length\": 249,\n \"num_lines\": 103,\n \"path\": \"/README.rst\",\n \"repo_name\": \"AFriemann/mdc\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"MDC\\n===\\n\\n.. image:: https://travis-ci.org/AFriemann/mdc.svg?branch=master\\n :target: https://travis-ci.org/AFriemann/mdc\\n\\nThis is thought to be an easy to use, import and go, library for Mapped Diagnostic Context style logging.\\n\\nLogs should include all necessary fields collected by the python logging library.\\nHelper methods are provided to add context fields where required.\\n\\n**NOTE** `1.2.0`: recent changes will result in changed json output if using the `MDCHandler` class:\\n\\n.. code:: python\\n\\n >>> with MDC(foobar=\\\"baz\\\"):\\n ... logging.warning(\\\"test\\\") \\n { \\\"message\\\": \\\"test\\\", \\\"foobar\\\": \\\"baz\\\" }\\n\\n\\nthis is a temporary solution to avoid breaking existing APIs. Please excuse any issues this might cause. In the future output handling should be done on the client side to avoid any possible breakage. The MDCHandler class will eventually be removed.\\n\\nInstallation\\n------------\\n\\nPyPi:\\n\\n.. code:: bash\\n\\n $ pip install --user mdc\\n\\nFrom source:\\n\\n.. code:: bash\\n\\n $ pip install --user .\\n\\nUsage\\n-----\\n\\nAdd a handler to the root logger or set the base handler with *logging.basicConfig*:\\n\\n.. code:: python\\n\\n >>> import logging\\n >>> from mdc import MDCHandler\\n\\n >>> # use the MDCHandler only\\n >>> logging.basicConfig(level=logging.DEBUG, handlers=[MDCHandler()])\\n\\n >>> # the MDC contextmanager\\n >>> with MDC(foo='bar'):\\n ... logging.warning('foobar')\\n { ..., \\\"mdc\\\": { \\\"foo\\\": \\\"bar\\\" }, \\\"extra\\\": {}, ... }\\n\\n >>> # the with_mdc decorator\\n >>> @with_mdc(test='123')\\n ... def foobar(ctx):\\n ... logging.warning('some warning')\\n\\n >>> foobar()\\n { ..., \\\"mdc\\\": { \\\"test\\\": \\\"123\\\" }, \\\"extra\\\": {}, ... }\\n\\n >>> # logging with extra fields\\n >>> logging.error('some error', extra=dict(foo='bar'))\\n { ..., \\\"mdc\\\": {}, \\\"extra\\\": { \\\"foo\\\": \\\"bar\\\" }, ... }\\n\\nBy default log messages will include the following fields:\\n\\n.. code:: json\\n\\n {\\n \\\"message\\\": \\\"deleting context b8321b4f-19ff-4c98-b011-5a97178e7ad6\\\",\\n \\\"logger\\\": \\\"mdc\\\",\\n \\\"timestamp\\\": \\\"2018-03-07T21:39:12.010851\\\",\\n \\\"level\\\": \\\"DEBUG\\\",\\n \\\"mdc\\\": {\\n \\\"foo\\\": \\\"bar\\\",\\n \\\"index\\\": 76\\n },\\n \\\"extra\\\": {},\\n \\\"python\\\": {\\n \\\"module\\\": \\\"__init__\\\",\\n \\\"function\\\": \\\"MDC\\\",\\n \\\"path\\\": \\\"/home/user/git/mdc/mdc/__init__.py\\\",\\n \\\"file\\\": \\\"__init__.py\\\",\\n \\\"line\\\": 56,\\n \\\"process\\\": {\\n \\\"name\\\": \\\"MainProcess\\\",\\n \\\"id\\\": 31678\\n },\\n \\\"thread\\\": {\\n \\\"name\\\": \\\"MainThread\\\",\\n \\\"id\\\": 140433289192768\\n }\\n }\\n }\\n\\nRunning tests:\\n\\n.. code:: bash\\n\\n $ tox\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6977491974830627,\n \"alphanum_fraction\": 0.6993569135665894,\n \"avg_line_length\": 23.39215660095215,\n \"blob_id\": \"fdf4690258d426e5b67f60171a6f2f334a05811d\",\n \"content_id\": \"f1eb2c927568636a38a44367aebe2fadf41c48c6\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1244,\n \"license_type\": \"permissive\",\n \"max_line_length\": 90,\n \"num_lines\": 51,\n \"path\": \"/mdc/__init__.py\",\n \"repo_name\": \"AFriemann/mdc\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n\\\"\\\"\\\"\\n.. module: mdc\\n.. moduleauthor:: Aljosha Friemann a.friemann@automate.wtf\\n\\\"\\\"\\\"\\nfrom __future__ import (absolute_import, division, print_function,\\n unicode_literals)\\n\\nimport inspect\\nimport logging\\nimport sys\\nfrom functools import wraps\\n\\nfrom future import standard_library\\nfrom mdc.context import *\\nfrom mdc.decorators import *\\nfrom pythonjsonlogger import jsonlogger\\n\\nstandard_library.install_aliases()\\n\\n\\nMDContext = new_log_context\\nMDC = new_log_context\\n\\nLOGGER = logging.getLogger(__name__)\\nLOGGER.setLevel(logging.ERROR)\\n\\n\\ndef patch(old_factory):\\n def record_factory(*args, **kwargs):\\n record = old_factory(*args, **kwargs)\\n\\n for key, value in get_mdc_fields().items():\\n setattr(record, key, value)\\n\\n return record\\n\\n return record_factory\\n\\n\\ntry:\\n logging.setLogRecordFactory(patch(logging.getLogRecordFactory()))\\nexcept AttributeError:\\n logging.LogRecord = patch(logging.LogRecord)\\n\\n\\n# legacy handler to avoid breaking existing implementations, this will be removed with 2.x\\nclass MDCHandler(logging.StreamHandler):\\n def __init__(self, *args, **kwargs):\\n logging.StreamHandler.__init__(self, *args, **kwargs)\\n self.setFormatter(jsonlogger.JsonFormatter())\\n\"\n },\n {\n \"alpha_fraction\": 0.5874326825141907,\n \"alphanum_fraction\": 0.5895870923995972,\n \"avg_line_length\": 22.20833396911621,\n \"blob_id\": \"67424458437edf5cbd6be624b142bcf49ae0aab9\",\n \"content_id\": \"f89662b758a663a071acc054e1cfc8cb695262aa\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2785,\n \"license_type\": \"permissive\",\n \"max_line_length\": 85,\n \"num_lines\": 120,\n \"path\": \"/tests/mdc_tests.py\",\n \"repo_name\": \"AFriemann/mdc\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import logging\\n\\nfrom mdc import with_mdc, MDC, log_value, new_log_context, log_argument, pass_context\\n\\n\\ndef assert_context_has(**kwargs):\\n record = logging.makeLogRecord({})\\n for key, value in kwargs.items():\\n assert hasattr(record, key)\\n assert getattr(record, key) == value\\n\\n\\ndef test_attaching_fields():\\n assert logging._mdc is not None\\n\\n with MDC(foo=\\\"bar\\\") as context:\\n assert context is not None\\n assert context.foo == \\\"bar\\\"\\n\\n logging.debug(\\\"abc\\\")\\n\\n with MDC(bar=\\\"foo\\\") as context2:\\n assert context2 is not None\\n assert context2.bar == \\\"foo\\\"\\n\\n try:\\n raise RuntimeError(\\\"test\\\")\\n except Exception as e:\\n logging.exception(e, extra=dict(foo=\\\"oink\\\"))\\n\\n\\ndef test_mdc_decorator():\\n @with_mdc(baz=\\\"bar\\\")\\n def foobar(x, ctx=None):\\n logging.debug(x)\\n\\n assert ctx.baz == \\\"bar\\\", ctx.baz\\n\\n foobar(1)\\n\\n\\ndef test_generator():\\n @log_value(inside=True)\\n def my_generator():\\n for i in range(5):\\n assert_context_has(inside=True)\\n yield i\\n\\n assert list(my_generator()) == list(range(5))\\n\\n with new_log_context(inside=False):\\n for _ in my_generator():\\n assert_context_has(inside=False)\\n\\n\\ndef test_log_value():\\n @log_value(foo=\\\"bar\\\")\\n def my_function():\\n assert_context_has(foo=\\\"bar\\\")\\n\\n assert not hasattr(logging.makeLogRecord({}), \\\"foo\\\")\\n my_function()\\n assert not hasattr(logging.makeLogRecord({}), \\\"foo\\\")\\n\\n\\ndef test_nested_contexts():\\n\\n with new_log_context(food=\\\"spam\\\"):\\n with new_log_context(food=\\\"ham\\\"):\\n with new_log_context(food=\\\"eggs\\\"):\\n assert_context_has(food=\\\"eggs\\\")\\n assert_context_has(food=\\\"ham\\\")\\n assert_context_has(food=\\\"spam\\\")\\n\\n\\ndef test_log_argument():\\n @log_argument(\\\"food\\\")\\n def eat(food):\\n assert_context_has(food=food)\\n\\n for food in [\\\"spam\\\", \\\"ham\\\", \\\"eggs\\\"]:\\n eat(food)\\n\\n\\ndef test_log_argument_with_destination():\\n @log_argument(\\\"food\\\", \\\"abcd\\\")\\n def eat(food):\\n assert_context_has(abcd=food)\\n\\n for food in [\\\"spam\\\", \\\"ham\\\", \\\"eggs\\\"]:\\n eat(food)\\n\\n\\ndef test_log_argument_with_formatter():\\n @log_argument(\\\"food\\\", formatter=str.upper)\\n def eat(food):\\n assert_context_has(food=str.upper(food))\\n\\n for food in [\\\"spam\\\", \\\"ham\\\", \\\"eggs\\\"]:\\n eat(food)\\n\\n\\ndef test_pass_context_in_args():\\n @pass_context(\\\"ctx\\\")\\n def eat(food, ctx):\\n ctx.food = food\\n assert_context_has(food=food)\\n\\n for food in [\\\"spam\\\", \\\"ham\\\", \\\"eggs\\\"]:\\n eat(food)\\n\\n\\ndef test_pass_context_in_kwargs():\\n @pass_context(\\\"ctx\\\")\\n def eat(food, ctx=None):\\n ctx.food = food\\n assert_context_has(food=food)\\n\\n for food in [\\\"spam\\\", \\\"ham\\\", \\\"eggs\\\"]:\\n eat(food)\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":6,"string":"6"}}},{"rowIdx":2979382,"cells":{"repo_name":{"kind":"string","value":"Paarmita/Space-shooter"},"repo_url":{"kind":"string","value":"https://github.com/Paarmita/Space-shooter"},"snapshot_id":{"kind":"string","value":"d0674a209b4199f1fa40d90e13c1e4dfd97fc90f"},"revision_id":{"kind":"string","value":"28b7ad4cb83795a9f107da2acebbea7e0bcf7ace"},"directory_id":{"kind":"string","value":"bdd68b7697458c5f875c3a55575c5ef97aac0408"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-03-18T17:30:59.842804","string":"2020-03-18T17:30:59.842804"},"revision_date":{"kind":"timestamp","value":"2018-06-02T15:13:07","string":"2018-06-02T15:13:07"},"committer_date":{"kind":"timestamp","value":"2018-06-02T15:13:07","string":"2018-06-02T15:13:07"},"github_id":{"kind":"number","value":135033095,"string":"135,033,095"},"star_events_count":{"kind":"number","value":2,"string":"2"},"fork_events_count":{"kind":"number","value":1,"string":"1"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6332970857620239,"alphanum_fraction":0.6387377381324768,"avg_line_length":26.02941131591797,"blob_id":"9d531177ba1f5dd301e25a881fc45b67f9890485","content_id":"ffcbc7844d02cca913ae5a42dcfb259aab0a587f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":919,"license_type":"no_license","max_line_length":80,"num_lines":34,"path":"/README.md","repo_name":"Paarmita/Space-shooter","src_encoding":"UTF-8","text":"# Space-shooter\nPython game using pygame\n\n\n## Controls\n\n| Control | Button |\n| ------------- |:-------------:|\n| Move left | left arrow | \n| Move right | right arrow | \n| Fire bullets | spacebar |\n| exit | esc |\n\n* Score 10 points for each alien hit and increases on each level\n\n## Run on linux\nPrerequisite : python and pygame installed\n\n```\n sudo apt-get update\n sudo apt-get install python3.6\n sudo apt-get install python-pygame\n```\n```\n git clone https://github.com/Paarmita/Space-shooter.git\n cd Space-shooter\n python alien_invasion.py\n```\n## Features to add\n- [ ] Add key press P to play\n- [ ] Game over msg after 3 ships\n- [ ] Display highscore and score labels\n- [ ] All time highScore by wrtitng score to a file\n- [ ] Sound effects on explosions and shooting sounds.\n"},{"alpha_fraction":0.7145499587059021,"alphanum_fraction":0.7250308394432068,"avg_line_length":27.98214340209961,"blob_id":"4983d419d0acb74af1b54c4c0583f983e59a7a0d","content_id":"e24890a4316bcf362b0b98b5b6487f4f5525d121","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1622,"license_type":"no_license","max_line_length":88,"num_lines":56,"path":"/alien_invasion.py","repo_name":"Paarmita/Space-shooter","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\nimport sys\nimport pygame\nimport game_functions as gf\nfrom settings import Settings\nfrom ship import Ship\nfrom alien import Alien\nfrom game_stats import GameStats\nfrom button import Button # to draw bullets to the screen \nfrom scoreboard import Scoreboard\nfrom pygame.sprite import Group\n\n\ndef run_game():\n\t# Initialize game, settings and create a screen object.\n\tpygame.init()\n\t# screen = pygame.display.set_mode((1200, 800))\n\tai_settings = Settings()\n\tscreen = pygame.display.set_mode((ai_settings.screen_width, ai_settings.screen_height))\n\tpygame.display.set_caption(\"Space-shooter\")\n\t \n\t# Make the Play button.\n\tplay_button = Button(ai_settings, screen, \"Play\")\n\t\n\t# Create an instance to store game statistics and create a scoreboard.\n\tstats = GameStats(ai_settings)\n\tsb = Scoreboard(ai_settings, screen, stats) \n\n\t# Make a ship.\n\tship = Ship(ai_settings, screen)\n\n\t# Make a group to store bullets in and group of aliens.\n\tbullets = Group()\n\taliens = Group()\n\n\t# Make an alien.\n\talien = Alien(ai_settings, screen)\n\n\t# Create the fleet of aliens.\n\tgf.create_fleet(ai_settings, screen, ship, aliens)\n\n\t# Set the background color RGB.\n\tbg_color = (230, 230, 230)\n\n\t# Start the main loop for the game.\n\twhile True:\n\t\tgf.check_events(ai_settings, screen, stats, sb, play_button, ship, aliens, bullets)\n\t\t \n\t\tif stats.game_active:\n\t\t\tship.update()\n\t\t\tgf.update_bullets(ai_settings, screen, stats, sb, ship, aliens, bullets)\n\t\t\tgf.update_aliens(ai_settings, screen, stats, sb, ship, aliens, bullets)\n\t\t\n\t\tgf.update_screen(ai_settings, screen, stats, sb, ship, aliens, bullets, play_button)\n\nrun_game()"},{"alpha_fraction":0.7205138802528381,"alphanum_fraction":0.7225147485733032,"avg_line_length":34.174072265625,"blob_id":"5a202b46979a7540f4e587205b8fd9a533c213ba","content_id":"648a79bce1c8e0e1e67910eee60610019ad05af2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":9498,"license_type":"no_license","max_line_length":109,"num_lines":270,"path":"/game_functions.py","repo_name":"Paarmita/Space-shooter","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\nimport sys\nimport pygame\nfrom time import sleep\nfrom bullet import Bullet\nfrom alien import Alien\n\ndef check_events(ai_settings, screen, stats, sb, play_button, ship, aliens, bullets):\n# Respond to keypresses and mouse events.\n# Watch for keyboard and mouse events.\n\tfor event in pygame.event.get():\n\t\tif event.type == pygame.QUIT:\n\t\t\t#to exit the game when the player quits\n\t\t\tsys.exit()\n\t\t# keydown->right->centre shift\n\t\telif event.type == pygame.KEYDOWN:\n\t\t\tcheck_keydown_events(event, ai_settings, screen, ship, bullets)\n\n\t\telif event.type == pygame.KEYUP:\n\t\t\tcheck_keyup_events(event, ship)\n\n\t\telif event.type == pygame.MOUSEBUTTONDOWN:\n\t\t\tmouse_x, mouse_y = pygame.mouse.get_pos()\n\t\t\t# to check mouse click on buttoon only from get_pos()\n\t\t\tcheck_play_button(ai_settings, screen, stats, sb, play_button, ship, aliens, bullets, mouse_x, mouse_y)\n\t\t\t\ndef update_screen(ai_settings, screen, stats, sb, ship, aliens, bullets, play_button):\n# Update images on the screen and flip to the new screen.\n# Redraw the screen during each pass through the loop.\n\t# screen.fill(bg_color)\n\tscreen.fill(ai_settings.bg_color)\n\n\tship.blitme()\n\taliens.draw(screen)\n\t# aliens.blitme()s\n\n\t# Redraw all bullets behind ship and aliens.\n\tfor bullet in bullets.sprites():\n\t\t# draw all fired bullets to the screen\n\t\tbullet.draw_bullet()\n\n\t# Draw the score information.\n\tsb.show_score()\n\t\n\t# Draw the play button if the game is inactive.\n\tif not stats.game_active:\n\t\tplay_button.draw_button()\n\n\t# Make the most recently drawn screen visible.\n\tpygame.display.flip()\n\ndef check_keydown_events(event, ai_settings, screen, ship, bullets):\n\t#Respond to keypresses.\n\tif event.key == pygame.K_RIGHT:\n\t\t# true when key pressed false when released\n\t\tship.moving_right = True\n\telif event.key == pygame.K_LEFT:\n\t\tship.moving_left = True\n\t\t# Move the ship to the right.\n\t\tship.rect.centerx += 1\n\telif event.key == pygame.K_SPACE:\n\t\tfire_bullet(ai_settings, screen, ship, bullets)\n\telif event.key == pygame.K_ESCAPE:\n\t\tsys.exit()\n\t\n\t\t\ndef check_keyup_events(event, ship):\n\t# Respond to key releases.\n\tif event.key == pygame.K_RIGHT:\n\t\t# set to false when keyup\n\t\tship.moving_right = False\n\telif event.key == pygame.K_LEFT:\n\t\tship.moving_left = False\n\ndef update_bullets(ai_settings, screen, stats, sb, ship, aliens, bullets):\n\t# Update position of bullets and get rid of old bullets\n\t# Update bullet positions.\n\tbullets.update()\n\n\t# # Get rid of bullets that have disappeared as they consume memory.\n\t# for bullet in bullets.copy():\n\t# \tif bullet.rect.bottom <= 0:\n\t# \t\tbullets.remove(bullet)\n\t# print(len(bullets))\n\n\tcheck_bullet_alien_collisions(ai_settings, screen, stats, sb, ship, aliens, bullets)\n\t\n# Whenever the rects of a bullet and alien overlap, \n# groupcollide() adds a key-value pair to the dictionary it returns. \n# The two True arguments tell Pygame whether to delete the bullets and aliens that have collided\n\n\ndef check_bullet_alien_collisions(ai_settings, screen, stats, sb, ship, aliens, bullets):\n\t# Respond to bullet-alien collisions.\n\t# Remove any bullets and aliens that have collided\n\t# Check for any bullets that have hit aliens.\n\t# If so, get rid of the bullet and the alien.\n\tcollisions = pygame.sprite.groupcollide(bullets, aliens, True, True)\n\n\tif collisions:\n\t\t# stats.score += ai_settings.alien_points\n\t\t# to ensure if 2 bullets collide at same time then score for two or more precise game\n\t\tfor aliens in collisions.values(): #collisions dictionary\n\t\t\tstats.score += ai_settings.alien_points * len(aliens)\n\t\t\tsb.prep_score()\n\t\t\t# call prep_score() to create a new image for the updated score\n\t\tcheck_high_score(stats, sb)\n\n\tif len(aliens) == 0: # check group aliens empty\n\t\t# Destroy existing bullets, speed up game and create new fleet.\n\t\tbullets.empty()\n\t\tai_settings.increase_speed()\n\n\t\t# If the entire fleet is destroyed, start a new level.\n\t\t# Increase level.\n\t\tstats.level += 1\n\t\tsb.prep_level()\t\t\n\n\t\tcreate_fleet(ai_settings, screen, ship, aliens)\n\t\tprint(\"Collision!!!\")\n\ndef fire_bullet(ai_settings, screen, ship, bullets):\n\t# Fire a bullet if limit not reached yet.\n\t# Create a new bullet and add it to the bullets group.\n\t# before firing check the no \n\t# if len(bullets) < ai_settings.bullets_allowed:\n\tnew_bullet = Bullet(ai_settings, screen, ship)\n\tbullets.add(new_bullet)\n\ndef get_number_aliens_x(ai_settings, alien_width):\n\t# Determine the number of aliens that fit in a row.\n\t# calculating horizontal space to fit \n\tavailable_space_x = ai_settings.screen_width - 2 * alien_width\n\tnumber_aliens_x = int(available_space_x / (2 * alien_width))\n\treturn number_aliens_x\n\ndef get_number_rows(ai_settings, ship_height, alien_height):\n\t# Determine the number of rows of aliens that fit on the screen.\n\tavailable_space_y = (ai_settings.screen_height - (2 * alien_height) - ship_height)\n\tnumber_rows = int(available_space_y / (2 * alien_height))\n\treturn number_rows\n\ndef create_alien(ai_settings, screen, aliens, alien_number, row_number):\n\t# Create an alien and place it in the row.\n\talien = Alien(ai_settings, screen)\n\t# Create an alien and find the number of aliens in a row.\n\t# Spacing between each alien is equal to one alien width.\n\talien_width = alien.rect.width\n\t# Create an alien and place it in the row.\n\talien = Alien(ai_settings, screen)\n\t# set it's x-coordinate value to place it in the row\n\talien.x = alien_width + 2 * alien_width * alien_number\n\t# pushed to the right one alien width from the left margin\n\talien.rect.x = alien.x\n\talien.rect.y = alien.rect.height + 2 * alien.rect.height * row_number\n\t# add to alien group\n\taliens.add(alien)\n\ndef create_fleet(ai_settings, screen, ship, aliens):\n\t# Create a full fleet of aliens.\n\talien = Alien(ai_settings, screen)\n\tnumber_aliens_x = get_number_aliens_x(ai_settings, alien.rect.width)\n\tnumber_rows = get_number_rows(ai_settings, ship.rect.height,alien.rect.height)\n\n\t# Create the fleet of aliens.\n\tfor row_number in range(number_rows):\n\t\t# Create the first row of aliens.\n\t\tfor alien_number in range(number_aliens_x):\n\t\t\tcreate_alien(ai_settings, screen, aliens, alien_number,row_number)\n\ndef check_fleet_edges(ai_settings, aliens):\n\t# Respond appropriately if any aliens have reached an edge.\"\"\"\n\tfor alien in aliens.sprites():\n\t\tif alien.check_edges():\n\t\t\tchange_fleet_direction(ai_settings, aliens)\n\t\t\t# print(\"Fleet direction changed!!!\")\n\t\t\tbreak\n\ndef change_fleet_direction(ai_settings, aliens):\n\t# Drop the entire fleet and change the fleet's direction.\"\"\"\n\tfor alien in aliens.sprites():\n\t\t# loop through all the aliens and drop each one using the setting fleet_drop_speed \n\t\talien.rect.y += ai_settings.fleet_drop_speed\n\tai_settings.fleet_direction *= -1\n\ndef ship_hit(ai_settings, screen, stats, sb, ship, aliens, bullets):\n\t# Respond to ship being hit by alien.\n\t# to check atleast 1 ship remaining\n\tif stats.ships_left > 0:\n\t\t# Decrement ships_left.\n\t\tstats.ships_left -= 1\n\n\t\t# Update scoreboard.\n\t\tsb.prep_ships()\n\n\t\t# Empty the list of aliens and bullets.\n\t\taliens.empty()\n\t\tbullets.empty()\n\n\t\t# Create a new fleet and center the ship.\n\t\tcreate_fleet(ai_settings, screen, ship, aliens)\n\t\tship.center_ship()\n\t\tprint(\"New fleet after hit!!!\")\n\n\t\t# Pause.\n\t\tsleep(0.5)\n\telse:\n\t\tstats.game_active = False\n\t\tpygame.mouse.set_visible(True)\n\ndef check_aliens_bottom(ai_settings, screen, stats, sb, ship, aliens, bullets):\n\t# Check if any aliens have reached the bottom of the screen.\n\tscreen_rect = screen.get_rect()\n\tfor alien in aliens.sprites():\n\t\tif alien.rect.bottom >= screen_rect.bottom:\n\t\t\t# Treat this the same as if the ship got hit.\n\t\t\tship_hit(ai_settings, screen, stats, sb, ship, aliens, bullets)\n\t\t\tprint(\"Aliens bottom!!!\")\n\t\t\tbreak\n\ndef update_aliens(ai_settings, screen, stats, sb, ship, aliens, bullets):\n\t# Check if the fleet is at an edge,\n\t# and then update the postions of all aliens in the fleet.\n\tcheck_fleet_edges(ai_settings, aliens)\n\t# Update the postions of all aliens in the fleet.\n\taliens.update()\n\t# Look for alien-ship collisions.\n\t# spritecollideany() looks for any member of the group that’s collided with \n\t# the sprite and stops looping through the group as soon as it finds one member \n\t# has collided with the sprite.\n\tif pygame.sprite.spritecollideany(ship, aliens):\n\t\tship_hit(ai_settings, screen, stats, sb, ship, aliens, bullets)\n\t\tprint(\"Ship hit!!!\")\n\t# Look for aliens hitting the bottom of the screen.\n\tcheck_aliens_bottom(ai_settings, screen, stats, sb, ship, aliens, bullets)\n\n# Start a new game when the player clicks Play.\ndef check_play_button(ai_settings, screen, stats, sb, play_button, ship, aliens, bullets, mouse_x, mouse_y):\t\n\tbutton_clicked = play_button.rect.collidepoint(mouse_x, mouse_y)\n\tif button_clicked and not stats.game_active:\n\t\t# Reset the game settings.\n\t\tai_settings.initialize_dynamic_settings()\n\n\t\t# Hide the mouse cursor after button click\n\t\tpygame.mouse.set_visible(False)\n\n\t\t# Reset the game statistics.\n\t\tstats.reset_stats() # gives player 3 new ships\n\t\tstats.game_active = True\n\n\t\t# Reset the scoreboard images.\n\t\tsb.prep_score()\n\t\tsb.prep_high_score()\n\t\tsb.prep_level()\n\t\t# To show the player how many ships they have to start with\n\t\tsb.prep_ships()\n\n\t\t# Empty the list of aliens and bullets.\n\t\taliens.empty()\n\t\tbullets.empty()\n\n\t\t# Create a new fleet and center the ship.\n\t\tcreate_fleet(ai_settings, screen, ship, aliens)\n\t\tship.center_ship()\n\ndef check_high_score(stats, sb):\n\t# Check to see if there's a new high score.\n\tif stats.score > stats.high_score:\n\t\tstats.high_score = stats.score\n\t\tsb.prep_high_score()"}],"string":"[\n {\n \"alpha_fraction\": 0.6332970857620239,\n \"alphanum_fraction\": 0.6387377381324768,\n \"avg_line_length\": 26.02941131591797,\n \"blob_id\": \"9d531177ba1f5dd301e25a881fc45b67f9890485\",\n \"content_id\": \"ffcbc7844d02cca913ae5a42dcfb259aab0a587f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 919,\n \"license_type\": \"no_license\",\n \"max_line_length\": 80,\n \"num_lines\": 34,\n \"path\": \"/README.md\",\n \"repo_name\": \"Paarmita/Space-shooter\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Space-shooter\\nPython game using pygame\\n\\n\\n## Controls\\n\\n| Control | Button |\\n| ------------- |:-------------:|\\n| Move left | left arrow | \\n| Move right | right arrow | \\n| Fire bullets | spacebar |\\n| exit | esc |\\n\\n* Score 10 points for each alien hit and increases on each level\\n\\n## Run on linux\\nPrerequisite : python and pygame installed\\n\\n```\\n sudo apt-get update\\n sudo apt-get install python3.6\\n sudo apt-get install python-pygame\\n```\\n```\\n git clone https://github.com/Paarmita/Space-shooter.git\\n cd Space-shooter\\n python alien_invasion.py\\n```\\n## Features to add\\n- [ ] Add key press P to play\\n- [ ] Game over msg after 3 ships\\n- [ ] Display highscore and score labels\\n- [ ] All time highScore by wrtitng score to a file\\n- [ ] Sound effects on explosions and shooting sounds.\\n\"\n },\n {\n \"alpha_fraction\": 0.7145499587059021,\n \"alphanum_fraction\": 0.7250308394432068,\n \"avg_line_length\": 27.98214340209961,\n \"blob_id\": \"4983d419d0acb74af1b54c4c0583f983e59a7a0d\",\n \"content_id\": \"e24890a4316bcf362b0b98b5b6487f4f5525d121\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1622,\n \"license_type\": \"no_license\",\n \"max_line_length\": 88,\n \"num_lines\": 56,\n \"path\": \"/alien_invasion.py\",\n \"repo_name\": \"Paarmita/Space-shooter\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\nimport sys\\nimport pygame\\nimport game_functions as gf\\nfrom settings import Settings\\nfrom ship import Ship\\nfrom alien import Alien\\nfrom game_stats import GameStats\\nfrom button import Button # to draw bullets to the screen \\nfrom scoreboard import Scoreboard\\nfrom pygame.sprite import Group\\n\\n\\ndef run_game():\\n\\t# Initialize game, settings and create a screen object.\\n\\tpygame.init()\\n\\t# screen = pygame.display.set_mode((1200, 800))\\n\\tai_settings = Settings()\\n\\tscreen = pygame.display.set_mode((ai_settings.screen_width, ai_settings.screen_height))\\n\\tpygame.display.set_caption(\\\"Space-shooter\\\")\\n\\t \\n\\t# Make the Play button.\\n\\tplay_button = Button(ai_settings, screen, \\\"Play\\\")\\n\\t\\n\\t# Create an instance to store game statistics and create a scoreboard.\\n\\tstats = GameStats(ai_settings)\\n\\tsb = Scoreboard(ai_settings, screen, stats) \\n\\n\\t# Make a ship.\\n\\tship = Ship(ai_settings, screen)\\n\\n\\t# Make a group to store bullets in and group of aliens.\\n\\tbullets = Group()\\n\\taliens = Group()\\n\\n\\t# Make an alien.\\n\\talien = Alien(ai_settings, screen)\\n\\n\\t# Create the fleet of aliens.\\n\\tgf.create_fleet(ai_settings, screen, ship, aliens)\\n\\n\\t# Set the background color RGB.\\n\\tbg_color = (230, 230, 230)\\n\\n\\t# Start the main loop for the game.\\n\\twhile True:\\n\\t\\tgf.check_events(ai_settings, screen, stats, sb, play_button, ship, aliens, bullets)\\n\\t\\t \\n\\t\\tif stats.game_active:\\n\\t\\t\\tship.update()\\n\\t\\t\\tgf.update_bullets(ai_settings, screen, stats, sb, ship, aliens, bullets)\\n\\t\\t\\tgf.update_aliens(ai_settings, screen, stats, sb, ship, aliens, bullets)\\n\\t\\t\\n\\t\\tgf.update_screen(ai_settings, screen, stats, sb, ship, aliens, bullets, play_button)\\n\\nrun_game()\"\n },\n {\n \"alpha_fraction\": 0.7205138802528381,\n \"alphanum_fraction\": 0.7225147485733032,\n \"avg_line_length\": 34.174072265625,\n \"blob_id\": \"5a202b46979a7540f4e587205b8fd9a533c213ba\",\n \"content_id\": \"648a79bce1c8e0e1e67910eee60610019ad05af2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 9498,\n \"license_type\": \"no_license\",\n \"max_line_length\": 109,\n \"num_lines\": 270,\n \"path\": \"/game_functions.py\",\n \"repo_name\": \"Paarmita/Space-shooter\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\nimport sys\\nimport pygame\\nfrom time import sleep\\nfrom bullet import Bullet\\nfrom alien import Alien\\n\\ndef check_events(ai_settings, screen, stats, sb, play_button, ship, aliens, bullets):\\n# Respond to keypresses and mouse events.\\n# Watch for keyboard and mouse events.\\n\\tfor event in pygame.event.get():\\n\\t\\tif event.type == pygame.QUIT:\\n\\t\\t\\t#to exit the game when the player quits\\n\\t\\t\\tsys.exit()\\n\\t\\t# keydown->right->centre shift\\n\\t\\telif event.type == pygame.KEYDOWN:\\n\\t\\t\\tcheck_keydown_events(event, ai_settings, screen, ship, bullets)\\n\\n\\t\\telif event.type == pygame.KEYUP:\\n\\t\\t\\tcheck_keyup_events(event, ship)\\n\\n\\t\\telif event.type == pygame.MOUSEBUTTONDOWN:\\n\\t\\t\\tmouse_x, mouse_y = pygame.mouse.get_pos()\\n\\t\\t\\t# to check mouse click on buttoon only from get_pos()\\n\\t\\t\\tcheck_play_button(ai_settings, screen, stats, sb, play_button, ship, aliens, bullets, mouse_x, mouse_y)\\n\\t\\t\\t\\ndef update_screen(ai_settings, screen, stats, sb, ship, aliens, bullets, play_button):\\n# Update images on the screen and flip to the new screen.\\n# Redraw the screen during each pass through the loop.\\n\\t# screen.fill(bg_color)\\n\\tscreen.fill(ai_settings.bg_color)\\n\\n\\tship.blitme()\\n\\taliens.draw(screen)\\n\\t# aliens.blitme()s\\n\\n\\t# Redraw all bullets behind ship and aliens.\\n\\tfor bullet in bullets.sprites():\\n\\t\\t# draw all fired bullets to the screen\\n\\t\\tbullet.draw_bullet()\\n\\n\\t# Draw the score information.\\n\\tsb.show_score()\\n\\t\\n\\t# Draw the play button if the game is inactive.\\n\\tif not stats.game_active:\\n\\t\\tplay_button.draw_button()\\n\\n\\t# Make the most recently drawn screen visible.\\n\\tpygame.display.flip()\\n\\ndef check_keydown_events(event, ai_settings, screen, ship, bullets):\\n\\t#Respond to keypresses.\\n\\tif event.key == pygame.K_RIGHT:\\n\\t\\t# true when key pressed false when released\\n\\t\\tship.moving_right = True\\n\\telif event.key == pygame.K_LEFT:\\n\\t\\tship.moving_left = True\\n\\t\\t# Move the ship to the right.\\n\\t\\tship.rect.centerx += 1\\n\\telif event.key == pygame.K_SPACE:\\n\\t\\tfire_bullet(ai_settings, screen, ship, bullets)\\n\\telif event.key == pygame.K_ESCAPE:\\n\\t\\tsys.exit()\\n\\t\\n\\t\\t\\ndef check_keyup_events(event, ship):\\n\\t# Respond to key releases.\\n\\tif event.key == pygame.K_RIGHT:\\n\\t\\t# set to false when keyup\\n\\t\\tship.moving_right = False\\n\\telif event.key == pygame.K_LEFT:\\n\\t\\tship.moving_left = False\\n\\ndef update_bullets(ai_settings, screen, stats, sb, ship, aliens, bullets):\\n\\t# Update position of bullets and get rid of old bullets\\n\\t# Update bullet positions.\\n\\tbullets.update()\\n\\n\\t# # Get rid of bullets that have disappeared as they consume memory.\\n\\t# for bullet in bullets.copy():\\n\\t# \\tif bullet.rect.bottom <= 0:\\n\\t# \\t\\tbullets.remove(bullet)\\n\\t# print(len(bullets))\\n\\n\\tcheck_bullet_alien_collisions(ai_settings, screen, stats, sb, ship, aliens, bullets)\\n\\t\\n# Whenever the rects of a bullet and alien overlap, \\n# groupcollide() adds a key-value pair to the dictionary it returns. \\n# The two True arguments tell Pygame whether to delete the bullets and aliens that have collided\\n\\n\\ndef check_bullet_alien_collisions(ai_settings, screen, stats, sb, ship, aliens, bullets):\\n\\t# Respond to bullet-alien collisions.\\n\\t# Remove any bullets and aliens that have collided\\n\\t# Check for any bullets that have hit aliens.\\n\\t# If so, get rid of the bullet and the alien.\\n\\tcollisions = pygame.sprite.groupcollide(bullets, aliens, True, True)\\n\\n\\tif collisions:\\n\\t\\t# stats.score += ai_settings.alien_points\\n\\t\\t# to ensure if 2 bullets collide at same time then score for two or more precise game\\n\\t\\tfor aliens in collisions.values(): #collisions dictionary\\n\\t\\t\\tstats.score += ai_settings.alien_points * len(aliens)\\n\\t\\t\\tsb.prep_score()\\n\\t\\t\\t# call prep_score() to create a new image for the updated score\\n\\t\\tcheck_high_score(stats, sb)\\n\\n\\tif len(aliens) == 0: # check group aliens empty\\n\\t\\t# Destroy existing bullets, speed up game and create new fleet.\\n\\t\\tbullets.empty()\\n\\t\\tai_settings.increase_speed()\\n\\n\\t\\t# If the entire fleet is destroyed, start a new level.\\n\\t\\t# Increase level.\\n\\t\\tstats.level += 1\\n\\t\\tsb.prep_level()\\t\\t\\n\\n\\t\\tcreate_fleet(ai_settings, screen, ship, aliens)\\n\\t\\tprint(\\\"Collision!!!\\\")\\n\\ndef fire_bullet(ai_settings, screen, ship, bullets):\\n\\t# Fire a bullet if limit not reached yet.\\n\\t# Create a new bullet and add it to the bullets group.\\n\\t# before firing check the no \\n\\t# if len(bullets) < ai_settings.bullets_allowed:\\n\\tnew_bullet = Bullet(ai_settings, screen, ship)\\n\\tbullets.add(new_bullet)\\n\\ndef get_number_aliens_x(ai_settings, alien_width):\\n\\t# Determine the number of aliens that fit in a row.\\n\\t# calculating horizontal space to fit \\n\\tavailable_space_x = ai_settings.screen_width - 2 * alien_width\\n\\tnumber_aliens_x = int(available_space_x / (2 * alien_width))\\n\\treturn number_aliens_x\\n\\ndef get_number_rows(ai_settings, ship_height, alien_height):\\n\\t# Determine the number of rows of aliens that fit on the screen.\\n\\tavailable_space_y = (ai_settings.screen_height - (2 * alien_height) - ship_height)\\n\\tnumber_rows = int(available_space_y / (2 * alien_height))\\n\\treturn number_rows\\n\\ndef create_alien(ai_settings, screen, aliens, alien_number, row_number):\\n\\t# Create an alien and place it in the row.\\n\\talien = Alien(ai_settings, screen)\\n\\t# Create an alien and find the number of aliens in a row.\\n\\t# Spacing between each alien is equal to one alien width.\\n\\talien_width = alien.rect.width\\n\\t# Create an alien and place it in the row.\\n\\talien = Alien(ai_settings, screen)\\n\\t# set it's x-coordinate value to place it in the row\\n\\talien.x = alien_width + 2 * alien_width * alien_number\\n\\t# pushed to the right one alien width from the left margin\\n\\talien.rect.x = alien.x\\n\\talien.rect.y = alien.rect.height + 2 * alien.rect.height * row_number\\n\\t# add to alien group\\n\\taliens.add(alien)\\n\\ndef create_fleet(ai_settings, screen, ship, aliens):\\n\\t# Create a full fleet of aliens.\\n\\talien = Alien(ai_settings, screen)\\n\\tnumber_aliens_x = get_number_aliens_x(ai_settings, alien.rect.width)\\n\\tnumber_rows = get_number_rows(ai_settings, ship.rect.height,alien.rect.height)\\n\\n\\t# Create the fleet of aliens.\\n\\tfor row_number in range(number_rows):\\n\\t\\t# Create the first row of aliens.\\n\\t\\tfor alien_number in range(number_aliens_x):\\n\\t\\t\\tcreate_alien(ai_settings, screen, aliens, alien_number,row_number)\\n\\ndef check_fleet_edges(ai_settings, aliens):\\n\\t# Respond appropriately if any aliens have reached an edge.\\\"\\\"\\\"\\n\\tfor alien in aliens.sprites():\\n\\t\\tif alien.check_edges():\\n\\t\\t\\tchange_fleet_direction(ai_settings, aliens)\\n\\t\\t\\t# print(\\\"Fleet direction changed!!!\\\")\\n\\t\\t\\tbreak\\n\\ndef change_fleet_direction(ai_settings, aliens):\\n\\t# Drop the entire fleet and change the fleet's direction.\\\"\\\"\\\"\\n\\tfor alien in aliens.sprites():\\n\\t\\t# loop through all the aliens and drop each one using the setting fleet_drop_speed \\n\\t\\talien.rect.y += ai_settings.fleet_drop_speed\\n\\tai_settings.fleet_direction *= -1\\n\\ndef ship_hit(ai_settings, screen, stats, sb, ship, aliens, bullets):\\n\\t# Respond to ship being hit by alien.\\n\\t# to check atleast 1 ship remaining\\n\\tif stats.ships_left > 0:\\n\\t\\t# Decrement ships_left.\\n\\t\\tstats.ships_left -= 1\\n\\n\\t\\t# Update scoreboard.\\n\\t\\tsb.prep_ships()\\n\\n\\t\\t# Empty the list of aliens and bullets.\\n\\t\\taliens.empty()\\n\\t\\tbullets.empty()\\n\\n\\t\\t# Create a new fleet and center the ship.\\n\\t\\tcreate_fleet(ai_settings, screen, ship, aliens)\\n\\t\\tship.center_ship()\\n\\t\\tprint(\\\"New fleet after hit!!!\\\")\\n\\n\\t\\t# Pause.\\n\\t\\tsleep(0.5)\\n\\telse:\\n\\t\\tstats.game_active = False\\n\\t\\tpygame.mouse.set_visible(True)\\n\\ndef check_aliens_bottom(ai_settings, screen, stats, sb, ship, aliens, bullets):\\n\\t# Check if any aliens have reached the bottom of the screen.\\n\\tscreen_rect = screen.get_rect()\\n\\tfor alien in aliens.sprites():\\n\\t\\tif alien.rect.bottom >= screen_rect.bottom:\\n\\t\\t\\t# Treat this the same as if the ship got hit.\\n\\t\\t\\tship_hit(ai_settings, screen, stats, sb, ship, aliens, bullets)\\n\\t\\t\\tprint(\\\"Aliens bottom!!!\\\")\\n\\t\\t\\tbreak\\n\\ndef update_aliens(ai_settings, screen, stats, sb, ship, aliens, bullets):\\n\\t# Check if the fleet is at an edge,\\n\\t# and then update the postions of all aliens in the fleet.\\n\\tcheck_fleet_edges(ai_settings, aliens)\\n\\t# Update the postions of all aliens in the fleet.\\n\\taliens.update()\\n\\t# Look for alien-ship collisions.\\n\\t# spritecollideany() looks for any member of the group that’s collided with \\n\\t# the sprite and stops looping through the group as soon as it finds one member \\n\\t# has collided with the sprite.\\n\\tif pygame.sprite.spritecollideany(ship, aliens):\\n\\t\\tship_hit(ai_settings, screen, stats, sb, ship, aliens, bullets)\\n\\t\\tprint(\\\"Ship hit!!!\\\")\\n\\t# Look for aliens hitting the bottom of the screen.\\n\\tcheck_aliens_bottom(ai_settings, screen, stats, sb, ship, aliens, bullets)\\n\\n# Start a new game when the player clicks Play.\\ndef check_play_button(ai_settings, screen, stats, sb, play_button, ship, aliens, bullets, mouse_x, mouse_y):\\t\\n\\tbutton_clicked = play_button.rect.collidepoint(mouse_x, mouse_y)\\n\\tif button_clicked and not stats.game_active:\\n\\t\\t# Reset the game settings.\\n\\t\\tai_settings.initialize_dynamic_settings()\\n\\n\\t\\t# Hide the mouse cursor after button click\\n\\t\\tpygame.mouse.set_visible(False)\\n\\n\\t\\t# Reset the game statistics.\\n\\t\\tstats.reset_stats() # gives player 3 new ships\\n\\t\\tstats.game_active = True\\n\\n\\t\\t# Reset the scoreboard images.\\n\\t\\tsb.prep_score()\\n\\t\\tsb.prep_high_score()\\n\\t\\tsb.prep_level()\\n\\t\\t# To show the player how many ships they have to start with\\n\\t\\tsb.prep_ships()\\n\\n\\t\\t# Empty the list of aliens and bullets.\\n\\t\\taliens.empty()\\n\\t\\tbullets.empty()\\n\\n\\t\\t# Create a new fleet and center the ship.\\n\\t\\tcreate_fleet(ai_settings, screen, ship, aliens)\\n\\t\\tship.center_ship()\\n\\ndef check_high_score(stats, sb):\\n\\t# Check to see if there's a new high score.\\n\\tif stats.score > stats.high_score:\\n\\t\\tstats.high_score = stats.score\\n\\t\\tsb.prep_high_score()\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":3,"string":"3"}}},{"rowIdx":2979383,"cells":{"repo_name":{"kind":"string","value":"origaddicted2u88y/overly-simple-encryptdecrypt"},"repo_url":{"kind":"string","value":"https://github.com/origaddicted2u88y/overly-simple-encryptdecrypt"},"snapshot_id":{"kind":"string","value":"f84939b936aa8aedd22cff484db9d3d65ad0a7b7"},"revision_id":{"kind":"string","value":"159a5fdd3bf36127b077db327a27a13353f56af7"},"directory_id":{"kind":"string","value":"b57488145cf46fe083b2f10d088d68c9ef0b4ba2"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-08-08T21:44:25.742176","string":"2021-08-08T21:44:25.742176"},"revision_date":{"kind":"timestamp","value":"2017-11-11T10:46:53","string":"2017-11-11T10:46:53"},"committer_date":{"kind":"timestamp","value":"2017-11-11T10:46:53","string":"2017-11-11T10:46:53"},"github_id":{"kind":"number","value":110337791,"string":"110,337,791"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.57334965467453,"alphanum_fraction":0.5831295847892761,"avg_line_length":20.394737243652344,"blob_id":"6d9681b26a0f2dce16baea973e7696ad7a62134c","content_id":"bd6779155159296fc1d03f9f1c0af4ca45ce3efa","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":818,"license_type":"no_license","max_line_length":75,"num_lines":38,"path":"/simpleencryptdecrypt.py","repo_name":"origaddicted2u88y/overly-simple-encryptdecrypt","src_encoding":"UTF-8","text":"#!/usr/bin/python\n\n#define encoding function\ndef encode(f):\n list2 = [\"b\", \"c\", \"d\", \"e\"]\n for n in f:\n if n > 0:\n f.append(list2)\n print (list2)\n return()\n \n#create empty list\nlist = []\n\n\n#enter number to be encoded one by one\nprint (\"enter number digit by digit, press enter after each digit\")\n#put the numbers into a list\nfor f in xrange(4):\n digit = raw_input()\n list.append(digit)\n print (list[0], \"ignore\")\n \n#start encode\nprint (\"confirm encrypt? if so type 1\")\na = raw_input()\nif a == 1:\n print (\"ok\")\nencode(list) \n\n\nprint (\"decrypt? yes or no\")\nb = raw_input()\nif b == \"yes\":\n print (list, \"original data, encrypted data\", \"end of demo\")\n\nif b == \"no\":\n print (\"ok, but your data wont be saved, please await future versions!\")\n\n "},{"alpha_fraction":0.8666666746139526,"alphanum_fraction":0.8666666746139526,"avg_line_length":30,"blob_id":"43cbbde84b229b10b2d1151860b0b7c30986de58","content_id":"740b0a808003596761e7bd8a42e77312c0c397d1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":30,"license_type":"no_license","max_line_length":30,"num_lines":1,"path":"/README.md","repo_name":"origaddicted2u88y/overly-simple-encryptdecrypt","src_encoding":"UTF-8","text":"# overly-simple-encryptdecrypt"}],"string":"[\n {\n \"alpha_fraction\": 0.57334965467453,\n \"alphanum_fraction\": 0.5831295847892761,\n \"avg_line_length\": 20.394737243652344,\n \"blob_id\": \"6d9681b26a0f2dce16baea973e7696ad7a62134c\",\n \"content_id\": \"bd6779155159296fc1d03f9f1c0af4ca45ce3efa\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 818,\n \"license_type\": \"no_license\",\n \"max_line_length\": 75,\n \"num_lines\": 38,\n \"path\": \"/simpleencryptdecrypt.py\",\n \"repo_name\": \"origaddicted2u88y/overly-simple-encryptdecrypt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/python\\n\\n#define encoding function\\ndef encode(f):\\n list2 = [\\\"b\\\", \\\"c\\\", \\\"d\\\", \\\"e\\\"]\\n for n in f:\\n if n > 0:\\n f.append(list2)\\n print (list2)\\n return()\\n \\n#create empty list\\nlist = []\\n\\n\\n#enter number to be encoded one by one\\nprint (\\\"enter number digit by digit, press enter after each digit\\\")\\n#put the numbers into a list\\nfor f in xrange(4):\\n digit = raw_input()\\n list.append(digit)\\n print (list[0], \\\"ignore\\\")\\n \\n#start encode\\nprint (\\\"confirm encrypt? if so type 1\\\")\\na = raw_input()\\nif a == 1:\\n print (\\\"ok\\\")\\nencode(list) \\n\\n\\nprint (\\\"decrypt? yes or no\\\")\\nb = raw_input()\\nif b == \\\"yes\\\":\\n print (list, \\\"original data, encrypted data\\\", \\\"end of demo\\\")\\n\\nif b == \\\"no\\\":\\n print (\\\"ok, but your data wont be saved, please await future versions!\\\")\\n\\n \"\n },\n {\n \"alpha_fraction\": 0.8666666746139526,\n \"alphanum_fraction\": 0.8666666746139526,\n \"avg_line_length\": 30,\n \"blob_id\": \"43cbbde84b229b10b2d1151860b0b7c30986de58\",\n \"content_id\": \"740b0a808003596761e7bd8a42e77312c0c397d1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 30,\n \"license_type\": \"no_license\",\n \"max_line_length\": 30,\n \"num_lines\": 1,\n \"path\": \"/README.md\",\n \"repo_name\": \"origaddicted2u88y/overly-simple-encryptdecrypt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# overly-simple-encryptdecrypt\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2979384,"cells":{"repo_name":{"kind":"string","value":"ltheodon/RandomGenerator"},"repo_url":{"kind":"string","value":"https://github.com/ltheodon/RandomGenerator"},"snapshot_id":{"kind":"string","value":"c2d96054c175c2342e8c3ade676fcefef45e19f5"},"revision_id":{"kind":"string","value":"095535f14202cf81c09561727061235abe7857a0"},"directory_id":{"kind":"string","value":"96efb1bd684a9ca0c8fa89ed7d0324ba0792808d"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-04-08T09:49:53.273877","string":"2021-04-08T09:49:53.273877"},"revision_date":{"kind":"timestamp","value":"2020-03-20T14:39:01","string":"2020-03-20T14:39:01"},"committer_date":{"kind":"timestamp","value":"2020-03-20T14:39:01","string":"2020-03-20T14:39:01"},"github_id":{"kind":"number","value":248764151,"string":"248,764,151"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.607569694519043,"alphanum_fraction":0.6633465886116028,"avg_line_length":15.193548202514648,"blob_id":"5d43402ea916bfc6e7394c47b94ad49b65f33f41","content_id":"1b2685ffb226447cd001c2acdea6d5da735749da","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1005,"license_type":"no_license","max_line_length":40,"num_lines":62,"path":"/simpleRandom.py","repo_name":"ltheodon/RandomGenerator","src_encoding":"UTF-8","text":"import math\nimport timeit\nstart = timeit.default_timer()\n\n\ndef simpleRandom(seed=1,c=12689):\n\t# Definition des constantes\n\t# a est un grand nombre\n\t# b est un grand nombre premier\n\t# c est également un nombre premier\n\ta = 10000\n\tb = 23459\n\treturn (a*seed+b)%c\n\n\n\nL = [simpleRandom(i) for i in range(10)]\nprint(L)\n\n\ndef simpleRandomSequence(seed=1,m=10):\n\tif m < 1:\n\t\treturn []\n\tL = [simpleRandom(seed)]\n\tfor i in range(1,m):\n\t\tL.append(simpleRandom(L[-1]))\n\treturn L\n\n\nL = simpleRandomSequence()\nprint(L)\nL = simpleRandomSequence(2)\nprint(L)\n\ndef timeRandomSequence(m=101):\n\ttimer = timeit.default_timer()\n\tt = math.floor(10000000 * timer)\n\tprint(t)\n\treturn simpleRandomSequence(t,m)\n\nL = timeRandomSequence()\nprint(L)\n\n\n\ndef basicRandom(n,r=3.57):\n\tx = 0.5\n\tfor _ in range(n):\n\t\tx = r * x * (1-x)\n\treturn x\n\ndef basicRandomSequence(m=10,r=3.57):\n\tif m < 1:\n\t\treturn []\n\tL = [basicRandom(100)]\n\tfor i in range(m):\n\t\tx = r * L[-1] * (1-L[-1])\n\t\tL.append(x)\n\treturn L\n\nL = basicRandomSequence(20)\nprint(L)\n"}],"string":"[\n {\n \"alpha_fraction\": 0.607569694519043,\n \"alphanum_fraction\": 0.6633465886116028,\n \"avg_line_length\": 15.193548202514648,\n \"blob_id\": \"5d43402ea916bfc6e7394c47b94ad49b65f33f41\",\n \"content_id\": \"1b2685ffb226447cd001c2acdea6d5da735749da\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1005,\n \"license_type\": \"no_license\",\n \"max_line_length\": 40,\n \"num_lines\": 62,\n \"path\": \"/simpleRandom.py\",\n \"repo_name\": \"ltheodon/RandomGenerator\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import math\\nimport timeit\\nstart = timeit.default_timer()\\n\\n\\ndef simpleRandom(seed=1,c=12689):\\n\\t# Definition des constantes\\n\\t# a est un grand nombre\\n\\t# b est un grand nombre premier\\n\\t# c est également un nombre premier\\n\\ta = 10000\\n\\tb = 23459\\n\\treturn (a*seed+b)%c\\n\\n\\n\\nL = [simpleRandom(i) for i in range(10)]\\nprint(L)\\n\\n\\ndef simpleRandomSequence(seed=1,m=10):\\n\\tif m < 1:\\n\\t\\treturn []\\n\\tL = [simpleRandom(seed)]\\n\\tfor i in range(1,m):\\n\\t\\tL.append(simpleRandom(L[-1]))\\n\\treturn L\\n\\n\\nL = simpleRandomSequence()\\nprint(L)\\nL = simpleRandomSequence(2)\\nprint(L)\\n\\ndef timeRandomSequence(m=101):\\n\\ttimer = timeit.default_timer()\\n\\tt = math.floor(10000000 * timer)\\n\\tprint(t)\\n\\treturn simpleRandomSequence(t,m)\\n\\nL = timeRandomSequence()\\nprint(L)\\n\\n\\n\\ndef basicRandom(n,r=3.57):\\n\\tx = 0.5\\n\\tfor _ in range(n):\\n\\t\\tx = r * x * (1-x)\\n\\treturn x\\n\\ndef basicRandomSequence(m=10,r=3.57):\\n\\tif m < 1:\\n\\t\\treturn []\\n\\tL = [basicRandom(100)]\\n\\tfor i in range(m):\\n\\t\\tx = r * L[-1] * (1-L[-1])\\n\\t\\tL.append(x)\\n\\treturn L\\n\\nL = basicRandomSequence(20)\\nprint(L)\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":2979385,"cells":{"repo_name":{"kind":"string","value":"syppik/GameOfCode"},"repo_url":{"kind":"string","value":"https://github.com/syppik/GameOfCode"},"snapshot_id":{"kind":"string","value":"75dd8d8eae1c4396b4db9a2bd1c5afd592d610cb"},"revision_id":{"kind":"string","value":"ad48c6598351aa62ac94e1cec5d3dd5af04db7e8"},"directory_id":{"kind":"string","value":"c73b435f81e9034d87f45bd3c7d5db770f12902c"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-04-27T18:29:21.760521","string":"2020-04-27T18:29:21.760521"},"revision_date":{"kind":"timestamp","value":"2019-03-23T15:14:31","string":"2019-03-23T15:14:31"},"committer_date":{"kind":"timestamp","value":"2019-03-23T15:14:31","string":"2019-03-23T15:14:31"},"github_id":{"kind":"number","value":174572657,"string":"174,572,657"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":2,"string":"2"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"timestamp","value":"2019-03-08T16:37:58","string":"2019-03-08T16:37:58"},"gha_updated_at":{"kind":"timestamp","value":"2019-03-09T15:26:29","string":"2019-03-09T15:26:29"},"gha_pushed_at":{"kind":"timestamp","value":"2019-03-09T15:31:48","string":"2019-03-09T15:31:48"},"gha_language":{"kind":"string","value":"Python"},"files":{"kind":"list like","value":[{"alpha_fraction":0.7906976938247681,"alphanum_fraction":0.8023256063461304,"avg_line_length":71.76923370361328,"blob_id":"edfd049860a5944cc40a26e193211f629f6797ae","content_id":"7913577f2193d1fddd909c8928f2e3293c415cb7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":947,"license_type":"no_license","max_line_length":388,"num_lines":13,"path":"/README.md","repo_name":"syppik/GameOfCode","src_encoding":"UTF-8","text":"# Game of Code 2019\n\nThis repository contains Python and R scripts for applying NLP to the XML Dublin Core files that can be downloaded from https://data.bnl.lu/data/historical-newspapers/\n\nThe scripts add new features to the data, perform Named Entitity Recognition and also recommend new articles based on cosine similarity of article content. \n\nEverything is then deployed into an ELK suite hosted on the cloud. You can find the app here: \nhttps://www.hoscheck.lu/bnl/\n\n\nThe app is an advanced search engine that allows historians to look for keywords and perform basic data analysis on Kibana (visualisations). There are 2 databases; the full one with around half a million articles on which we did not perform cosine similarity for article recommendation, and then a smaller database (around 50.000) on which 3 articles at most get recommended to the user. \n\nWe had to limit the computation of cosine similarity because of time constraints.\n"},{"alpha_fraction":0.6213808655738831,"alphanum_fraction":0.6403602361679077,"avg_line_length":22.849884033203125,"blob_id":"7e5b2ee5425cd689b49c436a941e0fd9a364f489","content_id":"551811435051e15d8f2c9371632dac70b78dbeed","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":10329,"license_type":"no_license","max_line_length":555,"num_lines":433,"path":"/NLP_analysis_for_BNL_gameofcode2019.py","repo_name":"syppik/GameOfCode","src_encoding":"UTF-8","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# # NLP for historical newspapers\nThe goal of this Notebook is to first create a parser that extracts all of the data contained in the Dublin Core XML files. Then we will perform Named Entity Recognition to extract relevant keywords about people, location and events from the text. To finish, we then create a content-based recommendation system for a sample of 47.850 articles. We do so by using the tf-idf method to map the content to a vector space and then compute the cosine similarity between articles. The top 3 articles, with at least 70% of likeness are recommended to the reader.\n# In[4]:\n\n\n# Load needed libraries\nimport pandas as pd\nimport os\nimport xml.etree.ElementTree\nfrom xml.dom import minidom\nimport glob\nfrom bs4 import BeautifulSoup\n\n\n# # I. Load all xml files\n\n# In[37]:\n\n\nfolder_path = 'data/export01-newspapers1841-1878/export01-newspapers1841-1878/' # PATH OF ALL DATA \n\n# This function reads in all the XML files in a directory (and sub-directories recursively) and returns a list of all the XML files\ndef search_files(directory='.', extension='',show = False):\n extension = extension.lower()\n list_files = []\n for dirpath, dirnames, files in os.walk(directory):\n for name in files:\n if extension and name.lower().endswith(extension):\n list_files.append(os.path.join(dirpath, name))\n if show:\n print(os.path.join(dirpath, name))\n elif not extension:\n list_files.append(os.path.join(dirpath, name))\n if show:\n print(os.path.join(dirpath, name))\n return list_files\n \nlist_files = search_files(folder_path)\nprint(\"Nombre de fichier xml:\",len(list_files))\n\n\n# In[38]:\n\n\nfilename = list_files[0]\n\n# This function parses the xml files and converts them json\ndef xml_file_to_json(filename):\n page = open(filename)\n soup = BeautifulSoup(page.read())\n # dc:relation: ID du journal\n # identifier: ID de l'article\n # filename: nom du fichier xml\n # dc:date: date publication\n # dc:description: contenu\n # dc:type: type (pub, article ...)\n # dc:language: langue detectée\n # dcterms:isPartOf: nom du journal (company)\n # dcterms:hasVersion: lien url vers l'article\n try:\n relation = soup.find('dc:relation').text\n except:\n relation = \"\"\n \n try:\n identifier = soup.find('identifier').text.split('-')[-1]\n except:\n identifier = \"\"\n \n try:\n file_name = filename\n except:\n file_name = \"\"\n \n try:\n dc_date = soup.find('dc:date').text\n except:\n dc_date = \"\"\n \n try:\n titre = soup.find('dc:title').text\n except:\n titre = \"\"\n \n try:\n description = soup.find('dc:description').text\n except:\n description = \"\"\n \n try:\n dc_type = soup.find('dc:type').text\n except:\n dc_type = \"\"\n \n try:\n language = soup.find('dc:language').text\n except:\n language = \"\"\n \n try:\n dctermispartof = soup.find('dcterms:ispartof').text\n except:\n dctermispartof = \"\"\n \n try:\n dctermhasversion = soup.find('dcterms:hasversion').text\n except:\n dctermhasversion = \"\"\n page.close()\n \n return {\"dc_relation\":relation,\"identifier\":identifier,\"filename\":file_name, \"dc_date\":dc_date,\"dc_title\":titre,\"dc_description\":description,\"dc_type\":dc_type, \"dc_language\":language,\"dcterms_ispartof\":dctermispartof,\"dcterms_hasversion\":dctermhasversion}\n\nxml_file_to_json(filename)\n\n\n# In[39]:\n\n\n# This function returns one dictionary made of all the XML files. The function from before is used in the body of this function\ndef all_file_to_dict(list_files):\n result = []\n i=0\n for file in list_files:\n i+=1\n result.append(xml_file_to_json(file))\n if i % 1000==0:\n print(i,\"/\",len(list_files))\n if i>1000:\n break\n return result\n\nresult = all_file_to_dict(list_files)\n\n\n# In[40]:\n\n\nresult\n\n\n# In[20]:\n\n\n# We now export the object from above to a csv file\nfile = open(\"export_json.csv\",\"w\")\ni=0\njson_data = []\nfor line in result:\n json_data.append(line)\nimport json\njson_to_export = json.dumps(json_data)\nfile.write(json_to_export)\nfile.close()\n\n\n# In[9]:\n\n\n# We now reload the file as a Data Frame and create a new column called ID made up of the relation and identifier nodes of the XML files\ndf = pd.DataFrame(result)\ndf['ID']=df['dc_relation']+\"_\"+df['identifier']\n\n\n# In[11]:\n\n\ndf\n\n\n# In[12]:\n\n\n# We export the data frame to a pipe separated values files. We already import this to ELK. This is the MVP.\ndf.to_csv(\"_export01-newspapers1841-1878.csv\", sep='|', encoding='utf-8',index = False)\n\n\n# # II. Named entity recognition\nHere, we perform Named Entity Recognition which allows to extract much more information from the text of the articles. This will make searching much faster, but also allows reader to glance at the entities that were extracted from the articles and thus have a good clue of the content of the artiles without needing to read it.\n# In[2]:\n\n\nimport pandas as pd\nimport os\nimport xml.etree.ElementTree\nfrom xml.dom import minidom\nimport glob\nfrom bs4 import BeautifulSoup\n\n\n# In[ ]:\n\n\n# Read in the data from before\ndf = pd.read_csv(\"_export01-newspapers1841-1878.csv\", sep='|', encoding='utf-8')\ndf = df[df['dc_type']=='ARTICLE'][df['dcterms_ispartof'] in [\"L'indépendence Luxembourgeoise\",\"L'UNION.\"]]\n\n\n# In[ ]:\n\n\nimport spacy\nfrom spacy import displacy\nfrom collections import Counter\n\n\n# In[ ]:\n\n\n# To load the model for french we first need to dowload it using \"python -m spacy download fr_core_news_md\"\nnlp = spacy.load('fr_core_news_md')\n\n\n# In[ ]:\n\n\n# Perform NER\ndf['Entities'] = df['dc_description'].apply(lambda x: [(X.text, X.label_) for X in nlp(x).ents])\n\n\n# In[4]:\n\n\nentities = df['Entities'].values.tolist()\n\n\n# In[5]:\n\n\nentities\n\n\n# In[6]:\n\n\nfrom ast import literal_eval\n\ndef tuple_to_tags(input_tuple):\n l = literal_eval(input_tuple)\n #print(l)\n LOC = []\n PER = []\n MISC = []\n ORG = []\n\n\n for t in l:\n if t[1]=='LOC':\n LOC.append(t[0])\n if t[1]=='PER':\n PER.append(t[0])\n if t[1]=='MISC':\n MISC.append(t[0])\n if t[1]=='ORG':\n ORG.append(t[0]) \n return [\"#[\"+'] #['.join(LOC)+\"]\",\"#[\"+'] #['.join(PER)+\"]\",\"#[\"+'] #['.join(MISC)+\"]\",\"#[\"+'] #['.join(ORG)+\"]\",' '.join(LOC+PER+MISC+ORG)]\n\n\ntuple_to_tags(entities[0])\n\n\n# In[7]:\n\n\nlist_tags = []\nfor i in range(len(entities)):\n if i%100 == 0:\n print(i,\"/\",len(entities))\n list_tags.append(tuple_to_tags(entities[i]))\n\n\n# In[8]:\n\n\nlist_LOC = list(zip(*list_tags))[0]\nlist_LOC\n\nlist_PER = list(zip(*list_tags))[1]\nlist_PER\n\nlist_MISC = list(zip(*list_tags))[2]\nlist_MISC\n\nlist_ORG = list(zip(*list_tags))[3]\nlist_ORG\n\nlist_all = list(zip(*list_tags))[4]\n\n\n# In[9]:\n\n\ndf['Tags_localisation']=list_LOC\ndf['Tags_personne']=list_PER\ndf['Tags_autres']=list_MISC\ndf['Tags_organisation']=list_ORG\ndf['All_key_word']=list_all\n\n\n# In[10]:\n\n\ndf.loc[df['Tags_localisation'] == \"#[]\", 'Tags_localisation'] = \"\"\ndf.loc[df['Tags_personne'] == \"#[]\", 'Tags_personne'] = \"\"\ndf.loc[df['Tags_autres'] == \"#[]\", 'Tags_autres'] = \"\"\ndf.loc[df['Tags_organisation'] == \"#[]\", 'Tags_organisation'] = \"\"\n\n\n# In[11]:\n\n\ndf = df.drop(['clean_description'],axis = 1)\ndf = df.drop(['Entities'],axis = 1)\n\n\n# In[12]:\n\n\ndf\n\n\n# In[13]:\n\n\ndf.to_csv(\"TAGS_export01-newspapers1841-1878.csv\", sep='|', encoding='utf-8',index = False)\n\n\n# # III. Content-based Recommender system (on article content) #AI #ML #predict\nThe Content-based recommender system is based on the article content. The idea is to identify the article which talk about the sames topics. To do that we need to vectorize the unstructured article text content. After that we will be able to calculate distance between vectors themself, which also mean : a \"distance\" or \"similarity\" between the differents articles. At the end we will be able to determine the most reliable articles for each article\n# In[1]:\n\n\nimport pandas as pd\nimport os\nimport xml.etree.ElementTree\nfrom xml.dom import minidom\nimport glob\nfrom bs4 import BeautifulSoup\nimport sklearn\nfrom sklearn.feature_extraction.text import TfidfVectorizer\n\n\n# In[2]:\n\n\ndf = pd.read_csv(\"TAGS_V2_export01-newspapers1841-1878.csv\", sep='|', encoding='utf-8')\ndf['Reco 1 - ID']=\"\"\ndf['Reco 1 - score']=\"\"\ndf['Reco 1 - Link']=\"\"\n\ndf['Reco 2 - ID']=\"\"\ndf['Reco 2 - score']=\"\"\ndf['Reco 2 - Link']=\"\"\n\ndf['Reco 3 - ID']=\"\"\ndf['Reco 3 - score']=\"\"\ndf['Reco 3 - Link']=\"\"\ndf\n\n\n# In[21]:\n\n\nN = 0\nSTEP = 10000\narticle_desc = df['dc_description'].values.tolist()[N:N+STEP]\nliste_id = df['ID'].values.tolist()\nurl = df['dcterms_hasversion'].values.tolist()\n\n\n# In[22]:\n\n\n# Text vectorization\ntfidf = TfidfVectorizer().fit_transform(article_desc)\n\n\n# In[23]:\n\n\n# Matrix of similarity generation\n# no need to normalize, since Vectorizer will return normalized tf-idf\npairwise_similarity = tfidf * tfidf.T\nmatrix_similarity = (tfidf * tfidf.T).A\nmatrix_similarity\n\n\n# In[24]:\n\n\nimport json\nimport heapq\n\n# Select 3 most reliable article for all of them\nfor i in range(len(matrix_similarity)):\n liste_recommandations = []\n #print(liste_id[i])\n for recommandations in heapq.nlargest(4, range(len(matrix_similarity[i])), matrix_similarity[i].__getitem__)[1:]:\n if matrix_similarity[i][recommandations] > 0.7:\n liste_recommandations.append({'ID':liste_id[recommandations],'dcterms_hasversion':url[recommandations],'Recommandation_score':matrix_similarity[i][recommandations]})\n #print(liste_recommandations)\n k=1\n for e in liste_recommandations:\n df.loc[df['ID']==liste_id[i+N],'Reco '+str(k)+' - ID'] = e['ID']\n df.loc[df['ID']==liste_id[i+N],'Reco '+str(k)+' - score'] = str(round(e['Recommandation_score']*100,2))+\"%\"\n df.loc[df['ID']==liste_id[i+N],'Reco '+str(k)+' - Link'] = e['dcterms_hasversion']\n k+=1\n\n\n# In[25]:\n\n\ndf\n\n\n# In[ ]:\n\n\n# Here run the R script to extract iso3 code from dc_title column\n\n\n# In[29]:\n\n\ndf\n\n\n# In[31]:\n\n\ndf.to_csv(\"RECOMMANDATIONS_&_TAGS_export01-newspapers1841-1878.csv\", sep='|', encoding='utf-8',index = False)\n"},{"alpha_fraction":0.6016548275947571,"alphanum_fraction":0.631205677986145,"avg_line_length":28.172412872314453,"blob_id":"797c5afbd0858303ec73fe2eb5a9c437cdf5bb98","content_id":"aca1b4cf477b13c0da0dd5eb9cbffeaedfb3db3b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"R","length_bytes":846,"license_type":"no_license","max_line_length":73,"num_lines":29,"path":"/add_iso3_column.R","repo_name":"syppik/GameOfCode","src_encoding":"UTF-8","text":"library(\"dplyr\")\nlibrary(\"tidyr\")\nlibrary(\"purrr\")\nlibrary(\"tidytext\")\nlibrary(\"udpipe\")\nlibrary(\"rvest\")\nlibrary(\"textrank\")\nlibrary(\"stopwords\")\nlibrary(\"chunked\")\nlibrary(\"furrr\")\nlibrary(\"stringr\")\n\nTAGS_export01 <- data.table::fread(\n \"~/Documents/bnl_data/TAGS_export01-newspapers1841-1878.csv\")\n\nto_iso <- function(country){\n case_when(str_detect(country, \"Belgique\") ~ \"BEL\",\n str_detect(country, \"France\") ~ \"FRA\",\n str_detect(country, \"Allemagne\") ~ \"DEU\",\n str_detect(country, \"Prusse\") ~ \"DEU\",\n str_detect(country, \"Angleterre\") ~ \"GBR\",\n TRUE ~ NA_character_)\n}\n\nplan(multiprocess, workers = 4)\nTAGS_export01_v2 <- TAGS_export01 %>% \n mutate(iso3 = future_map_chr(dc_title, to_iso))\n\nreadr::write_delim(TAGS_export01_v2, \"TAGS_export01_v2.csv\", delim = \"|\")\n"}],"string":"[\n {\n \"alpha_fraction\": 0.7906976938247681,\n \"alphanum_fraction\": 0.8023256063461304,\n \"avg_line_length\": 71.76923370361328,\n \"blob_id\": \"edfd049860a5944cc40a26e193211f629f6797ae\",\n \"content_id\": \"7913577f2193d1fddd909c8928f2e3293c415cb7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 947,\n \"license_type\": \"no_license\",\n \"max_line_length\": 388,\n \"num_lines\": 13,\n \"path\": \"/README.md\",\n \"repo_name\": \"syppik/GameOfCode\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Game of Code 2019\\n\\nThis repository contains Python and R scripts for applying NLP to the XML Dublin Core files that can be downloaded from https://data.bnl.lu/data/historical-newspapers/\\n\\nThe scripts add new features to the data, perform Named Entitity Recognition and also recommend new articles based on cosine similarity of article content. \\n\\nEverything is then deployed into an ELK suite hosted on the cloud. You can find the app here: \\nhttps://www.hoscheck.lu/bnl/\\n\\n\\nThe app is an advanced search engine that allows historians to look for keywords and perform basic data analysis on Kibana (visualisations). There are 2 databases; the full one with around half a million articles on which we did not perform cosine similarity for article recommendation, and then a smaller database (around 50.000) on which 3 articles at most get recommended to the user. \\n\\nWe had to limit the computation of cosine similarity because of time constraints.\\n\"\n },\n {\n \"alpha_fraction\": 0.6213808655738831,\n \"alphanum_fraction\": 0.6403602361679077,\n \"avg_line_length\": 22.849884033203125,\n \"blob_id\": \"7e5b2ee5425cd689b49c436a941e0fd9a364f489\",\n \"content_id\": \"551811435051e15d8f2c9371632dac70b78dbeed\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 10329,\n \"license_type\": \"no_license\",\n \"max_line_length\": 555,\n \"num_lines\": 433,\n \"path\": \"/NLP_analysis_for_BNL_gameofcode2019.py\",\n \"repo_name\": \"syppik/GameOfCode\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python\\n# coding: utf-8\\n\\n# # NLP for historical newspapers\\nThe goal of this Notebook is to first create a parser that extracts all of the data contained in the Dublin Core XML files. Then we will perform Named Entity Recognition to extract relevant keywords about people, location and events from the text. To finish, we then create a content-based recommendation system for a sample of 47.850 articles. We do so by using the tf-idf method to map the content to a vector space and then compute the cosine similarity between articles. The top 3 articles, with at least 70% of likeness are recommended to the reader.\\n# In[4]:\\n\\n\\n# Load needed libraries\\nimport pandas as pd\\nimport os\\nimport xml.etree.ElementTree\\nfrom xml.dom import minidom\\nimport glob\\nfrom bs4 import BeautifulSoup\\n\\n\\n# # I. Load all xml files\\n\\n# In[37]:\\n\\n\\nfolder_path = 'data/export01-newspapers1841-1878/export01-newspapers1841-1878/' # PATH OF ALL DATA \\n\\n# This function reads in all the XML files in a directory (and sub-directories recursively) and returns a list of all the XML files\\ndef search_files(directory='.', extension='',show = False):\\n extension = extension.lower()\\n list_files = []\\n for dirpath, dirnames, files in os.walk(directory):\\n for name in files:\\n if extension and name.lower().endswith(extension):\\n list_files.append(os.path.join(dirpath, name))\\n if show:\\n print(os.path.join(dirpath, name))\\n elif not extension:\\n list_files.append(os.path.join(dirpath, name))\\n if show:\\n print(os.path.join(dirpath, name))\\n return list_files\\n \\nlist_files = search_files(folder_path)\\nprint(\\\"Nombre de fichier xml:\\\",len(list_files))\\n\\n\\n# In[38]:\\n\\n\\nfilename = list_files[0]\\n\\n# This function parses the xml files and converts them json\\ndef xml_file_to_json(filename):\\n page = open(filename)\\n soup = BeautifulSoup(page.read())\\n # dc:relation: ID du journal\\n # identifier: ID de l'article\\n # filename: nom du fichier xml\\n # dc:date: date publication\\n # dc:description: contenu\\n # dc:type: type (pub, article ...)\\n # dc:language: langue detectée\\n # dcterms:isPartOf: nom du journal (company)\\n # dcterms:hasVersion: lien url vers l'article\\n try:\\n relation = soup.find('dc:relation').text\\n except:\\n relation = \\\"\\\"\\n \\n try:\\n identifier = soup.find('identifier').text.split('-')[-1]\\n except:\\n identifier = \\\"\\\"\\n \\n try:\\n file_name = filename\\n except:\\n file_name = \\\"\\\"\\n \\n try:\\n dc_date = soup.find('dc:date').text\\n except:\\n dc_date = \\\"\\\"\\n \\n try:\\n titre = soup.find('dc:title').text\\n except:\\n titre = \\\"\\\"\\n \\n try:\\n description = soup.find('dc:description').text\\n except:\\n description = \\\"\\\"\\n \\n try:\\n dc_type = soup.find('dc:type').text\\n except:\\n dc_type = \\\"\\\"\\n \\n try:\\n language = soup.find('dc:language').text\\n except:\\n language = \\\"\\\"\\n \\n try:\\n dctermispartof = soup.find('dcterms:ispartof').text\\n except:\\n dctermispartof = \\\"\\\"\\n \\n try:\\n dctermhasversion = soup.find('dcterms:hasversion').text\\n except:\\n dctermhasversion = \\\"\\\"\\n page.close()\\n \\n return {\\\"dc_relation\\\":relation,\\\"identifier\\\":identifier,\\\"filename\\\":file_name, \\\"dc_date\\\":dc_date,\\\"dc_title\\\":titre,\\\"dc_description\\\":description,\\\"dc_type\\\":dc_type, \\\"dc_language\\\":language,\\\"dcterms_ispartof\\\":dctermispartof,\\\"dcterms_hasversion\\\":dctermhasversion}\\n\\nxml_file_to_json(filename)\\n\\n\\n# In[39]:\\n\\n\\n# This function returns one dictionary made of all the XML files. The function from before is used in the body of this function\\ndef all_file_to_dict(list_files):\\n result = []\\n i=0\\n for file in list_files:\\n i+=1\\n result.append(xml_file_to_json(file))\\n if i % 1000==0:\\n print(i,\\\"/\\\",len(list_files))\\n if i>1000:\\n break\\n return result\\n\\nresult = all_file_to_dict(list_files)\\n\\n\\n# In[40]:\\n\\n\\nresult\\n\\n\\n# In[20]:\\n\\n\\n# We now export the object from above to a csv file\\nfile = open(\\\"export_json.csv\\\",\\\"w\\\")\\ni=0\\njson_data = []\\nfor line in result:\\n json_data.append(line)\\nimport json\\njson_to_export = json.dumps(json_data)\\nfile.write(json_to_export)\\nfile.close()\\n\\n\\n# In[9]:\\n\\n\\n# We now reload the file as a Data Frame and create a new column called ID made up of the relation and identifier nodes of the XML files\\ndf = pd.DataFrame(result)\\ndf['ID']=df['dc_relation']+\\\"_\\\"+df['identifier']\\n\\n\\n# In[11]:\\n\\n\\ndf\\n\\n\\n# In[12]:\\n\\n\\n# We export the data frame to a pipe separated values files. We already import this to ELK. This is the MVP.\\ndf.to_csv(\\\"_export01-newspapers1841-1878.csv\\\", sep='|', encoding='utf-8',index = False)\\n\\n\\n# # II. Named entity recognition\\nHere, we perform Named Entity Recognition which allows to extract much more information from the text of the articles. This will make searching much faster, but also allows reader to glance at the entities that were extracted from the articles and thus have a good clue of the content of the artiles without needing to read it.\\n# In[2]:\\n\\n\\nimport pandas as pd\\nimport os\\nimport xml.etree.ElementTree\\nfrom xml.dom import minidom\\nimport glob\\nfrom bs4 import BeautifulSoup\\n\\n\\n# In[ ]:\\n\\n\\n# Read in the data from before\\ndf = pd.read_csv(\\\"_export01-newspapers1841-1878.csv\\\", sep='|', encoding='utf-8')\\ndf = df[df['dc_type']=='ARTICLE'][df['dcterms_ispartof'] in [\\\"L'indépendence Luxembourgeoise\\\",\\\"L'UNION.\\\"]]\\n\\n\\n# In[ ]:\\n\\n\\nimport spacy\\nfrom spacy import displacy\\nfrom collections import Counter\\n\\n\\n# In[ ]:\\n\\n\\n# To load the model for french we first need to dowload it using \\\"python -m spacy download fr_core_news_md\\\"\\nnlp = spacy.load('fr_core_news_md')\\n\\n\\n# In[ ]:\\n\\n\\n# Perform NER\\ndf['Entities'] = df['dc_description'].apply(lambda x: [(X.text, X.label_) for X in nlp(x).ents])\\n\\n\\n# In[4]:\\n\\n\\nentities = df['Entities'].values.tolist()\\n\\n\\n# In[5]:\\n\\n\\nentities\\n\\n\\n# In[6]:\\n\\n\\nfrom ast import literal_eval\\n\\ndef tuple_to_tags(input_tuple):\\n l = literal_eval(input_tuple)\\n #print(l)\\n LOC = []\\n PER = []\\n MISC = []\\n ORG = []\\n\\n\\n for t in l:\\n if t[1]=='LOC':\\n LOC.append(t[0])\\n if t[1]=='PER':\\n PER.append(t[0])\\n if t[1]=='MISC':\\n MISC.append(t[0])\\n if t[1]=='ORG':\\n ORG.append(t[0]) \\n return [\\\"#[\\\"+'] #['.join(LOC)+\\\"]\\\",\\\"#[\\\"+'] #['.join(PER)+\\\"]\\\",\\\"#[\\\"+'] #['.join(MISC)+\\\"]\\\",\\\"#[\\\"+'] #['.join(ORG)+\\\"]\\\",' '.join(LOC+PER+MISC+ORG)]\\n\\n\\ntuple_to_tags(entities[0])\\n\\n\\n# In[7]:\\n\\n\\nlist_tags = []\\nfor i in range(len(entities)):\\n if i%100 == 0:\\n print(i,\\\"/\\\",len(entities))\\n list_tags.append(tuple_to_tags(entities[i]))\\n\\n\\n# In[8]:\\n\\n\\nlist_LOC = list(zip(*list_tags))[0]\\nlist_LOC\\n\\nlist_PER = list(zip(*list_tags))[1]\\nlist_PER\\n\\nlist_MISC = list(zip(*list_tags))[2]\\nlist_MISC\\n\\nlist_ORG = list(zip(*list_tags))[3]\\nlist_ORG\\n\\nlist_all = list(zip(*list_tags))[4]\\n\\n\\n# In[9]:\\n\\n\\ndf['Tags_localisation']=list_LOC\\ndf['Tags_personne']=list_PER\\ndf['Tags_autres']=list_MISC\\ndf['Tags_organisation']=list_ORG\\ndf['All_key_word']=list_all\\n\\n\\n# In[10]:\\n\\n\\ndf.loc[df['Tags_localisation'] == \\\"#[]\\\", 'Tags_localisation'] = \\\"\\\"\\ndf.loc[df['Tags_personne'] == \\\"#[]\\\", 'Tags_personne'] = \\\"\\\"\\ndf.loc[df['Tags_autres'] == \\\"#[]\\\", 'Tags_autres'] = \\\"\\\"\\ndf.loc[df['Tags_organisation'] == \\\"#[]\\\", 'Tags_organisation'] = \\\"\\\"\\n\\n\\n# In[11]:\\n\\n\\ndf = df.drop(['clean_description'],axis = 1)\\ndf = df.drop(['Entities'],axis = 1)\\n\\n\\n# In[12]:\\n\\n\\ndf\\n\\n\\n# In[13]:\\n\\n\\ndf.to_csv(\\\"TAGS_export01-newspapers1841-1878.csv\\\", sep='|', encoding='utf-8',index = False)\\n\\n\\n# # III. Content-based Recommender system (on article content) #AI #ML #predict\\nThe Content-based recommender system is based on the article content. The idea is to identify the article which talk about the sames topics. To do that we need to vectorize the unstructured article text content. After that we will be able to calculate distance between vectors themself, which also mean : a \\\"distance\\\" or \\\"similarity\\\" between the differents articles. At the end we will be able to determine the most reliable articles for each article\\n# In[1]:\\n\\n\\nimport pandas as pd\\nimport os\\nimport xml.etree.ElementTree\\nfrom xml.dom import minidom\\nimport glob\\nfrom bs4 import BeautifulSoup\\nimport sklearn\\nfrom sklearn.feature_extraction.text import TfidfVectorizer\\n\\n\\n# In[2]:\\n\\n\\ndf = pd.read_csv(\\\"TAGS_V2_export01-newspapers1841-1878.csv\\\", sep='|', encoding='utf-8')\\ndf['Reco 1 - ID']=\\\"\\\"\\ndf['Reco 1 - score']=\\\"\\\"\\ndf['Reco 1 - Link']=\\\"\\\"\\n\\ndf['Reco 2 - ID']=\\\"\\\"\\ndf['Reco 2 - score']=\\\"\\\"\\ndf['Reco 2 - Link']=\\\"\\\"\\n\\ndf['Reco 3 - ID']=\\\"\\\"\\ndf['Reco 3 - score']=\\\"\\\"\\ndf['Reco 3 - Link']=\\\"\\\"\\ndf\\n\\n\\n# In[21]:\\n\\n\\nN = 0\\nSTEP = 10000\\narticle_desc = df['dc_description'].values.tolist()[N:N+STEP]\\nliste_id = df['ID'].values.tolist()\\nurl = df['dcterms_hasversion'].values.tolist()\\n\\n\\n# In[22]:\\n\\n\\n# Text vectorization\\ntfidf = TfidfVectorizer().fit_transform(article_desc)\\n\\n\\n# In[23]:\\n\\n\\n# Matrix of similarity generation\\n# no need to normalize, since Vectorizer will return normalized tf-idf\\npairwise_similarity = tfidf * tfidf.T\\nmatrix_similarity = (tfidf * tfidf.T).A\\nmatrix_similarity\\n\\n\\n# In[24]:\\n\\n\\nimport json\\nimport heapq\\n\\n# Select 3 most reliable article for all of them\\nfor i in range(len(matrix_similarity)):\\n liste_recommandations = []\\n #print(liste_id[i])\\n for recommandations in heapq.nlargest(4, range(len(matrix_similarity[i])), matrix_similarity[i].__getitem__)[1:]:\\n if matrix_similarity[i][recommandations] > 0.7:\\n liste_recommandations.append({'ID':liste_id[recommandations],'dcterms_hasversion':url[recommandations],'Recommandation_score':matrix_similarity[i][recommandations]})\\n #print(liste_recommandations)\\n k=1\\n for e in liste_recommandations:\\n df.loc[df['ID']==liste_id[i+N],'Reco '+str(k)+' - ID'] = e['ID']\\n df.loc[df['ID']==liste_id[i+N],'Reco '+str(k)+' - score'] = str(round(e['Recommandation_score']*100,2))+\\\"%\\\"\\n df.loc[df['ID']==liste_id[i+N],'Reco '+str(k)+' - Link'] = e['dcterms_hasversion']\\n k+=1\\n\\n\\n# In[25]:\\n\\n\\ndf\\n\\n\\n# In[ ]:\\n\\n\\n# Here run the R script to extract iso3 code from dc_title column\\n\\n\\n# In[29]:\\n\\n\\ndf\\n\\n\\n# In[31]:\\n\\n\\ndf.to_csv(\\\"RECOMMANDATIONS_&_TAGS_export01-newspapers1841-1878.csv\\\", sep='|', encoding='utf-8',index = False)\\n\"\n },\n {\n \"alpha_fraction\": 0.6016548275947571,\n \"alphanum_fraction\": 0.631205677986145,\n \"avg_line_length\": 28.172412872314453,\n \"blob_id\": \"797c5afbd0858303ec73fe2eb5a9c437cdf5bb98\",\n \"content_id\": \"aca1b4cf477b13c0da0dd5eb9cbffeaedfb3db3b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"R\",\n \"length_bytes\": 846,\n \"license_type\": \"no_license\",\n \"max_line_length\": 73,\n \"num_lines\": 29,\n \"path\": \"/add_iso3_column.R\",\n \"repo_name\": \"syppik/GameOfCode\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"library(\\\"dplyr\\\")\\nlibrary(\\\"tidyr\\\")\\nlibrary(\\\"purrr\\\")\\nlibrary(\\\"tidytext\\\")\\nlibrary(\\\"udpipe\\\")\\nlibrary(\\\"rvest\\\")\\nlibrary(\\\"textrank\\\")\\nlibrary(\\\"stopwords\\\")\\nlibrary(\\\"chunked\\\")\\nlibrary(\\\"furrr\\\")\\nlibrary(\\\"stringr\\\")\\n\\nTAGS_export01 <- data.table::fread(\\n \\\"~/Documents/bnl_data/TAGS_export01-newspapers1841-1878.csv\\\")\\n\\nto_iso <- function(country){\\n case_when(str_detect(country, \\\"Belgique\\\") ~ \\\"BEL\\\",\\n str_detect(country, \\\"France\\\") ~ \\\"FRA\\\",\\n str_detect(country, \\\"Allemagne\\\") ~ \\\"DEU\\\",\\n str_detect(country, \\\"Prusse\\\") ~ \\\"DEU\\\",\\n str_detect(country, \\\"Angleterre\\\") ~ \\\"GBR\\\",\\n TRUE ~ NA_character_)\\n}\\n\\nplan(multiprocess, workers = 4)\\nTAGS_export01_v2 <- TAGS_export01 %>% \\n mutate(iso3 = future_map_chr(dc_title, to_iso))\\n\\nreadr::write_delim(TAGS_export01_v2, \\\"TAGS_export01_v2.csv\\\", delim = \\\"|\\\")\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":3,"string":"3"}}},{"rowIdx":2979386,"cells":{"repo_name":{"kind":"string","value":"IvanVazquezL/Competitive-Programming-Exercises"},"repo_url":{"kind":"string","value":"https://github.com/IvanVazquezL/Competitive-Programming-Exercises"},"snapshot_id":{"kind":"string","value":"3f20217689922068bae0f2c7b59e56213417d5aa"},"revision_id":{"kind":"string","value":"9ef9b995a2e58179d51acd98e0b07047f0aeb93a"},"directory_id":{"kind":"string","value":"9921767f58444c961018f3fe3e9c0d688453925b"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2023-01-04T02:12:46.892891","string":"2023-01-04T02:12:46.892891"},"revision_date":{"kind":"timestamp","value":"2020-10-26T01:07:57","string":"2020-10-26T01:07:57"},"committer_date":{"kind":"timestamp","value":"2020-10-26T01:07:57","string":"2020-10-26T01:07:57"},"github_id":{"kind":"number","value":285400759,"string":"285,400,759"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.47159841656684875,"alphanum_fraction":0.4768824279308319,"avg_line_length":16.204545974731445,"blob_id":"47bd611ee36a0ee1c408bd866fd22e3dd5fffcde","content_id":"88885b8875488945166ec7a931f49f6387f99975","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":757,"license_type":"no_license","max_line_length":51,"num_lines":44,"path":"/RepeatedString/string.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n\n# Complete the repeatedString function below.\ndef repeatedString(s, n):\n\n if set(s) == {\"a\"} :\n return n\n elif set(s) == {\"b\"} :\n return 0\n else:\n length = len(s)\n repeat = s.count('a')\n\n if length == n:\n return repeat\n else:\n times = n/length\n\n if (times % 2) == 0:\n return int(times * repeat)\n else:\n partial = int(times) * repeat\n\n cut = n - (int(times) * length)\n\n return partial + s[:cut].count('a')\n\n\n\nif __name__ == '__main__':\n s = input()\n\n n = int(input())\n\n result = repeatedString(s, n)\n\n print(result)\n"},{"alpha_fraction":0.6926829218864441,"alphanum_fraction":0.6975609660148621,"avg_line_length":16.08333396911621,"blob_id":"5da91029e30b3c3f5a71b0cc6aeebedbf7489a07","content_id":"bffaf52b98ae632fd3b036cc81c046aba0dd3f2a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":205,"license_type":"no_license","max_line_length":50,"num_lines":12,"path":"/AlphabeticalOrder/version1.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"## Version 1 read directly from the command prompt\ncontactList = []\n\nname = input();\nwhile name:\n contactList.append(name)\n name = input()\n\ncontactList.sort()\n\nfor name in contactList:\n print(name)\n"},{"alpha_fraction":0.491909384727478,"alphanum_fraction":0.491909384727478,"avg_line_length":21.071428298950195,"blob_id":"53d868f97b6636cea3056c6a4d1ab7ea9da3ffb4","content_id":"a395502cad7dfbb88d3733e240f31841dc020e12","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":618,"license_type":"no_license","max_line_length":113,"num_lines":28,"path":"/CaesarCypher/version1.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"alpha = [\"a\",\"b\",\"c\",\"d\",\"e\",\"f\",\"g\",\"h\",\"i\",\"j\",\"k\",\"l\",\"m\",\"n\",\"o\",\"p\",\"q\",\"r\",\"s\",\"t\",\"u\",\"v\",\"w\",\"x\",\"y\",\"z\"]\ntext = input()\nshift = input()\n\nfirstArray = alpha[:int(shift)]\nSecondArray = alpha[-(len(alpha)-int(shift)):]\n\nnewAlpha= SecondArray + firstArray\n\nprint(newAlpha)\n\nnewText = \"\"\n\nfor x in text:\n if x == \" \":\n newText += x\n elif x == \",\":\n newText += x\n elif x == \".\":\n newText += x\n elif x == \"!\":\n newText += x\n elif x.isupper():\n newText += newAlpha[alpha.index(x.lower())].upper()\n else:\n newText += newAlpha[alpha.index(x)]\n\nprint(newText)\n"},{"alpha_fraction":0.48778003454208374,"alphanum_fraction":0.5,"avg_line_length":15.931034088134766,"blob_id":"c928e34b611f77691e1ddc6d47f51a0f73f378da","content_id":"b84e3d1b346659cd9f182947becdbc1d71b8baee","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":982,"license_type":"no_license","max_line_length":50,"num_lines":58,"path":"/DictionarySherlock/anagram.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n# Complete the sherlockAndAnagrams function below.\ndef sherlockAndAnagrams(s):\n # dictAnagram = {}\n #\n # for element in s:\n # if element in dictAnagram:\n # dictAnagram[element] += 1\n # else:\n # dictAnagram[element] = 1\n #\n # if len(dictAnagram) == len(s):\n # return 0\n # print(dictAnagram)\n dict={}\n\n count=0\n\n for i in range(len(s)):\n\n for j in range(i+1,len(s)+1):\n\n list1= list(s[i:j].strip())\n\n list1.sort()\n\n transf=''.join(list1)\n\n\n if transf in dict:\n\n count+=dict[transf]\n\n dict[transf]=dict[transf]+1\n\n else:\n dict[transf]=1\n\n\n return count\n\nif __name__ == '__main__':\n\n q = int(input())\n\n for q_itr in range(q):\n s = input()\n\n result = sherlockAndAnagrams(s)\n\n print(result)\n"},{"alpha_fraction":0.6526032090187073,"alphanum_fraction":0.6570915579795837,"avg_line_length":36.13333511352539,"blob_id":"090ab967e519c09336b44e5511e609e99d6de861","content_id":"2bc193b87c7576142df46555a5bb2a8b9003079d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1114,"license_type":"no_license","max_line_length":79,"num_lines":30,"path":"/stringCompression.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"def stringCompression(stringRepeat):\n #initialize the counter for characters\n counter = 0\n #establish currentCharacter as the first character\n currentCharacter = stringRepeat[0]\n #initialize the new string\n compressed = \"\"\n #We will go through each character\n for i in stringRepeat:\n if currentCharacter is not i:\n #Add to the string the last character and their counter\n compressed += currentCharacter + str(counter)\n #The new character is now the currentCharacter\n currentCharacter = i\n #Reinitialize the counter\n counter = 0\n #Increment the counter in one for this first time\n counter += 1\n else:\n #Increment the counter\n counter += 1\n\n #Add to the string the last character and the last counter\n compressed += currentCharacter + str(counter)\n\n #If the new string is bigger than the original string, return the original\n return(stringRepeat if len(compressed) > len(stringRepeat) else compressed)\n\n\nprint(stringCompression(\"aabcccccaaa\"))\n"},{"alpha_fraction":0.5328031778335571,"alphanum_fraction":0.5427435636520386,"avg_line_length":14.71875,"blob_id":"2e171ab7bd2f209381eb045046697dfc8592b84f","content_id":"f4486710c4b6a92fb3ef5e95845c43e1e76d9c5e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":503,"license_type":"no_license","max_line_length":49,"num_lines":32,"path":"/sock.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n# Complete the sockMerchant function below.\ndef sockMerchant(n, ar):\n dic = {}\n pairs = 0\n\n for x in ar:\n if x in dic:\n dic[x] += 1\n else:\n dic[x] = 1\n\n for value in dic.values():\n pairs += int(value/2)\n\n return pairs\n\nif __name__ == '__main__':\n n = int(input())\n\n ar = list(map(int, input().rstrip().split()))\n\n result = sockMerchant(n, ar)\n\n print(result)\n"},{"alpha_fraction":0.5317646861076355,"alphanum_fraction":0.5529412031173706,"avg_line_length":17.478260040283203,"blob_id":"d49b7ccb5a89e0d40406a6d4ec1d07d620101a4f","content_id":"ab35d4e2ce23f8403ec3e4048ce167eac9ddf259","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":425,"license_type":"no_license","max_line_length":43,"num_lines":23,"path":"/BinarySearch/narce.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"def binaria(lista, busqueda):\n inicio =0\n fin = len(lista)-1\n\n while inicio<=fin:\n mitad= round((fin-inicio)/2)+inicio\n\n adivinar = lista[mitad]\n\n if adivinar == busqueda:\n return mitad\n elif adivinar > busqueda:\n fin = mitad - 1\n else:\n inicio = mitad + 1\n\n return None\n\nlista = list(range(1,11))\n\nbusqueda = 6\n\nprint(binaria(lista, busqueda))\n"},{"alpha_fraction":0.6936671733856201,"alphanum_fraction":0.6980854272842407,"avg_line_length":36.72222137451172,"blob_id":"22abb24c9c3bb6353d6d2c6dafa250d79f0d6aee","content_id":"7c3b654550569a2235f5763ebadf4743722f55e8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":679,"license_type":"no_license","max_line_length":109,"num_lines":18,"path":"/DictionaryUnique/unique.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"#Function receives a string as parameter\ndef unique(word):\n #All the characters of the string become keys of dictionary and their value is 0\n dictionary = dict.fromkeys(word,0)\n #If the length of the string is the same as the length of the dictionary\n #Then all characters are unique, if not the word has repeated characters\n return (\"All characters are unique\" if len(word) == len(dictionary) else \"The characters are not unique\")\n\n\n\nprint(unique(\"book\"))\n\n# def unique(word):\n# dictionary = dict.fromkeys(word,0)\n# if len(word) == len(dictionary):\n# return \"All characters are unique\"\n# else:\n# return \"The characters are not unique\"\n"},{"alpha_fraction":0.5285046696662903,"alphanum_fraction":0.5303738117218018,"avg_line_length":25.09756088256836,"blob_id":"36b8bac94ae0211b5765c892bb129cf96f37fce9","content_id":"6316198d7d6e6ff18786a6dadb98674a40498d48","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2140,"license_type":"no_license","max_line_length":80,"num_lines":82,"path":"/binaryTreeHeight.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"class Node:\n def __init__(self, info):\n self.info = info\n self.left = None\n self.right = None\n self.level = None\n\n def __str__(self):\n return str(self.info)\n\nclass BinarySearchTree:\n def __init__(self):\n self.root = None\n\n def create(self, val):\n if self.root == None:\n self.root = Node(val)\n else:\n current = self.root\n\n while True:\n if val < current.info:\n if current.left:\n current = current.left\n else:\n current.left = Node(val)\n break\n elif val > current.info:\n if current.right:\n current = current.right\n else:\n current.right = Node(val)\n break\n else:\n break\n\n# Enter your code here. Read input from STDIN. Print output to STDOUT\n'''\nclass Node:\n def __init__(self,info):\n self.info = info\n self.left = None\n self.right = None\n\n\n // this is a node of the tree , which contains info as data, left , right\n'''\ndef height(root):\n #Initialize counter of the height of the left side\n heightLeft = 0\n #Initialize counter of the height of the right side\n heightRight = 0\n #If the parent node has a left node\n if root.left:\n #Increment heightLeft with one and\n #Go into recursion with the left node as\n #the new parent node\n heightLeft = 1 + height(root.left)\n #If the parent node has a right node\n if root.right:\n #Increment heightRight with one and\n #Go into recursion with the right node as\n #the new parent node\n heightRight = 1 + height(root.right)\n\n #Return the highest of the heights\n if heightLeft > heightRight:\n return heightLeft\n else:\n return heightRight\n\n\n\ntree = BinarySearchTree()\nt = int(input())\n\narr = list(map(int, input().split()))\n\nfor i in range(t):\n tree.create(arr[i])\n\nprint(height(tree.root))\n"},{"alpha_fraction":0.6099843978881836,"alphanum_fraction":0.6411856412887573,"avg_line_length":19.03125,"blob_id":"44199b34f9127bceff4a78d2c65d295424093aac","content_id":"6c8d7e557f0ec4df7d0f1f0d38e3b405f8661692","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":641,"license_type":"no_license","max_line_length":62,"num_lines":32,"path":"/DictionaryTwoStrings/two.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n# Complete the twoStrings function below.\ndef twoStrings(s1, s2):\n #Turns the elements of a list into keys and their value is 0\n dictString1 = dict.fromkeys(s1,0)\n dictString2 = dict.fromkeys(s2,0)\n #Merge of two dictionaries into one dictionary\n dict3 = {**dictString1, **dictString2}\n\n if len(dict3) < len(dictString1)+ len(dictString2):\n return \"YES\"\n else:\n return \"NO\"\n\nif __name__ == '__main__':\n q = int(input())\n\n for q_itr in range(q):\n s1 = input()\n\n s2 = input()\n\n result = twoStrings(s1, s2)\n\n print(result)\n"},{"alpha_fraction":0.3214700222015381,"alphanum_fraction":0.40657639503479004,"avg_line_length":14.666666984558105,"blob_id":"98439749cabca3be7b3bbced7795606bef30f98f","content_id":"d5f035ce216fd380770252eb93f38fba2613a3c9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2585,"license_type":"no_license","max_line_length":52,"num_lines":165,"path":"/Bribe/bribe.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n# Complete the minimumBribes function below.\ndef minimumBribes(q,n):\n # rest = []\n # for x in range(n-1):\n # rest.append(q[x]-q[x+1])\n #\n # print(rest)\n # positive = [b for b in rest if b >= 0 ]\n #\n # print(positive)\n #\n # for c in positive:\n # if c > 2:\n # return \"Too chaotic\"\n #\n # return sum(positive)\n #\n #########\n # change = []\n # for x in range(n):\n # change.append((q[x]-1)-x)\n #\n # sum = 0\n # for x in range(len(change)):\n #\n # if change[x]>0:\n # if change[x]>2:\n # return \"Too chaotic\"\n # else:\n # sum += change[x]\n # elif change[x] < -1:\n # sum += 1\n # return sum\n ####################\n # change = []\n # for x in range(n):\n # change.append((q[x]-1)-x)\n #\n # # print(change)\n #\n # for c in change:\n # if c > 2:\n # return \"Too chaotic\"\n\n bribe = 0\n\n for a in range(len(q)):\n if ((q[a]-1)-a) > 2:\n return \"Too chaotic\"\n\n c=a+1\n for b in range(len(q)-c):\n print(q[a],\" > \", q[c+b])\n if q[a] > q[c+b]:\n bribe += 1\n\n\n print(bribe)\n\n\n\n\nif __name__ == '__main__':\n t = int(input())\n\n for t_itr in range(t):\n n = int(input())\n\n q = list(map(int, input().rstrip().split()))\n\n minimumBribes(q,n)\n\n\n# 9\n# 8\n# 1 2 3 5 4 6 7 8\n# 5\n# 2 1 5 3 4\n# 5\n# 2 5 1 3 4\n# 5\n# 2 1 5 4 3\n# 8\n# 1 2 5 3 4 7 8 6\n# 5\n# 2 1 5 3 4\n# 5\n# 2 5 1 3 4\n# 8\n# 5 1 2 3 7 8 6 4\n# 8\n# 1 2 5 3 7 8 6 4\n#\n# 9\n# 8\n# 1 2 3 5 4 6 7 8\n# [0, 0, 0, 1, -1, 0, 0, 0]\n# Moves: 1\n#\n# 5\n# 2 1 5 3 4\n# [1, -1, 2, -1, -1]\n# Moves: 3\n#\n# 5\n# 2 5 1 3 4\n# [1, 3, -2, -1, -1]\n# Moves: Too chaotic\n#\n# 5\n# 2 1 5 4 3\n# [1, -1, 2, 0, -2]\n# Moves: 4\n#\n# 8\n# 1 2 5 3 4 7 8 6\n# [0, 0, 2, -1, -1, 1, 1, -2]\n# Moves: 5\n#\n# 5\n# 2 1 5 3 4\n# [1, -1, 2, -1, -1]\n# Moves: 3\n#\n# 5\n# 2 5 1 3 4\n# [1, 3, -2, -1, -1]\n# Moves: Too chaotic\n#\n# 8\n# 5 1 2 3 7 8 6 4\n# [4, -1, -1, -1, 2, 2, -1, -4]\n# Moves: Too chaotic\n#\n# 8\n# 1 2 5 3 7 8 6 4\n# [0, 0, 2, -1, 2, 2, -1, -4]\n# Moves: 7\n\n# def minimumBribes(q,n):\n# change = []\n# for x in range(n):\n# change.append((q[x]-1)-x)\n#\n# for c in change:\n# if c > 2:\n# return \"Too chaotic\"\n#\n# bribe = 0\n#\n# for a in range(len(q)):\n# c=a+1\n# for b in range(len(q)-c):\n# if q[a] > q[c+b]:\n# bribe += 1\n#\n# return(bribe)\n"},{"alpha_fraction":0.5652680397033691,"alphanum_fraction":0.5780885815620422,"avg_line_length":15.188679695129395,"blob_id":"d8d73a8d159da5abe370512680cda6c4e74eddfa","content_id":"9ad2c8ccc8dd8de8bf2c23861b8c3b6bf716b28e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":858,"license_type":"no_license","max_line_length":63,"num_lines":53,"path":"/FlipBinary/flip.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\nbinary = []\n\ndef decimalToBinary(n):\n if n > 1:\n decimalToBinary(n//2)\n binary.append(n%2)\n\n# Complete the flippingBits function below.\ndef flippingBits(n):\n string = \"\"\n decimalToBinary(n)\n\n array = [1] * (32-len(binary))\n\n for index,element in enumerate(binary):\n\n if element == 1:\n binary[index] = 0\n else:\n binary[index] = 1\n\n string = string.join([str(elem) for elem in binary])\n\n stringOnes= \"\"\n stringOnes = stringOnes.join([str(elem) for elem in array])\n\n fullBinary = stringOnes + string\n\n return int(fullBinary,2)\n\n\n\nif __name__ == '__main__':\n\n\n q = int(input())\n\n for q_itr in range(q):\n n = int(input())\n\n result = flippingBits(n)\n\n binary = []\n\n print(result)\n"},{"alpha_fraction":0.5052316784858704,"alphanum_fraction":0.5261584520339966,"avg_line_length":15.317072868347168,"blob_id":"12f25bba2797afe8cf7003163fb22cc80c7fd483","content_id":"8e46e2a281dd2e6cd3b74432f22869bc26a2dff7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":669,"license_type":"no_license","max_line_length":48,"num_lines":41,"path":"/JumpingClouds/clouds.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n# Complete the jumpingOnClouds function below.\ndef jumpingOnClouds(c,n):\n jumps = 0\n index = 0\n\n while index < n-2 :\n oneJump = c[index+1]\n doubleJump = c[index+2]\n if doubleJump==0:\n jumps+=1\n index = index + 2\n continue\n elif oneJump==0:\n jumps+=1\n index = index + 1\n continue\n\n if index==(n-2):\n jumps+=1\n\n return jumps\n\n\n\nif __name__ == '__main__':\n\n n = int(input())\n\n c = list(map(int, input().rstrip().split()))\n\n result = jumpingOnClouds(c,n)\n\n print(result)\n"},{"alpha_fraction":0.6415094137191772,"alphanum_fraction":0.6698113083839417,"avg_line_length":31.615385055541992,"blob_id":"42dddb6cc1bde0824df776072f109d19e202b976","content_id":"b89e2aa7c96b1d328ff8873504e3f81802be7d78","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":424,"license_type":"no_license","max_line_length":94,"num_lines":13,"path":"/OneEditAway.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"def OneEdit(str1,str2):\n #Convert the strins into lists and then to sets\n set1 = set(list(str1))\n set2 = set(list(str2))\n #Subtract the sets\n setDifference = set1 - set2\n\n #If the difference is 1 or 0 elements, then it is one or 0 edits away\n #Else, it has more than one edit\n return(\"One or zero edits away\" if len(setDifference) <= 1 else \"More than one edit away\")\n\n\nprint(OneEdit(\"pale\",\"bale\"))\n"},{"alpha_fraction":0.5257731676101685,"alphanum_fraction":0.5567010045051575,"avg_line_length":8.699999809265137,"blob_id":"8e9e8acce034e1d73b571ec7d1a78fad5af595ac","content_id":"3a899b925eba252d97ae675c59c548cd4cd25b8f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":97,"license_type":"no_license","max_line_length":16,"num_lines":10,"path":"/SumNNumbers/version2.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"##Sum with a for\nn = int(input())\ni=1;\nsuma=0\n\nwhile i<=n:\n suma=suma+i\n i+=1\n\nprint(suma)\n"},{"alpha_fraction":0.5380333662033081,"alphanum_fraction":0.5408163070678711,"avg_line_length":33.774192810058594,"blob_id":"50c73927ca175f9d9fcac82fd7ee98d88cab7b5f","content_id":"64506b909e06c41a8163f29f70570ef0eb306325","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2156,"license_type":"no_license","max_line_length":71,"num_lines":62,"path":"/binaryTree.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"class Node:\n #Node Constructor\n def __init__(self, data=None):\n #Left node is none\n self.left = None\n #Right node is none\n self.right = None\n #Passed data is the value of the node\n self.data = data\n\n def insert(self, data):\n #If parent node value is not None\n if self.data:\n #If the passed value is less than the parent node value\n if data < self.data:\n #If the left node value is None\n if self.left is None:\n #Then create a left node for the parent node\n #with this value\n self.left = Node(data)\n else:\n #Recursion, keep looking for a place for this value\n #left node is now the parent node for this value\n self.left.insert(data)\n #If the passed value is bigger than the parent node value\n elif data > self.data:\n #If the right node value is None\n if self.right is None:\n #Then create a right node for the parent node\n #with this value\n self.right = Node(data)\n else:\n #Recursion, keep looking for a place for this value\n #right node is now the parent node for this value\n self.right.insert(data)\n #If parent node value is none\n else:\n #Give the root node this value\n self.data = data\n\n # Print the tree\n #It will print from the left most to the right most\n def PrintTree(self):\n #if parent node has a left node\n if self.left:\n #Recursion, left node is now the parent node\n self.left.PrintTree()\n #Print the parent node value\n print( self.data)\n #if parent node has a right\n if self.right:\n #Recursion, right node is now the parent node\n self.right.PrintTree()\n\n#Establish the root node\nroot = Node(12)\n#Insert the rest of the nodes\nroot.insert(6)\nroot.insert(14)\nroot.insert(3)\n\nroot.PrintTree()\n"},{"alpha_fraction":0.650668740272522,"alphanum_fraction":0.6608969569206238,"avg_line_length":37.51515197753906,"blob_id":"372c42f1bd3e6786d619cf9615fce949fdf87c00","content_id":"0b771993eac08f9d91619c9abaaf2529356e1416","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1271,"license_type":"no_license","max_line_length":71,"num_lines":33,"path":"/StringPermutations/perm.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"#Global dictionary used to store the permutations\ndictionary = dict()\n\ndef stringPermutation(word,lower,higher):\n #If lower has been incremented to the same value as higher\n if lower == higher:\n #Convert word list back to string\n newWord = ''.join(word)\n #Add the string to the dictionary as key and give it 0 as value\n dictionary[newWord] = 0\n else:\n #Loops from 0 to length of word\n for i in range(lower,higher+1):\n #Swaps characters\n word[lower], word[i] = word[i], word[lower]\n #Recursion, increment lower\n stringPermutation(word,lower+1,higher)\n #Swap the characters back to their place\n word[lower], word[i] = word[i], word[lower]\n\n#Receives two strings as parameters\ndef checkPermutation(str1,str2):\n #Converts str1 into a list to manipulate it\n word = list(str1)\n #The list of str1 is sent to check its permutations\n stringPermutation(word,0,len(word)-1)\n #Check if the second string is in the dictionary of permutations\n if str2 in dictionary:\n print(\"Second string is a permutation of the First string\")\n else:\n print(\"Second string is not a permutation of the First string\")\n\ncheckPermutation(\"abc\",\"bac\")\n"},{"alpha_fraction":0.6063745021820068,"alphanum_fraction":0.6127489805221558,"avg_line_length":22.240739822387695,"blob_id":"15c5ba948608869fe0309177e365708fe19f54c5","content_id":"eba6d22c9e474a9a6116f4e79253169dd1572350","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1255,"license_type":"no_license","max_line_length":64,"num_lines":54,"path":"/LLTail.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\nclass SinglyLinkedListNode:\n def __init__(self, node_data):\n self.data = node_data\n self.next = None\n\nclass SinglyLinkedList:\n def __init__(self):\n self.head = None\n\n\ndef insertNodeAtTail(head, data):\n #To prevent maximum recursion depth exceeded error\n sys.setrecursionlimit(1000000)\n #If the node is None\n if head == None:\n #return the created node for that data\n return SinglyLinkedListNode(data)\n else:\n #If the next value of the node is None\n if head.next == None:\n #create a node for data in the next node\n head.next = SinglyLinkedListNode(data)\n else:\n #Recursion sending the next node of the current node\n # and the data\n insertNodeAtTail(head.next,data)\n return head\n\n\n\nif __name__ == '__main__':\n\n llist_count = int(input())\n\n llist = SinglyLinkedList()\n\n for i in range(llist_count):\n llist_item = int(input())\n llist_head = insertNodeAtTail(llist.head, llist_item)\n llist.head = llist_head\n\n node = llist.head\n print(node.data)\n while node:\n print(node.data)\n node = node.next\n"},{"alpha_fraction":0.5595238208770752,"alphanum_fraction":0.5664682388305664,"avg_line_length":20.913043975830078,"blob_id":"9a2752b154fd9bf54353abc877b39b96bbc9cabd","content_id":"e34bd73e27769654fb3466a1eb5dffc4cef8d4a7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1008,"license_type":"no_license","max_line_length":66,"num_lines":46,"path":"/dictionaryTriplets.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n# Complete the countTriplets function below.\ndef countTriplets(arr, r):\n #Counter of triplets\n count = 0\n dict = {}\n dictPairs = {}\n\n #Looping through the array backwards\n for i in reversed(arr):\n #If i*r is in the dictPairs\n if i*r in dictPairs:\n #Increment the counter with the value of\n #i*r in dictPairs\n count += dictPairs[i*r]\n #If i*r is in dict\n if i*r in dict:\n #Insert i in dictPairs giving it the value of\n #dictPairs[i] plus dict[i*r]\n dictPairs[i] = dictPairs.get(i, 0) + dict[i*r]\n\n #Insert i in dict, giving it the value of dict[i] plus one\n dict[i] = dict.get(i, 0) + 1\n\n return count\n\nif __name__ == '__main__':\n\n nr = input().rstrip().split()\n\n n = int(nr[0])\n\n r = int(nr[1])\n\n arr = list(map(int, input().rstrip().split()))\n\n ans = countTriplets(arr, r)\n\n print(ans)\n"},{"alpha_fraction":0.5223880410194397,"alphanum_fraction":0.5362473130226135,"avg_line_length":16.370370864868164,"blob_id":"5e4192ab0e44f8366e2e5112a1216b06e00e0f19","content_id":"50e27860bebfe82fc79b620a3d30ad4e3b54c965","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":938,"license_type":"no_license","max_line_length":64,"num_lines":54,"path":"/ArrayManipulation/mani2.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n# Complete the arrayManipulation function below.\ndef arrayManipulation(n,m, queries):\n #To adjust the indexes with the queries ranges\n array = [0] * (n+2)\n\n #Make an array with the deltas between each range\n for x in range(m):\n start = queries[x][0]\n value = queries[x][2]\n next = queries[x][1]+1\n array[start] += value\n\n if next <= n:\n array[next] -= value\n\n print((array))\n\n max = 0\n sum = 0\n b = 1\n for a in range(n):\n sum += array[b]\n\n if max < sum:\n max = sum\n\n b +=1\n\n\n print((array))\n print(max)\n\nif __name__ == '__main__':\n\n nm = input().split()\n\n n = int(nm[0])\n\n m = int(nm[1])\n\n queries = []\n\n for _ in range(m):\n queries.append(list(map(int, input().rstrip().split())))\n\n result = arrayManipulation(n,m,queries)\n"},{"alpha_fraction":0.6722487807273865,"alphanum_fraction":0.6937798857688904,"avg_line_length":18.904762268066406,"blob_id":"8287f1f399e875f88c328e60207744b8181ea978","content_id":"d71db75c412c48420332091cdef8ffb2a738437c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":418,"license_type":"no_license","max_line_length":57,"num_lines":21,"path":"/BinarySearch/version1.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"## The user has to guess the number the computer selected\nimport random\n\nminNumber = 1\nmaxNumber = 100\n\nlistNumbers = list(range(minNumber,maxNumber+1))\n\nrandomNumber = random.randint(1, 100)\n\nguess = int(input())\n\nwhile guess!=randomNumber:\n if guess>randomNumber:\n print(\"mas bajo\")\n elif guess<randomNumber:\n print(\"mas alto\")\n\n guess = int(input())\n\nprint(\"YES! The number is\",randomNumber)\n"},{"alpha_fraction":0.6454917788505554,"alphanum_fraction":0.6516393423080444,"avg_line_length":27.705883026123047,"blob_id":"9844c9d509682937f3eccdc7b6f6d5653718b090","content_id":"c0254eef76a8bfb862a168291c1d64094be44c42","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":488,"license_type":"no_license","max_line_length":74,"num_lines":17,"path":"/AlphabeticalOrder/version3.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"##Version 3 solution without the sort function\ncontactList = []\nfile = open(\"sampleInput.txt\", \"r\")\n\nfor name in file:\n name = name.strip('\\n')\n contactList.append(name)\n\nfor i in range(len(contactList)):\n for j in range(i + 1, len(contactList)):\n\n if contactList[i] > contactList[j]:\n contactList[i], contactList[j] = contactList[j], contactList[i]\n\nwith open(\"outputVersion3.txt\", \"w\") as newFile:\n for name in contactList:\n newFile.write(name+\"\\n\")\n"},{"alpha_fraction":0.5773195624351501,"alphanum_fraction":0.6082473993301392,"avg_line_length":12.857142448425293,"blob_id":"254c7aca6f210e3eaf160fd25e15a058bb1b55ee","content_id":"44ad53417c162e53a80a48f678b34b3bcea7342c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":97,"license_type":"no_license","max_line_length":22,"num_lines":7,"path":"/SumNNumbers/version3.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"##Sum with a while\nn = int(input())\nsuma = 0\nfor i in range(1,n+1):\n suma=suma+i\n\nprint(suma)\n"},{"alpha_fraction":0.5470494627952576,"alphanum_fraction":0.5645933151245117,"avg_line_length":16.91428565979004,"blob_id":"ca3e39475b1f9d946300c6fecc350800403c1caa","content_id":"99923cafa15a53935e5a7955bb3dbd1177536367","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":627,"license_type":"no_license","max_line_length":64,"num_lines":35,"path":"/ArrayManipulation/mani.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n# Complete the arrayManipulation function below.\ndef arrayManipulation(n,m, queries):\n array = [0] * n\n\n for x in range(m):\n index = queries[x][0]-1\n for a in range(queries[x][1]-(queries[x][0]-1)):\n array[index] += queries[x][2]\n index +=1\n\n print(max(array))\n\n\nif __name__ == '__main__':\n\n nm = input().split()\n\n n = int(nm[0])\n\n m = int(nm[1])\n\n queries = []\n\n for _ in range(m):\n queries.append(list(map(int, input().rstrip().split())))\n\n result = arrayManipulation(n,m,queries)\n"},{"alpha_fraction":0.8857142925262451,"alphanum_fraction":0.8857142925262451,"avg_line_length":34,"blob_id":"b39ecaf6eb12baa7c5905ad6660655321ffc4abe","content_id":"0ca38d4472eb26c1fa5c2f96558056e311c9ccbc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":70,"license_type":"no_license","max_line_length":35,"num_lines":2,"path":"/README.md","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"# Competitive-Programming-Exercises\nCompetitive Programming exercises\n"},{"alpha_fraction":0.5685884952545166,"alphanum_fraction":0.5685884952545166,"avg_line_length":24.149999618530273,"blob_id":"ff15f1c036bc9fe100a4c8143d6f328f7bf88862","content_id":"36414a8c7eef7b0bd881f4274f0fa33fdc4f07a2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1509,"license_type":"no_license","max_line_length":60,"num_lines":60,"path":"/bstInsertion.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"class Node:\n def __init__(self, info):\n self.info = info\n self.left = None\n self.right = None\n self.level = None\n\n def __str__(self):\n return str(self.info)\n\ndef preOrder(root):\n if root == None:\n return\n print (root.info, end=\" \")\n preOrder(root.left)\n preOrder(root.right)\n\nclass BinarySearchTree:\n def __init__(self):\n self.root = None\n\n def insertion(self,cur,val):\n #If cur doesn't exist\n if not cur:\n #Create a node with the passed value\n cur = Node(val)\n #If the value of cur is bigger than the passed value\n elif cur.info > val:\n #Left node of cur is recursive function\n #With left empty node of cur sent and the value\n cur.left = self.insertion(cur.left,val)\n else:\n #Right node of cur is recursive function\n #With right empty node of cur sent and the value\n cur.right = self.insertion(cur.right,val)\n\n return cur\n\n def insert(self, val):\n #If node root is None\n if self.root==None:\n #Create a node with the passed value\n #And establish it as the root node\n self.root = Node(val)\n else:\n #Send root node and value to insertion function\n self.insertion(self.root,val)\n\n\n\n\ntree = BinarySearchTree()\nt = int(input())\n\narr = list(map(int, input().split()))\n\nfor i in range(t):\n tree.insert(arr[i])\n\npreOrder(tree.root)\n"},{"alpha_fraction":0.6412371397018433,"alphanum_fraction":0.6505154371261597,"avg_line_length":28.393939971923828,"blob_id":"39698dbb88ab79ce6433b67854f473e9059223cc","content_id":"87267016773ba4c11c3de2ae0b938edf10e0e4aa","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":970,"license_type":"no_license","max_line_length":56,"num_lines":33,"path":"/bstMinimal.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"class TreeNode(object):\n def __init__(self, x):\n self.val = x\n self.left = None\n self.right = None\n\ndef sorted_array_to_bst(nums):\n #If array is empty return None\n if not nums:\n return None\n #Get the middle element of the array\n mid_val = len(nums)//2\n #Create a new node with the middle element\n node = TreeNode(nums[mid_val])\n #Left node of node is recursion of this function\n #sending the array from the start until the element\n #before the middle element\n node.left = sorted_array_to_bst(nums[:mid_val])\n #Right node of node is recursion of this function\n #sending the array from the element after the middle\n #element until the end\n node.right = sorted_array_to_bst(nums[mid_val+1:])\n return node\n\ndef preOrder(node):\n if not node:\n return\n print(node.val)\n preOrder(node.left)\n preOrder(node.right)\n\nresult = sorted_array_to_bst([1, 2, 3, 4, 5, 6, 7])\npreOrder(result)\n"},{"alpha_fraction":0.6691297292709351,"alphanum_fraction":0.6806239485740662,"avg_line_length":31.91891860961914,"blob_id":"7bc76dcfb9ae21ebbc1f74ce1205929de5410cc5","content_id":"44c889f1780abc98052ea16683a2a24842d46de3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1218,"license_type":"no_license","max_line_length":71,"num_lines":37,"path":"/bstClosest.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"class TreeNode(object):\n def __init__(self, x):\n self.val = x\n self.left = None\n self.right = None\n\ndef closest_value(root, target):\n #a has the value of root\n a = root.val\n #if the target number is lower than the value of root\n #then kid is root left node else kid is root right node\n kid = root.left if target < a else root.right\n #If the nodes are None\n if not kid:\n #return the value of root\n return a\n #if the nodes are not None, b is recursion of this function\n #with the kid as the new root and send the target\n b = closest_value(kid, target)\n #lambda function to check the absolute value of the subtraction\n #of the target number with a parent node and one of its child nodes\n #the function return the node value associated with the lowest of\n #the subtractions\n return min((a,b), key=lambda x: abs(target-x))\n\nroot = TreeNode(8)\nroot.left = TreeNode(5)\nroot.right = TreeNode(14)\nroot.left.left = TreeNode(4)\nroot.left.right = TreeNode(6)\nroot.left.right.left = TreeNode(8)\nroot.left.right.right = TreeNode(7)\nroot.right.right = TreeNode(24)\nroot.right.right.left = TreeNode(22)\n\nresult = closest_value(root, 19)\nprint(result)\n"},{"alpha_fraction":0.6209093928337097,"alphanum_fraction":0.6295211911201477,"avg_line_length":31.80225944519043,"blob_id":"d58fb5795461ed4a1db9544ea0252c567bc99100","content_id":"5894320158ac22e03a2d58aa9004aa22b89ba0de","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5806,"license_type":"no_license","max_line_length":81,"num_lines":177,"path":"/LLSum.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"class Node:\n #Node constructor\n def __init__(self, dataval=None):\n #Give the new instance the passed value\n self.dataval = dataval\n #The next node of the node is none\n self.nextval = None\n\nclass SingleLinkedList:\n #Single Linked List constructor\n def __init__(self):\n #The node head value is none\n self.headval = None\n\n #Print LinkedList\n def listprint(self):\n #Get the the node head\n printval = self.headval\n while printval is not None:\n #Print the value of the node\n print (printval.dataval)\n #Printval is now the next node\n printval = printval.nextval\n\n #Insert a new node at the beginning\n def AtBeginning(self,newdata):\n #Create a new node with the passed data as its value\n NewNode = Node(newdata)\n # The next node is going to be the current node head\n NewNode.nextval = self.headval\n #The new node is going to be the new node head\n self.headval = NewNode\n\n # Insert a newnode at the end\n def AtEnd(self, newdata):\n #Create a new node with the passed data as its value\n NewNode = Node(newdata)\n #If the linked list is empty\n if self.headval is None:\n #Then the new node is the new head\n self.headval = NewNode\n return\n #Get the node head\n laste = self.headval\n #While the next node is not empty\n while(laste.nextval):\n #Get next node\n laste = laste.nextval\n #The next value of the last node is the new node\n laste.nextval=NewNode\n\n # Function to add a node between two nodes\n def Inbetween(self,middle_node,newdata):\n if middle_node is None:\n print(\"The mentioned node is absent\")\n return\n\n #Create a new node with the passed data as its value\n NewNode = Node(newdata)\n #The next value of the new node is the next value of the sent node\n NewNode.nextval = middle_node.nextval\n #the next value of the sent node is the new node\n middle_node.nextval = NewNode\n\n # Function to remove node\n def RemoveNode(self, Removekey):\n #Get the node head\n HeadVal = self.headval\n\n if (HeadVal is not None):\n #If the node head has the same value as the one to remove\n if (HeadVal.dataval == Removekey):\n #The new head is the next node of the node head\n self.headval = HeadVal.nextval\n #The past node head is now none\n HeadVal = None\n return\n\n #Loop through the linked list\n #While the node is not none\n while (HeadVal is not None):\n #If the node has the same value as the one to remove\n if HeadVal.dataval == Removekey:\n #End the while\n break\n #prev is the current node in the loop\n prev = HeadVal\n #HeadVal is now the next node\n HeadVal = HeadVal.nextval\n\n #If the value was not found\n if (HeadVal == None):\n return\n\n #the next node of the previous node that is going to be Removed\n #Is going to be now the next node of the node that is going to be removed\n prev.nextval = HeadVal.nextval\n\n #the removed node is now none\n HeadVal = None\n\n\ndef LLSum(list1,list2):\n #Get the head of the first list\n node1 = list1.headval\n #Create a stack to store the numbers\n listNum1 = []\n #Loop until the end of the linked list 1\n while node1:\n #Insert the value of the node in the stack\n listNum1.append(node1.dataval)\n #node1 is now the next node\n node1 = node1.nextval\n\n #Get the head of the second list\n node2 = list2.headval\n #Create a stack to store the numbers\n listNum2 = []\n #Loop until the end of the linked list 2\n while node2:\n #Insert the value of the node in the stack\n listNum2.append(node2.dataval)\n #node2 is now the next node\n node2 = node2.nextval\n\n #Create a list filling in the inverse order of the original linked list 1\n stringNum = [str(listNum1.pop()) for i in range(len(listNum1))]\n #Join all the elements of the list into a single string\n joinString = \"\".join(stringNum)\n #Convert that string into an integer\n stringInt1 = int(joinString)\n\n #Create a list filling in the inverse order of the original linked list 2\n stringNum = [str(listNum2.pop()) for i in range(len(listNum2))]\n #Join all the elements of the list into a single string\n joinString = \"\".join(stringNum)\n #Convert that string into an integer\n stringInt2 = int(joinString)\n\n #Sum both integers\n sumNumber = stringInt1 + stringInt2\n\n #Create a list eith each digit of the sum converted into a string\n sumStringNum = [int for int in str(sumNumber)]\n\n #Create the linked list where we will place the sum of the two other LLs\n sumList = SingleLinkedList()\n #The node head will be the first element of the sumStringNum list\n sumList.headval = Node(sumStringNum.pop(0))\n #nodeLoop is the node head\n nodeLoop = sumList.headval\n\n #Loop until the sumStringNum list is empty\n while sumStringNum:\n #The next node of nodeLoop will be the first element in the\n #sumStringNum list\n nodeLoop.nextval = Node(sumStringNum.pop(0))\n #NodeLoop is now the next node of NodeLoop\n nodeLoop = nodeLoop.nextval\n\n #Print the Linked List\n sumList.listprint()\n\n\n\n\nlist1 = SingleLinkedList()\nlist1.headval = Node(7)\nlist1.headval.nextval = Node(1)\nlist1.AtEnd(6)\n\nlist2 = SingleLinkedList()\nlist2.headval = Node(5)\nlist2.headval.nextval = Node(9)\nlist2.AtEnd(2)\n\nLLSum(list1,list2)\n"},{"alpha_fraction":0.7234042286872864,"alphanum_fraction":0.7234042286872864,"avg_line_length":22.5,"blob_id":"5b57b956e7e7f5843fe4f78c234792e4375a0b30","content_id":"9024663c8524a9ed988fb5fd747c64272b869ab2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":188,"license_type":"no_license","max_line_length":32,"num_lines":8,"path":"/Union/union.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"englishNumber = input()\nenglishStudents = input()\nfrenchNumber = input()\nfrenchStudents = input()\n\ne = set(englishStudents.split())\nf = set(frenchStudents.split())\nprint (len(e.union(f)))\n"},{"alpha_fraction":0.5757575631141663,"alphanum_fraction":0.6060606241226196,"avg_line_length":21,"blob_id":"27c2cbb7c4a94c075c296d2db2772ae85f6eef35","content_id":"754e4a014402f19f31423a95aa78f1c2f7f583d6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":66,"license_type":"no_license","max_line_length":25,"num_lines":3,"path":"/SumNNumbers/version1.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"##Sum with the formula\nn = int(input())\nprint(round((n*(n+1))/2))\n"},{"alpha_fraction":0.5591259598731995,"alphanum_fraction":0.5694087147712708,"avg_line_length":17.0930233001709,"blob_id":"269c0a023f2bbd5410edc8e9bc7aae4328c090d6","content_id":"630b3b6b8a61154229add3ae99bc4b33ecc2c1ff","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":778,"license_type":"no_license","max_line_length":50,"num_lines":43,"path":"/DictionaryCountingTriplets/version1.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n# Complete the countTriplets function below.\ndef countTriplets(arr, r):\n maxNumber = max(arr)\n dictNumbers = dict.fromkeys(arr,0)\n newList = []\n newList.append(1)\n\n for number in dictNumbers:\n if number is not maxNumber:\n newList.append(number*r)\n\n tripleList = []\n triple = []\n\n for i=0; i<=(len(newList)-3); i++:\n for a=i; a<=(a+3);a++:\n triple.append(newList[a])\n tripleList.append(triple)\n triple = []\n\n\n print(newList)\n\nif __name__ == '__main__':\n nr = input().rstrip().split()\n\n n = int(nr[0])\n\n r = int(nr[1])\n\n arr = list(map(int, input().rstrip().split()))\n\n ans = countTriplets(arr, r)\n\n print(ans)\n"},{"alpha_fraction":0.6601307392120361,"alphanum_fraction":0.6666666865348816,"avg_line_length":22.538461685180664,"blob_id":"4874ec1be356687f7b5f0a30b281a785e4bae6aa","content_id":"b4f9c6aa089471508dbb2d30278823724a2a449e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":306,"license_type":"no_license","max_line_length":48,"num_lines":13,"path":"/AlphabeticalOrder/version2.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"##Version 2 input and output with text files\ncontactList = []\nfile = open(\"sampleInput.txt\", \"r\")\n\nfor name in file:\n name = name.strip('\\n')\n contactList.append(name)\n\ncontactList.sort()\n\nwith open(\"outputVersion2.txt\", \"w\") as newFile:\n for name in contactList:\n newFile.write(name+\"\\n\")\n"},{"alpha_fraction":0.4566666781902313,"alphanum_fraction":0.47333332896232605,"avg_line_length":18.148935317993164,"blob_id":"c609b892916b6342c413d0980f0cf97bcab696a3","content_id":"248ca7b5ced80bc088c46beb2fcc2d153d0d4c65","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":900,"license_type":"no_license","max_line_length":69,"num_lines":47,"path":"/DictionaryFreqQueries.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n# Complete the freqQuery function below.\ndef freqQuery(queries):\n arr=[]\n ans=[]\n dictionary={}\n for query in queries:\n for j in range(1):\n if query[0]==1:\n arr.append(query[1])\n dictionary[query[1]] = dictionary.get(query[1],0) + 1\n elif query[0]==2:\n try:\n arr.remove(query[1])\n except:\n break\n else:\n if query[1] in dictionary.values():\n ans.append(1)\n else:\n ans.append(0)\n\n\n return ans\n\n\n\n\nif __name__ == '__main__':\n\n q = int(input().strip())\n\n queries = []\n\n for _ in range(q):\n queries.append(list(map(int, input().rstrip().split())))\n\n ans = freqQuery(queries)\n\n print(ans)\n"},{"alpha_fraction":0.45755693316459656,"alphanum_fraction":0.48033127188682556,"avg_line_length":17.576923370361328,"blob_id":"9b5b0fa1805e2ffb5d0679802c454b506c43e362","content_id":"30b1bcb95389fe46544bd18a5dca4479e9f81ec4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":483,"license_type":"no_license","max_line_length":43,"num_lines":26,"path":"/BinarySearch/version3.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"##corrected Book version play with computer\ndef binary_search(list, item):\n\n low = 0\n high = len(list)-1\n\n while low <= high:\n mid = (round((high-low+1)/2))+low\n guess = list[mid]\n\n print(guess)\n\n x = input()\n if x == \"yes\":\n return \" \"\n if x == \"mas bajo\":\n high = mid -1\n elif x == \"mas alto\":\n low = mid + 1\n\n\n print(\" \")\n\nmy_list = list(range(1,101))\n\nprint (binary_search(my_list,5))\n"},{"alpha_fraction":0.452160507440567,"alphanum_fraction":0.46759259700775146,"avg_line_length":16.052631378173828,"blob_id":"46b40589ea1ebab1d5ea2f67f8c8b6eda95a7374","content_id":"a90050668a543ddfdb6ae8d419c6fc52000ee2e9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":648,"license_type":"no_license","max_line_length":52,"num_lines":38,"path":"/Bribe/bribe2.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n# Complete the minimumBribes function below.\ndef minimumBribes(q,n):\n bribe = 0\n c = len(q)-1\n for a in range(len(q)):\n\n\n if (q[c] - (c + 1)) > 2:\n return \"Too chaotic\"\n\n b = max(0,q[c]-2)\n while b<c:\n print(q[b],\">\",q[c])\n if q[b] > q[c]:\n bribe += 1\n\n b +=1\n c += -1\n\n return bribe\n\nif __name__ == '__main__':\n t = int(input())\n\n for t_itr in range(t):\n n = int(input())\n\n q = list(map(int, input().rstrip().split()))\n\n print(minimumBribes(q,n))\n"},{"alpha_fraction":0.572311520576477,"alphanum_fraction":0.5741656422615051,"avg_line_length":18.493976593017578,"blob_id":"ea5cb26d40c440347f6f96a4d5e47ebddea9faa0","content_id":"d795861baffb326567eb9356b69c228a7a15ad9a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1618,"license_type":"no_license","max_line_length":65,"num_lines":83,"path":"/LlInsert.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\nclass SinglyLinkedListNode:\n def __init__(self, node_data):\n self.data = node_data\n self.next = None\n\nclass SinglyLinkedList:\n def __init__(self):\n self.head = None\n self.tail = None\n\n def insert_node(self, node_data):\n node = SinglyLinkedListNode(node_data)\n\n if not self.head:\n self.head = node\n else:\n self.tail.next = node\n\n\n self.tail = node\n\n def printList(self):\n node = self.head\n print(\" \")\n while node:\n print(node.data)\n node = node.next\n\ndef print_singly_linked_list(node, sep, fptr):\n while node:\n fptr.write(str(node.data))\n\n node = node.next\n\n if node:\n fptr.write(sep)\n\ndef insertNodeAtPosition(head, data, position):\n node = head\n if position==0:\n pastHead = head\n newHead = SinglyLinkedListNode(data)\n newHead.next = pastHead\n else:\n for i in range(position-1):\n node = node.next\n pastNode = node\n nextNode = node.next\n newNode = SinglyLinkedListNode(data)\n pastNode.next = newNode\n newNode.next = nextNode\n\n\n return head\n\n\n\nif __name__ == '__main__':\n\n\n llist_count = int(input())\n\n llist = SinglyLinkedList()\n\n for _ in range(llist_count):\n llist_item = int(input())\n llist.insert_node(llist_item)\n\n data = int(input())\n\n position = int(input())\n\n llist_head = insertNodeAtPosition(llist.head, data, position)\n\n llist_head.printList()\n"},{"alpha_fraction":0.5798165202140808,"alphanum_fraction":0.5871559381484985,"avg_line_length":17.16666603088379,"blob_id":"68842d43d8c0d14adc4d002949d153ba171de438","content_id":"8d7b944e26d7a7bc6cd3185e37bb9ff52a09ada8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":545,"license_type":"no_license","max_line_length":65,"num_lines":30,"path":"/Swap/swap.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n# Complete the minimumSwaps function below.\ndef minimumSwaps(arr):\n swap = 0\n arr = [x-1 for x in arr]\n\n for index,element in enumerate(arr):\n if element > index:\n newIndex = arr.index(index)\n arr[index], arr[newIndex] = arr[newIndex], arr[index]\n swap += 1\n\n print(arr)\n print(swap)\n\n\nif __name__ == '__main__':\n\n n = int(input())\n\n arr = list(map(int, input().rstrip().split()))\n\n res = minimumSwaps(arr)\n"},{"alpha_fraction":0.5017668008804321,"alphanum_fraction":0.5159010887145996,"avg_line_length":13.51282024383545,"blob_id":"ca81ef75285f69f5b1baa19630e6335c3c2320ed","content_id":"bdf85bd022622a3c15b272493bf3778c46e704c4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":566,"license_type":"no_license","max_line_length":46,"num_lines":39,"path":"/CountingValleys/valleys.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n# Complete the countingValleys function below.\ndef countingValleys(n, s):\n walk = [0]\n counter = 0\n valleys = 0\n\n for x in s:\n if x==\"D\" :\n if counter==0 :\n valleys += 1\n\n counter += -1\n walk.append(counter)\n\n else:\n counter += 1\n walk.append(counter)\n\n return valleys\n\n\n\nif __name__ == '__main__':\n\n n = int(input())\n\n s = input()\n\n result = countingValleys(n, s)\n\n print(result)\n"},{"alpha_fraction":0.5988608598709106,"alphanum_fraction":0.6037428975105286,"avg_line_length":25.148935317993164,"blob_id":"6a0af8337ad195a770f1abc032b3418e75c2ecbd","content_id":"23125830f2d1ce4b17bc7d241f6370afcc0d9601","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1229,"license_type":"no_license","max_line_length":65,"num_lines":47,"path":"/bstCheckIs.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"class TreeNode(object):\n def __init__(self, x):\n self.val = x\n self.left = None\n self.right = None\n\ndef is_BST(root):\n #Empty stack to store the nodes\n stack = []\n #previous node variable equal to None\n prev = None\n\n #While the node is not None or the stack is empty\n while root or stack:\n #While node is not None\n while root:\n #Append the node to the stack\n stack.append(root)\n #Root is now the left node of root\n root = root.left\n #Root is now the last element appended to the stack\n root = stack.pop()\n #If prev is not None and the root value is lower or equal\n #to the value of prev, return false\n if prev and root.val <= prev.val:\n return False\n #Prev is now root\n prev = root\n #Root is now the right node of root\n root = root.right\n #Return true when their is nothing in the stack or if the\n #variable root is None\n return True\n\nroot = TreeNode(2)\nroot.left = TreeNode(1)\nroot.right = TreeNode(3)\n\nresult = is_BST(root)\nprint(result)\n\nroot = TreeNode(1)\nroot.left = TreeNode(2)\nroot.right = TreeNode(3)\n\nresult = is_BST(root)\nprint(result)\n"},{"alpha_fraction":0.5941422581672668,"alphanum_fraction":0.5941422581672668,"avg_line_length":29.838708877563477,"blob_id":"7b89cc1cfc6d5a7d3f5d4513e46d12c9e57c5167","content_id":"a574cc272a0442a6b6569fb090394b8c631608cf","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":956,"license_type":"no_license","max_line_length":119,"num_lines":31,"path":"/bstCheck.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"\"\"\" Node is defined as\nclass node:\n def __init__(self, data):\n self.data = data\n self.left = None\n self.right = None\n\"\"\"\ndef checkBST(root):\n return checkBSTo(root, None, None)\n\ndef checkBSTo(root,min,max):\n if root==None:\n return \"Yes\"\n if ((min is not None and root.data <=min)) or ((max is not None and root.data > max)):\n return \"No\"\n if not(checkBSTo(root.left,min,root.data)) or not(checkBSTo(root.right,root.data,max)):\n return \"No\"\n return \"Yes\"\n###########\ndef checkBST(root):\n return check(root,float('inf'),float('-inf'))\n\ndef check(node,min,max):\n if(node==None):\n return \"Yes\"\n return min < node.data and node.data < max and check(node.left, min, node.data) and check(node.right,node.data,max)\n #############\n # if(node.data <= min or node.data >=max):\n # return \"No\"\n #\n # return check(node.left,min,node.data) and check(node.right,node.data,max)\n"},{"alpha_fraction":0.5178294777870178,"alphanum_fraction":0.5302325487136841,"avg_line_length":17.428571701049805,"blob_id":"c1725970028bcdb4d1e6c1016355d50207a9b97a","content_id":"ea704b33914eec30b22bdbb4d7e36417afd628c7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":645,"license_type":"no_license","max_line_length":54,"num_lines":35,"path":"/Bribe/bribe3.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n# Complete the minimumBribes function below.\ndef minimumBribes(q,n):\n bribe = 0\n q = [x-1 for x in q]\n\n for i,x in enumerate(q):\n\n #If any element of the array is ahead 2 places\n if x - i > 2:\n return \"Too chaotic\"\n\n #Who bribed x\n for j in range(max(x-1,0),i):\n if q[j] > x:\n bribe += 1\n\n return bribe\n\nif __name__ == '__main__':\n t = int(input())\n\n for t_itr in range(t):\n n = int(input())\n\n q = list(map(int, input().rstrip().split()))\n\n print(minimumBribes(q,n))\n"},{"alpha_fraction":0.46349745988845825,"alphanum_fraction":0.47877758741378784,"avg_line_length":19.310344696044922,"blob_id":"7ee9b1ca45ac87e779850d5fb3432751c204c807","content_id":"fe84f5e8fea7e6c66382a1d350d6577148b062d0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":589,"license_type":"no_license","max_line_length":39,"num_lines":29,"path":"/BinarySearch/version2.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"##corrected Book version\ndef binary_search(list, item):\n print(\" \")\n low = 0\n high = len(list)-1\n\n while low <= high:\n mid = (round((high-low)/2))+low\n guess = list[mid]\n print(\"item:\",item)\n print(\"low:\",low)\n print(\"high:\",high)\n print(\"mid:\",mid)\n print(\"guess:\",guess)\n print(\" \")\n\n if guess == item:\n return mid\n if guess > item:\n high = mid -1\n else:\n low = mid + 1\n\n return None\n print(\" \")\n\nmy_list = list(range(1,11))\n\nprint (binary_search(my_list,6))\n"},{"alpha_fraction":0.6216796636581421,"alphanum_fraction":0.6236327886581421,"avg_line_length":30.80124282836914,"blob_id":"b1c44e7772c64313917c5cd9efe1931706036057","content_id":"05a987f9d3d053395fb8ae93d188d9f52a61a9cd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5120,"license_type":"no_license","max_line_length":81,"num_lines":161,"path":"/linkedList.py","repo_name":"IvanVazquezL/Competitive-Programming-Exercises","src_encoding":"UTF-8","text":"class Node:\n #Node constructor\n def __init__(self, dataval=None):\n #Give the new instance the passed value\n self.dataval = dataval\n #The next node of the node is none\n self.nextval = None\n\nclass SingleLinkedList:\n #Single Linked List constructor\n def __init__(self):\n #The node head value is none\n self.headval = None\n\n #Print LinkedList\n def listprint(self):\n #Get the the node head\n printval = self.headval\n while printval is not None:\n #Print the value of the node\n print (printval.dataval)\n #Printval is now the next node\n printval = printval.nextval\n\n #Insert a new node at the beginning\n def AtBeginning(self,newdata):\n #Create a new node with the passed data as its value\n NewNode = Node(newdata)\n # The next node is going to be the current node head\n NewNode.nextval = self.headval\n #The new node is going to be the new node head\n self.headval = NewNode\n\n # Insert a newnode at the end\n def AtEnd(self, newdata):\n #Create a new node with the passed data as its value\n NewNode = Node(newdata)\n #If the linked list is empty\n if self.headval is None:\n #Then the new node is the new head\n self.headval = NewNode\n return\n #Get the node head\n laste = self.headval\n #While the next node is not empty\n while(laste.nextval):\n #Get next node\n laste = laste.nextval\n #The next value of the last node is the new node\n laste.nextval=NewNode\n\n # Function to add a node between two nodes\n def Inbetween(self,middle_node,newdata):\n if middle_node is None:\n print(\"The mentioned node is absent\")\n return\n\n #Create a new node with the passed data as its value\n NewNode = Node(newdata)\n #The next value of the new node is the next value of the sent node\n NewNode.nextval = middle_node.nextval\n #the next value of the sent node is the new node\n middle_node.nextval = NewNode\n\n # Function to remove node\n def RemoveNode(self, Removekey):\n #Get the node head\n HeadVal = self.headval\n\n if (HeadVal is not None):\n #If the node head has the same value as the one to remove\n if (HeadVal.dataval == Removekey):\n #The new head is the next node of the node head\n self.headval = HeadVal.nextval\n #The past node head is now none\n HeadVal = None\n return\n\n #Loop through the linked list\n #While the node is not none\n while (HeadVal is not None):\n #If the node has the same value as the one to remove\n if HeadVal.dataval == Removekey:\n #End the while\n break\n #prev is the current node in the loop\n prev = HeadVal\n #HeadVal is now the next node\n HeadVal = HeadVal.nextval\n\n #If the value was not found\n if (HeadVal == None):\n return\n\n #the next node of the previous node that is going to be Removed\n #Is going to be now the next node of the node that is going to be removed\n prev.nextval = HeadVal.nextval\n\n #the removed node is now none\n HeadVal = None\n\n#Create a new linked list\nlist = SingleLinkedList()\n#Establish the node head\nlist.headval = Node(\"Mon\")\n# Link first Node to second node\nlist.headval.nextval = Node(\"Tue\")\n# Link second Node to third node\nlist.headval.nextval.nextval = Node(\"Wed\")\n\nlist.AtBeginning(\"Sun\")\n\nlist.AtEnd(\"Thu\")\n\nlist.headval.nextval.nextval.nextval.nextval.nextval = Node(\"Sat\")\n\nlist.Inbetween(list.headval.nextval.nextval.nextval.nextval,\"Fri\")\n\nlist.AtEnd(\"Sam\")\n\nlist.RemoveNode(\"Sam\")\n\nlist.listprint()\n\ndef KtoLast(position):\n #if the linked list is empty\n if linkedList.headval == None:\n return None\n else:\n #current node is the node head\n currentNode = linkedList.headval\n #initialize counter\n counter = 0\n #while the currentNode is not None\n while(currentNode):\n #Increment the counter\n counter += 1\n #currentNode is now the next node\n currentNode = currentNode.nextval\n\n #current node is the node head\n currentNode = linkedList.headval\n #loop counter-position times\n # to get the kth to last node value\n for i in range(counter-position):\n #currentNode is now the next node\n currentNode = currentNode.nextval\n\n #return the value of the last currentNode\n return currentNode.dataval\n\n\nlinkedList = SingleLinkedList()\nlinkedList.headval = Node(1)\nlinkedList.headval.nextval = Node(2)\nlinkedList.headval.nextval.nextval = Node(8)\nlinkedList.headval.nextval.nextval.nextval = Node(3)\nlinkedList.headval.nextval.nextval.nextval.nextval = Node(7)\nlinkedList.headval.nextval.nextval.nextval.nextval.nextval = Node(0)\nlinkedList.AtEnd(4)\nprint(KtoLast(2))\n"}],"string":"[\n {\n \"alpha_fraction\": 0.47159841656684875,\n \"alphanum_fraction\": 0.4768824279308319,\n \"avg_line_length\": 16.204545974731445,\n \"blob_id\": \"47bd611ee36a0ee1c408bd866fd22e3dd5fffcde\",\n \"content_id\": \"88885b8875488945166ec7a931f49f6387f99975\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 757,\n \"license_type\": \"no_license\",\n \"max_line_length\": 51,\n \"num_lines\": 44,\n \"path\": \"/RepeatedString/string.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n\\n# Complete the repeatedString function below.\\ndef repeatedString(s, n):\\n\\n if set(s) == {\\\"a\\\"} :\\n return n\\n elif set(s) == {\\\"b\\\"} :\\n return 0\\n else:\\n length = len(s)\\n repeat = s.count('a')\\n\\n if length == n:\\n return repeat\\n else:\\n times = n/length\\n\\n if (times % 2) == 0:\\n return int(times * repeat)\\n else:\\n partial = int(times) * repeat\\n\\n cut = n - (int(times) * length)\\n\\n return partial + s[:cut].count('a')\\n\\n\\n\\nif __name__ == '__main__':\\n s = input()\\n\\n n = int(input())\\n\\n result = repeatedString(s, n)\\n\\n print(result)\\n\"\n },\n {\n \"alpha_fraction\": 0.6926829218864441,\n \"alphanum_fraction\": 0.6975609660148621,\n \"avg_line_length\": 16.08333396911621,\n \"blob_id\": \"5da91029e30b3c3f5a71b0cc6aeebedbf7489a07\",\n \"content_id\": \"bffaf52b98ae632fd3b036cc81c046aba0dd3f2a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 205,\n \"license_type\": \"no_license\",\n \"max_line_length\": 50,\n \"num_lines\": 12,\n \"path\": \"/AlphabeticalOrder/version1.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"## Version 1 read directly from the command prompt\\ncontactList = []\\n\\nname = input();\\nwhile name:\\n contactList.append(name)\\n name = input()\\n\\ncontactList.sort()\\n\\nfor name in contactList:\\n print(name)\\n\"\n },\n {\n \"alpha_fraction\": 0.491909384727478,\n \"alphanum_fraction\": 0.491909384727478,\n \"avg_line_length\": 21.071428298950195,\n \"blob_id\": \"53d868f97b6636cea3056c6a4d1ab7ea9da3ffb4\",\n \"content_id\": \"a395502cad7dfbb88d3733e240f31841dc020e12\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 618,\n \"license_type\": \"no_license\",\n \"max_line_length\": 113,\n \"num_lines\": 28,\n \"path\": \"/CaesarCypher/version1.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"alpha = [\\\"a\\\",\\\"b\\\",\\\"c\\\",\\\"d\\\",\\\"e\\\",\\\"f\\\",\\\"g\\\",\\\"h\\\",\\\"i\\\",\\\"j\\\",\\\"k\\\",\\\"l\\\",\\\"m\\\",\\\"n\\\",\\\"o\\\",\\\"p\\\",\\\"q\\\",\\\"r\\\",\\\"s\\\",\\\"t\\\",\\\"u\\\",\\\"v\\\",\\\"w\\\",\\\"x\\\",\\\"y\\\",\\\"z\\\"]\\ntext = input()\\nshift = input()\\n\\nfirstArray = alpha[:int(shift)]\\nSecondArray = alpha[-(len(alpha)-int(shift)):]\\n\\nnewAlpha= SecondArray + firstArray\\n\\nprint(newAlpha)\\n\\nnewText = \\\"\\\"\\n\\nfor x in text:\\n if x == \\\" \\\":\\n newText += x\\n elif x == \\\",\\\":\\n newText += x\\n elif x == \\\".\\\":\\n newText += x\\n elif x == \\\"!\\\":\\n newText += x\\n elif x.isupper():\\n newText += newAlpha[alpha.index(x.lower())].upper()\\n else:\\n newText += newAlpha[alpha.index(x)]\\n\\nprint(newText)\\n\"\n },\n {\n \"alpha_fraction\": 0.48778003454208374,\n \"alphanum_fraction\": 0.5,\n \"avg_line_length\": 15.931034088134766,\n \"blob_id\": \"c928e34b611f77691e1ddc6d47f51a0f73f378da\",\n \"content_id\": \"b84e3d1b346659cd9f182947becdbc1d71b8baee\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 982,\n \"license_type\": \"no_license\",\n \"max_line_length\": 50,\n \"num_lines\": 58,\n \"path\": \"/DictionarySherlock/anagram.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n# Complete the sherlockAndAnagrams function below.\\ndef sherlockAndAnagrams(s):\\n # dictAnagram = {}\\n #\\n # for element in s:\\n # if element in dictAnagram:\\n # dictAnagram[element] += 1\\n # else:\\n # dictAnagram[element] = 1\\n #\\n # if len(dictAnagram) == len(s):\\n # return 0\\n # print(dictAnagram)\\n dict={}\\n\\n count=0\\n\\n for i in range(len(s)):\\n\\n for j in range(i+1,len(s)+1):\\n\\n list1= list(s[i:j].strip())\\n\\n list1.sort()\\n\\n transf=''.join(list1)\\n\\n\\n if transf in dict:\\n\\n count+=dict[transf]\\n\\n dict[transf]=dict[transf]+1\\n\\n else:\\n dict[transf]=1\\n\\n\\n return count\\n\\nif __name__ == '__main__':\\n\\n q = int(input())\\n\\n for q_itr in range(q):\\n s = input()\\n\\n result = sherlockAndAnagrams(s)\\n\\n print(result)\\n\"\n },\n {\n \"alpha_fraction\": 0.6526032090187073,\n \"alphanum_fraction\": 0.6570915579795837,\n \"avg_line_length\": 36.13333511352539,\n \"blob_id\": \"090ab967e519c09336b44e5511e609e99d6de861\",\n \"content_id\": \"2bc193b87c7576142df46555a5bb2a8b9003079d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1114,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 30,\n \"path\": \"/stringCompression.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def stringCompression(stringRepeat):\\n #initialize the counter for characters\\n counter = 0\\n #establish currentCharacter as the first character\\n currentCharacter = stringRepeat[0]\\n #initialize the new string\\n compressed = \\\"\\\"\\n #We will go through each character\\n for i in stringRepeat:\\n if currentCharacter is not i:\\n #Add to the string the last character and their counter\\n compressed += currentCharacter + str(counter)\\n #The new character is now the currentCharacter\\n currentCharacter = i\\n #Reinitialize the counter\\n counter = 0\\n #Increment the counter in one for this first time\\n counter += 1\\n else:\\n #Increment the counter\\n counter += 1\\n\\n #Add to the string the last character and the last counter\\n compressed += currentCharacter + str(counter)\\n\\n #If the new string is bigger than the original string, return the original\\n return(stringRepeat if len(compressed) > len(stringRepeat) else compressed)\\n\\n\\nprint(stringCompression(\\\"aabcccccaaa\\\"))\\n\"\n },\n {\n \"alpha_fraction\": 0.5328031778335571,\n \"alphanum_fraction\": 0.5427435636520386,\n \"avg_line_length\": 14.71875,\n \"blob_id\": \"2e171ab7bd2f209381eb045046697dfc8592b84f\",\n \"content_id\": \"f4486710c4b6a92fb3ef5e95845c43e1e76d9c5e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 503,\n \"license_type\": \"no_license\",\n \"max_line_length\": 49,\n \"num_lines\": 32,\n \"path\": \"/sock.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n# Complete the sockMerchant function below.\\ndef sockMerchant(n, ar):\\n dic = {}\\n pairs = 0\\n\\n for x in ar:\\n if x in dic:\\n dic[x] += 1\\n else:\\n dic[x] = 1\\n\\n for value in dic.values():\\n pairs += int(value/2)\\n\\n return pairs\\n\\nif __name__ == '__main__':\\n n = int(input())\\n\\n ar = list(map(int, input().rstrip().split()))\\n\\n result = sockMerchant(n, ar)\\n\\n print(result)\\n\"\n },\n {\n \"alpha_fraction\": 0.5317646861076355,\n \"alphanum_fraction\": 0.5529412031173706,\n \"avg_line_length\": 17.478260040283203,\n \"blob_id\": \"d49b7ccb5a89e0d40406a6d4ec1d07d620101a4f\",\n \"content_id\": \"ab35d4e2ce23f8403ec3e4048ce167eac9ddf259\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 425,\n \"license_type\": \"no_license\",\n \"max_line_length\": 43,\n \"num_lines\": 23,\n \"path\": \"/BinarySearch/narce.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def binaria(lista, busqueda):\\n inicio =0\\n fin = len(lista)-1\\n\\n while inicio<=fin:\\n mitad= round((fin-inicio)/2)+inicio\\n\\n adivinar = lista[mitad]\\n\\n if adivinar == busqueda:\\n return mitad\\n elif adivinar > busqueda:\\n fin = mitad - 1\\n else:\\n inicio = mitad + 1\\n\\n return None\\n\\nlista = list(range(1,11))\\n\\nbusqueda = 6\\n\\nprint(binaria(lista, busqueda))\\n\"\n },\n {\n \"alpha_fraction\": 0.6936671733856201,\n \"alphanum_fraction\": 0.6980854272842407,\n \"avg_line_length\": 36.72222137451172,\n \"blob_id\": \"22abb24c9c3bb6353d6d2c6dafa250d79f0d6aee\",\n \"content_id\": \"7c3b654550569a2235f5763ebadf4743722f55e8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 679,\n \"license_type\": \"no_license\",\n \"max_line_length\": 109,\n \"num_lines\": 18,\n \"path\": \"/DictionaryUnique/unique.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#Function receives a string as parameter\\ndef unique(word):\\n #All the characters of the string become keys of dictionary and their value is 0\\n dictionary = dict.fromkeys(word,0)\\n #If the length of the string is the same as the length of the dictionary\\n #Then all characters are unique, if not the word has repeated characters\\n return (\\\"All characters are unique\\\" if len(word) == len(dictionary) else \\\"The characters are not unique\\\")\\n\\n\\n\\nprint(unique(\\\"book\\\"))\\n\\n# def unique(word):\\n# dictionary = dict.fromkeys(word,0)\\n# if len(word) == len(dictionary):\\n# return \\\"All characters are unique\\\"\\n# else:\\n# return \\\"The characters are not unique\\\"\\n\"\n },\n {\n \"alpha_fraction\": 0.5285046696662903,\n \"alphanum_fraction\": 0.5303738117218018,\n \"avg_line_length\": 25.09756088256836,\n \"blob_id\": \"36b8bac94ae0211b5765c892bb129cf96f37fce9\",\n \"content_id\": \"6316198d7d6e6ff18786a6dadb98674a40498d48\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2140,\n \"license_type\": \"no_license\",\n \"max_line_length\": 80,\n \"num_lines\": 82,\n \"path\": \"/binaryTreeHeight.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Node:\\n def __init__(self, info):\\n self.info = info\\n self.left = None\\n self.right = None\\n self.level = None\\n\\n def __str__(self):\\n return str(self.info)\\n\\nclass BinarySearchTree:\\n def __init__(self):\\n self.root = None\\n\\n def create(self, val):\\n if self.root == None:\\n self.root = Node(val)\\n else:\\n current = self.root\\n\\n while True:\\n if val < current.info:\\n if current.left:\\n current = current.left\\n else:\\n current.left = Node(val)\\n break\\n elif val > current.info:\\n if current.right:\\n current = current.right\\n else:\\n current.right = Node(val)\\n break\\n else:\\n break\\n\\n# Enter your code here. Read input from STDIN. Print output to STDOUT\\n'''\\nclass Node:\\n def __init__(self,info):\\n self.info = info\\n self.left = None\\n self.right = None\\n\\n\\n // this is a node of the tree , which contains info as data, left , right\\n'''\\ndef height(root):\\n #Initialize counter of the height of the left side\\n heightLeft = 0\\n #Initialize counter of the height of the right side\\n heightRight = 0\\n #If the parent node has a left node\\n if root.left:\\n #Increment heightLeft with one and\\n #Go into recursion with the left node as\\n #the new parent node\\n heightLeft = 1 + height(root.left)\\n #If the parent node has a right node\\n if root.right:\\n #Increment heightRight with one and\\n #Go into recursion with the right node as\\n #the new parent node\\n heightRight = 1 + height(root.right)\\n\\n #Return the highest of the heights\\n if heightLeft > heightRight:\\n return heightLeft\\n else:\\n return heightRight\\n\\n\\n\\ntree = BinarySearchTree()\\nt = int(input())\\n\\narr = list(map(int, input().split()))\\n\\nfor i in range(t):\\n tree.create(arr[i])\\n\\nprint(height(tree.root))\\n\"\n },\n {\n \"alpha_fraction\": 0.6099843978881836,\n \"alphanum_fraction\": 0.6411856412887573,\n \"avg_line_length\": 19.03125,\n \"blob_id\": \"44199b34f9127bceff4a78d2c65d295424093aac\",\n \"content_id\": \"6c8d7e557f0ec4df7d0f1f0d38e3b405f8661692\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 641,\n \"license_type\": \"no_license\",\n \"max_line_length\": 62,\n \"num_lines\": 32,\n \"path\": \"/DictionaryTwoStrings/two.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n# Complete the twoStrings function below.\\ndef twoStrings(s1, s2):\\n #Turns the elements of a list into keys and their value is 0\\n dictString1 = dict.fromkeys(s1,0)\\n dictString2 = dict.fromkeys(s2,0)\\n #Merge of two dictionaries into one dictionary\\n dict3 = {**dictString1, **dictString2}\\n\\n if len(dict3) < len(dictString1)+ len(dictString2):\\n return \\\"YES\\\"\\n else:\\n return \\\"NO\\\"\\n\\nif __name__ == '__main__':\\n q = int(input())\\n\\n for q_itr in range(q):\\n s1 = input()\\n\\n s2 = input()\\n\\n result = twoStrings(s1, s2)\\n\\n print(result)\\n\"\n },\n {\n \"alpha_fraction\": 0.3214700222015381,\n \"alphanum_fraction\": 0.40657639503479004,\n \"avg_line_length\": 14.666666984558105,\n \"blob_id\": \"98439749cabca3be7b3bbced7795606bef30f98f\",\n \"content_id\": \"d5f035ce216fd380770252eb93f38fba2613a3c9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2585,\n \"license_type\": \"no_license\",\n \"max_line_length\": 52,\n \"num_lines\": 165,\n \"path\": \"/Bribe/bribe.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n# Complete the minimumBribes function below.\\ndef minimumBribes(q,n):\\n # rest = []\\n # for x in range(n-1):\\n # rest.append(q[x]-q[x+1])\\n #\\n # print(rest)\\n # positive = [b for b in rest if b >= 0 ]\\n #\\n # print(positive)\\n #\\n # for c in positive:\\n # if c > 2:\\n # return \\\"Too chaotic\\\"\\n #\\n # return sum(positive)\\n #\\n #########\\n # change = []\\n # for x in range(n):\\n # change.append((q[x]-1)-x)\\n #\\n # sum = 0\\n # for x in range(len(change)):\\n #\\n # if change[x]>0:\\n # if change[x]>2:\\n # return \\\"Too chaotic\\\"\\n # else:\\n # sum += change[x]\\n # elif change[x] < -1:\\n # sum += 1\\n # return sum\\n ####################\\n # change = []\\n # for x in range(n):\\n # change.append((q[x]-1)-x)\\n #\\n # # print(change)\\n #\\n # for c in change:\\n # if c > 2:\\n # return \\\"Too chaotic\\\"\\n\\n bribe = 0\\n\\n for a in range(len(q)):\\n if ((q[a]-1)-a) > 2:\\n return \\\"Too chaotic\\\"\\n\\n c=a+1\\n for b in range(len(q)-c):\\n print(q[a],\\\" > \\\", q[c+b])\\n if q[a] > q[c+b]:\\n bribe += 1\\n\\n\\n print(bribe)\\n\\n\\n\\n\\nif __name__ == '__main__':\\n t = int(input())\\n\\n for t_itr in range(t):\\n n = int(input())\\n\\n q = list(map(int, input().rstrip().split()))\\n\\n minimumBribes(q,n)\\n\\n\\n# 9\\n# 8\\n# 1 2 3 5 4 6 7 8\\n# 5\\n# 2 1 5 3 4\\n# 5\\n# 2 5 1 3 4\\n# 5\\n# 2 1 5 4 3\\n# 8\\n# 1 2 5 3 4 7 8 6\\n# 5\\n# 2 1 5 3 4\\n# 5\\n# 2 5 1 3 4\\n# 8\\n# 5 1 2 3 7 8 6 4\\n# 8\\n# 1 2 5 3 7 8 6 4\\n#\\n# 9\\n# 8\\n# 1 2 3 5 4 6 7 8\\n# [0, 0, 0, 1, -1, 0, 0, 0]\\n# Moves: 1\\n#\\n# 5\\n# 2 1 5 3 4\\n# [1, -1, 2, -1, -1]\\n# Moves: 3\\n#\\n# 5\\n# 2 5 1 3 4\\n# [1, 3, -2, -1, -1]\\n# Moves: Too chaotic\\n#\\n# 5\\n# 2 1 5 4 3\\n# [1, -1, 2, 0, -2]\\n# Moves: 4\\n#\\n# 8\\n# 1 2 5 3 4 7 8 6\\n# [0, 0, 2, -1, -1, 1, 1, -2]\\n# Moves: 5\\n#\\n# 5\\n# 2 1 5 3 4\\n# [1, -1, 2, -1, -1]\\n# Moves: 3\\n#\\n# 5\\n# 2 5 1 3 4\\n# [1, 3, -2, -1, -1]\\n# Moves: Too chaotic\\n#\\n# 8\\n# 5 1 2 3 7 8 6 4\\n# [4, -1, -1, -1, 2, 2, -1, -4]\\n# Moves: Too chaotic\\n#\\n# 8\\n# 1 2 5 3 7 8 6 4\\n# [0, 0, 2, -1, 2, 2, -1, -4]\\n# Moves: 7\\n\\n# def minimumBribes(q,n):\\n# change = []\\n# for x in range(n):\\n# change.append((q[x]-1)-x)\\n#\\n# for c in change:\\n# if c > 2:\\n# return \\\"Too chaotic\\\"\\n#\\n# bribe = 0\\n#\\n# for a in range(len(q)):\\n# c=a+1\\n# for b in range(len(q)-c):\\n# if q[a] > q[c+b]:\\n# bribe += 1\\n#\\n# return(bribe)\\n\"\n },\n {\n \"alpha_fraction\": 0.5652680397033691,\n \"alphanum_fraction\": 0.5780885815620422,\n \"avg_line_length\": 15.188679695129395,\n \"blob_id\": \"d8d73a8d159da5abe370512680cda6c4e74eddfa\",\n \"content_id\": \"9ad2c8ccc8dd8de8bf2c23861b8c3b6bf716b28e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 858,\n \"license_type\": \"no_license\",\n \"max_line_length\": 63,\n \"num_lines\": 53,\n \"path\": \"/FlipBinary/flip.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\nbinary = []\\n\\ndef decimalToBinary(n):\\n if n > 1:\\n decimalToBinary(n//2)\\n binary.append(n%2)\\n\\n# Complete the flippingBits function below.\\ndef flippingBits(n):\\n string = \\\"\\\"\\n decimalToBinary(n)\\n\\n array = [1] * (32-len(binary))\\n\\n for index,element in enumerate(binary):\\n\\n if element == 1:\\n binary[index] = 0\\n else:\\n binary[index] = 1\\n\\n string = string.join([str(elem) for elem in binary])\\n\\n stringOnes= \\\"\\\"\\n stringOnes = stringOnes.join([str(elem) for elem in array])\\n\\n fullBinary = stringOnes + string\\n\\n return int(fullBinary,2)\\n\\n\\n\\nif __name__ == '__main__':\\n\\n\\n q = int(input())\\n\\n for q_itr in range(q):\\n n = int(input())\\n\\n result = flippingBits(n)\\n\\n binary = []\\n\\n print(result)\\n\"\n },\n {\n \"alpha_fraction\": 0.5052316784858704,\n \"alphanum_fraction\": 0.5261584520339966,\n \"avg_line_length\": 15.317072868347168,\n \"blob_id\": \"12f25bba2797afe8cf7003163fb22cc80c7fd483\",\n \"content_id\": \"8e46e2a281dd2e6cd3b74432f22869bc26a2dff7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 669,\n \"license_type\": \"no_license\",\n \"max_line_length\": 48,\n \"num_lines\": 41,\n \"path\": \"/JumpingClouds/clouds.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n# Complete the jumpingOnClouds function below.\\ndef jumpingOnClouds(c,n):\\n jumps = 0\\n index = 0\\n\\n while index < n-2 :\\n oneJump = c[index+1]\\n doubleJump = c[index+2]\\n if doubleJump==0:\\n jumps+=1\\n index = index + 2\\n continue\\n elif oneJump==0:\\n jumps+=1\\n index = index + 1\\n continue\\n\\n if index==(n-2):\\n jumps+=1\\n\\n return jumps\\n\\n\\n\\nif __name__ == '__main__':\\n\\n n = int(input())\\n\\n c = list(map(int, input().rstrip().split()))\\n\\n result = jumpingOnClouds(c,n)\\n\\n print(result)\\n\"\n },\n {\n \"alpha_fraction\": 0.6415094137191772,\n \"alphanum_fraction\": 0.6698113083839417,\n \"avg_line_length\": 31.615385055541992,\n \"blob_id\": \"42dddb6cc1bde0824df776072f109d19e202b976\",\n \"content_id\": \"b89e2aa7c96b1d328ff8873504e3f81802be7d78\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 424,\n \"license_type\": \"no_license\",\n \"max_line_length\": 94,\n \"num_lines\": 13,\n \"path\": \"/OneEditAway.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def OneEdit(str1,str2):\\n #Convert the strins into lists and then to sets\\n set1 = set(list(str1))\\n set2 = set(list(str2))\\n #Subtract the sets\\n setDifference = set1 - set2\\n\\n #If the difference is 1 or 0 elements, then it is one or 0 edits away\\n #Else, it has more than one edit\\n return(\\\"One or zero edits away\\\" if len(setDifference) <= 1 else \\\"More than one edit away\\\")\\n\\n\\nprint(OneEdit(\\\"pale\\\",\\\"bale\\\"))\\n\"\n },\n {\n \"alpha_fraction\": 0.5257731676101685,\n \"alphanum_fraction\": 0.5567010045051575,\n \"avg_line_length\": 8.699999809265137,\n \"blob_id\": \"8e9e8acce034e1d73b571ec7d1a78fad5af595ac\",\n \"content_id\": \"3a899b925eba252d97ae675c59c548cd4cd25b8f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 97,\n \"license_type\": \"no_license\",\n \"max_line_length\": 16,\n \"num_lines\": 10,\n \"path\": \"/SumNNumbers/version2.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"##Sum with a for\\nn = int(input())\\ni=1;\\nsuma=0\\n\\nwhile i<=n:\\n suma=suma+i\\n i+=1\\n\\nprint(suma)\\n\"\n },\n {\n \"alpha_fraction\": 0.5380333662033081,\n \"alphanum_fraction\": 0.5408163070678711,\n \"avg_line_length\": 33.774192810058594,\n \"blob_id\": \"50c73927ca175f9d9fcac82fd7ee98d88cab7b5f\",\n \"content_id\": \"64506b909e06c41a8163f29f70570ef0eb306325\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2156,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 62,\n \"path\": \"/binaryTree.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Node:\\n #Node Constructor\\n def __init__(self, data=None):\\n #Left node is none\\n self.left = None\\n #Right node is none\\n self.right = None\\n #Passed data is the value of the node\\n self.data = data\\n\\n def insert(self, data):\\n #If parent node value is not None\\n if self.data:\\n #If the passed value is less than the parent node value\\n if data < self.data:\\n #If the left node value is None\\n if self.left is None:\\n #Then create a left node for the parent node\\n #with this value\\n self.left = Node(data)\\n else:\\n #Recursion, keep looking for a place for this value\\n #left node is now the parent node for this value\\n self.left.insert(data)\\n #If the passed value is bigger than the parent node value\\n elif data > self.data:\\n #If the right node value is None\\n if self.right is None:\\n #Then create a right node for the parent node\\n #with this value\\n self.right = Node(data)\\n else:\\n #Recursion, keep looking for a place for this value\\n #right node is now the parent node for this value\\n self.right.insert(data)\\n #If parent node value is none\\n else:\\n #Give the root node this value\\n self.data = data\\n\\n # Print the tree\\n #It will print from the left most to the right most\\n def PrintTree(self):\\n #if parent node has a left node\\n if self.left:\\n #Recursion, left node is now the parent node\\n self.left.PrintTree()\\n #Print the parent node value\\n print( self.data)\\n #if parent node has a right\\n if self.right:\\n #Recursion, right node is now the parent node\\n self.right.PrintTree()\\n\\n#Establish the root node\\nroot = Node(12)\\n#Insert the rest of the nodes\\nroot.insert(6)\\nroot.insert(14)\\nroot.insert(3)\\n\\nroot.PrintTree()\\n\"\n },\n {\n \"alpha_fraction\": 0.650668740272522,\n \"alphanum_fraction\": 0.6608969569206238,\n \"avg_line_length\": 37.51515197753906,\n \"blob_id\": \"372c42f1bd3e6786d619cf9615fce949fdf87c00\",\n \"content_id\": \"0b771993eac08f9d91619c9abaaf2529356e1416\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1271,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 33,\n \"path\": \"/StringPermutations/perm.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#Global dictionary used to store the permutations\\ndictionary = dict()\\n\\ndef stringPermutation(word,lower,higher):\\n #If lower has been incremented to the same value as higher\\n if lower == higher:\\n #Convert word list back to string\\n newWord = ''.join(word)\\n #Add the string to the dictionary as key and give it 0 as value\\n dictionary[newWord] = 0\\n else:\\n #Loops from 0 to length of word\\n for i in range(lower,higher+1):\\n #Swaps characters\\n word[lower], word[i] = word[i], word[lower]\\n #Recursion, increment lower\\n stringPermutation(word,lower+1,higher)\\n #Swap the characters back to their place\\n word[lower], word[i] = word[i], word[lower]\\n\\n#Receives two strings as parameters\\ndef checkPermutation(str1,str2):\\n #Converts str1 into a list to manipulate it\\n word = list(str1)\\n #The list of str1 is sent to check its permutations\\n stringPermutation(word,0,len(word)-1)\\n #Check if the second string is in the dictionary of permutations\\n if str2 in dictionary:\\n print(\\\"Second string is a permutation of the First string\\\")\\n else:\\n print(\\\"Second string is not a permutation of the First string\\\")\\n\\ncheckPermutation(\\\"abc\\\",\\\"bac\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.6063745021820068,\n \"alphanum_fraction\": 0.6127489805221558,\n \"avg_line_length\": 22.240739822387695,\n \"blob_id\": \"15c5ba948608869fe0309177e365708fe19f54c5\",\n \"content_id\": \"eba6d22c9e474a9a6116f4e79253169dd1572350\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1255,\n \"license_type\": \"no_license\",\n \"max_line_length\": 64,\n \"num_lines\": 54,\n \"path\": \"/LLTail.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\nclass SinglyLinkedListNode:\\n def __init__(self, node_data):\\n self.data = node_data\\n self.next = None\\n\\nclass SinglyLinkedList:\\n def __init__(self):\\n self.head = None\\n\\n\\ndef insertNodeAtTail(head, data):\\n #To prevent maximum recursion depth exceeded error\\n sys.setrecursionlimit(1000000)\\n #If the node is None\\n if head == None:\\n #return the created node for that data\\n return SinglyLinkedListNode(data)\\n else:\\n #If the next value of the node is None\\n if head.next == None:\\n #create a node for data in the next node\\n head.next = SinglyLinkedListNode(data)\\n else:\\n #Recursion sending the next node of the current node\\n # and the data\\n insertNodeAtTail(head.next,data)\\n return head\\n\\n\\n\\nif __name__ == '__main__':\\n\\n llist_count = int(input())\\n\\n llist = SinglyLinkedList()\\n\\n for i in range(llist_count):\\n llist_item = int(input())\\n llist_head = insertNodeAtTail(llist.head, llist_item)\\n llist.head = llist_head\\n\\n node = llist.head\\n print(node.data)\\n while node:\\n print(node.data)\\n node = node.next\\n\"\n },\n {\n \"alpha_fraction\": 0.5595238208770752,\n \"alphanum_fraction\": 0.5664682388305664,\n \"avg_line_length\": 20.913043975830078,\n \"blob_id\": \"9a2752b154fd9bf54353abc877b39b96bbc9cabd\",\n \"content_id\": \"e34bd73e27769654fb3466a1eb5dffc4cef8d4a7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1008,\n \"license_type\": \"no_license\",\n \"max_line_length\": 66,\n \"num_lines\": 46,\n \"path\": \"/dictionaryTriplets.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n# Complete the countTriplets function below.\\ndef countTriplets(arr, r):\\n #Counter of triplets\\n count = 0\\n dict = {}\\n dictPairs = {}\\n\\n #Looping through the array backwards\\n for i in reversed(arr):\\n #If i*r is in the dictPairs\\n if i*r in dictPairs:\\n #Increment the counter with the value of\\n #i*r in dictPairs\\n count += dictPairs[i*r]\\n #If i*r is in dict\\n if i*r in dict:\\n #Insert i in dictPairs giving it the value of\\n #dictPairs[i] plus dict[i*r]\\n dictPairs[i] = dictPairs.get(i, 0) + dict[i*r]\\n\\n #Insert i in dict, giving it the value of dict[i] plus one\\n dict[i] = dict.get(i, 0) + 1\\n\\n return count\\n\\nif __name__ == '__main__':\\n\\n nr = input().rstrip().split()\\n\\n n = int(nr[0])\\n\\n r = int(nr[1])\\n\\n arr = list(map(int, input().rstrip().split()))\\n\\n ans = countTriplets(arr, r)\\n\\n print(ans)\\n\"\n },\n {\n \"alpha_fraction\": 0.5223880410194397,\n \"alphanum_fraction\": 0.5362473130226135,\n \"avg_line_length\": 16.370370864868164,\n \"blob_id\": \"5e4192ab0e44f8366e2e5112a1216b06e00e0f19\",\n \"content_id\": \"50e27860bebfe82fc79b620a3d30ad4e3b54c965\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 938,\n \"license_type\": \"no_license\",\n \"max_line_length\": 64,\n \"num_lines\": 54,\n \"path\": \"/ArrayManipulation/mani2.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n# Complete the arrayManipulation function below.\\ndef arrayManipulation(n,m, queries):\\n #To adjust the indexes with the queries ranges\\n array = [0] * (n+2)\\n\\n #Make an array with the deltas between each range\\n for x in range(m):\\n start = queries[x][0]\\n value = queries[x][2]\\n next = queries[x][1]+1\\n array[start] += value\\n\\n if next <= n:\\n array[next] -= value\\n\\n print((array))\\n\\n max = 0\\n sum = 0\\n b = 1\\n for a in range(n):\\n sum += array[b]\\n\\n if max < sum:\\n max = sum\\n\\n b +=1\\n\\n\\n print((array))\\n print(max)\\n\\nif __name__ == '__main__':\\n\\n nm = input().split()\\n\\n n = int(nm[0])\\n\\n m = int(nm[1])\\n\\n queries = []\\n\\n for _ in range(m):\\n queries.append(list(map(int, input().rstrip().split())))\\n\\n result = arrayManipulation(n,m,queries)\\n\"\n },\n {\n \"alpha_fraction\": 0.6722487807273865,\n \"alphanum_fraction\": 0.6937798857688904,\n \"avg_line_length\": 18.904762268066406,\n \"blob_id\": \"8287f1f399e875f88c328e60207744b8181ea978\",\n \"content_id\": \"d71db75c412c48420332091cdef8ffb2a738437c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 418,\n \"license_type\": \"no_license\",\n \"max_line_length\": 57,\n \"num_lines\": 21,\n \"path\": \"/BinarySearch/version1.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"## The user has to guess the number the computer selected\\nimport random\\n\\nminNumber = 1\\nmaxNumber = 100\\n\\nlistNumbers = list(range(minNumber,maxNumber+1))\\n\\nrandomNumber = random.randint(1, 100)\\n\\nguess = int(input())\\n\\nwhile guess!=randomNumber:\\n if guess>randomNumber:\\n print(\\\"mas bajo\\\")\\n elif guess<randomNumber:\\n print(\\\"mas alto\\\")\\n\\n guess = int(input())\\n\\nprint(\\\"YES! The number is\\\",randomNumber)\\n\"\n },\n {\n \"alpha_fraction\": 0.6454917788505554,\n \"alphanum_fraction\": 0.6516393423080444,\n \"avg_line_length\": 27.705883026123047,\n \"blob_id\": \"9844c9d509682937f3eccdc7b6f6d5653718b090\",\n \"content_id\": \"c0254eef76a8bfb862a168291c1d64094be44c42\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 488,\n \"license_type\": \"no_license\",\n \"max_line_length\": 74,\n \"num_lines\": 17,\n \"path\": \"/AlphabeticalOrder/version3.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"##Version 3 solution without the sort function\\ncontactList = []\\nfile = open(\\\"sampleInput.txt\\\", \\\"r\\\")\\n\\nfor name in file:\\n name = name.strip('\\\\n')\\n contactList.append(name)\\n\\nfor i in range(len(contactList)):\\n for j in range(i + 1, len(contactList)):\\n\\n if contactList[i] > contactList[j]:\\n contactList[i], contactList[j] = contactList[j], contactList[i]\\n\\nwith open(\\\"outputVersion3.txt\\\", \\\"w\\\") as newFile:\\n for name in contactList:\\n newFile.write(name+\\\"\\\\n\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.5773195624351501,\n \"alphanum_fraction\": 0.6082473993301392,\n \"avg_line_length\": 12.857142448425293,\n \"blob_id\": \"254c7aca6f210e3eaf160fd25e15a058bb1b55ee\",\n \"content_id\": \"44ad53417c162e53a80a48f678b34b3bcea7342c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 97,\n \"license_type\": \"no_license\",\n \"max_line_length\": 22,\n \"num_lines\": 7,\n \"path\": \"/SumNNumbers/version3.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"##Sum with a while\\nn = int(input())\\nsuma = 0\\nfor i in range(1,n+1):\\n suma=suma+i\\n\\nprint(suma)\\n\"\n },\n {\n \"alpha_fraction\": 0.5470494627952576,\n \"alphanum_fraction\": 0.5645933151245117,\n \"avg_line_length\": 16.91428565979004,\n \"blob_id\": \"ca3e39475b1f9d946300c6fecc350800403c1caa\",\n \"content_id\": \"99923cafa15a53935e5a7955bb3dbd1177536367\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 627,\n \"license_type\": \"no_license\",\n \"max_line_length\": 64,\n \"num_lines\": 35,\n \"path\": \"/ArrayManipulation/mani.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n# Complete the arrayManipulation function below.\\ndef arrayManipulation(n,m, queries):\\n array = [0] * n\\n\\n for x in range(m):\\n index = queries[x][0]-1\\n for a in range(queries[x][1]-(queries[x][0]-1)):\\n array[index] += queries[x][2]\\n index +=1\\n\\n print(max(array))\\n\\n\\nif __name__ == '__main__':\\n\\n nm = input().split()\\n\\n n = int(nm[0])\\n\\n m = int(nm[1])\\n\\n queries = []\\n\\n for _ in range(m):\\n queries.append(list(map(int, input().rstrip().split())))\\n\\n result = arrayManipulation(n,m,queries)\\n\"\n },\n {\n \"alpha_fraction\": 0.8857142925262451,\n \"alphanum_fraction\": 0.8857142925262451,\n \"avg_line_length\": 34,\n \"blob_id\": \"b39ecaf6eb12baa7c5905ad6660655321ffc4abe\",\n \"content_id\": \"0ca38d4472eb26c1fa5c2f96558056e311c9ccbc\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 70,\n \"license_type\": \"no_license\",\n \"max_line_length\": 35,\n \"num_lines\": 2,\n \"path\": \"/README.md\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Competitive-Programming-Exercises\\nCompetitive Programming exercises\\n\"\n },\n {\n \"alpha_fraction\": 0.5685884952545166,\n \"alphanum_fraction\": 0.5685884952545166,\n \"avg_line_length\": 24.149999618530273,\n \"blob_id\": \"ff15f1c036bc9fe100a4c8143d6f328f7bf88862\",\n \"content_id\": \"36414a8c7eef7b0bd881f4274f0fa33fdc4f07a2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1509,\n \"license_type\": \"no_license\",\n \"max_line_length\": 60,\n \"num_lines\": 60,\n \"path\": \"/bstInsertion.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Node:\\n def __init__(self, info):\\n self.info = info\\n self.left = None\\n self.right = None\\n self.level = None\\n\\n def __str__(self):\\n return str(self.info)\\n\\ndef preOrder(root):\\n if root == None:\\n return\\n print (root.info, end=\\\" \\\")\\n preOrder(root.left)\\n preOrder(root.right)\\n\\nclass BinarySearchTree:\\n def __init__(self):\\n self.root = None\\n\\n def insertion(self,cur,val):\\n #If cur doesn't exist\\n if not cur:\\n #Create a node with the passed value\\n cur = Node(val)\\n #If the value of cur is bigger than the passed value\\n elif cur.info > val:\\n #Left node of cur is recursive function\\n #With left empty node of cur sent and the value\\n cur.left = self.insertion(cur.left,val)\\n else:\\n #Right node of cur is recursive function\\n #With right empty node of cur sent and the value\\n cur.right = self.insertion(cur.right,val)\\n\\n return cur\\n\\n def insert(self, val):\\n #If node root is None\\n if self.root==None:\\n #Create a node with the passed value\\n #And establish it as the root node\\n self.root = Node(val)\\n else:\\n #Send root node and value to insertion function\\n self.insertion(self.root,val)\\n\\n\\n\\n\\ntree = BinarySearchTree()\\nt = int(input())\\n\\narr = list(map(int, input().split()))\\n\\nfor i in range(t):\\n tree.insert(arr[i])\\n\\npreOrder(tree.root)\\n\"\n },\n {\n \"alpha_fraction\": 0.6412371397018433,\n \"alphanum_fraction\": 0.6505154371261597,\n \"avg_line_length\": 28.393939971923828,\n \"blob_id\": \"39698dbb88ab79ce6433b67854f473e9059223cc\",\n \"content_id\": \"87267016773ba4c11c3de2ae0b938edf10e0e4aa\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 970,\n \"license_type\": \"no_license\",\n \"max_line_length\": 56,\n \"num_lines\": 33,\n \"path\": \"/bstMinimal.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class TreeNode(object):\\n def __init__(self, x):\\n self.val = x\\n self.left = None\\n self.right = None\\n\\ndef sorted_array_to_bst(nums):\\n #If array is empty return None\\n if not nums:\\n return None\\n #Get the middle element of the array\\n mid_val = len(nums)//2\\n #Create a new node with the middle element\\n node = TreeNode(nums[mid_val])\\n #Left node of node is recursion of this function\\n #sending the array from the start until the element\\n #before the middle element\\n node.left = sorted_array_to_bst(nums[:mid_val])\\n #Right node of node is recursion of this function\\n #sending the array from the element after the middle\\n #element until the end\\n node.right = sorted_array_to_bst(nums[mid_val+1:])\\n return node\\n\\ndef preOrder(node):\\n if not node:\\n return\\n print(node.val)\\n preOrder(node.left)\\n preOrder(node.right)\\n\\nresult = sorted_array_to_bst([1, 2, 3, 4, 5, 6, 7])\\npreOrder(result)\\n\"\n },\n {\n \"alpha_fraction\": 0.6691297292709351,\n \"alphanum_fraction\": 0.6806239485740662,\n \"avg_line_length\": 31.91891860961914,\n \"blob_id\": \"7bc76dcfb9ae21ebbc1f74ce1205929de5410cc5\",\n \"content_id\": \"44c889f1780abc98052ea16683a2a24842d46de3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1218,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 37,\n \"path\": \"/bstClosest.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class TreeNode(object):\\n def __init__(self, x):\\n self.val = x\\n self.left = None\\n self.right = None\\n\\ndef closest_value(root, target):\\n #a has the value of root\\n a = root.val\\n #if the target number is lower than the value of root\\n #then kid is root left node else kid is root right node\\n kid = root.left if target < a else root.right\\n #If the nodes are None\\n if not kid:\\n #return the value of root\\n return a\\n #if the nodes are not None, b is recursion of this function\\n #with the kid as the new root and send the target\\n b = closest_value(kid, target)\\n #lambda function to check the absolute value of the subtraction\\n #of the target number with a parent node and one of its child nodes\\n #the function return the node value associated with the lowest of\\n #the subtractions\\n return min((a,b), key=lambda x: abs(target-x))\\n\\nroot = TreeNode(8)\\nroot.left = TreeNode(5)\\nroot.right = TreeNode(14)\\nroot.left.left = TreeNode(4)\\nroot.left.right = TreeNode(6)\\nroot.left.right.left = TreeNode(8)\\nroot.left.right.right = TreeNode(7)\\nroot.right.right = TreeNode(24)\\nroot.right.right.left = TreeNode(22)\\n\\nresult = closest_value(root, 19)\\nprint(result)\\n\"\n },\n {\n \"alpha_fraction\": 0.6209093928337097,\n \"alphanum_fraction\": 0.6295211911201477,\n \"avg_line_length\": 31.80225944519043,\n \"blob_id\": \"d58fb5795461ed4a1db9544ea0252c567bc99100\",\n \"content_id\": \"5894320158ac22e03a2d58aa9004aa22b89ba0de\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5806,\n \"license_type\": \"no_license\",\n \"max_line_length\": 81,\n \"num_lines\": 177,\n \"path\": \"/LLSum.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Node:\\n #Node constructor\\n def __init__(self, dataval=None):\\n #Give the new instance the passed value\\n self.dataval = dataval\\n #The next node of the node is none\\n self.nextval = None\\n\\nclass SingleLinkedList:\\n #Single Linked List constructor\\n def __init__(self):\\n #The node head value is none\\n self.headval = None\\n\\n #Print LinkedList\\n def listprint(self):\\n #Get the the node head\\n printval = self.headval\\n while printval is not None:\\n #Print the value of the node\\n print (printval.dataval)\\n #Printval is now the next node\\n printval = printval.nextval\\n\\n #Insert a new node at the beginning\\n def AtBeginning(self,newdata):\\n #Create a new node with the passed data as its value\\n NewNode = Node(newdata)\\n # The next node is going to be the current node head\\n NewNode.nextval = self.headval\\n #The new node is going to be the new node head\\n self.headval = NewNode\\n\\n # Insert a newnode at the end\\n def AtEnd(self, newdata):\\n #Create a new node with the passed data as its value\\n NewNode = Node(newdata)\\n #If the linked list is empty\\n if self.headval is None:\\n #Then the new node is the new head\\n self.headval = NewNode\\n return\\n #Get the node head\\n laste = self.headval\\n #While the next node is not empty\\n while(laste.nextval):\\n #Get next node\\n laste = laste.nextval\\n #The next value of the last node is the new node\\n laste.nextval=NewNode\\n\\n # Function to add a node between two nodes\\n def Inbetween(self,middle_node,newdata):\\n if middle_node is None:\\n print(\\\"The mentioned node is absent\\\")\\n return\\n\\n #Create a new node with the passed data as its value\\n NewNode = Node(newdata)\\n #The next value of the new node is the next value of the sent node\\n NewNode.nextval = middle_node.nextval\\n #the next value of the sent node is the new node\\n middle_node.nextval = NewNode\\n\\n # Function to remove node\\n def RemoveNode(self, Removekey):\\n #Get the node head\\n HeadVal = self.headval\\n\\n if (HeadVal is not None):\\n #If the node head has the same value as the one to remove\\n if (HeadVal.dataval == Removekey):\\n #The new head is the next node of the node head\\n self.headval = HeadVal.nextval\\n #The past node head is now none\\n HeadVal = None\\n return\\n\\n #Loop through the linked list\\n #While the node is not none\\n while (HeadVal is not None):\\n #If the node has the same value as the one to remove\\n if HeadVal.dataval == Removekey:\\n #End the while\\n break\\n #prev is the current node in the loop\\n prev = HeadVal\\n #HeadVal is now the next node\\n HeadVal = HeadVal.nextval\\n\\n #If the value was not found\\n if (HeadVal == None):\\n return\\n\\n #the next node of the previous node that is going to be Removed\\n #Is going to be now the next node of the node that is going to be removed\\n prev.nextval = HeadVal.nextval\\n\\n #the removed node is now none\\n HeadVal = None\\n\\n\\ndef LLSum(list1,list2):\\n #Get the head of the first list\\n node1 = list1.headval\\n #Create a stack to store the numbers\\n listNum1 = []\\n #Loop until the end of the linked list 1\\n while node1:\\n #Insert the value of the node in the stack\\n listNum1.append(node1.dataval)\\n #node1 is now the next node\\n node1 = node1.nextval\\n\\n #Get the head of the second list\\n node2 = list2.headval\\n #Create a stack to store the numbers\\n listNum2 = []\\n #Loop until the end of the linked list 2\\n while node2:\\n #Insert the value of the node in the stack\\n listNum2.append(node2.dataval)\\n #node2 is now the next node\\n node2 = node2.nextval\\n\\n #Create a list filling in the inverse order of the original linked list 1\\n stringNum = [str(listNum1.pop()) for i in range(len(listNum1))]\\n #Join all the elements of the list into a single string\\n joinString = \\\"\\\".join(stringNum)\\n #Convert that string into an integer\\n stringInt1 = int(joinString)\\n\\n #Create a list filling in the inverse order of the original linked list 2\\n stringNum = [str(listNum2.pop()) for i in range(len(listNum2))]\\n #Join all the elements of the list into a single string\\n joinString = \\\"\\\".join(stringNum)\\n #Convert that string into an integer\\n stringInt2 = int(joinString)\\n\\n #Sum both integers\\n sumNumber = stringInt1 + stringInt2\\n\\n #Create a list eith each digit of the sum converted into a string\\n sumStringNum = [int for int in str(sumNumber)]\\n\\n #Create the linked list where we will place the sum of the two other LLs\\n sumList = SingleLinkedList()\\n #The node head will be the first element of the sumStringNum list\\n sumList.headval = Node(sumStringNum.pop(0))\\n #nodeLoop is the node head\\n nodeLoop = sumList.headval\\n\\n #Loop until the sumStringNum list is empty\\n while sumStringNum:\\n #The next node of nodeLoop will be the first element in the\\n #sumStringNum list\\n nodeLoop.nextval = Node(sumStringNum.pop(0))\\n #NodeLoop is now the next node of NodeLoop\\n nodeLoop = nodeLoop.nextval\\n\\n #Print the Linked List\\n sumList.listprint()\\n\\n\\n\\n\\nlist1 = SingleLinkedList()\\nlist1.headval = Node(7)\\nlist1.headval.nextval = Node(1)\\nlist1.AtEnd(6)\\n\\nlist2 = SingleLinkedList()\\nlist2.headval = Node(5)\\nlist2.headval.nextval = Node(9)\\nlist2.AtEnd(2)\\n\\nLLSum(list1,list2)\\n\"\n },\n {\n \"alpha_fraction\": 0.7234042286872864,\n \"alphanum_fraction\": 0.7234042286872864,\n \"avg_line_length\": 22.5,\n \"blob_id\": \"5b57b956e7e7f5843fe4f78c234792e4375a0b30\",\n \"content_id\": \"9024663c8524a9ed988fb5fd747c64272b869ab2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 188,\n \"license_type\": \"no_license\",\n \"max_line_length\": 32,\n \"num_lines\": 8,\n \"path\": \"/Union/union.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"englishNumber = input()\\nenglishStudents = input()\\nfrenchNumber = input()\\nfrenchStudents = input()\\n\\ne = set(englishStudents.split())\\nf = set(frenchStudents.split())\\nprint (len(e.union(f)))\\n\"\n },\n {\n \"alpha_fraction\": 0.5757575631141663,\n \"alphanum_fraction\": 0.6060606241226196,\n \"avg_line_length\": 21,\n \"blob_id\": \"27c2cbb7c4a94c075c296d2db2772ae85f6eef35\",\n \"content_id\": \"754e4a014402f19f31423a95aa78f1c2f7f583d6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 66,\n \"license_type\": \"no_license\",\n \"max_line_length\": 25,\n \"num_lines\": 3,\n \"path\": \"/SumNNumbers/version1.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"##Sum with the formula\\nn = int(input())\\nprint(round((n*(n+1))/2))\\n\"\n },\n {\n \"alpha_fraction\": 0.5591259598731995,\n \"alphanum_fraction\": 0.5694087147712708,\n \"avg_line_length\": 17.0930233001709,\n \"blob_id\": \"269c0a023f2bbd5410edc8e9bc7aae4328c090d6\",\n \"content_id\": \"630b3b6b8a61154229add3ae99bc4b33ecc2c1ff\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 778,\n \"license_type\": \"no_license\",\n \"max_line_length\": 50,\n \"num_lines\": 43,\n \"path\": \"/DictionaryCountingTriplets/version1.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n# Complete the countTriplets function below.\\ndef countTriplets(arr, r):\\n maxNumber = max(arr)\\n dictNumbers = dict.fromkeys(arr,0)\\n newList = []\\n newList.append(1)\\n\\n for number in dictNumbers:\\n if number is not maxNumber:\\n newList.append(number*r)\\n\\n tripleList = []\\n triple = []\\n\\n for i=0; i<=(len(newList)-3); i++:\\n for a=i; a<=(a+3);a++:\\n triple.append(newList[a])\\n tripleList.append(triple)\\n triple = []\\n\\n\\n print(newList)\\n\\nif __name__ == '__main__':\\n nr = input().rstrip().split()\\n\\n n = int(nr[0])\\n\\n r = int(nr[1])\\n\\n arr = list(map(int, input().rstrip().split()))\\n\\n ans = countTriplets(arr, r)\\n\\n print(ans)\\n\"\n },\n {\n \"alpha_fraction\": 0.6601307392120361,\n \"alphanum_fraction\": 0.6666666865348816,\n \"avg_line_length\": 22.538461685180664,\n \"blob_id\": \"4874ec1be356687f7b5f0a30b281a785e4bae6aa\",\n \"content_id\": \"b4f9c6aa089471508dbb2d30278823724a2a449e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 306,\n \"license_type\": \"no_license\",\n \"max_line_length\": 48,\n \"num_lines\": 13,\n \"path\": \"/AlphabeticalOrder/version2.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"##Version 2 input and output with text files\\ncontactList = []\\nfile = open(\\\"sampleInput.txt\\\", \\\"r\\\")\\n\\nfor name in file:\\n name = name.strip('\\\\n')\\n contactList.append(name)\\n\\ncontactList.sort()\\n\\nwith open(\\\"outputVersion2.txt\\\", \\\"w\\\") as newFile:\\n for name in contactList:\\n newFile.write(name+\\\"\\\\n\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.4566666781902313,\n \"alphanum_fraction\": 0.47333332896232605,\n \"avg_line_length\": 18.148935317993164,\n \"blob_id\": \"c609b892916b6342c413d0980f0cf97bcab696a3\",\n \"content_id\": \"248ca7b5ced80bc088c46beb2fcc2d153d0d4c65\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 900,\n \"license_type\": \"no_license\",\n \"max_line_length\": 69,\n \"num_lines\": 47,\n \"path\": \"/DictionaryFreqQueries.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n# Complete the freqQuery function below.\\ndef freqQuery(queries):\\n arr=[]\\n ans=[]\\n dictionary={}\\n for query in queries:\\n for j in range(1):\\n if query[0]==1:\\n arr.append(query[1])\\n dictionary[query[1]] = dictionary.get(query[1],0) + 1\\n elif query[0]==2:\\n try:\\n arr.remove(query[1])\\n except:\\n break\\n else:\\n if query[1] in dictionary.values():\\n ans.append(1)\\n else:\\n ans.append(0)\\n\\n\\n return ans\\n\\n\\n\\n\\nif __name__ == '__main__':\\n\\n q = int(input().strip())\\n\\n queries = []\\n\\n for _ in range(q):\\n queries.append(list(map(int, input().rstrip().split())))\\n\\n ans = freqQuery(queries)\\n\\n print(ans)\\n\"\n },\n {\n \"alpha_fraction\": 0.45755693316459656,\n \"alphanum_fraction\": 0.48033127188682556,\n \"avg_line_length\": 17.576923370361328,\n \"blob_id\": \"9b5b0fa1805e2ffb5d0679802c454b506c43e362\",\n \"content_id\": \"30b1bcb95389fe46544bd18a5dca4479e9f81ec4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 483,\n \"license_type\": \"no_license\",\n \"max_line_length\": 43,\n \"num_lines\": 26,\n \"path\": \"/BinarySearch/version3.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"##corrected Book version play with computer\\ndef binary_search(list, item):\\n\\n low = 0\\n high = len(list)-1\\n\\n while low <= high:\\n mid = (round((high-low+1)/2))+low\\n guess = list[mid]\\n\\n print(guess)\\n\\n x = input()\\n if x == \\\"yes\\\":\\n return \\\" \\\"\\n if x == \\\"mas bajo\\\":\\n high = mid -1\\n elif x == \\\"mas alto\\\":\\n low = mid + 1\\n\\n\\n print(\\\" \\\")\\n\\nmy_list = list(range(1,101))\\n\\nprint (binary_search(my_list,5))\\n\"\n },\n {\n \"alpha_fraction\": 0.452160507440567,\n \"alphanum_fraction\": 0.46759259700775146,\n \"avg_line_length\": 16.052631378173828,\n \"blob_id\": \"46b40589ea1ebab1d5ea2f67f8c8b6eda95a7374\",\n \"content_id\": \"a90050668a543ddfdb6ae8d419c6fc52000ee2e9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 648,\n \"license_type\": \"no_license\",\n \"max_line_length\": 52,\n \"num_lines\": 38,\n \"path\": \"/Bribe/bribe2.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n# Complete the minimumBribes function below.\\ndef minimumBribes(q,n):\\n bribe = 0\\n c = len(q)-1\\n for a in range(len(q)):\\n\\n\\n if (q[c] - (c + 1)) > 2:\\n return \\\"Too chaotic\\\"\\n\\n b = max(0,q[c]-2)\\n while b<c:\\n print(q[b],\\\">\\\",q[c])\\n if q[b] > q[c]:\\n bribe += 1\\n\\n b +=1\\n c += -1\\n\\n return bribe\\n\\nif __name__ == '__main__':\\n t = int(input())\\n\\n for t_itr in range(t):\\n n = int(input())\\n\\n q = list(map(int, input().rstrip().split()))\\n\\n print(minimumBribes(q,n))\\n\"\n },\n {\n \"alpha_fraction\": 0.572311520576477,\n \"alphanum_fraction\": 0.5741656422615051,\n \"avg_line_length\": 18.493976593017578,\n \"blob_id\": \"ea5cb26d40c440347f6f96a4d5e47ebddea9faa0\",\n \"content_id\": \"d795861baffb326567eb9356b69c228a7a15ad9a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1618,\n \"license_type\": \"no_license\",\n \"max_line_length\": 65,\n \"num_lines\": 83,\n \"path\": \"/LlInsert.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\nclass SinglyLinkedListNode:\\n def __init__(self, node_data):\\n self.data = node_data\\n self.next = None\\n\\nclass SinglyLinkedList:\\n def __init__(self):\\n self.head = None\\n self.tail = None\\n\\n def insert_node(self, node_data):\\n node = SinglyLinkedListNode(node_data)\\n\\n if not self.head:\\n self.head = node\\n else:\\n self.tail.next = node\\n\\n\\n self.tail = node\\n\\n def printList(self):\\n node = self.head\\n print(\\\" \\\")\\n while node:\\n print(node.data)\\n node = node.next\\n\\ndef print_singly_linked_list(node, sep, fptr):\\n while node:\\n fptr.write(str(node.data))\\n\\n node = node.next\\n\\n if node:\\n fptr.write(sep)\\n\\ndef insertNodeAtPosition(head, data, position):\\n node = head\\n if position==0:\\n pastHead = head\\n newHead = SinglyLinkedListNode(data)\\n newHead.next = pastHead\\n else:\\n for i in range(position-1):\\n node = node.next\\n pastNode = node\\n nextNode = node.next\\n newNode = SinglyLinkedListNode(data)\\n pastNode.next = newNode\\n newNode.next = nextNode\\n\\n\\n return head\\n\\n\\n\\nif __name__ == '__main__':\\n\\n\\n llist_count = int(input())\\n\\n llist = SinglyLinkedList()\\n\\n for _ in range(llist_count):\\n llist_item = int(input())\\n llist.insert_node(llist_item)\\n\\n data = int(input())\\n\\n position = int(input())\\n\\n llist_head = insertNodeAtPosition(llist.head, data, position)\\n\\n llist_head.printList()\\n\"\n },\n {\n \"alpha_fraction\": 0.5798165202140808,\n \"alphanum_fraction\": 0.5871559381484985,\n \"avg_line_length\": 17.16666603088379,\n \"blob_id\": \"68842d43d8c0d14adc4d002949d153ba171de438\",\n \"content_id\": \"8d7b944e26d7a7bc6cd3185e37bb9ff52a09ada8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 545,\n \"license_type\": \"no_license\",\n \"max_line_length\": 65,\n \"num_lines\": 30,\n \"path\": \"/Swap/swap.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n# Complete the minimumSwaps function below.\\ndef minimumSwaps(arr):\\n swap = 0\\n arr = [x-1 for x in arr]\\n\\n for index,element in enumerate(arr):\\n if element > index:\\n newIndex = arr.index(index)\\n arr[index], arr[newIndex] = arr[newIndex], arr[index]\\n swap += 1\\n\\n print(arr)\\n print(swap)\\n\\n\\nif __name__ == '__main__':\\n\\n n = int(input())\\n\\n arr = list(map(int, input().rstrip().split()))\\n\\n res = minimumSwaps(arr)\\n\"\n },\n {\n \"alpha_fraction\": 0.5017668008804321,\n \"alphanum_fraction\": 0.5159010887145996,\n \"avg_line_length\": 13.51282024383545,\n \"blob_id\": \"ca81ef75285f69f5b1baa19630e6335c3c2320ed\",\n \"content_id\": \"bdf85bd022622a3c15b272493bf3778c46e704c4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 566,\n \"license_type\": \"no_license\",\n \"max_line_length\": 46,\n \"num_lines\": 39,\n \"path\": \"/CountingValleys/valleys.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n# Complete the countingValleys function below.\\ndef countingValleys(n, s):\\n walk = [0]\\n counter = 0\\n valleys = 0\\n\\n for x in s:\\n if x==\\\"D\\\" :\\n if counter==0 :\\n valleys += 1\\n\\n counter += -1\\n walk.append(counter)\\n\\n else:\\n counter += 1\\n walk.append(counter)\\n\\n return valleys\\n\\n\\n\\nif __name__ == '__main__':\\n\\n n = int(input())\\n\\n s = input()\\n\\n result = countingValleys(n, s)\\n\\n print(result)\\n\"\n },\n {\n \"alpha_fraction\": 0.5988608598709106,\n \"alphanum_fraction\": 0.6037428975105286,\n \"avg_line_length\": 25.148935317993164,\n \"blob_id\": \"6a0af8337ad195a770f1abc032b3418e75c2ecbd\",\n \"content_id\": \"23125830f2d1ce4b17bc7d241f6370afcc0d9601\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1229,\n \"license_type\": \"no_license\",\n \"max_line_length\": 65,\n \"num_lines\": 47,\n \"path\": \"/bstCheckIs.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class TreeNode(object):\\n def __init__(self, x):\\n self.val = x\\n self.left = None\\n self.right = None\\n\\ndef is_BST(root):\\n #Empty stack to store the nodes\\n stack = []\\n #previous node variable equal to None\\n prev = None\\n\\n #While the node is not None or the stack is empty\\n while root or stack:\\n #While node is not None\\n while root:\\n #Append the node to the stack\\n stack.append(root)\\n #Root is now the left node of root\\n root = root.left\\n #Root is now the last element appended to the stack\\n root = stack.pop()\\n #If prev is not None and the root value is lower or equal\\n #to the value of prev, return false\\n if prev and root.val <= prev.val:\\n return False\\n #Prev is now root\\n prev = root\\n #Root is now the right node of root\\n root = root.right\\n #Return true when their is nothing in the stack or if the\\n #variable root is None\\n return True\\n\\nroot = TreeNode(2)\\nroot.left = TreeNode(1)\\nroot.right = TreeNode(3)\\n\\nresult = is_BST(root)\\nprint(result)\\n\\nroot = TreeNode(1)\\nroot.left = TreeNode(2)\\nroot.right = TreeNode(3)\\n\\nresult = is_BST(root)\\nprint(result)\\n\"\n },\n {\n \"alpha_fraction\": 0.5941422581672668,\n \"alphanum_fraction\": 0.5941422581672668,\n \"avg_line_length\": 29.838708877563477,\n \"blob_id\": \"7b89cc1cfc6d5a7d3f5d4513e46d12c9e57c5167\",\n \"content_id\": \"a574cc272a0442a6b6569fb090394b8c631608cf\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 956,\n \"license_type\": \"no_license\",\n \"max_line_length\": 119,\n \"num_lines\": 31,\n \"path\": \"/bstCheck.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\" Node is defined as\\nclass node:\\n def __init__(self, data):\\n self.data = data\\n self.left = None\\n self.right = None\\n\\\"\\\"\\\"\\ndef checkBST(root):\\n return checkBSTo(root, None, None)\\n\\ndef checkBSTo(root,min,max):\\n if root==None:\\n return \\\"Yes\\\"\\n if ((min is not None and root.data <=min)) or ((max is not None and root.data > max)):\\n return \\\"No\\\"\\n if not(checkBSTo(root.left,min,root.data)) or not(checkBSTo(root.right,root.data,max)):\\n return \\\"No\\\"\\n return \\\"Yes\\\"\\n###########\\ndef checkBST(root):\\n return check(root,float('inf'),float('-inf'))\\n\\ndef check(node,min,max):\\n if(node==None):\\n return \\\"Yes\\\"\\n return min < node.data and node.data < max and check(node.left, min, node.data) and check(node.right,node.data,max)\\n #############\\n # if(node.data <= min or node.data >=max):\\n # return \\\"No\\\"\\n #\\n # return check(node.left,min,node.data) and check(node.right,node.data,max)\\n\"\n },\n {\n \"alpha_fraction\": 0.5178294777870178,\n \"alphanum_fraction\": 0.5302325487136841,\n \"avg_line_length\": 17.428571701049805,\n \"blob_id\": \"c1725970028bcdb4d1e6c1016355d50207a9b97a\",\n \"content_id\": \"ea704b33914eec30b22bdbb4d7e36417afd628c7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 645,\n \"license_type\": \"no_license\",\n \"max_line_length\": 54,\n \"num_lines\": 35,\n \"path\": \"/Bribe/bribe3.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/python3\\n\\nimport math\\nimport os\\nimport random\\nimport re\\nimport sys\\n\\n# Complete the minimumBribes function below.\\ndef minimumBribes(q,n):\\n bribe = 0\\n q = [x-1 for x in q]\\n\\n for i,x in enumerate(q):\\n\\n #If any element of the array is ahead 2 places\\n if x - i > 2:\\n return \\\"Too chaotic\\\"\\n\\n #Who bribed x\\n for j in range(max(x-1,0),i):\\n if q[j] > x:\\n bribe += 1\\n\\n return bribe\\n\\nif __name__ == '__main__':\\n t = int(input())\\n\\n for t_itr in range(t):\\n n = int(input())\\n\\n q = list(map(int, input().rstrip().split()))\\n\\n print(minimumBribes(q,n))\\n\"\n },\n {\n \"alpha_fraction\": 0.46349745988845825,\n \"alphanum_fraction\": 0.47877758741378784,\n \"avg_line_length\": 19.310344696044922,\n \"blob_id\": \"7ee9b1ca45ac87e779850d5fb3432751c204c807\",\n \"content_id\": \"fe84f5e8fea7e6c66382a1d350d6577148b062d0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 589,\n \"license_type\": \"no_license\",\n \"max_line_length\": 39,\n \"num_lines\": 29,\n \"path\": \"/BinarySearch/version2.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"##corrected Book version\\ndef binary_search(list, item):\\n print(\\\" \\\")\\n low = 0\\n high = len(list)-1\\n\\n while low <= high:\\n mid = (round((high-low)/2))+low\\n guess = list[mid]\\n print(\\\"item:\\\",item)\\n print(\\\"low:\\\",low)\\n print(\\\"high:\\\",high)\\n print(\\\"mid:\\\",mid)\\n print(\\\"guess:\\\",guess)\\n print(\\\" \\\")\\n\\n if guess == item:\\n return mid\\n if guess > item:\\n high = mid -1\\n else:\\n low = mid + 1\\n\\n return None\\n print(\\\" \\\")\\n\\nmy_list = list(range(1,11))\\n\\nprint (binary_search(my_list,6))\\n\"\n },\n {\n \"alpha_fraction\": 0.6216796636581421,\n \"alphanum_fraction\": 0.6236327886581421,\n \"avg_line_length\": 30.80124282836914,\n \"blob_id\": \"b1c44e7772c64313917c5cd9efe1931706036057\",\n \"content_id\": \"05a987f9d3d053395fb8ae93d188d9f52a61a9cd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5120,\n \"license_type\": \"no_license\",\n \"max_line_length\": 81,\n \"num_lines\": 161,\n \"path\": \"/linkedList.py\",\n \"repo_name\": \"IvanVazquezL/Competitive-Programming-Exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Node:\\n #Node constructor\\n def __init__(self, dataval=None):\\n #Give the new instance the passed value\\n self.dataval = dataval\\n #The next node of the node is none\\n self.nextval = None\\n\\nclass SingleLinkedList:\\n #Single Linked List constructor\\n def __init__(self):\\n #The node head value is none\\n self.headval = None\\n\\n #Print LinkedList\\n def listprint(self):\\n #Get the the node head\\n printval = self.headval\\n while printval is not None:\\n #Print the value of the node\\n print (printval.dataval)\\n #Printval is now the next node\\n printval = printval.nextval\\n\\n #Insert a new node at the beginning\\n def AtBeginning(self,newdata):\\n #Create a new node with the passed data as its value\\n NewNode = Node(newdata)\\n # The next node is going to be the current node head\\n NewNode.nextval = self.headval\\n #The new node is going to be the new node head\\n self.headval = NewNode\\n\\n # Insert a newnode at the end\\n def AtEnd(self, newdata):\\n #Create a new node with the passed data as its value\\n NewNode = Node(newdata)\\n #If the linked list is empty\\n if self.headval is None:\\n #Then the new node is the new head\\n self.headval = NewNode\\n return\\n #Get the node head\\n laste = self.headval\\n #While the next node is not empty\\n while(laste.nextval):\\n #Get next node\\n laste = laste.nextval\\n #The next value of the last node is the new node\\n laste.nextval=NewNode\\n\\n # Function to add a node between two nodes\\n def Inbetween(self,middle_node,newdata):\\n if middle_node is None:\\n print(\\\"The mentioned node is absent\\\")\\n return\\n\\n #Create a new node with the passed data as its value\\n NewNode = Node(newdata)\\n #The next value of the new node is the next value of the sent node\\n NewNode.nextval = middle_node.nextval\\n #the next value of the sent node is the new node\\n middle_node.nextval = NewNode\\n\\n # Function to remove node\\n def RemoveNode(self, Removekey):\\n #Get the node head\\n HeadVal = self.headval\\n\\n if (HeadVal is not None):\\n #If the node head has the same value as the one to remove\\n if (HeadVal.dataval == Removekey):\\n #The new head is the next node of the node head\\n self.headval = HeadVal.nextval\\n #The past node head is now none\\n HeadVal = None\\n return\\n\\n #Loop through the linked list\\n #While the node is not none\\n while (HeadVal is not None):\\n #If the node has the same value as the one to remove\\n if HeadVal.dataval == Removekey:\\n #End the while\\n break\\n #prev is the current node in the loop\\n prev = HeadVal\\n #HeadVal is now the next node\\n HeadVal = HeadVal.nextval\\n\\n #If the value was not found\\n if (HeadVal == None):\\n return\\n\\n #the next node of the previous node that is going to be Removed\\n #Is going to be now the next node of the node that is going to be removed\\n prev.nextval = HeadVal.nextval\\n\\n #the removed node is now none\\n HeadVal = None\\n\\n#Create a new linked list\\nlist = SingleLinkedList()\\n#Establish the node head\\nlist.headval = Node(\\\"Mon\\\")\\n# Link first Node to second node\\nlist.headval.nextval = Node(\\\"Tue\\\")\\n# Link second Node to third node\\nlist.headval.nextval.nextval = Node(\\\"Wed\\\")\\n\\nlist.AtBeginning(\\\"Sun\\\")\\n\\nlist.AtEnd(\\\"Thu\\\")\\n\\nlist.headval.nextval.nextval.nextval.nextval.nextval = Node(\\\"Sat\\\")\\n\\nlist.Inbetween(list.headval.nextval.nextval.nextval.nextval,\\\"Fri\\\")\\n\\nlist.AtEnd(\\\"Sam\\\")\\n\\nlist.RemoveNode(\\\"Sam\\\")\\n\\nlist.listprint()\\n\\ndef KtoLast(position):\\n #if the linked list is empty\\n if linkedList.headval == None:\\n return None\\n else:\\n #current node is the node head\\n currentNode = linkedList.headval\\n #initialize counter\\n counter = 0\\n #while the currentNode is not None\\n while(currentNode):\\n #Increment the counter\\n counter += 1\\n #currentNode is now the next node\\n currentNode = currentNode.nextval\\n\\n #current node is the node head\\n currentNode = linkedList.headval\\n #loop counter-position times\\n # to get the kth to last node value\\n for i in range(counter-position):\\n #currentNode is now the next node\\n currentNode = currentNode.nextval\\n\\n #return the value of the last currentNode\\n return currentNode.dataval\\n\\n\\nlinkedList = SingleLinkedList()\\nlinkedList.headval = Node(1)\\nlinkedList.headval.nextval = Node(2)\\nlinkedList.headval.nextval.nextval = Node(8)\\nlinkedList.headval.nextval.nextval.nextval = Node(3)\\nlinkedList.headval.nextval.nextval.nextval.nextval = Node(7)\\nlinkedList.headval.nextval.nextval.nextval.nextval.nextval = Node(0)\\nlinkedList.AtEnd(4)\\nprint(KtoLast(2))\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":44,"string":"44"}}},{"rowIdx":2979387,"cells":{"repo_name":{"kind":"string","value":"nmscannell/ga-classification-assignment"},"repo_url":{"kind":"string","value":"https://github.com/nmscannell/ga-classification-assignment"},"snapshot_id":{"kind":"string","value":"b7caa0e4c9c84a50afe89a2d3397c2ec5e8fe58d"},"revision_id":{"kind":"string","value":"6fc8892d06323b2265ba4113e9a636e7225b2308"},"directory_id":{"kind":"string","value":"ca97ddca1911713432a2c24dbd2b01e07954c1ff"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-05-20T11:14:37.254852","string":"2021-05-20T11:14:37.254852"},"revision_date":{"kind":"timestamp","value":"2020-04-01T19:39:16","string":"2020-04-01T19:39:16"},"committer_date":{"kind":"timestamp","value":"2020-04-01T19:39:16","string":"2020-04-01T19:39:16"},"github_id":{"kind":"number","value":252270116,"string":"252,270,116"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5869918465614319,"alphanum_fraction":0.6227642297744751,"avg_line_length":42.92856979370117,"blob_id":"df8afdd53efcd18087ff6ef4fd16ed7e0f4997b2","content_id":"801ab201d8ef69e7826543a7e2bde3d74a2da955","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":615,"license_type":"no_license","max_line_length":89,"num_lines":14,"path":"/SearchDriver.py","repo_name":"nmscannell/ga-classification-assignment","src_encoding":"UTF-8","text":"from Searches import Searches\n\n\nwrite_file = \"Files/search_results.txt\"\nopen(write_file, 'w').close()\nwith open(write_file, 'a') as file:\n for i in range(8):\n file.write(\"Trial {}\".format(i+1))\n file.write(\"\\n\\nGreedy Search: \")\n file.write(\"\\n\" + Searches.greedy_search(\"Files/adPlac1.csv\", 100))\n file.write(\"\\n\" + Searches.greedy_search(\"Files/adPlac2.csv\", 5000))\n file.write(\"\\n\\nGenetic Algorithm: \")\n file.write(\"\\n\" + Searches.genetic_search(\"Files/adPlac1.csv\", 100, 7))\n file.write(\"\\n\" + Searches.genetic_search(\"Files/adPlac2.csv\", 5000, 6) + \"\\n\\n\")\n"},{"alpha_fraction":0.5950731039047241,"alphanum_fraction":0.6274057030677795,"avg_line_length":39.59375,"blob_id":"bc49d6caf5dc434c5fe0f5d23042659a93aed4c5","content_id":"36fe156e1b4ad38f64e49e4cf703edaf82c86665","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2598,"license_type":"no_license","max_line_length":178,"num_lines":64,"path":"/ClassDriver.py","repo_name":"nmscannell/ga-classification-assignment","src_encoding":"UTF-8","text":"from learning import *\nfrom probabilistic_learning import *\n\nzoo1train = DataSet(name='Files/zoo1train', exclude=[0])\nzoo2train = DataSet(name='Files/zoo2train', exclude=[0])\n\nzoo1test = DataSet(name='Files/zoo1test', exclude=[0])\nzoo2test = DataSet(name='Files/zoo2test', exclude=[0])\n\nDTL = DecisionTreeLearner(zoo1train)\nDTL2 = DecisionTreeLearner(zoo2train)\nnbd = NaiveBayesLearner(zoo1train, continuous=False)\nnbd2 = NaiveBayesLearner(zoo2train, continuous=False)\n\nattributes = [\"hair\", \"feathers\", \"eggs\", \"milk\", \"airborne\", \"aquatic\", \"predator\", \"toothed\", \"backbone\", \"breathes\", \"venomous\", \"fins\", \"legs\", \"tail\", \"domestic\", \"catsize\"]\n\nwrite_file = \"Files/classification_results.txt\"\nwith open(write_file, 'w') as file:\n file.write(\"Zoo1 test set: \\n\\n\")\n for i in range(6):\n file.write(\"\\n\" + str(zoo1test.examples[i]))\n file.write(\"\\n\\nDecision Tree Zoo1 results: \\n\")\n numCorrect = 0\n for i in range(6):\n file.write(\"\\n\" + DTL(zoo1test.examples[i]))\n if zoo1test.examples[i][17] == DTL(zoo1test.examples[i]):\n numCorrect += 1\n file.write(\"\\n\\nMaximum Depth of Tree: \" + str(DTL.max_depth()))\n file.write(\"\\n\\nPredictors: \")\n for i in DTL.get_predictors([]):\n file.write(attributes[i-1] + \" \")\n file.write(\"\\n\\nPercentage correct: \" + str(numCorrect*100/6))\n\n file.write(\"\\n\\nNaive Bayes Zoo1 results: \\n\")\n numCorrect = 0\n for i in range(6):\n file.write(\"\\n\" + nbd(zoo1test.examples[i]))\n if zoo1test.examples[i][17] == nbd(zoo1test.examples[i]):\n numCorrect += 1\n file.write(\"\\n\\nPercentage correct: \" + str(numCorrect * 100 / 6))\n\n file.write(\"\\n\\nZoo2 test set: \\n\\n\")\n for i in range(6):\n file.write(\"\\n\" + str(zoo2test.examples[i]))\n\n file.write(\"\\n\\nDecision Tree Zoo2 results: \\n\")\n numCorrect = 0\n for i in range(6):\n file.write(\"\\n\" + str(DTL2(zoo2test.examples[i])))\n if zoo2test.examples[i][17] == DTL2(zoo2test.examples[i]):\n numCorrect += 1\n file.write(\"\\n\\nMaximum Depth of Tree: \" + str(DTL2.max_depth()))\n file.write(\"\\n\\nPredictors: \")\n for i in DTL2.get_predictors([]):\n file.write(attributes[i - 1] + \" \")\n file.write(\"\\n\\nPercentage correct: \" + str(numCorrect * 100 / 6))\n\n file.write(\"\\n\\nNaive Bayes Zoo2 results: \\n\")\n numCorrect = 0\n for i in range(6):\n file.write(\"\\n\" + str(nbd2(zoo2test.examples[i])))\n if zoo2test.examples[i][17] == nbd2(zoo2test.examples[i]):\n numCorrect += 1\n file.write(\"\\n\\nPercentage correct: \" + str(numCorrect * 100 / 6))\n"},{"alpha_fraction":0.4438210725784302,"alphanum_fraction":0.4603487551212311,"avg_line_length":32.99484634399414,"blob_id":"62073b88bf1122bad9cd48f63948dfcb4bcf6fc5","content_id":"bd1b2b9b67e6efda325ca82ef73210deab4438c2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":6595,"license_type":"no_license","max_line_length":99,"num_lines":194,"path":"/Searches.py","repo_name":"nmscannell/ga-classification-assignment","src_encoding":"UTF-8","text":"import csv\nfrom utils import *\n\n\nclass Searches:\n\n @staticmethod\n def greedy_search(file, budget):\n q = PriorityQueue()\n remaining = budget\n reach = 0\n result = list()\n with open(file) as csvfile:\n readcsv = csv.reader(csvfile, delimiter=',')\n i = 0\n for row in readcsv:\n q.push(Node((i, row[0], row[1], row[2]), row[2]))\n result.append(0)\n i = i+1\n for j in range(q.size()):\n option = q.pop()\n if int(option.data[2]) <= remaining:\n remaining = remaining - int(option.data[2])\n result[option.data[0]] = 1\n reach += int(option.data[3])\n result_str = \"\"\n for i in result:\n result_str += str(i)\n result_str = result_str + \" cost: \" + str(budget - remaining) + \" reach: \" + str(reach)\n return result_str\n\n @staticmethod\n def genetic_search(file, budget, num_options, ngen=100, pmut=0.1):\n info = []\n with open(file) as csvfile:\n readcsv = csv.reader(csvfile, delimiter=',')\n i = 0\n for row in readcsv:\n info.append((i, row[0], row[1], row[2]))\n i = i + 1\n\n states = [[0 for i in range(num_options+1)] for j in range(2**num_options+1)]\n n = 1\n for i in range(1, 2**num_options):\n left = n\n if n % 2 == 1:\n states[i][num_options] = 1\n left -= 1\n j = 1\n while left > 0:\n k = left/(2**(num_options-j))\n if k >= 1:\n states[i][j] = 1\n left -= 2**(num_options-j)\n j += 1\n n += 1\n Searches.fitness(info, states[i], budget)\n random.shuffle(states)\n return Searches.genetic_algorithm(states[:100], budget, info, [0,1], ngen, pmut)\n\n @staticmethod\n def genetic_algorithm(population, budget, info, gene_pool=[0, 1], ngen=100, pmut=0.3):\n best_so_far = population[0]\n for i in range(100):\n for i in range(len(population)):\n x, y = Searches.select(2, population, info, budget)\n if Searches.fitness(info, x, budget) > Searches.fitness(info, best_so_far, budget):\n best_so_far = x\n if Searches.fitness(info, y, budget) > Searches.fitness(info, best_so_far, budget):\n best_so_far = y\n x, y = Searches.recombine(x, y)\n x = Searches.mutate(x, gene_pool, pmut, info, budget)\n y = Searches.mutate(y, gene_pool, pmut, info, budget)\n population.append(x)\n population.append(y)\n if Searches.fitness(info, x, budget) > Searches.fitness(info, best_so_far, budget):\n best_so_far = x\n if Searches.fitness(info, y, budget) > Searches.fitness(info, best_so_far, budget):\n best_so_far = y\n if len(population) > 100:\n k = len(population)\n l = 0\n ll = 0\n j = 0\n while k > 100 > j:\n if population[j][0] == 0:\n del population[j]\n k -= 1\n if population[j][0] < population[l][0]:\n ll = l\n l = j\n j += 1\n del population[l]\n del population[ll]\n\n q = PriorityQueue()\n for i in range(len(population)):\n q.push(Node(population[i][1:], population[i][0]))\n best = q.pop()\n if Searches.fitness(info, best_so_far, budget) < Searches.fitness(info, best.data, budget):\n best_so_far = best.data\n result = \"\"\n for i in range(1, len(best_so_far)):\n result += str(best_so_far[i])\n result += \" cost: \"\n cost = 0\n for i in range(1, len(best_so_far)):\n if best_so_far[i] == 1:\n cost += int(info[i-1][2])\n result = result + str(cost) + \" reach: \" + str(best_so_far[0])\n return result\n\n @staticmethod\n def fitness(info, state, threshold):\n cost = 0\n fit_lev = 0\n for i in range(1, len(state)):\n if state[i] == 1:\n cost += int(info[i-1][2])\n if cost > threshold:\n state[0] = 0\n return 0\n fit_lev += int(info[i-1][3])\n state[0] = fit_lev\n return fit_lev\n\n @staticmethod\n def mutate(x, gene_pool, pmut, info, budget):\n if random.uniform(0, 1) >= pmut:\n return x\n\n n = len(x)\n g = len(gene_pool)\n c = random.randrange(0,n)\n r = random.randrange(0,g)\n\n new_gene = gene_pool[r]\n new = x[:c] + [new_gene] + x[c+1:]\n new[0] = Searches.fitness(info, new, budget)\n return new\n\n @staticmethod\n def recombine(x, y):\n n = len(x)\n c = random.randrange(0, n)\n return x[:c] + y[c:], y[:c] + x[c:]\n\n @staticmethod\n def select(r, population, info, budget):\n fitnesses = []\n for i in range(len(population)):\n fitnesses.append(Searches.fitness(info, population[i], budget))\n sampler = weighted_sampler(population, fitnesses)\n return[sampler() for i in range(r)]\n\n\nclass Node:\n\n def __init__(self, data, priority):\n self.data = data\n self.priority = priority\n\n\nclass PriorityQueue:\n\n def __init__(self):\n self.queue = list()\n\n def push(self, node):\n if self.size() == 0:\n self.queue.append(node)\n else:\n for i in range(self.size()):\n if int(node.priority) < int(self.queue[i].priority):\n if i == self.size()-1:\n self.queue.insert(i+1, node)\n break\n else:\n continue\n elif int(node.priority) == int(self.queue[i].priority):\n if node.data[2] < self.queue[i].data[2]:\n self.queue.insert(i, node)\n else:\n self.queue.insert(i+1, node)\n break\n else:\n self.queue.insert(i, node)\n break\n\n def pop(self):\n return self.queue.pop(0)\n\n def size(self):\n return len(self.queue)\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5869918465614319,\n \"alphanum_fraction\": 0.6227642297744751,\n \"avg_line_length\": 42.92856979370117,\n \"blob_id\": \"df8afdd53efcd18087ff6ef4fd16ed7e0f4997b2\",\n \"content_id\": \"801ab201d8ef69e7826543a7e2bde3d74a2da955\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 615,\n \"license_type\": \"no_license\",\n \"max_line_length\": 89,\n \"num_lines\": 14,\n \"path\": \"/SearchDriver.py\",\n \"repo_name\": \"nmscannell/ga-classification-assignment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from Searches import Searches\\n\\n\\nwrite_file = \\\"Files/search_results.txt\\\"\\nopen(write_file, 'w').close()\\nwith open(write_file, 'a') as file:\\n for i in range(8):\\n file.write(\\\"Trial {}\\\".format(i+1))\\n file.write(\\\"\\\\n\\\\nGreedy Search: \\\")\\n file.write(\\\"\\\\n\\\" + Searches.greedy_search(\\\"Files/adPlac1.csv\\\", 100))\\n file.write(\\\"\\\\n\\\" + Searches.greedy_search(\\\"Files/adPlac2.csv\\\", 5000))\\n file.write(\\\"\\\\n\\\\nGenetic Algorithm: \\\")\\n file.write(\\\"\\\\n\\\" + Searches.genetic_search(\\\"Files/adPlac1.csv\\\", 100, 7))\\n file.write(\\\"\\\\n\\\" + Searches.genetic_search(\\\"Files/adPlac2.csv\\\", 5000, 6) + \\\"\\\\n\\\\n\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.5950731039047241,\n \"alphanum_fraction\": 0.6274057030677795,\n \"avg_line_length\": 39.59375,\n \"blob_id\": \"bc49d6caf5dc434c5fe0f5d23042659a93aed4c5\",\n \"content_id\": \"36fe156e1b4ad38f64e49e4cf703edaf82c86665\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2598,\n \"license_type\": \"no_license\",\n \"max_line_length\": 178,\n \"num_lines\": 64,\n \"path\": \"/ClassDriver.py\",\n \"repo_name\": \"nmscannell/ga-classification-assignment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from learning import *\\nfrom probabilistic_learning import *\\n\\nzoo1train = DataSet(name='Files/zoo1train', exclude=[0])\\nzoo2train = DataSet(name='Files/zoo2train', exclude=[0])\\n\\nzoo1test = DataSet(name='Files/zoo1test', exclude=[0])\\nzoo2test = DataSet(name='Files/zoo2test', exclude=[0])\\n\\nDTL = DecisionTreeLearner(zoo1train)\\nDTL2 = DecisionTreeLearner(zoo2train)\\nnbd = NaiveBayesLearner(zoo1train, continuous=False)\\nnbd2 = NaiveBayesLearner(zoo2train, continuous=False)\\n\\nattributes = [\\\"hair\\\", \\\"feathers\\\", \\\"eggs\\\", \\\"milk\\\", \\\"airborne\\\", \\\"aquatic\\\", \\\"predator\\\", \\\"toothed\\\", \\\"backbone\\\", \\\"breathes\\\", \\\"venomous\\\", \\\"fins\\\", \\\"legs\\\", \\\"tail\\\", \\\"domestic\\\", \\\"catsize\\\"]\\n\\nwrite_file = \\\"Files/classification_results.txt\\\"\\nwith open(write_file, 'w') as file:\\n file.write(\\\"Zoo1 test set: \\\\n\\\\n\\\")\\n for i in range(6):\\n file.write(\\\"\\\\n\\\" + str(zoo1test.examples[i]))\\n file.write(\\\"\\\\n\\\\nDecision Tree Zoo1 results: \\\\n\\\")\\n numCorrect = 0\\n for i in range(6):\\n file.write(\\\"\\\\n\\\" + DTL(zoo1test.examples[i]))\\n if zoo1test.examples[i][17] == DTL(zoo1test.examples[i]):\\n numCorrect += 1\\n file.write(\\\"\\\\n\\\\nMaximum Depth of Tree: \\\" + str(DTL.max_depth()))\\n file.write(\\\"\\\\n\\\\nPredictors: \\\")\\n for i in DTL.get_predictors([]):\\n file.write(attributes[i-1] + \\\" \\\")\\n file.write(\\\"\\\\n\\\\nPercentage correct: \\\" + str(numCorrect*100/6))\\n\\n file.write(\\\"\\\\n\\\\nNaive Bayes Zoo1 results: \\\\n\\\")\\n numCorrect = 0\\n for i in range(6):\\n file.write(\\\"\\\\n\\\" + nbd(zoo1test.examples[i]))\\n if zoo1test.examples[i][17] == nbd(zoo1test.examples[i]):\\n numCorrect += 1\\n file.write(\\\"\\\\n\\\\nPercentage correct: \\\" + str(numCorrect * 100 / 6))\\n\\n file.write(\\\"\\\\n\\\\nZoo2 test set: \\\\n\\\\n\\\")\\n for i in range(6):\\n file.write(\\\"\\\\n\\\" + str(zoo2test.examples[i]))\\n\\n file.write(\\\"\\\\n\\\\nDecision Tree Zoo2 results: \\\\n\\\")\\n numCorrect = 0\\n for i in range(6):\\n file.write(\\\"\\\\n\\\" + str(DTL2(zoo2test.examples[i])))\\n if zoo2test.examples[i][17] == DTL2(zoo2test.examples[i]):\\n numCorrect += 1\\n file.write(\\\"\\\\n\\\\nMaximum Depth of Tree: \\\" + str(DTL2.max_depth()))\\n file.write(\\\"\\\\n\\\\nPredictors: \\\")\\n for i in DTL2.get_predictors([]):\\n file.write(attributes[i - 1] + \\\" \\\")\\n file.write(\\\"\\\\n\\\\nPercentage correct: \\\" + str(numCorrect * 100 / 6))\\n\\n file.write(\\\"\\\\n\\\\nNaive Bayes Zoo2 results: \\\\n\\\")\\n numCorrect = 0\\n for i in range(6):\\n file.write(\\\"\\\\n\\\" + str(nbd2(zoo2test.examples[i])))\\n if zoo2test.examples[i][17] == nbd2(zoo2test.examples[i]):\\n numCorrect += 1\\n file.write(\\\"\\\\n\\\\nPercentage correct: \\\" + str(numCorrect * 100 / 6))\\n\"\n },\n {\n \"alpha_fraction\": 0.4438210725784302,\n \"alphanum_fraction\": 0.4603487551212311,\n \"avg_line_length\": 32.99484634399414,\n \"blob_id\": \"62073b88bf1122bad9cd48f63948dfcb4bcf6fc5\",\n \"content_id\": \"bd1b2b9b67e6efda325ca82ef73210deab4438c2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 6595,\n \"license_type\": \"no_license\",\n \"max_line_length\": 99,\n \"num_lines\": 194,\n \"path\": \"/Searches.py\",\n \"repo_name\": \"nmscannell/ga-classification-assignment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import csv\\nfrom utils import *\\n\\n\\nclass Searches:\\n\\n @staticmethod\\n def greedy_search(file, budget):\\n q = PriorityQueue()\\n remaining = budget\\n reach = 0\\n result = list()\\n with open(file) as csvfile:\\n readcsv = csv.reader(csvfile, delimiter=',')\\n i = 0\\n for row in readcsv:\\n q.push(Node((i, row[0], row[1], row[2]), row[2]))\\n result.append(0)\\n i = i+1\\n for j in range(q.size()):\\n option = q.pop()\\n if int(option.data[2]) <= remaining:\\n remaining = remaining - int(option.data[2])\\n result[option.data[0]] = 1\\n reach += int(option.data[3])\\n result_str = \\\"\\\"\\n for i in result:\\n result_str += str(i)\\n result_str = result_str + \\\" cost: \\\" + str(budget - remaining) + \\\" reach: \\\" + str(reach)\\n return result_str\\n\\n @staticmethod\\n def genetic_search(file, budget, num_options, ngen=100, pmut=0.1):\\n info = []\\n with open(file) as csvfile:\\n readcsv = csv.reader(csvfile, delimiter=',')\\n i = 0\\n for row in readcsv:\\n info.append((i, row[0], row[1], row[2]))\\n i = i + 1\\n\\n states = [[0 for i in range(num_options+1)] for j in range(2**num_options+1)]\\n n = 1\\n for i in range(1, 2**num_options):\\n left = n\\n if n % 2 == 1:\\n states[i][num_options] = 1\\n left -= 1\\n j = 1\\n while left > 0:\\n k = left/(2**(num_options-j))\\n if k >= 1:\\n states[i][j] = 1\\n left -= 2**(num_options-j)\\n j += 1\\n n += 1\\n Searches.fitness(info, states[i], budget)\\n random.shuffle(states)\\n return Searches.genetic_algorithm(states[:100], budget, info, [0,1], ngen, pmut)\\n\\n @staticmethod\\n def genetic_algorithm(population, budget, info, gene_pool=[0, 1], ngen=100, pmut=0.3):\\n best_so_far = population[0]\\n for i in range(100):\\n for i in range(len(population)):\\n x, y = Searches.select(2, population, info, budget)\\n if Searches.fitness(info, x, budget) > Searches.fitness(info, best_so_far, budget):\\n best_so_far = x\\n if Searches.fitness(info, y, budget) > Searches.fitness(info, best_so_far, budget):\\n best_so_far = y\\n x, y = Searches.recombine(x, y)\\n x = Searches.mutate(x, gene_pool, pmut, info, budget)\\n y = Searches.mutate(y, gene_pool, pmut, info, budget)\\n population.append(x)\\n population.append(y)\\n if Searches.fitness(info, x, budget) > Searches.fitness(info, best_so_far, budget):\\n best_so_far = x\\n if Searches.fitness(info, y, budget) > Searches.fitness(info, best_so_far, budget):\\n best_so_far = y\\n if len(population) > 100:\\n k = len(population)\\n l = 0\\n ll = 0\\n j = 0\\n while k > 100 > j:\\n if population[j][0] == 0:\\n del population[j]\\n k -= 1\\n if population[j][0] < population[l][0]:\\n ll = l\\n l = j\\n j += 1\\n del population[l]\\n del population[ll]\\n\\n q = PriorityQueue()\\n for i in range(len(population)):\\n q.push(Node(population[i][1:], population[i][0]))\\n best = q.pop()\\n if Searches.fitness(info, best_so_far, budget) < Searches.fitness(info, best.data, budget):\\n best_so_far = best.data\\n result = \\\"\\\"\\n for i in range(1, len(best_so_far)):\\n result += str(best_so_far[i])\\n result += \\\" cost: \\\"\\n cost = 0\\n for i in range(1, len(best_so_far)):\\n if best_so_far[i] == 1:\\n cost += int(info[i-1][2])\\n result = result + str(cost) + \\\" reach: \\\" + str(best_so_far[0])\\n return result\\n\\n @staticmethod\\n def fitness(info, state, threshold):\\n cost = 0\\n fit_lev = 0\\n for i in range(1, len(state)):\\n if state[i] == 1:\\n cost += int(info[i-1][2])\\n if cost > threshold:\\n state[0] = 0\\n return 0\\n fit_lev += int(info[i-1][3])\\n state[0] = fit_lev\\n return fit_lev\\n\\n @staticmethod\\n def mutate(x, gene_pool, pmut, info, budget):\\n if random.uniform(0, 1) >= pmut:\\n return x\\n\\n n = len(x)\\n g = len(gene_pool)\\n c = random.randrange(0,n)\\n r = random.randrange(0,g)\\n\\n new_gene = gene_pool[r]\\n new = x[:c] + [new_gene] + x[c+1:]\\n new[0] = Searches.fitness(info, new, budget)\\n return new\\n\\n @staticmethod\\n def recombine(x, y):\\n n = len(x)\\n c = random.randrange(0, n)\\n return x[:c] + y[c:], y[:c] + x[c:]\\n\\n @staticmethod\\n def select(r, population, info, budget):\\n fitnesses = []\\n for i in range(len(population)):\\n fitnesses.append(Searches.fitness(info, population[i], budget))\\n sampler = weighted_sampler(population, fitnesses)\\n return[sampler() for i in range(r)]\\n\\n\\nclass Node:\\n\\n def __init__(self, data, priority):\\n self.data = data\\n self.priority = priority\\n\\n\\nclass PriorityQueue:\\n\\n def __init__(self):\\n self.queue = list()\\n\\n def push(self, node):\\n if self.size() == 0:\\n self.queue.append(node)\\n else:\\n for i in range(self.size()):\\n if int(node.priority) < int(self.queue[i].priority):\\n if i == self.size()-1:\\n self.queue.insert(i+1, node)\\n break\\n else:\\n continue\\n elif int(node.priority) == int(self.queue[i].priority):\\n if node.data[2] < self.queue[i].data[2]:\\n self.queue.insert(i, node)\\n else:\\n self.queue.insert(i+1, node)\\n break\\n else:\\n self.queue.insert(i, node)\\n break\\n\\n def pop(self):\\n return self.queue.pop(0)\\n\\n def size(self):\\n return len(self.queue)\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":3,"string":"3"}}},{"rowIdx":2979388,"cells":{"repo_name":{"kind":"string","value":"moqingxinai/RST-OWE"},"repo_url":{"kind":"string","value":"https://github.com/moqingxinai/RST-OWE"},"snapshot_id":{"kind":"string","value":"dae0495912401dea1c588ba27fb9befbef00a9ed"},"revision_id":{"kind":"string","value":"edb838546140649b1690e40b4d1181852231d419"},"directory_id":{"kind":"string","value":"49be7f3efd12e2b8650476e3301598401b6d4319"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2023-02-13T23:01:42.816906","string":"2023-02-13T23:01:42.816906"},"revision_date":{"kind":"timestamp","value":"2021-01-13T10:18:15","string":"2021-01-13T10:18:15"},"committer_date":{"kind":"timestamp","value":"2021-01-13T10:18:15","string":"2021-01-13T10:18:15"},"github_id":{"kind":"null"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.4593437910079956,"alphanum_fraction":0.5106989741325378,"avg_line_length":27.040000915527344,"blob_id":"22c87fd1fd053d9d959bd2263a27e49de277e334","content_id":"899c7ae3bd215f5483f10090eb99d55fb3043664","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":701,"license_type":"no_license","max_line_length":79,"num_lines":25,"path":"/setup.py","repo_name":"moqingxinai/RST-OWE","src_encoding":"UTF-8","text":"from distutils.core import setup\n\nsetup(name='OWE',\n version='0.1a',\n description='Open World Extension for Knowledge Graph Completion Models',\n author='Haseeb Shah and Johannes Villmow',\n author_email='hshah1@ualberta.ca',\n license='gnu',\n packages=['owe'],\n url=\"https://www.aclweb.org/anthology/2020.textgraphs-1.9/\", \n install_requires=[\n 'torch==1.0.0',\n 'numpy==1.16.0',\n 'gensim==3.7.0',\n 'tabulate==0.8.2',\n 'matplotlib==3.0.2',\n 'nltk==3.4',\n 'annoy==1.15.0',\n 'tensorflow',\n 'tensorboardX==1.6',\n 'tqdm==4.30.0',\n 'attrs',\n ],\n # package_data={'owe': ['data/*.pickle', 'data/*.gz']}\n )\n"},{"alpha_fraction":0.7302284836769104,"alphanum_fraction":0.753954291343689,"avg_line_length":42.769229888916016,"blob_id":"9de7c9ae15c2b9e0c7456b36e2a68901b22c1460","content_id":"9855267c0c88839d9ca4428a515a36eea2592bdb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":3414,"license_type":"no_license","max_line_length":253,"num_lines":78,"path":"/README.md","repo_name":"moqingxinai/RST-OWE","src_encoding":"UTF-8","text":"# Relation Specific Transformations for Open World Knowledge Graph Completion\nThis repository contains the official Pytorch code for [Relation Specific Transformations for Open World Knowledge Graph Completion](https://www.aclweb.org/anthology/2020.textgraphs-1.9). This code is built on top of our original OWE codebase.\n\n## Setup\n\nResolve dependencies by executing the following command:\n```bash\npip install -e .\n```\n\nThen download the\n[FB15k-237-OWE](http://haseeb.ai/assets/FB15k-237-OWE.zip) and\n[DBPedia50](http://haseeb.ai/assets/dbpedia50.zip) datasets as\nrequired. These contain the datasets in format required for both OpenKE\nand our code.\n\n## Usage\n### Training the KGC model\n\nBefore using OWE you need to train a KGC model. You can use any knowledge graph embedding framework for training. \nWe used the PyTorch version of [OpenKE](https://github.com/thunlp/OpenKE/tree/OpenKE-PyTorch). \n\nTo use a previously trained KGC model with OWE you need to export the entity and relation matrices as \n*numpy arrays* using pickle:\n - For TransE and DistMult the matrices are called `entities.p` and `relations.p`\n - For CompLEx `entities_r.p`, `entities_i.p`, `relations_r.p` and `relations_i.p`. \n\nFurthermore you need to provide two more files: `entity2id.txt` and `relation2id.txt`, which contain a mapping\nof an entity/relation string to the corresponding index (id) in the embedding files.\n\nThe trained 300D ComplEx embeddings for FB15k-237-OWE can be obtained from [here](http://haseeb.ai/assets/FB15k-237-OWE-closed-world-embeddings.zip).\n\nFor closed-world evaluation:\n```bash\npython owe/run_closed_world.py -e -c <PATH_TO_DATASET> --<KGC_MODEL_NAME> <PATH_TO_KGC_EMBEDDINGS>\n```\nwhere `KGC_MODEL_NAME` can be `complex`, `distmult` or `transe`.\n\n#### Instructions for OpenKE\n\nFollow the instructions to install OpenKE. Under [scripts](scripts/openke_training) you can find exemplary training scripts (make sure you update the paths to the dataset in OpenKE format). As the API of OpenKE frequently changes, scripts work with the \n[following version of OpenKE](https://github.com/thunlp/OpenKE/tree/0a55399b3e800bc779582c4784cac96f00230fd8).\n\n### Training the Open-World Extention\n\nAn example config file is provided as config.ini in the root folder.\n\nFor training:\n```bash\npython owe/main.py -t -c <PATH_TO_DATASET> -d <PATH_TO_CONFIG_AND_OUTPUT_DIR> --<KGC_MODEL_NAME> <PATH_TO_KGC_EMBEDDINGS>\n```\n\nFor evaluation:\n```bash\npython owe/main.py -e -lb -c <PATH_TO_DATASET> -d <PATH_TO_CONFIG_AND_OUTPUT_DIR> --<KGC_MODEL_NAME> <PATH_TO_KGC_EMBEDDINGS>\n```\n\n#### Reproducing the paper experiments\nThe configs used to generated the experiments in the paper alongwith\ntheir respective output log files are provided in the\n`paper_cfgs_and_logs` folder. We recommend using those as a starting\npoint.\n\n## Citation\nIf you found our work helpful in your research, consider citing the following:\n```\n@inproceedings{shah-etal-2020-relation,\n title = \"Relation Specific Transformations for Open World Knowledge Graph Completion\",\n author = \"Shah, Haseeb and Villmow, Johannes and Ulges, Adrian\",\n booktitle = \"Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)\",\n month = dec,\n year = \"2020\",\n address = \"Barcelona, Spain (Online)\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/2020.textgraphs-1.9\",\n pages = \"79--84\",\n}\n```\n"}],"string":"[\n {\n \"alpha_fraction\": 0.4593437910079956,\n \"alphanum_fraction\": 0.5106989741325378,\n \"avg_line_length\": 27.040000915527344,\n \"blob_id\": \"22c87fd1fd053d9d959bd2263a27e49de277e334\",\n \"content_id\": \"899c7ae3bd215f5483f10090eb99d55fb3043664\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 701,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 25,\n \"path\": \"/setup.py\",\n \"repo_name\": \"moqingxinai/RST-OWE\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from distutils.core import setup\\n\\nsetup(name='OWE',\\n version='0.1a',\\n description='Open World Extension for Knowledge Graph Completion Models',\\n author='Haseeb Shah and Johannes Villmow',\\n author_email='hshah1@ualberta.ca',\\n license='gnu',\\n packages=['owe'],\\n url=\\\"https://www.aclweb.org/anthology/2020.textgraphs-1.9/\\\", \\n install_requires=[\\n 'torch==1.0.0',\\n 'numpy==1.16.0',\\n 'gensim==3.7.0',\\n 'tabulate==0.8.2',\\n 'matplotlib==3.0.2',\\n 'nltk==3.4',\\n 'annoy==1.15.0',\\n 'tensorflow',\\n 'tensorboardX==1.6',\\n 'tqdm==4.30.0',\\n 'attrs',\\n ],\\n # package_data={'owe': ['data/*.pickle', 'data/*.gz']}\\n )\\n\"\n },\n {\n \"alpha_fraction\": 0.7302284836769104,\n \"alphanum_fraction\": 0.753954291343689,\n \"avg_line_length\": 42.769229888916016,\n \"blob_id\": \"9de7c9ae15c2b9e0c7456b36e2a68901b22c1460\",\n \"content_id\": \"9855267c0c88839d9ca4428a515a36eea2592bdb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 3414,\n \"license_type\": \"no_license\",\n \"max_line_length\": 253,\n \"num_lines\": 78,\n \"path\": \"/README.md\",\n \"repo_name\": \"moqingxinai/RST-OWE\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Relation Specific Transformations for Open World Knowledge Graph Completion\\nThis repository contains the official Pytorch code for [Relation Specific Transformations for Open World Knowledge Graph Completion](https://www.aclweb.org/anthology/2020.textgraphs-1.9). This code is built on top of our original OWE codebase.\\n\\n## Setup\\n\\nResolve dependencies by executing the following command:\\n```bash\\npip install -e .\\n```\\n\\nThen download the\\n[FB15k-237-OWE](http://haseeb.ai/assets/FB15k-237-OWE.zip) and\\n[DBPedia50](http://haseeb.ai/assets/dbpedia50.zip) datasets as\\nrequired. These contain the datasets in format required for both OpenKE\\nand our code.\\n\\n## Usage\\n### Training the KGC model\\n\\nBefore using OWE you need to train a KGC model. You can use any knowledge graph embedding framework for training. \\nWe used the PyTorch version of [OpenKE](https://github.com/thunlp/OpenKE/tree/OpenKE-PyTorch). \\n\\nTo use a previously trained KGC model with OWE you need to export the entity and relation matrices as \\n*numpy arrays* using pickle:\\n - For TransE and DistMult the matrices are called `entities.p` and `relations.p`\\n - For CompLEx `entities_r.p`, `entities_i.p`, `relations_r.p` and `relations_i.p`. \\n\\nFurthermore you need to provide two more files: `entity2id.txt` and `relation2id.txt`, which contain a mapping\\nof an entity/relation string to the corresponding index (id) in the embedding files.\\n\\nThe trained 300D ComplEx embeddings for FB15k-237-OWE can be obtained from [here](http://haseeb.ai/assets/FB15k-237-OWE-closed-world-embeddings.zip).\\n\\nFor closed-world evaluation:\\n```bash\\npython owe/run_closed_world.py -e -c <PATH_TO_DATASET> --<KGC_MODEL_NAME> <PATH_TO_KGC_EMBEDDINGS>\\n```\\nwhere `KGC_MODEL_NAME` can be `complex`, `distmult` or `transe`.\\n\\n#### Instructions for OpenKE\\n\\nFollow the instructions to install OpenKE. Under [scripts](scripts/openke_training) you can find exemplary training scripts (make sure you update the paths to the dataset in OpenKE format). As the API of OpenKE frequently changes, scripts work with the \\n[following version of OpenKE](https://github.com/thunlp/OpenKE/tree/0a55399b3e800bc779582c4784cac96f00230fd8).\\n\\n### Training the Open-World Extention\\n\\nAn example config file is provided as config.ini in the root folder.\\n\\nFor training:\\n```bash\\npython owe/main.py -t -c <PATH_TO_DATASET> -d <PATH_TO_CONFIG_AND_OUTPUT_DIR> --<KGC_MODEL_NAME> <PATH_TO_KGC_EMBEDDINGS>\\n```\\n\\nFor evaluation:\\n```bash\\npython owe/main.py -e -lb -c <PATH_TO_DATASET> -d <PATH_TO_CONFIG_AND_OUTPUT_DIR> --<KGC_MODEL_NAME> <PATH_TO_KGC_EMBEDDINGS>\\n```\\n\\n#### Reproducing the paper experiments\\nThe configs used to generated the experiments in the paper alongwith\\ntheir respective output log files are provided in the\\n`paper_cfgs_and_logs` folder. We recommend using those as a starting\\npoint.\\n\\n## Citation\\nIf you found our work helpful in your research, consider citing the following:\\n```\\n@inproceedings{shah-etal-2020-relation,\\n title = \\\"Relation Specific Transformations for Open World Knowledge Graph Completion\\\",\\n author = \\\"Shah, Haseeb and Villmow, Johannes and Ulges, Adrian\\\",\\n booktitle = \\\"Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)\\\",\\n month = dec,\\n year = \\\"2020\\\",\\n address = \\\"Barcelona, Spain (Online)\\\",\\n publisher = \\\"Association for Computational Linguistics\\\",\\n url = \\\"https://www.aclweb.org/anthology/2020.textgraphs-1.9\\\",\\n pages = \\\"79--84\\\",\\n}\\n```\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2979389,"cells":{"repo_name":{"kind":"string","value":"shujaakbar2020/LMS"},"repo_url":{"kind":"string","value":"https://github.com/shujaakbar2020/LMS"},"snapshot_id":{"kind":"string","value":"aabfb056efb7db1ea1cca9e2fcaf073e81be531d"},"revision_id":{"kind":"string","value":"ad969950d818c239904149ad124d2fa9b6ec2b3c"},"directory_id":{"kind":"string","value":"bd30026da3dfee8de18f253b554c194a1631fbc0"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"timestamp","value":"2023-06-04T05:39:14.956931","string":"2023-06-04T05:39:14.956931"},"revision_date":{"kind":"timestamp","value":"2021-06-19T14:53:19","string":"2021-06-19T14:53:19"},"committer_date":{"kind":"timestamp","value":"2021-06-19T14:53:19","string":"2021-06-19T14:53:19"},"github_id":{"kind":"number","value":378449892,"string":"378,449,892"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5609756112098694,"alphanum_fraction":0.6202090382575989,"avg_line_length":30.88888931274414,"blob_id":"6ca33ae8072cc9af28dc11c939d7acd4a388e5a0","content_id":"1a6c63a4f30b8e2e12ebb0d67a86728927366436","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":574,"license_type":"no_license","max_line_length":230,"num_lines":18,"path":"/Management/migrations/0042_auto_20210527_1454.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-05-27 14:54\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0041_auto_20210527_1451'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='lmsuser',\n name='user_type',\n field=models.CharField(choices=[('student', 'student'), ('teacher', 'teacher'), ('admin', 'admin')], default='student', help_text='Please select this Correctly, Because you will be Checked.', max_length=20, null=True),\n ),\n ]\n"},{"alpha_fraction":0.5038363337516785,"alphanum_fraction":0.5907928347587585,"avg_line_length":20.72222137451172,"blob_id":"d2d7c087c215cecd48788f19fb5a42dc6f573358","content_id":"23357d43ba0d805a32e3a4ea39e6816335281232","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":391,"license_type":"no_license","max_line_length":51,"num_lines":18,"path":"/Management/migrations/0017_auto_20210424_1450.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-04-24 14:50\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0016_auto_20210424_1448'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='student',\n name='email',\n field=models.EmailField(max_length=64),\n ),\n ]\n"},{"alpha_fraction":0.5233415365219116,"alphanum_fraction":0.601965606212616,"avg_line_length":21.61111068725586,"blob_id":"7a73777f7a25981ca4f8383c35ccc1a7b03e12cf","content_id":"f5c6f4a62965bb05e6bd22be036ba691ff116f46","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":407,"license_type":"no_license","max_line_length":66,"num_lines":18,"path":"/Management/migrations/0024_auto_20210424_1513.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-04-24 15:13\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0023_auto_20210424_1513'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='course',\n name='subject',\n field=models.ManyToManyField(to='Management.Subject'),\n ),\n ]\n"},{"alpha_fraction":0.5855312943458557,"alphanum_fraction":0.6134136915206909,"avg_line_length":38.02941131591797,"blob_id":"b853e6703c70532c8331f4131718ea3699f6ac13","content_id":"4a2d31085a5ebcf7226627f37e88f91e1aae023a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1327,"license_type":"no_license","max_line_length":242,"num_lines":34,"path":"/Management/migrations/0041_auto_20210527_1451.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-05-27 14:51\n\nfrom django.conf import settings\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n ('Management', '0040_auto_20210521_1617'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='problem',\n name='problemAnswer',\n field=models.CharField(blank=True, max_length=64, null=True),\n ),\n migrations.AlterField(\n model_name='problem',\n name='studentFeedback',\n field=models.CharField(blank=True, max_length=64, null=True),\n ),\n migrations.CreateModel(\n name='LMSUser',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('user_type', models.CharField(choices=[('student', 'student'), ('teacher', 'teacher'), ('admin', 'admin')], default='student', help_text='Please select this Correctly, Because you will be Checked.', max_length=1, null=True)),\n ('user', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),\n ],\n ),\n ]\n"},{"alpha_fraction":0.5271317958831787,"alphanum_fraction":0.5604650974273682,"avg_line_length":29,"blob_id":"94de1eef6e641c4198b1e72d19261bee33a454cd","content_id":"b55a72df71cbd1b6ffe7bf159e5999de2d31a8cf","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1290,"license_type":"no_license","max_line_length":81,"num_lines":43,"path":"/Management/migrations/0033_auto_20210511_1648.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-05-11 16:48\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0032_auto_20210511_1636'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='course',\n name='courseCredit',\n field=models.DecimalField(decimal_places=1, max_digits=4, null=True),\n ),\n migrations.AddField(\n model_name='course',\n name='courseDescription',\n field=models.CharField(max_length=200, null=True),\n ),\n migrations.AddField(\n model_name='course',\n name='courseName',\n field=models.CharField(max_length=64, null=True),\n ),\n migrations.AddField(\n model_name='course',\n name='courseNature',\n field=models.CharField(max_length=64, null=True),\n ),\n migrations.AddField(\n model_name='course',\n name='experimentHours',\n field=models.SmallIntegerField(max_length=2, null=True),\n ),\n migrations.AddField(\n model_name='course',\n name='theoreticalHours',\n field=models.SmallIntegerField(max_length=2, null=True),\n ),\n ]\n"},{"alpha_fraction":0.48991936445236206,"alphanum_fraction":0.5725806355476379,"avg_line_length":26.55555534362793,"blob_id":"132a74565cc0ef4323cf4ec8c6c743180254b7a0","content_id":"b4da23ffd5f5d2bc95afae6122fc2463b02ef274","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":496,"license_type":"no_license","max_line_length":159,"num_lines":18,"path":"/Management/migrations/0020_auto_20210424_1458.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-04-24 14:58\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0019_auto_20210424_1458'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='class',\n name='year',\n field=models.CharField(blank=True, choices=[('1', 1), ('2', 2), ('3', 3), ('4', 4)], default='y', help_text='Select year for Class', max_length=1),\n ),\n ]\n"},{"alpha_fraction":0.6408450603485107,"alphanum_fraction":0.6408450603485107,"avg_line_length":31.769229888916016,"blob_id":"1879a7ebd83cf24fd4a789c1bf5c0a241a737a0e","content_id":"5f53d343e58c34c1d043690cd8cf973f5113aa76","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":426,"license_type":"no_license","max_line_length":59,"num_lines":13,"path":"/Management/urls.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"from django.urls import path\nfrom . import views\n\n\nurlpatterns = [\n path('', views.index, name=\"index\"),\n path('classes/', views.classes, name=\"classes\"),\n path('dash/', views.dash, name=\"dash\"),\n path('grades/', views.grades, name=\"grades\"),\n # path('login/', views.loginPage, name=\"login\"),\n path('signup/', views.registerPage, name=\"signup\"),\n path('students/', views.showStudents, name=\"students\"),\n]\n"},{"alpha_fraction":0.5410010814666748,"alphanum_fraction":0.5750798583030701,"avg_line_length":27.454545974731445,"blob_id":"f18136041d425decc3eddd76788230b0b8a8e959","content_id":"e03bfd5cd10f403014428b6d75e6a7351a385bbc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":939,"license_type":"no_license","max_line_length":66,"num_lines":33,"path":"/Management/migrations/0040_auto_20210521_1617.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-05-21 16:17\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0039_auto_20210521_1616'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='elective',\n name='courseNumber',\n field=models.ManyToManyField(to='Management.Course'),\n ),\n migrations.AlterField(\n model_name='elective',\n name='studentNumber',\n field=models.ManyToManyField(to='Management.Student'),\n ),\n migrations.AlterField(\n model_name='teach',\n name='courseNumber',\n field=models.ManyToManyField(to='Management.Course'),\n ),\n migrations.AlterField(\n model_name='teach',\n name='teacherNumber',\n field=models.ManyToManyField(to='Management.Teacher'),\n ),\n ]\n"},{"alpha_fraction":0.5028011202812195,"alphanum_fraction":0.5196078419685364,"avg_line_length":31.454545974731445,"blob_id":"0093bc534f54e6b9686c6237e46c1623a0f6adc2","content_id":"e17703af1ccae531e91d8d5f5dd5d192306d96dd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1428,"license_type":"no_license","max_line_length":114,"num_lines":44,"path":"/Management/migrations/0001_initial.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-04-23 07:40\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n initial = True\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='Class',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('year', models.IntegerField()),\n ],\n ),\n migrations.CreateModel(\n name='Course',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('title', models.CharField(max_length=64)),\n ('sem', models.IntegerField()),\n ],\n ),\n migrations.CreateModel(\n name='Student',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=64)),\n ('roll_no', models.CharField(max_length=64)),\n ],\n ),\n migrations.CreateModel(\n name='Teacher',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=64)),\n ],\n ),\n ]\n"},{"alpha_fraction":0.486868679523468,"alphanum_fraction":0.5717171430587769,"avg_line_length":26.5,"blob_id":"c358dcec9880138106e8b21bf8cb38f998606a26","content_id":"4e31ed15744d2f5dd201c3acbbea8b2c82fafe09","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":495,"license_type":"no_license","max_line_length":159,"num_lines":18,"path":"/Management/migrations/0028_course_year.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-04-27 02:19\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0027_auto_20210427_0215'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='course',\n name='year',\n field=models.CharField(choices=[('1', 1), ('2', 2), ('3', 3), ('4', 4)], default='1', help_text='Select year for course', max_length=1, null=True),\n ),\n ]\n"},{"alpha_fraction":0.5363214612007141,"alphanum_fraction":0.5564142465591431,"avg_line_length":42.86440658569336,"blob_id":"7496ef8a986e53e8dc58263308799036d97c2c7c","content_id":"2b8f40aaad6ca68ee7fef036010f1ad4b53e2d02","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2588,"license_type":"no_license","max_line_length":114,"num_lines":59,"path":"/Management/migrations/0037_answer_electives_proposed_teach_userinformation.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-05-12 06:12\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0036_problem'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Answer',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('teacherNumber', models.CharField(max_length=64, null=True)),\n ('problemNumber', models.CharField(max_length=64, null=True)),\n ('answerTime', models.CharField(max_length=64, null=True)),\n ],\n ),\n migrations.CreateModel(\n name='Electives',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('studentNumber', models.CharField(max_length=64, null=True)),\n ('courseNumber', models.CharField(max_length=64, null=True)),\n ],\n ),\n migrations.CreateModel(\n name='Proposed',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('studentNumber', models.CharField(max_length=64, null=True)),\n ('problemNumber', models.CharField(max_length=64, null=True)),\n ('proposedTime', models.CharField(max_length=64, null=True)),\n ],\n ),\n migrations.CreateModel(\n name='Teach',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('courseNumber', models.CharField(max_length=64, null=True)),\n ('teacherNumber', models.CharField(max_length=64, null=True)),\n ],\n ),\n migrations.CreateModel(\n name='UserInformation',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('userNumber', models.CharField(max_length=64, null=True)),\n ('userName', models.CharField(max_length=64, null=True)),\n ('loginPassword', models.CharField(max_length=64, null=True)),\n ('roleNumber', models.CharField(max_length=64, null=True)),\n ('roleName', models.CharField(max_length=64, null=True)),\n ('passwordProblem', models.CharField(max_length=64, null=True)),\n ],\n ),\n ]\n"},{"alpha_fraction":0.358638733625412,"alphanum_fraction":0.3926701545715332,"avg_line_length":37.29999923706055,"blob_id":"61dce8c81a061f52db01e8ca447632eec0f24d01","content_id":"43adb2f402f2bc52e36f5efa3e7b764410fb558e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"JavaScript","length_bytes":382,"license_type":"no_license","max_line_length":67,"num_lines":10,"path":"/static/js/testing.js","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"$(document).ready(function(){\n $('.col-4 .box').each(function(){\n var colors = 'rgba('+\n (Math.floor((180-0)*Math.random())) + ',' +\n (Math.floor((180-0)*Math.random())) + ',' +\n (Math.floor((180-0)*Math.random()))\n +')';\n $(this).css(\"background-color\", colors);\n });\n});"},{"alpha_fraction":0.8218390941619873,"alphanum_fraction":0.8218390941619873,"avg_line_length":31.625,"blob_id":"9869118703558c7c30dc2878ec85066d4cf25f3f","content_id":"dbdbddc1bab8baae674ab39dfe4933e24fe56aa9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":522,"license_type":"no_license","max_line_length":127,"num_lines":16,"path":"/Management/admin.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"from django.contrib import admin\nfrom .models import Course, Teacher, Student, TestQuestion, Problem, UserInformation, Elective, Teach, Proposed, Answer,LMSUser\n\n\n# Register your models here.\nadmin.site.register(Course)\nadmin.site.register(Teacher)\nadmin.site.register(TestQuestion)\nadmin.site.register(Student)\nadmin.site.register(Problem)\nadmin.site.register(UserInformation)\nadmin.site.register(Elective)\nadmin.site.register(Teach)\nadmin.site.register(Proposed)\nadmin.site.register(Answer)\nadmin.site.register(LMSUser)\n"},{"alpha_fraction":0.5007221698760986,"alphanum_fraction":0.5209436416625977,"avg_line_length":27.84722137451172,"blob_id":"0b520144621ad1a01d0cf054c9e5be0a5e2abe7c","content_id":"dae3665d93b2f80e1400364fc6a2a394d397e916","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2077,"license_type":"no_license","max_line_length":92,"num_lines":72,"path":"/Management/migrations/0030_auto_20210511_1608.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-05-11 16:08\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0029_auto_20210503_1540'),\n ]\n\n operations = [\n migrations.RemoveField(\n model_name='student',\n name='email',\n ),\n migrations.RemoveField(\n model_name='student',\n name='first_name',\n ),\n migrations.RemoveField(\n model_name='student',\n name='last_name',\n ),\n migrations.RemoveField(\n model_name='student',\n name='roll_no',\n ),\n migrations.RemoveField(\n model_name='student',\n name='semester',\n ),\n migrations.RemoveField(\n model_name='student',\n name='year',\n ),\n migrations.AddField(\n model_name='student',\n name='admitYear',\n field=models.IntegerField(blank=True, null=True),\n ),\n migrations.AddField(\n model_name='student',\n name='birthDate',\n field=models.DateField(blank=True, null=True),\n ),\n migrations.AddField(\n model_name='student',\n name='className',\n field=models.CharField(blank=True, max_length=64),\n ),\n migrations.AddField(\n model_name='student',\n name='collegeName',\n field=models.CharField(blank=True, max_length=64),\n ),\n migrations.AddField(\n model_name='student',\n name='professionalName',\n field=models.CharField(blank=True, max_length=64),\n ),\n migrations.AddField(\n model_name='student',\n name='studentName',\n field=models.CharField(blank=True, max_length=64),\n ),\n migrations.AddField(\n model_name='student',\n name='studentNumber',\n field=models.CharField(blank=True, help_text='Student Roll No.', max_length=64),\n ),\n ]\n"},{"alpha_fraction":0.5410885810852051,"alphanum_fraction":0.5880469679832458,"avg_line_length":36.47999954223633,"blob_id":"7841f4e44aa2428d6a0a727265049294276bffa9","content_id":"d203205aa1b8335b98172f153d9e9e1b87723136","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":937,"license_type":"no_license","max_line_length":114,"num_lines":25,"path":"/Management/migrations/0036_problem.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-05-12 06:03\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0035_auto_20210512_0554'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Problem',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('problemNumber', models.CharField(max_length=64, null=True)),\n ('problemCategory', models.CharField(max_length=64, null=True)),\n ('problemTitle', models.CharField(max_length=64, null=True)),\n ('problemContent', models.CharField(max_length=64, null=True)),\n ('problemAnswer', models.CharField(max_length=64, null=True)),\n ('studentFeedback', models.CharField(max_length=64, null=True)),\n ],\n ),\n ]\n"},{"alpha_fraction":0.6850379109382629,"alphanum_fraction":0.7059367299079895,"avg_line_length":36.03424835205078,"blob_id":"283c2c6ae2cbc1f6acd7714da2c2905cab59a886","content_id":"f3a7d708f44eaacf51e922a6069231ea578cea2b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5407,"license_type":"no_license","max_line_length":93,"num_lines":146,"path":"/Management/models.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"from django.db import models\nfrom django.db.models.base import Model\nfrom django.contrib.auth.models import User\n\n\nselect_year = (\n ('1', 1),\n ('2', 2),\n ('3', 3),\n ('4', 4),\n)\nselect_semester = (\n ('1', 1), ('2', 2), ('3', 3), ('4', 4), ('5', 5), ('6', 6), ('7', 7), ('8', 8)\n)\n\nselect_user = (\n ('student', 'student'),\n ('teacher', 'teacher'),\n ('admin', 'admin')\n)\n\n\n# Create your models here.\n\nclass Student(models.Model):\n studentNumber = models.CharField(max_length=64, help_text=\"Student Roll No.\", blank=True)\n studentName = models.CharField(max_length=64, blank=True)\n admitYear = models.IntegerField(blank=True, null=True)\n collegeName = models.CharField(max_length=64, blank=True)\n professionalName = models.CharField(max_length=64, blank=True)\n className = models.CharField(max_length=64, blank=True)\n birthDate = models.DateField(null=True)\n homeAddress = models.CharField(max_length=64, null=True)\n postCode = models.CharField(max_length=64, null=True)\n mobilePhone = models.CharField(max_length=64, null=True)\n emailAddress = models.EmailField(null=True)\n\n def __str__(self):\n return f\"{self.studentName} > {self.className} > {self.admitYear}\"\n\n\nclass Course(models.Model):\n courseNumber = models.IntegerField(null=True)\n courseName = models.CharField(max_length=64, null=True)\n courseNature = models.CharField(max_length=64, null=True)\n courseCredit = models.DecimalField(max_digits=4, decimal_places=1, null=True)\n theoreticalHours = models.SmallIntegerField(null=True)\n experimentHours = models.SmallIntegerField(null=True)\n courseDescription = models.CharField(max_length=200, null=True)\n\n def __str__(self) -> str:\n return f\"{self.courseName}\"\n\n\nclass Teacher(models.Model):\n teacherNumber = models.CharField(max_length=64, null=True)\n teacherName = models.CharField(max_length=64, null=True)\n graduationSchool = models.CharField(max_length=64, null=True)\n graduationTime = models.DateField(null=True)\n collegeName = models.CharField(max_length=64, null=True)\n professionalName = models.CharField(max_length=64, null=True)\n technicalTitle = models.CharField(max_length=64, null=True)\n finalDegree = models.CharField(max_length=64, null=True)\n finalEducation = models.CharField(max_length=64, null=True)\n mobilePhone = models.CharField(max_length=64, null=True)\n email = models.EmailField(null=True)\n jobResume = models.FileField(null=True)\n\n def __str__(self):\n return f\"{self.teacherName}\"\n\n\nclass TestQuestion(models.Model):\n question_no = models.IntegerField()\n courseNumber = models.CharField(max_length=64, null=True)\n knowledgePoint = models.CharField(max_length=64, null=True)\n chapterSection = models.CharField(max_length=64, null=True)\n testQuestionType = models.CharField(max_length=10, null=True)\n titleName = models.CharField(max_length=50, null=True)\n testQuestionContent = models.CharField(max_length=200, null=True)\n testQuestionAnwser = models.CharField(max_length=200, null=True)\n\n\nclass Problem(models.Model):\n problemNumber = models.CharField(max_length=64, null=True)\n problemCategory = models.CharField(max_length=64, null=True)\n problemTitle = models.CharField(max_length=64, null=True)\n problemContent = models.CharField(max_length=64, null=True)\n problemAnswer = models.CharField(max_length=64, null=True, blank=True)\n studentFeedback = models.CharField(max_length=64, null=True, blank=True)\n\n def __str__(self) -> str:\n return f\"{self.problemTitle}\"\n\n\nclass UserInformation(models.Model):\n userNumber = models.CharField(max_length=64, null=True)\n userName = models.CharField(max_length=64, null=True)\n loginPassword = models.CharField(max_length=64, null=True)\n roleNumber = models.CharField(max_length=64, null=True)\n roleName = models.CharField(max_length=64, null=True)\n passwordProblem = models.CharField(max_length=64, null=True)\n\n\nclass Elective(models.Model):\n studentNumber = models.ManyToManyField(Student)\n courseNumber = models.ManyToManyField(Course)\n\n def __str__(self):\n return f\"{self.studentNumber.studentName}\"\n\n\nclass Teach(models.Model):\n courseNumber = models.ManyToManyField(Course)\n teacherNumber = models.ManyToManyField(Teacher)\n # courseNumber = models.CharField(max_length=64, null=True)\n # teacherNumber = models.CharField(max_length=64, null=True)\n\n def __str__(self):\n return f\"{self.teacherNumber.teacherName}\"\n\n\nclass Proposed(models.Model):\n studentNumber = models.ForeignKey(Student, null=True, on_delete=models.CASCADE)\n problemNumber = models.ForeignKey(Problem, null=True, on_delete=models.CASCADE)\n proposedTime = models.DateTimeField(null=True)\n\n\nclass Answer(models.Model):\n teacherNumber = models.ForeignKey(Teacher, null=True, on_delete=models.CASCADE)\n problemNumber = models.ForeignKey(Problem,null=True, on_delete=models.CASCADE)\n answerTime = models.DateTimeField(null=True)\n\n\nclass LMSUser(models.Model):\n user = models.OneToOneField(User, on_delete=models.CASCADE)\n user_type = models.CharField(\n max_length=20,\n choices=select_user,\n null=True,\n default='student',\n help_text='Please select this Correctly, Because you will be Checked.'\n )\n\n def __str__(self):\n return f'{self.user_type}'\n"},{"alpha_fraction":0.5006250143051147,"alphanum_fraction":0.5262500047683716,"avg_line_length":27.070175170898438,"blob_id":"00ab5497acf5b3c5851b991a48e87c4abc8475af","content_id":"ca3db93818564ba897f6aea569c8375866ba9908","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1600,"license_type":"no_license","max_line_length":75,"num_lines":57,"path":"/Management/migrations/0031_auto_20210511_1636.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-05-11 16:36\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0030_auto_20210511_1608'),\n ]\n\n operations = [\n migrations.RemoveField(\n model_name='course',\n name='description',\n ),\n migrations.RemoveField(\n model_name='course',\n name='subjects',\n ),\n migrations.RemoveField(\n model_name='course',\n name='title',\n ),\n migrations.RemoveField(\n model_name='course',\n name='year',\n ),\n migrations.AddField(\n model_name='course',\n name='courseNumber',\n field=models.IntegerField(null=True),\n ),\n migrations.AddField(\n model_name='student',\n name='emailAddress',\n field=models.EmailField(blank=True, max_length=254, null=True),\n ),\n migrations.AddField(\n model_name='student',\n name='homeAddress',\n field=models.CharField(blank=True, max_length=64, null=True),\n ),\n migrations.AddField(\n model_name='student',\n name='mobilePhone',\n field=models.CharField(blank=True, max_length=64, null=True),\n ),\n migrations.AddField(\n model_name='student',\n name='postCode',\n field=models.CharField(blank=True, max_length=64, null=True),\n ),\n migrations.DeleteModel(\n name='Subject',\n ),\n ]\n"},{"alpha_fraction":0.5709515810012817,"alphanum_fraction":0.6043405532836914,"avg_line_length":25.04347801208496,"blob_id":"1091c6df92c9f056d46883aeac31fd7e8396bfae","content_id":"88cd895cdbdb139c697f051c92bf4ac3e0f998b1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":599,"license_type":"no_license","max_line_length":79,"num_lines":23,"path":"/Management/migrations/0038_auto_20210521_1454.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-05-21 14:54\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0037_answer_electives_proposed_teach_userinformation'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='course',\n name='experimentHours',\n field=models.SmallIntegerField(null=True),\n ),\n migrations.AlterField(\n model_name='course',\n name='theoreticalHours',\n field=models.SmallIntegerField(null=True),\n ),\n ]\n"},{"alpha_fraction":0.5066518783569336,"alphanum_fraction":0.5609756112098694,"avg_line_length":31.214284896850586,"blob_id":"0c4cc7dacb8ea26073e9333cdc42b77daff3f024","content_id":"7f8b7e9080da1e4f705b77fd589bf4e5ae2574a7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":902,"license_type":"no_license","max_line_length":202,"num_lines":28,"path":"/Management/migrations/0010_auto_20210424_1438.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-04-24 14:38\n\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0009_auto_20210424_1436'),\n ]\n\n operations = [\n migrations.RemoveField(\n model_name='class',\n name='students',\n ),\n migrations.AddField(\n model_name='class',\n name='students',\n field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='Management.Student'),\n ),\n migrations.AlterField(\n model_name='student',\n name='semester',\n field=models.CharField(blank=True, choices=[('1', 1), ('2', 2), ('3', 3), ('4', 4), ('5', 5), ('6', 6), ('7', 7), ('8', 8)], default='one', help_text='Please Select Semester', max_length=1),\n ),\n ]\n"},{"alpha_fraction":0.6098901033401489,"alphanum_fraction":0.6465201377868652,"avg_line_length":27.736841201782227,"blob_id":"9246bd93f888b625e2a7c3b690f4176a791b30ba","content_id":"2c130369aceef57e07505439dc6dbf73c7a561cd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":546,"license_type":"no_license","max_line_length":181,"num_lines":19,"path":"/Management/migrations/0029_auto_20210503_1540.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-05-03 15:40\n\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0028_course_year'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='class',\n name='course',\n field=models.ForeignKey(help_text='Select Course for this class', null=True, on_delete=django.db.models.deletion.CASCADE, related_name='course', to='Management.Course'),\n ),\n ]\n"},{"alpha_fraction":0.48918917775154114,"alphanum_fraction":0.5756756663322449,"avg_line_length":19.55555534362793,"blob_id":"1d5143724e6331a86eba42720e5f9abecdec6a43","content_id":"cf64b24c045f1f30d62588f0e1f0603425f3af5f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":370,"license_type":"no_license","max_line_length":50,"num_lines":18,"path":"/Management/migrations/0027_auto_20210427_0215.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-04-27 02:15\n\nfrom django.db import migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0026_auto_20210427_0215'),\n ]\n\n operations = [\n migrations.RenameField(\n model_name='course',\n old_name='subject',\n new_name='subjects',\n ),\n ]\n"},{"alpha_fraction":0.514018714427948,"alphanum_fraction":0.5523364543914795,"avg_line_length":27.157894134521484,"blob_id":"6482e8231605244e682ad569e17946e3c75b99b5","content_id":"18248f628bec53147eb8c73fb8bf5df0372fc7aa","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1070,"license_type":"no_license","max_line_length":63,"num_lines":38,"path":"/Management/migrations/0032_auto_20210511_1636.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-05-11 16:36\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0031_auto_20210511_1636'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='student',\n name='birthDate',\n field=models.DateField(null=True),\n ),\n migrations.AlterField(\n model_name='student',\n name='emailAddress',\n field=models.EmailField(max_length=254, null=True),\n ),\n migrations.AlterField(\n model_name='student',\n name='homeAddress',\n field=models.CharField(max_length=64, null=True),\n ),\n migrations.AlterField(\n model_name='student',\n name='mobilePhone',\n field=models.CharField(max_length=64, null=True),\n ),\n migrations.AlterField(\n model_name='student',\n name='postCode',\n field=models.CharField(max_length=64, null=True),\n ),\n ]\n"},{"alpha_fraction":0.57230144739151,"alphanum_fraction":0.637474536895752,"avg_line_length":24.842105865478516,"blob_id":"fb308e912f924eb9054e347b5f564e550c42ce42","content_id":"e7c9e45c8babeaa81e3091a2c27b952f9be98313","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":491,"license_type":"no_license","max_line_length":117,"num_lines":19,"path":"/Management/migrations/0004_auto_20210423_0954.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-04-23 09:54\n\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0003_auto_20210423_0954'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='class',\n name='students',\n field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='Management.Student'),\n ),\n ]\n"},{"alpha_fraction":0.5072463750839233,"alphanum_fraction":0.5637680888175964,"avg_line_length":29,"blob_id":"c4c0e79e070a221a42c800f8b3ce9f367bcf27ce","content_id":"914bbbc16242555a9930719f11f9fa572a10230f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":690,"license_type":"no_license","max_line_length":149,"num_lines":23,"path":"/Management/migrations/0008_auto_20210424_1430.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-04-24 14:30\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0007_auto_20210423_1449'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='subject',\n name='year',\n field=models.CharField(blank=True, choices=[('y', 1), ('e', 2), ('a', 3), ('r', 4)], default='y', help_text='Select Year', max_length=1),\n ),\n migrations.AlterField(\n model_name='subject',\n name='name',\n field=models.CharField(help_text='Enter Subject for this course e.g. DSA', max_length=64),\n ),\n ]\n"},{"alpha_fraction":0.5385281443595886,"alphanum_fraction":0.5800865888595581,"avg_line_length":37.5,"blob_id":"6fc4f8cc259aa095b9cc10e9ac4a4220a7fbca7e","content_id":"c3a01a8f01aabb55738f773c898f6baae37439a3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1155,"license_type":"no_license","max_line_length":114,"num_lines":30,"path":"/Management/migrations/0035_auto_20210512_0554.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-05-12 05:54\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0034_auto_20210512_0536'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='TestQuestion',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('question_no', models.IntegerField()),\n ('courseNumber', models.CharField(max_length=64, null=True)),\n ('knowledgePoint', models.CharField(max_length=64, null=True)),\n ('chapterSection', models.CharField(max_length=64, null=True)),\n ('testQuestionType', models.CharField(max_length=10, null=True)),\n ('titleName', models.CharField(max_length=50, null=True)),\n ('testQuestionContent', models.CharField(max_length=200, null=True)),\n ('testQuestionAnwser', models.CharField(max_length=200, null=True)),\n ],\n ),\n migrations.DeleteModel(\n name='Class',\n ),\n ]\n"},{"alpha_fraction":0.4957627058029175,"alphanum_fraction":0.5466101765632629,"avg_line_length":24.285715103149414,"blob_id":"2516ba471880b24181c2eaaca181019d87338de6","content_id":"0148c4ed1ff9fe1b27143f5aeffbc44d4cb6a2b0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":708,"license_type":"no_license","max_line_length":63,"num_lines":28,"path":"/Management/migrations/0016_auto_20210424_1448.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-04-24 14:48\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0015_auto_20210424_1446'),\n ]\n\n operations = [\n migrations.RenameField(\n model_name='student',\n old_name='name',\n new_name='last_name',\n ),\n migrations.AddField(\n model_name='student',\n name='email',\n field=models.EmailField(blank=True, max_length=64),\n ),\n migrations.AddField(\n model_name='student',\n name='first_name',\n field=models.CharField(max_length=64, null=True),\n ),\n ]\n"},{"alpha_fraction":0.5101671814918518,"alphanum_fraction":0.5323090553283691,"avg_line_length":29.3150691986084,"blob_id":"3a9f8508cd239422605c84f74193c2a14e858408","content_id":"761e96939ff60f8f62e31ebc96f21fb57d619882","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2213,"license_type":"no_license","max_line_length":63,"num_lines":73,"path":"/Management/migrations/0034_auto_20210512_0536.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-05-12 05:36\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0033_auto_20210511_1648'),\n ]\n\n operations = [\n migrations.RenameField(\n model_name='teacher',\n old_name='first_name',\n new_name='collegeName',\n ),\n migrations.RenameField(\n model_name='teacher',\n old_name='last_name',\n new_name='finalDegree',\n ),\n migrations.AddField(\n model_name='teacher',\n name='finalEducation',\n field=models.CharField(max_length=64, null=True),\n ),\n migrations.AddField(\n model_name='teacher',\n name='graduationSchool',\n field=models.CharField(max_length=64, null=True),\n ),\n migrations.AddField(\n model_name='teacher',\n name='graduationTime',\n field=models.DateField(null=True),\n ),\n migrations.AddField(\n model_name='teacher',\n name='jobResume',\n field=models.FileField(null=True, upload_to=''),\n ),\n migrations.AddField(\n model_name='teacher',\n name='mobilePhone',\n field=models.CharField(max_length=64, null=True),\n ),\n migrations.AddField(\n model_name='teacher',\n name='professionalName',\n field=models.CharField(max_length=64, null=True),\n ),\n migrations.AddField(\n model_name='teacher',\n name='teacherName',\n field=models.CharField(max_length=64, null=True),\n ),\n migrations.AddField(\n model_name='teacher',\n name='teacherNumber',\n field=models.CharField(max_length=64, null=True),\n ),\n migrations.AddField(\n model_name='teacher',\n name='technicalTitle',\n field=models.CharField(max_length=64, null=True),\n ),\n migrations.AlterField(\n model_name='teacher',\n name='email',\n field=models.EmailField(max_length=254, null=True),\n ),\n ]\n"},{"alpha_fraction":0.5476718544960022,"alphanum_fraction":0.618625283241272,"avg_line_length":24.05555534362793,"blob_id":"3da9e1b1e614005abe61e58df76a28ff77dcdd30","content_id":"00a16ecac070cb707c73a372eaea0534551b0b69","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":451,"license_type":"no_license","max_line_length":110,"num_lines":18,"path":"/Management/migrations/0015_auto_20210424_1446.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-04-24 14:46\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0014_auto_20210424_1445'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='class',\n name='students',\n field=models.ManyToManyField(help_text='Select students for this class', to='Management.Student'),\n ),\n ]\n"},{"alpha_fraction":0.4979838728904724,"alphanum_fraction":0.5725806355476379,"avg_line_length":26.55555534362793,"blob_id":"b9687617f3c3728c5ed56e7ff3517e4b74d18e80","content_id":"c428137dcb7c0ac3331d93bc862b67d179729346","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":496,"license_type":"no_license","max_line_length":159,"num_lines":18,"path":"/Management/migrations/0018_auto_20210424_1456.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-04-24 14:56\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0017_auto_20210424_1450'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='class',\n name='year',\n field=models.CharField(blank=True, choices=[('y', 1), ('e', 2), ('a', 3), ('r', 4)], default='y', help_text='Select year for Class', max_length=1),\n ),\n ]\n"},{"alpha_fraction":0.5031367540359497,"alphanum_fraction":0.5545796751976013,"avg_line_length":28.518518447875977,"blob_id":"af8e084998c13110052d2c80f4640413914618e4","content_id":"bd0958a54a4b53fb85e3797ef29efcee3caf4b91","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":797,"license_type":"no_license","max_line_length":149,"num_lines":27,"path":"/Management/migrations/0022_auto_20210424_1511.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-04-24 15:11\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0021_auto_20210424_1502'),\n ]\n\n operations = [\n migrations.RemoveField(\n model_name='course',\n name='teacher',\n ),\n migrations.AddField(\n model_name='subject',\n name='teacher',\n field=models.ManyToManyField(blank=True, related_name='Subject', to='Management.Teacher'),\n ),\n migrations.AlterField(\n model_name='subject',\n name='year',\n field=models.CharField(blank=True, choices=[('1', 1), ('2', 2), ('3', 3), ('4', 4)], default='y', help_text='Select Year', max_length=1),\n ),\n ]\n"},{"alpha_fraction":0.557441234588623,"alphanum_fraction":0.5861618518829346,"avg_line_length":28.461538314819336,"blob_id":"037e1a334b25ae02c2aa8330b170b76246d63982","content_id":"6af4c32f5439f5f6035b32e5b7a9e251fa33ccf3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":766,"license_type":"no_license","max_line_length":117,"num_lines":26,"path":"/Management/migrations/0003_auto_20210423_0954.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-04-23 09:54\n\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0002_class_students'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Subject',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=64)),\n ],\n ),\n migrations.AddField(\n model_name='course',\n name='subject',\n field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='Management.Subject'),\n ),\n ]\n"},{"alpha_fraction":0.5162659287452698,"alphanum_fraction":0.5643564462661743,"avg_line_length":25.185184478759766,"blob_id":"95ba47999ac848c089268884dff4435e57fd9dc7","content_id":"f6fcfa2e921b2c062e9b4f260e6abb51e00322a5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":707,"license_type":"no_license","max_line_length":101,"num_lines":27,"path":"/Management/migrations/0005_auto_20210423_1243.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-04-23 12:43\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0004_auto_20210423_0954'),\n ]\n\n operations = [\n migrations.RemoveField(\n model_name='course',\n name='sem',\n ),\n migrations.AddField(\n model_name='course',\n name='description',\n field=models.CharField(max_length=64, null=True),\n ),\n migrations.AddField(\n model_name='course',\n name='teacher',\n field=models.ManyToManyField(blank=True, related_name='Course', to='Management.Teacher'),\n ),\n ]\n"},{"alpha_fraction":0.5833333134651184,"alphanum_fraction":0.6439393758773804,"avg_line_length":26.789474487304688,"blob_id":"2ff65cce7b1a87eaf5a352e8270146b8c0301159","content_id":"ccb734c9bf593b614f3ccd05315e8289799cfff9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":528,"license_type":"no_license","max_line_length":158,"num_lines":19,"path":"/Management/migrations/0025_class_course.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-04-27 01:54\n\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0024_auto_20210424_1513'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='class',\n name='course',\n field=models.ForeignKey(help_text='Select Course for this class', null=True, on_delete=django.db.models.deletion.CASCADE, to='Management.Course'),\n ),\n ]\n"},{"alpha_fraction":0.5605765581130981,"alphanum_fraction":0.5730424523353577,"avg_line_length":34.16438293457031,"blob_id":"4a8d2a89167f248419698cd4ec714589136de3f6","content_id":"4d23ddead4cfef16e9c86872a86bd83644b7eec2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2567,"license_type":"no_license","max_line_length":117,"num_lines":73,"path":"/Management/migrations/0039_auto_20210521_1616.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-05-21 16:16\n\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0038_auto_20210521_1454'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Elective',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('courseNumber', models.ManyToManyField(null=True, to='Management.Course')),\n ('studentNumber', models.ManyToManyField(null=True, to='Management.Student')),\n ],\n ),\n migrations.DeleteModel(\n name='Electives',\n ),\n migrations.AlterField(\n model_name='answer',\n name='answerTime',\n field=models.DateTimeField(null=True),\n ),\n migrations.AlterField(\n model_name='answer',\n name='problemNumber',\n field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='Management.Problem'),\n ),\n migrations.AlterField(\n model_name='answer',\n name='teacherNumber',\n field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='Management.Teacher'),\n ),\n migrations.AlterField(\n model_name='proposed',\n name='problemNumber',\n field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='Management.Problem'),\n ),\n migrations.AlterField(\n model_name='proposed',\n name='proposedTime',\n field=models.DateTimeField(null=True),\n ),\n migrations.AlterField(\n model_name='proposed',\n name='studentNumber',\n field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='Management.Student'),\n ),\n migrations.RemoveField(\n model_name='teach',\n name='courseNumber',\n ),\n migrations.AddField(\n model_name='teach',\n name='courseNumber',\n field=models.ManyToManyField(null=True, to='Management.Course'),\n ),\n migrations.RemoveField(\n model_name='teach',\n name='teacherNumber',\n ),\n migrations.AddField(\n model_name='teach',\n name='teacherNumber',\n field=models.ManyToManyField(null=True, to='Management.Teacher'),\n ),\n ]\n"},{"alpha_fraction":0.6617324352264404,"alphanum_fraction":0.6617324352264404,"avg_line_length":28.901639938354492,"blob_id":"b81a7a82ab6bf0afcc4b8cab0d010edb9134014e","content_id":"09a45954a5fcdd88eaaa93f83e258a705a7dce8c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1824,"license_type":"no_license","max_line_length":78,"num_lines":61,"path":"/Management/views.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"from django.shortcuts import render, redirect\nfrom django.http import HttpResponse\nfrom django.contrib.auth import login, logout, authenticate\nfrom django.contrib.auth.models import User\nfrom Management.models import *\nfrom .forms import *\nfrom django.contrib.auth.decorators import *\n\n\n# Create your views here.\ndef loginPage(request):\n if request.user.is_authenticated:\n return redirect('index')\n else:\n if request.method == \"POST\":\n username = request.POST.get('username')\n password = request.POST.get('password')\n user = authenticate(request, usernsme=username, password=password)\n print(f'Username is {username} and Password is {password}')\n print(user)\n if user is not None:\n print('Shuja')\n login(request, user)\n return redirect('index')\n return render(request, 'Management/login.html')\n\n\ndef registerPage(request):\n form = CreateUserForm()\n\n if request.method == 'POST':\n form = CreateUserForm(request.POST)\n if form.is_valid():\n form.save()\n return redirect('index')\n\n context = {'form': form}\n return render(request, 'Management/signup.html', context)\n\n\ndef index(request):\n students = Student.objects.all()\n return render(request, 'Management/index.html', {'students': students})\n\n\ndef showStudents(request):\n students = Student.objects.all()\n return render(request, 'Management/students.html', {'students': students})\n\n\ndef classes(request):\n students = Student.objects.all()\n return render(request, 'Management/classes.html', {'students': students})\n\n\ndef dash(request):\n return render(request, 'Management/dashboard.html', {})\n\n\ndef grades(request):\n return render(request, 'Management/grades.html')\n"},{"alpha_fraction":0.49700599908828735,"alphanum_fraction":0.542514979839325,"avg_line_length":25.09375,"blob_id":"eec4392296e0deb39e359b993dad5ecdeb0cd189","content_id":"a59c2e297d3ae50d6079ea44d0e35afb104bf86b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":835,"license_type":"no_license","max_line_length":62,"num_lines":32,"path":"/Management/migrations/0021_auto_20210424_1502.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-04-24 15:02\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0020_auto_20210424_1458'),\n ]\n\n operations = [\n migrations.RemoveField(\n model_name='teacher',\n name='name',\n ),\n migrations.AddField(\n model_name='teacher',\n name='email',\n field=models.EmailField(max_length=64, null=True),\n ),\n migrations.AddField(\n model_name='teacher',\n name='first_name',\n field=models.CharField(max_length=64, null=True),\n ),\n migrations.AddField(\n model_name='teacher',\n name='last_name',\n field=models.CharField(max_length=64, null=True),\n ),\n ]\n"},{"alpha_fraction":0.5115384459495544,"alphanum_fraction":0.5730769038200378,"avg_line_length":22.636363983154297,"blob_id":"b7d2b4815cf27bc7c30edc66cd25fa547be14fcb","content_id":"f71d6a0cd52697714caae4edd732bbde1af1b889","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":520,"license_type":"no_license","max_line_length":77,"num_lines":22,"path":"/Management/migrations/0007_auto_20210423_1449.py","repo_name":"shujaakbar2020/LMS","src_encoding":"UTF-8","text":"# Generated by Django 2.2.17 on 2021-04-23 14:49\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('Management', '0006_auto_20210423_1448'),\n ]\n\n operations = [\n migrations.RemoveField(\n model_name='class',\n name='students',\n ),\n migrations.AddField(\n model_name='class',\n name='students',\n field=models.ManyToManyField(null=True, to='Management.Student'),\n ),\n ]\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5609756112098694,\n \"alphanum_fraction\": 0.6202090382575989,\n \"avg_line_length\": 30.88888931274414,\n \"blob_id\": \"6ca33ae8072cc9af28dc11c939d7acd4a388e5a0\",\n \"content_id\": \"1a6c63a4f30b8e2e12ebb0d67a86728927366436\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 574,\n \"license_type\": \"no_license\",\n \"max_line_length\": 230,\n \"num_lines\": 18,\n \"path\": \"/Management/migrations/0042_auto_20210527_1454.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-05-27 14:54\\n\\nfrom django.db import migrations, models\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0041_auto_20210527_1451'),\\n ]\\n\\n operations = [\\n migrations.AlterField(\\n model_name='lmsuser',\\n name='user_type',\\n field=models.CharField(choices=[('student', 'student'), ('teacher', 'teacher'), ('admin', 'admin')], default='student', help_text='Please select this Correctly, Because you will be Checked.', max_length=20, null=True),\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.5038363337516785,\n \"alphanum_fraction\": 0.5907928347587585,\n \"avg_line_length\": 20.72222137451172,\n \"blob_id\": \"d2d7c087c215cecd48788f19fb5a42dc6f573358\",\n \"content_id\": \"23357d43ba0d805a32e3a4ea39e6816335281232\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 391,\n \"license_type\": \"no_license\",\n \"max_line_length\": 51,\n \"num_lines\": 18,\n \"path\": \"/Management/migrations/0017_auto_20210424_1450.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-04-24 14:50\\n\\nfrom django.db import migrations, models\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0016_auto_20210424_1448'),\\n ]\\n\\n operations = [\\n migrations.AlterField(\\n model_name='student',\\n name='email',\\n field=models.EmailField(max_length=64),\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.5233415365219116,\n \"alphanum_fraction\": 0.601965606212616,\n \"avg_line_length\": 21.61111068725586,\n \"blob_id\": \"7a73777f7a25981ca4f8383c35ccc1a7b03e12cf\",\n \"content_id\": \"f5c6f4a62965bb05e6bd22be036ba691ff116f46\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 407,\n \"license_type\": \"no_license\",\n \"max_line_length\": 66,\n \"num_lines\": 18,\n \"path\": \"/Management/migrations/0024_auto_20210424_1513.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-04-24 15:13\\n\\nfrom django.db import migrations, models\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0023_auto_20210424_1513'),\\n ]\\n\\n operations = [\\n migrations.AlterField(\\n model_name='course',\\n name='subject',\\n field=models.ManyToManyField(to='Management.Subject'),\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.5855312943458557,\n \"alphanum_fraction\": 0.6134136915206909,\n \"avg_line_length\": 38.02941131591797,\n \"blob_id\": \"b853e6703c70532c8331f4131718ea3699f6ac13\",\n \"content_id\": \"4a2d31085a5ebcf7226627f37e88f91e1aae023a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1327,\n \"license_type\": \"no_license\",\n \"max_line_length\": 242,\n \"num_lines\": 34,\n \"path\": \"/Management/migrations/0041_auto_20210527_1451.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-05-27 14:51\\n\\nfrom django.conf import settings\\nfrom django.db import migrations, models\\nimport django.db.models.deletion\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n migrations.swappable_dependency(settings.AUTH_USER_MODEL),\\n ('Management', '0040_auto_20210521_1617'),\\n ]\\n\\n operations = [\\n migrations.AlterField(\\n model_name='problem',\\n name='problemAnswer',\\n field=models.CharField(blank=True, max_length=64, null=True),\\n ),\\n migrations.AlterField(\\n model_name='problem',\\n name='studentFeedback',\\n field=models.CharField(blank=True, max_length=64, null=True),\\n ),\\n migrations.CreateModel(\\n name='LMSUser',\\n fields=[\\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\\n ('user_type', models.CharField(choices=[('student', 'student'), ('teacher', 'teacher'), ('admin', 'admin')], default='student', help_text='Please select this Correctly, Because you will be Checked.', max_length=1, null=True)),\\n ('user', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),\\n ],\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.5271317958831787,\n \"alphanum_fraction\": 0.5604650974273682,\n \"avg_line_length\": 29,\n \"blob_id\": \"94de1eef6e641c4198b1e72d19261bee33a454cd\",\n \"content_id\": \"b55a72df71cbd1b6ffe7bf159e5999de2d31a8cf\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1290,\n \"license_type\": \"no_license\",\n \"max_line_length\": 81,\n \"num_lines\": 43,\n \"path\": \"/Management/migrations/0033_auto_20210511_1648.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-05-11 16:48\\n\\nfrom django.db import migrations, models\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0032_auto_20210511_1636'),\\n ]\\n\\n operations = [\\n migrations.AddField(\\n model_name='course',\\n name='courseCredit',\\n field=models.DecimalField(decimal_places=1, max_digits=4, null=True),\\n ),\\n migrations.AddField(\\n model_name='course',\\n name='courseDescription',\\n field=models.CharField(max_length=200, null=True),\\n ),\\n migrations.AddField(\\n model_name='course',\\n name='courseName',\\n field=models.CharField(max_length=64, null=True),\\n ),\\n migrations.AddField(\\n model_name='course',\\n name='courseNature',\\n field=models.CharField(max_length=64, null=True),\\n ),\\n migrations.AddField(\\n model_name='course',\\n name='experimentHours',\\n field=models.SmallIntegerField(max_length=2, null=True),\\n ),\\n migrations.AddField(\\n model_name='course',\\n name='theoreticalHours',\\n field=models.SmallIntegerField(max_length=2, null=True),\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.48991936445236206,\n \"alphanum_fraction\": 0.5725806355476379,\n \"avg_line_length\": 26.55555534362793,\n \"blob_id\": \"132a74565cc0ef4323cf4ec8c6c743180254b7a0\",\n \"content_id\": \"b4da23ffd5f5d2bc95afae6122fc2463b02ef274\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 496,\n \"license_type\": \"no_license\",\n \"max_line_length\": 159,\n \"num_lines\": 18,\n \"path\": \"/Management/migrations/0020_auto_20210424_1458.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-04-24 14:58\\n\\nfrom django.db import migrations, models\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0019_auto_20210424_1458'),\\n ]\\n\\n operations = [\\n migrations.AlterField(\\n model_name='class',\\n name='year',\\n field=models.CharField(blank=True, choices=[('1', 1), ('2', 2), ('3', 3), ('4', 4)], default='y', help_text='Select year for Class', max_length=1),\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.6408450603485107,\n \"alphanum_fraction\": 0.6408450603485107,\n \"avg_line_length\": 31.769229888916016,\n \"blob_id\": \"1879a7ebd83cf24fd4a789c1bf5c0a241a737a0e\",\n \"content_id\": \"5f53d343e58c34c1d043690cd8cf973f5113aa76\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 426,\n \"license_type\": \"no_license\",\n \"max_line_length\": 59,\n \"num_lines\": 13,\n \"path\": \"/Management/urls.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.urls import path\\nfrom . import views\\n\\n\\nurlpatterns = [\\n path('', views.index, name=\\\"index\\\"),\\n path('classes/', views.classes, name=\\\"classes\\\"),\\n path('dash/', views.dash, name=\\\"dash\\\"),\\n path('grades/', views.grades, name=\\\"grades\\\"),\\n # path('login/', views.loginPage, name=\\\"login\\\"),\\n path('signup/', views.registerPage, name=\\\"signup\\\"),\\n path('students/', views.showStudents, name=\\\"students\\\"),\\n]\\n\"\n },\n {\n \"alpha_fraction\": 0.5410010814666748,\n \"alphanum_fraction\": 0.5750798583030701,\n \"avg_line_length\": 27.454545974731445,\n \"blob_id\": \"f18136041d425decc3eddd76788230b0b8a8e959\",\n \"content_id\": \"e03bfd5cd10f403014428b6d75e6a7351a385bbc\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 939,\n \"license_type\": \"no_license\",\n \"max_line_length\": 66,\n \"num_lines\": 33,\n \"path\": \"/Management/migrations/0040_auto_20210521_1617.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-05-21 16:17\\n\\nfrom django.db import migrations, models\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0039_auto_20210521_1616'),\\n ]\\n\\n operations = [\\n migrations.AlterField(\\n model_name='elective',\\n name='courseNumber',\\n field=models.ManyToManyField(to='Management.Course'),\\n ),\\n migrations.AlterField(\\n model_name='elective',\\n name='studentNumber',\\n field=models.ManyToManyField(to='Management.Student'),\\n ),\\n migrations.AlterField(\\n model_name='teach',\\n name='courseNumber',\\n field=models.ManyToManyField(to='Management.Course'),\\n ),\\n migrations.AlterField(\\n model_name='teach',\\n name='teacherNumber',\\n field=models.ManyToManyField(to='Management.Teacher'),\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.5028011202812195,\n \"alphanum_fraction\": 0.5196078419685364,\n \"avg_line_length\": 31.454545974731445,\n \"blob_id\": \"0093bc534f54e6b9686c6237e46c1623a0f6adc2\",\n \"content_id\": \"e17703af1ccae531e91d8d5f5dd5d192306d96dd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1428,\n \"license_type\": \"no_license\",\n \"max_line_length\": 114,\n \"num_lines\": 44,\n \"path\": \"/Management/migrations/0001_initial.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-04-23 07:40\\n\\nfrom django.db import migrations, models\\n\\n\\nclass Migration(migrations.Migration):\\n\\n initial = True\\n\\n dependencies = [\\n ]\\n\\n operations = [\\n migrations.CreateModel(\\n name='Class',\\n fields=[\\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\\n ('year', models.IntegerField()),\\n ],\\n ),\\n migrations.CreateModel(\\n name='Course',\\n fields=[\\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\\n ('title', models.CharField(max_length=64)),\\n ('sem', models.IntegerField()),\\n ],\\n ),\\n migrations.CreateModel(\\n name='Student',\\n fields=[\\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\\n ('name', models.CharField(max_length=64)),\\n ('roll_no', models.CharField(max_length=64)),\\n ],\\n ),\\n migrations.CreateModel(\\n name='Teacher',\\n fields=[\\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\\n ('name', models.CharField(max_length=64)),\\n ],\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.486868679523468,\n \"alphanum_fraction\": 0.5717171430587769,\n \"avg_line_length\": 26.5,\n \"blob_id\": \"c358dcec9880138106e8b21bf8cb38f998606a26\",\n \"content_id\": \"4e31ed15744d2f5dd201c3acbbea8b2c82fafe09\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 495,\n \"license_type\": \"no_license\",\n \"max_line_length\": 159,\n \"num_lines\": 18,\n \"path\": \"/Management/migrations/0028_course_year.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-04-27 02:19\\n\\nfrom django.db import migrations, models\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0027_auto_20210427_0215'),\\n ]\\n\\n operations = [\\n migrations.AddField(\\n model_name='course',\\n name='year',\\n field=models.CharField(choices=[('1', 1), ('2', 2), ('3', 3), ('4', 4)], default='1', help_text='Select year for course', max_length=1, null=True),\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.5363214612007141,\n \"alphanum_fraction\": 0.5564142465591431,\n \"avg_line_length\": 42.86440658569336,\n \"blob_id\": \"7496ef8a986e53e8dc58263308799036d97c2c7c\",\n \"content_id\": \"2b8f40aaad6ca68ee7fef036010f1ad4b53e2d02\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2588,\n \"license_type\": \"no_license\",\n \"max_line_length\": 114,\n \"num_lines\": 59,\n \"path\": \"/Management/migrations/0037_answer_electives_proposed_teach_userinformation.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-05-12 06:12\\n\\nfrom django.db import migrations, models\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0036_problem'),\\n ]\\n\\n operations = [\\n migrations.CreateModel(\\n name='Answer',\\n fields=[\\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\\n ('teacherNumber', models.CharField(max_length=64, null=True)),\\n ('problemNumber', models.CharField(max_length=64, null=True)),\\n ('answerTime', models.CharField(max_length=64, null=True)),\\n ],\\n ),\\n migrations.CreateModel(\\n name='Electives',\\n fields=[\\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\\n ('studentNumber', models.CharField(max_length=64, null=True)),\\n ('courseNumber', models.CharField(max_length=64, null=True)),\\n ],\\n ),\\n migrations.CreateModel(\\n name='Proposed',\\n fields=[\\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\\n ('studentNumber', models.CharField(max_length=64, null=True)),\\n ('problemNumber', models.CharField(max_length=64, null=True)),\\n ('proposedTime', models.CharField(max_length=64, null=True)),\\n ],\\n ),\\n migrations.CreateModel(\\n name='Teach',\\n fields=[\\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\\n ('courseNumber', models.CharField(max_length=64, null=True)),\\n ('teacherNumber', models.CharField(max_length=64, null=True)),\\n ],\\n ),\\n migrations.CreateModel(\\n name='UserInformation',\\n fields=[\\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\\n ('userNumber', models.CharField(max_length=64, null=True)),\\n ('userName', models.CharField(max_length=64, null=True)),\\n ('loginPassword', models.CharField(max_length=64, null=True)),\\n ('roleNumber', models.CharField(max_length=64, null=True)),\\n ('roleName', models.CharField(max_length=64, null=True)),\\n ('passwordProblem', models.CharField(max_length=64, null=True)),\\n ],\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.358638733625412,\n \"alphanum_fraction\": 0.3926701545715332,\n \"avg_line_length\": 37.29999923706055,\n \"blob_id\": \"61dce8c81a061f52db01e8ca447632eec0f24d01\",\n \"content_id\": \"43adb2f402f2bc52e36f5efa3e7b764410fb558e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"JavaScript\",\n \"length_bytes\": 382,\n \"license_type\": \"no_license\",\n \"max_line_length\": 67,\n \"num_lines\": 10,\n \"path\": \"/static/js/testing.js\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"$(document).ready(function(){\\n $('.col-4 .box').each(function(){\\n var colors = 'rgba('+\\n (Math.floor((180-0)*Math.random())) + ',' +\\n (Math.floor((180-0)*Math.random())) + ',' +\\n (Math.floor((180-0)*Math.random()))\\n +')';\\n $(this).css(\\\"background-color\\\", colors);\\n });\\n});\"\n },\n {\n \"alpha_fraction\": 0.8218390941619873,\n \"alphanum_fraction\": 0.8218390941619873,\n \"avg_line_length\": 31.625,\n \"blob_id\": \"9869118703558c7c30dc2878ec85066d4cf25f3f\",\n \"content_id\": \"dbdbddc1bab8baae674ab39dfe4933e24fe56aa9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 522,\n \"license_type\": \"no_license\",\n \"max_line_length\": 127,\n \"num_lines\": 16,\n \"path\": \"/Management/admin.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.contrib import admin\\nfrom .models import Course, Teacher, Student, TestQuestion, Problem, UserInformation, Elective, Teach, Proposed, Answer,LMSUser\\n\\n\\n# Register your models here.\\nadmin.site.register(Course)\\nadmin.site.register(Teacher)\\nadmin.site.register(TestQuestion)\\nadmin.site.register(Student)\\nadmin.site.register(Problem)\\nadmin.site.register(UserInformation)\\nadmin.site.register(Elective)\\nadmin.site.register(Teach)\\nadmin.site.register(Proposed)\\nadmin.site.register(Answer)\\nadmin.site.register(LMSUser)\\n\"\n },\n {\n \"alpha_fraction\": 0.5007221698760986,\n \"alphanum_fraction\": 0.5209436416625977,\n \"avg_line_length\": 27.84722137451172,\n \"blob_id\": \"0b520144621ad1a01d0cf054c9e5be0a5e2abe7c\",\n \"content_id\": \"dae3665d93b2f80e1400364fc6a2a394d397e916\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2077,\n \"license_type\": \"no_license\",\n \"max_line_length\": 92,\n \"num_lines\": 72,\n \"path\": \"/Management/migrations/0030_auto_20210511_1608.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-05-11 16:08\\n\\nfrom django.db import migrations, models\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0029_auto_20210503_1540'),\\n ]\\n\\n operations = [\\n migrations.RemoveField(\\n model_name='student',\\n name='email',\\n ),\\n migrations.RemoveField(\\n model_name='student',\\n name='first_name',\\n ),\\n migrations.RemoveField(\\n model_name='student',\\n name='last_name',\\n ),\\n migrations.RemoveField(\\n model_name='student',\\n name='roll_no',\\n ),\\n migrations.RemoveField(\\n model_name='student',\\n name='semester',\\n ),\\n migrations.RemoveField(\\n model_name='student',\\n name='year',\\n ),\\n migrations.AddField(\\n model_name='student',\\n name='admitYear',\\n field=models.IntegerField(blank=True, null=True),\\n ),\\n migrations.AddField(\\n model_name='student',\\n name='birthDate',\\n field=models.DateField(blank=True, null=True),\\n ),\\n migrations.AddField(\\n model_name='student',\\n name='className',\\n field=models.CharField(blank=True, max_length=64),\\n ),\\n migrations.AddField(\\n model_name='student',\\n name='collegeName',\\n field=models.CharField(blank=True, max_length=64),\\n ),\\n migrations.AddField(\\n model_name='student',\\n name='professionalName',\\n field=models.CharField(blank=True, max_length=64),\\n ),\\n migrations.AddField(\\n model_name='student',\\n name='studentName',\\n field=models.CharField(blank=True, max_length=64),\\n ),\\n migrations.AddField(\\n model_name='student',\\n name='studentNumber',\\n field=models.CharField(blank=True, help_text='Student Roll No.', max_length=64),\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.5410885810852051,\n \"alphanum_fraction\": 0.5880469679832458,\n \"avg_line_length\": 36.47999954223633,\n \"blob_id\": \"7841f4e44aa2428d6a0a727265049294276bffa9\",\n \"content_id\": \"d203205aa1b8335b98172f153d9e9e1b87723136\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 937,\n \"license_type\": \"no_license\",\n \"max_line_length\": 114,\n \"num_lines\": 25,\n \"path\": \"/Management/migrations/0036_problem.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-05-12 06:03\\n\\nfrom django.db import migrations, models\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0035_auto_20210512_0554'),\\n ]\\n\\n operations = [\\n migrations.CreateModel(\\n name='Problem',\\n fields=[\\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\\n ('problemNumber', models.CharField(max_length=64, null=True)),\\n ('problemCategory', models.CharField(max_length=64, null=True)),\\n ('problemTitle', models.CharField(max_length=64, null=True)),\\n ('problemContent', models.CharField(max_length=64, null=True)),\\n ('problemAnswer', models.CharField(max_length=64, null=True)),\\n ('studentFeedback', models.CharField(max_length=64, null=True)),\\n ],\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.6850379109382629,\n \"alphanum_fraction\": 0.7059367299079895,\n \"avg_line_length\": 36.03424835205078,\n \"blob_id\": \"283c2c6ae2cbc1f6acd7714da2c2905cab59a886\",\n \"content_id\": \"f3a7d708f44eaacf51e922a6069231ea578cea2b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5407,\n \"license_type\": \"no_license\",\n \"max_line_length\": 93,\n \"num_lines\": 146,\n \"path\": \"/Management/models.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.db import models\\nfrom django.db.models.base import Model\\nfrom django.contrib.auth.models import User\\n\\n\\nselect_year = (\\n ('1', 1),\\n ('2', 2),\\n ('3', 3),\\n ('4', 4),\\n)\\nselect_semester = (\\n ('1', 1), ('2', 2), ('3', 3), ('4', 4), ('5', 5), ('6', 6), ('7', 7), ('8', 8)\\n)\\n\\nselect_user = (\\n ('student', 'student'),\\n ('teacher', 'teacher'),\\n ('admin', 'admin')\\n)\\n\\n\\n# Create your models here.\\n\\nclass Student(models.Model):\\n studentNumber = models.CharField(max_length=64, help_text=\\\"Student Roll No.\\\", blank=True)\\n studentName = models.CharField(max_length=64, blank=True)\\n admitYear = models.IntegerField(blank=True, null=True)\\n collegeName = models.CharField(max_length=64, blank=True)\\n professionalName = models.CharField(max_length=64, blank=True)\\n className = models.CharField(max_length=64, blank=True)\\n birthDate = models.DateField(null=True)\\n homeAddress = models.CharField(max_length=64, null=True)\\n postCode = models.CharField(max_length=64, null=True)\\n mobilePhone = models.CharField(max_length=64, null=True)\\n emailAddress = models.EmailField(null=True)\\n\\n def __str__(self):\\n return f\\\"{self.studentName} > {self.className} > {self.admitYear}\\\"\\n\\n\\nclass Course(models.Model):\\n courseNumber = models.IntegerField(null=True)\\n courseName = models.CharField(max_length=64, null=True)\\n courseNature = models.CharField(max_length=64, null=True)\\n courseCredit = models.DecimalField(max_digits=4, decimal_places=1, null=True)\\n theoreticalHours = models.SmallIntegerField(null=True)\\n experimentHours = models.SmallIntegerField(null=True)\\n courseDescription = models.CharField(max_length=200, null=True)\\n\\n def __str__(self) -> str:\\n return f\\\"{self.courseName}\\\"\\n\\n\\nclass Teacher(models.Model):\\n teacherNumber = models.CharField(max_length=64, null=True)\\n teacherName = models.CharField(max_length=64, null=True)\\n graduationSchool = models.CharField(max_length=64, null=True)\\n graduationTime = models.DateField(null=True)\\n collegeName = models.CharField(max_length=64, null=True)\\n professionalName = models.CharField(max_length=64, null=True)\\n technicalTitle = models.CharField(max_length=64, null=True)\\n finalDegree = models.CharField(max_length=64, null=True)\\n finalEducation = models.CharField(max_length=64, null=True)\\n mobilePhone = models.CharField(max_length=64, null=True)\\n email = models.EmailField(null=True)\\n jobResume = models.FileField(null=True)\\n\\n def __str__(self):\\n return f\\\"{self.teacherName}\\\"\\n\\n\\nclass TestQuestion(models.Model):\\n question_no = models.IntegerField()\\n courseNumber = models.CharField(max_length=64, null=True)\\n knowledgePoint = models.CharField(max_length=64, null=True)\\n chapterSection = models.CharField(max_length=64, null=True)\\n testQuestionType = models.CharField(max_length=10, null=True)\\n titleName = models.CharField(max_length=50, null=True)\\n testQuestionContent = models.CharField(max_length=200, null=True)\\n testQuestionAnwser = models.CharField(max_length=200, null=True)\\n\\n\\nclass Problem(models.Model):\\n problemNumber = models.CharField(max_length=64, null=True)\\n problemCategory = models.CharField(max_length=64, null=True)\\n problemTitle = models.CharField(max_length=64, null=True)\\n problemContent = models.CharField(max_length=64, null=True)\\n problemAnswer = models.CharField(max_length=64, null=True, blank=True)\\n studentFeedback = models.CharField(max_length=64, null=True, blank=True)\\n\\n def __str__(self) -> str:\\n return f\\\"{self.problemTitle}\\\"\\n\\n\\nclass UserInformation(models.Model):\\n userNumber = models.CharField(max_length=64, null=True)\\n userName = models.CharField(max_length=64, null=True)\\n loginPassword = models.CharField(max_length=64, null=True)\\n roleNumber = models.CharField(max_length=64, null=True)\\n roleName = models.CharField(max_length=64, null=True)\\n passwordProblem = models.CharField(max_length=64, null=True)\\n\\n\\nclass Elective(models.Model):\\n studentNumber = models.ManyToManyField(Student)\\n courseNumber = models.ManyToManyField(Course)\\n\\n def __str__(self):\\n return f\\\"{self.studentNumber.studentName}\\\"\\n\\n\\nclass Teach(models.Model):\\n courseNumber = models.ManyToManyField(Course)\\n teacherNumber = models.ManyToManyField(Teacher)\\n # courseNumber = models.CharField(max_length=64, null=True)\\n # teacherNumber = models.CharField(max_length=64, null=True)\\n\\n def __str__(self):\\n return f\\\"{self.teacherNumber.teacherName}\\\"\\n\\n\\nclass Proposed(models.Model):\\n studentNumber = models.ForeignKey(Student, null=True, on_delete=models.CASCADE)\\n problemNumber = models.ForeignKey(Problem, null=True, on_delete=models.CASCADE)\\n proposedTime = models.DateTimeField(null=True)\\n\\n\\nclass Answer(models.Model):\\n teacherNumber = models.ForeignKey(Teacher, null=True, on_delete=models.CASCADE)\\n problemNumber = models.ForeignKey(Problem,null=True, on_delete=models.CASCADE)\\n answerTime = models.DateTimeField(null=True)\\n\\n\\nclass LMSUser(models.Model):\\n user = models.OneToOneField(User, on_delete=models.CASCADE)\\n user_type = models.CharField(\\n max_length=20,\\n choices=select_user,\\n null=True,\\n default='student',\\n help_text='Please select this Correctly, Because you will be Checked.'\\n )\\n\\n def __str__(self):\\n return f'{self.user_type}'\\n\"\n },\n {\n \"alpha_fraction\": 0.5006250143051147,\n \"alphanum_fraction\": 0.5262500047683716,\n \"avg_line_length\": 27.070175170898438,\n \"blob_id\": \"00ab5497acf5b3c5851b991a48e87c4abc8475af\",\n \"content_id\": \"ca3db93818564ba897f6aea569c8375866ba9908\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1600,\n \"license_type\": \"no_license\",\n \"max_line_length\": 75,\n \"num_lines\": 57,\n \"path\": \"/Management/migrations/0031_auto_20210511_1636.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-05-11 16:36\\n\\nfrom django.db import migrations, models\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0030_auto_20210511_1608'),\\n ]\\n\\n operations = [\\n migrations.RemoveField(\\n model_name='course',\\n name='description',\\n ),\\n migrations.RemoveField(\\n model_name='course',\\n name='subjects',\\n ),\\n migrations.RemoveField(\\n model_name='course',\\n name='title',\\n ),\\n migrations.RemoveField(\\n model_name='course',\\n name='year',\\n ),\\n migrations.AddField(\\n model_name='course',\\n name='courseNumber',\\n field=models.IntegerField(null=True),\\n ),\\n migrations.AddField(\\n model_name='student',\\n name='emailAddress',\\n field=models.EmailField(blank=True, max_length=254, null=True),\\n ),\\n migrations.AddField(\\n model_name='student',\\n name='homeAddress',\\n field=models.CharField(blank=True, max_length=64, null=True),\\n ),\\n migrations.AddField(\\n model_name='student',\\n name='mobilePhone',\\n field=models.CharField(blank=True, max_length=64, null=True),\\n ),\\n migrations.AddField(\\n model_name='student',\\n name='postCode',\\n field=models.CharField(blank=True, max_length=64, null=True),\\n ),\\n migrations.DeleteModel(\\n name='Subject',\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.5709515810012817,\n \"alphanum_fraction\": 0.6043405532836914,\n \"avg_line_length\": 25.04347801208496,\n \"blob_id\": \"1091c6df92c9f056d46883aeac31fd7e8396bfae\",\n \"content_id\": \"88cd895cdbdb139c697f051c92bf4ac3e0f998b1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 599,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 23,\n \"path\": \"/Management/migrations/0038_auto_20210521_1454.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-05-21 14:54\\n\\nfrom django.db import migrations, models\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0037_answer_electives_proposed_teach_userinformation'),\\n ]\\n\\n operations = [\\n migrations.AlterField(\\n model_name='course',\\n name='experimentHours',\\n field=models.SmallIntegerField(null=True),\\n ),\\n migrations.AlterField(\\n model_name='course',\\n name='theoreticalHours',\\n field=models.SmallIntegerField(null=True),\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.5066518783569336,\n \"alphanum_fraction\": 0.5609756112098694,\n \"avg_line_length\": 31.214284896850586,\n \"blob_id\": \"0c4cc7dacb8ea26073e9333cdc42b77daff3f024\",\n \"content_id\": \"7f8b7e9080da1e4f705b77fd589bf4e5ae2574a7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 902,\n \"license_type\": \"no_license\",\n \"max_line_length\": 202,\n \"num_lines\": 28,\n \"path\": \"/Management/migrations/0010_auto_20210424_1438.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-04-24 14:38\\n\\nfrom django.db import migrations, models\\nimport django.db.models.deletion\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0009_auto_20210424_1436'),\\n ]\\n\\n operations = [\\n migrations.RemoveField(\\n model_name='class',\\n name='students',\\n ),\\n migrations.AddField(\\n model_name='class',\\n name='students',\\n field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='Management.Student'),\\n ),\\n migrations.AlterField(\\n model_name='student',\\n name='semester',\\n field=models.CharField(blank=True, choices=[('1', 1), ('2', 2), ('3', 3), ('4', 4), ('5', 5), ('6', 6), ('7', 7), ('8', 8)], default='one', help_text='Please Select Semester', max_length=1),\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.6098901033401489,\n \"alphanum_fraction\": 0.6465201377868652,\n \"avg_line_length\": 27.736841201782227,\n \"blob_id\": \"9246bd93f888b625e2a7c3b690f4176a791b30ba\",\n \"content_id\": \"2c130369aceef57e07505439dc6dbf73c7a561cd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 546,\n \"license_type\": \"no_license\",\n \"max_line_length\": 181,\n \"num_lines\": 19,\n \"path\": \"/Management/migrations/0029_auto_20210503_1540.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-05-03 15:40\\n\\nfrom django.db import migrations, models\\nimport django.db.models.deletion\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0028_course_year'),\\n ]\\n\\n operations = [\\n migrations.AlterField(\\n model_name='class',\\n name='course',\\n field=models.ForeignKey(help_text='Select Course for this class', null=True, on_delete=django.db.models.deletion.CASCADE, related_name='course', to='Management.Course'),\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.48918917775154114,\n \"alphanum_fraction\": 0.5756756663322449,\n \"avg_line_length\": 19.55555534362793,\n \"blob_id\": \"1d5143724e6331a86eba42720e5f9abecdec6a43\",\n \"content_id\": \"cf64b24c045f1f30d62588f0e1f0603425f3af5f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 370,\n \"license_type\": \"no_license\",\n \"max_line_length\": 50,\n \"num_lines\": 18,\n \"path\": \"/Management/migrations/0027_auto_20210427_0215.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-04-27 02:15\\n\\nfrom django.db import migrations\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0026_auto_20210427_0215'),\\n ]\\n\\n operations = [\\n migrations.RenameField(\\n model_name='course',\\n old_name='subject',\\n new_name='subjects',\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.514018714427948,\n \"alphanum_fraction\": 0.5523364543914795,\n \"avg_line_length\": 27.157894134521484,\n \"blob_id\": \"6482e8231605244e682ad569e17946e3c75b99b5\",\n \"content_id\": \"18248f628bec53147eb8c73fb8bf5df0372fc7aa\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1070,\n \"license_type\": \"no_license\",\n \"max_line_length\": 63,\n \"num_lines\": 38,\n \"path\": \"/Management/migrations/0032_auto_20210511_1636.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-05-11 16:36\\n\\nfrom django.db import migrations, models\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0031_auto_20210511_1636'),\\n ]\\n\\n operations = [\\n migrations.AlterField(\\n model_name='student',\\n name='birthDate',\\n field=models.DateField(null=True),\\n ),\\n migrations.AlterField(\\n model_name='student',\\n name='emailAddress',\\n field=models.EmailField(max_length=254, null=True),\\n ),\\n migrations.AlterField(\\n model_name='student',\\n name='homeAddress',\\n field=models.CharField(max_length=64, null=True),\\n ),\\n migrations.AlterField(\\n model_name='student',\\n name='mobilePhone',\\n field=models.CharField(max_length=64, null=True),\\n ),\\n migrations.AlterField(\\n model_name='student',\\n name='postCode',\\n field=models.CharField(max_length=64, null=True),\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.57230144739151,\n \"alphanum_fraction\": 0.637474536895752,\n \"avg_line_length\": 24.842105865478516,\n \"blob_id\": \"fb308e912f924eb9054e347b5f564e550c42ce42\",\n \"content_id\": \"e7c9e45c8babeaa81e3091a2c27b952f9be98313\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 491,\n \"license_type\": \"no_license\",\n \"max_line_length\": 117,\n \"num_lines\": 19,\n \"path\": \"/Management/migrations/0004_auto_20210423_0954.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-04-23 09:54\\n\\nfrom django.db import migrations, models\\nimport django.db.models.deletion\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0003_auto_20210423_0954'),\\n ]\\n\\n operations = [\\n migrations.AlterField(\\n model_name='class',\\n name='students',\\n field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='Management.Student'),\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.5072463750839233,\n \"alphanum_fraction\": 0.5637680888175964,\n \"avg_line_length\": 29,\n \"blob_id\": \"c4c0e79e070a221a42c800f8b3ce9f367bcf27ce\",\n \"content_id\": \"914bbbc16242555a9930719f11f9fa572a10230f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 690,\n \"license_type\": \"no_license\",\n \"max_line_length\": 149,\n \"num_lines\": 23,\n \"path\": \"/Management/migrations/0008_auto_20210424_1430.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-04-24 14:30\\n\\nfrom django.db import migrations, models\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0007_auto_20210423_1449'),\\n ]\\n\\n operations = [\\n migrations.AddField(\\n model_name='subject',\\n name='year',\\n field=models.CharField(blank=True, choices=[('y', 1), ('e', 2), ('a', 3), ('r', 4)], default='y', help_text='Select Year', max_length=1),\\n ),\\n migrations.AlterField(\\n model_name='subject',\\n name='name',\\n field=models.CharField(help_text='Enter Subject for this course e.g. DSA', max_length=64),\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.5385281443595886,\n \"alphanum_fraction\": 0.5800865888595581,\n \"avg_line_length\": 37.5,\n \"blob_id\": \"6fc4f8cc259aa095b9cc10e9ac4a4220a7fbca7e\",\n \"content_id\": \"c3a01a8f01aabb55738f773c898f6baae37439a3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1155,\n \"license_type\": \"no_license\",\n \"max_line_length\": 114,\n \"num_lines\": 30,\n \"path\": \"/Management/migrations/0035_auto_20210512_0554.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-05-12 05:54\\n\\nfrom django.db import migrations, models\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0034_auto_20210512_0536'),\\n ]\\n\\n operations = [\\n migrations.CreateModel(\\n name='TestQuestion',\\n fields=[\\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\\n ('question_no', models.IntegerField()),\\n ('courseNumber', models.CharField(max_length=64, null=True)),\\n ('knowledgePoint', models.CharField(max_length=64, null=True)),\\n ('chapterSection', models.CharField(max_length=64, null=True)),\\n ('testQuestionType', models.CharField(max_length=10, null=True)),\\n ('titleName', models.CharField(max_length=50, null=True)),\\n ('testQuestionContent', models.CharField(max_length=200, null=True)),\\n ('testQuestionAnwser', models.CharField(max_length=200, null=True)),\\n ],\\n ),\\n migrations.DeleteModel(\\n name='Class',\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.4957627058029175,\n \"alphanum_fraction\": 0.5466101765632629,\n \"avg_line_length\": 24.285715103149414,\n \"blob_id\": \"2516ba471880b24181c2eaaca181019d87338de6\",\n \"content_id\": \"0148c4ed1ff9fe1b27143f5aeffbc44d4cb6a2b0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 708,\n \"license_type\": \"no_license\",\n \"max_line_length\": 63,\n \"num_lines\": 28,\n \"path\": \"/Management/migrations/0016_auto_20210424_1448.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-04-24 14:48\\n\\nfrom django.db import migrations, models\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0015_auto_20210424_1446'),\\n ]\\n\\n operations = [\\n migrations.RenameField(\\n model_name='student',\\n old_name='name',\\n new_name='last_name',\\n ),\\n migrations.AddField(\\n model_name='student',\\n name='email',\\n field=models.EmailField(blank=True, max_length=64),\\n ),\\n migrations.AddField(\\n model_name='student',\\n name='first_name',\\n field=models.CharField(max_length=64, null=True),\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.5101671814918518,\n \"alphanum_fraction\": 0.5323090553283691,\n \"avg_line_length\": 29.3150691986084,\n \"blob_id\": \"3a9f8508cd239422605c84f74193c2a14e858408\",\n \"content_id\": \"761e96939ff60f8f62e31ebc96f21fb57d619882\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2213,\n \"license_type\": \"no_license\",\n \"max_line_length\": 63,\n \"num_lines\": 73,\n \"path\": \"/Management/migrations/0034_auto_20210512_0536.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-05-12 05:36\\n\\nfrom django.db import migrations, models\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0033_auto_20210511_1648'),\\n ]\\n\\n operations = [\\n migrations.RenameField(\\n model_name='teacher',\\n old_name='first_name',\\n new_name='collegeName',\\n ),\\n migrations.RenameField(\\n model_name='teacher',\\n old_name='last_name',\\n new_name='finalDegree',\\n ),\\n migrations.AddField(\\n model_name='teacher',\\n name='finalEducation',\\n field=models.CharField(max_length=64, null=True),\\n ),\\n migrations.AddField(\\n model_name='teacher',\\n name='graduationSchool',\\n field=models.CharField(max_length=64, null=True),\\n ),\\n migrations.AddField(\\n model_name='teacher',\\n name='graduationTime',\\n field=models.DateField(null=True),\\n ),\\n migrations.AddField(\\n model_name='teacher',\\n name='jobResume',\\n field=models.FileField(null=True, upload_to=''),\\n ),\\n migrations.AddField(\\n model_name='teacher',\\n name='mobilePhone',\\n field=models.CharField(max_length=64, null=True),\\n ),\\n migrations.AddField(\\n model_name='teacher',\\n name='professionalName',\\n field=models.CharField(max_length=64, null=True),\\n ),\\n migrations.AddField(\\n model_name='teacher',\\n name='teacherName',\\n field=models.CharField(max_length=64, null=True),\\n ),\\n migrations.AddField(\\n model_name='teacher',\\n name='teacherNumber',\\n field=models.CharField(max_length=64, null=True),\\n ),\\n migrations.AddField(\\n model_name='teacher',\\n name='technicalTitle',\\n field=models.CharField(max_length=64, null=True),\\n ),\\n migrations.AlterField(\\n model_name='teacher',\\n name='email',\\n field=models.EmailField(max_length=254, null=True),\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.5476718544960022,\n \"alphanum_fraction\": 0.618625283241272,\n \"avg_line_length\": 24.05555534362793,\n \"blob_id\": \"3da9e1b1e614005abe61e58df76a28ff77dcdd30\",\n \"content_id\": \"00a16ecac070cb707c73a372eaea0534551b0b69\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 451,\n \"license_type\": \"no_license\",\n \"max_line_length\": 110,\n \"num_lines\": 18,\n \"path\": \"/Management/migrations/0015_auto_20210424_1446.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-04-24 14:46\\n\\nfrom django.db import migrations, models\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0014_auto_20210424_1445'),\\n ]\\n\\n operations = [\\n migrations.AlterField(\\n model_name='class',\\n name='students',\\n field=models.ManyToManyField(help_text='Select students for this class', to='Management.Student'),\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.4979838728904724,\n \"alphanum_fraction\": 0.5725806355476379,\n \"avg_line_length\": 26.55555534362793,\n \"blob_id\": \"b9687617f3c3728c5ed56e7ff3517e4b74d18e80\",\n \"content_id\": \"c428137dcb7c0ac3331d93bc862b67d179729346\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 496,\n \"license_type\": \"no_license\",\n \"max_line_length\": 159,\n \"num_lines\": 18,\n \"path\": \"/Management/migrations/0018_auto_20210424_1456.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-04-24 14:56\\n\\nfrom django.db import migrations, models\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0017_auto_20210424_1450'),\\n ]\\n\\n operations = [\\n migrations.AlterField(\\n model_name='class',\\n name='year',\\n field=models.CharField(blank=True, choices=[('y', 1), ('e', 2), ('a', 3), ('r', 4)], default='y', help_text='Select year for Class', max_length=1),\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.5031367540359497,\n \"alphanum_fraction\": 0.5545796751976013,\n \"avg_line_length\": 28.518518447875977,\n \"blob_id\": \"af8e084998c13110052d2c80f4640413914618e4\",\n \"content_id\": \"bd0958a54a4b53fb85e3797ef29efcee3caf4b91\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 797,\n \"license_type\": \"no_license\",\n \"max_line_length\": 149,\n \"num_lines\": 27,\n \"path\": \"/Management/migrations/0022_auto_20210424_1511.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-04-24 15:11\\n\\nfrom django.db import migrations, models\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0021_auto_20210424_1502'),\\n ]\\n\\n operations = [\\n migrations.RemoveField(\\n model_name='course',\\n name='teacher',\\n ),\\n migrations.AddField(\\n model_name='subject',\\n name='teacher',\\n field=models.ManyToManyField(blank=True, related_name='Subject', to='Management.Teacher'),\\n ),\\n migrations.AlterField(\\n model_name='subject',\\n name='year',\\n field=models.CharField(blank=True, choices=[('1', 1), ('2', 2), ('3', 3), ('4', 4)], default='y', help_text='Select Year', max_length=1),\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.557441234588623,\n \"alphanum_fraction\": 0.5861618518829346,\n \"avg_line_length\": 28.461538314819336,\n \"blob_id\": \"037e1a334b25ae02c2aa8330b170b76246d63982\",\n \"content_id\": \"6af4c32f5439f5f6035b32e5b7a9e251fa33ccf3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 766,\n \"license_type\": \"no_license\",\n \"max_line_length\": 117,\n \"num_lines\": 26,\n \"path\": \"/Management/migrations/0003_auto_20210423_0954.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-04-23 09:54\\n\\nfrom django.db import migrations, models\\nimport django.db.models.deletion\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0002_class_students'),\\n ]\\n\\n operations = [\\n migrations.CreateModel(\\n name='Subject',\\n fields=[\\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\\n ('name', models.CharField(max_length=64)),\\n ],\\n ),\\n migrations.AddField(\\n model_name='course',\\n name='subject',\\n field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='Management.Subject'),\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.5162659287452698,\n \"alphanum_fraction\": 0.5643564462661743,\n \"avg_line_length\": 25.185184478759766,\n \"blob_id\": \"95ba47999ac848c089268884dff4435e57fd9dc7\",\n \"content_id\": \"f6fcfa2e921b2c062e9b4f260e6abb51e00322a5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 707,\n \"license_type\": \"no_license\",\n \"max_line_length\": 101,\n \"num_lines\": 27,\n \"path\": \"/Management/migrations/0005_auto_20210423_1243.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-04-23 12:43\\n\\nfrom django.db import migrations, models\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0004_auto_20210423_0954'),\\n ]\\n\\n operations = [\\n migrations.RemoveField(\\n model_name='course',\\n name='sem',\\n ),\\n migrations.AddField(\\n model_name='course',\\n name='description',\\n field=models.CharField(max_length=64, null=True),\\n ),\\n migrations.AddField(\\n model_name='course',\\n name='teacher',\\n field=models.ManyToManyField(blank=True, related_name='Course', to='Management.Teacher'),\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.5833333134651184,\n \"alphanum_fraction\": 0.6439393758773804,\n \"avg_line_length\": 26.789474487304688,\n \"blob_id\": \"2ff65cce7b1a87eaf5a352e8270146b8c0301159\",\n \"content_id\": \"ccb734c9bf593b614f3ccd05315e8289799cfff9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 528,\n \"license_type\": \"no_license\",\n \"max_line_length\": 158,\n \"num_lines\": 19,\n \"path\": \"/Management/migrations/0025_class_course.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-04-27 01:54\\n\\nfrom django.db import migrations, models\\nimport django.db.models.deletion\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0024_auto_20210424_1513'),\\n ]\\n\\n operations = [\\n migrations.AddField(\\n model_name='class',\\n name='course',\\n field=models.ForeignKey(help_text='Select Course for this class', null=True, on_delete=django.db.models.deletion.CASCADE, to='Management.Course'),\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.5605765581130981,\n \"alphanum_fraction\": 0.5730424523353577,\n \"avg_line_length\": 34.16438293457031,\n \"blob_id\": \"4a8d2a89167f248419698cd4ec714589136de3f6\",\n \"content_id\": \"4d23ddead4cfef16e9c86872a86bd83644b7eec2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2567,\n \"license_type\": \"no_license\",\n \"max_line_length\": 117,\n \"num_lines\": 73,\n \"path\": \"/Management/migrations/0039_auto_20210521_1616.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-05-21 16:16\\n\\nfrom django.db import migrations, models\\nimport django.db.models.deletion\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0038_auto_20210521_1454'),\\n ]\\n\\n operations = [\\n migrations.CreateModel(\\n name='Elective',\\n fields=[\\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\\n ('courseNumber', models.ManyToManyField(null=True, to='Management.Course')),\\n ('studentNumber', models.ManyToManyField(null=True, to='Management.Student')),\\n ],\\n ),\\n migrations.DeleteModel(\\n name='Electives',\\n ),\\n migrations.AlterField(\\n model_name='answer',\\n name='answerTime',\\n field=models.DateTimeField(null=True),\\n ),\\n migrations.AlterField(\\n model_name='answer',\\n name='problemNumber',\\n field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='Management.Problem'),\\n ),\\n migrations.AlterField(\\n model_name='answer',\\n name='teacherNumber',\\n field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='Management.Teacher'),\\n ),\\n migrations.AlterField(\\n model_name='proposed',\\n name='problemNumber',\\n field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='Management.Problem'),\\n ),\\n migrations.AlterField(\\n model_name='proposed',\\n name='proposedTime',\\n field=models.DateTimeField(null=True),\\n ),\\n migrations.AlterField(\\n model_name='proposed',\\n name='studentNumber',\\n field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='Management.Student'),\\n ),\\n migrations.RemoveField(\\n model_name='teach',\\n name='courseNumber',\\n ),\\n migrations.AddField(\\n model_name='teach',\\n name='courseNumber',\\n field=models.ManyToManyField(null=True, to='Management.Course'),\\n ),\\n migrations.RemoveField(\\n model_name='teach',\\n name='teacherNumber',\\n ),\\n migrations.AddField(\\n model_name='teach',\\n name='teacherNumber',\\n field=models.ManyToManyField(null=True, to='Management.Teacher'),\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.6617324352264404,\n \"alphanum_fraction\": 0.6617324352264404,\n \"avg_line_length\": 28.901639938354492,\n \"blob_id\": \"b81a7a82ab6bf0afcc4b8cab0d010edb9134014e\",\n \"content_id\": \"09a45954a5fcdd88eaaa93f83e258a705a7dce8c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1824,\n \"license_type\": \"no_license\",\n \"max_line_length\": 78,\n \"num_lines\": 61,\n \"path\": \"/Management/views.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.shortcuts import render, redirect\\nfrom django.http import HttpResponse\\nfrom django.contrib.auth import login, logout, authenticate\\nfrom django.contrib.auth.models import User\\nfrom Management.models import *\\nfrom .forms import *\\nfrom django.contrib.auth.decorators import *\\n\\n\\n# Create your views here.\\ndef loginPage(request):\\n if request.user.is_authenticated:\\n return redirect('index')\\n else:\\n if request.method == \\\"POST\\\":\\n username = request.POST.get('username')\\n password = request.POST.get('password')\\n user = authenticate(request, usernsme=username, password=password)\\n print(f'Username is {username} and Password is {password}')\\n print(user)\\n if user is not None:\\n print('Shuja')\\n login(request, user)\\n return redirect('index')\\n return render(request, 'Management/login.html')\\n\\n\\ndef registerPage(request):\\n form = CreateUserForm()\\n\\n if request.method == 'POST':\\n form = CreateUserForm(request.POST)\\n if form.is_valid():\\n form.save()\\n return redirect('index')\\n\\n context = {'form': form}\\n return render(request, 'Management/signup.html', context)\\n\\n\\ndef index(request):\\n students = Student.objects.all()\\n return render(request, 'Management/index.html', {'students': students})\\n\\n\\ndef showStudents(request):\\n students = Student.objects.all()\\n return render(request, 'Management/students.html', {'students': students})\\n\\n\\ndef classes(request):\\n students = Student.objects.all()\\n return render(request, 'Management/classes.html', {'students': students})\\n\\n\\ndef dash(request):\\n return render(request, 'Management/dashboard.html', {})\\n\\n\\ndef grades(request):\\n return render(request, 'Management/grades.html')\\n\"\n },\n {\n \"alpha_fraction\": 0.49700599908828735,\n \"alphanum_fraction\": 0.542514979839325,\n \"avg_line_length\": 25.09375,\n \"blob_id\": \"eec4392296e0deb39e359b993dad5ecdeb0cd189\",\n \"content_id\": \"a59c2e297d3ae50d6079ea44d0e35afb104bf86b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 835,\n \"license_type\": \"no_license\",\n \"max_line_length\": 62,\n \"num_lines\": 32,\n \"path\": \"/Management/migrations/0021_auto_20210424_1502.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-04-24 15:02\\n\\nfrom django.db import migrations, models\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0020_auto_20210424_1458'),\\n ]\\n\\n operations = [\\n migrations.RemoveField(\\n model_name='teacher',\\n name='name',\\n ),\\n migrations.AddField(\\n model_name='teacher',\\n name='email',\\n field=models.EmailField(max_length=64, null=True),\\n ),\\n migrations.AddField(\\n model_name='teacher',\\n name='first_name',\\n field=models.CharField(max_length=64, null=True),\\n ),\\n migrations.AddField(\\n model_name='teacher',\\n name='last_name',\\n field=models.CharField(max_length=64, null=True),\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.5115384459495544,\n \"alphanum_fraction\": 0.5730769038200378,\n \"avg_line_length\": 22.636363983154297,\n \"blob_id\": \"b7d2b4815cf27bc7c30edc66cd25fa547be14fcb\",\n \"content_id\": \"f71d6a0cd52697714caae4edd732bbde1af1b889\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 520,\n \"license_type\": \"no_license\",\n \"max_line_length\": 77,\n \"num_lines\": 22,\n \"path\": \"/Management/migrations/0007_auto_20210423_1449.py\",\n \"repo_name\": \"shujaakbar2020/LMS\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.2.17 on 2021-04-23 14:49\\n\\nfrom django.db import migrations, models\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('Management', '0006_auto_20210423_1448'),\\n ]\\n\\n operations = [\\n migrations.RemoveField(\\n model_name='class',\\n name='students',\\n ),\\n migrations.AddField(\\n model_name='class',\\n name='students',\\n field=models.ManyToManyField(null=True, to='Management.Student'),\\n ),\\n ]\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":37,"string":"37"}}},{"rowIdx":2979390,"cells":{"repo_name":{"kind":"string","value":"abuttonCH/ML-1"},"repo_url":{"kind":"string","value":"https://github.com/abuttonCH/ML-1"},"snapshot_id":{"kind":"string","value":"f6fd81510ac5f1eec2b280de684a21aa8862618f"},"revision_id":{"kind":"string","value":"e6433d04cee848fcbb033096ff0e1cd590c38dcb"},"directory_id":{"kind":"string","value":"a67092555e47d9f5f5210379f2ee5057a5c60057"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-01-24T11:09:41.781808","string":"2021-01-24T11:09:41.781808"},"revision_date":{"kind":"timestamp","value":"2016-10-07T16:19:55","string":"2016-10-07T16:19:55"},"committer_date":{"kind":"timestamp","value":"2016-10-07T16:19:55","string":"2016-10-07T16:19:55"},"github_id":{"kind":"number","value":70262446,"string":"70,262,446"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5947995781898499,"alphanum_fraction":0.6067172288894653,"avg_line_length":20.717647552490234,"blob_id":"afdcf7421d21422aadd61ccb2f7a607e0173bd26","content_id":"a416b82c41633c833767c4a19242b17c4dba4a15","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1846,"license_type":"no_license","max_line_length":120,"num_lines":85,"path":"/ML-test.py","repo_name":"abuttonCH/ML-1","src_encoding":"UTF-8","text":"#Test for loading the neurological data for the ML course\n\nimport nibabel as nib\nimport matplotlib.pyplot as plt\nimport os\nimport numpy as np\nfrom sklearn.decomposition import PCA\n\ntarget_list=[]\n\ntargets=open('targets.csv','r')\nfor lines in targets:\n target_list.append(int(lines.strip('\\n')))\n\nos.chdir('set_train')\nL=os.listdir('./')\n\ntarget_min=np.amin(target_list)\ntarget_max=np.amax(target_list)\n\nincrement=10\n\nbins=range(target_min,target_max,increment)\ntarget_bin = np.digitize(target_list, bins)\ntarget_bin = target_bin.tolist()\n\nfor j in list(set(target_bin)):\n indices=[]\n index=0\n for entry in target_bin:\n if entry == j:\n indices.append(index)\n index=index+1\n\n if j != np.amax(target_bin):\n print \"The age group is :\",bins[j-1], \"-\", bins[j], \" The number of samples are :\", len(indices)\n else:\n print \"The age group is :\", bins[j-1],\"-\", np.amax(bins)+increment, \" The number of samples are :\", len(indices)\n\n\n scans=[]\n\n#indices = range(278)\n\n for i in indices:\n img=nib.load(L[i])\n data=img.get_data()\n data=data[:,:,80,0]\n scans.append(data)\n\n print\n\n#mean_scan=np.average(np.array(scans),weights=target_list,axis=0)\n std_scans=np.std(np.array(scans),axis=0)\n mean_scans = np.mean(np.array(scans), axis=0)\n\n plt.imshow(mean_scans)\n#plt.plot(target_list)\n plt.show()\n\n\n########-PCA dimension reduction-#######\n\n#img = nib.load(L[i])\n#data = img.get_data()\n#data = data[:, :, 91, 0]\n\n\n#pca = PCA(n_components=10)\n#pca.fit(data)\n\n#print pca.explained_variance_ratio_\n#print pca.components_\n\n########################################\n\n\n\n############-distrubtion of scans-#############\n\n#plt.hist(target_list,20)\n#plt.title(\"Number of scans from different age groups\")\n#plt.show()\n\n###############################################\n"},{"alpha_fraction":0.7209302186965942,"alphanum_fraction":0.7674418687820435,"avg_line_length":20.5,"blob_id":"463551c85403a5bb78415811b5af3e3667be3a47","content_id":"5b56be2a97b0b53c8c81ee6d3210f93360fb051c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":43,"license_type":"no_license","max_line_length":35,"num_lines":2,"path":"/README.md","repo_name":"abuttonCH/ML-1","src_encoding":"UTF-8","text":"# ML-1\nwork for Machine learning project 1\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5947995781898499,\n \"alphanum_fraction\": 0.6067172288894653,\n \"avg_line_length\": 20.717647552490234,\n \"blob_id\": \"afdcf7421d21422aadd61ccb2f7a607e0173bd26\",\n \"content_id\": \"a416b82c41633c833767c4a19242b17c4dba4a15\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1846,\n \"license_type\": \"no_license\",\n \"max_line_length\": 120,\n \"num_lines\": 85,\n \"path\": \"/ML-test.py\",\n \"repo_name\": \"abuttonCH/ML-1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#Test for loading the neurological data for the ML course\\n\\nimport nibabel as nib\\nimport matplotlib.pyplot as plt\\nimport os\\nimport numpy as np\\nfrom sklearn.decomposition import PCA\\n\\ntarget_list=[]\\n\\ntargets=open('targets.csv','r')\\nfor lines in targets:\\n target_list.append(int(lines.strip('\\\\n')))\\n\\nos.chdir('set_train')\\nL=os.listdir('./')\\n\\ntarget_min=np.amin(target_list)\\ntarget_max=np.amax(target_list)\\n\\nincrement=10\\n\\nbins=range(target_min,target_max,increment)\\ntarget_bin = np.digitize(target_list, bins)\\ntarget_bin = target_bin.tolist()\\n\\nfor j in list(set(target_bin)):\\n indices=[]\\n index=0\\n for entry in target_bin:\\n if entry == j:\\n indices.append(index)\\n index=index+1\\n\\n if j != np.amax(target_bin):\\n print \\\"The age group is :\\\",bins[j-1], \\\"-\\\", bins[j], \\\" The number of samples are :\\\", len(indices)\\n else:\\n print \\\"The age group is :\\\", bins[j-1],\\\"-\\\", np.amax(bins)+increment, \\\" The number of samples are :\\\", len(indices)\\n\\n\\n scans=[]\\n\\n#indices = range(278)\\n\\n for i in indices:\\n img=nib.load(L[i])\\n data=img.get_data()\\n data=data[:,:,80,0]\\n scans.append(data)\\n\\n print\\n\\n#mean_scan=np.average(np.array(scans),weights=target_list,axis=0)\\n std_scans=np.std(np.array(scans),axis=0)\\n mean_scans = np.mean(np.array(scans), axis=0)\\n\\n plt.imshow(mean_scans)\\n#plt.plot(target_list)\\n plt.show()\\n\\n\\n########-PCA dimension reduction-#######\\n\\n#img = nib.load(L[i])\\n#data = img.get_data()\\n#data = data[:, :, 91, 0]\\n\\n\\n#pca = PCA(n_components=10)\\n#pca.fit(data)\\n\\n#print pca.explained_variance_ratio_\\n#print pca.components_\\n\\n########################################\\n\\n\\n\\n############-distrubtion of scans-#############\\n\\n#plt.hist(target_list,20)\\n#plt.title(\\\"Number of scans from different age groups\\\")\\n#plt.show()\\n\\n###############################################\\n\"\n },\n {\n \"alpha_fraction\": 0.7209302186965942,\n \"alphanum_fraction\": 0.7674418687820435,\n \"avg_line_length\": 20.5,\n \"blob_id\": \"463551c85403a5bb78415811b5af3e3667be3a47\",\n \"content_id\": \"5b56be2a97b0b53c8c81ee6d3210f93360fb051c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 43,\n \"license_type\": \"no_license\",\n \"max_line_length\": 35,\n \"num_lines\": 2,\n \"path\": \"/README.md\",\n \"repo_name\": \"abuttonCH/ML-1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# ML-1\\nwork for Machine learning project 1\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2979391,"cells":{"repo_name":{"kind":"string","value":"vsehgal1/Verilog_Parser"},"repo_url":{"kind":"string","value":"https://github.com/vsehgal1/Verilog_Parser"},"snapshot_id":{"kind":"string","value":"6885fe9ca996ae03a409fbd2aa2265d3b1646795"},"revision_id":{"kind":"string","value":"456d30decc77ef65925cf673eefc37bd4fe97da8"},"directory_id":{"kind":"string","value":"2a0d387b3ba5eee59827588dfc41d1226b6a4645"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2023-03-03T23:49:44.521596","string":"2023-03-03T23:49:44.521596"},"revision_date":{"kind":"timestamp","value":"2021-02-10T10:01:15","string":"2021-02-10T10:01:15"},"committer_date":{"kind":"timestamp","value":"2021-02-10T10:01:15","string":"2021-02-10T10:01:15"},"github_id":{"kind":"number","value":337681695,"string":"337,681,695"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5339228510856628,"alphanum_fraction":0.5933831334114075,"avg_line_length":33.345550537109375,"blob_id":"e775012b3bdd53bcb5acb4e71ed6d41adfc13486","content_id":"22b3b4733646cc894182a2dca15dbfa99c5d49ea","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":6559,"license_type":"no_license","max_line_length":90,"num_lines":191,"path":"/README.md","repo_name":"vsehgal1/Verilog_Parser","src_encoding":"UTF-8","text":"//****************************************************************************\n// Feel free to write this code in your favorite IDE and cut and paste\n// your final code into interviewzen.com. Your program will need to be\n// able to read this file.\n//\n// Instructions:\n// 1. Copy and paste the contents of this problem into a file called\n// TopCell.v. These comments can be included in the file.\n// 2. You will be writing a Python program that reads this file and\n// produces output as described below.\n// 3. Your program can simply hardcode the path to this file, or\n// optionally provide command line arguments to specify the file\n//\n//\n//\n// This is a simple verilog netlist. It contains several module definitions.\n// Each module contains input and output pins.\n// Some modules have wires.\n//\n// Each module also has instances of other modules or primitives.\n// The difference between a module and a primitive is that the primitives\n// do not have \"module\" definitions in this file. Primitives do not contain\n// instances (cellA is a module and contains instances, invN1 is a primitive\n// and doesn't contain any instances. It is the lowest level of hierarchy).\n//\n// Key words:\n// module, endmodule\n// output, input\n// wire\n//\n// Instance line description:\n// ==========================\n// bufferCell IbufferCellK0 (.in(in1), .out(net17));\n// bufferCell = module name\n// IbufferCellK0 = instance name (must be unique)\n// .in1(in1) = Pin in1 on instance IbufferCellK0 of module bufferCell\n// connects to a net named in1\n// .out(net17) = Pin out on instance IbufferCellK0 of module bufferCell\n// connects to a net named net17\n//\n//\n// Problem description:\n// ====================\n// cellB contains:\n// 4 instances of bufferCell\n// 2 instances of invN1 (a primitive)\n// 2 instances of nor2N1 (a primitive)\n// 1 instance of nand2N1 (a primitive)\n// bufferCell contains:\n// 2 instances of invN1 (a primitive)\n// This means that in the hierarchy of cellB, there are 10 instances\n// of invN1.\n//\n// Write a Python program to parse this file and store it in some kind\n// of structure. Assume that all information in the netlist (except\n// comments) may someday need to be accessed.\n// Write a function or method to count the total number of instances of\n// each module/primitive in the \"TopCell\" hierarchy (or any module)\n// by reading your structure.\n//\n// Example output for counting all of the instances in \"cellB\" hierarchy:\n// nand2N1 : 1 placements\n// nor2N1 : 2 placements\n// invN1 : 10 placements\n// bufferCell : 4 placements\n//\n// Tips:\n// If you are having trouble parsing the file with the instance\n// definitions split across multiple lines, then edit the input\n// so the entire statement is on one line.\n// Example:\n//\n// cellA IcellAK0 (.in1(inA), .in2(inB), .in3(inC), .in4(inD),\n// .out1(net22));\n// cellA IcellAK0 (.in1(inA), .in2(inB), .in3(inC), .in4(inD), .out1(net22));\n//\n//\n//****************************************************************************\n\n// Cell: designLib TopCell schematic\n// Last modified: Jun 14 12:01:37 2017\nmodule TopCell (data, out1, out2, out3, out4, out5);\n output out1;\n output out2;\n output out3;\n output out4;\n output out5;\n input [7:0] data;\n\n wire [7:0] data;\n\n cellA IcellAK0 (.in1(data[7]), .in2(data[6]), .in3(data[5]),\n .in4(data[4]), .out1(net14));\n cellC IcellCK0 (.inA(data[3]), .inB(data[2]), .inC(data[1]),\n .inD(data[0]), .outA(out2), .outB(out3));\n cellD IcellDK0 (.data(data[7:0]), .out1(out4), .out2(out5));\n invN1 X0 (.A(net14), .Y(out1));\n\nendmodule // TopCell\n\n// Cell: designLib bufferCell schematic\n// Last modified: Jun 14 11:00:42 2017\nmodule bufferCell (in, out);\n output out;\n input in;\n\n invN1 X0 (.A(in), .Y(net5));\n invN1 X1 (.A(net5), .Y(out));\nendmodule // bufferCell\n\n\n// Cell: designLib cellA schematic\n// Last modified: Jun 14 10:58:49 2017\nmodule cellA (in1, in2, in3, in4, out1);\n output out1;\n input in1;\n input in2;\n input in3;\n input in4;\n\n invN1 X0 (.A(in1), .Y(net13));\n invN1 X1 (.A(in2), .Y(net11));\n invN1 X2 (.A(in3), .Y(net12));\n invN1 X3 (.A(in4), .Y(net14));\n nand4N1 X4 (.A(net13), .B(net11), .C(net12), .D(net14), .Y(out1));\nendmodule // cellA\n\n\n// Cell: designLib cellB schematic\n// Last modified: Jun 14 11:09:05 2017\nmodule cellB (in1, in2, in3, in4, out1, out2);\n output out1;\n output out2;\n input in1;\n input in2;\n input in3;\n input in4;\n\n bufferCell IbufferCellK0 (.in(in1), .out(net17));\n bufferCell IbufferCellK1 (.in(in2), .out(net16));\n bufferCell IbufferCellK2 (.in(in3), .out(net15));\n bufferCell IbufferCellK3 (.in(in4), .out(net14));\n invN1 X0 (.A(net13), .Y(out2));\n invN1 X1 (.A(net19), .Y(net18));\n nand2N1 X2 (.A(net17), .B(net16), .Y(net19));\n nor2N1 X3 (.A(net15), .B(net14), .Y(net13));\n nor2N1 X4 (.A(net18), .B(net13), .Y(out1));\nendmodule // cellB\n\n\n// Cell: designLib cellC schematic\n// Last modified: Jun 14 11:16:40 2017\nmodule cellC (inA, inB, inC, inD, outA, outB);\n output outA;\n output outB;\n input inA;\n input inB;\n input inC;\n input inD;\n\n cellA IcellAK0 (.in1(inA), .in2(inB), .in3(inC), .in4(inD),\n .out1(net22));\n cellA IcellAK1 (.in1(inD), .in2(inA), .in3(inB), .in4(inC),\n .out1(net18));\n cellA IcellAK2 (.in1(inC), .in2(inD), .in3(inA), .in4(inB),\n .out1(net17));\n cellA IcellAK3 (.in1(inB), .in2(inC), .in3(inD), .in4(inA),\n .out1(net21));\n invN1 X0 (.A(net22), .Y(net20));\n invN1 X1 (.A(net21), .Y(net19));\n nand2N1 X2 (.A(net20), .B(net18), .Y(outA));\n nand2N1 X3 (.A(net17), .B(net19), .Y(outB));\nendmodule // cellC\n\n\n// Cell: designLib cellD schematic\n// Last modified: Jun 14 11:34:42 2017\nmodule cellD (data, out1, out2);\n output out1;\n output out2;\n input [7:0] data;\n\n wire [7:0] data;\n\n cellB IcellBK0 (.in1(data[0]), .in2(data[1]), .in3(data[2]),\n .in4(data[3]), .out1(net10), .out2(net8));\n cellB IcellBK1 (.in1(data[4]), .in2(data[5]), .in3(data[6]),\n .in4(data[7]), .out1(net7), .out2(net9));\n cellC IcellCK0 (.inA(net10), .inB(net8), .inC(net7), .inD(net9),\n .outA(out1), .outB(out2));\nendmodule // cellD"},{"alpha_fraction":0.5325067639350891,"alphanum_fraction":0.5395148396492004,"avg_line_length":35.37647247314453,"blob_id":"7f4930e55e0cf46da4797fb4c1794f9bf727c1fe","content_id":"400c537fc90236f90d929e66cdd7ea699bc4982f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":9275,"license_type":"no_license","max_line_length":114,"num_lines":255,"path":"/verilog_parser.py","repo_name":"vsehgal1/Verilog_Parser","src_encoding":"UTF-8","text":"# FUNCTION TO RETRIEVE CIRCUIT: getRawCircuit()\n# FUNCTION TO COUNT TOTAL INSTANCES/MODULES: totalInstances(MODU)\n# When testing please remove all new lines in instance definition.\n\n\nimport re\nimport copy\n\n# class to keep track of inner modules inside each module. \n# Points Parent module to child module which is also stored in circuit {} dictionary \n# and can be accessed through it.\nclass InnerModule:\n def __init__(self, module, instance, input_port, input_net, output_port, output_net):\n super().__init__()\n self.module = module\n self.instance = instance\n self.input_port = input_port\n self.input_net = input_net\n self.output_port = output_port\n self.output_net = output_net\n\n# class to keep track of inner primitives inside each module\nclass Primitives:\n def __init__(self, primitive, instance, input_port, input_net, output_port, output_net):\n super().__init__()\n self.primitive = primitive\n self.instance = instance\n self.input_port = input_port\n self.input_net = input_net\n self.output_port = output_port\n self.output_net = output_net\n\n\n# Main module class\nclass Module:\n def __init__(self, name='', inputs=None, outputs=None, wires=None, inner_modules=None, inner_primitives=None):\n # super().__init__()\n self.name = name\n if inputs is None:\n self.inputs = {}\n if outputs is None:\n self.outputs = {}\n if wires is None:\n self.wires = {}\n if inner_modules is None:\n self.inner_modules = []\n if inner_primitives is None:\n self.inner_primitives = []\n\n def addOutput(self, port):\n if len(port) == 1:\n self.outputs[port[0][:-1]] = [0, 0]\n else:\n bits = re.search('\\d+:\\d+', port[0])[0].split(':')\n self.outputs[port[-1][:-1]] = bits\n\n def addInputs(self, port):\n if len(port) == 1:\n self.inputs[port[0][:-1]] = [0, 0]\n else:\n bits = re.search('\\d+:\\d+', port[0])[0].split(':')\n self.inputs[port[-1][:-1]] = bits\n\n def addWire(self, wire):\n if len(wire) == 1:\n self.wires[wire[0][:-2]] = [0, 0]\n else:\n bits = re.search('\\d+:\\d+', wire[0])[0].split(':')\n self.wires[wire[-1][:-2]] = bits\n\n def addInnerModule(self, module, name, ports):\n input_ports = self.getPorts('input', ports)\n output_ports = self.getPorts('output', ports)\n input_net = self.getNet('input', ports)\n output_net = self.getNet('output', ports)\n self.inner_modules.append(InnerModule(\n module, name, input_ports, input_net, output_ports, output_net))\n\n def addInnerPrimitive(self, primitive, name, ports):\n inputP = self.getPrimitiveNet('input', ports)\n input_ports, input_net = inputP[0], inputP[1]\n outputP = self.getPrimitiveNet('output', ports)\n output_ports, output_net = outputP[0], outputP[1]\n self.inner_primitives.append(Primitives(\n primitive, name, input_ports, input_net, output_ports, output_net))\n\n def getName(self):\n return self.name\n\n def getPrimitiveNet(self, pType, ports):\n ports = [i[1:] for i in ports]\n ports = [i.split('(') for i in ports]\n if pType == 'input':\n port_list = [i for i in ports if 'out' not in i[1]]\n else:\n port_list = [i for i in ports if 'out' in i[1]]\n pPort = [i[0] for i in port_list]\n port_list = [i[1] for i in port_list]\n port_net = []\n for i in port_list:\n i = i.replace(')', '')\n i = i.replace(';', '')\n i = i.replace('\\n', '')\n port_net.append(i)\n return (pPort, port_net)\n\n def getPorts(self, pType, ports):\n port_string = ' '.join(ports)\n if pType == 'input':\n input_ports = re.findall('(in\\d*\\w*\\(|data\\()', port_string)\n input_ports = [i[:-1] for i in input_ports]\n return input_ports\n else:\n outputPorts = re.findall('(out\\d*\\w*\\(|data\\()', port_string)\n outputPorts = [i[:-1] for i in outputPorts]\n return outputPorts\n\n def getNet(self, pType, ports):\n if pType == 'input':\n port_net = [i for i in ports if 'in' in i or 'data' in i]\n else:\n port_net = [i for i in ports if 'out' in i]\n port_net = [i[1:-1] for i in port_net]\n port_net = [i.split('(')[1].split(')')[0] for i in port_net]\n return port_net\n\n def getInnerModule(self):\n return self.inner_modules\n\n def getPrimitive(self):\n return self.inner_primitives\n\n\nclass Circuit:\n def __init__(self, verilog, circuit=None):\n super().__init__()\n self.verilog = verilog\n if circuit is None:\n self.circuit = {}\n self.parse()\n\n def parse(self):\n primitives = ['invN\\d+', 'nand\\d+N\\d+', 'nor\\d+N\\d+', 'nand\\d+N\\d+']\n combined = \"(\" + \")|(\".join(primitives) + \")\"\n modules = []\n temp = []\n for line in self.verilog.readlines():\n if (line[0] == '/' and line[1] == '/') or line == '\\n':\n continue\n if 'endmodule' in line:\n temp.append(line)\n modules.append(temp)\n temp = []\n else:\n temp.append(line)\n # store each module\n for module in modules:\n declare = module[0].split(' ')\n new_module = Module(declare[1])\n for line in module[1:]:\n tokens = line.split(' ')\n tokens = list(filter(('').__ne__, tokens))\n if 'endmodule' in tokens:\n self.circuit[new_module.getName()] = new_module\n elif 'output' in tokens:\n new_module.addOutput(tokens[1:])\n elif 'input' in tokens:\n new_module.addInputs(tokens[1:])\n elif 'wire' in tokens:\n new_module.addWire(tokens[1:])\n elif re.match(combined, tokens[0]):\n new_module.addInnerPrimitive(\n tokens[0], tokens[1], tokens[2:])\n else:\n new_module.addInnerModule(tokens[0], tokens[1], tokens[2:])\n new_module = None\n\n def getRawCircuit(self):\n return self.circuit\n\n # Function to count all instances of modules/primitives\n def totalInstances(self, module):\n \n #reference to current module\n cur_module = self.circuit[module]\n module_count = {}\n inner_module_count = None\n\n # helper function to recurse through each module and adding all its modules to module_count\n def helper(head, acc):\n for i in self.circuit[head].getInnerModule():\n if i.module not in acc:\n acc[i.module] = 1\n # calling here for optimization. Only recurse once!\n helper(i.module, acc)\n else:\n acc[i.module] += 1\n for i in self.circuit[head].getPrimitive():\n if i.primitive not in acc:\n acc[i.primitive] = 1\n else:\n acc[i.primitive] += 1\n return acc\n\n for i in cur_module.getInnerModule():\n if i.module not in module_count:\n module_count[i.module] = 1\n # if more instance definition of same module we simply add whatever we got from the helper\n # instead of recursing again\n inner_module_count = helper(i.module, {})\n temp = copy.deepcopy(inner_module_count)\n else:\n for key in inner_module_count:\n inner_module_count[key] += temp[key]\n module_count[i.module] += 1\n\n for i in cur_module.getPrimitive():\n if i.primitive not in module_count:\n module_count[i.primitive] = 1\n else:\n module_count[i.primitive] += 1\n # if we did use recursion we add whatever we got through the helper to our main \n # module count dictionary\n if inner_module_count:\n for key in inner_module_count:\n if key in module_count:\n module_count[key] += inner_module_count[key]\n else:\n module_count[key] = inner_module_count[key]\n return module_count\n \n\n\ndef main():\n\n # ALL OPERATIONS GO INSIDE THIS BLOCK!\n with open('TopCell.v', 'r') as reader:\n circuit = Circuit(reader)\n\n # FUNCTION: getRawCircuit()\n # this function will return a dictionary of circuit modules\n # structure: {'Module_Name' => 'Module_Object'}\n # Module_Object is a class conataining all the relevant properties like\n # input and output pins and ports, wires, instances\n print(circuit.getRawCircuit)\n\n # FUNCTION: circuit.totalInstances(MODULE_NAMe)\n # this function will find all occuerences. It recursively goes through each module\n print(circuit.totalInstances('TopCell'))\n\n\n\n\nif __name__ == \"__main__\":\n main()"}],"string":"[\n {\n \"alpha_fraction\": 0.5339228510856628,\n \"alphanum_fraction\": 0.5933831334114075,\n \"avg_line_length\": 33.345550537109375,\n \"blob_id\": \"e775012b3bdd53bcb5acb4e71ed6d41adfc13486\",\n \"content_id\": \"22b3b4733646cc894182a2dca15dbfa99c5d49ea\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 6559,\n \"license_type\": \"no_license\",\n \"max_line_length\": 90,\n \"num_lines\": 191,\n \"path\": \"/README.md\",\n \"repo_name\": \"vsehgal1/Verilog_Parser\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"//****************************************************************************\\n// Feel free to write this code in your favorite IDE and cut and paste\\n// your final code into interviewzen.com. Your program will need to be\\n// able to read this file.\\n//\\n// Instructions:\\n// 1. Copy and paste the contents of this problem into a file called\\n// TopCell.v. These comments can be included in the file.\\n// 2. You will be writing a Python program that reads this file and\\n// produces output as described below.\\n// 3. Your program can simply hardcode the path to this file, or\\n// optionally provide command line arguments to specify the file\\n//\\n//\\n//\\n// This is a simple verilog netlist. It contains several module definitions.\\n// Each module contains input and output pins.\\n// Some modules have wires.\\n//\\n// Each module also has instances of other modules or primitives.\\n// The difference between a module and a primitive is that the primitives\\n// do not have \\\"module\\\" definitions in this file. Primitives do not contain\\n// instances (cellA is a module and contains instances, invN1 is a primitive\\n// and doesn't contain any instances. It is the lowest level of hierarchy).\\n//\\n// Key words:\\n// module, endmodule\\n// output, input\\n// wire\\n//\\n// Instance line description:\\n// ==========================\\n// bufferCell IbufferCellK0 (.in(in1), .out(net17));\\n// bufferCell = module name\\n// IbufferCellK0 = instance name (must be unique)\\n// .in1(in1) = Pin in1 on instance IbufferCellK0 of module bufferCell\\n// connects to a net named in1\\n// .out(net17) = Pin out on instance IbufferCellK0 of module bufferCell\\n// connects to a net named net17\\n//\\n//\\n// Problem description:\\n// ====================\\n// cellB contains:\\n// 4 instances of bufferCell\\n// 2 instances of invN1 (a primitive)\\n// 2 instances of nor2N1 (a primitive)\\n// 1 instance of nand2N1 (a primitive)\\n// bufferCell contains:\\n// 2 instances of invN1 (a primitive)\\n// This means that in the hierarchy of cellB, there are 10 instances\\n// of invN1.\\n//\\n// Write a Python program to parse this file and store it in some kind\\n// of structure. Assume that all information in the netlist (except\\n// comments) may someday need to be accessed.\\n// Write a function or method to count the total number of instances of\\n// each module/primitive in the \\\"TopCell\\\" hierarchy (or any module)\\n// by reading your structure.\\n//\\n// Example output for counting all of the instances in \\\"cellB\\\" hierarchy:\\n// nand2N1 : 1 placements\\n// nor2N1 : 2 placements\\n// invN1 : 10 placements\\n// bufferCell : 4 placements\\n//\\n// Tips:\\n// If you are having trouble parsing the file with the instance\\n// definitions split across multiple lines, then edit the input\\n// so the entire statement is on one line.\\n// Example:\\n//\\n// cellA IcellAK0 (.in1(inA), .in2(inB), .in3(inC), .in4(inD),\\n// .out1(net22));\\n// cellA IcellAK0 (.in1(inA), .in2(inB), .in3(inC), .in4(inD), .out1(net22));\\n//\\n//\\n//****************************************************************************\\n\\n// Cell: designLib TopCell schematic\\n// Last modified: Jun 14 12:01:37 2017\\nmodule TopCell (data, out1, out2, out3, out4, out5);\\n output out1;\\n output out2;\\n output out3;\\n output out4;\\n output out5;\\n input [7:0] data;\\n\\n wire [7:0] data;\\n\\n cellA IcellAK0 (.in1(data[7]), .in2(data[6]), .in3(data[5]),\\n .in4(data[4]), .out1(net14));\\n cellC IcellCK0 (.inA(data[3]), .inB(data[2]), .inC(data[1]),\\n .inD(data[0]), .outA(out2), .outB(out3));\\n cellD IcellDK0 (.data(data[7:0]), .out1(out4), .out2(out5));\\n invN1 X0 (.A(net14), .Y(out1));\\n\\nendmodule // TopCell\\n\\n// Cell: designLib bufferCell schematic\\n// Last modified: Jun 14 11:00:42 2017\\nmodule bufferCell (in, out);\\n output out;\\n input in;\\n\\n invN1 X0 (.A(in), .Y(net5));\\n invN1 X1 (.A(net5), .Y(out));\\nendmodule // bufferCell\\n\\n\\n// Cell: designLib cellA schematic\\n// Last modified: Jun 14 10:58:49 2017\\nmodule cellA (in1, in2, in3, in4, out1);\\n output out1;\\n input in1;\\n input in2;\\n input in3;\\n input in4;\\n\\n invN1 X0 (.A(in1), .Y(net13));\\n invN1 X1 (.A(in2), .Y(net11));\\n invN1 X2 (.A(in3), .Y(net12));\\n invN1 X3 (.A(in4), .Y(net14));\\n nand4N1 X4 (.A(net13), .B(net11), .C(net12), .D(net14), .Y(out1));\\nendmodule // cellA\\n\\n\\n// Cell: designLib cellB schematic\\n// Last modified: Jun 14 11:09:05 2017\\nmodule cellB (in1, in2, in3, in4, out1, out2);\\n output out1;\\n output out2;\\n input in1;\\n input in2;\\n input in3;\\n input in4;\\n\\n bufferCell IbufferCellK0 (.in(in1), .out(net17));\\n bufferCell IbufferCellK1 (.in(in2), .out(net16));\\n bufferCell IbufferCellK2 (.in(in3), .out(net15));\\n bufferCell IbufferCellK3 (.in(in4), .out(net14));\\n invN1 X0 (.A(net13), .Y(out2));\\n invN1 X1 (.A(net19), .Y(net18));\\n nand2N1 X2 (.A(net17), .B(net16), .Y(net19));\\n nor2N1 X3 (.A(net15), .B(net14), .Y(net13));\\n nor2N1 X4 (.A(net18), .B(net13), .Y(out1));\\nendmodule // cellB\\n\\n\\n// Cell: designLib cellC schematic\\n// Last modified: Jun 14 11:16:40 2017\\nmodule cellC (inA, inB, inC, inD, outA, outB);\\n output outA;\\n output outB;\\n input inA;\\n input inB;\\n input inC;\\n input inD;\\n\\n cellA IcellAK0 (.in1(inA), .in2(inB), .in3(inC), .in4(inD),\\n .out1(net22));\\n cellA IcellAK1 (.in1(inD), .in2(inA), .in3(inB), .in4(inC),\\n .out1(net18));\\n cellA IcellAK2 (.in1(inC), .in2(inD), .in3(inA), .in4(inB),\\n .out1(net17));\\n cellA IcellAK3 (.in1(inB), .in2(inC), .in3(inD), .in4(inA),\\n .out1(net21));\\n invN1 X0 (.A(net22), .Y(net20));\\n invN1 X1 (.A(net21), .Y(net19));\\n nand2N1 X2 (.A(net20), .B(net18), .Y(outA));\\n nand2N1 X3 (.A(net17), .B(net19), .Y(outB));\\nendmodule // cellC\\n\\n\\n// Cell: designLib cellD schematic\\n// Last modified: Jun 14 11:34:42 2017\\nmodule cellD (data, out1, out2);\\n output out1;\\n output out2;\\n input [7:0] data;\\n\\n wire [7:0] data;\\n\\n cellB IcellBK0 (.in1(data[0]), .in2(data[1]), .in3(data[2]),\\n .in4(data[3]), .out1(net10), .out2(net8));\\n cellB IcellBK1 (.in1(data[4]), .in2(data[5]), .in3(data[6]),\\n .in4(data[7]), .out1(net7), .out2(net9));\\n cellC IcellCK0 (.inA(net10), .inB(net8), .inC(net7), .inD(net9),\\n .outA(out1), .outB(out2));\\nendmodule // cellD\"\n },\n {\n \"alpha_fraction\": 0.5325067639350891,\n \"alphanum_fraction\": 0.5395148396492004,\n \"avg_line_length\": 35.37647247314453,\n \"blob_id\": \"7f4930e55e0cf46da4797fb4c1794f9bf727c1fe\",\n \"content_id\": \"400c537fc90236f90d929e66cdd7ea699bc4982f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 9275,\n \"license_type\": \"no_license\",\n \"max_line_length\": 114,\n \"num_lines\": 255,\n \"path\": \"/verilog_parser.py\",\n \"repo_name\": \"vsehgal1/Verilog_Parser\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# FUNCTION TO RETRIEVE CIRCUIT: getRawCircuit()\\n# FUNCTION TO COUNT TOTAL INSTANCES/MODULES: totalInstances(MODU)\\n# When testing please remove all new lines in instance definition.\\n\\n\\nimport re\\nimport copy\\n\\n# class to keep track of inner modules inside each module. \\n# Points Parent module to child module which is also stored in circuit {} dictionary \\n# and can be accessed through it.\\nclass InnerModule:\\n def __init__(self, module, instance, input_port, input_net, output_port, output_net):\\n super().__init__()\\n self.module = module\\n self.instance = instance\\n self.input_port = input_port\\n self.input_net = input_net\\n self.output_port = output_port\\n self.output_net = output_net\\n\\n# class to keep track of inner primitives inside each module\\nclass Primitives:\\n def __init__(self, primitive, instance, input_port, input_net, output_port, output_net):\\n super().__init__()\\n self.primitive = primitive\\n self.instance = instance\\n self.input_port = input_port\\n self.input_net = input_net\\n self.output_port = output_port\\n self.output_net = output_net\\n\\n\\n# Main module class\\nclass Module:\\n def __init__(self, name='', inputs=None, outputs=None, wires=None, inner_modules=None, inner_primitives=None):\\n # super().__init__()\\n self.name = name\\n if inputs is None:\\n self.inputs = {}\\n if outputs is None:\\n self.outputs = {}\\n if wires is None:\\n self.wires = {}\\n if inner_modules is None:\\n self.inner_modules = []\\n if inner_primitives is None:\\n self.inner_primitives = []\\n\\n def addOutput(self, port):\\n if len(port) == 1:\\n self.outputs[port[0][:-1]] = [0, 0]\\n else:\\n bits = re.search('\\\\d+:\\\\d+', port[0])[0].split(':')\\n self.outputs[port[-1][:-1]] = bits\\n\\n def addInputs(self, port):\\n if len(port) == 1:\\n self.inputs[port[0][:-1]] = [0, 0]\\n else:\\n bits = re.search('\\\\d+:\\\\d+', port[0])[0].split(':')\\n self.inputs[port[-1][:-1]] = bits\\n\\n def addWire(self, wire):\\n if len(wire) == 1:\\n self.wires[wire[0][:-2]] = [0, 0]\\n else:\\n bits = re.search('\\\\d+:\\\\d+', wire[0])[0].split(':')\\n self.wires[wire[-1][:-2]] = bits\\n\\n def addInnerModule(self, module, name, ports):\\n input_ports = self.getPorts('input', ports)\\n output_ports = self.getPorts('output', ports)\\n input_net = self.getNet('input', ports)\\n output_net = self.getNet('output', ports)\\n self.inner_modules.append(InnerModule(\\n module, name, input_ports, input_net, output_ports, output_net))\\n\\n def addInnerPrimitive(self, primitive, name, ports):\\n inputP = self.getPrimitiveNet('input', ports)\\n input_ports, input_net = inputP[0], inputP[1]\\n outputP = self.getPrimitiveNet('output', ports)\\n output_ports, output_net = outputP[0], outputP[1]\\n self.inner_primitives.append(Primitives(\\n primitive, name, input_ports, input_net, output_ports, output_net))\\n\\n def getName(self):\\n return self.name\\n\\n def getPrimitiveNet(self, pType, ports):\\n ports = [i[1:] for i in ports]\\n ports = [i.split('(') for i in ports]\\n if pType == 'input':\\n port_list = [i for i in ports if 'out' not in i[1]]\\n else:\\n port_list = [i for i in ports if 'out' in i[1]]\\n pPort = [i[0] for i in port_list]\\n port_list = [i[1] for i in port_list]\\n port_net = []\\n for i in port_list:\\n i = i.replace(')', '')\\n i = i.replace(';', '')\\n i = i.replace('\\\\n', '')\\n port_net.append(i)\\n return (pPort, port_net)\\n\\n def getPorts(self, pType, ports):\\n port_string = ' '.join(ports)\\n if pType == 'input':\\n input_ports = re.findall('(in\\\\d*\\\\w*\\\\(|data\\\\()', port_string)\\n input_ports = [i[:-1] for i in input_ports]\\n return input_ports\\n else:\\n outputPorts = re.findall('(out\\\\d*\\\\w*\\\\(|data\\\\()', port_string)\\n outputPorts = [i[:-1] for i in outputPorts]\\n return outputPorts\\n\\n def getNet(self, pType, ports):\\n if pType == 'input':\\n port_net = [i for i in ports if 'in' in i or 'data' in i]\\n else:\\n port_net = [i for i in ports if 'out' in i]\\n port_net = [i[1:-1] for i in port_net]\\n port_net = [i.split('(')[1].split(')')[0] for i in port_net]\\n return port_net\\n\\n def getInnerModule(self):\\n return self.inner_modules\\n\\n def getPrimitive(self):\\n return self.inner_primitives\\n\\n\\nclass Circuit:\\n def __init__(self, verilog, circuit=None):\\n super().__init__()\\n self.verilog = verilog\\n if circuit is None:\\n self.circuit = {}\\n self.parse()\\n\\n def parse(self):\\n primitives = ['invN\\\\d+', 'nand\\\\d+N\\\\d+', 'nor\\\\d+N\\\\d+', 'nand\\\\d+N\\\\d+']\\n combined = \\\"(\\\" + \\\")|(\\\".join(primitives) + \\\")\\\"\\n modules = []\\n temp = []\\n for line in self.verilog.readlines():\\n if (line[0] == '/' and line[1] == '/') or line == '\\\\n':\\n continue\\n if 'endmodule' in line:\\n temp.append(line)\\n modules.append(temp)\\n temp = []\\n else:\\n temp.append(line)\\n # store each module\\n for module in modules:\\n declare = module[0].split(' ')\\n new_module = Module(declare[1])\\n for line in module[1:]:\\n tokens = line.split(' ')\\n tokens = list(filter(('').__ne__, tokens))\\n if 'endmodule' in tokens:\\n self.circuit[new_module.getName()] = new_module\\n elif 'output' in tokens:\\n new_module.addOutput(tokens[1:])\\n elif 'input' in tokens:\\n new_module.addInputs(tokens[1:])\\n elif 'wire' in tokens:\\n new_module.addWire(tokens[1:])\\n elif re.match(combined, tokens[0]):\\n new_module.addInnerPrimitive(\\n tokens[0], tokens[1], tokens[2:])\\n else:\\n new_module.addInnerModule(tokens[0], tokens[1], tokens[2:])\\n new_module = None\\n\\n def getRawCircuit(self):\\n return self.circuit\\n\\n # Function to count all instances of modules/primitives\\n def totalInstances(self, module):\\n \\n #reference to current module\\n cur_module = self.circuit[module]\\n module_count = {}\\n inner_module_count = None\\n\\n # helper function to recurse through each module and adding all its modules to module_count\\n def helper(head, acc):\\n for i in self.circuit[head].getInnerModule():\\n if i.module not in acc:\\n acc[i.module] = 1\\n # calling here for optimization. Only recurse once!\\n helper(i.module, acc)\\n else:\\n acc[i.module] += 1\\n for i in self.circuit[head].getPrimitive():\\n if i.primitive not in acc:\\n acc[i.primitive] = 1\\n else:\\n acc[i.primitive] += 1\\n return acc\\n\\n for i in cur_module.getInnerModule():\\n if i.module not in module_count:\\n module_count[i.module] = 1\\n # if more instance definition of same module we simply add whatever we got from the helper\\n # instead of recursing again\\n inner_module_count = helper(i.module, {})\\n temp = copy.deepcopy(inner_module_count)\\n else:\\n for key in inner_module_count:\\n inner_module_count[key] += temp[key]\\n module_count[i.module] += 1\\n\\n for i in cur_module.getPrimitive():\\n if i.primitive not in module_count:\\n module_count[i.primitive] = 1\\n else:\\n module_count[i.primitive] += 1\\n # if we did use recursion we add whatever we got through the helper to our main \\n # module count dictionary\\n if inner_module_count:\\n for key in inner_module_count:\\n if key in module_count:\\n module_count[key] += inner_module_count[key]\\n else:\\n module_count[key] = inner_module_count[key]\\n return module_count\\n \\n\\n\\ndef main():\\n\\n # ALL OPERATIONS GO INSIDE THIS BLOCK!\\n with open('TopCell.v', 'r') as reader:\\n circuit = Circuit(reader)\\n\\n # FUNCTION: getRawCircuit()\\n # this function will return a dictionary of circuit modules\\n # structure: {'Module_Name' => 'Module_Object'}\\n # Module_Object is a class conataining all the relevant properties like\\n # input and output pins and ports, wires, instances\\n print(circuit.getRawCircuit)\\n\\n # FUNCTION: circuit.totalInstances(MODULE_NAMe)\\n # this function will find all occuerences. It recursively goes through each module\\n print(circuit.totalInstances('TopCell'))\\n\\n\\n\\n\\nif __name__ == \\\"__main__\\\":\\n main()\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2979392,"cells":{"repo_name":{"kind":"string","value":"skirt4aser/Store"},"repo_url":{"kind":"string","value":"https://github.com/skirt4aser/Store"},"snapshot_id":{"kind":"string","value":"21f332b3d8557f72702ff64a68442b8c339d68aa"},"revision_id":{"kind":"string","value":"5f53dceee9fd3158c89e1a8cacac957bc789f243"},"directory_id":{"kind":"string","value":"83376aa9490142128965e889351ebbc96017a52c"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-06-01T23:12:01.259284","string":"2020-06-01T23:12:01.259284"},"revision_date":{"kind":"timestamp","value":"2013-04-14T19:39:28","string":"2013-04-14T19:39:28"},"committer_date":{"kind":"timestamp","value":"2013-04-14T19:39:28","string":"2013-04-14T19:39:28"},"github_id":{"kind":"null"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.31015998125076294,"alphanum_fraction":0.3138958811759949,"avg_line_length":45.119850158691406,"blob_id":"c714b8737f780476dedf4e1a889f8e8bf375012d","content_id":"52c566e2d9ce4a869886bfb1d4da8f262c6bff93","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"HTML","length_bytes":12537,"license_type":"no_license","max_line_length":159,"num_lines":267,"path":"/templates/warehouse/writeoff.html","repo_name":"skirt4aser/Store","src_encoding":"UTF-8","text":"{% extends 'root.html' %}\n{% block title %}Списание{% endblock %}\n{% block content %}\n <div class=\"row-fluid\">\n <form class=\" no-margin\" method=\"POST\">\n <div class=\"span4\">\n <div class=\"widget\">\n <div class=\"widget-header\">\n <div class=\"title\">\n Списание\n </div>\n <span class=\"tools\">\n <a class=\"fs1\" aria-hidden=\"true\" data-icon=\"\"></a>\n </span>\n </div>\n <div class=\"widget-body\">\n {% csrf_token %}\n {{ form_writeoff.errors }}\n <div class=\"control-group\">\n <label class=\"control-label\" for=\"name\">\n Склад\n </label>\n <div class=\"controls controls-row\">\n {{ form_writeoff.warehouse }}\n </div>\n </div>\n <div class=\"control-group\">\n <label class=\"control-label\" for=\"name\">\n Причина\n </label>\n <div class=\"controls controls-row\">\n {{ form_writeoff.reason }}\n </div>\n </div>\n <div class=\"control-group\">\n <label class=\"control-label\" for=\"name\">\n Дата\n </label>\n <div class=\"controls controls-row\">\n {{ form_writeoff.date }}\n </div>\n </div>\n <div class=\"control-group\">\n <label class=\"control-label\" for=\"name\">\n Комментарий\n </label>\n <div class=\"controls controls-row\">\n {{ form_writeoff.comment }}\n </div>\n </div>\n </div>\n </div>\n </div>\n <div class=\"span4\">\n <div class=\"widget\">\n <div class=\"widget-header\">\n <div class=\"title\">\n Выбор блюда\n </div>\n <span class=\"tools\">\n <a class=\"fs1\" aria-hidden=\"true\" data-icon=\"\"></a>\n </span>\n </div>\n <div class=\"widget-body\">\n {{ form_dish.errors }}\n <div class=\"control-group\">\n <label class=\"control-label\" for=\"name\">\n Блюдо\n </label>\n <div class=\"controls controls-row\">\n {{ form_dish.dish }}\n </div>\n </div>\n <div class=\"control-group\">\n <label class=\"control-label\" for=\"name\">\n Количество\n </label>\n <div class=\"controls controls-row\">\n {{ form_dish.amount }}\n </div>\n </div>\n <div class=\"control-group\">\n <label class=\"control-label\" for=\"name\">\n Цена\n </label>\n <div class=\"controls controls-row\">\n {{ form_dish.price }}\n </div>\n </div>\n <div class=\"form-actions\">\n <button type=\"submit\" class=\"btn btn-info pull-right\" id=\"add_product\">\n Добавить блюдо\n </button>\n <div class=\"clearfix\">\n </div>\n </div>\n\n </div>\n </div>\n </div>\n <div class=\"span4\">\n <div class=\"widget\">\n <div class=\"widget-header\">\n <div class=\"title\">\n Выбор товара\n </div>\n <span class=\"tools\">\n <a class=\"fs1\" aria-hidden=\"true\" data-icon=\"\"></a>\n </span>\n </div>\n <div class=\"widget-body\">\n {{ form_product.errors }}\n <div class=\"control-group\">\n <label class=\"control-label\" for=\"name\">\n Товар\n </label>\n <div class=\"controls controls-row\">\n {{ form_product.product }}\n </div>\n </div>\n <div class=\"control-group\">\n <label class=\"control-label\" for=\"name\">\n Количество\n </label>\n <div class=\"controls controls-row\">\n {{ form_product.amount }}\n </div>\n </div>\n <div class=\"control-group\">\n <label class=\"control-label\" for=\"name\">\n Цена\n </label>\n <div class=\"controls controls-row\">\n {{ form_product.price }}\n </div>\n </div>\n <div class=\"form-actions\">\n <button type=\"submit\" class=\"btn btn-info pull-right\" id=\"add_product\">\n Добавить товар\n </button>\n <div class=\"clearfix\">\n </div>\n </div>\n\n </div>\n </div>\n </div>\n </form>\n </div>\n\n <div class=\"row-fluid\">\n <div class=\"span12\">\n <div class=\"widget\">\n <div class=\"widget-header\">\n <div class=\"title\">\n Товары\n </div>\n <span class=\"tools\">\n <a class=\"fs1\" aria-hidden=\"true\" data-icon=\"\"></a>\n </span>\n </div>\n <div class=\"widget-body\">\n <table class=\"table table-condensed table-striped table-bordered table-hover no-margin\">\n <thead>\n <tr>\n <th style=\"width:5%\">\n </th>\n <th style=\"width:15%\">\n Товар/Блюдо\n </th>\n <th style=\"width:15%\" class=\"hidden-phone\">\n Количество\n </th>\n <th style=\"width:15%\" class=\"hidden-phone\">\n Остаток\n </th>\n <th style=\"width:10%\" class=\"hidden-phone\">\n Цена\n </th>\n <th style=\"width:25%\" class=\"hidden-phone\">\n Сумма\n </th>\n <th style=\"width:10%\" class=\"hidden-phone\">\n Действия\n </th>\n </tr>\n </thead>\n <tbody>\n {% for product in products %}\n <tr>\n <td>{{ forloop.counter }}</td>\n <td class=\"hidden-phone\">{{ product.0.product }}</td>\n <td><span class=\"name\">{{ product.0.purchase_amount }}</span></td>\n <td><span class=\"name\">{{ product.0.product.measure_unit }}</span></td>\n <td class=\"hidden-phone\">{{ product.0.purchase_price }}</td>\n <td class=\"hidden-phone\">{{ product.1 }}</td>\n <td>\n {% if purchase.acceptance == None or purchase.acceptance == ''%}\n <div class=\"tools\">\n <div class=\"btn-group\">\n <a class=\"btn btn-small\" data-original-title=\"\" href=\"/purchase/{{ id_purchase }}/product/{{ product.0.pk }}/\">\n <i class=\"icon-edit\" data-original-title=\"Редактировать\">\n </i>\n </a>\n <a class=\"btn btn-small delete\" data-original-title=\"\" href=\"/purchase/product/{{ product.0.pk }}/delete/\">\n <i class=\"icon-trash\" data-original-title=\"Удалить\">\n </i>\n </a>\n </div>\n </div>\n {% endif %}\n </td>\n </tr>\n {% endfor %}\n <tr>\n <td></td>\n <td></td>\n <td></td>\n <td></td>\n <td><strong>Итого</strong></td>\n <td><strong>{{ total }}</strong></td>\n <td></td>\n </tr>\n </tbody>\n </table>\n <div class=\"form-actions no-margin\">\n {% if id_purchase != 0 %}\n {% if purchase.acceptance == None or purchase.acceptance == '' %}\n <a class=\"button btn btn-info pull-left\" href=\"/purchase/{{ id_purchase }}/delete/\" id=\"delete_purchase\">\n Удалить закуп\n </a>\n {% endif %}\n {% endif %}\n {% if purchase.acceptance == None or purchase.acceptance == '' %}\n <button type=\"button\" class=\"btn btn-info pull-right\" id=\"create\">\n Сохранить\n </button>\n {% endif %}\n <div class=\"clearfix\">\n </div>\n </div>\n </div>\n </div>\n </div>\n </div>\n{% endblock %}\n{% block js %}\n <script type=\"text/javascript\" src=\"/media/theme/js/bootstrap-datepicker.js\"></script>\n <script type=\"text/javascript\" src=\"/media/store/js/purchase.js\"></script>\n <script type=\"text/javascript\" src=\"/media/theme/js/select2.min.js\"></script>\n <script type=\"text/javascript\">\n $(document).ready(function(){\n $('#id_date').datepicker({\n format: 'yyyy-mm-dd'\n });\n $('#id_product').select2();\n $('#id_dish').select2();\n $.fn.purchase();\n $('a').tooltip('hide');\n $('i').tooltip('hide');\n });\n </script>\n{% endblock %}\n{% block css %}\n <link href=\"/media/theme/css/datepicker.css\" rel=\"stylesheet\">\n <link href=\"/media/theme/css/select2.css\" rel=\"stylesheet\">\n{% endblock %}"},{"alpha_fraction":0.5373784899711609,"alphanum_fraction":0.5411935448646545,"avg_line_length":50.44392395019531,"blob_id":"dd98d14d898a21c8ffe8f187f9f11c5be0a6b215","content_id":"035808bb04f78c904b6a3cb8de980e4cc658df7c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":22134,"license_type":"no_license","max_line_length":200,"num_lines":428,"path":"/warehouse/views.py","repo_name":"skirt4aser/Store","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\nimport datetime\nfrom math import *\nfrom django.forms.models import inlineformset_factory\nfrom django.http import HttpResponseRedirect, Http404\nfrom django.shortcuts import render_to_response, get_object_or_404\nfrom django.template.context import RequestContext\nfrom django.contrib.auth.decorators import login_required\nfrom database.models import Purchase, ProductOfPurchase, Acceptance, ProductAtWarehouse, ReturnModel\nfrom warehouse.forms import PurchaseForm, ProductOfPurchaseForm, AcceptanceForm, ReturnForm, WriteOffForm, ProductAtWarehouseForm, DishOfWarehouseForm\n\n__author__ = 'Ars'\n\n\n@login_required()\ndef purchases(request, page_pk):\n page_pk = int(page_pk)\n per_page = 10\n purchases = Purchase.objects.all().order_by('date_purchase', 'pk')\n all = purchases.count()\n pages = int(ceil(all/float(per_page)))\n if page_pk > pages and page_pk != 1:\n raise Http404()\n page_list = []\n for i in range(pages):\n page_list.append(i+1)\n prev = 1\n if page_pk!=1:\n prev = page_pk -1\n if page_pk==pages:\n next = page_pk\n else:\n next = page_pk + 1\n range_date = request.POST.get('report_range', '')\n if range_date!='':\n start = range_date[:10]\n end = range_date[14:]\n try:\n purchases = purchases.filter(date_purchase__gte=start, date_purchase__lte=end)\n except :\n print 'bad date request'\n purchases = purchases[per_page*page_pk-per_page:per_page*page_pk]\n purchase_list = []\n for purchase in purchases:\n sum = 0\n for product in purchase.productofpurchase_set.all():\n sum += product.purchase_amount * product.purchase_price\n purchase_list.append([purchase, sum])\n return render_to_response('warehouse/purchases.html',{'purchases' : purchase_list,\n 'range' : range_date,\n 'pages' : page_list,\n 'current' : page_pk,\n 'prev' : prev,\n 'next' : next\n }, RequestContext(request))\n\n@login_required()\ndef purchase(request, id_purchase):\n product_list = []\n total = 0\n purchase = ''\n if not id_purchase:\n form_purchase = PurchaseForm(request.POST or None)\n form_product = ProductOfPurchaseForm(request.POST or None)\n if request.method == 'POST' and form_purchase.is_valid() and form_product.is_valid():\n purchase = form_purchase.save(commit=False)\n purchase.created = datetime.date.today()\n purchase.author = request.user\n purchase.modified_date = datetime.date.today()\n purchase.modified_author = request.user\n purchase.save()\n product = form_product.save(commit=False)\n product.purchase = purchase\n product.save()\n return HttpResponseRedirect('/purchase/'+str(purchase.pk))\n else:\n purchase = get_object_or_404(Purchase, pk=id_purchase)\n issued = purchase.issued\n products = purchase.productofpurchase_set.all().order_by('pk')\n for product in products:\n total += product.purchase_amount * product.purchase_price\n product_list.append([product, product.purchase_amount * product.purchase_price])\n form_purchase = PurchaseForm(request.POST or None, instance=purchase)\n form_product = ProductOfPurchaseForm(request.POST or None)\n if request.method == 'POST' and form_purchase.is_valid() and form_product.is_valid():\n purchase = form_purchase.save(commit=False)\n purchase.modified_date = datetime.date.today()\n purchase.modified_author = request.user\n purchase.issued = issued\n purchase.save()\n product = form_product.save(commit=False)\n product.purchase = purchase\n product.save()\n return HttpResponseRedirect('/purchase/'+str(purchase.pk))\n return render_to_response('warehouse/purchase.html',{'form_purchase' : form_purchase,\n 'form_product' : form_product,\n 'products' : product_list,\n 'total' : total,\n 'id_purchase' : id_purchase,\n 'purchase' : purchase\n }, RequestContext(request))\n\n@login_required()\ndef purchase_product(request, id_purchase, id_product):\n product_list = []\n total = 0\n purchase = get_object_or_404(Purchase, pk=id_purchase)\n product = get_object_or_404(ProductOfPurchase, pk=id_product)\n issued = purchase.issued\n products = purchase.productofpurchase_set.all().order_by('pk')\n for prod in products:\n total += prod.purchase_amount * prod.purchase_price\n product_list.append([prod, prod.purchase_amount * prod.purchase_price])\n form_purchase = PurchaseForm(request.POST or None, instance=purchase)\n form_product = ProductOfPurchaseForm(request.POST or None, instance=product)\n if request.method == 'POST' and form_purchase.is_valid() and form_product.is_valid():\n purchase = form_purchase.save(commit=False)\n purchase.modified_date = datetime.date.today()\n purchase.modified_author = request.user\n purchase.issued = issued\n purchase.save()\n form_product.save()\n return HttpResponseRedirect('/purchase/'+str(purchase.pk))\n return render_to_response('warehouse/purchase.html',{'form_purchase' : form_purchase,\n 'form_product' : form_product,\n 'products' : product_list,\n 'total' : total,\n 'id_purchase' : id_purchase\n }, RequestContext(request))\n\n@login_required()\ndef purchase_product_delete(request, id_product):\n product = get_object_or_404(ProductOfPurchase, pk=id_product)\n purchase_pk = product.purchase.pk\n product.delete()\n return HttpResponseRedirect('/purchase/'+str(purchase_pk))\n\n@login_required()\ndef purchase_delete(request, id_purchase):\n purchase = get_object_or_404(Purchase, pk=id_purchase)\n purchase.delete()\n return HttpResponseRedirect('/purchases/page/1/')\n\n\n\n\n\n@login_required()\ndef acceptance(request, id_purchase):\n purchase = get_object_or_404(Purchase, pk=id_purchase)\n product_list = []\n old_product_list = []\n try:\n form_acceptance = AcceptanceForm(request.POST or None, instance=purchase.acceptance)\n exist = True\n for product in purchase.productofpurchase_set.all():\n old_product_list.append(product)\n except :\n form_acceptance = AcceptanceForm(request.POST or None)\n exist = False\n AcceptanceInlineFormSet = inlineformset_factory(Purchase,ProductOfPurchase, max_num=purchase.productofpurchase_set.all().count(), fields=('acceptance_amount','acceptance_price'),can_delete=False,)\n form_products = AcceptanceInlineFormSet(request.POST or None, instance=purchase)\n for i in range(len(form_products)):\n product_list.append([form_products.get_queryset()[i],form_products[i],\n form_products.get_queryset()[i].purchase_amount * form_products.get_queryset()[i].purchase_price])\n if form_acceptance.is_valid() and form_products.is_valid():\n acceptance = form_acceptance.save(commit=False)\n if not exist:\n acceptance.created = datetime.date.today()\n acceptance.author = request.user\n acceptance.modified_date = datetime.date.today()\n acceptance.modified_author = request.user\n acceptance.purchase = purchase\n acceptance.save()\n form_products.save()\n\n if exist:\n for old in old_product_list:\n try:\n prod_war = ProductAtWarehouse.objects.get(product=old.product, warehouse=old.purchase.warehouse, price=old.acceptance_price)\n prod_war.amount -= old.acceptance_amount\n prod_war.date = old.purchase.acceptance.date\n prod_war.save()\n if not prod_war.amount:\n prod_war.delete()\n except ProductAtWarehouse.DoesNotExist:\n ProductAtWarehouse.objects.create(product=old.product, warehouse=old.purchase.warehouse, amount=old.acceptance_amount, price=old.acceptance_price,\n date=old.purchase.acceptance.date)\n\n for product in purchase.productofpurchase_set.all():\n try:\n prod_war = ProductAtWarehouse.objects.get(product=product.product, warehouse=product.purchase.warehouse, price=product.acceptance_price)\n prod_war.amount += product.acceptance_amount\n prod_war.date = product.purchase.acceptance.date\n prod_war.save()\n if not prod_war.amount:\n prod_war.delete()\n except ProductAtWarehouse.DoesNotExist:\n ProductAtWarehouse.objects.create(product=product.product, warehouse=product.purchase.warehouse, amount=product.acceptance_amount, price=product.acceptance_price,\n date=product.purchase.acceptance.date)\n return HttpResponseRedirect('/acceptances/page/1')\n return render_to_response('warehouse/acceptance.html',{'form_acceptance' : form_acceptance,\n 'purchase' : purchase,\n 'products' : product_list,\n 'form_products' : form_products\n },RequestContext(request))\n\n\n@login_required()\ndef acceptances(request, page_pk):\n page_pk = int(page_pk)\n per_page = 10\n acceptances = Acceptance.objects.all().order_by('date','pk')\n all = acceptances.count()\n pages = int(ceil(all/float(per_page)))\n if page_pk>pages and page_pk!=1:\n raise Http404()\n page_list = []\n for i in range(pages):\n page_list.append(i+1)\n prev = 1\n if page_pk!=1:\n prev = page_pk -1\n if page_pk==pages:\n next = page_pk\n else:\n next = page_pk + 1\n range_date = request.POST.get('report_range', '')\n if range_date!='':\n start = range_date[:10]\n end = range_date[14:]\n try:\n acceptances = acceptances.filter(date__gte=start, date__lte=end)\n except :\n print 'bad date request'\n acceptances = acceptances[per_page*page_pk-per_page:per_page*page_pk]\n return render_to_response('warehouse/acceptances.html',{'acceptances' : acceptances,\n 'range' : range_date,\n 'pages' : page_list,\n 'current' : page_pk,\n 'prev' : prev,\n 'next' : next\n }, RequestContext(request))\n\n\n@login_required()\ndef return_view(request, id_acceptance):\n acceptance = get_object_or_404(Acceptance, pk=id_acceptance)\n old_product_list = []\n product_list = []\n failure = ''\n try:\n form_return = ReturnForm(request.POST or None, instance=acceptance.returnmodel)\n exist = True\n for product in acceptance.purchase.productofpurchase_set.all():\n old_product_list.append(product)\n except :\n form_return = ReturnForm(request.POST or None)\n exist = False\n AcceptanceInlineFormSet = inlineformset_factory(Purchase,ProductOfPurchase, max_num=acceptance.purchase.productofpurchase_set.all().count(), fields=('return_amount',),can_delete=False,)\n form_products = AcceptanceInlineFormSet(request.POST or None, instance=acceptance.purchase)\n for i in range(len(form_products)):\n product_list.append([form_products.get_queryset()[i],form_products[i],\n form_products.get_queryset()[i].purchase_amount * form_products.get_queryset()[i].purchase_price])\n if form_return.is_valid() and form_products.is_valid():\n return_m = form_return.save(commit=False)\n if not exist:\n return_m.created = datetime.date.today()\n return_m.author = request.user\n return_m.modified_date = datetime.date.today()\n return_m.modified_author = request.user\n return_m.acceptance = acceptance\n\n if exist:\n for old in old_product_list:\n try:\n prod_war = ProductAtWarehouse.objects.get(product=old.product, warehouse=old.purchase.warehouse, price=old.acceptance_price)\n prod_war.amount += old.return_amount\n prod_war.save()\n except ProductAtWarehouse.DoesNotExist:\n ProductAtWarehouse.objects.create(product=old.product,warehouse=old.purchase.warehouse,amount=old.return_amount,price=old.acceptance_price,date=old.purchase.acceptance.date)\n\n for product in form_products.get_queryset():\n try:\n prod_war = ProductAtWarehouse.objects.get(product=product.product, warehouse=product.purchase.warehouse, price=product.acceptance_price)\n if prod_war.amount - product.return_amount < 0:\n for old in old_product_list:\n try:\n prod_war = ProductAtWarehouse.objects.get(product=old.product, warehouse=old.purchase.warehouse, price=old.acceptance_price)\n prod_war.amount -= old.return_amount\n prod_war.save()\n if not prod_war.amount:\n prod_war.delete()\n except ProductAtWarehouse.DoesNotExist:\n print 'DoesNotExist'\n failure = u'На складе: ' + prod_war.warehouse.name + u' невозможно сделать возврат по товару: ' + prod_war.product.name + u' , так как его осталось ' + str(prod_war.amount)\n return render_to_response('warehouse/return.html',{'form_return' : form_return,\n 'acceptance' : acceptance,\n 'products' : product_list,\n 'form_products' : form_products,\n 'failure' : failure\n },RequestContext(request))\n except ProductAtWarehouse.DoesNotExist:\n for old in old_product_list:\n try:\n prod_war = ProductAtWarehouse.objects.get(product=old.product, warehouse=old.purchase.warehouse, price=old.acceptance_price)\n prod_war.amount -= old.return_amount\n prod_war.save()\n if not prod_war.amount:\n prod_war.delete()\n except ProductAtWarehouse.DoesNotExist:\n print 'DoesNotExist'\n failure = u'На складе: ' + product.purchase.warehouse.name + u' невозможно сделать возврат по товару: ' + product.product.name + u' , так как его не осталось '\n return render_to_response('warehouse/return.html',{'form_return' : form_return,\n 'acceptance' : acceptance,\n 'products' : product_list,\n 'form_products' : form_products,\n 'failure' : failure\n },RequestContext(request))\n return_m.save()\n form_products.save()\n\n for product in acceptance.purchase.productofpurchase_set.all():\n try:\n prod_war = ProductAtWarehouse.objects.get(product=product.product, warehouse=product.purchase.warehouse, price=product.acceptance_price)\n prod_war.amount -= product.return_amount\n prod_war.save()\n if not prod_war.amount:\n prod_war.delete()\n except ProductAtWarehouse.DoesNotExist:\n print 'DoesNotExist'\n return HttpResponseRedirect('/returns/page/1')\n return render_to_response('warehouse/return.html',{'form_return' : form_return,\n 'acceptance' : acceptance,\n 'products' : product_list,\n 'form_products' : form_products,\n 'failure' : failure\n },RequestContext(request))\n\n@login_required()\ndef returns(request, page_pk):\n page_pk = int(page_pk)\n per_page = 10\n returns = ReturnModel.objects.all().order_by('date','pk')\n all = returns.count()\n pages = int(ceil(all/float(per_page)))\n if page_pk>pages and page_pk!=1:\n raise Http404()\n page_list = []\n for i in range(pages):\n page_list.append(i+1)\n prev = 1\n if page_pk!=1:\n prev = page_pk -1\n if page_pk==pages:\n next = page_pk\n else:\n next = page_pk + 1\n range_date = request.POST.get('report_range', '')\n if range_date!='':\n start = range_date[:10]\n end = range_date[14:]\n try:\n returns = returns.filter(date__gte=start, date__lte=end)\n except :\n print 'bad date request'\n returns = returns[per_page*page_pk-per_page:per_page*page_pk]\n return render_to_response('warehouse/returns.html',{'returns' : returns,\n 'range' : range_date,\n 'pages' : page_list,\n 'current' : page_pk,\n 'prev' : prev,\n 'next' : next\n }, RequestContext(request))\n\n\n@login_required()\ndef writeoff(request, id_writeoff):\n if not id_writeoff:\n form_writeoff = WriteOffForm(request.POST or None)\n form_product = ProductAtWarehouseForm(request.POST or None)\n form_dish = DishOfWarehouseForm(request.POST or None)\n else:\n pass\n# product_list = []\n# total = 0\n# purchase = ''\n# if not id_purchase:\n# form_purchase = PurchaseForm(request.POST or None)\n# form_product = ProductOfPurchaseForm(request.POST or None)\n# if request.method == 'POST' and form_purchase.is_valid() and form_product.is_valid():\n# purchase = form_purchase.save(commit=False)\n# purchase.created = datetime.date.today()\n# purchase.author = request.user\n# purchase.modified_date = datetime.date.today()\n# purchase.modified_author = request.user\n# purchase.save()\n# product = form_product.save(commit=False)\n# product.purchase = purchase\n# product.save()\n# return HttpResponseRedirect('/purchase/'+str(purchase.pk))\n# else:\n# purchase = get_object_or_404(Purchase, pk=id_purchase)\n# issued = purchase.issued\n# products = purchase.productofpurchase_set.all().order_by('pk')\n# for product in products:\n# total += product.purchase_amount * product.purchase_price\n# product_list.append([product, product.purchase_amount * product.purchase_price])\n# form_purchase = PurchaseForm(request.POST or None, instance=purchase)\n# form_product = ProductOfPurchaseForm(request.POST or None)\n# if request.method == 'POST' and form_purchase.is_valid() and form_product.is_valid():\n# purchase = form_purchase.save(commit=False)\n# purchase.modified_date = datetime.date.today()\n# purchase.modified_author = request.user\n# purchase.issued = issued\n# purchase.save()\n# product = form_product.save(commit=False)\n# product.purchase = purchase\n# product.save()\n# return HttpResponseRedirect('/purchase/'+str(purchase.pk))\n return render_to_response('warehouse/writeoff.html',{'form_writeoff' : form_writeoff,\n 'form_product' : form_product,\n 'form_dish' : form_dish,\n# 'products' : product_list,\n# 'total' : total,\n# 'id_purchase' : id_purchase,\n# 'purchase' : purchase\n }, RequestContext(request))\n"},{"alpha_fraction":0.5814073085784912,"alphanum_fraction":0.5921931266784668,"avg_line_length":27.217391967773438,"blob_id":"4c9556c7b73fc909c95eaed843896a21a5dfaefd","content_id":"e3f54c3eeb36424e09421664e5f6db7d71ca2ae1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1947,"license_type":"no_license","max_line_length":127,"num_lines":69,"path":"/warehouse/forms.py","repo_name":"skirt4aser/Store","src_encoding":"UTF-8","text":"from django.forms import ModelForm\nfrom django.forms.widgets import Textarea, Select\nfrom database.models import Purchase, ProductOfPurchase, Acceptance, ReturnModel, WriteOff, ProductAtWarehouse, DishOfWarehouse\nfrom django import forms\n\n__author__ = 'Ars'\n\n\nclass PurchaseForm(ModelForm):\n class Meta:\n model = Purchase\n exclude = ('author', 'created', 'modified_date', 'modified_author')\n widgets = {\n 'comment': Textarea(attrs={'rows': '5', 'cols': '3'})\n }\n\n\nclass ProductOfPurchaseForm(ModelForm):\n purchase_amount = forms.DecimalField(max_digits=10, decimal_places=2, min_value=0)\n\n class Meta:\n model = ProductOfPurchase\n exclude = ('purchase')\n widgets = {\n 'product': Select(attrs={'style': 'width:220px;'}),\n }\n\n\nclass AcceptanceForm(ModelForm):\n class Meta:\n model = Acceptance\n exclude = ('author', 'created', 'modified_date', 'modified_author', 'purchase')\n widgets = {\n 'comment': Textarea(attrs={'rows': '5', 'cols': '3'}),\n }\n\n\nclass ReturnForm(ModelForm):\n class Meta:\n model = ReturnModel\n exclude = ('author', 'created', 'modified_date', 'modified_author', 'acceptance')\n widgets = {\n 'comment': Textarea(attrs={'rows': '5', 'cols': '3'}),\n }\n\n\nclass WriteOffForm(ModelForm):\n class Meta:\n model = WriteOff\n exclude = ('author', 'created', 'modified_date', 'modified_author')\n widgets = {\n 'comment': Textarea(attrs={'rows': '5', 'cols': '3'})\n }\n\n\nclass ProductAtWarehouseForm(ModelForm):\n class Meta:\n model = ProductAtWarehouse\n widgets = {\n 'product': Select(attrs={'style': 'width:220px;'}),\n }\n\n\nclass DishOfWarehouseForm(ModelForm):\n class Meta:\n model = DishOfWarehouse\n widgets = {\n 'dish': Select(attrs={'style': 'width:220px;'}),\n }\n"},{"alpha_fraction":0.44758734107017517,"alphanum_fraction":0.4532445967197418,"avg_line_length":35.192771911621094,"blob_id":"310b0af9f7835739a07a9bd7213eaf0bf2b36e60","content_id":"838fe81da6ac01858767f7231dba6259a3a06b86","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"JavaScript","length_bytes":3097,"license_type":"no_license","max_line_length":153,"num_lines":83,"path":"/media/store/js/acceptance.js","repo_name":"skirt4aser/Store","src_encoding":"UTF-8","text":"/**\n * Created with PyCharm.\n * User: Ars\n * Date: 02.04.13\n * Time: 1:30\n * To change this template use File | Settings | File Templates.\n */\n\n(function($) {\n\n var len;\n var issued;\n\n var methods = {\n init : function(options){\n len = options.product_len;\n issued = options.issued;\n for (var i=0;i<len;i++){\n var total = $('#id_productofpurchase_set-'+i+'-acceptance_amount').val() * $('#id_productofpurchase_set-'+i+'-acceptance_price').val();\n $('#total_product-'+i).text(total);\n }\n calc();\n $('input:not(#id_costs,#id_cash)').on('focusout',function(){\n var pk = this.id;\n pk = pk.substring(25, pk.indexOf('-acceptance'));\n var total = $('#id_productofpurchase_set-'+pk+'-acceptance_amount').val() * $('#id_productofpurchase_set-'+pk+'-acceptance_price').val();\n $('#total_product-'+pk).text(total);\n\n calc();\n });\n $('#id_costs, #id_cash').on('focusout',function(){\n calc();\n });\n $('#create').on('click',function(){\n var info = false;\n $('tbody input').each(function(){\n if ($(this).val()==''){\n info = true;\n }\n });\n var error = '';\n for (var i=0;i<len;i++){\n if (parseInt($('#purchase_amount_'+i).text()) < $('#id_productofpurchase_set-'+i+'-acceptance_amount').val()){\n error = 'Приемка(количество) не может быть больше закупа(количество)';\n }\n }\n if (info==true){\n alert('Заполните все поля в таблице');\n }else if(error!=''){\n alert(error);\n }else{\n $('#create_submit').click();\n }\n });\n }\n };\n\n function calc(){\n var main_total = 0;\n var main_total_purchase = 0;\n for (var i=0;i<len;i++){\n main_total += parseInt($('#total_product-'+i).text());\n main_total_purchase += parseInt($('#purchase_sum_'+i).text());\n }\n// var balance = main_total_purchase - main_total - $('#id_costs').val();\n var balance = parseInt(issued) - main_total - $('#id_costs').val();\n var debt = $('#id_cash').val()-balance;\n $('#total').text(main_total);\n $('#balance').text(balance);\n $('#debt').text(debt);\n }\n\n $.fn.acceptance = function( method ) {\n if ( methods[method] ) {\n return methods[method].apply( this, Array.prototype.slice.call( arguments, 1 ));\n } else if ( typeof method === 'object' || ! method ) {\n return methods.init.apply( this, arguments );\n } else {\n $.error( 'Метод ' + method + ' не существует в acceptance.js' );\n }\n };\n\n}(jQuery));\n\n"},{"alpha_fraction":0.7195122241973877,"alphanum_fraction":0.7195122241973877,"avg_line_length":24.230770111083984,"blob_id":"545237a4b8be3389be0a1bbc37717840c055ba5e","content_id":"57b36028d51a620d09101eb1795076ed7d8a2760","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":328,"license_type":"no_license","max_line_length":56,"num_lines":13,"path":"/catalog/forms.py","repo_name":"skirt4aser/Store","src_encoding":"UTF-8","text":"from django import forms\nfrom database.models import Supplier\n\nfrom bootstrap_toolkit.widgets import BootstrapTextInput\n#BootstrapUneditableInput, BootstrapDateInput\n\n\nclass SupplierForm(forms.ModelForm):\n class Meta:\n model = Supplier\n widgets = {\n 'name': BootstrapTextInput(prepend='P')\n }\n"},{"alpha_fraction":0.6490975022315979,"alphanum_fraction":0.6541516184806824,"avg_line_length":28.46808433532715,"blob_id":"eb1c701fc37c6cf3a66a59b76ac299cfadec879d","content_id":"882961789b4cded01238ed990805dae1a86c605a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1385,"license_type":"no_license","max_line_length":122,"num_lines":47,"path":"/catalog/ajax.py","repo_name":"skirt4aser/Store","src_encoding":"UTF-8","text":"from django.http import HttpResponse\nfrom django.shortcuts import render_to_response, get_object_or_404\nfrom django.template.context import RequestContext\nfrom database.models import Supplier\nfrom catalog.forms import SupplierForm\n#from django.utils import simplejson\n\n\ndef supplier(request):\n print('SUPPLIER GET')\n # returns populated form, if id is present,\n # blank form otherwise\n try:\n id = request.POST['id']\n except:\n print ('ERROR: id is not found...')\n else:\n print ('got id', id)\n obj = get_object_or_404(Supplier, pk=id)\n form = SupplierForm(instance=obj)\n return render_to_response('catalog/modal_supplier.html', {'form': form}, context_instance=RequestContext(request))\n\n form = SupplierForm()\n return render_to_response('catalog/modal_supplier.html', {'form': form}, context_instance=RequestContext(request))\n\n\ndef supplier_save(request):\n print('SUPPLIER SAVE')\n print(request)\n return HttpResponse('{success:true}')\n\n\ndef supplier_delete(request):\n print('SUPPLIER DELETE')\n\n\n# def ajax_test(request):\n# context = {}\n# try:\n# data = request.POST['text'].strip()\n# except:\n# context['text'] = 'error'\n# print (context['text'])\n# else:\n# context['text'] = data[::-1]\n# print (context['text'])\n# return HttpResponse(context['text'])\n"},{"alpha_fraction":0.42101043462753296,"alphanum_fraction":0.42782679200172424,"avg_line_length":33.15068435668945,"blob_id":"4bba6ec039d731734648bca5e081294ebb7c05f1","content_id":"f13a61e896b07051b5ea1f6bd8daa88db59c6787","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"JavaScript","length_bytes":2587,"license_type":"no_license","max_line_length":132,"num_lines":73,"path":"/media/store/js/return.js","repo_name":"skirt4aser/Store","src_encoding":"UTF-8","text":"/**\n * Created with PyCharm.\n * User: Ars\n * Date: 03.04.13\n * Time: 23:29\n * To change this template use File | Settings | File Templates.\n */\n\n(function($) {\n\n var len;\n\n var methods = {\n init : function(options){\n if ($('#failure').text()!=''){\n alert($('#failure').text());\n }\n len = options.product_len;\n for (var i=0;i<len;i++){\n var total = $('#id_productofpurchase_set-'+i+'-return_amount').val() * parseInt($('#acceptance_price_'+i).text());\n $('#total_product-'+i).text(total);\n }\n calc();\n $('table input').on('focusout',function(){\n var pk = this.id;\n pk = pk.substring(25, pk.indexOf('-return'));\n var total = $('#id_productofpurchase_set-'+pk+'-return_amount').val() * parseInt($('#acceptance_price_'+pk).text());\n $('#total_product-'+pk).text(total);\n calc();\n });\n $('#create').on('click',function(){\n var info = false;\n $('tbody input').each(function(){\n if ($(this).val()==''){\n info = true;\n }\n });\n var error = '';\n for (var i=0;i<len;i++){\n if (parseInt($('#acceptance_amount_'+i).text()) < $('#id_productofpurchase_set-'+i+'-return_amount').val()){\n error = 'Возврат(количество) не может быть больше приемки(количество)';\n }\n }\n if (info==true){\n alert('Заполните все поля в таблице');\n }else if(error!=''){\n alert(error);\n }else{\n $('#create_submit').click();\n }\n });\n }\n };\n\n function calc(){\n var main_total = 0;\n for (var i=0;i<len;i++){\n main_total += parseInt($('#total_product-'+i).text());\n }\n $('#total').text(main_total);\n }\n\n $.fn.return_ = function( method ) {\n if ( methods[method] ) {\n return methods[method].apply( this, Array.prototype.slice.call( arguments, 1 ));\n } else if ( typeof method === 'object' || ! method ) {\n return methods.init.apply( this, arguments );\n } else {\n $.error( 'Метод ' + method + ' не существует в return.js' );\n }\n };\n\n}(jQuery));\n\n"},{"alpha_fraction":0.6128364205360413,"alphanum_fraction":0.6197377443313599,"avg_line_length":38.18918991088867,"blob_id":"65108edcf59a52b49aa9f2d158d78079e808db24","content_id":"98b4abcf08a9c455a547375568779c1b78cd7743","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1473,"license_type":"no_license","max_line_length":86,"num_lines":37,"path":"/warehouse/ajax.py","repo_name":"skirt4aser/Store","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\nimport datetime\nfrom django.http import HttpResponseForbidden, HttpResponse\nfrom django.shortcuts import get_object_or_404\nfrom database.models import Purchase, Product\n\n__author__ = 'Ars'\n\ndef purchase_save(request):\n if request.is_ajax():\n purchase = get_object_or_404(Purchase, pk=request.POST.get('id_purchase', ''))\n text = 'ok'\n try:\n purchase.supplier_id = request.POST.get('id_supplier', '')\n purchase.purchaser_id = request.POST.get('id_purchaser', '')\n purchase.warehouse_id = request.POST.get('id_warehouse', '')\n purchase.date_purchase = request.POST.get('date_purchase', '')\n purchase.comment = request.POST.get('comment', '')\n issued = request.POST.get('issued')\n if issued=='':\n issued = None\n purchase.issued = issued\n purchase.modified_date = datetime.date.today()\n purchase.modified_author = request.user\n purchase.save()\n except :\n text = u'Неправильно заполнены поля'\n return HttpResponse(text,mimetype=\"text/html\")\n else:\n return HttpResponseForbidden()\n\ndef get_product_price(request):\n if request.is_ajax():\n product = get_object_or_404(Product, pk=request.POST.get('id_product', ''))\n return HttpResponse(product.price,mimetype=\"text/html\")\n else:\n return HttpResponseForbidden()"},{"alpha_fraction":0.8863636255264282,"alphanum_fraction":0.8863636255264282,"avg_line_length":14,"blob_id":"8a00ce10ba75f07149343782af1ffe06314333b0","content_id":"372b157fe3369157b761c482ace686853155df61","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":44,"license_type":"no_license","max_line_length":24,"num_lines":3,"path":"/requirements.txt","repo_name":"skirt4aser/Store","src_encoding":"UTF-8","text":"Django\ndjango-bootstrap-toolkit\nmysql-python"},{"alpha_fraction":0.66107577085495,"alphanum_fraction":0.662232518196106,"avg_line_length":41.17073059082031,"blob_id":"2689bea5e081fa082e82b68262dc62d7128fe3d5","content_id":"0ac6a95858533c67b70619c1b3bc4ae8983d7dac","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1729,"license_type":"no_license","max_line_length":164,"num_lines":41,"path":"/Store/urls.py","repo_name":"skirt4aser/Store","src_encoding":"UTF-8","text":"from django.conf.urls import patterns, include, url\nfrom django.contrib import admin\nfrom django.conf import settings\n\nfrom warehouse.views import purchases, purchase, purchase_product_delete, purchase_product, purchase_delete, acceptance, acceptances, return_view, returns, writeoff\nfrom catalog.views import suppliers\nfrom catalog.ajax import supplier, supplier_save, supplier_delete\n\nfrom warehouse.ajax import purchase_save, get_product_price\n\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n url(r'^admin/', include(admin.site.urls)),\n\n (r'^media/(?P<path>.*)$', 'django.views.static.serve', {'document_root': settings.MEDIA_ROOT}),\n (r'^static/(?P<path>.*)$', 'django.views.static.serve', {'document_root': settings.STATIC_ROOT}),\n\n (r'^purchases/page/(?P<page_pk>\\d+)/$', purchases),\n (r'^purchase/$', purchase, {'id_purchase': 0}),\n (r'^purchase/(?P<id_purchase>\\d+)/$', purchase),\n (r'^purchase/(?P<id_purchase>\\d+)/product/(?P<id_product>\\d+)/$', purchase_product),\n (r'^purchase/product/(?P<id_product>\\d+)/delete/$', purchase_product_delete),\n (r'^purchase/(?P<id_purchase>\\d+)/delete/$', purchase_delete),\n\n (r'^ajax/purchase/save/$', purchase_save),\n (r'^ajax/purchase/get_product_price/$', get_product_price),\n\n (r'^purchase/(?P<id_purchase>\\d+)/acceptance/$', acceptance),\n (r'^acceptances/page/(?P<page_pk>\\d+)/$', acceptances),\n\n (r'^acceptance/(?P<id_acceptance>\\d+)/return/$', return_view),\n (r'^returns/page/(?P<page_pk>\\d+)/$', returns),\n\n (r'^writeoff/$', writeoff, {'id_writeoff': 0}),\n\n (r'^suppliers/$', suppliers),\n (r'^ajax/supplier/$', supplier),\n (r'^ajax/supplier/save$', supplier_save),\n (r'^ajax/supplier/delete$', supplier_delete),\n)\n"},{"alpha_fraction":0.6819400787353516,"alphanum_fraction":0.6875419020652771,"avg_line_length":41.281375885009766,"blob_id":"6fd748a036ac368feba3ab5f5bd7e697c724a43a","content_id":"ddaf70119222d81e385fc02754effe1e84ac665d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":23073,"license_type":"no_license","max_line_length":166,"num_lines":494,"path":"/database/models.py","repo_name":"skirt4aser/Store","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\nfrom django.contrib.auth.models import User\nfrom django.db import models\nfrom django.db.models.signals import post_save\nfrom django.dispatch import receiver\nimport datetime\n\n\nclass Supplier(models.Model):\n name = models.CharField(max_length=255, verbose_name='Наименование')\n address = models.CharField(max_length=255, verbose_name='Адрес', blank=True)\n phone = models.CharField(max_length=255, verbose_name='Телефон', blank=True)\n contact = models.CharField(max_length=255, verbose_name='Контактное лицо, ФИО', blank=True)\n contact_phone = models.CharField(max_length=255, verbose_name='Контактное лицо, телефон', blank=True)\n comment = models.TextField(verbose_name='Комментарий', blank=True)\n contact_comment = models.TextField(verbose_name='Контактное лицо, комментарий', blank=True)\n\n def __unicode__(self):\n return self.name\n\n class Meta:\n db_table = 'store_supplier'\n ordering = ['name']\n verbose_name_plural = u'Поставщики'\n verbose_name = u'Поставщик'\n\nclass CategoryOfProduct(models.Model):\n name = models.CharField(max_length=255, verbose_name='Название')\n parent = models.ForeignKey('self', verbose_name='Родитель', blank=True, null=True)\n\n def __unicode__(self):\n return '%s' % self.name\n\n class Meta:\n db_table = 'store_product_categories'\n ordering = ['name']\n verbose_name_plural = u'Категории товара'\n verbose_name = u'Категория товара'\n\nclass Product(models.Model):\n name = models.CharField(max_length=255, verbose_name='Название')\n category = models.ForeignKey(CategoryOfProduct, verbose_name='Категория')\n measure_unit = models.CharField(max_length=255, verbose_name='Единица измерения')\n price = models.DecimalField(max_digits=10, decimal_places=2, verbose_name='Закупочная цена')\n supplier = models.ForeignKey(Supplier, blank=True, null=True, verbose_name='Поставщик')\n description = models.TextField(verbose_name='Описание', blank=True)\n\n def __unicode__(self):\n return '%s' % self.name\n\n class Meta:\n db_table = 'store_products'\n ordering = ['name']\n verbose_name_plural = u'Товары'\n verbose_name = u'Товар'\n\nclass Warehouse(models.Model):\n name = models.CharField(max_length=255, verbose_name='Название')\n address = models.CharField(max_length=255, verbose_name='Адрес', blank=True)\n comment = models.TextField(verbose_name='Комментарий', blank=True)\n\n def __unicode__(self):\n return '%s' % self.name\n\n class Meta:\n db_table = 'store_warehouses'\n ordering = ['name']\n verbose_name_plural = u'Склады'\n verbose_name = u'Склад'\n\nclass CategoryOfDish(models.Model):\n name = models.CharField(max_length=255, verbose_name='Название')\n parent = models.ForeignKey('self', verbose_name='Категория')\n archive = models.BooleanField(verbose_name='Признак архива')\n\n def __unicode__(self):\n return '%s' % self.name\n\n class Meta:\n db_table = 'store_dish_categories'\n ordering = ['name']\n verbose_name_plural = u'Категории калькуляционных карт'\n verbose_name = u'Категория калькуляционных карт'\n\nclass Dish(models.Model):\n name = models.CharField(max_length=255, verbose_name='Название')\n category = models.ForeignKey(CategoryOfDish, verbose_name='Категория')\n date_start = models.DateField(verbose_name='Действует с')\n date_end = models.DateField(verbose_name='Действует по', blank=True, null=True)\n portion = models.DecimalField(max_digits=10, decimal_places=2, verbose_name='Порция')\n master = models.BooleanField(verbose_name='выдан мастером или нет')\n created = models.DateTimeField(verbose_name='Дата создания')\n author = models.ForeignKey(User, verbose_name='Автор', related_name='dish_author')\n modified_date = models.DateTimeField(verbose_name='Дата изменения')\n modified_author = models.ForeignKey(User, verbose_name='Последний сотрудник внесший изменения ', related_name='dish_modified_author')\n comment = models.TextField(verbose_name='Комментарий', blank=True)\n\n\n def __unicode__(self):\n return '%s' % self.name\n\n class Meta:\n db_table = 'store_dishes'\n ordering = ['name']\n verbose_name_plural = u'Калькуляционные карты'\n verbose_name = u'Калькуляционная карта'\n\nclass ProductOfDish(models.Model):\n dish = models.ForeignKey(Dish, verbose_name='КК')\n product = models.ForeignKey(Product, verbose_name='Товар')\n measure_unit = models.CharField(max_length=255, verbose_name='Единица измерения')\n\n def __unicode__(self):\n return '%s' % self.product.name\n\n class Meta:\n db_table = 'store_dish_products'\n ordering = ['product']\n verbose_name_plural = u'Товары КК'\n verbose_name = u'Товар КК'\n\n\nclass SalePoint(models.Model):\n name = models.CharField(max_length=255, verbose_name='Название')\n warehouse = models.ForeignKey(Warehouse, verbose_name='Склад')\n address = models.CharField(max_length=255, verbose_name='Адрес', blank=True)\n comment = models.TextField(verbose_name='Комментарий', blank=True)\n created = models.DateField(verbose_name='Дата создания')\n author = models.ForeignKey(User, verbose_name='Автор', related_name='point_author')\n modified_date = models.DateField(verbose_name='Дата изменения')\n modified_author = models.ForeignKey(User, verbose_name='Автор изменений', related_name='point_modified_author')\n\n def __unicode__(self):\n return self.name\n\n class Meta:\n db_table = 'sale_salepoints'\n ordering = ['name']\n verbose_name_plural = u'Точки продаж'\n verbose_name = u'Точка продаж'\n\nclass Change(models.Model):\n point = models.ForeignKey(SalePoint, verbose_name='Точка продаж')\n cashier = models.ForeignKey(User, verbose_name='Кассир')\n start = models.DateField(verbose_name='Дата открытия')\n end = models.DateField(verbose_name='Дата закрытия', blank=True, null=True)\n\n def __unicode__(self):\n return self.point\n\n class Meta:\n db_table = 'sale_changes'\n ordering = ['point']\n verbose_name_plural = u'Смены'\n verbose_name = u'Смена'\n\nclass Sale(models.Model):\n point = models.ForeignKey(SalePoint, verbose_name='Точка продаж')\n cashier = models.ForeignKey(User, verbose_name='Кассир')\n change = models.ForeignKey(Change, verbose_name='Смена')\n discount = models.PositiveIntegerField(verbose_name='Скидка', blank=True, null=True)\n\n def __unicode__(self):\n return self.point\n\n class Meta:\n db_table = 'sale_sales'\n ordering = ['point']\n verbose_name_plural = u'Смены'\n verbose_name = u'Смена'\n\nclass DishOfSale(models.Model):\n sale = models.ForeignKey(Sale, verbose_name='Продажи')\n dish = models.ForeignKey(Dish, verbose_name='Блюда')\n amount = models.PositiveIntegerField(verbose_name='Количество')\n price = models.PositiveIntegerField(verbose_name='Цена')\n\n def __unicode__(self):\n return self.dish\n\n class Meta:\n db_table = 'sale_sale_dishes'\n ordering = ['dish']\n verbose_name_plural = u'Блюда для продажи'\n verbose_name = u'Блюдо для продажи'\n\nclass Purchase(models.Model):\n purchaser = models.ForeignKey(User, verbose_name='Закупщик', related_name='purchaser')\n supplier = models.ForeignKey(Supplier, verbose_name='Контрагент', blank=True, null=True)\n warehouse = models.ForeignKey(Warehouse, verbose_name='Склад')\n date_purchase = models.DateField(verbose_name='Дата закупа')\n comment = models.TextField(verbose_name='Комментарий', blank=True)\n created = models.DateTimeField(verbose_name='Время создания')\n issued = models.DecimalField(max_digits=10, decimal_places=2, verbose_name='Выдано', blank=True, null=True)\n author = models.ForeignKey(User, verbose_name='Автор', related_name='purchase_author')\n modified_date = models.DateTimeField(verbose_name='Дата изменения')\n modified_author = models.ForeignKey(User, verbose_name='Автор изменения', related_name='purchase_modified_author')\n\n def __unicode__(self):\n return '#%s - %s' % (str(self.pk),str(self.date_purchase))\n\n class Meta:\n db_table = 'wr_purchases'\n ordering = ['pk']\n verbose_name_plural = u'Закупы'\n verbose_name = u'Закуп'\n\nclass ProductOfPurchase(models.Model):\n purchase = models.ForeignKey(Purchase, verbose_name='Закуп')\n product = models.ForeignKey(Product, verbose_name='Товары')\n purchase_amount = models.DecimalField(max_digits=10, decimal_places=2, verbose_name='Количество, закуп')\n acceptance_amount = models.DecimalField(max_digits=10, decimal_places=2,verbose_name='Количество, приемка', blank=True, null=True)\n return_amount = models.DecimalField(max_digits=10, decimal_places=2,verbose_name='Количество, возврат', blank=True, null=True)\n purchase_price = models.DecimalField(max_digits=10, decimal_places=2,verbose_name='Цена закупа')\n acceptance_price = models.DecimalField(max_digits=10, decimal_places=2,verbose_name='Цена закупа', blank=True, null=True)\n return_price = models.DecimalField(max_digits=10, decimal_places=2, verbose_name='Цена закупа', blank=True, null=True)\n\n def __unicode__(self):\n return '%s' % self.product.name\n\n class Meta:\n db_table = 'wr_purchase_products'\n ordering = ['product']\n verbose_name_plural = u'Товары закупа'\n verbose_name = u'Товар закупа'\n\nclass Acceptance(models.Model):\n purchase = models.OneToOneField(Purchase, verbose_name='Закуп')\n date = models.DateField(verbose_name='Дата приемки')\n costs = models.DecimalField(max_digits=10, decimal_places=2,verbose_name='Расходы', blank=True, null=True)\n cash = models.DecimalField(max_digits=10, decimal_places=2,verbose_name='В кассу')\n comment = models.TextField(verbose_name='Комментарий', blank=True)\n created = models.DateTimeField(verbose_name='Время создания')\n author = models.ForeignKey(User, verbose_name='Автор', related_name='acceptance_author')\n modified_date = models.DateTimeField(verbose_name='Дата изменения')\n modified_author = models.ForeignKey(User, verbose_name='Автор изменения', related_name='acceptance_modified_author')\n\n def sum(self):\n products = ProductOfPurchase.objects.filter(purchase=self.purchase)\n sum = 0\n for product in products:\n sum += product.acceptance_price * product.acceptance_amount\n return sum\n\n def debt(self):\n debt = self.cash -(self.purchase.issued-self.sum()-self.costs)\n return debt\n\n def __unicode__(self):\n return '#%s - %s' % (str(self.pk),str(self.date))\n\n class Meta:\n db_table = 'wr_acceptances'\n ordering = ['pk']\n verbose_name_plural = u'Приемки'\n verbose_name = u'Приемка'\n\nclass ReturnModel(models.Model):\n acceptance = models.OneToOneField(Acceptance, verbose_name='Приемка')\n date = models.DateField(verbose_name='Дата возврата', blank=True, null=True)\n comment = models.TextField(verbose_name='Комментарий', blank=True)\n created = models.DateTimeField(verbose_name='Время создания')\n author = models.ForeignKey(User, verbose_name='Автор', related_name='return_author')\n modified_date = models.DateTimeField(verbose_name='Дата изменения')\n modified_author = models.ForeignKey(User, verbose_name='Автор изменения', related_name='return_modified_author')\n\n def sum(self):\n products = ProductOfPurchase.objects.filter(purchase=self.acceptance.purchase)\n sum = 0\n for product in products:\n sum += (product.acceptance_price * product.return_amount)\n return sum\n\n def __unicode__(self):\n return '#%s - %s' % (str(self.pk),str(self.date))\n\n class Meta:\n db_table = 'wr_returns'\n ordering = ['pk']\n verbose_name_plural = u'Возвраты'\n verbose_name = u'Возврат'\n\nclass WriteOff(models.Model):\n warehouse = models.ForeignKey(Warehouse, verbose_name='Склад')\n date = models.DateField(verbose_name='Дата')\n comment = models.TextField(verbose_name='Комментарий', blank=True)\n reason = models.CharField(max_length=100, verbose_name='Причина', choices=((u'Обеды сотрудников',u'Обеды сотрудников'),(u'Порча',u'Порча'),(u'Прочее',u'Прочее')))\n created = models.DateTimeField(verbose_name='Время создания')\n author = models.ForeignKey(User, verbose_name='Автор', related_name='writeoff_author')\n modified_date = models.DateTimeField(verbose_name='Дата изменения')\n modified_author = models.ForeignKey(User, verbose_name='Автор изменения', related_name='writeoff_modified_author')\n\n def __unicode__(self):\n return '#%s - %s' % (str(self.pk),str(self.date))\n\n class Meta:\n db_table = 'wr_writeoffs'\n ordering = ['pk']\n verbose_name_plural = u'Списания'\n verbose_name = u'Списание'\n\nclass ProductOfWriteOff(models.Model):\n writeoff = models.ForeignKey(WriteOff, verbose_name='Списание')\n product = models.ForeignKey(Product, verbose_name='Товары', blank=True, null=True)\n dish = models.ForeignKey(Dish, verbose_name='Товары', blank=True, null=True)\n amount = models.DecimalField(max_digits=10, decimal_places=2,verbose_name='Количество')\n price = models.DecimalField(max_digits=10, decimal_places=2,verbose_name='Цена')\n\n def __unicode__(self):\n return '%s' % self.product.name\n\n class Meta:\n db_table = 'wr_writeoff_products'\n ordering = ['product']\n verbose_name_plural = u'Товары списания'\n verbose_name = u'Товар списанияы'\n\nclass Inventory(models.Model):\n warehouse = models.ForeignKey(Warehouse, verbose_name='Склад')\n date = models.DateField(verbose_name='Дата')\n comment = models.TextField(verbose_name='Комментарий', blank=True)\n created = models.DateTimeField(verbose_name='Время создания')\n author = models.ForeignKey(User, verbose_name='Автор', related_name='inventory_author')\n modified_date = models.DateTimeField(verbose_name='Дата изменения')\n modified_author = models.ForeignKey(User, verbose_name='Автор изменения', related_name='inventory_modified_author')\n\n def __unicode__(self):\n return self.pk\n\n class Meta:\n db_table = 'wr_inventories'\n ordering = ['pk']\n verbose_name_plural = u'Инвентаризация'\n verbose_name = u'Инвентаризация'\n\nclass ProductOfInventory(models.Model):\n inventory = models.ForeignKey(Inventory, verbose_name='Инвентаризация')\n product = models.ForeignKey(Product, verbose_name='Товары')\n estimated_balance = models.PositiveIntegerField(verbose_name='Расчетный остаток')\n actual_balance = models.PositiveIntegerField(verbose_name='Фактический остаток')\n price = models.PositiveIntegerField(verbose_name='Цена')\n\n def __unicode__(self):\n return self.product\n\n class Meta:\n db_table = 'wr_inventory_products'\n ordering = ['product']\n verbose_name_plural = u'Товары инвентаризации'\n verbose_name = u'Товар инвентаризации'\n\nclass Movement(models.Model):\n warehouse_from = models.ForeignKey(Warehouse, verbose_name='Склад откуда', related_name='warehouse_from')\n warehouse_to = models.ForeignKey(Warehouse, verbose_name='Склад куда', related_name='warehouse_to')\n date = models.DateField(verbose_name='Дата')\n comment = models.TextField(verbose_name='Комментарий', blank=True)\n created = models.DateTimeField(verbose_name='Время создания')\n author = models.ForeignKey(User, verbose_name='Автор', related_name='movement_author')\n modified_date = models.DateTimeField(verbose_name='Дата изменения')\n modified_author = models.ForeignKey(User, verbose_name='Автор изменения', related_name='movement_modified_author')\n\n def __unicode__(self):\n return self.pk\n\n class Meta:\n db_table = 'wr_movements'\n ordering = ['pk']\n verbose_name_plural = u'Перемещения'\n verbose_name = u'Перемещение'\n\nclass ProductOfMovement(models.Model):\n movement = models.ForeignKey(Movement, verbose_name='Перемещение')\n product = models.ForeignKey(Product, verbose_name='Товары', blank=True, null=True)\n dish = models.ForeignKey(Dish, verbose_name='Блюдо', blank=True, null=True)\n amount = models.PositiveIntegerField(verbose_name='Количество')\n price = models.PositiveIntegerField(verbose_name='Цена')\n\n def __unicode__(self):\n return self.product\n\n class Meta:\n db_table = 'wr_movement_products'\n ordering = ['product']\n verbose_name_plural = u'Товары перемещения'\n verbose_name = u'Товар перемещения'\n\nclass Issuance(models.Model):\n warehouse = models.ForeignKey(Warehouse, verbose_name='Склад')\n master = models.ForeignKey(User, verbose_name='Мастер', related_name='master')\n date = models.DateField(verbose_name='Дата')\n comment = models.TextField(verbose_name='Комментарий', blank=True)\n created = models.DateTimeField(verbose_name='Время создания')\n author = models.ForeignKey(User, verbose_name='Автор', related_name='issuance_author')\n modified_date = models.DateTimeField(verbose_name='Дата изменения')\n modified_author = models.ForeignKey(User, verbose_name='Автор изменения', related_name='issuance_modified_author')\n\n def __unicode__(self):\n return self.pk\n\n class Meta:\n db_table = 'wr_issuances'\n ordering = ['pk']\n verbose_name_plural = u'Выдачи'\n verbose_name = u'Выдача'\n\nclass DishOfIssuance(models.Model):\n issuance = models.ForeignKey(Issuance, verbose_name='Выдача')\n dish = models.ForeignKey(Dish, verbose_name='Блюдо')\n amount = models.PositiveIntegerField(verbose_name='Количество')\n price = models.PositiveIntegerField(verbose_name='Цена')\n\n def __unicode__(self):\n return self.dish\n\n class Meta:\n db_table = 'wr_issuance_dishes'\n ordering = ['dish']\n verbose_name_plural = u'Блюда выдачи'\n verbose_name = u'Блюдо выдачи'\n\nclass DishOfWarehouse(models.Model):\n warehouse = models.ForeignKey(Warehouse, verbose_name='Склад')\n dish = models.ForeignKey(Dish, verbose_name='Блюдо')\n amount = models.PositiveIntegerField(verbose_name='Количество')\n price = models.PositiveIntegerField(verbose_name='Цена')\n date = models.DateField(verbose_name='Дата')\n\n def __unicode__(self):\n return '%s' % self.dish.name\n\n class Meta:\n db_table = 'store_warehouse_dishes'\n ordering = ['dish']\n verbose_name_plural = u'Блюда на складе'\n verbose_name = u'Блюдо на складе'\n\nclass Costs(models.Model):\n date = models.DateField(verbose_name='Дата')\n sum = models.PositiveIntegerField(verbose_name='Сумма')\n type = models.CharField(max_length=255, verbose_name='Тип расхода')\n comment = models.TextField(verbose_name='Комментарий', blank=True)\n\n def __unicode__(self):\n return self.pk\n\n class Meta:\n db_table = 'cst_costs'\n ordering = ['pk']\n verbose_name_plural = u'Расходы'\n verbose_name = u'Расход'\n\n\nclass ProductAtWarehouse(models.Model):\n product = models.ForeignKey(Product, verbose_name='Товар')\n warehouse = models.ForeignKey(Warehouse, verbose_name='Склад')\n acceptance = models.ForeignKey(Acceptance, verbose_name='Приемка')\n amount = models.DecimalField(max_digits=10, decimal_places=2, verbose_name='Количество')\n price = models.PositiveIntegerField(verbose_name='Цена приемки')\n\n def __unicode__(self):\n return '%s' % self.product.name\n\n class Meta:\n db_table = 'store_warehouse_products'\n ordering = ['product']\n verbose_name_plural = u'Товары на складе'\n verbose_name = u'Товар на складе'\n\n\nclass UserProfile(models.Model):\n user = models.OneToOneField(User, unique=True, verbose_name='Пользователь')\n patronymic = models.CharField(max_length=255, verbose_name='Отчество', blank=True)\n phone = models.CharField(max_length=255, verbose_name='Телефон', blank=True)\n position = models.CharField(max_length=255, blank=True, verbose_name='Должность')\n point = models.ForeignKey(SalePoint, verbose_name='Точка продаж', blank=True, null=True)\n active_from = models.DateField(verbose_name='Активен с')\n active_until = models.DateField(verbose_name='Активен до', blank=True, null=True)\n type = models.CharField(max_length=255, verbose_name='Тип оплаты')\n\n def __unicode__(self):\n return self.user\n\n class Meta:\n db_table = 'auth_user_profile'\n ordering = ['pk']\n verbose_name_plural = u'Профили'\n verbose_name = u'Профиль'\n\n@receiver(post_save, sender=User, dispatch_uid=\"gV3AQD1y3H6laRX5mx94KDWeqYrL9c0V\")\ndef create_profile(sender, instance, created, **kwargs):\n if created:\n UserProfile.objects.get_or_create(user=instance, active_from=datetime.date.today())\n\n# еще три таблицы по юзеру"},{"alpha_fraction":0.6290836930274963,"alphanum_fraction":0.629482090473175,"avg_line_length":40.83333206176758,"blob_id":"52488bea7e9252f02ac887291363ac2bbd000fb0","content_id":"1dc9f1d620a3ecc8b147633e581c5e6d4279b45d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2510,"license_type":"no_license","max_line_length":131,"num_lines":60,"path":"/catalog/views.py","repo_name":"skirt4aser/Store","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\nfrom math import *\nfrom django.http import HttpResponseRedirect\nfrom django.shortcuts import render_to_response\nfrom django.template.context import RequestContext\nfrom django.contrib.auth.decorators import login_required\n\nfrom database.models import Supplier\nfrom catalog.forms import SupplierForm\n\n\n@login_required()\ndef suppliers(request):\n suppliers = Supplier.objects.all()\n return render_to_response('catalog/suppliers.html', {'suppliers': suppliers}, RequestContext(request))\n\n# @login_required()\n# def supplier(request):\n# # if in a postback\n# if request.method == 'POST':\n# # getting form\n# form = SupplierForm(request.POST)\n# # validating form elements\n# if form.is_valid():\n# # creating and instance of a binded model object\n# supplier = form.save(commit=False)\n# # populating with form data\n# supplier.name = form.cleaned_data['name']\n# supplier.address = form.cleaned_data['address']\n# supplier.phone = form.cleaned_data['phone']\n# supplier.contact = form.cleaned_data['contact']\n# supplier.contact_phone = form.cleaned_data['contact_phone']\n# supplier.comment = form.cleaned_data['comment']\n# supplier.contact_comment = form.cleaned_data['contact_comment']\n# # saving to db\n# supplier = form.save()\n# # redirecting...\n# return HttpResponseRedirect('catalog/suppliers.html')\n# else:\n# form = SupplierForm()\n\n# return render_to_response('catalog/supplier.html', {'form': form}, RequestContext(request))\n\n\n# @login_required()\n# def suppliers(request):\n# errors = []\n# if request.method == 'POST':\n# if 'checkedRow' in request.POST and 'action' in request.POST:\n# checkedRow = request.POST['checkedRow']\n# action = request.POST['action']\n# if not checkedRow or not action:\n# # TODO: quit\n# errors.append('Error, some objects missing...')\n# else:\n# return render_to_response('catalog/supplier.html', {'action': action, 'id': checkedRow}, RequestContext(request))\n# else:\n# suppliers = Supplier.objects.all()\n# return render_to_response('catalog/suppliers.html', {'suppliers': suppliers}, RequestContext(request))\n# return render_to_response('catalog/suppliers.html', {'errors': errors}, RequestContext(request))\n"}],"string":"[\n {\n \"alpha_fraction\": 0.31015998125076294,\n \"alphanum_fraction\": 0.3138958811759949,\n \"avg_line_length\": 45.119850158691406,\n \"blob_id\": \"c714b8737f780476dedf4e1a889f8e8bf375012d\",\n \"content_id\": \"52c566e2d9ce4a869886bfb1d4da8f262c6bff93\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"HTML\",\n \"length_bytes\": 12537,\n \"license_type\": \"no_license\",\n \"max_line_length\": 159,\n \"num_lines\": 267,\n \"path\": \"/templates/warehouse/writeoff.html\",\n \"repo_name\": \"skirt4aser/Store\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"{% extends 'root.html' %}\\n{% block title %}Списание{% endblock %}\\n{% block content %}\\n <div class=\\\"row-fluid\\\">\\n <form class=\\\" no-margin\\\" method=\\\"POST\\\">\\n <div class=\\\"span4\\\">\\n <div class=\\\"widget\\\">\\n <div class=\\\"widget-header\\\">\\n <div class=\\\"title\\\">\\n Списание\\n </div>\\n <span class=\\\"tools\\\">\\n <a class=\\\"fs1\\\" aria-hidden=\\\"true\\\" data-icon=\\\"\\\"></a>\\n </span>\\n </div>\\n <div class=\\\"widget-body\\\">\\n {% csrf_token %}\\n {{ form_writeoff.errors }}\\n <div class=\\\"control-group\\\">\\n <label class=\\\"control-label\\\" for=\\\"name\\\">\\n Склад\\n </label>\\n <div class=\\\"controls controls-row\\\">\\n {{ form_writeoff.warehouse }}\\n </div>\\n </div>\\n <div class=\\\"control-group\\\">\\n <label class=\\\"control-label\\\" for=\\\"name\\\">\\n Причина\\n </label>\\n <div class=\\\"controls controls-row\\\">\\n {{ form_writeoff.reason }}\\n </div>\\n </div>\\n <div class=\\\"control-group\\\">\\n <label class=\\\"control-label\\\" for=\\\"name\\\">\\n Дата\\n </label>\\n <div class=\\\"controls controls-row\\\">\\n {{ form_writeoff.date }}\\n </div>\\n </div>\\n <div class=\\\"control-group\\\">\\n <label class=\\\"control-label\\\" for=\\\"name\\\">\\n Комментарий\\n </label>\\n <div class=\\\"controls controls-row\\\">\\n {{ form_writeoff.comment }}\\n </div>\\n </div>\\n </div>\\n </div>\\n </div>\\n <div class=\\\"span4\\\">\\n <div class=\\\"widget\\\">\\n <div class=\\\"widget-header\\\">\\n <div class=\\\"title\\\">\\n Выбор блюда\\n </div>\\n <span class=\\\"tools\\\">\\n <a class=\\\"fs1\\\" aria-hidden=\\\"true\\\" data-icon=\\\"\\\"></a>\\n </span>\\n </div>\\n <div class=\\\"widget-body\\\">\\n {{ form_dish.errors }}\\n <div class=\\\"control-group\\\">\\n <label class=\\\"control-label\\\" for=\\\"name\\\">\\n Блюдо\\n </label>\\n <div class=\\\"controls controls-row\\\">\\n {{ form_dish.dish }}\\n </div>\\n </div>\\n <div class=\\\"control-group\\\">\\n <label class=\\\"control-label\\\" for=\\\"name\\\">\\n Количество\\n </label>\\n <div class=\\\"controls controls-row\\\">\\n {{ form_dish.amount }}\\n </div>\\n </div>\\n <div class=\\\"control-group\\\">\\n <label class=\\\"control-label\\\" for=\\\"name\\\">\\n Цена\\n </label>\\n <div class=\\\"controls controls-row\\\">\\n {{ form_dish.price }}\\n </div>\\n </div>\\n <div class=\\\"form-actions\\\">\\n <button type=\\\"submit\\\" class=\\\"btn btn-info pull-right\\\" id=\\\"add_product\\\">\\n Добавить блюдо\\n </button>\\n <div class=\\\"clearfix\\\">\\n </div>\\n </div>\\n\\n </div>\\n </div>\\n </div>\\n <div class=\\\"span4\\\">\\n <div class=\\\"widget\\\">\\n <div class=\\\"widget-header\\\">\\n <div class=\\\"title\\\">\\n Выбор товара\\n </div>\\n <span class=\\\"tools\\\">\\n <a class=\\\"fs1\\\" aria-hidden=\\\"true\\\" data-icon=\\\"\\\"></a>\\n </span>\\n </div>\\n <div class=\\\"widget-body\\\">\\n {{ form_product.errors }}\\n <div class=\\\"control-group\\\">\\n <label class=\\\"control-label\\\" for=\\\"name\\\">\\n Товар\\n </label>\\n <div class=\\\"controls controls-row\\\">\\n {{ form_product.product }}\\n </div>\\n </div>\\n <div class=\\\"control-group\\\">\\n <label class=\\\"control-label\\\" for=\\\"name\\\">\\n Количество\\n </label>\\n <div class=\\\"controls controls-row\\\">\\n {{ form_product.amount }}\\n </div>\\n </div>\\n <div class=\\\"control-group\\\">\\n <label class=\\\"control-label\\\" for=\\\"name\\\">\\n Цена\\n </label>\\n <div class=\\\"controls controls-row\\\">\\n {{ form_product.price }}\\n </div>\\n </div>\\n <div class=\\\"form-actions\\\">\\n <button type=\\\"submit\\\" class=\\\"btn btn-info pull-right\\\" id=\\\"add_product\\\">\\n Добавить товар\\n </button>\\n <div class=\\\"clearfix\\\">\\n </div>\\n </div>\\n\\n </div>\\n </div>\\n </div>\\n </form>\\n </div>\\n\\n <div class=\\\"row-fluid\\\">\\n <div class=\\\"span12\\\">\\n <div class=\\\"widget\\\">\\n <div class=\\\"widget-header\\\">\\n <div class=\\\"title\\\">\\n Товары\\n </div>\\n <span class=\\\"tools\\\">\\n <a class=\\\"fs1\\\" aria-hidden=\\\"true\\\" data-icon=\\\"\\\"></a>\\n </span>\\n </div>\\n <div class=\\\"widget-body\\\">\\n <table class=\\\"table table-condensed table-striped table-bordered table-hover no-margin\\\">\\n <thead>\\n <tr>\\n <th style=\\\"width:5%\\\">\\n </th>\\n <th style=\\\"width:15%\\\">\\n Товар/Блюдо\\n </th>\\n <th style=\\\"width:15%\\\" class=\\\"hidden-phone\\\">\\n Количество\\n </th>\\n <th style=\\\"width:15%\\\" class=\\\"hidden-phone\\\">\\n Остаток\\n </th>\\n <th style=\\\"width:10%\\\" class=\\\"hidden-phone\\\">\\n Цена\\n </th>\\n <th style=\\\"width:25%\\\" class=\\\"hidden-phone\\\">\\n Сумма\\n </th>\\n <th style=\\\"width:10%\\\" class=\\\"hidden-phone\\\">\\n Действия\\n </th>\\n </tr>\\n </thead>\\n <tbody>\\n {% for product in products %}\\n <tr>\\n <td>{{ forloop.counter }}</td>\\n <td class=\\\"hidden-phone\\\">{{ product.0.product }}</td>\\n <td><span class=\\\"name\\\">{{ product.0.purchase_amount }}</span></td>\\n <td><span class=\\\"name\\\">{{ product.0.product.measure_unit }}</span></td>\\n <td class=\\\"hidden-phone\\\">{{ product.0.purchase_price }}</td>\\n <td class=\\\"hidden-phone\\\">{{ product.1 }}</td>\\n <td>\\n {% if purchase.acceptance == None or purchase.acceptance == ''%}\\n <div class=\\\"tools\\\">\\n <div class=\\\"btn-group\\\">\\n <a class=\\\"btn btn-small\\\" data-original-title=\\\"\\\" href=\\\"/purchase/{{ id_purchase }}/product/{{ product.0.pk }}/\\\">\\n <i class=\\\"icon-edit\\\" data-original-title=\\\"Редактировать\\\">\\n </i>\\n </a>\\n <a class=\\\"btn btn-small delete\\\" data-original-title=\\\"\\\" href=\\\"/purchase/product/{{ product.0.pk }}/delete/\\\">\\n <i class=\\\"icon-trash\\\" data-original-title=\\\"Удалить\\\">\\n </i>\\n </a>\\n </div>\\n </div>\\n {% endif %}\\n </td>\\n </tr>\\n {% endfor %}\\n <tr>\\n <td></td>\\n <td></td>\\n <td></td>\\n <td></td>\\n <td><strong>Итого</strong></td>\\n <td><strong>{{ total }}</strong></td>\\n <td></td>\\n </tr>\\n </tbody>\\n </table>\\n <div class=\\\"form-actions no-margin\\\">\\n {% if id_purchase != 0 %}\\n {% if purchase.acceptance == None or purchase.acceptance == '' %}\\n <a class=\\\"button btn btn-info pull-left\\\" href=\\\"/purchase/{{ id_purchase }}/delete/\\\" id=\\\"delete_purchase\\\">\\n Удалить закуп\\n </a>\\n {% endif %}\\n {% endif %}\\n {% if purchase.acceptance == None or purchase.acceptance == '' %}\\n <button type=\\\"button\\\" class=\\\"btn btn-info pull-right\\\" id=\\\"create\\\">\\n Сохранить\\n </button>\\n {% endif %}\\n <div class=\\\"clearfix\\\">\\n </div>\\n </div>\\n </div>\\n </div>\\n </div>\\n </div>\\n{% endblock %}\\n{% block js %}\\n <script type=\\\"text/javascript\\\" src=\\\"/media/theme/js/bootstrap-datepicker.js\\\"></script>\\n <script type=\\\"text/javascript\\\" src=\\\"/media/store/js/purchase.js\\\"></script>\\n <script type=\\\"text/javascript\\\" src=\\\"/media/theme/js/select2.min.js\\\"></script>\\n <script type=\\\"text/javascript\\\">\\n $(document).ready(function(){\\n $('#id_date').datepicker({\\n format: 'yyyy-mm-dd'\\n });\\n $('#id_product').select2();\\n $('#id_dish').select2();\\n $.fn.purchase();\\n $('a').tooltip('hide');\\n $('i').tooltip('hide');\\n });\\n </script>\\n{% endblock %}\\n{% block css %}\\n <link href=\\\"/media/theme/css/datepicker.css\\\" rel=\\\"stylesheet\\\">\\n <link href=\\\"/media/theme/css/select2.css\\\" rel=\\\"stylesheet\\\">\\n{% endblock %}\"\n },\n {\n \"alpha_fraction\": 0.5373784899711609,\n \"alphanum_fraction\": 0.5411935448646545,\n \"avg_line_length\": 50.44392395019531,\n \"blob_id\": \"dd98d14d898a21c8ffe8f187f9f11c5be0a6b215\",\n \"content_id\": \"035808bb04f78c904b6a3cb8de980e4cc658df7c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 22134,\n \"license_type\": \"no_license\",\n \"max_line_length\": 200,\n \"num_lines\": 428,\n \"path\": \"/warehouse/views.py\",\n \"repo_name\": \"skirt4aser/Store\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\nimport datetime\\nfrom math import *\\nfrom django.forms.models import inlineformset_factory\\nfrom django.http import HttpResponseRedirect, Http404\\nfrom django.shortcuts import render_to_response, get_object_or_404\\nfrom django.template.context import RequestContext\\nfrom django.contrib.auth.decorators import login_required\\nfrom database.models import Purchase, ProductOfPurchase, Acceptance, ProductAtWarehouse, ReturnModel\\nfrom warehouse.forms import PurchaseForm, ProductOfPurchaseForm, AcceptanceForm, ReturnForm, WriteOffForm, ProductAtWarehouseForm, DishOfWarehouseForm\\n\\n__author__ = 'Ars'\\n\\n\\n@login_required()\\ndef purchases(request, page_pk):\\n page_pk = int(page_pk)\\n per_page = 10\\n purchases = Purchase.objects.all().order_by('date_purchase', 'pk')\\n all = purchases.count()\\n pages = int(ceil(all/float(per_page)))\\n if page_pk > pages and page_pk != 1:\\n raise Http404()\\n page_list = []\\n for i in range(pages):\\n page_list.append(i+1)\\n prev = 1\\n if page_pk!=1:\\n prev = page_pk -1\\n if page_pk==pages:\\n next = page_pk\\n else:\\n next = page_pk + 1\\n range_date = request.POST.get('report_range', '')\\n if range_date!='':\\n start = range_date[:10]\\n end = range_date[14:]\\n try:\\n purchases = purchases.filter(date_purchase__gte=start, date_purchase__lte=end)\\n except :\\n print 'bad date request'\\n purchases = purchases[per_page*page_pk-per_page:per_page*page_pk]\\n purchase_list = []\\n for purchase in purchases:\\n sum = 0\\n for product in purchase.productofpurchase_set.all():\\n sum += product.purchase_amount * product.purchase_price\\n purchase_list.append([purchase, sum])\\n return render_to_response('warehouse/purchases.html',{'purchases' : purchase_list,\\n 'range' : range_date,\\n 'pages' : page_list,\\n 'current' : page_pk,\\n 'prev' : prev,\\n 'next' : next\\n }, RequestContext(request))\\n\\n@login_required()\\ndef purchase(request, id_purchase):\\n product_list = []\\n total = 0\\n purchase = ''\\n if not id_purchase:\\n form_purchase = PurchaseForm(request.POST or None)\\n form_product = ProductOfPurchaseForm(request.POST or None)\\n if request.method == 'POST' and form_purchase.is_valid() and form_product.is_valid():\\n purchase = form_purchase.save(commit=False)\\n purchase.created = datetime.date.today()\\n purchase.author = request.user\\n purchase.modified_date = datetime.date.today()\\n purchase.modified_author = request.user\\n purchase.save()\\n product = form_product.save(commit=False)\\n product.purchase = purchase\\n product.save()\\n return HttpResponseRedirect('/purchase/'+str(purchase.pk))\\n else:\\n purchase = get_object_or_404(Purchase, pk=id_purchase)\\n issued = purchase.issued\\n products = purchase.productofpurchase_set.all().order_by('pk')\\n for product in products:\\n total += product.purchase_amount * product.purchase_price\\n product_list.append([product, product.purchase_amount * product.purchase_price])\\n form_purchase = PurchaseForm(request.POST or None, instance=purchase)\\n form_product = ProductOfPurchaseForm(request.POST or None)\\n if request.method == 'POST' and form_purchase.is_valid() and form_product.is_valid():\\n purchase = form_purchase.save(commit=False)\\n purchase.modified_date = datetime.date.today()\\n purchase.modified_author = request.user\\n purchase.issued = issued\\n purchase.save()\\n product = form_product.save(commit=False)\\n product.purchase = purchase\\n product.save()\\n return HttpResponseRedirect('/purchase/'+str(purchase.pk))\\n return render_to_response('warehouse/purchase.html',{'form_purchase' : form_purchase,\\n 'form_product' : form_product,\\n 'products' : product_list,\\n 'total' : total,\\n 'id_purchase' : id_purchase,\\n 'purchase' : purchase\\n }, RequestContext(request))\\n\\n@login_required()\\ndef purchase_product(request, id_purchase, id_product):\\n product_list = []\\n total = 0\\n purchase = get_object_or_404(Purchase, pk=id_purchase)\\n product = get_object_or_404(ProductOfPurchase, pk=id_product)\\n issued = purchase.issued\\n products = purchase.productofpurchase_set.all().order_by('pk')\\n for prod in products:\\n total += prod.purchase_amount * prod.purchase_price\\n product_list.append([prod, prod.purchase_amount * prod.purchase_price])\\n form_purchase = PurchaseForm(request.POST or None, instance=purchase)\\n form_product = ProductOfPurchaseForm(request.POST or None, instance=product)\\n if request.method == 'POST' and form_purchase.is_valid() and form_product.is_valid():\\n purchase = form_purchase.save(commit=False)\\n purchase.modified_date = datetime.date.today()\\n purchase.modified_author = request.user\\n purchase.issued = issued\\n purchase.save()\\n form_product.save()\\n return HttpResponseRedirect('/purchase/'+str(purchase.pk))\\n return render_to_response('warehouse/purchase.html',{'form_purchase' : form_purchase,\\n 'form_product' : form_product,\\n 'products' : product_list,\\n 'total' : total,\\n 'id_purchase' : id_purchase\\n }, RequestContext(request))\\n\\n@login_required()\\ndef purchase_product_delete(request, id_product):\\n product = get_object_or_404(ProductOfPurchase, pk=id_product)\\n purchase_pk = product.purchase.pk\\n product.delete()\\n return HttpResponseRedirect('/purchase/'+str(purchase_pk))\\n\\n@login_required()\\ndef purchase_delete(request, id_purchase):\\n purchase = get_object_or_404(Purchase, pk=id_purchase)\\n purchase.delete()\\n return HttpResponseRedirect('/purchases/page/1/')\\n\\n\\n\\n\\n\\n@login_required()\\ndef acceptance(request, id_purchase):\\n purchase = get_object_or_404(Purchase, pk=id_purchase)\\n product_list = []\\n old_product_list = []\\n try:\\n form_acceptance = AcceptanceForm(request.POST or None, instance=purchase.acceptance)\\n exist = True\\n for product in purchase.productofpurchase_set.all():\\n old_product_list.append(product)\\n except :\\n form_acceptance = AcceptanceForm(request.POST or None)\\n exist = False\\n AcceptanceInlineFormSet = inlineformset_factory(Purchase,ProductOfPurchase, max_num=purchase.productofpurchase_set.all().count(), fields=('acceptance_amount','acceptance_price'),can_delete=False,)\\n form_products = AcceptanceInlineFormSet(request.POST or None, instance=purchase)\\n for i in range(len(form_products)):\\n product_list.append([form_products.get_queryset()[i],form_products[i],\\n form_products.get_queryset()[i].purchase_amount * form_products.get_queryset()[i].purchase_price])\\n if form_acceptance.is_valid() and form_products.is_valid():\\n acceptance = form_acceptance.save(commit=False)\\n if not exist:\\n acceptance.created = datetime.date.today()\\n acceptance.author = request.user\\n acceptance.modified_date = datetime.date.today()\\n acceptance.modified_author = request.user\\n acceptance.purchase = purchase\\n acceptance.save()\\n form_products.save()\\n\\n if exist:\\n for old in old_product_list:\\n try:\\n prod_war = ProductAtWarehouse.objects.get(product=old.product, warehouse=old.purchase.warehouse, price=old.acceptance_price)\\n prod_war.amount -= old.acceptance_amount\\n prod_war.date = old.purchase.acceptance.date\\n prod_war.save()\\n if not prod_war.amount:\\n prod_war.delete()\\n except ProductAtWarehouse.DoesNotExist:\\n ProductAtWarehouse.objects.create(product=old.product, warehouse=old.purchase.warehouse, amount=old.acceptance_amount, price=old.acceptance_price,\\n date=old.purchase.acceptance.date)\\n\\n for product in purchase.productofpurchase_set.all():\\n try:\\n prod_war = ProductAtWarehouse.objects.get(product=product.product, warehouse=product.purchase.warehouse, price=product.acceptance_price)\\n prod_war.amount += product.acceptance_amount\\n prod_war.date = product.purchase.acceptance.date\\n prod_war.save()\\n if not prod_war.amount:\\n prod_war.delete()\\n except ProductAtWarehouse.DoesNotExist:\\n ProductAtWarehouse.objects.create(product=product.product, warehouse=product.purchase.warehouse, amount=product.acceptance_amount, price=product.acceptance_price,\\n date=product.purchase.acceptance.date)\\n return HttpResponseRedirect('/acceptances/page/1')\\n return render_to_response('warehouse/acceptance.html',{'form_acceptance' : form_acceptance,\\n 'purchase' : purchase,\\n 'products' : product_list,\\n 'form_products' : form_products\\n },RequestContext(request))\\n\\n\\n@login_required()\\ndef acceptances(request, page_pk):\\n page_pk = int(page_pk)\\n per_page = 10\\n acceptances = Acceptance.objects.all().order_by('date','pk')\\n all = acceptances.count()\\n pages = int(ceil(all/float(per_page)))\\n if page_pk>pages and page_pk!=1:\\n raise Http404()\\n page_list = []\\n for i in range(pages):\\n page_list.append(i+1)\\n prev = 1\\n if page_pk!=1:\\n prev = page_pk -1\\n if page_pk==pages:\\n next = page_pk\\n else:\\n next = page_pk + 1\\n range_date = request.POST.get('report_range', '')\\n if range_date!='':\\n start = range_date[:10]\\n end = range_date[14:]\\n try:\\n acceptances = acceptances.filter(date__gte=start, date__lte=end)\\n except :\\n print 'bad date request'\\n acceptances = acceptances[per_page*page_pk-per_page:per_page*page_pk]\\n return render_to_response('warehouse/acceptances.html',{'acceptances' : acceptances,\\n 'range' : range_date,\\n 'pages' : page_list,\\n 'current' : page_pk,\\n 'prev' : prev,\\n 'next' : next\\n }, RequestContext(request))\\n\\n\\n@login_required()\\ndef return_view(request, id_acceptance):\\n acceptance = get_object_or_404(Acceptance, pk=id_acceptance)\\n old_product_list = []\\n product_list = []\\n failure = ''\\n try:\\n form_return = ReturnForm(request.POST or None, instance=acceptance.returnmodel)\\n exist = True\\n for product in acceptance.purchase.productofpurchase_set.all():\\n old_product_list.append(product)\\n except :\\n form_return = ReturnForm(request.POST or None)\\n exist = False\\n AcceptanceInlineFormSet = inlineformset_factory(Purchase,ProductOfPurchase, max_num=acceptance.purchase.productofpurchase_set.all().count(), fields=('return_amount',),can_delete=False,)\\n form_products = AcceptanceInlineFormSet(request.POST or None, instance=acceptance.purchase)\\n for i in range(len(form_products)):\\n product_list.append([form_products.get_queryset()[i],form_products[i],\\n form_products.get_queryset()[i].purchase_amount * form_products.get_queryset()[i].purchase_price])\\n if form_return.is_valid() and form_products.is_valid():\\n return_m = form_return.save(commit=False)\\n if not exist:\\n return_m.created = datetime.date.today()\\n return_m.author = request.user\\n return_m.modified_date = datetime.date.today()\\n return_m.modified_author = request.user\\n return_m.acceptance = acceptance\\n\\n if exist:\\n for old in old_product_list:\\n try:\\n prod_war = ProductAtWarehouse.objects.get(product=old.product, warehouse=old.purchase.warehouse, price=old.acceptance_price)\\n prod_war.amount += old.return_amount\\n prod_war.save()\\n except ProductAtWarehouse.DoesNotExist:\\n ProductAtWarehouse.objects.create(product=old.product,warehouse=old.purchase.warehouse,amount=old.return_amount,price=old.acceptance_price,date=old.purchase.acceptance.date)\\n\\n for product in form_products.get_queryset():\\n try:\\n prod_war = ProductAtWarehouse.objects.get(product=product.product, warehouse=product.purchase.warehouse, price=product.acceptance_price)\\n if prod_war.amount - product.return_amount < 0:\\n for old in old_product_list:\\n try:\\n prod_war = ProductAtWarehouse.objects.get(product=old.product, warehouse=old.purchase.warehouse, price=old.acceptance_price)\\n prod_war.amount -= old.return_amount\\n prod_war.save()\\n if not prod_war.amount:\\n prod_war.delete()\\n except ProductAtWarehouse.DoesNotExist:\\n print 'DoesNotExist'\\n failure = u'На складе: ' + prod_war.warehouse.name + u' невозможно сделать возврат по товару: ' + prod_war.product.name + u' , так как его осталось ' + str(prod_war.amount)\\n return render_to_response('warehouse/return.html',{'form_return' : form_return,\\n 'acceptance' : acceptance,\\n 'products' : product_list,\\n 'form_products' : form_products,\\n 'failure' : failure\\n },RequestContext(request))\\n except ProductAtWarehouse.DoesNotExist:\\n for old in old_product_list:\\n try:\\n prod_war = ProductAtWarehouse.objects.get(product=old.product, warehouse=old.purchase.warehouse, price=old.acceptance_price)\\n prod_war.amount -= old.return_amount\\n prod_war.save()\\n if not prod_war.amount:\\n prod_war.delete()\\n except ProductAtWarehouse.DoesNotExist:\\n print 'DoesNotExist'\\n failure = u'На складе: ' + product.purchase.warehouse.name + u' невозможно сделать возврат по товару: ' + product.product.name + u' , так как его не осталось '\\n return render_to_response('warehouse/return.html',{'form_return' : form_return,\\n 'acceptance' : acceptance,\\n 'products' : product_list,\\n 'form_products' : form_products,\\n 'failure' : failure\\n },RequestContext(request))\\n return_m.save()\\n form_products.save()\\n\\n for product in acceptance.purchase.productofpurchase_set.all():\\n try:\\n prod_war = ProductAtWarehouse.objects.get(product=product.product, warehouse=product.purchase.warehouse, price=product.acceptance_price)\\n prod_war.amount -= product.return_amount\\n prod_war.save()\\n if not prod_war.amount:\\n prod_war.delete()\\n except ProductAtWarehouse.DoesNotExist:\\n print 'DoesNotExist'\\n return HttpResponseRedirect('/returns/page/1')\\n return render_to_response('warehouse/return.html',{'form_return' : form_return,\\n 'acceptance' : acceptance,\\n 'products' : product_list,\\n 'form_products' : form_products,\\n 'failure' : failure\\n },RequestContext(request))\\n\\n@login_required()\\ndef returns(request, page_pk):\\n page_pk = int(page_pk)\\n per_page = 10\\n returns = ReturnModel.objects.all().order_by('date','pk')\\n all = returns.count()\\n pages = int(ceil(all/float(per_page)))\\n if page_pk>pages and page_pk!=1:\\n raise Http404()\\n page_list = []\\n for i in range(pages):\\n page_list.append(i+1)\\n prev = 1\\n if page_pk!=1:\\n prev = page_pk -1\\n if page_pk==pages:\\n next = page_pk\\n else:\\n next = page_pk + 1\\n range_date = request.POST.get('report_range', '')\\n if range_date!='':\\n start = range_date[:10]\\n end = range_date[14:]\\n try:\\n returns = returns.filter(date__gte=start, date__lte=end)\\n except :\\n print 'bad date request'\\n returns = returns[per_page*page_pk-per_page:per_page*page_pk]\\n return render_to_response('warehouse/returns.html',{'returns' : returns,\\n 'range' : range_date,\\n 'pages' : page_list,\\n 'current' : page_pk,\\n 'prev' : prev,\\n 'next' : next\\n }, RequestContext(request))\\n\\n\\n@login_required()\\ndef writeoff(request, id_writeoff):\\n if not id_writeoff:\\n form_writeoff = WriteOffForm(request.POST or None)\\n form_product = ProductAtWarehouseForm(request.POST or None)\\n form_dish = DishOfWarehouseForm(request.POST or None)\\n else:\\n pass\\n# product_list = []\\n# total = 0\\n# purchase = ''\\n# if not id_purchase:\\n# form_purchase = PurchaseForm(request.POST or None)\\n# form_product = ProductOfPurchaseForm(request.POST or None)\\n# if request.method == 'POST' and form_purchase.is_valid() and form_product.is_valid():\\n# purchase = form_purchase.save(commit=False)\\n# purchase.created = datetime.date.today()\\n# purchase.author = request.user\\n# purchase.modified_date = datetime.date.today()\\n# purchase.modified_author = request.user\\n# purchase.save()\\n# product = form_product.save(commit=False)\\n# product.purchase = purchase\\n# product.save()\\n# return HttpResponseRedirect('/purchase/'+str(purchase.pk))\\n# else:\\n# purchase = get_object_or_404(Purchase, pk=id_purchase)\\n# issued = purchase.issued\\n# products = purchase.productofpurchase_set.all().order_by('pk')\\n# for product in products:\\n# total += product.purchase_amount * product.purchase_price\\n# product_list.append([product, product.purchase_amount * product.purchase_price])\\n# form_purchase = PurchaseForm(request.POST or None, instance=purchase)\\n# form_product = ProductOfPurchaseForm(request.POST or None)\\n# if request.method == 'POST' and form_purchase.is_valid() and form_product.is_valid():\\n# purchase = form_purchase.save(commit=False)\\n# purchase.modified_date = datetime.date.today()\\n# purchase.modified_author = request.user\\n# purchase.issued = issued\\n# purchase.save()\\n# product = form_product.save(commit=False)\\n# product.purchase = purchase\\n# product.save()\\n# return HttpResponseRedirect('/purchase/'+str(purchase.pk))\\n return render_to_response('warehouse/writeoff.html',{'form_writeoff' : form_writeoff,\\n 'form_product' : form_product,\\n 'form_dish' : form_dish,\\n# 'products' : product_list,\\n# 'total' : total,\\n# 'id_purchase' : id_purchase,\\n# 'purchase' : purchase\\n }, RequestContext(request))\\n\"\n },\n {\n \"alpha_fraction\": 0.5814073085784912,\n \"alphanum_fraction\": 0.5921931266784668,\n \"avg_line_length\": 27.217391967773438,\n \"blob_id\": \"4c9556c7b73fc909c95eaed843896a21a5dfaefd\",\n \"content_id\": \"e3f54c3eeb36424e09421664e5f6db7d71ca2ae1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1947,\n \"license_type\": \"no_license\",\n \"max_line_length\": 127,\n \"num_lines\": 69,\n \"path\": \"/warehouse/forms.py\",\n \"repo_name\": \"skirt4aser/Store\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.forms import ModelForm\\nfrom django.forms.widgets import Textarea, Select\\nfrom database.models import Purchase, ProductOfPurchase, Acceptance, ReturnModel, WriteOff, ProductAtWarehouse, DishOfWarehouse\\nfrom django import forms\\n\\n__author__ = 'Ars'\\n\\n\\nclass PurchaseForm(ModelForm):\\n class Meta:\\n model = Purchase\\n exclude = ('author', 'created', 'modified_date', 'modified_author')\\n widgets = {\\n 'comment': Textarea(attrs={'rows': '5', 'cols': '3'})\\n }\\n\\n\\nclass ProductOfPurchaseForm(ModelForm):\\n purchase_amount = forms.DecimalField(max_digits=10, decimal_places=2, min_value=0)\\n\\n class Meta:\\n model = ProductOfPurchase\\n exclude = ('purchase')\\n widgets = {\\n 'product': Select(attrs={'style': 'width:220px;'}),\\n }\\n\\n\\nclass AcceptanceForm(ModelForm):\\n class Meta:\\n model = Acceptance\\n exclude = ('author', 'created', 'modified_date', 'modified_author', 'purchase')\\n widgets = {\\n 'comment': Textarea(attrs={'rows': '5', 'cols': '3'}),\\n }\\n\\n\\nclass ReturnForm(ModelForm):\\n class Meta:\\n model = ReturnModel\\n exclude = ('author', 'created', 'modified_date', 'modified_author', 'acceptance')\\n widgets = {\\n 'comment': Textarea(attrs={'rows': '5', 'cols': '3'}),\\n }\\n\\n\\nclass WriteOffForm(ModelForm):\\n class Meta:\\n model = WriteOff\\n exclude = ('author', 'created', 'modified_date', 'modified_author')\\n widgets = {\\n 'comment': Textarea(attrs={'rows': '5', 'cols': '3'})\\n }\\n\\n\\nclass ProductAtWarehouseForm(ModelForm):\\n class Meta:\\n model = ProductAtWarehouse\\n widgets = {\\n 'product': Select(attrs={'style': 'width:220px;'}),\\n }\\n\\n\\nclass DishOfWarehouseForm(ModelForm):\\n class Meta:\\n model = DishOfWarehouse\\n widgets = {\\n 'dish': Select(attrs={'style': 'width:220px;'}),\\n }\\n\"\n },\n {\n \"alpha_fraction\": 0.44758734107017517,\n \"alphanum_fraction\": 0.4532445967197418,\n \"avg_line_length\": 35.192771911621094,\n \"blob_id\": \"310b0af9f7835739a07a9bd7213eaf0bf2b36e60\",\n \"content_id\": \"838fe81da6ac01858767f7231dba6259a3a06b86\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"JavaScript\",\n \"length_bytes\": 3097,\n \"license_type\": \"no_license\",\n \"max_line_length\": 153,\n \"num_lines\": 83,\n \"path\": \"/media/store/js/acceptance.js\",\n \"repo_name\": \"skirt4aser/Store\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/**\\n * Created with PyCharm.\\n * User: Ars\\n * Date: 02.04.13\\n * Time: 1:30\\n * To change this template use File | Settings | File Templates.\\n */\\n\\n(function($) {\\n\\n var len;\\n var issued;\\n\\n var methods = {\\n init : function(options){\\n len = options.product_len;\\n issued = options.issued;\\n for (var i=0;i<len;i++){\\n var total = $('#id_productofpurchase_set-'+i+'-acceptance_amount').val() * $('#id_productofpurchase_set-'+i+'-acceptance_price').val();\\n $('#total_product-'+i).text(total);\\n }\\n calc();\\n $('input:not(#id_costs,#id_cash)').on('focusout',function(){\\n var pk = this.id;\\n pk = pk.substring(25, pk.indexOf('-acceptance'));\\n var total = $('#id_productofpurchase_set-'+pk+'-acceptance_amount').val() * $('#id_productofpurchase_set-'+pk+'-acceptance_price').val();\\n $('#total_product-'+pk).text(total);\\n\\n calc();\\n });\\n $('#id_costs, #id_cash').on('focusout',function(){\\n calc();\\n });\\n $('#create').on('click',function(){\\n var info = false;\\n $('tbody input').each(function(){\\n if ($(this).val()==''){\\n info = true;\\n }\\n });\\n var error = '';\\n for (var i=0;i<len;i++){\\n if (parseInt($('#purchase_amount_'+i).text()) < $('#id_productofpurchase_set-'+i+'-acceptance_amount').val()){\\n error = 'Приемка(количество) не может быть больше закупа(количество)';\\n }\\n }\\n if (info==true){\\n alert('Заполните все поля в таблице');\\n }else if(error!=''){\\n alert(error);\\n }else{\\n $('#create_submit').click();\\n }\\n });\\n }\\n };\\n\\n function calc(){\\n var main_total = 0;\\n var main_total_purchase = 0;\\n for (var i=0;i<len;i++){\\n main_total += parseInt($('#total_product-'+i).text());\\n main_total_purchase += parseInt($('#purchase_sum_'+i).text());\\n }\\n// var balance = main_total_purchase - main_total - $('#id_costs').val();\\n var balance = parseInt(issued) - main_total - $('#id_costs').val();\\n var debt = $('#id_cash').val()-balance;\\n $('#total').text(main_total);\\n $('#balance').text(balance);\\n $('#debt').text(debt);\\n }\\n\\n $.fn.acceptance = function( method ) {\\n if ( methods[method] ) {\\n return methods[method].apply( this, Array.prototype.slice.call( arguments, 1 ));\\n } else if ( typeof method === 'object' || ! method ) {\\n return methods.init.apply( this, arguments );\\n } else {\\n $.error( 'Метод ' + method + ' не существует в acceptance.js' );\\n }\\n };\\n\\n}(jQuery));\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7195122241973877,\n \"alphanum_fraction\": 0.7195122241973877,\n \"avg_line_length\": 24.230770111083984,\n \"blob_id\": \"545237a4b8be3389be0a1bbc37717840c055ba5e\",\n \"content_id\": \"57b36028d51a620d09101eb1795076ed7d8a2760\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 328,\n \"license_type\": \"no_license\",\n \"max_line_length\": 56,\n \"num_lines\": 13,\n \"path\": \"/catalog/forms.py\",\n \"repo_name\": \"skirt4aser/Store\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django import forms\\nfrom database.models import Supplier\\n\\nfrom bootstrap_toolkit.widgets import BootstrapTextInput\\n#BootstrapUneditableInput, BootstrapDateInput\\n\\n\\nclass SupplierForm(forms.ModelForm):\\n class Meta:\\n model = Supplier\\n widgets = {\\n 'name': BootstrapTextInput(prepend='P')\\n }\\n\"\n },\n {\n \"alpha_fraction\": 0.6490975022315979,\n \"alphanum_fraction\": 0.6541516184806824,\n \"avg_line_length\": 28.46808433532715,\n \"blob_id\": \"eb1c701fc37c6cf3a66a59b76ac299cfadec879d\",\n \"content_id\": \"882961789b4cded01238ed990805dae1a86c605a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1385,\n \"license_type\": \"no_license\",\n \"max_line_length\": 122,\n \"num_lines\": 47,\n \"path\": \"/catalog/ajax.py\",\n \"repo_name\": \"skirt4aser/Store\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.http import HttpResponse\\nfrom django.shortcuts import render_to_response, get_object_or_404\\nfrom django.template.context import RequestContext\\nfrom database.models import Supplier\\nfrom catalog.forms import SupplierForm\\n#from django.utils import simplejson\\n\\n\\ndef supplier(request):\\n print('SUPPLIER GET')\\n # returns populated form, if id is present,\\n # blank form otherwise\\n try:\\n id = request.POST['id']\\n except:\\n print ('ERROR: id is not found...')\\n else:\\n print ('got id', id)\\n obj = get_object_or_404(Supplier, pk=id)\\n form = SupplierForm(instance=obj)\\n return render_to_response('catalog/modal_supplier.html', {'form': form}, context_instance=RequestContext(request))\\n\\n form = SupplierForm()\\n return render_to_response('catalog/modal_supplier.html', {'form': form}, context_instance=RequestContext(request))\\n\\n\\ndef supplier_save(request):\\n print('SUPPLIER SAVE')\\n print(request)\\n return HttpResponse('{success:true}')\\n\\n\\ndef supplier_delete(request):\\n print('SUPPLIER DELETE')\\n\\n\\n# def ajax_test(request):\\n# context = {}\\n# try:\\n# data = request.POST['text'].strip()\\n# except:\\n# context['text'] = 'error'\\n# print (context['text'])\\n# else:\\n# context['text'] = data[::-1]\\n# print (context['text'])\\n# return HttpResponse(context['text'])\\n\"\n },\n {\n \"alpha_fraction\": 0.42101043462753296,\n \"alphanum_fraction\": 0.42782679200172424,\n \"avg_line_length\": 33.15068435668945,\n \"blob_id\": \"4bba6ec039d731734648bca5e081294ebb7c05f1\",\n \"content_id\": \"f13a61e896b07051b5ea1f6bd8daa88db59c6787\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"JavaScript\",\n \"length_bytes\": 2587,\n \"license_type\": \"no_license\",\n \"max_line_length\": 132,\n \"num_lines\": 73,\n \"path\": \"/media/store/js/return.js\",\n \"repo_name\": \"skirt4aser/Store\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/**\\n * Created with PyCharm.\\n * User: Ars\\n * Date: 03.04.13\\n * Time: 23:29\\n * To change this template use File | Settings | File Templates.\\n */\\n\\n(function($) {\\n\\n var len;\\n\\n var methods = {\\n init : function(options){\\n if ($('#failure').text()!=''){\\n alert($('#failure').text());\\n }\\n len = options.product_len;\\n for (var i=0;i<len;i++){\\n var total = $('#id_productofpurchase_set-'+i+'-return_amount').val() * parseInt($('#acceptance_price_'+i).text());\\n $('#total_product-'+i).text(total);\\n }\\n calc();\\n $('table input').on('focusout',function(){\\n var pk = this.id;\\n pk = pk.substring(25, pk.indexOf('-return'));\\n var total = $('#id_productofpurchase_set-'+pk+'-return_amount').val() * parseInt($('#acceptance_price_'+pk).text());\\n $('#total_product-'+pk).text(total);\\n calc();\\n });\\n $('#create').on('click',function(){\\n var info = false;\\n $('tbody input').each(function(){\\n if ($(this).val()==''){\\n info = true;\\n }\\n });\\n var error = '';\\n for (var i=0;i<len;i++){\\n if (parseInt($('#acceptance_amount_'+i).text()) < $('#id_productofpurchase_set-'+i+'-return_amount').val()){\\n error = 'Возврат(количество) не может быть больше приемки(количество)';\\n }\\n }\\n if (info==true){\\n alert('Заполните все поля в таблице');\\n }else if(error!=''){\\n alert(error);\\n }else{\\n $('#create_submit').click();\\n }\\n });\\n }\\n };\\n\\n function calc(){\\n var main_total = 0;\\n for (var i=0;i<len;i++){\\n main_total += parseInt($('#total_product-'+i).text());\\n }\\n $('#total').text(main_total);\\n }\\n\\n $.fn.return_ = function( method ) {\\n if ( methods[method] ) {\\n return methods[method].apply( this, Array.prototype.slice.call( arguments, 1 ));\\n } else if ( typeof method === 'object' || ! method ) {\\n return methods.init.apply( this, arguments );\\n } else {\\n $.error( 'Метод ' + method + ' не существует в return.js' );\\n }\\n };\\n\\n}(jQuery));\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6128364205360413,\n \"alphanum_fraction\": 0.6197377443313599,\n \"avg_line_length\": 38.18918991088867,\n \"blob_id\": \"65108edcf59a52b49aa9f2d158d78079e808db24\",\n \"content_id\": \"98b4abcf08a9c455a547375568779c1b78cd7743\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1473,\n \"license_type\": \"no_license\",\n \"max_line_length\": 86,\n \"num_lines\": 37,\n \"path\": \"/warehouse/ajax.py\",\n \"repo_name\": \"skirt4aser/Store\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\nimport datetime\\nfrom django.http import HttpResponseForbidden, HttpResponse\\nfrom django.shortcuts import get_object_or_404\\nfrom database.models import Purchase, Product\\n\\n__author__ = 'Ars'\\n\\ndef purchase_save(request):\\n if request.is_ajax():\\n purchase = get_object_or_404(Purchase, pk=request.POST.get('id_purchase', ''))\\n text = 'ok'\\n try:\\n purchase.supplier_id = request.POST.get('id_supplier', '')\\n purchase.purchaser_id = request.POST.get('id_purchaser', '')\\n purchase.warehouse_id = request.POST.get('id_warehouse', '')\\n purchase.date_purchase = request.POST.get('date_purchase', '')\\n purchase.comment = request.POST.get('comment', '')\\n issued = request.POST.get('issued')\\n if issued=='':\\n issued = None\\n purchase.issued = issued\\n purchase.modified_date = datetime.date.today()\\n purchase.modified_author = request.user\\n purchase.save()\\n except :\\n text = u'Неправильно заполнены поля'\\n return HttpResponse(text,mimetype=\\\"text/html\\\")\\n else:\\n return HttpResponseForbidden()\\n\\ndef get_product_price(request):\\n if request.is_ajax():\\n product = get_object_or_404(Product, pk=request.POST.get('id_product', ''))\\n return HttpResponse(product.price,mimetype=\\\"text/html\\\")\\n else:\\n return HttpResponseForbidden()\"\n },\n {\n \"alpha_fraction\": 0.8863636255264282,\n \"alphanum_fraction\": 0.8863636255264282,\n \"avg_line_length\": 14,\n \"blob_id\": \"8a00ce10ba75f07149343782af1ffe06314333b0\",\n \"content_id\": \"372b157fe3369157b761c482ace686853155df61\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 44,\n \"license_type\": \"no_license\",\n \"max_line_length\": 24,\n \"num_lines\": 3,\n \"path\": \"/requirements.txt\",\n \"repo_name\": \"skirt4aser/Store\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"Django\\ndjango-bootstrap-toolkit\\nmysql-python\"\n },\n {\n \"alpha_fraction\": 0.66107577085495,\n \"alphanum_fraction\": 0.662232518196106,\n \"avg_line_length\": 41.17073059082031,\n \"blob_id\": \"2689bea5e081fa082e82b68262dc62d7128fe3d5\",\n \"content_id\": \"0ac6a95858533c67b70619c1b3bc4ae8983d7dac\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1729,\n \"license_type\": \"no_license\",\n \"max_line_length\": 164,\n \"num_lines\": 41,\n \"path\": \"/Store/urls.py\",\n \"repo_name\": \"skirt4aser/Store\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.conf.urls import patterns, include, url\\nfrom django.contrib import admin\\nfrom django.conf import settings\\n\\nfrom warehouse.views import purchases, purchase, purchase_product_delete, purchase_product, purchase_delete, acceptance, acceptances, return_view, returns, writeoff\\nfrom catalog.views import suppliers\\nfrom catalog.ajax import supplier, supplier_save, supplier_delete\\n\\nfrom warehouse.ajax import purchase_save, get_product_price\\n\\nadmin.autodiscover()\\n\\nurlpatterns = patterns('',\\n url(r'^admin/', include(admin.site.urls)),\\n\\n (r'^media/(?P<path>.*)$', 'django.views.static.serve', {'document_root': settings.MEDIA_ROOT}),\\n (r'^static/(?P<path>.*)$', 'django.views.static.serve', {'document_root': settings.STATIC_ROOT}),\\n\\n (r'^purchases/page/(?P<page_pk>\\\\d+)/$', purchases),\\n (r'^purchase/$', purchase, {'id_purchase': 0}),\\n (r'^purchase/(?P<id_purchase>\\\\d+)/$', purchase),\\n (r'^purchase/(?P<id_purchase>\\\\d+)/product/(?P<id_product>\\\\d+)/$', purchase_product),\\n (r'^purchase/product/(?P<id_product>\\\\d+)/delete/$', purchase_product_delete),\\n (r'^purchase/(?P<id_purchase>\\\\d+)/delete/$', purchase_delete),\\n\\n (r'^ajax/purchase/save/$', purchase_save),\\n (r'^ajax/purchase/get_product_price/$', get_product_price),\\n\\n (r'^purchase/(?P<id_purchase>\\\\d+)/acceptance/$', acceptance),\\n (r'^acceptances/page/(?P<page_pk>\\\\d+)/$', acceptances),\\n\\n (r'^acceptance/(?P<id_acceptance>\\\\d+)/return/$', return_view),\\n (r'^returns/page/(?P<page_pk>\\\\d+)/$', returns),\\n\\n (r'^writeoff/$', writeoff, {'id_writeoff': 0}),\\n\\n (r'^suppliers/$', suppliers),\\n (r'^ajax/supplier/$', supplier),\\n (r'^ajax/supplier/save$', supplier_save),\\n (r'^ajax/supplier/delete$', supplier_delete),\\n)\\n\"\n },\n {\n \"alpha_fraction\": 0.6819400787353516,\n \"alphanum_fraction\": 0.6875419020652771,\n \"avg_line_length\": 41.281375885009766,\n \"blob_id\": \"6fd748a036ac368feba3ab5f5bd7e697c724a43a\",\n \"content_id\": \"ddaf70119222d81e385fc02754effe1e84ac665d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 23073,\n \"license_type\": \"no_license\",\n \"max_line_length\": 166,\n \"num_lines\": 494,\n \"path\": \"/database/models.py\",\n \"repo_name\": \"skirt4aser/Store\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\nfrom django.contrib.auth.models import User\\nfrom django.db import models\\nfrom django.db.models.signals import post_save\\nfrom django.dispatch import receiver\\nimport datetime\\n\\n\\nclass Supplier(models.Model):\\n name = models.CharField(max_length=255, verbose_name='Наименование')\\n address = models.CharField(max_length=255, verbose_name='Адрес', blank=True)\\n phone = models.CharField(max_length=255, verbose_name='Телефон', blank=True)\\n contact = models.CharField(max_length=255, verbose_name='Контактное лицо, ФИО', blank=True)\\n contact_phone = models.CharField(max_length=255, verbose_name='Контактное лицо, телефон', blank=True)\\n comment = models.TextField(verbose_name='Комментарий', blank=True)\\n contact_comment = models.TextField(verbose_name='Контактное лицо, комментарий', blank=True)\\n\\n def __unicode__(self):\\n return self.name\\n\\n class Meta:\\n db_table = 'store_supplier'\\n ordering = ['name']\\n verbose_name_plural = u'Поставщики'\\n verbose_name = u'Поставщик'\\n\\nclass CategoryOfProduct(models.Model):\\n name = models.CharField(max_length=255, verbose_name='Название')\\n parent = models.ForeignKey('self', verbose_name='Родитель', blank=True, null=True)\\n\\n def __unicode__(self):\\n return '%s' % self.name\\n\\n class Meta:\\n db_table = 'store_product_categories'\\n ordering = ['name']\\n verbose_name_plural = u'Категории товара'\\n verbose_name = u'Категория товара'\\n\\nclass Product(models.Model):\\n name = models.CharField(max_length=255, verbose_name='Название')\\n category = models.ForeignKey(CategoryOfProduct, verbose_name='Категория')\\n measure_unit = models.CharField(max_length=255, verbose_name='Единица измерения')\\n price = models.DecimalField(max_digits=10, decimal_places=2, verbose_name='Закупочная цена')\\n supplier = models.ForeignKey(Supplier, blank=True, null=True, verbose_name='Поставщик')\\n description = models.TextField(verbose_name='Описание', blank=True)\\n\\n def __unicode__(self):\\n return '%s' % self.name\\n\\n class Meta:\\n db_table = 'store_products'\\n ordering = ['name']\\n verbose_name_plural = u'Товары'\\n verbose_name = u'Товар'\\n\\nclass Warehouse(models.Model):\\n name = models.CharField(max_length=255, verbose_name='Название')\\n address = models.CharField(max_length=255, verbose_name='Адрес', blank=True)\\n comment = models.TextField(verbose_name='Комментарий', blank=True)\\n\\n def __unicode__(self):\\n return '%s' % self.name\\n\\n class Meta:\\n db_table = 'store_warehouses'\\n ordering = ['name']\\n verbose_name_plural = u'Склады'\\n verbose_name = u'Склад'\\n\\nclass CategoryOfDish(models.Model):\\n name = models.CharField(max_length=255, verbose_name='Название')\\n parent = models.ForeignKey('self', verbose_name='Категория')\\n archive = models.BooleanField(verbose_name='Признак архива')\\n\\n def __unicode__(self):\\n return '%s' % self.name\\n\\n class Meta:\\n db_table = 'store_dish_categories'\\n ordering = ['name']\\n verbose_name_plural = u'Категории калькуляционных карт'\\n verbose_name = u'Категория калькуляционных карт'\\n\\nclass Dish(models.Model):\\n name = models.CharField(max_length=255, verbose_name='Название')\\n category = models.ForeignKey(CategoryOfDish, verbose_name='Категория')\\n date_start = models.DateField(verbose_name='Действует с')\\n date_end = models.DateField(verbose_name='Действует по', blank=True, null=True)\\n portion = models.DecimalField(max_digits=10, decimal_places=2, verbose_name='Порция')\\n master = models.BooleanField(verbose_name='выдан мастером или нет')\\n created = models.DateTimeField(verbose_name='Дата создания')\\n author = models.ForeignKey(User, verbose_name='Автор', related_name='dish_author')\\n modified_date = models.DateTimeField(verbose_name='Дата изменения')\\n modified_author = models.ForeignKey(User, verbose_name='Последний сотрудник внесший изменения ', related_name='dish_modified_author')\\n comment = models.TextField(verbose_name='Комментарий', blank=True)\\n\\n\\n def __unicode__(self):\\n return '%s' % self.name\\n\\n class Meta:\\n db_table = 'store_dishes'\\n ordering = ['name']\\n verbose_name_plural = u'Калькуляционные карты'\\n verbose_name = u'Калькуляционная карта'\\n\\nclass ProductOfDish(models.Model):\\n dish = models.ForeignKey(Dish, verbose_name='КК')\\n product = models.ForeignKey(Product, verbose_name='Товар')\\n measure_unit = models.CharField(max_length=255, verbose_name='Единица измерения')\\n\\n def __unicode__(self):\\n return '%s' % self.product.name\\n\\n class Meta:\\n db_table = 'store_dish_products'\\n ordering = ['product']\\n verbose_name_plural = u'Товары КК'\\n verbose_name = u'Товар КК'\\n\\n\\nclass SalePoint(models.Model):\\n name = models.CharField(max_length=255, verbose_name='Название')\\n warehouse = models.ForeignKey(Warehouse, verbose_name='Склад')\\n address = models.CharField(max_length=255, verbose_name='Адрес', blank=True)\\n comment = models.TextField(verbose_name='Комментарий', blank=True)\\n created = models.DateField(verbose_name='Дата создания')\\n author = models.ForeignKey(User, verbose_name='Автор', related_name='point_author')\\n modified_date = models.DateField(verbose_name='Дата изменения')\\n modified_author = models.ForeignKey(User, verbose_name='Автор изменений', related_name='point_modified_author')\\n\\n def __unicode__(self):\\n return self.name\\n\\n class Meta:\\n db_table = 'sale_salepoints'\\n ordering = ['name']\\n verbose_name_plural = u'Точки продаж'\\n verbose_name = u'Точка продаж'\\n\\nclass Change(models.Model):\\n point = models.ForeignKey(SalePoint, verbose_name='Точка продаж')\\n cashier = models.ForeignKey(User, verbose_name='Кассир')\\n start = models.DateField(verbose_name='Дата открытия')\\n end = models.DateField(verbose_name='Дата закрытия', blank=True, null=True)\\n\\n def __unicode__(self):\\n return self.point\\n\\n class Meta:\\n db_table = 'sale_changes'\\n ordering = ['point']\\n verbose_name_plural = u'Смены'\\n verbose_name = u'Смена'\\n\\nclass Sale(models.Model):\\n point = models.ForeignKey(SalePoint, verbose_name='Точка продаж')\\n cashier = models.ForeignKey(User, verbose_name='Кассир')\\n change = models.ForeignKey(Change, verbose_name='Смена')\\n discount = models.PositiveIntegerField(verbose_name='Скидка', blank=True, null=True)\\n\\n def __unicode__(self):\\n return self.point\\n\\n class Meta:\\n db_table = 'sale_sales'\\n ordering = ['point']\\n verbose_name_plural = u'Смены'\\n verbose_name = u'Смена'\\n\\nclass DishOfSale(models.Model):\\n sale = models.ForeignKey(Sale, verbose_name='Продажи')\\n dish = models.ForeignKey(Dish, verbose_name='Блюда')\\n amount = models.PositiveIntegerField(verbose_name='Количество')\\n price = models.PositiveIntegerField(verbose_name='Цена')\\n\\n def __unicode__(self):\\n return self.dish\\n\\n class Meta:\\n db_table = 'sale_sale_dishes'\\n ordering = ['dish']\\n verbose_name_plural = u'Блюда для продажи'\\n verbose_name = u'Блюдо для продажи'\\n\\nclass Purchase(models.Model):\\n purchaser = models.ForeignKey(User, verbose_name='Закупщик', related_name='purchaser')\\n supplier = models.ForeignKey(Supplier, verbose_name='Контрагент', blank=True, null=True)\\n warehouse = models.ForeignKey(Warehouse, verbose_name='Склад')\\n date_purchase = models.DateField(verbose_name='Дата закупа')\\n comment = models.TextField(verbose_name='Комментарий', blank=True)\\n created = models.DateTimeField(verbose_name='Время создания')\\n issued = models.DecimalField(max_digits=10, decimal_places=2, verbose_name='Выдано', blank=True, null=True)\\n author = models.ForeignKey(User, verbose_name='Автор', related_name='purchase_author')\\n modified_date = models.DateTimeField(verbose_name='Дата изменения')\\n modified_author = models.ForeignKey(User, verbose_name='Автор изменения', related_name='purchase_modified_author')\\n\\n def __unicode__(self):\\n return '#%s - %s' % (str(self.pk),str(self.date_purchase))\\n\\n class Meta:\\n db_table = 'wr_purchases'\\n ordering = ['pk']\\n verbose_name_plural = u'Закупы'\\n verbose_name = u'Закуп'\\n\\nclass ProductOfPurchase(models.Model):\\n purchase = models.ForeignKey(Purchase, verbose_name='Закуп')\\n product = models.ForeignKey(Product, verbose_name='Товары')\\n purchase_amount = models.DecimalField(max_digits=10, decimal_places=2, verbose_name='Количество, закуп')\\n acceptance_amount = models.DecimalField(max_digits=10, decimal_places=2,verbose_name='Количество, приемка', blank=True, null=True)\\n return_amount = models.DecimalField(max_digits=10, decimal_places=2,verbose_name='Количество, возврат', blank=True, null=True)\\n purchase_price = models.DecimalField(max_digits=10, decimal_places=2,verbose_name='Цена закупа')\\n acceptance_price = models.DecimalField(max_digits=10, decimal_places=2,verbose_name='Цена закупа', blank=True, null=True)\\n return_price = models.DecimalField(max_digits=10, decimal_places=2, verbose_name='Цена закупа', blank=True, null=True)\\n\\n def __unicode__(self):\\n return '%s' % self.product.name\\n\\n class Meta:\\n db_table = 'wr_purchase_products'\\n ordering = ['product']\\n verbose_name_plural = u'Товары закупа'\\n verbose_name = u'Товар закупа'\\n\\nclass Acceptance(models.Model):\\n purchase = models.OneToOneField(Purchase, verbose_name='Закуп')\\n date = models.DateField(verbose_name='Дата приемки')\\n costs = models.DecimalField(max_digits=10, decimal_places=2,verbose_name='Расходы', blank=True, null=True)\\n cash = models.DecimalField(max_digits=10, decimal_places=2,verbose_name='В кассу')\\n comment = models.TextField(verbose_name='Комментарий', blank=True)\\n created = models.DateTimeField(verbose_name='Время создания')\\n author = models.ForeignKey(User, verbose_name='Автор', related_name='acceptance_author')\\n modified_date = models.DateTimeField(verbose_name='Дата изменения')\\n modified_author = models.ForeignKey(User, verbose_name='Автор изменения', related_name='acceptance_modified_author')\\n\\n def sum(self):\\n products = ProductOfPurchase.objects.filter(purchase=self.purchase)\\n sum = 0\\n for product in products:\\n sum += product.acceptance_price * product.acceptance_amount\\n return sum\\n\\n def debt(self):\\n debt = self.cash -(self.purchase.issued-self.sum()-self.costs)\\n return debt\\n\\n def __unicode__(self):\\n return '#%s - %s' % (str(self.pk),str(self.date))\\n\\n class Meta:\\n db_table = 'wr_acceptances'\\n ordering = ['pk']\\n verbose_name_plural = u'Приемки'\\n verbose_name = u'Приемка'\\n\\nclass ReturnModel(models.Model):\\n acceptance = models.OneToOneField(Acceptance, verbose_name='Приемка')\\n date = models.DateField(verbose_name='Дата возврата', blank=True, null=True)\\n comment = models.TextField(verbose_name='Комментарий', blank=True)\\n created = models.DateTimeField(verbose_name='Время создания')\\n author = models.ForeignKey(User, verbose_name='Автор', related_name='return_author')\\n modified_date = models.DateTimeField(verbose_name='Дата изменения')\\n modified_author = models.ForeignKey(User, verbose_name='Автор изменения', related_name='return_modified_author')\\n\\n def sum(self):\\n products = ProductOfPurchase.objects.filter(purchase=self.acceptance.purchase)\\n sum = 0\\n for product in products:\\n sum += (product.acceptance_price * product.return_amount)\\n return sum\\n\\n def __unicode__(self):\\n return '#%s - %s' % (str(self.pk),str(self.date))\\n\\n class Meta:\\n db_table = 'wr_returns'\\n ordering = ['pk']\\n verbose_name_plural = u'Возвраты'\\n verbose_name = u'Возврат'\\n\\nclass WriteOff(models.Model):\\n warehouse = models.ForeignKey(Warehouse, verbose_name='Склад')\\n date = models.DateField(verbose_name='Дата')\\n comment = models.TextField(verbose_name='Комментарий', blank=True)\\n reason = models.CharField(max_length=100, verbose_name='Причина', choices=((u'Обеды сотрудников',u'Обеды сотрудников'),(u'Порча',u'Порча'),(u'Прочее',u'Прочее')))\\n created = models.DateTimeField(verbose_name='Время создания')\\n author = models.ForeignKey(User, verbose_name='Автор', related_name='writeoff_author')\\n modified_date = models.DateTimeField(verbose_name='Дата изменения')\\n modified_author = models.ForeignKey(User, verbose_name='Автор изменения', related_name='writeoff_modified_author')\\n\\n def __unicode__(self):\\n return '#%s - %s' % (str(self.pk),str(self.date))\\n\\n class Meta:\\n db_table = 'wr_writeoffs'\\n ordering = ['pk']\\n verbose_name_plural = u'Списания'\\n verbose_name = u'Списание'\\n\\nclass ProductOfWriteOff(models.Model):\\n writeoff = models.ForeignKey(WriteOff, verbose_name='Списание')\\n product = models.ForeignKey(Product, verbose_name='Товары', blank=True, null=True)\\n dish = models.ForeignKey(Dish, verbose_name='Товары', blank=True, null=True)\\n amount = models.DecimalField(max_digits=10, decimal_places=2,verbose_name='Количество')\\n price = models.DecimalField(max_digits=10, decimal_places=2,verbose_name='Цена')\\n\\n def __unicode__(self):\\n return '%s' % self.product.name\\n\\n class Meta:\\n db_table = 'wr_writeoff_products'\\n ordering = ['product']\\n verbose_name_plural = u'Товары списания'\\n verbose_name = u'Товар списанияы'\\n\\nclass Inventory(models.Model):\\n warehouse = models.ForeignKey(Warehouse, verbose_name='Склад')\\n date = models.DateField(verbose_name='Дата')\\n comment = models.TextField(verbose_name='Комментарий', blank=True)\\n created = models.DateTimeField(verbose_name='Время создания')\\n author = models.ForeignKey(User, verbose_name='Автор', related_name='inventory_author')\\n modified_date = models.DateTimeField(verbose_name='Дата изменения')\\n modified_author = models.ForeignKey(User, verbose_name='Автор изменения', related_name='inventory_modified_author')\\n\\n def __unicode__(self):\\n return self.pk\\n\\n class Meta:\\n db_table = 'wr_inventories'\\n ordering = ['pk']\\n verbose_name_plural = u'Инвентаризация'\\n verbose_name = u'Инвентаризация'\\n\\nclass ProductOfInventory(models.Model):\\n inventory = models.ForeignKey(Inventory, verbose_name='Инвентаризация')\\n product = models.ForeignKey(Product, verbose_name='Товары')\\n estimated_balance = models.PositiveIntegerField(verbose_name='Расчетный остаток')\\n actual_balance = models.PositiveIntegerField(verbose_name='Фактический остаток')\\n price = models.PositiveIntegerField(verbose_name='Цена')\\n\\n def __unicode__(self):\\n return self.product\\n\\n class Meta:\\n db_table = 'wr_inventory_products'\\n ordering = ['product']\\n verbose_name_plural = u'Товары инвентаризации'\\n verbose_name = u'Товар инвентаризации'\\n\\nclass Movement(models.Model):\\n warehouse_from = models.ForeignKey(Warehouse, verbose_name='Склад откуда', related_name='warehouse_from')\\n warehouse_to = models.ForeignKey(Warehouse, verbose_name='Склад куда', related_name='warehouse_to')\\n date = models.DateField(verbose_name='Дата')\\n comment = models.TextField(verbose_name='Комментарий', blank=True)\\n created = models.DateTimeField(verbose_name='Время создания')\\n author = models.ForeignKey(User, verbose_name='Автор', related_name='movement_author')\\n modified_date = models.DateTimeField(verbose_name='Дата изменения')\\n modified_author = models.ForeignKey(User, verbose_name='Автор изменения', related_name='movement_modified_author')\\n\\n def __unicode__(self):\\n return self.pk\\n\\n class Meta:\\n db_table = 'wr_movements'\\n ordering = ['pk']\\n verbose_name_plural = u'Перемещения'\\n verbose_name = u'Перемещение'\\n\\nclass ProductOfMovement(models.Model):\\n movement = models.ForeignKey(Movement, verbose_name='Перемещение')\\n product = models.ForeignKey(Product, verbose_name='Товары', blank=True, null=True)\\n dish = models.ForeignKey(Dish, verbose_name='Блюдо', blank=True, null=True)\\n amount = models.PositiveIntegerField(verbose_name='Количество')\\n price = models.PositiveIntegerField(verbose_name='Цена')\\n\\n def __unicode__(self):\\n return self.product\\n\\n class Meta:\\n db_table = 'wr_movement_products'\\n ordering = ['product']\\n verbose_name_plural = u'Товары перемещения'\\n verbose_name = u'Товар перемещения'\\n\\nclass Issuance(models.Model):\\n warehouse = models.ForeignKey(Warehouse, verbose_name='Склад')\\n master = models.ForeignKey(User, verbose_name='Мастер', related_name='master')\\n date = models.DateField(verbose_name='Дата')\\n comment = models.TextField(verbose_name='Комментарий', blank=True)\\n created = models.DateTimeField(verbose_name='Время создания')\\n author = models.ForeignKey(User, verbose_name='Автор', related_name='issuance_author')\\n modified_date = models.DateTimeField(verbose_name='Дата изменения')\\n modified_author = models.ForeignKey(User, verbose_name='Автор изменения', related_name='issuance_modified_author')\\n\\n def __unicode__(self):\\n return self.pk\\n\\n class Meta:\\n db_table = 'wr_issuances'\\n ordering = ['pk']\\n verbose_name_plural = u'Выдачи'\\n verbose_name = u'Выдача'\\n\\nclass DishOfIssuance(models.Model):\\n issuance = models.ForeignKey(Issuance, verbose_name='Выдача')\\n dish = models.ForeignKey(Dish, verbose_name='Блюдо')\\n amount = models.PositiveIntegerField(verbose_name='Количество')\\n price = models.PositiveIntegerField(verbose_name='Цена')\\n\\n def __unicode__(self):\\n return self.dish\\n\\n class Meta:\\n db_table = 'wr_issuance_dishes'\\n ordering = ['dish']\\n verbose_name_plural = u'Блюда выдачи'\\n verbose_name = u'Блюдо выдачи'\\n\\nclass DishOfWarehouse(models.Model):\\n warehouse = models.ForeignKey(Warehouse, verbose_name='Склад')\\n dish = models.ForeignKey(Dish, verbose_name='Блюдо')\\n amount = models.PositiveIntegerField(verbose_name='Количество')\\n price = models.PositiveIntegerField(verbose_name='Цена')\\n date = models.DateField(verbose_name='Дата')\\n\\n def __unicode__(self):\\n return '%s' % self.dish.name\\n\\n class Meta:\\n db_table = 'store_warehouse_dishes'\\n ordering = ['dish']\\n verbose_name_plural = u'Блюда на складе'\\n verbose_name = u'Блюдо на складе'\\n\\nclass Costs(models.Model):\\n date = models.DateField(verbose_name='Дата')\\n sum = models.PositiveIntegerField(verbose_name='Сумма')\\n type = models.CharField(max_length=255, verbose_name='Тип расхода')\\n comment = models.TextField(verbose_name='Комментарий', blank=True)\\n\\n def __unicode__(self):\\n return self.pk\\n\\n class Meta:\\n db_table = 'cst_costs'\\n ordering = ['pk']\\n verbose_name_plural = u'Расходы'\\n verbose_name = u'Расход'\\n\\n\\nclass ProductAtWarehouse(models.Model):\\n product = models.ForeignKey(Product, verbose_name='Товар')\\n warehouse = models.ForeignKey(Warehouse, verbose_name='Склад')\\n acceptance = models.ForeignKey(Acceptance, verbose_name='Приемка')\\n amount = models.DecimalField(max_digits=10, decimal_places=2, verbose_name='Количество')\\n price = models.PositiveIntegerField(verbose_name='Цена приемки')\\n\\n def __unicode__(self):\\n return '%s' % self.product.name\\n\\n class Meta:\\n db_table = 'store_warehouse_products'\\n ordering = ['product']\\n verbose_name_plural = u'Товары на складе'\\n verbose_name = u'Товар на складе'\\n\\n\\nclass UserProfile(models.Model):\\n user = models.OneToOneField(User, unique=True, verbose_name='Пользователь')\\n patronymic = models.CharField(max_length=255, verbose_name='Отчество', blank=True)\\n phone = models.CharField(max_length=255, verbose_name='Телефон', blank=True)\\n position = models.CharField(max_length=255, blank=True, verbose_name='Должность')\\n point = models.ForeignKey(SalePoint, verbose_name='Точка продаж', blank=True, null=True)\\n active_from = models.DateField(verbose_name='Активен с')\\n active_until = models.DateField(verbose_name='Активен до', blank=True, null=True)\\n type = models.CharField(max_length=255, verbose_name='Тип оплаты')\\n\\n def __unicode__(self):\\n return self.user\\n\\n class Meta:\\n db_table = 'auth_user_profile'\\n ordering = ['pk']\\n verbose_name_plural = u'Профили'\\n verbose_name = u'Профиль'\\n\\n@receiver(post_save, sender=User, dispatch_uid=\\\"gV3AQD1y3H6laRX5mx94KDWeqYrL9c0V\\\")\\ndef create_profile(sender, instance, created, **kwargs):\\n if created:\\n UserProfile.objects.get_or_create(user=instance, active_from=datetime.date.today())\\n\\n# еще три таблицы по юзеру\"\n },\n {\n \"alpha_fraction\": 0.6290836930274963,\n \"alphanum_fraction\": 0.629482090473175,\n \"avg_line_length\": 40.83333206176758,\n \"blob_id\": \"52488bea7e9252f02ac887291363ac2bbd000fb0\",\n \"content_id\": \"1dc9f1d620a3ecc8b147633e581c5e6d4279b45d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2510,\n \"license_type\": \"no_license\",\n \"max_line_length\": 131,\n \"num_lines\": 60,\n \"path\": \"/catalog/views.py\",\n \"repo_name\": \"skirt4aser/Store\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\nfrom math import *\\nfrom django.http import HttpResponseRedirect\\nfrom django.shortcuts import render_to_response\\nfrom django.template.context import RequestContext\\nfrom django.contrib.auth.decorators import login_required\\n\\nfrom database.models import Supplier\\nfrom catalog.forms import SupplierForm\\n\\n\\n@login_required()\\ndef suppliers(request):\\n suppliers = Supplier.objects.all()\\n return render_to_response('catalog/suppliers.html', {'suppliers': suppliers}, RequestContext(request))\\n\\n# @login_required()\\n# def supplier(request):\\n# # if in a postback\\n# if request.method == 'POST':\\n# # getting form\\n# form = SupplierForm(request.POST)\\n# # validating form elements\\n# if form.is_valid():\\n# # creating and instance of a binded model object\\n# supplier = form.save(commit=False)\\n# # populating with form data\\n# supplier.name = form.cleaned_data['name']\\n# supplier.address = form.cleaned_data['address']\\n# supplier.phone = form.cleaned_data['phone']\\n# supplier.contact = form.cleaned_data['contact']\\n# supplier.contact_phone = form.cleaned_data['contact_phone']\\n# supplier.comment = form.cleaned_data['comment']\\n# supplier.contact_comment = form.cleaned_data['contact_comment']\\n# # saving to db\\n# supplier = form.save()\\n# # redirecting...\\n# return HttpResponseRedirect('catalog/suppliers.html')\\n# else:\\n# form = SupplierForm()\\n\\n# return render_to_response('catalog/supplier.html', {'form': form}, RequestContext(request))\\n\\n\\n# @login_required()\\n# def suppliers(request):\\n# errors = []\\n# if request.method == 'POST':\\n# if 'checkedRow' in request.POST and 'action' in request.POST:\\n# checkedRow = request.POST['checkedRow']\\n# action = request.POST['action']\\n# if not checkedRow or not action:\\n# # TODO: quit\\n# errors.append('Error, some objects missing...')\\n# else:\\n# return render_to_response('catalog/supplier.html', {'action': action, 'id': checkedRow}, RequestContext(request))\\n# else:\\n# suppliers = Supplier.objects.all()\\n# return render_to_response('catalog/suppliers.html', {'suppliers': suppliers}, RequestContext(request))\\n# return render_to_response('catalog/suppliers.html', {'errors': errors}, RequestContext(request))\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":12,"string":"12"}}},{"rowIdx":2979393,"cells":{"repo_name":{"kind":"string","value":"NickMazey/SupplyDemandSimulator"},"repo_url":{"kind":"string","value":"https://github.com/NickMazey/SupplyDemandSimulator"},"snapshot_id":{"kind":"string","value":"f66ec9dfe65647c130e0428f5869ea334310873c"},"revision_id":{"kind":"string","value":"c6c2b330463fea7c0e19dce8c92052f8ff50014a"},"directory_id":{"kind":"string","value":"df1d8440bdd6eb3d3d99e11034ae7fb83fa84651"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"timestamp","value":"2023-06-16T20:47:34.169995","string":"2023-06-16T20:47:34.169995"},"revision_date":{"kind":"timestamp","value":"2021-07-06T05:13:57","string":"2021-07-06T05:13:57"},"committer_date":{"kind":"timestamp","value":"2021-07-06T05:13:57","string":"2021-07-06T05:13:57"},"github_id":{"kind":"number","value":383278249,"string":"383,278,249"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.8397436141967773,"alphanum_fraction":0.8397436141967773,"avg_line_length":37.75,"blob_id":"2cddea3c551aa327a306ce889d86e44aad4ff31f","content_id":"b798c9c371c135793aa88d416a3ffec4e4052af1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":156,"license_type":"no_license","max_line_length":99,"num_lines":4,"path":"/README.md","repo_name":"NickMazey/SupplyDemandSimulator","src_encoding":"UTF-8","text":"# SupplyDemandSimulator\nA simple program that simulates the market forces of supply and demand and finds market equilibrium\n\nRequires numpy and matplotlib\n\n"},{"alpha_fraction":0.6181015372276306,"alphanum_fraction":0.6181015372276306,"avg_line_length":25.647058486938477,"blob_id":"4f5b6fa3a7fa8232d0c2d9027c74db1ff9eb85a5","content_id":"f8a479dd9973832e14a21b832cb4f24e3c214813","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":453,"license_type":"no_license","max_line_length":69,"num_lines":17,"path":"/utilities/classes.py","repo_name":"NickMazey/SupplyDemandSimulator","src_encoding":"UTF-8","text":"class consumer():\n \"\"\"\n Represents consumers in the market\n Consumers have a fixed maximum price they are willing to buy at\n \"\"\"\n def __init__(self,price):\n self.price = price\n self.bought = False\n\nclass producer():\n \"\"\"\n Represents producers in the market\n Producers have a fixed minimum price they are willing to sell for\n \"\"\"\n def __init__(self,price):\n self.price = price\n self.sold = False\n"},{"alpha_fraction":0.561820387840271,"alphanum_fraction":0.5751107335090637,"avg_line_length":30.846153259277344,"blob_id":"c6e3b198a8ce7270778fb5478668e6ce10e15c52","content_id":"87f9afa4c01ad102db311ac6abd30f2ef384f3b6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2483,"license_type":"no_license","max_line_length":133,"num_lines":78,"path":"/main.py","repo_name":"NickMazey/SupplyDemandSimulator","src_encoding":"UTF-8","text":"import utilities.generator as generator\nimport random\nimport numpy as np\nimport matplotlib.pyplot as plot\n\n\ndef main():\n consumers = generator.generateConsumers(50)\n producers = generator.generateProducers(50)\n\n # Maximum number of epochs\n numEpochs = 1000\n\n epoch_quantity_demanded = np.array([0 for i in range(numEpochs)])\n epoch_quantity_supplied = np.array([0 for i in range(numEpochs)])\n epoch_market_price = np.array([0 for i in range(numEpochs)])\n marketPrice = random.randrange(1, 100)\n\n for i in range(numEpochs):\n # Consumers purchasing goods\n for con in consumers:\n if con.price >= marketPrice:\n for prod in producers:\n if not prod.sold:\n if prod.price <= con.price and prod.price <= marketPrice:\n con.bought = True\n prod.sold = True\n break\n quantity_demanded = 0\n for con in consumers:\n if con.price >= marketPrice:\n quantity_demanded += 1\n\n quantity_supplied = 0\n for prod in producers:\n if prod.price <= marketPrice:\n quantity_supplied += 1\n\n if quantity_demanded > quantity_supplied:\n marketPrice += 1\n elif quantity_demanded < quantity_supplied:\n marketPrice -= 1\n\n np.put(epoch_quantity_demanded, i, quantity_demanded)\n np.put(epoch_quantity_supplied, i, quantity_supplied)\n np.put(epoch_market_price,i,marketPrice)\n\n # Reset status\n for prod in producers:\n prod.sold = False\n for con in consumers:\n con.bought = False\n\n demandQuantity = []\n priceLevel = []\n supplyQuantity = []\n\n for i in range(100):\n priceLevel.append(i)\n qd = 0\n qs = 0\n for con in consumers:\n if i <= con.price:\n qd += 1\n for prod in producers:\n if i >= prod.price:\n qs += 1\n demandQuantity.append(qd)\n supplyQuantity.append(qs)\n plot.plot(demandQuantity,priceLevel)\n plot.plot(supplyQuantity,priceLevel)\n plot.plot(epoch_quantity_demanded[numEpochs - 1] if epoch_quantity_demanded[numEpochs -1] < epoch_quantity_supplied[numEpochs -1]\n else epoch_quantity_supplied[numEpochs -1]\n ,epoch_market_price[numEpochs - 1], 'o-')\n plot.show()\n\nif __name__ == '__main__':\n main()"},{"alpha_fraction":0.7028824687004089,"alphanum_fraction":0.707317054271698,"avg_line_length":31.25,"blob_id":"8b70cce306c479dae08f5aae29c937afbf68a7f9","content_id":"01c8d46fd467a1765efe75b2ac27588deebe7dfd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":902,"license_type":"no_license","max_line_length":63,"num_lines":28,"path":"/utilities/generator.py","repo_name":"NickMazey/SupplyDemandSimulator","src_encoding":"UTF-8","text":"import random\nimport numpy as np\nfrom utilities.classes import consumer\nfrom utilities.classes import producer\ndef generateConsumers(number):\n \"\"\"\n Method to generate a number of consumers for the simulation\n :param number: the number of consumers to be generated\n :return: a list of generated consumers\n \"\"\"\n consumers = [None for i in range(number)]\n for i in range(number):\n consumers[i] = consumer(generatePrice())\n return np.array(consumers)\n\ndef generateProducers(number):\n \"\"\"\n Method to generate a number of producers for the simulation\n :param number: the number of producers to be generated\n :return: a list of generated producers\n \"\"\"\n producers = [None for i in range(number)]\n for i in range(number):\n producers[i] = producer(generatePrice())\n return np.array(producers)\n\ndef generatePrice():\n return random.randrange(1,100)"}],"string":"[\n {\n \"alpha_fraction\": 0.8397436141967773,\n \"alphanum_fraction\": 0.8397436141967773,\n \"avg_line_length\": 37.75,\n \"blob_id\": \"2cddea3c551aa327a306ce889d86e44aad4ff31f\",\n \"content_id\": \"b798c9c371c135793aa88d416a3ffec4e4052af1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 156,\n \"license_type\": \"no_license\",\n \"max_line_length\": 99,\n \"num_lines\": 4,\n \"path\": \"/README.md\",\n \"repo_name\": \"NickMazey/SupplyDemandSimulator\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# SupplyDemandSimulator\\nA simple program that simulates the market forces of supply and demand and finds market equilibrium\\n\\nRequires numpy and matplotlib\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6181015372276306,\n \"alphanum_fraction\": 0.6181015372276306,\n \"avg_line_length\": 25.647058486938477,\n \"blob_id\": \"4f5b6fa3a7fa8232d0c2d9027c74db1ff9eb85a5\",\n \"content_id\": \"f8a479dd9973832e14a21b832cb4f24e3c214813\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 453,\n \"license_type\": \"no_license\",\n \"max_line_length\": 69,\n \"num_lines\": 17,\n \"path\": \"/utilities/classes.py\",\n \"repo_name\": \"NickMazey/SupplyDemandSimulator\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class consumer():\\n \\\"\\\"\\\"\\n Represents consumers in the market\\n Consumers have a fixed maximum price they are willing to buy at\\n \\\"\\\"\\\"\\n def __init__(self,price):\\n self.price = price\\n self.bought = False\\n\\nclass producer():\\n \\\"\\\"\\\"\\n Represents producers in the market\\n Producers have a fixed minimum price they are willing to sell for\\n \\\"\\\"\\\"\\n def __init__(self,price):\\n self.price = price\\n self.sold = False\\n\"\n },\n {\n \"alpha_fraction\": 0.561820387840271,\n \"alphanum_fraction\": 0.5751107335090637,\n \"avg_line_length\": 30.846153259277344,\n \"blob_id\": \"c6e3b198a8ce7270778fb5478668e6ce10e15c52\",\n \"content_id\": \"87f9afa4c01ad102db311ac6abd30f2ef384f3b6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2483,\n \"license_type\": \"no_license\",\n \"max_line_length\": 133,\n \"num_lines\": 78,\n \"path\": \"/main.py\",\n \"repo_name\": \"NickMazey/SupplyDemandSimulator\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import utilities.generator as generator\\nimport random\\nimport numpy as np\\nimport matplotlib.pyplot as plot\\n\\n\\ndef main():\\n consumers = generator.generateConsumers(50)\\n producers = generator.generateProducers(50)\\n\\n # Maximum number of epochs\\n numEpochs = 1000\\n\\n epoch_quantity_demanded = np.array([0 for i in range(numEpochs)])\\n epoch_quantity_supplied = np.array([0 for i in range(numEpochs)])\\n epoch_market_price = np.array([0 for i in range(numEpochs)])\\n marketPrice = random.randrange(1, 100)\\n\\n for i in range(numEpochs):\\n # Consumers purchasing goods\\n for con in consumers:\\n if con.price >= marketPrice:\\n for prod in producers:\\n if not prod.sold:\\n if prod.price <= con.price and prod.price <= marketPrice:\\n con.bought = True\\n prod.sold = True\\n break\\n quantity_demanded = 0\\n for con in consumers:\\n if con.price >= marketPrice:\\n quantity_demanded += 1\\n\\n quantity_supplied = 0\\n for prod in producers:\\n if prod.price <= marketPrice:\\n quantity_supplied += 1\\n\\n if quantity_demanded > quantity_supplied:\\n marketPrice += 1\\n elif quantity_demanded < quantity_supplied:\\n marketPrice -= 1\\n\\n np.put(epoch_quantity_demanded, i, quantity_demanded)\\n np.put(epoch_quantity_supplied, i, quantity_supplied)\\n np.put(epoch_market_price,i,marketPrice)\\n\\n # Reset status\\n for prod in producers:\\n prod.sold = False\\n for con in consumers:\\n con.bought = False\\n\\n demandQuantity = []\\n priceLevel = []\\n supplyQuantity = []\\n\\n for i in range(100):\\n priceLevel.append(i)\\n qd = 0\\n qs = 0\\n for con in consumers:\\n if i <= con.price:\\n qd += 1\\n for prod in producers:\\n if i >= prod.price:\\n qs += 1\\n demandQuantity.append(qd)\\n supplyQuantity.append(qs)\\n plot.plot(demandQuantity,priceLevel)\\n plot.plot(supplyQuantity,priceLevel)\\n plot.plot(epoch_quantity_demanded[numEpochs - 1] if epoch_quantity_demanded[numEpochs -1] < epoch_quantity_supplied[numEpochs -1]\\n else epoch_quantity_supplied[numEpochs -1]\\n ,epoch_market_price[numEpochs - 1], 'o-')\\n plot.show()\\n\\nif __name__ == '__main__':\\n main()\"\n },\n {\n \"alpha_fraction\": 0.7028824687004089,\n \"alphanum_fraction\": 0.707317054271698,\n \"avg_line_length\": 31.25,\n \"blob_id\": \"8b70cce306c479dae08f5aae29c937afbf68a7f9\",\n \"content_id\": \"01c8d46fd467a1765efe75b2ac27588deebe7dfd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 902,\n \"license_type\": \"no_license\",\n \"max_line_length\": 63,\n \"num_lines\": 28,\n \"path\": \"/utilities/generator.py\",\n \"repo_name\": \"NickMazey/SupplyDemandSimulator\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import random\\nimport numpy as np\\nfrom utilities.classes import consumer\\nfrom utilities.classes import producer\\ndef generateConsumers(number):\\n \\\"\\\"\\\"\\n Method to generate a number of consumers for the simulation\\n :param number: the number of consumers to be generated\\n :return: a list of generated consumers\\n \\\"\\\"\\\"\\n consumers = [None for i in range(number)]\\n for i in range(number):\\n consumers[i] = consumer(generatePrice())\\n return np.array(consumers)\\n\\ndef generateProducers(number):\\n \\\"\\\"\\\"\\n Method to generate a number of producers for the simulation\\n :param number: the number of producers to be generated\\n :return: a list of generated producers\\n \\\"\\\"\\\"\\n producers = [None for i in range(number)]\\n for i in range(number):\\n producers[i] = producer(generatePrice())\\n return np.array(producers)\\n\\ndef generatePrice():\\n return random.randrange(1,100)\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":4,"string":"4"}}},{"rowIdx":2979394,"cells":{"repo_name":{"kind":"string","value":"elcritch/reactqt"},"repo_url":{"kind":"string","value":"https://github.com/elcritch/reactqt"},"snapshot_id":{"kind":"string","value":"2de560fa15c30bcb8a2ff2a08ae47bc8b7b4aa2a"},"revision_id":{"kind":"string","value":"ca4c48a8f73131c5ad0b62093ce4c4441351f0fc"},"directory_id":{"kind":"string","value":"a954cb820abfbf468545a7a4927e7ca0d0c91467"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-01-17T12:19:23.267229","string":"2021-01-17T12:19:23.267229"},"revision_date":{"kind":"timestamp","value":"2015-04-19T09:40:06","string":"2015-04-19T09:40:06"},"committer_date":{"kind":"timestamp","value":"2015-04-19T09:40:06","string":"2015-04-19T09:40:06"},"github_id":{"kind":"number","value":34195165,"string":"34,195,165"},"star_events_count":{"kind":"number","value":1,"string":"1"},"fork_events_count":{"kind":"number","value":1,"string":"1"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5228241086006165,"alphanum_fraction":0.5246500372886658,"avg_line_length":24.671875,"blob_id":"e42dd051829ba5df89a81c0423f51c958b789fff","content_id":"adbd8145b32e534b72719625e2cd3cabd6da72fd","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"JavaScript","length_bytes":1643,"license_type":"permissive","max_line_length":99,"num_lines":64,"path":"/www/src/navi.js","repo_name":"elcritch/reactqt","src_encoding":"UTF-8","text":"alert('jsx!');\n\n/** @jsx React.DOM */\n\nvar MenuExample = React.createClass({\n\n getInitialState: function(){\n return { focused: 0 };\n },\n\n clicked: function(index){\n\n // The click handler will update the state with\n // the index of the focused menu entry\n\n this.setState({focused: index});\n },\n\n render: function() {\n\n // Here we will read the items property, which was passed\n // as an attribute when the component was created\n\n var self = this;\n\n // The map method will loop over the array of menu entries,\n // and will return a new array with <li> elements.\n\n return (\n <div>\n <ul>{ this.props.items.map(function(m, index){\n\n var style = '';\n\n if(self.state.focused == index){\n style = 'focused';\n }\n\n // Notice the use of the bind() method. It makes the\n // index available to the clicked function:\n\n return <li className={style} onClick={self.clicked.bind(self, index)}>{m}</li>;\n\n }) }\n \n </ul>\n \n <p>Selected: {this.props.items[this.state.focused]}</p>\n </div>\n );\n\n }\n});\n\n// Render the menu component on the page, and pass an array with menu options\n// var root = React.createElement('ul', { className: 'my-list' }, child1, child2);\n\nReact.renderComponent(\n\t\n <MenuExample items={ ['Home', 'Services', 'About', 'Contact us'] } />,\n document.getElementById('timer')\n);\n\nalert('rendered navi');\n"},{"alpha_fraction":0.5453431606292725,"alphanum_fraction":0.5474439859390259,"avg_line_length":23.28936195373535,"blob_id":"ddae26494a5b4321abf7c0bc231c24c869c2c041","content_id":"c31fd750efb14e67117fcc42bddf0af99e54dcd2","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"JavaScript","length_bytes":5712,"license_type":"permissive","max_line_length":123,"num_lines":235,"path":"/www/src/trees.js","repo_name":"elcritch/reactqt","src_encoding":"UTF-8","text":"\n// http://react.rocks/example/react-bootstrap-treeview\n\n\nvar TreeView = React.createClass({\n\n propTypes: {\n levels: React.PropTypes.number,\n\n expandIcon: React.PropTypes.string,\n collapseIcon: React.PropTypes.string,\n emptyIcon: React.PropTypes.string,\n nodeIcon: React.PropTypes.string,\n\n color: React.PropTypes.string,\n backColor: React.PropTypes.string,\n borderColor: React.PropTypes.string,\n onhoverColor: React.PropTypes.string,\n selectedColor: React.PropTypes.string,\n selectedBackColor: React.PropTypes.string,\n\n enableLinks: React.PropTypes.bool,\n highlightSelected: React.PropTypes.bool,\n showBorder: React.PropTypes.bool,\n showTags: React.PropTypes.bool,\n\n nodes: React.PropTypes.arrayOf(React.PropTypes.number)\n },\n\n getDefaultProps: function () {\n return {\n levels: 2,\n\n expandIcon: 'glyphicon glyphicon-plus',\n collapseIcon: 'glyphicon glyphicon-minus',\n emptyIcon: 'glyphicon',\n nodeIcon: 'glyphicon glyphicon-stop',\n\n color: undefined,\n backColor: undefined,\n borderColor: undefined,\n onhoverColor: '#F5F5F5', // TODO Not implemented yet, investigate radium.js 'A toolchain for React component styling'\n selectedColor: '#FFFFFF',\n selectedBackColor: '#428bca',\n\n enableLinks: false,\n highlightSelected: true,\n showBorder: true,\n showTags: false,\n\n nodes: []\n }\n },\n\n setNodeId: function(node) {\n\n if (!node.nodes) return;\n\n var _this = this;\n node.nodes.forEach(function checkStates(node) {\n node.nodeId = _this.props.nodes.length;\n _this.props.nodes.push(node);\n _this.setNodeId(node);\n });\n },\n\n render: function() {\n\n this.setNodeId({ nodes: data });\n\n var children = [];\n if (data) {\n var _this = this;\n data.forEach(function (node) {\n children.push(<TreeNode node={node}\n level={1}\n visible={true}\n options={_this.props} />);\n });\n }\n\n return (\n <div id='treeview' className='treeview'>\n <ul className='list-group'>\n {children}\n </ul>\n </div>\n );\n }\n});\n\n\nvar TreeNode = React.createClass({\n\n getInitialState: function() {\n var node = this.props.node;\n return {\n expanded: (node.state && node.state.hasOwnProperty('expanded')) ?\n node.state.expanded :\n (this.props.level < this.props.options.levels) ?\n true :\n false,\n selected: (node.state && node.state.hasOwnProperty('selected')) ? \n node.state.selected :\n false\n }\n },\n\n toggleExpanded: function(id, event) {\n this.setState({ expanded: !this.state.expanded });\n event.stopPropagation();\n },\n\n toggleSelected: function(id, event) {\n this.setState({ selected: !this.state.selected });\n event.stopPropagation();\n },\n\n render: function() {\n\n var node = this.props.node;\n var options = this.props.options;\n\n var style;\n if (!this.props.visible) {\n\n style = { \n display: 'none' \n };\n }\n else {\n\n if (options.highlightSelected && this.state.selected) {\n style = {\n color: options.selectedColor,\n backgroundColor: options.selectedBackColor\n };\n }\n else {\n style = {\n color: node.color || options.color,\n backgroundColor: node.backColor || options.backColor\n };\n }\n\n if (!options.showBorder) {\n style.border = 'none';\n }\n else if (options.borderColor) {\n style.border = '1px solid ' + options.borderColor;\n }\n } \n\n var indents = [];\n for (var i = 0; i < this.props.level-1; i++) {\n indents.push(<span className='indent'></span>);\n }\n\n var expandCollapseIcon;\n if (node.nodes) {\n if (!this.state.expanded) {\n expandCollapseIcon = (\n <span className={options.expandIcon}\n onClick={this.toggleExpanded.bind(this, node.nodeId)}>\n </span>\n );\n }\n else {\n expandCollapseIcon = (\n <span className={options.collapseIcon}\n onClick={this.toggleExpanded.bind(this, node.nodeId)}>\n </span>\n );\n }\n }\n else {\n expandCollapseIcon = (\n <span className={options.emptyIcon}></span>\n );\n }\n\n var nodeIcon = (\n <span className='icon'>\n <i className={node.icon || options.nodeIcon}></i>\n </span>\n );\n\n var nodeText;\n if (options.enableLinks) {\n nodeText = (\n <a href={node.href} /*style=\"color:inherit;\"*/>\n {node.text}\n </a>\n );\n }\n else {\n nodeText = (\n <span>{node.text}</span>\n );\n }\n\n var badges;\n if (options.showTags && node.tags) {\n badges = node.tags.map(function (tag) {\n return (\n <span className='badge'>{tag}</span>\n );\n });\n }\n\n var children = [];\n if (node.nodes) {\n var _this = this;\n node.nodes.forEach(function (node) {\n children.push(<TreeNode node={node}\n level={_this.props.level+1}\n visible={_this.state.expanded && _this.props.visible}\n options={options} />);\n });\n }\n\n return (\n <li className='list-group-item'\n style={style}\n onClick={this.toggleSelected.bind(this, node.nodeId)}\n key={node.nodeId}>\n {indents}\n {expandCollapseIcon}\n {nodeIcon}\n {nodeText}\n {badges}\n {children}\n </li>\n );\n }\n});\n\n\n\n"},{"alpha_fraction":0.6666666865348816,"alphanum_fraction":0.8333333134651184,"avg_line_length":24,"blob_id":"e5900b63413f2fc0e150f435c7b864d65800a375","content_id":"53a91a47449f7633d5f30018977f58ad195de8f7","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"JavaScript","length_bytes":24,"license_type":"permissive","max_line_length":24,"num_lines":1,"path":"/www/scripts/JSXTransformer.js","repo_name":"elcritch/reactqt","src_encoding":"UTF-8","text":"JSXTransformer-0.13.2.js"},{"alpha_fraction":0.8333333134651184,"alphanum_fraction":0.8333333134651184,"avg_line_length":26,"blob_id":"feec41c2d98fe72809ab80eec15cfd289a663750","content_id":"ab9d2e384a6bf006fede7b6efedf486fcd48b08a","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":54,"license_type":"permissive","max_line_length":43,"num_lines":2,"path":"/README.md","repo_name":"elcritch/reactqt","src_encoding":"UTF-8","text":"# reactqt\nExample Application Mixing Pyside and React\n"},{"alpha_fraction":0.71875,"alphanum_fraction":0.7222222089767456,"avg_line_length":21.076923370361328,"blob_id":"ce414e91452cde4889ef09249dc0660af1581c15","content_id":"99c45d6ec5cf6f3998628e8f3a40bf46746f3f1b","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":288,"license_type":"permissive","max_line_length":51,"num_lines":13,"path":"/chromeReact.py","repo_name":"elcritch/reactqt","src_encoding":"UTF-8","text":"#!/usr/bin/env python3\n\nimport sys, os, collections\nfrom pathlib import Path\n\nimport webbrowser\n\nhtmlName = \"trees.html\"\nbaseHtml = Path(__file__).parent / \"www\" / htmlName\nbaseHtmlUrl = \"file://\"+baseHtml.as_posix()\n\n# chrome = webbrowser.get(\"chrome\")\nwebbrowser.open_new(baseHtmlUrl)\n\n"},{"alpha_fraction":0.6179653406143188,"alphanum_fraction":0.6185064911842346,"avg_line_length":23.95945930480957,"blob_id":"c6d1831a81b3b2987ca18b6fe4aba282d803e652","content_id":"075d75345b9f247ad8cb959aa5d801bbcb2b126d","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1848,"license_type":"permissive","max_line_length":86,"num_lines":74,"path":"/basic.py","repo_name":"elcritch/reactqt","src_encoding":"UTF-8","text":"#!/usr/bin/env python3\n\nimport sys, os, collections\nfrom pathlib import Path\n\nfrom PySide.QtCore import QObject, Slot, Signal\nfrom PySide.QtGui import QApplication\nfrom PySide.QtWebKit import QWebView, QWebSettings\nfrom PySide.QtNetwork import QNetworkRequest\n\nclass BasicHub(QObject):\n \n def __init__(self):\n super().__init__()\n \n @Slot(str)\n def display(self, config):\n print(config)\n self.on_client_event.emit(\"Howdy!\")\n \n @Slot(str)\n def disconnect(self, config):\n print(config)\n \n on_client_event = Signal(str)\n on_actor_event = Signal(str)\n on_connect = Signal(str)\n on_disconnect = Signal(str)\n\n\nclass BasicApplication(QWebView):\n \n def __init__(self):\n super().__init__()\n \n self.baseHtml = Path(__file__).parent / \"www\" / \"basic.html\"\n self.hub = BasicHub()\n \n def init(self):\n self.loadFinished.connect(self.onLoad)\n self.load(self.baseHtml.as_posix())\n self.show()\n \n def onLoad(self):\n \n # This is the body of a web browser tab\n self.page().settings().setAttribute(QWebSettings.DeveloperExtrasEnabled, True)\n \n # This is the actual context/frame a webpage is running in.\n # Other frames could include iframes or such.\n self.myFrame = self.page().mainFrame()\n \n # ATTENTION here's the magic that sets a bridge between Python to HTML\n self.myFrame.addToJavaScriptWindowObject(\"hub\", self.hub)\n \n # Tell the HTML side, we are open for business\n self.myFrame.evaluateJavaScript(\"ApplicationIsReady()\")\n\n\n\ndef main():\n \n #Kickoff the QT environment\n app = QApplication(sys.argv)\n\n reactApp = BasicApplication()\n reactApp.init()\n\n sys.exit(app.exec_())\n \n\n\nif __name__ == '__main__':\n main()\n\n"},{"alpha_fraction":0.620546817779541,"alphanum_fraction":0.6238608360290527,"avg_line_length":24.94623565673828,"blob_id":"b10469258844db5ff4f7296c2a74fba964582287","content_id":"e31959eeb7b86ea16f63fd8a817e2a10a760300d","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2414,"license_type":"permissive","max_line_length":86,"num_lines":93,"path":"/main.py","repo_name":"elcritch/reactqt","src_encoding":"UTF-8","text":"#!/usr/bin/env python3\n\nimport sys, os, collections\nfrom pathlib import Path\n\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtWidgets import QApplication\nfrom PyQt5.QtWebKitWidgets import QWebView\nfrom PyQt5.QtWebKit import QWebSettings\nfrom PyQt5.QtNetwork import QNetworkRequest\n\nSignal = pyqtSignal\nSlot = pyqtSlot\n\n\nclass ExampleScript(object):\n\n def __call__(self, input):\n return {\"result\": 1.2, \"input\": input}\n\nclass DataContainer(QObject):\n\n def __init__(self, data):\n self.data = data\n\n\nclass ReactHub(QObject):\n \n def __init__(self):\n super().__init__()\n self.exampleExecutor = ExampleScript()\n \n @Slot(str)\n def run(self, input, data):\n print(\"Executing input\", signalName, input)\n \n if hasattr(self, signalName):\n signal = getattr(self, signalName)\n signal.emit(DataContainer(data))\n \n on_client_event = Signal(str)\n signal_test = Signal(DataContainer)\n\n\nclass ReactApplication(QWebView):\n \n def __init__(self, htmlName=\"empty.html\"):\n super(ReactApplication, self).__init__()\n \n self.baseHtml = Path(__file__).parent / \"www\" / htmlName\n print(\"Base Html:\",self.baseHtml)\n self.hub = ReactHub()\n \n def init(self):\n self.loadFinished.connect(self.onLoad)\n self.load(QUrl(\"file://\"+self.baseHtml.as_posix()))\n self.show()\n \n def onLoad(self):\n \n # This is the body of a web browser tab\n self.page().settings().setAttribute(QWebSettings.DeveloperExtrasEnabled, True)\n \n # This is the actual context/frame a webpage is running in.\n # Other frames could include iframes or such.\n self.myFrame = self.page().mainFrame()\n \n # ATTENTION here's the magic that sets a bridge between Python to HTML\n self.myFrame.addToJavaScriptWindowObject(\"hub\", self.hub)\n \n # Tell the HTML side, we are open for business\n # self.myFrame.evaluateJavaScript(\"ApplicationIsReady()\")\n\n self.myFrame.evaluateJavaScript(\"\"\"\n \"\"\")\n\n\ndef main():\n \n #Kickoff the QT environment\n app = QApplication(sys.argv)\n\n # reactApp = ReactApplication(\"empty.html\")\n # reactApp = ReactApplication(\"timer.html\")\n reactApp = ReactApplication(\"trees.html\")\n reactApp.init()\n\n sys.exit(app.exec_())\n \n\n\nif __name__ == '__main__':\n main()\n\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5228241086006165,\n \"alphanum_fraction\": 0.5246500372886658,\n \"avg_line_length\": 24.671875,\n \"blob_id\": \"e42dd051829ba5df89a81c0423f51c958b789fff\",\n \"content_id\": \"adbd8145b32e534b72719625e2cd3cabd6da72fd\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"JavaScript\",\n \"length_bytes\": 1643,\n \"license_type\": \"permissive\",\n \"max_line_length\": 99,\n \"num_lines\": 64,\n \"path\": \"/www/src/navi.js\",\n \"repo_name\": \"elcritch/reactqt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"alert('jsx!');\\n\\n/** @jsx React.DOM */\\n\\nvar MenuExample = React.createClass({\\n\\n getInitialState: function(){\\n return { focused: 0 };\\n },\\n\\n clicked: function(index){\\n\\n // The click handler will update the state with\\n // the index of the focused menu entry\\n\\n this.setState({focused: index});\\n },\\n\\n render: function() {\\n\\n // Here we will read the items property, which was passed\\n // as an attribute when the component was created\\n\\n var self = this;\\n\\n // The map method will loop over the array of menu entries,\\n // and will return a new array with <li> elements.\\n\\n return (\\n <div>\\n <ul>{ this.props.items.map(function(m, index){\\n\\n var style = '';\\n\\n if(self.state.focused == index){\\n style = 'focused';\\n }\\n\\n // Notice the use of the bind() method. It makes the\\n // index available to the clicked function:\\n\\n return <li className={style} onClick={self.clicked.bind(self, index)}>{m}</li>;\\n\\n }) }\\n \\n </ul>\\n \\n <p>Selected: {this.props.items[this.state.focused]}</p>\\n </div>\\n );\\n\\n }\\n});\\n\\n// Render the menu component on the page, and pass an array with menu options\\n// var root = React.createElement('ul', { className: 'my-list' }, child1, child2);\\n\\nReact.renderComponent(\\n\\t\\n <MenuExample items={ ['Home', 'Services', 'About', 'Contact us'] } />,\\n document.getElementById('timer')\\n);\\n\\nalert('rendered navi');\\n\"\n },\n {\n \"alpha_fraction\": 0.5453431606292725,\n \"alphanum_fraction\": 0.5474439859390259,\n \"avg_line_length\": 23.28936195373535,\n \"blob_id\": \"ddae26494a5b4321abf7c0bc231c24c869c2c041\",\n \"content_id\": \"c31fd750efb14e67117fcc42bddf0af99e54dcd2\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"JavaScript\",\n \"length_bytes\": 5712,\n \"license_type\": \"permissive\",\n \"max_line_length\": 123,\n \"num_lines\": 235,\n \"path\": \"/www/src/trees.js\",\n \"repo_name\": \"elcritch/reactqt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n// http://react.rocks/example/react-bootstrap-treeview\\n\\n\\nvar TreeView = React.createClass({\\n\\n propTypes: {\\n levels: React.PropTypes.number,\\n\\n expandIcon: React.PropTypes.string,\\n collapseIcon: React.PropTypes.string,\\n emptyIcon: React.PropTypes.string,\\n nodeIcon: React.PropTypes.string,\\n\\n color: React.PropTypes.string,\\n backColor: React.PropTypes.string,\\n borderColor: React.PropTypes.string,\\n onhoverColor: React.PropTypes.string,\\n selectedColor: React.PropTypes.string,\\n selectedBackColor: React.PropTypes.string,\\n\\n enableLinks: React.PropTypes.bool,\\n highlightSelected: React.PropTypes.bool,\\n showBorder: React.PropTypes.bool,\\n showTags: React.PropTypes.bool,\\n\\n nodes: React.PropTypes.arrayOf(React.PropTypes.number)\\n },\\n\\n getDefaultProps: function () {\\n return {\\n levels: 2,\\n\\n expandIcon: 'glyphicon glyphicon-plus',\\n collapseIcon: 'glyphicon glyphicon-minus',\\n emptyIcon: 'glyphicon',\\n nodeIcon: 'glyphicon glyphicon-stop',\\n\\n color: undefined,\\n backColor: undefined,\\n borderColor: undefined,\\n onhoverColor: '#F5F5F5', // TODO Not implemented yet, investigate radium.js 'A toolchain for React component styling'\\n selectedColor: '#FFFFFF',\\n selectedBackColor: '#428bca',\\n\\n enableLinks: false,\\n highlightSelected: true,\\n showBorder: true,\\n showTags: false,\\n\\n nodes: []\\n }\\n },\\n\\n setNodeId: function(node) {\\n\\n if (!node.nodes) return;\\n\\n var _this = this;\\n node.nodes.forEach(function checkStates(node) {\\n node.nodeId = _this.props.nodes.length;\\n _this.props.nodes.push(node);\\n _this.setNodeId(node);\\n });\\n },\\n\\n render: function() {\\n\\n this.setNodeId({ nodes: data });\\n\\n var children = [];\\n if (data) {\\n var _this = this;\\n data.forEach(function (node) {\\n children.push(<TreeNode node={node}\\n level={1}\\n visible={true}\\n options={_this.props} />);\\n });\\n }\\n\\n return (\\n <div id='treeview' className='treeview'>\\n <ul className='list-group'>\\n {children}\\n </ul>\\n </div>\\n );\\n }\\n});\\n\\n\\nvar TreeNode = React.createClass({\\n\\n getInitialState: function() {\\n var node = this.props.node;\\n return {\\n expanded: (node.state && node.state.hasOwnProperty('expanded')) ?\\n node.state.expanded :\\n (this.props.level < this.props.options.levels) ?\\n true :\\n false,\\n selected: (node.state && node.state.hasOwnProperty('selected')) ? \\n node.state.selected :\\n false\\n }\\n },\\n\\n toggleExpanded: function(id, event) {\\n this.setState({ expanded: !this.state.expanded });\\n event.stopPropagation();\\n },\\n\\n toggleSelected: function(id, event) {\\n this.setState({ selected: !this.state.selected });\\n event.stopPropagation();\\n },\\n\\n render: function() {\\n\\n var node = this.props.node;\\n var options = this.props.options;\\n\\n var style;\\n if (!this.props.visible) {\\n\\n style = { \\n display: 'none' \\n };\\n }\\n else {\\n\\n if (options.highlightSelected && this.state.selected) {\\n style = {\\n color: options.selectedColor,\\n backgroundColor: options.selectedBackColor\\n };\\n }\\n else {\\n style = {\\n color: node.color || options.color,\\n backgroundColor: node.backColor || options.backColor\\n };\\n }\\n\\n if (!options.showBorder) {\\n style.border = 'none';\\n }\\n else if (options.borderColor) {\\n style.border = '1px solid ' + options.borderColor;\\n }\\n } \\n\\n var indents = [];\\n for (var i = 0; i < this.props.level-1; i++) {\\n indents.push(<span className='indent'></span>);\\n }\\n\\n var expandCollapseIcon;\\n if (node.nodes) {\\n if (!this.state.expanded) {\\n expandCollapseIcon = (\\n <span className={options.expandIcon}\\n onClick={this.toggleExpanded.bind(this, node.nodeId)}>\\n </span>\\n );\\n }\\n else {\\n expandCollapseIcon = (\\n <span className={options.collapseIcon}\\n onClick={this.toggleExpanded.bind(this, node.nodeId)}>\\n </span>\\n );\\n }\\n }\\n else {\\n expandCollapseIcon = (\\n <span className={options.emptyIcon}></span>\\n );\\n }\\n\\n var nodeIcon = (\\n <span className='icon'>\\n <i className={node.icon || options.nodeIcon}></i>\\n </span>\\n );\\n\\n var nodeText;\\n if (options.enableLinks) {\\n nodeText = (\\n <a href={node.href} /*style=\\\"color:inherit;\\\"*/>\\n {node.text}\\n </a>\\n );\\n }\\n else {\\n nodeText = (\\n <span>{node.text}</span>\\n );\\n }\\n\\n var badges;\\n if (options.showTags && node.tags) {\\n badges = node.tags.map(function (tag) {\\n return (\\n <span className='badge'>{tag}</span>\\n );\\n });\\n }\\n\\n var children = [];\\n if (node.nodes) {\\n var _this = this;\\n node.nodes.forEach(function (node) {\\n children.push(<TreeNode node={node}\\n level={_this.props.level+1}\\n visible={_this.state.expanded && _this.props.visible}\\n options={options} />);\\n });\\n }\\n\\n return (\\n <li className='list-group-item'\\n style={style}\\n onClick={this.toggleSelected.bind(this, node.nodeId)}\\n key={node.nodeId}>\\n {indents}\\n {expandCollapseIcon}\\n {nodeIcon}\\n {nodeText}\\n {badges}\\n {children}\\n </li>\\n );\\n }\\n});\\n\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6666666865348816,\n \"alphanum_fraction\": 0.8333333134651184,\n \"avg_line_length\": 24,\n \"blob_id\": \"e5900b63413f2fc0e150f435c7b864d65800a375\",\n \"content_id\": \"53a91a47449f7633d5f30018977f58ad195de8f7\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"JavaScript\",\n \"length_bytes\": 24,\n \"license_type\": \"permissive\",\n \"max_line_length\": 24,\n \"num_lines\": 1,\n \"path\": \"/www/scripts/JSXTransformer.js\",\n \"repo_name\": \"elcritch/reactqt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"JSXTransformer-0.13.2.js\"\n },\n {\n \"alpha_fraction\": 0.8333333134651184,\n \"alphanum_fraction\": 0.8333333134651184,\n \"avg_line_length\": 26,\n \"blob_id\": \"feec41c2d98fe72809ab80eec15cfd289a663750\",\n \"content_id\": \"ab9d2e384a6bf006fede7b6efedf486fcd48b08a\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 54,\n \"license_type\": \"permissive\",\n \"max_line_length\": 43,\n \"num_lines\": 2,\n \"path\": \"/README.md\",\n \"repo_name\": \"elcritch/reactqt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# reactqt\\nExample Application Mixing Pyside and React\\n\"\n },\n {\n \"alpha_fraction\": 0.71875,\n \"alphanum_fraction\": 0.7222222089767456,\n \"avg_line_length\": 21.076923370361328,\n \"blob_id\": \"ce414e91452cde4889ef09249dc0660af1581c15\",\n \"content_id\": \"99c45d6ec5cf6f3998628e8f3a40bf46746f3f1b\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 288,\n \"license_type\": \"permissive\",\n \"max_line_length\": 51,\n \"num_lines\": 13,\n \"path\": \"/chromeReact.py\",\n \"repo_name\": \"elcritch/reactqt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python3\\n\\nimport sys, os, collections\\nfrom pathlib import Path\\n\\nimport webbrowser\\n\\nhtmlName = \\\"trees.html\\\"\\nbaseHtml = Path(__file__).parent / \\\"www\\\" / htmlName\\nbaseHtmlUrl = \\\"file://\\\"+baseHtml.as_posix()\\n\\n# chrome = webbrowser.get(\\\"chrome\\\")\\nwebbrowser.open_new(baseHtmlUrl)\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6179653406143188,\n \"alphanum_fraction\": 0.6185064911842346,\n \"avg_line_length\": 23.95945930480957,\n \"blob_id\": \"c6d1831a81b3b2987ca18b6fe4aba282d803e652\",\n \"content_id\": \"075d75345b9f247ad8cb959aa5d801bbcb2b126d\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1848,\n \"license_type\": \"permissive\",\n \"max_line_length\": 86,\n \"num_lines\": 74,\n \"path\": \"/basic.py\",\n \"repo_name\": \"elcritch/reactqt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python3\\n\\nimport sys, os, collections\\nfrom pathlib import Path\\n\\nfrom PySide.QtCore import QObject, Slot, Signal\\nfrom PySide.QtGui import QApplication\\nfrom PySide.QtWebKit import QWebView, QWebSettings\\nfrom PySide.QtNetwork import QNetworkRequest\\n\\nclass BasicHub(QObject):\\n \\n def __init__(self):\\n super().__init__()\\n \\n @Slot(str)\\n def display(self, config):\\n print(config)\\n self.on_client_event.emit(\\\"Howdy!\\\")\\n \\n @Slot(str)\\n def disconnect(self, config):\\n print(config)\\n \\n on_client_event = Signal(str)\\n on_actor_event = Signal(str)\\n on_connect = Signal(str)\\n on_disconnect = Signal(str)\\n\\n\\nclass BasicApplication(QWebView):\\n \\n def __init__(self):\\n super().__init__()\\n \\n self.baseHtml = Path(__file__).parent / \\\"www\\\" / \\\"basic.html\\\"\\n self.hub = BasicHub()\\n \\n def init(self):\\n self.loadFinished.connect(self.onLoad)\\n self.load(self.baseHtml.as_posix())\\n self.show()\\n \\n def onLoad(self):\\n \\n # This is the body of a web browser tab\\n self.page().settings().setAttribute(QWebSettings.DeveloperExtrasEnabled, True)\\n \\n # This is the actual context/frame a webpage is running in.\\n # Other frames could include iframes or such.\\n self.myFrame = self.page().mainFrame()\\n \\n # ATTENTION here's the magic that sets a bridge between Python to HTML\\n self.myFrame.addToJavaScriptWindowObject(\\\"hub\\\", self.hub)\\n \\n # Tell the HTML side, we are open for business\\n self.myFrame.evaluateJavaScript(\\\"ApplicationIsReady()\\\")\\n\\n\\n\\ndef main():\\n \\n #Kickoff the QT environment\\n app = QApplication(sys.argv)\\n\\n reactApp = BasicApplication()\\n reactApp.init()\\n\\n sys.exit(app.exec_())\\n \\n\\n\\nif __name__ == '__main__':\\n main()\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.620546817779541,\n \"alphanum_fraction\": 0.6238608360290527,\n \"avg_line_length\": 24.94623565673828,\n \"blob_id\": \"b10469258844db5ff4f7296c2a74fba964582287\",\n \"content_id\": \"e31959eeb7b86ea16f63fd8a817e2a10a760300d\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2414,\n \"license_type\": \"permissive\",\n \"max_line_length\": 86,\n \"num_lines\": 93,\n \"path\": \"/main.py\",\n \"repo_name\": \"elcritch/reactqt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python3\\n\\nimport sys, os, collections\\nfrom pathlib import Path\\n\\nfrom PyQt5.QtCore import *\\nfrom PyQt5.QtWidgets import QApplication\\nfrom PyQt5.QtWebKitWidgets import QWebView\\nfrom PyQt5.QtWebKit import QWebSettings\\nfrom PyQt5.QtNetwork import QNetworkRequest\\n\\nSignal = pyqtSignal\\nSlot = pyqtSlot\\n\\n\\nclass ExampleScript(object):\\n\\n def __call__(self, input):\\n return {\\\"result\\\": 1.2, \\\"input\\\": input}\\n\\nclass DataContainer(QObject):\\n\\n def __init__(self, data):\\n self.data = data\\n\\n\\nclass ReactHub(QObject):\\n \\n def __init__(self):\\n super().__init__()\\n self.exampleExecutor = ExampleScript()\\n \\n @Slot(str)\\n def run(self, input, data):\\n print(\\\"Executing input\\\", signalName, input)\\n \\n if hasattr(self, signalName):\\n signal = getattr(self, signalName)\\n signal.emit(DataContainer(data))\\n \\n on_client_event = Signal(str)\\n signal_test = Signal(DataContainer)\\n\\n\\nclass ReactApplication(QWebView):\\n \\n def __init__(self, htmlName=\\\"empty.html\\\"):\\n super(ReactApplication, self).__init__()\\n \\n self.baseHtml = Path(__file__).parent / \\\"www\\\" / htmlName\\n print(\\\"Base Html:\\\",self.baseHtml)\\n self.hub = ReactHub()\\n \\n def init(self):\\n self.loadFinished.connect(self.onLoad)\\n self.load(QUrl(\\\"file://\\\"+self.baseHtml.as_posix()))\\n self.show()\\n \\n def onLoad(self):\\n \\n # This is the body of a web browser tab\\n self.page().settings().setAttribute(QWebSettings.DeveloperExtrasEnabled, True)\\n \\n # This is the actual context/frame a webpage is running in.\\n # Other frames could include iframes or such.\\n self.myFrame = self.page().mainFrame()\\n \\n # ATTENTION here's the magic that sets a bridge between Python to HTML\\n self.myFrame.addToJavaScriptWindowObject(\\\"hub\\\", self.hub)\\n \\n # Tell the HTML side, we are open for business\\n # self.myFrame.evaluateJavaScript(\\\"ApplicationIsReady()\\\")\\n\\n self.myFrame.evaluateJavaScript(\\\"\\\"\\\"\\n \\\"\\\"\\\")\\n\\n\\ndef main():\\n \\n #Kickoff the QT environment\\n app = QApplication(sys.argv)\\n\\n # reactApp = ReactApplication(\\\"empty.html\\\")\\n # reactApp = ReactApplication(\\\"timer.html\\\")\\n reactApp = ReactApplication(\\\"trees.html\\\")\\n reactApp.init()\\n\\n sys.exit(app.exec_())\\n \\n\\n\\nif __name__ == '__main__':\\n main()\\n\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":7,"string":"7"}}},{"rowIdx":2979395,"cells":{"repo_name":{"kind":"string","value":"Deisss/webservice-notification"},"repo_url":{"kind":"string","value":"https://github.com/Deisss/webservice-notification"},"snapshot_id":{"kind":"string","value":"2fce5a0a73fd6004333821716354e740b2f7bcec"},"revision_id":{"kind":"string","value":"73feddb22ad3a2543ad4f8047061d909c64fd75d"},"directory_id":{"kind":"string","value":"d7d594d642bb14673570eb853ddbf23d8f06e205"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-01-20T04:37:03.439096","string":"2021-01-20T04:37:03.439096"},"revision_date":{"kind":"timestamp","value":"2014-04-17T15:11:40","string":"2014-04-17T15:11:40"},"committer_date":{"kind":"timestamp","value":"2014-04-17T15:11:40","string":"2014-04-17T15:11:40"},"github_id":{"kind":"null"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.42338818311691284,"alphanum_fraction":0.42538875341415405,"avg_line_length":26.426433563232422,"blob_id":"74be245bc9f3db033f2ab44b0af1a52810d1d86f","content_id":"52320f7eff4b92b78e86ee93b030959faca5e287","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"JavaScript","length_bytes":10997,"license_type":"permissive","max_line_length":79,"num_lines":401,"path":"/client-example/resource/js/notification.js","repo_name":"Deisss/webservice-notification","src_encoding":"UTF-8","text":"/**\n * A notification system for grabbing important event threw the user session\n * lifetime\n *\n * @class notification\n * @static\n*/\nvar notification = {\n /**\n * Store the created socket instance\n * @property _socket\n * @type Object\n * @default null\n */\n _socket: null,\n\n /**\n * Store the 'all' type element\n * @property _allType\n * @type String\n * @default __all__\n */\n _allType: '__all__',\n\n /**\n * Keep a trace of all 'unseen' notifications\n * @property _unseen\n * @type Object\n * @default {}\n */\n _unseen: {},\n\n /**\n * Keep a trace of all notifications (include unseen)\n * @property _all\n * @type Object\n * @default {}\n */\n _all: {},\n\n /**\n * Store binded function when recieving something\n * @property _bind\n * @type Object\n * @default {}\n */\n _bind: {},\n\n\n /**\n * Get the object size (kind of length)\n *\n * @method _getObjectSize\n *\n * @param obj {Object} The object to check\n * @return {Integer} The count found\n */\n _getObjectSize: function(obj) {\n if(!obj) {\n return 0;\n }\n var key, count = 0;\n for(key in obj) {\n if(obj.hasOwnProperty(key)) {\n count++;\n }\n }\n return count;\n },\n\n\n /**\n * Raise a new event\n *\n * @method _raiseEvent\n * @private\n *\n * @param type {String} The notification type, or all\n * @param name {String} The event type (like 'seen',\n * 'delete')\n * @param el {Object} The concerned notification\n */\n _raiseEvent: function(type, name, el) {\n type = type || this._allType;\n\n if(this._bind[type]) {\n var tab = this._bind[type],\n i = tab.length;\n\n var unseenLength = 0,\n allLength = 0,\n seenLength = 0;\n\n // We select length globally\n if(type == this._allType) {\n unseenLength = this._getObjectSize(this._unseen);\n allLength = this._getObjectSize(this._all);\n\n // We select only the sub-length of some elements\n } else {\n var g = this._getObjectSize(this._all);\n\n while(g--) {\n var notification = this._all[g];\n if(notification['type'] === type) {\n var seen = notification['seen'];\n allLength++;\n\n if(seen == false || seen == 'false' || seen == 'unseen'\n || seen == '0' || seen == 0) {\n unseenLength++;\n }\n }\n }\n }\n\n // The seenLength is always the same...\n seenLength = allLength - unseenLength;\n\n while(i--) {\n var fct = tab[i];\n // Send the event\n setTimeout(function() {\n fct({\n event: name,\n element: el,\n notification: el,\n seen: seenLength,\n unseen: unseenLength,\n all: allLength,\n });\n }, 0);\n }\n }\n },\n\n /**\n * Start the notification system.\n *\n * @method connect\n *\n * @param login {String} The login to use\n * (same as global system)\n * @param password {String} The password to use\n * (same as global system => sha512)\n * @return {Object} The socket instance created\n */\n connect: function(login, password) {\n var instance = new socket('notification');\n\n // On every connect, the server 'loose' us\n // so we have to join again\n instance.on('connect', function() {\n // Everytime a connect appear, we have to logon again\n this.emit('join', {\n login: login,\n password: password\n });\n }, instance);\n\n // On seen, we remove from unseen only\n instance.on('seen', function(ids) {\n var elements = [];\n\n // Some element has been marked as seen\n for(var i=0, l=ids.length; i<l; ++i) {\n var id = ids[i];\n\n // We raise a new event\n if(this._unseen[id]) {\n elements.push(this._unseen[id]);\n delete this._unseen[id];\n }\n }\n\n for(var i=0, l=elements.length; i<l; ++i) {\n var element = elements[i];\n\n if(element) {\n if(element['type']) {\n this._raiseEvent(element['type'], 'seen', element);\n }\n\n this._raiseEvent(null, 'seen', element);\n }\n }\n }, this);\n\n // On delete, we try to remove elements existing inside\n instance.on('delete', function(ids) {\n var elements = [];\n\n for(var i=0, l=ids.length; i<l; ++i) {\n var id = ids[i];\n if(this._all[id]) {\n elements.push(this._all[id]);\n\n delete this._unseen[id];\n delete this._all[id];\n }\n }\n\n for(var i=0, l=elements.length; i<l; ++i) {\n var element = elements[i];\n\n if(element) {\n if(element['type']) {\n this._raiseEvent(element['type'], 'delete', element);\n }\n\n this._raiseEvent(null, 'delete', element);\n }\n }\n }, this);\n\n // On notification, we erase previous content or append\n instance.on('notification', function(notifications) {\n var elements = [];\n\n for(var i=0, l=notifications.length; i<l; ++i) {\n var notification = notifications[i],\n id = notification['_id'],\n seen = notification['seen'];\n\n elements.push(notification);\n\n // We try to append\n if(seen == false || seen == 'false' || seen == 'unseen'\n || seen == '0' || seen == 0) {\n this._unseen[id] = notification;\n }\n this._all[id] = notification;\n }\n\n for(var i=0, l=elements.length; i<l; ++i) {\n var element = elements[i];\n\n if(element) {\n if(element['type']) {\n this._raiseEvent(element['type'],\n 'notification', element);\n }\n\n this._raiseEvent(null, 'notification', element);\n }\n }\n }, this);\n\n // Start socket\n instance.connect();\n\n this._socket = instance;\n return instance;\n },\n\n /**\n * Mark as seen some ids.\n *\n * @method seen\n *\n * @param ids {Array} An array of ObjectId (string way)\n * to mark as seen\n */\n seen: function(ids) {\n if(this._socket !== null) {\n this._socket.emit('seen', {\n ids: ids\n });\n }\n },\n\n /**\n * Grab the unseen notifications from server (should not be needed at all)\n * As when user connect, it automatically retrieve unseen notifications...\n *\n * @method unseen\n */\n unseen: function() {\n if(this._socket !== null) {\n this._socket.emit('unseen', {});\n }\n },\n\n /**\n * Mark as deleted selected ids\n *\n * @method delete\n *\n * @param ids {Array} An array of ObjectId (string way)\n * to mark as deleted\n */\n delete: function(ids) {\n if(this._socket !== null) {\n this._socket.emit('delete', {\n ids: ids\n });\n }\n },\n\n /**\n * Ask the server to bring some existing content (page by page)\n *\n * @method content\n *\n * @param page {Integer} The page to recieve\n */\n content: function(page) {\n if(this._socket !== null) {\n this._socket.emit('content', {\n page: Math.min(0, page)\n });\n }\n },\n\n /**\n * Stop the notification system.\n *\n * @method disconnect\n */\n disconnect: function() {\n if(this._socket !== null) {\n this._socket.disconnect();\n this._socket = null;\n }\n },\n\n /**\n * Bind a function to a new notification arriving (regarding the type).\n * If you don't put any type to this function, the function will be binded\n * to all elements recieve.\n *\n * @method bind\n *\n * @param type {String} The notification type to bind\n * @param fct {Function} The function to respond in case of\n * new element arriving\n */\n bind: function(type, fct) {\n var f = 'function';\n\n if(typeof(type) === f) {\n fct = type;\n type = this._allType;\n }\n\n if(!this._bind[type]) {\n this._bind[type] = [];\n }\n\n if(typeof(fct) === f) {\n this._bind[type].push(fct);\n }\n },\n\n /**\n * Remove from binding a function previously binded using bind.\n * Like bind, if you specify only a function as parameter, the 'all' type\n * will be remove.\n *\n * @method unbind\n *\n * @param type {String} The notification type to bind\n * @param fct {Function} The function to remove\n */\n unbind: function(type, fct) {\n if(typeof(type) === 'function') {\n fct = type;\n type = this._allType;\n }\n\n if(this._bind[type]) {\n var tab = this._bind[type],\n l = tab.length;\n\n while(l--) {\n if(tab[l] === fct) {\n tab.splice(l, 1);\n }\n }\n\n if(tab.length == 0) {\n this.unbindAll(type);\n }\n }\n },\n\n /**\n * Remove all functions binded to a given type.\n * You can not specify type to unbind on 'all' type\n *\n * @method unbindAll\n *\n * @param type {String} The notification type to clear\n */\n unbindAll: function(type) {\n if(!type) {\n type = this._allType;\n }\n delete this._bind[type]; \n }\n};"},{"alpha_fraction":0.6566265225410461,"alphanum_fraction":0.6626505851745605,"avg_line_length":19.75,"blob_id":"6f9cb09d39e5ac492a6359423039ae479ca72573","content_id":"96471aa7d804b61186421e00764c0c47c381c1b7","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":166,"license_type":"permissive","max_line_length":47,"num_lines":8,"path":"/server/handler/HTTPServer.py","repo_name":"Deisss/webservice-notification","src_encoding":"UTF-8","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport tornado.web\n\nclass IndexHandler(tornado.web.RequestHandler):\n ''' Regular HTTP handler (unused) '''\n pass\n"},{"alpha_fraction":0.5703434944152832,"alphanum_fraction":0.5755221843719482,"avg_line_length":34.32926940917969,"blob_id":"e06b656ee45f95f48dbe256c57b9fcbb8ca73ad1","content_id":"0f743d750653f2b43ed6a01099b5b71a94a82c6f","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5793,"license_type":"permissive","max_line_length":102,"num_lines":164,"path":"/server/webservice.py","repo_name":"Deisss/webservice-notification","src_encoding":"UTF-8","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom sockjsroom import JsonDefaultHandler\nfrom pydispatch import dispatcher\nfrom datetime import datetime\n\nfrom system.CommonMongoDB import createNotification\nfrom system.ConfigLoader import getCfg\nfrom system.EmailNotification import send\n\nimport json, logging\n\n'''\nA notification is represented by:\n> automatically populated\n _id (ObjectId)\n createdAt (date)\n updatedAt (date)\n deletedAt (date)\n delete (Boolean)\n seen (Boolean)\n _userId (str) => the userid linked to...\n\n> defined by service\n type (String - a custom type)\n title (String)\n content (String)\n url (String)\n icon (String - an icon to show)\n image (String - an image to show, like user profile)\n\n> Except content, all of them are optionals, and system does't use them\nThey are here to help you define your own client look\n'''\n\ndef getBooleanRealValue(b):\n ''' Simple string to boolean convertion '''\n if b == True or b == '1' or b == 1 or b == 'true' or b == 'True' or b == 'yes' or b == 'Yes':\n return True\n else:\n return False\n\n\nclass ServiceRequestManager(JsonDefaultHandler):\n ''' Register a notification to user '''\n def set_default_headers(self):\n self.set_header('Access-Control-Allow-Origin', '*')\n self.set_header('Access-Control-Allow-Headers', 'origin, content-type, accept, authorization')\n self.set_header('Access-Control-Allow-Credentials', 'true')\n self.set_header('Access-Control-Allow-Methods', 'GET, POST, PUT, DELETE, OPTIONS, HEAD')\n # 1209600: 14 days\n self.set_header('Access-Control-Max-Age', '1209600')\n\n def post(self):\n # Before anything else, if APIKey is defined, we check it...\n APIKeyDefined = getCfg('APPLICATION', 'apikey')\n if(APIKeyDefined is not None) and (APIKeyDefined != ''):\n APIKeySubmitted = self.get_argument('apikey', '')\n if APIKeyDefined != APIKeySubmitted:\n self.set_status(400)\n self.finish('<html><body>API Key refused</body></html>')\n logging.error(\n 'notify: API Key refused, given: %s, allowed: %s' %\n (APIKeySubmitted, APIKeyDefined)\n )\n return\n\n\n # Will throw an HTTP 400 if missing\n content = self.get_argument('content')\n\n # TYPE: the notification type\n custom = self.get_argument('type', '')\n # TITLE: the notification title - can be null\n title = self.get_argument('title', '')\n # URL: a link to the given notification - can be null\n url = self.get_argument('url', '')\n # ICON: a class/image or something to show as icon - can be null\n icon = self.get_argument('icon', '')\n # IMAGE: an image to represent post (like user on fb) - can be null\n image = self.get_argument('image', '')\n # Users linked to the notification (user should recieve notification)\n users = json.loads(self.get_argument('users', '[]'))\n\n\n\n # Email stuff\n # System should send an email...\n canEmail = self.get_argument('email_send', False)\n forceEmail = self.get_argument('email_force', False)\n # Check everything is fine\n canEmail = getBooleanRealValue(canEmail)\n forceEmail = getBooleanRealValue(forceEmail)\n\n # ForceEmail will always override canEmail as it is mandatory\n if forceEmail == True:\n canEmail = True\n\n emailSubject = self.get_argument('email_subject', '')\n emailContent = self.get_argument('email_content', '')\n emailMime = self.get_argument('email_mime', '1.0')\n emailType = self.get_argument('email_type', 'text/html')\n\n\n\n # Debug (if needed)\n logging.debug('notify: new notification: ' +\n '\\n\\ttitle: %s\\n\\turl: %s\\n\\tcontent: %s\\n'\n % (title, url, content))\n\n # This element will recieve all notification to send to user\n notifications = []\n unsent = []\n\n for user in users:\n # Get user information\n userId = str(user['id'])\n userEmail = user['email']\n signal = 'user-%s' % userId\n\n # We check if there is one socket binded to given signal\n reciever = len(dispatcher.getReceivers(signal=signal))\n\n notifications.append({\n 'type': custom,\n 'title': title,\n 'content': content,\n 'url': url,\n 'icon': icon,\n 'image': image,\n '_userId': userId\n })\n\n # Append the user to next email campaign if needed\n if reciever <= 0 or forceEmail == True:\n unsent.append(userEmail)\n\n\n\n # We bulk insert everything in a single call to database\n # No matter if user is connected or not, we need to try...\n if len(notifications) > 0:\n createNotification(notifications)\n\n # For every item in notifications, we send them threw dispatcher\n # to let socket know if there is something to update\n for notification in notifications:\n data = {\n 'data': notification\n }\n userId = notification['_userId']\n signal = 'user-%s' % userId\n dispatcher.send(\n signal=signal,\n sender=dispatcher.Anonymous,\n **data\n )\n\n\n\n # We send email to people not connected\n if len(unsent) > 0 and canEmail == True:\n send(unsent, emailSubject, emailContent, emailType, emailMime)"},{"alpha_fraction":0.7690140604972839,"alphanum_fraction":0.7690140604972839,"avg_line_length":34.5,"blob_id":"1aa9dd66d66404ff3618a5cb9766b7f66a60dfe4","content_id":"006af5dd358f9fd81b2d8a9d72a7a56c74a47055","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":710,"license_type":"permissive","max_line_length":209,"num_lines":20,"path":"/server/README.md","repo_name":"Deisss/webservice-notification","src_encoding":"UTF-8","text":"# Server\n\nThis is the entry point for the notification server.\n\nThis server is a full python/MongoDB system. It needs the following pip installation:\n\n```\npip install tornado pymongo sockjs-tornado sockjsroom PyDispatcher\n```\n\nAnd a configuration file setted on ```config.ini```.\n\n\n## How to use\n\nFrom a server you already have, you want to implement a notification system on top of your existing system: you simply want to do a POST request on ```/notify``` to register a new notification to listed users.\n\nOn the other side (client side), users have to subscribe to websocket provided by this server.\n\nFor more information, just go to main [README.md file](https://github.com/Deisss/webservice-notification) "},{"alpha_fraction":0.5333333611488342,"alphanum_fraction":0.5352112650871277,"avg_line_length":23.79069709777832,"blob_id":"4b50b6c2ff85b809ad9dc12679c29a18d14ea234","content_id":"8a2ca655b118b574b0c3c1bd9a18f326e0374547","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1065,"license_type":"permissive","max_line_length":72,"num_lines":43,"path":"/server/system/CommonMySQL.py","repo_name":"Deisss/webservice-notification","src_encoding":"UTF-8","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom system.DBMysql import connect\nfrom system.ConfigLoader import getCfg\nimport logging\n\ndef loginUser(login, password):\n ''' Try to login a user regarding login/password '''\n userContent = None\n\n table = getCfg('MYSQL', 'table')\n tableId = getCfg('MYSQL', 'idField')\n tableLogin = getCfg('MYSQL', 'loginField')\n tablePassword = getCfg('MYSQL', 'passwordField')\n\n con = None\n try:\n # Starting\n con = connect()\n cur = con.cursor()\n\n cur.execute(\n 'SELECT ' + tableId + ' FROM ' + table +\n ' WHERE ' + tableLogin + '=%s AND ' + tablePassword + '=%s',\n (\n login,\n password\n )\n )\n\n userContent = cur.fetchone()\n\n if userContent is not None:\n userContent = userContent[0]\n \n except Exception as e:\n logging.error('loginUser: Error from MySQL => %s' % e)\n finally:\n if con:\n con.close()\n\n return userContent"},{"alpha_fraction":0.7493917346000671,"alphanum_fraction":0.7518247961997986,"avg_line_length":56.96154022216797,"blob_id":"284871dd06325c002b3a029b370d17652c76d3fe","content_id":"7f208effb5e3cd59e6ab0e1cac0f1f73233e33eb","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":4521,"license_type":"permissive","max_line_length":312,"num_lines":78,"path":"/client-example/README.md","repo_name":"Deisss/webservice-notification","src_encoding":"UTF-8","text":"# Client example\n\n\nThe client side is a simple example to manipulate two main functionnalities: the unseen notifications (all of them), and the first page only (as seen from API retrieve page by page).\n\n_Note_: this example will probably not work with your system as **the client send password encrypted in SHA-512**. So be carefull, and check file ```action.js``` before doing anything.\n\n_Note2_: jQuery is used only for DOM manipulation purpose, it's not required at all.\n\n_Note3_: On the other side, sockjs framework IS needed (see ```index.html``` to get from CDN).\n\nAs this is an example, the ```notification.js``` and ```socket.js``` can be reused without modification into your own application, they are both framework-less (except SockJS). The last file ```action.js``` is this time specific to this demo page.\nHere is a description of every files to get quickly into:\n\n\n## Socket.js\n\nA simple layer for [sockjsroom](https://github.com/Deisss/python-sockjsroom), this helps to use the multi-message and auto-reconnect capacities. It can be reused like this for every project you may have (the same file can be reused for all [sockjsroom](https://github.com/Deisss/python-sockjsroom) application).\n\n\n## Notification.js\n\nA layer for implementing API provided by this server, this should remains the same for your own application, it's not needed to change it as it is common to every application using this notification server.\nBasically, it's just a simple object helping to connect/bind events/retrieve messages specifically from this server. To summarize: ```socket.js``` is fully generic to all [sockjsroom](https://github.com/Deisss/python-sockjsroom) application, ```notification.js``` is generic to all application using this server.\n\n\n## Action.js\n\nThis file is _specific_ to this example, in this file you will find how to catch websocket events, and apply action to (basically DOM modification to graphically show notification to user).\n\nIn fact, a socket event (from ```notification.js```), will send on every example an object:\n```js\n\n// Bind to all notifications arriving\nnotification.bind(function(details) {\n // You can access to following content:\n details.element; // The given notification (system send them one by one)\n details.notification; // Same as element (alias)\n details.event; // The server event: 'seen', 'delete' or 'notification'\n details.seen; // Number of 'seen' notification currently stored on client side (see below)\n details.unseen; // Number of 'unseen' notification currently stored on client side (the most interesting)\n details.all; // Number of notifications (all of them) currently stored on client side\n});\n\n// Bind to only 'message' notification\nnotification.bind('message', function(details) {\n // Same details as above\n});\n```\n\n**What is important here**, is the result difference between two binding:\n**The second binding will only take care of notification with the 'message' type**. Others notifications will be ignored. This means that **details.seen, details.unseen, and details.all**, would be restricted to others type 'message', it means they will not have same number of elements compare to 'all' version.\n\nThis means you don't need to count how much items exist for every type, system will do it for you. And if you want to know all of them, just bind without any type...\n\n\n### Why this behavior ?\n\nThe main example, will be of course, facebook:\n * You have notification for message, counting how many message are waiting your reply.\n * You have notification for everything else: somebody type on your wall, like your comment...\n\nThey are both counted separately. On this system, you should do something like this:\n```js\nnotification.bind('message', function(details) {\n // Update facebook icon for messages\n});\n\nnotification.bind('other', function(details) {\n // Update the 'global' notification system\n // If notification popup is show, update also this content with notification content\n});\n```\n\nAnd on server side when you send a notification, you specify if it's a 'message' type or a 'other' type using the ```type``` parameter in ```/notify``` POST content.\n\nIf you understand this, you agree to the fact you don't want to have ALL unseen element for only message one, (you don't want to show 10 when you have 4 messages waiting, and 6 notifications, you want to show 4 message, and 6 notifications waiting instead).\nThis is why, if you bind only 'message' type, you will get only the statistics regarding this type.\n"},{"alpha_fraction":0.43438076972961426,"alphanum_fraction":0.4362291991710663,"avg_line_length":24.464706420898438,"blob_id":"848f6e07fa8cf8a23c557e72963917591486553c","content_id":"83503641792d17663cdcf2d859ed08c1dd6fc305","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"JavaScript","length_bytes":4328,"license_type":"permissive","max_line_length":78,"num_lines":170,"path":"/client-example/resource/js/socket.js","repo_name":"Deisss/webservice-notification","src_encoding":"UTF-8","text":"/**\n * Create a new SockJS instance.\n *\n * @class socket\n * @constructor\n *\n * @param namespace {String | null} The namespace to link SockJS with\n*/\nvar socket = function(namespace) {\n // Store events list\n this.events = {};\n // The base url\n this.url = '//' + window.location.hostname;\n // The base port\n this.port = 8587;\n // Store the SockJS instance\n this.instance = null;\n // Store the namespace\n this.namespace = namespace || '';\n // Should reconnect or not\n this.reconnect = true;\n};\n\n\n\nsocket.prototype = {\n /**\n * Bind a function to an event from server.\n *\n * @method on\n *\n * @param name {String} The message type\n * @param fct {Function} The function to call\n * @param scope {Object | null} The scope to apply for function\n */\n on: function(name, fct, scope) {\n var fn = fct;\n if(scope) {\n // We bind scope\n fn = function() {\n fct.apply(scope, arguments);\n };\n }\n // If it's not existing, we create\n if(!this.events[name]) {\n this.events[name] = [];\n }\n // Append event\n this.events[name].push(fn);\n },\n\n /**\n * Send data to server.\n *\n * @method emit\n *\n * @param name {String} The message type\n * @param data {Object} The linked data with message\n */\n emit: function(name, data) {\n this.instance.send(\n JSON.stringify({\n name: name,\n data: data\n })\n );\n },\n\n /**\n * Connect to server.\n *\n * @method connect\n */\n connect: function() {\n // Disconnect previous instance\n if(this.instance) {\n // Get auto-reconnect and re-setup\n var p = this.reconnect;\n this.disconnect();\n this.reconnect = p;\n }\n\n // Start new instance\n var base = (this.port != 80) ? this.url + ':' + this.port : this.url;\n var sckt = new SockJS(base + '/' + this.namespace, null, {\n debug : false,\n devel : false\n });\n\n var _this = this;\n\n /**\n * Parse event from server side, and dispatch it\n *\n * @method catchEvent\n *\n * @param response {Object} The data from server side\n */\n function catchEvent(response) {\n var name = (response.type) ? response.data.name : response.name,\n data = (response.type) ? response.data.data : response.data,\n evts = _this.events[name];\n if(evts) {\n var parsed = (typeof(data) === 'object' && data !== null) ?\n data : JSON.parse(data);\n\n for(var i=0, l=evts.length; i<l; ++i) {\n var fct = evts[i];\n if(typeof(fct) === 'function') {\n // Defer call on setTimeout\n (function(f) {\n setTimeout(function() {f(parsed);}, 0);\n })(fct);\n }\n }\n }\n };\n\n // Catch open\n sckt.onopen = function() {\n catchEvent({\n name: 'open',\n data: {}\n });\n catchEvent({\n name: 'connect',\n data: {}\n });\n };\n\n // Catch arriving message\n sckt.onmessage = function(data) {\n catchEvent(data);\n };\n\n // Catch close, and reconnect\n sckt.onclose = function() {\n catchEvent({\n name: 'close',\n data: {}\n });\n catchEvent({\n name: 'disconnect',\n data : {}\n });\n if(_this.reconnect) {\n _this.connect();\n }\n };\n\n // Link to server\n this.instance = sckt;\n },\n\n /**\n * Disconnect from server.\n *\n * @method disconnect\n */\n disconnect: function() {\n this.reconnect = false;\n\n if(!this.instance) {\n return;\n }\n\n this.instance.close();\n this.instance = null;\n }\n};"},{"alpha_fraction":0.5606180429458618,"alphanum_fraction":0.5629952549934387,"avg_line_length":23.504854202270508,"blob_id":"787e880c44b60c8fc68f59eb5703419348dd1796","content_id":"35819381d9737ffa719cea5fd63914e4c9e39ccb","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2524,"license_type":"permissive","max_line_length":79,"num_lines":103,"path":"/server/system/CommonMongoDB.py","repo_name":"Deisss/webservice-notification","src_encoding":"UTF-8","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom system.DBMongo import connect\nfrom system.ConfigLoader import getCfg\nfrom datetime import datetime\nfrom bson.objectid import ObjectId\n\n_collection = getCfg('MONGODB', 'db')\n\n\n#\n# ------------------------\n# INDEX\n# ------------------------\n#\ndef mongoIndex(db):\n ''' Try to generate indexes if needed '''\n db.ensure_index([('_userId', 1), ('seen', 1), ('delete', 1)], unique=False)\n\n\n#\n# ------------------------\n# NOTIFICATIONS\n# ------------------------\n#\ndef createNotification(notifications):\n ''' Register a new notification into database '''\n db = connect(_collection)\n mongoIndex(db)\n now = datetime.now()\n\n for notification in notifications:\n notification['createdAt'] = now\n notification['updatedAt'] = now\n notification['deletedAt'] = None\n notification['delete'] = False\n notification['seen'] = False\n\n db.insert(notifications, continue_on_error=True)\n\ndef getUserUnseenNotifications(userId):\n ''' Get the unseen notifications '''\n db = connect(_collection)\n mongoIndex(db)\n\n return db.find({\n '_userId': str(userId),\n 'seen': False,\n 'delete': False\n }).sort('createdAt', 1)\n\ndef getUserNotifications(userId, page, limit):\n ''' Get many notifications linked to a user '''\n db = connect(_collection)\n mongoIndex(db)\n\n return db.find({\n '_userId': str(userId),\n 'delete': False\n }).sort('createdAt', 1).skip(page * limit).limit(limit)\n\ndef seenNotification(userId, notificationsId):\n ''' Set many notifications as seen '''\n db = connect(_collection)\n mongoIndex(db)\n now = datetime.now()\n\n # We convert string ObjectId literals into ObjectId one\n resultsId = [ObjectId(str(x)) for x in notificationsId]\n\n db.update({\n '_id': {\n '$in': resultsId\n },\n '_userId': userId\n }, {\n '$set': {\n 'seen': True,\n 'updatedAt': now\n }\n })\n\ndef deleteNotification(userId, notificationsId):\n ''' Set many notifications as delete '''\n db = connect(_collection)\n mongoIndex(db)\n now = datetime.now()\n\n # We convert string ObjectId literals into ObjectId one\n resultsId = [ObjectId(str(x)) for x in notificationsId]\n\n db.update({\n '_id': {\n '$in': resultsId\n },\n '_userId': userId\n }, {\n '$set': {\n 'delete': True,\n 'deletedAt': now\n }\n })\n"},{"alpha_fraction":0.6008403301239014,"alphanum_fraction":0.605042040348053,"avg_line_length":20.636363983154297,"blob_id":"9e86715db74e6ebfa53004e128584918d3b77c59","content_id":"817dfd91f707706fad40d024f7e8a03d5fe73ccd","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":476,"license_type":"permissive","max_line_length":75,"num_lines":22,"path":"/server/system/DBMongo.py","repo_name":"Deisss/webservice-notification","src_encoding":"UTF-8","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport pymongo\n\nfrom system.ConfigLoader import getCfg\n\n\n\n\ndef connect(collection):\n ''' Connect to a database, and give back collection '''\n url = getCfg('MONGODB', 'url')\n name = getCfg('MONGODB', 'db')\n\n # Make url compatible in all cases with name\n if url[-1:] != '/':\n url += '/'\n\n con = pymongo.MongoClient(url + name, getCfg('MONGODB', 'port', 'int'))\n db = con[name]\n return db[collection]\n"},{"alpha_fraction":0.5362563133239746,"alphanum_fraction":0.5430017113685608,"avg_line_length":21.5,"blob_id":"d750824c3ae065a856fe30e09fc9f197e37b0327","content_id":"664e6f48dac72026a5cf288167d0015c80f2b17f","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":593,"license_type":"permissive","max_line_length":48,"num_lines":26,"path":"/server/system/DBMysql.py","repo_name":"Deisss/webservice-notification","src_encoding":"UTF-8","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\nimport MySQLdb as db\nfrom system.ConfigLoader import getCfg\n\n\n\n\ndef connect():\n ''' Create a new connection with MySQL '''\n # Set parameter format\n db.paramstyle = 'format'\n # Get connection\n return db.connect(\n host = getCfg('MYSQL', 'host'),\n user = getCfg('MYSQL', 'user'),\n passwd = getCfg('MYSQL', 'password'),\n db = getCfg('MYSQL', 'db'),\n port = getCfg('MYSQL', 'port', 'int'),\n\n # Force UTF 8\n charset= 'utf8',\n init_command='SET NAMES UTF8'\n )\n\n \n\n\n"},{"alpha_fraction":0.583512544631958,"alphanum_fraction":0.5863799452781677,"avg_line_length":25.846153259277344,"blob_id":"c0fd41fd5455948819cbc2baafe235d942b97320","content_id":"0170d8391c92bc7776a8da0456ec69fa644cdc6d","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1395,"license_type":"permissive","max_line_length":79,"num_lines":52,"path":"/server/system/EmailNotification.py","repo_name":"Deisss/webservice-notification","src_encoding":"UTF-8","text":"import smtplib, logging\n\nfrom ConfigLoader import getCfg\n\n'''\nSend an email to users:\n\nrecievers: a list of CC elements\nsubject: email subject\nmessage: email message\ncontentType: text/html by default\nmime: 1.0 by default\n'''\n\ndef send(recievers, subject, message, contentType='text/html', mime='1.0'):\n ''' Send an email to a group of users '''\n # The guy marked as 'the one :D'\n sender = getCfg('EMAIL', 'sender')\n\n content = \"From: %s\\r\\n\" % sender\n content+= \"BCC: %s\\r\\n\" % \",\".join(recievers)\n content+= \"MIME-Version: %s\\r\\n\" % mime\n content+= \"Content-type: %s\\r\\n\" % contentType\n content+= \"Subject: %s\\r\\n\" % subject\n content+= \"\\r\\n\"\n content+= message\n\n to = [sender] + recievers\n\n try:\n # start new connection\n server = smtplib.SMTP(getCfg('EMAIL', 'host'))\n\n # TLS REQUIRED\n if getCfg('EMAIL', 'tls', 'boolean') == True:\n server.ehlo()\n server.starttls()\n\n # LOGIN REQUIRED\n if getCfg('EMAIL', 'auth', 'boolean') == True:\n server.login(getCfg('EMAIL', 'login'), getCfg('EMAIL', 'password'))\n\n # save and exit\n server.sendmail(sender, to, content)\n server.quit()\n\n except smtplib.SMTPException:\n logging.error('SMTP: unable to send email')\n\n\nif __name__ == '__main__':\n send([sender], 'Super sujet', 'Super content')"},{"alpha_fraction":0.613822340965271,"alphanum_fraction":0.6183692216873169,"avg_line_length":23.62686538696289,"blob_id":"0116a7b3d67e358dfd01837026854ec5710687b3","content_id":"681a227af9b4669658baa147ebd2c62677456ac5","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3299,"license_type":"permissive","max_line_length":75,"num_lines":134,"path":"/server/server.py","repo_name":"Deisss/webservice-notification","src_encoding":"UTF-8","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport os, sys, logging, tornado.web\nfrom datetime import datetime\n\n# Getting folder\nfullPath = os.path.realpath(__file__)\ndirPath = os.path.dirname(fullPath)\n\n# Register path\nsys.path.append(os.path.join(dirPath, 'system' ))\nsys.path.append(os.path.join(dirPath, 'handler'))\n\n# Custom import\nfrom handler.HTTPServer import IndexHandler\nfrom sockjs.tornado import SockJSRouter\nfrom system.ConfigLoader import getCfg\n\n# Import sockets\nfrom websocket import NotificationSocketHandler\nfrom webservice import ServiceRequestManager\n\n\n\ndef getLogLevel():\n ''' Get the current application minimum log level '''\n level = getCfg('LOG', 'level').lower()\n if level == 'info' or level == 'information':\n return logging.INFO\n elif level == 'warn' or level == 'warning':\n return logging.WARN\n elif level == 'error' or level == 'err':\n return logging.ERROR\n else:\n return logging.DEBUG\n\n\n\ndef configureLogger(_logFolder, _logFile):\n ''' Start the logger instance and configure it '''\n # Set debug level\n logger = logging.getLogger()\n logger.setLevel(getLogLevel())\n\n # Format\n formatter = logging.Formatter(\n '%(asctime)s - %(levelname)s | %(name)s -> %(message)s',\n '%Y-%m-%d %H:%M:%S'\n )\n\n # Remove default handler to keep only clean one\n for hdlr in logger.handlers:\n logger.removeHandler(hdlr)\n\n # Create missing folder if needed\n if not os.path.exists(_logFolder):\n os.makedirs(_logFolder, 0700)\n\n #\n # ----------------------------\n # CREATE CONSOLE HANDLER\n # ----------------------------\n #\n\n # Create console handler\n consoleh = logging.StreamHandler()\n consoleh.setLevel(getLogLevel())\n consoleh.setFormatter(formatter)\n\n # Set our custom handler\n logger.addHandler(consoleh)\n\n #\n # ----------------------------\n # CREATE FILE HANDLER\n # ----------------------------\n #\n fileh = logging.FileHandler(_logFile, 'a')\n fileh.setLevel(getLogLevel())\n fileh.setFormatter(formatter)\n\n # Set our custom handler\n logger.addHandler(fileh)\n\n\n\ndef printWelcomeMessage(msg, place=10):\n ''' Print any welcome message '''\n logging.debug('*' * 30)\n welcome = ' ' * place\n welcome+= msg\n logging.debug(welcome)\n\n logging.debug('*' * 30 + '\\n')\n\n\n\n\n\n\n\nif __name__ == '__main__':\n logFile = getCfg('LOG', 'file')\n logFolder = os.path.dirname(logFile)\n configureLogger(logFolder, logFile)\n\n serverPort = getCfg('APPLICATION', 'port', 'int')\n\n # Print logger message\n logging.debug('\\n\\nSystem start at: %s\\nSystem log level: %s\\n' %\n (datetime.now(), getCfg('LOG', 'level')))\n\n printWelcomeMessage('STARTING', 11)\n printWelcomeMessage('SETUP ROUTES', 8)\n\n # Create route\n notifRouter = SockJSRouter(NotificationSocketHandler, '/notification')\n\n # Create tornado app\n app = tornado.web.Application([\n (r'/', IndexHandler),\n (r'/notify', ServiceRequestManager)\n ]\n + notifRouter.urls\n )\n\n # Listening to application\n app.listen(serverPort)\n\n printWelcomeMessage('SERVER RUNNING ON PORT %i' % serverPort, 1)\n\n # Start IOLoop\n tornado.ioloop.IOLoop.instance().start()"},{"alpha_fraction":0.73572838306427,"alphanum_fraction":0.7372398376464844,"avg_line_length":47.05586624145508,"blob_id":"0d8151e6ef1cf1deb878de8185fdd50af4c2a921","content_id":"144c367446c6755f2901775e3c9275319f6984ca","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":8601,"license_type":"permissive","max_line_length":305,"num_lines":179,"path":"/README.md","repo_name":"Deisss/webservice-notification","src_encoding":"UTF-8","text":"# webservice-notification\n\nA server to plug into your own system, to create a notification system between some server side event, and your users. This aims to provide something close to what you can found on facebook (for example), with a small footprint on your existing server architecture and client.\n\n\n\n## Installation\n\nAs it is a real system, get some time before starting (around 10/15min should be enough), here is the list of programs we need:\n\n * MongoDB\n * Python (with few packages)\n * MySQL (see below)\n\nThe system is using MongoDB as a primary database, and MySQL is here to show an example how to plug your logic (security) to this system (we will explain later).\n\n**The installation is describe for CentOS/RedHat system**\n\n\n\n### Database\nWe need only MongoDB:\n\n```\nyum -y install mongodb\n```\n\nFor mongodb you may need to use [MongoDB](http://docs.mongodb.org/manual/tutorial/install-mongodb-on-red-hat-centos-or-fedora-linux/) repository instead of default one.\n\nThat will be enough, of course if you want to run MySQL example, you will need also MySQL setup (package **mysql** and **mysql-server**).\n\n\n\n### Python\nThe main development is based on Python 2.x branch, but should work like a charm on Python 3.x branch. We need to install python and few dependencies:\n\n```\nyum -y install \\\n python python-devel \\\n python-setuptools python-pip \\\n MySQL-python\n```\n\n\n\n### PIP\nNow we have a Python instance ready and running, we need additional packages:\n```\npip install tornado pymongo sockjs-tornado sockjsroom PyDispatcher\n```\n\nWe install:\n * **tornado, sockjs-tornado, sockjsroom**: packages to manage server system, and real time server system,\n * **pymongo**: use MongoDB into python,\n * **PyDispatcher**: a PubSub system used between new notification, and currently connected user.\n\n \nNow we have the full system installed, we miss to clone this repository:\n```\nmkdir ws_notification\ncd ws_notification\ngit clone https://github.com/Deisss/webservice-notification.git .\n```\n\nWe are ready to configure.\n\n\n\n\n## Configure\n\nBefore doing anything else, it's quite recommended (required in fact) to setup configuration; from ```ws_notification```, go into ```server``` directory, and edit ```config.ini```:\n```\ncd ws_notification/server\nvim config.ini\n```\nFor every category (except APPLICATION), you have a DEBUG and RELEASE element, you should configure every part according to your own server configuration. All of them are 'classical' configuration, like email provider, database host, so we don't explain them here...\n\nNow we can start system.\n\n\n\n\n## Usage\n\nFirst of all, you need to launch the server. from ```ws_notification```, go into ```server``` directory, open a command prompt and type ```python server.py```:\n```\npython server.py\n```\nThat's enough, no need to do more to have server ready. Now we will explain API available (quite simple).\n\n\n\n\n## API - server\n\nServer(s) can send new notifications threw a single URL:\n * **POST** => **/notify**:\n - *type* | string: The notification type\n - *title* | string: The notification title\n - *content* | string: The notification content\n - *url* | string: The associated url\n - *icon* | string: A field to set icon\n - *image* | string: An image (like gravatar image)\n - *users* | string (json doc): A list of users who are concerned by this notification (see below)\n\n**/!\\ Except content, all elements are not required. This is simply due to the fact that server doesn't do anything with! The client may use some or all of them (you decide). Basically this server only make relation between notification and user, so it doesn't use them at all, only store them...**\n\n**/!\\ Remember also that users are important: if you specify none of them, nothing will happend/be done... As the server create one notification for one user in database.**\n\nThe **users** parameter is the most important here, it's a simple JSON document (in string format) indicating who is concerned by this notification, let's show an example as it's quite easy:\n```\n[\n {\n \"id\": 1,\n \"email\": \"myemail@test.com\"\n },\n {\n \"id\": 2,\n \"email\": \"second@test.com\"\n }\n]\n```\nWill try to find user with id 1 and 2 into websocket system (=currently connected), if at least, one of them is not found, it will use the related email to send an email instead (see below).\n\n\nThose elements represent a notification in most of existings system. But this system is able to do little more, so we provide extra parameters:\n * **email_send**: a boolean indicate is system should send an email if one or more user is not currently connected to websocket system (=not online)\n * **email_force**: no matter if user is connected or not, they should ALL recieve email notification\n * **email_subject**: A subject for the email\n * **email_content**: The email content\n * **email_mime** (default: 1.0): email MIME Version\n * **email_type** (default: text/html): The content type of the email\n\n*Note*: if email_send is not activated, system will never send email, so you need this parameter if you want to use email service.\n\n*Note2*: only one email is sent, with everybody in BCC.\n\n\nThis is the only entry point for sending notification to system. Of course this point should not be accessible from outside, only from your local server.\n\n**/!\\ Please keep in mind that this server does not provide ANY security check regarding what is sent to user. It means content, title and others fields are not escaped or checked against any traditionnal security trouble, you should take a lot care about this BEFORE sending to this system any content.**\n\n\n\n## API - client\n\nNow we know how to deal with adding new notification, it remains the client side: how user get new notification, and how you can use them.\n\nThe client API is only composed with a single websocket, so the whole API is based on that. Moreover, we use on top of [SockJS](https://github.com/mrjoes/sockjs-tornado), [sockjsroom](https://github.com/Deisss/python-sockjsroom) for creating multi-message system.\nAs websocket is bi-directionnal, we separate them:\n\n**From client to server:**\n * **unseen**: get the unseen notifications for the given user. It should not be so much needed, as everytime user connect to socket, you get exactly this return. Take no parameter.\n * **content**: get page by page existing notifications (no matter they are unseen or not). Take page (integer) as parameter.\n * **seen**: mark some notification as seen. Take an array of ObjectId's as parameter (the *_id* field from every notification you want to mark as seen).\n * **delete**: mark some notification as deleted. Take an array of ObjectId's as parameter (the *_id* field from every notification you want to mark as delete).\n\nWe strongly recommand you to check the example provided to have a working base to modify, and also check how to use those elements.\n\n**From server to client:**\n * **notification**: The server send some new notification, or send back data requested by user (like _unseen_ and _content_)\n * **seen**: Some notification have been marked as 'seen', we should apply modification...\n * **delete**: Some notification have been marked as 'deleted', we should remove them...\n\nThe notification is highly used by most of functions.\nFrom this API, now the client has to deal with information given, the seen and delete state should be enough to control the notification lifetime.\n\n\n\n## The case MySQL\n\nWe didn't talk until now about MySQL issue, here is. When user logon on webservice, the login and password are checked before allowing to use our notification system. This auth, is based on a MySQL db, as many system are using MySQL for user access instead of MongoDB.\nIf you plan to use something else (different auth method / different db), you can change this, by editing function ```loginUser``` into ```system/CommonMySQL.py``` (on server side of course) with your own authentification code.\nThis function is a simple \"give me a login/password, I will give you a unique ID to identify this user\". This id then, can be passed as parameter on ```/notify``` (see server API above)\n\nWe chose this way, as we wanted to provide a kind of 'full example' where you need only the minimum changes to integrate this system to your own existing system. This is also why you send on notify email content: we wanted to keep it with the minimum integration possible.\n\nFrom this point of view, this is the reason while we choose this: we provide a real example, on one of the most used DB. Allowing you to quickly understand how to modify it, and have fun !"},{"alpha_fraction":0.5337560772895813,"alphanum_fraction":0.5371130108833313,"avg_line_length":22.321739196777344,"blob_id":"954f0a5efe0fcb7aecb11718964bab362e0d5fe0","content_id":"52917b27576e14fc7831b6e3cf14370f2abbf4d2","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"JavaScript","length_bytes":2681,"license_type":"permissive","max_line_length":76,"num_lines":115,"path":"/client-example/resource/js/action.js","repo_name":"Deisss/webservice-notification","src_encoding":"UTF-8","text":"/**\n * Create a new socket instance and try to connect to it\n *\n * @method doLogin\n*/\nfunction doLogin() {\n var login = $('#signin #login').val(),\n password = $('#signin #password').val();\n\n if(login && password && login != '' && password != '') {\n notification.connect(login, CryptoJS.SHA512(password).toString());\n\n // We hide previous element and show new one\n $('#hide-after-login, #signin').hide();\n $('#hide-before-login, #signout').show();\n\n // Some notification are now marked as 'seen'\n notification.bind(function(details) {\n console.log('recieve events');\n console.log(details);\n\n if(details.event === 'notification') {\n var notification = details.element,\n seen = notification.seen;\n\n var container = $('#single-notification > div').clone();\n container.find('h4').html(notification.title);\n container.find('p').html(notification.content);\n container.find('button').data('objid', notification['_id']);\n\n // Empty or not valid url\n if(!notification.url) {\n container.find('a.link').css('display', 'none');\n } else {\n container.find('a.link').attr('href', notification.url);\n }\n\n if(seen == 'false' || seen == false) {\n container.addClass('unseen-notification');\n } else {\n container.addClass('seen-notification');\n }\n\n $('#content-show').append(container);\n }\n });\n }\n};\n\n\n\n/**\n * Logout, destroy and stop socket.\n *\n * @method doLogout\n*/\nfunction doLogout() {\n doClear();\n notification.unbindAll();\n notification.disconnect();\n $('#hide-before-login, #signout').hide();\n $('#hide-after-login, #signin').show();\n};\n\n\n/**\n * Clear existing content\n *\n * @method doClear\n*/\nfunction doClear() {\n $('#content-show').html('');\n};\n\n\n/**\n * Ask to get more notifications\n *\n * @method doContent\n*/\nfunction doContent() {\n notification.content(0);\n};\n\n\n/**\n * Get the unseen element\n *\n * @method doUnseen\n*/\nfunction doUnseen() {\n notification.unseen();\n};\n\n/**\n * Mark given id as seen\n *\n * @method doSeen\n*/\nfunction doSeen(el) {\n var id = $(el).data('objid');\n notification.seen([id]);\n};\n\n/**\n * Create a new notification for one or more users\n *\n * @method doNotify\n*/\nfunction doNotify() {\n // Send ajax request\n $.post('//' + window.location.hostname + ':8587/notify',\n $('form#notify').serialize(), function(data) {}\n );\n};"},{"alpha_fraction":0.5314711332321167,"alphanum_fraction":0.5325884819030762,"avg_line_length":32.14814758300781,"blob_id":"2291831459efddd1b84d8fa48d28f51c6951a90d","content_id":"bb27c274795105913108976bc78cd298b675f541","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5370,"license_type":"permissive","max_line_length":139,"num_lines":162,"path":"/server/websocket.py","repo_name":"Deisss/webservice-notification","src_encoding":"UTF-8","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom sockjsroom import SockJSDefaultHandler\nfrom bson.json_util import dumps\nfrom pydispatch import dispatcher\nfrom system.ConfigLoader import getCfg\nfrom system.CommonMySQL import loginUser\nfrom system.CommonMongoDB import createNotification, getUserUnseenNotifications, getUserNotifications, seenNotification, deleteNotification\n\nimport logging\n\ndef _debug(str):\n logging.debug('notification: ' + str)\n\n\nclass NotificationSocketHandler(SockJSDefaultHandler):\n ''' Basic notification system '''\n #\n # ---------------------------\n # CLASS INIT\n # ---------------------------\n #\n def initialize(self):\n ''' Initialize any new connection '''\n self.login = ''\n # As we use the str() function, the None is never really None\n # So we use this also here to keep test simple\n self.userId = 'None'\n\n def startPubSub(self):\n ''' Starting the PubSub system '''\n if self.userId != 'None':\n _debug('start pubsub for user #%s (%s)'\n % (self.userId, self.login))\n dispatcher.connect(\n self.pubsub,\n signal='user-%s' % self.userId\n )\n\n def stopPubSub(self):\n ''' Stopping the PubSub system '''\n if self.userId != 'None':\n _debug('stop pubsub for user #%s (%s)'\n % (self.userId, self.login))\n dispatcher.disconnect(\n self.pubsub,\n signal='user-%s' % self.userId\n )\n\n def jsonSend(self, name, param):\n ''' Publish as JSON data '''\n self.send({\n 'name': name,\n 'data': SockJSDefaultHandler._parser.encode(list(param))\n })\n\n #\n # ---------------------------\n # SOCKJS DEFAULT FUNCTION\n # ---------------------------\n #\n def on_open(self, info):\n pass\n\n def on_close(self):\n self.on_leave()\n\n #\n # ---------------------------\n # SOCKJS CUSTOM FUNCTION\n # ---------------------------\n #\n def on_join(self, data):\n ''' Join the notification system '''\n # data => login, password\n self.initialize()\n\n # Get the login and password, and check user on database\n self.login = str(data['login'])\n self.userId = str(loginUser(self.login, str(data['password'])))\n\n\n\n if self.userId != 'None':\n _debug('joining user #%s (%s)' % (self.userId, self.login))\n self.startPubSub()\n\n # We send back the unseen notifications\n self.on_unseen(None)\n else:\n _debug('ERROR joining user #%s (%s)' % (self.userId, self.login))\n self.initialize()\n\n\n def on_seen(self, data):\n ''' User seen a new notification, we mark them as seen '''\n # data => [id(s)], list of ids to mark as seen\n if self.userId != 'None':\n _debug('user #%s (%s) ask to mark as seen %s'\n % (self.userId, self.login, data['ids']))\n seenNotification(self.userId, data['ids'])\n self.jsonSend('seen', data['ids'])\n\n\n def on_delete(self, data):\n ''' User want to delete some notifications, we mark them as deleted '''\n # data => [id(s)], list of ids to mark as delete\n if self.userId != 'None':\n _debug('user #%s (%s) ask to mark as delete %s'\n % (self.userId, self.login, data['ids']))\n deleteNotification(self.userId, data['ids'])\n self.jsonSend('delete', data['ids'])\n\n\n def on_unseen(self, data):\n ''' Get the unseen content only '''\n # data => unused\n if self.userId != 'None':\n userNotification = getUserUnseenNotifications(self.userId)\n self.jsonSend('notification', userNotification)\n\n\n def on_content(self, data):\n ''' User ask to get latests notifications '''\n # data => page, limit\n if self.userId != 'None':\n page = int(data['page'])\n limit = getCfg('APPLICATION', 'limit', 'int')\n\n # Make elements fit requirements\n page = max(0, page)\n limit = max(1, limit)\n limit = min(100, limit)\n\n # Get latests notifications for user, and send it back\n userNotification = getUserNotifications(self.userId, page, limit)\n self.jsonSend('notification', userNotification)\n\n\n def pubsub(self, sender, data):\n '''\n This function send a notification from pubsub system\n to user, as the pubsub is not a socket element, we are\n not using the on_* pattern (we don't want user to be\n able to request himself this function threw client side)\n '''\n if self.userId != 'None':\n _debug('pubsub user #%s (%s):\\n\\tmessage:%s'\n % (self.userId, self.login, data))\n self.jsonSend('notification', [data])\n\n\n def on_leave(self):\n ''' Leave the notification system '''\n # Only if user has time to call self.initialize\n # (sometimes it's not the case)\n if self.userId != 'None':\n _debug('leaving user #%s (%s)' % (self.userId, self.login))\n self.stopPubSub()\n # Erasing data\n self.initialize()\n"},{"alpha_fraction":0.729394257068634,"alphanum_fraction":0.7591856718063354,"avg_line_length":19.33333396911621,"blob_id":"7cbe3b1e581b22e93d6e3e645c4b038c4e700cc5","content_id":"d0c038240be0a5287d599e50c72f3676bf22e6f2","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"INI","length_bytes":2014,"license_type":"permissive","max_line_length":73,"num_lines":99,"path":"/server/config.ini","repo_name":"Deisss/webservice-notification","src_encoding":"UTF-8","text":"; Config file\n\n; main application configuration\n[APPLICATION]\n; can be DEBUG or RELEASE\nconfiguration=DEBUG\nport=8587\n; number of notifications when requesting a page\nlimit=30\n; define an API Key for limiting access to /notify POST\n; this is of course not a huge security, but still better than nothing ;)\n; leave it blank to skip it...\napikey=\n\n; configure log level\n[LOG-DEBUG]\nfile=./log/server.log\nlevel=DEBUG\n\n; configure log level\n[LOG-RELEASE]\nfile=/var/log/ws_notification/server.log\nlevel=WARN\n\n\n; configure email service\n[EMAIL-DEBUG]\n; host can be 'localhost', 'smtp.gmail.com:587', ... (does include port)\nhost=smtp.gmail.com:587\n; indicate (true/false) if system use tls\ntls=true\n; indicate if system use auth\nauth=true\n; indicate login/password (only on auth)\nlogin=MyGMAILAccount\npassword=MyPASSWORD\n; the email which is sending...\nsender=MyNAME\n\n[EMAIL-RELEASE]\n; host can be 'localhost', 'smtp.gmail.com:587', ... (does include port)\nhost=smtp.gmail.com:587\n; indicate (true/false) if system use tls\ntls=true\n; indicate if system use auth\nauth=true\n; indicate login/password (only on auth)\nlogin=MyGMAILAccount\npassword=MyPASSWORD\n; the email which is sending...\nsender=MyEmail\n\n\n\n\n\n[MYSQL-DEBUG]\n; MySQL configuration\nhost=127.0.0.1\nuser=root\npassword=\nport=3306\ndb=MyDatabase\n; configure the request to send to authentificate user\n; WILL PRODUCE 'SELECT *idField* FROM *table* WHERE *loginField*=%s AND\n; *passwordField*=%s'\ntable=user\nidField=id\nloginField=login\npasswordField=password\n\n[MONGODB-DEBUG]\n; MongoDB configuration\nurl=mongodb://127.0.0.1/\nport=27017\ndb=ws_notification\n\n\n\n[MYSQL-RELEASE]\n; MySQL configuration\nhost=127.0.0.1\nuser=root\npassword=\nport=3306\ndb=MyDatabase\n; configure the request to send to authentificate user\n; WILL PRODUCE 'SELECT *idField* FROM *table* WHERE *loginField*=%s AND\n; *passwordField*=%s'\ntable=user\nidField=id\nloginField=login\npasswordField=password\n\n[MONGODB-RELEASE]\n; MongoDB configuration\nurl=mongodb://127.0.0.1/\nport=27017\ndb=ws_notification\n\n"}],"string":"[\n {\n \"alpha_fraction\": 0.42338818311691284,\n \"alphanum_fraction\": 0.42538875341415405,\n \"avg_line_length\": 26.426433563232422,\n \"blob_id\": \"74be245bc9f3db033f2ab44b0af1a52810d1d86f\",\n \"content_id\": \"52320f7eff4b92b78e86ee93b030959faca5e287\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"JavaScript\",\n \"length_bytes\": 10997,\n \"license_type\": \"permissive\",\n \"max_line_length\": 79,\n \"num_lines\": 401,\n \"path\": \"/client-example/resource/js/notification.js\",\n \"repo_name\": \"Deisss/webservice-notification\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/**\\n * A notification system for grabbing important event threw the user session\\n * lifetime\\n *\\n * @class notification\\n * @static\\n*/\\nvar notification = {\\n /**\\n * Store the created socket instance\\n * @property _socket\\n * @type Object\\n * @default null\\n */\\n _socket: null,\\n\\n /**\\n * Store the 'all' type element\\n * @property _allType\\n * @type String\\n * @default __all__\\n */\\n _allType: '__all__',\\n\\n /**\\n * Keep a trace of all 'unseen' notifications\\n * @property _unseen\\n * @type Object\\n * @default {}\\n */\\n _unseen: {},\\n\\n /**\\n * Keep a trace of all notifications (include unseen)\\n * @property _all\\n * @type Object\\n * @default {}\\n */\\n _all: {},\\n\\n /**\\n * Store binded function when recieving something\\n * @property _bind\\n * @type Object\\n * @default {}\\n */\\n _bind: {},\\n\\n\\n /**\\n * Get the object size (kind of length)\\n *\\n * @method _getObjectSize\\n *\\n * @param obj {Object} The object to check\\n * @return {Integer} The count found\\n */\\n _getObjectSize: function(obj) {\\n if(!obj) {\\n return 0;\\n }\\n var key, count = 0;\\n for(key in obj) {\\n if(obj.hasOwnProperty(key)) {\\n count++;\\n }\\n }\\n return count;\\n },\\n\\n\\n /**\\n * Raise a new event\\n *\\n * @method _raiseEvent\\n * @private\\n *\\n * @param type {String} The notification type, or all\\n * @param name {String} The event type (like 'seen',\\n * 'delete')\\n * @param el {Object} The concerned notification\\n */\\n _raiseEvent: function(type, name, el) {\\n type = type || this._allType;\\n\\n if(this._bind[type]) {\\n var tab = this._bind[type],\\n i = tab.length;\\n\\n var unseenLength = 0,\\n allLength = 0,\\n seenLength = 0;\\n\\n // We select length globally\\n if(type == this._allType) {\\n unseenLength = this._getObjectSize(this._unseen);\\n allLength = this._getObjectSize(this._all);\\n\\n // We select only the sub-length of some elements\\n } else {\\n var g = this._getObjectSize(this._all);\\n\\n while(g--) {\\n var notification = this._all[g];\\n if(notification['type'] === type) {\\n var seen = notification['seen'];\\n allLength++;\\n\\n if(seen == false || seen == 'false' || seen == 'unseen'\\n || seen == '0' || seen == 0) {\\n unseenLength++;\\n }\\n }\\n }\\n }\\n\\n // The seenLength is always the same...\\n seenLength = allLength - unseenLength;\\n\\n while(i--) {\\n var fct = tab[i];\\n // Send the event\\n setTimeout(function() {\\n fct({\\n event: name,\\n element: el,\\n notification: el,\\n seen: seenLength,\\n unseen: unseenLength,\\n all: allLength,\\n });\\n }, 0);\\n }\\n }\\n },\\n\\n /**\\n * Start the notification system.\\n *\\n * @method connect\\n *\\n * @param login {String} The login to use\\n * (same as global system)\\n * @param password {String} The password to use\\n * (same as global system => sha512)\\n * @return {Object} The socket instance created\\n */\\n connect: function(login, password) {\\n var instance = new socket('notification');\\n\\n // On every connect, the server 'loose' us\\n // so we have to join again\\n instance.on('connect', function() {\\n // Everytime a connect appear, we have to logon again\\n this.emit('join', {\\n login: login,\\n password: password\\n });\\n }, instance);\\n\\n // On seen, we remove from unseen only\\n instance.on('seen', function(ids) {\\n var elements = [];\\n\\n // Some element has been marked as seen\\n for(var i=0, l=ids.length; i<l; ++i) {\\n var id = ids[i];\\n\\n // We raise a new event\\n if(this._unseen[id]) {\\n elements.push(this._unseen[id]);\\n delete this._unseen[id];\\n }\\n }\\n\\n for(var i=0, l=elements.length; i<l; ++i) {\\n var element = elements[i];\\n\\n if(element) {\\n if(element['type']) {\\n this._raiseEvent(element['type'], 'seen', element);\\n }\\n\\n this._raiseEvent(null, 'seen', element);\\n }\\n }\\n }, this);\\n\\n // On delete, we try to remove elements existing inside\\n instance.on('delete', function(ids) {\\n var elements = [];\\n\\n for(var i=0, l=ids.length; i<l; ++i) {\\n var id = ids[i];\\n if(this._all[id]) {\\n elements.push(this._all[id]);\\n\\n delete this._unseen[id];\\n delete this._all[id];\\n }\\n }\\n\\n for(var i=0, l=elements.length; i<l; ++i) {\\n var element = elements[i];\\n\\n if(element) {\\n if(element['type']) {\\n this._raiseEvent(element['type'], 'delete', element);\\n }\\n\\n this._raiseEvent(null, 'delete', element);\\n }\\n }\\n }, this);\\n\\n // On notification, we erase previous content or append\\n instance.on('notification', function(notifications) {\\n var elements = [];\\n\\n for(var i=0, l=notifications.length; i<l; ++i) {\\n var notification = notifications[i],\\n id = notification['_id'],\\n seen = notification['seen'];\\n\\n elements.push(notification);\\n\\n // We try to append\\n if(seen == false || seen == 'false' || seen == 'unseen'\\n || seen == '0' || seen == 0) {\\n this._unseen[id] = notification;\\n }\\n this._all[id] = notification;\\n }\\n\\n for(var i=0, l=elements.length; i<l; ++i) {\\n var element = elements[i];\\n\\n if(element) {\\n if(element['type']) {\\n this._raiseEvent(element['type'],\\n 'notification', element);\\n }\\n\\n this._raiseEvent(null, 'notification', element);\\n }\\n }\\n }, this);\\n\\n // Start socket\\n instance.connect();\\n\\n this._socket = instance;\\n return instance;\\n },\\n\\n /**\\n * Mark as seen some ids.\\n *\\n * @method seen\\n *\\n * @param ids {Array} An array of ObjectId (string way)\\n * to mark as seen\\n */\\n seen: function(ids) {\\n if(this._socket !== null) {\\n this._socket.emit('seen', {\\n ids: ids\\n });\\n }\\n },\\n\\n /**\\n * Grab the unseen notifications from server (should not be needed at all)\\n * As when user connect, it automatically retrieve unseen notifications...\\n *\\n * @method unseen\\n */\\n unseen: function() {\\n if(this._socket !== null) {\\n this._socket.emit('unseen', {});\\n }\\n },\\n\\n /**\\n * Mark as deleted selected ids\\n *\\n * @method delete\\n *\\n * @param ids {Array} An array of ObjectId (string way)\\n * to mark as deleted\\n */\\n delete: function(ids) {\\n if(this._socket !== null) {\\n this._socket.emit('delete', {\\n ids: ids\\n });\\n }\\n },\\n\\n /**\\n * Ask the server to bring some existing content (page by page)\\n *\\n * @method content\\n *\\n * @param page {Integer} The page to recieve\\n */\\n content: function(page) {\\n if(this._socket !== null) {\\n this._socket.emit('content', {\\n page: Math.min(0, page)\\n });\\n }\\n },\\n\\n /**\\n * Stop the notification system.\\n *\\n * @method disconnect\\n */\\n disconnect: function() {\\n if(this._socket !== null) {\\n this._socket.disconnect();\\n this._socket = null;\\n }\\n },\\n\\n /**\\n * Bind a function to a new notification arriving (regarding the type).\\n * If you don't put any type to this function, the function will be binded\\n * to all elements recieve.\\n *\\n * @method bind\\n *\\n * @param type {String} The notification type to bind\\n * @param fct {Function} The function to respond in case of\\n * new element arriving\\n */\\n bind: function(type, fct) {\\n var f = 'function';\\n\\n if(typeof(type) === f) {\\n fct = type;\\n type = this._allType;\\n }\\n\\n if(!this._bind[type]) {\\n this._bind[type] = [];\\n }\\n\\n if(typeof(fct) === f) {\\n this._bind[type].push(fct);\\n }\\n },\\n\\n /**\\n * Remove from binding a function previously binded using bind.\\n * Like bind, if you specify only a function as parameter, the 'all' type\\n * will be remove.\\n *\\n * @method unbind\\n *\\n * @param type {String} The notification type to bind\\n * @param fct {Function} The function to remove\\n */\\n unbind: function(type, fct) {\\n if(typeof(type) === 'function') {\\n fct = type;\\n type = this._allType;\\n }\\n\\n if(this._bind[type]) {\\n var tab = this._bind[type],\\n l = tab.length;\\n\\n while(l--) {\\n if(tab[l] === fct) {\\n tab.splice(l, 1);\\n }\\n }\\n\\n if(tab.length == 0) {\\n this.unbindAll(type);\\n }\\n }\\n },\\n\\n /**\\n * Remove all functions binded to a given type.\\n * You can not specify type to unbind on 'all' type\\n *\\n * @method unbindAll\\n *\\n * @param type {String} The notification type to clear\\n */\\n unbindAll: function(type) {\\n if(!type) {\\n type = this._allType;\\n }\\n delete this._bind[type]; \\n }\\n};\"\n },\n {\n \"alpha_fraction\": 0.6566265225410461,\n \"alphanum_fraction\": 0.6626505851745605,\n \"avg_line_length\": 19.75,\n \"blob_id\": \"6f9cb09d39e5ac492a6359423039ae479ca72573\",\n \"content_id\": \"96471aa7d804b61186421e00764c0c47c381c1b7\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 166,\n \"license_type\": \"permissive\",\n \"max_line_length\": 47,\n \"num_lines\": 8,\n \"path\": \"/server/handler/HTTPServer.py\",\n \"repo_name\": \"Deisss/webservice-notification\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python\\n# -*- coding: utf-8 -*-\\n\\nimport tornado.web\\n\\nclass IndexHandler(tornado.web.RequestHandler):\\n ''' Regular HTTP handler (unused) '''\\n pass\\n\"\n },\n {\n \"alpha_fraction\": 0.5703434944152832,\n \"alphanum_fraction\": 0.5755221843719482,\n \"avg_line_length\": 34.32926940917969,\n \"blob_id\": \"e06b656ee45f95f48dbe256c57b9fcbb8ca73ad1\",\n \"content_id\": \"0f743d750653f2b43ed6a01099b5b71a94a82c6f\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5793,\n \"license_type\": \"permissive\",\n \"max_line_length\": 102,\n \"num_lines\": 164,\n \"path\": \"/server/webservice.py\",\n \"repo_name\": \"Deisss/webservice-notification\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python\\n# -*- coding: utf-8 -*-\\n\\nfrom sockjsroom import JsonDefaultHandler\\nfrom pydispatch import dispatcher\\nfrom datetime import datetime\\n\\nfrom system.CommonMongoDB import createNotification\\nfrom system.ConfigLoader import getCfg\\nfrom system.EmailNotification import send\\n\\nimport json, logging\\n\\n'''\\nA notification is represented by:\\n> automatically populated\\n _id (ObjectId)\\n createdAt (date)\\n updatedAt (date)\\n deletedAt (date)\\n delete (Boolean)\\n seen (Boolean)\\n _userId (str) => the userid linked to...\\n\\n> defined by service\\n type (String - a custom type)\\n title (String)\\n content (String)\\n url (String)\\n icon (String - an icon to show)\\n image (String - an image to show, like user profile)\\n\\n> Except content, all of them are optionals, and system does't use them\\nThey are here to help you define your own client look\\n'''\\n\\ndef getBooleanRealValue(b):\\n ''' Simple string to boolean convertion '''\\n if b == True or b == '1' or b == 1 or b == 'true' or b == 'True' or b == 'yes' or b == 'Yes':\\n return True\\n else:\\n return False\\n\\n\\nclass ServiceRequestManager(JsonDefaultHandler):\\n ''' Register a notification to user '''\\n def set_default_headers(self):\\n self.set_header('Access-Control-Allow-Origin', '*')\\n self.set_header('Access-Control-Allow-Headers', 'origin, content-type, accept, authorization')\\n self.set_header('Access-Control-Allow-Credentials', 'true')\\n self.set_header('Access-Control-Allow-Methods', 'GET, POST, PUT, DELETE, OPTIONS, HEAD')\\n # 1209600: 14 days\\n self.set_header('Access-Control-Max-Age', '1209600')\\n\\n def post(self):\\n # Before anything else, if APIKey is defined, we check it...\\n APIKeyDefined = getCfg('APPLICATION', 'apikey')\\n if(APIKeyDefined is not None) and (APIKeyDefined != ''):\\n APIKeySubmitted = self.get_argument('apikey', '')\\n if APIKeyDefined != APIKeySubmitted:\\n self.set_status(400)\\n self.finish('<html><body>API Key refused</body></html>')\\n logging.error(\\n 'notify: API Key refused, given: %s, allowed: %s' %\\n (APIKeySubmitted, APIKeyDefined)\\n )\\n return\\n\\n\\n # Will throw an HTTP 400 if missing\\n content = self.get_argument('content')\\n\\n # TYPE: the notification type\\n custom = self.get_argument('type', '')\\n # TITLE: the notification title - can be null\\n title = self.get_argument('title', '')\\n # URL: a link to the given notification - can be null\\n url = self.get_argument('url', '')\\n # ICON: a class/image or something to show as icon - can be null\\n icon = self.get_argument('icon', '')\\n # IMAGE: an image to represent post (like user on fb) - can be null\\n image = self.get_argument('image', '')\\n # Users linked to the notification (user should recieve notification)\\n users = json.loads(self.get_argument('users', '[]'))\\n\\n\\n\\n # Email stuff\\n # System should send an email...\\n canEmail = self.get_argument('email_send', False)\\n forceEmail = self.get_argument('email_force', False)\\n # Check everything is fine\\n canEmail = getBooleanRealValue(canEmail)\\n forceEmail = getBooleanRealValue(forceEmail)\\n\\n # ForceEmail will always override canEmail as it is mandatory\\n if forceEmail == True:\\n canEmail = True\\n\\n emailSubject = self.get_argument('email_subject', '')\\n emailContent = self.get_argument('email_content', '')\\n emailMime = self.get_argument('email_mime', '1.0')\\n emailType = self.get_argument('email_type', 'text/html')\\n\\n\\n\\n # Debug (if needed)\\n logging.debug('notify: new notification: ' +\\n '\\\\n\\\\ttitle: %s\\\\n\\\\turl: %s\\\\n\\\\tcontent: %s\\\\n'\\n % (title, url, content))\\n\\n # This element will recieve all notification to send to user\\n notifications = []\\n unsent = []\\n\\n for user in users:\\n # Get user information\\n userId = str(user['id'])\\n userEmail = user['email']\\n signal = 'user-%s' % userId\\n\\n # We check if there is one socket binded to given signal\\n reciever = len(dispatcher.getReceivers(signal=signal))\\n\\n notifications.append({\\n 'type': custom,\\n 'title': title,\\n 'content': content,\\n 'url': url,\\n 'icon': icon,\\n 'image': image,\\n '_userId': userId\\n })\\n\\n # Append the user to next email campaign if needed\\n if reciever <= 0 or forceEmail == True:\\n unsent.append(userEmail)\\n\\n\\n\\n # We bulk insert everything in a single call to database\\n # No matter if user is connected or not, we need to try...\\n if len(notifications) > 0:\\n createNotification(notifications)\\n\\n # For every item in notifications, we send them threw dispatcher\\n # to let socket know if there is something to update\\n for notification in notifications:\\n data = {\\n 'data': notification\\n }\\n userId = notification['_userId']\\n signal = 'user-%s' % userId\\n dispatcher.send(\\n signal=signal,\\n sender=dispatcher.Anonymous,\\n **data\\n )\\n\\n\\n\\n # We send email to people not connected\\n if len(unsent) > 0 and canEmail == True:\\n send(unsent, emailSubject, emailContent, emailType, emailMime)\"\n },\n {\n \"alpha_fraction\": 0.7690140604972839,\n \"alphanum_fraction\": 0.7690140604972839,\n \"avg_line_length\": 34.5,\n \"blob_id\": \"1aa9dd66d66404ff3618a5cb9766b7f66a60dfe4\",\n \"content_id\": \"006af5dd358f9fd81b2d8a9d72a7a56c74a47055\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 710,\n \"license_type\": \"permissive\",\n \"max_line_length\": 209,\n \"num_lines\": 20,\n \"path\": \"/server/README.md\",\n \"repo_name\": \"Deisss/webservice-notification\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Server\\n\\nThis is the entry point for the notification server.\\n\\nThis server is a full python/MongoDB system. It needs the following pip installation:\\n\\n```\\npip install tornado pymongo sockjs-tornado sockjsroom PyDispatcher\\n```\\n\\nAnd a configuration file setted on ```config.ini```.\\n\\n\\n## How to use\\n\\nFrom a server you already have, you want to implement a notification system on top of your existing system: you simply want to do a POST request on ```/notify``` to register a new notification to listed users.\\n\\nOn the other side (client side), users have to subscribe to websocket provided by this server.\\n\\nFor more information, just go to main [README.md file](https://github.com/Deisss/webservice-notification) \"\n },\n {\n \"alpha_fraction\": 0.5333333611488342,\n \"alphanum_fraction\": 0.5352112650871277,\n \"avg_line_length\": 23.79069709777832,\n \"blob_id\": \"4b50b6c2ff85b809ad9dc12679c29a18d14ea234\",\n \"content_id\": \"8a2ca655b118b574b0c3c1bd9a18f326e0374547\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1065,\n \"license_type\": \"permissive\",\n \"max_line_length\": 72,\n \"num_lines\": 43,\n \"path\": \"/server/system/CommonMySQL.py\",\n \"repo_name\": \"Deisss/webservice-notification\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python\\n# -*- coding: utf-8 -*-\\n\\nfrom system.DBMysql import connect\\nfrom system.ConfigLoader import getCfg\\nimport logging\\n\\ndef loginUser(login, password):\\n ''' Try to login a user regarding login/password '''\\n userContent = None\\n\\n table = getCfg('MYSQL', 'table')\\n tableId = getCfg('MYSQL', 'idField')\\n tableLogin = getCfg('MYSQL', 'loginField')\\n tablePassword = getCfg('MYSQL', 'passwordField')\\n\\n con = None\\n try:\\n # Starting\\n con = connect()\\n cur = con.cursor()\\n\\n cur.execute(\\n 'SELECT ' + tableId + ' FROM ' + table +\\n ' WHERE ' + tableLogin + '=%s AND ' + tablePassword + '=%s',\\n (\\n login,\\n password\\n )\\n )\\n\\n userContent = cur.fetchone()\\n\\n if userContent is not None:\\n userContent = userContent[0]\\n \\n except Exception as e:\\n logging.error('loginUser: Error from MySQL => %s' % e)\\n finally:\\n if con:\\n con.close()\\n\\n return userContent\"\n },\n {\n \"alpha_fraction\": 0.7493917346000671,\n \"alphanum_fraction\": 0.7518247961997986,\n \"avg_line_length\": 56.96154022216797,\n \"blob_id\": \"284871dd06325c002b3a029b370d17652c76d3fe\",\n \"content_id\": \"7f208effb5e3cd59e6ab0e1cac0f1f73233e33eb\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 4521,\n \"license_type\": \"permissive\",\n \"max_line_length\": 312,\n \"num_lines\": 78,\n \"path\": \"/client-example/README.md\",\n \"repo_name\": \"Deisss/webservice-notification\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Client example\\n\\n\\nThe client side is a simple example to manipulate two main functionnalities: the unseen notifications (all of them), and the first page only (as seen from API retrieve page by page).\\n\\n_Note_: this example will probably not work with your system as **the client send password encrypted in SHA-512**. So be carefull, and check file ```action.js``` before doing anything.\\n\\n_Note2_: jQuery is used only for DOM manipulation purpose, it's not required at all.\\n\\n_Note3_: On the other side, sockjs framework IS needed (see ```index.html``` to get from CDN).\\n\\nAs this is an example, the ```notification.js``` and ```socket.js``` can be reused without modification into your own application, they are both framework-less (except SockJS). The last file ```action.js``` is this time specific to this demo page.\\nHere is a description of every files to get quickly into:\\n\\n\\n## Socket.js\\n\\nA simple layer for [sockjsroom](https://github.com/Deisss/python-sockjsroom), this helps to use the multi-message and auto-reconnect capacities. It can be reused like this for every project you may have (the same file can be reused for all [sockjsroom](https://github.com/Deisss/python-sockjsroom) application).\\n\\n\\n## Notification.js\\n\\nA layer for implementing API provided by this server, this should remains the same for your own application, it's not needed to change it as it is common to every application using this notification server.\\nBasically, it's just a simple object helping to connect/bind events/retrieve messages specifically from this server. To summarize: ```socket.js``` is fully generic to all [sockjsroom](https://github.com/Deisss/python-sockjsroom) application, ```notification.js``` is generic to all application using this server.\\n\\n\\n## Action.js\\n\\nThis file is _specific_ to this example, in this file you will find how to catch websocket events, and apply action to (basically DOM modification to graphically show notification to user).\\n\\nIn fact, a socket event (from ```notification.js```), will send on every example an object:\\n```js\\n\\n// Bind to all notifications arriving\\nnotification.bind(function(details) {\\n // You can access to following content:\\n details.element; // The given notification (system send them one by one)\\n details.notification; // Same as element (alias)\\n details.event; // The server event: 'seen', 'delete' or 'notification'\\n details.seen; // Number of 'seen' notification currently stored on client side (see below)\\n details.unseen; // Number of 'unseen' notification currently stored on client side (the most interesting)\\n details.all; // Number of notifications (all of them) currently stored on client side\\n});\\n\\n// Bind to only 'message' notification\\nnotification.bind('message', function(details) {\\n // Same details as above\\n});\\n```\\n\\n**What is important here**, is the result difference between two binding:\\n**The second binding will only take care of notification with the 'message' type**. Others notifications will be ignored. This means that **details.seen, details.unseen, and details.all**, would be restricted to others type 'message', it means they will not have same number of elements compare to 'all' version.\\n\\nThis means you don't need to count how much items exist for every type, system will do it for you. And if you want to know all of them, just bind without any type...\\n\\n\\n### Why this behavior ?\\n\\nThe main example, will be of course, facebook:\\n * You have notification for message, counting how many message are waiting your reply.\\n * You have notification for everything else: somebody type on your wall, like your comment...\\n\\nThey are both counted separately. On this system, you should do something like this:\\n```js\\nnotification.bind('message', function(details) {\\n // Update facebook icon for messages\\n});\\n\\nnotification.bind('other', function(details) {\\n // Update the 'global' notification system\\n // If notification popup is show, update also this content with notification content\\n});\\n```\\n\\nAnd on server side when you send a notification, you specify if it's a 'message' type or a 'other' type using the ```type``` parameter in ```/notify``` POST content.\\n\\nIf you understand this, you agree to the fact you don't want to have ALL unseen element for only message one, (you don't want to show 10 when you have 4 messages waiting, and 6 notifications, you want to show 4 message, and 6 notifications waiting instead).\\nThis is why, if you bind only 'message' type, you will get only the statistics regarding this type.\\n\"\n },\n {\n \"alpha_fraction\": 0.43438076972961426,\n \"alphanum_fraction\": 0.4362291991710663,\n \"avg_line_length\": 24.464706420898438,\n \"blob_id\": \"848f6e07fa8cf8a23c557e72963917591486553c\",\n \"content_id\": \"83503641792d17663cdcf2d859ed08c1dd6fc305\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"JavaScript\",\n \"length_bytes\": 4328,\n \"license_type\": \"permissive\",\n \"max_line_length\": 78,\n \"num_lines\": 170,\n \"path\": \"/client-example/resource/js/socket.js\",\n \"repo_name\": \"Deisss/webservice-notification\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/**\\n * Create a new SockJS instance.\\n *\\n * @class socket\\n * @constructor\\n *\\n * @param namespace {String | null} The namespace to link SockJS with\\n*/\\nvar socket = function(namespace) {\\n // Store events list\\n this.events = {};\\n // The base url\\n this.url = '//' + window.location.hostname;\\n // The base port\\n this.port = 8587;\\n // Store the SockJS instance\\n this.instance = null;\\n // Store the namespace\\n this.namespace = namespace || '';\\n // Should reconnect or not\\n this.reconnect = true;\\n};\\n\\n\\n\\nsocket.prototype = {\\n /**\\n * Bind a function to an event from server.\\n *\\n * @method on\\n *\\n * @param name {String} The message type\\n * @param fct {Function} The function to call\\n * @param scope {Object | null} The scope to apply for function\\n */\\n on: function(name, fct, scope) {\\n var fn = fct;\\n if(scope) {\\n // We bind scope\\n fn = function() {\\n fct.apply(scope, arguments);\\n };\\n }\\n // If it's not existing, we create\\n if(!this.events[name]) {\\n this.events[name] = [];\\n }\\n // Append event\\n this.events[name].push(fn);\\n },\\n\\n /**\\n * Send data to server.\\n *\\n * @method emit\\n *\\n * @param name {String} The message type\\n * @param data {Object} The linked data with message\\n */\\n emit: function(name, data) {\\n this.instance.send(\\n JSON.stringify({\\n name: name,\\n data: data\\n })\\n );\\n },\\n\\n /**\\n * Connect to server.\\n *\\n * @method connect\\n */\\n connect: function() {\\n // Disconnect previous instance\\n if(this.instance) {\\n // Get auto-reconnect and re-setup\\n var p = this.reconnect;\\n this.disconnect();\\n this.reconnect = p;\\n }\\n\\n // Start new instance\\n var base = (this.port != 80) ? this.url + ':' + this.port : this.url;\\n var sckt = new SockJS(base + '/' + this.namespace, null, {\\n debug : false,\\n devel : false\\n });\\n\\n var _this = this;\\n\\n /**\\n * Parse event from server side, and dispatch it\\n *\\n * @method catchEvent\\n *\\n * @param response {Object} The data from server side\\n */\\n function catchEvent(response) {\\n var name = (response.type) ? response.data.name : response.name,\\n data = (response.type) ? response.data.data : response.data,\\n evts = _this.events[name];\\n if(evts) {\\n var parsed = (typeof(data) === 'object' && data !== null) ?\\n data : JSON.parse(data);\\n\\n for(var i=0, l=evts.length; i<l; ++i) {\\n var fct = evts[i];\\n if(typeof(fct) === 'function') {\\n // Defer call on setTimeout\\n (function(f) {\\n setTimeout(function() {f(parsed);}, 0);\\n })(fct);\\n }\\n }\\n }\\n };\\n\\n // Catch open\\n sckt.onopen = function() {\\n catchEvent({\\n name: 'open',\\n data: {}\\n });\\n catchEvent({\\n name: 'connect',\\n data: {}\\n });\\n };\\n\\n // Catch arriving message\\n sckt.onmessage = function(data) {\\n catchEvent(data);\\n };\\n\\n // Catch close, and reconnect\\n sckt.onclose = function() {\\n catchEvent({\\n name: 'close',\\n data: {}\\n });\\n catchEvent({\\n name: 'disconnect',\\n data : {}\\n });\\n if(_this.reconnect) {\\n _this.connect();\\n }\\n };\\n\\n // Link to server\\n this.instance = sckt;\\n },\\n\\n /**\\n * Disconnect from server.\\n *\\n * @method disconnect\\n */\\n disconnect: function() {\\n this.reconnect = false;\\n\\n if(!this.instance) {\\n return;\\n }\\n\\n this.instance.close();\\n this.instance = null;\\n }\\n};\"\n },\n {\n \"alpha_fraction\": 0.5606180429458618,\n \"alphanum_fraction\": 0.5629952549934387,\n \"avg_line_length\": 23.504854202270508,\n \"blob_id\": \"787e880c44b60c8fc68f59eb5703419348dd1796\",\n \"content_id\": \"35819381d9737ffa719cea5fd63914e4c9e39ccb\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2524,\n \"license_type\": \"permissive\",\n \"max_line_length\": 79,\n \"num_lines\": 103,\n \"path\": \"/server/system/CommonMongoDB.py\",\n \"repo_name\": \"Deisss/webservice-notification\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python\\n# -*- coding: utf-8 -*-\\n\\nfrom system.DBMongo import connect\\nfrom system.ConfigLoader import getCfg\\nfrom datetime import datetime\\nfrom bson.objectid import ObjectId\\n\\n_collection = getCfg('MONGODB', 'db')\\n\\n\\n#\\n# ------------------------\\n# INDEX\\n# ------------------------\\n#\\ndef mongoIndex(db):\\n ''' Try to generate indexes if needed '''\\n db.ensure_index([('_userId', 1), ('seen', 1), ('delete', 1)], unique=False)\\n\\n\\n#\\n# ------------------------\\n# NOTIFICATIONS\\n# ------------------------\\n#\\ndef createNotification(notifications):\\n ''' Register a new notification into database '''\\n db = connect(_collection)\\n mongoIndex(db)\\n now = datetime.now()\\n\\n for notification in notifications:\\n notification['createdAt'] = now\\n notification['updatedAt'] = now\\n notification['deletedAt'] = None\\n notification['delete'] = False\\n notification['seen'] = False\\n\\n db.insert(notifications, continue_on_error=True)\\n\\ndef getUserUnseenNotifications(userId):\\n ''' Get the unseen notifications '''\\n db = connect(_collection)\\n mongoIndex(db)\\n\\n return db.find({\\n '_userId': str(userId),\\n 'seen': False,\\n 'delete': False\\n }).sort('createdAt', 1)\\n\\ndef getUserNotifications(userId, page, limit):\\n ''' Get many notifications linked to a user '''\\n db = connect(_collection)\\n mongoIndex(db)\\n\\n return db.find({\\n '_userId': str(userId),\\n 'delete': False\\n }).sort('createdAt', 1).skip(page * limit).limit(limit)\\n\\ndef seenNotification(userId, notificationsId):\\n ''' Set many notifications as seen '''\\n db = connect(_collection)\\n mongoIndex(db)\\n now = datetime.now()\\n\\n # We convert string ObjectId literals into ObjectId one\\n resultsId = [ObjectId(str(x)) for x in notificationsId]\\n\\n db.update({\\n '_id': {\\n '$in': resultsId\\n },\\n '_userId': userId\\n }, {\\n '$set': {\\n 'seen': True,\\n 'updatedAt': now\\n }\\n })\\n\\ndef deleteNotification(userId, notificationsId):\\n ''' Set many notifications as delete '''\\n db = connect(_collection)\\n mongoIndex(db)\\n now = datetime.now()\\n\\n # We convert string ObjectId literals into ObjectId one\\n resultsId = [ObjectId(str(x)) for x in notificationsId]\\n\\n db.update({\\n '_id': {\\n '$in': resultsId\\n },\\n '_userId': userId\\n }, {\\n '$set': {\\n 'delete': True,\\n 'deletedAt': now\\n }\\n })\\n\"\n },\n {\n \"alpha_fraction\": 0.6008403301239014,\n \"alphanum_fraction\": 0.605042040348053,\n \"avg_line_length\": 20.636363983154297,\n \"blob_id\": \"9e86715db74e6ebfa53004e128584918d3b77c59\",\n \"content_id\": \"817dfd91f707706fad40d024f7e8a03d5fe73ccd\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 476,\n \"license_type\": \"permissive\",\n \"max_line_length\": 75,\n \"num_lines\": 22,\n \"path\": \"/server/system/DBMongo.py\",\n \"repo_name\": \"Deisss/webservice-notification\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python\\n# -*- coding: utf-8 -*-\\n\\nimport pymongo\\n\\nfrom system.ConfigLoader import getCfg\\n\\n\\n\\n\\ndef connect(collection):\\n ''' Connect to a database, and give back collection '''\\n url = getCfg('MONGODB', 'url')\\n name = getCfg('MONGODB', 'db')\\n\\n # Make url compatible in all cases with name\\n if url[-1:] != '/':\\n url += '/'\\n\\n con = pymongo.MongoClient(url + name, getCfg('MONGODB', 'port', 'int'))\\n db = con[name]\\n return db[collection]\\n\"\n },\n {\n \"alpha_fraction\": 0.5362563133239746,\n \"alphanum_fraction\": 0.5430017113685608,\n \"avg_line_length\": 21.5,\n \"blob_id\": \"d750824c3ae065a856fe30e09fc9f197e37b0327\",\n \"content_id\": \"664e6f48dac72026a5cf288167d0015c80f2b17f\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 593,\n \"license_type\": \"permissive\",\n \"max_line_length\": 48,\n \"num_lines\": 26,\n \"path\": \"/server/system/DBMysql.py\",\n \"repo_name\": \"Deisss/webservice-notification\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python\\n# -*- coding: utf-8 -*-\\n\\n\\nimport MySQLdb as db\\nfrom system.ConfigLoader import getCfg\\n\\n\\n\\n\\ndef connect():\\n ''' Create a new connection with MySQL '''\\n # Set parameter format\\n db.paramstyle = 'format'\\n # Get connection\\n return db.connect(\\n host = getCfg('MYSQL', 'host'),\\n user = getCfg('MYSQL', 'user'),\\n passwd = getCfg('MYSQL', 'password'),\\n db = getCfg('MYSQL', 'db'),\\n port = getCfg('MYSQL', 'port', 'int'),\\n\\n # Force UTF 8\\n charset= 'utf8',\\n init_command='SET NAMES UTF8'\\n )\\n\\n \\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.583512544631958,\n \"alphanum_fraction\": 0.5863799452781677,\n \"avg_line_length\": 25.846153259277344,\n \"blob_id\": \"c0fd41fd5455948819cbc2baafe235d942b97320\",\n \"content_id\": \"0170d8391c92bc7776a8da0456ec69fa644cdc6d\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1395,\n \"license_type\": \"permissive\",\n \"max_line_length\": 79,\n \"num_lines\": 52,\n \"path\": \"/server/system/EmailNotification.py\",\n \"repo_name\": \"Deisss/webservice-notification\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import smtplib, logging\\n\\nfrom ConfigLoader import getCfg\\n\\n'''\\nSend an email to users:\\n\\nrecievers: a list of CC elements\\nsubject: email subject\\nmessage: email message\\ncontentType: text/html by default\\nmime: 1.0 by default\\n'''\\n\\ndef send(recievers, subject, message, contentType='text/html', mime='1.0'):\\n ''' Send an email to a group of users '''\\n # The guy marked as 'the one :D'\\n sender = getCfg('EMAIL', 'sender')\\n\\n content = \\\"From: %s\\\\r\\\\n\\\" % sender\\n content+= \\\"BCC: %s\\\\r\\\\n\\\" % \\\",\\\".join(recievers)\\n content+= \\\"MIME-Version: %s\\\\r\\\\n\\\" % mime\\n content+= \\\"Content-type: %s\\\\r\\\\n\\\" % contentType\\n content+= \\\"Subject: %s\\\\r\\\\n\\\" % subject\\n content+= \\\"\\\\r\\\\n\\\"\\n content+= message\\n\\n to = [sender] + recievers\\n\\n try:\\n # start new connection\\n server = smtplib.SMTP(getCfg('EMAIL', 'host'))\\n\\n # TLS REQUIRED\\n if getCfg('EMAIL', 'tls', 'boolean') == True:\\n server.ehlo()\\n server.starttls()\\n\\n # LOGIN REQUIRED\\n if getCfg('EMAIL', 'auth', 'boolean') == True:\\n server.login(getCfg('EMAIL', 'login'), getCfg('EMAIL', 'password'))\\n\\n # save and exit\\n server.sendmail(sender, to, content)\\n server.quit()\\n\\n except smtplib.SMTPException:\\n logging.error('SMTP: unable to send email')\\n\\n\\nif __name__ == '__main__':\\n send([sender], 'Super sujet', 'Super content')\"\n },\n {\n \"alpha_fraction\": 0.613822340965271,\n \"alphanum_fraction\": 0.6183692216873169,\n \"avg_line_length\": 23.62686538696289,\n \"blob_id\": \"0116a7b3d67e358dfd01837026854ec5710687b3\",\n \"content_id\": \"681a227af9b4669658baa147ebd2c62677456ac5\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3299,\n \"license_type\": \"permissive\",\n \"max_line_length\": 75,\n \"num_lines\": 134,\n \"path\": \"/server/server.py\",\n \"repo_name\": \"Deisss/webservice-notification\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python\\n# -*- coding: utf-8 -*-\\n\\nimport os, sys, logging, tornado.web\\nfrom datetime import datetime\\n\\n# Getting folder\\nfullPath = os.path.realpath(__file__)\\ndirPath = os.path.dirname(fullPath)\\n\\n# Register path\\nsys.path.append(os.path.join(dirPath, 'system' ))\\nsys.path.append(os.path.join(dirPath, 'handler'))\\n\\n# Custom import\\nfrom handler.HTTPServer import IndexHandler\\nfrom sockjs.tornado import SockJSRouter\\nfrom system.ConfigLoader import getCfg\\n\\n# Import sockets\\nfrom websocket import NotificationSocketHandler\\nfrom webservice import ServiceRequestManager\\n\\n\\n\\ndef getLogLevel():\\n ''' Get the current application minimum log level '''\\n level = getCfg('LOG', 'level').lower()\\n if level == 'info' or level == 'information':\\n return logging.INFO\\n elif level == 'warn' or level == 'warning':\\n return logging.WARN\\n elif level == 'error' or level == 'err':\\n return logging.ERROR\\n else:\\n return logging.DEBUG\\n\\n\\n\\ndef configureLogger(_logFolder, _logFile):\\n ''' Start the logger instance and configure it '''\\n # Set debug level\\n logger = logging.getLogger()\\n logger.setLevel(getLogLevel())\\n\\n # Format\\n formatter = logging.Formatter(\\n '%(asctime)s - %(levelname)s | %(name)s -> %(message)s',\\n '%Y-%m-%d %H:%M:%S'\\n )\\n\\n # Remove default handler to keep only clean one\\n for hdlr in logger.handlers:\\n logger.removeHandler(hdlr)\\n\\n # Create missing folder if needed\\n if not os.path.exists(_logFolder):\\n os.makedirs(_logFolder, 0700)\\n\\n #\\n # ----------------------------\\n # CREATE CONSOLE HANDLER\\n # ----------------------------\\n #\\n\\n # Create console handler\\n consoleh = logging.StreamHandler()\\n consoleh.setLevel(getLogLevel())\\n consoleh.setFormatter(formatter)\\n\\n # Set our custom handler\\n logger.addHandler(consoleh)\\n\\n #\\n # ----------------------------\\n # CREATE FILE HANDLER\\n # ----------------------------\\n #\\n fileh = logging.FileHandler(_logFile, 'a')\\n fileh.setLevel(getLogLevel())\\n fileh.setFormatter(formatter)\\n\\n # Set our custom handler\\n logger.addHandler(fileh)\\n\\n\\n\\ndef printWelcomeMessage(msg, place=10):\\n ''' Print any welcome message '''\\n logging.debug('*' * 30)\\n welcome = ' ' * place\\n welcome+= msg\\n logging.debug(welcome)\\n\\n logging.debug('*' * 30 + '\\\\n')\\n\\n\\n\\n\\n\\n\\n\\nif __name__ == '__main__':\\n logFile = getCfg('LOG', 'file')\\n logFolder = os.path.dirname(logFile)\\n configureLogger(logFolder, logFile)\\n\\n serverPort = getCfg('APPLICATION', 'port', 'int')\\n\\n # Print logger message\\n logging.debug('\\\\n\\\\nSystem start at: %s\\\\nSystem log level: %s\\\\n' %\\n (datetime.now(), getCfg('LOG', 'level')))\\n\\n printWelcomeMessage('STARTING', 11)\\n printWelcomeMessage('SETUP ROUTES', 8)\\n\\n # Create route\\n notifRouter = SockJSRouter(NotificationSocketHandler, '/notification')\\n\\n # Create tornado app\\n app = tornado.web.Application([\\n (r'/', IndexHandler),\\n (r'/notify', ServiceRequestManager)\\n ]\\n + notifRouter.urls\\n )\\n\\n # Listening to application\\n app.listen(serverPort)\\n\\n printWelcomeMessage('SERVER RUNNING ON PORT %i' % serverPort, 1)\\n\\n # Start IOLoop\\n tornado.ioloop.IOLoop.instance().start()\"\n },\n {\n \"alpha_fraction\": 0.73572838306427,\n \"alphanum_fraction\": 0.7372398376464844,\n \"avg_line_length\": 47.05586624145508,\n \"blob_id\": \"0d8151e6ef1cf1deb878de8185fdd50af4c2a921\",\n \"content_id\": \"144c367446c6755f2901775e3c9275319f6984ca\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 8601,\n \"license_type\": \"permissive\",\n \"max_line_length\": 305,\n \"num_lines\": 179,\n \"path\": \"/README.md\",\n \"repo_name\": \"Deisss/webservice-notification\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# webservice-notification\\n\\nA server to plug into your own system, to create a notification system between some server side event, and your users. This aims to provide something close to what you can found on facebook (for example), with a small footprint on your existing server architecture and client.\\n\\n\\n\\n## Installation\\n\\nAs it is a real system, get some time before starting (around 10/15min should be enough), here is the list of programs we need:\\n\\n * MongoDB\\n * Python (with few packages)\\n * MySQL (see below)\\n\\nThe system is using MongoDB as a primary database, and MySQL is here to show an example how to plug your logic (security) to this system (we will explain later).\\n\\n**The installation is describe for CentOS/RedHat system**\\n\\n\\n\\n### Database\\nWe need only MongoDB:\\n\\n```\\nyum -y install mongodb\\n```\\n\\nFor mongodb you may need to use [MongoDB](http://docs.mongodb.org/manual/tutorial/install-mongodb-on-red-hat-centos-or-fedora-linux/) repository instead of default one.\\n\\nThat will be enough, of course if you want to run MySQL example, you will need also MySQL setup (package **mysql** and **mysql-server**).\\n\\n\\n\\n### Python\\nThe main development is based on Python 2.x branch, but should work like a charm on Python 3.x branch. We need to install python and few dependencies:\\n\\n```\\nyum -y install \\\\\\n python python-devel \\\\\\n python-setuptools python-pip \\\\\\n MySQL-python\\n```\\n\\n\\n\\n### PIP\\nNow we have a Python instance ready and running, we need additional packages:\\n```\\npip install tornado pymongo sockjs-tornado sockjsroom PyDispatcher\\n```\\n\\nWe install:\\n * **tornado, sockjs-tornado, sockjsroom**: packages to manage server system, and real time server system,\\n * **pymongo**: use MongoDB into python,\\n * **PyDispatcher**: a PubSub system used between new notification, and currently connected user.\\n\\n \\nNow we have the full system installed, we miss to clone this repository:\\n```\\nmkdir ws_notification\\ncd ws_notification\\ngit clone https://github.com/Deisss/webservice-notification.git .\\n```\\n\\nWe are ready to configure.\\n\\n\\n\\n\\n## Configure\\n\\nBefore doing anything else, it's quite recommended (required in fact) to setup configuration; from ```ws_notification```, go into ```server``` directory, and edit ```config.ini```:\\n```\\ncd ws_notification/server\\nvim config.ini\\n```\\nFor every category (except APPLICATION), you have a DEBUG and RELEASE element, you should configure every part according to your own server configuration. All of them are 'classical' configuration, like email provider, database host, so we don't explain them here...\\n\\nNow we can start system.\\n\\n\\n\\n\\n## Usage\\n\\nFirst of all, you need to launch the server. from ```ws_notification```, go into ```server``` directory, open a command prompt and type ```python server.py```:\\n```\\npython server.py\\n```\\nThat's enough, no need to do more to have server ready. Now we will explain API available (quite simple).\\n\\n\\n\\n\\n## API - server\\n\\nServer(s) can send new notifications threw a single URL:\\n * **POST** => **/notify**:\\n - *type* | string: The notification type\\n - *title* | string: The notification title\\n - *content* | string: The notification content\\n - *url* | string: The associated url\\n - *icon* | string: A field to set icon\\n - *image* | string: An image (like gravatar image)\\n - *users* | string (json doc): A list of users who are concerned by this notification (see below)\\n\\n**/!\\\\ Except content, all elements are not required. This is simply due to the fact that server doesn't do anything with! The client may use some or all of them (you decide). Basically this server only make relation between notification and user, so it doesn't use them at all, only store them...**\\n\\n**/!\\\\ Remember also that users are important: if you specify none of them, nothing will happend/be done... As the server create one notification for one user in database.**\\n\\nThe **users** parameter is the most important here, it's a simple JSON document (in string format) indicating who is concerned by this notification, let's show an example as it's quite easy:\\n```\\n[\\n {\\n \\\"id\\\": 1,\\n \\\"email\\\": \\\"myemail@test.com\\\"\\n },\\n {\\n \\\"id\\\": 2,\\n \\\"email\\\": \\\"second@test.com\\\"\\n }\\n]\\n```\\nWill try to find user with id 1 and 2 into websocket system (=currently connected), if at least, one of them is not found, it will use the related email to send an email instead (see below).\\n\\n\\nThose elements represent a notification in most of existings system. But this system is able to do little more, so we provide extra parameters:\\n * **email_send**: a boolean indicate is system should send an email if one or more user is not currently connected to websocket system (=not online)\\n * **email_force**: no matter if user is connected or not, they should ALL recieve email notification\\n * **email_subject**: A subject for the email\\n * **email_content**: The email content\\n * **email_mime** (default: 1.0): email MIME Version\\n * **email_type** (default: text/html): The content type of the email\\n\\n*Note*: if email_send is not activated, system will never send email, so you need this parameter if you want to use email service.\\n\\n*Note2*: only one email is sent, with everybody in BCC.\\n\\n\\nThis is the only entry point for sending notification to system. Of course this point should not be accessible from outside, only from your local server.\\n\\n**/!\\\\ Please keep in mind that this server does not provide ANY security check regarding what is sent to user. It means content, title and others fields are not escaped or checked against any traditionnal security trouble, you should take a lot care about this BEFORE sending to this system any content.**\\n\\n\\n\\n## API - client\\n\\nNow we know how to deal with adding new notification, it remains the client side: how user get new notification, and how you can use them.\\n\\nThe client API is only composed with a single websocket, so the whole API is based on that. Moreover, we use on top of [SockJS](https://github.com/mrjoes/sockjs-tornado), [sockjsroom](https://github.com/Deisss/python-sockjsroom) for creating multi-message system.\\nAs websocket is bi-directionnal, we separate them:\\n\\n**From client to server:**\\n * **unseen**: get the unseen notifications for the given user. It should not be so much needed, as everytime user connect to socket, you get exactly this return. Take no parameter.\\n * **content**: get page by page existing notifications (no matter they are unseen or not). Take page (integer) as parameter.\\n * **seen**: mark some notification as seen. Take an array of ObjectId's as parameter (the *_id* field from every notification you want to mark as seen).\\n * **delete**: mark some notification as deleted. Take an array of ObjectId's as parameter (the *_id* field from every notification you want to mark as delete).\\n\\nWe strongly recommand you to check the example provided to have a working base to modify, and also check how to use those elements.\\n\\n**From server to client:**\\n * **notification**: The server send some new notification, or send back data requested by user (like _unseen_ and _content_)\\n * **seen**: Some notification have been marked as 'seen', we should apply modification...\\n * **delete**: Some notification have been marked as 'deleted', we should remove them...\\n\\nThe notification is highly used by most of functions.\\nFrom this API, now the client has to deal with information given, the seen and delete state should be enough to control the notification lifetime.\\n\\n\\n\\n## The case MySQL\\n\\nWe didn't talk until now about MySQL issue, here is. When user logon on webservice, the login and password are checked before allowing to use our notification system. This auth, is based on a MySQL db, as many system are using MySQL for user access instead of MongoDB.\\nIf you plan to use something else (different auth method / different db), you can change this, by editing function ```loginUser``` into ```system/CommonMySQL.py``` (on server side of course) with your own authentification code.\\nThis function is a simple \\\"give me a login/password, I will give you a unique ID to identify this user\\\". This id then, can be passed as parameter on ```/notify``` (see server API above)\\n\\nWe chose this way, as we wanted to provide a kind of 'full example' where you need only the minimum changes to integrate this system to your own existing system. This is also why you send on notify email content: we wanted to keep it with the minimum integration possible.\\n\\nFrom this point of view, this is the reason while we choose this: we provide a real example, on one of the most used DB. Allowing you to quickly understand how to modify it, and have fun !\"\n },\n {\n \"alpha_fraction\": 0.5337560772895813,\n \"alphanum_fraction\": 0.5371130108833313,\n \"avg_line_length\": 22.321739196777344,\n \"blob_id\": \"954f0a5efe0fcb7aecb11718964bab362e0d5fe0\",\n \"content_id\": \"52917b27576e14fc7831b6e3cf14370f2abbf4d2\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"JavaScript\",\n \"length_bytes\": 2681,\n \"license_type\": \"permissive\",\n \"max_line_length\": 76,\n \"num_lines\": 115,\n \"path\": \"/client-example/resource/js/action.js\",\n \"repo_name\": \"Deisss/webservice-notification\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/**\\n * Create a new socket instance and try to connect to it\\n *\\n * @method doLogin\\n*/\\nfunction doLogin() {\\n var login = $('#signin #login').val(),\\n password = $('#signin #password').val();\\n\\n if(login && password && login != '' && password != '') {\\n notification.connect(login, CryptoJS.SHA512(password).toString());\\n\\n // We hide previous element and show new one\\n $('#hide-after-login, #signin').hide();\\n $('#hide-before-login, #signout').show();\\n\\n // Some notification are now marked as 'seen'\\n notification.bind(function(details) {\\n console.log('recieve events');\\n console.log(details);\\n\\n if(details.event === 'notification') {\\n var notification = details.element,\\n seen = notification.seen;\\n\\n var container = $('#single-notification > div').clone();\\n container.find('h4').html(notification.title);\\n container.find('p').html(notification.content);\\n container.find('button').data('objid', notification['_id']);\\n\\n // Empty or not valid url\\n if(!notification.url) {\\n container.find('a.link').css('display', 'none');\\n } else {\\n container.find('a.link').attr('href', notification.url);\\n }\\n\\n if(seen == 'false' || seen == false) {\\n container.addClass('unseen-notification');\\n } else {\\n container.addClass('seen-notification');\\n }\\n\\n $('#content-show').append(container);\\n }\\n });\\n }\\n};\\n\\n\\n\\n/**\\n * Logout, destroy and stop socket.\\n *\\n * @method doLogout\\n*/\\nfunction doLogout() {\\n doClear();\\n notification.unbindAll();\\n notification.disconnect();\\n $('#hide-before-login, #signout').hide();\\n $('#hide-after-login, #signin').show();\\n};\\n\\n\\n/**\\n * Clear existing content\\n *\\n * @method doClear\\n*/\\nfunction doClear() {\\n $('#content-show').html('');\\n};\\n\\n\\n/**\\n * Ask to get more notifications\\n *\\n * @method doContent\\n*/\\nfunction doContent() {\\n notification.content(0);\\n};\\n\\n\\n/**\\n * Get the unseen element\\n *\\n * @method doUnseen\\n*/\\nfunction doUnseen() {\\n notification.unseen();\\n};\\n\\n/**\\n * Mark given id as seen\\n *\\n * @method doSeen\\n*/\\nfunction doSeen(el) {\\n var id = $(el).data('objid');\\n notification.seen([id]);\\n};\\n\\n/**\\n * Create a new notification for one or more users\\n *\\n * @method doNotify\\n*/\\nfunction doNotify() {\\n // Send ajax request\\n $.post('//' + window.location.hostname + ':8587/notify',\\n $('form#notify').serialize(), function(data) {}\\n );\\n};\"\n },\n {\n \"alpha_fraction\": 0.5314711332321167,\n \"alphanum_fraction\": 0.5325884819030762,\n \"avg_line_length\": 32.14814758300781,\n \"blob_id\": \"2291831459efddd1b84d8fa48d28f51c6951a90d\",\n \"content_id\": \"bb27c274795105913108976bc78cd298b675f541\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5370,\n \"license_type\": \"permissive\",\n \"max_line_length\": 139,\n \"num_lines\": 162,\n \"path\": \"/server/websocket.py\",\n \"repo_name\": \"Deisss/webservice-notification\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python\\n# -*- coding: utf-8 -*-\\n\\nfrom sockjsroom import SockJSDefaultHandler\\nfrom bson.json_util import dumps\\nfrom pydispatch import dispatcher\\nfrom system.ConfigLoader import getCfg\\nfrom system.CommonMySQL import loginUser\\nfrom system.CommonMongoDB import createNotification, getUserUnseenNotifications, getUserNotifications, seenNotification, deleteNotification\\n\\nimport logging\\n\\ndef _debug(str):\\n logging.debug('notification: ' + str)\\n\\n\\nclass NotificationSocketHandler(SockJSDefaultHandler):\\n ''' Basic notification system '''\\n #\\n # ---------------------------\\n # CLASS INIT\\n # ---------------------------\\n #\\n def initialize(self):\\n ''' Initialize any new connection '''\\n self.login = ''\\n # As we use the str() function, the None is never really None\\n # So we use this also here to keep test simple\\n self.userId = 'None'\\n\\n def startPubSub(self):\\n ''' Starting the PubSub system '''\\n if self.userId != 'None':\\n _debug('start pubsub for user #%s (%s)'\\n % (self.userId, self.login))\\n dispatcher.connect(\\n self.pubsub,\\n signal='user-%s' % self.userId\\n )\\n\\n def stopPubSub(self):\\n ''' Stopping the PubSub system '''\\n if self.userId != 'None':\\n _debug('stop pubsub for user #%s (%s)'\\n % (self.userId, self.login))\\n dispatcher.disconnect(\\n self.pubsub,\\n signal='user-%s' % self.userId\\n )\\n\\n def jsonSend(self, name, param):\\n ''' Publish as JSON data '''\\n self.send({\\n 'name': name,\\n 'data': SockJSDefaultHandler._parser.encode(list(param))\\n })\\n\\n #\\n # ---------------------------\\n # SOCKJS DEFAULT FUNCTION\\n # ---------------------------\\n #\\n def on_open(self, info):\\n pass\\n\\n def on_close(self):\\n self.on_leave()\\n\\n #\\n # ---------------------------\\n # SOCKJS CUSTOM FUNCTION\\n # ---------------------------\\n #\\n def on_join(self, data):\\n ''' Join the notification system '''\\n # data => login, password\\n self.initialize()\\n\\n # Get the login and password, and check user on database\\n self.login = str(data['login'])\\n self.userId = str(loginUser(self.login, str(data['password'])))\\n\\n\\n\\n if self.userId != 'None':\\n _debug('joining user #%s (%s)' % (self.userId, self.login))\\n self.startPubSub()\\n\\n # We send back the unseen notifications\\n self.on_unseen(None)\\n else:\\n _debug('ERROR joining user #%s (%s)' % (self.userId, self.login))\\n self.initialize()\\n\\n\\n def on_seen(self, data):\\n ''' User seen a new notification, we mark them as seen '''\\n # data => [id(s)], list of ids to mark as seen\\n if self.userId != 'None':\\n _debug('user #%s (%s) ask to mark as seen %s'\\n % (self.userId, self.login, data['ids']))\\n seenNotification(self.userId, data['ids'])\\n self.jsonSend('seen', data['ids'])\\n\\n\\n def on_delete(self, data):\\n ''' User want to delete some notifications, we mark them as deleted '''\\n # data => [id(s)], list of ids to mark as delete\\n if self.userId != 'None':\\n _debug('user #%s (%s) ask to mark as delete %s'\\n % (self.userId, self.login, data['ids']))\\n deleteNotification(self.userId, data['ids'])\\n self.jsonSend('delete', data['ids'])\\n\\n\\n def on_unseen(self, data):\\n ''' Get the unseen content only '''\\n # data => unused\\n if self.userId != 'None':\\n userNotification = getUserUnseenNotifications(self.userId)\\n self.jsonSend('notification', userNotification)\\n\\n\\n def on_content(self, data):\\n ''' User ask to get latests notifications '''\\n # data => page, limit\\n if self.userId != 'None':\\n page = int(data['page'])\\n limit = getCfg('APPLICATION', 'limit', 'int')\\n\\n # Make elements fit requirements\\n page = max(0, page)\\n limit = max(1, limit)\\n limit = min(100, limit)\\n\\n # Get latests notifications for user, and send it back\\n userNotification = getUserNotifications(self.userId, page, limit)\\n self.jsonSend('notification', userNotification)\\n\\n\\n def pubsub(self, sender, data):\\n '''\\n This function send a notification from pubsub system\\n to user, as the pubsub is not a socket element, we are\\n not using the on_* pattern (we don't want user to be\\n able to request himself this function threw client side)\\n '''\\n if self.userId != 'None':\\n _debug('pubsub user #%s (%s):\\\\n\\\\tmessage:%s'\\n % (self.userId, self.login, data))\\n self.jsonSend('notification', [data])\\n\\n\\n def on_leave(self):\\n ''' Leave the notification system '''\\n # Only if user has time to call self.initialize\\n # (sometimes it's not the case)\\n if self.userId != 'None':\\n _debug('leaving user #%s (%s)' % (self.userId, self.login))\\n self.stopPubSub()\\n # Erasing data\\n self.initialize()\\n\"\n },\n {\n \"alpha_fraction\": 0.729394257068634,\n \"alphanum_fraction\": 0.7591856718063354,\n \"avg_line_length\": 19.33333396911621,\n \"blob_id\": \"7cbe3b1e581b22e93d6e3e645c4b038c4e700cc5\",\n \"content_id\": \"d0c038240be0a5287d599e50c72f3676bf22e6f2\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"INI\",\n \"length_bytes\": 2014,\n \"license_type\": \"permissive\",\n \"max_line_length\": 73,\n \"num_lines\": 99,\n \"path\": \"/server/config.ini\",\n \"repo_name\": \"Deisss/webservice-notification\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"; Config file\\n\\n; main application configuration\\n[APPLICATION]\\n; can be DEBUG or RELEASE\\nconfiguration=DEBUG\\nport=8587\\n; number of notifications when requesting a page\\nlimit=30\\n; define an API Key for limiting access to /notify POST\\n; this is of course not a huge security, but still better than nothing ;)\\n; leave it blank to skip it...\\napikey=\\n\\n; configure log level\\n[LOG-DEBUG]\\nfile=./log/server.log\\nlevel=DEBUG\\n\\n; configure log level\\n[LOG-RELEASE]\\nfile=/var/log/ws_notification/server.log\\nlevel=WARN\\n\\n\\n; configure email service\\n[EMAIL-DEBUG]\\n; host can be 'localhost', 'smtp.gmail.com:587', ... (does include port)\\nhost=smtp.gmail.com:587\\n; indicate (true/false) if system use tls\\ntls=true\\n; indicate if system use auth\\nauth=true\\n; indicate login/password (only on auth)\\nlogin=MyGMAILAccount\\npassword=MyPASSWORD\\n; the email which is sending...\\nsender=MyNAME\\n\\n[EMAIL-RELEASE]\\n; host can be 'localhost', 'smtp.gmail.com:587', ... (does include port)\\nhost=smtp.gmail.com:587\\n; indicate (true/false) if system use tls\\ntls=true\\n; indicate if system use auth\\nauth=true\\n; indicate login/password (only on auth)\\nlogin=MyGMAILAccount\\npassword=MyPASSWORD\\n; the email which is sending...\\nsender=MyEmail\\n\\n\\n\\n\\n\\n[MYSQL-DEBUG]\\n; MySQL configuration\\nhost=127.0.0.1\\nuser=root\\npassword=\\nport=3306\\ndb=MyDatabase\\n; configure the request to send to authentificate user\\n; WILL PRODUCE 'SELECT *idField* FROM *table* WHERE *loginField*=%s AND\\n; *passwordField*=%s'\\ntable=user\\nidField=id\\nloginField=login\\npasswordField=password\\n\\n[MONGODB-DEBUG]\\n; MongoDB configuration\\nurl=mongodb://127.0.0.1/\\nport=27017\\ndb=ws_notification\\n\\n\\n\\n[MYSQL-RELEASE]\\n; MySQL configuration\\nhost=127.0.0.1\\nuser=root\\npassword=\\nport=3306\\ndb=MyDatabase\\n; configure the request to send to authentificate user\\n; WILL PRODUCE 'SELECT *idField* FROM *table* WHERE *loginField*=%s AND\\n; *passwordField*=%s'\\ntable=user\\nidField=id\\nloginField=login\\npasswordField=password\\n\\n[MONGODB-RELEASE]\\n; MongoDB configuration\\nurl=mongodb://127.0.0.1/\\nport=27017\\ndb=ws_notification\\n\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":16,"string":"16"}}},{"rowIdx":2979396,"cells":{"repo_name":{"kind":"string","value":"pmichali/commit-coverage"},"repo_url":{"kind":"string","value":"https://github.com/pmichali/commit-coverage"},"snapshot_id":{"kind":"string","value":"34ac63bae15d3b41bab3befb713b8a821e4550ff"},"revision_id":{"kind":"string","value":"e6912f1d9cd605e9fa80154ced3494aeaa85bd21"},"directory_id":{"kind":"string","value":"de0fd72d6eb184086e6d5c6afe5183d083507c07"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-01-10T06:15:43.431141","string":"2021-01-10T06:15:43.431141"},"revision_date":{"kind":"timestamp","value":"2016-03-08T21:45:31","string":"2016-03-08T21:45:31"},"committer_date":{"kind":"timestamp","value":"2016-03-08T21:45:31","string":"2016-03-08T21:45:31"},"github_id":{"kind":"number","value":53350314,"string":"53,350,314"},"star_events_count":{"kind":"number","value":2,"string":"2"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6928218007087708,"alphanum_fraction":0.7059406042098999,"avg_line_length":41.978721618652344,"blob_id":"6a15d36fb73b966d8c102c2386052ec1e6e993ff","content_id":"3e86221ca2445b7664f525c6a8973ff3f0db3c02","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"reStructuredText","length_bytes":4040,"license_type":"permissive","max_line_length":90,"num_lines":94,"path":"/README.rst","repo_name":"pmichali/commit-coverage","src_encoding":"UTF-8","text":"commit-coverage\n===============\n\nThis allows you to assess the coverage of changes you have in a repo. It\nis intended for use, when preparing to push code up for review upstream.\n\nUsage\n-----\n\nAs a prerequisite to using this script, run coverage in the repo, to\nproduce coverage report files in a 'cover' directory at the root of the\nrepo's tree. This coverage should be done on the code that will be\nupstreamed (either what is in the working directory, or what has been\ncommitted to the local repo).\n\nBehavior\n--------\n\nAssuming you have a repo with uncommitted changes, you can run the command\nas follows::\n\n commit_coverage /opt/stack/networking-cisco\n\nThe argument must be the root of a git repo that has coverage data in a\n'cover' subdirectory.\n\nBy default, this will create diffs comparing the working directory, to the\nlatest commit (HEAD), with (up to) three lines of context. That output will\nbe checked against coverage data and a report produced. Here's what the\noutput looks like::\n\n devstack/csr1kv/cisco_neutron (No coverage data)\n\n networking_cisco/apps/saf/agent/dfa_agent.py (No added/changed lines)\n\n networking_cisco/apps/saf/server/dfa_server.py (run=1, mis=1, par=0, ign=0) 47%\n 32 run import time\n 33 \n 34 \n 35 run +from networking_cisco._i18n import _LE, _LI, _LW\n 36 run from oslo_serialization import jsonutils\n 37 \n 38 \n \n 382 # it is created by openstack.\n 383 run part_name = self.cfg.dcnm.default_partition_name\n 384 par if len(':'.join((proj_name, part_name))) > 32:\n 385 mis + LOG.error(_LE('Invalid project name length: %s. The length of '\n 386 'org:part name is greater than 32'),\n 387 len(':'.join((proj_name, part_name))))\n 388 mis return\n\nEach file from the diff will be reported. If the file was not processed\nby the coverage test, or there were no added or changed lines in the\ndiff for the file, this will be reported, as shown in the first two files.\n\nFor files with coverage data and added/changes lines, the output will\nlook like the third file. Each line number from the diff is shown, with\nthe coverage status, which can be:\n\n* 'run' The line was invoked as part of coverage run\n* 'mis' The line was not invoked during coverage\n* 'par' The line was partially covered\n* ' ' The line was not considered for coverage (e.g. blank, non-executable)\n\nNext, if the line was added/changed, a '+' is shown. If it was a context line\nfor the diff region, a ' ' is shown. Deleted lines are not shown. After that,\nthe source code is show.\n\nNext to the filename is summary information, ONLY for lines that were added\nor changed. In the example, there was one line run and one missing in the\nchange set (lines with a plus sign). At the end, we can see the overall\ncoverage report for the FILE - 47% in this example.\n\nThere are a few knobs that you can use with this script. First, you can change\nthe number of context lines shown by using the --context argument. The default\nis three, and can be zero or more. Note: if a diff region is at the start or\nend of a file, there may be fewer or no context lines.\n\nSecond, you can select which commits are used for the diff calculation, by\nspecifying the --which argument. The default is 'working', which will do a\ndiff between the working directory and latest commit (HEAD). Instead, you can\nprovide 'committed', which will compare the current commit against the\nprevious commit (HEAD^..HEAD). Otherwise, you can provide the commit versions\nto use for the diff, just make sure that the most recent corresponds to the\ncoverage report. For example, you can do::\n\n cd /opt/stack/neutron\n commit_coverage --context 5 --which HEAD~5..HEAD~ .\n\nThis runs the tool on a neutron repo, shows more context lines, and will\ndo a diff between HEAD~5 and HEAD~ commits.\n\nYou can use the -h option to see what the arguments are for this script.\n"},{"alpha_fraction":0.5890026688575745,"alphanum_fraction":0.6177027225494385,"avg_line_length":31.899141311645508,"blob_id":"df9d893a528855881634ef70c13002b290755185","content_id":"dbc8d783d20845c153c4345b13d8c232f0c6b31e","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":15331,"license_type":"permissive","max_line_length":82,"num_lines":466,"path":"/tests/test_commit_coverage.py","repo_name":"pmichali/commit-coverage","src_encoding":"UTF-8","text":"import mock\nimport os\nimport pytest\nimport shutil\nimport subprocess\nimport sys\nimport tempfile\n\nimport commit_coverage as cover\n\nif sys.version_info > (3, ):\n import builtins\nelse:\n import __builtin__ as builtins\n\n\n@pytest.fixture()\ndef fake_cover_project(request):\n cover_project_area = tempfile.mkdtemp()\n cwd = os.getcwd()\n os.chdir(cover_project_area)\n os.mkdir('cover')\n os.chdir(cwd)\n\n def fin():\n shutil.rmtree(cover_project_area)\n request.addfinalizer(fin)\n return cover_project_area\n\n\n@pytest.fixture()\ndef fake_project(request):\n project_area = tempfile.mkdtemp()\n\n def fin():\n shutil.rmtree(project_area)\n request.addfinalizer(fin)\n return project_area\n\n\ndef test_extract_no_added_line_diffs():\n diffs = \"\"\"diff --git a/whodunit.py b/whodunit.pyindex acd6edc..513b410 100644\n--- a/whodunit.py\n+++ b/whodunit.py\n@@ -1,3 +1,2 @@\n #!/usr/bin/env python\n-# whodunit\n #\n\"\"\"\n source_file, lines = cover.parse_diffs(diffs)\n expected = [cover.SourceLine(1), cover.SourceLine(2)]\n assert source_file == 'whodunit.py'\n assert lines == expected\n\n\ndef test_extract_single_added_line():\n diffs = \"\"\"diff --git a/path/file.py b/path/file.py\nindex 0fa872e..77ebfef 100644\n--- a/path/file.py\n+++ b/path/file.py\n@@ -59,6 +59,7 @@ INSTANCE_2 = 'testvm2'\n INSTANCE_PC = 'testpcvm'\n INSTANCE_DUAL = 'testdualvm'\n \n+NEXUS_BAREMETAL_PORT_1 = 'Ethernet 1/10'\n NEXUS_PORT_1 = 'ethernet:1/10'\n NEXUS_PORT_2 = 'ethernet:1/20'\n NEXUS_DUAL1 = 'ethernet:1/3'\n\"\"\"\n source_file, lines = cover.parse_diffs(diffs)\n expected = [\n cover.SourceLine(59), cover.SourceLine(60), cover.SourceLine(61),\n cover.SourceLine(62, False),\n cover.SourceLine(63), cover.SourceLine(64), cover.SourceLine(65)\n ]\n assert source_file == 'path/file.py'\n assert lines == expected\n assert lines[0].code == \"INSTANCE_PC = 'testpcvm'\"\n assert lines[1].code == \"INSTANCE_DUAL = 'testdualvm'\"\n assert lines[2].code == \"\"\n assert lines[3].code == \"NEXUS_BAREMETAL_PORT_1 = 'Ethernet 1/10'\"\n assert lines[4].code == \"NEXUS_PORT_1 = 'ethernet:1/10'\"\n assert lines[5].code == \"NEXUS_PORT_2 = 'ethernet:1/20'\"\n assert lines[6].code == \"NEXUS_DUAL1 = 'ethernet:1/3'\"\n\n\ndef test_extract_multiple_lines():\n diffs = \"\"\"diff --git a/path/constants.py b/path/constants.py\nindex 8c57e4a..983de98 100644\n--- a/path/constants.py\n+++ b/path/constants.py\n@@ -42,6 +42,7 @@ PORT_PROFILE_NAME_PREFIX = \"OS-PP-\"\n CLIENT_PROFILE_NAME_PREFIX = \"OS-CL-\"\n CLIENT_PROFILE_PATH_PREFIX = \"/cl-\"\n \n-ETH0 = \"/ether-eth0\"\n-ETH1 = \"/ether-eth1\"\n+ETH0 = \"eth0\"\n+ETH1 = \"eth1\"\n+ETH_PREFIX = \"/ether-\"\n DUPLICATE_EXCEPTION = \"object already exists\"\n\"\"\"\n source_file, lines = cover.parse_diffs(diffs)\n expected = [\n cover.SourceLine(42), cover.SourceLine(43), cover.SourceLine(44),\n cover.SourceLine(45, False), cover.SourceLine(46, False),\n cover.SourceLine(47, False),\n cover.SourceLine(48)\n ]\n assert source_file == 'path/constants.py'\n assert lines == expected\n\n\ndef test_extract_multiple_ranges_for_file():\n \"\"\"Check multiple ranges in a single file.\n\n Note: Final region is at end of file and does not have a trailing\n context line.\n \"\"\"\n diffs = \"\"\"diff --git a/whodunit.py b/whodunit.py\nindex 2af698e..79da120 100644\n--- a/whodunit.py\n+++ b/whodunit.py\n@@ -475,8 +484,8 @@ def build_owner(args):\n args.verbose, args.max)\n \n \n-def main(args):\n- args.root = os.path.abspath(args.root)\n+def main(parser):\n+ args = validate(parser)\n owners = build_owner(args)\n \n # Generators to get the owner info\n@@ -491,7 +500,7 @@ def main(args):\n all_authors += top_n\n # Don't alter ordering, as names in sort (date/size) order\n print(\"(%s)\" % ', '.join(top_n))\n- if args.details:\n+ if owners.details:\n owners.show_details(args.max)\n print(\"\\\\n\\\\nAll authors: %s\" % ', '.join(sort_by_name(all_authors)))\n \n@@ -517,5 +526,4 @@ def setup_parser():\n return parser\n \n if __name__ == \"__main__\":\n- parser = setup_parser()\n- main(parser.parse_args())\n+ main(setup_parser())\n\"\"\"\n source_file, lines = cover.parse_diffs(diffs)\n expected = [\n cover.SourceLine(484), cover.SourceLine(485), cover.SourceLine(486),\n cover.SourceLine(487, False), cover.SourceLine(488, False),\n cover.SourceLine(489), cover.SourceLine(490), cover.SourceLine(491),\n\n cover.SourceLine(500), cover.SourceLine(501), cover.SourceLine(502),\n cover.SourceLine(503, False),\n cover.SourceLine(504), cover.SourceLine(505), cover.SourceLine(506),\n\n cover.SourceLine(526), cover.SourceLine(527), cover.SourceLine(528),\n cover.SourceLine(529, False)\n ]\n assert source_file == 'whodunit.py'\n assert len(lines) == 19\n print(\"%r\" % lines)\n print(\"%r\" % expected)\n assert lines == expected\n\n\ndef test_extract_file_with_one_line_only():\n \"\"\"Special case test of file with no context lines available.\"\"\"\n diffs = \"\"\"diff --git a/dummy b/dummy\nnew file mode 100644\nindex 0000000..257cc56\n--- /dev/null\n+++ b/dummy\n@@ -0,0 +1 @@\n+foo\n\"\"\"\n source_file, lines = cover.parse_diffs(diffs)\n assert source_file == 'dummy'\n assert lines == [cover.SourceLine(1, is_context=False)]\n\n\ndef test_extract_no_context_no_added_lines():\n \"\"\"Context is set to zero and there are no added lines.\"\"\"\n diffs = \"\"\"diff --git a/dummy b/dummy\nindex 7a47da8..7ee1d92 100644\n--- a/dummy\n+++ b/dummy\n@@ -65 +35,0 @@ check_pot_files_errors () {\n-check_opinionated_shell\n\"\"\"\n source_file, lines = cover.parse_diffs(diffs)\n assert source_file == 'dummy'\n assert lines == []\n\n\ndef test_extract_one_line_deleted_and_added():\n \"\"\"Special case where both diff markers have no commas.\n\n Indicates that one line deleted and one line added.\n \"\"\"\n diffs = \"\"\"diff --git a/devstack/saf/cisco_saf b/devstack/saf/cisco_saf\nindex 2498062..efcc602 100644\n--- a/devstack/saf/cisco_saf\n+++ b/devstack/saf/cisco_saf\n@@ -1 +1 @@\n-#!/bin/bash\n+#!/usr/bin/env bash\n\"\"\"\n source_file, lines = cover.parse_diffs(diffs)\n assert source_file == 'devstack/saf/cisco_saf'\n assert lines == [cover.SourceLine(1, is_context=False)]\n\n\ndef test_collecting_diffs(monkeypatch):\n with mock.patch.object(subprocess, 'Popen', create=True) as popen:\n popen.return_value.communicate.side_effect = [('output1', ''),\n ('output2', '')]\n monkeypatch.setattr('os.chdir', lambda x: None)\n diffs = cover.collect_diffs_for_files('/some/path', versions=\"HEAD\",\n source_files=['foo', 'bar'],\n context_lines=5)\n assert list(diffs) == ['output1', 'output2']\n\n expected = [\n mock.call(['git', 'diff', '-U5', '-w', 'HEAD', '--', 'foo'],\n stderr=-1, stdout=-1),\n mock.call().communicate(),\n mock.call(['git', 'diff', '-U5', '-w', 'HEAD', '--', 'bar'],\n stderr=-1, stdout=-1),\n mock.call().communicate()\n ]\n assert popen.call_count == 2\n popen.assert_has_calls(expected)\n\n\ndef test_fail_collecting_diffs(monkeypatch):\n with mock.patch.object(subprocess, 'Popen', create=True) as popen:\n popen.return_value.communicate.return_value = ('', 'bad file')\n monkeypatch.setattr('os.chdir', lambda x: None)\n\n diffs = cover.collect_diffs_for_files('/some/path', versions=\"HEAD\",\n source_files=['foo'],\n context_lines=5)\n with pytest.raises(cover.DiffCollectionFailed) as excinfo:\n for diff in diffs:\n pass\n expected_msg = ('Unable to collect diffs for file /some/path/foo: '\n 'bad file')\n assert excinfo.value.args[0] == expected_msg\n\n\ndef test_collecting_diff_files(monkeypatch):\n \"\"\"Ignores files with leading period.\"\"\"\n with mock.patch.object(subprocess, 'Popen', create=True) as popen:\n popen.return_value.communicate.return_value = (\n 'foo.py\\n.ignored-file\\nbar.py\\n', '')\n monkeypatch.setattr('os.chdir', lambda x: None)\n\n diff_files = cover.collect_diff_files(fake_project, 'HEAD')\n assert list(diff_files) == ['foo.py', 'bar.py']\n\n\ndef test_fail_collecting_diff_files(monkeypatch):\n with mock.patch.object(subprocess, 'Popen', create=True) as popen:\n popen.return_value.communicate.return_value = ('foo.py', 'no file')\n monkeypatch.setattr('os.chdir', lambda x: None)\n \n diff_files = cover.collect_diff_files('/some/path', 'HEAD')\n with pytest.raises(cover.DiffCollectionFailed) as excinfo:\n for diff_file in diff_files:\n pass\n expected_msg = ('Unable to find diff files to examine in '\n '/some/path: no file')\n assert excinfo.value.args[0] == expected_msg\n\n\ndef test_determine_coverage_file_name():\n filename = \"relative/path/to/file.py\"\n module = cover.SourceModule(filename, lines=[])\n assert module.cover_file == 'relative_path_to_file_py.html'\n\n\ndef test_update_status_line():\n line = cover.SourceLine(10, is_context=False)\n module = cover.SourceModule('foo', [line])\n module.update_line_status(10, 'pln')\n assert line.status == ' '\n module.update_line_status(10, 'stm run')\n assert line.status == 'run'\n module.update_line_status(10, 'stm mis')\n assert line.status == 'mis'\n module.update_line_status(10, 'stm par')\n assert line.status == 'par'\n\n\ndef test_fail_update_status_no_matching_line():\n line = cover.SourceLine(10, is_context=False)\n module = cover.SourceModule('foo', [line])\n module.update_line_status(12, 'stm run')\n assert line.status == '???'\n\n\ndef test_check_coverage_no_lines():\n coverage_info = \"\"\"\n<p id=\"n1\" class=\"pln\"><a href=\"#n1\">1</a></p>\n<p id=\"n2\" class=\"pln\"><a href=\"#n2\">2</a></p>\n<p id=\"n3\" class=\"pln\"><a href=\"#n3\">3</a></p>\n\"\"\".splitlines()\n module = cover.SourceModule('foo.py', [])\n cover.check_coverage_status(coverage_info, module)\n assert module.lines == []\n\n\ndef test_coverage_status_collection():\n coverage_info = \"\"\"\n...\n<title>Coverage for foo.py: 81%</title>\n...\n<p id=\"n63\" class=\"pln\"><a href=\"#n63\">63</a></p>\n<p id=\"n64\" class=\"stm par run hide_run\"><a href=\"#n64\">64</a></p>\n<p id=\"n65\" class=\"stm mis\"><a href=\"#n65\">65</a></p>\n<p id=\"n66\" class=\"stm run hide_run\"><a href=\"#n66\">66</a></p>\n...\n </td>\n <td class=\"text\">\n\"\"\".splitlines()\n non_executable_line = cover.SourceLine(63)\n partial_covered_line = cover.SourceLine(64, is_context=False)\n missing_line = cover.SourceLine(65, is_context=False)\n covered_line = cover.SourceLine(66, is_context=False)\n module = cover.SourceModule('foo.py', [non_executable_line,\n partial_covered_line,\n missing_line,\n covered_line])\n cover.check_coverage_status(coverage_info, module)\n assert non_executable_line.status == ' '\n assert partial_covered_line.status == 'par'\n assert missing_line.status == 'mis'\n assert covered_line.status == 'run'\n assert module.coverage == '81%'\n\n\ndef test_missing_coverage_file(fake_cover_project):\n module = cover.SourceModule('foo.py', [])\n cover.check_coverage_file(fake_cover_project, module)\n assert not module.have_report\n\n\ndef test_checking_coverage_file(monkeypatch):\n coverage_info = \"\"\"\n...\n<title>Coverage for foo.py: 5%</title>\n...\n<p id=\"n2\" class=\"stm run\"><a href=\"#n2\">2</a></p>\n...\n </td>\n <td class=\"text\">\n\"\"\".splitlines()\n line = cover.SourceLine(2, is_context=False)\n module = cover.SourceModule('foo.py', [line])\n\n def is_a_file(filename):\n return True\n monkeypatch.setattr(os.path, 'isfile', is_a_file)\n\n with mock.patch.object(builtins, 'open', mock.mock_open(),\n create=True) as mock_open:\n mock_open.return_value.readlines.return_value = coverage_info\n cover.check_coverage_file('.', module)\n assert module.have_report\n assert line.status == 'run'\n assert module.coverage == '5%'\n\n\ndef test_report_non_coverage_files():\n module = cover.SourceModule('path/foo.py', [])\n assert module.report() == 'path/foo.py (No coverage data)\\n'\n\n\ndef test_report_no_no_added_lines():\n module = cover.SourceModule('path/foo.py', [])\n module.have_report = True\n assert module.report() == 'path/foo.py (No added/changed lines)\\n'\n\n lines = [cover.SourceLine(10, is_context=True),\n cover.SourceLine(12, is_context=True)]\n module = cover.SourceModule('deleted_lines_file', lines)\n module.have_report = True\n assert module.report() == 'deleted_lines_file (No added/changed lines)\\n'\n\n\ndef test_report_one_line():\n line = cover.SourceLine(10, is_context=False, code=' x = 1')\n module = cover.SourceModule('path/foo.py', [line])\n module.have_report = True\n module.coverage = '100%'\n line.status = 'run'\n expected = \"\"\"path/foo.py (run=1, mis=0, par=0, ign=0) 100%\n 10 run + x = 1\n\"\"\"\n assert module.report() == expected\n\n\ndef test_report_multiple_blocks():\n lines = [cover.SourceLine(10, is_context=False, code='x = 1'),\n cover.SourceLine(11, is_context=False, code='y = 2'),\n cover.SourceLine(20, is_context=False, code='z = 3'),\n cover.SourceLine(21, is_context=False, code='for i in [1, 2]')]\n lines[0].status = 'run'\n lines[1].status = 'mis'\n lines[2].status = 'par'\n lines[3].status = ' '\n module = cover.SourceModule('path/foo.py', lines)\n module.have_report = True\n module.coverage = '50%'\n\n expected = \"\"\"path/foo.py (run=1, mis=1, par=1, ign=1) 50%\n 10 run + x = 1\n 11 mis + y = 2\n\n 20 par + z = 3\n 21 + for i in [1, 2]\n\"\"\"\n assert module.report() == expected\n\n\ndef test_argument_parse_which(fake_cover_project):\n parser = cover.setup_parser()\n args = cover.validate(parser, [fake_cover_project])\n assert args.commits == 'HEAD'\n args = cover.validate(parser, ['-w', 'working', fake_cover_project])\n assert args.commits == 'HEAD'\n args = cover.validate(parser, ['-w', 'committed', fake_cover_project])\n assert args.commits == 'HEAD^..HEAD'\n args = cover.validate(parser, ['-w', 'HEAD~5..HEAD~3', fake_cover_project])\n assert args.commits == 'HEAD~5..HEAD~3'\n\n\ndef test_argument_parse_context(fake_cover_project):\n parser = cover.setup_parser()\n args = cover.validate(parser, ['-c', '10', fake_cover_project])\n assert args.context == 10\n\n\ndef test_validate_directory(fake_cover_project):\n parser = cover.setup_parser()\n assert cover.validate(parser, [fake_cover_project])\n\n\ndef test_validate_no_coverage_area(fake_project):\n parser = cover.setup_parser()\n with pytest.raises(SystemExit) as excinfo:\n cover.validate(parser, [fake_project])\n assert str(excinfo.value) == '2'\n\n\ndef test_validate_no_directory():\n parser = cover.setup_parser()\n with pytest.raises(SystemExit) as excinfo:\n cover.validate(parser, ['bogus-dir'])\n assert str(excinfo.value) == '2'\n"},{"alpha_fraction":0.6433566212654114,"alphanum_fraction":0.6503496766090393,"avg_line_length":14.88888931274414,"blob_id":"7ee327bff18a548ecce588bc4fb3176ac4ba5d03","content_id":"7eab6612885e0c05fb92ebf41b932c51ab43c816","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":143,"license_type":"permissive","max_line_length":72,"num_lines":9,"path":"/commit_coverage/__main__.py","repo_name":"pmichali/commit-coverage","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n\n\n\"\"\"commit_coverage.__main__: used when directory is called as script.\"\"\"\n\n\nfrom .commit_coverage import main\n\nmain()\n"},{"alpha_fraction":0.6008238196372986,"alphanum_fraction":0.603218674659729,"avg_line_length":36.282142639160156,"blob_id":"3cde73cedc1f4e9210874f393247ca2c1246e9b7","content_id":"eadd963cbb097db03416931195391122a435bea6","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":10439,"license_type":"permissive","max_line_length":79,"num_lines":280,"path":"/commit_coverage/__init__.py","repo_name":"pmichali/commit-coverage","src_encoding":"UTF-8","text":"#!/usr/bin/env python\n#\n# Copyright (c) 2016 Paul Michali, Cisco Systems Inc.\n# All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n#\n# commit_coverage\n#\n# Indicates the coverage for diffs specified in the command. It assumes that\n# coverage was run before using this tool (and report should be on what is\n# in the repo currently).\n#\n# Usage:\n# commit_coverage.py [-h] [-c CONTEXT] [-w WHICH] repo-dir\n# Where:\n# -h, --help Show this help message and exit.\n# -c CONTEXT, --context CONTEXT\n# Number of context lines around diff regions.\n# Default=3.\n# -w COMMITS, --which COMMITS\n# Which commit(s) to compare. Use 'working', 'committed',\n# or custom commit specification. Latest should be same\n# as cover run. Default='working'.\n#\n# For the commit selection, you want the latest to match what the coverage\n# was done on. This translates into the commit specification for 'git diff'.\n# If you have uncommitted changes, you can use (the default) 'working',\n# which will use 'HEAD'. If you've already committed, you can use 'commit',\n# which will use 'HEAD^'. Otherwise, you can use any valid 'git diff' commit\n# specification. Just make sure that the newer commit must match what was\n# used in the coverage report created.\n#\n# You can adjust the number of lines of context surrounding the diff lines,\n# just as is done with 'git diff'.\n#\n# The output will show each file from the diffs along with a report on\n# the coverage. Each line (including surrounding context lines) with line\n# number, coverage indication, diff indication, and the source line.\n#\n# The coverage indication is 'run' meaning the line is covered, 'mis'\n# meaning the line is missing coverage, 'par' meaning the line is partially\n# covered, or ' ' meaning the line is ignored w.r.t. coverage.\n#\n# There is a summary of covered, missing, partial, and ignored lines\n# that were added/changed (only - no context lines).\n#\n# The diff indication will be a '+' for lines added/changed by the diff,\n# or ' ' for context lines. Note: if a diff is at the start or end of a\n# file, there may not be coverage lines.\n#\n# If the file was not exercised as part of coverage, or if the file did\n# not have any added/changed lines, it will be indicated.\n\nfrom __future__ import print_function\n\nimport argparse\nfrom collections import Counter\nimport os\nimport re\nimport subprocess\n\nfile_re = re.compile(r'diff --git a/(\\S+)')\ndiff_region_re = re.compile(r'@@\\s[-]\\S+\\s[+](\\S+)\\s@@')\nsource_line_re = re.compile(r'<p id=\"n(\\d+)\" class=\"([^\"]+)\"')\ntitle_re = re.compile(r'\\s*<title>Coverage for [^:]+:\\s+(\\d+%)<\\/title>')\nsummary_end_re = re.compile(r'\\s+<td class=\"text\">')\n\n__version__ = \"0.7\"\n\n\nclass DiffCollectionFailed(Exception):\n pass\n\n\nclass SourceLine(object):\n\n def __init__(self, line_number, is_context=True, code=''):\n self.line_number = line_number\n self.is_context = is_context\n self.code = code\n self.status = '???'\n\n def __eq__(self, other):\n return (self.line_number == other.line_number and\n self.is_context == other.is_context)\n\n def __repr__(self):\n return \"SourceLine(line_number=%d, is_context=%s)\" % (self.line_number,\n self.is_context)\n\n\nclass SourceModule(object):\n\n def __init__(self, filename, lines):\n self.filename = filename\n self.lines = lines\n self.line_num_map = {l.line_number: l for l in lines}\n self.cover_file = (filename.replace('/', '_').replace('.', '_') +\n \".html\")\n self.have_report = False\n self.coverage = '??%'\n\n def update_line_status(self, line_number, status):\n if line_number in self.line_num_map:\n line = self.line_num_map[line_number]\n if status.startswith('pln'):\n line.status = ' '\n else:\n line.status = status[4:7]\n\n def report(self):\n output = self.filename\n if not self.have_report:\n return \"%s (No coverage data)\\n\" % output\n if not self.lines or all(l.is_context for l in self.lines):\n return \"%s (No added/changed lines)\\n\" % output\n stats = Counter([l.status for l in self.lines if not l.is_context])\n output += \" (run={}, mis={}, par={}, ign={}) {}\\n\".format(\n stats['run'], stats['mis'], stats['par'], stats[' '],\n self.coverage)\n last_line = None\n for line in self.lines:\n if last_line and line.line_number != (last_line + 1):\n output += \"\\n\"\n output += \"{:5d} {} {} {}\\n\".format(\n line.line_number, line.status,\n ' ' if line.is_context else '+', line.code)\n last_line = line.line_number\n return output\n\n\ndef check_coverage_status(coverage_info, module):\n for coverage_line in coverage_info:\n m = title_re.match(coverage_line)\n if m:\n module.coverage = m.group(1)\n continue\n if summary_end_re.match(coverage_line):\n return\n m = source_line_re.match(coverage_line)\n if m:\n line_num = int(m.group(1))\n status = m.group(2)\n module.update_line_status(line_num, status)\n\n\ndef check_coverage_file(root, module):\n \"\"\"Check the lines in coverage file and report coverage status.\"\"\"\n report_file = os.path.join(root, 'cover', module.cover_file)\n if not os.path.isfile(report_file):\n return # No coverage data for file\n with open(report_file) as coverage_info:\n coverage_lines = coverage_info.readlines()\n check_coverage_status(coverage_lines, module)\n module.have_report = True\n\n\ndef collect_diff_lines(diff_region, start, last):\n \"\"\"Find added and context lines in a diff region.\n\n Note: If the diff region is at the start or end of the file, there\n may not be context lines.\n \"\"\"\n lines = []\n line_num = start\n while line_num <= last:\n line = next(diff_region)\n if line.startswith('-'):\n continue\n lines.append(SourceLine(line_num, is_context=line.startswith(' '),\n code=line[1:]))\n line_num += 1\n return lines\n\n\ndef parse_diffs(diff_output):\n \"\"\"Collect the file and ranges of diffs added, if any.\"\"\"\n added_lines = []\n source_file = ''\n diff_lines = iter(diff_output.splitlines())\n for line in diff_lines:\n m = file_re.match(line)\n if m:\n source_file = m.group(1)\n continue\n m = diff_region_re.match(line)\n if m:\n start, comma, num = m.group(1).partition(',')\n start = int(start)\n if num:\n last = start + int(num) - 1\n else:\n last = start\n added_lines += collect_diff_lines(diff_lines, start, last)\n return (source_file, added_lines)\n\n\ndef collect_diffs_for_files(root, versions, source_files, context_lines):\n \"\"\"Generator to obtain the diffs for files.\"\"\"\n os.chdir(root)\n for filename in source_files:\n command = ['git', 'diff', '-U%d' % context_lines,\n '-w', versions, '--', filename]\n p = subprocess.Popen(command,\n stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n diff_lines, err = p.communicate()\n if err:\n raise DiffCollectionFailed(\n \"Unable to collect diffs for file %s/%s: %s\" %\n (root, filename, err))\n yield diff_lines\n\n\ndef collect_diff_files(root, versions):\n \"\"\"Generator to obtain all the diff files.\"\"\"\n command = ['git', 'diff', '--name-only', versions]\n os.chdir(root)\n p = subprocess.Popen(command,\n stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n out, err = p.communicate()\n if err:\n raise DiffCollectionFailed(\"Unable to find diff files to examine \"\n \"in %s: %s\" % (root, err))\n for filename in out.splitlines():\n if not os.path.basename(filename).startswith('.'):\n yield filename\n\n\ndef validate(parser, provided_args=None):\n args = parser.parse_args(provided_args)\n args.root = os.path.abspath(args.root)\n if not os.path.isdir(args.root):\n parser.error(\"The repo-dir must be a directory pointing to the top \"\n \"of the Git repo\")\n if not os.path.isdir(os.path.join(args.root, 'cover')):\n parser.error(\"Missing cover directory for project\")\n if args.commits == 'working':\n args.commits = 'HEAD'\n elif args.commits == 'committed':\n args.commits = 'HEAD^..HEAD'\n return args\n\n\ndef main():\n args = validate(setup_parser())\n files = collect_diff_files(args.root, args.commits)\n diff_files = collect_diffs_for_files(args.root, args.commits, files,\n args.context)\n for diffs in diff_files:\n source_file, lines = parse_diffs(diffs)\n module = SourceModule(source_file, lines)\n check_coverage_file(args.root, module)\n print(module.report())\n\n\ndef setup_parser():\n parser = argparse.ArgumentParser(\n description='Reports coverage for a commit in repo.')\n parser.add_argument(\n '-c', '--context', action='store', type=int, default=3,\n help='Number of context lines around diff regions. Default=3.')\n parser.add_argument(\n '-w', '--which', action='store', default=\"working\", dest='commits',\n help=\"Which commit(s) to compare. Use 'working', 'commit', or \"\n \"custom commit specification. Latest should be same as cover run. \"\n \"Default='working'.\")\n parser.add_argument(dest='root', metavar='repo-dir',\n help=\"Root of Git repo\")\n return parser\n"},{"alpha_fraction":0.5988888740539551,"alphanum_fraction":0.6061111092567444,"avg_line_length":27.125,"blob_id":"ee3b79b77891900ac05b8ae8dd14a2a29b67b9f7","content_id":"6da85da5e88d16b84fbe042b8c95a62ec0ebc174","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1800,"license_type":"permissive","max_line_length":77,"num_lines":64,"path":"/setup.py","repo_name":"pmichali/commit-coverage","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n\n\n\"\"\"setup.py: setuptools control.\"\"\"\n\n\nimport re\nfrom setuptools.command.test import test as TestCommand\nfrom setuptools import setup\n\n\nclass PyTest(TestCommand):\n def finalize_options(self):\n TestCommand.finalize_options(self)\n self.test_args = []\n self.test_suite = True\n\n def run_tests(self):\n # import here, cause outside the eggs aren't loaded\n import pytest\n pytest.main(self.test_args)\n\n\nversion = re.search(\n '^__version__\\s*=\\s*\"(.*)\"',\n open('commit_coverage/__init__.py').read(),\n re.M\n ).group(1)\n\n\nwith open(\"README.rst\", \"rb\") as f:\n long_descr = f.read()\n\n\nsetup(\n name=\"commit-coverage\",\n packages=[\"commit_coverage\"],\n entry_points={\n \"console_scripts\": ['commit_coverage = commit_coverage:main']\n },\n version=version,\n description=\"Reports on (previously run) coverage results for a commit.\",\n long_description=long_descr,\n author=\"Paul Michali\",\n author_email=\"pc@michali.net\",\n url=\"https://github.com/pmichali/commit-coverage\",\n license=\"Apache Software License\",\n platforms='any',\n tests_require=['pytest', 'mock'],\n cmdclass={'tests': PyTest},\n classifiers=[\n 'Development Status :: 3 - Alpha',\n 'Intended Audience :: Developers',\n 'Topic :: Software Development :: Build Tools',\n 'License :: OSI Approved :: Apache Software License',\n 'Natural Language :: English',\n 'Operating System :: OS Independent',\n 'Programming Language :: Python :: 2',\n 'Programming Language :: Python :: 2.7',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.2',\n 'Programming Language :: Python :: 3.3',\n 'Programming Language :: Python :: 3.4',\n ],)\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6928218007087708,\n \"alphanum_fraction\": 0.7059406042098999,\n \"avg_line_length\": 41.978721618652344,\n \"blob_id\": \"6a15d36fb73b966d8c102c2386052ec1e6e993ff\",\n \"content_id\": \"3e86221ca2445b7664f525c6a8973ff3f0db3c02\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"reStructuredText\",\n \"length_bytes\": 4040,\n \"license_type\": \"permissive\",\n \"max_line_length\": 90,\n \"num_lines\": 94,\n \"path\": \"/README.rst\",\n \"repo_name\": \"pmichali/commit-coverage\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"commit-coverage\\n===============\\n\\nThis allows you to assess the coverage of changes you have in a repo. It\\nis intended for use, when preparing to push code up for review upstream.\\n\\nUsage\\n-----\\n\\nAs a prerequisite to using this script, run coverage in the repo, to\\nproduce coverage report files in a 'cover' directory at the root of the\\nrepo's tree. This coverage should be done on the code that will be\\nupstreamed (either what is in the working directory, or what has been\\ncommitted to the local repo).\\n\\nBehavior\\n--------\\n\\nAssuming you have a repo with uncommitted changes, you can run the command\\nas follows::\\n\\n commit_coverage /opt/stack/networking-cisco\\n\\nThe argument must be the root of a git repo that has coverage data in a\\n'cover' subdirectory.\\n\\nBy default, this will create diffs comparing the working directory, to the\\nlatest commit (HEAD), with (up to) three lines of context. That output will\\nbe checked against coverage data and a report produced. Here's what the\\noutput looks like::\\n\\n devstack/csr1kv/cisco_neutron (No coverage data)\\n\\n networking_cisco/apps/saf/agent/dfa_agent.py (No added/changed lines)\\n\\n networking_cisco/apps/saf/server/dfa_server.py (run=1, mis=1, par=0, ign=0) 47%\\n 32 run import time\\n 33 \\n 34 \\n 35 run +from networking_cisco._i18n import _LE, _LI, _LW\\n 36 run from oslo_serialization import jsonutils\\n 37 \\n 38 \\n \\n 382 # it is created by openstack.\\n 383 run part_name = self.cfg.dcnm.default_partition_name\\n 384 par if len(':'.join((proj_name, part_name))) > 32:\\n 385 mis + LOG.error(_LE('Invalid project name length: %s. The length of '\\n 386 'org:part name is greater than 32'),\\n 387 len(':'.join((proj_name, part_name))))\\n 388 mis return\\n\\nEach file from the diff will be reported. If the file was not processed\\nby the coverage test, or there were no added or changed lines in the\\ndiff for the file, this will be reported, as shown in the first two files.\\n\\nFor files with coverage data and added/changes lines, the output will\\nlook like the third file. Each line number from the diff is shown, with\\nthe coverage status, which can be:\\n\\n* 'run' The line was invoked as part of coverage run\\n* 'mis' The line was not invoked during coverage\\n* 'par' The line was partially covered\\n* ' ' The line was not considered for coverage (e.g. blank, non-executable)\\n\\nNext, if the line was added/changed, a '+' is shown. If it was a context line\\nfor the diff region, a ' ' is shown. Deleted lines are not shown. After that,\\nthe source code is show.\\n\\nNext to the filename is summary information, ONLY for lines that were added\\nor changed. In the example, there was one line run and one missing in the\\nchange set (lines with a plus sign). At the end, we can see the overall\\ncoverage report for the FILE - 47% in this example.\\n\\nThere are a few knobs that you can use with this script. First, you can change\\nthe number of context lines shown by using the --context argument. The default\\nis three, and can be zero or more. Note: if a diff region is at the start or\\nend of a file, there may be fewer or no context lines.\\n\\nSecond, you can select which commits are used for the diff calculation, by\\nspecifying the --which argument. The default is 'working', which will do a\\ndiff between the working directory and latest commit (HEAD). Instead, you can\\nprovide 'committed', which will compare the current commit against the\\nprevious commit (HEAD^..HEAD). Otherwise, you can provide the commit versions\\nto use for the diff, just make sure that the most recent corresponds to the\\ncoverage report. For example, you can do::\\n\\n cd /opt/stack/neutron\\n commit_coverage --context 5 --which HEAD~5..HEAD~ .\\n\\nThis runs the tool on a neutron repo, shows more context lines, and will\\ndo a diff between HEAD~5 and HEAD~ commits.\\n\\nYou can use the -h option to see what the arguments are for this script.\\n\"\n },\n {\n \"alpha_fraction\": 0.5890026688575745,\n \"alphanum_fraction\": 0.6177027225494385,\n \"avg_line_length\": 31.899141311645508,\n \"blob_id\": \"df9d893a528855881634ef70c13002b290755185\",\n \"content_id\": \"dbc8d783d20845c153c4345b13d8c232f0c6b31e\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 15331,\n \"license_type\": \"permissive\",\n \"max_line_length\": 82,\n \"num_lines\": 466,\n \"path\": \"/tests/test_commit_coverage.py\",\n \"repo_name\": \"pmichali/commit-coverage\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import mock\\nimport os\\nimport pytest\\nimport shutil\\nimport subprocess\\nimport sys\\nimport tempfile\\n\\nimport commit_coverage as cover\\n\\nif sys.version_info > (3, ):\\n import builtins\\nelse:\\n import __builtin__ as builtins\\n\\n\\n@pytest.fixture()\\ndef fake_cover_project(request):\\n cover_project_area = tempfile.mkdtemp()\\n cwd = os.getcwd()\\n os.chdir(cover_project_area)\\n os.mkdir('cover')\\n os.chdir(cwd)\\n\\n def fin():\\n shutil.rmtree(cover_project_area)\\n request.addfinalizer(fin)\\n return cover_project_area\\n\\n\\n@pytest.fixture()\\ndef fake_project(request):\\n project_area = tempfile.mkdtemp()\\n\\n def fin():\\n shutil.rmtree(project_area)\\n request.addfinalizer(fin)\\n return project_area\\n\\n\\ndef test_extract_no_added_line_diffs():\\n diffs = \\\"\\\"\\\"diff --git a/whodunit.py b/whodunit.pyindex acd6edc..513b410 100644\\n--- a/whodunit.py\\n+++ b/whodunit.py\\n@@ -1,3 +1,2 @@\\n #!/usr/bin/env python\\n-# whodunit\\n #\\n\\\"\\\"\\\"\\n source_file, lines = cover.parse_diffs(diffs)\\n expected = [cover.SourceLine(1), cover.SourceLine(2)]\\n assert source_file == 'whodunit.py'\\n assert lines == expected\\n\\n\\ndef test_extract_single_added_line():\\n diffs = \\\"\\\"\\\"diff --git a/path/file.py b/path/file.py\\nindex 0fa872e..77ebfef 100644\\n--- a/path/file.py\\n+++ b/path/file.py\\n@@ -59,6 +59,7 @@ INSTANCE_2 = 'testvm2'\\n INSTANCE_PC = 'testpcvm'\\n INSTANCE_DUAL = 'testdualvm'\\n \\n+NEXUS_BAREMETAL_PORT_1 = 'Ethernet 1/10'\\n NEXUS_PORT_1 = 'ethernet:1/10'\\n NEXUS_PORT_2 = 'ethernet:1/20'\\n NEXUS_DUAL1 = 'ethernet:1/3'\\n\\\"\\\"\\\"\\n source_file, lines = cover.parse_diffs(diffs)\\n expected = [\\n cover.SourceLine(59), cover.SourceLine(60), cover.SourceLine(61),\\n cover.SourceLine(62, False),\\n cover.SourceLine(63), cover.SourceLine(64), cover.SourceLine(65)\\n ]\\n assert source_file == 'path/file.py'\\n assert lines == expected\\n assert lines[0].code == \\\"INSTANCE_PC = 'testpcvm'\\\"\\n assert lines[1].code == \\\"INSTANCE_DUAL = 'testdualvm'\\\"\\n assert lines[2].code == \\\"\\\"\\n assert lines[3].code == \\\"NEXUS_BAREMETAL_PORT_1 = 'Ethernet 1/10'\\\"\\n assert lines[4].code == \\\"NEXUS_PORT_1 = 'ethernet:1/10'\\\"\\n assert lines[5].code == \\\"NEXUS_PORT_2 = 'ethernet:1/20'\\\"\\n assert lines[6].code == \\\"NEXUS_DUAL1 = 'ethernet:1/3'\\\"\\n\\n\\ndef test_extract_multiple_lines():\\n diffs = \\\"\\\"\\\"diff --git a/path/constants.py b/path/constants.py\\nindex 8c57e4a..983de98 100644\\n--- a/path/constants.py\\n+++ b/path/constants.py\\n@@ -42,6 +42,7 @@ PORT_PROFILE_NAME_PREFIX = \\\"OS-PP-\\\"\\n CLIENT_PROFILE_NAME_PREFIX = \\\"OS-CL-\\\"\\n CLIENT_PROFILE_PATH_PREFIX = \\\"/cl-\\\"\\n \\n-ETH0 = \\\"/ether-eth0\\\"\\n-ETH1 = \\\"/ether-eth1\\\"\\n+ETH0 = \\\"eth0\\\"\\n+ETH1 = \\\"eth1\\\"\\n+ETH_PREFIX = \\\"/ether-\\\"\\n DUPLICATE_EXCEPTION = \\\"object already exists\\\"\\n\\\"\\\"\\\"\\n source_file, lines = cover.parse_diffs(diffs)\\n expected = [\\n cover.SourceLine(42), cover.SourceLine(43), cover.SourceLine(44),\\n cover.SourceLine(45, False), cover.SourceLine(46, False),\\n cover.SourceLine(47, False),\\n cover.SourceLine(48)\\n ]\\n assert source_file == 'path/constants.py'\\n assert lines == expected\\n\\n\\ndef test_extract_multiple_ranges_for_file():\\n \\\"\\\"\\\"Check multiple ranges in a single file.\\n\\n Note: Final region is at end of file and does not have a trailing\\n context line.\\n \\\"\\\"\\\"\\n diffs = \\\"\\\"\\\"diff --git a/whodunit.py b/whodunit.py\\nindex 2af698e..79da120 100644\\n--- a/whodunit.py\\n+++ b/whodunit.py\\n@@ -475,8 +484,8 @@ def build_owner(args):\\n args.verbose, args.max)\\n \\n \\n-def main(args):\\n- args.root = os.path.abspath(args.root)\\n+def main(parser):\\n+ args = validate(parser)\\n owners = build_owner(args)\\n \\n # Generators to get the owner info\\n@@ -491,7 +500,7 @@ def main(args):\\n all_authors += top_n\\n # Don't alter ordering, as names in sort (date/size) order\\n print(\\\"(%s)\\\" % ', '.join(top_n))\\n- if args.details:\\n+ if owners.details:\\n owners.show_details(args.max)\\n print(\\\"\\\\\\\\n\\\\\\\\nAll authors: %s\\\" % ', '.join(sort_by_name(all_authors)))\\n \\n@@ -517,5 +526,4 @@ def setup_parser():\\n return parser\\n \\n if __name__ == \\\"__main__\\\":\\n- parser = setup_parser()\\n- main(parser.parse_args())\\n+ main(setup_parser())\\n\\\"\\\"\\\"\\n source_file, lines = cover.parse_diffs(diffs)\\n expected = [\\n cover.SourceLine(484), cover.SourceLine(485), cover.SourceLine(486),\\n cover.SourceLine(487, False), cover.SourceLine(488, False),\\n cover.SourceLine(489), cover.SourceLine(490), cover.SourceLine(491),\\n\\n cover.SourceLine(500), cover.SourceLine(501), cover.SourceLine(502),\\n cover.SourceLine(503, False),\\n cover.SourceLine(504), cover.SourceLine(505), cover.SourceLine(506),\\n\\n cover.SourceLine(526), cover.SourceLine(527), cover.SourceLine(528),\\n cover.SourceLine(529, False)\\n ]\\n assert source_file == 'whodunit.py'\\n assert len(lines) == 19\\n print(\\\"%r\\\" % lines)\\n print(\\\"%r\\\" % expected)\\n assert lines == expected\\n\\n\\ndef test_extract_file_with_one_line_only():\\n \\\"\\\"\\\"Special case test of file with no context lines available.\\\"\\\"\\\"\\n diffs = \\\"\\\"\\\"diff --git a/dummy b/dummy\\nnew file mode 100644\\nindex 0000000..257cc56\\n--- /dev/null\\n+++ b/dummy\\n@@ -0,0 +1 @@\\n+foo\\n\\\"\\\"\\\"\\n source_file, lines = cover.parse_diffs(diffs)\\n assert source_file == 'dummy'\\n assert lines == [cover.SourceLine(1, is_context=False)]\\n\\n\\ndef test_extract_no_context_no_added_lines():\\n \\\"\\\"\\\"Context is set to zero and there are no added lines.\\\"\\\"\\\"\\n diffs = \\\"\\\"\\\"diff --git a/dummy b/dummy\\nindex 7a47da8..7ee1d92 100644\\n--- a/dummy\\n+++ b/dummy\\n@@ -65 +35,0 @@ check_pot_files_errors () {\\n-check_opinionated_shell\\n\\\"\\\"\\\"\\n source_file, lines = cover.parse_diffs(diffs)\\n assert source_file == 'dummy'\\n assert lines == []\\n\\n\\ndef test_extract_one_line_deleted_and_added():\\n \\\"\\\"\\\"Special case where both diff markers have no commas.\\n\\n Indicates that one line deleted and one line added.\\n \\\"\\\"\\\"\\n diffs = \\\"\\\"\\\"diff --git a/devstack/saf/cisco_saf b/devstack/saf/cisco_saf\\nindex 2498062..efcc602 100644\\n--- a/devstack/saf/cisco_saf\\n+++ b/devstack/saf/cisco_saf\\n@@ -1 +1 @@\\n-#!/bin/bash\\n+#!/usr/bin/env bash\\n\\\"\\\"\\\"\\n source_file, lines = cover.parse_diffs(diffs)\\n assert source_file == 'devstack/saf/cisco_saf'\\n assert lines == [cover.SourceLine(1, is_context=False)]\\n\\n\\ndef test_collecting_diffs(monkeypatch):\\n with mock.patch.object(subprocess, 'Popen', create=True) as popen:\\n popen.return_value.communicate.side_effect = [('output1', ''),\\n ('output2', '')]\\n monkeypatch.setattr('os.chdir', lambda x: None)\\n diffs = cover.collect_diffs_for_files('/some/path', versions=\\\"HEAD\\\",\\n source_files=['foo', 'bar'],\\n context_lines=5)\\n assert list(diffs) == ['output1', 'output2']\\n\\n expected = [\\n mock.call(['git', 'diff', '-U5', '-w', 'HEAD', '--', 'foo'],\\n stderr=-1, stdout=-1),\\n mock.call().communicate(),\\n mock.call(['git', 'diff', '-U5', '-w', 'HEAD', '--', 'bar'],\\n stderr=-1, stdout=-1),\\n mock.call().communicate()\\n ]\\n assert popen.call_count == 2\\n popen.assert_has_calls(expected)\\n\\n\\ndef test_fail_collecting_diffs(monkeypatch):\\n with mock.patch.object(subprocess, 'Popen', create=True) as popen:\\n popen.return_value.communicate.return_value = ('', 'bad file')\\n monkeypatch.setattr('os.chdir', lambda x: None)\\n\\n diffs = cover.collect_diffs_for_files('/some/path', versions=\\\"HEAD\\\",\\n source_files=['foo'],\\n context_lines=5)\\n with pytest.raises(cover.DiffCollectionFailed) as excinfo:\\n for diff in diffs:\\n pass\\n expected_msg = ('Unable to collect diffs for file /some/path/foo: '\\n 'bad file')\\n assert excinfo.value.args[0] == expected_msg\\n\\n\\ndef test_collecting_diff_files(monkeypatch):\\n \\\"\\\"\\\"Ignores files with leading period.\\\"\\\"\\\"\\n with mock.patch.object(subprocess, 'Popen', create=True) as popen:\\n popen.return_value.communicate.return_value = (\\n 'foo.py\\\\n.ignored-file\\\\nbar.py\\\\n', '')\\n monkeypatch.setattr('os.chdir', lambda x: None)\\n\\n diff_files = cover.collect_diff_files(fake_project, 'HEAD')\\n assert list(diff_files) == ['foo.py', 'bar.py']\\n\\n\\ndef test_fail_collecting_diff_files(monkeypatch):\\n with mock.patch.object(subprocess, 'Popen', create=True) as popen:\\n popen.return_value.communicate.return_value = ('foo.py', 'no file')\\n monkeypatch.setattr('os.chdir', lambda x: None)\\n \\n diff_files = cover.collect_diff_files('/some/path', 'HEAD')\\n with pytest.raises(cover.DiffCollectionFailed) as excinfo:\\n for diff_file in diff_files:\\n pass\\n expected_msg = ('Unable to find diff files to examine in '\\n '/some/path: no file')\\n assert excinfo.value.args[0] == expected_msg\\n\\n\\ndef test_determine_coverage_file_name():\\n filename = \\\"relative/path/to/file.py\\\"\\n module = cover.SourceModule(filename, lines=[])\\n assert module.cover_file == 'relative_path_to_file_py.html'\\n\\n\\ndef test_update_status_line():\\n line = cover.SourceLine(10, is_context=False)\\n module = cover.SourceModule('foo', [line])\\n module.update_line_status(10, 'pln')\\n assert line.status == ' '\\n module.update_line_status(10, 'stm run')\\n assert line.status == 'run'\\n module.update_line_status(10, 'stm mis')\\n assert line.status == 'mis'\\n module.update_line_status(10, 'stm par')\\n assert line.status == 'par'\\n\\n\\ndef test_fail_update_status_no_matching_line():\\n line = cover.SourceLine(10, is_context=False)\\n module = cover.SourceModule('foo', [line])\\n module.update_line_status(12, 'stm run')\\n assert line.status == '???'\\n\\n\\ndef test_check_coverage_no_lines():\\n coverage_info = \\\"\\\"\\\"\\n<p id=\\\"n1\\\" class=\\\"pln\\\"><a href=\\\"#n1\\\">1</a></p>\\n<p id=\\\"n2\\\" class=\\\"pln\\\"><a href=\\\"#n2\\\">2</a></p>\\n<p id=\\\"n3\\\" class=\\\"pln\\\"><a href=\\\"#n3\\\">3</a></p>\\n\\\"\\\"\\\".splitlines()\\n module = cover.SourceModule('foo.py', [])\\n cover.check_coverage_status(coverage_info, module)\\n assert module.lines == []\\n\\n\\ndef test_coverage_status_collection():\\n coverage_info = \\\"\\\"\\\"\\n...\\n<title>Coverage for foo.py: 81%</title>\\n...\\n<p id=\\\"n63\\\" class=\\\"pln\\\"><a href=\\\"#n63\\\">63</a></p>\\n<p id=\\\"n64\\\" class=\\\"stm par run hide_run\\\"><a href=\\\"#n64\\\">64</a></p>\\n<p id=\\\"n65\\\" class=\\\"stm mis\\\"><a href=\\\"#n65\\\">65</a></p>\\n<p id=\\\"n66\\\" class=\\\"stm run hide_run\\\"><a href=\\\"#n66\\\">66</a></p>\\n...\\n </td>\\n <td class=\\\"text\\\">\\n\\\"\\\"\\\".splitlines()\\n non_executable_line = cover.SourceLine(63)\\n partial_covered_line = cover.SourceLine(64, is_context=False)\\n missing_line = cover.SourceLine(65, is_context=False)\\n covered_line = cover.SourceLine(66, is_context=False)\\n module = cover.SourceModule('foo.py', [non_executable_line,\\n partial_covered_line,\\n missing_line,\\n covered_line])\\n cover.check_coverage_status(coverage_info, module)\\n assert non_executable_line.status == ' '\\n assert partial_covered_line.status == 'par'\\n assert missing_line.status == 'mis'\\n assert covered_line.status == 'run'\\n assert module.coverage == '81%'\\n\\n\\ndef test_missing_coverage_file(fake_cover_project):\\n module = cover.SourceModule('foo.py', [])\\n cover.check_coverage_file(fake_cover_project, module)\\n assert not module.have_report\\n\\n\\ndef test_checking_coverage_file(monkeypatch):\\n coverage_info = \\\"\\\"\\\"\\n...\\n<title>Coverage for foo.py: 5%</title>\\n...\\n<p id=\\\"n2\\\" class=\\\"stm run\\\"><a href=\\\"#n2\\\">2</a></p>\\n...\\n </td>\\n <td class=\\\"text\\\">\\n\\\"\\\"\\\".splitlines()\\n line = cover.SourceLine(2, is_context=False)\\n module = cover.SourceModule('foo.py', [line])\\n\\n def is_a_file(filename):\\n return True\\n monkeypatch.setattr(os.path, 'isfile', is_a_file)\\n\\n with mock.patch.object(builtins, 'open', mock.mock_open(),\\n create=True) as mock_open:\\n mock_open.return_value.readlines.return_value = coverage_info\\n cover.check_coverage_file('.', module)\\n assert module.have_report\\n assert line.status == 'run'\\n assert module.coverage == '5%'\\n\\n\\ndef test_report_non_coverage_files():\\n module = cover.SourceModule('path/foo.py', [])\\n assert module.report() == 'path/foo.py (No coverage data)\\\\n'\\n\\n\\ndef test_report_no_no_added_lines():\\n module = cover.SourceModule('path/foo.py', [])\\n module.have_report = True\\n assert module.report() == 'path/foo.py (No added/changed lines)\\\\n'\\n\\n lines = [cover.SourceLine(10, is_context=True),\\n cover.SourceLine(12, is_context=True)]\\n module = cover.SourceModule('deleted_lines_file', lines)\\n module.have_report = True\\n assert module.report() == 'deleted_lines_file (No added/changed lines)\\\\n'\\n\\n\\ndef test_report_one_line():\\n line = cover.SourceLine(10, is_context=False, code=' x = 1')\\n module = cover.SourceModule('path/foo.py', [line])\\n module.have_report = True\\n module.coverage = '100%'\\n line.status = 'run'\\n expected = \\\"\\\"\\\"path/foo.py (run=1, mis=0, par=0, ign=0) 100%\\n 10 run + x = 1\\n\\\"\\\"\\\"\\n assert module.report() == expected\\n\\n\\ndef test_report_multiple_blocks():\\n lines = [cover.SourceLine(10, is_context=False, code='x = 1'),\\n cover.SourceLine(11, is_context=False, code='y = 2'),\\n cover.SourceLine(20, is_context=False, code='z = 3'),\\n cover.SourceLine(21, is_context=False, code='for i in [1, 2]')]\\n lines[0].status = 'run'\\n lines[1].status = 'mis'\\n lines[2].status = 'par'\\n lines[3].status = ' '\\n module = cover.SourceModule('path/foo.py', lines)\\n module.have_report = True\\n module.coverage = '50%'\\n\\n expected = \\\"\\\"\\\"path/foo.py (run=1, mis=1, par=1, ign=1) 50%\\n 10 run + x = 1\\n 11 mis + y = 2\\n\\n 20 par + z = 3\\n 21 + for i in [1, 2]\\n\\\"\\\"\\\"\\n assert module.report() == expected\\n\\n\\ndef test_argument_parse_which(fake_cover_project):\\n parser = cover.setup_parser()\\n args = cover.validate(parser, [fake_cover_project])\\n assert args.commits == 'HEAD'\\n args = cover.validate(parser, ['-w', 'working', fake_cover_project])\\n assert args.commits == 'HEAD'\\n args = cover.validate(parser, ['-w', 'committed', fake_cover_project])\\n assert args.commits == 'HEAD^..HEAD'\\n args = cover.validate(parser, ['-w', 'HEAD~5..HEAD~3', fake_cover_project])\\n assert args.commits == 'HEAD~5..HEAD~3'\\n\\n\\ndef test_argument_parse_context(fake_cover_project):\\n parser = cover.setup_parser()\\n args = cover.validate(parser, ['-c', '10', fake_cover_project])\\n assert args.context == 10\\n\\n\\ndef test_validate_directory(fake_cover_project):\\n parser = cover.setup_parser()\\n assert cover.validate(parser, [fake_cover_project])\\n\\n\\ndef test_validate_no_coverage_area(fake_project):\\n parser = cover.setup_parser()\\n with pytest.raises(SystemExit) as excinfo:\\n cover.validate(parser, [fake_project])\\n assert str(excinfo.value) == '2'\\n\\n\\ndef test_validate_no_directory():\\n parser = cover.setup_parser()\\n with pytest.raises(SystemExit) as excinfo:\\n cover.validate(parser, ['bogus-dir'])\\n assert str(excinfo.value) == '2'\\n\"\n },\n {\n \"alpha_fraction\": 0.6433566212654114,\n \"alphanum_fraction\": 0.6503496766090393,\n \"avg_line_length\": 14.88888931274414,\n \"blob_id\": \"7ee327bff18a548ecce588bc4fb3176ac4ba5d03\",\n \"content_id\": \"7eab6612885e0c05fb92ebf41b932c51ab43c816\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 143,\n \"license_type\": \"permissive\",\n \"max_line_length\": 72,\n \"num_lines\": 9,\n \"path\": \"/commit_coverage/__main__.py\",\n \"repo_name\": \"pmichali/commit-coverage\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n\\n\\n\\\"\\\"\\\"commit_coverage.__main__: used when directory is called as script.\\\"\\\"\\\"\\n\\n\\nfrom .commit_coverage import main\\n\\nmain()\\n\"\n },\n {\n \"alpha_fraction\": 0.6008238196372986,\n \"alphanum_fraction\": 0.603218674659729,\n \"avg_line_length\": 36.282142639160156,\n \"blob_id\": \"3cde73cedc1f4e9210874f393247ca2c1246e9b7\",\n \"content_id\": \"eadd963cbb097db03416931195391122a435bea6\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 10439,\n \"license_type\": \"permissive\",\n \"max_line_length\": 79,\n \"num_lines\": 280,\n \"path\": \"/commit_coverage/__init__.py\",\n \"repo_name\": \"pmichali/commit-coverage\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python\\n#\\n# Copyright (c) 2016 Paul Michali, Cisco Systems Inc.\\n# All Rights Reserved.\\n#\\n# Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); you may\\n# not use this file except in compliance with the License. You may obtain\\n# a copy of the License at\\n#\\n# http://www.apache.org/licenses/LICENSE-2.0\\n#\\n# Unless required by applicable law or agreed to in writing, software\\n# distributed under the License is distributed on an \\\"AS IS\\\" BASIS, WITHOUT\\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\\n# License for the specific language governing permissions and limitations\\n# under the License.\\n#\\n# commit_coverage\\n#\\n# Indicates the coverage for diffs specified in the command. It assumes that\\n# coverage was run before using this tool (and report should be on what is\\n# in the repo currently).\\n#\\n# Usage:\\n# commit_coverage.py [-h] [-c CONTEXT] [-w WHICH] repo-dir\\n# Where:\\n# -h, --help Show this help message and exit.\\n# -c CONTEXT, --context CONTEXT\\n# Number of context lines around diff regions.\\n# Default=3.\\n# -w COMMITS, --which COMMITS\\n# Which commit(s) to compare. Use 'working', 'committed',\\n# or custom commit specification. Latest should be same\\n# as cover run. Default='working'.\\n#\\n# For the commit selection, you want the latest to match what the coverage\\n# was done on. This translates into the commit specification for 'git diff'.\\n# If you have uncommitted changes, you can use (the default) 'working',\\n# which will use 'HEAD'. If you've already committed, you can use 'commit',\\n# which will use 'HEAD^'. Otherwise, you can use any valid 'git diff' commit\\n# specification. Just make sure that the newer commit must match what was\\n# used in the coverage report created.\\n#\\n# You can adjust the number of lines of context surrounding the diff lines,\\n# just as is done with 'git diff'.\\n#\\n# The output will show each file from the diffs along with a report on\\n# the coverage. Each line (including surrounding context lines) with line\\n# number, coverage indication, diff indication, and the source line.\\n#\\n# The coverage indication is 'run' meaning the line is covered, 'mis'\\n# meaning the line is missing coverage, 'par' meaning the line is partially\\n# covered, or ' ' meaning the line is ignored w.r.t. coverage.\\n#\\n# There is a summary of covered, missing, partial, and ignored lines\\n# that were added/changed (only - no context lines).\\n#\\n# The diff indication will be a '+' for lines added/changed by the diff,\\n# or ' ' for context lines. Note: if a diff is at the start or end of a\\n# file, there may not be coverage lines.\\n#\\n# If the file was not exercised as part of coverage, or if the file did\\n# not have any added/changed lines, it will be indicated.\\n\\nfrom __future__ import print_function\\n\\nimport argparse\\nfrom collections import Counter\\nimport os\\nimport re\\nimport subprocess\\n\\nfile_re = re.compile(r'diff --git a/(\\\\S+)')\\ndiff_region_re = re.compile(r'@@\\\\s[-]\\\\S+\\\\s[+](\\\\S+)\\\\s@@')\\nsource_line_re = re.compile(r'<p id=\\\"n(\\\\d+)\\\" class=\\\"([^\\\"]+)\\\"')\\ntitle_re = re.compile(r'\\\\s*<title>Coverage for [^:]+:\\\\s+(\\\\d+%)<\\\\/title>')\\nsummary_end_re = re.compile(r'\\\\s+<td class=\\\"text\\\">')\\n\\n__version__ = \\\"0.7\\\"\\n\\n\\nclass DiffCollectionFailed(Exception):\\n pass\\n\\n\\nclass SourceLine(object):\\n\\n def __init__(self, line_number, is_context=True, code=''):\\n self.line_number = line_number\\n self.is_context = is_context\\n self.code = code\\n self.status = '???'\\n\\n def __eq__(self, other):\\n return (self.line_number == other.line_number and\\n self.is_context == other.is_context)\\n\\n def __repr__(self):\\n return \\\"SourceLine(line_number=%d, is_context=%s)\\\" % (self.line_number,\\n self.is_context)\\n\\n\\nclass SourceModule(object):\\n\\n def __init__(self, filename, lines):\\n self.filename = filename\\n self.lines = lines\\n self.line_num_map = {l.line_number: l for l in lines}\\n self.cover_file = (filename.replace('/', '_').replace('.', '_') +\\n \\\".html\\\")\\n self.have_report = False\\n self.coverage = '??%'\\n\\n def update_line_status(self, line_number, status):\\n if line_number in self.line_num_map:\\n line = self.line_num_map[line_number]\\n if status.startswith('pln'):\\n line.status = ' '\\n else:\\n line.status = status[4:7]\\n\\n def report(self):\\n output = self.filename\\n if not self.have_report:\\n return \\\"%s (No coverage data)\\\\n\\\" % output\\n if not self.lines or all(l.is_context for l in self.lines):\\n return \\\"%s (No added/changed lines)\\\\n\\\" % output\\n stats = Counter([l.status for l in self.lines if not l.is_context])\\n output += \\\" (run={}, mis={}, par={}, ign={}) {}\\\\n\\\".format(\\n stats['run'], stats['mis'], stats['par'], stats[' '],\\n self.coverage)\\n last_line = None\\n for line in self.lines:\\n if last_line and line.line_number != (last_line + 1):\\n output += \\\"\\\\n\\\"\\n output += \\\"{:5d} {} {} {}\\\\n\\\".format(\\n line.line_number, line.status,\\n ' ' if line.is_context else '+', line.code)\\n last_line = line.line_number\\n return output\\n\\n\\ndef check_coverage_status(coverage_info, module):\\n for coverage_line in coverage_info:\\n m = title_re.match(coverage_line)\\n if m:\\n module.coverage = m.group(1)\\n continue\\n if summary_end_re.match(coverage_line):\\n return\\n m = source_line_re.match(coverage_line)\\n if m:\\n line_num = int(m.group(1))\\n status = m.group(2)\\n module.update_line_status(line_num, status)\\n\\n\\ndef check_coverage_file(root, module):\\n \\\"\\\"\\\"Check the lines in coverage file and report coverage status.\\\"\\\"\\\"\\n report_file = os.path.join(root, 'cover', module.cover_file)\\n if not os.path.isfile(report_file):\\n return # No coverage data for file\\n with open(report_file) as coverage_info:\\n coverage_lines = coverage_info.readlines()\\n check_coverage_status(coverage_lines, module)\\n module.have_report = True\\n\\n\\ndef collect_diff_lines(diff_region, start, last):\\n \\\"\\\"\\\"Find added and context lines in a diff region.\\n\\n Note: If the diff region is at the start or end of the file, there\\n may not be context lines.\\n \\\"\\\"\\\"\\n lines = []\\n line_num = start\\n while line_num <= last:\\n line = next(diff_region)\\n if line.startswith('-'):\\n continue\\n lines.append(SourceLine(line_num, is_context=line.startswith(' '),\\n code=line[1:]))\\n line_num += 1\\n return lines\\n\\n\\ndef parse_diffs(diff_output):\\n \\\"\\\"\\\"Collect the file and ranges of diffs added, if any.\\\"\\\"\\\"\\n added_lines = []\\n source_file = ''\\n diff_lines = iter(diff_output.splitlines())\\n for line in diff_lines:\\n m = file_re.match(line)\\n if m:\\n source_file = m.group(1)\\n continue\\n m = diff_region_re.match(line)\\n if m:\\n start, comma, num = m.group(1).partition(',')\\n start = int(start)\\n if num:\\n last = start + int(num) - 1\\n else:\\n last = start\\n added_lines += collect_diff_lines(diff_lines, start, last)\\n return (source_file, added_lines)\\n\\n\\ndef collect_diffs_for_files(root, versions, source_files, context_lines):\\n \\\"\\\"\\\"Generator to obtain the diffs for files.\\\"\\\"\\\"\\n os.chdir(root)\\n for filename in source_files:\\n command = ['git', 'diff', '-U%d' % context_lines,\\n '-w', versions, '--', filename]\\n p = subprocess.Popen(command,\\n stdout=subprocess.PIPE, stderr=subprocess.PIPE)\\n diff_lines, err = p.communicate()\\n if err:\\n raise DiffCollectionFailed(\\n \\\"Unable to collect diffs for file %s/%s: %s\\\" %\\n (root, filename, err))\\n yield diff_lines\\n\\n\\ndef collect_diff_files(root, versions):\\n \\\"\\\"\\\"Generator to obtain all the diff files.\\\"\\\"\\\"\\n command = ['git', 'diff', '--name-only', versions]\\n os.chdir(root)\\n p = subprocess.Popen(command,\\n stdout=subprocess.PIPE, stderr=subprocess.PIPE)\\n out, err = p.communicate()\\n if err:\\n raise DiffCollectionFailed(\\\"Unable to find diff files to examine \\\"\\n \\\"in %s: %s\\\" % (root, err))\\n for filename in out.splitlines():\\n if not os.path.basename(filename).startswith('.'):\\n yield filename\\n\\n\\ndef validate(parser, provided_args=None):\\n args = parser.parse_args(provided_args)\\n args.root = os.path.abspath(args.root)\\n if not os.path.isdir(args.root):\\n parser.error(\\\"The repo-dir must be a directory pointing to the top \\\"\\n \\\"of the Git repo\\\")\\n if not os.path.isdir(os.path.join(args.root, 'cover')):\\n parser.error(\\\"Missing cover directory for project\\\")\\n if args.commits == 'working':\\n args.commits = 'HEAD'\\n elif args.commits == 'committed':\\n args.commits = 'HEAD^..HEAD'\\n return args\\n\\n\\ndef main():\\n args = validate(setup_parser())\\n files = collect_diff_files(args.root, args.commits)\\n diff_files = collect_diffs_for_files(args.root, args.commits, files,\\n args.context)\\n for diffs in diff_files:\\n source_file, lines = parse_diffs(diffs)\\n module = SourceModule(source_file, lines)\\n check_coverage_file(args.root, module)\\n print(module.report())\\n\\n\\ndef setup_parser():\\n parser = argparse.ArgumentParser(\\n description='Reports coverage for a commit in repo.')\\n parser.add_argument(\\n '-c', '--context', action='store', type=int, default=3,\\n help='Number of context lines around diff regions. Default=3.')\\n parser.add_argument(\\n '-w', '--which', action='store', default=\\\"working\\\", dest='commits',\\n help=\\\"Which commit(s) to compare. Use 'working', 'commit', or \\\"\\n \\\"custom commit specification. Latest should be same as cover run. \\\"\\n \\\"Default='working'.\\\")\\n parser.add_argument(dest='root', metavar='repo-dir',\\n help=\\\"Root of Git repo\\\")\\n return parser\\n\"\n },\n {\n \"alpha_fraction\": 0.5988888740539551,\n \"alphanum_fraction\": 0.6061111092567444,\n \"avg_line_length\": 27.125,\n \"blob_id\": \"ee3b79b77891900ac05b8ae8dd14a2a29b67b9f7\",\n \"content_id\": \"6da85da5e88d16b84fbe042b8c95a62ec0ebc174\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1800,\n \"license_type\": \"permissive\",\n \"max_line_length\": 77,\n \"num_lines\": 64,\n \"path\": \"/setup.py\",\n \"repo_name\": \"pmichali/commit-coverage\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n\\n\\n\\\"\\\"\\\"setup.py: setuptools control.\\\"\\\"\\\"\\n\\n\\nimport re\\nfrom setuptools.command.test import test as TestCommand\\nfrom setuptools import setup\\n\\n\\nclass PyTest(TestCommand):\\n def finalize_options(self):\\n TestCommand.finalize_options(self)\\n self.test_args = []\\n self.test_suite = True\\n\\n def run_tests(self):\\n # import here, cause outside the eggs aren't loaded\\n import pytest\\n pytest.main(self.test_args)\\n\\n\\nversion = re.search(\\n '^__version__\\\\s*=\\\\s*\\\"(.*)\\\"',\\n open('commit_coverage/__init__.py').read(),\\n re.M\\n ).group(1)\\n\\n\\nwith open(\\\"README.rst\\\", \\\"rb\\\") as f:\\n long_descr = f.read()\\n\\n\\nsetup(\\n name=\\\"commit-coverage\\\",\\n packages=[\\\"commit_coverage\\\"],\\n entry_points={\\n \\\"console_scripts\\\": ['commit_coverage = commit_coverage:main']\\n },\\n version=version,\\n description=\\\"Reports on (previously run) coverage results for a commit.\\\",\\n long_description=long_descr,\\n author=\\\"Paul Michali\\\",\\n author_email=\\\"pc@michali.net\\\",\\n url=\\\"https://github.com/pmichali/commit-coverage\\\",\\n license=\\\"Apache Software License\\\",\\n platforms='any',\\n tests_require=['pytest', 'mock'],\\n cmdclass={'tests': PyTest},\\n classifiers=[\\n 'Development Status :: 3 - Alpha',\\n 'Intended Audience :: Developers',\\n 'Topic :: Software Development :: Build Tools',\\n 'License :: OSI Approved :: Apache Software License',\\n 'Natural Language :: English',\\n 'Operating System :: OS Independent',\\n 'Programming Language :: Python :: 2',\\n 'Programming Language :: Python :: 2.7',\\n 'Programming Language :: Python :: 3',\\n 'Programming Language :: Python :: 3.2',\\n 'Programming Language :: Python :: 3.3',\\n 'Programming Language :: Python :: 3.4',\\n ],)\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":5,"string":"5"}}},{"rowIdx":2979397,"cells":{"repo_name":{"kind":"string","value":"PrateekCoder/ProjectEular.net"},"repo_url":{"kind":"string","value":"https://github.com/PrateekCoder/ProjectEular.net"},"snapshot_id":{"kind":"string","value":"5704cb2d346cad16ac4b65af62fec5b47fe1bff9"},"revision_id":{"kind":"string","value":"96c485f660fa764387d34d7f41ece3e464a93ed8"},"directory_id":{"kind":"string","value":"008df7525ab4b632ad0db0fe0ce57e3e4f165992"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-01-10T16:03:53.544089","string":"2021-01-10T16:03:53.544089"},"revision_date":{"kind":"timestamp","value":"2016-02-07T20:35:33","string":"2016-02-07T20:35:33"},"committer_date":{"kind":"timestamp","value":"2016-02-07T20:35:33","string":"2016-02-07T20:35:33"},"github_id":{"kind":"number","value":51265034,"string":"51,265,034"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.613095223903656,"alphanum_fraction":0.6607142686843872,"avg_line_length":27,"blob_id":"22e0d09829794a639e51d4af1223edccf6f735c6","content_id":"ab225546731fc6d1ee90a0a05908bfac12cbd8e5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":168,"license_type":"no_license","max_line_length":97,"num_lines":6,"path":"/01_Multiples_of_3_and_5.py","repo_name":"PrateekCoder/ProjectEular.net","src_encoding":"UTF-8","text":"'''\nThere were many different ways to print this answer, but I tried to make is as small as possible.\n'''\n\n\nprint sum([i for i in range(1000) if i%3 == 0 or i%5 == 0])\n"}],"string":"[\n {\n \"alpha_fraction\": 0.613095223903656,\n \"alphanum_fraction\": 0.6607142686843872,\n \"avg_line_length\": 27,\n \"blob_id\": \"22e0d09829794a639e51d4af1223edccf6f735c6\",\n \"content_id\": \"ab225546731fc6d1ee90a0a05908bfac12cbd8e5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 168,\n \"license_type\": \"no_license\",\n \"max_line_length\": 97,\n \"num_lines\": 6,\n \"path\": \"/01_Multiples_of_3_and_5.py\",\n \"repo_name\": \"PrateekCoder/ProjectEular.net\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"'''\\nThere were many different ways to print this answer, but I tried to make is as small as possible.\\n'''\\n\\n\\nprint sum([i for i in range(1000) if i%3 == 0 or i%5 == 0])\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":2979398,"cells":{"repo_name":{"kind":"string","value":"kanoekakihito/tidy_mhlw_data"},"repo_url":{"kind":"string","value":"https://github.com/kanoekakihito/tidy_mhlw_data"},"snapshot_id":{"kind":"string","value":"a846e43dd70cccca333e26d9fe79e79886ecb8bc"},"revision_id":{"kind":"string","value":"013ae1b2e034799e9e29bb78942d29170e6bdebc"},"directory_id":{"kind":"string","value":"c9cf111e2c714ff8bec8710d87ee6e11964896c5"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"timestamp","value":"2023-06-09T19:50:04.982167","string":"2023-06-09T19:50:04.982167"},"revision_date":{"kind":"timestamp","value":"2021-07-01T04:51:34","string":"2021-07-01T04:51:34"},"committer_date":{"kind":"timestamp","value":"2021-07-01T04:51:34","string":"2021-07-01T04:51:34"},"github_id":{"kind":"number","value":370073637,"string":"370,073,637"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5846201777458191,"alphanum_fraction":0.6170644164085388,"avg_line_length":31.74210548400879,"blob_id":"d41c3aea25e8af3883791317e2dd0b7be28895ae","content_id":"5b3458431bd52fce7c54358abcca9d7f9f617eb3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":8943,"license_type":"no_license","max_line_length":185,"num_lines":190,"path":"/20210523_新型コロナワクチン副反応CSV整形.py","repo_name":"kanoekakihito/tidy_mhlw_data","src_encoding":"UTF-8","text":"\"\"\"\r\n１）データソース\r\n第58回厚生科学審議会予防接種・ワクチン分科会副反応検討部会、令和３年度第５回薬事・食品衛生審議会薬事分科会医薬品等安全対策部会安全対策調査会（合同開催） 資料\r\nhttps://mhlw.go.jp/stf/newpage_18527.html\r\n\r\n資料１－１－２ 予防接種法に基づく医療機関からの副反応疑い報告状況について（報告症例一覧）\r\nhttps://www.mhlw.go.jp/content/10906000/000778300.pdf\r\n\r\n２）IMABARI ZINE(@imabarizine)さんによる加工済みデータ\r\nhttps://drive.google.com/file/d/1MUOKR0u8i_vkeG6NO4EaUrCgAqPgOWnn/view\r\n\r\n＊加工時ソースはhttps://twitter.com/imabarizine/status/1395369121034866690\r\n2021/05/23 22:48時点で確認したコード ここから：\r\n\r\nimport camelot\r\nimport pandas as pd\r\n\r\ntables = http://camelot.read_pdf(\"data.pdf\", pages=\"2-end\", split_text=True)\r\n\r\ndfs = [table.df for table in tables]\r\n\r\ndf = pd.concat(dfs)\r\n\r\ndf.to_csv(\"data.csv\", encoding=\"utf_8_sig\", index=False, header=False)\r\n...ここまで\r\n\r\n\r\n３）本コード。2をインプットにして整然データを書き出します。\r\n\r\n\r\n\"\"\"\r\n\r\n\r\nimport csv\r\nfrom functools import reduce\r\nfrom itertools import zip_longest\r\nimport re\r\nfrom tkinter.filedialog import askopenfilename, asksaveasfilename\r\n\r\n\r\ninput_filepath = \"000778300.csv\"\r\nwith open(input_filepath, encoding=\"utf-8-sig\") as input_file:\r\n input_content = [d for d in csv.DictReader(input_file)]\r\n\"\"\"\r\n調べた結果、\r\n１）発生日、症状名(PT名)、転帰日、転帰内容の列は各C個の値が格納されている。\r\n これは一部行をのぞき同行内なら値数Cが合致した。列数Cが合わない場合は上から値を割り振り、欠けが発生する欄は空欄とした。\r\n （多分RDBから出力したそのままのデータではないんだろう。）\r\n２）２行だけNo（おそらく主キー）の列にNo（\\n）※印の行を見つける。\r\n ※印の内容は備考で、\r\n 「※１ No.4611とNo.4842は、同一事例について、接種医療機関（No.4611）及び搬送先医療機関（No.4842）それぞれから副反応疑い報告がなされたもの。\r\n 資料1-1-1及び1-3における「死亡として報告された事例」においては1件として集計。\r\n また、資料1-1-1の集計においては、直接死亡を確認した搬送先医療機関からの報告内容に基づき分類している。」\r\n ・・・とのこと。\r\n 同一の人・事例に通し番号は基本一つにしたいが、勝手に触っていいのか分からないので手つかずにする。\r\n 特記事項欄に注意書きを付す。\r\n\r\n３）単位「歳」はどう考えても平均取ったり等には邪魔。外す。\r\n\r\n４）PT名とかSOCとかいうのは語彙が統一されているようだ（MedDRA）。上位カテゴリSOCと下位カテゴリPTとかいう風に見える。\r\n 参考：https://www.meddra.org/how-to-use/support-documentation/japanese\r\n 参考：https://admin.new.meddra.org/sites/default/files/guidance/file/intguide_24_0_Japanese.pdf\r\n 上位概念SOCに対して、PTは複数のSOCに属するようなツリー型構造をしているようだ。\r\n\r\n https://www.mhlw.go.jp/content/10906000/000778299.pdf p.8-\r\n ・・・を参照して症状名の対応表を作成。\r\n 一覧の症状名はたまに置き換えミスがあるので「症状名（原文ママ）」として残しつつPTから逆引きした「症状名」を付す。\r\n \r\n５）同日の症状別集計\r\n https://www.mhlw.go.jp/content/10906000/000778299.pdf p.8-\r\n のデータと整合しそうなのでPT名欄の値重複（例：悪心|悪心、とあれば２行になる）は排除していない。\r\n 必要であれば使う人が重複排除してほしい。\r\n \r\n\"\"\"\r\n\r\n# print(frozenset([i for i in map(lambda d: d[\"No\"], input_content) if \"\\n\" in i]))\r\n# 主キー列に\\n※が入っていることがある。\r\n\r\ntmp_content = []\r\ninput_keys = tuple(input_content[0].keys())\r\nnote = \"No.4611とNo.4842は、同一事例について、接種医療機関（No.4611）及び搬送先医療機関（No.4842）それぞれから副反応疑い報告がなされたもの。資料1-1-1及び1-3における「死亡として報告された事例」においては1件として集計。また、資料1-1-1の集計においては、直接死亡を確認した搬送先医療機関からの報告内容に基づき分類している。\"\r\nfor d in input_content:\r\n new_dict = dict()\r\n for key, value in d.items():\r\n if key == \"No\":\r\n if(\"\\n\" in d[\"No\"]):\r\n new_dict[\"No\"] = d[\"No\"].split(\"\\n\")[0]\r\n new_dict[\"特記事項\"] = note\r\n else:\r\n new_dict[\"No\"] = d[\"No\"]\r\n new_dict[\"特記事項\"] = \"\"\r\n\r\n elif key == \"年齢\":\r\n new_dict[\"年齢\"] = d[\"年齢\"].replace(\"歳\", \"\")\r\n else:\r\n new_dict[key.replace(\"\\n\", \"\")] = value\r\n tmp_content.append(new_dict)\r\n\r\nassert all([d[\"No\"].isdigit() for d in tmp_content]) == True, \"No列に数字以外の文字が含まれています。\"\r\n\r\n\r\n\"\"\"\r\nfor key in tmp_content[0].keys():\r\n print(key, frozenset(map(lambda d: d[key], tmp_content)))\r\n\"\"\"\r\n\"\"\"\r\nfor key in [\"発生日\", \"転帰日\"]:\r\n values = map(lambda d: set(d[key].split(\"\\n\")), tmp_content)\r\n assert all([re.fullmatch(\"\\d{4}/\\d{2}/\\d{2}\", date) for date in reduce(lambda a, b: a|b, values)]) == True, \\\r\n f\"{key}列に日付以外の文字が含まれています。\"\r\n\"\"\"\r\n\"\"\"\r\nsymptoms = set()\r\nfor d in tmp_content:\r\n symptoms |= {v.replace(\"\\n\", \"\") for v in re.split(\"(?<=[）)]\\n)\", d[\"症状名（PT名）\"])}\r\nassert all([re.fullmatch(\"[^（(]+[(（][^）)]+[)）]\", s) for s in symptoms]) == True, \\\r\n f\"{key}列に形式「症状名（PT名）」以外の文字列が含まれています。\"\r\n\r\n\"\"\"\r\n\r\npatient_attribute = (\r\n \"No\",\r\n \"年齢\",\r\n \"性別\",\r\n \"接種日\",\r\n \"接種から発生までの日数\",\r\n \"ワクチン名\",\r\n \"製造販売業者\",\r\n \"ロット番号\",\r\n \"因果関係（報告医評価）\",\r\n \"重篤度（報告医評価）\",\r\n \"特記事項\"\r\n)\r\n\r\nside_effect_attribute = (\"発生日\", \"症状名（PT名）\", \"転帰日\", \"転帰内容\")\r\nassert frozenset(patient_attribute + side_effect_attribute) == frozenset(tmp_content[0].keys()), \"新しい列が含まれています。\"\r\n\r\nsplit_content = []\r\nfor d in tmp_content:\r\n new_dict = {key: d[key] for key in patient_attribute}\r\n new_dict[\"発生日\"] = tuple(d[\"発生日\"].split(\"\\n\"))\r\n new_dict[\"症状名（PT名）\"] = tuple(map(lambda v: v.replace(\"\\n\", \"\"),\r\n re.split(\"(?<=[\\)）])\\n\", d[\"症状名（PT名）\"])))\r\n new_dict[\"転帰日\"] = tuple(d[\"転帰日\"].split(\"\\n\"))\r\n new_dict[\"転帰内容\"] = tuple(d[\"転帰内容\"].split(\"\\n\"))\r\n split_content.append(new_dict)\r\n\r\n\r\n# 参照元: https://www.mhlw.go.jp/content/10906000/000778299.pdf p.8-\r\nmeddra_filepath = \"meddra.csv\"\r\nwith open(meddra_filepath, encoding=\"utf-8\") as f:\r\n symptom_dict = [d for d in csv.DictReader(f)]\r\n # symptom_term = frozenset(map(lambda d: d[\"症状名\"], symptom_dict))\r\n pt_term = {d[\"PT名\"]:d for d in symptom_dict}\r\n\r\n \r\ntidy_content = []\r\nfor d in split_content:\r\n patient_dict = {key: d[key] for key in patient_attribute}\r\n side_effect_values = tuple(map(lambda key: d[key], side_effect_attribute))\r\n for row in zip_longest(*side_effect_values, fillvalue=\"\"):\r\n side_efect_dict = dict(zip(side_effect_attribute, map(lambda v: v.replace(\"\\n\", \"\"), row)))\r\n new_dict = dict()\r\n new_dict.update(patient_dict)\r\n new_dict.update(side_efect_dict)\r\n tidy_content.append(new_dict)\r\n\r\noutput_content = []\r\nfor d in tidy_content:\r\n tmp_symptom = \"\".join(re.split(\"(?<=[^（])(?=（)\", d[\"症状名（PT名）\"])[:-1])\r\n pt_name = re.split(\"(?<=[^（])(?=（)\", d[\"症状名（PT名）\"])[-1]\r\n for pt in re.sub(\"[（）]\", \"\", pt_name).split(\"|\"):\r\n assert pt in pt_term\r\n if pt_term[pt][\"症状名\"] != tmp_symptom:\r\n print(pt_term[pt][\"症状名\"], tmp_symptom)\r\n new_dict = dict()\r\n new_dict.update(d)\r\n new_dict.update({\r\n \"SOC名\": pt_term[pt][\"SOC名\"],\r\n \"症状名（原文ママ）\": tmp_symptom,\r\n \"症状名\": pt_term[pt][\"症状名\"],\r\n \"PT名\": pt\r\n })\r\n output_content.append(new_dict)\r\n\r\noutput_filepath = asksaveasfilename()\r\nwith open(output_filepath, mode=\"w\", encoding=\"utf-8-sig\") as output_file:\r\n csvfile = csv.DictWriter(output_file, output_content[0].keys(), lineterminator=\"\\n\")\r\n csvfile.writeheader()\r\n csvfile.writerows(output_content)\r\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5846201777458191,\n \"alphanum_fraction\": 0.6170644164085388,\n \"avg_line_length\": 31.74210548400879,\n \"blob_id\": \"d41c3aea25e8af3883791317e2dd0b7be28895ae\",\n \"content_id\": \"5b3458431bd52fce7c54358abcca9d7f9f617eb3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 8943,\n \"license_type\": \"no_license\",\n \"max_line_length\": 185,\n \"num_lines\": 190,\n \"path\": \"/20210523_新型コロナワクチン副反応CSV整形.py\",\n \"repo_name\": \"kanoekakihito/tidy_mhlw_data\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\r\\n１）データソース\\r\\n第58回厚生科学審議会予防接種・ワクチン分科会副反応検討部会、令和３年度第５回薬事・食品衛生審議会薬事分科会医薬品等安全対策部会安全対策調査会（合同開催） 資料\\r\\nhttps://mhlw.go.jp/stf/newpage_18527.html\\r\\n\\r\\n資料１－１－２ 予防接種法に基づく医療機関からの副反応疑い報告状況について（報告症例一覧）\\r\\nhttps://www.mhlw.go.jp/content/10906000/000778300.pdf\\r\\n\\r\\n２）IMABARI ZINE(@imabarizine)さんによる加工済みデータ\\r\\nhttps://drive.google.com/file/d/1MUOKR0u8i_vkeG6NO4EaUrCgAqPgOWnn/view\\r\\n\\r\\n＊加工時ソースはhttps://twitter.com/imabarizine/status/1395369121034866690\\r\\n2021/05/23 22:48時点で確認したコード ここから：\\r\\n\\r\\nimport camelot\\r\\nimport pandas as pd\\r\\n\\r\\ntables = http://camelot.read_pdf(\\\"data.pdf\\\", pages=\\\"2-end\\\", split_text=True)\\r\\n\\r\\ndfs = [table.df for table in tables]\\r\\n\\r\\ndf = pd.concat(dfs)\\r\\n\\r\\ndf.to_csv(\\\"data.csv\\\", encoding=\\\"utf_8_sig\\\", index=False, header=False)\\r\\n...ここまで\\r\\n\\r\\n\\r\\n３）本コード。2をインプットにして整然データを書き出します。\\r\\n\\r\\n\\r\\n\\\"\\\"\\\"\\r\\n\\r\\n\\r\\nimport csv\\r\\nfrom functools import reduce\\r\\nfrom itertools import zip_longest\\r\\nimport re\\r\\nfrom tkinter.filedialog import askopenfilename, asksaveasfilename\\r\\n\\r\\n\\r\\ninput_filepath = \\\"000778300.csv\\\"\\r\\nwith open(input_filepath, encoding=\\\"utf-8-sig\\\") as input_file:\\r\\n input_content = [d for d in csv.DictReader(input_file)]\\r\\n\\\"\\\"\\\"\\r\\n調べた結果、\\r\\n１）発生日、症状名(PT名)、転帰日、転帰内容の列は各C個の値が格納されている。\\r\\n これは一部行をのぞき同行内なら値数Cが合致した。列数Cが合わない場合は上から値を割り振り、欠けが発生する欄は空欄とした。\\r\\n （多分RDBから出力したそのままのデータではないんだろう。）\\r\\n２）２行だけNo（おそらく主キー）の列にNo（\\\\n）※印の行を見つける。\\r\\n ※印の内容は備考で、\\r\\n 「※１ No.4611とNo.4842は、同一事例について、接種医療機関（No.4611）及び搬送先医療機関（No.4842）それぞれから副反応疑い報告がなされたもの。\\r\\n 資料1-1-1及び1-3における「死亡として報告された事例」においては1件として集計。\\r\\n また、資料1-1-1の集計においては、直接死亡を確認した搬送先医療機関からの報告内容に基づき分類している。」\\r\\n ・・・とのこと。\\r\\n 同一の人・事例に通し番号は基本一つにしたいが、勝手に触っていいのか分からないので手つかずにする。\\r\\n 特記事項欄に注意書きを付す。\\r\\n\\r\\n３）単位「歳」はどう考えても平均取ったり等には邪魔。外す。\\r\\n\\r\\n４）PT名とかSOCとかいうのは語彙が統一されているようだ（MedDRA）。上位カテゴリSOCと下位カテゴリPTとかいう風に見える。\\r\\n 参考：https://www.meddra.org/how-to-use/support-documentation/japanese\\r\\n 参考：https://admin.new.meddra.org/sites/default/files/guidance/file/intguide_24_0_Japanese.pdf\\r\\n 上位概念SOCに対して、PTは複数のSOCに属するようなツリー型構造をしているようだ。\\r\\n\\r\\n https://www.mhlw.go.jp/content/10906000/000778299.pdf p.8-\\r\\n ・・・を参照して症状名の対応表を作成。\\r\\n 一覧の症状名はたまに置き換えミスがあるので「症状名（原文ママ）」として残しつつPTから逆引きした「症状名」を付す。\\r\\n \\r\\n５）同日の症状別集計\\r\\n https://www.mhlw.go.jp/content/10906000/000778299.pdf p.8-\\r\\n のデータと整合しそうなのでPT名欄の値重複（例：悪心|悪心、とあれば２行になる）は排除していない。\\r\\n 必要であれば使う人が重複排除してほしい。\\r\\n \\r\\n\\\"\\\"\\\"\\r\\n\\r\\n# print(frozenset([i for i in map(lambda d: d[\\\"No\\\"], input_content) if \\\"\\\\n\\\" in i]))\\r\\n# 主キー列に\\\\n※が入っていることがある。\\r\\n\\r\\ntmp_content = []\\r\\ninput_keys = tuple(input_content[0].keys())\\r\\nnote = \\\"No.4611とNo.4842は、同一事例について、接種医療機関（No.4611）及び搬送先医療機関（No.4842）それぞれから副反応疑い報告がなされたもの。資料1-1-1及び1-3における「死亡として報告された事例」においては1件として集計。また、資料1-1-1の集計においては、直接死亡を確認した搬送先医療機関からの報告内容に基づき分類している。\\\"\\r\\nfor d in input_content:\\r\\n new_dict = dict()\\r\\n for key, value in d.items():\\r\\n if key == \\\"No\\\":\\r\\n if(\\\"\\\\n\\\" in d[\\\"No\\\"]):\\r\\n new_dict[\\\"No\\\"] = d[\\\"No\\\"].split(\\\"\\\\n\\\")[0]\\r\\n new_dict[\\\"特記事項\\\"] = note\\r\\n else:\\r\\n new_dict[\\\"No\\\"] = d[\\\"No\\\"]\\r\\n new_dict[\\\"特記事項\\\"] = \\\"\\\"\\r\\n\\r\\n elif key == \\\"年齢\\\":\\r\\n new_dict[\\\"年齢\\\"] = d[\\\"年齢\\\"].replace(\\\"歳\\\", \\\"\\\")\\r\\n else:\\r\\n new_dict[key.replace(\\\"\\\\n\\\", \\\"\\\")] = value\\r\\n tmp_content.append(new_dict)\\r\\n\\r\\nassert all([d[\\\"No\\\"].isdigit() for d in tmp_content]) == True, \\\"No列に数字以外の文字が含まれています。\\\"\\r\\n\\r\\n\\r\\n\\\"\\\"\\\"\\r\\nfor key in tmp_content[0].keys():\\r\\n print(key, frozenset(map(lambda d: d[key], tmp_content)))\\r\\n\\\"\\\"\\\"\\r\\n\\\"\\\"\\\"\\r\\nfor key in [\\\"発生日\\\", \\\"転帰日\\\"]:\\r\\n values = map(lambda d: set(d[key].split(\\\"\\\\n\\\")), tmp_content)\\r\\n assert all([re.fullmatch(\\\"\\\\d{4}/\\\\d{2}/\\\\d{2}\\\", date) for date in reduce(lambda a, b: a|b, values)]) == True, \\\\\\r\\n f\\\"{key}列に日付以外の文字が含まれています。\\\"\\r\\n\\\"\\\"\\\"\\r\\n\\\"\\\"\\\"\\r\\nsymptoms = set()\\r\\nfor d in tmp_content:\\r\\n symptoms |= {v.replace(\\\"\\\\n\\\", \\\"\\\") for v in re.split(\\\"(?<=[）)]\\\\n)\\\", d[\\\"症状名（PT名）\\\"])}\\r\\nassert all([re.fullmatch(\\\"[^（(]+[(（][^）)]+[)）]\\\", s) for s in symptoms]) == True, \\\\\\r\\n f\\\"{key}列に形式「症状名（PT名）」以外の文字列が含まれています。\\\"\\r\\n\\r\\n\\\"\\\"\\\"\\r\\n\\r\\npatient_attribute = (\\r\\n \\\"No\\\",\\r\\n \\\"年齢\\\",\\r\\n \\\"性別\\\",\\r\\n \\\"接種日\\\",\\r\\n \\\"接種から発生までの日数\\\",\\r\\n \\\"ワクチン名\\\",\\r\\n \\\"製造販売業者\\\",\\r\\n \\\"ロット番号\\\",\\r\\n \\\"因果関係（報告医評価）\\\",\\r\\n \\\"重篤度（報告医評価）\\\",\\r\\n \\\"特記事項\\\"\\r\\n)\\r\\n\\r\\nside_effect_attribute = (\\\"発生日\\\", \\\"症状名（PT名）\\\", \\\"転帰日\\\", \\\"転帰内容\\\")\\r\\nassert frozenset(patient_attribute + side_effect_attribute) == frozenset(tmp_content[0].keys()), \\\"新しい列が含まれています。\\\"\\r\\n\\r\\nsplit_content = []\\r\\nfor d in tmp_content:\\r\\n new_dict = {key: d[key] for key in patient_attribute}\\r\\n new_dict[\\\"発生日\\\"] = tuple(d[\\\"発生日\\\"].split(\\\"\\\\n\\\"))\\r\\n new_dict[\\\"症状名（PT名）\\\"] = tuple(map(lambda v: v.replace(\\\"\\\\n\\\", \\\"\\\"),\\r\\n re.split(\\\"(?<=[\\\\)）])\\\\n\\\", d[\\\"症状名（PT名）\\\"])))\\r\\n new_dict[\\\"転帰日\\\"] = tuple(d[\\\"転帰日\\\"].split(\\\"\\\\n\\\"))\\r\\n new_dict[\\\"転帰内容\\\"] = tuple(d[\\\"転帰内容\\\"].split(\\\"\\\\n\\\"))\\r\\n split_content.append(new_dict)\\r\\n\\r\\n\\r\\n# 参照元: https://www.mhlw.go.jp/content/10906000/000778299.pdf p.8-\\r\\nmeddra_filepath = \\\"meddra.csv\\\"\\r\\nwith open(meddra_filepath, encoding=\\\"utf-8\\\") as f:\\r\\n symptom_dict = [d for d in csv.DictReader(f)]\\r\\n # symptom_term = frozenset(map(lambda d: d[\\\"症状名\\\"], symptom_dict))\\r\\n pt_term = {d[\\\"PT名\\\"]:d for d in symptom_dict}\\r\\n\\r\\n \\r\\ntidy_content = []\\r\\nfor d in split_content:\\r\\n patient_dict = {key: d[key] for key in patient_attribute}\\r\\n side_effect_values = tuple(map(lambda key: d[key], side_effect_attribute))\\r\\n for row in zip_longest(*side_effect_values, fillvalue=\\\"\\\"):\\r\\n side_efect_dict = dict(zip(side_effect_attribute, map(lambda v: v.replace(\\\"\\\\n\\\", \\\"\\\"), row)))\\r\\n new_dict = dict()\\r\\n new_dict.update(patient_dict)\\r\\n new_dict.update(side_efect_dict)\\r\\n tidy_content.append(new_dict)\\r\\n\\r\\noutput_content = []\\r\\nfor d in tidy_content:\\r\\n tmp_symptom = \\\"\\\".join(re.split(\\\"(?<=[^（])(?=（)\\\", d[\\\"症状名（PT名）\\\"])[:-1])\\r\\n pt_name = re.split(\\\"(?<=[^（])(?=（)\\\", d[\\\"症状名（PT名）\\\"])[-1]\\r\\n for pt in re.sub(\\\"[（）]\\\", \\\"\\\", pt_name).split(\\\"|\\\"):\\r\\n assert pt in pt_term\\r\\n if pt_term[pt][\\\"症状名\\\"] != tmp_symptom:\\r\\n print(pt_term[pt][\\\"症状名\\\"], tmp_symptom)\\r\\n new_dict = dict()\\r\\n new_dict.update(d)\\r\\n new_dict.update({\\r\\n \\\"SOC名\\\": pt_term[pt][\\\"SOC名\\\"],\\r\\n \\\"症状名（原文ママ）\\\": tmp_symptom,\\r\\n \\\"症状名\\\": pt_term[pt][\\\"症状名\\\"],\\r\\n \\\"PT名\\\": pt\\r\\n })\\r\\n output_content.append(new_dict)\\r\\n\\r\\noutput_filepath = asksaveasfilename()\\r\\nwith open(output_filepath, mode=\\\"w\\\", encoding=\\\"utf-8-sig\\\") as output_file:\\r\\n csvfile = csv.DictWriter(output_file, output_content[0].keys(), lineterminator=\\\"\\\\n\\\")\\r\\n csvfile.writeheader()\\r\\n csvfile.writerows(output_content)\\r\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":2979399,"cells":{"repo_name":{"kind":"string","value":"rums1605/Home-Automation-Product"},"repo_url":{"kind":"string","value":"https://github.com/rums1605/Home-Automation-Product"},"snapshot_id":{"kind":"string","value":"14d0c40c1c231506d252c40aa739c884dabfafdb"},"revision_id":{"kind":"string","value":"1d4fe72449a30dda993563a179ab4f6fe66a08ba"},"directory_id":{"kind":"string","value":"1ebfd68a5f975046e9bb373ac5913f125780e10c"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-12-03T04:13:05.161044","string":"2020-12-03T04:13:05.161044"},"revision_date":{"kind":"timestamp","value":"2017-06-30T01:04:47","string":"2017-06-30T01:04:47"},"committer_date":{"kind":"timestamp","value":"2017-06-30T01:04:47","string":"2017-06-30T01:04:47"},"github_id":{"kind":"number","value":95833302,"string":"95,833,302"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.4499332308769226,"alphanum_fraction":0.47396528720855713,"avg_line_length":12.814814567565918,"blob_id":"48b83a8df2902c08fbeb6a14b99a11fa2905cbe7","content_id":"c85ce6bf745623fe7a1f37c029c3e84cb031826f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":749,"license_type":"no_license","max_line_length":38,"num_lines":54,"path":"/Home Automation/ArduinoCode/ArduinoCode.ino","repo_name":"rums1605/Home-Automation-Product","src_encoding":"UTF-8","text":"#include<SoftwareSerial.h>\n#include<Servo.h>\n\nint enablePin = 5;\n\nServo servo;\n\nchar command;\nString string;\n#define led 12\n\n void setup()\n {\n digitalWrite(enablePin, HIGH);\n Serial.begin(9600);\n pinMode(led, OUTPUT);\n servo.attach(led);\n servo.write(90);\n }\n\n void loop()\n {\n digitalWrite(13, LOW);\n \n if (Serial.available() > 0) \n {string = \"\";}\n \n while(Serial.available() > 0)\n {\n command = ((byte)Serial.read());\n \n if(command == ':')\n {\n break;\n }\n \n else\n {\n string += command;\n }\n \n delay(1);\n }\n \n if(string == \"TO\")\n {\n servo.write(0);\n }\n\n if(string == \"TR\")\n {\n servo.write(120);\n }\n }\n \r\n"},{"alpha_fraction":0.8111110925674438,"alphanum_fraction":0.8111110925674438,"avg_line_length":179,"blob_id":"766af5f74d99efb2cd39063a17107bde21740112","content_id":"b5d4539ff711f9291f3db6eafec161865d5343b9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":360,"license_type":"no_license","max_line_length":333,"num_lines":2,"path":"/README.md","repo_name":"rums1605/Home-Automation-Product","src_encoding":"UTF-8","text":"# Home-Automation-Product\nA home automation product which makes use of IOT devices and enables theUsers to control the gate of his/her house via a mobile application. The Pictures Of the person standing outside the door are sent to the user in form of A Push Notification. On receiving the photos, Users can open/close the gate Through the mobile Application\n"},{"alpha_fraction":0.6405693888664246,"alphanum_fraction":0.6690391302108765,"avg_line_length":15.529411315917969,"blob_id":"84ef288d5d1cecf6d362c57636c0aac469394f2e","content_id":"b56b03932190d45cde9ce640716cb4474ba6f41a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":843,"license_type":"no_license","max_line_length":60,"num_lines":51,"path":"/Home Automation/sensor.py","repo_name":"rums1605/Home-Automation-Product","src_encoding":"UTF-8","text":"import RPi.GPIO as GPIO\nimport time\nfrom picamera import PiCamera\nfrom time import sleep\n\ncamera =PiCamera()\n\nGPIO.setmode(GPIO.BCM)\n\nTRIG = 23 \nECHO = 24\n\nprint(\"Distance Measurement In Progress\")\n\nGPIO.setup(TRIG,GPIO.OUT)\nGPIO.setup(ECHO,GPIO.IN)\n\ncnt = 0\nwhile True:\n GPIO.output(TRIG, False)\n print(\"Waiting For Sensor To Settle\")\n time.sleep(2)\n\n GPIO.output(TRIG, True)\n time.sleep(0.00001)\n GPIO.output(TRIG, False)\n\n while GPIO.input(ECHO)==0:\n pulse_start = time.time()\n\n while GPIO.input(ECHO)==1:\n pulse_end = time.time()\n\n pulse_duration = pulse_end - pulse_start\n\n distance = pulse_duration * 17150\n\n distance = round(distance, 2)\n\n \n if distance < 10:\n print(\"Distance:\",distance,\"cm\")\n sleep(5)\n camera.capture('/home/pi/Desktop/image'+str(cnt)+'.jpg')\n cnt = cnt+1\n \n \n\n\n \nGPIO.cleanup()\n"}],"string":"[\n {\n \"alpha_fraction\": 0.4499332308769226,\n \"alphanum_fraction\": 0.47396528720855713,\n \"avg_line_length\": 12.814814567565918,\n \"blob_id\": \"48b83a8df2902c08fbeb6a14b99a11fa2905cbe7\",\n \"content_id\": \"c85ce6bf745623fe7a1f37c029c3e84cb031826f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 749,\n \"license_type\": \"no_license\",\n \"max_line_length\": 38,\n \"num_lines\": 54,\n \"path\": \"/Home Automation/ArduinoCode/ArduinoCode.ino\",\n \"repo_name\": \"rums1605/Home-Automation-Product\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#include<SoftwareSerial.h>\\n#include<Servo.h>\\n\\nint enablePin = 5;\\n\\nServo servo;\\n\\nchar command;\\nString string;\\n#define led 12\\n\\n void setup()\\n {\\n digitalWrite(enablePin, HIGH);\\n Serial.begin(9600);\\n pinMode(led, OUTPUT);\\n servo.attach(led);\\n servo.write(90);\\n }\\n\\n void loop()\\n {\\n digitalWrite(13, LOW);\\n \\n if (Serial.available() > 0) \\n {string = \\\"\\\";}\\n \\n while(Serial.available() > 0)\\n {\\n command = ((byte)Serial.read());\\n \\n if(command == ':')\\n {\\n break;\\n }\\n \\n else\\n {\\n string += command;\\n }\\n \\n delay(1);\\n }\\n \\n if(string == \\\"TO\\\")\\n {\\n servo.write(0);\\n }\\n\\n if(string == \\\"TR\\\")\\n {\\n servo.write(120);\\n }\\n }\\n \\r\\n\"\n },\n {\n \"alpha_fraction\": 0.8111110925674438,\n \"alphanum_fraction\": 0.8111110925674438,\n \"avg_line_length\": 179,\n \"blob_id\": \"766af5f74d99efb2cd39063a17107bde21740112\",\n \"content_id\": \"b5d4539ff711f9291f3db6eafec161865d5343b9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 360,\n \"license_type\": \"no_license\",\n \"max_line_length\": 333,\n \"num_lines\": 2,\n \"path\": \"/README.md\",\n \"repo_name\": \"rums1605/Home-Automation-Product\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Home-Automation-Product\\nA home automation product which makes use of IOT devices and enables theUsers to control the gate of his/her house via a mobile application. The Pictures Of the person standing outside the door are sent to the user in form of A Push Notification. On receiving the photos, Users can open/close the gate Through the mobile Application\\n\"\n },\n {\n \"alpha_fraction\": 0.6405693888664246,\n \"alphanum_fraction\": 0.6690391302108765,\n \"avg_line_length\": 15.529411315917969,\n \"blob_id\": \"84ef288d5d1cecf6d362c57636c0aac469394f2e\",\n \"content_id\": \"b56b03932190d45cde9ce640716cb4474ba6f41a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 843,\n \"license_type\": \"no_license\",\n \"max_line_length\": 60,\n \"num_lines\": 51,\n \"path\": \"/Home Automation/sensor.py\",\n \"repo_name\": \"rums1605/Home-Automation-Product\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import RPi.GPIO as GPIO\\nimport time\\nfrom picamera import PiCamera\\nfrom time import sleep\\n\\ncamera =PiCamera()\\n\\nGPIO.setmode(GPIO.BCM)\\n\\nTRIG = 23 \\nECHO = 24\\n\\nprint(\\\"Distance Measurement In Progress\\\")\\n\\nGPIO.setup(TRIG,GPIO.OUT)\\nGPIO.setup(ECHO,GPIO.IN)\\n\\ncnt = 0\\nwhile True:\\n GPIO.output(TRIG, False)\\n print(\\\"Waiting For Sensor To Settle\\\")\\n time.sleep(2)\\n\\n GPIO.output(TRIG, True)\\n time.sleep(0.00001)\\n GPIO.output(TRIG, False)\\n\\n while GPIO.input(ECHO)==0:\\n pulse_start = time.time()\\n\\n while GPIO.input(ECHO)==1:\\n pulse_end = time.time()\\n\\n pulse_duration = pulse_end - pulse_start\\n\\n distance = pulse_duration * 17150\\n\\n distance = round(distance, 2)\\n\\n \\n if distance < 10:\\n print(\\\"Distance:\\\",distance,\\\"cm\\\")\\n sleep(5)\\n camera.capture('/home/pi/Desktop/image'+str(cnt)+'.jpg')\\n cnt = cnt+1\\n \\n \\n\\n\\n \\nGPIO.cleanup()\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":3,"string":"3"}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":29793,"numItemsPerPage":100,"numTotalItems":2984968,"offset":2979300,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3NDA5MjI0Nywic3ViIjoiL2RhdGFzZXRzL3BoYXJhb3VrL3N0YWNrLXYyLXB5dGhvbi13aXRoLWNvbnRlbnQtY2h1bmsxIiwiZXhwIjoxNzc0MDk1ODQ3LCJpc3MiOiJodHRwczovL2h1Z2dpbmdmYWNlLmNvIn0.vb0yO8Bi101ywFRloU6oZWIs2BuUOZz4T39wv3y4F5egWpN0Moqo2WgM4sU8GpFdDPdn97aSnzq7Kcf9rBZ4Bg","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":2984968,"numBytesParquet":51814563482}]},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
repo_name stringlengths 5 114 | repo_url stringlengths 24 133 | snapshot_id stringlengths 40 40 | revision_id stringlengths 40 40 | directory_id stringlengths 40 40 | branch_name stringclasses 209
values | visit_date timestamp[ns] | revision_date timestamp[ns] | committer_date timestamp[ns] | github_id int64 9.83k 683M ⌀ | star_events_count int64 0 22.6k | fork_events_count int64 0 4.15k | gha_license_id stringclasses 17
values | gha_created_at timestamp[ns] | gha_updated_at timestamp[ns] | gha_pushed_at timestamp[ns] | gha_language stringclasses 115
values | files listlengths 1 13.2k | num_files int64 1 13.2k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
marl0ny/split-operator-simulations | https://github.com/marl0ny/split-operator-simulations | 2043a6fb092f52f90e5edf1aa33f04cab32d1ab2 | 6f7f8fcaeee9b354c7bd2d4b9c83226f72deb5c5 | 205815be3883442ca3a4a0c38b3edbc58870182e | refs/heads/main | 2023-07-12T04:14:07.305214 | 2021-08-10T01:31:43 | 2021-08-10T01:31:43 | 360,713,463 | 4 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.902255654335022,
"alphanum_fraction": 0.902255654335022,
"avg_line_length": 65,
"blob_id": "378eca6101b851643821a317b65dfff3da0e9cd4",
"content_id": "e850fa8d65824dbff0b3c52324084aa5d2116e08",
"detected_licenses": [],
"is_generated": false,
"is_vendor": false,
... | 36 |
yikaigit/Mlstudy | https://github.com/yikaigit/Mlstudy | 1c9010f6e184ebc03cf7f3f720d7eafda6856905 | bdb0e55a0121e3db397dedf22fbf11e771264be3 | a869a919cc9a9e4179db9673b968b5a0888d5370 | refs/heads/master | 2020-03-30T03:40:18.449493 | 2018-11-14T01:42:50 | 2018-11-14T01:42:50 | 150,701,595 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.5963073968887329,
"alphanum_fraction": 0.6284930109977722,
"avg_line_length": 32.97457504272461,
"blob_id": "68859776b8f93df204899fa04ed7df3de1cd7fed",
"content_id": "cc1a9187894c25f4dbbf7bf3a3ac315e40e895a2",
"detected_licenses": [],
"is_generated": false,
"is_... | 1 |
zubu007/EEE299 | https://github.com/zubu007/EEE299 | 6689ed96c105d673462362a2757782382f84b79e | 5b3b9bbb24fe8e59f65a02174a44c53fb4f9cb6a | a1ec6aa444a1a0b70d3a4681960dbbccd9cf7cf6 | refs/heads/main | 2023-02-16T23:22:32.635172 | 2021-01-11T10:22:05 | 2021-01-11T10:22:05 | 328,624,100 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.5461783409118652,
"alphanum_fraction": 0.6178343892097473,
"avg_line_length": 21.185184478759766,
"blob_id": "830c27d2cfbad3ffcb817989d9735738e6f707e8",
"content_id": "43b83184dc2beb51e4d4bb98dd8de22e29952e79",
"detected_licenses": [],
"is_generated": false,
"is... | 2 |
jmart518/Home-Assistant | https://github.com/jmart518/Home-Assistant | 155096680773a72da6db92af5550d10ab1bbdc69 | 2656c2796bea800e8ae231a078763f9ef64eb67b | acdbe42fcff4339c8df635fe0f5d102845bf1e75 | refs/heads/master | 2017-12-07T08:07:43.279860 | 2017-02-23T18:58:28 | 2017-02-23T18:58:28 | 79,627,240 | 2 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.6576576828956604,
"alphanum_fraction": 0.6576576828956604,
"avg_line_length": 17.5,
"blob_id": "b5a395e0b1f9064a1e6a9d4c1d2a804493df5331",
"content_id": "ad1a18f0e487f43d006cff0726644b6ed3fd1f25",
"detected_licenses": [
"MIT"
],
"is_generated": false,
... | 2 |
IIKovalenko/instructor | https://github.com/IIKovalenko/instructor | e68da7c66e05ec86178fc34b85e6f9489646b99e | d880d5bce8b915c7d3fbe208eb63afec55f3ed1a | 1500b74a58a3e831dd543ffd17f4ab3d25ed8a04 | refs/heads/master | 2020-06-02T15:52:16.486942 | 2017-07-30T19:41:12 | 2017-07-30T19:41:12 | null | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.6224125623703003,
"alphanum_fraction": 0.6588151454925537,
"avg_line_length": 24.472726821899414,
"blob_id": "a71677b34fb4e389faa98baa5083e02a176a6e96",
"content_id": "c33f9d29811d1a57e917a4cf0ec8b5a3cd4fda67",
"detected_licenses": [
"MIT"
],
"is_generated... | 5 |
jakazmie/kubeflow | https://github.com/jakazmie/kubeflow | 4a4161b375b26c23259982b3e4dd14e8fa5785a3 | ef85f72c6603ad9c533d8790ff5003ffacadb7f5 | 0a391d1cf60f9f04bac9d12bbfd35fafc83b9341 | refs/heads/master | 2020-04-21T19:10:12.953626 | 2019-02-21T23:20:54 | 2019-02-21T23:20:54 | 169,797,299 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.48625829815864563,
"alphanum_fraction": 0.5566732287406921,
"avg_line_length": 44.62295150756836,
"blob_id": "abc592a55d13f79b33f86eb7f71bcc82c7a083bb",
"content_id": "5b6c4ffe9bbc48a31d71f0256d553b83cc102118",
"detected_licenses": [],
"is_generated": false,
"is... | 4 |
ashwin-ram03/Multilingual-Natural-Language-Processing | https://github.com/ashwin-ram03/Multilingual-Natural-Language-Processing | 81beee2a2e9801faa87a8e2a88cca53654d37738 | 76ac4e98aeb973270399e461d885215566c5f47c | f4a1ef7fed676bd1d555b10ae61d7079b9a6213e | refs/heads/main | 2023-08-11T12:20:18.177385 | 2021-09-17T22:21:00 | 2021-09-17T22:21:00 | 407,692,374 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.7088035941123962,
"alphanum_fraction": 0.7133182883262634,
"avg_line_length": 33.07692337036133,
"blob_id": "34f36050236103c2e7a6881bb218f1cd83242276",
"content_id": "1885e2984f9b438dadf36c8780e3e42b032d54fe",
"detected_licenses": [],
"is_generated": false,
"is_... | 2 |
SCGE/pamqp | https://github.com/SCGE/pamqp | d7bcf4a5fe78a3b80ceb070e1b831bf2f5b46e5d | cf638df74348ded410636af474ac438eeadbb626 | 9c0189b3e34ba3a6deef67d25a7ab07b898f39eb | refs/heads/master | 2020-03-11T20:31:07.619519 | 2018-04-19T16:07:56 | 2018-04-19T16:07:56 | 130,238,404 | 0 | 0 | null | 2018-04-19T15:44:06 | 2017-10-18T08:38:55 | 2015-06-26T02:43:20 | null | [
{
"alpha_fraction": 0.6699604988098145,
"alphanum_fraction": 0.676877498626709,
"avg_line_length": 25.63157844543457,
"blob_id": "c50d914ebb6976a8f9726fc68227a11d5f1830be",
"content_id": "9aaef830df5f8b9495ea8c20421208f525572887",
"detected_licenses": [
"BSD-3-Clause"
],
"is_ge... | 2 |
araujoG/abitarRevestimentos | https://github.com/araujoG/abitarRevestimentos | 5531db497d232d2fbb540116bd5b1ef0f7c457f2 | 8a028853c126211ad50b9a3b4392c8f1bc1b1002 | b8a7a62af0ba575ca041d4e05bce40500e3a982f | refs/heads/main | 2023-03-13T01:04:13.843794 | 2021-03-01T16:04:08 | 2021-03-01T16:04:08 | 335,955,143 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.7872340679168701,
"alphanum_fraction": 0.7895981073379517,
"avg_line_length": 37.54545593261719,
"blob_id": "a6f7206afe36d66a6815d914233ba2073d15cc96",
"content_id": "9f04cc44407f54e6a23fa01f0d694654aad22667",
"detected_licenses": [],
"is_generated": false,
"is_... | 19 |
mav10/dataVisualization | https://github.com/mav10/dataVisualization | f8aec5af2f56016a52f9e128ba16d01538fba44e | d3b3d6fc650792a07321f72507b977eaa58c0884 | 986f172b2de69a88f29b5c36fb1616106399dd64 | refs/heads/master | 2020-04-17T20:46:53.171891 | 2019-01-23T01:28:54 | 2019-01-23T01:28:54 | 166,920,360 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.6016216278076172,
"alphanum_fraction": 0.6016216278076172,
"avg_line_length": 20.776470184326172,
"blob_id": "4fbbe8aa1fdea2ba0e574adc9062fea256b93bfe",
"content_id": "670125088f8e43706e9cfd45c3d80ed0b9314cf5",
"detected_licenses": [
"MIT"
],
"is_generated... | 29 |
Pranav-Wadhwa/Lecture-Noted | https://github.com/Pranav-Wadhwa/Lecture-Noted | 67b97133e00c91a11e8e3aebe9081611b1e43df0 | 3315cf4a7fb8a0df291922da40ae2c96bef80fdb | b7803d28f4c9c57d3f60100a2004243b02a8411d | refs/heads/main | 2023-04-09T10:54:05.073224 | 2021-04-05T02:52:01 | 2021-04-05T02:52:01 | 352,126,742 | 3 | 3 | null | null | null | null | null | [
{
"alpha_fraction": 0.6772334575653076,
"alphanum_fraction": 0.679154634475708,
"avg_line_length": 33.71666717529297,
"blob_id": "a337ddb078bba35b8aa54b37a4d85f2f30d3b829",
"content_id": "f22870894a125d2236a1e1b93ed394b9ec4f2914",
"detected_licenses": [],
"is_generated": false,
"is_v... | 5 |
DuwanVargasSalas96/PINGen | https://github.com/DuwanVargasSalas96/PINGen | 0b2d5a7656ec84f870db2f81b11696d66eb62bb9 | bb787c7458b33fe9256beaedb4028938e2aebdbd | 2c6fe8860b1fba696af9f7596a666c91d5dbcdbe | refs/heads/main | 2023-07-19T07:03:33.446568 | 2021-09-07T03:56:56 | 2021-09-07T03:56:56 | 327,783,286 | 1 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.5910020470619202,
"alphanum_fraction": 0.5991820096969604,
"avg_line_length": 12.971428871154785,
"blob_id": "24d6231f0b7312f3b1117d1a568f61931bb7d2da",
"content_id": "f87457b6aed8ef4a02c0aba64f8e02b05ccdac46",
"detected_licenses": [],
"is_generated": false,
"is... | 2 |
CAMSROV/ROV2016 | https://github.com/CAMSROV/ROV2016 | 5fdf87f4e8ee50571c99c7d30c45015fdcb98d07 | a57191200e7a672197e4dfca834b6146cbacd7a3 | 563ef949f51a55625e1a63955290ecb50fd49cf9 | refs/heads/master | 2017-12-23T08:22:18.806109 | 2016-03-04T05:47:30 | 2016-03-04T05:47:30 | 51,205,927 | 2 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.6247379183769226,
"alphanum_fraction": 0.6540880799293518,
"avg_line_length": 29.774192810058594,
"blob_id": "1c1e5a88997b218ccd327fca100348aed9c6239e",
"content_id": "1507fb3b075651ed7e0b8092d21d20b6634f0540",
"detected_licenses": [],
"is_generated": false,
"is... | 10 |
veerajsolankee/Counting-Words | https://github.com/veerajsolankee/Counting-Words | d47c59567a177b04a653106172eef2d7be0b7d68 | 5d8abec1a0bc8958e8d744bf1f4f56ce8f068c54 | b4558e16e58ebb48f6e7cc498fe29acd1732fc43 | refs/heads/main | 2023-06-26T14:11:49.287107 | 2021-07-19T08:15:49 | 2021-07-19T08:15:49 | 387,389,741 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.5574468374252319,
"alphanum_fraction": 0.5617021322250366,
"avg_line_length": 17.75,
"blob_id": "a444adc16de5af2448bde4bc1820d94d5ac7adf6",
"content_id": "f93ebb73bd93b968ae1bfd9e4fdb8d4255798183",
"detected_licenses": [],
"is_generated": false,
"is_vendor": fal... | 1 |
valarauco/aprilchild | https://github.com/valarauco/aprilchild | 484e742e7a45ffb1c54d7ce4c7a186cb6dc44e97 | 46ae790f2e3092315179fe881f5fc6bd883b6def | 637d7e53f5cacde05bdf6afc039ca719b39008f0 | refs/heads/master | 2021-01-17T16:06:07.508202 | 2011-07-05T23:02:49 | 2011-07-05T23:02:49 | 1,818,882 | 3 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.5680751204490662,
"alphanum_fraction": 0.5744552612304688,
"avg_line_length": 36.5927619934082,
"blob_id": "d4730d8428b622aa778639f927e29c88417c0c65",
"content_id": "db5e4a54c93584600725181bf1282342b3bda89b",
"detected_licenses": [
"MIT"
],
"is_generated":... | 170 |
VilMe/shopping-site | https://github.com/VilMe/shopping-site | d34353144b99df34455c5f3a2988f27a950eadce | 4d70aec5418bc68a0d4c47851973a0adbcb9bee9 | 70f9a426ab107591ef048929e0f3424048586370 | refs/heads/master | 2020-04-27T23:38:41.418878 | 2019-04-30T22:40:45 | 2019-04-30T22:40:45 | 153,961,807 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.5170797109603882,
"alphanum_fraction": 0.5170797109603882,
"avg_line_length": 25.64285659790039,
"blob_id": "f86c619a7f7046fcf66ec199f0452e16dffe3fd4",
"content_id": "81427caa5e3d90b068d9aa12866b4ad8bce654db",
"detected_licenses": [],
"is_generated": false,
"is_... | 1 |
hillarysanders/cs_fundamentals | https://github.com/hillarysanders/cs_fundamentals | ef392a4b9ed7fdba2db2dac38f77318d8dc7fa53 | f43175c9050c868408e3356278a329f87fc69af9 | 7d77da1f9b6155d271105684b79e1bd136eabea9 | refs/heads/master | 2021-01-12T07:39:30.476266 | 2017-01-02T02:47:40 | 2017-01-02T02:47:40 | 76,993,454 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.6817888617515564,
"alphanum_fraction": 0.6832939386367798,
"avg_line_length": 28.814102172851562,
"blob_id": "919131ef33525a37743fed6e70786afef245ba92",
"content_id": "254e2f86069e8ecc3f67b70d78803d5f6cdd8f6c",
"detected_licenses": [],
"is_generated": false,
"is... | 2 |
TrendingTechnology/backprop | https://github.com/TrendingTechnology/backprop | 7b521c05099ef6cee4ea4b4269f5bac70a2e0f73 | 3b9aa4e7a8dc527799de7a80fb41351984f92524 | 3b6bd428141ff9e030ec596546480eaaeb17d8de | refs/heads/main | 2023-03-28T09:40:24.075860 | 2021-03-25T08:55:50 | 2021-03-25T08:55:50 | null | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.8263888955116272,
"alphanum_fraction": 0.8263888955116272,
"avg_line_length": 28,
"blob_id": "5d9dd34fe31a474a1d0219715d6f8dbc606cede5",
"content_id": "a28f5f402f9f6a7eef0c9669f4d2b5c2cbdd25ef",
"detected_licenses": [
"Apache-2.0"
],
"is_generated": false,... | 43 |
zhutong/misc | https://github.com/zhutong/misc | b66be63734cbc7ff464ca51d8def153319d004e1 | d16425472987f028f7e7d4c6f7a4e8292c4ca29c | f0345463a035dcb6babf921d8049a020a416f06e | refs/heads/master | 2021-01-22T13:13:28.730466 | 2014-03-20T16:42:04 | 2014-03-20T16:42:04 | 17,203,178 | 1 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.5078571438789368,
"alphanum_fraction": 0.5164285898208618,
"avg_line_length": 19.91044807434082,
"blob_id": "9a5dc1284a4b1e5f07eed7667e71a9cf2e04e72d",
"content_id": "a5a0dbc1bdc23024b8e63e591de68d5985c867f2",
"detected_licenses": [],
"is_generated": false,
"is_... | 7 |
prashantpk433/Covid19-World-Data-Downloader-GUI | https://github.com/prashantpk433/Covid19-World-Data-Downloader-GUI | eb32be0fb21ff1a89b3526b9ff1fb2e88ad26fe2 | 6a609d10d107c58683d8767f447c501706aa3470 | 86947d15ff9f411a57cff95ae59bb0079e0a4883 | refs/heads/master | 2022-12-02T21:53:25.634963 | 2020-08-16T03:55:46 | 2020-08-16T03:55:46 | 287,861,043 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.6824966073036194,
"alphanum_fraction": 0.7896879315376282,
"avg_line_length": 39.83333206176758,
"blob_id": "ffe0280dab96754d601e3f0ef6eb8be5add1e477",
"content_id": "63b8531759826c749b12ded1c3af5d85cbfc4e9f",
"detected_licenses": [],
"is_generated": false,
"is_... | 2 |
armen2009/study | https://github.com/armen2009/study | 192e6e0f2136ee9e23a3d3e361e7525c1e1a9c74 | 255e7ec19849d674ff0a2ef6cae968e6cffe5194 | 4b7c70f0f12b068e578550073e00123d1e69b8cf | refs/heads/main | 2023-02-10T15:29:03.527077 | 2021-01-06T15:33:57 | 2021-01-06T15:33:57 | 327,131,400 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.6834170818328857,
"alphanum_fraction": 0.7160804271697998,
"avg_line_length": 43.33333206176758,
"blob_id": "28df87a25ed7c81ae95f1aab4822995c1df396d7",
"content_id": "bdc0491be57c8a6e63c95067ef2b92070578a12c",
"detected_licenses": [],
"is_generated": false,
"is_... | 20 |
roni31/Job_Portal_Website | https://github.com/roni31/Job_Portal_Website | 9cc392eceb9da9022225ba75b6aabb2a3c0a1bd4 | 5931104a7b8687eedc7b2c007ffc512dcc73879b | 01745f6456ca8a1e3f2077afafc4e226f9a09aea | refs/heads/master | 2023-03-07T08:24:12.954576 | 2021-02-24T01:06:33 | 2021-02-24T01:06:33 | 341,737,981 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.5336134433746338,
"alphanum_fraction": 0.5644257664680481,
"avg_line_length": 29.382978439331055,
"blob_id": "134b94abb6eda63e72609d073e3eac9c6be56363",
"content_id": "1aaa9d4c3c4d7862335f85530d5597c3e77e8fba",
"detected_licenses": [],
"is_generated": false,
"is... | 10 |
WillMandil001/Thorvald_weed_spraying | https://github.com/WillMandil001/Thorvald_weed_spraying | d455bd3b1005bc1ddd8a9a57eafe83ebac0d96b4 | 0c1b9c417c3d56e11b30360de84236a0649548b3 | 11d7559280fe59fa3df584315530f3b3790403d7 | refs/heads/master | 2020-12-19T05:02:01.673428 | 2020-02-07T09:21:56 | 2020-02-07T09:21:56 | 235,628,258 | 1 | 0 | null | 2020-01-22T17:36:45 | 2020-02-06T21:21:52 | 2020-02-07T09:21:56 | Python | [
{
"alpha_fraction": 0.5198920369148254,
"alphanum_fraction": 0.5578768253326416,
"avg_line_length": 48.28078842163086,
"blob_id": "3c88c9440afaa68976dd8fe2c880e1776af28fde",
"content_id": "6d44fa9f04fc786422302834aa7e156415b8780e",
"detected_licenses": [],
"is_generated": false,
"is_... | 15 |
Rosary7/Python3_Demo | https://github.com/Rosary7/Python3_Demo | e56493c1387df9a791e380f65a4cc89f1b53536e | 3bb373cd0ac2a521bdcfffd3df55bb12b772b604 | a3bf5d27be6d2677ffbdac48a4d197b4b2c295ff | refs/heads/master | 2021-07-10T11:30:54.395434 | 2019-04-02T14:43:29 | 2019-04-02T14:43:29 | 149,006,278 | 0 | 3 | null | null | null | null | null | [
{
"alpha_fraction": 0.6100151538848877,
"alphanum_fraction": 0.6176024079322815,
"avg_line_length": 18.65625,
"blob_id": "7573691f528eb750e78041d3ed340cd2266a4ee9",
"content_id": "2a2a5bb73fdfd35a8f8fa9ae742a18968899be20",
"detected_licenses": [],
"is_generated": false,
"is_vendor": ... | 64 |
mitsumizo/scrayping | https://github.com/mitsumizo/scrayping | 96fde5be6a3763ead15b8fddde318c953b693baa | 87df2ced0974f19fad51546c7f5ae9a3baefed70 | 5c8a15d44e0b7bb79a871a358bd75d48adf5de9b | refs/heads/master | 2021-01-13T17:55:33.998958 | 2020-02-23T03:16:21 | 2020-02-23T03:16:21 | 242,448,191 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.6318492889404297,
"alphanum_fraction": 0.6352739930152893,
"avg_line_length": 23.375,
"blob_id": "d8591a436af507f8f19b832d03ece63497665293",
"content_id": "2dd644524d06ffd9add753ed167c8f6e92c06b22",
"detected_licenses": [],
"is_generated": false,
"is_vendor": fa... | 4 |
shaneZhang/SnapShotForVultrVPS | https://github.com/shaneZhang/SnapShotForVultrVPS | 76ea98c05302fa637af0bb8e56ae79d39f293d70 | 3c527e565f64d0635542286c9ba1f021effdf68b | 68a3789c601d405ab8681f3ddce6e0b9d790517a | refs/heads/master | 2021-01-01T19:53:13.662924 | 2020-11-19T04:03:42 | 2020-11-19T04:03:42 | 98,709,131 | 2 | 1 | null | null | null | null | null | [
{
"alpha_fraction": 0.7843137383460999,
"alphanum_fraction": 0.7843137383460999,
"avg_line_length": 16,
"blob_id": "bc0c3b57f725c7a379262ff1f3a3f216714a411e",
"content_id": "0d44d774ffe8c8b03c29324b6563318773c5d955",
"detected_licenses": [],
"is_generated": false,
"is_vendor": false,... | 2 |
aureliojgc/ProbeParticleModel | https://github.com/aureliojgc/ProbeParticleModel | 1b9834d83b71f020f5c890675f9a135c7a5077d9 | c93ffbc439b2cc7aa2d02dd9808d1fff2c5db76e | 060fc36c87721c9da3fa9ddb69107bb41d1d78df | refs/heads/master | 2022-11-26T11:54:49.909215 | 2019-10-29T10:30:06 | 2019-10-29T10:30:06 | 221,191,156 | 0 | 0 | MIT | 2019-11-12T10:28:50 | 2019-10-29T10:30:14 | 2019-11-04T17:12:15 | null | [
{
"alpha_fraction": 0.6140350699424744,
"alphanum_fraction": 0.6666666865348816,
"avg_line_length": 13.5,
"blob_id": "71396505033ea02da6040ae081de21469ab1b719",
"content_id": "ebb3fa64d80ee381f898ab56cd73eebb3daea103",
"detected_licenses": [
"MIT"
],
"is_generated": false,
... | 15 |
Ecpy/ecpy | https://github.com/Ecpy/ecpy | ea3f3dfd85a06e2d257de91f978184d28db4aca3 | 9ba74e54ba42471ad475acf8d2c98a6ff34d59ee | 73cab7c01337800686cc3c4c3b3f508e7e76baeb | refs/heads/main | 2021-05-22T09:51:03.672947 | 2021-04-27T13:19:30 | 2021-04-27T13:19:30 | 33,326,768 | 10 | 9 | null | null | null | null | null | [
{
"alpha_fraction": 0.6346336603164673,
"alphanum_fraction": 0.6508087515830994,
"avg_line_length": 32.36507797241211,
"blob_id": "e4e183d055970298669b718ec9196c1e266d8e9b",
"content_id": "48ab42e783cbba889be730d309233fce829184b2",
"detected_licenses": [
"BSD-3-Clause"
],
"is_g... | 1 |
spark8899/shell-scripts | https://github.com/spark8899/shell-scripts | 8a28d37bbe95a50de11e76b4502aaa18985cc435 | 04fc1ad6c5eea08b72cdbd2286bc49ca12d31b75 | 71eb09efb2a87eb07ead5c17879e04fdd96b5e7f | refs/heads/master | 2023-06-24T02:43:24.882545 | 2023-06-12T12:01:59 | 2023-06-12T12:01:59 | 19,594,283 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.5396332740783691,
"alphanum_fraction": 0.5684062242507935,
"avg_line_length": 24.141843795776367,
"blob_id": "ba346f2c65b64adfa6b3273b80e4f882f1ca1b73",
"content_id": "3ea6fd955ec1096ca19477b2b98b2403a11c3bde",
"detected_licenses": [],
"is_generated": false,
"is... | 10 |
TheKernelCorp/Fluorite | https://github.com/TheKernelCorp/Fluorite | 7543f7a98503be81b9d67b2824c9f7c9758bd522 | 8ef31652326dbdc0df7d6d61f0f419ce1c40b8de | 5479731894173669051a25eca9f5c895b5d6b987 | refs/heads/master | 2021-09-25T10:00:06.154699 | 2018-10-20T16:10:56 | 2018-10-20T16:10:56 | 113,212,389 | 17 | 4 | MIT | 2017-12-05T17:26:53 | 2018-10-18T05:08:26 | 2018-10-20T16:10:56 | Crystal | [
{
"alpha_fraction": 0.6152999401092529,
"alphanum_fraction": 0.6180517077445984,
"avg_line_length": 19.263940811157227,
"blob_id": "ca93033c2136c02bd2fc01bfe88dadce90b7a25c",
"content_id": "622939e4c8e46cd076154b10f2ee73f88eabdec8",
"detected_licenses": [
"MIT"
],
"is_generated... | 3 |
AnnWu/DeepLearnPython8Days | https://github.com/AnnWu/DeepLearnPython8Days | f8c10b6dd5ec0f4ec0ed5905e550bf503a529522 | dc007f84405dd7267a126a448672a75dc6d0bd9f | 68bc8001aa0df7b1c6ce3533e8f5ab96d7192a3b | refs/heads/master | 2020-03-29T20:18:13.374162 | 2018-09-26T16:13:35 | 2018-09-26T16:13:35 | 150,305,092 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.5704125165939331,
"alphanum_fraction": 0.5775249004364014,
"avg_line_length": 25.074073791503906,
"blob_id": "9415a853d068b952b8949de9561c1d94ef21ef74",
"content_id": "f3b0507047f2dcdc8af6fec190cb0cee562eb191",
"detected_licenses": [],
"is_generated": false,
"is... | 3 |
SgtMilk/garbage-project-1-backend | https://github.com/SgtMilk/garbage-project-1-backend | 5684a98ffc0b67d95cb7006481a9fbe837296fd3 | 852fbcee6ad05e8624f49c280098fb5698fa975a | c0ba6ad31478afef1fdd47af097aaf303d5a9ebf | refs/heads/main | 2023-08-06T19:09:07.522202 | 2021-10-03T14:25:51 | 2021-10-03T14:25:51 | 412,850,570 | 1 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.6263852119445801,
"alphanum_fraction": 0.6295514702796936,
"avg_line_length": 34.092594146728516,
"blob_id": "2033d23886539195b9a6d5eab9b4723b21f287cd",
"content_id": "540c4a17f4c00fde7839fc930703f91c84da74c3",
"detected_licenses": [
"MIT"
],
"is_generated... | 8 |
Facundo-Pfeffer/EDMNA-2020 | https://github.com/Facundo-Pfeffer/EDMNA-2020 | cfe1a2afa322969e7b4a7dc6af06c77af7a3f8fc | 2457a6049a9aef3917f2e55cdc3c535f967cdb0f | 9322cf4539ecb5f2869b1b4c4f08c9735299fbfa | refs/heads/master | 2023-06-23T09:06:21.821838 | 2020-12-11T13:47:10 | 2020-12-11T13:47:10 | null | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.3866328299045563,
"alphanum_fraction": 0.42216581106185913,
"avg_line_length": 36.5396842956543,
"blob_id": "00bfa1f0297355e68aafc02df198fa6ef6dbcf10",
"content_id": "aa3eff378529ac3a7797c8de3cc7da36fd5b0541",
"detected_licenses": [],
"is_generated": false,
"is_... | 6 |
mixinDennisBarzanoffMixin/smartDoor | https://github.com/mixinDennisBarzanoffMixin/smartDoor | ccd36757dd401506e7752c258c6b136c84e725b9 | fc226033b1c08463611a2789564a88ba16f0755d | f1301135976d7fb86503db8aafa2df4bc6bb2867 | refs/heads/master | 2022-12-29T13:40:01.491035 | 2020-10-18T20:38:56 | 2020-10-18T20:38:56 | null | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.7173144817352295,
"alphanum_fraction": 0.7750294208526611,
"avg_line_length": 43.68421173095703,
"blob_id": "3bc013bbd8440591ce76a8b22144fc6264d70ba2",
"content_id": "9a1b65c4a38988a3cc9328075102396b08ac33f9",
"detected_licenses": [],
"is_generated": false,
"is_... | 3 |
suzirui123/Theano-Tutorial | https://github.com/suzirui123/Theano-Tutorial | 0fc94b88289f2a9d8c9208e2b982ed09411092e0 | 60faca90fc0b2bfe3a1a91a7434772b83c399811 | f21008119e66ac306153aa317de83be0361da756 | refs/heads/master | 2020-06-13T10:04:05.745008 | 2016-12-06T08:23:07 | 2016-12-06T08:23:07 | 75,396,600 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.7777777910232544,
"alphanum_fraction": 0.7777777910232544,
"avg_line_length": 26,
"blob_id": "83502c76a6f57c1dcd0f732f580a25669531f7c9",
"content_id": "a0d262493c42714c47d76e50faba959edc349216",
"detected_licenses": [],
"is_generated": false,
"is_vendor": false,... | 3 |
JamesCullum/MISP-QRadar-Integration | https://github.com/JamesCullum/MISP-QRadar-Integration | 37ed7c28609a1e74fae3d07b2cfe4248dcf0259b | 3d584b451d3fa5160bb708869aeabb89c5bc7d81 | f06f7f2967e3be7b60bd69a833927f7c21961236 | refs/heads/master | 2022-01-14T20:05:50.140005 | 2022-01-05T15:39:41 | 2022-01-05T15:39:41 | 178,207,420 | 0 | 3 | null | 2019-03-28T13:17:59 | 2019-02-24T09:13:09 | 2019-02-18T13:09:45 | null | [
{
"alpha_fraction": 0.7158327698707581,
"alphanum_fraction": 0.7656486630439758,
"avg_line_length": 41.75,
"blob_id": "58d019091e71ccece6896fb0b87b332b3dcdee37",
"content_id": "811f851413c06ccac07d9ddf7052d34eef24fc9c",
"detected_licenses": [
"MIT"
],
"is_generated": false,
... | 3 |
giuscri/matasano | https://github.com/giuscri/matasano | 16eee00fb4caabff1c6e08adf84745256826f0f6 | ae209ba27d2484f456e8d0ff5456a082ac934cb4 | 2e7a4543c8f96f969f8041dbd6dc8c1fd51d2d05 | refs/heads/master | 2015-08-20T02:57:05.369639 | 2015-02-02T20:27:21 | 2015-02-02T20:27:21 | 30,192,722 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.5227963328361511,
"alphanum_fraction": 0.5471124649047852,
"avg_line_length": 31.254901885986328,
"blob_id": "e2da3d82ae79c17a778cc373dbc516f746e0a28b",
"content_id": "2d955730de128294c2f5a1221a3723a83298ba56",
"detected_licenses": [],
"is_generated": false,
"is... | 7 |
kilimanjarx/shopee-web-scraper | https://github.com/kilimanjarx/shopee-web-scraper | 964f8a9e8286f364446187790340bcf9f5cee10d | ae82b0278e15c35914a29dbe2191326f00d8a31c | d4a4b9754a2c0d7e52cbabae05ce4b138a4cb998 | refs/heads/master | 2023-05-30T03:25:53.732612 | 2021-06-12T01:22:14 | 2021-06-12T01:22:14 | 376,033,354 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.7280248403549194,
"alphanum_fraction": 0.7394002079963684,
"avg_line_length": 18.73469352722168,
"blob_id": "040dba7ff15f3f184c3d84a8dfd28cf397e69377",
"content_id": "e35b419490efb85b39185840b11f0884a428f07d",
"detected_licenses": [],
"is_generated": false,
"is_... | 2 |
DrVecctor/GameOfGo | https://github.com/DrVecctor/GameOfGo | 7c82a395af69d1bfe71b5044c16a2f901ad0a8dd | ab8b0313372fe380b44dd7021c00895c971b3e54 | 1998948102aa0ad6ab582ac7b48ca5a975e3eb3f | refs/heads/master | 2022-12-25T18:12:15.760989 | 2020-10-02T16:51:30 | 2020-10-02T16:51:30 | 294,747,593 | 0 | 1 | MIT | 2020-09-11T16:35:51 | 2020-10-02T16:22:45 | 2020-10-02T16:51:31 | Python | [
{
"alpha_fraction": 0.5403226017951965,
"alphanum_fraction": 0.5483871102333069,
"avg_line_length": 21.256410598754883,
"blob_id": "fdc169858a9110341ffaa0dafc74fdea9ba19213",
"content_id": "1c8d7a2dd3d2d17ce480359894e095ab3734c852",
"detected_licenses": [
"MIT"
],
"is_generated... | 16 |
derRAV3N/yadbot | https://github.com/derRAV3N/yadbot | dd6ec8974700b923f9101163ae87635fd15ce754 | f3fb93a19310318823b882d371e53bd59c5d18d2 | 373feaa9449c566b1305fc865a5ce549c2899797 | refs/heads/3.7 | 2020-03-25T20:14:35.417334 | 2020-03-19T18:04:57 | 2020-03-19T18:04:57 | 144,122,390 | 1 | 0 | null | 2018-08-09T08:20:09 | 2019-04-19T13:18:48 | 2019-10-07T20:51:43 | Python | [
{
"alpha_fraction": 0.6916666626930237,
"alphanum_fraction": 0.699999988079071,
"avg_line_length": 16.285715103149414,
"blob_id": "074187c029958aca0d7f19fd5e234cf44f4a04ec",
"content_id": "6098116f68b465730cfb2c529ed6135713d32f98",
"detected_licenses": [],
"is_generated": false,
"is_... | 10 |
jigglepuff/gdc_project | https://github.com/jigglepuff/gdc_project | fa220c66db55182017755410bded81af0c14f151 | 78d9691eb1b62c3158c882309893f8d1a0eb101a | dc945669c235a7888db8e9da7b5f6178effb1a03 | refs/heads/master | 2020-04-10T20:15:11.315422 | 2018-12-11T01:53:31 | 2018-12-11T01:53:31 | 161,126,297 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.7564767003059387,
"alphanum_fraction": 0.7772020697593689,
"avg_line_length": 31.33333396911621,
"blob_id": "0b41e247fc02ffadf4f44b8f5956fff216db7f08",
"content_id": "daeedfa77bc4077ad6879b9b426084720155dc30",
"detected_licenses": [],
"is_generated": false,
"is_... | 8 |
andrewphillipdoss/Selenium_Practice | https://github.com/andrewphillipdoss/Selenium_Practice | a9b1e6410effbe04ffa5bbc97c9d41253ead30db | ad3e279748b00963f08a8480b06fe734ee1ccce5 | a695ca4e13de9e6ed12e6d3942477ee2864d5d34 | refs/heads/master | 2020-07-04T05:25:09.998212 | 2019-08-13T15:28:28 | 2019-08-13T15:28:28 | 202,171,013 | 1 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.6666666865348816,
"alphanum_fraction": 0.6731034517288208,
"avg_line_length": 36.17948532104492,
"blob_id": "c51395486d7aacbbb9935ed36d0fe586a9d1a211",
"content_id": "2b83599d30d551cb03c8709be94076aec02041fe",
"detected_licenses": [],
"is_generated": false,
"is_... | 2 |
anandrajB/speedy-scanner | https://github.com/anandrajB/speedy-scanner | a97bfe16feef483db9e2fe77a2b1639e1dea8707 | fd5d4fd7b3ba600d975ae2aaf73ae81e1d0e3632 | 7a13a9def50e3d87d74f7d3a2b990cd9bc1acda1 | refs/heads/master | 2023-08-31T09:07:46.802433 | 2021-09-20T12:21:22 | 2021-09-20T12:21:22 | 374,634,888 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.6492753624916077,
"alphanum_fraction": 0.6539855003356934,
"avg_line_length": 27.163265228271484,
"blob_id": "2c4641d91677ef72006a281b64c493343df1c9c7",
"content_id": "c796b61e18541c3a67ae6e3944a7f6d8d480bd9b",
"detected_licenses": [],
"is_generated": false,
"is... | 6 |
alephjunio/simplemooc | https://github.com/alephjunio/simplemooc | f806a56a7526fd2dbd2df7d22f0509fb6b4b4a76 | 2291afa3671c5728ae6e2ebf311528637f44acf3 | c356ba31de4b530ded15915fdd1aaccccfbdad39 | refs/heads/master | 2022-04-30T10:05:23.174621 | 2017-07-07T22:52:05 | 2017-07-07T22:52:05 | 96,254,700 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.7001499533653259,
"alphanum_fraction": 0.7113943099975586,
"avg_line_length": 33.20512771606445,
"blob_id": "cb25958d40216b40ae6505e59e992f50f71a6fce",
"content_id": "0d80a72188369328322067b27b7b3b2b9842d1b7",
"detected_licenses": [],
"is_generated": false,
"is_... | 7 |
federico-arias/grades-scrapping | https://github.com/federico-arias/grades-scrapping | 146ca80581f0ba82cd2dd6fb100e024152bbf714 | 9069499208771d911cd848c96efaf5dc12ac519d | a596876a84a6787de91147989054b61afaef5a39 | refs/heads/master | 2020-07-04T04:59:15.036939 | 2018-01-03T16:15:05 | 2018-01-03T16:15:05 | 67,464,930 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.646291196346283,
"alphanum_fraction": 0.6648351550102234,
"avg_line_length": 23.266666412353516,
"blob_id": "a6d04026a47bba6922b7777a9dac53f69b282b56",
"content_id": "bf740e9d47ad327159468bbd7b3dfaeed163add0",
"detected_licenses": [],
"is_generated": false,
"is_... | 2 |
Yaroslav4Work/Observer-Pattern-Python | https://github.com/Yaroslav4Work/Observer-Pattern-Python | 376b649fdf634f11cbdb275fc422193142ddad37 | 6ae8684e3f1b1880148479f4ae1eebf8fd2649f1 | d6065daf62a504351fbe4da8f34ea379bb83b250 | refs/heads/master | 2023-01-18T17:40:22.762850 | 2020-11-24T13:31:47 | 2020-11-24T13:31:47 | 315,633,377 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.599071204662323,
"alphanum_fraction": 0.599071204662323,
"avg_line_length": 20.53333282470703,
"blob_id": "2feb87f62cb9fd4312a28c2524fcebc0cbb0d6ed",
"content_id": "e36117d482218c62f09924e8ecfc01a73d6d58fa",
"detected_licenses": [],
"is_generated": false,
"is_ve... | 2 |
akumuli/Akumuli | https://github.com/akumuli/Akumuli | 98956e9fe40e15a9d6c4815e7dfc21405a511d19 | c0803be819cf48203ed8776a30dd02c6c3c0ba2a | edff47e6548d8fbf15d560815a743b017c568e4c | refs/heads/master | 2022-08-21T09:48:41.543675 | 2020-05-19T13:02:08 | 2020-05-19T13:02:08 | 16,326,644 | 1,144 | 133 | Apache-2.0 | 2014-01-28T21:31:20 | 2022-07-26T21:49:05 | 2022-08-06T04:42:11 | C++ | [
{
"alpha_fraction": 0.5929036140441895,
"alphanum_fraction": 0.5985428690910339,
"avg_line_length": 26.358108520507812,
"blob_id": "97b908443ea02d59151f97e782eb514abdf6a2b9",
"content_id": "6a71fd88fc34d73fe26301f60250321290b28c68",
"detected_licenses": [
"Apache-2.0"
],
"is_ge... | 211 |
uds5501/Did-You-Say-That | https://github.com/uds5501/Did-You-Say-That | 7b16a6530d3175e622002e56cb7db7c58c53df47 | e4dc5f511947ddf04ce073ede0fa1510f4de5ca4 | f6705d8ca7990cbcfaf870f7d506bfb5213e504f | refs/heads/master | 2021-09-15T08:34:57.315584 | 2018-05-29T10:09:36 | 2018-05-29T10:09:36 | 119,049,902 | 6 | 2 | MIT | 2018-01-26T12:38:12 | 2018-01-27T05:32:45 | 2018-05-29T10:09:37 | Jupyter Notebook | [
{
"alpha_fraction": 0.645054280757904,
"alphanum_fraction": 0.6541013121604919,
"avg_line_length": 29.69444465637207,
"blob_id": "348be6b683c04a153caa2d7d40124e37da316e08",
"content_id": "afd8a6828b03df2c1ce443c9c262a11eeeab79af",
"detected_licenses": [
"MIT"
],
"is_generated":... | 3 |
jcbird/ks-dwarf | https://github.com/jcbird/ks-dwarf | 3ff162f12cc52500a72f203a186c73bc4151e277 | f9b1cd8ae98ef9831f55c4b15ef8ef9bf5ce09fc | 0d978593c9349fb44e88c6d152eec863f6464585 | refs/heads/master | 2021-01-10T08:56:47.384487 | 2015-11-30T15:44:05 | 2015-11-30T15:44:05 | 47,128,863 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.5532243251800537,
"alphanum_fraction": 0.5930268168449402,
"avg_line_length": 32.76041793823242,
"blob_id": "6f0760255583991beb3c672978c7e6faf2edbcba",
"content_id": "e10b2c95ec4ea9ce5fec84f7e5cb9bb48dbae1b8",
"detected_licenses": [],
"is_generated": false,
"is_... | 2 |
KevinSayers/snaketest | https://github.com/KevinSayers/snaketest | 6359d2055be3760d302b6752344537827ae8c6ff | 720eb2774e0dac7923f46b8e833a6de949ae81dd | 0f8f6d817f8bae244476e9a7e616084a02f3fb11 | refs/heads/master | 2021-04-09T10:20:44.782908 | 2018-03-16T13:45:15 | 2018-03-16T13:45:15 | 125,521,755 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.6410256624221802,
"alphanum_fraction": 0.6410256624221802,
"avg_line_length": 18.66666603088379,
"blob_id": "276bfcb71fed478a9cd07729267ff5c9aa854d89",
"content_id": "f788895d17e0fd1c1ec351d082174723a3b03fdf",
"detected_licenses": [],
"is_generated": false,
"is_... | 1 |
ambergoutam/Python-Json | https://github.com/ambergoutam/Python-Json | d5971df5f6f56fc4dbf0e7f85e252bbc393232d9 | 1a8bdec7557a5c5c1b4f9d901b59829a5f825c8d | 993105d64c696887542cce357e3896b3c3e4e3c0 | refs/heads/main | 2023-02-16T02:18:15.618652 | 2020-12-31T14:57:45 | 2020-12-31T14:57:45 | 325,460,274 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.8009950518608093,
"alphanum_fraction": 0.8009950518608093,
"avg_line_length": 99.5,
"blob_id": "9608cb8fe7f8ff5c283094ca32ee1f0b0c3cb78b",
"content_id": "110cb65522c7c9557006acc4ed27efe3e931a999",
"detected_licenses": [],
"is_generated": false,
"is_vendor": fals... | 2 |
rahiel/shellstats | https://github.com/rahiel/shellstats | c1524b20904113caf31da2ce289452201d1db513 | 1dae236049863cc5ce61c3e7a690d66c218c953c | 86aae480d533076bb7b3a96b800fb3cdb8f669d8 | refs/heads/master | 2020-05-17T14:40:59.587010 | 2014-11-29T16:55:20 | 2014-11-29T16:55:20 | 26,773,408 | 1 | 2 | null | null | null | null | null | [
{
"alpha_fraction": 0.45526695251464844,
"alphanum_fraction": 0.5050504803657532,
"avg_line_length": 27.26530647277832,
"blob_id": "61e8aec5132e35e60ed715c4a7b112dd744eeae2",
"content_id": "56f0a9de17a3ccad04b30df6e35f4230a9ab20b1",
"detected_licenses": [
"MIT"
],
"is_generated... | 4 |
CrazYoshi/Pyletudo | https://github.com/CrazYoshi/Pyletudo | 673d211c49ed6f9711b2479ffd24913ca84c8693 | 290fb4749206cc8ff2278fc58be74592822d5e66 | 241a120f0084f39a0d0e4b28842b6f008307ddef | refs/heads/master | 2020-05-26T05:16:03.296924 | 2019-09-11T22:51:25 | 2019-09-11T22:51:25 | 188,118,962 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.5617701411247253,
"alphanum_fraction": 0.6293792128562927,
"avg_line_length": 55.10344696044922,
"blob_id": "222b3dd0308c1512752134a39962fc3b4e5f98b7",
"content_id": "69d8cc665df9122a9f9f78071644343a1b22f227",
"detected_licenses": [],
"is_generated": false,
"is_... | 1 |
mazi76erX2/Black-Jack-Game | https://github.com/mazi76erX2/Black-Jack-Game | 7306f83163b2a9808ece948e7922fb33bb52ee66 | 4e8d5232e8758694fab3ac00158e5b8bbae37500 | 3fc8519a25d25ff9f48bd1d170bbe189a767f6e8 | refs/heads/master | 2023-06-03T10:34:54.420220 | 2021-06-23T19:52:02 | 2021-06-23T19:52:02 | 379,711,604 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.5179979205131531,
"alphanum_fraction": 0.5344532132148743,
"avg_line_length": 31.41111183166504,
"blob_id": "b2ce9236ceabdadd9adff7a57d7f68e784883137",
"content_id": "c1e53a8ffb6a36e15cae0999d39125b3e21fae32",
"detected_licenses": [],
"is_generated": false,
"is_... | 3 |
pytsite/plugin-tumblr | https://github.com/pytsite/plugin-tumblr | 29083eb6808657d318c62ab26e2795ee9af58885 | 60212d620e42c6539539f41f505c893e5c83ed54 | 0648f1b9515c83d1eb075fef117e761620c90ea5 | refs/heads/master | 2020-07-01T02:36:50.164157 | 2018-07-29T12:04:08 | 2018-07-29T12:04:08 | 74,557,168 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.654411792755127,
"alphanum_fraction": 0.654411792755127,
"avg_line_length": 11.952381134033203,
"blob_id": "aa3aa38af950e911d8f99d6593c85b07d7e2ee53",
"content_id": "ec79760ccdc3ac9b08e1fd5d6b5829a478ffcfdd",
"detected_licenses": [],
"is_generated": false,
"is_v... | 4 |
Benga22/CS2302-LAB1- | https://github.com/Benga22/CS2302-LAB1- | 6fb2af428426de6c4edd343d94223dba8b1c0e5d | f1293829b9a12f37c886671e24377da09b53edec | c70d75a774cd4f4823b3a94a3c7e5a2b416d1971 | refs/heads/master | 2020-04-21T20:13:49.809238 | 2019-05-13T04:21:55 | 2019-05-13T04:21:55 | 169,836,299 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.5523354411125183,
"alphanum_fraction": 0.5831663608551025,
"avg_line_length": 30.577890396118164,
"blob_id": "37b8029f3415cf615ca90805f33efceb6b0ab974",
"content_id": "70b465d5f89ccc386a205fcd780d324c75ed8c4f",
"detected_licenses": [],
"is_generated": false,
"is... | 5 |
araghuwanshi2017/Image_Metadata_WebApp | https://github.com/araghuwanshi2017/Image_Metadata_WebApp | 216c2e9a83ea024b5fb78406f345397efa9a6a92 | e34c897314264438e0a53c42e897eadb43d69b64 | 0f16ee7063284e234ce2535b2d47824a44a0e76e | refs/heads/main | 2023-01-07T08:01:04.019960 | 2020-11-11T10:48:59 | 2020-11-11T10:48:59 | 311,936,581 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.8091602921485901,
"alphanum_fraction": 0.8091602921485901,
"avg_line_length": 21,
"blob_id": "3d4898f59323ca152f8547479e7936a6dc3c00cd",
"content_id": "cbc82fbf8e0c15f4eb50a0959a94da3bfd8c1096",
"detected_licenses": [],
"is_generated": false,
"is_vendor": false,... | 3 |
kbjimenes/create-gephi-network-gexf | https://github.com/kbjimenes/create-gephi-network-gexf | 7c6d3205d6836b3196e934755d3ef4a467e2e665 | 1791923d76159d96337b6de5b663ee7e2c798f68 | 3c4a39f03cc4ccecca1ec0bba0e3045d18af6f4b | refs/heads/master | 2020-12-15T15:18:32.524229 | 2020-01-21T15:00:14 | 2020-01-21T15:00:14 | 235,153,832 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.7931034564971924,
"alphanum_fraction": 0.7931034564971924,
"avg_line_length": 28,
"blob_id": "c9dbeb68e8311632d9626ea1763e98993229c074",
"content_id": "c012651583f1b1816bd7ac207716aebd28482dc5",
"detected_licenses": [],
"is_generated": false,
"is_vendor": false,... | 2 |
waqarsaleem/python-games | https://github.com/waqarsaleem/python-games | 5e6e609b43bffc27007d7475068e56c9af008c28 | 43eae20c52174e7477ba4bc65f162d21e45a03eb | 784b4d87391a4e218de8fe8d59ea304383bfcef4 | refs/heads/master | 2020-12-01T12:40:51.704998 | 2019-12-30T10:05:00 | 2019-12-30T10:05:00 | 230,628,766 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.7279999852180481,
"alphanum_fraction": 0.7519999742507935,
"avg_line_length": 40.66666793823242,
"blob_id": "90830fca8f4f3cfe49e248f14f84b275151dd999",
"content_id": "570c936b4b46b83ea7979f0150c73f8e76d08f53",
"detected_licenses": [],
"is_generated": false,
"is_... | 3 |
hibaawad/TripPlanner | https://github.com/hibaawad/TripPlanner | 35b37dba7ac2960496ddeaddf0190531e40e6398 | c8d1a4f11d803ddd2b1d51d70aec2583e2ef3ff2 | 4ab1833fe951c174c0be15f911e674d7e84a3da8 | refs/heads/master | 2020-05-17T16:24:46.753234 | 2012-10-08T20:58:45 | 2012-10-08T20:58:45 | null | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.6718963384628296,
"alphanum_fraction": 0.6718963384628296,
"avg_line_length": 46.290321350097656,
"blob_id": "d1d564683c476c3d969ba2f8f7e32955f5539444",
"content_id": "972a7afe36ac20334b2f78c1424cc537a1580291",
"detected_licenses": [],
"is_generated": false,
"is... | 10 |
TeleClinic/swampdragon | https://github.com/TeleClinic/swampdragon | 3d5f7bc8c789848b8625a68ba5b4d9511b22c324 | b524d80acce855fe7e873a89a2522a5309196376 | d73b80f0222de3b0b8e693a26071d398dbe2e069 | refs/heads/master | 2017-05-30T09:17:36.593141 | 2016-05-24T17:21:00 | 2016-05-24T17:21:00 | 46,635,499 | 1 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.6565096974372864,
"alphanum_fraction": 0.6602031588554382,
"avg_line_length": 29.08333396911621,
"blob_id": "cc50be56e9283555a0109af466f533f2f894c83c",
"content_id": "41a405ae1ceca390210bc6c0f81f7b3c335ce3de",
"detected_licenses": [
"BSD-3-Clause"
],
"is_g... | 2 |
zhangbc07/Project | https://github.com/zhangbc07/Project | 25b9e7744fe2f0089bd92e1ab7252939bb47e1f7 | 68d7b411350e9fb04fd788fda38bbfa7865f1d24 | c0859588f13a3ad9729bac5907dfd42b83a48a08 | refs/heads/master | 2022-07-23T19:04:19.914211 | 2019-11-14T11:11:55 | 2019-11-14T11:11:55 | null | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.6338797807693481,
"alphanum_fraction": 0.6495446562767029,
"avg_line_length": 16.024845123291016,
"blob_id": "e8e015af63da92b36eef02ae6d968eccdbaf8dcb",
"content_id": "effcb9c5c88f832b490045c458cf46d43054f1c1",
"detected_licenses": [],
"is_generated": false,
"is... | 6 |
guiqiqi/DataAnalyser4Lab1 | https://github.com/guiqiqi/DataAnalyser4Lab1 | 7a222ab18f32d650dc737911e20df60b91a5a69a | 3dd7bc03cc42c1fabf8e4836fa3c75ca2d155a8b | 6868c17c2d1d9357217591e1ddd9fbbe0cea5869 | refs/heads/master | 2020-04-26T09:58:01.010413 | 2019-03-14T16:33:35 | 2019-03-14T16:33:35 | 173,473,558 | 1 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.6814516186714172,
"alphanum_fraction": 0.7163978219032288,
"avg_line_length": 20.257143020629883,
"blob_id": "e3e9e1ccec64eb592b39bd6e86128bb62481a2bb",
"content_id": "f7c8758c799fde26a1beac8f4a6601dcd21d1f4c",
"detected_licenses": [],
"is_generated": false,
"is... | 8 |
sergiojx/pactools | https://github.com/sergiojx/pactools | 9a29ea1247f36aa1cf6a6ed33821e879a27dd9c3 | f1ed7095e3dbd663001facaf5646c59c74aaec38 | 2d9acb85206069f88dababdc2a4197c823f10970 | refs/heads/master | 2020-08-04T02:26:18.166968 | 2019-07-01T21:44:53 | 2019-07-01T21:44:53 | null | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.6072889566421509,
"alphanum_fraction": 0.6162142157554626,
"avg_line_length": 39.74242401123047,
"blob_id": "291123c9d72cc7fb3943f913b332858924c1aa91",
"content_id": "c38b64025cd22953515b8adf8e8cb9c42f098b21",
"detected_licenses": [],
"is_generated": false,
"is_... | 1 |
Introduction-to-Programming-OSOWSKI/3-11-letter-position-AlexMartin23 | https://github.com/Introduction-to-Programming-OSOWSKI/3-11-letter-position-AlexMartin23 | 8e11c83661d1634cb5b92522ea71ee2e77702cd7 | f1372f68735168259a38700d5a32dbd5dc0eb71a | 45462c0605f2b8025a61a8c399df1e2be40a73e1 | refs/heads/master | 2023-03-06T10:56:18.846967 | 2021-02-04T18:33:28 | 2021-02-04T18:33:28 | 335,996,968 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.5175096988677979,
"alphanum_fraction": 0.533073902130127,
"avg_line_length": 28.375,
"blob_id": "8278e152fc05b41e82c12fde80fbc6764717f969",
"content_id": "55ebb6ab97442374508ae1fdc6da4c15a601cea1",
"detected_licenses": [],
"is_generated": false,
"is_vendor": fal... | 1 |
kalladhamodar/remote_addree_repository | https://github.com/kalladhamodar/remote_addree_repository | 170fe61017323a79b681d124bcbe5650d5a92751 | 44106a30f9b8667e1b3c2a9147c0eacf92124312 | 5d57f7415bbf6822d00c72735cdc9987b2a8230b | refs/heads/master | 2020-06-19T12:43:24.175659 | 2019-07-13T11:05:20 | 2019-07-13T11:05:20 | 196,712,196 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.7634408473968506,
"alphanum_fraction": 0.7634408473968506,
"avg_line_length": 17.600000381469727,
"blob_id": "9fb074395d984721ceb7f42401e07d7c4f15cc97",
"content_id": "060b9dbd4642447c2dad7b5a248b1d5b53658636",
"detected_licenses": [],
"is_generated": false,
"is... | 5 |
vinatsx/kattis | https://github.com/vinatsx/kattis | 023d6b0bd97eb04278e65d6216abc948da1a23e5 | 322bb2d1c48c587bfcee71fab3e755fb98cab206 | 63320876161f0933041d3cf047b2f751931655b4 | refs/heads/master | 2023-03-24T23:20:39.531263 | 2016-03-21T14:09:30 | 2016-03-21T14:09:30 | null | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.734375,
"alphanum_fraction": 0.75,
"avg_line_length": 41.66666793823242,
"blob_id": "8675d4746800e57fe44a350cdd95ccab9623e8e4",
"content_id": "e40ec3dba8a5b101441c7f0b17daf66fcd2104e9",
"detected_licenses": [],
"is_generated": false,
"is_vendor": false,
"lan... | 42 |
bijp/spj_08 | https://github.com/bijp/spj_08 | 976617c4a07a800abdd19055f4df15b5fb3b31e0 | 778943d2a2cc061bff8c7ebd3cd05a37b7975d71 | ba10320760e17cd79fe6b09f95b4cb706603ee3c | refs/heads/master | 2022-12-09T20:28:11.492676 | 2020-08-07T07:40:56 | 2020-08-07T07:40:56 | 285,766,198 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.6160950064659119,
"alphanum_fraction": 0.6187335252761841,
"avg_line_length": 19.486486434936523,
"blob_id": "9f3317fab674dfab231ed88cd237aa833321f439",
"content_id": "4b3341b6e07bf090918fb583196f82d0d45b453b",
"detected_licenses": [],
"is_generated": false,
"is... | 2 |
HongxuanWang/DE-ETL-Game | https://github.com/HongxuanWang/DE-ETL-Game | 3cc33ad19b0a872c329035b55dd199cfb278b35e | bb7acd9fdeff047618dabe68f34b4d762aebc4a7 | 641b33526e36f5fb3fc4959477bca740114a7abf | refs/heads/master | 2022-11-10T17:56:21.637969 | 2020-06-17T17:38:24 | 2020-06-17T17:38:24 | 265,392,095 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.5776081681251526,
"alphanum_fraction": 0.5788804292678833,
"avg_line_length": 35.738319396972656,
"blob_id": "cd8f2c2805b065d2bc41bc77f7c1835b8b20ed42",
"content_id": "afcd53bc03c8643d7841da8c6da24fd63eedf355",
"detected_licenses": [],
"is_generated": false,
"is... | 2 |
RenHao116/WURacing-tests | https://github.com/RenHao116/WURacing-tests | cb5e64516cecd58f67e6869624db81c3f08b3634 | 9558624f71ab04e9dbf86d92f83e5e262c9d2b5f | 4be944a746ceea62bff4d600a0423a15efb18e78 | refs/heads/master | 2021-07-26T11:58:54.559790 | 2017-11-04T22:16:41 | 2017-11-04T22:16:41 | 109,534,253 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.584725558757782,
"alphanum_fraction": 0.6252983212471008,
"avg_line_length": 27,
"blob_id": "a1f3c4892c994863e29ccb06194a7dba08415204",
"content_id": "8d9e1e817d438b245c6ffaaa5a0144c93d4a1849",
"detected_licenses": [],
"is_generated": false,
"is_vendor": false,
... | 3 |
kamal0072/Python-Code | https://github.com/kamal0072/Python-Code | d59b7885eeb1c97c8c14a6292b5319e4d659d1c1 | 4c567f2bf68a2572993e347fa658362b9875b11a | 3d6f5340a8c3c0300a01e30b0889ab12d70d5ec1 | refs/heads/master | 2023-06-05T03:18:00.701389 | 2021-06-17T20:01:43 | 2021-06-17T20:01:43 | 377,071,121 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.5199453830718994,
"alphanum_fraction": 0.5549180507659912,
"avg_line_length": 18.0625,
"blob_id": "b61f88e2bd06b6e6bc6740264c8ba1152312d0bf",
"content_id": "b02cb046129a357578ecfceec5c801e6cf0f31e8",
"detected_licenses": [],
"is_generated": false,
"is_vendor": f... | 1 |
heavensward-argentlux/ninjavan-cash-mgt | https://github.com/heavensward-argentlux/ninjavan-cash-mgt | d2b5cd1b1eeabf828e00939201605a460960872c | b892f43c8a25ad87eb80cfdbdc7335c21bf4d7f2 | efb72b246f3d04336233f11e1f71411ec3341e5f | refs/heads/main | 2023-03-24T14:42:29.454415 | 2021-10-18T03:25:52 | 2021-10-18T03:25:52 | 335,513,457 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.6009615659713745,
"alphanum_fraction": 0.6177884340286255,
"avg_line_length": 33.70833206176758,
"blob_id": "9628e9e35e7da00959040f7b82ba4c148764d9a9",
"content_id": "a363f6aae350069bd609e66f0da0d0bce72baf0d",
"detected_licenses": [
"MIT"
],
"is_generated"... | 3 |
LouiseMcMahon/UKCaveGIS | https://github.com/LouiseMcMahon/UKCaveGIS | 4dcdd1b352fcaba143008c5948cd0fa39e797536 | b943aa8ce15674267d83e19f9a152a9b73f2d910 | a8e3b7196bb67cdbee128ba556b44a0e1dd0a228 | refs/heads/master | 2020-08-02T00:12:39.762179 | 2020-01-14T20:02:00 | 2020-01-14T20:02:00 | 211,171,974 | 3 | 0 | MIT | 2019-09-26T20:02:43 | 2020-01-13T22:57:45 | 2020-01-14T20:02:01 | Python | [
{
"alpha_fraction": 0.5776138305664062,
"alphanum_fraction": 0.5932061672210693,
"avg_line_length": 32.72300338745117,
"blob_id": "a3b101439e194a09ccac3792850c4ba469a92008",
"content_id": "5eb6505b5716f7525054cf21ee42511e5b8ecde3",
"detected_licenses": [
"MIT"
],
"is_generated"... | 16 |
foy4748/university_fees | https://github.com/foy4748/university_fees | 4872edf56a8eb166ca6197ff129fff47a3cf2b32 | 4c5ab6e8dc94996320f6f85745f03c505722bafe | d6f5d896a9a3d677b101087d816b795c386f33df | refs/heads/main | 2023-07-13T02:48:46.260613 | 2021-08-21T18:54:32 | 2021-08-21T18:54:32 | 398,825,034 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.5382612943649292,
"alphanum_fraction": 0.5419052839279175,
"avg_line_length": 26.056337356567383,
"blob_id": "beac3b24d12820a14b3db1743b47cdf3ef683e7a",
"content_id": "86a66eaa11ed806d3673b3598cd052ab216fc9ab",
"detected_licenses": [],
"is_generated": false,
"is... | 2 |
ryantylerkane/Movie-Reviews | https://github.com/ryantylerkane/Movie-Reviews | 9e6cdd4329bd4b62549e15a52abf2963a9aaea9d | bb8a863e18b8707e85ecb4b539f76fc7b56c6246 | 5af1169cad32f7a23341e172317856dbf3605d53 | refs/heads/master | 2022-11-24T21:26:24.606234 | 2020-07-30T04:45:01 | 2020-07-30T04:45:01 | 277,402,726 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.6739332675933838,
"alphanum_fraction": 0.6783198118209839,
"avg_line_length": 55.869232177734375,
"blob_id": "82e8455e5ab937c42140fa2b0981e138d5e2a945",
"content_id": "ec79599ac9928f7c9d82b7655da67044cbd1c0bb",
"detected_licenses": [],
"is_generated": false,
"is... | 3 |
ghevcoul/fractal_app | https://github.com/ghevcoul/fractal_app | 540ff3422fc80df5ac0c8332359dddc56067a9c5 | 99c5833b90b668f8c91b3056583dd46eafd947f5 | 5c67255442702a93b2813a775fa014e4b87df0fc | refs/heads/master | 2021-01-01T04:04:54.382966 | 2016-05-04T19:51:30 | 2016-05-04T19:51:30 | 58,070,237 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.6301145553588867,
"alphanum_fraction": 0.6317512392997742,
"avg_line_length": 19.366666793823242,
"blob_id": "7b254f386e9aa9b36d0d3e411e7efe68c530ea4c",
"content_id": "56de958fef53f0b366f36e7036e182ec545c8638",
"detected_licenses": [],
"is_generated": false,
"is... | 4 |
naotohori/pca_imkl | https://github.com/naotohori/pca_imkl | 7fae1e9d95bd1435c3e6edaf0bf62f686599e550 | af9989a78391f72a36612b6ee3393739c8f2805d | f4fa4f1075769ae1b3b53cc462060b2e18cf01ea | refs/heads/master | 2021-01-25T04:02:54.846794 | 2017-03-22T19:46:52 | 2017-03-22T19:46:52 | 30,076,123 | 1 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.5234493017196655,
"alphanum_fraction": 0.5385779142379761,
"avg_line_length": 18.969696044921875,
"blob_id": "b7cd78ed68fc9478a599be54fc99e53b517467b5",
"content_id": "5c10eed1a4e9d646d78090d680b0b51eff061326",
"detected_licenses": [],
"is_generated": false,
"is... | 2 |
Sobhy92/Fyyur | https://github.com/Sobhy92/Fyyur | 023a5593a58db8cbfbd5e691e8da8af637f154ca | 6e3170a256105b3f98886ed551a062a573e86cb3 | 0a5c934b01baf6bf58639797498c4aae11931711 | refs/heads/main | 2023-01-19T00:00:58.781114 | 2020-11-27T11:24:34 | 2020-11-27T11:24:34 | 313,139,843 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.611474335193634,
"alphanum_fraction": 0.6158006191253662,
"avg_line_length": 32.67715072631836,
"blob_id": "a5cbf391a9a57158315fccb108ee7700ed627864",
"content_id": "8fa4c42384bdd35c15e1f15b2f4343b560e657c4",
"detected_licenses": [],
"is_generated": false,
"is_v... | 1 |
Telexine/RPI-IOT-HomeAppliance | https://github.com/Telexine/RPI-IOT-HomeAppliance | fed9b367e09acd45941c0bc0442ca95bd9d9fed8 | 540f1b1aa277112c873f90d7136e1938a510873e | 3d163eb8d3fde5202e80b24d2f794d6079309e84 | refs/heads/master | 2021-01-20T07:41:53.475772 | 2019-01-12T06:57:58 | 2019-01-12T06:57:58 | 101,553,217 | 0 | 1 | null | 2017-08-27T13:13:14 | 2017-09-01T07:38:22 | 2017-12-20T13:19:02 | CSS | [
{
"alpha_fraction": 0.6869972944259644,
"alphanum_fraction": 0.7319034934043884,
"avg_line_length": 22.3125,
"blob_id": "90b939ec725b2b5c33de8cf131cdf570df8ab1fa",
"content_id": "6b749bd49d8dee6910300f6cc3d2766af7d4de84",
"detected_licenses": [
"MIT"
],
"is_generated": false,
... | 2 |
januka36/dataviz-flights | https://github.com/januka36/dataviz-flights | a7bf4c5c0fd2e46e169dc890c660af4246c1e1be | 502e6e5065a6626896125d7086a98415d6934e5f | b62157d35074c959d8cbfa5c3201ab5664174601 | refs/heads/main | 2023-04-17T18:49:15.567257 | 2021-05-09T22:46:13 | 2021-05-09T22:46:13 | 365,846,136 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.6718232035636902,
"alphanum_fraction": 0.6872928142547607,
"avg_line_length": 31.518518447875977,
"blob_id": "135a13b9524f6c3e90a833752ec2e31b72a438c4",
"content_id": "60ef03b408bde7c5d4e5914bf026f631644f50bf",
"detected_licenses": [],
"is_generated": false,
"is... | 1 |
sirius0411/swagger-android-codegen | https://github.com/sirius0411/swagger-android-codegen | edce1cb62f9958636117dced885781cc0056f9d6 | 4718d18ac66c504929fae5dbe2f2c0cce31c3ba2 | 1114c8827d65f4feccbc86de4288839d5bc23546 | refs/heads/master | 2021-01-10T03:21:59.302523 | 2015-11-16T07:59:48 | 2015-11-16T07:59:48 | 46,255,806 | 3 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.6731391549110413,
"alphanum_fraction": 0.6731391549110413,
"avg_line_length": 17.235294342041016,
"blob_id": "97b29ed35d363db928c36d771c179bd7070c8428",
"content_id": "09bf1757c4950fbbbbe855de2b7b956514bf25a2",
"detected_licenses": [],
"is_generated": false,
"is... | 8 |
AFriemann/mdc | https://github.com/AFriemann/mdc | 516fbbce5ae2ec475485f732f90e537daffc99bd | 147c3de43f3da4afe56c9bbda3edb8d3c152e7ab | 2b30dfbbcc2ac90292a756ad56480a05f1582487 | refs/heads/master | 2021-08-29T03:31:55.423313 | 2019-09-09T06:48:28 | 2019-09-09T06:48:28 | 121,800,761 | 8 | 11 | BSD-3-Clause | 2018-02-16T21:06:46 | 2020-08-06T12:34:13 | 2021-08-02T16:08:34 | Python | [
{
"alpha_fraction": 0.5918854475021362,
"alphanum_fraction": 0.5918854475021362,
"avg_line_length": 14.55555534362793,
"blob_id": "6c0ed93668aac23a782e0de2f1a68f55f44b1ae7",
"content_id": "c3f8cb940583a8c2a745d9fe1446bae8f378e95e",
"detected_licenses": [
"BSD-3-Clause"
],
"is_g... | 6 |
Paarmita/Space-shooter | https://github.com/Paarmita/Space-shooter | d0674a209b4199f1fa40d90e13c1e4dfd97fc90f | 28b7ad4cb83795a9f107da2acebbea7e0bcf7ace | bdd68b7697458c5f875c3a55575c5ef97aac0408 | refs/heads/master | 2020-03-18T17:30:59.842804 | 2018-06-02T15:13:07 | 2018-06-02T15:13:07 | 135,033,095 | 2 | 1 | null | null | null | null | null | [
{
"alpha_fraction": 0.6332970857620239,
"alphanum_fraction": 0.6387377381324768,
"avg_line_length": 26.02941131591797,
"blob_id": "9d531177ba1f5dd301e25a881fc45b67f9890485",
"content_id": "ffcbc7844d02cca913ae5a42dcfb259aab0a587f",
"detected_licenses": [],
"is_generated": false,
"is_... | 3 |
origaddicted2u88y/overly-simple-encryptdecrypt | https://github.com/origaddicted2u88y/overly-simple-encryptdecrypt | f84939b936aa8aedd22cff484db9d3d65ad0a7b7 | 159a5fdd3bf36127b077db327a27a13353f56af7 | b57488145cf46fe083b2f10d088d68c9ef0b4ba2 | refs/heads/master | 2021-08-08T21:44:25.742176 | 2017-11-11T10:46:53 | 2017-11-11T10:46:53 | 110,337,791 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.57334965467453,
"alphanum_fraction": 0.5831295847892761,
"avg_line_length": 20.394737243652344,
"blob_id": "6d9681b26a0f2dce16baea973e7696ad7a62134c",
"content_id": "bd6779155159296fc1d03f9f1c0af4ca45ce3efa",
"detected_licenses": [],
"is_generated": false,
"is_v... | 2 |
ltheodon/RandomGenerator | https://github.com/ltheodon/RandomGenerator | c2d96054c175c2342e8c3ade676fcefef45e19f5 | 095535f14202cf81c09561727061235abe7857a0 | 96efb1bd684a9ca0c8fa89ed7d0324ba0792808d | refs/heads/master | 2021-04-08T09:49:53.273877 | 2020-03-20T14:39:01 | 2020-03-20T14:39:01 | 248,764,151 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.607569694519043,
"alphanum_fraction": 0.6633465886116028,
"avg_line_length": 15.193548202514648,
"blob_id": "5d43402ea916bfc6e7394c47b94ad49b65f33f41",
"content_id": "1b2685ffb226447cd001c2acdea6d5da735749da",
"detected_licenses": [],
"is_generated": false,
"is_... | 1 |
syppik/GameOfCode | https://github.com/syppik/GameOfCode | 75dd8d8eae1c4396b4db9a2bd1c5afd592d610cb | ad48c6598351aa62ac94e1cec5d3dd5af04db7e8 | c73b435f81e9034d87f45bd3c7d5db770f12902c | refs/heads/master | 2020-04-27T18:29:21.760521 | 2019-03-23T15:14:31 | 2019-03-23T15:14:31 | 174,572,657 | 0 | 2 | null | 2019-03-08T16:37:58 | 2019-03-09T15:26:29 | 2019-03-09T15:31:48 | Python | [
{
"alpha_fraction": 0.7906976938247681,
"alphanum_fraction": 0.8023256063461304,
"avg_line_length": 71.76923370361328,
"blob_id": "edfd049860a5944cc40a26e193211f629f6797ae",
"content_id": "7913577f2193d1fddd909c8928f2e3293c415cb7",
"detected_licenses": [],
"is_generated": false,
"is_... | 3 |
IvanVazquezL/Competitive-Programming-Exercises | https://github.com/IvanVazquezL/Competitive-Programming-Exercises | 3f20217689922068bae0f2c7b59e56213417d5aa | 9ef9b995a2e58179d51acd98e0b07047f0aeb93a | 9921767f58444c961018f3fe3e9c0d688453925b | refs/heads/master | 2023-01-04T02:12:46.892891 | 2020-10-26T01:07:57 | 2020-10-26T01:07:57 | 285,400,759 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.47159841656684875,
"alphanum_fraction": 0.4768824279308319,
"avg_line_length": 16.204545974731445,
"blob_id": "47bd611ee36a0ee1c408bd866fd22e3dd5fffcde",
"content_id": "88885b8875488945166ec7a931f49f6387f99975",
"detected_licenses": [],
"is_generated": false,
"i... | 44 |
nmscannell/ga-classification-assignment | https://github.com/nmscannell/ga-classification-assignment | b7caa0e4c9c84a50afe89a2d3397c2ec5e8fe58d | 6fc8892d06323b2265ba4113e9a636e7225b2308 | ca97ddca1911713432a2c24dbd2b01e07954c1ff | refs/heads/master | 2021-05-20T11:14:37.254852 | 2020-04-01T19:39:16 | 2020-04-01T19:39:16 | 252,270,116 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.5869918465614319,
"alphanum_fraction": 0.6227642297744751,
"avg_line_length": 42.92856979370117,
"blob_id": "df8afdd53efcd18087ff6ef4fd16ed7e0f4997b2",
"content_id": "801ab201d8ef69e7826543a7e2bde3d74a2da955",
"detected_licenses": [],
"is_generated": false,
"is_... | 3 |
moqingxinai/RST-OWE | https://github.com/moqingxinai/RST-OWE | dae0495912401dea1c588ba27fb9befbef00a9ed | edb838546140649b1690e40b4d1181852231d419 | 49be7f3efd12e2b8650476e3301598401b6d4319 | refs/heads/master | 2023-02-13T23:01:42.816906 | 2021-01-13T10:18:15 | 2021-01-13T10:18:15 | null | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.4593437910079956,
"alphanum_fraction": 0.5106989741325378,
"avg_line_length": 27.040000915527344,
"blob_id": "22c87fd1fd053d9d959bd2263a27e49de277e334",
"content_id": "899c7ae3bd215f5483f10090eb99d55fb3043664",
"detected_licenses": [],
"is_generated": false,
"is... | 2 |
shujaakbar2020/LMS | https://github.com/shujaakbar2020/LMS | aabfb056efb7db1ea1cca9e2fcaf073e81be531d | ad969950d818c239904149ad124d2fa9b6ec2b3c | bd30026da3dfee8de18f253b554c194a1631fbc0 | refs/heads/main | 2023-06-04T05:39:14.956931 | 2021-06-19T14:53:19 | 2021-06-19T14:53:19 | 378,449,892 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.5609756112098694,
"alphanum_fraction": 0.6202090382575989,
"avg_line_length": 30.88888931274414,
"blob_id": "6ca33ae8072cc9af28dc11c939d7acd4a388e5a0",
"content_id": "1a6c63a4f30b8e2e12ebb0d67a86728927366436",
"detected_licenses": [],
"is_generated": false,
"is_... | 37 |
abuttonCH/ML-1 | https://github.com/abuttonCH/ML-1 | f6fd81510ac5f1eec2b280de684a21aa8862618f | e6433d04cee848fcbb033096ff0e1cd590c38dcb | a67092555e47d9f5f5210379f2ee5057a5c60057 | refs/heads/master | 2021-01-24T11:09:41.781808 | 2016-10-07T16:19:55 | 2016-10-07T16:19:55 | 70,262,446 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.5947995781898499,
"alphanum_fraction": 0.6067172288894653,
"avg_line_length": 20.717647552490234,
"blob_id": "afdcf7421d21422aadd61ccb2f7a607e0173bd26",
"content_id": "a416b82c41633c833767c4a19242b17c4dba4a15",
"detected_licenses": [],
"is_generated": false,
"is... | 2 |
vsehgal1/Verilog_Parser | https://github.com/vsehgal1/Verilog_Parser | 6885fe9ca996ae03a409fbd2aa2265d3b1646795 | 456d30decc77ef65925cf673eefc37bd4fe97da8 | 2a0d387b3ba5eee59827588dfc41d1226b6a4645 | refs/heads/master | 2023-03-03T23:49:44.521596 | 2021-02-10T10:01:15 | 2021-02-10T10:01:15 | 337,681,695 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.5339228510856628,
"alphanum_fraction": 0.5933831334114075,
"avg_line_length": 33.345550537109375,
"blob_id": "e775012b3bdd53bcb5acb4e71ed6d41adfc13486",
"content_id": "22b3b4733646cc894182a2dca15dbfa99c5d49ea",
"detected_licenses": [],
"is_generated": false,
"is... | 2 |
skirt4aser/Store | https://github.com/skirt4aser/Store | 21f332b3d8557f72702ff64a68442b8c339d68aa | 5f53dceee9fd3158c89e1a8cacac957bc789f243 | 83376aa9490142128965e889351ebbc96017a52c | refs/heads/master | 2020-06-01T23:12:01.259284 | 2013-04-14T19:39:28 | 2013-04-14T19:39:28 | null | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.31015998125076294,
"alphanum_fraction": 0.3138958811759949,
"avg_line_length": 45.119850158691406,
"blob_id": "c714b8737f780476dedf4e1a889f8e8bf375012d",
"content_id": "52c566e2d9ce4a869886bfb1d4da8f262c6bff93",
"detected_licenses": [],
"is_generated": false,
"i... | 12 |
NickMazey/SupplyDemandSimulator | https://github.com/NickMazey/SupplyDemandSimulator | f66ec9dfe65647c130e0428f5869ea334310873c | c6c2b330463fea7c0e19dce8c92052f8ff50014a | df1d8440bdd6eb3d3d99e11034ae7fb83fa84651 | refs/heads/main | 2023-06-16T20:47:34.169995 | 2021-07-06T05:13:57 | 2021-07-06T05:13:57 | 383,278,249 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.8397436141967773,
"alphanum_fraction": 0.8397436141967773,
"avg_line_length": 37.75,
"blob_id": "2cddea3c551aa327a306ce889d86e44aad4ff31f",
"content_id": "b798c9c371c135793aa88d416a3ffec4e4052af1",
"detected_licenses": [],
"is_generated": false,
"is_vendor": fal... | 4 |
elcritch/reactqt | https://github.com/elcritch/reactqt | 2de560fa15c30bcb8a2ff2a08ae47bc8b7b4aa2a | ca4c48a8f73131c5ad0b62093ce4c4441351f0fc | a954cb820abfbf468545a7a4927e7ca0d0c91467 | refs/heads/master | 2021-01-17T12:19:23.267229 | 2015-04-19T09:40:06 | 2015-04-19T09:40:06 | 34,195,165 | 1 | 1 | null | null | null | null | null | [
{
"alpha_fraction": 0.5228241086006165,
"alphanum_fraction": 0.5246500372886658,
"avg_line_length": 24.671875,
"blob_id": "e42dd051829ba5df89a81c0423f51c958b789fff",
"content_id": "adbd8145b32e534b72719625e2cd3cabd6da72fd",
"detected_licenses": [
"MIT"
],
"is_generated": false,... | 7 |
Deisss/webservice-notification | https://github.com/Deisss/webservice-notification | 2fce5a0a73fd6004333821716354e740b2f7bcec | 73feddb22ad3a2543ad4f8047061d909c64fd75d | d7d594d642bb14673570eb853ddbf23d8f06e205 | refs/heads/master | 2021-01-20T04:37:03.439096 | 2014-04-17T15:11:40 | 2014-04-17T15:11:40 | null | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.42338818311691284,
"alphanum_fraction": 0.42538875341415405,
"avg_line_length": 26.426433563232422,
"blob_id": "74be245bc9f3db033f2ab44b0af1a52810d1d86f",
"content_id": "52320f7eff4b92b78e86ee93b030959faca5e287",
"detected_licenses": [
"MIT"
],
"is_generat... | 16 |
pmichali/commit-coverage | https://github.com/pmichali/commit-coverage | 34ac63bae15d3b41bab3befb713b8a821e4550ff | e6912f1d9cd605e9fa80154ced3494aeaa85bd21 | de0fd72d6eb184086e6d5c6afe5183d083507c07 | refs/heads/master | 2021-01-10T06:15:43.431141 | 2016-03-08T21:45:31 | 2016-03-08T21:45:31 | 53,350,314 | 2 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.6928218007087708,
"alphanum_fraction": 0.7059406042098999,
"avg_line_length": 41.978721618652344,
"blob_id": "6a15d36fb73b966d8c102c2386052ec1e6e993ff",
"content_id": "3e86221ca2445b7664f525c6a8973ff3f0db3c02",
"detected_licenses": [
"Apache-2.0"
],
"is_ge... | 5 |
PrateekCoder/ProjectEular.net | https://github.com/PrateekCoder/ProjectEular.net | 5704cb2d346cad16ac4b65af62fec5b47fe1bff9 | 96c485f660fa764387d34d7f41ece3e464a93ed8 | 008df7525ab4b632ad0db0fe0ce57e3e4f165992 | refs/heads/master | 2021-01-10T16:03:53.544089 | 2016-02-07T20:35:33 | 2016-02-07T20:35:33 | 51,265,034 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.613095223903656,
"alphanum_fraction": 0.6607142686843872,
"avg_line_length": 27,
"blob_id": "22e0d09829794a639e51d4af1223edccf6f735c6",
"content_id": "ab225546731fc6d1ee90a0a05908bfac12cbd8e5",
"detected_licenses": [],
"is_generated": false,
"is_vendor": false,
... | 1 |
kanoekakihito/tidy_mhlw_data | https://github.com/kanoekakihito/tidy_mhlw_data | a846e43dd70cccca333e26d9fe79e79886ecb8bc | 013ae1b2e034799e9e29bb78942d29170e6bdebc | c9cf111e2c714ff8bec8710d87ee6e11964896c5 | refs/heads/main | 2023-06-09T19:50:04.982167 | 2021-07-01T04:51:34 | 2021-07-01T04:51:34 | 370,073,637 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.5846201777458191,
"alphanum_fraction": 0.6170644164085388,
"avg_line_length": 31.74210548400879,
"blob_id": "d41c3aea25e8af3883791317e2dd0b7be28895ae",
"content_id": "5b3458431bd52fce7c54358abcca9d7f9f617eb3",
"detected_licenses": [],
"is_generated": false,
"is_... | 1 |
rums1605/Home-Automation-Product | https://github.com/rums1605/Home-Automation-Product | 14d0c40c1c231506d252c40aa739c884dabfafdb | 1d4fe72449a30dda993563a179ab4f6fe66a08ba | 1ebfd68a5f975046e9bb373ac5913f125780e10c | refs/heads/master | 2020-12-03T04:13:05.161044 | 2017-06-30T01:04:47 | 2017-06-30T01:04:47 | 95,833,302 | 0 | 0 | null | null | null | null | null | [
{
"alpha_fraction": 0.4499332308769226,
"alphanum_fraction": 0.47396528720855713,
"avg_line_length": 12.814814567565918,
"blob_id": "48b83a8df2902c08fbeb6a14b99a11fa2905cbe7",
"content_id": "c85ce6bf745623fe7a1f37c029c3e84cb031826f",
"detected_licenses": [],
"is_generated": false,
"i... | 3 |