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import numpy as np
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import time
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from math import *
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from collections import Counter
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from calendar import Calendar
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import datetime
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import pickle
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from flask import Flask, jsonify
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app = Flask(__name__)
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@app.route('/api/xiuos/entropy', methods=['GET'])
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def get_dict1():
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return dictLst[0]
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@app.route('/api/openharmony/entropy', methods=['GET'])
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def get_dict2():
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return dictLst[1]
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@app.route('/api/openeuler/entropy', methods=['GET'])
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def get_dict3():
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return dictLst[2]
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#from elasticsearch import Elasticsearch
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#es = Elasticsearch(['http://106.75.10.84:9200'])
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##def get_eventLst(repo):
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## ###获取repo的所有event
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## frm = 1
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## sz = 100
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## ##查询从frm开始的sz个event
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## eventLst = []
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## while 1:
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## query={"query":{"bool":{"must":[{"match":{"search_fields.repo":repo}},]}},'from':frm,'size':sz}
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## value = es.search(index="github_event_raw",body=query,_source=['data'])
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## if value['hits']['hits'] == []:
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## break
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## else:
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## frm += sz
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## for i in value['hits']['hits']:
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## item = {}
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## item['issue_number'] = i['_source']['data']['issue']['number']
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## item['event'] = i['_source']['data']['eventType']
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## item['create_at'] = i['_source']['data']['createdAt']
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## eventLst.append(item)
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## break
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## return eventLst
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def get_eventLst(repo):
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with open(repo+'.pkl','rb') as f:
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eventLst = list(pickle.load(f))
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return eventLst
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def filterEvent(startTime,endTime):
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filterEventLst = []
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for event in eventLst:
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if event['createdAt'] > startTime and event['createdAt'] < endTime:
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filterEventLst.append(event)
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issue2eventCount = {}
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for i in filterEventLst:
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if issue2eventCount.get(i['issue_number']):
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if issue2eventCount[i['issue_number']].get(i['eventType']):
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issue2eventCount[i['issue_number']][i['eventType']] += 1
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else:
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issue2eventCount[i['issue_number']][i['eventType']] = 1
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else:
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issue2eventCount[i['issue_number']]={i['eventType']:1}
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return issue2eventCount
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def day2timeStamp(string):
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if len(string) == 10:
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return int(time.mktime(time.strptime(string,"%Y-%m-%d")))
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elif len(string) == 20:
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return int(time.mktime(time.strptime(string,"%Y-%m-%dT%H:%M:%SZ")))
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else:
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return -1
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#计算熵的公式
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def lstToEntropy(lst):
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result = 0
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for i in lst:
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pi = i/sum(lst)
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result += -pi*log(pi,2)
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return result*sum(lst)
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#针对整理好的计算熵的数据，求每个时间段的熵，输出x、y，即时间和熵
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#input time2issuesEvent：{time:{issue_number:{event：count}}}
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#output {x:y}
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def dataToEntropy(time2issuesEvent):
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x = time2issuesEvent.keys()
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result = {}
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for i in x:
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issue2event2count = time2issuesEvent[i]
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if issue2event2count:
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tmpResult = 0
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for item in issue2event2count.values():
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tmpResult += lstToEntropy(list(item.values()) + [1])
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result[i] = tmpResult
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#result[i] = tmpResult/len(issue2event2count.values())
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else:
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result[i] = 0
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return result
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#汇总
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def timelstToEntropy(timelst):
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time2issuesEvent = {}
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for i in range(len(timelst) - 1):
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time2issuesEvent[timelst[i]] = filterEvent(timelst[i],timelst[i+1])
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result = dataToEntropy(time2issuesEvent)
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return result
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#####################################下面是执行函数#################################
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#计算某一个项目的激发熵随时间变化
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#input repo:str
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#output (x,y):(lst,lst)
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#timelst=['2016-09-29', '2016-09-30']时，计算2016-09-29当天产生的熵
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#timelst=['2016-09-29', '2016-09-30', '2016-10-01']时，计算2016-09-29, 2016-09-30两天产生的熵
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def calEntropy(repo,timelst):
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result = timelstToEntropy(timelst)
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x = list(result.keys())
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y = list(result.values())
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return (x,y)
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def calEntropy(repo,year):
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pass
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##获取计算熵的时间轴
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def get_date():
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c = Calendar()
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dateLst = []
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for year in range(2015,2025):
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for month in range(1,13):
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dateLst += [str(date) for date in c.itermonthdates(year,month)]
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dateLst = list(set(dateLst))
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dateLst.sort()
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return dateLst
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if __name__ == '__main__':
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repos = ['xiuos', 'openharmony', 'openeuler']
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global dictLst
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dictLst = []
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global eventLst
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for repo in repos:
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eventLst = get_eventLst(repo)
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today = str(datetime.date.today())
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min_date = min([i['createdAt'] for i in list(eventLst)])
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dateLst = get_date()
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dateLst = [
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i for i in dateLst
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if min_date < i < today
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]
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timelst = []
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for i in range(len(dateLst)):
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if i % 7 == 5:
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timelst.append(dateLst[i])
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result = timelstToEntropy(timelst)
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formatted_result = {key: round(value, 2) for key, value in result.items()}
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print(repo)
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print(formatted_result)
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dictLst.append(formatted_result)
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app.run(host='0.0.0.0', port=5000)
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