From aa69e89b9372b2d596f90bf37e9ac6613bd47cd6 Mon Sep 17 00:00:00 2001
From: sy00000 <745766988@qq.com>
Date: Thu, 7 Nov 2024 08:49:06 +0800
Subject: [PATCH] ADD file via upload

---
 calEntropyAPI.py | 171 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 171 insertions(+)
 create mode 100644 calEntropyAPI.py

diff --git a/calEntropyAPI.py b/calEntropyAPI.py
new file mode 100644
index 0000000..75c1614
--- /dev/null
+++ b/calEntropyAPI.py
@@ -0,0 +1,171 @@
+import numpy as np
+import time
+from math import *
+from collections import Counter
+from calendar import Calendar
+import datetime
+import pickle
+from flask import Flask, jsonify
+
+app = Flask(__name__)
+
+@app.route('/api/xiuos/entropy', methods=['GET'])
+def get_dict1():
+    return dictLst[0]
+
+@app.route('/api/openharmony/entropy', methods=['GET'])
+def get_dict2():
+    return dictLst[1]
+
+@app.route('/api/openeuler/entropy', methods=['GET'])
+def get_dict3():
+    return dictLst[2]
+
+
+#from elasticsearch import  Elasticsearch
+#es = Elasticsearch(['http://106.75.10.84:9200'])
+
+##def get_eventLst(repo):
+##    ###获取repo的所有event
+##    frm = 1
+##    sz = 100
+##    ##查询从frm开始的sz个event
+##    eventLst = []
+##    while 1:
+##        query={"query":{"bool":{"must":[{"match":{"search_fields.repo":repo}},]}},'from':frm,'size':sz}
+##        value = es.search(index="github_event_raw",body=query,_source=['data'])
+##        if value['hits']['hits'] == []:
+##            break
+##        else:
+##            frm += sz
+##            for i in value['hits']['hits']:
+##                item = {}
+##                item['issue_number'] = i['_source']['data']['issue']['number']
+##                item['event'] = i['_source']['data']['eventType']
+##                item['create_at'] = i['_source']['data']['createdAt']
+##                eventLst.append(item)
+##        break
+##    return eventLst
+
+def get_eventLst(repo):
+    with open(repo+'.pkl','rb') as f:
+        eventLst = list(pickle.load(f))
+    return eventLst
+    
+def filterEvent(startTime,endTime):
+    filterEventLst = []
+    for event in eventLst:
+        if event['createdAt'] > startTime and event['createdAt'] < endTime:
+            filterEventLst.append(event)
+    issue2eventCount = {}
+    for i in filterEventLst:
+        if issue2eventCount.get(i['issue_number']):
+            if issue2eventCount[i['issue_number']].get(i['eventType']):
+                issue2eventCount[i['issue_number']][i['eventType']] += 1
+            else:
+                issue2eventCount[i['issue_number']][i['eventType']] = 1
+        else:
+            issue2eventCount[i['issue_number']]={i['eventType']:1}
+    return issue2eventCount
+
+def day2timeStamp(string):
+    if len(string) == 10:
+        return int(time.mktime(time.strptime(string,"%Y-%m-%d")))
+    elif len(string) == 20:
+        return int(time.mktime(time.strptime(string,"%Y-%m-%dT%H:%M:%SZ")))
+    else:
+        return -1
+
+#计算熵的公式
+def lstToEntropy(lst):
+    result = 0
+    for i in lst:
+        pi = i/sum(lst)
+        result += -pi*log(pi,2)
+    return result*sum(lst)
+
+#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
+#input time2issuesEvent：{time:{issue_number:{event：count}}}
+#output {x:y}
+def dataToEntropy(time2issuesEvent):
+    x = time2issuesEvent.keys()
+    result = {}
+    for i in x:
+        issue2event2count = time2issuesEvent[i]
+        if issue2event2count:
+            tmpResult = 0
+            for item in issue2event2count.values():
+                tmpResult += lstToEntropy(list(item.values()) + [1])
+            result[i] = tmpResult
+            #result[i] = tmpResult/len(issue2event2count.values())
+        else:
+            result[i] = 0
+    return result
+
+#汇总
+def timelstToEntropy(timelst):
+    time2issuesEvent = {}
+    for i in range(len(timelst) - 1):
+        time2issuesEvent[timelst[i]] = filterEvent(timelst[i],timelst[i+1])
+    result = dataToEntropy(time2issuesEvent)
+    return result
+
+#####################################下面是执行函数#################################
+
+#计算某一个项目的激发熵随时间变化
+#input repo:str 
+#output (x,y):(lst,lst)
+#timelst=['2016-09-29', '2016-09-30']时，计算2016-09-29当天产生的熵
+#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
+def calEntropy(repo,timelst):
+    result = timelstToEntropy(timelst)
+    x = list(result.keys())
+    y = list(result.values())
+    return (x,y)
+
+def calEntropy(repo,year):
+    pass
+##获取计算熵的时间轴
+def get_date():
+    c = Calendar()
+    dateLst = []
+    for year in range(2015,2025):
+        for month in range(1,13):
+            dateLst += [str(date) for date in c.itermonthdates(year,month)]
+    dateLst = list(set(dateLst))
+    dateLst.sort()
+    return dateLst
+
+if __name__ == '__main__':
+    repos = ['xiuos', 'openharmony', 'openeuler']
+
+
+    global dictLst
+    dictLst = []
+    global eventLst
+    for repo in repos:
+        eventLst = get_eventLst(repo)
+        today = str(datetime.date.today())
+        min_date = min([i['createdAt'] for i in list(eventLst)])
+        dateLst = get_date()
+        
+        dateLst = [
+        i for i in dateLst 
+        if min_date < i < today
+    ]
+        timelst = []
+        for i in range(len(dateLst)):
+            if i % 7 == 5:
+                timelst.append(dateLst[i])
+
+        result = timelstToEntropy(timelst)
+        formatted_result = {key: round(value, 2) for key, value in result.items()}
+        print(repo)
+        print(formatted_result)
+        dictLst.append(formatted_result)
+    app.run(host='0.0.0.0', port=5000)
+
+
+
+
+