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Team.py
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import xlwt
from header import header, json, requests, os
# from PlayersSeansonStatistics import PlayerSeasonStatics
from Team_of_AllPlayers import Team_of_AllPlayer
import time
# from PlayerLifeStatics import PlayerLifeStatics
# from pubg_python import PUBG, Shard
# 框架：获取自身ID,获取队友ID及战绩，再获取所有对局成员的ID及战绩
# api = PUBG("Bearer eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJqdGkiOiJhMmZiNjI1MC0wZDQzLTAxMzgtNzE2NS0yZGY4YzdjNjQ2ZmYiLCJpc3MiOiJnYW1lbG9ja2VyIiwiaWF0IjoxNTc3NzE4Mjg0LCJwdWIiOiJibHVlaG9sZSIsInRpdGxlIjoicHViZyIsImFwcCI6ImE3Mzg1MzE1ODItZ21hIn0.ZqMmAbzh-nQbCWUuUuy2I86_CGKqGWyHZvZxVhDe4F0", Shard.PC_AS)
# 获取玩家比赛数据
# players = api.players().filter(player_names=['wan_zimu'])
All_Average_Rank = [] # 存储一位玩家最近14天所有玩家的分段
All_Average_Damage = [] # 存储一位玩家最近14天所有玩家的场伤
All_Average_Kill = [] # 存储一位玩家最近14天所有玩家的击杀
All_Average_Damages_of_Team = [] # 存储一位玩家最近14天所有对局的所有队伍平均场伤，一行表示一队平均场伤
All_Matches=[]#存储一位玩家最近14天所有玩家的本赛季游戏场数
print("绝地求生组队分析小工具 作者：小黑盒ID:10351265,QQ群：201359150")
print("请输入你的ID,区分大小写，确认按回车键")
# 获取Matches的数据并写入Player_jsondata_txt
#
# 此处要做输入错误校准
#
while (1):
player_name = input()
print("请输入组队的模式，四排TPP输入数字1，四排FPP输入数字2,双排TPP输入数字3，双排FPP输入数字4")
while (1):
Player_Model = int(input())
if Player_Model == 1 or Player_Model == 2 or Player_Model == 3 or Player_Model == 4:
# print("输入游戏的时间")
# Play_Time = input()
break
else:
print("输入不对，请重新输入")
# 拼凑链接
url = 'https://api.pubg.com/shards/steam/players?filter[playerNames]=' + player_name
# url = 'https://api.pubg.com/shards/steam/players?filter[playerNames]=NiWeihanii'
# url = 'https://api.pubg.com/shards/steam/players?filter[playerIds]=wan_zimu'
# 获取玩家Matche数据
Player = requests.get(url, headers=header)
Player_jsondata_requestCode = Player.status_code
print(Player_jsondata_requestCode)
if (Player_jsondata_requestCode == 200):
break
if (Player_jsondata_requestCode == 429):
print("向服务器请求次数太多，请等30后再输入ID")
print("输入ID不正确，请重新输入")
print("输入成功,请等待")
print("开始获取玩家数据")
# 打开与关闭文件Player_jsondata_txt
# PUBG的JSON中NONE需要改为"NONE"才能用JSON解析工具打开
Player_jsondata_txt = open(os.getcwd() + '\Player_jsondata.txt', 'w+', encoding="utf-8")
Player_jsondata = json.loads(Player.text)
print(Player_jsondata, file=Player_jsondata_txt) # 可以使用write的形式，但是write只能写入字符串，不能写入JSON格式，导致不易理解
Player_jsondata_txt.close()
Player_jsondata_txt_read = open(os.getcwd() + '\Player_jsondata.txt', 'r', encoding="utf-8")
Player_NONE = Player_jsondata_txt_read.read() # Player_NONE是需要替换的NONE
t = Player_NONE.replace("None", '"None"') # 注意要转义
Player_jsondata_txt_write = open(os.getcwd() + '\Player_jsondata.txt', 'w+', encoding="utf-8")
print(t, file=Player_jsondata_txt_write) # 可以使用write的形式，但是write只能写入字符串，不能写入JSON格式，导致不易理解
Player_jsondata_txt_read.close()
Player_jsondata_txt_write.close()
print(t)
# print输出到txt
# 使用for获取14天所有比赛的MatchID，并获取每场比赛的详细数据
MatchID = Player_jsondata['data'][0]['id'] # 对中括号部分读取数据需要使用[0]
print("玩家单场比赛ID获取成功")
# 获取比赛信息
# 根据玩家ID查询比赛的ID
MatchID = [(m['id']) for m in Player_jsondata['data'][0]['relationships']['matches']['data']] # 进行for循环
# 获得某人批量的匹配URL
MatchURL = [('https://api.pubg.com/shards/steam/matches/' + MatchIDURl) for MatchIDURl in MatchID]
print('一共有' + str(len(MatchURL)) + '场数据需要处理')
count_match = 0 # 数正在处理第几场比赛用
# 批量进行请求比赛数据
for matchurl in MatchURL:
Match = requests.get(matchurl, headers=header)
# PUBG的JSON中NONE需要改为"NONE"才能用JSON解析工具打开,但是不处理不影响代码的读取
# 打开与关闭文件Player_jsondata_txt
Match_jsondata_txt = open(os.getcwd() + '\Match_jsondata.txt', 'w+', encoding="utf-8")
Match_jsondata = json.loads(Match.text)
print(Match_jsondata, file=Match_jsondata_txt) # 可以使用write的形式，但是write只能写入字符串，不能写入JSON格式，导致不易理解
Match_jsondata_txt.close()
# 先保存文本，保存后用对象1读取，再用对象2保存，不能用同一个对象
Match_jsondata_txt_read = open(os.getcwd() + '\Match_jsondata.txt', 'r', encoding="utf-8")
Match_NONE = Match_jsondata_txt_read.read() # Match_NONE是需要替换的NONE
t = Match_NONE.replace("None", "\"None\"") # 注意要转义
Match_jsondata_txt_write = open(os.getcwd() + '\Match_jsondata.txt', 'w+', encoding="utf-8")
print(t, file=Match_jsondata_txt_write) # 可以使用write的形式，但是write只能写入字符串，不能写入JSON格式，导致不易理解
Match_jsondata_txt_read.close()
Match_jsondata_txt_write.close()
#print(t)
# print输出到txt
Match_jsondata_txt.close()
# 由于Include字段包括资源以及其他干扰项，因此需要将其分离
PlayersNames = [] #
PlayerIDs = [] # 一场比赛中所有玩家ID是一个数组
Player_Win_Places = [] # 一场比赛中所有玩家的排名，与PlayerIDs一一对应
PlayerCount = 0
# 在循环时不仅获取玩家的ID、名称、还获取玩家排名WinPlace
for Include in Match_jsondata['included']:
# print(Include)
if str(Include['type']) == 'participant':
PlayersNames.append(Include['attributes']['stats']['name'])
PlayerIDs.append(Include['attributes']['stats']['playerId'])
Player_Win_Places.append(Include['attributes']['stats']['winPlace'])
# AllMatches.append(Include['attributes']['stats']['roundsPlayed'])
PlayerCount += 1
# PlayerIDs.append(str(Include['type']))
# print(PlayerIDs)
# 获取某场比赛中所有玩家ID成功，需要将玩家ID、KD、分段、所有比赛场次等写入EXCEL中分析
# PlayerSeasonStatics(PlayerIDs,str(player_names),count_match,Player_Model)
# 只获取最近X场数据，由count_match控制
if count_match < 1:
# 统计比赛场次
count_match += 1
# 清空接受队列
Average_Rank = []
Average_Damage = []
Average_Kill = []
Average_Damages_of_Team = []
AllMatches=[]
#调用外部文件的方法对玩家赛季数据进行处理
(Average_Rank, Average_Damage, Average_Kill, Average_Damages_of_Team,AllMatches) = Team_of_AllPlayer(PlayerIDs,
Player_Win_Places,
str(player_name),
count_match,
Player_Model)
All_Average_Rank.append(Average_Rank)
All_Average_Damage.append(Average_Damage)
All_Average_Kill.append(Average_Kill)
All_Average_Damages_of_Team.append(Average_Damages_of_Team)
All_Matches.append(AllMatches)
print('本场比赛玩家数量：' + str(PlayerCount))
print(PlayersNames)
print(All_Average_Rank)
print(All_Average_Damage)
print(All_Average_Kill)
print(All_Average_Damages_of_Team)
# 列表写入的excel文件output_excel
# output_All_Average_Rank_excel=(os.getcwd()+'/'+player_name+'的分段表'+time.strftime('%Y-%m-%d',time.localtime(time.time()))+'.xls')
# output_All_Average_Damage_excel = (os.getcwd() + '/' + player_name + '的分段表' + time.strftime('%Y-%m-%d', time.localtime(time.time())) + '.xls')
# output_All_Average_Kill_excel=(os.getcwd()+'/'+player_name+'的分段表'+time.strftime('%Y-%m-%d',time.localtime(time.time()))+'.xls')
# output_All_Average_Damages_of_Team_excel=(os.getcwd()+'/'+player_name+'的分段表'+time.strftime('%Y-%m-%d',time.localtime(time.time()))+'.xls')
output_Data_excel = (os.getcwd() + '/' + player_name + '的匹配数据' + time.strftime('%Y-%m-%d', time.localtime(
time.time())) + '.xls')
wb = xlwt.Workbook(encoding='utf-8')
Rank_SheetName = '分段'
Damage_SheetName = '个人场伤'
Kill_SheetName = 'KD'
Damages_of_Team_SheetName = '队伍平均场伤'
AllMatches_SheetName='本赛季游戏场数'
#用sort方法让数据从小到大排列
Rank_Sort_SheetName = '分段顺序'
Damage_Sort_SheetName = '个人场伤顺序'
Kill_Sort_SheetName = 'KD顺序'
Damages_of_Team_Sort_SheetName = '队伍平均场伤顺序'
All_Matches_Sort_SheetName = '本赛季游戏场数顺序'
# 添加10张表
Rank_Excel = wb.add_sheet(Rank_SheetName)
Damage_Excel = wb.add_sheet(Damage_SheetName)
Kill_Excel = wb.add_sheet(Kill_SheetName)
Damages_of_Team_Excel = wb.add_sheet(Damages_of_Team_SheetName)
All_Matches_Excel=wb.add_sheet(AllMatches_SheetName)
Rank_Sort_Excel = wb.add_sheet(Rank_Sort_SheetName)
Damage_Sort_Excel = wb.add_sheet(Damage_Sort_SheetName)
Kill_Sort_Excel = wb.add_sheet(Kill_Sort_SheetName)
Damages_of_Team_Sort_Excel = wb.add_sheet(Damages_of_Team_Sort_SheetName)
All_Matches_Sort_Excel = wb.add_sheet(All_Matches_Sort_SheetName)
row = 0
col = 0
lines = 0; # 第几行
# 写入RANK数据
for row in range(len(All_Average_Rank)): # 读取行循环再列循环
for col in range(len(All_Average_Rank[row])): # 行列交换转置方便分析
Rank_Excel.write(col, row, All_Average_Rank[row][col])
# 写入Damage数据
for row in range(len(All_Average_Damage)): # 读取行循环再列循环
for col in range(len(All_Average_Damage[row])): # 行列交换转置方便分析
Damage_Excel.write(col, row, All_Average_Damage[row][col])
# 写入kd数据
for row in range(len(All_Average_Kill)): # 读取行循环再列循环
for col in range(len(All_Average_Kill[row])): # 行列交换转置方便分析
Kill_Excel.write(col, row, All_Average_Kill[row][col])
# 写入队伍平均伤害数据
for row in range(len(All_Average_Damages_of_Team)): # 读取行循环再列循环
for col in range(len(All_Average_Damages_of_Team[row])): # 行列交换转置方便分析
Damages_of_Team_Excel.write(col, row,
All_Average_Damages_of_Team[row][col])
# 写入游戏场数
for row in range(len(All_Matches)): # 读取行循环再列循环
for col in range(len(All_Matches[row])): # 行列交换转置方便分析
All_Matches_Excel.write(col, row,All_Matches[row][col])
#进行sort排序，排序后列表名称一样
# All_Average_Rank.sort()
# All_Average_Damage.sort()
# All_Average_Kill.sort()
# All_Average_Damages_of_Team.sort()
# All_Matches.sort()
# 写入RANK数据顺序
for row in range(len(All_Average_Rank)): # 读取行循环再列循环
All_Average_Rank[row].sort()
for col in range(len(All_Average_Rank[row])): # 行列交换转置方便分析
Rank_Sort_Excel.write(col, row, All_Average_Rank[row][col])
# 写入Damage数据顺序
for row in range(len(All_Average_Damage)): # 读取行循环再列循环
All_Average_Damage[row].sort()
for col in range(len(All_Average_Damage[row])): # 行列交换转置方便分析
Damage_Sort_Excel.write(col, row, All_Average_Damage[row][col])
# 写入kd数据顺序
for row in range(len(All_Average_Kill)): # 读取行循环再列循环
All_Average_Kill[row].sort()
for col in range(len(All_Average_Kill[row])): # 行列交换转置方便分析
Kill_Sort_Excel.write(col, row, All_Average_Kill[row][col])
# 写入队伍平均伤害数据顺序
for row in range(len(All_Average_Damages_of_Team)): # 读取行循环再列循环
All_Average_Damages_of_Team[row].sort()
for col in range(len(All_Average_Damages_of_Team[row])): # 行列交换转置方便分析
Damages_of_Team_Sort_Excel.write(col, row,
All_Average_Damages_of_Team[row][col])
# 写入游戏场数顺序
for row in range(len(All_Matches)): # 读取行循环再列循环
All_Matches[row].sort()
for col in range(len(All_Matches[row])): # 行列交换转置方便分析
All_Matches_Sort_Excel.write(col, row, All_Matches[row][col])
# 关闭文件
wb.save(output_Data_excel)
#后面不再执行
else:
print("玩家数据获取完成，请在本软件目录下查看Excle文件")
break