-
Notifications
You must be signed in to change notification settings - Fork 2
/
cloud_in_ims.py
executable file
·208 lines (173 loc) · 9.38 KB
/
cloud_in_ims.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
# -*-- encoding=utf-8 --*-
import pandas as pd
import xlsxwriter
import os
import platform
from pandas import ExcelWriter
from util import main_function,plot_trend
def clean_data(df_csv_datas):
'''
数据清洗
'''
row_length_before = df_csv_datas.shape[0]
print('\t清理--清除一些用不到的字段')
df_csv_datas = df_csv_datas.drop(['emmcid','上报时间','异常进程名','进程版本名','进程版本号','异常进程包名',
'软件系统类型','国家','异常类型','MBN版本信息','异常次数','日志路径'],axis=1)
df_csv_datas = df_csv_datas.fillna('-1')
print('\t地区码--只保留中国')
df_csv_datas = df_csv_datas.loc[df_csv_datas['地区码'] == 'china']
df_csv_datas = df_csv_datas.drop(['地区码'],axis=1)
print('\t运营商--移除测试的PLMN')
fp = open(os.path.join(os.path.abspath('.'),'config','remove_test_plmn.txt'),'r')
test_plmn_list = [plmn.strip() for plmn in fp.readlines()]
df_csv_datas = df_csv_datas[-df_csv_datas['运营商'].isin(test_plmn_list)]
fp.close()
print('\tIMEI--移除测试的IMEI')
fp = open(os.path.join(os.path.abspath('.'),'config','remove_test_imei.txt'),'r')
test_imei_list = [imei.strip() for imei in fp.readlines()]
df_csv_datas = df_csv_datas[-df_csv_datas['imei'].isin(test_imei_list)]
fp.close()
print('\t处理一些处理异常的字段')
df_csv_datas['省直辖市'] = df_csv_datas['省/直辖市']
df_csv_datas = df_csv_datas.drop(['省/直辖市'],axis=1)
df_csv_datas['县区'] = df_csv_datas['县/区']
df_csv_datas = df_csv_datas.drop(['县/区'],axis=1)
print('\t字段转义')
df_csv_datas['机型'] = df_csv_datas['外部机型'].str.cat(df_csv_datas['内部机型'],sep='/')
df_csv_datas = df_csv_datas.drop(['外部机型','内部机型'],axis=1)
print('\t发生时间--提取发生的小时')
df_csv_datas['发生时间1'] = pd.to_datetime(df_csv_datas['发生时间'],infer_datetime_format=True)
df_csv_datas['发生时间h'] = df_csv_datas['发生时间1'].apply(get_hour)
df_csv_datas = df_csv_datas.drop(['发生时间','发生时间1'],axis=1)
df_csv_datas['ImsRat'] = df_csv_datas['保留字段一']
df_csv_datas = df_csv_datas.drop(['保留字段一'],axis=1)
df_csv_datas['ExtraCode_ErroMSG'] = df_csv_datas['保留字段二']
df_csv_datas['ExtraCode'] = df_csv_datas['保留字段二'].apply(get_ExtraCode)
df_csv_datas['ErroMSG'] = df_csv_datas['保留字段二'].apply(get_ErroMSG)
df_csv_datas = df_csv_datas.drop(['保留字段二'],axis=1)
df_csv_datas['RSRP'] = df_csv_datas['log信息'].apply(get_rsrp)
df_csv_datas['RSRQ'] = df_csv_datas['log信息'].apply(get_rsrq)
df_csv_datas = df_csv_datas.drop(['log信息'],axis=1)
df_csv_datas['运营商1'] = df_csv_datas['运营商'].apply(get_plmn1)
df_csv_datas['运营商2'] = df_csv_datas['运营商'].apply(get_plmn2)
df_csv_datas = df_csv_datas.drop(['运营商'],axis=1)
print('\t合并一些共同分析的字段')
df_csv_datas['开始基站位置'] = df_csv_datas['运营商2'].str.cat(df_csv_datas['起呼位置码'],sep='/').str.cat(df_csv_datas['起呼基站编号'],sep='/')
df_csv_datas['结束基站位置'] = df_csv_datas['运营商2'].str.cat(df_csv_datas['结束位置码'],sep='/').str.cat(df_csv_datas['结束基站编号'],sep='/')
df_csv_datas = df_csv_datas.drop(['起呼位置码','起呼基站编号'],axis=1)
df_csv_datas = df_csv_datas.drop(['结束位置码','结束基站编号'],axis=1)
df_csv_datas['电话网络']=df_csv_datas['起呼电话网络'].str.cat(df_csv_datas['结束电话网络'],sep='/')
df_csv_datas['数据网络']=df_csv_datas['开始数据网络'].str.cat(df_csv_datas['结束数据网络'],sep='/')
df_csv_datas['网络']=df_csv_datas['电话网络'].str.cat(df_csv_datas['数据网络'],sep='/')
df_csv_datas = df_csv_datas.drop(['起呼电话网络','结束电话网络'],axis=1)
df_csv_datas = df_csv_datas.drop(['开始数据网络','结束数据网络'],axis=1)
df_csv_datas['运营商_电话网络'] = df_csv_datas['运营商2'].str.cat(df_csv_datas['电话网络'], sep='/')
df_csv_datas['运营商_数据网络'] = df_csv_datas['运营商2'].str.cat(df_csv_datas['数据网络'], sep='/')
df_csv_datas['运营商_网络'] = df_csv_datas['运营商2'].str.cat(df_csv_datas['网络'], sep='/')
df_csv_datas['运营商_网络_原因'] = df_csv_datas['运营商_网络'].str.cat(df_csv_datas['ExtraCode'], sep='/')
df_csv_datas['省市'] = df_csv_datas['省直辖市'].str.cat(df_csv_datas['市'],sep='/')
df_csv_datas['省市县区'] = df_csv_datas['省市'].str.cat(df_csv_datas['县区'],sep='/')
df_csv_datas = df_csv_datas.drop(['市'],axis=1)
df_csv_datas = df_csv_datas.drop(['县区'],axis=1)
row_length_after = df_csv_datas.shape[0]
print('\t数据清洗前后的数量='+str(row_length_after)+'/'+str(row_length_before)+',数据清洗比率='+str(row_length_after*100/row_length_before)+'%')
return df_csv_datas
def get_rsrp(name):
min_rsrp = -1
min_rsrq = -1
if('RSRP' in name and 'RSRQ' in name and ';' in name):
rsrp_rsrq_list = [rsrp_rsrq for rsrp_rsrq in name.split(';')]
for item in rsrp_rsrq_list:
rsrp_str = item.split(' ')[0][len('RSRP='):]
rsrq_str = item.split(' ')[-1]
try:
rsrp = eval(rsrp_str)
rsrq = eval(rsrq_str)
except:
rsrp = -1
rsrq = -1
if(rsrp < min_rsrp):
min_rsrp = rsrp
if(rsrq < min_rsrq):
min_rsrq = rsrq
return int(min_rsrp/5)*5
def get_rsrq(name):
min_rsrp = -1
min_rsrq = -1
if('RSRP' in name and 'RSRQ' in name and ';' in name):
rsrp_rsrq_list = [rsrp_rsrq for rsrp_rsrq in name.split(';')]
for item in rsrp_rsrq_list:
rsrp_str = item.split(' ')[0][len('RSRP='):]
rsrq_str = item.split(' ')[-1]
try:
rsrp = eval(rsrp_str)
rsrq = eval(rsrq_str)
except:
rsrp = -1
rsrq = -1
if(rsrp < min_rsrp):
min_rsrp = rsrp
if(rsrq < min_rsrq):
min_rsrq = rsrq
return int(min_rsrq)
def get_ImsRat(name):
if(';' in name):
return name.split(';')[0]
else:
return '-1'
def get_ExtraCode(name):
if(';' in name):
return name.split(';')[0]
else:
return '-1'
def get_ErroMSG(name):
if(';' in name):
return name.split(';')[1]
else:
return '-1'
def get_plmn1(name):
try:
plmn = ''.join(name.split(' ')[:-1])
if plmn == '':
return '-1'
else:
return plmn
return
except:
return '-1'
def get_plmn2(name):
try:
plmn = name.split(' ')[-1]
if plmn == '' or plmn == '-1':
return '-1'
else:
return plmn
except:
return '-1'
def get_hour(name):
returnName=name.to_pydatetime().hour
return returnName
def cloud_in_ims_main(path_raw_data,path_result):
main_function('云诊断内销IMS', path_raw_data, path_result, clean_data)
def cloud_in_ims_plot_trend(path_raw_data,path_result):
sheet_name_list=['SIM卡', '失败原因', '呼入或呼出', '运营商', '电话网络', '发生时间h', '机型', '系统版本', 'PLMN_CS']
trend_dics_list={}
trend_dics_list['出现异常的卡']=['卡1','卡2']
trend_dics_list['ExtraCode']=['ExtraCode=0','ExtraCode=804','ExtraCode=404','ExtraCode=805','ExtraCode=408','ExtraCode=480','ExtraCode=403','ExtraCode=500','ExtraCode=401','ExtraCode=503','ExtraCode=400']
trend_dics_list['失败原因']=['1000','5','2','4']
trend_dics_list['isim支持情况']=['ISIM=0;DUT=1','ISIM=0;DUT=0','ISIM=1;DUT=1','ISIM=1;DUT=0']
trend_dics_list['运营商2'] = ['46000','46001','46019','46011','46003']
trend_dics_list['VOLTE配置信息']=['CFG=3;ON=1','CFG=1;ON=1','CFG=1;ON=0']
trend_dics_list['机型']=['PD1710/X20Plus','PD1709/X20A','PD1708/Y79A','PD1635/X9sPlus','PD1616B/X9s','PD1610/Xplay6','PD1619/X9Plus','PD1624/X9i','PD1616/X9']
trend_dics_list['IMEI频次'] = ['PD1710/X20Plus','PD1709/X20A','PD1708/Y79A','PD1635/X9sPlus','PD1616B/X9s','PD1610/Xplay6','PD1619/X9Plus','PD1624/X9i','PD1616/X9','PD1718/PD1718','PD1621B/PD1621B']
trend_dics_list['省直辖市']=['广东省','河南省','甘肃省','江苏省','河北省','山西省','浙江省','新疆维吾尔自治区',
'广西壮族自治区','安徽省','山东省','福建省','湖南省','贵州省','陕西省','云南省',
'黑龙江省','四川省','吉林省','辽宁省','湖北省','内蒙古自治区','宁夏回族自治区',
'北京市','上海市','江西省','重庆市','青海省','海南省','天津市','西藏自治区']
plot_trend('云诊断内销IMS', path_raw_data, path_result, trend_dics_list)
if __name__ == '__main__':
path1=os.path.abspath('/opt/vivo-home/bigdata_cloud_datas/cloud_in_ims_rawdata/weeks')
path2=os.path.abspath('/opt/vivo-home/bigdata_cloud_datas/cloud_in_ims_resultdata/weeks')
cloud_in_callfail_main(path1,path2)
path3=os.path.join('/opt/vivo-home/bigdata_cloud_datas/cloud_in_ims_resultdata', '云诊断内销IMS周趋势.xlsx')
cloud_in_call_fail_plot_trend(path2,path3)