def GetData(coll_prefix):
fdata = []
for date, value in coll_prefix.items():
if ("traffic" in value):
traflist = value["traffic"]
else:
traflist = [""]
if ("ID" in value):
IDlist = value["ID"]
else:
IDlist = [""]
for t in traflist:
for i in IDlist:
W_coll = date + t + i
if (W_coll in coll_prefix["Exception"]):
continue
W_coll = W_coll + '-ProcessData'
mW = mongodb_api(user='******',
pwd='ubuntu',
database='wifi_diagnosis',
collection=W_coll)
found = mW.find(key_value={}, ftype='many')
c_print(W_coll + ' : ' + str(len(found)))
fdata = fdata + found
return fdata
SkipFirstN = 0
iptable = {
"AP": "10.144.24.24",
"STA": "10.144.24.23",
"10.144.24.24": "AP",
"10.144.24.23": "STA"
}
R_coll = date + '-TestData'
W_coll = date + '-ProcessData'
ML_coll = date + '-MLData'
ML_coll_pair = date + 'MLData-Pair'
mR = mongodb_api(user='******',
pwd='ubuntu',
database='wifi_diagnosis',
collection=R_coll)
mW = mongodb_api(user='******',
pwd='ubuntu',
database='wifi_diagnosis',
collection=W_coll)
mdb_ML = mongodb_api(user='******',
pwd='ubuntu',
database='wifi_diagnosis',
collection=ML_coll)
mdb_ML_pair = mongodb_api(user='******',
pwd='ubuntu',
database='wifi_diagnosis',
collection=ML_coll_pair)
# sorted_data = Testdata_sort(mR)
scanlist.append(tmp)
new_data_payload["Scan"] = scanlist
return new_data_payload
#new_data_payload["Time"] = Time
#return new_data_payload
if __name__ == '__main__':
date='1070207small-t1'
R_coll = date + '-RawData'
W_coll = date + '-TestData'
m = mongodb_api(user='******', pwd='ubuntu', database='wifi_diagnosis', collection=R_coll)
# find_reg = {"Topic":"CwmData/DumpAth9kAni"}
find_reg = {}
found_data = m.find(key_value = find_reg, ftype='many')
print(len(found_data))
m = mongodb_api(user='******', pwd='ubuntu', database='wifi_diagnosis', collection=W_coll)
m.remove(key_value = {}, justone=False)
casetype = "Start"
for data in found_data:
out, casetype = data_process(data,casetype)
import mongodb_api as db
import pandas as pd
import sys
import numpy as np
from sklearn.svm import SVR
from matplotlib import pyplot as plt
m = db.mongodb_api(user='******', pwd='ubuntu', collection="TestData1213")
found_data = m.find(ftype='many')
_found_data = {}
idx = 0
survey_dict = {
"survey_data": [],
"survey_mean": [],
"survey_std": [],
"survey_FER": []
}
spectralscan_data_dict = {
"Spectralscan_data": [],
"Spectralscan_mean": [],
"Spectralscan_std": [],
"Spectralscan_FER": []
}
survey_label = []
Spectralscan_label = []
survey_station_info = []
device = 'AP'
AP_data = []
for i in range(len(db_collections['collection'])):
collection_name = db_collections['collection'][i]
for j in range(db_collections['number'][i]):
if j==0:
db_collection = db_collections['collection'][i] + '-ProcessData'
else:
db_collection = db_collections['collection'][i] + '-'+ str(j+1) + '-ProcessData'
mW = db.mongodb_api(user='******', pwd='ubuntu', database='wifi_diagnosis',collection=db_collection)
fdata = mW.find(key_value = {}, ftype='many')
for k in range(len(fdata)):
AP_data.append(pd.Series(fdata[k]['AP']))
#tmp = pd.Series.transpose(tmp)
AP_data = pd.concat(AP_data, axis=1).transpose()
#Drop data with no delay values
AP_data = AP_data[AP_data.astype(str)['Delay']!='[]'].reset_index(drop='True')
AP_data = AP_data[AP_data.astype(str)['SS_Subval']!='[]'].reset_index(drop='True')
return train_data
output_folder = '../data/ProcessData1070208'
collections = ['1070222-clear-ProcessData']
"""
collections = ['1070208small-t1', '1070208small-t1-2',
'1070208small-t2', '1070208small-t2-2',
'1070208small-t3', '1070208small-t3-2'
]
"""
for c in collections:
m = db.mongodb_api(user='******', pwd='ubuntu', database='wifi_diagnosis', collection=c+'-MLData')
found_data = m.find(ftype='many')
output_file = c +'.h5'
output_path = os.path.join(output_folder,output_file)
if not os.path.isdir(output_folder):
os.mkdir(output_folder)
time_step = 1
time_stride = 1
train_data = gnerate_data_and_label(found_data, data_dict)
processed_data = pd.DataFrame.from_dict(train_data)
processed_data.to_hdf(output_path, 'raw_data', mode='w')
#label
"Ping_mean":[],
"Ping_std":[],
"FER":[],
}
train_data = {'AP':copy.deepcopy(data_dict), 'STA':copy.deepcopy(data_dict)}
test_data = {'AP':copy.deepcopy(data_dict), 'STA':copy.deepcopy(data_dict)}
devices_type = ['AP','STA']
#m = db.mongodb_api(user='******', pwd='ubuntu', collection="ProcessData1061228")
m = db.mongodb_api(user='******', pwd='ubuntu', collection="1070208small-t1")
found_data = m.find(ftype='many')
output_folder = '../data/ProcessData1070208'
output_file = 'training_data_mid_5.h5'
output_path = os.path.join(output_folder,output_file)
if not os.path.isdir(output_folder):
os.mkdir(output_folder)
training_portion = 0.0 #Portion for training data
time_step = 1
time_stride = 1
train_pairs = []
test_pairs = []
if rawdata[key] != test[key]: return False
return True
def test(self):
for idx in range(len(self.found_data)):
self.issucess = self.sucess_create_data(idx) and self.issucess
return self.issucess
#Init mogodb connection and find data
m = db.mongodb_api(user='******',
pwd='ubuntu',
collection='Fingerprint_171101')
found_data = m.find(ftype='many')
"""
#Simple function test
test = test_create_data(found_data)
print("Data test Result: ", test)
"""
#Create data dimensions to fix input dimensions
#Create label table for one got encode
key_pair = []
label_pair = {}
label_count = 0
for i in range(len(found_data)):
for key in found_data[i]["key"].keys():