def detect_anomaly( self, filename = '' ):
sys.stdout.write( '[INFO]: start detecting file:%s\n' % (filename) )
#load file
all_text = load_file( self.input_path+filename )
if all_text == False:
sys.stderr.write( "[ERROR]: load input file failed!" )
return
input_list = []
for line in all_text:
try:
inbound_data = json.loads( line.strip() )
events_num = self.get_event_num( inbound_data )
input_list.append(events_num)
except Exception:
sys.stderr.write( '[ERROR]: load json failed! %s' %s (line) )
#sort
input_list.sort(lambda x,y:cmp(x[1],y[1]), reverse=True)
#write result to file
f_write = open( self.output_path + 'res.'+ filename, 'a' )
for item in input_list:
f_write.write('%s,%s\n' % (item[0], item[1]) )
f_write.close()
sys.stdout.write( '[INFO]: done. write result into path:%s\n' % (self.output_path))
def trigger(self):
""" 添加触发事件 """
self.file1.clicked.connect(lambda: tools.choose_file(self.file_path1))
self.file2.clicked.connect(lambda: tools.choose_file(self.file_path2))
self.start.clicked.connect(lambda: tools.load_file(self.file_path1,
self.file_path2,
self.content1,
self.content2,
self.repetition_rate))
def set_localT(self, value):
self._localT = value
## do some other fancy stuff
Box._locaT = value
self.temperature_history.append(value) ## move to chain
for chain in self.chain_list:
chain.temperature_history.append(value)
## load new value of exp
if value in settings.EXP_TABLES:
settings.EXP_TABLE = settings.EXP_TABLES[value]
logging.info('change exp_table %f ' % value)
else:
settings.EXP_TABLE = tools.load_file(settings.ROOT_DIR + \
settings.FILES_TEMPLATE['exp'] % value, {})
settings.EXP_TABLES[value] = settings.EXP_TABLE
logging.info('load new exp_table %f' % value)
logging.info("Set temperature to: %f" % value)
def load_profile(self):
all_text = load_file( self.path )
if all_text == False:
sys.stderr.write( "[ERROR]: load profile failed!\n" )
return False
#load all the contents into tempory dictionary
profile_dict = {}
for line in all_text:
try:
profile = json.loads( line.strip() )
profile_dict[profile['type']] = {'thresholds': profile['thresholds'],
'window':profile['window']}
except Exception:
sys.stderr.write( "[ERROR] load json failed! %s\n" % (line))
if len(profile_dict) > 0:
sys.stdout.write( '[INFO]: load the device profile successfully!\n')
return profile_dict
else:
sys.stderr.write( '[ERROR]: load device profile failed!\n')
return False
import tools
import GSR
import numpy as np
# import matplotlib.pyplot as plt
import windowing as win
filename = "./data/GSR_F01_F.txt"
T1 = 0.75
T2 = 2
MX = 1
DELTA = 0.02
nFS = 16
gsr_data = tools.load_file(filename, header=8, sep=",") # 8 ","
#TODO GAUSSIANA
gsr_data = tools.downsampling(gsr_data, nFS)
# plt.figure(1)
# plt.plot(gsr_data[:,0], gsr_data[:,1])
# plt.xlabel("Time (s)")
# plt.ylabel("GSR (uS)")
# plt.title("Raw GSR")
# t_gsr, gsr = GSR.remove_spikes(gsr_data[:,1], nFS)
t_gsr = gsr_data[:, 0]
gsr = gsr_data[:, 1]
print gsr.shape
# print t_gsr.shape, gsr.shape, gsr_data.shape
t_driver, driver, phasic_d, tonic_d = GSR.estimate_drivers(
t_gsr, gsr, T1, T2, MX, DELTA)
def test_load_file_normal(self):
self.assertEqual( tools.load_file('test_load_file.txt'), ['abcd\n','ef\n'], '#2 test load_file failed' )
def test_load_file_empty(self):
self.assertEqual( tools.load_file('abc'), False, '#1 test load_file failed' )
def load_chat_texts():
patterns = tools.load_file('dialog-ger.md')
patterns.extend(tools.load_file('german-aixml.md'))
patterns.extend(tools.load_file('german-aixml-2.md'))
return patterns
"TIME", "ACCX", "ACCY", "ACCZ", "GYRX", "GYRY", "GYRZ", "MAGX", "MAGY",
"MAGZ", "LAB"
]
col_acc = ["ACCX", "ACCY", "ACCZ"]
col_gyr = ["GYRX", "GYRY", "GYRZ"]
col_mag = ["MAGX", "MAGY", "MAGZ"]
empaticaAccCoeff = 2 * 9.81 / 128
empaticafsamp = 32
sensAccCoeff = 8 * 9.81 / 32768
sensGyrCoeff = 2000 / 32768
sensMagCoeff = 0.007629
sensfsamp = 100
data = tools.load_file(filename, sep=',', header=1)
data = tools.downsampling(data, 50)
t = tools.selectCol(data, columns_in, "TIME")
acc = tools.selectCol(data, columns_in, col_acc)
gyr = tools.selectCol(data, columns_in, col_gyr)
mag = tools.selectCol(data, columns_in, col_mag)
lab = tools.selectCol(data, columns_in, "LAB")
acc = inertial.convert_units(acc, coeff=sensAccCoeff)
gyr = inertial.convert_units(gyr, coeff=sensGyrCoeff)
mag = inertial.convert_units(mag, coeff=sensMagCoeff)
# tools.array_labels_to_csv(np.column_stack([t, acc]), np.array(columns_in), "./output/preproc_"+filename[7:-4]+".csv")
import tools
import GSR
import matplotlib.pyplot as plt
import numpy as np
import windowing as win
filename="./data/GSR.csv"
T1=0.75
T2=2
MX=1
DELTA=0.02
FS=4
nFS=4
gsr_data = tools.load_file(filename, header=1, sep=";") # 8 ","
#TODO GAUSSIANA
# gsr_data=tools.downsampling(gsr_data, FS, nFS)
plt.figure(1)
plt.plot(gsr_data[:,0], gsr_data[:,1])
plt.xlabel("Time (s)")
plt.ylabel("GSR (uS)")
plt.title("Raw GSR")
# t_gsr, gsr = GSR.remove_spikes(gsr_data[:,1], nFS)
t_gsr = gsr_data[:,0]
gsr = gsr_data[:,1]
# print t_gsr.shape, gsr.shape, gsr_data.shape
t_driver, driver, phasic_d, tonic_d= GSR.estimate_drivers(t_gsr, gsr, T1, T2, MX, DELTA, FS=FS)
windows=win.generate_dummy_windows(len(phasic_d), 80, 10)
features = GSR.extract_features(phasic_d, t_driver, DELTA, windows)
columns_in=["TIME", "ACCX","ACCY","ACCZ", "GYRX","GYRY","GYRZ", "MAGX","MAGY","MAGZ", "LAB"] col_acc=["ACCX", "ACCY", "ACCZ"] col_gyr=["GYRX", "GYRY", "GYRZ"] col_mag=["MAGX", "MAGY", "MAGZ"] empaticaAccCoeff=2*9.81/128 empaticafsamp=32 sensAccCoeff=8*9.81/32768 sensGyrCoeff=2000/32768 sensMagCoeff=0.007629 sensfsamp=100 data = tools.load_file(filename, sep=',', header=1) data=tools.downsampling(data, 50) t=tools.selectCol(data, columns_in, "TIME") acc=tools.selectCol(data, columns_in, col_acc) gyr=tools.selectCol(data, columns_in, col_gyr) mag=tools.selectCol(data, columns_in, col_mag) lab=tools.selectCol(data, columns_in, "LAB") acc= inertial.convert_units(acc, coeff=sensAccCoeff) gyr= inertial.convert_units(gyr, coeff=sensGyrCoeff) mag= inertial.convert_units(mag, coeff=sensMagCoeff) # tools.array_labels_to_csv(np.column_stack([t, acc]), np.array(columns_in), "./output/preproc_"+filename[7:-4]+".csv")
def data_loader(flag):
data = load_file(flag)
mg, lg = (4, 4) if len(data) < 4 else (3, 2)
return data, mg, lg