def preproc(data,
cols,
T1=0.75,
T2=2,
MX=1,
DELTA_PEAK=0.02,
k_near=5,
grid_size=5,
s=0.2):
gsr = selectCol(data, cols, "GSR")
t_gsr = selectCol(data, cols, "TIME")
print "T_GSR", t_gsr.shape
try:
labs = selectCol(data, cols, "LAB")
except IndexError as e:
print "NO LAB:", e.message
labs = np.zeros(data.shape[0])
output_cols = ["TIME", "DRV", "PHA", "TNC", "LAB"]
TIME_DRV, DRV, PH_DRV, TN_DRV, labs = estimate_drivers(
t_gsr, gsr, labs, T1, T2, MX, DELTA_PEAK, k_near, grid_size, s)
print "T_DRV", TN_DRV.shape
print "LAB", labs.shape
result = np.column_stack((TIME_DRV, DRV, PH_DRV, TN_DRV, labs))
return result, output_cols
def preproc(data, cols, coeffAcc=1, coeffGyr=1, coeffMag=1):
'''
:param data: the data
:param cols: the olumns in input
:param coeff: the conversion rate
:param data_type: ACC, GYR or MAG
:return: aray and columns
'''
col_acc=["ACCX", "ACCY", "ACCZ"]
col_gyr=["GYRX", "GYRY", "GYRZ"]
col_mag=["MAGX", "MAGY", "MAGZ"]
time=selectCol(data, cols, "TIME")
try:
data_acc=selectCol(data, cols, col_acc)
present_acc=True
except IndexError as e:
print "NO ACC:", e.message
present_acc=False
try:
data_gyr=selectCol(data, cols, col_gyr)
present_gyr=True
except IndexError as e:
print "NO GYR:", e.message
present_gyr=False
try:
data_mag=selectCol(data, cols, col_mag)
present_mag=True
except IndexError as e:
print "NO MAG:", e.message
present_mag=False
try:
labs=selectCol(data, cols, "LAB")
except IndexError as e:
print "NO LAB:", e.message
print cols
labs=np.zeros(data.shape[0])
pass
result=time
columns=["TIME"]
if present_acc:
data_acc=convert_units(data_acc, coeffAcc)
result=np.column_stack((result,data_acc))
columns += col_acc
if present_gyr:
data_gyr=convert_units(data_gyr, coeffGyr)
result=np.column_stack((result,data_gyr))
columns += col_gyr
if present_mag:
data_mag=convert_units(data_mag, coeffMag)
result=np.column_stack((result,data_mag))
columns += col_mag
result=np.column_stack((result, labs))
columns += ["LAB"]
return result, columns
def preproc(data, cols, coeffAcc=1, coeffGyr=1, coeffMag=1):
'''
:param data: the data
:param cols: the olumns in input
:param coeff: the conversion rate
:param data_type: ACC, GYR or MAG
:return: aray and columns
'''
col_acc=["ACCX", "ACCY", "ACCZ"]
col_gyr=["GYRX", "GYRY", "GYRZ"]
col_mag=["MAGX", "MAGY", "MAGZ"]
time=selectCol(data, cols, "TIME")
try:
data_acc=selectCol(data, cols, col_acc)
present_acc=True
except IndexError as e:
print "NO ACC:", e.message
present_acc=False
try:
data_gyr=selectCol(data, cols, col_gyr)
present_gyr=True
except IndexError as e:
print "NO GYR:", e.message
present_gyr=False
try:
data_mag=selectCol(data, cols, col_mag)
present_mag=True
except IndexError as e:
print "NO MAG:", e.message
present_mag=False
try:
labs=selectCol(data, cols, "LAB")
except IndexError as e:
print "NO LAB:", e.message
print cols
labs=np.zeros(data.shape[0])
pass
result=time
columns=["TIME"]
if present_acc:
data_acc=convert_units(data_acc, coeffAcc)
result=np.column_stack((result,data_acc))
columns += col_acc
if present_gyr:
data_gyr=convert_units(data_gyr, coeffGyr)
result=np.column_stack((result,data_gyr))
columns += col_gyr
if present_mag:
data_mag=convert_units(data_mag, coeffMag)
result=np.column_stack((result,data_mag))
columns += col_mag
result=np.column_stack((result, labs))
columns += ["LAB"]
return result, columns
def preproc(data, cols, T1=0.75, T2=2, MX=1, DELTA_PEAK=0.02, k_near=5, grid_size=5, s=0.2):
gsr=selectCol(data, cols, "GSR")
t_gsr=selectCol(data, cols, "TIME")
print "T_GSR", t_gsr.shape
try:
labs=selectCol(data, cols, "LAB")
except IndexError as e:
print "NO LAB:", e.message
labs=np.zeros(data.shape[0])
output_cols=["TIME", "DRV", "PHA", "TNC", "LAB"]
TIME_DRV, DRV, PH_DRV, TN_DRV, labs = estimate_drivers(t_gsr, gsr, labs, T1, T2, MX, DELTA_PEAK, k_near, grid_size, s)
print "T_DRV", TN_DRV.shape
print "LAB", labs.shape
result=np.column_stack((TIME_DRV, DRV, PH_DRV, TN_DRV, labs))
return result, output_cols
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") #-----EXTRACT FEATURES----- windows, winlab = win.get_windows_no_mix(t, lab, 1, 0.5) feats_acc, fcol_acc = inertial.extract_features_acc(acc, t, col_acc, windows)
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") #-----EXTRACT FEATURES----- windows, winlab=win.get_windows_no_mix(t,lab , 1, 0.5) feats_acc, fcol_acc= inertial.extract_features_acc(acc, t, col_acc, windows)