def tilt_test(self):
ax=[]
ay=[]
az=[]
gx=[]
gy=[]
gz=[]
ax.append(0.9562395161)
ay.append(0.0913857527)
az.append(0.0079153226)
gx.append( -0.3661662395)
gy.append(-2.5631636763)
gz.append(-0.7323324789)
result = mytilt.mytilt(ax, ay, az, gx, gy, gz)
y_temp_angle = result[0]
z_temp_angle = result[1]
y_val = y_temp_angle[0]
z_val = z_temp_angle[0]
self.assertAlmostEqual(y_val,0.5465499145640936)
self.assertAlmostEqual(z_val,0.047210333793581472)
def featurescom (fullarray, micannot,act_state):
samp_rate = source_var.sampling_rate()
pre_win = [0, samp_rate] #pre-window
imp_win = [(samp_rate/2)-1, int(6.5 * samp_rate)] # impact window
post_win = [int((2.5 * samp_rate))-1, 12 * samp_rate] #post impact window
max_win = [(samp_rate/2)-1, int(1.5 * samp_rate)] # window for search max value
tilt_sample_impact = [(2*samp_rate)-1, 3*samp_rate]
tilt_sample_post = [(3 * samp_rate) - 1, 12 * samp_rate] # window for tilt-angle
datAr = [] #for vector magnitude
datXa= [] #for X values
datYa= [] #for Y values
datZa= [] #for Z values
datXg=[]
datYg=[]
datZg=[]
for tempdata in fullarray:
datAr.append(float(tempdata[6]))
datXa.append(float(tempdata[0]))
datYa.append(float(tempdata[1]))
datZa.append(float(tempdata[2]))
datXg.append(float(tempdata[3]))
datYg.append(float(tempdata[4]))
datZg.append(float(tempdata[5]))
#******************************************************************************************************************************
#minimum value feature
minVal = min(datAr[pre_win[0]:pre_win[1]])
#******************************************************************************************************************************
#maximum value feature
maxVal = max(datAr[max_win[0]:max_win[1]])
#******************************************************************************************************************************
#mean for pre-impact event
meanList1 = datAr[pre_win[0]:pre_win[1]]
meanVal1 = numpy.mean(meanList1)
#******************************************************************************************************************************
#mean for impact
meanList2 = datAr[imp_win[0]:imp_win[1]]
meanVal2 = numpy.mean(meanList2)
#******************************************************************************************************************************
#mean for post-impact
meanList3 = datAr[post_win[0]:post_win[1]]
meanVal3 = numpy.mean(meanList3)
#******************************************************************************************************************************
#Root mean square for pre-impact
rmsList1 = datAr[pre_win[0]:pre_win[1]]
rms1 = sqrt(mean(numpy.array(rmsList1)**2))
#Root mean square for impact
rmsList2 = datAr[imp_win[0]:imp_win[1]]
rms2 = sqrt(mean(numpy.array(rmsList2)**2))
#Root mean square for post-impact
rmsList3 = datAr[post_win[0]:post_win[1]]
rms3 = sqrt(mean(numpy.array(rmsList3)**2))
#******************************************************************************************************************************
#variance for pre-impact
variance1 = numpy.var(datAr[pre_win[0]:pre_win[1]],ddof=1)
#variance for impact
variance2 = numpy.var(datAr[imp_win[0]:imp_win[1]],ddof=1)
#variance for post-impact
variance3 = numpy.var(datAr[post_win[0]:post_win[1]],ddof=1)
#******************************************************************************************************************************
#velocity in pre-impact
velo1 = integrator.integrate(datAr[pre_win[0]:pre_win[1]])
#velocity in impact
velo2 = integrator.integrate(datAr[imp_win[0]:imp_win[1]])
#velocity in post-impact
velo3 = integrator.integrate(datAr[post_win[0]:post_win[1]])
#******************************************************************************************************************************
#energy in pre-impact
win1x = datXa[pre_win[0]:pre_win[1]]
win1y = datYa[pre_win[0]:pre_win[1]]
win1z = datZa[pre_win[0]:pre_win[1]]
energy1 = energyFeat.energyCalc(win1x,win1y,win1z)
#energy in impact
win2x = datXa[imp_win[0]:imp_win[1]]
win2y = datYa[imp_win[0]:imp_win[1]]
win2z = datZa[imp_win[0]:imp_win[1]]
energy2 = energyFeat.energyCalc(win2x,win2y,win2z)
#energy in post-impact
win3x = datXa[post_win[0]:post_win[1]]
win3y = datYa[post_win[0]:post_win[1]]
win3z = datZa[post_win[0]:post_win[1]]
energy3 = energyFeat.energyCalc(win3x,win3y,win3z)
#******************************************************************************************************************************
#signal magnitude are in pre-impact
sma1 = smaFeat.smafeat(datXa[pre_win[0]:pre_win[1]],datYa[pre_win[0]:pre_win[1]],datZa[pre_win[0]:pre_win[1]])
#signal magnitude are in impact
sma2 = smaFeat.smafeat(datXa[imp_win[0]:imp_win[1]],datYa[imp_win[0]:imp_win[1]],datZa[imp_win[0]:imp_win[1]])
#signal magnitude are in pre-impact
sma3 = smaFeat.smafeat(datXa[post_win[0]:post_win[1]],datYa[post_win[0]:post_win[1]],datZa[post_win[0]:post_win[1]])
#******************************************************************************************************************************
#exponential moving average in pre-impact
ewma1 = emafit.ema(datAr[pre_win[0]:pre_win[1]])
#exponential moving average in impact
ewma2 = emafit.ema(datAr[imp_win[0]:imp_win[1]])
#exponential moving average in post-impact
ewma3 = emafit.ema(datAr[post_win[0]:post_win[1]])
#******************************************************************************************************************************
#Tilt Angle in pre-impact
all_angle_1 = mytilt.mytilt(datXa[pre_win[0]:pre_win[1]],datYa[pre_win[0]:pre_win[1]],datZa[pre_win[0]:pre_win[1]],
datXg[pre_win[0]:pre_win[1]],datYg[pre_win[0]:pre_win[1]],datZg[pre_win[0]:pre_win[1]])
angle_y_1 = numpy.max(numpy.abs(numpy.array(all_angle_1[0])))
angle_z_1 = numpy.max(numpy.abs(numpy.array(all_angle_1[1])))
#Tilt Angle in impact
all_angle_2 = mytilt.mytilt(datXa[tilt_sample_impact[0]:tilt_sample_impact[1]],datYa[tilt_sample_impact[0]:tilt_sample_impact[1]],
datZa[tilt_sample_impact[0]:tilt_sample_impact[1]],datXg[tilt_sample_impact[0]:tilt_sample_impact[1]],
datYg[tilt_sample_impact[0]:tilt_sample_impact[1]],datZg[tilt_sample_impact[0]:tilt_sample_impact[1]])
angle_y_2 = numpy.max(numpy.abs(numpy.array(all_angle_2[0])))
angle_z_2 = numpy.max(numpy.abs(numpy.array(all_angle_2[1])))
#Tilt Angle in post-impact
all_angle_3 = mytilt.mytilt(datXa[tilt_sample_post[0]:tilt_sample_post[1]],datYa[tilt_sample_post[0]:tilt_sample_post[1]],
datZa[tilt_sample_post[0]:tilt_sample_post[1]],datXg[tilt_sample_post[0]:tilt_sample_post[1]],
datYg[tilt_sample_post[0]:tilt_sample_post[1]],datZg[tilt_sample_post[0]:tilt_sample_post[1]])
angle_y_3 = numpy.max(numpy.abs(numpy.array(all_angle_3[0])))
angle_z_3 = numpy.max(numpy.abs(numpy.array(all_angle_3[1])))
#******************************************************************************************************************************
annot = micannot
#datfeat = [minVal, maxVal, meanVal1, meanVal2, meanVal3, rms1, rms2, rms3, variance1, variance2, variance3, velo1,
# velo2, velo3, energy1, energy2, energy3, sma1, sma2, sma3, ewma1, ewma2, ewma3, act_state, annot]
datfeat = [minVal, maxVal, meanVal1, meanVal2, meanVal3, rms1, rms2, rms3, variance1, variance2, variance3, velo1,
velo2, velo3, energy1, energy2, energy3, sma1, sma2, sma3, ewma1, ewma2, ewma3, angle_y_1, angle_z_1,
angle_y_2, angle_z_2,angle_y_3,angle_z_3,act_state, annot]
return datfeat
