def sma_test(self):
"""This function is used to check the function for calculating signal magnitude area"""
sourcelist1 = [0.9398233871,0.9261432796,0.9179352151,0.9234072581,0.9097271505,0.9507674731,0.9398233871,0.9480314516,
0.9507674731,0.9343513441,0.9316153226,0.9288793011,0.9480314516,0.9343513441,0.9343513441,0.9234072581,
0.9343513441,0.9452954301,0.9425594086,0.9507674731,0.9398233871,0.9452954301,0.9261432796,0.9234072581,
0.9507674731,0.9370873656,0.9617115591,0.9480314516,0.9370873656,0.9343513441,0.9343513441,0.9398233871,
0.9343513441,0.9370873656,0.9234072581,0.9206712366,0.9316153226,0.9206712366,0.9206712366,0.9097271505,
0.9206712366,0.901519086,0.9042551075,0.9179352151,0.9261432796,0.9370873656,0.9425594086,0.9589755376,
0.9589755376,0.9535034946,0.9398233871,0.9343513441,0.9316153226,0.9370873656,0.9288793011,0.9699196237,
0.9726556452,0.9507674731,0.9452954301,0.9452954301,0.9562395161,0.9507674731,0.9726556452,0.9398233871,
0.9370873656,0.9452954301,0.9398233871,0.9206712366,0.9343513441,0.9261432796,0.9343513441,0.9370873656,
0.9425594086,0.9343513441,0.9398233871,0.9562395161,0.9452954301,0.9507674731,0.9507674731,0.9644475806,
0.9589755376,0.9452954301,0.9370873656,0.9343513441,0.9288793011,0.9288793011,0.9507674731,0.9452954301,
0.9617115591,0.9507674731,0.9562395161,0.9753916667,0.9562395161,0.9671836022,0.9343513441,0.9370873656,
0.9425594086,0.9507674731,0.9398233871,0.9562395161]
sourceList2 = [0.1540502688,0.1331620968,0.1305510753,0.1279400538,0.1331620968,0.1253290323,0.1148849462,0.1383841398,
0.1331620968,0.1279400538,0.1357731183,0.1305510753,0.1462172043,0.1409951613,0.1201069892,0.1305510753,
0.1279400538,0.1227180108,0.1279400538,0.1174959677,0.1227180108,0.1357731183,0.1201069892,0.1174959677,
0.1279400538,0.1044408602,0.1201069892,0.1331620968,0.1122739247,0.1227180108,0.1122739247,0.1227180108,
0.1462172043,0.1357731183,0.1227180108,0.1253290323,0.1201069892,0.1122739247,0.1305510753,0.1070518817,
0.1096629032,0.1253290323,0.1201069892,0.1201069892,0.1409951613,0.1227180108,0.1462172043,0.1201069892,
0.1540502688,0.1148849462,0.1148849462,0.1227180108,0.1462172043,0.1409951613,0.1227180108,0.1096629032,
0.1070518817,0.1253290323,0.1122739247,0.1227180108,0.1227180108,0.1148849462,0.1148849462,0.1227180108,
0.1331620968,0.1018298387,0.1201069892,0.1227180108,0.1279400538,0.1122739247,0.1279400538,0.1148849462,
0.1096629032,0.1279400538,0.1122739247,0.1070518817,0.0992188172,0.0992188172,0.0887747312,0.1122739247,
0.1018298387,0.1201069892,0.0887747312,0.0835526882,0.0678865591,0.0835526882,0.1044408602,0.1044408602,
0.1122739247,0.1122739247,0.1174959677,0.1070518817,0.1018298387,0.1148849462,0.1227180108,0.1096629032,
0.1122739247,0.1122739247,0.1096629032,0.1122739247]
sourceList3 = [-0.1714986559,-0.1741370968,-0.1926061828,-0.1583064516,-0.1820524194,-0.1794139785,-0.1794139785,
-0.1978830645,-0.1926061828,-0.1952446237,-0.2057983871,-0.1952446237,-0.1741370968,-0.1767755376,
-0.1820524194,-0.1899677419,-0.1688602151,-0.1767755376,-0.1926061828,-0.1767755376,-0.1846908602,
-0.2189905914,-0.1609448925,-0.1926061828,-0.1846908602,-0.1952446237,-0.1952446237,-0.1846908602,
-0.1873293011,-0.1767755376,-0.1820524194,-0.1846908602,-0.1846908602,-0.1794139785,-0.1794139785,
-0.1714986559,-0.1714986559,-0.1609448925,-0.1609448925,-0.1583064516,-0.1794139785,-0.1741370968,
-0.1662217742,-0.1609448925,-0.150391129,-0.1530295699,-0.1767755376,-0.1583064516,-0.1583064516,
-0.1767755376,-0.1899677419,-0.1978830645,-0.1873293011,-0.2242674731,-0.1556680108,-0.2005215054,
-0.1846908602,-0.1688602151,-0.1530295699,-0.1688602151,-0.1741370968,-0.1635833333,-0.1556680108,
-0.1609448925,-0.1635833333,-0.1530295699,-0.1530295699,-0.1609448925,-0.1635833333,-0.1451142473,
-0.1398373656,-0.1424758065,-0.1424758065,-0.131922043,-0.1371989247,-0.1530295699,-0.1556680108,
-0.1424758065,-0.1213682796,-0.1820524194,-0.1345604839,-0.1108145161,-0.1292836022,-0.1451142473,
-0.1530295699,-0.1556680108,-0.1424758065,-0.1424758065,-0.1662217742,-0.1556680108,-0.150391129,
-0.150391129,-0.1477526882,-0.1477526882,-0.1451142473,-0.1556680108,-0.1477526882,-0.1398373656,
-0.1530295699,-0.1477526882]
target = 0.0122690393012
result = smaFeat.smafeat(sourcelist1,sourceList2,sourceList3)
self.assertAlmostEqual(result,target)
def sma_test(self):
sourcelist1 = [0.9398233871,0.9261432796,0.9179352151,0.9234072581,0.9097271505,0.9507674731,0.9398233871,0.9480314516,
0.9507674731,0.9343513441,0.9316153226,0.9288793011,0.9480314516,0.9343513441,0.9343513441,0.9234072581,
0.9343513441,0.9452954301,0.9425594086,0.9507674731,0.9398233871,0.9452954301,0.9261432796,0.9234072581,
0.9507674731,0.9370873656,0.9617115591,0.9480314516,0.9370873656,0.9343513441,0.9343513441,0.9398233871,
0.9343513441,0.9370873656,0.9234072581,0.9206712366,0.9316153226,0.9206712366,0.9206712366,0.9097271505,
0.9206712366,0.901519086,0.9042551075,0.9179352151,0.9261432796,0.9370873656,0.9425594086,0.9589755376,
0.9589755376,0.9535034946,0.9398233871,0.9343513441,0.9316153226,0.9370873656,0.9288793011,0.9699196237,
0.9726556452,0.9507674731,0.9452954301,0.9452954301,0.9562395161,0.9507674731,0.9726556452,0.9398233871,
0.9370873656,0.9452954301,0.9398233871,0.9206712366,0.9343513441,0.9261432796,0.9343513441,0.9370873656,
0.9425594086,0.9343513441,0.9398233871,0.9562395161,0.9452954301,0.9507674731,0.9507674731,0.9644475806,
0.9589755376,0.9452954301,0.9370873656,0.9343513441,0.9288793011,0.9288793011,0.9507674731,0.9452954301,
0.9617115591,0.9507674731,0.9562395161,0.9753916667,0.9562395161,0.9671836022,0.9343513441,0.9370873656,
0.9425594086,0.9507674731,0.9398233871,0.9562395161]
sourceList2 = [0.1540502688,0.1331620968,0.1305510753,0.1279400538,0.1331620968,0.1253290323,0.1148849462,0.1383841398,
0.1331620968,0.1279400538,0.1357731183,0.1305510753,0.1462172043,0.1409951613,0.1201069892,0.1305510753,
0.1279400538,0.1227180108,0.1279400538,0.1174959677,0.1227180108,0.1357731183,0.1201069892,0.1174959677,
0.1279400538,0.1044408602,0.1201069892,0.1331620968,0.1122739247,0.1227180108,0.1122739247,0.1227180108,
0.1462172043,0.1357731183,0.1227180108,0.1253290323,0.1201069892,0.1122739247,0.1305510753,0.1070518817,
0.1096629032,0.1253290323,0.1201069892,0.1201069892,0.1409951613,0.1227180108,0.1462172043,0.1201069892,
0.1540502688,0.1148849462,0.1148849462,0.1227180108,0.1462172043,0.1409951613,0.1227180108,0.1096629032,
0.1070518817,0.1253290323,0.1122739247,0.1227180108,0.1227180108,0.1148849462,0.1148849462,0.1227180108,
0.1331620968,0.1018298387,0.1201069892,0.1227180108,0.1279400538,0.1122739247,0.1279400538,0.1148849462,
0.1096629032,0.1279400538,0.1122739247,0.1070518817,0.0992188172,0.0992188172,0.0887747312,0.1122739247,
0.1018298387,0.1201069892,0.0887747312,0.0835526882,0.0678865591,0.0835526882,0.1044408602,0.1044408602,
0.1122739247,0.1122739247,0.1174959677,0.1070518817,0.1018298387,0.1148849462,0.1227180108,0.1096629032,
0.1122739247,0.1122739247,0.1096629032,0.1122739247]
sourceList3 = [-0.1714986559,-0.1741370968,-0.1926061828,-0.1583064516,-0.1820524194,-0.1794139785,-0.1794139785,
-0.1978830645,-0.1926061828,-0.1952446237,-0.2057983871,-0.1952446237,-0.1741370968,-0.1767755376,
-0.1820524194,-0.1899677419,-0.1688602151,-0.1767755376,-0.1926061828,-0.1767755376,-0.1846908602,
-0.2189905914,-0.1609448925,-0.1926061828,-0.1846908602,-0.1952446237,-0.1952446237,-0.1846908602,
-0.1873293011,-0.1767755376,-0.1820524194,-0.1846908602,-0.1846908602,-0.1794139785,-0.1794139785,
-0.1714986559,-0.1714986559,-0.1609448925,-0.1609448925,-0.1583064516,-0.1794139785,-0.1741370968,
-0.1662217742,-0.1609448925,-0.150391129,-0.1530295699,-0.1767755376,-0.1583064516,-0.1583064516,
-0.1767755376,-0.1899677419,-0.1978830645,-0.1873293011,-0.2242674731,-0.1556680108,-0.2005215054,
-0.1846908602,-0.1688602151,-0.1530295699,-0.1688602151,-0.1741370968,-0.1635833333,-0.1556680108,
-0.1609448925,-0.1635833333,-0.1530295699,-0.1530295699,-0.1609448925,-0.1635833333,-0.1451142473,
-0.1398373656,-0.1424758065,-0.1424758065,-0.131922043,-0.1371989247,-0.1530295699,-0.1556680108,
-0.1424758065,-0.1213682796,-0.1820524194,-0.1345604839,-0.1108145161,-0.1292836022,-0.1451142473,
-0.1530295699,-0.1556680108,-0.1424758065,-0.1424758065,-0.1662217742,-0.1556680108,-0.150391129,
-0.150391129,-0.1477526882,-0.1477526882,-0.1451142473,-0.1556680108,-0.1477526882,-0.1398373656,
-0.1530295699,-0.1477526882]
target = 0.0122690393012
result = smaFeat.smafeat(sourcelist1,sourceList2,sourceList3)
self.assertAlmostEqual(result,target)
def calc_features(fullarray, micannot, samp_rate):
#vect_c not used
pre_win = [0, 100] #pre-window 1s
impact_max = [50, 150] #1s
imp_win = [50, 650] # impact window 6s
post_win = [300, 1200] #9 s, based on the paper it should be 250-1200
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]))
#if micannot == 2:
# print "fall"
#else:
# print "non-fall"
#plt.plot(datAr)
#plt.show()
#******************************************************************************************************************************
#minimum pre impact
min_pre = min(datAr[pre_win[0]:pre_win[1]])
#minimum value impact
#min_imp = min(datAr[imp_win[0]:imp_win[1]])
#minimum post imp
#min_post = min(datAr[post_win[0]:post_win[1]])
#******************************************************************************************************************************
#maximum value pre-impact
#max_pre = max(datAr[pre_win[0]:pre_win[1]])
#maximum value impact
max_imp = max(datAr[impact_max[0]:impact_max[1]])
#maximum value post
#max_post = max(datAr[post_win[0]:post_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]])
#******************************************************************************************************************************
datfeat = [
min_pre, max_imp, meanVal1, meanVal2, meanVal3, rms1, rms2, rms3,
variance1, variance2, variance3, velo1, velo2, velo3, energy1, energy2,
energy3, sma1, sma2, sma3, ewma1, ewma2, ewma3, micannot
]
return datfeat
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
