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