phalPin = b.phalPin ''' data fitting ''' #fitIndex = modE.fitMeasurements(calcB[0],d_index,(0,200)) #fitMiddle = modE.fitMeasurements(calcB[1],d_middle,(0,200)) #fitRing = modE.fitMeasurements(calcB[2],d_ring,(0,200)) #fitPinky = modE.fitMeasurements(calcB[3],d_pinky,(0,200)) # ## neglect the resting data #fitIndex = fitIndex[200:700] #fitMiddle = fitMiddle[200:700] #fitRing = fitRing[200:700] #fitPinky = fitPinky[200:700] avgIndex = datAc.moving_average3d(d_index,50) avgMiddle = datAc.moving_average3d(d_middle,50) avgRing = datAc.moving_average3d(d_ring,50) avgPinky = datAc.moving_average3d(d_pinky,50) ''' estimating the angles ''' #estAngInd = np.zeros((len(fitIndex[:,0]),3)) #estAngMid = np.zeros((len(fitIndex[:,0]),3)) #estAngRin = np.zeros((len(fitIndex[:,0]),3)) #estAngPin = np.zeros((len(fitIndex[:,0]),3)) # #bnds = ((0.0,np.pi/2), # index # (0.0,np.pi/2), # (0.0,np.pi/2), # (0.0,np.pi/2), # middle # (0.0,np.pi/2),
import numpy as np from scipy.optimize import * import plotting as plo import time,random ''' acquiring the data ''' cmd = "gatttool -t random -b E3:C0:07:76:53:70 --char-write-req --handle=0x000f --value=0300 --listen" #d = datAc.pipeAcquisition(cmd,4,measNr=199,fileName="151209_four") d = datAc.textAcquisition("151209_index2") #d = datAc.collectForTime(cmd,5,wait=0) #d_ind = datAc.moving_average3d(d[0],10) #d_mid = datAc.moving_average3d(d[1],10) #d_rin = datAc.moving_average3d(d[2],10) #d_pin = datAc.moving_average3d(d[3],10) index = datAc.moving_average3d(d[0],10) middle = datAc.moving_average3d(d[1],10) ring = datAc.moving_average3d(d[2],10) pinky = datAc.moving_average3d(d[3],10) #index = np.delete(index,np.s_[1],1) #middle = np.delete(middle,np.s_[1],1) #ring = np.delete(ring,np.s_[1],1) #pinky = np.delete(pinky,np.s_[1],1) sInd = [-0.03, -0.0, 0.024] # rack1 sMid = [-0.03, -0.022, 0.024] sRin = [-0.03, -0.044, 0.024] sPin = [-0.03, -0.066, 0.024] #sInd = [-0.03, -0.0, 0.024] # rack2 #sMid = [-0.05, -0.022, 0.024]
''' script for estimation with multiple sensors per magnet ''' import dataAcquisitionMulti as datAc import matplotlib.pyplot as plt import modelEqMultiCython as modE import numpy as np from scipy.optimize import * import plotting as plo import time ''' acquiring the data ''' cmd = "gatttool -t random -b E3:C0:07:76:53:70 --char-write-req --handle=0x000f --value=0300 --listen" #d = datAc.pipeAcquisition(cmd,4,measNr=199,fileName="151201_onefour1") d = datAc.textAcquisition("151201_onefour1") d_ind = datAc.moving_average3d(d[0],10) d_mid = datAc.moving_average3d(d[1],10) d_rin = datAc.moving_average3d(d[2],10) d_pin = datAc.moving_average3d(d[3],10) sInd = [-0.03, -0.0, 0.024] sMid = [-0.03, -0.022, 0.024] sRin = [-0.03, -0.044, 0.024] sPin = [-0.03, -0.066, 0.024] sInd2 = [-0.07, 0.0, 0.024] sMid2 = [-0.07, -0.022, 0.024] sRin2 = [-0.07, -0.044, 0.024] sPin2 = [-0.07, -0.066, 0.024]