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]
