#''' 12 ''' # startT = time.time() # estAngd_A = modDA.estimateSeries(s1_fit[:,:6], fingList, sensList[:2], jointList, bnds=True, met=1) # d_A = time.time()-startT # print "time d_A12: ", d_A # datAc.saveStates("../datasets/niceOnes/"+dayString+'_'+sstring+"dipA12.txt", estAngd_A) # # startT = time.time() # estAngc_A = modCA.estimateSeries(s1_fit[:,:6], fingList, sensList[:2], jointList, bnds=True, met=1) # c_A = time.time()-startT # datAc.saveStates("../datasets/niceOnes/"+dayString+'_'+sstring+"cylA12.txt", estAngc_A) # print "time c_A12: ", c_A # """ 14 """ startT = time.time() estAngd_A = modDA.estimateSeries(s1_fit, fingList, sensList, jointList, bnds=True, met=1) d_A = time.time() - startT print "time d_A14: ", d_A datAc.saveStates("../datasets/44/" + dayString + "_" + sstring + "dipA44.txt", estAngd_A) startT = time.time() estAngc_A = modCA.estimateSeries(s1_fit, fingList, sensList, jointList, bnds=True, met=1) c_A = time.time() - startT datAc.saveStates("../datasets/44/" + dayString + "_" + sstring + "cylA44.txt", estAngc_A) print "time c_A14: ", c_A plo.plotLeapVsMag((tim, estAngd_A), (tim, s1, s1_fit[:, :3]), head="estAngd_A vs B-field " + sstring) (timLeap, angInd, angMid, angRin, angPin) = datAc.readLeap("../datasets/160210/" + dayString + "_" + sstring + "_leap")
s3_fit = s3*scale+off (scale, off) = datAc.getScaleOff(b90_A[:,9:],s4[start:end]) s4_fit = s4*scale+off ## combining everything again... s1_fit = np.append(s1_fit,np.append(s2_fit,np.append(s3_fit,s4_fit,1),1),1) ''' estimating measurements ''' print "estimating ", sstring ''' 11 ''' print "11" # fi.write(sstring+',') startT = time.time() estAngd_A = modDA.estimateSeries(s1_fit[:,:3], fingList, [sensList[0]], jointList, bnds=True, met=1) d_A = time.time()-startT d_A /= len(s1_fit) # print "time d_A11: ", d_A # fi.write(str(d_A)+',') datAc.saveStates("../datasets/evalSets/estResults/160217_real/"+sstring+"_dipA11.txt", estAngd_A) (mean_A, var_A) = sf.getMeanVar(lInd_re,estAngd_A) # fi.write(str(mean_A)+','+str(var_A)+',') print "cyl mean_A %s var_A %s" % (mean_A, var_A) # neglecting ad-ab # (mean, var) = sf.getMeanVar(lInd_re[:,:3], estAngd_A[:,:3]) # fi.write(str(mean)+','+str(var)+',') # print "cyl mean %s var %s" % (mean, var) startT = time.time()
resString = "model: cylindrical, without adduction-abduction\n" resString += "total time[sec] needed: " + str(endT) + "\n" resString += "avg time per step[sec]: " + str(endT/len(simValues_A)) + "\n\n" # resString += "max fun value: " + str(max(fun_cyl)) + "\n\n" print resString f.write(resString) datAc.saveStates(folderStr+"estAng_cyl"+str(i), estAng_cyl) plo.plotter2d((estAng_cyl[:,:3], estAng_cyl[:,3:6]), ("model: cylindrical without Adduction index","middle"+methString)) # plo.plotter2d((estAng_cyl[:,:3], estAng_cyl[:,3:6], estAng_cyl[:,6:9], estAng_cyl[:,9:]), # ("model: cylindrical without Adduction index","middle"+methString,"ring","pinky")) plt.savefig(folderStr+str(i)+"cyl_nA.png") ''' dip with ad-ab ''' startT = time.time() estAng_dip_A = modDA.estimateSeries(b_cyl_A, fingList, sensList, jointList, bnds=True, met=i) endT = time.time()-startT resString = "model: dipole, with adduction-abduction\n" resString += "total time[sec] needed: " + str(endT) + "\n" resString += "avg time per step[sec]: " + str(endT/len(simValues_A)) + "\n\n" # resString += "max fun value: " + str(max(fun_dip_A)) + "\n\n" print resString f.write(resString) datAc.saveStates(folderStr+"estAng_dip_A"+str(i), estAng_dip_A) plo.plotter2d((estAng_dip_A[:,:4], estAng_dip_A[:,4:8]), ("model: dip with Adduction index","middle"+methString)) # plo.plotter2d((estAng_dip_A[:,:4], estAng_dip_A[:,4:8], estAng_dip_A[:,8:12], estAng_dip_A[:,12:]), # ("model: dip with Adduction index","middle"+methString,"ring","pinky")) plt.savefig(folderStr+str(i)+"dip_A.png")