import ezca import cdsutils import time import numpy e = ezca.Ezca() dt = 30. # suspension movements susp = ['SUS-ITMX_M0_OPTICALIGN_P_OFFSET', 'SUS-ETMX_M0_OPTICALIGN_P_OFFSET', 'SUS-TMSX_M1_OPTICALIGN_P_OFFSET', 'SUS-ITMY_M0_OPTICALIGN_P_OFFSET', 'SUS-ETMY_M0_OPTICALIGN_P_OFFSET', 'SUS-TMSY_M1_OPTICALIGN_P_OFFSET', 'SUS-ITMX_M0_OPTICALIGN_Y_OFFSET', 'SUS-ETMX_M0_OPTICALIGN_Y_OFFSET', 'SUS-TMSX_M1_OPTICALIGN_Y_OFFSET', 'SUS-ITMY_M0_OPTICALIGN_Y_OFFSET', 'SUS-ETMY_M0_OPTICALIGN_Y_OFFSET', 'SUS-TMSY_M1_OPTICALIGN_Y_OFFSET'] # QPD signals channels = ['H1:ASC-X_TR_A_PIT_INMON', 'H1:ASC-X_TR_B_PIT_INMON', 'H1:ASC-Y_TR_A_PIT_INMON', 'H1:ASC-Y_TR_B_PIT_INMON', 'H1:ASC-X_TR_A_YAW_INMON', 'H1:ASC-X_TR_B_YAW_INMON', 'H1:ASC-Y_TR_A_YAW_INMON', 'H1:ASC-Y_TR_B_YAW_INMON' ] # motion names motion_names = ['CSOFT_PIT', 'DSOFT_PIT', 'TMS X PIT', 'TMS Y PIT', 'CSOFT_YAW', 'DSOFT_YAW', 'TMS X YAW', 'TMS Y YAW'] # matrix of motions # ITMX ETMX TMSX ITMY ETMY TMSY motions = [[ 1.0, 0.87, 0.0, 1.0, 0.87, 0.0, # PIT # CSOFT_PIT 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], # YAW [ 1.0, 0.87, 0.0, -1.0, -0.87, 0.0, # PIT # DSOFT_PIT 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], # YAW [ 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, # PIT # TMS X PIT 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], # YAW [ 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, # PIT # TMS Y PIT 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], # YAW [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, # PIT # CSOFT YAW 1.0, -0.87, 0.0, -1.0, 0.87, 0.0], # YAW [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, # PIT # DSOFT YAW 1.0, -0.87, 0.0, 1.0, -0.87, 0.0], # YAW [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, # PIT # TMS X YAW 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], # YAW [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, # PIT # TMS Y YAW 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]] # YAW # rescaling factors for alignment offsets # CSOFT_PIT DSOFT_PIT TMSX_PIT TMSY_PIT CSOFT_YAW DSOFT_YAW TMSX_YAW TMSY_YAW scale = [ 0.3, 0.3, 0.5, 0.5, 0.3, 0.3, 0.5, 0.5, ] motions = numpy.array(motions) nmotions = motions.shape[0] nsusp = len(susp) nsensors = len(channels) # measurement matrix M = numpy.zeros((nsensors, nmotions)) # loop over all motions for i in range(nmotions): print('\n\n****** Measuring %s ******' % motion_names[i]) # initial positions pos0 = numpy.zeros((nsusp,)) for j,s in enumerate(susp): pos0[j] = e[s] # measure sensor values print('** Measuring initial values...') sens0 = cdsutils.avg(dt, channels) # move suspensions for sus,mot,p0 in zip(susp, motions[i,:], pos0): e[sus] = p0 + mot*scale[i] time.sleep(5) # taking another measurement print('** Measuring new values...') sens1 = cdsutils.avg(dt, channels) M[:,i] = (numpy.array(sens1) - numpy.array(sens0))/scale[i] # move suspensions back for sus,p0 in zip(susp, pos0): e[sus] = p0 time.sleep(5) # save measurement to file numpy.savetxt('trans_qpd_matrix.txt', M) # print full matrix import sys sys.stdout.write(' ' * len(channels[0]) + '\t') for j in range(M.shape[1]): sys.stdout.write('%s\t' % motion_names[j]) sys.stdout.write('\n') for i in range(M.shape[0]): sys.stdout.write('%s\t' % channels[i]) for j in range(M.shape[1]): sys.stdout.write('% f\t' % M[i,j]) sys.stdout.write('\n') # select pitch and yaw sub matrices Mp = M[0:4,0:4] My = M[4:8,4:8] # print pitch matrix print('') sys.stdout.write(' ' * len(channels[0]) + '\t') for j in range(Mp.shape[1]): sys.stdout.write('%s\t' % motion_names[j]) sys.stdout.write('\n') for i in range(Mp.shape[0]): sys.stdout.write('%s\t' % channels[i]) for j in range(Mp.shape[1]): sys.stdout.write('% f\t' % Mp[i,j]) sys.stdout.write('\n') # print yaw matrix print('') sys.stdout.write('\n') sys.stdout.write(' ' * len(channels[0]) + '\t') for j in range(Mp.shape[1]): sys.stdout.write('%s\t' % motion_names[j+4]) sys.stdout.write('\n') for i in range(My.shape[0]): sys.stdout.write('%s\t' % channels[i+4]) for j in range(My.shape[1]): sys.stdout.write('% f\t' % My[i,j]) sys.stdout.write('\n') # invert sub matrices from numpy.linalg import inv Mp_inv = inv(Mp) My_inv = inv(My) # print inverse pitch matrix print('') sys.stdout.write(' ' * len(motion_names[0]) + '\t') for j in range(Mp_inv.shape[1]): sys.stdout.write('%s\t' % channels[j]) sys.stdout.write('\n') for i in range(Mp_inv.shape[0]): sys.stdout.write('%s\t' % motion_names[i]) for j in range(Mp_inv.shape[1]): sys.stdout.write('% 23f\t' % Mp_inv[i,j]) sys.stdout.write('\n') # print inverse yaw matrix print('') sys.stdout.write(' ' * len(motion_names[0]) + '\t') for j in range(My_inv.shape[1]): sys.stdout.write('%s\t' % channels[j+4]) sys.stdout.write('\n') for i in range(My_inv.shape[0]): sys.stdout.write('%s\t' % motion_names[i+4]) for j in range(My_inv.shape[1]): sys.stdout.write('% 23f\t' % My_inv[i,j]) sys.stdout.write('\n') ## print HTML versions f = sys.stdout # print pitch matrix f.write('\n') f.write('\n') for j in range(Mp.shape[1]): f.write('' % motion_names[j]) f.write('\n') for i in range(Mp.shape[0]): f.write('' % channels[i]) for j in range(Mp.shape[1]): f.write('' % Mp[i,j]) f.write('\n') f.write('
%s
%s%f
') # print yaw matrix f.write('\n') f.write('\n') for j in range(My.shape[1]): f.write('' % motion_names[j+4]) f.write('\n') for i in range(My.shape[0]): f.write('' % channels[i+4]) for j in range(My.shape[1]): f.write('' % My[i,j]) f.write('\n') f.write('
%s
%s%f
') # print inverse pitch matrix f.write('\n') f.write('\n') for j in range(Mp_inv.shape[1]): f.write('' % channels[j]) f.write('\n') for i in range(Mp_inv.shape[0]): f.write('' % motion_names[i]) for j in range(Mp_inv.shape[1]): f.write('' % Mp_inv[i,j]) f.write('\n') f.write('
%s
%s%f
') # print inverse pitch matrix f.write('\n') f.write('\n') for j in range(My_inv.shape[1]): f.write('' % channels[j+4]) f.write('\n') for i in range(My_inv.shape[0]): f.write('' % motion_names[i+4]) for j in range(My_inv.shape[1]): f.write('' % My_inv[i,j]) f.write('\n') f.write('
%s
%s%f
')