displ_rz = ca.load_displ_grav(axis='RZ') displ_ry_calc, _ = np.gradient(displ_x, 0.5) # 0.5 mm if 'clip' not in globals(): clip = 20 if 'n_ss' not in globals(): n_ss = 5 # Optimize on X print print "** Optimize on X **" print coeffs_x, adj_x, M_2d_x, displ_x_clip = ca.calc_adj( ifuncs_x, displ_x, n_ss, clip) # print results # slope errors in az and ax from displ_x solution resid_x = displ_x_clip - adj_x print "Inputs" print "Displ_x stddev, mean: {:.4f},{:.4f}".format( displ_x.std(), displ_x.mean()) print "Displ_ry stddev, mean: {:.4f},{:.4f}".format( displ_ry.std(), displ_ry.mean()) print "Displ_rz stddev, mean: {:.4f},{:.4f}".format( displ_rz.std(), displ_rz.mean()) print "Displ_ry_calc stddev, mean: {:.4f},{:.4f}".format( displ_ry_calc.std(), displ_ry_calc.mean()) print
displ_x = calc_adj.load_file_legendre(ifuncs_x, slope=False, filename='data/exemplar_021312.dat') save = 'exemplar2_' else: displ_x = calc_adj.load_displ_legendre(ifuncs_x, 8, 4, 0.5) save = 'leg84_' # displ_ry = np.gradient(displ_x, 0.5 * 1000)[0] * RAD2ARCSEC # radians (for exemplar down by factor of 1000) displ_ry = np.gradient(displ_x, 0.5)[0] if corr_using == 'x': # coeffs from optimizing on amplitude coeffs, adj_2d, M_2d_all, displ_clip = calc_adj.calc_adj(ifuncs_x, displ_x, n_ss=5, clip=clip) else: # coeffs from optimizing on slope coeffs, adj_2d, M_2d_all, displ_clip = calc_adj.calc_adj(ifuncs_ry, displ_ry, n_ss=5, clip=clip) adj_x = calc_adj.calc_adj_coeffs(ifuncs_x, coeffs) adj_ry = calc_adj.calc_adj_coeffs(ifuncs_ry, coeffs) # adj_ry_dxdz = np.gradient(adj_x, 0.5)[0] fig1 = plt.figure(1, figsize=(6, 8)) fig2 = plt.figure(2, figsize=(6, 8)) calc_adj.make_plots(displ_x,
ifuncs_ry = calc_adj.load_ifuncs("RY", case="10+0_half") if 1: displ_x = calc_adj.load_file_legendre(ifuncs_x, slope=False, filename="data/exemplar_021312.dat") save = "exemplar2_" else: displ_x = calc_adj.load_displ_legendre(ifuncs_x, 8, 4, 0.5) save = "leg84_" # displ_ry = np.gradient(displ_x, 0.5 * 1000)[0] * RAD2ARCSEC # radians (for exemplar down by factor of 1000) displ_ry = np.gradient(displ_x, 0.5)[0] if corr_using == "x": # coeffs from optimizing on amplitude coeffs, adj_2d, M_2d_all, displ_clip = calc_adj.calc_adj(ifuncs_x, displ_x, n_ss=5, clip=clip) else: # coeffs from optimizing on slope coeffs, adj_2d, M_2d_all, displ_clip = calc_adj.calc_adj(ifuncs_ry, displ_ry, n_ss=5, clip=clip) adj_x = calc_adj.calc_adj_coeffs(ifuncs_x, coeffs) adj_ry = calc_adj.calc_adj_coeffs(ifuncs_ry, coeffs) # adj_ry_dxdz = np.gradient(adj_x, 0.5)[0] fig1 = plt.figure(1, figsize=(6, 8)) fig2 = plt.figure(2, figsize=(6, 8)) calc_adj.make_plots(displ_x, adj_x, fig1=fig1, fig2=fig2, clip=clip, save=save + corr_using + "_X") fig1 = plt.figure(3, figsize=(6, 8)) fig2 = plt.figure(4, figsize=(6, 8))