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))
