import numpy as np
import calc_adj as ca
import matplotlib.pyplot as plt
if 'ifuncs_x' not in globals():
ifuncs_ry = ca.load_ifuncs('RY', 'p', case='7+2')
ifuncs_x = ca.load_ifuncs('X', 'p', case='7+2')
# displ_x = ca.load_displ_legendre(ifuncs_x, 8, 4, rms=5.0)
# displ_ry, _ = np.gradient(displ_x, 0.5) # 0.5 mm
displ_x = ca.load_displ_grav(axis='X')
displ_ry = ca.load_displ_grav(axis='RY')
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
"""Run calc_adj for the exemplar data
"""
import numpy as np
import matplotlib.pyplot as plt
import calc_adj
RAD2ARCSEC = 206000. # convert to arcsec for better scale
clip = 20
scale_ry = RAD2ARCSEC / 1000. # ampl in microns, slope in arcsec
if 'ifuncs_x' not in globals() or 'ifuncs_ry' not in globals():
ifuncs_x = calc_adj.load_ifuncs('X', case='10+0_half')
ifuncs_ry = calc_adj.load_ifuncs('RY', case='10+0_half')
displ_x = calc_adj.load_file_legendre(
ifuncs_x, slope=False, filename='data/exemplar_021312.dat')
out = open('exemplar_displ_X.dat', 'w')
for i in range(displ_x.shape[0]):
for j in range(displ_x.shape[1]):
print >>out, i, j, '%.4f' % displ_x[i, j]
out.close()
import numpy as np
import matplotlib.pyplot as plt
import calc_adj
RAD2ARCSEC = 206000.0 # convert to arcsec for better scale
clip = 20
scale_ry = RAD2ARCSEC / 1000.0 # ampl in microns, slope in arcsec
if "save" not in globals():
save = None
if "corr_using" not in globals():
corr_using = "x"
if "ifuncs_x" not in globals() or "ifuncs_ry" not in globals():
ifuncs_x = calc_adj.load_ifuncs("X", case="10+0_half")
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
