def wavefront(rays, Nx, Ny, method='cubic', polar=False):
"""
Interpolate the beam slopes and then integrate the wavefront.
Rays assumed to be in the XY plane.
"""
#Interpolate slopes to regular grid
y, dx, dy = interpolateVec(rays, 5, Nx, Ny, method=method, polar=polar)
x, dx, dy = interpolateVec(rays, 4, Nx, Ny, method=method, polar=polar)
#Prepare arrays for integration
#Reconstruct requires nans be replaced by 100
#Fortran functions require arrays to be packed in
#fortran contiguous mode
x = man.padRect(x)
y = man.padRect(y)
phase = np.zeros(np.shape(x), order='F')
phase[np.isnan(x)] = 100.
x[np.isnan(x)] = 100.
y[np.isnan(y)] = 100.
y = np.array(y, order='F')
x = np.array(x, order='F')
#Reconstruct and remove border
phase = reconstruct.reconstruct(y, x, 1e-12, dx, phase)
phase[phase == 100] = np.nan
x[x == 100] = np.nan
y[y == 100] = np.nan
return phase[1:-1, 1:-1], x[1:-1, 1:-1], y[1:-1, 1:-1]
def shift_image(img, xshift, yshift):
ysize, xsize = shape(img)
buffer_ind = max([xshift, yshift])
expand_img = man.padRect(img, buffer_ind)
shift_img = expand_img[buffer_ind - yshift:buffer_ind + ysize - yshift,
buffer_ind - xshift:buffer_ind + xsize - xshift]
shift_img[isnan(shift_img)] = 0.0
return shift_img
def littrow():
"""
Trace rectangular beam into OPG in Littrow.
400 nm groove period
8.4 m groove convergence
"""
#Set up beam
rays = sources.rectArray(50., 50., 1e2)
#Trace to Littrow and diffract
tran.transform(rays, 0, 0, 0, 0, 52.28 * np.pi / 180, 0)
surf.flat(rays)
tran.transform(rays, 0, 8400., 0, 0, 0, 0)
tran.radgrat(rays, 400. / 8400., 1, 632.8)
#Steer out beam tilt
tran.steerX(rays)
tran.steerY(rays)
#Bring rays to common plane
surf.flat(rays)
#Interpolate slopes to regular grid
y, dx, dy = anal.interpolateVec(rays, 5, 200, 200)
x, dx, dy = anal.interpolateVec(rays, 4, 200, 200)
#Prepare arrays for integration
#Reconstruct requires nans be replaced by 100
#Fortran functions require arrays to be packed in
#fortran contiguous mode
x = man.padRect(x)
y = man.padRect(y)
phase = np.zeros(np.shape(x), order='F')
phase[np.isnan(x)] = 100.
x[np.isnan(x)] = 100.
y[np.isnan(y)] = 100.
y = np.array(y, order='F')
x = np.array(x, order='F')
#Reconstruct and remove border
phase = reconstruct.reconstruct(x, y, 1e-12, dx, phase)
phase[phase == 100] = np.nan
x[x == 100] = np.nan
y[y == 100] = np.nan
return man.stripnans(phase), man.stripnans(x), man.stripnans(y)
def make_aligned_images(measimg,
simimg,
final_size=[400, 200, 200, 200],
offset=18):
simimg = flipud(simimg)
simimg_padded = man.padRect(simimg, 1300)
simimg_padded[isnan(simimg_padded)] = 0.0
meas_profile_x, meas_profile_y = sum(measimg, axis=0), sum(measimg, axis=1)
meas_xCoM, meas_yCoM = get_CoM_img(measimg)
sim_xCoM, sim_yCoM = get_CoM_img(simimg_padded)
#pdb.set_trace()
meas_xCoM, meas_yCoM, sim_xCoM, sim_yCoM = int(meas_xCoM), int(
meas_yCoM), int(sim_xCoM), int(sim_yCoM) - offset
cut_meas_img = measimg[meas_yCoM - final_size[0]:meas_yCoM + final_size[1],
meas_xCoM - final_size[2]:meas_xCoM + final_size[3]]
cut_sim_img = simimg_padded[sim_yCoM - final_size[0]:sim_yCoM +
final_size[1], sim_xCoM -
final_size[2]:sim_xCoM + final_size[3]]
return rot90(cut_meas_img), rot90(cut_sim_img)