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)
#Get wavefront
r = anal.wavefront(rays, 200, 200)
return r
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)
trans.grat(control_rays,
period,
np.full(len(ind), 5, dtype=float),
np.full(len(ind), wave),
ind=ind)
for period in np.unique(expds):
ind = np.where(expds == period)[0]
trans.grat(experiment_rays,
period,
np.full(len(ind), 5, dtype=float),
np.full(len(ind), wave),
ind=ind)
# # Diffract rays.
trans.radgrat(rays, d / L, -int(5), wave)
# Go back to starting coordinate system.
rays = trans.applyT(rays, glob_coords, inverse=True)
experiment_rays = trans.applyT(experiment_rays, glob_coords, inverse=True)
control_rays = trans.applyT(control_rays, glob_coords, inverse=True)
# Focus rays.
surfaces.focusX(rays)
surfaces.focusX(experiment_rays)
surfaces.focusX(control_rays)
plt.figure()
plt.scatter(experiment_rays[1], experiment_rays[2], s=0.5)
plt.scatter(control_rays[1], control_rays[2], s=0.5)
plt.scatter(rays[1], rays[2], s=0.5)
# i.addComponent(w) # i.addComponent(g2) # i.simulate() # # prays.focusX() ## good_ind = np.where((rays[2] < 3300.) & (rays[2] > 3200.))[0] rays = [r[good_ind] for r in deepcopy(rays)] # Reflect rays. trans.reflect(rays) # Diffract rays. trans.radgrat(rays, d / L, -1, wave) # Go back to starting coordinate system. rays = trans.applyT(rays, glob_coords, inverse=True) # xcen = rays[1].min() + (rays[1].max() - rays[1].min()) / 2 # ycen = rays[2].min() + (rays[2].max() - rays[2].min()) / 2 # zcen = rays[3].min() + (rays[3].max() - rays[3].min()) / 2 # ind = np.argmin(np.abs(rays[1])) # dir = np.array([xcen - rays[1][ind], ycen - rays[2][ind], zcen - rays[3][ind]]) # # ind = np.argmin(np.abs(rays[2] - ycen)) # adir = np.array([xcen - rays[1][ind], ycen - rays[2][ind], zcen - rays[3][ind]]) # # norm = np.cross(dir,adir) * -1 #
ind = np.where((rays[2] > (hub - grat_length / 2))
& (rays[2] < (hub + grat_length / 2)))[0]
# Remove indices which have already been reflected/diffracted.
ind = ind[np.isin(ind, diff_inds, invert=True)]
ind = np.array(ind, dtype=int)
print(ind)
diff_inds = np.concatenate((ind, diff_inds))
# If there are no rays that interact with the grating, continue.
if len(ind) < 1:
rays = trans.applyT(rays, glob_coords, inverse=True)
continue
# Reflect photons which fall onto this grating.
trans.reflect(rays, ind=ind)
# Diffract photons.
trans.radgrat(rays, 1e6 / d / hub, 1, waves, ind=ind)
# trans.radgrat(rays,1e6/d/hub,1,waves)
# # Return back to original coordinate system.
rays = trans.applyT(rays, glob_coords, inverse=True)
# Only keep rays which have been diffracted.
diff_inds = np.array(diff_inds, dtype=int)
print(len(rays[0]), len(diff_inds))
rays = [r[diff_inds] for r in rays]
# With the rays propagated through the grating module, we will now go to the spectral focus.
# In[22]:
# Find optimal focus position.
grat_focus = surfaces.focusX(rays)