def multi_prop(n,lam, prefix, beam_ratio, RAVC=True, LS=True):
diam = 37.
print("lam: ", lam)
print("beam_ratio: ", beam_ratio)
npupil = math.ceil(n*beam_ratio) # compute the pupil size --> has to be ODD (proper puts the center in the up right pixel next to the grid center)
if npupil % 2 == 0:
npupil = npupil +1
## Non coronagraphic PSF --> simulate a non-coronagraphic psf
(wfo_noCoro, sampling) = proper.prop_run_multi('simple_telescope', lam, n, PASSVALUE={'prefix':prefix, 'charge':0, 'diam':diam,'beam_ratio':beam_ratio, 'RAVC':False, 'LS':False}, QUIET=True)
#(wfo_noCoro, sampling) = proper.prop_run('simple_telescope', lam, n, PASSVALUE={'prefix':prefix, 'charge':0, 'diam':37., 'RAVC':False, 'LS':False}, QUIET=True)
NoCoro_psf = (abs(wfo_noCoro))**2
fits.writeto(prefix+'psf_noCoro_nonorm.fits', NoCoro_psf, overwrite=True)#[int(n/2)-math.ceil(50./beam_ratio):int(n/2)+math.ceil(50./beam_ratio),int(n/2)-math.ceil(50./beam_ratio):int(n/2)+math.ceil(50./beam_ratio)], overwrite=True)
psf_noCoro_maxnorm = NoCoro_psf/np.max(NoCoro_psf)
fits.writeto(prefix+'_psf_noCoro_maxnorm.fits', psf_noCoro_maxnorm, overwrite=True)#[int(n/2)-math.ceil(50./beam_ratio):int(n/2)+math.ceil(50./beam_ratio),int(n/2)-math.ceil(50./beam_ratio):int(n/2)+math.ceil(50./beam_ratio)], overwrite=True)
psf_noCoro_flux1norm = NoCoro_psf/sum(sum(NoCoro_psf))
fits.writeto(prefix+'_psf_noCoro_flux1norm.fits', psf_noCoro_flux1norm, overwrite=True)#[int(n/2)-math.ceil(50./beam_ratio):int(n/2)+math.ceil(50./beam_ratio),int(n/2)-math.ceil(50./beam_ratio):int(n/2)+math.ceil(50./beam_ratio)], overwrite=True)
## No Vortex Si Mask --> simulate a non-coronagraphic-apodized psf: apodizer and Lyot Stop are present, but not the vortex --> as an off-axis psf
(wfo_offaxis, sampling) = proper.prop_run_multi('simple_telescope', lam, n, PASSVALUE={'prefix':prefix, 'charge':0, 'diam':diam,'beam_ratio':beam_ratio, 'RAVC':RAVC, 'LS':LS}, QUIET=True)
#(wfo_offaxis, sampling) = proper.prop_run('simple_telescope', lam, n, PASSVALUE={'prefix':prefix, 'charge':0, 'diam':37., 'RAVC':RAVC, 'LS':LS}, QUIET=True)
Offaxis_psf = (abs(wfo_offaxis))**2
fits.writeto(prefix+'psf_offaxis_nonorm.fits', Offaxis_psf, overwrite=True)#[int(n/2)-math.ceil(50./beam_ratio):int(n/2)+math.ceil(50./beam_ratio),int(n/2)-math.ceil(50./beam_ratio):int(n/2)+math.ceil(50./beam_ratio)], overwrite=True)
psf_noVortex_Mask_maxnorm = Offaxis_psf/np.max(NoCoro_psf)
fits.writeto(prefix+'psf_offaxis_maxnorm.fits', psf_noVortex_Mask_maxnorm, overwrite=True)#[int(n/2)-math.ceil(50./beam_ratio):int(n/2)+math.ceil(50./beam_ratio),int(n/2)-math.ceil(50./beam_ratio):int(n/2)+math.ceil(50./beam_ratio)], overwrite=True)
psf_noVortex_Mask_flux1norm = Offaxis_psf/sum(sum(NoCoro_psf))
fits.writeto(prefix+'psf_offaxis_flux1norm.fits', psf_noVortex_Mask_flux1norm, overwrite=True)#[int(n/2)-math.ceil(50./beam_ratio):int(n/2)+math.ceil(50./beam_ratio),int(n/2)-math.ceil(50./beam_ratio):int(n/2)+math.ceil(50./beam_ratio)], overwrite=True)
(wfo_Coro, sampling) = proper.prop_run_multi('simple_telescope', lam, n, PASSVALUE={'prefix':prefix, 'charge':2, 'diam':diam,'beam_ratio':beam_ratio, 'RAVC':RAVC, 'LS':LS}, QUIET=True)
#(wfo_Coro, sampling) = proper.prop_run('simple_telescope', lam, n, PASSVALUE={'prefix':prefix, 'charge':2, 'diam':37., 'RAVC':RAVC, 'LS':LS}, QUIET=True)
psf_Coro = (abs(wfo_Coro))**2
fits.writeto(prefix+'_psf_Coro_nonorm.fits', psf_Coro, overwrite=True)#[int(n/2)-math.ceil(50./beam_ratio):int(n/2)+math.ceil(50./beam_ratio),int(n/2)-math.ceil(50./beam_ratio):int(n/2)+math.ceil(50./beam_ratio)], overwrite=True)
psf_Coro_maxnorm = psf_Coro/np.max(Offaxis_psf)
fits.writeto(prefix+'_psf_Coro_maxnorm.fits', psf_Coro_maxnorm, overwrite=True)#[int(n/2)-math.ceil(50./beam_ratio):int(n/2)+math.ceil(50./beam_ratio),int(n/2)-math.ceil(50./beam_ratio):int(n/2)+math.ceil(50./beam_ratio)], overwrite=True)
psf_Coro_flux1norm = psf_Coro/sum(sum(NoCoro_psf))
fits.writeto(prefix+'_psf_Coro_flux1norm.fits', psf_Coro_flux1norm, overwrite=True)#[int(n/2)-math.ceil(50./beam_ratio):int(n/2)+math.ceil(50./beam_ratio),int(n/2)-math.ceil(50./beam_ratio):int(n/2)+math.ceil(50./beam_ratio)], overwrite=True)
return
def run_hlc_erkin():
nlam = 7
lam0 = 0.575
bandwidth = 0.1
minlam = lam0 * (1 - bandwidth/2)
maxlam = lam0 * (1 + bandwidth/2)
lam_array = np.linspace( minlam, maxlam, nlam )
n = 256 # output image dimension
final_sampling = 0.1 # output sampling in lam0/D
# compute coronagraphic field
print( "Computing coronagraphic field..." )
(fields, sampling) = proper.prop_run_multi('wfirst_phaseb', lam_array, n, QUIET=True, \
PASSVALUE={'cor_type':'hlc_erkin','use_errors':0,'zindex':[4],'zval_m':[0.19e-9],
'use_hlc_dm_patterns':1, 'final_sampling_lam0':final_sampling} )
images = np.abs(fields)**2
image = np.sum( images, 0 ) / nlam
# move source to 7 lam/D
print( "Computing offset source to compute NI..." )
(fields, sampling) = proper.prop_run_multi('wfirst_phaseb_compact', lam_array, n, QUIET=True, \
PASSVALUE={'cor_type':'hlc_erkin','source_x_offset':7.0,'final_sampling_lam0':final_sampling,
'use_hlc_dm_patterns':1} )
psfs = np.abs(fields)**2
psf = np.sum( psfs, 0 ) / nlam
ni = image / np.max(psf)
fig, ax = plt.subplots( figsize=(8,8) )
im = ax.imshow(ni, norm=LogNorm(vmin=1e-10,vmax=1e-7), cmap=plt.get_cmap('jet'))
circ = Circle((n/2,n/2),3/final_sampling,edgecolor='white', facecolor='none')
ax.add_patch(circ)
circ = Circle((n/2,n/2),9/final_sampling,edgecolor='white', facecolor='none')
ax.add_patch(circ)
fig.colorbar(im, shrink=0.5)
plt.show()
def form_multi_psf(prescription,
sources,
gridsize,
common_sampling,
npsf,
multi=True):
source_psfs = []
for source in sources:
settings = source['settings']
wavelengths = source['wavelengths']
wl_weights = source['weights']
if multi is True:
(wavefronts,
samplings) = proper.prop_run_multi(prescription,
wavelengths,
gridsize=gridsize,
QUIET=True,
PRINT_INTENSITY=False,
PASSVALUE=settings)
# prop_run_multi returns complex arrays, even when PSFs are intensity, so make real with abs
psfs = normalise_sampling(np.abs(wavefronts), samplings,
common_sampling, npsf)
else:
wavefronts = []
samplings = []
for wl in wavelengths:
(wavefront, sampling) = proper.prop_run(prescription,
wl,
gridsize=gridsize,
QUIET=True,
PRINT_INTENSITY=False,
PASSVALUE=settings)
wavefronts.append(wavefront)
samplings.append(sampling)
psfs = normalise_sampling(wavefronts, samplings, common_sampling,
npsf)
source_psfs.append(combine_psfs(psfs, wl_weights))
psf_all = combine_psfs(np.stack(source_psfs),
[1. for i in range(len(source_psfs))])
return psf_all
def run_hlc():
nlam = 7
lam0 = 0.575
bandwidth = 0.1
minlam = lam0 * (1 - bandwidth / 2)
maxlam = lam0 * (1 + bandwidth / 2)
lam_array = np.linspace(minlam, maxlam, nlam)
n = 256 # output image dimension (must be power of 2)
final_sampling = 0.1 # output sampling in lam0/D
# compute unaberrated coronagraphic field using Dwight's DM wavefront maps
print(
"Computing unaberrated coronagraphic field using DM wavefront map...")
(fields, sampling) = proper.prop_run_multi('wfirst_phaseb', lam_array, n, QUIET=True, \
PASSVALUE={'cor_type':'hlc','use_errors':0,'zindex':[4],'zval_m':[0.19e-9],
'use_hlc_dm_patterns':1,'final_sampling_lam0':final_sampling} )
images = np.abs(fields)**2
image = np.sum(images, 0) / nlam
# compute unaberrated coronagraphic field using DM actuator settings
print(
"Computing unaberrated coronagraphic field using DM actuator pistons..."
)
dm1 = proper.prop_fits_read(wfirst_phaseb_proper.lib_dir +
'/examples/hlc_dm1.fits')
dm2 = proper.prop_fits_read(wfirst_phaseb_proper.lib_dir +
'/examples/hlc_dm2.fits')
(fields, sampling) = proper.prop_run_multi('wfirst_phaseb', lam_array, n, QUIET=True, \
PASSVALUE={'cor_type':'hlc','use_errors':0,'zindex':[4],'zval_m':[0.19e-9],
'use_hlc_dm_patterns':0,'final_sampling_lam0':final_sampling,
'use_dm1':1, 'dm1_m':dm1, 'use_dm2':1, 'dm2_m':dm2} )
images = np.abs(fields)**2
image_dm = np.sum(images, 0) / nlam
# compute aberrated coronagraphic field using DM actuator settings
print(
"Computing aberrated coronagraphic field using DM actuator pistons...")
dm1 = proper.prop_fits_read(wfirst_phaseb_proper.lib_dir +
'/examples/hlc_with_aberrations_dm1.fits')
dm2 = proper.prop_fits_read(wfirst_phaseb_proper.lib_dir +
'/examples/hlc_with_aberrations_dm2.fits')
(fields, sampling) = proper.prop_run_multi('wfirst_phaseb', lam_array, n, QUIET=True, \
PASSVALUE={'cor_type':'hlc', 'use_errors':1, 'polaxis':10,
'use_hlc_dm_patterns':0, 'final_sampling_lam0':final_sampling,
'use_dm1':1, 'dm1_m':dm1, 'use_dm2':1, 'dm2_m':dm2} )
images = np.abs(fields)**2
image_ab = np.sum(images, 0) / nlam
# move source to 7 lam/D
print("Computing offset source to compute NI...")
(fields, sampling) = proper.prop_run_multi('wfirst_phaseb_compact', lam_array, n, QUIET=True, \
PASSVALUE={'cor_type':'hlc','source_x_offset':7.0,'use_hlc_dm_patterns':1,'final_sampling_lam0':final_sampling} )
psfs = np.abs(fields)**2
psf = np.sum(psfs, 0) / nlam
max_psf = np.max(psf)
ni = image / max_psf
ni_dm = image_dm / max_psf
ni_ab = image_ab / max_psf
fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(11, 4))
im = ax[0].imshow(ni,
norm=LogNorm(vmin=1e-10, vmax=1e-7),
cmap=plt.get_cmap('jet'))
circ_in = Circle((n / 2, n / 2),
3 / final_sampling,
edgecolor='white',
facecolor='none')
circ_out = Circle((n / 2, n / 2),
9 / final_sampling,
edgecolor='white',
facecolor='none')
ax[0].add_patch(circ_in)
ax[0].add_patch(circ_out)
ax[0].set_title('Unaberrated,\nUsing wavefront maps')
fig.colorbar(im, ax=ax[0], shrink=0.5)
im = ax[1].imshow(ni_dm,
norm=LogNorm(vmin=1e-10, vmax=1e-7),
cmap=plt.get_cmap('jet'))
circ_in = Circle((n / 2, n / 2),
3 / final_sampling,
edgecolor='white',
facecolor='none')
circ_out = Circle((n / 2, n / 2),
9 / final_sampling,
edgecolor='white',
facecolor='none')
ax[1].add_patch(circ_in)
ax[1].add_patch(circ_out)
ax[1].set_title('Unaberrated,\nUsing DM piston maps')
fig.colorbar(im, ax=ax[1], shrink=0.5)
im = ax[2].imshow(ni_ab,
norm=LogNorm(vmin=1e-10, vmax=1e-7),
cmap=plt.get_cmap('jet'))
circ_in = Circle((n / 2, n / 2),
3 / final_sampling,
edgecolor='white',
facecolor='none')
circ_out = Circle((n / 2, n / 2),
9 / final_sampling,
edgecolor='white',
facecolor='none')
ax[2].add_patch(circ_in)
ax[2].add_patch(circ_out)
ax[2].set_title('Aberrated,\nUsing DM piston maps')
fig.colorbar(im, ax=ax[2], shrink=0.5)
plt.show()
def run_flatten():
nlam = 7
lam0 = 0.575
bandwidth = 0.1
minlam = lam0 * (1 - bandwidth / 2)
maxlam = lam0 * (1 + bandwidth / 2)
lam_array = np.linspace(minlam, maxlam, nlam)
n = 256
final_sampling = 0.1
# compute field before flattening (use HLC DM WFE maps)
print("Computing field before flattening...")
fields, sampling = proper.prop_run_multi( 'wfirst_phaseb', lam_array, n, QUIET=True, \
PASSVALUE={'final_sampling_lam0':final_sampling, 'use_hlc_dm_patterns':1, 'use_errors':1, 'polaxis':10} )
images = np.abs(fields)**2
image_before = np.sum(images, 0) / nlam
# compute field after flattening
dm1 = proper.prop_fits_read(wfirst_phaseb_proper.lib_dir +
'/examples/errors_polaxis10_dm.fits')
print("Computing field after flattening...")
fields, sampling = proper.prop_run_multi( 'wfirst_phaseb', lam_array, n, QUIET=True, \
PASSVALUE={'final_sampling_lam0':final_sampling, 'use_dm1':1, 'dm1_m':dm1, 'use_hlc_dm_patterns':1, 'use_errors':1, 'polaxis':10} )
images = np.abs(fields)**2
image_after = np.sum(images, 0) / nlam
# move source by 7.0 lam/D
print("Computing offset source to compute NI...")
fields, sampling = proper.prop_run_multi( 'wfirst_phaseb_compact', lam_array, n, QUIET=True, \
PASSVALUE={'source_x_offset':7.0,'final_sampling_lam0':final_sampling,'use_hlc_dm_patterns':1} )
images = np.abs(fields)**2
psf = np.sum(images, 0) / nlam
max_psf = np.max(psf)
ni_before = image_before / max_psf
ni_after = image_after / max_psf
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(9, 4))
im = ax[0].imshow(ni_before,
norm=LogNorm(vmin=1e-7, vmax=1e-2),
cmap=plt.get_cmap('jet'))
circ = Circle((n / 2, n / 2),
3 / final_sampling,
edgecolor='white',
facecolor='none')
ax[0].add_patch(circ)
circ = Circle((n / 2, n / 2),
9 / final_sampling,
edgecolor='white',
facecolor='none')
ax[0].add_patch(circ)
ax[0].set_title('Before')
fig.colorbar(im, ax=ax[0], shrink=0.5)
im = ax[1].imshow(ni_after,
norm=LogNorm(vmin=1e-7, vmax=1e-2),
cmap=plt.get_cmap('jet'))
circ = Circle((n / 2, n / 2),
3 / final_sampling,
edgecolor='white',
facecolor='none')
ax[1].add_patch(circ)
circ = Circle((n / 2, n / 2),
9 / final_sampling,
edgecolor='white',
facecolor='none')
ax[1].add_patch(circ)
ax[1].set_title('After')
fig.colorbar(im, ax=ax[1], shrink=0.5)
plt.show()
def run_spc_wide_new():
nlam = 7
lam0 = 0.825
bandwidth = 0.1
minlam = lam0 * (1 - bandwidth / 2)
maxlam = lam0 * (1 + bandwidth / 2)
lam_array = np.linspace(minlam, maxlam, nlam)
n = 512 # output image dimension (must be power of 2)
final_sampling = 0.1 # output sampling in lam0/D
# compute unaberrated coronagraphic field
print("Computing unaberrated coronagraphic field using compact model...")
(fields, sampling) = proper.prop_run_multi('wfirst_phaseb_compact', lam_array, n, QUIET=True, \
PASSVALUE={'cor_type':'spc-wide','final_sampling_lam0':final_sampling} )
images = np.abs(fields)**2
image_noab = np.sum(images, 0) / nlam
# compute aberrated coronagraphic field using EFC-derived DM actuator settings
print(
"Computing aberrated coronagraphic field using DM actuator pistons...")
dm1 = proper.prop_fits_read(wfirst_phaseb_proper.lib_dir +
'/examples/spc-wide_with_aberrations_dm1.fits')
dm2 = proper.prop_fits_read(wfirst_phaseb_proper.lib_dir +
'/examples/spc-wide_with_aberrations_dm2.fits')
(fields, sampling) = proper.prop_run_multi('wfirst_phaseb', lam_array, n, QUIET=True, \
PASSVALUE={'cor_type':'spc-wide', 'use_errors':1, 'polaxis':10,
'final_sampling_lam0':final_sampling, 'use_dm1':1, 'dm1_m':dm1, 'use_dm2':1, 'dm2_m':dm2} )
images = np.abs(fields)**2
image_ab = np.sum(images, 0) / nlam
# move source to 10 lam/D
print("Computing offset source to compute NI...")
(fields, sampling) = proper.prop_run_multi('wfirst_phaseb_compact', lam_array, n, QUIET=True, \
PASSVALUE={'cor_type':'spc-wide','source_x_offset':10.0,'final_sampling_lam0':final_sampling} )
psfs = np.abs(fields)**2
psf = np.sum(psfs, 0) / nlam
max_psf = np.max(psf)
ni_noab = image_noab / max_psf
ni_ab = image_ab / max_psf
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))
im = ax[0].imshow(ni_noab,
norm=LogNorm(vmin=1e-10, vmax=1e-7),
cmap=plt.get_cmap('jet'))
circ_in = Circle((n / 2, n / 2),
5.4 / final_sampling,
edgecolor='white',
facecolor='none')
circ_out = Circle((n / 2, n / 2),
20 / final_sampling,
edgecolor='white',
facecolor='none')
ax[0].add_patch(circ_in)
ax[0].add_patch(circ_out)
ax[0].set_title('Unaberrated')
fig.colorbar(im, ax=ax[0], shrink=0.5)
im = ax[1].imshow(ni_ab,
norm=LogNorm(vmin=1e-10, vmax=1e-7),
cmap=plt.get_cmap('jet'))
circ_in = Circle((n / 2, n / 2),
5.4 / final_sampling,
edgecolor='white',
facecolor='none')
circ_out = Circle((n / 2, n / 2),
20 / final_sampling,
edgecolor='white',
facecolor='none')
ax[1].add_patch(circ_in)
ax[1].add_patch(circ_out)
ax[1].set_title('Aberrated,\nUsing DM piston maps')
fig.colorbar(im, ax=ax[1], shrink=0.5)
plt.show()
def run_hlc_input_fields():
nlam = 7
lam0 = 0.575
bandwidth = 0.1
minlam = lam0 * (1 - bandwidth / 2)
maxlam = lam0 * (1 + bandwidth / 2)
lam_array = np.linspace(minlam, maxlam, nlam)
n = 256 # output image dimension
final_sampling = 0.1 # output sampling in lam0/D
# compute coronagraphic field with full prescription
print('Computing field with full prescription')
t1 = time.time()
(fields, sampling) = proper.prop_run_multi('wfirst_phaseb', lam_array, n, QUIET=True, \
PASSVALUE={'use_errors':1,'polaxis':-2,'zindex':[4],'zval_m':[0.19e-9],'final_sampling_lam0':final_sampling} )
t2 = time.time()
print('Time (sec) for full prescription = ' + str(t2 - t1))
images = np.abs(fields)**2
image_full = np.sum(images, 0) / nlam
# write FPM exit pupil fields with full prescription
print('Writing FPM exit pupil fields using full prescription')
(fields, sampling) = proper.prop_run_multi('wfirst_phaseb', lam_array, n, QUIET=True, \
PASSVALUE={'use_errors':1,'polaxis':-2,'zindex':[4],'zval_m':[0.19e-9],'use_hlc_dm_patterns':0,'use_fpm':0,'end_at_fpm_exit_pupil':1, \
'output_field_rootname':'hlc_input_field'} )
# compute coronagraphic field with compact prescription and input fields
print(
'Computing field with compact prescription and pre-computed input fields'
)
t1 = time.time()
(fields, sampling) = proper.prop_run_multi('wfirst_phaseb_compact', lam_array, n, QUIET=True, \
PASSVALUE={'input_field_rootname':'hlc_input_field','polaxis':-2,'final_sampling_lam0':final_sampling} )
t2 = time.time()
print('Time (sec) for compact prescription with input fields = ' +
str(t2 - t1))
images = np.abs(fields)**2
image = np.sum(images, 0) / nlam
# move source to 7 lam/D
print('Computing offset source to compute NI')
(fields, sampling) = proper.prop_run_multi('wfirst_phaseb_compact', lam_array, n, QUIET=True, \
PASSVALUE={'source_x_offset':7.0,'final_sampling_lam0':final_sampling} )
psfs = np.abs(fields)**2
psf = np.sum(psfs, 0) / nlam
max_psf = np.max(psf)
ni_full = image_full / max_psf
ni = image / max_psf
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(9, 4))
im = ax[0].imshow(ni_full,
norm=LogNorm(vmin=1e-7, vmax=1e-2),
cmap=plt.get_cmap('jet'))
circ = Circle((n / 2, n / 2),
3 / final_sampling,
edgecolor='white',
facecolor='none')
ax[0].add_patch(circ)
circ = Circle((n / 2, n / 2),
9 / final_sampling,
edgecolor='white',
facecolor='none')
ax[0].add_patch(circ)
ax[0].set_title('Full prescription')
fig.colorbar(im, ax=ax[0], shrink=0.5)
im = ax[1].imshow(ni,
norm=LogNorm(vmin=1e-7, vmax=1e-2),
cmap=plt.get_cmap('jet'))
circ = Circle((n / 2, n / 2),
3 / final_sampling,
edgecolor='white',
facecolor='none')
ax[1].add_patch(circ)
circ = Circle((n / 2, n / 2),
9 / final_sampling,
edgecolor='white',
facecolor='none')
ax[1].add_patch(circ)
ax[1].set_title('Compact prescription')
fig.colorbar(im, ax=ax[1], shrink=0.5)
plt.show()