def contrast_jwst_ana_num(matdir,
matrix_mode="analytical",
rms=1. * u.nm,
im_pastis=False,
plotting=False):
"""
Calculate the contrast for an RMS WFE with image PASTIS, matrix PASTIS
:param matdir: data directory to use for matrix and calibration coefficients from
:param matrix_mode: use 'analytical or 'numerical' matrix
:param rms: RMS wavefront error in pupil to calculate contrast for; in NANOMETERS
:param im_pastis: default False, whether to also calculate contrast from image PASTIS
:param plotting: default False, whether to save E2E and PASTIS DH PSFs; works only if im_pastis=True
:return:
"""
from e2e_simulators import webbpsf_imaging as webbim
print("THIS ONLY WORKS FOR PISTON FOR NOW")
# Keep track of time
start_time = time.time() # runtime currently is around 12 min
# Parameters
dataDir = os.path.join(CONFIG_INI.get('local', 'local_data_path'), matdir)
which_tel = CONFIG_INI.get('telescope', 'name')
nb_seg = CONFIG_INI.getint(which_tel, 'nb_subapertures')
filter = CONFIG_INI.get(which_tel, 'filter_name')
fpm = CONFIG_INI.get(which_tel, 'focal_plane_mask') # focal plane mask
lyot_stop = CONFIG_INI.get(which_tel, 'pupil_plane_stop') # Lyot stop
inner_wa = CONFIG_INI.getint(which_tel, 'IWA')
outer_wa = CONFIG_INI.getint(which_tel, 'OWA')
tel_size_px = CONFIG_INI.getint('numerical', 'tel_size_px')
sampling = CONFIG_INI.getfloat('numerical', 'sampling')
#real_samp = sampling * tel_size_px / im_size
zern_number = CONFIG_INI.getint('calibration', 'zernike')
zern_mode = util.ZernikeMode(zern_number)
zern_max = CONFIG_INI.getint('zernikes', 'max_zern')
# Import PASTIS matrix
matrix_pastis = None
if matrix_mode == 'numerical':
filename = 'PASTISmatrix_num_' + zern_mode.name + '_' + zern_mode.convention + str(
zern_mode.index)
matrix_pastis = fits.getdata(
os.path.join(dataDir, 'matrix_numerical', filename + '.fits'))
elif matrix_mode == 'analytical':
filename = 'PASTISmatrix_' + zern_mode.name + '_' + zern_mode.convention + str(
zern_mode.index)
matrix_pastis = fits.getdata(
os.path.join(dataDir, 'matrix_analytical', filename + '.fits'))
# Create random aberration coefficients
aber = np.random.random([nb_seg]) # piston values in input units
#print('PISTON ABERRATIONS:', aber)
# Normalize to the RMS value I want
rms_init = util.rms(aber)
aber *= rms.value / rms_init
calc_rms = util.rms(aber) * u.nm
aber *= u.nm # making sure the aberration has the correct units
print("Calculated RMS:", calc_rms)
# Remove global piston
aber -= np.mean(aber)
# Make equivalent aberration array that goes into the WebbPSF function
Aber_WSS = np.zeros([nb_seg, zern_max])
Aber_WSS[:, 0] = aber.to(
u.m
).value # index "0" works because we're using piston currently; convert to meters
### BASELINE PSF - NO ABERRATIONS, NO CORONAGRAPH
print('Generating baseline PSF from E2E - no coronagraph, no aberrations')
psf_perfect = webbim.nircam_nocoro(filter, np.zeros_like(Aber_WSS))
normp = np.max(psf_perfect)
psf_perfect = psf_perfect / normp
### WEBBPSF
print('Generating E2E coro contrast')
start_webb = time.time()
# Set up NIRCam and coronagraph, get PSF
psf_webbpsf = webbim.nircam_coro(filter, fpm, lyot_stop, Aber_WSS)
psf_webbpsf = psf_webbpsf / normp
# Create dark hole
dh_area = util.create_dark_hole(psf_webbpsf, inner_wa, outer_wa, sampling)
# Get the mean contrast from the WebbPSF coronagraph
webb_dh_psf = psf_webbpsf * dh_area
contrast_webbpsf = np.mean(webb_dh_psf[np.where(webb_dh_psf != 0)])
end_webb = time.time()
#TODO: save plots of phase on segmented pupil
# Load in baseline contrast
contrastname = 'base-contrast_' + zern_mode.name + '_' + zern_mode.convention + str(
zern_mode.index)
contrast_base = float(
np.loadtxt(os.path.join(dataDir, 'calibration',
contrastname + '.txt')))
### IMAGE PASTIS
contrast_am = np.nan
if im_pastis:
print('Generating contrast from image-PASTIS')
start_impastis = time.time()
# Create calibrated image from analytical model
psf_am, full_psf = impastis.analytical_model(zern_number,
aber,
cali=True)
# Get the mean contrast from image PASTIS
contrast_am = np.mean(psf_am[np.where(psf_am != 0)]) + contrast_base
end_impastis = time.time()
### MATRIX PASTIS
print('Generating contrast from matrix-PASTIS')
start_matrixpastis = time.time()
# Get mean contrast from matrix PASTIS
contrast_matrix = util.pastis_contrast(
aber, matrix_pastis
) + contrast_base # calculating contrast with PASTIS matrix model
end_matrixpastis = time.time()
ratio = None
if im_pastis:
ratio = contrast_am / contrast_matrix
# Outputs
print('\n--- CONTRASTS: ---')
print('Mean contrast from E2E:', contrast_webbpsf)
print('Mean contrast with image PASTIS:', contrast_am)
print('Contrast from matrix PASTIS:', contrast_matrix)
print('Ratio image PASTIS / matrix PASTIS:', ratio)
print('\n--- RUNTIMES: ---')
print('E2E: ', end_webb - start_webb, 'sec =',
(end_webb - start_webb) / 60, 'min')
if im_pastis:
print('Image PASTIS: ', end_impastis - start_impastis, 'sec =',
(end_impastis - start_impastis) / 60, 'min')
print('Matrix PASTIS: ', end_matrixpastis - start_matrixpastis, 'sec =',
(end_matrixpastis - start_matrixpastis) / 60, 'min')
end_time = time.time()
runtime = end_time - start_time
print('Runtime for contrast_calculation_simple.py: {} sec = {} min'.format(
runtime, runtime / 60))
# Save the PSFs
if im_pastis:
if plotting:
# As fits files
util.write_fits(
util.zoom_cen(webb_dh_psf, psf_am.shape[0] / 2),
os.path.join(
dataDir, 'results', 'dh_images_' + matrix_mode,
'{:.2e}'.format(rms.value) + str(rms.unit) +
'RMS_e2e.fits'))
util.write_fits(
psf_am,
os.path.join(
dataDir, 'results', 'dh_images_' + matrix_mode,
'{:.2e}'.format(rms.value) + str(rms.unit) +
'RMS_am.fits'))
# As PDF plot
plt.clf()
plt.figure()
plt.suptitle('{:.2e}'.format(rms.value) + str(rms.unit) + " RMS")
plt.subplot(1, 2, 1)
plt.title("E2E")
plt.imshow(util.zoom_cen(webb_dh_psf, psf_am.shape[0] / 2),
norm=LogNorm())
plt.colorbar()
plt.subplot(1, 2, 2)
plt.title("PASTIS image")
plt.imshow(psf_am, norm=LogNorm())
plt.colorbar()
plt.savefig(
os.path.join(dataDir, 'results', 'dh_images_' + matrix_mode,
'{:.2e}'.format(rms.value) + 'DH_PSFs.pdf'))
#TODO: check image rotation, I think there is a 90 degree difference in them for the JWST simulations
return contrast_webbpsf, contrast_am, contrast_matrix
def ana_matrix_jwst():
# Keep track of time
start_time = time.time() # runtime is currently around 11 minutes
print('Building analytical matrix for JWST\n')
# Parameters
datadir = os.path.join(CONFIG_INI.get('local', 'local_data_path'),
'active')
which_tel = CONFIG_INI.get('telescope', 'name')
resDir = os.path.join(datadir, 'matrix_analytical')
nb_seg = CONFIG_INI.getint(which_tel, 'nb_subapertures')
nm_aber = CONFIG_INI.getfloat('calibration', 'single_aberration') * u.nm
zern_number = CONFIG_INI.getint('calibration',
'zernike') # Noll convention!
zern_mode = util.ZernikeMode(
zern_number) # Create Zernike mode object for easier handling
# If subfolder "matrix_analytical" doesn't exist yet, create it.
if not os.path.isdir(resDir):
os.mkdir(resDir)
#-# Generating the PASTIS matrix
matrix_direct = np.zeros(
[nb_seg,
nb_seg]) # Generate empty matrix for contrast values from loop.
all_ims = []
all_dhs = []
all_contrasts = []
for i in range(nb_seg):
for j in range(nb_seg):
print('STEP: {}-{} / {}-{}'.format(i + 1, j + 1, nb_seg, nb_seg))
# Putting aberration only on segments i and j
tempA = np.zeros([nb_seg])
tempA[i] = nm_aber.value
tempA[j] = nm_aber.value
tempA *= u.nm # making sure this array has the right units
# Create PASTIS image and save full image as well as DH image
temp_im_am, full_psf = impastis.analytical_model(zern_number,
tempA,
cali=True)
filename_psf = 'psf_' + zern_mode.name + '_' + zern_mode.convention + str(
zern_mode.index) + '_segs_' + str(i + 1) + '-' + str(j + 1)
util.write_fits(full_psf,
os.path.join(resDir, 'psfs',
filename_psf + '.fits'),
header=None,
metadata=None)
all_ims.append(full_psf)
filename_dh = 'dh_' + zern_mode.name + '_' + zern_mode.convention + str(
zern_mode.index) + '_segs_' + str(i + 1) + '-' + str(j + 1)
util.write_fits(temp_im_am,
os.path.join(resDir, 'darkholes',
filename_dh + '.fits'),
header=None,
metadata=None)
all_dhs.append(temp_im_am)
contrast = np.mean(temp_im_am[np.where(temp_im_am != 0)])
matrix_direct[i, j] = contrast
print('contrast =', contrast)
all_contrasts.append(contrast)
all_ims = np.array(all_ims)
all_dhs = np.array(all_dhs)
all_contrasts = np.array(all_contrasts)
# Filling the off-axis elements
matrix_two_N = np.copy(
matrix_direct
) # This is just an intermediary copy so that I don't mix things up.
matrix_pastis = np.copy(
matrix_direct) # This will be the final PASTIS matrix.
for i in range(nb_seg):
for j in range(nb_seg):
if i != j:
matrix_off_val = (matrix_two_N[i, j] - matrix_two_N[i, i] -
matrix_two_N[j, j]) / 2.
matrix_pastis[i, j] = matrix_off_val
print('Off-axis for i{}-j{}: {}'.format(
i + 1, j + 1, matrix_off_val))
# Normalize matrix for the input aberration
matrix_pastis /= np.square(nm_aber.value)
# Save matrix to file
filename = 'PASTISmatrix_' + zern_mode.name + '_' + zern_mode.convention + str(
zern_mode.index)
util.write_fits(matrix_pastis,
os.path.join(resDir, filename + '.fits'),
header=None,
metadata=None)
print('Matrix saved to:', os.path.join(resDir, filename + '.fits'))
# Save the PSF and DH image *cubes* as well (as opposed to each one individually)
util.write_fits(all_ims,
os.path.join(resDir, 'psfs', 'psf_cube' + '.fits'),
header=None,
metadata=None)
util.write_fits(all_dhs,
os.path.join(resDir, 'darkholes', 'dh_cube' + '.fits'),
header=None,
metadata=None)
np.savetxt(os.path.join(resDir, 'contrasts.txt'), all_contrasts, fmt='%e')
# Tell us how long it took to finish.
end_time = time.time()
print('Runtime for matrix_building.py:', end_time - start_time, 'sec =',
(end_time - start_time) / 60, 'min')
print('Data saved to {}'.format(resDir))
plt.xlabel("Surface RMS in " + str(u.nm))
plt.ylabel("Contrast")
plt.legend()
plt.savefig(os.path.join(resultdir, 'hockeystick_contrasts.pdf'))
end_time = time.time()
runtime = end_time - start_time
print(
'\nTotal runtime for pastis_vs_e2e_contrast_calc.py: {} sec = {} min'.
format(runtime, runtime / 60))
if __name__ == '__main__':
# Pick one to run
#hockeystick_jwst()
#hockeystick_hicat(matrixdir='/Users/ilaginja/Documents/Git/PASTIS/Jupyter Notebooks/HiCAT')
# LUVOIR
run_choice = 'active'
apod_design = 'small'
result_dir = os.path.join(CONFIG_INI.get('local', 'local_data_path'),
run_choice, 'results')
matrix_dir = os.path.join(CONFIG_INI.get('local', 'local_data_path'),
run_choice, 'matrix_numerical')
hockeystick_luvoir(apodizer_choice=apod_design,
matrixdir=matrix_dir,
resultdir=result_dir,
range_points=50,
no_realizations=10)
def hockeystick_jwst(range_points=3,
no_realizations=3,
matrix_mode='analytical'):
"""
Construct a PASTIS hockeystick contrast curve for validation of the PASTIS matrix.
The aberration range is a fixed parameter since it depends on the coronagraph (and telescope) used. We define how
many realizations of a specific rms error we want to run through, and also how many points we want to fill the
aberration range with. At each point we calculate the contrast for all realizations and plot the mean of this set
of results in a figure that shows contrast vs. rms phase error.
:param range_points: int, How many points of rms error to use in the predefined aberration range.
:param no_realizations: int, How many realizations per rms error should be calculated; the mean of the realizations
is used.
:param matrix_mode: string, Choice of PASTIS matrix to validate: 'analytical' or 'numerical'
:return:
"""
# Keep track of time
start_time = time.time()
##########################
WORKDIRECTORY = "active" # you can chose here what data directory to work in
# anything else than "active" works only with im_pastis=False
rms_range = np.logspace(-1, 3,
range_points) # Create range of RMS values to test
realiz = no_realizations # how many random realizations per RMS values to do
##########################
# Set up path for results
outDir = os.path.join(CONFIG_INI.get('local', 'local_data_path'),
WORKDIRECTORY, 'results')
if not os.path.isdir(outDir):
os.mkdir(outDir)
if not os.path.isdir(os.path.join(outDir, 'dh_images_' + matrix_mode)):
os.mkdir(os.path.join(outDir, 'dh_images_' + matrix_mode))
# Loop over different RMS values and calculate contrast with PASTIS and E2E simulation
e2e_contrasts = [] # contrasts from E2E sim
am_contrasts = [] # contrasts from image PASTIS
matrix_contrasts = [] # contrasts from matrix PASTIS
print("RMS range: {}".format(rms_range, fmt="%e"))
print("Random realizations: {}".format(realiz))
for i, rms in enumerate(rms_range):
rms *= u.nm # Making sure this has the correct units
e2e_rand = []
am_rand = []
matrix_rand = []
for j in range(realiz):
print("\n#####################################")
print("CALCULATING CONTRAST FOR {:.4f}".format(rms))
print("RMS {}/{}".format(i + 1, len(rms_range)))
print("Random realization: {}/{}".format(j + 1, realiz))
print("Total: {}/{}\n".format((i * realiz) + (j + 1),
len(rms_range) * realiz))
c_e2e, c_am, c_matrix = consim.contrast_jwst_ana_num(
matdir=WORKDIRECTORY,
matrix_mode=matrix_mode,
rms=rms,
im_pastis=True,
plotting=True)
e2e_rand.append(c_e2e)
am_rand.append(c_am)
matrix_rand.append(c_matrix)
e2e_contrasts.append(np.mean(e2e_rand))
am_contrasts.append(np.mean(am_rand))
matrix_contrasts.append(np.mean(matrix_rand))
e2e_contrasts = np.array(e2e_contrasts)
am_contrasts = np.array(am_contrasts)
matrix_contrasts = np.array(matrix_contrasts)
# Save results to txt file
df = pd.DataFrame({
'rms': rms_range,
'c_e2e': e2e_contrasts,
'c_am': am_contrasts,
'c_matrix': matrix_contrasts
})
df.to_csv(os.path.join(outDir, "contrasts_" + matrix_mode + ".txt"),
sep=' ',
na_rep='NaN')
# Plot
plt.clf()
plt.title("Contrast calculation")
plt.plot(rms_range, e2e_contrasts, label="E2E")
plt.plot(rms_range, am_contrasts, label="Image PASTIS")
plt.plot(rms_range, matrix_contrasts, label="Matrix PASTIS")
plt.semilogx()
plt.semilogy()
plt.xlabel("Surface RMS in " + str(u.nm))
plt.ylabel("Contrast")
plt.legend()
#plt.show()
plt.savefig(
os.path.join(outDir, "PASTIS_HOCKEY_STICK_" + matrix_mode + ".pdf"))
end_time = time.time()
runtime = end_time - start_time
print('Runtime for pastis_vs_e2e_contrast_calc.py: {} sec = {} min'.format(
runtime, runtime / 60))
def num_matrix_jwst():
"""
Generate a numerical PASTIS matrix for a JWST coronagraph.
All inputs are read from the (local) configfile and saved to the specified output directory.
"""
import webbpsf
from e2e_simulators import webbpsf_imaging as webbim
# Keep track of time
start_time = time.time() # runtime is currently around 21 minutes
print('Building numerical matrix for JWST\n')
# Parameters
resDir = os.path.join(CONFIG_INI.get('local', 'local_data_path'), 'active',
'matrix_numerical')
which_tel = CONFIG_INI.get('telescope', 'name')
nb_seg = CONFIG_INI.getint(which_tel, 'nb_subapertures')
im_size_e2e = CONFIG_INI.getint('numerical', 'im_size_px_webbpsf')
inner_wa = CONFIG_INI.getint(which_tel, 'IWA')
outer_wa = CONFIG_INI.getint(which_tel, 'OWA')
sampling = CONFIG_INI.getfloat('numerical', 'sampling')
fpm = CONFIG_INI.get(which_tel, 'focal_plane_mask') # focal plane mask
lyot_stop = CONFIG_INI.get(which_tel, 'pupil_plane_stop') # Lyot stop
filter = CONFIG_INI.get(which_tel, 'filter_name')
nm_aber = CONFIG_INI.getfloat('calibration', 'single_aberration') * u.nm
wss_segs = webbpsf.constants.SEGNAMES_WSS_ORDER
zern_max = CONFIG_INI.getint('zernikes', 'max_zern')
zern_number = CONFIG_INI.getint('calibration', 'zernike')
zern_mode = util.ZernikeMode(
zern_number) # Create Zernike mode object for easier handling
wss_zern_nb = util.noll_to_wss(
zern_number) # Convert from Noll to WSS framework
# If subfolder "matrix_numerical" doesn't exist yet, create it.
if not os.path.isdir(resDir):
os.mkdir(resDir)
# If subfolder "OTE_images" doesn't exist yet, create it.
if not os.path.isdir(os.path.join(resDir, 'OTE_images')):
os.mkdir(os.path.join(resDir, 'OTE_images'))
# If subfolder "psfs" doesn't exist yet, create it.
if not os.path.isdir(os.path.join(resDir, 'psfs')):
os.mkdir(os.path.join(resDir, 'psfs'))
# If subfolder "darkholes" doesn't exist yet, create it.
if not os.path.isdir(os.path.join(resDir, 'darkholes')):
os.mkdir(os.path.join(resDir, 'darkholes'))
# Create the dark hole mask.
pup_im = np.zeros([im_size_e2e, im_size_e2e
]) # this is just used for DH mask generation
dh_area = util.create_dark_hole(pup_im, inner_wa, outer_wa, sampling)
# Create a direct WebbPSF image for normalization factor
fake_aber = np.zeros([nb_seg, zern_max])
psf_perfect = webbim.nircam_nocoro(filter, fake_aber)
normp = np.max(psf_perfect)
psf_perfect = psf_perfect / normp
# Set up NIRCam coro object from WebbPSF
nc_coro = webbpsf.NIRCam()
nc_coro.filter = filter
nc_coro.image_mask = fpm
nc_coro.pupil_mask = lyot_stop
# Null the OTE OPDs for the PSFs, maybe we will add internal WFE later.
nc_coro, ote_coro = webbpsf.enable_adjustable_ote(
nc_coro) # create OTE for coronagraph
nc_coro.include_si_wfe = False # set SI internal WFE to zero
#-# Generating the PASTIS matrix and a list for all contrasts
matrix_direct = np.zeros([nb_seg, nb_seg]) # Generate empty matrix
all_psfs = []
all_dhs = []
all_contrasts = []
print('nm_aber: {}'.format(nm_aber))
for i in range(nb_seg):
for j in range(nb_seg):
print('\nSTEP: {}-{} / {}-{}'.format(i + 1, j + 1, nb_seg, nb_seg))
# Get names of segments, they're being addressed by their names in the ote functions.
seg_i = wss_segs[i].split('-')[0]
seg_j = wss_segs[j].split('-')[0]
# Put the aberration on the correct segments
Aber_WSS = np.zeros([
nb_seg, zern_max
]) # The Zernikes here will be filled in the WSS order!!!
# Because it goes into _apply_hexikes_to_seg().
Aber_WSS[i, wss_zern_nb - 1] = nm_aber.to(
u.m
).value # Aberration on the segment we're currently working on;
# convert to meters; -1 on the Zernike because Python starts
# numbering at 0.
Aber_WSS[j, wss_zern_nb - 1] = nm_aber.to(
u.m).value # same for other segment
# Putting aberrations on segments i and j
ote_coro.reset(
) # Making sure there are no previous movements on the segments.
ote_coro.zero() # set OTE for coronagraph to zero
# Apply both aberrations to OTE. If i=j, apply only once!
ote_coro._apply_hexikes_to_seg(seg_i, Aber_WSS[
i, :]) # set segment i (segment numbering starts at 1)
if i != j:
ote_coro._apply_hexikes_to_seg(seg_j,
Aber_WSS[j, :]) # set segment j
# If you want to display it:
# ote_coro.display_opd()
# plt.show()
# Save OPD images for testing
opd_name = 'opd_' + zern_mode.name + '_' + zern_mode.convention + str(
zern_mode.index) + '_segs_' + str(i + 1) + '-' + str(j + 1)
plt.clf()
ote_coro.display_opd()
plt.savefig(os.path.join(resDir, 'OTE_images', opd_name + '.pdf'))
print('Calculating WebbPSF image')
image = nc_coro.calc_psf(fov_pixels=int(im_size_e2e),
oversample=1,
nlambda=1)
psf = image[0].data / normp
# Save WebbPSF image to disk
filename_psf = 'psf_' + zern_mode.name + '_' + zern_mode.convention + str(
zern_mode.index) + '_segs_' + str(i + 1) + '-' + str(j + 1)
util.write_fits(psf,
os.path.join(resDir, 'psfs',
filename_psf + '.fits'),
header=None,
metadata=None)
all_psfs.append(psf)
print('Calculating mean contrast in dark hole')
dh_intensity = psf * dh_area
contrast = np.mean(dh_intensity[np.where(dh_intensity != 0)])
print('contrast:', contrast)
# Save DH image to disk and put current contrast in list
filename_dh = 'dh_' + zern_mode.name + '_' + zern_mode.convention + str(
zern_mode.index) + '_segs_' + str(i + 1) + '-' + str(j + 1)
util.write_fits(dh_intensity,
os.path.join(resDir, 'darkholes',
filename_dh + '.fits'),
header=None,
metadata=None)
all_dhs.append(dh_intensity)
all_contrasts.append(contrast)
# Fill according entry in the matrix
matrix_direct[i, j] = contrast
# Transform saved lists to arrays
all_psfs = np.array(all_psfs)
all_dhs = np.array(all_dhs)
all_contrasts = np.array(all_contrasts)
# Filling the off-axis elements
matrix_two_N = np.copy(
matrix_direct
) # This is just an intermediary copy so that I don't mix things up.
matrix_pastis = np.copy(
matrix_direct) # This will be the final PASTIS matrix.
for i in range(nb_seg):
for j in range(nb_seg):
if i != j:
matrix_off_val = (matrix_two_N[i, j] - matrix_two_N[i, i] -
matrix_two_N[j, j]) / 2.
matrix_pastis[i, j] = matrix_off_val
print('Off-axis for i{}-j{}: {}'.format(
i + 1, j + 1, matrix_off_val))
# Normalize matrix for the input aberration
matrix_pastis /= np.square(nm_aber.value)
# Save matrix to file
filename_matrix = 'PASTISmatrix_num_' + zern_mode.name + '_' + zern_mode.convention + str(
zern_mode.index)
util.write_fits(matrix_pastis,
os.path.join(resDir, filename_matrix + '.fits'),
header=None,
metadata=None)
print('Matrix saved to:', os.path.join(resDir, filename_matrix + '.fits'))
# Save the PSF and DH image *cubes* as well (as opposed to each one individually)
util.write_fits(all_psfs,
os.path.join(resDir, 'psfs', 'psf_cube' + '.fits'),
header=None,
metadata=None)
util.write_fits(all_dhs,
os.path.join(resDir, 'darkholes', 'dh_cube' + '.fits'),
header=None,
metadata=None)
np.savetxt(os.path.join(resDir, 'contrasts.txt'), all_contrasts, fmt='%e')
# Tell us how long it took to finish.
end_time = time.time()
print('Runtime for matrix_building.py:', end_time - start_time, 'sec =',
(end_time - start_time) / 60, 'min')
print('Data saved to {}'.format(resDir))
import numpy as np
import matplotlib.pyplot as plt
import astropy.units as u
import webbpsf
from config import CONFIG_INI
import util_pastis as util
import image_pastis as impastis
if __name__ == '__main__':
# Keep track of time
start_time = time.time() # runtime currently is around 3 minutes
# Parameters
outDir = os.path.join(CONFIG_INI.get('local', 'local_data_path'), 'active',
'calibration')
telescope = CONFIG_INI.get('telescope', 'name')
fpm = CONFIG_INI.get(telescope, 'focal_plane_mask') # focal plane mask
lyot_stop = CONFIG_INI.get(telescope, 'pupil_plane_stop') # Lyot stop
filter = CONFIG_INI.get(telescope, 'filter_name')
tel_size_px = CONFIG_INI.getint('numerical', 'tel_size_px')
im_size_e2e = CONFIG_INI.getint('numerical', 'im_size_px_webbpsf')
size_seg = CONFIG_INI.getint('numerical', 'size_seg')
nb_seg = CONFIG_INI.getint(telescope, 'nb_subapertures')
wss_segs = webbpsf.constants.SEGNAMES_WSS_ORDER
zern_max = CONFIG_INI.getint('zernikes', 'max_zern')
inner_wa = CONFIG_INI.getint(telescope, 'IWA')
outer_wa = CONFIG_INI.getint(telescope, 'OWA')
sampling = CONFIG_INI.getfloat('numerical', 'sampling')
"""
import os
import time
import numpy as np
import matplotlib.pyplot as plt
from astropy.io import fits
import astropy.units as u
import hcipy as hc
from config import CONFIG_INI
import util_pastis as util
from e2e_simulators.luvoir_imaging import LuvoirAPLC
# Set WebbPSF environment variable
os.environ['WEBBPSF_PATH'] = CONFIG_INI.get('local', 'webbpsf_data_path')
def num_matrix_jwst():
"""
Generate a numerical PASTIS matrix for a JWST coronagraph.
All inputs are read from the (local) configfile and saved to the specified output directory.
"""
import webbpsf
from e2e_simulators import webbpsf_imaging as webbim
# Keep track of time
start_time = time.time() # runtime is currently around 21 minutes
print('Building numerical matrix for JWST\n')
def num_matrix_luvoir(design):
"""
Generate a numerical PASTIS matrix for a LUVOIR A coronagraph.
All inputs are read from the (local) configfile and saved to the specified output directory.
The LUVOIR STDT delivery in May 2018 included three different apodizers
we can work with, so I will implement an easy way of making a choice between them.
small, medium and large
"""
# Keep track of time
start_time = time.time() # runtime is currently around 150 minutes
print('Building numerical matrix for LUVOIR\n')
### Parameters
# System parameters
resDir = os.path.join(CONFIG_INI.get('local', 'local_data_path'), 'active',
'matrix_numerical')
zern_number = CONFIG_INI.getint('calibration', 'zernike')
zern_mode = util.ZernikeMode(
zern_number) # Create Zernike mode object for easier handling
# General telescope parameters
nb_seg = CONFIG_INI.getint('LUVOIR', 'nb_subapertures')
wvln = CONFIG_INI.getfloat('LUVOIR', 'lambda') * 1e-9 # m
diam = CONFIG_INI.getfloat('LUVOIR', 'diameter') # m
nm_aber = CONFIG_INI.getfloat('calibration',
'single_aberration') * 1e-9 # m
# Image system parameters
im_lamD = 30 # image size in lambda/D
sampling = 4
# Print some of the defined parameters
print('LUVOIR apodizer design: {}'.format(design))
print()
print('Wavelength: {} m'.format(wvln))
print('Telescope diameter: {} m'.format(diam))
print('Number of segments: {}'.format(nb_seg))
print()
print('Image size: {} lambda/D'.format(im_lamD))
print('Sampling: {} px per lambda/D'.format(sampling))
### Setting up the paths
# If subfolder "matrix_numerical" doesn't exist yet, create it.
if not os.path.isdir(resDir):
os.mkdir(resDir)
# If subfolder "OTE_images" doesn't exist yet, create it.
if not os.path.isdir(os.path.join(resDir, 'OTE_images')):
os.mkdir(os.path.join(resDir, 'OTE_images'))
# If subfolder "psfs" doesn't exist yet, create it.
if not os.path.isdir(os.path.join(resDir, 'psfs')):
os.mkdir(os.path.join(resDir, 'psfs'))
### Instantiate Luvoir telescope with chosen apodizer design
optics_input = '/Users/ilaginja/Documents/LabWork/ultra/LUVOIR_delivery_May2019/'
luvoir = LuvoirAPLC(optics_input, design, sampling)
### Dark hole mask
dh_outer = hc.circular_aperture(2 * luvoir.apod_dict[design]['owa'] *
luvoir.lam_over_d)(luvoir.focal_det)
dh_inner = hc.circular_aperture(2 * luvoir.apod_dict[design]['iwa'] *
luvoir.lam_over_d)(luvoir.focal_det)
dh_mask = (dh_outer - dh_inner).astype('bool')
### Reference images for contrast normalization and coronagraph floor
unaberrated_coro_psf, ref = luvoir.calc_psf(ref=True,
display_intermediate=False,
return_intermediate=False)
norm = np.max(ref)
dh_intensity = unaberrated_coro_psf / norm * dh_mask
contrast_floor = np.mean(dh_intensity[np.where(dh_intensity != 0)])
print(contrast_floor)
### Generating the PASTIS matrix and a list for all contrasts
matrix_direct = np.zeros([nb_seg, nb_seg]) # Generate empty matrix
all_psfs = []
all_contrasts = []
print('nm_aber: {} m'.format(nm_aber))
for i in range(nb_seg):
for j in range(nb_seg):
print('\nSTEP: {}-{} / {}-{}'.format(i + 1, j + 1, nb_seg, nb_seg))
# Put aberration on correct segments. If i=j, apply only once!
luvoir.flatten()
luvoir.set_segment(i + 1, nm_aber / 2, 0, 0)
if i != j:
luvoir.set_segment(j + 1, nm_aber / 2, 0, 0)
print('Calculating coro image...')
image, inter = luvoir.calc_psf(ref=False,
display_intermediate=False,
return_intermediate='intensity')
# Normalize PSF by reference image
psf = image / norm
# Save image to disk
filename_psf = 'psf_' + zern_mode.name + '_' + zern_mode.convention + str(
zern_mode.index) + '_segs_' + str(i + 1) + '-' + str(j + 1)
hc.write_fits(psf,
os.path.join(resDir, 'psfs', filename_psf + '.fits'))
all_psfs.append(psf)
# Save OPD images for testing (are these actually surface images, not OPD?)
opd_name = 'opd_' + zern_mode.name + '_' + zern_mode.convention + str(
zern_mode.index) + '_segs_' + str(i + 1) + '-' + str(j + 1)
plt.clf()
hc.imshow_field(inter['seg_mirror'],
mask=luvoir.aperture,
cmap='RdBu')
plt.savefig(os.path.join(resDir, 'OTE_images', opd_name + '.pdf'))
print('Calculating mean contrast in dark hole')
dh_intensity = psf * dh_mask
contrast = np.mean(dh_intensity[np.where(dh_intensity != 0)])
print('contrast:', contrast)
all_contrasts.append(contrast)
# Fill according entry in the matrix and subtract baseline contrast
matrix_direct[i, j] = contrast - contrast_floor
# Transform saved lists to arrays
all_psfs = np.array(all_psfs)
all_contrasts = np.array(all_contrasts)
# Filling the off-axis elements
matrix_two_N = np.copy(
matrix_direct
) # This is just an intermediary copy so that I don't mix things up.
matrix_pastis = np.copy(
matrix_direct) # This will be the final PASTIS matrix.
for i in range(nb_seg):
for j in range(nb_seg):
if i != j:
matrix_off_val = (matrix_two_N[i, j] - matrix_two_N[i, i] -
matrix_two_N[j, j]) / 2.
matrix_pastis[i, j] = matrix_off_val
print('Off-axis for i{}-j{}: {}'.format(
i + 1, j + 1, matrix_off_val))
# Normalize matrix for the input aberration - the whole code is set up to be normalized to 1 nm, and even if
# the units entered are in m for the sake of HCIPy, everything else is assuming the baseline is 1nm, so the
# normalization can be taken out if we're working with exactly 1 nm for the aberration, even if entered in meters.
#matrix_pastis /= np.square(nm_aber)
# Save matrix to file
filename_matrix = 'PASTISmatrix_num_' + zern_mode.name + '_' + zern_mode.convention + str(
zern_mode.index)
hc.write_fits(matrix_pastis, os.path.join(resDir,
filename_matrix + '.fits'))
print('Matrix saved to:', os.path.join(resDir, filename_matrix + '.fits'))
# Save the PSF image *cube* as well (as opposed to each one individually)
hc.write_fits(
all_psfs,
os.path.join(resDir, 'psfs', 'psf_cube' + '.fits'),
)
np.savetxt(os.path.join(resDir, 'contrasts.txt'), all_contrasts, fmt='%e')
# Tell us how long it took to finish.
end_time = time.time()
print('Runtime for matrix_building.py:', end_time - start_time, 'sec =',
(end_time - start_time) / 60, 'min')
print('Data saved to {}'.format(resDir))
def analytical_model(zernike_pol, coef, cali=False):
"""
:param zernike_pol:
:param coef:
:param cali: bool; True if we already have calibration coefficients to use. False if we still need to create them.
:return:
"""
#-# Parameters
dataDir = os.path.join(CONFIG_INI.get('local', 'local_data_path'),
'active')
telescope = CONFIG_INI.get('telescope', 'name')
nb_seg = CONFIG_INI.getint(telescope, 'nb_subapertures')
tel_size_m = CONFIG_INI.getfloat(telescope, 'diameter') * u.m
real_size_seg = CONFIG_INI.getfloat(
telescope, 'flat_to_flat'
) # in m, size in meters of an individual segment flatl to flat
size_seg = CONFIG_INI.getint(
'numerical',
'size_seg') # pixel size of an individual segment tip to tip
wvln = CONFIG_INI.getint(telescope, 'lambda') * u.nm
inner_wa = CONFIG_INI.getint(telescope, 'IWA')
outer_wa = CONFIG_INI.getint(telescope, 'OWA')
tel_size_px = CONFIG_INI.getint(
'numerical', 'tel_size_px') # pupil diameter of telescope in pixels
im_size_pastis = CONFIG_INI.getint(
'numerical', 'im_size_px_pastis') # image array size in px
sampling = CONFIG_INI.getfloat('numerical', 'sampling') # sampling
size_px_tel = tel_size_m / tel_size_px # size of one pixel in pupil plane in m
px_sq_to_rad = (size_px_tel * np.pi / tel_size_m) * u.rad
zern_max = CONFIG_INI.getint('zernikes', 'max_zern')
sz = CONFIG_INI.getint('numerical', 'im_size_lamD_hcipy')
# Create Zernike mode object for easier handling
zern_mode = util.ZernikeMode(zernike_pol)
#-# Mean subtraction for piston
if zernike_pol == 1:
coef -= np.mean(coef)
#-# Generic segment shapes
if telescope == 'JWST':
# Load pupil from file
pupil = fits.getdata(
os.path.join(dataDir, 'segmentation', 'pupil.fits'))
# Put pupil in randomly picked, slightly larger image array
pup_im = np.copy(pupil) # remove if lines below this are active
#pup_im = np.zeros([tel_size_px, tel_size_px])
#lim = int((pup_im.shape[1] - pupil.shape[1])/2.)
#pup_im[lim:-lim, lim:-lim] = pupil
# test_seg = pupil[394:,197:315] # this is just so that I can display an individual segment when the pupil is 512
# test_seg = pupil[:203,392:631] # ... when the pupil is 1024
# one_seg = np.zeros_like(test_seg)
# one_seg[:110, :] = test_seg[8:, :] # this is the centered version of the individual segment for 512 px pupil
# Creat a mini-segment (one individual segment from the segmented aperture)
mini_seg_real = poppy.NgonAperture(
name='mini', radius=real_size_seg
) # creating real mini segment shape with poppy
#test = mini_seg_real.sample(wavelength=wvln, grid_size=flat_diam, return_scale=True) # fix its sampling with wavelength
mini_hdu = mini_seg_real.to_fits(wavelength=wvln,
npix=size_seg) # make it a fits file
mini_seg = mini_hdu[
0].data # extract the image data from the fits file
elif telescope == 'ATLAST':
# Create mini-segment
pupil_grid = hcipy.make_pupil_grid(dims=tel_size_px,
diameter=real_size_seg)
focal_grid = hcipy.make_focal_grid(
pupil_grid, sampling, sz, wavelength=wvln.to(
u.m).value) # fov = lambda/D radius of total image
prop = hcipy.FraunhoferPropagator(pupil_grid, focal_grid)
mini_seg_real = hcipy.hexagonal_aperture(circum_diameter=real_size_seg,
angle=np.pi / 2)
mini_seg_hc = hcipy.evaluate_supersampled(
mini_seg_real, pupil_grid, 4
) # the supersampling number doesn't really matter in context with the other numbers
mini_seg = mini_seg_hc.shaped # make it a 2D array
# Redefine size_seg if using HCIPy
size_seg = mini_seg.shape[0]
# Make stand-in pupil for DH array
pupil = fits.getdata(
os.path.join(dataDir, 'segmentation', 'pupil.fits'))
pup_im = np.copy(pupil)
#-# Generate a dark hole mask
#TODO: simplify DH generation and usage
dh_area = util.create_dark_hole(
pup_im, inner_wa, outer_wa, sampling
) # this might become a problem if pupil size is not same like pastis image size. fine for now though.
if telescope == 'ATLAST':
dh_sz = util.zoom_cen(dh_area, sz * sampling)
#-# Import information form segmentation script
Projection_Matrix = fits.getdata(
os.path.join(dataDir, 'segmentation', 'Projection_Matrix.fits'))
vec_list = fits.getdata(
os.path.join(dataDir, 'segmentation', 'vec_list.fits')) # in pixels
NR_pairs_list = fits.getdata(
os.path.join(dataDir, 'segmentation', 'NR_pairs_list_int.fits'))
# Figure out how many NRPs we're dealing with
NR_pairs_nb = NR_pairs_list.shape[0]
#-# Chose whether calibration factors to do the calibraiton with
if cali:
filename = 'calibration_' + zern_mode.name + '_' + zern_mode.convention + str(
zern_mode.index)
ck = fits.getdata(
os.path.join(dataDir, 'calibration', filename + '.fits'))
else:
ck = np.ones(nb_seg)
coef = coef * ck
#-# Generic coefficients
# the coefficients in front of the non redundant pairs, the A_q in eq. 13 in Leboulleux et al. 2018
generic_coef = np.zeros(
NR_pairs_nb
) * u.nm * u.nm # setting it up with the correct units this will have
for q in range(NR_pairs_nb):
for i in range(nb_seg):
for j in range(i + 1, nb_seg):
if Projection_Matrix[i, j, 0] == q + 1:
generic_coef[q] += coef[i] * coef[j]
#-# Constant sum and cosine sum - calculating eq. 13 from Leboulleux et al. 2018
if telescope == 'JWST':
i_line = np.linspace(-im_size_pastis / 2., im_size_pastis / 2.,
im_size_pastis)
tab_i, tab_j = np.meshgrid(i_line, i_line)
cos_u_mat = np.zeros(
(int(im_size_pastis), int(im_size_pastis), NR_pairs_nb))
elif telescope == 'ATLAST':
i_line = np.linspace(-(2 * sz * sampling) / 2.,
(2 * sz * sampling) / 2., (2 * sz * sampling))
tab_i, tab_j = np.meshgrid(i_line, i_line)
cos_u_mat = np.zeros((int((2 * sz * sampling)), int(
(2 * sz * sampling)), NR_pairs_nb))
# Calculating the cosine terms from eq. 13.
# The -1 with each NR_pairs_list is because the segment names are saved starting from 1, but Python starts
# its indexing at zero, so we have to make it start at zero here too.
for q in range(NR_pairs_nb):
# cos(b_q <dot> u): b_q with 1 <= q <= NR_pairs_nb is the basis of NRPS, meaning the distance vectors between
# two segments of one NRP. We can read these out from vec_list.
# u is the position (vector) in the detector plane. Here, those are the grids tab_i and tab_j.
# We need to calculate the dot product between all b_q and u, so in each iteration (for q), we simply add the
# x and y component.
cos_u_mat[:, :, q] = np.cos(
px_sq_to_rad *
(vec_list[NR_pairs_list[q, 0] - 1, NR_pairs_list[q, 1] - 1, 0] *
tab_i) + px_sq_to_rad *
(vec_list[NR_pairs_list[q, 0] - 1, NR_pairs_list[q, 1] - 1, 1] *
tab_j)) * u.dimensionless_unscaled
sum1 = np.sum(
coef**2
) # sum of all a_{k,l} in eq. 13 - this works only for single Zernikes (l fixed), because np.sum would sum over l too, which would be wrong.
if telescope == 'JWST':
sum2 = np.zeros(
(int(im_size_pastis), int(im_size_pastis))
) * u.nm * u.nm # setting it up with the correct units this will have
elif telescope == 'ATLAST':
sum2 = np.zeros(
(int(2 * sz * sampling), int(2 * sz * sampling))) * u.nm * u.nm
for q in range(NR_pairs_nb):
sum2 = sum2 + generic_coef[q] * cos_u_mat[:, :, q]
#-# Local Zernike
if telescope == 'JWST':
# Generate a basis of Zernikes with the mini segment being the support
isolated_zerns = zern.hexike_basis(nterms=zern_max,
npix=size_seg,
rho=None,
theta=None,
vertical=False,
outside=0.0)
# Calculate the Zernike that is currently being used and put it on one single subaperture, the result is Zer
# Apply the currently used Zernike to the mini-segment.
if zernike_pol == 1:
Zer = np.copy(mini_seg)
elif zernike_pol in range(2, zern_max - 2):
Zer = np.copy(mini_seg)
Zer = Zer * isolated_zerns[zernike_pol - 1]
# Fourier Transform of the Zernike - the global envelope
mf = mft.MatrixFourierTransform()
ft_zern = mf.perform(Zer, im_size_pastis / sampling, im_size_pastis)
elif telescope == 'ATLAST':
isolated_zerns = hcipy.make_zernike_basis(num_modes=zern_max,
D=real_size_seg,
grid=pupil_grid,
radial_cutoff=False)
Zer = hcipy.Wavefront(mini_seg_hc * isolated_zerns[zernike_pol - 1],
wavelength=wvln.to(u.m).value)
# Fourier transform the Zernike
ft_zern = prop(Zer)
#-# Final image
if telescope == 'JWST':
# Generating the final image that will get passed on to the outer scope, I(u) in eq. 13
intensity = np.abs(ft_zern)**2 * (sum1.value + 2. * sum2.value)
elif telescope == 'ATLAST':
intensity = ft_zern.intensity.shaped * (sum1.value + 2. * sum2.value)
# PASTIS is only valid inside the dark hole, so we cut out only that part
if telescope == 'JWST':
tot_dh_im_size = sampling * (outer_wa + 3)
intensity_zoom = util.zoom_cen(
intensity, tot_dh_im_size
) # zoom box is (owa + 3*lambda/D) wide, in terms of lambda/D
dh_area_zoom = util.zoom_cen(dh_area, tot_dh_im_size)
dh_psf = dh_area_zoom * intensity_zoom
elif telescope == 'ATLAST':
dh_psf = dh_sz * intensity
"""
# Create plots.
plt.subplot(1, 3, 1)
plt.imshow(pupil, origin='lower')
plt.title('JWST pupil and diameter definition')
plt.plot([46.5, 464.5], [101.5, 409.5], 'r-') # show how the diagonal of the pupil is defined
plt.subplot(1, 3, 2)
plt.imshow(mini_seg, origin='lower')
plt.title('JWST individual mini-segment')
plt.subplot(1, 3, 3)
plt.imshow(dh_psf, origin='lower')
plt.title('JWST dark hole')
plt.show()
"""
# dh_psf is the image of the dark hole only, the pixels outside of it are zero
# intensity is the entire final image
return dh_psf, intensity
# hc.imshow_field(psf, norm=LogNorm(), mask=dh_mask)
# plt.subplot(1, 3, 3)
# hc.imshow_field(psf, norm=LogNorm())
# plt.show()
rand_contrast = util.dh_mean(psf / ref.max() - psf_unaber / ref.max(),
dh_mask)
return rand_contrast
if __name__ == '__main__':
### Preparations
run_choice = 'active'
workdir = os.path.join(CONFIG_INI.get('local', 'local_data_path'),
run_choice)
# Which parts are we running?
calculate_modes = True
calculate_sigmas = True
calc_cumulative_contrast = True
calculate_mus = True
run_monte_carlo = True
# LUVOIR coronagraph parameters
sampling = 4
apodizer_design = 'small'
# Define contrast requirements
c_stat = 1e-10
plt.imshow(dh_psf, origin='lower')
plt.title('JWST dark hole')
plt.show()
"""
# dh_psf is the image of the dark hole only, the pixels outside of it are zero
# intensity is the entire final image
return dh_psf, intensity
if __name__ == '__main__':
"Testing the uncalibrated analytical model\n"
### Define the aberration coeffitients "coef"
telescope = CONFIG_INI.get('telescope', 'name')
nb_seg = CONFIG_INI.getint(telescope, 'nb_subapertures')
zern_max = CONFIG_INI.getint('zernikes', 'max_zern')
nm_aber = CONFIG_INI.getfloat(
'calibration',
'single_aberration') * u.nm # [nm] amplitude of aberration
zern_number = CONFIG_INI.getint(
'calibration',
'zernike') # Which (Noll) Zernike we are calibrating for
wss_zern_nb = util.noll_to_wss(
zern_number) # Convert from Noll to WSS framework
### What segmend are we aberrating? ###
i = 0 # segment 1 --> i=0, seg 2 --> i=1, etc.
cali = False # calibrated or not?
def seg_mirror_test():
"""
Testing the integrated energy of images produced by HCIPy vs Poppy segmented DMs.
This is now deprecated as I am using directly the hcipy SM, but specifically from an older commit:
from hcipy.optics.segmented_mirror import SegmentedMirror
"""
# Parameters
which_tel = CONFIG_INI.get('telescope', 'name')
NPIX = CONFIG_INI.getint('numerical', 'tel_size_px')
PUP_DIAMETER = CONFIG_INI.getfloat(which_tel, 'diameter')
GAPSIZE = CONFIG_INI.getfloat(which_tel, 'gaps')
FLATTOFLAT = CONFIG_INI.getfloat(which_tel, 'flat_to_flat')
wvln = 638e-9
lamD = 20
samp = 4
norm = False
fac = 6.55
# --------------------------------- #
#aber_rad = 6.2
aber_array = np.linspace(0, 2*np.pi, 50, True)
print('Aber in rad: \n{}'.format(aber_array))
print('Aber in m: \n{}'.format(util.aber_to_opd(aber_array, wvln)))
# --------------------------------- #
### HCIPy SM
# HCIPy grids and propagator
pupil_grid = hcipy.make_pupil_grid(dims=NPIX, diameter=PUP_DIAMETER)
focal_grid = hcipy.make_focal_grid(pupil_grid, samp, lamD, wavelength=wvln)
prop = hcipy.FraunhoferPropagator(pupil_grid, focal_grid)
# Generate an aperture
aper, seg_pos = get_atlast_aperture(normalized=norm)
aper = hcipy.evaluate_supersampled(aper, pupil_grid, 1)
# Instantiate the segmented mirror
hsm = SegmentedMirror(aper, seg_pos)
# Make a pupil plane wavefront from aperture
wf = hcipy.Wavefront(aper, wavelength=wvln)
### Poppy SM
psm = poppy.dms.HexSegmentedDeformableMirror(name='Poppy SM',
rings=3,
flattoflat=FLATTOFLAT * u.m,
gap=GAPSIZE * u.m,
center=False)
### Apply pistons
hc_ims = []
pop_ims = []
for aber_rad in aber_array:
# Flatten both SMs
hsm.flatten()
psm.flatten()
# HCIPy
for i in [19, 28]:
hsm.set_segment(i, util.aber_to_opd(aber_rad, wvln)/2, 0, 0)
# Poppy
for i in [34, 25]:
psm.set_actuator(i, util.aber_to_opd(aber_rad, wvln) * u.m, 0, 0) # 34 in poppy is 19 in HCIPy
### Propagate to image plane
### HCIPy
# Apply SM to pupil plane wf
wf_fp_pistoned = hsm(wf)
# Propagate from SM to image plane
im_pistoned_hc = prop(wf_fp_pistoned)
### Poppy
# Make an optical system with the Poppy SM and a detector
osys = poppy.OpticalSystem()
osys.add_pupil(psm)
pxscle = 0.0031 * fac # I'm tweaking pixelscale and fov_arcsec to match the HCIPy image
fovarc = 0.05 * fac
osys.add_detector(pixelscale=pxscle, fov_arcsec=fovarc, oversample=10)
# Calculate the PSF
psf = osys.calc_psf(wvln)
# Get the PSF as an array
im_pistoned_pop = psf[0].data
hc_ims.append(im_pistoned_hc.intensity.shaped/np.max(im_pistoned_hc.intensity))
pop_ims.append(im_pistoned_pop/np.max(im_pistoned_pop))
### Trying to do it with numbers
hc_ims = np.array(hc_ims)
pop_ims = np.array(pop_ims)
sum_hc = np.sum(hc_ims, axis=(1,2))
sum_pop = np.sum(pop_ims, axis=(1,2)) - 1.75 # the -1.75 is just there because I didn't bother about image normalization too much
plt.suptitle('Image degradation of SMs')
plt.plot(aber_array, sum_hc, label='HCIPy SM')
plt.plot(aber_array, sum_pop, label='Poppy SM')
plt.xlabel('rad')
plt.ylabel('image sum')
plt.legend()
plt.show()
"""
This is a module containing functions to generate the ATLAST pupil and simple coronagraphs from HCIPy.
"""
import os
import numpy as np
import matplotlib.pyplot as plt
import astropy.units as u
import hcipy
import poppy
from config import CONFIG_INI
import util_pastis as util
# Configfile imports
which_tel = CONFIG_INI.get('telescope', 'name')
pupil_size = CONFIG_INI.getint('numerical', 'tel_size_px')
PUP_DIAMETER = CONFIG_INI.getfloat(which_tel, 'diameter')
def get_atlast_aperture(normalized=False, with_segment_gaps=True, segment_transmissions=1, write_to_disk=False, outDir=None):
"""Make the ATLAST/HiCAT pupil mask.
This function is a copy of make_hicat_aperture(), except that it also returns the segment positions.
Parameters
----------
normalized : boolean
If this is True, the outer diameter will be scaled to 1. Otherwise, the
diameter of the pupil will be 15.0 meters.
with_segment_gaps : boolean
"""
import os
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
import astropy.units as u
import poppy
import webbpsf
from config import CONFIG_INI
import util_pastis as util
# Setting to ensure that PyCharm finds the webbpsf-data folder. If you don't know where it is, find it with:
# webbpsf.utils.get_webbpsf_data_path()
# --> e.g.: >>source activate astroconda >>ipython >>import webbpsf >>webbpsf.utils.get_webbpsf_data_path()
os.environ['WEBBPSF_PATH'] = CONFIG_INI.get('local', 'webbpsf_data_path')
which_tel = CONFIG_INI.get('telescope', 'name')
nb_seg = CONFIG_INI.getint(which_tel, 'nb_subapertures')
flat_to_flat = CONFIG_INI.getfloat(which_tel, 'flat_to_flat')
wvl = CONFIG_INI.getfloat(which_tel, 'lambda') * u.nm
im_size_pupil = CONFIG_INI.getint('numerical', 'tel_size_px')
flat_diam = CONFIG_INI.getfloat(which_tel, 'flat_diameter') * u.m
wss_segs = webbpsf.constants.SEGNAMES_WSS_ORDER
im_size_e2e = CONFIG_INI.getint('numerical', 'im_size_px_webbpsf')
fpm = CONFIG_INI.get(which_tel, 'focal_plane_mask') # focal plane mask
lyot_stop = CONFIG_INI.get(which_tel, 'pupil_plane_stop') # Lyot stop
filter = CONFIG_INI.get(which_tel, 'filter_name')
def get_jwst_coords(outDir):
def make_aperture_nrp():
# Keep track of time
start_time = time.time() # runtime currently is around 2 seconds for JWST, 9 minutes for ATLAST
# Parameters
telescope = CONFIG_INI.get('telescope', 'name').upper()
localDir = os.path.join(CONFIG_INI.get('local', 'local_data_path'), 'active')
outDir = os.path.join(localDir, 'segmentation')
nb_seg = CONFIG_INI.getint(telescope, 'nb_subapertures') # Number of apertures, without central obscuration
flat_diam = CONFIG_INI.getfloat(telescope, 'diameter') * u.m
im_size_pupil = CONFIG_INI.getint('numerical', 'tel_size_px')
m_to_px = im_size_pupil/flat_diam # for conversion from meters to pixels: 3 [m] = 3 * m_to_px [px]
print('Running aperture generation for {}\n'.format(telescope))
# If main subfolder "active" doesn't exist yet, create it.
if not os.path.isdir(localDir):
os.mkdir(localDir)
# If subfolder "segmentation" doesn't exist yet, create it.
if not os.path.isdir(outDir):
os.mkdir(outDir)
#-# Get the coordinates of the central pixel of each segment and save aperture to disk
print('Getting segment centers')
seg_position = np.zeros((nb_seg, 2))
if telescope == 'JWST':
from e2e_simulators import webbpsf_imaging as webbim
seg_position = webbim.get_jwst_coords(outDir)
elif telescope == 'ATLAST':
from e2e_simulators import atlast_imaging as atim
_aper, seg_coords = atim.get_atlast_aperture(normalized=False, write_to_disk=True, outDir=outDir)
seg_position[:,0] = seg_coords.x
seg_position[:,1] = seg_coords.y
# Save the segment center positions just in case we want to check them without running the code
np.savetxt(os.path.join(outDir, 'seg_position.txt'), seg_position, fmt='%2.2f')
# 18 segments, central segment (0) not included
#-# Make distance list with distances between all of the segment centers among each other - in meters
vec_list = np.zeros((nb_seg, nb_seg, 2))
for i in range(nb_seg):
for j in range(nb_seg):
vec_list[i,j,:] = seg_position[i,:] - seg_position[j,:]
vec_list *= u.m
# Save, but gotta save x and y coordinate separately because of the function I use for saving
np.savetxt(os.path.join(outDir, 'vec_list_x.txt'), vec_list[:,:,0], fmt='%2.2f') # x distance; units: meters
np.savetxt(os.path.join(outDir, 'vec_list_y.txt'), vec_list[:,:,1], fmt='%2.2f') # y distance; units: meters
#-# Nulling redundant vectors = setting redundant vectors in vec_list equal to zero
# This was really hard to figure out, so I simply went with exactly the same way like in IDL.
# Reshape vec_list array to one dimension so that we can implement the loop below
longshape = vec_list.shape[0] * vec_list.shape[1]
vec_flat = np.reshape(vec_list, (longshape, 2))
# Save for testing
np.savetxt(os.path.join(outDir, 'vec_flat.txt'), vec_flat)
# Create array that will hold the nulled coordinates
vec_null = np.copy(vec_flat)
ap = 0
rp = 0
print('Nulling redundant segment pairs')
for i in range(longshape):
for j in range(i): # Since i starts at 0, the case with i=0 & j=0 never happens, we start at i=1 & j=0
# With this loop setup, in all cases we have i != k, which is the baseline between a
# segment with itself - which is not a valid baseline, so these vectors are already set
# to 0 in vec_null (they're already 0 in vec_flat).
# Some print statements for testing
#print('i, j', i, j)
#print('vec_flat[i,:]: ', vec_flat[i,:])
#print('vec_flat[j,:]: ', vec_flat[j,:])
#print('norm diff: ', np.abs(np.linalg.norm(vec_flat[i,:]) - np.linalg.norm(vec_flat[j,:])))
#print('dir diff: ', np.linalg.norm(np.cross(vec_flat[i,:], vec_flat[j,:])))
ap += 1
# Check if length of two vectors is the same (within numerical limits)
if np.abs(np.linalg.norm(vec_flat[i,:]) - np.linalg.norm(vec_flat[j,:])) <= 1.e-10:
# Check if direction of two vectors is the same (within numerical limits)
if np.linalg.norm(np.cross(vec_flat[i,:], vec_flat[j,:])) <= 1.e-10:
# Some print statements for testing
#print('i, j', i, j)
#print('vec_flat[i,:]: ', vec_flat[i, :])
#print('vec_flat[j,:]: ', vec_flat[j, :])
#print('norm diff: ', np.abs(np.linalg.norm(vec_flat[i, :]) - np.linalg.norm(vec_flat[j, :])))
#print('dir diff: ', np.linalg.norm(np.cross(vec_flat[i, :], vec_flat[j, :])))
rp += 1
vec_null[i,:] = [0, 0]
# Reshape nulled array back into proper shape of vec_list
vec_list_nulled = np.reshape(vec_null, (vec_list.shape[0], vec_list.shape[1], 2))
# Save for testing
np.savetxt(os.path.join(outDir, 'vec_list_nulled_x.txt'), vec_list_nulled[:, :, 0], fmt='%2.2f')
np.savetxt(os.path.join(outDir, 'vec_list_nulled_y.txt'), vec_list_nulled[:, :, 1], fmt='%2.2f')
#-# Extract the (number of) non redundant vectors: NR_distance_list
# Create vector that holds distances between segments (instead of distance COORDINATES like in vec_list)
distance_list = np.square(vec_list_nulled[:,:,0]) + np.square(vec_list_nulled[:,:,1]) # We use square distances so that we don't miss out on negative values
nonzero = np.nonzero(distance_list) # get indices of non-redundant segment pairs
NR_distance_list = distance_list[nonzero] # extract the list of distances between segments of NR pairs
NR_pairs_nb = np.count_nonzero(distance_list) # Counting how many non-redundant (NR) pairs we have
# Save for testing
np.savetxt(os.path.join(outDir, 'NR_distance_list.txt'), NR_distance_list, fmt='%2.2f')
print('Number of non-redundant pairs: ' + str(NR_pairs_nb))
#-# Select non redundant vectors
# NR_pairs_list is a [NRP number, seg1, seg2] vector to hold non-redundant vector information.
# NRPs are numbered from 1 to NR_pairs_nb, but Python indexing starts at 0!
# Create the array of NRPs that will be the output
NR_pairs_list = np.zeros((NR_pairs_nb, 2)) # NRP are numbered from 1 to NR_pairs_nb, as are the segments!
# Loop over number of NRPs
for i in range(NR_pairs_nb):
# Since 'nonzero' holds the indices of segments, and Python indices start at 0, we have to add 1 to all the
# 'segment names' in the array that tells us which NRP they form.
NR_pairs_list[i,0] = nonzero[0][i] + 1
NR_pairs_list[i,1] = nonzero[1][i] + 1
# Again, NRP are numbered from 1 to NR_pairs_nb, and the segments are too!
NR_pairs_list = NR_pairs_list.astype(int)
# Save for testing
np.savetxt(os.path.join(outDir, 'NR_pairs_list.txt'), NR_pairs_list, fmt='%i')
#-# Generate projection matrix
# Set diagonal to zero (distance between a segment and itself will always be zero)
# Although I am pretty sure they already are. - yeah they are, vec_list is per definition a vector of distances
# between all segments between each other, and the distance of a segment with itself is always zero.
vec_list2 = np.copy(vec_list)
for i in range(nb_seg):
for j in range(nb_seg):
if i ==j:
vec_list2[i,j,:] = [0,0]
# Save for testing
np.savetxt(os.path.join(outDir, 'vec_list2_x.txt'), vec_list2[:, :, 0], fmt='%2.2f')
np.savetxt(os.path.join(outDir, 'vec_list2_y.txt'), vec_list2[:, :, 1], fmt='%2.2f')
# Initialize the projection matrix
Projection_Matrix_int = np.zeros((nb_seg, nb_seg, 3))
# Reshape arrays so that we can loop over them easier
vec2_long = vec_list2.shape[0] * vec_list2.shape[1]
vec2_flat = np.reshape(vec_list2, (vec2_long, 2))
matrix_long = Projection_Matrix_int.shape[0] * Projection_Matrix_int.shape[1]
matrix_flat = np.reshape(Projection_Matrix_int, (matrix_long, 3))
print('Creating projection matrix')
for i in range(np.square(nb_seg)):
# Compare segment pair in i against all available NRPs.
# Where it matches, record the NRP number in the matrix entry that corresponds to segments in i.
for k in range(NR_pairs_nb):
# Since the segment names (numbers) in NR_pairs_list assume we start numbering the segments at 1, we have to
# subtract 1 every time when we need to convert a segment number into an index.
# This means we write NR_pairs_list[k,0]-1 and NR_pairs_list[k,1]-1 .
# Figure out which NRP a segment distance vector corresponds to - first by length.
if np.abs(np.linalg.norm(vec2_flat[i, :]) - np.linalg.norm(vec_list[NR_pairs_list[k,0]-1, NR_pairs_list[k,1]-1, :])) <= 1.e-10:
# Figure out which NRP a segment distance vector corresponds to - now by direction.
if np.linalg.norm(np.cross(vec2_flat[i, :], vec_list[NR_pairs_list[k,0]-1, NR_pairs_list[k,1]-1, :])) <= 1.e-10:
matrix_flat[i, 0] = k + 1 # Again: NRP start their numbering at 1
matrix_flat[i, 1] = NR_pairs_list[k,1] + 1 # and segments start their numbering at 1 too
matrix_flat[i, 2] = NR_pairs_list[k,0] + 1 # (see pupil image!).
# Reshape matrix back to normal form
Projection_Matrix = np.reshape(matrix_flat, (Projection_Matrix_int.shape[0], Projection_Matrix_int.shape[1], 3))
# Convert the segment positions in vec_list from meters to pixels
vec_list_px = vec_list * m_to_px
#-# Save the arrays: vec_list, NR_pairs_list, Projection_Matrix
util.write_fits(vec_list_px.value, os.path.join(outDir, 'vec_list.fits'), header=None, metadata=None)
util.write_fits(NR_pairs_list, os.path.join(outDir, 'NR_pairs_list_int.fits'), header=None, metadata=None)
util.write_fits(Projection_Matrix, os.path.join(outDir, 'Projection_Matrix.fits'), header=None, metadata=None)
print('All outputs saved to {}'.format(outDir))
# Tell us how long it took to finish.
end_time = time.time()
print('Runtime for aperture_definition.py:', end_time - start_time, 'sec =', (end_time - start_time)/60, 'min')