def _jwst_matrix_one_pair(norm, wfe_aber, resDir, savepsfs, saveopds, segment_pair):
"""
Function to calculate JWST mean contrast of one aberrated segment pair in NIRCam; for num_matrix_luvoir_multiprocess().
:param norm: float, direct PSF normalization factor (peak pixel of direct PSF)
:param wfe_aber: calibration aberration per segment in m
:param resDir: str, directory for matrix calculations
:param savepsfs: bool, if True, all PSFs will be saved to disk individually, as fits files
:param saveopds: bool, if True, all pupil surface maps of aberrated segment pairs will be saved to disk as PDF
:param segment_pair: tuple, pair of segments to aberrate, 0-indexed. If same segment gets passed in both tuple
entries, the segment will be aberrated only once.
Note how JWST segments start numbering at 0 just because that's python indexing, with 0 being
the segment A1.
:return: contrast as float, and segment pair as tuple
"""
# Set up JWST simulator in coronagraphic state
jwst_instrument, jwst_ote = webbpsf_imaging.set_up_nircam()
jwst_instrument.image_mask = CONFIG_PASTIS.get('JWST', 'focal_plane_mask')
# Put aberration on correct segments. If i=j, apply only once!
log.info(f'PAIR: {segment_pair[0]}-{segment_pair[1]}')
# Identify the correct JWST segments
seg_i = webbpsf_imaging.WSS_SEGS[segment_pair[0]].split('-')[0]
seg_j = webbpsf_imaging.WSS_SEGS[segment_pair[1]].split('-')[0]
# Put aberration on correct segments. If i=j, apply only once!
jwst_ote.zero()
jwst_ote.move_seg_local(seg_i, piston=wfe_aber, trans_unit='m')
if segment_pair[0] != segment_pair[1]:
jwst_ote.move_seg_local(seg_j, piston=wfe_aber, trans_unit='m')
log.info('Calculating coro image...')
image = jwst_instrument.calc_psf(nlambda=1)
psf = image[0].data / norm
# Save PSF image to disk
if savepsfs:
filename_psf = f'psf_piston_Noll1_segs_{segment_pair[0]}-{segment_pair[1]}'
hcipy.write_fits(psf, os.path.join(resDir, 'psfs', filename_psf + '.fits'))
# Plot segmented mirror WFE and save to disk
if saveopds:
opd_name = f'opd_piston_Noll1_segs_{segment_pair[0]}-{segment_pair[1]}'
plt.clf()
plt.figure(figsize=(8, 8))
ax2 = plt.subplot(111)
jwst_ote.display_opd(ax=ax2, vmax=500, colorbar_orientation='horizontal', title='Aberrated segment pair')
plt.savefig(os.path.join(resDir, 'OTE_images', opd_name + '.pdf'))
log.info('Calculating mean contrast in dark hole')
iwa = CONFIG_PASTIS.getfloat('JWST', 'IWA')
owa = CONFIG_PASTIS.getfloat('JWST', 'OWA')
sampling = CONFIG_PASTIS.getfloat('JWST', 'sampling')
dh_mask = util.create_dark_hole(psf, iwa, owa, sampling)
contrast = util.dh_mean(psf, dh_mask)
return contrast, segment_pair
def _hicat_matrix_one_pair(norm, wfe_aber, resDir, savepsfs, saveopds, segment_pair):
"""
Function to calculate HiCAT mean contrast of one aberrated segment pair; for num_matrix_luvoir_multiprocess().
:param norm: float, direct PSF normalization factor (peak pixel of direct PSF)
:param wfe_aber: calibration aberration per segment in m
:param resDir: str, directory for matrix calculations
:param savepsfs: bool, if True, all PSFs will be saved to disk individually, as fits files
:param saveopds: bool, if True, all pupil surface maps of aberrated segment pairs will be saved to disk as PDF
:param segment_pair: tuple, pair of segments to aberrate, 0-indexed. If same segment gets passed in both tuple
entries, the segment will be aberrated only once.
Note how HiCAT segments start numbering at 0, with 0 being the center segment.
:return: contrast as float, and segment pair as tuple
"""
# Set up HiCAT simulator in correct state
hicat_sim = set_up_hicat(apply_continuous_dm_maps=True)
hicat_sim.include_fpm = True
# Put aberration on correct segments. If i=j, apply only once!
log.info(f'PAIR: {segment_pair[0]}-{segment_pair[1]}')
hicat_sim.iris_dm.flatten()
hicat_sim.iris_dm.set_actuator(segment_pair[0], wfe_aber, 0, 0)
if segment_pair[0] != segment_pair[1]:
hicat_sim.iris_dm.set_actuator(segment_pair[1], wfe_aber, 0, 0)
log.info('Calculating coro image...')
image, inter = hicat_sim.calc_psf(display=False, return_intermediates=True)
psf = image[0].data / norm
# Save PSF image to disk
if savepsfs:
filename_psf = f'psf_piston_Noll1_segs_{segment_pair[0]}-{segment_pair[1]}'
hcipy.write_fits(psf, os.path.join(resDir, 'psfs', filename_psf + '.fits'))
# Plot segmented mirror WFE and save to disk
if saveopds:
opd_name = f'opd_piston_Noll1_segs_{segment_pair[0]}-{segment_pair[1]}'
plt.clf()
plt.imshow(inter[1].phase)
plt.savefig(os.path.join(resDir, 'OTE_images', opd_name + '.pdf'))
log.info('Calculating mean contrast in dark hole')
iwa = CONFIG_PASTIS.getfloat('HiCAT', 'IWA')
owa = CONFIG_PASTIS.getfloat('HiCAT', 'OWA')
sampling = CONFIG_PASTIS.getfloat('HiCAT', 'sampling')
dh_mask = util.create_dark_hole(psf, iwa, owa, sampling)
contrast = util.dh_mean(psf, dh_mask)
return contrast, segment_pair
def _luvoir_matrix_one_pair(design, norm, wfe_aber, zern_mode, resDir, savepsfs, saveopds, segment_pair):
"""
Function to calculate LVUOIR-A mean contrast of one aberrated segment pair; for num_matrix_luvoir_multiprocess().
:param design: str, what coronagraph design to use - 'small', 'medium' or 'large'
:param norm: float, direct PSF normalization factor (peak pixel of direct PSF)
:param wfe_aber: float, calibration aberration per segment in m
:param zern_mode: Zernike mode object, local Zernike aberration
:param resDir: str, directory for matrix calculations
:param savepsfs: bool, if True, all PSFs will be saved to disk individually, as fits files
:param saveopds: bool, if True, all pupil surface maps of aberrated segment pairs will be saved to disk as PDF
:param segment_pair: tuple, pair of segments to aberrate, 0-indexed. If same segment gets passed in both tuple
entries, the segment will be aberrated only once.
Note how LUVOIR segments start numbering at 1, with 0 being the center segment that doesn't exist.
:return: contrast as float, and segment pair as tuple
"""
# Instantiate LUVOIR object
sampling = CONFIG_PASTIS.getfloat('LUVOIR', 'sampling')
optics_input = CONFIG_PASTIS.get('LUVOIR', 'optics_path')
luv = LuvoirAPLC(optics_input, design, sampling)
log.info(f'PAIR: {segment_pair[0]+1}-{segment_pair[1]+1}')
# Put aberration on correct segments. If i=j, apply only once!
luv.flatten()
luv.set_segment(segment_pair[0]+1, wfe_aber / 2, 0, 0)
if segment_pair[0] != segment_pair[1]:
luv.set_segment(segment_pair[1]+1, wfe_aber / 2, 0, 0)
log.info('Calculating coro image...')
image, inter = luv.calc_psf(ref=False, display_intermediate=False, return_intermediate='intensity')
# Normalize PSF by reference image
psf = image / norm
# Save PSF image to disk
if savepsfs:
filename_psf = f'psf_{zern_mode.name}_{zern_mode.convention + str(zern_mode.index)}_segs_{segment_pair[0]+1}-{segment_pair[1]+1}'
hcipy.write_fits(psf, os.path.join(resDir, 'psfs', filename_psf + '.fits'))
# Plot segmented mirror WFE and save to disk
if saveopds:
opd_name = f'opd_{zern_mode.name}_{zern_mode.convention + str(zern_mode.index)}_segs_{segment_pair[0]+1}-{segment_pair[1]+1}'
plt.clf()
hcipy.imshow_field(inter['seg_mirror'], grid=luv.aperture.grid, mask=luv.aperture, cmap='RdBu')
plt.savefig(os.path.join(resDir, 'OTE_images', opd_name + '.pdf'))
log.info('Calculating mean contrast in dark hole')
dh_intensity = psf * luv.dh_mask
contrast = np.mean(dh_intensity[np.where(luv.dh_mask != 0)])
log.info(f'contrast: {float(contrast)}') # contrast is a Field, here casting to normal float
return float(contrast), segment_pair
def plot_single_mode(mode_nr,
pastis_modes,
out_dir,
design,
figsize=(8.5, 8.5),
vmin=None,
vmax=None,
fname_suffix='',
save=False):
"""
Plot a single PASTIS mode.
:param mode_nr: int, mode index
:param pastis_modes: array, PASTIS modes [seg, mode] in nm
:param out_dir: str, output path to save the figure to if save=True
:param design: str, "small", "medium", or "large" LUVOIR-A APLC design
:param figsize: tuple, size of figure, default=(8.5,8.5)
:param vmin: matplotlib min extent of image, default is None
:param vmax: matplotlib max extent of image, default is None
:param fname_suffix: str, optional, suffix to add to the saved file name
:param save: bool, whether to save to disk or not, default is False
:return:
"""
fname = f'mode_{mode_nr}'
if fname_suffix != '':
fname += f'_{fname_suffix}'
# Create luvoir instance
sampling = CONFIG_PASTIS.getfloat('LUVOIR', 'sampling')
optics_input = CONFIG_PASTIS.get('LUVOIR', 'optics_path')
luvoir = LuvoirAPLC(optics_input, design, sampling)
plt.figure(figsize=figsize, constrained_layout=False)
one_mode = apply_mode_to_luvoir(pastis_modes[:, mode_nr - 1], luvoir)[0]
hcipy.imshow_field(one_mode.phase, cmap='RdBu', vmin=vmin, vmax=vmax)
plt.axis('off')
plt.annotate(f'{mode_nr}',
xy=(-7.1, -6.9),
fontweight='roman',
fontsize=43)
cbar = plt.colorbar(
fraction=0.046, pad=0.04
) # no clue what these numbers mean but it did the job of adjusting the colorbar size to the actual plot size
cbar.ax.tick_params(
labelsize=40) # this changes the numbers on the colorbar
plt.tight_layout()
if save:
plt.savefig(os.path.join(out_dir, '.'.join([fname, 'pdf'])))
def calculate_mode_phases(pastis_modes, design):
"""
Calculate the phase maps in radians of a set of PASTIS modes.
:param pastis_modes: array, PASTIS modes [seg, mode] in nm
:param design: str, "small", "medium", or "large" LUVOIR-A APLC design
:return: all_modes, array of phase pupil images
"""
# Create luvoir instance
sampling = CONFIG_PASTIS.getfloat('LUVOIR', 'sampling')
optics_input = CONFIG_PASTIS.get('LUVOIR', 'optics_path')
luvoir = LuvoirAPLC(optics_input, design, sampling)
# Calculate phases of all modes
all_modes = []
for mode in range(len(pastis_modes)):
all_modes.append(
apply_mode_to_luvoir(pastis_modes[:, mode], luvoir)[0].phase)
return all_modes
def modes_from_matrix(instrument, datadir, saving=True):
"""
Calculate mode basis and singular values from PASTIS matrix using an SVD. In the case of the PASTIS matrix,
this is equivalent to using an eigendecomposition, because the matrix is symmetric. Note how the SVD orders the
modes and singular values in reverse order compared to an eigendecomposition.
:param instrument: string, "LUVOIR", "HiCAT" or "JWST"
:param datadir: string, path to overall data directory containing matrix and results folder
:param saving: string, whether to save singular values, modes and their plots or not; default=True
:return: pastis modes (which are the singular vectors/eigenvectors), singular values/eigenvalues
"""
# Read matrix
matrix = fits.getdata(
os.path.join(datadir, 'matrix_numerical',
'PASTISmatrix_num_piston_Noll1.fits'))
# Get singular modes and values from SVD
pmodes, svals, vh = np.linalg.svd(matrix, full_matrices=True)
# Check for results directory and create if it doesn't exits
if not os.path.isdir(os.path.join(datadir, 'results')):
os.mkdir(os.path.join(datadir, 'results'))
# Save singular values and pastis modes (singular vectors)
if saving:
np.savetxt(os.path.join(datadir, 'results', 'eigenvalues.txt'), svals)
np.savetxt(os.path.join(datadir, 'results', 'pastis_modes.txt'),
pmodes)
# Plot singular values and save
if saving:
ppl.plot_eigenvalues(svals,
nseg=svals.shape[0],
wvln=CONFIG_PASTIS.getfloat(instrument, 'lambda'),
out_dir=os.path.join(datadir, 'results'),
save=True)
return pmodes, svals
def single_mode_error_budget(design,
run_choice,
c_target=1e-10,
single_mode=None):
"""
Calculate and plot single-mode error budget, for onde PASTIS mode.
Calculate the mode weight and consecutive contrast for a range of target contrasts
and plot the recovered contrasts against the target contrasts.
:param design: str, "small", "medium" or "large" LUVOIR-A APLC design
:param run_choice: str, path to data
:param c_target: float, target contrast
:param single_mode: int, mode index for single mode error budget
:return:
"""
# Data directory
workdir = os.path.join(CONFIG_PASTIS.get('local', 'local_data_path'),
run_choice)
# Info
log.info(f'Working on {design} coronagraph design.')
# Instantiate LUVOIR-A
optics_input = CONFIG_PASTIS.get('LUVOIR', 'optics_path')
sampling = CONFIG_PASTIS.getfloat('LUVOIR', 'sampling')
luvoir = LuvoirAPLC(optics_input, design, sampling)
luvoir.flatten()
# Generate baseline contrast
psf_unaber, ref = luvoir.calc_psf(ref=True)
norm = ref.max()
dh_intensity = psf_unaber / norm * luvoir.dh_mask
coronagraph_floor = np.mean(dh_intensity[np.where(dh_intensity != 0)])
log.info(f'coronagraph_floor: {coronagraph_floor}')
# Load PASTIS modes and eigenvalues
pmodes, svals = modes_from_file(workdir)
log.info('Single mode error budget')
# Calculate the mode weight
single_sigma = single_mode_sigma(c_target, coronagraph_floor,
svals[single_mode - 1])
log.info(f'Eigenvalue: {svals[single_mode-1]}')
log.info(f'single_sigma: {single_sigma}')
single_contrast = single_mode_contrasts(single_sigma, pmodes, single_mode,
luvoir)
log.info(f'contrast: {single_contrast}')
# Make array of target contrasts
c_list = [5e-11, 8e-11, 1e-10, 5e-10, 1e-9, 5e-9, 1e-8]
sigma_list = []
# Calculate according sigmas
for i, con in enumerate(c_list):
sigma_list.append(
single_mode_sigma(con, coronagraph_floor, svals[single_mode - 1]))
# Calculate recovered contrasts
c_recov = []
for i, sig in enumerate(sigma_list):
c_recov.append(single_mode_contrasts(sig, pmodes, single_mode, luvoir))
log.info(f'c_recov: {c_recov}')
np.savetxt(
os.path.join(workdir, 'results', 'single_mode_target_contrasts.txt'),
c_list)
np.savetxt(
os.path.join(workdir, 'results',
f'single_mode_recovered_contrasts_mode{single_mode}.txt'),
c_recov)
plt.plot(c_list, c_recov)
plt.title('Single-mode scaling')
plt.semilogy()
plt.semilogx()
plt.xlabel('Target contrast $c_{target}$')
plt.ylabel('Recovered contrast')
plt.savefig(
os.path.join(workdir, 'results',
f'single_mode_scaled_mode{single_mode}.pdf'))
try:
import webbpsf
# 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 pastis >>ipython >>import webbpsf >>webbpsf.utils.get_webbpsf_data_path()
os.environ['WEBBPSF_PATH'] = CONFIG_PASTIS.get('local', 'webbpsf_data_path')
WSS_SEGS = webbpsf.constants.SEGNAMES_WSS_ORDER
except ImportError:
log.info('WebbPSF was not imported.')
NB_SEG = CONFIG_PASTIS.getint('JWST', 'nb_subapertures')
FLAT_TO_FLAT = CONFIG_PASTIS.getfloat('JWST', 'flat_to_flat')
WVLN = CONFIG_PASTIS.getfloat('JWST', 'lambda') * u.nm
IM_SIZE_PUPIL = CONFIG_PASTIS.getint('numerical', 'tel_size_px')
FLAT_DIAM = CONFIG_PASTIS.getfloat('JWST', 'flat_diameter') * u.m
IM_SIZE_E2E = CONFIG_PASTIS.getint('numerical', 'im_size_px_webbpsf')
def get_jwst_coords(outDir):
#-# Generate the pupil with segments and spiders
# Use poppy to create JWST aperture without spiders
log.info('Creating and saving aperture')
jwst_pup = poppy.MultiHexagonAperture(rings=2, flattoflat=FLAT_TO_FLAT) # Create JWST pupil without spiders
jwst_pup.display(colorbar=False) # Show pupil (will be saved to file)
plt.title('JWST telescope pupil')
def ana_matrix_jwst():
# Keep track of time
start_time = time.time() # runtime is currently around 11 minutes
log.info('Building analytical matrix for JWST\n')
# Parameters
datadir = os.path.join(CONFIG_PASTIS.get('local', 'local_data_path'),
'active')
which_tel = CONFIG_PASTIS.get('telescope', 'name')
resDir = os.path.join(datadir, 'matrix_analytical')
nb_seg = CONFIG_PASTIS.getint(which_tel, 'nb_subapertures')
nm_aber = CONFIG_PASTIS.getfloat(which_tel,
'calibration_aberration') * u.nm
zern_number = CONFIG_PASTIS.getint('calibration',
'local_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):
log.info('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
log.info(f'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
log.info('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)
log.info(f'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, 'pair-wise_contrasts.txt'),
all_contrasts,
fmt='%e')
# Tell us how long it took to finish.
end_time = time.time()
log.info(
f'Runtime for matrix_building.py: {end_time - start_time}sec = {(end_time - start_time) / 60}min'
)
log.info('Data saved to {}'.format(resDir))
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_PASTIS.get('telescope', 'name').upper()
localDir = os.path.join(CONFIG_PASTIS.get('local', 'local_data_path'), 'active')
outDir = os.path.join(localDir, 'segmentation')
nb_seg = CONFIG_PASTIS.getint(telescope, 'nb_subapertures') # Number of apertures, without central obscuration
flat_diam = CONFIG_PASTIS.getfloat(telescope, 'diameter') * u.m
im_size_pupil = CONFIG_PASTIS.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]
log.info('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
log.info('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
log.info('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')
log.info(f'Number of non-redundant pairs: {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))
log.info('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)
log.info('All outputs saved to {}'.format(outDir))
# Tell us how long it took to finish.
end_time = time.time()
log.info(f'Runtime for aperture_definition.py: {end_time - start_time}sec = {(end_time - start_time)/60}min')
def full_modes_from_themselves(instrument,
pmodes,
datadir,
sim_instance,
saving=False):
"""
Put all modes onto the segmented mirror in the pupil and get full 2D pastis modes, in pupil plane and focal plane.
Take the pmodes array of all modes (shape [segnum, modenum] = [nseg, nseg]) and apply them onto a segmented mirror
in the pupil. This phase gets returned both as an array of 2D arrays.
Both the pupl plane and the focal plane modes get save into a PDF grid, and as a cube to fits. Optionally, you can
save the pupil plane modes individually to PDF files by setting saving=True.
:param instrument: string, 'LUVOIR', 'HiCAT' or 'JWST'
:param pmodes: array of PASTIS modes [segnum, modenum], expected in nanometers
:param datadir: string, path to overall data directory containing matrix and results folder
:param sim_instance: class instance of the simulator for "instrument"
:param saving: bool, whether to save the individual pupil plane modes as PDFs to disk, default=False
:return: cube of pupil plane modes as array of 2D arrays
"""
nseg = pmodes.shape[0]
seglist = util.get_segment_list(instrument)
### Put all modes sequentially on the segmented mirror and get them as a phase map, then convert to WFE map
all_modes = []
all_modes_focal_plane = []
for i, thismode in enumerate(seglist):
if instrument == 'LUVOIR':
log.info(f'Working on mode {thismode}/{nseg}.')
wf_sm, wf_detector = util.apply_mode_to_luvoir(
pmodes[:, i], sim_instance)
psf_detector = wf_detector.intensity.shaped
all_modes_focal_plane.append(psf_detector)
all_modes.append(
(wf_sm.phase / wf_sm.wavenumber).shaped
) # wf_sm.phase is in rad, so this converts it to meters
if instrument == 'HiCAT':
log.info(f'Working on mode {thismode}/{nseg-1}.')
for segnum in range(nseg):
sim_instance.iris_dm.set_actuator(segnum,
pmodes[segnum, i] / 1e9, 0,
0) # /1e9 converts to meters
psf_detector_data, inter = sim_instance.calc_psf(
return_intermediates=True)
psf_detector = psf_detector_data[0].data
all_modes_focal_plane.append(psf_detector)
phase_sm = inter[1].phase
hicat_wavenumber = 2 * np.pi / (CONFIG_PASTIS.getfloat(
'HiCAT', 'lambda') / 1e9) # /1e9 converts to meters
all_modes.append(
phase_sm / hicat_wavenumber
) # phase_sm is in rad, so this converts it to meters
if instrument == 'JWST':
log.info(f'Working on mode {thismode}/{nseg - 1}.')
sim_instance[1].zero()
for segnum in range(
nseg
): # TODO: there is probably a single function that puts the aberration on the OTE at once
seg_name = webbpsf_imaging.WSS_SEGS[segnum].split('-')[0]
sim_instance[1].move_seg_local(seg_name,
piston=pmodes[segnum, i],
trans_unit='nm')
psf_detector_data, inter = sim_instance[0].calc_psf(
nlambda=1, return_intermediates=True)
psf_detector = psf_detector_data[0].data
all_modes_focal_plane.append(psf_detector)
phase_ote = inter[1].phase
jwst_wavenumber = 2 * np.pi / (CONFIG_PASTIS.getfloat(
'JWST', 'lambda') / 1e9) # /1e9 converts to meters
all_modes.append(
phase_ote / jwst_wavenumber
) # phase_sm is in rad, so this converts it to meters
### Check for results directory structure and create if it doesn't exist
log.info('Creating data directories')
subdirs = [
os.path.join(datadir, 'results'),
os.path.join(datadir, 'results', 'modes'),
os.path.join(datadir, 'results', 'modes', 'pupil_plane'),
os.path.join(datadir, 'results', 'modes', 'focal_plane'),
os.path.join(datadir, 'results', 'modes', 'focal_plane', 'fits'),
os.path.join(datadir, 'results', 'modes', 'pupil_plane', 'fits'),
os.path.join(datadir, 'results', 'modes', 'pupil_plane', 'pdf')
]
for place in subdirs:
if not os.path.isdir(place):
os.mkdir(place)
### Plot all modes together and save as PDF (pupil plane)
log.info('Saving all PASTIS modes together as PDF (pupil plane)...')
plt.figure(figsize=(36, 30))
for i, thismode in enumerate(seglist):
if instrument == 'LUVOIR':
plt.subplot(12, 10, i + 1)
if instrument == 'HiCAT':
plt.subplot(8, 5, i + 1)
if instrument == 'JWST':
plt.subplot(6, 3, i + 1)
plt.imshow(all_modes[i], cmap='RdBu')
plt.axis('off')
plt.title(f'Mode {thismode}')
plt.savefig(
os.path.join(datadir, 'results', 'modes', 'pupil_plane',
'modes_piston.pdf'))
### Plot them individually and save as PDF (pupil plane)
if saving:
log.info(
'Saving all PASTIS modes into individual PDFs (pupil plane)...')
for i, thismode in enumerate(seglist):
# pdf
plt.clf()
plt.imshow(
all_modes[i], cmap='RdBu'
) # TODO: this is now super slow for LUVOIR, using hcipy was way faster. Change back in LUVOIR case?
plt.axis('off')
plt.title(f'Mode {thismode}', size=30)
if saving:
plt.savefig(
os.path.join(datadir, 'results', 'modes', 'pupil_plane',
'pdf', f'mode_{thismode}.pdf'))
### Save as fits cube (pupil plane)
log.info('Saving all PASTIS modes into fits cube (pupil plane)')
mode_cube = np.array(all_modes)
hcipy.write_fits(
mode_cube,
os.path.join(datadir, 'results', 'modes', 'pupil_plane', 'fits',
'cube_modes.fits'))
### Plot all modes together and save as PDF (focal plane)
log.info('Saving all PASTIS modes together as PDF (focal plane)...')
plt.figure(figsize=(36, 30))
for i, thismode in enumerate(seglist):
if instrument == 'LUVOIR':
plt.subplot(12, 10, i + 1)
if instrument == 'HiCAT':
plt.subplot(8, 5, i + 1)
if instrument == 'JWST':
plt.subplot(6, 3, i + 1)
plt.imshow(all_modes_focal_plane[i], cmap='inferno', norm=LogNorm())
plt.axis('off')
plt.title(f'Mode {thismode}')
plt.savefig(
os.path.join(datadir, 'results', 'modes', 'focal_plane',
'modes_piston.pdf'))
### Save as fits cube (focal plane)
log.info('Saving all PASTIS modes into fits cube (focal plane)')
psf_cube = np.array(all_modes_focal_plane)
hcipy.write_fits(
psf_cube,
os.path.join(datadir, 'results', 'modes', 'focal_plane', 'fits',
'cube_modes.fits'))
return mode_cube
def calculate_unaberrated_contrast_and_normalization(instrument, design=None, return_coro_simulator=True, save_coro_floor=False, save_psfs=False, outpath=''):
"""
Calculate the direct PSF peak and unaberrated coronagraph floor of an instrument.
:param instrument: string, 'LUVOIR', 'HiCAT' or 'JWST'
:param design: str, optional, default=None, which means we read from the configfile: what coronagraph design
to use - 'small', 'medium' or 'large'
:param return_coro_simulator: bool, whether to return the coronagraphic simulator as third return, default True
:param save: bool, if True, will save direct and coro PSF images to disk, default False
:param outpath: string, where to save outputs to if save=True
:return: contrast floor and PSF normalization factor, and optionally (by default) the simulator in coron mode
"""
if instrument == 'LUVOIR':
# Instantiate LuvoirAPLC class
sampling = CONFIG_PASTIS.getfloat(instrument, 'sampling')
optics_input = CONFIG_PASTIS.get('LUVOIR', 'optics_path')
if design is None:
design = CONFIG_PASTIS.get('LUVOIR', 'coronagraph_design')
luvoir = LuvoirAPLC(optics_input, design, sampling)
# Calculate reference images for contrast normalization and coronagraph floor
unaberrated_coro_psf, direct = luvoir.calc_psf(ref=True, display_intermediate=False, return_intermediate=False)
norm = np.max(direct)
direct_psf = direct.shaped
coro_psf = unaberrated_coro_psf.shaped / norm
# Return the coronagraphic simulator and DH mask
coro_simulator = luvoir
dh_mask = luvoir.dh_mask.shaped
if instrument == 'HiCAT':
# Set up HiCAT simulator in correct state
hicat_sim = set_up_hicat(apply_continuous_dm_maps=True)
# Calculate direct reference images for contrast normalization
hicat_sim.include_fpm = False
direct = hicat_sim.calc_psf()
direct_psf = direct[0].data
norm = direct_psf.max()
# Calculate unaberrated coronagraph image for contrast floor
hicat_sim.include_fpm = True
coro_image = hicat_sim.calc_psf()
coro_psf = coro_image[0].data / norm
iwa = CONFIG_PASTIS.getfloat('HiCAT', 'IWA')
owa = CONFIG_PASTIS.getfloat('HiCAT', 'OWA')
sampling = CONFIG_PASTIS.getfloat('HiCAT', 'sampling')
dh_mask = util.create_dark_hole(coro_psf, iwa, owa, sampling).astype('bool')
# Return the coronagraphic simulator
coro_simulator = hicat_sim
if instrument == 'JWST':
# Instantiate NIRCAM object
jwst_sim = webbpsf_imaging.set_up_nircam() # this returns a tuple of two: jwst_sim[0] is the nircam object, jwst_sim[1] its ote
# Calculate direct reference images for contrast normalization
jwst_sim[0].image_mask = None
direct = jwst_sim[0].calc_psf(nlambda=1)
direct_psf = direct[0].data
norm = direct_psf.max()
# Calculate unaberrated coronagraph image for contrast floor
jwst_sim[0].image_mask = CONFIG_PASTIS.get('JWST', 'focal_plane_mask')
coro_image = jwst_sim[0].calc_psf(nlambda=1)
coro_psf = coro_image[0].data / norm
iwa = CONFIG_PASTIS.getfloat('JWST', 'IWA')
owa = CONFIG_PASTIS.getfloat('JWST', 'OWA')
sampling = CONFIG_PASTIS.getfloat('JWST', 'sampling')
dh_mask = util.create_dark_hole(coro_psf, iwa, owa, sampling).astype('bool')
# Return the coronagraphic simulator (a tuple in the JWST case!)
coro_simulator = jwst_sim
# Calculate coronagraph floor in dark hole
contrast_floor = util.dh_mean(coro_psf, dh_mask)
log.info(f'contrast floor: {contrast_floor}')
if save_coro_floor:
# Save contrast floor to text file
with open(os.path.join(outpath, 'coronagraph_floor.txt'), 'w') as file:
file.write(f'Coronagraph floor: {contrast_floor}')
if save_psfs:
# Save direct PSF, unaberrated coro PSF and DH masked coro PSF as PDF
plt.figure(figsize=(18, 6))
plt.subplot(1, 3, 1)
plt.title("Direct PSF")
plt.imshow(direct_psf, norm=LogNorm())
plt.colorbar()
plt.subplot(1, 3, 2)
plt.title("Unaberrated coro PSF")
plt.imshow(coro_psf, norm=LogNorm())
plt.colorbar()
plt.subplot(1, 3, 3)
plt.title("Dark hole coro PSF")
plt.imshow(np.ma.masked_where(~dh_mask, coro_psf), norm=LogNorm())
plt.colorbar()
plt.savefig(os.path.join(outpath, 'unaberrated_dh.pdf'))
if return_coro_simulator:
return contrast_floor, norm, coro_simulator
else:
return contrast_floor, norm
def num_matrix_jwst():
"""
Generate a numerical PASTIS matrix for a JWST coronagraph.
-- Depracated function, the LUVOIR PASTIS matrix is better calculated with num_matrix_multiprocess(), which can
do this for your choice of one of the implemented instruments (LUVOIR, HiCAT, JWST). --
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
# Set WebbPSF environment variable
os.environ['WEBBPSF_PATH'] = CONFIG_PASTIS.get('local', 'webbpsf_data_path')
# Keep track of time
start_time = time.time() # runtime is currently around 21 minutes
log.info('Building numerical matrix for JWST\n')
# Parameters
overall_dir = util.create_data_path(CONFIG_PASTIS.get('local', 'local_data_path'), telescope='jwst')
resDir = os.path.join(overall_dir, 'matrix_numerical')
which_tel = CONFIG_PASTIS.get('telescope', 'name')
nb_seg = CONFIG_PASTIS.getint(which_tel, 'nb_subapertures')
im_size_e2e = CONFIG_PASTIS.getint('numerical', 'im_size_px_webbpsf')
inner_wa = CONFIG_PASTIS.getint(which_tel, 'IWA')
outer_wa = CONFIG_PASTIS.getint(which_tel, 'OWA')
sampling = CONFIG_PASTIS.getfloat(which_tel, 'sampling')
fpm = CONFIG_PASTIS.get(which_tel, 'focal_plane_mask') # focal plane mask
lyot_stop = CONFIG_PASTIS.get(which_tel, 'pupil_plane_stop') # Lyot stop
filter = CONFIG_PASTIS.get(which_tel, 'filter_name')
wfe_aber = CONFIG_PASTIS.getfloat(which_tel, 'calibration_aberration') * u.nm
wss_segs = webbpsf.constants.SEGNAMES_WSS_ORDER
zern_max = CONFIG_PASTIS.getint('zernikes', 'max_zern')
zern_number = CONFIG_PASTIS.getint('calibration', 'local_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
# Create necessary directories if they don't exist yet
os.makedirs(overall_dir, exist_ok=True)
os.makedirs(resDir, exist_ok=True)
os.makedirs(os.path.join(resDir, 'OTE_images'), exist_ok=True)
os.makedirs(os.path.join(resDir, 'psfs'), exist_ok=True)
os.makedirs(os.path.join(resDir, 'darkholes'), exist_ok=True)
# 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
contrast_matrix = np.zeros([nb_seg, nb_seg]) # Generate empty matrix
all_psfs = []
all_dhs = []
all_contrasts = []
log.info(f'wfe_aber: {wfe_aber}')
for i in range(nb_seg):
for j in range(nb_seg):
log.info(f'\nSTEP: {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] = wfe_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] = wfe_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 = f'opd_{zern_mode.name}_{zern_mode.convention + str(zern_mode.index)}_segs_{i+1}-{j+1}'
plt.clf()
ote_coro.display_opd()
plt.savefig(os.path.join(resDir, 'OTE_images', opd_name + '.pdf'))
log.info('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 = f'psf_{zern_mode.name}_{zern_mode.convention + str(zern_mode.index)}_segs_{i+1}-{j+1}'
util.write_fits(psf, os.path.join(resDir, 'psfs', filename_psf + '.fits'), header=None, metadata=None)
all_psfs.append(psf)
log.info('Calculating mean contrast in dark hole')
dh_intensity = psf * dh_area
contrast = np.mean(dh_intensity[np.where(dh_intensity != 0)])
log.info(f'contrast: {contrast}')
# Save DH image to disk and put current contrast in list
filename_dh = f'dh_{zern_mode.name}_{zern_mode.convention + str(zern_mode.index)}_segs_{i+1}-{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
contrast_matrix[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(contrast_matrix) # This is just an intermediary copy so that I don't mix things up.
matrix_pastis = np.copy(contrast_matrix) # 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
log.info(f'Off-axis for i{i+1}-j{j+1}: {matrix_off_val}')
# Normalize matrix for the input aberration
matrix_pastis /= np.square(wfe_aber.value)
# Save matrix to file
filename_matrix = f'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)
log.info(f'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, 'pair-wise_contrasts.txt'), all_contrasts, fmt='%e')
# Tell us how long it took to finish.
end_time = time.time()
log.info(f'Runtime for matrix_building.py: {end_time - start_time}sec = {(end_time - start_time) / 60}min')
log.info(f'Data saved to {resDir}')
def num_matrix_luvoir(design, savepsfs=False, saveopds=True):
"""
Generate a numerical PASTIS matrix for a LUVOIR A coronagraph.
-- Depracated function, the LUVOIR PASTIS matrix is better calculated with num_matrix_multiprocess(), which can
do this for your choice of one of the implemented instruments (LUVOIR, HiCAT, JWST). --
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, you pick which of the three you want with the 'design' parameter.
:param design: string, what coronagraph design to use - 'small', 'medium' or 'large'
:param savepsfs: bool, if True, all PSFs will be saved to disk individually, as fits files, additionally to the
total PSF cube. If False, the total cube will still get saved at the very end of the script.
:param saveopds: bool, if True, all pupil surface maps of aberrated segment pairs will be saved to disk as PDF
:return overall_dir: string, experiment directory
"""
# Keep track of time
start_time = time.time()
### Parameters
# System parameters
overall_dir = util.create_data_path(CONFIG_PASTIS.get('local', 'local_data_path'), telescope='luvoir-'+design)
os.makedirs(overall_dir, exist_ok=True)
resDir = os.path.join(overall_dir, 'matrix_numerical')
# Create necessary directories if they don't exist yet
os.makedirs(resDir, exist_ok=True)
os.makedirs(os.path.join(resDir, 'OTE_images'), exist_ok=True)
os.makedirs(os.path.join(resDir, 'psfs'), exist_ok=True)
# Set up logger
util.setup_pastis_logging(resDir, f'pastis_matrix_{design}')
log.info('Building numerical matrix for LUVOIR\n')
# Read calibration aberration
zern_number = CONFIG_PASTIS.getint('calibration', 'local_zernike')
zern_mode = util.ZernikeMode(zern_number) # Create Zernike mode object for easier handling
# General telescope parameters
nb_seg = CONFIG_PASTIS.getint('LUVOIR', 'nb_subapertures')
wvln = CONFIG_PASTIS.getfloat('LUVOIR', 'lambda') * 1e-9 # m
diam = CONFIG_PASTIS.getfloat('LUVOIR', 'diameter') # m
wfe_aber = CONFIG_PASTIS.getfloat('LUVOIR', 'calibration_aberration') * 1e-9 # m
# Image system parameters
sampling = CONFIG_PASTIS.getfloat('LUVOIR', 'sampling')
# Record some of the defined parameters
log.info(f'LUVOIR apodizer design: {design}')
log.info(f'Wavelength: {wvln} m')
log.info(f'Telescope diameter: {diam} m')
log.info(f'Number of segments: {nb_seg}')
log.info(f'Sampling: {sampling} px per lambda/D')
log.info(f'wfe_aber: {wfe_aber} m')
# Copy configfile to resulting matrix directory
util.copy_config(resDir)
### Instantiate Luvoir telescope with chosen apodizer design
optics_input = CONFIG_PASTIS.get('LUVOIR', 'optics_path')
luvoir = LuvoirAPLC(optics_input, design, sampling)
### 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) * luvoir.dh_mask
contrast_floor = np.mean(dh_intensity[np.where(luvoir.dh_mask != 0)])
log.info(f'contrast floor: {contrast_floor}')
### Generating the PASTIS matrix and a list for all contrasts
contrast_matrix = np.zeros([nb_seg, nb_seg]) # Generate empty matrix
all_psfs = []
all_contrasts = []
for i in range(nb_seg):
for j in range(nb_seg):
log.info(f'\nSTEP: {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, wfe_aber/2, 0, 0)
if i != j:
luvoir.set_segment(j+1, wfe_aber/2, 0, 0)
log.info('Calculating coro image...')
image, inter = luvoir.calc_psf(ref=False, display_intermediate=False, return_intermediate='intensity')
# Normalize PSF by reference image
psf = image / norm
all_psfs.append(psf.shaped)
# Save image to disk
if savepsfs: # TODO: I might want to change this to matplotlib images since I save the PSF cube anyway.
filename_psf = f'psf_{zern_mode.name}_{zern_mode.convention + str(zern_mode.index)}_segs_{i+1}-{j+1}'
hcipy.write_fits(psf, os.path.join(resDir, 'psfs', filename_psf + '.fits'))
# Save OPD images for testing
if saveopds:
opd_name = f'opd_{zern_mode.name}_{zern_mode.convention + str(zern_mode.index)}_segs_{i+1}-{j+1}'
plt.clf()
hcipy.imshow_field(inter['seg_mirror'], mask=luvoir.aperture, cmap='RdBu')
plt.savefig(os.path.join(resDir, 'OTE_images', opd_name + '.pdf'))
log.info('Calculating mean contrast in dark hole')
dh_intensity = psf * luvoir.dh_mask
contrast = np.mean(dh_intensity[np.where(luvoir.dh_mask != 0)])
log.info(f'contrast: {float(contrast)}') # contrast is a Field, here casting to normal float
all_contrasts.append(contrast)
# Fill according entry in the matrix and subtract baseline contrast
contrast_matrix[i,j] = contrast - contrast_floor
# Transform saved lists to arrays
all_psfs = np.array(all_psfs)
all_contrasts = np.array(all_contrasts)
# Save the PSF image *cube* as well (as opposed to each one individually)
hcipy.write_fits(all_psfs, os.path.join(resDir, 'psfs', 'psf_cube.fits'),)
np.savetxt(os.path.join(resDir, 'pair-wise_contrasts.txt'), all_contrasts, fmt='%e')
# Filling the off-axis elements
log.info('\nCalculating off-axis matrix elements...')
matrix_two_N = np.copy(contrast_matrix) # This is just an intermediary copy so that I don't mix things up.
matrix_pastis = np.copy(contrast_matrix) # 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
log.info(f'Off-axis for i{i+1}-j{j+1}: {matrix_off_val}')
# Normalize matrix for the input aberration - this defines what units the PASTIS matrix will be in. The PASTIS
# matrix propagation function (util.pastis_contrast()) then needs to take in the aberration vector in these same
# units. I have chosen to keep this to 1nm, so, we normalize the PASTIS matrix to units of nanometers.
matrix_pastis /= np.square(wfe_aber * 1e9) # 1e9 converts the calibration aberration back to nanometers
# Save matrix to file
filename_matrix = f'PASTISmatrix_num_{zern_mode.name}_{zern_mode.convention + str(zern_mode.index)}'
hcipy.write_fits(matrix_pastis, os.path.join(resDir, filename_matrix + '.fits'))
log.info(f'Matrix saved to: {os.path.join(resDir, filename_matrix + ".fits")}')
# Tell us how long it took to finish.
end_time = time.time()
log.info(f'Runtime for matrix_building.py: {end_time - start_time}sec = {(end_time - start_time) / 60}min')
log.info(f'Data saved to {resDir}')
return overall_dir
# Parameters
outDir = os.path.join(CONFIG_PASTIS.get('local', 'local_data_path'),
'active', 'calibration')
telescope = CONFIG_PASTIS.get('telescope', 'name')
fpm = CONFIG_PASTIS.get(telescope, 'focal_plane_mask') # focal plane mask
lyot_stop = CONFIG_PASTIS.get(telescope, 'pupil_plane_stop') # Lyot stop
filter = CONFIG_PASTIS.get(telescope, 'filter_name')
tel_size_px = CONFIG_PASTIS.getint('numerical', 'tel_size_px')
im_size_e2e = CONFIG_PASTIS.getint('numerical', 'im_size_px_webbpsf')
size_seg = CONFIG_PASTIS.getint('numerical', 'size_seg')
nb_seg = CONFIG_PASTIS.getint(telescope, 'nb_subapertures')
wss_segs = webbpsf.constants.SEGNAMES_WSS_ORDER
zern_max = CONFIG_PASTIS.getint('zernikes', 'max_zern')
inner_wa = CONFIG_PASTIS.getint(telescope, 'IWA')
outer_wa = CONFIG_PASTIS.getint(telescope, 'OWA')
sampling = CONFIG_PASTIS.getfloat(telescope, 'sampling')
if telescope == 'JWST':
# 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_PASTIS.get('local',
'webbpsf_data_path')
nm_aber = CONFIG_PASTIS.getfloat(
telescope,
'calibration_aberration') * u.nm # [nm] amplitude of aberration
zern_number = CONFIG_PASTIS.getint(
'calibration',
'local_zernike') # Which (Noll) Zernike we are calibrating for
wss_zern_nb = util.noll_to_wss(
def num_matrix_multiprocess(instrument, design=None, savepsfs=True, saveopds=True):
"""
Generate a numerical/semi-analytical PASTIS matrix.
Multiprocessed script to calculate PASTIS matrix. Implementation adapted from
hicat.scripts.stroke_minimization.calculate_jacobian
:param instrument: str, what instrument (LUVOIR, HiCAT, JWST) to generate the PASTIS matrix for
:param design: str, optional, default=None, which means we read from the configfile: what coronagraph design
to use - 'small', 'medium' or 'large'
:param savepsfs: bool, if True, all PSFs will be saved to disk individually, as fits files.
:param saveopds: bool, if True, all pupil surface maps of aberrated segment pairs will be saved to disk as PDF
:return: overall_dir: string, experiment directory
"""
# Keep track of time
start_time = time.time() # runtime is currently around 150 minutes
### Parameters
# Create directory names
tel_suffix = f'{instrument.lower()}'
if instrument == 'LUVOIR':
if design is None:
design = CONFIG_PASTIS.get('LUVOIR', 'coronagraph_design')
tel_suffix += f'-{design}'
overall_dir = util.create_data_path(CONFIG_PASTIS.get('local', 'local_data_path'), telescope=tel_suffix)
os.makedirs(overall_dir, exist_ok=True)
resDir = os.path.join(overall_dir, 'matrix_numerical')
# Create necessary directories if they don't exist yet
os.makedirs(resDir, exist_ok=True)
os.makedirs(os.path.join(resDir, 'OTE_images'), exist_ok=True)
os.makedirs(os.path.join(resDir, 'psfs'), exist_ok=True)
# Set up logger
util.setup_pastis_logging(resDir, f'pastis_matrix_{tel_suffix}')
log.info(f'Building numerical matrix for {tel_suffix}\n')
# Read calibration aberration
zern_number = CONFIG_PASTIS.getint('calibration', 'local_zernike')
zern_mode = util.ZernikeMode(zern_number) # Create Zernike mode object for easier handling
# General telescope parameters
nb_seg = CONFIG_PASTIS.getint(instrument, 'nb_subapertures')
seglist = util.get_segment_list(instrument)
wvln = CONFIG_PASTIS.getfloat(instrument, 'lambda') * 1e-9 # m
wfe_aber = CONFIG_PASTIS.getfloat(instrument, 'calibration_aberration') * 1e-9 # m
# Record some of the defined parameters
log.info(f'Instrument: {tel_suffix}')
log.info(f'Wavelength: {wvln} m')
log.info(f'Number of segments: {nb_seg}')
log.info(f'Segment list: {seglist}')
log.info(f'wfe_aber: {wfe_aber} m')
log.info(f'Total number of segment pairs in {instrument} pupil: {len(list(util.segment_pairs_all(nb_seg)))}')
log.info(f'Non-repeating pairs in {instrument} pupil calculated here: {len(list(util.segment_pairs_non_repeating(nb_seg)))}')
# Copy configfile to resulting matrix directory
util.copy_config(resDir)
# Calculate coronagraph floor, and normalization factor from direct image
contrast_floor, norm = calculate_unaberrated_contrast_and_normalization(instrument, design, return_coro_simulator=False,
save_coro_floor=True, save_psfs=False, outpath=overall_dir)
# Figure out how many processes is optimal and create a Pool.
# Assume we're the only one on the machine so we can hog all the resources.
# We expect numpy to use multithreaded math via the Intel MKL library, so
# we check how many threads MKL will use, and create enough processes so
# as to use 100% of the CPU cores.
# You might think we should divide number of cores by 2 to get physical cores
# to account for hyperthreading, however empirical testing on telserv3 shows that
# it is slightly more performant on telserv3 to use all logical cores
num_cpu = multiprocessing.cpu_count()
# try:
# import mkl
# num_core_per_process = mkl.get_max_threads()
# except ImportError:
# # typically this is 4, so use that as default
# log.info("Couldn't import MKL; guessing default value of 4 cores per process")
# num_core_per_process = 4
num_core_per_process = 1 # NOTE: this was changed by Scott Will in HiCAT and makes more sense, somehow
num_processes = int(num_cpu // num_core_per_process)
log.info(f"Multiprocess PASTIS matrix for {instrument} will use {num_processes} processes (with {num_core_per_process} threads per process)")
# Set up a function with all arguments fixed except for the last one, which is the segment pair tuple
if instrument == 'LUVOIR':
calculate_matrix_pair = functools.partial(_luvoir_matrix_one_pair, design, norm, wfe_aber, zern_mode, resDir,
savepsfs, saveopds)
if instrument == 'HiCAT':
# Copy used BostonDM maps to matrix folder
shutil.copytree(CONFIG_PASTIS.get('HiCAT', 'dm_maps_path'), os.path.join(resDir, 'hicat_boston_dm_commands'))
calculate_matrix_pair = functools.partial(_hicat_matrix_one_pair, norm, wfe_aber, resDir, savepsfs, saveopds)
if instrument == 'JWST':
calculate_matrix_pair = functools.partial(_jwst_matrix_one_pair, norm, wfe_aber, resDir, savepsfs, saveopds)
# Iterate over all segment pairs via a multiprocess pool
mypool = multiprocessing.Pool(num_processes)
t_start = time.time()
results = mypool.map(calculate_matrix_pair, util.segment_pairs_non_repeating(nb_seg)) # this util function returns a generator
t_stop = time.time()
log.info(f"Multiprocess calculation complete in {t_stop-t_start}sec = {(t_stop-t_start)/60}min")
# Unscramble results
# results is a list of tuples that contain the return from the partial function, in this case: result[i] = (c, (seg1, seg2))
contrast_matrix = np.zeros([nb_seg, nb_seg]) # Generate empty matrix
for i in range(len(results)):
# Fill according entry in the matrix and subtract baseline contrast
contrast_matrix[results[i][1][0], results[i][1][1]] = results[i][0] - contrast_floor
mypool.close()
# Save all contrasts to disk, WITH subtraction of coronagraph floor
hcipy.write_fits(contrast_matrix, os.path.join(resDir, 'pair-wise_contrasts.fits'))
plt.figure(figsize=(10, 10))
plt.imshow(contrast_matrix)
plt.colorbar()
plt.savefig(os.path.join(resDir, 'contrast_matrix.pdf'))
# Calculate the PASTIS matrix from the contrast matrix: off-axis elements and normalization
matrix_pastis = pastis_from_contrast_matrix(contrast_matrix, seglist, wfe_aber)
# Save matrix to file
filename_matrix = f'PASTISmatrix_num_{zern_mode.name}_{zern_mode.convention + str(zern_mode.index)}'
hcipy.write_fits(matrix_pastis, os.path.join(resDir, filename_matrix + '.fits'))
ppl.plot_pastis_matrix(matrix_pastis, wvln*1e9, out_dir=resDir, save=True) # convert wavelength to nm
log.info(f'Matrix saved to: {os.path.join(resDir, filename_matrix + ".fits")}')
# Tell us how long it took to finish.
end_time = time.time()
log.info(f'Runtime for matrix_building_numerical.py/multiprocess: {end_time - start_time}sec = {(end_time - start_time)/60}min')
log.info(f'Data saved to {resDir}')
return overall_dir
def run_full_pastis_analysis(instrument,
run_choice,
design=None,
c_target=1e-10,
n_repeat=100):
"""
Run a full PASTIS analysis on a given PASTIS matrix.
The first couple of lines contain switches to turn different parts of the analysis on and off. These include:
1. calculating the PASTIS modes
2. calculating the PASTIS mode weights sigma under assumption of a uniform contrast allocation across all modes
3. running an E2E Monte Carlo simulation on the modes with their weights sigma from the uniform contrast allocation
4. calculating a cumulative contrast plot from the sigmas of the uniform contrast allocation
5. calculating the segment constraints mu under assumption of uniform statistical contrast contribution across segments
6. running an E2E Monte Carlo simulation on the segments with their weights mu
7. calculating the segment- and mode-space covariance matrices Ca and Cb
8. analytically calculating the statistical mean contrast and its variance
9. calculting segment-based error budget
:param instrument: str, "LUVOIR", "HiCAT" or "JWST"
:param run_choice: str, path to data and where outputs will be saved
:param design: str, optional, default=None, which means we read from the configfile (if running for LUVOIR):
what coronagraph design to use - 'small', 'medium' or 'large'
:param c_target: float, target contrast
:param n_repeat: number of realizations in both Monte Carlo simulations (modes and segments), default=100
"""
# Which parts are we running?
calculate_modes = True
calculate_sigmas = True
run_monte_carlo_modes = True
calc_cumulative_contrast = True
calculate_mus = True
run_monte_carlo_segments = True
calculate_covariance_matrices = True
analytical_statistics = True
calculate_segment_based = True
# Data directory
workdir = os.path.join(CONFIG_PASTIS.get('local', 'local_data_path'),
run_choice)
nseg = CONFIG_PASTIS.getint(instrument, 'nb_subapertures')
wvln = CONFIG_PASTIS.getfloat(instrument, 'lambda') * 1e-9 # [m]
log.info('Setting up optics...')
log.info(f'Data folder: {workdir}')
log.info(f'Instrument: {instrument}')
# Set up simulator, calculate reference PSF and dark hole mask
# TODO: replace this section with calculate_unaberrated_contrast_and_normalization(). This will require to save out
# reference and unaberrated coronagraphic PSF already in matrix generation.
if instrument == "LUVOIR":
if design is None:
design = CONFIG_PASTIS.get('LUVOIR', 'coronagraph_design')
log.info(f'Coronagraph design: {design}')
sampling = CONFIG_PASTIS.getfloat('LUVOIR', 'sampling')
optics_input = CONFIG_PASTIS.get('LUVOIR', 'optics_path')
luvoir = LuvoirAPLC(optics_input, design, sampling)
# Generate reference PSF and unaberrated coronagraphic image
luvoir.flatten()
psf_unaber, ref = luvoir.calc_psf(ref=True, display_intermediate=False)
norm = ref.max()
psf_unaber = psf_unaber.shaped / norm
dh_mask = luvoir.dh_mask.shaped
sim_instance = luvoir
if instrument == 'HiCAT':
hicat_sim = set_up_hicat(apply_continuous_dm_maps=True)
# Generate reference PSF and unaberrated coronagraphic image
hicat_sim.include_fpm = False
direct = hicat_sim.calc_psf()
norm = direct[0].data.max()
hicat_sim.include_fpm = True
coro_image = hicat_sim.calc_psf()
psf_unaber = coro_image[0].data / norm
# Create DH mask
iwa = CONFIG_PASTIS.getfloat('HiCAT', 'IWA')
owa = CONFIG_PASTIS.getfloat('HiCAT', 'OWA')
sampling = CONFIG_PASTIS.getfloat('HiCAT', 'sampling')
dh_mask = util.create_dark_hole(psf_unaber, iwa, owa,
sampling).astype('bool')
sim_instance = hicat_sim
if instrument == 'JWST':
jwst_sim = webbpsf_imaging.set_up_nircam(
) # this returns a tuple of two: jwst_sim[0] is the nircam object, jwst_sim[1] its ote
# Generate reference PSF and unaberrated coronagraphic image
jwst_sim[0].image_mask = None
direct = jwst_sim[0].calc_psf(nlambda=1)
direct_psf = direct[0].data
norm = direct_psf.max()
jwst_sim[0].image_mask = CONFIG_PASTIS.get('JWST', 'focal_plane_mask')
coro_image = jwst_sim[0].calc_psf(nlambda=1)
psf_unaber = coro_image[0].data / norm
# Create DH mask
iwa = CONFIG_PASTIS.getfloat('JWST', 'IWA')
owa = CONFIG_PASTIS.getfloat('JWST', 'OWA')
sampling = CONFIG_PASTIS.getfloat('JWST', 'sampling')
dh_mask = util.create_dark_hole(psf_unaber, iwa, owa,
sampling).astype('bool')
sim_instance = jwst_sim
# TODO: this would also be part of the refactor mentioned above
# Calculate coronagraph contrast floor
coro_floor = util.dh_mean(psf_unaber, dh_mask)
log.info(f'Coronagraph floor: {coro_floor}')
# Read the PASTIS matrix
matrix = fits.getdata(
os.path.join(workdir, 'matrix_numerical',
'PASTISmatrix_num_piston_Noll1.fits'))
### Calculate PASTIS modes and singular values/eigenvalues
if calculate_modes:
log.info('Calculating all PASTIS modes')
pmodes, svals = modes_from_matrix(instrument, workdir)
### Get full 2D modes and save them
mode_cube = full_modes_from_themselves(instrument,
pmodes,
workdir,
sim_instance,
saving=True)
else:
log.info(f'Reading PASTIS modes from {workdir}')
pmodes, svals = modes_from_file(workdir)
### Calculate mode-based static constraints
if calculate_sigmas:
log.info('Calculating static sigmas')
sigmas = calculate_sigma(c_target, nseg, svals, coro_floor)
np.savetxt(
os.path.join(workdir, 'results',
f'mode_requirements_{c_target}_uniform.txt'), sigmas)
# Plot static mode constraints
ppl.plot_mode_weights_simple(sigmas,
wvln,
out_dir=os.path.join(workdir, 'results'),
c_target=c_target,
fname_suffix='uniform',
save=True)
else:
log.info(f'Reading sigmas from {workdir}')
sigmas = np.loadtxt(
os.path.join(workdir, 'results',
f'mode_requirements_{c_target}_uniform.txt'))
### Calculate Monte Carlo simulation for sigmas, with E2E
if run_monte_carlo_modes:
log.info('\nRunning Monte Carlo simulation for modes')
# Keep track of time
start_monte_carlo_modes = time.time()
all_contr_rand_modes = []
all_random_weight_sets = []
for rep in range(n_repeat):
log.info(f'Mode realization {rep + 1}/{n_repeat}')
random_weights, one_contrast_mode = calc_random_mode_configurations(
instrument, pmodes, sim_instance, sigmas, dh_mask, norm)
all_random_weight_sets.append(random_weights)
all_contr_rand_modes.append(one_contrast_mode)
# Empirical mean and standard deviation of the distribution
mean_modes = np.mean(all_contr_rand_modes)
stddev_modes = np.std(all_contr_rand_modes)
log.info(f'Mean of the Monte Carlo result modes: {mean_modes}')
log.info(
f'Standard deviation of the Monte Carlo result modes: {stddev_modes}'
)
end_monte_carlo_modes = time.time()
# Save Monte Carlo simulation
np.savetxt(
os.path.join(workdir, 'results', f'mc_mode_reqs_{c_target}.txt'),
all_random_weight_sets)
np.savetxt(
os.path.join(workdir, 'results',
f'mc_modes_contrasts_{c_target}.txt'),
all_contr_rand_modes)
ppl.plot_monte_carlo_simulation(all_contr_rand_modes,
out_dir=os.path.join(
workdir, 'results'),
c_target=c_target,
segments=False,
stddev=stddev_modes,
save=True)
### Calculate cumulative contrast plot with E2E simulator and matrix product
if calc_cumulative_contrast:
log.info(
'Calculating cumulative contrast plot, uniform contrast across all modes'
)
cumulative_e2e = cumulative_contrast_e2e(instrument, pmodes, sigmas,
sim_instance, dh_mask, norm)
cumulative_pastis = cumulative_contrast_matrix(pmodes, sigmas, matrix,
coro_floor)
np.savetxt(
os.path.join(workdir, 'results',
f'cumul_contrast_accuracy_e2e_{c_target}.txt'),
cumulative_e2e)
np.savetxt(
os.path.join(workdir, 'results',
f'cumul_contrast_accuracy_pastis_{c_target}.txt'),
cumulative_pastis)
# Plot the cumulative contrast from E2E simulator and matrix
ppl.plot_cumulative_contrast_compare_accuracy(cumulative_pastis,
cumulative_e2e,
out_dir=os.path.join(
workdir, 'results'),
c_target=c_target,
save=True)
else:
log.info('Loading uniform cumulative contrast from disk.')
cumulative_e2e = np.loadtxt(
os.path.join(workdir, 'results',
f'cumul_contrast_accuracy_e2e_{c_target}.txt'))
### Calculate segment-based static constraints
if calculate_mus:
log.info('Calculating segment-based constraints')
mus = calculate_segment_constraints(pmodes, matrix, c_target,
coro_floor)
np.savetxt(
os.path.join(workdir, 'results',
f'segment_requirements_{c_target}.txt'), mus)
ppl.plot_segment_weights(mus,
out_dir=os.path.join(workdir, 'results'),
c_target=c_target,
save=True)
ppl.plot_mu_map(instrument,
mus,
sim_instance,
out_dir=os.path.join(workdir, 'results'),
c_target=c_target,
save=True)
# Apply mu map directly and run through E2E simulator
mus *= u.nm
if instrument == 'LUVOIR':
sim_instance.flatten()
for seg, mu in enumerate(mus):
sim_instance.set_segment(seg + 1, mu.to(u.m).value / 2, 0, 0)
im_data = sim_instance.calc_psf()
psf_pure_mu_map = im_data.shaped
if instrument == 'HiCAT':
sim_instance.iris_dm.flatten()
for seg, mu in enumerate(mus):
sim_instance.iris_dm.set_actuator(seg, mu / 1e9, 0,
0) # /1e9 converts to meters
im_data = sim_instance.calc_psf()
psf_pure_mu_map = im_data[0].data
if instrument == 'JWST':
sim_instance[1].zero()
for seg, mu in enumerate(mus):
seg_num = webbpsf_imaging.WSS_SEGS[seg].split('-')[0]
sim_instance[1].move_seg_local(seg_num,
piston=mu.value,
trans_unit='nm')
im_data = sim_instance[0].calc_psf(nlambda=1)
psf_pure_mu_map = im_data[0].data
contrast_mu = util.dh_mean(psf_pure_mu_map / norm, dh_mask)
log.info(f'Contrast with pure mu-map: {contrast_mu}')
else:
log.info(f'Reading mus from {workdir}')
mus = np.loadtxt(
os.path.join(workdir, 'results',
f'segment_requirements_{c_target}.txt'))
mus *= u.nm
### Calculate Monte Carlo confirmation for segments, with E2E
if run_monte_carlo_segments:
log.info('\nRunning Monte Carlo simulation for segments')
# Keep track of time
start_monte_carlo_seg = time.time()
all_contr_rand_seg = []
all_random_maps = []
for rep in range(n_repeat):
log.info(f'Segment realization {rep + 1}/{n_repeat}')
random_map, one_contrast_seg = calc_random_segment_configuration(
instrument, sim_instance, mus, dh_mask, norm)
all_random_maps.append(random_map)
all_contr_rand_seg.append(one_contrast_seg)
# Empirical mean and standard deviation of the distribution
mean_segments = np.mean(all_contr_rand_seg)
stddev_segments = np.std(all_contr_rand_seg)
log.info(f'Mean of the Monte Carlo result segments: {mean_segments}')
log.info(
f'Standard deviation of the Monte Carlo result segments: {stddev_segments}'
)
with open(
os.path.join(workdir, 'results',
f'statistical_contrast_empirical_{c_target}.txt'),
'w') as file:
file.write(f'Empirical, statistical mean: {mean_segments}')
file.write(f'\nEmpirical variance: {stddev_segments**2}')
end_monte_carlo_seg = time.time()
log.info('\nRuntimes:')
log.info(
'Monte Carlo on segments with {} iterations: {} sec = {} min = {} h'
.format(n_repeat, end_monte_carlo_seg - start_monte_carlo_seg,
(end_monte_carlo_seg - start_monte_carlo_seg) / 60,
(end_monte_carlo_seg - start_monte_carlo_seg) / 3600))
# Save Monte Carlo simulation
np.savetxt(
os.path.join(workdir, 'results',
f'mc_segment_req_maps_{c_target}.txt'),
all_random_maps) # in m
np.savetxt(
os.path.join(workdir, 'results',
f'mc_segments_contrasts_{c_target}.txt'),
all_contr_rand_seg)
ppl.plot_monte_carlo_simulation(all_contr_rand_seg,
out_dir=os.path.join(
workdir, 'results'),
c_target=c_target,
segments=True,
stddev=stddev_segments,
save=True)
### Calculate covariance matrices
if calculate_covariance_matrices:
log.info('Calculating covariance matrices')
Ca = np.diag(np.square(mus.value))
hcipy.write_fits(
Ca,
os.path.join(
workdir, 'results',
f'cov_matrix_segments_Ca_{c_target}_segment-based.fits'))
Cb = np.dot(np.transpose(pmodes), np.dot(Ca, pmodes))
hcipy.write_fits(
Cb,
os.path.join(workdir, 'results',
f'cov_matrix_modes_Cb_{c_target}_segment-based.fits'))
ppl.plot_covariance_matrix(Ca,
os.path.join(workdir, 'results'),
c_target,
segment_space=True,
fname_suffix='segment-based',
save=True)
ppl.plot_covariance_matrix(Cb,
os.path.join(workdir, 'results'),
c_target,
segment_space=False,
fname_suffix='segment-based',
save=True)
else:
log.info('Loading covariance matrices from disk.')
Ca = fits.getdata(
os.path.join(
workdir, 'results',
f'cov_matrix_segments_Ca_{c_target}_segment-based.fits'))
Cb = fits.getdata(
os.path.join(workdir, 'results',
f'cov_matrix_modes_Cb_{c_target}_segment-based.fits'))
### Analytically calculate statistical mean contrast and its variance
if analytical_statistics:
log.info('Calculating analytical statistics.')
mean_stat_c = util.calc_statistical_mean_contrast(
matrix, Ca, coro_floor)
var_c = util.calc_variance_of_mean_contrast(matrix, Ca)
log.info(f'Analytical statistical mean: {mean_stat_c}')
log.info(f'Analytical standard deviation: {np.sqrt(var_c)}')
with open(
os.path.join(
workdir, 'results',
f'statistical_contrast_analytical_{c_target}.txt'),
'w') as file:
file.write(f'Analytical, statistical mean: {mean_stat_c}')
file.write(f'\nAnalytical variance: {var_c}')
### Calculate segment-based error budget
if calculate_segment_based:
log.info('Calculating segment-based error budget.')
# Extract segment-based mode weights
log.info('Calculate segment-based mode weights')
sigmas_opt = np.sqrt(np.diag(Cb))
np.savetxt(
os.path.join(workdir, 'results',
f'mode_requirements_{c_target}_segment-based.txt'),
sigmas_opt)
ppl.plot_mode_weights_simple(sigmas_opt,
wvln,
out_dir=os.path.join(workdir, 'results'),
c_target=c_target,
fname_suffix='segment-based',
save=True)
ppl.plot_mode_weights_double_axis(
(sigmas, sigmas_opt),
wvln,
os.path.join(workdir, 'results'),
c_target,
fname_suffix='segment-based-vs-uniform',
labels=('Uniform error budget', 'Segment-based error budget'),
alphas=(0.5, 1.),
linestyles=('--', '-'),
colors=('k', 'r'),
save=True)
# Calculate contrast per mode
log.info('Calculating contrast per mode')
per_mode_opt_e2e = cumulative_contrast_e2e(instrument,
pmodes,
sigmas_opt,
sim_instance,
dh_mask,
norm,
individual=True)
np.savetxt(
os.path.join(
workdir, 'results',
f'contrast_per_mode_{c_target}_e2e_segment-based.txt'),
per_mode_opt_e2e)
ppl.plot_contrast_per_mode(per_mode_opt_e2e,
coro_floor,
c_target,
pmodes.shape[0],
os.path.join(workdir, 'results'),
save=True)
# Calculate segment-based cumulative contrast
log.info('Calculating segment-based cumulative contrast')
cumulative_opt_e2e = cumulative_contrast_e2e(instrument, pmodes,
sigmas_opt, sim_instance,
dh_mask, norm)
np.savetxt(
os.path.join(
workdir, 'results',
f'cumul_contrast_allocation_e2e_{c_target}_segment-based.txt'),
cumulative_opt_e2e)
# Plot cumulative contrast from E2E simulator, segment-based vs. uniform error budget
ppl.plot_cumulative_contrast_compare_allocation(
cumulative_opt_e2e,
cumulative_e2e,
os.path.join(workdir, 'results'),
c_target,
fname_suffix='segment-based-vs-uniform',
save=True)
### Write full PDF report
title_page_list = util.collect_title_page(workdir, c_target)
util.create_title_page(instrument, workdir, title_page_list)
util.create_pdf_report(workdir, c_target)
### DONE
log.info(f"All saved in {os.path.join(workdir, 'results')}")
log.info('Good job')
def plot_mu_map(instrument,
mus,
sim_instance,
out_dir,
c_target,
limits=None,
fname_suffix='',
save=False):
"""
Plot the segment requirement map for a specific target contrast.
:param instrument: string, "LUVOIR", "HiCAT" or "JWST"
:param mus: array or list, segment requirements (standard deviations) in nm
:param sim_instance: class instance of the simulator for "instrument"
:param out_dir: str, output path to save the figure to if save=True
:param c_target: float, target contrast for which the segment requirements have been calculated
:param limits: tuple, colorbar limirs, deault is None
:param fname_suffix: str, optional, suffix to add to the saved file name
:param save: bool, whether to save to disk or not, default is False
:return:
"""
fname = f'segment_tolerance_map_{c_target}'
if fname_suffix != '':
fname += f'_{fname_suffix}'
if instrument == 'LUVOIR':
sim_instance.flatten()
wf_constraints = apply_mode_to_luvoir(mus, sim_instance)[0]
map_small = (wf_constraints.phase / wf_constraints.wavenumber *
1e12).shaped # in picometers
if instrument == 'HiCAT':
sim_instance.iris_dm.flatten()
for segnum in range(CONFIG_PASTIS.getint(instrument,
'nb_subapertures')):
sim_instance.iris_dm.set_actuator(segnum, mus[segnum] / 1e9, 0,
0) # /1e9 converts to meters
psf, inter = sim_instance.calc_psf(return_intermediates=True)
wf_sm = inter[1].phase
hicat_wavenumber = 2 * np.pi / (CONFIG_PASTIS.getfloat(
'HiCAT', 'lambda') / 1e9) # /1e9 converts to meters
map_small = (wf_sm / hicat_wavenumber) * 1e12 # in picometers
if instrument == 'JWST':
sim_instance[1].zero()
for segnum in range(
CONFIG_PASTIS.getint(instrument, 'nb_subapertures')
): # TODO: there is probably a single function that puts the aberration on the OTE at once
seg_name = webbpsf_imaging.WSS_SEGS[segnum].split('-')[0]
sim_instance[1].move_seg_local(seg_name,
piston=mus[segnum],
trans_unit='nm')
psf, inter = sim_instance[0].calc_psf(nlambda=1,
return_intermediates=True)
wf_sm = inter[1].phase
jwst_wavenumber = 2 * np.pi / (CONFIG_PASTIS.getfloat(
'JWST', 'lambda') / 1e9) # /1e9 converts to meters
map_small = (wf_sm / jwst_wavenumber) * 1e12 # in picometers
map_small = np.ma.masked_where(map_small == 0, map_small)
cmap_brev.set_bad(color='black')
plt.figure(figsize=(10, 10))
plt.imshow(map_small, cmap=cmap_brev)
cbar = plt.colorbar(
fraction=0.046, pad=0.04
) # no clue what these numbers mean but they did the job of adjusting the colorbar size to the actual plot size
cbar.ax.tick_params(
labelsize=30) # this changes the numbers on the colorbar
cbar.ax.yaxis.offsetText.set(
size=25) # this changes the base of ten on the colorbar
cbar.set_label('picometers', size=30)
if limits is not None:
plt.clim(limits[0] * 1e3, limits[1] * 1e3) # in pm
plt.tick_params(axis='both', which='both', length=6, width=2, labelsize=20)
plt.axis('off')
plt.tight_layout()
if save:
plt.savefig(os.path.join(out_dir, '.'.join([fname, 'pdf'])))
def hockeystick_curve(instrument,
apodizer_choice=None,
matrixdir='',
resultdir='',
range_points=3,
no_realizations=3):
"""
Construct a PASTIS hockeystick contrast curve for validation of the PASTIS matrix, for one particular instrument.
The aberration range is a fixed parameter in the function body since it depends on the coronagraph (and telescope)
used. We define how many realizations of a specific WFE 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. WFE rms error.
:param instrument: string, 'LUVOIR', 'HiCAT' or 'JWST'
:param apodizer_choice: string, needed if instrument='LUVOIR'; use "small", "medium" or "large" FPM coronagraph
:param matrixdir: string, Path to matrix that should be used.
:param resultdir: string, Path to directory where results will be saved.
:param range_points: int, How many points of WFE rms error to use in the predefined aberration range.
:param no_realizations: int, How many realizations per WFE rms error should be calculated; the mean of the realizations
is used in the plot
:return:
"""
if instrument == 'LUVOIR' and apodizer_choice is None:
raise ValueError(
'Need to specify apodizer_choice when working with LUVOIR instrument.'
)
# Keep track of time
start_time = time.time()
# Create range of WFE RMS values to test
rms_range = np.logspace(
CONFIG_PASTIS.getfloat(instrument, 'valid_range_lower'),
CONFIG_PASTIS.getfloat(instrument, 'valid_range_upper'), range_points)
# Create results directory if it doesn't exist yet
os.makedirs(resultdir, exist_ok=True)
# Calculate coronagraph floor, and normalization factor from direct image
contrast_floor, norm = calculate_unaberrated_contrast_and_normalization(
instrument, apodizer_choice, return_coro_simulator=False)
# Loop over different RMS values and calculate contrast with MATRIX PASTIS and E2E simulation
e2e_contrasts = [] # contrasts from E2E sim
matrix_contrasts = [] # contrasts from matrix PASTIS
log.info("WFE RMS range: {} nm".format(rms_range, fmt="%e"))
log.info(f"Random realizations: {no_realizations}")
for i, rms in enumerate(rms_range):
rms *= u.nm # Making sure this has the correct units
e2e_rand = []
matrix_rand = []
for j in range(no_realizations):
log.info("CALCULATING CONTRAST FOR {:.4f}".format(rms))
log.info(f"WFE RMS number {i + 1}/{len(rms_range)}")
log.info(f"Random realization: {j+1}/{no_realizations}")
log.info(
f"Total: {(i*no_realizations)+(j+1)}/{len(rms_range)*no_realizations}"
)
# Chose correct contrast propagation function for chose instrument
if instrument == 'LUVOIR':
c_e2e, c_matrix = consim.contrast_luvoir_num(
contrast_floor,
norm,
apodizer_choice,
matrix_dir=matrixdir,
rms=rms)
if instrument == 'HiCAT':
c_e2e, c_matrix = consim.contrast_hicat_num(
contrast_floor, norm, matrix_dir=matrixdir, rms=rms)
if instrument == 'JWST':
c_e2e, c_matrix = consim.contrast_jwst_num(
contrast_floor, norm, matrix_dir=matrixdir, rms=rms)
e2e_rand.append(c_e2e)
matrix_rand.append(c_matrix)
e2e_contrasts.append(np.mean(e2e_rand))
matrix_contrasts.append(np.mean(matrix_rand))
# Save contrasts and rms range
np.savetxt(os.path.join(resultdir, 'hockey_rms_range.txt'), rms_range)
np.savetxt(os.path.join(resultdir, 'hockey_e2e_contrasts.txt'),
e2e_contrasts)
np.savetxt(os.path.join(resultdir, 'hockey_matrix_contrasts.txt'),
matrix_contrasts)
# Plot
plt.clf()
ppl.plot_hockey_stick_curve(
rms_range,
matrix_contrasts,
e2e_contrasts,
wvln=CONFIG_PASTIS.getfloat(instrument, 'lambda'),
out_dir=resultdir,
fname_suffix=f'{no_realizations}_realizations_each',
save=True)
end_time = time.time()
runtime = end_time - start_time
log.info(
f'\nTotal runtime for pastis_vs_e2e_contrast_calc.py: {runtime} sec = {runtime/60} min'
)
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_PASTIS.get('local', 'local_data_path'),
'active')
telescope = CONFIG_PASTIS.get('telescope', 'name')
nb_seg = CONFIG_PASTIS.getint(telescope, 'nb_subapertures')
tel_size_m = CONFIG_PASTIS.getfloat(telescope, 'diameter') * u.m
real_size_seg = CONFIG_PASTIS.getfloat(
telescope, 'flat_to_flat'
) # in m, size in meters of an individual segment flatl to flat
size_seg = CONFIG_PASTIS.getint(
'numerical',
'size_seg') # pixel size of an individual segment tip to tip
wvln = CONFIG_PASTIS.getint(telescope, 'lambda') * u.nm
inner_wa = CONFIG_PASTIS.getint(telescope, 'IWA')
outer_wa = CONFIG_PASTIS.getint(telescope, 'OWA')
tel_size_px = CONFIG_PASTIS.getint(
'numerical', 'tel_size_px') # pupil diameter of telescope in pixels
im_size_pastis = CONFIG_PASTIS.getint(
'numerical', 'im_size_px_pastis') # image array size in px
sampling = CONFIG_PASTIS.getfloat(telescope, '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_PASTIS.getint('zernikes', 'max_zern')
sz = CONFIG_PASTIS.getint(
'ATLAST',
'im_size_lamD_hcipy') # image size in lam/D, only used in ATLAST case
# 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