def test_cube_derotate(imlib, interpolation, border_mode, edge_blend):
"""
Note: this calls most other routines defined in rotation.py
"""
# even
res = CUBE_even.copy()
angles = np.array([120, 90, 60, 45])
for i in range(24): # yields 360deg multiples for all derotation angles
res = cube_derotate(res,
angles,
imlib=imlib,
border_mode=border_mode,
edge_blend=edge_blend,
nproc=1 + (i % 2))
# just consider cropped image for evaluation
# to avoid differences due to edge effects
res = cube_crop_frames(res, 50)
CUBE_test = cube_crop_frames(CUBE_even, 50)
aarc(res, CUBE_test, rtol=1e-1, atol=1e-1)
# odd
res = CUBE_odd.copy()
angles = np.array([120, 90, 60, 45])
for i in range(24): # yields 360deg multiples for all derotation angles
res = cube_derotate(res,
angles,
imlib=imlib,
border_mode=border_mode,
edge_blend=edge_blend,
nproc=1 + (i % 2))
# just consider cropped image for evaluation
# to avoid differences due to edge effects
res = cube_crop_frames(res, 51)
CUBE_test = cube_crop_frames(CUBE_odd, 51)
aarc(res, CUBE_test, rtol=1e-1, atol=1e-1)
def _inject_FC(cube, psf, angle_list, plsc, inrad, outrad, flux_dist_theta,
k_list, sca, collapse_func, patch_size, lr_mode, interp, mode):
""" One patch residuals per injection
"""
cubefc, cox, coy = create_synt_cube(cube,
psf,
angle_list,
plsc,
flux=flux_dist_theta[0],
dist=flux_dist_theta[1],
theta=flux_dist_theta[2],
verbose=False)
cox = int(np.round(cox))
coy = int(np.round(coy))
cube_residuals = svd_decomp(cubefc,
angle_list,
patch_size,
inrad,
outrad,
sca,
k_list,
collapse_func,
neg_ang=False,
lr_mode=lr_mode,
nproc=1,
interp=interp,
mode=mode)
patch = cube_crop_frames(np.array(cube_residuals),
patch_size,
xy=(cox, coy),
verbose=False,
force=True)
return patch
def crop_cube(self, arcsecond_diameter=3.5, verbose=True, debug=False):
"""
Crops frames in the master cube after recentering and bad frame removal. Recommended for post-processing ie.
PCA in concentric annuli. If the provided arcsecond diameter happens to be larger than the cropping provided in
recentering, no cropping will occur.
Parameters
----------
arcsecond_diameter : float or int
Size of the frames diameter in arcseconds. Default of 3" for NaCO corresponds to 111x111 (x,y) pixel frames.
Note this is a diameter, not a radius.
verbose : bool optional
If True extra messages of completion are showed.
Writes to fits file
-------
cropped cube : numpy ndarray
Cube with cropped frames
"""
if not os.path.isfile(
self.outpath +
'{}_master_cube.fits'.format(self.dataset_dict['source'])):
raise NameError(
'Missing master cube from recentering and bad frame removal!')
master_cube = open_fits(
self.outpath +
'{}_master_cube.fits'.format(self.dataset_dict['source']),
verbose=debug)
nz, ny, nx = master_cube.shape
crop_size = int(
(arcsecond_diameter) / (self.dataset_dict['pixel_scale']))
if not crop_size % 2:
crop_size += 1
print('Crop size not odd, increased to {}'.format(crop_size))
if debug:
print('Input crop size is {} pixels'.format(crop_size))
if ny <= crop_size:
print(
'Crop size is larger than the frame size. Skipping cropping...'
)
else:
if verbose:
print('######### Running frame cropping #########')
master_cube = cube_crop_frames(master_cube,
crop_size,
force=False,
verbose=debug,
full_output=False)
write_fits(
self.outpath +
'{}_master_cube.fits'.format(self.dataset_dict['source']),
master_cube)
def test_cube_derotate_mask(imlib, interpolation, border_mode, edge_blend,
interp_zeros):
"""
Note: this calls most other routines defined in rotation.py
"""
# mask with nans
CUBE_odd_mask = mask_circle(CUBE_odd, 4, np.nan)
res = CUBE_odd_mask.copy()
angles = np.array([180, 120, 90, 60])
for i in range(6): # yields 360deg multiples for all derotation angles
res = cube_derotate(res,
angles,
border_mode=border_mode,
imlib=imlib,
interpolation=interpolation,
nproc=1 + (i % 2),
mask_val=np.nan,
edge_blend=edge_blend,
interp_zeros=False)
# just consider cropped image for evaluation
# to avoid differences due to edge effects
res = cube_crop_frames(res, 51)
CUBE_test = cube_crop_frames(CUBE_odd_mask, 51)
aarc(res, CUBE_test, rtol=1e-1, atol=1e-1)
# mask with zeros
CUBE_odd_mask = mask_circle(CUBE_odd, 4, 0)
res = CUBE_odd_mask.copy()
angles = np.array([180, 120, 90, 60])
for i in range(6): # yields 360deg multiples for all derotation angles
res = cube_derotate(res,
angles,
border_mode=border_mode,
imlib=imlib,
interpolation=interpolation,
nproc=1 + (i % 2),
mask_val=0,
edge_blend=edge_blend,
interp_zeros=interp_zeros)
# just consider cropped image for evaluation
# to avoid differences due to edge effects
res = cube_crop_frames(res, 51)
CUBE_test = cube_crop_frames(CUBE_odd_mask, 51)
aarc(res, CUBE_test, rtol=1e-1, atol=1e-1)
def example_dataset_rdi():
"""
Download example FITS cube from github + prepare HCIDataset object.
Returns
-------
dataset : HCIDataset
Notes
-----
Astropy's ``download_file`` uses caching, so the file is downloaded at most
once per test run.
"""
print("downloading data...")
url_prefix = "https://github.com/carlgogo/VIP_extras/raw/master/datasets"
f1 = download_file("{}/naco_betapic_cube.fits".format(url_prefix),
cache=True)
f2 = download_file("{}/naco_betapic_psf.fits".format(url_prefix),
cache=True)
f3 = download_file("{}/naco_betapic_pa.fits".format(url_prefix),
cache=True)
# load fits
cube = vip.fits.open_fits(f1)
angles = vip.fits.open_fits(f3).flatten() # shape (61,1) -> (61,)
psf = vip.fits.open_fits(f2)
# creating a flux screen
scr = vip.var.create_synth_psf('moff', (101, 101), fwhm=50)
scrcu = np.array([scr * i for i in np.linspace(1e3, 2e3, num=31)])
# upscaling (1.2) and taking half of the frames, reversing order
cube_upsc = cube_px_resampling(cube[::-1], 1.2, verbose=False)[::2]
# cropping and adding the flux screen
cube_ref = cube_crop_frames(cube_upsc, 101, verbose=False) + scrcu
# create dataset object
dataset = vip.Dataset(cube, angles=angles, psf=psf, cuberef=cube_ref,
px_scale=vip.conf.VLT_NACO['plsc'])
dataset.normalize_psf(size=20, force_odd=False)
# overwrite PSF for easy access
dataset.psf = dataset.psfn
return dataset
def prepare_patches(cube,
angle_list,
xy,
fwhm,
patch_size_px,
delta_rot=0.5,
normalization='slice',
imlib='opencv',
interpolation='bilinear',
debug=False):
""" Prepare patches for SODINN-PW.
"""
centy_fr, centx_fr = frame_center(cube[0])
angle_list = check_pa_vector(angle_list)
xy_dist = dist(centy_fr, centx_fr, xy[1], xy[0])
res = _pairwise_diff_residuals(cube,
angle_list,
ann_center=xy_dist,
fwhm=fwhm,
delta_rot=delta_rot,
debug=False)
res_der = cube_derotate(res,
angle_list,
imlib=imlib,
interpolation=interpolation)
res_der_crop = cube_crop_frames(res_der,
patch_size_px,
xy=xy,
verbose=False,
force=True)
patches = normalize_01_pw(res_der_crop, normalization)
if debug:
print('dist : {}'.format(xy_dist))
plot_frames(
tuple(patches),
axis=False,
colorbar=False,
)
return patches
def make_mlar_samples_ann_signal(input_array, angle_list, psf, n_samples,
cevr_thresh, n_ks, force_klen, inrad, outrad,
patch_size, flux_low, flux_high, plsc=0.01,
normalize='slice', nproc=1, nproc2=1,
interp='bilinear', lr_mode='eigen',
mode='mlar', kss_window=None, tss_window=None,
random_seed=42, verbose=False):
"""
n_samples : For ONEs, half_n_samples SVDs
mask is a list of tuples X,Y
inputarr is a 3d array or list of 3d arrays
orig_zero_patches : percentage of original zero patches
"""
dist_flux_p1 = flux_low
dist_flux_p2 = flux_high
collapse_func = np.mean
scaling = None # 'temp-mean'
random_state = np.random.RandomState(random_seed)
# making ones, injecting companions. The other half of n_samples
if verbose:
print("Creating the ONEs samples")
frsize = int(input_array.shape[1])
cube = input_array
# if frsize > outrad + outrad + patch_size + 2:
# frsize = int(outrad + outrad + patch_size + 2)
# cube = cube_crop_frames(input_array, frsize, force=True,
# verbose=False)
width = outrad - inrad
yy, xx = get_annulus_segments((frsize, frsize), inrad, width, 1)[0]
num_patches = yy.shape[0]
k_list = get_cumexpvar(cube, 'annular', inrad, outrad, patch_size,
k_list=None, cevr_thresh=cevr_thresh, n_ks=n_ks,
match_len=force_klen, verbose=False)
n_req_inject = n_samples
if mode == 'mlar':
# 4D: n_samples/2, n_k_list, patch_size, patch_size
X_ones_array = np.empty((n_req_inject, len(k_list), patch_size,
patch_size))
elif mode == 'tmlar':
nfr = cube.shape[0]
X_ones_array = np.empty((n_req_inject, nfr, patch_size, patch_size))
elif mode == 'tmlar4d':
nfr = cube.shape[0]
X_ones_array = np.empty((n_req_inject, nfr, len(k_list), patch_size,
patch_size))
if verbose:
print("{} injections".format(n_req_inject))
if not dist_flux_p2 > dist_flux_p1:
err_msg = 'dist_flux_p2 must be larger than dist_flux_p1'
raise ValueError(err_msg)
fluxes = random_state.uniform(dist_flux_p1, dist_flux_p2,
size=n_req_inject)
fluxes = np.sort(fluxes)
inds_inj = random_state.randint(0, num_patches, size=n_req_inject)
dists = []
thetas = []
for m in range(n_req_inject):
injx = xx[inds_inj[m]]
injy = yy[inds_inj[m]]
injx -= frame_center(cube[0])[1]
injy -= frame_center(cube[0])[0]
dist = np.sqrt(injx**2+injy**2)
theta = np.mod(np.arctan2(injy, injx) / np.pi * 180, 360)
dists.append(dist)
thetas.append(theta)
if not nproc:
nproc = int((cpu_count()/4))
if nproc == 1:
for m in range(n_req_inject):
cufc, cox, coy = create_synt_cube(cube, psf, angle_list,
plsc, theta=thetas[m],
flux=fluxes[m], dist=dists[m],
verbose=False)
cox = int(np.round(cox))
coy = int(np.round(coy))
cube_residuals = svd_decomp(cufc, angle_list, patch_size,
inrad, outrad, scaling, k_list,
collapse_func, neg_ang=False,
lr_mode=lr_mode, nproc=nproc2,
interp=interp, mode=mode)
# one patch residuals per injection
X_ones_array[m] = cube_crop_frames(np.asarray(cube_residuals),
patch_size, xy=(cox, coy),
verbose=False, force=True)
elif nproc > 1:
if lr_mode in ['cupy', 'randcupy', 'eigencupy']:
raise RuntimeError('CUPY does not play well with multiproc')
if get_start_method() == 'fork' and lr_mode in ['pytorch',
'eigenpytorch',
'randpytorch']:
raise RuntimeError("Cannot use pytorch and multiprocessing "
"outside main (i.e. from a jupyter cell). "
"See: http://pytorch.org/docs/0.3.1/notes/"
"multiprocessing.html.")
flux_dist_theta = zip(fluxes, dists, thetas)
res = pool_map(nproc, _inject_FC, cube, psf, angle_list, plsc,
inrad, outrad, iterable(flux_dist_theta), k_list,
scaling, collapse_func, patch_size, lr_mode, interp,
mode)
for m in range(n_req_inject):
X_ones_array[m] = res[m]
# Moving-subsampling
move_subsampling = 'median'
if mode == 'mlar' and kss_window is not None:
X_ones_array = cube_move_subsample(X_ones_array, kss_window, axis=1,
mode=move_subsampling)
elif mode == 'tmlar' and tss_window is not None:
X_ones_array = cube_move_subsample(X_ones_array, kss_window, axis=1,
mode=move_subsampling)
elif mode == 'tmlar4d':
if tss_window is not None:
X_ones_array = cube_move_subsample(X_ones_array, tss_window,
axis=1, mode=move_subsampling)
if kss_window is not None:
X_ones_array = cube_move_subsample(X_ones_array, kss_window,
axis=2, mode=move_subsampling)
if normalize is not None:
if mode == 'tmlar4d':
for i in range(X_ones_array.shape[0]):
X_ones_array[i] = normalize_01(X_ones_array[i], normalize)
else:
X_ones_array = normalize_01(X_ones_array, normalize)
return X_ones_array.astype('float32')
def make_mlar_samples_ann_noise(input_array, angle_list, cevr_thresh, n_ks,
force_klen, inrad, outrad, patch_size, fwhm=4,
normalize='slice', nproc2=1, interp='bilinear',
lr_mode='eigen', mode='mlar', kss_window=None,
tss_window=None, random_seed=42, nsamp_sep=None,
verbose=False):
"""
patch_size in pxs
mask is a list of tuples X,Y
inputarr is a 3d array or list of 3d arrays
orig_zero_patches : percentage of original zero patches
"""
collapse_func = np.mean
scaling = None # 'temp-mean'
random_state = np.random.RandomState(random_seed)
patches_list = []
frsize = int(input_array.shape[1])
if frsize > outrad + outrad + patch_size + 2:
frsize = int(outrad + outrad + patch_size + 2)
cube = cube_crop_frames(input_array, frsize, force=True, verbose=False)
# making zeros
if verbose:
print("Creating the ZEROs samples")
if not inrad >= int(patch_size/2.) + fwhm:
msg = "Warning: The patches are overlapping with the inner 1xFWHM "
msg += "annulus"
if not inrad > int(np.round(patch_size/2.)):
raise RuntimeError("Inner radius must be > half patch_size")
k_list = get_cumexpvar(cube, 'annular', inrad, outrad, patch_size,
k_list=None, cevr_thresh=cevr_thresh, n_ks=n_ks,
match_len=force_klen, verbose=False)
resdec = svd_decomp(cube, angle_list, patch_size, inrad, outrad,
scaling, k_list, collapse_func, interp=interp,
nproc=nproc2, lr_mode=lr_mode, neg_ang=True, mode=mode)
cube_residuals_negang = resdec
width = outrad - inrad
yy, xx = get_annulus_segments((frsize, frsize), inrad, width, 1)[0]
if nsamp_sep is None:
num_patches = yy.shape[0]
else:
num_patches = nsamp_sep
if num_patches < yy.shape[0]:
ind = random_state.choice(yy.shape[0], nsamp_sep, replace=False)
else:
ind = random_state.choice(yy.shape[0], nsamp_sep, replace=True)
yy = yy[ind]
xx = xx[ind]
cube_residuals_negang = np.asarray(cube_residuals_negang)
if verbose:
print("Total patches in annulus = {:}".format(num_patches))
for i in range(num_patches):
xy = (int(xx[i]), int(yy[i]))
patches_list.append(cube_crop_frames(cube_residuals_negang,
patch_size, xy=xy, verbose=False,
force=True))
# For MLAR and TMLAR X_zeros_array is 4d:
# [n_patches_annulus, n_k_list || n_time_steps, patch_size, patch_size]
# For TMLAR4D X_zeros_array is 5d:
# [n_patches_annulus, n_time_steps, n_k_list, patch_size, patch_size]
X_zeros_array = np.array(patches_list)
n_ks = len(k_list)
# Moving-subsampling
move_subsampling = 'median'
if mode == 'mlar' and kss_window is not None:
X_zeros_array = cube_move_subsample(X_zeros_array, kss_window, axis=1,
mode=move_subsampling)
elif mode == 'tmlar' and tss_window is not None:
X_zeros_array = cube_move_subsample(X_zeros_array, kss_window, axis=1,
mode=move_subsampling)
elif mode == 'tmlar4d':
if tss_window is not None:
X_zeros_array = cube_move_subsample(X_zeros_array, tss_window,
axis=1, mode=move_subsampling)
if kss_window is not None:
X_zeros_array = cube_move_subsample(X_zeros_array, kss_window,
axis=2, mode=move_subsampling)
# Normalization
if normalize is not None:
if mode == 'tmlar4d':
for i in range(X_zeros_array.shape[0]):
X_zeros_array[i] = normalize_01(X_zeros_array[i], normalize)
else:
X_zeros_array = normalize_01(X_zeros_array, normalize)
return X_zeros_array.astype('float32'), k_list, n_ks
def do_negfc(self, do_firstguess=True, guess_xy=None, mcmc_negfc=True, inject_neg=True, ncomp=1, algo='pca_annular',
nwalkers_ini=120, niteration_min=25, niteration_limit=10000, delta_rot=(0.5, 3), weights=False,
coronagraph=False, overwrite=True, save_plot=True, verbose=True):
"""
Module for estimating the location and flux of a planet. A sub-folder 'negfc' is created for storing all
output files.
Using a first guess from the (x,y) coordinates in pixels for the planet, we can estimate a preliminary guess for
the position and flux for each planet using a Nelder-Mead minimization. Saves the r, theta and flux
If using MCMC, runs an affine invariant MCMC sampling algorithm in order to determine the position and the flux
of the planet using the 'Negative Fake Companion' technique. The result of this procedure is a chain with the
samples from the posterior distributions of each of the 3 parameters.
Finally, we can inject a negative flux of the planet found in MCMC into the original dataset and apply
post processing to determine the residuals in the data.
Parameters:
***********
do_firstguess : bool
whether to determine a first guess for the position and the flux of a planet (not NEGFC)
guess_xy : tuple
if do_firstguess=True, this estimate of the source (x,y) location to be provided to firstguess(). Note
Python's zero-based indexing
mcmc_negfc : bool
whether to run MCMC NEGFC sampling (computationally intensive)
inject_neg : bool
whether to inject negative flux of the planet and post process the data without signal from the planet
ncomp : int, default 1
number of prinicple components to subtract
algo : 'pca_annulus', 'pca_annular', 'pca'. default 'pca_annular'
select which routine to be used to model and subtract the stellar PSF
nwalkers_ini : int, default 120
for MCMC, the number of Goodman & Weare 'walkers'
niteration_min : int, default 25
for MCMC, the simulation will run at least this number of steps per walker
niteration_limit : int, default 10000
for MCMC, stops simulation if this many steps run without having reached the convergence criterion
delta_rot : tuple
same as for postprocessing module. Threshold rotation angle for PCA annular
weights : bool
should only be used on unsaturated datasets, where the flux of the star can be measured in each image of
the cube. Applies a correction to each frame to account for variability of the adaptive optics, and hence
flux from the star (reference Christiaens et al. 2021 2021MNRAS.502.6117C)
coronagraph : bool
for MCMC and injecting a negative companion, True if the observation utilised a coronagraph (AGPM).
The known radial transmission of the NACO+AGPM coronagraph will be used
overwrite : bool, default True
whether to run a module and overwrite results if they already exist
save_plot : bool, default True
for firstguess the chi2 vs. flux plot is saved. For MCMC results are pickled and saved to the outpath
along with corner plots
verbose : bool
prints more output and interediate files when True
"""
print("======= Starting NEGFC....=======")
if guess_xy is None and do_firstguess is True:
raise ValueError("Enter an approximate location into guess_xy!")
if weights is True and coronagraph is True:
raise ValueError("Dataset cannot be both non-coronagraphic and coronagraphic!!")
outpath_sub = self.outpath + "negfc/"
if not isdir(outpath_sub):
os.system("mkdir " + outpath_sub)
if verbose:
print('Input path is {}'.format(self.inpath))
print('Output path is {}'.format(outpath_sub))
source = self.dataset_dict['source']
tn_shift = 0.568 # Launhardt et al. 2020, true North offset for NACO
ADI_cube_name = '{}_master_cube.fits' # template name for input master cube
derot_ang_name = 'derot_angles.fits' # template name for corresponding input derotation angles
psfn_name = "master_unsat_psf_norm.fits" # normalised PSF
ADI_cube = open_fits(self.inpath + ADI_cube_name.format(source), verbose=verbose)
derot_angles = open_fits(self.inpath + derot_ang_name, verbose=verbose) + tn_shift
psfn = open_fits(self.inpath + psfn_name, verbose=verbose)
ref_cube = None
if algo == 'pca_annular':
label_pca = 'pca_annular'
algo = pca_annular
elif algo == 'pca_annulus':
label_pca = 'pca_annulus'
algo = pca_annulus
elif algo == 'pca':
label_pca = 'pca'
algo = pca
else:
raise ValueError("Invalid algorithm. Select either pca_annular, pca_annulus or pca!")
opt_npc = ncomp
ap_rad = 1 * self.fwhm
f_range = np.geomspace(0.1, 201, 40)
asize = 3 * self.fwhm
if weights:
nfr = ADI_cube.shape[0] # number of frames
star_flux = np.zeros([nfr]) # for storing the star flux found in each frame
crop_sz_tmp = min(int(10 * self.fwhm),
ADI_cube.shape[1] - 2) # crop around star, either 10*FWHM or size - 2
if crop_sz_tmp % 2 == 0: # if it's not even, crop
crop_sz_tmp -= 1
for ii in range(nfr):
_, star_flux[ii], _ = normalize_psf(ADI_cube[ii], fwhm=self.fwhm, size=crop_sz_tmp,
full_output=True) # get star flux in 1*FWHM
weights = star_flux / np.median(star_flux)
star_flux = np.median(star_flux) # for use after MCMC when turning the chain into contrast
else:
weights = None
flux_psf_name = "master_unsat-stellarpsf_fluxes.fits" # flux in a FWHM aperture found in calibration
star_flux = open_fits(self.inpath + flux_psf_name, verbose=verbose)[1] # scaled fwhm flux
if coronagraph: # radial transmission of the coronagraph, 2 columns (pixels from centre, off-axis transmission)
# data provided by Valentin Christiaens. First entry in both columns was not zero, but VIP adds it in anyway
transmission = np.array([[0, 3.5894626e-10, 5.0611424e-01, 1.0122285e+00, 1.5183427e+00,
2.0244570e+00, 2.5305712e+00, 3.0366855e+00, 3.5427995e+00,
4.0489140e+00, 4.5550284e+00, 5.0611424e+00, 5.5672565e+00,
6.0733705e+00, 6.5794849e+00, 7.0855989e+00, 7.5917134e+00,
8.6039419e+00, 9.1100569e+00, 9.6161709e+00, 1.0628398e+01,
1.1134513e+01, 1.2146742e+01, 1.2652856e+01, 1.3665085e+01,
1.4677314e+01, 1.6195656e+01, 1.7207884e+01, 1.8220114e+01,
1.9738455e+01, 2.1256796e+01, 2.2775141e+01, 2.4293484e+01,
2.6317940e+01, 2.8342396e+01, 3.0366854e+01, 3.2897423e+01,
3.4921883e+01, 3.7452454e+01, 4.0489140e+01, 4.3525822e+01,
4.6562508e+01, 5.0105309e+01, 5.4154221e+01, 5.7697018e+01,
6.2252052e+01, 6.6807076e+01, 7.1868225e+01],
[0, 6.7836474e-05, 3.3822558e-03, 1.7766271e-02, 5.2646037e-02,
1.1413762e-01, 1.9890217e-01, 2.9460809e-01, 3.8605216e-01,
4.6217495e-01, 5.1963091e-01, 5.6185508e-01, 5.9548348e-01,
6.2670821e-01, 6.5912777e-01, 6.9335037e-01, 7.2783405e-01,
7.8866738e-01, 8.1227022e-01, 8.3128709e-01, 8.5912752e-01,
8.6968899e-01, 8.8677746e-01, 8.9409947e-01, 9.0848678e-01,
9.2426234e-01, 9.4704604e-01, 9.5787460e-01, 9.6538281e-01,
9.7379774e-01, 9.8088801e-01, 9.8751044e-01, 9.9255627e-01,
9.9640906e-01, 9.9917024e-01, 1.0009050e+00, 1.0021056e+00,
1.0026742e+00, 1.0027454e+00, 1.0027291e+00, 1.0023015e+00,
1.0016677e+00, 1.0009446e+00, 1.0000550e+00, 9.9953103e-01,
9.9917012e-01, 9.9915260e-01, 9.9922234e-01]])
else:
transmission = None
if (not isfile(outpath_sub + label_pca + "_npc{}_simplex_results.fits".format(opt_npc)) or overwrite) and do_firstguess:
# find r, theta based on the provided estimate location
cy, cx = frame_center(ADI_cube[0])
dy_pl = guess_xy[0][1] - cy
dx_pl = guess_xy[0][0] - cx
r_pl = np.sqrt(np.power(dx_pl, 2) + np.power(dy_pl, 2)) # pixel distance to the guess location
theta_pl = (np.rad2deg(np.arctan2(dy_pl, dx_pl))) % 360 # theta (angle) to the guess location
print("Estimated (r, theta) before first guess = ({:.1f},{:.1f})".format(r_pl, theta_pl))
ini_state = firstguess(ADI_cube, derot_angles, psfn, ncomp=opt_npc, plsc=self.pixel_scale,
planets_xy_coord=guess_xy, fwhm=self.fwhm,
annulus_width=12, aperture_radius=ap_rad, cube_ref=ref_cube,
svd_mode='lapack', scaling=None, fmerit='stddev', imlib='opencv',
interpolation='lanczos4', collapse='median', p_ini=None,
transmission=transmission, weights=weights, algo=algo,
f_range=f_range, simplex=True, simplex_options=None, plot=save_plot,
verbose=verbose, save=save_plot)
# when p_ini is set to None, it gets the value of planets_xy_coord
ini_state = np.array([ini_state[0][0], ini_state[1][0], ini_state[2][0]])
write_fits(outpath_sub + label_pca + "_npc{}_simplex_results.fits".format(opt_npc), ini_state,
verbose=verbose) # saves r, theta and flux. No print statement as firstguess() does that for us
if (not isfile(outpath_sub + "MCMC_results") or overwrite) and mcmc_negfc:
ini_state = open_fits(outpath_sub + label_pca + "_npc{}_simplex_results.fits".format(opt_npc),
verbose=verbose)
delta_theta_min = np.rad2deg(np.arctan(4./ r_pl)) # at least the angle corresponding to 2 azimuthal pixels
delta_theta = max(delta_theta_min, 5.)
bounds = [(max(r_pl - asize / 2., 1), r_pl + asize / 2.), # radius
(theta_pl - delta_theta, theta_pl + delta_theta), # angle
(0, 5 * abs(ini_state[2]))]
if ini_state[0] < bounds[0][0] or ini_state[0] > bounds[0][1] or ini_state[1] < bounds[1][0] or \
ini_state[1] > bounds[1][1] or ini_state[2] < bounds[2][0] or ini_state[2] > bounds[2][1]:
print("!!! WARNING: simplex results not in original bounds - NEGFC simplex MIGHT HAVE FAILED !!!")
ini_state = np.array([r_pl, theta_pl, abs(ini_state[2])])
if verbose is True:
verbosity = 2
print('MCMC NEGFC sampling is about to begin...')
else:
verbosity = 0
final_chain = mcmc_negfc_sampling(ADI_cube, derot_angles, psfn, ncomp=opt_npc, plsc=self.pixel_scale,
initial_state=ini_state, fwhm=self.fwhm, weights=weights,
annulus_width=12, aperture_radius=ap_rad, cube_ref=ref_cube,
svd_mode='lapack', scaling=None, fmerit='stddev',
imlib='opencv', interpolation='lanczos4', transmission=transmission,
collapse='median', nwalkers=nwalkers_ini, bounds=bounds, a=2.0,
ac_c=50, mu_sigma=(0, 1),
burnin=0.3, rhat_threshold=1.01, rhat_count_threshold=1, conv_test='ac',
# use autocorrelation 'ac' to ensure sufficient sampling. sample around
# the area of best likelihood to make distribution
niteration_min=niteration_min, niteration_limit=niteration_limit,
niteration_supp=0, check_maxgap=50, nproc=self.nproc, algo=algo,
output_dir=outpath_sub,
output_file="MCMC_results", display=False, verbosity=verbosity,
save=save_plot)
final_chain[:, :, 2] = final_chain[:, :, 2] / star_flux # dividing by the star flux converts to a contrast
show_walk_plot(final_chain, save=save_plot, output_dir=outpath_sub)
show_corner_plot(final_chain, burnin=0.5, save=save_plot, output_dir=outpath_sub)
# determine the highly probable value for each model parameter and the 1-sigma confidence interval
isamples_flat = final_chain[:,int(final_chain.shape[1]//(1/0.3)):,:].reshape((-1,3)) # 0.3 is the burnin
vals, err = confidence(isamples_flat, cfd=68.27, bins=100, gaussian_fit=False, weights=weights,
verbose=verbose, save=True, output_dir=outpath_sub, filename='confidence.txt',
plsc=self.pixel_scale)
labels = ['r', 'theta', 'f']
mcmc_res = np.zeros([3,3])
# pull the values and confidence interval out for saving
for i in range(3):
mcmc_res[i,0] = vals[labels[i]]
mcmc_res[i,1] = err[labels[i]][0]
mcmc_res[i,2] = err[labels[i]][1]
write_fits(outpath_sub + 'mcmc_results.fits', mcmc_res)
# now gaussian fit
gvals, gerr = confidence(isamples_flat, cfd=68.27, bins=100, gaussian_fit=True, weights=weights,
verbose=verbose, save=True, output_dir=outpath_sub,filename='confidence_gauss.txt',
plsc=self.pixel_scale)
mcmc_res = np.zeros([3,2])
for i in range(3):
mcmc_res[i,0] = gvals[i]
mcmc_res[i,1] = gerr[i]
write_fits(outpath_sub + 'mcmc_results_gauss.fits', mcmc_res)
if inject_neg:
pca_res = np.zeros([ADI_cube.shape[1], ADI_cube.shape[2]])
pca_res_emp = pca_res.copy()
planet_params = open_fits(outpath_sub+'mcmc_results.fits')
flux_psf_name = "master_unsat-stellarpsf_fluxes.fits"
star_flux = open_fits(self.inpath + flux_psf_name, verbose=verbose)[1]
ADI_cube_emp = cube_inject_companions(ADI_cube, psfn, derot_angles,
flevel=-planet_params[2, 0] * star_flux, plsc=self.pixel_scale,
rad_dists=[planet_params[0, 0]],
n_branches=1, theta=planet_params[1, 0],
imlib='opencv', interpolation='lanczos4',
verbose=verbose, transmission=transmission)
write_fits(outpath_sub+'ADI_cube_empty.fits', ADI_cube_emp) # the cube with the negative flux injected
if algo == pca_annular:
radius_int = int(np.floor(r_pl-asize/2)) # asize is 3 * FWHM, rounds down. To skip the inner region
# crop the cube to just larger than the annulus to improve the speed of PCA
crop_sz = int(2*np.ceil(r_pl+asize+1)) # rounds up
if not crop_sz % 2: # make sure the crop is odd
crop_sz += 1
if crop_sz < ADI_cube.shape[1] and crop_sz < ADI_cube.shape[2]: # crop if crop_sz is smaller than cube
pad = int((ADI_cube.shape[1]-crop_sz)/2)
crop_cube = cube_crop_frames(ADI_cube, crop_sz, verbose=verbose)
else:
crop_cube = ADI_cube # dont crop if the cube is already smaller
pca_res_tmp = pca_annular(crop_cube, derot_angles, cube_ref=ref_cube, radius_int=radius_int,
fwhm=self.fwhm, asize=asize, delta_rot=delta_rot, ncomp=opt_npc,
svd_mode='lapack', scaling=None, imlib='opencv', interpolation='lanczos4',
nproc=self.nproc, min_frames_lib=max(opt_npc, 10), verbose=verbose,
full_output=False)
pca_res = np.pad(pca_res_tmp, pad, mode='constant', constant_values=0)
write_fits(outpath_sub + 'pca_annular_res_npc{}.fits'.format(opt_npc), pca_res)
# emp
if crop_sz < ADI_cube_emp.shape[1] and crop_sz < ADI_cube_emp.shape[2]:
pad = int((ADI_cube_emp.shape[1]-crop_sz)/2)
crop_cube = cube_crop_frames(ADI_cube_emp, crop_sz, verbose=verbose)
else:
crop_cube = ADI_cube_emp
del ADI_cube_emp
del ADI_cube
pca_res_tmp = pca_annular(crop_cube, derot_angles, cube_ref=ref_cube, radius_int=radius_int,
fwhm=self.fwhm, asize=asize, delta_rot=delta_rot, ncomp=opt_npc,
svd_mode='lapack', scaling=None, imlib='opencv', interpolation='lanczos4',
nproc=self.nproc, min_frames_lib=max(opt_npc, 10), verbose=verbose,
full_output=False)
# pad again now
pca_res_emp = np.pad(pca_res_tmp, pad, mode='constant', constant_values=0)
write_fits(outpath_sub+'pca_annular_res_empty_npc{}.fits'.format(opt_npc), pca_res_emp)
def recenter(self,
nproc=1,
sigfactor=4,
subi_size=21,
crop_sz=None,
verbose=True,
debug=False,
plot=False,
coro=True):
"""
Recenters cropped science images by fitting a double Gaussian (negative+positive) to each median combined SCI cube,
or by fitting a single negative Gaussian to the coronagraph using the speckle pattern of each median combined SCI cube.
Parameters:
***********
sigfactor: float, default = 4
If thresholding is performed during 2gauss fitting, set the threshold in terms of gaussian sigma in the subimage (will depend on your cropping size)
subi_size: int, default = 21
Size of the square subimage sides in pixels.
crop_sz: int, optional, in units of pixels. None by default
Crops to this size after recentering for memory management purposes. Useful for very large datasets
verbose: True for False
To provide extra information about the progress and results of the pipeline
plot: True or False
Set to False when running on M3
coro: True for coronagraph data. False otherwise. Recentering requires coronagraphic data
Writes fits to file:
***********
x_shifts.fits # writes the x shifts to the file
y_shifts.fits # writes the y shifts to the file
{source}_master_cube.fits # makes the recentered master cube
derot_angles.fits # makes a vector of derotation angles
"""
if not coro:
if self.recenter_method != '2dfit':
raise ValueError('Recentering method invalid')
if self.recenter_model == '2gauss':
raise ValueError('2Gauss requires coronagraphic data')
if verbose:
print(len(self.sci_list), 'science cubes')
if debug:
print(self.sci_list)
ncubes = len(self.sci_list)
fwhm_all = open_fits(
self.inpath + 'fwhm.fits', verbose=debug
) # changed this to open the file as sometimes we wont run get_stellar_psf() or it may have already run
fwhm = fwhm_all[0] # fwhm is the first entry in the file
fwhm = fwhm.item(
) # changes from numpy.float32 to regular float so it will work in VIP
if verbose:
print('fwhm:', fwhm, 'of type', type(fwhm))
mem = np.zeros(len(self.sci_list))
# Creates a master science cube with just the median of each cube
bar = pyprind.ProgBar(
len(self.sci_list),
stream=1,
title=
'Creating master science cube (median of each science cube)....')
for sc, fits_name in enumerate(
self.sci_list): # enumerate over the list of all science cubes
tmp = open_fits(self.inpath + '4_sky_subtr_imlib_' + fits_name,
verbose=debug) #open cube as tmp
if sc == 0:
self.ndit, ny, nx = tmp.shape #dimensions of cube
tmp_tmp = np.zeros(
[ncubes, ny, nx]
) # template cube with the median of each SCI cube. np.zeros is array filled with zeros
#mem_msg = 'Set check_memory=False to override this memory check'
tmp_tmp[sc] = np.median(tmp, axis=0) # median frame of cube tmp
input_bytes = tmp.nbytes
memory = check_enough_memory(input_bytes, verbose=True)
tmp = None
mem[sc] = memory
bar.update()
write_fits(self.outpath + 'memory.fits', mem, verbose=debug)
if self.recenter_method == 'speckle':
# FOR GAUSSIAN
print('##### Recentering via speckle pattern #####')
#registered science sube, low+high pass filtered cube,cube with stretched values, x shifts, y shifts
tmp_tmp, cube_sci_lpf, cube_stret, sx, sy = cube_recenter_via_speckles(
tmp_tmp,
cube_ref=None,
alignment_iter=5,
gammaval=1,
min_spat_freq=0.5,
max_spat_freq=3,
fwhm=fwhm,
debug=debug,
recenter_median=True,
negative=coro,
fit_type='gaus',
crop=False,
subframesize=subi_size,
imlib='opencv',
interpolation='lanczos4',
plot=plot,
full_output=True)
del cube_sci_lpf
del cube_stret
elif self.recenter_method == '2dfit':
# DOUBLE GAUSSIAN
print('##### Recentering via 2dfit #####')
params_2g = {
'fwhm_neg': 0.8 * fwhm,
'fwhm_pos': 2 * fwhm,
'theta_neg': 48.,
'theta_pos': 135.,
'neg_amp': 0.8
}
res = cube_recenter_2dfit(tmp_tmp,
xy=None,
fwhm=fwhm,
subi_size=subi_size,
model=self.recenter_model,
nproc=nproc,
imlib='opencv',
interpolation='lanczos4',
offset=None,
negative=False,
threshold=True,
sigfactor=sigfactor,
fix_neg=False,
params_2g=params_2g,
save_shifts=False,
full_output=True,
verbose=verbose,
debug=debug,
plot=plot)
sy = res[1]
sx = res[2]
# true_agpm_cen = (res[4][0],res[3][0])
# true_fwhm_pos = (res[5][0],res[6][0])
# true_fwhm_neg = (res[7][0],res[8][0])
# true_theta_pos = res[9][0]
# true_theta_neg = res[10][0]
# amp_pos = res[11][0]
# amp_neg = res[12][0]
# true_neg_amp = amp_neg/amp_pos
# params_2g = {'fwhm_neg': true_fwhm_neg, 'fwhm_pos': true_fwhm_pos,
# 'theta_neg': true_theta_neg, 'theta_pos':true_theta_pos,
# 'neg_amp': true_neg_amp}
# # second: fixing params for neg gaussian - applied on individual frames. returns recentered array, and x-y shifts
# tmp_tmp, sy, sx = cube_recenter_2dfit(tmp_tmp, xy=true_agpm_cen,
# fwhm=self.fwhm, subi_size=subi_size,
# model=model, nproc=nproc, imlib='opencv',
# interpolation='lanczos4',
# offset=None, negative=False,
# threshold=True, sigfactor=sigfactor,
# fix_neg=True, params_2g=params_2g,
# save_shifts=False, full_output=True,
# verbose=verbose, debug=debug, plot=plot)
# LOAD IN REAL_NDIT_SCI
# Load original cubes, shift them, and create master cube
tmp_tmp = np.zeros(
[int(np.sum(self.real_ndit_sci)), ny, nx]
) #makes an array full of zeros, length of the sum of each entry in the sci dimensions file. we dont need our old tmp_tmp anymore
angles_1dvector = np.zeros([
int(np.sum(self.real_ndit_sci))
]) # makes empty array for derot angles, length of number of frames
for sc, fits_name in enumerate(self.sci_list):
tmp = open_fits(self.inpath + '4_sky_subtr_imlib_' + fits_name,
verbose=debug) #opens science cube
dim = int(self.real_ndit_sci[sc]
) #gets the integer dimensions of this science cube
for dd in range(dim): #dd goes from 0 to the largest dimension
tmp_tmp[int(
np.sum(self.real_ndit_sci[:sc])
) + dd] = frame_shift(
tmp[dd], shift_y=sy[sc], shift_x=sx[sc], imlib='opencv'
) #this line applies the shifts to all the science images in the cube the loop is currently on. it also converts all cubes to a single long cube by adding the first dd frames, then the next dd frames from the next cube and so on
angles_1dvector[int(
np.sum(self.real_ndit_sci[:sc])
) + dd] = self.derot_angles_cropped[sc][
dd] # turn 2d rotation file into a vector here same as for the mastercube above
# sc*ndit+dd i don't think this line works for variable sized cubes
tmp = None # memory management
pathlib.Path(self.outpath).mkdir(parents=True, exist_ok=True)
if crop_sz is not None:
if not crop_sz % 2:
crop_sz += 1
print('Crop size not odd, increased to {}'.format(crop_sz))
print('Cropping to {} pixels'.format(crop_sz))
tmp_tmp = cube_crop_frames(tmp_tmp,
crop_sz,
force=False,
verbose=debug,
full_output=False)
# write all the shifts
write_fits(self.outpath + 'x_shifts.fits',
sx) # writes the x shifts to the file
write_fits(self.outpath + 'y_shifts.fits',
sy) # writes the y shifts to the file
write_fits(self.outpath +
'{}_master_cube.fits'.format(self.dataset_dict['source']),
tmp_tmp) #makes the master cube
write_fits(self.outpath + 'derot_angles.fits',
angles_1dvector) # writes the 1D array of derotation angles
if verbose:
print('Shifts applied, master cube saved')
tmp_tmp = None
def bad_frame_removal(self,
pxl_shift_thres=0.5,
sub_frame_sz=31,
verbose=True,
debug=False,
plot='save'):
"""
For removing outlier frames often caused by AO errors. To be run after recentering is complete. Takes the
recentered mastercube and removes frames with a shift greater than a user defined pixel threshold in x or y above
the median shift. It then takes the median of those cubes and correlates them to the median combined mastercube.
Removes all those frames below the threshold from the mastercube and rotation file, then saves both as new files
for use in post processing
pxl_shift_thres: decimal, in units of pixels. Default is 0.5 pixels.
Any shifts in the x or y direction greater than this threshold will cause the frame/s
to be labelled as bad and thus removed
sub_frame_sz: integer, must be odd. Default is 31.
This sets the cropping during frame correlation to the median
plot: 'save' to write to file the correlation plot, None will not
"""
if verbose:
print('\n')
print('Beginning bad frame removal...')
print('\n')
angle_file = open_fits(self.outpath + 'derot_angles.fits',
verbose=debug) #opens the rotation file
recentered_cube = open_fits(
self.outpath +
'{}_master_cube.fits'.format(self.dataset_dict['source']),
verbose=debug) # loads the master cube
#open x shifts file for the respective method
x_shifts = open_fits(self.outpath + "x_shifts.fits", verbose=debug)
median_sx = np.median(x_shifts) #median of x shifts
#opens y shifts file for the respective method
y_shifts = open_fits(self.outpath + "y_shifts.fits", verbose=debug)
median_sy = np.median(y_shifts) #median of y shifts
#x_shifts_long = np.zeros([len(self.sci_list)*self.ndit]) # list with number of cubes times number of frames in each cube as the length
#y_shifts_long = np.zeros([len(self.sci_list)*self.ndit])
if not self.fast_reduction: # long are shifts to be apply to each frame in each cube. fast reduction only has one cube
x_shifts_long = np.zeros([int(np.sum(self.real_ndit_sci))])
y_shifts_long = np.zeros([int(np.sum(self.real_ndit_sci))])
for i in range(len(
self.sci_list)): # from 0 to the length of sci_list
ndit = self.real_ndit_sci[i] # gets the dimensions of the cube
x_shifts_long[i * ndit:(i + 1) * ndit] = x_shifts[
i] # sets the average shifts of all frames in that cube
y_shifts_long[i * ndit:(i + 1) * ndit] = y_shifts[i]
write_fits(self.outpath + 'x_shifts_long.fits',
x_shifts_long,
verbose=debug) # saves shifts to file
write_fits(self.outpath + 'y_shifts_long.fits',
y_shifts_long,
verbose=debug)
x_shifts = x_shifts_long
y_shifts = y_shifts_long
if verbose:
print("x shift median:", median_sx)
print("y shift median:", median_sy)
bad = []
good = []
# this system works, however it can let a couple of wild frames slip through at a 0.5 pixel threshold. may need a stricter threshold depending on the data and crop size
i = 0
shifts = list(zip(x_shifts, y_shifts))
bar = pyprind.ProgBar(len(x_shifts),
stream=1,
title='Running pixel shift check...')
for sx, sy in shifts: #iterate over the shifts to find any greater or less than pxl_shift_thres pixels from median
if abs(sx) < ((abs(median_sx)) + pxl_shift_thres) and abs(sx) > (
(abs(median_sx)) - pxl_shift_thres) and abs(sy) < (
(abs(median_sy)) + pxl_shift_thres) and abs(sy) > (
(abs(median_sy)) - pxl_shift_thres):
good.append(i)
else:
bad.append(i)
i += 1
bar.update()
# only keeps the files that weren't shifted above the threshold
frames_pxl_threshold = recentered_cube[good]
recentered_cube = None
if verbose:
print('Frames within pixel shift threshold:',
len(frames_pxl_threshold))
#recentered_cube = None
# only keeps the corresponding derotation entry for the frames that were kept
angle_pxl_threshold = angle_file[good]
if verbose:
print(
'########### Median combining {} frames for correlation check... ###########'
.format(len(frames_pxl_threshold)))
#makes array of good frames from the recentered mastercube
subarray = cube_crop_frames(
frames_pxl_threshold, size=sub_frame_sz,
verbose=verbose) # crops all the frames to a common size
frame_ref = np.median(
subarray, axis=0
) #median frame of remaining cropped frames, can be sped up with multi-processing
if verbose:
print('Running frame correlation check...')
# calculates correlation threshold using the median of the Pearson correlation of all frames, minus 1 standard deviation
#frame_ref = frame_crop(tmp_median, size = sub_frame_sz, verbose=verbose) # crops the median of all frames to a common size
distances = cube_distance(
subarray, frame_ref, mode='full', dist='pearson', plot=True
) # calculates the correlation of each frame to the median and saves as a list
if plot == 'save': # save a plot of distances compared to the median for each frame if set to 'save'
plt.savefig(self.outpath + 'distances.pdf')
correlation_thres = np.median(distances) - np.std(
distances
) # threshold is the median of the distances minus one stddev
good_frames, bad_frames = cube_detect_badfr_correlation(
subarray,
frame_ref=frame_ref,
dist='pearson',
threshold=correlation_thres,
plot=True,
verbose=False)
if plot == 'save':
plt.savefig(self.outpath + 'frame_correlation.pdf')
#only keeps the files that were above the correlation threshold
frames_threshold = frames_pxl_threshold[good_frames]
frames_pxl_threshold = None
if verbose:
print('Frames within correlation threshold:',
len(frames_threshold))
#frames_pxl_threshold = None
# only keeps the derotation entries for the good frames above the correlation threshold
angle_threshold = angle_pxl_threshold[good_frames]
# saves the good frames to a new file, and saves the derotation angles to a new file
write_fits(
self.outpath +
'{}_master_cube.fits'.format(self.dataset_dict['source']),
frames_threshold)
write_fits(self.outpath + 'derot_angles.fits', angle_threshold)
if verbose:
print('Saved good frames and their respective rotations to file')
frames_threshold = None
def correct_bad_pixels(self, verbose=True, debug=False):
sci_list = []
with open(self.outpath + "sci_list.txt", "r") as f:
tmp = f.readlines()
for line in tmp:
sci_list.append(line.split('\n')[0])
sky_list = []
with open(self.outpath + "sky_list.txt", "r") as f:
tmp = f.readlines()
for line in tmp:
sky_list.append(line.split('\n')[0])
unsat_list = []
with open(self.outpath + "unsat_list.txt", "r") as f:
tmp = f.readlines()
for line in tmp:
unsat_list.append(line.split('\n')[0])
n_sci = len(sci_list)
ndit_sci = fits_info.ndit_sci
n_sky = len(sky_list)
ndit_sky = fits_info.ndit_sky
tmp = open_fits(self.outpath + '1_crop_unsat_' + unsat_list[-1],
header=False)
nx_unsat_crop = tmp.shape[2]
master_flat_frame = open_fits(self.outpath + 'master_flat_field.fits')
# Create bpix map
bpix = np.where(np.abs(master_flat_frame - 1.09) >
0.41) # i.e. for QE < 0.68 and QE > 1.5
bpix_map = np.zeros([self.com_sz, self.com_sz])
bpix_map[bpix] = 1
if nx_unsat_crop < bpix_map.shape[1]:
bpix_map_unsat = frame_crop(bpix_map, nx_unsat_crop)
else:
bpix_map_unsat = bpix_map
#number of bad pixels
nbpix = int(np.sum(bpix_map))
ntotpix = self.com_sz**2
print("total number of bpix: ", nbpix)
print("total number of pixels: ", ntotpix)
print("=> {}% of bad pixels.".format(100 * nbpix / ntotpix))
write_fits(self.outpath + 'master_bpix_map.fits', bpix_map)
write_fits(self.outpath + 'master_bpix_map_unsat.fits', bpix_map_unsat)
plot_frames(bpix_map, bpix_map_unsat)
#update final crop size
final_sz = self.get_final_sz()
#crop frames to that size
for sc, fits_name in enumerate(sci_list):
tmp = open_fits(self.outpath + '2_nan_corr_' + fits_name,
verbose=False)
tmp_tmp = cube_crop_frames(tmp, final_sz, xy=self.agpm_pos)
write_fits(self.outpath + '2_crop_' + fits_name, tmp_tmp)
if not debug:
os.system("rm " + self.outpath + '2_nan_corr_' + fits_name)
for sk, fits_name in enumerate(sky_list):
tmp = open_fits(self.outpath + '2_nan_corr_' + fits_name,
verbose=False)
tmp_tmp = cube_crop_frames(tmp, final_sz, xy=self.agpm_pos)
write_fits(self.outpath + '2_crop_' + fits_name, tmp_tmp)
if not debug:
os.system("rm " + self.outpath + '2_nan_corr_' + fits_name)
if not debug:
tmp = open_fits(self.outpath + '2_crop_' + sci_list[0])[-1]
tmp_tmp = open_fits(self.outpath + '2_crop_' + sci_list[-1])[-1]
plot_frames(tmp, tmp_tmp)
else:
# COMPARE BEFORE AND AFTER NAN_CORR + CROP
old_tmp = open_fits(self.outpath + '2_ff_' + sci_list[0])[-1]
old_tmp_tmp = open_fits(self.outpath + '2_ff_' + sci_list[-1])[-1]
tmp = open_fits(self.outpath + '2_crop_' + sci_list[0])[-1]
tmp_tmp = open_fits(self.outpath + '2_crop_' + sci_list[-1])[-1]
plot_frames(old_tmp, tmp, old_tmp_tmp, tmp_tmp)
# Crop the bpix map in a same way
bpix_map = open_fits(self.outpath + 'master_bpix_map.fits')
bpix_map_2ndcrop = frame_crop(bpix_map, final_sz, cenxy=self.agpm_pos)
write_fits(self.outpath + 'master_bpix_map_2ndcrop.fits',
bpix_map_2ndcrop)
bpix_map = open_fits(self.outpath + 'master_bpix_map_2ndcrop.fits')
t0 = time_ini()
for sc, fits_name in enumerate(sci_list):
tmp = open_fits(self.outpath + '2_crop_' + fits_name,
verbose=False)
# first with the bp max defined from the flat field (without protecting radius)
tmp_tmp = cube_fix_badpix_isolated(tmp,
bpm_mask=bpix_map,
sigma_clip=7,
num_neig=5,
size=5,
protect_mask=True,
radius=9,
verbose=False,
debug=False)
write_fits(self.outpath + '2_bpix_corr_' + fits_name,
tmp_tmp,
verbose=False)
timing(t0)
# second, residual hot pixels
tmp_tmp, bpm = cube_fix_badpix_isolated(tmp_tmp,
bpm_mask=None,
sigma_clip=8,
num_neig=5,
size=5,
protect_mask=True,
radius=10,
verbose=False,
debug=False,
full_output=True)
write_fits(self.outpath + '2_bpix_corr2_' + fits_name, tmp_tmp)
write_fits(self.outpath + '2_bpix_corr2_map_' + fits_name, bpm)
timing(t0)
if not debug:
os.system("rm " + self.outpath + '2_crop_' + fits_name)
bpix_map = open_fits(self.outpath + 'master_bpix_map_2ndcrop.fits')
t0 = time_ini()
for sk, fits_name in enumerate(sky_list):
tmp = open_fits(self.outpath + '2_crop_' + fits_name,
verbose=False)
# first with the bp max defined from the flat field (without protecting radius)
tmp_tmp = cube_fix_badpix_isolated(tmp,
bpm_mask=bpix_map,
sigma_clip=7,
num_neig=5,
size=5,
protect_mask=True,
radius=9,
verbose=False,
debug=False)
write_fits(self.outpath + '2_bpix_corr_' + fits_name,
tmp_tmp,
verbose=False)
timing(t0)
# second, residual hot pixels
tmp_tmp, bpm = cube_fix_badpix_isolated(tmp_tmp,
bpm_mask=None,
sigma_clip=8,
num_neig=5,
size=5,
protect_mask=True,
radius=10,
verbose=False,
debug=False,
full_output=True)
write_fits(self.outpath + '2_bpix_corr2_' + fits_name, tmp_tmp)
write_fits(self.outpath + '2_bpix_corr2_map_' + fits_name, bpm)
timing(t0)
if not debug:
os.system("rm " + self.outpath + '2_crop_' + fits_name)
bpix_map_unsat = open_fits(self.outpath + 'master_bpix_map_unsat.fits')
t0 = time_ini()
for un, fits_name in enumerate(unsat_list):
tmp = open_fits(self.outpath + '2_nan_corr_unsat_' + fits_name,
verbose=False)
# first with the bp max defined from the flat field (without protecting radius)
tmp_tmp = cube_fix_badpix_isolated(tmp,
bpm_mask=bpix_map_unsat,
sigma_clip=7,
num_neig=5,
size=5,
protect_mask=True,
radius=9,
verbose=False,
debug=False)
write_fits(self.outpath + '2_bpix_corr_unsat_' + fits_name,
tmp_tmp)
timing(t0)
# second, residual hot pixels
tmp_tmp, bpm = cube_fix_badpix_isolated(tmp_tmp,
bpm_mask=None,
sigma_clip=8,
num_neig=5,
size=5,
protect_mask=True,
radius=10,
verbose=False,
debug=False,
full_output=True)
write_fits(self.outpath + '2_bpix_corr2_unsat_' + fits_name,
tmp_tmp)
write_fits(self.outpath + '2_bpix_corr2_map_unsat_' + fits_name,
bpm)
timing(t0)
if not debug:
os.system("rm " + self.outpath + '2_nan_corr_unsat_' +
fits_name)
# FIRST CREATE MASTER CUBE FOR SCI
tmp_tmp_tmp = open_fits(self.outpath + '2_bpix_corr2_' + sci_list[0],
verbose=False)
n_y = tmp_tmp_tmp.shape[1]
n_x = tmp_tmp_tmp.shape[2]
tmp_tmp_tmp = np.zeros([n_sci, n_y, n_x])
for sc, fits_name in enumerate(sci_list):
tmp_tmp_tmp[sc] = open_fits(
self.outpath + '2_bpix_corr2_' + fits_name,
verbose=False)[int(random.randrange(min(ndit_sci)))]
tmp_tmp_tmp = np.median(tmp_tmp_tmp, axis=0)
write_fits(self.outpath + 'TMP_2_master_median_SCI.fits', tmp_tmp_tmp)
# THEN CREATE MASTER CUBE FOR SKY
tmp_tmp_tmp = open_fits(self.outpath + '2_bpix_corr2_' + sky_list[0],
verbose=False)
n_y = tmp_tmp_tmp.shape[1]
n_x = tmp_tmp_tmp.shape[2]
tmp_tmp_tmp = np.zeros([n_sky, n_y, n_x])
for sk, fits_name in enumerate(sky_list):
tmp_tmp_tmp[sk] = open_fits(
self.outpath + '2_bpix_corr2_' + fits_name,
verbose=False)[int(random.randrange(min(ndit_sky)))]
tmp_tmp_tmp = np.median(tmp_tmp_tmp, axis=0)
write_fits(self.outpath + 'TMP_2_master_median_SKY.fits', tmp_tmp_tmp)
bpix_map_ori = open_fits(self.outpath + 'master_bpix_map_2ndcrop.fits')
bpix_map_sci_0 = open_fits(self.outpath + '2_bpix_corr2_map_' +
sci_list[0])
bpix_map_sci_1 = open_fits(self.outpath + '2_bpix_corr2_map_' +
sci_list[-1])
bpix_map_sky_0 = open_fits(self.outpath + '2_bpix_corr2_map_' +
sky_list[0])
bpix_map_sky_1 = open_fits(self.outpath + '2_bpix_corr2_map_' +
sky_list[-1])
bpix_map_unsat_0 = open_fits(self.outpath + '2_bpix_corr2_map_unsat_' +
unsat_list[0])
bpix_map_unsat_1 = open_fits(self.outpath + '2_bpix_corr2_map_unsat_' +
unsat_list[-1])
plot_frames(
bpix_map_ori,
bpix_map_sci_0,
bpix_map_sci_1, #tmp_tmp_tmp, #bpix_tmp,
bpix_map_sky_0,
bpix_map_sky_1, #, #tmp_tmp_tmp_tmp, #bpix_tmp_tmp, tmpSCI-tmpSKY
bpix_map_unsat_0,
bpix_map_unsat_1 #, #tmp_tmp_tmp_tmp, #bpix_tmp_tmp, tmpSCI-tmpSKY
)
tmpSCI = open_fits(self.outpath + 'TMP_2_master_median_SCI.fits')
tmpSKY = open_fits(self.outpath + 'TMP_2_master_median_SKY.fits')
if not debug:
tmp_tmp = open_fits(self.outpath + '2_bpix_corr2_' +
sci_list[1])[-1]
tmp_tmp2 = open_fits(self.outpath + '2_bpix_corr2_' +
sci_list[-1])[-1]
plot_frames( #tmp, tmp-tmpSKY, #tmp_tmp_tmp, #bpix_tmp,
tmp_tmp,
tmp_tmp -
tmpSKY, #, #tmp_tmp_tmp_tmp, #bpix_tmp_tmp, tmpSCI-tmpSKY
#tmp2, tmp2-tmpSKY, #tmp_tmp_tmp, #bpix_tmp,
tmp_tmp2,
tmp_tmp2 -
tmpSKY #, #tmp_tmp_tmp_tmp, #bpix_tmp_tmp, tmpSCI-tmpSKY
)
else:
# COMPARE BEFORE AND AFTER BPIX CORR (without sky subtr)
tmp = open_fits(self.outpath + '2_crop_' + sci_list[-1])[-1]
tmp_tmp = open_fits(self.outpath + '2_bpix_corr2_' +
sci_list[-1])[-1]
tmp2 = open_fits(self.outpath + '2_crop_' + sky_list[-1])[-1]
tmp_tmp2 = open_fits(self.outpath + '2_bpix_corr2_' +
sky_list[-1])[-1]
(
tmp,
tmp - tmpSKY, #tmp_tmp_tmp, #bpix_tmp,
tmp_tmp,
tmp_tmp -
tmpSKY, #, #tmp_tmp_tmp_tmp, #bpix_tmp_tmp, tmpSCI-tmpSKY
tmp2,
tmp2 - tmpSKY, #tmp_tmp_tmp, #bpix_tmp,
tmp_tmp2,
tmp_tmp2 -
tmpSKY #, #tmp_tmp_tmp_tmp, #bpix_tmp_tmp, tmpSCI-tmpSKY
)
def dark_subtract(self, verbose=True, debug=True):
sci_list = []
with open(self.outpath + "sci_list.txt", "r") as f:
tmp = f.readlines()
for line in tmp:
sci_list.append(line.split('\n')[0])
sky_list = []
with open(self.outpath + "sky_list.txt", "r") as f:
tmp = f.readlines()
for line in tmp:
sky_list.append(line.split('\n')[0])
unsat_list = []
with open(self.outpath + "unsat_list.txt", "r") as f:
tmp = f.readlines()
for line in tmp:
unsat_list.append(line.split('\n')[0])
unsat_dark_list = []
with open(self.outpath + "unsat_dark_list.txt", "r") as f:
tmp = f.readlines()
for line in tmp:
unsat_dark_list.append(line.split('\n')[0])
flat_list = []
with open(self.outpath + "flat_list.txt", "r") as f:
tmp = f.readlines()
for line in tmp:
flat_list.append(line.split('\n')[0])
flat_dark_list = []
with open(self.outpath + "flat_dark_list.txt", "r") as f:
tmp = f.readlines()
for line in tmp:
flat_dark_list.append(line.split('\n')[0])
sci_dark_list = []
with open(self.outpath + "sci_dark_list.txt", "r") as f:
tmp = f.readlines()
for line in tmp:
sci_dark_list.append(line.split('\n')[0])
pixel_scale = fits_info.pixel_scale
tmp = np.zeros([3, self.com_sz, self.com_sz])
#creating master dark cubes
for fd, fd_name in enumerate(flat_dark_list):
tmp_tmp = open_fits(self.inpath + fd_name,
header=False,
verbose=debug)
tmp[fd] = frame_crop(tmp_tmp,
self.com_sz,
force=True,
verbose=debug)
write_fits(self.outpath + 'flat_dark_cube.fits', tmp)
if verbose:
print('Flat dark cubes have been cropped and saved')
for sd, sd_name in enumerate(sci_dark_list):
tmp_tmp = open_fits(self.inpath + sd_name,
header=False,
verbose=debug)
n_dim = tmp_tmp.ndim
if sd == 0:
if n_dim == 2:
tmp = np.array([
frame_crop(tmp_tmp,
self.com_sz,
force=True,
verbose=debug)
])
else:
tmp = cube_crop_frames(tmp_tmp,
self.com_sz,
force=True,
verbose=debug)
else:
if n_dim == 2:
tmp = np.append(tmp, [
frame_crop(
tmp_tmp, self.com_sz, force=True, verbose=debug)
],
axis=0)
else:
tmp = np.append(tmp,
cube_crop_frames(tmp_tmp,
self.com_sz,
force=True,
verbose=debug),
axis=0)
write_fits(self.outpath + 'sci_dark_cube.fits', tmp)
if verbose:
print('Sci dark cubes have been cropped and saved')
#create an if stament for if the size is larger than sz and less than if less than crop by nx-1
for sd, sd_name in enumerate(unsat_dark_list):
tmp_tmp = open_fits(self.inpath + sd_name,
header=False,
verbose=debug)
tmp = np.zeros([
len(sci_dark_list) * tmp_tmp.shape[0], tmp_tmp.shape[1],
tmp_tmp.shape[2]
])
n_dim = tmp_tmp.ndim
if sd == 0:
if n_dim == 2:
ny, nx = tmp_tmp.shape
if nx <= self.com_sz:
tmp = np.array([
frame_crop(tmp_tmp,
nx - 1,
force=True,
verbose=debug)
])
else:
tmp = np.array(
[frame_crop(tmp_tmp, self.com_sz, verbose=debug)])
else:
nz, ny, nx = tmp_tmp.shape
if nx <= self.com_sz:
tmp = cube_crop_frames(tmp_tmp,
nx - 1,
force=True,
verbose=debug)
else:
tmp = cube_crop_frames(tmp_tmp,
self.com_sz,
force=True,
verbose=debug)
else:
if n_dim == 2:
ny, nx = tmp_tmp.shape
if nx <= self.com_sz:
tmp = np.append(tmp, [
frame_crop(
tmp_tmp, nx - 1, force=True, verbose=debug)
],
axis=0)
else:
tmp = np.append(tmp, [
frame_crop(tmp_tmp,
self.com_sz,
force=True,
verbose=debug)
],
axis=0)
else:
nz, ny, nx = tmp_tmp.shape
if nx <= self.com_sz:
tmp = cube_crop_frames(tmp,
nx - 1,
force=True,
verbose=debug)
tmp = np.append(tmp,
cube_crop_frames(tmp_tmp,
nx - 1,
force=True,
verbose=debug),
axis=0)
else:
tmp = np.append(tmp,
cube_crop_frames(tmp_tmp,
self.com_sz,
force=True,
verbose=debug),
axis=0)
tmp = np.zeros([
len(sci_dark_list) * tmp_tmp.shape[0], tmp_tmp.shape[1],
tmp_tmp.shape[2]
])
write_fits(self.outpath + 'unsat_dark_cube.fits', tmp)
if verbose:
print('Unsat dark cubes have been cropped and saved')
#defining the anulus (this is where we avoid correcting around the star)
cy, cx = find_agpm_list(self, sci_list)
self.agpm_pos = (cx, cy)
if verbose:
print(' The location of the AGPM has been calculated', 'cy = ', cy,
'cx = ', cx)
agpm_dedge = min(self.agpm_pos[0], self.agpm_pos[1],
self.com_sz - self.agpm_pos[0],
self.com_sz - self.agpm_pos[1])
mask_arr = np.ones([self.com_sz, self.com_sz])
cy, cx = frame_center(mask_arr)
mask_inner_rad = int(3.0 /
pixel_scale) # 3arcsec min to avoid star emission
mask_width = agpm_dedge - mask_inner_rad - 1
mask_AGPM_com = get_annulus_segments(mask_arr,
mask_inner_rad,
mask_width,
mode='mask')[0]
mask_AGPM_com = frame_shift(
mask_AGPM_com, self.agpm_pos[1] - cy, self.agpm_pos[0] -
cx) # agpm is not centered in the frame so shift the mask
if verbose:
print('AGPM mask has been defined')
if debug:
tmp = open_fits(self.outpath + sci_list[0])
#plot_frames(tmp[-1], circle = self.agpm_pos)
#now begin the dark subtraction useing PCA
npc_dark = 1 #val found this value gives the best result.
tmp_tmp = np.zeros([len(flat_list), self.com_sz, self.com_sz])
tmp_tmp_tmp = open_fits(self.outpath + 'flat_dark_cube.fits')
for fl, flat_name in enumerate(flat_list):
tmp = open_fits(self.inpath + flat_name,
header=False,
verbose=debug)
tmp_tmp[fl] = frame_crop(tmp,
self.com_sz,
force=True,
verbose=debug)
tmp_tmp = cube_subtract_sky_pca(tmp_tmp,
tmp_tmp_tmp,
mask_AGPM_com,
ref_cube=None,
ncomp=npc_dark)
write_fits(self.outpath + '1_crop_flat_cube.fits', tmp_tmp)
if verbose:
print('Dark has been subtracted from Flats')
if debug:
#plot the median of dark cube median of cube before subtraction median after subtraction
tmp_tmp_tmp = np.median(tmp_tmp_tmp,
axis=0) #flat_dark cube median
tmp = tmp #flat before subtraction
tmp_tmp = np.median(tmp_tmp,
axis=0) #median flat after dark subtract
plot_frames((tmp_tmp_tmp, tmp, tmp_tmp))
tmp_tmp_tmp = open_fits(self.outpath + 'sci_dark_cube.fits')
for sc, fits_name in enumerate(sci_list):
tmp = open_fits(self.inpath + fits_name,
header=False,
verbose=debug)
tmp = cube_crop_frames(tmp, self.com_sz, force=True, verbose=debug)
tmp_tmp = cube_subtract_sky_pca(tmp,
tmp_tmp_tmp,
mask_AGPM_com,
ref_cube=None,
ncomp=npc_dark)
write_fits(self.outpath + '1_crop_' + fits_name, tmp_tmp)
if verbose:
print('Dark has been subtracted from Sci')
if debug:
#plot the median of dark cube median of cube before subtraction median after subtraction
tmp_tmp_tmp = np.median(tmp_tmp_tmp, axis=0)
tmp = np.median(tmp, axis=0)
tmp_tmp = np.median(tmp_tmp, axis=0)
plot_frames((tmp_tmp_tmp, tmp, tmp_tmp))
tmp_tmp_tmp = open_fits(self.outpath + 'sci_dark_cube.fits')
for sc, fits_name in enumerate(sky_list):
tmp = open_fits(self.inpath + fits_name,
header=False,
verbose=debug)
tmp = cube_crop_frames(tmp, self.com_sz, force=True, verbose=debug)
tmp_tmp = cube_subtract_sky_pca(tmp,
tmp_tmp_tmp,
mask_AGPM_com,
ref_cube=None,
ncomp=npc_dark)
write_fits(self.outpath + '1_crop_' + fits_name, tmp_tmp)
if verbose:
print('Dark has been subtracted from Sky')
if debug:
#plot the median of dark cube median of cube before subtraction median after subtraction
tmp_tmp_tmp = np.median(tmp_tmp_tmp, axis=0)
tmp = np.median(tmp, axis=0)
tmp_tmp = np.median(tmp_tmp, axis=0)
plot_frames((tmp_tmp_tmp, tmp, tmp_tmp))
tmp_tmp_tmp = open_fits(self.outpath + 'master_unsat_dark.fits')
# no need to crop the unsat frame at the same size as the sci images if they are smaller
for un, fits_name in enumerate(unsat_list):
tmp = open_fits(self.inpath + fits_name, header=False)
#tmp = cube_crop_frames(tmp,nx_unsat_crop)
if tmp.shape[2] > self.com_sz:
nx_unsat_crop = self.com_sz
tmp_tmp = cube_crop_frames(tmp - tmp_tmp_tmp,
nx_unsat_crop,
force=True,
verbose=debug)
elif tmp.shape[2] % 2 == 0:
nx_unsat_crop = tmp.shape[2] - 1
tmp_tmp = cube_crop_frames(tmp - tmp_tmp_tmp,
nx_unsat_crop,
force=True,
verbose=debug)
else:
nx_unsat_crop = tmp.shape[2]
tmp_tmp = tmp - tmp_tmp_tmp
write_fits(self.outpath + '1_crop_unsat_' + fits_name, tmp_tmp)
if verbose:
print('unsat frames have been cropped')
if debug:
#plot the median of dark cube median of cube before subtraction median after subtraction
tmp_tmp_tmp = np.median(tmp_tmp_tmp, axis=0)
tmp = np.median(tmp, axis=0)
tmp_tmp = np.median(tmp_tmp, axis=0)
plot_frames((tmp_tmp_tmp, tmp, tmp_tmp))