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Adapted speckle-based recentering to add an option to avoid high/low-…
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…pass filtering - i.e. just log-scale recentering
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@VChristiaens
VChristiaens committed Aug 8, 2023
1 parent 5e245c8 commit c3e8ef8
Showing 1 changed file with 118 additions and 51 deletions.
169 changes: 118 additions & 51 deletions vip_hci/preproc/recentering.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1799,14 +1799,18 @@ def cube_recenter_via_speckles(cube_sci, cube_ref=None, alignment_iter=5,
if ref_star:
cube_ref_lpf = cube_ref_subframe.copy()

cube_sci_lpf = cube_sci_lpf + np.abs(np.min(cube_sci_lpf))
cube_sci_lpf = cube_sci_lpf -np.min(cube_sci_lpf)+1
if ref_star:
cube_ref_lpf = cube_ref_lpf + np.abs(np.min(cube_ref_lpf))
cube_ref_lpf = cube_ref_lpf - np.min(cube_ref_lpf)+1

median_size = int(fwhm * max_spat_freq)
# Remove spatial frequencies <0.5 lam/D and >3lam/D to isolate speckles
cube_sci_hpf = cube_filter_highpass(cube_sci_lpf, 'median-subt',
median_size=median_size, verbose=False)
if max_spat_freq>0:
median_size = int(fwhm * max_spat_freq)
# Remove spatial frequencies <0.5 lam/D and >3lam/D to isolate speckles
cube_sci_hpf = cube_filter_highpass(cube_sci_lpf, 'median-subt',
median_size=median_size,
verbose=False)
else:
cube_sci_hpf = cube_sci_lpf
if min_spat_freq > 0:
cube_sci_lpf = cube_filter_lowpass(cube_sci_hpf, 'gauss',
fwhm_size=min_spat_freq * fwhm,
Expand All @@ -1815,9 +1819,12 @@ def cube_recenter_via_speckles(cube_sci, cube_ref=None, alignment_iter=5,
cube_sci_lpf = cube_sci_hpf

if ref_star:
cube_ref_hpf = cube_filter_highpass(cube_ref_lpf, 'median-subt',
median_size=median_size,
verbose=False)
if max_spat_freq>0:
cube_ref_hpf = cube_filter_highpass(cube_ref_lpf, 'median-subt',
median_size=median_size,
verbose=False)
else:
cube_ref_hpf = cube_ref_lpf
if min_spat_freq > 0:
cube_ref_lpf = cube_filter_lowpass(cube_ref_hpf, 'gauss',
fwhm_size=min_spat_freq * fwhm,
Expand All @@ -1837,12 +1844,40 @@ def cube_recenter_via_speckles(cube_sci, cube_ref=None, alignment_iter=5,
cum_y_shifts = 0
cum_x_shifts = 0

for i in range(alignment_iter):
alignment_cube[0] = np.median(alignment_cube[1:(n + 1)], axis=0)
# no iteration => just align with respect to first frame => converges
if alignment_iter==1:
alignment_cube[0] = cube_sci_lpf[0]
# center the cube with stretched values
cube_stret = np.log10((np.abs(alignment_cube) + 1) ** gammaval)
if mask is not None and crop:
mask_tmp = frame_crop(mask, subframesize)
else:
mask_tmp = mask
res = cube_recenter_dft_upsampling(cube_stret, (ceny, cenx), fwhm=fwhm,
subi_size=None, full_output=True,
verbose=debug, plot=plot,
mask=mask_tmp, imlib=imlib,
interpolation=interpolation,
nproc=nproc)
cube_stret, y_shift, x_shift = res
sqsum_shifts = np.sum(np.sqrt(y_shift ** 2 + x_shift ** 2))
print('Square sum of shift vecs: ' + str(sqsum_shifts))

for j in range(1, n_frames):
alignment_cube[j] = frame_shift(alignment_cube[j], y_shift[j],
x_shift[j], imlib=imlib,
interpolation=interpolation,
border_mode=border_mode)
cum_y_shifts += y_shift
cum_x_shifts += x_shift

if recenter_median:
alignment_cube[0] = np.median(alignment_cube[1:(n + 1)], axis=0)
# Recenter the median frame using a 2d fit
if fit_type == 'gaus':
if fit_type == 'gaus' and negative:
crop_sz = int(fwhm)
elif fit_type == 'gaus':
crop_sz = int(3*fwhm)
else:
crop_sz = int(6*fwhm)
if not crop_sz % 2:
Expand All @@ -1867,44 +1902,78 @@ def cube_recenter_via_speckles(cube_sci, cube_ref=None, alignment_iter=5,
unc_in=2.)
yshift = ceny - (y1 + y_i)
xshift = cenx - (x1 + x_i)

alignment_cube[0] = frame_shift(alignment_cube[0, :, :], yshift,
xshift, imlib=imlib,
interpolation=interpolation,
border_mode=border_mode)

# center the cube with stretched values
cube_stret = np.log10((np.abs(alignment_cube) + 1) ** gammaval)
if mask is not None and crop:
mask_tmp = frame_crop(mask, subframesize)
else:
mask_tmp = mask
res = cube_recenter_dft_upsampling(cube_stret, (ceny, cenx), fwhm=fwhm,
subi_size=None, full_output=True,
verbose=False, plot=False,
mask=mask_tmp, imlib=imlib,
interpolation=interpolation, nproc=nproc)
_, y_shift, x_shift = res
sqsum_shifts = np.sum(np.sqrt(y_shift ** 2 + x_shift ** 2))
print('Square sum of shift vecs: ' + str(sqsum_shifts))

for j in range(1, n_frames):
alignment_cube[j] = frame_shift(alignment_cube[j], y_shift[j],
x_shift[j], imlib=imlib,
interpolation=interpolation,
border_mode=border_mode)

cum_y_shifts += y_shift
cum_x_shifts += x_shift

cum_y_shifts += yshift
cum_x_shifts += xshift
else:
for i in range(alignment_iter):
alignment_cube[0] = np.median(alignment_cube[1:(n + 1)], axis=0)
if recenter_median:
# Recenter the median frame using a 2d fit
if fit_type == 'gaus':
crop_sz = int(fwhm)
else:
crop_sz = int(6*fwhm)
if not crop_sz % 2:
# size should be odd and small, but at least 7 for 2D fit
if crop_sz > 7:
crop_sz -= 1
else:
crop_sz += 1
sub_image, y1, x1 = get_square(alignment_cube[0], size=crop_sz,
y=ceny, x=cenx, position=True)

if fit_type == 'gaus':
if negative:
sub_image = -sub_image + np.abs(np.min(-sub_image))
y_i, x_i = fit_2dgaussian(sub_image, crop=False,
threshold=False, sigfactor=1,
debug=debug, full_output=False)
elif fit_type == 'ann':
y_i, x_i, rad = _fit_2dannulus(sub_image, fwhm=fwhm, crop=False,
hole_rad=0.5, sampl_cen=0.1,
sampl_rad=0.2, ann_width=0.5,
unc_in=2.)
yshift = ceny - (y1 + y_i)
xshift = cenx - (x1 + x_i)

alignment_cube[0] = frame_shift(alignment_cube[0, :, :], yshift,
xshift, imlib=imlib,
interpolation=interpolation,
border_mode=border_mode)

# center the cube with stretched values
cube_stret = np.log10((np.abs(alignment_cube) + 1) ** gammaval)
if mask is not None and crop:
mask_tmp = frame_crop(mask, subframesize)
else:
mask_tmp = mask
res = cube_recenter_dft_upsampling(cube_stret, (ceny, cenx),
fwhm=fwhm, subi_size=None,
full_output=True,
verbose=False, plot=False,
mask=mask_tmp, imlib=imlib,
interpolation=interpolation,
nproc=nproc)
_, y_shift, x_shift = res
sqsum_shifts = np.sum(np.sqrt(y_shift ** 2 + x_shift ** 2))
print('Square sum of shift vecs: ' + str(sqsum_shifts))

for j in range(1, n_frames):
alignment_cube[j] = frame_shift(alignment_cube[j], y_shift[j],
x_shift[j], imlib=imlib,
interpolation=interpolation,
border_mode=border_mode)

cum_y_shifts += y_shift
cum_x_shifts += x_shift

cube_reg_sci = cube_sci.copy()
cum_y_shifts_sci = cum_y_shifts[1:(n + 1)]
cum_x_shifts_sci = cum_x_shifts[1:(n + 1)]
for i in range(n):
cube_reg_sci[i] = frame_shift(cube_sci[i], cum_y_shifts_sci[i],
cum_x_shifts_sci[i], imlib=imlib,
interpolation=interpolation,
border_mode=border_mode)
cube_reg_sci = cube_shift(cube_sci, cum_y_shifts_sci, cum_x_shifts_sci,
imlib=imlib, interpolation=interpolation,
border_mode=border_mode)

if plot:
plt.figure(figsize=vip_figsize)
Expand All @@ -1928,11 +1997,9 @@ def cube_recenter_via_speckles(cube_sci, cube_ref=None, alignment_iter=5,
cube_reg_ref = cube_ref.copy()
cum_y_shifts_ref = cum_y_shifts[(n + 1):]
cum_x_shifts_ref = cum_x_shifts[(n + 1):]
for i in range(nref):
cube_reg_ref[i] = frame_shift(cube_ref[i], cum_y_shifts_ref[i],
cum_x_shifts_ref[i], imlib=imlib,
interpolation=interpolation,
border_mode=border_mode)
cube_reg_ref = cube_shift(cube_ref, cum_y_shifts_ref, cum_x_shifts_ref,
imlib=imlib, interpolation=interpolation,
border_mode=border_mode)

if ref_star:
if full_output:
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