def AnnularPCA(cube, angs, fwhm, name_input):
print( "\n --- ANNULAR PCA")
print("Performing Annular PCA...")
ann_pca_output = vip.pca.pca_local.pca_annular(
cube, angs, fwhm=fwhm,
full_output = True, verbose = True
)
# Outputs three objects in this order:
# cube_out: cube of residuals
# cub_der: derotated cube of residuals
# frame: Annular PCA frame
hciplot.plot_frames(
ann_pca_output[2],
label = 'AnnPCA reduction of {name}'.format(name=name_input),
grid = False,
size_factor = 5
)
print( 'Would you like to save the Annular PCA reduced images?' )
questionsave = Checkfunction()
if questionsave == 0:
vip.fits.write_fits('AnnPCA_{name}.fits'.format(name=name_input), ann_pca_output[2], verbose=True)
vip.fits.write_fits('AnnPCA_residuals_{name}.fits'.format(name=name_input), ann_pca_output[1], verbose=True)
annpca_frame = ann_pca_output[2]
residuals_stim_input = ann_pca_output[1]
return annpca_frame, residuals_stim_input
def inspect_patch(self, xy, cmap='bone', dpi=40):
"""
"""
if self.probas is None:
raise RuntimeError("You must run the predictor first")
x_input, y_input = xy
if not isinstance(xy, tuple):
raise TypeError("`xy` must be a tuple")
for i, coord in enumerate(self.coords):
if coord[0] == y_input and coord[1] == x_input:
index = i
if index is None:
raise ValueError("Input coordinates not found")
prob = self.probas[index]
print("Proba : " + str(prob))
sample = np.squeeze(self.patches[index])
max_slices = sample.shape[0]
if self.sample_type == 'tmlar4d':
for i in range(sample.shape[1]):
plot_frames(tuple(sample[:, i]), axis=False,
colorbar=False, cmap=cmap, dpi=dpi, horsp=0.05)
else:
plot_frames(tuple(sample), axis=False, colorbar=False,
cmap=cmap, dpi=dpi, horsp=0.05)
def check_blobs(array, coords_temp, fwhm, debug):
y_temp = coords_temp[:, 0]
x_temp = coords_temp[:, 1]
coords = []
# Fitting a 2d gaussian to each local maxima position
for y, x in zip(y_temp, x_temp):
subsi = 3 * int(np.ceil(fwhm))
if subsi % 2 == 0:
subsi += 1
if mode in ('lpeaks', 'log', 'dog'):
scy = y + pad
scx = x + pad
elif mode in ('snrmap', 'snrmapf'):
scy = y
scx = x
subim, suby, subx = get_square(array,
subsi,
scy,
scx,
position=True,
force=True,
verbose=False)
cy, cx = frame_center(subim)
gauss = models.Gaussian2D(amplitude=subim.max(),
x_mean=cx,
y_mean=cy,
theta=0,
x_stddev=fwhm * gaussian_fwhm_to_sigma,
y_stddev=fwhm * gaussian_fwhm_to_sigma)
sy, sx = np.indices(subim.shape)
fitter = fitting.LevMarLSQFitter()
fit = fitter(gauss, sx, sy, subim)
# checking that the amplitude is positive > 0
# checking whether the x and y centroids of the 2d gaussian fit
# coincide with the center of the subimage (within 2px error)
# checking whether the mean of the fwhm in y and x of the fit
# are close to the FWHM_PSF with a margin of 3px
fwhm_y = fit.y_stddev.value * gaussian_sigma_to_fwhm
fwhm_x = fit.x_stddev.value * gaussian_sigma_to_fwhm
mean_fwhm_fit = np.mean([np.abs(fwhm_x), np.abs(fwhm_y)])
condyf = np.allclose(fit.y_mean.value, cy, atol=2)
condxf = np.allclose(fit.x_mean.value, cx, atol=2)
condmf = np.allclose(mean_fwhm_fit, fwhm, atol=3)
if fit.amplitude.value > 0 and condxf and condyf and condmf:
coords.append(
(suby + fit.y_mean.value, subx + fit.x_mean.value))
if debug:
print('Coordinates (Y,X): {:.3f},{:.3f}'.format(y, x))
print('fit peak = {:.3f}'.format(fit.amplitude.value))
msg = 'fwhm_y in px = {:.3f}, fwhm_x in px = {:.3f}'
print(msg.format(fwhm_y, fwhm_x))
print('mean fit fwhm = {:.3f}'.format(mean_fwhm_fit))
if plot:
plot_frames(subim, colorbar=True, axis=False, dpi=60)
return coords
def create_cube_with_satspots(n_frames=6, wh=31, star_fwhm=3, debug=False):
global seed
shape = (n_frames, wh, wh)
star = create_cube_with_gauss2d(shape=shape, mean=wh // 2, stddev=star_fwhm)
# make sure satspot is neither too close to star nor at the edge of the
# image
diagonal = seed.uniform(4 * star_fwhm, wh // 2)
d = diagonal / np.sqrt(2)
sat1_coords = (wh // 2 - d, wh // 2 + d)
sat2_coords = (wh // 2 + d, wh // 2 + d)
sat3_coords = (wh // 2 - d, wh // 2 - d)
sat4_coords = (wh // 2 + d, wh // 2 - d)
sat1 = create_cube_with_gauss2d(shape=shape, mean=sat1_coords, stddev=1)
sat2 = create_cube_with_gauss2d(shape=shape, mean=sat2_coords, stddev=1)
sat3 = create_cube_with_gauss2d(shape=shape, mean=sat3_coords, stddev=1)
sat4 = create_cube_with_gauss2d(shape=shape, mean=sat4_coords, stddev=1)
cube = star + sat1 + sat2 + sat3 + sat4
if debug:
hciplot.plot_frames(cube[0])
return cube, [sat1_coords, sat2_coords, sat3_coords, sat4_coords]
def check_blobs(array, coords_temp, fwhm, debug):
y_temp = coords_temp[:, 0]
x_temp = coords_temp[:, 1]
coords = []
# Fitting a 2d gaussian to each local maxima position
for y, x in zip(y_temp, x_temp):
subsi = 3 * int(np.ceil(fwhm))
if subsi % 2 == 0:
subsi += 1
if mode in ('lpeaks', 'log', 'dog'):
scy = y + pad
scx = x + pad
elif mode in ('snrmap', 'snrmapf'):
scy = y
scx = x
subim, suby, subx = get_square(array, subsi, scy, scx,
position=True, force=True,
verbose=False)
cy, cx = frame_center(subim)
gauss = models.Gaussian2D(amplitude=subim.max(), x_mean=cx,
y_mean=cy, theta=0,
x_stddev=fwhm*gaussian_fwhm_to_sigma,
y_stddev=fwhm*gaussian_fwhm_to_sigma)
sy, sx = np.indices(subim.shape)
fitter = fitting.LevMarLSQFitter()
fit = fitter(gauss, sx, sy, subim)
# checking that the amplitude is positive > 0
# checking whether the x and y centroids of the 2d gaussian fit
# coincide with the center of the subimage (within 2px error)
# checking whether the mean of the fwhm in y and x of the fit
# are close to the FWHM_PSF with a margin of 3px
fwhm_y = fit.y_stddev.value * gaussian_sigma_to_fwhm
fwhm_x = fit.x_stddev.value * gaussian_sigma_to_fwhm
mean_fwhm_fit = np.mean([np.abs(fwhm_x), np.abs(fwhm_y)])
condyf = np.allclose(fit.y_mean.value, cy, atol=2)
condxf = np.allclose(fit.x_mean.value, cx, atol=2)
condmf = np.allclose(mean_fwhm_fit, fwhm, atol=3)
if fit.amplitude.value > 0 and condxf and condyf and condmf:
coords.append((suby + fit.y_mean.value,
subx + fit.x_mean.value))
if debug:
print('Coordinates (Y,X): {:.3f},{:.3f}'.format(y, x))
print('fit peak = {:.3f}'.format(fit.amplitude.value))
msg = 'fwhm_y in px = {:.3f}, fwhm_x in px = {:.3f}'
print(msg.format(fwhm_y, fwhm_x))
print('mean fit fwhm = {:.3f}'.format(mean_fwhm_fit))
if plot:
plot_frames(subim, colorbar=True, axis=False, dpi=60)
return coords
def do_recenter(method, cube, shiftx, shifty, errormsg, mse=1e-2,
mse_skip_first=False, n_frames=6, debug=False, **kwargs):
#===== shift cube
shifted_cube = shift_cube(cube, shiftx, shifty)
if debug:
html("<h3>===== {}({}) =====</h3>".format(
method.__name__,
", ".join("{}={}".format(k, v) for k, v in kwargs.items())))
#===== recentering
rec_res = method(shifted_cube, debug=debug, **kwargs)
recentered_cube= rec_res[0]
unshifty= rec_res[1]
unshiftx= rec_res[2]
if debug:
hciplot.plot_frames(cube, title="input cube")
hciplot.plot_frames(shifted_cube, title="shifted cube")
hciplot.plot_frames(recentered_cube, title="recentered cube")
if debug:
hciplot.plot_frames(cube[1], recentered_cube[1], shifted_cube[1],
label=["cube[1]", "recentered[1]", "shifted[1]"])
if mse_skip_first:
if debug:
print("\033[33mfirst shift ignored for MSE\033[0m")
shiftx = shiftx[1:]
shifty = shifty[1:]
unshiftx = unshiftx[1:]
unshifty = unshifty[1:]
if debug:
try:
import pandas as pd
from IPython.display import display
p = pd.DataFrame(np.array([
shiftx,
-unshiftx,
shifty,
-unshifty
]).T, columns=["x", "un-x", "y", "un-y"])
print("\033[33mshifts:\033[0m")
display(p)
except:
print("\033[33mcalculated shifts:\033[0m", unshiftx)
print(" " * 18, unshifty)
print("\033[33moriginal shifts\033[0m: ", -shiftx)
print(" " * 18, -shifty)
print("\033[33merrors:\033[0m", mean_squared_error(
shiftx, -unshiftx), mean_squared_error(shifty, -unshifty))
#===== verify error
assert mean_squared_error(shiftx, -unshiftx) < mse, errormsg
assert mean_squared_error(shifty, -unshifty) < mse, errormsg
if debug:
print("\033[32mpassed.\033[0m")
def LLSG(cube, angs, fwhm, name_input):
print("\n --- LLSG")
rank = input("What rank of llsg would you like to do? (default = 6)\n")
rank = int(rank)
#Reduces the image using the LLSG algorithm then plots the image.
print(fwhm)
llsg_output = vip.llsg.llsg(cube,
angs,
fwhm=fwhm,
rank=rank,
thresh=2,
max_iter=20,
random_seed=10,
full_output=True)
# LLSG full output is:
#0 list_l_array_der
#1 list_s_array_der
#2 list_g_array_der
#3 frame_l
#4 frame_s
#5 frame_g
# Each is the frame and residual cube for the three noise types:
# l (low-rank), s (sparse), g (gaussian)
# Companions are in the Sparse noise.
# To get STIM map of LLSG, input 'list_s_array_der' into STIM Map
llsg_frame = llsg_output[4]
llsg_residual_sparse = np.asarray(llsg_output[1][0])
#list_s_array_der is a list of one member,
# this one member is another list of the 24 residual images
# So get that one element, and make it an array.
hciplot.plot_frames(
llsg_frame,
label='LLSG reduced image of {name}'.format(name=name_input))
#Loop asks user if the would like to save the image.
print('Would you like to save the LLSG reduced image?\n')
questionsave = Checkfunction()
if questionsave == 0:
vip.fits.write_fits('new_LLSG_{name}.fits'.format(name=name_input),
llsg_frame,
verbose=True)
return llsg_frame, llsg_residual_sparse, rank
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 StimMap(residuals_cube, name_input, origin_algo):
print("Computing STIM map using {algo} output...".format(algo=origin_algo))
stim_map = vip.metrics.compute_stim_map(residuals_cube)
hciplot.plot_frames(stim_map,
label='STIM map of {name} using {algo}'.format(
name=name_input, algo=origin_algo),
grid=False,
size_factor=5)
# Loop asks user if they would like to save the image.
print('Would you like to save the image?')
questionsave = Checkfunction()
if questionsave == 0:
vip.fits.write_fits('new_STIM_{algo}_{name}.fits'.format(
algo=origin_algo, name=name_input),
stim_map,
verbose=True)
def inspect_probmap(self, vmin_log=1e-10, labelsize=10, circlerad=10,
circlecolor='white', circlealpha=0.6, grid=True,
gridspacing=10, gridalpha=0.2, showcent=True,
print_info=True, **kwargs):
"""
from matplotlib.pyplot import hist, figure
vec = pred_svd.pmap.flatten()
_ = hist(vec[vec > 0], bins=np.sqrt(vec.shape[0]).astype(int))
figure()
_ = hist(np.log(vec[vec > 0]), bins=np.sqrt(vec.shape[0]).astype(int))
"""
if print_info:
self.print_info()
plot_frames((self.pmap, self.pmap), log=(False, True),
vmin=(0, vmin_log), vmax=(1, 1),
label=('Probmap', 'Probmap (logscale)'),
label_size=labelsize, circle_radius=circlerad,
circle_color=circlecolor, circle_alpha=circlealpha,
show_center=showcent, grid=grid, grid_alpha=gridalpha,
grid_spacing=gridspacing, **kwargs)
def find_star_unsat(self, unsat_list, verbose=True, debug=False):
self.unsat_star_pos = {}
y_star = []
x_star = []
for un, fits_name in enumerate(unsat_list):
tmp = np.median(open_fits(self.outpath + '1_crop_unsat_' +
fits_name,
header=False),
axis=0)
table_res = detection(tmp,
fwhm=1.2 * self.resel,
bkg_sigma=1,
mode='lpeaks',
matched_filter=False,
mask=True,
snr_thresh=10,
plot=debug,
debug=debug,
full_output=debug,
verbose=verbose)
y_star = np.append(x_star, table_res['y'][0])
x_star = np.append(y_star, table_res['x'][0])
self.unsat_star_pos['y'] = y_star
self.unsat_star_pos['x'] = x_star
self.unsat_star_pos['fname'] = unsat_list
if verbose:
print('The star has been located in the unsat cubes')
if debug:
snr_star = table_res['px_snr']
for un, fits_name in enumerate(unsat_list):
tmp = np.median(open_fits(self.outpath + '1_crop_unsat_' +
fits_name,
header=False),
axis=0)
plot_frames(tmp, circle=(y_star[un], x_star[un]))
def compute_binary_map(frame, thresholds, injections, fwhm, npix=1,
overlap_threshold=0.7, max_blob_fact=2, plot=False,
debug=False):
"""
Take a list of ``thresholds``, create binary maps and counts detections/fps.
A blob which is "too big" is split into apertures, and every aperture adds
one 'false positive'.
Parameters
----------
frame : numpy ndarray
Detection map.
thresholds : list or numpy ndarray
List of thresholds (detection criteria).
injections : tuple, list of tuples
Coordinates (x,y) of the injected companions. Also accepts 1d/2d
ndarrays.
fwhm : float
FWHM, used for obtaining the size of the circular aperture centered at
the injection position (and measuring the overlapping with found blobs).
The circular aperture has 2 * FWHM in diameter.
npix : int, optional
The number of connected pixels, each greater than the given threshold,
that an object must have to be detected. ``npix`` must be a positive
integer. Passed to ``detect_sources`` function from ``photutils``.
overlap_threshold : float
Percentage of overlap a blob has to have with the aperture around an
injection.
max_blob_fact : float
Maximum size of a blob (in multiples of the resolution element) before
it is considered as "too big" (= non-detection).
plot : bool, optional
If True, a final resulting plot summarizing the results will be shown.
debug : bool, optional
For showing optional information.
Returns
-------
list_detections : list of int
List of detection count for each threshold.
list_fps : list of int
List of false positives count for each threshold.
list_binmaps : list of 2d ndarray
List of binary maps: detection maps thresholded for each threshold
value.
"""
def _overlap_injection_blob(injection, fwhm, blob_mask):
"""
Parameters
----------
injection: tuple (y,x)
fwhm : float
blob_mask : 2d bool ndarray
Returns
-------
overlap_fact : float between 0 and 1
Percentage of the area overlap. If the blob is smaller than the
resolution element, this is ``intersection_area / blob_area``,
otherwise ``intersection_area / resolution_element``.
"""
if len(injections[0]) > 0:
injection_mask = get_circle(np.ones_like(blob_mask), radius=fwhm,
cy=injection[1], cx=injection[0],
mode="mask")
else:
injection_mask = np.zeros_like(blob_mask)
intersection = injection_mask & blob_mask
smallest_area = min(blob_mask.sum(), injection_mask.sum())
return intersection.sum() / smallest_area
# --------------------------------------------------------------------------
list_detections = []
list_fps = []
list_binmaps = []
sizey, sizex = frame.shape
cy, cx = frame_center(frame)
reselem_mask = get_circle(frame, radius=fwhm, cy=cy, cx=cx, mode="val")
npix_circ_aperture = reselem_mask.shape[0]
# normalize injections: accepts combinations of 1d/2d and tuple/list/array.
injections = np.asarray(injections)
if injections.ndim == 1:
injections = np.array([injections])
for ithr, threshold in enumerate(thresholds):
if debug:
print("\nprocessing threshold #{}: {}".format(ithr + 1, threshold))
segments = detect_sources(frame, threshold, npix, connectivity=4)
binmap = (segments.data != 0)
if debug:
plot_frames((segments.data, binmap), cmap=('tab20b', 'binary'),
circle=tuple(tuple(xy) for xy in injections),
circle_radius=fwhm, circle_alpha=0.6,
label=("segmentation map", "binary map"))
detections = 0
fps = 0
for segment in segments.segments:
label = segment.label
blob_mask = (segments.data == label)
blob_area = segment.area
if debug:
lab = "blob #{}, area={}px**2".format(label, blob_area)
plot_frames(blob_mask, circle_radius=fwhm, circle_alpha=0.6,
circle=tuple(tuple(xy) for xy in injections),
cmap='binary', label_size=8, label=lab,
size_factor=3)
for iinj, injection in enumerate(injections):
if len(injections[0]) > 0: # checking injections is not empty
if injection[0] > sizex or injection[1] > sizey:
raise ValueError("Wrong coordinates in `injections`")
if debug:
print("\ttesting injection #{} at {}".format(iinj + 1,
injection))
if blob_area > max_blob_fact * npix_circ_aperture:
number_of_apertures_in_blob = blob_area / npix_circ_aperture
fps += number_of_apertures_in_blob # float, rounded at end
if debug:
print("\tblob is too big (+{:.0f} fps)"
"".format(number_of_apertures_in_blob))
print("\tskipping all other injections")
# continue with next blob, do not check other injections
break
overlap = _overlap_injection_blob(injection, fwhm, blob_mask)
if overlap > overlap_threshold:
if debug:
print("\toverlap of {}! (+1 detection)"
"".format(overlap))
detections += 1
# continue with next blob, do not check other injections
break
if debug:
print("\toverlap of {} -> do nothing".format(overlap))
else:
if debug:
print("\tdid not find a matching injection for this "
"blob (+1 fps)")
fps += 1
if debug:
print("done with threshold #{}".format(ithr))
print("result: {} detections, {} fps".format(detections, fps))
fps = np.round(fps).astype(int).item() # -> python `int`
list_detections.append(detections)
list_binmaps.append(binmap)
list_fps.append(fps)
if plot:
labs = tuple(str(det) + ' detections' + '\n' + str(fps) +
' false positives' for det, fps in zip(list_detections,
list_fps))
if len(injections[0]) > 0:
circles = tuple(tuple(xy) for xy in injections)
else:
circles = None
plot_frames(tuple(list_binmaps), title='Final binary maps', label=labs,
label_size=8, cmap='binary', circle_alpha=0.8,
circle=circles, circle_radius=fwhm,
circle_color='deepskyblue', axis=False)
return list_detections, list_fps, list_binmaps
def plot_detmaps(self, i=None, thr=9, dpi=100,
axis=True, grid=False, vmin=-10, vmax='max',
plot_type="horiz"):
"""
Plot the detection maps for one injection.
Parameters
----------
i : int or None, optional
Index of the injection, between 0 and self.n_injections. If None,
takes the 30st injection, or if there are less injections, the
middle one.
thr : int, optional
Index of the threshold.
dpi, axis, grid, vmin, vmax
Passed to ``pp_subplots``
plot_type : {"horiz" or "vert"}, optional
Plot type.
``horiz``
One row per algorithm (frame, probmap, binmap)
``vert``
1 row for final frames, 1 row for probmaps and 1 row for binmaps
"""
# input parameters
if i is None:
if len(self.list_xy) > 30:
i = 30
else:
i = len(self.list_xy) // 2
if vmax == 'max':
# TODO: document this feature.
vmax = np.concatenate([m.frames[i] for m in self.methods if
hasattr(m, "frames") and
len(m.frames) >= i]).max()/2
# print information
print('X,Y: {}'.format(self.list_xy[i]))
print('dist: {:.3f}, flux: {:.3f}'.format(self.dists[i],
self.fluxes[i]))
print()
if plot_type in [1, "horiz"]:
for m in self.methods:
print('detection state: {} | false postives: {}'.format(
m.detections[i][thr], m.fps[i][thr]))
labels = ('{} frame'.format(m.name), '{} S/Nmap'.format(m.name),
'Thresholded at {:.1f}'.format(m.thresholds[thr]))
plot_frames((m.frames[i] if len(m.frames) >= i else
np.zeros((2, 2)), m.probmaps[i], m.bmaps[i][thr]),
label=labels, dpi=dpi, horsp=0.2, axis=axis,
grid=grid, cmap=['viridis', 'viridis', 'gray'])
elif plot_type in [2, "vert"]:
labels = tuple('{} frame'.format(m.name) for m in self.methods if
hasattr(m, "frames") and len(m.frames) >= i)
plot_frames(tuple(m.frames[i] for m in self.methods if
hasattr(m, "frames") and len(m.frames) >= i),
dpi=dpi, label=labels, vmax=vmax, vmin=vmin, axis=axis,
grid=grid)
plot_frames(tuple(m.probmaps[i] for m in self.methods), dpi=dpi,
label=tuple(['{} S/Nmap'.format(m.name) for m in
self.methods]), axis=axis, grid=grid)
for m in self.methods:
msg = '{} detection: {}, FPs: {}'
print(msg.format(m.name, m.detections[i][thr], m.fps[i][thr]))
labels = tuple('Thresholded at {:.1f}'.format(m.thresholds[thr])
for m in self.methods)
plot_frames(tuple(m.bmaps[i][thr] for m in self.methods),
dpi=dpi, label=labels, axis=axis, grid=grid,
colorbar=False, cmap='bone')
else:
raise ValueError("`plot_type` unknown")
def plot_traindata(T,
zeroind=None,
oneind=None,
full_info=False,
plot_pair=True,
dpi=100,
indices=None,
save_plot=False):
"""
"""
xarr = T.x
yarr = T.y
if 'xnor' in T:
xarrn = T.xnor
if zeroind is None:
zeroind = np.random.randint(0, xarr.shape[0] / 2.)
if oneind is None:
oneind = np.random.randint(xarr.shape[0] / 2., xarr.shape[0])
if full_info:
msg1 = 'N samples : {} | Runtime : {}'
print(msg1.format(T.nsamp, T.runtime))
msg2 = 'FWHM : {} | PLSC : {} | K list : {}'
print(msg2.format(T.fwhm, T.plsc, T.klist))
msg3 = 'In Rad : {} | Out Rad : {} | Patch size : {}'
print(msg3.format(T.inrad, T.outrad, T.sizepatch))
msg4 = 'Collapse func : {} | Scaling : {}'
print(msg4.format(T.collaf.__name__, T.scaling))
msg5 = 'N patches : {} | Perc orig zeros : {}'
print(msg5.format(T.npatches, T.perorigzeros))
msg6 = 'Flux distro : {} | Par1 : {} | Par2 : {}'
print(msg6.format(T.fluxdistro, T.fluxdistrop1, T.fluxdistrop2))
msg7 = 'N injections : {} | Perc aug ones : {}'
print(msg7.format(T.nsamp * 0.5 * T.peraugones, T.peraugones))
msg8 = 'Aug shifts : {} | Aug range rotat : {}'
print(msg8.format(T.shifts, T.rangerot))
figure(figsize=(12, 2))
subplot(1, 3, 1)
hist(T.fluxes, bins=int(np.sqrt(T.fluxes.shape[0])))
title('Fluxes histogram')
subplot(1, 3, 2)
hist(np.array(T.dists).flatten(), bins=int(np.sqrt(T.fluxes.shape[0])))
title('Distances histogram')
subplot(1, 3, 3)
hist(np.array(T.thetas).flatten(),
bins=int(np.sqrt(T.fluxes.shape[0])))
title('Thetas histogram')
show()
print()
npatches = xarr[zeroind].shape[0]
if plot_pair or save_plot:
if indices is not None:
zerarr = xarr[zeroind][indices]
onearr = xarr[oneind][indices]
if xarrn is not None: zerarrn = xarrn[zeroind][indices]
if xarrn is not None: onearrn = xarrn[oneind][indices]
else:
zerarr = xarr[zeroind]
onearr = xarr[oneind]
if xarrn is not None: zerarrn = xarrn[zeroind]
if xarrn is not None: onearrn = xarrn[oneind]
if save_plot:
print('{} | Sample {}'.format(int(yarr[zeroind]), zeroind))
plot_frames(zerarr,
dpi=dpi,
axis=False,
vmin=xarr[zeroind].min(),
vmax=xarr[zeroind].max(),
save='patch_zero.pdf',
colorbar=False,
horsp=0.1)
if xarrn is not None:
plot_frames(zerarrn,
axis=False,
dpi=dpi,
colorbar=False,
save='patch_zero_nor.pdf',
horsp=0.1)
print(int(yarr[oneind]), '| Sample', oneind)
plot_frames(onearr,
axis=False,
vmin=xarr[oneind].min(),
vmax=xarr[oneind].max(),
dpi=dpi,
save='patch_one.pdf',
colorbar=False,
horsp=0.1)
if xarrn is not None:
plot_frames(onearr,
axis=False,
dpi=dpi,
horsp=0.1,
save='patch_one_nor.pdf',
colorbar=False)
else:
plot_frames(zerarr,
title='Unnormalized ZERO multiK patch',
dpi=dpi,
axis=False,
vmin=xarr[zeroind].min(),
vmax=xarr[zeroind].max(),
horsp=0.1)
if xarrn is not None:
plot_frames(zerarrn,
title='Normalized ZERO multiK patch',
axis=False,
dpi=dpi,
horsp=0.1)
plot_frames(onearr,
title='Unnormalized ONE multiK patch',
axis=False,
vmin=xarr[oneind].min(),
vmax=xarr[oneind].max(),
dpi=dpi,
horsp=0.1)
if xarrn is not None:
plot_frames(onearrn,
title='Normalized ONE multiK patch',
axis=False,
dpi=dpi,
horsp=0.1)
def snrmap(array,
fwhm,
plot=False,
mode='sss',
source_mask=None,
nproc=None,
array2=None,
use2alone=False,
verbose=True,
**kwargs):
"""Parallel implementation of the S/N map generation function. Applies the
S/N function (small samples penalty) at each pixel.
Parameters
----------
array : numpy.ndarray
Input frame (2d array).
fwhm : float
Size in pixels of the FWHM.
plot : bool, optional
If True plots the S/N map. False by default.
mode : {'sss', 'peakstddev'}, string optional
'sss' uses the approach with the small sample statistics penalty and
'peakstddev' uses the peak(aperture)/std(annulus) version.
source_mask : array_like, optional
If exists, it takes into account existing sources. The mask is a ones
2d array, with the same size as the input frame. The centers of the
known sources have a zero value.
nproc : int or None
Number of processes for parallel computing.
array2 : numpy.ndarray, optional
Additional image (e.g. processed image with negative derotation angles)
enabling to have more noise samples. Should have the
same dimensions as array.
use2alone: bool, optional
Whether to use array2 alone to estimate the noise (might be useful to
estimate the snr of extended disk features).
verbose: bool, optional
Whether to print timing or not.
**kwargs : dictionary, optional
Arguments to be passed to ``plot_frames`` to customize the plot (and to
save it to disk).
Returns
-------
snrmap : 2d array_like
Frame with the same size as the input frame with each pixel.
"""
if verbose:
start_time = time_ini()
if array.ndim != 2:
raise TypeError('Input array is not a 2d array or image.')
if plot:
plt.close('snr')
sizey, sizex = array.shape
snrmap = np.zeros_like(array)
width = min(sizey, sizex) / 2 - 1.5 * fwhm
mask = get_annulus_segments(array, (fwhm / 2) + 2, width, mode="mask")[0]
mask = np.ma.make_mask(mask)
# by making a bool mask *after* applying the mask to the array, we also mask
# out zero values from the array. This logic cannot be simplified by using
# mode="ind"!
yy, xx = np.where(mask)
coords = zip(xx, yy)
if nproc is None:
nproc = cpu_count() // 2 # Hyper-threading doubles the # of cores
if mode == 'sss':
func = snr_ss
elif mode == 'peakstddev':
func = snr_peakstddev
else:
raise TypeError('\nMode not recognized.')
if source_mask is None:
res = pool_map(nproc, func, array, iterable(coords), fwhm, True,
array2, use2alone)
res = np.array(res)
yy = res[:, 0]
xx = res[:, 1]
snr = res[:, 2]
snrmap[yy.astype('int'), xx.astype('int')] = snr
else:
# checking the mask with the sources
if array.shape != source_mask.shape:
raise RuntimeError('Source mask has wrong size.')
if source_mask[source_mask == 0].shape[0] == 0:
msg = 'Input source mask is empty.'
raise RuntimeError(msg)
if source_mask[source_mask == 0].shape[0] > 20:
msg = 'Input source mask is too crowded. Check its validity.'
raise RuntimeError(msg)
soury, sourx = np.where(source_mask == 0)
sources = []
ciry = []
cirx = []
anny = []
annx = []
array_sources = array.copy()
centery, centerx = frame_center(array)
for (y, x) in zip(soury, sourx):
radd = dist(centery, centerx, y, x)
if int(radd) < centery - np.ceil(fwhm):
sources.append((y, x))
for source in sources:
y, x = source
radd = dist(centery, centerx, y, x)
tempay, tempax = get_annulus_segments(array, int(radd - fwhm),
int(np.ceil(2 * fwhm)))[0]
tempcy, tempcx = draw.circle(y, x, int(np.ceil(1 * fwhm)))
# masking the source position (using the MAD of pixels in annulus)
array_sources[tempcy, tempcx] = mad(array[tempay, tempax])
ciry += list(tempcy)
cirx += list(tempcx)
anny += list(tempay)
annx += list(tempax)
# coordinates of annulus without the sources
coor_ann = [(y, x) for (y, x) in zip(anny, annx)
if (y, x) not in zip(ciry, cirx)]
# coordinates of the rest of the frame without the annulus
coor_rest = [(y, x) for (y, x) in zip(yy, xx)
if (y, x) not in coor_ann]
res = pool_map(nproc, func, array, iterable(coor_rest), fwhm, True,
array2, use2alone)
res = np.array(res)
yy = res[:, 0]
xx = res[:, 1]
snr = res[:, 2]
snrmap[yy.astype('int'), xx.astype('int')] = snr
res = pool_map(nproc, func, array_sources, iterable(coor_ann), fwhm,
True, array2, use2alone)
res = np.array(res)
yy = res[:, 0]
xx = res[:, 1]
snr = res[:, 2]
snrmap[yy.astype('int'), xx.astype('int')] = snr
if plot:
plot_frames(snrmap, colorbar=True, title='S/N map', **kwargs)
if verbose:
print("S/N map created using {} processes.".format(nproc))
timing(start_time)
return snrmap
def LLSG(cube, angs, fwhm, name_input):
"""
----------------- LLSG -----------------
Adpated from vip_hci. Returns LLSG frame, residual cube for STIM map and the rank for running contrast curve.
When fulloutput == True, LLSG full output from VIP (0.9.11) is:
0 - list_l_array_der
1 - list_s_array_der
2 - list_g_array_der
3 - frame_l
4 - frame_s
5 - frame_g
Each is the frame and residual cube for the three noise types:
l (low-rank), s (sparse), g (gaussian)
Companions are in the Sparse noise.
To get STIM map of LLSG, input 'list_s_array_der' into STIM Map
Parameter:
cube : numpy ndarray, 3d
Input ADI cube.
angs : numpy ndarray, 1d
Corresponding parallactic angle for each frame. Extracted from header of each .fits file
fwhm : float
Known size of the FHWM in pixels to be used.
name_input : character string
Name of the star (obtained from the first input)
Return:
llsg_frame : numpy ndarray, 2d
Residual frame
llsg_residual_sparse : numpy ndarray, 3d
Residual Cube
Rank : integer
Expected rank for the L component
"""
print( "\n --- LLSG")
Rank = input("What rank of llsg would you like to do? (default =6)\n")
Rank = int(Rank)
print(fwhm)
# Reduces the image using the LLSG algorithm then plots the image.
llsg_output = vip.llsg.llsg(cube, angs, rank = Rank, fwhm=fwhm, thresh = 2, max_iter = 20, random_seed = 10,full_output = True)
# Store the sparse frame
llsg_frame = llsg_output[4]
# Store the sparse residual cube
llsg_residual_sparse = np.asarray( llsg_output[1][0] )
# list_s_array_der is a list of one member,
# this one member is another list of the 24 residual images
# So get that one element, and make it an array.
hciplot.plot_frames(llsg_frame, label ='LLSG reduced image of {name}'.format(name=name_input))
# Asks user if the would like to save the image.
print( 'Would you like to save the LLSG reduced image?\n' )
questionsave = Checkfunction()
if questionsave == 0:
vip.fits.write_fits('new_LLSG_{name}.fits'.format(name=name_input), llsg_frame, verbose=True)
return llsg_frame, llsg_residual_sparse, Rank
def flat_field_correction(self,
verbose=True,
debug_=False,
overwrite_basic=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])
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])
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])
flat_X = []
flat_X_values = []
tmp = open_fits(self.outpath + '1_crop_unsat_' + unsat_list[-1],
header=False)
nx_unsat_crop = tmp.shape[2]
for fl, flat_name in enumerate(flat_list):
tmp, header = open_fits(self.inpath + flat_list[fl],
header=True,
verbose=debug_)
flat_X.append(header['AIRMASS'])
if fl == 0:
flat_X_values.append(header['AIRMASS'])
else:
#creates a list rejecting airmass values that differ more than the tolerance.
list_occ = [
isclose(header['AIRMASS'], x, atol=0.1)
for x in flat_X_values
]
if True not in list_occ:
flat_X_values.append(header['AIRMASS'])
print(flat_X)
flat_X_values = np.sort(
flat_X_values
) # !!! VERY IMPORTANT, DO NOT COMMENT, OR IT WILL SCREW EVERYTHING UP!!!
print(flat_X_values)
if verbose:
print('The airmass values have been sorted into a list')
# There should be 15 twilight flats in total with NACO; 5 at each airmass. BUG SOMETIMES!
flat_tmp_cube_1 = np.zeros([5, self.com_sz, self.com_sz])
flat_tmp_cube_2 = np.zeros([5, self.com_sz, self.com_sz])
flat_tmp_cube_3 = np.zeros([5, self.com_sz, self.com_sz])
counter_1 = 0
counter_2 = 0
counter_3 = 0
flat_cube_3X = np.zeros([3, self.com_sz, self.com_sz])
# TAKE MEDIAN OF each group of 5 frames with SAME AIRMASS
flat_cube = open_fits(self.outpath + '1_crop_flat_cube.fits',
header=False,
verbose=debug_)
for fl, self.flat_name in enumerate(flat_list):
if find_nearest(
flat_X_values,
flat_X[fl]) == 0: #could create the function find_nearest
flat_tmp_cube_1[counter_1] = flat_cube[fl]
counter_1 += 1
elif find_nearest(flat_X_values, flat_X[fl]) == 1:
flat_tmp_cube_2[counter_2] = flat_cube[fl]
counter_2 += 1
elif find_nearest(flat_X_values, flat_X[fl]) == 2:
flat_tmp_cube_3[counter_3] = flat_cube[fl]
counter_3 += 1
flat_cube_3X[0] = np.median(flat_tmp_cube_1, axis=0)
flat_cube_3X[1] = np.median(flat_tmp_cube_2, axis=0)
flat_cube_3X[2] = np.median(flat_tmp_cube_3, axis=0)
if verbose:
print('The median flat cubes with same airmass have been created')
med_fl = np.zeros(3)
gains_all = np.zeros([3, self.com_sz, self.com_sz])
#the method for avoiding the bad quadrant is removed since it is fixed in the preproc
for ii in range(3):
med_fl[ii] = np.median(flat_cube_3X[ii])
gains_all[ii] = flat_cube_3X[ii] / med_fl[ii]
master_flat_frame = np.median(gains_all, axis=0)
if nx_unsat_crop < master_flat_frame.shape[1]:
master_flat_unsat = frame_crop(master_flat_frame, nx_unsat_crop)
else:
master_flat_unsat = master_flat_frame
write_fits(self.outpath + 'master_flat_field.fits', master_flat_frame)
write_fits(self.outpath + 'master_flat_field_unsat.fits',
master_flat_unsat)
#plots(master_flat_frame, master_flat_unsat)
if verbose:
print('master flat frames has been saved')
master_flat_frame = open_fits(self.outpath + 'master_flat_field.fits')
if overwrite_basic or not isfile(self.outpath + '2_ff_' +
sci_list[-1]):
for sc, fits_name in enumerate(sci_list):
tmp = open_fits(self.outpath + '1_crop_' + fits_name,
verbose=debug_)
tmp_tmp = np.zeros_like(tmp)
for jj in range(tmp.shape[0]):
tmp_tmp[jj] = tmp[jj] / master_flat_frame
write_fits(self.outpath + '2_ff_' + fits_name,
tmp_tmp,
verbose=debug_)
if not debug_:
os.system("rm " + self.outpath + '1_crop_' + fits_name)
if verbose:
print('Done scaling SCI frames with respects to ff')
if overwrite_basic or not isfile(self.outpath + '2_ff_' +
sky_list[-1]):
for sk, fits_name in enumerate(sky_list):
tmp = open_fits(self.outpath + '1_crop_' + fits_name,
verbose=debug_)
tmp_tmp = np.zeros_like(tmp)
for jj in range(tmp.shape[0]):
tmp_tmp[jj] = tmp[jj] / master_flat_frame
write_fits(self.outpath + '2_ff_' + fits_name,
tmp_tmp,
verbose=debug_)
if not debug_:
os.system("rm " + self.outpath + '1_crop_' + fits_name)
if verbose:
print('Done scaling SKY frames with respects to ff ')
# COMPARE BEFORE AND AFTER FLAT-FIELD
tmp = np.median(open_fits(self.outpath + '2_ff_' + sci_list[0]),
axis=0)
tmp_tmp = np.median(open_fits(self.outpath + '2_ff_' + sci_list[-1]),
axis=0)
if debug_:
old_tmp = np.median(open_fits(self.outpath + '1_crop_' +
sci_list[0]),
axis=0)
old_tmp_tmp = np.median(open_fits(self.outpath + '1_crop_' +
sci_list[-1]),
axis=0)
plot_frames(old_tmp, tmp, old_tmp_tmp, tmp_tmp)
else:
plot_frames(tmp, tmp_tmp)
master_flat_unsat = open_fits(self.outpath +
'master_flat_field_unsat.fits')
for un, fits_name in enumerate(unsat_list):
tmp = open_fits(self.outpath + '1_crop_unsat_' + fits_name,
verbose=debug_)
tmp_tmp = np.zeros_like(tmp)
for jj in range(tmp.shape[0]):
tmp_tmp[jj] = tmp[jj] / master_flat_unsat
write_fits(self.outpath + '2_ff_unsat_' + fits_name,
tmp_tmp,
verbose=debug_)
if not debug_:
os.system("rm " + self.outpath + '1_crop_unsat_' + fits_name)
if verbose:
print('Done scaling UNSAT frames with respects to ff')
# COMPARE BEFORE AND AFTER FLAT-FIELD
tmp = open_fits(self.outpath + '2_ff_unsat_' + unsat_list[0])[-1]
tmp_tmp = open_fits(self.outpath + '2_ff_unsat_' + unsat_list[-1])[-1]
if debug_:
old_tmp = open_fits(self.outpath + '1_crop_unsat_' +
unsat_list[0])[-1]
old_tmp_tmp = open_fits(self.outpath + '1_crop_unsat_' +
unsat_list[-1])[-1]
plot_frames(old_tmp, tmp, old_tmp_tmp, tmp_tmp)
else:
plot_frames(tmp, tmp_tmp)
def chisquare(modelParameters,
cube,
angs,
plsc,
psfs_norm,
fwhm,
annulus_width,
aperture_radius,
initialState,
ncomp,
cube_ref=None,
svd_mode='lapack',
scaling=None,
fmerit='sum',
collapse='median',
imlib='opencv',
interpolation='lanczos4',
debug=False):
"""
Calculate the reduced chi2:
\chi^2_r = \frac{1}{N-3}\sum_{j=1}^{N} |I_j|,
where N is the number of pixels within a circular aperture centered on the
first estimate of the planet position, and I_j the j-th pixel intensity.
Parameters
----------
modelParameters: tuple
The model parameters, typically (r, theta, flux).
cube: numpy.array
The cube of fits images expressed as a numpy.array.
angs: numpy.array
The parallactic angle fits image expressed as a numpy.array.
plsc: float
The platescale, in arcsec per pixel.
psfs_norm: numpy.array
The scaled psf expressed as a numpy.array.
fwhm : float
The FHWM in pixels.
annulus_width: int, optional
The width in terms of the FWHM of the annulus on which the PCA is done.
aperture_radius: int, optional
The radius of the circular aperture in terms of the FWHM.
initialState: numpy.array
Position (r, theta) of the circular aperture center.
ncomp: int
The number of principal components.
cube_ref : numpy ndarray, 3d, optional
Reference library cube. For Reference Star Differential Imaging.
svd_mode : {'lapack', 'randsvd', 'eigen', 'arpack'}, str optional
Switch for different ways of computing the SVD and selected PCs.
scaling : {'temp-mean', 'temp-standard'} or None, optional
With None, no scaling is performed on the input data before SVD. With
"temp-mean" then temporal px-wise mean subtraction is done and with
"temp-standard" temporal mean centering plus scaling to unit variance
is done.
fmerit : {'sum', 'stddev'}, string optional
Chooses the figure of merit to be used. stddev works better for close in
companions sitting on top of speckle noise.
collapse : {'median', 'mean', 'sum', 'trimmean', None}, str or None, optional
Sets the way of collapsing the frames for producing a final image. If
None then the cube of residuals is used when measuring the function of
merit (instead of a single final frame).
imlib : str, optional
See the documentation of the ``vip_hci.preproc.frame_shift`` function.
interpolation : str, optional
See the documentation of the ``vip_hci.preproc.frame_shift`` function.
Returns
-------
out: float
The reduced chi squared.
"""
try:
r, theta, flux = modelParameters
except TypeError:
msg = 'modelParameters must be a tuple, {} was given'
print(msg.format(type(modelParameters)))
# Create the cube with the negative fake companion injected
cube_negfc = cube_inject_companions(cube,
psfs_norm,
angs,
flevel=-flux,
plsc=plsc,
rad_dists=[r],
n_branches=1,
theta=theta,
imlib=imlib,
verbose=False,
interpolation=interpolation)
# Perform PCA and extract the zone of interest
res = get_values_optimize(cube_negfc,
angs,
ncomp,
annulus_width * fwhm,
aperture_radius * fwhm,
initialState[0],
initialState[1],
cube_ref=cube_ref,
svd_mode=svd_mode,
scaling=scaling,
collapse=collapse,
debug=debug)
if debug and collapse is not None:
values, frpca = res
plot_frames(frpca)
else:
values = res
# Function of merit
if fmerit == 'sum':
values = np.abs(values)
chi2 = np.sum(values[values > 0])
N = len(values[values > 0])
return chi2 / (N - 3)
elif fmerit == 'stddev':
return np.std(values[values != 0])
else:
raise RuntimeError('`fmerit` choice not recognized')
def FullFramePCA(cube, angs, fwhm, name_input):
print("\n --- FULL FRAME PCA")
print("Use DS9 to obtain the reference coordinates")
print(
"A 'reference' is simply any kind of stable bright spot across the images, like a static speckle or a candidate."
)
reference_xy[0] = input("Input the x coordinate of the reference: ")
reference_xy[0] = int(reference_xy[0])
reference_xy[1] = input("Input the y coordinate of the reference: ")
reference_xy[1] = int(reference_xy[1])
print('Applying Full Frame PCA...')
ffpca_output = vip.pca.pca_fullfr.pca(cube,
angs,
fwhm=fwhm,
source_xy=(reference_xy[0],
reference_xy[1]),
mask_center_px=None,
ncomp=(1, len(cube), 1),
full_output=True)
# vip_hci.pca.pca_fullfr.pca returns this outputs:
# (if full_output=True and source_xy != None)
# 0 - Final residuals cube (NOT TO USE IN STIM MAP)
# 1 - PCA Frame
# 2 - Pandas dataframe with PCs, S/Ns and fluxes
#
#<class 'numpy.ndarray'> 10,1024,1024
#<class 'numpy.ndarray'> 1024,1024
#<class 'pandas.core.frame.DataFrame'> 10 (columns = PCs, S/Ns, fluxes)
# Save FFPCA output
fr_pca1 = ffpca_output[1]
# ----- Getting Optimal Number of PCs
# Get pandas dataframe table output into a numpy array
pca_data = ffpca_output[2].rename_axis('ID').values
# Extract and save S/N ratio and PCs columns in arrays
snr_data = []
pcs_data = []
for i in range(0, len(pca_data)):
pcs_data.append(pca_data[i][0])
snr_data.append(pca_data[i][1])
# Get the index of the maximum value of the S/N column,
# and retrieve that same value in the PCs column
# This will be the optimal number of principal components
snr_max = np.argmax(snr_data, axis=0)
optimal_pcs = int(pcs_data[snr_max])
print("Optimal PCs", optimal_pcs)
hciplot.plot_frames(
fr_pca1,
label='FFPCA reduction of {name}'.format(name=name_input),
grid=False,
size_factor=5)
#Loop asks user if they would like to save the image.
print('Would you like to save the Full Frame PCA reduced images?')
questionsave = Checkfunction()
if questionsave == 0:
vip.fits.write_fits('new_FFPCA_{name}.fits'.format(name=name_input),
fr_pca1,
verbose=True)
return fr_pca1, optimal_pcs
def detection(array, fwhm=4, psf=None, mode='lpeaks', bkg_sigma=5,
matched_filter=False, mask=True, snr_thresh=5, nproc=1, plot=True,
debug=False, full_output=False, verbose=True, **kwargs):
""" Finds blobs in a 2d array. The algorithm is designed for automatically
finding planets in post-processed high contrast final frames. Blob can be
defined as a region of an image in which some properties are constant or
vary within a prescribed range of values. See ``Notes`` below to read about
the algorithm details.
Parameters
----------
array : numpy ndarray, 2d
Input frame.
fwhm : None or int, optional
Size of the FWHM in pixels. If None and a ``psf`` is provided, then the
FWHM is measured on the PSF image.
psf : numpy ndarray
Input PSF template. It must be normalized with the
``vip_hci.metrics.normalize_psf`` function.
mode : {'lpeaks', 'log', 'dog', 'snrmap', 'snrmapf'}, optional
Sets with algorithm to use. Each algorithm yields different results. See
notes for the details of each method.
bkg_sigma : int or float, optional
The number standard deviations above the clipped median for setting the
background level. Used when ``mode`` is either 'lpeaks', 'dog' or 'log'.
matched_filter : bool, optional
Whether to correlate with the psf of not. Used when ``mode`` is either
'lpeaks', 'dog' or 'log'.
mask : bool, optional
If True the central region (circular aperture of 2*FWHM radius) of the
image will be masked out.
snr_thresh : float, optional
S/N threshold for deciding whether the blob is a detection or not. Used
to threshold the S/N map when ``mode`` is set to 'snrmap' or 'snrmapf'.
nproc : None or int, optional
The number of processes for running the ``snrmap`` function.
plot : bool, optional
If True plots the frame showing the detected blobs on top.
debug : bool, optional
Whether to print and plot additional/intermediate results.
full_output : bool, optional
Whether to output just the coordinates of blobs that fulfill the SNR
constraint or a table with all the blobs and the peak pixels and SNR.
verbose : bool, optional
Whether to print to stdout information about found blobs.
**kwargs : dictionary, optional
Arguments to be passed to ``plot_frames`` to customize the plot (and to
save it to disk).
Returns
-------
yy, xx : numpy ndarray
Two vectors with the y and x coordinates of the centers of the sources
(potential planets).
If full_output is True then a table with all the candidates that passed the
2d Gaussian fit constrains and their S/N is returned.
Notes
-----
When ``mode`` is either 'lpeaks', 'dog' or 'log', the detection might happen
in the input frame or in a match-filtered version of it (by setting
``matched_filter`` to True and providing a PSF template, to run a
correlation filter). Filtering the image will smooth the noise and maximize
detectability of objects with a shape similar to the kernel. When ``mode``
is either 'snrmap' or 'snrmapf', the detection is done on an S/N map
directly.
When ``mode`` is set to:
'lpeaks' (Local maxima): The local peaks above the background (computed
using sigma clipped statistics) on the (correlated) frame are detected.
A maximum filter is used for finding local maxima. This operation
dilates the original image and merges neighboring local maxima closer
than the size of the dilation. Locations where the original image is
equal to the dilated image are returned as local maxima. The minimum
separation between the peaks is 1*FWHM.
'log' (Laplacian of Gaussian): It computes the Laplacian of Gaussian
images with successively increasing standard deviation and stacks them
up in a cube. Blobs are local maximas in this cube. LOG assumes that the
blobs are again assumed to be bright on dark.
'dog' (Difference of Gaussians): This is a faster approximation of the
Laplacian of Gaussian approach. In this case the image is blurred with
increasing standard deviations and the difference between two
successively blurred images are stacked up in a cube. DOG assumes that
the blobs are again assumed to be bright on dark.
'snrmap' or 'snrmapf': A threshold is applied to the S/N map, computed
with the ``snrmap`` function (``snrmapf`` calls ``snrmap`` with
``approximated`` set to True). The threshold is given by ``snr_thresh``
and local maxima are found as in the case of 'lpeaks'.
Finally, a 2d Gaussian fit is done on each of the potential blobs
constraining the position on a cropped sub-image and the sigma of the fit
(to match the input FWHM). Finally the blobs are filtered based on its S/N
value, according to ``snr_thresh``.
"""
def check_blobs(array, coords_temp, fwhm, debug):
y_temp = coords_temp[:, 0]
x_temp = coords_temp[:, 1]
coords = []
# Fitting a 2d gaussian to each local maxima position
for y, x in zip(y_temp, x_temp):
subsi = 3 * int(np.ceil(fwhm))
if subsi % 2 == 0:
subsi += 1
if mode in ('lpeaks', 'log', 'dog'):
scy = y + pad
scx = x + pad
elif mode in ('snrmap', 'snrmapf'):
scy = y
scx = x
subim, suby, subx = get_square(array, subsi, scy, scx,
position=True, force=True,
verbose=False)
cy, cx = frame_center(subim)
gauss = models.Gaussian2D(amplitude=subim.max(), x_mean=cx,
y_mean=cy, theta=0,
x_stddev=fwhm*gaussian_fwhm_to_sigma,
y_stddev=fwhm*gaussian_fwhm_to_sigma)
sy, sx = np.indices(subim.shape)
fitter = fitting.LevMarLSQFitter()
fit = fitter(gauss, sx, sy, subim)
# checking that the amplitude is positive > 0
# checking whether the x and y centroids of the 2d gaussian fit
# coincide with the center of the subimage (within 2px error)
# checking whether the mean of the fwhm in y and x of the fit
# are close to the FWHM_PSF with a margin of 3px
fwhm_y = fit.y_stddev.value * gaussian_sigma_to_fwhm
fwhm_x = fit.x_stddev.value * gaussian_sigma_to_fwhm
mean_fwhm_fit = np.mean([np.abs(fwhm_x), np.abs(fwhm_y)])
condyf = np.allclose(fit.y_mean.value, cy, atol=2)
condxf = np.allclose(fit.x_mean.value, cx, atol=2)
condmf = np.allclose(mean_fwhm_fit, fwhm, atol=3)
if fit.amplitude.value > 0 and condxf and condyf and condmf:
coords.append((suby + fit.y_mean.value,
subx + fit.x_mean.value))
if debug:
print('Coordinates (Y,X): {:.3f},{:.3f}'.format(y, x))
print('fit peak = {:.3f}'.format(fit.amplitude.value))
msg = 'fwhm_y in px = {:.3f}, fwhm_x in px = {:.3f}'
print(msg.format(fwhm_y, fwhm_x))
print('mean fit fwhm = {:.3f}'.format(mean_fwhm_fit))
if plot:
plot_frames(subim, colorbar=True, axis=False, dpi=60)
return coords
def print_coords(coords):
print('Blobs found:', len(coords))
print(' ycen xcen')
print('------ ------')
for j in range(len(coords[:, 0])):
print('{:.3f} \t {:.3f}'.format(coords[j, 0], coords[j, 1]))
def print_abort():
if verbose:
print(sep)
print('No potential sources found')
print(sep)
# --------------------------------------------------------------------------
if array.ndim != 2:
raise TypeError('Input array is not a frame or 2d array')
if psf is not None:
if psf.ndim != 2 and psf.shape[0] < array.shape[0]:
raise TypeError('Input psf is not a 2d array or has wrong size')
else:
if matched_filter:
raise ValueError('`psf` must be provided when `matched_filter` is '
'True')
if fwhm is None:
if psf is not None:
# Getting the FWHM from the PSF array
cenpsf = frame_center(psf)
outdf = fit_2dgaussian(psf, cent=(cenpsf), debug=debug,
full_output=True)
fwhm_x, fwhm_y = outdf['fwhm_x'], outdf['fwhm_y']
fwhm = np.mean([fwhm_x, fwhm_y])
if verbose:
print('FWHM = {:.2f} pxs\n'.format(fwhm))
if debug:
print('FWHM_y', fwhm_y)
print('FWHM_x', fwhm_x)
else:
raise ValueError('`fwhm` or `psf` must be provided')
# Masking the center, 2*lambda/D is the expected IWA
if mask:
array = mask_circle(array, radius=fwhm)
# Generating a detection map: Match-filtered frame or SNRmap
# For 'lpeaks', 'dog', 'log' it is possible to skip this step
if mode in ('lpeaks', 'log', 'dog'):
if matched_filter:
frame_det = correlate(array, psf)
else:
frame_det = array
if debug and plot and matched_filter:
print('Match-filtered frame:')
plot_frames(frame_det, colorbar=True)
# Estimation of background level
_, median, stddev = sigma_clipped_stats(frame_det, sigma=5,
maxiters=None)
bkg_level = median + (stddev * bkg_sigma)
if debug:
print('Sigma clipped median = {:.3f}'.format(median))
print('Sigma clipped stddev = {:.3f}'.format(stddev))
print('Background threshold = {:.3f}'.format(bkg_level), '\n')
elif mode in ('snrmap', 'snrmapf'):
if mode == 'snrmap':
approx = False
elif mode == 'snrmapf':
approx = True
frame_det = snrmap(array, fwhm=fwhm, approximated=approx, plot=False,
nproc=nproc, verbose=verbose)
if debug and plot:
print('Signal-to-noise ratio map:')
plot_frames(frame_det, colorbar=True)
if mode in ('lpeaks', 'log', 'dog'):
# Padding the image with zeros to avoid errors at the edges
pad = 10
array_padded = np.lib.pad(array, pad, 'constant', constant_values=0)
if mode in ('lpeaks', 'snrmap', 'snrmapf'):
if mode == 'lpeaks':
threshold = bkg_level
else:
threshold = snr_thresh
coords_temp = peak_local_max(frame_det, threshold_abs=threshold,
min_distance=int(np.ceil(fwhm)),
num_peaks=20)
if mode == 'lpeaks':
coords = check_blobs(array_padded, coords_temp, fwhm, debug)
else:
coords = check_blobs(array, coords_temp, fwhm, debug)
coords = np.array(coords)
if verbose and coords.shape[0] > 0:
print_coords(coords)
elif mode == 'log':
sigma = fwhm * gaussian_fwhm_to_sigma
coords = feature.blob_log(frame_det.astype('float'),
threshold=bkg_level, min_sigma=sigma-.5,
max_sigma=sigma+.5)
if len(coords) == 0:
print_abort()
return 0, 0
coords = coords[:, :2]
coords = check_blobs(array_padded, coords, fwhm, debug)
coords = np.array(coords)
if coords.shape[0] > 0 and verbose:
print_coords(coords)
elif mode == 'dog':
sigma = fwhm * gaussian_fwhm_to_sigma
coords = feature.blob_dog(frame_det.astype('float'),
threshold=bkg_level, min_sigma=sigma-.5,
max_sigma=sigma+.5)
if len(coords) == 0:
print_abort()
return 0, 0
coords = coords[:, :2]
coords = check_blobs(array_padded, coords, fwhm, debug)
coords = np.array(coords)
if coords.shape[0] > 0 and verbose:
print_coords(coords)
else:
raise ValueError('`mode` not recognized')
if coords.shape[0] == 0:
print_abort()
return 0, 0
yy = coords[:, 0]
xx = coords[:, 1]
yy_final = []
xx_final = []
yy_out = []
xx_out = []
snr_list = []
if mode in ('lpeaks', 'log', 'dog'):
xx -= pad
yy -= pad
# Checking S/N for potential sources
for i in range(yy.shape[0]):
y = yy[i]
x = xx[i]
if verbose:
print('')
print(sep)
print('X,Y = ({:.1f},{:.1f})'.format(x, y))
snr_value = snr(array, (x, y), fwhm, False, verbose=False)
snr_list.append(snr_value)
if snr_value >= snr_thresh:
if verbose:
_ = frame_report(array, fwhm, (x, y), verbose=verbose)
yy_final.append(y)
xx_final.append(x)
else:
yy_out.append(y)
xx_out.append(x)
if verbose:
msg = 'S/N constraint NOT fulfilled (S/N = {:.3f})'
print(msg.format(snr_value))
if debug:
_ = frame_report(array, fwhm, (x, y), verbose=verbose)
if verbose:
print(sep)
if debug or full_output:
table_full = pn.DataFrame({'y': yy.tolist(),
'x': xx.tolist(),
'px_snr': snr_list})
table_full.sort_values('px_snr')
yy_final = np.array(yy_final)
xx_final = np.array(xx_final)
yy_out = np.array(yy_out)
xx_out = np.array(xx_out)
table = pn.DataFrame({'y': yy_final.tolist(), 'x': xx_final.tolist()})
if plot:
coords = tuple(zip(xx_out.tolist() + xx_final.tolist(),
yy_out.tolist() + yy_final.tolist()))
circlealpha = [0.3] * len(xx_out)
circlealpha += [1] * len(xx_final)
plot_frames(array, dpi=120, circle=coords, circle_alpha=circlealpha,
circle_label=True, circle_radius=fwhm, **kwargs)
if debug:
print(table_full)
if full_output:
return table_full
else:
return table
def detection(array,
fwhm=4,
psf=None,
mode='lpeaks',
bkg_sigma=5,
matched_filter=False,
mask=True,
snr_thresh=5,
nproc=1,
plot=True,
debug=False,
full_output=False,
verbose=True,
**kwargs):
""" Finds blobs in a 2d array. The algorithm is designed for automatically
finding planets in post-processed high contrast final frames. Blob can be
defined as a region of an image in which some properties are constant or
vary within a prescribed range of values. See ``Notes`` below to read about
the algorithm details.
Parameters
----------
array : numpy ndarray, 2d
Input frame.
fwhm : None or int, optional
Size of the FWHM in pixels. If None and a ``psf`` is provided, then the
FWHM is measured on the PSF image.
psf : numpy ndarray
Input PSF template. It must be normalized with the
``vip_hci.metrics.normalize_psf`` function.
mode : {'lpeaks', 'log', 'dog', 'snrmap', 'snrmapf'}, optional
Sets with algorithm to use. Each algorithm yields different results. See
notes for the details of each method.
bkg_sigma : int or float, optional
The number standard deviations above the clipped median for setting the
background level. Used when ``mode`` is either 'lpeaks', 'dog' or 'log'.
matched_filter : bool, optional
Whether to correlate with the psf of not. Used when ``mode`` is either
'lpeaks', 'dog' or 'log'.
mask : bool, optional
If True the central region (circular aperture of 2*FWHM radius) of the
image will be masked out.
snr_thresh : float, optional
S/N threshold for deciding whether the blob is a detection or not. Used
to threshold the S/N map when ``mode`` is set to 'snrmap' or 'snrmapf'.
nproc : None or int, optional
The number of processes for running the ``snrmap`` function.
plot : bool, optional
If True plots the frame showing the detected blobs on top.
debug : bool, optional
Whether to print and plot additional/intermediate results.
full_output : bool, optional
Whether to output just the coordinates of blobs that fulfill the SNR
constraint or a table with all the blobs and the peak pixels and SNR.
verbose : bool, optional
Whether to print to stdout information about found blobs.
**kwargs : dictionary, optional
Arguments to be passed to ``plot_frames`` to customize the plot (and to
save it to disk).
Returns
-------
yy, xx : numpy ndarray
Two vectors with the y and x coordinates of the centers of the sources
(potential planets).
If full_output is True then a table with all the candidates that passed the
2d Gaussian fit constrains and their S/N is returned.
Notes
-----
When ``mode`` is either 'lpeaks', 'dog' or 'log', the detection might happen
in the input frame or in a match-filtered version of it (by setting
``matched_filter`` to True and providing a PSF template, to run a
correlation filter). Filtering the image will smooth the noise and maximize
detectability of objects with a shape similar to the kernel. When ``mode``
is either 'snrmap' or 'snrmapf', the detection is done on an S/N map
directly.
When ``mode`` is set to:
'lpeaks' (Local maxima): The local peaks above the background (computed
using sigma clipped statistics) on the (correlated) frame are detected.
A maximum filter is used for finding local maxima. This operation
dilates the original image and merges neighboring local maxima closer
than the size of the dilation. Locations where the original image is
equal to the dilated image are returned as local maxima. The minimum
separation between the peaks is 1*FWHM.
'log' (Laplacian of Gaussian): It computes the Laplacian of Gaussian
images with successively increasing standard deviation and stacks them
up in a cube. Blobs are local maximas in this cube. LOG assumes that the
blobs are again assumed to be bright on dark.
'dog' (Difference of Gaussians): This is a faster approximation of the
Laplacian of Gaussian approach. In this case the image is blurred with
increasing standard deviations and the difference between two
successively blurred images are stacked up in a cube. DOG assumes that
the blobs are again assumed to be bright on dark.
'snrmap' or 'snrmapf': A threshold is applied to the S/N map, computed
with the ``snrmap`` function (``snrmapf`` calls ``snrmap`` with
``approximated`` set to True). The threshold is given by ``snr_thresh``
and local maxima are found as in the case of 'lpeaks'.
Finally, a 2d Gaussian fit is done on each of the potential blobs
constraining the position on a cropped sub-image and the sigma of the fit
(to match the input FWHM). Finally the blobs are filtered based on its S/N
value, according to ``snr_thresh``.
"""
def check_blobs(array, coords_temp, fwhm, debug):
y_temp = coords_temp[:, 0]
x_temp = coords_temp[:, 1]
coords = []
# Fitting a 2d gaussian to each local maxima position
for y, x in zip(y_temp, x_temp):
subsi = 3 * int(np.ceil(fwhm))
if subsi % 2 == 0:
subsi += 1
if mode in ('lpeaks', 'log', 'dog'):
scy = y + pad
scx = x + pad
elif mode in ('snrmap', 'snrmapf'):
scy = y
scx = x
subim, suby, subx = get_square(array,
subsi,
scy,
scx,
position=True,
force=True,
verbose=False)
cy, cx = frame_center(subim)
gauss = models.Gaussian2D(amplitude=subim.max(),
x_mean=cx,
y_mean=cy,
theta=0,
x_stddev=fwhm * gaussian_fwhm_to_sigma,
y_stddev=fwhm * gaussian_fwhm_to_sigma)
sy, sx = np.indices(subim.shape)
fitter = fitting.LevMarLSQFitter()
fit = fitter(gauss, sx, sy, subim)
# checking that the amplitude is positive > 0
# checking whether the x and y centroids of the 2d gaussian fit
# coincide with the center of the subimage (within 2px error)
# checking whether the mean of the fwhm in y and x of the fit
# are close to the FWHM_PSF with a margin of 3px
fwhm_y = fit.y_stddev.value * gaussian_sigma_to_fwhm
fwhm_x = fit.x_stddev.value * gaussian_sigma_to_fwhm
mean_fwhm_fit = np.mean([np.abs(fwhm_x), np.abs(fwhm_y)])
condyf = np.allclose(fit.y_mean.value, cy, atol=2)
condxf = np.allclose(fit.x_mean.value, cx, atol=2)
condmf = np.allclose(mean_fwhm_fit, fwhm, atol=3)
if fit.amplitude.value > 0 and condxf and condyf and condmf:
coords.append(
(suby + fit.y_mean.value, subx + fit.x_mean.value))
if debug:
print('Coordinates (Y,X): {:.3f},{:.3f}'.format(y, x))
print('fit peak = {:.3f}'.format(fit.amplitude.value))
msg = 'fwhm_y in px = {:.3f}, fwhm_x in px = {:.3f}'
print(msg.format(fwhm_y, fwhm_x))
print('mean fit fwhm = {:.3f}'.format(mean_fwhm_fit))
if plot:
plot_frames(subim, colorbar=True, axis=False, dpi=60)
return coords
def print_coords(coords):
print('Blobs found:', len(coords))
print(' ycen xcen')
print('------ ------')
for j in range(len(coords[:, 0])):
print('{:.3f} \t {:.3f}'.format(coords[j, 0], coords[j, 1]))
def print_abort():
if verbose:
print(sep)
print('No potential sources found')
print(sep)
# --------------------------------------------------------------------------
if array.ndim != 2:
raise TypeError('Input array is not a frame or 2d array')
if psf is not None:
if psf.ndim != 2 and psf.shape[0] < array.shape[0]:
raise TypeError('Input psf is not a 2d array or has wrong size')
else:
if matched_filter:
raise ValueError('`psf` must be provided when `matched_filter` is '
'True')
if fwhm is None:
if psf is not None:
# Getting the FWHM from the PSF array
cenpsf = frame_center(psf)
outdf = fit_2dgaussian(psf,
cent=(cenpsf),
debug=debug,
full_output=True)
fwhm_x, fwhm_y = outdf['fwhm_x'], outdf['fwhm_y']
fwhm = np.mean([fwhm_x, fwhm_y])
if verbose:
print('FWHM = {:.2f} pxs\n'.format(fwhm))
if debug:
print('FWHM_y', fwhm_y)
print('FWHM_x', fwhm_x)
else:
raise ValueError('`fwhm` or `psf` must be provided')
# Masking the center, 2*lambda/D is the expected IWA
if mask:
array = mask_circle(array, radius=fwhm)
# Generating a detection map: Match-filtered frame or SNRmap
# For 'lpeaks', 'dog', 'log' it is possible to skip this step
if mode in ('lpeaks', 'log', 'dog'):
if matched_filter:
frame_det = correlate(array, psf)
else:
frame_det = array
if debug and plot and matched_filter:
print('Match-filtered frame:')
plot_frames(frame_det, colorbar=True)
# Estimation of background level
_, median, stddev = sigma_clipped_stats(frame_det,
sigma=5,
maxiters=None)
bkg_level = median + (stddev * bkg_sigma)
if debug:
print('Sigma clipped median = {:.3f}'.format(median))
print('Sigma clipped stddev = {:.3f}'.format(stddev))
print('Background threshold = {:.3f}'.format(bkg_level), '\n')
elif mode in ('snrmap', 'snrmapf'):
if mode == 'snrmap':
approx = False
elif mode == 'snrmapf':
approx = True
frame_det = snrmap(array,
fwhm=fwhm,
approximated=approx,
plot=False,
nproc=nproc,
verbose=verbose)
if debug and plot:
print('Signal-to-noise ratio map:')
plot_frames(frame_det, colorbar=True)
if mode in ('lpeaks', 'log', 'dog'):
# Padding the image with zeros to avoid errors at the edges
pad = 10
array_padded = np.lib.pad(array, pad, 'constant', constant_values=0)
if mode in ('lpeaks', 'snrmap', 'snrmapf'):
if mode == 'lpeaks':
threshold = bkg_level
else:
threshold = snr_thresh
coords_temp = peak_local_max(frame_det,
threshold_abs=threshold,
min_distance=int(np.ceil(fwhm)),
num_peaks=20)
if mode == 'lpeaks':
coords = check_blobs(array_padded, coords_temp, fwhm, debug)
else:
coords = check_blobs(array, coords_temp, fwhm, debug)
coords = np.array(coords)
if verbose and coords.shape[0] > 0:
print_coords(coords)
elif mode == 'log':
sigma = fwhm * gaussian_fwhm_to_sigma
coords = feature.blob_log(frame_det.astype('float'),
threshold=bkg_level,
min_sigma=sigma - .5,
max_sigma=sigma + .5)
if len(coords) == 0:
print_abort()
return 0, 0
coords = coords[:, :2]
coords = check_blobs(array_padded, coords, fwhm, debug)
coords = np.array(coords)
if coords.shape[0] > 0 and verbose:
print_coords(coords)
elif mode == 'dog':
sigma = fwhm * gaussian_fwhm_to_sigma
coords = feature.blob_dog(frame_det.astype('float'),
threshold=bkg_level,
min_sigma=sigma - .5,
max_sigma=sigma + .5)
if len(coords) == 0:
print_abort()
return 0, 0
coords = coords[:, :2]
coords = check_blobs(array_padded, coords, fwhm, debug)
coords = np.array(coords)
if coords.shape[0] > 0 and verbose:
print_coords(coords)
else:
raise ValueError('`mode` not recognized')
if coords.shape[0] == 0:
print_abort()
return 0, 0
yy = coords[:, 0]
xx = coords[:, 1]
yy_final = []
xx_final = []
yy_out = []
xx_out = []
snr_list = []
if mode in ('lpeaks', 'log', 'dog'):
xx -= pad
yy -= pad
# Checking S/N for potential sources
for i in range(yy.shape[0]):
y = yy[i]
x = xx[i]
if verbose:
print('')
print(sep)
print('X,Y = ({:.1f},{:.1f})'.format(x, y))
snr_value = snr(array, (x, y), fwhm, False, verbose=False)
snr_list.append(snr_value)
if snr_value >= snr_thresh:
if verbose:
_ = frame_report(array, fwhm, (x, y), verbose=verbose)
yy_final.append(y)
xx_final.append(x)
else:
yy_out.append(y)
xx_out.append(x)
if verbose:
msg = 'S/N constraint NOT fulfilled (S/N = {:.3f})'
print(msg.format(snr_value))
if debug:
_ = frame_report(array, fwhm, (x, y), verbose=verbose)
if verbose:
print(sep)
if debug or full_output:
table_full = pn.DataFrame({
'y': yy.tolist(),
'x': xx.tolist(),
'px_snr': snr_list
})
table_full.sort_values('px_snr')
yy_final = np.array(yy_final)
xx_final = np.array(xx_final)
yy_out = np.array(yy_out)
xx_out = np.array(xx_out)
table = pn.DataFrame({'y': yy_final.tolist(), 'x': xx_final.tolist()})
if plot:
coords = tuple(
zip(xx_out.tolist() + xx_final.tolist(),
yy_out.tolist() + yy_final.tolist()))
circlealpha = [0.3] * len(xx_out)
circlealpha += [1] * len(xx_final)
plot_frames(array,
dpi=120,
circle=coords,
circle_alpha=circlealpha,
circle_label=True,
circle_radius=fwhm,
**kwargs)
if debug:
print(table_full)
if full_output:
return table_full
else:
return table
def fit_2dgaussian(array, crop=False, cent=None, cropsize=15, fwhmx=4, fwhmy=4,
theta=0, threshold=False, sigfactor=6, full_output=True,
debug=True):
""" Fitting a 2D Gaussian to the 2D distribution of the data.
Parameters
----------
array : numpy ndarray
Input frame with a single PSF.
crop : bool, optional
If True an square sub image will be cropped.
cent : tuple of int, optional
X,Y integer position of source in the array for extracting the subimage.
If None the center of the frame is used for cropping the subframe (the
PSF is assumed to be ~ at the center of the frame).
cropsize : int, optional
Size of the subimage.
fwhmx, fwhmy : float, optional
Initial values for the standard deviation of the fitted Gaussian, in px.
theta : float, optional
Angle of inclination of the 2d Gaussian counting from the positive X
axis.
threshold : bool, optional
If True the background pixels (estimated using sigma clipped statistics)
will be replaced by small random Gaussian noise.
sigfactor : int, optional
The background pixels will be thresholded before fitting a 2d Gaussian
to the data using sigma clipped statistics. All values smaller than
(MEDIAN + sigfactor*STDDEV) will be replaced by small random Gaussian
noise.
full_output : bool, optional
If False it returns just the centroid, if True also returns the
FWHM in X and Y (in pixels), the amplitude and the rotation angle.
debug : bool, optional
If True, the function prints out parameters of the fit and plots the
data, model and residuals.
Returns
-------
mean_y : float
Source centroid y position on input array from fitting.
mean_x : float
Source centroid x position on input array from fitting.
If ``full_output`` is True it returns a Pandas dataframe containing the
following columns:
'amplitude' : Float value. Amplitude of the Gaussian.
'centroid_x' : Float value. X coordinate of the centroid.
'centroid_y' : Float value. Y coordinate of the centroid.
'fwhm_x' : Float value. FHWM in X [px].
'fwhm_y' : Float value. FHWM in Y [px].
'theta' : Float value. Rotation angle.
"""
check_array(array, dim=2, msg='array')
if crop:
if cent is None:
ceny, cenx = frame_center(array)
else:
cenx, ceny = cent
imside = array.shape[0]
psf_subimage, suby, subx = get_square(array, min(cropsize, imside),
ceny, cenx, position=True)
else:
psf_subimage = array.copy()
if threshold:
_, clipmed, clipstd = sigma_clipped_stats(psf_subimage, sigma=2)
indi = np.where(psf_subimage <= clipmed + sigfactor * clipstd)
subimnoise = np.random.randn(psf_subimage.shape[0],
psf_subimage.shape[1]) * clipstd
psf_subimage[indi] = subimnoise[indi]
# Creating the 2D Gaussian model
init_amplitude = np.ptp(psf_subimage)
xcom, ycom = photutils.centroid_com(psf_subimage)
gauss = models.Gaussian2D(amplitude=init_amplitude, theta=theta,
x_mean=xcom, y_mean=ycom,
x_stddev=fwhmx * gaussian_fwhm_to_sigma,
y_stddev=fwhmy * gaussian_fwhm_to_sigma)
# Levenberg-Marquardt algorithm
fitter = fitting.LevMarLSQFitter()
y, x = np.indices(psf_subimage.shape)
fit = fitter(gauss, x, y, psf_subimage)
if crop:
mean_y = fit.y_mean.value + suby
mean_x = fit.x_mean.value + subx
else:
mean_y = fit.y_mean.value
mean_x = fit.x_mean.value
fwhm_y = fit.y_stddev.value*gaussian_sigma_to_fwhm
fwhm_x = fit.x_stddev.value*gaussian_sigma_to_fwhm
amplitude = fit.amplitude.value
theta = np.rad2deg(fit.theta.value)
if debug:
if threshold:
label = ('Subimage thresholded', 'Model', 'Residuals')
else:
label = ('Subimage', 'Model', 'Residuals')
plot_frames((psf_subimage, fit(x, y), psf_subimage-fit(x, y)),
grid=True, grid_spacing=1, label=label)
print('FWHM_y =', fwhm_y)
print('FWHM_x =', fwhm_x, '\n')
print('centroid y =', mean_y)
print('centroid x =', mean_x)
print('centroid y subim =', fit.y_mean.value)
print('centroid x subim =', fit.x_mean.value, '\n')
print('amplitude =', amplitude)
print('theta =', theta)
if full_output:
return pd.DataFrame({'centroid_y': mean_y, 'centroid_x': mean_x,
'fwhm_y': fwhm_y, 'fwhm_x': fwhm_x,
'amplitude': amplitude, 'theta': theta}, index=[0])
else:
return mean_y, mean_x
def snrmap(array, fwhm, approximated=False, plot=False, known_sources=None,
nproc=None, array2=None, use2alone=False, verbose=True, **kwargs):
"""Parallel implementation of the S/N map generation function. Applies the
S/N function (small samples penalty) at each pixel.
Parameters
----------
array : numpy ndarray
Input frame (2d array).
fwhm : float
Size in pixels of the FWHM.
approximated : bool, optional
If True, an approximated S/N map is generated.
plot : bool, optional
If True plots the S/N map. False by default.
known_sources : None, tuple or tuple of tuples, optional
To take into account existing sources. It should be a tuple of float/int
or a tuple of tuples (of float/int) with the coordinate(s) of the known
sources.
nproc : int or None
Number of processes for parallel computing.
array2 : numpy ndarray, optional
Additional image (e.g. processed image with negative derotation angles)
enabling to have more noise samples. Should have the
same dimensions as array.
use2alone: bool, optional
Whether to use array2 alone to estimate the noise (might be useful to
estimate the snr of extended disk features).
verbose: bool, optional
Whether to print timing or not.
**kwargs : dictionary, optional
Arguments to be passed to ``plot_frames`` to customize the plot (and to
save it to disk).
Returns
-------
snrmap : 2d numpy ndarray
Frame with the same size as the input frame with each pixel.
"""
if verbose:
start_time = time_ini()
check_array(array, dim=2, msg='array')
sizey, sizex = array.shape
snrmap_array = np.zeros_like(array)
width = min(sizey, sizex) / 2 - 1.5 * fwhm
mask = get_annulus_segments(array, (fwhm / 2) + 2, width, mode="mask")[0]
mask = np.ma.make_mask(mask)
# by making a bool mask *after* applying the mask to the array, we also mask
# out zero values from the array. This logic cannot be simplified by using
# mode="ind"!
yy, xx = np.where(mask)
coords = zip(xx, yy)
if nproc is None:
nproc = cpu_count() // 2 # Hyper-threading doubles the # of cores
if known_sources is None:
# proxy to S/N calculation
if approximated:
cy, cx = frame_center(array)
tophat_kernel = Tophat2DKernel(fwhm / 2)
array = convolve(array, tophat_kernel)
width = min(sizey, sizex) / 2 - 1.5 * fwhm
mask = get_annulus_segments(array, (fwhm / 2) + 1, width - 1,
mode="mask")[0]
mask = np.ma.make_mask(mask)
yy, xx = np.where(mask)
coords = [(int(x), int(y)) for (x, y) in zip(xx, yy)]
res = pool_map(nproc, _snr_approx, array, iterable(coords), fwhm,
cy, cx)
res = np.array(res)
yy = res[:, 0]
xx = res[:, 1]
snr_value = res[:, 2]
snrmap_array[yy.astype(int), xx.astype(int)] = snr_value
# computing s/n map with Mawet+14 definition
else:
res = pool_map(nproc, snr, array, iterable(coords), fwhm, True,
array2, use2alone)
res = np.array(res)
yy = res[:, 0]
xx = res[:, 1]
snr_value = res[:, -1]
snrmap_array[yy.astype('int'), xx.astype('int')] = snr_value
# masking known sources
else:
if not isinstance(known_sources, tuple):
raise TypeError("`known_sources` must be a tuple or tuple of "
"tuples")
else:
source_mask = np.zeros_like(array)
if isinstance(known_sources[0], tuple):
for coor in known_sources:
source_mask[coor[::-1]] = 1
elif isinstance(known_sources[0], int):
source_mask[known_sources[1], known_sources[0]] = 1
else:
raise TypeError("`known_sources` seems to have wrong type. It "
"must be a tuple of ints or tuple of tuples "
"(of ints)")
# checking the mask with the sources
if source_mask[source_mask == 1].shape[0] > 50:
msg = 'Input source mask is too crowded (check its validity)'
raise RuntimeError(msg)
soury, sourx = np.where(source_mask == 1)
sources = []
coor_ann = []
arr_masked_sources = array.copy()
centery, centerx = frame_center(array)
for y, x in zip(soury, sourx):
radd = dist(centery, centerx, int(y), int(x))
if int(radd) < centery - np.ceil(fwhm):
sources.append((y, x))
for source in sources:
y, x = source
radd = dist(centery, centerx, int(y), int(x))
anny, annx = get_annulus_segments(array, int(radd-fwhm),
int(np.round(3 * fwhm)))[0]
ciry, cirx = draw.circle(y, x, int(np.ceil(fwhm)))
# masking the sources positions (using the MAD of pixels in annulus)
arr_masked_sources[ciry, cirx] = mad(array[anny, annx])
# S/Ns of annulus without the sources
coor_ann = [(x, y) for (x, y) in zip(annx, anny) if (x, y) not in
zip(cirx, ciry)]
res = pool_map(nproc, snr, arr_masked_sources, iterable(coor_ann),
fwhm, True, array2, use2alone)
res = np.array(res)
yy_res = res[:, 0]
xx_res = res[:, 1]
snr_value = res[:, 4]
snrmap_array[yy_res.astype('int'), xx_res.astype('int')] = snr_value
coor_ann += coor_ann
# S/Ns of the rest of the frame without the annulus
coor_rest = [(x, y) for (x, y) in zip(xx, yy) if (x, y) not in coor_ann]
res = pool_map(nproc, snr, array, iterable(coor_rest), fwhm, True,
array2, use2alone)
res = np.array(res)
yy_res = res[:, 0]
xx_res = res[:, 1]
snr_value = res[:, 4]
snrmap_array[yy_res.astype('int'), xx_res.astype('int')] = snr_value
if plot:
plot_frames(snrmap_array, colorbar=True, title='S/N map', **kwargs)
if verbose:
print("S/N map created using {} processes".format(nproc))
timing(start_time)
return snrmap_array
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 fit_2dairydisk(array,
crop=False,
cent=None,
cropsize=15,
fwhm=4,
threshold=False,
sigfactor=6,
full_output=True,
debug=True):
""" Fitting a 2D Airy to the 2D distribution of the data.
Parameters
----------
array : numpy ndarray
Input frame with a single PSF.
crop : bool, optional
If True a square sub image will be cropped equal to cropsize.
cent : tuple of int, optional
X,Y integer position of source in the array for extracting the subimage.
If None the center of the frame is used for cropping the subframe (the
PSF is assumed to be ~ at the center of the frame).
cropsize : int, optional
Size of the subimage.
fwhm : float, optional
Initial values for the FWHM of the fitted 2d Airy disk, in px.
threshold : bool, optional
If True the background pixels (estimated using sigma clipped statistics)
will be replaced by small random Gaussian noise.
sigfactor : int, optional
The background pixels will be thresholded before fitting a 2d Moffat
to the data using sigma clipped statistics. All values smaller than
(MEDIAN + sigfactor*STDDEV) will be replaced by small random Gaussian
noise.
full_output : bool, optional
If False it returns just the centroid, if True also returns the
FWHM in X and Y (in pixels), the amplitude and the rotation angle.
debug : bool, optional
If True, the function prints out parameters of the fit and plots the
data, model and residuals.
Returns
-------
mean_y : float
Source centroid y position on input array from fitting.
mean_x : float
Source centroid x position on input array from fitting.
If ``full_output`` is True it returns a Pandas dataframe containing the
following columns:
'amplitude' : Float value. Moffat Amplitude.
'centroid_x' : Float value. X coordinate of the centroid.
'centroid_y' : Float value. Y coordinate of the centroid.
'fwhm' : Float value. FHWM [px].
"""
check_array(array, dim=2, msg='array')
if crop:
if cent is None:
ceny, cenx = frame_center(array)
else:
cenx, ceny = cent
imside = array.shape[0]
psf_subimage, suby, subx = get_square(array,
min(cropsize, imside),
ceny,
cenx,
position=True)
else:
psf_subimage = array.copy()
if threshold:
_, clipmed, clipstd = sigma_clipped_stats(psf_subimage, sigma=2)
indi = np.where(psf_subimage <= clipmed + sigfactor * clipstd)
subimnoise = np.random.randn(psf_subimage.shape[0],
psf_subimage.shape[1]) * clipstd
psf_subimage[indi] = subimnoise[indi]
# Creating the 2d Airy disk model
init_amplitude = np.ptp(psf_subimage)
xcom, ycom = cen_com(psf_subimage)
diam_1st_zero = (fwhm * 2.44) / 1.028
airy = models.AiryDisk2D(amplitude=init_amplitude,
x_0=xcom,
y_0=ycom,
radius=diam_1st_zero / 2.)
# Levenberg-Marquardt algorithm
fitter = fitting.LevMarLSQFitter()
y, x = np.indices(psf_subimage.shape)
fit = fitter(airy, x, y, psf_subimage)
if crop:
mean_y = fit.y_0.value + suby
mean_x = fit.x_0.value + subx
else:
mean_y = fit.y_0.value
mean_x = fit.x_0.value
amplitude = fit.amplitude.value
radius = fit.radius.value
fwhm = ((radius * 1.028) / 2.44) * 2
# compute uncertainties
if fitter.fit_info['param_cov'] is not None:
perr = np.sqrt(np.diag(fitter.fit_info['param_cov']))
amplitude_err, mean_x_err, mean_y_err, radius_err = perr
fwhm_err = ((radius_err * 1.028) / 2.44) * 2
else:
amplitude_err, mean_x_err, mean_y_err = None, None, None
radius_err, fwhm_err = None, None
if debug:
if threshold:
label = ('Subimage thresholded', 'Model', 'Residuals')
else:
label = ('Subimage', 'Model', 'Residuals')
plot_frames((psf_subimage, fit(x, y), psf_subimage - fit(x, y)),
grid=True,
grid_spacing=1,
label=label)
print('FWHM =', fwhm)
print('centroid y =', mean_y)
print('centroid x =', mean_x)
print('centroid y subim =', fit.y_0.value)
print('centroid x subim =', fit.x_0.value, '\n')
print('amplitude =', amplitude)
print('radius =', radius)
if full_output:
return pd.DataFrame(
{
'centroid_y': mean_y,
'centroid_x': mean_x,
'fwhm': fwhm,
'radius': radius,
'amplitude': amplitude,
'centroid_y_err': mean_y_err,
'centroid_x_err': mean_x_err,
'fwhm_err': fwhm_err,
'radius_err': radius_err,
'amplitude_err': amplitude_err
},
index=[0])
else:
return mean_y, mean_x
def chisquare(modelParameters,
cube,
angs,
plsc,
psfs_norm,
fwhm,
annulus_width,
aperture_radius,
initialState,
ncomp,
cube_ref=None,
svd_mode='lapack',
scaling=None,
fmerit='sum',
collapse='median',
algo=pca_annulus,
delta_rot=1,
imlib='opencv',
interpolation='lanczos4',
algo_options={},
transmission=None,
mu_sigma=None,
weights=None,
force_rPA=False,
debug=False):
"""
Calculate the reduced chi2:
\chi^2_r = \frac{1}{N-3}\sum_{j=1}^{N} |I_j|,
where N is the number of pixels within a circular aperture centered on the
first estimate of the planet position, and I_j the j-th pixel intensity.
Parameters
----------
modelParameters: tuple
The model parameters, typically (r, theta, flux).
cube: numpy.array
The cube of fits images expressed as a numpy.array.
angs: numpy.array
The parallactic angle fits image expressed as a numpy.array.
plsc: float
The platescale, in arcsec per pixel.
psfs_norm: numpy.array
The scaled psf expressed as a numpy.array.
fwhm : float
The FHWM in pixels.
annulus_width: int, optional
The width in pixels of the annulus on which the PCA is done.
aperture_radius: int, optional
The radius of the circular aperture in terms of the FWHM.
initialState: numpy.array
Position (r, theta) of the circular aperture center.
ncomp: int or None
The number of principal components for PCA-based algorithms.
cube_ref : numpy ndarray, 3d, optional
Reference library cube. For Reference Star Differential Imaging.
svd_mode : {'lapack', 'randsvd', 'eigen', 'arpack'}, str optional
Switch for different ways of computing the SVD and selected PCs.
scaling : {'temp-mean', 'temp-standard'} or None, optional
With None, no scaling is performed on the input data before SVD. With
"temp-mean" then temporal px-wise mean subtraction is done and with
"temp-standard" temporal mean centering plus scaling to unit variance
is done.
fmerit : {'sum', 'stddev'}, string optional
Chooses the figure of merit to be used. stddev works better for close in
companions sitting on top of speckle noise.
collapse : {'median', 'mean', 'sum', 'trimmean', None}, str or None, optional
Sets the way of collapsing the frames for producing a final image. If
None then the cube of residuals is used when measuring the function of
merit (instead of a single final frame).
algo: python routine, opt {pca_annulus, pca_annular, pca, custom}
Routine to be used to model and subtract the stellar PSF. From an input
cube, derotation angles, and optional arguments, it should return a
post-processed frame.
delta_rot: float, optional
If algo is set to pca_annular, delta_rot is the angular threshold used
to select frames in the PCA library (see description of pca_annular).
imlib : str, optional
See the documentation of the ``vip_hci.preproc.frame_shift`` function.
interpolation : str, optional
See the documentation of the ``vip_hci.preproc.frame_shift`` function.
algo_options: dict, opt
Dictionary with additional parameters related to the algorithm
(e.g. tol, min_frames_lib, max_frames_lib). If 'algo' is not a vip
routine, this dict should contain all necessary arguments apart from
the cube and derotation angles. Note: arguments such as ncomp, svd_mode,
scaling, imlib, interpolation or collapse can also be included in this
dict (the latter are also kept as function arguments for consistency
with older versions of vip).
transmission: numpy array, optional
Array with 2 columns. First column is the radial separation in pixels.
Second column is the off-axis transmission (between 0 and 1) at the
radial separation given in column 1.
mu_sigma: tuple of 2 floats or None, opt
If set to None: not used, and falls back to original version of the
algorithm, using fmerit.
If set to anything but None: will compute the mean and standard
deviation of pixel intensities in an annulus centered on the location
of the companion, excluding the area directly adjacent to the companion.
weights : 1d array, optional
If provided, the negative fake companion fluxes will be scaled according
to these weights before injection in the cube. Can reflect changes in
the observing conditions throughout the sequence.
force_rPA: bool, optional
Whether to only search for optimal flux, provided (r,PA).
debug: bool, opt
Whether to debug and plot the post-processed frame after injection of
the negative fake companion.
Returns
-------
out: float
The reduced chi squared.
"""
if force_rPA:
r, theta = initialState
flux_tmp = modelParameters[0]
else:
try:
r, theta, flux_tmp = modelParameters
except TypeError:
msg = 'modelParameters must be a tuple, {} was given'
print(msg.format(type(modelParameters)))
if weights is None:
flux = -flux_tmp
norm_weights = weights
else:
flux = -flux_tmp * weights
norm_weights = weights / np.sum(weights)
# Create the cube with the negative fake companion injected
cube_negfc = cube_inject_companions(cube,
psfs_norm,
angs,
flevel=flux,
plsc=plsc,
rad_dists=[r],
n_branches=1,
theta=theta,
imlib=imlib,
verbose=False,
interpolation=interpolation,
transmission=transmission)
# Perform PCA and extract the zone of interest
res = get_values_optimize(cube_negfc,
angs,
ncomp,
annulus_width,
aperture_radius,
fwhm,
initialState[0],
initialState[1],
cube_ref=cube_ref,
svd_mode=svd_mode,
scaling=scaling,
algo=algo,
delta_rot=delta_rot,
collapse=collapse,
algo_options=algo_options,
weights=norm_weights,
debug=debug)
if debug and collapse is not None:
values, frpca = res
plot_frames(frpca)
else:
values = res
# Function of merit
if mu_sigma is None:
# old version - delete?
if fmerit == 'sum':
chi = np.sum(np.abs(values))
elif fmerit == 'stddev':
values = values[values != 0]
chi = np.std(values) * values.size
else:
raise RuntimeError('fmerit choice not recognized.')
else:
# true expression of a gaussian log probability
mu = mu_sigma[0]
sigma = mu_sigma[1]
chi = np.sum(np.power(mu - values, 2) / sigma**2)
return chi
def _pairwise_ann(ann,
n_annuli,
fwhm,
angles,
delta_rot,
metric,
dist_threshold,
n_similar,
radius_int,
asize,
ncomp,
imlib,
interpolation,
verbose,
debug=False):
"""
Helper functions for pair-wise subtraction for a single annulus.
"""
start_time = time_ini(False)
n_frames = array.shape[0]
pa_threshold, in_rad, ann_center = _define_annuli(angles, ann, n_annuli,
fwhm, radius_int, asize,
delta_rot, 1, verbose)
if ncomp is not None:
arrayin = pca_annulus(array,
None,
ncomp,
asize,
ann_center,
svd_mode='lapack',
scaling=None,
collapse=None)
else:
arrayin = array
yy, xx = get_annulus_segments(array[0],
inner_radius=in_rad,
width=asize,
nsegm=1)[0]
values = arrayin[:, yy, xx] # n_frames x n_pxs_annulus
if debug:
print('Done taking pixel intensities from annulus.')
timing(start_time)
mat_dists_ann_full = pairwise_distances(values, metric=metric)
if pa_threshold > 0:
mat_dists_ann = np.zeros_like(mat_dists_ann_full)
for i in range(n_frames):
ind_fr_i = _find_indices_adi(angles, i, pa_threshold, None, False)
mat_dists_ann[i][ind_fr_i] = mat_dists_ann_full[i][ind_fr_i]
else:
mat_dists_ann = mat_dists_ann_full
if debug:
msg = 'Done calculating the {} distance for annulus {}'
print(msg.format(metric, ann + 1))
timing(start_time)
threshold = np.percentile(mat_dists_ann[mat_dists_ann != 0],
dist_threshold)
mat_dists_ann[mat_dists_ann > threshold] = np.nan
mat_dists_ann[mat_dists_ann == 0] = np.nan
if not mat_dists_ann[~np.isnan(mat_dists_ann)].size > 0:
raise RuntimeError('No pairs left. Decrease thresholds')
if debug:
plot_frames(mat_dists_ann)
print('Done thresholding/checking distances.')
timing(start_time)
# median of n ``n_similar`` most similar patches
cube_res = []
if n_similar is not None:
if n_similar < 3:
raise ValueError("n_similar must be >= 3 or None")
for i in range(n_frames):
vector = pn.DataFrame(mat_dists_ann[i])
if vector.sum().values == 0:
continue
else:
vector_sorted = vector.i.sort_values()[:n_similar]
ind_n_similar = vector_sorted.index.values
# median subtraction
res = values[i] - np.median((values[ind_n_similar]), axis=0)
cube_res.append(res)
cube_res = np.array(cube_res)
# taking just the most similar frame
else:
ind = []
for i in range(n_frames):
vector = pn.DataFrame(mat_dists_ann[i])
if vector.sum().values == 0:
continue
else:
ind.append((i, vector.idxmin().tolist()[0]))
ind.append((vector.idxmin().tolist()[0], i))
if debug:
print('Done finding pairs. Total found: ', len(ind) / 2)
timing(start_time)
df = pn.DataFrame(ind) # sorting using pandas dataframe
df.columns = ['i', 'j']
df = df.sort_values('i')
indices = df.values
indices = indices.astype(int) # back to a ndarray int type
size = indices.shape[0]
angles_list = np.zeros((size))
for i in range(size):
angles_list[i] = angles[indices[i]
[0]] # filter of the angles vector
cube_res = np.zeros((size, yy.shape[0]))
# pair-wise subtraction
for i in range(size):
res = values[indices[i][0]] - values[indices[i][1]]
cube_res[i] = res
cube_out = np.zeros((array.shape[0], array.shape[1], array.shape[2]))
for i in range(n_frames):
cube_out[i, yy, xx] = cube_res[i]
return cube_out
def fit_2dairydisk(array, crop=False, cent=None, cropsize=15, fwhm=4,
threshold=False, sigfactor=6, full_output=True,
debug=True):
""" Fitting a 2D Moffat to the 2D distribution of the data.
Parameters
----------
array : numpy ndarray
Input frame with a single PSF.
crop : bool, optional
If True an square sub image will be cropped.
cent : tuple of int, optional
X,Y integer position of source in the array for extracting the subimage.
If None the center of the frame is used for cropping the subframe (the
PSF is assumed to be ~ at the center of the frame).
cropsize : int, optional
Size of the subimage.
fwhm : float, optional
Initial values for the FWHM of the fitted 2d Moffat, in px.
threshold : bool, optional
If True the background pixels (estimated using sigma clipped statistics)
will be replaced by small random Gaussian noise.
sigfactor : int, optional
The background pixels will be thresholded before fitting a 2d Moffat
to the data using sigma clipped statistics. All values smaller than
(MEDIAN + sigfactor*STDDEV) will be replaced by small random Gaussian
noise.
full_output : bool, optional
If False it returns just the centroid, if True also returns the
FWHM in X and Y (in pixels), the amplitude and the rotation angle.
debug : bool, optional
If True, the function prints out parameters of the fit and plots the
data, model and residuals.
Returns
-------
mean_y : float
Source centroid y position on input array from fitting.
mean_x : float
Source centroid x position on input array from fitting.
If ``full_output`` is True it returns a Pandas dataframe containing the
following columns:
'alpha': Float value. Alpha parameter.
'amplitude' : Float value. Moffat Amplitude.
'centroid_x' : Float value. X coordinate of the centroid.
'centroid_y' : Float value. Y coordinate of the centroid.
'fwhm' : Float value. FHWM [px].
'gamma' : Float value. Gamma parameter.
"""
check_array(array, dim=2, msg='array')
if crop:
if cent is None:
ceny, cenx = frame_center(array)
else:
cenx, ceny = cent
imside = array.shape[0]
psf_subimage, suby, subx = get_square(array, min(cropsize, imside),
ceny, cenx, position=True)
else:
psf_subimage = array.copy()
if threshold:
_, clipmed, clipstd = sigma_clipped_stats(psf_subimage, sigma=2)
indi = np.where(psf_subimage <= clipmed + sigfactor * clipstd)
subimnoise = np.random.randn(psf_subimage.shape[0],
psf_subimage.shape[1]) * clipstd
psf_subimage[indi] = subimnoise[indi]
# Creating the 2d Airy disk model
init_amplitude = np.ptp(psf_subimage)
xcom, ycom = photutils.centroid_com(psf_subimage)
diam_1st_zero = (fwhm * 2.44) / 1.028
airy = models.AiryDisk2D(amplitude=init_amplitude, x_0=xcom, y_0=ycom,
radius=diam_1st_zero/2.)
# Levenberg-Marquardt algorithm
fitter = fitting.LevMarLSQFitter()
y, x = np.indices(psf_subimage.shape)
fit = fitter(airy, x, y, psf_subimage)
if crop:
mean_y = fit.y_0.value + suby
mean_x = fit.x_0.value + subx
else:
mean_y = fit.y_0.value
mean_x = fit.x_0.value
amplitude = fit.amplitude.value
radius = fit.radius.value
fwhm = ((radius * 1.028) / 2.44) * 2
if debug:
if threshold:
label = ('Subimage thresholded', 'Model', 'Residuals')
else:
label = ('Subimage', 'Model', 'Residuals')
plot_frames((psf_subimage, fit(x, y), psf_subimage - fit(x, y)),
grid=True, grid_spacing=1, label=label)
print('FWHM =', fwhm)
print('centroid y =', mean_y)
print('centroid x =', mean_x)
print('centroid y subim =', fit.y_0.value)
print('centroid x subim =', fit.x_0.value, '\n')
print('amplitude =', amplitude)
print('radius =', radius)
if full_output:
return pd.DataFrame({'centroid_y': mean_y, 'centroid_x': mean_x,
'fwhm': fwhm, 'radius': radius,
'amplitude': amplitude}, index=[0])
else:
return mean_y, mean_x
def CenteringLoop(cube_orig, angs, psf):
#---------------------------------------------------------------
print("Recentering Cube")
print(" There are two ways to recenter the cube")
print(" 1. Use a 2D Gaussian Fit")
print(
" 2. Recenter manually. (Use if Gaussian fit doesn't work. Works if all images are already aligned but not in the center of the image)"
)
while True:
print(" Choose 1 (Gaussian fit) or 2 (manual fit): ")
fit_method = input()
if fit_method == '1' or fit_method == '2':
break
else:
print(" ->Option not recognised, please input '1' or '2'. ")
# Initialise variable
star_xy = [0.1, 0.1]
print("Using DS9, open any image in Newcube.fits")
star_xy[0] = input(
"Input the x coordinate of the central position of the star: ")
star_xy[0] = int(star_xy[0])
star_xy[1] = input(
"Input the x coordinate of the central position of the star: ")
star_xy[1] = int(star_xy[1])
#Opens the cube (using VIP) with cube_orig as HDU:0 and calls it cube_orig,
#and the parallactic angles from a text file.
#Uses VIP to also open the point spread function previously loaded in.
cube1 = cube_orig
# Gaussian Fit
if fit_method == '1':
print(" --2D Gaussian Fit")
print("Fitting a 2D gaussian to centre the images...")
#Uses VIP's 2D gaussian fitting algorithm to centre the cube.
cube1, shy1, shx1, fwhm = Gaussian_2d_Fit(psf, cube_orig, star_xy)
# Manual Fit
elif fit_method == '2':
print(" --Manual Fit")
# Calculate shifts here
image_centre = [512, 512]
print(" Image centre is at", image_centre)
shift_x = image_centre[0] - star_xy[0]
shift_y = image_centre[1] - star_xy[1]
cube1 = vip.preproc.recentering.cube_shift(cube_orig, shift_y, shift_x)
fwhm = Calculate_fwhm(psf)
#Writes the values of the centered cube into a fits file.
vip.fits.write_fits('centeredcube_{name}.fits'.format(name=name_input),
cube1,
verbose=True)
cube = cube1
#Loads up the centered cube.
#Plots the original cube vs the new centered cube.
im1 = vip.preproc.cosmetics.frame_crop(cube_orig[0], 1000, verbose=False)
im2 = vip.preproc.cosmetics.frame_crop(cube[0], 1000, verbose=False)
hciplot.plot_frames(
(im1, im2),
label=('Original first frame', 'First frame after recentering'),
grid=True,
size_factor=4)
print(
"Open DS9 and look through the centred data cube at each frame making sure it is centred."
)
print("If you're not happy with it, redo centring")
print("Redo Centring?")
cent_loop = Checkfunction()
return cube, cent_loop
def plot_detmaps(self, i=None, thr=9, dpi=100,
axis=True, grid=False, vmin=-10, vmax='max',
plot_type="horiz"):
"""
Plot the detection maps for one injection.
Parameters
----------
i : int or None, optional
Index of the injection, between 0 and self.n_injections. If None,
takes the 30st injection, or if there are less injections, the
middle one.
thr : int, optional
Index of the threshold.
dpi, axis, grid, vmin, vmax
Passed to ``pp_subplots``
plot_type : {"horiz" or "vert"}, optional
Plot type.
``horiz``
One row per algorithm (frame, probmap, binmap)
``vert``
1 row for final frames, 1 row for probmaps and 1 row for binmaps
"""
# input parameters
if i is None:
if len(self.list_xy) > 30:
i = 30
else:
i = len(self.list_xy) // 2
if vmax == 'max':
# TODO: document this feature.
vmax = np.concatenate([m.frames[i] for m in self.methods if
hasattr(m, "frames") and
len(m.frames) >= i]).max()/2
# print information
print('X,Y: {}'.format(self.list_xy[i]))
print('dist: {:.3f}, flux: {:.3f}'.format(self.dists[i],
self.fluxes[i]))
print()
if plot_type in [1, "horiz"]:
for m in self.methods:
print('detection state: {} | false postives: {}'.format(
m.detections[i][thr], m.fps[i][thr]))
labels = ('{} frame'.format(m.name), '{} S/Nmap'.format(m.name),
'Thresholded at {:.1f}'.format(m.thresholds[thr]))
plot_frames((m.frames[i] if len(m.frames) >= i else
np.zeros((2, 2)), m.probmaps[i], m.bmaps[i][thr]),
label=labels, dpi=dpi, horsp=0.2, axis=axis,
grid=grid, cmap=['viridis', 'viridis', 'gray'])
elif plot_type in [2, "vert"]:
labels = tuple('{} frame'.format(m.name) for m in self.methods if
hasattr(m, "frames") and len(m.frames) >= i)
plot_frames(tuple(m.frames[i] for m in self.methods if
hasattr(m, "frames") and len(m.frames) >= i),
dpi=dpi, label=labels, vmax=vmax, vmin=vmin, axis=axis,
grid=grid)
plot_frames(tuple(m.probmaps[i] for m in self.methods), dpi=dpi,
label=tuple(['{} S/Nmap'.format(m.name) for m in
self.methods]), axis=axis, grid=grid)
for m in self.methods:
msg = '{} detection: {}, FPs: {}'
print(msg.format(m.name, m.detections[i][thr], m.fps[i][thr]))
labels = tuple('Thresholded at {:.1f}'.format(m.thresholds[thr])
for m in self.methods)
plot_frames(tuple(m.bmaps[i][thr] for m in self.methods),
dpi=dpi, label=labels, axis=axis, grid=grid,
colorbar=False, cmap='bone')
else:
raise ValueError("`plot_type` unknown")
def _get_adi_snrs(psf,
angle_list,
fwhm,
plsc,
flux_dist_theta_all,
wavelengths=None,
spectrum=None,
mode='pca',
n_ks=3,
scaling='temp-standard',
svd_mode='randsvd',
debug=False):
""" Get the mean S/N (at 3 equidistant positions) for a given flux and
distance, on a residual frame.
"""
theta = flux_dist_theta_all[2]
flux = flux_dist_theta_all[0]
dist = flux_dist_theta_all[1]
if GARRAY.ndim == 3:
spectrum = 1
elif GARRAY.ndim == 4:
# grey spectrum (same flux in all wls)
if spectrum is None:
spectrum = np.ones((GARRAY.shape[0]))
snrs = []
# 3 equidistant azimuthal positions, 1 or several K values
for ang in [theta, theta + 120, theta + 240]:
cube_fc, pos = cube_inject_companions(GARRAY,
psf,
angle_list,
flevel=flux * spectrum,
plsc=plsc,
rad_dists=[dist],
theta=ang,
verbose=False,
full_output=True)
posy, posx = pos[0]
fr_temp = _compute_residual_frame(cube_fc, angle_list, dist, fwhm,
wavelengths, mode, n_ks, svd_mode,
scaling, 'median', 'opencv',
'bilinear')
# handling the case of mode='median'
if isinstance(fr_temp, np.ndarray):
fr_temp = [fr_temp]
snrs_ks = []
for i in range(len(fr_temp)):
res = snr(fr_temp[i],
source_xy=(posx, posy),
fwhm=fwhm,
exclude_negative_lobes=True)
snrs_ks.append(res)
maxsnr_ks = max(snrs_ks)
if np.isinf(maxsnr_ks) or np.isnan(maxsnr_ks) or maxsnr_ks < 0:
maxsnr_ks = 0.01
snrs.append(maxsnr_ks)
if debug:
print(' ')
cy, cx = frame_center(GARRAY[0])
label = 'Flux: {:.1f}, Max S/N: {:.2f}'.format(flux, maxsnr_ks)
hp.plot_frames(tuple(np.array(fr_temp)),
axis=False,
horsp=0.05,
colorbar=False,
circle=((posx, posy), (cx, cy)),
circle_radius=(5, dist),
label=label,
dpi=60)
# max of mean S/N at 3 equidistant positions
snr_value = np.max(snrs)
return flux, snr_value
def compute_binary_map(frame, thresholds, injections, fwhm, npix=1,
overlap_threshold=0.7, max_blob_fact=2, plot=False,
debug=False):
"""
Take a list of ``thresholds``, create binary maps and counts detections/fps.
A blob which is "too big" is split into apertures, and every aperture adds
one 'false positive'.
Parameters
----------
frame : numpy ndarray
Detection map.
thresholds : list or numpy ndarray
List of thresholds (detection criteria).
injections : tuple, list of tuples
Coordinates (x,y) of the injected companions. Also accepts 1d/2d
ndarrays.
fwhm : float
FWHM, used for obtaining the size of the circular aperture centered at
the injection position (and measuring the overlapping with found blobs).
The circular aperture has 2 * FWHM in diameter.
npix : int, optional
The number of connected pixels, each greater than the given threshold,
that an object must have to be detected. ``npix`` must be a positive
integer. Passed to ``detect_sources`` function from ``photutils``.
overlap_threshold : float
Percentage of overlap a blob has to have with the aperture around an
injection.
max_blob_fact : float
Maximum size of a blob (in multiples of the resolution element) before
it is considered as "too big" (= non-detection).
plot : bool, optional
If True, a final resulting plot summarizing the results will be shown.
debug : bool, optional
For showing optional information.
Returns
-------
list_detections : list of int
List of detection count for each threshold.
list_fps : list of int
List of false positives count for each threshold.
list_binmaps : list of 2d ndarray
List of binary maps: detection maps thresholded for each threshold
value.
"""
def _overlap_injection_blob(injection, fwhm, blob_mask):
"""
Parameters
----------
injection: tuple (y,x)
fwhm : float
blob_mask : 2d bool ndarray
Returns
-------
overlap_fact : float between 0 and 1
Percentage of the area overlap. If the blob is smaller than the
resolution element, this is ``intersection_area / blob_area``,
otherwise ``intersection_area / resolution_element``.
"""
injection_mask = get_circle(np.ones_like(blob_mask), radius=fwhm,
cy=injection[1], cx=injection[0],
mode="mask")
intersection = injection_mask & blob_mask
smallest_area = min(blob_mask.sum(), injection_mask.sum())
return intersection.sum() / smallest_area
# --------------------------------------------------------------------------
list_detections = []
list_fps = []
list_binmaps = []
sizey, sizex = frame.shape
cy, cx = frame_center(frame)
reselem_mask = get_circle(frame, radius=fwhm, cy=cy, cx=cx, mode="val")
npix_circ_aperture = reselem_mask.shape[0]
# normalize injections: accepts combinations of 1d/2d and tuple/list/array.
injections = np.asarray(injections)
if injections.ndim == 1:
injections = np.array([injections])
for ithr, threshold in enumerate(thresholds):
if debug:
print("\nprocessing threshold #{}: {}".format(ithr + 1, threshold))
segments = detect_sources(frame, threshold, npix, connectivity=4)
binmap = (segments.data != 0)
if debug:
plot_frames((segments.data, binmap), cmap=('tab20b', 'binary'),
circle=tuple(tuple(xy) for xy in injections),
circle_radius=fwhm, circle_alpha=0.6,
label=("segmentation map", "binary map"))
detections = 0
fps = 0
for segment in segments.segments:
label = segment.label
blob_mask = (segments.data == label)
blob_area = segment.area
if debug:
lab = "blob #{}, area={}px**2".format(label, blob_area)
plot_frames(blob_mask, circle_radius=fwhm, circle_alpha=0.6,
circle=tuple(tuple(xy) for xy in injections),
cmap='binary', label_size=8, label=lab,
size_factor=3)
for iinj, injection in enumerate(injections):
if injection[0] > sizex or injection[1] > sizey:
raise ValueError("Wrong coordinates in `injections`")
if debug:
print("\ttesting injection #{} at {}".format(iinj + 1,
injection))
if blob_area > max_blob_fact * npix_circ_aperture:
number_of_apertures_in_blob = blob_area / npix_circ_aperture
fps += number_of_apertures_in_blob # float, rounded at end
if debug:
print("\tblob is too big (+{:.0f} fps)"
"".format(number_of_apertures_in_blob))
print("\tskipping all other injections")
# continue with next blob, do not check other injections
break
overlap = _overlap_injection_blob(injection, fwhm, blob_mask)
if overlap > overlap_threshold:
if debug:
print("\toverlap of {}! (+1 detection)"
"".format(overlap))
detections += 1
# continue with next blob, do not check other injections
break
if debug:
print("\toverlap of {} -> do nothing".format(overlap))
else:
if debug:
print("\tdid not find a matching injection for this "
"blob (+1 fps)")
fps += 1
if debug:
print("done with threshold #{}".format(ithr))
print("result: {} detections, {} fps".format(detections, fps))
fps = np.round(fps).astype(int).item() # -> python `int`
list_detections.append(detections)
list_binmaps.append(binmap)
list_fps.append(fps)
if plot:
labs = tuple(str(det) + ' detections' + '\n' + str(fps) +
' false positives' for det, fps in zip(list_detections,
list_fps))
plot_frames(tuple(list_binmaps), title='Final binary maps', label=labs,
label_size=8, cmap='binary', circle_alpha=0.8,
circle=tuple(tuple(xy) for xy in injections),
circle_radius=fwhm, circle_color='deepskyblue', axis=False)
return list_detections, list_fps, list_binmaps
def fit_2dgaussian(array,
crop=False,
cent=None,
cropsize=15,
fwhmx=4,
fwhmy=4,
theta=0,
threshold=False,
sigfactor=6,
full_output=True,
debug=True):
""" Fitting a 2D Gaussian to the 2D distribution of the data.
Parameters
----------
array : numpy ndarray
Input frame with a single PSF.
crop : bool, optional
If True a square sub image will be cropped equal to cropsize.
cent : tuple of int, optional
X,Y integer position of source in the array for extracting the subimage.
If None the center of the frame is used for cropping the subframe (the
PSF is assumed to be ~ at the center of the frame).
cropsize : int, optional
Size of the subimage.
fwhmx, fwhmy : float, optional
Initial values for the standard deviation of the fitted Gaussian, in px.
theta : float, optional
Angle of inclination of the 2d Gaussian counting from the positive X
axis.
threshold : bool, optional
If True the background pixels (estimated using sigma clipped statistics)
will be replaced by small random Gaussian noise.
sigfactor : int, optional
The background pixels will be thresholded before fitting a 2d Gaussian
to the data using sigma clipped statistics. All values smaller than
(MEDIAN + sigfactor*STDDEV) will be replaced by small random Gaussian
noise.
full_output : bool, optional
If False it returns just the centroid, if True also returns the
FWHM in X and Y (in pixels), the amplitude and the rotation angle,
and the uncertainties on each parameter.
debug : bool, optional
If True, the function prints out parameters of the fit and plots the
data, model and residuals.
Returns
-------
mean_y : float
Source centroid y position on input array from fitting.
mean_x : float
Source centroid x position on input array from fitting.
If ``full_output`` is True it returns a Pandas dataframe containing the
following columns:
'centroid_y': Y coordinate of the centroid.
'centroid_x': X coordinate of the centroid.
'fwhm_y': Float value. FHWM in X [px].
'fwhm_x': Float value. FHWM in Y [px].
'amplitude': Amplitude of the Gaussian.
'theta': Float value. Rotation angle.
# and fit uncertainties on the above values:
'centroid_y_err'
'centroid_x_err'
'fwhm_y_err'
'fwhm_x_err'
'amplitude_err'
'theta_err'
"""
check_array(array, dim=2, msg='array')
if crop:
if cent is None:
ceny, cenx = frame_center(array)
else:
cenx, ceny = cent
imside = array.shape[0]
psf_subimage, suby, subx = get_square(array,
min(cropsize, imside),
ceny,
cenx,
position=True)
else:
psf_subimage = array.copy()
if threshold:
_, clipmed, clipstd = sigma_clipped_stats(psf_subimage, sigma=2)
indi = np.where(psf_subimage <= clipmed + sigfactor * clipstd)
subimnoise = np.random.randn(psf_subimage.shape[0],
psf_subimage.shape[1]) * clipstd
psf_subimage[indi] = subimnoise[indi]
# Creating the 2D Gaussian model
init_amplitude = np.ptp(psf_subimage)
xcom, ycom = cen_com(psf_subimage)
gauss = models.Gaussian2D(amplitude=init_amplitude,
theta=theta,
x_mean=xcom,
y_mean=ycom,
x_stddev=fwhmx * gaussian_fwhm_to_sigma,
y_stddev=fwhmy * gaussian_fwhm_to_sigma)
# Levenberg-Marquardt algorithm
fitter = fitting.LevMarLSQFitter()
y, x = np.indices(psf_subimage.shape)
fit = fitter(gauss, x, y, psf_subimage)
if crop:
mean_y = fit.y_mean.value + suby
mean_x = fit.x_mean.value + subx
else:
mean_y = fit.y_mean.value
mean_x = fit.x_mean.value
fwhm_y = fit.y_stddev.value * gaussian_sigma_to_fwhm
fwhm_x = fit.x_stddev.value * gaussian_sigma_to_fwhm
amplitude = fit.amplitude.value
theta = np.rad2deg(fit.theta.value)
# compute uncertainties
if fitter.fit_info['param_cov'] is not None:
perr = np.sqrt(np.diag(fitter.fit_info['param_cov']))
amplitude_e, mean_x_e, mean_y_e, fwhm_x_e, fwhm_y_e, theta_e = perr
fwhm_x_e /= gaussian_fwhm_to_sigma
fwhm_y_e /= gaussian_fwhm_to_sigma
else:
amplitude_e, theta_e, mean_x_e = None, None, None
mean_y_e, fwhm_x_e, fwhm_y_e = None, None, None
if debug:
if threshold:
label = ('Subimage thresholded', 'Model', 'Residuals')
else:
label = ('Subimage', 'Model', 'Residuals')
plot_frames((psf_subimage, fit(x, y), psf_subimage - fit(x, y)),
grid=True,
grid_spacing=1,
label=label)
print('FWHM_y =', fwhm_y)
print('FWHM_x =', fwhm_x, '\n')
print('centroid y =', mean_y)
print('centroid x =', mean_x)
print('centroid y subim =', fit.y_mean.value)
print('centroid x subim =', fit.x_mean.value, '\n')
print('amplitude =', amplitude)
print('theta =', theta)
if full_output:
return pd.DataFrame(
{
'centroid_y': mean_y,
'centroid_x': mean_x,
'fwhm_y': fwhm_y,
'fwhm_x': fwhm_x,
'amplitude': amplitude,
'theta': theta,
'centroid_y_err': mean_y_e,
'centroid_x_err': mean_x_e,
'fwhm_y_err': fwhm_y_e,
'fwhm_x_err': fwhm_x_e,
'amplitude_err': amplitude_e,
'theta_err': theta_e
},
index=[0],
dtype=np.float64)
else:
return mean_y, mean_x
def snrmap(array,
fwhm,
approximated=False,
plot=False,
known_sources=None,
nproc=None,
array2=None,
use2alone=False,
exclude_negative_lobes=False,
verbose=True,
**kwargs):
"""Parallel implementation of the S/N map generation function. Applies the
S/N function (small samples penalty) at each pixel.
The S/N is computed as in Mawet et al. (2014) for each radial separation.
https://ui.adsabs.harvard.edu/abs/2014ApJ...792...97M/abstract
*** DISCLAIMER ***
Signal-to-noise ratio is not significance! For a conversion from snr to
n-sigma (i.e. the equivalent confidence level of a Gaussian n-sigma), use
the significance() function.
Parameters
----------
array : numpy ndarray
Input frame (2d array).
fwhm : float
Size in pixels of the FWHM.
approximated : bool, optional
If True, an approximated S/N map is generated.
plot : bool, optional
If True plots the S/N map. False by default.
known_sources : None, tuple or tuple of tuples, optional
To take into account existing sources. It should be a tuple of float/int
or a tuple of tuples (of float/int) with the coordinate(s) of the known
sources.
nproc : int or None
Number of processes for parallel computing.
array2 : numpy ndarray, optional
Additional image (e.g. processed image with negative derotation angles)
enabling to have more noise samples. Should have the
same dimensions as array.
use2alone: bool, optional
Whether to use array2 alone to estimate the noise (might be useful to
estimate the snr of extended disk features).
verbose: bool, optional
Whether to print timing or not.
**kwargs : dictionary, optional
Arguments to be passed to ``plot_frames`` to customize the plot (and to
save it to disk).
Returns
-------
snrmap : 2d numpy ndarray
Frame with the same size as the input frame with each pixel.
"""
if verbose:
start_time = time_ini()
check_array(array, dim=2, msg='array')
sizey, sizex = array.shape
snrmap_array = np.zeros_like(array)
width = min(sizey, sizex) / 2 - 1.5 * fwhm
mask = get_annulus_segments(array, (fwhm / 2) + 2, width, mode="mask")[0]
mask = np.ma.make_mask(mask)
# by making a bool mask *after* applying the mask to the array, we also mask
# out zero values from the array. This logic cannot be simplified by using
# mode="ind"!
yy, xx = np.where(mask)
coords = zip(xx, yy)
if nproc is None:
nproc = cpu_count() // 2 # Hyper-threading doubles the # of cores
if known_sources is None:
# proxy to S/N calculation
if approximated:
cy, cx = frame_center(array)
tophat_kernel = Tophat2DKernel(fwhm / 2)
array = convolve(array, tophat_kernel)
width = min(sizey, sizex) / 2 - 1.5 * fwhm
mask = get_annulus_segments(array, (fwhm / 2) + 1,
width - 1,
mode="mask")[0]
mask = np.ma.make_mask(mask)
yy, xx = np.where(mask)
coords = [(int(x), int(y)) for (x, y) in zip(xx, yy)]
res = pool_map(nproc, _snr_approx, array, iterable(coords), fwhm,
cy, cx)
res = np.array(res)
yy = res[:, 0]
xx = res[:, 1]
snr_value = res[:, 2]
snrmap_array[yy.astype(int), xx.astype(int)] = snr_value
# computing s/n map with Mawet+14 definition
else:
res = pool_map(nproc, snr, array, iterable(coords), fwhm, True,
array2, use2alone, exclude_negative_lobes)
res = np.array(res)
yy = res[:, 0]
xx = res[:, 1]
snr_value = res[:, -1]
snrmap_array[yy.astype('int'), xx.astype('int')] = snr_value
# masking known sources
else:
if not isinstance(known_sources, tuple):
raise TypeError("`known_sources` must be a tuple or tuple of "
"tuples")
else:
source_mask = np.zeros_like(array)
if isinstance(known_sources[0], tuple):
for coor in known_sources:
source_mask[coor[::-1]] = 1
elif isinstance(known_sources[0], int):
source_mask[known_sources[1], known_sources[0]] = 1
else:
raise TypeError("`known_sources` seems to have wrong type. It "
"must be a tuple of ints or tuple of tuples "
"(of ints)")
# checking the mask with the sources
if source_mask[source_mask == 1].shape[0] > 50:
msg = 'Input source mask is too crowded (check its validity)'
raise RuntimeError(msg)
soury, sourx = np.where(source_mask == 1)
sources = []
coor_ann = []
arr_masked_sources = array.copy()
centery, centerx = frame_center(array)
for y, x in zip(soury, sourx):
radd = dist(centery, centerx, int(y), int(x))
if int(radd) < centery - np.ceil(fwhm):
sources.append((y, x))
for source in sources:
y, x = source
radd = dist(centery, centerx, int(y), int(x))
anny, annx = get_annulus_segments(array, int(radd - fwhm),
int(np.round(3 * fwhm)))[0]
ciry, cirx = disk((y, x), int(np.ceil(fwhm)))
# masking the sources positions (using the MAD of pixels in annulus)
arr_masked_sources[ciry, cirx] = mad(array[anny, annx])
# S/Ns of annulus without the sources
coor_ann = [(x, y) for (x, y) in zip(annx, anny)
if (x, y) not in zip(cirx, ciry)]
res = pool_map(nproc, snr, arr_masked_sources, iterable(coor_ann),
fwhm, True, array2, use2alone,
exclude_negative_lobes)
res = np.array(res)
yy_res = res[:, 0]
xx_res = res[:, 1]
snr_value = res[:, 4]
snrmap_array[yy_res.astype('int'),
xx_res.astype('int')] = snr_value
coor_ann += coor_ann
# S/Ns of the rest of the frame without the annulus
coor_rest = [(x, y) for (x, y) in zip(xx, yy)
if (x, y) not in coor_ann]
res = pool_map(nproc, snr, array, iterable(coor_rest), fwhm, True,
array2, use2alone, exclude_negative_lobes)
res = np.array(res)
yy_res = res[:, 0]
xx_res = res[:, 1]
snr_value = res[:, 4]
snrmap_array[yy_res.astype('int'), xx_res.astype('int')] = snr_value
if plot:
plot_frames(snrmap_array, colorbar=True, title='S/N map', **kwargs)
if verbose:
print("S/N map created using {} processes".format(nproc))
timing(start_time)
return snrmap_array
def fit_2d2gaussian(array,
crop=False,
cent=None,
cropsize=15,
fwhm_neg=4,
fwhm_pos=4,
theta_neg=0,
theta_pos=0,
neg_amp=1,
fix_neg=True,
threshold=False,
sigfactor=2,
full_output=False,
debug=True):
""" Fitting a 2D superimposed double Gaussian (negative and positive) to
the 2D distribution of the data (reproduce e.g. the effect of a coronagraph)
Parameters
----------
array : numpy ndarray
Input frame with a single PSF.
crop : bool, optional
If True a square sub image will be cropped equal to cropsize.
cent : tuple of float, optional
X,Y position of the source in the array for extracting the
subimage. If None the center of the frame is used for cropping the
subframe. If fix_neg is set to True, this will also be used as the
fixed position of the negative gaussian.
cropsize : int, optional
Size of the subimage.
fwhm_neg, fwhm_pos : float or tuple of floats, optional
Initial values for the FWHM of the fitted negative and positive
Gaussians, in px. If a tuple, should be the FWHM value along x and y.
theta_neg, theta_pos: float, optional
Angle of inclination of the 2d Gaussian counting from the positive X
axis (only matters if a tuple was provided for fwhm_neg or fwhm_pos).
neg_amp: float, optional
First guess on the amplitude of the negative gaussian, relative to the
amplitude of the positive gaussian (i.e. 1 means the negative gaussian
has the same amplitude as the positive gaussian)
fix_neg: bool, optional
Whether to fix the position and FWHM of the negative gaussian for a
fit with less free parameters. In that case, the center of the negative
gaussian is assumed to be cent
threshold : bool, optional
If True the background pixels (estimated using sigma clipped statistics)
will be replaced by small random Gaussian noise.
sigfactor : int, optional
The background pixels will be thresholded before fitting a 2d Gaussian
to the data using sigma clipped statistics. All values smaller than
(MEDIAN + sigfactor*STDDEV) will be replaced by small random Gaussian
noise.
full_output : bool, optional
If False it returns just the centroid, if True also returns the
FWHM in X and Y (in pixels), the amplitude and the rotation angle,
and the uncertainties on each parameter.
debug : bool, optional
If True, the function prints out parameters of the fit and plots the
data, model and residuals.
Returns
-------
mean_y : float
Source centroid y position on input array from fitting.
mean_x : float
Source centroid x position on input array from fitting.
If ``full_output`` is True it returns a Pandas dataframe containing the
following columns:
- for the positive gaussian:
'amplitude' : Float value. Amplitude of the Gaussian.
'centroid_x' : Float value. X coordinate of the centroid.
'centroid_y' : Float value. Y coordinate of the centroid.
'fwhm_x' : Float value. FHWM in X [px].
'fwhm_y' : Float value. FHWM in Y [px].
'theta' : Float value. Rotation angle of x axis
- for the negative gaussian:
'amplitude_neg' : Float value. Amplitude of the Gaussian.
'centroid_x_neg' : Float value. X coordinate of the centroid.
'centroid_y_neg' : Float value. Y coordinate of the centroid.
'fwhm_x_neg' : Float value. FHWM in X [px].
'fwhm_y_neg' : Float value. FHWM in Y [px].
'theta_neg' : Float value. Rotation angle of x axis
"""
if not array.ndim == 2:
raise TypeError('Input array is not a frame or 2d array')
if cent is None:
ceny, cenx = frame_center(array)
else:
cenx, ceny = cent
if crop:
x_sub_px = cenx % 1
y_sub_px = ceny % 1
imside = array.shape[0]
psf_subimage, suby, subx = get_square(array,
min(cropsize, imside),
int(ceny),
int(cenx),
position=True)
ceny, cenx = frame_center(psf_subimage)
ceny += y_sub_px
cenx += x_sub_px
else:
psf_subimage = array.copy()
if threshold:
_, clipmed, clipstd = sigma_clipped_stats(psf_subimage, sigma=2)
indi = np.where(psf_subimage <= clipmed + sigfactor * clipstd)
subimnoise = np.random.randn(psf_subimage.shape[0],
psf_subimage.shape[1]) * clipstd
psf_subimage[indi] = subimnoise[indi]
if isinstance(fwhm_neg, tuple):
fwhm_neg_x, fwhm_neg_y = fwhm_neg
else:
fwhm_neg_x = fwhm_neg
fwhm_neg_y = fwhm_neg
if isinstance(fwhm_pos, tuple):
fwhm_pos_x, fwhm_pos_y = fwhm_pos
else:
fwhm_pos_x = fwhm_pos
fwhm_pos_y = fwhm_pos
# Creating the 2D Gaussian model
init_amplitude = np.ptp(psf_subimage)
#xcom, ycom = cen_com(psf_subimage)
ycom, xcom = frame_center(psf_subimage)
fix_dico_pos = {'theta': True}
bounds_dico_pos = {
'amplitude': [0.8 * init_amplitude, 1.2 * init_amplitude],
'x_mean': [xcom - 3, xcom + 3],
'y_mean': [ycom - 3, ycom + 3],
'x_stddev': [
0.5 * fwhm_pos_x * gaussian_fwhm_to_sigma,
2 * fwhm_pos_x * gaussian_fwhm_to_sigma
],
'y_stddev': [
0.5 * fwhm_pos_y * gaussian_fwhm_to_sigma,
2 * fwhm_pos_y * gaussian_fwhm_to_sigma
]
}
gauss_pos = models.Gaussian2D(amplitude=init_amplitude,
x_mean=xcom,
y_mean=ycom,
x_stddev=fwhm_pos_x * gaussian_fwhm_to_sigma,
y_stddev=fwhm_pos_y * gaussian_fwhm_to_sigma,
theta=np.deg2rad(theta_pos) % (np.pi),
fixed=fix_dico_pos,
bounds=bounds_dico_pos)
if fix_neg:
fix_dico_neg = {
'x_mean': True,
'y_mean': True,
'x_stddev': True,
'y_stddev': True,
'theta': True
}
bounds_dico_neg = {
'amplitude':
[neg_amp * 0.5 * init_amplitude, neg_amp * 2 * init_amplitude]
}
else:
fix_dico_neg = {} #{'theta':True}
bounds_dico_neg = {
'amplitude':
[neg_amp * 0.5 * init_amplitude, neg_amp * 2 * init_amplitude],
'x_mean': [xcom - 3, xcom + 3],
'y_mean': [ycom - 3, ycom + 3],
'x_stddev': [
0.5 * fwhm_neg_x * gaussian_fwhm_to_sigma,
2 * fwhm_neg_x * gaussian_fwhm_to_sigma
],
'y_stddev': [
0.5 * fwhm_neg_y * gaussian_fwhm_to_sigma,
2 * fwhm_neg_y * gaussian_fwhm_to_sigma
],
'theta': [0, np.pi]
}
gauss_neg = models.Gaussian2D(amplitude=init_amplitude * neg_amp,
x_mean=cenx,
y_mean=ceny,
x_stddev=fwhm_neg_x * gaussian_fwhm_to_sigma,
y_stddev=fwhm_neg_y * gaussian_fwhm_to_sigma,
theta=np.deg2rad(theta_neg) % (np.pi),
fixed=fix_dico_neg,
bounds=bounds_dico_neg)
double_gauss = gauss_pos - gauss_neg
fitter = fitting.LevMarLSQFitter() #SLSQPLSQFitter() #LevMarLSQFitter()
y, x = np.indices(psf_subimage.shape)
fit = fitter(double_gauss, x, y, psf_subimage, maxiter=100000, acc=1e-08)
# positive gaussian
if crop:
mean_y = fit.y_mean_0.value + suby
mean_x = fit.x_mean_0.value + subx
else:
mean_y = fit.y_mean_0.value
mean_x = fit.x_mean_0.value
fwhm_y = fit.y_stddev_0.value * gaussian_sigma_to_fwhm
fwhm_x = fit.x_stddev_0.value * gaussian_sigma_to_fwhm
amplitude = fit.amplitude_0.value
theta = np.rad2deg(fit.theta_0.value)
# negative gaussian
if crop:
mean_y_neg = fit.y_mean_1.value + suby
mean_x_neg = fit.x_mean_1.value + subx
else:
mean_y_neg = fit.y_mean_1.value
mean_x_neg = fit.x_mean_1.value
fwhm_y_neg = fit.y_stddev_1.value * gaussian_sigma_to_fwhm
fwhm_x_neg = fit.x_stddev_1.value * gaussian_sigma_to_fwhm
amplitude_neg = fit.amplitude_1.value
theta_neg = np.rad2deg(fit.theta_1.value)
if debug:
if threshold:
label = ('Subimage thresholded', 'Model', 'Residuals')
else:
label = ('Subimage', 'Model', 'Residuals')
plot_frames((psf_subimage, fit(x, y), psf_subimage - fit(x, y)),
grid=True,
grid_spacing=1,
label=label)
print('FWHM_y =', fwhm_y)
print('FWHM_x =', fwhm_x, '\n')
print('centroid y =', mean_y)
print('centroid x =', mean_x)
print('centroid y subim =', fit.y_mean_0.value)
print('centroid x subim =', fit.x_mean_0.value, '\n')
print('amplitude =', amplitude)
print('theta =', theta)
print('FWHM_y (neg) =', fwhm_y_neg)
print('FWHM_x (neg) =', fwhm_x_neg, '\n')
print('centroid y (neg) =', mean_y_neg)
print('centroid x (neg) =', mean_x_neg)
print('centroid y subim (neg) =', fit.y_mean_1.value)
print('centroid x subim (neg) =', fit.x_mean_1.value, '\n')
print('amplitude (neg) =', amplitude_neg)
print('theta (neg) =', theta_neg)
if full_output:
return pd.DataFrame(
{
'centroid_y': mean_y,
'centroid_x': mean_x,
'fwhm_y': fwhm_y,
'fwhm_x': fwhm_x,
'amplitude': amplitude,
'theta': theta,
'centroid_y_neg': mean_y_neg,
'centroid_x_neg': mean_x_neg,
'fwhm_y_neg': fwhm_y_neg,
'fwhm_x_neg': fwhm_x_neg,
'amplitude_neg': amplitude_neg,
'theta_neg': theta_neg
},
index=[0],
dtype=np.float64)
else:
return mean_y, mean_x
def snrmap_fast(array, fwhm, nproc=None, plot=False, verbose=True, **kwargs):
""" Approximated S/N map generation. To be used as a quick proxy of the
S/N map generated using the small samples statistics definition.
Parameters
----------
array : 2d array_like
Input frame.
fwhm : float
Size in pixels of the FWHM.
nproc : int or None
Number of processes for parallel computing.
plot : bool, optional
If True plots the S/N map.
verbose: bool, optional
Whether to print timing or not.
**kwargs : dictionary, optional
Arguments to be passed to ``plot_frames`` to customize the plot (and to
save it to disk).
Returns
-------
snrmap : array_like
Frame with the same size as the input frame with each pixel.
"""
if verbose:
start_time = time_ini()
if array.ndim != 2:
raise TypeError('Input array is not a 2d array or image.')
cy, cx = frame_center(array)
tophat_kernel = Tophat2DKernel(fwhm / 2)
array = convolve(array, tophat_kernel)
sizey, sizex = array.shape
snrmap = np.zeros_like(array)
width = min(sizey, sizex) / 2 - 1.5 * fwhm
mask = get_annulus_segments(array, (fwhm / 2) + 1, width - 1,
mode="mask")[0]
mask = np.ma.make_mask(mask)
yy, xx = np.where(mask)
coords = [(x, y) for (x, y) in zip(xx, yy)]
if nproc is None:
nproc = cpu_count() // 2 # Hyper-threading doubles the # of cores
if nproc == 1:
for (y, x) in zip(yy, xx):
snrmap[y, x] = _snr_approx(array, (x, y), fwhm, cy, cx)[2]
elif nproc > 1:
res = pool_map(nproc, _snr_approx, array, iterable(coords), fwhm, cy,
cx)
res = np.array(res)
yy = res[:, 0]
xx = res[:, 1]
snr = res[:, 2]
snrmap[yy.astype(int), xx.astype(int)] = snr
if plot:
plot_frames(snrmap, colorbar=True, title='S/N map', **kwargs)
if verbose:
print("S/N map created using {} processes.".format(nproc))
timing(start_time)
return snrmap
def chisquare(modelParameters, cube, angs, plsc, psfs_norm, fwhm, annulus_width,
aperture_radius, initialState, ncomp, cube_ref=None,
svd_mode='lapack', scaling=None, fmerit='sum', collapse='median',
imlib='opencv', interpolation='lanczos4', debug=False):
"""
Calculate the reduced chi2:
\chi^2_r = \frac{1}{N-3}\sum_{j=1}^{N} |I_j|,
where N is the number of pixels within a circular aperture centered on the
first estimate of the planet position, and I_j the j-th pixel intensity.
Parameters
----------
modelParameters: tuple
The model parameters, typically (r, theta, flux).
cube: numpy.array
The cube of fits images expressed as a numpy.array.
angs: numpy.array
The parallactic angle fits image expressed as a numpy.array.
plsc: float
The platescale, in arcsec per pixel.
psfs_norm: numpy.array
The scaled psf expressed as a numpy.array.
fwhm : float
The FHWM in pixels.
annulus_width: int, optional
The width in terms of the FWHM of the annulus on which the PCA is done.
aperture_radius: int, optional
The radius of the circular aperture in terms of the FWHM.
initialState: numpy.array
Position (r, theta) of the circular aperture center.
ncomp: int
The number of principal components.
cube_ref : numpy ndarray, 3d, optional
Reference library cube. For Reference Star Differential Imaging.
svd_mode : {'lapack', 'randsvd', 'eigen', 'arpack'}, str optional
Switch for different ways of computing the SVD and selected PCs.
scaling : {'temp-mean', 'temp-standard'} or None, optional
With None, no scaling is performed on the input data before SVD. With
"temp-mean" then temporal px-wise mean subtraction is done and with
"temp-standard" temporal mean centering plus scaling to unit variance
is done.
fmerit : {'sum', 'stddev'}, string optional
Chooses the figure of merit to be used. stddev works better for close in
companions sitting on top of speckle noise.
collapse : {'median', 'mean', 'sum', 'trimmean', None}, str or None, optional
Sets the way of collapsing the frames for producing a final image. If
None then the cube of residuals is used when measuring the function of
merit (instead of a single final frame).
imlib : str, optional
See the documentation of the ``vip_hci.preproc.frame_shift`` function.
interpolation : str, optional
See the documentation of the ``vip_hci.preproc.frame_shift`` function.
Returns
-------
out: float
The reduced chi squared.
"""
try:
r, theta, flux = modelParameters
except TypeError:
msg = 'modelParameters must be a tuple, {} was given'
print(msg.format(type(modelParameters)))
# Create the cube with the negative fake companion injected
cube_negfc = cube_inject_companions(cube, psfs_norm, angs, flevel=-flux,
plsc=plsc, rad_dists=[r], n_branches=1,
theta=theta, imlib=imlib, verbose=False,
interpolation=interpolation)
# Perform PCA and extract the zone of interest
res = get_values_optimize(cube_negfc, angs, ncomp, annulus_width*fwhm,
aperture_radius*fwhm, initialState[0],
initialState[1], cube_ref=cube_ref,
svd_mode=svd_mode, scaling=scaling,
collapse=collapse, debug=debug)
if debug and collapse is not None:
values, frpca = res
plot_frames(frpca)
else:
values = res
# Function of merit
if fmerit == 'sum':
values = np.abs(values)
chi2 = np.sum(values[values > 0])
N = len(values[values > 0])
return chi2 / (N-3)
elif fmerit == 'stddev':
return np.std(values[values != 0])
else:
raise RuntimeError('`fmerit` choice not recognized')
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))
