def compute_regions(self, finalshape, box=200, corners=True):
regions = []
# A square of 100x100 in the center of the image
xref_cross = finalshape[1] // 2
yref_cross = finalshape[0] // 2
#
self.logger.debug("Reference position is (x,y) %d %d", xref_cross + 1,
yref_cross + 1)
self.logger.debug("Reference regions size is %d", 2 * box + 1)
region = image_box2d(xref_cross, yref_cross, finalshape, (box, box))
regions.append(region)
# corners
if corners:
xref_c = finalshape[1] // 4
yref_c = finalshape[0] // 4
for xi in [xref_c, 3 * xref_c]:
for yi in [yref_c, 3 * yref_c]:
self.logger.debug("Reference position is (x,y) %d %d",
xi + 1, yi + 1)
self.logger.debug("Reference regions size is %d",
2 * box + 1)
region = image_box2d(xi, yi, finalshape, (box, box))
regions.append(region)
return regions
def test_coor(images, order, refine):
arrs = images
shape = arrs[0].shape
xref_cross = shape[1] // 2
yref_cross = shape[0] // 2
box = 50
if refine:
result = numpy.array([
[0.00000000e+00, 0.00000000e+00],
[-1.99935691e+01, -1.00047803e+01],
[4.98913940e+00, -1.41737914e-02],
[2.93443492e-02, -3.53881751e-02]
]
)
else:
result = numpy.array([
[0.0, 0.0],
[-20.0, -10.0],
[5.0, 0.0],
[0.0, 0.0]
]
)
if order == 'xy':
result = result[:, ::-1]
region = utils.image_box2d(xref_cross, yref_cross, shape, (box, box))
computed = offsets_from_crosscor(arrs, region, refine=refine, order=order)
assert numpy.allclose(result, computed)
def test_coor(images, order, refine):
arrs = images
shape = arrs[0].shape
xref_cross = shape[1] // 2
yref_cross = shape[0] // 2
box = 50
if refine:
result = numpy.array([[0.00000000e+00, 0.00000000e+00],
[-1.99935691e+01, -1.00047803e+01],
[4.98913940e+00, -1.41737914e-02],
[2.93443492e-02, -3.53881751e-02]])
else:
result = numpy.array([[0.0, 0.0], [-20.0, -10.0], [5.0, 0.0],
[0.0, 0.0]])
if order == 'xy':
result = result[:, ::-1]
region = utils.image_box2d(xref_cross, yref_cross, shape, (box, box))
computed = offsets_from_crosscor(arrs, region, refine=refine, order=order)
assert numpy.allclose(result, computed)
def compute_regions_from_objs(self,
step,
arr,
finalshape,
box=50,
corners=True):
regions = []
# create catalog of objects skipping a border around the image
catalog, mask = self.create_object_catalog(arr, border=300)
self.save_intermediate_array(mask, 'objmask_i{}.fits'.format(step))
# with the catalog, compute the brightest NKEEP objects
LIMIT_AREA = 5000
NKEEP = 3
idx_small = catalog['npix'] < LIMIT_AREA
objects_small = catalog[idx_small]
idx_flux = objects_small['flux'].argsort()
objects_nth = objects_small[idx_flux][-NKEEP:]
for obj in objects_nth:
self.logger.debug('ref is (x,y) = (%s, %s)', obj['x'], obj['y'])
region = nautils.image_box2d(obj['x'], obj['y'], finalshape,
(box, box))
regions.append(region)
return regions
def test_coor_raises(images):
arrs = images
shape = arrs[0].shape
xref_cross = shape[1] // 2
yref_cross = shape[0] // 2
box = 50
region = utils.image_box2d(xref_cross, yref_cross, shape, (box, box))
with pytest.raises(ValueError):
offsets_from_crosscor(arrs, region, order="sksjd")
def test_coor_raises(images):
arrs = images
shape = arrs[0].shape
xref_cross = shape[1] // 2
yref_cross = shape[0] // 2
box = 50
region = utils.image_box2d(xref_cross, yref_cross, shape, (box, box))
with pytest.raises(ValueError):
offsets_from_crosscor(arrs, region, order="sksjd")
def compute_regions_from_objs(self, arr, finalshape, box=50, corners=True):
regions = []
catalog, mask = self.create_object_catalog(arr, border=300)
self.save_intermediate_array(mask, 'objmask.fits')
# with the catalog, compute 5 objects
LIMIT_AREA = 5000
NKEEP = 1
idx_small = catalog['npix'] < LIMIT_AREA
objects_small = catalog[idx_small]
idx_flux = objects_small['flux'].argsort()
objects_nth = objects_small[idx_flux][-NKEEP:]
for obj in objects_nth:
region = image_box2d(obj['x'], obj['y'], finalshape, (box, box))
regions.append(region)
return regions
def compute_regions_from_objs(self, arr, finalshape, box=50, corners=True):
regions = []
catalog, mask = self.create_object_catalog(arr, border=300)
self.save_intermediate_array(mask, 'objmask.fits')
# with the catalog, compute 5 objects
LIMIT_AREA = 5000
NKEEP = 1
idx_small = catalog['npix'] < LIMIT_AREA
objects_small = catalog[idx_small]
idx_flux = objects_small['flux'].argsort()
objects_nth = objects_small[idx_flux][-NKEEP:]
for obj in objects_nth:
print('ref is', obj['x'], obj['y'])
region = nautils.image_box2d(obj['x'], obj['y'], finalshape, (box, box))
print(region)
regions.append(region)
return regions
def moments(data, x0, y0, half_box):
sl1 = image_box2d(x0, y0, data.shape, half_box)
part1 = data[sl1]
norm = part1.sum()
Y1, X1 = np.mgrid[sl1]
xo = X1 - x0
yo = Y1 - y0
Mxx = (xo**2 * part1).sum() / norm
Myy = (yo**2 * part1).sum() / norm
Mxy = (xo * yo * part1).sum() / norm
e = math.sqrt((Mxx - Myy)**2 + (2 * Mxy)**2) / (Mxx + Myy)
pa = 0.5 * math.atan(2 * Mxy / (Mxx - Myy))
return Mxx, Myy, Mxy, e, pa
def rim(data, xinit, yinit,
recenter_half_box=5,
recenter_nloop=10,
recenter_maxdist=10.0, buff=3, width=5, niter=10, rplot=8):
fine_recentering = False
sigma0 = 1.0
rad = 3 * sigma0 * FWHM_G
box = recenter_half_box
recenter_half_box = (box, box)
plot_half_box = (3*box, 3*box)
print('C initial', xinit, yinit)
x0, y0, _1, _2, _3 = centering_centroid(data, xinit, yinit,
box=recenter_half_box,
maxdist=recenter_maxdist,
nloop=recenter_nloop
)
print('C final', x0, y0)
if fine_recentering:
print('Fine recentering')
print('C initial', x0, y0)
x1, y1, _back, _status, _msg = centering_centroid(
data,
x0,
y0,
box=(1, 1),
maxdist=2*math.sqrt(2),
nloop=1
)
print('C final', x1, y1)
sl = image_box2d(x0, y0, data.shape, plot_half_box)
part = data[sl]
xx0 = x0 - sl[1].start
yy0 = y0 - sl[0].start
Y, X = np.mgrid[sl]
# Photometry
D = np.sqrt((X-x0)**2 + (Y-y0)**2)
m = D < rplot
r1 = D[m]
fitter = fitting.LevMarLSQFitter()
model = models.Gaussian1D(amplitude=1.0, mean=0, stddev=1.0)
model.mean.fixed = True # Mean is always 0.0
irad = rad
for i in range(niter):
rs1 = rad + buff
rs2 = rs1 + width
bckestim = AnnulusBackgroundEstimator(r1=rs1, r2=rs2)
bck = bckestim(part, xx0, yy0)
part_s = part - bck
ca = CircularAperture([(xx0, yy0)], rad)
m = aperture_photometry(part_s, ca)
flux_aper = m['aperture_sum'][0]
f1 = part_s[m]
g1d_f = fitter(model, r1, f1, weights=(r1+1e-12)**-1)
rpeak = g1d_f.amplitude.value
# sometimes the fit is negative
rsigma = abs(g1d_f.stddev.value)
rfwhm = rsigma * FWHM_G
dpeak, dfwhm, smsg = compute_fwhm_enclosed_direct(part_s, xx0, yy0)
rad = 3 * dfwhm
if abs(rad-irad) < 1e-3:
# reached convergence
print('convergence in iter %d' % (i+1))
break
else:
irad = rad
else:
print('no convergence in photometric radius determination')
print('P, aper rad', rad, 'flux_aper', flux_aper[0])
print('P, annulus background:', bck, 'radii', rs1, rs2)
eamp, efwhm, epeak, emsg = compute_fwhm_enclosed_grow(
part_s, xx0, yy0, maxrad=rs1
)
print('Enclosed fit, peak:', epeak, 'fwhm', efwhm)
print('Radial fit, peak:', rpeak, 'fwhm', rfwhm)
print('Direct enclosed, peak:', dpeak, 'dfwhm', dfwhm)
lpeak, fwhm_x, fwhm_y = compute_fwhm_1d_simple(part_s, xx0, yy0)
print('Simple, peak:', lpeak, 'fwhm x', fwhm_x, 'fwhm y', fwhm_y)
# Fit in a smaller box
fit2d_rad = int(math.ceil(0.5 * rad))
fit2d_half_box = (fit2d_rad, fit2d_rad)
sl1 = image_box2d(x0, y0, data.shape, fit2d_half_box)
part1 = data[sl1]
Y1, X1 = np.mgrid[sl1]
g2d = models.Gaussian2D(amplitude=rpeak, x_mean=x0, y_mean=y0,
x_stddev=1.0, y_stddev=1.0)
g2d_f = fitter(g2d, X1, Y1, part1 - bck)
print('Gauss2D fit')
print(g2d_f)
moments_half_box = fit2d_half_box
Mxx, Myy, Mxy, e, pa = moments(data, x0, y0, moments_half_box)
print(Mxx, Myy, Mxy, e, pa)