def test_apertures_exact():
"""Test area as measured by exact aperture modes on array of ones"""
theta = np.random.uniform(-np.pi/2., np.pi/2., naper)
ratio = np.random.uniform(0.2, 1.0, naper)
r = 3.
for dt in SUPPORTED_IMAGE_DTYPES:
data = np.ones(data_shape, dtype=dt)
for r in [0.5, 1., 3.]:
flux, fluxerr, flag = sep.sum_circle(data, x, y, r, subpix=0)
assert_allclose(flux, np.pi*r**2)
rout = r*1.1
flux, fluxerr, flag = sep.sum_circann(data, x, y, r, rout,
subpix=0)
assert_allclose(flux, np.pi*(rout**2 - r**2))
flux, fluxerr, flag = sep.sum_ellipse(data, x, y, 1., ratio,
theta, r=r, subpix=0)
assert_allclose(flux, np.pi*ratio*r**2)
rout = r*1.1
flux, fluxerr, flag = sep.sum_ellipann(data, x, y, 1., ratio,
theta, r, rout, subpix=0)
assert_allclose(flux, np.pi*ratio*(rout**2 - r**2))
def test_apertures_all():
"""Test that aperture subpixel sampling works"""
data = np.random.rand(*data_shape)
r = 3.
rtol = 1.e-8
for subpix in [0, 1, 5]:
flux_ref, fluxerr_ref, flag_ref = sep.sum_circle(data,
x,
y,
r,
subpix=subpix)
flux, fluxerr, flag = sep.sum_circann(data, x, y, 0., r, subpix=subpix)
assert_allclose(flux, flux_ref, rtol=rtol)
flux, fluxerr, flag = sep.sum_ellipse(data,
x,
y,
r,
r,
0.,
subpix=subpix)
assert_allclose(flux, flux_ref, rtol=rtol)
flux, fluxerr, flag = sep.sum_ellipse(data,
x,
y,
1.,
1.,
0.,
r=r,
subpix=subpix)
assert_allclose(flux, flux_ref, rtol=rtol)
def auto_fit(image_data):
an_width = 3
temp_image_data = image_data
internal_image_data = temp_image_data.byteswap(True).newbyteorder()
bkg = sep.Background(internal_image_data)
thresh = 1.5 * bkg.globalback
objects = sep.extract(internal_image_data, thresh)
center_x = internal_image_data.shape[0]/2.0
center_y = internal_image_data.shape[1]/2.0
center = [center_x, center_y]
smallest_dFoc = 1000000000
radii = 21
count = 0
for i, j in zip(objects['x'], objects['y']):
pos = [i, j]
dFoc = math.sqrt(((pos[0]-center[0])**2) + ((pos[1]-center[1])**2))
if abs(dFoc) < smallest_dFoc:
smallest_dFoc = dFoc
minx = objects['xmin'][count]
miny = objects['ymin'][count]
maxx = objects['xmax'][count]
maxy = objects['ymax'][count]
radii = abs(math.sqrt(((maxx-minx)**2)+((maxy-miny)**2)))
else:
pass
count += 1
i = 0
while i < 25:
theta = 0
area = 0
while theta <= 2*pi:
area += (((i+.1)**2)/2) - ((i**2)/2)
theta += .001
flux, fluxerr, flag = sep.sum_circann(internal_image_data, internal_image_data.shape[0]/2, internal_image_data.shape[1]/2, i, i+an_width)
metric = (flux - bkg.globalback)/bkg.globalrms
metric /= area
if i > 1:
if metric < smallest_metric:
smallest_metric = metric
annulus = [i, i+an_width]
else:
smallest_metric = metric
annulus = [i, i+an_width]
i += 1
i = 1
an = [math.ceil(x*6) for x in annulus]
if radii > an[0]:
radii = an[0] - 0.5
if radii > 30:
radii = 30
else:
pass
return {'Ap': radii, 'InAn': an[0], 'OutAn': an[1]}
def sextract_magnitudes(
data,
aperture_radius,
buffer_radius,
background_radius,
bkg=None,
objects=None,
sigma=3.0,
):
aperture_area = np.pi * (aperture_radius**2)
buffer_area = np.pi * (buffer_radius**2)
background_area = (np.pi * (background_radius**2)) - buffer_area
if bkg is None:
data, bkg = sextract_bkg(data)
if objects is None:
objects = sextract_objects(data, sigma, bkg)
aperture_list, _, _ = sep.sum_circle(data,
objects["x"],
objects["y"],
aperture_radius,
err=bkg.globalrms,
gain=1.0)
background_list, _, _ = sep.sum_circann(
data,
objects["x"],
objects["y"],
buffer_radius,
background_radius,
err=bkg.globalrms,
gain=1.0,
)
background_densities = [
annulus_sum / background_area for annulus_sum in background_list
]
fluxes = np.array([
aperture_list[i] - (aperture_area * background_densities[i])
for i in range(len(aperture_list))
])
return fluxes
def test_apertures_all():
"""Test that aperture subpixel sampling works"""
data = np.random.rand(*data_shape)
r = 3.
rtol=1.e-8
for subpix in [0, 1, 5]:
flux_ref, fluxerr_ref, flag_ref = sep.sum_circle(data, x, y, r,
subpix=subpix)
flux, fluxerr, flag = sep.sum_circann(data, x, y, 0., r,
subpix=subpix)
assert_allclose(flux, flux_ref, rtol=rtol)
flux, fluxerr, flag = sep.sum_ellipse(data, x, y, r, r, 0.,
subpix=subpix)
assert_allclose(flux, flux_ref, rtol=rtol)
flux, fluxerr, flag = sep.sum_ellipse(data, x, y, 1., 1., 0., r=r,
subpix=subpix)
assert_allclose(flux, flux_ref, rtol=rtol)
minx = objects['xmin'][count]
miny = objects['ymin'][count]
maxx = objects['xmax'][count]
maxy = objects['ymax'][count]
radii = abs(math.sqrt(((maxx-minx)**2)+((maxy-miny)**2)))
else:
pass
count += 1
i = 0
while i < 25:
theta = 0
area = 0
while theta <= 2*pi:
area += (((i+.1)**2)/2) - ((i**2)/2)
theta += .001
flux, fluxerr, flag = sep.sum_circann(image_data, image_data.shape[0]/2, image_data.shape[1]/2, i, i+an_width)
metric = (flux - bkg.globalback)/bkg.globalrms
metric /= area
#print flux, metric
if i > 1:
if metric < smallest_metric:
smallest_metric = metric
annulus = [i, i+an_width]
else:
smallest_metric = metric
annulus = [i, i+an_width]
i += 1
i = 1
an = [math.ceil(x*6) for x in annulus]
if radii > an[0]:
radii = an[0] - 0.5
def extract_obj(img,
b=30,
f=5,
sigma=5,
pixel_scale=0.168,
minarea=5,
deblend_nthresh=32,
deblend_cont=0.005,
clean_param=1.0,
sky_subtract=False,
show_fig=True,
verbose=True,
flux_auto=True,
flux_aper=None):
'''Extract objects for a given image, using `sep`. This is from `slug`.
Parameters:
----------
img: 2-D numpy array
b: float, size of box
f: float, size of convolving kernel
sigma: float, detection threshold
pixel_scale: float
Returns:
-------
objects: astropy Table, containing the positions,
shapes and other properties of extracted objects.
segmap: 2-D numpy array, segmentation map
'''
# Subtract a mean sky value to achieve better object detection
b = 30 # Box size
f = 5 # Filter width
bkg = sep.Background(img, bw=b, bh=b, fw=f, fh=f)
data_sub = img - bkg.back()
sigma = sigma
if sky_subtract:
input_data = data_sub
else:
input_data = img
objects, segmap = sep.extract(input_data,
sigma,
err=bkg.globalrms,
segmentation_map=True,
filter_type='matched',
deblend_nthresh=deblend_nthresh,
deblend_cont=deblend_cont,
clean=True,
clean_param=clean_param,
minarea=minarea)
if verbose:
print("# Detect %d objects" % len(objects))
objects = Table(objects)
objects.add_column(Column(data=np.arange(len(objects)) + 1, name='index'))
# Maximum flux, defined as flux within six 'a' in radius.
objects.add_column(
Column(data=sep.sum_circle(input_data, objects['x'], objects['y'],
6. * objects['a'])[0],
name='flux_max'))
# Add FWHM estimated from 'a' and 'b'.
# This is suggested here: https://github.com/kbarbary/sep/issues/34
objects.add_column(
Column(data=2 *
np.sqrt(np.log(2) * (objects['a']**2 + objects['b']**2)),
name='fwhm_custom'))
# Use Kron radius to calculate FLUX_AUTO in SourceExtractor.
# Here PHOT_PARAMETER = 2.5, 3.5
if flux_auto:
kronrad, krflag = sep.kron_radius(input_data, objects['x'],
objects['y'], objects['a'],
objects['b'], objects['theta'], 6.0)
flux, fluxerr, flag = sep.sum_circle(input_data,
objects['x'],
objects['y'],
2.5 * (kronrad),
subpix=1)
flag |= krflag # combine flags into 'flag'
r_min = 1.75 # minimum diameter = 3.5
use_circle = kronrad * np.sqrt(objects['a'] * objects['b']) < r_min
cflux, cfluxerr, cflag = sep.sum_circle(input_data,
objects['x'][use_circle],
objects['y'][use_circle],
r_min,
subpix=1)
flux[use_circle] = cflux
fluxerr[use_circle] = cfluxerr
flag[use_circle] = cflag
objects.add_column(Column(data=flux, name='flux_auto'))
objects.add_column(Column(data=kronrad, name='kron_rad'))
if flux_aper is not None:
objects.add_column(
Column(data=sep.sum_circle(input_data, objects['x'], objects['y'],
flux_aper[0])[0],
name='flux_aper_1'))
objects.add_column(
Column(data=sep.sum_circle(input_data, objects['x'], objects['y'],
flux_aper[1])[0],
name='flux_aper_2'))
objects.add_column(
Column(data=sep.sum_circann(input_data, objects['x'], objects['y'],
flux_aper[0], flux_aper[1])[0],
name='flux_ann'))
'''
objects.add_column(Column(data=sep.sum_circle(input_data, objects['x'], objects['y'], flux_aper[0] * objects['a'])[0],
name='flux_aper_1'))
objects.add_column(Column(data=sep.sum_circle(input_data, objects['x'], objects['y'], flux_aper[1] * objects['a'])[0],
name='flux_aper_2'))
objects.add_column(Column(data=sep.sum_circann(input_data, objects['x'], objects['y'],
flux_aper[0] * objects['a'], flux_aper[1] * objects['a'])[0], name='flux_ann'))
'''
# plot background-subtracted image
if show_fig:
fig, ax = plt.subplots(1, 2, figsize=(12, 6))
ax[0] = display_single(data_sub,
ax=ax[0],
scale_bar=False,
pixel_scale=pixel_scale)
from matplotlib.patches import Ellipse
# plot an ellipse for each object
for obj in objects:
e = Ellipse(xy=(obj['x'], obj['y']),
width=8 * obj['a'],
height=8 * obj['b'],
angle=obj['theta'] * 180. / np.pi)
e.set_facecolor('none')
e.set_edgecolor('red')
ax[0].add_artist(e)
ax[1] = display_single(segmap, scale='linear', cmap=SEG_CMAP, ax=ax[1])
return objects, segmap
def compute_photometry(self, extname, extnum, r=3, r_in=14, r_out=16):
"""
Parameters
----------
extname
extnum
r
r_in
r_out
Returns
-------
"""
self.phot_catalog[f"{extname}{extnum}"] = \
self.source_catalog[f"{extname}{extnum}"]['id', 'x', 'y']
LOG.info('Computing local background within a circular annulus...')
area_annulus = np.pi * (r_out**2 - r_in**2)
bkg_sum, bkg_sum_err, flag = sep.sum_circann(
self.data[f"{extname}{extnum}"],
x=self.phot_catalog[f"{extname}{extnum}"]['x'],
y=self.phot_catalog[f"{extname}{extnum}"]['y'],
rin=14,
rout=16)
self.phot_catalog[f"{extname}{extnum}"][
'bkg_per_pix'] = bkg_sum / area_annulus
LOG.info('Computing aperture sum within a 3 pixel radius...')
# Note we use the non-background subtracted one
ap_sum, apsumerr, flag = sep.sum_circle(
self.data[f"{extname}{extnum}"],
self.phot_catalog[f"{extname}{extnum}"]['x'],
self.phot_catalog[f"{extname}{extnum}"]['y'],
r=3)
area_ap = np.pi * r**2
ap_sum_bkg_sub = \
ap_sum - \
area_ap * self.phot_catalog[f"{extname}{extnum}"]['bkg_per_pix']
# Very simplistic error computation
ap_sum_err_final = np.sqrt(
ap_sum +
area_ap * self.phot_catalog[f"{extname}{extnum}"]['bkg_per_pix'])
# Record the aperture sum
self.phot_catalog[f"{extname}{extnum}"][f'sum_{r:.0f}pix'] = \
ap_sum_bkg_sub
# Record the error in the aperture sum
self.phot_catalog[f"{extname}{extnum}"][f'sum_{r:.0f}pix_err'] = \
ap_sum_err_final
# Record the magnitude
self.phot_catalog[f"{extname}{extnum}"][f'mag_{r:.0f}pix'] = \
-2.5 * np.log10(ap_sum_bkg_sub)
nan_filter = np.isnan(
self.phot_catalog[f"{extname}{extnum}"][f"mag_{r:.0f}pix"])
# Record the error in the magnitude
self.phot_catalog[f"{extname}{extnum}"][f'mag_{r:.0f}pix_err'] = \
1.0857 * ap_sum_err_final / ap_sum_bkg_sub
# Conver the X/Y coords to RA/DEC
sky_coords = self.convert_to_sky(
self.phot_catalog[f"{extname}{extnum}"],
self.data[f"{extname}{extnum}_wcs"])
self.phot_catalog[f"{extname}{extnum}"]['ra'] = sky_coords.ra
self.phot_catalog[f"{extname}{extnum}"]['dec'] = sky_coords.dec
# Filter out all rows where any value is NaN
self.phot_catalog[f"{extname}{extnum}"] = \
self.phot_catalog[f"{extname}{extnum}"][~nan_filter]
def auto_fit(image_data, xpos, ypos):
"""
Refit algorithm
:param
image_data: fits image data 2D array {Numpy 2D array}
xpos: centered position of the regions on the x axis {integer}
ypos: centered position of the region on the y axis {integer}
##############################################################
:return: Dictionary of values
{
radii [Ap] -- Aperture radius {float}
an[0] [InAn] -- Inner annulus radius {float}
an[1] [OutAn] -- Outer annulus radius {float}
radii_disagree [disagree] -- The difference between the directly returned radius from SEP and the radius from SEP
flux sums {float}
}
"""
an_width = 18 # defines annulus width | TODO Make this a free parameter in the auto fit function
temp_image_data = image_data # creates non mutable version of array internally
internal_image_data = temp_image_data.byteswap(True).newbyteorder() # SEP requires that data be byte swaped
bkg = sep.Background(internal_image_data)
thresh = 1.5 * bkg.globalback
objects = sep.extract(internal_image_data, thresh)
center_x = internal_image_data.shape[0]/2.0
center_y = internal_image_data.shape[1]/2.0
center = [center_x, center_y]
smallest_dFoc = 1000000000 # big distance so it can only go down
radii = 21
radii_sep = 21
sdev = np.std(internal_image_data)
count = 0
# Finds radius of center target from the min and max x and y pixel locations of that target returned from SEP
for i, j in zip(objects['x'], objects['y']):
pos = [i, j]
dFoc = math.sqrt(((pos[0]-center[0])**2) + ((pos[1]-center[1])**2))
if abs(dFoc) < smallest_dFoc:
smallest_dFoc = dFoc
minx = objects['xmin'][count]
miny = objects['ymin'][count]
maxx = objects['xmax'][count]
maxy = objects['ymax'][count]
radii_sep = abs(math.sqrt(((maxx-minx)**2)+((maxy-miny)**2)))
else:
pass
count += 1
i = 1
# Finds optimal inner and outer annulus radii based off SEP flux sums
while i < 90:
area = (2*np.pi*((i+an_width)**2))-(2*np.pi*(i**2))
flux, fluxerr, flag = sep.sum_circann(internal_image_data, prev_x,
prev_y, i, i+an_width)
metric = ((flux/area) - bkg.globalback)
if i > 1:
if metric < smallest_metric:
smallest_metric = metric
annulus = [i, i+an_width]
else:
smallest_metric = metric
annulus = [i, i+an_width]
i += 1
i = 1
# Finds radius of center target using SEP flux sums
while i < 90:
area = 2*np.pi*(i**2)
flux, fluxerr, flag = sep.sum_circle(internal_image_data, prev_x,
prev_y, i)
flux = sum(flux)
metric = flux/area
if metric > sdev + bkg.globalback:
radii = i
i += 1
# used more pixels
an = [math.ceil(x) for x in annulus]
# sanity checks for size
if radii > an[0]:
radii = an[0] - 0.5
if radii > 30:
radii = 30
else:
pass
if an[0] > an[1]:
an[0] = an[1] - 0.1 * an[1]
radii_disagree = abs(radii-radii_sep)
return {'Ap': radii, 'InAn': an[0], 'OutAn': an[1], 'disagree': radii_disagree}
def process_file(filename, favor2=None, verbose=False, replace=False, dbname=None, dbhost=None, photodir='photometry'):
#### Some parameters
aper = 2.0
bkgann = None
order = 4
bg_order = 4
color_order = 2
sn = 5
if not posixpath.exists(filename):
return None
# Rough but fast checking of whether the file is already processed
if not replace and posixpath.exists(photodir + '/' + filename.split('/')[-2] + '/' + posixpath.splitext(posixpath.split(filename)[-1])[0] + '.cat'):
return
#### Preparation
header = fits.getheader(filename, -1)
if header['TYPE'] not in ['survey', 'imaging', 'widefield', 'Swift', 'Fermi', 'test']:
return
channel = header.get('CHANNEL ID')
fname = header.get('FILTER', 'unknown')
time = parse_time(header['TIME'])
shutter = header.get('SHUTTER', -1)
if fname not in ['Clear']:
return
if fname == 'Clear':
effective_fname = 'V'
else:
effective_fname = fname
night = get_night(time)
dirname = '%s/%s' % (photodir, night)
basename = posixpath.splitext(posixpath.split(filename)[-1])[0]
basename = dirname + '/' + basename
catname = basename + '.cat'
if not replace and posixpath.exists(catname):
return
if verbose:
print(filename, channel, night, fname, effective_fname)
image = fits.getdata(filename, -1).astype(np.double)
if favor2 is None:
favor2 = Favor2(dbname=options.db, dbhost=options.dbhost)
#### Basic calibration
darkname = favor2.find_image('masterdark', header=header, debug=False)
flatname = favor2.find_image('masterflat', header=header, debug=False)
if darkname:
dark = fits.getdata(darkname)
else:
dark = None
if flatname:
flat = fits.getdata(flatname)
else:
flat = None
if dark is None or flat is None:
survey.save_objects(catname, None)
return
image,header = calibrate.calibrate(image, header, dark=dark)
# Check whether the calibration failed
if 'SATURATE' not in header:
print('Calibration failed for', filename)
survey.save_objects(catname, None)
return
#### Basic masking
mask = image > 0.9*header['SATURATE']
fmask = ~np.isfinite(flat) | (flat < 0.5)
dmask = dark > 10.0*mad_std(dark) + np.nanmedian(dark)
if header.get('BLEMISHCORRECTION', 1):
# We have to mask blemished pixels
blemish = fits.getdata('calibrations/blemish_shutter_%d_channel_%d.fits' % (header['SHUTTER'], header['CHANNEL ID']))
if verbose:
print(100*np.sum(blemish>0)/blemish.shape[0]/blemish.shape[1], '% pixels blemished')
dmask |= blemish > 0
image[~fmask] *= np.median(flat[~fmask])/flat[~fmask]
#### WCS
wcs = WCS(header)
pixscale = np.hypot(wcs.pixel_scale_matrix[0,0], wcs.pixel_scale_matrix[0,1])
gain = 0.67 if header.get('SHUTTER') == 0 else 1.9
#### Background mask
mask_bg = np.zeros_like(mask)
mask_segm = np.zeros_like(mask)
# bg2 = sep.Background(image, mask=mask|mask_bg, bw=64, bh=64)
# for _ in xrange(3):
# bg1 = sep.Background(image, mask=mask|mask_bg, bw=256, bh=256)
# ibg = bg2.back() - bg1.back()
# tmp = np.abs(ibg - np.median(ibg)) > 5.0*mad_std(ibg)
# mask_bg |= survey.dilate(tmp, np.ones([50, 50]))
# mask_bg = survey.dilate(tmp, np.ones([50, 50]))
# Large objects?..
bg = sep.Background(image, mask=mask|dmask|fmask|mask_bg|mask_segm, bw=128, bh=128)
image1 = image - bg.back()
obj0,segm = sep.extract(image1, err=bg.rms(), thresh=10, minarea=10, mask=mask|dmask|fmask|mask_bg, filter_kernel=None, clean=False, segmentation_map=True)
mask_segm = np.isin(segm, [_+1 for _,npix in enumerate(obj0['npix']) if npix > 500])
mask_segm = survey.dilate(mask_segm, np.ones([20, 20]))
if np.sum(mask_bg|mask_segm|mask|fmask|dmask)/mask_bg.shape[0]/mask_bg.shape[1] > 0.4:
print(100*np.sum(mask_bg|mask_segm|mask|fmask|dmask)/mask_bg.shape[0]/mask_bg.shape[1], '% of image masked, skipping', filename)
survey.save_objects(catname, None)
return
elif verbose:
print(100*np.sum(mask_bg|mask_segm|mask|fmask|dmask)/mask_bg.shape[0]/mask_bg.shape[1], '% of image masked')
# Frame footprint at +10 pixels from the edge
ra,dec = wcs.all_pix2world([10, 10, image.shape[1]-10, image.shape[1]-10], [10, image.shape[0]-10, image.shape[0]-10, 10], 0)
footprint = "(" + ",".join(["(%g,%g)" % (_,__) for _,__ in zip(ra, dec)]) + ")"
#### Catalogue
ra0,dec0,sr0 = survey.get_frame_center(header=header)
cat = favor2.get_stars(ra0, dec0, sr0, catalog='gaia', extra=['g<14', 'q3c_poly_query(ra, dec, \'%s\'::polygon)' % footprint], limit=1000000)
if verbose:
print(len(cat['ra']), 'star positions from Gaia down to g=%.1f mag' % np.max(cat['g']))
## Detection of blended and not really needed stars in the catalogue
h = htm.HTM(10)
m = h.match(cat['ra'], cat['dec'], cat['ra'], cat['dec'], 2.0*aper*pixscale, maxmatch=0)
m = [_[m[2]>1e-5] for _ in m]
blended = np.zeros_like(cat['ra'], dtype=np.bool)
notneeded = np.zeros_like(cat['ra'], dtype=np.bool)
for i1,i2,dist in zip(*m):
if dist*3600 > 0.5*aper*pixscale:
if cat['g'][i1] - cat['g'][i2] < 3:
blended[i1] = True
blended[i2] = True
else:
# i1 is fainter by more than 3 mag
notneeded[i1] = True
if dist*3600 < 0.5*aper*pixscale:
if cat['g'][i1] > cat['g'][i2]:
notneeded[i1] = True
cat,blended = [_[~notneeded] for _ in cat,blended]
#### Background subtraction
bg = sep.Background(image, mask=mask|dmask|fmask|mask_bg|mask_segm, bw=128, bh=128)
# bg = sep.Background(image, mask=mask|dmask|fmask|mask_bg|mask_segm, bw=32, bh=32)
image1 = image - bg.back()
#### Detection of all objects on the frame
obj0,segm = sep.extract(image1, err=bg.rms(), thresh=2, minarea=3, mask=mask|dmask|fmask|mask_bg|mask_segm, filter_kernel=None, clean=False, segmentation_map=True)
obj0 = obj0[(obj0['x'] > 10) & (obj0['y'] > 10) & (obj0['x'] < image.shape[1]-10) & (obj0['y'] < image.shape[0]-10)]
obj0 = obj0[obj0['flag'] <= 1] # We keep only normal and blended oblects
fields = ['ra', 'dec', 'fluxerr', 'mag', 'magerr', 'flags', 'cat']
obj0 = np.lib.recfunctions.append_fields(obj0, fields, [np.zeros_like(obj0['x'], dtype=np.int if _ in ['flags', 'cat'] else np.double) for _ in fields], usemask=False)
obj0['ra'],obj0['dec'] = wcs.all_pix2world(obj0['x'], obj0['y'], 0)
obj0['flags'] = obj0['flag']
if verbose:
print(len(obj0['x']), 'objects detected on the frame')
## Filter out objects not coincident with catalogue positions
h = htm.HTM(10)
m = h.match(obj0['ra'], obj0['dec'], cat['ra'], cat['dec'], aper*pixscale)
nidx = np.isin(np.arange(len(obj0['ra'])), m[0], invert=True)
obj0 = obj0[nidx]
if verbose:
print(len(obj0['x']), 'are outside catalogue apertures')
# Catalogue stars
xc,yc = wcs.all_world2pix(cat['ra'], cat['dec'], 0)
obj = {'x':xc, 'y':yc, 'ra':cat['ra'], 'dec':cat['dec']}
obj['flags'] = np.zeros_like(xc, dtype=np.int)
obj['flags'][blended] |= FLAG_BLENDED
obj['cat'] = np.ones_like(xc, dtype=np.int)
for _ in ['mag', 'magerr', 'flux', 'fluxerr']:
obj[_] = np.zeros_like(xc)
# Merge detected objects
for _ in ['x', 'y', 'ra', 'dec', 'flags', 'mag', 'magerr', 'flux', 'fluxerr', 'cat']:
obj[_] = np.concatenate((obj[_], obj0[_]))
if verbose:
print(len(obj['x']), 'objects for photometry')
# Simple aperture photometry
obj['flux'],obj['fluxerr'],flag = sep.sum_circle(image1, obj['x'], obj['y'], aper, err=bg.rms(), gain=gain, mask=mask|dmask|fmask|mask_bg|mask_segm, bkgann=bkgann)
obj['flags'] |= flag
# Normalize flags
obj['flags'][obj['flags'] & sep.APER_TRUNC] |= FLAG_TRUNCATED
obj['flags'][obj['flags'] & sep.APER_ALLMASKED] |= FLAG_MASKED
obj['flags'] &= FLAG_NORMAL | FLAG_BLENDED | FLAG_TRUNCATED | FLAG_MASKED | FLAG_NO_BACKGROUND | FLAG_BAD_CALIBRATION
area,_,_ = sep.sum_circle(np.ones_like(image1), obj['x'], obj['y'], aper, err=bg.rms(), gain=gain, mask=mask|dmask|fmask|mask_bg|mask_segm, bkgann=bkgann)
# Simple local background estimation
bgflux,bgfluxerr,bgflag = sep.sum_circann(image1, obj['x'], obj['y'], 10, 15, err=bg.rms(), gain=gain, mask=mask|dmask|fmask|mask_bg|mask_segm|(segm>0))
bgarea,_,_ = sep.sum_circann(np.ones_like(image1), obj['x'], obj['y'], 10, 15, err=bg.rms(), gain=gain, mask=mask|dmask|fmask|mask_bg|mask_segm|(segm>0))
bgidx = np.isfinite(bgarea) & np.isfinite(area)
bgidx[bgidx] &= (bgarea[bgidx] > 10) & (area[bgidx] > 1)
obj['flux'][bgidx] -= bgflux[bgidx]*area[bgidx]/bgarea[bgidx]
obj['flags'][~bgidx] |= FLAG_NO_BACKGROUND # No local background
obj['deltabgflux'] = np.zeros_like(obj['x'])
obj['deltabgflux'][bgidx] = bgflux[bgidx]*area[bgidx]/bgarea[bgidx]
fidx = np.isfinite(obj['flux']) & np.isfinite(obj['fluxerr'])
fidx[fidx] &= (obj['flux'][fidx] > 0)
obj['mag'][fidx] = -2.5*np.log10(obj['flux'][fidx])
obj['magerr'][fidx] = 2.5/np.log(10)*obj['fluxerr'][fidx]/obj['flux'][fidx]
fidx[fidx] &= (obj['magerr'][fidx] > 0)
fidx[fidx] &= 1/obj['magerr'][fidx] > sn
for _ in obj.keys():
if hasattr(obj[_], '__len__'):
obj[_] = obj[_][fidx]
obj['aper'] = aper
if verbose:
print(len(obj['x']), 'objects with S/N >', sn)
if len(obj['x']) < 1000:
print('Only', len(obj['x']), 'objects on the frame, skipping', filename)
survey.save_objects(catname, None)
return
obj['fwhm'] = 2.0*sep.flux_radius(image1, obj['x'], obj['y'], 2.0*aper*np.ones_like(obj['x']), 0.5, mask=mask|dmask|fmask|mask_bg|mask_segm)[0]
#### Check FWHM of all objects and select only 'good' ones
idx = obj['flags'] == 0
idx &= obj['magerr'] < 1/20
fwhm0 = survey.fit_2d(obj['x'][idx], obj['y'][idx], obj['fwhm'][idx], obj['x'], obj['y'], weights=1/obj['magerr'][idx])
fwhm_idx = np.abs(obj['fwhm'] - fwhm0 - np.median((obj['fwhm'] - fwhm0)[idx])) < 3.0*mad_std((obj['fwhm'] - fwhm0)[idx])
obj['flags'][~fwhm_idx] |= FLAG_BLENDED
#### Catalogue matching
idx = obj['flags']
m = htm.HTM(10).match(obj['ra'], obj['dec'], cat['ra'], cat['dec'], 1e-5)
fidx = np.in1d(np.arange(len(cat['ra'])), m[1]) # Stars that got successfully measured and not blended
cidx = (cat['good'] == 1) & (cat['var'] == 0)
cidx &= np.isfinite(cat['B']) & np.isfinite(cat['V']) # & np.isfinite(cat['lum'])
cidx[cidx] &= ((cat['B'] - cat['V'])[cidx] > -0.5) & ((cat['B'] - cat['V'])[cidx] < 2.0)
# cidx[cidx] &= (cat['lum'][cidx] > 0.3) & (cat['lum'][cidx] < 30)
if np.sum(cidx & fidx & (cat['multi_70'] == 0)) > 2000:
cidx &= (cat['multi_70'] == 0)
obj['cat_multi'] = 70
elif np.sum(cidx & fidx & (cat['multi_45'] == 0)) > 1000:
cidx &= (cat['multi_45'] == 0)
obj['cat_multi'] = 45
else:
cidx &= (cat['multi_30'] == 0)
obj['cat_multi'] = 30
if verbose:
print(np.sum(obj['flags'] == 0), 'objects without flags')
print('Amount of good stars:',
np.sum(cidx & fidx & (cat['multi_70'] == 0)),
np.sum(cidx & fidx & (cat['multi_45'] == 0)),
np.sum(cidx & fidx & (cat['multi_30'] == 0)))
print('Using %d arcsec avoidance radius' % obj['cat_multi'])
# We match with very small SR to only account for manually placed apertures
if verbose:
print('Trying full fit:', len(obj['x']), 'objects,', np.sum(cidx), 'stars')
match = Match(width=image.shape[1], height=image.shape[0])
prev_ngoodstars = len(obj['x'])
for iter in range(10):
if not match.match(obj=obj, cat=cat[cidx], sr=1e-5, filter_name='V', order=order, bg_order=bg_order, color_order=color_order, verbose=False) or match.ngoodstars < 500:
if verbose:
print(match.ngoodstars, 'good matches, matching failed for', filename)
survey.save_objects(catname, None)
return
if verbose:
print(match.ngoodstars, 'good matches, std =', match.std)
if match.ngoodstars == prev_ngoodstars:
if verbose:
print('Converged on iteration', iter)
break
prev_ngoodstars = match.ngoodstars
# Match good objects with stars
oidx = obj['flags'] == 0
oidx1,cidx1,dist1 = htm.HTM(10).match(obj['ra'][oidx], obj['dec'][oidx], cat['ra'][cidx], cat['dec'][cidx], 1e-5)
x = obj['x'][oidx][oidx1]
y = obj['y'][oidx][oidx1]
cbv = match.color_term[oidx][oidx1]
cbv2 = match.color_term2[oidx][oidx1]
cbv3 = match.color_term3[oidx][oidx1]
bv = (cat['B'] - cat['V'])[cidx][cidx1]
cmag = cat[match.cat_filter_name][cidx][cidx1]
mag = match.mag[oidx][oidx1] + bv*cbv + bv**2*cbv2 + bv**3*cbv3
magerr = np.hypot(obj['magerr'][oidx][oidx1], 0.02)
dmag = mag-cmag
ndmag = ((mag-cmag)/magerr)
idx = cmag < match.mag_limit[oidx][oidx1]
x,y,cbv,cbv2,cbv3,bv,cmag,mag,magerr,dmag,ndmag = [_[idx] for _ in [x,y,cbv,cbv2,cbv3,bv,cmag,mag,magerr,dmag,ndmag]]
# Match all objects with good objects
xy = np.array([x,y]).T
xy0 = np.array([obj['x'], obj['y']]).T
kd = cKDTree(xy)
dist,m = kd.query(xy0, 101)
dist = dist[:,1:]
m = m[:,1:]
vchi2 = mad_std(ndmag[m]**2, axis=1)
# Mark regions of too sparse or too noisy matches as bad
obj['flags'][vchi2 > 5] |= FLAG_BAD_CALIBRATION
# obj['flags'][vchi2 > np.median(vchi2) + 5.0*mad_std(vchi2)] |= FLAG_BAD_CALIBRATION
obj['flags'][dist[:,10] > np.median(dist[:,10]) + 10.0*mad_std(dist[:,10])] |= FLAG_BAD_CALIBRATION
match.good_idx = (obj['flags'] & FLAG_BAD_CALIBRATION) == 0
if verbose:
print(np.sum(match.good_idx), 'of', len(match.good_idx), 'stars are good')
#### Store objects to file
try:
os.makedirs(dirname)
except:
pass
obj['mag_limit'] = match.mag_limit
obj['color_term'] = match.color_term
obj['color_term2'] = match.color_term2
obj['color_term3'] = match.color_term3
obj['filename'] = filename
obj['night'] = night
obj['channel'] = channel
obj['filter'] = fname
obj['cat_filter'] = match.cat_filter_name
obj['time'] = time
obj['mag_id'] = match.mag_id
obj['good_idx'] = match.good_idx
obj['calib_mag'] = match.mag
obj['calib_magerr'] = match.magerr
obj['std'] = match.std
obj['nstars'] = match.ngoodstars
survey.save_objects(catname, obj, header=header)
def aperture_photometry(img: Union[ndarray, MaskedArray], sources: ndarray,
background: Optional[Union[ndarray,
MaskedArray]] = None,
background_rms: Optional[Union[ndarray,
MaskedArray]] = None,
texp: float = 1,
gain: Union[float, ndarray, MaskedArray] = 1,
sat_level: float = 63000,
a: Optional[float] = None, b: Optional[float] = None,
theta: Optional[float] = 0,
a_in: Optional[float] = None,
a_out: Optional[float] = None,
b_out: Optional[float] = None,
theta_out: Optional[float] = None,
k: float = 0,
k_in: Optional[float] = None,
k_out: Optional[float] = None,
radius: float = 6,
fix_aper: bool = False,
fix_ell: bool = True,
fix_rot: bool = True,
apcorr_tol: float = 0.0001) -> ndarray:
"""
Do automatic or fixed aperture photometry
:param img: input 2D image array
:param sources: record array of sources extracted with
:func:`skylib.extraction.extract_sources`; should contain at least "x"
and "y" columns
:param background: optional sky background map; if omitted, extract
background from the annulus around the aperture, see `a_in` below
:param background_rms: optional sky background RMS map; if omitted,
calculate RMS over the annulus around the aperture
:param texp: exposure time in seconds
:param gain: electrons to data units conversion factor; used to estimate
photometric errors; for variable-gain images (e.g. mosaics), must be
an array of the same shape as the input data
:param sat_level: saturation level in ADUs; used to select only
non-saturated stars for adaptive aperture photometry and aperture
correction
:param a: fixed aperture radius or semi-major axis in pixels; default: use
automatic photometry with a = a_iso*k, where a_iso is the isophotal
semi-major axis
:param b: semi-minor axis in pixels when using a fixed aperture; default:
same as `a`
:param theta: rotation angle of semi-major axis in degrees CCW when using
a fixed aperture and `b` != `a`; default: 0
:param a_in: inner annulus radius or semi-major axis in pixels; used
to estimate the background if `background` or `background_rms` are not
provided, ignored otherwise; default: `a`*`k_in`
:param a_out: outer annulus radius or semi-major axis in pixels; default:
`a`*`k_out`
:param b_out: outer annulus semi-minor axis in pixels; default: `b`*`k_out`
:param theta_out: annulus orientation in degrees CCW; default: same
as `theta`
:param k: automatic aperture radius in units of isophotal radius; 0 means
find the optimal radius based on SNR; default: 0
:param k_in: inner annulus radius in units of aperture radius (fixed
aperture, i.e. `a` is provided) or isophotal radius (adaptive aperture);
default: 1.5*`k` or 3.75 if `k` is undefined and `a` = None
:param k_out: outer annulus radius in units of aperture radius (fixed
aperture) or isophotal radius (adaptive aperture); default: 2*`k` or
5 if `k` is undefined and `a` = None
:param radius: isophotal analysis radius in pixels used to compute automatic
aperture if ellipse parameters (a,b,theta) are missing
:param fix_aper: use the same aperture radius for all sources when doing
automatic photometry; calculated as flux-weighted median of aperture
sizes based on isophotal parameters
:param fix_ell: use the same major to minor aperture axis ratio for all
sources during automatic photometry; calculated as flux-weighted median
of all ellipticities
:param fix_rot: use the same aperture position angle for all sources during
automatic photometry; calculated as flux-weighted median
of all orientations
:param apcorr_tol: growth curve stopping tolerance for aperture correction;
0 = disable aperture correction
:return: record array containing the input sources, with the following
fields added or updated: "flux", "flux_err", "mag", "mag_err", "aper_a",
"aper_b", "aper_theta", "aper_a_in", "aper_a_out", "aper_b_out",
"aper_theta_out", "aper_area", "background_area", "background",
"background_rms", "phot_flag"
"""
if not len(sources):
return array([])
img = sep_compatible(img)
texp = float(texp)
if isscalar(gain):
gain = float(gain)
k = float(k)
if k <= 0.1:
k = 0 # temporary fix for k = 0 not being allowed in AgA
if k_in:
k_in = float(k_in)
if k_out:
k_out = float(k_out)
x, y = sources['x'] - 1, sources['y'] - 1
area_img = ones(img.shape, dtype=int32)
if isinstance(img, MaskedArray):
mask = img.mask
img = img.data
else:
mask = None
have_background = background is not None and background_rms is not None
if have_background:
background = sep_compatible(background)
if isinstance(background, MaskedArray):
if mask is None:
mask = background.mask
else:
mask |= background.mask
background = background.data
background_rms = sep_compatible(background_rms)
if isinstance(background_rms, MaskedArray):
if mask is None:
mask = background_rms.mask
else:
mask |= background_rms.mask
background_rms = background_rms.data
# Will need this to fill the newly added source table columns
z = zeros(len(sources), float)
fixed_aper = bool(a)
if fixed_aper:
# Use the same fixed aperture and annulus parameters for all sources
a = float(a)
if b:
b = float(b)
else:
b = a
if theta:
theta = float(theta % 180)*pi/180
if theta > pi/2:
theta -= pi
else:
theta = 0
if not have_background:
if theta_out:
theta_out = float(theta_out % 180)*pi/180
if theta_out > pi/2:
theta_out -= pi
elif theta_out != 0:
theta_out = theta
if a_in:
a_in = float(a_in)
else:
a_in = a*k_in if k_in else a*1.5*k if k else a*3.75
if a_out:
a_out = float(a_out)
else:
a_out = a*k_out if k_out else a*2*k if k else a*5
if b_out:
b_out = float(b_out)
else:
b_out = a_out*b/a
else:
# Use automatic apertures derived from ellipse axes; will need image
# with background subtracted
if background is None:
# Estimate background on the fly
tmp_back, tmp_rms = estimate_background(img, size=64)
else:
tmp_back, tmp_rms = background, background_rms
img_back = img - tmp_back
for name in ['a', 'b', 'theta', 'flux']:
if name not in sources.dtype.names:
sources = append_fields(sources, name, z, usemask=False)
a, b, theta = sources['a'], sources['b'], sources['theta']
flux = sources['flux']
bad = (a <= 0) | (b <= 0) | (flux <= 0)
if bad.any():
# Do isophotal analysis to compute ellipse parameters if missing
yy, xx = indices(img.shape)
for i in bad.nonzero()[0]:
ap = (xx - sources[i]['x'])**2 + (yy - sources[i]['y'])**2 <= \
radius**2
if ap.any():
yi, xi = ap.nonzero()
ap_data = img_back[ap].astype(float)
f = ap_data.sum()
if f > 0:
cx = (xi*ap_data).sum()/f
cy = (yi*ap_data).sum()/f
x2 = (xi**2*ap_data).sum()/f - cx**2
y2 = (yi**2*ap_data).sum()/f - cy**2
xy = (xi*yi*ap_data).sum()/f - cx*cy
else:
cx, cy = xi.mean(), yi.mean()
x2 = (xi**2).mean() - cx**2
y2 = (yi**2).mean() - cy**2
xy = (xi*yi).mean() - cx*cy
if x2 == y2:
thetai = 0
else:
thetai = arctan(2*xy/(x2 - y2))/2
if y2 > x2:
thetai += pi/2
m1 = (x2 + y2)/2
m2 = sqrt(max((x2 - y2)**2/4 + xy**2, 0))
ai = max(1/12, sqrt(max(m1 + m2, 0)))
bi = max(1/12, sqrt(max(m1 - m2, 0)))
if ai/bi > 2:
# Prevent too elongated apertures usually occurring for
# faint objects
bi = ai
else:
# Cannot obtain a,b,theta from isophotal analysis, assume
# circular aperture
ai, bi, thetai, f = radius, radius, 0, 0
a[i] = sources[i]['a'] = ai
b[i] = sources[i]['b'] = bi
theta[i] = sources[i]['theta'] = thetai
flux[i] = sources[i]['flux'] = f
bad = (a < b).nonzero()
a[bad], b[bad] = b[bad], a[bad]
theta[bad] += pi/2
theta %= pi
theta[theta > pi/2] -= pi
elongation = a/b
# Obtain the optimal aperture radius from the brightest non-saturated
# source
if not k:
for i in argsort(flux)[::-1]:
if sep.sum_ellipse(
img >= sat_level, [x[i]], [y[i]], a[i], b[i], theta[i],
1, subpix=0)[0][0]:
# Saturated source
continue
try:
# noinspection PyTypeChecker
res = minimize(
calc_flux_err, [a[i]*1.6],
(img_back, x[i], y[i], elongation[i], theta[i], tmp_rms,
mask, gain), bounds=[(1, None)], tol=1e-5)
except ValueError:
continue
if not res.success:
continue
k = res.x[0]/a[i]
break
if not k:
raise ValueError(
'Not enough data for automatic aperture factor; use '
'explicit aperture factor')
# Calculate weighted median of aperture sizes, elongations, and/or
# orientations if requested
r = sqrt(a*b)*k
if r.size > 1 and any([fix_aper, fix_ell, fix_rot]):
flux[flux < 0] = 0
if not flux.any():
raise ValueError(
'Not enough data for weighted median in fixed-aperture '
'automatic photometry; use static fixed-aperture or fully '
'adaptive automatic photometry instead')
if fix_aper:
r = weighted_median(r, flux)
if fix_ell:
elongation = weighted_median(elongation, flux)
if fix_rot:
theta = weighted_median(theta, flux, period=pi)
if theta > pi/2:
theta -= pi
# Calculate the final aperture and annulus sizes
sqrt_el = sqrt(elongation)
a, b = r*sqrt_el, r/sqrt_el
if not have_background:
if not k_in:
k_in = 1.5*k
if not k_out:
k_out = 2*k
a_in = a*(k_in/k)
a_out, b_out = a*(k_out/k), b*(k_out/k)
theta_out = theta
# Calculate mean and RMS of background; to get the pure sigma, set error
# to 1 and don't pass the gain
if have_background:
if fixed_aper and a == b:
bk_area = sep.sum_circle(area_img, x, y, a, mask=mask, subpix=0)[0]
bk_mean, bk_sigma = sep.sum_circle(
background, x, y, a, err=1, mask=mask, subpix=0)[:2]
else:
bk_area = sep.sum_ellipse(
area_img, x, y, a, b, theta, 1, mask=mask, subpix=0)[0]
bk_mean, bk_sigma = sep.sum_ellipse(
background, x, y, a, b, theta, 1, err=1, mask=mask,
subpix=0)[:2]
error = background_rms
elif fixed_aper and a_out == b_out:
bk_area = sep.sum_circann(
area_img, x, y, a_in, a_out, mask=mask, subpix=0)[0]
bk_mean, bk_sigma = sep.sum_circann(
img, x, y, a_in, a_out, err=1, mask=mask, subpix=0)[:2]
error = bk_sigma
else:
bk_area = sep.sum_ellipann(
area_img, x, y, a_out, b_out, theta_out, a_in/a_out, 1, mask=mask,
subpix=0)[0]
bk_mean, bk_sigma = sep.sum_ellipann(
img, x, y, a_out, b_out, theta_out, a_in/a_out, 1, err=1, mask=mask,
subpix=0)[:2]
error = bk_sigma
if have_background:
area = bk_area
elif fixed_aper and a == b:
area = sep.sum_circle(area_img, x, y, a, mask=mask, subpix=0)[0]
else:
area = sep.sum_ellipse(
area_img, x, y, a, b, theta, 1, mask=mask, subpix=0)[0]
if fixed_aper and a == b:
# Fixed circular aperture
if ndim(error) == 1:
# Separate scalar error for each source
flux, flux_err = empty([2, len(sources)], dtype=float)
flags = empty(len(sources), dtype=int)
for i, (_x, _y, _err) in enumerate(zip(x, y, error)):
flux[i], flux_err[i], flags[i] = sep.sum_circle(
img, [_x], [_y], a, err=_err, mask=mask, gain=gain,
subpix=0)
else:
flux, flux_err, flags = sep.sum_circle(
img, x, y, a, err=error, mask=mask, gain=gain, subpix=0)
else:
# Variable or elliptic aperture
if ndim(error) == 1:
# Separate scalar error for each source
flux, flux_err = empty([2, len(sources)], dtype=float)
flags = empty(len(sources), dtype=int)
if isscalar(a):
a = full_like(x, a)
if isscalar(b):
b = full_like(x, b)
if isscalar(theta):
theta = full_like(x, theta)
for i, (_x, _y, _err, _a, _b, _theta) in enumerate(zip(
x, y, error, a, b, theta)):
flux[i], flux_err[i], flags[i] = sep.sum_ellipse(
img, [_x], [_y], _a, _b, _theta, 1, err=_err, mask=mask,
gain=gain, subpix=0)
else:
flux, flux_err, flags = sep.sum_ellipse(
img, x, y, a, b, theta, 1, err=error, mask=mask, gain=gain,
subpix=0)
# Convert background sum to mean and subtract background from fluxes
if have_background:
# Background area equals aperture area
flux -= bk_mean
bk_mean = bk_mean/area
else:
# Background area equals annulus area
bk_mean = bk_mean/bk_area
flux -= bk_mean*area
# Convert ADUs to electrons
flux *= gain
flux_err *= gain
bk_mean *= gain
bk_sigma *= gain
# Calculate aperture correction for all aperture sizes from the brightest
# source
aper_corr = {}
if apcorr_tol > 0:
for i in argsort(flux)[::-1]:
xi, yi = x[i], y[i]
if isscalar(a):
ai = a
else:
ai = a[i]
if isscalar(b):
bi = b
else:
bi = b[i]
if isscalar(theta):
thetai = theta
else:
thetai = theta[i]
if ndim(error) == 1:
err = error[i]
else:
err = error
if ai == bi:
nsat = sep.sum_circle(
img >= sat_level, [xi], [yi], ai, subpix=0)[0][0]
else:
nsat = sep.sum_ellipse(
img >= sat_level, [xi], [yi], ai, bi, thetai, 1,
subpix=0)[0][0]
if nsat:
# Saturated source
continue
# Obtain total flux by increasing aperture size until it grows
# either more than before (i.e. a nearby source in the aperture) or
# less than the threshold (i.e. the growth curve reached saturation)
f0 = f_prev = flux[i]
dap = 0
f_tot = df_prev = None
while True:
dap += 0.1
if ai == bi:
f, f_err, fl = sep.sum_circle(
img, [xi], [yi], ai + dap, err=err, mask=mask,
gain=gain, subpix=0)
area_i = sep.sum_circle(
area_img, [xi], [yi], ai + dap, mask=mask,
subpix=0)[0][0]
else:
f, f_err, fl = sep.sum_ellipse(
img, [xi], [yi], ai + dap, bi*(1 + dap/ai), thetai, 1,
err=err, mask=mask, gain=gain, subpix=0)
area_i = sep.sum_ellipse(
area_img, [xi], [yi], ai + dap, bi + dap, thetai, 1,
mask=mask, subpix=0)[0][0]
f, fl = f[0], fl[0]
if fl:
break
f = (f - bk_mean[i]*area_i)*gain
if f <= 0:
break
df = f/f_prev
if df_prev is not None and df > df_prev:
# Increasing growth, nearby source hit
f_tot = f_prev
break
if df < 1 + apcorr_tol:
# Growth stopped to within the tolerance
f_tot = f
break
f_prev, df_prev = f, df
if f_tot is None:
continue
# Calculate fluxes for the chosen source for all unique aperture
# sizes used for other sources
fluxes_for_ap = {ai: f0}
if not isscalar(a):
for aj in set(a) - {ai}:
bj = aj*bi/ai
if aj == bj:
f, fl = sep.sum_circle(
img, [xi], [yi], aj, err=err, mask=mask, gain=gain,
subpix=0)[::2]
area_j = sep.sum_circle(
area_img, [xi], [yi], aj, mask=mask, subpix=0)[0][0]
else:
f, fl = sep.sum_ellipse(
img, [xi], [yi], aj, bj, thetai, 1, err=err,
mask=mask, gain=gain, subpix=0)[::2]
area_j = sep.sum_ellipse(
area_img, [xi], [yi], aj, bj, thetai, 1,
mask=mask, subpix=0)[0][0]
f, fl = f[0], fl[0]
f = (f - bk_mean[i]*area_j)*gain
if fl or f <= 0:
continue
fluxes_for_ap[aj] = f
# Calculate aperture corrections
for aj, f in fluxes_for_ap.items():
if f < f_tot:
aper_corr[aj] = -2.5*log10(f_tot/f)
break
if 'flux' in sources.dtype.names:
sources['flux'] = flux
else:
sources = append_fields(sources, 'flux', flux, usemask=False)
if 'flux_err' in sources.dtype.names:
sources['flux_err'] = flux_err
else:
sources = append_fields(sources, 'flux_err', flux_err, usemask=False)
if 'flag' in sources.dtype.names:
sources['flag'] |= flags
else:
sources = append_fields(sources, 'flag', flags, usemask=False)
for name in ['mag', 'mag_err', 'aper_a', 'aper_b', 'aper_theta',
'aper_a_in', 'aper_a_out', 'aper_b_out', 'aper_theta_out',
'aper_area', 'background_area', 'background',
'background_rms']:
if name not in sources.dtype.names:
sources = append_fields(sources, name, z, usemask=False)
good = (flux > 0).nonzero()
if len(good[0]):
sources['mag'][good] = -2.5*log10(flux[good]/texp)
sources['mag_err'][good] = 2.5*log10(1 + flux_err[good]/flux[good])
sources['aper_a'] = a
sources['aper_b'] = b
sources['aper_theta'] = theta
if not have_background:
sources['aper_a_in'] = a_in
sources['aper_a_out'] = a_out
sources['aper_b_out'] = b_out
sources['aper_theta_out'] = theta_out
sources['aper_area'] = area
sources['background_area'] = bk_area
sources['background'] = bk_mean
sources['background_rms'] = bk_sigma
# Apply aperture correction
for i in good[0]:
sources['mag'][i] += aper_corr.get(sources['aper_a'][i], 0)
return sources