def measure_rms(coord, data, img_wcs, annulus_radius=0.1 * u.arcsecond):
pixel_scale = np.abs(
img_wcs.pixel_scale_matrix.diagonal().prod())**0.5 * u.deg
annulus_radius_pix = (annulus_radius.to(u.degree) /
pixel_scale).decompose()
annulus_width = 15 #pix
center_coord_pix = coord.to_pixel(img_wcs)
cutout = Cutout2D(data,
center_coord_pix,
annulus_radius * 2.5,
img_wcs,
mode='partial')
cutout_center = regions.PixCoord(cutout.center_cutout[0],
cutout.center_cutout[1])
innerann_reg = regions.CirclePixelRegion(cutout_center,
annulus_radius_pix.value)
outerann_reg = regions.CirclePixelRegion(
cutout_center, annulus_radius_pix.value + annulus_width)
innerann_mask = innerann_reg.to_mask()
annulus_mask = mask(outerann_reg, cutout) - mask(innerann_reg, cutout)
# Calculate the SNR and aperture flux sums
pixels_in_annulus = cutout.data[annulus_mask.astype('bool')]
bg_rms = median_abs_deviation(pixels_in_annulus)
return bg_rms
def mlla_nondet():
MLLA_nondet = Table.read(
'/home/jotter/nrao/summer_research_2018/tables/IR_nondet_may21_full.fits'
)
b3_fl = fits.open(
'/home/jotter/nrao/images/Orion_SourceI_B3_continuum_r0.5.clean0.05mJy.allbaselines.huge.deepmask.image.tt0.pbcor.fits'
)
header = b3_fl[0].header
img_wcs = WCS(header)
data = b3_fl[0].data
beam = radio_beam.Beam.from_fits_header(header)
pixel_scale = np.abs(
img_wcs.pixel_scale_matrix.diagonal().prod())**0.5 * u.deg
#ppbeam = (beam.sr/(pixel_scale**2)).decompose().value
MLLA_coord = SkyCoord(ra=MLLA_nondet['RAJ2000'],
dec=MLLA_nondet['DEJ2000'],
unit=u.degree)
annulus_radius = 0.1 * u.arcsecond
annulus_radius_pix = (annulus_radius.to(u.degree) /
pixel_scale).decompose()
annulus_width = 15 #pix
ulim_fluxes = []
for ind in range(len(MLLA_coord)):
center_coord = MLLA_coord[ind]
center_coord_pix = center_coord.to_pixel(img_wcs)
cutout = Cutout2D(data,
center_coord_pix,
annulus_radius * 2.5,
img_wcs,
mode='partial')
cutout_center = regions.PixCoord(cutout.center_cutout[0],
cutout.center_cutout[1])
innerann_reg = regions.CirclePixelRegion(cutout_center,
annulus_radius_pix.value)
outerann_reg = regions.CirclePixelRegion(
cutout_center, annulus_radius_pix.value + annulus_width)
innerann_mask = innerann_reg.to_mask()
annulus_mask = mask(outerann_reg, cutout) - mask(innerann_reg, cutout)
# Calculate the SNR and aperture flux sums
pixels_in_annulus = cutout.data[annulus_mask.astype('bool')]
bg_rms = median_abs_deviation(pixels_in_annulus)
ulim_fluxes.append(3 * bg_rms)
ulim_fluxes = np.array(ulim_fluxes) * 1000 * u.mJy #in mJy
MLLA_nondet['B3_flux_ulim'] = ulim_fluxes
MLLA_nondet.write(
'/home/jotter/nrao/summer_research_2018/tables/IR_nondet_may21_full_ulim.fits',
overwrite=True)
def roi_to_reg(roi):
"""
Function to convert a ROI to a region
"""
if isinstance(roi, CircularROI):
return regions.CirclePixelRegion(center=(roi.xc, roi.yc, radius=roi.radius))
elif isinstance(roi, PointROI):
return regions.PointRegion(center=(roi.x, roi.y))
elif isinstance(roi, PolygonalROI):
return regions.PolygonPixelRegion(vertices=list(zip(roi.vx, roi.vy)))
else:
raise NotImplementedError("ROI {0} not recognized".format(roi))
def create_region(rtype, args, dx, dy):
if rtype in ["Rectangle", "Box"]:
xctr, yctr, xw, yw = args
x = xctr+dx
y = yctr+dy
center = regions.PixCoord(x=x, y=y)
reg = regions.RectanglePixelRegion(center=center, width=xw, height=yw)
bounds = [x-0.5*xw, x+0.5*xw, y-0.5*yw, y+0.5*yw]
elif rtype == "Circle":
xctr, yctr, radius = args
x = xctr+dx
y = yctr+dy
center = regions.PixCoord(x=x, y=y)
reg = regions.CirclePixelRegion(center=center, radius=radius)
bounds = [x-radius, x+radius, y-radius, y+radius]
elif rtype == "Polygon":
x = np.array(args[0])+dx
y = np.array(args[1])+dy
vertices = regions.PixCoord(x=x, y=y)
reg = regions.PolygonPixelRegion(vertices=vertices)
bounds = [x.min(), x.max(), y.min(), y.max()]
else:
raise NotImplementedError
return reg, bounds
def measure_fluxes_gaussfit_allsrcs(data,
img,
data_name,
name_start=27,
name_end=-21,
ref_name='B3'):
cat = Table.read(data)
RA_name = 'gauss_x_' + ref_name
ind = np.where(np.isnan(cat[RA_name]) == False)
cat = cat[ind]
col_names = fnmatch.filter(cat.colnames, 'ap_flux_r*')
col_names = [cn[8:] for cn in col_names]
fl = fits.open(img)
header = fl[0].header
img_data = fl[0].data.squeeze()
mywcs = WCS(header).celestial
beam = radio_beam.Beam.from_fits_header(header)
pixel_scale = np.abs(
mywcs.pixel_scale_matrix.diagonal().prod())**0.5 * u.deg
ppbeam = (beam.sr / (pixel_scale**2)).decompose().value
save_dir = '/lustre/aoc/students/jotter/gauss_diags/diff_imgs/' + ref_name + '/' + data_name + '/'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
reg_file = '/users/jotter/summer_research_2018/final_regs/misc_regs/' + data_name + '_reg_file_' + ref_name + '_apflux.reg'
with open(reg_file, 'w') as fh:
fh.write("fk5\n")
for ind in range(len(cat)):
fh.write(
'ellipse({x_cen}, {y_cen}, {maj}, {minr}, {ang}) #text={{{ID}}}\n'
.format(x_cen=cat['gauss_x_' + ref_name][ind],
y_cen=cat['gauss_y_' + ref_name][ind],
maj=(cat['FWHM_major_' + ref_name][ind] *
u.arcsec.to(u.degree)),
minr=(cat['FWHM_minor_' + ref_name][ind] *
u.arcsec.to(u.degree)),
ang=cat['position_angle_' + ref_name][ind],
ID=str(cat['D_ID'][ind])))
rad = Angle(1, 'arcsecond') #radius for region list
regs = []
for ind in range(len(cat)):
reg = regions.CircleSkyRegion(center=SkyCoord(
cat['gauss_x_' + ref_name][ind] * u.degree,
cat['gauss_y_' + ref_name][ind] * u.degree),
radius=rad,
meta={'text': str(cat['D_ID'][ind])})
reg_pix = reg.to_pixel(mywcs)
if reg_pix.center.x > 0 and reg_pix.center.x < len(img_data[0]):
if reg_pix.center.y > 0 and reg_pix.center.y < len(img_data):
if np.isnan(img_data[int(reg_pix.center.x),
int(reg_pix.center.y)]) == False:
regs.append(reg)
cat_r = Angle(0.5, 'arcsecond') #radius in gaussian fitting
gauss_cat = gaussfit_catalog(
img, regs, cat_r,
savepath=save_dir) #output is nested dictionary structure
gauss_fit_tab = Table(
names=('D_ID', 'FWHM_major_' + data_name, 'major_err_' + data_name,
'FWHM_minor_' + data_name, 'minor_err_' + data_name,
'pa_' + data_name, 'pa_err_' + data_name,
'g_amplitude_' + data_name, 'amp_err_' + data_name),
dtype=('i4', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8'))
for key in gauss_cat:
gauss_fit_tab.add_row(
(key, gauss_cat[key]['fwhm_major'], gauss_cat[key]['e_fwhm_major'],
gauss_cat[key]['fwhm_minor'], gauss_cat[key]['e_fwhm_minor'],
gauss_cat[key]['pa'], gauss_cat[key]['e_pa'],
gauss_cat[key]['amplitude'],
gauss_cat[key]['e_amplitude'])) #fill table
ap_flux_arr = []
circ_flux_arr = []
ap_flux_err_arr = []
circ_flux_err_arr = []
bg_median_arr = []
bg_ap_arr = []
bg_circ_arr = []
RA = []
DEC = []
RA_err = []
DEC_err = []
for row in range(len(cat)):
gauss_ind = np.where(gauss_fit_tab['D_ID'] == cat['D_ID'][row])[0]
if len(gauss_ind) > 0:
pix_major_fwhm = (
(cat['FWHM_major_' + ref_name][row] * u.arcsec).to(u.degree) /
pixel_scale).decompose()
pix_minor_fwhm = (
(cat['FWHM_minor_' + ref_name][row] * u.arcsec).to(u.degree) /
pixel_scale).decompose()
center_coord = SkyCoord(cat['gauss_x_' + ref_name][row],
cat['gauss_y_' + ref_name][row],
frame='icrs',
unit=(u.deg, u.deg))
center_coord_pix = center_coord.to_pixel(mywcs)
center_coord_pix_reg = regions.PixCoord(center_coord_pix[0],
center_coord_pix[1])
position_angle = cat['position_angle_' + ref_name][row] * u.deg
ellipse_reg = regions.EllipsePixelRegion(center_coord_pix_reg,
pix_major_fwhm * 2.,
pix_minor_fwhm * 2.,
angle=position_angle)
size = pix_major_fwhm * 2.1
ap_mask = ellipse_reg.to_mask()
cutout_mask = ap_mask.cutout(img_data)
aperture_flux = np.sum(cutout_mask[ap_mask.data == 1]) / ppbeam
npix = len(cutout_mask[ap_mask.data == 1])
ap_flux_arr.append(aperture_flux)
#now creating annulus around source to measure background and ap flux error
annulus_width = 15
annulus_radius = 0.1 * u.arcsecond
annulus_radius_pix = (annulus_radius.to(u.degree) /
pixel_scale).decompose()
# Cutout section of the image we care about, to speed up computation time
size = 2.5 * annulus_radius
cutout = Cutout2D(img_data,
center_coord_pix,
size,
mywcs,
mode='partial') #cutout of outer circle
cutout_center = regions.PixCoord(cutout.center_cutout[0],
cutout.center_cutout[1])
# Define the aperture regions needed for SNR
innerann_reg = regions.CirclePixelRegion(cutout_center,
annulus_radius_pix)
outerann_reg = regions.CirclePixelRegion(
cutout_center, annulus_radius_pix + annulus_width)
# Make masks from aperture regions
annulus_mask = mask(outerann_reg, cutout) - mask(
innerann_reg, cutout)
# Calculate the SNR and aperture flux sums
pixels_in_annulus = cutout.data[annulus_mask.astype(
'bool')] #pixels within annulus
bg_rms = rms(pixels_in_annulus)
ap_bg_rms = bg_rms / np.sqrt(
npix / ppbeam) #rms/sqrt(npix/ppbeam) - rms error per beam
bg_median = np.median(pixels_in_annulus)
pix_bg = bg_median * npix / ppbeam
ap_flux_err_arr.append(ap_bg_rms)
bg_median_arr.append(bg_median)
bg_ap_arr.append(pix_bg)
#now measure circle flux:
radius = 0.1 * u.arcsecond
radius_pix = annulus_radius_pix
circle_reg = regions.CirclePixelRegion(center_coord_pix_reg,
radius_pix)
circ_ap_mask = circle_reg.to_mask()
circ_cutout_mask = circ_ap_mask.cutout(img_data)
cutout_mask = ap_mask.cutout(img_data)
circ_aperture_flux = np.sum(
circ_cutout_mask[circ_ap_mask.data == 1]) / ppbeam
circ_npix = len(circ_cutout_mask[circ_ap_mask.data == 1])
circ_bg_rms = bg_rms / np.sqrt(circ_npix / ppbeam)
circ_flux_arr.append(circ_aperture_flux)
circ_flux_err_arr.append(circ_bg_rms)
bg_circ_arr.append(bg_median * circ_npix / ppbeam)
RA.append(cat['gauss_x_' + ref_name][row])
DEC.append(cat['gauss_y_' + ref_name][row])
RA_err.append(cat['x_err_' + ref_name][row])
DEC_err.append(cat['y_err_' + ref_name][row])
cols = [
'ap_flux_', 'ap_flux_err_', 'bg_median_', 'bg_ap_', 'circ_flux_',
'circ_flux_err_', 'bg_circ_'
]
arrs = [
ap_flux_arr, ap_flux_err_arr, bg_median_arr, bg_ap_arr, circ_flux_arr,
circ_flux_err_arr, bg_circ_arr
]
cols2 = ['RA_', 'DEC_', 'RA_err_',
'DEC_err_'] #seperate bc different naming
arrs2 = [RA, DEC, RA_err, DEC_err]
for j in range(len(cols)):
gauss_fit_tab[cols[j] + data_name] = arrs[j]
for c in range(len(cols2)):
gauss_fit_tab[cols2[c] + ref_name] = arrs2[c]
gauss_fit_tab.write('/lustre/aoc/students/jotter/dendro_catalogs/' +
data_name + '_500klplus_allsrcs_catalog_' + ref_name +
'_ref.txt',
format='ascii',
overwrite=True)
def measure_fluxes_gaussfit(data,
directory,
data_name,
name_start=27,
name_end=-21,
ref_name='B6'):
#This function takes a directory full of images and adds them all to a catalog with flux measurements and fits gaussians. 'data' is the catalog to get positions from, 'ref_name' is the name of the reference data (for positions), 'directory' is the directory with all the new images.
cat = Table.read(data)
RA_names = ['gauss_x_B3', 'gauss_x_B6', 'gauss_x_B7_hr']
inds = []
for fn in RA_names:
ind = np.where(np.isnan(cat[fn]) == False)
inds.append(ind)
detected = reduce(
np.intersect1d, inds
) #sources detected in B3, B6, B7 - only make measurements on these sources
cat = cat[detected]
imgs = glob.glob(directory + '*r-2.clean0.1mJy.500klplus.deepmask*')
img_names = [img[len(directory) + name_start:name_end] for img in imgs]
col_names = fnmatch.filter(cat.colnames, 'ap_flux_r*')
col_names = [cn[8:] for cn in col_names]
for j, img in enumerate(imgs):
name = img_names[j]
if len(name) > 54:
name = name[0:53]
if name not in col_names:
fl = fits.open(img)
header = fl[0].header
try:
img_data = fl[0].data.squeeze()
except TypeError:
print("error! img: " + img)
continue
mywcs = WCS(header).celestial
beam = radio_beam.Beam.from_fits_header(header)
pixel_scale = np.abs(
mywcs.pixel_scale_matrix.diagonal().prod())**0.5 * u.deg
ppbeam = (beam.sr / (pixel_scale**2)).decompose().value
save_dir = '/lustre/aoc/students/jotter/gauss_diags/diff_imgs/' + ref_name + '/' + name + '/'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
reg_file = '/users/jotter/summer_research_2018/final_regs/' + name + '_reg_file_B6_apflux.reg'
with open(reg_file, 'w') as fh:
fh.write("fk5\n")
for ind in range(len(cat)):
fh.write(
'ellipse({x_cen}, {y_cen}, {maj}, {minr}, {ang}) #text={{{ID}}}\n'
.format(x_cen=cat['gauss_x_' + ref_name][ind],
y_cen=cat['gauss_y_' + ref_name][ind],
maj=(cat['FWHM_major_' + ref_name][ind] *
u.arcsec.to(u.degree)),
minr=(cat['FWHM_minor_' + ref_name][ind] *
u.arcsec.to(u.degree)),
ang=cat['position_angle_' + ref_name][ind],
ID=str(cat['D_ID'][ind])))
rad = Angle(1, 'arcsecond') #radius for region list
regs = []
for ind in range(len(cat)):
reg = regions.CircleSkyRegion(center=SkyCoord(
cat['gauss_x_' + ref_name][ind] * u.degree,
cat['gauss_y_' + ref_name][ind] * u.degree),
radius=rad,
meta={
'text': str(cat['D_ID'][ind])
})
reg_pix = reg.to_pixel(mywcs)
if reg_pix.center.x > 0 and reg_pix.center.x < len(
img_data[0]):
if reg_pix.center.y > 0 and reg_pix.center.y < len(
img_data):
regs.append(reg)
cat_r = Angle(0.5, 'arcsecond') #radius in gaussian fitting
gauss_cat = gaussfit_catalog(
img, regs, cat_r,
savepath=save_dir) #output is nested dictionary structure
gauss_fit_tab = Table(
names=('D_ID', 'FWHM_major_' + name, 'major_err_' + name,
'FWHM_minor_' + name, 'minor_err_' + name, 'pa_' + name,
'pa_err_' + name, 'g_amplitude_' + name,
'amp_err_' + name),
dtype=('i4', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8'))
for key in gauss_cat:
gauss_fit_tab.add_row(
(key, gauss_cat[key]['fwhm_major'],
gauss_cat[key]['e_fwhm_major'],
gauss_cat[key]['fwhm_minor'],
gauss_cat[key]['e_fwhm_minor'], gauss_cat[key]['pa'],
gauss_cat[key]['e_pa'], gauss_cat[key]['amplitude'],
gauss_cat[key]['e_amplitude'])) #fill table
cat = join(cat, gauss_fit_tab, keys='D_ID', join_type='left')
ap_flux_arr = []
circ_flux_arr = []
ap_flux_err_arr = []
circ_flux_err_arr = []
bg_median_arr = []
bg_ap_arr = []
bg_circ_arr = []
for row in range(len(cat)):
pix_major_fwhm = ((cat['FWHM_major_' + ref_name][row] *
u.arcsec).to(u.degree) /
pixel_scale).decompose()
pix_minor_fwhm = ((cat['FWHM_minor_' + ref_name][row] *
u.arcsec).to(u.degree) /
pixel_scale).decompose()
center_coord = SkyCoord(cat['gauss_x_' + ref_name][row],
cat['gauss_y_' + ref_name][row],
frame='icrs',
unit=(u.deg, u.deg))
center_coord_pix = center_coord.to_pixel(mywcs)
center_coord_pix_reg = regions.PixCoord(
center_coord_pix[0], center_coord_pix[1])
position_angle = cat['position_angle_' + ref_name][row] * u.deg
print(center_coord_pix_reg, pix_major_fwhm, pix_minor_fwhm)
ellipse_reg = regions.EllipsePixelRegion(center_coord_pix_reg,
pix_major_fwhm * 2.,
pix_minor_fwhm * 2.,
angle=position_angle)
size = pix_major_fwhm * 2.1
ap_mask = ellipse_reg.to_mask()
cutout_mask = ap_mask.cutout(img_data)
aperture_flux = np.sum(cutout_mask[ap_mask.data == 1]) / ppbeam
npix = len(cutout_mask[ap_mask.data == 1])
ap_flux_arr.append(aperture_flux)
#now creating annulus around source to measure background and ap flux error
annulus_width = 15
annulus_radius = 0.1 * u.arcsecond
annulus_radius_pix = (annulus_radius.to(u.degree) /
pixel_scale).decompose()
# Cutout section of the image we care about, to speed up computation time
size = 2.5 * annulus_radius
cutout = Cutout2D(img_data,
center_coord_pix,
size,
mywcs,
mode='partial') #cutout of outer circle
cutout_center = regions.PixCoord(cutout.center_cutout[0],
cutout.center_cutout[1])
# Define the aperture regions needed for SNR
innerann_reg = regions.CirclePixelRegion(
cutout_center, annulus_radius_pix)
outerann_reg = regions.CirclePixelRegion(
cutout_center, annulus_radius_pix + annulus_width)
# Make masks from aperture regions
annulus_mask = mask(outerann_reg, cutout) - mask(
innerann_reg, cutout)
# Calculate the SNR and aperture flux sums
pixels_in_annulus = cutout.data[annulus_mask.astype(
'bool')] #pixels within annulus
bg_rms = rms(pixels_in_annulus)
ap_bg_rms = bg_rms / np.sqrt(
npix / ppbeam) #rms/sqrt(npix/ppbeam) - rms error per beam
bg_median = np.median(pixels_in_annulus)
pix_bg = bg_median * npix / ppbeam
ap_flux_err_arr.append(ap_bg_rms)
bg_median_arr.append(bg_median)
bg_ap_arr.append(pix_bg)
#now measure circle flux:
radius = 0.1 * u.arcsecond
radius_pix = annulus_radius_pix
circle_reg = regions.CirclePixelRegion(center_coord_pix_reg,
radius_pix)
circ_ap_mask = circle_reg.to_mask()
circ_cutout_mask = circ_ap_mask.cutout(img_data)
cutout_mask = ap_mask.cutout(img_data)
circ_aperture_flux = np.sum(
circ_cutout_mask[circ_ap_mask.data == 1]) / ppbeam
circ_npix = len(circ_cutout_mask[circ_ap_mask.data == 1])
circ_bg_rms = bg_rms / np.sqrt(circ_npix / ppbeam)
circ_flux_arr.append(circ_aperture_flux)
circ_flux_err_arr.append(circ_bg_rms)
bg_circ_arr.append(bg_median * circ_npix / ppbeam)
cols = [
'ap_flux_', 'ap_flux_err_', 'bg_median_', 'bg_ap_',
'circ_flux_', 'circ_flux_err_', 'bg_circ_'
]
arrs = [
ap_flux_arr, ap_flux_err_arr, bg_median_arr, bg_ap_arr,
circ_flux_arr, circ_flux_err_arr, bg_circ_arr
]
for j in range(len(cols)):
cat[cols[j] + name] = arrs[j]
cat.write('/lustre/aoc/students/jotter/dendro_catalogs/' + data_name +
'_500klplus_allsrcs_catalog_' + ref_name + '_ref.txt',
format='ascii',
overwrite=True)
def measure_ap_fluxes(data,
directory,
data_name,
name_start=27,
name_end=-21,
ref_name='B6'):
#This function takes a directory full of images and adds them all to a catalog with flux measurements. 'data' is the catalog to get positions and sizes from, 'ref_name' is the name of the reference data (for positions), 'directory' is the directory with all the new images.
#data = '/lustre/aoc/students/jotter/dendro_catalogs/IR_matched_catalog_B7.fits'
#directory = '/lustre/aoc/students/jotter/directory/OrionB3/'
cat = Table.read(data)
RA_names = ['gauss_x_B3', 'gauss_x_B6', 'gauss_x_B7_hr']
inds = []
for fn in RA_names:
ind = np.where(np.isnan(cat[fn]) == False)
inds.append(ind)
detected = reduce(
np.intersect1d, inds
) #sources detected in B3, B6, B7 - only make measurements on these sources
cat = cat[detected]
imgs = glob.glob(directory + '*')
img_names = [img[len(directory) + name_start:name_end] for img in imgs]
col_names = fnmatch.filter(cat.colnames, 'ap_flux_r*')
col_names = [cn[8:] for cn in col_names]
for j, img in enumerate(imgs):
name = img_names[j]
if len(name) > 54:
name = name[0:53]
if name not in col_names:
fl = fits.open(img)
header = fl[0].header
try:
img_data = fl[0].data.squeeze()
except TypeError:
print("ERROR! img: " + img)
continue
mywcs = WCS(header).celestial
beam = radio_beam.Beam.from_fits_header(header)
pixel_scale = np.abs(
mywcs.pixel_scale_matrix.diagonal().prod())**0.5 * u.deg
ppbeam = (beam.sr / (pixel_scale**2)).decompose().value
ap_flux_arr = []
circ_flux_arr = []
ap_flux_err_arr = []
circ_flux_err_arr = []
bg_median_arr = []
bg_ap_arr = []
bg_circ_arr = []
for row in range(len(cat)):
pix_major_fwhm = ((cat['FWHM_major_' + ref_name][row] *
u.arcsec).to(u.degree) /
pixel_scale).decompose()
pix_minor_fwhm = ((cat['FWHM_minor_' + ref_name][row] *
u.arcsec).to(u.degree) /
pixel_scale).decompose()
center_coord = SkyCoord(cat['gauss_x_' + ref_name][row],
cat['gauss_y_' + ref_name][row],
frame='icrs',
unit=(u.deg, u.deg))
center_coord_pix = center_coord.to_pixel(mywcs)
center_coord_pix_reg = regions.PixCoord(
center_coord_pix[0], center_coord_pix[1])
position_angle = cat['position_angle_' + ref_name][row] * u.deg
ellipse_reg = regions.EllipsePixelRegion(center_coord_pix_reg,
pix_major_fwhm * 2.,
pix_minor_fwhm * 2.,
angle=position_angle)
size = pix_major_fwhm * 2.1
ap_mask = ellipse_reg.to_mask()
cutout_mask = ap_mask.cutout(img_data)
aperture_flux = np.sum(cutout_mask[ap_mask.data == 1]) / ppbeam
npix = len(cutout_mask[ap_mask.data == 1])
ap_flux_arr.append(aperture_flux)
#now creating annulus around source to measure background and ap flux error
annulus_width = 15
annulus_radius = 0.1 * u.arcsecond
annulus_radius_pix = (annulus_radius.to(u.degree) /
pixel_scale).decompose()
# Cutout section of the image we care about, to speed up computation time
size = 2.5 * annulus_radius
cutout = Cutout2D(img_data,
center_coord_pix,
size,
mywcs,
mode='partial') #cutout of outer circle
cutout_center = regions.PixCoord(cutout.center_cutout[0],
cutout.center_cutout[1])
# Define the aperture regions needed for SNR
innerann_reg = regions.CirclePixelRegion(
cutout_center, annulus_radius_pix)
outerann_reg = regions.CirclePixelRegion(
cutout_center, annulus_radius_pix + annulus_width)
# Make masks from aperture regions
annulus_mask = mask(outerann_reg, cutout) - mask(
innerann_reg, cutout)
# Calculate the SNR and aperture flux sums
pixels_in_annulus = cutout.data[annulus_mask.astype(
'bool')] #pixels within annulus
bg_rms = rms(pixels_in_annulus)
ap_bg_rms = bg_rms / np.sqrt(
npix / ppbeam) #rms/sqrt(npix/ppbeam) - rms error per beam
bg_median = np.median(pixels_in_annulus)
pix_bg = bg_median * npix / ppbeam
ap_flux_err_arr.append(ap_bg_rms)
bg_median_arr.append(bg_median)
bg_ap_arr.append(pix_bg)
#now measure circle flux:
radius = 0.1 * u.arcsecond
radius_pix = annulus_radius_pix
circle_reg = regions.CirclePixelRegion(center_coord_pix_reg,
radius_pix)
circ_ap_mask = circle_reg.to_mask()
circ_cutout_mask = circ_ap_mask.cutout(img_data)
cutout_mask = ap_mask.cutout(img_data)
circ_aperture_flux = np.sum(
circ_cutout_mask[circ_ap_mask.data == 1]) / ppbeam
circ_npix = len(circ_cutout_mask[circ_ap_mask.data == 1])
circ_bg_rms = bg_rms / np.sqrt(circ_npix / ppbeam)
circ_flux_arr.append(circ_aperture_flux)
circ_flux_err_arr.append(circ_bg_rms)
bg_circ_arr.append(bg_median * circ_npix / ppbeam)
cols = [
'ap_flux_', 'ap_flux_err_', 'bg_median_', 'bg_ap_',
'circ_flux_', 'circ_flux_err_', 'bg_circ_'
]
arrs = [
ap_flux_arr, ap_flux_err_arr, bg_median_arr, bg_ap_arr,
circ_flux_arr, circ_flux_err_arr, bg_circ_arr
]
for j in range(len(cols)):
cat[cols[j] + name] = arrs[j]
cat.write('/lustre/aoc/students/jotter/dendro_catalogs/' + data_name +
'_diff_imgs_catalog_' + ref_name + '_ref.txt',
format='ascii',
overwrite=True)
def b6b7_catalog(B6_img, B6_name, B7_img, B7_name, cat_name, nonconv_B6_img=None, nonconv_B7_img=None):
#creates catalog from one image in each band
#B3_names, B6_names, B7_names only used for gaussian diag directory names
ref_data_name = '/home/jotter/nrao/summer_research_2018/tables/ref_catalog_may21.fits'
ref_data = Table.read(ref_data_name)
ref_arrs = [ref_data['B6_detect'], ref_data['B7_detect']]
band_imgs = [B6_img, B7_img]
band_names = ['B6', 'B7']
band_img_names = [B6_name, B7_name]
band_tables = []
for b in range(len(band_imgs)): #first loop through different bands
name = band_names[b]
img_name = band_img_names[b]
img = band_imgs[b]
fl = fits.open(img)
header = fl[0].header
img_data = fl[0].data.squeeze()
img_wcs = WCS(header).celestial
beam = radio_beam.Beam.from_fits_header(header)
pixel_scale = np.abs(img_wcs.pixel_scale_matrix.diagonal().prod())**0.5 * u.deg
ppbeam = (beam.sr/(pixel_scale**2)).decompose().value
if name == 'B6' and nonconv_B6_img is not None:
fl = fits.open(nonconv_B6_img)
header = fl[0].header
nonconv_beam = radio_beam.Beam.from_fits_header(header)
ppbeam = (nonconv_beam.sr/(pixel_scale**2)).decompose().value
if name == 'B7' and nonconv_B7_img is not None:
fl = fits.open(nonconv_B7_img)
header = fl[0].header
nonconv_beam = radio_beam.Beam.from_fits_header(header)
ppbeam = (nonconv_beam.sr/(pixel_scale**2)).decompose().value
#now get ready to fit gaussians
#start by setting up save directory for images
gauss_save_dir = '/home/jotter/nrao/gauss_diags_may21/'+img_name+'/'
if not os.path.exists(gauss_save_dir):
os.makedirs(gauss_save_dir)
#now make region list
rad = Angle(1, 'arcsecond') #radius used in region list
regs = []
src_inds = np.where(ref_arrs[b] == True)[0]
print(len(src_inds))
for ind in src_inds:
reg = regions.CircleSkyRegion(center=SkyCoord(ref_data['RA_B3'][ind]*u.degree, ref_data['DEC_B3'][ind]*u.degree), radius=rad, meta={'text':str(ref_data['B3_Seq'][ind])})
reg_pix = reg.to_pixel(img_wcs)
if reg_pix.center.x > 0 and reg_pix.center.x < len(img_data[0]):
if reg_pix.center.y > 0 and reg_pix.center.y < len(img_data):
if np.isnan(img_data[int(reg_pix.center.x), int(reg_pix.center.y)]) == False:
regs.append(reg)
cat_r = Angle(0.5, 'arcsecond')/2 #radius for gaussian fitting
print('ok')
gauss_cat = gaussfit_catalog(img, regs, cat_r, savepath=gauss_save_dir, max_offset_in_beams = 1, max_radius_in_beams = 5)
#table does not have all columns yet, add others later
img_table = Table(names=('Seq_B3', 'fwhm_maj_'+name, 'fwhm_maj_err_'+name, 'fwhm_min_'+name, 'fwhm_min_err_'+name, 'pa_'+name, 'pa_err_'+name, 'gauss_amp_'+name, 'gauss_amp_err_'+name,'RA_'+name,'RA_err_'+name, 'DEC_'+name, 'DEC_err_'+name, ), dtype=('i4', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8'))
for key in gauss_cat:
img_table.add_row((key, gauss_cat[key]['fwhm_major'], gauss_cat[key]['e_fwhm_major'], gauss_cat[key]['fwhm_minor'], gauss_cat[key]['e_fwhm_minor'], gauss_cat[key]['pa'], gauss_cat[key]['e_pa'], gauss_cat[key]['amplitude'], gauss_cat[key]['e_amplitude'], gauss_cat[key]['center_x'], gauss_cat[key]['e_center_x'], gauss_cat[key]['center_y'], gauss_cat[key]['e_center_y']))
#now measure deconvovled sizes and aperture flux measurements for each source
ap_flux_arr = []
ap_flux_err_arr = []
fwhm_maj_deconv_arr = []
fwhm_maj_deconv_err_arr = []
fwhm_min_deconv_arr = []
fwhm_min_deconv_err_arr = []
pa_deconv_arr = []
pa_deconv_err_arr = []
snr_arr = []
rms_arr = []
for row in range(len(img_table)): #now loop through sources in reference data and make measurements
ref_ind = np.where(ref_data['B3_Seq'] == img_table['Seq_B3'][row])[0]
if len(ref_ind > 0):
#now measuring deconvolved sizes
measured_source_size = radio_beam.Beam(major=img_table['fwhm_maj_'+name][row]*u.arcsec, minor=img_table['fwhm_min_'+name][row]*u.arcsec, pa=(img_table['pa_'+name][row]-90)*u.degree)
try:
deconv_size = measured_source_size.deconvolve(beam)
fwhm_maj_deconv_arr.append(deconv_size.major.value)
fwhm_min_deconv_arr.append(deconv_size.minor.value)
fwhm_maj_deconv_err_arr.append(img_table['fwhm_maj_err_'+name][row]) #same error as non deconvolved
fwhm_min_deconv_err_arr.append(img_table['fwhm_min_err_'+name][row])
pa_deconv_arr.append(deconv_size.pa.to(u.deg).value)
pa_deconv_err_arr.append(img_table['pa_err_'+name][row])
except ValueError:
fwhm_maj_deconv_arr.append(np.nan)
fwhm_min_deconv_arr.append(np.nan)
fwhm_maj_deconv_err_arr.append(np.nan)
fwhm_min_deconv_err_arr.append(np.nan)
pa_deconv_arr.append(np.nan)
pa_deconv_err_arr.append(np.nan)
pix_major_fwhm = ((img_table['fwhm_maj_'+name][row]*u.arcsec).to(u.degree)/pixel_scale).decompose()
pix_minor_fwhm = ((img_table['fwhm_min_'+name][row]*u.arcsec).to(u.degree)/pixel_scale).decompose()
center_coord = SkyCoord(img_table['RA_'+name][row], img_table['DEC_'+name][row], frame='icrs', unit=(u.deg, u.deg))
center_coord_pix = center_coord.to_pixel(img_wcs)
center_coord_pix_reg = regions.PixCoord(center_coord_pix[0], center_coord_pix[1])
pos_ang = (img_table['pa_'+name][row]-90)*u.deg #must subtract 90 to be consistent
ellipse_reg = regions.EllipsePixelRegion(center_coord_pix_reg, pix_major_fwhm.value*2, pix_minor_fwhm.value*2, angle=pos_ang)
ap_mask = ellipse_reg.to_mask()
cutout_mask = ap_mask.cutout(img_data)
aperture_flux = np.nansum(cutout_mask[ap_mask.data==1])/ppbeam
npix = len(cutout_mask[ap_mask.data==1])
#now make annulus for measuring background and error
annulus_width = 15 #pixels
annulus_radius = img_table['fwhm_maj_'+name][row]*u.arcsecond#+0.05*u.arcsecond
annulus_radius_pix = (annulus_radius.to(u.degree)/pixel_scale).decompose()
#cutout image
cutout = Cutout2D(img_data, center_coord_pix, annulus_radius*2.5, img_wcs, mode='partial')
cutout_center = regions.PixCoord(cutout.center_cutout[0], cutout.center_cutout[1])
#define aperture regions for SNR
innerann_reg = regions.CirclePixelRegion(cutout_center, annulus_radius_pix.value)
outerann_reg = regions.CirclePixelRegion(cutout_center, annulus_radius_pix.value+annulus_width)
#Make masks from aperture regions
annulus_mask = mask(outerann_reg, cutout) - mask(innerann_reg, cutout)
# Calculate the SNR and aperture flux sums
pixels_in_annulus = cutout.data[annulus_mask.astype('bool')]
bg_rms = median_abs_deviation(pixels_in_annulus)
print(img_table['Seq_B3'][row])
print('BG RMS: %f' % (bg_rms))
ap_bg_rms = bg_rms/np.sqrt(npix/ppbeam) #rms/sqrt(npix/ppbeam) - rms error per beam
bg_median = np.nanmedian(pixels_in_annulus)
pix_bg = bg_median*npix/ppbeam
ap_flux_bgcorrect = aperture_flux - pix_bg
ap_flux_correct = ap_flux_bgcorrect + ap_flux_bgcorrect*(1 - special.erf(2*np.sqrt(np.log(2)))) #flux correction for summing within 2*fwhm
ap_flux_err_arr.append(ap_bg_rms)
ap_flux_arr.append(ap_flux_correct)
snr_arr.append(img_table['gauss_amp_'+name][row]/bg_rms)
rms_arr.append(bg_rms)
cols = ['ap_flux_'+name, 'ap_flux_err_'+name, 'fwhm_maj_deconv_'+name, 'fwhm_maj_deconv_err_'+name, 'fwhm_min_deconv_'+name, 'fwhm_min_deconv_err_'+name, 'pa_deconv_'+name, 'pa_deconv_err_'+name, 'SNR_'+name, 'RMS_'+name]
arrs = [ap_flux_arr, ap_flux_err_arr, fwhm_maj_deconv_arr, fwhm_maj_deconv_err_arr, fwhm_min_deconv_arr, fwhm_min_deconv_err_arr, pa_deconv_arr, pa_deconv_err_arr, snr_arr, rms_arr]
for c in range(len(cols)):
img_table.add_column(Column(np.array(arrs[c])), name=cols[c])
img_table.add_column(Column(np.array(fwhm_maj_deconv_arr)/np.array(fwhm_min_deconv_arr)), name='ar_deconv_'+name)
band_tables.append(img_table) #list of tables for each image
B6B7 = join(band_tables[0], band_tables[1], keys='Seq_B3', join_type='outer')
B6B7.write('/home/jotter/nrao/summer_research_2018/tables/'+cat_name+'.fits', overwrite=True)
def reject(imfile, catfile, threshold):
"""Reject noisy detections.
Parameters
----------
imfile : str
The path to the radio image file
catfile : str
The path to the source catalog, as obtained from detect.py
threshold : float
The signal-to-noise threshold below which sources are rejected
"""
# Extract information from filename
outfile = os.path.basename(catfile).split('cat_')[1].split('.dat')[0]
region = outfile.split('region')[1].split('_band')[0]
band = outfile.split('band')[1].split('_val')[0]
min_value = outfile.split('val')[1].split('_delt')[0]
min_delta = outfile.split('delt')[1].split('_pix')[0]
min_npix = outfile.split('pix')[1]
print("\nSource rejection for region {} in band {}".format(region, band))
print("Loading image file")
contfile = fits.open(imfile)
data = contfile[0].data.squeeze()
mywcs = wcs.WCS(contfile[0].header).celestial
catalog = Table(Table.read(catfile, format='ascii'), masked=True)
beam = radio_beam.Beam.from_fits_header(contfile[0].header)
pixel_scale = np.abs(
mywcs.pixel_scale_matrix.diagonal().prod())**0.5 * u.deg
ppbeam = (beam.sr / (pixel_scale**2)).decompose().value
data = data / ppbeam
# Remove existing region files
if os.path.isfile('./reg/reg_' + outfile + '_annulus.reg'):
os.remove('./reg/reg_' + outfile + '_annulus.reg')
if os.path.isfile('./reg/reg_' + outfile + '_filtered.reg'):
os.remove('./reg/reg_' + outfile + '_filtered.reg')
# Load in manually accepted and rejected sources
override_accepted = []
override_rejected = []
if os.path.isfile('./.override/accept_' + outfile + '.txt'):
override_accepted = np.loadtxt('./.override/accept_' + outfile +
'.txt').astype('int')
if os.path.isfile('./.override/reject_' + outfile + '.txt'):
override_rejected = np.loadtxt('./.override/reject_' + outfile +
'.txt').astype('int')
print("\nManually accepted sources: ", set(override_accepted))
print("Manually rejected sources: ", set(override_rejected))
print('\nCalculating RMS values within aperture annuli')
pb = ProgressBar(len(catalog))
data_cube = []
masks = []
rejects = []
snr_vals = []
mean_backgrounds = []
for i in range(len(catalog)):
x_cen = catalog['x_cen'][i] * u.deg
y_cen = catalog['y_cen'][i] * u.deg
major_fwhm = catalog['major_fwhm'][i] * u.deg
minor_fwhm = catalog['minor_fwhm'][i] * u.deg
position_angle = catalog['position_angle'][i] * u.deg
dend_flux = catalog['dend_flux_band{}'.format(band)][i]
annulus_width = 1e-5 * u.deg
center_distance = 1e-5 * u.deg
# Define some ellipse properties in pixel coordinates
position = coordinates.SkyCoord(x_cen,
y_cen,
frame='icrs',
unit=(u.deg, u.deg))
pix_position = np.array(position.to_pixel(mywcs))
pix_major_fwhm = major_fwhm / pixel_scale
pix_minor_fwhm = minor_fwhm / pixel_scale
# Cutout section of the image we care about, to speed up computation time
size = (center_distance + annulus_width + major_fwhm) * 2.2
cutout = Cutout2D(data, position, size, mywcs, mode='partial')
cutout_center = regions.PixCoord(cutout.center_cutout[0],
cutout.center_cutout[1])
# Define the aperture regions needed for SNR
ellipse_reg = regions.EllipsePixelRegion(
cutout_center,
pix_major_fwhm * 2.,
pix_minor_fwhm * 2.,
angle=position_angle
) # Make sure you're running the dev version of regions, otherwise the position angles will be in radians!
innerann_reg = regions.CirclePixelRegion(
cutout_center, center_distance / pixel_scale + pix_major_fwhm)
outerann_reg = regions.CirclePixelRegion(
cutout_center, center_distance / pixel_scale + pix_major_fwhm +
annulus_width / pixel_scale)
# Make masks from aperture regions
ellipse_mask = mask(ellipse_reg, cutout)
annulus_mask = mask(outerann_reg, cutout) - mask(innerann_reg, cutout)
# Plot annulus and ellipse regions
data_cube.append(cutout.data)
masks.append([annulus_mask, ellipse_mask])
# Calculate the SNR and aperture flux sums
bg_rms = rms(cutout.data[annulus_mask.astype('bool')])
peak_flux = np.max(cutout.data[ellipse_mask.astype('bool')])
flux_rms_ratio = peak_flux / bg_rms
snr_vals.append(flux_rms_ratio)
# Reject bad sources below some SNR threshold
rejected = False
if flux_rms_ratio <= threshold:
rejected = True
# Process manual overrides
if catalog['_idx'][i] in override_accepted:
rejected = False
if catalog['_idx'][i] in override_rejected:
rejected = True
rejects.append(int(rejected))
# Add non-rejected source ellipses to a new region file
fname = './reg/reg_' + outfile + '_filtered.reg'
with open(fname, 'a') as fh:
if os.stat(fname).st_size == 0:
fh.write("icrs\n")
if not rejected:
fh.write("ellipse({}, {}, {}, {}, {}) # text={{{}}}\n".format(
x_cen.value, y_cen.value, major_fwhm.value,
minor_fwhm.value, position_angle.value, i))
pb.update()
# Plot the grid of sources
plot_grid(data_cube, masks, rejects, snr_vals, catalog['_idx'])
plt.suptitle(
'region={}, band={}, min_value={}, min_delta={}, min_npix={}, threshold={:.4f}'
.format(region, band, min_value, min_delta, min_npix, threshold))
plt.show(block=False)
# Get overrides from user
print(
'Manual overrides example: type "r319, a605" to manually reject source #319 and accept source #605.'
)
overrides = input(
"\nType manual override list, or press enter to continue:\n").split(
', ')
accepted_list = [
s[1:] for s in list(filter(lambda x: x.startswith('a'), overrides))
]
rejected_list = [
s[1:] for s in list(filter(lambda x: x.startswith('r'), overrides))
]
# Save the manually accepted and rejected sources
fname = './.override/accept_' + outfile + '.txt'
with open(fname, 'a') as fh:
for num in accepted_list:
fh.write('\n' + str(num))
fname = './.override/reject_' + outfile + '.txt'
with open(fname, 'a') as fh:
for num in rejected_list:
fh.write('\n' + str(num))
print(
"Manual overrides written to './.override/' and saved to source catalog. New overrides will be displayed the next time the rejection script is run."
)
# Process the new overrides, to be saved into the catalog
rejects = np.array(rejects)
acc = np.array([a[-2:] for a in accepted_list], dtype=int)
rej = np.array([r[-2:] for r in rejected_list], dtype=int)
rejects[acc] = 0
rejects[rej] = 1
# Save the catalog with new columns for SNR
catalog.add_column(Column(snr_vals), name='snr_band' + band)
catalog.add_column(np.invert(catalog.mask['snr_band' + band]).astype(int),
name='detected_band' + band)
catalog.add_column(Column(rejects), name='rejected')
catalog.write('./cat/cat_' + outfile + '_filtered.dat', format='ascii')
def fit_source(srcID,
img,
img_name,
band,
fit_bg=False,
bg_stddev_x=30,
bg_stddev_y=30,
bg_mean_x=0,
bg_mean_y=0,
zoom=1,
max_offset_in_beams=1,
max_radius_in_beams=5,
nonconv_img=None,
mask_size=1.5):
#this function fits a given source, and the background
#srcID : int
#name of source to fit in catalogs
#img : fits file
#fits file with source to fit
#img_name : str
#name of image for the directory where the fit plots will go
#band : str
#band of image to fit ('B3', 'B6', or 'B7')
#fit_bg : bool
#if False, do not fit background gaussian
#bg_stddev_x : float
#eyeballed estimate of stddev of the background source in pixels
#bg_stddev_y : float
#same as above in y direction
#bg_mean_x/y : float
#pixels away from center (origin) in x/y direction for background gaussian mean guess
#zoom : float
#amount of zoom, values greater than 1 are zoom ins
#ref_data_name = '/home/jotter/nrao/summer_research_2018/tables/dendro_ref_catalog_edited.fits'
ref_data_name = '/home/jotter/nrao/summer_research_2018/tables/ref_catalog_may21_b7.fits'
#ref_data_name = '/lustre/cv/observers/cv-12578/orion_disks/summer_research_2018/tables/ref_catalog_may21.fits'
ref_data = Table.read(ref_data_name)
fl = fits.open(img)
header = fl[0].header
img_data = fl[0].data.squeeze()
img_wcs = WCS(header).celestial
beam = radio_beam.Beam.from_fits_header(header)
pixel_scale = np.abs(
img_wcs.pixel_scale_matrix.diagonal().prod())**0.5 * u.deg
ppbeam = (beam.sr / (pixel_scale**2)).decompose().value
if nonconv_img is not None:
flnonconv = fits.open(nonconv_img)
nonconv_header = flnonconv[0].header
nonconv_beam = radio_beam.Beam.from_fits_header(nonconv_header)
ppbeam = (nonconv_beam.sr / (pixel_scale**2)).decompose().value
#now get ready to fit gaussians
#start by setting up save directory for images
gauss_save_dir = '/home/jotter/nrao/gauss_diags_may21/fitbg/' + img_name + '/'
#gauss_save_dir = f'/lustre/cv/observers/cv-12578/orion_disks/gauss_diags_may21/{img_name}/'
print('saving plots to ' + gauss_save_dir)
if not os.path.exists(gauss_save_dir):
os.makedirs(gauss_save_dir)
#now make region
rad = Angle(1, 'arcsecond') #radius used in region list
#src_ind = np.where(ref_data['D_ID']==srcID)[0]
src_ind = np.where(ref_data['B3_Seq'] == srcID)[0]
ra = ref_data['RA_B3'][src_ind].data[0]
dec = ref_data['DEC_B3'][src_ind].data[0]
center_reg = SkyCoord(ra, dec, unit='deg', frame='icrs')
reg = regions.CircleSkyRegion(
center=center_reg,
radius=1 * u.arcsecond,
meta={
'text':
str(ref_data['B3_Seq'][src_ind].data[0]) + '_xstddev_' +
str(bg_stddev_x) + '_ystddev_' + str(bg_stddev_y)
})
region_list = []
#valid_inds = np.where(np.isnan(ref_data[band+'_detect']) == False)[0]
for ind in range(len(ref_data)): #valid_inds:
if ref_data['B3_Seq'][ind] == srcID:
continue
ra_i = ref_data['RA_B3'][ind]
dec_i = ref_data['DEC_B3'][ind]
region_i = regions.CircleSkyRegion(center=SkyCoord(ra_i,
dec_i,
unit='deg',
frame='icrs'),
radius=1 * u.arcsecond)
region_list.append(region_i)
#print(region_list)
#print(reg)
cat_r = Angle(0.5, 'arcsecond') / zoom #radius for gaussian fitting
if fit_bg == True:
gauss_cat, fitim_bg = bg_gaussfit(
img,
reg,
region_list,
cat_r,
bg_stddev_x=bg_stddev_x,
bg_stddev_y=bg_stddev_y,
bg_mean_x=bg_mean_x,
bg_mean_y=bg_mean_y,
savepath=gauss_save_dir,
max_offset_in_beams=max_offset_in_beams,
max_offset_in_beams_bg=10,
max_radius_in_beams=max_radius_in_beams,
mask_size=mask_size)
#print('gauss_cat length ',len(gauss_cat))
#k = list(gauss_cat.keys())[0]
#if gauss_cat[k]['success'] == False:
# gauss_cat = gaussfit_cutoutim(img, fitim_bg, reg, region_list, cat_r, savepath=gauss_save_dir, max_offset_in_beams = max_offset_in_beams, max_radius_in_beams = max_radius_in_beams)
# success = gauss_cat[k]['success']
# print(F'ALTERNATIVE FIT SUCCESS: {success}')
else:
gauss_cat = gaussfit_catalog(img, [reg],
cat_r,
savepath=gauss_save_dir,
max_offset_in_beams=max_offset_in_beams,
max_radius_in_beams=max_radius_in_beams)
img_table = Table(names=('Seq', 'fwhm_maj_' + band, 'fwhm_maj_err_' + band,
'fwhm_min_' + band, 'fwhm_min_err_' + band,
'pa_' + band, 'pa_err_' + band,
'gauss_amp_' + band, 'gauss_amp_err_' + band,
'RA_' + band, 'RA_err_' + band, 'DEC_' + band,
'DEC_err_' + band),
dtype=('i4', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8',
'f8', 'f8', 'f8', 'f8', 'f8'))
for key in gauss_cat:
img_table.add_row(
(srcID, gauss_cat[key]['fwhm_major'],
gauss_cat[key]['e_fwhm_major'], gauss_cat[key]['fwhm_minor'],
gauss_cat[key]['e_fwhm_minor'], gauss_cat[key]['pa'],
gauss_cat[key]['e_pa'], gauss_cat[key]['amplitude'],
gauss_cat[key]['e_amplitude'], gauss_cat[key]['center_x'],
gauss_cat[key]['e_center_x'], gauss_cat[key]['center_y'],
gauss_cat[key]['e_center_y']))
#now measure deconvovled sizes and aperture flux measurements for each source
ap_flux_arr = []
ap_flux_err_arr = []
fwhm_maj_deconv_arr = []
fwhm_maj_deconv_err_arr = []
fwhm_min_deconv_arr = []
fwhm_min_deconv_err_arr = []
pa_deconv_arr = []
pa_deconv_err_arr = []
snr_arr = []
for row in range(
len(img_table)
): #now loop through sources in reference data and make measurements
ref_ind = np.where(ref_data['B3_Seq'] == img_table['Seq'][row])[0]
if True == True: #len(ref_ind > 0):
measured_source_size = radio_beam.Beam(
major=img_table['fwhm_maj_' + band][row] * u.arcsec,
minor=img_table['fwhm_min_' + band][row] * u.arcsec,
pa=(img_table['pa_' + band][row] - 90) * u.deg)
try:
deconv_size = measured_source_size.deconvolve(beam)
fwhm_maj_deconv_arr.append(deconv_size.major.value)
fwhm_min_deconv_arr.append(deconv_size.minor.value)
fwhm_maj_deconv_err_arr.append(img_table['fwhm_maj_err_' +
band][row])
fwhm_min_deconv_err_arr.append(img_table['fwhm_min_err_' +
band][row])
pa_deconv_arr.append(deconv_size.pa.value)
pa_deconv_err_arr.append(img_table['pa_err_' + band][row])
except ValueError:
fwhm_maj_deconv_arr.append(np.nan)
fwhm_min_deconv_arr.append(np.nan)
fwhm_maj_deconv_err_arr.append(np.nan)
fwhm_min_deconv_err_arr.append(np.nan)
pa_deconv_arr.append(np.nan)
pa_deconv_err_arr.append(np.nan)
pix_major_fwhm = (
(img_table['fwhm_maj_' + band][row] * u.arcsec).to(u.degree) /
pixel_scale).decompose()
pix_minor_fwhm = (
(img_table['fwhm_min_' + band][row] * u.arcsec).to(u.degree) /
pixel_scale).decompose()
center_coord = SkyCoord(img_table['RA_' + band][row],
img_table['DEC_' + band][row],
frame='icrs',
unit=(u.deg, u.deg))
center_coord_pix = center_coord.to_pixel(img_wcs)
center_coord_pix_reg = regions.PixCoord(center_coord_pix[0],
center_coord_pix[1])
pos_ang = (img_table['pa_' + band][row] - 90) * u.deg
ellipse_reg = regions.EllipsePixelRegion(center_coord_pix_reg,
pix_major_fwhm.value * 2,
pix_minor_fwhm.value * 2,
angle=pos_ang)
size = pix_major_fwhm * 2.1
ap_mask = ellipse_reg.to_mask()
cutout_mask = ap_mask.cutout(img_data)
aperture_flux = np.nansum(cutout_mask[ap_mask.data == 1]) / ppbeam
npix = len(cutout_mask[ap_mask.data == 1])
#now make annulus for measuring background and error
annulus_width = 15 #pixels
annulus_radius = img_table[
'fwhm_maj_' + band][row] * u.arcsecond #0.1*u.arcsecond
annulus_radius_pix = (annulus_radius.to(u.degree) /
pixel_scale).decompose()
#cutout image
cutout = Cutout2D(img_data,
center_coord_pix,
annulus_radius * 2.5,
img_wcs,
mode='partial')
cutout_center = regions.PixCoord(cutout.center_cutout[0],
cutout.center_cutout[1])
#define aperture regions for SNR
innerann_reg = regions.CirclePixelRegion(cutout_center,
annulus_radius_pix.value)
outerann_reg = regions.CirclePixelRegion(
cutout_center, annulus_radius_pix.value + annulus_width)
#Make masks from aperture regions
annulus_mask = mask(outerann_reg, cutout) - mask(
innerann_reg, cutout)
# Calculate the SNR and aperture flux sums
pixels_in_annulus = cutout.data[annulus_mask.astype(
'bool')] #pixels within annulus
bg_rms = median_abs_deviation(pixels_in_annulus)
ap_bg_rms = bg_rms / np.sqrt(
npix / ppbeam) #rms/sqrt(npix/ppbeam) - rms error per beam
bg_median = np.median(pixels_in_annulus)
pix_bg = bg_median * npix / ppbeam
#flux corrections
ap_flux_bgcorrect = aperture_flux - pix_bg
ap_flux_correct = ap_flux_bgcorrect + ap_flux_bgcorrect * (
1 - special.erf(2 * np.sqrt(np.log(2)))
) #flux correction for summing within 2*fwhm
print(
f'Background: {pix_bg}, Gauss amp: {img_table["gauss_amp_"+band][row]}'
)
print(f'peak pixel: {np.nanmax(cutout_mask[ap_mask.data==1])}')
ap_flux_err_arr.append(ap_bg_rms)
ap_flux_arr.append(ap_flux_correct)
snr_arr.append(img_table['gauss_amp_' + band][row] / bg_rms)
cols = [
'ap_flux_' + band, 'ap_flux_err_' + band, 'fwhm_maj_deconv_' + band,
'fwhm_maj_deconv_err_' + band, 'fwhm_min_deconv_' + band,
'fwhm_min_deconv_err_' + band, 'pa_deconv_' + band,
'pa_deconv_err_' + band, 'SNR_' + band
]
arrs = [
ap_flux_arr, ap_flux_err_arr, fwhm_maj_deconv_arr,
fwhm_maj_deconv_err_arr, fwhm_min_deconv_arr, fwhm_min_deconv_err_arr,
pa_deconv_arr, pa_deconv_err_arr, snr_arr
]
for c in range(len(cols)):
img_table.add_column(Column(np.array(arrs[c])), name=cols[c])
img_table.add_column(Column(img_table['fwhm_maj_deconv_' + band] /
img_table['fwhm_min_deconv_' + band]),
name='ar_deconv_' + band)
return img_table
def flux(region):
# import the catalog file, get names of bands
filename = glob('./cat/mastercat_region{}*'.format(region))[0]
catalog = Table(Table.read(filename, format='ascii'), masked=True)
catalog.sort('_idx')
bands = np.array(filename.split('bands_')[1].split('.dat')[0].split('_'),
dtype=int)
n_bands = len(bands)
n_rows = len(catalog)
ellipse_npix_col = MaskedColumn(length=len(catalog),
name='ellipse_npix',
mask=True)
circ1_npix_col = MaskedColumn(length=len(catalog),
name='circ1_npix',
mask=True)
circ2_npix_col = MaskedColumn(length=len(catalog),
name='circ2_npix',
mask=True)
circ3_npix_col = MaskedColumn(length=len(catalog),
name='circ3_npix',
mask=True)
n_rejected = 0
for i in range(n_bands):
band = bands[i]
# Load image file for this band
print("\nLoading image file for region {} in band {} (Image {}/{})".
format(region, band, i + 1, n_bands))
imfile = grabfileinfo(region, band)[0]
contfile = fits.open(imfile)
data = contfile[0].data.squeeze()
# Set up wcs, beam, and pixel scale for this image
mywcs = wcs.WCS(contfile[0].header).celestial
beam = radio_beam.Beam.from_fits_header(contfile[0].header)
pixel_scale = np.abs(
mywcs.pixel_scale_matrix.diagonal().prod())**0.5 * u.deg
ppbeam = (beam.sr / (pixel_scale**2)).decompose().value
print('ppbeam: ', ppbeam)
data = data / ppbeam
# Set up columns for each aperture
peak_flux_col = MaskedColumn(length=len(catalog),
name='peak_flux_band{}'.format(band),
mask=True)
annulus_median_col = MaskedColumn(
length=len(catalog),
name='annulus_median_band{}'.format(band),
mask=True)
annulus_rms_col = MaskedColumn(length=len(catalog),
name='annulus_rms_band{}'.format(band),
mask=True)
ellipse_flux_col = MaskedColumn(
length=len(catalog),
name='ellipse_flux_band{}'.format(band),
mask=True)
circ1_flux_col = MaskedColumn(length=len(catalog),
name='circ1_flux_band{}'.format(band),
mask=True)
circ2_flux_col = MaskedColumn(length=len(catalog),
name='circ2_flux_band{}'.format(band),
mask=True)
circ3_flux_col = MaskedColumn(length=len(catalog),
name='circ3_flux_band{}'.format(band),
mask=True)
ellipse_rms_col = MaskedColumn(length=len(catalog),
name='ellipse_rms_band{}'.format(band),
mask=True)
circ1_rms_col = MaskedColumn(length=len(catalog),
name='circ1_rms_band{}'.format(band),
mask=True)
circ2_rms_col = MaskedColumn(length=len(catalog),
name='circ2_rms_band{}'.format(band),
mask=True)
circ3_rms_col = MaskedColumn(length=len(catalog),
name='circ3_rms_band{}'.format(band),
mask=True)
circ1_r, circ2_r, circ3_r = 5e-6 * u.deg, 1e-5 * u.deg, 1.5e-5 * u.deg
print('Photometering sources')
pb = ProgressBar(len(catalog[np.where(catalog['rejected'] == 0)]))
masks = []
datacube = []
rejects = []
snr_vals = []
names = []
# Iterate over sources, extracting ellipse parameters
for j in range(n_rows):
if catalog['rejected'][j] == 1:
continue
source = catalog[j]
x_cen = source['x_cen'] * u.deg
y_cen = source['y_cen'] * u.deg
major = source['major_fwhm'] * u.deg
minor = source['minor_fwhm'] * u.deg
pa = source['position_angle'] * u.deg
annulus_width = 1e-5 * u.deg
center_distance = 1e-5 * u.deg
# Convert to pixel coordinates
position = coordinates.SkyCoord(x_cen,
y_cen,
frame='icrs',
unit=(u.deg, u.deg))
pix_position = np.array(position.to_pixel(mywcs))
pix_major = major / pixel_scale
pix_minor = minor / pixel_scale
# Create cutout
size = np.max([
circ3_r.value,
major.value + center_distance.value + annulus_width.value
]) * 2.2 * u.deg
try:
cutout = Cutout2D(data, position, size, mywcs, mode='partial')
except NoOverlapError:
catalog['rejected'][j] = 1
pb.update()
continue
cutout_center = regions.PixCoord(cutout.center_cutout[0],
cutout.center_cutout[1])
datacube.append(cutout.data)
# create all aperture shapes
ellipse_reg = regions.EllipsePixelRegion(cutout_center,
pix_major * 2.,
pix_minor * 2.,
angle=pa)
circ1_reg = regions.CirclePixelRegion(cutout_center,
circ1_r / pixel_scale)
circ2_reg = regions.CirclePixelRegion(cutout_center,
circ2_r / pixel_scale)
circ3_reg = regions.CirclePixelRegion(cutout_center,
circ3_r / pixel_scale)
innerann_reg = regions.CirclePixelRegion(
cutout_center, center_distance / pixel_scale + pix_major)
outerann_reg = regions.CirclePixelRegion(
cutout_center, center_distance / pixel_scale + pix_major +
annulus_width / pixel_scale)
annulus_mask = mask(outerann_reg, cutout) - mask(
innerann_reg, cutout)
# get flux information from regions
ellipse_flux, ellipse_rms, peak_flux, ellipse_mask, ellipse_npix = apsum(
ellipse_reg, cutout)
circ1_flux, circ1_rms, _, circ1_mask, circ1_npix = apsum(
circ1_reg, cutout)
circ2_flux, circ2_rms, _, circ2_mask, circ2_npix = apsum(
circ2_reg, cutout)
circ3_flux, circ3_rms, _, circ3_mask, circ3_npix = apsum(
circ3_reg, cutout)
annulus_rms = rms(cutout.data[annulus_mask.astype('bool')])
annulus_median = np.median(
cutout.data[annulus_mask.astype('bool')])
# Add grid plot mask to list
masklist = [
ellipse_mask, annulus_mask, circ1_mask, circ2_mask, circ3_mask
]
masks.append(masklist)
# add fluxes to appropriate columns
peak_flux_col[j] = peak_flux
ellipse_flux_col[j], ellipse_rms_col[j] = ellipse_flux, ellipse_rms
circ1_flux_col[j], circ1_rms_col[j] = circ1_flux, circ1_rms
circ2_flux_col[j], circ2_rms_col[j] = circ2_flux, circ2_rms
circ3_flux_col[j], circ3_rms_col[j] = circ3_flux, circ3_rms
ellipse_npix_col[j] = ellipse_npix
circ1_npix_col[j] = circ1_npix
circ2_npix_col[j] = circ2_npix
circ3_npix_col[j] = circ3_npix
annulus_median_col[j] = annulus_median
annulus_rms_col[j] = annulus_rms
catalog['snr_band' + str(band)][j] = peak_flux / annulus_rms
snr_vals.append(peak_flux / annulus_rms)
names.append(catalog['_idx'][j])
# Secondary rejection
rejected = 0
lowest_flux = np.min(
[ellipse_flux, circ1_flux, circ2_flux, circ3_flux])
#if lowest_flux <= annulus_median*ellipse_npix or lowest_flux < 0:
if lowest_flux < 0:
catalog['rejected'][j] = 1
n_rejected += 1
rejected = 1
rejects.append(rejected)
pb.update()
# Plot the grid of sources
plot_grid(datacube, masks, rejects, snr_vals, names)
plt.suptitle('region={}, band={}'.format(region, band))
plt.show(block=False)
# add columns to catalog
catalog.add_columns([
peak_flux_col,
ellipse_flux_col,
ellipse_rms_col,
circ1_flux_col,
circ1_rms_col,
circ2_flux_col,
circ2_rms_col,
circ3_flux_col,
circ3_rms_col,
])
catalog.add_columns([
ellipse_npix_col, circ1_npix_col, circ2_npix_col, circ3_npix_col,
annulus_median_col, annulus_rms_col
])
print("\n{} sources flagged for secondary rejection".format(n_rejected))
# save catalog
catalog = catalog[sorted(catalog.colnames)]
catalog.write(filename.split('.dat')[0] + '_photometered.dat',
format='ascii')
print("\nMaster catalog saved as '{}'".format(
filename.split('.dat')[0] + '_photometered.dat'))
def ginga_canvas_object_to_astropy_region(obj):
"""
Convert a Ginga canvas object to an AstroPy region object.
Parameters
----------
obj : subclass of `~ginga.canvas.CanvasObject`
The Ginga canvas object to be converted
Returns
-------
r : subclass of `~regions.PixelRegion`
The corresponding AstroPy region object
"""
if not HAVE_REGIONS:
raise ValueError(
"Please install the Astropy 'regions' package to use this function"
)
dc = get_canvas_types()
r = None
if isinstance(obj, (dc.Circle, )):
r = regions.CirclePixelRegion(center=regions.PixCoord(x=obj.x,
y=obj.y),
radius=obj.radius)
elif isinstance(obj, (dc.Ellipse, )):
r = regions.EllipsePixelRegion(center=regions.PixCoord(x=obj.x,
y=obj.y),
width=obj.xradius * 2,
height=obj.yradius * 2,
angle=obj.rot_deg * u.deg)
elif isinstance(obj, (dc.Text, )):
r = regions.TextPixelRegion(center=regions.PixCoord(x=obj.x, y=obj.y),
text=obj.text)
r.visual['textangle'] = str(obj.rot_deg)
elif isinstance(obj, (dc.Point, )):
r = regions.PointPixelRegion(center=regions.PixCoord(x=obj.x, y=obj.y))
style = pt_ginga.get(obj.style, '*')
r.visual['symbol'] = style
elif isinstance(obj, (dc.Line, )):
r = regions.LinePixelRegion(start=regions.PixCoord(x=obj.x1, y=obj.y1),
end=regions.PixCoord(x=obj.x2, y=obj.y2))
elif isinstance(obj, (dc.Box, )):
r = regions.RectanglePixelRegion(center=regions.PixCoord(x=obj.x,
y=obj.y),
width=obj.xradius * 2,
height=obj.yradius * 2,
angle=obj.rot_deg * u.deg)
elif isinstance(obj, (dc.Polygon, )):
x, y = np.asarray(obj.points).T
r = regions.PolygonPixelRegion(vertices=regions.PixCoord(x=x, y=y))
elif isinstance(obj, (dc.Annulus, )) and obj.atype == 'circle':
rin = obj.radius
rout = rin + obj.width
r = regions.CircleAnnulusPixelRegion(center=regions.PixCoord(x=obj.x,
y=obj.y),
inner_radius=rin,
outer_radius=rout)
elif isinstance(obj, (dc.Annulus2R, )) and obj.atype == 'ellipse':
r = regions.EllipseAnnulusPixelRegion(
center=regions.PixCoord(x=obj.x, y=obj.y),
inner_width=obj.xradius * 2,
inner_height=obj.yradius * 2,
outer_width=obj.xradius * 2 + obj.xwidth * 2,
outer_height=obj.yradius * 2 + obj.ywidth * 2,
angle=obj.rot_deg * u.deg)
elif isinstance(obj, (dc.Annulus2R, )) and obj.atype == 'box':
r = regions.RectangleAnnulusPixelRegion(
center=regions.PixCoord(x=obj.x, y=obj.y),
inner_width=obj.xradius * 2,
inner_height=obj.yradius * 2,
outer_width=obj.xradius * 2 + obj.xwidth * 2,
outer_height=obj.yradius * 2 + obj.ywidth * 2,
angle=obj.rot_deg * u.deg)
else:
raise ValueError("Don't know how to convert this object")
# Set visual styling attributes
r.visual['color'] = obj.color
if hasattr(obj, 'font'):
r.visual['font'] = obj.font
if obj.fontsize is not None:
r.visual['fontsize'] = str(obj.fontsize)
if hasattr(obj, 'linewidth'):
r.visual['linewidth'] = obj.linewidth
if hasattr(obj, 'fill'):
r.visual['fill'] = obj.fill = r.visual.get('fill', False)
# Limited support for other metadata
r.meta['edit'] = 1 if obj.editable else 0
meta = obj.get_data()
if meta is not None and meta.get('name', None) is not None:
r.meta['name'] = meta.get('name')
return r
def reject_sources(name,
catname,
datname,
min_snr=5,
max_size=None,
max_size_ID=None,
flux_type='peak'):
#catname: name of catalog of sources, with required columns 'gauss_x_'+name, 'gauss_y_'+name, 'FWHM_major_'+name, 'FWHM_minor_'+name, and 'position_angle_'+name
#datname: name of data fits image
#min_snr: minimum SNR value, all sources with SNR below this will be rejected
#max_size: maximum major axis radius for ellipse around source, in sigma
#max_size_ID: alternatively, give the index of a source to set that source's radius to the maximum
#flux_type: if 'peak', chooses the brightest pixel, if 'percentile', flux measured by average of top 10% of pixels
catalog = fits.getdata(catname)
catalog = Table(catalog)
bad_inds = np.where(np.isnan(catalog['ap_flux_' + name]) == True)
catalog.remove_rows(bad_inds)
fl = fits.open(datname)
data = fl[0].data.squeeze()
header = fl[0].header
mywcs = WCS(header).celestial
sigma_to_FWHM = 2 * np.sqrt(2 * np.log(2))
pixel_scale = np.abs(mywcs.pixel_scale_matrix.diagonal().prod()
)**0.5 * u.deg #for conversion to pixels
snr_vals = []
cutout_images = []
masks = []
bg_arr = []
bg_arr2 = []
reject = np.full(len(catalog), False)
for i in range(len(catalog)):
x_cen = catalog['gauss_x_' + name][i] * u.deg
y_cen = catalog['gauss_y_' + name][i] * u.deg
major_fwhm = (catalog['FWHM_major_' + name][i] * u.arcsec).to(u.degree)
minor_fwhm = (catalog['FWHM_minor_' + name][i] * u.arcsec).to(u.degree)
position_angle = catalog['position_angle_' + name][i] * u.deg
annulus_width = 15
center_pad = 10 #pad between ellipse and inner radius
# Define some ellipse properties in pixel coordinates
position = SkyCoord(x_cen, y_cen, frame='icrs', unit=(u.deg, u.deg))
pix_position = np.array(position.to_pixel(mywcs))
pix_major_fwhm = major_fwhm / pixel_scale
pix_minor_fwhm = minor_fwhm / pixel_scale
# Cutout section of the image we care about, to speed up computation time
size = (
(center_pad + annulus_width) * pixel_scale + major_fwhm
) * 2.2 #2.2 is arbitrary to get entire annulus and a little extra
cutout = Cutout2D(data, position, size, mywcs,
mode='partial') #cutout of outer circle
cutout_center = regions.PixCoord(
cutout.center_cutout[0],
cutout.center_cutout[1]) #center of the cutout in pixel coords
# Define the aperture regions needed for SNR
ellipse_reg = regions.EllipsePixelRegion(cutout_center,
pix_major_fwhm * 2.,
pix_minor_fwhm * 2.,
angle=position_angle)
innerann_reg = regions.CirclePixelRegion(cutout_center,
center_pad + pix_major_fwhm)
outerann_reg = regions.CirclePixelRegion(
cutout_center, center_pad + pix_major_fwhm + annulus_width)
# Make masks from aperture regions
ellipse_mask = mask(ellipse_reg, cutout)
annulus_mask = mask(outerann_reg, cutout) - mask(innerann_reg, cutout)
# Calculate the SNR and aperture flux sums
pixels_in_annulus = cutout.data[annulus_mask.astype(
'bool')] #pixels within annulus
pixels_in_ellipse = cutout.data[ellipse_mask.astype(
'bool')] #pixels in ellipse
bg_rms = rms(pixels_in_annulus)
bg_mean = np.mean(pixels_in_annulus)
bg_median = np.median(pixels_in_annulus)
if flux_type == 'peak':
peak_flux = catalog['peak_flux_' + name][i]
if flux_type == 'percentile':
top_percent = np.nanpercentile(pixels_in_ellipse, 90)
peak_flux = np.mean(
pixels_in_ellipse[pixels_in_ellipse > top_percent])
snr = peak_flux / bg_rms
catalog['ap_flux_err_' + name][i] = bg_rms
bg_arr.append(bg_mean)
bg_arr2.append(bg_median)
if snr < min_snr: #if low snr, reject
reject[i] = True
if max_size_ID is not None:
if catalog['major_sigma'][i] > catalog['major_sigma'][
max_size_ID] + 0.01 / 3600: #if too big a source, reject
reject[i] = True
if max_size is not None:
if catalog['major_sigma'][
i] > max_size: #if too big a source, reject
reject[i] = True
snr_vals.append(snr)
cutout_images.append(cutout.data)
masks.append(ellipse_mask + annulus_mask)
catalog['bg_mean_' + name] = bg_arr
catalog['bg_median_' + name] = bg_arr2
plot_grid(cutout_images, masks, reject, snr_vals, catalog['_idx_' + name])
plt.show(block=False)
line_remove = input(
'enter id values for sources to exclude from the catalog, seperated by whitespace: '
)
man_input_rem = np.array(line_remove.split(), dtype='int')
id_ind_true = np.where(
np.in1d(catalog['_idx_' + name], man_input_rem) == True)
reject[id_ind_true] = True
line_keep = input(
'enter id values for removed sources to include from the catalog, seperated by whitespace: '
)
man_input_keep = np.array(line_keep.split(), dtype='int')
id_ind_false = np.where(
np.in1d(catalog['_idx_' + name], man_input_keep) == True)
reject[id_ind_false] = False
rej_ind = np.where(reject == True)
catalog.remove_rows(rej_ind)
return catalog