def mass_photometry(fname, outfile):
filenames = glob('/lustre/aoc/students/bmcclell/w51/' + fname)
objs = []
print('Loading files')
pb = ProgressBar(len(filenames))
for f in filenames:
rs = dendrocat.RadioSource(fits.open(f))
objs.append(rs)
pb.update()
#n = np.shape(objs[0].data)[0]
#center = regions.PixCoord(n/2, n/2)
#reg = regions.CirclePixelRegion(center, 3200)
#mask = reg.to_mask()
#img = mask.to_image((n, n)).astype(bool)
#objs[0].data = np.where(img==True, objs[0].data, np.nan)
# Debugging
#plt.figure()
#plt.imshow(objs[0].data)
#plt.show()
#objs[0].threshold = 4.5
#objs[0].min_value = 1.1e-4
#objs[0].min_delta = 1.2*objs[0].min_value
#objs[0].to_catalog()
#objs[0].autoreject()
#objs[0].reject([44001, 44032])
#dendrocat.utils.save_regions(objs[0].catalog, '/users/bmcclell/nrao/reg/test_mass_photometry.reg')
#print('Autorejection complete')
t = Table.read('/users/bmcclell/nrao/cat/w51IRS2_photometered.dat',
format='ascii')
#for col in t.colnames:
# if 'GHz' in col:
# t.remove_column(col)
mc = dendrocat.MasterCatalog(*objs, catalog=t)
print('\nMaster Catalog made')
start = time.time()
mc.photometer(dendrocat.ellipse)
stop = time.time()
print('Ellipse apertures photometered. Time: {} s'.format(stop - start))
start = time.time()
mc.photometer(dendrocat.annulus)
stop = time.time()
print('Annulus apertures photometered. Time: {} s'.format(stop - start))
start = time.time()
mc.catalog.write(outfile, format='ascii', overwrite=True)
stop = time.time()
print('Catalog written. Time: {} s'.format(stop - start))
from astropy.table import Table
import astropy.units as u
from astropy.io import fits
import numpy as np
import dendrocat
t = Table(Table.read(
'/users/bmcclell/nrao/cat/43-93-226GHz_photometered_ellipse.dat',
format='ascii'),
masked=True)
rs1 = dendrocat.RadioSource(
fits.open(
'/lustre/aoc/students/bmcclell/w51/W51e2_cont_briggsSC_tclean.image.fits.gz'
))
rs2 = dendrocat.RadioSource(
fits.open(
'/lustre/aoc/students/bmcclell/w51/w51e2_sci.spw0_1_2_3_4_5_6_7_8_9_10_11_12_13_14_15_16_17_18_19.mfs.I.manual.image.tt0.pbcor.fits.gz'
))
rs3 = dendrocat.RadioSource(
fits.open(
'/lustre/aoc/students/bmcclell/w51/W51e2w_QbandAarray_cont_spws_continuum_cal_clean_2terms_robust0_incrementalselfcal8.image.tt0.pbcor.icrs.fits'
))
rs3.nu = 45 * u.GHz
rs3.freq_id = '45GHz'
rs3.set_metadata()
mc = dendrocat.MasterCatalog(rs1, rs2, rs3, catalog=t)
nus, fluxes, errs = mc.plot_sed(mc.catalog[23],
aperture='ellipse',
from astropy.table import Table
from astropy.io import fits
import dendrocat
import astropy.units as u
import numpy as np
import matplotlib.gridspec as gs
import matplotlib.pyplot as plt
from collections import OrderedDict
irs2 = Table.read('/users/bmcclell/nrao/cat/w51IRS2_photometered.dat',
format='ascii')
n1 = dendrocat.RadioSource(
fits.open(
'/users/bmcclell/Data/irs2/w51n_band6_226GHz_adjustedRADEC.fits'))
n2 = dendrocat.RadioSource(
fits.open(
'/users/bmcclell/Data/irs2/w51north_sci.spw0_1_2_3_4_5_6_7_8_9_10_11_12_13_14_15_16_17_18_19.mfs.I.manual.image.tt0.pbcor.fits'
))
n3 = dendrocat.RadioSource(
fits.open('/users/bmcclell/Data/irs2/w51n_bandQ_45GHz_adjustedRADEC.fits'))
n3.nu = 45.0 * u.GHz
n3.freq_id = '45.0GHz'
n3.set_metadata()
mc = dendrocat.MasterCatalog(n1, n2, n3, catalog=irs2)
accepted = mc.catalog[mc.catalog['rejected'] == 0]
for i in range(len(accepted)):
nus, fluxes, errs = mc.plotsedgrid(
from astropy.io import fits
from astropy.table import Table
import dendrocat
import astropy.units as u
t = Table(Table.read(
'/users/bmcclell/nrao/cat/45-93-226GHz_photometered_adjustedRADEC.dat',
format='ascii'),
masked=True)
rs1 = dendrocat.RadioSource(
fits.open(
'/lustre/aoc/students/bmcclell/w51/W51e2_cont_briggsSC_tclean.image.fits.gz'
))
rs2 = dendrocat.RadioSource(
fits.open('/users/bmcclell/Data/W51e2_band3_93GHz_adjustedRADEC.fits'))
rs3 = dendrocat.RadioSource(
fits.open('/users/bmcclell/Data/W51e2w_QbandAarray_adjustedRADEC.fits'))
rs3.nu = 45 * u.GHz
rs3.freq_id = '45GHz'
rs3.set_metadata()
mc = dendrocat.MasterCatalog(rs1, rs2, rs3, catalog=t)
ext = Table.read('/users/bmcclell/Data/EVLA_VLA_PointSourcePhotometry.ipac',
format='ipac')
ra = 'gracen'
dec = 'gdeccen'
freq = 'Frequency'
import dendrocat
from astropy.io import fits
import matplotlib.pyplot as plt
rs1 = dendrocat.RadioSource(
fits.open(
'/lustre/aoc/students/bmcclell/w51/W51e2_cont_briggsSC_tclean.image.fits.gz'
))
rs2 = dendrocat.RadioSource(
fits.open(
'/lustre/aoc/students/bmcclell/w51/w51e2_sci.spw0_1_2_3_4_5_6_7_8_9_10_11_12_13_14_15_16_17_18_19.mfs.I.manual.image.tt0.pbcor.fits.gz'
))
rs1.autoreject()
rs2.autoreject()
rs1.reject([0, 12, 59, 89, 98, 99, 106])
rs2.reject([8, 9, 14, 18, 21, 42])
mc = dendrocat.match(rs1, rs2)
dendrocat.ffplot(mc, rs2, rs1)
plt.show()
import dendrocat
from astropy.io import fits
import astropy.units as u
from astropy.table import Table
from dendrocat.aperture import *
rs1 = dendrocat.RadioSource(
fits.open(
'/media/connor/Lore/Summer2018/Data/W51e2_cont_briggsSC_tclean.image.fits.gz'
))
rs2 = dendrocat.RadioSource(
fits.open(
'/media/connor/Lore/Summer2018/Data/W51e2_band3_93GHz_adjustedRADEC.fits'
))
rs3 = dendrocat.RadioSource(
fits.open(
'/media/connor/Lore/Summer2018/Data/W51e2w_QbandAarray_adjustedRADEC.fits'
))
rs3.nu = 45.0 * u.GHz
rs3.freq_id = '45.0GHz'
rs3.set_metadata()
rs1.autoreject()
rs2.autoreject()
rs1.add_sources(
Table.read('/media/connor/Lore/Summer2018/nrao/cat/special_source.dat',
format='ascii'))
rs1.reject(
[226000, 226008, 226016, 226023, 226028, 226085, 226124, 226135, 226137])
import dendrocat
from astropy.io import fits
import astropy.units as u
import matplotlib.pyplot as plt
rs1 = dendrocat.RadioSource(
fits.open(
'/users/bmcclell/Data/e1e2/W51e2_cont_briggsSC_tclean.image.fits.gz'))
rs1.to_catalog()
rs1.autoreject()
circ = dendrocat.aperture.Circle([40, 40], 20 * u.pix)
rs1.plot_grid(source_aperture=circ)
plt.show()
def dendrocatify(regname,
fn,
threshold=4,
min_npix=100,
min_delta=1,
cutout=None,
cutout_header=None,
snr_threshold=5,
noiselevel_table='noise_levels.json'):
noisefilepath = f"{catalog_path}/{noiselevel_table}"
if os.path.exists(noisefilepath):
with open(noisefilepath, 'r') as fh:
noiselevel_table = json.load(fh)
else:
noiselevel_table = {}
print(f"Starting region {regname}: {fn}")
if cutout_header is None or cutout is None:
fh = fits.open(fn)
data = fh[0].data
header = fh[0].header
# LONPOLE isn't very relevant and LATPOLE is not part of the coordinate
# systems we're interested in. From Calabretta 2002: "LATPOLEa is never
# required for zenithal projections"
try:
del header['LONPOLE']
del header['LATPOLE']
except KeyError:
pass
ww = wcs.WCS(header)
pixscale = wcs.utils.proj_plane_pixel_area(ww)**0.5 * u.deg
reg = cutout_regions[regname].to_pixel(ww)
mask = reg.to_mask()
data_sm = convolve_fft(data,
Gaussian2DKernel(
(45 * u.arcsec / pixscale).decompose()),
allow_huge=True)
data_filtered = data - data_sm
err = mad_std(data_filtered)
cutout = mask.multiply(data_filtered)
frame = wcs.utils.wcs_to_celestial_frame(ww)
# TODO: check that this is working - the regions output seem to be offset?
ww_cutout = ww[mask.bbox.iymin:mask.bbox.iymax,
mask.bbox.ixmin:mask.bbox.ixmax]
# add beam parameters to header
cutout_header = ww_cutout.to_header()
cutout_header['BMAJ'] = mustang_beam_fwhm.to(u.deg).value
cutout_header['BMIN'] = mustang_beam_fwhm.to(u.deg).value
cutout_header['BUNIT'] = 'Jy/beam'
cutout_header['TELESCOP'] = 'GBT'
cutout_header['FREQ'] = mustang_central_frequency.to(u.Hz).value
avg_noise = mad_std(data, ignore_nan=True)
filt_avg_noise = mad_std(cutout, ignore_nan=True)
noiselevel_table[regname] = {
'noise': avg_noise,
'filtered_noise': filt_avg_noise
}
with open(noisefilepath, 'w') as fh:
json.dump(noiselevel_table, fh)
print(
f"Beginning cataloging. Noise is {avg_noise:0.4f} before and {filt_avg_noise:0.4f} after filtering"
)
radiosource = dendrocat.RadioSource(
[fits.PrimaryHDU(data=cutout, header=cutout_header)])
radiosource.nu = mustang_central_frequency
radiosource.freq_id = 'MUSTANG'
radiosource.set_metadata()
print("Running to_dendrogram")
radiosource.to_dendrogram(
min_value=err * threshold,
min_delta=err * min_delta,
min_npix=min_npix,
)
print("Making plot grid")
radiosource.plot_grid(
skip_rejects=False,
outfile=
f'{catalog_figure_path}/{regname}_dendrocat_thr{threshold}_minn{min_npix}_mind{min_delta}_prerejection.png',
figurekwargs={'num': 1},
)
pl.figure(1).clf()
#radiosource.autoreject(threshold=5.0)
rejected = radiosource.catalog[
'MUSTANG_dend_flux'] / filt_avg_noise < snr_threshold
#radiosource.catalog.add_column(Column(rejected, name='rejected'))
radiosource.catalog['rejected'] = rejected.astype('int')
print("Rejected {0}, kept {1}, of {2} total sources".format(
radiosource.catalog['rejected'].sum(),
(1 - radiosource.catalog['rejected']).sum(), len(radiosource.catalog)))
radiosource.plot_grid(
skip_rejects=False,
outfile=
f'{catalog_figure_path}/{regname}_dendrocat_thr{threshold}_minn{min_npix}_mind{min_delta}_postrejection.png',
figurekwargs={'num': 1},
)
pl.figure(1).clf()
#dend = astrodendro.Dendrogram.compute(cutout,
# min_value=err*threshold,
# min_delta=err*min_delta,
# verbose=True, min_npix=min_npix,
# wcs=ww_cutout)
dend = radiosource.dendrogram
pl.figure(2).clf()
ax = pl.gca()
ax.cla()
pl.imshow(cutout,
cmap='gray_r',
interpolation='none',
origin='lower',
vmax=0.01,
vmin=-0.001)
pltr = dend.plotter()
print("Contouring dendrogram leaves")
for struct in ProgressBar(dend.leaves):
pltr.plot_contour(ax,
structure=struct,
colors=['r'],
linewidths=[0.9],
zorder=5)
if struct.parent:
while struct.parent:
struct = struct.parent
pltr.plot_contour(ax,
structure=struct,
colors=[(0, 1, 0, 1)],
linewidths=[0.5])
cntr = pl.gca().collections
pl.setp([x for x in cntr if x.get_color()[0, 0] == 1], linewidth=0.25)
pl.setp([x for x in cntr if x.get_color()[0, 1] == 1], linewidth=0.25)
pl.savefig(
f'{catalog_figure_path}/{regname}_dend_contour_thr{threshold}_minn{min_npix}_mind{min_delta}.pdf'
)
# zoom only for G31
if regname == 'G31':
pl.axis((1125.4006254228616, 1670.3650637799306, 1291.6829155596627,
1871.8063499397681))
pl.setp([x for x in cntr if x.get_color()[0, 0] == 1],
linewidth=0.75) # Red
pl.setp([x for x in cntr if x.get_color()[0, 1] == 1],
linewidth=0.5) # Green
pl.savefig(
f'{catalog_figure_path}/{regname}_dend_contour_thr{threshold}_minn{min_npix}_mind{min_delta}_zoom.pdf'
)
#metadata = {'data_unit': u.Jy / u.beam,
# 'beam_major': 8*u.arcsec,
# 'beam_minor': 8*u.arcsec,
# 'wcs': ww_cutout,
# 'spatial_scale': wcs.utils.proj_plane_pixel_scales(ww_cutout).mean()*u.deg,
# }
#ppcat = astrodendro.pp_catalog(dend, metadata)
ppcat = radiosource.catalog
mastercatalog = dendrocat.MasterCatalog(radiosource, catalog=ppcat)
aperture1 = Circle([0, 0], 10 * u.arcsec, name='10as', frame=frame.name)
aperture2 = Circle([0, 0], 15 * u.arcsec, name='15as', frame=frame.name)
background = Annulus([0, 0],
inner=15 * u.arcsec,
outer=20 * u.arcsec,
name='background',
frame=frame.name)
print("Beginning photometry ...")
mastercatalog.photometer(aperture1, aperture2, background)
print()
source_center = coordinates.SkyCoord(mastercatalog.catalog['x_cen'],
mastercatalog.catalog['y_cen'],
unit=(u.deg, u.deg),
frame=frame.name)
source_name = [
"G{0:0.3f}{1:+0.3f}".format(sc.galactic.l.deg, sc.galactic.b.deg)
for sc in source_center
]
mastercatalog.catalog.add_column(
Column(name='SourceName', data=source_name))
mastercatalog.catalog.write(
f'{catalog_path}/{regname}_dend_contour_thr{threshold}_minn{min_npix}_mind{min_delta}.ipac',
format='ascii.ipac')
regs = ppcat_to_regions(ppcat, frame.name)
regions.write_ds9(
regions=regs,
filename=
f'{catalog_path}/{regname}_dend_contour_thr{threshold}_minn{min_npix}_mind{min_delta}.reg'
)
return radiosource, dend, mastercatalog, ppcat
