def writer(filename, catalog, fmt=None):
"""
construct a dict of the data
this method preserves the data types in the VOTable
"""
tab_dict = {}
name_list = []
for name in catalog[0].names:
col_name = name
if catalog[0].galactic:
if name.startswith('ra'):
col_name = 'lon' + name[2:]
elif name.endswith('ra'):
col_name = name[:-2] + 'lon'
elif name.startswith('dec'):
col_name = 'lat' + name[3:]
elif name.endswith('dec'):
col_name = name[:-3] + 'lat'
col_name = pre + col_name
tab_dict[col_name] = [getattr(c, name, None) for c in catalog]
name_list.append(col_name)
t = Table(tab_dict, meta=meta)
# re-order the columns
t = t[[n for n in name_list]]
if fmt is not None:
if fmt in ["vot", "vo", "xml"]:
vot = from_table(t)
# description of this votable
vot.description = repr(meta)
writetoVO(vot, filename)
elif fmt in ['hdf5']:
t.write(filename, path='data', overwrite=True)
elif fmt in ['fits']:
writeFITSTable(filename, t)
else:
ascii.write(t, filename, fmt, overwrite=True)
else:
ascii.write(t, filename, overwrite=True)
return
def writer(filename, catalog, fmt=None):
"""
construct a dict of the data
this method preserves the data types in the VOTable
"""
tab_dict = {}
name_list = []
for name in catalog[0].names:
col_name = name
if catalog[0].galactic:
if name.startswith('ra'):
col_name = 'lon'+name[2:]
elif name.endswith('ra'):
col_name = name[:-2] + 'lon'
elif name.startswith('dec'):
col_name = 'lat'+name[3:]
elif name.endswith('dec'):
col_name = name[:-3] + 'lat'
col_name = pre + col_name
tab_dict[col_name] = [getattr(c, name, None) for c in catalog]
name_list.append(col_name)
t = Table(tab_dict, meta=meta)
# re-order the columns
t = t[[n for n in name_list]]
if fmt is not None:
if fmt in ["vot", "vo", "xml"]:
vot = from_table(t)
# description of this votable
vot.description = repr(meta)
writetoVO(vot, filename)
elif fmt in ['hdf5']:
t.write(filename, path='data', overwrite=True)
elif fmt in ['fits']:
writeFITSTable(filename, t)
else:
ascii.write(t, filename, fmt, overwrite=True)
else:
ascii.write(t, filename, overwrite=True)
return
def writer(filename, catalog, fmt=None):
# construct a dict of the data
# this method preserves the data types in the VOTable
tab_dict = {}
for name in catalog[0].names:
tab_dict[name] = [getattr(c, name, None) for c in catalog]
t = Table(tab_dict,meta=meta)
# re-order the columns
t = t[[n for n in catalog[0].names]]
if fmt is not None:
if fmt in ["vot", "vo", "xml"]:
vot = from_table(t)
# description of this votable
vot.description = repr(meta)
writetoVO(vot, filename)
elif fmt in ['hdf5']:
t.write(filename,path='data',overwrite=True)
elif fmt in ['fits']:
writeFITSTable(filename,t)
else:
ascii.write(t, filename, fmt)
else:
ascii.write(t, filename)
return
def writer(filename, catalog, fmt=None):
# construct a dict of the data
# this method preserves the data types in the VOTable
tab_dict = {}
for name in catalog[0].names:
tab_dict[name] = [getattr(c, name, None) for c in catalog]
t = Table(tab_dict, meta=meta)
# re-order the columns
t = t[[n for n in catalog[0].names]]
if fmt is not None:
if fmt in ["vot", "vo", "xml"]:
vot = from_table(t)
# description of this votable
vot.description = repr(meta)
writetoVO(vot, filename)
elif fmt in ['hdf5']:
t.write(filename, path='data', overwrite=True)
elif fmt in ['fits']:
writeFITSTable(filename, t)
else:
ascii.write(t, filename, fmt)
else:
ascii.write(t, filename)
return
ax1 = fig.add_subplot(111,projection="polar")
ax1.set_title('Polar Beam Model Map')
c = ax1.scatter(Az0_samp[0],r,c=10**temp_poly_co[:,k_max-1],label='Order')
fig.colorbar(c)
plt.show()
"""
################################################################################
# Creating output VO table
################################################################################
#"""
print "Writing V0 table"
filename_body = 'model'
newvot = Table( [name, ra_str, dec_str, ra, dec, coeff_arr[:,1], S_centralfreq, beamvalue, beamvalue_approx, S_centralfreq_uncorrected,\
app_poly_co_tup_list,peak_flux_wide, int_flux_wide, a_wide, b_wide, pa_wide],\
names=('Name','ra_str','dec_str','ra','dec', 'alpha','S_centralfreq',
'beamvalue','beamvalue_approx','S_centralfreq_uncorrected','apparent_poly_coeff','peak_flux_wide','int_flux_wide',\
'a_wide', 'b_wide', 'pa_wide'), meta={'name': 'first table'} )
writetoVO(newvot, filename_body + "_morecolumns_temp.vot")
#"""
print 'Completed'
end0 = time.time()
print "Total runtime = ", np.round(end0 - start0, 3), "s\n"
hdu_in.writeto(outfits, clobber=True)
if options.average:
if options.scaleint:
print "Scaling the integrated fluxes in the catalogue downward by a direction-independent factor of " + str(
psf_ratio)
data['int_flux'] /= psf_ratio
else:
print "Scaling the peak fluxes in the catalogue upward by a direction-independent factor of " + str(
psf_ratio)
data['peak_flux'] *= psf_ratio
vot = Table(data)
# description of this votable
vot.description = "Corrected for position-independent PSF variation"
print "Writing to " + output
writetoVO(vot, output)
else:
print "Performing direction-dependent PSF correction."
psf_in = fits.open(psfimage)
w = wcs.WCS(psf_in[0].header, naxis=2)
unmara, unmadec = np.array(data['ra']).tolist(), np.array(
data['dec']).tolist()
rapix, decpix = w.wcs_world2pix(unmara, unmadec, 1)
raintpix, decintpix = np.array(np.round(rapix),
dtype=int), np.array(np.round(decpix),
dtype=int)
raclip, decclip = np.clip(raintpix, 0,
psf_in[0].data.shape[1] - 1), np.clip(
decintpix, 0,
psf_in[0].data.shape[0] - 1)
ands = ["a1_2.vot", "a2_3.vot", "a3_4.vot", "a4_1.vot"]
revands = ["a2_1.vot", "a3_2.vot", "a4_3.vot", "a1_4.vot"]
for amatch, rmatch in zip(ands, revands):
output = amatch.replace(".vot", "_best.vot")
if not os.path.exists(output):
print "Generating " + output
a = parse_single_table(amatch)
r = parse_single_table(rmatch)
data_a = a.array
data_r = r.array
indices_a = np.where(data_a['local_rms_1'] < data_a['local_rms_2'])
indices_r = np.where(data_r['local_rms_1'] < data_r['local_rms_2'])
data_x = np.ma.concatenate([data_a[indices_a], data_r[indices_r]])
vot = Table(data_x)
writetoVO(vot, 'temp.vot')
# Run through tpipe and keep the right columns (i.e. none of the _2 columns)
# os.system('stilts tpipe in=temp.vot cmd=\'keepcols "island source background local_rms ra_str dec_str ra err_ra dec err_dec peak_flux err_peak_flux int_flux err_int_flux a err_a b err_b pa err_pa flags residual_mean residual_std uuid"\' out='+output)
# For the older version of Aegean (no uuid)
os.system(
'stilts tpipe in=temp.vot cmd=\'keepcols "island source background local_rms ra_str dec_str ra err_ra dec err_dec peak_flux err_peak_flux int_flux err_int_flux a err_a b err_b pa err_pa flags residual_mean residual_std"\' out='
+ output)
# Not using XOR means we don't have to rename the columns later
# Crossmatch 1 with 2, 2 with 3, 3 with 4, 4 with 1, saving only things which DON'T overlap
if not os.path.exists('x1_2.vot'):
os.system(
'stilts tmatch2 matcher=skyellipse params=30 in1=' + table1 + ' in2=' +
table2 +
' out=x1_2.vot values1="ra dec a b pa" values2="ra dec a b pa" ofmt=votable find=all fixcols=none join=1not2'
)
os.remove('vlssr_temp.vot')
os.remove('vlssr_crop.vot')
os.remove('temp_vlssr_match.vot')
os.remove(Mmatchvot)
os.remove(Vmatchvot)
table = parse_single_table(outputfile)
data = table.array
else:
table = parse_single_table(sparse)
data = table.array
x = data['ra']
if max(x) > 360.:
print "RA goes higher than 360 degrees! Panic!"
sys.exit(1)
y = data['dec']
# Downselect to unresolved sources
mask = []
# Filter out any sources where Aegean's flags weren't zero
indices = np.where((data['flags'] == 0) & (
(data['peak_flux'] / data['local_rms']) >= options.minsnr))
mask.extend(indices[0])
mask = np.array(mask)
vot = Table(data[mask])
vot.description = "Sources selected for PSF calculation."
writetoVO(vot, outputfile)