def make_bins():
"""
Calls the select_size_bins2 and select_flux_bins2 and produces the .fits files
Deals with overlap.
"""
dataNN,dataMG = select_all_interesting()
tdata = vstack([dataNN,dataMG])
a = select_size_bins2(dataNN,dataMG,Write=True)
a = select_flux_bins2(tdata,Write=True)
for i in range(5):
size_data = Table(fits.open('./size_bins2_'+str(i)+'.fits')[1].data)
flux_data = Table(fits.open('./flux_bins2_'+str(i)+'.fits')[1].data)
size_data = deal_with_overlap(size_data)
flux_data = deal_with_overlap(flux_data)
size_data.write('./size_bins2_'+str(i)+'.fits',overwrite=True)
flux_data.write('./flux_bins2_'+str(i)+'.fits',overwrite=True)
def fix_data_latest():
"""
After initial source filtering, deal with overlap.
"""
dataNN_l, dataMG_l = select_all_interesting(iteration=2)
tdata_latest = vstack([dataNN_l,dataMG_l])
# Write it so nans appear in the MG columns
tdata_latest.write('/data1/osinga/value_added_catalog/source_filtering/biggest_selection_latest.fits',overwrite=True)
# Open it again
tdata_latest = Table(fits.open('/data1/osinga/value_added_catalog/source_filtering/biggest_selection_latest.fits')[1].data)
# Deal with the overlapping sources.
tdata_latest = deal_with_overlap(tdata_latest)
tdata_latest.write('/data1/osinga/value_added_catalog/source_filtering/biggest_selection_latest.fits',overwrite=True)
plt.ylabel('Mean angular radius (deg)')
plt.xlabel('n')
# plt.savefig('/data1/osinga/figures/statistics/show/angular_radius_vs_n_'+filename+'.png',overwrite=True)
plt.savefig('./angular_radius_vs_n_' + filename + '.pdf', overwrite=True)
plt.close()
return median
n = 180
for i in range(5):
# Open the size data
filename = 'size_bins2_' + str(i)
tdata = Table(fits.open('./' + filename + '.fits')[1].data)
tdata = deal_with_overlap(tdata)
Sn_mc = Table(fits.open('./Sn_monte_carlo_' + filename + '.fits')[1].data)
# Calculate the Sn and the plots
angular_radius = angular_radius_vs_n(tdata, filename, n)
Sn_data = angular_dispersion_vectorized_n(tdata, n)
Sn_vs_n(tdata, Sn_mc, Sn_data, filename, angular_radius, n)
break
# Open the flux data
filename = 'flux_bins2_' + str(i)
tdata = Table(fits.open('./' + filename + '.fits')[1].data)
tdata = deal_with_overlap(tdata)
Sn_mc = Table(fits.open('./Sn_monte_carlo_' + filename + '.fits')[1].data)
# Caluclate the Sn and the plots
Sn_data = angular_dispersion_vectorized_n(tdata, n)
tdata_BS = vstack([tdataNN, tdataMG])
print('Length of biggest selection before dealing with overlap:',
len(tdata_BS))
# have to write it out and read it again to prevent '--' from showing up in Table instead of NaN
tdata_BS.write('../biggest_selection_with_overlap.fits', overwrite=True)
tdata_BS = Table(
fits.open('../biggest_selection_with_overlap.fits')[1].data)
# fix the NN sources that are also MG sources and fix the nearest neighbour
# of NN sources that are also MG sources by visually classifying
# the results are saved in classtable.
classtable = '../NN_excluded_by_also_being_MG_classification.fits'
classtable = Table(fits.open(classtable)[1].data)
# classified=True makes it so it uses the visual classification to deal with overlap
tdata_BS = deal_with_overlap(tdata_BS,
classified=True,
classtable=classtable)
print('Length of biggest selection after dealing with overlap:',
len(tdata_BS))
# Then, check if we don't have double sources by imposing a
# minimum distance between to sources of 0.2 arcmin
tdata_BS = check_distance(tdata_BS)
print('Length of biggest selection after distance check:', len(tdata_BS))
NN_Peak_flux = find_NN_properties(tdata_BS, 'Peak_flux')
total_flux_NN = find_NN_properties(tdata_BS, 'Total_flux')
tdata_BS['new_NN_Peak_flux'] = NN_Peak_flux
tdata_BS['new_NN_Total_flux'] = total_flux_NN
tdata_BS.write('../biggest_selection.fits', overwrite=True)