def subsample_parameters(filename, equal_width=False):
''' print the number of matched and isolated lobes, and the distribution of PA
whether it is random according to the KStest etc. '''
tdata = Table(fits.open('../%s.fits' % filename)[1].data)
filename_original = filename
tdata_original = tdata
print_parameters(tdata, filename)
if equal_width:
fluxbins = select_flux_bins1(tdata_original)
else:
fluxbins = select_flux_bins_cuts_biggest_selection(tdata_original)
for key in sorted(fluxbins):
tdata = fluxbins[key]
filename = filename_original + 'flux%s' % key
print_parameters(tdata, filename)
if equal_width:
sizebins = select_size_bins1(tdata_original)
else:
sizebins = select_size_bins_cuts_biggest_selection(tdata_original)
for key in sorted(sizebins):
tdata = sizebins[key]
filename = filename_original + 'size%s' % key
print_parameters(tdata, filename)
def di_vs_right_ascension_declination():
n = 60
n = 140
file = '/data1/osinga/value_added_catalog_1_1b_thesis_cutoff069/value_added_selection_MG.fits'
# ############################################ VA selection MG only redshift sources, flux bin 3
tdata = Table(fits.open(file)[1].data)
# # With redshift
print('Using redshift..') ## edited for not actually using z
z_available = np.invert(np.isnan(tdata['z_best']))
z_zero = tdata['z_best'] == 0 #
# also remove sources with redshift 0, these dont have 3D positions
z_available = np.logical_xor(z_available, z_zero)
print('Number of sources with available redshift:', np.sum(z_available))
tdata = tdata[z_available]
tdata_original = tdata # use redshift data for power bins and size bins !!!
# ######################################### VA selection MG only redshift sources, flux bin 3
tdata['final_PA'] = tdata['position_angle']
print('Using flux bin 3')
tdata['RA'], tdata['DEC'] = tdata['RA_2'], tdata['DEC_2']
# tdata = only redshift sources flux bin 3
tdata = select_flux_bins_cuts_biggest_selection(tdata)['3']
# array of N,n for all sources N, with nearest neighbour n
di_max = dispersion_vectorized_n(tdata, n, redshift=False)
# index 0 = 1
di_max_n = di_max[:, n - 1]
ra, dec = tdata['RA'], tdata['DEC']
if n == 140:
cutoff = 0.17
else:
cutoff = 0.25
less_than_25 = np.where(di_max_n < cutoff)
more_than_25 = np.where(di_max_n > cutoff)
plt.scatter(ra[less_than_25],
dec[less_than_25],
c='k',
s=10,
label=r'$d_i$<%.2f' % cutoff)
plt.scatter(ra[more_than_25],
dec[more_than_25],
c='r',
s=10,
label=r'$d_i$>%.2f' % cutoff)
plt.xlabel('Righ ascension (degrees)', fontsize=14)
plt.ylabel('Declination (degrees)', fontsize=14)
# ax.legend(fontsize=14)
# plt.title(title,fontsize=16)
plt.tight_layout()
plt.ylim(45.058947246972785, 59)
plt.legend(loc='upper left')
plt.gca().set_aspect('equal', adjustable='box')
plt.show()
def helper_func(filename):
tdata = Table(fits.open('../%s.fits' % filename)[1].data)
# Take only the available redshift sources
z_available = np.invert(np.isnan(tdata['z_best']))
z_zero = tdata['z_best'] == 0 #
# also remove sources with redshift 0, these dont have 3D positions
z_available = np.logical_xor(z_available, z_zero)
tdata = tdata[z_available]
spectro_where = np.where(tdata['z_source'] == 1)
tdata = tdata[spectro_where]
fluxbin3 = select_flux_bins_cuts_biggest_selection(tdata)['3']
fluxbin3 = len(fluxbin3)
return fluxbin3
def plot_VA_MG_redshift_dist():
filename = 'value_added_selection_MG'
print(filename)
tdata = Table(fits.open('../%s.fits' % filename)[1].data)
print tdata['z_best_source']
# With redshift
print('Using redshift..') ## edited for not actually using z
z_available = np.invert(np.isnan(tdata['z_best']))
z_zero = tdata['z_best'] == 0 #
# also remove sources with redshift 0, these dont have 3D positions
z_available = np.logical_xor(z_available, z_zero)
print('Number of sources with available redshift:', np.sum(z_available))
tdata = tdata[z_available]
tdata_original = tdata # use redshift data for power bins and size bins !!!
######################################### VA selection MG only redshift sources, flux bin 3
# tdata['RA_2'], tdata['DEC_2'] = tdata['RA'], tdata['DEC']
tdata['final_PA'] = tdata['position_angle']
tdata = select_flux_bins_cuts_biggest_selection(tdata)['3']
spectroscopic = np.where(tdata['z_best_source'] == 1)
photometric = np.where(tdata['z_best_source'] == 0)
tdata_spec = tdata[spectroscopic]
tdata_phot = tdata[photometric]
print(' flux bin 3 VA MG')
print(len(tdata_spec), len(tdata_phot), len(tdata))
# Use fancy algorithms to determine the bins
from astropy import visualization
# visualization.hist(tdata_phot['z_best'],bins='scott',histtype=u'step',label='Photometric')
# visualization.hist(tdata_spec['z_best'],bins='scott',histtype=u'step',label='Spectroscopic')
plt.hist(tdata_phot['z_best'], histtype=u'step', label='Photometric')
plt.hist(tdata_spec['z_best'], histtype=u'step', label='Spectroscopic')
# plt.yscale('log')
plt.legend(fontsize=14)
plt.xlabel(r'Redshift', fontsize=14)
plt.ylabel('Counts', fontsize=14)
plt.tight_layout()
plt.show()
def helper_func(filename):
tdata = Table(fits.open('../%s.fits' % filename)[1].data)
# Take only the available redshift sources
z_available = np.invert(np.isnan(tdata['z_best']))
z_zero = tdata['z_best'] == 0 #
# also remove sources with redshift 0, these dont have 3D positions
z_available = np.logical_xor(z_available, z_zero)
tdata = tdata[z_available]
powerbin1 = select_power_bins_cuts_VA_selection(tdata)['1']
powerbin1 = len(powerbin1)
fluxbin3 = select_flux_bins_cuts_biggest_selection(tdata)['3']
fluxbin3 = len(fluxbin3)
print(fluxbin3)
sizebin0 = select_size_bins_cuts_biggest_selection(tdata)['0']
sizebin0 = len(sizebin0)
return powerbin1, fluxbin3, sizebin0
def investigate_strong_alignment_sources():
n = 60
n = 140
file = '/data1/osinga/value_added_catalog_1_1b_thesis_cutoff069/value_added_selection_MG.fits'
# ############################################ VA selection MG only redshift sources, flux bin 3
tdata = Table(fits.open(file)[1].data)
# # With redshift
print('Using redshift..') ## edited for not actually using z
z_available = np.invert(np.isnan(tdata['z_best']))
z_zero = tdata['z_best'] == 0 #
# also remove sources with redshift 0, these dont have 3D positions
z_available = np.logical_xor(z_available, z_zero)
print('Number of sources with available redshift:', np.sum(z_available))
tdata = tdata[z_available]
tdata_original = tdata # use redshift data for power bins and size bins !!!
# ######################################### VA selection MG only redshift sources, flux bin 3
tdata['final_PA'] = tdata['position_angle']
print('Using flux bin 3')
tdata['RA'], tdata['DEC'] = tdata['RA_2'], tdata['DEC_2']
# tdata = only redshift sources flux bin 3
tdata = select_flux_bins_cuts_biggest_selection(tdata)['3']
ra, dec = tdata['RA'], tdata['DEC']
pa = tdata['final_PA']
where = np.where((tdata['RA'] < 230) & (tdata['RA'] > 210)
& (tdata['DEC'] < 50) & (tdata['DEC'] > 45))
tdata_region = tdata[where]
plt.hist(pa[where], bins=180 / 5)
plt.ylabel('Counts', fontsize=14)
plt.xlabel('Position angle (degrees)', fontsize=14)
# ax.legend(fontsize=14)
# plt.title(title,fontsize=16)
plt.tight_layout()
plt.show()
def di_vs_right_ascension():
n = 60
n = 140
file = '/data1/osinga/value_added_catalog_1_1b_thesis_cutoff069/value_added_selection_MG.fits'
# ############################################ VA selection MG only redshift sources, flux bin 3
tdata = Table(fits.open(file)[1].data)
# # With redshift
print('Using redshift..') ## edited for not actually using z
z_available = np.invert(np.isnan(tdata['z_best']))
z_zero = tdata['z_best'] == 0 #
# also remove sources with redshift 0, these dont have 3D positions
z_available = np.logical_xor(z_available, z_zero)
print('Number of sources with available redshift:', np.sum(z_available))
tdata = tdata[z_available]
tdata_original = tdata # use redshift data for power bins and size bins !!!
# ######################################### VA selection MG only redshift sources, flux bin 3
tdata['final_PA'] = tdata['position_angle']
print('Using flux bin 3')
tdata['RA'], tdata['DEC'] = tdata['RA_2'], tdata['DEC_2']
# tdata = only redshift sources flux bin 3
tdata = select_flux_bins_cuts_biggest_selection(tdata)['3']
# array of N,n for all sources N, with nearest neighbour n
di_max = dispersion_vectorized_n(tdata, n, redshift=False)
# index 0 = 1
di_max_n = di_max[:, n - 1]
plt.scatter(tdata['RA'], di_max_n, c='k', s=10)
plt.xlabel('Righ ascension (degrees)', fontsize=14)
plt.ylabel('Dispersion', fontsize=14)
# ax.legend(fontsize=14)
# plt.title(title,fontsize=16)
plt.tight_layout()
plt.show()
def plot_all_position_angles():
def plot_position_angle(i, color):
"""
Plots the position angle for source (i) on the sky as a line with a radius proportional
to either the nearest neighbour distance or the major axis of a source.
"""
RA = tdata['RA_2'][i]
DEC = tdata['DEC_2'][i]
position_angle = tdata['final_PA'][i]
NN_dist = tdata['new_NN_distance(arcmin)'][i]
size = 160 # 80 #set fixed X*4 arcsec
# size = tdata['size_thesis'][i]
z_best = tdata['z_best'][i]
radius = size / 3600. # convert arcsec to deg
radius *= 4 # enhance radius by a factor
origin = (RA, DEC) # (coordinates of the source)
up_point = (RA, DEC + radius) # point above the origin (North)
down_point = (RA, DEC - radius) # point below the origin (South)
# rotate the line according to the position angle North through East (counterclockwise)
x_rot_up, y_rot_up = rotate_point(origin, up_point, position_angle)
x_rot_down, y_rot_down = rotate_point(origin, down_point,
position_angle)
x = x_rot_down
y = y_rot_down
dx = x_rot_up - x_rot_down
dy = y_rot_up - y_rot_down
# ax.arrow(x,y,dx,dy)
if color:
# make colorbar with the redshift data
plt.plot((x_rot_down, x_rot_up), (y_rot_down, y_rot_up),
c=color,
linewidth=0.4)
else:
plt.plot((x_rot_down, x_rot_up), (y_rot_down, y_rot_up),
linewidth=0.4,
c='k')
file = '/data1/osinga/value_added_catalog_1_1b_thesis_cutoff069/value_added_selection_MG.fits'
tdata = Table(fits.open(file)[1].data)
# With redshift
tdata = use_only_redshift(tdata)
tdata_original = tdata
tdata['final_PA'] = tdata['position_angle']
print('Using flux bin 3')
tdata = select_flux_bins_cuts_biggest_selection(tdata)['3']
f = plt.figure(figsize=(7, 7))
for i in range(len(tdata)):
plot_position_angle(i, color=False)
print('Number of sources in final plot: %i' % len(tdata))
plt.xlim(160, 250) #160,240
plt.ylim(45, 58)
print('Position angle distribution of ' + file[38:] + '')
# plt.title('Position angle distribution of ' + file[38:] + '')
# plt.xlabel('Right Ascension (degrees)',fontsize=14)
# plt.ylabel('Declination (degrees)',fontsize=14)
plt.gca().set_aspect('equal', adjustable='box')
# put spectro sources in same plot
spectroscopic = np.where(tdata['z_source'] == 1)
tdata = tdata[spectroscopic]
tdata_original = tdata
print('Using spectroscopic redshift only')
for i in range(len(tdata)):
# j = np.where(tdata['Mosaic_ID_2'][i].strip() == np.asarray(all_mosaicIDs))[0][0]
color = 'r'
plot_position_angle(i, color=color)
# for the legend
plt.plot((0, 0), (1, 1), c='r', linewidth=0.4, label='Spectroscopic-z')
plt.plot((0, 0), (1, 1), c='k', linewidth=0.4, label='Photometric-z')
plt.legend()
# also remove sources with redshift 0, these dont have 3D positions
z_available = np.logical_xor(z_available, z_zero)
print('Number of sources with available redshift:', np.sum(z_available))
# filename += '_only_redshift_sources_' ## edited for not actually using z
if position_angle: filename += '_PA' # only when using 'position_angle'
filename += '_redshift_'
filename += '10k'
tdata_original = tdata
filename_original = filename
tdata = tdata[z_available]
tdata_original = tdata # use redshift data for power bins and size bins !!
fluxbins = select_flux_bins_cuts_biggest_selection(tdata_original)
for key in fluxbins:
tdata = fluxbins[key]
# flux and redshift omg
filename = filename_original + 'flux%s' % key
z_available = np.invert(np.isnan(tdata['z_best']))
z_zero = tdata['z_best'] == 0 #
# also remove sources with redshift 0, these dont have 3D positions
z_available = np.logical_xor(z_available, z_zero)
print('Number of sources with available redshift:',
np.sum(z_available))
assert np.sum(np.invert(
z_available)) == 0, "These sources should all have redshift.."
tdata = tdata[z_available]
# also remove sources with redshift 0, these dont have 3D positions
z_available = np.logical_xor(z_available, z_zero)
print('Number of sources with available redshift:', np.sum(z_available))
tdata = tdata[z_available]
tdata_original = tdata # use redshift data for power bins and size bins !!!
# ######################################### VA selection MG only redshift sources, flux bin 3
try:
tdata['RA_2'], tdata['DEC_2'] = tdata['RA'], tdata['DEC']
except:
pass
tdata['final_PA'] = tdata['position_angle']
print('Using flux bin 3')
tdata = select_flux_bins_cuts_biggest_selection(tdata)['3']
# spectroscopic = np.where(tdata['z_source'] == 1)
# tdata = tdata[spectroscopic]
# tdata_original = tdata
# print ('Using spectroscopic redshift only')
# RAs, DECs, position_angles, Majs, NN_dists = load_in(file,'RA_2','DEC_2',
# 'final_PA','Maj_1','new_NN_distance(arcmin)')
# fluxbins11 = select_flux_bins11(tdata)
# RAs, DECs, position_angles, Majs, NN_dists = load_in_table(fluxbins11,'RA_2','DEC_2',
# 'final_PA','Maj_1','new_NN_distance(arcmin)')
# fluxbins = select_flux_bins1(tdata)
# tdata = fluxbins['0']
