def new_plots(pos, data, layer=None, plot_type='blazar', vmax=1, levels=[]):
ax0 = plt.subplot(1, 2, pos)
ax0.get_xaxis().set_ticks([])
ax0.get_yaxis().set_ticks([])
image_size = 60 * 2 # image is 2 * 2 arcminutes
len_x, len_y = data.shape
x0, x1 = len_x * (0.65 + 0.125), len_x * 0.9
y = len_y * 0.1
plt.text((x0 + x1) / 2,
y * (0.7),
str(int(image_size * 0.25 * 0.5)) + '"',
horizontalalignment='center',
verticalalignment='center',
fontsize=15,
color='w')
plt.plot([x0, x1], [y, y], 'w-', lw=1.5)
image = ax0.imshow(data,
origin='lower',
cmap='viridis',
vmax=vmax,
vmin=0,
norm=DS9Normalize(stretch='arcsinh'))
circle1 = plt.Circle([8, 8], (x1 - x0) * (6 / 15) / 2,
ls='--',
fc='None',
ec='w')
ax0.add_artist(circle1)
divider = make_axes_locatable(ax0)
cax0 = divider.append_axes('bottom', size='5%', pad=0.05)
cbar = plt.colorbar(image, cax=cax0, orientation='horizontal')
cbar.ax.tick_params(labelsize=15)
cbar.ax.set_xlabel(r'Jy beam$^{-1}$', fontsize=15)
if plot_type is 'blazar':
ax0.contour(data,
levels=levels,
origin='lower',
colors='white',
alpha=0.67)
elif plot_type is 'diffuse':
ax0.contour(layer,
levels=levels,
origin='lower',
colors='white',
alpha=0.67)
ax0.contour(data, levels=levels, origin='lower', colors='magenta'
) # in this case, data is the core subtracted array
x1s.append(x1)
x2s.append(x2)
y1s.append(y1)
y2s.append(y2)
distances = np.array(distances)
my_max = np.max(distances)
max_x1 = x1s[distances.argmax()]
max_x2 = x2s[distances.argmax()]
max_y1 = y1s[distances.argmax()]
max_y2 = y2s[distances.argmax()]
asec_max = my_max * 1.5 # 1.5" per pixel
centre = ((max_y1 + max_y2) / 2, (max_x1 + max_x2) / 2)
fig = plt.imshow(d, vmin=0, vmax=np.nanmax(d), origin='lower',
norm=DS9Normalize(stretch='arcsinh'))
plt.colorbar()
fig.axes.get_xaxis().set_visible(False)
fig.axes.get_yaxis().set_visible(False)
circle = plt.Circle(centre, my_max / 2, color='r', fill=False, alpha=0.5,
lw=2)
fig = plt.gcf()
ax = fig.gca()
ax.add_artist(circle)
plt.plot([max_y1, max_y2], [max_x1, max_x2], color='red', alpha=0.5, lw=2)
plt.plot([(max_y1 + max_y2) / 2], [(max_x1 + max_x2) / 2], marker='o',
color='red', alpha=0.5)
plt.title(f'{source_name}\n5\u03C3 = {threshold * 1000:.3f} mJy; d = ' +
f'{asec_max:.1f}"')
# plt.show()
def optical(sigma=4, my_directory='/data5/sean/ldr2'):
"""Plot Pan-STARRS data.
Parameters
----------
sigma : float or int
The threshold of the significance to set the mask, as a factor of the
local RMS. The default is 4.
my_directory : string
Working directory.
"""
df = pd.read_csv(f'{my_directory}/catalogues/final.csv')
plt.figure(figsize=(13.92, 8.60)).patch.set_facecolor('white')
plt.rcParams['font.family'] = 'serif'
plt.rcParams['mathtext.fontset'] = 'dejavuserif'
mpl.rcParams['xtick.major.size'] = 10
mpl.rcParams['xtick.major.width'] = 2
mpl.rcParams['xtick.minor.size'] = 5
mpl.rcParams['xtick.minor.width'] = 2
mpl.rcParams['ytick.major.size'] = 10
mpl.rcParams['ytick.major.width'] = 2
mpl.rcParams['ytick.minor.size'] = 5
mpl.rcParams['ytick.minor.width'] = 2
mpl.rcParams['axes.linewidth'] = 2
colors = ['#118ab2', '#06d6a0', '#ffd166', '#ef476f']
sbar_asec = 30 # desired length of scalebar in arcseconds
pix = 0.25 # arcseconds per pixel
for source_name, ra, dec, mosaic, rms, z, pf in zip(df['Name'],
df['BZCAT RA'],
df['BZCAT Dec'],
df['Mosaic_ID'],
df['Isl_rms'],
df['redshift'],
df['Peak_flux']):
source_name = '5BZB' + source_name
sky_position = SkyCoord(ra, dec, unit='deg')
if source_name == '5BZBJ1202+4444' or source_name == '5BZBJ1325+4115':
size = [3, 3] * u.arcmin
p = 54
elif (source_name == '5BZBJ1419+5423' or
source_name == '5BZBJ0945+5757'):
size = [4, 4] * u.arcmin
p = 72
else:
size = [2, 2] * u.arcmin
p = 36
print(source_name)
p_hdu = fits.open(f'{my_directory}/panstarrs/{source_name}.i.'
'fits')[0]
p_wcs = WCS(p_hdu.header)
p_cutout = Cutout2D(p_hdu.data, sky_position, size=size, wcs=p_wcs)
l_hdu = fits.open(f'{my_directory}/mosaics/{mosaic}-mosaic.fits')[0]
l_wcs = WCS(l_hdu.header, naxis=2)
l_cutout = Cutout2D(np.squeeze(l_hdu.data), sky_position, size=size,
wcs=l_wcs)
if 4 * rms < (pf / 50):
# see 2.2 of https://arxiv.org/pdf/1907.03726.pdf
levels = [level * (pf / 50) / 1000 for level in [1, 2, 4, 8]]
else:
levels = [level * rms / 1000 for level in [4, 8, 16, 32]]
ax = plt.subplot(projection=p_cutout.wcs)
im = ax.imshow(p_cutout.data, vmin=0, vmax=8000, cmap='cubehelix_r',
origin='lower', norm=DS9Normalize(stretch='arcsinh'),
interpolation='gaussian')
cbar = plt.colorbar(im)
cbar.set_label('Excess counts', size=20)
cbar.ax.tick_params(labelsize=20)
ax.contour(l_cutout.data, transform=ax.get_transform(l_cutout.wcs),
levels=levels, origin='lower', colors=colors)
plt.xlabel('Right ascension', fontsize=20, color='black')
plt.ylabel('Declination', fontsize=20, color='black')
ax.tick_params(axis='both', which='major', labelsize=20)
plt.minorticks_on()
ax.tick_params(which='minor', length=0)
kpc_per_asec = get_kpc_per_asec(z=z)
sbar = sbar_asec / pix # length of scalebar in pixels
kpc_per_pixel = kpc_per_asec * pix
s = p_cutout.data.shape[1] # plot scalebar
plt.plot([p, p + sbar], [s - p, s - p], marker='None', lw=2, color='b')
plt.text(p, s - p + 10 * (p / 36), f'{sbar_asec:.0f}" = '
f'{kpc_per_pixel * sbar:.0f} kpc', fontsize=20, color='b')
plt.savefig(f'{my_directory}/images/panstarrs-{source_name}.png')
plt.clf()
def __init__(self,filelist,
step=1.0,outdir='./nudged/',ext='',clobber=False):
self.filelist = filelist
# datalist holds current state of arrays
self.datalist = map(pyfits.getdata,filelist)
self.step = step
self.outdir = outdir
self.ext = ext
self.clobber = clobber
self.current = 0
self.orig_data = pyfits.getdata(filelist[0])
self.active_data = self.datalist[0]
# store total offsets
self.offsets = np.zeros((len(filelist),2))
# initialize norm
self.norm = DS9Normalize(stretch='linear')
# set up subparser
self.subparser=argparse.ArgumentParser(description='Parse window text.',prog='')
self.subparser.add_argument('-s',type=float,help='Step size (default=%.1f)' % self.step)
self.subparser.add_argument('--stretch',choices=['linear','sqrt','arcsinh','log','power','squared'],help='Choose image stretch (default = stretch)')
self.subparser.add_argument('--clip_lo',type=float,help='Clip minimum intensity percentile')
self.subparser.add_argument('--clip_hi',type=float,help='Clip maximum intensity percentile')
self.subparser.add_argument('-w',action='store_true',help="Write current frame to output directory (outdir=%s)"%self.outdir)
self.subparser.add_argument('-wa',action='store_true',help="Write all frames to output directory (outdir=%s)"%self.outdir)
self.subparser.add_argument('-wq',action='store_true',help="Write all frames to output directory and quit (outdir=%s)"%self.outdir)
self.subparser.add_argument('-r',action='store_true',help='Restore original')
self.subparser.add_argument('--c',action='store_true',help='Force clobber status to True on write')
self.subparser.add_argument('--q',action='store_true',help='Quit')
self.fig = plt.figure()
self.fig.canvas.mpl_disconnect(self.fig.canvas.manager.key_press_handler_id)
self.keycid = self.fig.canvas.mpl_connect('key_press_event',self.onkey)
self.fig.canvas.mpl_connect('button_press_event', self.onclick)
self.fig.canvas.mpl_connect('button_release_event', self.onrelease)
# Hold x,y drag coords
self.dragfrom = None
self.pausetext = '-'
self.pid = None
# Make directory if doesn't exist
try:
mkdir(self.outdir)
except OSError:
#directory exists. no big deal.
pass
print "Type directly into figure"
print "'left/right/up/down' to translate image"
print "'</>' to choose previous/next image in input list"
print "'-h' to see additional options"
print "'--q' to quit"
print
#Show initial
self.display(self.active_data)
self.displaytext('[0, 0], s=%.2f'%self.step,x=0.60)
plt.show()
def parsetext(self,text):
args = None
try:
# catch -h, or error exit
args = self.subparser.parse_args(text.split())
except SystemExit:
return
if not args:
return
if args.c:
self.clobber = True
print 'Force clobber on write'
if args.s:
self.step = args.s
print 'Step size changed to %.2f' % self.step
if args.stretch:
print 'Changed stretch to %s' % args.stretch
self.norm.stretch = args.stretch
self.display(self.active_data)
if args.clip_lo:
print 'Clip lo changed to %.2f' % args.clip_lo
self.norm.clip_lo = args.clip_lo
self.display(self.active_data)
if args.clip_hi:
print 'Clip hi changed to %.2f' % args.clip_hi
self.norm.clip_hi = args.clip_hi
self.display(self.active_data)
if args.r:
self.active_data = self.orig_data.copy()
self.offsets[self.current][0] = 0.0
self.offsets[self.current][1] = 0.0
self.norm = DS9Normalize(stretch='linear')
self.display(self.active_data)
print 'Restored image from %s' % self.filelist[self.current]
if args.w:
h = pyfits.getheader(self.filelist[self.current])
h['N_ORIG_F'] = (self.filelist[self.current],'Original file before nudge')
h['N_XS'] = (self.offsets[self.current][0],'Xshift of nudge')
h['N_YS'] = (self.offsets[self.current][1],'Yshift of nudge')
outfile = os.path.basename(self.filelist[self.current])
outfile = os.path.splitext(outfile)
outfile = ''.join([outfile[0],self.ext,outfile[1]])
outfile = os.path.join(self.outdir,outfile)
try:
pyfits.writeto(outfile,data=self.active_data,header=h,clobber=self.clobber)
except IOError as e:
print e, "'--c' to force overwrite"
else:
print '%s: written to disk, s = [%.2f, %.2f]' % (outfile,self.offsets[self.current][0],self.offsets[self.current][1])
if args.wq:
args.wa = True
args.q = True
if args.wa:
for idx in range(0,len(self.filelist)):
h = pyfits.getheader(self.filelist[idx])
h['N_ORIG_F'] = (self.filelist[idx],'Original file before nudge')
h['N_XS'] = (self.offsets[idx][0],'Xshift of nudge')
h['N_YS'] = (self.offsets[idx][1],'Yshift of nudge')
outfile = os.path.basename(self.filelist[idx])
outfile = os.path.splitext(outfile)
outfile = ''.join([outfile[0],self.ext,outfile[1]])
outfile = os.path.join(self.outdir,outfile)
try:
if idx == self.current:
pyfits.writeto(outfile,data=self.active_data,header=h,clobber=self.clobber)
else:
pyfits.writeto(outfile,data=self.datalist[idx],header=h,clobber=self.clobber)
except IOError as e:
print e, "'--c' to force overwrite"
args.q = False #don't quit if file fails to write
else:
print '%s: written to disk, s = [%.2f, %.2f]' % (outfile,self.offsets[idx][0],self.offsets[idx][1])
if args.q:
plt.close()
exit()
def loop_through_sources(sigma=4, my_directory='/data5/sean/ldr2'):
"""Plot postage stamp images of LDR2 BL Lacs.
Parameters
----------
sigma : float or integer
The threshold of the significance to set the mask, as a factor of the
local RMS. The default is 4.
my_directory : string
Working directory.
Returns
-------
string
The name of the CSV containing the results.
"""
df = pd.read_csv(f'{my_directory}/catalogues/pulsars-10asec-match.csv')
plt.figure(figsize=(13.92, 8.60)).patch.set_facecolor('white')
plt.rcParams['font.family'] = 'serif'
plt.rcParams['mathtext.fontset'] = 'dejavuserif'
mpl.rcParams['xtick.major.size'] = 10
mpl.rcParams['xtick.major.width'] = 2
mpl.rcParams['xtick.minor.size'] = 5
mpl.rcParams['xtick.minor.width'] = 2
mpl.rcParams['ytick.major.size'] = 10
mpl.rcParams['ytick.major.width'] = 2
mpl.rcParams['ytick.minor.size'] = 5
mpl.rcParams['ytick.minor.width'] = 2
mpl.rcParams['axes.linewidth'] = 2
dummy = 123456
sbar_asec = 30 # desired length of scalebar in arcseconds
pix = 1.5 # arcseconds per pixel
for source_name, ra, dec, mosaic, rms in zip(df['NAME'], df['RAJD'],
df['DECJD'], df['Mosaic_ID'],
df['Isl_rms']):
threshold = sigma * rms / 1000 # jansky
hdu = fits.open(f'{my_directory}/mosaics/{mosaic}-mosaic.fits')[0]
wcs = WCS(hdu.header, naxis=2)
sky_position = SkyCoord(ra, dec, unit='deg')
size = [2, 2] * u.arcmin
p = 6
cutout = Cutout2D(np.squeeze(hdu.data),
sky_position,
size=size,
wcs=wcs)
d = cutout.data
copy_d = np.copy(d)
another_copy_d = np.copy(d)
d[d < threshold] = 0
d[d >= threshold] = 1
rows, cols = d.shape
d = label(d) # label islands of emission
source_islands = nearest_to_centre(d, percent=0.1)
for source_island in source_islands:
d[d == source_island] = dummy
d[d != dummy] = 0
copy_d[d != dummy] = 0
set_to_nil = [] # identify values we can set to zero for being inside
for r in range(rows): # set to 0 if surrounded by non nans
for c in range(cols):
try:
if (d[r - 1, c - 1] != 0 and d[r - 1, c] != 0
and d[r - 1, c + 1] != 0 and d[r, c - 1] != 0
and d[r, c + 1] != 0 and d[r + 1, c - 1] != 0
and d[r + 1, c] != 0 and d[r + 1, c + 1] != 0):
set_to_nil.append((r, c))
except IndexError:
print(f'Index error for {source_name}.')
continue
for r, c in set_to_nil:
d[r, c] = 0 # needs separate loop to avoid checkered pattern
# d is an outline of the source (one) and everything else is zero
# copy_d is the source with flux values and everything else is zero
# another_copy_d has flux values throughout
good_cells = []
for r in range(rows):
for c in range(cols):
if d[r, c] != 0:
good_cells.append([r, c])
x, y, r = smallestenclosingcircle.make_circle(good_cells)
ax = plt.subplot(projection=cutout.wcs)
plt.xlabel('Right ascension', fontsize=20, color='black')
plt.ylabel('Declination', fontsize=20, color='black')
ax.tick_params(axis='both', which='major', labelsize=20)
plt.imshow(another_copy_d,
vmin=0,
vmax=np.nanmax(another_copy_d),
origin='lower',
norm=DS9Normalize(stretch='arcsinh'),
cmap='plasma_r') # interpolation='gaussian'
beam = Circle((6, 6),
radius=2,
linestyle='dashed',
lw=2,
fc='none',
edgecolor='blue')
diffuse = Circle((y + 0.5, x + 0.5),
radius=r,
fc='none',
edgecolor='k',
lw=2)
ax.add_patch(beam)
ax.add_patch(diffuse)
sbar = sbar_asec / pix # length of scalebar in pixels
s = cutout.data.shape[1] # plot scalebar
plt.plot([p, p + sbar], [s - p, s - p], marker='None', lw=2, color='b')
plt.text(p,
s - (5 * p / 6),
f'{sbar_asec:.0f}"',
fontsize=20,
color='b')
cbar = plt.colorbar()
cbar.set_label(r'Jy beam$^{-1}$', size=20)
cbar.ax.tick_params(labelsize=20)
plt.minorticks_on()
plt.tick_params(which='minor', length=0)
plt.contour(another_copy_d,
levels=[threshold],
origin='lower',
colors='w')
plt.contour(another_copy_d - copy_d,
levels=[threshold],
colors='grey',
origin='lower')
plt.savefig(f'{my_directory}/images/pulsar-{source_name}.png')
plt.clf()
print(f'{source_name}: {r * 1.5 * 2:.1f}"')
def measure_extent_radio_galaxies(my_dir='/data5/sean/deep-fields/catalogues/',
nah=False):
"""Measure the size of radio galaxies.
"""
results_csv = f'{my_dir}../results/extention-radio-galaxies.csv'
if nah:
return results_csv # do not do anything
df = pd.read_csv(f'{my_dir}radio.galaxies.ldr1.csv') # load data
fields = [f'/data1/lotss-data/{m}-mosaic.fits' for m in df['Mosaic_ID']]
thresholds = 5 * df['Isl_rms'] / 1000 # converting to jansky
plt.figure(figsize=(8, 8))
result_header = ('Name,Class,LM flux (mJy),LM size ("),Width ("),Width' +
' (kpc),5 * rms (mJy),Redshift,NAT,WAT,D-D\n')
if not os.path.exists(results_csv):
with open(results_csv, 'a') as f:
f.write(result_header)
# skipping these sources as they are too complicated
skip_list = ['ILTJ105155.09+552329.0', 'ILTJ104703.03+523023.4']
i = 0
for (source_name, ra, dec, field, threshold, fri, frii, redshift, nat, wat,
dd, lm_size, lm_flux) in zip(df['Source_Name_1'], df['RA_1'],
df['DEC_1'], fields, thresholds,
df['FR1'], df['FR2'], df['z_best'],
df['NAT'], df['WAT'], df['D-D'],
df['LM_dec_size'], df['LM_Flux']):
save = f'{my_dir}../images/extention-radio-galaxies/{source_name}.png'
if os.path.exists(save):
print(f'{save} already exists so it is being skipped.')
continue
if source_name in skip_list:
print(f'{source_name} is too complicated so it is being skipped.')
continue
# already removed small sources and ambiguous sources from the sample
source_type = 'FR-I' if fri else 'FR-II'
# if source_name != 'ILTJ130804.04+550835.4': # for testing one source
# continue
# try:
hdu = fits.open(field)[0]
# except FileNotFoundError: # fits might have different name
# print(f'{field} does not exist.')
# continue
wcs = WCS(hdu.header, naxis=2)
sky_position = SkyCoord(ra, dec, unit='deg')
s = math.ceil((lm_size * 2) / 60)
# already have adhoc masks for P196+55 field at [3, 3] arcmin but for
# the rest we take the size of the source from lomorph
size = [3, 3] if field[-19:-5] == 'P196+55-mosaic' else [s, s]
# some sources need a bigger cutout
if source_name == 'ILTJ130140.02+540825.9':
size = [4, 4]
elif source_name == 'ILTJ130331.30+540024.1':
size = [6, 6]
cutout = Cutout2D(np.squeeze(hdu.data),
sky_position,
size=size * u.arcmin,
wcs=wcs)
d = cutout.data
d[d < threshold] = np.nan
d = manual_mask(name=source_name, data=d)
rows, cols = d.shape
good_cells = []
for r in range(rows):
for c in range(cols):
if not np.isnan(d[r, c]):
good_cells.append([r, c])
# find distance between good_cell and all other good_cells
distances, x1s, x2s, y1s, y2s = [], [], [], [], []
for (x1, y1) in good_cells:
for (x2, y2) in good_cells:
distances.append(np.sqrt((x1 - x2)**2 + (y1 - y2)**2))
x1s.append(x1)
x2s.append(x2)
y1s.append(y1)
y2s.append(y2)
distances = np.array(distances)
# try:
# my_max = np.max(distances)
# except ValueError:
# print(f'Failed for {source_name}.')
# continue
max_x1 = x1s[distances.argmax()]
max_x2 = x2s[distances.argmax()]
max_y1 = y1s[distances.argmax()]
max_y2 = y2s[distances.argmax()]
# asec_max = my_max * 1.5 # 1.5" per pixel
# this removes highly curved sources
# if lm_size / asec_max > 1.75 or asec_max / lm_size > 1.75:
# print(f'{source_name} is too curved so it is being skipped.')
# continue
# for good cells find the distance from it to the point on the line, we
# assume we look down the longest line and the source is flat in the
# plane of the sky (https://stackoverflow.com/a/52756183/6386612)
p1 = np.array([max_x1, max_y1])
p2 = np.array([max_x2, max_y2])
widths = []
width_x, width_y = [], []
for (x, y) in good_cells:
p3 = np.array([x, y])
widths.append(np.cross(p2 - p1, p3 - p1) / np.linalg.norm(p2 - p1))
width_x.append(x)
width_y.append(y)
widths = np.array(widths)
my_max_width = np.max(widths)
my_min_width = np.min(widths)
width_x_max = width_x[widths.argmax()]
width_y_max = width_y[widths.argmax()]
width_x_min = width_x[widths.argmin()]
width_y_min = width_y[widths.argmin()]
asec_width = (my_max_width + np.abs(my_min_width)) * 1.5 # 1.5"/pixel
plt.imshow(d,
vmin=0,
vmax=np.nanmax(d),
origin='lower',
norm=DS9Normalize(stretch='arcsinh'))
plt.colorbar()
plt.plot([max_y1, max_y2], [max_x1, max_x2],
color='red',
alpha=0.5,
lw=2)
plt.plot([width_y_max], [width_x_max],
marker='o',
markersize=10,
color='red',
alpha=0.5)
plt.plot([width_y_min], [width_x_min],
marker='o',
markersize=10,
color='red',
alpha=0.5)
plt.minorticks_on()
plt.grid(which="both", linewidth=0.72, color="k")
plt.tick_params(which="minor", length=0)
my_string = ''
if nat:
my_string += 'NAT '
elif wat:
my_string += 'WAT '
if dd:
my_string += 'D-D '
plt.title(f'{source_name}\n{source_type}; {my_string}5\u03C3 = ' +
f'{threshold * 1000:.3f} mJy; width = {asec_width:.1f}"')
# plt.show()
plt.savefig(save)
plt.clf()
_, kpc = get_dl_and_kpc_per_asec(z=redshift)
kpc_width = kpc * asec_width
results = (f'{source_name},{source_type},{lm_flux},{lm_size},' +
f'{asec_width},{kpc_width},{threshold * 1000},{redshift},' +
f'{nat},{wat},{dd}\n')
with open(results_csv, 'a') as f:
f.write(results)
# build clean sample on visual inspection
i += 1
if i > 100:
return
def ghz(sigma=4, my_directory='/data5/sean/ldr2'):
"""Plot FIRST data.
Parameters
----------
sigma : float or int
The threshold of the significance to set the mask, as a factor of the
local RMS. The default is 4.
my_directory : string
Working directory.
"""
df = pd.read_csv(f'{my_directory}/catalogues/final.csv')
plt.figure(figsize=(13.92, 8.60)).patch.set_facecolor('white')
plt.rcParams['font.family'] = 'serif'
plt.rcParams['mathtext.fontset'] = 'dejavuserif'
mpl.rcParams['xtick.major.size'] = 10
mpl.rcParams['xtick.major.width'] = 2
mpl.rcParams['xtick.minor.size'] = 5
mpl.rcParams['xtick.minor.width'] = 2
mpl.rcParams['ytick.major.size'] = 10
mpl.rcParams['ytick.major.width'] = 2
mpl.rcParams['ytick.minor.size'] = 5
mpl.rcParams['ytick.minor.width'] = 2
mpl.rcParams['axes.linewidth'] = 2
colors = ['#118ab2', '#06d6a0', '#ffd166', '#ef476f']
sbar_asec = 30 # desired length of scalebar in arcseconds
pix = 1.8 # arcseconds per pixel
for source_name, ra, dec, mosaic, rms, z, f_rms, pf in zip(
df['Name'], df['BZCAT RA'], df['BZCAT Dec'], df['Mosaic_ID'],
df['Isl_rms'], df['redshift'], df['RMS'], df['Peak_flux']):
source_name = '5BZB' + source_name
sky_position = SkyCoord(ra, dec, unit='deg')
if source_name == '5BZBJ1202+4444' or source_name == '5BZBJ1325+4115':
size = [3, 3] * u.arcmin
x = 9 * (1.5 / pix) # to get the scalebar in the same place
elif (source_name == '5BZBJ1419+5423'
or source_name == '5BZBJ0945+5757'):
size = [4, 4] * u.arcmin
x = 12 * (1.5 / pix)
else:
size = [2, 2] * u.arcmin
x = 6 * (1.5 / pix)
f_hdu = fits.open(f'{my_directory}/first/{source_name}.fits')[0]
f_wcs = WCS(f_hdu.header, naxis=2)
print(f'{source_name}')
f_cutout = Cutout2D(np.squeeze(f_hdu.data),
sky_position,
size=size,
wcs=f_wcs)
l_hdu = fits.open(f'{my_directory}/mosaics/{mosaic}-mosaic.fits')[0]
l_wcs = WCS(l_hdu.header, naxis=2)
l_cutout = Cutout2D(np.squeeze(l_hdu.data),
sky_position,
size=size,
wcs=l_wcs)
if 4 * rms < (pf / 50):
# see 2.2 of https://arxiv.org/pdf/1907.03726.pdf
levels = [level * (pf / 50) / 1000 for level in [1, 2, 4, 8]]
else:
levels = [level * rms / 1000 for level in [4, 8, 16, 32]]
f_level = f_rms * 4 / 1000
ax = plt.subplot(projection=f_cutout.wcs)
im = ax.imshow(f_cutout.data,
vmin=0,
vmax=np.max(f_cutout.data),
cmap='cubehelix_r',
origin='lower',
interpolation='gaussian',
norm=DS9Normalize(stretch='arcsinh'))
ax.contour(f_cutout.data,
levels=[f_level],
origin='lower',
colors=['k'])
cbar = plt.colorbar(im)
cbar.set_label(r'Jy beam$^{-1}$', size=20)
cbar.ax.tick_params(labelsize=20)
ax.contour(l_cutout.data,
transform=ax.get_transform(l_cutout.wcs),
levels=levels,
origin='lower',
colors=colors)
beam = Circle((6, 6),
radius=1.5,
linestyle='dashed',
lw=2,
fc='none',
edgecolor='blue') # beam = 5.4" diameter, 1 pixel = 1.8"
ax.add_patch(beam)
plt.xlabel('Right ascension', fontsize=20, color='black')
plt.ylabel('Declination', fontsize=20, color='black')
ax.tick_params(axis='both', which='major', labelsize=20)
plt.minorticks_on()
ax.tick_params(which='minor', length=0)
plt.xlim(0, f_cutout.data.shape[0])
plt.ylim(0, f_cutout.data.shape[1])
kpc_per_asec = get_kpc_per_asec(z=z)
sbar = sbar_asec / pix # length of scalebar in pixels
kpc_per_pixel = kpc_per_asec * pix
s = f_cutout.data.shape[1]
plt.plot([x, x + sbar], [s - x, s - x], marker='None', lw=2, color='b')
plt.text(x,
s - (4.167 * x / 5), f'{sbar_asec:.0f}" = '
f'{kpc_per_pixel * sbar:.0f} kpc',
fontsize=20,
color='b')
plt.savefig(f'{my_directory}/images/first-{source_name}.png')
plt.clf()
def loop_through_sources(sigma=5, my_directory='/data5/sean/ldr2'):
"""Plot postage stamp images of LDR2 BL Lacs.
Parameters
----------
sigma : float or integer
The threshold of the significance to set the mask, as a factor of the
local RMS. The default is 5.
my_directory : string
Working directory.
Returns
-------
string
The name of the CSV containing the results.
"""
results_path = f'{my_directory}/measure_point_sources/'
if Path(results_path).exists() and Path(results_path).is_dir():
shutil.rmtree(results_path)
os.mkdir(results_path, 0o755)
results_csv = (f'{results_path}/measure_point_sources.csv')
result_header = ('BZB,Name,RA,Dec,S_peak (mJy),RMS (mJy/beam),D (asec)')
print(result_header)
with open(results_csv, 'a') as f:
f.write(f'{result_header}\n')
df = pd.read_csv(f'{my_directory}/S_Code=S,DC_Maj=0,_1deg - S_Code=S,'
'DC_Maj=0,_1deg.csv')
df = df[(df['PS SNR'] > 10) & (df['Degree separation'] < 0.5)]
df = df[(df['DC_Maj'] == 0) & (df['DC_Min'] == 0) & (df['S_Code'] == 'S')]
plt.figure(figsize=(13.92, 8.60)).patch.set_facecolor('white')
plt.rcParams['font.family'] = 'serif'
plt.rcParams['mathtext.fontset'] = 'dejavuserif'
mpl.rcParams['xtick.major.size'] = 10
mpl.rcParams['xtick.major.width'] = 2
mpl.rcParams['xtick.minor.size'] = 5
mpl.rcParams['xtick.minor.width'] = 2
mpl.rcParams['ytick.major.size'] = 10
mpl.rcParams['ytick.major.width'] = 2
mpl.rcParams['ytick.minor.size'] = 5
mpl.rcParams['ytick.minor.width'] = 2
mpl.rcParams['axes.linewidth'] = 2
dummy = 123456
pix = 1.5 # arcseconds per pixel
colors = ['#118ab2', '#06d6a0', '#ffd166', '#ef476f']
for bzb, bzb_mosaic, source_name, ra, dec, mosaic, rms, pf in zip(
df['BZB name'], df['BZB mosaic'], df['Source_Name'], df['RA'],
df['DEC'], df['Mosaic_ID'], df['Isl_rms'], df['Peak_flux']):
if not bzb_mosaic:
continue # don't use the source if it is from a different pointing
threshold = sigma * rms / 1000 # jansky
hdu = fits.open(f'{my_directory}/mosaics/{mosaic}-mosaic.fits')[0]
wcs = WCS(hdu.header, naxis=2)
sky_position = SkyCoord(ra, dec, unit='deg')
size = [1, 1] * u.arcmin
cutout = Cutout2D(np.squeeze(hdu.data), sky_position, size=size,
wcs=wcs)
d = cutout.data
copy_d = np.copy(d)
last_copy = np.copy(d)
another_copy_d = np.copy(d)
d[d < threshold] = 0
d[d >= threshold] = 1
rows, cols = d.shape
d = label(d)
source_islands = nearest_to_centre(d, percent=0.1)
for source_island in source_islands:
d[d == source_island] = dummy
d[d != dummy] = 0
copy_d[d != dummy] = 0
set_to_nil = [] # identify values we can set to zero for being inside
for r in range(rows): # set to 0 if surrounded by non nans
for c in range(cols):
try:
if (d[r - 1, c - 1] != 0 and d[r - 1, c] != 0 and
d[r - 1, c + 1] != 0 and d[r, c - 1] != 0 and
d[r, c + 1] != 0 and d[r + 1, c - 1] != 0 and
d[r + 1, c] != 0 and d[r + 1, c + 1] != 0):
set_to_nil.append((r, c))
except IndexError:
print(f'Index error for {source_name}.')
continue
for r, c in set_to_nil:
d[r, c] = 0 # needs separate loop to avoid checkered pattern
# d is an outline of the source (one) and everything else is zero
# copy_d is the source with flux values and everything else is zero
# another_copy_d has flux values throughout
good_cells = []
for r in range(rows):
for c in range(cols):
if d[r, c] != 0:
good_cells.append([r, c])
x, y, r = smallestenclosingcircle.make_circle(good_cells)
ax = plt.subplot(projection=cutout.wcs)
plt.xlabel('Right ascension', fontsize=20, color='black')
plt.ylabel('Declination', fontsize=20, color='black')
ax.tick_params(axis='both', which='major', labelsize=20)
plt.imshow(another_copy_d, vmin=0, vmax=np.nanmax(another_copy_d),
origin='lower', norm=DS9Normalize(stretch='arcsinh'),
cmap='cubehelix_r', interpolation='gaussian')
beam = Circle((6, 6), radius=2, linestyle='dashed', lw=2, fc='none',
edgecolor='blue') # radius=2 pixels -> 3" -> diameter=6"
diffuse = Circle((y + 0.5, x + 0.5), radius=r, fc='none',
edgecolor='k', lw=2)
ax.add_patch(beam)
ax.add_patch(diffuse)
cbar = plt.colorbar()
cbar.set_label(r'Jy beam$^{-1}$', size=20)
cbar.ax.tick_params(labelsize=20)
plt.minorticks_on()
plt.tick_params(which='minor', length=0)
levels = [level * threshold for level in [1, 2, 4, 8]]
plt.contour(another_copy_d, levels=levels, origin='lower',
colors=colors)
plt.contour(last_copy, levels=[-threshold * (3 / 5)], colors='grey',
origin='lower', linestyles='dashed')
saved = f'{results_path}/{source_name}.png'
plt.savefig(saved)
plt.clf()
# os.system(f'convert {saved} -trim {saved}') # removes whitespace
result = (f'{bzb},{source_name},{ra},{dec},{pf},{rms},'
f'{r * pix * 2:.1f}')
print(result)
with open(results_csv, 'a') as f:
f.write(f'{result}\n')
return results_csv
def loop_through_sources(sigma=5, my_directory='/data5/sean/ldr2'):
"""Plot postage stamp images of LDR2 BL Lacs.
Parameters
----------
sigma : float or integer
The threshold of the significance to set the mask, as a factor of the
local RMS. The default is 4.
my_directory : string
Working directory.
Returns
-------
string
The name of the CSV containing the results.
"""
results_csv = f'{my_directory}/images/ldr2.csv'
try:
os.remove(results_csv)
except OSError:
pass
df = pd.read_csv(f'{my_directory}/catalogues/final.csv')
result_header = ('Name,RA,Dec,RMS (uJy),Redshift,Width ("),Width (kpc),'
'Peak flux (mJy)\n')
with open(results_csv, 'a') as f:
f.write(result_header)
plt.figure(figsize=(13.92, 8.60)).patch.set_facecolor('white')
plt.rcParams['font.family'] = 'serif'
plt.rcParams['mathtext.fontset'] = 'dejavuserif'
mpl.rcParams['xtick.major.size'] = 10
mpl.rcParams['xtick.major.width'] = 2
mpl.rcParams['xtick.minor.size'] = 5
mpl.rcParams['xtick.minor.width'] = 2
mpl.rcParams['ytick.major.size'] = 10
mpl.rcParams['ytick.major.width'] = 2
mpl.rcParams['ytick.minor.size'] = 5
mpl.rcParams['ytick.minor.width'] = 2
mpl.rcParams['axes.linewidth'] = 2
dummy = 123456
sbar_asec = 30 # desired length of scalebar in arcseconds
pix = 1.5 # arcseconds per pixel
colors = ['#118ab2', '#06d6a0', '#ffd166', '#ef476f']
print('Name, asec, kpc, threshold, UL?')
for source_name, ra, dec, mosaic, rms, z, pf, comp in zip(
df['Name'], df['BZCAT RA'], df['BZCAT Dec'], df['Mosaic_ID'],
df['Isl_rms'], df['redshift'], df['Peak_flux'], df['Compact']):
source_name = '5BZB' + source_name
source_name = source_name.replace(' ', '')
threshold = sigma * rms / 1000 # jansky
thresh_ans = f'{sigma}sigma'
# if threshold < (pf / 50) / 1000:
# # see 2.2 of https://arxiv.org/pdf/1907.03726.pdf
# four_sigma = threshold
# threshold = (pf / 50) / 1000
# thresh_ans = '1/50 S_peak'
hdu = fits.open(f'{my_directory}/mosaics/{mosaic}-mosaic.fits')[0]
wcs = WCS(hdu.header, naxis=2)
sky_position = SkyCoord(ra, dec, unit='deg')
if source_name == '5BZBJ1202+4444' or source_name == '5BZBJ1325+4115':
size = [3, 3] * u.arcmin
p = 9
elif (source_name == '5BZBJ1419+5423'
or source_name == '5BZBJ0945+5757'):
size = [4, 4] * u.arcmin
p = 12
else:
size = [2, 2] * u.arcmin
p = 6
cutout = Cutout2D(np.squeeze(hdu.data),
sky_position,
size=size,
wcs=wcs)
d = cutout.data
copy_d = np.copy(d)
last_copy = np.copy(d)
another_copy_d = np.copy(d)
d[d < threshold] = 0
d[d >= threshold] = 1
rows, cols = d.shape
d = label(d) # label islands of emission
source_islands = nearest_to_centre(d, percent=0.1)
if source_name == '5BZBJ1000+5746':
source_islands = [1, 2, 3, 4]
elif source_name == '5BZBJ1202+4444':
source_islands = [1, 2, 3]
elif (source_name == '5BZBJ1203+5430'
or source_name == '5BZBJ1419+5423'):
source_islands = [1, 2]
elif source_name == '5BZBJ1409+5939':
source_islands = [2, 3]
elif source_name == '5BZBJ1203+5430':
source_islands = [1, 2]
for source_island in source_islands:
d[d == source_island] = dummy
d[d != dummy] = 0
copy_d[d != dummy] = 0
set_to_nil = [] # identify values we can set to zero for being inside
for r in range(rows): # set to 0 if surrounded by non nans
for c in range(cols):
try:
if (d[r - 1, c - 1] != 0 and d[r - 1, c] != 0
and d[r - 1, c + 1] != 0 and d[r, c - 1] != 0
and d[r, c + 1] != 0 and d[r + 1, c - 1] != 0
and d[r + 1, c] != 0 and d[r + 1, c + 1] != 0):
set_to_nil.append((r, c))
except IndexError:
print(f'Index error for {source_name}.')
continue
for r, c in set_to_nil:
d[r, c] = 0 # needs separate loop to avoid checkered pattern
# d is an outline of the source (one) and everything else is zero
# copy_d is the source with flux values and everything else is zero
# another_copy_d has flux values throughout
good_cells = []
for r in range(rows):
for c in range(cols):
if d[r, c] != 0:
good_cells.append([r, c])
x, y, r = smallestenclosingcircle.make_circle(good_cells)
ax = plt.subplot(projection=cutout.wcs)
plt.xlabel('Right ascension', fontsize=20, color='black')
plt.ylabel('Declination', fontsize=20, color='black')
ax.tick_params(axis='both', which='major', labelsize=20)
plt.imshow(another_copy_d,
vmin=0,
vmax=np.nanmax(another_copy_d),
origin='lower',
norm=DS9Normalize(stretch='arcsinh'),
cmap='cubehelix_r',
interpolation='gaussian')
beam = Circle((6, 6),
radius=2,
linestyle='dashed',
lw=2,
fc='none',
edgecolor='blue') # radius=2 pixels -> 3" -> diameter=6"
# diffuse = Circle((y + 0.5, x + 0.5), radius=r, fc='none',
# edgecolor='k', lw=2)
ax.add_patch(beam)
# if comp == False: # noqa
# ax.add_patch(diffuse)
kpc_per_asec = get_kpc_per_asec(z=z)
sbar = sbar_asec / pix # length of scalebar in pixels
kpc_per_pixel = kpc_per_asec * pix
s = cutout.data.shape[1] # plot scalebar
plt.plot([p, p + sbar], [s - p, s - p], marker='None', lw=2, color='b')
plt.text(p,
s - (5 * p / 6), f'{sbar_asec:.0f}" = '
f'{kpc_per_pixel * sbar:.0f} kpc',
fontsize=20,
color='b')
cbar = plt.colorbar()
cbar.set_label(r'Jy beam$^{-1}$', size=20)
cbar.ax.tick_params(labelsize=20)
plt.minorticks_on()
plt.tick_params(which='minor', length=0)
# levels = [level * threshold for level in [1, 2, 4, 8]]
levels = [level * threshold for level in [1, 2, 4, 8]]
plt.contour(another_copy_d,
levels=levels,
origin='lower',
colors=colors)
plt.contour(last_copy,
levels=[-threshold * (3 / 5)],
colors='grey',
origin='lower',
linestyles='dashed')
# plt.contour(another_copy_d - copy_d, levels=[threshold],
# colors='grey', origin='lower')
saved = f'{my_directory}/images/ldr2-{source_name}.png'
# if thresh_ans == '1/50 S_peak':
# plt.contour(last_copy, levels=[-four_sigma], colors='grey',
# origin='lower', linestyles='dashed')
# plt.contour(last_copy, levels=[four_sigma], colors='grey',
# origin='lower', linestyles='solid')
print('view me: gpicview ' + saved)
plt.savefig(saved)
plt.clf()
os.system(f'convert {saved} -trim {saved}') # removes whitespace
width = r * kpc_per_pixel * 2 # radius to diameter
result = (f'{source_name},{ra},{dec},{rms * 1e3},{z},'
f'{r * pix * 2:.1f}, {width:.1f}, {pf}\n')
print(f'{source_name[4:]}, {r * pix * 2:.4f}, {width:.4f}, '
f'{thresh_ans}, {comp}')
with open(results_csv, 'a') as f:
f.write(result)
return results_csv