def maths(image, cutoff, mask):
os.system('rm -r ' + mask)
maths = mirexec.TaskMaths()
maths.exp = "<" + image + ">"
maths.mask = "<" + image + ">" + ".gt." + str(cutoff)
maths.out = mask
tout = maths.snarf()
def maths(imname, cutoff, maskname):
os.system('rm -r ' + maskname)
maths = mirexec.TaskMaths()
maths.exp = imname
maths.mask = imname + '.gt.' + str(cutoff)
maths.out = maskname
maths.snarf()
def mkfmod():
'''
Uses image properties to cutout a single rectangular
region from a mask.
S.fipe = X,Y,xp,yp,l
X,Y: Size of the model image (pixels)
xp,yp: Pixel coords of the source to be peeled
l: Pixel length of displacement from source
'''
# First, the size of the image
S.fipe = S.fipe.split(',')
X = int(S.fipe[0])
Y = int(S.fipe[1])
xp = int(S.fipe[2])
yp = int(S.fipe[3])
l = int(S.fipe[4])
model = S.fipe[5]
polygon = 'polygon(1,1,' + str(xp - l) + ',1,' + str(xp -
l) + ',' + str(yp + l)
polygon = polygon + ',' + str(xp + l) + ',' + str(yp +
l) + ',' + str(xp +
l) + ',1,'
polygon = polygon + str(X) + ',1,' + str(X) + ',' + str(Y) + ',1,' + str(
Y) + ')'
maths = mirexec.TaskMaths()
maths.exp = model
maths.region = polygon
maths.out = 'fmod'
tout = maths.snarf()
acos.taskout(maths, tout, 'maths.txt')
def maths(image, cutoff, mask):
shrun('rm -r ' + mask)
maths = mirexec.TaskMaths()
maths.exp = image
maths.mask = image + ".gt." + str(cutoff)
maths.out = mask
tout = maths.snarf()
def maths(settings, fname):
settings.gt = round(settings.gt, 10)
maths = mirexec.TaskMaths()
maths.exp = settings.image
maths.mask = settings.image + '.gt.' + str(settings.gt)
maths.out = settings.mask
#maths.region=settings.clean_region;
tout = maths.snarf()
acos.taskout(maths, tout, fname)
def fp_models(ppoly, fpoly, model):
'''
Uses the vertices defined in pgon to
'''
maths = mirexec.TaskMaths()
maths.exp = model
maths.mask = model
maths.region = ppoly
maths.out = model.replace("src", "src_p")
tout = restor.snarf()
acos.taskout(maths, tout, 'maths.txt')
maths = mirexec.TaskMaths()
maths.exp = model
maths.mask = model
maths.region = fpoly
maths.out = model.replace("src", "src_f")
tout = restor.snarf()
acos.taskout(maths, tout, 'maths.txt')
def mk_lsm(options):
'''
mk_lsm: The module that makes the NVSS LSM using the miriad task IMGEN.
Needs options.infile (template) and options.outfile (name of output point source model)
'''
# NOTE: Classic cos^6 model of the WSRT PB used to calculate apparent fluxes. Here we use c=68.
PB = lambda c, v, r: (pl.cos(c * v * r))**6
# NOTE: Grab the central coordinates from the template file.
c = getradec(options.infile)
print c
ra0 = c.ra.deg
dec0 = c.dec.deg
# NOTE: Query the NVSS around the central pointing
L, M, N, S = query_nvss(options, ra0, dec0, s='>10.', proj='NCP')
L_asec = L * 3600.
M_asec = M * 3600.
pl.scatter(L_asec, M_asec, c=S * 1000, s=S)
pl.xlabel('L offset (arcsec)')
pl.ylabel('M offset (arcsec)')
pl.colorbar()
pl.savefig(options.infile + '-nvss-lsm.pdf', dpi=300)
pl.close()
n = 20
modfiles = ''
# NOTE: Make the LSM!
imgen = mirexec.TaskImGen()
for j in range(0, 1 + N / n):
imgen.in_ = options.infile
imgen.out = 'tmod' + str(j)
os.system('rm -r ' + str(imgen.out))
objs = ''
spars = ''
for i in range(0, n):
if (i + j * n < N):
objs += 'point,'
d2point = L[i + j * n]**2 + M[i + j * n]**2
S_app = S[i + j * n] * PB(c=68, v=1.420, r=d2point)
spars += str(S_app / 1e3) + ',' + str(
L_asec[i + j * n]) + ',' + str(M_asec[i + j * n]) + ','
imgen.factor = 0
imgen.object = objs[:-1]
imgen.spar = spars[:-1]
imgen.snarf()
modfiles += '<' + imgen.out + '>+'
maths = mirexec.TaskMaths()
maths.exp = modfiles[0:-1]
os.system('rm -r ' + options.outfile)
maths.out = options.outfile
maths.snarf()
os.system('rm -r tmod*')
