def worker(npos):
_noise = numpy.zeros(runs)
for i in range(runs):
radec = getpos(npos)
stack.log.info("Doing run {:d} of {:d}".format(i, runs))
stacked = stack.stack(catalog=radec)
mask = utils.elliptical_mask(stacked, stack.bmajPix/2., stack.bminPix/2., stack.bpa)
flux = utils.gauss_weights(stacked, stack.bmajPix/2., stack.bmajPix/2., mask=mask)
_noise[i] = flux.sum()
noise.append([ npos, _noise.std()])
def worker(npos):
_noise = numpy.zeros(runs)
for i in range(runs):
radec = getpos(npos)
stack.log.info("Doing run {:d} of {:d}".format(i, runs))
stacked = stack.stack(catalog=radec)
mask = utils.elliptical_mask(stacked, stack.bmajPix / 2.,
stack.bminPix / 2., stack.bpa)
flux = utils.gauss_weights(stacked,
stack.bmajPix / 2.,
stack.bmajPix / 2.,
mask=mask)
_noise[i] = flux.sum()
noise.append([npos, _noise.std()])
def stack(self):
nprofs = len(self.catalog)
self.log.info("Stacking {:d} line profiles".format(nprofs))
# Run these jobs in parallel
procs = []
range_ = range(10, 110, 10)
print("Progress:"),
counter = 0
while counter <= nprofs-1:
if self.active >= self.cores:
continue
ra, dec, cfreq, w, _id = self.catalog
proc = Process(target=self.profile, args = (ra, dec, cfreq, w, counter) )
proc.start()
procs.append(proc)
counter += 1
self.active.value += 1
nn = int(self.track.value/float(self.nprofs)*100)
if nn in range_:
print("..{:d}%".format(nn)),
range_.remove(nn)
for proc in procs:
proc.join()
print("..100%\n")
self.log.info("Have stackem all")
self.log.info("{:d} out of {:d} profiles were excluded because they \
were too close to an edge".format(self.excluded.value, nprofs))
stack = numpy.sum(self.profiles, 0)
if self.beam2pix:
mask = utils.elliptical_mask(stack[0], self.bmajPix/2, self.bminPix/2, self.bpa)
stack = utils.gauss_weights(stack, self.bmajPix/2, self.bminPix/2, mask=mask)
profile = stack.sum((1, 2))/self.weights.value
return profile
def stack(self):
nprofs = len(self.catalog)
self.log.info("Stacking {:d} line profiles".format(nprofs))
# Run these jobs in parallel
procs = []
range_ = range(10, 110, 10)
print("Progress:"),
for i, (ra, dec, cfreq, w, _id) in enumerate(self.catalog):
proc = Process(target=self.profile, args=(ra, dec, cfreq, w, i))
proc.start()
procs.append(proc)
nn = int(self.track.value / float(self.nprofs) * 100)
if nn in range_:
print("..{:d}%".format(nn)),
range_.remove(nn)
for proc in procs:
proc.join()
print("..100%\n")
self.log.info("Have stackem all")
self.log.info("{:d} out of {:d} profiles were excluded because they \
were too close to an edge".format(self.excluded.value, nprofs))
stack = numpy.sum(self.profiles, 0)
if self.beam2pix:
mask = utils.elliptical_mask(stack[0], self.bmajPix / 2,
self.bminPix / 2, self.bpa)
stack = utils.gauss_weights(stack,
self.bmajPix / 2,
self.bminPix / 2,
mask=mask)
profile = stack.sum((1, 2)) / self.weights.value
return profile
def main():
__version_info__ = (0, 0, 1)
__version__ = ".".join(map(str, __version_info__))
for i, arg in enumerate(sys.argv):
if (arg[0] == '-') and arg[1].isdigit(): sys.argv[i] = ' ' + arg
parser = ArgumentParser(
description=
'Image based stacking tool S Makhathini <*****@*****.**>')
add = parser.add_argument
add("-v",
"--version",
action='version',
version='{:s} version {:s}'.format(parser.prog, __version__))
add("-i", "--image", help="FITS image name")
add("-c",
"--catalog",
metavar="CATALOG_NAME:DELIMITER",
help=
"Catalog name. Default delimiter is a comma. Format: 'ra,dec,freq' ")
add("-p",
"--prefix",
default="gota_stackem_all",
help="Prefix for output products.")
add("-w",
"--width",
type=float,
default=0,
help=
"For line stacking: Band width [MHz] to sample across frequency (for line stacking). Default is 1."
"For continuum stacking: Width (in beams) of subregion to stack. Default is 10 beams"
)
add("-vbl",
"--vebosity-level",
dest="vbl",
choices=["0", "1", "2", "3"],
default="0",
help=
"Verbosity level. 0-> INFO, 1-> DEBUG, 2-> ERROR, 3-> CRITICAL. Default is 0"
)
add("-b",
"--beam",
metavar="BMIN[:BMIN:BPA]",
help="PSF (a.k.a dirty beam) FWHM in degrees. No default")
add("-b2p",
"--beam2pix",
action="store_true",
help="Do Jy/beam to Jy/pixel conversion")
add("-L", "--line", action="store_true", help="Do line stacking")
add("-C", "--cont", action="store_true", help="Do continuum stacking")
add("-mc",
"--monte-carlo",
metavar="SAMPLES:START:FINISH:N",
default=False,
help="Do a monte carlo analysis of the noise. That is, "
"stack on START random positions NSAMPLES times. "
"Repeat this N times with (FINISH-START)/N increments."
"if set -mc/--mont-carlo=yes, default is '400:1000:8000:7'")
args = parser.parse_args()
if args.catalog:
catalog_string = args.catalog.split(":")
if len(catalog_string) > 1:
catalgname, delimiter = catalog_string
else:
catalogname, delimiter = catalog_string[0], ","
if args.beam:
beam = args.beam.split(":")
if len(beam) == 1:
beam = float(beam[0])
elif len(beam) == 2:
beam = map(float, beam) + [0]
else:
beam = map(float, beam)
else:
beam = None
pylab.clf()
prefix = args.prefix or "stackem_default"
pylab.figure(figsize=(15, 10))
if args.line:
from Stackem import LineStacker
stack = LineStacker.load(args.image,
catalogname,
delimiter=delimiter,
beam=beam,
width=args.width,
beam2pix=args.beam2pix,
verbosity=args.vbl)
stacked_line = stack.stack() * 1e6 # convert to uJy
peak, nu, sigma = gfit_params = stack.fit_gaussian(stacked_line)
gfit = utils.gauss(range(stack.width), *gfit_params)
freqs = (numpy.linspace(-stack.width / 2, stack.width / 2, stack.width)
* stack.dfreq - stack.freq0) * 1e-9 # convert to GHz
# plot profile
pylab.plot(freqs, stacked_line, "r.", label="stacked profile")
pylab.plot(freqs, gfit, "k-", label="Gaussian fit")
pylab.grid()
pylab.xlim(freqs[0], freqs[-1])
pylab.xlabel("Frequency [GHz]")
pylab.ylabel("Flux density [mJy/{:s}]".format(
"beam" if args.beam2pix else "pixel"))
pylab.legend(loc=1)
pylab.savefig(prefix + "-line.png")
pylab.clf()
# save
numpy.savetxt(prefix + "-line.txt", stacked_line, delimiter=",")
with open(prefix + "-line_stats.txt", "w") as info:
tot = sigma * peak * math.sqrt(2 * math.pi)
stack.log.info("Gaussian Fit parameters.")
info.write("Gaussian Fit parameters\n")
info.write("Peak Flux : {:.4g} uJy\n".format(peak))
stack.log.info("Peak Flux : {:.4g} uJy".format(peak))
info.write("Integrated Flux : {:.4g} uJy\n".format(tot))
stack.log.info("Integrated Flux : {:.4g} uJy".format(tot))
info.write("Profile width : {:.4g} MHz \n".format(sigma *
stack.dfreq))
stack.log.info("Profile width : {:.4g} kHz".format(
sigma * stack.dfreq * 1e-3))
stack.log.info(
"Line stacking ran successfully. Find your outputs at {:s}-line*".
format(prefix))
if args.cont:
pylab.figure(figsize=(20, 20))
from Stackem import ContStacker
stack = ContStacker.load(args.image,
catalogname,
delimiter=delimiter,
beam=beam,
beam2pix=args.beam2pix,
width=int(args.width),
verbosity=args.vbl)
stacked = stack.stack()
mask = utils.elliptical_mask(stacked, stack.bmajPix / 2.,
stack.bminPix / 2., stack.bpa)
flux = utils.gauss_weights(stacked,
stack.bmajPix / 2.,
stack.bmajPix / 2.,
mask=mask)
flux = flux.sum()
rms = utils.negnoise(stacked)
with open(prefix + "-cont.txt", "w") as cont:
stack.log.info("Stacked flux: {:.3g} +/- {:.3g} uJy".format(
flux * 1e6, rms * 1e6))
cont.write("Stacked flux: {:.3g} +/- {:.3g} uJy/beam".format(
flux * 1e6, rms * 1e6))
# Plot stacked image, and cross-sections
rotated = numpy.rot90(stacked.T)
import matplotlib.gridspec as gridspec
gs = gridspec.GridSpec(2, 2, width_ratios=[4, 1], height_ratios=[1, 4])
gs.update(wspace=0.05, hspace=0.05)
ax1 = pylab.subplot(gs[2])
ax2 = pylab.subplot(gs[0], sharex=ax1)
ax3 = pylab.subplot(gs[3], sharey=ax1)
pylab.setp(ax3.get_yticklabels(), visible=False)
pylab.setp(ax3.get_xticklabels(), rotation=90)
pylab.setp(ax2.get_xticklabels(), visible=False)
ax1.imshow(stacked, interpolation="nearest")
ax1.set_aspect("auto")
ra0, dec0 = stack.wcs.getCentreWCSCoords()
ras = numpy.linspace(stack.width / 2, -stack.width / 2,
stack.width) * stack.cell - ra0
decs = numpy.linspace(-stack.width / 2, stack.width / 2,
stack.width) * stack.cell - dec0
ax1.set_xticklabels(map(lambda x: coords.decimal2hms(x, ":"), ras))
ax1.set_yticklabels(map(lambda x: coords.decimal2dms(x, ":"), decs))
ax1.set_xlabel("RA [hms]")
ax1.set_ylabel("DEC [dms]")
pylab.setp(ax1.get_xticklabels(), rotation=90)
ax2.plot(rotated[:, stack.width / 2] * 1e6)
ax3.plot(rotated[stack.width / 2, :][::-1] * 1e6, range(stack.width))
ax3.set_ylim(0, stack.width - 1)
ax2.set_xlim(0, stack.width - 1)
ax2.set_ylabel("Flux density [uJy/beam]")
ax3.set_xlabel("Flux density [uJy/beam]")
pylab.savefig(prefix + "-cont.png")
# save stacked image as fits
hdr = stack.hdr
hdr["crpix1"] = stack.width / 2
hdr["crpix2"] = stack.width / 2
pyfits.writeto(prefix + "-cont.fits", stacked, hdr, clobber=True)
stack.log.info(
"Continuum stacking ran successfully. Find your outputs at {:s}-line*"
.format(prefix))
if args.monte_carlo:
if args.monte_carlo in ["yes", "1", "True", "true"]:
runs, stacks = 400, xrange(1000, 8000, 1000)
else:
a, b, c, d = map(int, args.monte_carlo.split(":"))
runs, stacks = a, xrange(b, c, (c - b) / d)
from Stackem import ContStacker
noise = ContStacker.mc_noise_stack(args.image,
runs=runs,
stacks=stacks,
beam=beam,
beam2pix=args.beam2pix,
width=args.width,
verbosity=args.vbl)
pylab.clf()
pylab.loglog(stacks, noise)
pylab.grid()
pylab.ylabel("Log (Noise [Jy/beam])")
pylab.xlabel("Log (Random Stacks). {:d} Samples".format(runs))
pylab.savefig(prefix + "-cont_mc.png")
pylab.clf()
def main():
__version_info__ = (0,0,1)
__version__ = ".".join( map(str,__version_info__) )
for i, arg in enumerate(sys.argv):
if (arg[0] == '-') and arg[1].isdigit(): sys.argv[i] = ' ' + arg
parser = ArgumentParser(description='Image based stacking tool S Makhathini <*****@*****.**>')
add = parser.add_argument
add("-v","--version", action='version',version='{:s} version {:s}'.format(parser.prog, __version__))
add("-i", "--image",
help="FITS image name")
add("-c", "--catalog", metavar="CATALOG_NAME:DELIMITER",
help="Catalog name. Default delimiter is a comma. Format: 'ra,dec,freq' ")
add("-p", "--prefix", default="gota_stackem_all",
help="Prefix for output products.")
add("-w", "--width", type=float, default=0,
help="For line stacking: Band width [MHz] to sample across frequency (for line stacking). Default is 1."
"For continuum stacking: Width (in beams) of subregion to stack. Default is 10 beams")
add("-vbl", "--vebosity-level", dest="vbl", choices=["0", "1", "2", "3"], default="0",
help="Verbosity level. 0-> INFO, 1-> DEBUG, 2-> ERROR, 3-> CRITICAL. Default is 0")
add("-b", "--beam", metavar="BMIN[:BMIN:BPA]",
help="PSF (a.k.a dirty beam) FWHM in degrees. No default")
add("-b2p", "--beam2pix", action="store_true",
help="Do Jy/beam to Jy/pixel conversion")
add("-L", "--line", action="store_true",
help="Do line stacking")
add("-C", "--cont", action="store_true",
help="Do continuum stacking")
add("-mc", "--monte-carlo", metavar="SAMPLES:START:FINISH:N", default=False,
help="Do a monte carlo analysis of the noise. That is, "
"stack on START random positions NSAMPLES times. "
"Repeat this N times with (FINISH-START)/N increments."
"if set -mc/--mont-carlo=yes, default is '400:1000:8000:7'")
args = parser.parse_args()
if args.catalog:
catalog_string = args.catalog.split(":")
if len(catalog_string)>1:
catalgname, delimiter = catalog_string
else:
catalogname, delimiter = catalog_string[0], ","
if args.beam:
beam = args.beam.split(":")
if len(beam)==1:
beam = float(beam[0])
elif len(beam)==2:
beam = map(float, beam) + [0]
else:
beam = map(float, beam)
else:
beam = None
pylab.clf()
prefix = args.prefix or "stackem_default"
pylab.figure(figsize=(15,10))
if args.line:
from Stackem import LineStacker
stack = LineStacker.load(args.image, catalogname, delimiter=delimiter,
beam=beam, width=args.width, beam2pix=args.beam2pix,
verbosity=args.vbl)
stacked_line = stack.stack()*1e6 # convert to uJy
peak, nu, sigma = gfit_params = stack.fit_gaussian(stacked_line)
gfit = utils.gauss(range(stack.width), *gfit_params)
freqs = (numpy.linspace(-stack.width/2, stack.width/2, stack.width)*stack.dfreq - stack.freq0)*1e-9 # convert to GHz
# plot profile
pylab.plot(freqs, stacked_line, "r.", label="stacked profile")
pylab.plot(freqs, gfit, "k-", label="Gaussian fit")
pylab.grid()
pylab.xlim(freqs[0], freqs[-1])
pylab.xlabel("Frequency [GHz]")
pylab.ylabel("Flux density [mJy/{:s}]".format("beam" if args.beam2pix else "pixel"))
pylab.legend(loc=1)
pylab.savefig(prefix+"-line.png")
pylab.clf()
# save
numpy.savetxt(prefix+"-line.txt", stacked_line, delimiter=",")
with open(prefix+"-line_stats.txt", "w") as info:
tot = sigma*peak*math.sqrt(2*math.pi)
stack.log.info("Gaussian Fit parameters.")
info.write("Gaussian Fit parameters\n")
info.write("Peak Flux : {:.4g} uJy\n".format(peak))
stack.log.info("Peak Flux : {:.4g} uJy".format(peak))
info.write("Integrated Flux : {:.4g} uJy\n".format(tot))
stack.log.info("Integrated Flux : {:.4g} uJy".format(tot))
info.write("Profile width : {:.4g} MHz \n".format(sigma*stack.dfreq))
stack.log.info("Profile width : {:.4g} kHz".format(sigma*stack.dfreq*1e-3))
stack.log.info("Line stacking ran successfully. Find your outputs at {:s}-line*".format(prefix))
if args.cont:
pylab.figure(figsize=(20, 20))
from Stackem import ContStacker
stack = ContStacker.load(args.image, catalogname, delimiter=delimiter,
beam=beam, beam2pix=args.beam2pix, width=int(args.width),
verbosity=args.vbl)
stacked = stack.stack()
mask = utils.elliptical_mask(stacked, stack.bmajPix/2., stack.bminPix/2., stack.bpa)
flux = utils.gauss_weights(stacked, stack.bmajPix/2., stack.bmajPix/2., mask=mask)
flux = flux.sum()
rms = utils.negnoise(stacked)
with open(prefix+"-cont.txt", "w") as cont:
stack.log.info("Stacked flux: {:.3g} +/- {:.3g} uJy".format(flux*1e6, rms*1e6))
cont.write("Stacked flux: {:.3g} +/- {:.3g} uJy/beam".format(flux*1e6, rms*1e6))
# Plot stacked image, and cross-sections
rotated = numpy.rot90(stacked.T)
import matplotlib.gridspec as gridspec
gs = gridspec.GridSpec(2, 2,
width_ratios = [4,1],
height_ratios = [1,4])
gs.update(wspace=0.05, hspace=0.05)
ax1 = pylab.subplot(gs[2])
ax2 = pylab.subplot(gs[0], sharex=ax1)
ax3 = pylab.subplot(gs[3], sharey=ax1)
pylab.setp(ax3.get_yticklabels(), visible=False)
pylab.setp(ax3.get_xticklabels(), rotation=90)
pylab.setp(ax2.get_xticklabels(), visible=False)
ax1.imshow(stacked, interpolation="nearest")
ax1.set_aspect("auto")
ra0, dec0 = stack.wcs.getCentreWCSCoords()
ras = numpy.linspace(stack.width/2, -stack.width/2, stack.width)*stack.cell - ra0
decs = numpy.linspace(-stack.width/2, stack.width/2, stack.width)*stack.cell - dec0
ax1.set_xticklabels( map(lambda x: coords.decimal2hms(x, ":"), ras) )
ax1.set_yticklabels( map(lambda x: coords.decimal2dms(x, ":"), decs) )
ax1.set_xlabel("RA [hms]")
ax1.set_ylabel("DEC [dms]")
pylab.setp(ax1.get_xticklabels(), rotation=90)
ax2.plot(rotated[:, stack.width/2]*1e6)
ax3.plot(rotated[stack.width/2,:][::-1]*1e6, range(stack.width))
ax3.set_ylim(0, stack.width-1)
ax2.set_xlim(0, stack.width-1)
ax2.set_ylabel("Flux density [uJy/beam]")
ax3.set_xlabel("Flux density [uJy/beam]")
pylab.savefig(prefix+"-cont.png")
# save stacked image as fits
hdr = stack.hdr
hdr["crpix1"] = stack.width/2
hdr["crpix2"] = stack.width/2
pyfits.writeto(prefix+"-cont.fits", stacked, hdr, clobber=True)
stack.log.info("Continuum stacking ran successfully. Find your outputs at {:s}-line*".format(prefix))
if args.monte_carlo:
if args.monte_carlo in ["yes", "1", "True", "true"]:
runs, stacks = 400, xrange(1000, 8000, 1000)
else:
a, b, c, d = map(int, args.monte_carlo.split(":"))
runs, stacks = a, xrange(b, c, (c-b)/d)
from Stackem import ContStacker
noise = ContStacker.mc_noise_stack(args.image, runs=runs, stacks=stacks,
beam=beam, beam2pix=args.beam2pix, width=args.width,
verbosity=args.vbl)
pylab.clf()
pylab.loglog(stacks, noise)
pylab.grid()
pylab.ylabel("Log (Noise [Jy/beam])")
pylab.xlabel("Log (Random Stacks). {:d} Samples".format(runs))
pylab.savefig(prefix+"-cont_mc.png")
pylab.clf()