def compare_spectral_indices(finaliter_prefix_b3, finaliter_prefix_b6,
cutoutregion, fignum=1, stdthresh=5,
scalebarlength=5,
basepath='/home/adam/work/alma-imf/reduction/', ):
image_b3 = SpectralCube.read(f'{finaliter_prefix_b3}.image.tt0', format='casa_image').subcube_from_ds9region(cutoutregion)
try:
image_tt1_b3 = SpectralCube.read(f'{finaliter_prefix_b3}.image.tt1', format='casa_image').subcube_from_ds9region(cutoutregion)
except FileNotFoundError:
image_tt1_b3 = SpectralCube.read(f'{finaliter_prefix_b3.replace("cleanest", "cleanest/extras")}.image.tt1', format='casa_image').subcube_from_ds9region(cutoutregion)
image_b6 = SpectralCube.read(f'{finaliter_prefix_b6}.image.tt0', format='casa_image').subcube_from_ds9region(cutoutregion)
try:
image_tt1_b6 = SpectralCube.read(f'{finaliter_prefix_b6}.image.tt1', format='casa_image').subcube_from_ds9region(cutoutregion)
except FileNotFoundError:
image_tt1_b6 = SpectralCube.read(f'{finaliter_prefix_b6.replace("cleanest", "cleanest/extras")}.image.tt1', format='casa_image').subcube_from_ds9region(cutoutregion)
image_b3 = image_b3 * u.beam / image_b3.beam.sr
image_b6 = image_b6 * u.beam / image_b6.beam.sr
image_tt1_b3 = image_tt1_b3 * u.beam / image_tt1_b3.beam.sr
image_tt1_b6 = image_tt1_b6 * u.beam / image_tt1_b6.beam.sr
fieldname = os.path.basename(finaliter_prefix_b6).split("_")[0]
print(fieldname)
noise_region_b3 = regions.read_ds9(f"{basepath}/reduction/noise_estimation_regions/{fieldname}_B3_noise_sampling.reg")
noise_region_b6 = regions.read_ds9(f"{basepath}/reduction/noise_estimation_regions/{fieldname}_B6_noise_sampling.reg")
beams = radio_beam.Beams(major=u.Quantity([image_b3.beam.major, image_b6.beam.major]),
minor=u.Quantity([image_b3.beam.minor, image_b6.beam.minor]),
pa=u.Quantity([image_b3.beam.pa, image_b6.beam.pa]))
commonbeam = radio_beam.commonbeam.commonbeam(beams)
if image_b3.beam.sr < image_b6.beam.sr:
header = image_b6[0].header
ww = image_b6.wcs
else:
header = image_b3[0].header
ww = image_b3.wcs
image_b3_repr = image_b3.convolve_to(commonbeam)[0].reproject(header)
image_tt1_b3_repr = image_tt1_b3.convolve_to(commonbeam)[0].reproject(header)
image_b6_repr = image_b6.convolve_to(commonbeam)[0].reproject(header)
image_tt1_b6_repr = image_tt1_b6.convolve_to(commonbeam)[0].reproject(header)
alpha_b3 = image_tt1_b3_repr / image_b3_repr
alpha_b6 = image_tt1_b6_repr / image_b6_repr
noiseim_b3 = SpectralCube.read(f'{finaliter_prefix_b3}.image.tt0', format='casa_image', use_dask=True).subcube_from_regions(noise_region_b3)[0].convolve_to(commonbeam)
noiseim_b6 = SpectralCube.read(f'{finaliter_prefix_b3}.image.tt0', format='casa_image', use_dask=True).subcube_from_regions(noise_region_b6)[0].convolve_to(commonbeam)
b3_std = stats.mad_std(noiseim_b3.value, ignore_nan=True)
b6_std = stats.mad_std(noiseim_b6.value, ignore_nan=True)
mask = (image_b3_repr.value > stdthresh*b3_std) & (image_b6_repr.value > stdthresh*b6_std)
alpha_b3_b6 = (np.log(image_b3_repr / image_b6_repr) / np.log(image_b3.wcs.wcs.crval[2] / image_b6.wcs.wcs.crval[2])).value
alpha_b3_b6[~mask] = np.nan
alpha_b3[~mask] = np.nan
alpha_b6[~mask] = np.nan
fig = pl.figure(num=fignum, figsize=(12,4))
fig.clf()
ax = pl.subplot(1,3,1,label='B3')
ax.imshow(alpha_b3.value, vmax=4, vmin=-2)
ax.set_xticks([])
ax.set_yticks([])
ax.set_title("B3")
cb=pl.colorbar(mappable=pl.gca().images[0], cax=fig.add_axes([0.91,0.1,0.02,0.8]))
cb.set_label(r"Spectral Index $\alpha$")
ax = pl.subplot(1,3,2,label='B6')
ax.imshow(alpha_b6.value, vmax=4, vmin=-2)
ax.set_xticks([])
ax.set_yticks([])
ax.set_title("B6")
cb=pl.colorbar(mappable=pl.gca().images[0], cax=fig.add_axes([0.91,0.1,0.02,0.8]))
cb.set_label(r"Spectral Index $\alpha$")
# scalebar
cd = (ww.pixel_scale_matrix[1,1] * 3600)
ax.plot([10*scalebarlength, 10*scalebarlength+scalebarlength/cd], [5,5], color='k')
tx = ax.annotate(f'{scalebarlength}"', (10*scalebarlength+scalebarlength/2/cd, 9))
tx.set_horizontalalignment('center')
ax = pl.subplot(1,3,3,label='B3/B6')
ax.imshow(alpha_b3_b6, vmax=4, vmin=-2)
ax.set_xticks([])
ax.set_yticks([])
cb=pl.colorbar(mappable=pl.gca().images[0], cax=fig.add_axes([0.91,0.1,0.02,0.8]))
cb.set_label(r"Spectral Index $\alpha$")
ax.set_title("B6/B3")
pl.subplots_adjust(wspace=0.05)
def main():
"""Main function"""
#===========================
#== PARSE ARGS
#===========================
logger.info("Get script args ...")
try:
args = get_args()
except Exception as ex:
logger.error("Failed to get and parse options (err=%s)", str(ex))
return 1
filenames = [x.strip() for x in args.filenames.split(',')]
userbeam = args.userbeam
bmaj_user = args.bmaj
bmin_user = args.bmin
pa_user = args.pa
print("=== ARGS ===")
print("filenames")
print(filenames)
print("userbeam? %d" % userbeam)
if userbeam:
print("Beam (bmaj=%f arcsec,bmin=%f arcsec,pa=%f deg)" %
(bmaj_user, bmin_user, pa_user))
print("============")
#===========================
#== READ IMAGES
#===========================
filenames_out = []
data_list = []
header_list = []
pixsize_x = []
pixsize_y = []
beam_list = []
for filename in filenames:
# - Store output filename
filename_out = Utils.getBaseFileNoExt(filename) + '_conv.fits'
filenames_out.append(filename_out)
# - Get image data & header
#data_alldim, header= Utils.read_fits(filename)
#nchan = len(data_alldim.shape)
data, header = Utils.read_fits(filename)
nchan = len(data.shape)
logger.info("nchan=%d" % nchan)
#if nchan==4:
# data= data_alldim[0, 0, :, :]
#else:
# data= data_alldim
data[np.isnan(data)] = 0.0 # replace all NAN pixels with 0
data_list.append(data)
header_list.append(header)
# - Get beam and WCS info
wcs = WCS(header)
hasBeamInfo = Utils.hasBeamInfo(header)
xc = header['CRPIX1']
yc = header['CRPIX2']
if nchan == 4:
ra, dec = wcs.all_pix2world(xc, yc, 0, 0, 0, ra_dec_order=True)
else:
ra, dec = wcs.all_pix2world(xc, yc, 0, ra_dec_order=True)
print("ra=%f, dec=%f" % (ra, dec))
dx = abs(header['CDELT1']) # in deg
dy = abs(header['CDELT2']) # in deg
pixsize_x.append(dx)
pixsize_y.append(dy)
if hasBeamInfo:
bmaj = header['BMAJ'] # in deg
bmin = header['BMIN'] # in deg
pa = header['BPA'] if 'BPA' in header else 0 # in deg
beam = radio_beam.Beam(bmaj * u.deg, bmin * u.deg, pa * u.deg)
beam_list.append(beam)
else:
logger.error("No BMAJ/BMIN keyword present in file " + filename +
"!")
return -1
#===========================
#== SET COMMON BEAM
#===========================
if userbeam:
#common_beam_bmaj= bmaj_user.to(u.arcsec).value
#common_beam_bmin= bmin_user.to(u.arcsec).value
#common_beam_pa= pa_user.to(u.deg).value
common_beam_bmaj = bmaj_user
common_beam_bmin = bmin_user
common_beam_pa = pa_user
common_beam = radio_beam.Beam(bmaj_user * u.arcsec,
bmin_user * u.arcsec, pa_user * u.deg)
else:
beams = radio_beam.Beams(beams=beam_list)
common_beam = radio_beam.commonbeam.common_manybeams_mve(beams)
common_beam_bmaj = common_beam.major.to(u.arcsec).value
common_beam_bmin = common_beam.minor.to(u.arcsec).value
common_beam_pa = common_beam.pa.to(u.deg).value
logger.info(
"Convolving images to common beam size (bmaj,bmin,pa)=(%s,%s,%s) ..." %
(str(common_beam_bmaj), str(common_beam_bmin), str(common_beam_pa)))
#===========================
#== CONVOLVE IMAGES
#===========================
for index in range(0, len(data_list)):
# - Find convolving beam for this image
bmaj, bmin, pa = radio_beam.utils.deconvolve(common_beam,
beam_list[index])
bmaj_deg = bmaj.to(u.deg).value
bmin_deg = bmin.to(u.deg).value
pa_deg = pa.to(u.deg).value
conv_beam = radio_beam.Beam(bmaj_deg * u.deg, bmin_deg * u.deg,
pa_deg * u.deg)
bmaj_arcsec = bmaj.to(u.arcsec).value
bmin_arcsec = bmin.to(u.arcsec).value
ny = data_list[index].shape[0]
nx = data_list[index].shape[1]
# - Create convolution kernel
dx = pixsize_x[index]
dy = pixsize_y[index]
pixsize = max(dx, dy)
conv_kernel = conv_beam.as_kernel(pixsize * u.deg)
conv_kernel.normalize()
kernel = conv_kernel.array
logger.info("Convolution kernel size: %d x %d" %
(kernel.shape[0], kernel.shape[1]))
# - Convolve image
logger.info(
"Convolving image %d (size=%d,%d) by beam (bmaj,bmin,pa)=(%s,%s,%s) ..."
% (index + 1, nx, ny, str(bmaj_arcsec), str(bmin_arcsec),
str(pa_deg)))
data_conv = cv.filter2D(np.float64(data_list[index]),
-1,
kernel,
borderType=cv.BORDER_CONSTANT)
# - Write output FITS
logger.info("Saving convolved image to file %s ..." %
filenames_out[index])
Utils.write_fits(data_conv, filenames_out[index], header_list[index])
return 0
def alpha_hist(finaliter_prefix_b3, finaliter_prefix_b6, threshold=5,
basepath='/home/adam/work/alma-imf/reduction/', las=None):
image_b3 = SpectralCube.read(f'{finaliter_prefix_b3}.image.tt0.fits', use_dask=False, format='fits').minimal_subcube()
image_b6 = SpectralCube.read(f'{finaliter_prefix_b6}.image.tt0.fits', use_dask=False, format='fits').minimal_subcube()
image_b3 = image_b3 * u.beam / image_b3.beam.sr
image_b6 = image_b6 * u.beam / image_b6.beam.sr
fieldname = os.path.basename(finaliter_prefix_b6).split("_")[0]
print()
print(fieldname)
print(image_b3)
print(image_b6)
if las:
print(f"LAS {las} unsharp masking")
t0 = time.time()
smb3 = image_b3[0].convolve_to(radio_beam.Beam(las), allow_huge=True)
print(f"Convolution of b3: {time.time() - t0} seconds")
image_b3 = image_b3 - smb3
print(f"Subtraction of convolved slice: {time.time()-t0}")
smb6 = image_b6[0].convolve_to(radio_beam.Beam(las), allow_huge=True)
image_b6 = image_b6 - smb6
dt = time.time() - t0
print(f"LAS subtraction took {dt} seconds")
noise_region_b3 = regions.read_ds9(f"{basepath}/reduction/noise_estimation_regions/{fieldname}_B3_noise_sampling.reg")
noise_region_b6 = regions.read_ds9(f"{basepath}/reduction/noise_estimation_regions/{fieldname}_B6_noise_sampling.reg")
beams = radio_beam.Beams(major=u.Quantity([image_b3.beam.major, image_b6.beam.major]),
minor=u.Quantity([image_b3.beam.minor, image_b6.beam.minor]),
pa=u.Quantity([image_b3.beam.pa, image_b6.beam.pa]))
commonbeam = radio_beam.commonbeam.commonbeam(beams)
print(commonbeam)
if image_b3.beam.sr < image_b6.beam.sr:
header = image_b6[0].header
else:
header = image_b3[0].header
print("Convolution and Reprojection")
t0 = time.time()
image_b3_repr = image_b3[0].convolve_to(commonbeam, allow_huge=True).reproject(header)
print(f"B3 reprojection took {time.time()-t0} seconds")
t0 = time.time()
image_b6_repr = image_b6[0].convolve_to(commonbeam, allow_huge=True).reproject(header)
print(f"B6 reprojection took {time.time()-t0} seconds")
t0 = time.time()
noiseim_b3 = image_b3.subcube_from_regions(noise_region_b3)[0].convolve_to(commonbeam, allow_huge=True)
noiseim_b6 = image_b6.subcube_from_regions(noise_region_b6)[0].convolve_to(commonbeam, allow_huge=True)
print(f"Shape of noiseims; b3={noiseim_b3.shape}, b6={noiseim_b6.shape}. Subcubes took {time.time()-t0} seconds.")
t0 = time.time()
b3_std = stats.mad_std(noiseim_b3, ignore_nan=True)
b6_std = stats.mad_std(noiseim_b6, ignore_nan=True)
print(f"mad_std took {time.time()-t0} seconds")
t0 = time.time()
mask = (image_b3_repr > threshold*b3_std) & (image_b6_repr > threshold*b6_std)
alpha_b3_b6 = (np.log(image_b3_repr / image_b6_repr) / np.log(image_b3.wcs.wcs.crval[2] / image_b6.wcs.wcs.crval[2])).value
alpha_b3_b6[~mask] = np.nan
print(f"mask & alpha calc took {time.time()-t0} seconds")
pl.figure(2, figsize=(8,8)).clf()
ax = pl.gca()
ax.hist(alpha_b3_b6[mask], bins=np.linspace(-2,5), density=True)
ax.set_xlabel("Spectral Index $\\alpha$")
ax.set_ylabel("Fraction of Pixels")
return mask, alpha_b3_b6, image_b3_repr, image_b6_repr
'OrionSourceI_Unknown_2_robust0.5maskedclarkclean10000_medsub_K.fits',
'OrionSourceI_Unknown_3_robust0.5maskedclarkclean10000_medsub_K.fits',
'OrionSourceI_Unknown_4_robust0.5maskedclarkclean10000_medsub_K.fits',
'OrionSourceI_Unknown_5_robust0.5maskedclarkclean10000_medsub_K.fits',
'OrionSourceI_Unknown_8_robust0.5maskedclarkclean10000_medsub_K.fits',
'OrionSourceI_Unknown_9_robust0.5maskedclarkclean10000_medsub_K.fits',
'OrionSourceI_Unknown_10_robust0.5maskedclarkclean10000_medsub_K.fits',
'OrionSourceI_U229.682_robust0.5maskedclarkclean10000_medsub_K.fits',
]
allbeamlist = [
SpectralCube.read(paths.dpath('cubes/' + fn)).beams.common_beam()
for fn in files
]
allbeams = radio_beam.Beams(major=u.Quantity([x.major for x in allbeamlist]),
minor=u.Quantity([x.minor for x in allbeamlist]),
pa=u.Quantity([x.pa for x in allbeamlist]))
refbeam = allbeams.common_beam()
refcube = SpectralCube.read(paths.dpath('cubes/' + files[0]))
refcube = refcube.convolve_to(refbeam)
stackcube = [refcube]
for cubefn in files[1:]:
reproj_cube = (SpectralCube.read(
paths.dpath('cubes/' +
cubefn)).convolve_to(refbeam).spectral_interpolate(
refcube.spectral_axis))
stackcube.append(reproj_cube)
