def Snu(Te=default_te, nu=95*u.GHz, R=0.1*u.pc, Qlyc=1e45*u.s**-1, beam=4000*u.au,
angular_beam=0.5*u.arcsec):
tb = Tb(Te=Te, nu=nu, EM=EM(R=R, Qlyc=Qlyc))
if beam < R:
return tb.to(u.mJy,
u.brightness_temperature(radio_beam.Beam(angular_beam),
nu))
else:
return (tb * (R/beam)**2).to(u.mJy,
u.brightness_temperature(radio_beam.Beam(angular_beam),
nu))
def createmodelgal(path, skeletonfile, params):
# Start model
model = GM(skeletonfile)
# Initialize model
model.init(radii=params['radii'],
xpos=params['xpos'],
ypos=params['ypos'],
vsys=params['vsys'],
vrot=params['vrot'],
vdisp=params['vdisp'],
vrad=params['vrad'],
z0=params['z0'],
inc=params['inc'],
phi=params['phi'],
dens=np.array([0.8976, 0.983, 0.7064, 0.3329]))
model.set_options(ltype=params['ltype'])
# Compute
mymodel = model.compute()
temp_gal = SC.read(mymodel)
beam = rb.Beam(
major=params['radii'][-1] * params['beamfactor'][0] * u.arcsec,
minor=params['radii'][-1] * params['beamfactor'][1] * u.arcsec,
pa=params['phi'] * u.deg) # fitted radius must be last one
temp_gal = temp_gal.convolve_to(beam)
temp_gal.write(path, overwrite=True)
def spatial_smooth(filename,
major=None,
minor=None,
pa=0,
path_to_output='.',
suffix=None): # smooth image with 2D Gaussian
import radio_beam
from spectral_cube import SpectralCube
from astropy import units as u
cube = SpectralCube.read(filename)
beam = radio_beam.Beam(major=major * u.arcsec,
minor=minor * u.arcsec,
pa=pa * u.deg)
smoothcube = cube.convolve_to(beam)
if suffix is not None:
newname = filename.split('/')[-1].split('.fits')[0] + '_smooth' + str(
major) + '_arcsec' + suffix + '.fits'
else:
newname = filename.split('/')[-1].split('.fits')[0] + '_smooth_' + str(
major) + '_arcsec' + '.fits'
pathname = os.path.join(path_to_output, newname)
smoothcube.write(pathname, format='fits', overwrite=True)
print("\n\033[92mSAVED FILE:\033[0m '{}' in '{}'".format(
newname, path_to_output))
def add_noise(self, signal_to_noise):
print(
'In order to add noise please give a region of the signal in the format: (row1,row2,column1,column2)'
)
beam = rb.Beam(major=self.params['radii'][-1] *
self.params['beamfactor'][0] * u.arcsec,
minor=self.params['radii'][-1] *
self.params['beamfactor'][1] * u.arcsec,
pa=self.params['phi'] * u.deg)
pickregion(self.output + '/' + 'raw_' + self.name + '.fits', beam)
region = np.array(input().split(',')).astype(int)
self.region = region
print('Adding gaussian noise to galaxy.....')
self.signal_to_noise = signal_to_noise
rawfile = self.output + '/' + 'raw_' + self.name + '.fits'
noisefile = self.output + '/' + 'noise_' + self.name + '.fits'
SNR = getSNR(region, self.noise_amplifier, self.signal_to_noise,
rawfile, beam, noisefile)
# If the noise amplifier wasn't enough start from scratch
if abs(round(SNR[0], 1) - self.signal_to_noise) > 0.1:
N = 0
# Find the ideal noise amplifier to realise the SNR of user input
low = 1
high = 18
while abs(round(SNR[0], 1) -
self.signal_to_noise) > .1 and N < 100:
self.noise_amplifier = np.random.uniform(low=low, high=high)
SNR = getSNR(region, self.noise_amplifier,
self.signal_to_noise, rawfile, beam, noisefile)
if (SNR[0] - self.signal_to_noise) > 3: low += 0.2
if (SNR[0] - self.signal_to_noise) < 3: high -= 0.2
print(SNR[0], SNR[1], self.noise_amplifier)
N += 1
if N == 100: sys.exit('No convergence for noise')
SN = SNR[0]
self.params['std'] = SNR[1]
self.clean_mem()
print('Memory cleaned')
c_gal = SC.read(self.output + '/' + 'noise_' + self.name + '.fits')
# Results
print('Smoothened galaxy with beamsize ' +
'(Major Axis, Minor Axis) = ' + f"{self.params['beamfactor']}" +
f"x {self.params['radii'][-1]}" + 'arcseconds')
print('FINAL GALAXY written to ',
self.output + '/' + 'final_' + self.name + '.fits')
c_gal.write(self.output + '/' + 'final_' + self.name + '.fits',
overwrite=True)
# Store uncertainty
#self.params['std'] = getσ(c_gal.hdu.data)
# Final result!
self.c_gal = c_gal
def loglike(x, **params):
model = GM(
os.path.dirname(os.path.abspath(__file__)) + '/examples/ngc2403.fits')
if len(x) == 8:
model.init(radii=params['radii'],
xpos=params['xpos'],
ypos=params['ypos'],
vsys=params['vsys'],
vrot=x[0:4],
vrad=params['vrad'],
vdisp=x[4:8],
z0=params['z0'],
inc=params['inc'],
phi=params['phi'])
if len(x) == 9:
model.init(radii=params['radii'],
xpos=params['xpos'],
ypos=params['ypos'],
vsys=params['vsys'],
vrot=x[0:4],
vrad=params['vrad'],
vdisp=x[4:8],
z0=params['z0'],
inc=x[8],
phi=params['phi'])
if len(x) == 10:
model.init(radii=params['radii'],
xpos=params['xpos'],
ypos=params['ypos'],
vsys=params['vsys'],
vrot=x[0:4],
vrad=params['vrad'],
vdisp=x[4:8],
z0=params['z0'],
inc=x[8],
phi=x[9])
model.set_options(ltype=params['ltype'])
mymodel = model.compute()
temp_gal = SC.read(mymodel)
beam = rb.Beam(
major=params['radii'][-1] * params['beamfactor'][0] * u.arcsec,
minor=params['radii'][-1] * params['beamfactor'][1] * u.arcsec,
pa=params['phi'] * u.deg) # fitted radius must be last one
temp_gal = temp_gal.convolve_to(beam)
mdata = temp_gal.unmasked_data[:, :, :] / temp_gal.unit
if np.round((time.time() - params['start']) / 3600,
2) in np.arange(0, 10, 0.1):
gc.collect()
return -0.5 * np.sum([
np.sum(np.abs(params['data'][ch] - mdata[ch])) /
(params['std'] * params['Nsignal_pix'][ch]) for ch in params['nch']
]) # -len(data)*np.log(np.sqrt(2*np.pi*params['std']**2))-0.5*residual(data, mdata)/params['std']
def snu_dust(density=1e4*u.cm**-3, Td=40*u.K, radius=4000*u.au,
distance=8.4*u.kpc, cfreq=95*u.GHz):
mass = (density * 2.8 * u.Da * 4/3. * radius**3).to(u.M_sun)
print(mass)
beam = radio_beam.Beam((radius/distance).to(u.arcsec,
u.dimensionless_angles()))
flux = dust.snuofmass(nu=cfreq, mass=mass, beamomega=beam, temperature=Td,
distance=distance)
return flux
def prep(f='/Users/jkeown/Desktop/DR21_13CO.fits',
region='CygX_N',
line='13CO'):
if line == '13CO':
freq = 330588. * u.MHz
else:
freq = 329331. * u.MHz
header = fits.getheader(f)
beam = getres(freq=freq, diameter=15. * u.m)
header['BMIN'] = beam.to(u.deg).value # beam size in degrees
header['BMAJ'] = beam.to(u.deg).value # beam size in degrees
if region == 'W3(OH)' or region == 'W3Main' or region == 'M16':
del header['CD1_2']
del header['CD2_1']
data = fits.getdata(f)
data = data[1200:2200, :, :]
header['NAXIS3'] = data.shape[0]
fits.writeto(region + '_' + line + '_test.fits',
data=data,
header=header,
overwrite=True)
# If desired, convolve map with larger beam
# or load previously created convolved cube
cube = SpectralCube.read(region + '_' + line + '_test.fits')
cube_km_1 = cube.with_spectral_unit(u.km / u.s,
velocity_convention='radio')
beam = radio_beam.Beam(major=32 * u.arcsec,
minor=32 * u.arcsec,
pa=0 * u.deg)
cube_km = cube_km_1.convolve_to(beam)
cube_km.write(region + '_' + line + '_conv_test.fits',
format='fits',
overwrite=True)
#cube_km = SpectralCube.read(region+'_conv_test.fits')
# Then smooth spectrally
res = cube_km.spectral_axis[1] - cube_km.spectral_axis[2]
new_axis = np.arange(cube_km.spectral_axis[-1].value,
cube_km.spectral_axis[0].value,
res.value * 2) * u.km / u.s
fwhm_factor = np.sqrt(8 * np.log(2))
current_resolution = res
target_resolution = res * 2
pixel_scale = res
gaussian_width = ((target_resolution**2 - current_resolution**2)**0.5 /
pixel_scale / fwhm_factor)
kernel = Gaussian1DKernel(gaussian_width)
new_cube = cube_km.spectral_smooth(kernel)
interp_cube = new_cube.spectral_interpolate(new_axis,
suppress_smooth_warning=True)
interp_cube.write(region + '_' + line + '_conv_test_smooth.fits',
overwrite=True)
def ptsrcmodel(ptsrcx, ptsrcy, ptsrcamp, ptsrcwid, kernelpa=52):
ptsrc_wid_bm = radio_beam.Beam(ptsrcwid * u.arcsec, 0.00001 * u.arcsec,
(kernelpa + 90) * u.deg)
convbm = observed_beam.convolve(ptsrc_wid_bm)
assert convbm.pa.value != 0
ptsrc_bm = convbm.as_kernel(pixscale,
x_size=data.shape[1],
y_size=data.shape[0])
ptsrcmod = (shift.shift2d(ptsrc_bm, ptsrcx - data.shape[0] / 2,
ptsrcy - data.shape[1] / 2) / obsbm_max *
ptsrcamp)
return ptsrcmod
def convolve_to_beam(fitsfilename,
beam=radio_beam.Beam(0.04 * u.arcsec),
distance=5400 * u.pc):
hdr = fits.getheader(fitsfilename)
pix_area = (hdr['CDELT1'] * u.cm)**2
pix_area_arcsec = (pix_area / distance**2).to(u.arcsec**2,
u.dimensionless_angles())
kernel = beam.as_kernel(pix_area_arcsec**0.5)
data = fits.getdata(fitsfilename)
smoothed = convolve_fft(data, kernel)
return fits.PrimaryHDU(data=smoothed, header=hdr)
def snu_dust_of_density(density=1e4 * u.cm**-3,
Td=40 * u.K,
radius=4000 * u.au,
distance=distance,
cfreq=frequency):
# from the HII region notebook; not used here...
mass = (density * 2.8 * u.Da * 4 / 3. * radius**3).to(u.M_sun)
beam = radio_beam.Beam((radius / distance).to(u.arcsec,
u.dimensionless_angles()))
flux = dust.snuofmass(nu=cfreq,
mass=mass,
beamomega=beam,
temperature=Td,
distance=distance)
return flux
def non_deconvolved_sources(names, imgs):
all_data = fits.getdata(
'/lustre/aoc/students/jotter/dendro_catalogs/IR_matched_catalog_B7.fits'
)
beams = []
good_inds = [] #indices for each dataset with good gaussian fits
major_obs = []
minor_obs = []
pa = []
for i, data in enumerate(imgs):
fl = fits.open(data)
header = fl[0].header
beams.append(radio_beam.Beam.from_fits_header(header))
good_inds.append(np.where(all_data['fit_goodness_' + names[i]] == 'y'))
major_obs.append(all_data['FWHM_major_' + names[i]] * u.arcsecond)
minor_obs.append(all_data['FWHM_minor_' + names[i]] * u.arcsecond)
pa.append(all_data['position_angle_' + names[i]] * u.degree)
ind = reduce(np.intersect1d, good_inds)
maj_deconv = []
min_deconv = []
deconv_inds = []
for n in range(len(names)):
maj_deconv.append([])
min_deconv.append([])
deconv_inds.append([])
for j in ind:
obs_beam = radio_beam.Beam(major=major_obs[n][j],
minor=minor_obs[n][j],
pa=pa[n][j] - 90 * u.degree)
try:
src = obs_beam.deconvolve(beams[n])
maj_deconv[n].append(src.major.value)
min_deconv[n].append(src.minor.value)
deconv_inds[n].append(j)
except ValueError:
maj_deconv[n].append(np.nan)
min_deconv[n].append(np.nan)
int_deconv_inds = reduce(np.intersect1d, deconv_inds)
return int_deconv_inds
def calc_moments(mol='NH3_11'):
if mol == 'CS':
rms = 320 * u.mK
else:
rms = 120 * u.mK
v_lo = 83.0 * u.km / u.s
v_hi = 90.0 * u.km / u.s
# FIXME
filep = PDir.map_name('G285_mosaic', 'NH3_11')
cube = read_cube(filep)
beam = radio_beam.Beam.from_fits_header(cube.header)
cube = cube.spectral_slab(v_lo, v_hi)
cube = cube.spectral_smooth(convolution.Gaussian1DKernel(4 / 2.355))
spax = cube.spectral_axis
# smooth in area and velocity, create mask
bigger_beam = radio_beam.Beam(major=2 * beam.major,
minor=2 * beam.minor,
pa=beam.pa)
cube_s = cube.convolve_to(bigger_beam)
# calculate moments
filen_fmt = 'data/test_imaging/test_gbt_moments/G285_mosaic_gbt_NH3_11_{0}.fits'
mom0 = cube.with_mask(cube_s > 1 * rms).moment(order=0)
write_cube(mom0, PDir.IMG / Path(filen_fmt.format('mom0')))
mom1 = cube.with_mask(cube_s > 2 * rms).moment(order=1)
write_cube(mom1, PDir.IMG / Path(filen_fmt.format('mom1')))
mom2 = cube.with_mask(cube_s > 2 * rms).moment(order=2)
write_cube(mom2, PDir.IMG / Path(filen_fmt.format('mom2')))
mom3 = cube.with_mask(cube_s > 2 * rms).moment(order=3)
write_cube(mom3, PDir.IMG / Path(filen_fmt.format('mom3')))
momS = cube.with_mask(cube_s > 2 * rms).linewidth_sigma()
write_cube(momS, PDir.IMG / Path(filen_fmt.format('sigma')))
momM = cube.with_mask(cube_s > 1 * rms).max(axis=0)
write_cube(momM, PDir.IMG / Path(filen_fmt.format('max')))
momV = cube.with_mask(cube_s > 2 * rms).argmax(axis=0).astype(float)
momV[momV == 0] = np.nan
chans = np.unique(momV)
chans = chans[~np.isnan(chans)]
for ix in chans:
momV[momV == ix] = spax[int(ix)].value
momV = spectral_cube.lower_dimensional_structures.Projection(momV,
wcs=mom0.wcs)
write_cube(momV, PDir.IMG / Path(filen_fmt.format('vmax')))
import os
import glob
import paths
from spectral_cube import SpectralCube
from astropy import units as u
import radio_beam
sourcename = 'e2e8'
out_beam = radio_beam.Beam(0.35*u.arcsec, 0.35*u.arcsec)
for fn in glob.glob(paths.dpath('12m/cutouts/*.CH3OH*{0}*fits'.format(sourcename))):
outpath = fn.replace(".fits","_0.35arcsec.fits").replace("cutouts","cutouts/commonres")
if not os.path.exists(outpath):
vrcube = SpectralCube.read(fn)
cube = vrcube.convolve_to(out_beam)
cube.write(outpath)
cubes = [SpectralCube.read(fn) for fn in glob.glob(paths.dpath('12m/cutouts/commonres/W51_b6_12M.CH3OH*.image.pbcor_{0}cutout_0.35arcsec.fits'
.format(sourcename)))]
colfilename = datapath + '/column_maps/scuba_col_herscheltem.fits'
colfile = fits.open(colfilename)[0]
sgrb2contfile = fits.open(
paths.Fpath(
'merge/continuum/SgrB2_selfcal_full_TCTE7m_selfcal5_ampphase_taylorterms_multiscale_deeper_mask2.5mJy.image.tt0.pbcor.fits'
))
tbl = table.Table.read(
paths.tpath("continuum_photometry.ipac"),
format='ascii.ipac',
)
# Set the beam to be approximately the measured beam size
beam = radio_beam.Beam(11 * u.arcsec)
beam_rad = beam.major.to(u.deg).value
observed_region, _ = reproject.reproject_interp(sgrb2contfile, colfile.header)
# make the WCS grid
yy, xx = np.indices(colfile.data.shape)
inds = np.transpose((xx.ravel(), yy.ravel()))
ra, dec = wcs.WCS(colfile.header).celestial.wcs_pix2world(inds, 0).T
ra = ra.reshape(xx.shape)
dec = dec.reshape(yy.shape)
nonstarforming_mask = np.isfinite(observed_region)
for row in ProgressBar(tbl):
source_dist = ((ra - row['RA'])**2 + (dec - row['Dec'])**2)**0.5
def fit_source(srcID,
img,
img_name,
band,
fit_bg=False,
bg_stddev_x=30,
bg_stddev_y=30,
bg_mean_x=0,
bg_mean_y=0,
zoom=1,
max_offset_in_beams=1,
max_radius_in_beams=5,
nonconv_img=None,
mask_size=1.5):
#this function fits a given source, and the background
#srcID : int
#name of source to fit in catalogs
#img : fits file
#fits file with source to fit
#img_name : str
#name of image for the directory where the fit plots will go
#band : str
#band of image to fit ('B3', 'B6', or 'B7')
#fit_bg : bool
#if False, do not fit background gaussian
#bg_stddev_x : float
#eyeballed estimate of stddev of the background source in pixels
#bg_stddev_y : float
#same as above in y direction
#bg_mean_x/y : float
#pixels away from center (origin) in x/y direction for background gaussian mean guess
#zoom : float
#amount of zoom, values greater than 1 are zoom ins
#ref_data_name = '/home/jotter/nrao/summer_research_2018/tables/dendro_ref_catalog_edited.fits'
ref_data_name = '/home/jotter/nrao/summer_research_2018/tables/ref_catalog_may21_b7.fits'
#ref_data_name = '/lustre/cv/observers/cv-12578/orion_disks/summer_research_2018/tables/ref_catalog_may21.fits'
ref_data = Table.read(ref_data_name)
fl = fits.open(img)
header = fl[0].header
img_data = fl[0].data.squeeze()
img_wcs = WCS(header).celestial
beam = radio_beam.Beam.from_fits_header(header)
pixel_scale = np.abs(
img_wcs.pixel_scale_matrix.diagonal().prod())**0.5 * u.deg
ppbeam = (beam.sr / (pixel_scale**2)).decompose().value
if nonconv_img is not None:
flnonconv = fits.open(nonconv_img)
nonconv_header = flnonconv[0].header
nonconv_beam = radio_beam.Beam.from_fits_header(nonconv_header)
ppbeam = (nonconv_beam.sr / (pixel_scale**2)).decompose().value
#now get ready to fit gaussians
#start by setting up save directory for images
gauss_save_dir = '/home/jotter/nrao/gauss_diags_may21/fitbg/' + img_name + '/'
#gauss_save_dir = f'/lustre/cv/observers/cv-12578/orion_disks/gauss_diags_may21/{img_name}/'
print('saving plots to ' + gauss_save_dir)
if not os.path.exists(gauss_save_dir):
os.makedirs(gauss_save_dir)
#now make region
rad = Angle(1, 'arcsecond') #radius used in region list
#src_ind = np.where(ref_data['D_ID']==srcID)[0]
src_ind = np.where(ref_data['B3_Seq'] == srcID)[0]
ra = ref_data['RA_B3'][src_ind].data[0]
dec = ref_data['DEC_B3'][src_ind].data[0]
center_reg = SkyCoord(ra, dec, unit='deg', frame='icrs')
reg = regions.CircleSkyRegion(
center=center_reg,
radius=1 * u.arcsecond,
meta={
'text':
str(ref_data['B3_Seq'][src_ind].data[0]) + '_xstddev_' +
str(bg_stddev_x) + '_ystddev_' + str(bg_stddev_y)
})
region_list = []
#valid_inds = np.where(np.isnan(ref_data[band+'_detect']) == False)[0]
for ind in range(len(ref_data)): #valid_inds:
if ref_data['B3_Seq'][ind] == srcID:
continue
ra_i = ref_data['RA_B3'][ind]
dec_i = ref_data['DEC_B3'][ind]
region_i = regions.CircleSkyRegion(center=SkyCoord(ra_i,
dec_i,
unit='deg',
frame='icrs'),
radius=1 * u.arcsecond)
region_list.append(region_i)
#print(region_list)
#print(reg)
cat_r = Angle(0.5, 'arcsecond') / zoom #radius for gaussian fitting
if fit_bg == True:
gauss_cat, fitim_bg = bg_gaussfit(
img,
reg,
region_list,
cat_r,
bg_stddev_x=bg_stddev_x,
bg_stddev_y=bg_stddev_y,
bg_mean_x=bg_mean_x,
bg_mean_y=bg_mean_y,
savepath=gauss_save_dir,
max_offset_in_beams=max_offset_in_beams,
max_offset_in_beams_bg=10,
max_radius_in_beams=max_radius_in_beams,
mask_size=mask_size)
#print('gauss_cat length ',len(gauss_cat))
#k = list(gauss_cat.keys())[0]
#if gauss_cat[k]['success'] == False:
# gauss_cat = gaussfit_cutoutim(img, fitim_bg, reg, region_list, cat_r, savepath=gauss_save_dir, max_offset_in_beams = max_offset_in_beams, max_radius_in_beams = max_radius_in_beams)
# success = gauss_cat[k]['success']
# print(F'ALTERNATIVE FIT SUCCESS: {success}')
else:
gauss_cat = gaussfit_catalog(img, [reg],
cat_r,
savepath=gauss_save_dir,
max_offset_in_beams=max_offset_in_beams,
max_radius_in_beams=max_radius_in_beams)
img_table = Table(names=('Seq', 'fwhm_maj_' + band, 'fwhm_maj_err_' + band,
'fwhm_min_' + band, 'fwhm_min_err_' + band,
'pa_' + band, 'pa_err_' + band,
'gauss_amp_' + band, 'gauss_amp_err_' + band,
'RA_' + band, 'RA_err_' + band, 'DEC_' + band,
'DEC_err_' + band),
dtype=('i4', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8',
'f8', 'f8', 'f8', 'f8', 'f8'))
for key in gauss_cat:
img_table.add_row(
(srcID, gauss_cat[key]['fwhm_major'],
gauss_cat[key]['e_fwhm_major'], gauss_cat[key]['fwhm_minor'],
gauss_cat[key]['e_fwhm_minor'], gauss_cat[key]['pa'],
gauss_cat[key]['e_pa'], gauss_cat[key]['amplitude'],
gauss_cat[key]['e_amplitude'], gauss_cat[key]['center_x'],
gauss_cat[key]['e_center_x'], gauss_cat[key]['center_y'],
gauss_cat[key]['e_center_y']))
#now measure deconvovled sizes and aperture flux measurements for each source
ap_flux_arr = []
ap_flux_err_arr = []
fwhm_maj_deconv_arr = []
fwhm_maj_deconv_err_arr = []
fwhm_min_deconv_arr = []
fwhm_min_deconv_err_arr = []
pa_deconv_arr = []
pa_deconv_err_arr = []
snr_arr = []
for row in range(
len(img_table)
): #now loop through sources in reference data and make measurements
ref_ind = np.where(ref_data['B3_Seq'] == img_table['Seq'][row])[0]
if True == True: #len(ref_ind > 0):
measured_source_size = radio_beam.Beam(
major=img_table['fwhm_maj_' + band][row] * u.arcsec,
minor=img_table['fwhm_min_' + band][row] * u.arcsec,
pa=(img_table['pa_' + band][row] - 90) * u.deg)
try:
deconv_size = measured_source_size.deconvolve(beam)
fwhm_maj_deconv_arr.append(deconv_size.major.value)
fwhm_min_deconv_arr.append(deconv_size.minor.value)
fwhm_maj_deconv_err_arr.append(img_table['fwhm_maj_err_' +
band][row])
fwhm_min_deconv_err_arr.append(img_table['fwhm_min_err_' +
band][row])
pa_deconv_arr.append(deconv_size.pa.value)
pa_deconv_err_arr.append(img_table['pa_err_' + band][row])
except ValueError:
fwhm_maj_deconv_arr.append(np.nan)
fwhm_min_deconv_arr.append(np.nan)
fwhm_maj_deconv_err_arr.append(np.nan)
fwhm_min_deconv_err_arr.append(np.nan)
pa_deconv_arr.append(np.nan)
pa_deconv_err_arr.append(np.nan)
pix_major_fwhm = (
(img_table['fwhm_maj_' + band][row] * u.arcsec).to(u.degree) /
pixel_scale).decompose()
pix_minor_fwhm = (
(img_table['fwhm_min_' + band][row] * u.arcsec).to(u.degree) /
pixel_scale).decompose()
center_coord = SkyCoord(img_table['RA_' + band][row],
img_table['DEC_' + band][row],
frame='icrs',
unit=(u.deg, u.deg))
center_coord_pix = center_coord.to_pixel(img_wcs)
center_coord_pix_reg = regions.PixCoord(center_coord_pix[0],
center_coord_pix[1])
pos_ang = (img_table['pa_' + band][row] - 90) * u.deg
ellipse_reg = regions.EllipsePixelRegion(center_coord_pix_reg,
pix_major_fwhm.value * 2,
pix_minor_fwhm.value * 2,
angle=pos_ang)
size = pix_major_fwhm * 2.1
ap_mask = ellipse_reg.to_mask()
cutout_mask = ap_mask.cutout(img_data)
aperture_flux = np.nansum(cutout_mask[ap_mask.data == 1]) / ppbeam
npix = len(cutout_mask[ap_mask.data == 1])
#now make annulus for measuring background and error
annulus_width = 15 #pixels
annulus_radius = img_table[
'fwhm_maj_' + band][row] * u.arcsecond #0.1*u.arcsecond
annulus_radius_pix = (annulus_radius.to(u.degree) /
pixel_scale).decompose()
#cutout image
cutout = Cutout2D(img_data,
center_coord_pix,
annulus_radius * 2.5,
img_wcs,
mode='partial')
cutout_center = regions.PixCoord(cutout.center_cutout[0],
cutout.center_cutout[1])
#define aperture regions for SNR
innerann_reg = regions.CirclePixelRegion(cutout_center,
annulus_radius_pix.value)
outerann_reg = regions.CirclePixelRegion(
cutout_center, annulus_radius_pix.value + annulus_width)
#Make masks from aperture regions
annulus_mask = mask(outerann_reg, cutout) - mask(
innerann_reg, cutout)
# Calculate the SNR and aperture flux sums
pixels_in_annulus = cutout.data[annulus_mask.astype(
'bool')] #pixels within annulus
bg_rms = median_abs_deviation(pixels_in_annulus)
ap_bg_rms = bg_rms / np.sqrt(
npix / ppbeam) #rms/sqrt(npix/ppbeam) - rms error per beam
bg_median = np.median(pixels_in_annulus)
pix_bg = bg_median * npix / ppbeam
#flux corrections
ap_flux_bgcorrect = aperture_flux - pix_bg
ap_flux_correct = ap_flux_bgcorrect + ap_flux_bgcorrect * (
1 - special.erf(2 * np.sqrt(np.log(2)))
) #flux correction for summing within 2*fwhm
print(
f'Background: {pix_bg}, Gauss amp: {img_table["gauss_amp_"+band][row]}'
)
print(f'peak pixel: {np.nanmax(cutout_mask[ap_mask.data==1])}')
ap_flux_err_arr.append(ap_bg_rms)
ap_flux_arr.append(ap_flux_correct)
snr_arr.append(img_table['gauss_amp_' + band][row] / bg_rms)
cols = [
'ap_flux_' + band, 'ap_flux_err_' + band, 'fwhm_maj_deconv_' + band,
'fwhm_maj_deconv_err_' + band, 'fwhm_min_deconv_' + band,
'fwhm_min_deconv_err_' + band, 'pa_deconv_' + band,
'pa_deconv_err_' + band, 'SNR_' + band
]
arrs = [
ap_flux_arr, ap_flux_err_arr, fwhm_maj_deconv_arr,
fwhm_maj_deconv_err_arr, fwhm_min_deconv_arr, fwhm_min_deconv_err_arr,
pa_deconv_arr, pa_deconv_err_arr, snr_arr
]
for c in range(len(cols)):
img_table.add_column(Column(np.array(arrs[c])), name=cols[c])
img_table.add_column(Column(img_table['fwhm_maj_deconv_' + band] /
img_table['fwhm_min_deconv_' + band]),
name='ar_deconv_' + band)
return img_table
threecore_total = data[threecore_mask & (np.isfinite(data))].sum() / ppbeam
print("Total flux in the three 'main cores': {0}".format(threecore_total))
print("Fraction of total flux in the three 'main cores': {0}".format(
threecore_total / total_signal))
print(" Fraction of recovered flux from BGPS alpha=3.5: {0}".format(
threecore_total / bgps_scaled_225))
print(" Fraction of recovered flux from BGPS alpha=3: {0}".format(
threecore_total / bgps_scaled_225_3))
print(" Fraction of recovered flux from BGPS alpha=4: {0}".format(
threecore_total / bgps_scaled_225_4))
print("Area fraction in 'main cores': {0}".format(threecore_mask.sum() /
mask.sum()))
planck_217 = fits.open('../../planckwmap/PLCKI_C290.925+14.509_217GHz.fits')
# https://wiki.cosmos.esa.int/planckpla/index.php/Effective_Beams
beam_planck = radio_beam.Beam(4.990 * u.arcmin)
planck_217_flux = (planck_217[0].data * u.K).to(
u.Jy, beam_planck.jtok_equiv(217 * u.GHz))
pixel_area_planck = np.abs(
planck_217[0].header['CDELT1'] * planck_217[0].header['CDELT2']) * u.deg**2
ppbeam_planck = (beam_planck.sr / pixel_area_planck).decompose()
planck_12mptg_mask = regions.get_mask(planck_217[0])
planck_12mptg_total = planck_217_flux[planck_12mptg_mask].sum() / ppbeam_planck
print("Total Planck flux in 12m ptg area: {0}".format(planck_12mptg_total))
whole_w51_reg = pyregion.open(paths.rpath('whole_w51_cloud.reg'))
whole_w51_mask_planck = whole_w51_reg.get_mask(planck_217[0])
planck_whole_total = planck_217_flux[whole_w51_mask_planck].sum(
) / ppbeam_planck
print("Total Planck flux in entire cloud: {0}".format(planck_whole_total))
row['x_cen'], row['y_cen']))
#mask = rmask == name
#dend_inds = np.where(mask)
#view = (slice(None), # all spectral channels
# slice(dend_inds[0].min(), dend_inds[0].max()+1),
# slice(dend_inds[1].min(), dend_inds[1].max()+1),
# )
#sc = cube[view].with_mask(mask[view[1:]])
sc = cube.subcube_from_ds9region(SL)
spec = sc.mean(axis=(1, 2))
spec.meta['beam'] = radio_beam.Beam(
major=np.nanmedian([bm.major.to(u.deg).value
for bm in spec.beams]),
minor=np.nanmedian([bm.minor.to(u.deg).value
for bm in spec.beams]),
pa=np.nanmedian([bm.pa.to(u.deg).value for bm in spec.beams]),
)
spec.hdu.writeto("spectra/dendro{0:03d}_spw{1}_mean{2}.fits".format(
name, spw, suffix),
clobber=True)
bgSL = pyregion.parse("fk5; circle({0},{1},1.0\")".format(
row['x_cen'], row['y_cen']))
bgsc = cube.subcube_from_ds9region(bgSL)
npix = np.count_nonzero(np.isfinite(bgsc[0, :, :]))
bgspec = (bgsc.sum(axis=(1, 2)) - sc.sum(axis=(1, 2))) / npix
bgspec.meta['beam'] = radio_beam.Beam(
major=np.nanmedian([bm.major.to(u.deg).value
for bm in spec.beams]),
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
def b6b7_catalog(B6_img, B6_name, B7_img, B7_name, cat_name, nonconv_B6_img=None, nonconv_B7_img=None):
#creates catalog from one image in each band
#B3_names, B6_names, B7_names only used for gaussian diag directory names
ref_data_name = '/home/jotter/nrao/summer_research_2018/tables/ref_catalog_may21.fits'
ref_data = Table.read(ref_data_name)
ref_arrs = [ref_data['B6_detect'], ref_data['B7_detect']]
band_imgs = [B6_img, B7_img]
band_names = ['B6', 'B7']
band_img_names = [B6_name, B7_name]
band_tables = []
for b in range(len(band_imgs)): #first loop through different bands
name = band_names[b]
img_name = band_img_names[b]
img = band_imgs[b]
fl = fits.open(img)
header = fl[0].header
img_data = fl[0].data.squeeze()
img_wcs = WCS(header).celestial
beam = radio_beam.Beam.from_fits_header(header)
pixel_scale = np.abs(img_wcs.pixel_scale_matrix.diagonal().prod())**0.5 * u.deg
ppbeam = (beam.sr/(pixel_scale**2)).decompose().value
if name == 'B6' and nonconv_B6_img is not None:
fl = fits.open(nonconv_B6_img)
header = fl[0].header
nonconv_beam = radio_beam.Beam.from_fits_header(header)
ppbeam = (nonconv_beam.sr/(pixel_scale**2)).decompose().value
if name == 'B7' and nonconv_B7_img is not None:
fl = fits.open(nonconv_B7_img)
header = fl[0].header
nonconv_beam = radio_beam.Beam.from_fits_header(header)
ppbeam = (nonconv_beam.sr/(pixel_scale**2)).decompose().value
#now get ready to fit gaussians
#start by setting up save directory for images
gauss_save_dir = '/home/jotter/nrao/gauss_diags_may21/'+img_name+'/'
if not os.path.exists(gauss_save_dir):
os.makedirs(gauss_save_dir)
#now make region list
rad = Angle(1, 'arcsecond') #radius used in region list
regs = []
src_inds = np.where(ref_arrs[b] == True)[0]
print(len(src_inds))
for ind in src_inds:
reg = regions.CircleSkyRegion(center=SkyCoord(ref_data['RA_B3'][ind]*u.degree, ref_data['DEC_B3'][ind]*u.degree), radius=rad, meta={'text':str(ref_data['B3_Seq'][ind])})
reg_pix = reg.to_pixel(img_wcs)
if reg_pix.center.x > 0 and reg_pix.center.x < len(img_data[0]):
if reg_pix.center.y > 0 and reg_pix.center.y < len(img_data):
if np.isnan(img_data[int(reg_pix.center.x), int(reg_pix.center.y)]) == False:
regs.append(reg)
cat_r = Angle(0.5, 'arcsecond')/2 #radius for gaussian fitting
print('ok')
gauss_cat = gaussfit_catalog(img, regs, cat_r, savepath=gauss_save_dir, max_offset_in_beams = 1, max_radius_in_beams = 5)
#table does not have all columns yet, add others later
img_table = Table(names=('Seq_B3', 'fwhm_maj_'+name, 'fwhm_maj_err_'+name, 'fwhm_min_'+name, 'fwhm_min_err_'+name, 'pa_'+name, 'pa_err_'+name, 'gauss_amp_'+name, 'gauss_amp_err_'+name,'RA_'+name,'RA_err_'+name, 'DEC_'+name, 'DEC_err_'+name, ), dtype=('i4', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8', 'f8'))
for key in gauss_cat:
img_table.add_row((key, gauss_cat[key]['fwhm_major'], gauss_cat[key]['e_fwhm_major'], gauss_cat[key]['fwhm_minor'], gauss_cat[key]['e_fwhm_minor'], gauss_cat[key]['pa'], gauss_cat[key]['e_pa'], gauss_cat[key]['amplitude'], gauss_cat[key]['e_amplitude'], gauss_cat[key]['center_x'], gauss_cat[key]['e_center_x'], gauss_cat[key]['center_y'], gauss_cat[key]['e_center_y']))
#now measure deconvovled sizes and aperture flux measurements for each source
ap_flux_arr = []
ap_flux_err_arr = []
fwhm_maj_deconv_arr = []
fwhm_maj_deconv_err_arr = []
fwhm_min_deconv_arr = []
fwhm_min_deconv_err_arr = []
pa_deconv_arr = []
pa_deconv_err_arr = []
snr_arr = []
rms_arr = []
for row in range(len(img_table)): #now loop through sources in reference data and make measurements
ref_ind = np.where(ref_data['B3_Seq'] == img_table['Seq_B3'][row])[0]
if len(ref_ind > 0):
#now measuring deconvolved sizes
measured_source_size = radio_beam.Beam(major=img_table['fwhm_maj_'+name][row]*u.arcsec, minor=img_table['fwhm_min_'+name][row]*u.arcsec, pa=(img_table['pa_'+name][row]-90)*u.degree)
try:
deconv_size = measured_source_size.deconvolve(beam)
fwhm_maj_deconv_arr.append(deconv_size.major.value)
fwhm_min_deconv_arr.append(deconv_size.minor.value)
fwhm_maj_deconv_err_arr.append(img_table['fwhm_maj_err_'+name][row]) #same error as non deconvolved
fwhm_min_deconv_err_arr.append(img_table['fwhm_min_err_'+name][row])
pa_deconv_arr.append(deconv_size.pa.to(u.deg).value)
pa_deconv_err_arr.append(img_table['pa_err_'+name][row])
except ValueError:
fwhm_maj_deconv_arr.append(np.nan)
fwhm_min_deconv_arr.append(np.nan)
fwhm_maj_deconv_err_arr.append(np.nan)
fwhm_min_deconv_err_arr.append(np.nan)
pa_deconv_arr.append(np.nan)
pa_deconv_err_arr.append(np.nan)
pix_major_fwhm = ((img_table['fwhm_maj_'+name][row]*u.arcsec).to(u.degree)/pixel_scale).decompose()
pix_minor_fwhm = ((img_table['fwhm_min_'+name][row]*u.arcsec).to(u.degree)/pixel_scale).decompose()
center_coord = SkyCoord(img_table['RA_'+name][row], img_table['DEC_'+name][row], frame='icrs', unit=(u.deg, u.deg))
center_coord_pix = center_coord.to_pixel(img_wcs)
center_coord_pix_reg = regions.PixCoord(center_coord_pix[0], center_coord_pix[1])
pos_ang = (img_table['pa_'+name][row]-90)*u.deg #must subtract 90 to be consistent
ellipse_reg = regions.EllipsePixelRegion(center_coord_pix_reg, pix_major_fwhm.value*2, pix_minor_fwhm.value*2, angle=pos_ang)
ap_mask = ellipse_reg.to_mask()
cutout_mask = ap_mask.cutout(img_data)
aperture_flux = np.nansum(cutout_mask[ap_mask.data==1])/ppbeam
npix = len(cutout_mask[ap_mask.data==1])
#now make annulus for measuring background and error
annulus_width = 15 #pixels
annulus_radius = img_table['fwhm_maj_'+name][row]*u.arcsecond#+0.05*u.arcsecond
annulus_radius_pix = (annulus_radius.to(u.degree)/pixel_scale).decompose()
#cutout image
cutout = Cutout2D(img_data, center_coord_pix, annulus_radius*2.5, img_wcs, mode='partial')
cutout_center = regions.PixCoord(cutout.center_cutout[0], cutout.center_cutout[1])
#define aperture regions for SNR
innerann_reg = regions.CirclePixelRegion(cutout_center, annulus_radius_pix.value)
outerann_reg = regions.CirclePixelRegion(cutout_center, annulus_radius_pix.value+annulus_width)
#Make masks from aperture regions
annulus_mask = mask(outerann_reg, cutout) - mask(innerann_reg, cutout)
# Calculate the SNR and aperture flux sums
pixels_in_annulus = cutout.data[annulus_mask.astype('bool')]
bg_rms = median_abs_deviation(pixels_in_annulus)
print(img_table['Seq_B3'][row])
print('BG RMS: %f' % (bg_rms))
ap_bg_rms = bg_rms/np.sqrt(npix/ppbeam) #rms/sqrt(npix/ppbeam) - rms error per beam
bg_median = np.nanmedian(pixels_in_annulus)
pix_bg = bg_median*npix/ppbeam
ap_flux_bgcorrect = aperture_flux - pix_bg
ap_flux_correct = ap_flux_bgcorrect + ap_flux_bgcorrect*(1 - special.erf(2*np.sqrt(np.log(2)))) #flux correction for summing within 2*fwhm
ap_flux_err_arr.append(ap_bg_rms)
ap_flux_arr.append(ap_flux_correct)
snr_arr.append(img_table['gauss_amp_'+name][row]/bg_rms)
rms_arr.append(bg_rms)
cols = ['ap_flux_'+name, 'ap_flux_err_'+name, 'fwhm_maj_deconv_'+name, 'fwhm_maj_deconv_err_'+name, 'fwhm_min_deconv_'+name, 'fwhm_min_deconv_err_'+name, 'pa_deconv_'+name, 'pa_deconv_err_'+name, 'SNR_'+name, 'RMS_'+name]
arrs = [ap_flux_arr, ap_flux_err_arr, fwhm_maj_deconv_arr, fwhm_maj_deconv_err_arr, fwhm_min_deconv_arr, fwhm_min_deconv_err_arr, pa_deconv_arr, pa_deconv_err_arr, snr_arr, rms_arr]
for c in range(len(cols)):
img_table.add_column(Column(np.array(arrs[c])), name=cols[c])
img_table.add_column(Column(np.array(fwhm_maj_deconv_arr)/np.array(fwhm_min_deconv_arr)), name='ar_deconv_'+name)
band_tables.append(img_table) #list of tables for each image
B6B7 = join(band_tables[0], band_tables[1], keys='Seq_B3', join_type='outer')
B6B7.write('/home/jotter/nrao/summer_research_2018/tables/'+cat_name+'.fits', overwrite=True)
def flux_hist(finaliter_prefix_b3,
finaliter_prefix_b6,
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]
if las:
smb3 = image_b3[0].convolve_to(radio_beam.Beam(las), allow_huge=True)
image_b3 = image_b3 - smb3
smb6 = image_b6[0].convolve_to(radio_beam.Beam(las), allow_huge=True)
image_b6 = image_b6 - smb6
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"
)
noiseim_b3 = image_b3.subcube_from_regions(noise_region_b3)[0]
noiseim_b6 = image_b6.subcube_from_regions(noise_region_b6)[0]
b3_std = stats.mad_std(noiseim_b3, ignore_nan=True)
b6_std = stats.mad_std(noiseim_b6, ignore_nan=True)
print(fieldname, b3_std, b6_std)
fig = pl.figure(2, figsize=(12, 7))
fig.clf()
ax = pl.subplot(1, 2, 1)
b3data = image_b3[0].value
bins_b3 = np.linspace(np.nanmin(b3data), np.nanmax(b3data), 100)
bins_b3b = np.linspace(np.nanmin(b3data), np.nanmax(b3data), 10000)
H, L, P = ax.hist(b3data[np.isfinite(b3data)], bins=bins_b3, density=False)
#ax.hist(noiseim_b3.value.ravel(), bins=bins_b3)
ax.set_yscale('log')
ax.set_ylim(0.5, ax.get_ylim()[1])
ax.plot(bins_b3b,
H.max() * np.exp(-bins_b3b**2 / (2 * b3_std.value**2)), 'k')
ax.set_xlabel("$S_{3mm}$ [Jy/sr]")
ax.set_ylabel("Number of Pixels")
axin = fig.add_axes([0.25, 0.6, 0.20, 0.25])
bins = np.linspace(-5 * b3_std.value, 5 * b3_std.value, 100)
H, L, P = axin.hist(b3data[(b3data < 5 * b3_std.value)
& (b3data > -5 * b3_std.value)],
bins=bins,
density=False)
#axin.hist(noiseim_b3.value.ravel(), bins=bins)
gauss = H.max() * np.exp(-bins**2 / (2 * b3_std.value**2))
axin.plot(bins, gauss, 'k')
axin.set_xticklabels([])
axin.set_yticks(axin.get_yticks()[1:])
axin2 = fig.add_axes([0.25, 0.5, 0.2, 0.1])
loc = (L[1:] + L[:-1]) / 2
axin2.plot(loc,
H - H.max() * np.exp(-loc**2 / (2 * b3_std.value**2)),
drawstyle='steps',
color='k')
axin2.set_xlim(axin.get_xlim())
ax = pl.subplot(1, 2, 2)
b6data = image_b6[0].value
bins_b6 = np.linspace(np.nanmin(b6data), np.nanmax(b6data), 100)
bins_b6b = np.linspace(np.nanmin(b6data), np.nanmax(b6data), 10000)
H, L, P = ax.hist(b6data[np.isfinite(b6data)], bins=bins_b6, density=False)
ax.plot(bins_b6b,
H.max() * np.exp(-bins_b6b**2 / (2 * b6_std.value**2)), 'k')
#ax.hist(noiseim_b6.value.ravel(), bins=bins_b6)
ax.set_ylim(0.5, ax.get_ylim()[1])
ax.set_yscale('log')
ax.yaxis.set_label_position("right")
ax.yaxis.tick_right()
ax.set_xlabel("$S_{1mm}$ [Jy/sr]")
ax.set_ylabel("Number of Pixels")
axin = fig.add_axes([0.65, 0.6, 0.20, 0.25])
bins = np.linspace(-5 * b6_std.value, 5 * b6_std.value, 100)
H, L, P = axin.hist(b6data[(b6data < 5 * b6_std.value)
& (b6data > -5 * b6_std.value)],
bins=bins,
density=False)
#axin.hist(noiseim_b6.value.ravel(), bins=bins)
axin.plot(bins, H.max() * np.exp(-bins**2 / (2 * b6_std.value**2)), 'k')
axin.set_xticklabels([])
axin.set_yticks(axin.get_yticks()[1:])
axin2 = fig.add_axes([0.65, 0.5, 0.2, 0.1])
loc = (L[1:] + L[:-1]) / 2
axin2.plot(loc,
H - H.max() * np.exp(-loc**2 / (2 * b6_std.value**2)),
drawstyle='steps',
color='k')
axin2.set_xlim(axin.get_xlim())
#sharc_hdr['BUNIT'] = ('cm^-2', 'N(H2), column density of H2')
colmap_sharc_20 = dust_emissivity.dust.colofsnu(frequencies[0],
imagecube[0,:,:],
temperature=20*u.K,
beta=1.750,
)
colmap_sharc_50 = dust_emissivity.dust.colofsnu(frequencies[0],
imagecube[0,:,:],
temperature=50*u.K,
beta=1.750,
)
fits.writeto(filename=paths.cpath('column_maps/sharc_col_50K.fits'), data=colmap_sharc_50.value, header=scuba_hdr, overwrite=True)
fits.writeto(filename=paths.cpath('column_maps/sharc_col_20K.fits'), data=colmap_sharc_20.value, header=scuba_hdr, overwrite=True)
scuba_beam = radio_beam.Beam(8*u.arcsec)
colmap_scuba_20 = dust_emissivity.dust.colofsnu(frequencies[1],
imagecube[1,:,:],
beta=1.750,
temperature=20*u.K)
colmap_scuba_50 = dust_emissivity.dust.colofsnu(frequencies[1],
imagecube[1,:,:],
beta=1.750,
temperature=50*u.K)
fits.writeto(filename=paths.cpath('column_maps/scuba_col_50K.fits'), data=colmap_scuba_50.value, header=scuba_hdr, overwrite=True)
fits.writeto(filename=paths.cpath('column_maps/scuba_col_20K.fits'), data=colmap_scuba_20.value, header=scuba_hdr, overwrite=True)
pl.figure(1).clf()
pl.hist(colmap_sharc_50[np.isfinite(colmap_sharc_50)], bins=np.logspace(19,24),alpha=0.5, log=True)
def gauss_fitter(region='Cepheus_L1251',
snr_min=3.0,
mol='C2S',
vmin=5.0,
vmax=10.0,
convolve=False,
use_old_conv=False,
multicore=1,
file_extension=None):
"""
Fit a Gaussian to non-NH3 emission lines from GAS.
It creates a cube for the best-fit Gaussian, a cube
for the best-fit Gaussian with noise added back into
the spectrum, and a parameter map of Tpeak, Vlsr, and FWHM
Parameters
----------
region : str
Name of region to reduce
snr_min : float
Lowest signal-to-noise pixels to include in the line-fitting
mol : str
name of molecule to fit
vmin : numpy.float
Minimum centroid velocity, in km/s.
vmax : numpy.float
Maximum centroid velocity, in km/s.
convolve : bool or float
If not False, specifies the beam-size to convolve the original map with
Beam-size must be given in arcseconds
use_old_conv : bool
If True, use an already convolved map with name:
region + '_' + mol + file_extension + '_conv.fits'
This convolved map must be in units of km/s
multicore : int
Maximum number of simultaneous processes desired
file_extension: str
filename extension
"""
if file_extension:
root = file_extension
else:
# root = 'base{0}'.format(blorder)
root = 'all'
molecules = ['C2S', 'HC7N_22_21', 'HC7N_21_20', 'HC5N']
MolFile = '{0}/{0}_{2}_{1}.fits'.format(region, root, mol)
ConvFile = '{0}/{0}_{2}_{1}_conv.fits'.format(region, root, mol)
GaussOut = '{0}/{0}_{2}_{1}_gauss_cube.fits'.format(region, root, mol)
GaussNoiseOut = '{0}/{0}_{2}_{1}_gauss_cube_noise.fits'.format(
region, root, mol)
ParamOut = '{0}/{0}_{2}_{1}_param_cube.fits'.format(region, root, mol)
# Load the spectral cube and convert to velocity units
cube = SpectralCube.read(MolFile)
cube_km = cube.with_spectral_unit(u.km / u.s, velocity_convention='radio')
# If desired, convolve map with larger beam
# or load previously created convolved cube
if convolve:
cube = SpectralCube.read(MolFile)
cube_km_1 = cube.with_spectral_unit(u.km / u.s,
velocity_convention='radio')
beam = radio_beam.Beam(major=convolve * u.arcsec,
minor=convolve * u.arcsec,
pa=0 * u.deg)
cube_km = cube_km_1.convolve_to(beam)
cube_km.write(ConvFile, format='fits', overwrite=True)
if use_old_conv:
cube_km = SpectralCube.read(ConvFile)
# Define the spectral axis in km/s
spectra_x_axis_kms = np.array(cube_km.spectral_axis)
# Find the channel range corresponding to vmin and vmax
# -- This is a hold-over from when I originally set up the code to
# use a channel range rather than velocity range.
# Can change later, but this should work for now.
low_channel = np.where(spectra_x_axis_kms <= vmax
)[0][0] + 1 # Add ones to change index to channel
high_channel = np.where(spectra_x_axis_kms >= vmin
)[0][-1] + 1 # Again, hold-over from older setup
peak_channels = [low_channel, high_channel]
# Create cubes for storing the fitted Gaussian profiles
# and the Gaussians with noise added back into the spectrum
header = cube_km.header
cube_gauss = np.array(cube_km.unmasked_data[:, :, :])
cube_gauss_noise = np.array(cube_km.unmasked_data[:, :, :])
shape = np.shape(cube_gauss)
# Set up a cube for storing fitted parameters
param_cube = np.zeros((6, shape[1], shape[2]))
param_header = cube_km.header
# Define the Gaussian profile
def p_eval(x, a, x0, sigma):
return a * np.exp(-(x - x0)**2 / (2 * sigma**2))
# Create some arrays full of NANs
# To be used in output cubes if fits fail
nan_array = np.empty(shape[0]) # For gauss cubes
nan_array[:] = np.NAN
nan_array2 = np.empty(param_cube.shape[0]) # For param cubes
nan_array2[:] = np.NAN
# Loop through each pixel and find those
# with SNR above snr_min
x = []
y = []
pixels = 0
for (i, j), value in np.ndenumerate(cube_gauss[0]):
spectra = np.array(cube_km.unmasked_data[:, i, j])
if (False in np.isnan(spectra)):
rms = np.nanstd(
np.append(spectra[0:(peak_channels[0] - 1)],
spectra[(peak_channels[1] + 1):len(spectra)]))
if (max(spectra[peak_channels[0]:peak_channels[1]]) /
rms) > snr_min:
pixels += 1
x.append(i)
y.append(j)
else:
cube_gauss[:, i, j] = nan_array
param_cube[:, i, j] = nan_array2
cube_gauss_noise[:, i, j] = nan_array
print str(pixels) + ' Pixels above SNR=' + str(snr_min)
# Define a Gaussian fitting function for each pixel
# i, j are the x,y coordinates of the pixel being fit
def pix_fit(i, j):
spectra = np.array(cube_km.unmasked_data[:, i, j])
# Use the peak brightness Temp within specified channel
# range as the initial guess for Gaussian height
max_ch = np.argmax(spectra[peak_channels[0]:peak_channels[1]])
Tpeak = spectra[peak_channels[0]:peak_channels[1]][max_ch]
# Use the velocity of the brightness Temp peak as
# initial guess for Gaussian mean
vpeak = spectra_x_axis_kms[peak_channels[0]:peak_channels[1]][max_ch]
rms = np.std(
np.append(spectra[0:(peak_channels[0] - 1)],
spectra[(peak_channels[1] + 1):len(spectra)]))
err1 = np.zeros(shape[0]) + rms
# Create a noise spectrum based on rms of off-line channels
# This will be added to best-fit Gaussian to obtain a noisy Gaussian
noise = np.random.normal(0., rms, len(spectra_x_axis_kms))
# Define initial guesses for Gaussian fit
guess = [Tpeak, vpeak, 0.3] # [height, mean, sigma]
try:
coeffs, covar_mat = curve_fit(p_eval,
xdata=spectra_x_axis_kms,
ydata=spectra,
p0=guess,
sigma=err1,
maxfev=500)
gauss = np.array(
p_eval(spectra_x_axis_kms, coeffs[0], coeffs[1], coeffs[2]))
noisy_gauss = np.array(
p_eval(spectra_x_axis_kms, coeffs[0], coeffs[1],
coeffs[2])) + noise
params = np.append(coeffs, (covar_mat[0][0]**0.5, covar_mat[1][1]**
0.5, covar_mat[2][2]**0.5))
# params = ['Tpeak', 'VLSR','sigma','Tpeak_err','VLSR_err','sigma_err']
# Don't accept fit if fitted parameters are non-physical or too uncertain
if (params[0] < 0.01) or (params[3] > 1.0) or (
params[2] < 0.05) or (params[5] > 0.5) or (params[4] >
0.75):
noisy_gauss = nan_array
gauss = nan_array
params = nan_array2
# Don't accept fit if the SNR for fitted spectrum is less than SNR threshold
#if max(gauss)/rms < snr_min:
# noisy_gauss = nan_array
# gauss = nan_array
# params = nan_array2
except RuntimeError:
noisy_gauss = nan_array
gauss = nan_array
params = nan_array2
return i, j, gauss, params, noisy_gauss
# Parallel computation:
nproc = multicore # maximum number of simultaneous processes desired
queue = pprocess.Queue(limit=nproc)
calc = queue.manage(pprocess.MakeParallel(pix_fit))
tic = time.time()
counter = 0
# Uncomment to see some plots of the fitted spectra
#for i,j in zip(x,y):
#pix_fit(i,j)
#plt.plot(spectra_x_axis_kms, spectra, color='blue', drawstyle='steps')
#plt.plot(spectra_x_axis_kms, gauss, color='red')
#plt.show()
#plt.close()
# Begin parallel computations
# Store the best-fit Gaussians and parameters
# in their correct positions in the previously created cubes
for i, j in zip(x, y):
calc(i, j)
for i, j, gauss_spec, parameters, noisy_gauss_spec in queue:
cube_gauss[:, i, j] = gauss_spec
param_cube[:, i, j] = parameters
cube_gauss_noise[:, i, j] = noisy_gauss_spec
counter += 1
print str(counter) + ' of ' + str(pixels) + ' pixels completed \r',
sys.stdout.flush()
print "\n %f s for parallel computation." % (time.time() - tic)
# Save final cubes
# These will be in km/s units.
# Spectra will have larger values to the left, lower values to right
cube_final_gauss = SpectralCube(data=cube_gauss,
wcs=cube_km.wcs,
header=cube_km.header)
cube_final_gauss.write(GaussOut, format='fits', overwrite=True)
cube_final_gauss_noise = SpectralCube(data=cube_gauss_noise,
wcs=cube_km.wcs,
header=cube_km.header)
cube_final_gauss_noise.write(GaussNoiseOut, format='fits', overwrite=True)
# Construct appropriate header for param_cube
param_header['NAXIS3'] = len(nan_array2)
param_header['WCSAXES'] = 3
param_header['CRPIX3'] = 1
param_header['CDELT3'] = 1
param_header['CRVAL3'] = 0
param_header['PLANE1'] = 'Tpeak'
param_header['PLANE2'] = 'VLSR'
param_header['PLANE3'] = 'sigma'
param_header['PLANE5'] = 'Tpeak_err'
param_header['PLANE6'] = 'VLSR_err'
param_header['PLANE7'] = 'sigma_err'
fits.writeto(ParamOut, param_cube, header=param_header, clobber=True)
ided_linefreqs.value
])
linefreqs = u.Quantity(linefreqs, u.GHz)
flist = [fn] if 'fn' in locals() else glob.glob(
paths.dpath('stacked_spectra/OrionSourceI_*robust0.5.fits'))
for fn in flist:
basefn = os.path.split(fn)[-1]
if 'B7' in basefn and 'lb' not in basefn:
continue
print(fn)
beam = (radio_beam.Beam(0.1 * u.arcsec, 0.08 * u.arcsec) if 'B3' in fn else
radio_beam.Beam(0.043 * u.arcsec, 0.034 * u.arcsec) if 'B6' in fn
else radio_beam.Beam(0.029 * u.arcsec, 0.022 * u.arcsec))
sp_st = pyspeckit.Spectrum(fn)
jytok = beam.jtok(sp_st.xarr.mean())
sp_st.data *= jytok.value
sp_st.unit = u.K
pl.figure(0, figsize=(16, 6)).clf()
sp_st.plotter(figure=pl.figure(0, figsize=(16, 6)),
clear=True,
ymin=-0.0025 * jytok.value,
ymax=0.01 * jytok.value)
from constants import mustang_central_frequency, mustang_beam_fwhm
import paths
almafn = paths.root(
'SgrB2/SgrB2_selfcal_full_TCTE_selfcal5_ampphase_taylorterms_multiscale.image.tt0.pbcor.fits'
)
almafh = fits.open(almafn)[0]
loresfn = paths.root('SgrB2/SgrB2_precon_2_arcsec_pass_9.fits')
loresfn = paths.root('SgrB2/SgrB2_5pass_1_.0.2_10mJy_10mJy_final_smooth4.fits')
loresfn = '/Volumes/external/mgps/Feb5_2019/SgrB2_5pass_1_.0.2_10mJy_10mJy_w_session5_final_smooth4_PlanckCombined.fits'
loresfh_ = fits.open(loresfn)[0]
loresfwhm = mustang_beam_fwhm
loresbm = radio_beam.Beam(loresfwhm)
loresfh_header = loresfh_.header
if 'precon_2_arcsec_pass_9' in loresfn:
loresfh_header.update(
wcs.WCS(loresfh_.header)[1415:1815, 958:1358].to_header())
loresfh = fits.PrimaryHDU(data=loresfh_.data[1415:1815, 958:1358],
header=loresfh_header)
elif '5pass_1_.0.2_10mJy' in loresfn:
xc, yc = 1470, 1990
loresfh_header.update(
wcs.WCS(loresfh_.header)[yc - 350:yc + 350,
xc - 350:xc + 350].to_header())
loresfh = fits.PrimaryHDU(data=loresfh_.data[yc - 350:yc + 350,
xc - 350:xc + 350],
header=loresfh_header)
distance = (8100*u.pc).to(u.m)
# Some pixel & beam area stuff ...
bmaj = (head['bmaj']*u.deg).to(u.arcsec)
bmin = (head['bmin']*u.deg).to(u.arcsec)
bmaj_m = bmaj.to(u.rad).value * distance
bmin_m = bmin.to(u.rad).value * distance
pix_width = (head['cdelt2']*u.deg).to(u.rad)
pix_width_pc = pix_width.value * distance
pix_area = pix_width_pc**2
fwhm_to_sigma = 1./(8*np.log(2))**0.5
beam_area = 2.*np.pi*(bmaj_m*bmin_m*fwhm_to_sigma**2)
pixpbeam = beam_area/pix_area
# Convert to Brightness temperature (K)
beam = radio_beam.Beam(bmaj, bmin)
data_K = (data*(beam.jtok(v).value))
head.set('BUNIT', 'K')
fits.writeto('Brick_SiO_cube_K.fits', data_K, head)
# Convert to column density
Tex = 30*u.K
Jvtex = ((h*(v.to(u.Hz)))/k)/(np.e**(((h*(v.to(u.Hz))))/(k*Tex)) - 1 )
Jvtex = Jvtex.to(u.K)
Jvtbg = ((h*(v.to(u.Hz)))/k)/(np.e**(((h*(v.to(u.Hz))))/(k*Tbg)) - 1 )
Jvtbg = Jvtbg.to(u.K)
N = (1.6e11) *
(((Tex.value + 0.35)*(np.e**(31.26/Tex.value))) / (np.e**(10.4/Tex.value) - 1))
* (1/(Jvtex.value - Jvtbg.value))
data_Nsio = N * data_K
head.set('BUNIT', 'Nsio')
def smcube(snrcube,
header=None,
fwhm=None,
vsm=None,
vsm_type='gauss',
edgech=None,
huge=True):
"""
Smooth an SNRcube to produce a higher signal-to-noise SNRcube.
Parameters
----------
snrcube : SpectralCube or `~numpy.ndarray`
The image cube normalized by the estimated RMS noise. If a numpy array
is provided the header must also be provided.
header : `astropy.io.fits.Header`
The cube FITS header, required if a numpy array is given.
fwhm : float or :class:`~astropy.units.Quantity`, optional
Final spatial resolution to smooth to. If not astropy quantity, assumed
to be given in arcsec.
Default: 10 arcsec
vsm : float or :class:`~astropy.units.Quantity`, optional
Full width of the spectral smoothing kernel (or FWHM if gaussian).
If given as astropy quantity, should be given in velocity units.
If not given as astropy quantity, interpreted as number of channels.
Default: No spectral smoothing is applied.
vsm_type : string, optional
What type of spectral smoothing to employ. Currently three options:
(1) 'boxcar' - 1D boxcar smoothing, vsm rounded to integer # of chans.
(2) 'gauss' - 1D gaussian smoothing, vsm is the convolving gaussian FWHM.
(3) 'gaussfinal' - 1D gaussian smoothing, vsm is the gaussian FWHM
after convolution, assuming FWHM before convolution is 1 channel.
Default: 'gauss'
edgech : int, optional
Number of channels at left and right edges of each spectrum to use
for rms estimation.
Default is to use all channels.
Returns
-------
sm_snrcube : SpectralCube
A cube is SNR units after smoothing to the desired resolution.
"""
if isinstance(snrcube, SpectralCube):
hdr = snrcube.header
elif header is None:
raise NameError('A header must be provided to smcube procedure')
else:
snrcube = SpectralCube(data=snrcube,
header=header,
wcs=wcs.WCS(header))
hdr = header
print(snrcube)
# -- Spatial smoothing
if fwhm is not None:
# Requested final resolution
if not hasattr(fwhm, 'unit'):
fwhm = fwhm * u.arcsec
sm_beam = radio_beam.Beam(major=fwhm, minor=fwhm, pa=0 * u.deg)
print('Convolving to', sm_beam)
# From convolve_to method in spectral_cube
pixscale = wcs.utils.proj_plane_pixel_area(
snrcube.wcs.celestial)**0.5 * u.deg
if hasattr(snrcube, 'beam'):
print('Existing', snrcube.beam)
convolution_kernel = sm_beam.deconvolve(
snrcube.beam).as_kernel(pixscale)
else:
print('Warning: no existing beam found in input to smcube')
convolution_kernel = sm_beam.as_kernel(pixscale)
sm_snrcube = snrcube.spatial_smooth(convolution_kernel,
convolve_fft,
fill_value=0.0,
nan_treatment='fill',
preserve_nan=True,
parallel=False)
else:
sm_snrcube = snrcube
# -- Spectral smoothing
if vsm is not None:
fwhm_factor = np.sqrt(8 * np.log(2))
if hasattr(vsm, 'unit'):
delta_v = abs(hdr['CDELT3']) * u.m / u.s
vsm_ch = (vsm / delta_v).decompose().value
else:
vsm_ch = vsm
if vsm_type == 'gauss':
gaussian_width = vsm_ch / fwhm_factor
kernel = Gaussian1DKernel(gaussian_width)
print('Gaussian smoothing with stddev:', gaussian_width,
'channels')
elif vsm_type == 'boxcar':
box_width = round(vsm_ch)
kernel = Box1DKernel(box_width)
print('Boxcar smoothing with width:', box_width, 'channels')
elif vsm_type == 'gaussfinal':
if vsm_ch > 1:
gaussian_width = (vsm_ch**2 - 1)**0.5 / fwhm_factor
kernel = Gaussian1DKernel(gaussian_width)
print('Gaussian smoothing with stddev:', gaussian_width,
'channels')
else:
print('ERROR: requested resolution of', vsm_ch,
'chans is less than 1')
sm2_snrcube = sm_snrcube.spectral_smooth(kernel)
sm_snrcube = sm2_snrcube
# -- Renormalize by rms
newrms = makenoise(sm_snrcube, edge=edgech)
if huge:
sm_snrcube.allow_huge_operations = True
newrms.allow_huge_operations = True
sm_snrcube = sm_snrcube / newrms
return sm_snrcube
for spw in (0, 1, 2, 3):
try:
cube = SpectralCube.read(tmplt.format(spw, extra2, extra1))
except IOError:
print("didn't find {0}".format(
tmplt.format(spw, extra2, extra1)))
continue
print(cube)
try:
beam = radio_beam.Beam.from_fits_header(cube.header)
except TypeError:
if hasattr(cube, 'beams'):
beam = radio_beam.Beam(
major=np.nanmedian(
[bm.major.to(u.deg).value for bm in cube.beams]),
minor=np.nanmedian(
[bm.minor.to(u.deg).value for bm in cube.beams]),
pa=np.nanmedian(
[bm.pa.to(u.deg).value for bm in cube.beams]),
)
else:
beam = None
for row in tbl:
name = row['name']
print("Extracting {0} from {1}".format(name, spw))
coord = coordinates.SkyCoord(row['PeakRA'],
row['PeakDec'],
frame='fk5',
unit=(u.deg, u.deg))
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
distance_modulus = 5 * np.log10(5400) - 5
# 13.661968799114842
# http://www.pas.rochester.edu/~emamajek/EEM_dwarf_UBVIJHK_colors_Teff.txt
mkabso9v = -3.20
mko9v = distance_modulus + mkabso9v
# 10.461968799114842
# goldader 1994
ak = 2.6
mko9vext = mko9v + ak
# 13.061968799114842
# for e2e
snu_max = 0.6 * u.mJy
beam = radio_beam.Beam(0.34 * u.arcsec)
phys_radius = 0.34 * 5400 * u.au
tbmax = snu_max.to(u.K, u.brightness_temperature(beam, 14.5 * u.GHz))
qmax = qlyc_of_tb(tbmax, radius=phys_radius).decompose()
print("Upper limit continuum if thin = {0}".format(qmax))
# # plot of... not relevant
# pl.plot(np.logspace(40,50),
# Snu(Te=8500*u.K, nu=14.5*u.GHz, R=110*u.au, Qlyc=np.logspace(40,50)*u.s**-1,
# beam=phys_radius, angular_beam=beam.major))
# pl.plot(np.logspace(40,50), [0.6e-3]*50)
tbl = Table.read('pecaut_mamajek_table.txt', format='ascii')
wav = np.linspace(100 * u.AA, 10000 * u.AA, 100000)
lycfrac = []
# average mass of an H2
mh2 = 2.8 * u.Da
# ALMA freq
almafreq = 96 * u.GHz
# Bolocam freq
bolofreq = 271.1 * u.GHz
# distance to sgrb2
d_sgrb2 = 8 * u.kpc
beta = 2
ntau1_beta2 = (1 / (kappa271 * (almafreq / bolofreq)**beta) / mh2).to(u.cm**-2)
beta = 1
ntau1_beta1 = (1 / (kappa271 * (almafreq / bolofreq)**beta) / mh2).to(u.cm**-2)
print(f"column density at tau=1 for beta=2: {ntau1_beta2}")
print(f"column density at tau=1 for beta=1: {ntau1_beta1}")
beam = radio_beam.Beam(0.05 * u.arcsec)
massperbeam_beta1 = (ntau1_beta1 * mh2 * (beam.sr * d_sgrb2**2)).to(
u.M_sun, u.dimensionless_angles())
massperbeam_beta2 = (ntau1_beta2 * mh2 * (beam.sr * d_sgrb2**2)).to(
u.M_sun, u.dimensionless_angles())
print(f"mass per beam at tau=1 for beta=2: {massperbeam_beta2}")
print(f"mass per beam at tau=1 for beta=1: {massperbeam_beta1}")