cube_tpkrg = SpectralCube.read(tpkrg)
cube_tpkrg.with_spectral_unit(u.km/u.s,
rest_value=intcube.wcs.wcs.restfrq*u.Hz,
velocity_convention='radio').write(tpoutfn,
overwrite=True)
print("Single dish beam = {0}".format(cube_tpkrg.beam))
# intermediate work: test that a single frame has been properly combined
frq = intcube.wcs.wcs.restfrq*u.Hz * (1-13/3e5) # approximately 13 kms
closestchan = intcube.closest_spectral_channel(frq)
im = intcube[closestchan] * jtok[closestchan]
sdim = cube_tpkrg[cube_tpkrg.closest_spectral_channel(frq)]
im.write('singleframe_cubek_13kms.fits', overwrite=True)
sdim.write('singleframe_tpcube_k_rg_13kms.fits', overwrite=True)
combohdu, hdu2 = feather_simple(im.hdu, sdim.hdu, return_regridded_lores=True, return_hdu=True)
combohdu.writeto('singleframe_TP_7m_12m_feather_65kms.fits', clobber=True)
hdu2.writeto('singleframe_tpcube_k_spatialandspectralregrid_65kms.fits', clobber=True)
# "feather" the cubes together based on the low-resolution beam size
combhdu = fourier_combine_cubes(intcube_k, cube_tpkrg, return_hdu=True,
lowresfwhm=cube_tpkrg.beam.major)
log.info("Fourier combination completed.")
# restore all of the NaN values from the interferometer data back to NaN
combhdu.data[intcube.mask.exclude()] = np.nan
combhdu.writeto(outfilename, clobber=True)
log.info("Wrote file to {0}".format(outfilename))
# convert the cube spectral axis to km/s (...probably unnecessary now)
combcube = SpectralCube.read(combhdu).with_spectral_unit(u.km/u.s,
tpcube_k_ds_rg = tpcube_k_ds.spectral_interpolate(cube_k.spectral_axis)
#tpkrg = spectral_regrid(tpcube_k_ds, cube_k.spectral_axis)
log.debug("done regridding")
#tpkrg.writeto('HC3N_tp_freq_ds_interp.fits', clobber=True)
tpcube_k_ds_rg.write('HC3N_tp_freq_ds_interp_r05.fits', overwrite=True)
#
#cube_tpkrg = SpectralCube.read('HC3N_tp_freq_ds_interp.fits')
frq = 90.9662 * u.GHz
im = cube_k[cube_k.closest_spectral_channel(frq)]
sdim = tpcube_k_ds_rg[tpcube_k_ds_rg.closest_spectral_channel(frq)]
im.write('cubek_ch126_r05.fits', overwrite=True)
sdim.write('tpcube_k_rg_ch126_r05.fits', overwrite=True)
combohdu, hdu2 = feather_simple(im.hdu,
sdim.hdu,
return_regridded_lores=True,
return_hdu=True)
combohdu.writeto('HC3N_TP_7m_12m_feather_126_r05.fits', clobber=True)
# final goal
combhdu = fourier_combine_cubes(cube_k,
tpcube_k_ds_rg,
return_hdu=True,
lowresfwhm=tpcube_k_ds_rg.beam.major,
maximum_cube_size=3e9)
combhdu.header.update(cube_k.header)
combhdu.header.update(avbm.to_header_keywords())
combcube = SpectralCube.read(combhdu).with_spectral_unit(
u.km / u.s, velocity_convention='radio')
combcube.write('HC3N_TP_7m_12m_feather_r05.fits', overwrite=True)
frq7m,pow7m = fft_psd_tools.psds.pspec(fft_psd_tools.psds.PSD2(slc7m.value*jtok_7m[ch_65kms_7m]), view=False) frq12m,pow12m = fft_psd_tools.psds.pspec(fft_psd_tools.psds.PSD2(slc12m.value*jtok_12m[ch_65kms_12m]), view=False) frq12m_conv,pow12m_conv = fft_psd_tools.psds.pspec(fft_psd_tools.psds.PSD2(slc12m_conv_to_7m*jtok_12m[ch_65kms_12m]), view=False) frqTP,powTP = fft_psd_tools.psds.pspec(fft_psd_tools.psds.PSD2(slcTP), view=False) frq7m12m,pow7m12m = fft_psd_tools.psds.pspec(fft_psd_tools.psds.PSD2(slc7m12m.value*jtok_7m12m[ch_65kms_7m12m]), view=False) # some UVcombine tests; useful to compare to the above. Need psds still import uvcombine hdu7m = slc7m.hdu.copy() hdu7m.data *= jtok_7m[ch_65kms_7m] hdu7m.header['BUNIT'] = 'K' hdu12m = slc12m.hdu.copy() hdu12m.data *= jtok_12m[ch_65kms_12m] hdu12m.header['BUNIT'] = 'K' im7mTP = uvcombine.feather_simple(hdu7m, tp_fn) hdu7mTP = hdu7m.copy() hdu7mTP.data = im7mTP.real im12m7mTP = uvcombine.feather_simple(hdu12m, hdu7mTP) hdu12m7mTP = hdu12m.copy() hdu12m7mTP.data = im12m7mTP.real frq12m7mTP,pow12m7mTP = fft_psd_tools.psds.pspec(fft_psd_tools.psds.PSD2(hdu12m7mTP.data), view=False) frq7mTP,pow7mTP = fft_psd_tools.psds.pspec(fft_psd_tools.psds.PSD2(hdu7mTP.data), view=False) import pylab as pl pl.figure(1).clf() pl.loglog(pixscale7m.to(u.arcsec).value/frq7m, pow7m, label='7m')
sdim = cube_tpkrg[cube_tpkrg.closest_spectral_channel(frq)]
imJy.write('singleframes/{species}{suffix}_cubeJy_65kms.fits'.format(species=species, suffix=suffix), overwrite=True)
imK.write('singleframes/{species}{suffix}_cubek_65kms.fits'.format(species=species, suffix=suffix), overwrite=True)
sdim.write('singleframes/{species}{suffix}_tpcube_k_rg_65kms.fits'.format(species=species, suffix=suffix), overwrite=True)
# for sanity checking purposes, write out the Jy version too
sdim_Jy = sdim.to(u.Jy, tpcube.beam.jtok_equiv(frq))
sdim_Jy._unit = u.Jy/u.beam
sdim_Jy.write('singleframes/{species}{suffix}_tpcube_Jy_rg_65kms.fits'.format(species=species, suffix=suffix), overwrite=True)
imKhdu = imK.hdu
#imKhdu.data[imKhdu.data<0] = 0 # DELETE THIS IT'S WRONG
combohdu, hdu2 = feather_simple(imKhdu, sdim.hdu,
lowresscalefactor=1.0,
highresscalefactor=1.0,
highpassfilterSD=False,
replace_hires=False,
deconvSD=False,
return_regridded_lores=True,
return_hdu=True)
combohdu.writeto('singleframes/{species}{suffix}_TP_7m_12m_feather_65kms.fits'.format(species=species, suffix=suffix), clobber=True)
hdu2.header['BMAJ'] = tpcube_k_ds_hdu.header['BMAJ']
hdu2.header['BMIN'] = tpcube_k_ds_hdu.header['BMIN']
hdu2.header['BPA'] = tpcube_k_ds_hdu.header['BPA']
hdu2.writeto('singleframes/{species}{suffix}_tpcube_k_spatialandspectralregrid_65kms.fits'.format(species=species, suffix=suffix), clobber=True)
combohdu, hdu2 = feather_simple(imK.hdu, sdim.hdu, return_regridded_lores=True, return_hdu=True, replace_hires=0.5)
combohdu.writeto('singleframes/{species}{suffix}_TP_7m_12m_replacefeather_65kms.fits'.format(species=species, suffix=suffix), clobber=True)
if 'do_full_cube' not in locals() or do_full_cube:
# final goal
os.system('cp {0} {1}'.format(interferometer_fn, outfilename))
lowresfwhm=11 * u.arcsec,
hires_threshold=0.0005,
lores_threshold=0.001)
pl.savefig('pspec_threshold.png')
rslts_nothresh = uvcombine.feather_plot(alma_image,
lores=mgps_image,
lowresfwhm=11 * u.arcsec)
pl.savefig('pspec_nothreshold.png')
hdu_low, im_lowraw, header_low = file_in(lores)
hdu_hi, im_hi, header_hi = file_in(hires)
pl.figure(2).clf()
combined = uvcombine.feather_simple(alma_image,
mgps_image,
lowresfwhm=10 * u.arcsec)
pl.imshow(visualization.AsinhStretch()(combined.real),
vmax=0.1,
origin='lower',
interpolation='none')
hdr = fits.getheader(alma_image)
combined_default = uvcombine.feather_simple(alma_image, mgps_image)
fits.PrimaryHDU(data=np.abs(combined_default),
header=hdr).writeto('feathered_MGPS_ALMATCTE7m.fits',
overwrite=True)
combined_deconvsd = uvcombine.feather_simple(alma_image,
mgps_image,
loresfh.data > stats.mad_std(loresfh.data, ignore_nan=True) * 6)
pl.plot(repr_alma.flat, loresfh.data.flat, ',')
pl.plot(repr_alma.flat, unsharp_gb.flat, '.')
pl.plot(repr_alma[ok], unsharp_gb[ok], '.')
# TODO: do this using bayes_linear or ODR
scalefactor = np.nanmedian(unsharp_gb[ok] / repr_alma[ok])
pl.plot([0, 7.5], [0, 7.5 * scalefactor])
pl.subplot(2, 2, 2).imshow(loresfh.data, vmin=-0.1, vmax=1)
pl.subplot(2, 2, 4).imshow(loresfh.data - repr_alma * scalefactor,
vmin=-0.1,
vmax=1)
combined = uvcombine.feather_simple(almafn,
lores=loresfh,
lowresfwhm=loresfwhm,
highresscalefactor=1,
lowresscalefactor=1 / scalefactor)
hdr = fits.getheader(almafn)
fits.PrimaryHDU(data=repr_alma, header=loresfh.header).writeto(
paths.root('SgrB2/ALMAsmgridtoMGPS.fits'), overwrite=True)
fits.PrimaryHDU(data=sm_alma, header=almafh.header).writeto(
paths.root('SgrB2/ALMAsmtoMGPS.fits'), overwrite=True)
loresfh.writeto(paths.root('SgrB2/MGPS_SgrB2_zoom_fixed.fits'), overwrite=True)
fits.PrimaryHDU(data=combined.real, header=hdr).writeto(
paths.root('SgrB2/feathered_MGPS_ALMATCTE7m.fits'), overwrite=True)
pl.figure(1).clf()
# for sanity checking purposes, write out the Jy version too
sdim_Jy = sdim.to(u.Jy, tpcube.beam.jtok_equiv(frq))
sdim_Jy._unit = u.Jy / u.beam
sdim_Jy.write(
'singleframes/{species}{suffix}{robust}_tpcube_Jy_rg_65kms.fits'.
format(species=species, suffix=suffix, robust=robust),
overwrite=True)
imKhdu = imK.hdu
#imKhdu.data[imKhdu.data<0] = 0 # DELETE THIS IT'S WRONG
combohdu, hdu2 = feather_simple(imKhdu,
sdim.hdu,
lowresscalefactor=1.0,
highresscalefactor=1.0,
highpassfilterSD=False,
replace_hires=False,
deconvSD=False,
return_regridded_lores=True,
return_hdu=True)
combohdu.header.update(cube.header)
combohdu.header.update(avgbeam.to_header_keywords())
combohdu.writeto(
'singleframes/{species}{suffix}{robust}_TP_7m_12m_feather_65kms.fits'.
format(species=species, suffix=suffix, robust=robust),
clobber=True)
hdu2.header['BMAJ'] = tpcube_k_ds_hdu.header['BMAJ']
hdu2.header['BMIN'] = tpcube_k_ds_hdu.header['BMIN']
hdu2.header['BPA'] = tpcube_k_ds_hdu.header['BPA']
hdu2.writeto(
'singleframes/{species}{suffix}{robust}_tpcube_k_spatialandspectralregrid_65kms.fits'
planckfn = os.path.join(rootdir, 'planck/{0}_100GHz.fits'.format(regname))
planckfn_scaled = os.path.join(rootdir, 'planck/{0}_scaled_to_92.44GHz.fits'.format(regname))
if not os.path.exists(planckfn) or not os.path.exists(planckfn_scaled):
planck_image = SkyView.get_images(center, 'Planck 100')[0][0]
# bandpass information here: https://wiki.cosmos.esa.int/planckpla2015/index.php/The_RIMO#HFI_2
# adopt central frequency assuming spectral index = 3 giving nu_c for 100_avg = 104.225
reffreq_hz = 104.225e9
planck_image.header['REFFREQ'] = reffreq_hz
planck_image.header['BMAJ'] = 9.65/60
planck_image.header['BMIN'] = 9.65/60
planck_image.header['BUNIT'] = 'K'
planck_image.writeto(planckfn, overwrite=True)
# scale planck data to match MGPS assuming alpha=3
mustang_reffreq = 92.44e9
planck_image.data = planck_image.data * (reffreq_hz / mustang_reffreq)**-3
planck_image.header['REFFREQ'] = mustang_reffreq
planck_image.writeto(planckfn_scaled, overwrite=True)
fh[0].header['BMAJ'] = mustang_beam_fwhm.to(u.deg).value
fh[0].header['BMIN'] = mustang_beam_fwhm.to(u.deg).value
fh[0].header['REFFREQ'] = mustang_central_frequency.to(u.Hz).value
fh[0].header['BUNIT'] = 'Jy/beam'
rslt = feather_simple(fh[0], planckfn_scaled)
fh[0].data = rslt.real
fh.writeto(outfile, overwrite=True)
overwrite=True)
log.info("make Interferometric image")
intf_data = utils.interferometrically_observe_image(image=input_hdu.data,
pixel_scale=pixel_scale,
largest_angular_scale=40*u.arcsec,
smallest_angular_scale=2*u.arcsec)[0].real
intf_hdu = fits.PrimaryHDU(data=intf_data,
header=input_hdu.header
)
intf_fn = "input_image_sz512as_pl1.5_fwhm2as_scale1as_intf2to40as.fits"
convert_to_casa(intf_hdu).writeto(intf_fn, overwrite=True)
log.info("make SD image")
sd_header = input_hdu.header.copy()
sd_header['BMAJ'] = sd_header['BMIN'] = (33*u.arcsec).to(u.deg).value
sd_data = utils.singledish_observe_image(image=input_hdu.data,
pixel_scale=pixel_scale,
beam=radio_beam.Beam(33*u.arcsec))
sd_hdu = fits.PrimaryHDU(data=sd_data, header=sd_header)
sd_fn = "input_image_sz512as_pl1.5_fwhm2as_scale1as_sd33as.fits"
convert_to_casa(sd_hdu).writeto(sd_fn, overwrite=True)
log.info("Feather data")
feathered_hdu = feather_simple(hires=intf_hdu, lores=sd_hdu, return_hdu=True)
feathered_hdu.writeto("input_image_sz512as_pl1.5_fwhm2as_scale1as_intf2to40as_sd33as_feathered_MJySr.fits",
overwrite=True)
feathered_hdu = feather_simple(hires=intf_fn, lores=sd_fn, return_hdu=True)
feathered_hdu.writeto("input_image_sz512as_pl1.5_fwhm2as_scale1as_intf2to40as_sd33as_feathered_JyBeam.fits",
overwrite=True)
lowres=sd_fn_image.as_posix(),
sdfactor=sdfactor,
lowpassfiltersd=lowpassfilterSD,
)
# Right now read as spectralcube despite being a 2D image.
casa_feather_proj = SpectralCube.read(output_name)[0]
# casa_sd_proj = SpectralCube.read(sd_fn_image)[0]
# exportfits(imagename=output, fitsimage=output+".fits",
# overwrite=True, dropdeg=True)
# Feathering with uvcombine
feathered_hdu = feather_simple(hires=intf_proj,
lores=sd_proj,
lowresscalefactor=sdfactor,
lowpassfilterSD=lowpassfilterSD,
deconvSD=False,
return_hdu=True)
uvcomb_feather_proj = Projection.from_hdu(feathered_hdu)
diff = (uvcomb_feather_proj - casa_feather_proj).value
diff_orig_uvc = (input_proj.value - uvcomb_feather_proj.value)
diff_orig_casa = (input_proj.value - casa_feather_proj.value)
diffsq = (diff**2) / (casa_feather_proj.value**2)
diff_frac_orig = diff / input_proj.value
print("Proof that we have exactly reimplemented CASA's feather: ")
header['CRVAL2'] = 0.0
header['CRPIX2'] = 350.0
totalimg, weightimg = np.zeros([header['NAXIS2'], header['NAXIS1']]), np.zeros(
[header['NAXIS2'], header['NAXIS1']])
for fn in urls:
fh = fits.open(fn)
reproj, weight = reproject.reproject_interp(fh, header)
totalimg[weight.astype('bool')] += reproj[weight.astype('bool')]
weightimg += weight
header['BUNIT'] = 'Jy/beam'
rslt = fits.PrimaryHDU(data=totalimg / weightimg, header=header)
rslt.writeto(
os.path.expanduser('~/Dropbox/SMA_CMZ_FITS_files/BGPS_Mosaic.fits'),
overwrite=True)
sma_mosaic = fits.open(
os.path.expanduser(
'~/Dropbox/CMZoom_Data/continuum_images/mosaic_JySr.fits'))
#sma_mosaic[0].header['BUNIT'] = 'Jy/sr'
comb = uvcombine.feather_simple(sma_mosaic[0], rslt)
comb_hdu = fits.PrimaryHDU(data=comb.real, header=sma_mosaic[0].header)
comb_hdu.writeto(
os.path.expanduser(
'~/Dropbox/CMZoom_Data/continuum_images/sma_bgps_feather.fits'))