import numpy as np
import radio_beam
from astropy import units as u
from astropy import wcs
from astropy.io import fits
import higal_sedfitter
import paths
import reproject
import dust_emissivity
from astropy import convolution
import pylab as pl
sharc_fn = paths.Fpath('other/SHARC_Herschel_Feathered.fits')
scuba_fn = paths.Fpath('other/scuba_Herschel_Feathered.fits')
scuba_hdr = fits.getheader(scuba_fn)
pixscale = wcs.WCS(scuba_hdr).celestial.pixel_scale_matrix[1,1]*u.deg
sharc_data,_ = reproject.reproject_interp(sharc_fn, scuba_hdr)
scuba_data = fits.getdata(scuba_fn)
ratio_map_sharc_to_scuba = sharc_data/scuba_data
# Column density is independent of pixel scale; it is related to surface
# brightness
imagecube = u.Quantity([sharc_data,scuba_data], u.MJy/u.sr)
if False:
# don't need to fit it myself
pixelfitter = higal_sedfitter.fit.PixelFitter(bfixed=True)
import aplpy
import paths
from astropy.io import fits
from astropy.nddata import Cutout2D
from astropy import wcs
import paths
from spectral_cube import SpectralCube
from astropy.visualization import SqrtStretch, AsinhStretch
from astropy.visualization.mpl_normalize import ImageNormalize
pl.style.use('classic')
annotation_fontsize = 10
for cubefn, suffix, (mn, mx), species in (
(paths.Fpath('merge/lines/HNC.r2_TP_7m_12m_feather.fits'), '', (-5, 100),
'HNC'),
#(paths.Fpath('merge/lines/SgrB2_b3_7M_12M.HCN.image.pbcor_medsub.fits'), '_noTPnoTE', (-0.5,20), 'HCN'),
#(paths.Fpath('merge/lines/SgrB2_b3_7M_12M.HNC.image.pbcor_medsub.fits'), '_noTPnoTE', (-0.5,20), 'HNC'),
#(paths.Fpath('merge/lines/SgrB2_b3_7M_12M.HCOp.image.pbcor_medsub.fits'), '_noTPnoTE', (-0.5,20), 'HCOp'),
#(paths.Fpath('merge/lines/SgrB2_b3_7M_12M.HC3N.image.pbcor_medsub.fits'), '_noTP', (-0.5,20), 'HC3N'),
#(paths.Fpath('merge/lines/HC3N_TP_7m_12m_feather.fits'), '', (-2,200), 'HC3N'),
#(paths.Fpath('merge/lines/HC3N_TP_7m_12m_feather_r05.fits'), 'r05', (-10,400), 'HC3N')
):
sourcename = 'SgrB2'
dx = 5
slabs = [(x, x + dx) for x in range(5, 105, 5)] * u.km / u.s
cube = SpectralCube.read(cubefn)
from astropy import coordinates
from astropy import units as u
from astropy import constants
import regions
import latex_info
from constants import distance
import reproject
from astropy import wcs
from astropy.io import fits
# workaround for new regions API
arbitrary_wcs = wcs.WCS(
fits.getheader(
paths.Fpath(
'merge/continuum/SgrB2_selfcal_full_TCTE7m_selfcal5_ampphase_taylorterms_multiscale_deeper_mask2.5mJy.image.tt0.pbcor.fits'
)))
core_phot_tbl = Table.read(paths.tpath("continuum_photometry_withSIMBAD.ipac"),
format='ascii.ipac')
core_phot_tbl.add_column(
Column(name='radius', data=np.zeros(len(core_phot_tbl)) + np.nan))
# measured from core_flux_distributions
core_powerlaw_index = 1.94
kroupa = imf.Kroupa()
o_mmin = 8
hii_cutoff = 20
all_eigsvals = np.sort(all_eigsvals)[::-1]
eigims = []
for ii in range(20):
eigim = np.zeros(cube.shape[1:])
for channel in range(cube.shape[0]):
eigim += np.nan_to_num(data[channel] *
np.real_if_close(eigvecs[channel, ii]))
eigims.append(eigim)
hdul = fits.HDUList([
fits.PrimaryHDU(np.array(eigims)),
fits.ImageHDU(cov_matrix),
fits.ImageHDU(all_eigsvals)
])
hdul.writeto(paths.Fpath('eigenimages_SgrB2N_spw1.fits'), overwrite=True)
cov_matrix = var_cov_cube(data, mean_sub=True)
all_eigsvals, eigvecs = np.linalg.eigh(cov_matrix)
all_eigsvals = np.real_if_close(all_eigsvals)
eigvecs = eigvecs[:, np.argsort(all_eigsvals)[::-1]]
all_eigsvals = np.sort(all_eigsvals)[::-1]
eigims = []
for ii in range(20):
eigim = np.zeros(cube.shape[1:])
for channel in range(cube.shape[0]):
mean_value = np.nanmean(data[channel])
eigim += np.nan_to_num((data[channel] - mean_value) *
np.real_if_close(eigvecs[channel, ii]))
eigims.append(eigim)
import paths
from astropy import units as u
from astropy.io import fits
from astropy import wcs
import reproject
b3 = fits.open(paths.Fpath('SgrB2_B3_continuum_QA2_minimumcutout_K.fits'))
b6 = fits.open(
paths.Fpath('sgr_b2m.N.allspw.continuum.clean1000.image.tt0.pbcor_K.fits'))
b3r, _ = reproject.reproject_interp(
input_data=(b3[0].data.squeeze(), wcs.WCS(b3[0].header).celestial),
output_projection=wcs.WCS(b6[0].header).celestial,
shape_out=b6[0].data.shape)
ratio = b3r / b6[0].data.squeeze()
ratiohdu = fits.PrimaryHDU(data=ratio, header=b6[0].header)
ratiohdu.writeto(paths.Fpath('ratio_B3toB6_N.fits'), overwrite=True)
bottomleft = coordinates.SkyCoord(*mylims_fk5[0],
unit=(u.deg, u.deg),
frame='fk5')
scalebarpos = coordinates.SkyCoord("17:47:27",
"-28:26:15.0",
unit=(u.h, u.deg),
frame='fk5')
else:
raise Exception
if line in filenames:
hdu_line = fits.open(filenames[line])[0]
else:
hdu_line = fits.open(
paths.Fpath(
'merge/max/SgrB2_b3_7M_12M.{0}.image.pbcor_max_medsub.fits'
.format(line)))[0]
toplevel_wcs = wcs.WCS(hdu_line.header).celestial
#wcsaxes = WCSaxes(mywcs.to_header())
#toplevel_wcs = mywcs # hack for newer version of toplevel_wcs that is included in astropy
fig3 = pl.figure(3,
figsize=bigregion_parameters[regionname]['figsize'])
fig3.clf()
ax = fig3.add_axes([0.15, 0.1, 0.8, 0.8], projection=toplevel_wcs)
ra = ax.coords['ra']
ra.set_major_formatter('hh:mm:ss.s')
dec = ax.coords['dec']
ra.set_axislabel("RA (J2000)", fontsize=pl.rcParams['axes.labelsize'])
dec.set_axislabel("Dec (J2000)",
def rgbfig(
figfilename="SgrB2N_RGB.pdf",
lims=[([266.83404223, 266.83172659]), ([-28.373138, -28.3698755])],
scalebarx=coordinates.SkyCoord(266.833545 * u.deg,
-28.37283819 * u.deg),
redfn=paths.Fpath('SGRB2N-2012-Q.DePree_K.recentered.fits'),
greenfn=paths.
Fpath('sgr_b2m.N.B3.allspw.continuum.r0.5.clean1000.image.tt0.pbcor.fits'),
bluefn=paths.
Fpath('sgr_b2m.N.B6.allspw.continuum.r0.5.clean1000.image.tt0.pbcor.fits'),
redpercentile=99.99,
greenpercentile=99.99,
bluepercentile=99.99,
stretch=visualization.AsinhStretch(),
):
header = fits.getheader(redfn)
celwcs = wcs.WCS(header).celestial
redhdu = fits.open(redfn)
greenhdu = fits.open(greenfn)
bluehdu = fits.open(bluefn)
greendata, _ = reproject.reproject_interp(
(greenhdu[0].data, wcs.WCS(greenhdu[0].header).celestial),
celwcs,
shape_out=redhdu[0].data.squeeze().shape)
bluedata, _ = reproject.reproject_interp(
(bluehdu[0].data, wcs.WCS(bluehdu[0].header).celestial),
celwcs,
shape_out=redhdu[0].data.squeeze().shape)
#def rescale(x):
# return (x-np.nanmin(x))/(np.nanmax(x) - np.nanmin(x))
redrescale = visualization.PercentileInterval(redpercentile)
greenrescale = visualization.PercentileInterval(greenpercentile)
bluerescale = visualization.PercentileInterval(bluepercentile)
rgb = np.array([
stretch(redrescale(redhdu[0].data.squeeze())),
stretch(greenrescale(greendata)),
stretch(bluerescale(bluedata)),
]).swapaxes(0, 2).swapaxes(0, 1)
norm = visualization.ImageNormalize(
rgb, interval=visualization.MinMaxInterval(), stretch=stretch)
fig1 = pl.figure(1)
fig1.clf()
ax = fig1.add_subplot(1, 1, 1, projection=celwcs)
pl.imshow(rgb, origin='lower', interpolation='none', norm=norm)
(x1, x2), (y1, y2) = celwcs.wcs_world2pix(lims[0], lims[1], 0)
ax.axis((x1, x2, y1, y2))
visualization_tools.make_scalebar(ax,
left_side=scalebarx,
length=1.213 * u.arcsec,
label='0.05 pc')
pl.savefig(paths.fpath(figfilename), bbox_inches='tight')
pl.matplotlib.rc_file('pubfiguresrc')
pl.rcParams['figure.figsize'] = (12, 8)
pl.rcParams['figure.dpi'] = 75.
pl.rcParams['savefig.dpi'] = 300.
pl.rcParams['axes.labelsize'] = 9
pl.rcParams['xtick.labelsize'] = 8
pl.rcParams['ytick.labelsize'] = 8
tick_fontsize = 6
if matplotlib.__version__[0] == '1':
markersize = 10
elif matplotlib.__version__[0] == '2':
markersize = 0.5
hdu = fits.open(paths.Fpath('./other/scuba_Herschel_Feathered.fits'))[0]
mywcs = wcs.WCS(hdu.header).celestial
wcsaxes = mywcs # WCSaxes(mywcs.to_header())
fig3 = pl.figure(3)
fig3.clf()
ax = fig3.add_axes([0.15, 0.1, 0.8, 0.8], projection=wcsaxes)
ra = ax.coords['ra']
ra.set_major_formatter('hh:mm:ss.s')
dec = ax.coords['dec']
ra.set_axislabel("RA (J2000)", fontsize=pl.rcParams['axes.labelsize'])
dec.set_axislabel("Dec (J2000)",
fontsize=pl.rcParams['axes.labelsize'],
minpad=0)
ra.ticklabels.set_fontsize(tick_fontsize)
pl.matplotlib.rc_file('pubfiguresrc')
pl.rcParams['figure.figsize'] = (12,8)
pl.rcParams['figure.dpi'] = 75.
pl.rcParams['savefig.dpi'] = 300.
pl.rcParams['axes.labelsize'] = 15
pl.rcParams['xtick.labelsize'] = 14
pl.rcParams['ytick.labelsize'] = 14
tick_fontsize = 14
if matplotlib.__version__[0] == '1':
markersize = 6
elif matplotlib.__version__[0] == '2':
markersize = 0.5
hdu = fits.open(paths.Fpath('column_maps/scuba_col_herscheltem.fits'))[0]
mywcs = wcs.WCS(hdu.header).celestial
wcsaxes = mywcs # WCSaxes(mywcs.to_header())
fig3 = pl.figure(3)
fig3.clf()
ax = fig3.add_axes([0.15, 0.1, 0.8, 0.8], projection=wcsaxes)
ra = ax.coords['ra']
ra.set_major_formatter('hh:mm:ss.s')
dec = ax.coords['dec']
ra.set_axislabel("RA (J2000)", fontsize=pl.rcParams['axes.labelsize'])
dec.set_axislabel("Dec (J2000)", fontsize=pl.rcParams['axes.labelsize'], minpad=0)
ra.ticklabels.set_fontsize(tick_fontsize)
ra.set_ticks(exclude_overlapping=True)
dec.ticklabels.set_fontsize(tick_fontsize)
visualization_tools.make_scalebar(ax,
left_side=scalebarx,
length=1.213 * u.arcsec,
label='0.05 pc')
pl.savefig(paths.fpath(figfilename), bbox_inches='tight')
if __name__ == "__main__":
rgbfig(stretch=visualization.LinearStretch(), )
rgbfig(
figfilename='SgrB2M_RGB.pdf',
lims=[(266.8359, 266.8325), (-28.38600555, -28.3832)],
redfn=paths.Fpath('SGRB2M-2012-Q-MEAN.DePree.recentered.fits'),
greenfn=paths.Fpath(
'sgr_b2m.M.B3.allspw.continuum.r0.5.clean1000.image.tt0.pbcor.fits'
),
bluefn=paths.Fpath(
'sgr_b2m.M.B6.allspw.continuum.r0.5.clean1000.image.tt0.pbcor.fits'
),
scalebarx=coordinates.SkyCoord(266.8336007 * u.deg,
-28.38553839 * u.deg),
redpercentile=99.99,
greenpercentile=99.98,
bluepercentile=99.98,
stretch=visualization.AsinhStretch(),
)
import numpy as np
import paths
from astropy.io import fits
from astropy.utils.console import ProgressBar
import fil_finder
from spectral_cube import SpectralCube
cube = SpectralCube.read(
paths.Fpath('12m/SgrB2_b3_12M.HC3N.image.pbcor.contsub.fits'))
subcube = cube[100:155].minimal_subcube()
newcube = np.empty_like(subcube, dtype='int16')
for ii, imslice in enumerate(ProgressBar(subcube)):
img = imslice.value
hdr = header = imslice.header
try:
ff = fil_finder.fil_finder_2D(
img,
hdr,
beamwidth=3.5,
distance=8500,
skel_thresh=20,
branch_thresh=10,
glob_thresh=65,
adapt_thresh=10,
pad_size=1,
)
except ValueError:
print("FilFinder creation failed on iter {0}".format(ii))
continue
import numpy as np
from astropy import wcs
import aplpy
import paths
from astropy.io import fits
import reproject
from matplotlib.colors import Normalize, LogNorm
from matplotlib.colors import rgb_to_hsv, hsv_to_rgb
import PIL
from PIL import ImageEnhance
import pyavm
from astropy import log
if 'fncont' not in locals():
log.info("Loading and reprojecting FITS files")
fncont = paths.Fpath(
'merge/SgrB2_selfcal_full_TCTE7m_selfcal4_ampphase.image.pbcor.fits')
fnhc3n = paths.Fpath(
'merge/max/SgrB2_b3_7M_12M.HC3N.image.pbcor_max_medsub.fits')
fnhcn = paths.Fpath(
'merge/max/SgrB2_b3_7M_12M.HCN.image.pbcor_max_medsub.fits')
fnhnc = paths.Fpath(
'merge/max/SgrB2_b3_7M_12M.HNC.image.pbcor_max_medsub.fits')
fnhcop = paths.Fpath(
'merge/max/SgrB2_b3_7M_12M.HCOp.image.pbcor_max_medsub.fits')
outhdr = fits.getheader(fncont)
cont = fits.getdata(fncont)
hcn = reproject.reproject_interp(fnhcn, outhdr, order=1)[0]
hc3n = reproject.reproject_interp(fnhc3n, outhdr, order=1)[0]
hnc = reproject.reproject_interp(fnhnc, outhdr, order=1)[0]
hcop = reproject.reproject_interp(fnhcop, outhdr, order=1)[0]
from spectral_cube import SpectralCube, Projection
import paths
import files
import regions
import pylab as pl
regs = regions.read_ds9(paths.rpath('sio_masers.reg'))
v2maser = regs[2]
cont_b6 = fits.open(files.contb6_rm2)
cont_b3 = fits.open(files.contb3_rm2)
siov2j2im = paths.Fpath('SiO_v2_j2_peak_N.fits')
if os.path.exists(siov2j2im):
siov2j2 = Projection.from_hdu(fits.open(siov2j2im))
else:
region, spw, band, freq = 'N', 0, 3, 85.640452 * u.GHz
fn = paths.eFpath(
'sgr_b2m.{0}.spw{1}.B{2}.lines.clarkclean1000.robust0.5.image.pbcor.medsub.fits'
.format(region, spw, band))
siov2j2 = (SpectralCube.read(fn).with_spectral_unit(
u.km / u.s, velocity_convention='radio',
rest_value=freq).spectral_slab(55 * u.km / u.s,
110 * u.km / u.s).to(u.K)).max(axis=0)
siov2j2.write(siov2j2im)
hcnv3j1im = paths.Fpath('HCN_v3_j1_peak_N.fits')
if os.path.exists(hcnv3j1im):
})
print(freqs)
cubes = {}
mx = {}
for region in ("N", "M"):
for spw in (0, 1, 2, 3):
for band in (3, 6):
for line in freqs:
fn = 'full_SgrB2{0}_spw{1}_lines_cutout{0}_medsub.fits'.format(
region, spw)
fn = 'sgr_b2m.{0}.spw{1}.B{2}.lines.clarkclean1000.robust0.5.image.pbcor.medsub.fits'.format(
region, spw, band)
cube = SpectralCube.read(paths.Fpath(fn))
freq = freqs[line]
if cube.spectral_extrema[0] < freq < cube.spectral_extrema[1]:
print("Matched {line} to spw {spw}".format(line=line,
spw=spw))
subcube = (cube.with_spectral_unit(
u.km / u.s,
velocity_convention='radio',
rest_value=freq).spectral_slab(25 * u.km / u.s,
95 * u.km / u.s))
try:
subcube = subcube.to(u.K)
except Exception as ex:
print("Failed: {0}".format(ex))
if 'OBJECT' in subcube.meta:
subcube.meta['OBJECT'] = subcube.meta['OBJECT'] + line
from spectral_cube import SpectralCube
from astropy import coordinates
import paths
from astropy import units as u
sgb2m = coordinates.SkyCoord.from_name('Sgr B2 (M)')
cube = (SpectralCube.read(
paths.Fpath(
'merge/lines/HCN.r2_TP_7m_12m_feather.fits')).with_spectral_unit(
u.km / u.s, velocity_convention='radio'))
xp, yp = map(int,
cube.wcs.celestial.wcs_world2pix(sgb2m.ra.deg, sgb2m.dec.deg, 0))
s19cube = SpectralCube.read(paths.Fpath('tp/tp_concat.spw19.image.fits'))
s19cube = s19cube.to(u.K, s19cube.beam.jtok_equiv(s19cube.spectral_axis))
hcncube = s19cube.with_spectral_unit(u.km / u.s,
velocity_convention='radio',
rest_value=cube.wcs.wcs.restfrq * u.Hz)
hcnspec = hcncube.spectral_slab(-165 * u.km / u.s,
130 * u.km / u.s).mean(axis=(1, 2))
import pylab as pl
pl.clf()
(5 * hcnspec).quicklook(color='k')
hcnsgb2spec = cube[:, yp, xp]
(hcnsgb2spec - 4 * hcnsgb2spec.unit).quicklook(color='k')
otherhcnspec = cube[:, yp - 60, xp - 20]
yy,xx = np.indices([grid_size, grid_size])
x_centers,y_centers = mywcs.wcs_pix2world(xx.flat, yy.flat, 0)
grid_coords = coordinates.SkyCoord(x_centers*u.deg, y_centers*u.deg, frame='fk5')
cont_tbl = Table.read(paths.tpath('continuum_photometry.ipac'), format='ascii.ipac')
sgrb2_coords = coordinates.SkyCoord(cont_tbl['RA'], cont_tbl['Dec'],
unit=(u.deg, u.deg), frame='fk5',)
gridded_stars = np.histogram2d(sgrb2_coords.ra.deg, sgrb2_coords.dec.deg,
bins=[x_edges[::-1], y_edges])[0][::-1,:].T
hdu = fits.PrimaryHDU(data=gridded_stars, header=header)
hdu.writeto(paths.Fpath('stellar_density_grid.fits'), overwrite=True)
hdu.data *= mass_represented_by_a_source.value
hdu.writeto(paths.Fpath('stellar_mass_density_grid.fits'), overwrite=True)
_,nn11_grid,_ = coordinates.match_coordinates_sky(grid_coords, sgrb2_coords,
nthneighbor=11)
nn11_grid = nn11_grid.reshape([grid_size, grid_size])
nn11_grid_pc = (nn11_grid * distance).to(u.pc, u.dimensionless_angles())
herschel25 = fits.open(paths.cpath('gcmosaic_column_conv25.fits'))
herschel36 = fits.open(paths.cpath('gcmosaic_column_conv36.fits'))
herschel25reproj,_ = reproject.reproject_interp(herschel25, header)
herschel36reproj,_ = reproject.reproject_interp(herschel36, header)
import radio_beam
import paths
from restfreqs import restfreqs
import pylab as pl
pl.close('all')
apertures = ((1, 2, 3, 4) * u.pc / (8.4 * u.kpc)).to
velo = 62.5 * u.km / u.s
species_names = list(restfreqs.keys())
frequencies = u.Quantity([restfreqs[key] for key in species_names], unit=u.GHz)
for fn in glob.glob(paths.Fpath('tp/tp_concat*fits')):
basefn = os.path.basename(fn)
outf = paths.Fpath('tp/avgspectra/avg_{0}'.format(basefn))
if not os.path.exists(outf):
cube = SpectralCube.read(fn)
cube.allow_huge_operations = True
mad = cube.mad_std(axis=0)
sum = (cube * mad).sum(axis=(1, 2))
mean = sum / np.nansum(mad)
mean.write(paths.Fpath(
'tp/avgspectra/weighted_avg_{0}'.format(basefn)),
overwrite=True)
mn = cube.mean(axis=(1, 2))
mn.write(outf, overwrite=True)
for fn in glob.glob(paths.Fpath('tp/tp_concat*fits')):
import numpy as np
from constants import distance
import radio_beam
from astropy import units as u
from astropy import wcs
from astropy.io import fits
import higal_sedfitter
import paths
import reproject
import dust_emissivity
from astropy import convolution
import pylab as pl
herschel25_colfile = paths.Fpath(
'other/Herschel25umcolum_regridded_match_stellar.fits')
herschel36_colfile = paths.Fpath(
'other/Herschel36umcolum_regridded_match_stellar.fits')
herschel25 = fits.getdata(herschel25_colfile) * u.M_sun / u.pc**2
herschel36 = fits.getdata(herschel36_colfile) * u.M_sun / u.pc**2
sharc_colfile = paths.cpath('column_maps/sharc_col_herscheltem.fits')
scuba_colfile = paths.cpath('column_maps/scuba_col_herscheltem.fits')
sharc_col = fits.getdata(sharc_colfile) * u.cm**-2
scuba_col = fits.getdata(scuba_colfile) * u.cm**-2
sharc_beam = 10 * u.arcsec
scuba_beam = 10 * u.arcsec
scuba_beam_pc = (scuba_beam * distance).to(u.pc, u.dimensionless_angles())
h25bm_pc = (25 * u.arcsec * distance).to(u.pc, u.dimensionless_angles())
h36bm_pc = (36 * u.arcsec * distance).to(u.pc, u.dimensionless_angles())
FWHM_SCALE = np.sqrt(8 * np.log(2))
import paths
# old
#contfnpath = paths.tmpath('te/SgrB2_selfcal_full_TE_selfcal4_ampphase.image.pbcor.fits')
#contfnpath = paths.Fpath('merge/continuum/SgrB2_selfcal_full_TCTE7m_selfcal5_ampphase_taylorterms_multiscale_deeper_mask2.5mJy.image.tt0.pbcor.fits')
# contfilename = paths.Fpath('merge/continuum/SgrB2_selfcal_full_TCTE7m_selfcal6_ampphase_taylorterms_multiscale_deeper_mask1.5mJy.image.tt0.pbcor.fits')
# alphafilename = paths.Fpath('merge/continuum/SgrB2_selfcal_full_TCTE7m_selfcal5_ampphase_taylorterms_multiscale_deeper_mask2.5mJy.alpha.fits')
# alphaerrorfilename = paths.Fpath('merge/continuum/SgrB2_selfcal_full_TCTE7m_selfcal5_ampphase_taylorterms_multiscale_deeper_mask2.5mJy.alpha.error.fits')
contfilename = paths.Fpath(
'merge/continuum/SgrB2_selfcal_full_TCTE7m_try2_selfcal6_ampphase_deeper_mask1.5mJy.image.tt0.pbcor.fits'
)
alphafilename = paths.Fpath(
'merge/continuum/SgrB2_selfcal_full_TCTE7m_try2_selfcal6_ampphase_deeper_mask1.5mJy.alpha.fits'
)
alphaerrorfilename = paths.Fpath(
'merge/continuum/SgrB2_selfcal_full_TCTE7m_try2_selfcal6_ampphase_deeper_mask1.5mJy.alpha.error.fits'
)
import pyspeckit
import pyregion
import paths
import pylab as pl
import warnings
from latex_info import (latexdict, format_float, round_to_n,
strip_trailing_zeros, exp_to_tex)
latexdict = latexdict.copy()
warnings.filterwarnings('ignore', category=PossiblySlowWarning)
regs = pyregion.open(paths.rpath('SgrB2_1.3cm_hiiRegions_shiftedtoALMA.reg'))
cube = SpectralCube.read(
paths.Fpath(
'M_cutouts/SgrB2_b3_12M_TE.H41a.image.pbcor_M_cutout.medsub.fits'))
if 'fit_values' not in locals():
fit_values = {}
coords = {}
for reg in regs:
if 'text' not in reg.attr[1]:
continue
name = reg.attr[1]['text'].strip("{}")
try:
cutout = cube.subcube_from_ds9region(pyregion.ShapeList([reg])).to(
u.K,
cube.beam.jtok_equiv(
cube.with_spectral_unit(u.GHz).spectral_axis))
pl.rcParams['axes.labelsize'] = 8
pl.rcParams['axes.titlesize'] = 8
pl.rcParams['xtick.labelsize'] = 6
pl.rcParams['ytick.labelsize'] = 6
pl.rcParams['axes.linewidth'] = 0.15
tick_fontsize = 6
markersize = 3
else:
pl.rcParams['savefig.dpi'] = 300.
tick_fontsize = 10
markersize = 8
scalebarpos = coordinates.SkyCoord("17:47:32.", "-28:26:33.0", unit=(u.h, u.deg), frame='fk5')
contfnr2 = paths.Fpath('merge/continuum/SgrB2_selfcal_full_TCTE7m_try2_selfcal6_ampphase_taper1.5as_r2_mask5mJy.image.tt0.pbcor.fits')
contfnr0 = paths.Fpath('merge/continuum/SgrB2_selfcal_full_TCTE7m_try2_selfcal6_ampphase_deeper_mask1.5mJy.image.tt0.pbcor.fits')
for contfn,name,(vmin,vmax,truemax) in [(contfnr2, "taper", (-1,50,2000)), (contfnr0, "hires",(-1,5,200))]:
hdu = fits.open(contfn)[0]
mywcs = wcs.WCS(hdu.header).celestial
wcsaxes = mywcs # hack for newer version of wcsaxes that is included in astropy
fig3 = pl.figure(3)
fig3.clf()
ax = fig3.add_axes([0.15, 0.1, 0.8, 0.8], projection=wcsaxes)
ra = ax.coords['ra']
ra.set_major_formatter('hh:mm:ss.s')
dec = ax.coords['dec']
ra.set_axislabel("RA (J2000)", fontsize=pl.rcParams['axes.labelsize'])
import os
from astropy import constants, units as u, table, stats, coordinates, wcs, log, coordinates as coord
from astropy.io import fits
from spectral_cube import SpectralCube, Projection
import paths
import files
import regions
import pylab as pl
regs = regions.read_ds9(paths.rpath('sio_masers.reg'))
v2maser = regs[2]
bluefile = paths.Fpath('SgrB2_N_SiO_blue_20to50kms.fits')
redfile = paths.Fpath('SgrB2_N_SiO_blue_77to100kms.fits')
if os.path.exists(bluefile):
blue = Projection.from_hdu(fits.open(bluefile))
red = Projection.from_hdu(fits.open(redfile))
else:
siocube = (SpectralCube.read(
'/Volumes/external/sgrb2/full_SgrB2N_spw0_lines_cutoutN_medsub.fits').
with_spectral_unit(u.km / u.s,
velocity_convention='radio',
rest_value=217.10498 * u.GHz).spectral_slab(
-200 * u.km / u.s, 250 * u.km / u.s))
siocube.spectral_slab(0 * u.km / u.s, 120 * u.km / u.s).write(
'SgrB2_N_SiO_medsub_cutout.fits', overwrite=True)
import paths
contb6_rm2 = paths.Fpath(
'sgr_b2m.N.B6.allspw.continuum.r-2.cleanto2mJy_2terms.image.tt0.pbcor.fits'
)
# TODO: use clean version
contb3_rm2 = paths.Fpath(
'sgr_b2m.N.B3.allspw.continuum.r-2.dirty.image.tt0.pbcor.fits')
contb6_M_rm2 = paths.Fpath(
'sgr_b2m.M.B6.allspw.continuum.r-2.cleanto2mJy_2terms.image.tt0.pbcor.fits'
)
contb3_M_rm2 = paths.Fpath(
'sgr_b2m.M.B3.allspw.continuum.r-2.dirty.image.tt0.pbcor.fits')
import reproject
import pylab as pl
pl.matplotlib.rc_file('pubfiguresrc')
pl.rcParams['figure.figsize'] = (12, 8)
pl.rcParams['figure.dpi'] = 300.
pl.rcParams['savefig.dpi'] = 300.
pl.rcParams['axes.labelsize'] = 9
pl.rcParams['xtick.labelsize'] = 8
pl.rcParams['ytick.labelsize'] = 8
pl.rcParams['font.size'] = 6
tick_fontsize = 6
fnhc3n = paths.Fpath(
'merge/max/SgrB2_b3_7M_12M.HC3N.image.pbcor_max_medsub.fits')
fnhcn = paths.Fpath(
'merge/max/SgrB2_b3_7M_12M.HCN.image.pbcor_max_medsub.fits')
# FF2 = aplpy.FITSFigure(fnhc3n)
# FF2.show_grayscale(vmax=0.06, invert=True)
# FF2.show_contour(fnhcn, levels=[0.02,0.03,0.04,0.05,0.06,0.1,0.2,0.3,0.4],
# colors=[(1,0,0,x) for x in np.linspace(0,1,10)],
# filled=True, )
# FF2.save(paths.fpath("HC3N_grayscale_with_HCN_red_filled_contours.png"), dpi=150)
# FF2.recenter(266.8318615, -28.3940598, width=0.062, height=0.105)
# FF2.save(paths.fpath("HC3N_grayscale_with_HCN_red_filled_contours_zoom.png"), dpi=150)
hdu = fits.open(fnhc3n)[0]
mywcs = wcs.WCS(hdu.header).sub([wcs.WCSSUB_CELESTIAL])
wcsaxes = WCSaxes(mywcs.to_header())
from astropy.io import fits
from astropy import coordinates
import radio_beam
for fn in (
'SgrB2_B3_continuum_QA2_minimumcutout.fits',
'sgr_b2m.N.allspw.continuum.clean1000.image.tt0.pbcor.fits',
'sgr_b2m.N.B6.allspw.continuum.r-2.cleanto2mJy_2terms.image.tt0.pbcor.fits',
#'sgr_b2m.M.B3.allspw.continuum.r-2.dirty.image.tt0.pbcor.fits',
'sgr_b2m.N.B3.allspw.continuum.r-2.dirty.image.tt0.pbcor.fits',
#'sgr_b2m.M.B3.allspw.continuum.r0.5.clean1000.image.tt0.pbcor.fits',
#'sgr_b2m.N.B3.allspw.continuum.r0.5.clean1000.image.tt0.pbcor.fits',
#'sgr_b2m.M.B3.allspw.continuum.r-2.clean1000.image.tt0.pbcor.fits',
):
fh = fits.open(paths.Fpath(fn))
if fh[0].header['RADESYS'] == 'FK5':
refcen = coordinates.SkyCoord(fh[0].header['CRVAL1'] * u.deg,
fh[0].header['CRVAL2'] * u.deg,
frame='fk5').icrs
fh[0].header['CRVAL1'] = refcen.ra.deg
fh[0].header['CRVAL2'] = refcen.dec.deg
fh[0].header['RADESYS'] = 'ICRS'
beam = radio_beam.Beam.from_fits_header(fh[0].header)
fh[0].data = (fh[0].data * beam.jtok(fh[0].header['CRVAL3'] * u.Hz)).value
fh.writeto(paths.Fpath(fn.replace(".fits", "_K.fits")), overwrite=True)
