def make_flats(cubename,
vrange=[0, 10],
noisevrange=[-100, -50],
suffix='_sub.fits',
out_suffix=""):
cubefile = pyfits.open(cubename + suffix)
if not os.path.exists(cubename + suffix):
raise IOError(
"Missing file %s. This may be caused by a lack of starlink." %
(cubename + suffix))
cubefile[0].header = _fix_ms_kms_header(cubefile[0].header)
flathead = strip_headers.flatten_header(cubefile[0].header)
integrated = cubes.integ(cubefile, vrange, zunits='wcs',
dvmult=True)[0]
if integrated.shape != cubefile[0].data.shape[1:]:
raise ValueError(
"Cube integrated to incorrect size. Major error. Badness.")
flatimg = pyfits.PrimaryHDU(data=integrated, header=flathead)
flatimg.writeto(cubename.replace("cube", "integrated") + out_suffix +
".fits",
clobber=True)
noise = cubes.integ(cubefile,
noisevrange,
average=np.std,
zunits='wcs')[0]
flatimg.data = noise
flatimg.writeto(cubename.replace("cube", "noise") + out_suffix +
".fits",
clobber=True)
mincube = cubes.integ(cubefile, vrange, average=np.min,
zunits='wcs')[0]
flatimg.data = mincube
flatimg.writeto(cubename.replace("cube", "min") + out_suffix + ".fits",
clobber=True)
def integrate(vrange=vrange,suffix='_integrated.fits'):
for fn in ['LimaBean_H2CO11_taucube.fits',
'LimaBean_H2CO11_taucube_claw.fits',
'LimaBean_H2CO22_taucube.fits',]:
fn = datapath+fn
hdu = fits.open(fn)
hdr = hdu[0].header
xarr = (np.arange(hdr['NAXIS3'])-hdr['CRPIX3']+1)*hdr['CDELT3']+hdr['CRVAL3']
xr = [np.argmin(np.abs(xarr-x)) for x in vrange]
integ = hdu[0].data[xr[0]:xr[1],:,:].sum(axis=0)
integ *= hdr['CDELT3']
fhdr = strip_headers.flatten_header(hdr)
hdu[0].data = integ
hdu[0].header = fhdr
hdu.writeto(fn.replace(".fits",suffix),clobber=True)
def make_flats(cubename,vrange=[0,10],noisevrange=[-100,-50],suffix='_sub.fits',
out_suffix=""):
cubefile = pyfits.open(cubename+suffix)
if not os.path.exists(cubename+suffix):
raise IOError("Missing file %s. This may be caused by a lack of starlink." % (cubename+suffix))
cubefile[0].header = _fix_ms_kms_header(cubefile[0].header)
flathead = strip_headers.flatten_header(cubefile[0].header)
integrated = cubes.integ(cubefile,vrange,zunits='wcs',dvmult=True)[0]
if integrated.shape != cubefile[0].data.shape[1:]:
raise ValueError("Cube integrated to incorrect size. Major error. Badness.")
flatimg = pyfits.PrimaryHDU(data=integrated,header=flathead)
flatimg.writeto(cubename.replace("cube","integrated")+out_suffix+".fits",
clobber=True)
noise = cubes.integ(cubefile,noisevrange,average=np.std,zunits='wcs')[0]
flatimg.data = noise
flatimg.writeto(cubename.replace("cube","noise")+out_suffix+".fits",
clobber=True)
mincube = cubes.integ(cubefile,vrange,average=np.min,zunits='wcs')[0]
flatimg.data = mincube
flatimg.writeto(cubename.replace("cube","min")+out_suffix+".fits",
clobber=True)
import re
from paths import datapath_w51 as datapath
from paths import figurepath as figpath
from paths import h2co11taufn
import paths
import os
#matplotlib.rc('text', usetex=True)
#matplotlib.rcParams['text.latex.preamble'] = [r'\boldmath']
pl.mpl.rc_file(os.path.join(paths.source_root, 'plot_scripts/pubfiguresrc'))
h2co11 = fits.open(h2co11taufn)
co32 = fits.open(datapath+'w51_bieging_13co32.fits')
#hdr = fits.getheader(datapath+'h2co_integ.fits')
hdr = flatten_header(h2co11[0].header)
cohdr = flatten_header(co32[0].header)
h2co_45to55 = h2co11[0].data[95:105,:,:].sum(axis=0)
h2co_55to60 = h2co11[0].data[105:110,:,:].sum(axis=0)
h2co_60to65 = h2co11[0].data[110:115,:,:].sum(axis=0)
h2co_65to75 = h2co11[0].data[115:125,:,:].sum(axis=0)
co_45to55 = co32[0].data[31:51,:,:].sum(axis=0)
co_55to60 = co32[0].data[51:61,:,:].sum(axis=0)
co_60to65 = co32[0].data[61:71,:,:].sum(axis=0)
co_65to75 = co32[0].data[71:91,:,:].sum(axis=0)
rgb = np.array([h2co_55to60,h2co_60to65,h2co_65to75])
L,H = np.nanmin(rgb),np.nanmax(rgb)
def spectral_grid(cube11=pyspeckit.Cube(datapath+'LimaBean_H2CO11_cube_sub.fits'),
h213co11cube=pyspeckit.Cube(datapath+'LimaBean_H213CO_cube_sub.fits'),
cube22=pyspeckit.Cube(datapath+'LimaBean_H2CO22_cube_sub_smoothtoCband.fits'),
figure=pl.figure(1,figsize=(10,10)),
yrange=(-2.1,0.5),
nx=6,
ny=6,
dx=2,
dy=2,
xc=48,
yc=45,
xlabel="Velocity",
ylabel="$T_A^*$ [K]$",
ratio=False):
for c in [cube11,h213co11cube,cube22]:
c.xarr.convert_to_unit('km/s')
def some_plots(xx,yy,dolegend=False):
c13 = h213co11cube.get_spectrum(xx,yy)
c12 = cube11.get_spectrum(xx,yy)
c22 = cube22.get_spectrum(xx,yy)
for c in (c12,c13,c22):
c.plotter.autorefresh=False
c12.baseline(exclude=[-225,200],order=5)
c13.baseline(exclude=[-225,200],order=5)
c13.plotter(label='H$_{2}$$^{13}$CO 1-1', axis=pl.gca(), color='b',
alpha=0.5, refresh=False)
c12.plotter(axis=c13.plotter.axis, clear=False, color='k',
label="H$_{2}$CO 1-1", alpha=0.8, linewidth=1.5,
refresh=False)
#(c13*6).plotter(label='6$\\times$H$_{2}$$^{13}$CO',axis=pl.gca(),color='r',clear=False)
c22.plotter(axis=c13.plotter.axis,clear=False,color='r',linewidth=2,alpha=0.5,
label='H$_2$CO 2-2', refresh=False)
if ratio:
r = c12.copy()
r.data = c22.data/c12.data
r.data[(r.data>1)] = np.nan
r.data[(r.data<1/13.)] = np.nan
r.plotter(axis=c13.plotter.axis,clear=False,color='r', refresh=False)
if dolegend:
pl.legend(loc='best')
pl.gca().set_xlim(-100,100)
pl.clf()
pl.subplots_adjust(wspace=0,hspace=0,left=0.05,right=0.95,bottom=0.05,top=0.95)
#nx,ny = 6,6
#xc,yc = 52,48 # center
#xc,yc = 45,42 # bottom left
xl = xc - (nx/2.)*dx
yl = yc - (ny/2.)*dy
w = wcs.WCS(strip_headers.flatten_header(cube11.header))
for ii,(spy,spx) in enumerate(itertools.product(range(nx),range(ny))):
sp = pl.subplot(nx,ny,ii+1)
sp.zorder = nx-spx+ny*spy
some_plots(spx*dx+xl,(ny-spy-1)*dy+yl,dolegend=False) # ,dolegend=(ii==24))
sp.set_ylim(*yrange)
if spx > 0:
sp.set_yticks([])
else:
sp.set_yticks(sp.get_yticks()[1:])
if spy < ny-1:
sp.set_xticks([])
else:
sp.set_xticks(sp.get_xticks()[1:])
sp.set_xlabel("")
sp.set_ylabel("")
(l,b), = w.wcs_pix2world([[spx*dx+xl,(ny-spy-1)*dy+yl]],0)
sp.annotate('%0.3f %+0.3f' % (l,b),(0.5,0.9),xycoords='axes fraction',fontsize=12,
horizontalalignment='center')
sp.xaxis.set_tick_params(labelsize=14)
sp.yaxis.set_tick_params(labelsize=14)
pl.figtext(0.5,0.01,xlabel)
pl.figtext(-0.01,0.5,ylabel,rotation='vertical')
pl.draw()
ratioF[0].header['BUNIT'] = 'log volume density'
ratioF.writeto(datapath+'W51_H2CO11_to_22_logdensity_supersampled_textbg_sigma%0.1f.fits' % sigma,
clobber=True)
nfin = np.isfinite(ratio).sum(axis=0)
ratio[:,nfin < 2] = np.nan
from scipy.stats import nanmedian
rmax = np.nanmax(ratio, axis=0)[None,:,:]
rmin = np.nanmin(ratio, axis=0)[None,:,:]
rmid = nanmedian(ratio, axis=0)[None,:,:]
densc = fits.getdata(datapath+'W51_H2CO11_to_22_logdensity_supersampled_textbg_sigma%0.1f.fits' % sigma)
dens_peak = np.nanmax(densc,axis=0)
dens_peakf = fits.PrimaryHDU(data=dens_peak, header=flatten_header(header))
dens_peakf.writeto(datapath+'W51_H2CO_logdensity_textbg_peak.fits',clobber=True)
d_rmaxprojection = ratio_to_dens_slow(rmax, cont11, cont22)
d_rminprojection = ratio_to_dens_slow(rmin, cont11, cont22)
d_rmidprojection = ratio_to_dens_slow(rmid, cont11, cont22)
dens_rmaxf = fits.PrimaryHDU(data=d_rmaxprojection, header=flatten_header(header))
dens_rmaxf.writeto(datapath+'W51_H2CO_logdensity_textbg_max_ratio_sigma%0.1f.fits' % sigma,clobber=True)
dens_rminf = fits.PrimaryHDU(data=d_rminprojection, header=flatten_header(header))
dens_rminf.writeto(datapath+'W51_H2CO_logdensity_textbg_min_ratio_sigma%0.1f.fits' % sigma,clobber=True)
dens_rmidf = fits.PrimaryHDU(data=d_rmidprojection, header=flatten_header(header))
dens_rmidf.writeto(datapath+'W51_H2CO_logdensity_textbg_mid_ratio_sigma%0.1f.fits' % sigma,clobber=True)
dens_rmaxf = fits.PrimaryHDU(data=rmax, header=flatten_header(header))
dens_rmaxf.writeto(datapath+'W51_H2CO_max_ratio.fits',clobber=True)
savefig('W51_H2CO_2parfittry11_v1_justdensity%s.png' % extrastr,bbox_inches='tight')
dens1.show_markers(np.array(ysos['_Glon'][ysos['Cl1']=='I']),
np.array(ysos['_Glat'][ysos['Cl1']=='I']),
edgecolor='k',marker='x')
dens1.show_markers(np.array(masers['GLON']),
np.array(masers['GLAT']),
marker='+',edgecolor='k')
dens1.show_markers(glon,glat,marker='+',edgecolor='k')
dens1.refresh()
savefig('W51_H2CO_2parfittry11_v1_justdensity%s_withYSOs.png' % extrastr,bbox_inches='tight')
column1 = 10**parcubefile[0].data[1] * parcubefile[0].data[5]
column2 = 10**parcubefile[0].data[9] * parcubefile[0].data[13]
totalcolumn = np.log10(column1 + column2)
totalcolumn[totalcolumn == 0] = np.nan
colhdu = fits.PrimaryHDU(data=totalcolumn, header=flatten_header(parcubefile[0].header))
pl.figure(4)
pl.clf()
tcol = FITSFigure(colhdu, figure=pl.figure(4))
tcol.show_colorscale(cmap=cmhot)
tcol.colorbar._colorbar_axes.set_ylabel('log$_{10}$(N(H$_2$) cm$^{-2}$)')
savefig('W51_H2CO_2parfittry11_totalcolumn.png', bbox_inches='tight')
labels = {'dens':'log$_{10}$(n(H$_2$) cm$^{-3}$)',
'lindens':'n(H$_2$) [cm$^{-3}]$',
'velocity':'Velocity ($V_{LSR}$ km s$^{-1}$)',
'ratio':r'Ratio $\tau_{obs} 1-1 / \tau_{obs} 2-2$',
'width':'Line Width (km s$^{-1}$)',
'column':'log$_{10}$(N(H$_2$) cm$^{-2}$)'}
def spectral_grid(cube11=pyspeckit.Cube(datapath+'W51_H2CO11_taucube_supersampled.fits'),
h213co11cube=pyspeckit.Cube(datapath+'W51_H213CO11_taucube_supersampled.fits'),
cube22=pyspeckit.Cube(datapath+'W51_H2CO22_pyproc_taucube_lores_supersampled.fits'),
figure=pl.figure(1,figsize=(10,10)),
yrange=(-2.1,0.5),
vrange=(40,80),
ratio=False,
nx=6,
ny=6,
dx=4,
dy=4,
xc=94,
yc=75,
baseline=False
):
for c in [cube11,cube22]: # h213cocube
c.xarr.convert_to_unit('km/s')
def some_plots(xx,yy,dolegend=False):
c13 = h213co11cube.get_spectrum(xx,yy)
c12 = cube11.get_spectrum(xx,yy)
c22 = cube22.get_spectrum(xx,yy)
for c in (c12,c22):# c13,
c.plotter.autorefresh=False
if baseline:
c12.baseline(exclude=[-225,200],order=5)
c13.baseline(exclude=[-225,200],order=5)
c13.plotter(label='H$_{2}$$^{13}$CO 1-1',axis=pl.gca(),color='b',alpha=0.5)
#c12.plotter(axis=c13.plotter.axis,clear=False,color='k',label="H$_{2}$CO 1-1")
c12.plotter(axis=pl.gca(),color='k',label="H$_{2}$CO 1-1")
#(c13*6).plotter(label='6$\\times$H$_{2}$$^{13}$CO',axis=pl.gca(),color='r',clear=False)
c22.plotter(axis=c12.plotter.axis,clear=False,color='r',linewidth=2,alpha=0.8,
label='H$_2$CO 2-2')
if ratio:
r = c12.copy()
r.data = c22.data/c12.data
r.data[(r.data>1)] = np.nan
r.data[(r.data<1/13.)] = np.nan
r.plotter(axis=c12.plotter.axis,clear=False,color='r')
if dolegend:
pl.legend(loc='best')
pl.gca().set_xlim(*vrange)
pl.clf()
pl.subplots_adjust(wspace=0,hspace=0,left=0.05,right=0.95,bottom=0.05,top=0.95)
xl = xc - (nx/2.)*dx
yl = yc - (ny/2.)*dy
w = wcs.WCS(strip_headers.flatten_header(cube11.header))
for ii,(spy,spx) in enumerate(itertools.product(range(nx),range(ny))):
sp = pl.subplot(nx,ny,ii+1)
sp.zorder = nx-spx+ny*spy
some_plots(spx*dx+xl,(ny-spy-1)*dy+yl,dolegend=False)#,dolegend=(ii==24))
sp.set_ylim(*yrange)
if spx > 0:
sp.set_yticks([])
if spy < ny-1:
sp.set_xticks([])
(l,b), = w.wcs_pix2world([[spx*dx+xl,(ny-spy-1)*dy+yl]],0)
sp.annotate('%0.3f %+0.3f' % (l,b),(0.5,0.9),xycoords='axes fraction',fontsize=12,
horizontalalignment='center')
pl.draw()
reg_centers = {}
for freq,fn in files.items():
if freq not in fluxes:
fluxes[freq] = {}
peaks[freq] = {}
valleys[freq] = {}
cutouts[freq] = {}
mpfits[freq] = {}
gpars[freq] = {}
gparerrs[freq] = {}
gfits[freq] = {}
reg_centers[freq] = {}
data = fits.getdata(fn).squeeze()
header = flatten_header(fits.getheader(fn))
wcs = astropy.wcs.WCS(header).sub([astropy.wcs.WCSSUB_CELESTIAL])
beam = radio_beam.Beam.from_fits_header(header)
beams[freq] = beam
frequencies[freq] = header.get('CRVAL3') or header.get('ACRVAL3')
if wcs.wcs.radesys == 'FK4' or wcs.wcs.equinox == 1950:
(blx,bly),(tr_x,tr_y) = wcs.wcs_world2pix([(290.35090,14.42627),(290.34560,14.43126),],0)
else:
(blx,bly),(tr_x,tr_y) = wcs.wcs_world2pix([(290.92445,14.524376),(290.91912,14.529338)],0)
error[freq] = data[bly:tr_y,blx:tr_x].std()
obsdate[freq] = fits.getheader(fn)['DATE-OBS']
print("file: {0}".format(fn))
for reg in ProgressBar(reglist):
