def run_mctherm(self):
logger.info("Executing RADMC3D with mctherm mode")
if not os.path.isdir(self.radmc_dir):
msg = "radmc working directory not found: {self.radmc_dir}"
raise FileNotFoundError(msg)
tools.shell(
f"radmc3d mctherm setthreads {self.n_thread}",
cwd=self.radmc_dir,
error_keyword="ERROR",
log_prefix=" ",
)
cwd = os.getcwd()
os.chdir(self.radmc_dir)
self.rmcdata = rmca.readData(ispec=self.molname) # radmc3dData()
os.chdir(cwd)
def getGasAbundance(grid=None, ppar=None, ispec=''):
"""
Function to create the conversion factor from volume density to number density of molecule ispec.
The number density of a molecule is rhogas * abun
INPUT:
------
grid - An instance of the radmc3dGrid class containing the spatial and wavelength grid
ppar - Dictionary containing all parameters of the model
ispec - The name of the gas species whose abundance should be calculated
OUTPUT:
-------
returns the abundance as a Numpy array
"""
# Read the dust density and temperature
try:
data = readData(ddens=True, dtemp=True, binary=True)
except:
try:
data = readData(ddens=True, dtemp=True, binary=False)
except:
print 'WARNING!!'
print 'No data could be read in binary or in formatted ascii format'
print ' '
return 0
# Calculate continuum optical depth
data.getTau(axis='xy', wav=0.55)
nspec = len(ppar['gasspec_mol_name'])
if ppar['gasspec_mol_name'].__contains__(ispec):
sid = ppar['gasspec_mol_name'].index(ispec)
# Check where the radial and vertical optical depth is below unity
gasabun = np.zeros([grid.nx, grid.ny, grid.nz], dtype=np.float64)
for spec in range(nspec):
gasabun[:,:,:] = ppar['gasspec_mol_abun'][sid]
for iz in range(data.grid.nz):
for iy in range(data.grid.ny):
ii = (data.taux[:,iy,iz]<ppar['gasspec_mol_dissoc_taulim'][sid])
gasabun[ii,iy,iz] = 1e-90
ii = (data.dusttemp[:,iy,iz,0]<ppar['gasspec_mol_freezeout_temp'][sid])
gasabun[ii,iy,iz] = ppar['gasspec_mol_abun'][sid] * ppar['gasspec_mol_freezeout_dfact'][sid]
#for iz in range(data.grid.nz):
#for iy in range(data.grid.ny/2):
#ii = (data.tauy[:,iy,iz]<ppar['gasspec_mol_dissoc_taulim'][sid])
#gasabun[ii,iy,iz] = 1e-90
#gasabun[ii,data.grid.ny-1-iy,iz] = 1e-90
else:
gasabun = np.zeros([grid.nx, grid.ny, grid.nz], dtype=np.float64) + 1e-10
print 'WARNING !!!'
print 'Molecule name "'+ispec+'" is not found in gasspec_mol_name'
print 'A default 1e-10 abundance will be used'
print ' '
#gasabun = np.zeros([grid.nx, grid.ny, grid.nz], dtype=np.float64)
#gasabun[:,:,:] = ppar['gasspec_mol_abun'][0] / (2.4*mp)
return gasabun
def getGasAbundance(grid=None, ppar=None, ispec=''):
"""Calculates the molecular abundance.
The number density of a molecule is rhogas * abun
Parameters
----------
grid : radmc3dGrid
An instance of the radmc3dGrid class containing the spatial and wavelength grid
ppar : dictionary
Dictionary containing all parameters of the model
ispec : str
The name of the gas species whose abundance should be calculated
Returns
-------
Returns an ndarray containing the molecular abundance at each grid point
"""
# Read the dust density and temperature
try:
data = analyze.readData(ddens=True, dtemp=True, binary=True)
except:
try:
data = analyze.readData(ddens=True, dtemp=True, binary=False)
except:
print('WARNING!!')
print('No data could be read in binary or in formatted ascii format')
print(' ')
return 0
# Calculate continuum optical depth
data.getTau(axis='xy', wav=0.55)
nspec = len(ppar['gasspec_mol_name'])
if ppar['gasspec_mol_name'].__contains__(ispec):
sid = ppar['gasspec_mol_name'].index(ispec)
# Check where the radial and vertical optical depth is below unity
gasabun = np.zeros([grid.nx, grid.ny, grid.nz], dtype=np.float64)
for spec in range(nspec):
gasabun[:,:,:] = ppar['gasspec_mol_abun'][sid]
for iz in range(data.grid.nz):
for iy in range(data.grid.ny):
ii = (data.taux[:,iy,iz]<ppar['gasspec_mol_dissoc_taulim'][sid])
gasabun[ii,iy,iz] = 1e-90
ii = (data.dusttemp[:,iy,iz,0]<ppar['gasspec_mol_freezeout_temp'][sid])
gasabun[ii,iy,iz] = ppar['gasspec_mol_abun'][sid] * ppar['gasspec_mol_freezeout_dfact'][sid]
#for iz in range(data.grid.nz):
#for iy in range(data.grid.ny/2):
#ii = (data.tauy[:,iy,iz]<ppar['gasspec_mol_dissoc_taulim'][sid])
#gasabun[ii,iy,iz] = 1e-90
#gasabun[ii,data.grid.ny-1-iy,iz] = 1e-90
else:
gasabun = np.zeros([grid.nx, grid.ny, grid.nz], dtype=np.float64) + 1e-10
print('WARNING !!!')
print('Molecule name "'+ispec+'" is not found in gasspec_mol_name')
print('A default 1e-10 abundance will be used')
print(' ' )
#gasabun = np.zeros([grid.nx, grid.ny, grid.nz], dtype=np.float64)
#gasabun[:,:,:] = ppar['gasspec_mol_abun'][0] / (2.4*mp)
return gasabun
def commence(rundir, polunitlen=-2, dis=400, polmax=None,
dooutput_op=1, pltopwav=None,
dooutput_im=1, imlim=None, imUnits=None, imxlim=None, imylim=None,opltr=None,imaxisUnits='au',
dooutput_wavim=1, anglim=None, angcmap=None,
dooutput_xy=0, xyinx=None, tdxlim=None, tdylim=None,
dooutput_minor=0,
dooutput_stokes=0,
dooutput_conv=0, fwhm=None, pa=[[0]],
dooutput_alpha=0,
dooutput_fits=0, bwidthmhz=2000., coord='03h10m05s -10d05m30s',
dooutput_los=0, dokern=False,
dooutput_sed=0, pltsed=None,
dooutput_compreal=0
):
"""
dooutput_conv : bool
to output the convolved images
imlim : list
list of two elements for vmin, vmax of output_im image
imxlim : tuple
x coordinate limit for image in AU
imylim : tuple
y coordinate limit for image in AU
xyinx : tuple
2 element tuple to specify which image to plot xy.
In (a,b) where a is the index for inclination and b is for wavelength
tdxlim : tuple
tdylim : tuple
2 element tuple
fwhm : list
number of different resolutions by number of wavelengths in image.out
pa : list
number of different resolutions by number of wavelengths in image.out
pltsed : 2d array
[0,:] for wavelength
[1,:] for observed flux
opltr : list of floats
the radii that is desired for overplot in au
"""
if os.path.isdir(rundir) is False:
raise ValueError('rundir does not exist: %s'%rundir)
# stokes inputs for fits files
stokespref = ['I', 'Q', 'U', 'V']
# inclinations for image
fname = os.path.join(rundir, 'inp.imageinc')
imageinc = fntools.zylreadvec(fname)
ninc = len(imageinc)
# read parameter file
parobj = params.radmc3dPar()
parobj.readPar(fdir=rundir)
# opacity
op = dustopac.radmc3dDustOpac()
res = op.readMasterOpac(fdir=rundir)
op.readOpac(fdir=rundir, ext=res['ext'], scatmat=res['scatmat'], alignfact=res['align'][0])
fkabs = interpolate.interp1d(op.wav[0], op.kabs[0], kind='linear')
fksca = interpolate.interp1d(op.wav[0], op.ksca[0], kind='linear')
dinfo = op.readDustInfo()
if res['align'][0]:
kpara90 = op.kpara[0][0,-1]
korth90 = op.korth[0][0,-1]
else:
kpara90 = 1.
korth90 = 1.
# read dust density and temperature
dat = analyze.readData(fdir=rundir, binary=False, ddens=True, dtemp=True)
# see if heatsource.inp exists to read accretion luminosity
if os.path.isfile(os.path.join(rundir, 'heatsource.inp')):
qvisdat = analyze.readData(fdir=rundir, binary=False, qvis=True)
acclum = qvisdat.getHeatingLum()
else:
acclum = 0.
temp2d = np.squeeze(dat.dusttemp[:,:,0,0])
if dokern:
kern = np.array([[1,1,1,1,1],
[1,2,2,2,1],
[1,2,3,2,1],
[1,2,2,2,1],
[1,1,1,1,1]], dtype=np.float64)
kern = kern / sum(kern)
temp2d = convolve(temp2d, kern, boundary='fill', fill_value=0.)
rho2d = np.squeeze(dat.rhodust[:,:,0,0])
reg = rho2d < 1e-32
rho2d[reg] = 1e-32
rholog = np.log10(rho2d)
raxis = dat.grid.xi
rstg = dat.grid.x
ttaaxis = dat.grid.yi
ttastg = dat.grid.y
# output opacity
if dooutput_op:
pngname = rundir+'/out_opacity.png'
getOutput_op(op, res, dinfo, pngname, pltopwav=pltopwav)
# spectrum
if dooutput_sed and os.path.isfile('inp.spectruminc'):
star = star = radsources.radmc3dRadSources()
star.readStarsinp(fdir=rundir)
sedinc = fntools.zylreadvec('inp.spectruminc')
nsedinc = len(sedinc)
for ii in range(nsedinc):
fname = rundir + '/myspectrum.i%d.out'%(sedinc[ii])
spec = image.radmc3dImage()
spec.readSpectrum(fname=fname, dpc=dis)
pngname = rundir + '/out_sed.i%d.png'%(sedinc[ii])
getOutput_sed(spec, pngname, star=star, pltsed=pltsed)
# start iterating image related
for ii in range(ninc):
fname = os.path.join(rundir, 'myimage.i%d.out'%(imageinc[ii]))
im = image.readImage(fname=fname)
fname = os.path.join(rundir, 'mytausurf1.i%d.out'%(imageinc[ii]))
if os.path.isfile(fname):
tauim = image.readImage(fname=fname)
else:
tauim = None
fname = rundir + '/mytau3d.i%d.out'%(imageinc[ii])
if os.path.isfile(fname):
tau3d = image.readTauSurf3D(fname=fname)
else:
tau3d = None
fname = os.path.join(rundir,'myoptdepth.i%d.out'%(imageinc[ii]))
if os.path.isfile(fname):
optim = image.readImage(fname=fname)
else:
optim = None
camwavfile = os.path.join(rundir, 'camera_wavelength_micron.inp.image')
if os.path.isfile(camwavfile):
camwav = image.readCameraWavelength(fname=rundir+'/camera_wavelength_micron.inp.image')
else:
camwav = im.wav
ncamwav = len(camwav)
# line of sight properties along minor axis
if dooutput_minor or dooutput_los:
ylostemp = range(im.ny)
ylosdens = range(im.ny)
for iy in range(im.ny):
ym = im.y[iy]
lostemp = los.extract(imageinc[ii]*natconst.rad, ym, temp2d,
raxis, rstg,ttaaxis,ttastg,0.)
losdens = los.extract(imageinc[ii]*natconst.rad, ym, rholog,
raxis, rstg,ttaaxis,ttastg,-32.)
losdens['valcell'] = 10.**(losdens['valcell'])
losdens['valwall'] = 10.**(losdens['valwall'])
ylostemp[iy] = lostemp
ylosdens[iy] = losdens
# plot images through all wavelengths
if dooutput_wavim:
pngname = rundir + '/out_wavim.i%d.png'%imageinc[ii]
getOutput_wavim(im, dis, pngname, imTblim=imlim, imxlim=imxlim, imylim=imylim,
opltr=opltr, inc=imageinc[ii], anglim=anglim, angcmap=angcmap)
# plot spectral index through all wavelengths
if dooutput_alpha and (ncamwav > 0):
pngname = rundir + '/out_alpha.i%d.png'%(imageinc[ii])
getOutput_alpha(im, op, pngname, opltr=opltr, inc=imageinc[ii])
ifreq = 0
for ifreq in range(ncamwav):
kext = fkabs(camwav[ifreq]) + fksca(camwav[ifreq])
if dooutput_im:
pngname = rundir+'/out_im.i%d.f%d.png'%(imageinc[ii],camwav[ifreq])
getOutput_im(ifreq, dis, im, optim, tauim, polunitlen, fkabs, fksca,
pngname, polmax=polmax, imlim=None, imUnits='Tb',
xlim=imxlim, ylim=imylim,
opltr=opltr, inc=imageinc[ii], axisUnits=imaxisUnits)
if dooutput_xy:
if xyinx is not None:
if (xyinx[0] == ii) and (xyinx[1] == ifreq):
pngname = rundir+'/out_xy.i%d.f%d.png'%(imageinc[ii],camwav[ifreq])
getOutput_xy(ifreq, tau3d, dat, acclum, tdxlim, tdylim, pngname=pngname, parobj=parobj)
else:
pngname = rundir+'/out_xy.i%d.f%d.png'%(imageinc[ii],camwav[ifreq])
getOutput_xy(ifreq, tau3d, dat, acclum, tdxlim, tdylim, pngname=pngname, parobj=parobj)
if dooutput_minor:
pngname=rundir+'/out_minor.i%d.f%d.png'%(imageinc[ii],camwav[ifreq])
getOutput_minor(ifreq, im, optim, kpara90, korth90, kext,
ylostemp, ylosdens, pngname)
if dooutput_stokes:
pngname=rundir+'/out_stokes.i%d.f%d.png'%(imageinc[ii],camwav[ifreq])
getOutput_stokes(ifreq, dis, im, pngname, polmax=polmax)
if dooutput_los:
pngname = rundir + '/out_los.i%d.f%d.png'%(imageinc[ii],camwav[ifreq])
getOutput_los(camwav[ifreq], fkabs, fksca, losdens, lostemp, pngname)
if dooutput_fits:
# output to fits file
if im.stokes: #if stokes image
for isk in range(4):
fitsname = os.path.join(rundir, 'myimage.i%d.f%d.%s.fits'%(imageinc[ii], camwav[ifreq],stokespref[isk]) )
im.writeFits(fname=fitsname, dpc=dis, casa=True,
bandwidthmhz=bwidthmhz, coord=coord,
stokes=stokespref[isk], ifreq=ifreq, overwrite=True)
else:
fitsname = os.path.join(rundir, 'myimage.i%d.f%d.fits'%(imageinc[ii], camwav[ifreq]) )
im.writeFits(fname=fitsname, dpc=dis, casa=True,
bandwidthmhz=bwidthmhz, coord=coord,
stokes='I', ifreq=ifreq, overwrite=True)
if (dooutput_conv) & (fwhm is not None):
npa = len(pa)
for ipa in range(npa):
conv = im.imConv(dpc=dis, psfType='gauss', fwhm=fwhm[ipa], pa=pa[ipa])
# plot convolved image through all wavelengths
if dooutput_wavim:
pngname = rundir + '/out_wavim.i%d.b%d.png'%(imageinc[ii], ipa)
getOutput_wavim(conv, dis, pngname, imTblim=imlim,
imxlim=imxlim, imylim=imylim, opltr=opltr, inc=imageinc[ii],
anglim=anglim, angcmap=angcmap)
for ifreq in range(ncamwav):
if dooutput_im:
pngname = rundir+'/out_im.i%d.f%d.b%d.png'%(imageinc[ii],camwav[ifreq], ipa)
getOutput_im(ifreq, dis, conv, optim, tauim, polunitlen,
fkabs, fksca, pngname, polmax=polmax,
imlim=imlim, imUnits=imUnits,
xlim=imxlim, ylim=imylim, opltr=opltr, inc=imageinc[ii],
axisUnits=imaxisUnits)
# if dooutput_xy:
# pngname = rundir+'/out_xy.i%d.f%d.b%d.png'%(imageinc[ii],camwav[ifreq], ipa)
# getOutput_xy(ifreq, tauim, tau3d, dat, pngname)
if dooutput_minor:
pngname=rundir+'/out_minor.i%d.f%d.b%d.png'%(imageinc[ii],camwav[ifreq], ipa)
getOutput_minor(ifreq, conv, optim, kpara90, korth90, kext,
ylostemp, ylosdens, pngname)
if dooutput_stokes:
pngname=rundir+'/out_stokes.i%d.f%d.b%d.png'%(imageinc[ii],camwav[ifreq], ipa)
getOutput_stokes(ifreq, dis, conv, pngname, polmax=polmax)
if dooutput_fits:
if conv.stokes:
for isk in range(4):
fitsname = os.path.join(rundir, 'myimage.i%d.f%d.%s.b%d.fits'%(imageinc[ii], camwav[ifreq],stokespref[isk], ipa))
conv.writeFits(fname=fitsname, dpc=dis, casa=True,
bandwidthmhz=bwidthmhz, coord=coord,
stokes=stokespref[isk], ifreq=ifreq, overwrite=True)
else:
fitsname = os.path.join(rundir, 'myimage.i%d.f%d.b%d.fits'%(imageinc[ii], camwav[ifreq], ipa))
conv.writeFits(fname=fitsname, dpc=dis, casa=True,
bandwidthmhz=bwidthmhz, coord=coord,
stokes='I', ifreq=ifreq, overwrite=True)
# free up this memory
del conv
# free up the memory before going to next iteration
del im
del tauim
del tau3d
del optim