def model(visibilities, params, parameters, plot=False):
# Set the values of all of the parameters.
p = {}
for key in parameters:
if parameters[key]["fixed"]:
if parameters[key]["value"] in parameters.keys():
if parameters[parameters[key]["value"]]["fixed"]:
value = parameters[parameters[key]["value"]]["value"]
else:
value = params[parameters[key]["value"]]
else:
value = parameters[key]["value"]
else:
value = params[key]
if key[0:3] == "log":
p[key[3:]] = 10.**value
else:
p[key] = value
# Make sure alpha and beta are defined.
if p["disk_type"] == "exptaper":
t_rdisk = p["T0"] * (p["R_disk"] / 1.)**-p["q"]
p["h_0"] = ((k_b*(p["R_disk"]*AU)**3*t_rdisk) / (G*p["M_star"]*M_sun * \
2.37*m_p))**0.5 / AU
else:
p["h_0"] = ((k_b * AU**3 * p["T0"]) / (G*p["M_star"]*M_sun * \
2.37*m_p))**0.5 / AU
p["beta"] = 0.5 * (3 - p["q"])
p["alpha"] = p["gamma"] + p["beta"]
# Set up the dust.
dustopac = "pollack_new.hdf5"
dust_gen = dust.DustGenerator(dust.__path__[0] + "/data/" + dustopac)
ddust = dust_gen(p["a_max"] / 1e4, p["p"])
edust = dust_gen(1.0e-4, 3.5)
# Set up the gas.
gases = []
abundance = []
index = 1
while index > 0:
if "gas_file" + str(index) in p:
g = gas.Gas()
g.set_properties_from_lambda(gas.__path__[0]+"/data/"+\
p["gas_file"+str(index)])
gases.append(g)
abundance.append(p["abundance" + str(index)])
index += 1
else:
index = -1
# Make sure we are in a temp directory to not overwrite anything.
original_dir = os.environ["PWD"]
os.mkdir("/tmp/temp_{1:s}_{0:d}".format(comm.Get_rank(), source))
os.chdir("/tmp/temp_{1:s}_{0:d}".format(comm.Get_rank(), source))
# Write the parameters to a text file so it is easy to keep track of them.
f = open("params.txt", "w")
for key in p:
f.write("{0:s} = {1}\n".format(key, p[key]))
f.close()
# Set up the model.
m = modeling.YSOModel()
m.add_star(mass=p["M_star"],
luminosity=p["L_star"],
temperature=p["T_star"])
if p["envelope_type"] == "ulrich":
m.set_spherical_grid(p["R_in"], p["R_env"], 100, 51, 2, code="radmc3d")
else:
m.set_spherical_grid(p["R_in"], max(5*p["R_disk"],300), 100, 51, 2, \
code="radmc3d")
if p["disk_type"] == "exptaper":
m.add_pringle_disk(mass=p["M_disk"], rmin=p["R_in"], rmax=p["R_disk"], \
plrho=p["alpha"], h0=p["h_0"], plh=p["beta"], dust=ddust, \
t0=p["T0"], plt=p["q"], gas=gases, abundance=abundance,\
aturb=p["a_turb"])
else:
m.add_disk(mass=p["M_disk"], rmin=p["R_in"], rmax=p["R_disk"], \
plrho=p["alpha"], h0=p["h_0"], plh=p["beta"], dust=ddust, \
t0=p["T0"], plt=p["q"], gas=gases, abundance=abundance,\
aturb=p["a_turb"])
if p["envelope_type"] == "ulrich":
m.add_ulrich_envelope(mass=p["M_env"], rmin=p["R_in"], rmax=p["R_env"],\
cavpl=p["ksi"], cavrfact=p["f_cav"], dust=edust, \
t0=p["T0_env"], tpl=p["q_env"], gas=gases, abundance=abundance,\
aturb=p["a_turb_env"])
else:
pass
m.grid.set_wavelength_grid(0.1, 1.0e5, 500, log=True)
# Run the images/visibilities/SEDs.
for j in range(len(visibilities["file"])):
# Shift the wavelengths by the velocities.
b = p["v_sys"] * 1.0e5 / c
lam = c / visibilities["data"][j].freq / 1.0e-4
wave = lam * numpy.sqrt((1. - b) / (1. + b))
# Set the wavelengths for RADMC3D to use.
m.set_camera_wavelength(wave)
if p["docontsub"]:
m.run_image(name=visibilities["lam"][j], nphot=1e5, \
npix=visibilities["npix"][j], lam=None, \
pixelsize=visibilities["pixelsize"][j], tgas_eq_tdust=True,\
scattering_mode_max=0, incl_dust=True, incl_lines=True, \
loadlambda=True, incl=p["i"], pa=p["pa"], dpc=p["dpc"], \
code="radmc3d", verbose=False, writeimage_unformatted=True,\
setthreads=ncpus)
m.run_image(name="cont", nphot=1e5, \
npix=visibilities["npix"][j], lam=None, \
pixelsize=visibilities["pixelsize"][j], tgas_eq_tdust=True,\
scattering_mode_max=0, incl_dust=True, incl_lines=False, \
loadlambda=True, incl=p["i"], pa=p["pa"], dpc=p["dpc"], \
code="radmc3d", verbose=False, writeimage_unformatted=True,\
setthreads=ncpus)
m.images[visibilities["lam"][j]].image -= m.images["cont"].image
else:
m.run_image(name=visibilities["lam"][j], nphot=1e5, \
npix=visibilities["npix"][j], lam=None, \
pixelsize=visibilities["pixelsize"][j], tgas_eq_tdust=True,\
scattering_mode_max=0, incl_dust=False, incl_lines=True, \
loadlambda=True, incl=p["i"], pa=p["pa"], dpc=p["dpc"], \
code="radmc3d", verbose=False, writeimage_unformatted=True,\
setthreads=ncpus)
m.visibilities[visibilities["lam"][j]] = uv.interpolate_model(\
visibilities["data"][j].u, visibilities["data"][j].v, \
visibilities["data"][j].freq, \
m.images[visibilities["lam"][j]], dRA=-p["x0"], dDec=-p["y0"], \
nthreads=ncpus)
if plot:
lam = c / visibilities["image"][j].freq / 1.0e-4
wave = lam * numpy.sqrt((1. - b) / (1. + b))
m.set_camera_wavelength(wave)
if p["docontsub"]:
m.run_image(name=visibilities["lam"][j], nphot=1e5, \
npix=visibilities["image_npix"][j], lam=None, \
pixelsize=visibilities["image_pixelsize"][j], \
tgas_eq_tdust=True, scattering_mode_max=0, \
incl_dust=True, incl_lines=True, loadlambda=True, \
incl=p["i"], pa=-p["pa"], dpc=p["dpc"], code="radmc3d",\
verbose=False, setthreads=ncpus)
m.run_image(name="cont", nphot=1e5, \
npix=visibilities["image_npix"][j], lam=None, \
pixelsize=visibilities["image_pixelsize"][j], \
tgas_eq_tdust=True, scattering_mode_max=0, \
incl_dust=True, incl_lines=False, loadlambda=True, \
incl=p["i"], pa=-p["pa"], dpc=p["dpc"], code="radmc3d",\
verbose=False, setthreads=ncpus)
m.images[visibilities["lam"]
[j]].image -= m.images["cont"].image
else:
m.run_image(name=visibilities["lam"][j], nphot=1e5, \
npix=visibilities["image_npix"][j], lam=None, \
pixelsize=visibilities["image_pixelsize"][j], \
tgas_eq_tdust=True, scattering_mode_max=0, \
incl_dust=False, incl_lines=True, loadlambda=True, \
incl=p["i"], pa=-p["pa"], dpc=p["dpc"], code="radmc3d",\
verbose=False, setthreads=ncpus)
x, y = numpy.meshgrid(numpy.linspace(-256,255,512), \
numpy.linspace(-256,255,512))
beam = misc.gaussian2d(x, y, 0., 0., \
visibilities["image"][j].header["BMAJ"]/2.355/\
visibilities["image"][j].header["CDELT2"], \
visibilities["image"][j].header["BMIN"]/2.355/\
visibilities["image"][j].header["CDELT2"], \
(90-visibilities["image"][j].header["BPA"])*\
numpy.pi/180., 1.0)
for ind in range(len(wave)):
m.images[visibilities["lam"][j]].image[:,:,ind,0] = \
scipy.signal.fftconvolve(\
m.images[visibilities["lam"][j]].image[:,:,ind,0], \
beam, mode="same")
os.system("rm params.txt")
os.chdir(original_dir)
os.system("rmdir /tmp/temp_{1:s}_{0:d}".format(comm.Get_rank(), source))
return m
plt.ylim(-12,12)
plt.xlabel('$\Delta$ R.A. ["]', fontsize=14)
plt.ylabel('$\Delta$ Dec. ["]', fontsize=14)
plt.gca().tick_params(labelsize=14)
plt.show()
# Do the Fourier transform with TrIFT
u = numpy.linspace(100.,1.5e7,10000)
v = numpy.repeat(0.001,10000)
t1 = time.time()
vis = uv.interpolate_model(u, v, numpy.array([2.3e11]), m.images["image"], \
code="trift", dRA=0., dDec=0., nthreads=4)
t2 = time.time()
print(t2 - t1)
# Do a high resolution model to compare with.
m.run_visibilities(name="image", nphot=1e5, npix=1024, \
pixelsize=8*25./1024, lam="1000", phi=0, incl=0, code="radmc3d", \
dpc=100, verbose=False, nostar=True)
m.visibilities["image"] = uv.average(m.visibilities["image"], \
gridsize=1000000, binsize=1000, radial=True)
# Finally, plot the visibilities.
plt.loglog(u/1e3, vis.amp*1000, "k-", \
ax.yaxis.set_ticklabels([])
ax.tick_params(labelsize=14)
plt.show()
# Do the Fourier transform with TrIFT
ruv = numpy.linspace(100.,1.5e7,10000)
pa = 0.
u = ruv * numpy.cos(pa)
v = ruv * numpy.sin(pa)
t1 = time.time()
vis = uv.interpolate_model(u, v, m.images["image"].freq, m.images["image"], \
code="trift", dRA=0., dDec=0.)
t2 = time.time()
print(t2 - t1)
# Do a model with GALARIO to compare with.
npix = 1024
imsize = 25.
t1 = time.time()
m.run_image(name="galario", nphot=1e5, npix=npix, pixelsize=imsize/npix,\
lam=None, tgas_eq_tdust=True, scattering_mode_max=0, incl_dust=False, \
incl_lines=True, imolspec=1, iline=2, widthkms=10., linenlam=25, \
pa=0-180, incl=45, code="radmc3d", dpc=100, verbose=False, nostar=True)
t2 = time.time()
print(t2 - t1)
m.run_image(name="image", nphot=1e5, npix=25, pixelsize=1.0, lam=None, \
tgas_eq_tdust=True, scattering_mode_max=0, incl_dust=False, \
incl_lines=True, imolspec=1, iline=2, widthkms=10., linenlam=25, \
phi=0, incl=45, code="radmc3d", dpc=100, verbose=False, \
unstructured=True, camera_nrrefine=100, camera_refine_criterion=1, \
nostar=True)
# Do the Fourier transform with TrIFT
ruv = numpy.linspace(100., 1.5e7, 10000)
pa = 0.
u = ruv * numpy.cos(pa)
v = ruv * numpy.sin(pa)
vis = uv.interpolate_model(u, v, m.images["image"].freq, m.images["image"], \
code="trift", dRA=0., dDec=0.)
t2 = time.time()
# Calculate the effective resolution.
triang = tri.Triangulation(m.images["image"].x, m.images["image"].y)
trift_res = numpy.inf
for triangle in triang.triangles:
for i in range(3):
length = numpy.sqrt((m.images["image"].x[triangle[i]] - \
m.images["image"].x[triangle[i-1]])**2 + \
(m.images["image"].y[triangle[i]] - \
m.images["image"].y[triangle[i-1]])**2)
if length < trift_res:
trift_res = length
print(trift_res)