def export_to_fits(cli):
#
# Read in the model:
#
file = filename(cli, "plot")
file += ".rtout"
model = ModelOutput(file)
#
# Write fits file:
#
if(cli.mode == "images"):
los = [0 for i in range(3)]
los[0] = 'x'
los[1] = 'y'
los[2] = 'z'
for k in range(0, 3):
image = model.get_image(distance=1*pc, units='MJy/sr', inclination=0, component='total', group=k)
Nwavelength=image.val.shape[2]
for i in range(0, Nwavelength):
file = filename(cli, "fits")
file += "_wavelength=" + str(image.wav[i]) + "micron_los=" + los[k] + ".fits"
fits.writeto(file, image.val[:, :, i], clobber=True)
if(cli.verbose):
print(" The fits file was written to", file)
else:
print("ERROR: The specified mode", mode, "is not available. Use 'images' only.")
def plot_results(cli):
file = filename(cli, "plot")
file += ".rtout"
#
# Read in the model:
#
model = ModelOutput(file)
if(cli.mode == "images"):
#
# Extract the quantities
#
g = model.get_quantities()
#
# Get the wall positions:
#
ww = g.w_wall / pc
zw = g.z_wall / pc
pw = g.p_wall
grid_Nw = len(ww) - 1
grid_Nz = len(zw) - 1
grid_Np = len(pw) - 1
#
# Graphics:
#
fig = plt.figure()
los = [0 for i in range(3)]
los[0] = 'x'
los[1] = 'y'
los[2] = 'z'
#Imaxp = [0 for i in range(4)]
##Imaxp[0] = 1e-4
#Imaxp[1] = 1e-5
#Imaxp[2] = 1e-7
#Imaxp[3] = 1e-8
for k in range(0, 3):
if(cli.verbose):
print("Group: ", k)
image = model.get_image(distance=1*pc, units='MJy/sr', inclination=0, component='total', group=k)
source_emit = model.get_image(distance=1*pc, units='MJy/sr', inclination=0, component='source_emit', group=k)
dust_emit = model.get_image(distance=1*pc, units='MJy/sr', inclination=0, component='dust_emit' , group=k)
source_scat = model.get_image(distance=1*pc, units='MJy/sr', inclination=0, component='source_scat', group=k)
dust_scat = model.get_image(distance=1*pc, units='MJy/sr', inclination=0, component='dust_scat' , group=k)
if(cli.verbose):
print(" Data cube: ", image.val.shape)
print(" Wavelengths =", image.wav)
print(" Uncertainties =", image.unc)
image_Nx=image.val.shape[0]
image_Ny=image.val.shape[1]
Nwavelength=image.val.shape[2]
if(cli.verbose):
print(" Image Nx =", image_Nx)
print(" Image Ny =", image_Ny)
print(" Nwavelength =", Nwavelength)
for i in range(0, Nwavelength):
if(cli.verbose):
print(" Image #", i,":")
print(" Wavelength =", image.wav[i])
Imin = np.min(image.val[:, :, i])
Imax = np.max(image.val[:, :, i])
# TODO: compute the mean value as well and use this for specifying the maximum value/color?!
if(cli.verbose):
print(" Intensity min =", Imin)
print(" Intensity max =", Imax)
#Imax=Imaxp[i]
#ax = fig.add_subplot(2, 1, 2)
ax = fig.add_subplot(1, 1, 1)
if(image.wav[i] < 10.0):
ax.imshow(source_scat.val[:, :, i] + dust_scat.val[:, :, i], vmin=Imin, vmax=Imax/10, cmap=plt.cm.gist_heat, origin='lower')
else:
ax.imshow(image.val[:, :, i], vmin=Imin, vmax=Imax/10, cmap=plt.cm.gist_heat, origin='lower')
ax.set_xticks([0,100,200,300], minor=False)
ax.set_yticks([0,100,200,300], minor=False)
ax.set_xlabel('x (pixel)')
ax.set_ylabel('y (pixel)')
ax.set_title(str(image.wav[i]) + ' microns' + '\n' + los[k] + '-direction', y=0.88, x=0.5, color='white')
#ax = fig.add_subplot(2, 1, 1)
#ax.imshow([np.logspace(np.log10(Imin+1e-10),np.log10(Imax/10),100),np.logspace(np.log10(Imin+1e-10),np.log10(Imax/10),100)], vmin=Imin, vmax=Imax/10, cmap=plt.cm.gist_heat)
#ax.set_xticks(np.logspace(np.log10(Imin+1e-10),np.log10(Imax/10),1), minor=False)
##ax.set_xticks(np.linspace(np.log10(Imin+1e-10),np.log10(Imax/10),10), minor=False)
#ax.set_yticks([], minor=False)
#ax.set_xlabel('flux (MJy/sr)')
file = filename(cli, "plot")
file += "_wavelength=" + str(image.wav[i]) + "micron_los=" + los[k] + ".png"
fig.savefig(file, bbox_inches='tight')
if(cli.verbose):
print(" The image graphics was written to", file)
plt.clf()
elif(cli.mode == "sed"):
#
# Graphics:
#
fig = plt.figure()
z_center = [0 for i in range(3)]
z_center[0] = '2.5'
z_center[1] = '5.0'
z_center[2] = '7.5'
for k in range(0, 3):
if(cli.verbose):
print("Group: ", k)
sed = model.get_sed(distance=1*pc, inclination=0, aperture=-1, group=k)
ax = fig.add_subplot(1, 1, 1)
ax.loglog(sed.wav, sed.val)
ax.set_xlabel(r'$\lambda$ [$\mu$m]')
ax.set_ylabel(r'$\lambda F_\lambda$ [ergs/s/cm$^2$]')
ax.set_xlim(0.01, 2000.0)
#ax.set_ylim(2.e-16, 2.e-9)
file = filename(cli, "plot")
file += "_z=" + z_center[k] + ".png"
fig.savefig(file)
if(cli.verbose):
print(" The sed graphics was written to", file)
plt.clf()
else:
print("ERROR: The specified mode", mode, "is not available. Use 'images' or 'sed' only.")
# Resolution in pc:
cli.resolution = 100.0
# Photons per grid cell:
cli.photons_temperature = 1
cli.photons_raytracing = 1
cli.photons_imaging = 1
for cli.case in [1, 2]:
for cli.opticaldepth in [0.256434, 1.28217, 6.41084]:
for cli.mode in ["temperature", "images", "seds"]:
# Setup step:
setup_model(cli);
# Compute step:
file = filename(cli, cli.mode)
model = Model.read(file+".rtin")
model.write("temp.rtin")
model.run(filename=file+".rtout", logfile=file+".hyout", overwrite=True)
# Plotting step:
#TODO: if(cli.mode == "temperature"): visualize_setup + visualize_results => plot_setup
if(cli.mode != "temperature"):
plot_results(cli);
# Export to fits format:
if(cli.mode == "images"):
export_to_fits(cli);
def setup_model(cli):
lsun_TRUST = 3.839e33
#
# Hyperion setup:
#
model = Model()
if(cli.mode == "temperature"):
#
# Dust properties:
#
dust_properties = SphericalDust('dust_integrated_full_scattering.hdf5')
#
# Write dust properties:
#
dust_properties.write('dust_properties.hdf5')
dust_properties.plot('dust_properties.png')
#
# Specify galaxy setup:
#
hR = 4000.0*pc # [cm]
Rmax = 5.0*hR # [cm]
hz_oldstars = 350.0*pc # [cm]
hz_youngstars = 200.0*pc # [cm]
hz_dust = 200.0*pc # [cm]
zmax_oldstars = 5.0*hz_oldstars # [cm]
zmax_youngstars = 5.0*hz_youngstars # [cm]
zmax_dust = 5.0*hz_dust # [cm]
zmax = zmax_oldstars # [cm]
reff = 1600.0*pc # [cm]
n = 3.0
q = 0.6
bn = 2.0*n - 1.0/3.0 + 4.0/405.0/n + 46.0/25515.0/n/n + 131.0/1148175.0/n/n/n
temperature_oldstars = 3500.0 # [K]
temperature_youngstars = 10000.0 # [K]
temperature_bulge = 3500.0 # [K]
luminosity_oldstars = 4.0e+10*lsun_TRUST # [ergs/s]
luminosity_youngstars = 1.0e+10*lsun_TRUST # [ergs/s]
luminosity_bulge = 3.0e+10*lsun_TRUST # [ergs/s]
w_oldstars = 0.25
w_youngstars = 0.75
w_dust = 0.75
phi0_oldstars = 0.0
phi0_youngstars = 20.0 * pi/180.0
phi0_dust = 20.0 * pi/180.0
modes = 2
pitchangle = 20.0 * pi/180.0
#
# Grid setup:
#
grid_wmin = 0.0
grid_wmax = Rmax
grid_zmin = -zmax
grid_zmax = +zmax
grid_pmin = 0.0
grid_pmax = 2.0*pi
grid_dx = cli.resolution*pc
grid_dw = grid_dx # uniform resolution
grid_dz = grid_dx # uniform resolution
grid_dp = grid_dx # resolution at characteristic radial disk spatial scale hR = 4000.0 pc
grid_Nw = int((grid_wmax - grid_wmin) / grid_dw) + 1
grid_Nz = int((grid_zmax - grid_zmin) / grid_dz) + 1
if(cli.case == 1):
grid_Np = 1
if(cli.case == 2):
grid_Np = int((grid_pmax - grid_pmin) * hR / grid_dp)
if(cli.verbose):
print("Grid setup:")
print(" Grid resolution =",cli.resolution, "pc.")
print(" grid_Nw =",grid_Nw)
print(" grid_Nz =",grid_Nz)
print(" grid_Np =",grid_Np)
#grid_w = np.logspace(np.log10(grid_wmin), np.log10(grid_wmax), grid_Nw)
#grid_w = np.hstack([0., grid_w]) # add innermost cell interface at w=0
grid_w = np.linspace(grid_wmin, grid_wmax, grid_Nw+1)
grid_z = np.linspace(grid_zmin, grid_zmax, grid_Nz+1)
grid_p = np.linspace(grid_pmin, grid_pmax, grid_Np+1)
model.set_cylindrical_polar_grid(grid_w, grid_z, grid_p)
#
# Dust density and sources setup:
#
rho_oldstars = np.zeros(model.grid.shape)
rho_youngstars = np.zeros(model.grid.shape)
rho_bulge = np.zeros(model.grid.shape)
rho_dust = np.zeros(model.grid.shape)
for k in range(0, grid_Np):
for j in range(0, grid_Nz):
for i in range(0, grid_Nw):
R = model.grid.gw[k,j,i]
z = model.grid.gz[k,j,i]
m = math.sqrt(R*R + z*z/q/q)
rho_dust[k,j,i] = math.exp(- R/hR -abs(z)/hz_dust )
rho_oldstars[k,j,i] = math.exp(- R/hR -abs(z)/hz_oldstars )
rho_youngstars[k,j,i] = math.exp(- R/hR -abs(z)/hz_youngstars)
rho_bulge[k,j,i] = math.pow(m/reff, 0.5/n - 1.0) * math.exp(- bn * math.pow(m/reff, 1.0/n))
if(cli.case == 2):
phi = model.grid.gp[k,j,i]
perturb = math.sin(modes * (math.log(R/hR) / math.tan(pitchangle) - (phi - phi0_dust)))
rho_dust[k,j,i] *= (1.0 + w_dust * perturb)
perturb = math.sin(modes * (math.log(R/hR) / math.tan(pitchangle) - (phi - phi0_oldstars)))
rho_oldstars[k,j,i] *= (1.0 + w_oldstars * perturb)
perturb = math.sin(modes * (math.log(R/hR) / math.tan(pitchangle) - (phi - phi0_youngstars)))
rho_youngstars[k,j,i] *= (1.0 + w_youngstars * perturb)
rho_dust[model.grid.gw > grid_wmax] = 0
rho_dust[model.grid.gz < grid_zmin] = 0
rho_dust[model.grid.gz > grid_zmax] = 0
kappa_ref = dust_properties.optical_properties.interp_chi_wav(0.55693)
rho0 = cli.opticaldepth / (2.0 * hz_dust * kappa_ref)
rho_dust[:] *= rho0
model.add_density_grid(rho_dust, 'dust_properties.hdf5')
source_oldstars = model.add_map_source()
source_oldstars.luminosity = luminosity_oldstars
source_oldstars.temperature = temperature_oldstars
source_oldstars.map = rho_oldstars
source_youngstars = model.add_map_source()
source_youngstars.luminosity = luminosity_youngstars
source_youngstars.temperature = temperature_youngstars
source_youngstars.map = rho_youngstars
source_bulge = model.add_map_source()
source_bulge.luminosity = luminosity_bulge
source_bulge.temperature = temperature_bulge
source_bulge.map = rho_bulge
#
# Check face-on optical depth at 1.0 micron (per gram dust) through the dust disk:
#
tau = 0
k = 0
i = 0
for j in range(0, grid_Nz):
#print(model.grid.gz[k,j,i]/pc, rho_dust[k,j,i])
dz = model.grid.widths[1,k,j,i]
dtau = dz * rho_dust[k,j,i] * kappa_ref
tau += dtau
deviation = 100.0 * abs(cli.opticaldepth - tau) / cli.opticaldepth
if(cli.verbose):
print("Check optical depth of dust density setup:")
print(" kappa(0.55693 micron) = ", kappa_ref, "cm^2 g^-1")
print(" Numerical integration of the face-on optical depth at 0.55693 micron through the central dust disk yields tau = ", tau)
print(" This corresponds to a deviation to the chosen setup value of", deviation, "percent")
#
# Check central dust density:
#
rho_max = np.max(rho_dust)
if(cli.opticaldepth < 1.0):
rho_setup = 1.04366e-4 * msun/pc/pc/pc
if(cli.opticaldepth < 3.0):
rho_setup = 5.21829e-4 * msun/pc/pc/pc
else:
rho_setup = 2.60915e-3 * msun/pc/pc/pc
deviation = 100.0 * abs(rho_setup - rho_max) / rho_setup
if(cli.verbose):
print("Check value of central dust density:")
print(" rho_max = ", rho_max, "g cm^-3")
print(" This corresponds to a deviation to the chosen setup value of", deviation, "percent")
#
# To compute total photon numbers:
#
grid_N = grid_Nw * grid_Nz * grid_Np
if(cli.verbose):
print("Radiation setup:")
print(" photons_temperature / cell =", cli.photons_temperature)
print(" photons_temperature total =", grid_N * cli.photons_temperature)
file = filename(cli, "temperature")
file += ".rtin"
else:
file = filename(cli, "temperature")
file += ".rtout"
try:
with open(file):
if(cli.verbose):
print("Using the specific energy distribution from file", file)
model.use_geometry(file)
model.use_quantities(file, only_initial=False, copy=False)
model.use_sources(file)
except IOError:
print("ERROR: File '", file, "' cannot be found. \nERROR: This file, containing the specific energy density, has to be computed first via calling hyperion.")
exit(2)
#
# To compute total photon numbers:
#
grid_Nw = len(model.grid.gw[0,0,:])
grid_Nz = len(model.grid.gw[0,:,0])
grid_Np = len(model.grid.gw[:,0,0])
grid_N = grid_Nw * grid_Nz * grid_Np
if(cli.verbose):
print("Grid setup:")
print(" grid_Nw =",grid_Nw)
print(" grid_Nz =",grid_Nz)
print(" grid_Np =",grid_Np)
print("Radiation setup:")
print(" photons_temperature / cell =", cli.photons_temperature)
print(" photons_temperature total =", grid_N * cli.photons_temperature)
print(" photons_raytracing / cell =", cli.photons_raytracing)
print(" photons_raytracing total =", grid_N * cli.photons_raytracing)
print(" photons_imaging / cell =", cli.photons_imaging)
print(" photons_imaging total =", grid_N * cli.photons_imaging)
file = filename(cli, "")
file += ".rtin"
##
## Temperature, Images, and SEDs:
##
if(cli.mode == "temperature"):
model.set_raytracing(True)
model.set_n_photons(
initial = grid_N * cli.photons_temperature,
raytracing_sources = grid_N * cli.photons_raytracing,
raytracing_dust = grid_N * cli.photons_raytracing,
imaging = grid_N * cli.photons_imaging
)
elif(cli.mode == "images"):
model.set_n_initial_iterations(0)
model.set_raytracing(True)
# old setup: model.set_monochromatic(True, wavelengths=[0.4, 1.0, 10.0, 100.0, 500.0])
model.set_monochromatic(True, wavelengths=[0.45483, 1.2520, 26.114, 242.29])
model.set_n_photons(
raytracing_sources = grid_N * cli.photons_raytracing,
raytracing_dust = grid_N * cli.photons_raytracing,
imaging_sources = grid_N * cli.photons_imaging,
imaging_dust = grid_N * cli.photons_imaging
)
# group = 0
image1 = model.add_peeled_images(sed=False, image=True)
image1.set_image_size(501, 501)
image1.set_image_limits(-12500.0*pc, +12500.0*pc, -12500.0*pc, +12500.0*pc)
image1.set_viewing_angles([30],[0])
image1.set_uncertainties(True)
image1.set_output_bytes(8)
image1.set_track_origin('basic')
# group = 1
image2 = model.add_peeled_images(sed=False, image=True)
image2.set_image_size(501, 501)
image2.set_image_limits(-12500.0*pc, +12500.0*pc, -12500.0*pc, +12500.0*pc)
image2.set_viewing_angles([80],[90])
image2.set_uncertainties(True)
image2.set_output_bytes(8)
image2.set_track_origin('basic')
# group = 2
image3 = model.add_peeled_images(sed=False, image=True)
image3.set_image_size(501, 501)
image3.set_image_limits(-12500.0*pc, +12500.0*pc, -12500.0*pc, +12500.0*pc)
image3.set_viewing_angles([88],[0]) # mostly edge-on
image3.set_uncertainties(True)
image3.set_output_bytes(8)
image3.set_track_origin('basic')
elif(cli.mode == "seds"):
model.set_n_initial_iterations(0)
model.set_raytracing(True)
model.set_n_photons(
raytracing_sources = grid_N * cli.photons_raytracing,
raytracing_dust = grid_N * cli.photons_raytracing,
imaging = grid_N * cli.photons_imaging
)
# group = 0
sed1 = model.add_peeled_images(sed=True, image=False)
sed1.set_wavelength_range(47, 0.081333, 1106.56)
sed1.set_viewing_angles([30],[0])
sed1.set_peeloff_origin((0, 0, 0))
sed1.set_aperture_range(1, 25000.0*pc, 25000.0*pc)
sed1.set_uncertainties(True)
sed1.set_output_bytes(8)
sed1.set_track_origin('basic')
# group = 1
sed2 = model.add_peeled_images(sed=True, image=False)
sed2.set_wavelength_range(47, 0.081333, 1106.56)
sed2.set_viewing_angles([80],[0])
sed2.set_peeloff_origin((0, 0, 0))
sed2.set_aperture_range(1, 25000.0*pc, 25000.0*pc)
sed2.set_uncertainties(True)
sed2.set_output_bytes(8)
sed2.set_track_origin('basic')
# group = 2
sed3 = model.add_peeled_images(sed=True, image=False)
sed3.set_wavelength_range(47, 0.081333, 1106.56)
sed3.set_viewing_angles([88],[0])
sed3.set_peeloff_origin((0, 0, 0))
sed3.set_aperture_range(1, 25000.0*pc, 25000.0*pc)
sed3.set_uncertainties(True)
sed3.set_output_bytes(8)
sed3.set_track_origin('basic')
##
## Write model for hyperion runs:
##
model.conf.output.output_density = 'last'
model.conf.output.output_specific_energy = 'last'
model.conf.output.output_n_photons = 'last'
model.write(file)
if(cli.verbose):
print("The input file for hyperion was written to", file)
def setup_model(cli):
#
# Hyperion setup:
#
model = Model()
if(cli.mode == "temperature"):
#
# Dust properties:
#
dust_properties = SphericalDust('dust_integrated_full_scattering.hdf5')
#
# Write dust properties:
#
dust_properties.write('dust_properties.hdf5')
dust_properties.plot('dust_properties.png')
#
# Grid setup:
#
grid_wmin = 0
grid_wmax = 5.0*pc # 4.0*pc
grid_zmin = 0.0*pc
grid_zmax = 10.0*pc
grid_pmin = 0
grid_pmax = 2*pi
grid_dx = cli.resolution*pc
grid_dw = grid_dx # uniform resolution
grid_dz = grid_dx # uniform resolution
grid_dp = grid_dx # resolution at filament location at r = 1 pc
grid_Nw = int((grid_wmax - grid_wmin) / grid_dw)
grid_Nz = int((grid_zmax - grid_zmin) / grid_dz)
grid_Np = int(2*pi * 1.0*pc / grid_dp)
if(cli.verbose):
print("Grid setup:")
print(" Grid resolution =",cli.resolution, "pc.")
print(" grid_Nw =",grid_Nw)
print(" grid_Nz =",grid_Nz)
print(" grid_Np =",grid_Np)
#grid_w = np.logspace(np.log10(grid_wmin), np.log10(grid_wmax), grid_Nw)
#grid_w = np.hstack([0., grid_w]) # add innermost cell interface at w=0
grid_w = np.linspace(grid_wmin, grid_wmax, grid_Nw+1)
grid_z = np.linspace(grid_zmin, grid_zmax, grid_Nz+1)
grid_p = np.linspace(grid_pmin, grid_pmax, grid_Np+1)
model.set_cylindrical_polar_grid(grid_w, grid_z, grid_p)
#
# Dust density setup:
#
RC = 0.1*pc
nC = 6.6580e+03 # in cm^-3
nC *= cli.opticaldepth # the optical depth at 1 micron
nC *= m_h # in g cm^-3
nC /= 100.0 # converts from gas to dust density
rho = np.zeros(model.grid.shape)
#
# n(r) = nC / [ 1.0 + (r/RC)**2.0 ]
# x = -sin(2.0×pi×t) pc, y = +cos(2.0×pi×t) pc, z = 10.0×t pc, t = [0.0, 1.0]
# => t = m.grid.gz / (10*pc)
# => phi(t) = mod(360*t+270, 360)
#
for k in range(0, grid_Np):
for j in range(0, grid_Nz):
for i in range(0, grid_Nw):
t = model.grid.gz[k,j,i] / (10*pc)
if(cli.filament == "linear"):
filament_center_x = 0
filament_center_y = 0
elif(cli.filament == "spiraling"):
filament_center_x = - math.sin(2*pi*t)*pc
filament_center_y = + math.cos(2*pi*t)*pc
spherical_grid_r = model.grid.gw[k,j,i]
spherical_grid_phi = model.grid.gp[k,j,i]
cartesian_grid_x = spherical_grid_r * math.cos(spherical_grid_phi)
cartesian_grid_y = spherical_grid_r * math.sin(spherical_grid_phi)
rsquared = (
(cartesian_grid_x - filament_center_x)**2
+
(cartesian_grid_y - filament_center_y)**2
)
rho[k,j,i] = nC / (1.0 + (rsquared / (RC*RC)))
if rsquared**0.5 > 3*pc:
rho[k,j,i] = 0
rho[model.grid.gw > grid_wmax] = 0
rho[model.grid.gz < grid_zmin] = 0
rho[model.grid.gz > grid_zmax] = 0
model.add_density_grid(rho, 'dust_properties.hdf5')
#
# Check optical depth through the filament:
#
# (y,z = 0, 2.5 pc goes through the filament center in all setups)
#
# Determine index of closest grid cell to z = 2.5 pc:
#
dz_last = 2*abs(grid_zmax-grid_zmin)
for j in range(0, grid_Nz):
dz = abs(model.grid.gz[0,j,0] - 2.5*pc)
if(dz > dz_last):
j=j-1
break
else:
dz_last = dz
#
# Opacity at 1.0 micron (per gram dust):
#
chi = dust_properties.optical_properties.interp_chi_wav(1.0)
tau_max = 0
for k in range(0, grid_Np):
tau = 0
for i in range(0, grid_Nw):
dr = model.grid.widths[0,k,j,i]
dtau = dr * rho[k,j,i] * chi
tau += dtau
tau_max = max(tau_max, tau)
if(cli.filament == "linear"):
tau_max *= 2
dev = 100 * abs(cli.opticaldepth - tau_max) / cli.opticaldepth
if(cli.verbose):
print("Check:")
print(" Numerical integration of the optical depth through the filament center yields tau = ", tau_max)
print(" This corresponds to a deviation to the chosen setup value of", dev, "percent")
#
# Source:
#
if(cli.sources == "external"):
nu, jnu = np.loadtxt('bg_intensity_modified.txt', unpack=True)
source_R = 5*pc
source = model.add_external_spherical_source()
source.peeloff = False
source.position = (0, 0, 5.0*pc) # in a Cartesian frame
source.radius = source_R
source.spectrum = (nu, jnu)
#source_MeanIntensity_J = <integrate bg_intensity.txt>
#source_Area = 4.0 * pi * source_R*source_R
source.luminosity = 8237.0*lsun #source_Area * pi * source_MeanIntensity_J
elif(cli.sources == "stellar"):
source = model.add_point_source()
source.luminosity = 3.839e35 # in ergs s^-1
source.temperature = 10000.0 # in K
if(cli.filament == "linear"):
source.position = (3.0*pc, 0, 5.0*pc)
elif(cli.filament == "spiraling"):
source.position = (0 , 0, 3.0*pc)
#
# To compute total photon numbers:
#
grid_N = grid_Nw * grid_Nz * grid_Np
if(cli.verbose):
print("Radiation setup:")
print(" photons_temperature / cell =", cli.photons_temperature)
print(" photons_temperature total =", grid_N * cli.photons_temperature)
file = filename(cli, "temperature")
file += ".rtin"
else:
file = filename(cli, "temperature")
file += ".rtout"
try:
with open(file):
if(cli.verbose):
print("Using the specific energy distribution from file", file)
model.use_geometry(file)
model.use_quantities(file, only_initial=False, copy=False)
model.use_sources(file)
except IOError:
print("ERROR: File '", file, "' cannot be found. \nERROR: This file, containing the specific energy density, has to be computed first via calling hyperion.")
exit(2)
#
# To compute total photon numbers:
#
grid_Nw = len(model.grid.gw[0,0,:])
grid_Nz = len(model.grid.gw[0,:,0])
grid_Np = len(model.grid.gw[:,0,0])
grid_N = grid_Nw * grid_Nz * grid_Np
if(cli.verbose):
print("Grid setup:")
print(" grid_Nw =",grid_Nw)
print(" grid_Nz =",grid_Nz)
print(" grid_Np =",grid_Np)
print("Radiation setup:")
print(" photons_temperature / cell =", cli.photons_temperature)
print(" photons_temperature total =", grid_N * cli.photons_temperature)
print(" photons_raytracing / cell =", cli.photons_raytracing)
print(" photons_raytracing total =", grid_N * cli.photons_raytracing)
print(" photons_imaging / cell =", cli.photons_imaging)
print(" photons_imaging total =", grid_N * cli.photons_imaging)
file = filename(cli, "")
file += ".rtin"
##
## Temperature, Images, and SEDs:
##
if(cli.mode == "temperature"):
model.set_raytracing(True)
model.set_n_photons(
initial = grid_N * cli.photons_temperature,
raytracing_sources = grid_N * cli.photons_raytracing,
raytracing_dust = grid_N * cli.photons_raytracing,
imaging = grid_N * cli.photons_imaging
)
elif(cli.mode == "images"):
model.set_n_initial_iterations(0)
model.set_raytracing(True)
model.set_monochromatic(True, wavelengths=[100.0, 500.0, 0.55, 2.2])
model.set_n_photons(
raytracing_sources = grid_N * cli.photons_raytracing,
raytracing_dust = grid_N * cli.photons_raytracing,
imaging_sources = grid_N * cli.photons_imaging,
imaging_dust = grid_N * cli.photons_imaging
)
# group = 0
image1x = model.add_peeled_images(sed=False, image=True)
image1x.set_image_size(300, 300)
image1x.set_image_limits(-5*pc, +5*pc, 0, 10*pc)
image1x.set_viewing_angles([90],[0]) # along the x-direction
image1x.set_uncertainties(True)
image1x.set_output_bytes(8)
image1x.set_track_origin('basic')
# group = 1
image1y = model.add_peeled_images(sed=False, image=True)
image1y.set_image_size(300, 300)
image1y.set_image_limits(-5*pc, +5*pc, 0, 10*pc)
image1y.set_viewing_angles([90],[90]) # along the y-direction
image1y.set_uncertainties(True)
image1y.set_output_bytes(8)
image1y.set_track_origin('basic')
# group = 2
image1z = model.add_peeled_images(sed=False, image=True)
image1z.set_image_size(300, 300)
image1z.set_image_limits(-5*pc, +5*pc, -5*pc, +5*pc)
image1z.set_viewing_angles([0],[0]) # along the z-direction
image1z.set_uncertainties(True)
image1z.set_output_bytes(8)
image1z.set_track_origin('basic')
elif(cli.mode == "sed"):
model.set_n_initial_iterations(0)
model.set_raytracing(True)
model.set_n_photons(
raytracing_sources = grid_N * cli.photons_raytracing,
raytracing_dust = grid_N * cli.photons_raytracing,
imaging = grid_N * cli.photons_imaging
)
# group = 0
sed1 = model.add_peeled_images(sed=True, image=False)
sed1.set_wavelength_range(250, 0.01, 2000.0)
sed1.set_viewing_angles([90],[0]) # along the x-direction
sed1.set_peeloff_origin((0, 0, 2.5*pc))
sed1.set_aperture_range(1, 0.3*pc, 0.3*pc)
sed1.set_uncertainties(True)
sed1.set_output_bytes(8)
sed1.set_track_origin('basic')
# group = 1
sed2 = model.add_peeled_images(sed=True, image=False)
sed2.set_wavelength_range(250, 0.01, 2000.0)
sed2.set_viewing_angles([90],[0]) # along the x-direction
sed2.set_peeloff_origin((0, 0, 5.0*pc))
sed2.set_aperture_range(1, 0.3*pc, 0.3*pc)
sed2.set_uncertainties(True)
sed2.set_output_bytes(8)
sed2.set_track_origin('basic')
# group = 2
sed3 = model.add_peeled_images(sed=True, image=False)
sed3.set_wavelength_range(250, 0.01, 2000.0)
sed3.set_viewing_angles([90],[0]) # along the x-direction
sed3.set_peeloff_origin((0, 0, 7.5*pc))
sed3.set_aperture_range(1, 0.3*pc, 0.3*pc)
sed3.set_uncertainties(True)
sed3.set_output_bytes(8)
sed3.set_track_origin('basic')
##
## Write model for hyperion runs:
##
model.conf.output.output_density = 'last'
model.conf.output.output_specific_energy = 'last'
model.conf.output.output_n_photons = 'last'
model.write(file)
if(cli.verbose):
print("The input file for hyperion was written to", file)
def plot_results(cli):
file = filename(cli, "plot")
file += ".rtout"
#
# Read in the model:
#
model = ModelOutput(file)
los = [0 for k in range(3)]
los[0] = '30degree'
los[1] = '80degree'
los[2] = '88degree'
if(cli.mode == "images"):
#
# Extract the quantities
#
g = model.get_quantities()
#
# Get the wall positions:
#
ww = g.w_wall / pc
zw = g.z_wall / pc
pw = g.p_wall
grid_Nw = len(ww) - 1
grid_Nz = len(zw) - 1
grid_Np = len(pw) - 1
#
# Graphics:
#
fig = plt.figure()
Imaxp = [0 for i in range(5)]
Imaxp[0] = 1e-15 # in W/cm^2
Imaxp[1] = 1e-14 # in W/cm^2
Imaxp[2] = 1e-15 # in W/cm^2
Imaxp[3] = 1e-15 # in W/cm^2
Imaxp[4] = 1e-18 # in W/cm^2
for k in range(0, 3):
if(cli.verbose):
print("Group: ", k)
image = model.get_image(distance=1e+7*pc, units='ergs/cm^2/s', inclination=0, component='total', group=k)
#source_emit = model.get_image(distance=1e+7*pc, units='MJy/sr', inclination=0, component='source_emit', group=k)
#dust_emit = model.get_image(distance=1e+7*pc, units='MJy/sr', inclination=0, component='dust_emit' , group=k)
#source_scat = model.get_image(distance=1e+7*pc, units='MJy/sr', inclination=0, component='source_scat', group=k)
#dust_scat = model.get_image(distance=1e+7*pc, units='MJy/sr', inclination=0, component='dust_scat' , group=k)
if(cli.verbose):
print(" Data cube: ", image.val.shape)
print(" Wavelengths =", image.wav)
print(" Uncertainties =", image.unc)
image_Nx=image.val.shape[0]
image_Ny=image.val.shape[1]
Nwavelength=image.val.shape[2]
if(cli.verbose):
print(" Image Nx =", image_Nx)
print(" Image Ny =", image_Ny)
print(" Nwavelength =", Nwavelength)
for i in range(0, Nwavelength):
if(cli.verbose):
print(" Image #", i,":")
print(" Wavelength =", image.wav[i])
image.val[:, :, i] *= 1e-4 # in W/m^2
#Imin = np.min(image.val[:, :, i])
#Imax = np.max(image.val[:, :, i])
#Imax = Imaxp[i]
#Imin = Imax/1e+20
Imax = np.max(image.val[:, :, i])/5
Imin = 0.0
if(cli.verbose):
print(" Intensity min data values =", np.min(image.val[:, :, i]))
print(" Intensity max data values =", np.max(image.val[:, :, i]))
print(" Intensity min color-table =", Imin)
print(" Intensity max color-table =", Imax)
#ax = fig.add_subplot(2, 1, 2)
ax = fig.add_subplot(1, 1, 1)
# 'hot', see http://wiki.scipy.org/Cookbook/Matplotlib/Show_colormaps
ax.imshow(image.val[:, :, i], vmin=Imin, vmax=Imax, cmap=plt.cm.hot, origin='lower')
ax.set_xticks([0,100,200,300,400,500], minor=False)
ax.set_yticks([0,100,200,300,400,500], minor=False)
ax.set_xlabel('x (pixel)')
ax.set_ylabel('y (pixel)')
ax.set_title(str(image.wav[i]) + ' microns' + '\n' + los[k], y=0.88, x=0.5, color='white')
#ax = fig.add_subplot(2, 1, 1)
#ax.imshow([np.logspace(np.log10(Imin+1e-10),np.log10(Imax/10),100),np.logspace(np.log10(Imin+1e-10),np.log10(Imax/10),100)], vmin=Imin, vmax=Imax/10, cmap=plt.cm.gist_heat)
#ax.set_xticks(np.logspace(np.log10(Imin+1e-10),np.log10(Imax/10),1), minor=False)
##ax.set_xticks(np.linspace(np.log10(Imin+1e-10),np.log10(Imax/10),10), minor=False)
#ax.set_yticks([], minor=False)
#ax.set_xlabel('flux (MJy/sr)')
#x = plt.colorbar()
#print(x)
file = filename(cli, "plot")
file += "_wavelength=" + str(image.wav[i]) + "micron_los=" + los[k] + ".png"
fig.savefig(file, bbox_inches='tight')
if(cli.verbose):
print(" The image graphics was written to", file)
plt.clf()
elif(cli.mode == "seds"):
#
# Graphics:
#
fig = plt.figure()
for k in range(0, 3):
if(cli.verbose):
print("Group: ", k)
sed = model.get_sed(distance=1e+7*pc, inclination=0, aperture=-1, group=k)
#units='ergs/cm^2/s' # = default, if distance is specified
ax = fig.add_subplot(1, 1, 1)
ax.loglog(sed.wav, sed.val)
ax.set_xlabel(r'$\lambda$ [$\mu$m]')
ax.set_ylabel(r'$\lambda F_\lambda$ [ergs/s/cm$^2$]')
ax.set_xlim(0.09, 1000.0)
ax.set_ylim(1.e-13, 1.e-7)
file = filename(cli, "plot")
file += "_los=" + los[k] + ".png"
fig.savefig(file)
if(cli.verbose):
print(" The sed graphics was written to", file)
plt.clf()
#
# Data files:
#
for k in range(0, 3):
sed = model.get_sed(distance=1e+7*pc, inclination=0, aperture=-1, group=k)
file = filename(cli, "plot")
file += "_los=" + los[k] + ".dat"
sedtable = open(file, 'w')
sedtable.write("# wavelength [micron] - flux [erg cm^-2 s^-1]\n")
for lp in range(0, len(sed.wav)):
l = len(sed.wav)-lp-1
line = str("%.4e" % sed.wav[l]) + " " + str("%.4e" % sed.val[l]) + "\n"
sedtable.write(line)
sedtable.close()
else:
print("ERROR: The specified mode", mode, "is not available. Use 'images' or 'seds' only.")