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 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)
class YSOModelSim(object): def __init__(self,name,folder,T=9000,M_sun=5.6,L_sun=250,disk_mass=0.01,disk_rmax=100, env=True,env_type='power',rc=400,mdot=1e-8,env_mass=0.1,env_rmin=30,env_rmax=5000,cav=True,cav_r0=500,cav_rho_0=8e-24,cav_theta=25,env_power=-1.5, Npix=149,angles=[20.,45.,60.,80],angles2=[60.,60.,60.,60.], amb_dens=8e-24, disk="Flared",disk_rmin=1., amb_rmin=1., amb_rmax=1000., innerdustfile='OH5.hdf5', outerdustfile='d03_5.5_3.0_A.hdf5',beta=1.1): self.name=name self.folder=folder self.T=T self.M_sun=M_sun*msun self.L_sun=L_sun*lsun self.disk_mass=disk_mass*msun self.disk_rmax=disk_rmax*au self.disk_rmin=disk_rmin*au self.disk_h_0 = OptThinRadius(1600) self.env=env self.disk=disk self.env_type=env_type self.env_mass=env_mass*msun self.env_rmin=env_rmin*au self.env_rmax=env_rmax*au self.mdot=mdot #*msun/yr*self.M_sun # disk accretion rate self.rc=rc*au self.cav=cav self.cav_rho_0=cav_rho_0 self.cav_r0=cav_r0*au self.cav_theta=cav_theta self.Npix=Npix self.angles=angles self.angles2=angles2 self.amb_dens=amb_dens self.amb_rmin=amb_rmin self.amb_rmax=amb_rmax*au self.env_power=env_power self.dustfile=innerdustfile self.dustfile_out=outerdustfile self.limval = max(self.env_rmax,1000*au) self.beta = beta def modelDump(self): sp.call('rm %s.mod ' % (self.folder+self.name),shell=True) pickle.dump(self,open(self.folder+self.name+'.mod','wb')) time.sleep(2) def modelPrint(self): #string= self.folder+ self.name+'\n' string="T="+str(self.T)+"K"+'\n' string+= "M="+str(self.M_sun/msun)+'Msun'+'\n' string+= "L="+str(self.L_sun/lsun)+'Lsun'+'\n' string+= "Disk="+str(self.disk)+'\n' string+= "Disk_mass="+str(self.disk_mass/msun)+'Msun'+'\n' string+= "Disk_rmax="+str(self.disk_rmax/au)+'AU'+'\n' string+= "Disk_rmin="+str(self.disk_rmin/au)+'AU'+'\n' string+= "env="+str(self.env)+'\n' string+= "env_type="+self.env_type+'\n' string+= "env_mass="+str(self.env_mass/msun)+'Msun'+'\n' string+= "env_rmax="+str(self.env_rmax/au)+'AU'+'\n' string+= "env_rmin="+str(self.env_rmin/au)+'AU'+'\n' if self.env_type == 'ulrich' and self.env==True: string+= "mass_ulrich="+str((8.*np.pi*self.env_rho_0*self.rc**3*pow(self.env_rmax/self.rc,1.5)/(3.*np.sqrt(2)))/msun)+'Msun'+'\n' string+= "mdot="+str(self.mdot)+'Msun/yr'+'\n' # (only if env_type="Ulrich") string+= "rc="+str(self.rc/au)+'AU'+'\n' # (only if env_type="Ulrich") string+= "cav="+str(self.cav)+'\n' string+= "cav_theta="+str(self.cav_theta)+'\n' string+= "cav_r0="+str(self.cav_r0/au)+'\n' string+= "env_power="+str(self.env_power)+'\n' string+= "disk_h_0="+str(self.disk_h_0)+'\n' string+= "dustfile="+self.dustfile+'\n' string+= "dustfile_out="+self.dustfile_out+'\n' string+= "amb_dens="+str(self.amb_dens)+'\n' string+= "amb_rmin="+str(self.amb_rmin)+'\n' string+= "amb_rmax="+str(self.amb_rmax/au)+'\n' string+= "angles="+str(self.angles)+'\n' print string return string def dust_gen(self,dustfile,dustfile_out='d03_5.5_3.0_A.hdf5'): ### first, we need to load Tracy's dust files and manipulate them to feed to Hyperion ### wavelength (microns),Cext,Csca,Kappa,g,pmax,theta (ignored) ### albedo = Csca/Cext ### opacity kappa is in cm^2/gm, dust_gas extinction opactity (absorption+scattering) - assumes gas-to=dust raio of 100 ### see Whitney et al. 2003a # tracy_dust = np.loadtxt('Tracy_models/OH5.par') # ### format for dust: d = HenyeyGreensteinDust(nu, albedo, chi, g, p_lin_max) # nu = const.c.value/ (tracy_dust[:,0]*1e-6) # albedo = tracy_dust[:,2]/tracy_dust[:,1] # chi = tracy_dust[:,3] # g = tracy_dust[:,4] # p_lin_max = tracy_dust[:,5] # ### flip the table to have an increasing frequency # nu = nu[::-1] # albedo = albedo[::-1] # chi=chi[::-1] # g=g[::-1] # p_lin_max=p_lin_max[::-1] # ### create the dust model # d = HenyeyGreensteinDust(nu, albedo, chi, g, p_lin_max) # d.optical_properties.extrapolate_wav(0.001,1.e7) # d.plot('OH5.png') # d.write('OH5.hdf5') self.d = SphericalDust() self.d.read(dustfile) self.d.plot(str(dustfile.split(',')[:-1])+'.png') self.d_out = SphericalDust() self.d_out.read(dustfile_out) #self.d_out.read(dustfile) self.d_out.plot(str(dustfile_out.split(',')[:-1])+'.png') def initModel(self): ### Use Tracy parameter file to set up the model self.dust_gen(self.dustfile,self.dustfile_out) mi = AnalyticalYSOModel() mi.star.temperature = self.T mi.star.mass = self.M_sun mi.star.luminosity = self.L_sun mi.star.radius=np.sqrt(mi.star.luminosity/(4.0*np.pi*sigma*mi.star.temperature**4)) #m.star.luminosity = 4.0*np.pi*m.star.radius**2*sigma*m.star.temperature**4 print mi.star.luminosity/lsun self.luminosity=mi.star.luminosity/lsun if self.disk=="Flared": print "Adding flared disk" disk = mi.add_flared_disk() disk.dust=self.d if self.dustfile == 'd03_5.5_3.0_A.hdf5': disk.mass=self.disk_mass/100. else: disk.mass=self.disk_mass disk.rmin=OptThinRadius(1600) #self.disk_rmin print "disk.rmin = ",disk.rmin,disk.rmin/au disk.rmax=self.disk_rmax disk.r_0 = self.disk_rmin disk.h_0 = disk.r_0/10. #self.disk_h_0*au disk.beta=self.beta disk.p = -1. elif self.disk=="Alpha": print "Adding alpha disk" disk = mi.add_alpha_disk() disk.dust=self.d if self.dustfile == 'd03_5.5_3.0_A.hdf5': disk.mass=self.disk_mass/100. else: disk.mass=self.disk_mass disk.rmin=OptThinRadius(1600) disk.rmax=self.disk_rmax disk.r_0 = self.disk_rmin disk.h_0 = disk.r_0/10. #self.disk_h_0*au disk.beta=1.1 disk.p = -1 disk.mdot=self.mdot disk.star = mi.star #print 'Disk density:',disk.rho_0 if self.env==True and self.env_type=='power': envelope=mi.add_power_law_envelope() envelope.dust=self.d_out envelope.r_0=self.env_rmin #envelope.r_0 = OptThinRadius(1600) if self.dustfile_out == 'd03_5.5_3.0_A.hdf5': envelope.mass=self.env_mass/100. else: envelope.mass=self.env_mass envelope.rmin=self.env_rmin envelope.rmax=self.env_rmax envelope.power=self.env_power #print 'Envelope rho:',envelope.rho_0 elif self.env==True and self.env_type=='ulrich': envelope=mi.add_ulrich_envelope() envelope.dust=self.d_out envelope.mdot=1e-6*msun/yr # has little impact on the fluxes, so fixed envelope.rc=self.rc envelope.rmin=self.env_rmin envelope.rmax=self.env_rmax if self.env==True: self.env_rho_0 = envelope.rho_0 print 'Envelope rho:',envelope.rho_0 #print "Rho_0 = ",envelope.rho_0 if self.cav==True: cavity=envelope.add_bipolar_cavity() cavity.dust=self.d_out cavity.power=1.5 cavity.cap_to_envelope_density=True ### prevents the cavity density to go above the envelope's density cavity.r_0=self.cav_r0 cavity.theta_0=self.cav_theta cavity.rho_0=self.cav_rho_0 #in g/cm^3 cavity.rho_exp=0.0 # if self.env==True: # ambient=mi.add_ambient_medium(subtract=[envelope,disk]) # if self.dustfile_out == 'd03_5.5_3.0_A.hdf5': # ambient.rho=self.amb_dens/100. # else: ambient.rho=self.amb_dens # ambient.rmin=OptThinRadius(1600.) # ambient.rmax=self.env_rmax # ambient.dust=self.d_out '''*** Grid parameters ***''' mi.set_spherical_polar_grid_auto(199,49,1) # Specify that the specific energy and density are needed mi.conf.output.output_specific_energy = 'last' mi.conf.output.output_density = 'last' '''**** Output Data ****''' image = mi.add_peeled_images(sed=True,image=False) image.set_wavelength_range(150,1,3000) #image.set_image_size(self.Npix,self.Npix) #image.set_image_limits(-self.limval,self.limval,-self.limval,self.limval) image.set_aperture_range(1,100000.*au,100000.*au) image.set_viewing_angles(self.angles,self.angles2) #image.set_track_origin('detailed') image.set_uncertainties(True) ''' Use the modified random walk *** Advanced ***' YES = DIFFUSION = Whether to use the diffusion ''' if self.env==True: #mi.set_pda(True) mi.set_mrw(True) else: mi.set_pda(False) mi.set_mrw(False) # Use raytracing to improve s/n of thermal/source emission mi.set_raytracing(True) '''**** Preliminaries ****''' mi.set_n_initial_iterations(5) mi.set_n_photons(initial=1e6,imaging=1e6,raytracing_sources=1e5,raytracing_dust=1e6) mi.set_convergence(True, percentile=99.0, absolute=2.0, relative=1.1) self.m = mi def runModel(self): self.initModel() self.m.write(self.folder+self.name+'.rtin') self.m.run(self.folder+self.name+'.rtout', mpi=True,n_processes=6) def plotData(self,ax,sourcename): if sourcename != 'None': folder_export="/n/a2/mrizzo/Dropbox/SOFIA/Processed_Data/" sourcetable = pickle.load(open(folder_export+"totsourcetable_fits.data","r")) markers = ['v','p','D','^','h','o','*','x','d','<'] TwoMASS = ['j','h','ks'] uTwoMASS = ["e_"+col for col in TwoMASS] wlTwoMASS = [1.3,1.6,2.2] colTwoMASS = colors[0] markerTwoMASS = markers[0] labelTwoMASS = '2MASS' Spitzer = ['i1','i2','i3','i4','m1','m2'] uSpitzer = ["e_"+col for col in Spitzer] wlSpitzer = [3.6,4.5,5.8,8.,24,70] colSpitzer = colors[1] markerSpitzer = markers[1] labelSpitzer = 'Spitzer' WISE = ['w1','w2','w3','w4'] uWISE = ["e_"+col for col in WISE] wlWISE = [3.4,4.6,12,22] colWISE = colors[2] labelWISE = 'WISE' markerWISE = markers[2] SOFIA = ['F11','F19','F31','F37'] uSOFIA = ["e_"+col for col in SOFIA] wlSOFIA = [11.1,19.7,31.5,37.1] colSOFIA = colors[3] markerSOFIA = markers[3] labelSOFIA = 'SOFIA' IRAS = ['Fnu_12','Fnu_25','Fnu_60','Fnu_100'] uIRAS = ["e_"+col for col in IRAS] wlIRAS = [12,25,60,100] colIRAS = colors[4] markerIRAS = markers[4] labelIRAS = 'IRAS' AKARI = ['S65','S90','S140','S160'] uAKARI = ["e_"+col for col in AKARI] wlAKARI = [65,90,140,160] colAKARI = colors[5] markerAKARI = markers[5] labelAKARI = 'AKARI' ENOCH = ['Fp'] uENOCH = ["e_"+col for col in ENOCH] wlENOCH = [1300] colENOCH = colors[6] markerENOCH = markers[6] labelENOCH = 'ENOCH' HERSCHEL = ['H70','H160','H250','H350','H500'] uHERSCHEL = ["e_"+col for col in HERSCHEL] wlHERSCHEL = [70,160,250,350,500] colHERSCHEL = colors[7] markerHERSCHEL = markers[7] labelHERSCHEL = 'HERSCHEL' SCUBA = ['S450','S850','S1300'] uSCUBA = ["e_"+col for col in SCUBA] wlSCUBA = [450,850,1300] colSCUBA = colors[8] markerSCUBA = markers[8] labelSCUBA = 'SCUBA' alpha=1 sources = sourcetable.group_by('SOFIA_name') for key,sourcetable in zip(sources.groups.keys,sources.groups): if sourcename == sourcetable['SOFIA_name'][0]: #print sourcetable['SOFIA_name'][0] p.plotData(ax,sourcetable,markerTwoMASS,TwoMASS,uTwoMASS,wlTwoMASS,colTwoMASS,labelTwoMASS,alpha) p.plotData(ax,sourcetable,markerSpitzer,Spitzer,uSpitzer,wlSpitzer,colSpitzer,labelSpitzer,alpha) p.plotData(ax,sourcetable,markerWISE,WISE,uWISE,wlWISE,colWISE,labelWISE,alpha) p.plotData(ax,sourcetable,markerSOFIA,SOFIA,uSOFIA,wlSOFIA,colSOFIA,labelSOFIA,alpha) p.plotData(ax,sourcetable,markerIRAS,IRAS,uIRAS,wlIRAS,colIRAS,labelIRAS,alpha) p.plotData(ax,sourcetable,markerAKARI,AKARI,uAKARI,wlAKARI,colAKARI,labelAKARI,alpha) p.plotData(ax,sourcetable,markerENOCH,ENOCH,uENOCH,wlENOCH,colENOCH,labelENOCH,alpha) p.plotData(ax,sourcetable,markerHERSCHEL,HERSCHEL,uHERSCHEL,wlHERSCHEL,colHERSCHEL,labelHERSCHEL,alpha) p.plotData(ax,sourcetable,markerSCUBA,SCUBA,uSCUBA,wlSCUBA,colSCUBA,labelSCUBA,alpha) def calcChi2(self,dist_pc=140,extinction=0, sourcename='Oph.1'): self.dist=dist_pc*pc self.extinction=extinction chi = np.loadtxt('kmh94_3.1_full.chi') wav = np.loadtxt('kmh94_3.1_full.wav') Chi = interp1d(wav,chi,kind='linear') modelname = self.folder+self.name self.mo = ModelOutput(modelname+'.rtout') # get the sed of all inclination sed = self.mo.get_sed(aperture=-1, inclination='all', distance=self.dist,units='Jy') # calculate the optical depth at all wavelengths tau = self.extinction*Chi(sed.wav)/Chi(0.550)/1.086 # calculate extinction values ext = np.array([np.exp(-tau) for i in range(sed.val.shape[0])]) # apply extinction to model extinct_values = np.log10(sed.val.transpose()*ext.T) # data points and errors folder_export="/n/a2/mrizzo/Dropbox/SOFIA/Processed_Data/" sourcetable = pickle.load(open(folder_export+"totsourcetable_fits.data","r")) TwoMASS = ['j','h','ks'] uTwoMASS = ["e_"+col for col in TwoMASS] wlTwoMASS = [1.3,1.6,2.2] labelTwoMASS = '2MASS' Spitzer = ['i1','i2','i3','i4'] uSpitzer = ["e_"+col for col in Spitzer] wlSpitzer = [3.6,4.5,5.8,8.] labelSpitzer = 'Spitzer' SOFIA = ['F11','F19','F31','F37'] uSOFIA = ["e_"+col for col in SOFIA] wlSOFIA = [11.1,19.7,31.5,37.1] labelSOFIA = 'SOFIA' sources = sourcetable.group_by('SOFIA_name') for key,source in zip(sources.groups.keys,sources.groups): if sourcename == source['SOFIA_name'][0]: datapoints = source[TwoMASS+Spitzer+SOFIA] dataerrors = source[uTwoMASS+uSpitzer+uSOFIA] print p.nptable(datapoints),p.nptable(dataerrors) # calculate log10 of quantities required for chi squared logFnu = np.log10(p.nptable(datapoints))-0.5*(1./np.log(10.))*p.nptable(dataerrors)**2/p.nptable(datapoints)**2 varlogFnu = (1./np.log(10)/p.nptable(datapoints))**2*p.nptable(dataerrors)**2 print extinct_values,extinct_values.shape # for each inclination, calculate chi squared; need to interpolate to get model at required wavelengths Ninc = extinct_values.shape[1] chi2 = np.zeros(Ninc) wl=wlTwoMASS+wlSpitzer+wlSOFIA N = len(wl) for j in range(Ninc): interp_func = interp1d(sed.wav,extinct_values[:,j],kind='linear') interp_vals = interp_func(wl) chi2[j] = 1./N * np.sum((logFnu - interp_vals)**2/varlogFnu) print chi2 def plotModel(self,dist_pc=140,inc=3,extinction=0,show=False,sourcename='Oph.1'): self.dist=dist_pc*pc self.inc=inc self.extinction=extinction modelname = self.folder+self.name self.mo = ModelOutput(modelname+'.rtout') #tracy_dust = np.loadtxt('Tracy_models/OH5.par') chi = np.loadtxt('kmh94_3.1_full.chi') wav = np.loadtxt('kmh94_3.1_full.wav') Chi = interp1d(wav,chi,kind='linear') fig = plt.figure(figsize=(20,14)) ax=fig.add_subplot(2,3,1) sed = self.mo.get_sed(aperture=-1, inclination='all', distance=self.dist) #print tracy_dust[11,1],Cext(sed.wav[-1]),Cext(sed.wav[-1])/tracy_dust[11,1] tau = self.extinction*Chi(sed.wav)/Chi(0.550)/1.086 #print Cext(sed.wav)/tracy_dust[11,1] ext = np.array([np.exp(-tau) for i in range(sed.val.shape[0])]) #print tau,np.exp(-tau) ax.loglog(sed.wav, sed.val.transpose()*ext.T, color='black') ax.set_title(modelname+'_seds, Av='+str(self.extinction)) ax.set_xlim(sed.wav.min(), 1300) ax.set_ylim(1e-13, 1e-7) ax.set_xlabel(r'$\lambda$ [$\mu$m]') ax.set_ylabel(r'$\lambda F_\lambda$ [ergs/cm$^2/s$]') self.plotData(ax,sourcename) ax.set_xscale('log') ax.set_yscale('log') #ax.set_ylabel(r'$F_{Jy}$ [Jy]') #plt.legend(loc=4) ax=fig.add_subplot(2,3,2) sed = self.mo.get_sed(aperture=-1, inclination=self.inc, distance=self.dist) ext=np.exp(-tau) ax.loglog(sed.wav, sed.val.transpose()*ext.T, lw=3,color='black',label='source_total') ax.set_xlim(sed.wav.min(), 1300) ax.set_ylim(1e-13, 1e-7) ### for lamFlam sed = self.mo.get_sed(aperture=-1, inclination=self.inc, distance=self.dist,component='source_emit') ax.loglog(sed.wav, sed.val.transpose()*ext.T, color='blue',label='source_emit') sed = self.mo.get_sed(aperture=-1, inclination=self.inc, distance=self.dist,component='source_scat') ax.loglog(sed.wav, sed.val.transpose()*ext.T, color='teal',label='source_scat') sed = self.mo.get_sed(aperture=-1, inclination=self.inc, distance=self.dist,component='dust_emit') ax.loglog(sed.wav, sed.val.transpose()*ext.T, color='red',label='dust_emit') sed = self.mo.get_sed(aperture=-1, inclination=self.inc, distance=self.dist,component='dust_scat') ax.loglog(sed.wav, sed.val.transpose()*ext.T, color='orange',label='dust_scat') self.plotData(ax,sourcename) ax.set_xscale('log') ax.set_yscale('log') ax.set_title('seds_inc=inc') ax.set_xlabel(r'$\lambda$ [$\mu$m]') ax.set_ylabel(r'$\lambda F_\lambda$ [ergs/cm$^2/s$]') #ax.set_ylabel(r'$F_{Jy}$ [Jy]') leg = ax.legend(loc=4,fontsize='small') #leg = plt.gca().get_legend() #plt.setp(leg.get_text(),fontsize='small') # Extract the quantities g = self.mo.get_quantities() # Get the wall positions for r and theta rw, tw = g.r_wall / au, g.t_wall # Make a 2-d grid of the wall positions (used by pcolormesh) R, T = np.meshgrid(rw, tw) # Calculate the position of the cell walls in cartesian coordinates X, Z = R * np.sin(T), R * np.cos(T) # Make a plot in (x, z) space for different zooms from matplotlib.colors import LogNorm,PowerNorm # Make a plot in (r, theta) space ax = fig.add_subplot(2, 3, 3) if g.shape[-1]==2: c = ax.pcolormesh(X, Z, g['temperature'][0].array[0, :, :]+g['temperature'][1].array[0, :, :],norm=PowerNorm(gamma=0.5,vmin=1,vmax=500)) else : c = ax.pcolormesh(X, Z, g['temperature'][0].array[0, :, :],norm=PowerNorm(gamma=0.5,vmin=1,vmax=500)) #ax.set_xscale('log') #ax.set_yscale('log') ax.set_xlim(X.min(), X.max()/5.) ax.set_ylim(Z.min()/10., Z.max()/10.) ax.set_xlabel('x (au)') ax.set_ylabel('z (au)') #ax.set_yticks([np.pi, np.pi * 0.75, np.pi * 0.5, np.pi * 0.25, 0.]) #ax.set_yticklabels([r'$\pi$', r'$3\pi/4$', r'$\pi/2$', r'$\pi/4$', r'$0$']) cb = fig.colorbar(c) ax.set_title('Temperature structure') cb.set_label('Temperature (K)') #fig.savefig(modelname+'_temperature_spherical_rt.png', bbox_inches='tight') ax = fig.add_subplot(2, 3, 4) if g.shape[-1]==2: c = ax.pcolormesh(X, Z, g['density'][0].array[0, :, :]+g['density'][1].array[0, :, :],norm=LogNorm(vmin=1e-22,vmax=g['density'][0].array[0, :, :].max())) else : c = ax.pcolormesh(X, Z, g['density'][0].array[0, :, :],norm=LogNorm(vmin=1e-22,vmax=g['density'][0].array[0, :, :].max())) #ax.set_xscale('log') #ax.set_yscale('log') ax.set_xlim(X.min(), X.max()/5.) ax.set_ylim(Z.min()/10., Z.max()/10.) ax.set_xlabel('x (au)') ax.set_ylabel('z (au)') ax.set_title('Density structure') cb = fig.colorbar(c) cb.set_label('Density (g/cm2)') ### plot the convolved image with the 37 micron filter (manually set to slice 18 of the cube - this would change with wavelength coverage) ax = fig.add_subplot(2, 3, 5) self.image = self.mo.get_image(inclination=inc,distance=self.dist,units='Jy') fits.writeto(modelname+'_inc_'+str(inc)+'.fits',self.image.val.swapaxes(0,2).swapaxes(1,2),clobber=True) ### need to convolve the image with a Gaussian PSF pix = 2.*self.limval/au/self.Npix # in AU/pix pix_asec = pix/(self.dist/pc) # in asec/pix airy_asec = 3.5 #asec airy_pix = airy_asec/pix_asec # in pix gauss_pix = airy_pix/2.35 # in Gaussian std print "Gaussian std: ",gauss_pix from scipy.ndimage.filters import gaussian_filter as gauss #print [(i,sed.wav[i]) for i in range(len(sed.wav))] img37 = self.image.val[:,:,18] convol = gauss(img37,gauss_pix,mode='constant',cval=0.0) Nc = self.Npix/2 hw = min(int(20./pix_asec),Nc) #(max is Nc) #ax.imshow(img37,norm=LogNorm(vmin=1e-20,vmax=img37.max())) #ax.imshow(img37,interpolation='nearest') #ax.imshow(convol,norm=LogNorm(vmin=1e-20,vmax=img37.max())) #ax.imshow(convol,interpolation='nearest',norm=LogNorm(vmin=1e-20,vmax=img37.max())) ax.imshow(convol[Nc-hw:Nc+hw,Nc-hw:Nc+hw],interpolation='nearest',origin='lower',cmap=plt.get_cmap('gray')) airy_disk = plt.Circle((airy_pix*1.3,airy_pix*1.3),airy_pix,color=colors[3]) ax.add_artist(airy_disk) ax.text(airy_pix*3,airy_pix*1.3/2.0,'SOFIA 37um Airy disk',color=colors[3]) ax.set_title('Convolved image') fits.writeto(modelname+'_inc_'+str(inc)+'_convol37.fits',convol,clobber=True) ### draw a cross-section of the image to show the spatial extension in linear scale, to compare with what we observe in the model. ax = fig.add_subplot(2, 3, 6) ax.plot(range(Nc-hw,Nc+hw),convol[Nc-hw:Nc+hw,Nc-1],label='cross-section 1') ax.plot(range(Nc-hw,Nc+hw),convol[Nc-1,Nc-hw:Nc+hw],label='cross-section 2') maxconvol = convol[Nc-hw:Nc+hw,Nc-1].max() gauss = np.exp( -(np.array(range(-hw,hw))**2 / (2. * gauss_pix**2))) gauss/= gauss.max() gauss*=maxconvol ax.plot(range(Nc-hw,Nc+hw),gauss,label='SOFIA beam') leg = ax.legend(loc=2,fontsize='small') #leg = plt.gca().get_legend() #plt.setp(leg.get_text(),fontsize='small') ax.set_title('Cross section at the center') string=self.modelPrint() fig.text(0.0,0.14,string+'Av='+str(self.extinction)+'\n'+'dist='+str(self.dist/pc)+'\n',color='r') fig.savefig(modelname+'.png', bbox_inches='tight',dpi=300) if show: plt.show() def plotSim(self,dist_pc=140,inc=3,extinction=0,show=False): self.dist=dist_pc*pc self.inc=inc self.extinction=extinction modelname = self.folder+self.name self.mo = ModelOutput(modelname+'.rtout') #tracy_dust = np.loadtxt('Tracy_models/OH5.par') #chi = np.loadtxt('kmh94_3.1_full.chi') #wav = np.loadtxt('kmh94_3.1_full.wav') #Chi = interp1d(wav,chi,kind='linear') fig = plt.figure(figsize=(20,14)) ax=fig.add_subplot(1,3,1) sed = self.mo.get_sed(aperture=-1, inclination='all', distance=self.dist) #print tracy_dust[11,1],Cext(sed.wav[-1]),Cext(sed.wav[-1])/tracy_dust[11,1] #tau = self.extinction*Chi(sed.wav)/Chi(0.550)/1.086 #print Cext(sed.wav)/tracy_dust[11,1] #ext = np.array([np.exp(-tau) for i in range(sed.val.shape[0])]) #print tau,np.exp(-tau) ax.loglog(sed.wav, sed.val.transpose(), color='black') ax.set_title(modelname+'_seds, Av='+str(self.extinction)) ax.set_xlim(sed.wav.min(), 1300) ax.set_ylim(1e-13, 1e-7) ax.set_xlabel(r'$\lambda$ [$\mu$m]') ax.set_ylabel(r'$\lambda F_\lambda$ [ergs/cm$^2/s$]') #self.plotData(ax,sourcename) ax.set_xscale('log') ax.set_yscale('log') #ax.set_ylabel(r'$F_{Jy}$ [Jy]') #plt.legend(loc=4) # ax=fig.add_subplot(2,3,2) # sed = self.mo.get_sed(aperture=-1, inclination=self.inc, distance=self.dist) # ext=np.exp(-tau) # ax.loglog(sed.wav, sed.val.transpose()*ext.T, lw=3,color='black',label='source_total') # ax.set_xlim(sed.wav.min(), 1300) # ax.set_ylim(1e-13, 1e-7) ### for lamFlam # sed = self.mo.get_sed(aperture=-1, inclination=self.inc, distance=self.dist,component='source_emit') # ax.loglog(sed.wav, sed.val.transpose()*ext.T, color='blue',label='source_emit') # sed = self.mo.get_sed(aperture=-1, inclination=self.inc, distance=self.dist,component='source_scat') # ax.loglog(sed.wav, sed.val.transpose()*ext.T, color='teal',label='source_scat') # sed = self.mo.get_sed(aperture=-1, inclination=self.inc, distance=self.dist,component='dust_emit') # ax.loglog(sed.wav, sed.val.transpose()*ext.T, color='red',label='dust_emit') # sed = self.mo.get_sed(aperture=-1, inclination=self.inc, distance=self.dist,component='dust_scat') # ax.loglog(sed.wav, sed.val.transpose()*ext.T, color='orange',label='dust_scat') # #self.plotData(ax,sourcename) # ax.set_xscale('log') # ax.set_yscale('log') # ax.set_title('seds_inc=inc') # ax.set_xlabel(r'$\lambda$ [$\mu$m]') # ax.set_ylabel(r'$\lambda F_\lambda$ [ergs/cm$^2/s$]') # #ax.set_ylabel(r'$F_{Jy}$ [Jy]') # leg = ax.legend(loc=4,fontsize='small') #leg = plt.gca().get_legend() #plt.setp(leg.get_text(),fontsize='small') # Extract the quantities g = self.mo.get_quantities() # Get the wall positions for r and theta rw, tw = g.r_wall / au, g.t_wall # Make a 2-d grid of the wall positions (used by pcolormesh) R, T = np.meshgrid(rw, tw) # Calculate the position of the cell walls in cartesian coordinates X, Z = R * np.sin(T), R * np.cos(T) # Make a plot in (x, z) space for different zooms from matplotlib.colors import LogNorm,PowerNorm # Make a plot in (r, theta) space ax = fig.add_subplot(1, 3, 2) if g.shape[-1]==2: c = ax.pcolormesh(X, Z, g['temperature'][0].array[0, :, :]+g['temperature'][1].array[0, :, :],norm=PowerNorm(gamma=0.5,vmin=1,vmax=500)) else : c = ax.pcolormesh(X, Z, g['temperature'][0].array[0, :, :],norm=PowerNorm(gamma=0.5,vmin=1,vmax=500)) #ax.set_xscale('log') #ax.set_yscale('log') ax.set_xlim(X.min(), X.max()) ax.set_ylim(Z.min(), Z.max()) ax.set_xlabel('x (au)') ax.set_ylabel('z (au)') #ax.set_yticks([np.pi, np.pi * 0.75, np.pi * 0.5, np.pi * 0.25, 0.]) #ax.set_yticklabels([r'$\pi$', r'$3\pi/4$', r'$\pi/2$', r'$\pi/4$', r'$0$']) cb = fig.colorbar(c) ax.set_title('Temperature structure') cb.set_label('Temperature (K)') #fig.savefig(modelname+'_temperature_spherical_rt.png', bbox_inches='tight') ax = fig.add_subplot(1, 3, 3) if g.shape[-1]==2: c = ax.pcolormesh(X, Z, g['density'][0].array[0, :, :]+g['density'][1].array[0, :, :],norm=LogNorm(vmin=1e-22,vmax=g['density'][0].array[0, :, :].max())) else : c = ax.pcolormesh(X, Z, g['density'][0].array[0, :, :],norm=LogNorm(vmin=1e-22,vmax=g['density'][0].array[0, :, :].max())) #ax.set_xscale('log') #ax.set_yscale('log') ax.set_xlim(X.min(), X.max()) ax.set_ylim(Z.min(), Z.max()) ax.set_xlabel('x (au)') ax.set_ylabel('z (au)') ax.set_title('Density structure') cb = fig.colorbar(c) cb.set_label('Density (g/cm2)') # ### plot the convolved image with the 37 micron filter (manually set to slice 18 of the cube - this would change with wavelength coverage) # ax = fig.add_subplot(2, 3, 5) # self.image = self.mo.get_image(inclination=inc,distance=self.dist,units='Jy') # fits.writeto(modelname+'_inc_'+str(inc)+'.fits',self.image.val.swapaxes(0,2).swapaxes(1,2),clobber=True) # ### need to convolve the image with a Gaussian PSF # pix = 2.*self.limval/au/self.Npix # in AU/pix # pix_asec = pix/(self.dist/pc) # in asec/pix # airy_asec = 3.5 #asec # airy_pix = airy_asec/pix_asec # in pix # gauss_pix = airy_pix/2.35 # in Gaussian std # print "Gaussian std: ",gauss_pix # from scipy.ndimage.filters import gaussian_filter as gauss # #print [(i,sed.wav[i]) for i in range(len(sed.wav))] # img37 = self.image.val[:,:,18] # convol = gauss(img37,gauss_pix,mode='constant',cval=0.0) # Nc = self.Npix/2 # hw = min(int(20./pix_asec),Nc) #(max is Nc) # #ax.imshow(img37,norm=LogNorm(vmin=1e-20,vmax=img37.max())) # #ax.imshow(img37,interpolation='nearest') # #ax.imshow(convol,norm=LogNorm(vmin=1e-20,vmax=img37.max())) # #ax.imshow(convol,interpolation='nearest',norm=LogNorm(vmin=1e-20,vmax=img37.max())) # ax.imshow(convol[Nc-hw:Nc+hw,Nc-hw:Nc+hw],interpolation='nearest',origin='lower',cmap=plt.get_cmap('gray')) # airy_disk = plt.Circle((airy_pix*1.3,airy_pix*1.3),airy_pix,color=colors[3]) # ax.add_artist(airy_disk) # ax.text(airy_pix*3,airy_pix*1.3/2.0,'SOFIA 37um Airy disk',color=colors[3]) # ax.set_title('Convolved image') # fits.writeto(modelname+'_inc_'+str(inc)+'_convol37.fits',convol,clobber=True) # ### draw a cross-section of the image to show the spatial extension in linear scale, to compare with what we observe in the model. # ax = fig.add_subplot(2, 3, 6) # ax.plot(range(Nc-hw,Nc+hw),convol[Nc-hw:Nc+hw,Nc-1],label='cross-section 1') # ax.plot(range(Nc-hw,Nc+hw),convol[Nc-1,Nc-hw:Nc+hw],label='cross-section 2') # maxconvol = convol[Nc-hw:Nc+hw,Nc-1].max() # gauss = np.exp( -(np.array(range(-hw,hw))**2 / (2. * gauss_pix**2))) # gauss/= gauss.max() # gauss*=maxconvol # ax.plot(range(Nc-hw,Nc+hw),gauss,label='SOFIA beam') # leg = ax.legend(loc=2,fontsize='small') # #leg = plt.gca().get_legend() # #plt.setp(leg.get_text(),fontsize='small') # ax.set_title('Cross section at the center') string=self.modelPrint() fig.text(0.0,0.14,string+'Av='+str(self.extinction)+'\n'+'dist='+str(self.dist/pc)+'\n',color='r') fig.savefig(modelname+'.png', bbox_inches='tight',dpi=300) if show: plt.show()