Python Model.set_convergence方法代码示例

# 需要导入模块: from hyperion.model import Model [as 别名]
# 或者: from hyperion.model.Model import set_convergence [as 别名]

#.........这里部分代码省略.........
                        rho[ir,itheta,iphi] = rho_env[ir,itheta,iphi]
                    else:
                        rho[ir,itheta,iphi] = 1e-25
        rho_env  = rho_env  + 1e-40
        rho      = rho      + 1e-40

    # Call function to plot the density
    plot_density(rho, rc, thetac,'/Users/yaolun/bhr71/hyperion/', plotname='shell')
    # Insert the calculated grid and dust density profile into hyperion
    m.set_spherical_polar_grid(ri, thetai, phii)
    m.add_density_grid(rho.T, outdir+'oh5.hdf5')    # numpy read the array in reverse order

    # Define the luminsoity source
    source = m.add_spherical_source()
    source.luminosity = (4*PI*rstar**2)*sigma*(tstar**4)  # [ergs/s]
    source.radius = rstar  # [cm]
    source.temperature = tstar  # [K]
    source.position = (0., 0., 0.)
    print 'L_center =  % 5.2f L_sun' % ((4*PI*rstar**2)*sigma*(tstar**4)/LS)

    # Setting up the wavelength for monochromatic radiative transfer
    lambda0 = 0.1
    lambda1 = 2.0
    lambda2 = 50.0
    lambda3 = 95.0
    lambda4 = 200.0
    lambda5 = 314.0
    lambda6 = 670.0
    n01     = 10.0
    n12     = 20.0
    n23     = (lambda3-lambda2)/0.02
    n34     = (lambda4-lambda3)/0.03
    n45     = (lambda5-lambda4)/0.1
    n56     = (lambda6-lambda5)/0.1

    lam01   = lambda0 * (lambda1/lambda0)**(np.arange(n01)/n01)
    lam12   = lambda1 * (lambda2/lambda1)**(np.arange(n12)/n12)
    lam23   = lambda2 * (lambda3/lambda2)**(np.arange(n23)/n23)
    lam34   = lambda3 * (lambda4/lambda3)**(np.arange(n34)/n34)
    lam45   = lambda4 * (lambda5/lambda4)**(np.arange(n45)/n45)
    lam56   = lambda5 * (lambda6/lambda5)**(np.arange(n56+1)/n56)

    lam     = np.concatenate([lam01,lam12,lam23,lam34,lam45,lam56])
    nlam    = len(lam)

    # Create camera wavelength points
    n12     = 70.0
    n23     = 70.0
    n34     = 70.0
    n45     = 50.0
    n56     = 50.0
    
    lam12   = lambda1 * (lambda2/lambda1)**(np.arange(n12)/n12)
    lam23   = lambda2 * (lambda3/lambda2)**(np.arange(n23)/n23)
    lam34   = lambda3 * (lambda4/lambda3)**(np.arange(n34)/n34)
    lam45   = lambda4 * (lambda5/lambda4)**(np.arange(n45)/n45)
    lam56   = lambda5 * (lambda6/lambda5)**(np.arange(n56+1)/n56)

    lam_cam = np.concatenate([lam12,lam23,lam34,lam45,lam56])
    n_lam_cam = len(lam_cam)

    # Radiative transfer setting

    # number of photons for temp and image
    m.set_raytracing(True)
    m.set_monochromatic(True, wavelengths=[3.6, 4.5, 5.8, 8.0, 24, 70, 100, 160, 250, 350, 500])
    m.set_n_photons(initial=1000000, imaging_sources=1000000, imaging_dust=1000000,raytracing_sources=1000000, raytracing_dust=1000000)
    # imaging=100000, raytracing_sources=100000, raytracing_dust=100000
    # number of iteration to compute dust specific energy (temperature)
    m.set_n_initial_iterations(5)
    m.set_convergence(True, percentile=99., absolute=1.5, relative=1.02)
    m.set_mrw(True)   # Gamma = 1 by default
    # m.set_forced_first_scattering(forced_first_scattering=True)
    # Setting up images and SEDs
    image = m.add_peeled_images()
    # image.set_wavelength_range(300, 2.0, 670.0)
    # use the index of wavelength array used by the monochromatic radiative transfer
    image.set_wavelength_index_range(2,12)
    # pixel number
    image.set_image_size(300, 300)
    image.set_image_limits(-R_env_max, R_env_max, -R_env_max, R_env_max)
    image.set_viewing_angles([82.0], [0.0])
    image.set_uncertainties(True)
    # output as 64-bit
    image.set_output_bytes(8)

    # Output setting
    # Density
    m.conf.output.output_density = 'last'

    # Density difference (shows where dust was destroyed)
    m.conf.output.output_density_diff = 'none'

    # Energy absorbed (using pathlengths)
    m.conf.output.output_specific_energy = 'last'

    # Number of unique photons that passed through the cell
    m.conf.output.output_n_photons = 'last'

    m.write(outdir+outname+'.rtin')

# 需要导入模块: from hyperion.model import Model [as 别名]
# 或者: from hyperion.model.Model import set_convergence [as 别名]

#.........这里部分代码省略.........
    m.set_spherical_polar_grid(ri, thetai, phii)
    m.add_density_grid(rho_dust.T, d)

    # Define the luminsoity source
    source = m.add_spherical_source()
    source.luminosity = (4*PI*rstar**2)*sigma*(tstar**4)  # [ergs/s]
    source.radius = rstar  # [cm]
    source.temperature = tstar  # [K]
    source.position = (0., 0., 0.)
    print('L_center =  % 5.2f L_sun' % ((4*PI*rstar**2)*sigma*(tstar**4)/LS))

    # radiative transfer settigs
    m.set_raytracing(True)

    # determine the number of photons for imaging
    # the case of monochromatic
    if mono_wave != None:
        if (type(mono_wave) == int) or (type(mono_wave) == float) or (type(mono_wave) == str):
            mono_wave = float(mono_wave)
            mono_wave = [mono_wave]

        # Monochromatic radiative transfer setting
        m.set_monochromatic(True, wavelengths=mono_wave)
        m.set_n_photons(initial=mc_photons, imaging_sources=im_photon,
                        imaging_dust=im_photon, raytracing_sources=im_photon,
                        raytracing_dust=im_photon)
    # regular SED
    else:
        m.set_n_photons(initial=mc_photons, imaging=im_photon * wav_num,
                        raytracing_sources=im_photon,
                        raytracing_dust=im_photon)
    # number of iteration to compute dust specific energy (temperature)
    m.set_n_initial_iterations(20)
    m.set_convergence(True, percentile=dict_params['percentile'],
                            absolute=dict_params['absolute'],
                            relative=dict_params['relative'])
    m.set_mrw(True)   # Gamma = 1 by default

    # Setting up images and SEDs
    if not image_only:
        # SED setting
        # Infinite aperture
        syn_inf = m.add_peeled_images(image=False)
        # use the index of wavelength array used by the monochromatic radiative transfer
        if mono_wave == None:
            syn_inf.set_wavelength_range(wav_num, wav_min, wav_max)
        syn_inf.set_viewing_angles([dict_params['view_angle']], [0.0])
        syn_inf.set_uncertainties(True)
        syn_inf.set_output_bytes(8)

        # aperture
        # 7.2 in 10 um scaled by lambda / 10
        # flatten beyond 20 um
        # default aperture (should always specify a set of apertures)

        # assign wl_aper and aper from dictionary of aperture
        wl_aper = aperture['wave']
        aper    = aperture['aperture']
        # create the non-repetitive aperture list and index array
        aper_reduced = sorted(list(set(aper)))
        index_reduced = np.arange(1, len(aper_reduced)+1)

        dict_peel_sed = {}
        for i in range(0, len(aper_reduced)):
            aper_dum = aper_reduced[i]/2 * (1/3600.*np.pi/180.)*dstar*pc
            dict_peel_sed[str(index_reduced[i])] = m.add_peeled_images(image=False)

# 需要导入模块: from hyperion.model import Model [as 别名]
# 或者: from hyperion.model.Model import set_convergence [as 别名]
t = arange(nt)/(nt-1.)*pi
p = arange(np)/(np-1.)*2*pi

m.set_spherical_polar_grid(r, t, p)

dens = zeros((nr-1,nt-1,np-1)) + 1.0e-17

m.add_density_grid(dens, d)

source = m.add_spherical_source()
source.luminosity = lsun
source.radius = rsun
source.temperature = 4000.

m.set_n_photons(initial=1000000, imaging=0)
m.set_convergence(True, percentile=99., absolute=2., relative=1.02)

m.write("test_spherical.rtin")

m.run("test_spherical.rtout", mpi=False)

n = ModelOutput('test_spherical.rtout')

grid = n.get_quantities()

temp = grid.quantities['temperature'][0]

for i in range(9):
    plt.imshow(temp[i,:,:],origin="lower",interpolation="nearest", \
            vmin=temp.min(),vmax=temp.max())
    plt.colorbar()

# 需要导入模块: from hyperion.model import Model [as 别名]
# 或者: from hyperion.model.Model import set_convergence [as 别名]
    def run_thermal_hyperion(self, nphot=1e6, mrw=False, pda=False, \
            niterations=20, percentile=99., absolute=2.0, relative=1.02, \
            max_interactions=1e8, mpi=False, nprocesses=None):
        d = []
        for i in range(len(self.grid.dust)):
            d.append(IsotropicDust( \
                    self.grid.dust[i].nu[::-1].astype(numpy.float64), \
                    self.grid.dust[i].albedo[::-1].astype(numpy.float64), \
                    self.grid.dust[i].kext[::-1].astype(numpy.float64)))

        m = HypModel()
        if (self.grid.coordsystem == "cartesian"):
            m.set_cartesian_grid(self.grid.w1*AU, self.grid.w2*AU, \
                    self.grid.w3*AU)
        elif (self.grid.coordsystem == "cylindrical"):
            m.set_cylindrical_polar_grid(self.grid.w1*AU, self.grid.w3*AU, \
                    self.grid.w2)
        elif (self.grid.coordsystem == "spherical"):
            m.set_spherical_polar_grid(self.grid.w1*AU, self.grid.w2, \
                    self.grid.w3)

        for i in range(len(self.grid.density)):
            if (self.grid.coordsystem == "cartesian"):
                m.add_density_grid(numpy.transpose(self.grid.density[i], \
                        axes=(2,1,0)), d[i])
            if (self.grid.coordsystem == "cylindrical"):
                m.add_density_grid(numpy.transpose(self.grid.density[i], \
                        axes=(1,2,0)), d[i])
            if (self.grid.coordsystem == "spherical"):
                m.add_density_grid(numpy.transpose(self.grid.density[i], \
                        axes=(2,1,0)), d[i])

        sources = []
        for i in range(len(self.grid.stars)):
            sources.append(m.add_spherical_source())
            sources[i].luminosity = self.grid.stars[i].luminosity * L_sun
            sources[i].radius = self.grid.stars[i].radius * R_sun
            sources[i].temperature = self.grid.stars[i].temperature

        m.set_mrw(mrw)
        m.set_pda(pda)
        m.set_max_interactions(max_interactions)
        m.set_n_initial_iterations(niterations)
        m.set_n_photons(initial=nphot, imaging=0)
        m.set_convergence(True, percentile=percentile, absolute=absolute, \
                relative=relative)

        m.write("temp.rtin")

        m.run("temp.rtout", mpi=mpi, n_processes=nprocesses)

        n = ModelOutput("temp.rtout")

        grid = n.get_quantities()

        self.grid.temperature = []
        temperature = grid.quantities['temperature']
        for i in range(len(temperature)):
            if (self.grid.coordsystem == "cartesian"):
                self.grid.temperature.append(numpy.transpose(temperature[i], \
                        axes=(2,1,0)))
            if (self.grid.coordsystem == "cylindrical"):
                self.grid.temperature.append(numpy.transpose(temperature[i], \
                        axes=(2,0,1)))
            if (self.grid.coordsystem == "spherical"):
                self.grid.temperature.append(numpy.transpose(temperature[i], \
                        axes=(2,1,0)))

        os.system("rm temp.rtin temp.rtout")

# 需要导入模块: from hyperion.model import Model [as 别名]
# 或者: from hyperion.model.Model import set_convergence [as 别名]

#.........这里部分代码省略.........
                        #                 else:
                        #                     mu_o_dum = roots[imu]
                        #         if mu_o_dum == -0.5:
                        #             print 'Problem with cubic solving, roots are: ', roots
                        #     mu_o = mu_o_dum.real
                        #     rho_env[ir,itheta,iphi] = M_env_dot/(4*PI*(G*mstar*rcen**3)**0.5)*(rc[ir]/rcen)**(-3./2)*(1+mu/mu_o)**(-0.5)*(mu/mu_o+2*mu_o**2*rcen/rc[ir])**(-1)
                        # # Disk profile
                        # if ((w >= R_disk_min) and (w <= R_disk_max)) == True:
                        #     h = ((w/(100*AU))**beta)*h100
                        #     rho_disk[ir,itheta,iphi] = rho_0*(1-np.sqrt(rstar/w))*(rstar/w)**(beta+1)*np.exp(-0.5*(z/h)**2)
                        # # Combine envelope and disk
                        # rho[ir,itheta,iphi] = rho_disk[ir,itheta,iphi] + rho_env[ir,itheta,iphi]
                    else:
                        rho[ir,itheta,iphi] = 1e-30
        rho_env  = rho_env  + 1e-40
        rho_disk = rho_disk + 1e-40
        rho      = rho      + 1e-40
    else:
        for ir in range(0,len(rc)):
            for itheta in range(0,len(thetac)):
                for iphi in range(0,len(phic)):
                    # Envelope profile
                    w = abs(rc[ir]*np.cos(thetac[itheta]))
                    z = rc[ir]*np.sin(thetac[itheta])
                    z_cav = c*abs(w)**1.5
                    z_cav_wall = c*abs(w-wall)**1.5
                    if z_cav == 0:
                        z_cav = R_env_max
                    if abs(z) > abs(z_cav):
                        # rho_env[ir,itheta,iphi] = rho_cav
                        # Modification for using density gradient in the cavity
                        if rc[ir] <= 20*AU:
                            rho_env[ir,itheta,iphi] = rho_cav_center*((rc[ir]/AU)**2)
                        else:
                            rho_env[ir,itheta,iphi] = rho_cav_center*discont*(20*AU/rc[ir])**2
                        i += 1
                    elif (abs(z) > abs(z_cav_wall)) and (abs(z) < abs(z_cav)):
                        rho_env[ir,itheta,iphi] = rho_wall
                    else:
                        j += 1
                        mu = abs(np.cos(thetac[itheta]))
                        mu_o = np.abs(fsolve(func,[0.5,0.5,0.5],args=(rc[ir],rcen,mu))[0])
                        rho_env[ir,itheta,iphi] = M_env_dot/(4*PI*(G*mstar*rcen**3)**0.5)*(rc[ir]/rcen)**(-3./2)*(1+mu/mu_o)**(-0.5)*(mu/mu_o+2*mu_o**2*rcen/rc[ir])**(-1)
                    # Disk profile
                    if ((w >= R_disk_min) and (w <= R_disk_max)) == True:
                        h = ((w/(100*AU))**beta)*h100
                        rho_disk[ir,itheta,iphi] = rho_0*(1-np.sqrt(rstar/w))*(rstar/w)**(beta+1)*np.exp(-0.5*(z/h)**2)
                    # Combine envelope and disk
                    rho[ir,itheta,iphi] = rho_disk[ir,itheta,iphi] + rho_env[ir,itheta,iphi]
        rho_env  = rho_env  + 1e-40
        rho_disk = rho_disk + 1e-40
        rho      = rho      + 1e-40

    # Insert the calculated grid and dust density profile into hyperion
    m.set_spherical_polar_grid(ri, thetai, phii)
    m.add_density_grid(rho.T, outdir+'oh5.hdf5')    # numpy read the array in reverse order

    # Define the luminsoity source
    source = m.add_spherical_source()
    source.luminosity = (4*PI*rstar**2)*sigma*(tstar**4)  # [ergs/s]
    source.radius = rstar  # [cm]
    source.temperature = tstar  # [K]
    source.position = (0., 0., 0.)
    print 'L_center =  % 5.2f L_sun' % ((4*PI*rstar**2)*sigma*(tstar**4)/LS)

    # Setting up images and SEDs
    image = m.add_peeled_images()
    image.set_wavelength_range(300, 2.0, 670.0)
    # pixel number
    image.set_image_size(300, 300)
    image.set_image_limits(-R_env_max, R_env_max, -R_env_max, R_env_max)
    image.set_viewing_angles([82.0], [0.0])
    image.set_uncertainties(True)
    # output as 64-bit
    image.set_output_bytes(8)

    # Radiative transfer setting

    # number of photons for temp and image
    m.set_raytracing(True)
    m.set_n_photons(initial=1000000, imaging=1000000, raytracing_sources=1000000, raytracing_dust=1000000)
    # number of iteration to compute dust specific energy (temperature)
    m.set_n_initial_iterations(5)
    m.set_convergence(True, percentile=99., absolute=1.5, relative=1.02)
    m.set_mrw(True)   # Gamma = 1 by default

    # Output setting
    # Density
    m.conf.output.output_density = 'last'

    # Density difference (shows where dust was destroyed)
    m.conf.output.output_density_diff = 'none'

    # Energy absorbed (using pathlengths)
    m.conf.output.output_specific_energy = 'last'

    # Number of unique photons that passed through the cell
    m.conf.output.output_n_photons = 'last'

    m.write(outdir+'old_setup2.rtin')

# 需要导入模块: from hyperion.model import Model [as 别名]
# 或者: from hyperion.model.Model import set_convergence [as 别名]

#.........这里部分代码省略.........
    n56     = 50.0
    
    lam12   = lambda1 * (lambda2/lambda1)**(np.arange(n12)/n12)
    lam23   = lambda2 * (lambda3/lambda2)**(np.arange(n23)/n23)
    lam34   = lambda3 * (lambda4/lambda3)**(np.arange(n34)/n34)
    lam45   = lambda4 * (lambda5/lambda4)**(np.arange(n45)/n45)
    lam56   = lambda5 * (lambda6/lambda5)**(np.arange(n56+1)/n56)

    lam_cam = np.concatenate([lam12,lam23,lam34,lam45,lam56])
    n_lam_cam = len(lam_cam)

    # Radiative transfer setting

    # number of photons for temp and image
    lam_list = lam.tolist()
    # print lam_list
    m.set_raytracing(True)
    # option of using more photons for imaging
    if better_im == False:
        im_photon = 1e6
    else:
        im_photon = 5e7

    if mono == True:
        # Monechromatic radiative transfer setting
        m.set_monochromatic(True, wavelengths=lam_list)
        m.set_n_photons(initial=1000000, imaging_sources=im_photon, imaging_dust=im_photon,raytracing_sources=1000000, raytracing_dust=1000000)
    else:
        # regular wavelength grid setting
        m.set_n_photons(initial=1000000, imaging=im_photon,raytracing_sources=1000000, raytracing_dust=1000000)    
    # number of iteration to compute dust specific energy (temperature)
    m.set_n_initial_iterations(20)
    # m.set_convergence(True, percentile=95., absolute=1.5, relative=1.02)
    m.set_convergence(True, percentile=dict_params['percentile'], absolute=dict_params['absolute'], relative=dict_params['relative'])
    m.set_mrw(True)   # Gamma = 1 by default
    # m.set_forced_first_scattering(forced_first_scattering=True)

    # Setting up images and SEDs
    # SED setting

    # Infinite aperture
    syn_inf = m.add_peeled_images(image=False)
    # use the index of wavelength array used by the monochromatic radiative transfer
    if mono == False:
        syn_inf.set_wavelength_range(1400, 2.0, 1400.0)
    syn_inf.set_viewing_angles([dict_params['view_angle']], [0.0])
    syn_inf.set_uncertainties(True)
    syn_inf.set_output_bytes(8)

    # aperture
    # 7.2 in 10 um scaled by lambda / 10
    # flatten beyond 20 um
    # default aperture
    if aperture == None:    
        aperture = {'wave': [3.6, 4.5, 5.8, 8.0, 8.5, 9, 9.7, 10, 10.5, 11, 16, 20, 24, 35, 70, 100, 160, 250, 350, 500, 1300],\
                    'aperture': [7.2, 7.2, 7.2, 7.2, 7.2, 7.2, 7.2, 7.2, 7.2, 7.2, 20.4, 20.4, 20.4, 20.4, 24.5, 24.5, 24.5, 24.5, 24.5, 24.5, 101]}
    # assign wl_aper and aper from dictionary of aperture
    wl_aper = aperture['wave']
    aper    = aperture['aperture']
    # create the non-repetitive aperture list and index array
    aper_reduced = list(set(aper))
    index_reduced = np.arange(1, len(aper_reduced)+1)

    # name = np.arange(1,len(wl_aper)+1)
    # aper = np.empty_like(wl_aper)
    # for i in range(0, len(wl_aper)):