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 get_image(filename, dist): try: m = ModelOutput(filename) return m.get_image(inclination='all', distance=luminosity_distance, units='Jy') except (OSError, ValueError) as e: print("OS Error in reading in: " + filename) pass
def test_docs_example(self): import numpy as np from hyperion.model import ModelOutput from hyperion.util.constants import pc from fluxcompensator.cube import * from fluxcompensator.psf import * from fluxcompensator.utils.resolution import * # read in from HYPERION m = ModelOutput( os.path.join(os.path.dirname(__file__), 'hyperion_output.rtout')) array = m.get_image(group=0, inclination=0, distance=300 * pc, units='ergs/cm^2/s') # initial FluxCompensator array c = SyntheticCube(input_array=array, unit_out='ergs/cm^2/s', name='test_cube') # dered with provided extinction law ext = c.extinction(A_v=20.) # change resolution to 10-times of the initial zoom = ext.change_resolution(new_resolution=10 * ext.resolution['arcsec'], grid_plot=True) import fluxcompensator.database.missions as PSFs # call object from the psf database psf_object = getattr(PSFs, 'PACS1_PSF') # convolve with PSF psf = zoom.convolve_psf(psf_object) import fluxcompensator.database.missions as filters # call object from the filter database filter_input = getattr(filters, 'PACS1_FILTER') # convolve with filter filtered = psf.convolve_filter(filter_input, plot_rebin=None, plot_rebin_dpi=None) # add noise noise = filtered.add_noise(mu_noise=0, sigma_noise=5e-15, diagnostics=None)
def extract(model): # Extract model name model_name = os.path.basename(model).replace('.rtout', '').replace('external_', '') m = ModelOutput(model) wav, flux = m.get_image(group=0, units='MJy/sr', distance=1000. * kpc) # distance should not matter as long as it is large flux = flux[0, :, :, :] # Convolve with filters flux_conv = np.zeros((len(filters), flux.shape[0], flux.shape[1])) for i, filtname in enumerate(filters): transmission = rebin_filter(filtname, c / (wav * 1.e-4)) flux_conv[i, :, :] = np.sum(transmission[np.newaxis, np.newaxis:] * flux, axis=2) pyfits.writeto('models/external/external_%s.fits' % model_name, flux, clobber=True) pyfits.writeto('models/external/external_%s_conv.fits' % model_name, flux_conv, clobber=True)
def setup_method(self, method): import numpy as np from hyperion.model import ModelOutput from hyperion.util.constants import kpc from fluxcompensator.cube import * # read in from HYPERION m = ModelOutput( os.path.join(os.path.dirname(__file__), 'hyperion_output.rtout')) array = m.get_image(group=0, inclination=0, distance=10 * kpc, units='ergs/cm^2/s') # initial FluxCompensator array self.FC_object = SyntheticCube(input_array=array, unit_out='ergs/cm^2/s', name='test_cube')
import numpy as np import matplotlib.pyplot as plt from hyperion.model import ModelOutput from hyperion.util.integrate import integrate_loglog # Use LaTeX for plots plt.rc('text', usetex=True) # Open the output file m = ModelOutput('example_isrf.rtout') # Get an all-sky flux map image = m.get_image(units='ergs/cm^2/s/Hz', inclination=0) # Compute the frequency-integrated flux fint = np.zeros(image.val.shape[:-1]) for (j, i) in np.ndindex(fint.shape): fint[j, i] = integrate_loglog(image.nu, image.val[j, i, :]) # Find the area of each pixel l = np.radians(np.linspace(180., -180., fint.shape[1] + 1)) b = np.radians(np.linspace(-90., 90., fint.shape[0] + 1)) dl = l[1:] - l[:-1] db = np.sin(b[1:]) - np.sin(b[:-1]) DL, DB = np.meshgrid(dl, db) area = np.abs(DL * DB) # Compute the intensity intensity = fint / area
def hyperion_image(rtout, wave, plotdir, printname, dstar=178., group=0, marker=0, size='full', convolve=False, unit=None): # to avoid X server error import matplotlib as mpl mpl.use('Agg') import numpy as np import matplotlib.pyplot as plt import matplotlib as mpl import astropy.constants as const from hyperion.model import ModelOutput # Package for matching the colorbar from mpl_toolkits.axes_grid1 import make_axes_locatable pc = const.pc.cgs.value if unit == None: unit = r'$\rm{log(I_{\nu})\,[erg\,s^{-1}\,cm^{-2}\,Hz^{-1}\,sr^{-1}]}$' m = ModelOutput(rtout) # Extract the image. image = m.get_image(group=group, inclination=0, distance=dstar * pc, units='mJy') print np.shape(image.val) # Open figure and create axes fig = plt.figure(figsize=(8,8)) ax = fig.add_subplot(111) # Find the closest wavelength iwav = np.argmin(np.abs(wave - image.wav)) # Calculate the image width in arcseconds given the distance used above # get the max radius rmax = max(m.get_quantities().r_wall) w = np.degrees(rmax / image.distance) * 3600. # Image in the unit of MJy/sr # Change it into erg/s/cm2/Hz/sr # factor = 1e-23*1e6 factor = 1 # avoid zero in log # flip the image, because the setup of inclination is upside down val = image.val[::-1, :, iwav] * factor + 1e-30 if convolve: from astropy.convolution import convolve, Gaussian2DKernel img_res = 2*w/len(val[:,0]) kernel = Gaussian2DKernel(0.27/2.354/img_res) val = convolve(val, kernel) if size != 'full': pix_e2c = (w-size/2.)/w * len(val[:,0])/2 val = val[pix_e2c:-pix_e2c, pix_e2c:-pix_e2c] w = size/2. # This is the command to show the image. The parameters vmin and vmax are # the min and max levels for the colorscale (remove for default values). # cmap = sns.cubehelix_palette(start=0.1, rot=-0.7, gamma=0.2, as_cmap=True) cmap = plt.cm.CMRmap # im = ax.imshow(np.log10(val), vmin= -20, vmax= -15, # cmap=cmap, origin='lower', extent=[-w, w, -w, w], aspect=1) im = ax.imshow(val, cmap=cmap, origin='lower', extent=[-w, w, -w, w], aspect=1) print val.max() # plot the marker for center position by default or user input offset ax.plot([0],[-marker], '+', color='ForestGreen', markersize=10, mew=2) ax.set_xlim([-w,w]) ax.set_ylim([-w,w]) # ax.plot([0],[-10], '+', color='m', markersize=10, mew=2) # fix the tick label font ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',size=14) for label in ax.get_xticklabels(): label.set_fontproperties(ticks_font) for label in ax.get_yticklabels(): label.set_fontproperties(ticks_font) # Colorbar setting # create an axes on the right side of ax. The width of cax will be 5% # of ax and the padding between cax and ax will be fixed at 0.05 inch. divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cb = fig.colorbar(im, cax=cax) cb.solids.set_edgecolor("face") cb.ax.minorticks_on() cb.ax.set_ylabel(unit,fontsize=18) cb_obj = plt.getp(cb.ax.axes, 'yticklabels') plt.setp(cb_obj,fontsize=14) # fix the tick label font ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',size=14) for label in cb.ax.get_yticklabels(): label.set_fontproperties(ticks_font) ax.set_xlabel(r'$\rm{RA\,Offset\,(arcsec)}$', fontsize=18) ax.set_ylabel(r'$\rm{Dec\,Offset\,(arcsec)}$', fontsize=18) ax.tick_params(axis='both', which='major', labelsize=18) ax.text(0.7,0.88,str(wave) + r'$\rm{\,\mu m}$',fontsize=20,color='white', transform=ax.transAxes) fig.savefig(plotdir+printname+'_image_'+str(wave)+'.pdf', format='pdf', dpi=300, bbox_inches='tight') fig.clf()
def azimuthal_avg_radial_intensity(wave, imgpath, source_center, rtout, plotname, annulus_width=10, group=8, dstar=200.): import numpy as np import matplotlib as mpl # to avoid X server error mpl.use('Agg') from astropy.io import ascii, fits import matplotlib.pyplot as plt from photutils import aperture_photometry as ap from photutils import CircularAperture, CircularAnnulus from astropy import units as u from astropy.coordinates import SkyCoord from astropy import wcs from hyperion.model import ModelOutput import astropy.constants as const import os pc = const.pc.cgs.value AU = const.au.cgs.value # source_center = '12 01 36.3 -65 08 53.0' # Read in data and set up coversions im_hdu = fits.open(imgpath) im = im_hdu[1].data # error if (wave < 200.0) & (wave > 70.0): im_err = im_hdu[5].data elif (wave > 200.0) & (wave < 670.0): im_err = im_hdu[5].data else: im_err_exten = raw_input( 'The extension that includes the image error: ') im_err = im_hdu[int(im_err_exten)].data w = wcs.WCS(im_hdu[1].header) coord = SkyCoord(source_center, unit=(u.hourangle, u.deg)) pixcoord = w.wcs_world2pix(coord.ra.degree, coord.dec.degree, 1) pix2arcsec = abs(im_hdu[1].header['CDELT1']) * 3600. # convert intensity unit from MJy/sr to Jy/pixel factor = 1e6 / 4.25e10 * abs( im_hdu[1].header['CDELT1'] * im_hdu[1].header['CDELT2']) * 3600**2 # radial grid in arcsec # annulus_width = 10 r = np.arange(10, 200, annulus_width, dtype=float) I = np.empty_like(r[:-1]) I_err = np.empty_like(r[:-1]) # iteration for ir in range(len(r) - 1): aperture = CircularAnnulus((pixcoord[0], pixcoord[1]), r_in=r[ir] / pix2arcsec, r_out=r[ir + 1] / pix2arcsec) # print aperture.r_in phot = ap(im, aperture, error=im_err) I[ir] = phot['aperture_sum'].data * factor / aperture.area() I_err[ir] = phot['aperture_sum_err'].data * factor / aperture.area() # print r[ir], I[ir] # read in from RTout rtout = ModelOutput(rtout) # setting up parameters # dstar = 200. # group = 8 # wave = 500.0 im = rtout.get_image(group=group, inclination=0, distance=dstar * pc, units='Jy', uncertainties=True) # Find the closest wavelength iwav = np.argmin(np.abs(wave - im.wav)) # avoid zero when log, and flip the image val = im.val[::-1, :, iwav] unc = im.unc[::-1, :, iwav] w = np.degrees(max(rtout.get_quantities().r_wall) / im.distance) * 3600 npix = len(val[:, 0]) pix2arcsec = 2 * w / npix # radial grid in arcsec # annulus_width = 10 r = np.arange(10, 200, annulus_width, dtype=float) I_sim = np.empty_like(r[:-1]) I_sim_err = np.empty_like(r[:-1]) # iteration for ir in range(len(r) - 1): aperture = CircularAnnulus((npix / 2. + 0.5, npix / 2. + 0.5), r_in=r[ir] / pix2arcsec, r_out=r[ir + 1] / pix2arcsec) # print aperture.r_in phot = ap(val, aperture, error=unc) I_sim[ir] = phot['aperture_sum'].data / aperture.area() I_sim_err[ir] = phot['aperture_sum_err'].data / aperture.area() # print r[ir], I_sim[ir] # write the numbers into file foo = open(plotname + '_radial_profile_' + str(wave) + 'um.txt', 'w') # print some header info foo.write('# wavelength ' + str(wave) + ' um \n') foo.write('# image file ' + os.path.basename(imgpath) + ' \n') foo.write('# annulus width ' + str(annulus_width) + ' arcsec \n') # write profiles foo.write('r_in[arcsec] \t I \t I_err \t I_sim \t I_sim_err \n') for i in range(len(I)): foo.write('%f \t %e \t %e \t %e \t %e \n' % (r[i], I[i], I_err[i], I_sim[i], I_sim_err[i])) foo.close() # plot fig = plt.figure(figsize=(8, 6)) ax = fig.add_subplot(111) I_sim_hi = np.log10( (I_sim + I_sim_err) / I_sim.max()) - np.log10(I_sim / I_sim.max()) I_sim_low = np.log10(I_sim / I_sim.max()) - np.log10( (I_sim - I_sim_err) / I_sim.max()) I_hi = np.log10((I + I_err) / I.max()) - np.log10(I / I.max()) I_low = np.log10(I / I.max()) - np.log10((I - I_err) / I.max()) i_sim = ax.errorbar(np.log10(r[:-1] * dstar), np.log10(I_sim / I_sim.max()), yerr=(I_sim_low, I_sim_hi), marker='o', linestyle='-', mec='None', markersize=10) i = ax.errorbar(np.log10(r[:-1] * dstar), np.log10(I / I.max()), yerr=(I_low, I_hi), marker='o', linestyle='-', mec='None', markersize=10) ax.legend([i, i_sim], [r'$\rm{observation}$', r'$\rm{simulation}$'], fontsize=16, numpoints=1, loc='upper right') [ ax.spines[axis].set_linewidth(1.5) for axis in ['top', 'bottom', 'left', 'right'] ] ax.minorticks_on() ax.tick_params('both', labelsize=18, width=1.5, which='major', pad=10, length=5) ax.tick_params('both', labelsize=18, width=1.5, which='minor', pad=10, length=2.5) ax.set_xlabel(r'$\rm{log(Radius)\,[AU]}$', fontsize=18) ax.set_ylabel(r'$\rm{log(I\,/\,I_{max})}$', fontsize=18) # fix the tick label font ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral', size=18) for label in ax.get_xticklabels(): label.set_fontproperties(ticks_font) for label in ax.get_yticklabels(): label.set_fontproperties(ticks_font) fig.savefig(plotname + '_radial_profile_' + str(wave) + 'um.pdf', format='pdf', dpi=300, bbox_inches='tight') fig.clf()
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()
type=float, help='Minimum of colorbar scale, in units of ergs/s.') parser.add_argument('--vmax', type=float, help='Maximum of colorbar scale, in units of ergs/s.') args = parser.parse_args() m = ModelOutput(pathch(args.infile)) if args.outfile is None: args.outfile = os.path.dirname(args.infile) # Extract the image for the first inclination, and scale to 300pc. We # have to specify group=1 as there is no image in group 0. image = m.get_image(units='ergs/s') # Open figure and create axes fig = plt.figure() ax = fig.add_subplot(111) # Calculate the image width in kpc w = image.x_max * u.cm w = w.to(u.kpc) # Find the closest wavelength iwav = np.argmin(np.abs(args.wav - image.wav)) print('Input wavelength: {}'.format(args.wav)) print('Closest: {}'.format(image.wav[iwav])) image_data = image.val[0, :, :, iwav] default_image_suffix = '{:.4f}um'.format(image.wav[iwav])
def convolve(image_file, filterfilenames, filter_data): # Load the model output object m = ModelOutput(image_file) # Get the image image = m.get_image(units='ergs/s') # Get image bounds for correct scaling w = image.x_max * u.cm w = w.to(u.kpc) # This is where the convolved images will go image_data = [] # List the filters that shouldn't be used in convolution skip_conv = ['arbitrary.filter', 'pdfilters.dat'] # Loop through the filters and match wavelengths to those in the image for i in range(len(filterfilenames)): # Skip "arbitrary.filter" if it is selected if filterfilenames[i] in skip_conv: print(" Skipping convolution of default filter") continue print("\n Convolving filter {}...".format(filterfilenames[i])) wavs = filter_data[i][:, 0] # Figure out which indices of the image wavelengths correspond to # this filter indices = [] for wav in wavs: diffs = np.abs(image.wav - wav) # Make sure the closest wavelength is *really* close --- there # could be rounding errors, but we don't want to accidentally grab # the wrong wavelength if min(diffs) <= 1e-10: indices.append(diffs.argmin()) if len(indices) != len(wavs): raise ValueError( "Filter wavelength mismatch with available image wavelengths") # Get the monochromatic images at each wavelength in the filter images = [image.val[0, :, :, j] for j in indices] print(' Found {} monochromatic images'.format(len(images))) # Show wavelengths and weights from filter file wavelengths = [image.wav[j] for j in indices] weights = filter_data[i][:, 1] print('\n Wavelength Weight') print(' ---------- ------') for k in range(len(wavelengths)): print(' {:.2E} {:.2E}'.format( wavelengths[k], weights[k])) # Apply appropriate transmissivities from filter file image_data.append(np.average(images, axis=0, weights=weights)) # Save the image data and filter information as an .hdf5 file f = h5py.File( cfg.model.PD_output_dir + "convolved." + cfg.model.snapnum_str + ".hdf5", "w") f.create_dataset("image_data", data=image_data) f['image_data'].attrs['width'] = w.value f['image_data'].attrs['width_unit'] = np.bytes_('kpc') # Don't add the names of filters that were skipped trimmed_names = list(set(filterfilenames) - set(skip_conv)) f.create_dataset("filter_names", data=trimmed_names) for i in range(len(filterfilenames)): f.create_dataset(filterfilenames[i], data=filter_data[i]) f.close()
outdir = '/Users/yaolun/test/' dist = 178. wave = 500. from hyperion.model import ModelOutput import astropy.constants as const import numpy as np import matplotlib.pyplot as plt from matplotlib import font_manager from mpl_toolkits.axes_grid1 import make_axes_locatable # constant setup pc = const.pc.cgs.value m = ModelOutput(filename) image = m.get_image(group=22, inclination=0, distance=dist * pc, units='MJy/sr') # Find the closest wavelength iwav = np.argmin(np.abs(wave - image.wav)) # Calculate the image width in arcseconds given the distance used above # get the max radius rmax = max(m.get_quantities().r_wall) w = np.degrees(rmax / image.distance) * 3600. # Image in the unit of MJy/sr # Change it into erg/s/cm2/Hz/sr factor = 1e-23*1e6 # avoid zero in log # flip the image, because the setup of inclination is upside down val = image.val[::-1, :, iwav] * factor + 1e-30
import matplotlib.pyplot as plt from hyperion.model import ModelOutput from hyperion.util.constants import pc mo = ModelOutput('pure_scattering.rtout') image_fnu = mo.get_image(inclination=0, units='MJy/sr', distance=300. * pc) image_pol = mo.get_image(inclination=0, stokes='linpol') fig = plt.figure(figsize=(8, 8)) # Make total intensity sub-plot ax = fig.add_axes([0.1, 0.3, 0.4, 0.4]) ax.imshow(image_fnu.val[:, :, 0], extent=[-13, 13, -13, 13], interpolation='none', cmap=plt.cm.gist_heat, origin='lower', vmin=0., vmax=4e9) ax.set_xlim(-13., 13.) ax.set_ylim(-13., 13.) ax.set_xlabel("x (solar radii)") ax.set_ylabel("y (solar radii)") ax.set_title("Surface brightness") # Make linear polarization sub-plot ax = fig.add_axes([0.51, 0.3, 0.4, 0.4]) im = ax.imshow(image_pol.val[:, :, 0] * 100., extent=[-13, 13, -13, 13], interpolation='none', cmap=plt.cm.gist_heat, origin='lower', vmin=0., vmax=100.) ax.set_xlim(-13., 13.) ax.set_ylim(-13., 13.)
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.")
f2 = np.loadtxt(image_dat_file_2) diff = f1 - f2 OUTPUT_DIR = '/home/cmcclellan1010/pdwork/output/' np.savetxt(OUTPUT_DIR+'difference.dat', diff) # Image data path = '/home/cmcclellan1010/pdwork/output/manualconv/' filename = 'example.134.rtout.image' m = ModelOutput(path+filename) redshift = 3.1 image_width = 200 #kpc distance = Planck13.luminosity_distance(redshift).cgs.value image = m.get_image(distance=distance, units='mJy') w = image.x_max * u.cm w = w.to(u.kpc) # Plot the figure fig = plt.figure() ax = fig.add_subplot(111) cax = ax.imshow(diff, cmap=plt.cm.viridis, origin='lower', extent=[-w.value, w.value, -w.value, w.value]) plt.xlim([-image_width,image_width]) plt.ylim([-image_width,image_width]) ax.tick_params(axis='both', which='major', labelsize=10) ax.set_xlabel('x kpc') ax.set_xlabel('y kpc')
def extract(model): # Check that file is valid if not os.path.basename(model).startswith('basic_') or not os.path.basename(model).endswith('.rtout'): raise Exception("Only basic_*.rtout files should be specified") # Extract model name model_name = os.path.basename(model).replace('.rtout', '').replace('basic_', '') m = ModelOutput('models/basic/basic_%s.rtout' % model_name) for image_set in range(3): if image_set == 0: n_x = 1 n_y = 1 image_set_name = 'total' elif image_set == 1: n_x = 130 n_y = 1 image_set_name = 'lon' elif image_set == 2: n_x = 1 n_y = 40 image_set_name = 'lat' flux = np.zeros((n_y, n_x, n_groups, n_wav)) print "Direct source emission" try: wav, nufnu_all = m.get_image(group=image_set, component='source_emit', units='MJy/sr', source_id='all') except IOError: return for source_id in range(n_sources): nufnu = nufnu_all[source_id, 0, :, :, :] spec_type = t_default['Type'][source_id].strip().upper() group_id = group(spec_type) flux[:, :, group_id, :] += nufnu print "Direct dust emission" wav, nufnu_all = m.get_image(group=image_set, component='dust_emit', units='MJy/sr', dust_id='all') for dust_id in range(n_dust): nufnu = nufnu_all[dust_id, 0, :, :, :] flux[:, :, 5 + dust_id, :] += nufnu print "Scattered source emission" wav, nufnu = m.get_image(group=image_set, component='source_scat', units='MJy/sr') nufnu = nufnu[0, :, :, :] flux[:, :, 8, :] += nufnu print "Scattered dust emission" wav, nufnu = m.get_image(group=image_set, component='dust_scat', units='MJy/sr') nufnu = nufnu[0, :, :, :] flux[:, :, 8, :] += nufnu # Convolve with filters flux_conv = np.zeros((len(filters), n_y, n_x, n_groups)) for i, filtname in enumerate(filters): transmission = rebin_filter(filtname, c / (wav * 1.e-4)) flux_conv[i, :, :, :] = np.sum(transmission[np.newaxis, np.newaxis, np.newaxis, :] * flux, axis=3) pyfits.writeto('models/basic/images_%s_%s.fits' % (image_set_name, model_name), flux, clobber=True) pyfits.writeto('models/basic/images_%s_%s_conv.fits' % (image_set_name, model_name), flux_conv, clobber=True)
def azimuthal_simulation(rtout, beam_size, wave, dist=200., group=22): """ rtout: the filepath to the output file of Hyperion beam_size: the beam size used for the width of annulus dist: the physical distance to the source group: the group which contains image """ import numpy as np import matplotlib.pyplot as plt import astropy.constants as const from hyperion.model import ModelOutput # constant setup pc = const.pc.cgs.value au = const.au.cgs.value output = {'wave': wave, 'annuli': [], 'flux_annuli': []} # Read in the Hyperion output file m = ModelOutput(rtout) # get image image = m.get_image(group=5, inclination=0, distance=dist*pc, units='Jy') # Calculate the image width in arcsec given the distance to the source rmax = max(m.get_quantities().r_wall) w = np.degrees(rmax / image.distance) * 3600 # grid of radii of annulus annuli = np.linspace(beam_size/2., np.floor((w-beam_size/2.)/beam_size)*beam_size, np.floor((w-beam_size/2.)/beam_size)) # plot fig = plt.figure(figsize=(8,6)) ax = fig.add_subplot(111) # iternate through wavelength if type(wave) == int or type(wave) == float: wave = [wave] color_list = plt.cm.viridis(np.linspace(0, 1, len(wave)+1)) for i in range(len(wave)): wav = wave[i] # Find the closest wavelength iwav = np.argmin(np.abs(wav - image.wav)) # avoid zero when log, and flip the image val = image.val[::-1, :, iwav] # determine the center of the image npix = len(val[:,0]) center = np.array([npix/2. + 0.5, npix/2. + 0.5]) scale = 2*rmax/npix # create index array of the image x = np.empty_like(val) for j in range(len(val[0,:])): x[:,j] = j flux_annuli = np.empty_like(annuli) for k in range(len(annuli)): flux_annuli[k] = np.sum(val[(((x-center[0])**2+(x.T-center[1])**2)**0.5*2*w/npix >= annuli[k]-beam_size/2.) & \ (((x-center[0])**2+(x.T-center[1])**2)**0.5*2*w/npix < annuli[k]+beam_size/2.)]) output['annuli'].append(np.array(annuli)) output['flux_annuli'].append(flux_annuli) flux_annuli = flux_annuli/np.nanmax(flux_annuli) ax.plot(np.log10(annuli*dist), np.log10(flux_annuli), 'o-', color=color_list[i], \ markersize=3, mec='None', label=r'$\rm{'+str(wav)+'\,\mu m}$') ax.axvline(np.log10((w-beam_size/2.)*dist), linestyle='--', color='k') ax.axvline(np.log10(w*dist), linestyle='-', color='k') ax.legend(loc='best', fontsize=12, numpoints=1, ncol=2) ax.set_xlabel(r'$\rm{log(Radius)\,[AU]}$', fontsize=18) ax.set_ylabel(r'${\rm log(}F/F_{\rm max})$', fontsize=18) fig.gca().set_ylim(top=0.1) [ax.spines[axis].set_linewidth(1.5) for axis in ['top','bottom','left','right']] ax.minorticks_on() ax.tick_params('both',labelsize=18,width=1.5,which='major',pad=15,length=5) ax.tick_params('both',labelsize=18,width=1.5,which='minor',pad=15,length=2.5) fig.savefig('/Users/yaolun/test/annuli_profile.pdf', format='pdf', dpi=300, bbox_inches='tight') fig.clf() return output
import matplotlib.pyplot as plt from hyperion.model import ModelOutput from hyperion.util.constants import pc mo = ModelOutput('class1_example.rtout') sed = mo.get_sed(aperture=-1, distance=140. * pc) image = mo.get_image(inclination=0,distance=300*pc,units='Jy') fig = plt.figure(figsize=(5, 4)) ax = fig.add_subplot(1, 1, 1) ax.loglog(sed.wav, sed.val.transpose(), color='black') ax.set_xlim(0.03, 2000.) ax.set_ylim(2.e-15, 1e-8) ax.set_xlabel(r'$\lambda$ [$\mu$m]') ax.set_ylabel(r'$\lambda F_\lambda$ [ergs/cm$^2/s$]') #ax2 = fig_add_subplot(1,1,2) #ax.imshow(image,origin='lower') fig.savefig('class1_example_sed.png', bbox_inches='tight')
def extract_hyperion(filename, indir=None, outdir=None, dstar=200.0, aperture=None, save=True, filter_func=False, plot_all=False, clean=False, exclude_wl=[], log=True, image=True, obj='BHR71', print_data_w_aper=False, mag=1.5): """ filename: The path to Hyperion output file indir: The path to the directory which contains observations data outdir: The path to the directory for storing extracted plots and ASCII files """ def l_bol(wl, fv, dstar): import numpy as np import astropy.constants as const # wavelength unit: um # Flux density unit: Jy # constants setup # c = const.c.cgs.value pc = const.pc.cgs.value PI = np.pi SL = const.L_sun.cgs.value # Convert the unit from Jy to erg s-1 cm-2 Hz-1 fv = np.array(fv) * 1e-23 freq = c / (1e-4 * np.array(wl)) diff_dum = freq[1:] - freq[0:-1] freq_interpol = np.hstack( (freq[0:-1] + diff_dum / 2.0, freq[0:-1] + diff_dum / 2.0, freq[0], freq[-1])) freq_interpol = freq_interpol[np.argsort(freq_interpol)[::-1]] fv_interpol = np.empty(len(freq_interpol)) # calculate the histogram style of spectrum # for i in range(0, len(fv)): if i == 0: fv_interpol[i] = fv[i] else: fv_interpol[2 * i - 1] = fv[i - 1] fv_interpol[2 * i] = fv[i] fv_interpol[-1] = fv[-1] dv = freq_interpol[0:-1] - freq_interpol[1:] dv = np.delete(dv, np.where(dv == 0)) fv = fv[np.argsort(freq)] freq = freq[np.argsort(freq)] return (np.trapz(fv, freq) * 4. * PI * (dstar * pc)**2) / SL # function for properly calculating uncertainty of spectrophotometry value def unc_spectrophoto(wl, unc, trans): # adopting smiliar procedure as Trapezoidal rule # (b-a) * [ f(a) + f(b) ] / 2 # return (np.sum(trans[:-1]**2 * unc[:-1]**2 * (wl[1:] - wl[:-1])**2) / np.trapz(trans, x=wl)**2)**0.5 # to avoid X server error import matplotlib as mpl mpl.use('Agg') # import matplotlib.pyplot as plt import numpy as np import os from hyperion.model import ModelOutput, Model from scipy.interpolate import interp1d from hyperion.util.constants import pc, c, lsun, au from astropy.io import ascii import sys from phot_filter import phot_filter from get_obs import get_obs # Open the model m = ModelOutput(filename) # Read in the observation data and calculate the noise & variance if indir == None: indir = raw_input('Path to the observation data: ') if outdir == None: outdir = raw_input('Path for the output: ') # assign the file name from the input file print_name = os.path.splitext(os.path.basename(filename))[0] # use a canned function to extract observational data obs_data = get_obs(indir, obj=obj) # unit in um, Jy wl_tot, flux_tot, unc_tot = obs_data['spec'] flux_tot = flux_tot * 1e-23 # convert unit from Jy to erg s-1 cm-2 Hz-1 unc_tot = unc_tot * 1e-23 l_bol_obs = l_bol(wl_tot, flux_tot * 1e23, dstar) wl_phot, flux_phot, flux_sig_phot = obs_data['phot'] flux_phot = flux_phot * 1e-23 # convert unit from Jy to erg s-1 cm-2 Hz-1 flux_sig_phot = flux_sig_phot * 1e-23 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, 30, 70, 100, 160, 250, 350, 500, 850],\ '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, 24.5]} # 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) # '+1': the zeroth slice corresponds to infinite aperture # Create the plot fig = plt.figure(figsize=(8 * mag, 6 * mag)) ax_sed = fig.add_subplot(1, 1, 1) # Plot the observed SED if not clean: color_seq = ['Green', 'Red', 'Black'] else: color_seq = ['DimGray', 'DimGray', 'DimGray'] # plot the observations # plot in log scale if log: pacs, = ax_sed.plot( np.log10(wl_tot[(wl_tot > 40) & (wl_tot < 190.31)]), np.log10(c / (wl_tot[(wl_tot > 40) & (wl_tot < 190.31)] * 1e-4) * flux_tot[(wl_tot > 40) & (wl_tot < 190.31)]), '-', color=color_seq[0], linewidth=1.5 * mag, alpha=0.7) spire, = ax_sed.plot(np.log10(wl_tot[wl_tot > 194]), np.log10(c / (wl_tot[wl_tot > 194] * 1e-4) * flux_tot[wl_tot > 194]), '-', color=color_seq[1], linewidth=1.5 * mag, alpha=0.7) irs, = ax_sed.plot(np.log10(wl_tot[wl_tot < 40]), np.log10(c / (wl_tot[wl_tot < 40] * 1e-4) * flux_tot[wl_tot < 40]), '-', color=color_seq[2], linewidth=1.5 * mag, alpha=0.7) photometry, = ax_sed.plot(np.log10(wl_phot), np.log10(c / (wl_phot * 1e-4) * flux_phot), 's', mfc='DimGray', mec='k', markersize=8) # plot the observed photometry data ax_sed.errorbar( np.log10(wl_phot), np.log10(c / (wl_phot * 1e-4) * flux_phot), yerr=[ np.log10(c / (wl_phot * 1e-4) * flux_phot) - np.log10(c / (wl_phot * 1e-4) * (flux_phot - flux_sig_phot)), np.log10(c / (wl_phot * 1e-4) * (flux_phot + flux_sig_phot)) - np.log10(c / (wl_phot * 1e-4) * flux_phot) ], fmt='s', mfc='DimGray', mec='k', markersize=8) # plot in normal scale else: pacs, = ax_sed.plot( np.log10(wl_tot[(wl_tot > 40) & (wl_tot < 190.31)]), c / (wl_tot[(wl_tot > 40) & (wl_tot < 190.31)] * 1e-4) * flux_tot[(wl_tot > 40) & (wl_tot < 190.31)], '-', color=color_seq[0], linewidth=1.5 * mag, alpha=0.7) spire, = ax_sed.plot(np.log10(wl_tot[wl_tot > 194]), c / (wl_tot[wl_tot > 194] * 1e-4) * flux_tot[wl_tot > 194], '-', color=color_seq[1], linewidth=1.5 * mag, alpha=0.7) irs, = ax_sed.plot(np.log10(wl_tot[wl_tot < 40]), c / (wl_tot[wl_tot < 40] * 1e-4) * flux_tot[wl_tot < 40], '-', color=color_seq[2], linewidth=1.5 * mag, alpha=0.7) photometry, = ax_sed.plot(wl_phot, c / (wl_phot * 1e-4) * flux_phot, 's', mfc='DimGray', mec='k', markersize=8) # plot the observed photometry data ax_sed.errorbar( np.log10(wl_phot), c / (wl_phot * 1e-4) * flux_phot, yerr=[ c / (wl_phot * 1e-4) * flux_phot - c / (wl_phot * 1e-4) * (flux_phot - flux_sig_phot), c / (wl_phot * 1e-4) * (flux_phot + flux_sig_phot) - c / (wl_phot * 1e-4) * flux_phot ], fmt='s', mfc='DimGray', mec='k', markersize=8) # if keyword 'clean' is not set, print L_bol derived from observations at upper right corner. if not clean: ax_sed.text(0.75, 0.9, r'$\rm{L_{bol}= %5.2f L_{\odot}}$' % l_bol_obs, fontsize=mag * 16, transform=ax_sed.transAxes) # getting SED with infinite aperture sed_inf = m.get_sed(group=0, inclination=0, aperture=-1, distance=dstar * pc, uncertainties=True) # plot the simulated SED with infinite aperture if clean == False: sim, = ax_sed.plot(np.log10(sed_inf.wav), np.log10(sed_inf.val), '-', color='GoldenRod', linewidth=0.5 * mag) ax_sed.fill_between(np.log10(sed_inf.wav), np.log10(sed_inf.val - sed_inf.unc), np.log10(sed_inf.val + sed_inf.unc), color='GoldenRod', alpha=0.5) ####################################### # get fluxes with different apertures # ####################################### # this is non-reduced wavelength array because this is for printing out fluxes at all channels specified by users flux_aper = np.zeros_like(wl_aper, dtype=float) unc_aper = np.zeros_like(wl_aper, dtype=float) a = np.zeros_like(wl_aper) + 1 color_list = plt.cm.jet(np.linspace(0, 1, len(wl_aper) + 1)) for i in range(0, len(wl_aper)): # occasionally users might want not to report some wavelength channels if wl_aper[i] in exclude_wl: continue # getting simulated SED from Hyperion output. (have to match with the reduced index) sed_dum = m.get_sed( group=index_reduced[np.where(aper_reduced == aper[i])], inclination=0, aperture=-1, distance=dstar * pc, uncertainties=True) # plot the whole SED from this aperture (optional) if plot_all == True: ax_sed.plot(np.log10(sed_dum.wav), np.log10(sed_dum.val), '-', color=color_list[i]) ax_sed.fill_between(np.log10(sed_dum.wav), np.log10(sed_dum.val-sed_dum.unc), np.log10(sed_dum.val+sed_dum.unc),\ color=color_list[i], alpha=0.5) # Extracting spectrophotometry values from simulated SED # Not using the photometry filer function to extract spectrophotometry values # sort by wavelength first. sort_wl = np.argsort(sed_dum.wav) val_sort = sed_dum.val[sort_wl] unc_sort = sed_dum.unc[sort_wl] wav_sort = sed_dum.wav[sort_wl] # Before doing that, convert vSv to F_lambda flux_dum = val_sort / wav_sort unc_dum = unc_sort / wav_sort # If no using filter function to extract the spectrophotometry, # then use the spectral resolution. if filter_func == False: # use a rectangle function the average the simulated SED # apply the spectral resolution if (wl_aper[i] < 50.) & (wl_aper[i] >= 5): res = 60. elif wl_aper[i] < 5: res = 10. else: res = 1000. ind = np.where((wav_sort < wl_aper[i] * (1 + 1. / res)) & (wav_sort > wl_aper[i] * (1 - 1. / res))) if len(ind[0]) != 0: flux_aper[i] = np.mean(flux_dum[ind]) unc_aper[i] = np.mean(unc_dum[ind]) else: f = interp1d(wav_sort, flux_dum) f_unc = interp1d(wav_sort, unc_dum) flux_aper[i] = f(wl_aper[i]) unc_aper[i] = f_unc(wl_aper[i]) # Using photometry filter function to extract spectrophotometry values else: # apply the filter function # decide the filter name if wl_aper[i] == 70: fil_name = 'Herschel PACS 70um' elif wl_aper[i] == 100: fil_name = 'Herschel PACS 100um' elif wl_aper[i] == 160: fil_name = 'Herschel PACS 160um' elif wl_aper[i] == 250: fil_name = 'Herschel SPIRE 250um' elif wl_aper[i] == 350: fil_name = 'Herschel SPIRE 350um' elif wl_aper[i] == 500: fil_name = 'Herschel SPIRE 500um' elif wl_aper[i] == 3.6: fil_name = 'IRAC Channel 1' elif wl_aper[i] == 4.5: fil_name = 'IRAC Channel 2' elif wl_aper[i] == 5.8: fil_name = 'IRAC Channel 3' elif wl_aper[i] == 8.0: fil_name = 'IRAC Channel 4' elif wl_aper[i] == 24: fil_name = 'MIPS 24um' elif wl_aper[i] == 850: fil_name = 'SCUBA 850WB' else: fil_name = None if fil_name != None: filter_func = phot_filter(fil_name, indir) # Simulated SED should have enough wavelength coverage for applying photometry filters. f = interp1d(wav_sort, flux_dum) f_unc = interp1d(wav_sort, unc_dum) flux_aper[i] = np.trapz(f(filter_func['wave']/1e4)*\ filter_func['transmission'],x=filter_func['wave']/1e4 )/\ np.trapz(filter_func['transmission'], x=filter_func['wave']/1e4) # fix a bug unc_aper[i] = unc_spectrophoto( filter_func['wave'] / 1e4, f_unc(filter_func['wave'] / 1e4), filter_func['transmission']) else: # use a rectangle function the average the simulated SED # apply the spectral resolution if (wl_aper[i] < 50.) & (wl_aper[i] >= 5): res = 60. elif wl_aper[i] < 5: res = 10. else: res = 1000. ind = np.where((wav_sort < wl_aper[i] * (1 + 1. / res)) & (wav_sort > wl_aper[i] * (1 - 1. / res))) if len(ind[0]) != 0: flux_aper[i] = np.mean(flux_dum[ind]) unc_aper[i] = np.mean(unc_dum[ind]) else: f = interp1d(wav_sort, flux_dum) f_unc = interp1d(wav_sort, unc_dum) flux_aper[i] = f(wl_aper[i]) unc_aper[i] = f_unc(wl_aper[i]) # temperory step: solve issue of uncertainty greater than the value for i in range(len(wl_aper)): if unc_aper[i] >= flux_aper[i]: unc_aper[i] = flux_aper[i] - 1e-20 ########################### # Observations Extraction # ########################### # perform the same procedure of flux extraction of aperture flux with observed spectra # wl_aper = np.array(wl_aper, dtype=float) obs_aper_wl = wl_aper[(wl_aper >= min(wl_tot)) & (wl_aper <= max(wl_tot))] obs_aper_flux = np.zeros_like(obs_aper_wl) obs_aper_unc = np.zeros_like(obs_aper_wl) # have change the simulation part to work in F_lambda for fliter convolution # flux_tot and unc_tot have units of erg/s/cm2/Hz. Need to convert it to F_lambda (erg/s/cm2/um) fnu2fl = c / (wl_tot * 1e-4) / wl_tot # # wl_tot and flux_tot are already hstacked and sorted by wavelength for i in range(0, len(obs_aper_wl)): # sometime users want not report some wavelength channels if obs_aper_wl[i] in exclude_wl: continue if filter_func == False: # use a rectangle function the average the simulated SED # apply the spectral resolution if (obs_aper_wl[i] < 50.) & (obs_aper_wl[i] >= 5): res = 60. elif obs_aper_wl[i] < 5: res = 10. else: res = 1000. ind = np.where((wl_tot < obs_aper_wl[i] * (1 + 1. / res)) & (wl_tot > obs_aper_wl[i] * (1 - 1. / res))) if len(ind[0]) != 0: obs_aper_flux[i] = np.mean(fnu2fl[ind] * flux_tot[ind]) obs_aper_unc[i] = np.mean(fnu2fl[ind] * unc_tot[ind]) else: f = interp1d(wl_tot, fnu2fl * flux_tot) f_unc = interp1d(wl_tot, fnu2fl * unc_tot) obs_aper_flux[i] = f(obs_aper_wl[i]) obs_aper_unc[i] = f_unc(obs_aper_wl[i]) else: # apply the filter function # decide the filter name if obs_aper_wl[i] == 70: fil_name = 'Herschel PACS 70um' elif obs_aper_wl[i] == 100: fil_name = 'Herschel PACS 100um' elif obs_aper_wl[i] == 160: fil_name = 'Herschel PACS 160um' elif obs_aper_wl[i] == 250: fil_name = 'Herschel SPIRE 250um' elif obs_aper_wl[i] == 350: fil_name = 'Herschel SPIRE 350um' elif obs_aper_wl[i] == 500: fil_name = 'Herschel SPIRE 500um' elif obs_aper_wl[i] == 3.6: fil_name = 'IRAC Channel 1' elif obs_aper_wl[i] == 4.5: fil_name = 'IRAC Channel 2' elif obs_aper_wl[i] == 5.8: fil_name = 'IRAC Channel 3' elif obs_aper_wl[i] == 8.0: fil_name = 'IRAC Channel 4' elif obs_aper_wl[i] == 24: fil_name = 'MIPS 24um' elif obs_aper_wl[i] == 850: fil_name = 'SCUBA 850WB' # do not have SCUBA spectra else: fil_name = None if fil_name != None: filter_func = phot_filter(fil_name, indir) # Observed SED needs to be trimmed before applying photometry filters filter_func = filter_func[(filter_func['wave']/1e4 >= min(wl_tot))*\ ((filter_func['wave']/1e4 >= 54.8)+(filter_func['wave']/1e4 <= 36.0853))*\ ((filter_func['wave']/1e4 <= 95.05)+(filter_func['wave']/1e4 >=103))*\ ((filter_func['wave']/1e4 <= 190.31)+(filter_func['wave']/1e4 >= 195))*\ (filter_func['wave']/1e4 <= max(wl_tot))] f = interp1d(wl_tot, fnu2fl * flux_tot) f_unc = interp1d(wl_tot, fnu2fl * unc_tot) obs_aper_flux[i] = np.trapz(f(filter_func['wave']/1e4)*filter_func['transmission'], x=filter_func['wave']/1e4)/\ np.trapz(filter_func['transmission'], x=filter_func['wave']/1e4) obs_aper_unc[i] = unc_spectrophoto( filter_func['wave'] / 1e4, f_unc(filter_func['wave'] / 1e4), filter_func['transmission']) else: # use a rectangle function the average the simulated SED # apply the spectral resolution if (obs_aper_wl[i] < 50.) & (obs_aper_wl[i] >= 5): res = 60. elif obs_aper_wl[i] < 5: res = 10. else: res = 1000. ind = np.where((wl_tot < obs_aper_wl[i] * (1 + 1. / res)) & (wl_tot > obs_aper_wl[i] * (1 - 1. / res))) if len(ind[0]) != 0: obs_aper_flux[i] = np.mean(fnu2fl[ind] * flux_tot[ind]) obs_aper_unc[i] = np.mean(fnu2fl[ind] * unc_tot[ind]) else: f = interp1d(wl_tot, fnu2fl * flux_tot) f_unc = interp1d(wl_tot, fnu2fl * unc_tot) obs_aper_flux[i] = f(obs_aper_wl[i]) obs_aper_unc[i] = f_unc(obs_aper_wl[i]) # plot the aperture-extracted spectrophotometry fluxes from observed spectra and simulations # in log-scale if log: aper_obs = ax_sed.errorbar(np.log10(obs_aper_wl), np.log10(obs_aper_flux * obs_aper_wl ),\ yerr=[np.log10(obs_aper_flux*obs_aper_wl)-np.log10(obs_aper_flux*obs_aper_wl-obs_aper_unc*obs_aper_wl), np.log10(obs_aper_flux*obs_aper_wl+obs_aper_unc*obs_aper_wl)-np.log10(obs_aper_flux*obs_aper_wl)],\ fmt='s', mec='None', mfc='r', markersize=10, linewidth=1.5, ecolor='Red', elinewidth=3, capthick=3, barsabove=True) aper = ax_sed.errorbar(np.log10(wl_aper),np.log10(flux_aper*wl_aper),\ yerr=[np.log10(flux_aper*wl_aper)-np.log10(flux_aper*wl_aper-unc_aper*wl_aper), np.log10(flux_aper*wl_aper+unc_aper*wl_aper)-np.log10(flux_aper*wl_aper)],\ fmt='o', mec='Blue', mfc='None', color='b',markersize=12, markeredgewidth=2.5, linewidth=1.7, ecolor='Blue', elinewidth=3, barsabove=True) ax_sed.set_ylim([-14, -7]) ax_sed.set_xlim([0, 3.2]) # in normal scale (normal in y-axis) else: aper_obs = ax_sed.errorbar(np.log10(obs_aper_wl), obs_aper_flux*obs_aper_wl, yerr=obs_aper_unc*obs_aper_wl,\ fmt='s', mec='None', mfc='r', markersize=10, linewidth=1.5, ecolor='Red', elinewidth=3, capthick=3, barsabove=True) aper = ax_sed.errorbar(np.log10(wl_aper),flux_aper*wl_aper, yerr=unc_aper*wl_aper,\ fmt='o', mec='Blue', mfc='None', color='b',markersize=12, markeredgewidth=2.5, linewidth=1.7, ecolor='Blue', elinewidth=3, barsabove=True) ax_sed.set_xlim([0, 3.2]) # calculate the bolometric luminosity of the aperture # print flux_aper l_bol_sim = l_bol( wl_aper, flux_aper * wl_aper / (c / np.array(wl_aper) * 1e4) * 1e23, dstar) print 'Bolometric luminosity of simulated spectrum: %5.2f lsun' % l_bol_sim # print out the sed into ascii file for reading in later if save == True: # unapertured SED foo = open(outdir + print_name + '_sed_inf.txt', 'w') foo.write('%12s \t %12s \t %12s \n' % ('wave', 'vSv', 'sigma_vSv')) for i in range(0, len(sed_inf.wav)): foo.write('%12g \t %12g \t %12g \n' % (sed_inf.wav[i], sed_inf.val[i], sed_inf.unc[i])) foo.close() # SED with convolution of aperture sizes foo = open(outdir + print_name + '_sed_w_aperture.txt', 'w') foo.write('%12s \t %12s \t %12s \n' % ('wave', 'vSv', 'sigma_vSv')) for i in range(0, len(wl_aper)): foo.write('%12g \t %12g \t %12g \n' % (wl_aper[i], flux_aper[i] * wl_aper[i], unc_aper[i] * wl_aper[i])) foo.close() # print out the aperture-convolved fluxex from observations if print_data_w_aper: foo = open(outdir + print_name + '_obs_w_aperture.txt', 'w') foo.write('%12s \t %12s \t %12s \n' % ('wave', 'Jy', 'sigma_Jy')) for i in range(0, len(obs_aper_wl)): foo.write('%12g \t %12g \t %12g \n' % (obs_aper_wl[i], obs_aper_flux[i] * obs_aper_wl[i] / (c / obs_aper_wl[i] * 1e4) * 1e23, obs_aper_unc[i] * obs_aper_wl[i] / (c / obs_aper_wl[i] * 1e4) * 1e23)) foo.close() # read the input central luminosity by reading in the source information from output file dum = Model() dum.use_sources(filename) L_cen = dum.sources[0].luminosity / lsun # legend lg_data = ax_sed.legend([irs, photometry, aper, aper_obs], [ r'$\rm{observation}$', r'$\rm{photometry}$', r'$\rm{F_{aper,sim}}$', r'$\rm{F_{aper,obs}}$' ], loc='upper left', fontsize=14 * mag, numpoints=1, framealpha=0.3) if clean == False: lg_sim = ax_sed.legend([sim],[r'$\rm{L_{bol,sim}=%5.2f\,L_{\odot},\,L_{center}=%5.2f\,L_{\odot}}$' % (l_bol_sim, L_cen)], \ loc='lower right',fontsize=mag*16) plt.gca().add_artist(lg_data) # plot setting ax_sed.set_xlabel(r'$\rm{log\,\lambda\,[{\mu}m]}$', fontsize=mag * 20) ax_sed.set_ylabel(r'$\rm{log\,\nu S_{\nu}\,[erg\,s^{-1}\,cm^{-2}]}$', fontsize=mag * 20) [ ax_sed.spines[axis].set_linewidth(1.5 * mag) for axis in ['top', 'bottom', 'left', 'right'] ] ax_sed.minorticks_on() ax_sed.tick_params('both', labelsize=mag * 18, width=1.5 * mag, which='major', pad=15, length=5 * mag) ax_sed.tick_params('both', labelsize=mag * 18, width=1.5 * mag, which='minor', pad=15, length=2.5 * mag) # fix the tick label font ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral', size=mag * 18) for label in ax_sed.get_xticklabels(): label.set_fontproperties(ticks_font) for label in ax_sed.get_yticklabels(): label.set_fontproperties(ticks_font) # Write out the plot fig.savefig(outdir + print_name + '_sed.pdf', format='pdf', dpi=300, bbox_inches='tight') fig.clf() # option for suppress image plotting (for speed) if image: # Package for matching the colorbar from mpl_toolkits.axes_grid1 import make_axes_locatable, ImageGrid # Users may change the unit: mJy, Jy, MJy/sr, ergs/cm^2/s, ergs/cm^2/s/Hz # !!! image = m.get_image(group=len(aper_reduced) + 1, inclination=0, distance=dstar * pc, units='MJy/sr') # Open figure and create axes fig = plt.figure(figsize=(12, 12)) grid = ImageGrid(fig, 111, nrows_ncols=(3, 3), direction='row', add_all=True, label_mode='1', share_all=True, cbar_location='right', cbar_mode='single', cbar_size='3%', cbar_pad=0) for i, wav in enumerate([3.6, 8.0, 9.7, 24, 40, 100, 250, 500, 1000]): ax = grid[i] # Find the closest wavelength iwav = np.argmin(np.abs(wav - image.wav)) # Calculate the image width in arcseconds given the distance used above # get the max radius rmax = max(m.get_quantities().r_wall) w = np.degrees(rmax / image.distance) * 3600. # Image in the unit of MJy/sr # Change it into erg/s/cm2/Hz/sr factor = 1e-23 * 1e6 # avoid zero in log # flip the image, because the setup of inclination is upside down val = image.val[::-1, :, iwav] * factor + 1e-30 # This is the command to show the image. The parameters vmin and vmax are # the min and max levels for the colorscale (remove for default values). cmap = plt.cm.CMRmap im = ax.imshow(np.log10(val), vmin=-22, vmax=-12, cmap=cmap, origin='lower', extent=[-w, w, -w, w], aspect=1) ax.set_xlabel(r'$\rm{RA\,Offset\,[arcsec]}$', fontsize=14) ax.set_ylabel(r'$\rm{Dec\,Offset\,[arcsec]}$', fontsize=14) # fix the tick label font ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral', size=14) for label in ax.get_xticklabels(): label.set_fontproperties(ticks_font) for label in ax.get_yticklabels(): label.set_fontproperties(ticks_font) # Colorbar setting cb = ax.cax.colorbar(im) cb.solids.set_edgecolor('face') cb.ax.minorticks_on() cb.ax.set_ylabel( r'$\rm{log(I_{\nu})\,[erg\,s^{-1}\,cm^{-2}\,Hz^{-1}\,sr^{-1}]}$', fontsize=18) cb_obj = plt.getp(cb.ax.axes, 'yticklabels') plt.setp(cb_obj, fontsize=18) ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral', size=18) for label in cb.ax.get_yticklabels(): label.set_fontproperties(ticks_font) ax.tick_params(axis='both', which='major', labelsize=16) ax.text(0.7, 0.88, str(wav) + r'$\rm{\,\mu m}$', fontsize=16, color='white', transform=ax.transAxes) fig.savefig(outdir + print_name + '_image_gridplot.pdf', format='pdf', dpi=300, bbox_inches='tight') fig.clf()
import pyfits from hyperion.model import ModelOutput from hyperion.util.constants import pc # Open the model - we specify the name without the .rtout extension m = ModelOutput('tutorial_model.rtout') # Extract the image for the first inclination, and scale to 300pc. We # have to specify group=1 as there is no image in group 0 wav, nufnu = m.get_image(group=1, inclination=0, distance=300 * pc) # The image extracted above is a 3D array. We can write it out to FITS. # We need to swap some of the directions around so as to be able to use # the ds9 slider to change the wavelength of the image. pyfits.writeto('image_cube.fits', nufnu.swapaxes(0, 2).swapaxes(1, 2), \ clobber=True) # We can also just output one of the wavelengths pyfits.writeto('image_slice.fits', nufnu[:, :, 0], clobber=True)
from hyperion.model import ModelOutput from hyperion.util.constants import kpc from astropy.io import fits for tau in [0.1, 1.0, 20.]: input_file = 'bm1_slab_effgrain_tau_{tau:05.2f}_images.rtout'.format( tau=tau) m = ModelOutput(input_file) for iincl, theta in enumerate([0, 30, 60, 90, 120, 150, 180]): image = m.get_image(inclination=iincl, units='MJy/sr', distance=10. * kpc) for iwav, wav in enumerate([0.165, 0.570, 21.3, 161.6]): output_file = 'images/bm1_slab_effgrain_tau_{tau:06.2f}_theta_{theta:03d}_wave_{wav:07.3f}.fits'.format( tau=tau, theta=theta, wav=wav) fits.writeto(output_file, image.val[:, :, iwav], clobber=True)
import numpy as np import matplotlib.pyplot as plt from hyperion.model import ModelOutput from hyperion.util.constants import pc # Create output directory if it does not already exist if not os.path.exists('frames'): os.mkdir('frames') # Open model m = ModelOutput('flyaround_cube.rtout') # Read image from model image = m.get_image(distance=300 * pc, units='MJy/sr') # image.val is now an array with four dimensions (n_view, n_y, n_x, n_wav) for iview in range(image.val.shape[0]): # Open figure and create axes fig = plt.figure(figsize=(3, 3)) ax = fig.add_subplot(1, 1, 1) # This is the command to show the image. The parameters vmin and vmax are # the min and max levels for the grayscale (remove for default values). # The colormap is set here to be a heat map. Other possible heat maps # include plt.cm.gray (grayscale), plt.cm.gist_yarg (inverted grayscale), # plt.cm.jet (default, colorful). The np.sqrt() is used to plot the # images on a sqrt stretch.
def hyperion_image(rtout, wave, plotdir, printname, dstar=178., group=0, marker=0, size='full', convolve=False, unit=None): # to avoid X server error import matplotlib as mpl mpl.use('Agg') import numpy as np import matplotlib.pyplot as plt import matplotlib as mpl import astropy.constants as const from hyperion.model import ModelOutput # Package for matching the colorbar from mpl_toolkits.axes_grid1 import make_axes_locatable pc = const.pc.cgs.value if unit == None: unit = r'$\rm{log(I_{\nu})\,[erg\,s^{-1}\,cm^{-2}\,Hz^{-1}\,sr^{-1}]}$' m = ModelOutput(rtout) # Extract the image. image = m.get_image(group=group, inclination=0, distance=dstar * pc, units='mJy') print np.shape(image.val) # Open figure and create axes fig = plt.figure(figsize=(8, 8)) ax = fig.add_subplot(111) # Find the closest wavelength iwav = np.argmin(np.abs(wave - image.wav)) # Calculate the image width in arcseconds given the distance used above # get the max radius rmax = max(m.get_quantities().r_wall) w = np.degrees(rmax / image.distance) * 3600. # Image in the unit of MJy/sr # Change it into erg/s/cm2/Hz/sr # factor = 1e-23*1e6 factor = 1 # avoid zero in log # flip the image, because the setup of inclination is upside down val = image.val[::-1, :, iwav] * factor + 1e-30 if convolve: from astropy.convolution import convolve, Gaussian2DKernel img_res = 2 * w / len(val[:, 0]) kernel = Gaussian2DKernel(0.27 / 2.354 / img_res) val = convolve(val, kernel) if size != 'full': pix_e2c = (w - size / 2.) / w * len(val[:, 0]) / 2 val = val[pix_e2c:-pix_e2c, pix_e2c:-pix_e2c] w = size / 2. # This is the command to show the image. The parameters vmin and vmax are # the min and max levels for the colorscale (remove for default values). # cmap = sns.cubehelix_palette(start=0.1, rot=-0.7, gamma=0.2, as_cmap=True) cmap = plt.cm.CMRmap # im = ax.imshow(np.log10(val), vmin= -20, vmax= -15, # cmap=cmap, origin='lower', extent=[-w, w, -w, w], aspect=1) im = ax.imshow(val, cmap=cmap, origin='lower', extent=[-w, w, -w, w], aspect=1) print val.max() # plot the marker for center position by default or user input offset ax.plot([0], [-marker], '+', color='ForestGreen', markersize=10, mew=2) ax.set_xlim([-w, w]) ax.set_ylim([-w, w]) # ax.plot([0],[-10], '+', color='m', markersize=10, mew=2) # fix the tick label font ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral', size=14) for label in ax.get_xticklabels(): label.set_fontproperties(ticks_font) for label in ax.get_yticklabels(): label.set_fontproperties(ticks_font) # Colorbar setting # create an axes on the right side of ax. The width of cax will be 5% # of ax and the padding between cax and ax will be fixed at 0.05 inch. divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cb = fig.colorbar(im, cax=cax) cb.solids.set_edgecolor("face") cb.ax.minorticks_on() cb.ax.set_ylabel(unit, fontsize=18) cb_obj = plt.getp(cb.ax.axes, 'yticklabels') plt.setp(cb_obj, fontsize=14) # fix the tick label font ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral', size=14) for label in cb.ax.get_yticklabels(): label.set_fontproperties(ticks_font) ax.set_xlabel(r'$\rm{RA\,Offset\,(arcsec)}$', fontsize=18) ax.set_ylabel(r'$\rm{Dec\,Offset\,(arcsec)}$', fontsize=18) ax.tick_params(axis='both', which='major', labelsize=18) ax.text(0.7, 0.88, str(wave) + r'$\rm{\,\mu m}$', fontsize=20, color='white', transform=ax.transAxes) fig.savefig(plotdir + printname + '_image_' + str(wave) + '.pdf', format='pdf', dpi=300, bbox_inches='tight') fig.clf()
dist = 178. wave = 500. from hyperion.model import ModelOutput import astropy.constants as const import numpy as np import matplotlib.pyplot as plt from matplotlib import font_manager from mpl_toolkits.axes_grid1 import make_axes_locatable # constant setup pc = const.pc.cgs.value m = ModelOutput(filename) image = m.get_image(group=22, inclination=0, distance=dist * pc, units='MJy/sr') # Find the closest wavelength iwav = np.argmin(np.abs(wave - image.wav)) # Calculate the image width in arcseconds given the distance used above # get the max radius rmax = max(m.get_quantities().r_wall) w = np.degrees(rmax / image.distance) * 3600. # Image in the unit of MJy/sr # Change it into erg/s/cm2/Hz/sr factor = 1e-23 * 1e6 # avoid zero in log # flip the image, because the setup of inclination is upside down val = image.val[::-1, :, iwav] * factor + 1e-30
def extract_hyperion(filename, indir=None, outdir=None, dstar=178.0, wl_aper=None, save=True): def l_bol(wl, fv, dist=178.0): import numpy as np import astropy.constants as const # wavelength unit: um # Flux density unit: Jy # # constants setup # c = const.c.cgs.value pc = const.pc.cgs.value PI = np.pi SL = const.L_sun.cgs.value # Convert the unit from Jy to erg s-1 cm-2 Hz-1 fv = np.array(fv) * 1e-23 freq = c / (1e-4 * np.array(wl)) diff_dum = freq[1:] - freq[0:-1] freq_interpol = np.hstack( (freq[0:-1] + diff_dum / 2.0, freq[0:-1] + diff_dum / 2.0, freq[0], freq[-1])) freq_interpol = freq_interpol[np.argsort(freq_interpol)[::-1]] fv_interpol = np.empty(len(freq_interpol)) # calculate the histogram style of spectrum # for i in range(0, len(fv)): if i == 0: fv_interpol[i] = fv[i] else: fv_interpol[2 * i - 1] = fv[i - 1] fv_interpol[2 * i] = fv[i] fv_interpol[-1] = fv[-1] dv = freq_interpol[0:-1] - freq_interpol[1:] dv = np.delete(dv, np.where(dv == 0)) fv = fv[np.argsort(freq)] freq = freq[np.argsort(freq)] return (np.trapz(fv, freq) * 4. * PI * (dist * pc)**2) / SL import matplotlib.pyplot as plt import numpy as np import os from hyperion.model import ModelOutput from hyperion.model import Model from scipy.interpolate import interp1d from hyperion.util.constants import pc, c, lsun # Read in the observation data and calculate the noise & variance if indir == None: indir = '/Users/yaolun/bhr71/' if outdir == None: outdir = '/Users/yaolun/bhr71/hyperion/' # assign the file name from the input file print_name = os.path.splitext(os.path.basename(filename))[0] # [wl_pacs,flux_pacs,unc_pacs] = np.genfromtxt(indir+'BHR71_centralSpaxel_PointSourceCorrected_CorrectedYES_trim_continuum.txt',\ dtype='float',skip_header=1).T # Convert the unit from Jy to erg cm-2 Hz-1 flux_pacs = flux_pacs * 1e-23 [wl_spire, flux_spire] = np.genfromtxt(indir + 'BHR71_spire_corrected_continuum.txt', dtype='float', skip_header=1).T flux_spire = flux_spire * 1e-23 wl_obs = np.hstack((wl_pacs, wl_spire)) flux_obs = np.hstack((flux_pacs, flux_spire)) [wl_pacs_data,flux_pacs_data,unc_pacs_data] = np.genfromtxt(indir+'BHR71_centralSpaxel_PointSourceCorrected_CorrectedYES_trim.txt',\ dtype='float').T [wl_spire_data,flux_spire_data] = np.genfromtxt(indir+'BHR71_spire_corrected.txt',\ dtype='float').T [wl_pacs_flat,flux_pacs_flat,unc_pacs_flat] = np.genfromtxt(indir+'BHR71_centralSpaxel_PointSourceCorrected_CorrectedYES_trim_flat_spectrum.txt',\ dtype='float',skip_header=1).T [wl_spire_flat, flux_spire_flat ] = np.genfromtxt(indir + 'BHR71_spire_corrected_flat_spectrum.txt', dtype='float', skip_header=1).T # Convert the unit from Jy to erg cm-2 Hz-1 flux_pacs_flat = flux_pacs_flat * 1e-23 flux_spire_flat = flux_spire_flat * 1e-23 flux_pacs_data = flux_pacs_data * 1e-23 flux_spire_data = flux_spire_data * 1e-23 wl_pacs_noise = wl_pacs_data flux_pacs_noise = flux_pacs_data - flux_pacs - flux_pacs_flat wl_spire_noise = wl_spire_data flux_spire_noise = flux_spire_data - flux_spire - flux_spire_flat # Read in the Spitzer IRS spectrum [wl_irs, flux_irs] = (np.genfromtxt(indir + 'bhr71_spitzer_irs.txt', skip_header=2, dtype='float').T)[0:2] # Convert the unit from Jy to erg cm-2 Hz-1 flux_irs = flux_irs * 1e-23 # Remove points with zero or negative flux ind = flux_irs > 0 wl_irs = wl_irs[ind] flux_irs = flux_irs[ind] # Calculate the local variance (for spire), use the instrument uncertainty for pacs # wl_noise_5 = wl_spire_noise[(wl_spire_noise > 194) * (wl_spire_noise <= 304)] flux_noise_5 = flux_spire_noise[(wl_spire_noise > 194) * (wl_spire_noise <= 304)] wl_noise_6 = wl_spire_noise[wl_spire_noise > 304] flux_noise_6 = flux_spire_noise[wl_spire_noise > 304] wl_noise = [wl_pacs_data[wl_pacs_data <= 190.31], wl_noise_5, wl_noise_6] flux_noise = [unc_pacs[wl_pacs_data <= 190.31], flux_noise_5, flux_noise_6] sig_num = 20 sigma_noise = [] for i in range(0, len(wl_noise)): sigma_dum = np.zeros([len(wl_noise[i])]) for iwl in range(0, len(wl_noise[i])): if iwl < sig_num / 2: sigma_dum[iwl] = np.std( np.hstack((flux_noise[i][0:sig_num / 2], flux_noise[i][0:sig_num / 2 - iwl]))) elif len(wl_noise[i]) - iwl < sig_num / 2: sigma_dum[iwl] = np.std( np.hstack( (flux_noise[i][iwl:], flux_noise[i][len(wl_noise[i]) - sig_num / 2:]))) else: sigma_dum[iwl] = np.std(flux_noise[i][iwl - sig_num / 2:iwl + sig_num / 2]) sigma_noise = np.hstack((sigma_noise, sigma_dum)) sigma_noise = np.array(sigma_noise) # Read in the photometry data phot = np.genfromtxt(indir + 'bhr71.txt', dtype=None, skip_header=1, comments='%') wl_phot = [] flux_phot = [] flux_sig_phot = [] note = [] for i in range(0, len(phot)): wl_phot.append(phot[i][0]) flux_phot.append(phot[i][1]) flux_sig_phot.append(phot[i][2]) note.append(phot[i][4]) wl_phot = np.array(wl_phot) # Convert the unit from Jy to erg cm-2 Hz-1 flux_phot = np.array(flux_phot) * 1e-23 flux_sig_phot = np.array(flux_sig_phot) * 1e-23 # Print the observed L_bol wl_tot = np.hstack((wl_irs, wl_obs, wl_phot)) flux_tot = np.hstack((flux_irs, flux_obs, flux_phot)) flux_tot = flux_tot[np.argsort(wl_tot)] wl_tot = wl_tot[np.argsort(wl_tot)] l_bol_obs = l_bol(wl_tot, flux_tot * 1e23) # Open the model m = ModelOutput(filename) if wl_aper == None: wl_aper = [ 3.6, 4.5, 5.8, 8.0, 10, 16, 20, 24, 35, 70, 100, 160, 250, 350, 500, 850 ] # Create the plot mag = 1.5 fig = plt.figure(figsize=(8 * mag, 6 * mag)) ax_sed = fig.add_subplot(1, 1, 1) # Plot the observed SED # plot the observed spectra pacs, = ax_sed.plot(np.log10(wl_pacs), np.log10(c / (wl_pacs * 1e-4) * flux_pacs), '-', color='DimGray', linewidth=1.5 * mag, alpha=0.7) spire, = ax_sed.plot(np.log10(wl_spire), np.log10(c / (wl_spire * 1e-4) * flux_spire), '-', color='DimGray', linewidth=1.5 * mag, alpha=0.7) irs, = ax_sed.plot(np.log10(wl_irs), np.log10(c / (wl_irs * 1e-4) * flux_irs), '-', color='DimGray', linewidth=1.5 * mag, alpha=0.7) # ax_sed.text(0.75,0.9,r'$\rm{L_{bol}= %5.2f L_{\odot}}$' % l_bol_obs,fontsize=mag*16,transform=ax_sed.transAxes) # plot the observed photometry data photometry, = ax_sed.plot(np.log10(wl_phot), np.log10(c / (wl_phot * 1e-4) * flux_phot), 's', mfc='DimGray', mec='k', markersize=8) ax_sed.errorbar(np.log10(wl_phot),np.log10(c/(wl_phot*1e-4)*flux_phot),\ yerr=[np.log10(c/(wl_phot*1e-4)*flux_phot)-np.log10(c/(wl_phot*1e-4)*(flux_phot-flux_sig_phot)),\ np.log10(c/(wl_phot*1e-4)*(flux_phot+flux_sig_phot))-np.log10(c/(wl_phot*1e-4)*flux_phot)],\ fmt='s',mfc='DimGray',mec='k',markersize=8) # Extract the SED for the smallest inclination and largest aperture, and # scale to 300pc. In Python, negative indices can be used for lists and # arrays, and indicate the position from the end. So to get the SED in the # largest aperture, we set aperture=-1. # aperture group is aranged from smallest to infinite sed_inf = m.get_sed(group=0, inclination=0, aperture=-1, distance=dstar * pc) # l_bol_sim = l_bol(sed_inf.wav, sed_inf.val/(c/sed_inf.wav*1e4)*1e23) # print sed.wav, sed.val # print 'Bolometric luminosity of simulated spectrum: %5.2f lsun' % l_bol_sim # plot the simulated SED # sim, = ax_sed.plot(np.log10(sed_inf.wav), np.log10(sed_inf.val), '-', color='k', linewidth=1.5*mag, alpha=0.7) # get flux at different apertures flux_aper = np.empty_like(wl_aper) unc_aper = np.empty_like(wl_aper) for i in range(0, len(wl_aper)): sed_dum = m.get_sed(group=i + 1, inclination=0, aperture=-1, distance=dstar * pc) # use a rectangle function the average the simulated SED # apply the spectral resolution if (wl_aper[i] < 50.) & (wl_aper[i] >= 5): res = 60. elif wl_aper[i] < 5: res = 10. else: res = 1000. ind = np.where((sed_dum.wav < wl_aper[i] * (1 + 1. / res)) & (sed_dum.wav > wl_aper[i] * (1 - 1. / res))) if len(ind[0]) != 0: flux_aper[i] = np.mean(sed_dum.val[ind]) else: f = interp1d(sed_dum.wav, sed_dum.val) flux_aper[i] = f(wl_aper[i]) # perform the same procedure of flux extraction of aperture flux with observed spectra wl_aper = np.array(wl_aper) obs_aper_wl = wl_aper[(wl_aper >= min(wl_irs)) & (wl_aper <= max(wl_spire))] obs_aper_sed = np.empty_like(obs_aper_wl) sed_tot = c / (wl_tot * 1e-4) * flux_tot # wl_tot and flux_tot are already hstacked and sorted by wavelength for i in range(0, len(obs_aper_wl)): if (obs_aper_wl[i] < 50.) & (obs_aper_wl[i] >= 5): res = 60. elif obs_aper_wl[i] < 5: res = 10. else: res = 1000. ind = np.where((wl_tot < obs_aper_wl[i] * (1 + 1. / res)) & (wl_tot > obs_aper_wl[i] * (1 - 1. / res))) if len(ind[0]) != 0: obs_aper_sed[i] = np.mean(sed_tot[ind]) else: f = interp1d(wl_tot, sed_tot) obs_aper_sed[i] = f(wl_aper[i]) aper_obs, = ax_sed.plot(np.log10(obs_aper_wl), np.log10(obs_aper_sed), 's-', mec='None', mfc='r', color='r', markersize=10, linewidth=1.5) # # interpolate the uncertainty (maybe not the best way to do this) # print sed_dum.unc # f = interp1d(sed_dum.wav, sed_dum.unc) # unc_aper[i] = f(wl_aper[i]) # if wl_aper[i] == 9.7: # ax_sed.plot(np.log10(sed_dum.wav), np.log10(sed_dum.val), '-', linewidth=1.5*mag) # print l_bol(sed_dum.wav, sed_dum.val/(c/sed_dum.wav*1e4)*1e23) aper, = ax_sed.plot(np.log10(wl_aper), np.log10(flux_aper), 'o-', mec='Blue', mfc='None', color='b', markersize=12, markeredgewidth=3, linewidth=1.7) # calculate the bolometric luminosity of the aperture l_bol_sim = l_bol(wl_aper, flux_aper / (c / np.array(wl_aper) * 1e4) * 1e23) print 'Bolometric luminosity of simulated spectrum: %5.2f lsun' % l_bol_sim # print out the sed into ascii file for reading in later if save == True: # unapertured SED foo = open(outdir + print_name + '_sed_inf.txt', 'w') foo.write('%12s \t %12s \n' % ('wave', 'vSv')) for i in range(0, len(sed_inf.wav)): foo.write('%12g \t %12g \n' % (sed_inf.wav[i], sed_inf.val[i])) foo.close() # SED with convolution of aperture sizes foo = open(outdir + print_name + '_sed_w_aperture.txt', 'w') foo.write('%12s \t %12s \n' % ('wave', 'vSv')) for i in range(0, len(wl_aper)): foo.write('%12g \t %12g \n' % (wl_aper[i], flux_aper[i])) foo.close() # Read in and plot the simulated SED produced by RADMC-3D using the same parameters # [wl,fit] = np.genfromtxt(indir+'hyperion/radmc_comparison/spectrum.out',dtype='float',skip_header=3).T # l_bol_radmc = l_bol(wl,fit*1e23/dstar**2) # radmc, = ax_sed.plot(np.log10(wl),np.log10(c/(wl*1e-4)*fit/dstar**2),'-',color='DimGray', linewidth=1.5*mag, alpha=0.5) # print the L bol of the simulated SED (both Hyperion and RADMC-3D) # lg_sim = ax_sed.legend([sim,radmc],[r'$\rm{L_{bol,sim}=%5.2f~L_{\odot},~L_{center}=9.18~L_{\odot}}$' % l_bol_sim, \ # r'$\rm{L_{bol,radmc3d}=%5.2f~L_{\odot},~L_{center}=9.18~L_{\odot}}$' % l_bol_radmc],\ # loc='lower right',fontsize=mag*16) # read the input central luminosity by reading in the source information from output file dum = Model() dum.use_sources(filename) L_cen = dum.sources[0].luminosity / lsun # lg_sim = ax_sed.legend([sim],[r'$\rm{L_{bol,sim}=%5.2f~L_{\odot},~L_{center}=%5.2f~L_{\odot}}$' % (l_bol_sim, L_cen)], \ # loc='lower right',fontsize=mag*16) # lg_sim = ax_sed.legend([sim],[r'$\rm{L_{bol,sim}=%5.2f~L_{\odot},~L_{bol,obs}=%5.2f~L_{\odot}}$' % (l_bol_sim, l_bol_obs)], \ # loc='lower right',fontsize=mag*16) # text = ax_sed.text(0.2 ,0.05 ,r'$\rm{L_{bol,simulation}=%5.2f~L_{\odot},~L_{bol,observation}=%5.2f~L_{\odot}}$' % (l_bol_sim, l_bol_obs),fontsize=mag*16,transform=ax_sed.transAxes) # text.set_bbox(dict( edgecolor='k',facecolor='None',alpha=0.3,pad=10.0)) # plot setting ax_sed.set_xlabel(r'$\rm{log\,\lambda\,({\mu}m)}$', fontsize=mag * 20) ax_sed.set_ylabel(r'$\rm{log\,\nu S_{\nu}\,(erg\,cm^{-2}\,s^{-1})}$', fontsize=mag * 20) [ ax_sed.spines[axis].set_linewidth(1.5 * mag) for axis in ['top', 'bottom', 'left', 'right'] ] ax_sed.minorticks_on() ax_sed.tick_params('both', labelsize=mag * 18, width=1.5 * mag, which='major', pad=15, length=5 * mag) ax_sed.tick_params('both', labelsize=mag * 18, width=1.5 * mag, which='minor', pad=15, length=2.5 * mag) ax_sed.set_ylim([-13, -7.5]) ax_sed.set_xlim([0, 3]) # lg_data = ax_sed.legend([sim, aper], [r'$\rm{w/o~aperture}$', r'$\rm{w/~aperture}$'], \ # loc='upper left', fontsize=14*mag, framealpha=0.3, numpoints=1) lg_data = ax_sed.legend([irs, photometry, aper, aper_obs],\ [r'$\rm{observation}$',\ r'$\rm{photometry}$',r'$\rm{F_{aper,sim}}$',r'$\rm{F_{aper,obs}}$'],\ loc='upper left',fontsize=14*mag,numpoints=1,framealpha=0.3) # plt.gca().add_artist(lg_sim) # Write out the plot fig.savefig(outdir + print_name + '_sed.pdf', format='pdf', dpi=300, bbox_inches='tight') fig.clf() # Package for matching the colorbar from mpl_toolkits.axes_grid1 import make_axes_locatable # Extract the image for the first inclination, and scale to 300pc. We # have to specify group=1 as there is no image in group 0. image = m.get_image(group=len(wl_aper) + 1, inclination=0, distance=dstar * pc, units='MJy/sr') # image = m.get_image(group=14, inclination=0, distance=dstar * pc, units='MJy/sr') # Open figure and create axes # fig = plt.figure(figsize=(8, 8)) fig, axarr = plt.subplots(3, 3, sharex='col', sharey='row', figsize=(13.5, 12)) # Pre-set maximum for colorscales VMAX = {} # VMAX[3.6] = 10. # VMAX[24] = 100. # VMAX[160] = 2000. # VMAX[500] = 2000. VMAX[100] = 10. VMAX[250] = 100. VMAX[500] = 2000. VMAX[1000] = 2000. # We will now show four sub-plots, each one for a different wavelength # for i, wav in enumerate([3.6, 24, 160, 500]): # for i, wav in enumerate([100, 250, 500, 1000]): # for i, wav in enumerate([4.5, 9.7, 24, 40, 70, 100, 250, 500, 1000]): for i, wav in enumerate([3.6, 8.0, 9.7, 24, 40, 100, 250, 500, 1000]): # ax = fig.add_subplot(3, 3, i + 1) ax = axarr[i / 3, i % 3] # Find the closest wavelength iwav = np.argmin(np.abs(wav - image.wav)) # Calculate the image width in arcseconds given the distance used above rmax = max(m.get_quantities().r_wall) w = np.degrees(rmax / image.distance) * 3600. # w = np.degrees((1.5 * pc) / image.distance) * 60. # Image in the unit of MJy/sr # Change it into erg/s/cm2/Hz/sr factor = 1e-23 * 1e6 # avoid zero in log val = image.val[:, :, iwav] * factor + 1e-30 # This is the command to show the image. The parameters vmin and vmax are # the min and max levels for the colorscale (remove for default values). im = ax.imshow(np.log10(val), vmin=-22, vmax=-12, cmap=plt.cm.jet, origin='lower', extent=[-w, w, -w, w], aspect=1) # Colorbar setting # create an axes on the right side of ax. The width of cax will be 5% # of ax and the padding between cax and ax will be fixed at 0.05 inch. if (i + 1) % 3 == 0: divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cb = fig.colorbar(im, cax=cax) cb.solids.set_edgecolor("face") cb.ax.minorticks_on() cb.ax.set_ylabel( r'$\rm{log(I_{\nu})\,[erg\,s^{-2}\,cm^{-2}\,Hz^{-1}\,sr^{-1}]}$', fontsize=12) cb_obj = plt.getp(cb.ax.axes, 'yticklabels') plt.setp(cb_obj, fontsize=12) if (i + 1) == 7: # Finalize the plot ax.set_xlabel('RA Offset (arcsec)', fontsize=14) ax.set_ylabel('Dec Offset (arcsec)', fontsize=14) ax.tick_params(axis='both', which='major', labelsize=16) ax.set_adjustable('box-forced') ax.text(0.7, 0.88, str(wav) + r'$\rm{\,\mu m}$', fontsize=18, color='white', weight='bold', transform=ax.transAxes) fig.subplots_adjust(hspace=0, wspace=-0.2) # Adjust the spaces between the subplots # plt.tight_layout() fig.savefig(outdir + print_name + '_cube_plot.png', format='png', dpi=300, bbox_inches='tight') fig.clf()
def extract_hyperion(filename,indir=None,outdir=None,dstar=178.0,wl_aper=None,save=True): def l_bol(wl,fv,dist=178.0): import numpy as np import astropy.constants as const # wavelength unit: um # Flux density unit: Jy # # constants setup # c = const.c.cgs.value pc = const.pc.cgs.value PI = np.pi SL = const.L_sun.cgs.value # Convert the unit from Jy to erg s-1 cm-2 Hz-1 fv = np.array(fv)*1e-23 freq = c/(1e-4*np.array(wl)) diff_dum = freq[1:]-freq[0:-1] freq_interpol = np.hstack((freq[0:-1]+diff_dum/2.0,freq[0:-1]+diff_dum/2.0,freq[0],freq[-1])) freq_interpol = freq_interpol[np.argsort(freq_interpol)[::-1]] fv_interpol = np.empty(len(freq_interpol)) # calculate the histogram style of spectrum # for i in range(0,len(fv)): if i == 0: fv_interpol[i] = fv[i] else: fv_interpol[2*i-1] = fv[i-1] fv_interpol[2*i] = fv[i] fv_interpol[-1] = fv[-1] dv = freq_interpol[0:-1]-freq_interpol[1:] dv = np.delete(dv,np.where(dv==0)) fv = fv[np.argsort(freq)] freq = freq[np.argsort(freq)] return (np.trapz(fv,freq)*4.*PI*(dist*pc)**2)/SL import matplotlib.pyplot as plt import numpy as np import os from hyperion.model import ModelOutput from hyperion.model import Model from scipy.interpolate import interp1d from hyperion.util.constants import pc, c, lsun # Read in the observation data and calculate the noise & variance if indir == None: indir = '/Users/yaolun/bhr71/' if outdir == None: outdir = '/Users/yaolun/bhr71/hyperion/' # assign the file name from the input file print_name = os.path.splitext(os.path.basename(filename))[0] # [wl_pacs,flux_pacs,unc_pacs] = np.genfromtxt(indir+'BHR71_centralSpaxel_PointSourceCorrected_CorrectedYES_trim_continuum.txt',\ dtype='float',skip_header=1).T # Convert the unit from Jy to erg cm-2 Hz-1 flux_pacs = flux_pacs*1e-23 [wl_spire,flux_spire] = np.genfromtxt(indir+'BHR71_spire_corrected_continuum.txt',dtype='float',skip_header=1).T flux_spire = flux_spire*1e-23 wl_obs = np.hstack((wl_pacs,wl_spire)) flux_obs = np.hstack((flux_pacs,flux_spire)) [wl_pacs_data,flux_pacs_data,unc_pacs_data] = np.genfromtxt(indir+'BHR71_centralSpaxel_PointSourceCorrected_CorrectedYES_trim.txt',\ dtype='float').T [wl_spire_data,flux_spire_data] = np.genfromtxt(indir+'BHR71_spire_corrected.txt',\ dtype='float').T [wl_pacs_flat,flux_pacs_flat,unc_pacs_flat] = np.genfromtxt(indir+'BHR71_centralSpaxel_PointSourceCorrected_CorrectedYES_trim_flat_spectrum.txt',\ dtype='float',skip_header=1).T [wl_spire_flat,flux_spire_flat] = np.genfromtxt(indir+'BHR71_spire_corrected_flat_spectrum.txt',dtype='float',skip_header=1).T # Convert the unit from Jy to erg cm-2 Hz-1 flux_pacs_flat = flux_pacs_flat*1e-23 flux_spire_flat = flux_spire_flat*1e-23 flux_pacs_data = flux_pacs_data*1e-23 flux_spire_data = flux_spire_data*1e-23 wl_pacs_noise = wl_pacs_data flux_pacs_noise = flux_pacs_data-flux_pacs-flux_pacs_flat wl_spire_noise = wl_spire_data flux_spire_noise = flux_spire_data-flux_spire-flux_spire_flat # Read in the Spitzer IRS spectrum [wl_irs, flux_irs]= (np.genfromtxt(indir+'bhr71_spitzer_irs.txt',skip_header=2,dtype='float').T)[0:2] # Convert the unit from Jy to erg cm-2 Hz-1 flux_irs = flux_irs*1e-23 # Remove points with zero or negative flux ind = flux_irs > 0 wl_irs = wl_irs[ind] flux_irs = flux_irs[ind] # Calculate the local variance (for spire), use the instrument uncertainty for pacs # wl_noise_5 = wl_spire_noise[(wl_spire_noise > 194)*(wl_spire_noise <= 304)] flux_noise_5 = flux_spire_noise[(wl_spire_noise > 194)*(wl_spire_noise <= 304)] wl_noise_6 = wl_spire_noise[wl_spire_noise > 304] flux_noise_6 = flux_spire_noise[wl_spire_noise > 304] wl_noise = [wl_pacs_data[wl_pacs_data<=190.31],wl_noise_5,wl_noise_6] flux_noise = [unc_pacs[wl_pacs_data<=190.31],flux_noise_5,flux_noise_6] sig_num = 20 sigma_noise = [] for i in range(0,len(wl_noise)): sigma_dum = np.zeros([len(wl_noise[i])]) for iwl in range(0,len(wl_noise[i])): if iwl < sig_num/2: sigma_dum[iwl] = np.std(np.hstack((flux_noise[i][0:sig_num/2],flux_noise[i][0:sig_num/2-iwl]))) elif len(wl_noise[i])-iwl < sig_num/2: sigma_dum[iwl] = np.std(np.hstack((flux_noise[i][iwl:],flux_noise[i][len(wl_noise[i])-sig_num/2:]))) else: sigma_dum[iwl] = np.std(flux_noise[i][iwl-sig_num/2:iwl+sig_num/2]) sigma_noise = np.hstack((sigma_noise,sigma_dum)) sigma_noise = np.array(sigma_noise) # Read in the photometry data phot = np.genfromtxt(indir+'bhr71.txt',dtype=None,skip_header=1,comments='%') wl_phot = [] flux_phot = [] flux_sig_phot = [] note = [] for i in range(0,len(phot)): wl_phot.append(phot[i][0]) flux_phot.append(phot[i][1]) flux_sig_phot.append(phot[i][2]) note.append(phot[i][4]) wl_phot = np.array(wl_phot) # Convert the unit from Jy to erg cm-2 Hz-1 flux_phot = np.array(flux_phot)*1e-23 flux_sig_phot = np.array(flux_sig_phot)*1e-23 # Print the observed L_bol wl_tot = np.hstack((wl_irs,wl_obs,wl_phot)) flux_tot = np.hstack((flux_irs,flux_obs,flux_phot)) flux_tot = flux_tot[np.argsort(wl_tot)] wl_tot = wl_tot[np.argsort(wl_tot)] l_bol_obs = l_bol(wl_tot,flux_tot*1e23) # Open the model m = ModelOutput(filename) if wl_aper == None: wl_aper = [3.6, 4.5, 5.8, 8.0, 10, 16, 20, 24, 35, 70, 100, 160, 250, 350, 500, 850] # Create the plot mag = 1.5 fig = plt.figure(figsize=(8*mag,6*mag)) ax_sed = fig.add_subplot(1, 1, 1) # Plot the observed SED # plot the observed spectra pacs, = ax_sed.plot(np.log10(wl_pacs),np.log10(c/(wl_pacs*1e-4)*flux_pacs),'-',color='DimGray',linewidth=1.5*mag, alpha=0.7) spire, = ax_sed.plot(np.log10(wl_spire),np.log10(c/(wl_spire*1e-4)*flux_spire),'-',color='DimGray',linewidth=1.5*mag, alpha=0.7) irs, = ax_sed.plot(np.log10(wl_irs),np.log10(c/(wl_irs*1e-4)*flux_irs),'-',color='DimGray',linewidth=1.5*mag, alpha=0.7) # ax_sed.text(0.75,0.9,r'$\rm{L_{bol}= %5.2f L_{\odot}}$' % l_bol_obs,fontsize=mag*16,transform=ax_sed.transAxes) # plot the observed photometry data photometry, = ax_sed.plot(np.log10(wl_phot),np.log10(c/(wl_phot*1e-4)*flux_phot),'s',mfc='DimGray',mec='k',markersize=8) ax_sed.errorbar(np.log10(wl_phot),np.log10(c/(wl_phot*1e-4)*flux_phot),\ yerr=[np.log10(c/(wl_phot*1e-4)*flux_phot)-np.log10(c/(wl_phot*1e-4)*(flux_phot-flux_sig_phot)),\ np.log10(c/(wl_phot*1e-4)*(flux_phot+flux_sig_phot))-np.log10(c/(wl_phot*1e-4)*flux_phot)],\ fmt='s',mfc='DimGray',mec='k',markersize=8) # Extract the SED for the smallest inclination and largest aperture, and # scale to 300pc. In Python, negative indices can be used for lists and # arrays, and indicate the position from the end. So to get the SED in the # largest aperture, we set aperture=-1. # aperture group is aranged from smallest to infinite sed_inf = m.get_sed(group=0, inclination=0, aperture=-1, distance=dstar * pc) # l_bol_sim = l_bol(sed_inf.wav, sed_inf.val/(c/sed_inf.wav*1e4)*1e23) # print sed.wav, sed.val # print 'Bolometric luminosity of simulated spectrum: %5.2f lsun' % l_bol_sim # plot the simulated SED # sim, = ax_sed.plot(np.log10(sed_inf.wav), np.log10(sed_inf.val), '-', color='k', linewidth=1.5*mag, alpha=0.7) # get flux at different apertures flux_aper = np.empty_like(wl_aper) unc_aper = np.empty_like(wl_aper) for i in range(0, len(wl_aper)): sed_dum = m.get_sed(group=i+1, inclination=0, aperture=-1, distance=dstar * pc) # use a rectangle function the average the simulated SED # apply the spectral resolution if (wl_aper[i] < 50.) & (wl_aper[i] >= 5): res = 60. elif wl_aper[i] < 5: res = 10. else: res = 1000. ind = np.where((sed_dum.wav < wl_aper[i]*(1+1./res)) & (sed_dum.wav > wl_aper[i]*(1-1./res))) if len(ind[0]) != 0: flux_aper[i] = np.mean(sed_dum.val[ind]) else: f = interp1d(sed_dum.wav, sed_dum.val) flux_aper[i] = f(wl_aper[i]) # perform the same procedure of flux extraction of aperture flux with observed spectra wl_aper = np.array(wl_aper) obs_aper_wl = wl_aper[(wl_aper >= min(wl_irs)) & (wl_aper <= max(wl_spire))] obs_aper_sed = np.empty_like(obs_aper_wl) sed_tot = c/(wl_tot*1e-4)*flux_tot # wl_tot and flux_tot are already hstacked and sorted by wavelength for i in range(0, len(obs_aper_wl)): if (obs_aper_wl[i] < 50.) & (obs_aper_wl[i] >= 5): res = 60. elif obs_aper_wl[i] < 5: res = 10. else: res = 1000. ind = np.where((wl_tot < obs_aper_wl[i]*(1+1./res)) & (wl_tot > obs_aper_wl[i]*(1-1./res))) if len(ind[0]) != 0: obs_aper_sed[i] = np.mean(sed_tot[ind]) else: f = interp1d(wl_tot, sed_tot) obs_aper_sed[i] = f(wl_aper[i]) aper_obs, = ax_sed.plot(np.log10(obs_aper_wl),np.log10(obs_aper_sed), 's-', mec='None', mfc='r', color='r',markersize=10, linewidth=1.5) # # interpolate the uncertainty (maybe not the best way to do this) # print sed_dum.unc # f = interp1d(sed_dum.wav, sed_dum.unc) # unc_aper[i] = f(wl_aper[i]) # if wl_aper[i] == 9.7: # ax_sed.plot(np.log10(sed_dum.wav), np.log10(sed_dum.val), '-', linewidth=1.5*mag) # print l_bol(sed_dum.wav, sed_dum.val/(c/sed_dum.wav*1e4)*1e23) aper, = ax_sed.plot(np.log10(wl_aper),np.log10(flux_aper),'o-', mec='Blue', mfc='None', color='b',markersize=12, markeredgewidth=3, linewidth=1.7) # calculate the bolometric luminosity of the aperture l_bol_sim = l_bol(wl_aper, flux_aper/(c/np.array(wl_aper)*1e4)*1e23) print 'Bolometric luminosity of simulated spectrum: %5.2f lsun' % l_bol_sim # print out the sed into ascii file for reading in later if save == True: # unapertured SED foo = open(outdir+print_name+'_sed_inf.txt','w') foo.write('%12s \t %12s \n' % ('wave','vSv')) for i in range(0, len(sed_inf.wav)): foo.write('%12g \t %12g \n' % (sed_inf.wav[i], sed_inf.val[i])) foo.close() # SED with convolution of aperture sizes foo = open(outdir+print_name+'_sed_w_aperture.txt','w') foo.write('%12s \t %12s \n' % ('wave','vSv')) for i in range(0, len(wl_aper)): foo.write('%12g \t %12g \n' % (wl_aper[i], flux_aper[i])) foo.close() # Read in and plot the simulated SED produced by RADMC-3D using the same parameters # [wl,fit] = np.genfromtxt(indir+'hyperion/radmc_comparison/spectrum.out',dtype='float',skip_header=3).T # l_bol_radmc = l_bol(wl,fit*1e23/dstar**2) # radmc, = ax_sed.plot(np.log10(wl),np.log10(c/(wl*1e-4)*fit/dstar**2),'-',color='DimGray', linewidth=1.5*mag, alpha=0.5) # print the L bol of the simulated SED (both Hyperion and RADMC-3D) # lg_sim = ax_sed.legend([sim,radmc],[r'$\rm{L_{bol,sim}=%5.2f~L_{\odot},~L_{center}=9.18~L_{\odot}}$' % l_bol_sim, \ # r'$\rm{L_{bol,radmc3d}=%5.2f~L_{\odot},~L_{center}=9.18~L_{\odot}}$' % l_bol_radmc],\ # loc='lower right',fontsize=mag*16) # read the input central luminosity by reading in the source information from output file dum = Model() dum.use_sources(filename) L_cen = dum.sources[0].luminosity/lsun # lg_sim = ax_sed.legend([sim],[r'$\rm{L_{bol,sim}=%5.2f~L_{\odot},~L_{center}=%5.2f~L_{\odot}}$' % (l_bol_sim, L_cen)], \ # loc='lower right',fontsize=mag*16) # lg_sim = ax_sed.legend([sim],[r'$\rm{L_{bol,sim}=%5.2f~L_{\odot},~L_{bol,obs}=%5.2f~L_{\odot}}$' % (l_bol_sim, l_bol_obs)], \ # loc='lower right',fontsize=mag*16) # text = ax_sed.text(0.2 ,0.05 ,r'$\rm{L_{bol,simulation}=%5.2f~L_{\odot},~L_{bol,observation}=%5.2f~L_{\odot}}$' % (l_bol_sim, l_bol_obs),fontsize=mag*16,transform=ax_sed.transAxes) # text.set_bbox(dict( edgecolor='k',facecolor='None',alpha=0.3,pad=10.0)) # plot setting ax_sed.set_xlabel(r'$\rm{log\,\lambda\,({\mu}m)}$',fontsize=mag*20) ax_sed.set_ylabel(r'$\rm{log\,\nu S_{\nu}\,(erg\,cm^{-2}\,s^{-1})}$',fontsize=mag*20) [ax_sed.spines[axis].set_linewidth(1.5*mag) for axis in ['top','bottom','left','right']] ax_sed.minorticks_on() ax_sed.tick_params('both',labelsize=mag*18,width=1.5*mag,which='major',pad=15,length=5*mag) ax_sed.tick_params('both',labelsize=mag*18,width=1.5*mag,which='minor',pad=15,length=2.5*mag) ax_sed.set_ylim([-13,-7.5]) ax_sed.set_xlim([0,3]) # lg_data = ax_sed.legend([sim, aper], [r'$\rm{w/o~aperture}$', r'$\rm{w/~aperture}$'], \ # loc='upper left', fontsize=14*mag, framealpha=0.3, numpoints=1) lg_data = ax_sed.legend([irs, photometry, aper, aper_obs],\ [r'$\rm{observation}$',\ r'$\rm{photometry}$',r'$\rm{F_{aper,sim}}$',r'$\rm{F_{aper,obs}}$'],\ loc='upper left',fontsize=14*mag,numpoints=1,framealpha=0.3) # plt.gca().add_artist(lg_sim) # Write out the plot fig.savefig(outdir+print_name+'_sed.pdf',format='pdf',dpi=300,bbox_inches='tight') fig.clf() # Package for matching the colorbar from mpl_toolkits.axes_grid1 import make_axes_locatable # Extract the image for the first inclination, and scale to 300pc. We # have to specify group=1 as there is no image in group 0. image = m.get_image(group=len(wl_aper)+1, inclination=0, distance=dstar * pc, units='MJy/sr') # image = m.get_image(group=14, inclination=0, distance=dstar * pc, units='MJy/sr') # Open figure and create axes # fig = plt.figure(figsize=(8, 8)) fig, axarr = plt.subplots(3, 3, sharex='col', sharey='row',figsize=(13.5,12)) # Pre-set maximum for colorscales VMAX = {} # VMAX[3.6] = 10. # VMAX[24] = 100. # VMAX[160] = 2000. # VMAX[500] = 2000. VMAX[100] = 10. VMAX[250] = 100. VMAX[500] = 2000. VMAX[1000] = 2000. # We will now show four sub-plots, each one for a different wavelength # for i, wav in enumerate([3.6, 24, 160, 500]): # for i, wav in enumerate([100, 250, 500, 1000]): # for i, wav in enumerate([4.5, 9.7, 24, 40, 70, 100, 250, 500, 1000]): for i, wav in enumerate([3.6, 8.0, 9.7, 24, 40, 100, 250, 500, 1000]): # ax = fig.add_subplot(3, 3, i + 1) ax = axarr[i/3, i%3] # Find the closest wavelength iwav = np.argmin(np.abs(wav - image.wav)) # Calculate the image width in arcseconds given the distance used above rmax = max(m.get_quantities().r_wall) w = np.degrees(rmax / image.distance) * 3600. # w = np.degrees((1.5 * pc) / image.distance) * 60. # Image in the unit of MJy/sr # Change it into erg/s/cm2/Hz/sr factor = 1e-23*1e6 # avoid zero in log val = image.val[:, :, iwav] * factor + 1e-30 # This is the command to show the image. The parameters vmin and vmax are # the min and max levels for the colorscale (remove for default values). im = ax.imshow(np.log10(val), vmin= -22, vmax= -12, cmap=plt.cm.jet, origin='lower', extent=[-w, w, -w, w], aspect=1) # Colorbar setting # create an axes on the right side of ax. The width of cax will be 5% # of ax and the padding between cax and ax will be fixed at 0.05 inch. if (i+1) % 3 == 0: divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cb = fig.colorbar(im, cax=cax) cb.solids.set_edgecolor("face") cb.ax.minorticks_on() cb.ax.set_ylabel(r'$\rm{log(I_{\nu})\,[erg\,s^{-2}\,cm^{-2}\,Hz^{-1}\,sr^{-1}]}$',fontsize=12) cb_obj = plt.getp(cb.ax.axes, 'yticklabels') plt.setp(cb_obj,fontsize=12) if (i+1) == 7: # Finalize the plot ax.set_xlabel('RA Offset (arcsec)', fontsize=14) ax.set_ylabel('Dec Offset (arcsec)', fontsize=14) ax.tick_params(axis='both', which='major', labelsize=16) ax.set_adjustable('box-forced') ax.text(0.7,0.88,str(wav) + r'$\rm{\,\mu m}$',fontsize=18,color='white',weight='bold',transform=ax.transAxes) fig.subplots_adjust(hspace=0,wspace=-0.2) # Adjust the spaces between the subplots # plt.tight_layout() fig.savefig(outdir+print_name+'_cube_plot.png', format='png', dpi=300, bbox_inches='tight') fig.clf()
from hyperion.model import ModelOutput from hyperion.util.constants import kpc from astropy.io import fits for tau in [0.1, 1.0, 20.]: input_file = 'bm1_slab_effgrain_tau_{tau:05.2f}_images.rtout'.format(tau=tau) m = ModelOutput(input_file) for iincl, theta in enumerate([0, 30, 60, 90, 120, 150, 180]): image = m.get_image(inclination=iincl, units='MJy/sr', distance=10. * kpc) for iwav, wav in enumerate([0.165, 0.570, 21.3, 161.6]): output_file = 'images/bm1_slab_effgrain_tau_{tau:06.2f}_theta_{theta:03d}_wave_{wav:07.3f}.fits'.format(tau=tau, theta=theta, wav=wav) fits.writeto(output_file, image.val[:, :, iwav], clobber=True)
def extract_hyperion(filename,indir=None,outdir=None,dstar=200.0,aperture=None, save=True,filter_func=False,plot_all=False,clean=False, exclude_wl=[],log=True,image=True,obj='BHR71', print_data_w_aper=False,mag=1.5): """ filename: The path to Hyperion output file indir: The path to the directory which contains observations data outdir: The path to the directory for storing extracted plots and ASCII files """ def l_bol(wl,fv,dstar): import numpy as np import astropy.constants as const # wavelength unit: um # Flux density unit: Jy # constants setup # c = const.c.cgs.value pc = const.pc.cgs.value PI = np.pi SL = const.L_sun.cgs.value # Convert the unit from Jy to erg s-1 cm-2 Hz-1 fv = np.array(fv)*1e-23 freq = c/(1e-4*np.array(wl)) diff_dum = freq[1:]-freq[0:-1] freq_interpol = np.hstack((freq[0:-1]+diff_dum/2.0,freq[0:-1]+diff_dum/2.0,freq[0],freq[-1])) freq_interpol = freq_interpol[np.argsort(freq_interpol)[::-1]] fv_interpol = np.empty(len(freq_interpol)) # calculate the histogram style of spectrum # for i in range(0,len(fv)): if i == 0: fv_interpol[i] = fv[i] else: fv_interpol[2*i-1] = fv[i-1] fv_interpol[2*i] = fv[i] fv_interpol[-1] = fv[-1] dv = freq_interpol[0:-1]-freq_interpol[1:] dv = np.delete(dv,np.where(dv==0)) fv = fv[np.argsort(freq)] freq = freq[np.argsort(freq)] return (np.trapz(fv,freq)*4.*PI*(dstar*pc)**2)/SL # function for properly calculating uncertainty of spectrophotometry value def unc_spectrophoto(wl, unc, trans): # adopting smiliar procedure as Trapezoidal rule # (b-a) * [ f(a) + f(b) ] / 2 # return ( np.sum( trans[:-1]**2 * unc[:-1]**2 * (wl[1:]-wl[:-1])**2 ) / np.trapz(trans, x=wl)**2 )**0.5 # to avoid X server error import matplotlib as mpl mpl.use('Agg') # import matplotlib.pyplot as plt import numpy as np import os from hyperion.model import ModelOutput, Model from scipy.interpolate import interp1d from hyperion.util.constants import pc, c, lsun, au from astropy.io import ascii import sys from phot_filter import phot_filter from get_obs import get_obs # Open the model m = ModelOutput(filename) # Read in the observation data and calculate the noise & variance if indir == None: indir = raw_input('Path to the observation data: ') if outdir == None: outdir = raw_input('Path for the output: ') # assign the file name from the input file print_name = os.path.splitext(os.path.basename(filename))[0] # use a canned function to extract observational data obs_data = get_obs(indir, obj=obj) # unit in um, Jy wl_tot, flux_tot, unc_tot = obs_data['spec'] flux_tot = flux_tot*1e-23 # convert unit from Jy to erg s-1 cm-2 Hz-1 unc_tot = unc_tot*1e-23 l_bol_obs = l_bol(wl_tot, flux_tot*1e23, dstar) wl_phot, flux_phot, flux_sig_phot = obs_data['phot'] flux_phot = flux_phot*1e-23 # convert unit from Jy to erg s-1 cm-2 Hz-1 flux_sig_phot = flux_sig_phot*1e-23 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, 850],\ '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, 24.5]} # 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) # '+1': the zeroth slice corresponds to infinite aperture # Create the plot fig = plt.figure(figsize=(8*mag,6*mag)) ax_sed = fig.add_subplot(1, 1, 1) # Plot the observed SED if not clean: color_seq = ['Green','Red','Black'] else: color_seq = ['DimGray','DimGray','DimGray'] # plot the observations # plot in log scale if log: pacs, = ax_sed.plot(np.log10(wl_tot[(wl_tot>40) & (wl_tot<190.31)]), np.log10(c/(wl_tot[(wl_tot>40) & (wl_tot<190.31)]*1e-4)*flux_tot[(wl_tot>40) & (wl_tot<190.31)]), '-',color=color_seq[0],linewidth=1.5*mag, alpha=0.7) spire, = ax_sed.plot(np.log10(wl_tot[wl_tot > 194]),np.log10(c/(wl_tot[wl_tot > 194]*1e-4)*flux_tot[wl_tot > 194]), '-',color=color_seq[1],linewidth=1.5*mag, alpha=0.7) irs, = ax_sed.plot(np.log10(wl_tot[wl_tot < 40]),np.log10(c/(wl_tot[wl_tot < 40]*1e-4)*flux_tot[wl_tot < 40]), '-',color=color_seq[2],linewidth=1.5*mag, alpha=0.7) photometry, = ax_sed.plot(np.log10(wl_phot),np.log10(c/(wl_phot*1e-4)*flux_phot),'s',mfc='DimGray',mec='k',markersize=8) # plot the observed photometry data ax_sed.errorbar(np.log10(wl_phot),np.log10(c/(wl_phot*1e-4)*flux_phot), yerr=[np.log10(c/(wl_phot*1e-4)*flux_phot)-np.log10(c/(wl_phot*1e-4)*(flux_phot-flux_sig_phot)), np.log10(c/(wl_phot*1e-4)*(flux_phot+flux_sig_phot))-np.log10(c/(wl_phot*1e-4)*flux_phot)], fmt='s',mfc='DimGray',mec='k',markersize=8) # plot in normal scale else: pacs, = ax_sed.plot(np.log10(wl_tot[(wl_tot>40) & (wl_tot<190.31)]), c/(wl_tot[(wl_tot>40) & (wl_tot<190.31)]*1e-4)*flux_tot[(wl_tot>40) & (wl_tot<190.31)], '-',color=color_seq[0],linewidth=1.5*mag, alpha=0.7) spire, = ax_sed.plot(np.log10(wl_tot[wl_tot > 194]),c/(wl_tot[wl_tot > 194]*1e-4)*flux_tot[wl_tot > 194], '-',color=color_seq[1],linewidth=1.5*mag, alpha=0.7) irs, = ax_sed.plot(np.log10(wl_tot[wl_tot < 40]),c/(wl_tot[wl_tot < 40]*1e-4)*flux_tot[wl_tot < 40], '-',color=color_seq[2],linewidth=1.5*mag, alpha=0.7) photometry, = ax_sed.plot(wl_phot,c/(wl_phot*1e-4)*flux_phot,'s',mfc='DimGray',mec='k',markersize=8) # plot the observed photometry data ax_sed.errorbar(np.log10(wl_phot),c/(wl_phot*1e-4)*flux_phot, yerr=[c/(wl_phot*1e-4)*flux_phot-c/(wl_phot*1e-4)*(flux_phot-flux_sig_phot), c/(wl_phot*1e-4)*(flux_phot+flux_sig_phot)-c/(wl_phot*1e-4)*flux_phot], fmt='s',mfc='DimGray',mec='k',markersize=8) # if keyword 'clean' is not set, print L_bol derived from observations at upper right corner. if not clean: ax_sed.text(0.75,0.9,r'$\rm{L_{bol}= %5.2f L_{\odot}}$' % l_bol_obs, fontsize=mag*16,transform=ax_sed.transAxes) # getting SED with infinite aperture sed_inf = m.get_sed(group=0, inclination=0, aperture=-1, distance=dstar*pc, uncertainties=True) # plot the simulated SED with infinite aperture if clean == False: sim, = ax_sed.plot(np.log10(sed_inf.wav), np.log10(sed_inf.val), '-', color='GoldenRod', linewidth=0.5*mag) ax_sed.fill_between(np.log10(sed_inf.wav), np.log10(sed_inf.val-sed_inf.unc), np.log10(sed_inf.val+sed_inf.unc),color='GoldenRod', alpha=0.5) ####################################### # get fluxes with different apertures # ####################################### # this is non-reduced wavelength array because this is for printing out fluxes at all channels specified by users flux_aper = np.zeros_like(wl_aper, dtype=float) unc_aper = np.zeros_like(wl_aper, dtype=float) a = np.zeros_like(wl_aper) + 1 color_list = plt.cm.jet(np.linspace(0, 1, len(wl_aper)+1)) for i in range(0, len(wl_aper)): # occasionally users might want not to report some wavelength channels if wl_aper[i] in exclude_wl: continue # getting simulated SED from Hyperion output. (have to match with the reduced index) sed_dum = m.get_sed(group=index_reduced[np.where(aper_reduced == aper[i])], inclination=0,aperture=-1,distance=dstar*pc, uncertainties=True) # plot the whole SED from this aperture (optional) if plot_all == True: ax_sed.plot(np.log10(sed_dum.wav), np.log10(sed_dum.val),'-', color=color_list[i]) ax_sed.fill_between(np.log10(sed_dum.wav), np.log10(sed_dum.val-sed_dum.unc), np.log10(sed_dum.val+sed_dum.unc),\ color=color_list[i], alpha=0.5) # Extracting spectrophotometry values from simulated SED # Not using the photometry filer function to extract spectrophotometry values # sort by wavelength first. sort_wl = np.argsort(sed_dum.wav) val_sort = sed_dum.val[sort_wl] unc_sort = sed_dum.unc[sort_wl] wav_sort = sed_dum.wav[sort_wl] # Before doing that, convert vSv to F_lambda flux_dum = val_sort / wav_sort unc_dum = unc_sort / wav_sort # If no using filter function to extract the spectrophotometry, # then use the spectral resolution. if filter_func == False: # use a rectangle function the average the simulated SED # apply the spectral resolution if (wl_aper[i] < 50.) & (wl_aper[i] >= 5): res = 60. elif wl_aper[i] < 5: res = 10. else: res = 1000. ind = np.where((wav_sort < wl_aper[i]*(1+1./res)) & (wav_sort > wl_aper[i]*(1-1./res))) if len(ind[0]) != 0: flux_aper[i] = np.mean(flux_dum[ind]) unc_aper[i] = np.mean(unc_dum[ind]) else: f = interp1d(wav_sort, flux_dum) f_unc = interp1d(wav_sort, unc_dum) flux_aper[i] = f(wl_aper[i]) unc_aper[i] = f_unc(wl_aper[i]) # Using photometry filter function to extract spectrophotometry values else: # apply the filter function # decide the filter name if wl_aper[i] == 70: fil_name = 'Herschel PACS 70um' elif wl_aper[i] == 100: fil_name = 'Herschel PACS 100um' elif wl_aper[i] == 160: fil_name = 'Herschel PACS 160um' elif wl_aper[i] == 250: fil_name = 'Herschel SPIRE 250um' elif wl_aper[i] == 350: fil_name = 'Herschel SPIRE 350um' elif wl_aper[i] == 500: fil_name = 'Herschel SPIRE 500um' elif wl_aper[i] == 3.6: fil_name = 'IRAC Channel 1' elif wl_aper[i] == 4.5: fil_name = 'IRAC Channel 2' elif wl_aper[i] == 5.8: fil_name = 'IRAC Channel 3' elif wl_aper[i] == 8.0: fil_name = 'IRAC Channel 4' elif wl_aper[i] == 24: fil_name = 'MIPS 24um' elif wl_aper[i] == 850: fil_name = 'SCUBA 850WB' else: fil_name = None if fil_name != None: filter_func = phot_filter(fil_name) # Simulated SED should have enough wavelength coverage for applying photometry filters. f = interp1d(wav_sort, flux_dum) f_unc = interp1d(wav_sort, unc_dum) flux_aper[i] = np.trapz(f(filter_func['wave']/1e4)*\ filter_func['transmission'],x=filter_func['wave']/1e4 )/\ np.trapz(filter_func['transmission'], x=filter_func['wave']/1e4) # fix a bug unc_aper[i] = unc_spectrophoto(filter_func['wave']/1e4, f_unc(filter_func['wave']/1e4), filter_func['transmission']) else: # use a rectangle function the average the simulated SED # apply the spectral resolution if (wl_aper[i] < 50.) & (wl_aper[i] >= 5): res = 60. elif wl_aper[i] < 5: res = 10. else: res = 1000. ind = np.where((wav_sort < wl_aper[i]*(1+1./res)) & (wav_sort > wl_aper[i]*(1-1./res))) if len(ind[0]) != 0: flux_aper[i] = np.mean(flux_dum[ind]) unc_aper[i] = np.mean(unc_dum[ind]) else: f = interp1d(wav_sort, flux_dum) f_unc = interp1d(wav_sort, unc_dum) flux_aper[i] = f(wl_aper[i]) unc_aper[i] = f_unc(wl_aper[i]) # temperory step: solve issue of uncertainty greater than the value for i in range(len(wl_aper)): if unc_aper[i] >= flux_aper[i]: unc_aper[i] = flux_aper[i] - 1e-20 ########################### # Observations Extraction # ########################### # perform the same procedure of flux extraction of aperture flux with observed spectra # wl_aper = np.array(wl_aper, dtype=float) obs_aper_wl = wl_aper[(wl_aper >= min(wl_tot)) & (wl_aper <= max(wl_tot))] obs_aper_flux = np.zeros_like(obs_aper_wl) obs_aper_unc = np.zeros_like(obs_aper_wl) # have change the simulation part to work in F_lambda for fliter convolution # flux_tot and unc_tot have units of erg/s/cm2/Hz. Need to convert it to F_lambda (erg/s/cm2/um) fnu2fl = c/(wl_tot*1e-4)/wl_tot # # wl_tot and flux_tot are already hstacked and sorted by wavelength for i in range(0, len(obs_aper_wl)): # sometime users want not report some wavelength channels if obs_aper_wl[i] in exclude_wl: continue if filter_func == False: # use a rectangle function the average the simulated SED # apply the spectral resolution if (obs_aper_wl[i] < 50.) & (obs_aper_wl[i] >= 5): res = 60. elif obs_aper_wl[i] < 5: res = 10. else: res = 1000. ind = np.where((wl_tot < obs_aper_wl[i]*(1+1./res)) & (wl_tot > obs_aper_wl[i]*(1-1./res))) if len(ind[0]) != 0: obs_aper_flux[i] = np.mean(fnu2fl[ind]*flux_tot[ind]) obs_aper_unc[i] = np.mean(fnu2fl[ind]*unc_tot[ind]) else: f = interp1d(wl_tot, fnu2fl*flux_tot) f_unc = interp1d(wl_tot, fnu2fl*unc_tot) obs_aper_flux[i] = f(obs_aper_wl[i]) obs_aper_unc[i] = f_unc(obs_aper_wl[i]) else: # apply the filter function # decide the filter name if obs_aper_wl[i] == 70: fil_name = 'Herschel PACS 70um' elif obs_aper_wl[i] == 100: fil_name = 'Herschel PACS 100um' elif obs_aper_wl[i] == 160: fil_name = 'Herschel PACS 160um' elif obs_aper_wl[i] == 250: fil_name = 'Herschel SPIRE 250um' elif obs_aper_wl[i] == 350: fil_name = 'Herschel SPIRE 350um' elif obs_aper_wl[i] == 500: fil_name = 'Herschel SPIRE 500um' elif obs_aper_wl[i] == 3.6: fil_name = 'IRAC Channel 1' elif obs_aper_wl[i] == 4.5: fil_name = 'IRAC Channel 2' elif obs_aper_wl[i] == 5.8: fil_name = 'IRAC Channel 3' elif obs_aper_wl[i] == 8.0: fil_name = 'IRAC Channel 4' elif obs_aper_wl[i] == 24: fil_name = 'MIPS 24um' elif obs_aper_wl[i] == 850: fil_name = 'SCUBA 850WB' # do not have SCUBA spectra else: fil_name = None if fil_name != None: filter_func = phot_filter(fil_name) # Observed SED needs to be trimmed before applying photometry filters filter_func = filter_func[(filter_func['wave']/1e4 >= min(wl_tot))*\ ((filter_func['wave']/1e4 >= 54.8)+(filter_func['wave']/1e4 <= 36.0853))*\ ((filter_func['wave']/1e4 <= 95.05)+(filter_func['wave']/1e4 >=103))*\ ((filter_func['wave']/1e4 <= 190.31)+(filter_func['wave']/1e4 >= 195))*\ (filter_func['wave']/1e4 <= max(wl_tot))] f = interp1d(wl_tot, fnu2fl*flux_tot) f_unc = interp1d(wl_tot, fnu2fl*unc_tot) obs_aper_flux[i] = np.trapz(f(filter_func['wave']/1e4)*filter_func['transmission'], x=filter_func['wave']/1e4)/\ np.trapz(filter_func['transmission'], x=filter_func['wave']/1e4) obs_aper_unc[i] = unc_spectrophoto(filter_func['wave']/1e4, f_unc(filter_func['wave']/1e4), filter_func['transmission']) else: # use a rectangle function the average the simulated SED # apply the spectral resolution if (obs_aper_wl[i] < 50.) & (obs_aper_wl[i] >= 5): res = 60. elif obs_aper_wl[i] < 5: res = 10. else: res = 1000. ind = np.where((wl_tot < obs_aper_wl[i]*(1+1./res)) & (wl_tot > obs_aper_wl[i]*(1-1./res))) if len(ind[0]) != 0: obs_aper_flux[i] = np.mean(fnu2fl[ind]*flux_tot[ind]) obs_aper_unc[i] = np.mean(fnu2fl[ind]*unc_tot[ind]) else: f = interp1d(wl_tot, fnu2fl*flux_tot) f_unc = interp1d(wl_tot, fnu2fl*unc_tot) obs_aper_flux[i] = f(obs_aper_wl[i]) obs_aper_unc[i] = f_unc(obs_aper_wl[i]) # plot the aperture-extracted spectrophotometry fluxes from observed spectra and simulations # in log-scale if log: aper_obs = ax_sed.errorbar(np.log10(obs_aper_wl), np.log10(obs_aper_flux * obs_aper_wl ),\ yerr=[np.log10(obs_aper_flux*obs_aper_wl)-np.log10(obs_aper_flux*obs_aper_wl-obs_aper_unc*obs_aper_wl), np.log10(obs_aper_flux*obs_aper_wl+obs_aper_unc*obs_aper_wl)-np.log10(obs_aper_flux*obs_aper_wl)],\ fmt='s', mec='None', mfc='r', markersize=10, linewidth=1.5, ecolor='Red', elinewidth=3, capthick=3, barsabove=True) aper = ax_sed.errorbar(np.log10(wl_aper),np.log10(flux_aper*wl_aper),\ yerr=[np.log10(flux_aper*wl_aper)-np.log10(flux_aper*wl_aper-unc_aper*wl_aper), np.log10(flux_aper*wl_aper+unc_aper*wl_aper)-np.log10(flux_aper*wl_aper)],\ fmt='o', mec='Blue', mfc='None', color='b',markersize=12, markeredgewidth=2.5, linewidth=1.7, ecolor='Blue', elinewidth=3, barsabove=True) ax_sed.set_ylim([-14,-7]) ax_sed.set_xlim([0,3.2]) # in normal scale (normal in y-axis) else: aper_obs = ax_sed.errorbar(np.log10(obs_aper_wl), obs_aper_flux*obs_aper_wl, yerr=obs_aper_unc*obs_aper_wl,\ fmt='s', mec='None', mfc='r', markersize=10, linewidth=1.5, ecolor='Red', elinewidth=3, capthick=3, barsabove=True) aper = ax_sed.errorbar(np.log10(wl_aper),flux_aper*wl_aper, yerr=unc_aper*wl_aper,\ fmt='o', mec='Blue', mfc='None', color='b',markersize=12, markeredgewidth=2.5, linewidth=1.7, ecolor='Blue', elinewidth=3, barsabove=True) ax_sed.set_xlim([0,3.2]) # calculate the bolometric luminosity of the aperture # print flux_aper l_bol_sim = l_bol(wl_aper, flux_aper*wl_aper/(c/np.array(wl_aper)*1e4)*1e23, dstar) print 'Bolometric luminosity of simulated spectrum: %5.2f lsun' % l_bol_sim # print out the sed into ascii file for reading in later if save == True: # unapertured SED foo = open(outdir+print_name+'_sed_inf.txt','w') foo.write('%12s \t %12s \t %12s \n' % ('wave','vSv','sigma_vSv')) for i in range(0, len(sed_inf.wav)): foo.write('%12g \t %12g \t %12g \n' % (sed_inf.wav[i], sed_inf.val[i], sed_inf.unc[i])) foo.close() # SED with convolution of aperture sizes foo = open(outdir+print_name+'_sed_w_aperture.txt','w') foo.write('%12s \t %12s \t %12s \n' % ('wave','vSv','sigma_vSv')) for i in range(0, len(wl_aper)): foo.write('%12g \t %12g \t %12g \n' % (wl_aper[i], flux_aper[i]*wl_aper[i], unc_aper[i]*wl_aper[i])) foo.close() # print out the aperture-convolved fluxex from observations if print_data_w_aper: foo = open(outdir+print_name+'_obs_w_aperture.txt','w') foo.write('%12s \t %12s \t %12s \n' % ('wave','Jy','sigma_Jy')) for i in range(0, len(obs_aper_wl)): foo.write('%12g \t %12g \t %12g \n' % (obs_aper_wl[i], obs_aper_flux[i]*obs_aper_wl[i]/(c/obs_aper_wl[i]*1e4)*1e23, obs_aper_unc[i]*obs_aper_wl[i]/(c/obs_aper_wl[i]*1e4)*1e23)) foo.close() # read the input central luminosity by reading in the source information from output file dum = Model() dum.use_sources(filename) L_cen = dum.sources[0].luminosity/lsun # legend lg_data = ax_sed.legend([irs, photometry, aper, aper_obs], [r'$\rm{observation}$', r'$\rm{photometry}$',r'$\rm{F_{aper,sim}}$',r'$\rm{F_{aper,obs}}$'], loc='upper left',fontsize=14*mag,numpoints=1,framealpha=0.3) if clean == False: lg_sim = ax_sed.legend([sim],[r'$\rm{L_{bol,sim}=%5.2f\,L_{\odot},\,L_{center}=%5.2f\,L_{\odot}}$' % (l_bol_sim, L_cen)], \ loc='lower right',fontsize=mag*16) plt.gca().add_artist(lg_data) # plot setting ax_sed.set_xlabel(r'$\rm{log\,\lambda\,[{\mu}m]}$',fontsize=mag*20) ax_sed.set_ylabel(r'$\rm{log\,\nu S_{\nu}\,[erg\,s^{-1}\,cm^{-2}]}$',fontsize=mag*20) [ax_sed.spines[axis].set_linewidth(1.5*mag) for axis in ['top','bottom','left','right']] ax_sed.minorticks_on() ax_sed.tick_params('both',labelsize=mag*18,width=1.5*mag,which='major',pad=15,length=5*mag) ax_sed.tick_params('both',labelsize=mag*18,width=1.5*mag,which='minor',pad=15,length=2.5*mag) # fix the tick label font ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',size=mag*18) for label in ax_sed.get_xticklabels(): label.set_fontproperties(ticks_font) for label in ax_sed.get_yticklabels(): label.set_fontproperties(ticks_font) # Write out the plot fig.savefig(outdir+print_name+'_sed.pdf',format='pdf',dpi=300,bbox_inches='tight') fig.clf() # option for suppress image plotting (for speed) if image: # Package for matching the colorbar from mpl_toolkits.axes_grid1 import make_axes_locatable, ImageGrid # Users may change the unit: mJy, Jy, MJy/sr, ergs/cm^2/s, ergs/cm^2/s/Hz # !!! image = m.get_image(group=len(aper_reduced)+1, inclination=0, distance=dstar*pc, units='MJy/sr') # Open figure and create axes fig = plt.figure(figsize=(12,12)) grid = ImageGrid(fig, 111,nrows_ncols=(3,3),direction='row', add_all=True,label_mode='1',share_all=True, cbar_location='right',cbar_mode='single', cbar_size='3%',cbar_pad=0) for i, wav in enumerate([3.6, 8.0, 9.7, 24, 40, 100, 250, 500, 1000]): ax = grid[i] # Find the closest wavelength iwav = np.argmin(np.abs(wav - image.wav)) # Calculate the image width in arcseconds given the distance used above # get the max radius rmax = max(m.get_quantities().r_wall) w = np.degrees(rmax / image.distance) * 3600. # Image in the unit of MJy/sr # Change it into erg/s/cm2/Hz/sr factor = 1e-23*1e6 # avoid zero in log # flip the image, because the setup of inclination is upside down val = image.val[::-1, :, iwav] * factor + 1e-30 # This is the command to show the image. The parameters vmin and vmax are # the min and max levels for the colorscale (remove for default values). cmap = plt.cm.CMRmap im = ax.imshow(np.log10(val), vmin= -22, vmax= -12, cmap=cmap, origin='lower', extent=[-w, w, -w, w], aspect=1) ax.set_xlabel(r'$\rm{RA\,Offset\,[arcsec]}$', fontsize=14) ax.set_ylabel(r'$\rm{Dec\,Offset\,[arcsec]}$', fontsize=14) # fix the tick label font ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',size=14) for label in ax.get_xticklabels(): label.set_fontproperties(ticks_font) for label in ax.get_yticklabels(): label.set_fontproperties(ticks_font) # Colorbar setting cb = ax.cax.colorbar(im) cb.solids.set_edgecolor('face') cb.ax.minorticks_on() cb.ax.set_ylabel(r'$\rm{log(I_{\nu})\,[erg\,s^{-1}\,cm^{-2}\,Hz^{-1}\,sr^{-1}]}$',fontsize=18) cb_obj = plt.getp(cb.ax.axes, 'yticklabels') plt.setp(cb_obj,fontsize=18) ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',size=18) for label in cb.ax.get_yticklabels(): label.set_fontproperties(ticks_font) ax.tick_params(axis='both', which='major', labelsize=16) ax.text(0.7,0.88,str(wav) + r'$\rm{\,\mu m}$',fontsize=16,color='white', transform=ax.transAxes) fig.savefig(outdir+print_name+'_image_gridplot.pdf', format='pdf', dpi=300, bbox_inches='tight') fig.clf()
ax_top.set_xticklabels(r_tick_labels) ax_top.tick_params('x', labelsize=14) else: r_ticks = scale(np.array([0, 1, 2, 3, 4]), (np.log10(0.14), np.log10(41253)), (-w, w)) ax_top = grid[i].twiny() ax_top.set_xticks(r_ticks) ax_top.set_xticklabels([]) grid[i].tick_params('both', labelsize=14) for i in range(4, 8): # get the H-band simulated image m = ModelOutput(filename[i - 4]) image = m.get_image(group=0, inclination=0, distance=178 * pc, units='MJy/sr') # Find the closest wavelength iwav = np.argmin(np.abs(wave - image.wav)) # Calculate the image width in arcseconds given the distance used above # get the max radius rmax = max(m.get_quantities().r_wall) w = np.degrees(rmax / image.distance) * 3600. # Image in the unit of MJy/sr # Change it into erg/s/cm2/Hz/sr factor = 1e-23 * 1e6 # avoid zero in log # flip the image, because the setup of inclination is upside down
def azimuthal_simulation(rtout, beam_size, wave, dist=200., group=22): """ rtout: the filepath to the output file of Hyperion beam_size: the beam size used for the width of annulus dist: the physical distance to the source group: the group which contains image """ import numpy as np import matplotlib.pyplot as plt import astropy.constants as const from hyperion.model import ModelOutput # constant setup pc = const.pc.cgs.value au = const.au.cgs.value output = {'wave': wave, 'annuli': [], 'flux_annuli': []} # Read in the Hyperion output file m = ModelOutput(rtout) # get image image = m.get_image(group=5, inclination=0, distance=dist * pc, units='Jy') # Calculate the image width in arcsec given the distance to the source rmax = max(m.get_quantities().r_wall) w = np.degrees(rmax / image.distance) * 3600 # grid of radii of annulus annuli = np.linspace(beam_size / 2., np.floor( (w - beam_size / 2.) / beam_size) * beam_size, np.floor((w - beam_size / 2.) / beam_size)) # plot fig = plt.figure(figsize=(8, 6)) ax = fig.add_subplot(111) # iternate through wavelength if type(wave) == int or type(wave) == float: wave = [wave] color_list = plt.cm.viridis(np.linspace(0, 1, len(wave) + 1)) for i in range(len(wave)): wav = wave[i] # Find the closest wavelength iwav = np.argmin(np.abs(wav - image.wav)) # avoid zero when log, and flip the image val = image.val[::-1, :, iwav] # determine the center of the image npix = len(val[:, 0]) center = np.array([npix / 2. + 0.5, npix / 2. + 0.5]) scale = 2 * rmax / npix # create index array of the image x = np.empty_like(val) for j in range(len(val[0, :])): x[:, j] = j flux_annuli = np.empty_like(annuli) for k in range(len(annuli)): flux_annuli[k] = np.sum(val[(((x-center[0])**2+(x.T-center[1])**2)**0.5*2*w/npix >= annuli[k]-beam_size/2.) & \ (((x-center[0])**2+(x.T-center[1])**2)**0.5*2*w/npix < annuli[k]+beam_size/2.)]) output['annuli'].append(np.array(annuli)) output['flux_annuli'].append(flux_annuli) flux_annuli = flux_annuli / np.nanmax(flux_annuli) ax.plot(np.log10(annuli*dist), np.log10(flux_annuli), 'o-', color=color_list[i], \ markersize=3, mec='None', label=r'$\rm{'+str(wav)+'\,\mu m}$') ax.axvline(np.log10((w - beam_size / 2.) * dist), linestyle='--', color='k') ax.axvline(np.log10(w * dist), linestyle='-', color='k') ax.legend(loc='best', fontsize=12, numpoints=1, ncol=2) ax.set_xlabel(r'$\rm{log(Radius)\,[AU]}$', fontsize=18) ax.set_ylabel(r'${\rm log(}F/F_{\rm max})$', fontsize=18) fig.gca().set_ylim(top=0.1) [ ax.spines[axis].set_linewidth(1.5) for axis in ['top', 'bottom', 'left', 'right'] ] ax.minorticks_on() ax.tick_params('both', labelsize=18, width=1.5, which='major', pad=15, length=5) ax.tick_params('both', labelsize=18, width=1.5, which='minor', pad=15, length=2.5) fig.savefig('/Users/yaolun/test/annuli_profile.pdf', format='pdf', dpi=300, bbox_inches='tight') fig.clf() return output
import matplotlib as mpl mpl.use('Agg') import matplotlib.pyplot as plt from hyperion.model import ModelOutput from hyperion.util.constants import pc # Create output directory if it does not already exist if not os.path.exists('frames'): os.mkdir('frames') # Open model m = ModelOutput('tutorial_model.rtout') # Read image from model wav, nufnu = m.get_image(group=2, distance=300 * pc) # nufnu is now an array with four dimensions (n_view, n_wav, n_y, n_x) # Fix the wavelength to the first one and cycle through viewing angles iwav = 0 print "Wavelength is %g microns" % wav[iwav] for iview in range(nufnu.shape[0]): # Open figure and create axes fig = plt.figure() ax = fig.add_subplot(1, 1, 1) # This is the command to show the image. The parameters vmin and vmax are # the min and max levels for the grayscale (remove for default values).
ax_top.set_xlabel(r'$\rm{log(radius)\,[AU]}$', fontsize=16) ax_top.set_xticks(r_ticks) ax_top.set_xticklabels(r_tick_labels) ax_top.tick_params('x', labelsize=14) else: r_ticks = scale(np.array([0,1,2,3,4]), (np.log10(0.14), np.log10(41253)), (-w,w)) ax_top = grid[i].twiny() ax_top.set_xticks(r_ticks) ax_top.set_xticklabels([]) grid[i].tick_params('both',labelsize=14) for i in range(4,8): # get the H-band simulated image m = ModelOutput(filename[i-4]) image = m.get_image(group=0, inclination=0, distance=178 * pc, units='MJy/sr') # Find the closest wavelength iwav = np.argmin(np.abs(wave - image.wav)) # Calculate the image width in arcseconds given the distance used above # get the max radius rmax = max(m.get_quantities().r_wall) w = np.degrees(rmax / image.distance) * 3600. # Image in the unit of MJy/sr # Change it into erg/s/cm2/Hz/sr factor = 1e-23*1e6 # avoid zero in log # flip the image, because the setup of inclination is upside down val = image.val[::-1, :, iwav] * factor + 1e-30
def azimuthal_avg_radial_intensity(wave, rtout, plotname, dstar, annulus_width=10, rrange=[10,200], group=8, obs=None, other_obs=None): """ The 'obs' option only works for Herschel PACS/SPIRE image. The 'obs' option now accept """ import numpy as np import matplotlib as mpl # to avoid X server error mpl.use('Agg') from astropy.io import ascii, fits import matplotlib.pyplot as plt from photutils import aperture_photometry as ap from photutils import CircularAperture, CircularAnnulus from astropy import units as u from astropy.coordinates import SkyCoord from astropy import wcs from hyperion.model import ModelOutput import astropy.constants as const import os pc = const.pc.cgs.value AU = const.au.cgs.value # radial grid in arcsec # make the annulus center on r = np.arange(rrange[0], rrange[1], annulus_width, dtype=float) - annulus_width*0.5 # r = np.arange(rrange[0], rrange[1], annulus_width, dtype=float) - annulus_width*0.55 # source_center = '12 01 36.3 -65 08 53.0' def ExtractIntensityObs(rrange, annulus_width, obs): import numpy as np from astropy.io import fits from astropy.coordinates import SkyCoord from astropy import wcs from photutils import aperture_photometry as ap from photutils import CircularAperture, CircularAnnulus r = np.arange(rrange[0], rrange[1], annulus_width, dtype=float) - annulus_width*0.5 # r = np.arange(rrange[0], rrange[1], annulus_width, dtype=float) - annulus_width*0.55 imgpath = obs['imgpath'] source_center = obs['source_center'] # Read in data and set up coversions im_hdu = fits.open(imgpath) im = im_hdu[1].data wave = im_hdu[0].header['WAVELNTH'] # error if (wave < 200.0) & (wave > 70.0): im_err = im_hdu[5].data elif (wave > 200.0) & (wave < 670.0): im_err = im_hdu[2].data else: im_err_exten = raw_input('The extension that includes the image error: ') im_err = im_hdu[int(im_err_exten)].data w = wcs.WCS(im_hdu[1].header) coord = SkyCoord(source_center, unit=(u.hourangle, u.deg)) pixcoord = w.wcs_world2pix(coord.ra.degree, coord.dec.degree, 1) pix2arcsec = abs(im_hdu[1].header['CDELT1'])*3600. # determine whether need to convert the unit factor = 1 print 'Image unit is ', im_hdu[1].header['BUNIT'] if im_hdu[1].header['BUNIT'] != 'Jy/pixel': print 'Image unit is ', im_hdu[1].header['BUNIT'] if im_hdu[1].header['BUNIT'] == 'MJy/sr': # convert intensity unit from MJy/sr to Jy/pixel factor = 1e6/4.25e10*abs(im_hdu[1].header['CDELT1']*im_hdu[1].header['CDELT2'])*3600**2 else: factor = raw_input('What is the conversion factor to Jy/pixel?') I = np.empty_like(r[:-1]) I_low = np.empty_like(r[:-1]) I_hi = np.empty_like(r[:-1]) I_err = np.empty_like(r[:-1]) # for calculating the uncertainty from the variation within each annulus # construct the x- and y-matrix grid_x, grid_y = np.meshgrid(np.linspace(0,len(im[0,:])-1,len(im[0,:])), np.linspace(0,len(im[:,0])-1,len(im[:,0]))) grid_dist = ((grid_x-pixcoord[0])**2+(grid_y-pixcoord[1])**2)**0.5 # iteration for ir in range(len(r)-1): aperture = CircularAnnulus((pixcoord[0],pixcoord[1]), r_in=r[ir]/pix2arcsec, r_out=r[ir+1]/pix2arcsec) phot = ap(im, aperture, error=im_err) I[ir] = phot['aperture_sum'].data * factor / aperture.area() # uncertainty im_dum = np.where((grid_dist < r[ir+1]/pix2arcsec) & (grid_dist >= r[ir]/pix2arcsec), im, np.nan) # estimate the uncertainty by offsetting the annulus by +/- 1 pixel offset = -1 if r[ir]/pix2arcsec + offset < 0: offset = -r[ir]/pix2arcsec aperture = CircularAnnulus((pixcoord[0],pixcoord[1]), r_in=r[ir]/pix2arcsec + offset, r_out=r[ir+1]/pix2arcsec + offset) phot = ap(im, aperture, error=im_err) I_low[ir] = phot['aperture_sum'].data * factor / aperture.area() offset = 1 aperture = CircularAnnulus((pixcoord[0],pixcoord[1]), r_in=r[ir]/pix2arcsec + offset, r_out=r[ir+1]/pix2arcsec + offset) phot = ap(im, aperture, error=im_err) I_hi[ir] = phot['aperture_sum'].data * factor / aperture.area() I_err = (abs(I_low - I) + abs(I_hi - I))/2. return r, I, I_err if obs != None: I_obs = [] for o in obs: if 'label' not in o.keys(): label_dum = r'$\rm{observation}$' color_dum = 'g' linestyle_dum = '-' rrange_dum = rrange annulus_width_dum = annulus_width else: label_dum = o['label'] color_dum = o['plot_color'] linestyle_dum = o['plot_linestyle'] rrange_dum = o['rrange'] annulus_width_dum = o['annulus_width'] r_dum, I_dum, I_err_dum = ExtractIntensityObs(rrange_dum, annulus_width_dum, o) # determine the label I_obs.append({'imgpath':o['imgpath'], 'r':r_dum, 'I':I_dum, 'I_err':I_err_dum, 'label': label_dum, 'plot_color':color_dum, 'plot_linestyle':linestyle_dum}) # The first image should be the one to be compared primarily, and written out I = I_obs[0]['I'] I_err = I_obs[0]['I_err'] imgpath = I_obs[0]['imgpath'] # # read in from RTout rtout = ModelOutput(rtout) im = rtout.get_image(group=group, inclination=0, distance=dstar*pc, units='Jy', uncertainties=True) factor = 1 # Find the closest wavelength iwav = np.argmin(np.abs(wave - im.wav)) # avoid zero when log, and flip the image val = im.val[::-1, :, iwav] unc = im.unc[::-1, :, iwav] w = np.degrees(max(rtout.get_quantities().r_wall) / im.distance) * 3600 npix = len(val[:,0]) pix2arcsec = 2*w/npix I_sim = np.empty_like(r[:-1]) I_sim_hi = np.empty_like(r[:-1]) I_sim_low = np.empty_like(r[:-1]) I_sim_err = np.empty_like(r[:-1]) # for calculating the uncertainty from the variation within each annulus # construct the x- and y-matrix grid_x, grid_y = np.meshgrid(np.linspace(0,npix-1,npix), np.linspace(0,npix-1,npix)) dist_x = abs(grid_x - ((npix-1)/2.)) dist_y = abs(grid_y - ((npix-1)/2.)) grid_dist = (dist_x**2+dist_y**2)**0.5 # iteration for ir in range(len(r)-1): aperture = CircularAnnulus((npix/2.+0.5, npix/2.+0.5), r_in=r[ir]/pix2arcsec, r_out=r[ir+1]/pix2arcsec) phot = ap(val, aperture, error=unc) I_sim[ir] = phot['aperture_sum'].data / aperture.area() # uncertainty im_dum = np.where((grid_dist < r[ir+1]/pix2arcsec) & (grid_dist >= r[ir]/pix2arcsec), val, np.nan) # I_sim_err[ir] = phot['aperture_sum_err'].data / aperture.area() # I_sim_err[ir] = (np.nanstd(im_dum)**2+phot['aperture_sum_err'].data**2)**0.5 * factor / aperture.area() offset = -1 aperture = CircularAnnulus((npix/2.+0.5, npix/2.+0.5), r_in=r[ir]/pix2arcsec + offset, r_out=r[ir+1]/pix2arcsec + offset) phot = ap(val, aperture, error=unc) I_sim_low[ir] = phot['aperture_sum'].data * factor / aperture.area() offset = 1 aperture = CircularAnnulus((npix/2.+0.5, npix/2.+0.5), r_in=r[ir]/pix2arcsec + offset, r_out=r[ir+1]/pix2arcsec + offset) phot = ap(val, aperture, error=unc) I_sim_hi[ir] = phot['aperture_sum'].data * factor / aperture.area() I_sim_err = (abs(I_sim_low - I_sim)+ abs(I_sim_hi - I_sim))/2. if obs != None: # write the numbers into file foo = open(plotname+'_radial_profile_'+str(wave)+'um.txt', 'w') # print some header info foo.write('# wavelength '+str(wave)+' um \n') foo.write('# image file '+os.path.basename(imgpath)+' \n') foo.write('# annulus width '+str(annulus_width)+' arcsec \n') # write profiles foo.write('r_in \t I \t I_err \t I_sim \t I_sim_err \n') foo.write('# [arcsec] \t [Jy/pixel] \t [Jy/pixel] \t [Jy/pixel] \t [Jy/pixel] \n') for i in range(len(I)): foo.write('%f \t %e \t %e \t %e \t %e \n' % (r[i]+annulus_width/2., I[i], I_err[i], I_sim[i], I_sim_err[i])) foo.close() else: # write the numbers into file foo = open(plotname+'_radial_profile_'+str(wave)+'um.txt', 'w') # print some header info foo.write('# wavelength '+str(wave)+' um \n') foo.write('# annulus width '+str(annulus_width)+' arcsec \n') # write profiles foo.write('r_in \t I_sim \t I_sim_err \n') foo.write('# [arcsec] \t [Jy/pixel] \t [Jy/pixel] \n') for i in range(len(I_sim)): foo.write('%f \t %e \t %e \n' % (r[i]+annulus_width/2., I_sim[i], I_sim_err[i])) foo.close() # plot fig = plt.figure(figsize=(8,6)) ax = fig.add_subplot(111) I_sim_hi = np.log10((I_sim+I_sim_err)/I_sim.max())-np.log10(I_sim/I_sim.max()) I_sim_low = np.log10(I_sim/I_sim.max())-np.log10((I_sim-I_sim_err)/I_sim.max()) i_sim = ax.errorbar(np.log10(r[:-1]*dstar), np.log10(I_sim/I_sim.max()), color='b', yerr=(I_sim_low, I_sim_hi), marker='o', linestyle='-', mec='None', markersize=5, ecolor='b', elinewidth=1.5, capthick=1.5, barsabove=True) if obs != None: plot_profile = [] plot_label = [] for o in I_obs: I_hi = np.log10((o['I']+o['I_err'])/o['I'].max())-np.log10(o['I']/o['I'].max()) I_low = np.log10(o['I']/o['I'].max())-np.log10((o['I']-o['I_err'])/o['I'].max()) i = ax.errorbar(np.log10(o['r'][:-1]*dstar), np.log10(o['I']/o['I'].max()), color=o['plot_color'], yerr=(I_low, I_hi), marker='o', linestyle=o['plot_linestyle'], mec='None', markersize=5, ecolor=o['plot_color'], elinewidth=1.5, capthick=1.5, barsabove=True) plot_profile.append(i) plot_label.append(o['label']) plot_profile.append(i_sim) plot_label.append(r'$\rm{simulation}$') ax.legend(plot_profile, plot_label, fontsize=16, numpoints=1, loc='best') else: ax.legend([i_sim], [r'$\rm{simulation}$'], fontsize=16, numpoints=1, loc='best') # limit radius ax.axvline([np.log10(100*dstar)], color='k', linestyle='--', linewidth=1) # [ax.spines[axis].set_linewidth(1.5) for axis in ['top','bottom','left','right']] ax.minorticks_on() ax.tick_params('both',labelsize=18,width=1.5,which='major',pad=10,length=5) ax.tick_params('both',labelsize=18,width=1.5,which='minor',pad=10,length=2.5) ax.set_xlabel(r'$\rm{log(\it{b})\,[\rm{AU}]}$', fontsize=18) ax.set_ylabel(r'$\rm{log(I\,/\,I_{max})}$', fontsize=18) # fix the tick label font ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',size=18) for label in ax.get_xticklabels(): label.set_fontproperties(ticks_font) for label in ax.get_yticklabels(): label.set_fontproperties(ticks_font) fig.savefig(plotname+'_radial_profile_'+str(wave)+'um.pdf', format='pdf', dpi=300, bbox_inches='tight') fig.clf()
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.")
def hyperion_image(rtout, wave, plotdir, printname, dstar=200., group=0, marker=0, size='full', convolve=False, unit=None, scalebar=None): # to avoid X server error import matplotlib as mpl mpl.use('Agg') import numpy as np import matplotlib.pyplot as plt import matplotlib as mpl import astropy.constants as const from hyperion.model import ModelOutput # Package for matching the colorbar from mpl_toolkits.axes_grid1 import make_axes_locatable pc = const.pc.cgs.value if unit == None: unit = 'erg\,s^{-1}\,cm^{-2}\,Hz^{-1}\,sr^{-1}' m = ModelOutput(rtout) # Extract the image. image = m.get_image(group=group, inclination=0, distance=dstar * pc, units='MJy/sr') # print np.shape(image.val) # Open figure and create axes fig = plt.figure(figsize=(8,8)) ax = fig.add_subplot(111) # Find the closest wavelength iwav = np.argmin(np.abs(wave - image.wav)) # Calculate the image width in arcseconds given the distance used above # get the max radius rmax = max(m.get_quantities().r_wall) w = np.degrees(rmax / image.distance) * 3600. # Image in the unit of MJy/sr # Change it into erg/s/cm2/Hz/sr # factor = 1e-23*1e6 factor = 1 # avoid zero in log # flip the image, because the setup of inclination is upside down val = image.val[::-1, :, iwav] * factor + 1e-30 if convolve: from astropy.convolution import convolve, Gaussian2DKernel img_res = 2*w/len(val[:,0]) kernel = Gaussian2DKernel(0.27/2.354/img_res) val = convolve(val, kernel) if size != 'full': pix_e2c = (w-size/2.)/w * len(val[:,0])/2 val = val[pix_e2c:-pix_e2c, pix_e2c:-pix_e2c] w = size/2. # This is the command to show the image. The parameters vmin and vmax are # the min and max levels for the colorscale (remove for default values). # cmap = sns.cubehelix_palette(start=0.1, rot=-0.7, gamma=0.2, as_cmap=True) cmap = plt.cm.CMRmap im = ax.imshow(val, # norm=mpl.colors.LogNorm(vmin=1.515e-01, vmax=4.118e+01), norm=mpl.colors.LogNorm(vmin=1e-04, vmax=1e+01), cmap=cmap, origin='lower', extent=[-w, w, -w, w], aspect=1) # draw the flux extraction regions # x = 100 # y = 100 # area = x*y / 4.25e10 # offset = 50 # # pos_n = (len(val[0,:])/2.-1,len(val[0,:])/2.-1 + offset*len(val[0,:])/2/w) # pos_s = (len(val[0,:])/2.-1,len(val[0,:])/2.-1 - offset*len(val[0,:])/2/w) # # import matplotlib.patches as patches # ax.add_patch(patches.Rectangle((-x/2, -y), x, y, fill=False, edgecolor='lime')) # ax.add_patch(patches.Rectangle((-x/2, 0), x, y, fill=False, edgecolor='lime')) # plot the marker for center position by default or user input offset ax.plot([0],[-marker], '+', color='lime', markersize=10, mew=2) ax.set_xlim([-w,w]) ax.set_ylim([-w,w]) # ax.plot([0],[-10], '+', color='m', markersize=10, mew=2) print(w) # plot scalebar if scalebar != None: ax.plot([0.85*w-scalebar, 0.85*w], [-0.8*w, -0.8*w], color='w', linewidth=3) # add text ax.text(0.85*w-scalebar/2, -0.9*w, r'$\rm{'+str(scalebar)+"\,arcsec}$", color='w', fontsize=18, fontweight='bold', ha='center') # fix the tick label font ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',size=16) for label in ax.get_xticklabels(): label.set_fontproperties(ticks_font) for label in ax.get_yticklabels(): label.set_fontproperties(ticks_font) # Colorbar setting # create an axes on the right side of ax. The width of cax will be 5% # of ax and the padding between cax and ax will be fixed at 0.05 inch. divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cb = fig.colorbar(im, cax=cax) cb.solids.set_edgecolor("face") cb.ax.minorticks_on() cb.ax.set_ylabel(r'$\rm{Intensity\,['+unit+']}$',fontsize=16) cb.ax.tick_params('both', width=1.5, which='major', length=3) cb.ax.tick_params('both', width=1.5, which='minor', length=2) cb_obj = plt.getp(cb.ax.axes, 'yticklabels') plt.setp(cb_obj,fontsize=18) # fix the tick label font ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',size=18) for label in cb.ax.get_yticklabels(): label.set_fontproperties(ticks_font) ax.set_xlabel(r'$\rm{RA\,Offset\,[arcsec]}$', fontsize=16) ax.set_ylabel(r'$\rm{Dec\,Offset\,[arcsec]}$', fontsize=16) # set the frame color ax.spines['bottom'].set_color('white') ax.spines['top'].set_color('white') ax.spines['left'].set_color('white') ax.spines['right'].set_color('white') ax.tick_params(axis='both', which='major', width=1.5, labelsize=18, color='white', length=5) ax.text(0.7,0.88,str(wave) + r'$\rm{\,\mu m}$',fontsize=20,color='white', transform=ax.transAxes) fig.savefig(plotdir+printname+'_image_'+str(wave)+'.pdf', format='pdf', dpi=300, bbox_inches='tight') fig.clf()
import numpy as np from PIL import Image from hyperion.model import ModelOutput from hyperion.util.constants import pc m = ModelOutput('simple_cube.rtout') image = m.get_image(inclination=0, distance=300 * pc, units='MJy/sr') # Extract the slices we want to use for red, green, and blue r = image.val[:, :, 17] g = image.val[:, :, 18] b = image.val[:, :, 19] # Now we need to rescale the values we want to the range 0 to 255, clip values # outside the range, and convert to unsigned 8-bit integers. We also use a sqrt # stretch (hence the ** 0.5) r = np.clip((r / 0.5)**0.5 * 255., 0., 255.) r = np.array(r, dtype=np.uint8) g = np.clip((g / 2)**0.5 * 255., 0., 255.) g = np.array(g, dtype=np.uint8) b = np.clip((b / 4.)**0.5 * 255., 0., 255.) b = np.array(b, dtype=np.uint8) # We now convert to image objects image_r = Image.fromarray(r) image_g = Image.fromarray(g) image_b = Image.fromarray(b)
def hyperion_image(rtout, wave, plotdir, printname, dstar=200., group=0, marker=0, size='full', convolve=False, unit=None, scalebar=None): # to avoid X server error import matplotlib as mpl mpl.use('Agg') import numpy as np import matplotlib.pyplot as plt import matplotlib as mpl import astropy.constants as const from hyperion.model import ModelOutput # Package for matching the colorbar from mpl_toolkits.axes_grid1 import make_axes_locatable pc = const.pc.cgs.value if unit == None: unit = 'erg\,s^{-1}\,cm^{-2}\,Hz^{-1}\,sr^{-1}' m = ModelOutput(rtout) # Extract the image. image = m.get_image(group=group, inclination=0, distance=dstar * pc, units='MJy/sr') # print np.shape(image.val) # Open figure and create axes fig = plt.figure(figsize=(8, 8)) ax = fig.add_subplot(111) # Find the closest wavelength iwav = np.argmin(np.abs(wave - image.wav)) # Calculate the image width in arcseconds given the distance used above # get the max radius rmax = max(m.get_quantities().r_wall) w = np.degrees(rmax / image.distance) * 3600. # Image in the unit of MJy/sr # Change it into erg/s/cm2/Hz/sr # factor = 1e-23*1e6 factor = 1 # avoid zero in log # flip the image, because the setup of inclination is upside down val = image.val[::-1, :, iwav] * factor + 1e-30 if convolve: from astropy.convolution import convolve, Gaussian2DKernel img_res = 2 * w / len(val[:, 0]) kernel = Gaussian2DKernel(0.27 / 2.354 / img_res) val = convolve(val, kernel) if size != 'full': pix_e2c = (w - size / 2.) / w * len(val[:, 0]) / 2 val = val[pix_e2c:-pix_e2c, pix_e2c:-pix_e2c] w = size / 2. # This is the command to show the image. The parameters vmin and vmax are # the min and max levels for the colorscale (remove for default values). # cmap = sns.cubehelix_palette(start=0.1, rot=-0.7, gamma=0.2, as_cmap=True) cmap = plt.cm.CMRmap im = ax.imshow( val, # norm=mpl.colors.LogNorm(vmin=1.515e-01, vmax=4.118e+01), norm=mpl.colors.LogNorm(vmin=1e-04, vmax=1e+01), cmap=cmap, origin='lower', extent=[-w, w, -w, w], aspect=1) # draw the flux extraction regions # x = 100 # y = 100 # area = x*y / 4.25e10 # offset = 50 # # pos_n = (len(val[0,:])/2.-1,len(val[0,:])/2.-1 + offset*len(val[0,:])/2/w) # pos_s = (len(val[0,:])/2.-1,len(val[0,:])/2.-1 - offset*len(val[0,:])/2/w) # # import matplotlib.patches as patches # ax.add_patch(patches.Rectangle((-x/2, -y), x, y, fill=False, edgecolor='lime')) # ax.add_patch(patches.Rectangle((-x/2, 0), x, y, fill=False, edgecolor='lime')) # plot the marker for center position by default or user input offset ax.plot([0], [-marker], '+', color='lime', markersize=10, mew=2) ax.set_xlim([-w, w]) ax.set_ylim([-w, w]) # ax.plot([0],[-10], '+', color='m', markersize=10, mew=2) print(w) # plot scalebar if scalebar != None: ax.plot([0.85 * w - scalebar, 0.85 * w], [-0.8 * w, -0.8 * w], color='w', linewidth=3) # add text ax.text(0.85 * w - scalebar / 2, -0.9 * w, r'$\rm{' + str(scalebar) + "\,arcsec}$", color='w', fontsize=18, fontweight='bold', ha='center') # fix the tick label font ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral', size=16) for label in ax.get_xticklabels(): label.set_fontproperties(ticks_font) for label in ax.get_yticklabels(): label.set_fontproperties(ticks_font) # Colorbar setting # create an axes on the right side of ax. The width of cax will be 5% # of ax and the padding between cax and ax will be fixed at 0.05 inch. divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cb = fig.colorbar(im, cax=cax) cb.solids.set_edgecolor("face") cb.ax.minorticks_on() cb.ax.set_ylabel(r'$\rm{Intensity\,[' + unit + ']}$', fontsize=16) cb.ax.tick_params('both', width=1.5, which='major', length=3) cb.ax.tick_params('both', width=1.5, which='minor', length=2) cb_obj = plt.getp(cb.ax.axes, 'yticklabels') plt.setp(cb_obj, fontsize=18) # fix the tick label font ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral', size=18) for label in cb.ax.get_yticklabels(): label.set_fontproperties(ticks_font) ax.set_xlabel(r'$\rm{RA\,Offset\,[arcsec]}$', fontsize=16) ax.set_ylabel(r'$\rm{Dec\,Offset\,[arcsec]}$', fontsize=16) # set the frame color ax.spines['bottom'].set_color('white') ax.spines['top'].set_color('white') ax.spines['left'].set_color('white') ax.spines['right'].set_color('white') ax.tick_params(axis='both', which='major', width=1.5, labelsize=18, color='white', length=5) ax.text(0.7, 0.88, str(wave) + r'$\rm{\,\mu m}$', fontsize=20, color='white', transform=ax.transAxes) fig.savefig(plotdir + printname + '_image_' + str(wave) + '.pdf', format='pdf', dpi=300, bbox_inches='tight') fig.clf()
import matplotlib.pyplot as plt from hyperion.model import ModelOutput from hyperion.util.constants import pc mo = ModelOutput('pure_scattering.rtout') wav, fnu = mo.get_image(inclination=0, units='MJy/sr', distance=300. * pc) wav, pol = mo.get_image(inclination=0, stokes='linpol') fig = plt.figure(figsize=(8, 8)) # Make total intensity sub-plot ax = fig.add_axes([0.1, 0.3, 0.4, 0.4]) ax.imshow(fnu[:, :, 0], extent=[-13, 13, -13, 13], interpolation='none', cmap=plt.cm.gist_heat, origin='lower', vmin=0., vmax=4e9) ax.set_xlim(-13., 13.) ax.set_ylim(-13., 13.) ax.set_xlabel("x (solar radii)") ax.set_ylabel("y (solar radii)") ax.set_title("Surface brightness") # Make linear polarization sub-plot ax = fig.add_axes([0.51, 0.3, 0.4, 0.4]) im = ax.imshow(pol[:, :, 0] * 100., extent=[-13, 13, -13, 13], interpolation='none', cmap=plt.cm.gist_heat, origin='lower', vmin=0., vmax=100.) ax.set_xlim(-13., 13.) ax.set_ylim(-13., 13.)
def extract_hyperion(filename,indir=None,outdir=None,dstar=178.0,wl_aper=None,save=True,filter_func=False,\ plot_all=False,clean=False,exclude_wl=[],log=True): def l_bol(wl, fv, dist=178.0): import numpy as np import astropy.constants as const # wavelength unit: um # Flux density unit: Jy # # constants setup # c = const.c.cgs.value pc = const.pc.cgs.value PI = np.pi SL = const.L_sun.cgs.value # Convert the unit from Jy to erg s-1 cm-2 Hz-1 fv = np.array(fv) * 1e-23 freq = c / (1e-4 * np.array(wl)) diff_dum = freq[1:] - freq[0:-1] freq_interpol = np.hstack( (freq[0:-1] + diff_dum / 2.0, freq[0:-1] + diff_dum / 2.0, freq[0], freq[-1])) freq_interpol = freq_interpol[np.argsort(freq_interpol)[::-1]] fv_interpol = np.empty(len(freq_interpol)) # calculate the histogram style of spectrum # for i in range(0, len(fv)): if i == 0: fv_interpol[i] = fv[i] else: fv_interpol[2 * i - 1] = fv[i - 1] fv_interpol[2 * i] = fv[i] fv_interpol[-1] = fv[-1] dv = freq_interpol[0:-1] - freq_interpol[1:] dv = np.delete(dv, np.where(dv == 0)) fv = fv[np.argsort(freq)] freq = freq[np.argsort(freq)] return (np.trapz(fv, freq) * 4. * PI * (dist * pc)**2) / SL # to avoid X server error import matplotlib as mpl mpl.use('Agg') # import matplotlib.pyplot as plt import numpy as np import os from hyperion.model import ModelOutput, Model from scipy.interpolate import interp1d from hyperion.util.constants import pc, c, lsun, au from astropy.io import ascii import sys sys.path.append(os.path.expanduser('~') + '/programs/spectra_analysis/') from phot_filter import phot_filter from get_bhr71_obs import get_bhr71_obs # seaborn colormap, because jet is bad obviously import seaborn.apionly as sns # Read in the observation data and calculate the noise & variance if indir == None: indir = '/Users/yaolun/bhr71/' if outdir == None: outdir = '/Users/yaolun/bhr71/hyperion/' # assign the file name from the input file print_name = os.path.splitext(os.path.basename(filename))[0] # use a canned function to extract BHR71 observational data bhr71 = get_bhr71_obs(indir) # unit in um, Jy wl_tot, flux_tot, unc_tot = bhr71['spec'] flux_tot = flux_tot * 1e-23 # convert unit from Jy to erg s-1 cm-2 Hz-1 unc_tot = unc_tot * 1e-23 l_bol_obs = l_bol(wl_tot, flux_tot * 1e23) wl_phot, flux_phot, flux_sig_phot = bhr71['phot'] flux_phot = flux_phot * 1e-23 # convert unit from Jy to erg s-1 cm-2 Hz-1 flux_sig_phot = flux_sig_phot * 1e-23 # Print the observed L_bol # wl_tot = np.hstack((wl_irs,wl_obs,wl_phot)) # flux_tot = np.hstack((flux_irs,flux_obs,flux_phot)) # flux_tot = flux_tot[np.argsort(wl_tot)] # wl_tot = wl_tot[np.argsort(wl_tot)] # l_bol_obs = l_bol(wl_tot,flux_tot*1e23) # Open the model m = ModelOutput(filename) if wl_aper == None: wl_aper = [ 3.6, 4.5, 5.8, 8.0, 10, 16, 20, 24, 35, 70, 100, 160, 250, 350, 500, 850 ] # Create the plot mag = 1.5 fig = plt.figure(figsize=(8 * mag, 6 * mag)) ax_sed = fig.add_subplot(1, 1, 1) # Plot the observed SED # plot the observed spectra if not clean: color_seq = ['Green', 'Red', 'Blue'] else: color_seq = ['DimGray', 'DimGray', 'DimGray'] # plot the observations if log: pacs, = ax_sed.plot(np.log10(wl_tot[(wl_tot>40) & (wl_tot<190.31)]),\ np.log10(c/(wl_tot[(wl_tot>40) & (wl_tot<190.31)]*1e-4)*flux_tot[(wl_tot>40) & (wl_tot<190.31)]),\ '-',color=color_seq[0],linewidth=1.5*mag, alpha=0.7) spire, = ax_sed.plot(np.log10(wl_tot[wl_tot > 194]),np.log10(c/(wl_tot[wl_tot > 194]*1e-4)*flux_tot[wl_tot > 194]),\ '-',color=color_seq[1],linewidth=1.5*mag, alpha=0.7) irs, = ax_sed.plot(np.log10(wl_tot[wl_tot < 40]),np.log10(c/(wl_tot[wl_tot < 40]*1e-4)*flux_tot[wl_tot < 40]),\ '-',color=color_seq[2],linewidth=1.5*mag, alpha=0.7) photometry, = ax_sed.plot(np.log10(wl_phot), np.log10(c / (wl_phot * 1e-4) * flux_phot), 's', mfc='DimGray', mec='k', markersize=8) # plot the observed photometry data ax_sed.errorbar(np.log10(wl_phot),np.log10(c/(wl_phot*1e-4)*flux_phot),\ yerr=[np.log10(c/(wl_phot*1e-4)*flux_phot)-np.log10(c/(wl_phot*1e-4)*(flux_phot-flux_sig_phot)),\ np.log10(c/(wl_phot*1e-4)*(flux_phot+flux_sig_phot))-np.log10(c/(wl_phot*1e-4)*flux_phot)],\ fmt='s',mfc='DimGray',mec='k',markersize=8) else: pacs, = ax_sed.plot(np.log10(wl_tot[(wl_tot>40) & (wl_tot<190.31)]),\ c/(wl_tot[(wl_tot>40) & (wl_tot<190.31)]*1e-4)*flux_tot[(wl_tot>40) & (wl_tot<190.31)],\ '-',color=color_seq[0],linewidth=1.5*mag, alpha=0.7) spire, = ax_sed.plot(np.log10(wl_tot[wl_tot > 194]),c/(wl_tot[wl_tot > 194]*1e-4)*flux_tot[wl_tot > 194],\ '-',color=color_seq[1],linewidth=1.5*mag, alpha=0.7) irs, = ax_sed.plot(np.log10(wl_tot[wl_tot < 40]),c/(wl_tot[wl_tot < 40]*1e-4)*flux_tot[wl_tot < 40],\ '-',color=color_seq[2],linewidth=1.5*mag, alpha=0.7) photometry, = ax_sed.plot(wl_phot, c / (wl_phot * 1e-4) * flux_phot, 's', mfc='DimGray', mec='k', markersize=8) # plot the observed photometry data ax_sed.errorbar(np.log10(wl_phot),c/(wl_phot*1e-4)*flux_phot,\ yerr=[c/(wl_phot*1e-4)*flux_phot-c/(wl_phot*1e-4)*(flux_phot-flux_sig_phot),\ c/(wl_phot*1e-4)*(flux_phot+flux_sig_phot)-c/(wl_phot*1e-4)*flux_phot],\ fmt='s',mfc='DimGray',mec='k',markersize=8) if not clean: ax_sed.text(0.75, 0.9, r'$\rm{L_{bol}= %5.2f L_{\odot}}$' % l_bol_obs, fontsize=mag * 16, transform=ax_sed.transAxes) # else: # pacs, = ax_sed.plot(np.log10(wl_tot[(wl_tot>40) & (wl_tot<190.31)]),\ # np.log10(c/(wl_tot[(wl_tot>40) & (wl_tot<190.31)]*1e-4)*flux_tot[(wl_tot>40) & (wl_tot<190.31)]),\ # '-',color='DimGray',linewidth=1.5*mag, alpha=0.7) # spire, = ax_sed.plot(np.log10(wl_tot[wl_tot > 194]),np.log10(c/(wl_tot[wl_tot > 194]*1e-4)*flux_tot[wl_tot > 194]),\ # '-',color='DimGray',linewidth=1.5*mag, alpha=0.7) # irs, = ax_sed.plot(np.log10(wl_tot[wl_tot < 40]),np.log10(c/(wl_tot[wl_tot < 40]*1e-4)*flux_tot[wl_tot < 40]),\ # '-',color='DimGray',linewidth=1.5*mag, alpha=0.7) # Extract the SED for the smallest inclination and largest aperture, and # scale to 300pc. In Python, negative indices can be used for lists and # arrays, and indicate the position from the end. So to get the SED in the # largest aperture, we set aperture=-1. # aperture group is aranged from smallest to infinite sed_inf = m.get_sed(group=0, inclination=0, aperture=-1, distance=dstar * pc, uncertainties=True) # plot the simulated SED if clean == False: sim, = ax_sed.plot(np.log10(sed_inf.wav), np.log10(sed_inf.val), '-', color='GoldenRod', linewidth=0.5 * mag) ax_sed.fill_between(np.log10(sed_inf.wav), np.log10(sed_inf.val-sed_inf.unc), np.log10(sed_inf.val+sed_inf.unc),\ color='GoldenRod', alpha=0.5) # get flux at different apertures flux_aper = np.zeros_like(wl_aper, dtype=float) unc_aper = np.zeros_like(wl_aper, dtype=float) a = np.zeros_like(wl_aper) + 1 color_list = plt.cm.jet(np.linspace(0, 1, len(wl_aper) + 1)) for i in range(0, len(wl_aper)): if wl_aper[i] in exclude_wl: continue # if (wl_aper[i] == 5.8) or (wl_aper[i] == 8.0) or (wl_aper[i] == 10.5) or (wl_aper[i] == 11): # continue sed_dum = m.get_sed(group=i + 1, inclination=0, aperture=-1, distance=dstar * pc, uncertainties=True) if plot_all == True: ax_sed.plot(np.log10(sed_dum.wav), np.log10(sed_dum.val), '-', color=color_list[i]) ax_sed.fill_between(np.log10(sed_dum.wav), np.log10(sed_dum.val-sed_dum.unc), np.log10(sed_dum.val+sed_dum.unc),\ color=color_list[i], alpha=0.5) if filter_func == False: # use a rectangle function the average the simulated SED # apply the spectral resolution if (wl_aper[i] < 50.) & (wl_aper[i] >= 5): res = 60. elif wl_aper[i] < 5: res = 10. else: res = 1000. ind = np.where((sed_dum.wav < wl_aper[i] * (1 + 1. / res)) & (sed_dum.wav > wl_aper[i] * (1 - 1. / res))) if len(ind[0]) != 0: flux_aper[i] = np.mean(sed_dum.val[ind]) unc_aper[i] = np.mean(sed_dum.unc[ind]) else: f = interp1d(sed_dum.wav, sed_dum.val) f_unc = interp1d(sed_dum.wav, sed_dum.unc) flux_aper[i] = f(wl_aper[i]) unc_aper[i] = f_unc(wl_aper[i]) else: # apply the filter function # decide the filter name if wl_aper[i] == 70: fil_name = 'Herschel PACS 70um' elif wl_aper[i] == 100: fil_name = 'Herschel PACS 100um' elif wl_aper[i] == 160: fil_name = 'Herschel PACS 160um' elif wl_aper[i] == 250: fil_name = 'Herschel SPIRE 250um' elif wl_aper[i] == 350: fil_name = 'Herschel SPIRE 350um' elif wl_aper[i] == 500: fil_name = 'Herschel SPIRE 500um' elif wl_aper[i] == 3.6: fil_name = 'IRAC Channel 1' elif wl_aper[i] == 4.5: fil_name = 'IRAC Channel 2' elif wl_aper[i] == 5.8: fil_name = 'IRAC Channel 3' elif wl_aper[i] == 8.0: fil_name = 'IRAC Channel 4' elif wl_aper[i] == 24: fil_name = 'MIPS 24um' elif wl_aper[i] == 850: fil_name = 'SCUBA 850WB' else: fil_name = None if fil_name != None: filter_func = phot_filter(fil_name) # Simulated SED should have enough wavelength coverage for applying photometry filters. f = interp1d(sed_dum.wav, sed_dum.val) f_unc = interp1d(sed_dum.wav, sed_dum.unc) flux_aper[i] = np.trapz( filter_func['wave'] / 1e4, f(filter_func['wave'] / 1e4) * filter_func['transmission']) / np.trapz( filter_func['wave'] / 1e4, filter_func['transmission']) unc_aper[i] = abs( np.trapz((filter_func['wave'] / 1e4)**2, (f_unc(filter_func['wave'] / 1e4) * filter_func['transmission'])**2))**0.5 / abs( np.trapz(filter_func['wave'] / 1e4, filter_func['transmission'])) else: # use a rectangle function the average the simulated SED # apply the spectral resolution if (wl_aper[i] < 50.) & (wl_aper[i] >= 5): res = 60. elif wl_aper[i] < 5: res = 10. else: res = 1000. ind = np.where((sed_dum.wav < wl_aper[i] * (1 + 1. / res)) & (sed_dum.wav > wl_aper[i] * (1 - 1. / res))) if len(ind[0]) != 0: flux_aper[i] = np.mean(sed_dum.val[ind]) unc_aper[i] = np.mean(sed_dum.unc[ind]) else: f = interp1d(sed_dum.wav, sed_dum.val) f_unc = interp1d(sed_dum.wav, sed_dum.unc) flux_aper[i] = f(wl_aper[i]) unc_aper[i] = f_unc(wl_aper[i]) # temperory step: solve issue of uncertainty greater than the value for i in range(len(wl_aper)): if unc_aper[i] >= flux_aper[i]: unc_aper[i] = flux_aper[i] - 1e-20 # perform the same procedure of flux extraction of aperture flux with observed spectra wl_aper = np.array(wl_aper, dtype=float) obs_aper_wl = wl_aper[(wl_aper >= min(wl_tot)) & (wl_aper <= max(wl_tot))] obs_aper_sed = np.zeros_like(obs_aper_wl) obs_aper_sed_unc = np.zeros_like(obs_aper_wl) sed_tot = c / (wl_tot * 1e-4) * flux_tot sed_unc_tot = c / (wl_tot * 1e-4) * unc_tot # wl_tot and flux_tot are already hstacked and sorted by wavelength for i in range(0, len(obs_aper_wl)): if obs_aper_wl[i] in exclude_wl: continue if filter_func == False: # use a rectangle function the average the simulated SED # apply the spectral resolution if (obs_aper_wl[i] < 50.) & (obs_aper_wl[i] >= 5): res = 60. elif obs_aper_wl[i] < 5: res = 10. else: res = 1000. ind = np.where((wl_tot < obs_aper_wl[i] * (1 + 1. / res)) & (wl_tot > obs_aper_wl[i] * (1 - 1. / res))) if len(ind[0]) != 0: obs_aper_sed[i] = np.mean(sed_tot[ind]) obs_aper_sed_unc[i] = np.mean(sed_unc_tot[ind]) else: f = interp1d(wl_tot, sed_tot) f_unc = interp1d(wl_tot, sed_unc_tot) obs_aper_sed[i] = f(obs_aper_wl[i]) obs_aper_sed_unc[i] = f_unc(obs_aper_wl[i]) else: # apply the filter function # decide the filter name if obs_aper_wl[i] == 70: fil_name = 'Herschel PACS 70um' elif obs_aper_wl[i] == 100: fil_name = 'Herschel PACS 100um' elif obs_aper_wl[i] == 160: fil_name = 'Herschel PACS 160um' elif obs_aper_wl[i] == 250: fil_name = 'Herschel SPIRE 250um' elif obs_aper_wl[i] == 350: fil_name = 'Herschel SPIRE 350um' elif obs_aper_wl[i] == 500: fil_name = 'Herschel SPIRE 500um' elif obs_aper_wl[i] == 3.6: fil_name = 'IRAC Channel 1' elif obs_aper_wl[i] == 4.5: fil_name = 'IRAC Channel 2' elif obs_aper_wl[i] == 5.8: fil_name = 'IRAC Channel 3' elif obs_aper_wl[i] == 8.0: fil_name = 'IRAC Channel 4' elif obs_aper_wl[i] == 24: fil_name = 'MIPS 24um' # elif obs_aper_wl[i] == 850: # fil_name = 'SCUBA 850WB' # do not have SCUBA spectra else: fil_name = None # print obs_aper_wl[i], fil_name if fil_name != None: filter_func = phot_filter(fil_name) # Observed SED needs to be trimmed before applying photometry filters filter_func = filter_func[(filter_func['wave']/1e4 >= min(wl_tot))*\ ((filter_func['wave']/1e4 >= 54.8)+(filter_func['wave']/1e4 <= 36.0853))*\ ((filter_func['wave']/1e4 <= 95.05)+(filter_func['wave']/1e4 >=103))*\ ((filter_func['wave']/1e4 <= 190.31)+(filter_func['wave']/1e4 >= 195))*\ (filter_func['wave']/1e4 <= max(wl_tot))] f = interp1d(wl_tot, sed_tot) f_unc = interp1d(wl_tot, sed_unc_tot) obs_aper_sed[i] = np.trapz( filter_func['wave'] / 1e4, f(filter_func['wave'] / 1e4) * filter_func['transmission']) / np.trapz( filter_func['wave'] / 1e4, filter_func['transmission']) obs_aper_sed_unc[i] = abs( np.trapz((filter_func['wave'] / 1e4)**2, (f_unc(filter_func['wave'] / 1e4) * filter_func['transmission'])**2))**0.5 / abs( np.trapz(filter_func['wave'] / 1e4, filter_func['transmission'])) else: # use a rectangle function the average the simulated SED # apply the spectral resolution if (obs_aper_wl[i] < 50.) & (obs_aper_wl[i] >= 5): res = 60. elif obs_aper_wl[i] < 5: res = 10. else: res = 1000. ind = np.where((wl_tot < obs_aper_wl[i] * (1 + 1. / res)) & (wl_tot > obs_aper_wl[i] * (1 - 1. / res))) if len(ind[0]) != 0: obs_aper_sed[i] = np.mean(sed_tot[ind]) obs_aper_sed_unc[i] = np.mean(sed_unc_tot[ind]) else: f = interp1d(wl_tot, sed_tot) f_unc = interp1d(wl_tot, sed_unc_tot) obs_aper_sed[i] = f(obs_aper_wl[i]) obs_aper_sed_unc[i] = f_unc(obs_aper_wl[i]) # if clean == False: # if log: # aper_obs = ax_sed.errorbar(np.log10(obs_aper_wl), np.log10(obs_aper_sed), \ # yerr=[np.log10(obs_aper_sed)-np.log10(obs_aper_sed-obs_aper_sed_unc), np.log10(obs_aper_sed+obs_aper_sed_unc)-np.log10(obs_aper_sed)],\ # fmt='s', mec='Magenta', mfc='Magenta', markersize=10, elinewidth=3, ecolor='Magenta',capthick=3,barsabove=True) # aper = ax_sed.errorbar(np.log10(wl_aper), np.log10(flux_aper),\ # yerr=[np.log10(flux_aper)-np.log10(flux_aper-unc_aper), np.log10(flux_aper+unc_aper)-np.log10(flux_aper)],\ # fmt='o', mfc='None', mec='k', ecolor='Black', markersize=12, markeredgewidth=3, elinewidth=3, barsabove=True) # else: # aper_obs = ax_sed.errorbar(obs_aper_wl, obs_aper_sed, yerr=obs_aper_sed_unc,\ # fmt='s', mec='Magenta', mfc='Magenta', markersize=10, elinewidth=3, ecolor='Magenta',capthick=3,barsabove=True) # aper = ax_sed.errorbar(wl_aper, flux_aper, yerr=unc_aper,\ # fmt='o', mfc='None', mec='k', ecolor='Black', markersize=12, markeredgewidth=3, elinewidth=3, barsabove=True) # else: if log: aper_obs = ax_sed.errorbar(np.log10(obs_aper_wl), np.log10(obs_aper_sed),\ yerr=[np.log10(obs_aper_sed)-np.log10(obs_aper_sed-obs_aper_sed_unc), np.log10(obs_aper_sed+obs_aper_sed_unc)-np.log10(obs_aper_sed)],\ fmt='s', mec='None', mfc='r', markersize=10, linewidth=1.5, ecolor='Red', elinewidth=3, capthick=3, barsabove=True) aper = ax_sed.errorbar(np.log10(wl_aper),np.log10(flux_aper),\ yerr=[np.log10(flux_aper)-np.log10(flux_aper-unc_aper), np.log10(flux_aper+unc_aper)-np.log10(flux_aper)],\ fmt='o', mec='Blue', mfc='None', color='b',markersize=12, markeredgewidth=2.5, linewidth=1.7, ecolor='Blue', elinewidth=3, barsabove=True) ax_sed.set_ylim([-14, -7]) ax_sed.set_xlim([0, 3]) else: aper_obs = ax_sed.errorbar(np.log10(obs_aper_wl), obs_aper_sed, yerr=obs_aper_sed_unc,\ fmt='s', mec='None', mfc='r', markersize=10, linewidth=1.5, ecolor='Red', elinewidth=3, capthick=3, barsabove=True) aper = ax_sed.errorbar(np.log10(wl_aper),flux_aper, yerr=unc_aper,\ fmt='o', mec='Blue', mfc='None', color='b',markersize=12, markeredgewidth=2.5, linewidth=1.7, ecolor='Blue', elinewidth=3, barsabove=True) # ax_sed.set_xlim([1, 1000]) ax_sed.set_xlim([0, 3]) # ax_sed.set_ylim([1e-14, 1e-8]) # calculate the bolometric luminosity of the aperture # print flux_aper l_bol_sim = l_bol(wl_aper, flux_aper / (c / np.array(wl_aper) * 1e4) * 1e23) print 'Bolometric luminosity of simulated spectrum: %5.2f lsun' % l_bol_sim # print out the sed into ascii file for reading in later if save == True: # unapertured SED foo = open(outdir + print_name + '_sed_inf.txt', 'w') foo.write('%12s \t %12s \t %12s \n' % ('wave', 'vSv', 'sigma_vSv')) for i in range(0, len(sed_inf.wav)): foo.write('%12g \t %12g \t %12g \n' % (sed_inf.wav[i], sed_inf.val[i], sed_inf.unc[i])) foo.close() # SED with convolution of aperture sizes foo = open(outdir + print_name + '_sed_w_aperture.txt', 'w') foo.write('%12s \t %12s \t %12s \n' % ('wave', 'vSv', 'sigma_vSv')) for i in range(0, len(wl_aper)): foo.write('%12g \t %12g \t %12g \n' % (wl_aper[i], flux_aper[i], unc_aper[i])) foo.close() # Read in and plot the simulated SED produced by RADMC-3D using the same parameters # [wl,fit] = np.genfromtxt(indir+'hyperion/radmc_comparison/spectrum.out',dtype='float',skip_header=3).T # l_bol_radmc = l_bol(wl,fit*1e23/dstar**2) # radmc, = ax_sed.plot(np.log10(wl),np.log10(c/(wl*1e-4)*fit/dstar**2),'-',color='DimGray', linewidth=1.5*mag, alpha=0.5) # print the L bol of the simulated SED (both Hyperion and RADMC-3D) # lg_sim = ax_sed.legend([sim,radmc],[r'$\rm{L_{bol,sim}=%5.2f\,L_{\odot},\,L_{center}=9.18\,L_{\odot}}$' % l_bol_sim, \ # r'$\rm{L_{bol,radmc3d}=%5.2f\,L_{\odot},\,L_{center}=9.18\,L_{\odot}}$' % l_bol_radmc],\ # loc='lower right',fontsize=mag*16) # read the input central luminosity by reading in the source information from output file dum = Model() dum.use_sources(filename) L_cen = dum.sources[0].luminosity / lsun # legend lg_data = ax_sed.legend([irs, photometry, aper, aper_obs],\ [r'$\rm{observation}$',\ r'$\rm{photometry}$',r'$\rm{F_{aper,sim}}$',r'$\rm{F_{aper,obs}}$'],\ loc='upper left',fontsize=14*mag,numpoints=1,framealpha=0.3) if clean == False: lg_sim = ax_sed.legend([sim],[r'$\rm{L_{bol,sim}=%5.2f\,L_{\odot},\,L_{center}=%5.2f\,L_{\odot}}$' % (l_bol_sim, L_cen)], \ loc='lower right',fontsize=mag*16) plt.gca().add_artist(lg_data) # plot setting ax_sed.set_xlabel(r'$\rm{log\,\lambda\,({\mu}m)}$', fontsize=mag * 20) ax_sed.set_ylabel(r'$\rm{log\,\nu S_{\nu}\,(erg/cm^{2}/s)}$', fontsize=mag * 20) [ ax_sed.spines[axis].set_linewidth(1.5 * mag) for axis in ['top', 'bottom', 'left', 'right'] ] ax_sed.minorticks_on() ax_sed.tick_params('both', labelsize=mag * 18, width=1.5 * mag, which='major', pad=15, length=5 * mag) ax_sed.tick_params('both', labelsize=mag * 18, width=1.5 * mag, which='minor', pad=15, length=2.5 * mag) # fix the tick label font ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral', size=mag * 18) for label in ax_sed.get_xticklabels(): label.set_fontproperties(ticks_font) for label in ax_sed.get_yticklabels(): label.set_fontproperties(ticks_font) # if clean == False: # lg_data = ax_sed.legend([irs, pacs, spire,photometry],[r'$\rm{{\it Spitzer}-IRS}$',r'$\rm{{\it Herschel}-PACS}$',r'$\rm{{\it Herschel}-SPIRE}$',r'$\rm{Photometry}$'],\ # loc='upper left',fontsize=14*mag,numpoints=1,framealpha=0.3) # plt.gca().add_artist(lg_sim) # else: # lg_data = ax_sed.legend([irs, photometry, aper, aper_obs],\ # [r'$\rm{observation}$',\ # r'$\rm{photometry}$',r'$\rm{F_{aper,sim}}$',r'$\rm{F_{aper,obs}}$'],\ # loc='upper left',fontsize=14*mag,numpoints=1,framealpha=0.3) # Write out the plot fig.savefig(outdir + print_name + '_sed.pdf', format='pdf', dpi=300, bbox_inches='tight') fig.clf() # Package for matching the colorbar from mpl_toolkits.axes_grid1 import make_axes_locatable # Extract the image for the first inclination, and scale to 300pc. We # have to specify group=1 as there is no image in group 0. image = m.get_image(group=len(wl_aper) + 1, inclination=0, distance=dstar * pc, units='MJy/sr') # image = m.get_image(group=14, inclination=0, distance=dstar * pc, units='MJy/sr') # Open figure and create axes # fig = plt.figure(figsize=(8, 8)) fig, axarr = plt.subplots(3, 3, sharex='col', sharey='row', figsize=(13.5, 12)) # Pre-set maximum for colorscales VMAX = {} # VMAX[3.6] = 10. # VMAX[24] = 100. # VMAX[160] = 2000. # VMAX[500] = 2000. VMAX[100] = 10. VMAX[250] = 100. VMAX[500] = 2000. VMAX[1000] = 2000. # We will now show four sub-plots, each one for a different wavelength # for i, wav in enumerate([3.6, 24, 160, 500]): # for i, wav in enumerate([100, 250, 500, 1000]): # for i, wav in enumerate([4.5, 9.7, 24, 40, 70, 100, 250, 500, 1000]): for i, wav in enumerate([3.6, 8.0, 9.7, 24, 40, 100, 250, 500, 1000]): # ax = fig.add_subplot(3, 3, i + 1) ax = axarr[i / 3, i % 3] # Find the closest wavelength iwav = np.argmin(np.abs(wav - image.wav)) # Calculate the image width in arcseconds given the distance used above # get the max radius rmax = max(m.get_quantities().r_wall) w = np.degrees(rmax / image.distance) * 3600. # Image in the unit of MJy/sr # Change it into erg/s/cm2/Hz/sr factor = 1e-23 * 1e6 # avoid zero in log # flip the image, because the setup of inclination is upside down val = image.val[::-1, :, iwav] * factor + 1e-30 # val = image.val[:, :, iwav] * factor + 1e-30 # This is the command to show the image. The parameters vmin and vmax are # the min and max levels for the colorscale (remove for default values). # cmap = sns.cubehelix_palette(start=0.1, rot=-0.7, gamma=0.2, as_cmap=True) cmap = plt.cm.CMRmap im = ax.imshow(np.log10(val), vmin=-22, vmax=-12, cmap=cmap, origin='lower', extent=[-w, w, -w, w], aspect=1) # fix the tick label font ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral', size=14) for label in ax.get_xticklabels(): label.set_fontproperties(ticks_font) for label in ax.get_yticklabels(): label.set_fontproperties(ticks_font) # Colorbar setting # create an axes on the right side of ax. The width of cax will be 5% # of ax and the padding between cax and ax will be fixed at 0.05 inch. if (i + 1) % 3 == 0: divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cb = fig.colorbar(im, cax=cax) cb.solids.set_edgecolor("face") cb.ax.minorticks_on() cb.ax.set_ylabel( r'$\rm{log(I_{\nu})\,[erg\,s^{-1}\,cm^{-2}\,Hz^{-1}\,sr^{-1}]}$', fontsize=12) cb_obj = plt.getp(cb.ax.axes, 'yticklabels') plt.setp(cb_obj, fontsize=12) # fix the tick label font ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral', size=12) for label in cb.ax.get_yticklabels(): label.set_fontproperties(ticks_font) if (i + 1) == 7: # Finalize the plot ax.set_xlabel(r'$\rm{RA\,Offset\,(arcsec)}$', fontsize=14) ax.set_ylabel(r'$\rm{Dec\,Offset\,(arcsec)}$', fontsize=14) ax.tick_params(axis='both', which='major', labelsize=16) ax.set_adjustable('box-forced') ax.text(0.7, 0.88, str(wav) + r'$\rm{\,\mu m}$', fontsize=16, color='white', transform=ax.transAxes) fig.subplots_adjust(hspace=0, wspace=-0.2) # Adjust the spaces between the subplots # plt.tight_layout() fig.savefig(outdir + print_name + '_cube_plot.png', format='png', dpi=300, bbox_inches='tight') fig.clf()
def alma_cavity(freq, outdir, vlim, units='MJy/sr', pix=300, filename=None, label=None): import numpy as np import matplotlib.pyplot as plt import astropy.constants as const from hyperion.model import ModelOutput from matplotlib.ticker import MaxNLocator # constants setup c = const.c.cgs.value pc = const.pc.cgs.value au = const.au.cgs.value # Image in the unit of MJy/sr # Change it into erg/s/cm2/Hz/sr if units == 'erg/s/cm2/Hz/sr': factor = 1e-23 * 1e6 cb_label = r'$\rm{I_{\nu}\,(erg\,s^{-1}\,cm^{-2}\,Hz^{-1}\,sr^{-1})}$' elif units == 'MJy/sr': factor = 1 cb_label = r'$\rm{I_{\nu}\,(MJy\,sr^{-1})}$' if filename == None: # input files setup filename_reg = '/Users/yaolun/test/model12.rtout' filename_r2 = '/Users/yaolun/test/model13.rtout' filename_r15 = '/Users/yaolun/test/model17.rtout' filename_uni = '/Users/yaolun/test/model62.rtout' else: filename_reg = filename['reg'] filename_r2 = filename['r2'] filename_r15 = filename['r15'] filename_uni = filename['uni'] if label == None: label_reg = r'$\rm{const.+r^{-2}}$' label_r2 = r'$\rm{r^{-2}}$' label_r15 = r'$\rm{r^{-1.5}}$' label_uni = r'$\rm{uniform}$' else: label_reg = label['reg'] label_r2 = label['r2'] label_r15 = label['r15'] label_uni = label['uni'] wl_aper = [ 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, 850 ] wav = c / freq / 1e9 * 1e4 # wav = 40 # read in # regular cavity setting m_reg = ModelOutput(filename_reg) image_reg = m_reg.get_image(group=len(wl_aper) + 1, inclination=0, distance=178.0 * pc, units='MJy/sr') # Calculate the image width in arcseconds given the distance used above rmax = max(m_reg.get_quantities().r_wall) w = np.degrees(rmax / image_reg.distance) * 3600. # w = np.degrees((1.5 * pc) / image_reg.distance) * 60. pix_num = len(image_reg.val[:, 0, 0]) pix2arcsec = 2 * w / pix_num pix2au = np.radians(2 * w / pix_num / 3600.) * image_reg.distance / au iwav = np.argmin(np.abs(wav - image_reg.wav)) # avoid zero in log val_reg = image_reg.val[:, :, iwav] * factor + 1e-30 # r^-2 cavity setting m_r2 = ModelOutput(filename_r2) image_r2 = m_r2.get_image(group=len(wl_aper) + 1, inclination=0, distance=178.0 * pc, units='MJy/sr') # Calculate the image width in arcseconds given the distance used above rmax = max(m_r2.get_quantities().r_wall) w = np.degrees(rmax / image_r2.distance) * 3600. pix_num = len(image_reg.val[:, 0, 0]) pix2arcsec = 2 * w / pix_num pix2au = np.radians(2 * w / pix_num / 3600.) * image_reg.distance / au iwav = np.argmin(np.abs(wav - image_r2.wav)) # avoid zero in log val_r2 = image_r2.val[:, :, iwav] * factor + 1e-30 # r^-1.5 cavity setting m_r15 = ModelOutput(filename_r15) image_r15 = m_r15.get_image(group=len(wl_aper) + 1, inclination=0, distance=178.0 * pc, units='MJy/sr') # Calculate the image width in arcseconds given the distance used above rmax = max(m_r15.get_quantities().r_wall) w = np.degrees(rmax / image_r15.distance) * 3600. pix_num = len(image_reg.val[:, 0, 0]) pix2arcsec = 2 * w / pix_num pix2au = np.radians(2 * w / pix_num / 3600.) * image_reg.distance / au iwav = np.argmin(np.abs(wav - image_r15.wav)) # avoid zero in log val_r15 = image_r15.val[:, :, iwav] * factor + 1e-30 # uniform cavity setting m_uni = ModelOutput(filename_uni) image_uni = m_uni.get_image(group=len(wl_aper) + 1, inclination=0, distance=178.0 * pc, units='MJy/sr') # Calculate the image width in arcseconds given the distance used above rmax = max(m_uni.get_quantities().r_wall) w = np.degrees(rmax / image_uni.distance) * 3600. print w pix_num = len(image_reg.val[:, 0, 0]) pix2arcsec = 2 * w / pix_num pix2au = np.radians(2 * w / pix_num / 3600.) * image_reg.distance / au iwav = np.argmin(np.abs(wav - image_uni.wav)) # avoid zero in log val_uni = image_uni.val[:, :, iwav] * factor + 1e-30 # 1-D radial intensity profile # get y=0 plane, and plot it fig = plt.figure(figsize=(8, 6)) ax = fig.add_subplot(111) reg, = ax.plot(np.linspace(-pix / 2, pix / 2, num=pix) * pix2arcsec, val_reg[:, pix / 2 - 1], color='b', linewidth=2) r2, = ax.plot(np.linspace(-pix / 2, pix / 2, num=pix) * pix2arcsec, val_r2[:, pix / 2 - 1], color='r', linewidth=1.5) r15, = ax.plot(np.linspace(-pix / 2, pix / 2, num=pix) * pix2arcsec, val_r15[:, pix / 2 - 1], '--', color='r', linewidth=1.5) uni, = ax.plot(np.linspace(-pix / 2, pix / 2, num=pix) * pix2arcsec, val_uni[:, pix / 2 - 1], color='k', linewidth=1.5) ax.legend([reg, r2, r15, uni], [label_reg, label_r2, label_r15, label_uni],\ numpoints=1, loc='lower center', fontsize=18) ax.set_xlim([-1, 1]) ax.set_xlabel(r'$\rm{offset\,(arcsec)}$', fontsize=24) # ax.set_ylabel(r'$\rm{I_{\nu}~(erg~s^{-1}~cm^{-2}~Hz^{-1}~sr^{-1})}$', fontsize=16) ax.set_ylabel(cb_label, fontsize=24) [ ax.spines[axis].set_linewidth(2) for axis in ['top', 'bottom', 'left', 'right'] ] ax.minorticks_on() ax.tick_params('both', labelsize=16, width=2, which='major', pad=10, length=5) ax.tick_params('both', labelsize=16, width=2, which='minor', pad=10, length=2.5) fig.savefig(outdir + 'cavity_intensity_' + str(freq) + '.pdf', format='pdf', dpi=300, bbox_inches='tight') # 2-D intensity map from mpl_toolkits.axes_grid1 import AxesGrid image_grid = [val_reg, val_uni, val_r2, val_r15] label_grid = [label_reg, label_r2, label_r15, label_uni] fig = plt.figure(figsize=(30, 30)) grid = AxesGrid( fig, 142, # similar to subplot(142) nrows_ncols=(2, 2), axes_pad=0, share_all=True, label_mode="L", cbar_location="right", cbar_mode="single", ) for i in range(4): offset = np.linspace(-pix / 2, pix / 2, num=pix) * pix2arcsec trim = np.where(abs(offset) <= 2) im = grid[i].pcolor(np.linspace(-pix/2,pix/2,num=pix)*pix2arcsec, np.linspace(-pix/2,pix/2,num=pix)*pix2arcsec,\ image_grid[i], cmap=plt.cm.jet, vmin=vlim[0], vmax=vlim[1])#vmin=(image_grid[i][trim,trim]).min(), vmax=(image_grid[i][trim,trim]).max()) grid[i].set_xlim([-20, 20]) grid[i].set_ylim([-20, 20]) grid[i].set_xlabel(r'$\rm{RA\,offset\,(arcsec)}$', fontsize=14) grid[i].set_ylabel(r'$\rm{Dec\,offset\,(arcsec)}$', fontsize=14) # lg = grid[i].legend([label_grid[i]], loc='upper center', numpoints=1, fontsize=16) # for text in lg.get_texts(): # text.set_color('w') grid[i].text(0.5, 0.8, label_grid[i], color='w', weight='heavy', fontsize=18, transform=grid[i].transAxes, ha='center') grid[i].locator_params(axis='x', nbins=5) grid[i].locator_params(axis='y', nbins=5) [ grid[i].spines[axis].set_linewidth(1.2) for axis in ['top', 'bottom', 'left', 'right'] ] grid[i].tick_params('both', labelsize=12, width=1.2, which='major', pad=10, color='white', length=5) grid[i].tick_params('both', labelsize=12, width=1.2, which='minor', pad=10, color='white', length=2.5) # fix the overlap tick labels if i != 0: x_nbins = len(grid[i].get_xticklabels()) y_nbins = len(grid[i].get_yticklabels()) grid[i].yaxis.set_major_locator(MaxNLocator(nbins=5, prune='upper')) if i != 2: grid[i].xaxis.set_major_locator( MaxNLocator(nbins=5, prune='lower')) [grid[0].spines[axis].set_color('white') for axis in ['bottom', 'right']] [grid[1].spines[axis].set_color('white') for axis in ['bottom', 'left']] [grid[2].spines[axis].set_color('white') for axis in ['top', 'right']] [grid[3].spines[axis].set_color('white') for axis in ['top', 'left']] # ax.set_aspect('equal') cb = grid.cbar_axes[0].colorbar(im) cb.solids.set_edgecolor("face") cb.ax.minorticks_on() cb.ax.set_ylabel(cb_label, fontsize=12) cb_obj = plt.getp(cb.ax.axes, 'yticklabels') plt.setp(cb_obj, fontsize=12) # fig.text(0.5, -0.05 , r'$\rm{RA~offset~(arcsec)}$', fontsize=12, ha='center') # fig.text(0, 0.5, r'$\rm{Dec~offset~(arcsec)}$', fontsize=12, va='center', rotation='vertical') fig.savefig(outdir + 'cavity_2d_intensity_' + str(freq) + '.png', format='png', dpi=300, bbox_inches='tight')
import numpy as np import matplotlib.pyplot as plt from hyperion.model import ModelOutput from hyperion.util.constants import pc # Open the model m = ModelOutput('simple_cube.rtout') # Extract the image for the first inclination, and scale to 300pc. We # have to specify group=1 as there is no image in group 0. image = m.get_image(inclination=0, distance=300 * pc, units='MJy/sr') # Open figure and create axes fig = plt.figure(figsize=(8, 8)) # Pre-set maximum for colorscales VMAX = {} VMAX[1] = 10. VMAX[30] = 100. VMAX[100] = 2000. VMAX[300] = 2000. # We will now show four sub-plots, each one for a different wavelength for i, wav in enumerate([1, 30, 100, 300]): ax = fig.add_subplot(2, 2, i + 1) # Find the closest wavelength iwav = np.argmin(np.abs(wav - image.wav))
def setup_method(self, method): path = os.path.join(os.path.dirname(__file__), 'hyperion_output.rtout') m = ModelOutput(path) self.image = m.get_image(group=0, inclination=0, distance=8.5*kpc, units='ergs/cm^2/s')