def dump_data(pf,model):
ad = pf.all_data()
particle_fh2 = ad["gasfh2"]
particle_fh1 = np.ones(len(particle_fh2))-particle_fh2
particle_gas_mass = ad["gasmasses"]
particle_star_mass = ad["starmasses"]
particle_star_metallicity = ad["starmetals"]
particle_stellar_formation_time = ad["starformationtime"]
particle_sfr = ad['gassfr'].in_units('Msun/yr')
#these are in try/excepts in case we're not dealing with gadget and yt 3.x
try: grid_gas_mass = ad["gassmoothedmasses"]
except: grid_gas_mass = -1
try: grid_gas_metallicity = ad["gassmoothedmetals"]
except: grid_gas_metallicity = -1
try: grid_star_mass = ad["starsmoothedmasses"]
except: grid_star_mass = -1
#get tdust
m = ModelOutput(model.outputfile+'.sed')
oct = m.get_quantities()
tdust_pf = oct.to_yt()
tdust_ad = tdust_pf.all_data()
tdust = tdust_ad[ ('gas', 'temperature')]
try: outfile = cfg.model.PD_output_dir+"grid_physical_properties."+cfg.model.snapnum_str+'_galaxy'+cfg.model.galaxy_num_str+".npz"
except:
outfile = cfg.model.PD_output_dir+"grid_physical_properties."+cfg.model.snapnum_str+".npz"
np.savez(outfile,particle_fh2=particle_fh2,particle_fh1 = particle_fh1,particle_gas_mass = particle_gas_mass,particle_star_mass = particle_star_mass,particle_star_metallicity = particle_star_metallicity,particle_stellar_formation_time = particle_stellar_formation_time,grid_gas_metallicity = grid_gas_metallicity,grid_gas_mass = grid_gas_mass,grid_star_mass = grid_star_mass,particle_sfr = particle_sfr,tdust = tdust)
def get_spectrum(self, fname, gal_id, stype='out'):
"""
For combined spec files
"""
# if fname is None:
# run = glob.glob('{0}/snap*.galaxy*.rt{1}.sed'.format(directory,stype))
# else:
# run = glob.glob('{0}/{1}'.format(directory,fname))
# if len(run) > 1:
# raise ValueError('More than one spectrum in directory')
# elif len(run) == 0:
# raise ValueError('No output spectrum in this directory')
if gal_id is None:
m = ModelOutput(filename=fname)
else:
m = ModelOutput(filename=fname, group=gal_id)
wav, lum = m.get_sed(inclination='all', aperture=-1)
# set units
wav = np.asarray(wav) * u.micron
lum = np.asarray(lum) * u.erg / u.s
return (wav, lum)
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 load_obs(sed_file):
from hyperion.model import ModelOutput
from astropy.cosmology import Planck15
from astropy import units as u
from astropy import constants
m = ModelOutput(sed_file)
wav,flux = m.get_sed(inclination='all',aperture=-1)
wav = np.asarray(wav)*u.micron #wav is in micron
wav = wav.to(u.AA)
#wav *= (1.+2.)
flux = np.asarray(flux)*u.erg/u.s
dl = 10.0*u.pc
#dl = Planck15.luminosity_distance(2.0)
dl = dl.to(u.cm)
flux /= (4.*3.14*dl**2.)
nu = constants.c.cgs/(wav.to(u.cm))
nu = nu.to(u.Hz)
flux /= nu
flux = flux.to(u.Jy)
maggies = flux[0] / 3631.
return maggies.value, wav
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 build_obs(**kwargs):
from hyperion.model import ModelOutput
from astropy import units as u
from astropy import constants
print('galaxy: ', sys.argv[1])
m = ModelOutput(
"/ufrc/narayanan/s.lower/pd_runs/simba_m25n512/snap305_dustscreen/snap305/snap305.galaxy"
+ str(sys.argv[1]) + ".rtout.sed")
wav, flux = m.get_sed(inclination=0, aperture=-1)
wav = np.asarray(wav) * u.micron #wav is in micron
wav = wav.to(u.AA)
flux = np.asarray(flux) * u.erg / u.s
dl = (10. * u.pc).to(u.cm)
flux /= (4. * 3.14 * dl**2.)
nu = constants.c.cgs / (wav.to(u.cm))
nu = nu.to(u.Hz)
flux /= nu
flux = flux.to(u.Jy)
maggies = flux / 3631.
filters_unsorted = load_filters(filternames)
waves_unsorted = [x.wave_mean for x in filters_unsorted]
filters = [x for _, x in sorted(zip(waves_unsorted, filters_unsorted))]
flx = []
flxe = []
for i in range(len(filters)):
flux_range = []
wav_range = []
for j in filters[i].wavelength:
flux_range.append(maggies[find_nearest(wav.value, j)].value)
wav_range.append(wav[find_nearest(wav.value, j)].value)
a = np.trapz(wav_range * filters[i].transmission * flux_range,
wav_range,
axis=-1)
b = np.trapz(wav_range * filters[i].transmission, wav_range)
flx.append(a / b)
flxe.append(0.03 * flx[i])
flx = np.asarray(flx)
flxe = np.asarray(flxe)
flux_mag = flx
unc_mag = flxe
obs = {}
obs['filters'] = filters
obs['maggies'] = flux_mag
obs['maggies_unc'] = unc_mag
obs['phot_mask'] = np.isfinite(flux_mag)
obs['wavelength'] = None
obs['spectrum'] = None
return obs
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 __init__(self,
rtout,
velfile,
cs,
age,
omega,
rmin=0,
mmw=2.37,
g2d=100,
truncate=None,
debug=False,
load_full=True,
fix_tsc=True,
hybrid_tsc=False,
interpolate=False,
TSC_dir='',
tsc_outdir=''):
self.rtout = rtout
self.velfile = velfile
if load_full:
self.hyperion = ModelOutput(rtout)
self.hy_grid = self.hyperion.get_quantities()
self.rmin = rmin * 1e2 # rmin defined in LIME, which use SI unit
self.mmw = mmw
self.g2d = g2d
self.cs = cs # in km/s
self.age = age # in year
# YLY update - add omega
self.omega = omega
self.r_inf = self.cs * 1e5 * self.age * yr # in cm
# option to truncate the sphere to be a cylinder
# the value is given in au to specify the radius of the truncated cylinder viewed from the observer
self.truncate = truncate
# debug option: print out every call to getDensity, getVelocity and getAbundance
self.debug = debug
# option to use simple Trapezoid rule average for getting density, temperature, and velocity
self.interpolate = interpolate
self.tsc2d = getTSC(age,
cs,
omega,
velfile=velfile,
TSC_dir=TSC_dir,
outdir=tsc_outdir,
outname='tsc_regrid')
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 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')
def test_docs_sed(self):
import numpy as np
from hyperion.model import ModelOutput
from hyperion.util.constants import pc
from fluxcompensator.sed import *
# read in from HYPERION
m = ModelOutput(
os.path.join(os.path.dirname(__file__), 'B5_class2_45.rtout'))
array = m.get_sed(group=0,
inclination=0,
distance=300 * pc,
units='ergs/cm^2/s')
# initial FluxCompensator array
s = SyntheticSED(input_array=array,
unit_out='ergs/cm^2/s',
name='test_sed')
def getRadialDensity(rtout, angle, plotdir):
"""
"""
import numpy as np
from hyperion.model import ModelOutput
m = ModelOutput(rtout)
q = m.get_quantities()
r_wall = q.r_wall; theta_wall = q.t_wall; phi_wall = q.p_wall
# get the cell coordinates
rc = r_wall[0:len(r_wall)-1] + 0.5*(r_wall[1:len(r_wall)]-r_wall[0:len(r_wall)-1])
thetac = theta_wall[0:len(theta_wall)-1] + \
0.5*(theta_wall[1:len(theta_wall)]-theta_wall[0:len(theta_wall)-1])
phic = phi_wall[0:len(phi_wall)-1] + \
0.5*(phi_wall[1:len(phi_wall)]-phi_wall[0:len(phi_wall)-1])
#
rho = q['density'].array[0]
# find the closest angle in the thetac grid
ind = np.argsort(abs(thetac-angle*np.pi/180.))[0]
return rc, rho[0,ind,:]
def load_data(self,
key,
file_name=None,
source=None,
incl=None,
angle=None,
dtype=None):
"""Load data for key.
Parameters:
key: data to load.
filename: file to open.
source: source object.
incl: inclination index.
angle: inclination angle (model config must be pre-loaded)
dtype: data type.
"""
if key == 'model':
assert os.path.isfile(file_name)
self.data[key] = ModelOutput(file_name)
elif file_name and dtype:
assert os.path.isfile(file_name)
self.data[key] = load_data_by_type(file_name, dtype.lower(),
REGISTERED_CLASSES)
elif key == 'sed':
assert incl is not None or (angle is not None and \
self.config is not None)
wlg, F = self.data['model'].get_sed(
group=0,
distance=source.distance.cgs.value,
inclination=incl,
units='Jy')
data = np.array(zip(wlg, F[0]),
dtype=[('wlg', float), ('F', float)])
self.data[key] = SED(data=data,
units={
'wlg': 1. * u.micron,
'F': 1. * u.Jy
})
else:
raise NotImplementedError
name = ['model']
angles=np.arccos(np.linspace(0,1.,20))*180./np.pi
inclinations=angles[::-1]
d = SphericalDust()
d.read('d03_5.5_3.0_A.hdf5')
chi = d.optical_properties.chi
chi = chi[::-1]
wav = d.optical_properties.wav
wav = wav[::-1]
Chi = interp1d(wav,chi,kind='linear')
sorted_grid = pickle.load(open(folder[0]+name[0]+"_"+target+".grid.dat",'r'))
best_model_fname = folder[0]+sorted_grid['name'][0]+'.rtout'
best_model = ModelOutput(fname)
inc = int(np.argwhere(inclinations==sorted_grid['inc'][0]))
sed = best_model.get_sed(aperture=-1, inclination=inc, distance=dist,units='Jy')
N = len(sed.wav)
vec = np.zeros(N,len(target_list)+1)
vec[:,0] = sed.wav
for i in range(len(target_list)):
target = target_list[i]
sorted_grid = pickle.load(open(folder[0]+name[0]+"_"+target+".grid.dat",'r'))
best_model_fname = folder[0]+sorted_grid['name'][0]+'.rtout'
best_model = ModelOutput(fname)
extinction = sorted_grid['ext'][0]
# get inclination
inc = int(np.argwhere(inclinations==sorted_grid['inc'][0]))
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
from hyperion.model import ModelOutput
from hyperion.util.constants import au, lsun
RES = 256
mo = ModelOutput('bm2_eff_vor_temperature.rtout')
g = mo.get_quantities()
from scipy.spatial import cKDTree
sites = np.array([g.x, g.y, g.z]).transpose()
tree = cKDTree(sites)
ymin, ymax = 0 * au, 60 * au
zmin, zmax = 0 * au, 60 * au
y = np.linspace(ymin, ymax, RES)
z = np.linspace(zmin, zmax, RES)
Y, Z = np.meshgrid(y, z)
YR = Y.ravel()
ZR = Z.ravel()
for x_cut in [10 * au, 26.666667 * au]:
XR = np.ones(YR.shape) * x_cut
map_sites = np.array([XR, YR, ZR]).transpose()
dust_mass_Msun = []
sfr100 = []
metallicity_logzsol = []
gal_count = []
filter_list = []
pd_list = glob.glob(pd_dir+'/*.rtout.sed')
snap_num = int(pd_list[0].split('snap')[2].split('.')[0])
print('loading galaxy list')
galaxy_list = []
for i in pd_list:
galaxy_list.append(int(i.split('.')[2].split('galaxy')[1]))
for galaxy in tqdm.tqdm(galaxy_list):
m = ModelOutput(pd_dir+'/snap'+str(snap_num)+'.galaxy'+str(galaxy)+'.rtout.sed')
ds = yt.load(snaps_dir+'/galaxy_'+str(galaxy)+'.hdf5', 'r')
wave, flx = m.get_sed(inclination=0, aperture=-1)
gal_count.append(galaxy)
#get mock photometry
wave = np.asarray(wave)*u.micron
if obs_frame:
wav = wave[::-1].to(u.AA)*(1 + float(z))
else:
wav = wave[::-1].to(u.AA)
flux = np.asarray(flx)[::-1]*u.erg/u.s
if float(z) == 0.0:
dl = (10*u.pc).to(u.cm)
else:
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)
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$^2/s$]')
fig.savefig('class1_example_sed.png', bbox_inches='tight')
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)
matplotlib.use('Agg')
import numpy as np
from astropy.io import fits
from hyperion.model import ModelOutput
from hyperion.util.constants import kpc
import matplotlib.pyplot as plt
if not os.path.exists('seds'):
os.mkdir('seds')
for model_path in glob.glob(os.path.join('models', '*_seds.rtout')):
m = ModelOutput(model_path)
model_name = os.path.basename(model_path).replace('_seds.rtout', '')
for iincl, theta in enumerate([0, 30, 60, 90, 120, 150, 180]):
sed_total = m.get_sed(inclination=iincl, units='Jy', distance=10. * kpc, aperture=-1, group=0, component='total')
sed_semit = m.get_sed(inclination=iincl, units='Jy', distance=10. * kpc, aperture=-1, group=0, component='source_emit')
sed_sscat = m.get_sed(inclination=iincl, units='Jy', distance=10. * kpc, aperture=-1, group=0, component='source_scat')
sed_demit = m.get_sed(inclination=iincl, units='Jy', distance=10. * kpc, aperture=-1, group=0, component='dust_emit')
sed_dscat = m.get_sed(inclination=iincl, units='Jy', distance=10. * kpc, aperture=-1, group=0, component='dust_scat')
sed_trans = m.get_sed(inclination=iincl, units='Jy', distance=10. * kpc, aperture=-1, group=1, component='source_emit')
output_file = 'seds/{name}_i{theta:03d}a000.sed'.format(name=model_name, theta=theta)
type=float,
help='A single wavelength in microns, if producing a monochromatic image.')
parser.add_argument('-d',
'--dat',
action='store_true',
help='If enabled, saves a ".dat" file with image data.')
parser.add_argument('--vmin',
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)
filename = '/Users/yaolun/bhr71/hyperion/cycle9/model34.rtout'
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
Filter_Library = pyphot.get_library(fname="Ampere_FiterProfile_Library.hdf5") #Getting Instrument Filters for Filter Convolution of the SED
#Filter names of the image wavelengths as given in the Ampere Filter porifles library.
#'HERSCHEL_PACS_BLUE', 'HERSCHEL_PACS_RED', 'JCMT_SCUBA2_450', 'JCMT_SCUBA2_850'
#Needed for Beam Conv
model_max_envelope_size = 80 #Values used in RT modelling
model_arcsec_size = model_max_envelope_size * 3 #Values used in RT modelling
model_pix_size = 400 #Values used in RT modelling - (400 x 400 pixels)
model_pix_arcsec = model_arcsec_size / model_pix_size #Size of each pixel in model image in arcsecs
for filename in OutPutFiles:
model = ModelOutput(filename)
wavelength_group = np.array([1, 2, 3, 4]) #wavelength group in data cube to exract the image at the wanted wavelength. Groups (in the order written out in RT modelling code) =>> 0 - SED (ignore here as we need the images) // 1 - PACS 70; 2 - PACS 160; 3 - SCUBA2 450; 4 - SCUBA2 850
for group_val in wavelength_group:
#print(group_val)
#Applying Filer convolution to the Model image at each wavelength
Filter_Name, Filter_ConvImage = Image_FilterConvolve(model, Source_Distance, Filter_Library, group_val)
#Creating .fits files of the filter convolved image at each wavelength
hdu = fits.PrimaryHDU(Filter_ConvImage)
hdulist = fits.HDUList([hdu])
hdulist[0].header['Filter'] = Filter_Name[0]
hdulist[0].header['BUNIT'] = "Jy"
import matplotlib.pyplot as plt
from hyperion.model import ModelOutput
from hyperion.util.constants import pc
m = ModelOutput('class2_sed.rtout')
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
# Total SED
sed = m.get_sed(inclination=0, aperture=-1, distance=300 * pc)
ax.loglog(sed.wav, sed.val, color='black', lw=3, alpha=0.5)
# Direct stellar photons
sed = m.get_sed(inclination=0, aperture=-1, distance=300 * pc,
component='source_emit')
ax.loglog(sed.wav, sed.val, color='blue')
# Scattered stellar photons
sed = m.get_sed(inclination=0, aperture=-1, distance=300 * pc,
component='source_scat')
ax.loglog(sed.wav, sed.val, color='teal')
# Direct dust photons
sed = m.get_sed(inclination=0, aperture=-1, distance=300 * pc,
component='dust_emit')
ax.loglog(sed.wav, sed.val, color='red')
# Scattered dust photons
sed = m.get_sed(inclination=0, aperture=-1, distance=300 * pc,
import os
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
source_lam_downsampled[i] = source_lam[idx].value
lum_source_downsampled[i] = lum_source[idx].value
return source_lam_downsampled, lum_source_downsampled
#check if path exists - if not, create it
#if not os.path.exists(output_directory): os.makedirs(output_directory)
sed_file = glob(sed_directory + '/*galaxy' + str(galaxy_num) + '.rtout.sed')[0]
#print(sed_file)
stellar_file = glob(sources_directory + '/*.galaxy' + str(galaxy_num) +
'.rtout.sed')[0]
comp_sed = ModelOutput(sed_file)
wav_rest_sed, dum_lum_obs_sed = comp_sed.get_sed(inclination='all',
aperture=-1)
wav_rest_sed = wav_rest_sed * u.micron #wav is in micron
nu_rest_sed = constants.c.cgs / wav_rest_sed.cgs
lum_obs_sed = dum_lum_obs_sed
lum_obs_sed = lum_obs_sed * u.erg / u.s
nu_rest_sed = constants.c.cgs / (wav_rest_sed.to(u.cm))
fnu_obs_sed = lum_obs_sed.to(u.Lsun)
fnu_obs_sed /= nu_rest_sed.to(u.Hz)
fnu_obs_sed = fnu_obs_sed.to(u.Lsun / u.Hz)
#stellar_sed = np.load(stellar_file)
#nu_rest_stellar = stellar_sed['nu'] #Hz
#fnu_rest_stellar = stellar_sed['fnu'] #Lsun/Hz
#fnu_rest_stellar = fnu_rest_stellar * u.Lsun/u.Hz
import matplotlib.pyplot as plt
from hyperion.model import ModelOutput
from hyperion.util.constants import pc
m = ModelOutput('class2_sed.rtout')
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
# Extract all SEDs
sed = m.get_sed(inclination='all', aperture=-1, distance=300 * pc)
# Plot SED for each inclination
for i in range(sed.val.shape[0]):
ax.loglog(sed.wav, sed.val[i, :], color='black')
ax.set_xlabel(r'$\lambda$ [$\mu$m]')
ax.set_ylabel(r'$\lambda F_\lambda$ [ergs/s/cm$^2$]')
ax.set_xlim(0.1, 2000.)
ax.set_ylim(2.e-16, 2.e-9)
fig.savefig('class2_sed_plot_incl.png')
import numpy as np
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)
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()
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 hyperion_sedcom(modellist, outdir, plotname, obs_data=None, labellist=None, lbol=False, legend=True, mag=1.5,\
obs_preset='sh', dstar=1, aper=[3.6, 4.5, 5.8, 8.0, 10, 20, 24, 70, 160, 250, 350, 500, 850]):
"""
obs_data: dictionary which obs_data['spec'] is spectrum and obs_data['phot'] is photometry
obs_data['label'] = (wave, Fv, err) in um and Jy by default
"""
import numpy as np
import os
import matplotlib.pyplot as plt
import astropy.constants as const
from hyperion.model import ModelOutput
from scipy.interpolate import interp1d
from l_bol import l_bol
import seaborn as sb
# from seaborn import color_palette
# from seaborn_color import seaborn_color
# constant setup
c = const.c.cgs.value
pc = const.pc.cgs.value
if labellist == None:
if legend == True:
print 'Model labels are not provided. Use their filename instead.'
labellist = []
for i in range(0, len(modellist)):
labellist.append(r'$\mathrm{'+os.path.splitext(os.path.basename(modellist[i]))[0]+'}$')
# cm = seaborn_color('colorblind',len(modellist))
sb.set(style="white")
cm = sb.color_palette('husl', len(modellist))
# create figure object
fig = plt.figure(figsize=(8*mag,6*mag))
ax = fig.add_subplot(111)
# sb.set_style('ticks')
print 'plotting with aperture at ', aper, 'um'
# if the obs_data is provided than plot the observation first. In this way, models won't be blocked by data
if obs_data != None:
if 'spec' in obs_data.keys():
(wave, fv, err) = obs_data['spec']
vfv = c/(wave*1e-4)*fv*1e-23
l_bol_obs = l_bol(wave, fv, dstar)
if legend == True:
ax.text(0.75,0.9,r'$\mathrm{L_{bol}= %5.2f L_{\odot}}$' % l_bol_obs,fontsize=mag*16,transform=ax.transAxes)
# general plotting scheme
if obs_preset == None:
spec, = ax.plot(np.log10(wave),np.log10(vfv),'-',color='k',linewidth=1.5*mag, label=r'$\mathrm{observations}$')
# plot spitzer, Herschel pacs and spire in different colors
elif obs_preset == 'sh':
# spitzer
spitz, = ax.plot(np.log10(wave[wave < 50]),np.log10(vfv[wave < 50]),'-',color='b',linewidth=1*mag,\
label=r'$\mathrm{\it Spitzer}$')
# herschel
pacs, = ax.plot(np.log10(wave[(wave < 190.31) & (wave > 50)]),np.log10(vfv[(wave < 190.31) & (wave > 50)]),'-',\
color='Green',linewidth=1*mag, label=r'$\mathrm{{\it Herschel}-PACS}$')
spire, = ax.plot(np.log10(wave[wave >= 190.31]),np.log10(vfv[wave >= 190.31]),'-',color='k',linewidth=1*mag,\
label=r'$\mathrm{{\it Herschel}-SPIRE}$')
spec = [spitz, pacs, spire]
if 'phot' in obs_data.keys():
(wave_p, fv_p, err_p) = obs_data['phot']
vfv_p = c/(wave_p*1e-4)*fv_p*1e-23
vfv_p_err = c/(wave_p*1e-4)*err_p*1e-23
phot, = ax.plot(np.log10(wave_p),np.log10(vfv_p),'s',mfc='DimGray',mec='k',markersize=8)
ax.errorbar(np.log10(wave_p),np.log10(vfv_p),yerr=[np.log10(vfv_p)-np.log10(vfv_p-vfv_p_err), np.log10(vfv_p+vfv_p_err)-np.log10(vfv_p)],\
fmt='s',mfc='DimGray',mec='k',markersize=8)
modplot = dict()
for imod in range(0, len(modellist)):
m = ModelOutput(modellist[imod])
# if not specified, distance of the star will be taken as 1 pc.
if aper == None:
sed_dum = m.get_sed(group=0, inclination=0, aperture=-1, distance=dstar * pc)
modplot['mod'+str(imod+1)], = ax_sed.plot(np.log10(sed_dum.wav), np.log10(sed_dum.val), '-', color='GoldenRod', linewidth=1.5*mag)
else:
vfv_aper = np.empty_like(aper)
for i in range(0, len(aper)):
sed_dum = m.get_sed(group=i+1, inclination=0, aperture=-1, distance=dstar * pc)
f = interp1d(sed_dum.wav, sed_dum.val)
vfv_aper[i] = f(aper[i])
modplot['mod'+str(imod+1)], = ax.plot(np.log10(aper),np.log10(vfv_aper),'o',mfc='None',mec=cm[imod],markersize=12,\
markeredgewidth=3, label=labellist[imod], linestyle='-',color=cm[imod],linewidth=1.5*mag)
# plot fine tune
ax.set_xlabel(r'$\mathrm{log~\lambda~({\mu}m)}$',fontsize=mag*20)
ax.set_ylabel(r'$\mathrm{log~\nu S_{\nu}~(erg/cm^{2}/s)}$',fontsize=mag*20)
[ax.spines[axis].set_linewidth(1.5*mag) for axis in ['top','bottom','left','right']]
ax.minorticks_on()
ax.tick_params('both',labelsize=mag*18,width=1.5*mag,which='major',pad=15,length=5*mag)
ax.tick_params('both',labelsize=mag*18,width=1.5*mag,which='minor',pad=15,length=2.5*mag)
if obs_preset == 'sh':
ax.set_ylim([-14,-7])
ax.set_xlim([0,3])
if legend == True:
lg = ax.legend(loc='best',fontsize=14*mag,numpoints=1,framealpha=0.3)
# Write out the plot
fig.savefig(outdir+plotname+'.pdf',format='pdf',dpi=300,bbox_inches='tight')
fig.clf()
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 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 DIG_source_add(m,reg,df_nu):
print("--------------------------------\n")
print("Adding DIG to Source List in source_creation\n")
print("--------------------------------\n")
print ("Getting specific energy dumped in each grid cell")
try:
rtout = cfg.model.outputfile + '.sed'
try:
grid_properties = np.load(cfg.model.PD_output_dir+"/grid_physical_properties."+cfg.model.snapnum_str+'_galaxy'+cfg.model.galaxy_num_str+".npz")
except:
grid_properties = np.load(cfg.model.PD_output_dir+"/grid_physical_properties."+cfg.model.snapnum_str+".npz")
cell_info = np.load(cfg.model.PD_output_dir+"/cell_info."+cfg.model.snapnum_str+"_"+cfg.model.galaxy_num_str+".npz")
except:
print ("ERROR: Can't proceed with DIG nebular emission calculation. Code is unable to find the required files.")
print ("Make sure you have the rtout.sed, grid_physical_properties.npz and cell_info.npz for the corresponding galaxy.")
return
m_out = ModelOutput(rtout)
oct = m_out.get_quantities()
grid = oct
order = find_order(grid.refined)
refined = grid.refined[order]
quantities = {}
for field in grid.quantities:
quantities[('gas', field)] = grid.quantities[field][0][order][~refined]
cell_width = cell_info["fw1"][:,0]
mass = (quantities['gas','density']*units.g/units.cm**3).value * (cell_width**3)
met = grid_properties["grid_gas_metallicity"]
specific_energy = (quantities['gas','specific_energy']*units.erg/units.s/units.g).value
specific_energy = (specific_energy * mass) # in ergs/s
# Black 1987 curve has a integrated ergs/s/cm2 of 0.0278 so the factor we need to multiply it by is given by this value
factor = specific_energy/(cell_width**2)/(0.0278)
mask1 = np.where(mass != 0 )[0]
mask = np.where((mass != 0 ) & (factor >= cfg.par.DIG_min_factor))[0] # Masking out all grid cells that have no gas mass and where the specific emergy is too low
print (len(factor), len(mask1), len(mask))
factor = factor[mask]
cell_width = cell_width[mask]
cell_x = (cell_info["xmax"] - cell_info["xmin"])[mask]
cell_y = (cell_info["ymax"] - cell_info["ymin"])[mask]
cell_z = (cell_info["zmax"] - cell_info["zmin"])[mask]
pos = np.vstack([cell_x, cell_y, cell_z]).transpose()
met = grid_properties["grid_gas_metallicity"][:, mask]
met = np.transpose(met)
fnu_arr = sg.get_dig_seds(factor, cell_width, met)
dat = np.load(cfg.par.pd_source_dir + "/powderday/nebular_emission/data/black_1987.npz")
spec_lam = dat["lam"]
nu = 1.e8 * constants.c.cgs.value / spec_lam
for i in range(len(factor)):
fnu = fnu_arr[i,:]
nu, fnu = wavelength_compress(nu,fnu,df_nu)
nu = nu[::-1]
fnu = fnu[::-1]
lum = np.absolute(np.trapz(fnu,x=nu))*constants.L_sun.cgs.value
source = m.add_point_source()
source.luminosity = lum # [ergs/s]
source.spectrum = (nu,fnu)
source.position = pos[i] # [cm]
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()
from astropy import constants
import h5py
import fsps
#SKIRT STUFF
sedfile = '/home/desika.narayanan/SKIRT/run/pd_test.dust_i90_sed.dat'
run = '/ufrc/narayanan/desika.narayanan/pd/tests/SKIRT/mw_zoom/pd_skirt_comparison.134.rtout.sed'
data = np.loadtxt(sedfile)
skirt_lam = data[:, 0]
skirt_flambda = data[:, 1] * u.W / u.m**2 / u.micron
skirt_flambda = skirt_flambda.to(u.erg / u.s / u.cm**2. / u.angstrom)
#PD stuff
m = ModelOutput(run)
pd_wav, pd_flux = m.get_sed(inclination='all', aperture=-1, units='ergs/s')
distance = 1. * u.Mpc.to(u.cm)
pd_flux *= u.erg / u.s
pd_flux /= (4. * np.pi * distance**2.
) #SKIRT seems to say F = L/d^2 instead of F = L/(4*pi*d^2)
pd_wav *= u.micron
pd_flux /= pd_wav.to(u.angstrom)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.loglog(pd_wav, pd_flux[0, :], label='powderday', lw=3)
ax.loglog(skirt_lam, skirt_flambda, label='SKIRT')
plt.legend()
ax.set_xlim([0.05, 1000])
ax.set_ylim([1.e-17, 1.e-8])
class Hyperion2LIME:
"""
Class for importing Hyperion result to LIME
IMPORTANT: LIME uses SI units, while Hyperion uses CGS units.
"""
def __init__(self,
rtout,
velfile,
cs,
age,
omega,
rmin=0,
mmw=2.37,
g2d=100,
truncate=None,
debug=False,
load_full=True,
fix_tsc=True,
hybrid_tsc=False,
interpolate=False,
TSC_dir='',
tsc_outdir=''):
self.rtout = rtout
self.velfile = velfile
if load_full:
self.hyperion = ModelOutput(rtout)
self.hy_grid = self.hyperion.get_quantities()
self.rmin = rmin * 1e2 # rmin defined in LIME, which use SI unit
self.mmw = mmw
self.g2d = g2d
self.cs = cs # in km/s
self.age = age # in year
# YLY update - add omega
self.omega = omega
self.r_inf = self.cs * 1e5 * self.age * yr # in cm
# option to truncate the sphere to be a cylinder
# the value is given in au to specify the radius of the truncated cylinder viewed from the observer
self.truncate = truncate
# debug option: print out every call to getDensity, getVelocity and getAbundance
self.debug = debug
# option to use simple Trapezoid rule average for getting density, temperature, and velocity
self.interpolate = interpolate
self.tsc2d = getTSC(age,
cs,
omega,
velfile=velfile,
TSC_dir=TSC_dir,
outdir=tsc_outdir,
outname='tsc_regrid')
# # velocity grid construction
# if load_full:
# # ascii.read() fails for large file. Use pandas instead
# self.tsc = pd.read_csv(velfile, skiprows=1, delim_whitespace=True, header=None)
# self.tsc.columns = ['lp', 'xr', 'theta', 'ro', 'ur', 'utheta', 'uphi']
#
# self.xr = np.unique(self.tsc['xr']) # reduce radius: x = r/(a*t) = r/r_inf
# self.xr_wall = np.hstack(([2*self.xr[0]-self.xr[1]],
# (self.xr[:-1]+self.xr[1:])/2,
# [2*self.xr[-1]-self.xr[-2]]))
# self.theta = np.unique(self.tsc['theta'])
# self.theta_wall = np.hstack(([2*self.theta[0]-self.theta[1]],
# (self.theta[:-1]+self.theta[1:])/2,
# [2*self.theta[-1]-self.theta[-2]]))
# self.nxr = len(self.xr)
# self.ntheta = len(self.theta)
#
# # the output of TSC fortran binary is in mass density
# self.tsc_rho2d = 1/(4*np.pi*G*(self.age*yr)**2)/mh/mmw * np.array(self.tsc['ro']).reshape([self.nxr, self.ntheta])
#
# # self.vr2d = np.array(self.tsc['ur']).reshape([self.nxr, self.ntheta]) * self.cs*1e5
# # self.vtheta2d = np.array(self.tsc['utheta']).reshape([self.nxr, self.ntheta]) * self.cs*1e5
# # self.vphi2d = np.array(self.tsc['uphi']).reshape([self.nxr, self.ntheta]) * self.cs*1e5
#
# # in unit of km/s
# self.vr2d = np.reshape(self.tsc['ur'].to_numpy(), (self.nxr, self.ntheta)) * np.float64(self.cs)
# self.vtheta2d = np.reshape(self.tsc['utheta'].to_numpy(), (self.nxr, self.ntheta)) * np.float64(self.cs)
# self.vphi2d = np.reshape(self.tsc['uphi'].to_numpy(), (self.nxr, self.ntheta)) * np.float64(self.cs)
#
# if fix_tsc:
# # fix the discontinuity in v_r
# # vr = vr + offset * log(xr)/log(xr_break) for xr >= xr_break
# for i in range(self.ntheta):
# dvr = abs((self.vr2d[1:,i] - self.vr2d[:-1,i])/self.vr2d[1:,i])
# break_pt = self.xr[1:][(dvr > 0.05) & (self.xr[1:] > 1e-3) & (self.xr[1:] < 1-2e-3)]
# if len(break_pt) > 0:
# offset = self.vr2d[(self.xr < break_pt),i].max() - self.vr2d[(self.xr > break_pt),i].min()
# self.vr2d[(self.xr >= break_pt),i] = self.vr2d[(self.xr >= break_pt),i] + offset*np.log10(self.xr[self.xr >= break_pt])/np.log10(break_pt)
# # YLY update - 091118
# # fix the discontinuity in v_phi
# for i in range(self.ntheta):
# dvr = abs((self.vphi2d[1:,i] - self.vphi2d[:-1,i])/self.vphi2d[1:,i])
# break_pt = self.xr[1:][(dvr > 0.1) & (self.xr[1:] > 1e-3) & (self.xr[1:] < 1-2e-3)]
# if len(break_pt) > 0:
# offset = self.vphi2d[(self.xr < break_pt),i].min() - self.vphi2d[(self.xr > break_pt),i].max()
# self.vphi2d[(self.xr >= break_pt),i] = self.vphi2d[(self.xr >= break_pt),i] + offset*np.log10(self.xr[self.xr >= break_pt])/np.log10(break_pt)
#
# # hybrid TSC kinematics that switches to angular momentum conservation within the centrifugal radius
# if hybrid_tsc:
# from scipy.interpolate import interp1d
# for i in range(self.ntheta):
# rCR = self.omega**2 * G**3 * (0.975*(self.cs*1e5)**3/G*(self.age*3600*24*365))**3 * np.sin(self.theta[i])**4 / (16*(self.cs*1e5)**8)
# if rCR/self.r_inf >= self.xr.min():
# f_vr = interp1d(self.xr, self.vr2d[:,i])
# vr_rCR = f_vr(rCR/self.r_inf)
# f_vphi = interp1d(self.xr, self.vphi2d[:,i])
# vphi_rCR = f_vphi(rCR/self.r_inf)
#
# # radius in cylinderical coordinates
# wCR = np.sin(self.theta[i]) * rCR
# J = vphi_rCR * wCR
# M = (vr_rCR**2 + vphi_rCR**2) * wCR / (2*G)
#
# w = self.xr*np.sin(self.theta[i])*self.r_inf
# self.vr2d[(self.xr <= rCR/self.r_inf), i] = -( 2*G*M/w[self.xr <= rCR/self.r_inf] - J**2/(w[self.xr <= rCR/self.r_inf])**2 )**0.5
# self.vphi2d[(self.xr <= rCR/self.r_inf), i] = J/(w[self.xr <= rCR/self.r_inf])
#
# self.tsc2d = {'vr2d': self.vr2d, 'vtheta2d': self.vtheta2d, 'vphi2d': self.vphi2d}
def Cart2Spherical(self, x, y, z, unit_convert=True):
"""
if unit_convert, the inputs (x, y, z) are meter.
The outputs are in cm.
"""
if unit_convert:
x, y, z = x * 1e2, y * 1e2, z * 1e2
r_in = (x**2 + y**2 + z**2)**0.5
if r_in != 0:
t_in = np.arccos(z / r_in)
else:
t_in = 0
# if x != 0:
# p_in = np.sign(y)*np.arctan(y/x) # the input phi is irrelevant in axisymmetric model
# else:
# p_in = np.sign(y)*np.pi/2
p_in = np.arctan2(y, x)
if r_in < self.rmin:
r_in = self.rmin
return (r_in, t_in, p_in)
def Spherical2Cart(self, r, t, p):
"""
This is only valid for axisymmetric model
"""
x = r * np.sin(t) * np.cos(p)
y = r * np.sin(t) * np.sin(p)
z = r * np.cos(t)
return (x, y, z)
def Spherical2Cart_vector(self, coord_sph, v_sph):
r, theta, phi = coord_sph
vr, vt, vp = v_sph
transform = np.matrix([[
np.sin(theta) * np.cos(phi),
np.cos(theta) * np.cos(phi), -np.sin(phi)
],
[
np.sin(theta) * np.sin(phi),
np.cos(theta) * np.sin(phi),
np.cos(phi)
], [np.cos(theta), -np.sin(theta), 0]])
v_cart = transform.dot(np.array([vr, vt, vp]))
return list(map(float, np.asarray(v_cart).flatten()))
def locateCell(self, coord, wall_grid):
"""
return the indice of cell at given coordinates
"""
r, t, p = coord
r_wall, t_wall, p_wall = wall_grid
r_ind = min(np.argsort(abs(r_wall - r))[:2])
t_ind = min(np.argsort(abs(t_wall - t))[:2])
p_ind = min(np.argsort(abs(p_wall - p))[:2])
return (r_ind, t_ind, p_ind)
# def interpolateCell(self, coord, cube, wall_grid):
# """
# return the interpolated value at given data cube at given coordinates
# """
# r, t, p = coord
# r_wall, t_wall, p_wall = wall_grid
#
# # the cell center
# r_ind = min(np.argsort(abs(r_wall-r))[:2])
# t_ind = min(np.argsort(abs(t_wall-t))[:2])
# p_ind = min(np.argsort(abs(p_wall-p))[:2])
#
# # simple Trapezoid rule
# val_dum = 0
# for ri in r_ind:
# for ti in t_ind:
# for pi in p_ind:
# val_dum += cube[ri, ti, pi]
# val = val_dum/8.0
#
# return val
def locateCell2d(self, coord, wall_grid):
"""
return the indice of cell at given coordinates
"""
r, t = coord
r_wall, t_wall = wall_grid
r_ind = min(np.argsort(abs(r_wall - r))[:2])
t_ind = min(np.argsort(abs(t_wall - t))[:2])
return (r_ind, t_ind)
# def interpolateCell2d(self, coord, cube, wall_grid):
# """
# return the interpolated value at given data cube at given coordinates
# """
# r, t= coord
# r_wall, t_wall = wall_grid
#
# r_ind = np.argsort(abs(r_wall-r))[:2]
# t_ind = np.argsort(abs(t_wall-t))[:2]
#
# # simple Trapezoid rule
# val = (cube[r_ind[0], t_ind[0]] + cube[r_ind[0], t_ind[1]] +
# cube[r_ind[1], t_ind[0]] + cube[r_ind[1], t_ind[1]])/4.0
#
# return val
def getDensity(self, x, y, z, version='gridding', theta_cav=None):
(r_in, t_in, p_in) = self.Cart2Spherical(x, y, z)
if self.truncate != None:
if (y**2 + z**2)**0.5 > self.truncate * au_si:
return 0.0
if version == 'hyperion':
r_wall = self.hy_grid.r_wall
t_wall = self.hy_grid.t_wall
p_wall = self.hy_grid.p_wall
self.rho = self.hy_grid.quantities['density'][0].T
if not self.interpolate:
indice = self.locateCell((r_in, t_in, p_in),
(r_wall, t_wall, p_wall))
rho = self.rho[indice]
else:
rho = self.interpolateCell((r_in, t_in, p_in), self.rho,
(r_wall, t_wall, p_wall))
# LIME needs molecule number density per cubic meter
# if self.debug:
# foo = open('density.log', 'a')
# foo.write('%e \t %e \t %e \t %e\n' % (x,y,z,float(self.rho[indice])*self.g2d/mh/self.mmw*1e6))
# foo.close()
return float(rho) * self.g2d / mh / self.mmw * 1e6
elif version == 'gridding':
# check for cavity
# determine whether the cell is in the cavity
# if (theta_cav != None) and (theta_cav != 0):
# # using R = 10000 AU as the reference point
# c0 = (10000.*au_cgs)**(-0.5)*\
# np.sqrt(1/np.sin(np.radians(theta_cav))**3-1/np.sin(np.radians(theta_cav)))
#
# # related coordinates
# w = abs(r_in*np.cos(np.pi/2 - t_in))
# _z = r_in*np.sin(np.pi/2 - t_in)
#
# # condition for open cavity
# z_cav = c0*abs(w)**1.5
# cav_con = abs(_z) > abs(z_cav)
#
# if cav_con:
# # this is still wrong, because in the "correct" model setup. The cavity does not have zero density.
# rho = 0.0
# return float(rho)
# isothermal solution
if r_in > self.r_inf:
rho = (self.cs * 1e5)**2 / (2 * np.pi * G *
(r_in)**2) / mh / self.mmw * 1e6
# TSC solution
else:
if not self.interpolate:
ind = self.locateCell2d(
(r_in, t_in), (self.tsc2d['xr_wall'] * self.r_inf,
self.tsc2d['theta_wall']))
rho = self.tsc2d['rho2d'][
ind] * 1e6 # has been divided by "mh" and "mmw"
else:
rho = self.interpolateCell2d(
(r_in, t_in), self.tsc2d['rho2d'],
(self.tsc2d['xr_wall'] * self.r_inf,
self.tsc2d['theta_wall'])) * 1e6
return float(rho)
def getTemperature(self, x, y, z, external_heating=False, r_break=None):
r_wall = self.hy_grid.r_wall
t_wall = self.hy_grid.t_wall
p_wall = self.hy_grid.p_wall
self.temp = self.hy_grid.quantities['temperature'][0].T
if self.truncate != None:
if (y**2 + z**2)**0.5 > self.truncate * au_si:
return 0.0
(r_in, t_in, p_in) = self.Cart2Spherical(x, y, z)
if not self.interpolate:
indice = self.locateCell((r_in, t_in, p_in),
(r_wall, t_wall, p_wall))
temp = self.temp[indice]
else:
temp = self.interpolateCell((r_in, t_in, p_in), cube,
(r_wall, t_wall, p_wall))
# if external_heating:
# # get the temperature at the outermost radius
# indice_lowT = self.locateCell(((r_wall[-1]+r_wall[-2])/2, t_in, p_in), (r_wall, t_wall, p_wall))
# lowT = self.temp[indice_lowT]
# # the inner radius where the temperature correction starts to apply
# # User-defined value
# # r_break = 13000*au_cgs
# # r_break = 2600*au_cgs
# r_break = r_break*au_cgs
#
# if (lowT < 15) and (r_in >= r_break):
# dT = (r_in - r_break)*(15-lowT)/((r_wall[-1]+r_wall[-2])/2 - r_break)
# if float(temp) + float(dT) >= 0.0:
# return float(temp) + float(dT)
# else:
# return 0.0
# test for a different approach of external heating
if external_heating:
from scipy.interpolate import interp1d
# get the temperature at the outermost radius
rc = (r_wall[1:] + r_wall[:-1]) / 2
indice_Tmin = self.locateCell((rc.max(), t_in, p_in),
(r_wall, t_wall, p_wall))
Tmin = self.temp[indice_Tmin]
# set an inner radius that the external heating will apply for skipping the disk, where temperature may be lower than 10 K
# take two times the centrifugal radius
rCen = self.omega**2 * G**3 * (0.975 * (self.cs * 1e5)**3 / G *
(self.age * yr))**3 / (
16 * (self.cs * 1e5)**8)
r_ext_min = 2 * rCen
if (temp < 10.0) and (r_in >= r_ext_min) and (Tmin < 15.0):
rc = (r_wall[1:] + r_wall[:-1]) / 2
f_temp = interp1d(
self.temp[(rc > r_ext_min), indice_Tmin[1],
indice_Tmin[2]], rc[rc > r_ext_min])
r10K = f_temp(10.0)
dT = (r_in - r10K) / (rc.max() - r10K) * (15.0 - Tmin)
temp = float(temp) + float(dT)
if float(temp) >= 0.0:
return float(temp)
else:
return 0.0
def getVelocity(self,
x,
y,
z,
sph=False,
unit_convert=True,
vr_factor=1.0,
vr_offset=0.0):
"""
cs: effecitve sound speed in km/s;
age: the time since the collapse began in year.
vr_offset: in km/s
"""
(r_in, t_in, p_in) = self.Cart2Spherical(x,
y,
z,
unit_convert=unit_convert)
if self.truncate != None:
if (y**2 + z**2)**0.5 > self.truncate * au_si:
v_out = [0.0, 0.0, 0.0]
return v_out
# outside of infall radius, the envelope is static
# if r_in > self.r_inf:
# v_sph = [0.0+vr_offset*1e3, 0.0, 0.0]
# v_out = self.Spherical2Cart_vector((r_in, t_in, p_in), v_sph)
# return v_out
# if the input radius is smaller than the minimum in xr array,
# use the minimum in xr array instead.
# UPDATE (081518): return zero velocity instead
if r_in < self.tsc2d['xr_wall'].min() * self.r_inf:
r_in = self.tsc2d['xrc'].min() * self.r_inf
v_out = [0.0, 0.0, 0.0]
return v_out
if not self.interpolate:
ind = self.locateCell2d(
(r_in, t_in),
(self.tsc2d['xr_wall'] * self.r_inf, self.tsc2d['theta_wall']))
v_sph = list(
map(float, [
self.tsc2d['vr2d'][ind] * 1e5 / 1e2,
self.tsc2d['vtheta2d'][ind] * 1e5 / 1e2,
self.tsc2d['vphi2d'][ind] * 1e5 / 1e2
]))
else:
vr = self.interpolateCell2d((r_in, t_in), self.tsc2d['vr2d'],
(self.tsc2d['xr_wall'] * self.r_inf,
self.tsc2d['theta_wall'])) * 1e5
vtheta = self.interpolateCell2d(
(r_in, t_in), self.tsc2d['vtheta2d'],
(self.tsc2d['xr_wall'] * self.r_inf,
self.tsc2d['theta_wall'])) * 1e5
vphi = self.interpolateCell2d((r_in, t_in), self.tsc2d['vphi2d'],
(self.tsc2d['xr_wall'] * self.r_inf,
self.tsc2d['theta_wall'])) * 1e5
v_sph = list(map(float, [vr / 1e2, vtheta / 1e2, vphi / 1e2]))
# test for artifically reducing the radial velocity
v_sph[0] = v_sph[0] * vr_factor # + vr_offset*1e3
# flatten out at the vr_offset
# if v_sph[0] > vr_offset*1e3:
# v_sph[0] = vr_offset*1e3 # Note infall velocity should be negative
# A hybrid outer envelope model: -0.5 km/s uniformly within 1e4 AU and static beyond.
# static envelope beyond 3000 AU
# the vr_offset has a parabolic curve as a function of radius (e.g. Keto+2015)
# parameter is taken from Keto+2015. y = a(r - r_max)^2
# vr is negative
if v_sph[0] > vr_offset * 1e3:
v_sph[0] = 50.0 * (r_in - (r_wall[-1] + r_wall[-2]) / 2)**2 * 1e3
if sph:
return v_sph
v_out = self.Spherical2Cart_vector((r_in, t_in, p_in), v_sph)
if self.debug:
foo = open('velocity.log', 'a')
foo.write('%e \t %e \t %e \t %f \t %f \t %f\n' %
(x, y, z, v_out[0], v_out[1], v_out[2]))
foo.close()
return v_out
def getFFVelocity(self,
x,
y,
z,
J,
M,
sph=False,
unit_convert=True,
vr_factor=1.0):
"""
cs: effecitve sound speed in km/s;
age: the time since the collapse began in year.
"""
(r_in, t_in, p_in) = self.Cart2Spherical(x,
y,
z,
unit_convert=unit_convert)
if self.truncate != None:
if (y**2 + z**2)**0.5 > self.truncate * au_si:
v_out = [0.0, 0.0, 0.0]
return v_out
# if the input radius is smaller than the minimum in xr array,
# use the minimum in xr array instead.
# UPDATE: return zero velocity instead
if r_in < self.xr_wall.min() * self.r_inf:
r_in = self.xr.min() * self.r_inf
v_out = [0.0, 0.0, 0.0]
return v_out
# use the Sakai model
M = M * MS
# centrifugal barrier
cb = J**2 / (2 * G * M)
if 2 * G * M / r_in - J**2 / r_in**2 >= 0:
vr = (2 * G * M / r_in - J**2 / r_in**2)**0.5 * vr_factor
else:
vr = 0.0
# let vk = vp at CB
M_k = J**2 / (G * cb)
vp = J / r_in
vk = (G * M_k / r_in)**0.5
if r_in >= cb:
v_sph = [-vr / 1e2, 0.0, vp / 1e2]
else:
v_sph = [-vr / 1e2, 0.0, vk / 1e2]
if sph:
return v_sph
v_out = self.Spherical2Cart_vector((r_in, t_in, p_in), v_sph)
if self.debug:
foo = open('velocity.log', 'a')
foo.write('%e \t %e \t %e \t %f \t %f \t %f\n' %
(x, y, z, v_out[0], v_out[1], v_out[2]))
foo.close()
return v_out
def getVelocity2(self, x, y, z, sph=False, unit_convert=True):
"""
new method to interpolate the velocity
cs: effecitve sound speed in km/s;
age: the time since the collapse began in year.
"""
(r_in, t_in, p_in) = self.Cart2Spherical(x,
y,
z,
unit_convert=unit_convert)
if self.truncate != None:
if (y**2 + z**2)**0.5 > self.truncate * au_si:
v_out = [0.0, 0.0, 0.0]
return v_out
# outside of infall radius, the envelope is static
if r_in > self.r_inf:
v_out = [0.0, 0.0, 0.0]
return v_out
# if the input radius is smaller than the minimum in xr array,
# use the minimum in xr array instead.
if r_in < self.tsc2d['xr_wall'].min() * self.r_inf:
r_in = self.tsc2d['xrc'].min() * self.r_inf
# TODO: raise warning
# r, t = 10*au, np.radians(30.)
# print(r, t)
r_corners = np.argsort(abs(r_in - self.tsc2d['xrc'] * self.r_inf))[:2]
theta_corners = np.argsort(abs(t_in - self.tsc2d['thetac']))[:2]
# print(r_corners, theta_corners)
# initialize the velocity vector in spherical coordinates
# TODO: use scipy interp2d
v_sph = []
for k in ['vr2d', 'vtheta2d', 'vphi2d']:
f = interp2d(self.tsc2d['xrc'][r_corners] * self.r_inf,
self.tsc2d['thetac'][theta_corners],
self.tsc2d[k][np.ix_(r_corners, theta_corners)])
v_sph.append(float(f(r_in, t_in) * 1e5 / 1e2))
# v_r, v_theta, v_phi = 0.0, 0.0, 0.0
# for rc in r_corners:
# for tc in theta_corners:
# v_r += self.vr2d[rc, tc]
# v_theta += self.vtheta2d[rc, tc]
# v_phi += self.vphi2d[rc, tc]
# v_r = v_r/4
# v_theta = v_theta/4
# v_phi = v_phi/4
#
# v_sph = list(map(float, [v_r/1e2, v_theta/1e2, v_phi/1e2])) # convert to SI unit (meter)
if sph:
return v_sph
v_out = self.Spherical2Cart_vector((r_in, t_in, p_in), v_sph)
if self.debug:
foo = open('velocity.log', 'a')
foo.write('%e \t %e \t %e \t %f \t %f \t %f\n' %
(x, y, z, v_out[0], v_out[1], v_out[2]))
foo.close()
return v_out
def getAbundance(self, x, y, z, config, tol=10, theta_cav=None):
# tol: the size (in AU) of the linear region between two steps
# (try to avoid "cannot find cell" problem in LIME)
# a_params = [abundance at outer region,
# fraction of outer abundance to the inner abundance,
# the ratio of the outer radius of the inner region to the infall radius]
# abundances = [3.5e-8, 3.5e-9] # inner, outer
if self.truncate != None:
if (y**2 + z**2)**0.5 > self.truncate * au_si:
return 0.0
tol = tol * au_cgs
(r_in, t_in, p_in) = self.Cart2Spherical(x, y, z)
# determine whether the cell is in the cavity
if (theta_cav != None) and (theta_cav != 0):
# using R = 10000 AU as the reference point
c0 = (10000.*au_cgs)**(-0.5)*\
np.sqrt(1/np.sin(np.radians(theta_cav))**3-1/np.sin(np.radians(theta_cav)))
# related coordinates
w = abs(r_in * np.cos(np.pi / 2 - t_in))
_z = r_in * np.sin(np.pi / 2 - t_in)
# condition for open cavity
z_cav = c0 * abs(w)**1.5
cav_con = abs(_z) > abs(z_cav)
if cav_con:
abundance = 0.0
return float(abundance)
# single negative drop case
# TODO: adopt a more generic model name, but keep backward compatability.
if (config['a_model'] == 'neg_step1') or (config['a_model']
== 'step1'):
a0 = float(config['a_params0'])
a1 = float(config['a_params1'])
a2 = float(config['a_params2'])
if (r_in - a2 * self.r_inf) > tol / 2:
abundance = a0
elif abs(r_in - a2 * self.r_inf) <= tol / 2:
abundance = a0 * a1 + (r_in - (a2 * self.r_inf - tol / 2)) * (
a0 - a0 * a1) / tol
else:
abundance = a0 * a1
elif (config['a_model'] == 'neg_step2') or (config['a_model']
== 'step2'):
a0 = float(config['a_params0'])
a1 = float(config['a_params1'])
a2 = float(config['a_params2'])
a3 = float(config['a_params3'])
a4 = float(config['a_params4'])
if (r_in - a2 * self.r_inf) > tol / 2:
abundance = a0
# linear interpolation from the outer region to the first step
elif abs(r_in - a2 * self.r_inf) <= tol / 2:
abundance = a0 * a1 + (r_in - (a2 * self.r_inf - tol / 2)) * (
a0 - a0 * a1) / tol
# first step
elif (r_in - a4 * au_cgs) > tol / 5 / 2 and (a2 * self.r_inf -
r_in) > tol / 2:
abundance = a0 * a1
# linear interpolation from the first step to the second step
elif abs(r_in - a4 * au_cgs) <= tol / 5 / 2:
abundance = a0 * a3 + (r_in - (a4 * au_cgs - tol / 5 / 2)) * (
a0 * a1 - a0 * a3) / (tol / 5)
else:
abundance = a0 * a3
elif (config['a_model'] == 'drop'):
a0 = float(config['a_params0'])
a1 = float(config['a_params1'])
a2 = float(config['a_params2'])
a3 = float(config['a_params3'])
a4 = float(config['a_params4'])
if (r_in - a2 * au_cgs) > tol / 2:
abundance = a0
# linear interpolation from the outer region to the first step
elif abs(r_in - a2 * au_cgs) <= tol / 2:
abundance = a1 + (r_in -
(a2 * au_cgs - tol / 2)) * (a0 - a1) / tol
# first step
elif (r_in - a4 * au_cgs) > tol / 5 / 2 and (a2 * au_cgs -
r_in) > tol / 2:
abundance = a1
# linear interpolation from the first step to the second step
elif abs(r_in - a4 * au_cgs) <= tol / 5 / 2:
abundance = a3 + (r_in - (a4 * au_cgs - tol / 5 / 2)) * (
a1 - a3) / (tol / 5)
else:
abundance = a3
elif (config['a_model'] == 'drop2'):
a0 = float(config['a_params0'])
a1 = float(config['a_params1'])
a2 = float(config['a_params2'])
a3 = float(config['a_params3'])
a4 = float(config['a_params4'])
if (r_in - a2 * au_cgs) > tol / 2:
abundance = a0
# linear interpolation from the outer region to the first step
elif abs(r_in - a2 * au_cgs) <= tol / 2:
abundance = a1 + (r_in -
(a2 * au_cgs - tol / 2)) * (a0 - a1) / tol
# first step
elif (r_in - a4 * au_cgs) > tol / 5 / 2 and (a2 * au_cgs -
r_in) > tol / 2:
abundance = a1
# linear interpolation from the first step to the second step
elif abs(r_in - a4 * au_cgs) <= tol / 5 / 2:
abundance = a3 + (r_in - (a4 * au_cgs - tol / 5 / 2)) * (
a1 - a3) / (tol / 5)
elif r_in >= 13 * au_cgs:
abundance = a3
else:
abundance = 1e-20
elif (config['a_model'] == 'drop3'):
a0 = float(config['a_params0']) # undelepted abundance
a1 = float(config['a_params1']) # depleted abundance
a2 = float(config['a_params2']) # evaporation temperature (K)
a3 = float(config['a_params3']) # depletion density (cm-3)
a4 = float(config['a_params4']
) # the temperature H2O starts to destory HCO+
if a4 == -1:
a4 = np.inf
temp = self.getTemperature(x, y, z)
density = self.getDensity(x, y, z) / 1e6
if (temp <= a2) and (density >= a3):
abundance = a1
elif (temp <= a4):
abundance = a0
else:
abundance = 1e-20
elif config['a_model'] == 'uniform':
abundance = float(config['a_params0'])
elif config['a_model'] == 'lognorm':
a0 = float(config['a_params0'])
a1 = float(config['a_params1'])
a2 = float(config['a_params2'])
a3 = float(config['a_params3']) # r_in for power law decrease
if r_in >= a2 * self.r_inf:
abundance = a0
elif (r_in < a2 * self.r_inf) & (r_in > a3 * au_cgs):
abundance = a0 * a1 + a0 * (1 - a1) / (
np.log10(self.r_inf * a2) - np.log10(a3 * au_cgs)) * (
np.log10(r_in) - np.log10(a3 * au_cgs))
else:
abundance = a0 * a1
elif config['a_model'] == 'powerlaw':
a0 = float(config['a_params0'])
a1 = float(config['a_params1'])
a2 = float(config['a_params2'])
a3 = float(config['a_params3'])
a4 = float(config['a_params4'])
# re-define rMin
# rmin = 100*au_cgs
rmin = self.rmin
if r_in >= a2 * self.r_inf:
abundance = a0
elif (r_in >= rmin) and (r_in < a2 * self.r_inf):
# y = Ax^a3+B
A = a0 * (1 - a1) / ((a2 * self.r_inf)**a3 - rmin**a3)
B = a0 - a0 * (1 - a1) * (a2 * self.r_inf)**a3 / (
(a2 * self.r_inf)**a3 - rmin**a3)
abundance = A * r_in**a3 + B
else:
abundance = a0 * a1
# option to cap the maximum value of abundance
if a4 > 0:
if abundance > abs(a4):
abundance = abs(a4)
elif config['a_model'] == 'powerlaw2':
a0 = float(config['a_params0'])
a1 = float(config['a_params1'])
a2 = float(config['a_params2'])
a3 = float(config['a_params3'])
a4 = float(config['a_params4'])
# re-define rMin
# rmin = 100*au_cgs
rmin = self.rmin
if r_in >= a2 * self.r_inf:
abundance = a0
elif (r_in >= rmin) and (r_in < a2 * self.r_inf):
# y = Ax^a3+B
A = a0 * (1 - a1) / ((a2 * self.r_inf)**a3 - rmin**a3)
B = a0 - a0 * (1 - a1) * (a2 * self.r_inf)**a3 / (
(a2 * self.r_inf)**a3 - rmin**a3)
abundance = A * r_in**a3 + B
else:
abundance = a0 * a1
# add the evaporation zone
# TODO: parametrize the setup
if (r_in <= 100 * au_cgs) and (r_in >= 13 * au_cgs):
abundance = 1e-10
# option to cap the maximum value of abundance
if a4 > 0:
if abundance > abs(a4):
abundance = abs(a4)
elif config['a_model'] == 'chem':
a0 = float(config['a_params0']) # peak abundance
a1 = float(config['a_params1']) # inner abundance
a2 = float(config['a_params2']) # peak radius
a3 = float(config['a_params3']) # inner decrease power
a4 = float(config['a_params4']) # outer decrease power
# radius of the evaporation front, determined by the extent of COM emission
rCOM = 100 * au_cgs
if r_in >= a2 * self.r_inf:
# y = Ax^a, a < 0
A_out = a0 / (a2 * self.r_inf)**a4
abundance = A_out * r_in**a4
elif (r_in >= rCOM) and (r_in < a2 * self.r_inf):
# y = Ax^a, a > 0
A_in = a0 / (a2 * self.r_inf)**a3
abundance = A_in * r_in**a3
else:
abundance = a1
elif config['a_model'] == 'chem2':
a0 = float(config['a_params0']) # peak abundance
a1 = float(config['a_params1']) # inner abundance
a2 = float(config['a_params2']) # inner peak radius [AU]
a3 = float(config['a_params3']) # outer peak radius [AU]
a4 = config[
'a_params4'] # inner/outer radius of the evaporation region
# radius of the evaporation front, determined by the extent of COM emission
if (a4 == '-1') or (a4
== '2.0/-2.0'): # for backward compatability
rCOM = 100 * au_cgs
rCen = 13 * au_cgs
else:
rCen = float(a4.split(',')[0]) * au_cgs
rCOM = float(a4.split(',')[1]) * au_cgs
# innerExpo, outerExpo = [float(i) for i in config['a_params4'].split('/')]
# fix the decreasing/increasing powers
innerExpo = 2.0
outerExpo = -2.0
if r_in >= a3 * au_cgs:
# y = Ax^a, a < 0
A_out = a0 / (a3 * au_cgs)**outerExpo
abundance = A_out * r_in**outerExpo
elif (r_in < a3 * au_cgs) and (r_in >= a2 * au_cgs):
abundance = a0
elif (r_in >= rCOM) and (r_in < a2 * au_cgs):
# y = Ax^a, a > 0
A_in = a0 / (a2 * au_cgs)**innerExpo
abundance = A_in * r_in**innerExpo
elif (r_in >= rCen) and (r_in < rCOM): # centrifugal radius
abundance = a1
else:
abundance = 1e-20
elif config['a_model'] == 'chem3':
a0 = float(config['a_params0']) # peak abundance
a1 = float(config['a_params1']) # inner abundance
a2 = list(map(float, config['a_params2'].split(
','))) # inner/outer radius for the maximum abundance [AU]
a3 = list(map(float, config['a_params3'].split(
','))) # inner/outer radius for the evaporation zone [AU]
a4 = list(map(float, config['a_params4'].split(
','))) # inner/outer decreasing power
# radius of the evaporation front, determined by the extent of COM emission
rEvap_inner = a3[0] * au_cgs
rEvap_outer = a3[1] * au_cgs
# test the case of a broken power law for the freeze-out zone
# In this case, there will be three values for both a2 and a4
# The input powers are stored as -
# innerExpo for all powers except for the last one
# outerExpo for the last power
# fix the decreasing/increasing powers
innerExpo = a4[:-1]
outerExpo = a4[-1]
# calculate the constants for each freeze-out zone
A = []
for i, (r_out,
pow) in enumerate(zip(a2[:-1][::-1], innerExpo[::-1])):
if i == 0:
previous_pow = 0.0
_A = (r_out * au_cgs)**(-pow)
else:
_A = _A * (r_out * au_cgs)**(previous_pow - pow)
previous_pow = pow
A.append(a0 * _A)
A = A[::-1]
if r_in >= a2[-1] * au_cgs:
# y = Ax^a, a < 0
A_out = a0 / (a2[-1] * au_cgs)**outerExpo
abundance = A_out * r_in**outerExpo
elif (r_in < a2[-1] * au_cgs) and (r_in >= a2[-2] * au_cgs):
abundance = a0
# freeze-out zone
elif (r_in >= rEvap_outer) and (r_in < a2[-2] * au_cgs):
# y = Ax^a, a > 0
# determine which freeze-out zone
ind_zone = a2[:-1].index(
min([
rr for i, rr in enumerate(a2[:-1])
if rr * au_cgs - r_in > 0
]))
A_in = A[ind_zone]
Expo = innerExpo[ind_zone]
# A_in = a0 / (a2[0]*au_cgs)**innerExpo
# abundance = A_in * r_in**innerExpo
abundance = A_in * r_in**Expo
elif (r_in >= rEvap_inner) and (r_in <
rEvap_outer): # centrifugal radius
abundance = a1
else:
abundance = 0.0
elif config[
'a_model'] == 'chem4': # mostly for CS, which has two evaporation fronts, one for CO, and one for CS.
a0 = float(config['a_params0']) # peak abundance
a1 = list(map(
float, config['a_params1'].split(','))) # TWO inner abundance
a2 = list(map(float, config['a_params2'].split(
','))) # inner/outer radius for the maximum abundance [AU]
a3 = list(
map(float, config['a_params3'].split(','))
) # inner/middle/outer radius for the evaporation zone [AU]
a4 = list(map(float, config['a_params4'].split(
','))) # inner/outer decreasing power
# radius of the evaporation front, determined by the extent of COM emission
rEvap_inner = a3[0] * au_cgs
rEvap_middle = a3[1] * au_cgs
rEvap_outer = a3[2] * au_cgs
# test the case of a broken power law for the freeze-out zone
# In this case, there will be three values for both a2 and a4
# The input powers are stored as -
# innerExpo for all powers except for the last one
# outerExpo for the last power
# fix the decreasing/increasing powers
innerExpo = a4[:-1]
outerExpo = a4[-1]
# calculate the constants for each freeze-out zone
A = []
for i, (r_out,
pow) in enumerate(zip(a2[:-1][::-1], innerExpo[::-1])):
if i == 0:
previous_pow = 0.0
_A = (r_out * au_cgs)**(-pow)
else:
_A = _A * (r_out * au_cgs)**(previous_pow - pow)
previous_pow = pow
A.append(a0 * _A)
A = A[::-1]
if r_in >= a2[-1] * au_cgs:
# y = Ax^a, a < 0
A_out = a0 / (a2[-1] * au_cgs)**outerExpo
abundance = A_out * r_in**outerExpo
elif (r_in < a2[-1] * au_cgs) and (r_in >= a2[-2] * au_cgs):
abundance = a0
# freeze-out zone
elif (r_in >= rEvap_outer) and (r_in < a2[-2] * au_cgs):
# y = Ax^a, a > 0
# determine which freeze-out zone
ind_zone = a2[:-1].index(
min([
rr for i, rr in enumerate(a2[:-1])
if rr * au_cgs - r_in > 0
]))
A_in = A[ind_zone]
Expo = innerExpo[ind_zone]
# A_in = a0 / (a2[0]*au_cgs)**innerExpo
# abundance = A_in * r_in**innerExpo
abundance = A_in * r_in**Expo
elif (r_in >= rEvap_middle) and (
r_in < rEvap_outer): # 1st evaporation zone
abundance = a1[1]
elif (r_in >= rEvap_inner) and (
r_in < rEvap_middle): # 1st evaporation zone
abundance = a1[0]
else:
abundance = 0.0
elif config['a_model'] == 'interp':
filename = config['a_params0']
adata = io.ascii.read(filename, names=['radius', 'abundance'])
f_a = interp1d(adata['radius'], adata['abundance'])
if (r_in < adata['radius'].min() * au_cgs) or (
r_in > adata['radius'].max() * au_cgs):
abundance = 1e-40
else:
abundance = f_a(r_in / au_cgs)
else:
print('Cannot recognize the input a_model', config['a_model'])
return False
if self.debug:
foo = open('abundance.log', 'a')
foo.write('%e \t %e \t %e \t %f\n' % (x, y, z, abundance))
foo.close()
# uniform abundance
# abundance = 3.5e-9
return float(abundance)
def radialSmoothing(self,
x,
y,
z,
variable,
kernel='boxcar',
smooth_factor=2,
config=None):
# convert the coordinates from Cartian to spherical
(r_in, t_in, p_in) = self.Cart2Spherical(x, y, z)
# r-array for smoothing
smoothL = r_in / smooth_factor * au_cgs
r_arr = np.arange(r_in - smoothL / 2, r_in + smoothL / 2,
smoothL / 50) # 50 bins
# setup the smoothing kernel
# it is not really a smoothing kernel, more like a local mean
def averageKernel(kernel, r, var):
if kernel == 'boxcar':
out = np.mean(var)
return out
# run the corresponding look-up function for the desired variable
var_arr = np.empty_like(r_arr)
for i, r in enumerate(r_arr):
(xd, yd, zd) = self.Spherical2Cart(r, t_in, p_in)
if variable == 'abundance':
var_arr[i] = self.getAbundance(xd / 1e2, yd / 1e2, zd / 1e2,
config)
var = averageKernel(kernel, r, var_arr)
return float(var)
def temp_hyperion(rtout,outdir, bb_dust=False):
import numpy as np
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import os
from hyperion.model import ModelOutput
import astropy.constants as const
from matplotlib.colors import LogNorm
# seaborn colormap
import seaborn.apionly as sns
# constants setup
AU = const.au.cgs.value
# misc variable setup
print_name = os.path.splitext(os.path.basename(rtout))[0]
m = ModelOutput(rtout)
q = m.get_quantities()
# get the grid info
ri, thetai = q.r_wall, q.t_wall
rc = 0.5*( ri[0:len(ri)-1] + ri[1:len(ri)] )
thetac = 0.5*( thetai[0:len(thetai)-1] + thetai[1:len(thetai)] )
# get the temperature profile
# and average across azimuthal angle
# temperature array in [phi, theta, r]
temp = q['temperature'][0].array.T
temp2d = np.sum(temp**2, axis=2)/np.sum(temp, axis=2)
temp2d_exp = np.hstack((temp2d,temp2d,temp2d[:,0:1]))
thetac_exp = np.hstack((thetac-np.pi/2, thetac+np.pi/2, thetac[0]-np.pi/2))
mag = 1
fig = plt.figure(figsize=(mag*8,mag*6))
ax = fig.add_subplot(111, projection='polar')
# cmap = sns.cubehelix_palette(light=1, as_cmap=True)
cmap = plt.cm.CMRmap
im = ax.pcolormesh(thetac_exp, rc/AU, temp2d_exp, cmap=cmap, norm=LogNorm(vmin=5, vmax=100))
#
# cmap = plt.cm.RdBu_r
# im = ax.pcolormesh(thetac_exp, np.log10(rc/AU), temp2d_exp/10, cmap=cmap, norm=LogNorm(vmin=0.1, vmax=10))
#
print temp2d_exp.min(), temp2d_exp.max()
im.set_edgecolor('face')
ax.set_xlabel(r'$\rm{Polar\,angle\,(Degree)}$',fontsize=20)
# ax.set_ylabel(r'$\rm{Radius\,(AU)}$',fontsize=20, labelpad=-140, color='grey')
# ax.set_ylabel('',fontsize=20, labelpad=-140, color='grey')
ax.tick_params(labelsize=16)
ax.tick_params(axis='y', colors='grey')
ax.set_yticks(np.hstack((np.arange(0,(int(max(rc)/AU/10000.)+1)*10000, 10000),max(rc)/AU)))
#
# ax.set_yticks(np.log10(np.array([1, 10, 100, 1000, 10000, max(rc)/AU])))
#
ax.set_yticklabels([])
ax.grid(True, color='LightGray', linewidth=1.5)
# ax.grid(True, color='k', linewidth=1)
ax.set_xticklabels([r'$\rm{90^{\circ}}$',r'$\rm{45^{\circ}}$',r'$\rm{0^{\circ}}$',r'$\rm{-45^{\circ}}$',\
r'$\rm{-90^{\circ}}$',r'$\rm{-135^{\circ}}$',r'$\rm{180^{\circ}}$',r'$\rm{135^{\circ}}$'])
cb = fig.colorbar(im, pad=0.1)
cb.ax.set_ylabel(r'$\rm{Averaged\,Temperature\,(K)}$',fontsize=20)
cb.set_ticks([5,10,20,30,40,50,60,70,80,90,100])
cb.set_ticklabels([r'$\rm{5}$',r'$\rm{10}$',r'$\rm{20}$',r'$\rm{30}$',r'$\rm{40}$',r'$\rm{50}$',r'$\rm{60}$',r'$\rm{70}$',r'$\rm{80}$',r'$\rm{90}$',r'$\rm{>100}$'])
#
# cb.ax.set_ylabel(r'$\rm{log(T/10)}$',fontsize=20)
# cb.set_ticks([0.1, 10**-0.5, 1, 10**0.5, 10])
# cb.set_ticklabels([r'$\rm{-1}$',r'$\rm{-0.5}$',r'$\rm{0}$',r'$\rm{0.5}$',r'$\rm{\geq 1}$'])
#
cb_obj = plt.getp(cb.ax.axes, 'yticklabels')
plt.setp(cb_obj,fontsize=20)
# fix the tick label font
ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',size=20)
for label in ax.get_yticklabels():
label.set_fontproperties(ticks_font)
fig.savefig(outdir+print_name+'_temperature.png', format='png', dpi=300, bbox_inches='tight')
fig.clf()
# Plot the radial temperature profile
fig = plt.figure(figsize=(12,9))
ax = fig.add_subplot(111)
plot_grid = [0,99,199]
label_grid = [r'$\rm{outflow}$', r'$\rm{45^{\circ}}$', r'$\rm{midplane}$']
alpha = np.linspace(0.3,1.0,len(plot_grid))
color_list = [[0.8507598215729224, 0.6322174528970308, 0.6702243543099417],\
[0.5687505862870377, 0.3322661256969763, 0.516976691731939],\
[0.1750865648952205, 0.11840023306916837, 0.24215989137836502]]
for i in plot_grid:
temp_rad, = ax.plot(np.log10(rc/AU), np.log10(temp2d[:,i]),'-',color=color_list[plot_grid.index(i)],\
linewidth=2, markersize=3,label=label_grid[plot_grid.index(i)])
# plot the theoretical prediction for black body dust without considering the extinction
if bb_dust == True:
from hyperion.model import Model
sigma = const.sigma_sb.cgs.value
lsun = const.L_sun.cgs.value
dum = Model()
dum.use_sources(rtout)
L_cen = dum.sources[0].luminosity/lsun
t_bbdust = (L_cen*lsun/(16*np.pi*sigma*rc**2))**(0.25)
temp_bbdust, = ax.plot(np.log10(rc/AU), np.log10(t_bbdust), '--', color='r', linewidth=2.5,label=r'$\rm{blackbody\,dust}$')
ax.legend(loc='upper right', numpoints=1, fontsize=24)
ax.set_xlabel(r'$\rm{log\,R\,(AU)}$',fontsize=24)
ax.set_ylabel(r'$\rm{log\,T\,(K)}$',fontsize=24)
[ax.spines[axis].set_linewidth(2) for axis in ['top','bottom','left','right']]
ax.minorticks_on()
ax.tick_params('both',labelsize=24,width=2,which='major',pad=15,length=5)
ax.tick_params('both',labelsize=24,width=2,which='minor',pad=15,length=2.5)
# fix the tick label font
ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',size=24)
for label in ax.get_xticklabels():
label.set_fontproperties(ticks_font)
for label in ax.get_yticklabels():
label.set_fontproperties(ticks_font)
ax.set_ylim([0,4])
fig.gca().set_xlim(left=np.log10(0.05))
# ax.set_xlim([np.log10(0.8),np.log10(10000)])
fig.savefig(outdir+print_name+'_temp_radial.pdf',format='pdf',dpi=300,bbox_inches='tight')
fig.clf()
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()
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 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()
import matplotlib.pyplot as plt
from hyperion.model import ModelOutput
from hyperion.util.constants import pc
m = ModelOutput("class2_sed.rtout")
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
# Total SED
wav, nufnu = m.get_sed(inclination=0, aperture=-1, distance=300 * pc)
ax.loglog(wav, nufnu, color="black", lw=3, alpha=0.5)
# Direct stellar photons
wav, nufnu = m.get_sed(inclination=0, aperture=-1, distance=300 * pc, component="source_emit")
ax.loglog(wav, nufnu, color="blue")
# Scattered stellar photons
wav, nufnu = m.get_sed(inclination=0, aperture=-1, distance=300 * pc, component="source_scat")
ax.loglog(wav, nufnu, color="teal")
# Direct dust photons
wav, nufnu = m.get_sed(inclination=0, aperture=-1, distance=300 * pc, component="dust_emit")
ax.loglog(wav, nufnu, color="red")
# Scattered dust photons
wav, nufnu = m.get_sed(inclination=0, aperture=-1, distance=300 * pc, component="dust_scat")
ax.loglog(wav, nufnu, color="orange")
print "No grid reference file found!" oldparams = ['name', 'folder','T','M_sun','env_rmax','env_rmin','disk','disk_mass','disk_rmax',\ 'cav_theta','innerdustfile','outerdustfile','beta','L_sun','env_mass','env_power'] oldtypes = ['<S30','<S30','f8','f8','f8','f8','<S30','f8','f8','f8','<S30','<S30','f8','f8','f8','f8'] newgrid = Table(names=oldparams+['ext','inc']+names,dtype=oldtypes+['f8','f8']+['f8' for val in names]) # calculate value for each band for each model, extinction value and inclination for i in range(len(grid)): # load model fname = folder[0]+grid['name'][i]+'.rtout' if i%10 ==0: print "Model: ",fname if os.path.exists(fname):# and grid['env_rmax'][i]==5000.0 and grid['disk_mass'][i]==0.003: #print "Model found!" mo = ModelOutput(fname) # load sed from model sed = mo.get_sed(aperture=-1, inclination='all', distance=100.*pc,units='Jy') for extinction in extinctions: # calculate optical depth tau_ext1 = Chi(sed.wav)/Chi(0.550)/1.086 tau = tau_ext1*extinction #print "tau,",tau # calculate extinction for all inclinations ext = np.array([np.exp(-tau) for shape in range(sed.val.shape[0])]) #print "ext,",ext
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)
m.set_spherical_polar_grid(r, t, p)
dens = zeros((nr - 1, nt - 1, np - 1)) + 1.0e-17
m.add_density_grid(dens, d)
source = m.add_spherical_source()
source.luminosity = lsun
source.radius = rsun
source.temperature = 4000.
m.set_n_photons(initial=1000000, imaging=0)
m.set_convergence(True, percentile=99., absolute=2., relative=1.02)
m.write("test_spherical.rtin")
m.run("test_spherical.rtout", mpi=False)
n = ModelOutput('test_spherical.rtout')
grid = n.get_quantities()
temp = grid.quantities['temperature'][0]
for i in range(9):
plt.imshow(temp[i,:,:],origin="lower",interpolation="nearest", \
vmin=temp.min(),vmax=temp.max())
plt.colorbar()
plt.show()
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.)
wav = np.loadtxt('kmh94_3.1_full.wav')
Chi = interp1d(wav,chi,kind='linear')
# now load up the grid
name = ['IRAS20050']
folder = ['/cardini3/mrizzo/2012SOFIA/SED_Models/hyperion/IRAS20050_new/']
filename = folder[0]+name[0]+"_"+target+".grid.dat"
if os.path.exists(filename):
grid = pickle.load(open(filename,'r'))
# for each fit that is desired, search for the right line in the grid
fitnames = grid.group_by('name')
for fitkey,fitname in zip(fitnames.groups.keys,fitnames.groups):
name = fitkey['name']
#print name
fname = folder[0]+name+'.rtout'
mo = ModelOutput(fname)
sed = mo.get_sed(aperture=-1, inclination='all', distance=dist,units='Jy')
if name in plotlist:
# sort table according to chi2
fitname.sort('chi2')
# the first line is then the best fit. let's select the extinction
extinction = fitname['ext'][0]
print "extinction = ",extinction
# inclination
angles=np.arccos(np.linspace(0,1.,20))*180./np.pi
incs=angles[::-1]
#incs = [0.,10.,20.,30.,40.,50.,60.,70.,80.,90.]
import os
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
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
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
from hyperion.model import ModelOutput
from hyperion.util.constants import pc
# Open the model - we specify the name without the .rtout extension
m = ModelOutput('test_disc.rtout')
# Create the plot
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
# 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.
wav, nufnu = m.get_sed(inclination=0, aperture=-1, distance=300 * pc)
# Plot the SED. The loglog command is similar to plot, but automatically
# sets the x and y axes to be on a log scale.
ax.loglog(wav, nufnu)
# Add some axis labels (we are using LaTeX here)
ax.set_xlabel(r'$\lambda$ [$\mu$m]')
ax.set_ylabel(r'$\lambda F_\lambda$ [ergs/s/cm$^2$]')
# Set view limits
ax.set_xlim(0.1, 5000.)
ax.set_ylim(1.e-15, 2.e-10)
ax_top = grid[i].twiny()
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
g2d = 100
mmw = 2.37
mh = const.m_p.cgs.value + const.m_e.cgs.value
AU = const.au.cgs.value
model = np.arange(99,133).astype('str')
# color map
cmap = plt.cm.viridis
color_array = [cmap(np.linspace(0, 0.9, len(model))[i]) for i in range(len(model))]
fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
for i in range(len(model)):
m = ModelOutput('/home/bettyjo/yaolun/hyperion/bhr71/controlled/model'+model[i]+'/model'+model[i]+'.rtout')
q = m.get_quantities()
r = q.r_wall
rc = 0.5*(r[0:len(r)-1]+r[1:len(r)])
rho = q['density'][0].array
rho2d = np.sum(rho**2,axis=0)/np.sum(rho,axis=0)
plt.plot(np.log10(rc[rc > 0.14*AU]/AU), np.log10(rho2d[199,rc > 0.14*AU]/g2d/mmw/mh)-0.1*i, '-',
color=color_array[i], linewidth=1)
ax.set_ylim([-2,9])
ax.set_xlabel(r'$\rm{log(Radius)\,(AU)}$',fontsize=20)
ax.set_ylabel(r'$\rm{log(Dust\,Density)\,(cm^{-3})}$',fontsize=20)
[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)
import numpy as np
from hyperion.model import ModelOutput
import matplotlib.pyplot as plt
from yt.mods import write_bitmap, ColorTransferFunction
plt.rcParams['font.family'] = 'Arial'
# Read in model from Hyperion
m = ModelOutput('pla704850_lev7_129.rtout')
grid = m.get_quantities()
# Convert quantities to yt
pf = grid.to_yt()
# Instantiate the ColorTransferfunction.
tmin, tmax = 1.3, 2.3
tf_temp = ColorTransferFunction((tmin, tmax))
dmin, dmax = -20, -16
tf_dens = ColorTransferFunction((dmin, dmax))
# Set up the camera parameters: center, looking direction, width, resolution
c = (pf.domain_right_edge + pf.domain_left_edge) / 2.0
L = np.array([1.0, 1.0, 1.0])
W = 0.7 / pf["unitary"]
N = 512
# Create camera objects
cam_temp = pf.h.camera(c, L, W, N, tf_temp,
#Script showing how to extract some rtout files that were run on an
#octree format
from __future__ import print_function
from hyperion.model import ModelOutput
from hyperion.grid.yt3_wrappers import find_order
import astropy.units as u
import numpy as np
run = '/home/desika.narayanan/pd_git/tests/SKIRT/gizmo_mw_zoom/pd_skirt_comparison.134.rtout.sed'
m = ModelOutput(run)
oct = m.get_quantities()
#ds = oct.to_yt()
#ripped from hyperion/grid/yt3_wrappers.py -- we do this because
#something about load_octree in yt4.x is only returning the first cell
grid = oct
order = find_order(grid.refined)
refined = grid.refined[order]
quantities = {}
for field in grid.quantities:
quantities[('gas', field)] = np.atleast_2d(grid.quantities[field][0][order][~refined]).transpose()
specific_energy = quantities['gas','specific_energy']*u.erg/u.s/u.g
dust_temp = quantities['gas','temperature']*u.K
dust_density = quantities['gas','density']*u.g/u.cm**3
oldparams = ['name', 'folder','T','M_sun','env_rmax','env_rmin','disk','disk_mass','disk_rmax',\ 'cav_theta','innerdustfile','outerdustfile','beta','L_sun','env_mass','env_power'] oldtypes = ['<S30','<S30','f8','f8','f8','f8','<S30','f8','f8','f8','<S30','<S30','f8','f8','f8','f8'] newgrid = Table(names=oldparams+['ext','inc','chi2','chi2_old','n'],dtype=oldtypes+['f8','f8','f8','f8','i8']) # calculate chi squared metric for each run of the grid #len(grid)-150, for i in range(len(grid)): #for i in range(1): # load model fname = folder[0]+grid['name'][i]+'.rtout' if i%10 ==0: print "Model: ",fname if os.path.exists(fname):# and grid['env_rmax'][i]==5000.0 and grid['disk_mass'][i]==0.003: #print "Model found!" mo = ModelOutput(fname) # load sed from model sed = mo.get_sed(aperture=-1, inclination='all', distance=distance(target),units='Jy') for extinction in extinctions: # calculate optical depth tau_ext1 = Chi(sed.wav)/Chi(0.550)/1.086 tau = tau_ext1*extinction #print "tau,",tau # calculate extinction for all inclinations ext = np.array([np.exp(-tau) for shape in range(sed.val.shape[0])]) #print "ext,",ext