def setUp(self):
super(VelocitySpaceTest, self).setUp()
ds = load(COSMO_PLUS_SINGLE)
line_list = ['H I 1216', 'H I 1026']
make_simple_ray(ds, start_position=ds.domain_left_edge,
end_position=ds.domain_right_edge,
data_filename='ray.h5',
lines=line_list, ftype='gas')
self.line_list = line_list
def test_light_ray_redshift_coverage_grid(self):
"""
Tests to assure a light ray covers the full redshift range appropriate
for that comoving line of sight distance. Was not always so!
"""
ds = load(COSMO_PLUS_SINGLE)
ray = make_simple_ray(ds, start_position=ds.domain_left_edge, end_position=ds.domain_right_edge, lines=['H'])
assert_almost_equal(ray.r['redshift'][0], 6.99900695e-03, decimal=8)
assert_almost_equal(ray.r['redshift'][-1], -1.08961751e-02, decimal=8)
def test_light_ray_redshift_coverage(self):
"""
Tests to assure a light ray covers the full redshift range appropriate
for that comoving line of sight distance. Was not always so!
"""
ds = load(GIZMO_COSMO_SINGLE)
ray = make_simple_ray(ds, start_position=ds.domain_left_edge, end_position=ds.domain_right_edge, lines=['H'])
assert_almost_equal(ray.r['redshift'][0], 0.00489571, decimal=8)
assert_almost_equal(ray.r['redshift'][-1], -0.00416831, decimal=8)
def test_light_ray_redshift_monotonic(self):
"""
Tests to assure a light ray redshift decreases monotonically
when ray extends outside the domain.
"""
ds = load(COSMO_PLUS_SINGLE)
ray = make_simple_ray(ds, start_position=ds.domain_center,
end_position=ds.domain_center+ds.domain_width)
assert((np.diff(ray.data['redshift']) < 0).all())
def generate_spectrum_plot_data(index_start, index_end, output):
ds = yt.load('/nobackupp2/ibutsky/simulations/romulusC/romulusC.%06d'%(output))
ion_list = ['H I', 'C II', 'C III', 'C IV', 'Si II', 'Si III', 'Si IV', 'O VI']
trident.add_ion_fields(ds, ions=ion_list)
ds.add_field(("gas", "particle_H_nuclei_density"), function = ytf._H_nuc, \
particle_type = True, force_override = True, units = "cm**(-3)")
y_start = ds.domain_left_edge[2]
y_end = ds.domain_right_edge[2]
center = rom.get_romulusC_center(output)
center_x = center[0]
center_y = center[1]
center_z = center[2]
print(center_x, center_y, center_z)
ray_id, x_list, z_list = np.loadtxt('/nobackupp2/ibutsky/data/YalePaper/spectra/coordinate_list.dat',\
skiprows = 1, unpack=True)
for i in range(index_start, index_end):
h5file = h5.File('/nobackup/ibutsky/data/YalePaper/romulusC.%06d_sightline_%i_plot_data.h5'%(output, i), 'w')
# note: the plus is important. x_list, z_list counted from center
x = ((x_list[i] + center_x) / ds.length_unit).d
z = ((z_list[i] + center_z) / ds.length_unit).d
print(x_list[i], z_list[i], ds.length_unit)
ycen = (center_y / ds.length_unit).d
# y from -2.5 mpc of center to 2.5 mpc from center
ylen = (2500. / ds.length_unit).d
ray_start = [x, ycen-ylen , z]
ray_end = [x, ycen+ylen, z]
ad = ds.r[ray_start:ray_end]
ray = trident.make_simple_ray(ds,
start_position = ray_start,
end_position = ray_end,
lines=ion_list)
ad_ray = ray.all_data()
field_list = ['y', 'temperature', 'density', 'metallicity', 'dl']
source_list = [ad, ad, ad, ad, ad_ray]
unit_list = ['kpc', 'K', 'g/cm**3', 'Zsun', 'cm']
yt_ion_list = ipd.generate_ion_field_list(ion_list, 'number_density', full_name = False)
yt_ion_list[0] = 'H_number_density'
field_list = np.append(field_list, yt_ion_list)
for j in range(len(yt_ion_list)):
unit_list.append('cm**-3')
source_list.append(ad_ray)
for field,source,unit in zip(field_list, source_list, unit_list):
if field not in h5file.keys():
h5file.create_dataset(field, data = source[('gas', field)].in_units(unit))
h5file.flush()
h5file.create_dataset('y_lr', data = ad_ray['y'].in_units('kpc'))
h5file.flush()
print("saved sightline data %i\n"%(i))
def generate_trident_spectrum(ds, line_list, ray_start, ray_end, spec_name, lambda_rest, vpos):
import trident
print("Generating trident spectrum...")
# Generate ray through box
TINIT = time.time()
ray = trident.make_simple_ray(ds,
start_position=ray_start,
end_position=ray_end,
data_filename="ray.h5",
lines=line_list,
ftype='PartType0')
print('Trident spectrum done [t=%g s]'%(np.round(time.time()-TINIT,2)))
#sg = trident.SpectrumGenerator(lambda_min=lambda_min.value, lambda_max=lambda_max, n_lambda=Nbins)
sg = trident.SpectrumGenerator('COS-G130M') # convolves with COS line spread fcn, gives COS resolution
with h5py.File('./spectra/{}.h5'.format(spec_name), 'w') as hf:
for i in range(len(line_list)):
line = line_list[i]
l_rest = lambda_rest[i]
print('Saving data for line ' + line)
name = line.replace(' ', '_')
sg.make_spectrum(ray, lines=[line])
# Get fields and convert wavelengths to velocities. Note that the velocities are negative compared to pygad!
taus = np.array(sg.tau_field)
fluxes = np.array(sg.flux_field)
wavelengths = np.array(sg.lambda_field)
velocities = wave_to_vel(wavelengths, l_rest, c, ds.current_redshift)
plt.plot(velocities, fluxes)
plt.axvline(vpos, linewidth=1, c='k')
plt.xlabel('Velocity (km/s)')
plt.ylabel('Flux')
plt.savefig('./plots/'+spec_name+'_'+name+'.png')
plt.clf()
#Save spectrum to hdf5 format
hf.create_dataset(name+'_flux', data=np.array(fluxes))
hf.create_dataset(name+'_tau', data=np.array(taus))
sigma_noise = 1.0/snr
noise = np.random.normal(0.0, sigma_noise, len(wavelengths))
#Save spectrum to hdf5 format
hf.create_dataset('velocity', data=np.array(velocities))
hf.create_dataset('wavelength', data=np.array(wavelengths))
hf.create_dataset('noise', data=np.array(noise))
del ray; gc.collect()
return
def generate_random_rays(ds, halo_center, **kwargs):
'''
generate some random rays
'''
low_impact = kwargs.get("low_impact", 10.)
high_impact = kwargs.get("high_impact", 200.)
Nrays = kwargs.get("Nrays",50)
haloname = kwargs.get("haloname","somehalo")
# line_list = kwargs.get("line_list", ['H I 1216', 'Si II 1260', 'C II 1334', 'Mg II 2796', 'C III 977', 'Si III 1207','C IV 1548', 'O VI 1032'])
line_list = kwargs.get("line_list", ['H I 1216', 'Si II 1260', 'C II 1335', 'C III 977', 'Si III 1207','C IV 1548', 'O VI 1032'])
## for now, assume all are z-axis
axis = "z"
np.random.seed(17)
impacts = np.random.uniform(low=low_impact, high=high_impact, size=Nrays)
angles = np.random.uniform(low=0, high=2*pi, size=Nrays)
out_ray_basename = ds.basename + "_ray_" + axis
for i in range(Nrays):
this_out_ray_basename = out_ray_basename + "_imp"+"{:05.1f}".format(impacts[i]) + \
"_ang"+"{:4.2f}".format(angles[i])
out_ray_name = this_out_ray_basename + ".h5"
out_fits_name = "hlsp_misty_foggie_"+haloname+"_"+ds.basename.lower()+"_i"+"{:05.1f}".format(impacts[i]) + \
"-a"+"{:4.2f}".format(angles[i])+"_v2_los.fits"
rs, re = get_ray_endpoints(ds, halo_center, impact=impacts[i], angle=angles[i], axis=axis)
rs = ds.arr(rs, "code_length")
re = ds.arr(re, "code_length")
ray = ds.ray(rs, re)
ray.save_as_dataset(out_ray_name)
out_tri_name = this_out_ray_basename + "_tri.h5"
triray = trident.make_simple_ray(ds, start_position=rs.copy(),
end_position=re.copy(),
data_filename=out_tri_name,
lines=line_list,
ftype='gas')
ray_start = triray.light_ray_solution[0]['start']
ray_end = triray.light_ray_solution[0]['end']
filespecout_base = this_out_ray_basename + '_spec'
print ray_start, ray_end, filespecout_base
hdulist = MISTY.write_header(triray,start_pos=ray_start,end_pos=ray_end,
lines=line_list, impact=impacts[i], angle=angles[i])
tmp = MISTY.write_parameter_file(ds,hdulist=hdulist)
for line in line_list:
sg = MISTY.generate_line(triray,line,write=True,hdulist=hdulist)
filespecout = filespecout_base+'_'+line.replace(" ", "_")+'.png'
## if we write our own plotting routine, we can overplot the spectacle fits
sg.plot_spectrum(filespecout,flux_limits=(0.0,1.0))
MISTY.write_out(hdulist,filename=out_fits_name)
def get_spec(ds, halo_center, rho_ray):
proper_box_size = get_proper_box_size(ds)
line_list = ['H', 'O', 'C', 'N', 'Si']
# big rectangle box
ray_start = [
halo_center[0] - 250. / proper_box_size,
halo_center[1] + 71. / proper_box_size,
halo_center[2] - 71. / proper_box_size
]
ray_end = [
halo_center[0] + 250. / proper_box_size,
halo_center[1] + 71. / proper_box_size,
halo_center[2] - 71. / proper_box_size
]
ray = trident.make_simple_ray(ds,
start_position=ray_start,
end_position=ray_end,
data_filename="ray.h5",
lines=line_list,
ftype='gas')
sg = trident.SpectrumGenerator(lambda_min=1020,
lambda_max=1045,
dlambda=0.002)
trident.add_ion_fields(ds, ions=['C IV', 'O VI', 'H I', 'Si III'])
sg.make_spectrum(ray, lines=line_list)
sg.plot_spectrum('spec_final.png')
sg.save_spectrum('spec_final.txt')
# this makes for convenient field extraction from the ray
ad = ray.all_data()
dl = ad["dl"] # line element length
out_dict = {
'nhi': np.sum(ad['H_number_density'] * dl),
'no6': np.sum(ad['O_p5_number_density'] * dl),
'nsi3': np.sum(ad['Si_p2_number_density'] * dl)
}
print np.sum(ad['H_number_density'] * dl)
print np.sum(ad['O_p5_number_density'] * dl)
print np.sum(ad['Si_p2_number_density'] * dl)
return ray, out_dict
def calculateSpectra(runName, savefolder, file_list):
filePrefix = '../Files/' + runName + '/KH_hdf5_chk_'
for chk_num in file_list:
file_name = filePrefix + chk_num
dataset = yt.load(file_name)
dataset.add_field(('gas', 'metallicity'),
function=_metallicity,
display_name="Metallicity",
units='Zsun')
#make a ray object
ray = tri.make_simple_ray(dataset,
redshift=0.0,
start_position=ray_start,
end_position=ray_end,
lines=line_list,
ftype='gas')
#plot and save density projection to look at the path of the ray
p = yt.ProjectionPlot(dataset, 'z', 'density', origin='native')
p.annotate_ray(ray)
p.save(savefolder + 'projection' + chk_num + '.png')
print("Saved Projection images as " + savefolder + "/projection" +
chk_num + ".png")
#create a spectrumGenerator; you may make your own spectrograph settings
sg = tri.SpectrumGenerator(
lambda_min=500, lambda_max=1600, dlambda=0.01
) #uses default spectrograph settings for "Cosmic Origins Spectrograph"
sg.make_spectrum(ray, lines=line_list)
#save and plot the spectrum
sg.save_spectrum(savefolder + 'spec_raw' + chk_num + '.h5')
sg.plot_spectrum(savefolder + 'spec_raw' + chk_num + '.png',
features=line_features,
title=title + chk_num,
flux_limits=(0, 1.5))
print("Saved Spectrum as " + savefolder + "spec_raw" + chk_num +
"_NV.h5")
print("Saved Spectrum image as " + savefolder + "spec_raw" + chk_num +
"_NV.png")
print("Finished with " + runName)
def _get_coldens_helper(dsparamsvectorions):
try:
ds = dsparamsvectorions[0]
scanparams = dsparamsvectorions[1]
vector = dsparamsvectorions[2]
ions = dsparamsvectorions[3]
print(str(current_process()))
ident = str(current_process()).split(",")[0]
if ident[-2:] == "ss":
ident = ""
else:
ident = ident.split("-")[1]
start = vector[5:8]
end = vector[8:11]
ray = trident.make_simple_ray(ds,
start_position=start,
end_position=end,
data_filename="ray"+ident+".h5",
fields = [('gas',"metallicity")],
ftype='gas')
trident.add_ion_fields(ray,ions)
field_data = ray.all_data()
for i in range(len(ions)):
ion = ions[i]
cdens = np.sum(field_data[("gas",ion_to_field_name(ion))] * field_data['dl'])
#outcdens = np.sum((field_data['radial_velocity']>0)*field_data[ion_to_field_name(ion)]*field_data['dl'])
#incdens = np.sum((field_data['radial_velocity']<0)*field_data[ion_to_field_name(ion)]*field_data['dl'])
vector[11+i] = cdens
#vector[12+3*i+1] = outcdens
#vector[12+3*i+2] = incdens
Z = np.average(field_data[('gas',"metallicity")],weights=field_data['dl'])
vector[-1] = Z
if _platform == 'darwin':
foldername = "/Users/claytonstrawn/Desktop/astroresearch/code/ready_for_pleiades/quasarlines"
else:
foldername = "/u/cstrawn/quasarlines/galaxy_catalogs/"
except Exception:
logging.exception("failed")
try:
os.remove(foldername+"/"+"ray"+ident+".h5")
except:
pass
print("vector = "+str(vector))
return vector
def test_create_simple_grid_ray_with_lines():
"""
Test to create a simple ray from a grid dataset using the lines kwargs
Then generate a spectrum from it with those lines included.
"""
dirpath = tempfile.mkdtemp()
filename = os.path.join(dirpath, 'ray.h5')
ds = tri.make_onezone_dataset()
ray_start = ds.arr([0., 0., 0.], 'unitary')
ray_end = ds.arr([1., 1., 1.], 'unitary')
ray = tri.make_simple_ray(ds,
start_position=ray_start,
end_position=ray_end,
data_filename=filename,
lines=['H', 'C IV'],
ftype='gas')
sg = tri.SpectrumGenerator(lambda_min=1000, lambda_max=1400., dlambda=0.1)
sg.make_spectrum(ray, lines=['H', 'C IV'])
sg.plot_spectrum(os.path.join(dirpath, 'spec.png'))
shutil.rmtree(dirpath)
def test_absorption_spectrum_with_continuum(self):
"""
This test generates an absorption spectrum then does a default fit.
Uses the example in the docs as a template:
https://trident.readthedocs.io/en/latest/absorption_spectrum_fit.html
"""
ds = load(ISO_GALAXY)
line_list = ['O VI']
ray = make_simple_ray(ds,
start_position=ds.domain_left_edge,
end_position=ds.domain_right_edge,
data_filename='ray.h5',
lines=line_list,
ftype='gas')
sg = SpectrumGenerator('COS')
sg.make_spectrum(ray, lines=line_list)
wavelength = sg.lambda_field
flux = sg.flux_field
OVI_parameters = {
'name': 'OVI',
'f': [.1325, .06580],
'Gamma': [4.148E8, 4.076E8],
'wavelength': [1031.9261, 1037.6167],
'numLines': 2,
'maxN': 1E17,
'minN': 1E11,
'maxb': 300,
'minb': 1,
'maxz': 6,
'minz': 0,
'init_b': 20,
'init_N': 1E12
}
speciesDicts = {'OVI': OVI_parameters}
orderFits = ['OVI']
fitted_lines, fitted_flux = generate_total_fit(wavelength, flux,
orderFits, speciesDicts)
def create_misty_spectrum():
my_line_list = ['H I 1216', 'C II 1335', 'C IV 1548', 'O VI 1032']
if len(args.ray) == 0:
## read in example data
ds = yt.load(args.sim)
print "ray start and end are hardcoded and should be passed in !!!!!!"
## create ray start and end
ray_start = [1, 0, 1]
ray_end = [1, 2, 1]
ray = trident.make_simple_ray(ds,
start_position=ray_start,
end_position=ray_end,
lines=my_line_list,
ftype='gas')
filespecout_base = 'spectrum_' + ds.basename
else:
ray = yt.load(args.ray)
ray_start = ray.light_ray_solution[0]['start']
ray_end = ray.light_ray_solution[0]['end']
filespecout_base = 'spectrum_' + ray.light_ray_solution[0]['filename']
print ray_start, ray_end, filespecout_base
hdulist = MISTY.write_header(ray,
start_pos=ray_start,
end_pos=ray_end,
lines=my_line_list,
author=args.author)
## put parameter file into the fits file
MISTY.write_parameter_file(args.paramfile, hdulist=hdulist)
for line in my_line_list:
sg = MISTY.generate_line(ray, line, write=True, hdulist=hdulist)
filespecout = filespecout_base + '_' + line.replace(" ", "_") + '.png'
sg.plot_spectrum(filespecout, flux_limits=(0.0, 1.0))
MISTY.write_out(hdulist, filename='lauren_advance_spectrum.fits')
def extract_spectra(ds, impact, read=False):
out_fits_name = "temp.fits"
if read:
hdulist = fits.open(out_fits_name)
return hdulist, fits[0].header['RAYSTART'], fits[0].header['RAYEND']
else:
proper_box_size = ds.get_parameter('CosmologyComovingBoxSize') / ds.get_parameter('CosmologyHubbleConstantNow') * 1000. # in kpc
line_list = ['H I 1216', 'Si II 1260', 'C IV 1548', 'O VI 1032']
ray_start = np.zeros(3)
ray_end = np.zeros(3)
ray_start[0] = xmin
ray_end[0] = xmax
ray_start[1] = halo_center[1] - (impact/proper_box_size)
ray_end[1] = ray_start[1]
ray_start[2] = halo_center[2]
ray_end[2] = ray_start[2]
rs, re = np.array(ray_start), np.array(ray_end)
# out_fits_name = "hlsp_misty_foggie_"+haloname+"_"+ds.basename.lower()+"_i"+"{:05.1f}".format(impacts[i]) + \
# "_dx"+"{:4.2f}".format(dx[i])+"_v2_los.fits"
rs = ds.arr(rs, "code_length")
re = ds.arr(re, "code_length")
ray = ds.ray(rs, re)
triray = trident.make_simple_ray(ds, start_position=rs.copy(),
end_position=re.copy(),
data_filename="test.h5",
lines=line_list,
ftype='gas')
hdulist = MISTY.write_header(triray,start_pos=ray_start,end_pos=ray_end,
lines=line_list, impact=impact)
tmp = MISTY.write_parameter_file(ds,hdulist=hdulist)
for line in line_list:
sg = MISTY.generate_line(triray,line,write=True,use_spectacle=False,hdulist=hdulist)
MISTY.write_out(hdulist,filename=out_fits_name)
return hdulist, ray_start, ray_end
trident.add_ion_fields(ds, ['C', "O", "O IV", "O VI", "C IV"], ftype='gas')
for field in ds.derived_field_list:
print (field)
pos = ad['particle_position'][ad['particle_mass'] \
== ad['particle_mass'].max()][0].to('unitary')
r = 4.0*ad['virial_radius'][ad['particle_mass'] \
== ad['particle_mass'].max()][0].to('unitary')
sp = ds.sphere(pos, r)
fields = ['H_p1_density','O_p4_density','C_p0_density', 'H_density']
labels = ['HI', "OIV", "C", "H"]
## make a ray that intersects this most massive halo ##
ray = trident.make_simple_ray(ds, start_position=[pos[0], pos[1],0], end_position=[pos[0], pos[1], 1],\
lines='H')
width = ds.domain_width[0]
lbase = 1215.67
dz = ds.cosmology.z_from_t(ds.current_time-width/ds.quan(2.998*10**8, 'm/s'))
lmin = lbase*(z-dz+1)
lmax = lbase*(z+1)
sg = trident.SpectrumGenerator(lambda_min = lmin, lambda_max = lmax, dlambda=0.01)
sg.make_spectrum(ray, lines='H')
sg.save_spectrum('/home/azton/simulations/analysis/%s/spectrum_%0.3f.png'%(simname, ds.current_redshift)
for field in range(len(fields)):
prj = yt.ProfilePlot(sp, "radius",fields[field], label=labels[field]\
, weight_field='density', n_bins=100)
prj.annotate_ray(ray, arrow=True)
prj.save('/home/azton/simulations/analysis/%s/radialProfile'%simname)
def test_enzo_small_simple(self):
"""
This is an answer test, which compares the results of this test
against answers generated from a previous version of the code.
This test generates a COS spectrum from a single Enzo dataset
using a simple ray and compare the ray and spectral output data
against a known answer.
"""
# Set the dataset filename, load it into yt and define the trajectory
# of the LightRay to cross the box from one corner to the other.
ds = load(os.path.join(enzo_small, 'RD0009/RD0009'))
ray_start = ds.domain_left_edge
ray_end = ds.domain_right_edge
# Make a LightRay object including all necessary fields so you can add
# all H, C, N, O, and Mg fields to the resulting spectrum from your dataset.
# Save LightRay to ray.h5 and use it locally as ray object.
ray_fn = 'enzo_small_simple_ray.h5'
ray = make_simple_ray(ds,
start_position=ray_start,
end_position=ray_end,
data_filename=ray_fn,
lines=['H', 'C', 'N', 'O', 'Mg'],
ftype='gas')
# Now use the ray object to actually generate an absorption spectrum
# Use the settings (spectral range, LSF, and spectral resolution) for COS
# And save it as an output hdf5 file and plot it to an image.
sg = SpectrumGenerator('COS')
sg.make_spectrum(ray, lines=['H', 'C', 'N', 'O', 'Mg'])
raw_file = 'enzo_small_simple_spec_raw.h5'
raw_file_compare = os.path.join(test_results_dir, raw_file)
sg.save_spectrum(raw_file)
sg.plot_spectrum('enzo_small_simple_spec_raw.png')
# "Final" spectrum with added quasar, MW background, applied line-spread
# function, and added gaussian noise (SNR=30)
# Save as a text file and plot it to an image.
sg.add_qso_spectrum()
sg.add_milky_way_foreground()
sg.apply_lsf()
sg.add_gaussian_noise(30, seed=1)
final_file = 'enzo_small_simple_spec_final.h5'
final_file_compare = os.path.join(test_results_dir, final_file)
sg.save_spectrum(final_file)
sg.plot_spectrum('enzo_small_simple_spec_final.png')
if generate_results:
os.rename(raw_file, raw_file_compare)
os.rename(final_file, final_file_compare)
else:
old_spec = h5py.File(raw_file_compare, 'r')
new_spec = h5py.File(raw_file, 'r')
for key in old_spec.keys():
assert_almost_equal(new_spec[key].value, old_spec[key].value, \
decimal=err_precision,
err_msg='Raw spectrum array does not match '+\
'for enzo_small_simple answer test')
old_spec.close()
new_spec.close()
old_spec = h5py.File(final_file_compare, 'r')
new_spec = h5py.File(final_file, 'r')
for key in old_spec.keys():
assert_almost_equal(new_spec[key].value, old_spec[key].value, \
decimal=err_precision,
err_msg='Final spectrum array does not match '+\
'for enzo_small_simple answer test')
old_spec.close()
new_spec.close()
def make_light_ray_plots(filename,
plotting=False,
ray_start=[0, 0, 0],
ray_direction=[1, 0, 0]):
'''
This function computes the dispersion measure along a light ray
going through a simulated galaxy.
---------Arguments------------
plotting: If Galaxy plots should be made
ray_start: The position of the start of the ray
with respect to the center of the Halo
ray_direction: The position of the end of the ray
with respect to the start of the ray
'''
ds, center_Halo, sp, ray_direction_conv, ray_start_conv =\
load_data(ray_start, ray_direction)
# RAY CREATION
ray_end = list(center_Halo + ray_start_conv + ray_direction_conv)
ray = trident.make_simple_ray(ds,
start_position=ray_start_conv + center_Halo,
end_position=ray_end,
fields=['_ElectronDensity', 'density'],
data_filename="{}.h5".format(filename))
# GALAXY PLOTS
if plotting:
# Creates three on-axis projection plots
axes = ['x', 'y', 'z']
for axis in axes:
prj_x = yt.ProjectionPlot(ds,
axis,
'density',
width=10 * kpc,
data_source=sp,
center=center_Halo)
prj_x.annotate_ray(ray)
prj_x.save(filename + '_galaxy_{}.pdf'.format(str(axis)))
# COMPUTING THE DM
length = np.linalg.norm(np.array(ray_end)-np.array(center_Halo)-\
np.array(ray_start_conv))
int_elec_dens = []
int_total = 0
# Finding the entry where arc_length>1kpc:
for i in range(len(ray.data['_ElectronDensity'])):
# Convert in pc/cm^-3 from kpc/h
int_total += ray.data['_ElectronDensity'][i]*\
ray.data['dl'][i]*60000*1000/0.7
int_elec_dens.append(float(int_total))
# LIGHT RAY DATA PLOTS
fig, axs = pl.subplots(3)
axs[0].plot(np.linspace(0, length, len(ray.data['density'])),
np.log10(ray.data['density']))
axs[0].set_xlabel('Arc Length (kpc/h)')
axs[0].set_ylabel('Density ' + r'$\log\left(\frac{g}{cm^3}\right)$')
axs[1].plot(np.linspace(0, length, len(ray.data['_ElectronDensity'])),
np.log10(ray.data['_ElectronDensity']))
axs[1].set_xlabel('Arc Length (kpc/h)')
axs[1].set_ylabel('Electron Density ' + r'$\log(cm^{-3})$')
axs[2].title.set_text('DM = {}'.format(round(int_elec_dens[-1], 2)))
axs[2].plot(np.linspace(0, length, len(int_elec_dens)),
np.log10(int_elec_dens))
axs[2].set_xlabel('Arc Length (kpc/h)')
axs[2].set_ylabel('DM ' + r'$\log(cm^{-2})$')
pl.subplots_adjust(left=0.20,
right=0.9,
bottom=0.2,
top=0.9,
wspace=0.4,
hspace=1.5)
fig.savefig(filename + '_subplots.pdf')
return int_elec_dens[-1], ray.data['dl']
def generate_random_rays(ds, halo_center, **kwargs):
'''
generate some random rays
'''
low_impact = kwargs.get("low_impact", 10.)
high_impact = kwargs.get("high_impact", 45.)
track = kwargs.get("track", "halo_track")
Nrays = kwargs.get("Nrays", 2)
seed = kwargs.get("seed", 17)
axis = kwargs.get("axis", 'x')
output_dir = kwargs.get("output_dir", ".")
haloname = kwargs.get("haloname", "somehalo")
line_list = kwargs.get("line_list",
['H I 1216', 'Si II 1260', 'O VI 1032'])
proper_box_size = get_proper_box_size(ds)
zsnap = ds.get_parameter('CosmologyCurrentRedshift')
refine_box, refine_box_center, x_width = get_refine_box(ds, zsnap, track)
proper_x_width = x_width * proper_box_size
np.random.seed(seed)
high_impact = 0.45 * proper_x_width
impacts = np.random.uniform(low=low_impact, high=high_impact, size=Nrays)
print('impacts = ', impacts)
angles = np.random.uniform(low=0, high=2 * pi, size=Nrays)
out_ray_basename = ds.basename + "_ray_" + axis
for i in range(Nrays):
os.chdir(output_dir)
this_out_ray_basename = out_ray_basename + "_i"+"{:05.1f}".format(impacts[i]) + \
"-a"+"{:4.2f}".format(angles[i])
out_ray_name = this_out_ray_basename + ".h5"
rs, re = get_refined_ray_endpoints(ds,
halo_center,
track,
impact=impacts[i])
out_name_base = "hlsp_misty_foggie_"+haloname+"_"+ds.basename.lower()+"_ax"+axis+"_i"+"{:05.1f}".format(impacts[i]) + \
"-a"+"{:4.2f}".format(angles[i])+"_v4_los"
out_fits_name = out_name_base + ".fits.gz"
out_plot_name = out_name_base + ".png"
rs = ds.arr(rs, "code_length")
re = ds.arr(re, "code_length")
if args.velocities:
trident.add_ion_fields(ds,
ions=[
'Si II', 'Si III', 'Si IV', 'C II',
'C III', 'C IV', 'O VI', 'Mg II',
'Ne VIII'
])
ray = ds.ray(rs, re)
ray["x-velocity"] = ray["x-velocity"].in_units('km / s')
ray.save_as_dataset(out_ray_name,
fields=["density", "temperature", "metallicity"])
ray["x-velocity"] = ray["x-velocity"].in_units('km / s')
if args.velocities:
ray['x-velocity'] = ray['x-velocity'].convert_to_units('km/s')
ray['y-velocity'] = ray['y-velocity'].convert_to_units('km/s')
ray['z-velocity'] = ray['z-velocity'].convert_to_units('km/s')
ray['cell_mass'] = ray['cell_mass'].convert_to_units('Msun')
ray_df = ray.to_dataframe([
"x", "y", "z", "density", "temperature", "metallicity",
"HI_Density", "cell_mass", "x-velocity", "y-velocity",
"z-velocity", "C_p2_number_density", "C_p3_number_density",
"H_p0_number_density", "Mg_p1_number_density",
"O_p5_number_density", "Si_p2_number_density",
"Si_p1_number_density", "Si_p3_number_density",
"Ne_p7_number_density"
])
out_tri_name = this_out_ray_basename + "_tri.h5"
triray = trident.make_simple_ray(ds,
start_position=rs.copy(),
end_position=re.copy(),
data_filename=out_tri_name,
lines=line_list,
ftype='gas')
ray_start = triray.light_ray_solution[0]['start']
ray_end = triray.light_ray_solution[0]['end']
print("final start, end = ", ray_start, ray_end)
filespecout_base = this_out_ray_basename + '_spec'
print(ray_start, ray_end, filespecout_base)
hdulist = MISTY.write_header(triray,
start_pos=ray_start,
end_pos=ray_end,
lines=line_list,
impact=impacts[i],
redshift=ds.current_redshift)
tmp = MISTY.write_parameter_file(ds, hdulist=hdulist)
for line in line_list:
sg = MISTY.generate_line(triray,
line,
zsnap=ds.current_redshift,
write=True,
hdulist=hdulist,
use_spectacle=False,
resample=True)
MISTY.write_out(hdulist, filename=out_fits_name)
if args.velocities:
print('making the velphase plot....')
sv.show_velphase(ds, ray_df, rs, re, hdulist, out_name_base)
if args.plot:
plot_misty_spectra(hdulist, outname=out_plot_name)
print("done with generate_random_rays")
def extract_spectra(ds, impact, **kwargs):
read_fits_file = kwargs.get('read_fits_file', False)
out_fits_name = kwargs.get('out_fits_name', "temp.fits")
xmin = kwargs.get("xmin", 0)
xmax = kwargs.get("xmax", 1)
halo_center = kwargs.get("halo_center", [0.5, 0.5, 0.5])
refine_box_center = kwargs.get("refine_box_center", halo_center)
if read_fits_file:
print "opening ", out_fits_name
hdulist = fits.open(out_fits_name)
ray_start_str, ray_end_str = hdulist[0].header['RAYSTART'], hdulist[
0].header['RAYEND']
ray_start = [float(ray_start_str.split(",")[0]), \
float(ray_start_str.split(",")[1]), \
float(ray_start_str.split(",")[2])]
ray_end = [float(ray_end_str.split(",")[0]), \
float(ray_end_str.split(",")[1]), \
float(ray_end_str.split(",")[2])]
return hdulist, ray_start, ray_end
else:
proper_box_size = get_proper_box_size(ds)
# line_list = ['H I 1216', 'Ly b', 'Ly c', 'Ly d', 'Ly 10', 'Si II 1260', 'C IV 1548', 'O VI 1032']
line_list = [
'H I 1216', 'H I 1026', 'H I 973', 'H I 950', 'H I 919',
'Si II 1260', 'Si III 1207', 'C II 1335', 'C IV 1548', 'O VI 1032'
]
ray_start = np.zeros(3)
ray_end = np.zeros(3)
ray_start[0] = xmin
ray_end[0] = xmax
ray_start[1] = halo_center[1] + (impact / proper_box_size)
ray_end[1] = halo_center[1] + (impact / proper_box_size)
ray_start[2] = halo_center[2]
ray_end[2] = halo_center[2]
rs, re = np.array(ray_start), np.array(ray_end)
# out_fits_name = "hlsp_misty_foggie_"+haloname+"_"+ds.basename.lower()+"_i"+"{:05.1f}".format(impacts[i]) + \
# "_dx"+"{:4.2f}".format(dx[i])+"_v2_los.fits"
rs = ds.arr(rs, "code_length")
re = ds.arr(re, "code_length")
ray = ds.ray(rs, re)
triray = trident.make_simple_ray(ds,
start_position=rs.copy(),
end_position=re.copy(),
data_filename="test.h5",
lines=line_list,
ftype='gas')
hdulist = MISTY.write_header(triray,
start_pos=ray_start,
end_pos=ray_end,
lines=line_list,
impact=impact)
tmp = MISTY.write_parameter_file(ds, hdulist=hdulist)
for line in line_list:
sg = MISTY.generate_line(triray,
line,
write=True,
use_spectacle=True,
hdulist=hdulist)
MISTY.write_out(hdulist, filename=out_fits_name)
return hdulist, ray_start, ray_end
def make_ray(dataset_file,
ray_start,
ray_end,
line_list=["H I", "H II"],
field_list=None,
filename="ray.h5",
return_ray=False,
**kwargs):
"""
A wrapper for the trident.make_simple_ray function to generate rays in
SPH simulations.
Parameters
----------
dataset_file : string or YT Dataset object
Either a YT dataset object or the filename of a dataset on disk.
ray_start, ray_end : list of floats
The coordinates of the starting and end positions of the ray. The
coordinates are assumed to be in code length units.
line_list : list of strings, optional
The list that will determine which fields that will be
added to output ray. The format of these is important, they can
include elements (e.g. "C" for Carbon), ions (e.g. "He II" for
specifically the Helium II line) or wavelengths (e.g. "Mg II #####"
where # is the integer wavelength of the line). These fields will
appear in the output ray dataset in the form of "H_p0_number_density"
(H plus 0 = H I). Default: ["H I", "H II"]
field_list : list of string, optional
The list of which additional fields to add to the output light ray.
Default: None
filename : string, optional
The output file name for the ray data stored as a HDF5 file.
The ray must be saved. Default: "ray.h5"
return_ray : boolean, optional
If true, make_ray will return the generated YTDataLightRayDataset,
If false, make_ray will return None. Default: False
Returns
-------
ray
return_ray = True: Returns the generated YTDataLightRayDataset
None
return_ray = False: Returns None
"""
# If supplied a path name, load the snapshot first
if isinstance(dataset_file, str):
ds = yt.load(dataset_file)
else:
ds = dataset_file
if field_list is None:
field_list = []
field_list.extend([('PartType0', 'SmoothingLength'),
('PartType0', 'ParticleIDs')])
trident.add_ion_fields(ds, ions=line_list)
ray = trident.make_simple_ray(ds,
start_position=ray_start,
end_position=ray_end,
lines=line_list,
ftype='PartType0',
fields=field_list,
data_filename=filename,
**kwargs)
if return_ray:
return ray
else:
return None
def run_sightlines(outputfilename,save_after_num,parallel,\
simulation_dest = None,run = 'default',throwerrors = 'warn'):
if run not in ['default', 'test']:
print('unknown option for "run" %s.'%run+\
' Please restart with "run = default"'+\
' or "run = test".')
#do not print out anything from yt (it prints plenty)
yt.funcs.mylog.setLevel(50)
if parallel:
yt.enable_parallelism()
readvalsoutput = simulation_quasar_sphere.read_values(outputfilename)
#by creating a QuasarSphere, it knows all its metadata and other
#information from simparams and scanparams (first lines of file at
#'filename')
q = simulation_quasar_sphere.SimQuasarSphere(
start_up_info_packet=readvalsoutput)
if q.simparams[6] is None:
if simulation_dest:
q.simparams[6] = simulation_dest
else:
raise NoSimulationError('Simulation file location unknown, '+\
'run with "simulation_dest" to process')
else:
simulation_dest = q.simparams[6]
ds,fields_to_keep = code_specific_setup.load_and_setup(simulation_dest,\
q.fullname,ions = q.ions,\
redshift = q.redshift)
set_up_general(ds, q.code, q.center, q.bulk_velocity, q.Rvir)
code_specific_setup.check_redshift(ds, outputfilename=outputfilename)
num_bin_vars = q.gasbins.get_length()
#Can start at a position further than 0 if reached
starting_point = q.length_reached
bins = np.append(np.arange(starting_point, q.length, save_after_num),
q.length)
#first for loop is non-parallel. If 32 processors available, it will break up
#into bins of size 32 at a time for example. At end, saves data from all 32.
#this is (~12 bins) in usual circumstances
for i in range(0, len(bins) - 1):
current_info = q.info[bins[i]:bins[i + 1]]
if yt.is_root():
tprint("%s-%s /%s" % (bins[i], bins[i + 1], len(q.info)))
my_storage = {}
#2nd for loop is parallel. Each vector goes to a different processor, and creates
#a separate trident sightline (~32 sightlines [in a bin]).
#the longest processing step is ray = trident.make_simple_ray, and it's
#the only step which actually takes any time (below two for loops go by fast)
for sto, in_vec in yt.parallel_objects(current_info,
storage=my_storage):
vector = np.copy(in_vec)
index = vector[0]
toprint = "line %s, (r = %.0f) densities " % (str(
int(index)), vector[3])
tprint("<line %d, starting process> " % index)
ident = str(index)
start = ds.arr(tuple(vector[5:8]), 'unitary')
end = ds.arr(tuple(vector[8:11]), 'unitary')
try:
ray = trident.make_simple_ray(ds,
start_position=start,
end_position=end,
data_filename="ray" + ident +
".h5",
fields=fields_to_keep,
ftype='gas')
except KeyboardInterrupt:
print('skipping sightline %s ...' % index)
print('Interrupt again within 5 seconds to *actually* end')
time.sleep(5)
continue
except ValueError as e:
throw_errors_if_allowed(
e, throwerrors,
'ray has shape %s - %s, but size 0' % (start, end))
except Exception as e:
throw_errors_if_allowed(e, throwerrors,
'problem with making ray')
continue
trident.add_ion_fields(ray, q.ions)
field_data = ray.all_data()
dl = field_data['gas', 'dl']
#3rd for loop is for processing each piece of info about each ion
#including how much that ion is in each bin according to gasbinning
#here just process topline data (column densities and ion fractions)
#(~10 ions)
for j in range(len(q.ions)):
ion = q.ions[j]
ionfield = field_data["gas", ion_to_field_name(ion)]
cdens = np.sum((ionfield * dl).in_units('cm**-2')).value
vector[11 + j * (num_bin_vars + 2)] = cdens
total_nucleus = np.sum(ionfield[ionfield>0]/\
field_data["gas",ion_to_field_name(ion,'ion_fraction')][ionfield>0]\
* dl[ionfield>0])
vector[11 + j * (num_bin_vars + 2) + 1] = cdens / total_nucleus
#4th for loop is processing each gasbin for the current ion
#(~20 bins)
for k in range(num_bin_vars):
try:
variable_name, edges, units = q.gasbins.get_field_binedges_for_num(
k, ion)
if variable_name is None:
vector[11 + j * (num_bin_vars + 2) + k +
2] = np.nan
elif variable_name in ray.derived_field_list:
if units:
data = field_data[variable_name].in_units(
units)
else:
data = field_data[variable_name]
abovelowerbound = data > edges[0]
belowupperbound = data < edges[1]
withinbounds = np.logical_and(
abovelowerbound, belowupperbound)
coldens_in_line = (ionfield[withinbounds]) * (
dl[withinbounds])
coldens_in_bin = np.sum(coldens_in_line)
vector[11 + j * (num_bin_vars + 2) + k +
2] = coldens_in_bin / cdens
else:
print(
str(variable_name) +
" not in ray.derived_field_list")
except Exception as e:
throw_errors_if_allowed(
e, throwerrors,
'Could not bin into %s with edges %s' %
(variable_name, edges))
toprint += "%s:%e " % (ion, cdens)
#gets some more information from the general sightline.
#metallicity, average density (over the whole sightline)
#mass-weighted temperature
try:
if ('gas', "H_nuclei_density") in ray.derived_field_list:
Z = np.sum(field_data['gas',"metal_density"]*dl)/ \
np.sum(field_data['gas',"H_nuclei_density"]*mh*dl)
else:
Z = np.sum(field_data['gas',"metal_density"]*dl)/ \
np.sum(field_data['gas',"number_density"]*mh*dl)
vector[-1] = Z
except Exception as e:
throw_errors_if_allowed(e, throwerrors,
'problem with average metallicity')
try:
n = np.sum(
field_data['gas', 'number_density'] * dl) / np.sum(dl)
vector[-2] = n
except Exception as e:
throw_errors_if_allowed(e, throwerrors,
'problem with average density')
try:
T = np.average(field_data['gas','temperature'],\
weights=field_data['gas','density']*dl)
vector[-3] = T
except Exception as e:
throw_errors_if_allowed(e, throwerrors,
'problem with average temperature')
try:
os.remove("ray" + ident + ".h5")
except:
pass
tprint(toprint)
#'vector' now contains real data, not just '-1's
sto.result_id = index
sto.result = vector
#save all parallel sightlines after they finish (every 32 lines are saved at once)
if yt.is_root():
keys = my_storage.keys()
for key in keys:
q.info[int(key)] = my_storage[key]
q.scanparams[6] += (bins[i + 1] - bins[i])
q.length_reached = q.scanparams[6]
if run != 'test':
outputfilename = q.save_values(oldfilename=outputfilename)
tprint("file saved to " + outputfilename + ".")
datafile = sys.argv[1]
start_position = sys.argv[2]
end_position = sys.argv[3]
savename = sys.argv[4]
line_list = ['C','N','O']
ds = yt.load(datafile) # Loading the dataset
if start_position == 'left_edge':
start_position = ds.domain_left_edge
if end_position == 'right_edge':
end_position = ds.domain_right_edge
trident.add_ion_fields(ds,ions=line_list)
actual_ray = trident.make_simple_ray(ds,start_position = np.array([0.,0.,0.]),end_position = np.array([128.,128.,128.]),data_filename="mray.h5",lines=line_list,ftype='gas')
#lr = trident.LightRay(ds)
#lr.make_light_ray(ds,start_position = start_position,end_position = end_position,data_filename="mray.h5",fields=['temperature','density'])
sg = trident.SpectrumGenerator('COS')
#sg = trident.SpectrumGenerator(lambda_min='auto', lambda_max='auto',dlambda=0.01)
sg.make_spectrum(actual_ray, lines=line_list)
sg.save_spectrum(savename + '.txt')
sg.plot_spectrum(savename + '.png')
def construct_rays(ds_file,
start_points,
end_points,
fld_params=None,
line_list=None,
other_fields=None,
ftype='gas',
out_dir='./'):
"""
Construct rays given a set of starting points and end points.
Parameters
----------
ds_file : str or YT dataset
path to dataset to be used to create rays
start_points : numpy array
1d array of starting points for each ray (code_length)
end_points : numpy array
1d array of end points for each ray (code_length)
fld_params: dict, optional
Dictionary of parameters that will be passed to the lightrays. (ie
`center`, `bulk_velocity`).
Default: None
line_list : list
list of ions to add to light rays. None defaults to
H I, C IV, and O VI
other_fields : list
other yt fields to add to light rays. None defaults
to density, metallicity, and temperature
ftype : str
The field to be passed to trident that ion fields will be added to, i.e.
('gas', 'H_p0_number_density'). 'gas' should work for most grid-based
simulations. For particle-based simulations this will not work and needs
to be changed. 'PartType0' often works though it varies.
See trident.make_simple_ray() for more information
out_dir : str/path
where to save all of the lightrays
"""
comm = MPI.COMM_WORLD
#set defaults
if line_list is None:
line_list = ['H I', 'C IV', 'O VI']
if other_fields is None:
other_fields = ['density', 'metallicity', 'temperature']
n_rays = start_points.shape[0]
#set padding for filenames
pad = np.floor(np.log10(n_rays))
pad = int(pad) + 1
# distribute rays to proccesors
my_ray_nums = np.arange(n_rays)
#split ray numbers then take a portion based on rank
split_ray_nums = np.array_split(my_ray_nums, comm.size)
my_ray_nums = split_ray_nums[comm.rank]
for i in my_ray_nums:
#construct ray
ray_filename = f"{out_dir}/ray{i:0{pad}d}.h5"
trident.make_simple_ray(ds_file,
start_points[i],
end_points[i],
lines=line_list,
fields=other_fields,
ftype=ftype,
field_parameters=fld_params,
data_filename=ray_filename)
comm.Barrier()
def make_random_spectrum(model, output, num_spectra = 1, spectrum_directory = '.', \
ion_list = 'all', rmin = 10, rmax = 100, ray_len = 500, redshift = None):
# load data set with yt, galaxy center, and the bulk velocity of the halo
ds, gcenter, bulk_velocity = spg.load_simulation_properties(model, output)
ad = ds.all_data()
# set field parameters so that trident knows to subtract off bulk velocity
ad.set_field_parameter('bulk_velocity', bulk_velocity)
ad.set_field_parameter('center', gcenter)
# either specify redshift manually, or determine redshift from the redshift of the simulation
if redshift is None:
redshift = round(ds.current_redshift, 2)
print(gcenter, gcenter[0], bulk_velocity)
gcenter = np.array(gcenter.d)
kpc_unit = (ds.domain_right_edge[0].d - ds.domain_left_edge[0].d) \
/ (ds.domain_right_edge.in_units('kpc')[0].d - ds.domain_left_edge.in_units('kpc')[0].d)
ikpc_unit = 1.0 / kpc_unit
for i in range(num_spectra):
# generate the coordinates of the random sightline
impact_parameter, ray_start, ray_end = spg.generate_random_ray_coordinates(gcenter*kpc_unit, rmin*kpc_unit, rmax*kpc_unit, ray_len*kpc_unit)
ray_id, ray_fn = spg.get_next_ray_id(model, redshift, spectrum_directory = spectrum_directory)
# write ray id, impact parameter, bulk velocity, and start/end coordinates out to file
ray_outfile = open(ray_fn, 'a')
ray_outfile.write('%i %.2f %.2f %.2f %.2f %e %e %e %e %e %e %e %e %e\n'%(ray_id, impact_parameter*ikpc_unit, \
bulk_velocity[0].in_units('km/s').d, bulk_velocity[1].in_units('km/s').d, bulk_velocity[2].in_units('km/s').d,\
ray_start[0]*ikpc_unit, ray_start[1]*ikpc_unit, ray_start[2]*ikpc_unit, \
ray_end[0]*ikpc_unit, ray_end[1]*ikpc_unit, ray_end[2]*ikpc_unit,\
gcenter[0].astype(float), gcenter[1].astype(float), gcenter[2].astype(float)))
ray_outfile.close()
# generate sightline using Trident
ray = trident.make_simple_ray(ds,
start_position = ray_start,
end_position = ray_end,
lines=ion_list,
ftype='gas',
field_parameters=ad.field_parameters,
# the current redshift of the simulation, calculated above, rounded to two decimal places
redshift=redshift,
data_filename='%s/ray_%s_%s_%i.h5'%(spectrum_directory, model, output, ray_id))
# create spectrum from sightline for both G130M and G160M instruments,
# for now, save these two spectra as temporary separate files
for instrument in ['COS-G130M', 'COS-G160M']:
sg = trident.SpectrumGenerator(instrument, line_database = 'pyigm_line_list.txt')
sg.make_spectrum(ray, lines = ion_list)
sg.apply_lsf()
sg.add_gaussian_noise(10)
temp_name = '%s.fits'%(instrument)
sg.save_spectrum(temp_name, format = 'FITS')
# stitch the spectra together in the format you'll need for veeper
spec_name = '%s/COS-FUV_%s_z%.2f_%i.fits'%(spectrum_directory, model, redshift, int(ray_id))
spg.stitch_g130m_g160m_spectra('COS-G130M.fits', 'COS-G160M.fits', spec_name)
os.remove('COS-G130M.fits')
os.remove('COS-G160M.fits')
def generate_ray_data(model, output, ray_data_file, data_loc = '.', \
ion_list = 'all', redshift = None):
# load data set with yt, galaxy center, and the bulk velocity of the halo
if model == 'P0':
ds = yt.load('/nobackup/ibutsky/tmp/pioneer.%06d' % (output))
trident.add_ion_fields(ds, ions=ion_list)
# for annoying reasons... need to convert ray positions to "code units"
code_unit_conversion = ds.domain_right_edge.d / ds.domain_right_edge.in_units(
'kpc').d
ray_id_list, impact, bvx, bvy, bvz, xi, yi, zi, xf, yf, zf, cx, cy, cz =\
np.loadtxt('../../data/P0_z0.25_ray_data.dat', skiprows = 1, unpack = True)
gcenter_kpc = [cx[0], cy[0], cz[0]
] # assuming galaxy center is the same for all sightlines
gcenter = gcenter_kpc * code_unit_conversion
bulk_velocity = YTArray([bvx[0], bvy[0], bvz[0]], 'km/s')
# set field parameters so that trident knows to subtract off bulk velocity
ad = ds.all_data()
ad.set_field_parameter('bulk_velocity', bulk_velocity)
ad.set_field_parameter('center', gcenter)
ray_start_list = np.ndarray(shape=(0, 3))
ray_end_list = np.ndarray(shape=(0, 3))
for i in range(len(xi)):
ray_start_list = np.vstack(
(ray_start_list, [xi[i], yi[i], zi[i]] * code_unit_conversion))
ray_end_list = np.vstack(
(ray_end_list, [xf[i], yf[i], zf[i]] * code_unit_conversion))
# either specify redshift manually, or determine redshift from the redshift of the simulation
if redshift is None:
redshift = round(ds.current_redshift, 2)
print(gcenter, gcenter[0], bulk_velocity)
for i in range(1, 150):
# generate the coordinates of the random sightline
# write ray id, impact parameter, bulk velocity, and start/end coordinates out to file
h5file = h5.File(
'%s/ray_%s_%i_%i.h5' % (data_loc, model, output, ray_id_list[i]),
'a')
# generate sightline using Trident
ray = trident.make_simple_ray(
ds,
start_position=ray_start_list[i],
end_position=ray_end_list[i],
lines=ion_list,
ftype='gas',
field_parameters=ad.field_parameters,
# the current redshift of the simulation, calculated above, rounded to two decimal places
redshift=redshift)
ad_ray = ray.all_data()
# generating the list of all of the
field_list = ['y', 'temperature', 'density', 'metallicity', 'dl']
source_list = [ad, ad, ad, ad, ad_ray]
unit_list = ['kpc', 'K', 'g/cm**3', 'Zsun', 'cm']
yt_ion_list = ipd.generate_ion_field_list(ion_list,
'number_density',
full_name=False)
yt_ion_list[0] = 'H_number_density'
field_list = np.append(field_list, yt_ion_list)
for j in range(len(yt_ion_list)):
unit_list.append('cm**-3')
source_list.append(ad_ray)
for field, source, unit in zip(field_list, source_list, unit_list):
if field not in h5file.keys():
h5file.create_dataset(field,
data=source[('gas',
field)].in_units(unit))
h5file.flush()
h5file.create_dataset('y_lr', data=ad_ray['y'].in_units('kpc'))
h5file.flush()
h5file.close()
print("saved sightline data %i\n" % (i))
# Set the dataset filename, load it into yt and define the trajectory
# of the LightRay. Define desired spectral features to include
# all H, C, N, O, and Mg lines.
fn = 'enzo_cosmology_plus/RD0009/RD0009'
ds = yt.load(fn)
ray_start = ds.domain_left_edge
ray_end = ds.domain_right_edge
line_list = ['H', 'C', 'N', 'O', 'Mg']
# Make a LightRay object including all necessary fields so you can add
# all H, C, N, O, and Mg fields to the resulting spectrum from your dataset.
# Save LightRay to ray.h5 and use it locally as ray object.
ray = trident.make_simple_ray(ds,
start_position=ray_start,
end_position=ray_end,
data_filename='ray.h5',
lines=line_list,
ftype='gas')
# Create a projection of the dataset in density along the x axis,
# overplot the trajectory of the ray, and save it.
p = yt.ProjectionPlot(ds, 'x', 'density')
p.annotate_ray(ray, arrow=True)
p.save('projection.png')
# Now use the ray object to actually generate an absorption spectrum
# Use the settings (spectral range, LSF, and spectral resolution) for COS
# And save it as an output text file and plot it to an image.
sg = trident.SpectrumGenerator('COS')
sg.make_spectrum(ray, lines=line_list)
sg.save_spectrum('spec_raw.txt')
savefolder = '../Spectra/'+runName+savefolderEnd
filePrefix = '../Files/'+runName+'/KH_hdf5_chk_'
#cdfile_list = ['0006']
for chk_num in file_list:
file_name = filePrefix+chk_num
dataset = yt.load(file_name)
dataset.add_field(('gas', 'metallicity'), function=_metallicity, display_name="Metallicity", units='Zsun')
#make a ray object
ray = tri.make_simple_ray(dataset,
redshift=0.0,
start_position=ray_start,
end_position=ray_end,
lines=line_list,
ftype = 'gas')
#plot and save density projection to look at the path of the ray
p = yt.ProjectionPlot(dataset, 'z', 'density', origin='native')
p.annotate_ray(ray)
p.save(savefolder+'projection'+chk_num+'.png')
print("Saved Projection images as "+savefolder+"/projection"+chk_num+".png")
#create a spectrumGenerator; you may make your own spectrograph settings
sg = tri.SpectrumGenerator(lambda_min= 500, lambda_max=1600, dlambda=0.01) #uses default spectrograph settings for "Cosmic Origins Spectrograph"
sg.make_spectrum(ray, lines=line_list)
#save and plot the spectrum
sg.save_spectrum(savefolder+'spec_raw'+chk_num+'.h5')
theta = x[i]
phi = y[i]
dx = R * np.cos(theta) * np.sin(phi)
dy = R * np.sin(theta) * np.sin(phi)
dz = R * np.cos(phi)
dv = YTArray([dx, dy, dz], 'kpc')
ray_end = ray_start + dv
rayfile = 'ray_files/ray' + str(ds) + '_' + str(i) + '_' + '.h5'
ray = trident.make_simple_ray(ds,
start_position=ray_start,
end_position=ray_end,
data_filename=rayfile,
fields=field_list,
ftype='gas')
ray_data = ray.all_data()
path_length = ray_data['dl'].convert_to_units('kpc').d
# Remove the first 5 kpc from the ray data
path = np.zeros(len(path_length))
for h in range(len(path_length) - 1):
dl = path_length[h]
p = path[h]
path[h + 1] = dl + p
def make_random_spectrum(model, output = 3195, spectrum_directory = '../../data/unanalyzed_spectra', \
ion_list = 'all', redshift = None, write_spectra = True):
ds, gcenter, bulk_velocity = spg.load_simulation_properties(model)
ad = ds.all_data()
ad.set_field_parameter('bulk_velocity', bulk_velocity)
ad.set_field_parameter('center', gcenter)
if redshift is None:
redshift = round(ds.current_redshift, 2)
print(gcenter, gcenter[0], bulk_velocity)
field_list = ['temperature', 'density', 'metallicity', 'velocity_x', 'velocity_y', 'velocity_z', 'pressure']
if model == 'P0_agncr':
field_list.append('cr_pressure')
ray_id_list, impact_list, xs, ys, zs, xe, ye, ze = np.loadtxt('../../data/random_sightlines.dat', skiprows=1, unpack = True)
spectrum_data_filename = '%s/%s_z%0.2f_ray_data.dat'%(spectrum_directory, model, redshift)
total_column_filename = '%s/%s_total_column.dat'%(spectrum_directory, model)
if not os.path.isfile(spectrum_data_filename):
spectrum_data_outfile = open(spectrum_data_filename, 'w')
spectrum_data_outfile.write('ray_id, impact_parameter(kpc), bulk velocity(km/s)[vx, vy, vz], ray_start(kpc)[x, y, z], \
ray_end(kpc)[x, y, z], sim_center(kpc)[x,y,z] \n')
else:
spectrum_data_outfile = open(spectrum_data_filename, 'a')
if not os.path.isfile(total_column_filename):
col_outfile = open(total_column_filename, 'w')
col_outfile.write('#ray_id, impact_parameter(kpc), OVI col, SiIII col, HI col\n')
else:
col_outfile = open(total_column_filename, 'a')
for i, ray_id in enumerate(ray_id_list):
spec_name = '%s/COS-FUV_%s_z%.2f_%04d.fits'%(spectrum_directory, model, redshift, int(ray_id))
ray_name = '%s/ray_%s_%s_%i.h5'%(spectrum_directory, model, output, ray_id)
if not os.path.isfile(ray_name):
ray_start = ds.arr(gcenter.in_units('kpc').d + np.array([xs[i], ys[i], zs[i]]), 'kpc')
ray_end = ds.arr(gcenter.in_units('kpc').d + np.array([xe[i], ye[i], ze[i]]), 'kpc')
spectrum_data_outfile.write('%i %.2f %.2f %.2f %.2f %e %e %e %e %e %e %e %e %e\n'%(ray_id, impact_list[i],
bulk_velocity[0], bulk_velocity[1], bulk_velocity[2], ray_start[0],
ray_start[1], ray_start[2], ray_end[0], ray_end[1], ray_end[2],
gcenter[0].astype(float), gcenter[1].astype(float), gcenter[2].astype(float)))
spectrum_data_outfile.flush()
ray = trident.make_simple_ray(ds,
start_position = ray_start,
end_position = ray_end,
lines=ion_list,
ftype='gas',
fields = field_list,
field_parameters=ad.field_parameters,
# the current redshift of the simulation, calculated above, rounded to two decimal places
redshift=redshift,
data_filename=ray_name)
if write_spectra:
rd = ray.all_data()
dl = rd['dl']
ocol = np.cumsum(rd['O_p5_number_density']*dl)[-1]
sicol = np.cumsum(rd['Si_p2_number_density']*dl)[-1]
hcol = np.cumsum(rd['H_p0_number_density']*dl)[-1]
col_outfile.write("%i %.2f %.2f %.2f %.2f\n"%(ray_id, impact_list[i], np.log10(ocol), np.log10(sicol), np.log10(hcol)))
col_outfile.flush()
for instrument in ['COS-G130M', 'COS-G160M']:
sg = trident.SpectrumGenerator(instrument, line_database = 'pyigm_line_list.txt')
sg.make_spectrum(ray, lines = ion_list)
sg.apply_lsf()
sg.add_gaussian_noise(10)
temp_name = '%s_%s.fits'%(instrument, model)
sg.save_spectrum(temp_name, format = 'FITS')
# stitch the spectra together in the format you'll need for veeper
spg.stitch_g130m_g160m_spectra('COS-G130M_%s.fits'%model, 'COS-G160M_%s.fits'%model, spec_name)
os.remove('COS-G130M_%s.fits'%model)
os.remove('COS-G160M_%s.fits'%model)
spectrum_data_outfile.close()
col_outfile.close()
comm.Barrier()
for i in locals:
startTime = time.time()
if dataLoc == "comet":
rFile = "%s/lin_rays/ray_%d.h5" % (dataRepo, i)
else:
rFile = '%s/rayData/%s/RD%04d/lin_rays/ray_%d.h5'\
%(dataRepo,simname, d, i)
row = (i // dim)
col = (i % dim)
xstart = (row + 0.5) * dx
ystart = (col + 0.5) * dx
start = np.array([xstart, ystart, 0.0])
end = start + np.array([5 * dx, 5 * dx, 1.0])
ray = trident.make_simple_ray(ds, start_position=start,\
end_position=end, data_filename=rFile,\
lines=['H I 1216'], ftype='gas',\
fields = ['density','metallicity','temperature','H_p0_number_density'])
count += 1
counts = comm.gather(count, root=0)
if rank == 0:
if count % 50 == 0:
counts = sum(counts)
print('%d light-ray objects created!'%(counts)\
+'\nIntermediate pipelinefile being created')
f = open('lin_%s_RD%04d_pipeline.info' % (simname, d), 'w')
f.write('%s\n%d\n%d\n%s\n%d\n%d\n'\
%(simname,d,dim,sys.argv[4], size, counts ))
f.close()
os.system('cd %s; tar -cvf lin_rays.tar lin_rays;'%(dataRepo)\
+' mv lin_rays.tar %s/rayData/%s/RD%04d/'\
