def test_total_baryon_mass():
# gather most recent data set
sim = sim_dir_load(_pf_name, path=_dir_name, find_outputs=True)
if (sim.parameters['CosmologySimulationOmegaBaryonNow'] == 0.0):
return
sim.get_time_series()
ds = sim[-1]
data = ds.all_data()
# sum masses
Mstar = np.sum(
data['particle_mass'][data['particle_type'] == 2].to('Msun'))
Mgas = np.sum(data['cell_mass'].to('Msun'))
output_data = {'masses': Mstar + Mgas}
# save
filename = "baryon_mass_results.h5"
save_filename = os.path.join(_dir_name, filename)
yt.save_as_dataset(ds, save_filename, output_data)
compare_filename = os.path.join(test_data_dir, filename)
if generate_answers:
os.rename(save_filename, compare_filename)
return
ds_comp = yt.load(compare_filename)
assert_rel_equal(output_data['masses'], ds_comp.data['masses'], tolerance)
def test_dark_matter_mass():
# gather most recent data set
sim = sim_dir_load(_pf_name, path=_dir_name, find_outputs=True)
sim.get_time_series()
ds = sim[-1]
data = ds.all_data()
# sum masses
MDM = np.sum(
data[('all',
'particle_mass')][data[('all',
'particle_type')] == 1].to('Msun'))
output_data = {('data', 'mass'): MDM}
# save
filename = "DM_mass_results.h5"
save_filename = os.path.join(_dir_name, filename)
yt.save_as_dataset(ds, save_filename, output_data)
compare_filename = os.path.join(test_data_dir, filename)
if generate_answers:
os.rename(save_filename, compare_filename)
return
ds_comp = yt.load(compare_filename)
assert_rel_equal(output_data[('data', 'mass')],
ds_comp.data[('data', 'mass')], tolerance)
def test_unequal_bin_field_profile(self):
density = np.random.random(128)
temperature = np.random.random(127)
mass = np.random.random((128, 128))
my_data = {
("gas", "density"): density,
("gas", "temperature"): temperature,
("gas", "mass"): mass,
}
fake_ds_med = {"current_time": yt.YTQuantity(10, "Myr")}
field_types = {field: "gas" for field in my_data.keys()}
yt.save_as_dataset(fake_ds_med,
"mydata.h5",
my_data,
field_types=field_types)
ds = yt.load("mydata.h5")
with assert_raises(YTProfileDataShape):
yt.PhasePlot(
ds.data,
("gas", "temperature"),
("gas", "density"),
("gas", "mass"),
)
def test_phase():
es = sim_dir_load(_pf_name, path=_dir_name)
es.get_time_series(redshifts=[0])
ds = es[-1]
ad = ds.all_data()
profile = ad.profile([("gas", "density")], [("gas", "temperature"),
("gas", "cell_mass")])
profile1 = ad.profile([("gas", "density")], [("gas", "temperature"),
("gas", "cooling_time")],
weight_field=('gas', 'cell_mass'))
density = profile.x
temperature = profile[('gas', 'temperature')]
cooling_time = profile1[('gas', 'cooling_time')]
cell_mass = profile[('gas', 'cell_mass')]
filename = 'phase_data.h5'
save_filename = os.path.join(_dir_name, filename)
data = {
('data', 'density'): density,
('data', 'temperature'): temperature,
('data', 'cooling_time'): cooling_time,
('data', 'cell_mass'): cell_mass
}
yt.save_as_dataset(ds, save_filename, data)
pp = yt.PhasePlot(ad, ('gas', 'density'), ('gas', 'temperature'),
('gas', 'cell_mass'))
pp.set_unit(('gas', 'cell_mass'), 'Msun')
pp.save(_dir_name)
pp1 = yt.PhasePlot(ad, ('gas', 'density'), ('gas', 'temperature'),
('gas', 'cooling_time'),
weight_field=('gas', 'cell_mass'))
pp1.save(_dir_name)
compare_filename = os.path.join(test_data_dir, filename)
if generate_answers:
os.rename(save_filename, compare_filename)
return
# do the comparison
ds_comp = yt.load(compare_filename)
# assert quality to 8 decimals
assert_rel_equal(data[('data', 'density')],
ds_comp.data[('data', 'density')], 8)
assert_rel_equal(data[('data', 'temperature')],
ds_comp.data[('data', 'temperature')], 8)
assert_rel_equal(data[('data', 'cooling_time')],
ds_comp.data[('data', 'cooling_time')], 8)
assert_rel_equal(data[('data', 'cell_mass')],
ds_comp.data[('data', 'cell_mass')], 8)
def test_max_density_halo_quantities():
ds = yt.load(os.path.join(_dir_name, 'RD0009/RD0009'))
# Find the point of maximum density, center a sphere of radius
# 1 Mpc around it, and sum the masses inside
val, pos = ds.find_max('Density')
sp = ds.sphere(pos, (1000., 'kpc'))
ct = sp['creation_time']
dm = (ct < 0)
dm_mass = np.sum(sp['particle_mass'][dm]).in_units('Msun')
gas_mass = np.sum(sp['cell_mass'].in_units('Msun'))
# Also look at the radial profiles of density and temperature
# within these spheres. The bin size is chosen to make the profiles
# smooth and for each bin to be larger than the cell size.
ptest0 = yt.create_profile(sp, "radius", "density", n_bins=[20])
ptest1 = yt.create_profile(sp, "radius", "temperature", n_bins=[20])
# Save the quantities to be compared
data = {
"dm_mass": dm_mass,
"gas_mass": gas_mass,
"max_position": pos,
"density_profile": ptest0['density'],
"temperature_profile": ptest1['temperature']
}
# save your results file
filename = "max_density_halo_quantities.h5"
save_filename = os.path.join(_dir_name, filename)
yt.save_as_dataset(ds, save_filename, data)
compare_filename = os.path.join(test_data_dir, filename)
if generate_answers:
os.rename(save_filename, compare_filename)
return
ds_comp = yt.load(compare_filename)
assert_rel_equal(data["dm_mass"], ds_comp.data["dm_mass"], tolerance)
assert_rel_equal(data["gas_mass"], ds_comp.data["gas_mass"], tolerance)
assert_rel_equal(data["max_position"], ds_comp.data["max_position"],
tolerance)
assert_rel_equal(data["density_profile"], ds_comp.data["density_profile"],
tolerance)
assert_rel_equal(data["temperature_profile"],
ds_comp.data["temperature_profile"], tolerance)
def test_unequal_bin_field_profile(self):
density = np.random.random(128)
temperature = np.random.random(127)
cell_mass = np.random.random((128, 128))
my_data = {
"density": density,
"temperature": temperature,
"cell_mass": cell_mass}
fake_ds_med = {"current_time": yt.YTQuantity(10, "Myr")}
yt.save_as_dataset(fake_ds_med, "mydata.h5", my_data)
ds = yt.load('mydata.h5')
assert_raises(
YTProfileDataShape,
yt.PhasePlot, ds.data, 'temperature', 'density', 'cell_mass')
def test_hmf():
es = sim_dir_load(_pf_name, path=_dir_name)
es.get_time_series()
ds = es[-1]
hc = HaloCatalog(data_ds=ds,
finder_method='fof',
output_dir=os.path.join(_dir_name,
"halo_catalogs/catalog"))
hc.create()
masses = hc.data_source['particle_mass'].in_units('Msun')
h = ds.hubble_constant
mtot = np.log10(masses * 1.2) - np.log10(h)
masses_sim = np.sort(mtot)
sim_volume = ds.domain_width.in_units('Mpccm').prod()
n_cumulative_sim = np.arange(len(mtot), 0, -1)
masses_sim, unique_indices = np.unique(masses_sim, return_index=True)
n_cumulative_sim = n_cumulative_sim[unique_indices] / sim_volume
filename = 'hmf.h5'
save_filename = os.path.join(_dir_name, filename)
data = {'masses': masses_sim, 'n_sim': n_cumulative_sim}
yt.save_as_dataset(ds, save_filename, data)
# make a plot
fig = plt.figure(figsize=(8, 8))
plt.semilogy(masses_sim, n_cumulative_sim, '-')
plt.ylabel('Cumulative Halo Number Density $\mathrm{Mpc}^{-3}$',
fontsize=16)
plt.xlabel('log Mass/$\mathrm{M}_{\odot}$', fontsize=16)
plt.tick_params(labelsize=16)
plt.savefig(os.path.join(_dir_name, 'hmf.png'), format='png')
compare_filename = os.path.join(test_data_dir, filename)
if generate_answers:
os.rename(save_filename, compare_filename)
return
# do the comparison
ds_comp = yt.load(compare_filename)
# assert quality to 8 decimals
assert_rel_equal(data['masses'], ds_comp.data['masses'], 8)
assert_rel_equal(data['n_sim'], ds_comp.data['n_sim'], 8)
def test_output_number():
ds = yt.load(os.path.join(_dir_name, 'DD0000/DD0000'))
DDnum = len(glob.glob(os.path.join(_dir_name, 'DD????/DD????')))
RDnum = len(glob.glob(os.path.join(_dir_name, 'RD????/RD????')))
output_data = {'number_of_files': np.array([DDnum, RDnum])}
filename = "outputnum_results.h5"
save_filename = os.path.join(_dir_name, filename)
yt.save_as_dataset(ds, save_filename, output_data)
compare_filename = os.path.join(test_data_dir, filename)
if generate_answers:
os.rename(save_filename, compare_filename)
return
ds_comp = yt.load(compare_filename)
assert_equal(output_data['number_of_files'],
ds_comp.data['number_of_files'])
def save_simulation(es, filename=None):
def to_arr(my_list):
if hasattr(my_list[0], "units"):
f = es.arr
else:
f = np.array
return f(my_list)
fields = ["filename", "time"]
ex_keys = ["box_size", "initial_time", "final_time"]
if es.cosmological_simulation:
fields.append("redshift")
ex_keys.extend(["initial_redshift", "final_redshift"])
data = dict((field, to_arr([d[field] for d in es.all_outputs]))
for field in fields)
for i in range(data["filename"].size):
if data["filename"][i].startswith("./"):
data["filename"][i] = data["filename"][i][2:]
if filename is None:
filename = str(es)
filename = filename[:filename.rfind(".")] + ".h5"
extra_attrs = dict((field, getattr(es, field))
for field in ex_keys)
yt.save_as_dataset(es, filename, data, extra_attrs=extra_attrs)
def wrapper(*args):
# name the file after the function
filename = "%s.h5" % func.__name__
result_filename = os.path.join(test_results_dir, filename)
# check that answers exist
if not generate_results:
assert os.path.exists(result_filename), \
"Result file, %s, not found!" % result_filename
data = func(*args)
fn = yt.save_as_dataset({}, filename=filename, data=data)
# if generating, move files to results dir
if generate_results:
os.rename(filename, result_filename)
# if comparing, run the comparison
else:
ytdataset_compare(
filename, result_filename,
compare_func=compare_func, **kwargs)
bhinfo["dt"].append(
es.data["time"][min(i + 1, fns.size - 1)] -
es.data["time"][i])
print("In clump %s (cell %d): %d." %
(str(my_clump), d.argmin(), bhids[ib]))
break
clump_dmin.append(min_sep.to("pc"))
# save dataset with bh-clump distances
if cfn == os.path.join(data_dir,
"%s/RD0077/halo_2170858_clumps.h5"):
distance_fn = "%s_distances.h5" % cfn[:-3]
distance_data = \
{"particle_index": bhids,
"clump_distances": ds.arr(clump_dmin)}
yt.save_as_dataset(ds, distance_fn, distance_data)
my_dmin.extend(clump_dmin)
del ds
pbar.update(1)
pbar.finish()
sto.result_id = i
if my_dmin:
my_dmin = yt.YTArray(my_dmin)
print(my_dmin.min(), my_dmin.max())
sto.result = [contained, get_distribution(my_dmin)]
if yt.is_root():
ids = []
all_dmin = []
def find_stars(ds, filename, min_level=4):
fields = [
"particle_mass", "particle_index", "particle_type",
"particle_position_x", "particle_position_y", "particle_position_z",
"particle_velocity_x", "particle_velocity_y", "particle_velocity_z",
"creation_time", "metallicity_fraction"
]
bfields = [
"density", "temperature", "sound_speed", "velocity_x", "velocity_y",
"velocity_z", "dx"
]
data = defaultdict(list)
Zcr = ds.parameters['PopIIIMetalCriticalFraction']
ns = 0
if yt.is_root():
pbar = yt.get_pbar("Reading grids", ds.index.grids.size, parallel=True)
for i, grid in enumerate(ds.index.grids):
if ds.index.grid_levels[i][0] >= min_level:
ct = grid["creation_time"]
stars = (ct > 0)
if not stars.any():
grid.clear_data()
continue
Zfr = grid["metallicity_fraction"]
stars &= (Zfr < Zcr)
if not stars.any():
grid.clear_data()
continue
pt = grid["particle_type"]
stars &= ((pt == 1) | (pt == 5))
if not stars.any():
grid.clear_data()
continue
# mass is multiplied by 1e-20 when main-sequence lifetime is over
mass = grid["particle_mass"].to("Msun")
mass[mass < 1e-9] *= 1e20
stars &= (((mass >= 25) & (mass <= 140)) | (mass >= 260))
if stars.any():
ns += stars.sum()
for field in fields:
data[field].append(grid[field][stars])
grid.clear_data()
if yt.is_root():
pbar.update(i)
if yt.is_root():
pbar.finish()
ndata = {}
if len(data["particle_mass"]) > 0:
for field in fields:
a = ds.arr(np.empty(ns), data[field][0].units)
ip = 0
for chunk in data[field]:
a[ip:ip + chunk.size] = chunk
ip += chunk.size
ndata[field] = a
yt.mylog.info("Getting %d point field values for %s." %
(ndata["particle_mass"].size, str(ds)))
p = ds.arr([ndata["particle_position_%s" % ax] for ax in "xyz"])
p = np.rollaxis(p, 1)
bdatal = ds.find_field_values_at_points(bfields, p)
bdata = dict((field, bdatal[i]) for i, field in enumerate(bfields))
ndata.update(bdata)
con_args = ["center", "left_edge", "right_edge"]
extra_attrs = dict(
(field, getattr(ds, "domain_%s" % field)) for field in con_args)
extra_attrs["con_args"] = con_args
extra_attrs["data_type"] = "yt_data_container"
extra_attrs["container_type"] = "region"
extra_attrs["dimensionality"] = 3
ftypes = dict((field, "star") for field in fields + bfields)
yt.save_as_dataset(ds,
filename,
ndata,
field_types=ftypes,
extra_attrs=extra_attrs)
if __name__ == "__main__":
es = yt.load(sys.argv[1])
fns = es.data['filename'].astype(str)[::-1]
data_dir = es.directory
for fn in yt.parallel_objects(fns, njobs=-1, dynamic=True):
ds = yt.load(os.path.join(data_dir, fn))
output_file = os.path.join("pop3", f"{ds.basename}.h5")
if os.path.exists(output_file):
continue
add_p2p_particle_filters(ds)
region = ds.box(ds.parameters["RefineRegionLeftEdge"],
ds.parameters["RefineRegionRightEdge"])
fields = [
"particle_mass", "particle_index", "particle_type",
"particle_position_x", "particle_position_y",
"particle_position_z", "particle_velocity_x",
"particle_velocity_y", "particle_velocity_z", "creation_time",
"metallicity_fraction"
]
data = dict((field, region[('pop3', field)]) for field in fields)
ftypes = dict((field, 'pop3') for field in fields)
yt.save_as_dataset(ds,
filename=output_file,
data=data,
field_types=ftypes)
from collections import defaultdict
import numpy as np
import sys
import yt
if __name__ == "__main__":
fns = [line.strip() for line in open('pfs.dat', 'r').readlines()]
parameters = ['RefineRegionLeftEdge', 'RefineRegionRightEdge']
data = defaultdict(list)
for fn in fns:
ds = yt.load(fn)
data['filename'].append(fn)
data['time'].append(ds.current_time.to('Myr'))
data['redshift'].append(ds.current_redshift)
for par in parameters:
data[par].append(ds.parameters[par])
data['filename'] = np.array(data['filename'])
data['time'] = ds.arr(data['time'])
data['redshift'] = np.array(data['redshift'])
data['RefineRegionLeftEdge'] = ds.arr(data['RefineRegionLeftEdge'],
'unitary')
data['RefineRegionRightEdge'] = ds.arr(data['RefineRegionRightEdge'],
'unitary')
yt.save_as_dataset(ds, filename='simulation.h5', data=data)
if os.environ.get("METAL_COOLING", 0) == "1":
plots.extend(
pyplot.loglog(data["density"],
data["dust_temperature"],
color="black",
linestyle="--",
label="T$_{dust}$"))
pyplot.xlabel("$\\rho$ [g/cm$^{3}$]")
pyplot.ylabel("T [K]")
pyplot.twinx()
plots.extend(
pyplot.loglog(data["density"],
data["H2I"] / data["density"],
color="red",
label="f$_{H2}$"))
pyplot.ylabel("H$_{2}$ fraction")
pyplot.legend(plots, [plot.get_label() for plot in plots],
loc="lower right")
if os.environ.get("METAL_COOLING", 0) == "1":
output = "freefall_metal"
else:
output = "freefall"
pyplot.tight_layout()
pyplot.savefig("%s.png" % output)
# save data arrays as a yt dataset
yt.save_as_dataset({}, "%s.h5" % output, data)
# timestepping safety factor
safety_factor = 0.01
# let gas cool at constant density
data = evolve_constant_density(fc,
final_time=final_time,
safety_factor=safety_factor)
p1, = pyplot.loglog(data["time"].to("Myr"),
data["temperature"],
color="black",
label="T")
pyplot.xlabel("Time [Myr]")
pyplot.ylabel("T [K]")
data["mu"] = data["temperature"] / \
(data["energy"] * (my_chemistry.Gamma - 1.) *
fc.chemistry_data.temperature_units)
pyplot.twinx()
p2, = pyplot.semilogx(data["time"].to("Myr"),
data["mu"],
color="red",
label="$\\mu$")
pyplot.ylabel("$\\mu$")
pyplot.legend([p1, p2], ["T", "$\\mu$"], fancybox=True, loc="center left")
pyplot.savefig("cooling_cell.png")
# save data arrays as a yt dataset
yt.save_as_dataset({}, "cooling_cell.h5", data)
def cooling_cell(density=12.2,
initial_temperature=2.0E4,
final_time=30.0,
metal_fraction=4.0E-4,
make_plot=False,
save_output=False,
primordial_chemistry=2,
outname=None,
save_H2_fraction=False,
return_result=False,
verbose=False,
H2_converge=None,
*args,
**kwargs):
current_redshift = 0.
# Set solver parameters
my_chemistry = chemistry_data()
my_chemistry.use_grackle = 1
my_chemistry.with_radiative_cooling = 1
my_chemistry.primordial_chemistry = primordial_chemistry
my_chemistry.metal_cooling = 1
my_chemistry.UVbackground = 1
my_chemistry.self_shielding_method = 3
if primordial_chemistry > 1:
my_chemistry.H2_self_shielding = 2
my_chemistry.h2_on_dust = 1
my_chemistry.three_body_rate = 4
grackle_dir = "/home/aemerick/code/grackle-emerick/"
my_chemistry.grackle_data_file = os.sep.join( #['/home/aemerick/code/grackle-emerick/input/CloudyData_UVB=HM2012.h5'])
[grackle_dir, "input", "CloudyData_UVB=HM2012_shielded.h5"])
# set the factors
my_chemistry.LW_factor = kwargs.get("LW_factor", 1.0)
my_chemistry.k27_factor = kwargs.get("k27_factor", 1.0)
#if 'LW_factor' in kwargs.keys():
# my_chemistry.LW_factor = kwargs['LW_factor']
#else:
# my_chemistry.LW_factor = 1.0
#if 'k27_factor' in kwargs.keys():
# my_chemistry.k27_factor = kwargs['k27_factor']
#else:
# my_chemistry.k27_factor = 1.0
# Set units
my_chemistry.comoving_coordinates = 0 # proper units
my_chemistry.a_units = 1.0
my_chemistry.a_value = 1. / (1. + current_redshift) / \
my_chemistry.a_units
my_chemistry.density_units = mass_hydrogen_cgs # rho = 1.0 is 1.67e-24 g
my_chemistry.length_units = cm_per_mpc # 1 Mpc in cm
my_chemistry.time_units = sec_per_Myr # 1 Myr in s
my_chemistry.velocity_units = my_chemistry.a_units * \
(my_chemistry.length_units / my_chemistry.a_value) / \
my_chemistry.time_units
rval = my_chemistry.initialize()
fc = FluidContainer(my_chemistry, 1)
fc["density"][:] = density
if my_chemistry.primordial_chemistry > 0:
fc["HI"][:] = 0.76 * fc["density"]
fc["HII"][:] = tiny_number * fc["density"]
fc["HeI"][:] = (1.0 - 0.76) * fc["density"]
fc["HeII"][:] = tiny_number * fc["density"]
fc["HeIII"][:] = tiny_number * fc["density"]
if my_chemistry.primordial_chemistry > 1:
fc["H2I"][:] = tiny_number * fc["density"]
fc["H2II"][:] = tiny_number * fc["density"]
fc["HM"][:] = tiny_number * fc["density"]
fc["de"][:] = tiny_number * fc["density"]
fc['H2_self_shielding_length'][:] = 1.8E-6
if my_chemistry.primordial_chemistry > 2:
fc["DI"][:] = 2.0 * 3.4e-5 * fc["density"]
fc["DII"][:] = tiny_number * fc["density"]
fc["HDI"][:] = tiny_number * fc["density"]
if my_chemistry.metal_cooling == 1:
fc["metal"][:] = metal_fraction * fc["density"] * \
my_chemistry.SolarMetalFractionByMass
fc["x-velocity"][:] = 0.0
fc["y-velocity"][:] = 0.0
fc["z-velocity"][:] = 0.0
fc["energy"][:] = initial_temperature / \
fc.chemistry_data.temperature_units
fc.calculate_temperature()
fc["energy"][:] *= initial_temperature / fc["temperature"]
# timestepping safety factor
safety_factor = 0.001
# let gas cool at constant density
#if verbose:
print("Beginning Run")
data = evolve_constant_density(fc,
final_time=final_time,
H2_converge=H2_converge,
safety_factor=safety_factor,
verbose=verbose)
#else:
# print "Beginning Run"
# with NoStdStreams():
# data = evolve_constant_density(
# fc, final_time=final_time, H2_converge = 1.0E-6,
# safety_factor=safety_factor)
# print "Ending Run"
if make_plot:
p1, = plt.loglog(data["time"].to("Myr"),
data["temperature"],
color="black",
label="T")
plt.xlabel("Time [Myr]")
plt.ylabel("T [K]")
data["mu"] = data["temperature"] / \
(data["energy"] * (my_chemistry.Gamma - 1.) *
fc.chemistry_data.temperature_units)
plt.twinx()
p2, = plt.semilogx(data["time"].to("Myr"),
data["mu"],
color="red",
label="$\\mu$")
plt.ylabel("$\\mu$")
plt.legend([p1, p2], ["T", "$\\mu$"], fancybox=True, loc="center left")
plt.savefig("cooling_cell.png")
# save data arrays as a yt dataset
if outname is None:
outname = 'cooling_cell_%.2f_%.2f' % (my_chemistry.k27_factor,
my_chemistry.LW_factor)
if save_output:
yt.save_as_dataset({}, outname + '.h5', data)
if my_chemistry.primordial_chemistry > 1:
H2_fraction = (data['H2I'] + data['H2II']) / data['density']
else:
H2_fraction = np.zeros(np.size(data['density']))
if save_H2_fraction:
#np.savetxt(outname + ".dat", [data['time'], H2_fraction])
f = open("all_runs_d_%.2f.dat" % (density), "a")
# f.write("# k27 LW f_H2 T time\n")
f.write("%8.8E %8.8E %8.8E %8.8E %8.8E \n" %
(my_chemistry.k27_factor, my_chemistry.LW_factor,
H2_fraction[-1], data['temperature'][-1], data['time'][-1]))
f.close()
if return_result:
return data
else:
return
def make_onezone_ray(density=1e-26,
temperature=1000,
metallicity=0.3,
length=10,
redshift=0,
filename='ray.h5',
column_densities=None):
"""
Create a one-zone ray object for use as test data. The ray
consists of a single absorber of hydrodynamic characteristics
specified in the function kwargs. It makes an excellent test dataset
to test Trident's capabilities for making absorption spectra.
You can specify the column densities of different ions explicitly using
the column_densities keyword, or you can let Trident calculate the
different ion columns internally from the density, temperature, and
metallicity fields.
Using the defaults will produce a ray that should result in a spectrum
with a good number of absorption features.
**Parameters**
:density: float, optional
The gas density value of the ray in g/cm**3
Default: 1e-26
:temperature: float, optional
The gas temperature value of the ray in K
Default: 10**3
:metallicity: float, optional
The gas metallicity value of the ray in Zsun
Default: 0.3
:length: float, optional
The length of the ray in kpc
Default: 10.
:redshift: float, optional
The redshift of the ray
Default: 0
:filename: string, optional
The filename to which to save the ray to disk. Due to the
mechanism for passing rays, the ray data must be saved to disk.
Default: 'ray.h5'
:column_densities: dict, optional
The user can create a dictionary which adds more number density ion
fields to the ray. Each key in the dictionary should be the desired
ion field name according to the field name format:
i.e. "<ELEMENT>_p<IONSTATE>_number_density"
e.g. neutral hydrogen = "H_p0_number_density".
The corresponding value for each key should be the desired column
density of that ion in cm**-2. See example below.
Default: None
**Returns**
A YT LightRay object
**Example**
Create a one-zone ray, and generate a COS spectrum from that ray.
>>> import trident
>>> ray = trident.make_onezone_ray()
>>> sg = trident.SpectrumGenerator('COS')
>>> sg.make_spectrum(ray)
>>> sg.plot_spectrum('spec_raw.png')
Create a one-zone ray with an HI column density of 1e21 (DLA) and generate
a COS spectrum from that ray for just the Lyman alpha line.
>>> import trident
>>> ds = trident.make_onezone_ray(column_densities={'H_number_density': 1e21})
>>> sg = trident.SpectrumGenerator('COS')
>>> sg.make_spectrum(ray, lines=['Ly a'])
>>> sg.plot_spectrum('spec_raw.png')
"""
from yt import save_as_dataset
length = YTArray([length], "kpc")
data = {
"density": YTArray([density], "g/cm**3"),
"metallicity": YTArray([metallicity], "Zsun"),
"dl": length,
"temperature": YTArray([temperature], "K"),
"redshift": np.array([redshift]),
"redshift_eff": np.array([redshift]),
"velocity_los": YTArray([0.], "cm/s"),
"x": length / 2,
"dx": length,
"y": length / 2,
"dy": length,
"z": length / 2,
"dz": length
}
extra_attrs = {"data_type": "yt_light_ray", "dimensionality": 3}
field_types = dict([(field, "grid") for field in data.keys()])
# Add additional number_density fields to dataset
if column_densities:
for k, v in six.iteritems(column_densities):
# Assure we add X_number_density for neutral ions
# instead of X_p0_number_density
key_string_list = k.split('_')
if key_string_list[1] == 'p0':
k = '_'.join([
key_string_list[0], key_string_list[2], key_string_list[3]
])
v = YTArray([v], 'cm**-2')
data[k] = v / length
field_types[k] = 'grid'
ds = {
"current_time": 0.,
"current_redshift": 0.,
"cosmological_simulation": 0.,
"domain_left_edge": np.zeros(3) * length,
"domain_right_edge": np.ones(3) * length,
"periodicity": [True] * 3
}
save_as_dataset(ds,
filename,
data,
field_types=field_types,
extra_attrs=extra_attrs)
# load dataset and make spectrum
ray = load(filename)
return ray
cooling_rate = fc.chemistry_data.cooling_units * fc["energy"] / \
np.abs(fc["cooling_time"]) / density_proper
data = {}
t_sort = np.argsort(fc["temperature"])
for field in fc.density_fields:
data[field] = yt.YTArray(
fc[field][t_sort] * my_chemistry.density_units, "g/cm**3")
data["energy"] = yt.YTArray(
fc["energy"][t_sort] * my_chemistry.energy_units, "erg/g")
data["temperature"] = yt.YTArray(fc["temperature"][t_sort], "K")
data["pressure"] = yt.YTArray(
fc["pressure"][t_sort] * my_chemistry.pressure_units, "dyne/cm**2")
data["cooling_time"] = yt.YTArray(fc["cooling_time"][t_sort], "s")
data["cooling_rate"] = yt.YTArray(cooling_rate[t_sort], "erg*cm**3/s")
pyplot.loglog(data["temperature"], data["cooling_rate"], color="black")
pyplot.xlabel('T [K]')
pyplot.ylabel('$\\Lambda$ [erg s$^{-1}$ cm$^{3}$]')
# save data arrays as a yt dataset
if 'PRIMORDIAL_CHEM' in os.environ:
ds_name = 'cooling_rate.pc%s.h5' % os.environ['PRIMORDIAL_CHEM']
im_name = 'cooling_rate.pc%s.png' % os.environ['PRIMORDIAL_CHEM']
else:
ds_name = 'cooling_rate.h5'
im_name = 'cooling_rate.png'
pyplot.tight_layout()
pyplot.savefig(im_name)
yt.save_as_dataset({}, ds_name, data)
black_holes[pid]["time"].append(ds.current_time)
mbh[i] = black_holes[pid]["mass"][-1]
mdot = bondi_hoyle_accretion_rate(ds.r, mbh)
for i, pid in enumerate(pids):
if pid not in black_holes:
continue
black_holes[pid]["mdot"].append(mdot[i])
black_holes[pid]["density"].append(ds.r["density"][i])
black_holes[pid]["temperature"].append(ds.r["temperature"][i])
for pid in black_holes:
if black_holes[pid]["time"][-1] >= ds.current_time:
continue
t_el = ds.current_time - black_holes[pid]["time"][-1]
m_new = black_holes[pid]["mass"][-1] + \
black_holes[pid]["mdot"][-1] * t_el
black_holes[pid]["mass"].append(m_new)
black_holes[pid]["time"].append(ds.current_time)
data = {}
for bh in black_holes:
for field in black_holes[bh]:
key = ("p_%d_%s" % (bh, field))
data[key] = es.arr(black_holes[bh][field])
ftypes=dict((field, "star") for field in data)
yt.save_as_dataset(es, os.path.join(data_dir, "black_holes_bh.h5"),
data, field_types=ftypes)
