def run(self):
curdir = os.getcwd()
tmpdir = tempfile.mkdtemp()
os.chdir(tmpdir)
dds = data_dir_load(self.data_ds_fn)
hds = data_dir_load(self.halos_ds_fn)
hc = HaloCatalog(data_ds=dds,
halos_ds=hds,
output_dir=os.path.join(tmpdir, str(dds)))
hc.add_callback("sphere")
hc.add_quantity("nstars")
hc.create()
fn = os.path.join(tmpdir, str(dds), "%s.0.h5" % str(dds))
ds = load(fn)
ad = ds.all_data()
mi, ma = ad.quantities.extrema("nstars")
mean = ad.quantities.weighted_average_quantity("nstars",
"particle_ones")
os.chdir(curdir)
shutil.rmtree(tmpdir)
return np.array([mean, mi, ma])
import yt
from yt.analysis_modules.halo_analysis.api import HaloCatalog
from yt.analysis_modules.halo_analysis.halo_callbacks import add_callback
ds = yt.load(directory + '/' + directory)
halos_ds = yt.load('halo_catalogs/catalog/catalog.0.h5')
hc = HaloCatalog(data_ds=ds, halos_ds=halos_ds)
hc.load()
def count_particles(halo):
"""
Creates a 30kpc sphere around a center of mass and counts the number of contained particles.
"""
x = halo.quantities['particle_position_x']
y = halo.quantities['particle_position_y']
z = halo.quantities['particle_position_z']
sphere = halo.halo_catalog.data_ds.sphere([x, y, z], (30, 'kpc'))
n_particles = len(sphere['x'])
halo.particle_count = n_particles
print halo.particle_count
add_callback('count_particles', count_particles)
# Run the analysis.
hc.add_callback('count_particles')
hc.create()
'gas': ('gas', 'density'),
'stars': ('deposit', 'stars_density'),
'dark_matter': ('deposit', 'dark_matter_density')
}
def stars(pfilter, data):
filter = data[(pfilter.filtered_type, 'particle_type')] == 2
return filter
def dark_matter(pfilter, data):
filter = data[(pfilter.filtered_type, 'particle_type')] == 1
return filter
yt.add_particle_filter('stars', function=stars, filtered_type='all', requires=['particle_type'])
yt.add_particle_filter('dark_matter', function=dark_matter, filtered_type='all', requires=['particle_type'])
ds = yt.load(directory+'/'+directory)
ds.add_particle_filter('stars')
ds.add_particle_filter('dark_matter')
halos_ds = yt.load('halo_catalogs/catalog/catalog.0.h5')
hc = HaloCatalog(data_ds=ds, halos_ds=halos_ds)
hc.add_callback("sphere", factor=2.0)
hc.add_callback("profile", ["radius"], [("gas", "overdensity")], weight_field="cell_volume", accumulation=True, storage="virial_quantities_profiles")
hc.add_callback("virial_quantities", ["radius"], profile_storage = "virial_quantities_profiles")
hc.add_callback('sphere', radius_field='radius_200', factor=5, field_parameters=dict(virial_radius=('quantity', 'radius_200')))
hc.add_callback('profile', 'virial_radius_fraction', [('gas','temperature')], storage='virial_profiles', weight_field='cell_mass', accumulation=False, output_dir='profiles')
hc.load()
args = parser.parse_args()
directory = args.directory
import yt
from yt.analysis_modules.halo_analysis.api import HaloCatalog
from yt.analysis_modules.halo_analysis.halo_callbacks import add_callback
ds = yt.load(directory+'/'+directory)
halos_ds = yt.load('halo_catalogs/catalog/catalog.0.h5')
hc = HaloCatalog(data_ds=ds, halos_ds=halos_ds)
hc.load()
def count_particles(halo):
"""
Creates a 30kpc sphere around a center of mass and counts the number of contained particles.
"""
x = halo.quantities['particle_position_x']
y = halo.quantities['particle_position_y']
z = halo.quantities['particle_position_z']
sphere = halo.halo_catalog.data_ds.sphere([x, y, z], (30, 'kpc'))
n_particles = len(sphere['x'])
halo.particle_count = n_particles
print halo.particle_count
add_callback('count_particles', count_particles)
# Run the analysis.
hc.add_callback('count_particles')
hc.create()
from yt.analysis_modules.halo_analysis.api import HaloCatalog
# Load the data set with the full simulation information
# and rockstar halos
data_ds = yt.load('Enzo_64/RD0006/RedshiftOutput0006')
halos_ds = yt.load('rockstar_halos/halos_0.0.bin')
# Instantiate a catalog using those two paramter files
hc = HaloCatalog(data_ds=data_ds, halos_ds=halos_ds)
# Filter out less massive halos
hc.add_filter("quantity_value", "particle_mass", ">", 1e14, "Msun")
# attach a sphere object to each halo whose radius extends
# to twice the radius of the halo
hc.add_callback("sphere", factor=2.0)
# use the sphere to calculate radial profiles of gas density
# weighted by cell volume in terms of the virial radius
hc.add_callback("profile",
x_field="radius",
y_fields=[("gas", "overdensity")],
weight_field="cell_volume",
accumulation=False,
storage="virial_quantities_profiles")
hc.add_callback("virial_quantities", ["radius"],
profile_storage="virial_quantities_profiles")
hc.add_callback('delete_attribute', 'virial_quantities_profiles')
field_params = dict(virial_radius=('quantity', 'radius_200'))
path_man = path_manager(args.root_data_dir,
args.root_output_dir,
sim_num=args.sim_number,
snap_name=args.time_slice,
data_dir_prefix=args.data_prefix)
if not dataset_finished(path_man.get_exp_path()):
print("Sim number %s is not yet finished. Skipping." % args.sim_number)
sys.exit(0)
if yt.is_root():
path_man.ensure_directories()
ds = yt.load(path_man.get_dataset_path())
halos_ds = yt.load(path_man.get_rockstar_halo_dirname() + "/halos_0.0.bin")
hc = HaloCatalog(data_ds=ds,
halos_ds=halos_ds,
output_dir=path_man.get_rockstar_catalogue_dirname())
hc.add_callback('save_quantities',
prefix='orig',
fields=[
'virial_radius', 'particle_position_x',
'particle_position_y', 'particle_position_z',
'particle_mass'
])
hc.add_callback('get_additional_halo_properties')
# hc.add_callback('recenter_halo', radius_field='virial_radius', units='pc')
hc.add_recipe('calculate_virial_quantities', ['radius', 'matter_mass'])
hc.create()
x = halo.quantities['particle_position_x']
y = halo.quantities['particle_position_y']
z = halo.quantities['particle_position_z']
return (lower_bound < x < upper_bound) and (lower_bound < y < upper_bound) and (lower_bound < z < upper_bound)
def filter_particles(halo):
"""
Checks to see if there are more than a certain number of particles within a sphere.
"""
minimum_particles = 50000
x = halo.quantities['particle_position_x']
y = halo.quantities['particle_position_y']
z = halo.quantities['particle_position_z']
sphere = halo.halo_catalog.data_ds.sphere([x, y, z], (30, 'kpc'))
n_particles = len(sphere['x'])
return n_particles > minimum_particles
# Add to the filter registry.
add_filter('position', filter_position)
add_filter('particles', filter_particles)
# Run the analysis.
hc.add_filter('quantity_value', 'particle_mass', '<', 1e13, 'Msun')
hc.add_callback('iterative_center_of_mass')
hc.add_filter('position')
hc.add_filter('particles')
hc.create()
data_ds = yt.load('Enzo_64/RD0006/RedshiftOutput0006')
halos_ds = yt.load('rockstar_halos/halos_0.0.bin')
# Instantiate a catalog using those two paramter files
hc = HaloCatalog(data_ds=data_ds, halos_ds=halos_ds)
# Filter out less massive halos
hc.add_filter("quantity_value", "particle_mass", ">", 1e14, "Msun")
# This recipe creates a spherical data container, computes
# radial profiles, and calculates r_200 and M_200.
hc.add_recipe("calculate_virial_quantities", ["radius", "matter_mass"])
# Create a sphere container with radius 5x r_200.
field_params = dict(virial_radius=('quantity', 'radius_200'))
hc.add_callback('sphere', radius_field='radius_200', factor=5,
field_parameters=field_params)
# Compute profiles of T vs. r/r_200
hc.add_callback('profile', ['virial_radius_fraction'],
[('gas', 'temperature')],
storage='virial_profiles',
weight_field='cell_mass',
accumulation=False, output_dir='profiles')
# Save the profiles
hc.add_callback("save_profiles", storage="virial_profiles",
output_dir="profiles")
hc.create()
