return data["particle_mass"] <= 1.01 * min_dm_mass
add_particle_filter("max_res_dark_matter", function=MaxResDarkMatter, \
filtered_type='dark_matter', requires=["particle_mass"])
ds.add_particle_filter('max_res_dark_matter')
# If desired, we can see the total number of DM and High-res DM particles
#if yt.is_root():
# print("Simulation has %d DM particles." %
# ad['dark_matter','particle_type'].shape)
# print("Simulation has %d Highest Res DM particles." %
# ad['max_res_dark_matter', 'particle_type'].shape)
# Run the halo catalog on the dataset only on the highest resolution dark matter
# particles
hc = HaloCatalog(data_ds=ds, finder_method='rockstar', \
finder_kwargs={'dm_only':True, 'particle_type':'max_res_dark_matter'})
hc.create()
# Or alternatively, just run the RockstarHaloFinder and later import the
# output file as necessary. You can skip this step if you've already run it
# once, but be careful since subsequent halo finds will overwrite this data.
#rhf = RockstarHaloFinder(ds, particle_type="max_res_dark_matter")
#rhf.run()
# Load the halo list from a rockstar output for this dataset
# Create a projection with the halos overplot on top
#halos = yt.load('rockstar_halos/halos_0.0.bin')
#hc = HaloCatalog(halos_ds=halos)
#hc.load()
# Regardless of your method of creating the halo catalog, use it to overplot the
# halos on a projection.
# and apply it to dataset
def MaxResDarkMatter(pfilter, data):
return data["particle_mass"] <= 1.01 * min_dm_mass
add_particle_filter("max_res_dark_matter", function=MaxResDarkMatter, \
filtered_type='dark_matter', requires=["particle_mass"])
ds.add_particle_filter('max_res_dark_matter')
# If desired, we can see the total number of DM and High-res DM particles
#if yt.is_root():
# print "Simulation has %d DM particles." % ad['dark_matter','particle_type'].shape
# print "Simulation has %d Highest Res DM particles." % ad['max_res_dark_matter', 'particle_type'].shape
# Run the halo catalog on the dataset only on the highest resolution dark matter
# particles
hc = HaloCatalog(data_ds=ds, finder_method='rockstar', \
finder_kwargs={'dm_only':True, 'particle_type':'max_res_dark_matter'})
hc.create()
# Or alternatively, just run the RockstarHaloFinder and later import the
# output file as necessary. You can skip this step if you've already run it
# once, but be careful since subsequent halo finds will overwrite this data.
#rhf = RockstarHaloFinder(ds, particle_type="max_res_dark_matter")
#rhf.run()
# Load the halo list from a rockstar output for this dataset
# Create a projection with the halos overplot on top
#halos = yt.load('rockstar_halos/halos_0.0.bin')
#hc = HaloCatalog(halos_ds=halos)
#hc.load()
# Regardless of your method of creating the halo catalog, use it to overplot the
# halos on a projection.
import yt
from yt.analysis_modules.halo_analysis.halo_catalog import HaloCatalog
# Load the dataset
ds = yt.load("Enzo_64/RD0006/RedshiftOutput0006")
# Load the halo list from a rockstar output for this dataset
halos = yt.load('rockstar_halos/halos_0.0.bin')
# Create the halo catalog from this halo list
hc = HaloCatalog(halos_ds=halos)
hc.load()
# Create a projection with the halos overplot on top
p = yt.ProjectionPlot(ds, "x", "density")
p.annotate_halos(hc)
p.save()
