def get_circular_velocity( self,
galaxy_cut,
length_scale,
metafile_dir,
ptypes = data_constants.STANDARD_PTYPES,
):
'''Get the circular velocity at galaxy_cut*length_scale.
Args:
galaxy_cut (float) :
galaxy_cut*length_scale defines the radius around the center of the halo within which to get the mass.
length_scale (str) :
galaxy_cut*length_scale defines the radius around the center of the halo within which to get the mass.
metafile_dir (str) :
Directory containing metafile data, for getting out the redshift given a snapshot.
ptypes (list of strs) :
Particle types to count the mass inside the halo of.
Returns:
v_circ (np.ndarray)
Circular velocity at galaxy_cut*length_scale using mass from the given ptypes.
'''
# Get the redshift, for converting the radius to pkpc/h.
metafile_reader = read_metafile.MetafileReader( metafile_dir )
metafile_reader.get_snapshot_times()
redshift = metafile_reader.snapshot_times['redshift'][self.halos_snum]
# Get the radius in pkpc/h
try:
radius = galaxy_cut*self.halos[length_scale]
except KeyError:
radius = galaxy_cut*self.halos_add[length_scale]
radius /= ( 1. + redshift )
# Get the mass in Msun/h
masses = []
for ptype in ptypes:
mass_key = self.key_parser.get_enclosed_mass_key( ptype, galaxy_cut, length_scale )
try:
ptype_mass = self.halos_add[mass_key]
except:
ptype_mass = self.halos[mass_key]
masses.append( ptype_mass )
mass = np.array( masses ).sum( axis=0 )
# Now get the circular velocity out
# (note that we don't need to bother with converting out the 1/h's, because in this particular case they'll cancel)
v_circ = astro_utils.circular_velocity( radius, mass )
return v_circ
def save_custom_mtree_halos( self, snums, halo_ids, metafile_dir, ):
'''Save a custom merger tree.
Args:
snums (array-like or int) :
What snapshots to generate the custom merger tree for.
If a single integer, then snums will start at that integer and count backwards by single snapshots for the
length of halo_ids
halo_ids (array-like) :
halo_ids[i] is the AHF_halos halo ID for the merger tree halo at snums[i].
metafile_dir (str) :
Directory for the metafile (used to get simulation redshift).
Modifies:
self.data_dir/halo_00000_custom.dat (text file) : Saves the custom merger tree at this location.
'''
if isinstance( snums, int ):
snums = np.arange( snums, snums - len( halo_ids ), -1 )
# Concatenate the data
ahf_frames = []
for snum, halo_id in zip( snums, halo_ids, ):
print( "Getting data for snapshot {}".format( snum ) )
self.get_halos( snum )
ahf_frames.append( self.halos.loc[halo_id:halo_id] )
custom_mtree_halo = pd.concat( ahf_frames )
# Make sure to store the IDs too
custom_mtree_halo['ID'] = halo_ids
# Add in the snapshots, and use them as the index
custom_mtree_halo['snum'] = snums
custom_mtree_halo = custom_mtree_halo.set_index( 'snum', )
# Get and save the redshift
metafile_reader = read_metafile.MetafileReader( metafile_dir )
metafile_reader.get_snapshot_times()
custom_mtree_halo['redshift'] = metafile_reader.snapshot_times['redshift'][snums]
# Save the data
save_filepath = os.path.join( self.data_dir, 'halo_00000_custom.dat' )
custom_mtree_halo.to_csv( save_filepath, sep='\t' )
def get_accurate_redshift( self, metafile_dir ):
'''Get a better values of the redshift than what's stored in the Halo filename, by loading them from an external file.
Args:
metafile_dir (str): The directory the snapshot_times are stored in.
Modifies:
self.mtree_halos (dict of pd.DataFrames): Updates the redshift column
'''
# Get the redshift data out
metafile_reader = read_metafile.MetafileReader( metafile_dir )
metafile_reader.get_snapshot_times()
# Replace the old data
for halo_id in self.mtree_halos.keys():
mtree_halo = self.mtree_halos[ halo_id ]
# Read and replace the new redshift
new_redshift = metafile_reader.snapshot_times['redshift'][ mtree_halo.index ]
mtree_halo['redshift'] = new_redshift
def get_analytic_concentration( self, metafile_dir, type_of_halo_id='merger_tree' ):
'''Get analytic values for the halo concentration, using colossus, Benedikt Diemer's cosmology code
( https://bitbucket.org/bdiemer/colossus ; http://www.benediktdiemer.com/code/colossus/ ).
Assumptions:
- We're using the default formula of Diemer&Kravtstov15
- We're using the Bryan&Norman1998 version of the virial radius.
Args:
metafile_dir (str): The directory the snapshot_times are stored in.
type_of_halo_id (str): 'merger_tree' if the halo id is a merger tree halo id.
'halos' if the halo id is a *.AHF_halos halo id.
Returns
c_vir (np.array of floats): The concentration, defined as R_vir/r_scale.
'''
# Include imports here, because this function may not in general work if colossus is not available,
# and the rest of the module should still be made useable
# There may be some warnings here about the version of scipy colossus uses, as opposed to the version galaxy_dive uses
import colossus.cosmology.cosmology as co_cosmology
import colossus.halo.concentration as co_concentration
# Get simulation parameters, for use in creating a cosmology
metafile_reader = read_metafile.MetafileReader( metafile_dir )
metafile_reader.get_used_parameters()
# Setup the cosmology used by the simulations
sim_cosmo = {
'flat': True,
'H0' : float( metafile_reader.used_parameters['HubbleParam'] )*100.,
'Om0' : float( metafile_reader.used_parameters['Omega0'] ),
'Ob0' : float( metafile_reader.used_parameters['OmegaBaryon'] ),
'sigma8' : co_cosmology.cosmologies['WMAP9']['sigma8'], # Use WMAP9 for values we don't store in our simulations explicitly.
'ns' : co_cosmology.cosmologies['WMAP9']['ns'], # Use WMAP9 for values we don't store in our simulations explicitly.
}
cosmo = co_cosmology.setCosmology( 'sim_cosmo', sim_cosmo )
if type_of_halo_id == 'merger_tree':
# Loop over all mt halos
for halo_id in self.mtree_halos.keys():
# Load the data
mtree_halo = self.mtree_halos[ halo_id ]
# Get the concentration out
c_vir = []
for m_vir, z in zip( mtree_halo['Mvir'], mtree_halo['redshift'] ):
c = co_concentration.concentration( m_vir, 'vir', z, model='diemer15', statistic='median')
c_vir.append( c )
# Turn the concentration into an array
c_vir = np.array( c_vir )
# Save the concentration
mtree_halo['cAnalytic'] = c_vir
elif type_of_halo_id == 'halos':
# Get the redshift for the halo file.
metafile_reader.get_snapshot_times()
redshift = metafile_reader.snapshot_times['redshift'][self.halos_snum]
# Get the concentration
c = co_concentration.concentration( self.halos['Mvir'], 'vir', redshift, model='diemer15', statistic='median')
return c
def setUp(self):
self.metafile_reader = read_metafile.MetafileReader(sdir)