def main_worker(haloi,ffo, stardata,snapshot,min_snap):
hpath = "/bigbang/data/AnnaGroup/caterpillar/halos/middle_mass_halos/H1387186/H1387186_EB_Z127_P7_LN7_LX14_O4_NV4/"
z_current = htils.get_z_snap(hpath,snapshot)[0]
#print "Current redshift",z_current
#---# case empty
if ffo['key'] == 0 :
if snapshot != min_snap:
# LW to be computed here
# Way it is done now is to lookback one snapshot, then find galaxies with stars in them, track them to rhe start
# and make arrays of star mass and ages for that 'one' position at the last snapshot. Thus you create an SED
# accordingly and then compute ht ekde/kdi/Jlw
gal_mass, gal_age, pos_gal = makelw.lwgal(snapshot, stardata)
kde , kdi , jlw = lwlib.sedcompute(gal_mass,gal_age,pos_gal)
jlw_global = lwlib.lwglobal(z_current)
# Global J is computed as a fit
total_lw = jlw + jlw_global
#step2: Pop III or no Pop III
make_PopIII = popiii.checkpopiii_lw(mvir, total_lw)
#step3: pass final output
if make_PopIII == 'yes':
smass_iii = popiii.makepopiii(niii)
mstar = smass_iii
# NEED TO PRINT THIS TO A FILE: GLOBALSTARCAT
print "MADE IT"
key_update = 3
#---# case DCBH
elif ffo['key'] == 1 :
#do nothing here
ffo['bh_switch'] = 1
#---# case Pop II stars already exist
# here is where the prog_info comes in handy
elif ffo['key'] == 2 :
popii.makepopii(haloi['snapshot'],haloi['mvir'],haloi['vmax'],haloi['rvir'],ffo['mvir_prog'],ffo['coldgas'],ffo['hotgas'],ffo['blowout'],ffo['mstar'])
# NEED TO PRINT THIS TO A FILE: GLOBALSTARCAT
ffo['key'] = 2
#---# case Pop III formed here at some point , make Pop II now
elif ffo['key'] == 3 :
popiii.makepopiii(haloi['snapshot'],haloi['mvir'],haloi['vmax'],haloi['rvir'],ffo['mvir_prog'],ffo['coldgas'],ffo['hotgas'],ffo['blowout'],ffo['mstar'])
# NEED TO PRINT THIS TO A FILE: GLOBALSTARCAT
ffo['key'] = 2
if ffo['key'] != 1:
ffo['bh_switch'] = 0
#return the output fields
return ffo['key'], ffo['bh_switch'], ffo['coldgas'], ffo['hotgas'], ffo['blowout'], ffo['mstar']
'key': o_key,
'bh_switch': o_bh_swtich,
'coldgas': o_coldgas,
'hotgas': o_hotgas,
'blowout': 0,
'mstar': o_mstar
},
index=first_data.index)
first_data_with_ffo = first_data.join(ffo_data)
first_data_with_ffo.to_csv(first_out_filename, sep=' ')
print "> Now looping through snapshots (%i > %i)" % (min_snap + 1, max_snap)
for snapi in np.arange(min_snap + 1, max_snap):
z_current = htils.get_z_snap(hpath, snapi)[0]
print " >> Snapshot: %i | %3.2f" % (snapi, z_current)
print " >> Reading input_data..."
input_file_name = input_cat_path + input_cat_file_prefix + str(snapi)
input_data = pd.read_csv(input_file_name,
delim_whitespace=True,
names=input_cols)
snapi_prev = snapi - 1
print " >> Reading output_catalogue..."
output_cat_file_name = output_cat_path + output_cat_file_prefix + str(
snapi_prev)
output_cat_data = pd.read_csv(output_cat_file_name, delim_whitespace=True)
#sys.exit()
def convert_tree(hpath, tree, treeoutputpath, verbose=True):
# Maps to go up and down trees
# desc_map is analogous to merger_tree_ytree.add_descendants()
# mmp = "most massive progenitor" flag
start = time.time()
desc_map = tree.get_desc_map()
if verbose:
print " Time to precompute tree maps: {:.1f}".format(time.time() -
start)
sys.stdout.flush()
num_nodes_in_tree = len(tree.data)
num_nodes_processed = 0
## This gets the scale factors etc for Caterpillar
snaps = np.unique(tree["snap"])
times = haloutils.get_t_snap(hpath, snaps) * 1e9 #Gyr
redshifts = haloutils.get_z_snap(hpath, snaps)
## Convert some things to lists for list.index() method
snaps = list(snaps)
# NOTE: a branch is a multi-snapshot segment of the tree that experiences no mergers
# Declare the arrays
br_halo_ID = [] # List of connected halo IDs (in redshift order)
br_age = [] # Age of the branch
br_z = [] # Redshift of the branch
br_t_merge = [
] # Duration of the branches (delay between formation and merger)
br_ID_merge = [] # Last halo ID of the branch (once it has merged)
br_m_halo = [] # Array of dark matter halo masses
br_r_vir = [] # Array of dark matter halo radii
br_is_sub = [
] # Array of True if the halo is a subhalo, False if is a host halo
br_is_prim = [] # True or False depending whether the branch is primordial
# Create an entry for each redshift
for i_z in range(0, len(redshifts)):
br_halo_ID.append([])
br_age.append([])
br_z.append([])
br_t_merge.append([])
br_ID_merge.append([])
br_m_halo.append([])
br_r_vir.append([])
br_is_sub.append([])
br_is_prim.append([])
start = time.time()
## Loop through all nodes of the tree that are branch points
for irow, row in enumerate(tree.data):
i_z = snaps.index(row["snap"])
## If exactly one progenitor, this is not a branching point, so skip it
if row["num_prog"] == 1: continue
# Create a new branch for the considered redshift
br_halo_ID[i_z].append([])
br_age[i_z].append([])
br_z[i_z].append([])
br_t_merge[i_z].append(0.0)
br_ID_merge[i_z].append(0.0)
br_m_halo[i_z].append([])
br_r_vir[i_z].append([])
br_is_sub[i_z].append([])
## Assign whether or not this is a primordial branch
br_is_prim[i_z].append(row["num_prog"] == 0)
## Fill the start of the branch
## Fill the halo ID, age, mass, and radius
# Note: the ID is the mtid!!! not sure if this is what we actually want
# To access that object in the halo catalogs, we need origid and snapshot
fill_branch_info(br_halo_ID, br_age, br_z, br_m_halo, br_r_vir,
row["id"], i_z, i_z, times, redshifts, row, br_is_sub)
num_nodes_processed += 1
## Step down the tree
desc_irow = tree.getDesc(irow, desc_map)
if desc_irow is None: continue # reached the root
desc_row = tree[desc_irow]
## Loop through subsequent parts of the branch, defined as single-progenitor
while desc_row["num_prog"] == 1:
## Fill next part of the branch
i_z_cur = snaps.index(desc_row["snap"])
fill_branch_info(br_halo_ID, br_age, br_z, br_m_halo, br_r_vir,
desc_row["id"], i_z, i_z_cur, times, redshifts,
desc_row, br_is_sub)
num_nodes_processed += 1
## Step down the tree
desc_irow = tree.getDesc(desc_irow, desc_map)
if desc_irow is None: break # reached the root
desc_row = tree[desc_irow]
# Calculate the time before merger
i_z_last = snaps.index(desc_row["snap"])
br_t_merge[i_z][-1] = times[i_z_last] - times[i_z]
# Copy the last halo ID (when the branch has merged)
br_ID_merge[i_z][-1] = desc_row["id"]
if verbose:
print " Time to convert: {:.1f}".format(time.time() - start)
sys.stdout.flush()
if num_nodes_processed != num_nodes_in_tree:
raise ValueError(
"ERROR! num nodes processed != num nodes in tree ({} != {})".
format(num_nodes_processed, num_nodes_in_tree))
start = time.time()
np.save(treeoutputpath,[br_halo_ID, br_age, br_z, br_t_merge, br_ID_merge, \
br_m_halo, br_r_vir, br_is_prim, redshifts, times, tree[0]['id'], br_is_sub])
if verbose:
print " Time to save: {:.1f}".format(time.time() - start)
sys.stdout.flush()
print
first_out_filename = output_cat_path + output_cat_file_prefix + str(min_snap)
ffo_data = pd.DataFrame({'key':o_key,
'bh_switch':o_bh_swtich,
'coldgas':o_coldgas, 'hotgas':o_hotgas,
'blowout':0,
'mstar':o_mstar},index=first_data.index)
first_data_with_ffo = first_data.join(ffo_data)
first_data_with_ffo.to_csv(first_out_filename,sep=' ')
print "> Now looping through snapshots (%i > %i)" % (min_snap+1,max_snap)
for snapi in np.arange(min_snap + 1, max_snap):
z_current = htils.get_z_snap(hpath,snapi)[0]
print " >> Snapshot: %i | %3.2f" % (snapi,z_current)
print " >> Reading input_data..."
input_file_name = input_cat_path + input_cat_file_prefix + str(snapi)
input_data = pd.read_csv(input_file_name, delim_whitespace=True, names=input_cols)
snapi_prev = snapi - 1
print " >> Reading output_catalogue..."
output_cat_file_name = output_cat_path + output_cat_file_prefix + str(snapi_prev)
output_cat_data = pd.read_csv(output_cat_file_name, delim_whitespace=True)
#sys.exit()
#z_current = zl.zreturn(snapi)
# matching has to happen here for each halo in the current input file