def extract_halo():
# Resimulation mode
#mode='2048'
mode='1024'
base_path = '/media/edoardo/Elements/CLUES/DATA/LGF/' + mode + '/'
# Configure the LG model and subpaths
if mode == '2048':
code_run = cfg.simu_runs()
sub_run = cfg.sub_runs()
elif mode == '1024':
num_run = cfg.gen_runs(0, 80)
sub_run = cfg.gen_runs(0, 30)
# Read csv list of LGs
lgs_csv = 'output/lg_pairs_' + mode + '.csv'
df_lgs = pd.read_csv(lgs_csv)
print('TotLen: ', len(df_lgs))
x_cols = ['Xc_LG', 'Yc_LG', 'Zc_LG']
x_cols_ahf = ['Xc(6)', 'Yc(7)', 'Zc(8)']
box_center = np.array([5e+4] * 3)
r_max = 10000.0
'''
# Do some LG filtering
v_max = - 0.0
d_max = 7000.0
df_lgs = df_lgs[df_lgs['Vrad'] < v_max]
print('TotLen: ', len(df_lgs))
# Select LGs depending on their distance from the box center
'''
#file_ahf = 'snapshot_full_054.z0.000.AHF_halos'
#file_ahf = 'snapshot_full_054.z0.001.AHF_halos'
file_ahf = 'snapshot_054.z0.000.AHF_halos'
# Loop on all the simulations and gather data
if mode == '2048':
for code in code_run:
for sub in sub_run:
this_path = base_path + code + '/' + sub + '/'
this_ahf = this_path + file_ahf
# Check that file exists
if os.path.isfile(this_ahf):
print('Reading AHF file: ', this_ahf)
halo_df = rf.read_ahf_halo(this_ahf, file_mpi=False)
halo_select_df = halo_df[halo_df['Mvir(4)'] > m_thresh]
out_all_halo_csv = out_base + code + '_' + sub + '.csv'
halo_select_df.to_csv(out_all_halo_csv)
elif mode == '1024':
for ilg, row in df_lgs.iterrows():
num = str('%02d' % int(row['simu_code']))
sub = str('%02d' % int(row['sub_code']))
this_ahf = base_path + num + '_' + sub + '/' + file_ahf
this_x = row[x_cols].values
#print(this_x)
if os.path.isfile(this_ahf):
halo_df = rf.read_ahf_halo(this_ahf, file_mpi=True)
halo_df['Dist'] = halo_df[x_cols_ahf].T.apply(lambda x: t.distance(x, this_x))
halo_df = halo_df[halo_df['Dist'] < r_max]
#print(halo_df.head())
f_out = 'output/LG_' + mode + '/lg_center_' + num + '_' + sub + '.' + str(ilg) + '.csv'
print('Saving to: ', f_out)
halo_df.to_csv(f_out)
def sub_mass_function():
'''
Compute the subhalo mass functions
'''
# Read the | Gyr / z / a | time conversion table
time = rf.read_time(data_path)
all_halo_mah = []
# Output file base path, save all relevant halo MAHs here
out_base_pkl = 'saved/lg_mahs_'
# Now loop on all the simulations and gather data
for code in code_run[10:11]:
this_model = model_run[dict_model[code]]
this_model.info(dump=True)
for sub in sub_run:
this_path = base_path + code + '/' + sub + '/'
this_path_mah = base_path_mah + code + '/' + sub + '/'
this_ahf = this_path + file_ahf
this_mah = this_path_mah + file_mah
# Check that file exists
if os.path.isfile(this_ahf):
out_file_pkl = out_base_pkl + code + '_' + sub + '.pkl'
if os.path.isfile(out_file_pkl):
print('Loading MAHs from .pkl file: ', out_file_pkl)
out_f_pkl = open(out_file_pkl, 'rb')
mahs, lg = pkl.load(out_f_pkl)
out_f_pkl.close()
print('LG properties: ')
lg.info()
else:
print('Reading AHF file: ', this_ahf)
halos, halo_df = rf.read_ahf_halo(this_ahf)
print('Looking for Local Group candidates...')
# The local group model might eventually find more than one pair in the given volume
these_lgs = hu.find_lg(halo_df, this_model, cat_center, cat_radius)
# SELECT ONE LG
print('Found ', len(these_lgs), ' LG candidates with properties: ')
try:
these_lgs[0].info()
lg = these_lgs[0]
# Define a gather radius for halos around the LG
lg_radius = lg.LG1.r() + lg.LG2.r() + lg.r_halos()
lg_radius = 1.2 * lg_radius
# TODO there might be more LGs in the area but here I am selecting only one
#for this_lg in these_lgs:
lg_center = lg.geo_com()
id_list = hu.halo_ids_around_center(halos, lg_center, lg_radius)
print('Reading MAHs for ', len(id_list), ' halos at ', lg_radius, ' kpc/h around the LG center of mass... ')
mahs = rf.read_mah_halo(id_list, this_mah, time)
print('Done.')
print('Saving MAHs output to file: ', out_file_pkl)
out_f_pkl = open(out_file_pkl, 'wb')
pkl.dump([mahs, lg], out_f_pkl)
out_f_pkl.close()
except:
print('No LGs found for ', code, sub)
this_lgs = lgs_base + '.csv'
print('LGS FILE: ', this_lgs)
df_lgs_orig = pd.read_csv(this_lgs)
n_lgs_pre = len(df_lgs_orig)
print('N pre: ', n_lgs_pre)
df_lgs = df_lgs_orig.drop_duplicates(subset=['M_M31'])
df_lgs = df_lgs[df_lgs['M_M31'] > m_min]
df_lgs = df_lgs[df_lgs['Vrad'] < v_max]
print('N post: ', len(df_lgs))
# Read AHF and export it to CSV
if mode == 'export_csv':
print('AHF FILE: ', this_ahf)
df_ahf = rf.read_ahf_halo(this_ahf)
df_ahf = t.periodic_boundaries(data=df_ahf, slab_size=r_max, box=box_size)
print('AHF FILE TO CSV: ', this_ahf_csv)
df_ahf.to_csv(this_ahf_csv)
# Read CSV ahf files and extract mass functions around LG candidates
elif mode == 'read_csv':
print('AHF CSV FILE: ', this_ahf_csv)
df_ahf = pd.read_csv(this_ahf_csv)
print(df_ahf[x_col_ahf])
# Initialize a string containing all the radii - this will be the dataframe column with the max halo mass
radii_str = []
for rad in radii:
r_str = 'R_' + str(rad)
radii_str.append(r_str)
'''
Python Routines for COsmology and Data I/ (PyRCODIO) v0.2
Edoardo Carlesi 2020
[email protected]
ahf2csv.py: convert (and compress) AHF halo catalogs to csv files
'''
import pandas as pd
import sys
sys.path.insert(1, '/home/edoardo/CLUES/PyRCODIO/')
import read_files as rf
this_ahf = sys.argv[1]
mpi = sys.argv[2]
out_file = this_ahf + '.csv'
halo_df = rf.read_ahf_halo(this_ahf, file_mpi=mpi)
halo_df.to_csv(out_file)
this_mah = this_path_mah + file_mah
# Check that file exists
if os.path.isfile(this_ahf):
out_file_pkl = out_base_pkl + code + '_' + sub + '.pkl'
if os.path.isfile(out_file_pkl):
print('Loading MAHs from .pkl file: ', out_file_pkl)
out_f_pkl = open(out_file_pkl, 'rb')
mahs, lg = pkl.load(out_f_pkl)
out_f_pkl.close()
print('LG properties: ')
lg.info()
else:
print('Reading AHF file: ', this_ahf)
halos, halo_df = rf.read_ahf_halo(this_ahf)
print('Looking for Local Group candidates...')
# The local group model might eventually find more than one pair in the given volume
these_lgs = hu.find_lg(halo_df, this_model, cat_center, cat_radius)
# SELECT ONE LG
print('Found ', len(these_lgs), ' LG candidates with properties: ')
try:
these_lgs[0].info()
lg = these_lgs[0]
# Define a gather radius for halos around the LG
lg_radius = lg.LG1.r() + lg.LG2.r() + lg.r_halos()