"""
#import dependencies
import numpy as np
import MAS_library as MASL
import sys, os, h5py, time
import units_library as UL
import matplotlib.pyplot as plt
import h5py
dims = 2048
MAS = 'CIC'
#import illustris_python as il
delta = np.zeros((dims, dims, dims), dtype=np.float32)
U = UL.units()
rho_crit = U.rho_crit #h^2 Msun/Mpc^3
# read header
FILE_TYPE = '/hydro/' #or subhalos
#TODO: Before this, cp from fof_files into a new folder made @ ../dat/illustris_tng/hydro/groupcat/groups_099
# Once done, you can use TNG's illustris-python code to read in this data for hod computation
root = '../dat/illustris_tng/' + FILE_TYPE + 'groupcat/groups_099/'
basePath = '../dat/illustris_tng' + FILE_TYPE + 'groupcat'
# /scratch/gpfs/nk11/dm2gal/dat/illustris-data/hydrosim
# this part you'll need to download.
#sys.path.append("/home/nk11")
sys.path.append('/scratch/nsk367/pytorch-use/research')
from mpi4py import MPI import numpy as np import snapshot as sn import readsnapHDF5 as rs import units_library as UL import HI_library as HIL import sys,os,glob,h5py import groupcat ####### MPI DEFINITIONS ####### comm = MPI.COMM_WORLD nprocs = comm.Get_size() myrank = comm.Get_rank() U = UL.units(); rho_crit = U.rho_crit #h^2 Msun/Mpc^3 TREECOOL_file = '/n/home09/fvillaescusa/Illustris/HI/TREECOOL_fg_dec11' ################################ INPUT ######################################## run = '/n/hernquistfs3/IllustrisTNG/Runs/L75n1820TNG' fout = 'M_HI_new_75_1820.txt' snaps = np.array([17, 21, 25, 33, 50, 99]) ############################################################################### # find offset_root and snapshot_root snapshot_root = '%s/output/'%run # find the numbers each cpu will work on array = np.arange(0, len(snaps)) numbers = np.where(array%nprocs==myrank)[0] numbers = snaps[numbers]
import numpy as np
import MAS_library as MASL
import sys,os,h5py,time
import units_library as UL
import HI_library as HIL
U = UL.units(); rho_crit = U.rho_crit #h^2 Msun/Mpc^3
################################ INPUT ########################################
snapnums = np.array([85, 68, 60])
runs = ['/simons/scratch/sgenel/Illustris_IllustrisTNG_public_data_release/L75n1820FP']
fouts = ['Illustris100-1_matter']
dims = 2048
MAS = 'CIC'
##############################################################################
for fout,run in zip(fouts,runs):
# find whether it is an N-body or an hydro sims
if run[-2:]=='DM': Nbody = True
else: Nbody = False
# do a loop over the different redshifts
for snapnum in snapnums:
# define the array hosting delta_HI and delta_m
delta_m = np.zeros((dims,dims,dims), dtype=np.float32)
# read header
snapshot = '%s/output/snapdir_%03d/snap_%03d'%(run,snapnum, snapnum)
f.close()
density = np.zeros((dims,dims), dtype=np.float64)
pos2D = np.ascontiguousarray(pos[:,0:2])
if component=='HI':
MASL.voronoi_RT_2D(density, pos2D, M_HI, radii,
x_min, y_min, 0, 1, x_max-x_min,
False)
if component in ['gas','CDM']:
MASL.voronoi_RT_2D(density, pos2D, mass, radii,
x_min, y_min, 0, 1, x_max-x_min,
False)
# convert (Msun/h)/(Mpc/h)^2 to cm^{-2}
factor = h*(1.0+redshift)**2*\
(UL.units().Msun_g)/(UL.units().mH_g)/(UL.units().Mpc_cm)**2
####### CAUTION!!!! ########
#factor/=(1.0+redshift)**2
###########################
density *= factor
# save density field to file
f = h5py.File(fout_df, 'w')
f.create_dataset('df', data=density)
f.close()
import numpy as np
import sys,os
import mass_function_library as MFL
import units_library as UL
import integration_library as IL
############################ CONSTANTS ############################
pi = np.pi
rho_crit = (UL.units()).rho_crit #h^2 Msun/Mpc^3
deltac = 1.686
###################################################################
##############################################################################
# This functions computes the halo bias, b(M), in a set of different halo masses
# k_in --------> input k
# Pk_in -------> input Pk
# OmegaM ------> value of Omega_m
# Masses ------> array with the halo masses where to evaluate the halo bias
# author ------> SMT01', 'Tinker'
def bias(k_in, Pk_in, OmegaM, Masses, author, bins=10000):
rhoM = rho_crit*OmegaM
# sort the input k-array
indexes = np.argsort(k_in)
k_in = k_in[indexes]; Pk_in = Pk_in[indexes]
# interpolate the input k-array into bins points
k = np.logspace(np.log10(k_in[0]), np.log10(k_in[-1]), bins)
Pk = np.interp(np.log(k), np.log(k_in), Pk_in)
# This routine returns the circular velocity of a halo of mass M at redshift z
def Vcirc(M,z,Omega_m,Omega_l):
x = Omega_m*(1.0+z)**3/(Omega_m*(1.0+z)**3 + Omega_l) - 1.0
delta_c = 18*np.pi**2 + 82.0*x - 39.0*x**2
rho_crit_z = rho_crit*(Omega_m*(1.0+z)**3 + Omega_l)
Hz = 100.0*np.sqrt(Omega_m*(1.0+z)**3 + Omega_l) #km/s/(Mpc/h)
R = ((3.0*M)/(4.0*np.pi*delta_c*rho_crit_z))**(1.0/3.0) #Mpc/h
V = np.sqrt(0.5*Hz**2*delta_c*R**2)
return V
rho_crit = UL.units().rho_crit
############################### INPUT #######################################
Omega_m = 0.3089
Omega_l = 1.0-Omega_m
threshold = 0.98 #find Mmin such as lighter halos only host 1-threshold % of HI
#############################################################################
for z in [0.000, 0.997, 2.002, 3.008, 4.008, 4.996]:
f = h5py.File('M_HI_new_75_1820_z=%.3f.hdf5'%z, 'r')
M_HI = f['M_HI'][:]
M = f['Mass'][:]
R = f['R'][:]
f.close()
# This scripts computes the value of Omega_HI and HI shot-noise
# assuming a M_HI(M) = M_0*(M/Mmin)^alpha*exp(-Mmin/M)
# P_SN = 1/(rho_c^0*Omega_HI)^2*\int_0^oo n(M)*M_HI(M)^2dM
# Omega_HI = 1/rho_c^0*\int_0^oo n(M)*M_HI(M) dM
import numpy as np
import sys, os
import mass_function_library as MFL
import units_library as UL
import integration_library as IL
rho_crit = UL.units().rho_crit
################################# INPUT ######################################
# input Pk at wanted MF redshift. For neutrinos use CDM+B Pk
f_Pk = '../../CAMB/illustris_matterpower_z=0.dat'
bins_k = 10000 #number of bins to use in the input Pk
# For neutrinos use Omega_{CDM+B} instead of Omega_m
Omega_m = 0.3089
M_min = 1e8 #Msun/h
M_max = 1e16 #Msun/h
bins_MF = 800 #number of bins in the HMF
author = 'ST'
f_out = 'ST_MF_z=0.dat'
# optional arguments
z = 0.0 # only for 'Tinker', 'Tinker10' and Crocce
delta = 200 # only for 'Tinker' and 'Tinker10'
# M_HI parameters (M0 only needed for Omega_HI, not P_SN)
alpha, M0, Mmin = 0.63, 4.7e8, 2.2e10 #z=0
import numpy as np
import sys, os
import mass_function_library as MFL
import units_library as UL
import integration_library as IL
############################ CONSTANTS ############################
pi = np.pi
rho_crit = (UL.units()).rho_crit #h^2 Msun/Mpc^3
deltac = 1.686
###################################################################
##############################################################################
# This functions computes the halo bias, b(M), in a set of different halo masses
# k_in --------> input k
# Pk_in -------> input Pk
# OmegaM ------> value of Omega_m
# Masses ------> array with the halo masses where to evaluate the halo bias
# author ------> SMT01', 'Tinker'
def bias(k_in, Pk_in, OmegaM, Masses, author, bins=10000):
rhoM = rho_crit * OmegaM
# sort the input k-array
indexes = np.argsort(k_in)
k_in = k_in[indexes]
Pk_in = Pk_in[indexes]
# interpolate the input k-array into bins points
import numpy as np
import readsnap
import redshift_space_library as RSL
import MAS_library as MASL
import units_library as UL
import Pk_library as PKL
import sys,os
########### routines ############
# Pk_comp(snapshot_fname,ptype,dims,do_RSD,axis,cpus)
# Pk_Gadget(snapshot_fname,dims,particle_type,do_RSD,axis,cpus)
#################################
U = UL.units(); rho_crit = U.rho_crit
# dictionary for files name
name_dict = {'0' :'GAS', '01':'GCDM', '02':'GNU', '04':'Gstars',
'1' :'CDM', '12':'CDMNU', '14':'CDMStars',
'2' :'NU', '24':'NUStars',
'4' :'Stars',
'-1':'matter'}
###############################################################################
# This routine computes the power spectrum of either a single species or of all
# species from a Gadget routine
# snapshot_fname -----------> name of the Gadget snapshot
# ptype --------------------> scalar: 0-GAS, 1-CDM, 2-NU, 4-Stars, -1:ALL
# dims ---------------------> Total number of cells is dims^3 to compute Pk
# do_RSD -------------------> Pk in redshift-space (True) or real-space (False)
# axis ---------------------> axis along which move particles in redshift-space
import numpy as np
import sys, os, h5py
import Pk_library as PKL
import units_library as UL
rho_crit = UL.units().rho_crit #critical density of the Universe at z=0
##################################### INPUT #############################################
prefixes = ['TNG100', 'TNG100DM', 'TNG300', 'TNG300DM']
BoxSizes = [75.0, 75.0, 205.0, 205.0] #Mpc/h
redshifts = [0.0, 0.2, 0.4, 0.7, 1.0, 1.5, 2.0, 3.0, 4.0]
axis = 0 #axis along which RSD are placed (no matter in our case)
MAS = 'CIC'
threads = 48 #openmp threads
#########################################################################################
# do a loop over the different simulations
for prefix, BoxSize in zip(prefixes, BoxSizes):
# do a loop over the different redshifts
for z in redshifts[::-1]:
# find the name of the output file
fout = 'Pk_%s_z=%.1f.txt' % (prefix, z)
if os.path.exists(fout): continue
# read the file containing the mass per voxel
fin = '%s_z=%.1f.hdf5' % (prefix, z)
f = h5py.File(fin, 'r')
delta = f['density'][:]
f.close()
