mirror of
https://github.com/Richard-Sti/csiborgtools.git
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add read particle file
This commit is contained in:
rstiskalek
parent
016be32634
commit
b119d21acf
2 changed files with 159 additions and 408 deletions
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@ -1,234 +0,0 @@
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import numpy as np
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import math
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import matplotlib.pyplot as plt
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import fortranfile as ff
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from os import listdir
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import random
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outnr1 = str(184).zfill(5)
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outnr2 = str(2).zfill(5)
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srcdir1 = '/mnt/extraspace/hdesmond/IC_test3/output_'+outnr1
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srcdir2 = '/mnt/extraspace/hdesmond/IC_test3_inv/output_'+outnr2
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for i in range(2):
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print("Starting ", i)
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if i==0:
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srcdir = srcdir1
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outnr = outnr1
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else:
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srcdir = srcdir2
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outnr = outnr2
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infofile = srcdir+'/info_'+outnr+'.txt'
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f = open(infofile, 'r')
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ncpuline = f.readline()
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line = ncpuline.split()
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ncpu = int(line[-1])
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print("ncpu:", ncpu)
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print("Reading in particles of output", int(srcdir[-5:]))
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srcdirlist = listdir(srcdir)
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if 'unbinding_'+srcdir[-5:]+'.out00001' not in srcdirlist:
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print("Couldn't find unbinding_"+srcdir[-5:]+".out00001 in", srcdir)
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print("use mergertreeplot.py -h or --help to print help message.")
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quit()
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#-----------------------
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# First read headers
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#-----------------------
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nparts = np.zeros(ncpu, dtype='int32')
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partfiles = [0]*ncpu
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for cpu in range(ncpu):
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srcfile = srcdir+'/part_'+srcdir[-5:]+'.out'+str(cpu+1).zfill(5)
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partfiles[cpu] = ff.FortranFile(srcfile)
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ncpu = partfiles[cpu].readInts()
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ndim = partfiles[cpu].readInts()
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nparts[cpu] = partfiles[cpu].readInts()
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localseed = partfiles[cpu].readInts()
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nstar_tot = partfiles[cpu].readInts()
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mstar_tot = partfiles[cpu].readReals('d')
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mstar_lost = partfiles[cpu].readReals('d')
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nsink = partfiles[cpu].readInts()
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del ndim, localseed, nstar_tot, mstar_tot, mstar_lost, nsink
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#-------------------
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# Allocate arrays
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#-------------------
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nparttot = nparts.sum()
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dum = np.zeros(nparttot, dtype='float16')
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if i==0:
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#x = np.zeros(nparttot, dtype='float16')
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#y = np.zeros(nparttot, dtype='float16')
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#z = np.zeros(nparttot, dtype='float16')
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mass = np.zeros(nparttot, dtype='float16')
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ID = np.zeros(nparttot, dtype='int32')
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level = np.zeros(nparttot, dtype='int32')
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clumpid = np.zeros(nparttot, dtype='int32')
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else:
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#x_inv = np.zeros(nparttot, dtype='float16')
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#y_inv = np.zeros(nparttot, dtype='float16')
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#z_inv = np.zeros(nparttot, dtype='float16')
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mass_inv = np.zeros(nparttot, dtype='float16')
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ID_inv = np.zeros(nparttot, dtype='int32')
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level_inv = np.zeros(nparttot, dtype='int32')
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clumpid_inv = np.zeros(nparttot, dtype='int32')
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#----------------------
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# Read particle data
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#----------------------
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#read(1)ncpu2 # What you would do in fortran
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#read(1)ndim2
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#read(1)npart2
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#read(1)
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#read(1)
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#read(1)
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#read(1)
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#read(1)
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#do i=1,ndim
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#read(1)m
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#x(1:npart2,i)=m
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#end do
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#! Skip velocity
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#do i=1,ndim
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#read(1)m
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#end do
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#! Read mass
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#read(1)m
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#if(nstar>0)then
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#read(1) ! Skip identity
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#read(1) ! Skip level
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#read(1)family
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#read(1)tag
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#read(1)age
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start_ind = np.zeros(ncpu, dtype='int')
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for cpu in range(ncpu-1):
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start_ind[cpu+1] = nparts[cpu] + start_ind[cpu]
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for cpu in range(ncpu):
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unbfile = srcdir+'/unbinding_'+srcdir[-5:]+'.out'+str(cpu+1).zfill(5)
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unbffile = ff.FortranFile(unbfile)
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if i==0:
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dum[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d') # Think they're stored as double so must read as double
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dum[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d') # Positions
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dum[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d')
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dum[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d') # Velocities; this all just overwrites itself
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dum[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d')
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dum[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d')
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mass[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d') # Mass
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#vx[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d')
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#vy[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d')
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#vz[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d')
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#mass[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d')
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ID[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readInts()
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level[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readInts()
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clumpid[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = unbffile.readInts()
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else:
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dum[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d')
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dum[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d')
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dum[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d')
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dum[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d')
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dum[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d')
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dum[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d')
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mass_inv[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d')
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#vx[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d')
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#vy[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d')
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#vz[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d')
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#mass[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d')
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ID_inv[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readInts()
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level_inv[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readInts()
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clumpid_inv[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = unbffile.readInts()
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del dum
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if i==0:
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print("Minimum clump ID:", np.min(clumpid)) # This is the clump a particle has been assigned to, so min should be 0 which means not in clump
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clumpid = np.absolute(clumpid) # Not sure why this is here...
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else:
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print("Minimum inv clump ID:", np.min(clumpid_inv))
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clumpid_inv = np.absolute(clumpid_inv)
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#random.shuffle(ID); random.shuffle(ID_inv) # If the IDs are randomised but not the clumpIDs then all of the below should be random
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print(np.min(ID), np.median(ID), np.mean(ID), np.max(ID))
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print(np.min(ID_inv), np.median(ID_inv), np.mean(ID_inv), np.max(ID_inv))
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print(np.min(level), np.median(level), np.mean(level), np.max(level))
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print(np.min(level_inv), np.median(level_inv), np.mean(level_inv), np.max(level_inv))
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print(np.min(mass), np.median(mass), np.mean(mass), np.max(mass))
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print(np.min(mass_inv), np.median(mass_inv), np.mean(mass_inv), np.max(mass_inv))
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#plt.clf()
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#plt.hist(mass)
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#plt.show()
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#index lev parent(2) ncell peak_x peak_y(5) peak_z rho- rho+(8) rho_av mass_cl relevance(11)
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clumparr = np.genfromtxt(srcdir1+"/clump_"+outnr1+".dat")
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clumparr_inv = np.genfromtxt(srcdir2+"/clump_"+outnr2+".dat")
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clumpID, parent, Mclump = clumparr[:,0].astype(int), clumparr[:,2].astype(int), clumparr[:,10]
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clumpID_inv, parent_inv, Mclump_inv = clumparr_inv[:,0].astype(int), clumparr_inv[:,2].astype(int), clumparr_inv[:,10]
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#clumpID_main = clumpID[clumpID==parent] # IDs of main halos only from the clump file
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#clumpID_main_inv = clumpID_inv[clumpID_inv==parent_inv]
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#clumpID_big = clumpID[Mclump>np.median(Mclump)] # IDs of halos more massive than the median
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#clumpID_big_inv = clumpID_inv[Mclump_inv>np.median(Mclump_inv)]
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clumpID_big = clumpID[Mclump>np.percentile(Mclump, 90)]
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clumpID_big_inv = clumpID_inv[Mclump_inv>np.percentile(Mclump_inv, 90)]
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#clumpID_small = clumpID[Mclump<=np.median(Mclump)] # IDs of halos more massive than the median
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#clumpID_small_inv = clumpID_inv[Mclump_inv<=np.median(Mclump_inv)]
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#[np.where(clumpid==x) for x in clumpID_main]
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print("CHECK:", len(ID), len(ID_inv), len(np.intersect1d(ID, ID_inv)), "(should all be the same)")
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print("Total number of clumps in the two sims:", len(clumparr), len(clumparr_inv))
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print("Total number of particles in the two sims:", len(ID), len(ID_inv), "(should be the same)")
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print("Fraction of particles within halos in the two sims:", round(len(ID[clumpid!=0])/float(len(ID)), 6), round(len(ID_inv[clumpid_inv!=0])/float(len(ID)), 6))
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print("Fraction of particles within halos in *both* sims:", round(len(np.intersect1d(ID[clumpid!=0], ID_inv[clumpid_inv!=0]))/float(len(np.intersect1d(ID, ID_inv))), 6), "(random value =", round(len(ID[clumpid!=0])/float(len(ID)) * len(ID_inv[clumpid_inv!=0])/float(len(ID_inv)), 6), "), ratio =", round(len(np.intersect1d(ID[clumpid!=0], ID_inv[clumpid_inv!=0]))/float(len(np.intersect1d(ID, ID_inv))) / (len(ID[clumpid!=0])/float(len(ID)) * len(ID_inv[clumpid_inv!=0])/float(len(ID_inv))), 3))
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print("Fraction of particles in massive halos in the two sims:", round(np.sum(np.in1d(clumpid, clumpID_big))/float(len(ID)), 6), round(np.sum(np.in1d(clumpid_inv, clumpID_big_inv))/float(len(ID)), 6))
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print("Fraction of particles in massive halos in *both* sims:", round(len(np.intersect1d(ID[np.in1d(clumpid, clumpID_big)], ID_inv[np.in1d(clumpid_inv, clumpID_big_inv)]))/float(len(np.intersect1d(ID, ID_inv))), 6), "(random value =", round(np.sum(np.in1d(clumpid, clumpID_big))/float(len(ID)) * np.sum(np.in1d(clumpid_inv, clumpID_big_inv))/float(len(ID)), 6), "), ratio =", round(len(np.intersect1d(ID[np.in1d(clumpid, clumpID_big)], ID_inv[np.in1d(clumpid_inv, clumpID_big_inv)]))/float(len(np.intersect1d(ID, ID_inv))) / (np.sum(np.in1d(clumpid, clumpID_big))/float(len(ID)) * np.sum(np.in1d(clumpid_inv, clumpID_big_inv))/float(len(ID_inv))), 3))
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@ -1,194 +1,179 @@
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import numpy as np
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import fortranfile as ff
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# PUT HARRY HERE
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import numpy
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from scipy.io import FortranFile
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from os import listdir
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outnr = str(184).zfill(5)
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srcdir = '/mnt/extraspace/hdesmond/IC_test3/output_'+outnr
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from os.path import join
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from tqdm import tqdm
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infofile = srcdir+'/info_'+outnr+'.txt'
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f = open(infofile, 'r')
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ncpuline = f.readline()
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line = ncpuline.split()
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def get_sim_path(n, fname="ramses_out_{}", srcdir="/mnt/extraspace/hdesmond"):
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"""
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Get a path to a CSiBORG simulation.
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ncpu = int(line[-1])
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print("ncpu:", ncpu)
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Parameters
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----------
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n : int
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The index of the initial conditions (IC) realisation.
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fname : str, optional
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The file name. By default `ramses_out_{}`, where `n` is the IC index.
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srcdir : str, optional
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The file path to the folder where realisations of the ICs are stored.
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print("Reading in particles of output", int(srcdir[-5:]))
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srcdirlist = listdir(srcdir)
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if 'unbinding_'+srcdir[-5:]+'.out00001' not in srcdirlist:
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print("Couldn't find unbinding_"+srcdir[-5:]+".out00001 in", srcdir)
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print("use mergertreeplot.py -h or --help to print help message.")
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quit()
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Returns
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-------
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path : str
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The complete path to the `n`th CSiBORG simulation.
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"""
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return join(srcdir, fname.format(n))
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#-----------------------
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# First read headers
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#-----------------------
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nparts = np.zeros(ncpu, dtype='int32')
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partfiles = [0]*ncpu
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def open_particle(n, simpath, verbose=True):
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"""
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Open particle files to a given CSiBORG simulation.
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for cpu in range(ncpu):
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srcfile = srcdir+'/part_'+srcdir[-5:]+'.out'+str(cpu+1).zfill(5)
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partfiles[cpu] = ff.FortranFile(srcfile)
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Parameters
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----------
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n : int
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The index of a redshift snapshot.
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simpath : str
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The complete path to the CSiBORG simulation.
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verbose : bool, optional
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Verbosity flag.
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ncpu = partfiles[cpu].readInts()
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ndim = partfiles[cpu].readInts()
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nparts[cpu] = partfiles[cpu].readInts()
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localseed = partfiles[cpu].readInts()
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nstar_tot = partfiles[cpu].readInts()
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mstar_tot = partfiles[cpu].readReals('d')
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mstar_lost = partfiles[cpu].readReals('d')
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nsink = partfiles[cpu].readInts()
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Returns
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-------
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nparts : 1-dimensional array
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Number of parts assosiated with each CPU.
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partfiles : list of `scipy.io.FortranFile`
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Opened part files.
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"""
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# Zeros filled snapshot number and the snapshot path
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nout = str(n).zfill(5)
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snappath = join(simpath, "output_{}".format(nout))
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infopath = join(snappath, "info_{}.txt".format(nout))
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del ndim, localseed, nstar_tot, mstar_tot, mstar_lost, nsink
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with open(infopath, "r") as f:
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ncpu = int(f.readline().split()[-1])
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if verbose:
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print("Reading in output `{}` with ncpu = `{}`.".format(nout, ncpu))
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# Check whether the unbinding file exists.
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snapdirlist = listdir(snappath)
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unbinding_file = "unbinding_{}.out00001".format(nout)
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if unbinding_file not in snapdirlist:
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raise FileNotFoundError(
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"Couldn't find `{}` in `{}`. Use mergertreeplot.py -h or --help to "
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"print help message.".format(unbinding_file, snappath))
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# First read the headers. Reallocate arrays and fill them.
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nparts = numpy.zeros(ncpu, dtype=int)
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partfiles = [None] * ncpu
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for cpu in range(ncpu):
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cpu_str = str(cpu + 1).zfill(5)
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fpath = join(snappath, "part_{}.out{}".format(nout, cpu_str))
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f = FortranFile(fpath)
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# Read in this order
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ncpuloc = f.read_ints()
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if ncpuloc != ncpu:
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raise ValueError("`ncpu = {}` of `{}` disagrees with `ncpu = {}` "
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"of `{}`.".format(ncpu, infopath, ncpuloc, fpath))
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ndim = f.read_ints()
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nparts[cpu] = f.read_ints()
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localseed = f.read_ints()
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nstar_tot = f.read_ints()
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mstar_tot = f.read_reals('d')
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mstar_lost = f.read_reals('d')
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nsink = f.read_ints()
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partfiles[cpu] = f
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return nparts, partfiles
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#-------------------
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# Allocate arrays
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#-------------------
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nparttot = nparts.sum()
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def read_sp(dtype, partfile):
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"""
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Utility function to read a single particle file, depending on the dtype.
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dum = np.zeros(nparttot, dtype='float16')
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Parameters
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----------
|
||||
dtype : str
|
||||
The dtype of the part file to be read now.
|
||||
partfile : `scipy.io.FortranFile`
|
||||
Part file to read from.
|
||||
|
||||
if i==0:
|
||||
#x = np.zeros(nparttot, dtype='float16')
|
||||
#y = np.zeros(nparttot, dtype='float16')
|
||||
#z = np.zeros(nparttot, dtype='float16')
|
||||
|
||||
mass = np.zeros(nparttot, dtype='float16')
|
||||
ID = np.zeros(nparttot, dtype='int32')
|
||||
|
||||
level = np.zeros(nparttot, dtype='int32')
|
||||
|
||||
clumpid = np.zeros(nparttot, dtype='int32')
|
||||
|
||||
else:
|
||||
#x_inv = np.zeros(nparttot, dtype='float16')
|
||||
#y_inv = np.zeros(nparttot, dtype='float16')
|
||||
#z_inv = np.zeros(nparttot, dtype='float16')
|
||||
|
||||
mass_inv = np.zeros(nparttot, dtype='float16')
|
||||
ID_inv = np.zeros(nparttot, dtype='int32')
|
||||
|
||||
level_inv = np.zeros(nparttot, dtype='int32')
|
||||
|
||||
clumpid_inv = np.zeros(nparttot, dtype='int32')
|
||||
Returns
|
||||
-------
|
||||
out : 1-dimensional array
|
||||
The data read from the part file.
|
||||
n : int
|
||||
The index of the initial conditions (IC) realisation.
|
||||
simpath : str
|
||||
The complete path to the CSiBORG simulation.
|
||||
"""
|
||||
if dtype in ["float16", "float32", "float64"]:
|
||||
return partfile.read_reals('d')
|
||||
elif dtype in ["int32", "int64"]:
|
||||
return partfile.read_ints()
|
||||
else:
|
||||
raise TypeError("Unexpected dtype `{}`.".format(dtype))
|
||||
|
||||
|
||||
#----------------------
|
||||
# Read particle data
|
||||
#----------------------
|
||||
def read_particle(pars_extract, n, simpath, verbose=True):
|
||||
"""
|
||||
Read particle files of a simulation at a given snapshot and return
|
||||
values of `pars_extract`.
|
||||
|
||||
#read(1)ncpu2 # What you would do in fortran
|
||||
#read(1)ndim2
|
||||
#read(1)npart2
|
||||
#read(1)
|
||||
#read(1)
|
||||
#read(1)
|
||||
#read(1)
|
||||
#read(1)
|
||||
#do i=1,ndim
|
||||
#read(1)m
|
||||
#x(1:npart2,i)=m
|
||||
#end do
|
||||
#! Skip velocity
|
||||
#do i=1,ndim
|
||||
#read(1)m
|
||||
#end do
|
||||
#! Read mass
|
||||
#read(1)m
|
||||
#if(nstar>0)then
|
||||
#read(1) ! Skip identity
|
||||
#read(1) ! Skip level
|
||||
#read(1)family
|
||||
#read(1)tag
|
||||
#read(1)age
|
||||
Parameters
|
||||
----------
|
||||
pars_extract : list of str
|
||||
Parameters to be extacted.
|
||||
n : int
|
||||
The index of the redshift snapshot.
|
||||
simpath : str
|
||||
The complete path to the CSiBORG simulation.
|
||||
verbose : bool, optional
|
||||
Verbosity flag while for reading the CPU outputs.
|
||||
|
||||
start_ind = np.zeros(ncpu, dtype='int')
|
||||
for cpu in range(ncpu-1):
|
||||
start_ind[cpu+1] = nparts[cpu] + start_ind[cpu]
|
||||
Returns
|
||||
-------
|
||||
out : structured array
|
||||
The data read from the particle file.
|
||||
"""
|
||||
# Open the particle files
|
||||
nparts, partfiles = open_particle(n, simpath)
|
||||
ncpu = nparts.size
|
||||
# Order in which the particles are written in the FortranFile
|
||||
forder = [("x", "float16"), ("y", "float16"), ("z", "float16"),
|
||||
("vx", "float16"), ("vy", "float16"), ("vz", "float16"),
|
||||
("M", "float16"), ("ID", "int32"), ("level", "int32")]
|
||||
fnames = [fp[0] for fp in forder]
|
||||
fdtypes = [fp[1] for fp in forder]
|
||||
# Check there are no strange parameters
|
||||
for p in pars_extract:
|
||||
if p not in fnames:
|
||||
raise ValueError("Undefined parameter `{}`. Must be one of `{}`."
|
||||
.format(p, fnames))
|
||||
|
||||
for cpu in range(ncpu):
|
||||
unbfile = srcdir+'/unbinding_'+srcdir[-5:]+'.out'+str(cpu+1).zfill(5)
|
||||
unbffile = ff.FortranFile(unbfile)
|
||||
npart_tot = numpy.sum(nparts)
|
||||
# A dummy array is necessary for reading the fortran files.
|
||||
dum = numpy.full(npart_tot, numpy.nan, dtype="float16")
|
||||
# These are the data we read along with types
|
||||
dtype = {"names": pars_extract,
|
||||
"formats": [forder[fnames.index(p)][1] for p in pars_extract]}
|
||||
# Allocate the output structured array
|
||||
out = numpy.full(npart_tot, numpy.nan, dtype)
|
||||
# Loop indices
|
||||
start_ind = numpy.zeros(ncpu, dtype=int)
|
||||
start_ind[1:] = numpy.cumsum(nparts)[:-1]
|
||||
iters = tqdm(range(ncpu)) if verbose else range(ncpu)
|
||||
for cpu in iters:
|
||||
i = start_ind[cpu]
|
||||
j = nparts[cpu]
|
||||
for (fname, fdtype) in zip(fnames, fdtypes):
|
||||
if fname in pars_extract:
|
||||
out[fname][i:i + j] = read_sp(fdtype, partfiles[cpu])
|
||||
else:
|
||||
dum[i:i + j] = read_sp(fdtype, partfiles[cpu])
|
||||
|
||||
dum[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d') # Think they're stored as double so must read as double
|
||||
dum[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d') # Positions
|
||||
dum[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d')
|
||||
|
||||
dum[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d') # Velocities; this all just overwrites itself
|
||||
dum[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d')
|
||||
dum[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d')
|
||||
|
||||
mass[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d') # Mass
|
||||
|
||||
#vx[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d')
|
||||
#vy[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d')
|
||||
#vz[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d')
|
||||
|
||||
#mass[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readReals('d')
|
||||
ID[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readInts()
|
||||
|
||||
level[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = partfiles[cpu].readInts()
|
||||
|
||||
clumpid[start_ind[cpu]:start_ind[cpu]+nparts[cpu]] = unbffile.readInts()
|
||||
|
||||
del dum
|
||||
|
||||
|
||||
print("Minimum clump ID:", np.min(clumpid)) # This is the clump a particle has been assigned to, so min should be 0 which means not in clump
|
||||
clumpid = np.absolute(clumpid) # Not sure why this is here...
|
||||
|
||||
|
||||
#random.shuffle(ID); random.shuffle(ID_inv) # If the IDs are randomised but not the clumpIDs then all of the below should be random
|
||||
|
||||
print(np.min(ID), np.median(ID), np.mean(ID), np.max(ID))
|
||||
print(np.min(ID_inv), np.median(ID_inv), np.mean(ID_inv), np.max(ID_inv))
|
||||
|
||||
print(np.min(level), np.median(level), np.mean(level), np.max(level))
|
||||
print(np.min(level_inv), np.median(level_inv), np.mean(level_inv), np.max(level_inv))
|
||||
|
||||
print(np.min(mass), np.median(mass), np.mean(mass), np.max(mass))
|
||||
print(np.min(mass_inv), np.median(mass_inv), np.mean(mass_inv), np.max(mass_inv))
|
||||
|
||||
#plt.clf()
|
||||
#plt.hist(mass)
|
||||
#plt.show()
|
||||
|
||||
|
||||
#index lev parent(2) ncell peak_x peak_y(5) peak_z rho- rho+(8) rho_av mass_cl relevance(11)
|
||||
clumparr = np.genfromtxt(srcdir1+"/clump_"+outnr1+".dat")
|
||||
clumparr_inv = np.genfromtxt(srcdir2+"/clump_"+outnr2+".dat")
|
||||
|
||||
clumpID, parent, Mclump = clumparr[:,0].astype(int), clumparr[:,2].astype(int), clumparr[:,10]
|
||||
clumpID_inv, parent_inv, Mclump_inv = clumparr_inv[:,0].astype(int), clumparr_inv[:,2].astype(int), clumparr_inv[:,10]
|
||||
|
||||
#clumpID_main = clumpID[clumpID==parent] # IDs of main halos only from the clump file
|
||||
#clumpID_main_inv = clumpID_inv[clumpID_inv==parent_inv]
|
||||
|
||||
#clumpID_big = clumpID[Mclump>np.median(Mclump)] # IDs of halos more massive than the median
|
||||
#clumpID_big_inv = clumpID_inv[Mclump_inv>np.median(Mclump_inv)]
|
||||
|
||||
clumpID_big = clumpID[Mclump>np.percentile(Mclump, 90)]
|
||||
clumpID_big_inv = clumpID_inv[Mclump_inv>np.percentile(Mclump_inv, 90)]
|
||||
|
||||
#clumpID_small = clumpID[Mclump<=np.median(Mclump)] # IDs of halos more massive than the median
|
||||
#clumpID_small_inv = clumpID_inv[Mclump_inv<=np.median(Mclump_inv)]
|
||||
|
||||
|
||||
#[np.where(clumpid==x) for x in clumpID_main]
|
||||
|
||||
print("CHECK:", len(ID), len(ID_inv), len(np.intersect1d(ID, ID_inv)), "(should all be the same)")
|
||||
|
||||
print("Total number of clumps in the two sims:", len(clumparr), len(clumparr_inv))
|
||||
|
||||
print("Total number of particles in the two sims:", len(ID), len(ID_inv), "(should be the same)")
|
||||
|
||||
print("Fraction of particles within halos in the two sims:", round(len(ID[clumpid!=0])/float(len(ID)), 6), round(len(ID_inv[clumpid_inv!=0])/float(len(ID)), 6))
|
||||
print("Fraction of particles within halos in *both* sims:", round(len(np.intersect1d(ID[clumpid!=0], ID_inv[clumpid_inv!=0]))/float(len(np.intersect1d(ID, ID_inv))), 6), "(random value =", round(len(ID[clumpid!=0])/float(len(ID)) * len(ID_inv[clumpid_inv!=0])/float(len(ID_inv)), 6), "), ratio =", round(len(np.intersect1d(ID[clumpid!=0], ID_inv[clumpid_inv!=0]))/float(len(np.intersect1d(ID, ID_inv))) / (len(ID[clumpid!=0])/float(len(ID)) * len(ID_inv[clumpid_inv!=0])/float(len(ID_inv))), 3))
|
||||
|
||||
print("Fraction of particles in massive halos in the two sims:", round(np.sum(np.in1d(clumpid, clumpID_big))/float(len(ID)), 6), round(np.sum(np.in1d(clumpid_inv, clumpID_big_inv))/float(len(ID)), 6))
|
||||
print("Fraction of particles in massive halos in *both* sims:", round(len(np.intersect1d(ID[np.in1d(clumpid, clumpID_big)], ID_inv[np.in1d(clumpid_inv, clumpID_big_inv)]))/float(len(np.intersect1d(ID, ID_inv))), 6), "(random value =", round(np.sum(np.in1d(clumpid, clumpID_big))/float(len(ID)) * np.sum(np.in1d(clumpid_inv, clumpID_big_inv))/float(len(ID)), 6), "), ratio =", round(len(np.intersect1d(ID[np.in1d(clumpid, clumpID_big)], ID_inv[np.in1d(clumpid_inv, clumpID_big_inv)]))/float(len(np.intersect1d(ID, ID_inv))) / (np.sum(np.in1d(clumpid, clumpID_big))/float(len(ID)) * np.sum(np.in1d(clumpid_inv, clumpID_big_inv))/float(len(ID_inv))), 3))
|
||||
return out
|
||||
|
|
|
|||
Loading…
Reference in a new issue