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rstiskalek
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4
.gitignore
vendored
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4
.gitignore
vendored
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venv_galomatch/
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share/
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bin/
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.bashrc
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37
data/descr.txt
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37
data/descr.txt
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Various column names
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Clump file columns:
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"index", "lev", "parent", "ncell", "peak_x", "peak_y", "peak_z", "rho-", "rho+", "rho_av", "mass_cl", "relevance"
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Mergertree file columns:
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"clump", "progenitor", "prog outputnr", "desc mass", "desc npart", "desc x", "desc y", "desc z", "desc vx", "desc vy", "desc vz"
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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The merger trees are stored in `output_XXXXX/mergertree_XXXXX.txtYYYYY` files. Each file contains 11 columns:
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* `clump`: clump ID of a clump at this output number
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* `progenitor`: the progenitor clump ID in output number "prog_outputnr"
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* `prog_outputnr`: the output number of when the progenitor was an alive clump
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* `desc mass`: mass of the current clump.
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* `desc npart`: number of particles of the current clump.
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* `desc x,y,z`: x, y, z position of current clump.
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* `desc vx, vy, vz`: x, y, z velocities of current clump.
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desc_mass and desc_npart will be either inclusive or exclusive, depending on how you set the `use_exclusive_mass` parameter.
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(See below for details)
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**How to read the output:**
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* A clump > 0 has progenitor > 0: Standard case. A direct progenitor from the adjacent previous snapshot was identified for this clump.
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* A clump > 0 has progenitor = 0: no progenitor could be established and the clump is treated as newly formed.
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* A clump > 0 has progenitor < 0: it means that no direct progenitor could be found in the adjacent previous snapshot, but a progenitor was identified from an earlier, non-adjacent snapshot.
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* A clump < 0 has progenitor > 0: this progenitor merged into this clump, but is not this clump's main progenitor.
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* A clump < 0 has progenitor < 0: this shouldn't happen.
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### Visualisation
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`ramses/utils/py/mergertreeplot.py` is a python 2 script to plot the merger trees as found by this patch.
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Details on options and usage are at the start of the script as a comment.
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14
galomatch/__init__.py
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14
galomatch/__init__.py
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# Copyright (C) 2022 Richard Stiskalek
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# This program is free software; you can redistribute it and/or modify it
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# under the terms of the GNU General Public License as published by the
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# Free Software Foundation; either version 3 of the License, or (at your
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# option) any later version.
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#
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# This program is distributed in the hope that it will be useful, but
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# WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
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# Public License for more details.
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#
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# You should have received a copy of the GNU General Public License along
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# with this program; if not, write to the Free Software Foundation, Inc.,
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# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
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14
galomatch/io/__init__.py
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14
galomatch/io/__init__.py
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# Copyright (C) 2022 Richard Stiskalek
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# This program is free software; you can redistribute it and/or modify it
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# under the terms of the GNU General Public License as published by the
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# Free Software Foundation; either version 3 of the License, or (at your
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# option) any later version.
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#
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# This program is distributed in the hope that it will be useful, but
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# WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
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# Public License for more details.
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#
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# You should have received a copy of the GNU General Public License along
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# with this program; if not, write to the Free Software Foundation, Inc.,
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# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
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234
galomatch/io/_readsim.py
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234
galomatch/io/_readsim.py
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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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49
scripts/readsim.ipynb
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49
scripts/readsim.ipynb
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{
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"cells": [
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{
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||||
"cell_type": "code",
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||||
"execution_count": null,
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||||
"metadata": {},
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"outputs": [
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{
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"ename": "",
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"evalue": "",
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"output_type": "error",
|
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"traceback": [
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"\u001b[1;31mThe kernel failed to start as '_psutil_linux' could not be imported from 'most likely due to a circular import'.\n",
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"\u001b[1;31mClick <a href='https://aka.ms/kernelFailuresModuleImportErrFromFile'>here</a> for more info."
|
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]
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||||
}
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||||
],
|
||||
"source": [
|
||||
"a"
|
||||
]
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||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.8.0 64-bit",
|
||||
"language": "python",
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||||
"name": "python3"
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||||
},
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||||
"language_info": {
|
||||
"name": "python",
|
||||
"version": "3.8.0"
|
||||
},
|
||||
"orig_nbformat": 4,
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "df0893f56f349688326838aaeea0de204df53a132722cbd565e54b24a8fec5f6"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
0
test.py
0
test.py
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