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velociraptor_python_tools.py
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velociraptor_python_tools.py
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# Make backwards compatible with python 2, ignored in python 3
from __future__ import print_function
import sys
import os
import subprocess
import struct
import os.path
import string
import time
import re
import math
import operator
import numpy as np
import h5py
# import hdf5 interface
# import tables as pytb #import pytables
#import pandas as pd
import copy
from collections import deque
import itertools
import scipy.interpolate as scipyinterp
import scipy.spatial as spatial
import multiprocessing as mp
#import mpi4py as mpi
from collections import deque
import pandas as pd
#import cython
#from cython.parallel import prange, parallel
# would be good to compile these routines with cython
# try to speed up search
# cimport numpy as np
"""
Routines for reading velociraptor output
"""
"""
IO Routines
"""
def ReadPropertyFile(basefilename, ibinary=0, iseparatesubfiles=0, iverbose=0, desiredfields=[], isiminfo=True, iunitinfo=True, iconfiginfo=True):
"""
VELOCIraptor/STF files in various formats
for example ascii format contains
a header with
filenumber number_of_files
numhalos_in_file nnumhalos_in_total
followed by a header listing the information contain. An example would be
ID(1) ID_mbp(2) hostHaloID(3) numSubStruct(4) npart(5) Mvir(6) Xc(7) Yc(8) Zc(9) Xcmbp(10) Ycmbp(11) Zcmbp(12) VXc(13) VYc(14) VZc(15) VXcmbp(16) VYcmbp(17) VZcmbp(18) Mass_tot(19) Mass_FOF(20) Mass_200mean(21) Mass_200crit(22) Mass_BN97(23) Efrac(24) Rvir(25) R_size(26) R_200mean(27) R_200crit(28) R_BN97(29) R_HalfMass(30) Rmax(31) Vmax(32) sigV(33) veldisp_xx(34) veldisp_xy(35) veldisp_xz(36) veldisp_yx(37) veldisp_yy(38) veldisp_yz(39) veldisp_zx(40) veldisp_zy(41) veldisp_zz(42) lambda_B(43) Lx(44) Ly(45) Lz(46) q(47) s(48) eig_xx(49) eig_xy(50) eig_xz(51) eig_yx(52) eig_yy(53) eig_yz(54) eig_zx(55) eig_zy(56) eig_zz(57) cNFW(58) Krot(59) Ekin(60) Epot(61) n_gas(62) M_gas(63) Xc_gas(64) Yc_gas(65) Zc_gas(66) VXc_gas(67) VYc_gas(68) VZc_gas(69) Efrac_gas(70) R_HalfMass_gas(71) veldisp_xx_gas(72) veldisp_xy_gas(73) veldisp_xz_gas(74) veldisp_yx_gas(75) veldisp_yy_gas(76) veldisp_yz_gas(77) veldisp_zx_gas(78) veldisp_zy_gas(79) veldisp_zz_gas(80) Lx_gas(81) Ly_gas(82) Lz_gas(83) q_gas(84) s_gas(85) eig_xx_gas(86) eig_xy_gas(87) eig_xz_gas(88) eig_yx_gas(89) eig_yy_gas(90) eig_yz_gas(91) eig_zx_gas(92) eig_zy_gas(93) eig_zz_gas(94) Krot_gas(95) T_gas(96) Zmet_gas(97) SFR_gas(98) n_star(99) M_star(100) Xc_star(101) Yc_star(102) Zc_star(103) VXc_star(104) VYc_star(105) VZc_star(106) Efrac_star(107) R_HalfMass_star(108) veldisp_xx_star(109) veldisp_xy_star(110) veldisp_xz_star(111) veldisp_yx_star(112) veldisp_yy_star(113) veldisp_yz_star(114) veldisp_zx_star(115) veldisp_zy_star(116) veldisp_zz_star(117) Lx_star(118) Ly_star(119) Lz_star(120) q_star(121) s_star(122) eig_xx_star(123) eig_xy_star(124) eig_xz_star(125) eig_yx_star(126) eig_yy_star(127) eig_yz_star(128) eig_zx_star(129) eig_zy_star(130) eig_zz_star(131) Krot_star(132) tage_star(133) Zmet_star(134)
then followed by data
Note that a file will indicate how many files the total output has been split into
Not all fields need be read in. If only want specific fields, can pass a string of desired fields like
['ID', 'Mass_FOF', 'Krot']
#todo still need checks to see if fields not present and if so, not to include them or handle the error
"""
# this variable is the size of the char array in binary formated data that stores the field names
CHARSIZE = 40
start = time.process_time()
inompi = True
if (iverbose):
print("reading properties file", basefilename)
filename = basefilename+".properties"
# load header
if (os.path.isfile(filename) == True):
numfiles = 0
else:
filename = basefilename+".properties"+".0"
inompi = False
if (os.path.isfile(filename) == False):
print("Could not find VELOCIraptor file as either",basefilename+".properties or",filename)
return []
byteoffset = 0
# used to store fields, their type, etc
fieldnames = []
fieldtype = []
fieldindex = []
if (ibinary == 0):
# load ascii file
halofile = open(filename, 'r')
# read header information
[filenum, numfiles] = halofile.readline().split()
filenum = int(filenum)
numfiles = int(numfiles)
[numhalos, numtothalos] = halofile.readline().split()
numhalos = np.uint64(numhalos)
numtothalos = np.uint64(numtothalos)
names = ((halofile.readline())).split()
# remove the brackets in ascii file names
fieldnames = [fieldname.split("(")[0] for fieldname in names]
for i in np.arange(fieldnames.__len__()):
fieldname = fieldnames[i]
if fieldname in ["ID", "numSubStruct", "npart", "n_gas", "n_star", "Structuretype"]:
fieldtype.append(np.uint64)
elif fieldname in ["ID_mbp", "hostHaloID"]:
fieldtype.append(np.int64)
else:
fieldtype.append(np.float64)
halofile.close()
# if desiredfields is NULL load all fields
# but if this is passed load only those fields
if (len(desiredfields) > 0):
lend = len(desiredfields)
fieldindex = np.zeros(lend, dtype=int)
desiredfieldtype = [[] for i in range(lend)]
for i in range(lend):
fieldindex[i] = fieldnames.index(desiredfields[i])
desiredfieldtype[i] = fieldtype[fieldindex[i]]
fieldtype = desiredfieldtype
fieldnames = desiredfields
# to store the string containing data format
fieldtypestring = ''
for i in np.arange(fieldnames.__len__()):
if fieldtype[i] == np.uint64:
fieldtypestring += 'u8, '
elif fieldtype[i] == np.int64:
fieldtypestring += 'i8, '
elif fieldtype[i] == np.float64:
fieldtypestring += 'f8, '
elif (ibinary == 1):
# load binary file
halofile = open(filename, 'rb')
[filenum, numfiles] = np.fromfile(halofile, dtype=np.int32, count=2)
[numhalos, numtothalos] = np.fromfile(
halofile, dtype=np.uint64, count=2)
headersize = np.fromfile(halofile, dtype=np.int32, count=1)[0]
byteoffset = np.dtype(np.int32).itemsize*3 + \
np.dtype(np.uint64).itemsize*2+4*headersize
for i in range(headersize):
fieldnames.append(struct.unpack('s', halofile.read(CHARSIZE)).strip())
for i in np.arange(fieldnames.__len__()):
fieldname = fieldnames[i]
if fieldname in ["ID", "numSubStruct", "npart", "n_gas", "n_star", "Structuretype"]:
fieldtype.append(np.uint64)
elif fieldname in ["ID_mbp", "hostHaloID"]:
fieldtype.append(np.int64)
else:
fieldtype.append(np.float64)
halofile.close()
# if desiredfields is NULL load all fields
# but if this is passed load only those fields
if (len(desiredfields) > 0):
lend = len(desiredfields)
fieldindex = np.zeros(lend, dtype=int)
desiredfieldtype = [[] for i in range(lend)]
for i in range(lend):
fieldindex[i] = fieldnames.index(desiredfields[i])
desiredfieldtype[i] = fieldtype[fieldindex[i]]
fieldtype = desiredfieldtype
fieldnames = desiredfields
# to store the string containing data format
fieldtypestring = ''
for i in np.arange(fieldnames.__len__()):
if fieldtype[i] == np.uint64:
fieldtypestring += 'u8, '
elif fieldtype[i] == np.int64:
fieldtypestring += 'i8, '
elif fieldtype[i] == np.float64:
fieldtypestring += 'f8, '
elif (ibinary == 2):
# load hdf file
halofile = h5py.File(filename, 'r')
filenum = int(halofile["File_id"][0])
numfiles = int(halofile["Num_of_files"][0])
numhalos = np.uint64(halofile["Num_of_groups"][0])
numtothalos = np.uint64(halofile["Total_num_of_groups"][0])
#atime = np.float(halofile.attrs["Time"])
fieldnames = [str(n) for n in halofile.keys()]
# clean of header info
fieldnames.remove("File_id")
fieldnames.remove("Num_of_files")
fieldnames.remove("Num_of_groups")
fieldnames.remove("Total_num_of_groups")
if ('Configuration' in fieldnames): fieldnames.remove('Configuration')
if ('SimulationInfo' in fieldnames): fieldnames.remove('SimulationInfo')
if ('UnitInfo' in fieldnames): fieldnames.remove('UnitInfo')
fieldtype = [halofile[fieldname].dtype for fieldname in fieldnames]
# if the desiredfields argument is passed only these fieds are loaded
if (len(desiredfields) > 0):
if (iverbose):
print("Loading subset of all fields in property file ",
len(desiredfields), " instead of ", len(fieldnames))
fieldnames = desiredfields
fieldtype = [halofile[fieldname].dtype for fieldname in fieldnames]
halofile.close()
# allocate memory that will store the halo dictionary
catalog = {fieldnames[i]: np.zeros(
numtothalos, dtype=fieldtype[i]) for i in range(len(fieldnames))}
noffset = np.uint64(0)
for ifile in range(numfiles):
if (inompi == True):
filename = basefilename+".properties"
else:
filename = basefilename+".properties"+"."+str(ifile)
if (iverbose):
print("reading ", filename)
if (ibinary == 0):
halofile = open(filename, 'r')
halofile.readline()
numhalos = np.uint64(halofile.readline().split()[0])
halofile.close()
if (numhalos > 0):
htemp = np.loadtxt(filename, skiprows=3, usecols=fieldindex,
dtype=fieldtypestring, unpack=True, ndmin=1)
elif(ibinary == 1):
halofile = open(filename, 'rb')
np.fromfile(halofile, dtype=np.int32, count=2)
numhalos = np.fromfile(halofile, dtype=np.uint64, count=2)[0]
# halofile.seek(byteoffset);
if (numhalos > 0):
htemp = np.fromfile(
halofile, usecols=fieldindex, dtype=fieldtypestring, unpack=True)
halofile.close()
elif(ibinary == 2):
# here convert the hdf information into a numpy array
halofile = h5py.File(filename, 'r')
numhalos = np.uint64(halofile["Num_of_groups"][0])
if (numhalos > 0):
htemp = [np.array(halofile[catvalue])
for catvalue in fieldnames]
halofile.close()
#numhalos = len(htemp[0])
for i in range(len(fieldnames)):
catvalue = fieldnames[i]
if (numhalos > 0):
catalog[catvalue][noffset:noffset+numhalos] = htemp[i]
noffset += numhalos
# if subhalos are written in separate files, then read them too
if (iseparatesubfiles == 1):
for ifile in range(numfiles):
if (inompi == True):
filename = basefilename+".sublevels"+".properties"
else:
filename = basefilename+".sublevels" + \
".properties"+"."+str(ifile)
if (iverbose):
print("reading ", filename)
if (ibinary == 0):
halofile = open(filename, 'r')
halofile.readline()
numhalos = np.uint64(halofile.readline().split()[0])
halofile.close()
if (numhalos > 0):
htemp = np.loadtxt(
filename, skiprows=3, usecols=fieldindex, dtype=fieldtypestring, unpack=True, ndmin=1)
elif(ibinary == 1):
halofile = open(filename, 'rb')
# halofile.seek(byteoffset);
np.fromfile(halofile, dtype=np.int32, count=2)
numhalos = np.fromfile(halofile, dtype=np.uint64, count=2)[0]
if (numhalos > 0):
htemp = np.fromfile(
halofile, usecols=fieldindex, dtype=fieldtypestring, unpack=True)
halofile.close()
elif(ibinary == 2):
halofile = h5py.File(filename, 'r')
numhalos = np.uint64(halofile["Num_of_groups"][0])
if (numhalos > 0):
htemp = [np.array(halofile[catvalue])
for catvalue in fieldnames]
halofile.close()
#numhalos = len(htemp[0])
for i in range(len(fieldnames)):
catvalue = fieldnames[i]
if (numhalos > 0):
catalog[catvalue][noffset:noffset+numhalos] = htemp[i]
noffset += numhalos
# load associated simulation info, time, units and configuration options
if (isiminfo):
if(iverbose):
print("reading ",basefilename+".siminfo")
catalog['SimulationInfo'] = ReadSimInfo(basefilename)
if (iunitinfo):
if(iverbose):
print("reading ",basefilename+".unitinfo")
catalog['UnitInfo'] = ReadUnitInfo(basefilename)
if (iconfiginfo):
if(iverbose):
print("reading ",basefilename+".configuration")
catalog['ConfigurationInfo'] = ReadConfigInfo(basefilename)
if (iverbose):
print("done reading properties file ", time.process_time()-start)
return catalog, numtothalos
def ReadPropertyFileMultiWrapper(basefilename, index, halodata, numhalos, atime, ibinary=0, iseparatesubfiles=0, iverbose=0, desiredfields=[]):
"""
Wrapper for multithreaded reading
"""
# call read routine and store the data
halodata[index], numhalos[index] = ReadPropertyFile(
basefilename, ibinary, iseparatesubfiles, iverbose, desiredfields)
def ReadPropertyFileMultiWrapperNamespace(index, basefilename, ns, ibinary=0, iseparatesubfiles=0, iverbose=0, desiredfields=[]):
# call read routine and store the data
ns.hdata[index], ns.ndata[index] = ReadPropertyFile(
basefilename, ibinary, iseparatesubfiles, iverbose, desiredfields)
def ReadHaloMergerTree(treefilename, ibinary=0, iverbose=0, imerit=False, inpart=False):
"""
VELOCIraptor/STF merger tree in ascii format contains
a header with
number_of_snapshots
a description of how the tree was built
total number of halos across all snapshots
then followed by data
for each snapshot
snapshotvalue numhalos
haloid_1 numprogen_1
progenid_1
progenid_2
...
progenid_numprogen_1
haloid_2 numprogen_2
.
.
.
one can also have an output format that has an additional field for each progenitor, the meritvalue
"""
start = time.process_time()
tree = {}
if (iverbose):
print("reading Tree file", treefilename, os.path.isfile(treefilename))
if (os.path.isfile(treefilename) == False):
print("Error, file not found")
return tree
# if ascii format
if (ibinary == 0):
treefile = open(treefilename, 'r')
numsnap = int(treefile.readline())
treefile.close()
elif(ibinary == 2):
if (iverbose):
print("Reading HDF5 input")
snaptreelist = open(treefilename, 'r')
snaptreename = snaptreelist.readline().strip()+".tree"
numsnaplist = sum(1 for line in snaptreelist) +1
treedata = h5py.File(snaptreename, "r")
numsnap = treedata.attrs['Number_of_snapshots']
#Check if the treefrog number of snapshots and the number of files in the list is consistent
if(numsnap!=numsnaplist):
print("Error, the number of snapshots reported by the TreeFrog output is different to the number of filenames supplied. \nPlease update this.")
return tree
#Lets extract te header information
tree["Header"] = dict()
for field in treedata.attrs.keys():
tree["Header"][field] = treedata.attrs[field]
treedata.close()
snaptreelist.close()
else:
print("Unknown format, returning null")
numsnap = 0
return tree
tree.update({i: {"haloID": [], "Num_progen": [], "Progen": []}
for i in range(numsnap)})
if (imerit):
for i in range(numsnap):
tree[i]['Merit'] = []
if (inpart):
for i in range(numsnap):
tree[i]['Npart'] = []
tree[i]['Npart_progen'] = []
# if ascii format
if (ibinary == 0):
treefile = open(treefilename, 'r')
numsnap = int(treefile.readline())
descrip = treefile.readline().strip()
tothalos = int(treefile.readline())
offset = 0
totalnumprogen = 0
for i in range(numsnap):
[snapval, numhalos] = treefile.readline().strip().split('\t')
snapval = int(snapval)
numhalos = int(numhalos)
# if really verbose
if (iverbose == 2):
print(snapval, numhalos)
tree[i]["haloID"] = np.zeros(numhalos, dtype=np.int64)
tree[i]["Num_progen"] = np.zeros(numhalos, dtype=np.uint32)
tree[i]["Progen"] = [[] for j in range(numhalos)]
if (imerit):
tree[i]["Merit"] = [[] for j in range(numhalos)]
if (inpart):
tree[i]["Npart"] = np.zeros(numhalos, dtype=np.uint32)
tree[i]["Npart_progen"] = [[] for j in range(numhalos)]
for j in range(numhalos):
data = treefile.readline().strip().split('\t')
hid = np.int64(data[0])
nprog = np.uint32(data[1])
tree[i]["haloID"][j] = hid
tree[i]["Num_progen"][j] = nprog
if (inpart):
tree[i]["Npart"][j] = np.uint32(data[2])
totalnumprogen += nprog
if (nprog > 0):
tree[i]["Progen"][j] = np.zeros(nprog, dtype=np.int64)
if (imerit):
tree[i]["Merit"][j] = np.zeros(nprog, dtype=np.float32)
if (inpart):
tree[i]["Npart_progen"][j] = np.zeros(
nprog, dtype=np.uint32)
for k in range(nprog):
data = treefile.readline().strip().split(' ')
tree[i]["Progen"][j][k] = np.int64(data[0])
if (imerit):
tree[i]["Merit"][j][k] = np.float32(data[1])
if (inpart):
tree[i]["Npart_progen"][j][k] = np.uint32(data[2])
elif(ibinary == 2):
if("HaloID_snapshot_offset" in tree["Header"]):
snapshotoffset = tree["Header"]["HaloID_snapshot_offset"]
else:
print("Warning: you are using older TreeFrog output (version<=1.2) which does not contain information about which snapshot the halo catalog starts at\nAssuming that it starts at snapshot = 0.\nPlease use a TreeFrog version>1.2 if you require this feature")
snapshotoffset = 0
snaptreelist = open(treefilename, 'r')
for snap in range(snapshotoffset,snapshotoffset+numsnap):
snaptreename = snaptreelist.readline().strip()+".tree"
if (iverbose):
print("Reading", snaptreename)
treedata = h5py.File(snaptreename, "r")
tree[snap]["haloID"] = np.asarray(treedata["ID"])
tree[snap]["Num_progen"] = np.asarray(treedata["NumProgen"])
if(inpart):
tree[snap]["Npart"] = np.asarray(treedata["Npart"])
# See if the dataset exits
if("ProgenOffsets" in treedata.keys()):
# Find the indices to split the array
split = np.add(np.asarray(
treedata["ProgenOffsets"]), tree[snap]["Num_progen"], dtype=np.uint64, casting="unsafe")
# Read in the progenitors, splitting them as reading them in
tree[snap]["Progen"] = np.split(
treedata["Progenitors"][:], split[:-1])
if(inpart):
tree[snap]["Npart_progen"] = np.split(
treedata["ProgenNpart"], split[:-1])
if(imerit):
tree[snap]["Merit"] = np.split(
treedata["Merits"], split[:-1])
snaptreelist.close()
if (iverbose):
print("done reading tree file ", time.process_time()-start)
return tree
class MinStorageList():
"""
This uses two smaller arrays to access a large contiguous array and return subchunks
"""
Num,Offset=None,None
Data=None
def __init__(self,nums,offsets,rawdata):
self.Num=nums
self.Offset=offsets
self.Data=rawdata
def __getitem__(self, index):
if self.Num[index] == 0 :
return np.array([])
else :
return self.Data[self.Offset[index]:self.Offset[index]+self.Num[index]]
def GetBestRanks(self, ):
return np.array(self.Data[self.Offset],copy=True)
def ReadHaloMergerTreeDescendant(treefilename, ireverseorder=False, ibinary=0,
iverbose=0, imerit=False, inpart=False,
ireducedtobestranks=False, meritlimit=0.025,
ireducemem=True,iprimarydescen=False):
"""
VELOCIraptor/STF descendant based merger tree in ascii format contains
a header with
number_of_snapshots
a description of how the tree was built
total number of halos across all snapshots
then followed by data
for each snapshot
snapshotvalue numhalos
haloid_1 numprogen_1
progenid_1
progenid_2
...
progenid_numprogen_1
haloid_2 numprogen_2
.
.
.
one can also have an output format that has an additional field for each progenitor, the meritvalue
"""
start = time.process_time()
tree = {}
if (iverbose):
print("reading Tree file", treefilename, os.path.isfile(treefilename))
if (os.path.isfile(treefilename) == False):
print("Error, file not found")
return tree
# fine out how many snapshots there are
# if ascii format
if (ibinary == 0):
if (iverbose):
print("Reading ascii input")
treefile = open(treefilename, 'r')
numsnap = int(treefile.readline())
treefile.close()
# hdf format, input file is a list of filenames can also extract the header info
elif(ibinary == 2):
if (iverbose):
print("Reading HDF5 input")
snaptreelist = open(treefilename, 'r')
snaptreename = snaptreelist.readline().strip()+".tree"
numsnap = sum(1 for line in snaptreelist) +1
treedata = h5py.File(snaptreename, "r")
#Lets extract te header information
tree["Header"] = dict()
for field in treedata.attrs.keys():
tree["Header"][field] = treedata.attrs[field]
treedata.close()
snaptreelist.close()
else:
print("Unknown format, returning null")
numsnap = 0
return tree
#Check for flag compatibility
if(ireducemem & iprimarydescen):
print("Warning: Both the ireducemem and iprimarydescen are set to True. The ireducemem is not needed for iprimarydescen flag \nas it already had reduced memory footprint, due to only primiary descendants being extracted")
ireducemem=False
#Update the dictionary with the tree information
tree.update({i: {"haloID": [], "Num_descen": [], "Descen": [], "Rank": []}
for i in range(numsnap)})
if (imerit):
for i in range(numsnap):
tree[i]['Merit'] = []
if (inpart):
for i in range(numsnap):
tree[i]['Npart'] = []
tree[i]['Npart_descen'] = []
if (ibinary == 0):
treefile = open(treefilename, 'r')
numsnap = int(treefile.readline())
descrip = treefile.readline().strip()
tothalos = int(treefile.readline())
offset = 0
totalnumdescen = 0
for i in range(numsnap):
ii = i
if (ireverseorder):
ii = numsnap-1-i
[snapval, numhalos] = treefile.readline().strip().split('\t')
snapval = int(snapval)
numhalos = int(numhalos)
# if really verbose
if (iverbose == 2):
print(snapval, numhalos)
tree[ii]["haloID"] = np.zeros(numhalos, dtype=np.int64)
tree[ii]["Num_descen"] = np.zeros(numhalos, dtype=np.uint32)
tree[ii]["Descen"] = [[] for j in range(numhalos)]
tree[ii]["Rank"] = [[] for j in range(numhalos)]
if (imerit):
tree[ii]["Merit"] = [[] for j in range(numhalos)]
if (inpart):
tree[i]["Npart"] = np.zeros(numhalos, dtype=np.uint32)
tree[ii]["Npart_descen"] = [[] for j in range(numhalos)]
for j in range(numhalos):
data = treefile.readline().strip().split('\t')
hid = np.int64(data[0])
ndescen = np.uint32(data[1])
tree[ii]["haloID"][j] = hid
tree[ii]["Num_descen"][j] = ndescen
if (inpart):
tree[ii]["Npart"][j] = np.uint32(data[2])
totalnumdescen += ndescen
if (ndescen > 0):
tree[ii]["Descen"][j] = np.zeros(ndescen, dtype=np.int64)
tree[ii]["Rank"][j] = np.zeros(ndescen, dtype=np.uint32)
if (imerit):
tree[ii]["Merit"][j] = np.zeros(
ndescen, dtype=np.float32)
if (inpart):
tree[ii]["Npart_descen"][j] = np.zeros(
ndescen, dtype=np.float32)
for k in range(ndescen):
data = treefile.readline().strip().split(' ')
tree[ii]["Descen"][j][k] = np.int64(data[0])
tree[ii]["Rank"][j][k] = np.uint32(data[1])
if (imerit):
tree[ii]["Merit"][j][k] = np.float32(data[2])
if (inpart):
tree[ii]["Npart_descen"][j][k] = np.uint32(data[3])
if (ireducedtobestranks):
halolist=np.where(tree[ii]["Num_descen"]>1)[0]
for ihalo in halolist:
numdescen = 1
if (imerit):
numdescen = np.int32(np.max([1,np.argmax(tree[ii]["Merit"][ihalo]<meritlimit)]))
tree[ii]["Num_descen"][ihalo] = numdescen
tree[ii]["Descen"][ihalo] = np.array([tree[ii]["Descen"][ihalo][:numdescen]])
tree[ii]["Rank"][ihalo] = np.array([tree[ii]["Rank"][ihalo][:numdescen]])
if (imerit):
tree[ii]["Merit"][ihalo] = np.array([tree[ii]["Merit"][ihalo][:numdescen]])
if (inpart):
tree[ii]["Npart_descen"][ihalo] = np.array([tree[ii]["Npart_descen"][ihalo][:numdescen]])
# hdf format
elif(ibinary == 2):
snaptreelist = open(treefilename, 'r')
snaptreenames=[[] for snap in range(numsnap)]
for snap in range(numsnap):
snaptreenames[snap] = snaptreelist.readline().strip()+".tree"
snaptreelist.close()
if("HaloID_snapshot_offset" in tree["Header"]):
snapshotoffset = tree["Header"]["HaloID_snapshot_offset"]
else:
print("Warning: you are using older TreeFrog output (version<=1.2) which does not contain information about which snapshot the halo catalog starts at\nAssuming that it starts at snapshot = 0.\nPlease use a TreeFrog version>1.2 if you require this feature")
snapshotoffset = 0
if (iverbose):
snaptreename = snaptreenames[0]
treedata = h5py.File(snaptreename, "r")
numhalos = treedata.attrs["Total_number_of_halos"]
memsize = np.uint64(1).nbytes*2.0+np.uint16(0).nbytes*2.0
if (inpart):
memsize += np.int32(0).nbytes*2.0
if (imerit):
memsize += np.float32(0).nbytes
memsize *= numhalos
if (ireducemem):
print("Reducing memory, changes api.")
print("Data contains ", numhalos, "halos and will likley need a minimum of", memsize/1024.**3.0, "GB of memory")
elif(iprimarydescen):
print("Extracting just the primary descendants")
print("Data contains ", numhalos, "halos and will likley need a minimum of", memsize/1024.**3.0, "GB of memory")
else:
print("Contains ", numhalos, "halos and will likley minimum ", memsize/1024.**3.0, "GB of memory")
print("Plus overhead to store list of arrays, with likely need a minimum of ",100*numhalos/1024**3.0, "GB of memory ")
treedata.close()
for snap in range(snapshotoffset,snapshotoffset+numsnap):
snaptreename = snaptreenames[snap]
if (iverbose):
print("Reading", snaptreename)
treedata = h5py.File(snaptreename, "r")
tree[snap]["haloID"] = np.asarray(treedata["ID"])
tree[snap]["Num_descen"] = np.asarray(treedata["NumDesc"],np.uint16)
numhalos=tree[snap]["haloID"].size
if(inpart):
tree[snap]["Npart"] = np.asarray(treedata["Npart"],np.int32)
# See if the dataset exits
if("DescOffsets" in treedata.keys()):
# Find the indices to split the array
if (ireducemem):
tree[snap]["_Offsets"] = np.asarray(treedata["DescOffsets"],dtype=np.uint64)
elif(iprimarydescen):
offsets = np.asarray(treedata["DescOffsets"],dtype=np.uint64)
#Only include the offset for the last halo in the array if it has a descendant
if(offsets.size):
if(tree[snap]["Num_descen"][-1]==0): offsets = offsets[:-1]
else:
descenoff=np.asarray(treedata["DescOffsets"],dtype=np.uint64)
split = np.add(descenoff, tree[snap]["Num_descen"], dtype=np.uint64, casting="unsafe")[:-1]
descenoff=None
# Read in the data splitting it up as reading it in
# if reducing memory then store all the values in the _ keys
# and generate class that returns the appropriate subchunk as an array when using the [] operaotor
# otherwise generate lists of arrays
if (ireducemem):
tree[snap]["_Ranks"] = np.asarray(treedata["Ranks"],dtype=np.int16)
tree[snap]["_Descens"] = np.asarray(treedata["Descendants"],dtype=np.uint64)
tree[snap]["Rank"] = MinStorageList(tree[snap]["Num_descen"],tree[snap]["_Offsets"],tree[snap]["_Ranks"])
tree[snap]["Descen"] = MinStorageList(tree[snap]["Num_descen"],tree[snap]["_Offsets"],tree[snap]["_Descens"])
elif(iprimarydescen):
tree[snap]["Rank"] = np.asarray(treedata["Ranks"],dtype=np.uint16)[offsets]
tree[snap]["Descen"] = np.asarray(treedata["Descendants"],dtype=np.uint64)[offsets]
else:
tree[snap]["Rank"] = np.split(np.asarray(treedata["Ranks"],dtype=np.uint16), split)
tree[snap]["Descen"] = np.split(np.asarray(treedata["Descendants"],dtype=np.uint64), split)
if(inpart):
if (ireducemem):
tree[snap]["_Npart_descens"] = np.asarray(treedata["DescenNpart"],np.uint64)
tree[snap]["Npart_descen"] = MinStorageList(tree[snap]["Num_descen"],tree[snap]["_Offsets"],tree[snap]["_Npart_descens"])
elif(iprimarydescen):
tree[snap]["Npart_descen"] = np.asarray(treedata["DescenNpart"],np.uint64)[offsets]
else:
tree[snap]["Npart_descen"] = np.split(np.asarray(treedata["DescenNpart"],np.uint64), split)
if(imerit):
if (ireducemem):
tree[snap]["_Merits"] = np.asarray(treedata["Merits"],np.float32)
tree[snap]["Merit"] = MinStorageList(tree[snap]["Num_descen"],tree[snap]["_Offsets"],tree[snap]["_Merits"])
elif(iprimarydescen):
tree[snap]["Merit"] = np.asarray(treedata["Merits"],np.float32)[offsets]
else:
tree[snap]["Merit"] = np.split(np.asarray(treedata["Merits"],np.float32), split)
#if reducing stuff down to best ranks, then only keep first descendant
#unless also reading merit and then keep first descendant and all other descendants that are above a merit limit
if (ireducedtobestranks==True and ireducemem==False and iprimarydescen==False):
halolist = np.where(tree[snap]["Num_descen"]>1)[0]
if (iverbose):
print('Reducing memory needed. At snap ', snap, ' with %d total halos and alter %d halos. '% (len(tree[snap]['Num_descen']), len(halolist)))
print(np.percentile(tree[snap]['Num_descen'][halolist],[50.0,99.0]))
for ihalo in halolist:
numdescen = 1
if (imerit):
numdescen = np.int32(np.max([1,np.argmax(tree[snap]["Merit"][ihalo]<meritlimit)]))
tree[snap]["Num_descen"][ihalo] = numdescen
tree[snap]["Descen"][ihalo] = np.asarray([tree[snap]["Descen"][ihalo][:numdescen]])
tree[snap]["Rank"][ihalo] = np.asarray([tree[snap]["Rank"][ihalo][:numdescen]])
if (imerit):
tree[snap]["Merit"][ihalo] = np.asarray([tree[snap]["Merit"][ihalo][:numdescen]])
if (inpart):
tree[snap]["Npart_descen"][ihalo] = np.asarray([tree[snap]["Npart_descen"][ihalo][:numdescen]])
split=None
treedata.close()
if (iverbose):
print("done reading tree file ", time.process_time()-start)
return tree
def ReadHaloPropertiesAcrossSnapshots(numsnaps, snaplistfname, inputtype, iseperatefiles, iverbose=0, desiredfields=[]):
"""
read halo data from snapshots listed in file with snaplistfname file name
"""
halodata = [dict() for j in range(numsnaps)]
ngtot = [0 for j in range(numsnaps)]
atime = [0 for j in range(numsnaps)]
start = time.process_time()
print("reading data")
# if there are a large number of snapshots to read, read in parallel
# only read in parallel if worthwhile, specifically if large number of snapshots and snapshots are ascii
iparallel = (numsnaps > 20 and inputtype == 2)
if (iparallel):
# determine maximum number of threads
nthreads = min(mp.cpu_count(), numsnaps)
nchunks = int(np.ceil(numsnaps/float(nthreads)))
print("Using", nthreads, "threads to parse ",
numsnaps, " snapshots in ", nchunks, "chunks")
# load file names
snapnamelist = open(snaplistfname, 'r')
catfilename = ["" for j in range(numsnaps)]
for j in range(numsnaps):
catfilename[j] = snapnamelist.readline().strip()
# allocate a manager
manager = mp.Manager()
# use manager to specify the dictionary and list that can be accessed by threads
hdata = manager.list([manager.dict() for j in range(numsnaps)])
ndata = manager.list([0 for j in range(numsnaps)])
adata = manager.list([0 for j in range(numsnaps)])
# now for each chunk run a set of proceses
for j in range(nchunks):
offset = j*nthreads
# if last chunk then must adjust nthreads
if (j == nchunks-1):
nthreads = numsnaps-offset
# when calling a process pass manager based proxies, which then are used to copy data back
processes = [mp.Process(target=ReadPropertyFileMultiWrapper, args=(catfilename[offset+k], k+offset,
hdata, ndata, adata, inputtype, iseperatefiles, iverbose, desiredfields)) for k in range(nthreads)]
# start each process
# store the state of each thread, alive or not, and whether it has finished
activethreads = [[True, False] for k in range(nthreads)]
count = 0
for p in processes:
print("reading", catfilename[offset+count])
p.start()
# space threads apart (join's time out is 0.25 seconds
p.join(0.2)
count += 1
totactivethreads = nthreads
while(totactivethreads > 0):
count = 0
for p in processes:
# join thread and see if still active
p.join(0.5)
if (p.is_alive() == False):
# if thread nolonger active check if its been processed
if (activethreads[count][1] == False):
# make deep copy of manager constructed objects that store data
#halodata[i][offset+count] = copy.deepcopy(hdata[offset+count])
# try instead init a dictionary
halodata[offset+count] = dict(hdata[offset+count])
ngtot[offset+count] = ndata[offset+count]
atime[offset+count] = adata[offset+count]
# effectively free the data in manager dictionary
hdata[offset+count] = []
activethreads[count][0] = False
activethreads[count][1] = True
totactivethreads -= 1
count += 1
# terminate threads
for p in processes:
p.terminate()
else:
snapnamelist = open(snaplistfname, 'r')
for j in range(0, numsnaps):
catfilename = snapnamelist.readline().strip()
print("reading ", catfilename)
halodata[j], ngtot[j], atime[j] = ReadPropertyFile(
catfilename, inputtype, iseperatefiles, iverbose, desiredfields)
print("data read in ", time.process_time()-start)
return halodata, ngtot, atime
def ReadCrossCatalogList(fname, meritlim=0.1, iverbose=0):
"""
Reads a cross catalog produced by halomergertree,
also allows trimming of cross catalog using a higher merit threshold than one used to produce catalog
"""
return []
"""
start = time.process_time()
if (iverbose):
print("reading cross catalog")
dfile = open(fname, "r")
dfile.readline()
dfile.readline()
dataline = (dfile.readline().strip()).split('\t')
ndata = np.int32(dataline[1])
pdata = CrossCatalogList(ndata)
for i in range(0, ndata):
data = (dfile.readline().strip()).split('\t')
nmatches = np.int32(data[1])
for j in range(0, nmatches):
data = (dfile.readline().strip()).split(' ')
meritval = np.float32(data[1])
nsharedval = np.float32(data[2])
if(meritval > meritlim):
nmatchid = np.int64(data[0])
pdata.matches[i].append(nmatchid)
pdata.matches[i].append(meritval)
pdata.nsharedfrac[i].append(nsharedval)
pdata.nmatches[i] += 1
dfile.close()
if (iverbose):
print("done reading cross catalog ", time.process_time()-start)
return pdata
"""
def ReadSimInfo(basefilename):
"""
Reads in the information in the .siminfo file and returns it as a dictionary
"""
filename = basefilename + ".siminfo"
if (os.path.isfile(filename) == False):
print(filename,"not found")
return {}
cosmodata = {}
siminfofile = open(filename, "r")
for i,l in enumerate(siminfofile):
line = l.strip()
#See if this a comment
if(line==""):
continue
if(line[0]=="#"):
continue
try:
field, value = line.replace(" ","").split(':')
except ValueError:
print("Cannot read line",i,"of",filename,"continuing")
continue
#See if the datatype is present
if("#" in value):
value, datatype = value.split("#")
try:
typefunc = np.dtype(datatype).type
except:
print("Cannot interpret",datatype,"for field",field,"on line",i,"in",filename,"as a valid datatype, interpreting the value as float64")
typefunc = np.float64
else:
typefunc = np.float64
try:
#Find if the value is a list of values
if("," in value):
value = value.split(",")
#Remove any empty strings
value = list(filter(None,value))
cosmodata[field] = np.array(value,dtype=typefunc)
else:
cosmodata[field] = typefunc(value)
except ValueError:
print("Cannot interpret",value,"as a",np.dtype(typefunc))
siminfofile.close()
return cosmodata
def ReadUnitInfo(basefilename):
"""
Reads in the information in the .units file and returns it as a dictionary
"""
filename = basefilename + ".units"
if (os.path.isfile(filename) == False):
print(filename,"not found")
return {}
unitsfile = open(filename, 'r')
unitdata = dict()
for i,l in enumerate(unitsfile):
line = l.strip()
#See if this a comment
if(line==""):
continue
if(line[0]=="#"):
continue
try:
field, value = line.replace(" ","").split(':')
except ValueError:
print("Cannot read line",i,"of",filename,"continuing")
continue
#See if the datatype is present
if("#" in value):
value, datatype = value.split("#")
try:
typefunc = np.dtype(datatype).type
except:
print("Cannot interpret",datatype,"for field",field,"on line",i,"in",filename,"as a valid datatype, interpreting the value as float64")
typefunc = np.float64
else:
typefunc = np.float64
try:
#Find if the value is a list of values
if("," in value):
value = value.split(",")
#Remove any empty strings
value = list(filter(None,value))
unitdata[field] = np.array(value,dtype=typefunc)
else:
unitdata[field] = typefunc(value)
except ValueError:
print("Cannot interpret",value,"as a",np.dtype(typefunc))
unitsfile.close()
return unitdata