def __init__(self, basedir, snapnum):
self.filename = basedir + "tree_offsets_subgroup_" + str(
snapnum) + "_135.hdf5"
self.snapnum = snapnum
f = hdf5lib.OpenFile(self.filename)
self.TreeFile = hdf5lib.GetData(f, "TreeFile")[:]
self.TreeIndex = hdf5lib.GetData(f, "TreeIndex")[:]
self.TreeNum = hdf5lib.GetData(f, "TreeNum")[:]
f.close()
def mass_from_mergers(base, snap=135):
snaptag='000'+str(snap)
snaptag=snaptag[-3:]
f=hdf5lib.OpenFile(base+'/postprocessing/StellarAssembly/galaxies_'+snaptag+'.hdf5', mode ='r' )
delta=hdf5lib.GetData(f, "StellarMassFromMergers")[:]
f.close()
return np.array(delta)
def mass_from_minor_mergers(base, snap=135):
snaptag='000'+str(snap)
snaptag=snaptag[-3:]
f=hdf5lib.OpenFile(base+'/postprocessing/MergerHistory/merger_history_'+snaptag+'.hdf5', mode ='r' )
data=hdf5lib.GetData(f, "StellarMassFromMinorMergers")[:]
f.close()
return np.array(data)
def subhalo_overdensity(base, snap = 135):
snaptag='000'+str(snap)
snaptag=snaptag[-3:]
f=hdf5lib.OpenFile(base+'/postprocessing/environment/environment_'+snaptag+'.hdf5', mode ='r' )
delta=hdf5lib.GetData(f, "delta")[:]
f.close()
return np.array(delta)
def number_of_minor_mergers(base, snap=135):
snaptag='000'+str(snap)
snaptag=snaptag[-3:]
f=hdf5lib.OpenFile(base+'/postprocessing/MergerHistory/merger_history_'+snaptag+'.hdf5', mode ='r' )
data=hdf5lib.GetData(f, "NumMinorMergersTotal")[:]
f.close()
return np.array(data)
def subhalo_insitu_fraction(snap = 135):
snaptag='000'+str(snap)
snaptag=snaptag[-3:]
f=hdf5lib.OpenFile('/n/ghernquist/Illustris/Runs/Illustris-1/postprocessing/InSituFraction/insitu_stellar_fraction_'+snaptag+'.hdf5', mode ='r' )
data=hdf5lib.GetData(f, "InSitu")[:]
f.close()
return np.array(data)
def __init__(self,
basedir,
skipfac,
snapnum,
filenum=0,
tree_start=-1,
tree_num=-1,
keysel=None):
self.filebase = basedir + "trees_sf" + str(skipfac) + "_" + str(
snapnum).zfill(3)
self.basedir = basedir
self.filenum = filenum
filename = self.filebase + "." + str(filenum) + ".hdf5"
f = hdf5lib.OpenFile(filename)
self.NtreesPerFile = hdf5lib.GetAttr(f, "Header", "NtreesPerFile")
self.NumberOfOutputFiles = hdf5lib.GetAttr(f, "Header",
"NumberOfOutputFiles")
self.ParticleMass = hdf5lib.GetAttr(f, "Header", "ParticleMass")
if self.ParticleMass == 0:
print "WARNING: ParticleMass = 0, needed for merger rate calculation"
self.TreeNHalos = hdf5lib.GetData(f, "Header/TreeNHalos")[:]
self.TotNsubhalos = hdf5lib.GetData(f, "Header/TotNsubhalos")[:]
self.Redshifts = hdf5lib.GetData(f, "Header/Redshifts")[:]
if (tree_start == -1) | (tree_num == -1):
tree_start = 0
tree_num = self.NtreesPerFile
# self.trees = np.empty(tree_num - tree_start, dtype='object')
self.trees = np.empty(tree_num, dtype='object')
self.tree_start = tree_start
self.tree_num = tree_num
for ntree in range(tree_start, tree_start + tree_num):
list = []
if keysel is None:
for datablock in mergertree_datablocks.keys():
data = hdf5lib.GetData(
f, "Tree" + str(ntree) + "/" + datablock)[:]
list.append((datablock, data))
else:
for datablock in keysel:
if hdf5lib.Contains(f, "Tree" + str(ntree), datablock):
data = hdf5lib.GetData(
f, "Tree" + str(ntree) + "/" + datablock)[:]
list.append((datablock, data))
print ntree, tree_start
self.trees[ntree - tree_start] = dict(list)
f.close()
def get_offsets(cat, part_types=[0, 1, 4, 5], snap=None, run=None):
if snap and run:
group_file = "/n/ghernquist/Illustris/Runs/%s/postprocessing/offsets/snap_offsets_group_%s.hdf5" % (run, snap)
halo_file = "/n/ghernquist/Illustris/Runs/%s/postprocessing/offsets/snap_offsets_subhalo_%s.hdf5" % (run, snap)
if os.path.isfile(group_file) and os.path.isfile(halo_file):
print "READSUBF: found pretabulated offsets to read"
f = hdf5lib.OpenFile(group_file)
group_offsets = hdf5lib.GetData(f, "Offsets")[:]
f.close()
f = hdf5lib.OpenFile(halo_file)
halo_offsets = hdf5lib.GetData(f, "Offsets")[:]
f.close()
return np.array(group_offsets), np.array(halo_offsets)
else:
# /n/hernquistfs3/IllustrisTNG/Runs/L75n910TNG/postprocessing/offsets/
group_file = "/n/hernquistfs3/IllustrisTNG/Runs/%s/postprocessing/offsets/offsets_%s.hdf5" % (run, str(snap).zfill(3))
# sys.exit()
if os.path.isfile(group_file):
f = hdf5lib.OpenFile(group_file)
group_offsets = np.copy(hdf5lib.GetData(f, "Group/SnapByType"))
halo_offsets = np.copy(hdf5lib.GetData(f, "Subhalo/SnapByType"))
return group_offsets, halo_offsets
GroupOffset = np.zeros((cat.ngroups, 6), dtype="int64")
HaloOffset = np.zeros((cat.nsubs, 6), dtype="int64")
for parttype in part_types:
print "Calculating offsets for PartType: %d" % parttype
k = 0
for i in range(0, cat.ngroups):
if i > 0:
GroupOffset[i, parttype] = GroupOffset[i-1, parttype] + cat.GroupLenType[i-1, parttype]
if cat.GroupNsubs[i] > 0:
HaloOffset[k, parttype] = GroupOffset[i, parttype]
k += 1
for j in range(1, cat.GroupNsubs[i]):
HaloOffset[k, parttype] = HaloOffset[k-1, parttype] + cat.SubhaloLenType[k-1, parttype]
k += 1
if k != cat.nsubs:
print "READHALO: problem with offset table", k, cat.nsubs
sys.exit()
return np.array(GroupOffset), np.array(HaloOffset)
def subhalo_gas_kinematics(base, snap = 135, which="v_5"):
snaptag=str(snap)
file = base+'/postprocessing/gas_kinematics/gas_kinematic_info_'+snaptag+'.hdf5'
print file
f=hdf5lib.OpenFile(file, mode ='r' )
data=hdf5lib.GetData(f, "Subhalo/"+which)[:]
f.close()
return np.array(data)
def subhalo_gas_z_grad(base, snap = 135, which="GradMetallicity_5"):
snaptag=str(snap)
file = base+'/postprocessing/gas_metallicity/gas_metallicity_info_'+snaptag+'.hdf5'
print file
f=hdf5lib.OpenFile(file, mode ='r' )
data=hdf5lib.GetData(f, "Subhalo/"+which)[:]
f.close()
return np.array(data)
def subhalo_petrosian_radius(snap = 135):
file = '/n/ghernquist/Illustris/Runs/Illustris-1/postprocessing/PhotometricMorphologies/nonparmorphs_iSDSS_135.hdf5'
if os.path.exists(file):
f=hdf5lib.OpenFile(file,mode='r')
data0=np.array(hdf5lib.GetData(f, "RP_cam0")[:])
data = np.zeros( (4 , data0.shape[0]) )
data[1,:] = np.array(hdf5lib.GetData(f, "RP_cam1")[:])
data[2,:] = np.array(hdf5lib.GetData(f, "RP_cam2")[:])
data[3,:] = np.array(hdf5lib.GetData(f, "RP_cam3")[:])
data = np.median( data, axis=0 )
f.close()
else:
data = None
return data
def subhalo_circularities(base, snap = 135):
snaptag='000'+str(snap)
snaptag=snaptag[-3:]
print base+'/postprocessing/circularities/circularities_'+snaptag+'.hdf5'
f=hdf5lib.OpenFile(base+'/postprocessing/circularities/circularities_'+snaptag+'.hdf5', mode ='r' )
data=np.array(hdf5lib.GetData(f, "CircAbove05Frac")[:])
data=np.reshape(data, -1)
f.close()
return data
def subhalo_stellar_vel_disp(base, snap = 135, which="StellarVelDisp_HalfMassRad"):
#snaptag='000'+str(snap)
#snaptag=snaptag[-3:]
snaptag=str(snap)
print base+'/postprocessing/stellar_vel_disp/stellar_vel_disp_'+snaptag+'.hdf5'
f=hdf5lib.OpenFile(base+'/postprocessing/stellar_vel_disp/stellar_vel_disp_'+snaptag+'.hdf5', mode ='r' )
delta=hdf5lib.GetData(f, "Subhalo/"+which)[:]
f.close()
return np.array(delta)
def subhalo_stellar_age(snap = 135):
snaptag='000'+str(snap)
snaptag=snaptag[-3:]
file='/n/ghernquist/Illustris/Runs/Illustris-1/postprocessing/galprop/galprop_'+snaptag+'.hdf5'
if os.path.exists(file):
f=hdf5lib.OpenFile('/n/ghernquist/Illustris/Runs/Illustris-1/postprocessing/galprop/galprop_'+snaptag+'.hdf5', mode='r')
data=np.array(hdf5lib.GetData(f, "stellar_age_inrad")[:])
f.close()
else:
data = None
return data
def __init__(self,
basedir,
snapnum,
long_ids=False,
name="fof_subhalo_tab",
verbose=False):
self.filebase = basedir + "/groups_" + str(snapnum).zfill(
3) + "/" + name + "_" + str(snapnum).zfill(3) + "."
if long_ids: self.id_type = np.uint64
else: self.id_type = np.uint32
filenum = 0
doneflag = False
skip_ids = 0
while not doneflag:
curfile = self.filebase + str(filenum) + ".hdf5"
if not os.path.exists(curfile):
self.filebase = basedir + "/" + name + "_" + str(
snapnum).zfill(3)
curfile = self.filebase + ".hdf5"
if not os.path.exists(curfile):
print "file not found:", curfile
sys.exit()
f = hdf5lib.OpenFile(curfile)
nfiles = hdf5lib.GetAttr(f, "Header", "NumFiles")
nids = hdf5lib.GetAttr(f, "Header", "Nids_ThisFile")
idlen = hdf5lib.GetAttr(f, "Header", "Nids_Total")
if filenum == 0:
if not long_ids:
self.IDs = np.empty(idlen, dtype=np.uint32)
else:
self.IDs = np.empty(idlen, dtype=np.uint64)
# TODO: This call seems to fail when FOF_STOREIDS is not
# switched on in Config.sh, since the dataset IDs/ID
# will not exist.
self.IDs[skip_ids:skip_ids + nids] = hdf5lib.GetData(f, "IDs/ID")
skip_ids += nids
f.close()
filenum += 1
if filenum == self.nfiles: doneflag = True
def __init__(self, basedir, snapnum, keysel=None, long_ids=False):
if long_ids: id_type = np.uint64
else: id_type = np.uint32
vardict = {}
if keysel is None:
keysel = galprop_datablocks.items()
file=naming.return_galprop_file(basedir, snapnum)
if os.path.exists(file):
f=hdf5lib.OpenFile(file, mode='r')
for key in keysel:
if hdf5lib.Contains(f, "", key):
val = galprop_datablocks[key]
type = val[0]
dim = val[1]
vars(self)[key]=np.array(hdf5lib.GetData(f, key)[:])
f.close()
else:
print "Galprop File Not Found..."
def read_block_single_file(filename,
block_name,
dim2,
parttype=-1,
no_mass_replicate=False,
fill_block_name="",
slab_start=-1,
slab_len=-1,
ids=-1,
verbose=False):
if os.path.exists(filename):
curfilename = filename
elif os.path.exists(filename + ".hdf5"):
curfilename = filename + ".hdf5"
elif os.path.exists(filename + ".0.hdf5"):
curfilename = filename + ".0.hdf5"
else:
print "[error] file not found : ", filename
sys.stdout.flush()
sys.exit()
if verbose:
print "[single] reading file : ", curfilename
print "[single] reading : ", block_name
sys.stdout.flush()
head = snapshot_header(curfilename)
npart = head.npart
massarr = head.massarr
nall = head.nall
filenum = head.filenum
doubleflag = head.double
if (parttype != -1):
if (head.npart[parttype] == 0):
return [0, False]
else:
if (head.npart.sum() == 0):
return [0, False]
del head
#construct indices for partial access
idsflag = False
if (slab_start != -1) and (slab_len != -1):
data_slice = slice(slab_start, (slab_start + slab_len))
elif type(ids) == np.ndarray:
idsflag = True
data_slice = ids
else:
data_slice = slice(None, None, 1)
if verbose:
print "[single] data slice: ", data_slice
sys.stdout.flush()
f = hdf5lib.OpenFile(curfilename)
#read specific particle type (parttype>=0, non-default)
if parttype >= 0:
if verbose:
print "[single] parttype : ", parttype
sys.stdout.flush()
if ((block_name == "Masses") and (npart[parttype] > 0)
and (massarr[parttype] > 0)):
if verbose:
print "[single] replicate mass block"
sys.stdout.flush()
ret_val = np.repeat(massarr[parttype], npart[parttype])[data_slice]
else:
part_name = 'PartType' + str(parttype)
if idsflag:
ret_val = hdf5lib.GetData(f, part_name + "/" +
block_name)[:][[data_slice]]
else:
ret_val = hdf5lib.GetData(f, part_name + "/" +
block_name)[data_slice]
if verbose:
print "[single] read particles (total) : ", ret_val.shape[0] / dim2
sys.stdout.flush()
#read all particle types (parttype=-1, default)
if parttype == -1:
first = True
dim1 = long(0)
for parttype in range(0, 6):
part_name = 'PartType' + str(parttype)
if hdf5lib.Contains(f, "", part_name):
if verbose:
print "[single] parttype : ", parttype
print "[single] massarr : ", massarr
print "[single] npart : ", npart
sys.stdout.flush()
#replicate mass block per default (unless no_mass_replicate is set)
if ((block_name == "Masses") and (npart[parttype] > 0)
and (massarr[parttype] > 0)
and (no_mass_replicate == False)):
if (verbose):
print "[single] replicate mass block"
sys.stdout.flush()
if (first):
data = np.repeat(massarr[parttype], npart[parttype])
dim1 += long(data.shape[0])
ret_val = data
first = False
else:
data = np.repeat(massarr[parttype], npart[parttype])
dim1 += long(data.shape[0])
ret_val = np.append(ret_val, data)
if (verbose):
print "[single] read particles (total) : ", ret_val.shape[
0] / dim2
sys.stdout.flush()
if (doubleflag == 0):
ret_val = ret_val.astype("float32")
#fill fill_block_name with zeros if fill_block_name is set and particle type is present and fill_block_name not already stored in file for that particle type
if ((block_name == fill_block_name)
and (block_name != "Masses") and (npart[parttype] > 0)
and
(hdf5lib.Contains(f, part_name, block_name) == False)):
if (verbose):
print "[single] replicate block name", fill_block_name
sys.stdout.flush()
if (first):
data = np.repeat(0.0, npart[parttype] * dim2)
dim1 += long(data.shape[0])
ret_val = data
first = False
else:
data = np.repeat(0.0, npart[parttype] * dim2)
dim1 += long(data.shape[0])
ret_val = np.append(ret_val, data)
if (verbose):
print "[single] read particles (total) : ", ret_val.shape[
0] / dim2
sys.stdout.flush()
if (doubleflag == 0):
ret_val = ret_val.astype("float32")
#default: just read the block
if (hdf5lib.Contains(f, part_name, block_name)):
if (first):
data = hdf5lib.GetData(f,
part_name + "/" + block_name)[:]
dim1 += long(data.shape[0])
ret_val = data
first = False
else:
data = hdf5lib.GetData(f,
part_name + "/" + block_name)[:]
dim1 += long(data.shape[0])
ret_val = np.append(ret_val, data)
if (verbose):
print "[single] read particles (total) : ", ret_val.shape[
0] / dim2
sys.stdout.flush()
if ((dim1 > 0) & (dim2 > 1)):
ret_val = ret_val.reshape(dim1, dim2)
if (verbose):
print "[single] reshape done: ", ret_val.shape
sys.stdout.flush()
f.close()
gc.collect()
return [ret_val, True]
def read_block(filename,
block,
parttype=-1,
no_mass_replicate=False,
fill_block="",
slab_start=-1,
slab_len=-1,
ids=-1,
verbose=False,
multiple_files=False):
if os.path.exists(filename):
curfilename = filename
elif os.path.exists(filename + ".hdf5"):
curfilename = filename + ".hdf5"
elif os.path.exists(filename + ".0.hdf5"):
curfilename = filename + ".0.hdf5"
multiple_files = True
else:
print "[error] file not found : ", filename
sys.stdout.flush()
sys.exit()
if (verbose):
print "reading block : ", block
sys.stdout.flush()
if parttype not in [-1, 0, 1, 2, 3, 4, 5]:
print "[error] wrong parttype given"
sys.stdout.flush()
sys.exit()
slabflag = False
if ((slab_start != -1) | (slab_len != -1)):
slabflag = True
if (parttype == -1):
print "[error] slabs only supported for specific parttype"
sys.stdout.flush()
sys.exit()
idsflag = False
if type(ids) != int:
idsflag = True
ids = np.array(ids)
if parttype == -1:
print "[error] id list only supported for specific parttype"
sys.stdout.flush()
sys.exit()
if np.sum(ids == np.sort(ids)) < len(ids):
print "[error] input ids not sorted. must be in order!"
return
if (verbose):
print curfilename
head = snapshot_header(curfilename)
filenum = head.filenum
npart_all = np.array(head.nall)
highword = head.nall_highword
npart_all.astype(long)
# Need to determine size of array and data type to pre-allocate data.
if idsflag == True:
length = len(ids)
if slabflag == True:
length = slab_len
if idsflag == False and slabflag == False:
if parttype != -1:
length = head.nall[parttype]
if highword[parttype] == 1:
length += 2**32
else:
length = head.nall.sum()
if (verbose):
print "Length of data allocation:", length #GREG
if (datablocks.has_key(block)):
block_name = datablocks[block][0]
dim2 = datablocks[block][1]
first = True
if (verbose):
print "Reading HDF5 : ", block_name
print "Data dimension : ", dim2
print "Multiple file : ", multiple_files
print "Slab data : ", slabflag
sys.stdout.flush()
else:
print "[error] Block type ", block, "not known!"
sys.stdout.flush()
sys.exit()
fill_block_name = ""
if (fill_block != ""):
if (datablocks.has_key(fill_block)):
fill_block_name = datablocks[fill_block][0]
dim2 = datablocks[fill_block][1]
if (verbose):
print "Block filling active : ", fill_block_name
sys.stdout.flush()
# set alloc_type here. read in example item to determine data type.
alloc_type = None
# need to loop over all files until block is found, or no more files left.
if not multiple_files:
filepaths = [curfilename]
else:
filepaths = [
filename + "." + str(subfile) + ".hdf5"
for subfile in np.arange(filenum)
]
for filepath in filepaths:
g = hdf5lib.OpenFile(filepath)
if parttype == -1:
for ptype in range(0, 6):
try:
contains = hdf5lib.Contains(g, 'PartType' + str(ptype),
block_name)
except:
contains = False
if contains:
alloc_type = str(
hdf5lib.GetData(
g, 'PartType' + str(ptype) + '/' +
block_name)[0:1].dtype)
break
else:
try:
contains = hdf5lib.Contains(g, 'PartType' + str(parttype),
block_name)
except:
contains = False
if contains:
alloc_type = str(
hdf5lib.GetData(
g, 'PartType' + str(parttype) + '/' +
block_name)[0:1].dtype)
g.close()
gc.collect()
if contains == True:
break
# if block does not exist
if alloc_type == None:
if block == "ID ":
alloc_type = np.uint32
#else:
# alloc_type=np.float32
elif block == "MASS":
alloc_type = np.float32 #default to float32 for MASS
else:
print "[error] block : ", block, "of parttype : ", parttype, "not found"
sys.stdout.flush()
sys.exit()
if dim2 > 1:
ret_val = np.ndarray((length, dim2), alloc_type)
else:
ret_val = np.ndarray((length, ), alloc_type)
if (verbose):
print "Allocated array"
if (multiple_files):
if slabflag == False and idsflag == False:
first = True
dim1 = long(0)
for num in range(0, filenum):
curfilename = filename + "." + str(num) + ".hdf5"
if (verbose):
print "Reading file : ", num, curfilename
sys.stdout.flush()
data, succ = read_block_single_file(curfilename,
block_name,
dim2,
parttype,
no_mass_replicate,
fill_block_name,
slab_start,
slab_len,
verbose=False)
if succ == True:
if dim2 > 1:
ret_val[dim1:dim1 + len(data), :] = data
else:
ret_val[dim1:dim1 + len(data)] = data
dim1 += long(data.shape[0])
if (verbose):
if (succ):
print "Read particles (total) : ", ret_val.shape[0]
sys.stdout.flush()
else:
print "Read particles (total) : none"
sys.stdout.flush()
# Implementation of reading specified particle positions.
if (idsflag == True):
dim1 = long(0)
for num in range(0, filenum):
curfilename = filename + "." + str(num) + ".hdf5"
head = snapshot_header(curfilename)
nloc = head.npart[parttype]
low = ids[0] # First particle to read
high = ids[-1] # Last particle to read
if (nloc > low): # Something to read in this file
toread = ids[
ids <
nloc] # Need to get subset of ids still in this file
if (verbose):
print "Reading file : ", num, curfilename
sys.stdout.flush()
data, succ = read_block_single_file(curfilename,
block_name,
dim2,
parttype,
no_mass_replicate,
fill_block_name,
ids=toread,
verbose=verbose)
if (succ == True):
if dim2 > 1:
ret_val[dim1:dim1 + len(data), :] = data
else:
ret_val[dim1:dim1 + len(data)] = data
dim1 += data.shape[0]
if (verbose):
if (succ):
# No longer need to divide by dim2. append would flatten array, not now.
print "Read particles (total) : ", ret_val.shape[0]
sys.stdout.flush()
else:
print "Read particles (total) : none"
sys.stdout.flush()
ids -= nloc
ids = ids[ids >= 0] # Only keep particles not yet read
if (len(ids) == 0 or high < 0):
break
if (slabflag == True):
off = slab_start
left = slab_len
first = True
dim1 = long(0)
for num in range(0, filenum):
curfilename = filename + "." + str(num) + ".hdf5"
head = snapshot_header(curfilename)
nloc = head.npart[parttype]
if (nloc > off):
start = off
if (nloc - off > left):
count = left
else:
count = nloc - off
if (verbose):
print "Reading file : ", num, curfilename, start, count
sys.stdout.flush()
data, succ = read_block_single_file(curfilename,
block_name,
dim2,
parttype,
no_mass_replicate,
fill_block_name,
slab_start=start,
slab_len=count,
verbose=verbose)
if (succ == True):
if dim2 > 1:
ret_val[dim1:dim1 + len(data), :] = data
else:
ret_val[dim1:dim1 + len(data)] = data
dim1 += data.shape[0]
if (verbose):
if (succ):
# No longer need to divide by dim2, append would flatten array, not now.
print "Read particles (total) : ", ret_val.shape[0]
sys.stdout.flush()
else:
print "Read particles (total) : none"
sys.stdout.flush()
left -= count
off += count
if (left == 0):
break
off -= nloc
if (verbose):
print "all partial files read in"
sys.stdout.flush()
else:
ret_val, succ = read_block_single_file(curfilename, block_name, dim2,
parttype, no_mass_replicate,
fill_block_name, slab_start,
slab_len, verbose)
return ret_val
def subhalo_offsets(snap = 135, run='Illustris-1'):
snaptag=str(snap)
f=hdf5lib.OpenFile('/n/ghernquist/Illustris/Runs/'+run+'/postprocessing/offsets/snap_offsets_subhalo_'+snaptag+'.hdf5', mode ='r' )
data=hdf5lib.GetData(f, "Offsets")[:]
f.close()
return np.array(data)
def read_insitu():
file = "/n/ghernquist/vrodrigu/StellarAssembly/output/Illustris/L75n1820FP/stars_135.hdf5"
f = hdf5lib.OpenFile(file, mode='r')
data = hdf5lib.GetData(f, "InSitu")[:]
f.close()
return np.array(data)
def __init__(self, basedir, snapnum, long_ids=False, double_output=False, grpcat=True, subcat=True, name="fof_subhalo_tab", keysel=[]):
if long_ids: self.id_type = np.uint64
else: self.id_type = np.uint32
if double_output: self.double_type = np.float32
else: self.double_type = np.float64
filenum = 0
doneflag = False
skip_gr = 0
skip_sub = 0
vardict = {}
if keysel is None:
keysel = grp_datablocks.items()
while not doneflag:
self.filebase, curfile = naming.return_subfind_filebase(basedir, snapnum, name, filenum)
self.firstfile = curfile
f=hdf5lib.OpenFile(curfile)
ngroups = hdf5lib.GetAttr(f, "Header", "Ngroups_ThisFile")
nsubs = hdf5lib.GetAttr(f, "Header", "Nsubgroups_ThisFile")
nfiles = hdf5lib.GetAttr(f, "Header", "NumFiles")
if filenum == 0:
self.ngroups = hdf5lib.GetAttr(f, "Header", "Ngroups_Total")
self.nids = hdf5lib.GetAttr(f, "Header", "Nids_Total")
self.nsubs = hdf5lib.GetAttr(f, "Header", "Nsubgroups_Total")
#GROUPS
if grpcat:
for key in keysel:
if hdf5lib.Contains(f, "Group", key):
val = grp_datablocks[key]
type = val[0]
dim = val[1]
if (type=='FLOAT'):
vars(self)[key]=np.empty(self.ngroups, dtype=np.dtype((self.double_type,dim)))
if (type=='INT'):
vars(self)[key]=np.empty(self.ngroups, dtype=np.dtype((np.int32,dim)))
if (type=='INT64'):
vars(self)[key]=np.empty(self.ngroups, dtype=np.dtype((np.int64,dim)))
if (type=='ID'):
vars(self)[key]=np.empty(self.ngroups, dtype=np.dtype((self.id_type,dim)))
vardict[key]=vars(self)[key]
#SUBHALOS
if subcat:
for key in keysel:
if hdf5lib.Contains(f, "Subhalo", key):
val = sub_datablocks[key]
type = val[0]
dim = val[1]
if (type=='FLOAT'):
vars(self)[key]=np.empty(self.nsubs, dtype=np.dtype((self.double_type,dim)))
if (type=='INT'):
vars(self)[key]=np.empty(self.nsubs, dtype=np.dtype((np.int32,dim)))
if (type=='INT64'):
vars(self)[key]=np.empty(self.nsubs, dtype=np.dtype((np.int64,dim)))
if (type=='ID'):
vars(self)[key]=np.empty(self.nsubs, dtype=np.dtype((self.id_type,dim)))
vardict[key]=vars(self)[key]
#GROUPS
if grpcat:
if ngroups > 0:
for key in keysel:
if hdf5lib.Contains(f, "Group", key):
val = grp_datablocks[key]
type = val[0]
dim = val[1]
a=hdf5lib.GetData(f, "Group/"+key)
if dim==1:
vardict[key][skip_gr:skip_gr + ngroups]=a[:]
else:
for d in range(0,dim):
vardict[key][skip_gr:skip_gr + ngroups,d]=a[:,d]
skip_gr += ngroups
#SUBHALOS
if subcat:
if nsubs > 0:
for key in keysel:
if hdf5lib.Contains(f, "Subhalo", key):
val = sub_datablocks[key]
type = val[0]
dim = val[1]
a=hdf5lib.GetData(f, "Subhalo/"+key)
if dim==1:
vardict[key][skip_sub:skip_sub + nsubs]=a[:]
else:
for d in range(0,dim):
vardict[key][skip_sub:skip_sub + nsubs,d]=a[:,d]
skip_sub += nsubs
f.close()
filenum += 1
if filenum == nfiles: doneflag = True