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_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 __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.tree_start = tree_start
self.tree_num = tree_num
for ntree in range(tree_start, tree_start + tree_num):
list = []
if (keysel == None):
for datablock in list(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))
self.trees[ntree - tree_start] = dict(list)
f.close()
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_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 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 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 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_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 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_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 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 __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 __init__(self,
basedir,
snapnum,
long_ids=False,
double_output=False,
grpcat=True,
subcat=True,
name="fof_subhalo_tab",
keysel=None):
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
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 = {}
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)
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 == True):
if (keysel == None):
for key, val in grp_datablocks.items():
if hdf5lib.Contains(f, "Group", 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]
else:
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 == True):
if (keysel == None):
for key, val in sub_datablocks.items():
if hdf5lib.Contains(f, "Subhalo", 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.int32,
dim)))
if (type == 'ID'):
vars(self)[key] = np.empty(self.nsubs,
dtype=np.dtype(
(self.id_type,
dim)))
vardict[key] = vars(self)[key]
else:
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 == True):
if ngroups > 0:
if (keysel == None):
for key, val in grp_datablocks.items():
if hdf5lib.Contains(f, "Group", 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]
else:
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 == True):
if nsubs > 0:
if (keysel == None):
for key, val in sub_datablocks.items():
if hdf5lib.Contains(f, "Subhalo", 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]
else:
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
def read_block_single_file(filename, block_name, dim2, parttype=-1, no_mass_replicate=False, fill_block_name="", slab_start=-1, slab_len=-1, verbose=False):
if (verbose):
print("[single] reading file : ", filename )
print("[single] reading : ", block_name)
sys.stdout.flush()
head = snapshot_header(filename)
npart = head.npart
massarr = head.massarr
nall = head.nall
filenum = head.filenum
doubleflag = head.double #GADGET-2 change
#doubleflag = 0 #GADGET-2 change
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
if (slab_start!=-1) & (slab_len!=-1):
data_slice = slice(slab_start, (slab_start+slab_len))
else:
data_slice = slice(None, None, 1)
if (verbose):
print("[single] data slice: ", data_slice)
sys.stdout.flush()
f=hdf5lib.OpenFile(filename)
#read specific particle type (parttype>=0, non-default)
if parttype>=0:
if (verbose):
print("[single] parttype : ", parttype )
sys.stdout.flush()
if ((block_name=="Masses") & (npart[parttype]>0) & (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)
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") & (npart[parttype]>0) & (massarr[parttype]>0) & (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) & (block_name!="Masses") & (npart[parttype]>0) & (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()
return [ret_val, True]
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 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 : {:s} in 'read_block' ".format( 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
'SubhaloGasMetallicitySfr'
])
stellar_masses = cat.SubhaloMassType[:,
4] * 1e10 / little_h # units of M_solar
sfr = cat.SubhaloSFR # units of M_solar / yr
gas_z = cat.SubhaloGasMetallicity # unitless metallicity
gas_sfr_z = cat.SubhaloGasMetallicitySfr # unitless metallicity
star_z = readsubfHDF5.subhalo_stellar_metallicities(snap=snap[0])
# read this guy in manually...
file = '/n/home01/ptorrey/ReviewPlots/cold_gas_masses_z' + str(
redshift) + '.hdf5'
f = hdf5lib.OpenFile(file, mode='r')
cold_gas_mass = np.array(
hdf5lib.GetData(f, "ColdGasMass")
[:]) # should already be in units of M_solar (per cold_gas_catalog.py)
f.close()
output_cgm = open(
'./data/cold_gas_mass_vs_stellar_mass_z' + str(redshift) + '.txt', 'w')
output_cgm.write(
'# Dense gas mass vs stellar mass (note, I used "cold" to describe this data throughout,\n'
)
output_cgm.write(
'# but its really only a density cut!!! per Romeels email) \n')
output_cgm.write('# col1 = stellar mass (in solar masses) \n')
output_cgm.write('# col2 = dense gas mass (in solar masses)\n')
output_cgm.write('# col3 = dense gas mass / stellar mass (unitless) \n')
output_cgm.write('\n')
output_sfr = open('./data/sfr_vs_stellar_mass_z' + str(redshift) + '.txt',
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
