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1e169d1424
csiborgtools/galomatch/io/readsim.py
rstiskalek 1e169d1424 add little h conversion
2022-10-11 16:32:23 +01:00

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# Copyright (C) 2022 Richard Stiskalek, Harry Desmond
# This program is free software; you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the
# Free Software Foundation; either version 3 of the License, or (at your
# option) any later version.
#
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
# Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
"""Functions to read in the particle and clump files."""
import numpy
from scipy.io import FortranFile
from os import listdir
from os.path import (join, isfile)
from tqdm import tqdm
F16 = numpy.float16
F32 = numpy.float32
F64 = numpy.float64
I32 = numpy.int32
I64 = numpy.int64
little_h = 0.705
BOXSIZE = 677.7 / little_h # Mpc. Otherwise positions in [0, 1].
BOXMASS = 3.749e19 # Msun
def get_sim_path(n, fname="ramses_out_{}", srcdir="/mnt/extraspace/hdesmond"):
"""
Get a path to a CSiBORG simulation.
Parameters
----------
n : int
The index of the initial conditions (IC) realisation.
fname : str, optional
The file name. By default `ramses_out_{}`, where `n` is the IC index.
srcdir : str, optional
The file path to the folder where realisations of the ICs are stored.
Returns
-------
path : str
The complete path to the `n`th CSiBORG simulation.
"""
return join(srcdir, fname.format(n))
def open_particle(n, simpath, verbose=True):
"""
Open particle files to a given CSiBORG simulation.
Parameters
----------
n : int
The index of a redshift snapshot.
simpath : str
The complete path to the CSiBORG simulation.
verbose : bool, optional
Verbosity flag.
Returns
-------
nparts : 1-dimensional array
Number of parts assosiated with each CPU.
partfiles : list of `scipy.io.FortranFile`
Opened part files.
"""
# Zeros filled snapshot number and the snapshot path
nout = str(n).zfill(5)
snappath = join(simpath, "output_{}".format(nout))
infopath = join(snappath, "info_{}.txt".format(nout))
with open(infopath, "r") as f:
ncpu = int(f.readline().split()[-1])
if verbose:
print("Reading in output `{}` with ncpu = `{}`.".format(nout, ncpu))
# Check whether the unbinding file exists.
snapdirlist = listdir(snappath)
unbinding_file = "unbinding_{}.out00001".format(nout)
if unbinding_file not in snapdirlist:
raise FileNotFoundError(
"Couldn't find `{}` in `{}`. Use mergertreeplot.py -h or --help to "
"print help message.".format(unbinding_file, snappath))
# First read the headers. Reallocate arrays and fill them.
nparts = numpy.zeros(ncpu, dtype=int)
partfiles = [None] * ncpu
for cpu in range(ncpu):
cpu_str = str(cpu + 1).zfill(5)
fpath = join(snappath, "part_{}.out{}".format(nout, cpu_str))
f = FortranFile(fpath)
# Read in this order
ncpuloc = f.read_ints()
if ncpuloc != ncpu:
raise ValueError("`ncpu = {}` of `{}` disagrees with `ncpu = {}` "
"of `{}`.".format(ncpu, infopath, ncpuloc, fpath))
ndim = f.read_ints()
nparts[cpu] = f.read_ints()
localseed = f.read_ints()
nstar_tot = f.read_ints()
mstar_tot = f.read_reals('d')
mstar_lost = f.read_reals('d')
nsink = f.read_ints()
partfiles[cpu] = f
return nparts, partfiles
def read_sp(dtype, partfile):
"""
Utility function to read a single particle file, depending on the dtype.
Parameters
----------
dtype : str
The dtype of the part file to be read now.
partfile : `scipy.io.FortranFile`
Part file to read from.
Returns
-------
out : 1-dimensional array
The data read from the part file.
n : int
The index of the initial conditions (IC) realisation.
simpath : str
The complete path to the CSiBORG simulation.
"""
if dtype in [F16, F32, F64]:
return partfile.read_reals('d')
elif dtype in [I32]:
return partfile.read_ints()
else:
raise TypeError("Unexpected dtype `{}`.".format(dtype))
def nparts_to_start_ind(nparts):
"""
Convert `nparts` array to starting indices in a pre-allocated array for looping over the CPU number.
Parameters
----------
nparts : 1-dimensional array
Number of parts assosiated with each CPU.
Returns
-------
start_ind : 1-dimensional array
The starting indices calculated as a cumulative sum starting at 0.
"""
return numpy.hstack([[0], numpy.cumsum(nparts[:-1])])
def read_particle(pars_extract, n, simpath, verbose=True):
"""
Read particle files of a simulation at a given snapshot and return
values of `pars_extract`.
Parameters
----------
pars_extract : list of str
Parameters to be extacted.
n : int
The index of the redshift snapshot.
simpath : str
The complete path to the CSiBORG simulation.
verbose : bool, optional
Verbosity flag while for reading the CPU outputs.
Returns
-------
out : structured array
The data read from the particle file.
"""
# Open the particle files
nparts, partfiles = open_particle(n, simpath)
if verbose:
print("Opened {} particle files.".format(nparts.size))
ncpu = nparts.size
# Order in which the particles are written in the FortranFile
forder = [("x", F16), ("y", F16), ("z", F16),
("vx", F16), ("vy", F16), ("vz", F16),
("M", F32), ("ID", I32), ("level", I32)]
fnames = [fp[0] for fp in forder]
fdtypes = [fp[1] for fp in forder]
# Check there are no strange parameters
for p in pars_extract:
if p not in fnames:
raise ValueError("Undefined parameter `{}`. Must be one of `{}`."
.format(p, fnames))
npart_tot = numpy.sum(nparts)
# A dummy array is necessary for reading the fortran files.
dum = numpy.full(npart_tot, numpy.nan, dtype=F16)
# These are the data we read along with types
dtype = {"names": pars_extract,
"formats": [forder[fnames.index(p)][1] for p in pars_extract]}
# Allocate the output structured array
out = numpy.full(npart_tot, numpy.nan, dtype)
start_ind = nparts_to_start_ind((nparts))
iters = tqdm(range(ncpu)) if verbose else range(ncpu)
for cpu in iters:
i = start_ind[cpu]
j = nparts[cpu]
for (fname, fdtype) in zip(fnames, fdtypes):
if fname in pars_extract:
out[fname][i:i + j] = read_sp(fdtype, partfiles[cpu])
else:
dum[i:i + j] = read_sp(fdtype, partfiles[cpu])
return out
def open_unbinding(cpu, n, simpath):
"""
Open particle files to a given CSiBORG simulation. Note that to be consistent CPU is incremented by 1.
Parameters
----------
cpu : int
The CPU index.
n : int
The index of a redshift snapshot.
simpath : str
The complete path to the CSiBORG simulation.
Returns
-------
unbinding : `scipy.io.FortranFile`
The opened unbinding FortranFile.
"""
nout = str(n).zfill(5)
cpu = str(cpu + 1).zfill(5)
fpath = join(simpath, "output_{}".format(nout),
"unbinding_{}.out{}".format(nout, cpu))
return FortranFile(fpath)
def read_clumpid(n, simpath, verbose=True):
"""
Read clump IDs from unbinding files.
Parameters
----------
n : int
The index of a redshift snapshot.
simpath : str
The complete path to the CSiBORG simulation.
verbose : bool, optional
Verbosity flag while for reading the CPU outputs.
Returns
-------
clumpid : 1-dimensional array
The array of clump IDs.
"""
nparts, __ = open_particle(n, simpath, verbose)
start_ind = nparts_to_start_ind(nparts)
ncpu = nparts.size
clumpid = numpy.full(numpy.sum(nparts), numpy.nan)
iters = tqdm(range(ncpu)) if verbose else range(ncpu)
for cpu in iters:
i = start_ind[cpu]
j = nparts[cpu]
ff = open_unbinding(cpu, n, simpath)
clumpid[i:i + j] = ff.read_ints()
return clumpid
def read_clumps(n, simpath):
"""
Read in a precomputed clump file `clump_N.dat`.
Parameters
----------
n : int
The index of a redshift snapshot.
simpath : str
The complete path to the CSiBORG simulation.
Returns
-------
out : structured array
Structured array of the clumps.
"""
n = str(n).zfill(5)
fname = join(simpath, "output_{}".format(n), "clump_{}.dat".format(n))
# Check the file exists.
if not isfile(fname):
raise FileExistsError("Clump file `{}` does not exist.".format(fname))
# Read in the clump array. This is how the columns must be written!
arr = numpy.genfromtxt(fname)
cols = [("index", I64), ("level", I64), ("parent", I64), ("ncell", F64),
("peak_x", F64), ("peak_y", F64), ("peak_z", F64),
("rho-", F64), ("rho+", F64), ("rho_av", F64),
("mass_cl", F64), ("relevance", F64)]
# Write to a structured array
dtype = {"names": [col[0] for col in cols],
"formats": [col[1] for col in cols]}
out = numpy.full(arr.shape[0], numpy.nan, dtype=dtype)
for i, name in enumerate(dtype["names"]):
out[name] = arr[:, i]
return out
def convert_mass_cols(arr, cols):
"""
Convert mass columns from box units to :math:`M_{odot}`. `arr` is passed by
reference and is not explicitly returned back.
Parameters
----------
arr : structured array
The array whose columns are to be converted.
cols : str or list of str
The mass columns to be converted.
Returns
-------
None
"""
cols = [cols] if isinstance(cols, str) else cols
for col in cols:
arr[col] *= BOXMASS
def convert_position_cols(arr, cols, zero_centered=False):
"""
Convert position columns from box units to :math:`\mathrm{Mpc}`. `arr` is
passed by reference and is not explicitly returned back.
Parameters
----------
arr : structured array
The array whose columns are to be converted.
cols : str or list of str
The mass columns to be converted.
zero_centered : bool, optional
Whether to translate the well-resolved origin in the centre of the
simulation to the :math:`(0, 0 , 0)` point.
Returns
-------
None
"""
cols = [cols] if isinstance(cols, str) else cols
for col in cols:
arr[col] *= BOXSIZE
if zero_centered:
arr[col] -= BOXSIZE / 2
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