csiborgtools/scripts/cluster_knn_auto.py

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# Copyright (C) 2022 Richard Stiskalek
# 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.
"""
A script to calculate the KNN-CDF for a set of halo catalogues.
"""
from argparse import ArgumentParser
from datetime import datetime
from distutils.util import strtobool
import joblib
import numpy
import yaml
from mpi4py import MPI
from sklearn.neighbors import NearestNeighbors
from taskmaster import work_delegation
try:
import csiborgtools
except ModuleNotFoundError:
import sys
sys.path.append("../")
import csiborgtools
from utils import open_catalogues
def do_auto(args, config, cats, nsim, paths):
"""
Calculate the kNN-CDF single catalogue auto-correlation.
Parameters
----------
args : argparse.Namespace
Command line arguments.
config : dict
Configuration dictionary.
cats : dict
Dictionary of halo catalogues. Keys are simulation indices, values are
the catalogues.
nsim : int
Simulation index.
paths : csiborgtools.paths.Paths
Paths object.
Returns
-------
None
"""
cat = cats[nsim]
rvs_gen = csiborgtools.clustering.RVSinsphere(args.Rmax, cat.boxsize)
knncdf = csiborgtools.clustering.kNN_1DCDF()
knn = cat.knn(in_initial=False, subtract_observer=False, periodic=True)
rs, cdf = knncdf(
knn, rvs_gen=rvs_gen, nneighbours=config["nneighbours"],
rmin=config["rmin"], rmax=config["rmax"],
nsamples=int(config["nsamples"]), neval=int(config["neval"]),
batch_size=int(config["batch_size"]), random_state=config["seed"])
totvol = (4 / 3) * numpy.pi * args.Rmax ** 3
fout = paths.knnauto(args.simname, args.run, nsim)
if args.verbose:
print(f"Saving output to `{fout}`.")
joblib.dump({"rs": rs, "cdf": cdf, "ndensity": len(cat) / totvol}, fout)
def do_cross_rand(args, config, cats, nsim, paths):
"""
Calculate the kNN-CDF cross catalogue random correlation.
Parameters
----------
args : argparse.Namespace
Command line arguments.
config : dict
Configuration dictionary.
cats : dict
Dictionary of halo catalogues. Keys are simulation indices, values are
the catalogues.
nsim : int
Simulation index.
paths : csiborgtools.paths.Paths
Paths object.
Returns
-------
None
"""
cat = cats[nsim]
rvs_gen = csiborgtools.clustering.RVSinsphere(args.Rmax, cat.boxsize)
knn1 = cat.knn(in_initial=False, subtract_observer=False, periodic=True)
knn2 = NearestNeighbors()
pos2 = rvs_gen(len(cat).shape[0])
knn2.fit(pos2)
knncdf = csiborgtools.clustering.kNN_1DCDF()
rs, cdf0, cdf1, joint_cdf = knncdf.joint(
knn1, knn2, rvs_gen=rvs_gen, nneighbours=int(config["nneighbours"]),
rmin=config["rmin"], rmax=config["rmax"],
nsamples=int(config["nsamples"]), neval=int(config["neval"]),
batch_size=int(config["batch_size"]), random_state=config["seed"])
corr = knncdf.joint_to_corr(cdf0, cdf1, joint_cdf)
fout = paths.knnauto(args.simname, args.run, nsim)
if args.verbose:
print(f"Saving output to `{fout}`.", flush=True)
joblib.dump({"rs": rs, "corr": corr}, fout)
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--run", type=str, help="Run name.")
parser.add_argument("--simname", type=str, choices=["csiborg", "quijote"],
help="Simulation name")
parser.add_argument("--nsims", type=int, nargs="+", default=None,
help="Indices of simulations to cross. If `-1` processes all simulations.") # noqa
parser.add_argument("--Rmax", type=float, default=155,
help="High-resolution region radius") # noqa
parser.add_argument("--verbose", type=lambda x: bool(strtobool(x)),
default=False)
args = parser.parse_args()
with open("./cluster_knn_auto.yml", "r") as file:
config = yaml.safe_load(file)
comm = MPI.COMM_WORLD
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
cats = open_catalogues(args, config, paths, comm)
if args.verbose and comm.Get_rank() == 0:
print(f"{datetime.now()}: starting to calculate the kNN statistic.")
def do_work(nsim):
if "random" in args.run:
do_cross_rand(args, config, cats, nsim, paths)
else:
do_auto(args, config, cats, nsim, paths)
nsims = list(cats.keys())
work_delegation(do_work, nsims, comm, master_verbose=args.verbose)
comm.Barrier()
if comm.Get_rank() == 0:
print(f"{datetime.now()}: all finished. Quitting.")