mirror of
https://github.com/Richard-Sti/csiborgtools.git
synced 2024-12-22 22:28:03 +00:00
522ee709c9
* Add joint kNN CDF * add jointKNN calculation * change sub script * Update readme * update sub * Small changes * comments * update nb * Update submisison script
158 lines
5.9 KiB
Python
158 lines
5.9 KiB
Python
# 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 CSiBORG halo catalogues."""
|
|
from os.path import join
|
|
from argparse import ArgumentParser
|
|
from copy import deepcopy
|
|
from datetime import datetime
|
|
from itertools import combinations
|
|
from mpi4py import MPI
|
|
from TaskmasterMPI import master_process, worker_process
|
|
from sklearn.neighbors import NearestNeighbors
|
|
import joblib
|
|
try:
|
|
import csiborgtools
|
|
except ModuleNotFoundError:
|
|
import sys
|
|
sys.path.append("../")
|
|
import csiborgtools
|
|
|
|
|
|
###############################################################################
|
|
# MPI and arguments #
|
|
###############################################################################
|
|
comm = MPI.COMM_WORLD
|
|
rank = comm.Get_rank()
|
|
nproc = comm.Get_size()
|
|
|
|
parser = ArgumentParser()
|
|
parser.add_argument("--rmin", type=float)
|
|
parser.add_argument("--rmax", type=float)
|
|
parser.add_argument("--nneighbours", type=int)
|
|
parser.add_argument("--nsamples", type=int)
|
|
parser.add_argument("--neval", type=int)
|
|
parser.add_argument("--batch_size", type=int)
|
|
parser.add_argument("--seed", type=int, default=42)
|
|
args = parser.parse_args()
|
|
|
|
Rmax = 155 / 0.705 # Mpc/h high resolution region radius
|
|
mass_threshold = [1e12, 1e13, 1e14] # Msun
|
|
ics = [7444, 7468, 7492, 7516, 7540, 7564, 7588, 7612, 7636, 7660, 7684,
|
|
7708, 7732, 7756, 7780, 7804, 7828, 7852, 7876, 7900, 7924, 7948,
|
|
7972, 7996, 8020, 8044, 8068, 8092, 8116, 8140, 8164, 8188, 8212,
|
|
8236, 8260, 8284, 8308, 8332, 8356, 8380, 8404, 8428, 8452, 8476,
|
|
8500, 8524, 8548, 8572, 8596, 8620, 8644, 8668, 8692, 8716, 8740,
|
|
8764, 8788, 8812, 8836, 8860, 8884, 8908, 8932, 8956, 8980, 9004,
|
|
9028, 9052, 9076, 9100, 9124, 9148, 9172, 9196, 9220, 9244, 9268,
|
|
9292, 9316, 9340, 9364, 9388, 9412, 9436, 9460, 9484, 9508, 9532,
|
|
9556, 9580, 9604, 9628, 9652, 9676, 9700, 9724, 9748, 9772, 9796,
|
|
9820, 9844]
|
|
dumpdir = "/mnt/extraspace/rstiskalek/csiborg/knn"
|
|
fout_auto = join(dumpdir, "auto", "knncdf_{}.p")
|
|
fout_cross = join(dumpdir, "cross", "knncdf_{}_{}.p")
|
|
|
|
|
|
###############################################################################
|
|
# Analysis #
|
|
###############################################################################
|
|
knncdf = csiborgtools.match.kNN_CDF()
|
|
|
|
|
|
def do_auto(ic):
|
|
out = {}
|
|
cat = csiborgtools.read.HaloCatalogue(ic, max_dist=Rmax)
|
|
|
|
for i, mmin in enumerate(mass_threshold):
|
|
knn = NearestNeighbors()
|
|
knn.fit(cat.positions[cat["totpartmass"] > mmin, ...])
|
|
|
|
rs, cdf = knncdf(knn, nneighbours=args.nneighbours, Rmax=Rmax,
|
|
rmin=args.rmin, rmax=args.rmax, nsamples=args.nsamples,
|
|
neval=args.neval, batch_size=args.batch_size,
|
|
random_state=args.seed, verbose=False)
|
|
out.update({"cdf_{}".format(i): cdf})
|
|
|
|
out.update({"rs": rs, "mass_threshold": mass_threshold})
|
|
joblib.dump(out, fout_auto.format(ic))
|
|
|
|
|
|
def do_cross(ics):
|
|
out = {}
|
|
cat1 = csiborgtools.read.HaloCatalogue(ics[0], max_dist=Rmax)
|
|
cat2 = csiborgtools.read.HaloCatalogue(ics[1], max_dist=Rmax)
|
|
|
|
for i, mmin in enumerate(mass_threshold):
|
|
knn1 = NearestNeighbors()
|
|
knn1.fit(cat1.positions[cat1["totpartmass"] > mmin, ...])
|
|
|
|
knn2 = NearestNeighbors()
|
|
knn2.fit(cat2.positions[cat2["totpartmass"] > mmin, ...])
|
|
|
|
rs, cdf0, cdf1, joint_cdf = knncdf.joint(
|
|
knn1, knn2, nneighbours=args.nneighbours, Rmax=Rmax,
|
|
rmin=args.rmin, rmax=args.rmax, nsamples=args.nsamples,
|
|
neval=args.neval, batch_size=args.batch_size,
|
|
random_state=args.seed)
|
|
|
|
corr = knncdf.joint_to_corr(cdf0, cdf1, joint_cdf)
|
|
|
|
out.update({"corr_{}".format(i): corr})
|
|
|
|
out.update({"rs": rs, "mass_threshold": mass_threshold})
|
|
joblib.dump(out, fout_cross.format(*ics))
|
|
|
|
|
|
|
|
###############################################################################
|
|
# Autocorrelation calculation #
|
|
###############################################################################
|
|
|
|
|
|
if nproc > 1:
|
|
if rank == 0:
|
|
tasks = deepcopy(ics)
|
|
master_process(tasks, comm, verbose=True)
|
|
else:
|
|
worker_process(do_auto, comm, verbose=False)
|
|
else:
|
|
tasks = deepcopy(ics)
|
|
for task in tasks:
|
|
print("{}: completing task `{}`.".format(datetime.now(), task))
|
|
do_auto(task)
|
|
comm.Barrier()
|
|
|
|
|
|
###############################################################################
|
|
# Crosscorrelation calculation #
|
|
###############################################################################
|
|
|
|
|
|
if nproc > 1:
|
|
if rank == 0:
|
|
tasks = list(combinations(ics, 2))
|
|
master_process(tasks, comm, verbose=True)
|
|
else:
|
|
worker_process(do_cross, comm, verbose=False)
|
|
else:
|
|
tasks = deepcopy(ics)
|
|
for task in tasks:
|
|
print("{}: completing task `{}`.".format(datetime.now(), task))
|
|
do_cross(task)
|
|
comm.Barrier()
|
|
|
|
|
|
if rank == 0:
|
|
print("{}: all finished.".format(datetime.now()))
|
|
quit() # Force quit the script
|