mirror of
https://github.com/Richard-Sti/csiborgtools.git
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522ee709c9
* Add joint kNN CDF * add jointKNN calculation * change sub script * Update readme * update sub * Small changes * comments * update nb * Update submisison script
159 lines
5.9 KiB
Python
159 lines
5.9 KiB
Python
# Copyright (C) 2022 Richard Stiskalek
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# This program is free software; you can redistribute it and/or modify it
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# under the terms of the GNU General Public License as published by the
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# Free Software Foundation; either version 3 of the License, or (at your
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# option) any later version.
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#
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# This program is distributed in the hope that it will be useful, but
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# WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
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# Public License for more details.
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#
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# You should have received a copy of the GNU General Public License along
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# with this program; if not, write to the Free Software Foundation, Inc.,
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# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
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"""A script to calculate the KNN-CDF for a set of CSiBORG halo catalogues."""
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from os.path import join
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from argparse import ArgumentParser
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from copy import deepcopy
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from datetime import datetime
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from itertools import combinations
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from mpi4py import MPI
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from TaskmasterMPI import master_process, worker_process
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from sklearn.neighbors import NearestNeighbors
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import joblib
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try:
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import csiborgtools
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except ModuleNotFoundError:
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import sys
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sys.path.append("../")
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import csiborgtools
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###############################################################################
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# MPI and arguments #
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###############################################################################
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comm = MPI.COMM_WORLD
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rank = comm.Get_rank()
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nproc = comm.Get_size()
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parser = ArgumentParser()
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parser.add_argument("--rmin", type=float)
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parser.add_argument("--rmax", type=float)
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parser.add_argument("--nneighbours", type=int)
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parser.add_argument("--nsamples", type=int)
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parser.add_argument("--neval", type=int)
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parser.add_argument("--batch_size", type=int)
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parser.add_argument("--seed", type=int, default=42)
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args = parser.parse_args()
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Rmax = 155 / 0.705 # Mpc/h high resolution region radius
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mass_threshold = [1e12, 1e13, 1e14] # Msun
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ics = [7444, 7468, 7492, 7516, 7540, 7564, 7588, 7612, 7636, 7660, 7684,
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7708, 7732, 7756, 7780, 7804, 7828, 7852, 7876, 7900, 7924, 7948,
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7972, 7996, 8020, 8044, 8068, 8092, 8116, 8140, 8164, 8188, 8212,
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8236, 8260, 8284, 8308, 8332, 8356, 8380, 8404, 8428, 8452, 8476,
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8500, 8524, 8548, 8572, 8596, 8620, 8644, 8668, 8692, 8716, 8740,
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8764, 8788, 8812, 8836, 8860, 8884, 8908, 8932, 8956, 8980, 9004,
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9028, 9052, 9076, 9100, 9124, 9148, 9172, 9196, 9220, 9244, 9268,
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9292, 9316, 9340, 9364, 9388, 9412, 9436, 9460, 9484, 9508, 9532,
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9556, 9580, 9604, 9628, 9652, 9676, 9700, 9724, 9748, 9772, 9796,
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9820, 9844]
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dumpdir = "/mnt/extraspace/rstiskalek/csiborg/knn"
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fout_auto = join(dumpdir, "auto", "knncdf_{}.p")
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fout_cross = join(dumpdir, "cross", "knncdf_{}_{}.p")
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###############################################################################
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# Analysis #
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###############################################################################
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knncdf = csiborgtools.match.kNN_CDF()
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def do_auto(ic):
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out = {}
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cat = csiborgtools.read.HaloCatalogue(ic, max_dist=Rmax)
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for i, mmin in enumerate(mass_threshold):
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knn = NearestNeighbors()
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knn.fit(cat.positions[cat["totpartmass"] > mmin, ...])
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rs, cdf = knncdf(knn, nneighbours=args.nneighbours, Rmax=Rmax,
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rmin=args.rmin, rmax=args.rmax, nsamples=args.nsamples,
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neval=args.neval, batch_size=args.batch_size,
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random_state=args.seed, verbose=False)
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out.update({"cdf_{}".format(i): cdf})
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out.update({"rs": rs, "mass_threshold": mass_threshold})
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joblib.dump(out, fout_auto.format(ic))
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def do_cross(ics):
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out = {}
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cat1 = csiborgtools.read.HaloCatalogue(ics[0], max_dist=Rmax)
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cat2 = csiborgtools.read.HaloCatalogue(ics[1], max_dist=Rmax)
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for i, mmin in enumerate(mass_threshold):
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knn1 = NearestNeighbors()
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knn1.fit(cat1.positions[cat1["totpartmass"] > mmin, ...])
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knn2 = NearestNeighbors()
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knn2.fit(cat2.positions[cat2["totpartmass"] > mmin, ...])
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rs, cdf0, cdf1, joint_cdf = knncdf.joint(
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knn1, knn2, nneighbours=args.nneighbours, Rmax=Rmax,
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rmin=args.rmin, rmax=args.rmax, nsamples=args.nsamples,
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neval=args.neval, batch_size=args.batch_size,
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random_state=args.seed)
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corr = knncdf.joint_to_corr(cdf0, cdf1, joint_cdf)
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out.update({"corr_{}".format(i): corr})
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out.update({"rs": rs, "mass_threshold": mass_threshold})
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joblib.dump(out, fout_cross.format(*ics))
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###############################################################################
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# Autocorrelation calculation #
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###############################################################################
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if nproc > 1:
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if rank == 0:
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tasks = deepcopy(ics)
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master_process(tasks, comm, verbose=True)
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else:
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worker_process(do_auto, comm, verbose=False)
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else:
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tasks = deepcopy(ics)
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for task in tasks:
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print("{}: completing task `{}`.".format(datetime.now(), task))
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do_auto(task)
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comm.Barrier()
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###############################################################################
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# Crosscorrelation calculation #
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###############################################################################
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if nproc > 1:
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if rank == 0:
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tasks = list(combinations(ics, 2))
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master_process(tasks, comm, verbose=True)
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else:
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worker_process(do_cross, comm, verbose=False)
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else:
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tasks = deepcopy(ics)
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for task in tasks:
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print("{}: completing task `{}`.".format(datetime.now(), task))
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do_cross(task)
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comm.Barrier()
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if rank == 0:
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print("{}: all finished.".format(datetime.now()))
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quit() # Force quit the script
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