mirror of
https://github.com/Richard-Sti/csiborgtools.git
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e0d3854277
* ignore venv * ignore pylians if unavaible * Edit loading paths * Edit paths * Stop having default paths * Add nb * Add glamdring paths --------- Co-authored-by: Richard Stiskalek <dc-stis1@login8b.pri.cosma7.alces.network> Co-authored-by: Richard Stiskalek <dc-stis1@login8a.pri.cosma7.alces.network>
182 lines
6.6 KiB
Python
182 lines
6.6 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 warnings import warn
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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 mpi4py import MPI
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from TaskmasterMPI import master_process, worker_process
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import numpy
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from sklearn.neighbors import NearestNeighbors
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import joblib
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import yaml
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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("--runs", type=str, nargs="+")
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args = parser.parse_args()
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with open('../scripts/knn_auto.yml', 'r') as file:
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config = yaml.safe_load(file)
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Rmax = 155 / 0.705 # Mpc (h = 0.705) high resolution region radius
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totvol = 4 * numpy.pi * Rmax**3 / 3
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minmass = 1e12
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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 = join(dumpdir, "auto", "knncdf_{}_{}.p")
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paths = csiborgtools.read.CSiBORGPaths(**csiborgtools.paths_glamdring)
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knncdf = csiborgtools.clustering.kNN_CDF()
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###############################################################################
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# Analysis #
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###############################################################################
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def read_single(selection, cat):
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"""Positions for single catalogue auto-correlation."""
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mmask = numpy.ones(len(cat), dtype=bool)
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pos = cat.positions(False)
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# Primary selection
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psel = selection["primary"]
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pmin, pmax = psel.get("min", None), psel.get("max", None)
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if pmin is not None:
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mmask &= (cat[psel["name"]] >= pmin)
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if pmax is not None:
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mmask &= (cat[psel["name"]] < pmax)
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pos = pos[mmask, ...]
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# Secondary selection
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if "secondary" not in selection:
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return pos
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smask = numpy.ones(pos.shape[0], dtype=bool)
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ssel = selection["secondary"]
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smin, smax = ssel.get("min", None), ssel.get("max", None)
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prop = cat[ssel["name"]][mmask]
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if ssel.get("toperm", False):
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prop = numpy.random.permutation(prop)
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if ssel.get("marked", True):
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x = cat[psel["name"]][mmask]
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prop = csiborgtools.clustering.normalised_marks(
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x, prop, nbins=config["nbins_marks"])
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if smin is not None:
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smask &= (prop >= smin)
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if smax is not None:
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smask &= (prop < smax)
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return pos[smask, ...]
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def do_auto(run, cat, ic):
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"""Calculate the kNN-CDF single catalgoue autocorrelation."""
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_config = config.get(run, None)
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if _config is None:
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warn("No configuration for run {}.".format(run))
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return
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rvs_gen = csiborgtools.clustering.RVSinsphere(Rmax)
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pos = read_single(_config, cat)
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knn = NearestNeighbors()
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knn.fit(pos)
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rs, cdf = knncdf(
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knn, rvs_gen=rvs_gen, nneighbours=config["nneighbours"],
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rmin=config["rmin"], rmax=config["rmax"],
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nsamples=int(config["nsamples"]), neval=int(config["neval"]),
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batch_size=int(config["batch_size"]), random_state=config["seed"])
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joblib.dump({"rs": rs, "cdf": cdf, "ndensity": pos.shape[0] / totvol},
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fout.format(str(ic).zfill(5), run))
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def do_cross_rand(run, cat, ic):
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"""Calculate the kNN-CDF cross catalogue random correlation."""
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_config = config.get(run, None)
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if _config is None:
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warn("No configuration for run {}.".format(run))
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return
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rvs_gen = csiborgtools.clustering.RVSinsphere(Rmax)
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knn1, knn2 = NearestNeighbors(), NearestNeighbors()
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pos1 = read_single(_config, cat)
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knn1.fit(pos1)
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pos2 = rvs_gen(pos1.shape[0])
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knn2.fit(pos2)
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rs, cdf0, cdf1, joint_cdf = knncdf.joint(
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knn1, knn2, rvs_gen=rvs_gen, nneighbours=int(config["nneighbours"]),
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rmin=config["rmin"], rmax=config["rmax"],
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nsamples=int(config["nsamples"]), neval=int(config["neval"]),
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batch_size=int(config["batch_size"]), random_state=config["seed"])
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corr = knncdf.joint_to_corr(cdf0, cdf1, joint_cdf)
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joblib.dump({"rs": rs, "corr": corr}, fout.format(str(ic).zfill(5), run))
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def do_runs(ic):
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cat = csiborgtools.read.HaloCatalogue(ic, paths, max_dist=Rmax,
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min_mass=minmass)
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for run in args.runs:
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if "random" in run:
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do_cross_rand(run, cat, ic)
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else:
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do_auto(run, cat, ic)
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###############################################################################
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# MPI task delegation #
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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_runs, 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_runs(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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