mirror of
https://github.com/Richard-Sti/csiborgtools.git
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f48eb6dcb0
* add radial position path * pep8 * Add basic fit profile dumping * pep8 * pep8 * pep8 * pep8 * pep8 * pep8 * Update TODO * Fix parts is None bug * Update nb
141 lines
4.5 KiB
Python
141 lines
4.5 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 auto-2PCF of CSiBORG catalogues."""
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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 warnings import warn
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import joblib
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import numpy
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import yaml
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from mpi4py import MPI
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from taskmaster import master_process, worker_process
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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/tpcf_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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paths = csiborgtools.read.CSiBORGPaths()
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ics = paths.get_ics(False)
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tpcf = csiborgtools.clustering.Mock2PCF()
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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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)
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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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_config = config.get(run, None)
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if _config is None:
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warn("No configuration for run {}.".format(run), stacklevel=1)
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return
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rvs_gen = csiborgtools.clustering.RVSinsphere(Rmax)
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bins = numpy.logspace(
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numpy.log10(config["rpmin"]),
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numpy.log10(config["rpmax"]),
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config["nrpbins"] + 1,
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)
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pos = read_single(_config, cat)
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nrandom = int(config["randmult"] * pos.shape[0])
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rp, wp = tpcf(pos, rvs_gen, nrandom, bins)
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joblib.dump({"rp": rp, "wp": wp}, paths.tpcfauto_path(run, ic))
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def do_runs(ic):
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cat = csiborgtools.read.ClumpsCatalogue(ic, paths, maxdist=Rmax)
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for run in args.runs:
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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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