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Remove halo fitting. (#83)
* Rename file * Remove old content * Remove halo fit * Completely remove fits * Add utils here * Account for renaming
This commit is contained in:
Richard Stiskalek
GitHub
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7 changed files with 148 additions and 718 deletions
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# 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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"""
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A script to fit FoF halos (concentration, ...). The CSiBORG particle array of
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each realisation must have been processed in advance by `pre_dumppart.py`.
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Quijote is not supported yet
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"""
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from argparse import ArgumentParser
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import numpy
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from mpi4py import MPI
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from taskmaster import work_delegation
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from tqdm import trange
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from utils import get_nsims
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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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def fit_halo(particles, box):
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"""
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Fit a single halo from the particle array. Only halos with more than 100
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particles are fitted.
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Parameters
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----------
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particles : 2-dimensional array of shape `(n_particles, 3)`
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Particle array. The columns must be `x`, `y`, `z`, `vx`, `vy`, `vz`,
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`M`.
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box : object derived from :py:class`csiborgtools.read.BaseBox`
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Box object.
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Returns
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-------
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out : dict
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"""
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halo = csiborgtools.fits.Halo(particles, box)
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out = {}
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out["npart"] = len(halo)
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out["totpartmass"] = numpy.sum(halo["M"])
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for i, v in enumerate(["vx", "vy", "vz"]):
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out[v] = numpy.average(halo.vel[:, i], weights=halo["M"])
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if out["npart"] < 100:
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return out
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cm, dist = halo.center_of_mass()
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m200c, r200c = halo.spherical_overdensity_mass(dist, 200)
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angmom = halo.angular_momentum(dist, cm, r200c)
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out["m200c"] = m200c
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out["r200c"] = r200c
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out["lambda200c"] = halo.lambda_bullock(angmom, m200c, r200c)
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out["conc"] = halo.nfw_concentration(dist, r200c)
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return out
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def _main(nsim, simname, verbose):
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"""
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Fit the FoF halos.
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Parameters
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----------
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nsim : int
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IC realisation index.
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simname : str
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Simulation name.
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verbose : bool
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Verbosity flag.
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"""
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cols = [("index", numpy.int32),
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("npart", numpy.int32),
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("totpartmass", numpy.float32),
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("vx", numpy.float32),
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("vy", numpy.float32),
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("vz", numpy.float32),
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("conc", numpy.float32),
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("r200c", numpy.float32),
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("m200c", numpy.float32),
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("lambda200c", numpy.float32),]
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nsnap = max(paths.get_snapshots(nsim, simname))
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if simname == "csiborg":
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box = csiborgtools.read.CSiBORGBox(nsnap, nsim, paths)
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cat = csiborgtools.read.CSiBORGHaloCatalogue(
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nsim, paths, bounds=None, load_fitted=False, load_initial=False)
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else:
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box = csiborgtools.read.QuijoteBox(nsnap, nsim, paths)
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cat = csiborgtools.read.QuijoteHaloCatalogue(
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nsim, paths, nsnap, bounds=None, load_fitted=False,
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load_initial=False)
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# Particle archive
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f = csiborgtools.read.read_h5(paths.particles(nsim, simname))
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particles = f["particles"]
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halo_map = f["halomap"]
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hid2map = {hid: i for i, hid in enumerate(halo_map[:, 0])}
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out = csiborgtools.read.cols_to_structured(len(cat), cols)
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for i in trange(len(cat)) if verbose else range(len(cat)):
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hid = cat["index"][i]
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out["index"][i] = hid
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part = csiborgtools.read.load_halo_particles(hid, particles, halo_map,
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hid2map)
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# Skip if no particles.
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if part is None:
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continue
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_out = fit_halo(part, box)
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for key in _out.keys():
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out[key][i] = _out.get(key, numpy.nan)
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fout = paths.structfit(nsnap, nsim, simname)
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if verbose:
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print(f"Saving to `{fout}`.", flush=True)
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numpy.save(fout, out)
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if __name__ == "__main__":
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parser = ArgumentParser()
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parser.add_argument("--simname", type=str, default="csiborg",
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choices=["csiborg", "quijote", "quijote_full"],
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help="Simulation name")
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parser.add_argument("--nsims", type=int, nargs="+", default=None,
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help="IC realisations. If `-1` processes all.")
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args = parser.parse_args()
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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nsims = get_nsims(args, paths)
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def main(nsim):
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_main(nsim, args.simname, MPI.COMM_WORLD.Get_size() == 1)
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work_delegation(main, nsims, MPI.COMM_WORLD)
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@ -61,7 +61,7 @@ def get_counts(nsim, bins, paths, parser_args):
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cat = csiborgtools.read.CSiBORGHaloCatalogue(
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nsim, paths, bounds=bounds, load_fitted=False, load_initial=False)
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logmass = numpy.log10(cat["fof_totpartmass"])
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counts = csiborgtools.fits.number_counts(logmass, bins)
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counts = csiborgtools.number_counts(logmass, bins)
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elif simname == "quijote":
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cat0 = csiborgtools.read.QuijoteHaloCatalogue(
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nsim, paths, nsnap=4, load_fitted=False, load_initial=False)
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@ -72,12 +72,12 @@ def get_counts(nsim, bins, paths, parser_args):
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for nobs in range(nmax):
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cat = cat0.pick_fiducial_observer(nobs, rmax=parser_args.Rmax)
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logmass = numpy.log10(cat["group_mass"])
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counts[nobs, :] = csiborgtools.fits.number_counts(logmass, bins)
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counts[nobs, :] = csiborgtools.number_counts(logmass, bins)
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elif simname == "quijote_full":
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cat = csiborgtools.read.QuijoteHaloCatalogue(
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nsim, paths, nsnap=4, load_fitted=False, load_initial=False)
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logmass = numpy.log10(cat["group_mass"])
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counts = csiborgtools.fits.number_counts(logmass, bins)
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counts = csiborgtools.number_counts(logmass, bins)
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else:
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raise ValueError(f"Unknown simulation name `{simname}`.")
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@ -91,14 +91,14 @@ def _main(nsim, simname, verbose):
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pos, mass = part[:, :3], part[:, 3]
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# Calculate the centre of mass and the Lagrangian patch size.
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cm = csiborgtools.fits.center_of_mass(pos, mass, boxsize=1.0)
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distances = csiborgtools.fits.periodic_distance(pos, cm, boxsize=1.0)
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cm = csiborgtools.center_of_mass(pos, mass, boxsize=1.0)
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distances = csiborgtools.periodic_distance(pos, cm, boxsize=1.0)
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out["x"][i], out["y"][i], out["z"][i] = cm
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out["lagpatch_size"][i] = numpy.percentile(distances, 99)
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# Calculate the number of cells with > 0 density.
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delta = overlapper.make_delta(pos, mass, subbox=True)
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out["lagpatch_ncells"][i] = csiborgtools.fits.delta2ncells(delta)
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out["lagpatch_ncells"][i] = csiborgtools.delta2ncells(delta)
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# Now save it
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fout = paths.initmatch(nsim, simname, "fit")
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