mirror of
https://github.com/Richard-Sti/csiborgtools.git
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eccd8e3507
* Improve calculations * Improve flags * Add smoothed options * Remove some old comments * Edit little things * Save smoothed * Move files * Edit imports * Edit imports * Renaming imports * Renaming imports * Sort imports * Sort files * Sorting * Optionally make copies of the field * Add quijote backup check * Add direct field smoothing * Shorten stupid documentation * Shorten stupid docs * Update conversion * Add particles to ASCII conversion * Add a short comment * Add SDSS uncorrected distance * Adjust comment * Add FITS index to galaxies * Remove spare space * Remove a stupid line * Remove blank line * Make space separated * Add interpolated field path * Add field sampling * Sort imports * Return density in cells * Clear out observer velocity * Add 170817 sampling * Fix normalization * Update plot
218 lines
8.7 KiB
Python
218 lines
8.7 KiB
Python
# 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 calculate overlap between two IC realisations of the same
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simulation.
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"""
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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 distutils.util import strtobool
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import numpy
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from scipy.ndimage import gaussian_filter
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import csiborgtools
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def pair_match_max(nsim0, nsimx, simname, min_logmass, mult, verbose):
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"""
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Match a pair of simulations using the Max method.
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Parameters
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----------
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nsim0, nsimx : int
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The reference and cross simulation IC index.
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simname : str
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Simulation name.
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min_logmass : float
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Minimum log halo mass.
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mult : float
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Multiplicative factor for search radius.
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verbose : bool
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Verbosity flag.
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"""
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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if simname == "csiborg":
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mass_kind = "fof_totpartmass"
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maxdist = 155
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periodic = False
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bounds = {"dist": (0, maxdist), mass_kind: (10**min_logmass, None)}
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cat0 = csiborgtools.read.CSiBORGHaloCatalogue(
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nsim0, paths, bounds=bounds, load_fitted=True, load_initial=False)
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catx = csiborgtools.read.CSiBORGHaloCatalogue(
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nsimx, paths, bounds=bounds, load_fitted=True, load_initial=False)
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elif simname == "quijote":
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mass_kind = "group_mass"
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maxdist = None
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periodic = True
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bounds = {mass_kind: (10**min_logmass, None)}
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cat0 = csiborgtools.read.QuijoteHaloCatalogue(
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nsim0, paths, 4, bounds=bounds, load_fitted=True,
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load_initial=False)
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catx = csiborgtools.read.QuijoteHaloCatalogue(
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nsimx, paths, 4, bounds=bounds, load_fitted=True,
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load_initial=False)
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else:
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raise ValueError(f"Unknown simulation `{simname}`.")
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reader = csiborgtools.summary.PairOverlap(cat0, catx, paths, min_logmass,
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maxdist=maxdist)
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out = csiborgtools.match.matching_max(
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cat0, catx, mass_kind, mult=mult, periodic=periodic,
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overlap=reader.overlap(from_smoothed=True),
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match_indxs=reader["match_indxs"], verbose=verbose)
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fout = paths.match_max(simname, nsim0, nsimx, min_logmass, mult)
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if verbose:
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print(f"{datetime.now()}: saving to ... `{fout}`.", flush=True)
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numpy.savez(fout, **{p: out[p] for p in out.dtype.names})
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def pair_match(nsim0, nsimx, simname, min_logmass, sigma, verbose):
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"""
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Calculate overlaps between two simulations.
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Parameters
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----------
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nsim0 : int
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The reference simulation IC index.
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nsimx : int
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The cross simulation IC index.
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simname : str
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Simulation name.
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min_logmass : float
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Minimum log halo mass.
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sigma : float
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Smoothing scale in number of grid cells.
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verbose : bool
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Verbosity flag.
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Returns
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-------
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None
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"""
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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smooth_kwargs = {"sigma": sigma, "mode": "constant", "cval": 0}
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if simname == "csiborg":
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overlapper_kwargs = {"box_size": 2048, "bckg_halfsize": 512}
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mass_kind = "fof_totpartmass"
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bounds = {"dist": (0, 155), mass_kind: (10**min_logmass, None)}
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cat0 = csiborgtools.read.CSiBORGHaloCatalogue(
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nsim0, paths, bounds=bounds, load_fitted=False,
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with_lagpatch=True)
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catx = csiborgtools.read.CSiBORGHaloCatalogue(
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nsimx, paths, bounds=bounds, load_fitted=False,
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with_lagpatch=True)
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elif simname == "quijote":
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overlapper_kwargs = {"box_size": 512, "bckg_halfsize": 256}
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mass_kind = "group_mass"
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bounds = {mass_kind: (10**min_logmass, None)}
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cat0 = csiborgtools.read.QuijoteHaloCatalogue(
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nsim0, paths, 4, bounds=bounds, load_fitted=False,
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with_lagpatch=True)
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catx = csiborgtools.read.QuijoteHaloCatalogue(
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nsimx, paths, 4, bounds=bounds, load_fitted=False,
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with_lagpatch=True)
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else:
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raise ValueError(f"Unknown simulation name: `{simname}`.")
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halomap0 = csiborgtools.read.read_h5(
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paths.particles(nsim0, simname))["halomap"]
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parts0 = csiborgtools.read.read_h5(
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paths.initmatch(nsim0, simname, "particles"))["particles"]
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hid2map0 = {hid: i for i, hid in enumerate(halomap0[:, 0])}
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halomapx = csiborgtools.read.read_h5(
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paths.particles(nsimx, simname))["halomap"]
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partsx = csiborgtools.read.read_h5(
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paths.initmatch(nsimx, simname, "particles"))["particles"]
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hid2mapx = {hid: i for i, hid in enumerate(halomapx[:, 0])}
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overlapper = csiborgtools.match.ParticleOverlap(**overlapper_kwargs)
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delta_bckg = overlapper.make_bckg_delta(parts0, halomap0, hid2map0, cat0,
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verbose=verbose)
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delta_bckg = overlapper.make_bckg_delta(partsx, halomapx, hid2mapx, catx,
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delta=delta_bckg, verbose=verbose)
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matcher = csiborgtools.match.RealisationsMatcher(
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mass_kind=mass_kind, **overlapper_kwargs)
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match_indxs, ngp_overlap = matcher.cross(cat0, catx, parts0, partsx,
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halomap0, halomapx, delta_bckg,
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verbose=verbose)
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# We want to store the halo IDs of the matches, not their array positions
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# in the catalogues.
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match_hids = deepcopy(match_indxs)
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for i, matches in enumerate(match_indxs):
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for j, match in enumerate(matches):
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match_hids[i][j] = catx["index"][match]
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fout = paths.overlap(simname, nsim0, nsimx, min_logmass, smoothed=False)
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if verbose:
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print(f"{datetime.now()}: saving to ... `{fout}`.", flush=True)
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numpy.savez(fout, ref_hids=cat0["index"], match_hids=match_hids,
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ngp_overlap=ngp_overlap)
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if not sigma > 0:
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return
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if verbose:
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print(f"{datetime.now()}: smoothing the background field.", flush=True)
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gaussian_filter(delta_bckg, output=delta_bckg, **smooth_kwargs)
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# We calculate the smoothed overlap for the pairs whose NGP overlap is > 0.
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smoothed_overlap = matcher.smoothed_cross(cat0, catx, parts0, partsx,
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halomap0, halomapx, delta_bckg,
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match_indxs, smooth_kwargs,
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verbose=verbose)
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fout = paths.overlap(simname, nsim0, nsimx, min_logmass, smoothed=True)
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if verbose:
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print(f"{datetime.now()}: saving to ... `{fout}`.", flush=True)
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numpy.savez(fout, smoothed_overlap=smoothed_overlap, sigma=sigma)
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if __name__ == "__main__":
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parser = ArgumentParser()
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parser.add_argument("--kind", type=str, required=True,
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choices=["overlap", "max"], help="Kind of matching.")
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parser.add_argument("--nsim0", type=int, required=True,
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help="Reference simulation IC index.")
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parser.add_argument("--nsimx", type=int, required=True,
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help="Cross simulation IC index.")
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parser.add_argument("--simname", type=str, required=True,
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help="Simulation name.")
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parser.add_argument("--min_logmass", type=float, required=True,
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help="Minimum log halo mass.")
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parser.add_argument("--mult", type=float, default=5,
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help="Search radius multiplier for Max's method.")
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parser.add_argument("--sigma", type=float, default=0,
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help="Smoothing scale in number of grid cells.")
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parser.add_argument("--verbose", type=lambda x: bool(strtobool(x)),
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default=False, help="Verbosity flag.")
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args = parser.parse_args()
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if args.kind == "overlap":
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pair_match(args.nsim0, args.nsimx, args.simname, args.min_logmass,
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args.sigma, args.verbose)
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elif args.kind == "max":
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pair_match_max(args.nsim0, args.nsimx, args.simname, args.min_logmass,
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args.mult, args.verbose)
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else:
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raise ValueError(f"Unknown matching kind: `{args.kind}`.")
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