mirror of
https://github.com/Richard-Sti/csiborgtools_public.git
synced 2025-05-13 14:11:11 +00:00
Fixing overlaps and halo definitions. (#80)
* Add imports * Refactor code * Rename fof velocities * Clean up and add Quijote * Edit docstrings * Update submission script * Fix bug * Start loading fitted properties * Edit docstrings * Update fitting for new `halo` * Update CM definition and R200c * Tune the minimum number of particles * Enforce crossing threshold & tune hypers * Fix periodiity when calculating angmom * Doc strings * Relax checkip * Minor edit * Fix old kwarg bug * Fix CSiBORG bounds * Catch warnings! * Add `mass_kind` and new boundaries
This commit is contained in:
Richard Stiskalek
GitHub
parent
169a5e5bd7
commit
344ff8e091
10 changed files with 543 additions and 388 deletions
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@ -37,7 +37,8 @@ except ModuleNotFoundError:
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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.
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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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@ -59,12 +60,17 @@ def fit_halo(particles, box):
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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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m200c, r200c, cm = halo.spherical_overdensity_mass(200, kind="crit",
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maxiter=100)
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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(cm, r200c)
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out["conc"] = halo.nfw_concentration(cm, 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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@ -81,9 +87,6 @@ def _main(nsim, simname, verbose):
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verbose : bool
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Verbosity flag.
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"""
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# if simname == "quijote":
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# raise NotImplementedError("Quijote not implemented yet.")
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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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@ -116,7 +119,6 @@ def _main(nsim, simname, verbose):
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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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# print("i = ", i)
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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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@ -125,7 +127,7 @@ def _main(nsim, simname, verbose):
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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[key]
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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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@ -66,7 +66,7 @@ def _main(nsim, simname, verbose):
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if simname == "csiborg":
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cat = csiborgtools.read.CSiBORGHaloCatalogue(
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nsim, paths, rawdata=True, load_fitted=False, load_initial=False)
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nsim, paths, bounds=None, load_fitted=False, load_initial=False)
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else:
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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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@ -11,10 +11,7 @@
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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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Script to match all pairs of CSiBORG simulations. Mathches main haloes whose
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mass is above 1e12 solar masses.
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"""
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"""A script to match all IC pairs of a simulation."""
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from argparse import ArgumentParser
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from distutils.util import strtobool
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from itertools import combinations
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@ -34,10 +31,15 @@ except ModuleNotFoundError:
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import csiborgtools
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def get_combs():
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def get_combs(simname):
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"""
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Get the list of all pairs of simulations, then permute them with a known
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seed to minimise loading the same files simultaneously.
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Get the list of all pairs of IC indices and permute them with a fixed
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seed.
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Parameters
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----------
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simname : str
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Simulation name.
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Returns
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-------
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@ -45,38 +47,49 @@ def get_combs():
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List of pairs of simulations.
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"""
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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ics = paths.get_ics("csiborg")
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combs = list(combinations(ics, 2))
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combs = list(combinations(paths.get_ics(simname), 2))
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Random(42).shuffle(combs)
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return combs
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def do_work(comb):
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def main(comb, simname, sigma, verbose):
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"""
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Match a pair of simulations.
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Parameters
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----------
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comb : tuple
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Pair of simulations.
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Pair of simulation IC indices.
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simname : str
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Simulation name.
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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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nsim0, nsimx = comb
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pair_match(nsim0, nsimx, args.sigma, args.smoothen, args.verbose)
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pair_match(nsim0, nsimx, simname, sigma, verbose)
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if __name__ == "__main__":
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parser = ArgumentParser()
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parser.add_argument("--sigma", type=float, default=None)
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parser.add_argument("--smoothen", type=lambda x: bool(strtobool(x)),
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default=None)
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parser.add_argument("--simname", type=str, help="Simulation name.",
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choices=["csiborg", "quijote"])
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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)
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default=False, help="Verbosity flag.")
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args = parser.parse_args()
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comm = MPI.COMM_WORLD
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combs = get_combs()
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work_delegation(do_work, combs, comm, master_verbose=True)
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def _main(comb):
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main(comb, args.simname, args.sigma, args.verbose)
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work_delegation(_main, combs, MPI.COMM_WORLD)
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@ -11,7 +11,13 @@
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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 overlap between two CSiBORG realisations."""
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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. The matching is performed for haloes whose total particles mass is
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- CSiBORG: > 1e13 Msun/h,
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- Quijote: > 1e14 Msun/h,
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since Quijote has much lower resolution than CSiBORG.
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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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@ -29,95 +35,123 @@ except ModuleNotFoundError:
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import csiborgtools
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def pair_match(nsim0, nsimx, sigma, smoothen, verbose):
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# TODO fix this.
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simname = "csiborg"
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overlapper_kwargs = {"box_size": 512, "bckg_halfsize": 475}
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from csiborgtools.read import CSiBORGHaloCatalogue, read_h5
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def pair_match(nsim0, nsimx, simname, 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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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": 0.0}
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overlapper = csiborgtools.match.ParticleOverlap(**overlapper_kwargs)
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matcher = csiborgtools.match.RealisationsMatcher(**overlapper_kwargs)
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smooth_kwargs = {"sigma": sigma, "mode": "wrap"}
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# Load the raw catalogues (i.e. no selection) including the initial CM
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# positions and the particle archives.
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bounds = {"totpartmass": (1e12, None)}
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cat0 = CSiBORGHaloCatalogue(nsim0, paths, load_initial=True, bounds=bounds,
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with_lagpatch=True, load_clumps_cat=True)
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catx = CSiBORGHaloCatalogue(nsimx, paths, load_initial=True, bounds=bounds,
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with_lagpatch=True, load_clumps_cat=True)
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if simname == "csiborg":
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overlapper_kwargs = {"box_size": 2048, "bckg_halfsize": 475}
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mass_kind = "fof_totpartmass"
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bounds = {mass_kind: (1e13, 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: (1e14, None)}
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cat0 = csiborgtools.read.QuijoteHaloCatalogue(
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nsim0, paths, 4, load_fitted=False, with_lagpatch=True)
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catx = csiborgtools.read.QuijoteHaloCatalogue(
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nsimx, paths, 4, load_fitted=False, with_lagpatch=True)
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else:
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raise ValueError(f"Unknown simulation name: `{simname}`.")
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clumpmap0 = read_h5(paths.particles(nsim0, simname))["clumpmap"]
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parts0 = read_h5(paths.initmatch(nsim0, simname, "particles"))["particles"]
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clid2map0 = {clid: i for i, clid in enumerate(clumpmap0[:, 0])}
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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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clumpmapx = read_h5(paths.particles(nsimx, simname))["clumpmap"]
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partsx = read_h5(paths.initmatch(nsimx, simname, "particles"))["particles"]
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clid2mapx = {clid: i for i, clid in enumerate(clumpmapx[:, 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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# We generate the background density fields. Loads halos's particles one by
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# one from the archive, concatenates them and calculates the NGP density
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# field.
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if verbose:
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print(f"{datetime.now()}: generating the background density fields.",
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print(f"{datetime.now()}: calculating the background density fields.",
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flush=True)
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delta_bckg = overlapper.make_bckg_delta(parts0, clumpmap0, clid2map0, cat0,
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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, clumpmapx, clid2mapx, catx,
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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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# We calculate the overlap between the NGP fields.
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if verbose:
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print(f"{datetime.now()}: crossing the simulations.", flush=True)
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print(f"{datetime.now()}: NGP crossing the simulations.", flush=True)
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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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clumpmap0, clumpmapx, delta_bckg,
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halomap0, halomapx, delta_bckg,
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verbose=verbose)
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# We wish to store the halo IDs of the matches, not their array positions
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# in the catalogues
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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(nsim0, nsimx, 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 verbose:
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print(f"{datetime.now()}: calculated NGP overlap, saved to {fout}.",
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flush=True)
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if not smoothen:
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quit()
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if not sigma > 0:
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return
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# We now smoothen up the background density field for the smoothed overlap
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# calculation.
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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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clumpmap0, clumpmapx, delta_bckg,
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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(nsim0, nsimx, smoothed=True)
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numpy.savez(fout, smoothed_overlap=smoothed_overlap, sigma=sigma)
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if verbose:
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print(f"{datetime.now()}: calculated smoothing, saved to {fout}.",
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flush=True)
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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("--nsim0", type=int)
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parser.add_argument("--nsimx", type=int)
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parser.add_argument("--sigma", type=float, default=None)
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parser.add_argument("--smoothen", type=lambda x: bool(strtobool(x)),
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default=None)
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parser.add_argument("--nsim0", type=int,
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help="Reference simulation IC index.")
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parser.add_argument("--nsimx", type=int,
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help="Cross simulation IC index.")
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parser.add_argument("--simname", type=str, help="Simulation name.")
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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)
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default=False, help="Verbosity flag.")
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args = parser.parse_args()
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pair_match(args.nsim0, args.nsimx, args.sigma, args.smoothen, args.verbose)
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pair_match(args.nsim0, args.nsimx, args.simname, args.sigma, args.verbose)
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