mirror of
https://github.com/Richard-Sti/csiborgtools.git
synced 2024-12-22 22:58:02 +00:00
2185846e90
* Updat ebounds * fix mistake * add plot script * fix which sims * Add Poisson * Just docs * Hide things to __main__ * Rename paths * Move old script * Remove radpos * Paths renaming * Paths renaming * Remove trunk stuff * Add import * Add nearest neighbour search * Add Quijote fiducial indices * Add final snapshot matching * Add fiducial observer selection * add boxsizes * Add reading functions * Little stuff * Bring back the fiducial observer * Add arguments * Add quijote paths * Add notes * Get this running * Add yaml * Remove Poisson stuff * Get the 2PCF script running * Add not finished htings * Remove comment * Verbosity only on 0th rank! * Update plotting style * Add nearest neighbour CDF * Save radial distance too * Add centres * Add basic plotting
154 lines
5 KiB
Python
154 lines
5 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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Script to load in the simulation particles, load them by their clump ID and
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dump into a HDF5 file. Stores the first and last index of each clump in the
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particle array. This can be used for fast slicing of the array to acces
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particles of a single clump.
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"""
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from datetime import datetime
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from gc import collect
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import h5py
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import numba
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import numpy
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from mpi4py import MPI
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from tqdm import trange
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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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from argparse import ArgumentParser
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# We set up the MPI
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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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# And next parse all the arguments and set up CSiBORG objects
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parser = ArgumentParser()
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parser.add_argument("--ics", type=int, nargs="+", default=None,
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help="IC realisations. If `-1` processes all simulations.")
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args = parser.parse_args()
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verbose = nproc == 1
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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partreader = csiborgtools.read.ParticleReader(paths)
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# Keep "ID" as the last column!
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pars_extract = ['x', 'y', 'z', 'vx', 'vy', 'vz', 'M', "ID"]
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if args.ics is None or args.ics[0] == -1:
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ics = paths.get_ics("csiborg")
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else:
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ics = args.ics
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@numba.jit(nopython=True)
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def minmax_clump(clid, clump_ids, start_loop=0):
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"""
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Find the start and end index of a clump in a sorted array of clump IDs.
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This is much faster than using `numpy.where` and then `numpy.min` and
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`numpy.max`.
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"""
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start = None
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end = None
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for i in range(start_loop, clump_ids.size):
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n = clump_ids[i]
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if n == clid:
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if start is None:
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start = i
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end = i
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elif n > clid:
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break
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return start, end
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# MPI loop over individual simulations. We read in the particles from RAMSES
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# files and dump them to a HDF5 file.
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jobs = csiborgtools.fits.split_jobs(len(ics), nproc)[rank]
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for i in jobs:
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nsim = ics[i]
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nsnap = max(paths.get_snapshots(nsim))
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fname = paths.particles(nsim)
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# We first read in the clump IDs of the particles and infer the sorting.
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# Right away we dump the clump IDs to a HDF5 file and clear up memory.
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print(f"{datetime.now()}: rank {rank} loading particles {nsim}.",
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flush=True)
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part_cids = partreader.read_clumpid(nsnap, nsim, verbose=verbose)
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sort_indxs = numpy.argsort(part_cids).astype(numpy.int32)
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part_cids = part_cids[sort_indxs]
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with h5py.File(fname, "w") as f:
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f.create_dataset("clump_ids", data=part_cids)
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f.close()
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del part_cids
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collect()
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# Next we read in the particles and sort them by their clump ID.
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# We cannot directly read this as an unstructured array because the float32
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# precision is insufficient to capture the clump IDs.
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parts, pids = partreader.read_particle(
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nsnap, nsim, pars_extract, return_structured=False, verbose=verbose)
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# Now we in two steps save the particles and particle IDs.
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print(f"{datetime.now()}: rank {rank} dumping particles from {nsim}.",
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flush=True)
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parts = parts[sort_indxs]
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pids = pids[sort_indxs]
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del sort_indxs
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collect()
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with h5py.File(fname, "r+") as f:
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f.create_dataset("particle_ids", data=pids)
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f.close()
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del pids
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collect()
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with h5py.File(fname, "r+") as f:
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f.create_dataset("particles", data=parts)
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f.close()
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del parts
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collect()
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print(f"{datetime.now()}: rank {rank} creating clump mapping for {nsim}.",
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flush=True)
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# Load clump IDs back to memory
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with h5py.File(fname, "r") as f:
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part_cids = f["clump_ids"][:]
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# We loop over the unique clump IDs.
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unique_clump_ids = numpy.unique(part_cids)
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clump_map = numpy.full((unique_clump_ids.size, 3), numpy.nan,
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dtype=numpy.int32)
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start_loop = 0
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niters = unique_clump_ids.size
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for i in trange(niters) if verbose else range(niters):
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clid = unique_clump_ids[i]
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k0, kf = minmax_clump(clid, part_cids, start_loop=start_loop)
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clump_map[i, 0] = clid
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clump_map[i, 1] = k0
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clump_map[i, 2] = kf
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start_loop = kf
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# We save the mapping to a HDF5 file
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with h5py.File(paths.particles(nsim), "r+") as f:
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f.create_dataset("clumpmap", data=clump_map)
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f.close()
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del part_cids
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collect()
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