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Add better diagnostics & plotting (#67)
* Add caching functions * Add limts * Add new mass runs * Update .gitignore * Edit which CDFs are loaded * Stop saving cross hindxs * Change dist to half precision * New nearest path * Add neighbour counting * Add neighbour kwargs * Update work in progress * Add new counting * Add how dist is built * Collect dist to 1 file * Update reading routine * Delete Quijote files * Remove file * Back to float32 * Fix bugs * Rename utils * Remove neighbuor kwargs * Rename file * Fix bug * Rename plt utils * Change where nghb kwargs from * line length * Remove old notebooks * Move survey * Add white space * Update TODO * Update CDF calculation * Update temporarily plotting * Merge branch 'add_diagnostics' of github.com:Richard-Sti/csiborgtools into add_diagnostics * Start adding documentation to plotting * Remove comments * Better code documentation * Some work on tidal tensor * Better plotting * Add comment * Remove nb * Remove comment * Add documentation * Update plotting * Update submission * Update KL vs KS plots * Update the plotting routine * Update plotting * Update plotting routines
This commit is contained in:
Richard Stiskalek
GitHub
parent
004d9629a2
commit
ccbbbd24b4
20 changed files with 1075 additions and 32121 deletions
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@ -19,12 +19,14 @@ MPI parallelized over the reference simulations.
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from argparse import ArgumentParser
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from datetime import datetime
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from distutils.util import strtobool
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from os import remove
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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 work_delegation
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from tqdm import trange
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from utils import open_catalogues
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try:
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@ -36,7 +38,7 @@ except ModuleNotFoundError:
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import csiborgtools
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def find_neighbour(args, nsim, cats, paths, comm):
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def find_neighbour(args, nsim, cats, paths, comm, save_kind):
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"""
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Find the nearest neighbour of each halo in the given catalogue.
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@ -53,23 +55,78 @@ def find_neighbour(args, nsim, cats, paths, comm):
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Paths object.
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comm : mpi4py.MPI.Comm
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MPI communicator.
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save_kind : str
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Kind of data to save. Must be either `dist` or `bin_dist`.
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Returns
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-------
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None
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"""
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assert save_kind in ["dist", "bin_dist"]
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ndist, cross_hindxs = csiborgtools.match.find_neighbour(nsim, cats)
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mass_key = "totpartmass" if args.simname == "csiborg" else "group_mass"
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cat0 = cats[nsim]
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mass = cat0[mass_key]
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rdist = cat0.radial_distance(in_initial=False)
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fout = paths.cross_nearest(args.simname, args.run, nsim)
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# Distance is saved optionally, whereas binned distance is always saved.
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if save_kind == "dist":
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out = {"ndist": ndist,
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"cross_hindxs": cross_hindxs,
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"mass": cat0[mass_key],
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"ref_hindxs": cat0["index"],
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"rdist": rdist}
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fout = paths.cross_nearest(args.simname, args.run, "dist", nsim)
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if args.verbose:
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print(f"Rank {comm.Get_rank()} writing to `{fout}`.", flush=True)
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numpy.savez(fout, **out)
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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reader = csiborgtools.read.NearestNeighbourReader(
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paths=paths, **csiborgtools.neighbour_kwargs)
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counts = numpy.zeros((reader.nbins_radial, reader.nbins_neighbour),
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dtype=numpy.float32)
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counts = reader.count_neighbour(counts, ndist, rdist)
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out = {"counts": counts}
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fout = paths.cross_nearest(args.simname, args.run, "bin_dist", nsim)
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if args.verbose:
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print(f"Rank {comm.Get_rank()} writing to `{fout}`.", flush=True)
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numpy.savez(fout, ndist=ndist, cross_hindxs=cross_hindxs, mass=mass,
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ref_hindxs=cat0["index"], rdist=rdist)
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numpy.savez(fout, **out)
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def collect_dist(args, paths):
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"""
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Collect the binned nearest neighbour distances into a single file.
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Parameters
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----------
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args : argparse.Namespace
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Command line arguments.
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paths : csiborgtools.paths.Paths
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Paths object.
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Returns
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-------
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"""
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fnames = paths.cross_nearest(args.simname, args.run, "bin_dist")
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if args.verbose:
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print("Collecting counts into a single file.", flush=True)
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for i in trange(len(fnames)) if args.verbose else range(len(fnames)):
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fname = fnames[i]
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data = numpy.load(fname)
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if i == 0:
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out = data["counts"]
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else:
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out += data["counts"]
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remove(fname)
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fout = paths.cross_nearest(args.simname, args.run, "tot_counts",
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nsim=0, nobs=0)
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if args.verbose:
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print(f"Writing the summed counts to `{fout}`.", flush=True)
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numpy.savez(fout, tot_counts=out)
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if __name__ == "__main__":
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@ -87,16 +144,23 @@ if __name__ == "__main__":
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with open("./match_finsnap.yml", "r") as file:
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config = yaml.safe_load(file)
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if args.simname == "csiborg":
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save_kind = "dist"
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else:
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save_kind = "bin_dist"
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comm = MPI.COMM_WORLD
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rank = comm.Get_rank()
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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cats = open_catalogues(args, config, paths, comm)
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def do_work(nsim):
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return find_neighbour(args, nsim, cats, paths, comm)
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return find_neighbour(args, nsim, cats, paths, comm, save_kind)
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work_delegation(do_work, list(cats.keys()), comm,
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master_verbose=args.verbose)
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comm.Barrier()
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if comm.Get_rank() == 0:
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if rank == 0:
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collect_dist(args, paths)
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print(f"{datetime.now()}: all finished. Quitting.")
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@ -18,20 +18,77 @@ nbins_marks: 10
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name:
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- totpartmass
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- group_mass
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min: 1.e+12
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max: 1.e+13
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min: 12.4
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max: 12.8
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islog: true
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"mass002":
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primary:
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name:
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- totpartmass
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- group_mass
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min: 1.e+13
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max: 1.e+14
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min: 12.6
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max: 13.0
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islog: true
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"mass003":
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primary:
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name:
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- totpartmass
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- group_mass
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min: 1.e+14
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min: 12.8
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max: 13.2
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islog: true
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"mass004":
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primary:
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name:
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- totpartmass
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- group_mass
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min: 13.0
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max: 13.4
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islog: true
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"mass005":
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primary:
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name:
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- totpartmass
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- group_mass
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min: 13.2
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max: 13.6
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islog: true
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"mass006":
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primary:
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name:
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- totpartmass
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- group_mass
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min: 13.4
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max: 13.8
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islog: true
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"mass007":
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primary:
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name:
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- totpartmass
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- group_mass
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min: 13.6
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max: 14.0
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islog: true
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"mass008":
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primary:
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name:
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- totpartmass
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- group_mass
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min: 13.8
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max: 14.2
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islog: true
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"mass009":
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primary:
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name:
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- totpartmass
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- group_mass
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min: 14.0
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islog: true
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@ -106,8 +106,12 @@ def read_single_catalogue(args, config, nsim, run, rmax, paths, nobs=None):
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pname = _name
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if pname is None:
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raise KeyError(f"Invalid names `{sel['name']}`.")
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cat.apply_bounds({pname: (sel.get("min", None), sel.get("max", None))})
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xmin = sel.get("min", None)
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xmax = sel.get("max", None)
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if sel.get("islog", False):
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xmin = 10**xmin if xmin is not None else None
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xmax = 10**xmax if xmax is not None else None
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cat.apply_bounds({pname: (xmin, xmax)})
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# Now the secondary selection bounds. If needed transfrom the secondary
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# property before applying the bounds.
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