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https://github.com/Richard-Sti/csiborgtools_public.git
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Quijote kNN adding (#62)
* Fix small bug * Add fiducial observers * Rename 1D knn * Add new bounds system * rm whitespace * Add boudns * Add simname to paths * Add fiducial obserevrs * apply bounds only if not none * Add TODO * add simnames * update script * Fix distance bug * update yaml * Update file reading * Update gitignore * Add plots * add check if empty list * add func to obtaining cross * Update nb * Remove blank lines * update ignroes * loop over a few ics * update gitignore * add comments
This commit is contained in:
Richard Stiskalek
GitHub
parent
7971fe2bc1
commit
255bec9710
16 changed files with 635 additions and 231 deletions
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@ -43,58 +43,76 @@ nproc = comm.Get_size()
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parser = ArgumentParser()
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parser.add_argument("--runs", type=str, nargs="+")
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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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parser.add_argument("--simname", type=str, choices=["csiborg", "quijote"])
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args = parser.parse_args()
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with open("../scripts/knn_auto.yml", "r") as file:
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with open("../scripts/cluster_knn_auto.yml", "r") as file:
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config = yaml.safe_load(file)
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Rmax = 155 / 0.705 # Mpc (h = 0.705) high resolution region radius
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totvol = 4 * numpy.pi * Rmax**3 / 3
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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ics = paths.get_ics()
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knncdf = csiborgtools.clustering.kNN_CDF()
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knncdf = csiborgtools.clustering.kNN_1DCDF()
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if args.ics is None or args.ics[0] == -1:
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if args.simname == "csiborg":
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ics = paths.get_ics()
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else:
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ics = paths.get_quijote_ics()
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else:
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ics = args.ics
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###############################################################################
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# Analysis #
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###############################################################################
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def read_single(selection, cat):
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"""Positions for single catalogue auto-correlation."""
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mmask = numpy.ones(len(cat), dtype=bool)
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pos = cat.positions(False)
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# Primary selection
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psel = selection["primary"]
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pmin, pmax = psel.get("min", None), psel.get("max", None)
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if pmin is not None:
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mmask &= cat[psel["name"]] >= pmin
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if pmax is not None:
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mmask &= cat[psel["name"]] < pmax
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pos = pos[mmask, ...]
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def read_single(nsim, selection, nobs=None):
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# We first read the full catalogue without applying any bounds.
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if args.simname == "csiborg":
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cat = csiborgtools.read.HaloCatalogue(nsim, paths)
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else:
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cat = csiborgtools.read.QuijoteHaloCatalogue(nsim, paths, nsnap=4,
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origin=nobs)
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# Secondary selection
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if "secondary" not in selection:
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return pos
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smask = numpy.ones(pos.shape[0], dtype=bool)
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ssel = selection["secondary"]
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smin, smax = ssel.get("min", None), ssel.get("max", None)
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prop = cat[ssel["name"]][mmask]
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if ssel.get("toperm", False):
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prop = numpy.random.permutation(prop)
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if ssel.get("marked", True):
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x = cat[psel["name"]][mmask]
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prop = csiborgtools.clustering.normalised_marks(
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x, prop, nbins=config["nbins_marks"]
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)
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cat.apply_bounds({"dist": (0, Rmax)})
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# We then first read off the primary selection bounds.
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sel = selection["primary"]
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pname = None
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xs = sel["names"] if isinstance(sel["names"], list) else [sel["names"]]
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for _name in xs:
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if _name in cat.keys:
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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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if smin is not None:
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smask &= prop >= smin
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if smax is not None:
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smask &= prop < smax
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cat.apply_bounds({pname: (sel.get("min", None), sel.get("max", None))})
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return pos[smask, ...]
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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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if "secondary" in selection:
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sel = selection["secondary"]
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sname = None
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xs = sel["names"] if isinstance(sel["names"], list) else [sel["names"]]
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for _name in xs:
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if _name in cat.keys:
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sname = _name
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if sname is None:
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raise KeyError(f"Invalid names `{sel['name']}`.")
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if sel.get("toperm", False):
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cat[sname] = numpy.random.permutation(cat[sname])
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if sel.get("marked", False):
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cat[sname] = csiborgtools.clustering.normalised_marks(
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cat[pname], cat[sname], nbins=config["nbins_marks"])
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cat.apply_bounds({sname: (sel.get("min", None), sel.get("max", None))})
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return cat
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def do_auto(run, cat, ic):
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def do_auto(run, nsim, nobs=None):
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"""Calculate the kNN-CDF single catalgoue autocorrelation."""
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_config = config.get(run, None)
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if _config is None:
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@ -102,22 +120,20 @@ def do_auto(run, cat, ic):
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return
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rvs_gen = csiborgtools.clustering.RVSinsphere(Rmax)
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pos = read_single(_config, cat)
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knn = NearestNeighbors()
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knn.fit(pos)
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cat = read_single(nsim, _config, nobs=nobs)
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knn = cat.knn(in_initial=False)
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rs, cdf = knncdf(
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knn, rvs_gen=rvs_gen, nneighbours=config["nneighbours"],
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rmin=config["rmin"], rmax=config["rmax"],
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nsamples=int(config["nsamples"]), neval=int(config["neval"]),
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batch_size=int(config["batch_size"]), random_state=config["seed"])
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joblib.dump(
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{"rs": rs, "cdf": cdf, "ndensity": pos.shape[0] / totvol},
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paths.knnauto_path(run, ic),
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)
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fout = paths.knnauto_path(args.simname, run, nsim, nobs)
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print(f"Saving output to `{fout}`.")
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joblib.dump({"rs": rs, "cdf": cdf, "ndensity": len(cat) / totvol}, fout)
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def do_cross_rand(run, cat, ic):
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def do_cross_rand(run, nsim, nobs=None):
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"""Calculate the kNN-CDF cross catalogue random correlation."""
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_config = config.get(run, None)
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if _config is None:
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@ -125,31 +141,32 @@ def do_cross_rand(run, cat, ic):
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return
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rvs_gen = csiborgtools.clustering.RVSinsphere(Rmax)
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knn1, knn2 = NearestNeighbors(), NearestNeighbors()
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cat = read_single(nsim, _config)
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knn1 = cat.knn(in_initial=False)
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pos1 = read_single(_config, cat)
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knn1.fit(pos1)
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pos2 = rvs_gen(pos1.shape[0])
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knn2 = NearestNeighbors()
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pos2 = rvs_gen(len(cat).shape[0])
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knn2.fit(pos2)
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rs, cdf0, cdf1, joint_cdf = knncdf.joint(
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knn1, knn2, rvs_gen=rvs_gen, nneighbours=int(config["nneighbours"]),
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rmin=config["rmin"], rmax=config["rmax"],
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nsamples=int(config["nsamples"]), neval=int(config["neval"]),
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batch_size=int(config["batch_size"]), random_state=config["seed"],
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)
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batch_size=int(config["batch_size"]), random_state=config["seed"])
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corr = knncdf.joint_to_corr(cdf0, cdf1, joint_cdf)
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joblib.dump({"rs": rs, "corr": corr}, paths.knnauto_path(run, ic))
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fout = paths.knnauto_path(args.simname, run, nsim, nobs)
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print(f"Saving output to `{fout}`.")
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joblib.dump({"rs": rs, "corr": corr}, fout)
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def do_runs(ic):
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cat = csiborgtools.read.ClumpsCatalogue(ic, paths, maxdist=Rmax)
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def do_runs(nsim):
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for run in args.runs:
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if "random" in run:
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do_cross_rand(run, cat, ic)
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else:
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do_auto(run, cat, ic)
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iters = range(27) if args.simname == "quijote" else [None]
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for nobs in iters:
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if "random" in run:
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do_cross_rand(run, nsim, nobs)
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else:
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do_auto(run, nsim, nobs)
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###############################################################################
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