mirror of
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kNN-CDF secondary halo bias (#40)
* Add seperate autoknn script & config file * edit ics * Edit submission script * Add threshold values * Edit batch sizign * Remove print * edit * Rename files * Rename * Update nb * edit runs * Edit submit * Add median threshold * add new auto reader * editt submit * edit submit * Edit submit * Add mean prk * Edit runs * Remove correlation file * Move split to clutering * Add init * Remove import * Add the file * Add correlation reading * Edit scripts * Add below and above median permutation for cross * Update imports * Move rvs_in_sphere * Create utils * Split * Add import * Add normalised marks * Add import * Edit readme * Clean up submission file * Stop tracking submit files * Update gitignore * Add poisson field analytical expression * Add abstract generators * Add generators * Pass in the generator * Add a check for if there are any files * Start saving average density * Update nb * Update readme * Update units * Edit jobs * Update submits * Update reader * Add random crossing * Update crossing script * Add crossing with random * Update readme * Update notebook
This commit is contained in:
Richard Stiskalek
GitHub
parent
826ab61d2d
commit
5784011de0
28 changed files with 2563 additions and 486 deletions
182
scripts/knn_auto.py
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182
scripts/knn_auto.py
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# Copyright (C) 2022 Richard Stiskalek
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# 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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"""A script to calculate the KNN-CDF for a set of CSiBORG halo catalogues."""
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from os.path import join
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from warnings import warn
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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 mpi4py import MPI
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from TaskmasterMPI import master_process, worker_process
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import numpy
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from sklearn.neighbors import NearestNeighbors
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import joblib
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import yaml
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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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###############################################################################
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# MPI and arguments #
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###############################################################################
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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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parser = ArgumentParser()
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parser.add_argument("--runs", type=str, nargs="+")
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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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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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minmass = 1e12
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ics = [7444, 7468, 7492, 7516, 7540, 7564, 7588, 7612, 7636, 7660, 7684,
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7708, 7732, 7756, 7780, 7804, 7828, 7852, 7876, 7900, 7924, 7948,
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7972, 7996, 8020, 8044, 8068, 8092, 8116, 8140, 8164, 8188, 8212,
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8236, 8260, 8284, 8308, 8332, 8356, 8380, 8404, 8428, 8452, 8476,
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8500, 8524, 8548, 8572, 8596, 8620, 8644, 8668, 8692, 8716, 8740,
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8764, 8788, 8812, 8836, 8860, 8884, 8908, 8932, 8956, 8980, 9004,
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9028, 9052, 9076, 9100, 9124, 9148, 9172, 9196, 9220, 9244, 9268,
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9292, 9316, 9340, 9364, 9388, 9412, 9436, 9460, 9484, 9508, 9532,
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9556, 9580, 9604, 9628, 9652, 9676, 9700, 9724, 9748, 9772, 9796,
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9820, 9844]
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dumpdir = "/mnt/extraspace/rstiskalek/csiborg/knn"
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fout = join(dumpdir, "auto", "knncdf_{}_{}.p")
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paths = csiborgtools.read.CSiBORGPaths()
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knncdf = csiborgtools.clustering.kNN_CDF()
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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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# 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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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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return pos[smask, ...]
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def do_auto(run, cat, ic):
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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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warn("No configuration for run {}.".format(run))
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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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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({"rs": rs, "cdf": cdf, "ndensity": pos.shape[0] / totvol},
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fout.format(str(ic).zfill(5), run))
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def do_cross_rand(run, cat, ic):
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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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warn("No configuration for run {}.".format(run))
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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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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.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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corr = knncdf.joint_to_corr(cdf0, cdf1, joint_cdf)
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joblib.dump({"rs": rs, "corr": corr}, fout.format(str(ic).zfill(5), run))
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def do_runs(ic):
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cat = csiborgtools.read.HaloCatalogue(ic, paths, max_dist=Rmax,
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min_mass=minmass)
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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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###############################################################################
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# MPI task delegation #
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###############################################################################
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if nproc > 1:
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if rank == 0:
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tasks = deepcopy(ics)
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master_process(tasks, comm, verbose=True)
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else:
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worker_process(do_runs, comm, verbose=False)
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else:
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tasks = deepcopy(ics)
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for task in tasks:
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print("{}: completing task `{}`.".format(datetime.now(), task))
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do_runs(task)
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comm.Barrier()
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if rank == 0:
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print("{}: all finished.".format(datetime.now()))
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quit() # Force quit the script
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144
scripts/knn_auto.yml
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144
scripts/knn_auto.yml
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rmin: 0.1
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rmax: 100
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nneighbours: 64
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nsamples: 1.e+7
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batch_size: 1.e+6
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neval: 10000
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seed: 42
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nbins_marks: 10
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################################################################################
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# totpartmass #
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################################################################################
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"mass001":
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primary:
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name: totpartmass
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min: 1.e+12
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max: 1.e+13
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"mass002":
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primary:
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name: totpartmass
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min: 1.e+13
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max: 1.e+14
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"mass003":
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primary:
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name: totpartmass
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min: 1.e+14
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################################################################################
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# totpartmass + lambda200c #
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################################################################################
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"mass001_spinlow":
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primary:
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name: totpartmass
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min: 1.e+12
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max: 1.e+13
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secondary:
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name: lambda200c
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toperm: false
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marked: false
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max: 0.5
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"mass001_spinhigh":
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primary:
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name: totpartmass
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min: 1.e+12
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max: 1.e+13
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secondary:
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name: lambda200c
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toperm: false
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marked: true
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min: 0.5
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"mass001_spinmedian_perm":
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primary:
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name: totpartmass
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min: 1.e+12
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max: 1.e+13
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secondary:
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name: lambda200c
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toperm: true
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marked : true
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min: 0.5
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"mass002_spinlow":
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primary:
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name: totpartmass
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min: 1.e+13
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max: 1.e+14
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secondary:
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name: lambda200c
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toperm: false
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marked: false
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max: 0.5
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"mass002_spinhigh":
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primary:
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name: totpartmass
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min: 1.e+13
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max: 1.e+14
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secondary:
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name: lambda200c
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toperm: false
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marked: true
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min: 0.5
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"mass002_spinmedian_perm":
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primary:
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name: totpartmass
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min: 1.e+13
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max: 1.e+14
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secondary:
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name: lambda200c
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toperm: true
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marked : true
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min: 0.5
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"mass003_spinlow":
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primary:
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name: totpartmass
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min: 1.e+14
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secondary:
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name: lambda200c
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toperm: false
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marked: false
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max: 0.5
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"mass003_spinhigh":
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primary:
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name: totpartmass
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min: 1.e+14
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secondary:
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name: lambda200c
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toperm: false
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marked: true
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min: 0.5
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"mass003_spinmedian_perm":
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primary:
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name: totpartmass
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min: 1.e+14
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secondary:
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name: lambda200c
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toperm: true
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marked : true
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min: 0.5
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################################################################################
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# Cross with random #
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################################################################################
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"mass001_random":
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primary:
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name: totpartmass
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min: 1.e+12
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max: 1.e+13
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@ -13,6 +13,7 @@
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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 the KNN-CDF for a set of CSiBORG halo catalogues."""
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from warnings import warn
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from os.path import join
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from argparse import ArgumentParser
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from copy import deepcopy
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@ -20,8 +21,10 @@ from datetime import datetime
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from itertools import combinations
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from mpi4py import MPI
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from TaskmasterMPI import master_process, worker_process
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import numpy
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from sklearn.neighbors import NearestNeighbors
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import joblib
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import yaml
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try:
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import csiborgtools
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except ModuleNotFoundError:
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@ -38,17 +41,13 @@ rank = comm.Get_rank()
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nproc = comm.Get_size()
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parser = ArgumentParser()
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parser.add_argument("--rmin", type=float)
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parser.add_argument("--rmax", type=float)
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parser.add_argument("--nneighbours", type=int)
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parser.add_argument("--nsamples", type=int)
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parser.add_argument("--neval", type=int)
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parser.add_argument("--batch_size", type=int)
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parser.add_argument("--seed", type=int, default=42)
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parser.add_argument("--runs", type=str, nargs="+")
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args = parser.parse_args()
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with open('../scripts/knn_cross.yml', 'r') as file:
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config = yaml.safe_load(file)
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Rmax = 155 / 0.705 # Mpc/h high resolution region radius
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mass_threshold = [1e12, 1e13, 1e14] # Msun
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Rmax = 155 / 0.705 # Mpc (h = 0.705) high resolution region radius
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minmass = 1e12
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ics = [7444, 7468, 7492, 7516, 7540, 7564, 7588, 7612, 7636, 7660, 7684,
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7708, 7732, 7756, 7780, 7804, 7828, 7852, 7876, 7900, 7924, 7948,
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7972, 7996, 8020, 8044, 8068, 8092, 8116, 8140, 8164, 8188, 8212,
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@ -59,80 +58,58 @@ ics = [7444, 7468, 7492, 7516, 7540, 7564, 7588, 7612, 7636, 7660, 7684,
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9292, 9316, 9340, 9364, 9388, 9412, 9436, 9460, 9484, 9508, 9532,
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9556, 9580, 9604, 9628, 9652, 9676, 9700, 9724, 9748, 9772, 9796,
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9820, 9844]
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dumpdir = "/mnt/extraspace/rstiskalek/csiborg/knn"
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fout_auto = join(dumpdir, "auto", "knncdf_{}.p")
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fout_cross = join(dumpdir, "cross", "knncdf_{}_{}.p")
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paths = csiborgtools.read.CSiBORGPaths()
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dumpdir = "/mnt/extraspace/rstiskalek/csiborg/knn"
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fout = join(dumpdir, "cross", "knncdf_{}_{}_{}.p")
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knncdf = csiborgtools.clustering.kNN_CDF()
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###############################################################################
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# Analysis #
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###############################################################################
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knncdf = csiborgtools.match.kNN_CDF()
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def read_single(selection, cat):
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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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return pos[mmask, ...]
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def do_auto(ic):
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out = {}
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cat = csiborgtools.read.HaloCatalogue(ic, paths, max_dist=Rmax)
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def do_cross(run, ics):
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_config = config.get(run, None)
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if _config is None:
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warn("No configuration for run {}.".format(run))
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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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for i, mmin in enumerate(mass_threshold):
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knn = NearestNeighbors()
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knn.fit(cat.positions(False)[cat["totpartmass"] > mmin, ...])
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rs, cdf = knncdf(knn, nneighbours=args.nneighbours, Rmax=Rmax,
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rmin=args.rmin, rmax=args.rmax, nsamples=args.nsamples,
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neval=args.neval, batch_size=args.batch_size,
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random_state=args.seed, verbose=False)
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out.update({"cdf_{}".format(i): cdf})
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out.update({"rs": rs, "mass_threshold": mass_threshold})
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joblib.dump(out, fout_auto.format(ic))
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def do_cross(ics):
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out = {}
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cat1 = csiborgtools.read.HaloCatalogue(ics[0], paths, max_dist=Rmax)
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pos1 = read_single(_config, cat1)
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knn1.fit(pos1)
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cat2 = csiborgtools.read.HaloCatalogue(ics[1], paths, max_dist=Rmax)
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pos2 = read_single(_config, cat2)
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knn2.fit(pos2)
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for i, mmin in enumerate(mass_threshold):
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knn1 = NearestNeighbors()
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knn1.fit(cat1.positions()[cat1["totpartmass"] > mmin, ...])
|
||||
rs, cdf0, cdf1, joint_cdf = knncdf.joint(
|
||||
knn1, knn2, rvs_gen=rvs_gen, nneighbours=int(config["nneighbours"]),
|
||||
rmin=config["rmin"], rmax=config["rmax"],
|
||||
nsamples=int(config["nsamples"]), neval=int(config["neval"]),
|
||||
batch_size=int(config["batch_size"]), random_state=config["seed"])
|
||||
|
||||
knn2 = NearestNeighbors()
|
||||
knn2.fit(cat2.positions()[cat2["totpartmass"] > mmin, ...])
|
||||
corr = knncdf.joint_to_corr(cdf0, cdf1, joint_cdf)
|
||||
|
||||
rs, cdf0, cdf1, joint_cdf = knncdf.joint(
|
||||
knn1, knn2, nneighbours=args.nneighbours, Rmax=Rmax,
|
||||
rmin=args.rmin, rmax=args.rmax, nsamples=args.nsamples,
|
||||
neval=args.neval, batch_size=args.batch_size,
|
||||
random_state=args.seed)
|
||||
joblib.dump({"rs": rs, "corr": corr},
|
||||
fout.format(str(ics[0]).zfill(5), str(ics[1]).zfill(5), run))
|
||||
|
||||
corr = knncdf.joint_to_corr(cdf0, cdf1, joint_cdf)
|
||||
|
||||
out.update({"corr_{}".format(i): corr})
|
||||
|
||||
out.update({"rs": rs, "mass_threshold": mass_threshold})
|
||||
joblib.dump(out, fout_cross.format(*ics))
|
||||
|
||||
|
||||
|
||||
###############################################################################
|
||||
# Autocorrelation calculation #
|
||||
###############################################################################
|
||||
|
||||
|
||||
if nproc > 1:
|
||||
if rank == 0:
|
||||
tasks = deepcopy(ics)
|
||||
master_process(tasks, comm, verbose=True)
|
||||
else:
|
||||
worker_process(do_auto, comm, verbose=False)
|
||||
else:
|
||||
tasks = deepcopy(ics)
|
||||
for task in tasks:
|
||||
print("{}: completing task `{}`.".format(datetime.now(), task))
|
||||
do_auto(task)
|
||||
comm.Barrier()
|
||||
def do_runs(ics):
|
||||
print(ics)
|
||||
for run in args.runs:
|
||||
do_cross(run, ics)
|
||||
|
||||
|
||||
###############################################################################
|
||||
|
|
@ -145,12 +122,12 @@ if nproc > 1:
|
|||
tasks = list(combinations(ics, 2))
|
||||
master_process(tasks, comm, verbose=True)
|
||||
else:
|
||||
worker_process(do_cross, comm, verbose=False)
|
||||
worker_process(do_runs, comm, verbose=False)
|
||||
else:
|
||||
tasks = deepcopy(ics)
|
||||
tasks = list(combinations(ics, 2))
|
||||
for task in tasks:
|
||||
print("{}: completing task `{}`.".format(datetime.now(), task))
|
||||
do_cross(task)
|
||||
do_runs(task)
|
||||
comm.Barrier()
|
||||
|
||||
|
||||
29
scripts/knn_cross.yml
Normal file
29
scripts/knn_cross.yml
Normal file
|
|
@ -0,0 +1,29 @@
|
|||
rmin: 0.1
|
||||
rmax: 100
|
||||
nneighbours: 64
|
||||
nsamples: 1.e+7
|
||||
batch_size: 1.e+6
|
||||
neval: 10000
|
||||
seed: 42
|
||||
|
||||
|
||||
################################################################################
|
||||
# totpartmass #
|
||||
################################################################################
|
||||
|
||||
"mass001":
|
||||
primary:
|
||||
name: totpartmass
|
||||
min: 1.e+12
|
||||
max: 1.e+13
|
||||
|
||||
"mass002":
|
||||
primary:
|
||||
name: totpartmass
|
||||
min: 1.e+13
|
||||
max: 1.e+14
|
||||
|
||||
"mass003":
|
||||
primary:
|
||||
name: totpartmass
|
||||
min: 1.e+14
|
||||
|
|
@ -1,46 +0,0 @@
|
|||
#!/bin/bash -l
|
||||
echo =========================================================
|
||||
echo Job submitted date = Fri Mar 31 16:17:57 BST 2023
|
||||
date_start=`date +%s`
|
||||
echo $SLURM_JOB_NUM_NODES nodes \( $SMP processes per node \)
|
||||
echo $SLURM_JOB_NUM_NODES hosts used: $SLURM_JOB_NODELIST
|
||||
echo Job output begins
|
||||
echo -----------------
|
||||
echo
|
||||
#hostname
|
||||
|
||||
# Need to set the max locked memory very high otherwise IB can't allocate enough and fails with "UCX ERROR Failed to allocate memory pool chunk: Input/output error"
|
||||
ulimit -l unlimited
|
||||
|
||||
# To allow mvapich to run ok
|
||||
export MV2_SMP_USE_CMA=0
|
||||
|
||||
#which mpirun
|
||||
export OMP_NUM_THEADS=1
|
||||
/usr/local/shared/slurm/bin/srun -u -n 5 --mpi=pmi2 --mem-per-cpu=7168 nice -n 10 /mnt/zfsusers/rstiskalek/csiborgtools/venv_galomatch/bin/python run_knn.py --rmin 0.05 --rmax 50 --nsamples 100000 --neval 10000
|
||||
# If we've been checkpointed
|
||||
#if [ -n "${DMTCP_CHECKPOINT_DIR}" ]; then
|
||||
if [ -d "${DMTCP_CHECKPOINT_DIR}" ]; then
|
||||
# echo -n "Job was checkpointed at "
|
||||
# date
|
||||
# echo
|
||||
sleep 1
|
||||
# fi
|
||||
echo -n
|
||||
else
|
||||
echo ---------------
|
||||
echo Job output ends
|
||||
date_end=`date +%s`
|
||||
seconds=$((date_end-date_start))
|
||||
minutes=$((seconds/60))
|
||||
seconds=$((seconds-60*minutes))
|
||||
hours=$((minutes/60))
|
||||
minutes=$((minutes-60*hours))
|
||||
echo =========================================================
|
||||
echo PBS job: finished date = `date`
|
||||
echo Total run time : $hours Hours $minutes Minutes $seconds Seconds
|
||||
echo =========================================================
|
||||
fi
|
||||
if [ ${SLURM_NTASKS} -eq 1 ]; then
|
||||
rm -f $fname
|
||||
fi
|
||||
|
|
@ -1,14 +0,0 @@
|
|||
nthreads=20
|
||||
memory=40
|
||||
queue="berg"
|
||||
env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_galomatch/bin/python"
|
||||
file="run_crosspk.py"
|
||||
grid=1024
|
||||
halfwidth=0.13
|
||||
|
||||
cm="addqueue -q $queue -n $nthreads -m $memory $env $file --grid $grid --halfwidth $halfwidth"
|
||||
|
||||
echo "Submitting:"
|
||||
echo $cm
|
||||
echo
|
||||
$cm
|
||||
|
|
@ -1,14 +0,0 @@
|
|||
nthreads=10
|
||||
memory=32
|
||||
queue="berg"
|
||||
env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_galomatch/bin/python"
|
||||
file="run_fieldprop.py"
|
||||
# grid=1024
|
||||
# halfwidth=0.1
|
||||
|
||||
cm="addqueue -q $queue -n $nthreads -m $memory $env $file"
|
||||
|
||||
echo "Submitting:"
|
||||
echo $cm
|
||||
echo
|
||||
$cm
|
||||
|
|
@ -1,12 +0,0 @@
|
|||
nthreads=100
|
||||
memory=3
|
||||
queue="berg"
|
||||
env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_galomatch/bin/python"
|
||||
file="run_fit_halos.py"
|
||||
|
||||
cm="addqueue -q $queue -n $nthreads -m $memory $env $file"
|
||||
|
||||
echo "Submitting:"
|
||||
echo $cm
|
||||
echo
|
||||
$cm
|
||||
|
|
@ -1,14 +0,0 @@
|
|||
nthreads=15 # There isn't too much benefit going to too many CPUs...
|
||||
memory=32
|
||||
queue="berg"
|
||||
env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_galomatch/bin/python"
|
||||
file="run_initmatch.py"
|
||||
|
||||
dump_clumps="false"
|
||||
|
||||
cm="addqueue -q $queue -n $nthreads -m $memory $env $file --dump_clumps $dump_clumps"
|
||||
|
||||
echo "Submitting:"
|
||||
echo $cm
|
||||
echo
|
||||
$cm
|
||||
|
|
@ -1,23 +0,0 @@
|
|||
nthreads=151
|
||||
memory=4
|
||||
queue="cmb"
|
||||
env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_galomatch/bin/python"
|
||||
file="run_knn.py"
|
||||
|
||||
rmin=0.01
|
||||
rmax=100
|
||||
nneighbours=8
|
||||
nsamples=100000000
|
||||
batch_size=1000000
|
||||
neval=10000
|
||||
|
||||
pythoncm="$env $file --rmin $rmin --rmax $rmax --nneighbours $nneighbours --nsamples $nsamples --batch_size $batch_size --neval $neval"
|
||||
|
||||
# echo $pythoncm
|
||||
# $pythoncm
|
||||
|
||||
cm="addqueue -q $queue -n $nthreads -m $memory $pythoncm"
|
||||
echo "Submitting:"
|
||||
echo $cm
|
||||
echo
|
||||
$cm
|
||||
|
|
@ -1,36 +0,0 @@
|
|||
#!/bin/bash
|
||||
# nthreads=1
|
||||
memory=16
|
||||
queue="berg"
|
||||
env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_galomatch/bin/python"
|
||||
file="run_singlematch.py"
|
||||
|
||||
nmult=1.
|
||||
sigma=1.
|
||||
|
||||
sims=(7468 7588 8020 8452 8836)
|
||||
nsims=${#sims[@]}
|
||||
|
||||
for i in $(seq 0 $((nsims-1))); do
|
||||
for j in $(seq 0 $((nsims-1))); do
|
||||
if [ $i -eq $j ]; then
|
||||
continue
|
||||
elif [ $i -gt $j ]; then
|
||||
continue
|
||||
else
|
||||
:
|
||||
fi
|
||||
|
||||
nsim0=${sims[$i]}
|
||||
nsimx=${sims[$j]}
|
||||
|
||||
pythoncm="$env $file --nsim0 $nsim0 --nsimx $nsimx --nmult $nmult --sigma $sigma"
|
||||
|
||||
cm="addqueue -q $queue -n 1x1 -m $memory $pythoncm"
|
||||
echo "Submitting:"
|
||||
echo $cm
|
||||
echo
|
||||
$cm
|
||||
sleep 0.05
|
||||
|
||||
done; done
|
||||
|
|
@ -1,12 +0,0 @@
|
|||
nthreads=1
|
||||
memory=30
|
||||
queue="cmb"
|
||||
env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_galomatch/bin/python"
|
||||
file="run_split_halos.py"
|
||||
|
||||
cm="addqueue -q $queue -n $nthreads -m $memory $env $file"
|
||||
|
||||
echo "Submitting:"
|
||||
echo $cm
|
||||
echo
|
||||
$cm
|
||||
Loading…
Add table
Add a link
Reference in a new issue