mirror of
https://github.com/Richard-Sti/csiborgtools.git
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d578c71b83
* Move files * Move files * Add galactic to RA/dec * Update sky maps * Add projected fields * Remove old import * Quick update * Add IO * Add imports * Update imports * Add basic file
217 lines
8.6 KiB
Python
217 lines
8.6 KiB
Python
# Copyright (C) 2023 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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"""
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A script to calculate the bulk flow in Quijote simulations from either
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particles or FoF haloes and to also save the resulting smaller halo catalogues.
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If `Rmin > 0` the bulk flows computed from projected radial velocities are
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wrong, but the 3D volume average bulk flows are still correct.
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"""
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from datetime import datetime
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from os.path import join
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import csiborgtools
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import numpy as np
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from mpi4py import MPI
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from taskmaster import work_delegation # noqa
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from warnings import catch_warnings, simplefilter
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from h5py import File
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from sklearn.neighbors import NearestNeighbors
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###############################################################################
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# Read in information about the simulation #
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###############################################################################
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def t():
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return datetime.now()
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def get_data(nsim, verbose=True):
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if verbose:
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print(f"{t()}: reading particles of simulation `{nsim}`.")
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reader = csiborgtools.read.QuijoteSnapshot(nsim, 4, paths)
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part_pos = reader.coordinates().astype(np.float64)
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part_vel = reader.velocities().astype(np.float64)
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if verbose:
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print(f"{t()}: reading haloes of simulation `{nsim}`.")
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reader = csiborgtools.read.QuijoteCatalogue(nsim)
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halo_pos = reader.coordinates
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halo_vel = reader.velocities
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halo_mass = reader.totmass
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return part_pos, part_vel, halo_pos, halo_vel, halo_mass
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def volume_bulk_flow(rdist, mass, vel, distances):
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out = csiborgtools.field.particles_enclosed_momentum(
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rdist, mass, vel, distances)
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with catch_warnings():
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simplefilter("ignore", category=RuntimeWarning)
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out /= csiborgtools.field.particles_enclosed_mass(
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rdist, mass, distances)[:, np.newaxis]
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return out
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###############################################################################
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# Main & command line interface #
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###############################################################################
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def main(nsim, folder, fname_basis, Rmin, Rmax, subtract_observer_velocity,
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verbose=True):
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boxsize = csiborgtools.simname2boxsize("quijote")
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observers = csiborgtools.read.fiducial_observers(boxsize, Rmax)
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distances = np.linspace(0, Rmax, 101)[1:]
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part_pos, part_vel, halo_pos, halo_vel, halo_mass = get_data(nsim, verbose)
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if verbose:
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print(f"{t()}: Fitting the particle and halo trees of simulation `{nsim}`.") # noqa
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part_tree = NearestNeighbors().fit(part_pos)
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halo_tree = NearestNeighbors().fit(halo_pos)
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samples = {}
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bf_volume_part = np.full((len(observers), len(distances), 3), np.nan)
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bf_volume_halo = np.full_like(bf_volume_part, np.nan)
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bf_volume_halo_uniform = np.full_like(bf_volume_part, np.nan)
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bf_vrad_weighted_part = np.full_like(bf_volume_part, np.nan)
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bf_vrad_weighted_halo_uniform = np.full_like(bf_volume_part, np.nan)
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bf_vrad_weighted_halo = np.full_like(bf_volume_part, np.nan)
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obs_vel = np.full((len(observers), 3), np.nan)
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for i in range(len(observers)):
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print(f"{t()}: Calculating bulk flow for observer {i + 1} of simulation {nsim}.") # noqa
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# Select particles within Rmax of the observer
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rdist_part, indxs = part_tree.radius_neighbors(
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np.asarray(observers[i]).reshape(1, -1), Rmax,
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return_distance=True, sort_results=True)
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rdist_part, indxs = rdist_part[0], indxs[0]
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# And only the ones that are above Rmin
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mask = rdist_part > Rmin
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rdist_part = rdist_part[mask]
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indxs = indxs[mask]
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part_pos_current = part_pos[indxs] - observers[i]
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part_vel_current = part_vel[indxs]
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# Quijote particle masses are all equal
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part_mass = np.ones_like(rdist_part)
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# Select haloes within Rmax of the observer
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rdist_halo, indxs = halo_tree.radius_neighbors(
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np.asarray(observers[i]).reshape(1, -1), Rmax,
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return_distance=True, sort_results=True)
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rdist_halo, indxs = rdist_halo[0], indxs[0]
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mask = rdist_halo > Rmin
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rdist_halo = rdist_halo[mask]
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indxs = indxs[mask]
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halo_pos_current = halo_pos[indxs] - observers[i]
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halo_vel_current = halo_vel[indxs]
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halo_mass_current = halo_mass[indxs]
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# Subtract the observer velocity
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rscale = 2.0 # Mpc / h
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weights = np.exp(-0.5 * (rdist_part / rscale)**2)
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obs_vel_x = np.average(part_vel_current[:, 0], weights=weights)
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obs_vel_y = np.average(part_vel_current[:, 1], weights=weights)
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obs_vel_z = np.average(part_vel_current[:, 2], weights=weights)
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obs_vel[i, 0] = obs_vel_x
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obs_vel[i, 1] = obs_vel_y
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obs_vel[i, 2] = obs_vel_z
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if subtract_observer_velocity:
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part_vel_current[:, 0] -= obs_vel_x
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part_vel_current[:, 1] -= obs_vel_y
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part_vel_current[:, 2] -= obs_vel_z
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halo_vel_current[:, 0] -= obs_vel_x
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halo_vel_current[:, 1] -= obs_vel_y
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halo_vel_current[:, 2] -= obs_vel_z
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# Calculate the volume average bulk flows
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bf_volume_part[i, ...] = volume_bulk_flow(
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rdist_part, part_mass, part_vel_current, distances)
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bf_volume_halo[i, ...] = volume_bulk_flow(
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rdist_halo, halo_mass_current, halo_vel_current, distances)
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bf_volume_halo_uniform[i, ...] = volume_bulk_flow(
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rdist_halo, np.ones_like(halo_mass_current), halo_vel_current,
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distances)
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bf_vrad_weighted_part[i, ...] = csiborgtools.field.bulkflow_peery2018(
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rdist_part, part_mass, part_pos_current, part_vel_current,
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distances, weights="1/r^2", verbose=False)
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# Calculate the bulk flow from projected velocities w. 1/r^2 weights
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bf_vrad_weighted_halo_uniform[i, ...] = csiborgtools.field.bulkflow_peery2018( # noqa
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rdist_halo, np.ones_like(halo_mass_current), halo_pos_current,
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halo_vel_current, distances, weights="1/r^2", verbose=False)
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bf_vrad_weighted_halo[i, ...] = csiborgtools.field.bulkflow_peery2018(
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rdist_halo, halo_mass_current, halo_pos_current,
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halo_vel_current, distances, weights="1/r^2", verbose=False)
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# Store the haloes around this observer
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samples[i] = {
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"halo_pos": halo_pos_current,
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"halo_vel": halo_vel_current,
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"halo_mass": halo_mass_current}
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# Finally save the output
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fname = join(folder, f"{fname_basis}_{nsim}.hdf5")
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if verbose:
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print(f"Saving to `{fname}`.")
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with File(fname, 'w') as f:
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f["distances"] = distances
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f["bf_volume_part"] = bf_volume_part
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f["bf_volume_halo"] = bf_volume_halo
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f["bf_vrad_weighted_part"] = bf_vrad_weighted_part
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f["bf_volume_halo_uniform"] = bf_volume_halo_uniform
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f["bf_vrad_weighted_halo_uniform"] = bf_vrad_weighted_halo_uniform
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f["bf_vrad_weighted_halo"] = bf_vrad_weighted_halo
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f["obs_vel"] = obs_vel
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for i in range(len(observers)):
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g = f.create_group(f"obs_{str(i)}")
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g["halo_pos"] = samples[i]["halo_pos"]
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g["halo_vel"] = samples[i]["halo_vel"]
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g["halo_mass"] = samples[i]["halo_mass"]
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if __name__ == "__main__":
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Rmin = 0
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Rmax = 150
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subtract_observer_velocity = True
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folder = "/mnt/extraspace/rstiskalek/quijote/BulkFlow_fiducial"
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fname_basis = "sBF_nsim" if subtract_observer_velocity else "BF_nsim"
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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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nsims = list(paths.get_ics("quijote"))
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def main_wrapper(nsim):
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main(nsim, folder, fname_basis, Rmin, Rmax, subtract_observer_velocity,
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verbose=rank == 0)
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if rank == 0:
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print(f"Running with {len(nsims)} Quijote simulations.")
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comm.Barrier()
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work_delegation(main_wrapper, nsims, comm, master_verbose=True)
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