mirror of
https://github.com/Richard-Sti/csiborgtools.git
synced 2024-12-23 11:51:53 +00:00
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
174 lines
6.5 KiB
Python
174 lines
6.5 KiB
Python
# Copyright (C) 2024 Richard Stiskalek
|
|
# This program is free software; you can redistribute it and/or modify it
|
|
# under the terms of the GNU General Public License as published by the
|
|
# Free Software Foundation; either version 3 of the License, or (at your
|
|
# option) any later version.
|
|
#
|
|
# This program is distributed in the hope that it will be useful, but
|
|
# WITHOUT ANY WARRANTY; without even the implied warranty of
|
|
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
|
|
# Public License for more details.
|
|
#
|
|
# You should have received a copy of the GNU General Public License along
|
|
# with this program; if not, write to the Free Software Foundation, Inc.,
|
|
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
|
|
"""
|
|
Script to calculate cosmological redshifts from observed redshifts assuming
|
|
the Carrick+2015 peculiar velocity model. In the future this may be extended
|
|
to include other peculiar velocity models.
|
|
"""
|
|
from datetime import datetime
|
|
from os import remove
|
|
from os.path import join
|
|
|
|
import csiborgtools
|
|
import numpy as np
|
|
from csiborgtools import fprint
|
|
from h5py import File
|
|
from mpi4py import MPI
|
|
from taskmaster import work_delegation # noqa
|
|
from tqdm import tqdm
|
|
|
|
SPEED_OF_LIGHT = 299792.458 # km / s
|
|
|
|
|
|
def t():
|
|
return datetime.now().strftime("%H:%M:%S")
|
|
|
|
|
|
def load_calibration(catalogue, simname, ksmooth, sample_beta,
|
|
verbose=False):
|
|
"""Load the pre-computed calibration samples."""
|
|
fname = f"/mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/samples_{simname}_{catalogue}_ksmooth{ksmooth}.hdf5" # noqa
|
|
if sample_beta:
|
|
fname = fname.replace(".hdf5", "_sample_beta.hdf5")
|
|
|
|
keys = ["Vext", "sigma_v", "alpha", "beta"]
|
|
calibration_samples = {}
|
|
with File(fname, 'r') as f:
|
|
for n, key in enumerate(keys):
|
|
# In case alpha wasn't sampled just set to 1
|
|
if key == "alpha" and "alpha" not in f["samples"].keys():
|
|
calibration_samples[key] = np.full_like(
|
|
calibration_samples["sigma_v"], 1.0)
|
|
continue
|
|
|
|
# NOTE: here the posterior samples are down-sampled
|
|
calibration_samples[key] = f[f"samples/{key}"][:][::10]
|
|
|
|
if n == 0:
|
|
num_samples_original = len(f[f"samples/{key}"])
|
|
num_samples_final = len(calibration_samples[key])
|
|
|
|
fprint(f"downsampling calibration samples from {num_samples_original} to {num_samples_final}.", verbose=verbose) # noqa
|
|
return calibration_samples
|
|
|
|
|
|
def main(loader, nsim, model, indxs, fdir, fname, num_split, verbose):
|
|
out = np.full(
|
|
len(indxs), np.nan,
|
|
dtype=[("mean_zcosmo", float), ("std_zcosmo", float)])
|
|
|
|
# Process each galaxy in this split
|
|
for i, n in enumerate(tqdm(indxs, desc=f"Split {num_split}",
|
|
disable=not verbose)):
|
|
x, y = model.posterior_zcosmo(
|
|
loader.cat["zcmb"][n], loader.cat["RA"][n], loader.cat["DEC"][n],
|
|
loader.los_density[nsim, n], loader.los_radial_velocity[nsim, n],
|
|
extra_sigma_v=loader.cat["e_zcmb"][n] * SPEED_OF_LIGHT,
|
|
verbose=False)
|
|
|
|
mu, std = model.posterior_mean_std(x, y)
|
|
out["mean_zcosmo"][i], out["std_zcosmo"][i] = mu, std
|
|
|
|
# Save the results of this rank
|
|
fname = join(fdir, f"{fname}_{num_split}.hdf5")
|
|
with File(fname, 'w') as f:
|
|
f.create_dataset("mean_zcosmo", data=out["mean_zcosmo"])
|
|
f.create_dataset("std_zcosmo", data=out["std_zcosmo"])
|
|
f.create_dataset("indxs", data=indxs)
|
|
|
|
|
|
###############################################################################
|
|
# Command line interface #
|
|
###############################################################################
|
|
|
|
|
|
if __name__ == "__main__":
|
|
comm = MPI.COMM_WORLD
|
|
rank, size = comm.Get_rank(), comm.Get_size()
|
|
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
|
|
|
# Calibration parameters
|
|
simname = "Carrick2015"
|
|
ksmooth = 0
|
|
nsim = 0
|
|
catalogue_calibration = "Pantheon+_zSN"
|
|
|
|
# Galaxy sample parameters
|
|
catalogue = "UPGLADE"
|
|
fpath_data = "/mnt/users/rstiskalek/csiborgtools/data/upglade_all_z0p05_new_PROCESSED.h5" # noqa
|
|
|
|
# Number of splits for MPI
|
|
nsplits = 1000
|
|
|
|
# Folder to save the results
|
|
fdir = "/mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/UPGLADE" # noqa
|
|
fname = f"zcosmo_{catalogue}"
|
|
|
|
# Load in the data, calibration samples and the model
|
|
loader = csiborgtools.flow.DataLoader(
|
|
simname, nsim, catalogue, fpath_data, paths, ksmooth=ksmooth,
|
|
verbose=rank == 0)
|
|
calibration_samples = load_calibration(
|
|
catalogue_calibration, simname, ksmooth, sample_beta=True,
|
|
verbose=rank == 0)
|
|
|
|
model = csiborgtools.flow.Observed2CosmologicalRedshift(
|
|
calibration_samples, loader.rdist, loader._Omega_m)
|
|
|
|
fprint(f"catalogue size is {loader.cat['zcmb'].size}.", verbose=rank == 0)
|
|
fprint("loaded calibration samples and model.", verbose=rank == 0)
|
|
|
|
# Decide how to split up the job
|
|
if rank == 0:
|
|
indxs = np.arange(loader.cat["zcmb"].size)
|
|
split_indxs = np.array_split(indxs, nsplits)
|
|
else:
|
|
indxs = None
|
|
split_indxs = None
|
|
indxs = comm.bcast(indxs, root=0)
|
|
split_indxs = comm.bcast(split_indxs, root=0)
|
|
|
|
# Process all splits with MPI, the rank 0 delegates the jobs.
|
|
def main_wrapper(n):
|
|
main(loader, nsim, model, split_indxs[n], fdir, fname, n,
|
|
verbose=size == 1)
|
|
|
|
comm.Barrier()
|
|
work_delegation(
|
|
main_wrapper, list(range(nsplits)), comm, master_verbose=True)
|
|
comm.Barrier()
|
|
|
|
# Combine the results to a single file
|
|
if rank == 0:
|
|
print("Combining results from all ranks.", flush=True)
|
|
mean_zcosmo = np.full(loader.cat["zcmb"].size, np.nan)
|
|
std_zcosmo = np.full_like(mean_zcosmo, np.nan)
|
|
|
|
for n in range(nsplits):
|
|
fname_current = join(fdir, f"{fname}_{n}.hdf5")
|
|
with File(fname_current, 'r') as f:
|
|
mask = f["indxs"][:]
|
|
mean_zcosmo[mask] = f["mean_zcosmo"][:]
|
|
std_zcosmo[mask] = f["std_zcosmo"][:]
|
|
|
|
remove(fname_current)
|
|
|
|
# Save the results
|
|
fname = join(fdir, f"{fname}.hdf5")
|
|
print(f"Saving results to `{fname}`.")
|
|
with File(fname, 'w') as f:
|
|
f.create_dataset("mean_zcosmo", data=mean_zcosmo)
|
|
f.create_dataset("std_zcosmo", data=std_zcosmo)
|
|
f.create_dataset("indxs", data=indxs)
|