diff --git a/.gitignore b/.gitignore index 88a9991..ec1090d 100644 --- a/.gitignore +++ b/.gitignore @@ -1,32 +1,38 @@ +# Python virtual environments +venv/ venv_csiborg/ -share/ -bin/ -.bashrc + +# Compiled Python files *.pyc + +# Jupyter Notebook checkpoints */.ipynb_checkpoints/ -plots/* -.vscode/settings.json -csiborgtools/fits/_halo_profile.py -csiborgtools/fits/_filenames.py -csiborgtools/fits/analyse_voids_25.py -scripts/*.out -build/* + +# Egg-info directories .eggs/* csiborgtools.egg-info/* -Pylians3/* -scripts/plot_correlation.ipynb -scripts/*.sh -venv/ -.trunk/* -scripts_test/ -scripts_plots/python.sh -scripts_plots/submit.sh -scripts_plots/*.out -scripts_plots/*.sh -notebooks/test.ipynb -scripts/mgtree.py -scripts/makemerger.py -*.out -*/python.sh +# Build directories +build/* +bin/* +share/* + +# Scripts and their outputs +scripts/*.out +scripts_plots/*.out scripts_independent/clear.sh + +# Specific script files +*python.sh +*python3.sh + +# IDE settings +.vscode/settings.json + +# Miscellaneous +.bashrc +*.out + +# Generated plots +plots/* + diff --git a/csiborgtools/__init__.py b/csiborgtools/__init__.py index ba09388..b5376af 100644 --- a/csiborgtools/__init__.py +++ b/csiborgtools/__init__.py @@ -18,7 +18,7 @@ from .utils import (center_of_mass, delta2ncells, number_counts, periodic_distance, periodic_distance_two_points, # noqa binned_statistic, cosine_similarity, fprint, # noqa hms_to_degrees, dms_to_degrees, great_circle_distance, # noqa - radec_to_cartesian) # noqa + radec_to_cartesian, cartesian_to_radec) # noqa from .params import paths_glamdring, simname2boxsize # noqa diff --git a/csiborgtools/params.py b/csiborgtools/params.py index 50b5c8f..7e6cd7f 100644 --- a/csiborgtools/params.py +++ b/csiborgtools/params.py @@ -36,7 +36,8 @@ def simname2boxsize(simname): "csiborg2_random": 676.6, "borg1": 677.7, "borg2": 676.6, - "quijote": 1000. + "quijote": 1000., + "TNG300-1": 205. } boxsize = d.get(simname, None) @@ -54,6 +55,7 @@ paths_glamdring = { "csiborg2_random_srcdir": "/mnt/extraspace/rstiskalek/csiborg2_random", # noqa "postdir": "/mnt/extraspace/rstiskalek/csiborg_postprocessing/", "quijote_dir": "/mnt/extraspace/rstiskalek/quijote", + "borg1_dir": "/mnt/users/hdesmond/BORG_final", "borg2_dir": "/mnt/extraspace/rstiskalek/BORG_STOPYRA_2023", "tng300_1_dir": "/mnt/extraspace/rstiskalek/TNG300-1/", } diff --git a/csiborgtools/read/__init__.py b/csiborgtools/read/__init__.py index 9fb99b7..304a663 100644 --- a/csiborgtools/read/__init__.py +++ b/csiborgtools/read/__init__.py @@ -16,7 +16,7 @@ from .catalogue import (CSiBORG1Catalogue, CSiBORG2Catalogue, CSiBORG2MergerTreeReader, QuijoteCatalogue) # noqa from .snapshot import (CSiBORG1Snapshot, CSiBORG2Snapshot, QuijoteSnapshot, # noqa CSiBORG1Field, CSiBORG2Field, QuijoteField, BORG2Field, # noqa - BORG1Field) # noqa + BORG1Field, TNG300_1Field) # noqa from .obs import (SDSS, MCXCClusters, PlanckClusters, TwoMPPGalaxies, # noqa TwoMPPGroups, ObservedCluster, match_array_to_no_masking, # noqa cols_to_structured) # noqa diff --git a/csiborgtools/read/paths.py b/csiborgtools/read/paths.py index 6d1031e..0d3a823 100644 --- a/csiborgtools/read/paths.py +++ b/csiborgtools/read/paths.py @@ -467,9 +467,10 @@ class Paths: fname = f"observer_peculiar_velocity_{simname}_{MAS}_{str(nsim).zfill(5)}_{grid}.npz" # noqa return join(fdir, fname) - def field_interpolated(self, survey, simname, nsim, kind, MAS, grid): + def field_interpolated(self, survey, simname, nsim, kind, MAS, grid, + radial_scatter=None): """ - Path to the files containing the CSiBORG interpolated field for a given + Path to the files containing the interpolated field for a given survey. Parameters @@ -486,13 +487,16 @@ class Paths: Mass-assignment scheme. grid : int Grid size. + radial_scatter : float, optional + Radial scatter added to the galaxy positions, only supported for + TNG300-1. Returns ------- str """ - if "csiborg" not in simname: - raise ValueError("Interpolated field only available for CSiBORG.") + + # # In case the galaxy positions of TNG300-1 were scattered.. if kind not in ["density", "potential", "radvel"]: raise ValueError("Unsupported field type.") @@ -501,7 +505,12 @@ class Paths: try_create_directory(fdir) nsim = str(nsim).zfill(5) - return join(fdir, f"{survey}_{simname}_{kind}_{MAS}_{nsim}_{grid}.npz") + fname = join(fdir, f"{survey}_{simname}_{kind}_{MAS}_{nsim}_{grid}.npz") # noqa + + if simname == "TNG300-1" and radial_scatter is not None: + fname = fname.replace(".npz", f"_scatter{radial_scatter}.npz") + + return fname def cross_nearest(self, simname, run, kind, nsim=None, nobs=None): """ diff --git a/csiborgtools/read/snapshot.py b/csiborgtools/read/snapshot.py index 929755a..35fd253 100644 --- a/csiborgtools/read/snapshot.py +++ b/csiborgtools/read/snapshot.py @@ -1004,7 +1004,7 @@ class TNG300_1Field(BaseField): return density def density_field(self, MAS, grid): - fpath = join(self.paths.tng300_1, "postprocessing", "density_field", + fpath = join(self.paths.tng300_1(), "postprocessing", "density_field", f"rho_dm_099_{grid}_{MAS}.npy") return numpy.load(fpath) diff --git a/notebooks/test.ipynb b/notebooks/test.ipynb new file mode 100644 index 0000000..a3119b5 --- /dev/null +++ b/notebooks/test.ipynb @@ -0,0 +1,129 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import numpy\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import h5py\n", + "%matplotlib inline\n", + "\n", + "\n", + "import csiborgtools\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "d0 = np.load(\"/mnt/extraspace/rstiskalek/csiborg_postprocessing/field_interpolated/TNG300-1_TNG300-1_density_PCS_00000_1024_scatter0.0.npz\")\n", + "d1 = np.load(\"/mnt/extraspace/rstiskalek/csiborg_postprocessing/field_interpolated/TNG300-1_TNG300-1_density_PCS_00000_1024_scatter1.0.npz\")\n", + "d2 = np.load(\"/mnt/extraspace/rstiskalek/csiborg_postprocessing/field_interpolated/TNG300-1_TNG300-1_density_PCS_00000_1024_scatter2.0.npz\")\n", + "\n", + "val0 = d0[\"val\"]\n", + "val1 = d1[\"val\"]\n", + "val2 = d2[\"val\"]\n", + "\n", + "\n", + "with h5py.File(\"/mnt/extraspace/rstiskalek/TNG300-1/postprocessing/subhalo_catalogue_099.hdf5\", 'r') as f:\n", + " mstar = f[\"SubhaloMassType\"][:, 4] * 1e10\n", + " mhi = f[\"m_neutral_H\"][:]\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SignificanceResult(statistic=-0.1217955937334526, pvalue=0.0)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.stats import spearmanr\n", + "\n", + "k = 1\n", + "\n", + "x = mhi\n", + "y = val1[:, k]\n", + "\n", + "m = (x > 0) & (y > 0)\n", + "x = x[m]\n", + "y = y[m]\n", + "\n", + "\n", + "\n", + "plt.figure()\n", + "print(spearmanr(x, y))\n", + "plt.scatter(x, y, s=0.1)\n", + "\n", + "plt.xscale(\"log\")\n", + "plt.yscale(\"log\")\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv_csiborg", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/scripts/clear.sh b/scripts/clear.sh new file mode 100755 index 0000000..bc0d2e7 --- /dev/null +++ b/scripts/clear.sh @@ -0,0 +1,2 @@ +cm="rm *.out" +$cm \ No newline at end of file diff --git a/scripts/field_prop.sh b/scripts/field_prop.sh new file mode 100755 index 0000000..c358286 --- /dev/null +++ b/scripts/field_prop.sh @@ -0,0 +1,24 @@ +nthreads=6 +memory=64 +on_login=${1} +queue="berg" +env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_csiborg/bin/python" +file="field_prop.py" +kind="radvel" +simname="csiborg2_random" +nsims="-1" +MAS="SPH" +grid=1024 + + +pythoncm="$env $file --nsims $nsims --simname $simname --kind $kind --MAS $MAS --grid $grid" +if [ $on_login -eq 1 ]; then + echo $pythoncm + $pythoncm +else + cm="addqueue -q $queue -n $nthreads -m $memory $pythoncm" + echo "Submitting:" + echo $cm + echo + eval $cm +fi diff --git a/scripts/field_sample.py b/scripts/field_sample.py index ca61503..52d0547 100644 --- a/scripts/field_sample.py +++ b/scripts/field_sample.py @@ -26,11 +26,58 @@ from h5py import File from mpi4py import MPI from taskmaster import work_delegation from tqdm import tqdm +from numba import jit from utils import get_nsims -def open_galaxy_positions(survey_name, comm): +@jit(nopython=True, fastmath=True, boundscheck=False) +def scatter_along_radial_direction(pos, scatter, boxsize): + """ + Scatter galaxy positions along the radial direction. Enforces that the + radial position is always on the same side of the box and that the galaxy + is still inside the box. + + Parameters + ---------- + pos : 2-dimensional array + Galaxy positions in the form of (distance, RA, DEC). + scatter : float + Scatter to add to the radial positions of galaxies in same units as + `distance` (Mpc / h). + boxsize : float + Box size in `Mpc / h`. + """ + pos_new = numpy.copy(pos) + + for i in range(len(pos)): + r0, ra, dec = pos[i] + # Convert to radians + ra *= numpy.pi / 180 + dec *= numpy.pi / 180 + + # Convert to normalized Cartesian coordinates + xnorm = numpy.cos(dec) * numpy.cos(ra) + ynorm = numpy.cos(dec) * numpy.sin(ra) + znorm = numpy.sin(dec) + + while True: + rnew = numpy.random.normal(r0, scatter) + if rnew < 0: + continue + + xnew = rnew * xnorm + boxsize / 2 + ynew = rnew * ynorm + boxsize / 2 + znew = rnew * znorm + boxsize / 2 + + if 0 <= xnew < boxsize and 0 <= ynew < boxsize and 0 <= znew < boxsize: # noqa + pos_new[i, 0] = rnew + break + + return pos_new + + +def open_galaxy_positions(survey_name, comm, scatter=None): """ Load the survey's galaxy positions , broadcasting them to all ranks. @@ -40,6 +87,9 @@ def open_galaxy_positions(survey_name, comm): Name of the survey. comm : mpi4py.MPI.Comm MPI communicator. + scatter : float + Scatter to add to the radial positions of galaxies, supportted only in + TNG300-1. Returns ------- @@ -71,6 +121,24 @@ def open_galaxy_positions(survey_name, comm): samples["ra"][:] * 180 / numpy.pi, samples["dec"][:] * 180 / numpy.pi], ).T + elif survey_name == "TNG300-1": + with File("/mnt/extraspace/rstiskalek/TNG300-1/postprocessing/subhalo_catalogue_099.hdf5", 'r') as f: # noqa + pos = numpy.vstack([f["SubhaloPos"][:, 0], + f["SubhaloPos"][:, 1], + f["SubhaloPos"][:, 2]], + ).T + boxsize = csiborgtools.simname2boxsize("TNG300-1") + pos -= boxsize / 2 + pos = csiborgtools.cartesian_to_radec(pos) + if scatter is not None: + if scatter < 0: + raise ValueError("Scatter must be positive.") + if scatter > 0: + print(f"Adding scatter of {scatter} Mpc / h.", + flush=True) + pos = scatter_along_radial_direction(pos, scatter, + boxsize) + else: raise NotImplementedError(f"Survey `{survey_name}` not " "implemented.") @@ -118,7 +186,7 @@ def evaluate_field(field, pos, boxsize, smooth_scales, verbose=True): field, scale * mpc2box, boxsize=1, make_copy=True) else: field_smoothed = numpy.copy(field) - + print("Going to evaluate the field....") val[:, i] = csiborgtools.field.evaluate_sky( field_smoothed, pos=pos, mpc2box=mpc2box) @@ -182,6 +250,8 @@ def main(nsim, parser_args, pos, verbose): elif "csiborg2" in parser_args.simname: kind = parser_args.simname.split("_")[-1] freader = csiborgtools.read.CSiBORG2Field(nsim, kind) + elif parser_args.simname == "TNG300-1": + freader = csiborgtools.read.TNG300_1Field() else: raise NotImplementedError(f"Simulation `{parser_args.simname}` is not supported.") # noqa @@ -199,13 +269,19 @@ def main(nsim, parser_args, pos, verbose): "/mnt/extraspace/rstiskalek/GWLSS/", f"{parser_args.kind}_{parser_args.MAS}_{parser_args.grid}_{nsim}_H1L1V1-EXTRACT_POSTERIOR_GW170817-1187008600-400.npz") # noqa else: + if parser_args.simname == "TNG300-1": + scatter = parser_args.scatter + else: + scatter = None + fout = paths.field_interpolated( parser_args.survey, parser_args.simname, nsim, parser_args.kind, - parser_args.MAS, parser_args.grid) + parser_args.MAS, parser_args.grid, scatter) # The survey above had some cuts, however for compatibility we want # the same shape as the `uncut` survey - val = match_to_no_selection(val, parser_args) + if parser_args.survey != "TNG300-1": + val = match_to_no_selection(val, parser_args) if verbose: print(f"Saving to ... `{fout}`.") @@ -218,10 +294,10 @@ if __name__ == "__main__": parser.add_argument("--nsims", type=int, nargs="+", default=None, help="IC realisations. If `-1` processes all.") parser.add_argument("--simname", type=str, default="csiborg1", - choices=["csiborg1", "csiborg2_main", "csiborg2_random", "csiborg2_varysmall"], # noqa + choices=["csiborg1", "csiborg2_main", "csiborg2_random", "csiborg2_varysmall", "TNG300-1"], # noqa help="Simulation name") parser.add_argument("--survey", type=str, required=True, - choices=["SDSS", "SDSSxALFALFA", "GW170817"], + choices=["SDSS", "SDSSxALFALFA", "GW170817", "TNG300-1"], # noqa help="Galaxy survey") parser.add_argument("--smooth_scales", type=float, nargs="+", default=None, help="Smoothing scales in Mpc / h.") @@ -233,12 +309,20 @@ if __name__ == "__main__": choices=["NGP", "CIC", "TSC", "PCS", "SPH"], help="Mass assignment scheme.") parser.add_argument("--grid", type=int, help="Grid resolution.") + parser.add_argument("--scatter", type=float, default=None, + help="Scatter to add to the radial positions of galaxies, supportted only in TNG300-1.") # noqa args = parser.parse_args() - paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring) - nsims = get_nsims(args, paths) + if args.simname == "TNG300-1" and args.survey != "TNG300-1": + raise ValueError("TNG300-1 simulation is only supported for TNG300-1 survey.") # noqa - pos = open_galaxy_positions(args.survey, MPI.COMM_WORLD) + paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring) + if args.simname == "TNG300-1": + nsims = [0] + else: + nsims = get_nsims(args, paths) + + pos = open_galaxy_positions(args.survey, MPI.COMM_WORLD, args.scatter) def _main(nsim): main(nsim, args, pos, verbose=MPI.COMM_WORLD.Get_size() == 1) diff --git a/scripts/field_sample.sh b/scripts/field_sample.sh new file mode 100755 index 0000000..912c3c1 --- /dev/null +++ b/scripts/field_sample.sh @@ -0,0 +1,29 @@ +nthreads=1 +memory=32 +on_login=${1} +queue="berg" +env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_csiborg/bin/python" +file="field_sample.py" + + +nsims="-1" +simname="TNG300-1" +survey="TNG300-1" +smooth_scales="0 2 4 8 16" +kind="density" +MAS="PCS" +grid=1024 +scatter=0 + + +pythoncm="$env $file --nsims $nsims --simname $simname --survey $survey --smooth_scales $smooth_scales --kind $kind --MAS $MAS --grid $grid --scatter $scatter" +if [ $on_login -eq 1 ]; then + echo $pythoncm + $pythoncm +else + cm="addqueue -q $queue -n $nthreads -m $memory $pythoncm" + echo "Submitting:" + echo $cm + echo + eval $cm +fi diff --git a/scripts/field_shells.sh b/scripts/field_shells.sh new file mode 100755 index 0000000..bd6a519 --- /dev/null +++ b/scripts/field_shells.sh @@ -0,0 +1,24 @@ +nthreads=1 +memory=32 +on_login=${1} +queue="berg" +env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_csiborg/bin/python" +file="field_shells.py" + +field="overdensity" +simname="borg2" +MAS="SPH" +grid=1024 + + +pythoncm="$env $file --field $field --simname $simname --MAS $MAS --grid $grid" +if [ $on_login -eq 1 ]; then + echo $pythoncm + $pythoncm +else + cm="addqueue -q $queue -n $nthreads -m $memory $pythoncm" + echo "Submitting:" + echo $cm + echo + eval $cm +fi diff --git a/scripts/fit_init.sh b/scripts/fit_init.sh new file mode 100755 index 0000000..82520a4 --- /dev/null +++ b/scripts/fit_init.sh @@ -0,0 +1,23 @@ +nthreads=6 +memory=16 +on_login=${1} +queue="berg" +env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_csiborg/bin/python" +file="fit_init.py" + + +simname="csiborg2_varysmall" +nsims="-1" + + +pythoncm="$env $file --nsims $nsims --simname $simname" +if [ $on_login -eq 1 ]; then + echo $pythoncm + $pythoncm +else + cm="addqueue -q $queue -n $nthreads -m $memory $pythoncm" + echo "Submitting:" + echo $cm + echo + eval $cm +fi diff --git a/scripts/mass_enclosed.sh b/scripts/mass_enclosed.sh new file mode 100755 index 0000000..7997ea7 --- /dev/null +++ b/scripts/mass_enclosed.sh @@ -0,0 +1,21 @@ +nthreads=1 +memory=32 +on_login=${1} +queue="berg" +env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_csiborg/bin/python" +file="mass_enclosed.py" + +simname="borg2" + + +pythoncm="$env $file --simname $simname" +if [ $on_login -eq 1 ]; then + echo $pythoncm + $pythoncm +else + cm="addqueue -q $queue -n $nthreads -m $memory $pythoncm" + echo "Submitting:" + echo $cm + echo + eval $cm +fi diff --git a/scripts/match_knn.sh b/scripts/match_knn.sh new file mode 100755 index 0000000..610dbaa --- /dev/null +++ b/scripts/match_knn.sh @@ -0,0 +1,29 @@ +#!/bin/bash +nthreads=50 +memory=7 +queue="cmb" +env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_csiborg/bin/python" +file="match_finsnap.py" +verbose="true" +nsims="-1" +onlogin=false + +# for run in "mass001" "mass003" "mass005" "mass007" "mass009" +for run in "mass002" "mass004" "mass006" "mass008" +do +for simname in "csiborg" +do +pythoncm="$env $file --simname $simname --run $run --nsims $nsims --verbose $verbose" + +if $onlogin +then + $pythoncm +else + cm="addqueue -q $queue -n $nthreads -m $memory $pythoncm" + echo "Submitting:" + echo $cm + echo + $cm +fi +done +done diff --git a/scripts/match_overlap_all.sh b/scripts/match_overlap_all.sh new file mode 100755 index 0000000..b2c11db --- /dev/null +++ b/scripts/match_overlap_all.sh @@ -0,0 +1,24 @@ +#!/bin/bash +nthreads=11 +memory=4 +queue="cmb" +env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_csiborg/bin/python" +file="match_all.py" + +simname="quijote" +min_logmass=13.25 +nsim0=0 +kind="max" +mult=10 +sigma=1 +verbose="false" + + +pythoncm="$env $file --kind $kind --simname $simname --nsim0 $nsim0 --min_logmass $min_logmass --mult $mult --sigma $sigma --verbose $verbose" +# $pythoncm + +cm="addqueue -q $queue -n $nthreads -m $memory $pythoncm" +echo "Submitting:" +echo $cm +echo +$cm diff --git a/scripts/match_overlap_single.sh b/scripts/match_overlap_single.sh new file mode 100755 index 0000000..f4aadc9 --- /dev/null +++ b/scripts/match_overlap_single.sh @@ -0,0 +1,50 @@ +#!/bin/bash +nthreads=1 +memory=7 +queue="berg" +env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_csiborg/bin/python" +verbose="true" +file="match_overlap_single.py" + +simname="csiborg2_main" +kind="overlap" +min_logmass=13.25 +mult=5 +sigma=1 + +# sims=(7444 7468) +sims=(16417 16517) +# sims=(0 1) +# sims=(7468 7588) +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 --kind $kind --nsim0 $nsim0 --nsimx $nsimx --simname $simname --min_logmass $min_logmass --sigma $sigma --mult $mult --verbose $verbose" + + # $pythoncm + + cm="addqueue -q $queue -n 1x1 -m $memory $pythoncm" + echo "Submitting:" + echo $cm + echo + $cm + sleep 0.05 + + done +done diff --git a/scripts_independent/catalogue_tng.py b/scripts_independent/catalogue_tng.py new file mode 100644 index 0000000..2c71cd6 --- /dev/null +++ b/scripts_independent/catalogue_tng.py @@ -0,0 +1,64 @@ +# Copyright (C) 2023 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 iteratively load particles of a TNG simulation and construct the DM +density field. +""" +from os.path import join +import numpy as np +from h5py import File +import illustris_python as il + + +if __name__ == "__main__": + fdir = "/mnt/extraspace/rstiskalek/TNG300-1/" + basepath = join(fdir, "output") + out_fname = join(fdir, "postprocessing/subhalo_catalogue_099.hdf5") + + # SUBFIND catalogue + fields = ["SubhaloFlag", "SubhaloPos", "SubhaloMassType", + "SubhaloGasMetallicity", "SubhaloStarMetallicity", + "SubhaloSFR", "SubhaloSpin", "SubhaloStellarPhotometrics"] + + print("Loading the data.....") + data = il.groupcat.loadSubhalos(basepath, 99, fields=fields) + data["SubhaloPos"] /= 1000. # Convert to Mpc/h + print("Finished loading!") + + # Take only galaxies with stellar mass more than 10^9 Msun / h + mask = (data["SubhaloFlag"] == 1) & (data["SubhaloMassType"][:, 4] > 0.1) + + print(f"Writing the subfind dataset to '{out_fname}'") + with File(out_fname, 'w') as f: + for key in fields: + if key == "SubhaloFlag": + continue + + f.create_dataset(key, data=data[key][mask]) + + # HIH2 supplemetary catalogue + print("Loading the HI & H2 supplementary catalogue.") + fname = join(fdir, "postprocessing/hih2/hih2_galaxy_099.hdf5") + with File(fname, "r") as f: + _m_neutral_H = f["m_neutral_H"][:] + _id_subhalo = np.array(f["id_subhalo"][:], dtype=int) + + m_neutral_H = np.full(data["count"], np.nan, dtype=float) + for i, j in enumerate(_id_subhalo): + m_neutral_H[j] = _m_neutral_H[i] + + print("Adding the HI & H2 supplementary catalogue.") + with File(out_fname, 'r+') as f: + f.create_dataset("m_neutral_H", data=m_neutral_H[mask]) diff --git a/scripts_plots/submit.sh b/scripts_plots/submit.sh new file mode 100755 index 0000000..7f58f6a --- /dev/null +++ b/scripts_plots/submit.sh @@ -0,0 +1,20 @@ +#!/bin/bash + +if [ "$#" -ne 1 ]; then + echo "Usage: ./script.sh " + exit 1 +fi + +env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_csiborg/bin/python" +queue="berg" +nthreads=1 +memory=7 + +file="$1" + +cm="addqueue -q $queue -n $nthreads -m $memory $env $file" + +echo "Submitting:" +echo $cm +echo +$cm