mirror of
https://github.com/Richard-Sti/csiborgtools.git
synced 2024-12-23 01:18:02 +00:00
dbf93b9416
* Rename lagpatch * Fix old bug * Fix small bug * Add number of cells calculation * Fix a small bug * Rename column * Move file * Small changes * Edit style * Add plot script * Add delta2ncells * Add HMF calculation * Move definition around * Add HMF plot * pep8 * Update HMF plotting routine * Small edit
178 lines
6.7 KiB
Python
178 lines
6.7 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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from os.path import join
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from argparse import ArgumentParser
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import matplotlib.pyplot as plt
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import numpy
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import scienceplots # noqa
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import utils
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from cache_to_disk import cache_to_disk, delete_disk_caches_for_function # noqa
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from tqdm import tqdm
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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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def open_csiborg(nsim):
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"""
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Open a CSiBORG halo catalogue.
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"""
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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bounds = {"totpartmass": (None, None), "dist": (0, 155/0.705)}
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return csiborgtools.read.HaloCatalogue(nsim, paths, bounds=bounds)
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def open_quijote(nsim, nobs=None):
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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cat = csiborgtools.read.QuijoteHaloCatalogue(nsim, paths, nsnap=4)
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if nobs is not None:
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cat = cat.pick_fiducial_observer(nobs, rmax=155.5 / 0.705)
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return cat
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def plot_mass_vs_ncells(nsim, pdf=False):
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cat = open_csiborg(nsim)
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mpart = 4.38304044e+09
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with plt.style.context(utils.mplstyle):
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plt.figure()
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plt.scatter(cat["totpartmass"], cat["lagpatch_ncells"], s=0.25,
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rasterized=True)
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plt.xscale("log")
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plt.yscale("log")
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for n in [1, 10, 100]:
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plt.axvline(n * 512 * mpart, c="black", ls="--", zorder=0, lw=0.8)
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plt.xlabel(r"$M_{\rm tot} / M_\odot$")
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plt.ylabel(r"$N_{\rm cells}$")
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for ext in ["png"] if pdf is False else ["png", "pdf"]:
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fout = join(utils.fout, f"init_mass_vs_ncells_{nsim}.{ext}")
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print(f"Saving to `{fout}`.")
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plt.savefig(fout, dpi=utils.dpi, bbox_inches="tight")
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plt.close()
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def process_counts(counts):
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mean = numpy.mean(counts, axis=0)
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std = numpy.std(counts, axis=0)
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return mean, std
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def plot_hmf(pdf=False):
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print("Plotting the HMF...", flush=True)
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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csiborg_nsims = paths.get_ics("csiborg")
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print("Loading CSiBORG halo counts.", flush=True)
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for i, nsim in enumerate(tqdm(csiborg_nsims)):
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data = numpy.load(paths.halo_counts("csiborg", nsim))
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if i == 0:
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bins = data["bins"]
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csiborg_counts = numpy.full((len(csiborg_nsims), len(bins) - 1),
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numpy.nan, dtype=numpy.float32)
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csiborg_counts[i, :] = data["counts"]
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csiborg_counts /= numpy.diff(bins).reshape(1, -1)
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print("Loading Quijote halo counts.", flush=True)
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quijote_nsims = paths.get_ics("quijote")
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for i, nsim in enumerate(tqdm(quijote_nsims)):
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data = numpy.load(paths.halo_counts("quijote", nsim))
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if i == 0:
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bins = data["bins"]
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nmax = data["counts"].shape[0]
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quijote_counts = numpy.full(
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(len(quijote_nsims) * nmax, len(bins) - 1), numpy.nan,
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dtype=numpy.float32)
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quijote_counts[i * nmax:(i + 1) * nmax, :] = data["counts"]
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quijote_counts /= numpy.diff(bins).reshape(1, -1)
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x = 10**(0.5 * (bins[1:] + bins[:-1]))
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# Edit lower limits
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csiborg_counts[:, x < 1e12] = numpy.nan
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quijote_counts[:, x < 8e12] = numpy.nan
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# Edit upper limits
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csiborg_counts[:, x > 4e15] = numpy.nan
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quijote_counts[:, x > 4e15] = numpy.nan
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with plt.style.context(utils.mplstyle):
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cols = plt.rcParams["axes.prop_cycle"].by_key()["color"]
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fig, ax = plt.subplots(nrows=2, sharex=True,
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figsize=(3.5, 2.625 * 1.25),
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gridspec_kw={"height_ratios": [1, 0.65]})
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fig.subplots_adjust(hspace=0, wspace=0)
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mean_csiborg, std_csiborg = process_counts(csiborg_counts)
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ax[0].plot(x, mean_csiborg, label="CSiBORG")
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ax[0].fill_between(x, mean_csiborg - std_csiborg,
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mean_csiborg + std_csiborg, alpha=0.5)
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mean_quijote, std_quijote = process_counts(quijote_counts)
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ax[0].plot(x, mean_quijote, label="Quijote")
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ax[0].fill_between(x, mean_quijote - std_quijote,
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mean_quijote + std_quijote, alpha=0.5)
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log_y = numpy.log10(mean_csiborg / mean_quijote)
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err = numpy.sqrt((std_csiborg / mean_csiborg / numpy.log(10))**2
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+ (std_quijote / mean_quijote / numpy.log(10))**2)
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ax[1].plot(x, 10**log_y, c=cols[2])
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ax[1].fill_between(x, 10**(log_y - err), 10**(log_y + err), alpha=0.5,
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color=cols[2])
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ax[1].axhline(1, color="k", ls=plt.rcParams["lines.linestyle"],
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lw=0.5 * plt.rcParams["lines.linewidth"], zorder=0)
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ax[0].set_ylabel(r"$\frac{\mathrm{d} n}{\mathrm{d}\log M_{\rm h}}~\mathrm{dex}^{-1}$") # noqa
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ax[1].set_xlabel(r"$M_{\rm h}$ [$M_\odot$]")
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ax[1].set_ylabel(r"$\mathrm{CSiBORG} / \mathrm{Quijote}$")
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ax[0].set_xscale("log")
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ax[0].set_yscale("log")
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ax[1].set_yscale("log")
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ax[0].legend()
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fig.tight_layout(h_pad=0, w_pad=0)
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for ext in ["png"] if pdf is False else ["png", "pdf"]:
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fout = join(utils.fout, f"hmf_comparison.{ext}")
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print(f"Saving to `{fout}`.")
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fig.savefig(fout, dpi=utils.dpi, bbox_inches="tight")
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plt.close()
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###############################################################################
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# Command line interface #
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###############################################################################
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if __name__ == "__main__":
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parser = ArgumentParser()
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parser.add_argument('-c', '--clean', action='store_true')
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args = parser.parse_args()
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cached_funcs = []
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if args.clean:
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for func in cached_funcs:
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print(f"Cleaning cache for function {func}.")
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delete_disk_caches_for_function(func)
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# plot_mass_vs_occupancy(7444)
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# plot_mass_vs_normcells(7444 + 24 * 4, pdf=False)
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# plot_mass_vs_ncells(7444, pdf=True)
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plot_hmf(pdf=True)
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