2023-05-21 23:46:28 +02:00
|
|
|
# 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.
|
|
|
|
|
|
|
|
from argparse import ArgumentParser
|
2023-05-22 14:42:10 +02:00
|
|
|
from os.path import join
|
2023-05-21 23:46:28 +02:00
|
|
|
|
|
|
|
import matplotlib.pyplot as plt
|
2023-05-22 14:42:10 +02:00
|
|
|
import numpy
|
2023-05-21 23:46:28 +02:00
|
|
|
import scienceplots # noqa
|
|
|
|
from cache_to_disk import cache_to_disk, delete_disk_caches_for_function
|
|
|
|
|
|
|
|
import utils
|
|
|
|
|
|
|
|
try:
|
|
|
|
import csiborgtools
|
|
|
|
except ModuleNotFoundError:
|
|
|
|
import sys
|
|
|
|
sys.path.append("../")
|
|
|
|
import csiborgtools
|
|
|
|
|
|
|
|
|
|
|
|
@cache_to_disk(7)
|
|
|
|
def read_cdf(simname, run, kwargs):
|
2023-05-22 14:42:10 +02:00
|
|
|
"""Read the CDFs. Caches them to disk"""
|
2023-05-21 23:46:28 +02:00
|
|
|
paths = csiborgtools.read.Paths(**kwargs["paths_kind"])
|
|
|
|
reader = csiborgtools.read.NearestNeighbourReader(**kwargs, paths=paths)
|
|
|
|
return reader.build_cdf(simname, run, verbose=True)
|
|
|
|
|
|
|
|
|
2023-05-22 14:42:10 +02:00
|
|
|
def plot_cdf(run, kwargs):
|
|
|
|
"""
|
|
|
|
Plot the CDF of the nearest neighbour distance for Quijote and CSiBORG.
|
|
|
|
"""
|
2023-05-21 23:46:28 +02:00
|
|
|
print("Plotting the CDFs.", flush=True)
|
|
|
|
paths = csiborgtools.read.Paths(**kwargs["paths_kind"])
|
|
|
|
reader = csiborgtools.read.NearestNeighbourReader(**kwargs, paths=paths)
|
|
|
|
x = reader.bin_centres("neighbour")
|
|
|
|
|
2023-05-22 14:42:10 +02:00
|
|
|
y_quijote = read_cdf("quijote", run, kwargs)
|
|
|
|
y_csiborg = read_cdf("csiborg", run, kwargs)
|
2023-05-21 23:46:28 +02:00
|
|
|
ncdf = y_quijote.shape[0]
|
|
|
|
|
|
|
|
with plt.style.context(utils.mplstyle):
|
|
|
|
plt.figure()
|
|
|
|
for i in range(ncdf):
|
|
|
|
if i == 0:
|
|
|
|
label1 = "Quijote"
|
|
|
|
label2 = "CSiBORG"
|
|
|
|
else:
|
|
|
|
label1 = None
|
|
|
|
label2 = None
|
|
|
|
plt.plot(x, y_quijote[i], c="C0", label=label1)
|
|
|
|
plt.plot(x, y_csiborg[i], c="C1", label=label2)
|
|
|
|
plt.xlim(0, 75)
|
|
|
|
plt.ylim(0, 1)
|
2023-05-22 14:42:48 +02:00
|
|
|
plt.xlabel(r"$r_{1\mathrm{NN}}~[\mathrm{Mpc}]$")
|
|
|
|
plt.ylabel(r"$\mathrm{CDF}(r_{1\mathrm{NN}})$")
|
2023-05-21 23:46:28 +02:00
|
|
|
plt.legend()
|
|
|
|
|
|
|
|
plt.tight_layout()
|
2023-05-22 14:42:10 +02:00
|
|
|
for ext in ["png"]:
|
|
|
|
fout = join(utils.fout, f"1nn_cdf_{run}.{ext}")
|
|
|
|
print(f"Saving to `{fout}`.")
|
|
|
|
plt.savefig(fout, dpi=utils.dpi, bbox_inches="tight")
|
|
|
|
plt.close()
|
|
|
|
|
|
|
|
|
|
|
|
def plot_significance_hist(run, nsim, kwargs):
|
|
|
|
"""
|
|
|
|
Plot the histogram of the significance of the 1NN distance for CSiBORG.
|
|
|
|
"""
|
|
|
|
paths = csiborgtools.read.Paths(**kwargs["paths_kind"])
|
|
|
|
reader = csiborgtools.read.NearestNeighbourReader(**kwargs, paths=paths)
|
|
|
|
|
|
|
|
cdf = read_cdf("quijote", run, kwargs)
|
|
|
|
|
|
|
|
x = reader.calc_significance("csiborg", run, nsim, cdf)
|
|
|
|
x = x[numpy.isfinite(x)]
|
|
|
|
|
|
|
|
with plt.style.context(utils.mplstyle):
|
|
|
|
plt.figure()
|
|
|
|
plt.hist(x, bins="auto")
|
|
|
|
|
2023-05-22 14:42:48 +02:00
|
|
|
plt.xlabel(r"$r_{1\mathrm{NN}}$ significance $\mathrm{[\sigma]}$")
|
2023-05-22 14:42:10 +02:00
|
|
|
plt.ylabel(r"Counts")
|
|
|
|
plt.tight_layout()
|
|
|
|
for ext in ["png"]:
|
|
|
|
fout = join(utils.fout, f"sigma_{run}_{str(nsim).zfill(5)}.{ext}")
|
|
|
|
print(f"Saving to `{fout}`.")
|
|
|
|
plt.savefig(fout, dpi=utils.dpi, bbox_inches="tight")
|
2023-05-21 23:46:28 +02:00
|
|
|
plt.close()
|
|
|
|
|
|
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
|
parser = ArgumentParser()
|
|
|
|
parser.add_argument('-c', '--clean', action='store_true')
|
|
|
|
args = parser.parse_args()
|
|
|
|
|
|
|
|
kwargs = {"rmax_radial": 155 / 0.705,
|
|
|
|
"nbins_radial": 20,
|
|
|
|
"rmax_neighbour": 100.,
|
|
|
|
"nbins_neighbour": 150,
|
|
|
|
"paths_kind": csiborgtools.paths_glamdring}
|
|
|
|
|
|
|
|
cached_funcs = ["read_cdf"]
|
|
|
|
if args.clean:
|
|
|
|
for func in cached_funcs:
|
|
|
|
print(f"Cleaning cache for function {func}.")
|
|
|
|
delete_disk_caches_for_function(func)
|
|
|
|
|
2023-05-22 14:42:10 +02:00
|
|
|
# paths = csiborgtools.read.Paths(**kwargs["paths_kind"])
|
|
|
|
# reader = csiborgtools.read.NearestNeighbourReader(**kwargs, paths=paths)
|
|
|
|
|
|
|
|
plot_significance_hist("mass003", 7444, kwargs)
|