csiborgtools/scripts_plots/plot_knn.py

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# 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 os.path import join
import matplotlib.pyplot as plt
import numpy
import scienceplots # noqa
import plt_utils
try:
import csiborgtools
except ModuleNotFoundError:
import sys
sys.path.append("../")
import csiborgtools
###############################################################################
# Probability of matching a reference simulation halo #
###############################################################################
def plot_knn(runname):
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"""
Plot the kNN CDF for a given runname.
Parameters
----------
runname : str
Returns
-------
None
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"""
print(f"Plotting kNN CDF for {runname}.")
cols = plt.rcParams["axes.prop_cycle"].by_key()["color"]
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
reader = csiborgtools.read.kNNCDFReader(paths)
with plt.style.context(plt_utils.mplstyle):
plt.figure()
# Quijote kNN
rs, cdf, ndensity = reader.read("quijote", runname, kind="auto")
pk = reader.prob_k(cdf)
pk_poisson = reader.poisson_prob_k(rs, numpy.arange(pk.shape[1]),
ndensity)
for k in range(3):
mu = numpy.mean(pk[:, k, :], axis=0)
std = numpy.std(pk[:, k, :], axis=0)
plt.plot(rs, mu, label=r"$k = {}$, Quijote".format(k + 1),
c=cols[k % len(cols)])
# plt.fill_between(rs, mu - std, mu + std, alpha=0.15,
# color=cols[k % len(cols)], zorder=0)
mu = numpy.mean(pk_poisson[:, k, :], axis=0)
std = numpy.std(pk_poisson[:, k, :], axis=0)
plt.plot(rs, mu, c=cols[k % len(cols)], ls="dashed",
label=r"$k = {}$, Poisson analytical".format(k + 1))
# plt.fill_between(rs, mu - std, mu + std, alpha=0.15,
# color=cols[k % len(cols)], zorder=0, hatch="\\")
# Quijote poisson kNN
rs, cdf, ndensity = reader.read("quijote", "mass003_poisson",
kind="auto")
pk = reader.prob_k(cdf)
for k in range(3):
mu = numpy.mean(pk[:, k, :], axis=0)
std = numpy.std(pk[:, k, :], axis=0)
plt.plot(rs, mu, label=r"$k = {}$, Poisson Quijote".format(k + 1),
c=cols[k % len(cols)], ls="dotted")
# plt.fill_between(rs, mu - std, mu + std, alpha=0.15,
# color=cols[k % len(cols)], zorder=0)
# # CSiBORG kNN
# rs, cdf, ndensity = reader.read("csiborg", runname, kind="auto")
# pk = reader.mean_prob_k(cdf)
# for k in range(2):
# mu = pk[k, :, 0]
# std = pk[k, :, 1]
# plt.plot(rs, mu, ls="--", c=cols[k % len(cols)])
# plt.fill_between(rs, mu - std, mu + std, alpha=0.15, hatch="\\",
# color=cols[k % len(cols)], zorder=0)
plt.legend()
plt.xlabel(r"$r~[\mathrm{Mpc}]$")
plt.ylabel(r"$P(k | V = 4 \pi r^3 / 3)$")
for ext in ["png"]:
fout = join(plt_utils.fout, f"knn_{runname}.{ext}")
print("Saving to `{fout}`.".format(fout=fout))
plt.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
plt.close()
if __name__ == "__main__":
plot_knn("mass003")