mirror of
https://github.com/Richard-Sti/csiborgtools.git
synced 2024-12-22 21:28:03 +00:00
27e1c181a2
* Drag several fields at once * add env in RSP * Add docstrings * Make __main__ * Fix bug * Fix plotting little bug
114 lines
4.1 KiB
Python
114 lines
4.1 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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import matplotlib.pyplot as plt
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import numpy
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import scienceplots # noqa
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import plt_utils
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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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###############################################################################
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# Probability of matching a reference simulation halo #
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###############################################################################
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def plot_knn(runname):
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"""
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Plot the kNN CDF for a given runname.
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Parameters
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----------
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runname : str
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Returns
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-------
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None
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"""
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print(f"Plotting kNN CDF for {runname}.")
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cols = plt.rcParams["axes.prop_cycle"].by_key()["color"]
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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reader = csiborgtools.read.kNNCDFReader(paths)
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with plt.style.context(plt_utils.mplstyle):
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plt.figure()
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# Quijote kNN
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rs, cdf, ndensity = reader.read("quijote", runname, kind="auto")
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pk = reader.prob_k(cdf)
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pk_poisson = reader.poisson_prob_k(rs, numpy.arange(pk.shape[1]),
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ndensity)
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for k in range(3):
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mu = numpy.mean(pk[:, k, :], axis=0)
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std = numpy.std(pk[:, k, :], axis=0)
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plt.plot(rs, mu, label=r"$k = {}$, Quijote".format(k + 1),
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c=cols[k % len(cols)])
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# plt.fill_between(rs, mu - std, mu + std, alpha=0.15,
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# color=cols[k % len(cols)], zorder=0)
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mu = numpy.mean(pk_poisson[:, k, :], axis=0)
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std = numpy.std(pk_poisson[:, k, :], axis=0)
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plt.plot(rs, mu, c=cols[k % len(cols)], ls="dashed",
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label=r"$k = {}$, Poisson analytical".format(k + 1))
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# plt.fill_between(rs, mu - std, mu + std, alpha=0.15,
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# color=cols[k % len(cols)], zorder=0, hatch="\\")
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# Quijote poisson kNN
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rs, cdf, ndensity = reader.read("quijote", "mass003_poisson",
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kind="auto")
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pk = reader.prob_k(cdf)
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for k in range(3):
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mu = numpy.mean(pk[:, k, :], axis=0)
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std = numpy.std(pk[:, k, :], axis=0)
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plt.plot(rs, mu, label=r"$k = {}$, Poisson Quijote".format(k + 1),
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c=cols[k % len(cols)], ls="dotted")
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# plt.fill_between(rs, mu - std, mu + std, alpha=0.15,
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# color=cols[k % len(cols)], zorder=0)
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# # CSiBORG kNN
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# rs, cdf, ndensity = reader.read("csiborg", runname, kind="auto")
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# pk = reader.mean_prob_k(cdf)
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# for k in range(2):
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# mu = pk[k, :, 0]
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# std = pk[k, :, 1]
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# plt.plot(rs, mu, ls="--", c=cols[k % len(cols)])
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# plt.fill_between(rs, mu - std, mu + std, alpha=0.15, hatch="\\",
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# color=cols[k % len(cols)], zorder=0)
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plt.legend()
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plt.xlabel(r"$r~[\mathrm{Mpc}]$")
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plt.ylabel(r"$P(k | V = 4 \pi r^3 / 3)$")
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for ext in ["png"]:
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fout = join(plt_utils.fout, f"knn_{runname}.{ext}")
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print("Saving to `{fout}`.".format(fout=fout))
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plt.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
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plt.close()
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if __name__ == "__main__":
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plot_knn("mass003")
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