mirror of
https://github.com/Richard-Sti/csiborgtools.git
synced 2024-10-18 16:25:06 +02:00
Merge plotting scritps
This commit is contained in:
rstiskalek
parent
48cd5da88c
commit
7c2d7a86f5
@ -13,15 +13,16 @@
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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 argparse import ArgumentParser
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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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from cache_to_disk import cache_to_disk, delete_disk_caches_for_function
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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
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from tqdm import tqdm
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try:
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import csiborgtools
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@ -31,6 +32,119 @@ except ModuleNotFoundError:
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import csiborgtools
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###############################################################################
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# IC overlap plotting #
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###############################################################################
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def open_cat(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": (1e12, None)}
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return csiborgtools.read.HaloCatalogue(nsim, paths, bounds=bounds)
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@cache_to_disk(7)
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def get_overlap(nsim0):
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"""
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Calculate the summed overlap and probability of no match for a single
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reference simulation.
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"""
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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nsimxs = csiborgtools.read.get_cross_sims(nsim0, paths, smoothed=True)
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cat0 = open_cat(nsim0)
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catxs = []
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for nsimx in tqdm(nsimxs):
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catxs.append(open_cat(nsimx))
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reader = csiborgtools.read.NPairsOverlap(cat0, catxs, paths)
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x = reader.cat0("totpartmass")
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summed_overlap = reader.summed_overlap(True)
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prob_nomatch = reader.prob_nomatch(True)
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return x, summed_overlap, prob_nomatch
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def plot_summed_overlap(nsim0):
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"""
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Plot the summed overlap and probability of no matching for a single
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reference simulation as a function of the reference halo mass.
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"""
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x, summed_overlap, prob_nomatch = get_overlap(nsim0)
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mean_overlap = numpy.mean(summed_overlap, axis=1)
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std_overlap = numpy.std(summed_overlap, axis=1)
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mean_prob_nomatch = numpy.mean(prob_nomatch, axis=1)
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# std_prob_nomatch = numpy.std(prob_nomatch, axis=1)
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mask = mean_overlap > 0
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x = x[mask]
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mean_overlap = mean_overlap[mask]
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std_overlap = std_overlap[mask]
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mean_prob_nomatch = mean_prob_nomatch[mask]
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# Mean summed overlap
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with plt.style.context(utils.mplstyle):
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plt.figure()
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plt.hexbin(x, mean_overlap, mincnt=1, xscale="log", bins="log",
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gridsize=50)
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plt.colorbar(label="Counts in bins")
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plt.xlabel(r"$M_{\rm tot} / M_\odot$")
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plt.ylabel(r"$\langle \mathcal{O}_{a}^{\mathcal{A} \mathcal{B}} \rangle_{\mathcal{B}}$") # noqa
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plt.ylim(0., 1.)
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plt.tight_layout()
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for ext in ["png", "pdf"]:
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fout = join(utils.fout, f"overlap_mean_{nsim0}.{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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# Std summed overlap
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with plt.style.context(utils.mplstyle):
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plt.figure()
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plt.hexbin(x, std_overlap, mincnt=1, xscale="log", bins="log",
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gridsize=50)
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plt.colorbar(label="Counts in bins")
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plt.xlabel(r"$M_{\rm tot} / M_\odot$")
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plt.ylabel(r"$\delta \left( \mathcal{O}_{a}^{\mathcal{A} \mathcal{B}} \right)_{\mathcal{B}}$") # noqa
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plt.ylim(0., 1.)
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plt.tight_layout()
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for ext in ["png", "pdf"]:
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fout = join(utils.fout, f"overlap_std_{nsim0}.{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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# 1 - mean summed overlap vs mean prob nomatch
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with plt.style.context(utils.mplstyle):
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plt.figure()
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plt.scatter(1 - mean_overlap, mean_prob_nomatch, c=numpy.log10(x), s=2,
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rasterized=True)
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plt.colorbar(label=r"$\log_{10} M_{\rm halo} / M_\odot$")
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t = numpy.linspace(0.3, 1, 100)
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plt.plot(t, t, color="red", linestyle="--")
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plt.xlabel(r"$1 - \langle \mathcal{O}_a^{\mathcal{A} \mathcal{B}} \rangle_{\mathcal{B}}$") # noqa
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plt.ylabel(r"$\langle \eta_a^{\mathcal{A} \mathcal{B}} \rangle_{\mathcal{B}}$") # noqa
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plt.tight_layout()
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for ext in ["png", "pdf"]:
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fout = join(utils.fout, f"overlap_vs_prob_nomatch_{nsim0}.{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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###############################################################################
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# Nearest neighbour plotting #
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###############################################################################
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@cache_to_disk(7)
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def read_dist(simname, run, kind, kwargs):
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paths = csiborgtools.read.Paths(**kwargs["paths_kind"])
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@ -199,32 +313,22 @@ if __name__ == "__main__":
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parser.add_argument('-c', '--clean', action='store_true')
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args = parser.parse_args()
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kwargs = {"rmax_radial": 155 / 0.705,
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"nbins_radial": 20,
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"rmax_neighbour": 100.,
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"nbins_neighbour": 150,
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"paths_kind": csiborgtools.paths_glamdring}
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cached_funcs = ["read_dist", "make_kl", "make_ks"]
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cached_funcs = ["get_overlap", "read_dist", "make_kl", "make_ks"]
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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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print(f"Cleaning cache for function {func}.")
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delete_disk_caches_for_function(func)
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paths = csiborgtools.read.Paths(**kwargs["paths_kind"])
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reader = csiborgtools.read.NearestNeighbourReader(**kwargs, paths=paths)
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neighbour_kwargs = {"rmax_radial": 155 / 0.705,
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"nbins_radial": 20,
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"rmax_neighbour": 100.,
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"nbins_neighbour": 150,
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"paths_kind": csiborgtools.paths_glamdring}
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paths = csiborgtools.read.Paths(**neighbour_kwargs["paths_kind"])
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nn_reader = csiborgtools.read.NearestNeighbourReader(**neighbour_kwargs,
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paths=paths)
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run = "mass003"
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# for kind in ["pdf", "cdf"]:
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# plot_dist(run, kind, kwargs)
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# for kind in ["kl", "ks"]:
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# # plot_significance_hist("csiborg", run, 7444, nobs=None, kind=kind,
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# # kwargs=kwargs)
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# plot_significance_mass("quijote", run, 0, nobs=0, kind=kind,
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# kwargs=kwargs)
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# plot_significance_mass("quijote", run, 0, nobs=0, kind="ks",
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# kwargs=kwargs)
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plot_kl_vs_ks("quijote", run, 0, nobs=0, kwargs=kwargs)
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plot_kl_vs_ks("csiborg", run, 7444, nobs=None, kwargs=kwargs)
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# for ic in [7444, 8812, 9700]:
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# plot_summed_overlap(ic)
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@ -1,156 +0,0 @@
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# 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
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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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###############################################################################
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# Probability of matching a reference simulation halo #
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###############################################################################
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def open_cat(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": (1e12, None)}
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return csiborgtools.read.HaloCatalogue(nsim, paths, bounds=bounds)
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@cache_to_disk(7)
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def get_overlap(nsim0):
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"""
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Calculate the summed overlap and probability of no match for a single
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reference simulation.
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"""
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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nsimxs = csiborgtools.read.get_cross_sims(nsim0, paths, smoothed=True)
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cat0 = open_cat(nsim0)
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catxs = []
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for nsimx in tqdm(nsimxs):
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catxs.append(open_cat(nsimx))
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reader = csiborgtools.read.NPairsOverlap(cat0, catxs, paths)
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x = reader.cat0("totpartmass")
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summed_overlap = reader.summed_overlap(True)
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prob_nomatch = reader.prob_nomatch(True)
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return x, summed_overlap, prob_nomatch
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def plot_summed_overlap(nsim0):
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"""
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Plot the summed overlap and probability of no matching for a single
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reference simulation as a function of the reference halo mass.
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"""
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x, summed_overlap, prob_nomatch = get_overlap(nsim0)
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mean_overlap = numpy.mean(summed_overlap, axis=1)
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std_overlap = numpy.std(summed_overlap, axis=1)
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mean_prob_nomatch = numpy.mean(prob_nomatch, axis=1)
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# std_prob_nomatch = numpy.std(prob_nomatch, axis=1)
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mask = mean_overlap > 0
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x = x[mask]
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mean_overlap = mean_overlap[mask]
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std_overlap = std_overlap[mask]
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mean_prob_nomatch = mean_prob_nomatch[mask]
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# Mean summed overlap
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with plt.style.context(utils.mplstyle):
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plt.figure()
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plt.hexbin(x, mean_overlap, mincnt=1, xscale="log", bins="log",
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gridsize=50)
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plt.colorbar(label="Counts in bins")
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plt.xlabel(r"$M_{\rm tot} / M_\odot$")
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plt.ylabel(r"$\langle \mathcal{O}_{a}^{\mathcal{A} \mathcal{B}} \rangle_{\mathcal{B}}$") # noqa
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plt.ylim(0., 1.)
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plt.tight_layout()
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for ext in ["png", "pdf"]:
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fout = join(utils.fout, f"overlap_mean_{nsim0}.{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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# Std summed overlap
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with plt.style.context(utils.mplstyle):
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plt.figure()
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plt.hexbin(x, std_overlap, mincnt=1, xscale="log", bins="log",
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gridsize=50)
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plt.colorbar(label="Counts in bins")
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plt.xlabel(r"$M_{\rm tot} / M_\odot$")
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plt.ylabel(r"$\delta \left( \mathcal{O}_{a}^{\mathcal{A} \mathcal{B}} \right)_{\mathcal{B}}$") # noqa
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plt.ylim(0., 1.)
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plt.tight_layout()
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for ext in ["png", "pdf"]:
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fout = join(utils.fout, f"overlap_std_{nsim0}.{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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# 1 - mean summed overlap vs mean prob nomatch
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with plt.style.context(utils.mplstyle):
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plt.figure()
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plt.scatter(1 - mean_overlap, mean_prob_nomatch, c=numpy.log10(x), s=2,
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rasterized=True)
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plt.colorbar(label=r"$\log_{10} M_{\rm halo} / M_\odot$")
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t = numpy.linspace(0.3, 1, 100)
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plt.plot(t, t, color="red", linestyle="--")
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plt.xlabel(r"$1 - \langle \mathcal{O}_a^{\mathcal{A} \mathcal{B}} \rangle_{\mathcal{B}}$") # noqa
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plt.ylabel(r"$\langle \eta_a^{\mathcal{A} \mathcal{B}} \rangle_{\mathcal{B}}$") # noqa
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plt.tight_layout()
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for ext in ["png", "pdf"]:
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fout = join(utils.fout, f"overlap_vs_prob_nomatch_{nsim0}.{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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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 = ["get_overlap"]
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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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for ic in [7444, 8812, 9700]:
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plot_summed_overlap(ic)
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