mirror of
https://github.com/Richard-Sti/csiborgtools.git
synced 2024-10-18 18:15:05 +02:00
28e93e917f
* Add a new plot * Add a binned trend * Fix bug * Improve plot further * Add new plotting * add max overlap * edit get_overlap * Add max overlap plot * Update plot * Add max overlap key * add max dist flag * Improve plotting
1523 lines
52 KiB
Python
1523 lines
52 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 argparse import ArgumentParser
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from gc import collect
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from os.path import join
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import matplotlib as mpl
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import matplotlib.pyplot as plt
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from mpl_toolkits.axes_grid1.inset_locator import inset_axes
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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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from scipy.stats import kendalltau
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from tqdm import trange, tqdm
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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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# 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. Applies only mass selection.
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Parameters
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----------
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nsim : int
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Simulation index.
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Returns
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-------
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cat : csiborgtools.read.HaloCatalogue
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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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def plot_mass_vs_pairoverlap(nsim0, nsimx):
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"""
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Plot the pair overlap of a reference simulation with a single cross
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simulation as a function of the reference halo mass.
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Parameters
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----------
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nsim0 : int
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Reference simulation index.
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nsimx : int
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Cross simulation index.
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"""
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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cat0 = open_cat(nsim0)
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catx = open_cat(nsimx)
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reader = csiborgtools.read.PairOverlap(cat0, catx, paths)
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x = reader.copy_per_match("totpartmass")
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y = reader.overlap(True)
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x = numpy.log10(numpy.concatenate(x))
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y = numpy.concatenate(y)
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with plt.style.context(plt_utils.mplstyle):
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plt.figure()
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plt.hexbin(x, y, mincnt=1, 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"$\log M_{\rm tot} / M_\odot$")
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plt.ylabel("Pair overlap")
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plt.ylim(0., 1.)
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plt.tight_layout()
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for ext in ["png"]:
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fout = join(plt_utils.fout, f"mass_vs_pair_overlap{nsim0}.{ext}")
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print(f"Saving to `{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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def plot_mass_vs_maxpairoverlap(nsim0, nsimx):
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"""
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Plot the maximum pair overlap of a reference simulation haloes with a
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single cross simulation.
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Parameters
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----------
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nsim0 : int
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Reference simulation index.
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nsimx : int
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Cross simulation index.
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"""
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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cat0 = open_cat(nsim0)
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catx = open_cat(nsimx)
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reader = csiborgtools.read.PairOverlap(cat0, catx, paths)
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x = numpy.log10(cat0["totpartmass"])
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y = reader.overlap(True)
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def get_max(y_):
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if len(y_) == 0:
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return numpy.nan
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return numpy.max(y_)
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y = numpy.array([get_max(y_) for y_ in y])
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with plt.style.context(plt_utils.mplstyle):
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plt.figure()
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plt.hexbin(x, y, mincnt=1, 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"$\log M_{\rm tot} / M_\odot$")
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plt.ylabel("Maximum pair overlap")
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plt.ylim(0., 1.)
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plt.tight_layout()
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for ext in ["png"]:
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fout = join(plt_utils.fout, f"mass_vs_maxpairoverlap{nsim0}.{ext}")
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print(f"Saving to `{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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@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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Parameters
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----------
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nsim0 : int
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Simulation index.
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Returns
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-------
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mass : 1-dimensional array
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Mass of halos in the reference simulation.
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hindxs : 1-dimensional array
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Halo indices in the reference simulation.
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max_overlap : 2-dimensional array
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Maximum overlap for each halo in the reference simulation.
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summed_overlap : 2-dimensional array
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Summed overlap for each halo in the reference simulation.
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prob_nomatch : 2-dimensional array
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Probability of no match for each halo in the 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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print("Opening catalogues...", flush=True)
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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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mass = reader.cat0("totpartmass")
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hindxs = reader.cat0("index")
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summed_overlap = reader.summed_overlap(True)
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max_overlap = reader.max_overlap(True)
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prob_nomatch = reader.prob_nomatch(True)
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return mass, hindxs, max_overlap, summed_overlap, prob_nomatch
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def plot_mass_vsmedmaxoverlap(nsim0):
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"""
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Plot the mean maximum overlap of a reference simulation haloes with all the
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cross simulations.
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Parameters
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----------
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nsim0 : int
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Reference simulation index.
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"""
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x, __, max_overlap, __, __ = get_overlap(nsim0)
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for i in trange(max_overlap.shape[0]):
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if numpy.sum(numpy.isnan(max_overlap[i, :])) > 0:
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max_overlap[i, :] = numpy.nan
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x = numpy.log10(x)
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with plt.style.context(plt_utils.mplstyle):
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fig, axs = plt.subplots(ncols=3, figsize=(3.5 * 2, 2.625))
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im1 = axs[0].hexbin(x, numpy.nanmean(max_overlap, axis=1), gridsize=30,
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mincnt=1, bins="log")
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im2 = axs[1].hexbin(x, numpy.nanstd(max_overlap, axis=1), gridsize=30,
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mincnt=1, bins="log")
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im3 = axs[2].hexbin(numpy.nanmean(max_overlap, axis=1),
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numpy.nanstd(max_overlap, axis=1), gridsize=30,
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C=x, reduce_C_function=numpy.nanmean)
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axs[0].set_xlabel(r"$\log M_{\rm tot} / M_\odot$")
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axs[0].set_ylabel(r"Mean max. pair overlap")
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axs[1].set_xlabel(r"$\log M_{\rm tot} / M_\odot$")
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axs[1].set_ylabel(r"Uncertainty of max. pair overlap")
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axs[2].set_xlabel(r"Mean max. pair overlap")
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axs[2].set_ylabel(r"Uncertainty of max. pair overlap")
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label = ["Bin counts", "Bin counts", r"$\log M_{\rm tot} / M_\odot$"]
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ims = [im1, im2, im3]
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for i in range(3):
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axins = inset_axes(axs[i], width="100%", height="5%",
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loc='upper center', borderpad=-0.75)
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fig.colorbar(ims[i], cax=axins, orientation="horizontal",
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label=label[i])
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axins.xaxis.tick_top()
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axins.xaxis.set_tick_params(labeltop=True)
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axins.xaxis.set_label_position("top")
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fig.tight_layout()
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for ext in ["png"]:
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fout = join(plt_utils.fout, f"maxpairoverlap_{nsim0}.{ext}")
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print(f"Saving to `{fout}`.")
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fig.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
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plt.close()
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def plot_summed_overlap_vs_mass(nsim0):
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"""
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Plot the summed overlap of probability of no matching for a single
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reference simulations as a function of the reference halo mass, along with
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their comparison.
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Parameters
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----------
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nsim0 : int
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Simulation index.
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Returns
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-------
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None
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"""
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x, __, __, summed_overlap, prob_nomatch = get_overlap(nsim0)
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del __
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collect()
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for i in trange(summed_overlap.shape[0]):
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if numpy.sum(numpy.isnan(summed_overlap[i, :])) > 0:
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summed_overlap[i, :] = numpy.nan
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x = numpy.log10(x)
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mean_overlap = numpy.nanmean(summed_overlap, axis=1)
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std_overlap = numpy.nanstd(summed_overlap, axis=1)
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mean_prob_nomatch = numpy.nanmean(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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with plt.style.context(plt_utils.mplstyle):
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fig, axs = plt.subplots(ncols=3, figsize=(3.5 * 2, 2.625))
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im1 = axs[0].hexbin(x, mean_overlap, mincnt=1, bins="log",
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gridsize=30)
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im2 = axs[1].hexbin(x, std_overlap, mincnt=1, bins="log",
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gridsize=30)
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im3 = axs[2].scatter(1 - mean_overlap, mean_prob_nomatch, c=x, s=2,
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rasterized=True)
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t = numpy.linspace(0.3, 1, 100)
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axs[2].plot(t, t, color="red", linestyle="--")
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axs[0].set_ylim(0.)
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axs[1].set_ylim(0.)
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axs[0].set_xlabel(r"$\log M_{\rm tot} / M_\odot$")
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axs[0].set_ylabel("Mean summed overlap")
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axs[1].set_xlabel(r"$\log M_{\rm tot} / M_\odot$")
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axs[1].set_ylabel("Uncertainty of summed overlap")
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axs[2].set_xlabel(r"$1 - $ mean summed overlap")
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axs[2].set_ylabel("Mean prob. of no match")
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label = ["Bin counts", "Bin counts", r"$\log M_{\rm tot} / M_\odot$"]
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ims = [im1, im2, im3]
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for i in range(3):
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axins = inset_axes(axs[i], width="100%", height="5%",
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loc='upper center', borderpad=-0.75)
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fig.colorbar(ims[i], cax=axins, orientation="horizontal",
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label=label[i])
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axins.xaxis.tick_top()
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axins.xaxis.set_tick_params(labeltop=True)
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axins.xaxis.set_label_position("top")
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fig.tight_layout()
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for ext in ["png"]:
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fout = join(plt_utils.fout, f"overlap_stat_{nsim0}.{ext}")
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print(f"Saving to `{fout}`.")
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fig.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
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plt.close()
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def plot_mass_vs_separation(nsim0, nsimx, plot_std=False, min_overlap=0.0):
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"""
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Plot the mass of a reference halo against the weighted separation of
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its counterparts.
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Parameters
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----------
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nsim0 : int
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Reference simulation index.
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nsimx : int
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Cross simulation index.
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plot_std : bool, optional
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Whether to plot thestd instead of mean.
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min_overlap : float, optional
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Minimum overlap to consider.
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Returns
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-------
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None
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"""
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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cat0 = open_cat(nsim0)
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catx = open_cat(nsimx)
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reader = csiborgtools.read.PairOverlap(cat0, catx, paths,
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maxdist=155 / 0.705)
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mass = numpy.log10(reader.cat0("totpartmass"))
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dist = reader.dist(in_initial=False, norm_kind="r200c")
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overlap = reader.overlap(True)
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dist = csiborgtools.read.weighted_stats(dist, overlap,
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min_weight=min_overlap)
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mask = numpy.isfinite(dist[:, 0])
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mass = mass[mask]
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dist = dist[mask, :]
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dist = numpy.log10(dist)
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if not plot_std:
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p = numpy.polyfit(mass, dist[:, 0], 1)
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else:
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p = numpy.polyfit(mass, dist[:, 1], 1)
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xrange = numpy.linspace(numpy.min(mass), numpy.max(mass), 1000)
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txt = r"$m = {}$, $c = {}$".format(*plt_utils.latex_float(*p, n=3))
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with plt.style.context(plt_utils.mplstyle):
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fig, ax = plt.subplots()
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ax.set_title(txt, fontsize="small")
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if not plot_std:
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cx = ax.hexbin(mass, dist[:, 0], mincnt=1, bins="log", gridsize=50)
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ax.set_ylabel(r"$\log \langle \Delta R / R_{\rm 200c}\rangle$")
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else:
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cx = ax.hexbin(mass, dist[:, 1], mincnt=1, bins="log", gridsize=50)
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ax.set_ylabel(
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r"$\delta \log \langle \Delta R / R_{\rm 200c}\rangle$")
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ax.plot(xrange, numpy.polyval(p, xrange), color="red",
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linestyle="--")
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fig.colorbar(cx, label="Bin counts")
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ax.set_xlabel(r"$\log M_{\rm tot} / M_\odot$")
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ax.set_ylabel(r"$\log \langle \Delta R / R_{\rm 200c}\rangle$")
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fig.tight_layout()
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for ext in ["png"]:
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fout = join(plt_utils.fout,
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f"mass_vs_sep_{nsim0}_{nsimx}_{min_overlap}.{ext}")
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if plot_std:
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fout = fout.replace(f".{ext}", f"_std.{ext}")
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print(f"Saving to `{fout}`.")
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fig.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
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plt.close()
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@cache_to_disk(7)
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def get_max_key(nsim0, key):
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"""
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Get the value of a maximum overlap halo's property.
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Parameters
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----------
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nsim0 : int
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Reference simulation index.
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key : str
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Property to get.
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Returns
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-------
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mass0 : 1-dimensional array
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Mass of the reference haloes.
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key_val : 1-dimensional array
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Value of the property of the reference haloes.
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max_overlap : 2-dimensional array
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Maximum overlap of the reference haloes.
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stat : 2-dimensional array
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Value of the property of the maximum overlap halo.
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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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nsimxs = nsimxs
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cat0 = open_cat(nsim0)
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catxs = []
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print("Opening catalogues...", flush=True)
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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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mass0 = reader.cat0("totpartmass")
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key_val = reader.cat0(key)
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max_overlap = reader.max_overlap(True)
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stat = reader.max_overlap_key(key, True)
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return mass0, key_val, max_overlap, stat
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def plot_maxoverlap_mass(nsim0):
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"""
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Plot the mass of the reference haloes against the mass of the maximum
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overlap haloes.
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Parameters
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----------
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nsim0 : int
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Reference simulation index.
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"""
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mass0, __, __, stat = get_max_key(nsim0, "totpartmass")
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mu = numpy.mean(stat, axis=1)
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std = numpy.std(numpy.log10(stat), axis=1)
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mu = numpy.log10(mu)
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mass0 = numpy.log10(mass0)
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with plt.style.context(plt_utils.mplstyle):
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fig, axs = plt.subplots(ncols=2, figsize=(3.5 * 1.75, 2.625))
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im0 = axs[0].hexbin(mass0, mu, mincnt=1, bins="log", gridsize=50)
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im1 = axs[1].hexbin(mass0, std, mincnt=1, bins="log", gridsize=50)
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m = numpy.isfinite(mass0) & numpy.isfinite(mu)
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print("True to expectation corr: ", kendalltau(mass0[m], mu[m]))
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t = numpy.linspace(*numpy.percentile(mass0, [0, 100]), 1000)
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axs[0].plot(t, t, color="red", linestyle="--")
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axs[0].plot(t, t + 0.2, color="red", linestyle="--", alpha=0.5)
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axs[0].plot(t, t - 0.2, color="red", linestyle="--", alpha=0.5)
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axs[0].set_xlabel(r"$\log M_{\rm tot} / M_\odot$")
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axs[1].set_xlabel(r"$\log M_{\rm tot} / M_\odot$")
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axs[0].set_ylabel(r"Max. overlap mean of $\log M_{\rm tot} / M_\odot$")
|
|
axs[1].set_ylabel(r"Max. overlap std. of $\log M_{\rm tot} / M_\odot$")
|
|
|
|
ims = [im0, im1]
|
|
for i in range(2):
|
|
axins = inset_axes(axs[i], width="100%", height="5%",
|
|
loc='upper center', borderpad=-0.75)
|
|
fig.colorbar(ims[i], cax=axins, orientation="horizontal",
|
|
label="Bin counts")
|
|
axins.xaxis.tick_top()
|
|
axins.xaxis.set_tick_params(labeltop=True)
|
|
axins.xaxis.set_label_position("top")
|
|
|
|
fig.tight_layout()
|
|
for ext in ["png"]:
|
|
fout = join(plt_utils.fout,
|
|
f"max_totpartmass_{nsim0}.{ext}")
|
|
print(f"Saving to `{fout}`.")
|
|
fig.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
|
|
plt.close()
|
|
|
|
|
|
def plot_maxoverlapstat(nsim0, key):
|
|
"""
|
|
Plot the mass of the reference haloes against the value of the maximum
|
|
overlap statistic.
|
|
|
|
Parameters
|
|
----------
|
|
nsim0 : int
|
|
Reference simulation index.
|
|
key : str
|
|
Property to get.
|
|
"""
|
|
assert key != "totpartmass"
|
|
mass0, key_val, __, stat = get_max_key(nsim0, key)
|
|
|
|
xlabels = {"lambda200c": r"\log \lambda_{\rm 200c}"}
|
|
key_label = xlabels.get(key, key)
|
|
|
|
mass0 = numpy.log10(mass0)
|
|
key_val = numpy.log10(key_val)
|
|
|
|
mu = numpy.mean(stat, axis=1)
|
|
std = numpy.std(numpy.log10(stat), axis=1)
|
|
mu = numpy.log10(mu)
|
|
|
|
with plt.style.context(plt_utils.mplstyle):
|
|
fig, axs = plt.subplots(ncols=3, figsize=(3.5 * 2, 2.625))
|
|
|
|
im0 = axs[0].hexbin(mass0, mu, mincnt=1, bins="log", gridsize=30)
|
|
im1 = axs[1].hexbin(mass0, std, mincnt=1, bins="log", gridsize=30)
|
|
im2 = axs[2].hexbin(key_val, mu, mincnt=1, bins="log", gridsize=30)
|
|
m = numpy.isfinite(key_val) & numpy.isfinite(mu)
|
|
print("True to expectation corr: ", kendalltau(key_val[m], mu[m]))
|
|
|
|
axs[0].set_xlabel(r"$\log M_{\rm tot} / M_\odot$")
|
|
axs[0].set_ylabel(r"Max. overlap mean of ${}$".format(key_label))
|
|
axs[1].set_xlabel(r"$\log M_{\rm tot} / M_\odot$")
|
|
axs[1].set_ylabel(r"Max. overlap std. of ${}$".format(key_label))
|
|
axs[2].set_xlabel(r"${}$".format(key_label))
|
|
axs[2].set_ylabel(r"Max. overlap mean of ${}$".format(key_label))
|
|
|
|
ims = [im0, im1, im2]
|
|
for i in range(3):
|
|
axins = inset_axes(axs[i], width="100%", height="5%",
|
|
loc='upper center', borderpad=-0.75)
|
|
fig.colorbar(ims[i], cax=axins, orientation="horizontal",
|
|
label="Bin counts")
|
|
axins.xaxis.tick_top()
|
|
axins.xaxis.set_tick_params(labeltop=True)
|
|
axins.xaxis.set_label_position("top")
|
|
|
|
fig.tight_layout()
|
|
for ext in ["png"]:
|
|
fout = join(plt_utils.fout,
|
|
f"max_{key}_{nsim0}.{ext}")
|
|
print(f"Saving to `{fout}`.")
|
|
fig.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
|
|
plt.close()
|
|
|
|
|
|
@cache_to_disk(7)
|
|
def get_expected_mass(nsim0, min_overlap):
|
|
"""
|
|
Get the expected mass of a reference halo given its overlap with halos
|
|
from other simulations.
|
|
|
|
Parameters
|
|
----------
|
|
nsim0 : int
|
|
Reference simulation index.
|
|
min_overlap : float
|
|
Minimum overlap to consider between a pair of haloes.
|
|
|
|
Returns
|
|
-------
|
|
mass : 1-dimensional array
|
|
Mass of the reference haloes.
|
|
mu : 1-dimensional array
|
|
Expected mass of the matched haloes.
|
|
std : 1-dimensional array
|
|
Standard deviation of the expected mass of the matched haloes.
|
|
prob_nomatch : 2-dimensional array
|
|
Probability of not matching the reference halo.
|
|
"""
|
|
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
|
nsimxs = csiborgtools.read.get_cross_sims(nsim0, paths, smoothed=True)
|
|
nsimxs = nsimxs
|
|
cat0 = open_cat(nsim0)
|
|
|
|
catxs = []
|
|
print("Opening catalogues...", flush=True)
|
|
for nsimx in tqdm(nsimxs):
|
|
catxs.append(open_cat(nsimx))
|
|
|
|
reader = csiborgtools.read.NPairsOverlap(cat0, catxs, paths)
|
|
mass = reader.cat0("totpartmass")
|
|
|
|
mu, std = reader.counterpart_mass(True, overlap_threshold=min_overlap,
|
|
in_log=False, return_full=False)
|
|
prob_nomatch = reader.prob_nomatch(True)
|
|
return mass, mu, std, prob_nomatch
|
|
|
|
|
|
def plot_mass_vs_expected_mass(nsim0, min_overlap=0, max_prob_nomatch=1):
|
|
"""
|
|
Plot the mass of a reference halo against the expected mass of its
|
|
counterparts.
|
|
|
|
Parameters
|
|
----------
|
|
nsim0 : int
|
|
Reference simulation index.
|
|
min_overlap : float, optional
|
|
Minimum overlap between a pair of haloes to consider.
|
|
max_prob_nomatch : float, optional
|
|
Maximum probability of no match to consider.
|
|
"""
|
|
mass, mu, std, prob_nomatch = get_expected_mass(nsim0, min_overlap)
|
|
|
|
std = std / mu / numpy.log(10)
|
|
mass = numpy.log10(mass)
|
|
mu = numpy.log10(mu)
|
|
prob_nomatch = numpy.nanmedian(prob_nomatch, axis=1)
|
|
|
|
# bins = numpy.arange(numpy.min(mass), numpy.max(mass), 0.2)
|
|
mask = numpy.isfinite(mass) & numpy.isfinite(mu)
|
|
mask &= (prob_nomatch < max_prob_nomatch)
|
|
# stat_x, stat_mu, stat_std = plt_utils.binned_trend(
|
|
# mass[mask], mu[mask], 1 - prob_nomatch[mask], bins)
|
|
|
|
with plt.style.context(plt_utils.mplstyle):
|
|
fig, axs = plt.subplots(ncols=3, figsize=(3.5 * 2, 2.625))
|
|
|
|
im0 = axs[0].hexbin(mass[mask], mu[mask], mincnt=1, bins="log",
|
|
gridsize=50,)
|
|
im1 = axs[1].hexbin(mass[mask], std[mask], mincnt=1, bins="log",
|
|
gridsize=50)
|
|
im2 = axs[2].hexbin(1 - prob_nomatch[mask], mass[mask] - mu[mask],
|
|
gridsize=50, C=mass[mask],
|
|
reduce_C_function=numpy.nanmedian)
|
|
axs[2].axhline(0, color="red", linestyle="--", alpha=0.5)
|
|
|
|
axs[0].set_xlabel(r"True $\log M_{\rm tot} / M_\odot$")
|
|
axs[0].set_ylabel(r"Expected $\log M_{\rm tot} / M_\odot$")
|
|
axs[1].set_xlabel(r"True $\log M_{\rm tot} / M_\odot$")
|
|
axs[1].set_ylabel(r"Std. of $\sigma_{\log M_{\rm tot}}$")
|
|
axs[2].set_xlabel(r"1 - median prob. of no match")
|
|
axs[2].set_ylabel(r"$\log M_{\rm tot} - \log M_{\rm tot, exp}$")
|
|
|
|
t = numpy.linspace(*numpy.percentile(mass[mask], [0, 100]), 1000)
|
|
axs[0].plot(t, t, color="red", linestyle="--")
|
|
axs[0].plot(t, t + 0.2, color="red", linestyle="--", alpha=0.5)
|
|
axs[0].plot(t, t - 0.2, color="red", linestyle="--", alpha=0.5)
|
|
|
|
ims = [im0, im1, im2]
|
|
labels = ["Bin counts", "Bin counts", r"$\log M_{\rm tot}$"]
|
|
for i in range(3):
|
|
axins = inset_axes(axs[i], width="100%", height="5%",
|
|
loc='upper center', borderpad=-0.75)
|
|
fig.colorbar(ims[i], cax=axins, orientation="horizontal",
|
|
label=labels[i])
|
|
axins.xaxis.tick_top()
|
|
axins.xaxis.set_tick_params(labeltop=True)
|
|
axins.xaxis.set_label_position("top")
|
|
|
|
fig.tight_layout()
|
|
for ext in ["png"]:
|
|
fout = join(plt_utils.fout,
|
|
f"mass_vs_expmass_{nsim0}_{max_prob_nomatch}.{ext}")
|
|
print(f"Saving to `{fout}`.")
|
|
fig.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
|
|
plt.close()
|
|
|
|
|
|
###############################################################################
|
|
# Nearest neighbour plotting #
|
|
###############################################################################
|
|
|
|
|
|
def read_dist(simname, run, kind, kwargs):
|
|
"""
|
|
Read PDF/CDF of a nearest neighbour distribution.
|
|
|
|
Parameters
|
|
----------
|
|
simname : str
|
|
Simulation name. Must be either `csiborg` or `quijote`.
|
|
run : str
|
|
Run name.
|
|
kind : str
|
|
Kind of distribution. Must be either `pdf` or `cdf`.
|
|
kwargs : dict
|
|
Nearest neighbour reader keyword arguments.
|
|
|
|
Returns
|
|
-------
|
|
dist : 2-dimensional array
|
|
Distribution of distances in radial and neighbour bins.
|
|
"""
|
|
paths = csiborgtools.read.Paths(**kwargs["paths_kind"])
|
|
reader = csiborgtools.read.NearestNeighbourReader(**kwargs, paths=paths)
|
|
|
|
fpath = paths.cross_nearest(simname, run, "tot_counts", nsim=0, nobs=0)
|
|
counts = numpy.load(fpath)["tot_counts"]
|
|
return reader.build_dist(counts, kind)
|
|
|
|
|
|
def pull_cdf(x, fid_cdf, test_cdf):
|
|
"""
|
|
Pull a CDF so that it matches the fiducial CDF at 0.5. Rescales the x-axis,
|
|
while keeping the corresponding CDF values fixed.
|
|
|
|
Parameters
|
|
----------
|
|
x : 1-dimensional array
|
|
The x-axis of the CDF.
|
|
fid_cdf : 1-dimensional array
|
|
The fiducial CDF.
|
|
test_cdf : 1-dimensional array
|
|
The test CDF to be pulled.
|
|
|
|
Returns
|
|
-------
|
|
xnew : 1-dimensional array
|
|
The new x-axis of the test CDF.
|
|
test_cdf : 1-dimensional array
|
|
The new test CDF.
|
|
"""
|
|
xnew = x * numpy.interp(0.5, fid_cdf, x) / numpy.interp(0.5, test_cdf, x)
|
|
return xnew, test_cdf
|
|
|
|
|
|
def plot_dist(run, kind, kwargs, runs_to_mass, pulled_cdf=False, r200=None):
|
|
r"""
|
|
Plot the PDF or CDF of the nearest neighbour distance for CSiBORG and
|
|
Quijote.
|
|
|
|
Parameters
|
|
----------
|
|
run : str
|
|
Run name.
|
|
kind : str
|
|
Kind of distribution. Must be either `pdf` or `cdf`.
|
|
kwargs : dict
|
|
Nearest neighbour reader keyword arguments.
|
|
runs_to_mass : dict
|
|
Dictionary mapping run names to halo mass range.
|
|
pulled_cdf : bool, optional
|
|
Whether to pull the CDFs of CSiBORG and Quijote so that they match
|
|
(individually) at 0.5. Default is `False`.
|
|
r200 : float, optional
|
|
Halo radial size :math:`R_{200}`. If set, the x-axis will be scaled by
|
|
it.
|
|
|
|
Returns
|
|
-------
|
|
None
|
|
"""
|
|
assert kind in ["pdf", "cdf"]
|
|
print(f"Plotting the {kind} for {run}...", flush=True)
|
|
reader = csiborgtools.read.NearestNeighbourReader(
|
|
**kwargs, paths=csiborgtools.read.Paths(**kwargs["paths_kind"]))
|
|
raddist = reader.bin_centres("radial")
|
|
r = reader.bin_centres("neighbour")
|
|
r = r / r200 if r200 is not None else r
|
|
|
|
y_csiborg = read_dist("csiborg", run, kind, kwargs)
|
|
y_quijote = read_dist("quijote", run, kind, kwargs)
|
|
|
|
with plt.style.context(plt_utils.mplstyle):
|
|
norm = mpl.colors.Normalize(vmin=numpy.min(raddist),
|
|
vmax=numpy.max(raddist))
|
|
cmap = mpl.cm.ScalarMappable(norm=norm, cmap=mpl.cm.viridis)
|
|
cmap.set_array([])
|
|
|
|
fig, ax = plt.subplots()
|
|
if run != "mass009":
|
|
ax.set_title(r"${} \leq \log M_{{\rm tot}} / M_\odot < {}$"
|
|
.format(*runs_to_mass[run]), fontsize="small")
|
|
else:
|
|
ax.set_title(r"$\log M_{{\rm tot}} / M_\odot \geq {}$"
|
|
.format(runs_to_mass[run][0]), fontsize="small")
|
|
# Plot data
|
|
nrad = y_csiborg.shape[0]
|
|
for i in range(nrad):
|
|
if pulled_cdf:
|
|
x1, y1 = pull_cdf(r, y_csiborg[0], y_csiborg[i])
|
|
x2, y2 = pull_cdf(r, y_quijote[0], y_quijote[i])
|
|
else:
|
|
x1, y1 = r, y_csiborg[i]
|
|
x2, y2 = r, y_quijote[i]
|
|
|
|
ax.plot(x1, y1, c=cmap.to_rgba(raddist[i]),
|
|
label="CSiBORG" if i == 0 else None)
|
|
ax.plot(x2, y2, c="gray", ls="--",
|
|
label="Quijote" if i == 0 else None)
|
|
|
|
fig.colorbar(cmap, ax=ax, label=r"$R_{\rm dist}~[\mathrm{Mpc}]$")
|
|
ax.grid(alpha=0.5, lw=0.4)
|
|
# Plot labels
|
|
if pulled_cdf:
|
|
if r200 is None:
|
|
ax.set_xlabel(r"$\tilde{r}_{1\mathrm{NN}}~[\mathrm{Mpc}]$")
|
|
if kind == "pdf":
|
|
ax.set_ylabel(r"$p(\tilde{r}_{1\mathrm{NN}})$")
|
|
else:
|
|
ax.set_ylabel(r"$\mathrm{CDF}(\tilde{r}_{1\mathrm{NN}})$")
|
|
else:
|
|
ax.set_xlabel(r"$\tilde{r}_{1\mathrm{NN}} / R_{200c}$")
|
|
if kind == "pdf":
|
|
ax.set_ylabel(r"$p(\tilde{r}_{1\mathrm{NN}} / R_{200c})$")
|
|
else:
|
|
ax.set_ylabel(r"$\mathrm{CDF}(\tilde{r}_{1\mathrm{NN}} / R_{200c})$") # noqa
|
|
else:
|
|
if r200 is None:
|
|
ax.set_xlabel(r"$r_{1\mathrm{NN}}~[\mathrm{Mpc}]$")
|
|
if kind == "pdf":
|
|
ax.set_ylabel(r"$p(r_{1\mathrm{NN}})$")
|
|
else:
|
|
ax.set_ylabel(r"$\mathrm{CDF}(r_{1\mathrm{NN}})$")
|
|
else:
|
|
ax.set_xlabel(r"$r_{1\mathrm{NN}} / R_{200c}$")
|
|
if kind == "pdf":
|
|
ax.set_ylabel(r"$p(r_{1\mathrm{NN}} / R_{200c})$")
|
|
else:
|
|
ax.set_ylabel(r"$\mathrm{CDF}(r_{1\mathrm{NN}} / R_{200c})$") # noqa
|
|
|
|
if kind == "cdf":
|
|
xmax = numpy.min(r[numpy.isclose(y_quijote[-1, :], 1.)])
|
|
if xmax > 0:
|
|
ax.set_xlim(0, xmax)
|
|
ax.set_ylim(0, 1)
|
|
|
|
ax.legend(fontsize="small")
|
|
fig.tight_layout()
|
|
for ext in ["png"]:
|
|
if pulled_cdf:
|
|
fout = join(plt_utils.fout, f"1nn_{kind}_{run}_pulled.{ext}")
|
|
else:
|
|
fout = join(plt_utils.fout, f"1nn_{kind}_{run}.{ext}")
|
|
print(f"Saving to `{fout}`.")
|
|
fig.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
|
|
plt.close()
|
|
|
|
|
|
def get_cdf_diff(x, y_csiborg, y_quijote, pulled_cdf):
|
|
"""
|
|
Get difference between the two CDFs as a function of radial distance.
|
|
|
|
Parameters
|
|
----------
|
|
x : 1-dimensional array
|
|
The x-axis of the CDFs.
|
|
y_csiborg : 2-dimensional array
|
|
The CDFs of CSiBORG.
|
|
y_quijote : 2-dimensional array
|
|
The CDFs of Quijote.
|
|
pulled_cdf : bool
|
|
Whether to pull the CDFs of CSiBORG and Quijote.
|
|
|
|
Returns
|
|
-------
|
|
dy : 2-dimensional array
|
|
The difference between the two CDFs.
|
|
"""
|
|
dy = numpy.full_like(y_csiborg, numpy.nan)
|
|
for i in range(y_csiborg.shape[0]):
|
|
if pulled_cdf:
|
|
x1, y1 = pull_cdf(x, y_csiborg[0], y_csiborg[i])
|
|
y1 = numpy.interp(x, x1, y1, left=0., right=1.)
|
|
x2, y2 = pull_cdf(x, y_quijote[0], y_quijote[i])
|
|
y2 = numpy.interp(x, x2, y2, left=0., right=1.)
|
|
dy[i] = y1 - y2
|
|
else:
|
|
dy[i] = y_csiborg[i] - y_quijote[i]
|
|
return dy
|
|
|
|
|
|
def plot_cdf_diff(runs, kwargs, pulled_cdf, runs_to_mass):
|
|
"""
|
|
Plot the CDF difference between Quijote and CSiBORG.
|
|
|
|
Parameters
|
|
----------
|
|
runs : list of str
|
|
Run names.
|
|
kwargs : dict
|
|
Nearest neighbour reader keyword arguments.
|
|
pulled_cdf : bool
|
|
Whether to pull the CDFs of CSiBORG and Quijote.
|
|
runs_to_mass : dict
|
|
Dictionary mapping run names to halo mass range.
|
|
|
|
Returns
|
|
-------
|
|
None
|
|
"""
|
|
print("Plotting the CDF difference...", flush=True)
|
|
paths = csiborgtools.read.Paths(**kwargs["paths_kind"])
|
|
reader = csiborgtools.read.NearestNeighbourReader(**kwargs, paths=paths)
|
|
r = reader.bin_centres("neighbour")
|
|
runs_to_mass = [numpy.mean(runs_to_mass[run]) for run in runs]
|
|
|
|
with plt.style.context(plt_utils.mplstyle):
|
|
norm = mpl.colors.Normalize(vmin=min(runs_to_mass),
|
|
vmax=max(runs_to_mass))
|
|
cmap = mpl.cm.ScalarMappable(norm=norm, cmap=mpl.cm.viridis)
|
|
cmap.set_array([])
|
|
|
|
fig, ax = plt.subplots()
|
|
for i, run in enumerate(runs):
|
|
y_quijote = read_dist("quijote", run, "cdf", kwargs)
|
|
y_csiborg = read_dist("csiborg", run, "cdf", kwargs)
|
|
|
|
dy = get_cdf_diff(r, y_csiborg, y_quijote, pulled_cdf)
|
|
ax.plot(r, numpy.median(dy, axis=0),
|
|
c=cmap.to_rgba(runs_to_mass[i]))
|
|
ax.fill_between(r, *numpy.percentile(dy, [16, 84], axis=0),
|
|
alpha=0.5, color=cmap.to_rgba(runs_to_mass[i]))
|
|
fig.colorbar(cmap, ax=ax, ticks=runs_to_mass,
|
|
label=r"$\log M_{\rm tot} / M_\odot$")
|
|
ax.set_xlim(0.0, 55)
|
|
ax.set_ylim(0)
|
|
|
|
ax.grid(alpha=1/3, lw=0.4)
|
|
|
|
# Plot labels
|
|
if pulled_cdf:
|
|
ax.set_xlabel(r"$\tilde{r}_{1\mathrm{NN}}~[\mathrm{Mpc}]$")
|
|
else:
|
|
ax.set_xlabel(r"$r_{1\mathrm{NN}}~[\mathrm{Mpc}]$")
|
|
ax.set_ylabel(r"$\Delta \mathrm{CDF}(r_{1\mathrm{NN}})$")
|
|
|
|
# Plot labels
|
|
if pulled_cdf:
|
|
ax.set_xlabel(r"$\tilde{r}_{1\mathrm{NN}}~[\mathrm{Mpc}]$")
|
|
ax.set_ylabel(r"$\Delta \mathrm{CDF}(\tilde{r}_{1\mathrm{NN}})$")
|
|
else:
|
|
ax.set_xlabel(r"$r_{1\mathrm{NN}}~[\mathrm{Mpc}]$")
|
|
ax.set_ylabel(r"$\Delta \mathrm{CDF}(r_{1\mathrm{NN}})$")
|
|
|
|
fig.tight_layout()
|
|
for ext in ["png"]:
|
|
if pulled_cdf:
|
|
fout = join(plt_utils.fout, f"1nn_diff_pulled.{ext}")
|
|
else:
|
|
fout = join(plt_utils.fout, f"1nn_diff.{ext}")
|
|
print(f"Saving to `{fout}`.")
|
|
fig.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
|
|
plt.close()
|
|
|
|
|
|
@cache_to_disk(7)
|
|
def make_kl(simname, run, nsim, nobs, kwargs):
|
|
"""
|
|
Calculate the KL divergence between the distribution of nearest neighbour
|
|
distances of haloes in a reference simulation with respect to Quijote.
|
|
|
|
Parameters
|
|
----------
|
|
simname : str
|
|
Simulation name. Must be either `csiborg` or `quijote`.
|
|
run : str
|
|
Run name.
|
|
nsim : int
|
|
Simulation index.
|
|
nobs : int
|
|
Fiducial Quijote observer index. For CSiBORG must be set to `None`.
|
|
kwargs : dict
|
|
Nearest neighbour reader keyword arguments.
|
|
|
|
Returns
|
|
-------
|
|
kl : 1-dimensional array
|
|
KL divergence of the distribution of nearest neighbour distances
|
|
of each halo in the reference simulation.
|
|
"""
|
|
paths = csiborgtools.read.Paths(**kwargs["paths_kind"])
|
|
reader = csiborgtools.read.NearestNeighbourReader(**kwargs, paths=paths)
|
|
# This is the reference PDF. Must be Quijote!
|
|
pdf = read_dist("quijote", run, "pdf", kwargs)
|
|
return reader.kl_divergence(simname, run, nsim, pdf, nobs=nobs)
|
|
|
|
|
|
@cache_to_disk(7)
|
|
def make_ks(simname, run, nsim, nobs, kwargs):
|
|
"""
|
|
Calculate the KS significance between the distribution of nearest neighbour
|
|
distances of haloes in a reference simulation with respect to Quijote.
|
|
|
|
Parameters
|
|
----------
|
|
simname : str
|
|
Simulation name. Must be either `csiborg` or `quijote`.
|
|
run : str
|
|
Run name.
|
|
nsim : int
|
|
Simulation index.
|
|
nobs : int
|
|
Fiducial Quijote observer index. For CSiBORG must be set to `None`.
|
|
kwargs : dict
|
|
Nearest neighbour reader keyword arguments.
|
|
|
|
Returns
|
|
-------
|
|
ks : 1-dimensional array
|
|
KS significance of the distribution of nearest neighbour distances of
|
|
each halo in the reference simulation.
|
|
"""
|
|
paths = csiborgtools.read.Paths(**kwargs["paths_kind"])
|
|
reader = csiborgtools.read.NearestNeighbourReader(**kwargs, paths=paths)
|
|
# This is the reference CDF. Must be Quijote!
|
|
cdf = read_dist("quijote", run, "cdf", kwargs)
|
|
return reader.ks_significance(simname, run, nsim, cdf, nobs=nobs)
|
|
|
|
|
|
def get_cumulative_significance(simname, runs, nsim, nobs, kind, kwargs):
|
|
"""
|
|
Calculate the cumulative significance of the distribution of nearest
|
|
neighbours and evaluate it at the same points for all runs.
|
|
|
|
Parameters
|
|
----------
|
|
simname : str
|
|
Simulation name. Must be either `csiborg` or `quijote`.
|
|
runs : list of str
|
|
Run names.
|
|
nsim : int
|
|
Simulation index.
|
|
nobs : int
|
|
Fiducial Quijote observer index. For CSiBORG must be set to `None`.
|
|
kind : str
|
|
Must be either `kl` (Kullback-Leibler diverge) or `ks`
|
|
(Kolmogorov-Smirnov p-value).
|
|
kwargs : dict
|
|
Nearest neighbour reader keyword arguments.
|
|
|
|
Returns
|
|
-------
|
|
z : 1-dimensional array
|
|
Points where the cumulative significance is evaluated.
|
|
cumsum : 2-dimensional array of shape `(len(runs), len(z)))`
|
|
Cumulative significance of the distribution of nearest neighbours.
|
|
"""
|
|
significances = []
|
|
for run in runs:
|
|
if kind == "kl":
|
|
x = make_kl(simname, run, nsim, nobs, kwargs)
|
|
else:
|
|
x = make_ks(simname, run, nsim, nobs, kwargs)
|
|
x = numpy.log10(x)
|
|
x = x[numpy.isfinite(x)]
|
|
x = numpy.sort(x)
|
|
significances.append(x)
|
|
z = numpy.hstack(significances).reshape(-1, )
|
|
|
|
if kind == "ks":
|
|
zmin, zmax = numpy.percentile(z, [1, 100])
|
|
else:
|
|
zmin, zmax = numpy.percentile(z, [0.0, 99.9])
|
|
z = numpy.linspace(zmin, zmax, 1000, dtype=numpy.float32)
|
|
|
|
cumsum = numpy.full((len(runs), z.size), numpy.nan, dtype=numpy.float32)
|
|
for i, run in enumerate(runs):
|
|
x = significances[i]
|
|
y = numpy.linspace(0, 1, x.size)
|
|
cumsum[i, :] = numpy.interp(z, x, y, left=0, right=1)
|
|
|
|
return z, cumsum
|
|
|
|
|
|
def plot_significance(simname, runs, nsim, nobs, kind, kwargs, runs_to_mass):
|
|
"""
|
|
Plot cumulative significance of the 1NN distribution.
|
|
|
|
Parameters
|
|
----------
|
|
simname : str
|
|
Simulation name. Must be either `csiborg` or `quijote`.
|
|
runs : list of str
|
|
Run names.
|
|
nsim : int
|
|
Simulation index.
|
|
nobs : int
|
|
Fiducial Quijote observer index. For CSiBORG must be set to `None`.
|
|
kind : str
|
|
Must be either `kl` (Kullback-Leibler diverge) or `ks`
|
|
(Kolmogorov-Smirnov p-value).
|
|
kwargs : dict
|
|
Nearest neighbour reader keyword arguments.
|
|
runs_to_mass : dict
|
|
Dictionary mapping run names to total halo mass range.
|
|
upper_threshold : bool, optional
|
|
|
|
Returns
|
|
-------
|
|
None
|
|
"""
|
|
assert kind in ["kl", "ks"]
|
|
runs_to_mass = [numpy.mean(runs_to_mass[run]) for run in runs]
|
|
|
|
with plt.style.context(plt_utils.mplstyle):
|
|
norm = mpl.colors.Normalize(vmin=min(runs_to_mass),
|
|
vmax=max(runs_to_mass))
|
|
cmap = mpl.cm.ScalarMappable(norm=norm, cmap=mpl.cm.viridis)
|
|
cmap.set_array([])
|
|
|
|
fig, ax = plt.subplots(figsize=(3.5, 2.625 * 1.2), nrows=2,
|
|
sharex=True, height_ratios=[1, 0.5])
|
|
fig.subplots_adjust(hspace=0, wspace=0)
|
|
z, cumsum = get_cumulative_significance(simname, runs, nsim, nobs,
|
|
kind, kwargs)
|
|
|
|
for i in range(len(runs)):
|
|
ax[0].plot(z, cumsum[i, :], color=cmap.to_rgba(runs_to_mass[i]))
|
|
|
|
dy = cumsum[-1, :] - cumsum[i, :]
|
|
if kind == "kl":
|
|
dy *= -1
|
|
ax[1].plot(z, dy, color=cmap.to_rgba(runs_to_mass[i]))
|
|
|
|
cbar_ax = fig.add_axes([1.0, 0.125, 0.035, 0.85])
|
|
fig.colorbar(cmap, cax=cbar_ax, ticks=runs_to_mass,
|
|
label=r"$\log M_{\rm tot} / M_\odot$")
|
|
|
|
ax[0].set_xlim(z[0], z[-1])
|
|
ax[0].set_ylim(1e-5, 1.)
|
|
if kind == "ks":
|
|
ax[1].set_xlabel(r"$\log p$-value of $r_{1\mathrm{NN}}$ distribution") # noqa
|
|
else:
|
|
ax[1].set_xlabel(r"$D_{\mathrm{KL}}$ of $r_{1\mathrm{NN}}$ distribution") # noqa
|
|
ax[0].set_ylabel(r"Cumulative norm. counts")
|
|
ax[1].set_ylabel(r"$\Delta f$")
|
|
|
|
fig.tight_layout(h_pad=0, w_pad=0)
|
|
for ext in ["png"]:
|
|
if simname == "quijote":
|
|
paths = csiborgtools.read.Paths(**kwargs["paths_kind"])
|
|
nsim = paths.quijote_fiducial_nsim(nsim, nobs)
|
|
nsim = str(nsim).zfill(5)
|
|
fout = join(
|
|
plt_utils.fout,
|
|
f"significance_{kind}_{simname}_{nsim}_{runs}.{ext}")
|
|
print(f"Saving to `{fout}`.")
|
|
fig.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
|
|
plt.close()
|
|
|
|
|
|
def make_binlims(run, runs_to_mass, upper_threshold=None):
|
|
"""
|
|
Make bin limits for the 1NN distance runs, corresponding to the first half
|
|
of the log-mass bin.
|
|
|
|
Parameters
|
|
----------
|
|
run : str
|
|
Run name.
|
|
runs_to_mass : dict
|
|
Dictionary mapping run names to total halo mass range.
|
|
upper_threshold : float, optional
|
|
Bin width in dex. If set to `None`, the bin width is taken from the
|
|
`runs_to_mass` dictionary.
|
|
|
|
Returns
|
|
-------
|
|
xmin, xmax : floats
|
|
"""
|
|
xmin, xmax = runs_to_mass[run]
|
|
if upper_threshold is not None:
|
|
xmax = xmin + upper_threshold
|
|
|
|
xmin, xmax = 10**xmin, 10**xmax
|
|
if run == "mass009":
|
|
xmax = numpy.infty
|
|
return xmin, xmax
|
|
|
|
|
|
def plot_significance_vs_mass(simname, runs, nsim, nobs, kind, kwargs,
|
|
runs_to_mass, upper_threshold=False):
|
|
"""
|
|
Plot significance of the 1NN distance as a function of the total mass.
|
|
|
|
Parameters
|
|
----------
|
|
simname : str
|
|
Simulation name. Must be either `csiborg` or `quijote`.
|
|
runs : list of str
|
|
Run names.
|
|
nsim : int
|
|
Simulation index.
|
|
nobs : int
|
|
Fiducial Quijote observer index. For CSiBORG must be set to `None`.
|
|
kind : str
|
|
Must be either `kl` (Kullback-Leibler diverge) or `ks`
|
|
(Kolmogorov-Smirnov p-value).
|
|
kwargs : dict
|
|
Nearest neighbour reader keyword arguments.
|
|
runs_to_mass : dict
|
|
Dictionary mapping run names to total halo mass range.
|
|
upper_threshold : bool, optional
|
|
Whether to enforce an upper threshold on halo mass.
|
|
|
|
Returns
|
|
-------
|
|
None
|
|
"""
|
|
print(f"Plotting {kind} significance vs mass.")
|
|
assert kind in ["kl", "ks"]
|
|
paths = csiborgtools.read.Paths(**kwargs["paths_kind"])
|
|
reader = csiborgtools.read.NearestNeighbourReader(**kwargs, paths=paths)
|
|
|
|
with plt.style.context(plt_utils.mplstyle):
|
|
plt.figure()
|
|
xs, ys = [], []
|
|
for run in runs:
|
|
x = reader.read_single(simname, run, nsim, nobs)["mass"]
|
|
if kind == "kl":
|
|
y = make_kl(simname, run, nsim, nobs, kwargs)
|
|
else:
|
|
y = numpy.log10(make_ks(simname, run, nsim, nobs, kwargs))
|
|
|
|
xmin, xmax = make_binlims(run, runs_to_mass, upper_threshold)
|
|
|
|
mask = (x >= xmin) & (x < xmax)
|
|
xs.append(numpy.log10(x[mask]))
|
|
ys.append(y[mask])
|
|
|
|
xs = numpy.concatenate(xs)
|
|
ys = numpy.concatenate(ys)
|
|
|
|
plt.hexbin(xs, ys, gridsize=75, mincnt=1, bins="log")
|
|
mask = numpy.isfinite(xs) & numpy.isfinite(ys)
|
|
corr = plt_utils.latex_float(*kendalltau(xs[mask], ys[mask]))
|
|
plt.title(r"$\tau = {}, p = {}$".format(*corr), fontsize="small")
|
|
|
|
plt.xlabel(r"$\log M_{\rm tot} / M_\odot$")
|
|
if kind == "ks":
|
|
plt.ylabel(r"$\log p$-value of $r_{1\mathrm{NN}}$ distribution")
|
|
plt.ylim(top=0)
|
|
else:
|
|
plt.ylabel(r"$D_{\mathrm{KL}}$ of $r_{1\mathrm{NN}}$ distribution")
|
|
plt.ylim(bottom=0)
|
|
plt.colorbar(label="Bin counts")
|
|
|
|
plt.tight_layout()
|
|
for ext in ["png"]:
|
|
if simname == "quijote":
|
|
nsim = paths.quijote_fiducial_nsim(nsim, nobs)
|
|
nsim = str(nsim).zfill(5)
|
|
fout = f"sgnf_vs_mass_{kind}_{simname}_{nsim}_{runs}.{ext}"
|
|
if upper_threshold:
|
|
fout = fout.replace(f".{ext}", f"_upper_threshold.{ext}")
|
|
fout = join(plt_utils.fout, fout)
|
|
print(f"Saving to `{fout}`.")
|
|
plt.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
|
|
plt.close()
|
|
|
|
|
|
def plot_kl_vs_ks(simname, runs, nsim, nobs, kwargs, runs_to_mass,
|
|
upper_threshold=False):
|
|
"""
|
|
Plot Kullback-Leibler divergence vs Kolmogorov-Smirnov statistic p-value.
|
|
|
|
Parameters
|
|
----------
|
|
simname : str
|
|
Simulation name. Must be either `csiborg` or `quijote`.
|
|
runs : str
|
|
Run names.
|
|
nsim : int
|
|
Simulation index.
|
|
nobs : int
|
|
Fiducial Quijote observer index. For CSiBORG must be set to `None`.
|
|
kwargs : dict
|
|
Nearest neighbour reader keyword arguments.
|
|
runs_to_mass : dict
|
|
Dictionary mapping run names to total halo mass range.
|
|
upper_threshold : bool, optional
|
|
Whether to enforce an upper threshold on halo mass.
|
|
|
|
Returns
|
|
-------
|
|
None
|
|
"""
|
|
paths = csiborgtools.read.Paths(**kwargs["paths_kind"])
|
|
reader = csiborgtools.read.NearestNeighbourReader(**kwargs, paths=paths)
|
|
|
|
xs, ys, cs = [], [], []
|
|
for run in runs:
|
|
c = reader.read_single(simname, run, nsim, nobs)["mass"]
|
|
x = make_kl(simname, run, nsim, nobs, kwargs)
|
|
y = make_ks(simname, run, nsim, nobs, kwargs)
|
|
|
|
cmin, cmax = make_binlims(run, runs_to_mass)
|
|
mask = (c >= cmin) & (c < cmax if upper_threshold else True)
|
|
xs.append(x[mask])
|
|
ys.append(y[mask])
|
|
cs.append(c[mask])
|
|
|
|
xs = numpy.concatenate(xs)
|
|
ys = numpy.log10(numpy.concatenate(ys))
|
|
cs = numpy.log10(numpy.concatenate(cs))
|
|
|
|
with plt.style.context(plt_utils.mplstyle):
|
|
plt.figure()
|
|
plt.hexbin(xs, ys, C=cs, gridsize=50, mincnt=0,
|
|
reduce_C_function=numpy.median)
|
|
mask = numpy.isfinite(xs) & numpy.isfinite(ys)
|
|
corr = plt_utils.latex_float(*kendalltau(xs[mask], ys[mask]))
|
|
plt.title(r"$\tau = {}, p = {}$".format(*corr), fontsize="small")
|
|
plt.colorbar(label=r"$\log M_{\rm tot} / M_\odot$")
|
|
|
|
plt.xlabel(r"$D_{\mathrm{KL}}$ of $r_{1\mathrm{NN}}$ distribution")
|
|
plt.ylabel(r"$\log p$-value of $r_{1\mathrm{NN}}$ distribution")
|
|
|
|
plt.tight_layout()
|
|
for ext in ["png"]:
|
|
if simname == "quijote":
|
|
nsim = paths.quijote_fiducial_nsim(nsim, nobs)
|
|
nsim = str(nsim).zfill(5)
|
|
fout = join(
|
|
plt_utils.fout,
|
|
f"kl_vs_ks_{simname}_{run}_{nsim}_{runs}.{ext}")
|
|
if upper_threshold:
|
|
fout = fout.replace(f".{ext}", f"_upper_threshold.{ext}")
|
|
print(f"Saving to `{fout}`.")
|
|
plt.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
|
|
plt.close()
|
|
|
|
|
|
def plot_kl_vs_overlap(runs, nsim, kwargs, runs_to_mass, plot_std=True,
|
|
upper_threshold=False):
|
|
"""
|
|
Plot KL divergence vs overlap for CSiBORG.
|
|
|
|
Parameters
|
|
----------
|
|
runs : str
|
|
Run names.
|
|
nsim : int
|
|
Simulation index.
|
|
kwargs : dict
|
|
Nearest neighbour reader keyword arguments.
|
|
runs_to_mass : dict
|
|
Dictionary mapping run names to total halo mass range.
|
|
plot_std : bool, optional
|
|
Whether to plot the standard deviation of the overlap distribution.
|
|
upper_threshold : bool, optional
|
|
Whether to enforce an upper threshold on halo mass.
|
|
|
|
Returns
|
|
-------
|
|
None
|
|
"""
|
|
paths = csiborgtools.read.Paths(**kwargs["paths_kind"])
|
|
nn_reader = csiborgtools.read.NearestNeighbourReader(**kwargs, paths=paths)
|
|
|
|
xs, ys1, ys2, cs = [], [], [], []
|
|
for run in runs:
|
|
nn_data = nn_reader.read_single("csiborg", run, nsim, nobs=None)
|
|
nn_hindxs = nn_data["ref_hindxs"]
|
|
mass, overlap_hindxs, __, summed_overlap, prob_nomatch = get_overlap(nsim) # noqa
|
|
|
|
# We need to match the hindxs between the two.
|
|
hind2overlap_array = {hind: i for i, hind in enumerate(overlap_hindxs)}
|
|
mask = numpy.asanyarray([hind2overlap_array[hind]
|
|
for hind in nn_hindxs])
|
|
summed_overlap = summed_overlap[mask]
|
|
prob_nomatch = prob_nomatch[mask]
|
|
mass = mass[mask]
|
|
|
|
x = make_kl("csiborg", run, nsim, nobs=None, kwargs=kwargs)
|
|
y1 = 1 - numpy.mean(prob_nomatch, axis=1)
|
|
y2 = numpy.std(prob_nomatch, axis=1)
|
|
cmin, cmax = make_binlims(run, runs_to_mass, upper_threshold)
|
|
mask = (mass >= cmin) & (mass < cmax if upper_threshold else True)
|
|
xs.append(x[mask])
|
|
ys1.append(y1[mask])
|
|
ys2.append(y2[mask])
|
|
cs.append(numpy.log10(mass[mask]))
|
|
|
|
xs = numpy.concatenate(xs)
|
|
ys1 = numpy.concatenate(ys1)
|
|
ys2 = numpy.concatenate(ys2)
|
|
cs = numpy.concatenate(cs)
|
|
|
|
with plt.style.context(plt_utils.mplstyle):
|
|
plt.figure()
|
|
plt.hexbin(xs, ys1, C=cs, gridsize=50, mincnt=0,
|
|
reduce_C_function=numpy.median)
|
|
mask = numpy.isfinite(xs) & numpy.isfinite(ys1)
|
|
corr = plt_utils.latex_float(*kendalltau(xs[mask], ys1[mask]))
|
|
plt.title(r"$\tau = {}, p = {}$".format(*corr), fontsize="small")
|
|
|
|
plt.colorbar(label=r"$\log M_{\rm tot} / M_\odot$")
|
|
plt.xlabel(r"$D_{\mathrm{KL}}$ of $r_{1\mathrm{NN}}$ distribution")
|
|
plt.ylabel("1 - mean prob. of no match")
|
|
|
|
plt.tight_layout()
|
|
for ext in ["png"]:
|
|
nsim = str(nsim).zfill(5)
|
|
fout = join(plt_utils.fout,
|
|
f"kl_vs_overlap_mean_{nsim}_{runs}.{ext}")
|
|
if upper_threshold:
|
|
fout = fout.replace(f".{ext}", f"_upper_threshold.{ext}")
|
|
print(f"Saving to `{fout}`.")
|
|
plt.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
|
|
plt.close()
|
|
|
|
if not plot_std:
|
|
return
|
|
|
|
with plt.style.context(plt_utils.mplstyle):
|
|
plt.figure()
|
|
plt.hexbin(xs, ys2, C=cs, gridsize=50, mincnt=0,
|
|
reduce_C_function=numpy.median)
|
|
plt.colorbar(label=r"$\log M_{\rm tot} / M_\odot$")
|
|
plt.xlabel(r"$D_{\mathrm{KL}}$ of $r_{1\mathrm{NN}}$ distribution")
|
|
plt.ylabel(r"Ensemble std of summed overlap")
|
|
mask = numpy.isfinite(xs) & numpy.isfinite(ys2)
|
|
corr = plt_utils.latex_float(*kendalltau(xs[mask], ys2[mask]))
|
|
plt.title(r"$\tau = {}, p = {}$".format(*corr), fontsize="small")
|
|
|
|
plt.tight_layout()
|
|
for ext in ["png"]:
|
|
nsim = str(nsim).zfill(5)
|
|
fout = join(plt_utils.fout,
|
|
f"kl_vs_overlap_std_{nsim}_{runs}.{ext}")
|
|
if upper_threshold:
|
|
fout = fout.replace(f".{ext}", f"_upper_threshold.{ext}")
|
|
print(f"Saving to `{fout}`.")
|
|
plt.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
|
|
plt.close()
|
|
|
|
|
|
###############################################################################
|
|
# Command line interface #
|
|
###############################################################################
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = ArgumentParser()
|
|
parser.add_argument('-c', '--clean', action='store_true')
|
|
args = parser.parse_args()
|
|
neighbour_kwargs = csiborgtools.neighbour_kwargs
|
|
|
|
runs_to_mass = {
|
|
"mass001": (12.4, 12.8),
|
|
"mass002": (12.6, 13.0),
|
|
"mass003": (12.8, 13.2),
|
|
"mass004": (13.0, 13.4),
|
|
"mass005": (13.2, 13.6),
|
|
"mass006": (13.4, 13.8),
|
|
"mass007": (13.6, 14.0),
|
|
"mass008": (13.8, 14.2),
|
|
"mass009": (14.0, 14.4), # There is no upper limit.
|
|
}
|
|
|
|
# cached_funcs = ["get_overlap", "read_dist", "make_kl", "make_ks"]
|
|
cached_funcs = ["get_max_key"]
|
|
if args.clean:
|
|
for func in cached_funcs:
|
|
print(f"Cleaning cache for function {func}.")
|
|
delete_disk_caches_for_function(func)
|
|
|
|
if False:
|
|
plot_mass_vs_pairoverlap(7444 + 24, 8956 + 24 * 3)
|
|
|
|
if False:
|
|
plot_mass_vs_maxpairoverlap(7444 + 24, 8956 + 24 * 3)
|
|
|
|
if False:
|
|
plot_mass_vsmedmaxoverlap(7444)
|
|
|
|
if False:
|
|
plot_summed_overlap_vs_mass(7444)
|
|
|
|
if True:
|
|
plot_mass_vs_separation(7444 + 24, 8956 + 24 * 3, min_overlap=0.0)
|
|
|
|
if False:
|
|
plot_maxoverlap_mass(7444)
|
|
|
|
if False:
|
|
plot_maxoverlapstat(7444, "lambda200c")
|
|
|
|
if False:
|
|
plot_maxoverlapstat(7444, "totpartmass")
|
|
|
|
if False:
|
|
plot_mass_vs_expected_mass(7444, max_prob_nomatch=1.0)
|
|
|
|
# Plot 1NN distance distributions.
|
|
if False:
|
|
for i in range(1, 10):
|
|
run = f"mass00{i}"
|
|
for pulled_cdf in [True, False]:
|
|
plot_dist(run, "cdf", neighbour_kwargs, runs_to_mass,
|
|
pulled_cdf=pulled_cdf,)
|
|
plot_dist(run, "pdf", neighbour_kwargs, runs_to_mass)
|
|
|
|
# Plot 1NN CDF differences.
|
|
if False:
|
|
runs = [f"mass00{i}" for i in range(1, 10)]
|
|
for pulled_cdf in [True, False]:
|
|
plot_cdf_diff(runs, neighbour_kwargs, pulled_cdf=pulled_cdf,
|
|
runs_to_mass=runs_to_mass)
|
|
if False:
|
|
runs = [f"mass00{i}" for i in range(1, 9)]
|
|
for kind in ["kl", "ks"]:
|
|
plot_significance("csiborg", runs, 7444, nobs=None, kind=kind,
|
|
kwargs=neighbour_kwargs,
|
|
runs_to_mass=runs_to_mass)
|
|
|
|
if False:
|
|
# runs = [[f"mass00{i}"] for i in range(1, 10)]
|
|
runs = [[f"mass00{i}"] for i in [4]]
|
|
for runs_ in runs:
|
|
# runs = ["mass007"]
|
|
for kind in ["kl"]:
|
|
plot_significance_vs_mass("csiborg", runs_, 7444, nobs=None,
|
|
kind=kind, kwargs=neighbour_kwargs,
|
|
runs_to_mass=runs_to_mass,
|
|
upper_threshold=100)
|
|
|
|
if False:
|
|
# runs = [f"mass00{i}" for i in range(1, 10)]
|
|
runs = ["mass004"]
|
|
plot_kl_vs_ks("csiborg", runs, 7444, None, kwargs=neighbour_kwargs,
|
|
runs_to_mass=runs_to_mass, upper_threshold=100)
|
|
|
|
if False:
|
|
# runs = [f"mass00{i}" for i in range(1, 10)]
|
|
runs = ["mass007"]
|
|
plot_kl_vs_overlap(runs, 7444, neighbour_kwargs, runs_to_mass,
|
|
upper_threshold=100, plot_std=False)
|