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17badf2652
* add xray reading * add import * add more comments * add data * add nb * add MCXC shortcut * add match indxs to Planck of MCXC * add import * update TODO * update readme * add famous clusters * add 2mpp_groups * shorten paths * add 2M++ groups * add import * Update TODO
523 KiB
523 KiB
In [1]:
import numpy as np
%matplotlib notebook
import matplotlib.pyplot as plt
try:
import csiborgtools
except ModuleNotFoundError:
import sys
sys.path.append("../")
import csiborgtools
import utils
%load_ext autoreload
%autoreload 2
import joblib
from os.path import join
In [4]:
Nsim = 9844
Nsnap = 1016
data = utils.load_processed(Nsim, Nsnap)
In [11]:
bins = np.arange(11.8, 15.4, 0.2)
plt.figure()
x, mu, std = csiborgtools.match.number_density(data, "m200", bins, 200, True)
plt.errorbar(x, mu, std, capsize=4, label=r"$M_{200c}$")
x, mu, std = csiborgtools.match.number_density(data, "m500", bins, 200, True)
plt.errorbar(x, mu, std, capsize=4, label=r"$M_{500c}$")
x, mu, std = csiborgtools.match.number_density(data, "totpartmass", bins, 200, True)
plt.errorbar(x, mu, std, capsize=4, label=r"$M_{\rm tot}$")
x, mu, std = csiborgtools.match.number_density(data, "mass_mmain", bins, 200, True)
plt.errorbar(x, mu, std, capsize=4, label=r"$M_{\rm mmain}$")
plt.legend()
plt.yscale("log")
plt.xscale("log")
plt.ylabel(r"$\phi / (\mathrm{Mpc}^{-3})~\mathrm{dex}$")
plt.xlabel(r"$M_{\rm x}$")
plt.tight_layout()
plt.savefig("../plots/HMF.png", dpi=450)
plt.show()
In [8]:
nfw = csiborgtools.fits.NFWProfile()
m200_nfw = nfw.enclosed_mass(data["r200"], data["Rs"], data["rho0"])
plt.figure()
plt.scatter(data["m200"], m200_nfw, s=1)
t = np.linspace(1e11, 1e15)
plt.plot(t, t, c="red", ls="--", lw=1.5)
plt.xscale("log")
plt.yscale("log")
plt.xlabel(r"$M_{200c}$")
plt.ylabel(r"$M_{\mathrm{NFW}, 200c}$")
plt.tight_layout()
plt.savefig("../plots/enclosed_vs_nfw.png", dpi=450)
plt.show()
In [ ]:
In [9]:
logm200 = np.log10(data["m200"])
conc = data["conc"]
N = 10
bins = np.linspace(logm200.min(), logm200.max(), N)
x = [0.5*(bins[i] + bins[i + 1]) for i in range(N-1)]
y = np.full((N - 1, 3), np.nan)
for i in range(N - 1):
mask = (logm200 >= bins[i]) & (logm200 < bins[i + 1]) & np.isfinite(conc)
y[i, :] = np.percentile(conc[mask], [14, 50, 84])
fig, axs = plt.subplots(nrows=2, sharex=True, figsize=(6.4, 6.4 * 1))
fig.subplots_adjust(hspace=0)
axs[0].plot(x, y[:, 1], c="red", marker="o")
axs[0].fill_between(x, y[:, 0], y[:, 2], color="red", alpha=0.25)
axs[1].hist(logm200, bins="auto", log=True)
for b in bins:
for i in range(2):
axs[i].axvline(b, c="orange", lw=0.5)
axs[0].set_ylim(2, 10)
axs[1].set_xlabel(r"$M_{200c}$")
axs[0].set_ylabel(r"$c_{200c}$")
axs[1].set_ylabel(r"Counts")
plt.tight_layout(h_pad=0)
plt.savefig("../plots/mass_concentration.png", dpi=450)
plt.show()
In [ ]: