csiborgtools/notebooks/flow/quijote_Cell.ipynb

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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

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from astropy.cosmology import FlatLambdaCDM, z_at_value
from astropy import units

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Nsim = 9844
Nsnap = 1016
# data, box = utils.load_processed(Nsim, Nsnap)
data, box = utils.load_processed(Nsim, Nsnap)

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X = np.vstack([data["peak_{}".format(p)] for p in ("x", "y", "z")]).T

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from sklearn.neighbors import NearestNeighbors

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neighbors = NearestNeighbors()

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neighbors.fit(X)

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NearestNeighbors()

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

NearestNeighbors

NearestNeighbors()

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p = X[0, :]

neighbors.kneighbors(p.reshape(-1,3))

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(array([[0.        , 0.00441153, 0.00459373, 0.00568435, 0.00596298]]),
 array([[   0,  186,  513,  130, 1795]]))

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Out[16]:

array([-0.04183578, -0.21976852, -0.05943659])

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data["peak_y"]

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array([-0.21976852, -0.15894653, -0.16384361, ..., -0.23763191,
       -0.23351993, -0.23352051])

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zcosmo = box.box2cosmoredshift(data["dist"])
z

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zpec = box.box2pecredshift(*[data[p] for p in ["vx", "vy", "vz", "peak_x", "peak_y", "peak_z"]])
zobs = box.box2obsredshift(*[data[p] for p in ["vx", "vy", "vz", "peak_x", "peak_y", "peak_z"]])

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m = zcosmo < 0.05

plt.figure()

# plt.scatter(zcosmo[m], zcosmo[m] - zobs[m], s=0.05)
plt.scatter(zcosmo[m], zpec[m], s=0.05, rasterized=True)
t = np.linspace(0, zcosmo[m].max())

plt.axhline(0, c="red", ls="--")

plt.xlabel(r"$z_{\rm cosmo}$")
plt.ylabel(r"$z_{\rm pec}$")
plt.tight_layout()
plt.savefig("../plots/redshift.png", dpi=450)
plt.show()

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data[""]

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cosmo = FlatLambdaCDM(H0=70, Om0=0.3, Ob0=0.05)

cosmo.comoving_distance()

x = 10000 * units.Mpc

z_at_value(cosmo.comoving_distance, x)

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from astropy import constants

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constants.c.value * 1e-8

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n_sims = csiborgtools.io.get_csiborg_ids("/mnt/extraspace/hdesmond")

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np.where(n_sims == 7660)

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for n_sim in n_sims:
    simpath = csiborgtools.io.get_sim_path(n_sim)
    maxsnap = csiborgtools.io.get_maximum_snapshot(simpath)
    box = csiborgtools.units.BoxUnits(maxsnap, simpath)
    print(maxsnap, box._aexp)

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simpath = csiborgtools.io.get_sim_path(7660)

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simpath

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box = csiborgtools.units.BoxUnits(999, simpath)

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box._aexp

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planck = utils.load_planck2015(200)
mcxc = utils.load_mcxc(200)

indxs = csiborgtools.io.match_planck_to_mcxc(planck, mcxc)

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groups = utils.load_2mpp_groups()

Nsim = 9844
Nsnap = 1016

data = utils.load_processed(Nsim, Nsnap)

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plt.figure()

m = data["m500"] > 1e13
plt.scatter(data["ra"][m], data["dec"][m], label="CSiBORG", s=3)
plt.scatter(groups["RA"], groups["DEC"], label="2M++ galaxy groups", s=3, marker="x")



plt.show()

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RAcoma = (12 +  59/60 + 48.7 / 60**2) * 15
DECcoma = 27 + 58 / 60 + 50 / 60**2


RAvirgo = (12 + 27 / 60) * 15
DECvirgo = 12 + 43/60



plt.figure()


plt.scatter(mcxc["RAdeg"], mcxc["DEdeg"], label="MCXC")
plt.scatter(planck["RA"], planck["DEC"], label="Plank",s=8, c="red")

plt.scatter(RAcoma, DECcoma, label="Coma", s=30, marker="x")
plt.scatter(RAvirgo, DECvirgo, label="Virgo", s=30, marker="x")

plt.legend(framealpha=0.5)
plt.xlabel("RA")
plt.ylabel("DEC")
plt.title("Clusters below 200 Mpc")
plt.savefig("../plots/clusters_radec.png", dpi=450)

plt.show()

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plt.figure()

plt.scatter(mcxc["COMDIST"], mcxc["M500"], label="MCXC")
# yerr = np.vstack([planck["MSZ"] - planck["MSZ_ERR_LOW"],
#                   planck["MSZ_ERR_UP"] - planck["MSZ"]])
yerr = np.vstack([planck["MSZ_ERR_LOW"], planck["MSZ_ERR_UP"]])
plt.errorbar(planck["COMDIST"], planck["MSZ"], yerr, label="Plank", fmt=" ", capsize=3, color="red")

plt.yscale("log")
plt.legend()
plt.title("Clusters below 200 Mpc")
plt.xlabel(r"$D_{\rm c} / \mathrm{Mpc}$")
plt.ylabel(r"$M_{\rm 500c} / M_\odot$")
# plt.savefig("../plots/clusters_mass_dist.png", dpi=450)

plt.show()

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d["RAdeg"]

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d.dtype.names

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d["Cat"]

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type(d["MCXC"])

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np.asanyarray(["aasdasdaasdasdasdad", "bsdfadadfasddsgasdg"]).dtype

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d["MCXC"]

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plt.figure()
# plt.scatter(d["RAdeg"], d["DEdeg"], s=1)
plt.scatter(d["z"], d["M500"], s=1)

plt.yscale("log")
plt.show()

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Nsim = 9844
Nsnap = 1016

data = utils.load_processed(Nsim, Nsnap)

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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()

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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()

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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()

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