csiborgtools/notebooks/flow/quijote_Cell.ipynb

Matching of haloes to clusters¶

In [144]:

import numpy as np
import matplotlib.pyplot as plt
from astropy.cosmology import FlatLambdaCDM

import pandas as pd

%load_ext autoreload
%autoreload 2
%matplotlib inline

The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

Load in the data¶

Harry: the exact routine may not work, I had to edit the raw .txt file a little

In [124]:

cosmo = FlatLambdaCDM(H0=100, Om0=0.307)

data0 = pd.read_csv("/mnt/users/rstiskalek/csiborgtools/data/top10_pwave_coords.txt", sep='\s+')
data = {}

data["id"] = np.array([int(x.split("_")[-1]) for x in data0["halo_ID"].values])
data["l"] = data0["l[deg]"].values
data["b"] = data0["b[deg]"].values
data["dist"] = cosmo.comoving_distance(data0["z"].values).value

print(data["dist"])
data0

[34.14606699  9.28692846 11.11279198 16.85641068  3.71636485 33.43032196
 24.65595192 29.91007806 40.31604335  9.346801  ]

Out[124]:

	halo_ID	KIND	l[deg]	b[deg]	d[kpc]	z	Rdelta[kpc]	rhos[Msol/kpc^3]	rs[kpc]	prof[keywrd]	#1	#2	#3	FOVDiam[deg]
0	halo_647110	CLUSTER	-47.66	29.96	-1	0.01142	4523.71	643822.01573	663.92743	kZHAO	1.0	3.0	1.0	37.74875
1	halo_10128802	CLUSTER	-90.76	25.97	-1	0.00310	1975.98	611499.11522	294.56973	kZHAO	1.0	3.0	1.0	60.00762
2	halo_1338057	CLUSTER	-35.42	-6.15	-1	0.00371	2738.50	593219.40743	413.73423	kZHAO	1.0	3.0	1.0	69.19885
3	halo_20419495	CLUSTER	87.42	88.09	-1	0.00563	3082.73	596355.51967	465.36921	kZHAO	1.0	3.0	1.0	51.83070
4	halo_20355327	CLUSTER	-94.48	75.29	-1	0.00124	1644.94	635981.23866	240.83028	kZHAO	1.0	3.0	1.0	119.66328
5	halo_2503990	CLUSTER	-35.24	-12.06	-1	0.01118	3164.45	600754.77263	478.21044	kZHAO	1.0	3.0	1.0	27.04669
6	halo_21251859	CLUSTER	31.15	44.45	-1	0.00824	2731.34	595715.63380	413.32640	kZHAO	1.0	3.0	1.0	31.59913
7	halo_745775	CLUSTER	-47.49	36.08	-1	0.01000	2878.14	597011.32933	435.71418	kZHAO	1.0	3.0	1.0	27.49495
8	halo_3003328	CLUSTER	39.76	-46.41	-1	0.01349	3172.97	602266.09230	479.82208	kZHAO	1.0	3.0	1.0	22.50342
9	halo_4200359	CLUSTER	-28.67	-22.77	-1	0.00312	1826.88	621061.51007	270.55674	kZHAO	1.0	3.0	1.0	55.35257

Load in the halo catalogue¶

In [126]:

boxsize = 677.7

halos = np.load("/users/hdesmond/Mmain/Mmain_9844.npy")
names = ["id", "x", "y", "z", "M"]
halos = {k: halos[:, i] for i, k in enumerate(names)}
halos["id"] = halos["id"].astype(int)
# Coordinates are in box units. Convert to Mpc/h
for p in ("x", "y", "z"):
    halos[p] = halos[p] * boxsize

halos["dist"] = np.sqrt((halos["x"] - boxsize/2)**2 + (halos["y"] - boxsize/2)**2 + (halos["z"] - boxsize/2)**2)

In [142]:

# Find which item in the catalogue matches the nth halo in the .txt file
n = 0
k = np.where(data["id"][n] == halos["id"])[0][0]
print(k)

5791

In [143]:

data["dist"][n], halos["dist"][k]

Out[143]:

(34.14606698507264, 149.24046381654668)