mirror of
https://github.com/Richard-Sti/csiborgtools_public.git
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eb8d070fff
* Add moving FoF membership files * add FoF membership path * Add notes where its PHEW * Add FoF catalogue path * Correct typo * Add more functionalities * Make work with halo IDs from FoF * Edit print statement * Fix copy bug * copy * Add FoF catalogue reading * Clean up script * Fix typo * Little edits * Fix naming convention * Rename key * Remove loading substructure particles * Rename CSiBORG Cat * Rename clumps cat * Rename cat * Remove misplaced import * Switch to halos * rm import * structfit of only halos * Add FoF halo reading * Add a short comment * Fix __getitem__ to work with int * Fix problems * Improve __getitem__ * Add more conversion * Fix indexing * Fix __getitem__ assertion * Fix numbers * Rename * Fix verbosity flags * Add full Quijote HMF option * Add plot of Quijote only * Add quijote full paths * Fix the fit_init script * Renam arg * Update .gitignore * add default argument name * Change default verbosity flag * Modernise script structure * Fix dictionary * Fix reading to include m200c * Modernise script * Add args
25 KiB
25 KiB
In [2]:
import sys
import numpy as np
import matplotlib.pyplot as plt
import scienceplots
import astroquery
from tqdm import trange, tqdm
sys.path.append("../")
import csiborgtools
%matplotlib widget
%load_ext autoreload
%autoreload 2
In [38]:
# # Norma
cluster = {"RA": (16 + 15 / 60 + 32.8 / 60**2) * 15,
"DEC": -60 + 54 / 60 + 30 / 60**2,
"DIST": 67.8}
Xclust = np.array([cluster["DIST"], cluster["RA"], cluster["DEC"]]).reshape(1, -1)
In [39]:
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
nsims = paths.get_ics(False)
In [29]:
Xclust = np.array([cluster["DIST"], cluster["RA"], cluster["DEC"]]).reshape(1, -1)
In [33]:
matches = np.full(len(nsims), np.nan)
for ii in trange(101):
cat = csiborgtools.read.CSiBORGHaloCatalogue(nsims[ii], paths, minmass=('M', 1e13))
dist, ind = cat.angular_neighbours(Xclust, ang_radius=5, rad_tolerance=10)
dist = dist[0]
ind = ind[0]
if ind.size > 0:
matches[ii] = np.max(cat['M'][ind])
In [37]:
x = np.log10(matches[~np.isnan(matches)])
plt.figure()
plt.hist(x, bins=10)
plt.show()
In [ ]: