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5784011de0
* Add seperate autoknn script & config file * edit ics * Edit submission script * Add threshold values * Edit batch sizign * Remove print * edit * Rename files * Rename * Update nb * edit runs * Edit submit * Add median threshold * add new auto reader * editt submit * edit submit * Edit submit * Add mean prk * Edit runs * Remove correlation file * Move split to clutering * Add init * Remove import * Add the file * Add correlation reading * Edit scripts * Add below and above median permutation for cross * Update imports * Move rvs_in_sphere * Create utils * Split * Add import * Add normalised marks * Add import * Edit readme * Clean up submission file * Stop tracking submit files * Update gitignore * Add poisson field analytical expression * Add abstract generators * Add generators * Pass in the generator * Add a check for if there are any files * Start saving average density * Update nb * Update readme * Update units * Edit jobs * Update submits * Update reader * Add random crossing * Update crossing script * Add crossing with random * Update readme * Update notebook
717 KiB
717 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.HaloCatalogue(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 [ ]: