mirror of
https://github.com/Richard-Sti/csiborgtools.git
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fdb0df8d4c
* Move paths to a separate file * Add mmain reader * Add a verbosity flag * Fix imports * Fix bug * Rename files * Return ultimate parents * Add script to generate mmain * Remove mmain path * edit path * Add mmain path * Change function name * Rename function * Turn off verbose * Fix list requirement * Edit init match paths * Fix init pathing * Edit paths docs * Edit dumpdir name * Rename path * Fix split paths * Remove unused import * Add comment * Update readme * remove read mmain * Rename haloatalogue * Fix minor bugs * Update nbs * Add create directory option * Move split jobs * Move spliot jobs * Remove splitting * Add import * Edit script * Deeper split folder * Fix paths bug * Rename catalogue * Rename Catalogue * Add new clumpread * Edit paths * add knn paths * Update commenting * Update imports * Add more conversions * Update temp file * Add a note * Add catalogue * Cooment * Update TODO * Update script * add nb * Update * pep8 * edit paths & pep8 * Fix knn auto paths * add paths docs * Add auto and cross knn paths * Add new paths * Simplify tpcf reading * pep8 patch * update readme * Update progress * pep8 * pep8 * pep8 * pep8 * pep8 * pep8 * pep8 * pep8 * pep8 * pep8 * pep8 * pep8 * pep8 * pep8 * pep8 * Pep 8 and restructure * add lambda spin * add clump and halo * add checks * Edit halo profile fit * Update gitignore * backup script
6 KiB
6 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 [ ]: