csiborgtools/notebooks/flow/process_PV.ipynb
Richard Stiskalek 779f2e76ac
Calculate upglade redshifts (#128)
* Update redshift reading

* Add helio to CMB redshift

* Update imports

* Update nb

* Run for Quijote

* Add script

* Update

* Update .gitignore

* Update imports

* Add Peery estimator

* Add bulk flow scripts

* Update typs

* Add comment

* Add blank space

* Update submission script

* Update description

* Add barriers

* Update nb

* Update nb

* Rename script

* Move to old

* Update imports

* Add nb

* Update script

* Fix catalogue key

* Update script

* Update submit

* Update comment

* Update .gitignore

* Update nb

* Update for stationary obsrevers

* Update submission

* Add nb

* Add better verbose control

* Update nb

* Update submit

* Update nb

* Add SN errors

* Add draft of the script

* Update verbosity flags

* Add submission script

* Debug script

* Quickfix

* Remove comment

* Update nb

* Update submission

* Update nb

* Processed UPGLADE
2024-06-20 14:33:00 +01:00

41 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])
100%|██████████| 101/101 [00:44<00:00,  2.25it/s]
In [37]:
x = np.log10(matches[~np.isnan(matches)])


plt.figure()
plt.hist(x, bins=10)
plt.show()
Figure
No description has been provided for this image
In [ ]: