csiborgtools/notebooks/flow/flow_mapping.ipynb
Richard Stiskalek ee222cd010
Fix overlap runs (#125)
* Update nb

* Update script

* Update script

* Rename

* Update script

* Update script

* Remove warning

* Ignore minors when extracting MAH

* Fix paths bug

* Move notebooks

* Move files

* Rename and delete things

* Rename file

* Move file

* Rename things

* Remove old print statement

* Add basic MAH plot

* Add random MAH path

* Output snapshot numbers

* Add MAH random extraction

* Fix redshift bug

* Edit script

* Add extracting random MAH

* Little updates

* Add CB2 redshift

* Add some caching

* Add diagnostic plots

* Add caching

* Minor updates

* Update nb

* Update notebook

* Update script

* Add Sorce randoms

* Add CB2 varysmall

* Update nb

* Update nb

* Update nb

* Use catalogue HMF

* Move definition of radec2galactic

* Update nb

* Update import

* Update import

* Add galatic coords to catalogues

* Update nb
2024-04-08 11:23:21 +02:00

46 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])
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
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In [ ]: