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https://github.com/Richard-Sti/csiborgtools.git
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779f2e76ac
* 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
1.1 MiB
1.1 MiB
In [1]:
import numpy as np
%matplotlib notebook
import matplotlib.pyplot as plt
# Local imports
try:
import csiborgtools
except ModuleNotFoundError:
import sys
sys.path.append("../")
import csiborgtools
import utils
%load_ext autoreload
%autoreload 2
In [2]:
obs = utils.load_2mpp()
cols = ["ra", "dec", "mass_cl", "dist"]
mmains = utils.load_mmains(1)
mmains = csiborgtools.utils.list_to_ndarray(mmains, cols)
sim = mmains[0, ...]
sim = csiborgtools.utils.array_to_structured(sim ,cols)
planck = utils.load_planck2015()
In [3]:
dx = 20
dmin = 125
dmax = dmin + dx
mask_obs = (dmin < obs["CDIST_CMB"]) & (obs["CDIST_CMB"] < dmax)
mask_sim = (dmin < sim["dist"]) & (sim["dist"] < dmax) & (sim["mass_cl"] > 1e12)
width = 6.4
plt.figure(figsize=(width, width*0.75))
plt.scatter(obs["RA"][mask_obs], obs["DEC"][mask_obs], s=1.5, label="2M++")
plt.scatter(sim["ra"][mask_sim] , sim["dec"][mask_sim], s=1.5, label="CSiBORG")
plt.scatter(planck["RA"], planck["DEC"], label="Planck SZ clusters < 214 MPc", c="red")
plt.legend()
plt.xlabel("RA")
plt.ylabel("dec")
plt.tight_layout()
# plt.savefig("../plots/2mpp_overlap.png", dpi=450)
plt.show()
In [ ]: