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a65e3cb15b
* Add imports * Add field LOS paths * Add basic flow model * Edit script * Add nb * Add nb * Update nb * Add some docs * Add RA reading * Add imoprts * Updates to the flow model * Update script * Bring back A2 * Update imports * Update imports * Add Carrick to ICs * Add Carrick boxsize * Add Carrick and fix minor bugs * Add Carrick box * Update script * Edit imports * Add fixed flow! * Update omega_m and add it * Update nb * Update nb * Update nb * Remove old print statements * Update params * Add thinning of chains * Add import * Add flow validation script * Add submit script * Add ksmooth * Update nb * Update params * Update script * Update string * Move where distributions are defined * Add density bias parameter * Add lognorm mean * Update scripts * Update script
4 KiB
4 KiB
In [1]:
import matplotlib.pyplot as plt
import numpy
import scienceplots
from h5py import File
import plt_utils
%load_ext autoreload
%autoreload 2
In [12]:
with File("/mnt/extraspace/rstiskalek/csiborg_postprocessing/ACL/BORG2_0.25.hdf5", 'r') as f:
voxel_acl = f['voxel_acl'][...].flatten()
voxel_dist = f['voxel_dist'][...].flatten()
In [28]:
bins = numpy.linspace(0, 100, 10)
plt.figure()
mask = voxel_dist < 20
plt.hist(voxel_acl[mask], bins="auto", histtype='step', density=1, label=r"$0 < R / (\mathrm{Mpc} / h) < 20$")
mask = (voxel_dist > 20) & (voxel_dist < 40)
plt.hist(voxel_acl[mask], bins="auto", histtype='step', density=1, label=r"$20 < R / (\mathrm{Mpc} / h) < 40$")
mask = (voxel_dist > 40) & (voxel_dist < 60)
plt.hist(voxel_acl[mask], bins="auto", histtype='step', density=1, label=r"$40 < R / (\mathrm{Mpc} / h) < 60$")
# plt.scatter(voxel_dist.flatten(), voxel_acl.flatten(), s=0.1)
plt.legend()
plt.title("ACL of individual voxels")
plt.xlabel(r"$\mathrm{ACL}$")
plt.ylabel(r"Normalized bin counts")
plt.tight_layout()
plt.savefig("../plots/BORG_Stephen_ACL.png", dpi=450)
plt.show()
In [ ]: