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aaa14fc880
* Add RAMSES2HDF5 conversion * Upload changes * Clean up * More clean up * updates * Little change * pep9 * Add basic SPH calculation for a snapshot * Add submit script * Remove echo * Little changes * Send off changes * Little formatting * Little updates * Add nthreads argument * Upload chagnes * Add nthreads arguemnts * Some local changes.. * Update scripts * Add submission script * Update script * Update params * Rename CSiBORGBox to CSiBORG1box * Rename CSiBORG1 reader * Move files * Rename folder again * Add basic plotting here * Add new skeletons * Move def * Update nbs * Edit directories * Rename files * Add units to converted snapshots * Fix empty dataset bug * Delete file * Edits to submission scripts * Edit paths * Update .gitignore * Fix attrs * Update weighting * Fix RA/dec bug * Add FORNAX cluster * Little edit * Remove boxes since will no longer need * Move func back * Edit to include sort by membership * Edit paths * Purge basic things * Start removing * Bring file back * Scratch * Update the rest * Improve the entire file * Remove old things * Remove old * Delete old things * Fully updates * Rename file * Edit submit script * Little things * Add print statement * Add here cols_to_structured * Edit halo cat * Remove old import * Add comment * Update paths manager * Move file * Remove file * Add chains
1,009 KiB
1,009 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 [ ]: