mirror of
https://github.com/glatterf42/sims_python_files.git
synced 2024-09-19 16:13:45 +02:00
69 lines
2.5 KiB
Python
69 lines
2.5 KiB
Python
|
#!/usr/bin/env python3
|
||
|
|
# -*- coding: utf-8 -*-
|
||
|
|
"""
|
||
|
|
Created on Wed Apr 27 11:55:06 2022
|
||
|
|
|
||
|
|
@author: ben
|
||
|
|
"""
|
||
|
|
|
||
|
|
import h5py
|
||
|
|
from pathlib import Path
|
||
|
|
import numpy as np
|
||
|
|
import matplotlib.pyplot as plt
|
||
|
|
from matplotlib.colors import LogNorm
|
||
|
|
|
||
|
|
directory = Path(r"/home/ben/sims/swiftsim/examples/zoom_tests/auriga6_halo7_8_10")
|
||
|
|
Nres = 128 # 233.45 #for arj
|
||
|
|
Lbox = 100
|
||
|
|
|
||
|
|
snap_number = 7 # 7 for our tests, 0 for arj
|
||
|
|
|
||
|
|
fof_file = h5py.File(directory / f"fof_output_000{snap_number}.hdf5", "r")
|
||
|
|
file = h5py.File(directory / f'output_000{snap_number}.hdf5', 'r')
|
||
|
|
|
||
|
|
PartType1 = file["PartType1"]
|
||
|
|
highres_groupids = PartType1['FOFGroupIDs'][:]
|
||
|
|
highres_coordinates = np.array(PartType1['Coordinates'][:])
|
||
|
|
highres_masses = PartType1['Masses'][:]
|
||
|
|
|
||
|
|
total_highres_particles = len(highres_groupids)
|
||
|
|
unique_groups, particle_count = np.unique(highres_groupids, return_counts=True)
|
||
|
|
# print(f'Group IDs: {unique_groups}, Number in them: {particle_count}')
|
||
|
|
|
||
|
|
# all particles belonging to no group have group id 2147483647
|
||
|
|
|
||
|
|
highres_x, highres_y, highres_z = highres_coordinates[:, 0], highres_coordinates[:, 1], highres_coordinates[:, 2]
|
||
|
|
|
||
|
|
groups = fof_file['Groups']
|
||
|
|
|
||
|
|
# for key in groups.keys():
|
||
|
|
# print(key)
|
||
|
|
|
||
|
|
centres = groups['Centres'][:]
|
||
|
|
groupids = groups['GroupIDs'][:]
|
||
|
|
masses = groups['Masses'][:]
|
||
|
|
number_of_members = groups['Sizes'][:]
|
||
|
|
|
||
|
|
# table_width = 4
|
||
|
|
|
||
|
|
# separate_unique_counter = 0
|
||
|
|
# # for i in range(len(groupids)-1):
|
||
|
|
# for i in range(11):
|
||
|
|
# if np.isin(groupids[i], unique_groups):
|
||
|
|
# highres_members = particle_count[separate_unique_counter]
|
||
|
|
# contamination = (1 - highres_members / number_of_members[i])
|
||
|
|
# print(f'Group: {groupids[i]:{table_width}} | Mass: {masses[i]:{table_width + 1}.{table_width}} | Highres Members: {highres_members:{table_width + 1}} | Total Members: {number_of_members[i]:{table_width}} | Contamination: {contamination * 100:.{table_width}}%\n')
|
||
|
|
# separate_unique_counter += 1
|
||
|
|
|
||
|
|
main_group_origin = centres[np.min(unique_groups) - 1]
|
||
|
|
main_group_mass = masses[np.min(unique_groups) - 1]
|
||
|
|
origin_x, origin_y, origin_z = main_group_origin[0], main_group_origin[1], main_group_origin[2]
|
||
|
|
print(np.mean(highres_masses))
|
||
|
|
|
||
|
|
plt.title('Histogram of Auriga 6 Centre Levelmax 10')
|
||
|
|
plt.xlabel('x [Mpc]')
|
||
|
|
plt.ylabel('y [Mpc]')
|
||
|
|
i = np.logical_and( highres_z>origin_z-0.5, highres_z<origin_z+0.5 )
|
||
|
|
plt.hist2d(x=highres_x[i], y=highres_y[i], bins=256, range=[[origin_x - 0.5, origin_x + 0.5], [origin_y - 0.5, origin_y + 0.5]], weights=highres_masses[i], norm=LogNorm())
|
||
|
|
plt.colorbar()
|
||
|
|
plt.show()
|