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124 lines
4.1 KiB
Python
124 lines
4.1 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Tue Mar 8 11:16:06 2022
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@author: ben
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"""
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import h5py
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from pathlib import Path
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import numpy as np
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import matplotlib.pyplot as plt
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def V(r):
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return 4 * np.pi * r**3 / 3
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directory = Path(r"/home/ben/sims/swiftsim/examples/zoom_tests/auriga6_halo_arj")
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snap_number = 0 # 7 for our tests
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fof_file = h5py.File(directory / f"fof_output_000{snap_number}.hdf5", "r")
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file = h5py.File(directory / f'output_000{snap_number}.hdf5', 'r')
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PartType1 = file["PartType1"]
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highres_groupids = PartType1['FOFGroupIDs'][:]
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highres_coordinates = PartType1['Coordinates'][:]
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highres_masses = PartType1['Masses'][:]
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total_highres_particles = len(highres_groupids)
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unique_groups, particle_count = np.unique(highres_groupids, return_counts=True)
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print(f'Group IDs: {unique_groups}, Number in them: {particle_count}')
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# all particles belonging to no group have group id 2147483647
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# for key in fof_file.keys():
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# print(key) #for finding all header entries, which are:
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groups = fof_file['Groups']
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# for key in groups.keys():
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# print(key)
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centres = groups['Centres'][:]
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groupids = groups['GroupIDs'][:]
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masses = groups['Masses'][:]
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number_of_members = groups['Sizes'][:]
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table_width = 4
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separate_unique_counter = 0
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# for i in range(len(groupids)-1):
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for i in range(11):
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if np.isin(groupids[i], unique_groups):
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highres_members = particle_count[separate_unique_counter]
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contamination = (1 - highres_members / number_of_members[i])
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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')
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separate_unique_counter += 1
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main_group_origin = centres[0]
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main_group_mass = masses[0]
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distances = []
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# masses = []
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for j in range(len(highres_groupids)):
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if highres_groupids[j] == np.min(unique_groups):
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distance = np.linalg.norm(main_group_origin - highres_coordinates[j])
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# mass = highres_masses[j]
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distances.append(distance)
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# masses.append(mass)
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group_radius = np.max(np.array(distances))
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normalised_distances = np.array(distances) / group_radius
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num_bins = 30
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log_radial_bins = np.geomspace(0.01, 2, num_bins)
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particle_mass = highres_masses[0] # Careful: this assumes that all highres particles have the same mass and that it's exclusively them in the main group,
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# so keep an eye on its contamination and adjust accordingly!
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counts_in_radial_bins = []
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for k in range(len(log_radial_bins) - 1):
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count = 0
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for member_distance in normalised_distances:
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if log_radial_bins[k] <= member_distance < log_radial_bins[k + 1]:
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count += 1
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volume = V(log_radial_bins[k + 1] * group_radius) - V(log_radial_bins[k] * group_radius)
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count /= volume
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counts_in_radial_bins.append(count)
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plot_log_radial_bins = log_radial_bins[0:len(log_radial_bins) - 1] # there is one fewer bin then there are bin borders
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masses_in_radial_bins = np.array(counts_in_radial_bins) * particle_mass
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Nres = 128
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Lbox = 100
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softening = Lbox / Nres / 30
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plt.axvline(4 * softening / group_radius, linestyle='--', color='grey')
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plt.title('Density profile Auriga 6 ARJ')
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plt.xlabel(r'R / $\mathrm{R}_\mathrm{group}$')
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plt.ylabel(r'ρ [$10^{10}\ \mathrm{M}_\odot\ /\ \mathrm{Mpc}^3$]')
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plt.loglog(plot_log_radial_bins, masses_in_radial_bins)
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plt.show()
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# kumulatives Dichteprofil -> sollte bei grossen Massen praktisch gleich sein, am besten direkt uebereinander plotten.
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# plt.hist2D mit x und y, jeweils Zentrum der Gruppe +- 0.5 Mpc anschauen, alles andere wegwerfen, sollte direkt Struktur erkennen koennen, ob sie gleich ist.
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# for i in range(10):
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# print(f'Group: {groupids[i]} | Mass: {masses[i]} | Size: {sizes[i]} | Centre: {centres[i]}\n')
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# print(list(parameters.attrs.items()))
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# use list(Header.attr.keys()) to list all attributes contained in the Header and .attr.values() to see their values
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# even otherwise empty things like ICs_parameters can contain such attributes
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