path = "/share/data2/lls/regression/local_inertia/tensor/"
ran_5k = np.load("/share/data2/lls/regression/local_inertia/tensor/ran_5k.npy")
def pool_local_inertia(particle_id):
li, eigi = In.get_local_inertia_single_id(particle_id, snapshot,
r_smoothing, rho)
np.save(
path + "random/inertia_tensor_particle_" + str(particle_id) + ".npy",
li)
np.save(path + "random/eigenvalues_particle_" + str(particle_id) + ".npy",
eigi)
# print("Done and saved particle " + str(particle_id))
return li
ic = parameters.InitialConditionsParameters(load_final=True)
In = inertia.LocalInertia(ran_5k, initial_parameters=ic)
snapshot = ic.initial_conditions
rho = snapshot["rho"]
filtering_scales = In.filt_scales
r_smoothing = In.filter_parameters.smoothing_radii.in_units(
snapshot["pos"].units)[filtering_scales]
pool = Pool(processes=40)
li_particles = pool.map(pool_local_inertia, ran_5k)
pool.close()
pool.join()
def pool_local_inertia(particle_id):
li = In.get_local_inertia_single_id(particle_id, snapshot, r_smoothing,
rho)
np.save(path + "ids/inertia_tensor_particle_" + str(particle_id) + ".npy",
li)
# print("Done and saved particle " + str(particle_id))
return li
path = "/share/data2/lls/regression/local_inertia/tensor/"
tr_ids = np.load(path + "subset_ids.npy")
ic = parameters.InitialConditionsParameters(load_final=True)
In = inertia.LocalInertia(tr_ids, initial_parameters=ic)
snapshot = ic.initial_conditions
rho = snapshot["rho"]
# rho_mean = (np.sum(snapshot["mass"]) / snapshot.properties["boxsize"] ** 3).in_units("Msol kpc**-3")
# C = rho - rho_mean
filtering_scales = In.filt_scales
r_smoothing = In.filter_parameters.smoothing_radii.in_units(
snapshot["pos"].units)[filtering_scales]
ids_remaining = tr_ids[:]
pool = Pool(processes=40)
li_particles = pool.map(pool_local_inertia, ids_remaining)
pool.close()
pool.join()
li, eigi = In.get_local_inertia_single_id(particle_id, snapshot,
r_smoothing, rho)
np.save(
path + "testing/ids/inertia_tensor_particle_" + str(particle_id) +
".npy", li)
np.save(
path + "testing/ids/eigenvalues_particle_" + str(particle_id) + ".npy",
eigi)
# print("Done and saved particle " + str(particle_id))
return li
testing_ids = training_ind
ic = parameters.InitialConditionsParameters(load_final=True)
In = inertia.LocalInertia(testing_ids, initial_parameters=ic)
snapshot = ic.initial_conditions
rho = snapshot["rho"]
# rho_mean = (np.sum(snapshot["mass"]) / snapshot.properties["boxsize"] ** 3).in_units("Msol kpc**-3")
# C = rho - rho_mean
filtering_scales = In.filt_scales
r_smoothing = In.filter_parameters.smoothing_radii.in_units(
snapshot["pos"].units)[filtering_scales]
pool = Pool(processes=40)
li_particles = pool.map(pool_local_inertia, testing_ids)
pool.close()
pool.join()
# training_ids = np.load("/share/data1/lls/regression/in_halos_only/log_m_output/even_radii_and_random/training_ids.npy")
#
# a = []
# for filename in os.listdir(path + "ids_50scales/"):
# a.append(int(get_numbers_from_filename(filename)))
#
# a = np.array(a)
# ids_remaining = training_ids[~np.in1d(training_ids, a)]
# del a
#ids_remaining = np.load(path + "training_high_mass.npy")
ids_remaining = np.load(path + "ran_above_137.npy")
ic = parameters.InitialConditionsParameters(load_final=True)
#In = inertia.LocalInertia(training_ids, initial_parameters=ic)
In = inertia.LocalInertia(ids_remaining, initial_parameters=ic)
snapshot = ic.initial_conditions
rho = snapshot["rho"]
filtering_scales = In.filt_scales
r_smoothing = In.filter_parameters.smoothing_radii.in_units(snapshot["pos"].units)[filtering_scales]
pool = Pool(processes=40)
li_particles = pool.map(pool_local_inertia, ids_remaining)
pool.close()
pool.join()
# n_tot, b_tot, p_tot = plt.hist(np.log10(halo_mass[halo_mass!=0]), bins=50)
# n_tr, b_tr, p_tr = plt.hist(np.log10(halo_mass[t_train]), bins=b_tot)