def test_mass_assignment_sharp_k():
r = pynbody.array.SimArray([20])
r.units = "Mpc a h^-1"
ic = parameters.InitialConditionsParameters(path=path)
w_sk = window.WindowParameters(initial_parameters=ic, volume="sharp-k")
mass_sk = w_sk.get_mass_from_radius(ic, r, ic.mean_density)
w_th = window.WindowParameters(initial_parameters=ic, volume="sphere")
mass_th = w_th.get_mass_from_radius(ic, r, ic.mean_density)
assert mass_sk == (9 * np.pi /2) * mass_th
def test_radius_assignment_sharp_k():
m = pynbody.array.SimArray([1e13])
m.units = "Msol h^-1"
ic = parameters.InitialConditionsParameters(path=path)
w_sk = window.WindowParameters(initial_parameters=ic, volume="sharp-k")
r_sk = w_sk.get_smoothing_radius_corresponding_to_filtering_mass(ic, m)
w_th = window.WindowParameters(initial_parameters=ic, volume="sphere")
r_th = w_th.get_smoothing_radius_corresponding_to_filtering_mass(ic, m)
r_sk_test = (2 / (9 * np.pi))**(1/3) * r_th
assert np.allclose(r_sk, r_sk_test), "Th sharp k radius is" + str(r_sk) + "and the testing one is " + str(r_sk_test)
def importances_plot(imp,
initial_parameters=None,
save=False,
yerr=None,
label=None,
subplots=1,
figsize=(7.5, 6)):
if initial_parameters is None:
initial_parameters = parameters.InitialConditionsParameters(
path="/Users/lls/Documents/CODE", load_final=True)
w = window.WindowParameters(initial_parameters=initial_parameters)
mass = w.smoothing_masses
plot.plot_importances_vs_mass_scale(imp,
mass,
save=save,
yerr=yerr,
label=label,
path=".",
title=None,
width=0.5,
log=False,
subplots=subplots,
figsize=figsize,
frameon=False,
legend_fontsize=None,
ecolor="k")
def __init__(self,
ids_particles,
initial_parameters=None,
num_filtering_scales=50,
path=None):
"""
Instantiates :class:`Inertia` given the
Args:
"""
if initial_parameters is None:
initial_parameters = parameters.InitialConditionsParameters(
path=path)
self.initial_parameters = initial_parameters
self.filter_parameters = window.WindowParameters(
initial_parameters=initial_parameters,
num_filtering_scales=num_filtering_scales)
# self.delta_class = density.DensityContrasts(initial_parameters=initial_parameters,
# num_filtering_scales=num_filtering_scales,
# window_function=window_function)
# self.shear_class = shear.Shear(initial_parameters=initial_parameters,
# num_filtering_scales=num_filtering_scales,
# number_of_processors=number_of_processors)
#
# Inertia.__init__(self, initial_parameters=initial_parameters, density_contrasts=density_contrasts,
#
# num_filtering_scales=num_filtering_scales, path=path, window_function=window_function)
self.filt_scales = np.arange(50)
self.ids_particles = ids_particles
def get_in_range_indices_of_trajectories(mass_range="in", initial_parameters=None):
if initial_parameters is None:
initial_parameters = parameters.InitialConditionsParameters(path="/Users/lls/Documents/CODE")
w = window.WindowParameters(initial_parameters=initial_parameters, num_filtering_scales=50)
if mass_range == "in":
range_mass_IN = np.intersect1d(np.where(w.smoothing_masses >
initial_parameters.halo[initial_parameters.max_halo_number]['mass'].sum()),
np.where(w.smoothing_masses <
initial_parameters.halo[initial_parameters.min_halo_number]['mass'].sum()))
return range_mass_IN
else:
NameError("Enter a valid mass range of trajectories")
def test_correct_units():
initial_parameters = parameters.InitialConditionsParameters(path=path)
snapshot = initial_parameters.initial_conditions
smoothing_radii = np.linspace(0.0057291, 0.20, 1500)
r_a_h = smoothing_radii/snapshot.properties['a'] * snapshot.properties['h']
r_a_h = r_a_h.view(pynbody.array.SimArray)
r_a_h.units = "Mpc a h^-1"
th = pynbody.analysis.hmf.TophatFilter(snapshot)
m = th.R_to_M(r_a_h)
m.units = "Msol h^-1"
rho_M = pynbody.analysis.cosmology.rho_M(snapshot, unit="Msol Mpc**-3")
w = window.WindowParameters(initial_parameters=initial_parameters, volume="sphere")
filtering_masses = w.get_mass_from_radius(initial_parameters, smoothing_radii, rho_M)
filtering_masses_with_h = filtering_masses * snapshot.properties['h']
filtering_masses_with_h.units = "Msol h^-1"
np.testing.assert_allclose(filtering_masses_with_h, m)
sys.path.append("/home/lls/mlhalos_code/")
import numpy as np
from mlhalos import parameters
from mlhalos import window
from mlhalos import density
import pynbody
from regression.adaboost import gbm_04_only as gbm_fun
import matplotlib.pyplot as plt
from sklearn.externals import joblib
# path="/Users/lls/Documents/mlhalos_files/"
saving_path_traj = "/share/data2/lls/regression/gradboost/randomly_sampled_training/ic_traj_smoothed_above_1e15Msol/"
ic = parameters.InitialConditionsParameters()
w = window.WindowParameters(initial_parameters=ic)
d = density.DensityContrasts(initial_parameters=ic)
m = np.linspace(np.log10(3e10), np.log10(1e15), 50)
width = np.append(np.diff(m), np.diff(m)[-1])
m_all_p = ic.initial_conditions["mass"].sum()
m1 = np.arange(np.log10(1e15), np.log10(m_all_p*5000), step=width[-1])[1:]
M = pynbody.array.SimArray(10**m1)
M.units = "Msol"
r_smoothing = w.get_smoothing_radius_corresponding_to_filtering_mass(ic, M)
den = d.get_smooth_density_for_radii_list(ic, r_smoothing)
traj_high_smoothing = den/ic.mean_density
np.save(saving_path_traj + "density_contrasts_smoothed_above_1e15Msol.npy", traj_high_smoothing)
import numpy as np
import matplotlib.pyplot as plt
from mlhalos import parameters
from mlhalos import window
ic = parameters.InitialConditionsParameters(path="/Users/lls/Documents/CODE",
load_final=True)
w = window.WindowParameters(initial_parameters=ic, num_filtering_scales=50)
halo_mass = np.load(
"/Users/lls/Documents/mlhalos_files/stored_files/halo_mass_particles.npy")
in_ids = np.where(halo_mass > 0)[0]
path_lowz = "/Users/lls/Documents/mlhalos_files/regression/lowz_density/"
lowz_den = np.lib.format.open_memmap(path_lowz + "z8_density_contrasts.npy")
ics_den = np.lib.format.open_memmap(path_lowz + "ics_density_contrasts.npy")
lowz_den_in_ids = lowz_den[in_ids]
ics_in_ids = ics_den[in_ids]
h_m_in_ids = halo_mass[in_ids]
f_1 = np.vstack((lowz_den_in_ids.transpose(), np.log10(h_m_in_ids)))
corr = np.corrcoef(f_1)
f_2 = np.vstack((ics_in_ids.transpose(), np.log10(h_m_in_ids)))
corr_ics = np.corrcoef(f_2)
f_3 = np.vstack((ics_in_ids.transpose(), lowz_den_in_ids.transpose()))
corr_dens = np.corrcoef(f_3)
x_min = np.log10(w.smoothing_masses).min()