normed=True,
cumulative=True,
histtype="step",
bins=100)
plt.xlabel(r"$(M_{\mathrm{period.fix}} - M_{\mathrm{no fix}}) \%$")
plt.ylabel("CDF")
plt.savefig(
"/Users/lls/Documents/mlhalos_files/den_only_periodicity_fix/cdf_difference_predicted_masses.png"
)
plt.clf()
# Compare 2d histograms of true vs predicted w and w/o periddicity fix
pf.compare_2d_histograms(x,
y_fixed_den,
x,
y_den,
title1="periodicity fix",
title2="no period. fix")
plt.savefig(
"/Users/lls/Documents/mlhalos_files/den_only_periodicity_fix/2d_hist.png"
)
# Compare violin plots
b_t = np.linspace(x.min(), x.max(), 15, endpoint=True)
pf.compare_violin_plots(y_fixed_den,
x,
y_den,
x,
b_t,
label1="periodicity fix",
import sys
sys.path.append("/Users/lls/Documents/mlhalos_code")
import numpy as np
import matplotlib.pyplot as plt
from regression.plots import plotting_functions as pf
y_lowz = np.load("/Users/lls/Documents/mlhalos_files/z0_test/z0_only/predicted_log_halo_mass.npy")
# y_lowz = np.load("/Users/lls/Documents/mlhalos_files/lowz_density/predicted_log_halo_mass.npy")
y_fixed_den = np.load("/Users/lls/Documents/mlhalos_files/den_only_periodicity_fix/predicted_log_halo_mass.npy")
x = np.load("/Users/lls/Documents/mlhalos_files/lowz_density/true_mass_test_set.npy")
bins_plotting = np.linspace(x.min(), x.max(), 15, endpoint=True)
# 2D HISTOGRAM
pf.compare_2d_histograms(x, y_lowz, x, y_fixed_den,
title1="ics + low-z density", title2="ics density", save_path=None)
# VIOLINS
pf.compare_violin_plots(y_lowz, x, y_fixed_den, x,
bins_plotting, label1="ics + low-z density", label2="ics density", color1="g", color2="r")
pf.compare_violin_plots(y_lowz, x, y_fixed_den, x,
bins_plotting, label1="$z=0$ density", label2="ics density", color1="g", color2="r")
# 2D histograms of low/mid/high mass halos
high_mass = np.where(x>=13)[0]
mid_mass = np.where((x<13) & (x>12))[0]
low_mass = np.where(x<=12)[0]
pf.compare_2d_histograms(x[high_mass], y_lowz[high_mass], x[high_mass], y_fixed_den[high_mass],
# path_density = "/Users/lls/Documents/mlhalos_files/regression/in_halos_only/log_m_output/even_radii_and_random/"
path_density = "/Users/lls/Documents/mlhalos_files/den_only_periodicity_fix/"
log_den_true = np.load(
"/Users/lls/Documents/mlhalos_files/lowz_density/true_mass_test_set.npy"
)
log_den_predicted = np.load(path_density + "predicted_log_halo_mass.npy")
# log_den_true = np.log10(den_true)
# log_den_predicted = np.log10(den_predicted)
# 2D HISTOGRAM
pf.compare_2d_histograms(all_log_true_mass,
all_log_predicted_mass,
log_den_true,
log_den_predicted,
title1="density+shear",
title2="density",
save_path=None)
# VIOLINS
pf.compare_violin_plots(all_log_predicted_mass,
all_log_true_mass,
log_den_predicted,
log_den_true,
bins_plotting,
label1="den+shear",
label2="density",
color1="b",
color2="r",
perc_den0, perc_den1 = np.percentile(y_den_z0[ind], [16, 84])
plt.plot([perc_den0, perc_den1], [3000, 3000], color="g", lw=1.5)
plt.scatter(np.median(y_den_z0[ind]), 3000, color="g", s=10)
plt.legend(loc="best")
plt.xlabel(r"$\log (M /\mathrm{M}_{\odot})$", size=17)
# difference in distributions
plt.plot((bden[1:] + bden[:-1]) / 2, n0 - nden)
plt.xlabel(r"$\log (M /\mathrm{M}_{\odot})$", size=17)
plt.ylabel("N(ics + $z=0$) - N (ics)", size=17)
# 2d histograms
pf.compare_2d_histograms(x[ind],
y_den_z0[ind],
x[ind],
y_fixed_den[ind],
title2="ics density",
title1="ics + $z=0$ density",
x_max=x[ind].max(),
x_min=x[ind].min(),
y_max=13,
y_min=y_fixed_den[ind].min())
pf.compare_2d_histograms(x,
y_den_z0,
x,
y_fixed_den,
title2="ics density",
title1="ics + $z=0$ density")
"/Users/lls/Documents/mlhalos_files/truth_feature/predicted_log_halo_mass.npy"
)
y_den = np.load(
"/Users/lls/Documents/mlhalos_files/den_only_periodicity_fix/predicted_log_halo_mass.npy"
)
x = np.load(
"/Users/lls/Documents/mlhalos_files/lowz_density/true_mass_test_set.npy")
bins_plotting = np.linspace(x.min(), x.max(), 15, endpoint=True)
# VIOLINS
pf.compare_violin_plots(y_w_truth_feat,
x,
y_den,
x,
bins_plotting,
label1="ics density + truth",
label2="ics density",
color1="g",
color2="r")
# 2D HISTOGRAM
pf.compare_2d_histograms(x,
y_w_truth_feat,
x,
y_den,
title1="ics density + truth",
title2="ics density",
save_path=None)