Aquila-Consortium/csiborgtools
0
0
Fork 0
mirror of https://github.com/Richard-Sti/csiborgtools.git synced 2024-12-22 17:48:01 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

Add KL vs KS plot

Browse source
This commit is contained in:
rstiskalek 2023-05-25 15:47:15 +01:00
parent 10fdf6d40e
commit 48cd5da88c
1 changed files with 43 additions and 2 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

45
scripts_plots/plot_nearest.py
View file

@ -164,6 +164,36 @@ def plot_significance_mass(simname, run, nsim, nobs, kind, kwargs):
plt.close() plt.close()
def plot_kl_vs_ks(simname, run, nsim, nobs, kwargs):
"""
Plot Kullback-Leibler divergence vs Kolmogorov-Smirnov statistic p-value.
"""
paths = csiborgtools.read.Paths(**kwargs["paths_kind"])
reader = csiborgtools.read.NearestNeighbourReader(**kwargs, paths=paths)
x = reader.read_single(simname, run, nsim, nobs)["mass"]
y_kl = make_kl(simname, run, nsim, nobs, kwargs)
y_ks = make_ks(simname, run, nsim, nobs, kwargs)
with plt.style.context(utils.mplstyle):
plt.figure()
plt.scatter(y_kl, y_ks, c=numpy.log10(x))
plt.colorbar(label=r"$\log M_{\rm tot} / M_\odot$")
plt.xlabel(r"$D_{\mathrm{KL}}$ of $r_{1\mathrm{NN}}$ distribution")
plt.ylabel(r"$p$-value of $r_{1\mathrm{NN}}$ distribution")
plt.yscale("log")
plt.tight_layout()
for ext in ["png"]:
if simname == "quijote":
nsim = paths.quijote_fiducial_nsim(nsim, nobs)
fout = join(utils.fout, f"kl_vs_ks{simname}_{run}_{str(nsim).zfill(5)}.{ext}") # noqa
print(f"Saving to `{fout}`.")
plt.savefig(fout, dpi=utils.dpi, bbox_inches="tight")
plt.close()
if __name__ == "__main__": if __name__ == "__main__":
parser = ArgumentParser() parser = ArgumentParser()
parser.add_argument('-c', '--clean', action='store_true') parser.add_argument('-c', '--clean', action='store_true')
@ -185,5 +215,16 @@ if __name__ == "__main__":
reader = csiborgtools.read.NearestNeighbourReader(**kwargs, paths=paths) reader = csiborgtools.read.NearestNeighbourReader(**kwargs, paths=paths)
run = "mass003" run = "mass003"
plot_significance_mass("quijote", run, 0, nobs=0, kind="ks", # for kind in ["pdf", "cdf"]:
kwargs=kwargs) # plot_dist(run, kind, kwargs)
# for kind in ["kl", "ks"]:
# # plot_significance_hist("csiborg", run, 7444, nobs=None, kind=kind,
# # kwargs=kwargs)
# plot_significance_mass("quijote", run, 0, nobs=0, kind=kind,
# kwargs=kwargs)
# plot_significance_mass("quijote", run, 0, nobs=0, kind="ks",
# kwargs=kwargs)
plot_kl_vs_ks("quijote", run, 0, nobs=0, kwargs=kwargs)
plot_kl_vs_ks("csiborg", run, 7444, nobs=None, kwargs=kwargs)