From 371b4bd0579b9302262e5e618dc5945f57c69817 Mon Sep 17 00:00:00 2001 From: rstiskalek Date: Wed, 24 May 2023 11:25:22 +0100 Subject: [PATCH] Update plotting routines --- scripts_plots/plot_nearest.py | 121 ++++++++++++++++++++++++++-------- 1 file changed, 92 insertions(+), 29 deletions(-) diff --git a/scripts_plots/plot_nearest.py b/scripts_plots/plot_nearest.py index 53ee5d3..1297440 100644 --- a/scripts_plots/plot_nearest.py +++ b/scripts_plots/plot_nearest.py @@ -32,25 +32,43 @@ except ModuleNotFoundError: @cache_to_disk(7) -def read_cdf(simname, run, kwargs): - """Read the CDFs. Caches them to disk""" +def read_dist(simname, run, kind, kwargs): paths = csiborgtools.read.Paths(**kwargs["paths_kind"]) reader = csiborgtools.read.NearestNeighbourReader(**kwargs, paths=paths) - return reader.build_cdf(simname, run, verbose=True) + return reader.build_dist(simname, run, kind, verbose=True) -def plot_cdf(run, kwargs): +@cache_to_disk(7) +def make_kl(simname, run, nsim, nobs, kwargs): + paths = csiborgtools.read.Paths(**kwargs["paths_kind"]) + reader = csiborgtools.read.NearestNeighbourReader(**kwargs, paths=paths) + + pdf = read_dist("quijote", run, "pdf", kwargs) + return reader.kl_divergence(simname, run, nsim, pdf, nobs=nobs) + + +@cache_to_disk(7) +def make_ks(simname, run, nsim, nobs, kwargs): + paths = csiborgtools.read.Paths(**kwargs["paths_kind"]) + reader = csiborgtools.read.NearestNeighbourReader(**kwargs, paths=paths) + + cdf = read_dist("quijote", run, "cdf", kwargs) + return reader.ks_significance(simname, run, nsim, cdf, nobs=nobs) + + +def plot_dist(run, kind, kwargs): """ - Plot the CDF of the nearest neighbour distance for Quijote and CSiBORG. + Plot the PDF/CDF of the nearest neighbour distance for Quijote and CSiBORG. """ - print("Plotting the CDFs.", flush=True) + assert kind in ["pdf", "cdf"] + print(f"Plotting the {kind}.", flush=True) paths = csiborgtools.read.Paths(**kwargs["paths_kind"]) reader = csiborgtools.read.NearestNeighbourReader(**kwargs, paths=paths) x = reader.bin_centres("neighbour") - y_quijote = read_cdf("quijote", run, kwargs) - y_csiborg = read_cdf("csiborg", run, kwargs) - ncdf = y_quijote.shape[0] + y_quijote = read_dist("quijote", run, kind, kwargs) + y_csiborg = read_dist("csiborg", run, kind, kwargs) + ncdf = y_csiborg.shape[0] with plt.style.context(utils.mplstyle): plt.figure() @@ -64,40 +82,83 @@ def plot_cdf(run, kwargs): plt.plot(x, y_quijote[i], c="C0", label=label1) plt.plot(x, y_csiborg[i], c="C1", label=label2) plt.xlim(0, 75) - plt.ylim(0, 1) plt.xlabel(r"$r_{1\mathrm{NN}}~[\mathrm{Mpc}]$") - plt.ylabel(r"$\mathrm{CDF}(r_{1\mathrm{NN}})$") + if kind == "pdf": + plt.ylabel(r"$p(r_{1\mathrm{NN}})$") + else: + plt.ylabel(r"$\mathrm{CDF}(r_{1\mathrm{NN}})$") + plt.ylim(0, 1) plt.legend() - plt.tight_layout() for ext in ["png"]: - fout = join(utils.fout, f"1nn_cdf_{run}.{ext}") + fout = join(utils.fout, f"1nn_{kind}_{run}.{ext}") print(f"Saving to `{fout}`.") plt.savefig(fout, dpi=utils.dpi, bbox_inches="tight") plt.close() -def plot_significance_hist(run, nsim, kwargs): - """ - Plot the histogram of the significance of the 1NN distance for CSiBORG. - """ +def plot_significance_hist(simname, run, nsim, nobs, kind, kwargs): + """Plot a histogram of the significance of the 1NN distance.""" + assert kind in ["kl", "ks"] paths = csiborgtools.read.Paths(**kwargs["paths_kind"]) - reader = csiborgtools.read.NearestNeighbourReader(**kwargs, paths=paths) - - cdf = read_cdf("quijote", run, kwargs) - - x = reader.calc_significance("csiborg", run, nsim, cdf) + if kind == "kl": + x = make_kl(simname, run, nsim, nobs, kwargs) + else: + x = make_ks(simname, run, nsim, nobs, kwargs) + x = numpy.log10(x) x = x[numpy.isfinite(x)] with plt.style.context(utils.mplstyle): plt.figure() plt.hist(x, bins="auto") - plt.xlabel(r"$r_{1\mathrm{NN}}$ significance $\mathrm{[\sigma]}$") + if kind == "ks": + plt.xlabel(r"$\log p$-value of $r_{1\mathrm{NN}}$ distribution") + else: + plt.xlabel(r"$D_{\mathrm{KL}}$ of $r_{1\mathrm{NN}}$ distribution") plt.ylabel(r"Counts") plt.tight_layout() + for ext in ["png"]: - fout = join(utils.fout, f"sigma_{run}_{str(nsim).zfill(5)}.{ext}") + if simname == "quijote": + nsim = paths.quijote_fiducial_nsim(nsim, nobs) + fout = join(utils.fout, f"significance_{kind}_{simname}_{run}_{str(nsim).zfill(5)}.{ext}") # noqa + print(f"Saving to `{fout}`.") + plt.savefig(fout, dpi=utils.dpi, bbox_inches="tight") + plt.close() + + +def plot_significance_mass(simname, run, nsim, nobs, kind, kwargs): + """ + Plot significance of the 1NN distance as a function of the total mass. + """ + assert kind in ["kl", "ks"] + paths = csiborgtools.read.Paths(**kwargs["paths_kind"]) + reader = csiborgtools.read.NearestNeighbourReader(**kwargs, paths=paths) + + x = reader.read_single(simname, run, nsim, nobs)["mass"] + if kind == "kl": + y = make_kl(simname, run, nsim, nobs, kwargs) + else: + y = make_ks(simname, run, nsim, nobs, kwargs) + + with plt.style.context(utils.mplstyle): + plt.figure() + plt.scatter(x, y) + + plt.xscale("log") + plt.xlabel(r"$M_{\rm tot} / M_\odot$") + if kind == "ks": + plt.ylabel(r"$p$-value of $r_{1\mathrm{NN}}$ distribution") + plt.yscale("log") + else: + plt.ylabel(r"$D_{\mathrm{KL}}$ of $r_{1\mathrm{NN}}$ distribution") + + plt.tight_layout() + for ext in ["png"]: + if simname == "quijote": + nsim = paths.quijote_fiducial_nsim(nsim, nobs) + fout = join(utils.fout, f"significance_vs_mass_{kind}_{simname}_{run}_{str(nsim).zfill(5)}.{ext}") # noqa print(f"Saving to `{fout}`.") plt.savefig(fout, dpi=utils.dpi, bbox_inches="tight") plt.close() @@ -114,13 +175,15 @@ if __name__ == "__main__": "nbins_neighbour": 150, "paths_kind": csiborgtools.paths_glamdring} - cached_funcs = ["read_cdf"] + cached_funcs = ["read_dist", "make_kl", "make_ks"] if args.clean: for func in cached_funcs: - print(f"Cleaning cache for function {func}.") + print(f"Cleaning cache for function `{func}`.") delete_disk_caches_for_function(func) - # paths = csiborgtools.read.Paths(**kwargs["paths_kind"]) - # reader = csiborgtools.read.NearestNeighbourReader(**kwargs, paths=paths) + paths = csiborgtools.read.Paths(**kwargs["paths_kind"]) + reader = csiborgtools.read.NearestNeighbourReader(**kwargs, paths=paths) + run = "mass003" - plot_significance_hist("mass003", 7444, kwargs) + plot_significance_mass("quijote", run, 0, nobs=0, kind="ks", + kwargs=kwargs)