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Add plotting (#64)

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* Add verbosity statements

* More verbosity

* Save masses too

* Add CDF new plot

* Blank line

* Fix RVS sampling bug

* Add R200 conversion

* Simplify plotting routines

* Remove imoprt
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Richard Stiskalek 2023-05-27 00:08:39 +01:00 • committed by GitHub
parent 7c2d7a86f5
commit f1dbe6f03f
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6 changed files with 123 additions and 12 deletions
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97
scripts_plots/plot_match.py
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@ -56,14 +56,16 @@ def get_overlap(nsim0):
cat0 = open_cat(nsim0)
catxs = []
print("Opening catalogues...", flush=True)
for nsimx in tqdm(nsimxs):
catxs.append(open_cat(nsimx))
reader = csiborgtools.read.NPairsOverlap(cat0, catxs, paths)
x = reader.cat0("totpartmass")
mass = reader.cat0("totpartmass")
hindxs = reader.cat0("index")
summed_overlap = reader.summed_overlap(True)
prob_nomatch = reader.prob_nomatch(True)
return x, summed_overlap, prob_nomatch
return mass, hindxs, summed_overlap, prob_nomatch
def plot_summed_overlap(nsim0):
@ -71,7 +73,7 @@ def plot_summed_overlap(nsim0):
Plot the summed overlap and probability of no matching for a single
reference simulation as a function of the reference halo mass.
"""
x, summed_overlap, prob_nomatch = get_overlap(nsim0)
x, __, summed_overlap, prob_nomatch = get_overlap(nsim0)
mean_overlap = numpy.mean(summed_overlap, axis=1)
std_overlap = numpy.std(summed_overlap, axis=1)
@ -170,7 +172,7 @@ def make_ks(simname, run, nsim, nobs, kwargs):
return reader.ks_significance(simname, run, nsim, cdf, nobs=nobs)
def plot_dist(run, kind, kwargs):
def plot_dist(run, kind, kwargs, r200):
"""
Plot the PDF/CDF of the nearest neighbour distance for Quijote and CSiBORG.
"""
@ -179,6 +181,8 @@ def plot_dist(run, kind, kwargs):
paths = csiborgtools.read.Paths(**kwargs["paths_kind"])
reader = csiborgtools.read.NearestNeighbourReader(**kwargs, paths=paths)
x = reader.bin_centres("neighbour")
if r200 is not None:
x /= r200
y_quijote = read_dist("quijote", run, kind, kwargs)
y_csiborg = read_dist("csiborg", run, kind, kwargs)
@ -196,7 +200,10 @@ def plot_dist(run, kind, 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.xlabel(r"$r_{1\mathrm{NN}}~[\mathrm{Mpc}]$")
if r200 is None:
plt.xlabel(r"$r_{1\mathrm{NN}}~[\mathrm{Mpc}]$")
else:
plt.xlabel(r"$r_{1\mathrm{NN}} / R_{200c}$")
if kind == "pdf":
plt.ylabel(r"$p(r_{1\mathrm{NN}})$")
else:
@ -308,6 +315,63 @@ def plot_kl_vs_ks(simname, run, nsim, nobs, kwargs):
plt.close()
def plot_kl_vs_overlap(run, nsim, kwargs):
"""
Plot KL divergence vs overlap.
"""
paths = csiborgtools.read.Paths(**kwargs["paths_kind"])
nn_reader = csiborgtools.read.NearestNeighbourReader(**kwargs, paths=paths)
nn_data = nn_reader.read_single("csiborg", run, nsim, nobs=None)
nn_hindxs = nn_data["ref_hindxs"]
mass, overlap_hindxs, summed_overlap, prob_nomatch = get_overlap(nsim)
# We need to match the hindxs between the two.
hind2overlap_array = {hind: i for i, hind in enumerate(overlap_hindxs)}
mask = numpy.asanyarray([hind2overlap_array[hind] for hind in nn_hindxs])
summed_overlap = summed_overlap[mask]
prob_nomatch = prob_nomatch[mask]
mass = mass[mask]
kl = make_kl("csiborg", run, nsim, nobs=None, kwargs=kwargs)
with plt.style.context(utils.mplstyle):
plt.figure()
mu = numpy.mean(prob_nomatch, axis=1)
plt.scatter(kl, 1 - mu, c=numpy.log10(mass))
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"$1 - \langle \eta^{\mathcal{B}}_a \rangle_{\mathcal{B}}$")
plt.tight_layout()
for ext in ["png"]:
fout = join(utils.fout, f"kl_vs_overlap_mean_{run}_{str(nsim).zfill(5)}.{ext}") # noqa
print(f"Saving to `{fout}`.")
plt.savefig(fout, dpi=utils.dpi, bbox_inches="tight")
plt.close()
with plt.style.context(utils.mplstyle):
plt.figure()
std = numpy.std(prob_nomatch, axis=1)
plt.scatter(kl, std, c=numpy.log10(mass))
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"$\langle \left(\eta^{\mathcal{B}}_a - \langle \eta^{\mathcal{B}^\prime}_a \rangle_{\mathcal{B}^\prime}\right)^2\rangle_{\mathcal{B}}^{1/2}$") # noqa
plt.tight_layout()
for ext in ["png"]:
fout = join(utils.fout, f"kl_vs_overlap_std_{run}_{str(nsim).zfill(5)}.{ext}") # noqa
print(f"Saving to `{fout}`.")
plt.savefig(fout, dpi=utils.dpi, bbox_inches="tight")
plt.close()
###############################################################################
# Command line interface #
###############################################################################
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument('-c', '--clean', action='store_true')
@ -320,15 +384,30 @@ if __name__ == "__main__":
delete_disk_caches_for_function(func)
neighbour_kwargs = {"rmax_radial": 155 / 0.705,
"nbins_radial": 20,
"nbins_radial": 50,
"rmax_neighbour": 100.,
"nbins_neighbour": 150,
"paths_kind": csiborgtools.paths_glamdring}
run = "mass003"
# plot_dist("mass003", "pdf", neighbour_kwargs)
paths = csiborgtools.read.Paths(**neighbour_kwargs["paths_kind"])
nn_reader = csiborgtools.read.NearestNeighbourReader(**neighbour_kwargs,
paths=paths)
run = "mass003"
# for ic in [7444, 8812, 9700]:
# plot_summed_overlap(ic)
# sizes = numpy.full(2700, numpy.nan)
# from tqdm import trange
# k = 0
# for nsim in trange(100):
# for nobs in range(27):
# d = nn_reader.read_single("quijote", run, nsim, nobs)
# sizes[k] = d["mass"].size
# k += 1
# print(sizes)
# print(numpy.mean(sizes), numpy.std(sizes))
# plot_kl_vs_overlap("mass003", 7444, neighbour_kwargs)
# plot_cdf_r200("mass003", neighbour_kwargs)