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* Remove old file

* Add velocity plotting

* add smooth scale

* Fix bug

* Improve paths

* Edit plotting

* Add smoothed density

* Update boundaries

* Add basics

* Further docs

* Remove blank

* Better catalog broadcasting

* Update high res size

* Update plotting routines

* Update routine

* Update plotting

* Fix field saving name

* Add better colormap for environemnt
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Richard Stiskalek 2023-06-17 19:52:26 +01:00 • committed by GitHub
parent 73687fd8cc
commit 35ccfb5c67
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9 changed files with 343 additions and 169 deletions
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91
scripts_plots/plot_match.py
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@ -648,7 +648,32 @@ def plot_significance(simname, runs, nsim, nobs, kind, kwargs, runs_to_mass):
plt.close()
def plot_significance_vs_mass(simname, runs, nsim, nobs, kind, kwargs):
def make_binlims(run, runs_to_mass):
"""
Make bin limits for the 1NN distance runs, corresponding to the first half
of the log-mass bin.
Parameters
----------
run : str
Run name.
runs_to_mass : dict
Dictionary mapping run names to total halo mass range.
Returns
-------
xmin, xmax : floats
"""
xmin, xmax = runs_to_mass[run]
xmax = xmin + (xmax - xmin) / 2
xmin, xmax = 10**xmin, 10**xmax
if run == "mass009":
xmax = numpy.infty
return xmin, xmax
def plot_significance_vs_mass(simname, runs, nsim, nobs, kind, kwargs,
runs_to_mass):
"""
Plot significance of the 1NN distance as a function of the total mass.
@ -667,6 +692,8 @@ def plot_significance_vs_mass(simname, runs, nsim, nobs, kind, kwargs):
(Kolmogorov-Smirnov p-value).
kwargs : dict
Nearest neighbour reader keyword arguments.
runs_to_mass : dict
Dictionary mapping run names to total halo mass range.
Returns
-------
@ -686,8 +713,12 @@ def plot_significance_vs_mass(simname, runs, nsim, nobs, kind, kwargs):
y = make_kl(simname, run, nsim, nobs, kwargs)
else:
y = numpy.log10(make_ks(simname, run, nsim, nobs, kwargs))
xs.append(x)
ys.append(y)
xmin, xmax = make_binlims(run, runs_to_mass)
mask = (x >= xmin) & (x < xmax)
xs.append(x[mask])
ys.append(y[mask])
xs = numpy.concatenate(xs)
ys = numpy.concatenate(ys)
@ -715,7 +746,7 @@ def plot_significance_vs_mass(simname, runs, nsim, nobs, kind, kwargs):
plt.close()
def plot_kl_vs_ks(simname, runs, nsim, nobs, kwargs):
def plot_kl_vs_ks(simname, runs, nsim, nobs, kwargs, runs_to_mass):
"""
Plot Kullback-Leibler divergence vs Kolmogorov-Smirnov statistic p-value.
@ -731,6 +762,8 @@ def plot_kl_vs_ks(simname, runs, nsim, nobs, kwargs):
Fiducial Quijote observer index. For CSiBORG must be set to `None`.
kwargs : dict
Nearest neighbour reader keyword arguments.
runs_to_mass : dict
Dictionary mapping run names to total halo mass range.
Returns
-------
@ -741,9 +774,16 @@ def plot_kl_vs_ks(simname, runs, nsim, nobs, kwargs):
xs, ys, cs = [], [], []
for run in runs:
cs.append(reader.read_single(simname, run, nsim, nobs)["mass"])
xs.append(make_kl(simname, run, nsim, nobs, kwargs))
ys.append(make_ks(simname, run, nsim, nobs, kwargs))
c = reader.read_single(simname, run, nsim, nobs)["mass"]
x = make_kl(simname, run, nsim, nobs, kwargs)
y = make_ks(simname, run, nsim, nobs, kwargs)
cmin, cmax = make_binlims(run, runs_to_mass)
mask = (c >= cmin) & (c < cmax)
xs.append(x[mask])
ys.append(y[mask])
cs.append(c[mask])
xs = numpy.concatenate(xs)
ys = numpy.log10(numpy.concatenate(ys))
cs = numpy.log10(numpy.concatenate(cs))
@ -768,7 +808,7 @@ def plot_kl_vs_ks(simname, runs, nsim, nobs, kwargs):
plt.close()
def plot_kl_vs_overlap(runs, nsim, kwargs):
def plot_kl_vs_overlap(runs, nsim, kwargs, runs_to_mass):
"""
Plot KL divergence vs overlap for CSiBORG.
@ -780,6 +820,8 @@ def plot_kl_vs_overlap(runs, nsim, kwargs):
Simulation index.
kwargs : dict
Nearest neighbour reader keyword arguments.
runs_to_mass : dict
Dictionary mapping run names to total halo mass range.
Returns
-------
@ -802,12 +844,15 @@ def plot_kl_vs_overlap(runs, nsim, kwargs):
prob_nomatch = prob_nomatch[mask]
mass = mass[mask]
kl = make_kl("csiborg", run, nsim, nobs=None, kwargs=kwargs)
xs.append(kl)
ys1.append(1 - numpy.mean(prob_nomatch, axis=1))
ys2.append(numpy.std(prob_nomatch, axis=1))
cs.append(numpy.log10(mass))
x = make_kl("csiborg", run, nsim, nobs=None, kwargs=kwargs)
y1 = 1 - numpy.mean(prob_nomatch, axis=1)
y2 = numpy.std(prob_nomatch, axis=1)
cmin, cmax = make_binlims(run, runs_to_mass)
mask = (mass >= cmin) & (mass < cmax)
xs.append(x[mask])
ys1.append(y1[mask])
ys2.append(y2[mask])
cs.append(numpy.log10(mass[mask]))
xs = numpy.concatenate(xs)
ys1 = numpy.concatenate(ys1)
@ -877,7 +922,7 @@ if __name__ == "__main__":
delete_disk_caches_for_function(func)
# Plot 1NN distance distributions.
if False:
if True:
for i in range(1, 10):
run = f"mass00{i}"
for pulled_cdf in [True, False]:
@ -886,12 +931,12 @@ if __name__ == "__main__":
plot_dist(run, "pdf", neighbour_kwargs, runs_to_mass)
# Plot 1NN CDF differences.
if False:
if True:
runs = [f"mass00{i}" for i in range(1, 10)]
for pulled_cdf in [True, False]:
plot_cdf_diff(runs, neighbour_kwargs, pulled_cdf=pulled_cdf,
runs_to_mass=runs_to_mass)
if False:
if True:
runs = [f"mass00{i}" for i in range(1, 9)]
for kind in ["kl", "ks"]:
plot_significance("csiborg", runs, 7444, nobs=None, kind=kind,
@ -902,12 +947,14 @@ if __name__ == "__main__":
runs = [f"mass00{i}" for i in range(1, 10)]
for kind in ["kl", "ks"]:
plot_significance_vs_mass("csiborg", runs, 7444, nobs=None,
kind=kind, kwargs=neighbour_kwargs)
kind=kind, kwargs=neighbour_kwargs,
runs_to_mass=runs_to_mass)
if False:
if True:
runs = [f"mass00{i}" for i in range(1, 10)]
plot_kl_vs_ks("csiborg", runs, 7444, None, kwargs=neighbour_kwargs)
plot_kl_vs_ks("csiborg", runs, 7444, None, kwargs=neighbour_kwargs,
runs_to_mass=runs_to_mass)
if False:
if True:
runs = [f"mass00{i}" for i in range(1, 10)]
plot_kl_vs_overlap(runs, 7444, neighbour_kwargs)
plot_kl_vs_overlap(runs, 7444, neighbour_kwargs, runs_to_mass)