mirror of
https://github.com/Richard-Sti/csiborgtools.git
synced 2024-12-22 21:18:04 +00:00
eccd8e3507
* Improve calculations * Improve flags * Add smoothed options * Remove some old comments * Edit little things * Save smoothed * Move files * Edit imports * Edit imports * Renaming imports * Renaming imports * Sort imports * Sort files * Sorting * Optionally make copies of the field * Add quijote backup check * Add direct field smoothing * Shorten stupid documentation * Shorten stupid docs * Update conversion * Add particles to ASCII conversion * Add a short comment * Add SDSS uncorrected distance * Adjust comment * Add FITS index to galaxies * Remove spare space * Remove a stupid line * Remove blank line * Make space separated * Add interpolated field path * Add field sampling * Sort imports * Return density in cells * Clear out observer velocity * Add 170817 sampling * Fix normalization * Update plot
597 lines
23 KiB
Python
597 lines
23 KiB
Python
# Copyright (C) 2023 Richard Stiskalek
|
|
# This program is free software; you can redistribute it and/or modify it
|
|
# under the terms of the GNU General Public License as published by the
|
|
# Free Software Foundation; either version 3 of the License, or (at your
|
|
# option) any later version.
|
|
#
|
|
# This program is distributed in the hope that it will be useful, but
|
|
# WITHOUT ANY WARRANTY; without even the implied warranty of
|
|
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
|
|
# Public License for more details.
|
|
#
|
|
# You should have received a copy of the GNU General Public License along
|
|
# with this program; if not, write to the Free Software Foundation, Inc.,
|
|
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
|
|
|
|
from os.path import join
|
|
from argparse import ArgumentParser
|
|
|
|
import matplotlib as mpl
|
|
import matplotlib.pyplot as plt
|
|
import numpy
|
|
from h5py import File
|
|
import healpy
|
|
|
|
import scienceplots # noqa
|
|
import plt_utils
|
|
from cache_to_disk import cache_to_disk, delete_disk_caches_for_function # noqa
|
|
from tqdm import tqdm
|
|
|
|
try:
|
|
import csiborgtools
|
|
except ModuleNotFoundError:
|
|
import sys
|
|
sys.path.append("../")
|
|
import csiborgtools
|
|
|
|
|
|
def open_csiborg(nsim):
|
|
"""
|
|
Open a CSiBORG halo catalogue. Applies mass and distance selection.
|
|
"""
|
|
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
|
bounds = {"totpartmass": (None, None), "dist": (0, 155)}
|
|
return csiborgtools.read.CSiBORGHaloCatalogue(
|
|
nsim, paths, bounds=bounds, load_fitted=True, load_initial=True,
|
|
with_lagpatch=False)
|
|
|
|
|
|
def open_quijote(nsim, nobs=None):
|
|
"""
|
|
Open a Quijote halo catalogue. Applies mass and distance selection.
|
|
"""
|
|
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
|
cat = csiborgtools.read.QuijoteHaloCatalogue(
|
|
nsim, paths, nsnap=4, load_fitted=True, load_initial=True,
|
|
with_lagpatch=False)
|
|
if nobs is not None:
|
|
cat = cat.pick_fiducial_observer(nobs, rmax=155.5)
|
|
return cat
|
|
|
|
|
|
def plot_mass_vs_ncells(nsim, pdf=False):
|
|
"""
|
|
Plot the halo mass vs. number of occupied cells in the initial snapshot.
|
|
"""
|
|
cat = open_csiborg(nsim)
|
|
mpart = 4.38304044e+09
|
|
|
|
with plt.style.context(plt_utils.mplstyle):
|
|
plt.figure()
|
|
plt.scatter(cat["totpartmass"], cat["lagpatch_ncells"], s=0.25,
|
|
rasterized=True)
|
|
plt.xscale("log")
|
|
plt.yscale("log")
|
|
for n in [1, 10, 100]:
|
|
plt.axvline(n * 512 * mpart, c="black", ls="--", zorder=0, lw=0.8)
|
|
plt.xlabel(r"$M_{\rm tot} ~ [M_\odot$ / h]")
|
|
plt.ylabel(r"$N_{\rm cells}$")
|
|
|
|
for ext in ["png"] if pdf is False else ["png", "pdf"]:
|
|
fout = join(plt_utils.fout, f"init_mass_vs_ncells_{nsim}.{ext}")
|
|
print(f"Saving to `{fout}`.")
|
|
plt.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
|
|
plt.close()
|
|
|
|
|
|
###############################################################################
|
|
# HMF plot #
|
|
###############################################################################
|
|
|
|
|
|
def plot_hmf(pdf=False):
|
|
"""
|
|
Plot the FoF halo mass function of CSiBORG and Quijote.
|
|
"""
|
|
print("Plotting the HMF...", flush=True)
|
|
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
|
|
|
csiborg_nsims = paths.get_ics("csiborg")
|
|
print("Loading CSiBORG halo counts.", flush=True)
|
|
for i, nsim in enumerate(tqdm(csiborg_nsims)):
|
|
data = numpy.load(paths.halo_counts("csiborg", nsim))
|
|
if i == 0:
|
|
bins = data["bins"]
|
|
csiborg_counts = numpy.full((len(csiborg_nsims), len(bins) - 1),
|
|
numpy.nan, dtype=numpy.float32)
|
|
csiborg_counts[i, :] = data["counts"]
|
|
# csiborg_counts /= numpy.diff(bins).reshape(1, -1)
|
|
|
|
csiborg5511 = numpy.load(paths.halo_counts("csiborg", 5511))["counts"]
|
|
# csiborg5511 /= numpy.diff(data["bins"])
|
|
|
|
print("Loading Quijote halo counts.", flush=True)
|
|
quijote_nsims = paths.get_ics("quijote")
|
|
for i, nsim in enumerate(tqdm(quijote_nsims)):
|
|
data = numpy.load(paths.halo_counts("quijote", nsim))
|
|
if i == 0:
|
|
bins = data["bins"]
|
|
nmax = data["counts"].shape[0]
|
|
quijote_counts = numpy.full(
|
|
(len(quijote_nsims) * nmax, len(bins) - 1), numpy.nan,
|
|
dtype=numpy.float32)
|
|
quijote_counts[i * nmax:(i + 1) * nmax, :] = data["counts"]
|
|
# quijote_counts /= numpy.diff(bins).reshape(1, -1)
|
|
|
|
# vol = 155.5**3
|
|
# csiborg_counts /= vol
|
|
# quijote_counts /= vol
|
|
# csiborg5511 /= vol
|
|
|
|
x = 10**(0.5 * (bins[1:] + bins[:-1]))
|
|
# Edit lower limits
|
|
csiborg_counts[:, x < 1e12] = numpy.nan
|
|
quijote_counts[:, x < 10**(13.1)] = numpy.nan
|
|
# Edit upper limits
|
|
csiborg_counts[:, x > 3e15] = numpy.nan
|
|
quijote_counts[:, x > 3e15] = numpy.nan
|
|
csiborg5511[x > 3e15] = numpy.nan
|
|
|
|
with plt.style.context(plt_utils.mplstyle):
|
|
cols = plt.rcParams["axes.prop_cycle"].by_key()["color"]
|
|
fig, ax = plt.subplots(nrows=2, sharex=True,
|
|
figsize=(3.5, 2.625 * 1.25),
|
|
gridspec_kw={"height_ratios": [1, 0.45]})
|
|
fig.subplots_adjust(hspace=0, wspace=0)
|
|
|
|
# Upper panel data
|
|
mean_csiborg = numpy.mean(csiborg_counts, axis=0)
|
|
std_csiborg = numpy.std(csiborg_counts, axis=0)
|
|
ax[0].plot(x, mean_csiborg, label="CSiBORG", c=cols[0])
|
|
ax[0].fill_between(x, mean_csiborg - std_csiborg,
|
|
mean_csiborg + std_csiborg,
|
|
alpha=0.5, color=cols[0])
|
|
|
|
mean_quijote = numpy.mean(quijote_counts, axis=0)
|
|
std_quijote = numpy.std(quijote_counts, axis=0)
|
|
ax[0].plot(x, mean_quijote, label="Quijote", c=cols[1])
|
|
ax[0].fill_between(x, mean_quijote - std_quijote,
|
|
mean_quijote + std_quijote, alpha=0.5,
|
|
color=cols[1])
|
|
|
|
ax[0].plot(x, csiborg5511, label="CSiBORG 5511", c="k", ls="--")
|
|
std5511 = numpy.sqrt(csiborg5511)
|
|
ax[0].fill_between(x, csiborg5511 - std_csiborg, csiborg5511 + std5511,
|
|
alpha=0.2, color="k")
|
|
# Lower panel data
|
|
log_y = numpy.log10(mean_csiborg / mean_quijote)
|
|
err = numpy.sqrt((std_csiborg / mean_csiborg / numpy.log(10))**2
|
|
+ (std_quijote / mean_quijote / numpy.log(10))**2)
|
|
ax[1].plot(x, 10**log_y, c=cols[0])
|
|
ax[1].fill_between(x, 10**(log_y - err), 10**(log_y + err), alpha=0.5,
|
|
color=cols[0])
|
|
|
|
ax[1].plot(x, csiborg5511 / mean_quijote, c="k", ls="--")
|
|
|
|
# Labels and accesories
|
|
ax[1].axhline(1, color="k", ls="--",
|
|
lw=0.5 * plt.rcParams["lines.linewidth"], zorder=0)
|
|
# ax[0].set_ylabel(r"$\frac{\mathrm{d}^2 N}{\mathrm{d} V \mathrm{d}\log M_{\rm tot}}~[\mathrm{dex}^{-1} (\mathrm{Mpc} / h)^{-3}]$", # noqa
|
|
# fontsize="small")
|
|
ax[0].set_ylabel("Counts in bins")
|
|
ax[1].set_xlabel(r"$M_{\rm tot}~[M_\odot / h]$", fontsize="small")
|
|
ax[1].set_ylabel(r"$\mathrm{CSiBORG} / \mathrm{Quijote}$",
|
|
fontsize="small")
|
|
|
|
ax[0].set_xscale("log")
|
|
ax[0].set_yscale("log")
|
|
ax[1].set_ylim(0.5, 2.0)
|
|
# ax[1].set_yscale("log")
|
|
ax[0].legend(fontsize="small")
|
|
|
|
fig.tight_layout(h_pad=0, w_pad=0)
|
|
for ext in ["png"] if pdf is False else ["png", "pdf"]:
|
|
fout = join(plt_utils.fout, f"hmf_comparison.{ext}")
|
|
print(f"Saving to `{fout}`.")
|
|
fig.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
|
|
plt.close()
|
|
|
|
|
|
def plot_hmf_quijote_full(pdf=False):
|
|
"""
|
|
Plot the FoF halo mass function of Quijote full run.
|
|
|
|
Returns
|
|
-------
|
|
None
|
|
"""
|
|
print("Plotting the HMF...", flush=True)
|
|
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
|
|
|
print("Loading Quijote halo counts.", flush=True)
|
|
quijote_nsims = paths.get_ics("quijote")
|
|
for i, nsim in enumerate(tqdm(quijote_nsims)):
|
|
data = numpy.load(paths.halo_counts("quijote_full", nsim))
|
|
if i == 0:
|
|
bins = data["bins"]
|
|
counts = numpy.full((len(quijote_nsims), len(bins) - 1), numpy.nan,
|
|
dtype=numpy.float32)
|
|
counts[i, :] = data["counts"]
|
|
counts /= numpy.diff(bins).reshape(1, -1)
|
|
counts /= 1000**3
|
|
|
|
x = 10**(0.5 * (bins[1:] + bins[:-1]))
|
|
# Edit lower and upper limits
|
|
counts[:, x < 10**(12.4)] = numpy.nan
|
|
counts[:, x > 4e15] = numpy.nan
|
|
|
|
with plt.style.context(plt_utils.mplstyle):
|
|
cols = plt.rcParams["axes.prop_cycle"].by_key()["color"]
|
|
fig, ax = plt.subplots(nrows=2, sharex=True,
|
|
figsize=(3.5, 2.625 * 1.25),
|
|
gridspec_kw={"height_ratios": [1, 0.65]})
|
|
fig.subplots_adjust(hspace=0, wspace=0)
|
|
|
|
# Upper panel data
|
|
mean = numpy.mean(counts, axis=0)
|
|
std = numpy.std(counts, axis=0)
|
|
ax[0].plot(x, mean)
|
|
ax[0].fill_between(x, mean - std, mean + std, alpha=0.5)
|
|
# Lower panel data
|
|
for i in range(counts.shape[0]):
|
|
ax[1].plot(x, counts[i, :] / mean, c=cols[0])
|
|
|
|
# Labels and accesories
|
|
ax[1].axhline(1, color="k", ls="--",
|
|
lw=0.5 * plt.rcParams["lines.linewidth"], zorder=0)
|
|
ax[0].set_ylabel(r"$\frac{\mathrm{d}^2 n}{\mathrm{d}\log M_{\rm tot} \mathrm{d} V}~[\mathrm{dex}^{-1} (\mathrm{Mpc / h})^{-3}]$", # noqa
|
|
fontsize="small")
|
|
ax[1].set_xlabel(r"$M_{\rm tot}~[$M_\odot / h]$", fontsize="small")
|
|
ax[1].set_ylabel(r"$\mathrm{HMF} / \langle \mathrm{HMF} \rangle$",
|
|
fontsize="small")
|
|
|
|
ax[0].set_xscale("log")
|
|
ax[0].set_yscale("log")
|
|
ax[0].legend()
|
|
|
|
fig.tight_layout(h_pad=0, w_pad=0)
|
|
for ext in ["png"] if pdf is False else ["png", "pdf"]:
|
|
fout = join(plt_utils.fout, f"hmf_quijote_full.{ext}")
|
|
print(f"Saving to `{fout}`.")
|
|
fig.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
|
|
plt.close()
|
|
|
|
|
|
def load_field(kind, nsim, grid, MAS, in_rsp=False, smooth_scale=None):
|
|
r"""
|
|
Load a single field.
|
|
"""
|
|
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
|
return numpy.load(paths.field(kind, MAS, grid, nsim, in_rsp=in_rsp,
|
|
smooth_scale=smooth_scale))
|
|
|
|
|
|
###############################################################################
|
|
# Projected field #
|
|
###############################################################################
|
|
|
|
|
|
def plot_projected_field(kind, nsim, grid, in_rsp, smooth_scale, MAS="PCS",
|
|
vel_component=0, highres_only=True, slice_find=None,
|
|
pdf=False):
|
|
"""
|
|
Plot the mean projected field, however can also plot a single slice.
|
|
"""
|
|
print(f"Plotting projected field `{kind}`. ", flush=True)
|
|
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
|
nsnap = max(paths.get_snapshots(nsim, "csiborg"))
|
|
box = csiborgtools.read.CSiBORGBox(nsnap, nsim, paths)
|
|
|
|
if kind == "overdensity":
|
|
field = load_field("density", nsim, grid, MAS=MAS, in_rsp=in_rsp)
|
|
density_gen = csiborgtools.field.DensityField(box, MAS)
|
|
field = density_gen.overdensity_field(field) + 2
|
|
|
|
field = numpy.log10(field)
|
|
elif kind == "borg_density":
|
|
field = File(paths.borg_mcmc(nsim), 'r')
|
|
field = field["scalars"]["BORG_final_density"][...]
|
|
else:
|
|
field = load_field(kind, nsim, grid, MAS=MAS, in_rsp=in_rsp,
|
|
smooth_scale=smooth_scale)
|
|
|
|
if kind == "velocity":
|
|
field = field[vel_component, ...]
|
|
field = box.box2vel(field)
|
|
|
|
if highres_only:
|
|
csiborgtools.field.fill_outside(field, numpy.nan, rmax=155.5,
|
|
boxsize=677.7)
|
|
start = round(field.shape[0] * 0.27)
|
|
end = round(field.shape[0] * 0.73)
|
|
field = field[start:end, start:end, start:end]
|
|
|
|
if kind == "environment":
|
|
cmap = mpl.colors.ListedColormap(
|
|
['red', 'lightcoral', 'limegreen', 'khaki'])
|
|
else:
|
|
cmap = "viridis"
|
|
|
|
labels = [r"$y-z$", r"$x-z$", r"$x-y$"]
|
|
with plt.style.context(plt_utils.mplstyle):
|
|
fig, ax = plt.subplots(figsize=(3.5 * 2, 2.625), ncols=3, sharey=True,
|
|
sharex="col")
|
|
fig.subplots_adjust(hspace=0, wspace=0)
|
|
for i in range(3):
|
|
if slice_find is None:
|
|
img = numpy.nanmean(field, axis=i)
|
|
else:
|
|
ii = int(field.shape[i] * slice_find)
|
|
img = numpy.take(field, ii, axis=i)
|
|
|
|
if i == 0:
|
|
vmin, vmax = numpy.nanpercentile(img, [1, 99])
|
|
im = ax[i].imshow(img, vmin=vmin, vmax=vmax, cmap=cmap)
|
|
else:
|
|
ax[i].imshow(img, vmin=vmin, vmax=vmax, cmap=cmap)
|
|
|
|
k = img.shape[0] // 2
|
|
ax[i].scatter(k, k, marker="x", s=5, zorder=2, c="red")
|
|
|
|
frad = 155.5 / 677.7
|
|
R = 155.5 / 677.7 * grid
|
|
if slice_find is None:
|
|
rad = R
|
|
plot_circle = True
|
|
elif (not highres_only and 0.5 - frad < slice_find < 0.5 + frad):
|
|
z = (slice_find - 0.5) * grid
|
|
rad = R * numpy.sqrt(1 - z**2 / R**2)
|
|
plot_circle = True
|
|
else:
|
|
plot_circle = False
|
|
|
|
if not highres_only and plot_circle:
|
|
theta = numpy.linspace(0, 2 * numpy.pi, 100)
|
|
ax[i].plot(rad * numpy.cos(theta) + grid // 2,
|
|
rad * numpy.sin(theta) + grid // 2,
|
|
lw=0.75 * plt.rcParams["lines.linewidth"], zorder=1,
|
|
c="red", ls="--")
|
|
|
|
ax[i].set_title(labels[i])
|
|
|
|
if highres_only:
|
|
ncells = end - start
|
|
size = ncells / grid * 677.7
|
|
else:
|
|
ncells = grid
|
|
size = 677.7
|
|
|
|
# Get beautiful ticks
|
|
yticks = numpy.linspace(0, ncells, 6).astype(int)
|
|
yticks = numpy.append(yticks, ncells // 2)
|
|
ax[0].set_yticks(yticks)
|
|
ax[0].set_yticklabels((yticks * size / ncells - size / 2).astype(int))
|
|
ax[0].set_ylabel(r"$x_i ~ [\mathrm{Mpc} / h]$")
|
|
|
|
for i in range(3):
|
|
xticks = numpy.linspace(0, ncells, 6).astype(int)
|
|
xticks = numpy.append(xticks, ncells // 2)
|
|
xticks = numpy.sort(xticks)
|
|
if i < 2:
|
|
xticks = xticks[:-1]
|
|
ax[i].set_xticks(xticks)
|
|
ax[i].set_xticklabels(
|
|
(xticks * size / ncells - size / 2).astype(int))
|
|
ax[i].set_xlabel(r"$x_j ~ [\mathrm{Mpc} / h]$")
|
|
|
|
cbar_ax = fig.add_axes([0.982, 0.155, 0.025, 0.75],
|
|
transform=ax[2].transAxes)
|
|
if slice_find is None:
|
|
clabel = "Mean projected field"
|
|
else:
|
|
clabel = "Sliced field"
|
|
|
|
if kind == "environment":
|
|
bounds = [0, 1, 2, 3, 4]
|
|
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
|
|
cbar = fig.colorbar(
|
|
mpl.cm.ScalarMappable(cmap=cmap, norm=norm), cax=cbar_ax,
|
|
ticks=[0.5, 1.5, 2.5, 3.5])
|
|
cbar.ax.set_yticklabels(["knot", "filament", "sheet", "void"],
|
|
rotation=90, va="center")
|
|
else:
|
|
fig.colorbar(im, cax=cbar_ax, label=clabel)
|
|
|
|
fig.tight_layout(h_pad=0, w_pad=0)
|
|
for ext in ["png"] if pdf is False else ["png", "pdf"]:
|
|
fout = join(
|
|
plt_utils.fout,
|
|
f"field_{kind}_{nsim}_rsp{in_rsp}_hres{highres_only}.{ext}")
|
|
if smooth_scale is not None and smooth_scale > 0:
|
|
smooth_scale = float(smooth_scale)
|
|
fout = fout.replace(f".{ext}", f"_smooth{smooth_scale}.{ext}")
|
|
print(f"Saving to `{fout}`.")
|
|
fig.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
|
|
plt.close()
|
|
|
|
|
|
###############################################################################
|
|
# Sky distribution #
|
|
###############################################################################
|
|
|
|
|
|
def get_sky_label(kind, volume_weight: bool):
|
|
"""
|
|
Get the sky label for a given field kind.
|
|
"""
|
|
if volume_weight:
|
|
if kind == "density":
|
|
label = r"$\log \int_{0}^{R} r^2 \rho(r, \mathrm{RA}, \mathrm{dec}) \mathrm{d} r$" # noqa
|
|
if kind == "overdensity":
|
|
label = r"$\log \int_{0}^{R} r^2 \left[\delta(r, \mathrm{RA}, \mathrm{dec}) + 1\right] \mathrm{d} r$" # noqa
|
|
elif kind == "potential":
|
|
label = r"$\int_{0}^{R} r^2 \phi(r, \mathrm{RA}, \mathrm{dec}) \mathrm{d} r$" # noqa
|
|
elif kind == "radvel":
|
|
label = r"$\int_{0}^{R} r^2 v_r(r, \mathrm{RA}, \mathrm{dec}) \mathrm{d} r$" # noqa
|
|
else:
|
|
label = None
|
|
else:
|
|
if kind == "density":
|
|
label = r"$\log \int_{0}^{R} \rho(r, \mathrm{RA}, \mathrm{dec}) \mathrm{d} r$" # noqa
|
|
if kind == "overdensity":
|
|
label = r"$\log \int_{0}^{R} \left[\delta(r, \mathrm{RA}, \mathrm{dec}) + 1\right] \mathrm{d} r$" # noqa
|
|
elif kind == "potential":
|
|
label = r"$\int_{0}^{R} \phi(r, \mathrm{RA}, \mathrm{dec}) \mathrm{d} r$" # noqa
|
|
elif kind == "radvel":
|
|
label = r"$\int_{0}^{R} v_r(r, \mathrm{RA}, \mathrm{dec}) \mathrm{d} r$" # noqa
|
|
else:
|
|
label = None
|
|
return label
|
|
|
|
|
|
def plot_sky_distribution(field, nsim, grid, nside, smooth_scale=None,
|
|
MAS="PCS", plot_groups=True, dmin=0, dmax=220,
|
|
plot_halos=None, volume_weight=True, pdf=False):
|
|
r"""
|
|
Plot the sky distribution of a given field kind on the sky along with halos
|
|
and selected observations.
|
|
|
|
TODO
|
|
----
|
|
- Add distance for groups.
|
|
|
|
Parameters
|
|
----------
|
|
field : str
|
|
Field kind.
|
|
nsim : int
|
|
Simulation index.
|
|
grid : int
|
|
Grid size.
|
|
nside : int
|
|
Healpix nside of the sky projection.
|
|
smooth_scale : float
|
|
Smoothing scale in :math:`\mathrm{Mpc} / h`.
|
|
MAS : str, optional
|
|
Mass assignment scheme.
|
|
plot_groups : bool, optional
|
|
Whether to plot the 2M++ groups.
|
|
dmin : float, optional
|
|
Minimum projection distance in :math:`\mathrm{Mpc}/h`.
|
|
dmax : float, optional
|
|
Maximum projection distance in :math:`\mathrm{Mpc}/h`.
|
|
plot_halos : list, optional
|
|
Minimum halo mass to plot in :math:`M_\odot`.
|
|
volume_weight : bool, optional
|
|
Whether to volume weight the field.
|
|
pdf : bool, optional
|
|
Whether to save the figure as a pdf.
|
|
"""
|
|
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
|
nsnap = max(paths.get_snapshots(nsim, "csiborg"))
|
|
box = csiborgtools.read.CSiBORGBox(nsnap, nsim, paths)
|
|
|
|
if field == "overdensity":
|
|
field = load_field("density", nsim, grid, MAS=MAS, in_rsp=False,
|
|
smooth_scale=smooth_scale)
|
|
density_gen = csiborgtools.field.DensityField(box, MAS)
|
|
field = density_gen.overdensity_field(field) + 1
|
|
else:
|
|
field = load_field(kind, nsim, grid, MAS=MAS, in_rsp=False,
|
|
smooth_scale=smooth_scale)
|
|
|
|
angpos = csiborgtools.field.nside2radec(nside)
|
|
dist = numpy.linspace(dmin, dmax, 500)
|
|
out = csiborgtools.field.make_sky(field, angpos=angpos, dist=dist, box=box,
|
|
volume_weight=volume_weight)
|
|
|
|
with plt.style.context(plt_utils.mplstyle):
|
|
label = get_sky_label(kind, volume_weight)
|
|
if kind in ["density", "overdensity"]:
|
|
out = numpy.log10(out)
|
|
healpy.mollview(out, fig=0, title="", unit=label, rot=90)
|
|
|
|
if plot_halos is not None:
|
|
bounds = {"dist": (dmin, dmax),
|
|
"totpartmass": (plot_halos, None)}
|
|
cat = csiborgtools.read.CSiBORGHaloCatalogue(nsim, paths,
|
|
bounds=bounds)
|
|
X = cat.position(cartesian=False)
|
|
healpy.projscatter(numpy.deg2rad(X[:, 2] + 90),
|
|
numpy.deg2rad(X[:, 1]),
|
|
s=5, c="red", label="CSiBORG haloes")
|
|
|
|
if plot_groups:
|
|
groups = csiborgtools.read.TwoMPPGroups(fpath="/mnt/extraspace/rstiskalek/catalogs/2M++_group_catalog.dat") # noqa
|
|
healpy.projscatter(numpy.deg2rad(groups["DEC"] + 90),
|
|
numpy.deg2rad(groups["RA"]), s=1, c="blue",
|
|
label="2M++ groups")
|
|
|
|
if plot_halos is not None or plot_groups:
|
|
plt.legend(markerscale=5)
|
|
|
|
for ext in ["png"] if pdf is False else ["png", "pdf"]:
|
|
fout = join(plt_utils.fout, f"sky_{kind}_{nsim}_from_{dmin}_to_{dmax}_vol{volume_weight}.{ext}") # noqa
|
|
print(f"Saving to `{fout}`.")
|
|
plt.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
|
|
plt.close()
|
|
|
|
|
|
###############################################################################
|
|
# Command line interface #
|
|
###############################################################################
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = ArgumentParser()
|
|
parser.add_argument('-c', '--clean', action='store_true')
|
|
args = parser.parse_args()
|
|
|
|
cached_funcs = ["load_field"]
|
|
if args.clean:
|
|
for func in cached_funcs:
|
|
print(f"Cleaning cache for function {func}.")
|
|
delete_disk_caches_for_function(func)
|
|
|
|
if False:
|
|
plot_mass_vs_ncells(7444, pdf=False)
|
|
|
|
if False:
|
|
plot_hmf(pdf=False)
|
|
|
|
if False:
|
|
plot_hmf_quijote_full(pdf=False)
|
|
|
|
if False:
|
|
kind = "overdensity"
|
|
grid = 1024
|
|
plot_sky_distribution(kind, 7444, grid, nside=64,
|
|
plot_groups=False, dmin=45, dmax=60,
|
|
plot_halos=5e13, volume_weight=True)
|
|
|
|
if True:
|
|
kind = "environment"
|
|
grid = 512
|
|
smooth_scale = 8.0
|
|
# plot_projected_field("overdensity", 7444, grid, in_rsp=True,
|
|
# highres_only=False)
|
|
# nsims = [7444 + n * 24 for n in range(101)]
|
|
nsim = 7444
|
|
|
|
for in_rsp in [False]:
|
|
plot_projected_field(kind, nsim, grid, in_rsp=in_rsp,
|
|
smooth_scale=smooth_scale, slice_find=0.5,
|
|
MAS="PCS", highres_only=True)
|
|
|
|
if False:
|
|
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
|
|
|
d = csiborgtools.read.read_h5(paths.particles(7444, "csiborg"))
|
|
d = d["particles"]
|
|
|
|
plt.figure()
|
|
plt.hist(d[:100000, 4], bins="auto")
|
|
|
|
plt.tight_layout()
|
|
plt.savefig("../plots/velocity_distribution.png", dpi=450,
|
|
bbox_inches="tight")
|