csiborgtools/scripts_plots/plot_vobs.py

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# 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
import matplotlib.pyplot as plt
import numpy
import scienceplots # noqa
from tqdm import tqdm
import csiborgtools
import plt_utils
def observer_peculiar_velocity(MAS, grid, ext="png"):
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
nsims = paths.get_ics("csiborg")
for n, nsim in enumerate(nsims):
fpath = paths.observer_peculiar_velocity(MAS, grid, nsim)
f = numpy.load(fpath)
if n == 0:
data = numpy.full((len(nsims), *f["observer_vp"].shape), numpy.nan)
smooth_scales = f["smooth_scales"]
data[n] = f["observer_vp"]
for n, smooth_scale in enumerate(tqdm(smooth_scales,
desc="Plotting smooth scales")):
with plt.style.context(plt_utils.mplstyle):
fig, axs = plt.subplots(ncols=3, figsize=(3.5 * 3, 2.625),
sharey=True)
fig.subplots_adjust(wspace=0)
for i, ax in enumerate(axs):
ax.hist(data[:, n, i], bins="auto", histtype="step")
mu, sigma = numpy.mean(data[:, n, i]), numpy.std(data[:, n, i])
ax.set_title(r"$V_{{\rm obs, i}} = {:.3f} \pm {:.3f} ~ \mathrm{{km}} / \mathrm{{s}}$".format(mu, sigma)) # noqa
axs[0].set_xlabel(r"$V_{\rm obs, x} ~ [\mathrm{km} / \mathrm{s}]$")
axs[1].set_xlabel(r"$V_{\rm obs, y} ~ [\mathrm{km} / \mathrm{s}]$")
axs[2].set_xlabel(r"$V_{\rm obs, z} ~ [\mathrm{km} / \mathrm{s}]$")
axs[0].set_ylabel(r"Counts")
fig.suptitle(r"$N_{{\rm grid}} = {}$, $\sigma_{{\rm smooth}} = {:.2f} ~ [\mathrm{{Mpc}} / h]$".format(grid, smooth_scale)) # noqa
fig.tight_layout(w_pad=0)
fout = join(plt_utils.fout,
f"observer_vp_{grid}_{smooth_scale}.{ext}")
fig.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
plt.close()
with plt.style.context(plt_utils.mplstyle):
fig, axs = plt.subplots(ncols=3, figsize=(3.5 * 3, 2.625))
for i, ax in enumerate(axs):
ymu = numpy.mean(data[..., i], axis=0)
ystd = numpy.std(data[..., i], axis=0)
ylow, yupp = ymu - ystd, ymu + ystd
ax.plot(smooth_scales, ymu, c="k")
ax.fill_between(smooth_scales, ylow, yupp, color="k", alpha=0.2)
ax.set_xlabel(r"$\sigma_{{\rm smooth}} ~ [\mathrm{{Mpc}} / h]$")
axs[0].set_ylabel(r"$V_{\rm obs, x} ~ [\mathrm{km} / \mathrm{s}]$")
axs[1].set_ylabel(r"$V_{\rm obs, y} ~ [\mathrm{km} / \mathrm{s}]$")
axs[2].set_ylabel(r"$V_{\rm obs, z} ~ [\mathrm{km} / \mathrm{s}]$")
fig.suptitle(r"$N_{{\rm grid}} = {}$".format(grid))
fig.tight_layout(w_pad=0)
fout = join(plt_utils.fout, f"observer_vp_summary_{grid}.{ext}")
fig.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
plt.close()
if __name__ == "__main__":
observer_peculiar_velocity("PCS", 512)