mirror of
https://github.com/Richard-Sti/csiborgtools.git
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ae92fd9b72
* Continue if r200c not defined * Remove smooth scale * Remove smooth scale * Edit Max Matching plot * Add peculiar velocity * Add Vobs calculation * Edit docs * Add Vobs plot * Improve plotting * Edit naming convention * Make a note * Add new cat options * Update density field RSP calculation * Update field 2 rsp * Move functions and shorten documentation * Improve transforms and comments * Update docs * Update imports * Edit calculation * Add docs * Remove imports * Add Quijote flags * Edit documentation * Shorten documentation * Edit func calls * Shorten * Docs edits * Edit docs * Shorten docs * Short docs edits * Simplify docs a little bit * Save plotting * Update env
90 lines
3.7 KiB
Python
90 lines
3.7 KiB
Python
# Copyright (C) 2023 Richard Stiskalek
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# This program is free software; you can redistribute it and/or modify it
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# under the terms of the GNU General Public License as published by the
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# Free Software Foundation; either version 3 of the License, or (at your
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# option) any later version.
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#
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# This program is distributed in the hope that it will be useful, but
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# WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
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# Public License for more details.
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#
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# You should have received a copy of the GNU General Public License along
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# with this program; if not, write to the Free Software Foundation, Inc.,
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# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
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from os.path import join
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import matplotlib.pyplot as plt
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import numpy
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import scienceplots # noqa
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from tqdm import tqdm
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import csiborgtools
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import plt_utils
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def observer_peculiar_velocity(MAS, grid, ext="png"):
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paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
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nsims = paths.get_ics("csiborg")
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for n, nsim in enumerate(nsims):
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fpath = paths.observer_peculiar_velocity(MAS, grid, nsim)
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f = numpy.load(fpath)
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if n == 0:
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data = numpy.full((len(nsims), *f["observer_vp"].shape), numpy.nan)
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smooth_scales = f["smooth_scales"]
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data[n] = f["observer_vp"]
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for n, smooth_scale in enumerate(tqdm(smooth_scales,
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desc="Plotting smooth scales")):
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with plt.style.context(plt_utils.mplstyle):
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fig, axs = plt.subplots(ncols=3, figsize=(3.5 * 3, 2.625),
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sharey=True)
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fig.subplots_adjust(wspace=0)
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for i, ax in enumerate(axs):
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ax.hist(data[:, n, i], bins="auto", histtype="step")
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mu, sigma = numpy.mean(data[:, n, i]), numpy.std(data[:, n, i])
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ax.set_title(r"$V_{{\rm obs, i}} = {:.3f} \pm {:.3f} ~ \mathrm{{km}} / \mathrm{{s}}$".format(mu, sigma)) # noqa
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axs[0].set_xlabel(r"$V_{\rm obs, x} ~ [\mathrm{km} / \mathrm{s}]$")
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axs[1].set_xlabel(r"$V_{\rm obs, y} ~ [\mathrm{km} / \mathrm{s}]$")
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axs[2].set_xlabel(r"$V_{\rm obs, z} ~ [\mathrm{km} / \mathrm{s}]$")
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axs[0].set_ylabel(r"Counts")
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fig.suptitle(r"$N_{{\rm grid}} = {}$, $\sigma_{{\rm smooth}} = {:.2f} ~ [\mathrm{{Mpc}} / h]$".format(grid, smooth_scale)) # noqa
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fig.tight_layout(w_pad=0)
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fout = join(plt_utils.fout,
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f"observer_vp_{grid}_{smooth_scale}.{ext}")
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fig.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
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plt.close()
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with plt.style.context(plt_utils.mplstyle):
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fig, axs = plt.subplots(ncols=3, figsize=(3.5 * 3, 2.625))
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for i, ax in enumerate(axs):
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ymu = numpy.mean(data[..., i], axis=0)
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ystd = numpy.std(data[..., i], axis=0)
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ylow, yupp = ymu - ystd, ymu + ystd
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ax.plot(smooth_scales, ymu, c="k")
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ax.fill_between(smooth_scales, ylow, yupp, color="k", alpha=0.2)
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ax.set_xlabel(r"$\sigma_{{\rm smooth}} ~ [\mathrm{{Mpc}} / h]$")
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axs[0].set_ylabel(r"$V_{\rm obs, x} ~ [\mathrm{km} / \mathrm{s}]$")
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axs[1].set_ylabel(r"$V_{\rm obs, y} ~ [\mathrm{km} / \mathrm{s}]$")
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axs[2].set_ylabel(r"$V_{\rm obs, z} ~ [\mathrm{km} / \mathrm{s}]$")
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fig.suptitle(r"$N_{{\rm grid}} = {}$".format(grid))
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fig.tight_layout(w_pad=0)
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fout = join(plt_utils.fout, f"observer_vp_summary_{grid}.{ext}")
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fig.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
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plt.close()
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if __name__ == "__main__":
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observer_peculiar_velocity("PCS", 512)
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