csiborgtools/notebooks/flow/reconstruction_comparison.ipynb

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# Copyright (C) 2024 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.
import numpy as np
import matplotlib.pyplot as plt
from h5py import File
import csiborgtools

SPEED_OF_LIGHT = 299792.458  # km / s


%load_ext autoreload
%autoreload 2
%matplotlib inline

paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)

Read in upGLADE¶

• Mask out galaxies with bad redshifts
• Convert heliocentric redshifts to the CMB frame.

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fname = "/mnt/users/rstiskalek/csiborgtools/data/upglade_all_z0p05_new.h5"
data = {}
with File(fname, "r") as f:
    for i, key in enumerate(["RA", "dec", "zhelio", "e_zhelio"]):
        data[key] = f["data"]["block0_values"][:, i]
data["DEC"] = data.pop("dec")

print(f"Initially, we have {data['zhelio'].size} objects.")

# Ask about this
mask = (data["e_zhelio"] < 0) | (data["zhelio"] < 0)
print(f"Masking {mask.sum()} objects that have `e_zhelio` < 0 or `zhelio` < 0.")
for key in data.keys():
    data[key][mask] = np.nan

mask = (data["e_zhelio"] / data["zhelio"] > 0.15)
print(f"Masking {mask.sum()} objects that have `e_zhelio` / `zhelio` > 0.15.")
for key in data.keys():
    data[key][mask] = np.nan

print(f"Finally, we have {np.sum(np.isfinite(data['zhelio']))} objects.")

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plt.figure()
plt.scatter(data["zhelio"], data["e_zhelio"], s=0.1)

plt.yscale("log")
plt.xscale("log")
plt.xlabel(r"$z_{\rm helio}$")
plt.ylabel(r"$\sigma_{z_{\rm helio}}$")

plt.tight_layout()
plt.show()

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zcmb, e_zcmb = csiborgtools.heliocentric_to_cmb(data["zhelio"], data["RA"], data["DEC"], data["e_zhelio"])
data["zcmb"] = zcmb
data["e_zcmb"] = e_zcmb


mask = (data["zcmb"] > 0.06) #& ~np.isnan(data["zhelio"])
print(f"Masking {mask.sum()} objects that have `zcmb` > 0.06.")
for key in data.keys():
    data[key][mask] = np.nan

print(f"Finally, we have {np.sum(np.isfinite(data['zhelio']))} objects.")

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plt.figure()

plt.scatter(data["zcmb"], data["e_zcmb"], s=0.001)
plt.xlabel(r"$z_{\rm CMB}$")
plt.ylabel(r"$\sigma_{z_{\rm CMB}}$")

plt.xlim(0, 0.05)
plt.ylim(0, 0.008)

plt.tight_layout()
plt.savefig("../../plots/UPGLADE_zcmb_vs_ezcmb.png")
plt.show()

• Write only masked galaxies to this file, but also save the mask.

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fname_here = fname.replace(".h5", "_PROCESSED.h5")
mask = np.isfinite(data["RA"])
print(f"Writing {mask.sum()} objects to `{fname_here}`.")

with File(fname_here, "w") as f:
    for key in data.keys():
        f[key] = data[key][mask]

    f["mask"] = mask

• Having generated this file, next step is to run field_los.py to evaluate the density and velocity field along the LOS of each object that is not masked.
• Then, the next step is to run post_upglade.py to calculate the cosmological redshift of each object in UPGLADE.
  • Based on Carrick2015 samples calibrated against Pantheon+

Results verification¶

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fname = "/mnt/extraspace/rstiskalek/csiborg_postprocessing/peculiar_velocity/UPGLADE/zcosmo_UPGLADE.hdf5"

with File(fname, "r") as f:
    indxs = f["indxs"][:]
    mean_zcosmo = f["mean_zcosmo"][:]
    std_zcosmo = f["std_zcosmo"][:]


plt.figure()
plt.scatter(mean_zcosmo, std_zcosmo, s=0.001)

plt.xlabel(r"$z_{\rm cosmo}$")
plt.ylabel(r"$\sigma_{z_{\rm cosmo}}$")

plt.xlim(0, 0.05)
plt.ylim(0, 0.006)
plt.tight_layout()
plt.savefig("../../plots/UPGLADE_zcosmo_vs_ezcosmo.png")


plt.show()

Combine datasets¶

i.e. match to the original UPGLADE file

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data["zcosmo"] = np.zeros_like(data["RA"])
data["zcosmo"][mask] = mean_zcosmo

data["e_zcosmo"] = np.zeros_like(data["RA"])
data["e_zcosmo"][mask] = std_zcosmo

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plt.figure()
plt.scatter(data["zcmb"], data["zcosmo"], s=0.01)
plt.axline([0, 0], slope=1, color="red", ls="--")

plt.xlabel(r"$z_{\rm CMB}$")
plt.ylabel(r"$z_{\rm cosmo}$")
plt.xlim(0)
plt.ylim(0)

plt.tight_layout()
plt.savefig("../../plots/UPGLADE_zcmb_vs_zcosmo.png")

plt.show()

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plt.figure()
plt.scatter(data["zcmb"], (data["zcosmo"] - data["zcmb"]) * SPEED_OF_LIGHT, s=0.001)

plt.xlabel(r"$z_{\rm CMB}$")
plt.ylabel(r"$c (z_{\rm cosmo} - z_{\rm CMB}) ~ [\mathrm{km} / \mathrm{s}]$")
plt.xlim(0)
plt.ylim(-1000, 1000)

plt.tight_layout()
plt.savefig("../../plots/UPGLADE_zcmb_vs_dzcosmo.png")
plt.show()

Save the data¶

• In a format matching what Gergely shared.

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fname = "/mnt/users/rstiskalek/csiborgtools/data/upglade_z_0p05_all_WZCOSMO.h5"
print(f"Writing to `{fname}`.")

with File(fname, "w") as f:
    for key in data.keys():
        f.create_dataset(key, data=data[key])

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