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Add mocks generator
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csiborgtools/flow/mocks.py
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csiborgtools/flow/mocks.py
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# Copyright (C) 2024 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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"""Mock data generators."""
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import numpy as np
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from ..field.interp import evaluate_cartesian_regular
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from ..params import SPEED_OF_LIGHT
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from ..utils import cartesian_to_radec, radec_to_cartesian, radec_to_galactic
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from .cosmography import distmod2dist, distmod2redshift
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###############################################################################
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# Mock Quijote observations #
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###############################################################################
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def mock_Carrick2MTF(velocity_field, boxsize, RA_2MTF, DEC_2MTF,
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a_TF=-22.8, b_TF=-7.2, sigma_TF=0.25, sigma_v=100.,
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Vext=[150.0, 10.0, -100.0], beta=0.4,
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mean_eta=0.069, std_eta=0.078, mean_e_eta=0.012,
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mean_mag=10.31, std_mag=0.83, mean_e_mag=0.044,
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seed=42, Om0=0.3, **kwargs):
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"""
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Mock TFR catalogue build against the Carrick velocity field and the
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2MTF sky distribution to avoid recomputing the LOS velocities.
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"""
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nsamples = len(RA_2MTF)
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# Convert Vext from ICRS to Galactic coordinates.
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Vext = np.asarray(Vext).reshape(1, 3)
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Vext_mag, Vext_RA, Vext_DEC = cartesian_to_radec(Vext).reshape(-1, )
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Vext_l, Vext_b = radec_to_galactic(Vext_RA, Vext_DEC)
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Vext_galactic = np.asanyarray([Vext_mag, Vext_l, Vext_b]).reshape(1, 3)
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Vext = radec_to_cartesian(Vext_galactic).reshape(-1, )
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truths = {"a": a_TF, "b": b_TF, "e_mu": sigma_TF, "sigma_v": sigma_v,
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"Vext": Vext,
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"mean_eta": mean_eta, "std_eta": std_eta,
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"mean_mag": mean_mag, "std_mag": std_mag,
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}
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gen = np.random.default_rng(seed)
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# The Carrick box is in the Galactic coordinates.
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l, b = radec_to_galactic(RA_2MTF, DEC_2MTF)
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gal_phi = np.deg2rad(l)
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gal_theta = np.pi / 2 - np.deg2rad(b)
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# Sample the linewidth of each galaxy from a Gaussian distribution to mimic
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# the MNR procedure.
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eta_true = gen.normal(mean_eta, std_eta, nsamples)
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eta_obs = gen.normal(eta_true, mean_e_eta)
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# Subtract the mean of the observed linewidths, so that they are
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# centered around zero. For consistency subtract from both observed
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# and true values.
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eta_mean_sampled = np.mean(eta_obs)
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eta_true -= eta_mean_sampled
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eta_obs -= eta_mean_sampled
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# Sample the magnitude from some Gaussian distribution to replicate MNR.
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mag_true = gen.normal(mean_mag, std_mag, nsamples)
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mag_obs = gen.normal(mag_true, mean_e_mag)
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# Calculate the 'true' distance modulus and redshift from the TFR distance.
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mu_TFR = mag_true - (a_TF + b_TF * eta_true)
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mu_true = gen.normal(mu_TFR, sigma_TF)
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# Convert the true distance modulus to true distance and cosmological
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# redshift.
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r = distmod2dist(mu_true, Om0)
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zcosmo = distmod2redshift(mu_true, Om0)
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# Calculate the Cartesian coordinates of each galaxy. This is initially
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# centered at (0, 0, 0).
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pos = r * np.asarray([
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np.sin(gal_theta) * np.cos(gal_phi),
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np.sin(gal_theta) * np.sin(gal_phi),
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np.cos(gal_theta)])
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pos = pos.T
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pos_box = pos / boxsize + 0.5
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vel = evaluate_cartesian_regular(
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velocity_field[0], velocity_field[1], velocity_field[2],
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pos=pos_box, smooth_scales=None, method="cubic")
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vel = beta * np.vstack(vel).T
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for i in range(3):
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vel[:, i] += Vext[i]
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# Compute the radial velocity.
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Vr = np.sum(vel * pos, axis=1) / np.linalg.norm(pos, axis=1)
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# The true redshift of the source.
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zCMB_true = (1 + zcosmo) * (1 + Vr / SPEED_OF_LIGHT) - 1
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zCMB_obs = gen.normal(zCMB_true, sigma_v / SPEED_OF_LIGHT)
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# These galaxies will be masked out when LOS is read it because they are
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# too far away.
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distance_mask = r < 125
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truths["distance_mask"] = distance_mask
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sample = {"RA": RA_2MTF,
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"DEC": DEC_2MTF,
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"z_CMB": zCMB_obs,
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"eta": eta_obs,
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"mag": mag_obs,
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"e_eta": np.ones(nsamples) * mean_e_eta,
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"e_mag": np.ones(nsamples) * mean_e_mag,
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"r": r,
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"distmod_true": mu_true,
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"distmod_TFR": mu_TFR}
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return sample, truths
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