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Matching of observations (#127)

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* Add list of clusters

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Richard Stiskalek 2024-04-23 12:02:09 +01:00 committed by GitHub
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16
csiborgtools/__init__.py
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@ -14,6 +14,7 @@
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
from csiborgtools import clustering, field, flow, halo, match, read, summary # noqa
from .clusters import clusters # noqa
from .utils import (center_of_mass, delta2ncells, number_counts, # noqa
periodic_distance, periodic_distance_two_points, # noqa
binned_statistic, cosine_similarity, fprint, # noqa
@ -58,18 +59,3 @@ class SDSSxALFALFA:
survey = read.SDSS(fpath, h=1, sel_steps=sel_steps)
survey.name = "SDSSxALFALFA"
return survey
###############################################################################
# Clusters #
###############################################################################
clusters = {"Virgo": read.ObservedCluster(RA=hms_to_degrees(12, 27),
dec=dms_to_degrees(12, 43),
dist=16.5 * 0.7,
name="Virgo"),
"Fornax": read.ObservedCluster(RA=hms_to_degrees(3, 38),
dec=dms_to_degrees(-35, 27),
dist=19 * 0.7,
name="Fornax"),
}

105
csiborgtools/clusters.py Normal file
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@ -0,0 +1,105 @@
# 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.
"""
Database of a few nearby observed clusters. Can be augmented with the list
compiled in https://arxiv.org/abs/2402.01834 or some eROSITA clusters?
"""
from csiborgtools.read import ObservedCluster
from .utils import hms_to_degrees, dms_to_degrees
# https://arxiv.org/abs/astro-ph/0702510
# https://arxiv.org/abs/2002.12820
# https://en.wikipedia.org/wiki/Virgo_Cluster
VIRGO = ObservedCluster(
RA=hms_to_degrees(12, 27), dec=dms_to_degrees(12, 43), dist=16.5 * 0.73,
mass=6.3e14 * 0.73, name="Virgo")
# https://arxiv.org/abs/astro-ph/0702320
# https://en.wikipedia.org/wiki/Fornax_Cluster
FORNAX = ObservedCluster(
RA=hms_to_degrees(3, 35), dec=-35.7, dist=19.3 * 0.7,
mass=7e13 * 0.73, name="Fornax")
# https://en.wikipedia.org/wiki/Coma_Cluster
# https://arxiv.org/abs/2311.08603
COMA = ObservedCluster(
RA=hms_to_degrees(12, 59, 48.7), dec=dms_to_degrees(27, 58, 50),
dist=102.975 * 0.705, mass=1.2e15 * 0.73, name="Coma")
# https://en.wikipedia.org/wiki/Perseus_Cluster
# https://ui.adsabs.harvard.edu/abs/2020MNRAS.494.1681A/abstract
PERSEUS = ObservedCluster(
RA=hms_to_degrees(3, 18), dec=dms_to_degrees(41, 30),
dist=73.6 * 0.705, mass=1.2e15 * 0.7, name="Perseus")
# https://en.wikipedia.org/wiki/Centaurus_Cluster
# Not sure about the mass, couldn't find a good estimate. Some paper claimed
# 3e13 Msun, but that seems a little low?
CENTAURUS = ObservedCluster(
RA=hms_to_degrees(12, 48, 51.8), dec=dms_to_degrees(-41, 18, 21),
dist=52.4 * 0.705, mass=2e14 * 0.7, name="Centaurus")
# https://en.wikipedia.org/wiki/Shapley_Supercluster
# https://arxiv.org/abs/0805.0596
SHAPLEY = ObservedCluster(
RA=hms_to_degrees(13, 25), dec=dms_to_degrees(-30),
dist=136, mass=1e16 * 0.7, name="Shapley")
# https://en.wikipedia.org/wiki/Norma_Cluster
# https://arxiv.org/abs/0706.2227
NORMA = ObservedCluster(
RA=hms_to_degrees(16, 15, 32.8), dec=dms_to_degrees(-60, 53, 30),
dist=67.8 * 0.705, mass=1e15 * 0.7, name="Norma")
# Wikipedia seems to give the wrong distance.
# https://en.wikipedia.org/wiki/Leo_Cluster
# https://arxiv.org/abs/astro-ph/0406367
LEO = ObservedCluster(
RA=hms_to_degrees(11, 44, 36.5), dec=dms_to_degrees(19, 43, 32),
dist=91.3 * 0.705, mass=7e14 * 0.7, name="Leo")
# https://en.wikipedia.org/wiki/Hydra_Cluster
HYDRA = ObservedCluster(
RA=hms_to_degrees(9, 18), dec=dms_to_degrees(-12, 5),
dist=58.3 * 0.705, mass=4e15 * 0.7, name="Hydra")
# I think this is Pisces? Not very sure about its mass.
# https://en.wikipedia.org/wiki/Abell_262
# https://arxiv.org/abs/0911.1774
PISCES = ObservedCluster(
RA=hms_to_degrees(1, 52, 50.4), dec=dms_to_degrees(36, 8, 46),
dist=68.8 * 0.705, mass=2e14 * 0.7, name="Pisces")
# This one is in the ZOA
# https://en.wikipedia.org/wiki/Ophiuchus_Supercluster
# https://arxiv.org/abs/1509.00986
OPICHIUS = ObservedCluster(
RA=hms_to_degrees(17, 10, 0), dec=dms_to_degrees(-22),
dist=83.4, mass=1e15 * 0.7, name="Ophiuchus")
clusters = {"Virgo": VIRGO,
"Fornax": FORNAX,
"Coma": COMA,
"Perseus": PERSEUS,
"Centaurus": CENTAURUS,
"Shapley": SHAPLEY,
"Norma": NORMA,
"Leo": LEO,
"Hydra": HYDRA,
"Pisces": PISCES,
"Opichius": OPICHIUS,
}

33
csiborgtools/flow/flow_model.py
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@ -212,7 +212,12 @@ class DataLoader:
raise ValueError("Invalid simulation index.")
nsim = nsims[ksim]
with File(paths.field_los(simname, catalogue), 'r') as f:
if "Pantheon+" in catalogue:
fpath = paths.field_los(simname, "Pantheon+")
else:
fpath = paths.field_los(simname, catalogue)
with File(fpath, 'r') as f:
has_smoothed = True if f[f"density_{nsim}"].ndim > 2 else False
if has_smoothed and (ksmooth is None or not isinstance(ksmooth, int)): # noqa
raise ValueError("The output contains smoothed field but "
@ -236,9 +241,14 @@ class DataLoader:
for key in f.keys():
arr[key] = f[key][:]
elif catalogue in ["LOSS", "Foundation", "SFI_gals", "2MTF",
"Pantheon+", "SFI_gals_masked", "SFI_groups"]:
"Pantheon+", "SFI_gals_masked", "SFI_groups",
"Pantheon+_groups", "Pantheon+_groups_zSN",
"Pantheon+_zSN"]:
with File(catalogue_fpath, 'r') as f:
grp = f[catalogue]
if "Pantheon+" in catalogue:
grp = f["Pantheon+"]
else:
grp = f[catalogue]
dtype = [(key, np.float32) for key in grp.keys()]
arr = np.empty(len(grp["RA"]), dtype=dtype)
@ -1381,15 +1391,26 @@ def get_model(loader, zcmb_max=None, verbose=True):
los_overdensity[mask], los_velocity[mask], RA[mask], dec[mask],
zCMB[mask], mB[mask], x1[mask], c[mask], e_mB[mask], e_x1[mask],
e_c[mask], loader.rdist, loader._Omega_m)
elif kind == "Pantheon+":
elif "Pantheon+" in kind:
keys = ["RA", "DEC", "zCMB", "mB", "x1", "c", "biasCor_m_b", "mBERR",
"x1ERR", "cERR", "biasCorErr_m_b"]
"x1ERR", "cERR", "biasCorErr_m_b", "zCMB_SN", "zCMB_Group"]
RA, dec, zCMB, mB, x1, c, bias_corr_mB, e_mB, e_x1, e_c, e_bias_corr_mB = (loader.cat[k] for k in keys) # noqa
RA, dec, zCMB, mB, x1, c, bias_corr_mB, e_mB, e_x1, e_c, e_bias_corr_mB, zCMB_SN, zCMB_Group = (loader.cat[k] for k in keys) # noqa
mB -= bias_corr_mB
e_mB = np.sqrt(e_mB**2 + e_bias_corr_mB**2)
mask = (zCMB < zcmb_max)
if kind == "Pantheon+_groups":
mask &= np.isfinite(zCMB_Group)
if kind == "Pantheon+_groups_zSN":
mask &= np.isfinite(zCMB_Group)
zCMB = zCMB_SN
if kind == "Pantheon+_zSN":
zCMB = zCMB_SN
model = SN_PV_validation_model(
los_overdensity[mask], los_velocity[mask], RA[mask], dec[mask],
zCMB[mask], mB[mask], x1[mask], c[mask], e_mB[mask], e_x1[mask],

5
csiborgtools/match/__init__.py
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@ -12,5 +12,6 @@
# 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 .match import (ParticleOverlap, RealisationsMatcher, calculate_overlap, # noqa
find_neighbour, matching_max) # noqa
from .overlap import (ParticleOverlap, RealisationsMatcher, calculate_overlap, # noqa
find_neighbour, matching_max) # noqa
from .obs_to_box import (MatchingProbability, MatchCatalogues) # noqa

396
csiborgtools/match/obs_to_box.py Normal file
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@ -0,0 +1,396 @@
# 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.
"""
Code to match observations to a constrained simulation.
"""
from abc import ABC
import numpy as np
from colossus.cosmology import cosmology
from colossus.lss import mass_function
from scipy.interpolate import interp1d
from sklearn.neighbors import NearestNeighbors
from tqdm import trange
###############################################################################
# Matching probability class #
###############################################################################
class BaseMatchingProbability(ABC):
"""Base class for `MatchingProbability`."""
@property
def halo_pos(self):
"""
Halo positions in the constrained simulation.
Returns
-------
2-dimensional array of shape `(n, 3)`
"""
return self._halo_pos
@halo_pos.setter
def halo_pos(self, x):
if not isinstance(x, np.ndarray) and x.ndim == 2 and x.shape[1] == 3:
raise ValueError("Invalid halo positions.")
self._halo_pos = x
@property
def halo_log_mass(self):
"""
Halo log mass in the constrained simulation.
Returns
-------
1-dimensional array of shape `(n,)`
"""
return self._halo_log_mass
@halo_log_mass.setter
def halo_log_mass(self, x):
if not isinstance(x, np.ndarray) and x.ndim == 1 and len(x) != len(self.halo_pos): # noqa
raise ValueError("Invalid halo log mass.")
self._halo_log_mass = x
@property
def nhalo(self):
""""
Number of haloes in the constrained simulation that are used for
matching.
Returns
-------
int
"""
return self.halo_log_mass.size
def HMF(self, log_mass):
"""
Evaluate the halo mass function at a given mass.
Parameters
----------
log_mass : float
Logarithmic mass of the halo in `Msun / h`.
Returns
-------
HMF : float
The HMF in `h^3 Mpc^-3 dex^-1`.
"""
return self._hmf(log_mass)
class MatchingProbability(BaseMatchingProbability):
""""
Matching probability by calculating the CDF of finding a halo of a certain
mass at a given distance from a reference point. Calibrated against a HMF,
by assuming that the haloes are uniformly distributed. This is only
approximate treatment, as the haloes are not uniformly distributed, however
it is sufficient for the present purposes.
NOTE: The method currently does not account for uncertainty in distance.
Parameters
----------
halo_pos : 2-dimensional array of shape `(n, 3)`
Halo positions in the constrained simulation in `Mpc / h`.
halo_log_mass : 1-dimensional array of shape `(n,)`
Halo log mass in the constrained simulation in `Msun / h`.
mdef : str, optional
Definition of the halo mass. Default is 'fof'.
cosmo_params : dict, optional
Cosmological parameters of the constrained simulation.
"""
def __init__(self, halo_pos, halo_log_mass, mdef="fof",
cosmo_params={'flat': True, 'H0': 67.66, 'Om0': 0.3111,
'Ob0': 0.0489, 'sigma8': 0.8101, 'ns': 0.9665}):
self.halo_pos = halo_pos
self.halo_log_mass = halo_log_mass
# Define the kNN object and fit it to the halo positions, so that we
# can quickly query distances to an arbitrary point.
self._knn = NearestNeighbors()
self._knn.fit(halo_pos)
# Next, get the HMF from colossus and create its interpolant.
cosmology.addCosmology("myCosmo", **cosmo_params)
cosmology.setCosmology("myCosmo")
x = np.logspace(10, 16, 10000)
y = mass_function.massFunction(
x, 0.0, mdef=mdef, model="angulo12", q_out="dndlnM") * np.log(10)
self._hmf = interp1d(np.log10(x), y, kind="cubic")
def pdf(self, r, log_mass):
"""
Calculate the PDF of finding a halo of a given mass at a given distance
from a random point.
Parameters
----------
r : float
Distance from the random point in `Mpc / h`.
log_mass : float
Logarithmic mass of the halo in `Msun / h`.
Returns
-------
float
"""
nd = self.HMF(log_mass)
return 4 * np.pi * r**2 * nd * np.exp(-4 / 3 * np.pi * r**3 * nd)
def cdf(self, r, log_mass):
"""
Calculate the CDF of finding a halo of a given mass at a given distance
from a random point.
Parameters
----------
r : float
Distance from the random point in `Mpc / h`.
log_mass : float
Logarithmic mass of the halo in `Msun / h`.
Returns
-------
float
"""
nd = self.HMF(log_mass)
return 1 - np.exp(-4 / 3 * np.pi * r**3 * nd)
def inverse_cdf(self, cdf, log_mass):
"""
Calculate the inverse CDF of finding a halo of a given mass at a given
distance from a random point.
Parameters
----------
cdf : float
CDF of finding a halo of a given mass at a given distance.
log_mass : float
Logarithmic mass of the halo in `Msun / h`.
Returns
-------
float
"""
nd = self.HMF(log_mass)
return (np.log(1 - cdf) / (-4 / 3 * np.pi * nd))**(1 / 3)
def cdf_per_halo(self, refpos, ref_log_mass=None, rmax=50,
return_full=True):
"""
Calculate the CDF per each halo in the constrained simulation.
Parameters
----------
refpos : 1-dimensional array of shape `(3,)`
Reference position in `Mpc / h`.
ref_log_mass : float, optional
Reference log mass, used to calculate the difference in log mass
between the reference and each halo.
rmax : float, optional
Maximum distance from the reference point to consider. Below this,
the CDF is simply set to 1.
return_full : bool, optional
If `True`, return the CDF, dlogmass and indxs for all haloes,
otherwise return only the haloes within `rmax`.
Returns
-------
cdf : 1-dimensional array of shape `(nhalo,)`
CDF per halo.
dlogmass : 1-dimensional array of shape `(nhalo,)`
Difference in log mass between the reference and each halo.
indxs : 1-dimensional array of shape `(nhalo,)`
Indices of the haloes.
"""
if not (isinstance(refpos, np.ndarray) and refpos.ndim == 1):
raise ValueError("Invalid reference position.")
if ref_log_mass is not None and not isinstance(ref_log_mass, (float, int, np.float32, np.float64)): # noqa
raise ValueError("Invalid reference log mass.")
# Use the kNN to pick out the haloes within `rmax` of the reference
# point.
dist, indxs = self._knn.radius_neighbors(
refpos.reshape(-1, 3), rmax, return_distance=True)
dist, indxs = dist[0], indxs[0]
cdf_ = self.cdf(dist, self.halo_log_mass[indxs])
if ref_log_mass is not None:
dlogmass_ = self.halo_log_mass[indxs] - ref_log_mass
else:
dlogmass_ = None
if return_full:
cdf = np.ones(self.nhalo)
cdf[indxs] = cdf_
if ref_log_mass is not None:
dlogmass = np.full(self.nhalo, np.infty)
dlogmass[indxs] = dlogmass_
else:
dlogmass = dlogmass_
indxs = np.arange(self.nhalo)
else:
cdf, dlogmass = cdf_, dlogmass_
return cdf, dlogmass, indxs
def match_halo(self, refpos, ref_log_mass, pvalue_threshold=0.005,
max_absdlogmass=1., rmax=50, verbose=True,
catalogue_index=0):
"""
Match a halo in the constrained simulation to a reference halo.
Considers match the highest significance halo within `rmax` and
within `max_absdlogmass` of the reference halo mass. In case of no
match, returns `None`.
Parameters
----------
refpos : 1-dimensional array of shape `(3,)`
Reference position.
ref_log_mass : float
Reference log mass.
pvalue_threshold : float, optional
Threshold for the CDF to be considered a match.
max_absdlogmass : float, optional
Maximum difference in log mass between the reference and the
matched halo.
rmax : float, optional
Maximum distance from the reference point to consider.
verbose : bool, optional
If `True`, print information about the match.
catalogue_index : int, optional
Optional catalogue index for more informative printing.
Returns
-------
cdf : float, or None
CDF of the matched halo (significance), if any.
index : int, or None
Index of the matched halo, if any.
"""
cdf, dlogmass, indxs = self.cdf_per_halo(
refpos, ref_log_mass, rmax, return_full=False)
dlogmass = np.abs(dlogmass)
ks = np.argsort(cdf)
cdf, dlogmass, indxs = cdf[ks], dlogmass[ks], indxs[ks]
matches = np.where(
(cdf < pvalue_threshold) & (dlogmass < max_absdlogmass))[0]
if len(matches) == 0:
return None, None
if verbose and len(matches) > 1:
print(f"Found {len(matches)} plausible matches in catalogue {catalogue_index}.") # noqa
for i, k in enumerate(matches):
j = indxs[k]
logM = self.halo_log_mass[j]
dx = np.linalg.norm(self.halo_pos[j] - refpos)
print(f" {i + 1}: CDF = {cdf[k]:.3e}, index = {j}, logM = {logM:.3f} Msun / h, dx = {dx:.3f} Mpc / h.") # noqa
print(flush=True)
k = matches[0]
return cdf[k], indxs[k]
class MatchCatalogues:
"""
A wrapper for `MatchingProbability` that allows to match observed clusters
to haloes in multiple catalogues.
Parameters
----------
catalogues : list
List of halo catalogues of constrained simulations.
cosmo_params : dict, optional
Cosmological parameters of the constrained simulation to calculate
the corresponding FOF mass function.
"""
def __init__(self, catalogues,
cosmo_params={'flat': True, 'H0': 67.66, 'Om0': 0.3111,
'Ob0': 0.0489, 'sigma8': 0.8101, 'ns': 0.9665}):
mdef = "fof"
self._catalogues = catalogues
self._prob_models = [None] * len(catalogues)
for i in trange(len(catalogues)):
pos = catalogues[i]["cartesian_pos"]
log_mass = np.log10(catalogues[i]["totmass"])
self._prob_models[i] = MatchingProbability(
pos, log_mass, mdef, cosmo_params)
def __getitem__(self, index):
return self._prob_models[index]
def __len__(self):
return len(self._catalogues)
def __call__(self, refpos, ref_log_mass, pvalue_threshold=0.05,
max_absdlogmass=1., rmax=50, verbose=True):
"""
Calculate the CDFs of finding a halo of a certain mass at a given
distance from a reference point for all catalogues.
Parameters
----------
refpos : 1-dimensional array of shape `(3,)`
Reference position.
ref_log_mass : float
Reference log mass.
pvalue_threshold : float, optional
Threshold for the CDF to be considered a match.
max_absdlogmass : float, optional
Maximum difference in log mass between the reference and the
matched halo.
rmax : float, optional
Maximum distance from the reference point to consider.
verbose : bool, optional
Verbosity flag.
Returns
-------
cdfs : dict
Dictionary of CDFs per halo, with keys being the simulation
indices.
indxs : dict
Dictionary of indices of the matched haloes, with keys being the
simulation indices.
"""
cdfs, indxs = {}, {}
for i in trange(len(self), desc="Matching catalogues",
disable=not verbose):
cdf, indx = self._prob_models[i].match_halo(
refpos, ref_log_mass, pvalue_threshold, max_absdlogmass, rmax,
verbose, i)
if cdf is not None:
cdfs[i] = cdf
indxs[i] = indx
n = len(self) - len(cdfs)
if n > 0 and verbose:
print(f"Failed to assign haloes in {n} catalogues.")
return cdfs, indxs

0
csiborgtools/match/match.py → csiborgtools/match/overlap.py
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43
csiborgtools/read/obs.py
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@ -26,6 +26,8 @@ from astropy.io import fits
from astropy.cosmology import FlatLambdaCDM
from scipy import constants
from ..utils import radec_to_cartesian
###############################################################################
# Text survey base class #
@ -756,6 +758,42 @@ class BaseSingleObservation(ABC):
self._spherical_pos = pos
@property
def mass(self):
"""
Total mass estimate in Msun / h.
Returns
-------
float
"""
if self._mass is None:
raise ValueError("`mass` is not set!")
return self._mass
@mass.setter
def mass(self, mass):
if not isinstance(mass, (int, float)):
raise ValueError("`mass` must be a float.")
self._mass = mass
def cartesian_pos(self, boxsize):
"""
Cartesian position of the observation in Mpc / h, assuming the observer
is in the centre of the box.
Parameters
----------
boxsize : float
Box size in Mpc / h.
Returns
-------
1-dimensional array of shape (3,)
"""
return radec_to_cartesian(
self.spherical_pos.reshape(1, 3)).reshape(-1,) + boxsize / 2
@property
def name(self):
"""
@ -788,13 +826,16 @@ class ObservedCluster(BaseSingleObservation):
Declination in degrees.
dist : float
Distance in Mpc / h.
mass : float
Total mass estimate in Msun / h.
name : str
Cluster name.
"""
def __init__(self, RA, dec, dist, name):
def __init__(self, RA, dec, dist, mass, name):
super().__init__()
self.name = name
self.spherical_pos = [dist, RA, dec]
self.mass = mass
###############################################################################

362
notebooks/flow/PV_process.ipynb
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629
notebooks/flow/flow_calibration.ipynb
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9
notebooks/flow/flow_calibration.py
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@ -38,7 +38,7 @@ def read_samples(catalogue, simname, ksmooth, include_calibration=False,
Vx, Vy, Vz, beta, sigma_v, alpha = [], [], [], [], [], []
BIC, AIC, logZ, chi2 = [], [], [], []
if catalogue in ["LOSS", "Foundation", "Pantheon+"]:
if catalogue in ["LOSS", "Foundation"] or "Pantheon+" in catalogue:
alpha_cal, beta_cal, mag_cal, e_mu_intrinsic = [], [], [], []
elif catalogue in ["2MTF", "SFI_gals", "SFI_gals_masked"]:
a, b, e_mu_intrinsic = [], [], []
@ -86,7 +86,7 @@ def read_samples(catalogue, simname, ksmooth, include_calibration=False,
except KeyError:
chi2.append([0.])
if catalogue in ["LOSS", "Foundation", "Pantheon+"]:
if catalogue in ["LOSS", "Foundation"] or "Pantheon+" in catalogue: # noqa
alpha_cal.append(f[f"sim_{nsim}/alpha_cal"][:])
beta_cal.append(f[f"sim_{nsim}/beta_cal"][:])
mag_cal.append(f[f"sim_{nsim}/mag_cal"][:])
@ -106,7 +106,7 @@ def read_samples(catalogue, simname, ksmooth, include_calibration=False,
gof = np.hstack(BIC), np.hstack(AIC), np.hstack(logZ), np.hstack(chi2)
if catalogue in ["LOSS", "Foundation", "Pantheon+"]:
if catalogue in ["LOSS", "Foundation"] or "Pantheon+" in catalogue:
alpha_cal, beta_cal, mag_cal, e_mu_intrinsic = np.hstack(alpha_cal), np.hstack(beta_cal), np.hstack(mag_cal), np.hstack(e_mu_intrinsic) # noqa
elif catalogue in ["2MTF", "SFI_gals", "SFI_gals_masked"]:
a, b, e_mu_intrinsic = np.hstack(a), np.hstack(b), np.hstack(e_mu_intrinsic) # noqa
@ -118,6 +118,7 @@ def read_samples(catalogue, simname, ksmooth, include_calibration=False,
raise ValueError(f"Catalogue {catalogue} not recognized.")
# Calculate magnitude of V_ext
Vmag = np.sqrt(Vx**2 + Vy**2 + Vz**2)
# Calculate direction in galactic coordinates of V_ext
V = np.vstack([Vx, Vy, Vz]).T
@ -128,7 +129,7 @@ def read_samples(catalogue, simname, ksmooth, include_calibration=False,
names = ["alpha", "beta", "Vmag", "l", "b", "sigma_v"]
if include_calibration:
if catalogue in ["LOSS", "Foundation", "Pantheon+"]:
if catalogue in ["LOSS", "Foundation"] or "Pantheon+" in catalogue:
data += [alpha_cal, beta_cal, mag_cal, e_mu_intrinsic]
names += ["alpha_cal", "beta_cal", "mag_cal", "e_mu_intrinsic"]
elif catalogue in ["2MTF", "SFI_gals", "SFI_gals_masked"]:

436
notebooks/flow/process_PV.ipynb Normal file
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368
notebooks/match_observation/match_clusters.ipynb Normal file
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25
notebooks/match_observation/match_clusters.py Normal file
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@ -0,0 +1,25 @@
import csiborgtools
from tqdm import tqdm
def open_cat(nsim, simname, bounds):
if "csiborg1" in simname:
cat = csiborgtools.read.CSiBORG1Catalogue(nsim, bounds=bounds)
elif "csiborg2" in simname:
cat = csiborgtools.read.CSiBORG2Catalogue(
nsim, 99, simname.split("_")[-1], bounds=bounds)
else:
raise ValueError(f"Unknown simulation name: {simname}.")
return cat
def open_cats(simname, bounds):
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
nsims = paths.get_ics(simname)
catalogues = [None] * len(nsims)
for i, nsim in enumerate(tqdm(nsims, desc="Opening catalogues")):
catalogues[i] = open_cat(nsim, simname, bounds)
return catalogues

4
scripts/field_los.py
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@ -61,7 +61,7 @@ def get_los(catalogue_name, simname, comm):
if catalogue_name in ["LOSS", "Foundation", "SFI_gals",
"SFI_gals_masked", "SFI_groups", "2MTF",
"Pantheon+"]:
fpath = join(folder, "PV_compilation_Supranta2019.hdf5")
fpath = join(folder, "PV_compilation.hdf5")
with File(fpath, 'r') as f:
grp = f[catalogue_name]
RA = grp["RA"][:]
@ -286,7 +286,7 @@ if __name__ == "__main__":
rmax = 200
dr = 0.5
smooth_scales = [0, 2]
smooth_scales = [0, 2, 4]
comm = MPI.COMM_WORLD
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)

6
scripts/flow_validation.py
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@ -52,8 +52,10 @@ def get_model(args, nsim_iterator, get_model_kwargs):
if args.catalogue == "A2":
fpath = join(folder, "A2.h5")
elif args.catalogue in ["LOSS", "Foundation", "Pantheon+", "SFI_gals",
"2MTF", "SFI_groups", "SFI_gals_masked"]:
fpath = join(folder, "PV_compilation_Supranta2019.hdf5")
"2MTF", "SFI_groups", "SFI_gals_masked",
"Pantheon+_groups", "Pantheon+_groups_zSN",
"Pantheon+_zSN"]:
fpath = join(folder, "PV_compilation.hdf5")
elif "CB2_" in args.catalogue:
kind = args.catalogue.split("_")[-1]
fpath = join(folder, f"PV_mock_CB2_17417_{kind}.hdf5")

4
scripts/flow_validation.sh
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@ -7,8 +7,8 @@ queue="berg"
env="/mnt/users/rstiskalek/csiborgtools/venv_csiborg/bin/python"
file="flow_validation.py"
catalogue="CB2_small"
simname="csiborg2_main"
catalogue="Pantheon+_groups"
simname="csiborg2_varysmall"
pythoncm="$env $file --catalogue $catalogue --simname $simname --ksmooth $ksmooth"