mirror of
https://github.com/Richard-Sti/csiborgtools.git
synced 2024-12-22 15:58:03 +00:00
Remove halo fitting. (#83)
* Rename file * Remove old content * Remove halo fit * Completely remove fits * Add utils here * Account for renaming
This commit is contained in:
Richard Stiskalek
GitHub
parent
ff395af148
commit
f4a7cb0d16
7 changed files with 148 additions and 718 deletions
|
|
@ -12,7 +12,8 @@
|
||||||
# You should have received a copy of the GNU General Public License along
|
# 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.,
|
# with this program; if not, write to the Free Software Foundation, Inc.,
|
||||||
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
|
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
|
||||||
from csiborgtools import clustering, field, fits, match, read # noqa
|
from csiborgtools import clustering, field, match, read # noqa
|
||||||
|
from .utils import (center_of_mass, delta2ncells, periodic_distance, number_counts) # noqa
|
||||||
|
|
||||||
# Arguments to csiborgtools.read.Paths.
|
# Arguments to csiborgtools.read.Paths.
|
||||||
paths_glamdring = {"srcdir": "/mnt/extraspace/hdesmond/",
|
paths_glamdring = {"srcdir": "/mnt/extraspace/hdesmond/",
|
||||||
|
|
|
||||||
|
|
@ -1,16 +0,0 @@
|
||||||
# Copyright (C) 2022 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 .halo import (Halo, delta2ncells, center_of_mass, # noqa
|
|
||||||
periodic_distance, shift_to_center_of_box, number_counts) # noqa
|
|
||||||
|
|
@ -1,542 +0,0 @@
|
||||||
# Copyright (C) 2022 Richard Stiskalek, Deaglan Bartlett
|
|
||||||
# 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.
|
|
||||||
"""A halo object."""
|
|
||||||
from abc import ABC
|
|
||||||
|
|
||||||
import numpy
|
|
||||||
from numba import jit
|
|
||||||
from scipy.optimize import minimize
|
|
||||||
|
|
||||||
GRAV = 4.300917270069976e-09 # G in (Msun / h)^-1 (Mpc / h) (km / s)^2
|
|
||||||
|
|
||||||
|
|
||||||
class BaseStructure(ABC):
|
|
||||||
"""
|
|
||||||
Basic structure object for handling operations on its particles.
|
|
||||||
"""
|
|
||||||
_particles = None
|
|
||||||
_box = None
|
|
||||||
|
|
||||||
@property
|
|
||||||
def particles(self):
|
|
||||||
"""
|
|
||||||
Particle array.
|
|
||||||
|
|
||||||
Returns
|
|
||||||
-------
|
|
||||||
particles : structured array
|
|
||||||
"""
|
|
||||||
return self._particles
|
|
||||||
|
|
||||||
@particles.setter
|
|
||||||
def particles(self, particles):
|
|
||||||
assert particles.ndim == 2 and particles.shape[1] == 7
|
|
||||||
self._particles = particles
|
|
||||||
|
|
||||||
@property
|
|
||||||
def box(self):
|
|
||||||
"""
|
|
||||||
Box object handling unit conversion.
|
|
||||||
|
|
||||||
Returns
|
|
||||||
-------
|
|
||||||
box : Object derived from :py:class:`csiborgtools.units.BaseBox`
|
|
||||||
"""
|
|
||||||
return self._box
|
|
||||||
|
|
||||||
@box.setter
|
|
||||||
def box(self, box):
|
|
||||||
try:
|
|
||||||
assert box._name == "box_units"
|
|
||||||
self._box = box
|
|
||||||
except AttributeError as err:
|
|
||||||
raise TypeError from err
|
|
||||||
|
|
||||||
@property
|
|
||||||
def pos(self):
|
|
||||||
"""
|
|
||||||
Cartesian particle coordinates in the box coordinate system.
|
|
||||||
|
|
||||||
Returns
|
|
||||||
-------
|
|
||||||
pos : 2-dimensional array of shape `(n_particles, 3)`.
|
|
||||||
"""
|
|
||||||
return numpy.vstack([self[p] for p in ('x', 'y', 'z')]).T
|
|
||||||
|
|
||||||
@property
|
|
||||||
def vel(self):
|
|
||||||
"""
|
|
||||||
Cartesian particle velocity components.
|
|
||||||
|
|
||||||
Returns
|
|
||||||
-------
|
|
||||||
vel : 2-dimensional array of shape (`n_particles, 3`)
|
|
||||||
"""
|
|
||||||
return numpy.vstack([self[p] for p in ("vx", "vy", "vz")]).T
|
|
||||||
|
|
||||||
def center_of_mass(self, npart_min=30, shrink_factor=0.98):
|
|
||||||
r"""
|
|
||||||
Calculate the center of mass of a halo via the shrinking sphere
|
|
||||||
procedure. Iteratively reduces initial radius and calculates the CM of
|
|
||||||
enclosed particles while the number of enclosed particles is greater
|
|
||||||
than a set minimum.
|
|
||||||
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
npart_min : int, optional
|
|
||||||
Minimum number of enclosed particles above which to continue
|
|
||||||
shrinking the sphere.
|
|
||||||
shrink_factor : float, optional
|
|
||||||
Factor by which to shrink the sphere radius at each iteration.
|
|
||||||
|
|
||||||
Returns
|
|
||||||
-------
|
|
||||||
cm : 1-dimensional array of shape `(3, )`
|
|
||||||
Center of mass in box units.
|
|
||||||
dist : 1-dimensional array of shape `(n_particles, )`
|
|
||||||
Distance of each particle from the center of mass in box units.
|
|
||||||
"""
|
|
||||||
pos, mass = self.pos, self["M"]
|
|
||||||
|
|
||||||
cm = center_of_mass(pos, mass, boxsize=1)
|
|
||||||
rad = None
|
|
||||||
|
|
||||||
while True:
|
|
||||||
dist = periodic_distance(pos, cm, boxsize=1)
|
|
||||||
|
|
||||||
if rad is None:
|
|
||||||
rad = numpy.max(dist)
|
|
||||||
|
|
||||||
within_rad = dist <= rad
|
|
||||||
|
|
||||||
cm = center_of_mass(pos[within_rad], mass[within_rad], boxsize=1)
|
|
||||||
|
|
||||||
if numpy.sum(within_rad) < npart_min:
|
|
||||||
return cm, periodic_distance(pos, cm, boxsize=1)
|
|
||||||
|
|
||||||
rad *= shrink_factor
|
|
||||||
|
|
||||||
def spherical_overdensity_mass(self, dist, delta_mult, kind="crit"):
|
|
||||||
r"""
|
|
||||||
Calculate spherical overdensity mass and radius around a CM, defined as
|
|
||||||
the inner-most radius where the density falls below a given threshold.
|
|
||||||
The exact radius is found via linear interpolation between the two
|
|
||||||
particles enclosing the threshold.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
dist : 1-dimensional array of shape `(n_particles, )`
|
|
||||||
Distance of each particle from the centre of mass in box units.
|
|
||||||
delta_mult : int or float
|
|
||||||
Overdensity multiple.
|
|
||||||
kind : str, optional
|
|
||||||
Either `crit` or `matter`, for critical or matter overdensity
|
|
||||||
|
|
||||||
Returns
|
|
||||||
-------
|
|
||||||
mass : float
|
|
||||||
Overdensity mass in (Msun / h).
|
|
||||||
rad : float
|
|
||||||
Overdensity radius in box units.
|
|
||||||
"""
|
|
||||||
if kind not in ["crit", "matter"]:
|
|
||||||
raise ValueError("kind must be either `crit` or `matter`.")
|
|
||||||
|
|
||||||
rho = delta_mult * self.box.rho_crit0
|
|
||||||
rho *= self.box.Om if kind == "matter" else 1.
|
|
||||||
|
|
||||||
argsort = numpy.argsort(dist)
|
|
||||||
dist = self.box.box2mpc(dist[argsort])
|
|
||||||
|
|
||||||
norm_density = numpy.cumsum(self['M'][argsort])
|
|
||||||
totmass = norm_density[-1]
|
|
||||||
with numpy.errstate(divide="ignore"):
|
|
||||||
norm_density /= (4. / 3. * numpy.pi * dist**3)
|
|
||||||
norm_density /= rho
|
|
||||||
|
|
||||||
# This ensures that the j - 1 index is also just above 1, therefore the
|
|
||||||
# expression below strictly interpolates.
|
|
||||||
j = find_first_below_threshold(norm_density, 1.)
|
|
||||||
|
|
||||||
if j is None:
|
|
||||||
return numpy.nan, numpy.nan
|
|
||||||
|
|
||||||
i = j - 1
|
|
||||||
|
|
||||||
rad = (dist[j] - dist[i])
|
|
||||||
rad *= (1. - norm_density[i]) / (norm_density[j] - norm_density[i])
|
|
||||||
rad += dist[i]
|
|
||||||
|
|
||||||
mass = radius_to_mass(rad, rho)
|
|
||||||
rad = self.box.mpc2box(rad)
|
|
||||||
|
|
||||||
if mass > totmass:
|
|
||||||
return numpy.nan, numpy.nan
|
|
||||||
|
|
||||||
return mass, rad
|
|
||||||
|
|
||||||
def angular_momentum(self, dist, cm, rad, npart_min=10):
|
|
||||||
r"""
|
|
||||||
Calculate angular momentum around a centre of mass using all particles
|
|
||||||
within a radius. Accounts for periodicity of the box and units are
|
|
||||||
(Msun / h) * (Mpc / h) * (km / s).
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
dist : 1-dimensional array of shape `(n_particles, )`
|
|
||||||
Distance of each particle from center of mass in box units.
|
|
||||||
cm : 1-dimensional array of shape `(3, )`
|
|
||||||
Reference point in box units.
|
|
||||||
rad : float
|
|
||||||
Radius around the reference point in box units.
|
|
||||||
npart_min : int, optional
|
|
||||||
Minimum number of enclosed particles for a radius to be
|
|
||||||
considered trustworthy.
|
|
||||||
|
|
||||||
Returns
|
|
||||||
-------
|
|
||||||
angmom : 1-dimensional array or shape `(3, )`
|
|
||||||
"""
|
|
||||||
mask = dist < rad
|
|
||||||
|
|
||||||
if numpy.sum(mask) < npart_min:
|
|
||||||
return numpy.full(3, numpy.nan, numpy.float32)
|
|
||||||
|
|
||||||
mass, pos, vel = self["M"][mask], self.pos[mask], self.vel[mask]
|
|
||||||
|
|
||||||
pos = shift_to_center_of_box(pos, cm, 1.0, set_cm_to_zero=True)
|
|
||||||
pos = self.box.box2mpc(pos)
|
|
||||||
vel -= numpy.average(vel, axis=0, weights=mass)
|
|
||||||
|
|
||||||
return numpy.sum(mass[:, numpy.newaxis] * numpy.cross(pos, vel),
|
|
||||||
axis=0)
|
|
||||||
|
|
||||||
def lambda_bullock(self, angmom, mass, rad):
|
|
||||||
"""
|
|
||||||
Calculate the Bullock spin, see Eq. 5 in [1].
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
angmom : 1-dimensional array of shape `(3, )`
|
|
||||||
Angular momentum in (Msun / h) * (Mpc / h) * (km / s).
|
|
||||||
ref : 1-dimensional array of shape `(3, )`
|
|
||||||
Reference point in box units.
|
|
||||||
rad : float
|
|
||||||
Radius around the reference point in box units.
|
|
||||||
|
|
||||||
Returns
|
|
||||||
-------
|
|
||||||
lambda_bullock : float
|
|
||||||
|
|
||||||
References
|
|
||||||
----------
|
|
||||||
[1] A Universal Angular Momentum Profile for Galactic Halos; 2001;
|
|
||||||
Bullock, J. S.; Dekel, A.; Kolatt, T. S.; Kravtsov, A. V.;
|
|
||||||
Klypin, A. A.; Porciani, C.; Primack, J. R.
|
|
||||||
"""
|
|
||||||
out = numpy.linalg.norm(angmom)
|
|
||||||
return out / numpy.sqrt(2 * GRAV * mass**3 * self.box.box2mpc(rad))
|
|
||||||
|
|
||||||
def nfw_concentration(self, dist, rad, conc_min=1e-3, npart_min=10):
|
|
||||||
"""
|
|
||||||
Calculate the NFW concentration parameter in a given radius around a
|
|
||||||
reference point.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
dist : 1-dimensional array of shape `(n_particles, )`
|
|
||||||
Distance of each particle from center of mass in box units.
|
|
||||||
rad : float
|
|
||||||
Radius around the reference point in box units.
|
|
||||||
conc_min : float
|
|
||||||
Minimum concentration limit.
|
|
||||||
npart_min : int, optional
|
|
||||||
Minimum number of enclosed particles to calculate the
|
|
||||||
concentration.
|
|
||||||
|
|
||||||
Returns
|
|
||||||
-------
|
|
||||||
conc : float
|
|
||||||
"""
|
|
||||||
mask = dist < rad
|
|
||||||
if numpy.sum(mask) < npart_min:
|
|
||||||
return numpy.nan
|
|
||||||
|
|
||||||
dist, weight = dist[mask], self["M"][mask]
|
|
||||||
weight /= weight[0]
|
|
||||||
|
|
||||||
res = minimize(negll_nfw_concentration, x0=1.,
|
|
||||||
args=(dist / rad, weight, ), method='Nelder-Mead',
|
|
||||||
bounds=((numpy.log10(conc_min), 5),))
|
|
||||||
|
|
||||||
if not res.success:
|
|
||||||
return numpy.nan
|
|
||||||
|
|
||||||
conc = 10**res["x"][0]
|
|
||||||
if conc < conc_min or numpy.isclose(conc, conc_min):
|
|
||||||
return numpy.nan
|
|
||||||
|
|
||||||
return conc
|
|
||||||
|
|
||||||
def __getitem__(self, key):
|
|
||||||
key_to_index = {'x': 0, 'y': 1, 'z': 2,
|
|
||||||
'vx': 3, 'vy': 4, 'vz': 5, 'M': 6}
|
|
||||||
if key not in key_to_index:
|
|
||||||
raise RuntimeError(f"Invalid key `{key}`!")
|
|
||||||
return self.particles[:, key_to_index[key]]
|
|
||||||
|
|
||||||
def __len__(self):
|
|
||||||
return self.particles.shape[0]
|
|
||||||
|
|
||||||
|
|
||||||
class Halo(BaseStructure):
|
|
||||||
"""
|
|
||||||
Halo object to handle operations on its particles.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
particles : structured array
|
|
||||||
Particle array. Must contain `['x', 'y', 'z', 'vx', 'vy', 'vz', 'M']`.
|
|
||||||
info : structured array
|
|
||||||
Array containing information from the halo finder.
|
|
||||||
box : :py:class:`csiborgtools.read.CSiBORGBox`
|
|
||||||
Box units object.
|
|
||||||
"""
|
|
||||||
|
|
||||||
def __init__(self, particles, box):
|
|
||||||
self.particles = particles
|
|
||||||
self.box = box
|
|
||||||
|
|
||||||
|
|
||||||
###############################################################################
|
|
||||||
# Other, supplementary functions #
|
|
||||||
###############################################################################
|
|
||||||
|
|
||||||
|
|
||||||
@jit(nopython=True, fastmath=True, boundscheck=False)
|
|
||||||
def center_of_mass(points, mass, boxsize):
|
|
||||||
"""
|
|
||||||
Calculate the center of mass of a halo while assuming periodic boundary
|
|
||||||
conditions of a cubical box. Assuming that particle positions are in
|
|
||||||
`[0, boxsize)` range. This is a JIT implementation.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
points : 2-dimensional array of shape (n_particles, 3)
|
|
||||||
Particle position array.
|
|
||||||
mass : 1-dimensional array of shape `(n_particles, )`
|
|
||||||
Particle mass array.
|
|
||||||
boxsize : float
|
|
||||||
Box size in the same units as `parts` coordinates.
|
|
||||||
|
|
||||||
Returns
|
|
||||||
-------
|
|
||||||
cm : 1-dimensional array of shape `(3, )`
|
|
||||||
"""
|
|
||||||
cm = numpy.zeros(3, dtype=points.dtype)
|
|
||||||
totmass = sum(mass)
|
|
||||||
|
|
||||||
# Convert positions to unit circle coordinates in the complex plane,
|
|
||||||
# calculate the weighted average and convert it back to box coordinates.
|
|
||||||
for i in range(3):
|
|
||||||
cm_i = sum(mass * numpy.exp(2j * numpy.pi * points[:, i] / boxsize))
|
|
||||||
cm_i /= totmass
|
|
||||||
|
|
||||||
cm_i = numpy.arctan2(cm_i.imag, cm_i.real) * boxsize / (2 * numpy.pi)
|
|
||||||
|
|
||||||
if cm_i < 0:
|
|
||||||
cm_i += boxsize
|
|
||||||
cm[i] = cm_i
|
|
||||||
|
|
||||||
return cm
|
|
||||||
|
|
||||||
|
|
||||||
@jit(nopython=True)
|
|
||||||
def periodic_distance(points, reference, boxsize):
|
|
||||||
"""
|
|
||||||
Compute the 3D distance between multiple points and a reference point using
|
|
||||||
periodic boundary conditions. This is an optimized JIT implementation.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
points : 2-dimensional array of shape `(n_points, 3)`
|
|
||||||
Points to calculate the distance from the reference point.
|
|
||||||
reference : 1-dimensional array of shape `(3, )`
|
|
||||||
Reference point.
|
|
||||||
boxsize : float
|
|
||||||
Box size.
|
|
||||||
|
|
||||||
Returns
|
|
||||||
-------
|
|
||||||
dist : 1-dimensional array of shape `(n_points, )`
|
|
||||||
"""
|
|
||||||
npoints = len(points)
|
|
||||||
half_box = boxsize / 2
|
|
||||||
|
|
||||||
dist = numpy.zeros(npoints, dtype=points.dtype)
|
|
||||||
for i in range(npoints):
|
|
||||||
for j in range(3):
|
|
||||||
dist_1d = abs(points[i, j] - reference[j])
|
|
||||||
|
|
||||||
if dist_1d > (half_box):
|
|
||||||
dist_1d = boxsize - dist_1d
|
|
||||||
|
|
||||||
dist[i] += dist_1d**2
|
|
||||||
|
|
||||||
dist[i] = dist[i]**0.5
|
|
||||||
|
|
||||||
return dist
|
|
||||||
|
|
||||||
|
|
||||||
def shift_to_center_of_box(points, cm, boxsize, set_cm_to_zero=False):
|
|
||||||
"""
|
|
||||||
Shift the positions such that the CM is at the center of the box, while
|
|
||||||
accounting for periodic boundary conditions.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
points : 2-dimensional array of shape `(n_points, 3)`
|
|
||||||
Points to shift.
|
|
||||||
cm : 1-dimensional array of shape `(3, )`
|
|
||||||
Center of mass.
|
|
||||||
boxsize : float
|
|
||||||
Box size.
|
|
||||||
set_cm_to_zero : bool, optional
|
|
||||||
If `True`, set the CM to zero.
|
|
||||||
|
|
||||||
Returns
|
|
||||||
-------
|
|
||||||
shifted_positions : 2-dimensional array of shape `(n_points, 3)`
|
|
||||||
"""
|
|
||||||
pos = (points + (boxsize / 2 - cm)) % boxsize
|
|
||||||
if set_cm_to_zero:
|
|
||||||
pos -= boxsize / 2
|
|
||||||
return pos
|
|
||||||
|
|
||||||
|
|
||||||
@jit(nopython=True, fastmath=True, boundscheck=False)
|
|
||||||
def radius_to_mass(radius, rho):
|
|
||||||
"""
|
|
||||||
Compute the mass of a sphere with a given radius and density.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
radius : float
|
|
||||||
Radius of the sphere.
|
|
||||||
rho : float
|
|
||||||
Density of the sphere.
|
|
||||||
|
|
||||||
Returns
|
|
||||||
-------
|
|
||||||
mass : float
|
|
||||||
"""
|
|
||||||
return ((4 * numpy.pi * rho) / 3) * radius**3
|
|
||||||
|
|
||||||
|
|
||||||
@jit(nopython=True, fastmath=True, boundscheck=False)
|
|
||||||
def find_first_below_threshold(x, threshold):
|
|
||||||
"""
|
|
||||||
Find index of first element in `x` that is below `threshold`. The index
|
|
||||||
must be greater than 0. If no such element is found, return `None`.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
x : 1-dimensional array
|
|
||||||
Array to search in.
|
|
||||||
threshold : float
|
|
||||||
Threshold value.
|
|
||||||
|
|
||||||
Returns
|
|
||||||
-------
|
|
||||||
index : int or None
|
|
||||||
"""
|
|
||||||
for i in range(1, len(x)):
|
|
||||||
if 1 < x[i - 1] and x[i] < threshold:
|
|
||||||
return i
|
|
||||||
return None
|
|
||||||
|
|
||||||
|
|
||||||
@jit(nopython=True, fastmath=True, boundscheck=False)
|
|
||||||
def negll_nfw_concentration(log_c, xs, w):
|
|
||||||
"""
|
|
||||||
Negative log-likelihood of the NFW concentration parameter.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
log_c : float
|
|
||||||
Logarithm of the concentration parameter.
|
|
||||||
xs : 1-dimensional array
|
|
||||||
Normalised radii.
|
|
||||||
w : 1-dimensional array
|
|
||||||
Weights.
|
|
||||||
|
|
||||||
Returns
|
|
||||||
------
|
|
||||||
negll : float
|
|
||||||
"""
|
|
||||||
c = 10**log_c
|
|
||||||
ll = xs / (1 + c * xs)**2 * c**2
|
|
||||||
ll *= (1 + c) / ((1 + c) * numpy.log(1 + c) - c)
|
|
||||||
ll = numpy.sum(numpy.log(w * ll))
|
|
||||||
return -ll
|
|
||||||
|
|
||||||
|
|
||||||
@jit(nopython=True, fastmath=True, boundscheck=False)
|
|
||||||
def delta2ncells(delta):
|
|
||||||
"""
|
|
||||||
Calculate the number of cells in `delta` that are non-zero.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
delta : 3-dimensional array
|
|
||||||
Halo density field.
|
|
||||||
|
|
||||||
Returns
|
|
||||||
-------
|
|
||||||
ncells : int
|
|
||||||
Number of non-zero cells.
|
|
||||||
"""
|
|
||||||
tot = 0
|
|
||||||
imax, jmax, kmax = delta.shape
|
|
||||||
for i in range(imax):
|
|
||||||
for j in range(jmax):
|
|
||||||
for k in range(kmax):
|
|
||||||
if delta[i, j, k] > 0:
|
|
||||||
tot += 1
|
|
||||||
return tot
|
|
||||||
|
|
||||||
|
|
||||||
@jit(nopython=True, fastmath=True, boundscheck=False)
|
|
||||||
def number_counts(x, bin_edges):
|
|
||||||
"""
|
|
||||||
Calculate counts of samples in bins.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
x : 1-dimensional array
|
|
||||||
Samples' values.
|
|
||||||
bin_edges : 1-dimensional array
|
|
||||||
Bin edges.
|
|
||||||
|
|
||||||
Returns
|
|
||||||
-------
|
|
||||||
counts : 1-dimensional array
|
|
||||||
Bin counts.
|
|
||||||
"""
|
|
||||||
out = numpy.full(bin_edges.size - 1, numpy.nan, dtype=numpy.float32)
|
|
||||||
for i in range(bin_edges.size - 1):
|
|
||||||
out[i] = numpy.sum((x >= bin_edges[i]) & (x < bin_edges[i + 1]))
|
|
||||||
return out
|
|
||||||
140
csiborgtools/utils.py
Normal file
140
csiborgtools/utils.py
Normal file
|
|
@ -0,0 +1,140 @@
|
||||||
|
# Copyright (C) 2022 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.
|
||||||
|
"""Collection of stand-off utility functions used in the scripts."""
|
||||||
|
import numpy
|
||||||
|
from numba import jit
|
||||||
|
|
||||||
|
|
||||||
|
@jit(nopython=True, fastmath=True, boundscheck=False)
|
||||||
|
def center_of_mass(points, mass, boxsize):
|
||||||
|
"""
|
||||||
|
Calculate the center of mass of a halo while assuming periodic boundary
|
||||||
|
conditions of a cubical box. Assuming that particle positions are in
|
||||||
|
`[0, boxsize)` range. This is a JIT implementation.
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
points : 2-dimensional array of shape (n_particles, 3)
|
||||||
|
Particle position array.
|
||||||
|
mass : 1-dimensional array of shape `(n_particles, )`
|
||||||
|
Particle mass array.
|
||||||
|
boxsize : float
|
||||||
|
Box size in the same units as `parts` coordinates.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
cm : 1-dimensional array of shape `(3, )`
|
||||||
|
"""
|
||||||
|
cm = numpy.zeros(3, dtype=points.dtype)
|
||||||
|
totmass = sum(mass)
|
||||||
|
|
||||||
|
# Convert positions to unit circle coordinates in the complex plane,
|
||||||
|
# calculate the weighted average and convert it back to box coordinates.
|
||||||
|
for i in range(3):
|
||||||
|
cm_i = sum(mass * numpy.exp(2j * numpy.pi * points[:, i] / boxsize))
|
||||||
|
cm_i /= totmass
|
||||||
|
|
||||||
|
cm_i = numpy.arctan2(cm_i.imag, cm_i.real) * boxsize / (2 * numpy.pi)
|
||||||
|
|
||||||
|
if cm_i < 0:
|
||||||
|
cm_i += boxsize
|
||||||
|
cm[i] = cm_i
|
||||||
|
|
||||||
|
return cm
|
||||||
|
|
||||||
|
|
||||||
|
@jit(nopython=True)
|
||||||
|
def periodic_distance(points, reference, boxsize):
|
||||||
|
"""
|
||||||
|
Compute the 3D distance between multiple points and a reference point using
|
||||||
|
periodic boundary conditions. This is an optimized JIT implementation.
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
points : 2-dimensional array of shape `(n_points, 3)`
|
||||||
|
Points to calculate the distance from the reference point.
|
||||||
|
reference : 1-dimensional array of shape `(3, )`
|
||||||
|
Reference point.
|
||||||
|
boxsize : float
|
||||||
|
Box size.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
dist : 1-dimensional array of shape `(n_points, )`
|
||||||
|
"""
|
||||||
|
npoints = len(points)
|
||||||
|
half_box = boxsize / 2
|
||||||
|
|
||||||
|
dist = numpy.zeros(npoints, dtype=points.dtype)
|
||||||
|
for i in range(npoints):
|
||||||
|
for j in range(3):
|
||||||
|
dist_1d = abs(points[i, j] - reference[j])
|
||||||
|
|
||||||
|
if dist_1d > (half_box):
|
||||||
|
dist_1d = boxsize - dist_1d
|
||||||
|
|
||||||
|
dist[i] += dist_1d**2
|
||||||
|
|
||||||
|
dist[i] = dist[i]**0.5
|
||||||
|
|
||||||
|
return dist
|
||||||
|
|
||||||
|
|
||||||
|
@jit(nopython=True, fastmath=True, boundscheck=False)
|
||||||
|
def delta2ncells(delta):
|
||||||
|
"""
|
||||||
|
Calculate the number of cells in `delta` that are non-zero.
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
delta : 3-dimensional array
|
||||||
|
Halo density field.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
ncells : int
|
||||||
|
Number of non-zero cells.
|
||||||
|
"""
|
||||||
|
tot = 0
|
||||||
|
imax, jmax, kmax = delta.shape
|
||||||
|
for i in range(imax):
|
||||||
|
for j in range(jmax):
|
||||||
|
for k in range(kmax):
|
||||||
|
if delta[i, j, k] > 0:
|
||||||
|
tot += 1
|
||||||
|
return tot
|
||||||
|
|
||||||
|
|
||||||
|
@jit(nopython=True, fastmath=True, boundscheck=False)
|
||||||
|
def number_counts(x, bin_edges):
|
||||||
|
"""
|
||||||
|
Calculate counts of samples in bins.
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
x : 1-dimensional array
|
||||||
|
Samples' values.
|
||||||
|
bin_edges : 1-dimensional array
|
||||||
|
Bin edges.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
counts : 1-dimensional array
|
||||||
|
Bin counts.
|
||||||
|
"""
|
||||||
|
out = numpy.full(bin_edges.size - 1, numpy.nan, dtype=numpy.float32)
|
||||||
|
for i in range(bin_edges.size - 1):
|
||||||
|
out[i] = numpy.sum((x >= bin_edges[i]) & (x < bin_edges[i + 1]))
|
||||||
|
return out
|
||||||
|
|
@ -1,153 +0,0 @@
|
||||||
# Copyright (C) 2022 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.
|
|
||||||
"""
|
|
||||||
A script to fit FoF halos (concentration, ...). The CSiBORG particle array of
|
|
||||||
each realisation must have been processed in advance by `pre_dumppart.py`.
|
|
||||||
Quijote is not supported yet
|
|
||||||
"""
|
|
||||||
from argparse import ArgumentParser
|
|
||||||
|
|
||||||
import numpy
|
|
||||||
from mpi4py import MPI
|
|
||||||
from taskmaster import work_delegation
|
|
||||||
from tqdm import trange
|
|
||||||
|
|
||||||
from utils import get_nsims
|
|
||||||
|
|
||||||
try:
|
|
||||||
import csiborgtools
|
|
||||||
except ModuleNotFoundError:
|
|
||||||
import sys
|
|
||||||
|
|
||||||
sys.path.append("../")
|
|
||||||
import csiborgtools
|
|
||||||
|
|
||||||
|
|
||||||
def fit_halo(particles, box):
|
|
||||||
"""
|
|
||||||
Fit a single halo from the particle array. Only halos with more than 100
|
|
||||||
particles are fitted.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
particles : 2-dimensional array of shape `(n_particles, 3)`
|
|
||||||
Particle array. The columns must be `x`, `y`, `z`, `vx`, `vy`, `vz`,
|
|
||||||
`M`.
|
|
||||||
box : object derived from :py:class`csiborgtools.read.BaseBox`
|
|
||||||
Box object.
|
|
||||||
|
|
||||||
Returns
|
|
||||||
-------
|
|
||||||
out : dict
|
|
||||||
"""
|
|
||||||
halo = csiborgtools.fits.Halo(particles, box)
|
|
||||||
|
|
||||||
out = {}
|
|
||||||
out["npart"] = len(halo)
|
|
||||||
out["totpartmass"] = numpy.sum(halo["M"])
|
|
||||||
for i, v in enumerate(["vx", "vy", "vz"]):
|
|
||||||
out[v] = numpy.average(halo.vel[:, i], weights=halo["M"])
|
|
||||||
|
|
||||||
if out["npart"] < 100:
|
|
||||||
return out
|
|
||||||
|
|
||||||
cm, dist = halo.center_of_mass()
|
|
||||||
m200c, r200c = halo.spherical_overdensity_mass(dist, 200)
|
|
||||||
angmom = halo.angular_momentum(dist, cm, r200c)
|
|
||||||
|
|
||||||
out["m200c"] = m200c
|
|
||||||
out["r200c"] = r200c
|
|
||||||
out["lambda200c"] = halo.lambda_bullock(angmom, m200c, r200c)
|
|
||||||
out["conc"] = halo.nfw_concentration(dist, r200c)
|
|
||||||
return out
|
|
||||||
|
|
||||||
|
|
||||||
def _main(nsim, simname, verbose):
|
|
||||||
"""
|
|
||||||
Fit the FoF halos.
|
|
||||||
|
|
||||||
Parameters
|
|
||||||
----------
|
|
||||||
nsim : int
|
|
||||||
IC realisation index.
|
|
||||||
simname : str
|
|
||||||
Simulation name.
|
|
||||||
verbose : bool
|
|
||||||
Verbosity flag.
|
|
||||||
"""
|
|
||||||
cols = [("index", numpy.int32),
|
|
||||||
("npart", numpy.int32),
|
|
||||||
("totpartmass", numpy.float32),
|
|
||||||
("vx", numpy.float32),
|
|
||||||
("vy", numpy.float32),
|
|
||||||
("vz", numpy.float32),
|
|
||||||
("conc", numpy.float32),
|
|
||||||
("r200c", numpy.float32),
|
|
||||||
("m200c", numpy.float32),
|
|
||||||
("lambda200c", numpy.float32),]
|
|
||||||
|
|
||||||
nsnap = max(paths.get_snapshots(nsim, simname))
|
|
||||||
if simname == "csiborg":
|
|
||||||
box = csiborgtools.read.CSiBORGBox(nsnap, nsim, paths)
|
|
||||||
cat = csiborgtools.read.CSiBORGHaloCatalogue(
|
|
||||||
nsim, paths, bounds=None, load_fitted=False, load_initial=False)
|
|
||||||
else:
|
|
||||||
box = csiborgtools.read.QuijoteBox(nsnap, nsim, paths)
|
|
||||||
cat = csiborgtools.read.QuijoteHaloCatalogue(
|
|
||||||
nsim, paths, nsnap, bounds=None, load_fitted=False,
|
|
||||||
load_initial=False)
|
|
||||||
|
|
||||||
# Particle archive
|
|
||||||
f = csiborgtools.read.read_h5(paths.particles(nsim, simname))
|
|
||||||
particles = f["particles"]
|
|
||||||
halo_map = f["halomap"]
|
|
||||||
hid2map = {hid: i for i, hid in enumerate(halo_map[:, 0])}
|
|
||||||
|
|
||||||
out = csiborgtools.read.cols_to_structured(len(cat), cols)
|
|
||||||
for i in trange(len(cat)) if verbose else range(len(cat)):
|
|
||||||
hid = cat["index"][i]
|
|
||||||
out["index"][i] = hid
|
|
||||||
part = csiborgtools.read.load_halo_particles(hid, particles, halo_map,
|
|
||||||
hid2map)
|
|
||||||
# Skip if no particles.
|
|
||||||
if part is None:
|
|
||||||
continue
|
|
||||||
|
|
||||||
_out = fit_halo(part, box)
|
|
||||||
for key in _out.keys():
|
|
||||||
out[key][i] = _out.get(key, numpy.nan)
|
|
||||||
|
|
||||||
fout = paths.structfit(nsnap, nsim, simname)
|
|
||||||
if verbose:
|
|
||||||
print(f"Saving to `{fout}`.", flush=True)
|
|
||||||
numpy.save(fout, out)
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
parser = ArgumentParser()
|
|
||||||
parser.add_argument("--simname", type=str, default="csiborg",
|
|
||||||
choices=["csiborg", "quijote", "quijote_full"],
|
|
||||||
help="Simulation name")
|
|
||||||
parser.add_argument("--nsims", type=int, nargs="+", default=None,
|
|
||||||
help="IC realisations. If `-1` processes all.")
|
|
||||||
args = parser.parse_args()
|
|
||||||
|
|
||||||
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
|
||||||
nsims = get_nsims(args, paths)
|
|
||||||
|
|
||||||
def main(nsim):
|
|
||||||
_main(nsim, args.simname, MPI.COMM_WORLD.Get_size() == 1)
|
|
||||||
|
|
||||||
work_delegation(main, nsims, MPI.COMM_WORLD)
|
|
||||||
|
|
@ -61,7 +61,7 @@ def get_counts(nsim, bins, paths, parser_args):
|
||||||
cat = csiborgtools.read.CSiBORGHaloCatalogue(
|
cat = csiborgtools.read.CSiBORGHaloCatalogue(
|
||||||
nsim, paths, bounds=bounds, load_fitted=False, load_initial=False)
|
nsim, paths, bounds=bounds, load_fitted=False, load_initial=False)
|
||||||
logmass = numpy.log10(cat["fof_totpartmass"])
|
logmass = numpy.log10(cat["fof_totpartmass"])
|
||||||
counts = csiborgtools.fits.number_counts(logmass, bins)
|
counts = csiborgtools.number_counts(logmass, bins)
|
||||||
elif simname == "quijote":
|
elif simname == "quijote":
|
||||||
cat0 = csiborgtools.read.QuijoteHaloCatalogue(
|
cat0 = csiborgtools.read.QuijoteHaloCatalogue(
|
||||||
nsim, paths, nsnap=4, load_fitted=False, load_initial=False)
|
nsim, paths, nsnap=4, load_fitted=False, load_initial=False)
|
||||||
|
|
@ -72,12 +72,12 @@ def get_counts(nsim, bins, paths, parser_args):
|
||||||
for nobs in range(nmax):
|
for nobs in range(nmax):
|
||||||
cat = cat0.pick_fiducial_observer(nobs, rmax=parser_args.Rmax)
|
cat = cat0.pick_fiducial_observer(nobs, rmax=parser_args.Rmax)
|
||||||
logmass = numpy.log10(cat["group_mass"])
|
logmass = numpy.log10(cat["group_mass"])
|
||||||
counts[nobs, :] = csiborgtools.fits.number_counts(logmass, bins)
|
counts[nobs, :] = csiborgtools.number_counts(logmass, bins)
|
||||||
elif simname == "quijote_full":
|
elif simname == "quijote_full":
|
||||||
cat = csiborgtools.read.QuijoteHaloCatalogue(
|
cat = csiborgtools.read.QuijoteHaloCatalogue(
|
||||||
nsim, paths, nsnap=4, load_fitted=False, load_initial=False)
|
nsim, paths, nsnap=4, load_fitted=False, load_initial=False)
|
||||||
logmass = numpy.log10(cat["group_mass"])
|
logmass = numpy.log10(cat["group_mass"])
|
||||||
counts = csiborgtools.fits.number_counts(logmass, bins)
|
counts = csiborgtools.number_counts(logmass, bins)
|
||||||
else:
|
else:
|
||||||
raise ValueError(f"Unknown simulation name `{simname}`.")
|
raise ValueError(f"Unknown simulation name `{simname}`.")
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -91,14 +91,14 @@ def _main(nsim, simname, verbose):
|
||||||
|
|
||||||
pos, mass = part[:, :3], part[:, 3]
|
pos, mass = part[:, :3], part[:, 3]
|
||||||
# Calculate the centre of mass and the Lagrangian patch size.
|
# Calculate the centre of mass and the Lagrangian patch size.
|
||||||
cm = csiborgtools.fits.center_of_mass(pos, mass, boxsize=1.0)
|
cm = csiborgtools.center_of_mass(pos, mass, boxsize=1.0)
|
||||||
distances = csiborgtools.fits.periodic_distance(pos, cm, boxsize=1.0)
|
distances = csiborgtools.periodic_distance(pos, cm, boxsize=1.0)
|
||||||
out["x"][i], out["y"][i], out["z"][i] = cm
|
out["x"][i], out["y"][i], out["z"][i] = cm
|
||||||
out["lagpatch_size"][i] = numpy.percentile(distances, 99)
|
out["lagpatch_size"][i] = numpy.percentile(distances, 99)
|
||||||
|
|
||||||
# Calculate the number of cells with > 0 density.
|
# Calculate the number of cells with > 0 density.
|
||||||
delta = overlapper.make_delta(pos, mass, subbox=True)
|
delta = overlapper.make_delta(pos, mass, subbox=True)
|
||||||
out["lagpatch_ncells"][i] = csiborgtools.fits.delta2ncells(delta)
|
out["lagpatch_ncells"][i] = csiborgtools.delta2ncells(delta)
|
||||||
|
|
||||||
# Now save it
|
# Now save it
|
||||||
fout = paths.initmatch(nsim, simname, "fit")
|
fout = paths.initmatch(nsim, simname, "fit")
|
||||||
|
|
|
||||||
Loading…
Reference in a new issue