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Fixing overlaps and halo definitions. (#80)

Browse source 
* Add imports

* Refactor code

* Rename fof velocities

* Clean up and add Quijote

* Edit docstrings

* Update submission script

* Fix bug

* Start loading fitted properties

* Edit docstrings

* Update fitting for new `halo`

* Update CM definition and R200c

* Tune the minimum number of particles

* Enforce crossing threshold & tune hypers

* Fix periodiity when calculating angmom

* Doc strings

* Relax checkip

* Minor edit

* Fix old kwarg bug

* Fix CSiBORG bounds

* Catch warnings!

* Add `mass_kind` and new boundaries
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Richard Stiskalek 2023-07-31 16:13:21 +02:00 committed by GitHub
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328
csiborgtools/fits/halo.py
View file

@ -19,17 +19,13 @@ import numpy
from numba import jit from numba import jit
from scipy.optimize import minimize from scipy.optimize import minimize
GRAV = 4.300917270069976e-09 # G in (Msun / h)^-1 (Mpc / h) (km / s)^2
GRAV = 6.6743e-11 # m^3 kg^-1 s^-2
MSUN = 1.988409870698051e+30 # kg
MPC2M = 3.0856775814671916e+22 # 1 Mpc is this many meters
class BaseStructure(ABC): class BaseStructure(ABC):
""" """
Basic structure object for handling operations on its particles. Basic structure object for handling operations on its particles.
""" """
_particles = None _particles = None
_box = None _box = None
@ -90,94 +86,119 @@ class BaseStructure(ABC):
""" """
return numpy.vstack([self[p] for p in ("vx", "vy", "vz")]).T return numpy.vstack([self[p] for p in ("vx", "vy", "vz")]).T
def spherical_overdensity_mass(self, delta_mult, kind="crit", rtol=1e-8, def center_of_mass(self, npart_min=30, shrink_factor=0.98):
maxiter=100, npart_min=10):
r""" r"""
Calculate spherical overdensity mass and radius via the iterative Calculate the center of mass of a halo via the shrinking sphere
shrinking sphere method. 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 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 delta_mult : int or float
Overdensity multiple. Overdensity multiple.
kind : str, optional kind : str, optional
Either `crit` or `matter`, for critical or matter overdensity Either `crit` or `matter`, for critical or matter overdensity
rtol : float, optional
Tolerance for the change in the center of mass or radius.
maxiter : int, optional
Maximum number of iterations.
npart_min : int, optional
Minimum number of enclosed particles to reset the iterator.
Returns Returns
------- -------
mass : float mass : float
The requested spherical overdensity mass in :math:`M_\odot / h`. Overdensity mass in (Msun / h).
rad : float rad : float
The radius of the sphere enclosing the requested overdensity in box Overdensity radius in box units.
units.
cm : 1-dimensional array of shape `(3, )`
The center of mass of the sphere enclosing the requested
overdensity in box units.
""" """
assert kind in ["crit", "matter"] if kind not in ["crit", "matter"]:
raise ValueError("kind must be either `crit` or `matter`.")
# Calculate density based on the provided kind
rho = delta_mult * self.box.rho_crit0 rho = delta_mult * self.box.rho_crit0
if kind == "matter": rho *= self.box.Om if kind == "matter" else 1.
rho *= self.box.Om
pos, mass = self.pos, self["M"] argsort = numpy.argsort(dist)
dist = self.box.box2mpc(dist[argsort])
# Initial estimates for center of mass and radius norm_density = numpy.cumsum(self['M'][argsort])
init_cm = center_of_mass(pos, mass, boxsize=1) totmass = norm_density[-1]
init_rad = self.box.mpc2box(mass_to_radius(numpy.sum(mass), rho) * 1.5) with numpy.errstate(divide="ignore"):
norm_density /= (4. / 3. * numpy.pi * dist**3)
norm_density /= rho
rad, cm = init_rad, numpy.copy(init_cm) # 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.)
for _ in range(maxiter): if j is None:
dist = periodic_distance(pos, cm, boxsize=1) return numpy.nan, numpy.nan
within_rad = dist <= rad
# Heuristic reset if too few enclosed particles i = j - 1
if numpy.sum(within_rad) < npart_min:
js = numpy.random.choice(len(self), len(self), replace=True)
cm = center_of_mass(pos[js], mass[js], boxsize=1)
rad = init_rad * (0.75 + numpy.random.rand())
dist = periodic_distance(pos, cm, boxsize=1)
within_rad = dist <= rad
# If there are still too few particles, then skip this rad = (dist[j] - dist[i])
# iteration. rad *= (1. - norm_density[i]) / (norm_density[j] - norm_density[i])
if numpy.sum(within_rad) < npart_min: rad += dist[i]
continue
enclosed_mass = numpy.sum(mass[within_rad]) mass = radius_to_mass(rad, rho)
new_rad = self.box.mpc2box(mass_to_radius(enclosed_mass, rho)) rad = self.box.mpc2box(rad)
new_cm = center_of_mass(pos[within_rad], mass[within_rad],
boxsize=1)
# Check convergence based on center of mass and radius if mass > totmass:
cm_conv = numpy.linalg.norm(cm - new_cm) < rtol return numpy.nan, numpy.nan
rad_conv = abs(rad - new_rad) < rtol
if cm_conv or rad_conv: return mass, rad
return enclosed_mass, rad, cm
cm, rad = new_cm, new_rad def angular_momentum(self, dist, cm, rad, npart_min=10):
# Return NaN values if no convergence after max iterations
return numpy.nan, numpy.nan, numpy.full(3, numpy.nan, numpy.float32)
def angular_momentum(self, ref, rad, npart_min=10):
r""" r"""
Calculate angular momentum around a reference point using all particles Calculate angular momentum around a centre of mass using all particles
within a radius. Units are within a radius. Accounts for periodicity of the box and units are
:math:`(M_\odot / h) (\mathrm{Mpc} / h) \mathrm{km} / \mathrm{s}`. (Msun / h) * (Mpc / h) * (km / s).
Parameters Parameters
---------- ----------
ref : 1-dimensional array of shape `(3, )` 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. Reference point in box units.
rad : float rad : float
Radius around the reference point in box units. Radius around the reference point in box units.
@ -189,31 +210,28 @@ class BaseStructure(ABC):
------- -------
angmom : 1-dimensional array or shape `(3, )` angmom : 1-dimensional array or shape `(3, )`
""" """
# Calculate the distance of each particle from the reference point. mask = dist < rad
distances = periodic_distance(self.pos, ref, boxsize=1)
# Filter particles within the provided radius.
mask = distances < rad
if numpy.sum(mask) < npart_min: if numpy.sum(mask) < npart_min:
return numpy.full(3, numpy.nan, numpy.float32) return numpy.full(3, numpy.nan, numpy.float32)
mass, pos, vel = self["M"][mask], self.pos[mask], self.vel[mask] mass, pos, vel = self["M"][mask], self.pos[mask], self.vel[mask]
# Convert positions to Mpc / h and center around the reference point. pos = shift_to_center_of_box(pos, cm, 1.0, set_cm_to_zero=True)
pos = self.box.box2mpc(pos) - ref pos = self.box.box2mpc(pos)
# Adjust velocities to be in the CM frame.
vel -= numpy.average(vel, axis=0, weights=mass) vel -= numpy.average(vel, axis=0, weights=mass)
# Calculate angular momentum.
return numpy.sum(mass[:, numpy.newaxis] * numpy.cross(pos, vel), return numpy.sum(mass[:, numpy.newaxis] * numpy.cross(pos, vel),
axis=0) axis=0)
def lambda_bullock(self, ref, rad): def lambda_bullock(self, angmom, mass, rad):
r""" """
Bullock spin, see Eq. 5 in [1], in a given radius around a reference Calculate the Bullock spin, see Eq. 5 in [1].
point.
Parameters Parameters
---------- ----------
angmom : 1-dimensional array of shape `(3, )`
Angular momentum in (Msun / h) * (Mpc / h) * (km / s).
ref : 1-dimensional array of shape `(3, )` ref : 1-dimensional array of shape `(3, )`
Reference point in box units. Reference point in box units.
rad : float rad : float
@ -229,28 +247,18 @@ class BaseStructure(ABC):
Bullock, J. S.; Dekel, A.; Kolatt, T. S.; Kravtsov, A. V.; Bullock, J. S.; Dekel, A.; Kolatt, T. S.; Kravtsov, A. V.;
Klypin, A. A.; Porciani, C.; Primack, J. R. Klypin, A. A.; Porciani, C.; Primack, J. R.
""" """
# Filter particles within the provided radius out = numpy.linalg.norm(angmom)
mask = periodic_distance(self.pos, ref, boxsize=1) < rad return out / numpy.sqrt(2 * GRAV * mass**3 * self.box.box2mpc(rad))
# Calculate the total mass of the enclosed particles
enclosed_mass = numpy.sum(self["M"][mask])
# Convert the radius from box units to Mpc/h
rad_mpc = self.box.box2mpc(rad)
# Circular velocity in km/s
circvel = (GRAV * enclosed_mass * MSUN / (rad_mpc * MPC2M))**0.5 * 1e-3
# Magnitude of the angular momentum
l_norm = numpy.linalg.norm(self.angular_momentum(ref, rad))
# Compute and return the Bullock spin parameter
return l_norm / (numpy.sqrt(2) * enclosed_mass * circvel * rad_mpc)
def nfw_concentration(self, ref, rad, conc_min=1e-3, npart_min=10): def nfw_concentration(self, dist, rad, conc_min=1e-3, npart_min=10):
""" """
Calculate the NFW concentration parameter in a given radius around a Calculate the NFW concentration parameter in a given radius around a
reference point. reference point.
Parameters Parameters
---------- ----------
ref : 1-dimensional array of shape `(3, )` dist : 1-dimensional array of shape `(n_particles, )`
Reference point in box units. Distance of each particle from center of mass in box units.
rad : float rad : float
Radius around the reference point in box units. Radius around the reference point in box units.
conc_min : float conc_min : float
@ -263,36 +271,25 @@ class BaseStructure(ABC):
------- -------
conc : float conc : float
""" """
dist = periodic_distance(self.pos, ref, boxsize=1)
mask = dist < rad mask = dist < rad
if numpy.sum(mask) < npart_min: if numpy.sum(mask) < npart_min:
return numpy.nan return numpy.nan
dist, weight = dist[mask], self["M"][mask] dist, weight = dist[mask], self["M"][mask]
weight /= numpy.mean(weight) weight /= weight[0]
# Objective function for minimization res = minimize(negll_nfw_concentration, x0=1.,
def negll_nfw_concentration(log_c, xs, w):
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
initial_guess = 1.5
res = minimize(negll_nfw_concentration, x0=initial_guess,
args=(dist / rad, weight, ), method='Nelder-Mead', args=(dist / rad, weight, ), method='Nelder-Mead',
bounds=((numpy.log10(conc_min), 5),)) bounds=((numpy.log10(conc_min), 5),))
if not res.success: if not res.success:
return numpy.nan return numpy.nan
conc_value = 10**res["x"][0] conc = 10**res["x"][0]
if conc_value < conc_min or numpy.isclose(conc_value, conc_min): if conc < conc_min or numpy.isclose(conc, conc_min):
return numpy.nan return numpy.nan
return conc_value return conc
def __getitem__(self, key): def __getitem__(self, key):
key_to_index = {'x': 0, 'y': 1, 'z': 2, key_to_index = {'x': 0, 'y': 1, 'z': 2,
@ -329,11 +326,12 @@ class Halo(BaseStructure):
############################################################################### ###############################################################################
@jit(nopython=True, fastmath=True, boundscheck=False)
def center_of_mass(points, mass, boxsize): def center_of_mass(points, mass, boxsize):
""" """
Calculate the center of mass of a halo, while assuming for periodic Calculate the center of mass of a halo while assuming periodic boundary
boundary conditions of a cubical box. Assuming that particle positions are conditions of a cubical box. Assuming that particle positions are in
in `[0, boxsize)` range. `[0, boxsize)` range. This is a JIT implementation.
Parameters Parameters
---------- ----------
@ -348,20 +346,29 @@ def center_of_mass(points, mass, boxsize):
------- -------
cm : 1-dimensional array of shape `(3, )` cm : 1-dimensional array of shape `(3, )`
""" """
# Convert positions to unit circle coordinates in the complex plane cm = numpy.zeros(3, dtype=points.dtype)
pos = numpy.exp(2j * numpy.pi * points / boxsize) totmass = sum(mass)
# Compute weighted average of these coordinates, convert it back to
# box coordinates and fix any negative positions due to angle calculations. # Convert positions to unit circle coordinates in the complex plane,
cm = numpy.angle(numpy.average(pos, axis=0, weights=mass)) # calculate the weighted average and convert it back to box coordinates.
cm *= boxsize / (2 * numpy.pi) for i in range(3):
cm[cm < 0] += boxsize 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 return cm
@jit(nopython=True)
def periodic_distance(points, reference, boxsize): def periodic_distance(points, reference, boxsize):
""" """
Compute the periodic distance between multiple points and a reference Compute the 3D distance between multiple points and a reference point using
point. periodic boundary conditions. This is an optimized JIT implementation.
Parameters Parameters
---------- ----------
@ -376,9 +383,22 @@ def periodic_distance(points, reference, boxsize):
------- -------
dist : 1-dimensional array of shape `(n_points, )` dist : 1-dimensional array of shape `(n_points, )`
""" """
delta = numpy.abs(points - reference) npoints = len(points)
delta = numpy.where(delta > boxsize / 2, boxsize - delta, delta) half_box = boxsize / 2
return numpy.linalg.norm(delta, axis=1)
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): def shift_to_center_of_box(points, cm, boxsize, set_cm_to_zero=False):
@ -407,26 +427,74 @@ def shift_to_center_of_box(points, cm, boxsize, set_cm_to_zero=False):
return pos return pos
def mass_to_radius(mass, rho): @jit(nopython=True, fastmath=True, boundscheck=False)
def radius_to_mass(radius, rho):
""" """
Compute the radius of a sphere with a given mass and density. Compute the mass of a sphere with a given radius and density.
Parameters Parameters
---------- ----------
mass : float radius : float
Mass of the sphere. Radius of the sphere.
rho : float rho : float
Density of the sphere. Density of the sphere.
Returns Returns
------- -------
rad : float mass : float
Radius of the sphere.
""" """
return ((3 * mass) / (4 * numpy.pi * rho))**(1./3) return ((4 * numpy.pi * rho) / 3) * radius**3
@jit(nopython=True) @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): def delta2ncells(delta):
""" """
Calculate the number of cells in `delta` that are non-zero. Calculate the number of cells in `delta` that are non-zero.
@ -451,7 +519,7 @@ def delta2ncells(delta):
return tot return tot
@jit(nopython=True) @jit(nopython=True, fastmath=True, boundscheck=False)
def number_counts(x, bin_edges): def number_counts(x, bin_edges):
""" """
Calculate counts of samples in bins. Calculate counts of samples in bins.

4
csiborgtools/match/__init__.py
View file

@ -13,5 +13,5 @@
# 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 .match import (ParticleOverlap, RealisationsMatcher, # noqa from .match import (ParticleOverlap, RealisationsMatcher, # noqa
calculate_overlap, calculate_overlap_indxs, calculate_overlap, calculate_overlap_indxs, pos2cell,
cosine_similarity, find_neighbour) cosine_similarity, find_neighbour, get_halo_cell_limits)

360
csiborgtools/match/match.py
View file

@ -21,8 +21,9 @@ from functools import lru_cache
from math import ceil from math import ceil
import numpy import numpy
from numba import jit
from scipy.ndimage import gaussian_filter from scipy.ndimage import gaussian_filter
from numba import jit
from tqdm import tqdm, trange from tqdm import tqdm, trange
from ..read import load_halo_particles from ..read import load_halo_particles
@ -45,34 +46,39 @@ class BaseMatcher(ABC):
box_size : int box_size : int
""" """
if self._box_size is None: if self._box_size is None:
raise RuntimeError("`box_size` is not set.") raise RuntimeError("`box_size` has not been set.")
return self._box_size return self._box_size
@box_size.setter @box_size.setter
def box_size(self, value): def box_size(self, value):
assert isinstance(value, int) if not (isinstance(value, int) and value > 0):
assert value > 0 raise ValueError("`box_size` must be a positive integer.")
if not value != 0 and (value & (value - 1) == 0):
raise ValueError("`box_size` must be a power of 2.")
self._box_size = value self._box_size = value
@property @property
def bckg_halfsize(self): def bckg_halfsize(self):
""" """
Number of to each side of the centre of the box to calculate the Background half-size for density field calculation. This is the
density field. This is because in CSiBORG we are only interested in the grid distance from the center of the box to each side over which to
high-resolution region. evaluate the background density field. Must be less than or equal to
half the box size.
Returns Returns
------- -------
bckg_halfsize : int bckg_halfsize : int
""" """
if self._bckg_halfsize is None: if self._bckg_halfsize is None:
raise RuntimeError("`bckg_halfsize` is not set.") raise RuntimeError("`bckg_halfsize` has not been set.")
return self._bckg_halfsize return self._bckg_halfsize
@bckg_halfsize.setter @bckg_halfsize.setter
def bckg_halfsize(self, value): def bckg_halfsize(self, value):
assert isinstance(value, int) if not (isinstance(value, int) and value > 0):
assert value > 0 raise ValueError("`bckg_halfsize` must be a positive integer.")
if value > self.box_size // 2:
raise ValueError("`bckg_halfsize` must be <= half the box size.")
self._bckg_halfsize = value self._bckg_halfsize = value
@ -83,26 +89,26 @@ class BaseMatcher(ABC):
class RealisationsMatcher(BaseMatcher): class RealisationsMatcher(BaseMatcher):
""" """
A tool to match haloes between IC realisations. Matches haloes between IC realisations.
Parameters Parameters
---------- ----------
box_size : int box_size : int
Number of cells in the box. Number of cells in the box.
bckg_halfsize : int bckg_halfsize : int
Number of to each side of the centre of the box to calculate the Background half-size for density field calculation. This is the
density field. This is because in CSiBORG we are only interested in the grid distance from the center of the box to each side over which to
high-resolution region. evaluate the background density field. Must be less than or equal to
half the box size.
nmult : float or int, optional nmult : float or int, optional
Multiple of the sum of pair initial Lagrangian patch sizes Multiplier of the sum of the initial Lagrangian patch sizes of a halo
within which to return neighbours. By default 1. pair. Determines the range within which neighbors are returned.
dlogmass : float, optional dlogmass : float, optional
Tolerance on the absolute logarithmic mass difference of potential Tolerance on the absolute logarithmic mass difference of potential
matches. By default 2. matches.
mass_kind : str, optional mass_kind : str, optional
The mass kind whose similarity is to be checked. Must be a valid Mass kind whose similarity is to be checked. Must be a valid key in the
catalogue key. By default `totpartmass`, i.e. the total particle halo catalogue.
mass associated with a halo.
""" """
_nmult = None _nmult = None
_dlogmass = None _dlogmass = None
@ -111,21 +117,19 @@ class RealisationsMatcher(BaseMatcher):
def __init__(self, box_size, bckg_halfsize, nmult=1.0, dlogmass=2.0, def __init__(self, box_size, bckg_halfsize, nmult=1.0, dlogmass=2.0,
mass_kind="totpartmass"): mass_kind="totpartmass"):
assert nmult > 0
assert dlogmass > 0
assert isinstance(mass_kind, str)
self.box_size = box_size self.box_size = box_size
self.halfsize = bckg_halfsize self.bckg_halfsize = bckg_halfsize
self._nmult = nmult self.nmult = nmult
self._dlogmass = dlogmass self.dlogmass = dlogmass
self._mass_kind = mass_kind self.mass_kind = mass_kind
self._overlapper = ParticleOverlap(box_size, bckg_halfsize) self._overlapper = ParticleOverlap(box_size, bckg_halfsize)
@property @property
def nmult(self): def nmult(self):
""" """
Multiple of the sum of pair initial Lagrangian patch sizes within which Multiplier of the sum of the initial Lagrangian patch sizes of a halo
to return neighbours. pair. Determines the range within which neighbors are returned.
Returns Returns
------- -------
@ -133,6 +137,12 @@ class RealisationsMatcher(BaseMatcher):
""" """
return self._nmult return self._nmult
@nmult.setter
def nmult(self, value):
if not (value > 0 and isinstance(value, (int, float))):
raise ValueError("`nmult` must be a positive integer or float.")
self._nmult = float(value)
@property @property
def dlogmass(self): def dlogmass(self):
""" """
@ -145,10 +155,17 @@ class RealisationsMatcher(BaseMatcher):
""" """
return self._dlogmass return self._dlogmass
@dlogmass.setter
def dlogmass(self, value):
if not (value > 0 and isinstance(value, (float, int))):
raise ValueError("`dlogmass` must be a positive float.")
self._dlogmass = float(value)
@property @property
def mass_kind(self): def mass_kind(self):
""" """
Mass kind whose similarity is to be checked. Mass kind whose similarity is to be checked. Must be a valid key in the
halo catalogue.
Returns Returns
------- -------
@ -156,6 +173,12 @@ class RealisationsMatcher(BaseMatcher):
""" """
return self._mass_kind return self._mass_kind
@mass_kind.setter
def mass_kind(self, value):
if not isinstance(value, str):
raise ValueError("`mass_kind` must be a string.")
self._mass_kind = value
@property @property
def overlapper(self): def overlapper(self):
""" """
@ -172,34 +195,33 @@ class RealisationsMatcher(BaseMatcher):
r""" r"""
Find all neighbours whose CM separation is less than `nmult` times the Find all neighbours whose CM separation is less than `nmult` times the
sum of their initial Lagrangian patch sizes and calculate their sum of their initial Lagrangian patch sizes and calculate their
overlap. Enforces that the neighbours' are similar in mass up to overlap. Enforces that the neighbours are similar in mass up to
`dlogmass` dex. `dlogmass` dex.
Parameters Parameters
---------- ----------
cat0 : :py:class:`csiborgtools.read.CSiBORGHaloCatalogue` cat0 : instance of :py:class:`csiborgtools.read.BaseCatalogue`
Halo catalogue of the reference simulation. Halo catalogue of the reference simulation.
catx : :py:class:`csiborgtools.read.CSiBORGHaloCatalogue` catx : instance of :py:class:`csiborgtools.read.BaseCatalogue`
Halo catalogue of the cross simulation. Halo catalogue of the cross simulation.
particles0 : 2-dimensional array particles0 : 2-dimensional array
Array of particles in box units in the reference simulation. Particles archive file of the reference simulation. The columns
The columns must be `x`, `y`, `z` and `M`. must be `x`, `y`, `z` and `M`.
particlesx : 2-dimensional array particlesx : 2-dimensional array
Array of particles in box units in the cross simulation. Particles archive file of the cross simulation. The columns must be
The columns must be `x`, `y`, `z` and `M`. `x`, `y`, `z` and `M`.
halo_map0 : 2-dimensional array halo_map0 : 2-dimensional array
Halo map of the reference simulation. Halo map of the reference simulation.
halo_mapx : 2-dimensional array halo_mapx : 2-dimensional array
Halo map of the cross simulation. Halo map of the cross simulation.
delta_bckg : 3-dimensional array delta_bckg : 3-dimensional array
Summed background density field of the reference and cross Summed background density field of the reference and cross
simulations calculated with particles assigned to haloes at the simulations calculated with particles assigned to halos at the
final snapshot. Assumed to only be sampled in cells final snapshot. Calculated on a grid determined by `bckg_halfsize`.
:math:`[512, 1536)^3`.
cache_size : int, optional cache_size : int, optional
Caching size for loading the cross simulation halos. Caching size for loading the cross simulation halos.
verbose : bool, optional verbose : bool, optional
iterator verbosity flag. by default `true`. Iterator verbosity flag. By default `true`.
Returns Returns
------- -------
@ -279,21 +301,21 @@ class RealisationsMatcher(BaseMatcher):
halo_mapx, delta_bckg, match_indxs, smooth_kwargs, halo_mapx, delta_bckg, match_indxs, smooth_kwargs,
cache_size=10000, verbose=True): cache_size=10000, verbose=True):
r""" r"""
Calculate the smoothed overlaps for pair previously identified via Calculate the smoothed overlaps for pairs previously identified via
`self.cross(...)` to have a non-zero overlap. `self.cross(...)` to have a non-zero NGP overlap.
Parameters Parameters
---------- ----------
cat0 : :py:class:`csiborgtools.read.CSiBORGHaloCatalogue` cat0 : instance of :py:class:`csiborgtools.read.BaseCatalogue`
Halo catalogue of the reference simulation. Halo catalogue of the reference simulation.
catx : :py:class:`csiborgtools.read.CSiBORGHaloCatalogue` catx : instance of :py:class:`csiborgtools.read.BaseCatalogue`
Halo catalogue of the cross simulation. Halo catalogue of the cross simulation.
particles0 : 2-dimensional array particles0 : 2-dimensional array
Array of particles in box units in the reference simulation. Particles archive file of the reference simulation. The columns
The columns must be `x`, `y`, `z` and `M`. must be `x`, `y`, `z` and `M`.
particlesx : 2-dimensional array particlesx : 2-dimensional array
Array of particles in box units in the cross simulation. Particles archive file of the cross simulation. The columns must be
The columns must be `x`, `y`, `z` and `M`. `x`, `y`, `z` and `M`.
halo_map0 : 2-dimensional array halo_map0 : 2-dimensional array
Halo map of the reference simulation. Halo map of the reference simulation.
halo_mapx : 2-dimensional array halo_mapx : 2-dimensional array
@ -301,8 +323,7 @@ class RealisationsMatcher(BaseMatcher):
delta_bckg : 3-dimensional array delta_bckg : 3-dimensional array
Smoothed summed background density field of the reference and cross Smoothed summed background density field of the reference and cross
simulations calculated with particles assigned to halos at the simulations calculated with particles assigned to halos at the
final snapshot. Assumed to only be sampled in cells final snapshot. Calculated on a grid determined by `bckg_halfsize`.
:math:`[512, 1536)^3`.
match_indxs : 1-dimensional array of arrays match_indxs : 1-dimensional array of arrays
Indices of halo counterparts in the cross catalogue. Indices of halo counterparts in the cross catalogue.
smooth_kwargs : kwargs smooth_kwargs : kwargs
@ -310,7 +331,7 @@ class RealisationsMatcher(BaseMatcher):
cache_size : int, optional cache_size : int, optional
Caching size for loading the cross simulation halos. Caching size for loading the cross simulation halos.
verbose : bool, optional verbose : bool, optional
Iterator verbosity flag. By default `True`. Iterator verbosity flag. By default `true`.
Returns Returns
------- -------
@ -328,8 +349,8 @@ class RealisationsMatcher(BaseMatcher):
if verbose: if verbose:
print(f"{datetime.now()}: calculating smoothed overlaps.", print(f"{datetime.now()}: calculating smoothed overlaps.",
flush=True) flush=True)
indxs = cat0["index"]
cross = [numpy.asanyarray([], dtype=numpy.float32)] * match_indxs.size cross = [numpy.asanyarray([], dtype=numpy.float32)] * match_indxs.size
indxs = cat0["index"]
for i, k0 in enumerate(tqdm(indxs) if verbose else indxs): for i, k0 in enumerate(tqdm(indxs) if verbose else indxs):
pos0, mass0, __, mins0, maxs0 = load_processed_halo( pos0, mass0, __, mins0, maxs0 = load_processed_halo(
k0, particles0, halo_map0, hid2map0, nshift=nshift, k0, particles0, halo_map0, hid2map0, nshift=nshift,
@ -348,41 +369,10 @@ class RealisationsMatcher(BaseMatcher):
############################################################################### ###############################################################################
# Matching statistics # # Overlap calculator #
############################################################################### ###############################################################################
def cosine_similarity(x, y):
r"""
Calculate the cosine similarity between two Cartesian vectors. Defined
as :math:`\Sum_{i} x_i y_{i} / (|x| * |y|)`.
Parameters
----------
x : 1-dimensional array
The first vector.
y : 1- or 2-dimensional array
The second vector. Can be 2-dimensional of shape `(n_samples, 3)`,
in which case the calculation is broadcasted.
Returns
-------
out : float or 1-dimensional array
The cosine similarity. If y is 1-dimensinal returns only a float.
"""
# Quick check of dimensions
if x.ndim != 1:
raise ValueError("`x` must be a 1-dimensional array.")
y = y.reshape(-1, 3) if y.ndim == 1 else y
out = numpy.sum(x * y, axis=1)
out /= numpy.linalg.norm(x) * numpy.linalg.norm(y, axis=1)
if out.size == 1:
return out[0]
return out
class ParticleOverlap(BaseMatcher): class ParticleOverlap(BaseMatcher):
r""" r"""
Halo overlaps calculator. The density field calculation is based on the Halo overlaps calculator. The density field calculation is based on the
@ -394,9 +384,10 @@ class ParticleOverlap(BaseMatcher):
box_size : int box_size : int
Number of cells in the box. Number of cells in the box.
bckg_halfsize : int bckg_halfsize : int
Number of to each side of the centre of the box to calculate the Background half-size for density field calculation. This is the
density field. This is because in CSiBORG we are only interested in the grid distance from the center of the box to each side over which to
high-resolution region. evaluate the background density field. Must be less than or equal to
half the box size.
""" """
def __init__(self, box_size, bckg_halfsize): def __init__(self, box_size, bckg_halfsize):
@ -414,16 +405,16 @@ class ParticleOverlap(BaseMatcher):
Parameters Parameters
---------- ----------
particles : 2-dimensional array particles : 2-dimensional array
Array of particles. Particles archive file. The columns must be `x`, `y`, `z` and `M`.
halo_map : 2-dimensional array halo_map : 2-dimensional array
Array containing start and end indices in the particle array Array containing start and end indices in the particle array
corresponding to each halo. corresponding to each halo.
hid2map : dict hid2map : dict
Dictionary mapping halo IDs to `halo_map` array positions. Dictionary mapping halo IDs to `halo_map` array positions.
halo_cat: :py:class:`csiborgtools.read.CSiBORGHaloCatalogue` halo_cat : instance of :py:class:`csiborgtools.read.BaseCatalogue`
Halo catalogue. Halo catalogue.
delta : 3-dimensional array, optional delta : 3-dimensional array, optional
Array to store the density field in. If `None` a new array is Array to store the density field. If `None` a new array is
created. created.
verbose : bool, optional verbose : bool, optional
Verbosity flag for loading the halos' particles. Verbosity flag for loading the halos' particles.
@ -449,6 +440,7 @@ class ParticleOverlap(BaseMatcher):
pos, mass = pos[:, :3], pos[:, 3] pos, mass = pos[:, :3], pos[:, 3]
pos = pos2cell(pos, self.box_size) pos = pos2cell(pos, self.box_size)
# We mask out particles outside the cubical high-resolution region # We mask out particles outside the cubical high-resolution region
mask = numpy.all((cellmin <= pos) & (pos < cellmax), axis=1) mask = numpy.all((cellmin <= pos) & (pos < cellmax), axis=1)
pos = pos[mask] pos = pos[mask]
@ -465,14 +457,13 @@ class ParticleOverlap(BaseMatcher):
Parameters Parameters
---------- ----------
pos : 2-dimensional array pos : 2-dimensional array
Halo particle position array. Halo's particles position array.
mass : 1-dimensional array mass : 1-dimensional array
Halo particle mass array. Halo's particles mass array.
mins, maxs : 1-dimensional arrays of shape `(3,)` mins, maxs : 1-dimensional arrays of shape `(3,)`
Minimun and maximum cell numbers along each dimension. Minimun and maximum cell numbers along each dimension.
subbox : bool, optional subbox : bool, optional
Whether to calculate the density field on a grid strictly enclosing Whether to calculate the field on a grid enclosing the halo.
the halo.
smooth_kwargs : kwargs, optional smooth_kwargs : kwargs, optional
Kwargs to be passed to :py:func:`scipy.ndimage.gaussian_filter`. Kwargs to be passed to :py:func:`scipy.ndimage.gaussian_filter`.
If `None` no smoothing is applied. If `None` no smoothing is applied.
@ -483,25 +474,25 @@ class ParticleOverlap(BaseMatcher):
""" """
nshift = read_nshift(smooth_kwargs) nshift = read_nshift(smooth_kwargs)
cells = pos2cell(pos, self.box_size) cells = pos2cell(pos, self.box_size)
# Check that minima and maxima are integers
if not (mins is None and maxs is None): if not (mins is None and maxs is None):
assert mins.dtype.char in numpy.typecodes["AllInteger"] assert mins.dtype.char in numpy.typecodes["AllInteger"]
assert maxs.dtype.char in numpy.typecodes["AllInteger"] assert maxs.dtype.char in numpy.typecodes["AllInteger"]
if subbox: if subbox:
if mins is None or maxs is None: if mins is None or maxs is None:
mins, maxs = get_halolims(cells, self.box_size, nshift) mins, maxs = get_halo_cell_limits(cells, self.box_size, nshift)
ncells = maxs - mins + 1
ncells = maxs - mins + 1 # To get the number of cells
else: else:
mins = [0, 0, 0] mins = [0, 0, 0]
ncells = (self.box_size, ) * 3 ncells = (self.box_size, ) * 3
# Preallocate and fill the array
delta = numpy.zeros(ncells, dtype=numpy.float32) delta = numpy.zeros(ncells, dtype=numpy.float32)
fill_delta(delta, cells[:, 0], cells[:, 1], cells[:, 2], *mins, mass) fill_delta(delta, cells[:, 0], cells[:, 1], cells[:, 2], *mins, mass)
if smooth_kwargs is not None: if smooth_kwargs is not None:
gaussian_filter(delta, output=delta, **smooth_kwargs) gaussian_filter(delta, output=delta, **smooth_kwargs)
return delta return delta
def make_deltas(self, pos1, pos2, mass1, mass2, mins1=None, maxs1=None, def make_deltas(self, pos1, pos2, mass1, mass2, mins1=None, maxs1=None,
@ -543,6 +534,7 @@ class ParticleOverlap(BaseMatcher):
nshift = read_nshift(smooth_kwargs) nshift = read_nshift(smooth_kwargs)
pos1 = pos2cell(pos1, self.box_size) pos1 = pos2cell(pos1, self.box_size)
pos2 = pos2cell(pos2, self.box_size) pos2 = pos2cell(pos2, self.box_size)
xc1, yc1, zc1 = [pos1[:, i] for i in range(3)] xc1, yc1, zc1 = [pos1[:, i] for i in range(3)]
xc2, yc2, zc2 = [pos2[:, i] for i in range(3)] xc2, yc2, zc2 = [pos2[:, i] for i in range(3)]
@ -551,6 +543,7 @@ class ParticleOverlap(BaseMatcher):
xmin = min(numpy.min(xc1), numpy.min(xc2)) - nshift xmin = min(numpy.min(xc1), numpy.min(xc2)) - nshift
ymin = min(numpy.min(yc1), numpy.min(yc2)) - nshift ymin = min(numpy.min(yc1), numpy.min(yc2)) - nshift
zmin = min(numpy.min(zc1), numpy.min(zc2)) - nshift zmin = min(numpy.min(zc1), numpy.min(zc2)) - nshift
# Make sure shifting does not go beyond boundaries # Make sure shifting does not go beyond boundaries
xmin, ymin, zmin = [max(px, 0) for px in (xmin, ymin, zmin)] xmin, ymin, zmin = [max(px, 0) for px in (xmin, ymin, zmin)]
@ -558,6 +551,7 @@ class ParticleOverlap(BaseMatcher):
xmax = max(numpy.max(xc1), numpy.max(xc2)) + nshift xmax = max(numpy.max(xc1), numpy.max(xc2)) + nshift
ymax = max(numpy.max(yc1), numpy.max(yc2)) + nshift ymax = max(numpy.max(yc1), numpy.max(yc2)) + nshift
zmax = max(numpy.max(zc1), numpy.max(zc2)) + nshift zmax = max(numpy.max(zc1), numpy.max(zc2)) + nshift
# Make sure shifting does not go beyond boundaries # Make sure shifting does not go beyond boundaries
xmax, ymax, zmax = [min(px, self.box_size - 1) xmax, ymax, zmax = [min(px, self.box_size - 1)
for px in (xmax, ymax, zmax)] for px in (xmax, ymax, zmax)]
@ -565,10 +559,9 @@ class ParticleOverlap(BaseMatcher):
xmin, ymin, zmin = [min(mins1[i], mins2[i]) for i in range(3)] xmin, ymin, zmin = [min(mins1[i], mins2[i]) for i in range(3)]
xmax, ymax, zmax = [max(maxs1[i], maxs2[i]) for i in range(3)] xmax, ymax, zmax = [max(maxs1[i], maxs2[i]) for i in range(3)]
cellmins = (xmin, ymin, zmin) # Cell minima cellmins = (xmin, ymin, zmin)
ncells = xmax - xmin + 1, ymax - ymin + 1, zmax - zmin + 1 # Num cells ncells = (xmax - xmin + 1, ymax - ymin + 1, zmax - zmin + 1,)
# Preallocate and fill the arrays
delta1 = numpy.zeros(ncells, dtype=numpy.float32) delta1 = numpy.zeros(ncells, dtype=numpy.float32)
delta2 = numpy.zeros(ncells, dtype=numpy.float32) delta2 = numpy.zeros(ncells, dtype=numpy.float32)
@ -590,6 +583,7 @@ class ParticleOverlap(BaseMatcher):
if smooth_kwargs is not None: if smooth_kwargs is not None:
gaussian_filter(delta1, output=delta1, **smooth_kwargs) gaussian_filter(delta1, output=delta1, **smooth_kwargs)
gaussian_filter(delta2, output=delta2, **smooth_kwargs) gaussian_filter(delta2, output=delta2, **smooth_kwargs)
return delta1, delta2, cellmins, nonzero return delta1, delta2, cellmins, nonzero
def __call__(self, pos1, pos2, mass1, mass2, delta_bckg, def __call__(self, pos1, pos2, mass1, mass2, delta_bckg,
@ -644,9 +638,10 @@ class ParticleOverlap(BaseMatcher):
if smooth_kwargs is not None: if smooth_kwargs is not None:
return calculate_overlap(delta1, delta2, cellmins, delta_bckg, return calculate_overlap(delta1, delta2, cellmins, delta_bckg,
self.box_size, self.bckg_halfsize) self.box_size, self.bckg_halfsize)
# Calculate masses not given
totmass1 = numpy.sum(mass1) if totmass1 is None else totmass1 totmass1 = numpy.sum(mass1) if totmass1 is None else totmass1
totmass2 = numpy.sum(mass2) if totmass2 is None else totmass2 totmass2 = numpy.sum(mass2) if totmass2 is None else totmass2
return calculate_overlap_indxs(delta1, delta2, cellmins, delta_bckg, return calculate_overlap_indxs(delta1, delta2, cellmins, delta_bckg,
nonzero, totmass1, totmass2, nonzero, totmass1, totmass2,
self.box_size, self.bckg_halfsize) self.box_size, self.bckg_halfsize)
@ -681,29 +676,26 @@ def pos2cell(pos, ncells):
def read_nshift(smooth_kwargs): def read_nshift(smooth_kwargs):
""" """
Read off the number of cells to pad the density field if smoothing is Determine the number of cells to pad the density field if smoothing is
applied. Defaults to the ceiling of twice of the smoothing scale. applied. It defaults to the ceiling of three times the smoothing scale.
Parameters Parameters
---------- ----------
smooth_kwargs : kwargs, optional smooth_kwargs : dict or None
Kwargs to be passed to :py:func:`scipy.ndimage.gaussian_filter`. Arguments to be passed to :py:func:`scipy.ndimage.gaussian_filter`.
If `None` no smoothing is applied. If `None`, no smoothing is applied.
Returns Returns
------- -------
nshift : int nshift : int
""" """
if smooth_kwargs is None: return 0 if smooth_kwargs is None else ceil(3 * smooth_kwargs["sigma"])
return 0
else:
return ceil(2 * smooth_kwargs["sigma"])
@jit(nopython=True) @jit(nopython=True)
def fill_delta(delta, xcell, ycell, zcell, xmin, ymin, zmin, weights): def fill_delta(delta, xcell, ycell, zcell, xmin, ymin, zmin, weights):
""" """
Fill array `delta` at the specified indices with their weights. This is a Fill array `delta` by adding `weights` to the specified cells. This is a
JIT implementation. JIT implementation.
Parameters Parameters
@ -715,20 +707,23 @@ def fill_delta(delta, xcell, ycell, zcell, xmin, ymin, zmin, weights):
xmin, ymin, zmin : ints xmin, ymin, zmin : ints
Minimum cell IDs of particles. Minimum cell IDs of particles.
weights : 1-dimensional arrays weights : 1-dimensional arrays
Particle mass. Weights
Returns Returns
------- -------
None None
""" """
for n in range(xcell.size): n_particles = xcell.size
delta[xcell[n] - xmin, ycell[n] - ymin, zcell[n] - zmin] += weights[n]
for n in range(n_particles):
i, j, k = xcell[n] - xmin, ycell[n] - ymin, zcell[n] - zmin
delta[i, j, k] += weights[n]
@jit(nopython=True) @jit(nopython=True)
def fill_delta_indxs(delta, xcell, ycell, zcell, xmin, ymin, zmin, weights): def fill_delta_indxs(delta, xcell, ycell, zcell, xmin, ymin, zmin, weights):
""" """
Fill array `delta` at the specified indices with their weights and return Fill array `delta` by adding `weights` to the specified cells and return
indices where `delta` was assigned a value. This is a JIT implementation. indices where `delta` was assigned a value. This is a JIT implementation.
Parameters Parameters
@ -740,36 +735,41 @@ def fill_delta_indxs(delta, xcell, ycell, zcell, xmin, ymin, zmin, weights):
xmin, ymin, zmin : ints xmin, ymin, zmin : ints
Minimum cell IDs of particles. Minimum cell IDs of particles.
weights : 1-dimensional arrays weights : 1-dimensional arrays
Particle mass. Weights.
Returns Returns
------- -------
cells : 1-dimensional array cells : 1-dimensional array
Indices where `delta` was assigned a value. Indices where `delta` was assigned a value.
""" """
# Array to count non-zero cells n_particles = xcell.size
cells = numpy.full((xcell.size, 3), numpy.nan, numpy.int32) cells = numpy.full((n_particles, 3), numpy.nan, numpy.int32)
count_nonzero = 0 count_nonzero = 0
for n in range(xcell.size):
for n in range(n_particles):
i, j, k = xcell[n] - xmin, ycell[n] - ymin, zcell[n] - zmin i, j, k = xcell[n] - xmin, ycell[n] - ymin, zcell[n] - zmin
# If a cell is zero add it
if delta[i, j, k] == 0: if delta[i, j, k] == 0:
cells[count_nonzero, :] = i, j, k cells[count_nonzero] = i, j, k
count_nonzero += 1 count_nonzero += 1
delta[i, j, k] += weights[n] delta[i, j, k] += weights[n]
return cells[:count_nonzero, :] # Cutoff unassigned places return cells[:count_nonzero]
def get_halolims(pos, ncells, nshift=None): @jit(nopython=True)
def get_halo_cell_limits(pos, ncells, nshift=0):
""" """
Get the lower and upper limit of a halo's positions or cell numbers. Get the lower and upper limit of a halo's cell numbers. Optionally,
floating point positions are also supported. However, in this case `nshift`
must be 0. Be careful, no error will be raised.
Parameters Parameters
---------- ----------
pos : 2-dimensional array pos : 2-dimensional array
Halo particle array. Columns must be `x`, `y`, `z`. Halo particle array. The first three columns must be the cell numbers
corresponding to `x`, `y`, `z`.
ncells : int ncells : int
Number of grid cells of the box along a single dimension. Number of grid cells of the box along a single dimension.
nshift : int, optional nshift : int, optional
@ -778,16 +778,12 @@ def get_halolims(pos, ncells, nshift=None):
Returns Returns
------- -------
mins, maxs : 1-dimensional arrays of shape `(3, )` mins, maxs : 1-dimensional arrays of shape `(3, )`
Minimum and maximum along each axis.
""" """
# Check that in case of `nshift` we have integer positions.
dtype = pos.dtype dtype = pos.dtype
if nshift is not None and dtype.char not in numpy.typecodes["AllInteger"]:
raise TypeError("`nshift` supported only positions are cells.")
nshift = 0 if nshift is None else nshift # To simplify code below
mins = numpy.full(3, numpy.nan, dtype=dtype) mins = numpy.full(3, numpy.nan, dtype=dtype)
maxs = numpy.full(3, numpy.nan, dtype=dtype) maxs = numpy.full(3, numpy.nan, dtype=dtype)
for i in range(3): for i in range(3):
mins[i] = max(numpy.min(pos[:, i]) - nshift, 0) mins[i] = max(numpy.min(pos[:, i]) - nshift, 0)
maxs[i] = min(numpy.max(pos[:, i]) + nshift, ncells - 1) maxs[i] = min(numpy.max(pos[:, i]) + nshift, ncells - 1)
@ -810,27 +806,29 @@ def calculate_overlap(delta1, delta2, cellmins, delta_bckg, box_size,
delta2 : 3-dimensional array delta2 : 3-dimensional array
Density field of the second halo. Density field of the second halo.
cellmins : len-3 tuple cellmins : len-3 tuple
Tuple of left-most cell ID in the full box. Tuple of lower cell ID in the full box.
delta_bckg : 3-dimensional array delta_bckg : 3-dimensional array
Summed background density field of the reference and cross simulations Summed background density field of the reference and cross simulations
calculated with particles assigned to halos at the final snapshot. calculated with particles assigned to halos at the final snapshot.
Assumed to only be sampled in cells :math:`[512, 1536)^3`. Calculated on a grid determined by `bckg_halfsize`.
box_size : int box_size : int
Number of cells in the box. Number of cells in the box.
bckg_halfsize : int bckg_halfsize : int
Number of to each side of the centre of the box to calculate the Background half-size for density field calculation. This is the
density field. This is because in CSiBORG we are only interested in the grid distance from the center of the box to each side over which to
high-resolution region. evaluate the background density field. Must be less than or equal to
half the box size.
Returns Returns
------- -------
overlap : float overlap : float
""" """
totmass = 0.0 # Total mass of halo 1 and halo 2 totmass = 0.0
intersect = 0.0 # Weighted intersecting mass intersect = 0.0
i0, j0, k0 = cellmins # Unpack things
bckg_size = 2 * bckg_halfsize bckg_size = 2 * bckg_halfsize
bckg_offset = box_size // 2 - bckg_halfsize bckg_offset = box_size // 2 - bckg_halfsize
i0, j0, k0 = cellmins
imax, jmax, kmax = delta1.shape imax, jmax, kmax = delta1.shape
for i in range(imax): for i in range(imax):
@ -868,11 +866,11 @@ def calculate_overlap_indxs(delta1, delta2, cellmins, delta_bckg, nonzero,
delta2 : 3-dimensional array delta2 : 3-dimensional array
Density field of the second halo. Density field of the second halo.
cellmins : len-3 tuple cellmins : len-3 tuple
Tuple of left-most cell ID in the full box. Tuple of lower cell ID in the full box.
delta_bckg : 3-dimensional array delta_bckg : 3-dimensional array
Summed background density field of the reference and cross simulations Summed background density field of the reference and cross simulations
calculated with particles assigned to halos at the final snapshot. calculated with particles assigned to halos at the final snapshot.
Assumed to only be sampled in cells :math:`[512, 1536)^3`. Calculated on a grid determined by `bckg_halfsize`.
nonzero : 2-dimensional array of shape `(n_cells, 3)` nonzero : 2-dimensional array of shape `(n_cells, 3)`
Indices of cells that are non-zero of the lower mass halo. Expected to Indices of cells that are non-zero of the lower mass halo. Expected to
be precomputed from `fill_delta_indxs`. be precomputed from `fill_delta_indxs`.
@ -882,19 +880,21 @@ def calculate_overlap_indxs(delta1, delta2, cellmins, delta_bckg, nonzero,
box_size : int box_size : int
Number of cells in the box. Number of cells in the box.
bckg_halfsize : int bckg_halfsize : int
Number of to each side of the centre of the box to calculate the Background half-size for density field calculation. This is the
density field. This is because in CSiBORG we are only interested in the grid distance from the center of the box to each side over which to
high-resolution region. evaluate the background density field. Must be less than or equal to
half the box size.
Returns Returns
------- -------
overlap : float overlap : float
""" """
intersect = 0.0 # Weighted intersecting mass intersect = 0.0
i0, j0, k0 = cellmins # Unpack cell minimas
bckg_size = 2 * bckg_halfsize bckg_size = 2 * bckg_halfsize
bckg_offset = box_size // 2 - bckg_halfsize bckg_offset = box_size // 2 - bckg_halfsize
i0, j0, k0 = cellmins
for n in range(nonzero.shape[0]): for n in range(nonzero.shape[0]):
i, j, k = nonzero[n, :] i, j, k = nonzero[n, :]
m1, m2 = delta1[i, j, k], delta2[i, j, k] m1, m2 = delta1[i, j, k], delta2[i, j, k]
@ -933,9 +933,9 @@ def load_processed_halo(hid, particles, halo_map, hid2map, ncells, nshift):
hid2map : dict hid2map : dict
Dictionary mapping halo IDs to `halo_map` array positions. Dictionary mapping halo IDs to `halo_map` array positions.
ncells : int ncells : int
Number of cells in the original density field. Typically 2048. Number of cells in the box density field.
nshift : int nshift : int
Number of cells to pad the density field. Cell padding for the density field.
Returns Returns
------- -------
@ -952,29 +952,28 @@ def load_processed_halo(hid, particles, halo_map, hid2map, ncells, nshift):
""" """
pos = load_halo_particles(hid, particles, halo_map, hid2map) pos = load_halo_particles(hid, particles, halo_map, hid2map)
pos, mass = pos[:, :3], pos[:, 3] pos, mass = pos[:, :3], pos[:, 3]
pos = pos2cell(pos, ncells) pos = pos2cell(pos, ncells)
totmass = numpy.sum(mass) mins, maxs = get_halo_cell_limits(pos, ncells=ncells, nshift=nshift)
mins, maxs = get_halolims(pos, ncells=ncells, nshift=nshift) return pos, mass, numpy.sum(mass), mins, maxs
return pos, mass, totmass, mins, maxs
def radius_neighbours(knn, X, radiusX, radiusKNN, nmult=1.0, def radius_neighbours(knn, X, radiusX, radiusKNN, nmult=1.0,
enforce_int32=False, verbose=True): enforce_int32=False, verbose=True):
""" """
Find all neigbours of a trained KNN model whose center of mass separation Find all neigbours of a fitted kNN model whose center of mass separation
is less than `nmult` times the sum of their respective radii. is less than `nmult` times the sum of their respective radii.
Parameters Parameters
---------- ----------
knn : :py:class:`sklearn.neighbors.NearestNeighbors` knn : :py:class:`sklearn.neighbors.NearestNeighbors`
Fitted nearest neighbour search. Fitted nearest neighbour search.
X : 2-dimensional array X : 2-dimensional array of shape `(n_samples, 3)`
Array of shape `(n_samples, 3)`, where the latter axis represents Array of halo positions from the cross simulation.
`x`, `y` and `z`.
radiusX: 1-dimensional array of shape `(n_samples, )` radiusX: 1-dimensional array of shape `(n_samples, )`
Patch radii corresponding to haloes in `X`. Lagrangian patch radii corresponding to haloes in `X`.
radiusKNN : 1-dimensional array radiusKNN : 1-dimensional array
Patch radii corresponding to haloes used to train `knn`. Lagrangian patch radii corresponding to haloes used to train the kNN.
nmult : float, optional nmult : float, optional
Multiple of the sum of two radii below which to consider a match. Multiple of the sum of two radii below which to consider a match.
enforce_int32 : bool, optional enforce_int32 : bool, optional
@ -988,22 +987,24 @@ def radius_neighbours(knn, X, radiusX, radiusKNN, nmult=1.0,
indxs : 1-dimensional array `(n_samples,)` of arrays indxs : 1-dimensional array `(n_samples,)` of arrays
Matches to `X` from `knn`. Matches to `X` from `knn`.
""" """
assert X.ndim == 2 and X.shape[1] == 3 # shape of X ok? if X.shape != (radiusX.size, 3):
assert X.shape[0] == radiusX.size # patchX matches X? raise ValueError("Mismatch in shape of `X` or `radiusX`")
assert radiusKNN.size == knn.n_samples_fit_ # patchknn matches the knn? if radiusKNN.size != knn.n_samples_fit_:
raise ValueError("Mismatch in shape of `radiusKNN` or `knn`")
nsamples = X.shape[0] nsamples = len(X)
indxs = [None] * nsamples indxs = [None] * nsamples
patchknn_max = numpy.max(radiusKNN) # Maximum for completeness patchknn_max = numpy.max(radiusKNN)
for i in trange(nsamples) if verbose else range(nsamples): for i in trange(nsamples) if verbose else range(nsamples):
dist, indx = knn.radius_neighbors( dist, indx = knn.radius_neighbors(
X[i, :].reshape(-1, 3), radiusX[i] + patchknn_max, X[i].reshape(1, -1), radiusX[i] + patchknn_max,
sort_results=True) sort_results=True)
# Note that `dist` and `indx` are wrapped in 1-element arrays # Note that `dist` and `indx` are wrapped in 1-element arrays
# so we take the first item where appropriate # so we take the first item where appropriate
mask = (dist[0] / (radiusX[i] + radiusKNN[indx[0]])) < nmult mask = (dist[0] / (radiusX[i] + radiusKNN[indx[0]])) < nmult
indxs[i] = indx[0][mask] indxs[i] = indx[0][mask]
if enforce_int32: if enforce_int32:
indxs[i] = indxs[i].astype(numpy.int32) indxs[i] = indxs[i].astype(numpy.int32)
@ -1048,3 +1049,32 @@ def find_neighbour(nsim0, cats):
cross_hindxs[:, i] = catx["index"][numpy.ravel(ind)] cross_hindxs[:, i] = catx["index"][numpy.ravel(ind)]
return dists, cross_hindxs return dists, cross_hindxs
def cosine_similarity(x, y):
r"""
Calculate the cosine similarity between two Cartesian vectors. Defined
as :math:`\Sum_{i} x_i y_{i} / (|x| * |y|)`.
Parameters
----------
x : 1-dimensional array
The first vector.
y : 1- or 2-dimensional array
The second vector. Can be 2-dimensional of shape `(n_samples, 3)`,
in which case the calculation is broadcasted.
Returns
-------
out : float or 1-dimensional array
"""
if x.ndim != 1:
raise ValueError("`x` must be a 1-dimensional array.")
if y.ndim == 1:
y = y.reshape(1, -1)
out = numpy.sum(x * y, axis=1)
out /= numpy.linalg.norm(x) * numpy.linalg.norm(y, axis=1)
return out[0] if out.size == 1 else out

6
csiborgtools/read/box_units.py
View file

@ -263,8 +263,10 @@ class QuijoteBox(BaseBox):
---------- ----------
nsnap : int nsnap : int
Snapshot number. Snapshot number.
**kwargs : dict nsim : int
Empty keyword arguments. For backwards compatibility. IC realisation index.
paths : py:class`csiborgtools.read.Paths`
Paths manager
""" """
def __init__(self, nsnap, nsim, paths): def __init__(self, nsnap, nsim, paths):

15
csiborgtools/read/halo_cat.py
View file

@ -58,7 +58,8 @@ class BaseCatalogue(ABC):
@nsim.setter @nsim.setter
def nsim(self, nsim): def nsim(self, nsim):
assert isinstance(nsim, int) if not isinstance(nsim, (int, numpy.integer)):
raise TypeError("`nsim` must be an integer!")
self._nsim = nsim self._nsim = nsim
@abstractproperty @abstractproperty
@ -614,9 +615,9 @@ class QuijoteHaloCatalogue(BaseCatalogue):
SFR=False, read_IDs=False) SFR=False, read_IDs=False)
cols = [("x", numpy.float32), ("y", numpy.float32), cols = [("x", numpy.float32), ("y", numpy.float32),
("z", numpy.float32), ("vx", numpy.float32), ("z", numpy.float32), ("fof_vx", numpy.float32),
("vy", numpy.float32), ("vz", numpy.float32), ("fof_vy", numpy.float32), ("fof_vz", numpy.float32),
("group_mass", numpy.float32), ("npart", numpy.int32), ("group_mass", numpy.float32), ("fof_npart", numpy.int32),
("index", numpy.int32)] ("index", numpy.int32)]
data = cols_to_structured(fof.GroupLen.size, cols) data = cols_to_structured(fof.GroupLen.size, cols)
@ -624,9 +625,9 @@ class QuijoteHaloCatalogue(BaseCatalogue):
vel = fof.GroupVel * (1 + self.redshift) vel = fof.GroupVel * (1 + self.redshift)
for i, p in enumerate(["x", "y", "z"]): for i, p in enumerate(["x", "y", "z"]):
data[p] = pos[:, i] data[p] = pos[:, i]
data["v" + p] = vel[:, i] data["fof_v" + p] = vel[:, i]
data["group_mass"] = fof.GroupMass * 1e10 data["group_mass"] = fof.GroupMass * 1e10
data["npart"] = fof.GroupLen data["fof_npart"] = fof.GroupLen
# We want to start indexing from 1. Index 0 is reserved for # We want to start indexing from 1. Index 0 is reserved for
# particles unassigned to any FoF group. # particles unassigned to any FoF group.
data["index"] = 1 + numpy.arange(data.size, dtype=numpy.int32) data["index"] = 1 + numpy.arange(data.size, dtype=numpy.int32)
@ -634,7 +635,7 @@ class QuijoteHaloCatalogue(BaseCatalogue):
if load_initial: if load_initial:
data = self.load_initial(data, paths, "quijote") data = self.load_initial(data, paths, "quijote")
if load_fitted: if load_fitted:
assert nsnap == 4 data = self.load_fitted(data, paths, "quijote")
if load_initial and with_lagpatch: if load_initial and with_lagpatch:
data = data[numpy.isfinite(data["lagpatch_size"])] data = data[numpy.isfinite(data["lagpatch_size"])]

7
csiborgtools/read/paths.py
View file

@ -366,6 +366,7 @@ class Paths:
snapshots : 1-dimensional array snapshots : 1-dimensional array
""" """
simpath = self.snapshots(nsim, simname, tonew=False) simpath = self.snapshots(nsim, simname, tonew=False)
if simname == "csiborg": if simname == "csiborg":
# Get all files in simpath that start with output_ # Get all files in simpath that start with output_
snaps = glob(join(simpath, "output_*")) snaps = glob(join(simpath, "output_*"))
@ -456,6 +457,8 @@ class Paths:
else: else:
raise ValueError(f"Unknown simulation name `{simname}`.") raise ValueError(f"Unknown simulation name `{simname}`.")
try_create_directory(fdir)
fname = f"out_{str(nsim).zfill(5)}_{str(nsnap).zfill(5)}.npy" fname = f"out_{str(nsim).zfill(5)}_{str(nsnap).zfill(5)}.npy"
return join(fdir, fname) return join(fdir, fname)
@ -477,6 +480,7 @@ class Paths:
path : str path : str
""" """
fdir = join(self.postdir, "overlap") fdir = join(self.postdir, "overlap")
try_create_directory(fdir) try_create_directory(fdir)
fname = f"overlap_{str(nsim0).zfill(5)}_{str(nsimx).zfill(5)}.npz" fname = f"overlap_{str(nsim0).zfill(5)}_{str(nsimx).zfill(5)}.npz"
@ -508,9 +512,10 @@ class Paths:
------- -------
path : str path : str
""" """
fdir = join(self.postdir, "environment")
assert kind in ["density", "velocity", "potential", "radvel", assert kind in ["density", "velocity", "potential", "radvel",
"environment"] "environment"]
fdir = join(self.postdir, "environment")
try_create_directory(fdir) try_create_directory(fdir)
if in_rsp: if in_rsp:

22
scripts/fit_halos.py
View file

@ -37,7 +37,8 @@ except ModuleNotFoundError:
def fit_halo(particles, box): def fit_halo(particles, box):
""" """
Fit a single halo from the particle array. Fit a single halo from the particle array. Only halos with more than 100
particles are fitted.
Parameters Parameters
---------- ----------
@ -59,12 +60,17 @@ def fit_halo(particles, box):
for i, v in enumerate(["vx", "vy", "vz"]): for i, v in enumerate(["vx", "vy", "vz"]):
out[v] = numpy.average(halo.vel[:, i], weights=halo["M"]) out[v] = numpy.average(halo.vel[:, i], weights=halo["M"])
m200c, r200c, cm = halo.spherical_overdensity_mass(200, kind="crit", if out["npart"] < 100:
maxiter=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["m200c"] = m200c
out["r200c"] = r200c out["r200c"] = r200c
out["lambda200c"] = halo.lambda_bullock(cm, r200c) out["lambda200c"] = halo.lambda_bullock(angmom, m200c, r200c)
out["conc"] = halo.nfw_concentration(cm, r200c) out["conc"] = halo.nfw_concentration(dist, r200c)
return out return out
@ -81,9 +87,6 @@ def _main(nsim, simname, verbose):
verbose : bool verbose : bool
Verbosity flag. Verbosity flag.
""" """
# if simname == "quijote":
# raise NotImplementedError("Quijote not implemented yet.")
cols = [("index", numpy.int32), cols = [("index", numpy.int32),
("npart", numpy.int32), ("npart", numpy.int32),
("totpartmass", numpy.float32), ("totpartmass", numpy.float32),
@ -116,7 +119,6 @@ def _main(nsim, simname, verbose):
for i in trange(len(cat)) if verbose else range(len(cat)): for i in trange(len(cat)) if verbose else range(len(cat)):
hid = cat["index"][i] hid = cat["index"][i]
out["index"][i] = hid out["index"][i] = hid
# print("i = ", i)
part = csiborgtools.read.load_halo_particles(hid, particles, halo_map, part = csiborgtools.read.load_halo_particles(hid, particles, halo_map,
hid2map) hid2map)
# Skip if no particles. # Skip if no particles.
@ -125,7 +127,7 @@ def _main(nsim, simname, verbose):
_out = fit_halo(part, box) _out = fit_halo(part, box)
for key in _out.keys(): for key in _out.keys():
out[key][i] = _out[key] out[key][i] = _out.get(key, numpy.nan)
fout = paths.structfit(nsnap, nsim, simname) fout = paths.structfit(nsnap, nsim, simname)
if verbose: if verbose:

2
scripts/fit_init.py
View file

@ -66,7 +66,7 @@ def _main(nsim, simname, verbose):
if simname == "csiborg": if simname == "csiborg":
cat = csiborgtools.read.CSiBORGHaloCatalogue( cat = csiborgtools.read.CSiBORGHaloCatalogue(
nsim, paths, rawdata=True, load_fitted=False, load_initial=False) nsim, paths, bounds=None, load_fitted=False, load_initial=False)
else: else:
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)

49
scripts/match_all.py
View file

@ -11,10 +11,7 @@
# 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.
""" """A script to match all IC pairs of a simulation."""
Script to match all pairs of CSiBORG simulations. Mathches main haloes whose
mass is above 1e12 solar masses.
"""
from argparse import ArgumentParser from argparse import ArgumentParser
from distutils.util import strtobool from distutils.util import strtobool
from itertools import combinations from itertools import combinations
@ -34,10 +31,15 @@ except ModuleNotFoundError:
import csiborgtools import csiborgtools
def get_combs(): def get_combs(simname):
""" """
Get the list of all pairs of simulations, then permute them with a known Get the list of all pairs of IC indices and permute them with a fixed
seed to minimise loading the same files simultaneously. seed.
Parameters
----------
simname : str
Simulation name.
Returns Returns
------- -------
@ -45,38 +47,49 @@ def get_combs():
List of pairs of simulations. List of pairs of simulations.
""" """
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring) paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
ics = paths.get_ics("csiborg") combs = list(combinations(paths.get_ics(simname), 2))
combs = list(combinations(ics, 2))
Random(42).shuffle(combs) Random(42).shuffle(combs)
return combs return combs
def do_work(comb): def main(comb, simname, sigma, verbose):
""" """
Match a pair of simulations. Match a pair of simulations.
Parameters Parameters
---------- ----------
comb : tuple comb : tuple
Pair of simulations. Pair of simulation IC indices.
simname : str
Simulation name.
sigma : float
Smoothing scale in number of grid cells.
verbose : bool
Verbosity flag.
Returns Returns
------- -------
None None
""" """
nsim0, nsimx = comb nsim0, nsimx = comb
pair_match(nsim0, nsimx, args.sigma, args.smoothen, args.verbose) pair_match(nsim0, nsimx, simname, sigma, verbose)
if __name__ == "__main__": if __name__ == "__main__":
parser = ArgumentParser() parser = ArgumentParser()
parser.add_argument("--sigma", type=float, default=None) parser.add_argument("--simname", type=str, help="Simulation name.",
parser.add_argument("--smoothen", type=lambda x: bool(strtobool(x)), choices=["csiborg", "quijote"])
default=None) parser.add_argument("--sigma", type=float, default=0,
help="Smoothing scale in number of grid cells.")
parser.add_argument("--verbose", type=lambda x: bool(strtobool(x)), parser.add_argument("--verbose", type=lambda x: bool(strtobool(x)),
default=False) default=False, help="Verbosity flag.")
args = parser.parse_args() args = parser.parse_args()
comm = MPI.COMM_WORLD
combs = get_combs() combs = get_combs()
work_delegation(do_work, combs, comm, master_verbose=True)
def _main(comb):
main(comb, args.simname, args.sigma, args.verbose)
work_delegation(_main, combs, MPI.COMM_WORLD)

136
scripts/match_singlematch.py
View file

@ -11,7 +11,13 @@
# 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.
"""A script to calculate overlap between two CSiBORG realisations.""" """
A script to calculate overlap between two IC realisations of the same
simulation. The matching is performed for haloes whose total particles mass is
- CSiBORG: > 1e13 Msun/h,
- Quijote: > 1e14 Msun/h,
since Quijote has much lower resolution than CSiBORG.
"""
from argparse import ArgumentParser from argparse import ArgumentParser
from copy import deepcopy from copy import deepcopy
from datetime import datetime from datetime import datetime
@ -29,95 +35,123 @@ except ModuleNotFoundError:
import csiborgtools import csiborgtools
def pair_match(nsim0, nsimx, sigma, smoothen, verbose): def pair_match(nsim0, nsimx, simname, sigma, verbose):
# TODO fix this. """
simname = "csiborg" Calculate overlaps between two simulations.
overlapper_kwargs = {"box_size": 512, "bckg_halfsize": 475}
from csiborgtools.read import CSiBORGHaloCatalogue, read_h5
Parameters
----------
nsim0 : int
The reference simulation IC index.
nsimx : int
The cross simulation IC index.
simname : str
Simulation name.
sigma : float
Smoothing scale in number of grid cells.
verbose : bool
Verbosity flag.
Returns
-------
None
"""
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring) paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
smooth_kwargs = {"sigma": sigma, "mode": "constant", "cval": 0.0} smooth_kwargs = {"sigma": sigma, "mode": "wrap"}
overlapper = csiborgtools.match.ParticleOverlap(**overlapper_kwargs)
matcher = csiborgtools.match.RealisationsMatcher(**overlapper_kwargs)
# Load the raw catalogues (i.e. no selection) including the initial CM if simname == "csiborg":
# positions and the particle archives. overlapper_kwargs = {"box_size": 2048, "bckg_halfsize": 475}
bounds = {"totpartmass": (1e12, None)} mass_kind = "fof_totpartmass"
cat0 = CSiBORGHaloCatalogue(nsim0, paths, load_initial=True, bounds=bounds, bounds = {mass_kind: (1e13, None)}
with_lagpatch=True, load_clumps_cat=True) cat0 = csiborgtools.read.CSiBORGHaloCatalogue(
catx = CSiBORGHaloCatalogue(nsimx, paths, load_initial=True, bounds=bounds, nsim0, paths, bounds=bounds, load_fitted=False,
with_lagpatch=True, load_clumps_cat=True) with_lagpatch=True)
catx = csiborgtools.read.CSiBORGHaloCatalogue(
nsimx, paths, bounds=bounds, load_fitted=False,
with_lagpatch=True)
elif simname == "quijote":
overlapper_kwargs = {"box_size": 512, "bckg_halfsize": 256}
mass_kind = "group_mass"
bounds = {mass_kind: (1e14, None)}
cat0 = csiborgtools.read.QuijoteHaloCatalogue(
nsim0, paths, 4, load_fitted=False, with_lagpatch=True)
catx = csiborgtools.read.QuijoteHaloCatalogue(
nsimx, paths, 4, load_fitted=False, with_lagpatch=True)
else:
raise ValueError(f"Unknown simulation name: `{simname}`.")
clumpmap0 = read_h5(paths.particles(nsim0, simname))["clumpmap"] halomap0 = csiborgtools.read.read_h5(
parts0 = read_h5(paths.initmatch(nsim0, simname, "particles"))["particles"] paths.particles(nsim0, simname))["halomap"]
clid2map0 = {clid: i for i, clid in enumerate(clumpmap0[:, 0])} parts0 = csiborgtools.read.read_h5(
paths.initmatch(nsim0, simname, "particles"))["particles"]
hid2map0 = {hid: i for i, hid in enumerate(halomap0[:, 0])}
clumpmapx = read_h5(paths.particles(nsimx, simname))["clumpmap"] halomapx = csiborgtools.read.read_h5(
partsx = read_h5(paths.initmatch(nsimx, simname, "particles"))["particles"] paths.particles(nsimx, simname))["halomap"]
clid2mapx = {clid: i for i, clid in enumerate(clumpmapx[:, 0])} partsx = csiborgtools.read.read_h5(
paths.initmatch(nsimx, simname, "particles"))["particles"]
hid2mapx = {hid: i for i, hid in enumerate(halomapx[:, 0])}
# We generate the background density fields. Loads halos's particles one by
# one from the archive, concatenates them and calculates the NGP density
# field.
if verbose: if verbose:
print(f"{datetime.now()}: generating the background density fields.", print(f"{datetime.now()}: calculating the background density fields.",
flush=True) flush=True)
delta_bckg = overlapper.make_bckg_delta(parts0, clumpmap0, clid2map0, cat0, overlapper = csiborgtools.match.ParticleOverlap(**overlapper_kwargs)
delta_bckg = overlapper.make_bckg_delta(parts0, halomap0, hid2map0, cat0,
verbose=verbose) verbose=verbose)
delta_bckg = overlapper.make_bckg_delta(partsx, clumpmapx, clid2mapx, catx, delta_bckg = overlapper.make_bckg_delta(partsx, halomapx, hid2mapx, catx,
delta=delta_bckg, verbose=verbose) delta=delta_bckg, verbose=verbose)
# We calculate the overlap between the NGP fields.
if verbose: if verbose:
print(f"{datetime.now()}: crossing the simulations.", flush=True) print(f"{datetime.now()}: NGP crossing the simulations.", flush=True)
matcher = csiborgtools.match.RealisationsMatcher(
mass_kind=mass_kind, **overlapper_kwargs)
match_indxs, ngp_overlap = matcher.cross(cat0, catx, parts0, partsx, match_indxs, ngp_overlap = matcher.cross(cat0, catx, parts0, partsx,
clumpmap0, clumpmapx, delta_bckg, halomap0, halomapx, delta_bckg,
verbose=verbose) verbose=verbose)
# We wish to store the halo IDs of the matches, not their array positions
# in the catalogues # We want to store the halo IDs of the matches, not their array positions
# in the catalogues.
match_hids = deepcopy(match_indxs) match_hids = deepcopy(match_indxs)
for i, matches in enumerate(match_indxs): for i, matches in enumerate(match_indxs):
for j, match in enumerate(matches): for j, match in enumerate(matches):
match_hids[i][j] = catx["index"][match] match_hids[i][j] = catx["index"][match]
fout = paths.overlap(nsim0, nsimx, smoothed=False) fout = paths.overlap(nsim0, nsimx, smoothed=False)
if verbose:
print(f"{datetime.now()}: saving to ... `{fout}`.", flush=True)
numpy.savez(fout, ref_hids=cat0["index"], match_hids=match_hids, numpy.savez(fout, ref_hids=cat0["index"], match_hids=match_hids,
ngp_overlap=ngp_overlap) ngp_overlap=ngp_overlap)
if verbose:
print(f"{datetime.now()}: calculated NGP overlap, saved to {fout}.",
flush=True)
if not smoothen: if not sigma > 0:
quit() return
# We now smoothen up the background density field for the smoothed overlap
# calculation.
if verbose: if verbose:
print(f"{datetime.now()}: smoothing the background field.", flush=True) print(f"{datetime.now()}: smoothing the background field.", flush=True)
gaussian_filter(delta_bckg, output=delta_bckg, **smooth_kwargs) gaussian_filter(delta_bckg, output=delta_bckg, **smooth_kwargs)
# We calculate the smoothed overlap for the pairs whose NGP overlap is > 0. # We calculate the smoothed overlap for the pairs whose NGP overlap is > 0.
smoothed_overlap = matcher.smoothed_cross(cat0, catx, parts0, partsx, smoothed_overlap = matcher.smoothed_cross(cat0, catx, parts0, partsx,
clumpmap0, clumpmapx, delta_bckg, halomap0, halomapx, delta_bckg,
match_indxs, smooth_kwargs, match_indxs, smooth_kwargs,
verbose=verbose) verbose=verbose)
fout = paths.overlap(nsim0, nsimx, smoothed=True) fout = paths.overlap(nsim0, nsimx, smoothed=True)
numpy.savez(fout, smoothed_overlap=smoothed_overlap, sigma=sigma)
if verbose: if verbose:
print(f"{datetime.now()}: calculated smoothing, saved to {fout}.", print(f"{datetime.now()}: saving to ... `{fout}`.", flush=True)
flush=True) numpy.savez(fout, smoothed_overlap=smoothed_overlap, sigma=sigma)
if __name__ == "__main__": if __name__ == "__main__":
parser = ArgumentParser() parser = ArgumentParser()
parser.add_argument("--nsim0", type=int) parser.add_argument("--nsim0", type=int,
parser.add_argument("--nsimx", type=int) help="Reference simulation IC index.")
parser.add_argument("--sigma", type=float, default=None) parser.add_argument("--nsimx", type=int,
parser.add_argument("--smoothen", type=lambda x: bool(strtobool(x)), help="Cross simulation IC index.")
default=None) parser.add_argument("--simname", type=str, help="Simulation name.")
parser.add_argument("--sigma", type=float, default=0,
help="Smoothing scale in number of grid cells.")
parser.add_argument("--verbose", type=lambda x: bool(strtobool(x)), parser.add_argument("--verbose", type=lambda x: bool(strtobool(x)),
default=False) default=False, help="Verbosity flag.")
args = parser.parse_args() args = parser.parse_args()
pair_match(args.nsim0, args.nsimx, args.sigma, args.smoothen, args.verbose) pair_match(args.nsim0, args.nsimx, args.simname, args.sigma, args.verbose)