mirror of
https://github.com/Richard-Sti/csiborgtools.git
synced 2024-12-22 07:08:01 +00:00
Update units to be consistent. (#78)
* Fix Quijote units * Updates to units * Fix how things are loaded * Updating definitions & conventions * Clear up how fiducial observers in quijote work * Refactorize array manip * Move function definition * More code refactoring * Remove unused argument * Remove `convert_from_box` * Make a note * Converting particle units * Add notes about units * Remove box constants * Add rho_crit0 * Fix spherical overdensity mass units * Refactor more code * Edit catalogue kwargs * Edit the docstring * Edit bounds * Add new checks for empty array * Remove unused import * Remove old code * Remove old function * Update real 2 redshift * Clear up the RSP conv * Add comments * Add some units
This commit is contained in:
Richard Stiskalek
GitHub
parent
fb4b4edf19
commit
acb8d9571c
23 changed files with 695 additions and 1079 deletions
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@ -181,6 +181,8 @@ class DensityField(BaseField):
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vel[:, [0, 2]] = vel[:, [2, 0]]
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if in_rsp:
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raise NotImplementedError("Redshift space needs to be fixed.")
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# TODO change how called + units.
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pos = real2redshift(pos, vel, [0.5, 0.5, 0.5], self.box,
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in_box_units=True, periodic_wrap=True,
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make_copy=False)
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@ -255,6 +255,8 @@ def field2rsp(*fields, parts, vobs, box, nbatch=30, flip_partsxz=True,
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pos[:, [0, 2]] = pos[:, [2, 0]]
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vel[:, [0, 2]] = vel[:, [2, 0]]
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# Then move the particles to redshift space.
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# TODO here the function is now called differently and pos assumes
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# different units.
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rsp_pos = real2redshift(pos, vel, [0.5, 0.5, 0.5], box,
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in_box_units=True, periodic_wrap=True,
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make_copy=True)
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@ -14,4 +14,3 @@
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# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
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from .halo import (Halo, delta2ncells, center_of_mass, # noqa
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periodic_distance, shift_to_center_of_box, number_counts) # noqa
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from .utils import split_jobs # noqa
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@ -20,6 +20,11 @@ from numba import jit
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from scipy.optimize import minimize
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GRAV = 6.6743e-11 # m^3 kg^-1 s^-2
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MSUN = 1.988409870698051e+30 # kg
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MPC2M = 3.0856775814671916e+22 # 1 Mpc is this many meters
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class BaseStructure(ABC):
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"""
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Basic structure object for handling operations on its particles.
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@ -85,7 +90,7 @@ class BaseStructure(ABC):
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"""
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return numpy.vstack([self[p] for p in ("vx", "vy", "vz")]).T
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def spherical_overdensity_mass(self, delta_mult, kind="crit", tol=1e-8,
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def spherical_overdensity_mass(self, delta_mult, kind="crit", rtol=1e-8,
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maxiter=100, npart_min=10):
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r"""
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Calculate spherical overdensity mass and radius via the iterative
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@ -97,7 +102,7 @@ class BaseStructure(ABC):
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Overdensity multiple.
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kind : str, optional
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Either `crit` or `matter`, for critical or matter overdensity
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tol : float, optional
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rtol : float, optional
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Tolerance for the change in the center of mass or radius.
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maxiter : int, optional
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Maximum number of iterations.
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@ -107,33 +112,34 @@ class BaseStructure(ABC):
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Returns
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-------
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mass : float
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The requested spherical overdensity mass.
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The requested spherical overdensity mass in :math:`M_\odot / h`.
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rad : float
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The radius of the sphere enclosing the requested overdensity.
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The radius of the sphere enclosing the requested overdensity in box
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units.
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cm : 1-dimensional array of shape `(3, )`
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The center of mass of the sphere enclosing the requested
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overdensity.
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overdensity in box units.
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"""
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assert kind in ["crit", "matter"]
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rho = delta_mult * self.box.box_rhoc
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# Calculate density based on the provided kind
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rho = delta_mult * self.box.rho_crit0
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if kind == "matter":
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rho *= self.box.Om
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pos = self.pos
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mass = self["M"]
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# Initial guesses
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pos, mass = self.pos, self["M"]
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# Initial estimates for center of mass and radius
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init_cm = center_of_mass(pos, mass, boxsize=1)
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init_rad = mass_to_radius(numpy.sum(mass), rho) * 1.5
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init_rad = self.box.mpc2box(mass_to_radius(numpy.sum(mass), rho) * 1.5)
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rad = init_rad
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cm = numpy.copy(init_cm)
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rad, cm = init_rad, numpy.copy(init_cm)
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success = False
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for __ in range(maxiter):
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# Calculate the distance of each particle from the current guess.
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for _ in range(maxiter):
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dist = periodic_distance(pos, cm, boxsize=1)
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within_rad = dist <= rad
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# Heuristic reset if there are too few enclosed particles.
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# Heuristic reset if too few enclosed particles
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if numpy.sum(within_rad) < npart_min:
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js = numpy.random.choice(len(self), len(self), replace=True)
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cm = center_of_mass(pos[js], mass[js], boxsize=1)
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@ -141,41 +147,40 @@ class BaseStructure(ABC):
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dist = periodic_distance(pos, cm, boxsize=1)
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within_rad = dist <= rad
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# Calculate the enclosed mass for the current CM and radius.
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enclosed_mass = numpy.sum(mass[within_rad])
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# If there are still too few particles, then skip this
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# iteration.
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if numpy.sum(within_rad) < npart_min:
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continue
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# Calculate the new CM and radius from this mass.
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new_rad = mass_to_radius(enclosed_mass, rho)
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enclosed_mass = numpy.sum(mass[within_rad])
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new_rad = self.box.mpc2box(mass_to_radius(enclosed_mass, rho))
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new_cm = center_of_mass(pos[within_rad], mass[within_rad],
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boxsize=1)
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# Update the CM and radius
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prev_cm, cm = cm, new_cm
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prev_rad, rad = rad, new_rad
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# Check convergence based on center of mass and radius
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cm_conv = numpy.linalg.norm(cm - new_cm) < rtol
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rad_conv = abs(rad - new_rad) < rtol
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# Check if the change in CM and radius is small enough.
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dcm = numpy.linalg.norm(cm - prev_cm)
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drad = abs(rad - prev_rad)
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if dcm < tol or drad < tol:
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success = True
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break
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if cm_conv or rad_conv:
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return enclosed_mass, rad, cm
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if not success:
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return numpy.nan, numpy.nan, numpy.full(3, numpy.nan)
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cm, rad = new_cm, new_rad
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return enclosed_mass, rad, cm
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# Return NaN values if no convergence after max iterations
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return numpy.nan, numpy.nan, numpy.full(3, numpy.nan, numpy.float32)
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def angular_momentum(self, ref, rad, npart_min=10):
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"""
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r"""
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Calculate angular momentum around a reference point using all particles
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within a radius. The angular momentum is returned in box units.
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within a radius. Units are
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:math:`(M_\odot / h) (\mathrm{Mpc} / h) \mathrm{km} / \mathrm{s}`.
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Parameters
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----------
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ref : 1-dimensional array of shape `(3, )`
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Reference point.
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Reference point in box units.
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rad : float
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Radius around the reference point.
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Radius around the reference point in box units.
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npart_min : int, optional
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Minimum number of enclosed particles for a radius to be
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considered trustworthy.
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@ -184,16 +189,21 @@ class BaseStructure(ABC):
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-------
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angmom : 1-dimensional array or shape `(3, )`
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"""
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pos = self.pos
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mask = periodic_distance(pos, ref, boxsize=1) < rad
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if numpy.sum(mask) < npart_min:
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return numpy.full(3, numpy.nan)
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# Calculate the distance of each particle from the reference point.
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distances = periodic_distance(self.pos, ref, boxsize=1)
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mass = self["M"][mask]
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pos = pos[mask]
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vel = self.vel[mask]
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# Velocitities in the object CM frame
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# Filter particles within the provided radius.
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mask = distances < rad
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if numpy.sum(mask) < npart_min:
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return numpy.full(3, numpy.nan, numpy.float32)
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mass, pos, vel = self["M"][mask], self.pos[mask], self.vel[mask]
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# Convert positions to Mpc / h and center around the reference point.
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pos = self.box.box2mpc(pos) - ref
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# Adjust velocities to be in the CM frame.
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vel -= numpy.average(vel, axis=0, weights=mass)
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# Calculate angular momentum.
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return numpy.sum(mass[:, numpy.newaxis] * numpy.cross(pos, vel),
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axis=0)
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@ -205,9 +215,9 @@ class BaseStructure(ABC):
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Parameters
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----------
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ref : 1-dimensional array of shape `(3, )`
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Reference point.
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Reference point in box units.
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rad : float
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Radius around the reference point.
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Radius around the reference point in box units.
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Returns
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-------
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@ -219,12 +229,18 @@ class BaseStructure(ABC):
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Bullock, J. S.; Dekel, A.; Kolatt, T. S.; Kravtsov, A. V.;
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Klypin, A. A.; Porciani, C.; Primack, J. R.
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"""
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pos = self.pos
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mask = periodic_distance(pos, ref, boxsize=1) < rad
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mass = numpy.sum(self["M"][mask])
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circvel = numpy.sqrt(self.box.box_G * mass / rad)
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angmom_norm = numpy.linalg.norm(self.angular_momentum(ref, rad))
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return angmom_norm / (numpy.sqrt(2) * mass * circvel * rad)
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# Filter particles within the provided radius
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mask = periodic_distance(self.pos, ref, boxsize=1) < rad
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# Calculate the total mass of the enclosed particles
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enclosed_mass = numpy.sum(self["M"][mask])
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# Convert the radius from box units to Mpc/h
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rad_mpc = self.box.box2mpc(rad)
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# Circular velocity in km/s
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circvel = (GRAV * enclosed_mass * MSUN / (rad_mpc * MPC2M))**0.5 * 1e-3
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# Magnitude of the angular momentum
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l_norm = numpy.linalg.norm(self.angular_momentum(ref, rad))
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# Compute and return the Bullock spin parameter
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return l_norm / (numpy.sqrt(2) * enclosed_mass * circvel * rad_mpc)
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def nfw_concentration(self, ref, rad, conc_min=1e-3, npart_min=10):
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"""
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@ -234,9 +250,9 @@ class BaseStructure(ABC):
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Parameters
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----------
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ref : 1-dimensional array of shape `(3, )`
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Reference point.
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Reference point in box units.
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rad : float
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Radius around the reference point.
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Radius around the reference point in box units.
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conc_min : float
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Minimum concentration limit.
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npart_min : int, optional
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@ -247,42 +263,43 @@ class BaseStructure(ABC):
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-------
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conc : float
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"""
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pos = self.pos
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dist = periodic_distance(pos, ref, boxsize=1)
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dist = periodic_distance(self.pos, ref, boxsize=1)
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mask = dist < rad
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if numpy.sum(mask) < npart_min:
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return numpy.nan
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dist = dist[mask]
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weight = self["M"][mask]
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dist, weight = dist[mask], self["M"][mask]
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weight /= numpy.mean(weight)
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# We do the minimization in log space
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def negll_nfw_concentration(log_c, xs, weight):
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# Objective function for minimization
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def negll_nfw_concentration(log_c, xs, w):
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c = 10**log_c
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ll = xs / (1 + c * xs)**2 * c**2
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ll *= (1 + c) / ((1 + c) * numpy.log(1 + c) - c)
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ll = numpy.sum(numpy.log(weight * ll))
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ll = numpy.sum(numpy.log(w * ll))
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return -ll
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res = minimize(negll_nfw_concentration, x0=1.5,
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initial_guess = 1.5
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res = minimize(negll_nfw_concentration, x0=initial_guess,
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args=(dist / rad, weight, ), method='Nelder-Mead',
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bounds=((numpy.log10(conc_min), 5),))
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if not res.success:
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return numpy.nan
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res = 10**res["x"][0]
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if res < conc_min or numpy.isclose(res, conc_min):
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conc_value = 10**res["x"][0]
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if conc_value < conc_min or numpy.isclose(conc_value, conc_min):
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return numpy.nan
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return res
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return conc_value
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def __getitem__(self, key):
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keys = ['x', 'y', 'z', 'vx', 'vy', 'vz', 'M']
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if key not in keys:
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key_to_index = {'x': 0, 'y': 1, 'z': 2,
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'vx': 3, 'vy': 4, 'vz': 5, 'M': 6}
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if key not in key_to_index:
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raise RuntimeError(f"Invalid key `{key}`!")
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return self.particles[:, keys.index(key)]
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return self.particles[:, key_to_index[key]]
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def __len__(self):
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return self.particles.shape[0]
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@ -304,7 +321,6 @@ class Halo(BaseStructure):
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def __init__(self, particles, box):
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self.particles = particles
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# self.info = info
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self.box = box
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@ -1,43 +0,0 @@
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# Copyright (C) 2022 Richard Stiskalek, Deaglan Bartlett
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# This program is free software; you can redistribute it and/or modify it
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# under the terms of the GNU General Public License as published by the
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# Free Software Foundation; either version 3 of the License, or (at your
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# option) any later version.
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#
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# This program is distributed in the hope that it will be useful, but
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# WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
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# Public License for more details.
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#
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# You should have received a copy of the GNU General Public License along
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# with this program; if not, write to the Free Software Foundation, Inc.,
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# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
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"""Fitting utility functions."""
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import numpy
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def split_jobs(njobs, ncpu):
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"""
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Split `njobs` amongst `ncpu`.
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Parameters
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----------
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njobs : int
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Number of jobs.
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ncpu : int
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Number of CPUs.
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Returns
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-------
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jobs : list of lists of integers
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Outer list of each CPU and inner lists for CPU's jobs.
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"""
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njobs_per_cpu, njobs_remainder = divmod(njobs, ncpu)
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jobs = numpy.arange(njobs_per_cpu * ncpu).reshape((njobs_per_cpu, ncpu)).T
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jobs = jobs.tolist()
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for i in range(njobs_remainder):
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jobs[i].append(njobs_per_cpu * ncpu + i)
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return jobs
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@ -14,6 +14,4 @@
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# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
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from .match import (ParticleOverlap, RealisationsMatcher, # noqa
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calculate_overlap, calculate_overlap_indxs,
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cosine_similarity)
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from .nearest_neighbour import find_neighbour # noqa
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from .utils import concatenate_parts # noqa
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cosine_similarity, find_neighbour)
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@ -1008,3 +1008,43 @@ def radius_neighbours(knn, X, radiusX, radiusKNN, nmult=1.0,
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indxs[i] = indxs[i].astype(numpy.int32)
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return numpy.asarray(indxs, dtype=object)
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def find_neighbour(nsim0, cats):
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"""
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Find the nearest neighbour of halos from a reference catalogue indexed
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`nsim0` in the remaining simulations.
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Parameters
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----------
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nsim0 : int
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Index of the reference simulation.
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cats : dict
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Dictionary of halo catalogues. Keys must be the simulation indices.
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Returns
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-------
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dists : 2-dimensional array of shape `(nhalos, len(cats) - 1)`
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Distances to the nearest neighbour.
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cross_hindxs : 2-dimensional array of shape `(nhalos, len(cats) - 1)`
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Halo indices of the nearest neighbour.
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"""
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cat0 = cats[nsim0]
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X = cat0.position(in_initial=False, subtract_observer=True)
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nhalos = X.shape[0]
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num_cats = len(cats) - 1
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dists = numpy.full((nhalos, num_cats), numpy.nan, dtype=numpy.float32)
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cross_hindxs = numpy.full((nhalos, num_cats), numpy.nan, dtype=numpy.int32)
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# Filter out the reference simulation from the dictionary
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filtered_cats = {k: v for k, v in cats.items() if k != nsim0}
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for i, catx in enumerate(filtered_cats):
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dist, ind = catx.nearest_neighbours(X, radius=1, in_initial=False,
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knearest=True)
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dists[:, i] = numpy.ravel(dist)
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cross_hindxs[:, i] = catx["index"][numpy.ravel(ind)]
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return dists, cross_hindxs
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@ -1,56 +0,0 @@
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# Copyright (C) 2022 Richard Stiskalek
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# This program is free software; you can redistribute it and/or modify it
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# under the terms of the GNU General Public License as published by the
|
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# Free Software Foundation; either version 3 of the License, or (at your
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# option) any later version.
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#
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# This program is distributed in the hope that it will be useful, but
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# WITHOUT ANY WARRANTY; without even the implied warranty of
|
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
|
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# Public License for more details.
|
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#
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||||
# You should have received a copy of the GNU General Public License along
|
||||
# with this program; if not, write to the Free Software Foundation, Inc.,
|
||||
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
|
||||
"""
|
||||
Tools for finding the nearest neighbours of reference simulation haloes from
|
||||
cross simulations.
|
||||
"""
|
||||
import numpy
|
||||
|
||||
|
||||
def find_neighbour(nsim0, cats):
|
||||
"""
|
||||
Find the nearest neighbour of halos in `cat0` in `catx`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
nsim0 : int
|
||||
Index of the reference simulation.
|
||||
cats : dict
|
||||
Dictionary of halo catalogues. Keys must be the simulation indices.
|
||||
|
||||
Returns
|
||||
-------
|
||||
dists : 2-dimensional array of shape `(nhalos, len(cats) - 1)`
|
||||
Distances to the nearest neighbour.
|
||||
cross_hindxs : 2-dimensional array of shape `(nhalos, len(cats) - 1)`
|
||||
Halo indices of the nearest neighbour.
|
||||
"""
|
||||
cat0 = cats[nsim0]
|
||||
X = cat0.position(in_initial=False)
|
||||
shape = (X.shape[0], len(cats) - 1)
|
||||
dists = numpy.full(shape, numpy.nan, dtype=numpy.float32)
|
||||
cross_hindxs = numpy.full(shape, numpy.nan, dtype=numpy.int32)
|
||||
|
||||
i = 0
|
||||
for nsimx, catx in cats.items():
|
||||
if nsimx == nsim0:
|
||||
continue
|
||||
dist, ind = catx.nearest_neighbours(X, radius=1, in_initial=False,
|
||||
knearest=True)
|
||||
dists[:, i] = dist.reshape(-1,)
|
||||
cross_hindxs[:, i] = catx["index"][ind.reshape(-1,)]
|
||||
i += 1
|
||||
|
||||
return dists, cross_hindxs
|
||||
|
|
@ -1,67 +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.
|
||||
"""Useful functions."""
|
||||
import numpy
|
||||
|
||||
|
||||
def concatenate_parts(list_parts, include_velocities=False):
|
||||
"""
|
||||
Concatenate a list of particle arrays into a single array.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
list_parts : list of structured arrays
|
||||
List of particle arrays.
|
||||
include_velocities : bool, optional
|
||||
Whether to include velocities in the output array.
|
||||
|
||||
Returns
|
||||
-------
|
||||
parts_out : structured array
|
||||
"""
|
||||
# Count how large array will be needed
|
||||
N = 0
|
||||
for part in list_parts:
|
||||
N += part.size
|
||||
# Infer dtype of positions
|
||||
if list_parts[0]["x"].dtype.char in numpy.typecodes["AllInteger"]:
|
||||
posdtype = numpy.int32
|
||||
else:
|
||||
posdtype = numpy.float32
|
||||
|
||||
# We pre-allocate an empty array. By default, we include just particle
|
||||
# positions, which may be specified by cell IDs if integers, and masses.
|
||||
# Additionally also outputs velocities.
|
||||
if include_velocities:
|
||||
dtype = {
|
||||
"names": ["x", "y", "z", "vx", "vy", "vz", "M"],
|
||||
"formats": [posdtype] * 3 + [numpy.float32] * 4,
|
||||
}
|
||||
else:
|
||||
dtype = {
|
||||
"names": ["x", "y", "z", "M"],
|
||||
"formats": [posdtype] * 3 + [numpy.float32],
|
||||
}
|
||||
parts_out = numpy.full(N, numpy.nan, dtype)
|
||||
|
||||
# Fill it one clump by another
|
||||
start = 0
|
||||
for parts in list_parts:
|
||||
end = start + parts.size
|
||||
for p in dtype["names"]:
|
||||
parts_out[p][start:end] = parts[p]
|
||||
start = end
|
||||
|
||||
return parts_out
|
||||
|
|
@ -26,6 +26,5 @@ from .pk_summary import PKReader # noqa
|
|||
from .readsim import (MmainReader, CSiBORGReader, QuijoteReader, halfwidth_mask, # noqa
|
||||
load_halo_particles) # noqa
|
||||
from .tpcf_summary import TPCFReader # noqa
|
||||
from .utils import (M200_to_R200, cartesian_to_radec, # noqa
|
||||
cols_to_structured, radec_to_cartesian, read_h5,
|
||||
real2redshift) # noqa
|
||||
from .utils import (cartesian_to_radec, cols_to_structured, radec_to_cartesian, # noqa
|
||||
read_h5, real2redshift) # noqa
|
||||
|
|
|
|||
|
|
@ -17,29 +17,12 @@ Simulation box unit transformations.
|
|||
"""
|
||||
from abc import ABC, abstractmethod, abstractproperty
|
||||
|
||||
import numpy
|
||||
from astropy import constants, units
|
||||
from astropy.cosmology import LambdaCDM
|
||||
|
||||
from .readsim import CSiBORGReader, QuijoteReader
|
||||
|
||||
|
||||
CSIBORG_CONV_NAME = {
|
||||
"length": ["x", "y", "z", "peak_x", "peak_y", "peak_z", "Rs", "rmin",
|
||||
"rmax", "r200c", "r500c", "r200m", "r500m", "x0", "y0", "z0",
|
||||
"lagpatch_size"],
|
||||
"velocity": ["vx", "vy", "vz"],
|
||||
"mass": ["mass_cl", "totpartmass", "m200c", "m500c", "mass_mmain", "M",
|
||||
"m200m", "m500m"],
|
||||
"density": ["rho0"]
|
||||
}
|
||||
|
||||
QUIJOTE_CONV_NAME = {
|
||||
"length": ["x", "y", "z", "x0", "y0", "z0", "Rs", "r200c", "r500c",
|
||||
"r200m", "r500m", "lagpatch_size"],
|
||||
"mass": ["group_mass", "totpartmass", "m200c", "m500c", "m200m", "m500m"],
|
||||
}
|
||||
|
||||
###############################################################################
|
||||
# Base box #
|
||||
###############################################################################
|
||||
|
|
@ -77,6 +60,18 @@ class BaseBox(ABC):
|
|||
"""
|
||||
return self.cosmo.H0.value
|
||||
|
||||
@property
|
||||
def rho_crit0(self):
|
||||
r"""
|
||||
Present-day critical density in :math:`M_\odot h^2 / \mathrm{cMpc}^3`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
rho_crit0 : float
|
||||
"""
|
||||
rho_crit0 = self.cosmo.critical_density0
|
||||
return rho_crit0.to_value(units.solMass / units.Mpc**3)
|
||||
|
||||
@property
|
||||
def h(self):
|
||||
r"""
|
||||
|
|
@ -86,7 +81,7 @@ class BaseBox(ABC):
|
|||
-------
|
||||
h : float
|
||||
"""
|
||||
return self.H0 / 100
|
||||
return self._h
|
||||
|
||||
@property
|
||||
def Om0(self):
|
||||
|
|
@ -111,31 +106,70 @@ class BaseBox(ABC):
|
|||
pass
|
||||
|
||||
@abstractmethod
|
||||
def convert_from_box(self, data, names):
|
||||
def mpc2box(self, length):
|
||||
r"""
|
||||
Convert columns named `names` in array `data` from box units to
|
||||
physical units, such that
|
||||
- length -> :math:`Mpc`,
|
||||
- mass -> :math:`M_\odot`,
|
||||
- velocity -> :math:`\mathrm{km} / \mathrm{s}`,
|
||||
- density -> :math:`M_\odot / \mathrm{Mpc}^3`.
|
||||
|
||||
Any other conversions are currently not implemented. Note that the
|
||||
array is passed by reference and directly modified, even though it is
|
||||
also explicitly returned. Additionally centres the box coordinates on
|
||||
the observer, if they are being transformed.
|
||||
Convert length from :math:`\mathrm{cMpc} / h` to box units.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
data : structured array
|
||||
Input array.
|
||||
names : list of str
|
||||
Columns to be converted.
|
||||
length : float
|
||||
Length in :math:`\mathrm{cMpc}`
|
||||
|
||||
Returns
|
||||
-------
|
||||
data : structured array
|
||||
Input array with converted columns.
|
||||
length : float
|
||||
Length in box units.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def box2mpc(self, length):
|
||||
r"""
|
||||
Convert length from box units to :math:`\mathrm{cMpc} / h`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
length : float
|
||||
Length in box units.
|
||||
|
||||
Returns
|
||||
-------
|
||||
length : float
|
||||
Length in :math:`\mathrm{cMpc} / h`
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def solarmass2box(self, mass):
|
||||
r"""
|
||||
Convert mass from :math:`M_\odot / h` to box units.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
mass : float
|
||||
Mass in :math:`M_\odot / h`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
mass : float
|
||||
Mass in box units.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def box2solarmass(self, mass):
|
||||
r"""
|
||||
Convert mass from box units to :math:`M_\odot / h`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
mass : float
|
||||
Mass in box units.
|
||||
|
||||
Returns
|
||||
-------
|
||||
mass : float
|
||||
Mass in :math:`M_\odot / h`.
|
||||
"""
|
||||
pass
|
||||
|
||||
|
|
@ -169,124 +203,31 @@ class CSiBORGBox(BaseBox):
|
|||
"omega_k", "omega_b", "unit_l", "unit_d", "unit_t"]
|
||||
for par in pars:
|
||||
setattr(self, "_" + par, info[par])
|
||||
|
||||
self._cosmo = LambdaCDM(H0=self._H0, Om0=self._omega_m,
|
||||
self._h = self._H0 / 100
|
||||
self._cosmo = LambdaCDM(H0=100, Om0=self._omega_m,
|
||||
Ode0=self._omega_l, Tcmb0=2.725 * units.K,
|
||||
Ob0=self._omega_b)
|
||||
self._Msuncgs = constants.M_sun.cgs.value # Solar mass in grams
|
||||
|
||||
@property
|
||||
def box_G(self):
|
||||
"""
|
||||
Gravitational constant :math:`G` in box units. Given everything else
|
||||
it looks like `self.unit_t` is in seconds.
|
||||
|
||||
Returns
|
||||
-------
|
||||
G : float
|
||||
"""
|
||||
return constants.G.cgs.value * (self._unit_d * self._unit_t**2)
|
||||
|
||||
@property
|
||||
def box_H0(self):
|
||||
"""
|
||||
Present time Hubble constant :math:`H_0` in box units.
|
||||
|
||||
Returns
|
||||
-------
|
||||
H0 : float
|
||||
"""
|
||||
return self.H0 * 1e5 / units.Mpc.to(units.cm) * self._unit_t
|
||||
|
||||
@property
|
||||
def box_c(self):
|
||||
"""
|
||||
Speed of light in box units.
|
||||
|
||||
Returns
|
||||
-------
|
||||
c : float
|
||||
"""
|
||||
return constants.c.cgs.value * self._unit_t / self._unit_l
|
||||
|
||||
@property
|
||||
def box_rhoc(self):
|
||||
"""
|
||||
Critical density in box units.
|
||||
|
||||
Returns
|
||||
-------
|
||||
rhoc : float
|
||||
"""
|
||||
return 3 * self.box_H0**2 / (8 * numpy.pi * self.box_G)
|
||||
|
||||
def box2kpc(self, length):
|
||||
r"""
|
||||
Convert length from box units to :math:`\mathrm{ckpc}` (with
|
||||
:math:`h=0.705`).
|
||||
|
||||
Parameters
|
||||
----------
|
||||
length : float
|
||||
Length in box units.
|
||||
|
||||
Returns
|
||||
-------
|
||||
length : float
|
||||
Length in :math:`\mathrm{ckpc}`
|
||||
"""
|
||||
return length * (self._unit_l / units.kpc.to(units.cm) / self._aexp)
|
||||
|
||||
def kpc2box(self, length):
|
||||
r"""
|
||||
Convert length from :math:`\mathrm{ckpc}` (with :math:`h=0.705`) to
|
||||
box units.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
length : float
|
||||
Length in :math:`\mathrm{ckpc}`
|
||||
|
||||
Returns
|
||||
-------
|
||||
length : float
|
||||
Length in box units.
|
||||
"""
|
||||
return length / (self._unit_l / units.kpc.to(units.cm) / self._aexp)
|
||||
|
||||
def mpc2box(self, length):
|
||||
r"""
|
||||
Convert length from :math:`\mathrm{cMpc}` (with :math:`h=0.705`) to
|
||||
box units.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
length : float
|
||||
Length in :math:`\mathrm{cMpc}`
|
||||
|
||||
Returns
|
||||
-------
|
||||
length : float
|
||||
Length in box units.
|
||||
"""
|
||||
return self.kpc2box(length * 1e3)
|
||||
conv = (self._unit_l / units.kpc.to(units.cm) / self._aexp) * 1e-3
|
||||
conv *= self._h
|
||||
return length / conv
|
||||
|
||||
def box2mpc(self, length):
|
||||
r"""
|
||||
Convert length from box units to :math:`\mathrm{cMpc}` (with
|
||||
:math:`h=0.705`).
|
||||
conv = (self._unit_l / units.kpc.to(units.cm) / self._aexp) * 1e-3
|
||||
conv *= self._h
|
||||
return length * conv
|
||||
|
||||
Parameters
|
||||
----------
|
||||
length : float
|
||||
Length in box units.
|
||||
def solarmass2box(self, mass):
|
||||
conv = (self._unit_d * self._unit_l**3) / self._Msuncgs
|
||||
conv *= self.h
|
||||
return mass / conv
|
||||
|
||||
Returns
|
||||
-------
|
||||
length : float
|
||||
Length in :math:`\mathrm{ckpc}`
|
||||
"""
|
||||
return self.box2kpc(length) * 1e-3
|
||||
def box2solarmass(self, mass):
|
||||
conv = (self._unit_d * self._unit_l**3) / self._Msuncgs
|
||||
conv *= self.h
|
||||
return mass * conv
|
||||
|
||||
def box2vel(self, vel):
|
||||
r"""
|
||||
|
|
@ -304,105 +245,6 @@ class CSiBORGBox(BaseBox):
|
|||
"""
|
||||
return vel * (1e-2 * self._unit_l / self._unit_t / self._aexp) * 1e-3
|
||||
|
||||
def solarmass2box(self, mass):
|
||||
r"""
|
||||
Convert mass from :math:`M_\odot` (with :math:`h=0.705`) to box units.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
mass : float
|
||||
Mass in :math:`M_\odot`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
mass : float
|
||||
Mass in box units.
|
||||
"""
|
||||
return mass / (self._unit_d * self._unit_l**3) * self._Msuncgs
|
||||
|
||||
def box2solarmass(self, mass):
|
||||
r"""
|
||||
Convert mass from box units to :math:`M_\odot` (with :math:`h=0.705`).
|
||||
It appears that `self.unit_d` is density in units of
|
||||
:math:`\mathrm{g}/\mathrm{cm}^3`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
mass : float
|
||||
Mass in box units.
|
||||
|
||||
Returns
|
||||
-------
|
||||
mass : float
|
||||
Mass in :math:`M_\odot`.
|
||||
"""
|
||||
return mass * (self._unit_d * self._unit_l**3) / self._Msuncgs
|
||||
|
||||
def box2dens(self, density):
|
||||
r"""
|
||||
Convert density from box units to :math:`M_\odot / \mathrm{Mpc}^3`
|
||||
(with :math:`h=0.705`).
|
||||
|
||||
Parameters
|
||||
----------
|
||||
density : float
|
||||
Density in box units.
|
||||
|
||||
Returns
|
||||
-------
|
||||
density : float
|
||||
Density in :math:`M_\odot / \mathrm{pc}^3`.
|
||||
"""
|
||||
return (density * self._unit_d
|
||||
/ self._Msuncgs * (units.Mpc.to(units.cm)) ** 3)
|
||||
|
||||
def dens2box(self, density):
|
||||
r"""
|
||||
Convert density from :math:`M_\odot / \mathrm{Mpc}^3`
|
||||
(with :math:`h=0.705`) to box units.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
density : float
|
||||
Density in :math:`M_\odot / \mathrm{pc}^3`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
density : float
|
||||
Density in box units.
|
||||
"""
|
||||
return (density / self._unit_d * self._Msuncgs
|
||||
/ (units.Mpc.to(units.cm)) ** 3)
|
||||
|
||||
def convert_from_box(self, data, names):
|
||||
names = [names] if isinstance(names, str) else names
|
||||
transforms = {"length": self.box2mpc,
|
||||
"mass": self.box2solarmass,
|
||||
"velocity": self.box2vel,
|
||||
"density": self.box2dens}
|
||||
|
||||
for name in names:
|
||||
if name not in data.dtype.names:
|
||||
continue
|
||||
|
||||
# Convert
|
||||
found = False
|
||||
for unittype, suppnames in CSIBORG_CONV_NAME.items():
|
||||
if name in suppnames:
|
||||
data[name] = transforms[unittype](data[name])
|
||||
found = True
|
||||
continue
|
||||
# If nothing found
|
||||
if not found:
|
||||
raise NotImplementedError(
|
||||
f"Conversion of `{name}` is not defined.")
|
||||
|
||||
# Center at the observer
|
||||
if name in ["x0", "y0", "z0"]:
|
||||
data[name] -= transforms["length"](0.5)
|
||||
|
||||
return data
|
||||
|
||||
@property
|
||||
def boxsize(self):
|
||||
return self.box2mpc(1.)
|
||||
|
|
@ -428,10 +270,10 @@ class QuijoteBox(BaseBox):
|
|||
def __init__(self, nsnap, nsim, paths):
|
||||
zdict = {4: 0.0, 3: 0.5, 2: 1.0, 1: 2.0, 0: 3.0}
|
||||
assert nsnap in zdict.keys(), f"`nsnap` must be in {zdict.keys()}."
|
||||
self._aexp = 1 / (1 + zdict[nsnap])
|
||||
|
||||
info = QuijoteReader(paths).read_info(nsnap, nsim)
|
||||
self._cosmo = LambdaCDM(H0=info["Hubble"], Om0=info["Omega_m"],
|
||||
self._aexp = 1 / (1 + zdict[nsnap])
|
||||
self._h = info["h"]
|
||||
self._cosmo = LambdaCDM(H0=100, Om0=info["Omega_m"],
|
||||
Ode0=info["Omega_l"], Tcmb0=2.725 * units.K)
|
||||
self._info = info
|
||||
|
||||
|
|
@ -440,35 +282,9 @@ class QuijoteBox(BaseBox):
|
|||
return self._info["BoxSize"]
|
||||
|
||||
def box2mpc(self, length):
|
||||
r"""
|
||||
Convert length from box units to :math:`\mathrm{cMpc} / h`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
length : float
|
||||
Length in box units.
|
||||
|
||||
Returns
|
||||
-------
|
||||
length : float
|
||||
Length in :math:`\mathrm{cMpc} / h`
|
||||
"""
|
||||
return length * self.boxsize
|
||||
|
||||
def mpc2box(self, length):
|
||||
r"""
|
||||
Convert length from :math:`\mathrm{cMpc} / h` to box units.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
length : float
|
||||
Length in :math:`\mathrm{cMpc} / h`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
length : float
|
||||
Length in box units.
|
||||
"""
|
||||
return length / self.boxsize
|
||||
|
||||
def solarmass2box(self, mass):
|
||||
|
|
@ -502,33 +318,3 @@ class QuijoteBox(BaseBox):
|
|||
Mass in :math:`M_\odot / h`.
|
||||
"""
|
||||
return mass * self._info["TotMass"]
|
||||
|
||||
def convert_from_box(self, data, names):
|
||||
names = [names] if isinstance(names, str) else names
|
||||
transforms = {"length": self.box2mpc,
|
||||
"mass": self.box2solarmass,
|
||||
# "velocity": self.box2vel,
|
||||
# "density": self.box2dens,
|
||||
}
|
||||
|
||||
for name in names:
|
||||
if name not in data.dtype.names:
|
||||
continue
|
||||
|
||||
# Convert
|
||||
found = False
|
||||
for unittype, suppnames in QUIJOTE_CONV_NAME.items():
|
||||
if name in suppnames:
|
||||
data[name] = transforms[unittype](data[name])
|
||||
found = True
|
||||
continue
|
||||
# If nothing found
|
||||
if not found:
|
||||
raise NotImplementedError(
|
||||
f"Conversion of `{name}` is not defined.")
|
||||
|
||||
# # Center at the observer
|
||||
# if name in ["x0", "y0", "z0"]:
|
||||
# data[name] -= transforms["length"](0.5)
|
||||
|
||||
return data
|
||||
|
|
|
|||
|
|
@ -24,6 +24,7 @@ from itertools import product
|
|||
from math import floor
|
||||
|
||||
import numpy
|
||||
|
||||
from readfof import FoF_catalog
|
||||
from sklearn.neighbors import NearestNeighbors
|
||||
|
||||
|
|
@ -57,6 +58,7 @@ class BaseCatalogue(ABC):
|
|||
|
||||
@nsim.setter
|
||||
def nsim(self, nsim):
|
||||
assert isinstance(nsim, int)
|
||||
self._nsim = nsim
|
||||
|
||||
@abstractproperty
|
||||
|
|
@ -98,19 +100,9 @@ class BaseCatalogue(ABC):
|
|||
data : structured array
|
||||
"""
|
||||
if self._data is None:
|
||||
raise RuntimeError("Catalogue data not loaded!")
|
||||
raise RuntimeError("`data` is not set!")
|
||||
return self._data
|
||||
|
||||
def apply_bounds(self, bounds):
|
||||
for key, (xmin, xmax) in bounds.items():
|
||||
xmin = -numpy.inf if xmin is None else xmin
|
||||
xmax = numpy.inf if xmax is None else xmax
|
||||
if key == "dist":
|
||||
x = self.radial_distance(in_initial=False)
|
||||
else:
|
||||
x = self[key]
|
||||
self._data = self._data[(x > xmin) & (x <= xmax)]
|
||||
|
||||
@abstractproperty
|
||||
def box(self):
|
||||
"""
|
||||
|
|
@ -122,72 +114,156 @@ class BaseCatalogue(ABC):
|
|||
"""
|
||||
pass
|
||||
|
||||
def position(self, in_initial=False, cartesian=True):
|
||||
def load_initial(self, data, paths, simname):
|
||||
"""
|
||||
Load initial snapshot fits from the script `fit_init.py`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
data : structured array
|
||||
The catalogue to which append the new data.
|
||||
paths : :py:class:`csiborgtools.read.Paths`
|
||||
Paths manager.
|
||||
simname : str
|
||||
Simulation name.
|
||||
|
||||
Returns
|
||||
-------
|
||||
data : structured array
|
||||
"""
|
||||
fits = numpy.load(paths.initmatch(self.nsim, simname, "fit"))
|
||||
X, cols = [], []
|
||||
|
||||
for col in fits.dtype.names:
|
||||
if col == "index":
|
||||
continue
|
||||
cols.append(col + "0" if col in ['x', 'y', 'z'] else col)
|
||||
X.append(fits[col])
|
||||
|
||||
data = add_columns(data, X, cols)
|
||||
for p in ('x0', 'y0', 'z0', 'lagpatch_size'):
|
||||
data[p] = self.box.box2mpc(data[p])
|
||||
|
||||
return data
|
||||
|
||||
def load_fitted(self, data, paths, simname):
|
||||
"""
|
||||
Load halo fits from the script `fit_halos.py`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
data : structured array
|
||||
The catalogue to which append the new data.
|
||||
paths : :py:class:`csiborgtools.read.Paths`
|
||||
Paths manager.
|
||||
simname : str
|
||||
Simulation name.
|
||||
|
||||
Returns
|
||||
-------
|
||||
data : structured array
|
||||
"""
|
||||
fits = numpy.load(paths.structfit(self.nsnap, self.nsim, simname))
|
||||
|
||||
cols = [col for col in fits.dtype.names if col != "index"]
|
||||
X = [fits[col] for col in cols]
|
||||
data = add_columns(data, X, cols)
|
||||
box = self.box
|
||||
|
||||
data["r200c"] = box.box2mpc(data["r200c"])
|
||||
|
||||
return data
|
||||
|
||||
def filter_data(self, data, bounds):
|
||||
"""
|
||||
Filters data based on specified bounds for each key.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
data : structured array
|
||||
The data to be filtered.
|
||||
bounds : dict
|
||||
A dictionary with keys corresponding to data columns or `dist` and
|
||||
values as a tuple of `(xmin, xmax)`. If `xmin` or `xmax` is `None`,
|
||||
it defaults to negative infinity and positive infinity,
|
||||
respectively.
|
||||
|
||||
Returns
|
||||
-------
|
||||
data : structured array
|
||||
The filtered data based on the provided bounds.
|
||||
"""
|
||||
for key, (xmin, xmax) in bounds.items():
|
||||
if key == "dist":
|
||||
pos = numpy.vstack([data[p] - self.observer_location[i]
|
||||
for i, p in enumerate("xyz")]).T
|
||||
values_to_filter = numpy.linalg.norm(pos, axis=1)
|
||||
else:
|
||||
values_to_filter = data[key]
|
||||
|
||||
min_bound = xmin if xmin is not None else -numpy.inf
|
||||
max_bound = xmax if xmax is not None else numpy.inf
|
||||
|
||||
data = data[(values_to_filter > min_bound)
|
||||
& (values_to_filter <= max_bound)]
|
||||
|
||||
return data
|
||||
|
||||
@property
|
||||
def observer_location(self):
|
||||
r"""
|
||||
Position components. If not Cartesian, then RA is in :math:`[0, 360)`
|
||||
degrees and DEC is in :math:`[-90, 90]` degrees.
|
||||
Location of the observer in units :math:`\mathrm{Mpc} / h`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
obs_pos : 1-dimensional array of shape `(3,)`
|
||||
"""
|
||||
if self._observer_location is None:
|
||||
raise RuntimeError("`observer_location` is not set!")
|
||||
return self._observer_location
|
||||
|
||||
@observer_location.setter
|
||||
def observer_location(self, obs_pos):
|
||||
assert isinstance(obs_pos, (list, tuple, numpy.ndarray))
|
||||
obs_pos = numpy.asanyarray(obs_pos)
|
||||
assert obs_pos.shape == (3,)
|
||||
self._observer_location = obs_pos
|
||||
|
||||
def position(self, in_initial=False, cartesian=True,
|
||||
subtract_observer=False):
|
||||
r"""
|
||||
Return position components (Cartesian or RA/DEC).
|
||||
|
||||
Parameters
|
||||
----------
|
||||
in_initial : bool, optional
|
||||
Whether to return the initial snapshot positions.
|
||||
If True, return positions from the initial snapshot, otherwise the
|
||||
final snapshot.
|
||||
cartesian : bool, optional
|
||||
Whether to return the Cartesian or spherical position components.
|
||||
By default Cartesian.
|
||||
If True, return Cartesian positions. Otherwise, return dist/RA/DEC
|
||||
centered at the observer.
|
||||
subtract_observer : bool, optional
|
||||
If True, subtract the observer's location from the returned
|
||||
positions. This is only relevant if `cartesian` is True.
|
||||
|
||||
Returns
|
||||
-------
|
||||
pos : 2-dimensional array of shape `(nobjects, 3)`
|
||||
pos : ndarray, shape `(nobjects, 3)`
|
||||
Position components.
|
||||
"""
|
||||
if in_initial:
|
||||
ps = ["x0", "y0", "z0"]
|
||||
else:
|
||||
ps = ["x", "y", "z"]
|
||||
pos = numpy.vstack([self[p] for p in ps]).T
|
||||
if not cartesian:
|
||||
pos = cartesian_to_radec(pos)
|
||||
return pos
|
||||
suffix = '0' if in_initial else ''
|
||||
component_keys = [f"{comp}{suffix}" for comp in ('x', 'y', 'z')]
|
||||
|
||||
def velocity(self):
|
||||
r"""
|
||||
Cartesian velocity components in :math:`\mathrm{km} / \mathrm{s}`.
|
||||
pos = numpy.vstack([self[key] for key in component_keys]).T
|
||||
|
||||
Returns
|
||||
-------
|
||||
vel : 2-dimensional array of shape `(nobjects, 3)`
|
||||
"""
|
||||
return numpy.vstack([self["v{}".format(p)] for p in ("x", "y", "z")]).T
|
||||
if subtract_observer or not cartesian:
|
||||
pos -= self.observer_location
|
||||
|
||||
def redshift_space_position(self, cartesian=True):
|
||||
r"""
|
||||
Redshift space position components. If Cartesian, then in
|
||||
:math:`\mathrm{cMpc}`. If spherical, then radius is in
|
||||
:math:`\mathrm{cMpc}`, RA in :math:`[0, 360)` degrees and DEC in
|
||||
:math:`[-90, 90]` degrees. Note that the position is defined as the
|
||||
minimum of the gravitationl potential.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
cartesian : bool, optional
|
||||
Whether to return the Cartesian or spherical position components.
|
||||
By default Cartesian.
|
||||
|
||||
Returns
|
||||
-------
|
||||
pos : 2-dimensional array of shape `(nobjects, 3)`
|
||||
"""
|
||||
pos = self.position(cartesian=True)
|
||||
vel = self.velocity()
|
||||
origin = [0., 0., 0.]
|
||||
rsp = real2redshift(pos, vel, origin, self.box, in_box_units=False,
|
||||
make_copy=False)
|
||||
if not cartesian:
|
||||
rsp = cartesian_to_radec(rsp)
|
||||
return rsp
|
||||
return cartesian_to_radec(pos) if not cartesian else pos
|
||||
|
||||
def radial_distance(self, in_initial=False):
|
||||
r"""
|
||||
Distance of haloes from the origin.
|
||||
Distance of haloes from the observer in :math:`\mathrm{cMpc}`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
|
|
@ -198,9 +274,41 @@ class BaseCatalogue(ABC):
|
|||
-------
|
||||
radial_distance : 1-dimensional array of shape `(nobjects,)`
|
||||
"""
|
||||
pos = self.position(in_initial=in_initial, cartesian=True)
|
||||
pos = self.position(in_initial=in_initial, cartesian=True,
|
||||
subtract_observer=True)
|
||||
return numpy.linalg.norm(pos, axis=1)
|
||||
|
||||
def velocity(self):
|
||||
r"""
|
||||
Return Cartesian velocity in :math:`\mathrm{km} / \mathrm{s}`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
vel : 2-dimensional array of shape `(nobjects, 3)`
|
||||
"""
|
||||
return numpy.vstack([self["v{}".format(p)] for p in ("x", "y", "z")]).T
|
||||
|
||||
def redshift_space_position(self, cartesian=True):
|
||||
"""
|
||||
Calculates the position of objects in redshift space. Positions can be
|
||||
returned in either Cartesian coordinates (default) or spherical
|
||||
coordinates (dist/RA/dec).
|
||||
|
||||
Parameters
|
||||
----------
|
||||
cartesian : bool, optional
|
||||
Returns position in Cartesian coordinates if True, else in
|
||||
spherical coordinates.
|
||||
|
||||
Returns
|
||||
-------
|
||||
pos : 2-dimensional array of shape `(nobjects, 3)`
|
||||
Position of objects in the desired coordinate system.
|
||||
"""
|
||||
rsp = real2redshift(self.position(cartesian=True), self.velocity(),
|
||||
self.observer_location, self.box, make_copy=False)
|
||||
return rsp if cartesian else cartesian_to_radec(rsp)
|
||||
|
||||
def angmomentum(self):
|
||||
"""
|
||||
Cartesian angular momentum components of halos in the box coordinate
|
||||
|
|
@ -214,8 +322,9 @@ class BaseCatalogue(ABC):
|
|||
|
||||
@lru_cache(maxsize=2)
|
||||
def knn(self, in_initial):
|
||||
"""
|
||||
kNN object fitted on all catalogue objects. Caches the kNN object.
|
||||
r"""
|
||||
kNN object for catalogue objects with caching. Positions are centered
|
||||
on the observer.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
|
|
@ -225,51 +334,49 @@ class BaseCatalogue(ABC):
|
|||
Returns
|
||||
-------
|
||||
knn : :py:class:`sklearn.neighbors.NearestNeighbors`
|
||||
kNN object fitted with object positions.
|
||||
"""
|
||||
knn = NearestNeighbors()
|
||||
return knn.fit(self.position(in_initial=in_initial))
|
||||
pos = self.position(in_initial=in_initial)
|
||||
return NearestNeighbors().fit(pos)
|
||||
|
||||
def nearest_neighbours(self, X, radius, in_initial, knearest=False,
|
||||
return_mass=False, masss_key=None):
|
||||
return_mass=False, mass_key=None):
|
||||
r"""
|
||||
Sorted nearest neigbours within `radius` of `X` in the initial or final
|
||||
snapshot. However, if `knearest` is `True` then the `radius` is assumed
|
||||
to be the integer number of nearest neighbours to return.
|
||||
Return nearest neighbours within `radius` of `X` in a given snapshot.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
X : 2-dimensional array of shape `(n_queries, 3)`
|
||||
Cartesian query position components in :math:`\mathrm{cMpc}`.
|
||||
X : 2D array, shape `(n_queries, 3)`
|
||||
Query positions in :math:`\mathrm{cMpc} / h`. Expected to be
|
||||
centered on the observer.
|
||||
radius : float or int
|
||||
Limiting neighbour distance. If `knearest` is `True` then this is
|
||||
the number of nearest neighbours to return.
|
||||
Limiting distance or number of neighbours, depending on `knearest`.
|
||||
in_initial : bool
|
||||
Whether to define the kNN on the initial or final snapshot.
|
||||
Use the initial or final snapshot for kNN.
|
||||
knearest : bool, optional
|
||||
Whether `radius` is the number of nearest neighbours to return.
|
||||
If True, `radius` is the number of neighbours to return.
|
||||
return_mass : bool, optional
|
||||
Whether to return the masses of the nearest neighbours.
|
||||
masss_key : str, optional
|
||||
Key of the mass column in the catalogue. Must be provided if
|
||||
`return_mass` is `True`.
|
||||
Return masses of the nearest neighbours.
|
||||
mass_key : str, optional
|
||||
Mass column key. Required if `return_mass` is True.
|
||||
|
||||
Returns
|
||||
-------
|
||||
dist : list of 1-dimensional arrays
|
||||
List of length `n_queries` whose elements are arrays of distances
|
||||
to the nearest neighbours.
|
||||
knns : list of 1-dimensional arrays
|
||||
List of length `n_queries` whose elements are arrays of indices of
|
||||
nearest neighbours in this catalogue.
|
||||
dist : list of arrays
|
||||
Distances to the nearest neighbours for each query.
|
||||
indxs : list of arrays
|
||||
Indices of nearest neighbours for each query.
|
||||
mass (optional): list of arrays
|
||||
Masses of the nearest neighbours for each query.
|
||||
"""
|
||||
if not (X.ndim == 2 and X.shape[1] == 3):
|
||||
raise TypeError("`X` must be an array of shape `(n_samples, 3)`.")
|
||||
if knearest:
|
||||
assert isinstance(radius, int)
|
||||
if return_mass:
|
||||
assert masss_key is not None
|
||||
knn = self.knn(in_initial)
|
||||
if X.shape != (len(X), 3):
|
||||
raise ValueError("`X` must be of shape `(n_samples, 3)`.")
|
||||
if knearest and not isinstance(radius, int):
|
||||
raise ValueError("`radius` must be an integer if `knearest`.")
|
||||
if return_mass and not mass_key:
|
||||
raise ValueError("`mass_key` must be provided if `return_mass`.")
|
||||
|
||||
knn = self.knn(in_initial)
|
||||
if knearest:
|
||||
dist, indxs = knn.kneighbors(X, radius)
|
||||
else:
|
||||
|
|
@ -278,35 +385,26 @@ class BaseCatalogue(ABC):
|
|||
if not return_mass:
|
||||
return dist, indxs
|
||||
|
||||
if knearest:
|
||||
mass = numpy.copy(dist)
|
||||
for i in range(dist.shape[0]):
|
||||
mass[i, :] = self[masss_key][indxs[i]]
|
||||
else:
|
||||
mass = deepcopy(dist)
|
||||
for i in range(dist.size):
|
||||
mass[i] = self[masss_key][indxs[i]]
|
||||
|
||||
mass = [self[mass_key][indx] for indx in indxs]
|
||||
return dist, indxs, mass
|
||||
|
||||
def angular_neighbours(self, X, ang_radius, in_rsp, rad_tolerance=None):
|
||||
r"""
|
||||
Find nearest neighbours within `ang_radius` of query points `X`.
|
||||
Optionally applies radial tolerance, which is expected to be in
|
||||
:math:`\mathrm{cMpc}`.
|
||||
Find nearest neighbours within `ang_radius` of query points `X` in the
|
||||
final snaphot. Optionally applies radial distance tolerance, which is
|
||||
expected to be in :math:`\mathrm{cMpc} / h`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
X : 2-dimensional array of shape `(n_queries, 2)` or `(n_queries, 3)`
|
||||
Query positions. If 2-dimensional, then RA and DEC in degrees.
|
||||
If 3-dimensional, then radial distance in :math:`\mathrm{cMpc}`,
|
||||
RA and DEC in degrees.
|
||||
Query positions. Either RA/dec in degrees or dist/RA/dec with
|
||||
distance in :math:`\mathrm{cMpc} / h`.
|
||||
in_rsp : bool
|
||||
Whether to use redshift space positions of haloes.
|
||||
If True, use redshift space positions of haloes.
|
||||
ang_radius : float
|
||||
Angular radius in degrees.
|
||||
rad_tolerance : float, optional
|
||||
Radial tolerance in :math:`\mathrm{cMpc}`.
|
||||
Radial distance tolerance in :math:`\mathrm{cMpc} / h`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
|
|
@ -316,46 +414,50 @@ class BaseCatalogue(ABC):
|
|||
Indices of each neighbour in this catalogue.
|
||||
"""
|
||||
assert X.ndim == 2
|
||||
# We first get positions of haloes in this catalogue, store their
|
||||
# radial distance and normalise them to unit vectors.
|
||||
|
||||
# Get positions of haloes in this catalogue
|
||||
if in_rsp:
|
||||
# TODO what to do with subtracting the observer here?
|
||||
pos = self.redshift_space_position(cartesian=True)
|
||||
else:
|
||||
pos = self.position(in_initial=False, cartesian=True)
|
||||
pos = self.position(in_initial=False, cartesian=True,
|
||||
subtract_observer=True)
|
||||
|
||||
# Convert halo positions to unit vectors.
|
||||
raddist = numpy.linalg.norm(pos, axis=1)
|
||||
pos /= raddist.reshape(-1, 1)
|
||||
# We convert RAdec query positions to unit vectors. If no radial
|
||||
# distance provided add it.
|
||||
|
||||
# Convert RA/dec query positions to unit vectors. If no radial
|
||||
# distance is provided artificially add it.
|
||||
if X.shape[1] == 2:
|
||||
X = numpy.vstack([numpy.ones_like(X[:, 0]), X[:, 0], X[:, 1]]).T
|
||||
radquery = None
|
||||
else:
|
||||
radquery = X[:, 0]
|
||||
|
||||
X = radec_to_cartesian(X)
|
||||
|
||||
# Find neighbours
|
||||
knn = NearestNeighbors(metric="cosine")
|
||||
knn.fit(pos)
|
||||
# Convert angular radius to cosine difference.
|
||||
metric_maxdist = 1 - numpy.cos(numpy.deg2rad(ang_radius))
|
||||
dist, ind = knn.radius_neighbors(X, radius=metric_maxdist,
|
||||
sort_results=True)
|
||||
# And the cosine difference to angular distance.
|
||||
|
||||
# Convert cosine difference to angular distance
|
||||
for i in range(X.shape[0]):
|
||||
dist[i] = numpy.rad2deg(numpy.arccos(1 - dist[i]))
|
||||
|
||||
# Apply the radial tolerance
|
||||
if rad_tolerance is not None:
|
||||
assert radquery is not None
|
||||
# Apply radial tolerance
|
||||
if rad_tolerance and radquery:
|
||||
for i in range(X.shape[0]):
|
||||
mask = numpy.abs(raddist[ind[i]] - radquery) < rad_tolerance
|
||||
dist[i] = dist[i][mask]
|
||||
ind[i] = ind[i][mask]
|
||||
mask = numpy.abs(raddist[ind[i]] - radquery[i]) < rad_tolerance
|
||||
dist[i], ind[i] = dist[i][mask], ind[i][mask]
|
||||
|
||||
return dist, ind
|
||||
|
||||
@property
|
||||
def keys(self):
|
||||
"""
|
||||
Catalogue keys.
|
||||
Return catalogue keys.
|
||||
|
||||
Returns
|
||||
-------
|
||||
|
|
@ -364,11 +466,12 @@ class BaseCatalogue(ABC):
|
|||
return self.data.dtype.names
|
||||
|
||||
def __getitem__(self, key):
|
||||
# If key is an integer, return the corresponding row.
|
||||
if isinstance(key, (int, numpy.integer)):
|
||||
assert key >= 0
|
||||
return self.data[key]
|
||||
if key not in self.keys:
|
||||
elif key not in self.keys():
|
||||
raise KeyError(f"Key '{key}' not in catalogue.")
|
||||
|
||||
return self.data[key]
|
||||
|
||||
def __len__(self):
|
||||
|
|
@ -382,7 +485,10 @@ class BaseCatalogue(ABC):
|
|||
|
||||
class CSiBORGHaloCatalogue(BaseCatalogue):
|
||||
r"""
|
||||
CSiBORG FoF halo catalogue.
|
||||
CSiBORG FoF halo catalogue with units:
|
||||
- Length: :math:`cMpc / h`
|
||||
- Velocity: :math:`km / s`
|
||||
- Mass: :math:`M_\odot / h`
|
||||
|
||||
Parameters
|
||||
----------
|
||||
|
|
@ -390,74 +496,52 @@ class CSiBORGHaloCatalogue(BaseCatalogue):
|
|||
IC realisation index.
|
||||
paths : py:class`csiborgtools.read.Paths`
|
||||
Paths object.
|
||||
observer_location : array, optional
|
||||
Observer's location in :math:`\mathrm{Mpc} / h`.
|
||||
bounds : dict
|
||||
Parameter bounds to apply to the catalogue. The keys are the parameter
|
||||
names and the items are a len-2 tuple of (min, max) values. In case of
|
||||
no minimum or maximum, use `None`. For radial distance from the origin
|
||||
use `dist`.
|
||||
Parameter bounds; keys as names, values as (min, max) tuples. Use
|
||||
`dist` for radial distance, `None` for no bound.
|
||||
load_fitted : bool, optional
|
||||
Whether to load fitted quantities.
|
||||
Load fitted quantities.
|
||||
load_initial : bool, optional
|
||||
Whether to load initial positions.
|
||||
Load initial positions.
|
||||
with_lagpatch : bool, optional
|
||||
Whether to only load halos with a resolved Lagrangian patch.
|
||||
rawdata : bool, optional
|
||||
Whether to return the raw data. In this case applies no cuts and
|
||||
transformations.
|
||||
Load halos with a resolved Lagrangian patch.
|
||||
"""
|
||||
|
||||
def __init__(self, nsim, paths, bounds={"dist": (0, 155.5 / 0.705)},
|
||||
load_fitted=True, load_initial=True, with_lagpatch=True,
|
||||
rawdata=False):
|
||||
def __init__(self, nsim, paths, observer_location=[338.85, 338.85, 338.85],
|
||||
bounds={"dist": (0, 155.5)},
|
||||
load_fitted=True, load_initial=True, with_lagpatch=False):
|
||||
self.nsim = nsim
|
||||
self.paths = paths
|
||||
self.observer_location = observer_location
|
||||
reader = CSiBORGReader(paths)
|
||||
self._data = reader.read_fof_halos(self.nsim)
|
||||
data = reader.read_fof_halos(self.nsim)
|
||||
box = self.box
|
||||
|
||||
if load_fitted:
|
||||
fits = numpy.load(paths.structfit(self.nsnap, nsim, "csiborg"))
|
||||
cols = [col for col in fits.dtype.names if col != "index"]
|
||||
X = [fits[col] for col in cols]
|
||||
self._data = add_columns(self._data, X, cols)
|
||||
# We want coordinates to be [0, 677.7] in Mpc / h
|
||||
for p in ('x', 'y', 'z'):
|
||||
data[p] = data[p] * box.h + box.box2mpc(1) / 2
|
||||
# Similarly mass in units of Msun / h
|
||||
data["fof_totpartmass"] *= box.h
|
||||
data["fof_m200c"] *= box.h
|
||||
# Because of a RAMSES bug, we must flip the x and z coordinates
|
||||
flip_cols(data, 'x', 'z')
|
||||
|
||||
if load_initial:
|
||||
fits = numpy.load(paths.initmatch(nsim, "csiborg", "fit"))
|
||||
X, cols = [], []
|
||||
for col in fits.dtype.names:
|
||||
if col == "index":
|
||||
continue
|
||||
if col in ['x', 'y', 'z']:
|
||||
cols.append(col + "0")
|
||||
else:
|
||||
cols.append(col)
|
||||
X.append(fits[col])
|
||||
data = self.load_initial(data, paths, "csiborg")
|
||||
flip_cols(data, "x0", "z0")
|
||||
if load_fitted:
|
||||
data = self.load_fitted(data, paths, "csiborg")
|
||||
flip_cols(data, "vx", "vz")
|
||||
|
||||
self._data = add_columns(self._data, X, cols)
|
||||
if load_initial and with_lagpatch:
|
||||
data = data[numpy.isfinite(data["lagpatch_size"])]
|
||||
|
||||
if rawdata:
|
||||
for p in ('x', 'y', 'z'):
|
||||
self._data[p] = self.box.mpc2box(self._data[p]) + 0.5
|
||||
else:
|
||||
if with_lagpatch:
|
||||
self._data = self._data[numpy.isfinite(self["lagpatch_size"])]
|
||||
# Flip positions and convert from code units to cMpc. Convert M too
|
||||
flip_cols(self._data, "x", "z")
|
||||
if load_fitted:
|
||||
flip_cols(self._data, "vx", "vz")
|
||||
names = ["totpartmass", "rho0", "r200c",
|
||||
"r500c", "m200c", "m500c", "r200m", "m200m",
|
||||
"r500m", "m500m", "vx", "vy", "vz"]
|
||||
self._data = self.box.convert_from_box(self._data, names)
|
||||
if bounds is not None:
|
||||
data = self.filter_data(data, bounds)
|
||||
|
||||
if load_initial:
|
||||
flip_cols(self._data, "x0", "z0")
|
||||
for p in ("x0", "y0", "z0"):
|
||||
self._data[p] -= 0.5
|
||||
names = ["x0", "y0", "z0", "lagpatch_size"]
|
||||
self._data = self.box.convert_from_box(self._data, names)
|
||||
|
||||
if bounds is not None:
|
||||
self.apply_bounds(bounds)
|
||||
self._data = data
|
||||
|
||||
@property
|
||||
def nsnap(self):
|
||||
|
|
@ -481,8 +565,11 @@ class CSiBORGHaloCatalogue(BaseCatalogue):
|
|||
|
||||
|
||||
class QuijoteHaloCatalogue(BaseCatalogue):
|
||||
"""
|
||||
Quijote FoF halo catalogue.
|
||||
r"""
|
||||
Quijote FoF halo catalogue with units:
|
||||
- Length: :math:`cMpc / h`
|
||||
- Velocity: :math:`km / s`
|
||||
- Mass: :math:`M_\odot / h`
|
||||
|
||||
Parameters
|
||||
----------
|
||||
|
|
@ -492,34 +579,30 @@ class QuijoteHaloCatalogue(BaseCatalogue):
|
|||
Paths object.
|
||||
nsnap : int
|
||||
Snapshot index.
|
||||
origin : len-3 tuple, optional
|
||||
Where to place the origin of the box. In units of :math:`cMpc / h`.
|
||||
observer_location : array, optional
|
||||
Observer's location in :math:`\mathrm{Mpc} / h`.
|
||||
bounds : dict
|
||||
Parameter bounds to apply to the catalogue. The keys are the parameter
|
||||
names and the items are a len-2 tuple of (min, max) values. In case of
|
||||
no minimum or maximum, use `None`. For radial distance from the origin
|
||||
use `dist`.
|
||||
Parameter bounds; keys as parameter names, values as (min, max)
|
||||
tuples. Use `dist` for radial distance, `None` for no bound.
|
||||
load_fitted : bool, optional
|
||||
Load fitted quantities from `fit_halos.py`.
|
||||
load_initial : bool, optional
|
||||
Whether to load initial positions.
|
||||
Load initial positions from `fit_init.py`.
|
||||
with_lagpatch : bool, optional
|
||||
Whether to only load halos with a resolved Lagrangian patch.
|
||||
rawdata : bool, optional
|
||||
Whether to return the raw data. In this case applies no cuts and
|
||||
transformations.
|
||||
**kwargs : dict
|
||||
Keyword arguments for backward compatibility.
|
||||
Load halos with a resolved Lagrangian patch.
|
||||
"""
|
||||
_nsnap = None
|
||||
_origin = None
|
||||
|
||||
def __init__(self, nsim, paths, nsnap, origin=[0., 0., 0.],
|
||||
bounds=None, load_initial=True, with_lagpatch=True,
|
||||
rawdata=False, **kwargs):
|
||||
def __init__(self, nsim, paths, nsnap,
|
||||
observer_location=[500., 500., 500.],
|
||||
bounds=None, load_fitted=True, load_initial=True,
|
||||
with_lagpatch=False):
|
||||
self.nsim = nsim
|
||||
self.paths = paths
|
||||
self.nsnap = nsnap
|
||||
self.origin = origin
|
||||
self.observer_location = observer_location
|
||||
self._box = QuijoteBox(nsnap, nsim, paths)
|
||||
self._boxwidth = self.box.boxsize
|
||||
|
||||
fpath = self.paths.fof_cat(nsim, "quijote")
|
||||
fof = FoF_catalog(fpath, self.nsnap, long_ids=False, swap=False,
|
||||
|
|
@ -532,44 +615,29 @@ class QuijoteHaloCatalogue(BaseCatalogue):
|
|||
("index", numpy.int32)]
|
||||
data = cols_to_structured(fof.GroupLen.size, cols)
|
||||
|
||||
pos = self.box.mpc2box(fof.GroupPos / 1e3)
|
||||
pos = fof.GroupPos / 1e3
|
||||
vel = fof.GroupVel * (1 + self.redshift)
|
||||
for i, p in enumerate(["x", "y", "z"]):
|
||||
data[p] = pos[:, i] - self.origin[i]
|
||||
data[p] = pos[:, i]
|
||||
data["v" + p] = vel[:, i]
|
||||
data["group_mass"] = self.box.solarmass2box(fof.GroupMass * 1e10)
|
||||
data["group_mass"] = fof.GroupMass * 1e10
|
||||
data["npart"] = fof.GroupLen
|
||||
# We want to start indexing from 1. Index 0 is reserved for
|
||||
# particles unassigned to any FoF group.
|
||||
data["index"] = 1 + numpy.arange(data.size, dtype=numpy.int32)
|
||||
|
||||
if load_initial:
|
||||
fits = numpy.load(paths.initmatch(nsim, "quijote", "fit"))
|
||||
X, cols = [], []
|
||||
for col in fits.dtype.names:
|
||||
if col == "index":
|
||||
continue
|
||||
if col in ['x', 'y', 'z']:
|
||||
cols.append(col + "0")
|
||||
else:
|
||||
cols.append(col)
|
||||
X.append(fits[col])
|
||||
data = add_columns(data, X, cols)
|
||||
data = self.load_initial(data, paths, "quijote")
|
||||
if load_fitted:
|
||||
assert nsnap == 4
|
||||
|
||||
if load_initial and with_lagpatch:
|
||||
data = data[numpy.isfinite(data["lagpatch_size"])]
|
||||
|
||||
if bounds is not None:
|
||||
data = self.filter_data(data, bounds)
|
||||
|
||||
self._data = data
|
||||
if not rawdata:
|
||||
if with_lagpatch:
|
||||
mask = numpy.isfinite(self._data["lagpatch_size"])
|
||||
self._data = self._data[mask]
|
||||
|
||||
names = ["x", "y", "z", "group_mass"]
|
||||
self._data = self.box.convert_from_box(self._data, names)
|
||||
if load_initial:
|
||||
names = ["x0", "y0", "z0", "lagpatch_size"]
|
||||
self._data = self.box.convert_from_box(self._data, names)
|
||||
|
||||
if bounds is not None:
|
||||
self.apply_bounds(bounds)
|
||||
|
||||
@property
|
||||
def nsnap(self):
|
||||
|
|
@ -596,40 +664,12 @@ class QuijoteHaloCatalogue(BaseCatalogue):
|
|||
-------
|
||||
redshift : float
|
||||
"""
|
||||
z_dict = {4: 0.0, 3: 0.5, 2: 1.0, 1: 2.0, 0: 3.0}
|
||||
return z_dict[self.nsnap]
|
||||
return {4: 0.0, 3: 0.5, 2: 1.0, 1: 2.0, 0: 3.0}[self.nsnap]
|
||||
|
||||
@property
|
||||
def box(self):
|
||||
"""
|
||||
Quijote box object.
|
||||
|
||||
Returns
|
||||
-------
|
||||
box : instance of :py:class:`csiborgtools.units.BaseBox`
|
||||
"""
|
||||
return self._box
|
||||
|
||||
@property
|
||||
def origin(self):
|
||||
"""
|
||||
Origin of the box with respect to the initial box units.
|
||||
|
||||
Returns
|
||||
-------
|
||||
origin : len-3 tuple
|
||||
"""
|
||||
if self._origin is None:
|
||||
raise ValueError("`origin` is not set.")
|
||||
return self._origin
|
||||
|
||||
@origin.setter
|
||||
def origin(self, origin):
|
||||
if isinstance(origin, (list, tuple)):
|
||||
origin = numpy.asanyarray(origin)
|
||||
assert origin.ndim == 1 and origin.size == 3
|
||||
self._origin = origin
|
||||
|
||||
def pick_fiducial_observer(self, n, rmax):
|
||||
r"""
|
||||
Return a copy of itself, storing only halos within `rmax` of the new
|
||||
|
|
@ -640,22 +680,15 @@ class QuijoteHaloCatalogue(BaseCatalogue):
|
|||
n : int
|
||||
Fiducial observer index.
|
||||
rmax : float
|
||||
Maximum distance from the fiducial observer in :math:`cMpc`.
|
||||
Max. distance from the fiducial obs. in :math:`\mathrm{cMpc} / h`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
cat : instance of csiborgtools.read.QuijoteHaloCatalogue
|
||||
"""
|
||||
new_origin = fiducial_observers(self.box.boxsize, rmax)[n]
|
||||
# We make a copy of the catalogue to avoid modifying the original.
|
||||
# Then, we shift coordinates back to the original box frame and then to
|
||||
# the new origin.
|
||||
cat = deepcopy(self)
|
||||
for i, p in enumerate(('x', 'y', 'z')):
|
||||
cat._data[p] += self.origin[i]
|
||||
cat._data[p] -= new_origin[i]
|
||||
|
||||
cat.apply_bounds({"dist": (0, rmax)})
|
||||
cat.observer_location = fiducial_observers(self.box.boxsize, rmax)[n]
|
||||
cat._data = cat.filter_data(cat._data, {"dist": (0, rmax)})
|
||||
return cat
|
||||
|
||||
|
||||
|
|
@ -666,26 +699,20 @@ class QuijoteHaloCatalogue(BaseCatalogue):
|
|||
|
||||
def fiducial_observers(boxwidth, radius):
|
||||
"""
|
||||
Positions of fiducial observers in a box, such that that the box is
|
||||
subdivided among them into spherical regions.
|
||||
Compute observer positions in a box, subdivided into spherical regions.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
boxwidth : float
|
||||
Box width.
|
||||
Width of the box.
|
||||
radius : float
|
||||
Radius of the spherical regions.
|
||||
|
||||
Returns
|
||||
-------
|
||||
origins : list of len-3 lists
|
||||
Positions of the observers.
|
||||
origins : list of lists
|
||||
Positions of the observers, with each position as a len-3 list.
|
||||
"""
|
||||
nobs = floor(boxwidth / (2 * radius)) # Number of observers per dimension
|
||||
|
||||
origins = list(product([1, 3, 5], repeat=nobs))
|
||||
for i in range(len(origins)):
|
||||
origins[i] = list(origins[i])
|
||||
for j in range(nobs):
|
||||
origins[i][j] *= radius
|
||||
return origins
|
||||
nobs = floor(boxwidth / (2 * radius))
|
||||
return [[val * radius for val in position]
|
||||
for position in product([1, 3, 5], repeat=nobs)]
|
||||
|
|
|
|||
|
|
@ -531,6 +531,8 @@ class MmainReader:
|
|||
the position of the parent, the summed mass and the fraction of mass in
|
||||
substructure. Corresponds to the PHEW Halo finder.
|
||||
|
||||
NOTE: this code is no longer used and the units may be inconsistent.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
nsim : int
|
||||
|
|
@ -642,8 +644,8 @@ class QuijoteReader:
|
|||
|
||||
if verbose:
|
||||
print(f"{datetime.now()}: reading particle velocities.")
|
||||
# NOTE convert to box units.
|
||||
vel = readgadget.read_block(snapshot, "VEL ", ptype) # km/s
|
||||
# Unlike the positions, we keep velocities in km/s
|
||||
vel = readgadget.read_block(snapshot, "VEL ", ptype)
|
||||
vel *= (1 + info["redshift"])
|
||||
|
||||
for i, v in enumerate(['vx', 'vy', 'vz']):
|
||||
|
|
@ -657,9 +659,9 @@ class QuijoteReader:
|
|||
if verbose:
|
||||
print(f"{datetime.now()}: reading particle masses.")
|
||||
if return_structured:
|
||||
out["M"] = info["PartMass"] / info["TotMass"]
|
||||
out["M"] = info["PartMass"]
|
||||
else:
|
||||
out[:, 6] = info["PartMass"] / info["TotMass"]
|
||||
out[:, 6] = info["PartMass"]
|
||||
|
||||
return out, pids
|
||||
|
||||
|
|
|
|||
|
|
@ -18,7 +18,6 @@ Various coordinate transformations.
|
|||
from os.path import isfile
|
||||
|
||||
import numpy
|
||||
from astropy import units
|
||||
from h5py import File
|
||||
|
||||
###############################################################################
|
||||
|
|
@ -81,7 +80,7 @@ def radec_to_cartesian(X, isdeg=True):
|
|||
return numpy.vstack([x, y, z]).T
|
||||
|
||||
|
||||
def real2redshift(pos, vel, origin, box, in_box_units, periodic_wrap=True,
|
||||
def real2redshift(pos, vel, observer_location, box, periodic_wrap=True,
|
||||
make_copy=True):
|
||||
r"""
|
||||
Convert real-space position to redshift space position.
|
||||
|
|
@ -89,18 +88,13 @@ def real2redshift(pos, vel, origin, box, in_box_units, periodic_wrap=True,
|
|||
Parameters
|
||||
----------
|
||||
pos : 2-dimensional array `(nsamples, 3)`
|
||||
Real-space Cartesian position components.
|
||||
Real-space Cartesian components in :math:`\mathrm{cMpc} / h`.
|
||||
vel : 2-dimensional array `(nsamples, 3)`
|
||||
Cartesian velocity components.
|
||||
origin : 1-dimensional array `(3,)`
|
||||
Origin of the coordinate system in the `pos` reference frame.
|
||||
Cartesian velocity in :math:`\mathrm{km} \mathrm{s}^{-1}`.
|
||||
observer_location: 1-dimensional array `(3,)`
|
||||
Observer location in :math:`\mathrm{cMpc} / h`.
|
||||
box : py:class:`csiborg.read.CSiBORGBox`
|
||||
Box units.
|
||||
in_box_units: bool
|
||||
Whether `pos` and `vel` are in box units. If not, position is assumed
|
||||
to be in :math:`\mathrm{Mpc}`, velocity in
|
||||
:math:`\mathrm{km} \mathrm{s}^{-1}` and math:`h=0.705`, or otherwise
|
||||
matching the box.
|
||||
periodic_wrap : bool, optional
|
||||
Whether to wrap around the box, particles may be outside the default
|
||||
bounds once RSD is applied.
|
||||
|
|
@ -110,55 +104,30 @@ def real2redshift(pos, vel, origin, box, in_box_units, periodic_wrap=True,
|
|||
Returns
|
||||
-------
|
||||
pos : 2-dimensional array `(nsamples, 3)`
|
||||
Redshift-space Cartesian position components, with an observer assumed
|
||||
at the `origin`.
|
||||
Redshift-space Cartesian position in :math:`\mathrm{cMpc} / h`.
|
||||
"""
|
||||
a = box._aexp
|
||||
H0 = box.box_H0 if in_box_units else box.H0
|
||||
|
||||
if make_copy:
|
||||
pos = numpy.copy(pos)
|
||||
for i in range(3):
|
||||
pos[:, i] -= origin[i]
|
||||
|
||||
# Place the observer at the origin
|
||||
pos -= observer_location
|
||||
# Dot product of position vector and velocity
|
||||
vr_dot = numpy.sum(pos * vel, axis=1)
|
||||
# Compute the norm squared of the displacement
|
||||
norm2 = numpy.sum(pos**2, axis=1)
|
||||
dot = numpy.einsum("ij,ij->i", pos, vel)
|
||||
pos *= (1 + a / H0 * dot / norm2).reshape(-1, 1)
|
||||
|
||||
for i in range(3):
|
||||
pos[:, i] += origin[i]
|
||||
pos *= (1 + box._aexp / box.H0 * vr_dot / norm2).reshape(-1, 1)
|
||||
# Place the observer back at the original location
|
||||
pos += observer_location
|
||||
|
||||
if periodic_wrap:
|
||||
boxsize = 1. if in_box_units else box.box2mpc(1.)
|
||||
# Wrap around the box: x > 1 -> x - 1, x < 0 -> x + 1
|
||||
pos[pos > boxsize] -= boxsize
|
||||
pos[pos < 0] += boxsize
|
||||
boxsize = box.box2mpc(1.)
|
||||
# Wrap around the box.
|
||||
pos = numpy.where(pos > boxsize, pos - boxsize, pos)
|
||||
pos = numpy.where(pos < 0, pos + boxsize, pos)
|
||||
|
||||
return pos
|
||||
|
||||
|
||||
def M200_to_R200(M200, cosmo):
|
||||
r"""
|
||||
Convert :math:M_{200} to :math:`R_{200}`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
M200 : float
|
||||
:math:`M_{200}` in :math:`M_{\odot}`.
|
||||
cosmo : astropy cosmology object
|
||||
Cosmology.
|
||||
|
||||
Returns
|
||||
-------
|
||||
R200 : float
|
||||
:math:`R_{200}` in :math:`\mathrm{Mpc}`.
|
||||
"""
|
||||
Msun = 1.98847e30
|
||||
M200 = 1e14 * Msun * units.kg
|
||||
rhoc = cosmo.critical_density0
|
||||
R200 = (M200 / (4 * numpy.pi / 3 * 200 * rhoc))**(1. / 3)
|
||||
return R200.to(units.Mpc).value
|
||||
|
||||
|
||||
###############################################################################
|
||||
# Array manipulation #
|
||||
###############################################################################
|
||||
|
|
@ -173,215 +142,121 @@ def cols_to_structured(N, cols):
|
|||
N : int
|
||||
Structured array size.
|
||||
cols: list of tuples
|
||||
Column names and dtypes. Each tuple must written as `(name, dtype)`.
|
||||
Column names and dtypes. Each tuple must be written as `(name, dtype)`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : structured array
|
||||
Initialised structured array.
|
||||
Initialized structured array.
|
||||
"""
|
||||
if not isinstance(cols, list) and all(isinstance(c, tuple) for c in cols):
|
||||
raise TypeError("`cols` must be a list of tuples.")
|
||||
if not (isinstance(cols, list)
|
||||
and all(isinstance(c, tuple) and len(c) == 2 for c in cols)):
|
||||
raise TypeError("`cols` must be a list of (name, dtype) tuples.")
|
||||
|
||||
names, formats = zip(*cols)
|
||||
dtype = {"names": names, "formats": formats}
|
||||
|
||||
dtype = {"names": [col[0] for col in cols],
|
||||
"formats": [col[1] for col in cols]}
|
||||
return numpy.full(N, numpy.nan, dtype=dtype)
|
||||
|
||||
|
||||
def add_columns(arr, X, cols):
|
||||
"""
|
||||
Add new columns to a record array `arr`. Creates a new array.
|
||||
Add new columns `X` to a record array `arr`. Creates a new array.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
arr : record array
|
||||
Record array to add columns to.
|
||||
X : (list of) 1-dimensional array(s) or 2-dimensional array
|
||||
arr : structured array
|
||||
Structured array to add columns to.
|
||||
X : (list of) 1-dimensional array(s)
|
||||
Columns to be added.
|
||||
cols : str or list of str
|
||||
Column names to be added.
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : record array
|
||||
out : structured array
|
||||
"""
|
||||
# Make sure cols is a list of str and X a 2D array
|
||||
cols = [cols] if isinstance(cols, str) else cols
|
||||
|
||||
# Convert X to a list of 1D arrays for consistency
|
||||
if isinstance(X, numpy.ndarray) and X.ndim == 1:
|
||||
X = X.reshape(-1, 1)
|
||||
if isinstance(X, list) and all(x.ndim == 1 for x in X):
|
||||
X = numpy.vstack([X]).T
|
||||
if len(cols) != X.shape[1]:
|
||||
raise ValueError("Number of columns of `X` does not match `cols`.")
|
||||
if arr.size != X.shape[0]:
|
||||
raise ValueError("Number of rows of `X` does not match size of `arr`.")
|
||||
X = [X]
|
||||
elif isinstance(X, numpy.ndarray):
|
||||
raise ValueError("`X` should be a 1D array or a list of 1D arrays.")
|
||||
|
||||
# Get the new data types
|
||||
dtype = arr.dtype.descr
|
||||
for i, col in enumerate(cols):
|
||||
dtype.append((col, X[i, :].dtype.descr[0][1]))
|
||||
if len(X) != len(cols):
|
||||
raise ValueError("Mismatch between `X` and `cols` lengths.")
|
||||
|
||||
# Fill in the old array
|
||||
out = numpy.full(arr.size, numpy.nan, dtype=dtype)
|
||||
if not all(isinstance(x, numpy.ndarray) and x.ndim == 1 for x in X):
|
||||
raise ValueError("All elements of `X` should be 1D arrays.")
|
||||
|
||||
if not all(x.size == arr.size for x in X):
|
||||
raise ValueError("All arrays in `X` must have the same size as `arr`.")
|
||||
|
||||
# Define new dtype
|
||||
dtype = list(arr.dtype.descr) + [(col, x.dtype) for col, x in zip(cols, X)]
|
||||
|
||||
# Create a new array and fill in values
|
||||
out = numpy.empty(arr.size, dtype=dtype)
|
||||
for col in arr.dtype.names:
|
||||
out[col] = arr[col]
|
||||
for i, col in enumerate(cols):
|
||||
out[col] = X[:, i]
|
||||
for col, x in zip(cols, X):
|
||||
out[col] = x
|
||||
|
||||
return out
|
||||
|
||||
|
||||
def rm_columns(arr, cols):
|
||||
"""
|
||||
Remove columns `cols` from a record array `arr`. Creates a new array.
|
||||
Remove columns `cols` from a structured array `arr`. Allocates a new array.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
arr : record array
|
||||
Record array to remove columns from.
|
||||
arr : structured array
|
||||
Structured array to remove columns from.
|
||||
cols : str or list of str
|
||||
Column names to be removed.
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : record array
|
||||
out : structured array
|
||||
"""
|
||||
# Check columns we wish to delete are in the array
|
||||
# Ensure cols is a list
|
||||
cols = [cols] if isinstance(cols, str) else cols
|
||||
for col in cols:
|
||||
if col not in arr.dtype.names:
|
||||
raise ValueError("Column `{}` not in `arr`.".format(col))
|
||||
|
||||
# Get a new dtype without the cols to be deleted
|
||||
new_dtype = []
|
||||
for dtype, name in zip(arr.dtype.descr, arr.dtype.names, strict=True):
|
||||
if name not in cols:
|
||||
new_dtype.append(dtype)
|
||||
# Check columns we wish to delete are in the array
|
||||
missing_cols = [col for col in cols if col not in arr.dtype.names]
|
||||
if missing_cols:
|
||||
raise ValueError(f"Columns `{missing_cols}` not in `arr`.")
|
||||
|
||||
# Allocate a new array and fill it in.
|
||||
out = numpy.full(arr.size, numpy.nan, new_dtype)
|
||||
# Define new dtype without the cols to be deleted
|
||||
new_dtype = [(n, dt) for n, dt in arr.dtype.descr if n not in cols]
|
||||
|
||||
# Allocate a new array and fill in values
|
||||
out = numpy.empty(arr.size, dtype=new_dtype)
|
||||
for name in out.dtype.names:
|
||||
out[name] = arr[name]
|
||||
|
||||
return out
|
||||
|
||||
|
||||
def list_to_ndarray(arrs, cols):
|
||||
"""
|
||||
Convert a list of structured arrays of CSiBORG simulation catalogues to
|
||||
an 3-dimensional array.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
arrs : list of structured arrays
|
||||
List of CSiBORG catalogues.
|
||||
cols : str or list of str
|
||||
Columns to be extracted from the CSiBORG catalogues.
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : 3-dimensional array
|
||||
Catalogue array of shape `(n_realisations, n_samples, n_cols)`, where
|
||||
`n_samples` is the maximum number of samples over the CSiBORG
|
||||
catalogues.
|
||||
"""
|
||||
if not isinstance(arrs, list):
|
||||
raise TypeError("`arrs` must be a list of structured arrays.")
|
||||
cols = [cols] if isinstance(cols, str) else cols
|
||||
|
||||
Narr = len(arrs)
|
||||
Nobj_max = max([arr.size for arr in arrs])
|
||||
Ncol = len(cols)
|
||||
# Preallocate the array and fill it
|
||||
out = numpy.full((Narr, Nobj_max, Ncol), numpy.nan)
|
||||
for i in range(Narr):
|
||||
Nobj = arrs[i].size
|
||||
for j in range(Ncol):
|
||||
out[i, :Nobj, j] = arrs[i][cols[j]]
|
||||
return out
|
||||
|
||||
|
||||
def array_to_structured(arr, cols):
|
||||
"""
|
||||
Create a structured array from a 2-dimensional array.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
arr : 2-dimensional array
|
||||
Original array of shape `(n_samples, n_cols)`.
|
||||
cols : list of str
|
||||
Columns of the structured array
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : structured array
|
||||
Output structured array.
|
||||
"""
|
||||
cols = [cols] if isinstance(cols, str) else cols
|
||||
if arr.ndim != 2 and arr.shape[1] != len(cols):
|
||||
raise TypeError("`arr` must be a 2D array `(n_samples, n_cols)`.")
|
||||
|
||||
dtype = {"names": cols, "formats": [arr.dtype] * len(cols)}
|
||||
out = numpy.full(arr.shape[0], numpy.nan, dtype=dtype)
|
||||
for i, col in enumerate(cols):
|
||||
out[col] = arr[:, i]
|
||||
|
||||
return out
|
||||
|
||||
|
||||
def flip_cols(arr, col1, col2):
|
||||
"""
|
||||
Flip values in columns `col1` and `col2`. `arr` is passed by reference and
|
||||
is not explicitly returned back.
|
||||
Flip values in columns `col1` and `col2`. `arr` is modified in place.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
arr : structured array
|
||||
Array whose columns are to be converted.
|
||||
Array whose columns are to be flipped.
|
||||
col1 : str
|
||||
First column name.
|
||||
col2 : str
|
||||
Second column name.
|
||||
|
||||
Returns
|
||||
-------
|
||||
None
|
||||
"""
|
||||
dum = numpy.copy(arr[col1])
|
||||
arr[col1] = arr[col2]
|
||||
arr[col2] = dum
|
||||
if col1 not in arr.dtype.names or col2 not in arr.dtype.names:
|
||||
raise ValueError(f"Both `{col1}` and `{col2}` must exist in `arr`.")
|
||||
|
||||
|
||||
def extract_from_structured(arr, cols):
|
||||
"""
|
||||
Extract columns `cols` from a structured array. The array dtype is set
|
||||
to be that of the first column in `cols`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
arr : structured array
|
||||
Array from which to extract columns.
|
||||
cols : list of str or str
|
||||
Column to extract.
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : 2- or 1-dimensional array
|
||||
Array with shape `(n_particles, len(cols))`. If `len(cols)` is 1
|
||||
flattens the array.
|
||||
"""
|
||||
cols = [cols] if isinstance(cols, str) else cols
|
||||
for col in cols:
|
||||
if col not in arr.dtype.names:
|
||||
raise ValueError(f"Invalid column `{col}`!")
|
||||
# Preallocate an array and populate it
|
||||
out = numpy.zeros((arr.size, len(cols)), dtype=arr[cols[0]].dtype)
|
||||
for i, col in enumerate(cols):
|
||||
out[:, i] = arr[col]
|
||||
# Optionally flatten
|
||||
if len(cols) == 1:
|
||||
return out.reshape(-1, )
|
||||
return out
|
||||
arr[col1], arr[col2] = numpy.copy(arr[col2]), numpy.copy(arr[col1])
|
||||
|
||||
|
||||
###############################################################################
|
||||
|
|
|
|||
|
|
@ -99,12 +99,12 @@ def _main(nsim, simname, verbose):
|
|||
if simname == "csiborg":
|
||||
box = csiborgtools.read.CSiBORGBox(nsnap, nsim, paths)
|
||||
cat = csiborgtools.read.CSiBORGHaloCatalogue(
|
||||
nsim, paths, with_lagpatch=False, load_initial=False, rawdata=True,
|
||||
load_fitted=False)
|
||||
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, load_initial=False, rawdata=True)
|
||||
nsim, paths, nsnap, bounds=None, load_fitted=False,
|
||||
load_initial=False)
|
||||
|
||||
# Particle archive
|
||||
f = csiborgtools.read.read_h5(paths.particles(nsim, simname))
|
||||
|
|
@ -116,6 +116,7 @@ def _main(nsim, simname, verbose):
|
|||
for i in trange(len(cat)) if verbose else range(len(cat)):
|
||||
hid = cat["index"][i]
|
||||
out["index"][i] = hid
|
||||
# print("i = ", i)
|
||||
part = csiborgtools.read.load_halo_particles(hid, particles, halo_map,
|
||||
hid2map)
|
||||
# Skip if no particles.
|
||||
|
|
|
|||
|
|
@ -59,12 +59,12 @@ def get_counts(nsim, bins, paths, parser_args):
|
|||
|
||||
if simname == "csiborg":
|
||||
cat = csiborgtools.read.CSiBORGHaloCatalogue(
|
||||
nsim, paths, bounds=bounds, with_lagpatch=False,
|
||||
load_initial=False)
|
||||
nsim, paths, bounds=bounds, load_initial=False)
|
||||
logmass = numpy.log10(cat["totpartmass"])
|
||||
counts = csiborgtools.fits.number_counts(logmass, bins)
|
||||
elif simname == "quijote":
|
||||
cat0 = csiborgtools.read.QuijoteHaloCatalogue(nsim, paths, nsnap=4)
|
||||
cat0 = csiborgtools.read.QuijoteHaloCatalogue(nsim, paths, nsnap=4,
|
||||
load_initial=False)
|
||||
nmax = int(cat0.box.boxsize // (2 * parser_args.Rmax))**3
|
||||
counts = numpy.full((nmax, len(bins) - 1), numpy.nan,
|
||||
dtype=numpy.float32)
|
||||
|
|
@ -74,7 +74,8 @@ def get_counts(nsim, bins, paths, parser_args):
|
|||
logmass = numpy.log10(cat["group_mass"])
|
||||
counts[nobs, :] = csiborgtools.fits.number_counts(logmass, bins)
|
||||
elif simname == "quijote_full":
|
||||
cat = csiborgtools.read.QuijoteHaloCatalogue(nsim, paths, nsnap=4)
|
||||
cat = csiborgtools.read.QuijoteHaloCatalogue(nsim, paths, nsnap=4,
|
||||
load_initial=False)
|
||||
logmass = numpy.log10(cat["group_mass"])
|
||||
counts = csiborgtools.fits.number_counts(logmass, bins)
|
||||
else:
|
||||
|
|
|
|||
|
|
@ -68,7 +68,8 @@ def _main(nsim, simname, verbose):
|
|||
cat = csiborgtools.read.CSiBORGHaloCatalogue(
|
||||
nsim, paths, rawdata=True, load_fitted=False, load_initial=False)
|
||||
else:
|
||||
cat = csiborgtools.read.QuijoteHaloCatalogue(nsim, paths, nsnap=4)
|
||||
cat = csiborgtools.read.QuijoteHaloCatalogue(
|
||||
nsim, paths, nsnap=4, load_fitted=False, load_initial=False)
|
||||
hid2map = {hid: i for i, hid in enumerate(halo_map[:, 0])}
|
||||
|
||||
# Initialise the overlapper.
|
||||
|
|
|
|||
|
|
@ -11,11 +11,16 @@
|
|||
# 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.
|
||||
"""
|
||||
r"""
|
||||
Script to load in the simulation particles, sort them by their FoF halo ID and
|
||||
dump into a HDF5 file. Stores the first and last index of each halo in the
|
||||
particle array. This can be used for fast slicing of the array to acces
|
||||
particles of a single clump.
|
||||
|
||||
Ensures the following units:
|
||||
- Positions in box units.
|
||||
- Velocities in :math:`\mathrm{km} / \mathrm{s}`.
|
||||
- Masses in :math:`M_\odot / h`.
|
||||
"""
|
||||
from argparse import ArgumentParser
|
||||
from datetime import datetime
|
||||
|
|
@ -118,6 +123,14 @@ def main(nsim, simname, verbose):
|
|||
pars_extract = None
|
||||
parts, pids = partreader.read_particle(
|
||||
nsnap, nsim, pars_extract, return_structured=False, verbose=verbose)
|
||||
|
||||
# In case of CSiBORG, we need to convert the mass and velocities from
|
||||
# box units.
|
||||
if simname == "csiborg":
|
||||
box = csiborgtools.read.CSiBORGBox(nsnap, nsim, paths)
|
||||
parts[:, [3, 4, 5]] = box.box2vel(parts[:, [3, 4, 5]])
|
||||
parts[:, 6] = box.box2solarmass(parts[:, 6])
|
||||
|
||||
# Now we in two steps save the particles and particle IDs.
|
||||
if verbose:
|
||||
print(f"{datetime.now()}: dumping particles from {nsim}.", flush=True)
|
||||
|
|
|
|||
|
|
@ -12,9 +12,13 @@
|
|||
# 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.
|
||||
"""
|
||||
r"""
|
||||
Script to sort the initial snapshot particles according to their final
|
||||
snapshot ordering, which is sorted by the halo IDs.
|
||||
|
||||
Ensures the following units:
|
||||
- Positions in box units.
|
||||
- Masses in :math:`M_\odot / h`.
|
||||
"""
|
||||
from argparse import ArgumentParser
|
||||
from datetime import datetime
|
||||
|
|
@ -75,6 +79,13 @@ def _main(nsim, simname, verbose):
|
|||
nsnap = -1
|
||||
part0, pid0 = partreader.read_particle(
|
||||
nsnap, nsim, pars_extract, return_structured=False, verbose=verbose)
|
||||
|
||||
# In CSiBORG we need to convert particle masses from box units.
|
||||
if simname == "csiborg":
|
||||
box = csiborgtools.read.CSiBORGBox(
|
||||
max(paths.get_snapshots(nsim, simname)), nsim, paths)
|
||||
part0[:, 3] = box.box2solarmass(part0[:, 3])
|
||||
|
||||
# Quijote's initial snapshot information also contains velocities but we
|
||||
# don't need those.
|
||||
if simname == "quijote":
|
||||
|
|
|
|||
|
|
@ -89,9 +89,13 @@ def read_single_catalogue(args, config, nsim, run, rmax, paths, nobs=None):
|
|||
raise KeyError(f"No configuration for run {run}.")
|
||||
# We first read the full catalogue without applying any bounds.
|
||||
if args.simname == "csiborg":
|
||||
cat = csiborgtools.read.CSiBORGHaloCatalogue(nsim, paths)
|
||||
cat = csiborgtools.read.CSiBORGHaloCatalogue(
|
||||
nsim, paths, load_fitted=True, load_inital=True,
|
||||
with_lagpatch=False)
|
||||
else:
|
||||
cat = csiborgtools.read.QuijoteHaloCatalogue(nsim, paths, nsnap=4)
|
||||
cat = csiborgtools.read.QuijoteHaloCatalogue(
|
||||
nsim, paths, nsnap=4, load_fitted=True, load_initial=True,
|
||||
with_lagpatch=False)
|
||||
if nobs is not None:
|
||||
# We may optionally already here pick a fiducial observer.
|
||||
cat = cat.pick_fiducial_observer(nobs, args.Rmax)
|
||||
|
|
|
|||
|
|
@ -49,8 +49,10 @@ def open_csiborg(nsim):
|
|||
cat : csiborgtools.read.CSiBORGHaloCatalogue
|
||||
"""
|
||||
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
||||
bounds = {"totpartmass": (None, None), "dist": (0, 155/0.705)}
|
||||
return csiborgtools.read.CSiBORGHaloCatalogue(nsim, paths, bounds=bounds)
|
||||
bounds = {"totpartmass": (None, None), "dist": (0, 155)}
|
||||
return csiborgtools.read.CSiBORGHaloCatalogue(
|
||||
nsim, paths, bounds=bounds, load_fitted=True, load_initial=True,
|
||||
with_lagpatch=False)
|
||||
|
||||
|
||||
def open_quijote(nsim, nobs=None):
|
||||
|
|
@ -69,9 +71,11 @@ def open_quijote(nsim, nobs=None):
|
|||
cat : csiborgtools.read.QuijoteHaloCatalogue
|
||||
"""
|
||||
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
||||
cat = csiborgtools.read.QuijoteHaloCatalogue(nsim, paths, nsnap=4)
|
||||
cat = csiborgtools.read.QuijoteHaloCatalogue(
|
||||
nsim, paths, nsnap=4, load_fitted=True, load_initial=True,
|
||||
with_lagpatch=False)
|
||||
if nobs is not None:
|
||||
cat = cat.pick_fiducial_observer(nobs, rmax=155.5 / 0.705)
|
||||
cat = cat.pick_fiducial_observer(nobs, rmax=155.5)
|
||||
return cat
|
||||
|
||||
|
||||
|
|
@ -101,7 +105,7 @@ def plot_mass_vs_ncells(nsim, pdf=False):
|
|||
plt.yscale("log")
|
||||
for n in [1, 10, 100]:
|
||||
plt.axvline(n * 512 * mpart, c="black", ls="--", zorder=0, lw=0.8)
|
||||
plt.xlabel(r"$M_{\rm tot} / M_\odot$")
|
||||
plt.xlabel(r"$M_{\rm tot} ~ [M_\odot$ / h]")
|
||||
plt.ylabel(r"$N_{\rm cells}$")
|
||||
|
||||
for ext in ["png"] if pdf is False else ["png", "pdf"]:
|
||||
|
|
@ -198,7 +202,7 @@ def plot_hmf(pdf=False):
|
|||
ax[1].axhline(1, color="k", ls=plt.rcParams["lines.linestyle"],
|
||||
lw=0.5 * plt.rcParams["lines.linewidth"], zorder=0)
|
||||
ax[0].set_ylabel(r"$\frac{\mathrm{d} n}{\mathrm{d}\log M_{\rm h}}~\mathrm{dex}^{-1}$") # noqa
|
||||
ax[1].set_xlabel(r"$M_{\rm h}$ [$M_\odot$]")
|
||||
ax[1].set_xlabel(r"$M_{\rm h}~[M_\odot / h]$")
|
||||
ax[1].set_ylabel(r"$\mathrm{CSiBORG} / \mathrm{Quijote}$")
|
||||
|
||||
ax[0].set_xscale("log")
|
||||
|
|
@ -268,7 +272,7 @@ def plot_hmf_quijote_full(pdf=False):
|
|||
lw=0.5 * plt.rcParams["lines.linewidth"], zorder=0)
|
||||
ax[0].set_ylabel(r"$\frac{\mathrm{d}^2 n}{\mathrm{d}\log M_{\rm h} \mathrm{d} V}~[\mathrm{dex}^{-1} (\mathrm{Mpc / h})^{-3}]$", # noqa
|
||||
fontsize="small")
|
||||
ax[1].set_xlabel(r"$M_{\rm h}$ [$M_\odot$]")
|
||||
ax[1].set_xlabel(r"$M_{\rm h}~[$M_\odot / h]$", fontsize="small")
|
||||
ax[1].set_ylabel(r"$\mathrm{HMF} / \langle \mathrm{HMF} \rangle$",
|
||||
fontsize="small")
|
||||
|
||||
|
|
|
|||
|
|
@ -99,7 +99,7 @@ def plot_knn(runname):
|
|||
# color=cols[k % len(cols)], zorder=0)
|
||||
|
||||
plt.legend()
|
||||
plt.xlabel(r"$r~[\mathrm{Mpc}]$")
|
||||
plt.xlabel(r"$r~[\mathrm{Mpc} / h]$")
|
||||
plt.ylabel(r"$P(k | V = 4 \pi r^3 / 3)$")
|
||||
|
||||
for ext in ["png"]:
|
||||
|
|
|
|||
|
|
@ -54,7 +54,7 @@ def open_cat(nsim):
|
|||
cat : csiborgtools.read.CSiBORGHaloCatalogue
|
||||
"""
|
||||
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
|
||||
bounds = {"totpartmass": (1e12, None)}
|
||||
bounds = {"dist": (0, 155), "totpartmass": (1e12, None)}
|
||||
return csiborgtools.read.CSiBORGHaloCatalogue(nsim, paths, bounds=bounds)
|
||||
|
||||
|
||||
|
|
@ -86,7 +86,7 @@ def plot_mass_vs_pairoverlap(nsim0, nsimx):
|
|||
plt.hexbin(x, y, mincnt=1, bins="log",
|
||||
gridsize=50)
|
||||
plt.colorbar(label="Counts in bins")
|
||||
plt.xlabel(r"$\log M_{\rm tot} / M_\odot$")
|
||||
plt.xlabel(r"$\log M_{\rm tot} ~ [M_\odot / h]$")
|
||||
plt.ylabel("Pair overlap")
|
||||
plt.ylim(0., 1.)
|
||||
|
||||
|
|
@ -130,7 +130,7 @@ def plot_mass_vs_maxpairoverlap(nsim0, nsimx):
|
|||
plt.hexbin(x, y, mincnt=1, bins="log",
|
||||
gridsize=50)
|
||||
plt.colorbar(label="Counts in bins")
|
||||
plt.xlabel(r"$\log M_{\rm tot} / M_\odot$")
|
||||
plt.xlabel(r"$\log M_{\rm tot} ~ [M_\odot / h]$")
|
||||
plt.ylabel("Maximum pair overlap")
|
||||
plt.ylim(0., 1.)
|
||||
|
||||
|
|
@ -214,9 +214,9 @@ def plot_mass_vsmedmaxoverlap(nsim0):
|
|||
numpy.nanstd(max_overlap, axis=1), gridsize=30,
|
||||
C=x, reduce_C_function=numpy.nanmean)
|
||||
|
||||
axs[0].set_xlabel(r"$\log M_{\rm tot} / M_\odot$")
|
||||
axs[0].set_xlabel(r"$\log M_{\rm tot} ~ [M_\odot / h]$")
|
||||
axs[0].set_ylabel(r"Mean max. pair overlap")
|
||||
axs[1].set_xlabel(r"$\log M_{\rm tot} / M_\odot$")
|
||||
axs[1].set_xlabel(r"$\log M_{\rm tot} ~ [M_\odot / h]$")
|
||||
axs[1].set_ylabel(r"Uncertainty of max. pair overlap")
|
||||
axs[2].set_xlabel(r"Mean max. pair overlap")
|
||||
axs[2].set_ylabel(r"Uncertainty of max. pair overlap")
|
||||
|
|
@ -287,14 +287,15 @@ def plot_summed_overlap_vs_mass(nsim0):
|
|||
axs[2].plot(t, t, color="red", linestyle="--")
|
||||
axs[0].set_ylim(0.)
|
||||
axs[1].set_ylim(0.)
|
||||
axs[0].set_xlabel(r"$\log M_{\rm tot} / M_\odot$")
|
||||
axs[0].set_xlabel(r"$\log M_{\rm tot} ~ [M_\odot / h]$")
|
||||
axs[0].set_ylabel("Mean summed overlap")
|
||||
axs[1].set_xlabel(r"$\log M_{\rm tot} / M_\odot$")
|
||||
axs[1].set_xlabel(r"$\log M_{\rm tot} ~ [M_\odot / h]$")
|
||||
axs[1].set_ylabel("Uncertainty of summed overlap")
|
||||
axs[2].set_xlabel(r"$1 - $ mean summed overlap")
|
||||
axs[2].set_ylabel("Mean prob. of no match")
|
||||
|
||||
label = ["Bin counts", "Bin counts", r"$\log M_{\rm tot} / M_\odot$"]
|
||||
label = ["Bin counts", "Bin counts",
|
||||
r"$\log M_{\rm tot} ~ [M_\odot / h]$"]
|
||||
ims = [im1, im2, im3]
|
||||
for i in range(3):
|
||||
axins = inset_axes(axs[i], width="100%", height="5%",
|
||||
|
|
@ -338,7 +339,7 @@ def plot_mass_vs_separation(nsim0, nsimx, plot_std=False, min_overlap=0.0):
|
|||
catx = open_cat(nsimx)
|
||||
|
||||
reader = csiborgtools.read.PairOverlap(cat0, catx, paths,
|
||||
maxdist=155 / 0.705)
|
||||
maxdist=155)
|
||||
mass = numpy.log10(reader.cat0("totpartmass"))
|
||||
dist = reader.dist(in_initial=False, norm_kind="r200c")
|
||||
overlap = reader.overlap(True)
|
||||
|
|
@ -373,7 +374,7 @@ def plot_mass_vs_separation(nsim0, nsimx, plot_std=False, min_overlap=0.0):
|
|||
ax.plot(xrange, numpy.polyval(p, xrange), color="red",
|
||||
linestyle="--")
|
||||
fig.colorbar(cx, label="Bin counts")
|
||||
ax.set_xlabel(r"$\log M_{\rm tot} / M_\odot$")
|
||||
ax.set_xlabel(r"$\log M_{\rm tot} ~ [M_\odot / h]$")
|
||||
ax.set_ylabel(r"$\log \langle \Delta R / R_{\rm 200c}\rangle$")
|
||||
|
||||
fig.tight_layout()
|
||||
|
|
@ -460,10 +461,10 @@ def plot_maxoverlap_mass(nsim0):
|
|||
axs[0].plot(t, t + 0.2, color="red", linestyle="--", alpha=0.5)
|
||||
axs[0].plot(t, t - 0.2, color="red", linestyle="--", alpha=0.5)
|
||||
|
||||
axs[0].set_xlabel(r"$\log M_{\rm tot} / M_\odot$")
|
||||
axs[1].set_xlabel(r"$\log M_{\rm tot} / M_\odot$")
|
||||
axs[0].set_ylabel(r"Max. overlap mean of $\log M_{\rm tot} / M_\odot$")
|
||||
axs[1].set_ylabel(r"Max. overlap std. of $\log M_{\rm tot} / M_\odot$")
|
||||
axs[0].set_xlabel(r"$\log M_{\rm tot} ~ [M_\odot / h]$")
|
||||
axs[1].set_xlabel(r"$\log M_{\rm tot} ~ [M_\odot / h]$")
|
||||
axs[0].set_ylabel(r"Max. overlap mean of $\log M_{\rm tot} ~ [M_\odot / h]$")
|
||||
axs[1].set_ylabel(r"Max. overlap std. of $\log M_{\rm tot} ~ [M_\odot / h]$")
|
||||
|
||||
ims = [im0, im1]
|
||||
for i in range(2):
|
||||
|
|
@ -518,9 +519,9 @@ def plot_maxoverlapstat(nsim0, key):
|
|||
m = numpy.isfinite(key_val) & numpy.isfinite(mu)
|
||||
print("True to expectation corr: ", kendalltau(key_val[m], mu[m]))
|
||||
|
||||
axs[0].set_xlabel(r"$\log M_{\rm tot} / M_\odot$")
|
||||
axs[0].set_xlabel(r"$\log M_{\rm tot} ~ [M_\odot / h]$")
|
||||
axs[0].set_ylabel(r"Max. overlap mean of ${}$".format(key_label))
|
||||
axs[1].set_xlabel(r"$\log M_{\rm tot} / M_\odot$")
|
||||
axs[1].set_xlabel(r"$\log M_{\rm tot} ~ [M_\odot / h]$")
|
||||
axs[1].set_ylabel(r"Max. overlap std. of ${}$".format(key_label))
|
||||
axs[2].set_xlabel(r"${}$".format(key_label))
|
||||
axs[2].set_ylabel(r"Max. overlap mean of ${}$".format(key_label))
|
||||
|
|
@ -622,10 +623,9 @@ def plot_mass_vs_expected_mass(nsim0, min_overlap=0, max_prob_nomatch=1):
|
|||
gridsize=50, C=mass[mask],
|
||||
reduce_C_function=numpy.nanmedian)
|
||||
axs[2].axhline(0, color="red", linestyle="--", alpha=0.5)
|
||||
|
||||
axs[0].set_xlabel(r"True $\log M_{\rm tot} / M_\odot$")
|
||||
axs[0].set_ylabel(r"Expected $\log M_{\rm tot} / M_\odot$")
|
||||
axs[1].set_xlabel(r"True $\log M_{\rm tot} / M_\odot$")
|
||||
axs[0].set_xlabel(r"True $\log M_{\rm tot} ~ [M_\odot / h]$")
|
||||
axs[0].set_ylabel(r"Expected $\log M_{\rm tot} ~ [M_\odot / h]$")
|
||||
axs[1].set_xlabel(r"True $\log M_{\rm tot} ~ [M_\odot / h]$")
|
||||
axs[1].set_ylabel(r"Std. of $\sigma_{\log M_{\rm tot}}$")
|
||||
axs[2].set_xlabel(r"1 - median prob. of no match")
|
||||
axs[2].set_ylabel(r"$\log M_{\rm tot} - \log M_{\rm tot, exp}$")
|
||||
|
|
@ -636,7 +636,8 @@ def plot_mass_vs_expected_mass(nsim0, min_overlap=0, max_prob_nomatch=1):
|
|||
axs[0].plot(t, t - 0.2, color="red", linestyle="--", alpha=0.5)
|
||||
|
||||
ims = [im0, im1, im2]
|
||||
labels = ["Bin counts", "Bin counts", r"$\log M_{\rm tot}$"]
|
||||
labels = ["Bin counts", "Bin counts",
|
||||
r"$\log M_{\rm tot} ~ [M_\odot / h]$"]
|
||||
for i in range(3):
|
||||
axins = inset_axes(axs[i], width="100%", height="5%",
|
||||
loc='upper center', borderpad=-0.75)
|
||||
|
|
@ -758,10 +759,10 @@ def plot_dist(run, kind, kwargs, runs_to_mass, pulled_cdf=False, r200=None):
|
|||
|
||||
fig, ax = plt.subplots()
|
||||
if run != "mass009":
|
||||
ax.set_title(r"${} \leq \log M_{{\rm tot}} / M_\odot < {}$"
|
||||
ax.set_title(r"${} \leq \log M_{{\rm tot}} / (M_\odot h) < {}$"
|
||||
.format(*runs_to_mass[run]), fontsize="small")
|
||||
else:
|
||||
ax.set_title(r"$\log M_{{\rm tot}} / M_\odot \geq {}$"
|
||||
ax.set_title(r"$\log M_{{\rm tot}} / (M_\odot h) \geq {}$"
|
||||
.format(runs_to_mass[run][0]), fontsize="small")
|
||||
# Plot data
|
||||
nrad = y_csiborg.shape[0]
|
||||
|
|
@ -778,12 +779,12 @@ def plot_dist(run, kind, kwargs, runs_to_mass, pulled_cdf=False, r200=None):
|
|||
ax.plot(x2, y2, c="gray", ls="--",
|
||||
label="Quijote" if i == 0 else None)
|
||||
|
||||
fig.colorbar(cmap, ax=ax, label=r"$R_{\rm dist}~[\mathrm{Mpc}]$")
|
||||
fig.colorbar(cmap, ax=ax, label=r"$R_{\rm dist}~[\mathrm{Mpc} / h]$")
|
||||
ax.grid(alpha=0.5, lw=0.4)
|
||||
# Plot labels
|
||||
if pulled_cdf:
|
||||
if r200 is None:
|
||||
ax.set_xlabel(r"$\tilde{r}_{1\mathrm{NN}}~[\mathrm{Mpc}]$")
|
||||
ax.set_xlabel(r"$\tilde{r}_{1\mathrm{NN}}~[\mathrm{Mpc} / h]$")
|
||||
if kind == "pdf":
|
||||
ax.set_ylabel(r"$p(\tilde{r}_{1\mathrm{NN}})$")
|
||||
else:
|
||||
|
|
@ -796,7 +797,7 @@ def plot_dist(run, kind, kwargs, runs_to_mass, pulled_cdf=False, r200=None):
|
|||
ax.set_ylabel(r"$\mathrm{CDF}(\tilde{r}_{1\mathrm{NN}} / R_{200c})$") # noqa
|
||||
else:
|
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if r200 is None:
|
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ax.set_xlabel(r"$r_{1\mathrm{NN}}~[\mathrm{Mpc}]$")
|
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ax.set_xlabel(r"$r_{1\mathrm{NN}}~[\mathrm{Mpc} / h]$")
|
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if kind == "pdf":
|
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ax.set_ylabel(r"$p(r_{1\mathrm{NN}})$")
|
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else:
|
||||
|
|
@ -901,7 +902,7 @@ def plot_cdf_diff(runs, kwargs, pulled_cdf, runs_to_mass):
|
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ax.fill_between(r, *numpy.percentile(dy, [16, 84], axis=0),
|
||||
alpha=0.5, color=cmap.to_rgba(runs_to_mass[i]))
|
||||
fig.colorbar(cmap, ax=ax, ticks=runs_to_mass,
|
||||
label=r"$\log M_{\rm tot} / M_\odot$")
|
||||
label=r"$\log M_{\rm tot} ~ [M_\odot / h]$")
|
||||
ax.set_xlim(0.0, 55)
|
||||
ax.set_ylim(0)
|
||||
|
||||
|
|
@ -909,17 +910,17 @@ def plot_cdf_diff(runs, kwargs, pulled_cdf, runs_to_mass):
|
|||
|
||||
# Plot labels
|
||||
if pulled_cdf:
|
||||
ax.set_xlabel(r"$\tilde{r}_{1\mathrm{NN}}~[\mathrm{Mpc}]$")
|
||||
ax.set_xlabel(r"$\tilde{r}_{1\mathrm{NN}}~[\mathrm{Mpc} / h]$")
|
||||
else:
|
||||
ax.set_xlabel(r"$r_{1\mathrm{NN}}~[\mathrm{Mpc}]$")
|
||||
ax.set_xlabel(r"$r_{1\mathrm{NN}}~[\mathrm{Mpc} / h]$")
|
||||
ax.set_ylabel(r"$\Delta \mathrm{CDF}(r_{1\mathrm{NN}})$")
|
||||
|
||||
# Plot labels
|
||||
if pulled_cdf:
|
||||
ax.set_xlabel(r"$\tilde{r}_{1\mathrm{NN}}~[\mathrm{Mpc}]$")
|
||||
ax.set_xlabel(r"$\tilde{r}_{1\mathrm{NN}}~[\mathrm{Mpc} / h]$")
|
||||
ax.set_ylabel(r"$\Delta \mathrm{CDF}(\tilde{r}_{1\mathrm{NN}})$")
|
||||
else:
|
||||
ax.set_xlabel(r"$r_{1\mathrm{NN}}~[\mathrm{Mpc}]$")
|
||||
ax.set_xlabel(r"$r_{1\mathrm{NN}}~[\mathrm{Mpc} / h]$")
|
||||
ax.set_ylabel(r"$\Delta \mathrm{CDF}(r_{1\mathrm{NN}})$")
|
||||
|
||||
fig.tight_layout()
|
||||
|
|
@ -1104,7 +1105,7 @@ def plot_significance(simname, runs, nsim, nobs, kind, kwargs, runs_to_mass):
|
|||
|
||||
cbar_ax = fig.add_axes([1.0, 0.125, 0.035, 0.85])
|
||||
fig.colorbar(cmap, cax=cbar_ax, ticks=runs_to_mass,
|
||||
label=r"$\log M_{\rm tot} / M_\odot$")
|
||||
label=r"$\log M_{\rm tot} ~ [M_\odot / h]$")
|
||||
|
||||
ax[0].set_xlim(z[0], z[-1])
|
||||
ax[0].set_ylim(1e-5, 1.)
|
||||
|
|
@ -1216,7 +1217,7 @@ def plot_significance_vs_mass(simname, runs, nsim, nobs, kind, kwargs,
|
|||
corr = plt_utils.latex_float(*kendalltau(xs[mask], ys[mask]))
|
||||
plt.title(r"$\tau = {}, p = {}$".format(*corr), fontsize="small")
|
||||
|
||||
plt.xlabel(r"$\log M_{\rm tot} / M_\odot$")
|
||||
plt.xlabel(r"$\log M_{\rm tot} ~ [M_\odot / h]$")
|
||||
if kind == "ks":
|
||||
plt.ylabel(r"$\log p$-value of $r_{1\mathrm{NN}}$ distribution")
|
||||
plt.ylim(top=0)
|
||||
|
|
|
|||
Loading…
Reference in a new issue