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Richard Stiskalek 2023-04-04 15:22:00 +01:00 committed by GitHub
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commit 8d34b832af
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1
csiborgtools/__init__.py
View file

@ -12,5 +12,4 @@
# You should have received a copy of the GNU General Public License along # You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc., # with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. # 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
from csiborgtools import (read, match, utils, units, fits, field) # noqa from csiborgtools import (read, match, utils, units, fits, field) # noqa

1
csiborgtools/field/__init__.py
View file

@ -12,5 +12,4 @@
# You should have received a copy of the GNU General Public License along # You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc., # with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. # 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
from .density import DensityField # noqa from .density import DensityField # noqa

125
csiborgtools/field/density.py
View file

@ -15,12 +15,12 @@
""" """
Density field and cross-correlation calculations. Density field and cross-correlation calculations.
""" """
from warnings import warn
from tqdm import trange
import numpy import numpy
import MAS_library as MASL import MAS_library as MASL
import Pk_library as PKL import Pk_library as PKL
import smoothing_library as SL import smoothing_library as SL
from warnings import warn
from tqdm import trange
from ..units import (BoxUnits, radec_to_cartesian) from ..units import (BoxUnits, radec_to_cartesian)
@ -55,9 +55,12 @@ class DensityField:
def __init__(self, particles, boxsize, box, MAS="CIC"): def __init__(self, particles, boxsize, box, MAS="CIC"):
self.particles = particles self.particles = particles
assert boxsize > 0
self._boxsize = boxsize
assert isinstance(box, BoxUnits)
self.box = box self.box = box
self.boxsize = boxsize assert MAS in ["NGP", "CIC", "TSC", "PCS"]
self.MAS = MAS self._MAS = MAS
@property @property
def particles(self): def particles(self):
@ -89,15 +92,10 @@ class DensityField:
""" """
return self._boxsize return self._boxsize
@boxsize.setter
def boxsize(self, boxsize):
"""Set `self.boxsize`."""
self._boxsize = boxsize
@property @property
def box(self): def box(self):
""" """
The simulation box information and transformations. Simulation box information and transformations.
Returns Returns
------- -------
@ -105,17 +103,10 @@ class DensityField:
""" """
return self._box return self._box
@box.setter
def box(self, box):
"""Set the simulation box."""
if not isinstance(box, BoxUnits):
raise TypeError("`box` must be `BoxUnits` instance.")
self._box = box
@property @property
def MAS(self): def MAS(self):
""" """
The mass-assignment scheme. Mass-assignment scheme.
Returns Returns
------- -------
@ -123,15 +114,6 @@ class DensityField:
""" """
return self._MAS return self._MAS
@MAS.setter
def MAS(self, MAS):
"""Sets `self.MAS`."""
opts = ["NGP", "CIC", "TSC", "PCS"]
if MAS not in opts:
raise ValueError("Invalid MAS `{}`. Options are: `{}`."
.format(MAS, opts))
self._MAS = MAS
@staticmethod @staticmethod
def _force_f32(x, name): def _force_f32(x, name):
if x.dtype != numpy.float32: if x.dtype != numpy.float32:
@ -147,17 +129,16 @@ class DensityField:
Parameters Parameters
---------- ----------
grid : int grid : int
The grid size. Grid size.
smooth_scale : float, optional smooth_scale : float, optional
Scale to smoothen the density field, in units matching Scale to smoothen the density field, in units matching
`self.boxsize`. By default `None`, i.e. no smoothing is applied. `self.boxsize`. By default no smoothing is applied.
verbose : float, optional verbose : bool
A verbosity flag. By default `True`. Verbosity flag.
Returns Returns
------- -------
rho : 3-dimensional array of shape `(grid, grid, grid)`. rho : 3-dimensional array of shape `(grid, grid, grid)`.
Density field.
References References
---------- ----------
@ -185,17 +166,16 @@ class DensityField:
Parameters Parameters
---------- ----------
grid : int grid : int
The grid size. Grid size.
smooth_scale : float, optional smooth_scale : float, optional
Scale to smoothen the density field, in units matching Scale to smoothen the density field, in units matching
`self.boxsize`. By default `None`, i.e. no smoothing is applied. `self.boxsize`. By default no smoothing is applied.
verbose : float, optional verbose : bool
A verbosity flag. By default `True`. Verbosity flag.
Returns Returns
------- -------
overdensity : 3-dimensional array of shape `(grid, grid, grid)`. overdensity : 3-dimensional array of shape `(grid, grid, grid)`.
Overdensity field.
""" """
# Get the overdensity # Get the overdensity
delta = self.density_field(grid, smooth_scale, verbose) delta = self.density_field(grid, smooth_scale, verbose)
@ -210,17 +190,16 @@ class DensityField:
Parameters Parameters
---------- ----------
grid : int grid : int
The grid size. Grid size.
smooth_scale : float, optional smooth_scale : float, optional
Scale to smoothen the original density field, in units matching Scale to smoothen the density field, in units matching
`self.boxsize`. By default `None`, i.e. no smoothing is applied. `self.boxsize`. By default no smoothing is applied.
verbose : float, optional verbose : bool
A verbosity flag. By default `True`. Verbosity flag.
Returns Returns
------- -------
potential : 3-dimensional array of shape `(grid, grid, grid)`. potential : 3-dimensional array of shape `(grid, grid, grid)`.
Potential field.
""" """
delta = self.overdensity_field(grid, smooth_scale, verbose) delta = self.overdensity_field(grid, smooth_scale, verbose)
if verbose: if verbose:
@ -236,12 +215,12 @@ class DensityField:
Parameters Parameters
---------- ----------
grid : int grid : int
The grid size. Grid size.
smooth_scale : float, optional smooth_scale : float, optional
Scale to smoothen the original density field, in units matching Scale to smoothen the density field, in units matching
`self.boxsize`. By default `None`, i.e. no smoothing is applied. `self.boxsize`. By default no smoothing is applied.
verbose : float, optional verbose : bool
A verbosity flag. By default `True`. Verbosity flag.
Returns Returns
------- -------
@ -260,12 +239,12 @@ class DensityField:
Parameters Parameters
---------- ----------
grid : int grid : int
The grid size. Grid size.
smooth_scale : float, optional smooth_scale : float, optional
Scale to smoothen the original density field, in units matching Scale to smoothen the density field, in units matching
`self.boxsize`. By default `None`, i.e. no smoothing is applied. `self.boxsize`. By default no smoothing is applied.
verbose : float, optional verbose : bool, optional
A verbosity flag. By default `True`. A verbosity flag.
Returns Returns
------- -------
@ -284,24 +263,23 @@ class DensityField:
Parameters Parameters
---------- ----------
grid : int grid : int
The grid size. Grid size.
smooth_scale : float, optional smooth_scale : float, optional
Scale to smoothen the original density field, in units matching Scale to smoothen the density field, in units matching
`self.boxsize`. By default `None`, i.e. no smoothing is applied. `self.boxsize`. By default no smoothing is applied.
verbose : float, optional verbose : bool, optional
A verbosity flag. By default `True`. Verbosity flag.
Returns Returns
------- -------
pk : py:class`Pk_library.Pk` pk : py:class`Pk_library.Pk`
Power spectrum object.
""" """
delta = self.overdensity_field(grid, smooth_scale, verbose) delta = self.overdensity_field(grid, smooth_scale, verbose)
return PKL.Pk( return PKL.Pk(
delta, self.boxsize, axis=1, MAS=self.MAS, threads=1, delta, self.boxsize, axis=1, MAS=self.MAS, threads=1,
verbose=verbose) verbose=verbose)
def smooth_field(self, field, scale, threads=1): def smooth_field(self, field, smooth_scale, threads=1):
""" """
Smooth a field with a Gaussian filter. Smooth a field with a Gaussian filter.
@ -309,9 +287,9 @@ class DensityField:
---------- ----------
field : 3-dimensional array of shape `(grid, grid, grid)` field : 3-dimensional array of shape `(grid, grid, grid)`
The field to be smoothed. The field to be smoothed.
scale : float smooth_scale : float, optional
The smoothing scale of the Gaussian filter. Units must match that Scale to smoothen the density field, in units matching
of `self.boxsize`. `self.boxsize`. By default no smoothing is applied.
threads : int, optional threads : int, optional
Number of threads. By default 1. Number of threads. By default 1.
@ -322,17 +300,17 @@ class DensityField:
Filter = "Gaussian" Filter = "Gaussian"
grid = field.shape[0] grid = field.shape[0]
# FFT of the filter # FFT of the filter
W_k = SL.FT_filter(self.boxsize, scale, grid, Filter, threads) W_k = SL.FT_filter(self.boxsize, smooth_scale, grid, Filter, threads)
return SL.field_smoothing(field, W_k, threads) return SL.field_smoothing(field, W_k, threads)
def evaluate_field(self, *field, pos): def evaluate_field(self, *field, pos):
""" """
Evaluate the field at Cartesian coordinates. Evaluate the field at Cartesian coordinates using CIC interpolation.
Parameters Parameters
---------- ----------
field : (list of) 3-dimensional array of shape `(grid, grid, grid)` field : (list of) 3-dimensional array of shape `(grid, grid, grid)`
The density field that is to be interpolated. Density field that is to be interpolated.
pos : 2-dimensional array of shape `(n_samples, 3)` pos : 2-dimensional array of shape `(n_samples, 3)`
Positions to evaluate the density field. The coordinates span range Positions to evaluate the density field. The coordinates span range
of [0, boxsize]. of [0, boxsize].
@ -340,7 +318,6 @@ class DensityField:
Returns Returns
------- -------
interp_field : (list of) 1-dimensional array of shape `(n_samples,). interp_field : (list of) 1-dimensional array of shape `(n_samples,).
Interpolated fields at `pos`.
""" """
self._force_f32(pos, "pos") self._force_f32(pos, "pos")
@ -353,12 +330,13 @@ class DensityField:
def evaluate_sky(self, *field, pos, isdeg=True): def evaluate_sky(self, *field, pos, isdeg=True):
""" """
Evaluate the field at given distance, right ascension and declination. Evaluate the field at given distance, right ascension and declination.
Assumes that the observed is in the centre of the box. Assumes that the observed is in the centre of the box and uses CIC
interpolation.
Parameters Parameters
---------- ----------
field : (list of) 3-dimensional array of shape `(grid, grid, grid)` field : (list of) 3-dimensional array of shape `(grid, grid, grid)`
The density field that is to be interpolated. Assumed to be defined Density field that is to be interpolated. Assumed to be defined
on a Cartesian grid. on a Cartesian grid.
pos : 2-dimensional array of shape `(n_samples, 3)` pos : 2-dimensional array of shape `(n_samples, 3)`
Spherical coordinates to evaluate the field. Should be distance, Spherical coordinates to evaluate the field. Should be distance,
@ -369,7 +347,6 @@ class DensityField:
Returns Returns
------- -------
interp_field : (list of) 1-dimensional array of shape `(n_samples,). interp_field : (list of) 1-dimensional array of shape `(n_samples,).
Interpolated fields at `pos`.
""" """
self._force_f32(pos, "pos") self._force_f32(pos, "pos")
X = numpy.vstack( X = numpy.vstack(
@ -387,12 +364,11 @@ class DensityField:
Parameters Parameters
---------- ----------
gx, gy, gz : 1-dimensional arrays of shape `(n_samples,)` gx, gy, gz : 1-dimensional arrays of shape `(n_samples,)`
Gravitational field components. Gravitational field Cartesian components.
Returns Returns
------- -------
g : 1-dimensional array of shape `(n_samples,)` g : 1-dimensional array of shape `(n_samples,)`
Gravitational field norm.
""" """
return numpy.sqrt(gx * gx + gy * gy + gz * gz) return numpy.sqrt(gx * gx + gy * gy + gz * gz)
@ -410,7 +386,6 @@ class DensityField:
Returns Returns
------- -------
eigvals : 2-dimensional array of shape `(n_samples, 3)` eigvals : 2-dimensional array of shape `(n_samples, 3)`
Eigenvalues of each sample.
""" """
n_samples = T00.size n_samples = T00.size
# Fill array of shape `(n_samples, 3, 3)` to calculate eigvals # Fill array of shape `(n_samples, 3, 3)` to calculate eigvals
@ -446,14 +421,14 @@ class DensityField:
Directions to evaluate the field. Assumes `dec` is in [-90, 90] Directions to evaluate the field. Assumes `dec` is in [-90, 90]
degrees (or equivalently in radians). degrees (or equivalently in radians).
field : 3-dimensional array of shape `(grid, grid, grid)` field : 3-dimensional array of shape `(grid, grid, grid)`
The density field that is to be interpolated. Assumed to be defined Density field that is to be interpolated. Assumed to be defined
on a Cartesian grid `[0, self.boxsize]^3`. on a Cartesian grid `[0, self.boxsize]^3`.
dist_marg : 1-dimensional array dist_marg : 1-dimensional array
Radial distances to evaluate the field. Radial distances to evaluate the field.
isdeg : bool, optional isdeg : bool, optional
Whether `ra` and `dec` are in degres. By default `True`. Whether `ra` and `dec` are in degres. By default `True`.
verbose : bool, optional verbose : bool, optional
Verbosity flag. By default `True`. Verbosity flag.
Returns Returns
------- -------

1
csiborgtools/fits/__init__.py
View file

@ -12,7 +12,6 @@
# You should have received a copy of the GNU General Public License along # You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc., # with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. # 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
from .haloprofile import (NFWProfile, NFWPosterior) # noqa from .haloprofile import (NFWProfile, NFWPosterior) # noqa
from .halo import (distribute_halos, clump_with_particles, # noqa from .halo import (distribute_halos, clump_with_particles, # noqa
dump_split_particles, load_split_particles, # noqa dump_split_particles, load_split_particles, # noqa

253
csiborgtools/fits/halo.py
View file

@ -15,12 +15,10 @@
""" """
Tools for splitting the particles and a clump object. Tools for splitting the particles and a clump object.
""" """
import numpy
from os import remove from os import remove
from warnings import warn from warnings import warn
from os.path import join from os.path import join
import numpy
from tqdm import trange from tqdm import trange
from ..read import ParticleReader from ..read import ParticleReader
@ -34,11 +32,11 @@ def clump_with_particles(particle_clumps, clumps):
particle_clumps : 1-dimensional array particle_clumps : 1-dimensional array
Array of particles' clump IDs. Array of particles' clump IDs.
clumps : structured array clumps : structured array
The clumps array. Clumps array.
Returns Returns
------- -------
with_particles : 1-dimensional array with_particles : 1-dimensional boolean array
Array of whether a clump has any particles. Array of whether a clump has any particles.
""" """
return numpy.isin(clumps["index"], particle_clumps) return numpy.isin(clumps["index"], particle_clumps)
@ -54,13 +52,12 @@ def distribute_halos(n_splits, clumps):
n_splits : int n_splits : int
Number of splits. Number of splits.
clumps : structured array clumps : structured array
The clumps array. Clumps array.
Returns Returns
------- -------
splits : 2-dimensional array splits : 2-dimensional array of shape `(njobs, 2)`
Array of starting and ending indices of each CPU of shape Array of starting and ending indices of each CPU.
`(njobs, 2)`.
""" """
# Make sure these are unique IDs # Make sure these are unique IDs
indxs = clumps["index"] indxs = clumps["index"]
@ -75,7 +72,7 @@ def distribute_halos(n_splits, clumps):
def dump_split_particles(particles, particle_clumps, clumps, n_splits, def dump_split_particles(particles, particle_clumps, clumps, n_splits,
paths, verbose=True): nsnap, nsim, paths, verbose=True):
""" """
Save the data needed for each split so that a process does not have to load Save the data needed for each split so that a process does not have to load
everything. everything.
@ -90,6 +87,10 @@ def dump_split_particles(particles, particle_clumps, clumps, n_splits,
The clumps array. The clumps array.
n_splits : int n_splits : int
Number of times to split the clumps. Number of times to split the clumps.
nsnap : int
Snapshot index.
nsim : int
IC realisation index.
paths : py:class`csiborgtools.read.CSiBORGPaths` paths : py:class`csiborgtools.read.CSiBORGPaths`
CSiBORG paths-handling object with set `n_sim` and `n_snap`. CSiBORG paths-handling object with set `n_sim` and `n_snap`.
verbose : bool, optional verbose : bool, optional
@ -112,9 +113,8 @@ def dump_split_particles(particles, particle_clumps, clumps, n_splits,
splits = distribute_halos(n_splits, clumps) splits = distribute_halos(n_splits, clumps)
fname = join(paths.temp_dumpdir, "out_{}_snap_{}_{}.npz") fname = join(paths.temp_dumpdir, "out_{}_snap_{}_{}.npz")
iters = trange(n_splits) if verbose else range(n_splits)
tot = 0 tot = 0
for n in iters: for n in trange(n_splits) if verbose else range(n_splits):
# Lower and upper array index of the clumps array # Lower and upper array index of the clumps array
i, j = splits[n, :] i, j = splits[n, :]
# Clump indices in this split # Clump indices in this split
@ -130,7 +130,7 @@ def dump_split_particles(particles, particle_clumps, clumps, n_splits,
"with no particles.".format(n, indxs.size, npart_unique)) "with no particles.".format(n, indxs.size, npart_unique))
# Dump it! # Dump it!
tot += mask.sum() tot += mask.sum()
fout = fname.format(paths.n_sim, paths.n_snap, n) fout = fname.format(nsim, nsnap, n)
numpy.savez(fout, particles[mask], particle_clumps[mask], clumps[i:j]) numpy.savez(fout, particles[mask], particle_clumps[mask], clumps[i:j])
# There are particles whose clump ID is > 1 and have no counterpart in the # There are particles whose clump ID is > 1 and have no counterpart in the
@ -166,7 +166,7 @@ def split_jobs(njobs, ncpu):
return jobs return jobs
def load_split_particles(n_split, paths, remove_split=False): def load_split_particles(nsplit, nsnap, nsim, paths, remove_split=False):
""" """
Load particles of a split saved by `dump_split_particles`. Load particles of a split saved by `dump_split_particles`.
@ -174,6 +174,10 @@ def load_split_particles(n_split, paths, remove_split=False):
-------- --------
n_split : int n_split : int
Split index. Split index.
nsnap : int
Snapshot index.
nsim : int
IC realisation index.
paths : py:class`csiborgtools.read.CSiBORGPaths` paths : py:class`csiborgtools.read.CSiBORGPaths`
CSiBORG paths-handling object with set `n_sim` and `n_snap`. CSiBORG paths-handling object with set `n_sim` and `n_snap`.
remove_split : bool, optional remove_split : bool, optional
@ -188,9 +192,8 @@ def load_split_particles(n_split, paths, remove_split=False):
clumps : 1-dimensional array clumps : 1-dimensional array
Clumps belonging to this split. Clumps belonging to this split.
""" """
fname = join( fname = join(paths.temp_dumpdir,
paths.temp_dumpdir, "out_{}_snap_{}_{}.npz".format( "out_{}_snap_{}_{}.npz".format(nsim, nsnap, nsplit))
paths.n_sim, paths.n_snap, n_split))
file = numpy.load(fname) file = numpy.load(fname)
particles, clump_indxs, clumps = (file[f] for f in file.files) particles, clump_indxs, clumps = (file[f] for f in file.files)
if remove_split: if remove_split:
@ -228,6 +231,11 @@ def pick_single_clump(n, particles, particle_clumps, clumps):
return particles[mask], clumps[n] return particles[mask], clumps[n]
###############################################################################
# Clump object #
###############################################################################
class Clump: class Clump:
r""" r"""
A clump (halo) object to handle the particles and their clump's data. A clump (halo) object to handle the particles and their clump's data.
@ -264,28 +272,31 @@ class Clump:
G : float, optional G : float, optional
The gravitational constant :math:`G` in box units. By default not set. The gravitational constant :math:`G` in box units. By default not set.
""" """
_r = None
_rmin = None
_rmax = None
_pos = None _pos = None
_clump_pos = None _clump_pos = None
_clump_mass = None _clump_mass = None
_vel = None _vel = None
_Npart = None
_index = None _index = None
_rhoc = None _rhoc = None
_G = None _G = None
def __init__(self, x, y, z, m, x0, y0, z0, clump_mass=None, def __init__(self, x, y, z, m, x0, y0, z0, clump_mass=None,
vx=None, vy=None, vz=None, index=None, rhoc=None, G=None): vx=None, vy=None, vz=None, index=None, rhoc=None, G=None):
self.pos = (x, y, z, x0, y0, z0) self._pos = numpy.vstack([x - x0, y - y0, z - z0]).T
self.clump_pos = (x0, y0, z0) self._clump_pos = numpy.asarray((x0, y0, z0))
self.clump_mass = clump_mass assert clump_mass is None or isinstance(clump_mass, float)
self.vel = (vx, vy, vz) self._clump_mass = clump_mass
self.m = m if all(v is not None for v in (vx, vy, vz)):
self.index = index self._vel = numpy.vstack([vx, vy, vz]).T
self.rhoc = rhoc assert self._vel.shape == self.pos.shape
self.G = G assert m.ndim == 1 and m.size == self.Npart
self._m = m
assert index is None or (isinstance(index, (int, numpy.int64)) and index >= 0) # noqa
self._index = index
assert rhoc is None or rhoc > 0
self._rhoc = rhoc
assert G is None or G > 0
self._G = G
@property @property
def pos(self): def pos(self):
@ -298,15 +309,16 @@ class Clump:
""" """
return self._pos return self._pos
@pos.setter @property
def pos(self, X): def Npart(self):
"""Sets `pos` and calculates radial distance.""" """
x, y, z, x0, y0, z0 = X Number of particles associated with this clump.
self._pos = numpy.vstack([x - x0, y - y0, z - z0]).T
self._r = numpy.sum(self.pos**2, axis=1)**0.5 Returns
self._rmin = numpy.min(self._r) -------
self._rmax = numpy.max(self._r) Npart : int
self._Npart = self._r.size """
return self.pos.shape[0]
@property @property
def r(self): def r(self):
@ -317,7 +329,7 @@ class Clump:
------- -------
r : 1-dimensional array of shape `(n_particles, )`. r : 1-dimensional array of shape `(n_particles, )`.
""" """
return self._r return numpy.sum(self.pos**2, axis=1)**0.5
@property @property
def rmin(self): def rmin(self):
@ -328,7 +340,7 @@ class Clump:
------- -------
rmin : float rmin : float
""" """
return self._rmin return numpy.min(self.r)
@property @property
def rmax(self): def rmax(self):
@ -339,23 +351,12 @@ class Clump:
------- -------
rmin : float rmin : float
""" """
return self._rmax return numpy.max(self.r)
@property
def Npart(self):
"""
Number of particles associated with this clump.
Returns
-------
Npart : int
"""
return self._Npart
@property @property
def clump_pos(self): def clump_pos(self):
""" """
Cartesian clump coordinates. Cartesian position components of the clump.
Returns Returns
------- -------
@ -363,14 +364,6 @@ class Clump:
""" """
return self._clump_pos return self._clump_pos
@clump_pos.setter
def clump_pos(self, pos):
"""Sets `clump_pos`. Makes sure it is the correct shape."""
pos = numpy.asarray(pos)
if pos.shape != (3,):
raise TypeError("Invalid clump position `{}`".format(pos.shape))
self._clump_pos = pos
@property @property
def clump_mass(self): def clump_mass(self):
""" """
@ -384,17 +377,10 @@ class Clump:
raise ValueError("Clump mass `clump_mass` has not been set.") raise ValueError("Clump mass `clump_mass` has not been set.")
return self._clump_mass return self._clump_mass
@clump_mass.setter
def clump_mass(self, mass):
"""Sets `clump_mass`, making sure it is a float."""
if mass is not None and not isinstance(mass, float):
raise ValueError("`clump_mass` must be a float.")
self._clump_mass = mass
@property @property
def vel(self): def vel(self):
""" """
Cartesian particle velocities. Throws an error if they are not set. Cartesian velocity components of the clump.
Returns Returns
------- -------
@ -404,16 +390,6 @@ class Clump:
raise ValueError("Velocities `vel` have not been set.") raise ValueError("Velocities `vel` have not been set.")
return self._vel return self._vel
@vel.setter
def vel(self, V):
"""Sets the particle velocities, making sure the shape is OK."""
if any(v is None for v in V):
return
vx, vy, vz = V
self._vel = numpy.vstack([vx, vy, vz]).T
if self.pos.shape != self.vel.shape:
raise ValueError("Different `pos` and `vel` arrays!")
@property @property
def m(self): def m(self):
""" """
@ -425,18 +401,11 @@ class Clump:
""" """
return self._m return self._m
@m.setter
def m(self, m):
"""Sets particle masses `m`, ensuring it is the right size."""
if not isinstance(m, numpy.ndarray) and m.size != self.r.size:
raise TypeError("`r` and `m` must be equal size 1-dim arrays.")
self._m = m
@property @property
def center_mass(self): def center_mass(self):
""" """
Clump center of mass. Note that this is already in a frame centered at Cartesian position components of the clump centre of mass. Note that
the clump's potential minimum. this is already in a frame centered at the clump's potential minimum.
Returns Returns
------- -------
@ -444,10 +413,43 @@ class Clump:
""" """
return numpy.average(self.pos, axis=0, weights=self.m) return numpy.average(self.pos, axis=0, weights=self.m)
@property
def angular_momentum(self):
"""
Clump angular momentum in the box coordinates.
Returns
-------
J : 1-dimensional array or shape `(3, )`
"""
J = numpy.cross(self.pos - self.center_mass, self.vel)
return numpy.einsum("i,ij->j", self.m, J)
@property
def lambda200c(self):
r"""
Clump Bullock spin, see Eq. 5 in [1], in a radius of
:math:`R_{\rm 200c}`.
References
----------
[1] A Universal Angular Momentum Profile for Galactic Halos; 2001;
Bullock, J. S.; Dekel, A.; Kolatt, T. S.; Kravtsov, A. V.;
Klypin, A. A.; Porciani, C.; Primack, J. R.
Returns
-------
lambda200c : float
"""
J = self.angular_momentum
R, M = self.spherical_overdensity_mass(200)
V = numpy.sqrt(self.G * M / R)
return numpy.linalg.norm(J) / (numpy.sqrt(2) * M * V * R)
@property @property
def index(self): def index(self):
""" """
The halo finder clump index. Halo finder clump index.
Returns Returns
------- -------
@ -457,17 +459,10 @@ class Clump:
raise ValueError("Halo index `hindex` has not been set.") raise ValueError("Halo index `hindex` has not been set.")
return self._index return self._index
@index.setter
def index(self, n):
"""Sets the halo index, making sure it is an integer."""
if n is not None and not (isinstance(n, (int, numpy.int64)) and n > 0):
raise ValueError("Halo index `index` must be an integer > 0.")
self._index = n
@property @property
def rhoc(self): def rhoc(self):
r""" r"""
The critical density :math:`\rho_c` at this snapshot in box units. Critical density :math:`\rho_c` at this snapshot in box units.
Returns Returns
------- -------
@ -477,17 +472,10 @@ class Clump:
raise ValueError("The critical density `rhoc` has not been set.") raise ValueError("The critical density `rhoc` has not been set.")
return self._rhoc return self._rhoc
@rhoc.setter
def rhoc(self, rhoc):
"""Sets the critical density. Makes sure it is > 0."""
if rhoc is not None and not rhoc > 0:
raise ValueError("Critical density `rho_c` must be > 0.")
self._rhoc = rhoc
@property @property
def G(self): def G(self):
r""" r"""
The gravitational constant :math:`G` in box units. Gravitational constant :math:`G` in box units.
Returns Returns
------- -------
@ -497,13 +485,6 @@ class Clump:
raise ValueError("The grav. constant `G` has not been set.") raise ValueError("The grav. constant `G` has not been set.")
return self._G return self._G
@G.setter
def G(self, G):
"""Sets the gravitational constant. Makes sure it is > 0."""
if G is not None and not G > 0:
raise ValueError("Gravitational constant `G` must be > 0.")
self._G = G
@property @property
def total_particle_mass(self): def total_particle_mass(self):
""" """
@ -528,8 +509,7 @@ class Clump:
def enclosed_spherical_mass(self, rmax, rmin=0): def enclosed_spherical_mass(self, rmax, rmin=0):
""" """
The enclosed spherical mass between two radii. All quantities remain Enclosed spherical mass between two radii in box units.
in the box units.
Parameters Parameters
---------- ----------
@ -547,14 +527,14 @@ class Clump:
def enclosed_spherical_volume(self, rmax, rmin=0): def enclosed_spherical_volume(self, rmax, rmin=0):
""" """
Enclosed volume within two radii. Enclosed spherical volume within two radii in box units.
Parameters Parameters
---------- ----------
rmax : float rmax : float
The maximum radial distance. Maximum radial distance.
rmin : float, optional rmin : float, optional
The minimum radial distance. By default 0. Minimum radial distance. By default 0.
Returns Returns
------- -------
@ -581,15 +561,15 @@ class Clump:
The :math:`\delta_{\rm x}` parameters where :math:`\mathrm{x}` is The :math:`\delta_{\rm x}` parameters where :math:`\mathrm{x}` is
the overdensity multiple. the overdensity multiple.
n_particles_min : int n_particles_min : int
The minimum number of enclosed particles for a radius to be Minimum number of enclosed particles for a radius to be
considered trustworthy. considered trustworthy.
Returns Returns
------- -------
rx : float rx : float
The radius where the enclosed density reaches required value. Radius where the enclosed density reaches required value.
mx : float mx : float
The corresponding spherical enclosed mass. Corresponding spherical enclosed mass.
""" """
# If single `delta` turn to list # If single `delta` turn to list
delta = [delta] if isinstance(delta, (float, int)) else delta delta = [delta] if isinstance(delta, (float, int)) else delta
@ -636,39 +616,6 @@ class Clump:
return rfound, mfound return rfound, mfound
@property
def angular_momentum(self):
"""
The clump angular momentum in the box coordinates.
Returns
-------
J : 1-dimensional array or shape `(3, )`
"""
J = numpy.cross(self.pos - self.center_mass, self.vel)
return numpy.einsum("i,ij->j", self.m, J)
@property
def lambda200c(self):
r"""
The clump Bullock spin, see Eq. 5 in [1], in a radius of
:math:`R_{\rm 200c}`.
References
----------
[1] A Universal Angular Momentum Profile for Galactic Halos; 2001;
Bullock, J. S.; Dekel, A.; Kolatt, T. S.; Kravtsov, A. V.;
Klypin, A. A.; Porciani, C.; Primack, J. R.
Returns
-------
lambda200c : float
"""
J = self.angular_momentum
R, M = self.spherical_overdensity_mass(200)
V = numpy.sqrt(self.G * M / R)
return numpy.linalg.norm(J) / (numpy.sqrt(2) * M * V * R)
@classmethod @classmethod
def from_arrays(cls, particles, clump, rhoc=None, G=None): def from_arrays(cls, particles, clump, rhoc=None, G=None):
r""" r"""

80
csiborgtools/fits/haloprofile.py
View file

@ -15,37 +15,14 @@
""" """
Halo profiles functions and posteriors. Halo profiles functions and posteriors.
""" """
import numpy
from jax import numpy as jnumpy from jax import numpy as jnumpy
from jax import grad from jax import grad
import numpy
from scipy.optimize import minimize_scalar from scipy.optimize import minimize_scalar
from scipy.stats import uniform from scipy.stats import uniform
from .halo import Clump from .halo import Clump
def jax_switch(f1, f2, use_jax):
"""
Return function `f1` if `use_jax` else return `f2`.
Parameters
----------
f1 : function
The JAX function.
f2 : function
The non-JAX function.
use_jax : bool
Whether to use the JAX function.
Returns
-------
chosen_func : function
The chosen function.
"""
return f1 if use_jax else f2
class NFWProfile: class NFWProfile:
r""" r"""
The Navarro-Frenk-White (NFW) density profile defined as The Navarro-Frenk-White (NFW) density profile defined as
@ -62,10 +39,6 @@ class NFWProfile:
rho0 : float rho0 : float
NFW density parameter :math:`\rho_0`. NFW density parameter :math:`\rho_0`.
""" """
def __init__(self):
pass
@staticmethod @staticmethod
def profile(r, Rs, rho0): def profile(r, Rs, rho0):
r""" r"""
@ -109,7 +82,7 @@ class NFWProfile:
logdensity : float or 1-dimensional array logdensity : float or 1-dimensional array
Logarithmic density of the NFW profile at :math:`r`. Logarithmic density of the NFW profile at :math:`r`.
""" """
log = jax_switch(jnumpy.log, numpy.log, use_jax) log = jnumpy.log if use_jax else numpy.log
x = r / Rs x = r / Rs
return log(rho0) - log(x) - 2 * log(1 + x) return log(rho0) - log(x) - 2 * log(1 + x)
@ -134,8 +107,9 @@ class NFWProfile:
M : float or 1-dimensional array M : float or 1-dimensional array
The enclosed mass. The enclosed mass.
""" """
log = jnumpy.log if use_jax else numpy.log
x = r / Rs x = r / Rs
out = jax_switch(jnumpy.log, numpy.log, use_jax)(1 + x) - x / (1 + x) out = log(1 + x) - x / (1 + x)
return 4 * numpy.pi * rho0 * Rs**3 * out return 4 * numpy.pi * rho0 * Rs**3 * out
def bounded_enclosed_mass(self, rmin, rmax, Rs, rho0, use_jax=False): def bounded_enclosed_mass(self, rmin, rmax, Rs, rho0, use_jax=False):
@ -145,9 +119,9 @@ class NFWProfile:
Parameters Parameters
---------- ----------
rmin : float rmin : float
The minimum radius. Minimum radius.
rmax : float rmax : float
The maximum radius. Maximum radius.
Rs : float Rs : float
Scale radius :math:`R_s`. Scale radius :math:`R_s`.
rho0 : float rho0 : float
@ -158,7 +132,7 @@ class NFWProfile:
Returns Returns
------- -------
M : float M : float
The enclosed mass within the radial range. Enclosed mass within the radial range.
""" """
return (self.enclosed_mass(rmax, Rs, rho0, use_jax) return (self.enclosed_mass(rmax, Rs, rho0, use_jax)
- self.enclosed_mass(rmin, Rs, rho0, use_jax)) - self.enclosed_mass(rmin, Rs, rho0, use_jax))
@ -182,9 +156,9 @@ class NFWProfile:
Rs : float Rs : float
Scale radius :math:`R_s`. Scale radius :math:`R_s`.
rmin : float rmin : float
The minimum radius. Minimum radius.
rmax : float rmax : float
The maximum radius. Maximum radius.
Returns Returns
------- -------
@ -202,9 +176,9 @@ class NFWProfile:
Parameters Parameters
---------- ----------
rmin : float rmin : float
The minimum radius. Minimum radius.
rmax : float rmax : float
The maximum radius. Maximum radius.
Rs : float Rs : float
Scale radius :math:`R_s`. Scale radius :math:`R_s`.
N : int, optional N : int, optional
@ -271,7 +245,6 @@ class NFWPosterior(NFWProfile):
Returns Returns
------- -------
clump : `Clump` clump : `Clump`
The clump object.
""" """
return self._clump return self._clump
@ -284,7 +257,6 @@ class NFWPosterior(NFWProfile):
Returns Returns
------- -------
r : 1-dimensional array r : 1-dimensional array
Array of shape `(n_particles, )`.
""" """
return self._r return self._r
@ -297,7 +269,6 @@ class NFWPosterior(NFWProfile):
Returns Returns
------- -------
Npart : int Npart : int
Number of particles.
""" """
return self._Npart return self._Npart
@ -310,7 +281,6 @@ class NFWPosterior(NFWProfile):
Returns Returns
------- -------
r : 1-dimensional array r : 1-dimensional array
Array of shape `(n_particles, )`.
""" """
return self._m return self._m
@ -322,7 +292,6 @@ class NFWPosterior(NFWProfile):
Returns Returns
------- -------
rmin : float rmin : float
The minimum distance.
""" """
return self._rmin return self._rmin
@ -335,7 +304,6 @@ class NFWPosterior(NFWProfile):
Returns Returns
------- -------
rmax : float rmax : float
The R200c radius.
""" """
return self._rmax return self._rmax
@ -384,7 +352,6 @@ class NFWPosterior(NFWProfile):
Returns Returns
------- -------
rho0: float rho0: float
The NFW density parameter.
""" """
Mtot = numpy.exp(self._logMtot) Mtot = numpy.exp(self._logMtot)
Mnfw_norm = self.bounded_enclosed_mass(self.rmin, self.rmax, Rs, 1) Mnfw_norm = self.bounded_enclosed_mass(self.rmin, self.rmax, Rs, 1)
@ -401,8 +368,7 @@ class NFWPosterior(NFWProfile):
Returns Returns
------- -------
ll : float lp : float
The logarithmic prior.
""" """
if not self._logrmin < logRs < self._logrmax: if not self._logrmin < logRs < self._logrmax:
return - numpy.infty return - numpy.infty
@ -422,16 +388,14 @@ class NFWPosterior(NFWProfile):
Returns Returns
------- -------
ll : float ll : float
The logarithmic likelihood.
""" """
Rs = 10**logRs Rs = 10**logRs
log = jnumpy.log if use_jax else numpy.log log = jnumpy.log if use_jax else numpy.log
# Expected enclosed mass from a NFW # Expected enclosed mass from a NFW
Mnfw = self.bounded_enclosed_mass(self.rmin, self.rmax, Mnfw = self.bounded_enclosed_mass(self.rmin, self.rmax,
Rs, 1, use_jax) Rs, 1, use_jax)
ll = jax_switch(jnumpy.sum, numpy.sum, use_jax)(self.logprofile( fsum = jnumpy.sum if use_jax else numpy.sum
self.r, Rs, 1, use_jax)) ll = fsum(self.logprofile(self.r, Rs, 1, use_jax)) + self._ll0
ll += self._ll0
return ll - self.Npart * log(Mnfw) return ll - self.Npart * log(Mnfw)
@property @property
@ -444,7 +408,6 @@ class NFWPosterior(NFWProfile):
Returns Returns
------- -------
initlogRs : float initlogRs : float
The initial guess of :math:`\log R_{\rm s}`.
""" """
bins = numpy.linspace(self.rmin, self.rmax, bins = numpy.linspace(self.rmin, self.rmax,
self._binsguess) self._binsguess)
@ -465,7 +428,6 @@ class NFWPosterior(NFWProfile):
Returns Returns
------- -------
lpost : float lpost : float
The logarithmic posterior.
""" """
lp = self.logprior(logRs) lp = self.logprior(logRs)
if not numpy.isfinite(lp): if not numpy.isfinite(lp):
@ -475,7 +437,7 @@ class NFWPosterior(NFWProfile):
def uncertainty_at_maxpost(self, logRs_max): def uncertainty_at_maxpost(self, logRs_max):
r""" r"""
Calculate Gaussian approximation of the uncertainty at `logRs_max`, the Calculate Gaussian approximation of the uncertainty at `logRs_max`, the
maximum a-posteriori esitmate. This is the square root of the negative maximum a-posteriori estimate. This is the square root of the negative
inverse 2nd derivate of the logarithimic posterior with respect to the inverse 2nd derivate of the logarithimic posterior with respect to the
logarithm of the scale factor. This is only valid `logRs_max` is the logarithm of the scale factor. This is only valid `logRs_max` is the
maximum of the posterior! maximum of the posterior!
@ -492,7 +454,6 @@ class NFWPosterior(NFWProfile):
Returns Returns
------- -------
uncertainty : float uncertainty : float
The uncertainty calculated as outlined above.
""" """
def f(x): def f(x):
return self(x, use_jax=True) return self(x, use_jax=True)
@ -506,9 +467,10 @@ class NFWPosterior(NFWProfile):
def maxpost_logRs(self, calc_err=False, eps=1e-4): def maxpost_logRs(self, calc_err=False, eps=1e-4):
r""" r"""
Maximum a-posterio estimate of the scale radius :math:`\log R_{\rm s}`. Maximum a-posteriori estimate of the scale radius
Returns the scale radius if the fit converged, otherwise `numpy.nan`. :math:`\log R_{\rm s}`. Returns the scale radius if the fit converged,
Checks whether :math:`log r_{\rm max} / R_{\rm s} > \epsilon`, where otherwise `numpy.nan`. Checks whether
:math:`log r_{\rm max} / R_{\rm s} > \epsilon`, where
to ensure that the scale radius is not too close to the boundary which to ensure that the scale radius is not too close to the boundary which
occurs if the fit fails. occurs if the fit fails.
@ -521,9 +483,9 @@ class NFWPosterior(NFWProfile):
Returns Returns
------- -------
logRs: float logRs: float
The log scale radius. Log scale radius.
uncertainty : float uncertainty : float
The uncertainty on the scale radius. Calculated following Uncertainty on the scale radius. Calculated following
`self.uncertainty_at_maxpost`. `self.uncertainty_at_maxpost`.
""" """
# Loss function to optimize # Loss function to optimize

2
csiborgtools/match/__init__.py
View file

@ -13,7 +13,7 @@
# with this program; if not, write to the Free Software Foundation, Inc., # with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. # 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
from .match import (brute_spatial_separation, RealisationsMatcher, cosine_similarity, # noqa from .match import (RealisationsMatcher, cosine_similarity, # noqa
ParticleOverlap, get_clumplims, fill_delta, fill_delta_indxs, # noqa ParticleOverlap, get_clumplims, fill_delta, fill_delta_indxs, # noqa
calculate_overlap, calculate_overlap_indxs, # noqa calculate_overlap, calculate_overlap_indxs, # noqa
dist_centmass, dist_percentile) # noqa dist_centmass, dist_percentile) # noqa

1
csiborgtools/match/correlation.py
View file

@ -12,7 +12,6 @@
# You should have received a copy of the GNU General Public License along # You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc., # with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. # 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
import numpy import numpy
from Corrfunc.mocks import DDtheta_mocks from Corrfunc.mocks import DDtheta_mocks
from Corrfunc.utils import convert_3d_counts_to_cf from Corrfunc.utils import convert_3d_counts_to_cf

9
csiborgtools/match/knn.py
View file

@ -129,7 +129,7 @@ class kNN_CDF:
return corr return corr
def brute_cdf(self, knn, nneighbours, Rmax, nsamples, rmin, rmax, neval, def brute_cdf(self, knn, nneighbours, Rmax, nsamples, rmin, rmax, neval,
random_state=42, dtype=numpy.float32): random_state=42, dtype=numpy.float32):
""" """
Calculate the CDF for a kNN of CSiBORG halo catalogues without batch Calculate the CDF for a kNN of CSiBORG halo catalogues without batch
sizing. This can become memory intense for large numbers of randoms sizing. This can become memory intense for large numbers of randoms
@ -234,7 +234,7 @@ class kNN_CDF:
jointdist = numpy.zeros((batch_size, 2), dtype=dtype) jointdist = numpy.zeros((batch_size, 2), dtype=dtype)
for j in range(nbatches): for j in range(nbatches):
rand = self.rvs_in_sphere(batch_size, Rmax, rand = self.rvs_in_sphere(batch_size, Rmax,
random_state=random_state + j) random_state=random_state + j)
dist0, __ = knn0.kneighbors(rand, nneighbours) dist0, __ = knn0.kneighbors(rand, nneighbours)
dist1, __ = knn1.kneighbors(rand, nneighbours) dist1, __ = knn1.kneighbors(rand, nneighbours)
@ -258,7 +258,6 @@ class kNN_CDF:
range=(rmin, rmax)) range=(rmin, rmax))
cdf1[k, :] += _counts cdf1[k, :] += _counts
joint_cdf = numpy.cumsum(joint_cdf, axis=-1) joint_cdf = numpy.cumsum(joint_cdf, axis=-1)
cdf0 = numpy.cumsum(cdf0, axis=-1) cdf0 = numpy.cumsum(cdf0, axis=-1)
cdf1 = numpy.cumsum(cdf1, axis=-1) cdf1 = numpy.cumsum(cdf1, axis=-1)
@ -271,8 +270,8 @@ class kNN_CDF:
return rs, cdf0, cdf1, joint_cdf return rs, cdf0, cdf1, joint_cdf
def __call__(self, *knns, nneighbours, Rmax, nsamples, rmin, rmax, neval, def __call__(self, *knns, nneighbours, Rmax, nsamples, rmin, rmax, neval,
batch_size=None, verbose=True, random_state=42, batch_size=None, verbose=True, random_state=42,
dtype=numpy.float32): dtype=numpy.float32):
""" """
Calculate the CDF for a set of kNNs of CSiBORG halo catalogues. Calculate the CDF for a set of kNNs of CSiBORG halo catalogues.

156
csiborgtools/match/match.py
View file

@ -12,64 +12,21 @@
# You should have received a copy of the GNU General Public License along # You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc., # with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. # 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
"""
Support for matching halos between CSiBORG IC realisations.
"""
from gc import collect
import numpy import numpy
from scipy.ndimage import gaussian_filter from scipy.ndimage import gaussian_filter
from tqdm import (tqdm, trange)
from astropy.coordinates import SkyCoord
from numba import jit from numba import jit
from gc import collect from tqdm import (tqdm, trange)
from ..read import concatenate_clumps from ..read import concatenate_clumps
from ..utils import now from ..utils import now
def brute_spatial_separation(c1, c2, angular=False, N=None, verbose=False): ###############################################################################
""" # Realisations matcher for calculating overlaps #
Calculate for each point in `c1` the `N` closest points in `c2`. ###############################################################################
Parameters
----------
c1 : `astropy.coordinates.SkyCoord`
Coordinates of the first set of points.
c2 : `astropy.coordinates.SkyCoord`
Coordinates of the second set of points.
angular : bool, optional
Whether to calculate angular separation or 3D separation. By default
`False` and 3D separation is calculated.
N : int, optional
Number of closest points in `c2` to each object in `c1` to return.
verbose : bool, optional
Verbosity flag. By default `False`.
Returns
-------
sep : 1-dimensional array
Separation of each object in `c1` to `N` closest objects in `c2`. The
array shape is `(c1.size, N)`. Separation is in units of `c1`.
indxs : 1-dimensional array
Indexes of the closest objects in `c2` for each object in `c1`. The
array shape is `(c1.size, N)`.
"""
if not (isinstance(c1, SkyCoord) and isinstance(c2, SkyCoord)):
raise TypeError("`c1` & `c2` must be `astropy.coordinates.SkyCoord`.")
N1 = c1.size
N2 = c2.size if N is None else N
# Pre-allocate arrays
sep = numpy.full((N1, N2), numpy.nan)
indxs = numpy.full((N1, N2), numpy.nan, dtype=int)
iters = tqdm(range(N1)) if verbose else range(N1)
for i in iters:
if angular:
dist = c1[i].separation(c2).value
else:
dist = c1[i].separation_3d(c2).value
# Sort the distances
sort = numpy.argsort(dist)[:N2]
indxs[i, :] = sort
sep[i, :] = dist[sort]
return sep, indxs
class RealisationsMatcher: class RealisationsMatcher:
@ -101,9 +58,12 @@ class RealisationsMatcher:
def __init__(self, nmult=1., dlogmass=2., mass_kind="totpartmass", def __init__(self, nmult=1., dlogmass=2., mass_kind="totpartmass",
overlapper_kwargs={}): overlapper_kwargs={}):
self.nmult = nmult assert nmult > 0
self.dlogmass = dlogmass assert dlogmass > 0
self.mass_kind = mass_kind assert isinstance(mass_kind, str)
self._nmult = nmult
self._dlogmass = dlogmass
self._mass_kind = mass_kind
self._overlapper = ParticleOverlap(**overlapper_kwargs) self._overlapper = ParticleOverlap(**overlapper_kwargs)
@property @property
@ -118,12 +78,6 @@ class RealisationsMatcher:
""" """
return self._nmult return self._nmult
@nmult.setter
def nmult(self, nmult):
"""Set `nmult`."""
assert nmult > 0
self._nmult = nmult
@property @property
def dlogmass(self): def dlogmass(self):
""" """
@ -136,16 +90,10 @@ class RealisationsMatcher:
""" """
return self._dlogmass return self._dlogmass
@dlogmass.setter
def dlogmass(self, dlogmass):
"""Set `dlogmass`."""
assert dlogmass > 0
self._dlogmass = dlogmass
@property @property
def mass_kind(self): def mass_kind(self):
""" """
The mass kind whose similarity is to be checked. Mass kind whose similarity is to be checked.
Returns Returns
------- -------
@ -153,12 +101,6 @@ class RealisationsMatcher:
""" """
return self._mass_kind return self._mass_kind
@mass_kind.setter
def mass_kind(self, mass_kind):
"""Set `mass_kind`."""
assert isinstance(mass_kind, str)
self._mass_kind = mass_kind
@property @property
def overlapper(self): def overlapper(self):
""" """
@ -213,7 +155,7 @@ class RealisationsMatcher:
# Query the KNN # Query the KNN
verbose and print("{}: querying the KNN.".format(now()), flush=True) verbose and print("{}: querying the KNN.".format(now()), flush=True)
match_indxs = radius_neighbours( match_indxs = radius_neighbours(
catx.knn(select_initial=True), cat0.positions0, catx.knn(select_initial=True), cat0.positions(in_initial=True),
radiusX=cat0["lagpatch"], radiusKNN=catx["lagpatch"], radiusX=cat0["lagpatch"], radiusKNN=catx["lagpatch"],
nmult=self.nmult, enforce_in32=True, verbose=verbose) nmult=self.nmult, enforce_in32=True, verbose=verbose)
@ -381,10 +323,6 @@ class ParticleOverlap:
the nearest grid position particle assignment scheme, with optional the nearest grid position particle assignment scheme, with optional
Gaussian smoothing. Gaussian smoothing.
""" """
_inv_clength = None
_clength = None
_nshift = None
def __init__(self): def __init__(self):
# Inverse cell length in box units. By default :math:`2^11`, which # Inverse cell length in box units. By default :math:`2^11`, which
# matches the initial RAMSES grid resolution. # matches the initial RAMSES grid resolution.
@ -679,6 +617,11 @@ class ParticleOverlap:
nonzero, mass1, mass2) nonzero, mass1, mass2)
###############################################################################
# Halo matching supplementary functions #
###############################################################################
@jit(nopython=True) @jit(nopython=True)
def fill_delta(delta, xcell, ycell, zcell, xmin, ymin, zmin, weights): def fill_delta(delta, xcell, ycell, zcell, xmin, ymin, zmin, weights):
""" """
@ -757,7 +700,7 @@ def get_clumplims(clumps, ncells, nshift=None):
Returns Returns
------- -------
mins, maxs : 2-dimensional arrays of shape `(n_samples, 3)` mins, maxs : 2-dimensional arrays of shape `(n_samples, 3)`
The minimum and maximum along each axis. Minimum and maximum along each axis.
""" """
dtype = clumps[0][0]['x'].dtype # dtype of the first clump's 'x' dtype = clumps[0][0]['x'].dtype # dtype of the first clump's 'x'
# Check that for real positions we cannot apply nshift # Check that for real positions we cannot apply nshift
@ -983,3 +926,58 @@ def radius_neighbours(knn, X, radiusX, radiusKNN, nmult=1., enforce_in32=False,
indxs[i] = indxs[i].astype(numpy.int32) indxs[i] = indxs[i].astype(numpy.int32)
return numpy.asarray(indxs, dtype=object) return numpy.asarray(indxs, dtype=object)
###############################################################################
# Sky mathing #
###############################################################################
# def brute_spatial_separation(c1, c2, angular=False, N=None, verbose=False):
# """
# Calculate for each point in `c1` the `N` closest points in `c2`.
# Parameters
# ----------
# c1 : `astropy.coordinates.SkyCoord`
# Coordinates of the first set of points.
# c2 : `astropy.coordinates.SkyCoord`
# Coordinates of the second set of points.
# angular : bool, optional
# Whether to calculate angular separation or 3D separation. By default
# `False` and 3D separation is calculated.
# N : int, optional
# Number of closest points in `c2` to each object in `c1` to return.
# verbose : bool, optional
# Verbosity flag. By default `False`.
# Returns
# -------
# sep : 1-dimensional array
# Separation of each object in `c1` to `N` closest objects in `c2`. The
# array shape is `(c1.size, N)`. Separation is in units of `c1`.
# indxs : 1-dimensional array
# Indexes of the closest objects in `c2` for each object in `c1`. The
# array shape is `(c1.size, N)`.
# """
# if not (isinstance(c1, SkyCoord) and isinstance(c2, SkyCoord)):
# raise TypeError(
# "`c1` & `c2` must be `astropy.coordinates.SkyCoord`.")
# N1 = c1.size
# N2 = c2.size if N is None else N
# # Pre-allocate arrays
# sep = numpy.full((N1, N2), numpy.nan)
# indxs = numpy.full((N1, N2), numpy.nan, dtype=int)
# iters = tqdm(range(N1)) if verbose else range(N1)
# for i in iters:
# if angular:
# dist = c1[i].separation(c2).value
# else:
# dist = c1[i].separation_3d(c2).value
# # Sort the distances
# sort = numpy.argsort(dist)[:N2]
# indxs[i, :] = sort
# sep[i, :] = dist[sort]
# return sep, indxs

1
csiborgtools/match/num_density.py
View file

@ -15,7 +15,6 @@
""" """
Calculation of number density functions. Calculation of number density functions.
""" """
import numpy import numpy

4
csiborgtools/read/__init__.py
View file

@ -17,6 +17,6 @@ from .readsim import (CSiBORGPaths, ParticleReader, read_mmain, read_initcm, hal
from .make_cat import (HaloCatalogue, concatenate_clumps) # noqa from .make_cat import (HaloCatalogue, concatenate_clumps) # noqa
from .readobs import (PlanckClusters, MCXCClusters, TwoMPPGalaxies, # noqa from .readobs import (PlanckClusters, MCXCClusters, TwoMPPGalaxies, # noqa
TwoMPPGroups, SDSS) # noqa TwoMPPGroups, SDSS) # noqa
from .outsim import (dump_split, combine_splits, make_ascii_powmes) # noqa from .outsim import (dump_split, combine_splits) # noqa
from .summaries import (PKReader, kNNCDFReader, PairOverlap, NPairsOverlap, from .summaries import (PKReader, kNNCDFReader, PairOverlap, NPairsOverlap, # noqa
binned_resample_mean) # noqa binned_resample_mean) # noqa

209
csiborgtools/read/make_cat.py
View file

@ -29,8 +29,10 @@ class HaloCatalogue:
Parameters Parameters
---------- ----------
paths : py:class:`csiborgtools.read.CSiBORGPaths` nsim : int
CSiBORG paths-handling object with set `n_sim` and `n_snap`. IC realisation index.
paths : py:class`csiborgtools.read.CSiBORGPaths`
CSiBORG paths object.
min_mass : float, optional min_mass : float, optional
The minimum :math:`M_{rm tot} / M_\odot` mass. By default no threshold. The minimum :math:`M_{rm tot} / M_\odot` mass. By default no threshold.
max_dist : float, optional max_dist : float, optional
@ -38,20 +40,49 @@ class HaloCatalogue:
load_init : bool, optional load_init : bool, optional
Whether to load the initial snapshot information. By default False. Whether to load the initial snapshot information. By default False.
""" """
_box = None
_paths = None _paths = None
_nsim = None
_data = None _data = None
_selmask = None _selmask = None
def __init__(self, nsim, min_mass=None, max_dist=None, load_init=False): def __init__(self, nsim, paths, min_mass=None, max_dist=None,
# Set up paths load_init=False):
paths = CSiBORGPaths(n_sim=nsim) assert isinstance(paths, CSiBORGPaths)
paths.n_snap = paths.get_maximum_snapshot() self._nsim = nsim
self._paths = paths
self._box = BoxUnits(paths)
self._paths = paths self._paths = paths
self._set_data(min_mass, max_dist, load_init) self._set_data(min_mass, max_dist, load_init)
@property
def nsim(self):
return self._nsim
@property
def paths(self):
return self._paths
@property
def nsnap(self):
"""
Catalogue's snapshot index corresponding to the maximum simulation
snapshot index.
Returns
-------
nsnap : int
"""
return max(self.paths.get_snapshots(self.nsim))
@property
def box(self):
"""
CSiBORG box object handling unit conversion.
Returns
-------
box : :py:class:`csiborgtools.units.BoxUnits`
"""
return BoxUnits(self.nsnap, self.nsim, self.paths)
@property @property
def data(self): def data(self):
""" """
@ -61,81 +92,19 @@ class HaloCatalogue:
------- -------
cat : structured array cat : structured array
""" """
if self._data is None:
raise ValueError("`data` is not set!")
return self._data return self._data
@property
def box(self):
"""
Box object, useful for change of units.
Returns
-------
box : :py:class:`csiborgtools.units.BoxUnits`
"""
return self._box
@property
def paths(self):
"""
The paths-handling object.
Returns
-------
paths : :py:class:`csiborgtools.read.CSiBORGPaths`
"""
return self._paths
@property
def n_snap(self):
"""
The snapshot ID.
Returns
-------
n_snap : int
"""
return self.paths.n_snap
@property
def n_sim(self):
"""
The initial condition (IC) realisation ID.
Returns
-------
n_sim : int
"""
return self.paths.n_sim
def knn(self, select_initial):
"""
kNN object of all halo positions.
Parameters
----------
select_initial : bool
Whether to define the KNN on the initial or final snapshot.
Returns
-------
knn : :py:class:`sklearn.neighbors.NearestNeighbors`
"""
knn = NearestNeighbors()
return knn.fit(self.positions0 if select_initial else self.positions)
def _set_data(self, min_mass, max_dist, load_init): def _set_data(self, min_mass, max_dist, load_init):
""" """
Loads the data, merges with mmain, does various coordinate transforms. Loads the data, merges with mmain, does various coordinate transforms.
""" """
# Load the processed data # Load the processed data
fname = "ramses_out_{}_{}.npy".format( fname = "ramses_out_{}_{}.npy".format(
str(self.n_sim).zfill(5), str(self.n_snap).zfill(5)) str(self.nsim).zfill(5), str(self.nsnap).zfill(5))
data = numpy.load(join(self.paths.dumpdir, fname)) data = numpy.load(join(self.paths.dumpdir, fname))
# Load the mmain file and add it to the data # Load the mmain file and add it to the data
mmain = read_mmain(self.n_sim, self.paths.mmain_path) mmain = read_mmain(self.nsim, self.paths.mmain_path)
data = self.merge_mmain_to_clumps(data, mmain) data = self.merge_mmain_to_clumps(data, mmain)
flip_cols(data, "peak_x", "peak_z") flip_cols(data, "peak_x", "peak_z")
@ -144,7 +113,7 @@ class HaloCatalogue:
# Now also load the initial positions # Now also load the initial positions
if load_init: if load_init:
initcm = read_initcm(self.n_sim, self.paths.initmatch_path) initcm = read_initcm(self.nsim, self.paths.initmatch_path)
if initcm is not None: if initcm is not None:
data = self.merge_initmatch_to_clumps(data, initcm) data = self.merge_initmatch_to_clumps(data, initcm)
flip_cols(data, "x0", "z0") flip_cols(data, "x0", "z0")
@ -185,7 +154,7 @@ class HaloCatalogue:
formats.append(numpy.float32) formats.append(numpy.float32)
dtype = numpy.dtype({"names": names, "formats": formats}) dtype = numpy.dtype({"names": names, "formats": formats})
# Apply cuts on distance and min500 if any # Apply cuts on distance and total particle mass if any
data = data[data["dist"] < max_dist] if max_dist is not None else data data = data[data["dist"] < max_dist] if max_dist is not None else data
data = (data[data["totpartmass"] > min_mass] data = (data[data["totpartmass"] > min_mass]
if min_mass is not None else data) if min_mass is not None else data)
@ -247,76 +216,75 @@ class HaloCatalogue:
X[:, i] = initcat[p] X[:, i] = initcat[p]
return add_columns(clumps, X, ["x0", "y0", "z0", "lagpatch"]) return add_columns(clumps, X, ["x0", "y0", "z0", "lagpatch"])
@property def positions(self, in_initial=False):
def positions(self):
r""" r"""
3D positions of halos in :math:`\mathrm{cMpc}`. Cartesian position components of halos in :math:`\mathrm{cMpc}`.
Parameters
----------
in_initial : bool, optional
Whether to define the kNN on the initial or final snapshot.
Returns Returns
------- -------
X : 2-dimensional array pos : 2-dimensional array of shape `(nhalos, 3)`
Array of shape `(n_halos, 3)`, where the latter axis represents
`x`, `y` and `z`.
""" """
return numpy.vstack( if in_initial:
[self._data["peak_{}".format(p)] for p in ("x", "y", "z")]).T ps = ["x0", "y0", "z0"]
else:
ps = ["peak_x", "peak_y", "peak_z"]
return numpy.vstack([self[p] for p in ps]).T
@property
def positions0(self):
r"""
3D positions of halos in the initial snapshot in :math:`\mathrm{cMpc}`.
Returns
-------
X : 2-dimensional array
Array of shape `(n_halos, 3)`, where the latter axis represents
`x`, `y` and `z`.
"""
try:
return numpy.vstack(
[self._data["{}".format(p)] for p in ("x0", "y0", "z0")]).T
except KeyError:
raise RuntimeError("Initial positions are not set!")
@property
def velocities(self): def velocities(self):
""" """
Cartesian velocities of halos. Cartesian velocity components of halos. Likely in box units.
Returns Returns
------- -------
vel : 2-dimensional array vel : 2-dimensional array of shape `(nhalos, 3)`
Array of shape `(n_halos, 3)`.
""" """
return numpy.vstack([self["v{}".format(p)] for p in ("x", "y", "z")]).T return numpy.vstack([self["v{}".format(p)] for p in ("x", "y", "z")]).T
@property
def angmomentum(self): def angmomentum(self):
""" """
Angular momentum of halos in the box coordinate system. Cartesian angular momentum components of halos in the box coordinate
system. Likely in box units.
Returns Returns
------- -------
angmom : 2-dimensional array angmom : 2-dimensional array of shape `(nhalos, 3)`
Array of shape `(n_halos, 3)`.
""" """
return numpy.vstack([self["L{}".format(p)] for p in ("x", "y", "z")]).T return numpy.vstack([self["L{}".format(p)] for p in ("x", "y", "z")]).T
def radius_neigbours(self, X, radius, select_initial=True): def knn(self, in_initial):
"""
kNN object of all halo positions.
Parameters
----------
in_initial : bool
Whether to define the kNN on the initial or final snapshot.
Returns
-------
knn : :py:class:`sklearn.neighbors.NearestNeighbors`
"""
knn = NearestNeighbors()
return knn.fit(self.positions(in_initial))
def radius_neigbours(self, X, radius, in_initial):
r""" r"""
Return sorted nearest neigbours within `radius` of `X` in the initial Sorted nearest neigbours within `radius` of `X` in the initial
or final snapshot. or final snapshot.
Parameters Parameters
---------- ----------
X : 2-dimensional array X : 2-dimensional array of shape `(n_queries, 3)`
Array of shape `(n_queries, 3)`, where the latter axis represents Cartesian query position components in :math:`\mathrm{cMpc}`.
`x`, `y` and `z`.
radius : float radius : float
Limiting distance of neighbours. Limiting neighbour distance.
select_initial : bool, optional in_initial : bool
Whether to search for neighbours in the initial or final snapshot. Whether to define the kNN on the initial or final snapshot.
By default `True`, i.e. the final snapshot.
Returns Returns
------- -------
@ -329,7 +297,7 @@ class HaloCatalogue:
""" """
if not (X.ndim == 2 and X.shape[1] == 3): if not (X.ndim == 2 and X.shape[1] == 3):
raise TypeError("`X` must be an array of shape `(n_samples, 3)`.") raise TypeError("`X` must be an array of shape `(n_samples, 3)`.")
knn = self.knn(select_initial) knn = self.knn(in_initial)
return knn.radius_neighbors(X, radius, sort_results=True) return knn.radius_neighbors(X, radius, sort_results=True)
@property @property
@ -347,6 +315,11 @@ class HaloCatalogue:
return self.data.size return self.data.size
###############################################################################
# Useful functions #
###############################################################################
def concatenate_clumps(clumps): def concatenate_clumps(clumps):
""" """
Concatenate an array of clumps to a single array containing all particles. Concatenate an array of clumps to a single array containing all particles.

82
csiborgtools/read/outsim.py
View file

@ -15,19 +15,13 @@
""" """
I/O functions for analysing the CSiBORG realisations. I/O functions for analysing the CSiBORG realisations.
""" """
import numpy import numpy
from os.path import (join, isfile) from os.path import join
from os import remove from os import remove
from tqdm import trange from tqdm import trange
from astropy.table import Table
I64 = numpy.int64
F64 = numpy.float64
def dump_split(arr, n_split, paths): def dump_split(arr, nsplit, nsnap, nsim, paths):
""" """
Dump an array from a split. Dump an array from a split.
@ -35,30 +29,26 @@ def dump_split(arr, n_split, paths):
---------- ----------
arr : n-dimensional or structured array arr : n-dimensional or structured array
Array to be saved. Array to be saved.
n_split: int nsplit : int
The split index. Split index.
nsnap : int
Snapshot index.
nsim : int
IC realisation index.
paths : py:class`csiborgtools.read.CSiBORGPaths` paths : py:class`csiborgtools.read.CSiBORGPaths`
CSiBORG paths-handling object with set `n_sim` and `n_snap`. CSiBORG paths-handling object with set `n_sim` and `n_snap`.
n_sim : int
The CSiBORG realisation index.
n_snap : int
The index of a redshift snapshot.
outdir : string
Directory where to save the temporary files.
Returns Returns
------- -------
None None
""" """
n_sim = str(paths.n_sim).zfill(5)
n_snap = str(paths.n_snap).zfill(5)
fname = join(paths.temp_dumpdir, "ramses_out_{}_{}_{}.npy" fname = join(paths.temp_dumpdir, "ramses_out_{}_{}_{}.npy"
.format(n_sim, n_snap, n_split)) .format(str(nsim).zfill(5), str(nsnap).zfill(5), nsplit))
numpy.save(fname, arr) numpy.save(fname, arr)
def combine_splits(n_splits, part_reader, cols_add, remove_splits=False, def combine_splits(nsplits, nsnap, nsim, part_reader, cols_add,
verbose=True): remove_splits=False, verbose=True):
""" """
Combine results of many splits saved from `dump_split`. Identifies to which Combine results of many splits saved from `dump_split`. Identifies to which
clump the clumps in the split correspond to by matching their index. clump the clumps in the split correspond to by matching their index.
@ -67,8 +57,12 @@ def combine_splits(n_splits, part_reader, cols_add, remove_splits=False,
Paramaters Paramaters
---------- ----------
n_splits : int nsplits : int
The total number of clump splits. Total number of clump splits.
nsnap : int
Snapshot index.
nsim : int
IC realisation index.
part_reader : py:class`csiborgtools.read.ParticleReadear` part_reader : py:class`csiborgtools.read.ParticleReadear`
CSiBORG particle reader. CSiBORG particle reader.
cols_add : list of `(str, dtype)` cols_add : list of `(str, dtype)`
@ -84,26 +78,20 @@ def combine_splits(n_splits, part_reader, cols_add, remove_splits=False,
out : structured array out : structured array
Clump array with appended results from the splits. Clump array with appended results from the splits.
""" """
# Load clumps to see how many there are and will add to this array clumps = part_reader.read_clumps(nsnap, nsim, cols=None)
n_sim = part_reader.paths.n_sim
n_snap = part_reader.paths.n_snap
clumps = part_reader.read_clumps(cols=None)
# Get the old + new dtypes and create an empty array # Get the old + new dtypes and create an empty array
descr = clumps.dtype.descr + cols_add descr = clumps.dtype.descr + cols_add
out = numpy.full(clumps.size, numpy.nan, dtype=descr) out = numpy.full(clumps.size, numpy.nan, dtype=descr)
# Now put the old values into the array for par in clumps.dtype.names: # Now put the old values into the array
for par in clumps.dtype.names:
out[par] = clumps[par] out[par] = clumps[par]
# Filename of splits data # Filename of splits data
froot = "ramses_out_{}_{}".format( froot = "ramses_out_{}_{}".format(str(nsim).zfill(5), str(nsnap).zfill(5))
str(n_sim).zfill(5), str(n_snap).zfill(5))
fname = join(part_reader.paths.temp_dumpdir, froot + "_{}.npy") fname = join(part_reader.paths.temp_dumpdir, froot + "_{}.npy")
# Iterate over splits and add to the output array # Iterate over splits and add to the output array
cols_add_names = [col[0] for col in cols_add] cols_add_names = [col[0] for col in cols_add]
iters = trange(n_splits) if verbose else range(n_splits) iters = trange(nsplits) if verbose else range(nsplits)
for n in iters: for n in iters:
fnamesplit = fname.format(n) fnamesplit = fname.format(n)
arr = numpy.load(fnamesplit) arr = numpy.load(fnamesplit)
@ -121,31 +109,3 @@ def combine_splits(n_splits, part_reader, cols_add, remove_splits=False,
remove(fnamesplit) remove(fnamesplit)
return out return out
def make_ascii_powmes(particles, fout):
"""
Write an ASCII file with appropriate formatting for POWMES.
Parameters
----------
particles : structured array
Array of particles.
fout : str
File path to store the ASCII file.
Returns
-------
None
"""
out = Table()
for p in ('x', 'y', 'z', 'M'):
out[p] = particles[p]
Npart = particles.size
# If fout exists, remove
if isfile(fout):
remove(fout)
with open(fout, 'wb') as f:
numpy.savetxt(f, out, delimiter=',', header=str(Npart), comments='')

651
csiborgtools/read/readsim.py
View file

@ -15,23 +15,15 @@
""" """
Functions to read in the particle and clump files. Functions to read in the particle and clump files.
""" """
import numpy
from scipy.io import FortranFile
from os.path import (join, isfile, isdir) from os.path import (join, isfile, isdir)
from glob import glob from glob import glob
from tqdm import tqdm
from warnings import warn from warnings import warn
import numpy
from scipy.io import FortranFile
from tqdm import tqdm
from ..utils import (cols_to_structured) from ..utils import (cols_to_structured)
F16 = numpy.float16
F32 = numpy.float32
F64 = numpy.float64
I32 = numpy.int32
I64 = numpy.int64
############################################################################### ###############################################################################
# Paths manager # # Paths manager #
############################################################################### ###############################################################################
@ -43,397 +35,174 @@ class CSiBORGPaths:
Parameters Parameters
---------- ----------
n_sim : int, optional
CSiBORG IC realisation index. By default not set.
n_snap : int, optional
Snapshot index. By default not set.
srcdir : str, optional srcdir : str, optional
The file path to the folder where realisations of the ICs are stored. Path to the folder where CSiBORG simulations are stored.
By default `/mnt/extraspace/hdesmond/`.
dumpdir : str, optional dumpdir : str, optional
Path to where files from `run_fit_halos` are stored. By default Path to the folder where files from `run_fit_halos` are stored.
`/mnt/extraspace/rstiskalek/csiborg/`.
mmain_path : str, optional mmain_path : str, optional
Path to where mmain files are stored. By default Path to folder where mmain files are stored.
`/mnt/zfsusers/hdesmond/Mmain`.
initmatch_path : str, optional initmatch_path : str, optional
Path to where match between the first and final snapshot is stored. By Path to the folder where particle ID match between the first and final
default `/mnt/extraspace/rstiskalek/csiborg/initmatch/`. snapshot is stored.
to_new : bool, optional
Whether the paths should point to `new` files, for example
`ramses_out_8452_new`.
""" """
_srcdir = None _srcdir = None
_n_sim = None
_n_snap = None
_dumpdir = None _dumpdir = None
_mmain_path = None _mmain_path = None
_initmatch_path = None _initmatch_path = None
_to_new = None
def __init__(self, n_sim=None, n_snap=None, srcdir=None, dumpdir=None, def __init__(self, srcdir="/mnt/extraspace/hdesmond/",
mmain_path=None, initmatch_path=None, to_new=False): dumpdir="/mnt/extraspace/rstiskalek/csiborg/",
if srcdir is None: mmain_path="/mnt/zfsusers/hdesmond/Mmain",
srcdir = "/mnt/extraspace/hdesmond/" initmatch_path="/mnt/extraspace/rstiskalek/csiborg/initmatch/"): # noqa
self.srcdir = srcdir for path in [srcdir, dumpdir, mmain_path, initmatch_path]:
if dumpdir is None: self._check_directory(path)
dumpdir = "/mnt/extraspace/rstiskalek/csiborg/" self._srcdir = srcdir
self.dumpdir = dumpdir self._dumpdir = dumpdir
if mmain_path is None: self._mmain_path = mmain_path
mmain_path = "/mnt/zfsusers/hdesmond/Mmain" self._initmatch_path = initmatch_path
self.mmain_path = mmain_path
if initmatch_path is None: @staticmethod
initmatch_path = "/mnt/extraspace/rstiskalek/csiborg/initmatch/" def _check_directory(path):
self.initmatch_path = initmatch_path if not isdir(path):
self.to_new = to_new raise IOError("Invalid directory `{}`!".format(path))
if n_sim is not None and n_snap is not None:
self.set_info(n_sim, n_snap)
if n_sim is not None:
self.n_sim = n_sim
@property @property
def srcdir(self): def srcdir(self):
""" """
Folder where CSiBORG simulations are stored. Path to the folder where CSiBORG simulations are stored.
Returns Returns
------- -------
srcdir : int path : str
""" """
return self._srcdir return self._srcdir
@srcdir.setter
def srcdir(self, srcdir):
"""
Set `srcdir`, check that the directory exists.
"""
if not isdir(srcdir):
raise IOError("Invalid directory `{}`!".format(srcdir))
self._srcdir = srcdir
@property @property
def dumpdir(self): def dumpdir(self):
""" """
Folder where files from `run_fit_halos` are stored. Path to the folder where files from `run_fit_halos` are stored.
Returns Returns
------- -------
dumpdir : str path : str
""" """
return self._dumpdir return self._dumpdir
@property @property
def temp_dumpdir(self): def temp_dumpdir(self):
""" """
Temporary dumping directory. Path to a temporary dumping folder.
Returns
-------
temp_dumpdir : str
"""
fpath = join(self.dumpdir, "temp")
if not isdir(fpath):
raise IOError("Invalid directory `{}`!".format(fpath))
return fpath
@dumpdir.setter
def dumpdir(self, dumpdir):
"""
Set `dumpdir`, check that the directory exists.
"""
if not isdir(dumpdir):
raise IOError("Invalid directory `{}`!".format(dumpdir))
self._dumpdir = dumpdir
@property
def mmain_path(self):
"""
Path where mmain files are stored.
Returns
-------
mmain_path : str
"""
return self._mmain_path
@mmain_path.setter
def mmain_path(self, mmain_path):
"""
Set `mmain_path`, check that the directory exists.
"""
if not isdir(mmain_path):
raise IOError("Invalid directory `{}`!".format(mmain_path))
self._mmain_path = mmain_path
@property
def initmatch_path(self):
"""
Path to where match between the first and final snapshot is stored.
Returns
-------
initmach_path : str
"""
return self._initmatch_path
@initmatch_path.setter
def initmatch_path(self, initmatch_path):
"""
Set `initmatch_path`, check that the directory exists.
"""
if not isdir(initmatch_path):
raise IOError("Invalid directory `{}`!".format(initmatch_path))
self._initmatch_path = initmatch_path
@property
def to_new(self):
"""
Flag whether paths should point to `new` files, for example
`ramses_out_8452_new`.
Returns
-------
to_new : bool
"""
return self._to_new
@to_new.setter
def to_new(self, to_new):
"""Set `to_new`."""
if not isinstance(to_new, bool):
raise TypeError("`to_new` must be be a bool")
self._to_new = to_new
@property
def n_sim(self):
"""
The IC realisation index set by the user.
Returns
-------
n_sim : int
"""
if self._n_sim is None:
raise ValueError(
"`self.n_sim` is not set! Either provide a value directly "
"or set it using `self.set_info(...)`")
return self._n_sim
@n_sim.setter
def n_sim(self, n_sim):
"""Set `n_sim`, ensure it is a valid simulation index."""
if n_sim not in self.ic_ids:
raise ValueError(
"`{}` is not a valid IC realisation index.".format(n_sim))
self._n_sim = n_sim
@property
def n_snap(self):
"""
The snapshot index of a IC realisation set by the user.
Returns
-------
n_snap: int
"""
if self._n_snap is None:
raise ValueError(
"`self.n_sim` is not set! Either provide a value directly "
"or set it using `self.set_info(...)`")
return self._n_snap
@n_snap.setter
def n_snap(self, n_snap):
"""Set `n_snap`."""
self._n_snap = n_snap
def set_info(self, n_sim, n_snap):
"""
Convenience function for setting `n_sim` and `n_snap`.
Parameters
----------
n_sim : int
CSiBORG IC realisation index.
n_snap : int
Snapshot index.
"""
self.n_sim = n_sim
if n_snap not in self.get_snapshots(n_sim):
raise ValueError(
"Invalid snapshot number `{}` for IC realisation `{}`."
.format(n_snap, n_sim))
self.n_snap = n_snap
def reset_info(self):
"""
Reset `self.n_sim` and `self.n_snap`.
"""
self._n_sim = None
self._n_snap = None
def get_n_sim(self, n_sim):
"""
Get `n_sim`. If `self.n_sim` return it, otherwise returns `n_sim`.
"""
if n_sim is None:
return self.n_sim
return n_sim
def get_n_snap(self, n_snap):
"""
Get `n_snap`. If `self.n_snap` return it, otherwise returns `n_snap`.
"""
if n_snap is None:
return self.n_snap
return n_snap
@property
def ic_ids(self):
"""
CSiBORG initial condition (IC) simulation IDs from the list of folders
in `self.srcdir`.
Returns
-------
ids : 1-dimensional array
Array of CSiBORG simulation IDs.
"""
if self.to_new:
return self._ic_ids_new
return self._ic_ids
@property
def _ic_ids(self):
"""
IC simulation IDs.
Returns
-------
ids : 1-dimensional array
"""
files = glob(join(self.srcdir, "ramses_out*"))
# Select only file names
files = [f.split("/")[-1] for f in files]
# Remove files with inverted ICs
files = [f for f in files if "_inv" not in f]
# Remove the new files with z = 70 only
files = [f for f in files if "_new" not in f]
# Remove the filename with _old
files = [f for f in files if "OLD" not in f]
ids = [int(f.split("_")[-1]) for f in files]
try:
ids.remove(5511)
except ValueError:
pass
return numpy.sort(ids)
@property
def _ic_ids_new(self):
"""
ICs simulation IDs denoted as `new` with recoved :math:`z = 70`
particle information.
Returns
-------
ids : 1-dimensional array
"""
files = glob(join(self.srcdir, "ramses_out*"))
# Select only file names
files = [f.split("/")[-1] for f in files]
# Only _new files
files = [f for f in files if "_new" in f]
# Take the ICs
ids = [int(f.split("_")[2]) for f in files]
return numpy.sort(ids)
def ic_path(self, n_sim=None):
"""
Path to `n_sim`th CSiBORG IC realisation.
Parameters
----------
n_sim : int, optional
The index of the initial conditions (IC) realisation. By default
`None` and the set value is attempted to be used.
Returns Returns
------- -------
path : str path : str
""" """
n_sim = self.get_n_sim(n_sim) fpath = join(self.dumpdir, "temp")
fname = "ramses_out_{}" if not isdir(fpath):
if self.to_new: raise IOError("Invalid directory `{}`.".format(fpath))
fname += "_new" return fpath
return join(self.srcdir, fname.format(n_sim))
def get_snapshots(self, n_sim=None): @property
def mmain_path(self):
""" """
List of snapshots for the `n_sim`th IC realisation. Path to the folder where mmain files are stored.
Returns
-------
path : str
"""
return self._mmain_path
@property
def initmatch_path(self):
"""
Path to the folder where the match between the first and final
snapshot is stored.
Returns
-------
path : str
"""
return self._initmatch_path
def ic_ids(self, tonew):
"""
CSiBORG IC realisation IDs from the list of folders in `self.srcdir`.
Parameters Parameters
---------- ----------
n_sim : int tonew : bool
The index of the initial conditions (IC) realisation. By default If `True`, path to the '_new' ICs is returned.
`None` and the set value is attempted to be used. Returns
-------
ids : 1-dimensional array
"""
files = glob(join(self.srcdir, "ramses_out*"))
files = [f.split("/")[-1] for f in files] # Select only file names
if tonew:
files = [f for f in files if "_new" in f]
ids = [int(f.split("_")[2]) for f in files] # Take the IC IDs
else:
files = [f for f in files if "_inv" not in f] # Remove inv. ICs
files = [f for f in files if "_new" not in f] # Remove _new
files = [f for f in files if "OLD" not in f] # Remove _old
ids = [int(f.split("_")[-1]) for f in files]
try:
ids.remove(5511)
except ValueError:
pass
return numpy.sort(ids)
def ic_path(self, nsim, tonew=False):
"""
Path to a CSiBORG IC realisation folder.
Parameters
----------
nsim : int
IC realisation index.
tonew : bool, optional
Whether to return the path to the '_new' IC realisation.
Returns
-------
path : str
"""
fname = "ramses_out_{}"
if tonew:
fname += "_new"
return join(self.srcdir, fname.format(nsim))
def get_snapshots(self, nsim):
"""
List of available snapshots of a CSiBORG IC realisation.
Parameters
----------
nsim : int
IC realisation index.
Returns Returns
------- -------
snapshots : 1-dimensional array snapshots : 1-dimensional array
""" """
n_sim = self.get_n_sim(n_sim) simpath = self.ic_path(nsim, tonew=False)
simpath = self.ic_path(n_sim)
# Get all files in simpath that start with output_ # Get all files in simpath that start with output_
snaps = glob(join(simpath, "output_*")) snaps = glob(join(simpath, "output_*"))
# Take just the last _00XXXX from each file and strip zeros # Take just the last _00XXXX from each file and strip zeros
snaps = [int(snap.split('_')[-1].lstrip('0')) for snap in snaps] snaps = [int(snap.split('_')[-1].lstrip('0')) for snap in snaps]
return numpy.sort(snaps) return numpy.sort(snaps)
def get_maximum_snapshot(self, n_sim=None):
"""
Return the maximum snapshot of an IC realisation.
Parameters
----------
n_sim : int
The index of the initial conditions (IC) realisation. By default
`None` and the set value is attempted to be used.
Returns
-------
maxsnap : float
"""
n_sim = self.get_n_sim(n_sim)
return max(self.get_snapshots(n_sim))
def get_minimum_snapshot(self, n_sim=None):
"""
Return the maximum snapshot of an IC realisation.
Parameters
----------
n_sim : int
The index of the initial conditions (IC) realisation. By default
`None` and the set value is attempted to be used.
Returns
-------
minsnap : float
"""
n_sim = self.get_n_sim(n_sim)
return min(self.get_snapshots(n_sim))
def clump0_path(self, nsim): def clump0_path(self, nsim):
""" """
Path to a single dumped clump's particles. This is expected to point Path to a single dumped clump's particles. Expected to point to a
to a dictonary whose keys are the clump indices and items structured dictonary whose keys are the clump indices and items structured
arrays with the clump's particles in the initial snapshot. arrays with the clump's particles in the initial snapshot.
Parameters Parameters
---------- ----------
nsim : int nsim : int
Index of the initial conditions (IC) realisation. IC realisation index.
Returns Returns
------- -------
@ -442,27 +211,25 @@ class CSiBORGPaths:
cdir = join(self.dumpdir, "initmatch") cdir = join(self.dumpdir, "initmatch")
return join(cdir, "clump_{}_{}.npy".format(nsim, "particles")) return join(cdir, "clump_{}_{}.npy".format(nsim, "particles"))
def snapshot_path(self, n_snap=None, n_sim=None): def snapshot_path(self, nsnap, nsim, tonew=False):
""" """
Path to a CSiBORG IC realisation snapshot. Path to a CSiBORG IC realisation snapshot.
Parameters Parameters
---------- ----------
n_snap : int nsnap : int
Snapshot index. By default `None` and the set value is attempted Snapshot index.
to be used. nsim : int
n_sim : str IC realisation index.
Corresponding CSiBORG IC realisation index. By default `None` and tonew : bool, optional
the set value is attempted to be used. Whether to return the path to the '_new' IC realisation.
Returns Returns
------- -------
snappath : str snappath : str
""" """
n_snap = self.get_n_snap(n_snap) simpath = self.ic_path(nsim, tonew=tonew)
n_sim = self.get_n_sim(n_sim) return join(simpath, "output_{}".format(str(nsnap).zfill(5)))
simpath = self.ic_path(n_sim)
return join(simpath, "output_{}".format(str(n_snap).zfill(5)))
############################################################################### ###############################################################################
@ -472,56 +239,41 @@ class CSiBORGPaths:
class ParticleReader: class ParticleReader:
""" """
Tools to read in particle files alon with their corresponding clumps. Shortcut to read in particle files along with their corresponding clumps.
Parameters Parameters
---------- ----------
paths : py:class`csiborgtools.read.CSiBORGPaths` paths : py:class`csiborgtools.read.CSiBORGPaths`
CSiBORG paths-handling object with set `n_sim` and `n_snap`.
""" """
_paths = None _paths = None
def __init__(self, paths): def __init__(self, paths):
self.paths = paths assert isinstance(paths, CSiBORGPaths)
self._paths = paths
@property @property
def paths(self): def paths(self):
"""
The paths-handling object.
Returns
-------
paths : :py:class:`csiborgtools.read.CSiBORGPaths`
"""
return self._paths return self._paths
@paths.setter def read_info(self, nsnap, nsim):
def paths(self, paths):
"""
Set `paths`. Makes sure it is the right object and `n_sim` and `n_snap`
are both set.
"""
if not isinstance(paths, CSiBORGPaths):
raise TypeError("`paths` must be of type `CSiBORGPaths`.")
if paths.n_sim is None or paths.n_snap is None:
raise ValueError(
"`paths` must have set both `n_sim` and `n_snap`!")
self._paths = paths
def read_info(self):
""" """
Read CSiBORG simulation snapshot info. Read CSiBORG simulation snapshot info.
Parameters
----------
nsnap : int
Snapshot index.
nsim : int
IC realisation index.
Returns Returns
------- -------
info : dict info : dict
Dictionary of info paramaters. Note that both keys and values are Dictionary of information paramaters. Note that both keys and
strings. values are strings.
""" """
# Open the info file snappath = self.paths.snapshot_path(nsnap, nsim)
n_snap = self.paths.n_snap filename = join(snappath, "info_{}.txt".format(str(nsnap).zfill(5)))
snappath = self.paths.snapshot_path()
filename = join(snappath, "info_{}.txt".format(str(n_snap).zfill(5)))
with open(filename, "r") as f: with open(filename, "r") as f:
info = f.read().split() info = f.read().split()
# Throw anything below ordering line out # Throw anything below ordering line out
@ -533,12 +285,16 @@ class ParticleReader:
vals = info[eqs + 1] vals = info[eqs + 1]
return {key: val for key, val in zip(keys, vals)} return {key: val for key, val in zip(keys, vals)}
def open_particle(self, verbose=True): def open_particle(self, nsnap, nsim, verbose=True):
""" """
Open particle files to a given CSiBORG simulation. Open particle files to a given CSiBORG simulation.
Parameters Parameters
---------- ----------
nsnap : int
Snapshot index.
nsim : int
IC realisation index.
verbose : bool, optional verbose : bool, optional
Verbosity flag. Verbosity flag.
@ -549,28 +305,25 @@ class ParticleReader:
partfiles : list of `scipy.io.FortranFile` partfiles : list of `scipy.io.FortranFile`
Opened part files. Opened part files.
""" """
n_snap = self.paths.n_snap snappath = self.paths.snapshot_path(nsnap, nsim)
# Zeros filled snapshot number and the snapshot path
nout = str(n_snap).zfill(5)
snappath = self.paths.snapshot_path()
ncpu = int(self.read_info()["ncpu"]) ncpu = int(self.read_info()["ncpu"])
nsnap = str(nsnap).zfill(5)
if verbose: if verbose:
print("Reading in output `{}` with ncpu = `{}`." print("Reading in output `{}` with ncpu = `{}`."
.format(nout, ncpu)) .format(nsnap, ncpu))
# First read the headers. Reallocate arrays and fill them. # First read the headers. Reallocate arrays and fill them.
nparts = numpy.zeros(ncpu, dtype=int) nparts = numpy.zeros(ncpu, dtype=int)
partfiles = [None] * ncpu partfiles = [None] * ncpu
for cpu in range(ncpu): for cpu in range(ncpu):
cpu_str = str(cpu + 1).zfill(5) cpu_str = str(cpu + 1).zfill(5)
fpath = join(snappath, "part_{}.out{}".format(nout, cpu_str)) fpath = join(snappath, "part_{}.out{}".format(nsnap, cpu_str))
f = FortranFile(fpath) f = FortranFile(fpath)
# Read in this order # Read in this order
ncpuloc = f.read_ints() ncpuloc = f.read_ints()
if ncpuloc != ncpu: if ncpuloc != ncpu:
infopath = join(snappath, "info_{}.txt".format(nout)) infopath = join(snappath, "info_{}.txt".format(nsnap))
raise ValueError( raise ValueError(
"`ncpu = {}` of `{}` disagrees with `ncpu = {}` " "`ncpu = {}` of `{}` disagrees with `ncpu = {}` "
"of `{}`.".format(ncpu, infopath, ncpuloc, fpath)) "of `{}`.".format(ncpu, infopath, ncpuloc, fpath))
@ -590,8 +343,7 @@ class ParticleReader:
@staticmethod @staticmethod
def read_sp(dtype, partfile): def read_sp(dtype, partfile):
""" """
Utility function to read a single particle file, depending on its Utility function to read a single particle file.
dtype.
Parameters Parameters
---------- ----------
@ -609,9 +361,9 @@ class ParticleReader:
simpath : str simpath : str
The complete path to the CSiBORG simulation. The complete path to the CSiBORG simulation.
""" """
if dtype in [F16, F32, F64]: if dtype in [numpy.float16, numpy.float32, numpy.float64]:
return partfile.read_reals('d') return partfile.read_reals('d')
elif dtype in [I32]: elif dtype in [numpy.int32]:
return partfile.read_ints() return partfile.read_ints()
else: else:
raise TypeError("Unexpected dtype `{}`.".format(dtype)) raise TypeError("Unexpected dtype `{}`.".format(dtype))
@ -620,7 +372,8 @@ class ParticleReader:
def nparts_to_start_ind(nparts): def nparts_to_start_ind(nparts):
""" """
Convert `nparts` array to starting indices in a pre-allocated array for Convert `nparts` array to starting indices in a pre-allocated array for
looping over the CPU number. looping over the CPU number. The starting indices calculated as a
cumulative sum starting at 0.
Parameters Parameters
---------- ----------
@ -630,40 +383,40 @@ class ParticleReader:
Returns Returns
------- -------
start_ind : 1-dimensional array start_ind : 1-dimensional array
The starting indices calculated as a cumulative sum starting at 0.
""" """
return numpy.hstack([[0], numpy.cumsum(nparts[:-1])]) return numpy.hstack([[0], numpy.cumsum(nparts[:-1])])
def read_particle(self, pars_extract, verbose=True): def read_particle(self, nsnap, nsim, pars_extract, verbose=True):
""" """
Read particle files of a simulation at a given snapshot and return Read particle files of a simulation at a given snapshot and return
values of `pars_extract`. values of `pars_extract`.
Parameters Parameters
---------- ----------
nsnap : int
Snapshot index.
nsim : int
IC realisation index.
pars_extract : list of str pars_extract : list of str
Parameters to be extacted. Parameters to be extacted.
Nsnap : int
The index of the redshift snapshot.
simpath : str
The complete path to the CSiBORG simulation.
verbose : bool, optional verbose : bool, optional
Verbosity flag while for reading the CPU outputs. Verbosity flag while for reading the CPU outputs.
Returns Returns
------- -------
out : structured array out : structured array
The data read from the particle file.
""" """
# Open the particle files # Open the particle files
nparts, partfiles = self.open_particle(verbose=verbose) nparts, partfiles = self.open_particle(nsnap, nsim, verbose=verbose)
if verbose: if verbose:
print("Opened {} particle files.".format(nparts.size)) print("Opened {} particle files.".format(nparts.size))
ncpu = nparts.size ncpu = nparts.size
# Order in which the particles are written in the FortranFile # Order in which the particles are written in the FortranFile
forder = [("x", F32), ("y", F32), ("z", F32), forder = [("x", numpy.float32), ("y", numpy.float32),
("vx", F32), ("vy", F32), ("vz", F32), ("z", numpy.float32), ("vx", numpy.float32),
("M", F32), ("ID", I32), ("level", I32)] ("vy", numpy.float32), ("vz", numpy.float32),
("M", numpy.float32), ("ID", numpy.int32),
("level", numpy.int32)]
fnames = [fp[0] for fp in forder] fnames = [fp[0] for fp in forder]
fdtypes = [fp[1] for fp in forder] fdtypes = [fp[1] for fp in forder]
# Check there are no strange parameters # Check there are no strange parameters
@ -677,7 +430,7 @@ class ParticleReader:
npart_tot = numpy.sum(nparts) npart_tot = numpy.sum(nparts)
# A dummy array is necessary for reading the fortran files. # A dummy array is necessary for reading the fortran files.
dum = numpy.full(npart_tot, numpy.nan, dtype=F16) dum = numpy.full(npart_tot, numpy.nan, dtype=numpy.float16)
# These are the data we read along with types # These are the data we read along with types
dtype = {"names": pars_extract, dtype = {"names": pars_extract,
"formats": [forder[fnames.index(p)][1] for p in pars_extract]} "formats": [forder[fnames.index(p)][1] for p in pars_extract]}
@ -699,13 +452,17 @@ class ParticleReader:
return out return out
def open_unbinding(self, cpu): def open_unbinding(self, nsnap, nsim, cpu):
""" """
Open particle files to a given CSiBORG simulation. Note that to be Open particle files to a given CSiBORG simulation. Note that to be
consistent CPU is incremented by 1. consistent CPU is incremented by 1.
Parameters Parameters
---------- ----------
nsnap : int
Snapshot index.
nsim : int
IC realisation index.
cpu : int cpu : int
The CPU index. The CPU index.
@ -714,18 +471,23 @@ class ParticleReader:
unbinding : `scipy.io.FortranFile` unbinding : `scipy.io.FortranFile`
The opened unbinding FortranFile. The opened unbinding FortranFile.
""" """
nout = str(self.paths.n_snap).zfill(5) nsnap = str(nsnap).zfill(5)
cpu = str(cpu + 1).zfill(5) cpu = str(cpu + 1).zfill(5)
fpath = join(self.paths.ic_path(), "output_{}".format(nout), fpath = join(self.paths.ic_path(nsim, to_new=False),
"unbinding_{}.out{}".format(nout, cpu)) "output_{}".format(nsnap),
"unbinding_{}.out{}".format(nsnap, cpu))
return FortranFile(fpath) return FortranFile(fpath)
def read_clumpid(self, verbose=True): def read_clumpid(self, nsnap, nsim, verbose=True):
""" """
Read clump IDs of particles from unbinding files. Read clump IDs of particles from unbinding files.
Parameters Parameters
---------- ----------
nsnap : int
Snapshot index.
nsim : int
IC realisation index.
verbose : bool, optional verbose : bool, optional
Verbosity flag while for reading the CPU outputs. Verbosity flag while for reading the CPU outputs.
@ -734,16 +496,16 @@ class ParticleReader:
clumpid : 1-dimensional array clumpid : 1-dimensional array
The array of clump IDs. The array of clump IDs.
""" """
nparts, __ = self.open_particle(verbose) nparts, __ = self.open_particle(nsnap, nsim, verbose)
start_ind = self.nparts_to_start_ind(nparts) start_ind = self.nparts_to_start_ind(nparts)
ncpu = nparts.size ncpu = nparts.size
clumpid = numpy.full(numpy.sum(nparts), numpy.nan, dtype=I32) clumpid = numpy.full(numpy.sum(nparts), numpy.nan, dtype=numpy.int32)
iters = tqdm(range(ncpu)) if verbose else range(ncpu) iters = tqdm(range(ncpu)) if verbose else range(ncpu)
for cpu in iters: for cpu in iters:
i = start_ind[cpu] i = start_ind[cpu]
j = nparts[cpu] j = nparts[cpu]
ff = self.open_unbinding(cpu) ff = self.open_unbinding(nsnap, nsim, cpu)
clumpid[i:i + j] = ff.read_ints() clumpid[i:i + j] = ff.read_ints()
# Close # Close
ff.close() ff.close()
@ -772,12 +534,17 @@ class ParticleReader:
mask = clump_ids != 0 mask = clump_ids != 0
return clump_ids[mask], particles[mask] return clump_ids[mask], particles[mask]
def read_clumps(self, cols=None): def read_clumps(self, nsnap, nsim, cols=None):
""" """
Read in a clump file `clump_Nsnap.dat`. Read in a clump file `clump_Nsnap.dat`.
Parameters Parameters
---------- ----------
nsnap : int
Snapshot index.
nsim : int
IC realisation index.
cols : list of str, optional. cols : list of str, optional.
Columns to extract. By default `None` and all columns are Columns to extract. By default `None` and all columns are
extracted. extracted.
@ -787,9 +554,10 @@ class ParticleReader:
out : structured array out : structured array
Structured array of the clumps. Structured array of the clumps.
""" """
n_snap = str(self.paths.n_snap).zfill(5) nsnap = str(nsnap).zfill(5)
fname = join(self.paths.ic_path(), "output_{}".format(n_snap), fname = join(self.paths.ic_path(nsim, to_new=False),
"clump_{}.dat".format(n_snap)) "output_{}".format(nsnap),
"clump_{}.dat".format(nsnap))
# Check the file exists. # Check the file exists.
if not isfile(fname): if not isfile(fname):
raise FileExistsError( raise FileExistsError(
@ -797,10 +565,12 @@ class ParticleReader:
# Read in the clump array. This is how the columns must be written! # Read in the clump array. This is how the columns must be written!
data = numpy.genfromtxt(fname) data = numpy.genfromtxt(fname)
clump_cols = [("index", I64), ("level", I64), ("parent", I64), clump_cols = [("index", numpy.int64), ("level", numpy.int64),
("ncell", F64), ("peak_x", F64), ("peak_y", F64), ("parent", numpy.int64), ("ncell", numpy.float64),
("peak_z", F64), ("rho-", F64), ("rho+", F64), ("peak_x", numpy.float64), ("peak_y", numpy.float64),
("rho_av", F64), ("mass_cl", F64), ("relevance", F64)] ("peak_z", numpy.float64), ("rho-", numpy.float64),
("rho+", numpy.float64), ("rho_av", numpy.float64),
("mass_cl", numpy.float64), ("relevance", numpy.float64)]
out0 = cols_to_structured(data.shape[0], clump_cols) out0 = cols_to_structured(data.shape[0], clump_cols)
for i, name in enumerate(out0.dtype.names): for i, name in enumerate(out0.dtype.names):
out0[name] = data[:, i] out0[name] = data[:, i]
@ -824,19 +594,19 @@ class ParticleReader:
return out return out
def read_mmain(n, srcdir, fname="Mmain_{}.npy"): def read_mmain(nsim, srcdir, fname="Mmain_{}.npy"):
""" """
Read `mmain` numpy arrays of central halos whose mass contains their Read `mmain` numpy arrays of central halos whose mass contains their
substracture contribution. substracture contribution.
Parameters Parameters
---------- ----------
n : int nsim : int
The index of the initial conditions (IC) realisation. IC realisation index.
srcdir : str srcdir : str
The path to the folder containing the files. Path to the folder containing the files.
fname : str, optional fname : str, optional
The file name convention. By default `Mmain_{}.npy`, where the File name convention. By default `Mmain_{}.npy`, where the
substituted value is `n`. substituted value is `n`.
Returns Returns
@ -844,11 +614,12 @@ def read_mmain(n, srcdir, fname="Mmain_{}.npy"):
out : structured array out : structured array
Array with the central halo information. Array with the central halo information.
""" """
fpath = join(srcdir, fname.format(n)) fpath = join(srcdir, fname.format(nsim))
arr = numpy.load(fpath) arr = numpy.load(fpath)
cols = [("index", I64), ("peak_x", F64), ("peak_y", F64), cols = [("index", numpy.int64), ("peak_x", numpy.float64),
("peak_z", F64), ("mass_cl", F64), ("sub_frac", F64)] ("peak_y", numpy.float64), ("peak_z", numpy.float64),
("mass_cl", numpy.float64), ("sub_frac", numpy.float64)]
out = cols_to_structured(arr.shape[0], cols) out = cols_to_structured(arr.shape[0], cols)
for i, name in enumerate(out.dtype.names): for i, name in enumerate(out.dtype.names):
out[name] = arr[:, i] out[name] = arr[:, i]
@ -856,26 +627,26 @@ def read_mmain(n, srcdir, fname="Mmain_{}.npy"):
return out return out
def read_initcm(n, srcdir, fname="clump_{}_cm.npy"): def read_initcm(nsim, srcdir, fname="clump_{}_cm.npy"):
""" """
Read `clump_cm`, i.e. the center of mass of a clump at redshift z = 70. Read `clump_cm`, i.e. the center of mass of a clump at redshift z = 70.
If the file does not exist returns `None`. If the file does not exist returns `None`.
Parameters Parameters
---------- ----------
n : int nsim : int
The index of the initial conditions (IC) realisation. IC realisation index.
srcdir : str srcdir : str
The path to the folder containing the files. Path to the folder containing the files.
fname : str, optional fname : str, optional
The file name convention. By default `clump_cm_{}.npy`, where the File name convention. By default `clump_cm_{}.npy`, where the
substituted value is `n`. substituted value is `nsim`.
Returns Returns
------- -------
out : structured array out : structured array
""" """
fpath = join(srcdir, fname.format(n)) fpath = join(srcdir, fname.format(nsim))
try: try:
return numpy.load(fpath) return numpy.load(fpath)
except FileNotFoundError: except FileNotFoundError:

19
csiborgtools/read/summaries.py
View file

@ -15,13 +15,18 @@
""" """
Tools for summarising various results. Tools for summarising various results.
""" """
import numpy
import joblib
from os.path import (join, isfile) from os.path import (join, isfile)
from glob import glob from glob import glob
import numpy
import joblib
from tqdm import tqdm from tqdm import tqdm
###############################################################################
# PKReader #
###############################################################################
class PKReader: class PKReader:
""" """
A shortcut object for reading in the power spectrum files. A shortcut object for reading in the power spectrum files.
@ -170,6 +175,11 @@ class PKReader:
return ks, xpks return ks, xpks
###############################################################################
# PKReader #
###############################################################################
class kNNCDFReader: class kNNCDFReader:
""" """
Shortcut object to read in the kNN CDF data. Shortcut object to read in the kNN CDF data.
@ -304,6 +314,11 @@ class kNNCDFReader:
return [file for file in glob(join(folder, "*")) if str(ic) in file] return [file for file in glob(join(folder, "*")) if str(ic) in file]
###############################################################################
# PKReader #
###############################################################################
class PairOverlap: class PairOverlap:
r""" r"""
A shortcut object for reading in the results of matching two simulations. A shortcut object for reading in the results of matching two simulations.

2
csiborgtools/units/__init__.py
View file

@ -14,4 +14,4 @@
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. # 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
from .transforms import cartesian_to_radec, radec_to_cartesian # noqa from .transforms import cartesian_to_radec, radec_to_cartesian # noqa
from .box_units import (BoxUnits) # noqa from .box_units import BoxUnits # noqa

45
csiborgtools/units/box_units.py
View file

@ -15,7 +15,6 @@
""" """
Simulation box unit transformations. Simulation box unit transformations.
""" """
import numpy import numpy
from scipy.interpolate import interp1d from scipy.interpolate import interp1d
from astropy.cosmology import LambdaCDM from astropy.cosmology import LambdaCDM
@ -38,17 +37,21 @@ class BoxUnits:
Paramaters Paramaters
---------- ----------
nsnap : int
Snapshot index.
nsim : int
IC realisation index.
paths : py:class`csiborgtools.read.CSiBORGPaths` paths : py:class`csiborgtools.read.CSiBORGPaths`
CSiBORG paths-handling object with set `n_sim` and `n_snap`. CSiBORG paths object.
""" """
_cosmo = None _cosmo = None
def __init__(self, paths): def __init__(self, nsnap, nsim, paths):
""" """
Read in the snapshot info file and set the units from it. Read in the snapshot info file and set the units from it.
""" """
partreader = ParticleReader(paths) partreader = ParticleReader(paths)
info = partreader.read_info() info = partreader.read_info(nsnap, nsim)
pars = ["boxlen", "time", "aexp", "H0", pars = ["boxlen", "time", "aexp", "H0",
"omega_m", "omega_l", "omega_k", "omega_b", "omega_m", "omega_l", "omega_k", "omega_b",
"unit_l", "unit_d", "unit_t"] "unit_l", "unit_d", "unit_t"]
@ -68,7 +71,6 @@ class BoxUnits:
Returns Returns
------- -------
cosmo : `astropy.cosmology.LambdaCDM` cosmo : `astropy.cosmology.LambdaCDM`
The CSiBORG cosmology.
""" """
return self._cosmo return self._cosmo
@ -81,7 +83,6 @@ class BoxUnits:
Returns Returns
------- -------
H0 : float H0 : float
Hubble constant.
""" """
return self._H0 return self._H0
@ -93,7 +94,6 @@ class BoxUnits:
Returns Returns
------- -------
h : float h : float
The little h
""" """
return self._H0 / 100 return self._H0 / 100
@ -106,7 +106,6 @@ class BoxUnits:
Returns Returns
------- -------
G : float G : float
The gravitational constant.
""" """
return constants.G.cgs.value * (self._unit_d * self._unit_t ** 2) return constants.G.cgs.value * (self._unit_d * self._unit_t ** 2)
@ -118,7 +117,6 @@ class BoxUnits:
Returns Returns
------- -------
H0 : float H0 : float
The Hubble constant.
""" """
return self.H0 * 1e5 / units.Mpc.to(units.cm) * self._unit_t return self.H0 * 1e5 / units.Mpc.to(units.cm) * self._unit_t
@ -130,7 +128,6 @@ class BoxUnits:
Returns Returns
------- -------
c : float c : float
The speed of light.
""" """
return constants.c.cgs.value * self._unit_t / self._unit_l return constants.c.cgs.value * self._unit_t / self._unit_l
@ -142,7 +139,6 @@ class BoxUnits:
Returns Returns
------- -------
rhoc : float rhoc : float
The critical density.
""" """
return 3 * self.box_H0 ** 2 / (8 * numpy.pi * self.box_G) return 3 * self.box_H0 ** 2 / (8 * numpy.pi * self.box_G)
@ -150,8 +146,7 @@ class BoxUnits:
def box2kpc(self, length): def box2kpc(self, length):
r""" r"""
Convert length from box units to :math:`\mathrm{ckpc}` (with Convert length from box units to :math:`\mathrm{ckpc}` (with
:math:`h=0.705`). It appears that `self.unit_l` must be in :math:`h=0.705`).
:math:`\mathrm{cm}`.
Parameters Parameters
---------- ----------
@ -224,12 +219,12 @@ class BoxUnits:
Parameters Parameters
---------- ----------
dist : float dist : float
Dist in box units. Distance in box units.
Returns Returns
------- -------
cosmo_redshift : foat cosmo_redshift : foat
The cosmological redshift. Cosmological redshift.
""" """
x = numpy.linspace(0., 1., 5001) x = numpy.linspace(0., 1., 5001)
y = self.cosmo.comoving_distance(x) y = self.cosmo.comoving_distance(x)
@ -244,17 +239,17 @@ class BoxUnits:
Parameters Parameters
---------- ----------
vx, vy, vz : 1-dimensional arrays vx, vy, vz : 1-dimensional arrays
The Cartesian velocity components. Cartesian velocity components.
px, py, pz : 1-dimensional arrays px, py, pz : 1-dimensional arrays
The Cartesian position vectors components. Cartesian position vectors components.
p0x, p0y, p0z : floats p0x, p0y, p0z : floats
The centre of the box. By default 0, in which it is assumed that Centre of the box coordinates. By default 0, in which it is assumed
the coordinates are already centred. that the coordinates are already centred.
Returns Returns
------- -------
pec_redshift : 1-dimensional array pec_redshift : 1-dimensional array
The peculiar redshift. Peculiar redshift.
""" """
# Peculiar velocity along the radial distance # Peculiar velocity along the radial distance
r = numpy.vstack([px - p0x, py - p0y, pz - p0z]).T r = numpy.vstack([px - p0x, py - p0y, pz - p0z]).T
@ -275,17 +270,17 @@ class BoxUnits:
Parameters Parameters
---------- ----------
vx, vy, vz : 1-dimensional arrays vx, vy, vz : 1-dimensional arrays
The Cartesian velocity components. Cartesian velocity components.
px, py, pz : 1-dimensional arrays px, py, pz : 1-dimensional arrays
The Cartesian position vectors components. Cartesian position vectors components.
p0x, p0y, p0z : floats p0x, p0y, p0z : floats
The centre of the box. By default 0, in which it is assumed that Centre of the box coordinates. By default 0, in which it is assumed
the coordinates are already centred. that the coordinates are already centred.
Returns Returns
------- -------
obs_redshift : 1-dimensional array obs_redshift : 1-dimensional array
The observed redshift. Observed redshift.
""" """
r = numpy.vstack([px - p0x, py - p0y, pz - p0z]).T r = numpy.vstack([px - p0x, py - p0y, pz - p0z]).T
zcosmo = self.box2cosmoredshift(numpy.sum(r**2, axis=1)**0.5) zcosmo = self.box2cosmoredshift(numpy.sum(r**2, axis=1)**0.5)

3
csiborgtools/units/transforms.py
View file

@ -15,7 +15,6 @@
""" """
Various coordinate transformations. Various coordinate transformations.
""" """
import numpy import numpy
@ -49,7 +48,7 @@ def radec_to_cartesian(dist, ra, dec, isdeg=True):
Parameters Parameters
---------- ----------
dist, ra, dec : 1-dimensional arrays dist, ra, dec : 1-dimensional arrays
The spherical coordinates. Spherical coordinates.
isdeg : bool, optional isdeg : bool, optional
Whether `ra` and `dec` are in degres. By default `True`. Whether `ra` and `dec` are in degres. By default `True`.

18
csiborgtools/utils/recarray_manip.py
View file

@ -15,8 +15,6 @@
""" """
Utilility functions for manipulation structured arrays. Utilility functions for manipulation structured arrays.
""" """
import numpy import numpy
@ -51,7 +49,7 @@ def add_columns(arr, X, cols):
Parameters Parameters
---------- ----------
arr : record array arr : record array
The record array to add columns to. Record array to add columns to.
X : (list of) 1-dimensional array(s) or 2-dimensional array X : (list of) 1-dimensional array(s) or 2-dimensional array
Columns to be added. Columns to be added.
cols : str or list of str cols : str or list of str
@ -60,7 +58,6 @@ def add_columns(arr, X, cols):
Returns Returns
------- -------
out : record array out : record array
The new record array with added values.
""" """
# Make sure cols is a list of str and X a 2D array # Make sure cols is a list of str and X a 2D array
cols = [cols] if isinstance(cols, str) else cols cols = [cols] if isinstance(cols, str) else cols
@ -95,14 +92,13 @@ def rm_columns(arr, cols):
Parameters Parameters
---------- ----------
arr : record array arr : record array
The record array to remove columns from. Record array to remove columns from.
cols : str or list of str cols : str or list of str
Column names to be removed. Column names to be removed.
Returns Returns
------- -------
out : record array out : record array
Record array with removed columns.
""" """
# Check columns we wish to delete are in the array # Check columns we wish to delete are in the array
cols = [cols] if isinstance(cols, str) else cols cols = [cols] if isinstance(cols, str) else cols
@ -173,7 +169,7 @@ def array_to_structured(arr, cols):
Returns Returns
------- -------
out : structured array out : structured array
The output structured array. Output structured array.
""" """
cols = [cols] if isinstance(cols, str) else cols cols = [cols] if isinstance(cols, str) else cols
if arr.ndim != 2 and arr.shape[1] != len(cols): if arr.ndim != 2 and arr.shape[1] != len(cols):
@ -196,15 +192,15 @@ def flip_cols(arr, col1, col2):
Parameters Parameters
---------- ----------
arr : structured array arr : structured array
The array whose columns are to be converted. Array whose columns are to be converted.
col1 : str col1 : str
The first column name. First column name.
col2 : str col2 : str
The second column name. Second column name.
Returns Returns
------- -------
nothing None
""" """
dum = numpy.copy(arr[col1]) dum = numpy.copy(arr[col1])
arr[col1] = arr[col2] arr[col1] = arr[col2]

4242
notebooks/knn.ipynb
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File diff suppressed because one or more lines are too long

68
scripts/run_asciipos.py
View file

@ -1,68 +0,0 @@
# Copyright (C) 2022 Richard Stiskalek
# This program is free software; you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the
# Free Software Foundation; either version 3 of the License, or (at your
# option) any later version.
#
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
# Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
"""A script to dump or remove files for POWMES."""
from os.path import join, exists
from argparse import ArgumentParser
import numpy
from datetime import datetime
from os import remove
from mpi4py import MPI
try:
import csiborgtools
except ModuleNotFoundError:
import sys
sys.path.append("../")
import csiborgtools
import utils
parser = ArgumentParser()
parser.add_argument("--mode", type=str, choices=["dump", "remove"])
args = parser.parse_args()
F64 = numpy.float64
I64 = numpy.int64
# Get MPI things
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
nproc = comm.Get_size()
dumpdir = join(utils.dumpdir, "temp_powmes")
fout = join(dumpdir, "out_{}_{}.ascii")
paths = csiborgtools.read.CSiBORGPaths()
n_sims = paths.ic_ids[:1]
for i in csiborgtools.fits.split_jobs(len(n_sims), nproc)[rank]:
print("{}: calculating {}th simulation.".format(datetime.now(), i))
n_sim = n_sims[i]
n_snap = paths.get_maximum_snapshot(n_sim)
paths.set_info(n_sim, n_snap)
f = fout.format(n_sim, n_snap)
if args.mode == "dump":
# Read the particles
reader = csiborgtools.read.ParticleReader(paths)
particles = reader.read_particle(["x", "y", "z", "M"])
csiborgtools.read.make_ascii_powmes(particles, f, verbose=True)
else:
if exists(f):
remove(f)
comm.Barrier()
if rank == 0:
print("All finished! See you!")

13
scripts/run_asciipos.sh
View file

@ -1,13 +0,0 @@
nthreads=1
memory=75
queue="berg"
env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_galomatch/bin/python"
file="run_asciipos.py"
mode="dump"
cm="addqueue -q $queue -n $nthreads -m $memory $env $file --mode $mode"
echo "Submitting:"
echo $cm
echo
$cm

71
scripts/run_crossmatch.py
View file

@ -1,71 +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.
"""
MPI script to run the CSiBORG realisations matcher.
TODO
----
- [ ] Update this script
"""
import numpy
from datetime import datetime
from mpi4py import MPI
from os.path import join
try:
import csiborgtools
except ModuleNotFoundError:
import sys
sys.path.append("../")
import csiborgtools
import utils
# Get MPI things
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
nproc = comm.Get_size()
# File paths
fperm = join(utils.dumpdir, "overlap", "cross_{}.npy")
# fperm = join(utils.dumpdir, "match", "cross_matches.npy")
# Set up the catalogue
paths = csiborgtools.read.CSiBORGPaths(to_new=False)
print("{}: started reading in the combined catalogue.".format(datetime.now()),
flush=True)
cat = csiborgtools.read.CombinedHaloCatalogue(
paths, min_m500=None, max_dist=None, verbose=False)
print("{}: finished reading in the combined catalogue with `{}`."
.format(datetime.now(), cat.n_sims), flush=True)
matcher = csiborgtools.match.RealisationsMatcher(cat)
for i in csiborgtools.fits.split_jobs(len(cat.n_sims), nproc)[rank]:
n = cat.n_sims[i]
print("{}: rank {} working on simulation `{}`."
.format(datetime.now(), rank, n), flush=True)
out = matcher.cross_knn_position_single(
i, nmult=15, dlogmass=2, init_dist=True, overlap=False, verbose=False,
overlapper_kwargs={"smooth_scale": 1})
# Dump the result
fout = fperm.format(n)
print("Saving results to `{}`.".format(fout))
with open(fout, "wb") as f:
numpy.save(fout, out)
comm.Barrier()
if rank == 0:
print("All finished.")

17
scripts/run_crossmatch.sh
View file

@ -1,17 +0,0 @@
nthreads=1
memory=32
queue="berg"
env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_galomatch/bin/python"
file="run_crossmatch.py"
pythoncm="$env $file"
# echo "Submitting:"
# echo $pythoncm
# echo
# $pythoncm
cm="addqueue -q $queue -n $nthreads -m $memory $pythoncm"
echo "Submitting:"
echo $cm
echo
$cm

43
scripts/run_crosspk.py
View file

@ -16,14 +16,14 @@
MPI script to calculate the matter cross power spectrum between CSiBORG MPI script to calculate the matter cross power spectrum between CSiBORG
IC realisations. Units are Mpc/h. IC realisations. Units are Mpc/h.
""" """
from gc import collect
from argparse import ArgumentParser from argparse import ArgumentParser
from os import remove
from os.path import join
from itertools import combinations
from datetime import datetime
import numpy import numpy
import joblib import joblib
from datetime import datetime
from itertools import combinations
from os.path import join
from os import remove
from gc import collect
from mpi4py import MPI from mpi4py import MPI
import Pk_library as PKL import Pk_library as PKL
try: try:
@ -32,9 +32,9 @@ except ModuleNotFoundError:
import sys import sys
sys.path.append("../") sys.path.append("../")
import csiborgtools import csiborgtools
import utils
dumpdir = "/mnt/extraspace/rstiskalek/csiborg/"
parser = ArgumentParser() parser = ArgumentParser()
parser.add_argument("--grid", type=int) parser.add_argument("--grid", type=int)
parser.add_argument("--halfwidth", type=float, default=0.5) parser.add_argument("--halfwidth", type=float, default=0.5)
@ -47,27 +47,24 @@ nproc = comm.Get_size()
MAS = "CIC" # mass asignment scheme MAS = "CIC" # mass asignment scheme
paths = csiborgtools.read.CSiBORGPaths() paths = csiborgtools.read.CSiBORGPaths()
ics = paths.ic_ids box = csiborgtools.units.BoxUnits(paths)
n_sims = len(ics) reader = csiborgtools.read.ParticleReader(paths)
ics = paths.ic_ids(tonew=False)
nsims = len(ics)
# File paths # File paths
ftemp = join(utils.dumpdir, "temp_crosspk", ftemp = join(dumpdir, "temp_crosspk",
"out_{}_{}" + "_{}".format(args.halfwidth)) "out_{}_{}" + "_{}".format(args.halfwidth))
fout = join(utils.dumpdir, "crosspk", fout = join(dumpdir, "crosspk",
"out_{}_{}" + "_{}.p".format(args.halfwidth)) "out_{}_{}" + "_{}.p".format(args.halfwidth))
jobs = csiborgtools.fits.split_jobs(n_sims, nproc)[rank] jobs = csiborgtools.fits.split_jobs(nsims, nproc)[rank]
for n in jobs: for n in jobs:
print("Rank {}@{}: saving {}th delta.".format(rank, datetime.now(), n)) print("Rank {}@{}: saving {}th delta.".format(rank, datetime.now(), n))
# Set the paths nsim = ics[n]
n_sim = ics[n] particles = reader.read_particle(max(paths.get_snapshots(nsim)), nsim,
paths.set_info(n_sim, paths.get_maximum_snapshot(n_sim)) ["x", "y", "z", "M"], verbose=False)
# Set reader and the box
reader = csiborgtools.read.ParticleReader(paths)
box = csiborgtools.units.BoxUnits(paths)
# Read particles
particles = reader.read_particle(["x", "y", "z", "M"], verbose=False)
# Halfwidth -- particle selection # Halfwidth -- particle selection
if args.halfwidth < 0.5: if args.halfwidth < 0.5:
particles = csiborgtools.read.halfwidth_select( particles = csiborgtools.read.halfwidth_select(
@ -85,9 +82,9 @@ for n in jobs:
collect() collect()
# Dump the results # Dump the results
with open(ftemp.format(n_sim, "delta") + ".npy", "wb") as f: with open(ftemp.format(nsim, "delta") + ".npy", "wb") as f:
numpy.save(f, delta) numpy.save(f, delta)
joblib.dump([aexp, length], ftemp.format(n_sim, "lengths") + ".p") joblib.dump([aexp, length], ftemp.format(nsim, "lengths") + ".p")
# Try to clean up memory # Try to clean up memory
del delta del delta
@ -97,8 +94,8 @@ for n in jobs:
comm.Barrier() comm.Barrier()
# Get off-diagonal elements and append the diagoal # Get off-diagonal elements and append the diagoal
combs = [c for c in combinations(range(n_sims), 2)] combs = [c for c in combinations(range(nsims), 2)]
for i in range(n_sims): for i in range(nsims):
combs.append((i, i)) combs.append((i, i))
prev_delta = [-1, None, None, None] # i, delta, aexp, length prev_delta = [-1, None, None, None] # i, delta, aexp, length

41
scripts/run_fieldprop.py
View file

@ -16,11 +16,11 @@
MPI script to evaluate field properties at the galaxy positions. MPI script to evaluate field properties at the galaxy positions.
""" """
from argparse import ArgumentParser from argparse import ArgumentParser
import numpy
from datetime import datetime
from mpi4py import MPI
from os.path import join from os.path import join
from os import remove from os import remove
from datetime import datetime
import numpy
from mpi4py import MPI
try: try:
import csiborgtools import csiborgtools
except ModuleNotFoundError: except ModuleNotFoundError:
@ -29,6 +29,7 @@ except ModuleNotFoundError:
import csiborgtools import csiborgtools
import utils import utils
dumpdir = "/mnt/extraspace/rstiskalek/csiborg/"
parser = ArgumentParser() parser = ArgumentParser()
parser.add_argument("--survey", type=str, choices=["SDSS"]) parser.add_argument("--survey", type=str, choices=["SDSS"])
parser.add_argument("--grid", type=int) parser.add_argument("--grid", type=int)
@ -52,30 +53,28 @@ pos = pos.astype(numpy.float32)
# File paths # File paths
fname = "out_{}_{}_{}_{}_{}".format( fname = "out_{}_{}_{}_{}_{}".format(
survey.name, args.grid, args.MAS, args.halfwidth, args.smooth_scale) survey.name, args.grid, args.MAS, args.halfwidth, args.smooth_scale)
ftemp = join(utils.dumpdir, "temp_fields", fname + "_{}.npy") ftemp = join(dumpdir, "temp_fields", fname + "_{}.npy")
fperm = join(utils.dumpdir, "fields", fname + ".npy") fperm = join(dumpdir, "fields", fname + ".npy")
# Edit depending on what is calculated # Edit depending on what is calculated
dtype = {"names": ["delta", "phi"], "formats": [numpy.float32] * 2} dtype = {"names": ["delta", "phi"], "formats": [numpy.float32] * 2}
# CSiBORG simulation paths # CSiBORG simulation paths
paths = csiborgtools.read.CSiBORGPaths() paths = csiborgtools.read.CSiBORGPaths()
ics = paths.ic_ids ics = paths.ic_ids(tonew=False)
n_sims = len(ics) nsims = len(ics)
for n in csiborgtools.fits.split_jobs(n_sims, nproc)[rank]: for n in csiborgtools.fits.split_jobs(nsims, nproc)[rank]:
print("Rank {}@{}: working on {}th IC.".format(rank, datetime.now(), n), print("Rank {}@{}: working on {}th IC.".format(rank, datetime.now(), n),
flush=True) flush=True)
# Set the paths nsim = ics[n]
n_sim = ics[n] nsnap = max(paths.get_snapshots(nsim))
paths.set_info(n_sim, paths.get_maximum_snapshot(n_sim))
# Set reader and the box
reader = csiborgtools.read.ParticleReader(paths) reader = csiborgtools.read.ParticleReader(paths)
box = csiborgtools.units.BoxUnits(paths) box = csiborgtools.units.BoxUnits(nsnap, nsim, paths)
# Read particles and select a subset of them # Read particles and select a subset of them
particles = reader.read_particle(["x", "y", "z", "M"], verbose=False) particles = reader.read_particle(nsnap, nsim, ["x", "y", "z", "M"],
verbose=False)
if args.halfwidth < 0.5: if args.halfwidth < 0.5:
particles = csiborgtools.read.halfwidth_select( particles = csiborgtools.read.halfwidth_select(
args.halfwidth, particles) args.halfwidth, particles)
@ -101,7 +100,7 @@ for n in csiborgtools.fits.split_jobs(n_sims, nproc)[rank]:
# ... # ...
# Dump the results # Dump the results
with open(ftemp.format(n_sim), "wb") as f: with open(ftemp.format(nsim), "wb") as f:
numpy.save(f, out) numpy.save(f, out)
# Wait for all ranks to finish # Wait for all ranks to finish
@ -109,16 +108,16 @@ comm.Barrier()
if rank == 0: if rank == 0:
print("Collecting files...", flush=True) print("Collecting files...", flush=True)
out = numpy.full((n_sims, pos.shape[0]), numpy.nan, dtype=dtype) out = numpy.full((nsims, pos.shape[0]), numpy.nan, dtype=dtype)
for n in range(n_sims): for n in range(nsims):
n_sim = ics[n] nsim = ics[n]
with open(ftemp.format(n_sim), "rb") as f: with open(ftemp.format(nsim), "rb") as f:
fin = numpy.load(f, allow_pickle=True) fin = numpy.load(f, allow_pickle=True)
for name in dtype["names"]: for name in dtype["names"]:
out[name][n, ...] = fin[name] out[name][n, ...] = fin[name]
# Remove the temporary file # Remove the temporary file
remove(ftemp.format(n_sim)) remove(ftemp.format(nsim))
print("Saving results to `{}`.".format(fperm), flush=True) print("Saving results to `{}`.".format(fperm), flush=True)
with open(fperm, "wb") as f: with open(fperm, "wb") as f:

69
scripts/run_fit_halos.py
View file

@ -14,12 +14,11 @@
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. # 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
""" """
A script to fit halos (concentration, ...). The particle array of each CSiBORG A script to fit halos (concentration, ...). The particle array of each CSiBORG
realisation must have been split in advance by `run_split_halos`. realisation must have been split in advance by `runsplit_halos`.
""" """
import numpy
from datetime import datetime
from os.path import join from os.path import join
from datetime import datetime
import numpy
from mpi4py import MPI from mpi4py import MPI
try: try:
import csiborgtools import csiborgtools
@ -29,47 +28,48 @@ except ModuleNotFoundError:
import csiborgtools import csiborgtools
import utils import utils
F64 = numpy.float64
I64 = numpy.int64
# Get MPI things # Get MPI things
comm = MPI.COMM_WORLD comm = MPI.COMM_WORLD
rank = comm.Get_rank() rank = comm.Get_rank()
nproc = comm.Get_size() nproc = comm.Get_size()
dumpdir = utils.dumpdir
loaddir = join(utils.dumpdir, "temp")
cols_collect = [("npart", I64), ("totpartmass", F64), ("Rs", F64),
("vx", F64), ("vy", F64), ("vz", F64),
("Lx", F64), ("Ly", F64), ("Lz", F64),
("rho0", F64), ("conc", F64), ("rmin", F64),
("rmax", F64), ("r200", F64), ("r500", F64),
("m200", F64), ("m500", F64), ("lambda200c", F64)]
paths = csiborgtools.read.CSiBORGPaths() paths = csiborgtools.read.CSiBORGPaths()
dumpdir = "/mnt/extraspace/rstiskalek/csiborg/"
loaddir = join(dumpdir, "temp")
cols_collect = [("npart", numpy.int64), ("totpartmass", numpy.float64),
("Rs", numpy.float64), ("vx", numpy.float64),
("vy", numpy.float64), ("vz", numpy.float64),
("Lx", numpy.float64), ("Ly", numpy.float64),
("Lz", numpy.float64), ("rho0", numpy.float64),
("conc", numpy.float64), ("rmin", numpy.float64),
("rmax", numpy.float64), ("r200", numpy.float64),
("r500", numpy.float64), ("m200", numpy.float64),
("m500", numpy.float64), ("lambda200c", numpy.float64)]
for i, n_sim in enumerate(paths.ic_ids):
for i, nsim in enumerate(paths.ic_ids(tonew=False)):
if rank == 0: if rank == 0:
print("{}: calculating {}th simulation.".format(datetime.now(), i)) print("{}: calculating {}th simulation.".format(datetime.now(), i))
# Correctly set the paths! nsnap = max(paths.get_snapshots(nsim))
n_snap = paths.get_maximum_snapshot(n_sim) box = csiborgtools.units.BoxUnits(nsnap, nsim, paths)
paths.set_info(n_sim, n_snap)
box = csiborgtools.units.BoxUnits(paths)
jobs = csiborgtools.fits.split_jobs(utils.Nsplits, nproc)[rank] jobs = csiborgtools.fits.split_jobs(utils.Nsplits, nproc)[rank]
for n_split in jobs: for nsplit in jobs:
parts, part_clumps, clumps = csiborgtools.fits.load_split_particles( parts, part_clumps, clumps = csiborgtools.fits.load_split_particles(
n_split, paths, remove_split=False) nsplit, nsnap, nsim, paths, remove_split=False)
N = clumps.size N = clumps.size
cols = [("index", I64), ("npart", I64), ("totpartmass", F64), cols = [("index", numpy.int64), ("npart", numpy.int64),
("Rs", F64), ("rho0", F64), ("conc", F64), ("lambda200c", F64), ("totpartmass", numpy.float64), ("Rs", numpy.float64),
("vx", F64), ("vy", F64), ("vz", F64), ("rho0", numpy.float64), ("conc", numpy.float64),
("Lx", F64), ("Ly", F64), ("Lz", F64), ("lambda200c", numpy.float64), ("vx", numpy.float64),
("rmin", F64), ("rmax", F64), ("vy", numpy.float64), ("vz", numpy.float64),
("r200", F64), ("r500", F64), ("m200", F64), ("m500", F64)] ("Lx", numpy.float64), ("Ly", numpy.float64),
("Lz", numpy.float64), ("rmin", numpy.float64),
("rmax", numpy.float64), ("r200", numpy.float64),
("r500", numpy.float64), ("m200", numpy.float64),
("m500", numpy.float64)]
out = csiborgtools.utils.cols_to_structured(N, cols) out = csiborgtools.utils.cols_to_structured(N, cols)
out["index"] = clumps["index"] out["index"] = clumps["index"]
@ -106,7 +106,7 @@ for i, n_sim in enumerate(paths.ic_ids):
out["rho0"][n] = nfwpost.rho0_from_Rs(Rs) out["rho0"][n] = nfwpost.rho0_from_Rs(Rs)
out["conc"][n] = out["r200"][n] / Rs out["conc"][n] = out["r200"][n] / Rs
csiborgtools.read.dump_split(out, n_split, paths) csiborgtools.read.dump_split(out, nsplit, nsnap, nsim, paths)
# Wait until all jobs finished before moving to another simulation # Wait until all jobs finished before moving to another simulation
comm.Barrier() comm.Barrier()
@ -116,11 +116,10 @@ for i, n_sim in enumerate(paths.ic_ids):
print("Collecting results!") print("Collecting results!")
partreader = csiborgtools.read.ParticleReader(paths) partreader = csiborgtools.read.ParticleReader(paths)
out_collected = csiborgtools.read.combine_splits( out_collected = csiborgtools.read.combine_splits(
utils.Nsplits, partreader, cols_collect, remove_splits=True, utils.Nsplits, nsnap, nsim, partreader, cols_collect,
verbose=False) remove_splits=True, verbose=False)
fname = join(paths.dumpdir, "ramses_out_{}_{}.npy" fname = join(paths.dumpdir, "ramses_out_{}_{}.npy"
.format(str(paths.n_sim).zfill(5), .format(str(nsim).zfill(5), str(nsnap).zfill(5)))
str(paths.n_snap).zfill(5)))
print("Saving results to `{}`.".format(fname)) print("Saving results to `{}`.".format(fname))
numpy.save(fname, out_collected) numpy.save(fname, out_collected)

35
scripts/run_initmatch.py
View file

@ -19,14 +19,14 @@ are grouped in a clump at present redshift.
Optionally also dumps the clumps information, however watch out as this will Optionally also dumps the clumps information, however watch out as this will
eat up a lot of memory. eat up a lot of memory.
""" """
import numpy from gc import collect
from argparse import ArgumentParser
from distutils.util import strtobool
from datetime import datetime
from mpi4py import MPI
from os.path import join from os.path import join
from os import remove from os import remove
from gc import collect from argparse import ArgumentParser
from datetime import datetime
from distutils.util import strtobool
import numpy
from mpi4py import MPI
try: try:
import csiborgtools import csiborgtools
except ModuleNotFoundError: except ModuleNotFoundError:
@ -44,9 +44,8 @@ parser = ArgumentParser()
parser.add_argument("--dump_clumps", type=lambda x: bool(strtobool(x))) parser.add_argument("--dump_clumps", type=lambda x: bool(strtobool(x)))
args = parser.parse_args() args = parser.parse_args()
init_paths = csiborgtools.read.CSiBORGPaths(to_new=True) paths = csiborgtools.read.CSiBORGPaths()
fin_paths = csiborgtools.read.CSiBORGPaths(to_new=False) nsims = paths.ic_ids(tonew=True)
nsims = init_paths.ic_ids
# Output files # Output files
dumpdir = "/mnt/extraspace/rstiskalek/csiborg/" dumpdir = "/mnt/extraspace/rstiskalek/csiborg/"
@ -58,22 +57,18 @@ for nsim in nsims:
if rank == 0: if rank == 0:
print("{}: reading simulation {}.".format(datetime.now(), nsim), print("{}: reading simulation {}.".format(datetime.now(), nsim),
flush=True) flush=True)
nsnap_min = min(paths.get_snapshots(nsim))
# Set the snapshot numbers nsnap_max = max(paths.get_snapshots(nsim))
init_paths.set_info(nsim, init_paths.get_minimum_snapshot(nsim)) reader = csiborgtools.read.ParticleReader(paths)
fin_paths.set_info(nsim, fin_paths.get_maximum_snapshot(nsim))
# Set the readers
init_reader = csiborgtools.read.ParticleReader(init_paths)
fin_reader = csiborgtools.read.ParticleReader(fin_paths)
# Read and sort the initial particle files by their particle IDs # Read and sort the initial particle files by their particle IDs
part0 = init_reader.read_particle(["x", "y", "z", "M", "ID"], part0 = reader.read_particle(nsnap_min, nsim, ["x", "y", "z", "M", "ID"],
verbose=False) verbose=False)
part0 = part0[numpy.argsort(part0["ID"])] part0 = part0[numpy.argsort(part0["ID"])]
# Order the final snapshot clump IDs by the particle IDs # Order the final snapshot clump IDs by the particle IDs
pid = fin_reader.read_particle(["ID"], verbose=False)["ID"] pid = reader.read_particle(nsnap_max, nsim, ["ID"], verbose=False)["ID"]
clump_ids = fin_reader.read_clumpid(verbose=False) clump_ids = reader.read_clumpid(nsnap_max, nsim, verbose=False)
clump_ids = clump_ids[numpy.argsort(pid)] clump_ids = clump_ids[numpy.argsort(pid)]
del pid del pid

13
scripts/run_knn.py
View file

@ -62,6 +62,7 @@ ics = [7444, 7468, 7492, 7516, 7540, 7564, 7588, 7612, 7636, 7660, 7684,
dumpdir = "/mnt/extraspace/rstiskalek/csiborg/knn" dumpdir = "/mnt/extraspace/rstiskalek/csiborg/knn"
fout_auto = join(dumpdir, "auto", "knncdf_{}.p") fout_auto = join(dumpdir, "auto", "knncdf_{}.p")
fout_cross = join(dumpdir, "cross", "knncdf_{}_{}.p") fout_cross = join(dumpdir, "cross", "knncdf_{}_{}.p")
paths = csiborgtools.read.CSiBORGPaths()
############################################################################### ###############################################################################
@ -72,11 +73,11 @@ knncdf = csiborgtools.match.kNN_CDF()
def do_auto(ic): def do_auto(ic):
out = {} out = {}
cat = csiborgtools.read.HaloCatalogue(ic, max_dist=Rmax) cat = csiborgtools.read.HaloCatalogue(ic, paths, max_dist=Rmax)
for i, mmin in enumerate(mass_threshold): for i, mmin in enumerate(mass_threshold):
knn = NearestNeighbors() knn = NearestNeighbors()
knn.fit(cat.positions[cat["totpartmass"] > mmin, ...]) knn.fit(cat.positions(False)[cat["totpartmass"] > mmin, ...])
rs, cdf = knncdf(knn, nneighbours=args.nneighbours, Rmax=Rmax, rs, cdf = knncdf(knn, nneighbours=args.nneighbours, Rmax=Rmax,
rmin=args.rmin, rmax=args.rmax, nsamples=args.nsamples, rmin=args.rmin, rmax=args.rmax, nsamples=args.nsamples,
@ -90,15 +91,15 @@ def do_auto(ic):
def do_cross(ics): def do_cross(ics):
out = {} out = {}
cat1 = csiborgtools.read.HaloCatalogue(ics[0], max_dist=Rmax) cat1 = csiborgtools.read.HaloCatalogue(ics[0], paths, max_dist=Rmax)
cat2 = csiborgtools.read.HaloCatalogue(ics[1], max_dist=Rmax) cat2 = csiborgtools.read.HaloCatalogue(ics[1], paths, max_dist=Rmax)
for i, mmin in enumerate(mass_threshold): for i, mmin in enumerate(mass_threshold):
knn1 = NearestNeighbors() knn1 = NearestNeighbors()
knn1.fit(cat1.positions[cat1["totpartmass"] > mmin, ...]) knn1.fit(cat1.positions()[cat1["totpartmass"] > mmin, ...])
knn2 = NearestNeighbors() knn2 = NearestNeighbors()
knn2.fit(cat2.positions[cat2["totpartmass"] > mmin, ...]) knn2.fit(cat2.positions()[cat2["totpartmass"] > mmin, ...])
rs, cdf0, cdf1, joint_cdf = knncdf.joint( rs, cdf0, cdf1, joint_cdf = knncdf.joint(
knn1, knn2, nneighbours=args.nneighbours, Rmax=Rmax, knn1, knn2, nneighbours=args.nneighbours, Rmax=Rmax,

9
scripts/run_singlematch.py
View file

@ -35,6 +35,7 @@ parser.add_argument("--sigma", type=float)
args = parser.parse_args() args = parser.parse_args()
# File paths # File paths
paths = csiborgtools.read.CSiBORGPaths()
fout = join(utils.dumpdir, "overlap", fout = join(utils.dumpdir, "overlap",
"cross_{}_{}.npz".format(args.nsim0, args.nsimx)) "cross_{}_{}.npz".format(args.nsim0, args.nsimx))
smooth_kwargs = {"sigma": args.sigma, "mode": "constant", "cval": 0.0} smooth_kwargs = {"sigma": args.sigma, "mode": "constant", "cval": 0.0}
@ -43,18 +44,18 @@ overlapper = csiborgtools.match.ParticleOverlap()
# Load catalogues # Load catalogues
print("{}: loading catalogues {} and {}." print("{}: loading catalogues {} and {}."
.format(datetime.now(), args.nsim0, args.nsimx), flush=True) .format(datetime.now(), args.nsim0, args.nsimx), flush=True)
cat0 = csiborgtools.read.HaloCatalogue(args.nsim0) cat0 = csiborgtools.read.HaloCatalogue(args.nsim0, paths)
catx = csiborgtools.read.HaloCatalogue(args.nsimx) catx = csiborgtools.read.HaloCatalogue(args.nsimx, paths)
print("{}: loading simulation {} and converting positions to cell numbers." print("{}: loading simulation {} and converting positions to cell numbers."
.format(datetime.now(), args.nsim0), flush=True) .format(datetime.now(), args.nsim0), flush=True)
with open(cat0.paths.clump0_path(args.nsim0), "rb") as f: with open(paths.clump0_path(args.nsim0), "rb") as f:
clumps0 = numpy.load(f, allow_pickle=True) clumps0 = numpy.load(f, allow_pickle=True)
overlapper.clumps_pos2cell(clumps0) overlapper.clumps_pos2cell(clumps0)
print("{}: loading simulation {} and converting positions to cell numbers." print("{}: loading simulation {} and converting positions to cell numbers."
.format(datetime.now(), args.nsimx), flush=True) .format(datetime.now(), args.nsimx), flush=True)
with open(catx.paths.clump0_path(args.nsimx), 'rb') as f: with open(paths.clump0_path(args.nsimx), 'rb') as f:
clumpsx = numpy.load(f, allow_pickle=True) clumpsx = numpy.load(f, allow_pickle=True)
overlapper.clumps_pos2cell(clumpsx) overlapper.clumps_pos2cell(clumpsx)

28
scripts/run_split_halos.py
View file

@ -15,9 +15,8 @@
""" """
Script to split particles into smaller files according to their clump Script to split particles into smaller files according to their clump
membership for faster manipulation. Currently does this for the maximum membership for faster manipulation. Currently does this for the maximum
snapshot of each simulation. Running this will require a lot of memory. snapshot of each simulation. Running this requires a lot of memory.
""" """
from mpi4py import MPI from mpi4py import MPI
from datetime import datetime from datetime import datetime
try: try:
@ -34,27 +33,26 @@ rank = comm.Get_rank()
nproc = comm.Get_size() nproc = comm.Get_size()
paths = csiborgtools.read.CSiBORGPaths() paths = csiborgtools.read.CSiBORGPaths()
n_sims = paths.ic_ids[:1] sims = paths.ic_ids(False)
partcols = ["x", "y", "z", "vx", "vy", "vz", "M", "level"] partcols = ["x", "y", "z", "vx", "vy", "vz", "M", "level"]
jobs = csiborgtools.fits.split_jobs(len(n_sims), nproc)[rank] jobs = csiborgtools.fits.split_jobs(len(sims), nproc)[rank]
for icount, sim_index in enumerate(jobs): for icount, sim_index in enumerate(jobs):
print("{}: rank {} working {} / {} jobs.".format(datetime.now(), rank, print("{}: rank {} working {} / {} jobs."
icount + 1, len(jobs))) .format(datetime.now(), rank, icount + 1, len(jobs)), flush=True)
n_sim = n_sims[sim_index] nsim = sims[sim_index]
n_snap = paths.get_maximum_snapshot(n_sim) nsnap = max(paths.get_snapshots(nsim))
# Set paths and inifitalise a particle reader
paths.set_info(n_sim, n_snap)
partreader = csiborgtools.read.ParticleReader(paths) partreader = csiborgtools.read.ParticleReader(paths)
# Load the clumps, particles' clump IDs and particles. # Load the clumps, particles' clump IDs and particles.
clumps = partreader.read_clumps() clumps = partreader.read_clumps(nsnap, nsim)
particle_clumps = partreader.read_clumpid(verbose=False) particle_clumps = partreader.read_clumpid(nsnap, nsim, verbose=False)
particles = partreader.read_particle(partcols, verbose=False) particles = partreader.read_particle(nsnap, nsim, partcols, verbose=False)
# Drop all particles whose clump index is 0 (not assigned to any halo) # Drop all particles whose clump index is 0 (not assigned to any halo)
particle_clumps, particles = partreader.drop_zero_indx( particle_clumps, particles = partreader.drop_zero_indx(
particle_clumps, particles) particle_clumps, particles)
# Dump it! # Dump it!
csiborgtools.fits.dump_split_particles(particles, particle_clumps, clumps, csiborgtools.fits.dump_split_particles(particles, particle_clumps, clumps,
utils.Nsplits, paths, verbose=False) utils.Nsplits, nsnap, nsim, paths,
verbose=False)
print("All finished!") print("All finished!", flush=True)