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Extend enclosed density to all CB2 (#126)

Browse source 
* Add borg2 flag

* add borg2_all

* Undo a comment?

* ADd borg2

* Update notebook

* Update nb

* Add external halo catalogue

* Add MDPL2

* Add BORG2 density profile plot

* Add more params

* Add comments
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Richard Stiskalek 2024-04-10 11:34:07 +02:00 committed by GitHub
parent 7330e535f7
commit 3876985f26
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9 changed files with 387 additions and 62 deletions
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4
csiborgtools/params.py
View file

@ -92,6 +92,7 @@ def simname2boxsize(simname):
"csiborg2_random": 676.6,
"borg1": 677.7,
"borg2": 676.6,
"borg2_all": 676.6,
"quijote": 1000.,
"TNG300-1": 205.,
"Carrick2015": 400.,
@ -123,6 +124,8 @@ def simname2Omega_m(simname):
"csiborg2_random": 0.3111,
"csiborg2_varysmall": 0.3111,
"borg1": 0.307,
"borg2": 0.3111,
"borg2_all": 0.3111,
"Carrick2015": 0.3,
}
@ -144,6 +147,7 @@ paths_glamdring = {
"borg1_dir": "/mnt/users/hdesmond/BORG_final",
"borg2_dir": "/mnt/extraspace/rstiskalek/BORG_STOPYRA_2023",
"tng300_1_dir": "/mnt/extraspace/rstiskalek/TNG300-1/",
"aux_cat_dir": "/mnt/extraspace/rstiskalek/catalogs",
}

3
csiborgtools/read/__init__.py
View file

@ -14,7 +14,8 @@
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
from .catalogue import (CSiBORG1Catalogue, CSiBORG2Catalogue, # noqa
CSiBORG2SUBFINDCatalogue, # noqa
CSiBORG2MergerTreeReader, QuijoteCatalogue) # noqa
CSiBORG2MergerTreeReader, QuijoteCatalogue, # noqa
MDPL2Catalogue) # noqa
from .snapshot import (CSiBORG1Snapshot, CSiBORG2Snapshot, QuijoteSnapshot, # noqa
CSiBORG1Field, CSiBORG2Field, QuijoteField, BORG2Field, # noqa
BORG1Field, TNG300_1Field) # noqa

107
csiborgtools/read/catalogue.py
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@ -582,6 +582,29 @@ class BaseCatalogue(ABC):
"""
return self._properties + self._custom_keys
def pick_fiducial_observer(self, n, rmax):
r"""
Select a new fiducial observer in the box.
Parameters
----------
n : int
Fiducial observer index.
rmax : float
Max. distance from the fiducial obs. in :math:`\mathrm{cMpc} / h`.
"""
self.clear_cache()
print(fiducial_observers(self.boxsize, rmax))
self.observer_location = fiducial_observers(self.boxsize, rmax)[n]
self.observer_velocity = None
if self._bounds is None:
bounds = {"dist": (0, rmax)}
else:
bounds = {**self._bounds, "dist": (0, rmax)}
self._make_mask(bounds)
def __getitem__(self, key):
# For internal calls we don't want to load the filtered data and use
# the __ prefixed keys. The internal calls are not being cached.
@ -1366,27 +1389,83 @@ class QuijoteCatalogue(BaseCatalogue):
fpath = self.paths.initial_lagpatch(self.nsim, self.simname)
return numpy.load(fpath)["lagpatch_size"]
def pick_fiducial_observer(self, n, rmax):
###############################################################################
# External halo catalogues #
###############################################################################
class MDPL2Catalogue(BaseCatalogue):
r"""
Select a new fiducial observer in the box.
XXX
Parameters
----------
n : int
Fiducial observer index.
rmax : float
Max. distance from the fiducial obs. in :math:`\mathrm{cMpc} / h`.
nsim : int
IC realisation index.
paths : py:class`csiborgtools.read.Paths`, optional
Paths object.
snapshot : subclass of py:class:`BaseSnapshot`, optional
Snapshot object corresponding to the catalogue.
bounds : dict
Parameter bounds; keys as parameter names, values as (min, max)
tuples. Use `dist` for radial distance, `None` for no bound.
observer_velocity : array, optional
Observer's velocity in :math:`\mathrm{km} / \mathrm{s}`.
cache_maxsize : int, optional
Maximum number of cached arrays.
"""
self.clear_cache()
self.observer_location = fiducial_observers(self.box.boxsize, rmax)[n]
self.observer_velocity = None
def __init__(self, paths=None, bounds=None, cache_maxsize=64):
boxsize = 1000.
super().__init__()
x0 = boxsize / 2
super().init_with_snapshot(
"MDPL2", 0, 125, paths, None, bounds, boxsize, [x0, x0, x0], None,
False, cache_maxsize)
if self._bounds is None:
bounds = {"dist": (0, rmax)}
else:
bounds = {**self._bounds, "dist": (0, rmax)}
self._custom_keys = []
self._bounds = bounds
self._make_mask(bounds)
def _read_fof_catalogue(self, kind):
fpath = self.paths.external_halo_catalogue(self.simname)
with File(fpath, 'r') as f:
if kind == "index":
return numpy.arange(len(f["x"]))
if kind not in f.keys():
raise ValueError(f"FoF catalogue key '{kind}' not available. Available keys are: {list(f.keys())}") # noqa
out = f[kind][...]
return out
@property
def coordinates(self):
return numpy.vstack(
[self._read_fof_catalogue(key) for key in ["x", "y", "z"]]).T
@property
def velocities(self):
return numpy.vstack(
[self._read_fof_catalogue(key) for key in ["vx", "vy", "vz"]]).T
@property
def totmass(self):
return self._read_fof_catalogue("mass")
@property
def npart(self):
raise RuntimeError("Number of particles is not available.")
@property
def index(self):
return self._read_fof_catalogue("index")
@property
def lagpatch_coordinates(self):
raise RuntimeError("Lagrangian patch information is not available")
@property
def lagpatch_radius(self):
raise RuntimeError("Lagrangian patch information is not available")
###############################################################################

27
csiborgtools/read/paths.py
View file

@ -59,6 +59,8 @@ class Paths:
Path to the BORG2 simulation directory.
tng300_1_dir : str
Path to the TNG300-1 simulation directory.
aux_cat_dir : str
Path to the directory containing auxiliary catalogues.
"""
def __init__(self,
csiborg1_srcdir,
@ -69,7 +71,8 @@ class Paths:
quijote_dir,
borg1_dir,
borg2_dir,
tng300_1_dir
tng300_1_dir,
aux_cat_dir
):
self.csiborg1_srcdir = csiborg1_srcdir
self.csiborg2_main_srcdir = csiborg2_main_srcdir
@ -80,6 +83,7 @@ class Paths:
self.borg2_dir = borg2_dir
self.tng300_1_dir = tng300_1_dir
self.postdir = postdir
self.aux_cat_dir = aux_cat_dir
def get_ics(self, simname):
"""
@ -100,6 +104,9 @@ class Paths:
elif simname == "csiborg2_main" or simname == "borg2":
files = glob(join(self.csiborg2_main_srcdir, "chain_*"))
files = [int(search(r'chain_(\d+)', f).group(1)) for f in files]
elif simname == "borg2_all":
files = glob(join(self.borg2_dir, "mcmc_*"))
files = [int(search(r'mcmc_(\d+)', f).group(1)) for f in files]
elif simname == "csiborg2_random":
files = glob(join(self.csiborg2_random_srcdir, "chain_*"))
files = [int(search(r'chain_(\d+)', f).group(1)) for f in files]
@ -246,6 +253,24 @@ class Paths:
else:
raise ValueError(f"Unknown simulation name `{simname}`.")
def external_halo_catalogue(self, name):
"""
Path to an external halo catalogue.
Parameters
----------
name : str
Catalogue name.
Returns
-------
str
"""
if name == "MDPL2":
return join(self.aux_cat_dir, "MDPL2_FOF_125.hdf5")
else:
raise ValueError(f"Unknown external FOF catalogue `{name}`.")
def initial_lagpatch(self, nsim, simname):
"""
Path to the Lagrangain patch information of a simulation for halos

2
csiborgtools/read/snapshot.py
View file

@ -969,7 +969,7 @@ class BORG2Field(BaseField):
rho_mean = omega0 * 277.53662724583074 # h^2 Msun / kpc^3
field += 1
field *= rho_mean
# return field
return field
def velocity_field(self, MAS, grid):
raise RuntimeError("The velocity field is not available.")

168
csiborgtools/utils.py
View file

@ -35,6 +35,19 @@ def center_of_mass(particle_positions, particles_mass, boxsize):
"""
Calculate the center of mass of a halo while assuming periodic boundary
conditions of a cubical box.
Parameters
----------
particle_positions : 2-dimensional array of shape `(nparticles, 3)`
Particle positions in the box.
particles_mass : 1-dimensional array of shape `(nparticles,)`
Particle masses.
boxsize : float
Box size.
Returns
-------
1-dimensional array of shape `(3,)`
"""
cm = np.zeros(3, dtype=particle_positions.dtype)
totmass = sum(particles_mass)
@ -60,28 +73,46 @@ def periodic_distance(points, reference_point, boxsize):
"""
Compute the 3D distance between multiple points and a reference point using
periodic boundary conditions.
Parameters
----------
points : 2-dimensional array of shape `(npoints, 3)`
Points to calculate the distance from.
reference_point : 1-dimensional array of shape `(3,)`
Reference point.
boxsize : float
Box size.
Returns
-------
1-dimensional array of shape `(npoints,)`
"""
npoints = len(points)
half_box = boxsize / 2
dist = np.zeros(npoints, dtype=points.dtype)
for i in range(npoints):
for j in range(3):
dist_1d = abs(points[i, j] - reference_point[j])
if dist_1d > (half_box):
dist_1d = boxsize - dist_1d
dist[i] += dist_1d**2
dist[i] = dist[i]**0.5
dist[i] = periodic_distance_two_points(
points[i], reference_point, boxsize)
return dist
@jit(nopython=True, fastmath=True, boundscheck=False)
def periodic_distance_two_points(p1, p2, boxsize):
"""Compute the 3D distance between two points in a periodic box."""
"""
Compute the 3D distance between two points in a periodic box.
Parameters
----------
p1, p2 : 1-dimensional array of shape `(3,)`
Points to calculate the distance between.
boxsize : float
Box size.
Returns
-------
float
"""
half_box = boxsize / 2
dist = 0
@ -98,7 +129,20 @@ def periodic_distance_two_points(p1, p2, boxsize):
@jit(nopython=True, boundscheck=False)
def periodic_wrap_grid(pos, boxsize=1):
"""Wrap positions in a periodic box."""
"""
Wrap positions in a periodic box. Overwrites the input array.
Parameters
----------
pos : 2-dimensional array of shape `(npoints, 3)`
Positions to wrap.
boxsize : float, optional
Box size.
Returns
-------
2-dimensional array of shape `(npoints, 3)`
"""
for n in range(pos.shape[0]):
for i in range(3):
if pos[n, i] > boxsize:
@ -113,6 +157,15 @@ def periodic_wrap_grid(pos, boxsize=1):
def delta2ncells(field):
"""
Calculate the number of cells in `field` that are non-zero.
Parameters
----------
field : 3-dimensional array of shape `(nx, ny, nz)`
Field to calculate the number of non-zero cells.
Returns
-------
int
"""
tot = 0
imax, jmax, kmax = field.shape
@ -124,20 +177,44 @@ def delta2ncells(field):
return tot
def cartesian_to_radec(X):
def cartesian_to_radec(X, return_degrees=True, origin=[0., 0., 0.]):
"""
Calculate the radial distance, RA [0, 360) deg and dec [-90, 90] deg.
Parameters
----------
X : 2-dimensional array of shape `(npoints, 3)`
Cartesian coordinates.
return_degrees : bool, optional
Whether to return the angles in degrees.
origin : 1-dimensional array of shape `(3,)`, optional
Origin of the coordinate system.
Returns
-------
out : 2-dimensional array of shape `(npoints, 3)`
Spherical coordinates: distance, RA and dec.
"""
x, y, z = X[:, 0], X[:, 1], X[:, 2]
x -= origin[0]
y -= origin[1]
z -= origin[2]
dist = np.linalg.norm(X, axis=1)
dec = np.arcsin(z / dist)
ra = np.arctan2(y, x)
ra[ra < 0] += 2 * np.pi
if return_degrees:
ra *= 180 / np.pi
dec *= 180 / np.pi
# Place the origin back
x += origin[0]
y += origin[1]
z += origin[2]
return np.vstack([dist, ra, dec]).T
@ -145,6 +222,15 @@ def radec_to_cartesian(X):
"""
Calculate Cartesian coordinates from radial distance, RA [0, 360) deg and
dec [-90, 90] deg.
Parameters
----------
X : 2-dimensional array of shape `(npoints, 3)`
Spherical coordinates: distance, RA and dec.
Returns
-------
2-dimensional array of shape `(npoints, 3)`
"""
dist, ra, dec = X[:, 0], X[:, 1], X[:, 2]
@ -175,23 +261,43 @@ def radec_to_galactic(ra, dec):
@jit(nopython=True, fastmath=True, boundscheck=False)
def great_circle_distance(x1, x2):
def great_circle_distance(x1, x2, in_degrees=True):
"""
Great circle distance between two points on a sphere, defined by RA and
dec, both in degrees.
Parameters
----------
x1, x2 : 1-dimensional arrays of shape `(2,)`
RA and dec in degrees.
in_degrees : bool, optional
Whether the input is in degrees.
Returns
-------
float
"""
ra1, dec1 = x1
ra2, dec2 = x2
if in_degrees:
ra1 *= np.pi / 180
dec1 *= np.pi / 180
ra2 *= np.pi / 180
dec2 *= np.pi / 180
return 180 / np.pi * np.arccos(
np.sin(dec1) * np.sin(dec2)
+ np.cos(dec1) * np.cos(dec2) * np.cos(ra1 - ra2)
)
dist = np.arccos(np.sin(dec1) * np.sin(dec2)
+ np.cos(dec1) * np.cos(dec2) * np.cos(ra1 - ra2))
# Convert to degrees and ensure the inputs are unchanged.
if in_degrees:
dist *= 180 / np.pi
ra1 *= 180 / np.pi
dec1 *= 180 / np.pi
ra2 *= 180 / np.pi
dec2 *= 180 / np.pi
return dist
def cosine_similarity(x, y):
@ -316,6 +422,17 @@ def real2redshift(pos, vel, observer_location, observer_velocity, boxsize,
def number_counts(x, bin_edges):
"""
Calculate counts of samples in bins.
Parameters
----------
x : 1-dimensional array
Samples to bin.
bin_edges : 1-dimensional array
Bin edges.
Returns
-------
1-dimensional array
"""
out = np.full(bin_edges.size - 1, np.nan, dtype=np.float32)
for i in range(bin_edges.size - 1):
@ -326,6 +443,21 @@ def number_counts(x, bin_edges):
def binned_statistic(x, y, left_edges, bin_width, statistic):
"""
Calculate a binned statistic.
Parameters
----------
x, y : 1-dimensional arrays
Values by which to bin and calculate the statistic on, respectively.
left_edges : 1-dimensional array
Left edges of the bins.
bin_width : float
Width of the bins.
statistic : callable
Function to calculate the statistic, must be `f(x)`.
Returns
-------
1-dimensional array
"""
out = np.full(left_edges.size, np.nan, dtype=x.dtype)

108
notebooks/flow/flow_bulk.ipynb
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File diff suppressed because one or more lines are too long

4
scripts/field_bulk.py
View file

@ -148,7 +148,7 @@ def main_borg(args, folder):
if args.simname == "borg1":
reader = csiborgtools.read.BORG1Field(nsim)
field = reader.density_field()
elif args.simname == "borg2":
elif args.simname == "borg2" or args.simname == "borg2_all":
reader = csiborgtools.read.BORG2Field(nsim)
field = reader.density_field()
else:
@ -204,7 +204,7 @@ def main_csiborg(args, folder):
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--simname", type=str, help="Simulation name.",
choices=["csiborg1", "csiborg2_main", "csiborg2_varysmall", "csiborg2_random", "borg1", "borg2"]) # noqa
choices=["csiborg1", "csiborg2_main", "csiborg2_varysmall", "csiborg2_random", "borg1", "borg2", "borg2_all"]) # noqa
args = parser.parse_args()
folder = "/mnt/extraspace/rstiskalek/csiborg_postprocessing/field_shells"

6
scripts/field_bulk.sh
View file

@ -1,9 +1,9 @@
nthreads=1
memory=40
memory=12
on_login=0
queue="cmb"
queue="berg"
env="/mnt/zfsusers/rstiskalek/csiborgtools/venv_csiborg/bin/python"
file="mass_enclosed.py"
file="field_bulk.py"
simname=${1}