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Periodic neighbours (#84)

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* Edit the HMF plot

* Add periodic dist 2 points

* Add boxsize to RVSSphere

* Add periodic distance

* Adding periodic distance

* Add imports

* Change arguments

* Update bounds

* Lower min number of particles

* Change kwargs

* Add paths overlap quijote

* Add some comments
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Richard Stiskalek 2023-08-08 12:19:40 +02:00 committed by GitHub
parent c7e447df01
commit c7b600d0ad
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14 changed files with 196 additions and 61 deletions
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4
csiborgtools/__init__.py
View file

@ -13,7 +13,9 @@
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
from csiborgtools import clustering, field, match, read # noqa
from .utils import (center_of_mass, delta2ncells, periodic_distance, number_counts) # noqa
from .utils import (center_of_mass, delta2ncells, number_counts,
periodic_distance, periodic_distance_two_points) # noqa
# Arguments to csiborgtools.read.Paths.
paths_glamdring = {"srcdir": "/mnt/extraspace/hdesmond/",

10
csiborgtools/clustering/utils.py
View file

@ -51,16 +51,20 @@ class BaseRVS(ABC):
class RVSinsphere(BaseRVS):
"""
Generator of uniform RVS in a sphere of radius `R` in Cartesian
coordinates centered at the origin.
coordinates centered at the centre of the box.
Parameters
----------
R : float
Radius of the sphere.
boxsize : float
Box size
"""
def __init__(self, R):
def __init__(self, R, boxsize):
assert R > 0, "Radius must be positive."
assert boxsize > 0, "Box size must be positive."
self.R = R
self.boxsize = boxsize
BaseRVS.__init__(self)
def __call__(self, nsamples, random_state=42, dtype=numpy.float32):
@ -73,7 +77,7 @@ class RVSinsphere(BaseRVS):
x = r * numpy.sin(theta) * numpy.cos(phi)
y = r * numpy.sin(theta) * numpy.sin(phi)
z = r * numpy.cos(theta)
return numpy.vstack([x, y, z]).T
return numpy.vstack([x, y, z]).T + self.boxsize / 2
class RVSinbox(BaseRVS):

12
csiborgtools/match/match.py
View file

@ -239,7 +239,8 @@ class RealisationsMatcher(BaseMatcher):
if verbose:
print(f"{datetime.now()}: querying the KNN.", flush=True)
match_indxs = radius_neighbours(
catx.knn(in_initial=True), cat0.position(in_initial=True),
catx.knn(in_initial=True, subtract_observer=False, periodic=True),
cat0.position(in_initial=True),
radiusX=cat0["lagpatch_size"], radiusKNN=catx["lagpatch_size"],
nmult=self.nmult, enforce_int32=True, verbose=verbose)
@ -515,7 +516,7 @@ class ParticleOverlap(BaseMatcher):
Minimun and maximum cell numbers along each dimension of `halo1`.
Optional.
mins2, maxs2 : 1-dimensional arrays of shape `(3,)`
Minimun and maximum cell numbers along each dimension of `halo2`.
Minimum and maximum cell numbers along each dimension of `halo2`.
Optional.
smooth_kwargs : kwargs, optional
Kwargs to be passed to :py:func:`scipy.ndimage.gaussian_filter`.
@ -1014,7 +1015,8 @@ def radius_neighbours(knn, X, radiusX, radiusKNN, nmult=1.0,
def find_neighbour(nsim0, cats):
"""
Find the nearest neighbour of halos from a reference catalogue indexed
`nsim0` in the remaining simulations.
`nsim0` in the remaining simulations. Note that this must be the same
simulation suite.
Parameters
----------
@ -1030,8 +1032,10 @@ def find_neighbour(nsim0, cats):
cross_hindxs : 2-dimensional array of shape `(nhalos, len(cats) - 1)`
Halo indices of the nearest neighbour.
"""
assert all(isinstance(cat, type(cats[nsim0])) for cat in cats.values())
cat0 = cats[nsim0]
X = cat0.position(in_initial=False, subtract_observer=True)
X = cat0.position(in_initial=False)
nhalos = X.shape[0]
num_cats = len(cats) - 1

47
csiborgtools/read/halo_cat.py
View file

@ -33,6 +33,7 @@ from .paths import Paths
from .readsim import CSiBORGReader
from .utils import (add_columns, cartesian_to_radec, cols_to_structured,
flip_cols, radec_to_cartesian, real2redshift)
from ..utils import periodic_distance_two_points
class BaseCatalogue(ABC):
@ -73,6 +74,17 @@ class BaseCatalogue(ABC):
"""
pass
@abstractproperty
def simname(self):
"""
Simulation name.
Returns
-------
simname : str
"""
pass
@property
def paths(self):
"""
@ -327,7 +339,7 @@ class BaseCatalogue(ABC):
return numpy.vstack([self["L{}".format(p)] for p in ("x", "y", "z")]).T
@lru_cache(maxsize=2)
def knn(self, in_initial):
def knn(self, in_initial, subtract_observer, periodic):
r"""
kNN object for catalogue objects with caching. Positions are centered
on the observer.
@ -336,14 +348,32 @@ class BaseCatalogue(ABC):
----------
in_initial : bool
Whether to define the kNN on the initial or final snapshot.
subtract_observer : bool
Whether to subtract the observer's location from the positions.
periodic : bool
Whether to use periodic boundary conditions.
Returns
-------
knn : :py:class:`sklearn.neighbors.NearestNeighbors`
kNN object fitted with object positions.
"""
pos = self.position(in_initial=in_initial)
return NearestNeighbors().fit(pos)
if subtract_observer and periodic:
raise ValueError("Subtracting observer is not supported for "
"periodic boundary conditions.")
pos = self.position(in_initial=in_initial,
subtract_observer=subtract_observer)
if periodic:
L = self.box.boxsize
knn = NearestNeighbors(
metric=lambda a, b: periodic_distance_two_points(a, b, L))
else:
knn = NearestNeighbors()
knn.fit(pos)
return knn
def nearest_neighbours(self, X, radius, in_initial, knearest=False,
return_mass=False, mass_key=None):
@ -382,7 +412,8 @@ class BaseCatalogue(ABC):
if return_mass and not mass_key:
raise ValueError("`mass_key` must be provided if `return_mass`.")
knn = self.knn(in_initial)
knn = self.knn(in_initial, subtract_observer=False, periodic=True)
if knearest:
dist, indxs = knn.kneighbors(X, radius)
else:
@ -564,6 +595,10 @@ class CSiBORGHaloCatalogue(BaseCatalogue):
"""
return CSiBORGBox(self.nsnap, self.nsim, self.paths)
@property
def simname(self):
return "csiborg"
###############################################################################
# Quijote halo catalogue #
@ -661,6 +696,10 @@ class QuijoteHaloCatalogue(BaseCatalogue):
assert nsnap in [0, 1, 2, 3, 4]
self._nsnap = nsnap
@property
def simname(self):
return "quijote"
@property
def redshift(self):
"""

19
csiborgtools/read/overlap_summary.py
View file

@ -47,6 +47,10 @@ class PairOverlap:
_data = None
def __init__(self, cat0, catx, paths, maxdist=None):
if cat0.simname != catx.simname:
raise ValueError("The two catalogues must be from the same "
"simulation.")
self._cat0 = cat0
self._catx = catx
self.load(cat0, catx, paths, maxdist)
@ -77,8 +81,8 @@ class PairOverlap:
# We first load in the output files. We need to find the right
# combination of the reference and cross simulation.
fname = paths.overlap(nsim0, nsimx, smoothed=False)
fname_inv = paths.overlap(nsimx, nsim0, smoothed=False)
fname = paths.overlap(cat0.simname, nsim0, nsimx, smoothed=False)
fname_inv = paths.overlap(cat0.simname, nsimx, nsim0, smoothed=False)
if isfile(fname):
data_ngp = numpy.load(fname, allow_pickle=True)
to_invert = False
@ -89,7 +93,8 @@ class PairOverlap:
else:
raise FileNotFoundError(f"No file found for {nsim0} and {nsimx}.")
fname_smooth = paths.overlap(cat0.nsim, catx.nsim, smoothed=True)
fname_smooth = paths.overlap(cat0.simname, cat0.nsim, catx.nsim,
smoothed=True)
data_smooth = numpy.load(fname_smooth, allow_pickle=True)
# Create mapping from halo indices to array positions in the catalogue.
@ -764,13 +769,15 @@ class NPairsOverlap:
###############################################################################
def get_cross_sims(nsim0, paths, smoothed):
def get_cross_sims(simname, nsim0, paths, smoothed):
"""
Get the list of cross simulations for a given reference simulation for
which the overlap has been calculated.
Parameters
----------
simname : str
Simulation name.
nsim0 : int
Reference simulation number.
paths : :py:class:`csiborgtools.paths.Paths`
@ -782,8 +789,8 @@ def get_cross_sims(nsim0, paths, smoothed):
for nsimx in paths.get_ics("csiborg"):
if nsimx == nsim0:
continue
f1 = paths.overlap(nsim0, nsimx, smoothed)
f2 = paths.overlap(nsimx, nsim0, smoothed)
f1 = paths.overlap(simname, nsim0, nsimx, smoothed)
f2 = paths.overlap(simname, nsimx, nsim0, smoothed)
if isfile(f1) or isfile(f2):
nsimxs.append(nsimx)
return nsimxs

11
csiborgtools/read/paths.py
View file

@ -462,12 +462,14 @@ class Paths:
fname = f"out_{str(nsim).zfill(5)}_{str(nsnap).zfill(5)}.npy"
return join(fdir, fname)
def overlap(self, nsim0, nsimx, smoothed):
def overlap(self, simname, nsim0, nsimx, smoothed):
"""
Path to the overlap files between two CSiBORG simulations.
Parameters
----------
simname : str
Simulation name. Must be one of `csiborg` or `quijote`.
nsim0 : int
IC realisation index of the first simulation.
nsimx : int
@ -479,7 +481,12 @@ class Paths:
-------
path : str
"""
fdir = join(self.postdir, "overlap")
if simname == "csiborg":
fdir = join(self.postdir, "overlap")
elif simname == "quijote":
fdir = join(self.quijote_dir, "overlap")
else:
raise ValueError(f"Unknown simulation name `{simname}`.")
try_create_directory(fdir)

33
csiborgtools/utils.py
View file

@ -92,6 +92,39 @@ def periodic_distance(points, reference, 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 using periodic boundary
conditions. This is an optimized JIT implementation.
Parameters
----------
p1 : 1-dimensional array of shape `(3, )`
First point.
p2 : 1-dimensional array of shape `(3, )`
Second point.
boxsize : float
Box size.
Returns
-------
dist : 1-dimensional array of shape `(n_points, )`
"""
half_box = boxsize / 2
dist = 0
for i in range(3):
dist_1d = abs(p1[i] - p2[i])
if dist_1d > (half_box):
dist_1d = boxsize - dist_1d
dist += dist_1d**2
return dist**0.5
@jit(nopython=True, fastmath=True, boundscheck=False)
def delta2ncells(delta):
"""

12
scripts/cluster_knn_auto.py
View file

@ -59,10 +59,10 @@ def do_auto(args, config, cats, nsim, paths):
-------
None
"""
rvs_gen = csiborgtools.clustering.RVSinsphere(args.Rmax)
knncdf = csiborgtools.clustering.kNN_1DCDF()
cat = cats[nsim]
knn = cat.knn(in_initial=False)
rvs_gen = csiborgtools.clustering.RVSinsphere(args.Rmax, cat.boxsize)
knncdf = csiborgtools.clustering.kNN_1DCDF()
knn = cat.knn(in_initial=False, subtract_observer=False, periodic=True)
rs, cdf = knncdf(
knn, rvs_gen=rvs_gen, nneighbours=config["nneighbours"],
rmin=config["rmin"], rmax=config["rmax"],
@ -97,9 +97,9 @@ def do_cross_rand(args, config, cats, nsim, paths):
-------
None
"""
rvs_gen = csiborgtools.clustering.RVSinsphere(args.Rmax)
cat = cats[nsim]
knn1 = cat.knn(in_initial=False)
rvs_gen = csiborgtools.clustering.RVSinsphere(args.Rmax, cat.boxsize)
knn1 = cat.knn(in_initial=False, subtract_observer=False, periodic=True)
knn2 = NearestNeighbors()
pos2 = rvs_gen(len(cat).shape[0])
@ -126,7 +126,7 @@ if __name__ == "__main__":
help="Simulation name")
parser.add_argument("--nsims", type=int, nargs="+", default=None,
help="Indices of simulations to cross. If `-1` processes all simulations.") # noqa
parser.add_argument("--Rmax", type=float, default=155/0.705,
parser.add_argument("--Rmax", type=float, default=155,
help="High-resolution region radius") # noqa
parser.add_argument("--verbose", type=lambda x: bool(strtobool(x)),
default=False)

6
scripts/cluster_tpcf_auto.py
View file

@ -37,12 +37,12 @@ except ModuleNotFoundError:
def do_auto(args, config, cats, nsim, paths):
cat = cats[nsim]
tpcf = csiborgtools.clustering.Mock2PCF()
rvs_gen = csiborgtools.clustering.RVSinsphere(args.Rmax)
rvs_gen = csiborgtools.clustering.RVSinsphere(args.Rmax, cat.boxsize)
bins = numpy.logspace(
numpy.log10(config["rpmin"]), numpy.log10(config["rpmax"]),
config["nrpbins"] + 1,)
cat = cats[nsim]
pos = cat.position(in_initial=False, cartesian=True)
nrandom = int(config["randmult"] * pos.shape[0])
@ -59,7 +59,7 @@ if __name__ == "__main__":
help="Simulation name")
parser.add_argument("--nsims", type=int, nargs="+", default=None,
help="Indices of simulations to cross. If `-1` processes all simulations.") # noqa
parser.add_argument("--Rmax", type=float, default=155/0.705,
parser.add_argument("--Rmax", type=float, default=155,
help="High-resolution region radius.")
parser.add_argument("--verbose", type=lambda x: bool(strtobool(x)),
default=False, help="Verbosity flag.")

9
scripts/fit_init.py
View file

@ -14,8 +14,9 @@
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
"""
Script to calculate the particle centre of mass, Lagrangian patch size in the
initial snapshot. The initial snapshot particles are read from the sorted
files.
initial snapshot.
The initial snapshot particles are read from the sorted files.
"""
from argparse import ArgumentParser
from datetime import datetime
@ -74,7 +75,7 @@ def _main(nsim, simname, verbose):
# Initialise the overlapper.
if simname == "csiborg":
kwargs = {"box_size": 2048, "bckg_halfsize": 475}
kwargs = {"box_size": 2048, "bckg_halfsize": 512}
else:
kwargs = {"box_size": 512, "bckg_halfsize": 256}
overlapper = csiborgtools.match.ParticleOverlap(**kwargs)
@ -86,7 +87,7 @@ def _main(nsim, simname, verbose):
hid2map)
# Skip if the halo has no particles or is too small.
if part is None or part.size < 100:
if part is None or part.size < 40:
continue
pos, mass = part[:, :3], part[:, 3]

20
scripts/match_singlematch.py
View file

@ -57,12 +57,13 @@ def pair_match(nsim0, nsimx, simname, sigma, verbose):
None
"""
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
smooth_kwargs = {"sigma": sigma, "mode": "wrap"}
smooth_kwargs = {"sigma": sigma, "mode": "constant", "cval": 0}
if simname == "csiborg":
overlapper_kwargs = {"box_size": 2048, "bckg_halfsize": 475}
overlapper_kwargs = {"box_size": 2048, "bckg_halfsize": 512}
mass_kind = "fof_totpartmass"
bounds = {mass_kind: (1e13, None)}
bounds = {"dist": (0, 155), mass_kind: (10**13.25, None)}
cat0 = csiborgtools.read.CSiBORGHaloCatalogue(
nsim0, paths, bounds=bounds, load_fitted=False,
with_lagpatch=True)
@ -72,11 +73,14 @@ def pair_match(nsim0, nsimx, simname, sigma, verbose):
elif simname == "quijote":
overlapper_kwargs = {"box_size": 512, "bckg_halfsize": 256}
mass_kind = "group_mass"
bounds = {mass_kind: (1e14, None)}
bounds = {mass_kind: (10**13.25, None)}
cat0 = csiborgtools.read.QuijoteHaloCatalogue(
nsim0, paths, 4, load_fitted=False, with_lagpatch=True)
nsim0, paths, 4, bounds=bounds, load_fitted=False,
with_lagpatch=True)
catx = csiborgtools.read.QuijoteHaloCatalogue(
nsimx, paths, 4, load_fitted=False, with_lagpatch=True)
nsimx, paths, 4, bounds=bounds, load_fitted=False,
with_lagpatch=True)
else:
raise ValueError(f"Unknown simulation name: `{simname}`.")
@ -116,7 +120,7 @@ def pair_match(nsim0, nsimx, simname, sigma, verbose):
for j, match in enumerate(matches):
match_hids[i][j] = catx["index"][match]
fout = paths.overlap(nsim0, nsimx, smoothed=False)
fout = paths.overlap(simname, nsim0, nsimx, smoothed=False)
if verbose:
print(f"{datetime.now()}: saving to ... `{fout}`.", flush=True)
numpy.savez(fout, ref_hids=cat0["index"], match_hids=match_hids,
@ -135,7 +139,7 @@ def pair_match(nsim0, nsimx, simname, sigma, verbose):
match_indxs, smooth_kwargs,
verbose=verbose)
fout = paths.overlap(nsim0, nsimx, smoothed=True)
fout = paths.overlap(simname, nsim0, nsimx, smoothed=True)
if verbose:
print(f"{datetime.now()}: saving to ... `{fout}`.", flush=True)
numpy.savez(fout, smoothed_overlap=smoothed_overlap, sigma=sigma)

15
scripts_plots/plot_data.py
View file

@ -178,6 +178,7 @@ def plot_hmf(pdf=False):
csiborg5511[x > 3e15] = numpy.nan
with plt.style.context(plt_utils.mplstyle):
cols = plt.rcParams["axes.prop_cycle"].by_key()["color"]
fig, ax = plt.subplots(nrows=2, sharex=True,
figsize=(3.5, 2.625 * 1.25),
gridspec_kw={"height_ratios": [1, 0.45]})
@ -186,15 +187,15 @@ def plot_hmf(pdf=False):
# Upper panel data
mean_csiborg = numpy.mean(csiborg_counts, axis=0)
std_csiborg = numpy.std(csiborg_counts, axis=0)
ax[0].plot(x, mean_csiborg, label="CSiBORG")
ax[0].plot(x, mean_csiborg, label="CSiBORG", c=cols[0])
ax[0].fill_between(x, mean_csiborg - std_csiborg,
mean_csiborg + std_csiborg, alpha=0.5)
mean_csiborg + std_csiborg, alpha=0.5, color=cols[0])
mean_quijote = numpy.mean(quijote_counts, axis=0)
std_quijote = numpy.std(quijote_counts, axis=0)
ax[0].plot(x, mean_quijote, label="Quijote")
ax[0].plot(x, mean_quijote, label="Quijote", c=cols[1])
ax[0].fill_between(x, mean_quijote - std_quijote,
mean_quijote + std_quijote, alpha=0.5)
mean_quijote + std_quijote, alpha=0.5, color=cols[1])
ax[0].plot(x, csiborg5511, label="CSiBORG 5511", c="k", ls="--")
std5511 = numpy.sqrt(csiborg5511)
@ -204,9 +205,11 @@ def plot_hmf(pdf=False):
log_y = numpy.log10(mean_csiborg / mean_quijote)
err = numpy.sqrt((std_csiborg / mean_csiborg / numpy.log(10))**2
+ (std_quijote / mean_quijote / numpy.log(10))**2)
ax[1].plot(x, 10**log_y, c="gray")
ax[1].plot(x, 10**log_y, c=cols[0])
ax[1].fill_between(x, 10**(log_y - err), 10**(log_y + err), alpha=0.5,
color="gray")
color=col[0])
ax[1].plot(x, csiborg5511 / mean_quijote, c="k", ls="--")
# Labels and accesories
ax[1].axhline(1, color="k", ls="--",

40
scripts_plots/plot_match.py
View file

@ -143,13 +143,15 @@ def plot_mass_vs_maxpairoverlap(nsim0, nsimx):
@cache_to_disk(7)
def get_overlap(nsim0):
def get_overlap(simname, nsim0):
"""
Calculate the summed overlap and probability of no match for a single
reference simulation.
Parameters
----------
simname : str
Simulation name.
nsim0 : int
Simulation index.
@ -167,7 +169,8 @@ def get_overlap(nsim0):
Probability of no match for each halo in the reference simulation.
"""
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
nsimxs = csiborgtools.read.get_cross_sims(nsim0, paths, smoothed=True)
nsimxs = csiborgtools.read.get_cross_sims(simname, nsim0, paths,
smoothed=True)
cat0 = open_cat(nsim0)
catxs = []
@ -195,7 +198,7 @@ def plot_mass_vsmedmaxoverlap(nsim0):
nsim0 : int
Reference simulation index.
"""
x, __, max_overlap, __, __ = get_overlap(nsim0)
x, __, max_overlap, __, __ = get_overlap("csiborg", nsim0)
for i in trange(max_overlap.shape[0]):
if numpy.sum(numpy.isnan(max_overlap[i, :])) > 0:
@ -255,7 +258,7 @@ def plot_summed_overlap_vs_mass(nsim0):
-------
None
"""
x, __, __, summed_overlap, prob_nomatch = get_overlap(nsim0)
x, __, __, summed_overlap, prob_nomatch = get_overlap("csiborg", nsim0)
del __
collect()
@ -389,12 +392,14 @@ def plot_mass_vs_separation(nsim0, nsimx, plot_std=False, min_overlap=0.0):
@cache_to_disk(7)
def get_max_key(nsim0, key):
def get_max_key(simname, nsim0, key):
"""
Get the value of a maximum overlap halo's property.
Parameters
----------
simname : str
Simulation name.
nsim0 : int
Reference simulation index.
key : str
@ -412,7 +417,8 @@ def get_max_key(nsim0, key):
Value of the property of the maximum overlap halo.
"""
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
nsimxs = csiborgtools.read.get_cross_sims(nsim0, paths, smoothed=True)
nsimxs = csiborgtools.read.get_cross_sims(simname, nsim0, paths,
smoothed=True)
nsimxs = nsimxs
cat0 = open_cat(nsim0)
@ -440,7 +446,7 @@ def plot_maxoverlap_mass(nsim0):
nsim0 : int
Reference simulation index.
"""
mass0, __, __, stat = get_max_key(nsim0, "totpartmass")
mass0, __, __, stat = get_max_key("csiborg", nsim0, "totpartmass")
mu = numpy.mean(stat, axis=1)
std = numpy.std(numpy.log10(stat), axis=1)
@ -463,8 +469,10 @@ def plot_maxoverlap_mass(nsim0):
axs[0].set_xlabel(r"$\log M_{\rm tot} ~ [M_\odot / h]$")
axs[1].set_xlabel(r"$\log M_{\rm tot} ~ [M_\odot / h]$")
axs[0].set_ylabel(r"Max. overlap mean of $\log M_{\rm tot} ~ [M_\odot / h]$")
axs[1].set_ylabel(r"Max. overlap std. of $\log M_{\rm tot} ~ [M_\odot / h]$")
axs[0].set_ylabel(
r"Max. overlap mean of $\log M_{\rm tot} ~ [M_\odot / h]$")
axs[1].set_ylabel(
r"Max. overlap std. of $\log M_{\rm tot} ~ [M_\odot / h]$")
ims = [im0, im1]
for i in range(2):
@ -498,7 +506,7 @@ def plot_maxoverlapstat(nsim0, key):
Property to get.
"""
assert key != "totpartmass"
mass0, key_val, __, stat = get_max_key(nsim0, key)
mass0, key_val, __, stat = get_max_key("csiborg", nsim0, key)
xlabels = {"lambda200c": r"\log \lambda_{\rm 200c}"}
key_label = xlabels.get(key, key)
@ -546,13 +554,15 @@ def plot_maxoverlapstat(nsim0, key):
@cache_to_disk(7)
def get_expected_mass(nsim0, min_overlap):
def get_expected_mass(simname, nsim0, min_overlap):
"""
Get the expected mass of a reference halo given its overlap with halos
from other simulations.
Parameters
----------
simname : str
Simulation name.
nsim0 : int
Reference simulation index.
min_overlap : float
@ -570,7 +580,8 @@ def get_expected_mass(nsim0, min_overlap):
Probability of not matching the reference halo.
"""
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
nsimxs = csiborgtools.read.get_cross_sims(nsim0, paths, smoothed=True)
nsimxs = csiborgtools.read.get_cross_sims(simname, nsim0, paths,
smoothed=True)
nsimxs = nsimxs
cat0 = open_cat(nsim0)
@ -602,7 +613,8 @@ def plot_mass_vs_expected_mass(nsim0, min_overlap=0, max_prob_nomatch=1):
max_prob_nomatch : float, optional
Maximum probability of no match to consider.
"""
mass, mu, std, prob_nomatch = get_expected_mass(nsim0, min_overlap)
mass, mu, std, prob_nomatch = get_expected_mass("csiborg", nsim0,
min_overlap)
std = std / mu / numpy.log(10)
mass = numpy.log10(mass)
@ -1343,7 +1355,7 @@ def plot_kl_vs_overlap(runs, nsim, kwargs, runs_to_mass, plot_std=True,
for run in runs:
nn_data = nn_reader.read_single("csiborg", run, nsim, nobs=None)
nn_hindxs = nn_data["ref_hindxs"]
mass, overlap_hindxs, __, summed_overlap, prob_nomatch = get_overlap(nsim) # noqa
mass, overlap_hindxs, __, summed_overlap, prob_nomatch = get_overlap("csiborg", nsim) # noqa
# We need to match the hindxs between the two.
hind2overlap_array = {hind: i for i, hind in enumerate(overlap_hindxs)}

19
setup.py
View file

@ -1,5 +1,24 @@
from setuptools import find_packages, setup
# List of dependencies:
# - Corrfunc -> To be moved to a separate package.
# - NumPy
# - SciPy
# - Numba
# - Pylians
# - tqdm
# - healpy
# - astropy
# - scikit-learn
# - joblib
# - h5py
# - MPI
# - pyyaml
# - taskmaster
# - matplotlib
# - scienceplots
# - cache_to_disk
BUILD_REQ = ["numpy", "scipy"]
INSTALL_REQ = BUILD_REQ