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Add better diagnostics & plotting (#67)

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* Add caching functions

* Add limts

* Add new mass runs

* Update .gitignore

* Edit which CDFs are loaded

* Stop saving cross hindxs

* Change dist to half precision

* New nearest path

* Add neighbour counting

* Add neighbour kwargs

* Update work in progress

* Add new counting

* Add how dist is built

* Collect dist to 1 file

* Update reading routine

* Delete Quijote files

* Remove file

* Back to float32

* Fix bugs

* Rename utils

* Remove neighbuor kwargs

* Rename file

* Fix bug

* Rename plt utils

* Change where nghb kwargs from

* line length

* Remove old notebooks

* Move survey

* Add white space

* Update TODO

* Update CDF calculation

* Update temporarily plotting

* Merge branch 'add_diagnostics' of github.com:Richard-Sti/csiborgtools into add_diagnostics

* Start adding documentation to plotting

* Remove comments

* Better code documentation

* Some work on tidal tensor

* Better plotting

* Add comment

* Remove nb

* Remove comment

* Add documentation

* Update plotting

* Update submission

* Update KL vs KS plots

* Update the plotting routine

* Update plotting

* Update plotting routines
This commit is contained in:
Richard Stiskalek 2023-06-16 14:33:27 +01:00 committed by GitHub
parent 004d9629a2
commit ccbbbd24b4
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GPG Key ID: 4AEE18F83AFDEB23
20 changed files with 1075 additions and 32121 deletions
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1
.gitignore vendored
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@ -22,3 +22,4 @@ scripts_test/
scripts_plots/python.sh
scripts_plots/submit.sh
scripts_plots/*.out
scripts_plots/*.sh

23
csiborgtools/__init__.py
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@ -19,3 +19,26 @@ paths_glamdring = {"srcdir": "/mnt/extraspace/hdesmond/",
"postdir": "/mnt/extraspace/rstiskalek/CSiBORG/",
"quijote_dir": "/mnt/extraspace/rstiskalek/Quijote",
}
neighbour_kwargs = {"rmax_radial": 155 / 0.705,
"nbins_radial": 50,
"rmax_neighbour": 100.,
"nbins_neighbour": 150,
"paths_kind": paths_glamdring}
###############################################################################
# Surveys #
###############################################################################
class SDSS:
@staticmethod
def steps(cls):
return [(lambda x: cls[x], ("IN_DR7_LSS",)),
(lambda x: cls[x] < 17.6, ("ELPETRO_APPMAG_r", )),
(lambda x: cls[x] < 155, ("DIST", ))
]
def __call__(self):
return read.SDSS(h=1, sel_steps=self.steps)

51
csiborgtools/field/density.py
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@ -14,9 +14,6 @@
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
"""
Density field and cross-correlation calculations.
TODO:
- [ ] Project the velocity field along the line of sight.
"""
from abc import ABC
@ -370,9 +367,9 @@ class TidalTensorField(BaseField):
box : :py:class:`csiborgtools.read.CSiBORGBox`
The simulation box information and transformations.
MAS : str
Mass assignment scheme. Options are Options are: 'NGP' (nearest grid
point), 'CIC' (cloud-in-cell), 'TSC' (triangular-shape cloud), 'PCS'
(piecewise cubic spline).
Mass assignment scheme used to calculate the density field. Options
are: 'NGP' (nearest grid point), 'CIC' (cloud-in-cell), 'TSC'
(triangular-shape cloud), 'PCS' (piecewise cubic spline).
"""
def __init__(self, box, MAS):
self.box = box
@ -384,8 +381,6 @@ class TidalTensorField(BaseField):
Calculate eigenvalues of the tidal tensor field, sorted in increasing
order.
TODO: evaluate this on a grid instead.
Parameters
----------
tidal_tensor : :py:class:`MAS_library.tidal_tensor`
@ -396,20 +391,14 @@ class TidalTensorField(BaseField):
-------
eigvals : 3-dimensional array of shape `(grid, grid, grid)`
"""
n_samples = tidal_tensor.T00.size
# We create a array and then calculate the eigenvalues.
Teval = numpy.full((n_samples, 3, 3), numpy.nan, dtype=numpy.float32)
Teval[:, 0, 0] = tidal_tensor.T00
Teval[:, 0, 1] = tidal_tensor.T01
Teval[:, 0, 2] = tidal_tensor.T02
Teval[:, 1, 1] = tidal_tensor.T11
Teval[:, 1, 2] = tidal_tensor.T12
Teval[:, 2, 2] = tidal_tensor.T22
# TODO needs to be checked further
grid = tidal_tensor.T00.shape[0]
eigvals = numpy.full((grid, grid, grid, 3), numpy.nan,
dtype=numpy.float32)
dummy = numpy.full((3, 3), numpy.nan, dtype=numpy.float32)
eigvals = numpy.full((n_samples, 3), numpy.nan, dtype=numpy.float32)
for i in range(n_samples):
eigvals[i, :] = numpy.linalg.eigvalsh(Teval[i, ...], 'U')
eigvals[i, :] = numpy.sort(eigvals[i, :])
# FILL IN THESER ARGUMENTS
tidal_tensor_to_eigenvalues(eigvals, dummy, ...)
return eigvals
@ -430,3 +419,23 @@ class TidalTensorField(BaseField):
"""
return MASL.tidal_tensor(overdensity_field, self.box._omega_m,
self.box._aexp, self.MAS)
@jit(nopython=True)
def tidal_tensor_to_eigenvalues(eigvals, dummy, T00, T01, T02, T11, T12, T22):
"""
TODO: needs to be checked further.
"""
grid = T00.shape[0]
for i in range(grid):
for j in range(grid):
for k in range(grid):
dummy[0, 0] = T00[i, j, k]
dummy[0, 1] = T01[i, j, k]
dummy[0, 2] = T02[i, j, k]
dummy[1, 1] = T11[i, j, k]
dummy[1, 2] = T12[i, j, k]
dummy[2, 2] = T22[i, j, k]
eigvals[i, j, k, :] = numpy.linalg.eigvalsh(dummy, 'U')
eigvals[i, j, k, :] = numpy.sort(eigvals[i, j, k, :])
return eigvals

2
csiborgtools/field/interp.py
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@ -260,7 +260,6 @@ def fill_outside(field, fill_value, rmax, boxsize):
N = imax
# Squared radial distance from the center of the box in box units.
rmax_box2 = (N * rmax / boxsize)**2
# print("Box ", rmax_box2)
for i in range(N):
idist2 = (i - 0.5 * (N - 1))**2
@ -268,7 +267,6 @@ def fill_outside(field, fill_value, rmax, boxsize):
jdist2 = (j - 0.5 * (N - 1))**2
for k in range(N):
kdist2 = (k - 0.5 * (N - 1))**2
# print(idist2 + jdist2 + kdist2 > rmax_box2)
if idist2 + jdist2 + kdist2 > rmax_box2:
field[i, j, k] = fill_value
return field

1
csiborgtools/fits/halo.py
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@ -58,7 +58,6 @@ class BaseStructure(ABC):
@info.setter
def info(self, info):
# TODO turn this into a structured array and add some checks
self._info = info
@property

72
csiborgtools/read/nearest_neighbour_summary.py
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@ -19,10 +19,11 @@ the final snapshot.
from math import floor
import numpy
from numba import jit
from scipy.integrate import quad
from scipy.integrate import cumulative_trapezoid, quad
from scipy.interpolate import interp1d
from scipy.stats import gaussian_kde, kstest
from numba import jit
from tqdm import tqdm
@ -205,54 +206,57 @@ class NearestNeighbourReader:
Archive with keys `ndist`, `rdist`, `mass`, `cross_hindxs``
"""
assert simname in ["csiborg", "quijote"]
fpath = self.paths.cross_nearest(simname, run, nsim, nobs)
fpath = self.paths.cross_nearest(simname, run, "dist", nsim, nobs)
return numpy.load(fpath)
def build_dist(self, simname, run, kind, verbose=True):
def count_neighbour(self, out, ndist, rdist):
"""
Build the a PDF or a CDF for the nearest neighbour distribution.
Counts the binned number of neighbour for each halo as a funtion of its
radial distance from the centre of the high-resolution region.
Count the number of neighbours for each halo as a function of its
radial distance.
Parameters
----------
simname : str
Simulation name. Must be either `csiborg` or `quijote`.
run : str
Run name.
kind : str
Distribution kind. Either `pdf` or `cdf`.
verbose : bool, optional
Verbosity flag.
out : 2-dimensional array of shape `(nbins_radial, nbins_neighbour)`
Output array to write to. Results are added to this array.
ndist : 2-dimensional array of shape `(nhalos, ncross_simulations)`
Distance of each halo to its nearest neighbour from a cross
simulation.
rdist : 1-dimensional array of shape `(nhalos, )`
Distance of each halo to the centre of the high-resolution region.
Returns
-------
out : 2-dimensional array of shape `(nbins_radial, nbins_neighbour)`
"""
return count_neighbour(out, ndist, rdist, self.radial_bin_edges,
self.rmax_neighbour, self.nbins_neighbour)
def build_dist(self, counts, kind):
"""
Build the a PDF or a CDF for the nearest neighbour distribution from
binned counts as a function of radial distance from the centre of the
high-resolution region.
Parameters
----------
counts : 2-dimensional array of shape `(nbins_radial, nbins_neighbour)`
Binned counts of the number of neighbours as a function of
radial distance.
Returns
-------
dist : 2-dimensional array of shape `(nbins_radial, nbins_neighbour)`
"""
assert simname in ["csiborg", "quijote"]
assert kind in ["pdf", "cdf"]
rbin_edges = self.radial_bin_edges
# We first bin the distances as a function of each reference halo
# radial distance and then its nearest neighbour distance.
fpaths = self.paths.cross_nearest(simname, run)
if simname == "quijote":
fpaths = fpaths
out = numpy.zeros((self.nbins_radial, self.nbins_neighbour),
dtype=numpy.float32)
for fpath in tqdm(fpaths) if verbose else fpaths:
data = numpy.load(fpath)
out = count_neighbour(
out, data["ndist"], data["rdist"], rbin_edges,
self.rmax_neighbour, self.nbins_neighbour)
if kind == "pdf":
neighbour_bin_edges = self.neighbour_bin_edges
dx = neighbour_bin_edges[1] - neighbour_bin_edges[0]
out /= numpy.sum(dx * out, axis=1).reshape(-1, 1)
counts /= numpy.sum(dx * counts, axis=1).reshape(-1, 1)
else:
out = numpy.cumsum(out, axis=1, out=out)
out /= out[:, -1].reshape(-1, 1)
return out
x = self.bin_centres("neighbour")
counts = cumulative_trapezoid(counts, x, axis=1, initial=0)
counts /= counts[:, -1].reshape(-1, 1)
return counts
def kl_divergence(self, simname, run, nsim, pdf, nobs=None, verbose=True):
r"""

10
csiborgtools/read/paths.py
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@ -410,7 +410,7 @@ class Paths:
fname = f"halo_counts_{simname}_{str(nsim).zfill(5)}.npz"
return join(fdir, fname)
def cross_nearest(self, simname, run, nsim=None, nobs=None):
def cross_nearest(self, simname, run, kind, nsim=None, nobs=None):
"""
Path to the files containing distance from a halo in a reference
simulation to the nearest halo from a cross simulation.
@ -421,6 +421,9 @@ class Paths:
Simulation name. Must be one of: `csiborg`, `quijote`.
run : str
Run name.
kind : str
Whether raw distances or counts in bins. Must be one of `dist`,
`bin_dist` or `tot_counts`.
nsim : int, optional
IC realisation index.
nobs : int, optional
@ -431,6 +434,7 @@ class Paths:
path : str
"""
assert simname in ["csiborg", "quijote"]
assert kind in ["dist", "bin_dist", "tot_counts"]
fdir = join(self.postdir, "nearest_neighbour")
if not isdir(fdir):
makedirs(fdir)
@ -440,9 +444,9 @@ class Paths:
nsim = str(nsim).zfill(5)
else:
nsim = self.quijote_fiducial_nsim(nsim, nobs)
return join(fdir, f"{simname}_nn_{nsim}_{run}.npz")
return join(fdir, f"{simname}_nn_{kind}_{nsim}_{run}.npz")
files = glob(join(fdir, f"{simname}_nn_*"))
files = glob(join(fdir, f"{simname}_nn_{kind}_*"))
run = "_" + run
return [f for f in files if run in f]

2
csiborgtools/read/pk_summary.py
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@ -36,7 +36,7 @@ class PKReader:
Output precision. By default `numpy.float32`.
"""
def __init__(self, ics, hw, fskel=None, dtype=numpy.float32):
self.ics= ics
self.ics = ics
self.hw = hw
if fskel is None:
fskel = "/mnt/extraspace/rstiskalek/csiborg/crosspk/out_{}_{}_{}.p"

12419
notebooks/fits.ipynb
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12560
notebooks/matching.ipynb
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2474
notebooks/playground_field.ipynb
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File diff suppressed because one or more lines are too long

3134
notebooks/plot_galaxy_distribution.ipynb
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File diff suppressed because one or more lines are too long

1262
notebooks/plot_mass_function.ipynb
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File diff suppressed because one or more lines are too long

82
scripts/match_finsnap.py
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@ -19,12 +19,14 @@ MPI parallelized over the reference simulations.
from argparse import ArgumentParser
from datetime import datetime
from distutils.util import strtobool
from os import remove
import numpy
import yaml
from mpi4py import MPI
from taskmaster import work_delegation
from tqdm import trange
from utils import open_catalogues
try:
@ -36,7 +38,7 @@ except ModuleNotFoundError:
import csiborgtools
def find_neighbour(args, nsim, cats, paths, comm):
def find_neighbour(args, nsim, cats, paths, comm, save_kind):
"""
Find the nearest neighbour of each halo in the given catalogue.
@ -53,23 +55,78 @@ def find_neighbour(args, nsim, cats, paths, comm):
Paths object.
comm : mpi4py.MPI.Comm
MPI communicator.
save_kind : str
Kind of data to save. Must be either `dist` or `bin_dist`.
Returns
-------
None
"""
assert save_kind in ["dist", "bin_dist"]
ndist, cross_hindxs = csiborgtools.match.find_neighbour(nsim, cats)
mass_key = "totpartmass" if args.simname == "csiborg" else "group_mass"
cat0 = cats[nsim]
mass = cat0[mass_key]
rdist = cat0.radial_distance(in_initial=False)
fout = paths.cross_nearest(args.simname, args.run, nsim)
# Distance is saved optionally, whereas binned distance is always saved.
if save_kind == "dist":
out = {"ndist": ndist,
"cross_hindxs": cross_hindxs,
"mass": cat0[mass_key],
"ref_hindxs": cat0["index"],
"rdist": rdist}
fout = paths.cross_nearest(args.simname, args.run, "dist", nsim)
if args.verbose:
print(f"Rank {comm.Get_rank()} writing to `{fout}`.", flush=True)
numpy.savez(fout, **out)
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
reader = csiborgtools.read.NearestNeighbourReader(
paths=paths, **csiborgtools.neighbour_kwargs)
counts = numpy.zeros((reader.nbins_radial, reader.nbins_neighbour),
dtype=numpy.float32)
counts = reader.count_neighbour(counts, ndist, rdist)
out = {"counts": counts}
fout = paths.cross_nearest(args.simname, args.run, "bin_dist", nsim)
if args.verbose:
print(f"Rank {comm.Get_rank()} writing to `{fout}`.", flush=True)
numpy.savez(fout, ndist=ndist, cross_hindxs=cross_hindxs, mass=mass,
ref_hindxs=cat0["index"], rdist=rdist)
numpy.savez(fout, **out)
def collect_dist(args, paths):
"""
Collect the binned nearest neighbour distances into a single file.
Parameters
----------
args : argparse.Namespace
Command line arguments.
paths : csiborgtools.paths.Paths
Paths object.
Returns
-------
"""
fnames = paths.cross_nearest(args.simname, args.run, "bin_dist")
if args.verbose:
print("Collecting counts into a single file.", flush=True)
for i in trange(len(fnames)) if args.verbose else range(len(fnames)):
fname = fnames[i]
data = numpy.load(fname)
if i == 0:
out = data["counts"]
else:
out += data["counts"]
remove(fname)
fout = paths.cross_nearest(args.simname, args.run, "tot_counts",
nsim=0, nobs=0)
if args.verbose:
print(f"Writing the summed counts to `{fout}`.", flush=True)
numpy.savez(fout, tot_counts=out)
if __name__ == "__main__":
@ -87,16 +144,23 @@ if __name__ == "__main__":
with open("./match_finsnap.yml", "r") as file:
config = yaml.safe_load(file)
if args.simname == "csiborg":
save_kind = "dist"
else:
save_kind = "bin_dist"
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
cats = open_catalogues(args, config, paths, comm)
def do_work(nsim):
return find_neighbour(args, nsim, cats, paths, comm)
return find_neighbour(args, nsim, cats, paths, comm, save_kind)
work_delegation(do_work, list(cats.keys()), comm,
master_verbose=args.verbose)
comm.Barrier()
if comm.Get_rank() == 0:
if rank == 0:
collect_dist(args, paths)
print(f"{datetime.now()}: all finished. Quitting.")

67
scripts/match_finsnap.yml
View File

@ -18,20 +18,77 @@ nbins_marks: 10
name:
- totpartmass
- group_mass
min: 1.e+12
max: 1.e+13
min: 12.4
max: 12.8
islog: true
"mass002":
primary:
name:
- totpartmass
- group_mass
min: 1.e+13
max: 1.e+14
min: 12.6
max: 13.0
islog: true
"mass003":
primary:
name:
- totpartmass
- group_mass
min: 1.e+14
min: 12.8
max: 13.2
islog: true
"mass004":
primary:
name:
- totpartmass
- group_mass
min: 13.0
max: 13.4
islog: true
"mass005":
primary:
name:
- totpartmass
- group_mass
min: 13.2
max: 13.6
islog: true
"mass006":
primary:
name:
- totpartmass
- group_mass
min: 13.4
max: 13.8
islog: true
"mass007":
primary:
name:
- totpartmass
- group_mass
min: 13.6
max: 14.0
islog: true
"mass008":
primary:
name:
- totpartmass
- group_mass
min: 13.8
max: 14.2
islog: true
"mass009":
primary:
name:
- totpartmass
- group_mass
min: 14.0
islog: true

8
scripts/utils.py
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@ -106,8 +106,12 @@ def read_single_catalogue(args, config, nsim, run, rmax, paths, nobs=None):
pname = _name
if pname is None:
raise KeyError(f"Invalid names `{sel['name']}`.")
cat.apply_bounds({pname: (sel.get("min", None), sel.get("max", None))})
xmin = sel.get("min", None)
xmax = sel.get("max", None)
if sel.get("islog", False):
xmin = 10**xmin if xmin is not None else None
xmax = 10**xmax if xmax is not None else None
cat.apply_bounds({pname: (xmin, xmax)})
# Now the secondary selection bounds. If needed transfrom the secondary
# property before applying the bounds.

216
scripts_plots/plot_data.py
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@ -21,7 +21,7 @@ import numpy
import healpy
import scienceplots # noqa
import utils
import plt_utils
from cache_to_disk import cache_to_disk, delete_disk_caches_for_function # noqa
from tqdm import tqdm
@ -35,7 +35,16 @@ except ModuleNotFoundError:
def open_csiborg(nsim):
"""
Open a CSiBORG halo catalogue.
Open a CSiBORG halo catalogue. Applies mass and distance selection.
Parameters
----------
nsim : int
Simulation index.
Returns
-------
cat : csiborgtools.read.HaloCatalogue
"""
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
bounds = {"totpartmass": (None, None), "dist": (0, 155/0.705)}
@ -43,6 +52,20 @@ def open_csiborg(nsim):
def open_quijote(nsim, nobs=None):
"""
Open a Quijote halo catalogue. Applies mass and distance selection.
Parameters
----------
nsim : int
Simulation index.
nobs : int, optional
Fiducial observer index.
Returns
-------
cat : csiborgtools.read.QuijoteHaloCatalogue
"""
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
cat = csiborgtools.read.QuijoteHaloCatalogue(nsim, paths, nsnap=4)
if nobs is not None:
@ -51,10 +74,24 @@ def open_quijote(nsim, nobs=None):
def plot_mass_vs_ncells(nsim, pdf=False):
"""
Plot the halo mass vs. number of occupied cells in the initial snapshot.
Parameters
----------
nsim : int
Simulation index.
pdf : bool, optional
Whether to save the figure as a PDF file.
Returns
-------
None
"""
cat = open_csiborg(nsim)
mpart = 4.38304044e+09
with plt.style.context(utils.mplstyle):
with plt.style.context(plt_utils.mplstyle):
plt.figure()
plt.scatter(cat["totpartmass"], cat["lagpatch_ncells"], s=0.25,
rasterized=True)
@ -66,9 +103,9 @@ def plot_mass_vs_ncells(nsim, pdf=False):
plt.ylabel(r"$N_{\rm cells}$")
for ext in ["png"] if pdf is False else ["png", "pdf"]:
fout = join(utils.fout, f"init_mass_vs_ncells_{nsim}.{ext}")
fout = join(plt_utils.fout, f"init_mass_vs_ncells_{nsim}.{ext}")
print(f"Saving to `{fout}`.")
plt.savefig(fout, dpi=utils.dpi, bbox_inches="tight")
plt.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
plt.close()
@ -77,13 +114,19 @@ def plot_mass_vs_ncells(nsim, pdf=False):
###############################################################################
def process_counts(counts):
mean = numpy.mean(counts, axis=0)
std = numpy.std(counts, axis=0)
return mean, std
def plot_hmf(pdf=False):
"""
Plot the (ultimate paretn) halo mass function of CSiBORG and Quijote.
Parameters
----------
pdf : bool, optional
Whether to save the figure as a PDF file.
Returns
-------
None
"""
print("Plotting the HMF...", flush=True)
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
@ -114,35 +157,39 @@ def plot_hmf(pdf=False):
x = 10**(0.5 * (bins[1:] + bins[:-1]))
# Edit lower limits
csiborg_counts[:, x < 1e12] = numpy.nan
quijote_counts[:, x < 8e12] = numpy.nan
quijote_counts[:, x < 10**(12.4)] = numpy.nan
# Edit upper limits
csiborg_counts[:, x > 4e15] = numpy.nan
quijote_counts[:, x > 4e15] = numpy.nan
with plt.style.context(utils.mplstyle):
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, 0.65]})
fig.subplots_adjust(hspace=0, wspace=0)
mean_csiborg, std_csiborg = process_counts(csiborg_counts)
# 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].fill_between(x, mean_csiborg - std_csiborg,
mean_csiborg + std_csiborg, alpha=0.5)
mean_quijote, std_quijote = process_counts(quijote_counts)
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].fill_between(x, mean_quijote - std_quijote,
mean_quijote + std_quijote, alpha=0.5)
# Lower panel data
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=cols[2])
ax[1].fill_between(x, 10**(log_y - err), 10**(log_y + err), alpha=0.5,
color=cols[2])
# Labels and accesories
ax[1].axhline(1, color="k", ls=plt.rcParams["lines.linestyle"],
lw=0.5 * plt.rcParams["lines.linewidth"], zorder=0)
ax[0].set_ylabel(r"$\frac{\mathrm{d} n}{\mathrm{d}\log M_{\rm h}}~\mathrm{dex}^{-1}$") # noqa
@ -156,14 +203,33 @@ def plot_hmf(pdf=False):
fig.tight_layout(h_pad=0, w_pad=0)
for ext in ["png"] if pdf is False else ["png", "pdf"]:
fout = join(utils.fout, f"hmf_comparison.{ext}")
fout = join(plt_utils.fout, f"hmf_comparison.{ext}")
print(f"Saving to `{fout}`.")
fig.savefig(fout, dpi=utils.dpi, bbox_inches="tight")
fig.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
plt.close()
@cache_to_disk(7)
def load_field(kind, nsim, grid, MAS, in_rsp=False):
"""
Load a single field.
Parameters
----------
kind : str
Field kind.
nsim : int
Simulation index.
grid : int
Grid size.
MAS : str
Mass assignment scheme.
in_rsp : bool, optional
Whether to load the field in redshift space.
Returns
-------
field : n-dimensional array
"""
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
return numpy.load(paths.field(kind, MAS, grid, nsim, in_rsp=in_rsp))
@ -175,6 +241,30 @@ def load_field(kind, nsim, grid, MAS, in_rsp=False):
def plot_projected_field(kind, nsim, grid, in_rsp, MAS="PCS",
highres_only=True, pdf=False):
"""
Plot the mean projected field.
Parameters
----------
kind : str
Field kind.
nsim : int
Simulation index.
grid : int
Grid size.
in_rsp : bool
Whether to load the field in redshift space.
MAS : str, optional
Mass assignment scheme.
highres_only : bool, optional
Whether to only plot the high-resolution region.
pdf : bool, optional
Whether to save the figure as a PDF.
Returns
-------
None
"""
print(f"Plotting projected field `{kind}`. ", flush=True)
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
nsnap = max(paths.get_snapshots(nsim))
@ -190,14 +280,12 @@ def plot_projected_field(kind, nsim, grid, in_rsp, MAS="PCS",
if highres_only:
csiborgtools.field.fill_outside(field, numpy.nan, rmax=155.5,
boxsize=677.7)
# start = field.shape[0] // 4
start = round(field.shape[0] * 0.27)
end = round(field.shape[0] * 0.73)
# end = field.shape[0] - start
field = field[start:end, start:end, start:end]
labels = [r"$y-z$", r"$x-z$", r"$x-y$"]
with plt.style.context(utils.mplstyle):
with plt.style.context(plt_utils.mplstyle):
fig, ax = plt.subplots(figsize=(3.5 * 2, 2.625), ncols=3, sharey=True,
sharex=True)
fig.subplots_adjust(hspace=0, wspace=0)
@ -216,9 +304,10 @@ def plot_projected_field(kind, nsim, grid, in_rsp, MAS="PCS",
fig.tight_layout(h_pad=0, w_pad=0)
for ext in ["png"] if pdf is False else ["png", "pdf"]:
fout = join(utils.fout, f"field_{kind}_{nsim}_rsp{in_rsp}.{ext}")
fout = join(plt_utils.fout,
f"field_{kind}_{nsim}_rsp{in_rsp}.{ext}")
print(f"Saving to `{fout}`.")
fig.savefig(fout, dpi=utils.dpi, bbox_inches="tight")
fig.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
plt.close()
###############################################################################
@ -227,6 +316,20 @@ def plot_projected_field(kind, nsim, grid, in_rsp, MAS="PCS",
def get_sky_label(kind, volume_weight):
"""
Get the sky label for a given field kind.
Parameters
----------
kind : str
Field kind.
volume_weight : bool
Whether to volume weight the field.
Returns
-------
label : str
"""
if volume_weight:
if kind == "density":
label = r"$\log \int_{0}^{R} r^2 \rho(r, \mathrm{RA}, \mathrm{dec}) \mathrm{d} r$" # noqa
@ -255,8 +358,38 @@ def get_sky_label(kind, volume_weight):
def plot_sky_distribution(kind, nsim, grid, nside, MAS="PCS", plot_groups=True,
dmin=0, dmax=220, plot_halos=None,
volume_weight=True, pdf=False):
"""
NOTE: add distance for groups.
r"""
Plot the sky distribution of a given field kind on the sky along with halos
and selected observations.
TODO
----
- Add distance for groups.
Parameters
----------
field : str
Field kind.
nsim : int
Simulation index.
grid : int
Grid size.
nside : int
Healpix nside of the sky projection.
MAS : str, optional
Mass assignment scheme.
plot_groups : bool, optional
Whether to plot the 2M++ groups.
dmin : float, optional
Minimum projection distance in :math:`\mathrm{Mpc}/h`.
dmax : float, optional
Maximum projection distance in :math:`\mathrm{Mpc}/h`.
plot_halos : list, optional
Minimum halo mass to plot in :math:`M_\odot`.
volume_weight : bool, optional
Whether to volume weight the field.
pdf : bool, optional
Whether to save the figure as a pdf.
"""
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
nsnap = max(paths.get_snapshots(nsim))
@ -274,7 +407,7 @@ def plot_sky_distribution(kind, nsim, grid, nside, MAS="PCS", plot_groups=True,
out = csiborgtools.field.make_sky(field, angpos=angpos, dist=dist, box=box,
volume_weight=volume_weight)
with plt.style.context(utils.mplstyle):
with plt.style.context(plt_utils.mplstyle):
label = get_sky_label(kind, volume_weight)
if kind in ["density", "overdensity"]:
out = numpy.log10(out)
@ -299,9 +432,9 @@ def plot_sky_distribution(kind, nsim, grid, nside, MAS="PCS", plot_groups=True,
plt.legend(markerscale=10)
for ext in ["png"] if pdf is False else ["png", "pdf"]:
fout = join(utils.fout, f"sky_{kind}_{nsim}_from_{dmin}_to_{dmax}_vol{volume_weight}.{ext}") # noqa
fout = join(plt_utils.fout, f"sky_{kind}_{nsim}_from_{dmin}_to_{dmax}_vol{volume_weight}.{ext}") # noqa
print(f"Saving to `{fout}`.")
plt.savefig(fout, dpi=utils.dpi, bbox_inches="tight")
plt.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
plt.close()
@ -321,12 +454,19 @@ if __name__ == "__main__":
print(f"Cleaning cache for function {func}.")
delete_disk_caches_for_function(func)
# plot_mass_vs_occupancy(7444)
# plot_mass_vs_normcells(7444 + 24 * 4, pdf=False)
# plot_mass_vs_ncells(7444, pdf=True)
# plot_hmf(pdf=True)
# plot_sky_distribution("radvel", 7444, 256, nside=64,
# plot_groups=False, dmin=50, dmax=100,
# plot_halos=5e13, volume_weight=False)
if False:
plot_mass_vs_ncells(7444, pdf=False)
plot_projected_field("potential", 7444, 256, in_rsp=True)
if False:
plot_hmf(pdf=False)
if False:
plot_sky_distribution("radvel", 7444, 256, nside=64,
plot_groups=False, dmin=50, dmax=100,
plot_halos=5e13, volume_weight=False)
if True:
plot_projected_field("overdensity", 7444, 1024, in_rsp=True,
highres_only=False)
plot_projected_field("overdensity", 7444, 1024, in_rsp=False,
highres_only=False)

8
scripts_plots/plot_knn.py
View File

@ -19,7 +19,7 @@ import matplotlib.pyplot as plt
import numpy
import scienceplots # noqa
import utils
import plt_utils
try:
import csiborgtools
@ -40,7 +40,7 @@ def plot_knn(runname):
paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
reader = csiborgtools.read.kNNCDFReader(paths)
with plt.style.context(utils.mplstyle):
with plt.style.context(plt_utils.mplstyle):
plt.figure()
# Quijote kNN
@ -92,9 +92,9 @@ def plot_knn(runname):
plt.ylabel(r"$P(k | V = 4 \pi r^3 / 3)$")
for ext in ["png"]:
fout = join(utils.fout, f"knn_{runname}.{ext}")
fout = join(plt_utils.fout, f"knn_{runname}.{ext}")
print("Saving to `{fout}`.".format(fout=fout))
plt.savefig(fout, dpi=utils.dpi, bbox_inches="tight")
plt.savefig(fout, dpi=plt_utils.dpi, bbox_inches="tight")
plt.close()

804
scripts_plots/plot_match.py
View File

File diff suppressed because it is too large Load Diff

0
scripts_plots/utils.py → scripts_plots/plt_utils.py
View File