mirror of
https://github.com/Richard-Sti/csiborgtools.git
synced 2024-12-22 12:18:01 +00:00
Remove overly verbose code (#129)
* Remove not necessary comments * Simplify * Remove unnecessary comments * Remove verbose comments * Remove unnecessary verbosity * Update params * Remove verbose * Simplify verbosity * Simploify comments * Remove more silly verbosity
This commit is contained in:
Richard Stiskalek
GitHub
parent
779f2e76ac
commit
d3b4bfd29c
11 changed files with 212 additions and 1306 deletions
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@ -138,17 +138,6 @@ def wrapRA(ra, indeg):
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"""
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Wrap RA from :math:`[-180, 180)` to :math`[0, 360)` degrees if `indeg` or
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equivalently in radians otherwise.
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Paramaters
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----------
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ra : 1-dimensional array
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Right ascension.
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indeg : bool
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Whether the right ascension is in degrees.
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Returns
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-------
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wrapped_ra : 1-dimensional array
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"""
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mask = ra < 0
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if numpy.sum(mask) == 0:
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@ -22,9 +22,7 @@ import healpy
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def force_single_precision(x):
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"""
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Attempt to convert an array `x` to float 32.
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"""
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"""Attempt to convert an array `x` to float32."""
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if x.dtype != numpy.float32:
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x = x.astype(numpy.float32)
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return x
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@ -32,9 +30,7 @@ def force_single_precision(x):
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@jit(nopython=True)
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def divide_nonzero(field0, field1):
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"""
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Perform in-place `field0 /= field1` but only where `field1 != 0`.
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"""
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"""Perform in-place `field0 /= field1` but only where `field1 != 0`."""
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assert field0.shape == field1.shape, "Field shapes must match."
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imax, jmax, kmax = field0.shape
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@ -47,17 +43,8 @@ def divide_nonzero(field0, field1):
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def nside2radec(nside):
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"""
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Generate RA [0, 360] deg. and declination [-90, 90] deg for HEALPix pixel
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Generate RA [0, 360] deg and declination [-90, 90] deg for HEALPix pixel
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centres at a given nside.
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Parameters
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----------
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nside : int
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HEALPix nside.
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Returns
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-------
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angpos : 2-dimensional array of shape (npix, 2)
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"""
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pixs = numpy.arange(healpy.nside2npix(nside))
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theta, phi = healpy.pix2ang(nside, pixs)
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@ -13,14 +13,15 @@
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# with this program; if not, write to the Free Software Foundation, Inc.,
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# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
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"""
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Validation of the CSiBORG velocity field against PV measurements. Based on [1].
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Validation of the CSiBORG velocity field against PV measurements. A lot here
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is based on [1], though with many modifications. Throughout, comoving distances
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are in `Mpc / h` and velocities in `km / s`.
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References
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----------
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[1] https://arxiv.org/abs/1912.09383.
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"""
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from abc import ABC, abstractmethod
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from datetime import datetime
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from warnings import catch_warnings, simplefilter, warn
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import numpy as np
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@ -40,17 +41,12 @@ from scipy.optimize import fmin_powell
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from sklearn.model_selection import KFold
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from tqdm import trange
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from ..params import simname2Omega_m
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from ..utils import radec_to_galactic
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from ..params import SPEED_OF_LIGHT, simname2Omega_m
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from ..utils import fprint, radec_to_galactic
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SPEED_OF_LIGHT = 299792.458 # km / s
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H0 = 100 # km / s / Mpc
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def t():
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return datetime.now().strftime("%H:%M:%S")
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###############################################################################
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# Data loader #
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###############################################################################
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@ -83,13 +79,11 @@ class DataLoader:
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"""
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def __init__(self, simname, ksim, catalogue, catalogue_fpath, paths,
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ksmooth=None, store_full_velocity=False, verbose=True):
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if verbose:
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print(f"{t()}: reading the catalogue.", flush=True)
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fprint("reading the catalogue,", verbose)
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self._cat = self._read_catalogue(catalogue, catalogue_fpath)
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self._catname = catalogue
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if verbose:
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print(f"{t()}: reading the interpolated field.", flush=True)
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fprint("reading the interpolated field,", verbose)
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self._field_rdist, self._los_density, self._los_velocity = self._read_field( # noqa
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simname, ksim, catalogue, ksmooth, paths)
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@ -106,8 +100,7 @@ class DataLoader:
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raise ValueError("The number of objects in the catalogue does not "
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"match the number of objects in the field.")
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if verbose:
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print(f"{t()}: calculating the radial velocity.", flush=True)
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fprint("calculating the radial velocity.", verbose)
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nobject = len(self._los_density)
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dtype = self._los_density.dtype
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@ -144,74 +137,37 @@ class DataLoader:
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@property
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def cat(self):
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"""
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The distance indicators catalogue.
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Returns
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-------
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structured array
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"""
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"""The distance indicators catalogue (structured array)."""
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return self._cat[self._mask]
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@property
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def catname(self):
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"""
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Name of the catalogue.
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Returns
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-------
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str
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"""
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"""Catalogue name."""
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return self._catname
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@property
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def rdist(self):
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"""
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Radial distances where the field was interpolated for each object.
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Returns
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-------
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1-dimensional array
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"""
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"""Radial distances at which the field was interpolated."""
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return self._field_rdist
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@property
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def los_density(self):
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"""
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Density field along the line of sight.
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Returns
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----------
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2-dimensional array of shape (n_objects, n_steps)
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"""
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"""Density field along the line of sight `(n_objects, n_steps)`."""
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return self._los_density[self._mask]
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@property
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def los_velocity(self):
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"""
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Velocity field along the line of sight.
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Returns
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-------
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3-dimensional array of shape (3, n_objects, n_steps)
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"""
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"""Velocity field along the line of sight `(3, n_objects, n_steps)`."""
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if self._los_velocity is None:
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raise ValueError("The 3D velocities were not stored.")
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return self._los_velocity[self._mask]
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@property
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def los_radial_velocity(self):
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"""
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Radial velocity along the line of sight.
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Returns
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-------
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2-dimensional array of shape (n_objects, n_steps)
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"""
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"""Radial velocity along the line of sight `(n_objects, n_steps)`."""
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return self._los_radial_velocity[self._mask]
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def _read_field(self, simname, ksim, catalogue, ksmooth, paths):
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"""Read in the interpolated field."""
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nsims = paths.get_ics(simname)
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if not (0 <= ksim < len(nsims)):
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raise ValueError("Invalid simulation index.")
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@ -236,7 +192,6 @@ class DataLoader:
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return rdist, los_density, los_velocity
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def _read_catalogue(self, catalogue, catalogue_fpath):
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"""Read in the distance indicator catalogue."""
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if catalogue == "A2":
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with File(catalogue_fpath, 'r') as f:
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dtype = [(key, np.float32) for key in f.keys()]
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@ -279,20 +234,8 @@ class DataLoader:
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def make_jackknife_mask(self, i, n_splits, seed=42):
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"""
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Set the jackknife mask to exclude the `i`-th split.
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Parameters
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----------
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i : int
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Index of the split to exclude.
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n_splits : int
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Number of splits.
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seed : int, optional
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Random seed.
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Returns
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-------
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None, sets `mask` internally.
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Set the internal jackknife mask to exclude the `i`-th split out of
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`n_splits`.
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"""
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cv = KFold(n_splits=n_splits, shuffle=True, random_state=seed)
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n = len(self._cat)
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@ -320,20 +263,8 @@ class DataLoader:
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def radial_velocity_los(los_velocity, ra, dec):
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"""
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Calculate the radial velocity along the line of sight.
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Parameters
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----------
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los_velocity : 2-dimensional array of shape (3, n_steps)
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Line of sight velocity field.
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ra, dec : floats
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Right ascension and declination of the line of sight.
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is_degrees : bool, optional
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Whether the angles are in degrees.
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Returns
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-------
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1-dimensional array of shape (n_steps)
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Calculate the radial velocity along the LOS from the 3D velocity
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along the LOS `(3, n_steps)`.
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"""
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types = (float, np.float32, np.float64)
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if not isinstance(ra, types) and not isinstance(dec, types):
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@ -359,15 +290,6 @@ def lognorm_mean_std_to_loc_scale(mu, std):
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"""
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Calculate the location and scale parameters for the log-normal distribution
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from the mean and standard deviation.
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Parameters
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----------
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mu, std : float
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Mean and standard deviation.
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Returns
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-------
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loc, scale : float
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"""
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loc = np.log(mu) - 0.5 * np.log(1 + (std / mu) ** 2)
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scale = np.sqrt(np.log(1 + (std / mu) ** 2))
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@ -378,17 +300,6 @@ def simps(y, dx):
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"""
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Simpson's rule 1D integration, assuming that the number of steps is even
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and that the step size is constant.
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Parameters
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----------
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y : 1-dimensional array
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Function values.
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dx : float
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Step size.
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Returns
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-------
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float
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"""
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if len(y) % 2 == 0:
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raise ValueError("The number of steps must be odd.")
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@ -400,17 +311,6 @@ def dist2redshift(dist, Omega_m):
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"""
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Convert comoving distance to cosmological redshift if the Universe is
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flat and z << 1.
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Parameters
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----------
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dist : float or 1-dimensional array
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Comoving distance in `Mpc / h`.
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Omega_m : float
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Matter density parameter.
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Returns
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-------
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float or 1-dimensional array
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"""
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eta = 3 * Omega_m / 2
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return 1 / eta * (1 - (1 - 2 * H0 * dist / SPEED_OF_LIGHT * eta)**0.5)
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@ -420,17 +320,6 @@ def redshift2dist(z, Omega_m):
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"""
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Convert cosmological redshift to comoving distance if the Universe is
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flat and z << 1.
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Parameters
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----------
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z : float or 1-dimensional array
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Cosmological redshift.
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Omega_m : float
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Matter density parameter.
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Returns
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-------
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float or 1-dimensional array
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"""
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q0 = 3 * Omega_m / 2 - 1
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return SPEED_OF_LIGHT * z / (2 * H0) * (2 - z * (1 + q0))
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@ -440,37 +329,13 @@ def gradient_redshift2dist(z, Omega_m):
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"""
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Gradient of the redshift to comoving distance conversion if the Universe is
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flat and z << 1.
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Parameters
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----------
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z : float or 1-dimensional array
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Cosmological redshift.
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Omega_m : float
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Matter density parameter.
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Returns
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-------
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float or 1-dimensional array
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"""
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q0 = 3 * Omega_m / 2 - 1
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return SPEED_OF_LIGHT / H0 * (1 - z * (1 + q0))
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def dist2distmodulus(dist, Omega_m):
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"""
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Convert comoving distance to distance modulus, assuming z << 1.
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Parameters
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----------
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dist : float or 1-dimensional array
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Comoving distance in `Mpc / h`.
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Omega_m : float
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Matter density parameter.
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Returns
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-------
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float or 1-dimensional array
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"""
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"""Convert comoving distance to distance modulus, assuming z << 1."""
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zcosmo = dist2redshift(dist, Omega_m)
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luminosity_distance = dist * (1 + zcosmo)
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return 5 * jnp.log10(luminosity_distance) + 25
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@ -479,9 +344,8 @@ def dist2distmodulus(dist, Omega_m):
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def distmodulus2dist(mu, Omega_m, ninterp=10000, zmax=0.1, mu2comoving=None,
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return_interpolator=False):
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"""
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Convert distance modulus to comoving distance. Note that this is a costly
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implementation, as it builts up the interpolator every time it is called
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unless it is provided.
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Convert distance modulus to comoving distance. This is costly as it builds
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up the interpolator every time it is called, unless it is provided.
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Parameters
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----------
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@ -520,9 +384,8 @@ def distmodulus2dist(mu, Omega_m, ninterp=10000, zmax=0.1, mu2comoving=None,
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def distmodulus2redsfhit(mu, Omega_m, ninterp=10000, zmax=0.1, mu2z=None,
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return_interpolator=False):
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"""
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Convert distance modulus to cosmological redshift. Note that this is a
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costly implementation, as it builts up the interpolator every time it is
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called unless it is provided.
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Convert distance modulus to cosmological redshift. This is costly as it
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builts up the interpolator every time it is called, unless it is provided.
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Parameters
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----------
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@ -556,49 +419,18 @@ def distmodulus2redsfhit(mu, Omega_m, ninterp=10000, zmax=0.1, mu2z=None,
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return mu2z(mu)
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def project_Vext(Vext_x, Vext_y, Vext_z, RA, dec):
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"""
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Project the external velocity onto the line of sight along direction
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specified by RA/dec. Note that the angles must be in radians.
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Parameters
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----------
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Vext_x, Vext_y, Vext_z : floats
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Components of the external velocity.
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RA, dec : floats
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Right ascension and declination in radians
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Returns
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-------
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float
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"""
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cos_dec = jnp.cos(dec)
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return (Vext_x * jnp.cos(RA) * cos_dec
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+ Vext_y * jnp.sin(RA) * cos_dec
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+ Vext_z * jnp.sin(dec))
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def project_Vext(Vext_x, Vext_y, Vext_z, RA_radians, dec_radians):
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"""Project the external velocity vector onto the line of sight."""
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cos_dec = jnp.cos(dec_radians)
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return (Vext_x * jnp.cos(RA_radians) * cos_dec
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+ Vext_y * jnp.sin(RA_radians) * cos_dec
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+ Vext_z * jnp.sin(dec_radians))
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def predict_zobs(dist, beta, Vext_radial, vpec_radial, Omega_m):
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"""
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Predict the observed redshift at a given comoving distance given some
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velocity field.
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Parameters
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----------
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dist : float
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Comoving distance in `Mpc / h`.
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beta : float
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Velocity bias parameter.
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Vext_radial : float
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Radial component of the external velocity along the LOS.
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vpec_radial : float
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Radial component of the peculiar velocity along the LOS.
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Omega_m : float
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Matter density parameter.
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Returns
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-------
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float
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"""
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zcosmo = dist2redshift(dist, Omega_m)
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@ -613,7 +445,7 @@ def predict_zobs(dist, beta, Vext_radial, vpec_radial, Omega_m):
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def calculate_ptilde_wo_bias(xrange, mu, err, r_squared_xrange=None,
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is_err_squared=False):
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"""
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Calculate `ptilde(r)` without any bias.
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Calculate `ptilde(r)` without (im)homogeneous Malmquist bias.
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Parameters
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----------
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@ -648,19 +480,6 @@ def calculate_likelihood_zobs(zobs, zobs_pred, sigma_v):
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"""
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Calculate the likelihood of the observed redshift given the predicted
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redshift.
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Parameters
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----------
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zobs : float
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Observed redshift.
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zobs_pred : float
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Predicted redshift.
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sigma_v : float
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Velocity uncertainty.
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Returns
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-------
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float
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"""
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dcz = SPEED_OF_LIGHT * (zobs - zobs_pred)
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return jnp.exp(-0.5 * (dcz / sigma_v)**2) / jnp.sqrt(2 * np.pi) / sigma_v
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@ -668,7 +487,7 @@ def calculate_likelihood_zobs(zobs, zobs_pred, sigma_v):
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def stack_normal(mus, stds):
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"""
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Stack the normal distributions and approximate the stacked distribution
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Stack normal distributions and approximate the stacked distribution
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by a single Gaussian.
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Parameters
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@ -690,9 +509,6 @@ def stack_normal(mus, stds):
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|
||||
class BaseFlowValidationModel(ABC):
|
||||
"""
|
||||
Base class for the flow validation models.
|
||||
"""
|
||||
|
||||
@property
|
||||
def ndata(self):
|
||||
|
|
@ -813,17 +629,16 @@ class SD_PV_validation_model(BaseFlowValidationModel):
|
|||
|
||||
def __init__(self, los_density, los_velocity, RA, dec, z_obs,
|
||||
r_hMpc, e_r_hMpc, r_xrange, Omega_m):
|
||||
dt = jnp.float32
|
||||
# Convert everything to JAX arrays.
|
||||
self._los_density = jnp.asarray(los_density, dtype=dt)
|
||||
self._los_velocity = jnp.asarray(los_velocity, dtype=dt)
|
||||
self._los_density = jnp.asarray(los_density)
|
||||
self._los_velocity = jnp.asarray(los_velocity)
|
||||
|
||||
self._RA = jnp.asarray(np.deg2rad(RA), dtype=dt)
|
||||
self._dec = jnp.asarray(np.deg2rad(dec), dtype=dt)
|
||||
self._z_obs = jnp.asarray(z_obs, dtype=dt)
|
||||
self._RA = jnp.asarray(np.deg2rad(RA))
|
||||
self._dec = jnp.asarray(np.deg2rad(dec))
|
||||
self._z_obs = jnp.asarray(z_obs)
|
||||
|
||||
self._r_hMpc = jnp.asarray(r_hMpc, dtype=dt)
|
||||
self._e2_rhMpc = jnp.asarray(e_r_hMpc**2, dtype=dt)
|
||||
self._r_hMpc = jnp.asarray(r_hMpc)
|
||||
self._e2_rhMpc = jnp.asarray(e_r_hMpc**2)
|
||||
|
||||
# Get radius squared
|
||||
r2_xrange = r_xrange**2
|
||||
|
|
@ -941,26 +756,24 @@ class SN_PV_validation_model(BaseFlowValidationModel):
|
|||
|
||||
def __init__(self, los_density, los_velocity, RA, dec, z_obs,
|
||||
e_zobs, mB, x1, c, e_mB, e_x1, e_c, r_xrange, Omega_m):
|
||||
dt = jnp.float32
|
||||
# Convert everything to JAX arrays.
|
||||
self._los_density = jnp.asarray(los_density, dtype=dt)
|
||||
self._los_velocity = jnp.asarray(los_velocity, dtype=dt)
|
||||
self._los_density = jnp.asarray(los_density)
|
||||
self._los_velocity = jnp.asarray(los_velocity)
|
||||
|
||||
self._RA = jnp.asarray(np.deg2rad(RA), dtype=dt)
|
||||
self._dec = jnp.asarray(np.deg2rad(dec), dtype=dt)
|
||||
self._z_obs = jnp.asarray(z_obs, dtype=dt)
|
||||
self._RA = jnp.asarray(np.deg2rad(RA))
|
||||
self._dec = jnp.asarray(np.deg2rad(dec))
|
||||
self._z_obs = jnp.asarray(z_obs)
|
||||
if e_zobs is not None:
|
||||
self._e2_cz_obs = jnp.asarray(
|
||||
(SPEED_OF_LIGHT * e_zobs)**2, dtype=dt)
|
||||
self._e2_cz_obs = jnp.asarray((SPEED_OF_LIGHT * e_zobs)**2)
|
||||
else:
|
||||
self._e2_cz_obs = jnp.zeros_like(self._z_obs)
|
||||
|
||||
self._mB = jnp.asarray(mB, dtype=dt)
|
||||
self._x1 = jnp.asarray(x1, dtype=dt)
|
||||
self._c = jnp.asarray(c, dtype=dt)
|
||||
self._e2_mB = jnp.asarray(e_mB**2, dtype=dt)
|
||||
self._e2_x1 = jnp.asarray(e_x1**2, dtype=dt)
|
||||
self._e2_c = jnp.asarray(e_c**2, dtype=dt)
|
||||
self._mB = jnp.asarray(mB)
|
||||
self._x1 = jnp.asarray(x1)
|
||||
self._c = jnp.asarray(c)
|
||||
self._e2_mB = jnp.asarray(e_mB**2)
|
||||
self._e2_x1 = jnp.asarray(e_x1**2)
|
||||
self._e2_c = jnp.asarray(e_c**2)
|
||||
|
||||
# Get radius squared
|
||||
r2_xrange = r_xrange**2
|
||||
|
|
@ -1001,32 +814,12 @@ class SN_PV_validation_model(BaseFlowValidationModel):
|
|||
|
||||
def mu(self, mag_cal, alpha_cal, beta_cal):
|
||||
"""
|
||||
Distance modulus of each object the given SALT2 calibration parameters.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
mag_cal, alpha_cal, beta_cal : floats
|
||||
SALT2 calibration parameters.
|
||||
|
||||
Returns
|
||||
-------
|
||||
1-dimensional array
|
||||
Distance modulus of each object given SALT2 calibration parameters.
|
||||
"""
|
||||
return self._mB - mag_cal + alpha_cal * self._x1 - beta_cal * self._c
|
||||
|
||||
def squared_e_mu(self, alpha_cal, beta_cal, e_mu_intrinsic):
|
||||
"""
|
||||
Linearly-propagated squared error on the SALT2 distance modulus.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
alpha_cal, beta_cal, e_mu_intrinsic : floats
|
||||
SALT2 calibration parameters.
|
||||
|
||||
Returns
|
||||
-------
|
||||
1-dimensional array
|
||||
"""
|
||||
"""Linearly-propagated squared error on the SALT2 distance modulus."""
|
||||
return (self._e2_mB + alpha_cal**2 * self._e2_x1
|
||||
+ beta_cal**2 * self._e2_c + e_mu_intrinsic**2)
|
||||
|
||||
|
|
@ -1122,10 +915,6 @@ class SN_PV_validation_model(BaseFlowValidationModel):
|
|||
sample_beta : bool, optional
|
||||
Whether to sample the velocity bias parameter `beta`, otherwise
|
||||
it is fixed to 1.
|
||||
|
||||
Returns
|
||||
-------
|
||||
None
|
||||
"""
|
||||
Vx = numpyro.sample("Vext_x", self._Vext)
|
||||
Vy = numpyro.sample("Vext_y", self._Vext)
|
||||
|
|
@ -1192,19 +981,18 @@ class TF_PV_validation_model(BaseFlowValidationModel):
|
|||
|
||||
def __init__(self, los_density, los_velocity, RA, dec, z_obs,
|
||||
mag, eta, e_mag, e_eta, r_xrange, Omega_m):
|
||||
dt = jnp.float32
|
||||
# Convert everything to JAX arrays.
|
||||
self._los_density = jnp.asarray(los_density, dtype=dt)
|
||||
self._los_velocity = jnp.asarray(los_velocity, dtype=dt)
|
||||
self._los_density = jnp.asarray(los_density)
|
||||
self._los_velocity = jnp.asarray(los_velocity)
|
||||
|
||||
self._RA = jnp.asarray(np.deg2rad(RA), dtype=dt)
|
||||
self._dec = jnp.asarray(np.deg2rad(dec), dtype=dt)
|
||||
self._z_obs = jnp.asarray(z_obs, dtype=dt)
|
||||
self._RA = jnp.asarray(np.deg2rad(RA))
|
||||
self._dec = jnp.asarray(np.deg2rad(dec))
|
||||
self._z_obs = jnp.asarray(z_obs)
|
||||
|
||||
self._mag = jnp.asarray(mag, dtype=dt)
|
||||
self._eta = jnp.asarray(eta, dtype=dt)
|
||||
self._e2_mag = jnp.asarray(e_mag**2, dtype=dt)
|
||||
self._e2_eta = jnp.asarray(e_eta**2, dtype=dt)
|
||||
self._mag = jnp.asarray(mag)
|
||||
self._eta = jnp.asarray(eta)
|
||||
self._e2_mag = jnp.asarray(e_mag**2)
|
||||
self._e2_eta = jnp.asarray(e_eta**2)
|
||||
|
||||
# Get radius squared
|
||||
r2_xrange = r_xrange**2
|
||||
|
|
@ -1243,34 +1031,11 @@ class TF_PV_validation_model(BaseFlowValidationModel):
|
|||
self._r_xrange = r_xrange
|
||||
|
||||
def mu(self, a, b):
|
||||
"""
|
||||
Distance modulus of each object the given Tully-Fisher calibration.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
a, b : floats
|
||||
Tully-Fisher calibration parameters.
|
||||
|
||||
Returns
|
||||
-------
|
||||
1-dimensional array
|
||||
"""
|
||||
|
||||
"""Distance modulus of each object given the TFR calibration."""
|
||||
return self._mag - (a + b * self._eta)
|
||||
|
||||
def squared_e_mu(self, b, e_mu_intrinsic):
|
||||
"""
|
||||
Squared error on the Tully-Fisher distance modulus.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
b, e_mu_intrinsic : floats
|
||||
Tully-Fisher calibration parameters.
|
||||
|
||||
Returns
|
||||
-------
|
||||
1-dimensional array
|
||||
"""
|
||||
"""Linearly propagated squared error on the TFR distance modulus."""
|
||||
return (self._e2_mag + b**2 * self._e2_eta + e_mu_intrinsic**2)
|
||||
|
||||
def predict_zcosmo_from_calibration(self, **kwargs):
|
||||
|
|
@ -1331,10 +1096,6 @@ class TF_PV_validation_model(BaseFlowValidationModel):
|
|||
sample_beta : bool, optional
|
||||
Whether to sample the velocity bias parameter `beta`, otherwise
|
||||
it is fixed to 1.
|
||||
|
||||
Returns
|
||||
-------
|
||||
None
|
||||
"""
|
||||
Vx = numpyro.sample("Vext_x", self._Vext)
|
||||
Vy = numpyro.sample("Vext_y", self._Vext)
|
||||
|
|
@ -1671,7 +1432,6 @@ def _posterior_element(r, beta, Vext_radial, los_velocity, Omega_m, zobs,
|
|||
class BaseObserved2CosmologicalRedshift(ABC):
|
||||
"""Base class for `Observed2CosmologicalRedshift`."""
|
||||
def __init__(self, calibration_samples, r_xrange):
|
||||
dt = jnp.float32
|
||||
# Check calibration samples input.
|
||||
for i, key in enumerate(calibration_samples.keys()):
|
||||
x = calibration_samples[key]
|
||||
|
|
@ -1687,14 +1447,13 @@ class BaseObserved2CosmologicalRedshift(ABC):
|
|||
if len(x) != ncalibratrion:
|
||||
raise ValueError("Calibration samples do not have the same length.") # noqa
|
||||
|
||||
# Enforce the same data type.
|
||||
calibration_samples[key] = jnp.asarray(x, dtype=dt)
|
||||
calibration_samples[key] = jnp.asarray(x)
|
||||
|
||||
if "alpha" not in calibration_samples:
|
||||
calibration_samples["alpha"] = jnp.ones(ncalibratrion, dtype=dt)
|
||||
calibration_samples["alpha"] = jnp.ones(ncalibratrion)
|
||||
|
||||
if "beta" not in calibration_samples:
|
||||
calibration_samples["beta"] = jnp.ones(ncalibratrion, dtype=dt)
|
||||
calibration_samples["beta"] = jnp.ones(ncalibratrion)
|
||||
|
||||
# Get the stepsize, we need it to be constant for Simpson's rule.
|
||||
dr = np.diff(r_xrange)
|
||||
|
|
@ -1714,18 +1473,7 @@ class BaseObserved2CosmologicalRedshift(ABC):
|
|||
self._simps = jit(lambda y: simps(y, dr))
|
||||
|
||||
def get_calibration_samples(self, key):
|
||||
"""
|
||||
Get calibration samples for a given key.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
key : str
|
||||
Key of the calibration samples.
|
||||
|
||||
Returns
|
||||
-------
|
||||
1-dimensional array
|
||||
"""
|
||||
"""Get calibration samples for a given key."""
|
||||
if key not in self._calibration_samples:
|
||||
raise ValueError(f"Key `{key}` not found in calibration samples. Available keys are: `{self.calibration_keys}`.") # noqa
|
||||
|
||||
|
|
@ -1733,24 +1481,12 @@ class BaseObserved2CosmologicalRedshift(ABC):
|
|||
|
||||
@property
|
||||
def ncalibration_samples(self):
|
||||
"""
|
||||
Number of calibration samples.
|
||||
|
||||
Returns
|
||||
-------
|
||||
int
|
||||
"""
|
||||
"""Number of calibration samples."""
|
||||
return self._ncalibration_samples
|
||||
|
||||
@property
|
||||
def calibration_keys(self):
|
||||
"""
|
||||
Calibration sample keys.
|
||||
|
||||
Returns
|
||||
-------
|
||||
list of str
|
||||
"""
|
||||
"""Calibration sample keys."""
|
||||
return list(self._calibration_samples.keys())
|
||||
|
||||
|
||||
|
|
@ -1785,22 +1521,8 @@ class Observed2CosmologicalRedshift(BaseObserved2CosmologicalRedshift):
|
|||
def posterior_mean_std(self, x, px):
|
||||
"""
|
||||
Calculate the mean and standard deviation of a 1-dimensional PDF.
|
||||
Assumes that the PDF is already normalized. Assumes that the PDF
|
||||
spacing is that of `r_xrange` which is inferred when initializing this
|
||||
class.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
x : 1-dimensional array
|
||||
Values at which the PDF is evaluated. Note that the PDF must be
|
||||
normalized.
|
||||
px : 1-dimensional array
|
||||
PDF values.
|
||||
dx
|
||||
|
||||
Returns
|
||||
-------
|
||||
mu, std : floats
|
||||
Assumes that the PDF is already normalized and that the spacing is that
|
||||
of `r_xrange` which is inferred when initializing this class.
|
||||
"""
|
||||
mu = self._simps(x * px)
|
||||
std = (self._simps(x**2 * px) - mu**2)**0.5
|
||||
|
|
|
|||
|
|
@ -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.
|
||||
"""
|
||||
Code to match observations to a constrained simulation.
|
||||
Code to match observations to a constrained simulation. Throughout, masses are
|
||||
assumed to be in `Msun / h`, distances in `Mpc / h` and the HMF in
|
||||
`h^3 Mpc^-3 dex^-1`.
|
||||
"""
|
||||
from abc import ABC
|
||||
|
||||
|
|
@ -30,17 +32,10 @@ from tqdm import trange
|
|||
|
||||
|
||||
class BaseMatchingProbability(ABC):
|
||||
"""Base class for `MatchingProbability`."""
|
||||
|
||||
@property
|
||||
def halo_pos(self):
|
||||
"""
|
||||
Halo positions in the constrained simulation.
|
||||
|
||||
Returns
|
||||
-------
|
||||
2-dimensional array of shape `(n, 3)`
|
||||
"""
|
||||
"""Halo positions in the constrained simulation."""
|
||||
return self._halo_pos
|
||||
|
||||
@halo_pos.setter
|
||||
|
|
@ -51,13 +46,7 @@ class BaseMatchingProbability(ABC):
|
|||
|
||||
@property
|
||||
def halo_log_mass(self):
|
||||
"""
|
||||
Halo log mass in the constrained simulation.
|
||||
|
||||
Returns
|
||||
-------
|
||||
1-dimensional array of shape `(n,)`
|
||||
"""
|
||||
"""Halo logarithmic mass in the constrained simulation."""
|
||||
return self._halo_log_mass
|
||||
|
||||
@halo_log_mass.setter
|
||||
|
|
@ -68,30 +57,11 @@ class BaseMatchingProbability(ABC):
|
|||
|
||||
@property
|
||||
def nhalo(self):
|
||||
""""
|
||||
Number of haloes in the constrained simulation that are used for
|
||||
matching.
|
||||
|
||||
Returns
|
||||
-------
|
||||
int
|
||||
"""
|
||||
""""Number of haloes in the constrained simulation."""
|
||||
return self.halo_log_mass.size
|
||||
|
||||
def HMF(self, log_mass):
|
||||
"""
|
||||
Evaluate the halo mass function at a given mass.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
log_mass : float
|
||||
Logarithmic mass of the halo in `Msun / h`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
HMF : float
|
||||
The HMF in `h^3 Mpc^-3 dex^-1`.
|
||||
"""
|
||||
"""Evaluate the halo mass function at a given log mass."""
|
||||
return self._hmf(log_mass)
|
||||
|
||||
|
||||
|
|
@ -140,17 +110,6 @@ class MatchingProbability(BaseMatchingProbability):
|
|||
"""
|
||||
Calculate the PDF of finding a halo of a given mass at a given distance
|
||||
from a random point.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
r : float
|
||||
Distance from the random point in `Mpc / h`.
|
||||
log_mass : float
|
||||
Logarithmic mass of the halo in `Msun / h`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
float
|
||||
"""
|
||||
nd = self.HMF(log_mass)
|
||||
return 4 * np.pi * r**2 * nd * np.exp(-4 / 3 * np.pi * r**3 * nd)
|
||||
|
|
@ -159,17 +118,6 @@ class MatchingProbability(BaseMatchingProbability):
|
|||
"""
|
||||
Calculate the CDF of finding a halo of a given mass at a given distance
|
||||
from a random point.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
r : float
|
||||
Distance from the random point in `Mpc / h`.
|
||||
log_mass : float
|
||||
Logarithmic mass of the halo in `Msun / h`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
float
|
||||
"""
|
||||
nd = self.HMF(log_mass)
|
||||
return 1 - np.exp(-4 / 3 * np.pi * r**3 * nd)
|
||||
|
|
@ -178,17 +126,6 @@ class MatchingProbability(BaseMatchingProbability):
|
|||
"""
|
||||
Calculate the inverse CDF of finding a halo of a given mass at a given
|
||||
distance from a random point.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
cdf : float
|
||||
CDF of finding a halo of a given mass at a given distance.
|
||||
log_mass : float
|
||||
Logarithmic mass of the halo in `Msun / h`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
float
|
||||
"""
|
||||
nd = self.HMF(log_mass)
|
||||
return (np.log(1 - cdf) / (-4 / 3 * np.pi * nd))**(1 / 3)
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@
|
|||
# with this program; if not, write to the Free Software Foundation, Inc.,
|
||||
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
|
||||
"""
|
||||
Support for matching halos between CSiBORG IC realisations based on their
|
||||
Code for matching halos between CSiBORG IC realisations based on their
|
||||
Lagrangian patch overlap.
|
||||
"""
|
||||
from abc import ABC
|
||||
|
|
@ -112,10 +112,6 @@ class RealisationsMatcher(BaseMatcher):
|
|||
"""
|
||||
Multiplier of the sum of the initial Lagrangian patch sizes of a halo
|
||||
pair. Determines the range within which neighbors are returned.
|
||||
|
||||
Returns
|
||||
-------
|
||||
float
|
||||
"""
|
||||
return self._nmult
|
||||
|
||||
|
|
@ -130,10 +126,6 @@ class RealisationsMatcher(BaseMatcher):
|
|||
"""
|
||||
Tolerance on the absolute logarithmic mass difference of potential
|
||||
matches.
|
||||
|
||||
Returns
|
||||
-------
|
||||
float
|
||||
"""
|
||||
return self._dlogmass
|
||||
|
||||
|
|
@ -148,10 +140,6 @@ class RealisationsMatcher(BaseMatcher):
|
|||
"""
|
||||
Mass key whose similarity is to be checked. Must be a valid key in the
|
||||
halo catalogue.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
return self._mass_key
|
||||
|
||||
|
|
|
|||
|
|
@ -16,6 +16,7 @@
|
|||
Various user parameters for CSiBORGTools.
|
||||
"""
|
||||
|
||||
SPEED_OF_LIGHT = 299792.458 # km / s
|
||||
CB2_REDSHIFT = [69.0000210000063, 40.250007218751264, 28.24050991940438,
|
||||
21.6470609550175, 17.480001404480106, 14.608109099433955,
|
||||
12.508772664512199, 10.90721705951751, 9.64516173673259,
|
||||
|
|
@ -53,39 +54,18 @@ CB2_REDSHIFT = [69.0000210000063, 40.250007218751264, 28.24050991940438,
|
|||
|
||||
|
||||
def snap2redshift(snapnum, simname):
|
||||
"""
|
||||
Convert a snapshot number to redshift.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
snapnum : int
|
||||
Snapshot number.
|
||||
simname : str
|
||||
Simulation name.
|
||||
|
||||
Returns
|
||||
-------
|
||||
float
|
||||
"""
|
||||
"""Convert a snapshot number to redshift."""
|
||||
if "csiborg2_" in simname:
|
||||
try:
|
||||
return CB2_REDSHIFT[snapnum]
|
||||
except KeyError:
|
||||
raise ValueError(f"Unknown snapshot: `{snapnum}`.")
|
||||
else:
|
||||
raise ValueError(f"Unknown simname: {simname}")
|
||||
raise ValueError(f"Unsupported simulation: `{simname}`.")
|
||||
|
||||
|
||||
def simname2boxsize(simname):
|
||||
"""
|
||||
Return boxsize in `Mpc/h` for a given simname.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
simname : str
|
||||
Simulation name.
|
||||
|
||||
Returns
|
||||
-------
|
||||
boxsize : float
|
||||
"""
|
||||
"""Return boxsize in `Mpc/h` for a given simulation."""
|
||||
d = {"csiborg1": 677.7,
|
||||
"csiborg2_main": 676.6,
|
||||
"csiborg2_varysmall": 676.6,
|
||||
|
|
@ -97,28 +77,16 @@ def simname2boxsize(simname):
|
|||
"TNG300-1": 205.,
|
||||
"Carrick2015": 400.,
|
||||
}
|
||||
|
||||
boxsize = d.get(simname, None)
|
||||
|
||||
if boxsize is None:
|
||||
raise ValueError("Unknown simname: {}".format(simname))
|
||||
raise ValueError(f"Unknown simulation: `{simname}`.")
|
||||
|
||||
return boxsize
|
||||
|
||||
|
||||
def simname2Omega_m(simname):
|
||||
"""
|
||||
Return Omega_m for a given simname.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
simname : str
|
||||
Simulation name.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Omega_m: float
|
||||
"""
|
||||
"""Return `Omega_m` for a given simulation"""
|
||||
d = {"csiborg1": 0.307,
|
||||
"csiborg2_main": 0.3111,
|
||||
"csiborg2_random": 0.3111,
|
||||
|
|
@ -132,7 +100,7 @@ def simname2Omega_m(simname):
|
|||
omega_m = d.get(simname, None)
|
||||
|
||||
if omega_m is None:
|
||||
raise ValueError("Unknown simname: {}".format(simname))
|
||||
raise ValueError(f"Unknown simulation: `{simname}`.")
|
||||
|
||||
return omega_m
|
||||
|
||||
|
|
@ -141,7 +109,7 @@ paths_glamdring = {
|
|||
"csiborg1_srcdir": "/mnt/extraspace/rstiskalek/csiborg1",
|
||||
"csiborg2_main_srcdir": "/mnt/extraspace/rstiskalek/csiborg2_main",
|
||||
"csiborg2_varysmall_srcdir": "/mnt/extraspace/rstiskalek/csiborg2_varysmall", # noqa
|
||||
"csiborg2_random_srcdir": "/mnt/extraspace/rstiskalek/csiborg2_random", # noqa
|
||||
"csiborg2_random_srcdir": "/mnt/extraspace/rstiskalek/csiborg2_random",
|
||||
"postdir": "/mnt/extraspace/rstiskalek/csiborg_postprocessing/",
|
||||
"quijote_dir": "/mnt/extraspace/rstiskalek/quijote",
|
||||
"borg1_dir": "/mnt/users/hdesmond/BORG_final",
|
||||
|
|
@ -149,10 +117,3 @@ paths_glamdring = {
|
|||
"tng300_1_dir": "/mnt/extraspace/rstiskalek/TNG300-1/",
|
||||
"aux_cat_dir": "/mnt/extraspace/rstiskalek/catalogs",
|
||||
}
|
||||
|
||||
|
||||
# neighbour_kwargs = {"rmax_radial": 155 / 0.705,
|
||||
# "nbins_radial": 50,
|
||||
# "rmax_neighbour": 100.,
|
||||
# "nbins_neighbour": 150,
|
||||
# "paths_kind": paths_glamdring}
|
||||
|
|
|
|||
|
|
@ -17,7 +17,7 @@ Unified interface for simulation catalogues. Currently supports CSiBORG1,
|
|||
CSiBORG2 and Quijote. For specific implementation always check the relevant
|
||||
classes in this module.
|
||||
"""
|
||||
from abc import ABC, abstractproperty
|
||||
from abc import ABC, abstractmethod
|
||||
from collections import OrderedDict
|
||||
from functools import lru_cache
|
||||
from gc import collect
|
||||
|
|
@ -104,13 +104,7 @@ class BaseCatalogue(ABC):
|
|||
|
||||
@property
|
||||
def simname(self):
|
||||
"""
|
||||
Simulation name.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
"""Simulation name."""
|
||||
if self._simname is None:
|
||||
raise RuntimeError("`simname` is not set!")
|
||||
return self._simname
|
||||
|
|
@ -123,13 +117,7 @@ class BaseCatalogue(ABC):
|
|||
|
||||
@property
|
||||
def nsim(self):
|
||||
"""
|
||||
Simulation IC realisation index.
|
||||
|
||||
Returns
|
||||
-------
|
||||
int
|
||||
"""
|
||||
"""Simulation IC realisation index."""
|
||||
if self._nsim is None:
|
||||
raise RuntimeError("`nsim` is not set!")
|
||||
return self._nsim
|
||||
|
|
@ -142,13 +130,7 @@ class BaseCatalogue(ABC):
|
|||
|
||||
@property
|
||||
def nsnap(self):
|
||||
"""
|
||||
Catalogue snapshot index.
|
||||
|
||||
Returns
|
||||
-------
|
||||
int
|
||||
"""
|
||||
"""Catalogue snapshot index."""
|
||||
if self._nsnap is None:
|
||||
raise RuntimeError("`nsnap` is not set!")
|
||||
return self._nsnap
|
||||
|
|
@ -186,26 +168,14 @@ class BaseCatalogue(ABC):
|
|||
|
||||
@property
|
||||
def paths(self):
|
||||
"""
|
||||
Paths manager.
|
||||
|
||||
Returns
|
||||
-------
|
||||
py:class:`csiborgtools.read.Paths`
|
||||
"""
|
||||
"""Paths manager."""
|
||||
if self._paths is None:
|
||||
return Paths(**paths_glamdring)
|
||||
return self._paths
|
||||
|
||||
@property
|
||||
def boxsize(self):
|
||||
"""
|
||||
Box size in `cMpc / h`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
float
|
||||
"""
|
||||
"""Box size in `cMpc / h`."""
|
||||
if self._boxsize is None:
|
||||
raise RuntimeError("`boxsize` is not set!")
|
||||
return self._boxsize
|
||||
|
|
@ -221,10 +191,6 @@ class BaseCatalogue(ABC):
|
|||
"""
|
||||
Whether to flip the x- and z-coordinates to undo the MUSIC bug to match
|
||||
observations.
|
||||
|
||||
Returns
|
||||
-------
|
||||
bool
|
||||
"""
|
||||
return self._flip_xz
|
||||
|
||||
|
|
@ -236,13 +202,7 @@ class BaseCatalogue(ABC):
|
|||
|
||||
@property
|
||||
def cache_maxsize(self):
|
||||
"""
|
||||
Maximum length of the cache dictionary.
|
||||
|
||||
Returns
|
||||
-------
|
||||
int
|
||||
"""
|
||||
"""Maximum length of the cache dictionary."""
|
||||
if self._cache_maxsize is None:
|
||||
raise RuntimeError("`cache_maxsize` is not set!")
|
||||
return self._cache_maxsize
|
||||
|
|
@ -253,111 +213,57 @@ class BaseCatalogue(ABC):
|
|||
self._cache_maxsize = cache_maxsize
|
||||
|
||||
def cache_keys(self):
|
||||
"""
|
||||
Current keys of the cache dictionary.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
list of str
|
||||
"""
|
||||
"""Current keys of the cache dictionary."""
|
||||
return list(self._cache.keys())
|
||||
|
||||
def cache_length(self):
|
||||
"""
|
||||
Current length of the cache dictionary.
|
||||
|
||||
Returns
|
||||
-------
|
||||
int
|
||||
"""
|
||||
"""Current length of the cache dictionary."""
|
||||
return len(self._cache)
|
||||
|
||||
@abstractproperty
|
||||
@property
|
||||
@abstractmethod
|
||||
def coordinates(self):
|
||||
"""
|
||||
Halo coordinates.
|
||||
|
||||
Returns
|
||||
-------
|
||||
2-dimensional array
|
||||
"""
|
||||
"""Halo coordinates."""
|
||||
pass
|
||||
|
||||
@abstractproperty
|
||||
@property
|
||||
@abstractmethod
|
||||
def velocities(self):
|
||||
"""
|
||||
Halo peculiar velocities.
|
||||
|
||||
Returns
|
||||
-------
|
||||
2-dimensional array
|
||||
"""
|
||||
"""Halo peculiar velocities."""
|
||||
pass
|
||||
|
||||
@abstractproperty
|
||||
@property
|
||||
@abstractmethod
|
||||
def npart(self):
|
||||
"""
|
||||
Number of particles in a halo.
|
||||
|
||||
Returns
|
||||
-------
|
||||
1-dimensional array
|
||||
"""
|
||||
"""Number of particles in a halo."""
|
||||
pass
|
||||
|
||||
@abstractproperty
|
||||
@property
|
||||
@abstractmethod
|
||||
def totmass(self):
|
||||
"""
|
||||
Total particle mass of a halo.
|
||||
|
||||
Returns
|
||||
-------
|
||||
1-dimensional array
|
||||
"""
|
||||
"""Total particle mass of a halo."""
|
||||
pass
|
||||
|
||||
@abstractproperty
|
||||
@property
|
||||
@abstractmethod
|
||||
def index(self):
|
||||
"""
|
||||
Halo index.
|
||||
|
||||
Returns
|
||||
-------
|
||||
1-dimensional array
|
||||
"""
|
||||
"""Halo index."""
|
||||
pass
|
||||
|
||||
@abstractproperty
|
||||
@property
|
||||
@abstractmethod
|
||||
def lagpatch_coordinates(self):
|
||||
"""
|
||||
Lagrangian patch coordinates.
|
||||
|
||||
Returns
|
||||
-------
|
||||
2-dimensional array
|
||||
"""
|
||||
"""Lagrangian patch coordinates."""
|
||||
pass
|
||||
|
||||
@abstractproperty
|
||||
@property
|
||||
@abstractmethod
|
||||
def lagpatch_radius(self):
|
||||
"""
|
||||
Lagrangian patch radius.
|
||||
|
||||
Returns
|
||||
-------
|
||||
1-dimensional array
|
||||
"""
|
||||
"""Lagrangian patch radius."""
|
||||
pass
|
||||
|
||||
@property
|
||||
def observer_location(self):
|
||||
"""
|
||||
Observer location.
|
||||
|
||||
Returns
|
||||
-------
|
||||
1-dimensional array
|
||||
"""
|
||||
if self._observer_location is None:
|
||||
raise RuntimeError("`observer_location` is not set!")
|
||||
return self._observer_location
|
||||
|
|
@ -371,13 +277,6 @@ class BaseCatalogue(ABC):
|
|||
|
||||
@property
|
||||
def observer_velocity(self):
|
||||
"""
|
||||
Observer velocity.
|
||||
|
||||
Returns
|
||||
-------
|
||||
1-dimensional array
|
||||
"""
|
||||
if self._observer_velocity is None:
|
||||
raise RuntimeError("`observer_velocity` is not set!")
|
||||
return self._observer_velocity
|
||||
|
|
@ -562,24 +461,12 @@ class BaseCatalogue(ABC):
|
|||
self._load_filtered = True
|
||||
|
||||
def clear_cache(self):
|
||||
"""
|
||||
Clear the cache dictionary.
|
||||
|
||||
Returns
|
||||
-------
|
||||
None
|
||||
"""
|
||||
"""Clear the cache dictionary."""
|
||||
self._cache.clear()
|
||||
collect()
|
||||
|
||||
def keys(self):
|
||||
"""
|
||||
Catalogue keys.
|
||||
|
||||
Returns
|
||||
-------
|
||||
list
|
||||
"""
|
||||
"""Catalogue keys."""
|
||||
return self._properties + self._custom_keys
|
||||
|
||||
def pick_fiducial_observer(self, n, rmax):
|
||||
|
|
@ -945,13 +832,7 @@ class CSiBORG2MergerTreeReader:
|
|||
|
||||
@property
|
||||
def simname(self):
|
||||
"""
|
||||
Simulation name.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
"""Simulation name."""
|
||||
if self._simname is None:
|
||||
raise RuntimeError("`simname` is not set!")
|
||||
return self._simname
|
||||
|
|
@ -964,13 +845,7 @@ class CSiBORG2MergerTreeReader:
|
|||
|
||||
@property
|
||||
def nsim(self):
|
||||
"""
|
||||
Simulation IC realisation index.
|
||||
|
||||
Returns
|
||||
-------
|
||||
int
|
||||
"""
|
||||
"""Simulation IC realisation index."""
|
||||
if self._nsim is None:
|
||||
raise RuntimeError("`nsim` is not set!")
|
||||
return self._nsim
|
||||
|
|
@ -983,24 +858,12 @@ class CSiBORG2MergerTreeReader:
|
|||
|
||||
@property
|
||||
def kind(self):
|
||||
"""
|
||||
Simulation kind.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
"""Simulation kind."""
|
||||
return self._simname.split("_")[-1]
|
||||
|
||||
@property
|
||||
def paths(self):
|
||||
"""
|
||||
Paths manager.
|
||||
|
||||
Returns
|
||||
-------
|
||||
py:class:`csiborgtools.read.Paths`
|
||||
"""
|
||||
"""Paths manager."""
|
||||
if self._paths is None:
|
||||
return Paths(**paths_glamdring)
|
||||
return self._paths
|
||||
|
|
@ -1207,13 +1070,7 @@ class CSiBORG2SUBFINDCatalogue(BaseCatalogue):
|
|||
|
||||
@property
|
||||
def kind(self):
|
||||
"""
|
||||
Simulation kind.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
"""Simulation kind."""
|
||||
return self._simname.split("_")[-1]
|
||||
|
||||
def _read_subfind_catalogue(self, kind):
|
||||
|
|
@ -1396,24 +1253,19 @@ class QuijoteCatalogue(BaseCatalogue):
|
|||
|
||||
class MDPL2Catalogue(BaseCatalogue):
|
||||
r"""
|
||||
XXX
|
||||
MDPL2 (FoF) halo catalogue at `z = 0`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
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.
|
||||
"""
|
||||
|
||||
def __init__(self, paths=None, bounds=None, cache_maxsize=64):
|
||||
boxsize = 1000.
|
||||
super().__init__()
|
||||
|
|
|
|||
|
|
@ -15,7 +15,7 @@
|
|||
"""
|
||||
Scripts to read in observation.
|
||||
"""
|
||||
from abc import ABC, abstractproperty
|
||||
from abc import ABC, abstractmethod
|
||||
from os.path import join
|
||||
from warnings import warn
|
||||
|
||||
|
|
@ -191,26 +191,14 @@ class FitsSurvey(ABC):
|
|||
|
||||
@property
|
||||
def file(self):
|
||||
"""
|
||||
The survey FITS file.
|
||||
|
||||
Returns
|
||||
-------
|
||||
file : py:class:`astropy.io.fits.hdu.hdulist.HDUList`
|
||||
"""
|
||||
"""The survey FITS file."""
|
||||
if self._file is None:
|
||||
raise ValueError("`file` is not set!")
|
||||
return self._file
|
||||
|
||||
@property
|
||||
def h(self):
|
||||
"""
|
||||
Little h.
|
||||
|
||||
Returns
|
||||
-------
|
||||
h : float
|
||||
"""
|
||||
"""Little h."""
|
||||
return self._h
|
||||
|
||||
@h.setter
|
||||
|
|
@ -240,15 +228,10 @@ class FitsSurvey(ABC):
|
|||
"""
|
||||
return self._routines
|
||||
|
||||
@abstractproperty
|
||||
@property
|
||||
@abstractmethod
|
||||
def size(self):
|
||||
"""
|
||||
Return the number of samples in the catalogue.
|
||||
|
||||
Returns
|
||||
-------
|
||||
size : int
|
||||
"""
|
||||
"""Number of samples in the catalogue."""
|
||||
pass
|
||||
|
||||
@property
|
||||
|
|
@ -259,18 +242,11 @@ class FitsSurvey(ABC):
|
|||
|
||||
@property
|
||||
def selection_mask(self):
|
||||
"""
|
||||
Selection mask, generated with `fmask` when initialised.
|
||||
|
||||
Returns
|
||||
-------
|
||||
mask : 1-dimensional boolean array
|
||||
"""
|
||||
"""Selection mask, generated with `fmask` when initialised."""
|
||||
return self._selection_mask
|
||||
|
||||
@selection_mask.setter
|
||||
def selection_mask(self, mask):
|
||||
"""Set the selection mask."""
|
||||
if not (isinstance(mask, numpy.ndarray)
|
||||
and mask.ndim == 1
|
||||
and mask.dtype == bool):
|
||||
|
|
@ -280,50 +256,21 @@ class FitsSurvey(ABC):
|
|||
|
||||
@property
|
||||
def fits_keys(self):
|
||||
"""
|
||||
Keys of the FITS file `self.file`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
keys : list of str
|
||||
"""
|
||||
"""Keys of the FITS file `self.file`."""
|
||||
return self.file[1].data.columns.names
|
||||
|
||||
@property
|
||||
def routine_keys(self):
|
||||
"""
|
||||
Routine keys.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
keys : list of str
|
||||
"""
|
||||
"""Routine keys."""
|
||||
return list(self.routines.keys())
|
||||
|
||||
def get_fitsitem(self, key):
|
||||
"""
|
||||
Get a column `key` from the FITS file `self.file`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
key : str
|
||||
FITS key.
|
||||
|
||||
Returns
|
||||
-------
|
||||
col : 1-dimensional array
|
||||
"""
|
||||
"""Get a column `key` from the FITS file `self.file`."""
|
||||
return self.file[1].data[key]
|
||||
|
||||
@property
|
||||
def keys(self):
|
||||
"""
|
||||
Routine and FITS keys.
|
||||
|
||||
Returns
|
||||
-------
|
||||
keys : list of str
|
||||
"""
|
||||
"""Routine and FITS keys."""
|
||||
return self.routine_keys + self.fits_keys + ["INDEX"]
|
||||
|
||||
def make_mask(self, steps):
|
||||
|
|
@ -760,13 +707,7 @@ class BaseSingleObservation(ABC):
|
|||
|
||||
@property
|
||||
def mass(self):
|
||||
"""
|
||||
Total mass estimate in Msun / h.
|
||||
|
||||
Returns
|
||||
-------
|
||||
float
|
||||
"""
|
||||
"""Total mass estimate in Msun / h."""
|
||||
if self._mass is None:
|
||||
raise ValueError("`mass` is not set!")
|
||||
return self._mass
|
||||
|
|
@ -779,30 +720,15 @@ class BaseSingleObservation(ABC):
|
|||
|
||||
def cartesian_pos(self, boxsize):
|
||||
"""
|
||||
Cartesian position of the observation in Mpc / h, assuming the observer
|
||||
is in the centre of the box.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
boxsize : float
|
||||
Box size in Mpc / h.
|
||||
|
||||
Returns
|
||||
-------
|
||||
1-dimensional array of shape (3,)
|
||||
Cartesian position in Mpc / h, assuming the observer is in the centre
|
||||
of the box.
|
||||
"""
|
||||
return radec_to_cartesian(
|
||||
self.spherical_pos.reshape(1, 3)).reshape(-1,) + boxsize / 2
|
||||
|
||||
@property
|
||||
def name(self):
|
||||
"""
|
||||
Observated object name.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
"""Object name."""
|
||||
if self._name is None:
|
||||
raise ValueError("`name` is not set!")
|
||||
return self._name
|
||||
|
|
|
|||
|
|
@ -86,18 +86,7 @@ class Paths:
|
|||
self.aux_cat_dir = aux_cat_dir
|
||||
|
||||
def get_ics(self, simname):
|
||||
"""
|
||||
Get available IC realisation IDs for a given simulation.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
simname : str
|
||||
Simulation name.
|
||||
|
||||
Returns
|
||||
-------
|
||||
ids : 1-dimensional array
|
||||
"""
|
||||
"""Get available IC realisation IDs for a given simulation."""
|
||||
if simname == "csiborg1" or simname == "borg1":
|
||||
files = glob(join(self.csiborg1_srcdir, "chain_*"))
|
||||
files = [int(search(r'chain_(\d+)', f).group(1)) for f in files]
|
||||
|
|
@ -126,20 +115,7 @@ class Paths:
|
|||
return numpy.sort(files)
|
||||
|
||||
def get_snapshots(self, nsim, simname):
|
||||
"""
|
||||
List of available snapshots of simulation.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
nsim : int
|
||||
IC realisation index.
|
||||
simname : str
|
||||
Simulation name.
|
||||
|
||||
Returns
|
||||
-------
|
||||
snapshots : 1-dimensional array
|
||||
"""
|
||||
"""List available snapshots of simulation."""
|
||||
if simname == "csiborg1":
|
||||
snaps = glob(join(self.csiborg1_srcdir, f"chain_{nsim}",
|
||||
"snapshot_*"))
|
||||
|
|
@ -178,22 +154,7 @@ class Paths:
|
|||
return snaps
|
||||
|
||||
def snapshot(self, nsnap, nsim, simname):
|
||||
"""
|
||||
Path to a simulation snapshot.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
nsnap : int
|
||||
Snapshot index. For Quijote, `ICs` indicates the IC snapshot.
|
||||
nsim : int
|
||||
IC realisation index.
|
||||
simname : str
|
||||
Simulation name.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
"""Path to a simulation snapshot."""
|
||||
if simname == "csiborg1":
|
||||
fpath = join(self.csiborg1_srcdir, f"chain_{nsim}",
|
||||
f"snapshot_{str(nsnap).zfill(5)}.hdf5")
|
||||
|
|
@ -217,22 +178,7 @@ class Paths:
|
|||
return fpath
|
||||
|
||||
def snapshot_catalogue(self, nsnap, nsim, simname):
|
||||
"""
|
||||
Path to the halo catalogue of a simulation snapshot.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
nsnap : int
|
||||
Snapshot index.
|
||||
nsim : int
|
||||
IC realisation index.
|
||||
simname : str
|
||||
Simulation name.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
"""Path to the halo catalogue of a simulation snapshot."""
|
||||
if simname == "csiborg1":
|
||||
return join(self.csiborg1_srcdir, f"chain_{nsim}",
|
||||
f"fof_{str(nsnap).zfill(5)}.hdf5")
|
||||
|
|
@ -254,18 +200,7 @@ class Paths:
|
|||
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
|
||||
"""
|
||||
"""Path to an external halo catalogue."""
|
||||
if name == "MDPL2":
|
||||
return join(self.aux_cat_dir, "MDPL2_FOF_125.hdf5")
|
||||
else:
|
||||
|
|
@ -275,17 +210,6 @@ class Paths:
|
|||
"""
|
||||
Path to the Lagrangain patch information of a simulation for halos
|
||||
defined at z = 0.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
nsim : int
|
||||
IC realisation index.
|
||||
simname : str
|
||||
Simulation name.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
if simname == "csiborg1":
|
||||
return join(self.csiborg1_srcdir, f"chain_{nsim}",
|
||||
|
|
@ -306,20 +230,7 @@ class Paths:
|
|||
raise ValueError(f"Unknown simulation name `{simname}`.")
|
||||
|
||||
def trees(self, nsim, simname):
|
||||
"""
|
||||
Path to the halo trees of a simulation snapshot.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
nsim : int
|
||||
IC realisation index.
|
||||
simname : str
|
||||
Simulation name.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
"""Path to the halo trees of a simulation snapshot."""
|
||||
if simname == "csiborg1":
|
||||
raise ValueError("Trees not available for CSiBORG1.")
|
||||
elif simname == "csiborg2_main":
|
||||
|
|
@ -377,20 +288,7 @@ class Paths:
|
|||
return join(fdir, fname)
|
||||
|
||||
def random_mah(self, simname, nsim):
|
||||
"""
|
||||
Path to the files containing MAHs from random simulations.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
simname : str
|
||||
Simulation name.
|
||||
nsim0 : int
|
||||
IC realisation index of the simulation.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
"""Path to the files containing MAHs from random simulations."""
|
||||
fdir = join(self.postdir, "random_mah")
|
||||
try_create_directory(fdir)
|
||||
|
||||
|
|
@ -441,26 +339,7 @@ class Paths:
|
|||
return join(fdir, fname)
|
||||
|
||||
def field(self, kind, MAS, grid, nsim, simname):
|
||||
r"""
|
||||
Path to the files containing the calculated fields in CSiBORG.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
kind : str
|
||||
Field type.
|
||||
MAS : str
|
||||
Mass-assignment scheme.
|
||||
grid : int
|
||||
Grid size.
|
||||
nsim : int
|
||||
IC realisation index.
|
||||
simname : str
|
||||
Simulation name.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
"""Path to the files containing the calculated fields."""
|
||||
if simname == "borg2":
|
||||
return join(self.borg2_dir, f"mcmc_{nsim}.h5")
|
||||
|
||||
|
|
@ -493,22 +372,8 @@ class Paths:
|
|||
|
||||
def observer_peculiar_velocity(self, MAS, grid, nsim, simname):
|
||||
"""
|
||||
Path to the files containing the observer peculiar velocity.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
MAS : str
|
||||
Mass-assignment scheme.
|
||||
grid : int
|
||||
Grid size.
|
||||
nsim : int
|
||||
IC realisation index.
|
||||
simname : str
|
||||
Simulation name.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
Path to the files containing the observer peculiar velocity defined as
|
||||
the velocity of the centre of the box.
|
||||
"""
|
||||
fdir = join(self.postdir, "environment")
|
||||
try_create_directory(fdir)
|
||||
|
|
|
|||
|
|
@ -17,7 +17,7 @@ Classes for reading in snapshots and unifying the snapshot interface. Here
|
|||
should be implemented things such as flipping x- and z-axes, to make sure that
|
||||
observed RA-dec can be mapped into the simulation box.
|
||||
"""
|
||||
from abc import ABC, abstractmethod, abstractproperty
|
||||
from abc import ABC, abstractmethod
|
||||
from os.path import join
|
||||
|
||||
import numpy
|
||||
|
|
@ -62,59 +62,29 @@ class BaseSnapshot(ABC):
|
|||
|
||||
@property
|
||||
def nsim(self):
|
||||
"""
|
||||
Simulation index.
|
||||
|
||||
Returns
|
||||
-------
|
||||
int
|
||||
"""
|
||||
"""Simulation index."""
|
||||
return self._nsim
|
||||
|
||||
@property
|
||||
def nsnap(self):
|
||||
"""
|
||||
Snapshot index.
|
||||
|
||||
Returns
|
||||
-------
|
||||
int
|
||||
"""
|
||||
"""Snapshot index."""
|
||||
return self._nsnap
|
||||
|
||||
@property
|
||||
def simname(self):
|
||||
"""
|
||||
Simulation name.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
"""
|
||||
"""Simulation name."""
|
||||
if self._simname is None:
|
||||
raise ValueError("Simulation name not set.")
|
||||
return self._simname
|
||||
|
||||
@property
|
||||
def boxsize(self):
|
||||
"""
|
||||
Simulation boxsize in `cMpc/h`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
float
|
||||
"""
|
||||
"""Simulation boxsize in `cMpc/h`."""
|
||||
return simname2boxsize(self.simname)
|
||||
|
||||
@property
|
||||
def paths(self):
|
||||
"""
|
||||
Paths manager.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Paths
|
||||
"""
|
||||
"""Paths manager."""
|
||||
if self._paths is None:
|
||||
self._paths = Paths(**paths_glamdring)
|
||||
return self._paths
|
||||
|
|
@ -124,10 +94,6 @@ class BaseSnapshot(ABC):
|
|||
"""
|
||||
Whether to keep the snapshot file open when reading halo particles.
|
||||
This is useful for repeated access to the snapshot.
|
||||
|
||||
Returns
|
||||
-------
|
||||
bool
|
||||
"""
|
||||
return self._keep_snapshot_open
|
||||
|
||||
|
|
@ -136,61 +102,37 @@ class BaseSnapshot(ABC):
|
|||
"""
|
||||
Whether to flip the x- and z-axes to undo the MUSIC bug so that the
|
||||
coordinates are consistent with observations.
|
||||
|
||||
Returns
|
||||
-------
|
||||
bool
|
||||
"""
|
||||
return self._flip_xz
|
||||
|
||||
@abstractproperty
|
||||
@property
|
||||
@abstractmethod
|
||||
def coordinates(self):
|
||||
"""
|
||||
Return the particle coordinates.
|
||||
|
||||
Returns
|
||||
-------
|
||||
coords : 2-dimensional array
|
||||
"""
|
||||
"""Particle coordinates."""
|
||||
pass
|
||||
|
||||
@abstractproperty
|
||||
@property
|
||||
@abstractmethod
|
||||
def velocities(self):
|
||||
"""
|
||||
Return the particle velocities.
|
||||
|
||||
Returns
|
||||
-------
|
||||
vel : 2-dimensional array
|
||||
"""
|
||||
"""Particle velocities."""
|
||||
pass
|
||||
|
||||
@abstractproperty
|
||||
@property
|
||||
@abstractmethod
|
||||
def masses(self):
|
||||
"""
|
||||
Return the particle masses.
|
||||
|
||||
Returns
|
||||
-------
|
||||
mass : 1-dimensional array
|
||||
"""
|
||||
"""Particle masses."""
|
||||
pass
|
||||
|
||||
@abstractproperty
|
||||
@property
|
||||
@abstractmethod
|
||||
def particle_ids(self):
|
||||
"""
|
||||
Return the particle IDs.
|
||||
|
||||
Returns
|
||||
-------
|
||||
ids : 1-dimensional array
|
||||
"""
|
||||
"""Particle IDs."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def halo_coordinates(self, halo_id, is_group):
|
||||
"""
|
||||
Return the halo particle coordinates.
|
||||
Halo particle coordinates.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
|
|
@ -253,19 +195,12 @@ class BaseSnapshot(ABC):
|
|||
|
||||
@abstractmethod
|
||||
def _make_hid2offset(self):
|
||||
"""
|
||||
Private class function to make the halo ID to offset dictionary.
|
||||
"""
|
||||
pass
|
||||
|
||||
def open_snapshot(self):
|
||||
"""
|
||||
Open the snapshot file, particularly used in the context of loading in
|
||||
particles of individual haloes.
|
||||
|
||||
Returns
|
||||
-------
|
||||
h5py.File
|
||||
"""
|
||||
if not self.keep_snapshot_open:
|
||||
# Check if the snapshot path is set
|
||||
|
|
@ -283,10 +218,6 @@ class BaseSnapshot(ABC):
|
|||
def close_snapshot(self):
|
||||
"""
|
||||
Close the snapshot file opened with `open_snapshot`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
None
|
||||
"""
|
||||
if not self.keep_snapshot_open:
|
||||
return
|
||||
|
|
@ -648,24 +579,12 @@ class BaseField(ABC):
|
|||
|
||||
@property
|
||||
def nsim(self):
|
||||
"""
|
||||
Simulation index.
|
||||
|
||||
Returns
|
||||
-------
|
||||
int
|
||||
"""
|
||||
"""Simulation index."""
|
||||
return self._nsim
|
||||
|
||||
@property
|
||||
def paths(self):
|
||||
"""
|
||||
Paths manager.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Paths
|
||||
"""
|
||||
"""Paths manager."""
|
||||
if self._paths is None:
|
||||
self._paths = Paths(**paths_glamdring)
|
||||
return self._paths
|
||||
|
|
@ -675,65 +594,22 @@ class BaseField(ABC):
|
|||
"""
|
||||
Whether to flip the x- and z-axes to undo the MUSIC bug so that the
|
||||
coordinates are consistent with observations.
|
||||
|
||||
Returns
|
||||
-------
|
||||
bool
|
||||
"""
|
||||
return self._flip_xz
|
||||
|
||||
@abstractmethod
|
||||
def density_field(self, MAS, grid):
|
||||
"""
|
||||
Return the pre-computed density field.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
MAS : str
|
||||
Mass assignment scheme.
|
||||
grid : int
|
||||
Grid size.
|
||||
|
||||
Returns
|
||||
-------
|
||||
field : 3-dimensional array
|
||||
"""
|
||||
"""Precomputed density field."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def velocity_field(self, MAS, grid):
|
||||
"""
|
||||
Return the pre-computed velocity field.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
MAS : str
|
||||
Mass assignment scheme.
|
||||
grid : int
|
||||
Grid size.
|
||||
|
||||
Returns
|
||||
-------
|
||||
field : 4-dimensional array
|
||||
"""
|
||||
"""Precomputed velocity field."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def radial_velocity_field(self, MAS, grid):
|
||||
"""
|
||||
Return the pre-computed radial velocity field.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
MAS : str
|
||||
Mass assignment scheme.
|
||||
grid : int
|
||||
Grid size.
|
||||
|
||||
Returns
|
||||
-------
|
||||
field : 3-dimensional array
|
||||
"""
|
||||
"""Precomputed radial velocity field."""
|
||||
pass
|
||||
|
||||
|
||||
|
|
@ -1046,5 +922,4 @@ def is_instance_of_base_snapshot_subclass(obj):
|
|||
Check if `obj` is an instance of a subclass of `BaseSnapshot`.
|
||||
"""
|
||||
return isinstance(obj, BaseSnapshot) and any(
|
||||
issubclass(cls, BaseSnapshot) for cls in obj.__class__.__bases__
|
||||
)
|
||||
issubclass(cls, BaseSnapshot) for cls in obj.__class__.__bases__)
|
||||
|
|
|
|||
|
|
@ -14,6 +14,10 @@
|
|||
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
|
||||
"""
|
||||
Collection of stand-off utility functions used in the scripts.
|
||||
|
||||
Right ascension and declination is always assumed to be in degrees such that
|
||||
RA is in [0, 360) and dec is in [-90, 90]. Galactic coordinates are also in
|
||||
degrees.
|
||||
"""
|
||||
from copy import deepcopy
|
||||
from datetime import datetime
|
||||
|
|
@ -32,23 +36,7 @@ from scipy.stats import multivariate_normal
|
|||
|
||||
@jit(nopython=True, fastmath=True, boundscheck=False)
|
||||
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,)`
|
||||
"""
|
||||
"""Calculate the CM, assuming periodic boundary conditions in a cube."""
|
||||
cm = np.zeros(3, dtype=particle_positions.dtype)
|
||||
totmass = sum(particles_mass)
|
||||
|
||||
|
|
@ -71,21 +59,8 @@ def center_of_mass(particle_positions, particles_mass, boxsize):
|
|||
@jit(nopython=True, fastmath=True, boundscheck=False)
|
||||
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,)`
|
||||
Compute the 3D distance between multiple points and a reference point
|
||||
using periodic boundary conditions.
|
||||
"""
|
||||
npoints = len(points)
|
||||
|
||||
|
|
@ -99,20 +74,7 @@ def periodic_distance(points, reference_point, boxsize):
|
|||
|
||||
@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.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
p1, p2 : 1-dimensional array of shape `(3,)`
|
||||
Points to calculate the distance between.
|
||||
boxsize : float
|
||||
Box size.
|
||||
|
||||
Returns
|
||||
-------
|
||||
float
|
||||
"""
|
||||
"""Compute the 3D distance between two points in a periodic box."""
|
||||
half_box = boxsize / 2
|
||||
|
||||
dist = 0
|
||||
|
|
@ -129,20 +91,7 @@ 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. 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)`
|
||||
"""
|
||||
"""Wrap positions in a periodic box. Overwrites the input array."""
|
||||
for n in range(pos.shape[0]):
|
||||
for i in range(3):
|
||||
if pos[n, i] > boxsize:
|
||||
|
|
@ -156,16 +105,8 @@ def periodic_wrap_grid(pos, boxsize=1):
|
|||
@jit(nopython=True, fastmath=True, boundscheck=False)
|
||||
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
|
||||
Calculate the number of cells in `field` of shape `(nx, ny, nz)` that are
|
||||
non-zero.
|
||||
"""
|
||||
tot = 0
|
||||
imax, jmax, kmax = field.shape
|
||||
|
|
@ -178,23 +119,7 @@ def delta2ncells(field):
|
|||
|
||||
|
||||
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.
|
||||
"""
|
||||
"""Calculate the radial distance, RA and deg."""
|
||||
x, y, z = X[:, 0], X[:, 1], X[:, 2]
|
||||
|
||||
x -= origin[0]
|
||||
|
|
@ -204,6 +129,7 @@ def cartesian_to_radec(X, return_degrees=True, origin=[0., 0., 0.]):
|
|||
dist = np.linalg.norm(X, axis=1)
|
||||
dec = np.arcsin(z / dist)
|
||||
ra = np.arctan2(y, x)
|
||||
# Wrapping to ensure RA is in [0, 2pi) (later converted to degrees).
|
||||
ra[ra < 0] += 2 * np.pi
|
||||
|
||||
if return_degrees:
|
||||
|
|
@ -220,17 +146,8 @@ def cartesian_to_radec(X, return_degrees=True, origin=[0., 0., 0.]):
|
|||
|
||||
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)`
|
||||
Calculate Cartesian coordinates from radial distance, RA and dec
|
||||
`(npoints, 3)`.
|
||||
"""
|
||||
dist, ra, dec = X[:, 0], X[:, 1], X[:, 2]
|
||||
|
||||
|
|
@ -243,19 +160,7 @@ def radec_to_cartesian(X):
|
|||
|
||||
|
||||
def radec_to_galactic(ra, dec):
|
||||
"""
|
||||
Convert right ascension and declination to galactic coordinates (all in
|
||||
degrees.)
|
||||
|
||||
Parameters
|
||||
----------
|
||||
ra, dec : float or 1-dimensional array
|
||||
Right ascension and declination in degrees.
|
||||
|
||||
Returns
|
||||
-------
|
||||
l, b : float or 1-dimensional array
|
||||
"""
|
||||
"""Convert right ascension and declination to galactic coordinates."""
|
||||
c = SkyCoord(ra=ra*u.degree, dec=dec*u.degree, frame='icrs')
|
||||
return c.galactic.l.degree, c.galactic.b.degree
|
||||
|
||||
|
|
@ -263,19 +168,8 @@ def radec_to_galactic(ra, dec):
|
|||
@jit(nopython=True, fastmath=True, boundscheck=False)
|
||||
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
|
||||
Great circle distance between two points, each of shape `(2,)`, specified
|
||||
by RA an dec.
|
||||
"""
|
||||
ra1, dec1 = x1
|
||||
ra2, dec2 = x2
|
||||
|
|
@ -303,19 +197,8 @@ def great_circle_distance(x1, x2, in_degrees=True):
|
|||
def cosine_similarity(x, y):
|
||||
r"""
|
||||
Calculate the cosine similarity between two Cartesian vectors. Defined
|
||||
as :math:`\Sum_{i} x_i y_{i} / (|x| * |y|)`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
x : 1-dimensional array
|
||||
The first vector.
|
||||
y : 1- or 2-dimensional array
|
||||
The second vector. Can be 2-dimensional of shape `(n_samples, 3)`,
|
||||
in which case the calculation is broadcasted.
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : float or 1-dimensional array
|
||||
as :math:`\Sum_{i} x_i y_{i} / (|x| * |y|)`. Optionally, `y` can be a
|
||||
2-dimensional array of shape `(n_samples, 3)`.
|
||||
"""
|
||||
if x.ndim != 1:
|
||||
raise ValueError("`x` must be a 1-dimensional array.")
|
||||
|
|
@ -330,39 +213,19 @@ def cosine_similarity(x, y):
|
|||
|
||||
|
||||
def hms_to_degrees(hours, minutes=None, seconds=None):
|
||||
"""
|
||||
Convert hours, minutes and seconds to degrees.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
hours, minutes, seconds : float
|
||||
|
||||
Returns
|
||||
-------
|
||||
float
|
||||
"""
|
||||
"""Convert hours, minutes and seconds to degrees."""
|
||||
return hours * 15 + (minutes or 0) / 60 * 15 + (seconds or 0) / 3600 * 15
|
||||
|
||||
|
||||
def dms_to_degrees(degrees, arcminutes=None, arcseconds=None):
|
||||
"""
|
||||
Convert degrees, arcminutes and arcseconds to decimal degrees.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
degrees, arcminutes, arcseconds : float
|
||||
|
||||
Returns
|
||||
-------
|
||||
float
|
||||
"""
|
||||
"""Convert degrees, arcminutes and arcseconds to decimal degrees."""
|
||||
return degrees + (arcminutes or 0) / 60 + (arcseconds or 0) / 3600
|
||||
|
||||
|
||||
def real2redshift(pos, vel, observer_location, observer_velocity, boxsize,
|
||||
periodic_wrap=True, make_copy=True):
|
||||
r"""
|
||||
Convert real-space position to redshift space position.
|
||||
Convert real space position to redshift space position.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
|
|
@ -461,20 +324,7 @@ def heliocentric_to_cmb(z_helio, RA, dec, e_z_helio=None):
|
|||
|
||||
@jit(nopython=True, fastmath=True, boundscheck=False)
|
||||
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
|
||||
"""
|
||||
"""Calculate counts of samples in bins."""
|
||||
out = np.full(bin_edges.size - 1, np.nan, dtype=np.float32)
|
||||
for i in range(bin_edges.size - 1):
|
||||
out[i] = np.sum((x >= bin_edges[i]) & (x < bin_edges[i + 1]))
|
||||
|
|
@ -483,22 +333,7 @@ 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
|
||||
Calculate a binned statistic, `statistic` must be a callable `f(x)`.
|
||||
"""
|
||||
out = np.full(left_edges.size, np.nan, dtype=x.dtype)
|
||||
|
||||
|
|
@ -525,15 +360,6 @@ def calculate_acf(data):
|
|||
"""
|
||||
Calculates the autocorrelation of some data. Taken from `epsie` package
|
||||
written by Collin Capano.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
data : 1-dimensional array
|
||||
The data to calculate the autocorrelation of.
|
||||
|
||||
Returns
|
||||
-------
|
||||
acf : 1-dimensional array
|
||||
"""
|
||||
# zero the mean
|
||||
data = data - data.mean()
|
||||
|
|
@ -553,19 +379,9 @@ def calculate_acf(data):
|
|||
def calculate_acl(data):
|
||||
"""
|
||||
Calculate the autocorrelation length of some data. Taken from `epsie`
|
||||
package written by Collin Capano. Algorithm used is from:
|
||||
N. Madras and A.D. Sokal, J. Stat. Phys. 50, 109 (1988).
|
||||
|
||||
Parameters
|
||||
----------
|
||||
data : 1-dimensional array
|
||||
The data to calculate the autocorrelation length of.
|
||||
|
||||
Returns
|
||||
-------
|
||||
acl : int
|
||||
package written by Collin Capano. Algorithm used is from: N. Madras and
|
||||
A.D. Sokal, J. Stat. Phys. 50, 109 (1988).
|
||||
"""
|
||||
# calculate the acf
|
||||
acf = calculate_acf(data)
|
||||
# now the ACL: Following from Sokal, this is estimated
|
||||
# as the first point where M*tau[k] <= k, where
|
||||
|
|
@ -585,20 +401,8 @@ def calculate_acl(data):
|
|||
|
||||
def thin_samples_by_acl(samples):
|
||||
"""
|
||||
Thin MCMC samples by the autocorrelation length of each chain.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
samples : dict
|
||||
Dictionary of samples. Each value is a 2-dimensional array of shape
|
||||
`(nchains, nsamples)`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
thinned_samples : dict
|
||||
Dictionary of thinned samples. Each value is a 1-dimensional array of
|
||||
shape `(n_thinned_samples)`, where the samples are concatenated across
|
||||
the chain.
|
||||
Thin MCMC samples (dictionary with arrays of shape `(nchains, nsamples)`)
|
||||
by the autocorrelation length of each chain and concatenate the chains.
|
||||
"""
|
||||
keys = list(samples.keys())
|
||||
nchains = 1 if samples[keys[0]].ndim == 1 else samples[keys[0]].shape[0]
|
||||
|
|
|
|||
Loading…
Reference in a new issue