mirror of
https://github.com/Richard-Sti/csiborgtools_public.git
synced 2025-05-13 14:11:11 +00:00
Basic matching (#2)
* add recarray manipulations * add cart to radec * add behav so x can be a list * add import * create empty files * ignore plots file * add planck data * add read_mmain file * add cols_to_structured import * use cols_to_structured * add cols_to_structued * add read_mmain import * add reading planck * add mass conversion * add brute force separation calculation * update nb * rename & int dtype * add func to get csiborg ids * add list to nd array conversion * add utils * rename file * add 2M++ * add read 2mpp * add 2mpp shortcut * add randoms generator * Change range of RA [0, 360] * fix ang wrapping * add code for sphere 2pcf * rm wrapping * optionally load only a few borgs * update nb
This commit is contained in:
Richard Stiskalek
GitHub
parent
d2c1f3294a
commit
53a0629d90
17 changed files with 74697 additions and 37 deletions
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@ -13,4 +13,4 @@
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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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from galomatch import io
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from galomatch import (io, match, utils)
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@ -13,6 +13,8 @@
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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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from .readsim import (get_sim_path, open_particle, open_unbinding,
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read_particle, read_clumpid, read_clumps,
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from .readsim import (get_csiborg_ids, get_sim_path, open_particle,
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open_unbinding, read_particle, read_clumpid, read_clumps,
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read_mmain,
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convert_mass_cols, convert_position_cols, flip_cols)
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from .readobs import (read_planck2015, read_2mpp)
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95
galomatch/io/readobs.py
Normal file
95
galomatch/io/readobs.py
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@ -0,0 +1,95 @@
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# Copyright (C) 2022 Richard Stiskalek
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# This program is free software; you can redistribute it and/or modify it
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# under the terms of the GNU General Public License as published by the
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# Free Software Foundation; either version 3 of the License, or (at your
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# option) any later version.
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#
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# This program is distributed in the hope that it will be useful, but
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# WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
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# Public License for more details.
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#
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# You should have received a copy of the GNU General Public License along
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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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import numpy
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from astropy.io import fits
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from ..utils import (add_columns, cols_to_structured)
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def read_planck2015(fpath, dist_cosmo, max_comdist=None):
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"""
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Read the Planck 2nd Sunyaev-Zeldovich source catalogue [1]. The following
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is performed:
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- removes clusters without a redshift estimate,
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- calculates the comoving distance with the provided cosmology.
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- Converts `MSZ` from units of :math:`1e14 M_\odot` to :math:`M_\odot`
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Parameters
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----------
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fpath : str
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Path to the source catalogue.
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dist_cosmo : `astropy.cosmology` object
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The cosmology to calculate cluster comoving distance from redshift.
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max_comdist : float, optional
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Maximum comoving distance threshold in units of :math:`\mathrm{MPc}`.
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By default `None` and no threshold is applied.
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References
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----------
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[1] https://heasarc.gsfc.nasa.gov/W3Browse/all/plancksz2.html
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Returns
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-------
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out : `astropy.io.fits.FITS_rec`
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The catalogue structured array.
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"""
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data = fits.open(fpath)[1].data
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# Convert FITS to a structured array
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out = numpy.full(data.size, numpy.nan, dtype=data.dtype.descr)
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for name in out.dtype.names:
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out[name] = data[name]
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# Take only clusters with redshifts
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out = out[out["REDSHIFT"] >= 0]
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# Add comoving distance
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dist = dist_cosmo.comoving_distance(out["REDSHIFT"]).value
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out = add_columns(out, dist, "COMDIST")
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# Convert masses
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for p in ("MSZ", "MSZ_ERR_UP", "MSZ_ERR_LOW"):
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out[p] *= 1e14
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# Distance threshold
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if max_comdist is not None:
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out = out[out["COMDIST"] < max_comdist]
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return out
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def read_2mpp(fpath):
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"""
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Read in the 2M++ galaxy redshift catalogue [1], with the catalogue at [2].
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Removes fake galaxies used to fill the zone of avoidance.
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Parameters
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----------
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fpath : str
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File path to the catalogue.
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Returns
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-------
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out : structured array
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The catalogue.
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"""
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# Read the catalogue and select non-fake galaxies
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cat = numpy.genfromtxt(fpath, delimiter="|", )
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cat = cat[cat[:, 12] == 0, :]
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F64 = numpy.float64
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cols = [("RA", F64), ("DEC", F64), ("Ksmag", F64)]
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out = cols_to_structured(cat.shape[0], cols)
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out["RA"] = cat[:, 1] - 180
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out["DEC"] = cat[:, 2]
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out["Ksmag"] = cat[:, 5]
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return out
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@ -19,8 +19,11 @@ import numpy
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from scipy.io import FortranFile
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from os import listdir
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from os.path import (join, isfile)
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from glob import glob
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from tqdm import tqdm
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from ..utils import cols_to_structured
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F16 = numpy.float16
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F32 = numpy.float32
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@ -32,6 +35,37 @@ BOXSIZE = 677.7 / little_h # Mpc. Otherwise positions in [0, 1].
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BOXMASS = 3.749e19 # Msun
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def get_csiborg_ids(srcdir):
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"""
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Get CSiBORG simulation IDs from the list of folders in `srcdir`.
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Assumes that the folders look like `ramses_out_X` and extract the `X`
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integer. Removes `5511` from the list of IDs.
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Parameters
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----------
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srcdir : string
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The folder where CSiBORG simulations are stored.
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Returns
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-------
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ids : 1-dimensional array
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Array of CSiBORG simulation IDs.
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"""
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files = glob(join(srcdir, "ramses_out*"))
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# Select only file names
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files = [f.split("/")[-1] for f in files]
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# Remove files with inverted ICs
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files = [f for f in files if "_inv" not in f]
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# Remove the filename with _old
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files = [f for f in files if "OLD" not in f]
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ids = [int(f.split("_")[-1]) for f in files]
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try:
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ids.remove(5511)
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except ValueError:
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pass
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return numpy.sort(ids)
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def get_sim_path(n, fname="ramses_out_{}", srcdir="/mnt/extraspace/hdesmond"):
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"""
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Get a path to a CSiBORG simulation.
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@ -309,15 +343,45 @@ def read_clumps(n, simpath):
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("peak_x", F64), ("peak_y", F64), ("peak_z", F64),
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("rho-", F64), ("rho+", F64), ("rho_av", F64),
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("mass_cl", F64), ("relevance", F64)]
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# Write to a structured array
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dtype = {"names": [col[0] for col in cols],
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"formats": [col[1] for col in cols]}
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out = numpy.full(arr.shape[0], numpy.nan, dtype=dtype)
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for i, name in enumerate(dtype["names"]):
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out = cols_to_structured(arr.shape[0], cols)
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for i, name in enumerate(out.dtype.names):
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out[name] = arr[:, i]
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return out
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def read_mmain(n, srcdir, fname="Mmain_{}.npy"):
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"""
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Read `mmain` numpy arrays of central halos whose mass contains their
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substracture contribution.
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Parameters
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----------
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n : int
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The index of the initial conditions (IC) realisation.
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srcdir : str
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The path to the folder containing the files.
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fname : str, optional
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The file name convention. By default `Mmain_{}.npy`, where the
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substituted value is `n`.
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Returns
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-------
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out : structured array
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Array with the central halo information.
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"""
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fpath = join(srcdir, fname.format(n))
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arr = numpy.load(fpath)
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cols = [("index", I64), ("peak_x", F64), ("peak_y", F64),
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("peak_z", F64), ("mass_cl", F64), ("sub_frac", F64)]
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out = cols_to_structured(arr.shape[0], cols)
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for i, name in enumerate(out.dtype.names):
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out[name] = arr[:, i]
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return out
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def convert_mass_cols(arr, cols):
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"""
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Convert mass columns from box units to :math:`M_{odot}`. `arr` is passed by
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17
galomatch/match/__init__.py
Normal file
17
galomatch/match/__init__.py
Normal file
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@ -0,0 +1,17 @@
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# Copyright (C) 2022 Richard Stiskalek
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# This program is free software; you can redistribute it and/or modify it
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# under the terms of the GNU General Public License as published by the
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# Free Software Foundation; either version 3 of the License, or (at your
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# option) any later version.
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#
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# This program is distributed in the hope that it will be useful, but
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# WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
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# Public License for more details.
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#
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# You should have received a copy of the GNU General Public License along
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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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from .match import brute_spatial_separation
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from .correlation import (get_randoms_sphere, angular_tpcf)
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131
galomatch/match/correlation.py
Normal file
131
galomatch/match/correlation.py
Normal file
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@ -0,0 +1,131 @@
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# Copyright (C) 2022 Richard Stiskalek
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# This program is free software; you can redistribute it and/or modify it
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# under the terms of the GNU General Public License as published by the
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# Free Software Foundation; either version 3 of the License, or (at your
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# option) any later version.
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#
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# This program is distributed in the hope that it will be useful, but
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# WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
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# Public License for more details.
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#
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# You should have received a copy of the GNU General Public License along
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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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import numpy
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from Corrfunc.mocks import DDtheta_mocks
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from Corrfunc.utils import convert_3d_counts_to_cf
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from warnings import warn
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def get_randoms_sphere(N, seed=42):
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"""
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Generate random points on a sphere.
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Parameters
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----------
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N : int
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Number of points.
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seed : int
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Random seed.
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Returns
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-------
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ra : 1-dimensional array
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Right ascension in :math:`[0, 360)` degrees.
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dec : 1-dimensional array
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Declination in :math:`[-90, 90]` degrees.
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"""
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gen = numpy.random.default_rng(seed)
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ra = gen.random(N) * 360
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dec = numpy.rad2deg(numpy.arcsin(2 * (gen.random(N) - 0.5)))
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return ra, dec
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def wrapRA(ra, degrees=True):
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"""
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Wrap the right ascension from :math:`[-180, 180)` to :math`[0, 360)`
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degrees or equivalently if `degrees=False` in radians.
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Paramaters
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----------
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ra : 1-dimensional array
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Right ascension values.
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degrees : float, optional
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Whether the right ascension is in degrees.
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Returns
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-------
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ra : 1-dimensional array
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Wrapped around right ascension.
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"""
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mask = ra < 0
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if numpy.sum(mask) == 0:
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warn("No negative right ascension found.")
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ra[mask] += 360 if degrees else 2 * numpy.pi
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return ra
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def sphere_angular_tpcf(bins, RA1, DEC1, RA2=None, DEC2=None, nthreads=1,
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Nmult=5, seed1=42, seed2=666):
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"""
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Calculate the angular two-point correlation function. The coordinates must
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be provided in degrees. With the right ascension and degrees being
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in range of :math:`[-180, 180]` and :math:`[-90, 90]` degrees, respectively.
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If `RA2` and `DEC2` are provided cross-correlates the first data set with
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the second. Creates a uniformly sampled randoms on the surface of a sphere
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of size `Nmult` times the corresponding number of data points. Uses the
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Landy-Szalay estimator.
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Parameters
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----------
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bins : 1-dimensional array
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Angular bins to calculate the angular twop-point correlation function.
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RA1 : 1-dimensional array
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Right ascension of the 1st data set, in degrees.
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DEC1 : 1-dimensional array
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Declination of the 1st data set, in degrees.
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RA2 : 1-dimensional array, optional
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Right ascension of the 2nd data set, in degrees.
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DEC2 : 1-dimensional array, optional
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Declination of the 2nd data set, in degrees.
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nthreads : int, optional
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Number of threads, by default 1.
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Nmult : int, optional
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Relative randoms size with respect to the data set. By default 5.
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seed1 : int, optional
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Seed to generate the first set of randoms.
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seed2 : int, optional
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Seed to generate the second set of randoms.
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Returns
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-------
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cf : 1-dimensional array
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The angular 2-point correlation function.
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"""
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# If not provided calculate autocorrelation
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if RA2 is None:
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RA2 = RA1
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DEC2 = DEC1
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# Get the array sizes
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ND1 = RA1.size
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ND2 = RA2.size
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NR1 = ND1 * Nmult
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NR2 = ND2 * Nmult
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# Generate randoms. Note that these are over the sphere!
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randRA1, randDEC1 = get_randoms_sphere(NR1, seed1)
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randRA2, randDEC2 = get_randoms_sphere(NR2, seed2)
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# Wrap RA
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RA1 = wrapRA(numpy.copy(RA1))
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RA2 = wrapRA(numpy.copy(RA2))
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# Calculate pairs
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D1D2 = DDtheta_mocks(0, nthreads, bins, RA1, DEC1, RA2=RA2, DEC2=DEC2)
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D1R2 = DDtheta_mocks(0, nthreads, bins, RA1, DEC1,
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RA2=randRA2, DEC2=randDEC2)
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D2R1 = DDtheta_mocks(0, nthreads, bins, RA2, DEC2,
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RA2=randRA1, DEC2=randDEC1)
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R1R2 = DDtheta_mocks(0, nthreads, bins, randRA1, randDEC1,
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RA2=randRA2, DEC2=randDEC2)
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# Convert to the CF
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return convert_3d_counts_to_cf(ND1, ND2, NR1, NR2, D1D2, D1R2, D2R1, R1R2)
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67
galomatch/match/match.py
Normal file
67
galomatch/match/match.py
Normal file
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@ -0,0 +1,67 @@
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# Copyright (C) 2022 Richard Stiskalek
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# This program is free software; you can redistribute it and/or modify it
|
||||
# under the terms of the GNU General Public License as published by the
|
||||
# Free Software Foundation; either version 3 of the License, or (at your
|
||||
# option) any later version.
|
||||
#
|
||||
# This program is distributed in the hope that it will be useful, but
|
||||
# WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
|
||||
# Public License for more details.
|
||||
#
|
||||
# You should have received a copy of the GNU General Public License along
|
||||
# with this program; if not, write to the Free Software Foundation, Inc.,
|
||||
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
|
||||
|
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import numpy
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from tqdm import tqdm
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from astropy.coordinates import SkyCoord
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def brute_spatial_separation(c1, c2, angular=False, N=None, verbose=False):
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"""
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Calculate for each point in `c1` the `N` closest points in `c2`.
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Parameters
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----------
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c1 : `astropy.coordinates.SkyCoord`
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Coordinates of the first set of points.
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c2 : `astropy.coordinates.SkyCoord`
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Coordinates of the second set of points.
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angular : bool, optional
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Whether to calculate angular separation or 3D separation. By default
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`False` and 3D separation is calculated.
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N : int, optional
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Number of closest points in `c2` to each object in `c1` to return.
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verbose : bool, optional
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Verbosity flag. By default `False`.
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Returns
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-------
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sep : 1-dimensional array
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Separation of each object in `c1` to `N` closest objects in `c2`. The
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array shape is `(c1.size, N)`. Separation is in units of `c1`.
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indxs : 1-dimensional array
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Indexes of the closest objects in `c2` for each object in `c1`. The
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array shape is `(c1.size, N)`.
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"""
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if not (isinstance(c1, SkyCoord) and isinstance(c2, SkyCoord)):
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raise TypeError("`c1` & `c2` must be `astropy.coordinates.SkyCoord`.")
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N1 = c1.size
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N2 = c2.size if N is None else N
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# Pre-allocate arrays
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sep = numpy.full((N1, N2), numpy.nan)
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indxs = numpy.full((N1, N2), numpy.nan, dtype=int)
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iters = tqdm(range(N1)) if verbose else range(N1)
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for i in iters:
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if angular:
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dist = c1[i].separation(c2).value
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else:
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dist = c1[i].separation_3d(c2).value
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# Sort the distances
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sort = numpy.argsort(dist)[:N2]
|
||||
indxs[i, :] = sort
|
||||
sep[i, :] = dist[sort]
|
||||
|
||||
return sep, indxs
|
||||
18
galomatch/utils/__init__.py
Normal file
18
galomatch/utils/__init__.py
Normal file
|
|
@ -0,0 +1,18 @@
|
|||
# Copyright (C) 2022 Richard Stiskalek
|
||||
# This program is free software; you can redistribute it and/or modify it
|
||||
# under the terms of the GNU General Public License as published by the
|
||||
# Free Software Foundation; either version 3 of the License, or (at your
|
||||
# option) any later version.
|
||||
#
|
||||
# This program is distributed in the hope that it will be useful, but
|
||||
# WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
|
||||
# Public License for more details.
|
||||
#
|
||||
# You should have received a copy of the GNU General Public License along
|
||||
# with this program; if not, write to the Free Software Foundation, Inc.,
|
||||
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
|
||||
|
||||
from .recarray_manip import (cols_to_structured, add_columns, rm_columns,
|
||||
list_to_ndarray)
|
||||
from .transforms import cartesian_to_radec
|
||||
155
galomatch/utils/recarray_manip.py
Normal file
155
galomatch/utils/recarray_manip.py
Normal file
|
|
@ -0,0 +1,155 @@
|
|||
# Copyright (C) 2022 Richard Stiskalek
|
||||
# This program is free software; you can redistribute it and/or modify it
|
||||
# under the terms of the GNU General Public License as published by the
|
||||
# Free Software Foundation; either version 3 of the License, or (at your
|
||||
# option) any later version.
|
||||
#
|
||||
# This program is distributed in the hope that it will be useful, but
|
||||
# WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
|
||||
# Public License for more details.
|
||||
#
|
||||
# You should have received a copy of the GNU General Public License along
|
||||
# with this program; if not, write to the Free Software Foundation, Inc.,
|
||||
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
|
||||
|
||||
"""Utilility functions for manipulation structured arrays."""
|
||||
|
||||
import numpy
|
||||
|
||||
def cols_to_structured(N, cols):
|
||||
"""
|
||||
Allocate a structured array from `cols`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
N : int
|
||||
Structured array size.
|
||||
cols: list of tuples
|
||||
Column names and dtypes. Each tuple must written as `(name, dtype)`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : structured array
|
||||
Initialised structured array.
|
||||
"""
|
||||
if not isinstance(cols, list) and all(isinstance(c, tuple) for c in cols):
|
||||
raise TypeError("`cols` must be a list of tuples.")
|
||||
|
||||
dtype = {"names": [col[0] for col in cols],
|
||||
"formats": [col[1] for col in cols]}
|
||||
return numpy.full(N, numpy.nan, dtype=dtype)
|
||||
|
||||
|
||||
def add_columns(arr, X, cols):
|
||||
"""
|
||||
Add new columns to a record array `arr`. Creates a new array.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
arr : record array
|
||||
The record array to add columns to.
|
||||
X : (list of) 1-dimensional array(s) or 2-dimensional array
|
||||
Columns to be added.
|
||||
cols : str or list of str
|
||||
Column names to be added.
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : record array
|
||||
The new record array with added values.
|
||||
"""
|
||||
# Make sure cols is a list of str and X a 2D array
|
||||
cols = [cols] if isinstance(cols, str) else cols
|
||||
if isinstance(X, numpy.ndarray) and X.ndim == 1:
|
||||
X = X.reshape(-1, 1)
|
||||
if isinstance(X, list) and all(x.ndim == 1 for x in X):
|
||||
X = numpy.vstack([X]).T
|
||||
if len(cols) != X.shape[1]:
|
||||
raise ValueError("Number of columns of `X` does not match `cols`.")
|
||||
if arr.size != X.shape[0]:
|
||||
raise ValueError("Number of rows of `X` does not match size of `arr`.")
|
||||
|
||||
# Get the new data types
|
||||
dtype = arr.dtype.descr
|
||||
for i, col in enumerate(cols):
|
||||
dtype.append((col, X[i, :].dtype.descr[0][1]))
|
||||
|
||||
# Fill in the old array
|
||||
out = numpy.full(arr.size, numpy.nan, dtype=dtype)
|
||||
for col in arr.dtype.names:
|
||||
out[col] = arr[col]
|
||||
for i, col in enumerate(cols):
|
||||
out[col] = X[:, i]
|
||||
|
||||
return out
|
||||
|
||||
def rm_columns(arr, cols):
|
||||
"""
|
||||
Remove columns `cols` from a record array `arr`. Creates a new array.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
arr : record array
|
||||
The record array to remove columns from.
|
||||
cols : str or list of str
|
||||
Column names to be removed.
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : record array
|
||||
Record array with removed columns.
|
||||
"""
|
||||
# Check columns we wish to delete are in the array
|
||||
cols = [cols] if isinstance(cols, str) else cols
|
||||
for col in cols:
|
||||
if col not in arr.dtype.names:
|
||||
raise ValueError("Column `{}` not in `arr`.".format(col))
|
||||
|
||||
# Get a new dtype without the cols to be deleted
|
||||
new_dtype = []
|
||||
for dtype, name in zip(arr.dtype.descr, arr.dtype.names):
|
||||
if name not in cols:
|
||||
new_dtype.append(dtype)
|
||||
|
||||
# Allocate a new array and fill it in.
|
||||
out = numpy.full(arr.size, numpy.nan, new_dtype)
|
||||
for name in out.dtype.names:
|
||||
out[name] = arr[name]
|
||||
|
||||
return out
|
||||
|
||||
|
||||
def list_to_ndarray(arrs, cols):
|
||||
"""
|
||||
Convert a list of structured arrays of CSiBORG simulation catalogues to
|
||||
an 3-dimensional array.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
arrs : list of structured arrays
|
||||
List of CSiBORG catalogues.
|
||||
cols : str or list of str
|
||||
Columns to be extracted from the CSiBORG catalogues.
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : 3-dimensional array
|
||||
Catalogue array of shape `(n_realisations, n_samples, n_cols)`, where
|
||||
`n_samples` is the maximum number of samples over the CSiBORG
|
||||
catalogues.
|
||||
"""
|
||||
if not isinstance(arrs, list):
|
||||
raise TypeError("`arrs` must be a list of structured arrays.")
|
||||
cols = [cols] if isinstance(cols, str) else cols
|
||||
|
||||
Narr = len(arrs)
|
||||
Nobj_max = max([arr.size for arr in arrs])
|
||||
Ncol = len(cols)
|
||||
# Preallocate the array and fill it
|
||||
out = numpy.full((Narr, Nobj_max, Ncol), numpy.nan)
|
||||
for i in range(Narr):
|
||||
Nobj = arrs[i].size
|
||||
for j in range(Ncol):
|
||||
out[i, :Nobj, j] = arrs[i][cols[j]]
|
||||
return out
|
||||
56
galomatch/utils/transforms.py
Normal file
56
galomatch/utils/transforms.py
Normal file
|
|
@ -0,0 +1,56 @@
|
|||
# Copyright (C) 2022 Richard Stiskalek
|
||||
# This program is free software; you can redistribute it and/or modify it
|
||||
# under the terms of the GNU General Public License as published by the
|
||||
# Free Software Foundation; either version 3 of the License, or (at your
|
||||
# option) any later version.
|
||||
#
|
||||
# This program is distributed in the hope that it will be useful, but
|
||||
# WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
|
||||
# Public License for more details.
|
||||
#
|
||||
# You should have received a copy of the GNU General Public License along
|
||||
# with this program; if not, write to the Free Software Foundation, Inc.,
|
||||
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
|
||||
|
||||
import numpy
|
||||
|
||||
|
||||
def cartesian_to_radec(arr, xpar="peak_x", ypar="peak_y", zpar="peak_z", degrees=True):
|
||||
"""
|
||||
Extract `x`, `y`, and `z` coordinates from a record array `arr` and
|
||||
calculate their spherical coordinates representation.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
arr : record array
|
||||
Record array with the Cartesian coordinates.
|
||||
xpar : str, optional
|
||||
Name of the x coordinate in the record array.
|
||||
ypar : str, optional
|
||||
Name of the y coordinate in the record array.
|
||||
zpar : str, optional
|
||||
Name of the z coordinate in the record array.
|
||||
degrees : bool, optional
|
||||
Whether to return angles in degrees. By default `True`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
dist : 1-dimensional array
|
||||
Radial distance.
|
||||
ra : 1-dimensional array
|
||||
Right ascension.
|
||||
dec : 1-dimensional array
|
||||
Declination.
|
||||
"""
|
||||
x, y, z = arr[xpar], arr[ypar], arr[zpar]
|
||||
|
||||
dist = numpy.sqrt(x**2 + y**2 + z**2)
|
||||
dec = numpy.arcsin(z / dist)
|
||||
ra = numpy.arctan2(y, x)
|
||||
|
||||
if degrees:
|
||||
dec = numpy.rad2deg(dec)
|
||||
ra = numpy.rad2deg(ra)
|
||||
|
||||
return dist, ra, dec
|
||||
Loading…
Add table
Add a link
Reference in a new issue