mirror of
https://github.com/Richard-Sti/csiborgtools.git
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68 lines
2.5 KiB
Python
68 lines
2.5 KiB
Python
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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 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]
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indxs[i, :] = sort
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sep[i, :] = dist[sort]
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return sep, indxs
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