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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
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17 changed files with 74697 additions and 37 deletions
17
galomatch/match/__init__.py
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17
galomatch/match/__init__.py
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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
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galomatch/match/correlation.py
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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
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galomatch/match/match.py
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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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