associationUtils.py

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# This file is part of pipe_tasks.
#
# Developed for the LSST Data Management System.
# This product includes software developed by the LSST Project
# (https://www.lsst.org).
# See the COPYRIGHT file at the top-level directory of this distribution
# for details of code ownership.
#
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"""Utilities for interfacing with hpgeom. Originally implemented in
http://github.com/LSSTDESC/dia_pipe and then translated to hpgeom.
"""
 
__all__ = ["toIndex", "toRaDec", "eq2xyz", "eq2xyzVec", "convert_spherical",
           "convert_spherical_array", "query_disc"]
 
import hpgeom as hpg
import numpy as np
 
 
def toIndex(nside, ra, dec):
    """Return healpix index given ra, dec in degrees
 
    Parameters
    ----------
    nside : `int`
        Power of 2 nside healpix resolution.
    ra : `float`
        RA in degrees.
    dec : `float`
        Declination in degrees
 
    Returns
    -------
    index : `int`
        Unique healpix pixel ID containing point RA, DEC at resolution nside.
    """
    return hpg.angle_to_pixel(nside, ra, dec, nest=False)
 
 
def toRaDec(nside, index):
    """Convert from healpix index to ra,dec in degrees
 
    Parameters
    ----------
    nside : `int`
        Resolution of healpixel "grid".
    index : `int`
        Index of the healpix pixel we want to find the location of.
 
    Returns
    -------
    pos : `numpy.ndarray`, (2,)
        RA and DEC of healpix pixel location in degrees.
    """
    ra, dec = hpg.pixel_to_angle(nside, index, nest=False)
    return np.dstack((ra, dec))[0]
 
 
def eq2xyz(ra, dec):
    """Convert from equatorial ra,dec in degrees to x,y,z on unit sphere.
 
    Parameters
    ----------
    ra : `float`
        RA in degrees.
    dec : `float`
        Declination in degrees
 
    Returns
    -------
    xyz : `numpy.ndarray`, (3,)
        Float xyz positions on the unit sphere.
    """
    phi = np.deg2rad(ra)
    theta = np.pi/2 - np.deg2rad(dec)
    sintheta = np.sin(theta)
    x = sintheta*np.cos(phi)
    y = sintheta*np.sin(phi)
    z = np.cos(theta)
    return np.array([x, y, z])
 
 
def eq2xyzVec(ra, dec):
    """Convert equatorial ra,dec in degrees to x,y,z on the unit sphere
    parameters
 
    Vectorized version of ``eq2xyz``
 
    Parameters
    ----------
    ra : array_like, (N,)
        Array of RA in degrees.
    dec : array_like, (N,)
        Declination in degrees
 
    Returns
    -------
    vec : `numpy.ndarray`, (N,3)
        Array of unitsphere 3-vectors.
    """
    ra = np.array(ra, dtype='f8', ndmin=1, copy=False)
    dec = np.array(dec, dtype='f8', ndmin=1, copy=False)
    if ra.size != dec.size:
        raise ValueError("ra,dec not same size: %s,%s" % (ra.size, dec.size))
 
    vec = eq2xyz(ra, dec)
 
    return vec
 
 
def convert_spherical(ra, dec):
    """Convert from ra,dec to spherical coordinates.
 
    Used in query_disc.
 
    Parameters
    ----------
    ra : `float`
        RA in radians.
    dec : `float`
        Declination in radians
    """
    return np.dstack([np.cos(dec*np.pi/180)*np.cos(ra*np.pi/180),
                      np.cos(dec*np.pi/180)*np.sin(ra*np.pi/180),
                      np.sin(dec*np.pi/180)])[0]
 
 
def convert_spherical_array(array):
    """Convert from and a array ra,dec to spherical coordinates.
 
    Used in query_disc
 
    Parameters
    ----------
    array : `numpy.ndarray`, (N, 2)
        (N, 2) Array of RA, DEC values.
 
    Returns
    -------
    vecs : `numpy.ndarray`, (N, 3)
        Vectors on the unit sphere
    """
    ra = array[:, 0]
    dec = array[:, 1]
    return convert_spherical(ra, dec)
 
 
def query_disc(nside, ra, dec, max_rad, min_rad=0):
    """Get the list of healpix indices within max_rad, min_rad given in radians
    around ra,dec given in degrees
 
    Parameters
    ----------
    nside : `int`
        Resolution of the healpixels to search/return.
    ra : `float`
        RA in degrees.
    dec : `float`
        Declination in degrees
    max_rad : `float`
        Max distance in radians to search nearby healpixels.
    min_rad : `float`, optional
        Minimum distance in radians to search healpixels. Default = 0.
    """
    ra = np.atleast_1d(ra)
    dec = np.atleast_1d(dec)
 
    max_rad_deg = np.rad2deg(max_rad)
 
    pixels = np.unique(
        [hpg.query_circle(nside, a, b, max_rad_deg, nest=False)
         for (a, b) in zip(ra, dec)])
 
    if min_rad > 0 and len(pixels) > 0:
        vec0 = convert_spherical(ra, dec)
        min_rad2 = min_rad**2
        vecs = convert_spherical_array(toRaDec(nside, pixels))
        dsq = np.sum((vecs - vec0)**2, axis=1)
        match = dsq > min_rad2
        pixels = pixels[match]
 
    return pixels