# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Celestial coordinate utility functions.
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np
from astropy.coordinates.angle_utilities import angular_separation
__all__ = ["minimum_separation", "pair_correlation"]
[docs]def minimum_separation(lon1, lat1, lon2, lat2):
"""Compute minimum distance of each (lon1, lat1) to any (lon2, lat2).
Parameters
----------
lon1, lat1 : array_like
Primary coordinates of interest
lon2, lat2 : array_like
Counterpart coordinate array
unit : {'deg', 'rad'}
Units of input and output coordinates
Returns
-------
theta_min : array
Minimum distance
"""
lon1 = np.asanyarray(lon1)
lat1 = np.asanyarray(lat1)
theta_min = np.empty_like(lon1, dtype=np.float64)
for i1 in range(lon1.size):
thetas = angular_separation(lon1[i1], lat1[i1], lon2, lat2)
theta_min[i1] = thetas.min()
return theta_min
[docs]def pair_correlation(lon, lat, theta_bins):
"""Compute pair correlation function for points on the sphere.
Parameters
----------
lon, lat : array_like
Coordinate arrays
theta_bins : array_like
Array defining the ``theta`` binning.
``theta`` is the angular offset between positions.
unit : {'deg', 'rad'}
Units of input and output coordinates
Returns
-------
counts : array
Array of point separations per ``theta`` bin.
"""
# TODO: Implement speedups:
# - use radians
# - avoid processing each pair twice (distance a to b and b to a)
counts = np.zeros(shape=len(theta_bins) - 1, dtype=int)
# If there are many points this should have acceptable performance
# because the inner loop is in np.histogram, not in Python
for ii in range(len(lon)):
theta = angular_separation(lon[ii], lat[ii], lon, lat)
hist = np.histogram(theta, theta_bins)[0]
counts += hist
return counts