coord_utils#
This module contains utility functions for dealing with sky coordinates, sky distances, and healpix grids.
Module Contents#
Functions#
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Uniform distribution of points around location. |
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Upgrade (superresolve) a healpix into finer ones |
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Compute the distance between two angular positions given in degrees |
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Get the ra, dec corresponding to centers of the healpixels with given IDs |
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Get the maximum nside (finest healpix grid) whose centers along |
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Get the indices of corners of a set of finer healpixes making up |
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Get the boolean mask for whether points are inside provided bounds |
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Create an astropy.coordinates.SkyCoord object |
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Get the NSIDE corresponding to the number of sub-healpixels |
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Match gridpoints to a catalog based on distance threshold |
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Uniformly sample points around a zero coordinate on the celestial sphere |
Attributes#
- coord_utils.uniform_around(centre, area, size)[source]#
Uniform distribution of points around location. Draws randomly distributed points from a circular region of the given area around the centre point.
Parameters#
- centre~astropy.coordinates.SkyCoord
Centre of the sampling region.
- area~astropy.units.Quantity
Area of the sampling region as a ~astropy.units.Quantity in units of solid angle.
- sizeint
Number of points to draw.
Returns#
- coords~astropy.coordinates.SkyCoord
Randomly distributed points around the centre. The coordinates are returned in the same frame as the input.
Examples#
See User Documentation. Modified from the skypy implementation: skypyproject/skypy
- coord_utils.upgrade_healpix(pix_id, nested, nside_in, nside_out)[source]#
Upgrade (superresolve) a healpix into finer ones
Parameters#
- pix_idint
coarse healpix ID to upgrade
- nestedbool
whether pix_id is given in NESTED scheme
- nside_inint
NSIDE of pix_id
- nside_outint
desired NSIDE of finer healpix
Returns#
- np.array
the upgraded healpix IDs in the NESTED scheme
- coord_utils.get_distance(ra_i, dec_i, ra_f, dec_f)[source]#
Compute the distance between two angular positions given in degrees
- coord_utils.get_healpix_centers(pix_id, nside, nest)[source]#
Get the ra, dec corresponding to centers of the healpixels with given IDs
Parameters#
- pix_idint or array-like
IDs of healpixels to evaluate centers. Must be in NESTED scheme
- nside_inint
NSIDE of pix_id
- coord_utils.get_padded_nside(padding, nside_in)[source]#
Get the maximum nside (finest healpix grid) whose centers along the boundary of the input nside are located sufficiently far away from the boundaries
Parameters#
- paddingfloat
Padding in arcmin
- nside_inint
NSIDE of the healpix to upgrade
- coord_utils.get_corners(n_pix, counterclockwise=False)[source]#
Get the indices of corners of a set of finer healpixes making up a big healpix, e.g. the output of upgrade_healpix
Parameters#
- n_pixint
Number of finer healpixes
- clockwise: bool
Ordering of the corners are counterclockwise. If False, ordering follows healpix ordering. Default: False
Returns#
- list
Indices of four corners that can be used to slice a list of RA, Dec
- coord_utils.is_inside(ra, dec, ra_bounds, dec_bounds)[source]#
Get the boolean mask for whether points are inside provided bounds
Parameters#
- ranp.ndarray
RA of candidate positions, of shape [N,]
- decnp.ndarray
Dec of candidate positions, of shape [N,]
- ra_boundsnp.ndarray
RA of bounds, of shape [4,]
- dec_boundsnp.ndarray
Dec of bounds, of shape [4,]
Returns#
- np.ndarray
Boolean mask over ra, dec, whose elements are true if corresponding points are inside bounds
- coord_utils.get_skycoord(ra, dec)[source]#
Create an astropy.coordinates.SkyCoord object
Parameters#
- ranp.array
RA in deg
- decnp.array
dec in deg
- coord_utils.get_target_nside(n_pix, nside_in=2**5)[source]#
Get the NSIDE corresponding to the number of sub-healpixels
Parameters#
- n_pixint
desired number of pixels
- nside_inint
input NSIDE to subsample
- coord_utils.match(ra_grid, dec_grid, ra_cat, dec_cat, threshold)[source]#
Match gridpoints to a catalog based on distance threshold
Parameters#
ra_grid : np.array dec_grid : np.array ra_cat : np.array dec_cat : np.array gridpoints : astropy.SkyCoord instance threshold : float
matching distance threshold in deg
- extra_constraintnp.array of type bool
another set of constraints, aside from separation constraint. Ordering must be based on gridpoints
Returns#
- sep_constraintnp.array of shape same as ra/dec_grid and type bool
whether each gridpoint was matched to a catalog within sep limit
- passing_i_catnp.array of length same as ra_grid[sep_constraint]
catalog idx (value) corresponding to each successfully matched gridpoint (position)
- passing_distnp.array of shape same as passing_i_cat
distance (value) corresponding to each successfully matched gridpoint (position)