Multi-Order HEALPix Maps (ligo.skymap.moc)

Support for HEALPix UNIQ pixel indexing [1] and multi-order coverage (MOC) maps [2].

References

[1]

Reinecke & Hivon, 2015. “Efficient data structures for masks on 2D grids.” AA 580, A132. doi:10.1051/0004-6361/201526549

[2]

Boch et al., 2014. “MOC - HEALPix Multi-Order Coverage map.” IVOA Recommendation <http://ivoa.net/documents/MOC/>.

ligo.skymap.moc.nest2uniq(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])

Convert a pixel index from NESTED to NUNIQ ordering.

Parameters:

ordernumpy.ndarray

HEALPix resolution order, the logarithm base 2 of nside

ipixnumpy.ndarray

NESTED pixel index

Returns:

uniqnumpy.ndarray

NUNIQ pixel index

ligo.skymap.moc.uniq2nest(x, [out1, out2, ]/, [out=(None, None), ]*, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])

Convert a pixel index from NUNIQ to NESTED ordering.

Parameters:

uniqnumpy.ndarray

NUNIQ pixel index

Returns:

ordernumpy.ndarray

HEALPix resolution order (logarithm base 2 of nside)

ipixnumpy.ndarray

NESTED pixel index

ligo.skymap.moc.uniq2order(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])

Determine the HEALPix resolution order of a HEALPix NESTED index.

Parameters:

uniqnumpy.ndarray

NUNIQ pixel index

Returns:

ordernumpy.ndarray

HEALPix resolution order, the logarithm base 2 of nside

ligo.skymap.moc.uniq2pixarea(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])

Determine the area of a HEALPix NESTED index.

Parameters:

uniqnumpy.ndarray

NUNIQ pixel index

Returns:

areanumpy.ndarray

The pixel’s area in steradians

ligo.skymap.moc.rasterize(moc_data, order=None)

Convert a multi-order HEALPix dataset to fixed-order NESTED ordering.

Parameters:

moc_datanumpy.ndarray

A multi-order HEALPix dataset stored as a Numpy record array whose first column is called UNIQ and contains the NUNIQ pixel index. Every point on the unit sphere must be contained in exactly one pixel in the dataset.

orderint, optional

The desired output resolution order, or None for the maximum resolution present in the dataset.

Returns:

nested_datanumpy.ndarray

A fixed-order, NESTED-ordering HEALPix dataset with all of the columns that were in moc_data, with the exception of the UNIQ column.

ligo.skymap.moc.bayestar_adaptive_grid(probdensity, *args, top_nside=16, rounds=8, **kwargs)

Create a sky map by evaluating a function on an adaptive grid.

Perform the BAYESTAR adaptive mesh refinement scheme as described in Section VI of Singer & Price 2016, PRD, 93, 024013 doi:10.1103/PhysRevD.93.024013. This computes the sky map using a provided analytic function and refines the grid, dividing the highest 25% into subpixels and then recalculating their values. The extra given args and kwargs will be passed to the given probdensity function.

Parameters:

probdensitycallable

Probability density function. The first argument consists of column-stacked array of right ascension and declination in radians. The return value must be a 1D array of the probability density in inverse steradians with the same length as the argument.

top_nsideint

HEALPix NSIDE resolution of initial evaluation of the sky map

roundsint

Number of refinement rounds, including the initial sky map evaluation

Returns:

skymapastropy.table.Table

An astropy Table with UNIQ and PROBDENSITY columns, representing a multi-ordered sky map