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The current positions_from_delta() function carries out a large number of steps:
Apply bias model to given delta map.
Convert biased delta to mean number count in each map pixel.
Apply a visibility map.
Compute observed number counts from a distribution (e.g., Poisson).
Accumulate the galaxies in the number count map into batches of a given size (e.g., 1_000_000)
Sample random positions for each galaxy within its pixel.
This structure is very rigid, and makes it difficult to implement things such as #136.
We should split this up into individual functions, so that they can be mixed and matched with different implementations:
fori, deltainenumerate(matter):
# mean number count in each pixelnbar=glass.nbar_from_density(arcmin2=ngal[i], nside=nside)
# galaxy density from linear bias model, could be a complicated modelgal=nbar* (1+beff[i] *delta)
# apply visibility map for this shell, could be a complicated modelgal*=vmap[i]
# Poisson-sample galaxy indices from map in batchesforipixinglass.batched_poisson(gal, rng=rng):
# randomly sample galaxy positions within each pixellon, lat=glass.random_healpix_positions(nside, ipix)
This needs a bit of experimentation for multi-dimensional arrays, but in principle seems feasible.
The text was updated successfully, but these errors were encountered:
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The current
positions_from_delta()
function carries out a large number of steps:delta
map.delta
to mean number count in each map pixel.This structure is very rigid, and makes it difficult to implement things such as #136.
We should split this up into individual functions, so that they can be mixed and matched with different implementations:
This needs a bit of experimentation for multi-dimensional arrays, but in principle seems feasible.
The text was updated successfully, but these errors were encountered: