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With overlapping radial window functions (#118, #119), there is no longer a unique way to distribute galaxies among each shell to recover some given galaxy distribution $dN/dz$.
If $w_i$ are the radial window functions, $f = dN/dz$ is the target function, and $z_i$ is a redshift grid, we want a solution $x_i$ of the following overdetermined matrix equation:
Change the `partition()` function to return an array of weights such
that the weighted sum of window functions approximates the given input
function. This can be used to directly obtain e.g. the galaxy densities
in each shell to match a target distribution $dN/dz$:
# the galaxy density in each shell to match dndz
ngal = partition(z, dndz, shells)
For overlapping window functions, there are in general many ways to
combine shells to match a given function. The `partition()` function
currently implements least-squares (`method="lstsq"`) and the
restriction of the target function to the shell, followed by integration
(`method="restrict"`). The latter was previously the default procedure
for tophat windows.
Closes: #121
Changed: The `partition()` function now returns an array of weights to
approximate the given function by the windows.
With overlapping radial window functions (#118, #119), there is no longer a unique way to distribute galaxies among each shell to recover some given galaxy distribution$dN/dz$ .
If$w_i$ are the radial window functions, $f = dN/dz$ is the target function, and $z_i$ is a redshift grid, we want a solution $x_i$ of the following overdetermined matrix equation:
Linear least squares is one obvious method. A weighting$\Delta z_i$ can ensure that finer-sampled regions do not overly influence the result:
The$\Delta z_i$ could be the weights of the trapezoidal rule.
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