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optionally return counts as well

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rstiskalek 2022-11-06 20:58:57 +00:00
parent d69012424e
commit d4f89fc343
1 changed files with 12 additions and 2 deletions
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14
csiborgtools/match/num_density.py
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@ -51,7 +51,7 @@ def binned_counts(x, bins):
return centres, out return centres, out
def number_density(data, feat, bins, max_dist, to_log10): def number_density(data, feat, bins, max_dist, to_log10, return_counts=False):
""" """
Calculate volume-limited number density of a feature `feat` from array Calculate volume-limited number density of a feature `feat` from array
`data`, normalised also by the bin width. `data`, normalised also by the bin width.
@ -70,6 +70,9 @@ def number_density(data, feat, bins, max_dist, to_log10):
to_log10 : bool to_log10 : bool
Whether to take a logarithm of base 10 of the feature. If so, then the Whether to take a logarithm of base 10 of the feature. If so, then the
bins must also be logarithmic. bins must also be logarithmic.
return_counts : bool, optional
Whether to also return number counts in each bin. By default `False`.
Returns Returns
------- -------
@ -80,6 +83,9 @@ def number_density(data, feat, bins, max_dist, to_log10):
Number density of shape `(n_edges - 1, )`. Number density of shape `(n_edges - 1, )`.
nd_err : 1-dimensional array nd_err : 1-dimensional array
Poissonian uncertainty of `nd` of shape `(n_edges - 1, )`. Poissonian uncertainty of `nd` of shape `(n_edges - 1, )`.
counts: 1-dimensional array, optional
Counts in each bin of shape `(n_edges - 1, )`. Returned only if
`return_counts`.
""" """
# Extract the param and optionally convert to log10 # Extract the param and optionally convert to log10
x = data[feat] x = data[feat]
@ -104,4 +110,8 @@ def number_density(data, feat, bins, max_dist, to_log10):
# Convert bins to linear space if log10 # Convert bins to linear space if log10
if to_log10: if to_log10:
bin_centres = 10**bin_centres bin_centres = 10**bin_centres
return bin_centres, nd, nd_err
out = (bin_centres, nd, nd_err)
if return_counts:
out += counts
return out