KVCorrelation: Scalar-vector correlations
- class treecorr.KVCorrelation(config=None, *, logger=None, **kwargs)[source]
Bases:
BaseKZCorrelation
This class handles the calculation and storage of a 2-point scalar-vector correlation function.
See the doc string of
Corr3
for a description of how the triangles are binned along with the attributes related to the different binning options.In addition to the attributes common to all
Corr3
subclasses, objects of this class hold the following attributes:- Attributes:
xi – The real component of the correlation function, \(\xi(r) = \langle \kappa\, v_R\rangle\).
xi_im – The imaginary comonent of \(\xi(r)\).
varxi – An estimate of the variance of \(\xi\)
cov – An estimate of the full covariance matrix.
Note
The default method for estimating the variance and covariance attributes (
varxi
, andcov
) is ‘shot’, which only includes the shape noise propagated into the final correlation. This does not include sample variance, so it is always an underestimate of the actual variance. To get better estimates, you need to setvar_method
to something else and use patches in the input catalog(s). cf. Covariance Estimates.The typical usage pattern is as follows:
>>> kv = treecorr.KVCorrelation(config) >>> kv.process(cat1,cat2) # Calculate the cross-correlation >>> kv.write(file_name) # Write out to a file. >>> xi = kv.xi # Or access the correlation function directly.
- Parameters:
config (dict) – A configuration dict that can be used to pass in kwargs if desired. This dict is allowed to have addition entries besides those listed in
Corr2
, which are ignored here. (default: None)logger – If desired, a logger object for logging. (default: None, in which case one will be built according to the config dict’s verbose level.)
- Keyword Arguments:
**kwargs – See the documentation for
Corr2
for the list of allowed keyword arguments, which may be passed either directly or in the config dict.
- finalize(vark, varv)[source]
Finalize the calculation of the correlation function.
The
Corr2.process_cross
command accumulates values in each bin, so it can be called multiple times if appropriate. Afterwards, this command finishes the calculation by dividing each column by the total weight.- Parameters:
vark (float) – The variance of the scaler field.
varv (float) – The variance per component of the vector field.
- write(file_name, *, file_type=None, precision=None, write_patch_results=False, write_cov=False)[source]
Write the correlation function to the file, file_name.
The output file will include the following columns:
Column
Description
r_nom
The nominal center of the bin in r
meanr
The mean value \(\langle r\rangle\) of pairs that fell into each bin
meanlogr
The mean value \(\langle \log(r)\rangle\) of pairs that fell into each bin
xi
The real component of the correlation function, \(xi(r) = \langle \kappa\, v_R\rangle\)
xi_im
The imaginary component of the correlation function.
sigma
The sqrt of the variance estimate of both of these
weight
The total weight contributing to each bin
npairs
The total number of pairs in each bin
If
sep_units
was given at construction, then the distances will all be in these units. Otherwise, they will be in either the same units as x,y,z (for flat or 3d coordinates) or radians (for spherical coordinates).- Parameters:
file_name (str) – The name of the file to write to.
file_type (str) – The type of file to write (‘ASCII’ or ‘FITS’). (default: determine the type automatically from the extension of file_name.)
precision (int) – For ASCII output catalogs, the desired precision. (default: 4; this value can also be given in the constructor in the config dict.)
write_patch_results (bool) – Whether to write the patch-based results as well. (default: False)
write_cov (bool) – Whether to write the covariance matrix as well. (default: False)