VVCorrelation: Vector-vector correlations

class treecorr.VVCorrelation(config=None, *, logger=None, **kwargs)[source]

Bases: BaseZZCorrelation

This class handles the calculation and storage of a 2-point vector-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 Corr2 subclasses, objects of this class hold the following attributes:

Attributes:

  • xip – The correlation function, \(\xi_+(r)\).

  • xim – The correlation function, \(\xi_-(r)\).

  • xip_im – The imaginary part of \(\xi_+(r)\).

  • xim_im – The imaginary part of \(\xi_-(r)\).

  • varxip – An estimate of the variance of \(\xi_+(r)\)

  • varxim – An estimate of the variance of \(\xi_-(r)\)

  • cov – An estimate of the full covariance matrix for the data vector with \(\xi_+\) first and then \(\xi_-\).

Note

The default method for estimating the variance and covariance attributes (varxip, varxim, and cov) 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 set var_method to something else and use patches in the input catalog(s). cf. Covariance Estimates.

The typical usage pattern is as follows:

>>> vv = treecorr.VVCorrelation(config)
>>> vv.process(cat)         # For auto-correlation.
>>> vv.process(cat1,cat2)   # For cross-correlation.
>>> vv.write(file_name)     # Write out to a file.
>>> xip = vv.xip            # 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(varv1, varv2)[source]

Finalize the calculation of the correlation function.

The Corr2.process_auto and Corr2.process_cross commands accumulate values in each bin, so they can be called multiple times if appropriate. Afterwards, this command finishes the calculation by dividing each column by the total weight.

Parameters:

  • varv1 (float) – The variance per component of the first vector field.

  • varv2 (float) – The variance per component of the second vector field.