KZCorrelation: Scalar-complex correlations

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

Bases: BaseKZCorrelation

This class handles the calculation and storage of a 2-point scalar-complex correlation function, where the complex field is taken to have spin-0 rotational properties. If the spin-0 field is real, you should instead use KKCorrelation as it will be faster. This class is intended for correlations of a scalar field with complex values that don’t change with orientation.

See the docstring of Corr2 for a description of how the pairs 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:

In addition, the following attributes are numpy arrays of length (nbins):

Attributes:

  • xi – The correlation function, \(\xi(r) = \langle \kappa\, z\rangle\).

  • xi_im – The imaginary part of \(\xi(r)\).

  • varxi – An estimate of the variance of each component of \(\xi\)

  • cov – An estimate of the full covariance matrix.

Note

The default method for estimating the variance and covariance attributes (varxi, 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:

>>> kz = treecorr.KZCorrelation(config)
>>> kz.process(cat1, cat2)         # Compute the cross-correlation.
>>> kz.write(file_name)            # Write out to a file.
>>> xi, xi_im = kz.xi, kz.xi_im    # Or access the correlation function directly.

See also: NZCorrelation, ZZCorrelation, KVCorrelation.

Parameters:

  • config (dict) – A configuration dict that can be used to pass in kwargs if desired. This dict is allowed to have additional entries besides those listed in Corr2, which are ignored here. (default: None)

  • logger (logging.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, varz)[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.

  • varz (float) – The variance per component of the complex 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 part of the correlation function, \(xi(r) = \langle \kappa\, z\rangle\)

xi_im

The imaginary part 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 files, 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)

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

Bases: Corr2

This class is a base class for all the K?Correlation classes, where ? is one of the complex fields of varying spin.

A lot of the implementation is shared among those types, so whenever possible the shared implementation is done in this class.

__init__(config=None, *, logger=None, **kwargs)[source]