ZZCorrelation: Complex-complex correlations
- class treecorr.ZZCorrelation(config=None, *, logger=None, **kwargs)[source]
Bases:
BaseZZCorrelation
This class handles the calculation and storage of a 2-point correlation function of two complex spin-0 fields. If either spin-0 field is real, you should instead use
KZCorrelation
as it will be faster, and if both are real, you should useKKCorrelation
. This class is intended for correlations of scalar fields with a complex values that don’t change with orientation.To be consistent with the other spin correlation functions, we compute two quantities:
\[\xi_+ = \langle z_1 z_2^* \rangle \xi_- = \langle z_1 z_2 \rangle\]There is no projection along the line connecting the two points as there is for the other complex fields, since the field values don’t change with orientation.
Ojects of this class holds the following attributes:
- Attributes:
nbins – The number of bins in logr
bin_size – The size of the bins in logr
min_sep – The minimum separation being considered
max_sep – The maximum separation being considered
In addition, the following attributes are numpy arrays of length (nbins):
- Attributes:
logr – The nominal center of the bin in log(r) (the natural logarithm of r).
rnom – The nominal center of the bin converted to regular distance. i.e. r = exp(logr).
meanr – The (weighted) mean value of r for the pairs in each bin. If there are no pairs in a bin, then exp(logr) will be used instead.
meanlogr – The (weighted) mean value of log(r) for the pairs in each bin. If there are no pairs in a bin, then logr will be used instead.
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 \(\xi_-(r)\)
weight – The total weight in each bin.
npairs – The number of pairs going into each bin (including pairs where one or both objects have w=0).
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
, 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.If
sep_units
are given (either in the config dict or as a named kwarg) then the distances will all be in these units.Note
If you separate out the steps of the
Corr2.process
command and useBaseZZCorrelation.process_auto
and/orCorr2.process_cross
, then the units will not be applied tomeanr
ormeanlogr
until theBaseZZCorrelation.finalize
function is called.The typical usage pattern is as follows:
>>> zz = treecorr.ZZCorrelation(config) >>> zz.process(cat) # For auto-correlation. >>> zz.process(cat1,cat2) # For cross-correlation. >>> zz.write(file_name) # Write out to a file. >>> xip = zz.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.
- __init__(config=None, *, logger=None, **kwargs)[source]
Initialize
ZZCorrelation
. See class doc for details.
- class treecorr.BaseZZCorrelation(config=None, *, logger=None, **kwargs)[source]
Bases:
Corr2
This class is a base class for all the ??Correlation classes, where both ?’s are 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.
- finalize(varz1, varz2)[source]
Finalize the calculation of the correlation function.
The
process_auto
andCorr2.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:
varz1 (float) – The variance per component of the first field.
varz2 (float) – The variance per component of the second field.
- getStat()[source]
The standard statistic for the current correlation object as a 1-d array.
In this case, this is the concatenation of self.xip and self.xim (raveled if necessary).
- getWeight()[source]
The weight array for the current correlation object as a 1-d array.
This is the weight array corresponding to
getStat
. In this case, the weight is duplicated to account for both xip and xim returned as part of getStat().
- process_auto(cat, *, metric=None, num_threads=None)[source]
Process a single catalog, accumulating the auto-correlation.
This accumulates the weighted sums into the bins, but does not finalize the calculation by dividing by the total weight at the end. After calling this function as often as desired, the
finalize
command will finish the calculation.- Parameters:
cat (Catalog) – The catalog to process
metric (str) – Which metric to use. See Metrics for details. (default: ‘Euclidean’; this value can also be given in the constructor in the config dict.)
num_threads (int) – How many OpenMP threads to use during the calculation. (default: use the number of cpu cores; this value can also be given in the constructor in the config dict.)
- 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
xip
The real part of the \(\xi_+\) correlation function
xim
The real part of the \(\xi_-\) correlation function
xip_im
The imag part of the \(\xi_+\) correlation function
xim_im
The imag part of the \(\xi_-\) correlation function
sigma_xip
The sqrt of the variance estimate of \(\xi_+\)
sigma_xim
The sqrt of the variance estimate of \(\xi_-\)
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)