# Copyright (c) 2003-2024 by Mike Jarvis
#
# TreeCorr is free software: redistribution and use in source and binary forms,
# with or without modification, are permitted provided that the following
# conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions, and the disclaimer given in the accompanying LICENSE
# file.
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions, and the disclaimer given in the documentation
# and/or other materials provided with the distribution.
"""
.. module:: kvcorrelation
"""
import numpy as np
from . import _treecorr
from .catalog import calculateVarZ, calculateVarK
from .corr2base import Corr2
from .util import make_writer
from .config import make_minimal_config
[docs]class BaseKZCorrelation(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.
"""
_sig1 = 'sig_k'
_sig2 = 'sig_sn (per component)'
[docs] def __init__(self, config=None, *, logger=None, **kwargs):
super().__init__(config, logger=logger, **kwargs)
self._xi1 = np.zeros_like(self.rnom, dtype=float)
self._xi2 = np.zeros_like(self.rnom, dtype=float)
self._xi3 = self._xi4 = np.array([])
self._varxi = None
self.logger.debug('Finished building %s', self._cls)
@property
def xi(self):
return self._xi1
@property
def xi_im(self):
return self._xi2
def finalize(self, vark, varz):
self._finalize()
self._var_num = vark * varz
@property
def varxi(self):
if self._varxi is None:
self._varxi = np.zeros_like(self.rnom, dtype=float)
if self._var_num != 0:
self._varxi.ravel()[:] = self.cov_diag
return self._varxi
def _clear(self):
"""Clear the data vectors
"""
super()._clear()
self._varxi = None
def _sum(self, others):
# Equivalent to the operation of:
# self._clear()
# for other in others:
# self += other
# but no sanity checks and use numpy.sum for faster calculation.
np.sum([c._xi1 for c in others], axis=0, out=self._xi1)
np.sum([c._xi2 for c in others], axis=0, out=self._xi2)
np.sum([c.meanr for c in others], axis=0, out=self.meanr)
np.sum([c.meanlogr for c in others], axis=0, out=self.meanlogr)
np.sum([c.weight for c in others], axis=0, out=self.weight)
np.sum([c.npairs for c in others], axis=0, out=self.npairs)
self._varxi = None
self._cov = None
def write(self, file_name, file_type=None, precision=None, write_patch_results=False,
write_cov=False):
self.logger.info(f'Writing {self._letters} correlations to %s', file_name)
precision = self.config.get('precision', 4) if precision is None else precision
with make_writer(file_name, precision, file_type, self.logger) as writer:
self._write(writer, None, write_patch_results, write_cov=write_cov)
@property
def _write_col_names(self):
return ['r_nom','meanr','meanlogr',self._xireal,self._xiimag,'sigma','weight','npairs']
@property
def _write_data(self):
data = [ self.rnom, self.meanr, self.meanlogr,
self.xi, self.xi_im, np.sqrt(self.varxi),
self.weight, self.npairs ]
data = [ col.flatten() for col in data ]
return data
def _read_from_data(self, data, params):
super()._read_from_data(data, params)
s = self.logr.shape
self._xi1 = data[self._xireal].reshape(s)
self._xi2 = data[self._xiimag].reshape(s)
self._varxi = data['sigma'].reshape(s)**2
[docs]class KZCorrelation(BaseKZCorrelation):
r"""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 a complex values that
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.
xi: The correlation function, :math:`\xi(r) = \langle \kappa\, z\rangle`.
xi_im: The imaginary part of :math:`\xi(r)`.
varxi: An estimate of the variance of each component of :math:`\xi`
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.
.. 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`.
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 use `Corr2.process_cross`,
then the units will not be applied to ``meanr`` or ``meanlogr`` until the `finalize`
function is called.
The typical usage pattern is as follows:
>>> kv = treecorr.KZCorrelation(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.
"""
_cls = 'KZCorrelation'
_letter1 = 'K'
_letter2 = 'Z'
_letters = 'KZ'
_builder = _treecorr.KZCorr
_calculateVar1 = staticmethod(calculateVarK)
_calculateVar2 = staticmethod(calculateVarZ)
_xireal = 'xi'
_xiimag = 'xi_im'
[docs] def __init__(self, config=None, *, logger=None, **kwargs):
"""Initialize `KZCorrelation`. See class doc for details.
"""
super().__init__(config, logger=logger, **kwargs)
[docs] def finalize(self, vark, varz):
"""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.
"""
super().finalize(vark, varz)
[docs] def write(self, file_name, *, file_type=None, precision=None, write_patch_results=False,
write_cov=False):
r"""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 :math:`\langle r\rangle` of pairs that
fell into each bin
meanlogr The mean value :math:`\langle \log(r)\rangle` of pairs
that fell into each bin
xi The real part of correlation function,
:math:`xi(r) = \langle \kappa\, z\rangle`
xi_im The imaginary part of 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)
"""
super().write(file_name, file_type, precision, write_patch_results, write_cov)