# 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:: ngcorrelation
"""
import numpy as np
from . import _treecorr
from .catalog import calculateVarQ
from .nzcorrelation import BaseNZCorrelation
from .util import make_writer, make_reader
from .config import make_minimal_config
[docs]class NQCorrelation(BaseNZCorrelation):
r"""This class handles the calculation and storage of a 2-point count-quatrefoil correlation
function, where a quatrefoil is any field with spin-4 rotational properties.
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 q_R\rangle`.
xi_im: The imaginary part of :math:`\xi(r)`.
varxi: An estimate of the variance 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.
raw_xi: The raw value of xi, uncorrected by an RQ calculation. cf. `calculateXi`
raw_xi_im: The raw value of xi_im, uncorrected by an RQ calculation. cf. `calculateXi`
raw_varxi: The raw value of varxi, uncorrected by an RQ calculation. cf. `calculateXi`
.. 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:
>>> nq = treecorr.NQCorrelation(config)
>>> nq.process(cat1,cat2) # Compute the cross-correlation.
>>> nq.write(file_name) # Write out to a file.
>>> xi = nq.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 = 'NQCorrelation'
_letter1 = 'N'
_letter2 = 'Q'
_letters = 'NQ'
_builder = _treecorr.NQCorr
_calculateVar1 = lambda *args, **kwargs: None
_calculateVar2 = staticmethod(calculateVarQ)
_zreal = 'qR'
_zimag = 'qR_im'
[docs] def __init__(self, config=None, *, logger=None, **kwargs):
"""Initialize `NQCorrelation`. See class doc for details.
"""
super().__init__(config, logger=logger, **kwargs)
[docs] def finalize(self, varq):
"""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:
varq (float): The variance per component of the quatrefoil field.
"""
super().finalize(varq)
[docs] def calculateXi(self, *, rq=None):
r"""Calculate the correlation function possibly given another correlation function
that uses random points for the foreground objects.
- If rq is None, the simple correlation function :math:`\langle q_R\rangle` is
returned.
- If rq is not None, then a compensated calculation is done:
:math:`\langle q_R\rangle = (DQ - RQ)`, where DQ represents the mean radial quatrefoil
field around the data points and RQ represents the mean radial quatrefoil field around
random points.
After calling this function, the attributes ``xi``, ``xi_im``, ``varxi``, and ``cov`` will
correspond to the compensated values (if rq is provided). The raw, uncompensated values
are available as ``rawxi``, ``raw_xi_im``, and ``raw_varxi``.
Parameters:
rq (NQCorrelation): The cross-correlation using random locations as the lenses
(RQ), if desired. (default: None)
Returns:
Tuple containing
- xi = array of the real part of :math:`\xi(R)`
- xi_im = array of the imaginary part of :math:`\xi(R)`
- varxi = array of the variance estimates of the above values
"""
return super().calculateXi(rz=rq)
[docs] def write(self, file_name, *, rq=None, file_type=None, precision=None,
write_patch_results=False, write_cov=False):
r"""Write the correlation function to the file, file_name.
- If rq is None, the simple correlation function :math:`\langle q_R\rangle` is used.
- If rq is not None, then a compensated calculation is done:
:math:`\langle q_R\rangle = (DQ - RQ)`, where DQ represents the mean quatrefoil field.
around the data points and RQ represents the mean quatrefoil field around random points.
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
qR The mean real part of the quatrefoil field relative to the
center points
qR_im The mean imaginary part of the quatrefoil field relative to the
center points
sigma The sqrt of the variance estimate of either 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.
rq (NQCorrelation): The cross-correlation using random locations as the lenses
(RQ), if desired. (default: None)
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, rq, file_type, precision, write_patch_results, write_cov)