lsst.meas.algorithms.noise_covariance.CorrelationMatrix Class Reference

Public Member Functions
tuple[int, int]	shape (self)
float	__call__ (self, int x, int y)

Static Public Attributes
np	array .ndarray

Detailed Description

A class holding correlation coefficients for a set of background pixels.

CorrelationMatrix is a dataclass that is initialized with a numpy ndarray
and provides some convenience methods for accessing the matrix elements.
A CorrelationMatrix instance is callable wth two integer values x and y,
which returns the <I(m,n) I(m+x, n+y) / sqrt( V(m,n) V(m+x,n+y) )>, where
I is the image, V is the variance plane and < > denotes the expectation
operator.

Parameters
----------
array : `numpy.ndarray`
    The matrix of correlation coefficients.

Definition at line 41 of file noise_covariance.py.

Member Function Documentation

__call__()

float lsst.meas.algorithms.noise_covariance.CorrelationMatrix.__call__	(		self,
		int	x,
		int	y )

Definition at line 64 of file noise_covariance.py.

    def __call__(self, x: int, y: int) -> float:
        return self.array[x, y]
 
 

shape()

tuple[int, int] lsst.meas.algorithms.noise_covariance.CorrelationMatrix.shape

(

self

)

The shape of the correlation matrix.

Definition at line 60 of file noise_covariance.py.

    def shape(self) -> tuple[int, int]:
        """The shape of the correlation matrix."""
        return self.array.shape
 

Member Data Documentation

array

np lsst.meas.algorithms.noise_covariance.CorrelationMatrix.array .ndarray

static

Definition at line 57 of file noise_covariance.py.

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The documentation for this class was generated from the following file:

• /j/snowflake/release/lsstsw/stack/lsst-scipipe-8.0.0/Linux64/meas_algorithms/ge6526c86ff+d4c1d4bfef/python/lsst/meas/algorithms/noise_covariance.py