template<typename dataT>
class mx::improc::eigenCube< dataT >

An image cube with an Eigen-like API.

Definition at line 29 of file eigenCube.hpp.

#include <improc/eigenCube.hpp>

Public Member Functions
	eigenCube (Index nrows, Index ncols, Index nplanes)
	C'tor which will allocate space..

	eigenCube (dataT *ndata, size_t nrows, size_t ncols, size_t nplanes)
	C'tor taking an existing array as an argument.

void	clear ()
	De-allocate and set all sizes to 0.

Eigen::Map< Eigen::Array< dataT, Eigen::Dynamic, Eigen::Dynamic > >	cube ()
	Returns a 2D Eigen::Eigen::Map pointed at the entire cube.

Eigen::Map< Eigen::Array< dataT, Eigen::Dynamic, Eigen::Dynamic > >	image (Index n)
	Returns a 2D Eigen::Eigen::Map pointed at the specified image.

const Eigen::Map< Eigen::Array< dataT, Eigen::Dynamic, Eigen::Dynamic > >	image (Index n) const
	Returns a 2D Eigen::Eigen::Map pointed at the specified image.

Eigen::Map< Eigen::Array< dataT, Eigen::Dynamic, Eigen::Dynamic >, Eigen::Unaligned, Eigen::Stride< Eigen::Dynamic, Eigen::Dynamic > >	pixel (Index i, Index j)
	Returns an Eigen::Eigen::Map-ed vector of the pixels at the given coordinate.

Eigen::Map< Eigen::Array< dataT, Eigen::Dynamic, Eigen::Dynamic > >	asVectors ()
	Return an Eigen::Eigen::Map of the cube where each image is a vector.

void	Covar (Eigen::Matrix< dataT, Eigen::Dynamic, Eigen::Dynamic > &cv)
	Calculate the covariance matrix of the images in the cube.

template<typename eigenT >
void	sum (eigenT &mim)
	Calculate the sum image of the cube.

template<typename eigenT >
void	mean (eigenT &mim)
	Calculate the mean image of the cube.

template<typename eigenT , typename eigenCubeT >
void	mean (eigenT &mim, eigenCubeT &mask, double minGoodFract=0.0)
	Calculate the mean image of the cube with a mask.

template<typename eigenT >
void	mean (eigenT &mim, std::vector< dataT > &weights)
	Calculate the weighted mean image of the cube.

template<typename eigenT , typename eigenCubeT >
void	mean (eigenT &mim, std::vector< dataT > &weights, eigenCubeT &mask, double minGoodFract=0.0)
	Calculate the weighted mean image of the cube, with a mask cube.

template<typename eigenT >
void	median (eigenT &mim)
	Calculate the median image of the cube.

template<typename eigenT >
void	sigmaMean (eigenT &mim, Scalar sigma)
	Calculate the sigma clipped mean image of the cube.

template<typename eigenT , typename eigenCubeT >
void	sigmaMean (eigenT &mim, eigenCubeT &mask, Scalar sigma, double minGoodFract=0.0)
	Calculate the sigma clipped mean image of the cube, with a mask cube.

template<typename eigenT >
void	sigmaMean (eigenT &mim, std::vector< dataT > &weights, Scalar sigma)
	Calculate the sigma clipped weighted mean image of the cube.

template<typename eigenT , typename eigenCubeT >
void	sigmaMean (eigenT &mim, std::vector< dataT > &weights, eigenCubeT &mask, Scalar sigma, double minGoodFract=0.0)
	Calculate the sigma clipped weighted mean image of the cube, with a mask cube.

Constructor & Destructor Documentation

◆ eigenCube() [1/2]

template<typename dataT >

mx::improc::eigenCube< dataT >::eigenCube	(	Index	nrows,
		Index	ncols,
		Index	nplanes
	)

C'tor which will allocate space..

Parameters

[in]	nrows	Number of rows in the cube
[in]	ncols	Number of columns the cube
[in]	nplanes	Number of planes in the cube

Definition at line 233 of file eigenCube.hpp.

◆ eigenCube() [2/2]

template<typename dataT >

mx::improc::eigenCube< dataT >::eigenCube	(	dataT *	ndata,
		size_t	nrows,
		size_t	ncols,
		size_t	nplanes
	)

C'tor taking an existing array as an argument.

The existing array is used as is, as in a map, and ownership is not taken. You are responsible for memory management, e.g. free-ing this array.

Parameters

[in]	ndata	Allocated array with a cube of nrows x ncols x nplanes
[in]	nrows	Number of rows in the cube
[in]	ncols	Number of columns the cube
[in]	nplanes	Number of planes in the cube

Definition at line 244 of file eigenCube.hpp.

Member Function Documentation

◆ asVectors()

template<typename dataT >

Eigen::Map< Eigen::Array< dataT, Eigen::Dynamic, Eigen::Dynamic > > mx::improc::eigenCube< dataT >::asVectors ( )

Return an Eigen::Eigen::Map of the cube where each image is a vector.

Definition at line 406 of file eigenCube.hpp.

Referenced by mx::AO::ifPInv(), and mx::AO::m2cMatrix().

◆ clear()

template<typename dataT >

void mx::improc::eigenCube< dataT >::clear ( )

De-allocate and set all sizes to 0.

Definition at line 311 of file eigenCube.hpp.

◆ Covar()

template<typename dataT >

void mx::improc::eigenCube< dataT >::Covar ( Eigen::Matrix< dataT, Eigen::Dynamic, Eigen::Dynamic > & cv )

Calculate the covariance matrix of the images in the cube.

Definition at line 412 of file eigenCube.hpp.

◆ cube()

template<typename dataT >

Eigen::Map< Eigen::Array< dataT, Eigen::Dynamic, Eigen::Dynamic > > mx::improc::eigenCube< dataT >::cube ( )

Returns a 2D Eigen::Eigen::Map pointed at the entire cube.

Definition at line 376 of file eigenCube.hpp.

◆ image() [1/2]

template<typename dataT >

Eigen::Map< Eigen::Array< dataT, Eigen::Dynamic, Eigen::Dynamic > > mx::improc::eigenCube< dataT >::image ( Index n )

Returns a 2D Eigen::Eigen::Map pointed at the specified image.

Parameters

[in] n the image number

Definition at line 382 of file eigenCube.hpp.

Referenced by mx::AO::influenceFunctionsGaussian(), mx::AO::analysis::fourierTemporalPSD< _realT, aosysT >::intensityPSD(), mx::wfp::lyotCoronagraph< _realT, _fpmaskFloatT >::loadFocalMask(), mx::AO::makeModfBasis(), mx::AO::makeZernikeBasis(), mx::AO::modesOnDM(), mx::AO::orthogonalizeBasis(), and SCENARIO().

◆ image() [2/2]

template<typename dataT >

const Eigen::Map< Eigen::Array< dataT, Eigen::Dynamic, Eigen::Dynamic > > mx::improc::eigenCube< dataT >::image ( Index n ) const

Returns a 2D Eigen::Eigen::Map pointed at the specified image.

Parameters

[in] n the image number

Definition at line 388 of file eigenCube.hpp.

◆ mean() [1/4]

template<typename dataT >

template<typename eigenT >

void mx::improc::eigenCube< dataT >::mean ( eigenT & mim )

Calculate the mean image of the cube.

Template Parameters

eigenT an Eigen-like type.

Parameters

[out] mim the resultant mean image. Is resized.

Definition at line 435 of file eigenCube.hpp.

◆ mean() [2/4]

template<typename dataT >

template<typename eigenT , typename eigenCubeT >

void mx::improc::eigenCube< dataT >::mean	(	eigenT &	mim,
		eigenCubeT &	mask,
		double	minGoodFract = `0.0`
	)

Calculate the mean image of the cube with a mask.

Template Parameters

eigenT	an Eigen-like type.
eigenCubeT	an eigenCube type.

Parameters

[out]	mim	the resultant mean image. Is resized.
[in]	mask	a mask cube. Only pixels with value 1 are included in the mean calculation.
[in]	minGoodFract	[optional] the minimum fraction of good pixels, if not met then the pixel is NaN-ed.

Definition at line 451 of file eigenCube.hpp.

References mx::math::vectorMean().

◆ mean() [3/4]

template<typename dataT >

template<typename eigenT >

void mx::improc::eigenCube< dataT >::mean	(	eigenT &	mim,
		std::vector< dataT > &	weights
	)

Calculate the weighted mean image of the cube.

Template Parameters

eigenT an Eigen-like type.

Parameters

[out]	mim	the resultant mean image. Is resized.
[in]	weights	a vector of weights to use for calculating the mean

Definition at line 490 of file eigenCube.hpp.

References mx::math::vectorMean().

◆ mean() [4/4]

template<typename dataT >

template<typename eigenT , typename eigenCubeT >

void mx::improc::eigenCube< dataT >::mean	(	eigenT &	mim,
		std::vector< dataT > &	weights,
		eigenCubeT &	mask,
		double	minGoodFract = `0.0`
	)

Calculate the weighted mean image of the cube, with a mask cube.

Template Parameters

eigenT	an Eigen-like type.
eigenCubeT	an eigenCube type.

Parameters

[out]	mim	the resultant mean image. Is resized.
[in]	weights	a vector of weights to use for calculating the mean
[in]	mask	a mask cube. Only pixels with value 1 are included in the mean calculation.
[in]	minGoodFract	[optional] the minimum fraction of good pixels, if not met then the pixel is NaN-ed.

Definition at line 516 of file eigenCube.hpp.

References mx::math::vectorMean().

◆ median()

template<typename dataT >

template<typename eigenT >

void mx::improc::eigenCube< dataT >::median ( eigenT & mim )

Calculate the median image of the cube.

Template Parameters

eigenT an Eigen-like type.

Parameters

[out] mim the resultant median image. Is resized.

Definition at line 555 of file eigenCube.hpp.

References mx::improc::imageMedian().

◆ pixel()

template<typename dataT >

Eigen::Map< Eigen::Array< dataT, Eigen::Dynamic, Eigen::Dynamic >, Eigen::Unaligned, Eigen::Stride< Eigen::Dynamic, Eigen::Dynamic > > mx::improc::eigenCube< dataT >::pixel	(	Index	i,
		Index	j
	)

Returns an Eigen::Eigen::Map-ed vector of the pixels at the given coordinate.

Definition at line 397 of file eigenCube.hpp.

Referenced by SCENARIO().

◆ sigmaMean() [1/4]

template<typename dataT >

template<typename eigenT , typename eigenCubeT >

void mx::improc::eigenCube< dataT >::sigmaMean	(	eigenT &	mim,
		eigenCubeT &	mask,
		Scalar	sigma,
		double	minGoodFract = `0.0`
	)

Calculate the sigma clipped mean image of the cube, with a mask cube.

Template Parameters

eigenT	an Eigen-like type.
eigenCubeT	an eigenCube type.

Parameters

[out]	mim	the resultant mean image. Is resized.
[in]	mask	a mask cube. Only pixels with value 1 are included in the mean calculation.
[in]	sigma	the sigma value at which to clip.
[in]	minGoodFract	[optional] the minimum fraction of good pixels, if not met then the pixel is NaN-ed.

Definition at line 598 of file eigenCube.hpp.

References mx::math::vectorSigmaMean().

◆ sigmaMean() [2/4]

template<typename dataT >

template<typename eigenT >

void mx::improc::eigenCube< dataT >::sigmaMean	(	eigenT &	mim,
		Scalar	sigma
	)

Calculate the sigma clipped mean image of the cube.

Template Parameters

eigenT an Eigen-like type.

Parameters

[out]	mim	the resultant mean image
[in]	sigma	the sigma value at which to clip.

Definition at line 573 of file eigenCube.hpp.

References mx::math::vectorSigmaMean().

◆ sigmaMean() [3/4]

template<typename dataT >

template<typename eigenT , typename eigenCubeT >

void mx::improc::eigenCube< dataT >::sigmaMean	(	eigenT &	mim,
		std::vector< dataT > &	weights,
		eigenCubeT &	mask,
		Scalar	sigma,
		double	minGoodFract = `0.0`
	)

Calculate the sigma clipped weighted mean image of the cube, with a mask cube.

Template Parameters

eigenT	an Eigen-like type.
eigenCubeT	an eigenCube type.

Parameters

[out]	mim	the resultant mean image. Is resized.
[in]	weights	a vector of weights to use for calculating the mean
[in]	mask	a mask cube. Only pixels with value 1 are included in the mean calculation.
[in]	sigma	the sigma value at which to clip.
[in]	minGoodFract	[optional] the minimum fraction of good pixels, if not met then the pixel is NaN-ed.

Definition at line 663 of file eigenCube.hpp.

References mx::math::vectorSigmaMean().

◆ sigmaMean() [4/4]

template<typename dataT >

template<typename eigenT >

void mx::improc::eigenCube< dataT >::sigmaMean	(	eigenT &	mim,
		std::vector< dataT > &	weights,
		Scalar	sigma
	)

Calculate the sigma clipped weighted mean image of the cube.

Template Parameters

eigenT an Eigen-like type.

Parameters

[out]	mim	the resultant mean image. Is resized.
[in]	weights	a vector of weights to use for calculating the mean
[in]	sigma	the sigma value at which to clip.

Definition at line 637 of file eigenCube.hpp.

References mx::math::vectorSigmaMean().

◆ sum()

template<typename dataT >

template<typename eigenT >

void mx::improc::eigenCube< dataT >::sum ( eigenT & mim )

Calculate the sum image of the cube.

Template Parameters

eigenT an Eigen-like type.

Parameters

[out] mim the resultant sum image. Is resized.

Definition at line 419 of file eigenCube.hpp.

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

improc/eigenCube.hpp

Public Member Functions

Constructor & Destructor Documentation

◆ eigenCube() [1/2]

◆ eigenCube() [2/2]

Member Function Documentation

◆ asVectors()

◆ clear()

◆ Covar()

◆ cube()

◆ image() [1/2]

◆ image() [2/2]

◆ mean() [1/4]

◆ mean() [2/4]

◆ mean() [3/4]

◆ mean() [4/4]

◆ median()

◆ pixel()

◆ sigmaMean() [1/4]

◆ sigmaMean() [2/4]

◆ sigmaMean() [3/4]

◆ sigmaMean() [4/4]

◆ sum()