Functions
void	initializeModel (Model *model, int X, int Y, int M, int Z)
void	initializeOutputModel (Model *model, MAP_DEPTH_MATRIX kernelTdm)
void	initializeForwardOutputModel (Model *model, MAP_DEPTH_MATRIX kernelTdm)
void	initializeCalcQOutputModel (Model *model, MAP_DEPTH_MATRIX w)
void	freeModel (Model *model)
void	useNoMaps (Model *model)
void	setParameters (Model *model, Float mu, Float sigma)
void	setParameters2 (Model model, Float values, int nValues)
void	clearParameters (Model *model)
void	saveW (Model *model, MATRIX w, int kx, int ky)
void	saveB (Model *model, MATRIX b, int nStep)
MATRIX	calculateQK (MATRIX wk, MAP_DEPTH_MATRIX tau, int kx, int ky)
MATRIX	calculateAvgK (MATRIX wk, MAP_DEPTH_MATRIX K, int kx, int ky)
MATRIX	calculateNoiseK (MATRIX wk, MAP_DEPTH_MATRIX Lambda, int kx, int ky)
void	calculateQValues (Model *model, MAP_DEPTH_MATRIX tau, bool DValueShift)
void	calculateDerivedValues (Model *model, MAP_DEPTH_MATRIX K, MAP_DEPTH_MATRIX tau, MAP_DEPTH_MATRIX Lambda, bool DValueShift)
void	calculateDerivedValuesNoQ (Model *model, MAP_DEPTH_MATRIX K, MAP_DEPTH_MATRIX Lambda, bool DValueShift)
void	writeQ (Model model, BaseDirectory baseName)
void	writeW (Model model, BaseDirectory baseName)
void	writeAvgKern (Model model, BaseDirectory baseName)
void	writeAVectors1d (Model model, BaseDirectory baseName, Float lambda)
void	writeNoise (Model model, BaseDirectory baseName)
void	writeCovVectors1d (Model model, BaseDirectory baseName, Float lambda)
void	writeBVectors1d (Model model, BaseDirectory baseName)
void	writeBVectors3d (Model model, BaseDirectory baseName)
void	saveGridValues (Model *m, int r, int c, Float kx, Float ky, Float lambda)
void	writeGridValues3d (Model model, BaseDirectory baseName)
void	writeDTildaValues3d (Model model, BaseDirectory baseName, bool useFT)
void	writeAvgKernValues3d (Model model, BaseDirectory baseName)

Function Documentation

MATRIX calculateAvgK	(	MATRIX	wk,
		MAP_DEPTH_MATRIX	K,
		int	kx,
		int	ky
	)

Calculate the averaging kernel at a given kx, ky in wave space.

Parameters:

`[in]`	wk	A MATRIX containing the solution weights at a specific kx,ky in wave space.
`[in]`	K	A MAP_DEPTH_MATRIX containing the kernels.
`[in]`	kx	The x-coordinate in wave space.
`[in]`	ky	The y-coordinate in wave space.

Returns:: a Matrix containing the averaging kernel at a particular kx,ky.

void calculateDerivedValues	(	Model *	model,
		MAP_DEPTH_MATRIX	K,
		MAP_DEPTH_MATRIX	tau,
		MAP_DEPTH_MATRIX	Lambda,
		bool	DValueShift
	)

Calculate q, Averaging Kernel and noise squared.

Parameters:

`[in]`	model	A Model object.
`[in]`	K	Kernel matrix
`[in]`	tau	Travel-map matrix
`[in]`	Lambda	Travel-map noise covariance matrix
`[in]`	DValueShift	Boolean indicating whether to shift output matrices q, and averaging kernel, and noise.

void calculateDerivedValuesNoQ	(	Model *	model,
		MAP_DEPTH_MATRIX	K,
		MAP_DEPTH_MATRIX	Lambda,
		bool	DValueShift
	)

MATRIX calculateNoiseK	(	MATRIX	wk,
		MAP_DEPTH_MATRIX	Lambda,
		int	kx,
		int	ky
	)

Calculate the noise at a given kx, ky in wave space.

Parameters:

`[in]`	wk	A MATRIX containing the solution weights at a specific kx,ky in wave space.
`[in]`	Lambda	A MAP_DEPTH_MATRIX containing the input covariance.
`[in]`	kx	The x-coordinate in wave space.
`[in]`	ky	The y-coordinate in wave space.

Returns:: a Matrix containing the averaging kernel at a particular kx,ky.

MATRIX calculateQK	(	MATRIX	wk,
		MAP_DEPTH_MATRIX	tau,
		int	kx,
		int	ky
	)

Calculate the model, q, at a given kx, ky in wave space.

Parameters:

`[in]`	wk	A MATRIX containing the solution weights at a specific kx,ky in wave space.
`[in]`	tau	Travel-map matrix
`[in]`	kx	The x-coordinate in wave space.
`[in]`	ky	The y-coordinate in wave space.

Returns:: a Matrix containing the model, q, at a particular kx,ky.

void calculateQValues	(	Model *	model,
		MAP_DEPTH_MATRIX	tau,
		bool	DValueShift
	)

Calculate q

Parameters:

`[in]`	model	A Model object.
`[in]`	tau	Travel-map matrix
`[in]`	DValueShift	Boolean indicating whether to shift output matrix q.

void clearParameters ( Model * model )

Clear parameter values (mu and sigma) in the model. Output FITS files will not have sigma and mu parameters in the header.

Parameters:

[in,out] model A Model object.

void freeModel ( Model * model )

Free all memory associated with outputModel.

void initializeCalcQOutputModel	(	Model *	model,
		MAP_DEPTH_MATRIX	w
	)

void initializeForwardOutputModel	(	Model *	model,
		MAP_DEPTH_MATRIX	kernelTdm
	)

Initialize output model for use by forward calculation

Parameters:

`[in,out]`	model	Pointer to Model structure
`[in]`	kernelTdm	Matrix containing 3D Kernel.

void initializeModel	(	Model *	model,
		int	X,
		int	Y,
		int	M,
		int	Z
	)

Initialize Model object, allocate space for data and constant values. Use for OLA inversions.

Parameters:

`[in,out]`	model	Initialize this Model.
`[in]`	X	Number of grid points, Nx
`[in]`	Y	Number of grid points, Ny
`[in]`	M	Number of measurements
`[in]`	Z	Number of grid points, Nz

void initializeOutputModel	(	Model *	model,
		MAP_DEPTH_MATRIX	kernelTdm
	)

Initialize output model for use by RLS inversion

Parameters:

`[in,out]`	model	Pointer to Model structure
`[in]`	kernelTdm	Matrix containing 3D RLS Kernel.

void saveB	(	Model *	model,
		MATRIX	b,
		int	nStep
	)

Save inversion solution, b, for RLS 1D.

Parameters:

`[in,out]`	model	A Model object.
`[in]`	b	Solution matrix.
`[in]`	nStep	Parameter step number.

void saveGridValues	(	Model *	m,
		int	r,
		int	c,
		Float	kx,
		Float	ky,
		Float	lambda
	)

Save lambda values and kx, ky values for each wave number (RLS 3D inversion).

Parameters:

`[in,out]`	m	A Model object.
`[in]`	r	Row (index into matrix).
`[in]`	c	Column (index into matrix).
`[in]`	kx	Value of kx at (x,y)
`[in]`	ky	Value of ky at (x,y)
`[in]`	lambda	Value of lambda (regularization) parameter.

void saveW	(	Model *	model,
		MATRIX	w,
		int	kx,
		int	ky
	)

Save inversion weights, w, for wave number, kx, ky.

Parameters:

`[in,out]`	model	A Model object.
`[in]`	w	Inversion weights.
`[in]`	kx	X-index in Fourier space.
`[in]`	ky	Y-index in Fourier space.

void setParameters	(	Model *	model,
		Float	mu,
		Float	sigma
	)

Record parameter values in the model.

Parameters:

`[in,out]`	model	A Model object.
`[in]`	mu	Value for mu parameter.
`[in]`	sigma	Value for sigma parameter.

void setParameters2	(	Model *	model,
		Float *	values,
		int	nValues
	)

Record multiple parameter values in the model.

Parameters:

`[in,out]`	model	A Model object.
`[in]`	values	Pointer to array of parameter values. NOTE: there is a special ordering to the parameters, mu, sigma, then nu (which is optional).
`[in]`	nValues	How many parameter values.

void useNoMaps ( Model * model )

void writeAVectors1d	(	Model *	model,
		BaseDirectory *	baseName,
		Float	lambda
	)

Write the Averaging Kernel data for 1d problem.

Parameters:

`[in]`	model	The OutputModel containing the Averaging Kernel.
`[in]`	baseName	BaseDirectory structure for managing output file name.
`[in]`	lambda	Which lambda value was used for this solution.

void writeAvgKern	(	Model *	model,
		BaseDirectory *	baseName
	)

Write averaging kernel to file for OLA inversion.

Parameters:

`[in,out]`	model	A Model object.
`[in]`	baseName	BaseDirectory structure for managing output file name.

void writeAvgKernValues3d	(	Model *	model,
		BaseDirectory *	baseName
	)

Write averaging kernels and error covariance matrices for RLS 3D inversion.

Parameters:

`[in,out]`	model	A Model object.
`[in]`	baseName	BaseDirectory structure for managing output file name.

void writeBVectors1d	(	Model *	model,
		BaseDirectory *	baseName
	)

Write solution matrix for RLS 1D inversion.

Parameters:

`[in,out]`	model	A Model object.
`[in]`	baseName	BaseDirectory structure for managing output file name.

void writeBVectors3d	(	Model *	model,
		BaseDirectory *	baseName
	)

Write solution B vectors to FITS files matrix for RLS 3D inversion.

Parameters:

`[in,out]`	model	A Model object.
`[in]`	baseName	BaseDirectory structure for managing output file name.

void writeCovVectors1d	(	Model *	model,
		BaseDirectory *	baseName,
		Float	lambda
	)

Write the Error Covariance data for 1d problem.

Parameters:

`[in]`	model	Model structure containing the Error Covariance.
`[in]`	baseName	Which lambda step was used for this solution.
`[in]`	lambda	Which lambda value was used for this solution.

void writeDTildaValues3d	(	Model *	model,
		BaseDirectory *	baseName,
		bool	useFT
	)

Write d~ matrices for RLS 3D inversion.

Parameters:

`[in,out]`	model	A Model object.
`[in]`	baseName	BaseDirectory structure for managing output file name.
`[in]`	useFT	Boolean indicates if d~ matrices should be Fourier transformed.

void writeGridValues3d	(	Model *	model,
		BaseDirectory *	baseName
	)

Write lambda values and kx, ky values for each wave number (RLS 3D inversion). Write kxGridValues, kyGridValues and lambdaGridValues at each grid point to FITS files matrix for RLS 3D inversion.

Parameters:

`[in,out]`	model	A Model object.
`[in]`	baseName	BaseDirectory structure for managing output file name.

void writeNoise	(	Model *	model,
		BaseDirectory *	baseName
	)

Write noise estimate to file for OLA inversion.

Parameters:

`[in,out]`	model	A Model object.
`[in]`	baseName	BaseDirectory structure for managing output file name.

void writeQ	(	Model *	model,
		BaseDirectory *	baseName
	)

Write solution, q, for OLA inversion.

Parameters:

`[in,out]`	model	A Model object.
`[in]`	baseName	BaseDirectory structure for managing output file name.

void writeW	(	Model *	model,
		BaseDirectory *	baseName
	)

Write OLA inversion weights, w, to file.

Parameters:

`[in,out]`	model	A Model object.
`[in]`	baseName	BaseDirectory structure for managing output file name.

src/inversion/Model.c File Reference

Functions

Function Documentation