linear auto-regressive predictive filter More...

#include <stdint.h>
#include <string.h>
#include <stdio.h>
#include <ctype.h>
#include <malloc.h>
#include <math.h>
#include <stdlib.h>
#include <semaphore.h>
#include <sched.h>
#include <assert.h>
#include <sys/stat.h>
#include <gsl/gsl_multimin.h>
#include <gsl/gsl_multifit.h>
#include <fitsio.h>
#include <gsl/gsl_vector.h>
#include <gsl/gsl_matrix.h>
#include <gsl/gsl_math.h>
#include <gsl/gsl_eigen.h>
#include <gsl/gsl_cblas.h>
#include <gsl/gsl_blas.h>
#include <time.h>
#include "CommandLineInterface/CLIcore.h"
#include "00CORE/00CORE.h"
#include "COREMOD_tools/COREMOD_tools.h"
#include "COREMOD_memory/COREMOD_memory.h"
#include "COREMOD_arith/COREMOD_arith.h"
#include "COREMOD_iofits/COREMOD_iofits.h"
#include "statistic/statistic.h"
#include "info/info.h"
#include "linopt_imtools/linopt_imtools.h"
#include "linARfilterPred/linARfilterPred.h"

Include dependency graph for linARfilterPred.c:

Macros
#define	MODULE_SHORTNAME_DEFAULT ""

#define	MODULE_DESCRIPTION "Linear auto-regressive predictive filters"

#define	MODULE_APPLICATION "milk"

Functions
errno_t	LINARFILTERPRED_LoadASCIIfiles_cli ()

errno_t	LINARFILTERPRED_SelectBlock_cli ()

errno_t	linARfilterPred_repeat_shift_X_cli ()

errno_t	LINARFILTERPRED_Build_LinPredictor_cli ()

errno_t	LINARFILTERPRED_Apply_LinPredictor_cli ()

errno_t	LINARFILTERPRED_ScanGain_cli ()

errno_t	LINARFILTERPRED_PF_updatePFmatrix_cli ()

errno_t	LINARFILTERPRED_PF_RealTimeApply_cli ()

static errno_t	init_module_CLI ()

int	NBwords (const char sentence[])

long	LINARFILTERPRED_LoadASCIIfiles (double tstart, double dt, long NBpt, long NBfr, const char *IDoutname)
	load ascii file(s) into image cube More...

imageID	LINARFILTERPRED_SelectBlock (const char IDin_name, const char IDblknb_name, long blkNB, const char *IDout_name)

imageID	linARfilterPred_repeat_shift_X (const char IDin_name, long NBstep, const char IDout_name)
	Expand 2D image/matrix in X direction by repeat and shift. More...

imageID	LINARFILTERPRED_Build_LinPredictor (const char IDin_name, long PForder, float PFlag, double SVDeps, double RegLambda, const char IDoutPF_name, __attribute__((unused)) int outMode, int LOOPmode, float LOOPgain, int testmode)

imageID	LINARFILTERPRED_Apply_LinPredictor_RT (const char IDfilt_name, const char IDin_name, const char *IDout_name)

imageID	LINARFILTERPRED_Apply_LinPredictor (const char IDfilt_name, const char IDin_name, float PFlag, const char *IDout_name)

imageID	LINARFILTERPRED_PF_updatePFmatrix (const char IDPF_name, const char IDPFM_name, float alpha)

imageID	LINARFILTERPRED_PF_RealTimeApply (const char IDmodevalIN_name, long IndexOffset, int semtrig, const char IDPFM_name, long NBPFstep, const char *IDPFout_name, int nbGPU, long loop, long NBiter, int SAVEMODE, float tlag, long PFindex)

float	LINARFILTERPRED_ScanGain (char *IDin_name, float multfact, float framelag)

Detailed Description

linear auto-regressive predictive filter

Implements Empirical Orthogonal Functions

Macro Definition Documentation

◆ MODULE_APPLICATION

#define MODULE_APPLICATION "milk"

◆ MODULE_DESCRIPTION

#define MODULE_DESCRIPTION "Linear auto-regressive predictive filters"

◆ MODULE_SHORTNAME_DEFAULT

#define MODULE_SHORTNAME_DEFAULT ""

Function Documentation

◆ init_module_CLI()

static errno_t init_module_CLI ( )

static

◆ LINARFILTERPRED_Apply_LinPredictor()

imageID LINARFILTERPRED_Apply_LinPredictor	(	const char *	IDfilt_name,
		const char *	IDin_name,
		float	PFlag,
		const char *	IDout_name
	)

◆ LINARFILTERPRED_Apply_LinPredictor_cli()

errno_t LINARFILTERPRED_Apply_LinPredictor_cli ( )

◆ LINARFILTERPRED_Apply_LinPredictor_RT()

imageID LINARFILTERPRED_Apply_LinPredictor_RT	(	const char *	IDfilt_name,
		const char *	IDin_name,
		const char *	IDout_name
	)

◆ LINARFILTERPRED_Build_LinPredictor()

imageID LINARFILTERPRED_Build_LinPredictor	(	const char *	IDin_name,
		long	PForder,
		float	PFlag,
		double	SVDeps,
		double	RegLambda,
		const char *	IDoutPF_name,
		__attribute__((unused)) int	outMode,
		int	LOOPmode,
		float	LOOPgain,
		int	testmode
	)

Purpose

Build predictive filter from real-time AO telemetry

Masking

Optional input and output pixel masks select active input & output

Loop mode

If LOOPmode = 1, operate in a loop, and re-run filter computation everytime IDin_name changes

Input parameters: dynamic mode

if <IFoutPF_name>_PFparam image exist, read parameters from it: PFlag, SVDeps, RegLambda, LOOPgain create it in shared memory by default

Returns: If testmode=2, write 3D output filter; output filter image indentifier

Code Description

Reading Parameters from Image

If image named <IDoutPF_name>_PFparam exists, the predictive filter parameters are read from it instead of the function arguments.
This mode is particularly useful in LOOP mode if the user needs to change the parameters between LOOP iterations.

Selecting input values

The goal of this function is to build a linear link between input and output variables.
Input variables values are provided by the input telemetry image which is first read to measure dimensions, and allocate memory.
Note that an optional variable selection step allows only a subset of the telemetry variables to be considered.

Read input telemetry image IDin_name to measure xsize, ysize and number of samples

If 2D image:

xysize <- size[0] is number of variables
nbspl <- size[1] is number of samples

If 3D image

xysize <- size[0] * size[1] is number of variables
nbspl <- size[2] is number of samples

Once input telemetry size measured, arrays are created:

pixarray_x : x coordinate of each variable (useful to keep track of spatial coordinates)
pixarray_y : y coordinate of each variable (useful to keep track of spatial coordinates)
pixarray_xy : combined index (avoids re-computing index frequently)
ave_inarray : time averaged value, useful because the predictive filter often needs average to be zero, so we will remove it

Select input variables from mask (optional)

If image "inmask" exists, use it to select which variables are active. Otherwise, all variables are active
The number of active input variables is stored in NBpixin.

Selecting Output Variables

By default, the output variables are the same as the input variables, so the prediction is performed on the same variables as the input.
With inmask and outmask, input AND output variables can be selected amond the telemetry.

Arrays are created:

outpixarray_x : x coordinate of each output variable (useful to keep track of spatial coordinates)
outpixarray_y : y coordinate of each output variable (useful to keep track of spatial coordinates)
outpixarray_xy : combined output index (avoids re-computing index frequently)

Reading PFlag from image (optional)

PFlag_run needs to be read before entering the loop as some array sizes depend on its value.

Build Empty Data Matrix

Note: column / row description follows FITS file viewing conventions.
The data matrix is build from the telemetry. Each column (= time sample) of the data matrix consists of consecutives columns (= time sample) of the input telemetry.
Variable naming:

NBmvec is the number of telemetry vectors (each corresponding to a different time) in the data matrix.
mvecsize is the size of each vector, equal to NBpixin times PForder

Data matrix is stored as image of size NBmvec x mvecsize, to be fed to routine compute_SVDpseudoInverse in linopt_imtools (CPU mode) or in cudacomp (GPU mode)

Regularization can be added to penalize strong coefficients in the predictive filter. It is optionally implemented by adding extra columns at the end of the data matrix.

Data matrix conventions :

each column (ii = cst) is a measurement
m index is measurement
dt*NBpixin+pix index is pixel

Predictive Filter Computation

In LOOP mode, LOOP STARTS HERE

Prepare data matrix PFmatD

STEP: Read parameters from external image (optional)

STEP: In loop mode, wait for input data to arrive

STEP: Copy IDin to IDincp

Necessary as input may be continuously changing between consecutive loop iterations.

STEP: if DC_MODE==1, compute average value from each variable

STEP: Fill up data matrix PFmatD from input telemetry

STEP: Write regularization coefficients (optional)

Compute pseudo-inverse of PFmatD

STEP: Compute Pseudo-Inverse of PFmatD

If using MAGMA, call function CUDACOMP_magma_compute_SVDpseudoInverse()
Otherwise, call function linopt_compute_SVDpseudoInverse()

Result (pseudoinverse) is stored in image PFmatC

Assemble Predictive Filter

In LOOP mode, LOOP ENDS HERE

◆ LINARFILTERPRED_Build_LinPredictor_cli()

errno_t LINARFILTERPRED_Build_LinPredictor_cli ( )

◆ LINARFILTERPRED_LoadASCIIfiles()

long LINARFILTERPRED_LoadASCIIfiles	(	double	tstart,
		double	dt,
		long	NBpt,
		long	NBfr,
		const char *	IDoutname
	)

load ascii file(s) into image cube

resamples sequence(s) of data points INPUT FILES HAVE TO BE NAMED seq000.dat, seq001.dat etc...

file starts at tstart, sampling = dt NBpt per file NBfr files

◆ LINARFILTERPRED_LoadASCIIfiles_cli()

errno_t LINARFILTERPRED_LoadASCIIfiles_cli ( )

◆ LINARFILTERPRED_PF_RealTimeApply()

imageID LINARFILTERPRED_PF_RealTimeApply	(	const char *	IDmodevalIN_name,
		long	IndexOffset,
		int	semtrig,
		const char *	IDPFM_name,
		long	NBPFstep,
		const char *	IDPFout_name,
		int	nbGPU,
		long	loop,
		long	NBiter,
		int	SAVEMODE,
		float	tlag,
		long	PFindex
	)

measure time

◆ LINARFILTERPRED_PF_RealTimeApply_cli()

errno_t LINARFILTERPRED_PF_RealTimeApply_cli ( )

◆ LINARFILTERPRED_PF_updatePFmatrix()

imageID LINARFILTERPRED_PF_updatePFmatrix	(	const char *	IDPF_name,
		const char *	IDPFM_name,
		float	alpha
	)

◆ LINARFILTERPRED_PF_updatePFmatrix_cli()

errno_t LINARFILTERPRED_PF_updatePFmatrix_cli ( )

◆ linARfilterPred_repeat_shift_X()

imageID linARfilterPred_repeat_shift_X	(	const char *	IDin_name,
		long	NBstep,
		const char *	IDout_name
	)

Expand 2D image/matrix in X direction by repeat and shift.

◆ linARfilterPred_repeat_shift_X_cli()

errno_t linARfilterPred_repeat_shift_X_cli ( )

◆ LINARFILTERPRED_ScanGain()

float LINARFILTERPRED_ScanGain	(	char *	IDin_name,
		float	multfact,
		float	framelag
	)

◆ LINARFILTERPRED_ScanGain_cli()

errno_t LINARFILTERPRED_ScanGain_cli ( )

◆ LINARFILTERPRED_SelectBlock()

imageID LINARFILTERPRED_SelectBlock	(	const char *	IDin_name,
		const char *	IDblknb_name,
		long	blkNB,
		const char *	IDout_name
	)

◆ LINARFILTERPRED_SelectBlock_cli()

errno_t LINARFILTERPRED_SelectBlock_cli ( )

◆ NBwords()

int NBwords ( const char sentence[] )

Macros

Functions