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bayesGrad.c
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Sun, Dec 1, 13:25

bayesGrad.c
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#include<stdio.h>
#include<signal.h>
#include<stdlib.h>
#include<string.h>
#include<math.h>
#include "dimension.h"
#include "structure.h"
#include "constant.h"
#include "fonction.h"
#include "bayesChires.h"
/******************************************************************************
* Name: bayesGrad.c
* Authors: EJ
* Date: 03/01/08
*
* Analyse a bayes.dat file and for each model, compute the deflection angle
* due to each lens for each image. Return the largest deflection produced
* (generally for the closest image) in a bayesGrad.dat file, to be analysed
* with the bayesResults.pl script.
*
* syntax : bayesGrad <.par>
*
* The bayes.dat file in the current directory is used.
* Output : bayesGrad.dat
*
******************************************************************************/
typedef void (*sighandler_t)(int);
static void signalReset();
int optInterrupt;
void help_msg()
{
fprintf(stderr, "Syntax : bayesGrad [OPTION] <.par>\n");
fprintf(stderr, "Available OPTIONS:\n");
fprintf(stderr, " -i <potential id> : compute gradients due to a specific potential\n");
exit(-1);
}
/* Print a line of bayesGrad.dat
*/
void printLine(FILE *bayes, long int ilens)
{
int i, j;
long int k;
double dx, dy;
struct point grad;
for ( i = 0 ; i < I.n_mult; i++ )
for ( j = 0 ; j < I.mult[i] ; j++ )
{
dx = dy = 0.;
if( ilens != -1 )
{
grad = e_grad_pot(&multi[i][j].C, ilens );
dx = multi[i][j].dr * grad.x;
dy = multi[i][j].dr * grad.y;
}
else
for (k = 0; k < G.nlens; k++ )
{
grad = e_grad_pot(&multi[i][j].C, k );
dx += multi[i][j].dr * grad.x;
dy += multi[i][j].dr * grad.y;
}
fprintf( bayes, "%lf ", sqrt(dx * dx + dy * dy));
}
fprintf( bayes, "\n" );
}
int main( int argc, char** argv )
{
extern struct pot lens[];
double **array; // contains the bayes.dat data
int nParam, j; // size of array
long int nVal, iVal, i;
FILE *bayes;
double *index; // list of bayes.dat lines
int seed; // random seed
int tmp;
// Check the arguments
if ( argc < 2 )
help_msg();
if ( strstr(argv[1], ".par") == NULL )
help_msg;
char id_lens[IDSIZE];
id_lens[0] = 0; // default empty
if( !strcmp(argv[1], "-i" ) )
{
strcpy(id_lens, argv[2]);
for( i = 1; i < argc-1 ; i++ )
argv[i]=argv[i+2];
argc-=2;
}
// Read the .par file
init_grille( argv[1], 1);
// look for lens id in lens[] array
long int ilens = -1;
if( id_lens[0] != 0 )
{
i = 0;
while( i < G.nlens && strcmp(id_lens, lens[i].n)) i++;
ilens = i;
}
// Read constraints
readConstraints();
if ( G.nmsgrid != G.nlens )
{
prep_non_param();
}
// Read the bayes.dat file
array = readBayesModels(&nParam, &nVal);
if ( array == NULL )
{
fprintf(stderr, "ERROR: bayes.dat file not found\n");
return -1;
}
// Write the header of the bayesGrad.dat file
bayes = fopen( "bayesGrad.dat", "w" );
fprintf( bayes, "#Nsample\n");
fprintf( bayes, "#Chi2\n");
for ( i = 0 ; i < I.n_mult ; i++ )
for ( j = 0; j < I.mult[i]; j++ )
fprintf( bayes, "#Image %s\n", multi[i][j].n);
// Prepare the index list
index = (double *) malloc((unsigned) nVal * sizeof(double));
for ( i = 0 ; i < nVal ; i++ ) index[i] = i;
seed = -2;
// Handle CTRL-C to interrupt the optimisation but not lenstool
signal(SIGINT, signalReset);
optInterrupt = 0;
// Loop over each line
for ( i = 0; i < nVal && !optInterrupt; i++ )
{
// Randomly draw a line from index array
// tmp = (int) floor(d_random(&seed) * (nVal - i));
// iVal = index[i + tmp];
// and swap the indexes in the index list
// index[tmp] = i;
iVal = i;
// Set the lens parameters from <array>
setBayesModel( iVal, nVal, array );
printf( "INFO: Compute the bayesGrad.dat file %ld/%ld [CTRL-C to interrupt]\r", i + 1, nVal);
fflush(stdout);
fprintf( bayes, "%ld %lf ", iVal, array[0][iVal] );
printLine(bayes, ilens);
fflush(bayes);
}
printf( "\n" );
fclose(bayes);
free( array );
return 0;
}
static void signalReset()
{
signal(SIGINT, SIG_DFL);
optInterrupt = 1;
printf( "\nINFO: Optimisation interrupted by CTRL-C\r");
}

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