stats.c

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/* 
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of this software and associated documentation files (the "Software"), to deal
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 */


#include "extern.h"

#include "channels.h"

#include "proto.h"
#include "caclib_proto.h"

int feed;

struct DATASET dataset;

int mySort(a, b)
struct CHANNELS *a;
struct CHANNELS *b;
{
  return( (*a).x < (*b).x ? 1 : (*a).x > (*b).x ? -1 : 0 );
}


/* Prepare & flag bad channel data */
int prepareData(p1, p2)
struct SCAN_INFO *p1;
struct SCAN_INFO *p2;
{
  int i;

  st_report("PD: Preparing Data");

  p1->len = p1->head.datalen / sizeof(float);
  p2->len = p2->head.datalen / sizeof(float);

/* Assume all data good for the first pass */
  for(i=0;i<p1->len;i++)
  {
    if(fabs(p1->data[i]) > 1.0e-20)
    {
      p1->good[i] = 1;
    }
    else
    {
      p1->good[i] = 0;
      st_report("PD: Flagging X-Chan %3d: Value: %10.6f", i, p1->data[i]);
    }

    if(fabs(p2->data[i]) > 1.0e-20)
    {
      p2->good[i] = 1;
    }
    else
    {
      p2->good[i] = 0;
      st_report("PD: Flagging Y-Chan %3d: Value: %10.6f", i, p2->data[i]);
    }
  }

  return(0);
}


/* Find mean of datasets */
int findMean(p1, p2, pass)
struct SCAN_INFO *p1;
struct SCAN_INFO *p2;
int pass;
{
  int i, j;

  st_report(" ");

  p1->mean = p2->mean = 0.0;

  st_report("CM: Computing Pass: %d Mean", pass);

  for(i=0,j=0;i<p1->len;i++)
  {
    if(p1->good[i] && p2->good[i])
    {
      p1->mean   += p1->data[i];
      p2->mean   += p2->data[i];
      j++;
    }
  }

  p1->mean /= (double)j;
  p2->mean /= (double)j;

  st_report("CM: First-Pass: Mean-X = %10.6f, Mean-Y = %10.6f, %4d Good Channels", 
                        p1->mean, p2->mean, j);

  return(j);
}


int remove10X(p1, p2, pass)
struct SCAN_INFO *p1;
struct SCAN_INFO *p2;
int pass;
{
  int i;

  st_report("Removing any Channels 10X Mean: Pass %d", pass);
  /* First cut at removing wild bad channels */
  for(i=0;i<p1->len;i++)
  {
    if(p1->good[i])
    {
     /* If any channel is 10 time worst then mean */
      if((fabs(p1->data[i]) / fabs(p1->mean)) > 10.0)
      {
        st_report("Flagging X-Chan %3d: Value: %10.6f: Factor: %.2f", 
                                        i, p1->data[i], fabs(p1->data[i]) / fabs(p1->mean));
        p1->good[i] = 0; /* Flag as bad */
      }
    }

    if(p2->good[i])
    {
      if((fabs(p2->data[i]) / fabs(p2->mean)) > 10.0)
      {
        st_report("Flagging Y-Chan %3d: Value: %10.6f: Factor: %.2f", 
                                        i, p2->data[i], fabs(p2->data[i]) / fabs(p2->mean));
        p2->good[i] = 0; /* Flag as bad */
      }
    }
  }

  return(0);
}


int correlateScans()
{
  int i, j, n, m, good, write_good, g;
  double linear;
  struct SCAN_INFO *p1, *p2;
  struct CHANNELS *chans;


  st_report("Entering correlateScans(): %.2f", h0->head.scan);

  bzero((char *)&dataset, sizeof(dataset));

  p1 = &dataset.info[0];
  p2 = &dataset.info[1];
  chans = &dataset.chans[0];

  /* Load the data into the struct */

  /* Channel #1 */
  bcopy((char *)&h0->head, (char *)&p1->head, sizeof(struct HEADER));
  bcopy((char *)&h0->head, (char *)&p2->head, sizeof(struct HEADER));

  p1->len = h0->head.noint;
  for(i=0;i<p1->len;i++)
  {
    p1->data[i] = h0->yplot1[i];
  }
  p1->rms = (2.0 * p1->head.stsys) / sqrt(p1->head.inttime * fabs(p1->head.freqres * 1e6));

  /* Channel #2 */
  p2->len = h0->head.noint;
  for(i=0;i<p2->len;i++)
  {
    p2->data[i] = h0->yplot2[i];
  }
  p2->rms = (2.0 * p2->head.stsys) / sqrt(p2->head.inttime * fabs(p2->head.freqres * 1e6));

  prepareData(p1, p2);

  good = findMean(p1, p2, 1);

  /* Now loop until no change in mean's or 5 times */
  for(m=0;m<5;m++)
  {
    remove10X(p1, p2, 2+m);

    g = findMean(p1, p2, 2+m);

    if(g == good) /* No change */
    {
      st_report("Converged on %d tries", m+2);
      break;
    }
    good = g;
  }

  st_report(" ");
  st_report("Now Remove any Channels 5X the rms");

  /* If still good, copy to sort array */

  n = p1->len;
  for(i=0,j=0;i<n;i++)
  {
    if(p1->good[i] && p2->good[i]) /* Already Flagged bad? */
    {
      if((p1->data[i] > (p1->mean - p1->rms * 5.0)) && (p1->data[i] < (p1->mean + p1->rms * 5.0)))
      {
        if((p2->data[i] > (p2->mean - p2->rms * 5.0)) && (p2->data[i] < (p2->mean + p2->rms * 5.0)))
        {
          chans[j].x    = (double)p1->data[i];
          chans[j].y    = (double)p2->data[i];
          chans[j].c    = (double)i;
          chans[j].good = 1;
          j++;
        }
        else
        {
          st_report("Y-Channel %4d: %10.6f > Mean-Y: %10.6f", i, p2->data[i], p2->mean*5.0);
        }
      }
      else
      {
        st_report("X-Channel %4d: %10.6f > Mean-X: %10.6f", i, p1->data[i], p1->mean*5.0);
      }
    }
    else
    {
      st_report("Skipping Already Flagged Channel: %4d, %14.6f %14.6f", 
                                        i, p1->data[i], p2->data[i]);
    }
  }

  /* From now on only use j channels */

  st_report(" ");
  st_report("Sorting %d channels", j);

  qsort(chans, j, sizeof(struct CHANNELS), (__compar_fn_t)mySort);

  for(i=0;i<j;i++)
  {
    dataset.xd[i] = chans[i].x;
    dataset.yd[i] = chans[i].y;
    dataset.sig[i] = 1.0;
  }

  st_report(" ");
  st_report("Fitting linear regression using grace_fit()");

  fitcurve(dataset.xd, dataset.yd, j, 1, dataset.coeff);

//  linear_regression(j, dataset.xd, dataset.yd, dataset.coeff);

  st_report("Ignoring Results with vaules 1.5 times rms: %10.6f", p2->rms);

  p2->rms *= 1.5;

  /* Copy results into H3 */

  hzero(&H3);  /* Clear it out */
  _copyImage(h0, &H3);

  write_good = 0;
  for(i=0;i<j;i++)
  {
    if(!chans[i].good)
    {
      st_report("Found a Bad one: %d:", chans[i].c);

      continue;
    }

    linear = dataset.coeff[1] * chans[i].x + dataset.coeff[0];

    if(chans[i].y < linear-p2->rms)
    {
      st_report("Ignoring channel[%4d]: data %10.6f < %10.6f", 
                        (int)chans[i].c, chans[i].y, linear-p2->rms);
      continue;
    }

    if(chans[i].y > linear+p2->rms)
    {
      st_report("Ignoring channel[%4d]: data %10.6f > %10.6f", 
                        (int)chans[i].c, chans[i].y, linear+p2->rms);
      continue;
    }

    if(verboseLevel & VERBOSE_STATS)
    {
      st_print("# Channel %4d\n", (int)chans[i].c);
      st_print("%10.6f %10.6f %10.6f %10.6f %10.6f\n", 
                        chans[i].x,  
                        chans[i].y, 
                        linear, 
                        linear + p2->rms, 
                        linear - p2->rms);
    }

    H3.bplot[i]  = chans[i].x;          /* Put the x-axis in the bplot array */
    H3.yplot1[i] = chans[i].y;
    H3.yplot2[i] = linear;
    H3.yplot3[i] = linear + p2->rms;
    H3.yplot4[i] = linear - p2->rms;

    write_good++;
  }

  H3.mode         = MODE_XCOR;
  H3.plotwin      = 1;
  H3.narray       = 4;
  H3.nsize        = j * sizeof(float);
  H3.pinfo.bplot  = j;

  _copyImage(&H3, &H0); /* Copy it int the default holder */

  _h0();  /* Point to H0 */

  sendImageStruct(&H0, MODE_XCOR);

  st_report("Wrote %d data points", write_good);

  return(0);
}