src/gromacs/gmxana/dens_filter.cpp

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  35
  36 #include "gmxpre.h"
  37
  38 /* dens_filter.c
  39  * Routines for Filters and convolutions
  40  */
  41
  42 #include "dens_filter.h"
  43
  44 #include <cmath>
  45
  46 #include "gromacs/math/vec.h"
  47 #include "gromacs/utility/smalloc.h"
  48
  49 gmx_bool convolution(int dataSize, real *x, int kernelSize, const real* kernel)
  50 {
  51     int   i, j, k;
  52     real *out;
  53     snew(out, dataSize);
  54     /* check validity of params */
  55     if (!x || !kernel)
  56     {
  57         return FALSE;
  58     }
  59     if (dataSize <= 0 || kernelSize <= 0)
  60     {
  61         return FALSE;
  62     }
  63
  64     /* start convolution from out[kernelSize-1] to out[dataSize-1] (last) */
  65     for (i = kernelSize-1; i < dataSize; ++i)
  66     {
  67         for (j = i, k = 0; k < kernelSize; --j, ++k)
  68         {
  69             out[i] += x[j] * kernel[k];
  70         }
  71     }
  72
  73     /* convolution from out[0] to out[kernelSize-2] */
  74     for (i = 0; i < kernelSize - 1; ++i)
  75     {
  76         for (j = i, k = 0; j >= 0; --j, ++k)
  77         {
  78             out[i] += x[j] * kernel[k];
  79         }
  80     }
  81
  82     for (i = 0; i < dataSize; i++)
  83     {
  84         x[i] = out[i];
  85     }
  86     sfree(out);
  87     return TRUE;
  88 }
  89
  90 /* Assuming kernel is shorter than x */
  91
  92 gmx_bool periodic_convolution(int datasize, real *x, int kernelsize,
  93                               const real *kernel)
  94 {
  95     int   i, j, idx;
  96     real *filtered;
  97
  98     if (!x || !kernel)
  99     {
 100         return FALSE;
 101     }
 102     if (kernelsize <= 0 || datasize <= 0 || kernelsize > datasize)
 103     {
 104         return FALSE;
 105     }
 106
 107     snew(filtered, datasize);
 108
 109     for (i = 0; (i < datasize); i++)
 110     {
 111         for (j = 0; (j < kernelsize); j++)
 112         {
 113             // add datasize in case i-j is <0
 114             idx          = i-j + datasize;
 115             filtered[i] += kernel[j]*x[idx % datasize];
 116         }
 117     }
 118     for (i = 0; i < datasize; i++)
 119     {
 120         x[i] = filtered[i];
 121     }
 122     sfree(filtered);
 123
 124     return TRUE;
 125 }
 126
 127
 128 /* returns discrete gaussian kernel of size n in *out, where n=2k+1=3,5,7,9,11 and k=1,2,3 is the order
 129  * NO checks are performed
 130  */
 131 void gausskernel(real *out, int n, real var)
 132 {
 133     int  i, j = 0, k;
 134     real arg, tot = 0;
 135     k = n/2;
 136
 137     for (i = -k; i <= k; i++)
 138     {
 139         arg  = (i*i)/(2*var);
 140         tot += out[j++] = std::exp(-arg);
 141     }
 142     for (i = 0; i < j; i++)
 143     {
 144         out[i] /= tot;
 145     }
 146 }