This example calculates the grouping and quality arrays that represent the input data (here the contents of the y array) after it has been adaptively grouped to at least 20 counts per group.

Here we take the function

y = 3 + 30 * exp( -(x-2)^2 / 0.1 )

and adaptively group it by 15 counts per group. The plot shows the original data (the solid line and the crosses) and the grouped data (as the red squares); the latter has been normalised by the width of each group and is displayed at the left-edge of each group.

Unlike the simple grouping done by grpNumCounts() - where only the end element(s) may have non-zero quality values - the adaptive grouping scheme can create groups with non-zero quality anywhere in the array. The code below identifies these points and marks them with a solid-yellow circle.

chips> i = where( g == 1 )
chips> j = where( q[i] != 0 )
chips> curve( x[i][j], yavg[j] )
chips> symbol bigpoint
chips> symbol yellow