In article
<f6219865-59f4-4bf8-8718-67884c9df226@[EMAIL PROTECTED]
>,
Conor <cmancone@[EMAIL PROTECTED]
> wrote:
> Arbitrary bin sizes should be pretty easy to program. You just need
> to map your data points appropriately. For instance if you had the
> data set:
>
> x = randomu(seed,100)
>
> and you wanted bins from:
> [0-.1,.1-.3,.3-.35,.35-.8,.8-1]
>
> you might do something like this:
>
> x = randomu(seed,100)
> bins = [ [0,.1], [.1,.3], [.3,.35], [.35,.8], [.8,1] ]
> newx = fltarr(n_elements(x))
> for i=0,n_elements(bins[0,*])-1 do begin
> w = where( x ge bins[0,i] and x lt bins[1,i], c )
> if c gt 0 then newx[w] = i+.5
> endfor
>
> hist = histogram(newx,binsize=1.0,min=0)
> plothist,newx
This will work, but will be extremely slow because you test every value
in the input array once for every bin.
The VALUE_LOCATE approach will be much faster, particularly for large
numbers of bins, as it does a binary search.
Ken Bowman
|
