On Thu, Aug 23, 2012 at 4:28 PM, Tom Grydeland <tom.grydeland@gmail.com>wrote:

Hi all,

I'm working on a system to translate (EXELIS/ITTVIS/RSI) IDL to Python/Numpy. The results are close to usable for many purposes, but some simple constructs are causing snags I was hoping you could help me with.

IDL's MAX function supports a second (output) argument which will contain the index of the maximum value. One can write

PRINT, MAX(x, id), id

to get the value and index of the maximum element. In Numpy, I could do

id = np.argmax(x); print x[id], id

to get the same results in two statements. However, I can give MAX an extra keyword /NAN to tell it to ignore NaN and infinities. In Numpy, I have np.nanmax, but no np.nanargmax.

In addition, MAX has the output keywords MIN=value and SUBSCRIPT_MIN=index to return the minimum value and its index from the same function call. Similarly, MIN has output keywords MAX=value and SUBSCRIPT_MAX=index, so I can get max/min values and their indices (ignoring NaNs) in a single call -- in Numpy I would need two calls, two indexing operations, one temporary array of values and one of indices.

Would it be hard to add similar functionality to Numpy's min() and max() functions? Is it desirable?

You may get more responses on the numpy mailing list. I think adding nanargmin/nanargmax is fine, and shouldn't be hard. Putting min/argmin keywords into max and vice versa looks weird to me. Ralf