Pearu Peterson said:

On Tue, 28 May 2002, Larry Denneau wrote:

...

Hello,

I recently discovered the following behavior when fetching values from a Numeric array. Can somebody offer some insight?

#1)

import Numeric

a = Numeric.zeros((2, 2), 'i') n = a[1, 1] # fetch interesting value from array print n a[1, 1] = 10 # change array print n # blam print type(n) # huh

[bash]$ python 1.py 0 10

[ deleted]

Use

a[1][1] = 10

and the output will be

0 0

I find it is an useful feature in Numeric to have both behaviours of either using a[1,1] or a[1][1]. You may want to dig into Numeric's userguide to get a more detailed explanation of the differences.

Regards, Pearu

Hi Pearu, I assume you mean n = a[1][1] which produces the expected behavior. All the Numpy documentation examples (see http://pfdubois.com/numpy/html2/numpy-6.html#pgfId-36033, "Getting and Stting Array Values") use the [x, y] notation instead of [x][y], so I would consider this a bug in the documentation, since the [x, y] method leads to unexpected behavior. I'm still curious what happens to the original array when n=a[1, 1] del(a) but that may have to wait until I have time to peruse the Numeric source. Thanks, Larry

Pearu Peterson writes:

an array with 0 rank. It seems that the Numeric documentation is missing (though, I didn't look too hard) the following rules of thumb:

If `a' is rank 1 array, then a[i] is Python scalar or object. [MISSING]

Or rather: - If `a' is rank 1 array with elements of type Int, Float, or Complex, then a[i] is Python scalar or object. [MISSING] - If `a' is rank 1 array with elements of type Int16, Int32, Float32, or Complex32, then a[i] is a rank 0 array. [MISSING] - If `a' is rank > 1 array, then a[i] is a sub-array a[i,...] The rank-0 arrays are the #1 question topic for users of my netCDF interface (for portability reasons, netCDF integer arrays map to Int32, not Int, so scalar integers read from a netCDF array are always rank-0 arrays), and almost everybody initially claims that it's a bug, so some education seems necessary. Konrad. -- ------------------------------------------------------------------------------- Konrad Hinsen | E-Mail: hinsen@cnrs-orleans.fr Centre de Biophysique Moleculaire (CNRS) | Tel.: +33-2.38.25.56.24 Rue Charles Sadron | Fax: +33-2.38.63.15.17 45071 Orleans Cedex 2 | Deutsch/Esperanto/English/ France | Nederlands/Francais -------------------------------------------------------------------------------

I don't think education is the answer here. We need to change Numeric to have uniform behavior across all typecodes.

I agree that this would be the better solution. But until this is done...

Having alternative behaviors for indexing based on the typecode can lead to very difficult to find bugs. Generic routines meant to work

The differences are not that important, in most circumstances rank-0 arrays and scalars behave in the same way. The problems occur mostly with code that does explicit type checking. The best solution, in my opinion, is to provide scalar objects corresponding to low-precision ints and floats, as part of NumPy. Konrad. -- ------------------------------------------------------------------------------- Konrad Hinsen | E-Mail: hinsen@cnrs-orleans.fr Centre de Biophysique Moleculaire (CNRS) | Tel.: +33-2.38.25.56.24 Rue Charles Sadron | Fax: +33-2.38.63.15.17 45071 Orleans Cedex 2 | Deutsch/Esperanto/English/ France | Nederlands/Francais -------------------------------------------------------------------------------

On Mon, 2002-06-03 at 10:54, Paul F Dubois wrote:

Konrad said:

...

The best solution, in my opinion, is to provide scalar objects corresponding to low-precision ints and floats, as part of NumPy.

Konrad.

This seems like a good idea. It's been an old source of confusion. On a related note, how does the community feel about retrofitting Numeric with unsigned shorts and unsigned ints. I've got the code to do it already written. -Travis