...

This is a difficult question for me, but I think the answer is that

the

...

use of _bytestride is very limited. _bytestride is used to compute

the

...

"natural" strides of an almost contiguous array, e.g. a field of a recarray. That is, given a bytestride and a shape, the strides

of a

...

field of a contiguous RecArray are implied.

Yes, I was trying to use _bytestride mainly in RecArray contexts. It

just

happens that I've used a NumArray object to check the _bytestride

behaviour.

...

However, once we start slicing (say in more than one dimension), _strides contains more and more information and is no longer implied

by

...

just the shape and bytestride but also by the history of slicing.

From

...

that perspective, it's not clear what _bytestride can be relied

upon

...

for in general or that it needs to be (or can be) kept up to date

during

...

slicing.

Well, for the time being it seems that RecArray does not support multidimensionalty, so I can just use _bytestride as a shortcut of _strides[0] here.

I wouldn't.

I had a veritable stroke of genius and talked to JC Hsu... He suggested doing concrete examples and also indicated that full multi-dimensional RecArray support is almost all (except for repr'ing) there.

To be clear, in general, _bytestride != _itemsize. For a RecArray field (of a contiguous RecArray) where each row contains just one element, generally _strides[-1] == _bytestride != _itemsize:

...

...

r = rec.array(buffer=None, formats="i2,(3,4)f4,a3", shape=10) r.field(0).info()

class: <class 'numarray.numarraycore.NumArray'> shape: (10,) strides: (53,) byteoffset: 0 bytestride: 53 itemsize: 2 aligned: 0 contiguous: 0 data: <memory at 0x08256c10 with size:0x00000212 held by object 0x40764db8 aliasing object 0x00000000> byteorder: little byteswap: 0 type: Int16

For a RecArray field where each row contains more than one element, _strides[-1] == itemsize:

...

...

r.field(1).info()

class: <class 'numarray.numarraycore.NumArray'> shape: (10, 3, 4) strides: (53, 16, 4) byteoffset: 2 bytestride: 53 itemsize: 4 aligned: 0 contiguous: 0 data: <memory at 0x08256978 with size:0x00000212 held by object 0x407613a8 aliasing object 0x00000000> byteorder: little byteswap: 0 type: Float32

The situation gets more complicated still when someone starts slicing the field or RecArray:

...

...

x[::3].info()

class: <class 'numarray.numarraycore.NumArray'> shape: (4, 3, 4) strides: (159, 16, 4) byteoffset: 2 bytestride: 53 itemsize: 4 aligned: 0 contiguous: 0 data: <memory at 0x08256c10 with size:0x00000212 held by object 0x40764db8 aliasing object 0x00000000> byteorder: little byteswap: 0 type: Float32

Multi-dimensional RecArrays add their own twist:

...

...

r = rec.array(buffer=None, formats="i2,(3,4)f4,a3",shape=(10,10,10)) r.field(1).info()

class: <class 'numarray.numarraycore.NumArray'> shape: (10, 10, 10, 3, 4) strides: (5300, 480, 48, 16, 4) byteoffset: 2 bytestride: 53 itemsize: 4 aligned: 0 contiguous: 0 data: <memory at 0x08257dd0 with size:0x0000cf08 held by object 0x40763a70 aliasing object 0x00000000> byteorder: little byteswap: 0 type: Float32

HTH, Todd