• Alex (13/13) May 20 2019 given some array, is there some way to easily impose structure on
• Dennis (9/12) May 20 2019 It sounds like you're looking for ndslide from mir
• 9il (14/27) May 26 2019 Slightly updated version of the prev. example.

Alex <AJ gmail.com> writes:

given some array, is there some way to easily impose structure on 
that array at runtime?

void* data;

auto x = cast(byte[A,B,C])data;

X is then an AxBxC matrix.

I'm having to compute the index myself and it just seems 
unnecessary. A and B are not known at compile time though.

Obviously it should be just as efficient as computing the offset 
manually.

I could probably do this with a struct and override opIndex but 
I'm wondering what is already out there. If it's slower or 
consumes more memory than manual it's not worth it(since my code 
already calcs the index).

Dennis <dkorpel gmail.com> writes:

On Monday, 20 May 2019 at 12:09:02 UTC, Alex wrote:
 void* data;

 auto x = cast(byte[A,B,C])data;

 X is then an AxBxC matrix.

It sounds like you're looking for ndslide from mir
http://code.dlang.org/packages/mir-algorithm

```
ubyte data[];
auto x = x.sliced(A, B, C);
```

It should be pretty efficient, but I'm not certain whether it's 
as good as your hand-written code.

9il <ilyayaroshenko gmail.com> writes:

On Monday, 20 May 2019 at 12:09:02 UTC, Alex wrote:
 given some array, is there some way to easily impose structure 
 on that array at runtime?

 void* data;

 auto x = cast(byte[A,B,C])data;

 X is then an AxBxC matrix.

 I'm having to compute the index myself and it just seems 
 unnecessary. A and B are not known at compile time though.

 Obviously it should be just as efficient as computing the 
 offset manually.

 I could probably do this with a struct and override opIndex but 
 I'm wondering what is already out there. If it's slower or 
 consumes more memory than manual it's not worth it(since my 
 code already calcs the index).

Slightly updated version of the prev. example.
```
import mir.ndslice;
byte data[];
...
// canonical is `optional`
auto tensor = data.sliced(A, B, C).canonical;
...
byte elem = tensor[i, j, k];
auto matrix = tensor[i, j];
auto otherKindOfMatrix = tensor[0..$, i, j];
```

It is efficient as handwritten code.