question #318

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1. adamreichold commented on Apr 12, 2022

   

   adamreichold

   on Apr 12, 2022

   Member

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   I think the easiest way to achieve this is to use ndarray's rows or Columns iterators and collect those, e.g.

   fn array2_to_vec2(array: PyReadonlyArray2<f64>) -> Vec<Vec<f64>> {
       array
           .as_array()
           .rows()
           .into_iter()
           .map(|row| row.iter().copied().collect::<Vec<_>>())
           .collect::<Vec<_>>()
   }

   But that said, this is an usual conversion as the resulting memory layout is relatively inefficient compared to store the two-dimensional array in a single allocation. Maybe you could expand a little on why you need Vec<Vec<_>> and we can find a way to reduce the number of separate allocations?
2. PyO3

   locked and limited conversation to collaborators on Apr 12, 2022
3. messense

   converted this issue into a discussion #320 on Apr 12, 2022