da.zeros_like instanciated arrays are not writeable when map_blocks is run over them Summary Dask arrays created with da.zeros_like are not writeable when used alongside with map_blocks. Trying to write in-place the received block array inside of the mapped function will produce the following error…

All input data of dask functions must be treated at all times as strictly immutable, no ifs, no buts. When immutability is not enforceable (e.g. a list or dict input parameter) and you do modify your input, things will go horribly wrong if you need to re-run your task at a later time or if the same…

[image] eschalkargans: Or, in other words: can we modify a chunk in-place? Well in some cases you can, and in others you cannot. Overall, I think it’s best to assume that Dask Array are immutables and blocks should be copied if modified. In any case, there is at least some lack of documentat…

Hello, Thanks for your answer! This is quite a limitation to updating existing Python code with Numpy to make it use Dask, if the existing code performs of a lot of in-place mutations on its arrays. For example, a single array that go through lots of successive updates in a processing chain. A wo…

[image] eschalkargans: However this is the only way to keep immutable inputs while reuse complex Numpy code, using dask more as a “wrapper” of Numpy code applied on chunks (allowing code-reuse). I think this is the correct approach. [image] eschalkargans: From my current experiences, try…

`da.zeros_like` instanciated arrays are not writeable when `map_blocks` is run over them

Dask Array

eschalkargans April 22, 2024, 9:13am 2

PS: I found a similar open issue from 4 years ago on the Dask’s GitHub: np.asarray returns non-writable array #6520

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`da.zeros_like` instanciated arrays are not writeable when `map_blocks` is run over them

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