pydata / pydata/sparse

Support assignment of DOK to DOK subarray

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enhancement
Dominant language
Python
Stars
668
Forks
141
Avg merge
2d 8h
Merged PRs (30d)
4

Description

Is your feature request related to a problem? Please describe.
Allow assignment of DOK subarray without requiring creation of dense array.

Describe the solution you'd like

>>> import sparse
>>> import numpy as np
>>> array = sparse.DOK([3,4,5], dtype=np.int8)
>>> subarray = sparse.DOK([4,5], dtype=np.int8)
>>> subarray[1,1] = 1
>>> array[0] = subarray
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/tyler/lib/anaconda3/envs/glia/lib/python3.8/site-packages/sparse/_dok.py", line 317, in __setitem__
    value = np.asanyarray(value)
  File "/home/tyler/lib/anaconda3/envs/glia/lib/python3.8/site-packages/numpy/core/_asarray.py", line 136, in asanyarray
    return array(a, dtype, copy=False, order=order, subok=True)
  File "/home/tyler/lib/anaconda3/envs/glia/lib/python3.8/site-packages/sparse/_sparse_array.py", line 222, in __array__
    raise RuntimeError(
RuntimeError: Cannot convert a sparse array to dense automatically. To manually densify, use the todense method.

Describe alternatives you've considered

>>> array[0] = subarray.todense()
>>>

Additional context
If subarray is huge and very sparse, todense is not feasible.

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start with DOK.setitem in sparse/_dok.py and the sparse-array conversion behavior in sparse/_sparse_array.py, using the issue's example to reproduce the failure. Done means assigning a sparse DOK subarray succeeds without implicitly converting it to a dense array; the issue does not mention a specific test file.

Written by the indexing model from the issue text.

Assessment

Tech stack
numpy, python
Domain
data
Issue type
Feature
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
35/100

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