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[pre-commit.ci] pre-commit autoupdate (#268)
* [pre-commit.ci] pre-commit autoupdate updates: - [github.com/astral-sh/ruff-pre-commit: v0.0.276 → v0.0.292](astral-sh/ruff-pre-commit@v0.0.276...v0.0.292) - [github.com/psf/black: 23.3.0 → 23.9.1](psf/black@23.3.0...23.9.1) - [github.com/executablebooks/mdformat: 0.7.16 → 0.7.17](hukkin/mdformat@0.7.16...0.7.17) - [github.com/codespell-project/codespell: v2.2.5 → v2.2.6](codespell-project/codespell@v2.2.5...v2.2.6) - [github.com/abravalheri/validate-pyproject: v0.13 → v0.14](abravalheri/validate-pyproject@v0.13...v0.14) * fix typos --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Deepak Cherian <[email protected]>
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.pre-commit-config.yaml

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repos:
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- repo: https://github.com/astral-sh/ruff-pre-commit
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# Ruff version.
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rev: 'v0.0.276'
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rev: 'v0.0.292'
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hooks:
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- id: ruff
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args: ["--fix"]
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- id: check-docstring-first
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- repo: https://github.com/psf/black
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rev: 23.3.0
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rev: 23.9.1
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hooks:
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- id: black
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- repo: https://github.com/executablebooks/mdformat
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rev: 0.7.16
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rev: 0.7.17
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hooks:
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- id: mdformat
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additional_dependencies:
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args: [--extra-keys=metadata.kernelspec metadata.language_info.version]
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- repo: https://github.com/codespell-project/codespell
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rev: v2.2.5
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rev: v2.2.6
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hooks:
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- id: codespell
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additional_dependencies:
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- tomli
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- repo: https://github.com/abravalheri/validate-pyproject
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rev: v0.13
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rev: v0.14
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hooks:
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- id: validate-pyproject

docs/source/implementation.md

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1. Group labels must be known at graph construction time, so this only works for numpy arrays.
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1. This does require more tasks and a more complicated graph, but the communication overhead can be significantly lower.
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1. The detection of "cohorts" is currently slow but could be improved.
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1. The extra effort of detecting cohorts and mul;tiple copying of intermediate blocks may be worthwhile only if the chunk sizes are small
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1. The extra effort of detecting cohorts and multiple copying of intermediate blocks may be worthwhile only if the chunk sizes are small
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relative to the approximate period of group labels, or small relative to the size of spatially localized groups.
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### Example : sensitivity to chunking

docs/source/user-stories/custom-aggregations.ipynb

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" # The next are for dask inputs and describe how to reduce\n",
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" # the data in parallel\n",
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" chunk=(\"sum\", \"nanlen\"), # first compute these blockwise : (grouped_sum, grouped_count)\n",
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" combine=(\"sum\", \"sum\"), # reduce intermediate reuslts (sum the sums, sum the counts)\n",
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" combine=(\"sum\", \"sum\"), # reduce intermediate results (sum the sums, sum the counts)\n",
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" finalize=lambda sum_, count: sum_ / count, # final mean value (divide sum by count)\n",
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"\n",
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" fill_value=(0, 0), # fill value for intermediate sums and counts when groups have no members\n",

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