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Merge pull request #248 from jeromekelleher/last-docs-updates-and-release
Last docs updates and release
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docs/vcf2zarr/tutorial.md

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@@ -18,6 +18,17 @@ convert your data, basically providing different levels of
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convenience and flexibility corresponding to what you might
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need for small, intermediate and large datasets.
2020

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:::{warning}
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The documentation of vcf2zarr is under development, and
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some bits are more polished than others. This "tutorial"
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is experimental, and will likely evolve into a slightly
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different format in the near future. It is
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a work in progress and incomplete. The
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{ref}`sec-vcf2zarr-cli-ref` should be complete
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and authoritative, however.
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:::
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## Small dataset
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The simplest way to convert VCF data to Zarr is to use the
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Once ``dexplode-init`` is done and we know how many partitions we have,
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we need to call ``dexplode-partition`` this number of times.
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we need to call
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{ref}`dexplode-partition<cmd-vcf2zarr-dexplode-partition>` this number of times:
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```{code-cell}
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vcf2zarr dexplode-partition sample-dist.icf 0
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vcf2zarr dexplode-partition sample-dist.icf 1
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vcf2zarr dexplode-partition sample-dist.icf 2
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```
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This is not how it would be done in practise of course: you would
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use your cluster scheduler of choice to dispatch these operations.
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:::{todo}
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Document how to do this conveniently over some popular schedulers.
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:::
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:::{tip}
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Use the ``--one-based`` argument in cases in which it's more convenient
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to index the partitions from 1 to n, rather than 0 to n - 1.
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:::
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Finally we need to call
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{ref}`dexplode-finalise<cmd-vcf2zarr-dexplode-finalise>`:
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```{code-cell}
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vcf2zarr dexplode-finalise sample-dist.icf
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```
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:::{todo}
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Document the process for dencode, noting the information output about
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memory requirements.
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:::

pyproject.toml

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]
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requires-python = ">=3.9"
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classifiers = [
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"Development Status :: 3 - Alpha",
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"Development Status :: 4 - Beta",
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"License :: OSI Approved :: Apache Software License",
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"Operating System :: POSIX",
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"Operating System :: POSIX :: Linux",

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