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switch from storage_type to interface_type
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README.md

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@@ -69,7 +69,7 @@ There are some (relatively obsolete) documents from our exploration of zarr inte
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## Storage types
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PyActiveStorage is designed to interact with various storage backends.
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The storage backend is automatically detected, but can still be specified using the `storage_type` argument to the `Active` constructor.
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The storage backend is automatically detected, but can still be specified using the `interface_type` argument to the `Active` constructor.
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There are two main integration points for a storage backend:
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#. Load netCDF metadata
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### Local file
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The default storage backend is a local file.
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To use a local file, use a `storage_type` of `None`, which is its default value.
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To use a local file, use a `interface_type` of `None`, which is its default value.
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netCDF metadata is loaded using the [netCDF4](https://pypi.org/project/netCDF4/) library.
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The chunk reductions are implemented in `activestorage.storage` using NumPy.
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We now have support for Active runs with netCDF4 files on S3, from [PR 89](https://github.com/NCAS-CMS/PyActiveStorage/pull/89).
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To achieve this we integrate with [Reductionist](https://github.com/stackhpc/reductionist-rs), an S3 Active Storage Server.
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Reductionist is typically deployed "near" to an S3-compatible object store and provides an API to perform numerical reductions on object data.
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To use Reductionist, use a `storage_type` of `s3`.
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To use Reductionist, use a `interface_type` of `s3`.
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To load metadata, netCDF files are opened using `s3fs`, with `h5netcdf` used to put the open file (which is nothing more than a memory view of the netCDF file) into an hdf5/netCDF-like object format.
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Chunk reductions are implemented in `activestorage.reductionist`, with each operation resulting in an API request to the Reductionist server.

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