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docs/guide.md

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@@ -600,24 +600,25 @@ If the `slice_source` setting is _not_ specified, the slice items passed as `sli
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argument to the [`zappend`](api.md) Python function can be one of the types described
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in the following subsections.
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#### `str` and `zappend.api.FileObj`
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#### Types `str` and `FileObj`
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A slice object of type `str` is interpreted as local file path or URI, in the case
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the path has a protocol prefix, such as `s3://`.
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An alternative to providing the slice dataset as path or URI is using the `FileObj`
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class, which combines a URI with dedicated filesystem storage options.
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An alternative to providing the slice dataset as path or URI is using the
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`zappend.api.FileObj` class, which combines a URI with dedicated filesystem
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storage options.
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```python
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from zappend.api import FileObj
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slice_obj = FileObj(slice_uri, storage_options=dict(...))
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```
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#### `xarray.Dataset`
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#### Type `Dataset`
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In-memory slice objects can be passed as [xarray.Dataset](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html) objects.
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Such objects may originate from opening datasets from some storage
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In-memory slice objects can be passed as [`xarray.Dataset`](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html)
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objects. Such objects may originate from opening datasets from some storage
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```python
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import xarray as xr
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appending them to the target dataset. It may prevent expensive re-computation of chunks
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at the cost of additional i/o. It therefore defaults to `false`.
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#### `zappend.api.SliceSource`
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#### Type `SliceSource`
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Often you want to perform some custom cleanup after a slice has been processed and
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appended to the target dataset. In this case you can write your own
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Slice source instances are supposed to be created by _slice factories_, see
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subsection below.
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#### `zappend.api.SliceFactory`
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#### Type `SliceFactory`
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A slice factory is a 1-argument function that receives a processing context of type
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`zappend.api.Context` and yields a slice dataset object of one of the types

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