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Install xarray from main (#106)
* Install xarray from main now that [#8872](pydata/xarray#8872) has merged * Remove note in docs
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docs/usage.md

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This concatenation property is what will allow us to combine the data from multiple netCDF files on disk into a single Zarr store containing arrays of many chunks.
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```{note}
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As a single Zarr array has only one array-level set of compression codecs by definition, concatenation of arrays from files saved to disk with differing codecs cannot be achieved through concatenation of `ManifestArray` objects. Implementing this feature will require a more abstract and general notion of concatentation, see [GH issue #5](https://github.com/TomNicholas/VirtualiZarr/issues/5).
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As a single Zarr array has only one array-level set of compression codecs by definition, concatenation of arrays from files saved to disk with differing codecs cannot be achieved through concatenation of `ManifestArray` objects. Implementing this feature will require a more abstract and general notion of concatenation, see [GH issue #5](https://github.com/TomNicholas/VirtualiZarr/issues/5).
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```
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Remember that you cannot load values from a `ManifestArray` directly.
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title: 4x daily NMC reanalysis (1948)
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```
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```{note}
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Concatenation without indexes like this will only work if you use a [specific branch of xarray](https://github.com/pydata/xarray/pull/8872), as it requires an in-progress PR, see [GH issue #14](https://github.com/TomNicholas/VirtualiZarr/issues/14#issuecomment-2018369470).
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```
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We can see that the resulting combined manifest has two chunks, as expected.
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```python
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The keyword arguments `coords='minimal', compat='override'` are currently necessary because the default behaviour of xarray will attempt to load coordinates in order to check their compatibility with one another. In future this [default will be changed](https://github.com/pydata/xarray/issues/8778), such that passing these two arguments explicitly will become unnecessary.
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```
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The general multi-dimensional version of this contatenation-by-order-supplied can be achieved using `xarray.combine_nested`.
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The general multi-dimensional version of this concatenation-by-order-supplied can be achieved using `xarray.combine_nested`.
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```python
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combined_vds = xr.combine_nested([vds1, vds2], concat_dim=['time'], coords='minimal', compat='override')
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You can see that the dataset contains a mixture of virtual variables backed by `ManifestArray` objects, and loadable variables backed by (lazy) numpy arrays.
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Loading variables can be useful in a few scenarios:
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1. You need to look at the actual values of a muilti-dimensional variable in order to decide what to do next,
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1. You need to look at the actual values of a multi-dimensional variable in order to decide what to do next,
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2. Storing a variable on-disk as a set of references would be inefficient, e.g. because it's a very small array (saving the values like this is similar to kerchunk's concept of "inlining" data),
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3. The variable has encoding, and the simplest way to decode it correctly is to let xarray's standard decoding machinery load it into memory and apply the decoding.
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pyproject.toml

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requires-python = ">=3.9"
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dynamic = ["version"]
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dependencies = [
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"xarray@git+https://github.com/TomNicholas/xarray.git@concat-avoid-index-auto-creation#egg=xarray",
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"xarray@git+https://github.com/pydata/xarray.git@main#egg=xarray",
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"kerchunk==0.2.2",
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"h5netcdf",
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"pydantic",

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