Documentation of zarr compression

[observingClouds]

Explain the different approaches how BitRounding and Zarr can be used and explain why we have chosen not the alternative approach below:

import numcodecs
encoding = {
    "var1": {
        "filters": [numcodecs.BitRound(keepbits=14)],
        "compressor": numcodecs.Blosc("zstd",shuffle=numcodecs.Blosc.BITSHUFFLE)
    }
}

ds.to_zarr("out.zarr", encoding=encoding)

But as you mentioned, the issue with this solution is that "filters" are saved in the zarr files (.zattrs and .zmetadata) which makes it necessary for users to have this filter installed on their machine when opening the dataset. However, this particular filter can be seen as a one way filter and does not need a decoder for reading.

Applying bitrounding directly to the Dataset circumvents the writing of the filters information, but still keeps the information about the number of keepbits in the _QuantizeBitRoundNumberOfSignificantDigits attribute.

Originally posted by @observingClouds in #71 (comment)

[observingClouds]

I wait with this until we have the template set up ( #51 )

[aaronspring]

No need to wait. A notebook or rst file both work