124 lines
4.0 KiB
Markdown
124 lines
4.0 KiB
Markdown
---
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jupytext:
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text_representation:
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extension: .md
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format_name: myst
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format_version: 0.13
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jupytext_version: 1.16.4
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kernelspec:
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display_name: Python 3
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language: python
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name: python3
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---
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## Qubed
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# Datacubes, Trees and Compressed trees
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This first part is essentially a abridged version of the [datacube spec](https://github.com/ecmwf/datacube-spec), see that document for more detail and the canonical source of truth on the matter.
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Qubed is primarily geared towards dealing with datafiles uniquely labeled by sets of key value pairs. We'll call a set of key value pairs that uniquely labels some data an `identifier`. Here's an example:
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```python
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{
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'class': 'd1',
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'dataset': 'climate-dt',
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'generation': '1',
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'date': '20241102',
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'resolution': 'high',
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'time': '0000',
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}
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```
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Unfortunately, we have more than one data file. If we are lucky, the set of identifiers that current exists might form a dense datacube that we could represent like this:
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```python
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{
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'class': ['d1', 'd2'],
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'dataset': 'climate-dt',
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'generation': ['1','2','3'],
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'model': 'icon',
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'date': ['20241102','20241103'],
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'resolution': ['high','low'],
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'time': ['0000', '0600', '1200', '1800'],
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}
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```
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with the property that any particular choice for a value for any key will correspond to datafile that exists. So this object represents `2x1x3x1x2x2x4 = 96` different datafiles.
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To save space I will also represent this same thing like this:
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```
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- class=d1/d2, dataset=climate-dt, generation=1/2/3, ..., time=0000/0600/1200/1800
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```
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Unfortunately, we are not lucky and our datacubes are not always dense. In this case we might instead represent which data exists using a tree:
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```{code-cell} python3
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from qubed import Qube
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q = Qube.from_dict({
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"class=od" : {
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"expver=0001": {"param=1":{}, "param=2":{}},
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"expver=0002": {"param=1":{}, "param=2":{}},
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},
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"class=rd" : {
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"expver=0001": {"param=1":{}, "param=2":{}, "param=3":{}},
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"expver=0002": {"param=1":{}, "param=2":{}},
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},
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})
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q.print()
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```
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But it's clear that the above tree contains a lot of redundant information. Many of the subtrees are identical for example. Indeed in practice a lot of our data turns out to be 'nearly dense' in that it contains many dense datacubes within it.
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There are many valid ways one could compress this tree. If we add the restriction that no identical key=value pairs can be adjacent then here is the compressed tree we might get:
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```{code-cell} python3
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q.compress().print()
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````
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Without the above restriction we could, for example, have:
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```
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root
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├── class=od, expver=0001/0002, param=1/2
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└── class=rd
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├── expver=0001, param=3
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└── expver=0001/0002, param=1/2
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```
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but we do not allow this because it would mean we would have to take multiple branches in order to find data with `expver=0001`.
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What we have now is a tree of dense datacubes which represents a single larger sparse datacube in a more compact manner. For want of a better word we'll call it a Qube.
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## API
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Qubed will provide a core compressed tree data structure called a Qube with:
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Methods to convert to and from:
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- [x] A human readable representation like those seen above.
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- [x] An HTML version where subtrees can be collapsed.
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- [ ] An compact protobuf-based binary format
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- [x] Nested python dictionaries or JSON
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- [/] The output of [fdb list](https://confluence.ecmwf.int/display/FDB/fdb-list)
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- [ ] [mars list][mars list]
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- [ ] [constraints.json][constraints]
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[constraints]: (https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/reanalysis-era5-land/constraints_a0ae5b42d67869674e13fba9fd055640bcffc37c24578be1f465d7d5ab2c7ee5.json
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[mars list]: https://git.ecmwf.int/projects/CDS/repos/cads-forms-reanalysis/browse/reanalysis-era5-single-levels/gecko-config/mars.list?at=refs%2Fheads%2Fprod
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Useful algorithms:
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- [x] Compression
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- [/] Union/Intersection/Difference
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Performant Membership Queries
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- Identifier membership
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- Datacube query (selection)
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Metadata Storage
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