Add notes about data structures to documentation #329
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@mikapfl I think these are the essential aspects of our research. Let me know if you think of anything else. |
mikapfl
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Apr 9, 2025
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| <table> | ||
| <tr> | ||
| <th>Pro</th> | ||
| <th>Con</th> | ||
| </tr> | ||
| <tr> | ||
| <td> | ||
| <ul> | ||
| <li>Supports conversion from/to NetCDF and Zarr.</li> | ||
| <li>Allows us to stay within the xarray ecosystem (as opposed to SQL or pandas).</li> | ||
| <li>All pre-processing steps can be covered in DataTree.</li> | ||
| </ul> | ||
| </td> | ||
| <td> | ||
| <ul> | ||
| <li>Limited examples and documentation available (as of April 2025).</li> | ||
| <li>Requires making our primap2 functions compatible with DataTree.</li> | ||
| <li>Processing DataTrees may involve looping through sub-datasets. Is it faster than merging once?</li> | ||
| <li>The hierarchical concept does not align well with our use case. All country datasets would be "siblings," but this is not necessarily a problem.</li> | ||
| </ul> | ||
| </td> | ||
| </tr> | ||
| </table> |
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Suggested change
| <table> | |
| <tr> | |
| <th>Pro</th> | |
| <th>Con</th> | |
| </tr> | |
| <tr> | |
| <td> | |
| <ul> | |
| <li>Supports conversion from/to NetCDF and Zarr.</li> | |
| <li>Allows us to stay within the xarray ecosystem (as opposed to SQL or pandas).</li> | |
| <li>All pre-processing steps can be covered in DataTree.</li> | |
| </ul> | |
| </td> | |
| <td> | |
| <ul> | |
| <li>Limited examples and documentation available (as of April 2025).</li> | |
| <li>Requires making our primap2 functions compatible with DataTree.</li> | |
| <li>Processing DataTrees may involve looping through sub-datasets. Is it faster than merging once?</li> | |
| <li>The hierarchical concept does not align well with our use case. All country datasets would be "siblings," but this is not necessarily a problem.</li> | |
| </ul> | |
| </td> | |
| </tr> | |
| </table> | |
| | Pro | Con | | |
| |---|---| | |
| | Supports conversion from/to NetCDF and Zarr. | Limited examples and documentation available (as of April 2025). | | |
| | Allows us to stay within the xarray ecosystem (as opposed to SQL or pandas). | Requires making our primap2 functions compatible with DataTree. | | |
| | All pre-processing steps can be covered in DataTree. | Processing DataTrees may involve looping through sub-datasets. Is it faster than merging once? | | |
| | | The hierarchical concept does not align well with out use case. All country datasets would "siblings," but this is not necessarily a problem. | |
It's not exactly the same result, but if possible avoid html and use markdown instead.
| Example: A script like `src/unfccc_ghg_data/unfccc_crf_reader/crf_raw_for_year.py` compiles all CRF (or CRT) datasets for a submission year into one dataset. Since countries report at different levels of detail, categories unique to one dataset result in NaNs in others, making datasets sparse and memory-intensive. We aim to significantly reduce memory usage. | ||
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| * **Select** | ||
| Filtering datasets by dimensions such as country, sector, or gas is essential. The `primap2` `.loc` function already supports this. |
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Suggested change
| Filtering datasets by dimensions such as country, sector, or gas is essential. The `primap2` `.loc` function already supports this. | |
| Filtering datasets by dimensions such as country, sector, or gas is essential. The {py:meth}`xarray.Dataset.pr.set` function already supports this, but not for DataTrees. |
We can add a proper link using the {py:meth} syntax.
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| ### Data structures for primap2 | |||
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Suggested change
| Here, we document an unfinished proposal for more advanced data structures for sparse data within primap2. |
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Also, maybe "Data Structures for primap" is a bit too general as a title. Maybe "Sparse data ideas" instead, to make clear in the title already that this is not currently implemented functionality? |
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{pr}.thing.mdfile in the directorychangelogadded - see changelog/README.md for detailsDescription
Add the notes on data structure research so they don't get lost