Comments (4)
Also potentially challenging wrt to relative_file_path
and absolute_file_path
, depending on how these are made under the hood.
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Should perhaps be seen in relations to #136. Very different data, but possibly similar implementation.
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Refinement 13 jan 2022 @jsorb @jcrivenaes
Requirements
- fmu-dataio needs to be able to produce metadata for aggregated data. E.g. when given an aggregated surface + necessary arguments, return that aggregated surface to disk along with metadata.
- Aggregations can be multi-ensemble, multi-case, etc.
- For now, limit to aggregations across realizations within one iteration within one case.
Assumptions
- A set of realizations exists.
- Compliant metadata already exists.
- Calculation of aggregated data is not responsibility of fmu-dataio
- Fmu-dataio is to be used by other calculators to produce metadata only
Currently (in x installations):
- One realization (0 if present) is used as metadata template
- Aggregation functions modify the realization metadata into aggregation metadata + checks consistency across all realizations.
Proposed input to fmu-dataio:
- List of all metadata dictionaries for all inputs to the aggregation.
- Type of aggregation
Initial tests:
- Given a surface + a list of metadata, fmu-dataio produce aggregated metadata according to schema.
- Create surfaces with known values, create metadata with fmu-dataio, aggregate, check that resulting metadata is according to spec.
- Look for corner cases as we go along.
from fmu-dataio.
Notes from discussion 2022-02-28:
At least two situations to be covered:
-
Metadata exists and is available for a set of input data objects.
1 a) fmu-dataio shall use existing metadata as template for new metadata
1 b) fmu-dataio shall not use existing metadata, build metadata from nothing (through input args as in .export()) -
Metadata does not exist/is not available for a set of input objects.
--> Use existing metadata as input, OR leave it to the aggregation service to handle existing metadata, and provide only key arguments in. From the perspective of fmu-dataio, it will be like generating metadata from nothing.
Is each post-process/pre-process "special" enough to justify separate functionality? Or are "most" generic, with a few special? Or is none special?
Use of existing metadata: Risk of handling metadata logic outside fmu-dataio is that updates to metadata becomes very difficult.
Possibly smart to mitigate conceptual discussion by having context-specific (public) classes in fmu-dataio.
Try to rebuild existing ExportData
to handle also aggregations (and other pre/post-processed data). Under the hood call other methods? Start with writing examples.
from fmu-dataio.
Related Issues (20)
- Create a release template
- Discuss fmu-dataio scope - also _read_ results from storage?
- Add maps and 3d grids as output from export_rms_volumetrics()
- Test code, consider monkeypatch.chdir() in stead of os.chdir()
- One-liner for export of regions
- Faultroom export lacks mapping of juxtapositions to SMDA names
- Guard imports used only for typing checking under `TYPE_CHECKING`
- Restructure and rename `datastructure` package
- Update the `datastructure._internal` naming conventions
- Have `aggregation.py` validate metadata with Pydantic model
- Align enum member capitalization
- Decide on a way forward for content="property"
- Address test warnings regarding deprecated behavior HOT 1
- Display enum classes in Pydantic model docs
- Move `rms_oneliners.rst` into `docs/src`
- Add a `--force` option to `tools/update_schema` HOT 1
- Investigate alternatives to xtgeo proxies in `types.Inferrable`
- Remove `parameters` from `Aggregation`
- Deprecate `AggregatedData.generate_aggregation_metadata`
- Collect and document the list of deprecations
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