feat(io.yaml): factor model_from_yaml_data out of read_yaml_model + back-port docs / known-issues / localization / scripts (#11)

* Project scaffold: pyproject + package skeleton + README + LICENSE

* Add GitHub Actions CI and the maintainer-scripts README

* Add the foundation utilities: GPR, balance, parse, sort, validate

* Add the model-manipulation layer (add, remove, transport, merge, etc.)

* Add binary + data resolvers for external tools and published artefacts

* Add YAML and SIF model I/O

* Add Excel export and the Standard-GEM git-layout export

* Add BLAST and DIAMOND wrappers for protein-homology searches

* Add the homology-based draft model builder (getModelFromHomology port)

* Add KEGG download, dump parser and taxonomy parser

* Add KEGG HMM-library build and HMM-based KO assignment

* Add KEGG species-model assembly (per-organism reconstruction)

* Add KEGG artefact-build scripts and HMM-cutoff calibration docs

* Add metabolic-task parsing and the check_tasks validator

* Add connectivity gap-filling (MILP) against template models

* Add the tINIT (INIT) MILP and its supporting machinery

* Add the ftINIT pipeline and task-aware gap-filling

* Add Human-GEM validation, parameter studies and cross-solver tests

* Add HPA omics ingestion (proteomics + RNA-seq)

* Add FSEOF, reporter metabolites and flux sampling

* Add N-model comparison (presence + Jaccard + optional task check)

* Add subcellular-localisation prediction (MILP) with pluggable predictors

* Add the yeast-GEM localization benchmark (real-data validation)

* Add the documentation index, RAVEN migration map and CHANGELOG

* Add known-issues catalogue with closed sweep A–F regression notes

* Add the consolidated MATLAB RAVEN back-port proposals doc

* feat(io.yaml): factor model_from_yaml_data out of read_yaml_model

read_yaml_model now opens+parses the file then delegates the
post-parse work (capturing per-entry side-fields onto notes,
restoring legacy metaData id/name, stashing unknown top-level
sections onto model.notes['_yaml_sections']) to a new
model_from_yaml_data(raw: dict) helper.

This lets downstream packages that need to pre-normalise their YAML
before cobra reads it (e.g. geckopy, which lifts legacy MATLAB
ec-model quirks like top-level per-metabolite `smiles` into
`annotation` and merges bare-`-` sequence-of-single-key-maps
back to a mapping) hand the cleaned dict directly to the post-parse
pipeline, without round-tripping through a temp file.

Both functions are exported from raven_python.io.yaml. Pure
refactor on the read side; no behaviour change for existing
read_yaml_model callers.

* fix(io.yaml): drop metaData/version/_yaml_sections from doc['notes']

cobra's model_to_dict serialises model.notes verbatim into the output
doc as the 'notes' section. write_yaml_model already pops these three
management keys from a local copy of model.notes to use them as
top-level YAML fields, but the originals remained on model.notes and
therefore also leaked into doc['notes'], producing duplicate sections
in the file (the legitimate top-level emit AND a nested copy inside
notes).

Strip them from doc['notes'] post-model_to_dict and drop the notes
section entirely when nothing else is left. Discovered while
round-tripping a geckopy ecModel (it stashes ec-rxns / ec-enzymes /
gecko_light on model.notes['_yaml_sections']); was visible as
duplicated GECKO sections in the written YAML.

Author: edkerk