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fiona

Guides Fiona workflows for GDAL-backed vector data I/O, GeoJSON-like features and schemas, CRS transforms, local virtual files, cloud-session boundaries, and the fio CLI.

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含まれるファイル(24)

  • SKILL.md3.4 KB
  • references/repo-provenance.md1.5 KB
  • references/repo-routing-metadata.json313 B
  • references/troubleshooting.md2.0 KB
  • sub-skills/cli/references/cli-reference.md2.0 KB
  • sub-skills/cli/references/pipeline-recipes.md1.5 KB
  • sub-skills/cli/references/troubleshooting.md1.6 KB
  • sub-skills/cli/SKILL.md1.1 KB
  • sub-skills/crs-transform/references/api-reference.md1.5 KB
  • sub-skills/crs-transform/references/integrations.md959 B
  • sub-skills/crs-transform/references/troubleshooting.md1.3 KB
  • sub-skills/crs-transform/references/workflows.md1.5 KB
  • sub-skills/crs-transform/SKILL.md1.1 KB
  • sub-skills/environment-cloud/references/environment-api.md1.5 KB
  • sub-skills/environment-cloud/references/installation-and-build.md1.7 KB
  • sub-skills/environment-cloud/references/troubleshooting.md2.0 KB
  • sub-skills/environment-cloud/references/virtual-files-and-cloud.md1.7 KB
  • sub-skills/environment-cloud/scripts/check_runtime.py1.1 KB
  • sub-skills/environment-cloud/SKILL.md1.4 KB
  • sub-skills/vector-io/references/api-reference.md3.0 KB
  • sub-skills/vector-io/references/data-model-and-schema.md2.2 KB
  • sub-skills/vector-io/references/troubleshooting.md1.8 KB
  • sub-skills/vector-io/references/workflows.md2.7 KB
  • sub-skills/vector-io/SKILL.md2.3 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Fiona repository skill

Fiona is a Python interface for reading and writing vector data through GDAL/OGR. Use this graph when a task mentions Fiona, fiona.open, Collection, MemoryFile, Feature, Geometry, CRS, fio, GeoPackage, Shapefile, GeoJSON conversion, VSI paths, or Fiona/GDAL installation problems.

Start here

  1. Confirm the installed Fiona/GDAL/PROJ combination and supported drivers. For installation and runtime failures, read troubleshooting and the provenance snapshot.
  2. Choose one focused route:
    • vector-io: Python read/write/append, collections, schemas, models, layers, encodings, MemoryFile, and local VSI.
    • cli: fio inspection, streaming, conversion, metadata, bounds, and safe pipelines.
    • crs-transform: CRS definitions, coordinate/geometry transforms, and PyProj/Shapely boundaries.
    • environment-cloud: compiled installation, GDAL/PROJ data, drivers, Env, openers, and optional cloud sessions.
  3. For a task crossing routes, keep one explicit source/destination CRS and one schema owner; link the relevant sibling route rather than duplicating details.

Installation and smoke check

For a normal public install:

python -m pip install fiona
python -c "import fiona; print(fiona.__version__, fiona.__gdal_version__)"

Use a compatible conda-forge or source build when you need format drivers not present in a wheel. Do not mix incompatible GDAL binary stacks. The checked repository line requires GDAL >=3.1 at source-build time; current public releases may document newer minimums, so inspect the installed package before pinning a deployment.

For a generic diagnostic that does not expose local paths or credentials, run the bundled runtime checker.

Operating rules

  • Use context managers for collections and memory files; close external resources deterministically.
  • In write mode, provide a supported driver and schema, and usually a CRS. Reopen outputs and validate driver, schema, CRS, bounds, and representative features instead of checking only file existence.
  • Treat collections as streams: iteration consumes the cursor and dataset IDs are GDAL-controlled, not necessarily contiguous Python indexes.
  • Fiona transports feature data; it does not replace Shapely for geometry operations or PyProj for advanced transformation control.
  • Treat HTTP/S3/GS paths, requester-pays access, credentials, and destructive fio rm as explicit side-effect boundaries. Stop for missing authorization.

Scope and limitations

This graph covers the public package and CLI surfaces evidenced by the source, docs, examples, and representative tests. It intentionally excludes vendored implementation, CI/release automation, destructive verification, unapproved network/credential use, and long-running or large-data operations. Read troubleshooting for those boundaries.

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