Exports Schema Utils¶
Module: spectralbridge.exports.schema_utils.
Direct-module reference; consult the linked workflow for processing context.
API glossary ยท Workflow / scientific context
Signatures and documentation below are collected from the current source. Advanced objects are not newly declared stable by inclusion here.
Audience: advanced.
infer_stage_from_name ¶
infer_stage_from_name(name: str) -> str
Infer the stage key (column prefix) from a filename.
Files with an _undarkened_envi suffix are treated as convolution-only
cubes (no brightness offset). They inherit the base stage key and append
_undarkened so they remain distinct from the darkened products.
Audience: advanced.
sort_and_rename_spectral_columns ¶
sort_and_rename_spectral_columns(df: DataFrame, stage_key: str, wavelengths_nm: List[int]) -> pd.DataFrame
Rebuild DataFrame with non-spectral first, then spectral columns sorted by wavelength
and renamed as:
Audience: advanced.
parse_envi_wavelengths_nm ¶
parse_envi_wavelengths_nm(hdr_text: str) -> Optional[List[int]]
Parse ENVI header text for numeric wavelengths, return nm ints or None.
Audience: advanced.
compute_lonlat ¶
compute_lonlat(xs: ndarray, ys: ndarray, src_epsg: int | str) -> Tuple[np.ndarray, np.ndarray]
Transform planar x,y to lon,lat. Returns (lon, lat).
Audience: advanced.
ensure_coord_columns ¶
ensure_coord_columns(df: DataFrame, transform, crs_epsg: int | str) -> pd.DataFrame
Make sure df has x, y, lon, lat columns. If missing, compute from row/col with affine transform and the CRS.
Audience: advanced.
write_parquet_standardized ¶
write_parquet_standardized(df: DataFrame, out_path: Path, hdr_path: Path, transform=None, crs_epsg: int | str = 32613)
Standardized Parquet writer:
- ensures x,y,lon,lat present
- ensures spectral columns sorted and renamed as