libera_utils.footprint_matching.readers.brdf.VIIRSBRDFReader#

class libera_utils.footprint_matching.readers.brdf.VIIRSBRDFReader(file_paths: Sequence[Path])#

Bases: StaticReader

Read VIIRS surface BRDF model parameters from a VJ143C1 HDF5 file.

Loads nine RTLS kernel parameter fields (3 bands x 3 parameters) from a VJ143C1 VIIRS daily BRDF product as a (9, n_lat, n_lon) grid stacked in VARIABLES order. The product is a 16-day composite issued daily with no time axis, so the staged day is the time match (TIME_MATCH = DAILY).

Class Attributes#

READER_KEYstr: Registry key "VIIRS_BRDF".
RESOLUTION_KMfloat: 5.6 km (0.05° CMG grid spacing at equator).
VARIABLEStuple[VariableSpec, …]: Nine BRDF kernel parameter variables (3 bands × 3 kernel weights), all float32 with weighted_mean aggregation, carried by FMATCH-IMAGER and FMATCH-IMAGER-CAMTIME.

param file_paths:: The one VJ143C1 HDF5 (HDF-EOS5) file for the processing day.
type file_paths:: Sequence[Path]

__init__(file_paths: Sequence[Path]) → None#

Methods

`preload`()	Load and index the whole source now, rather than on the first query.
`product_stacks`()	Return every stacked product variable this reader contributes.
`product_variable_specs`()	Return every variable this reader contributes to the product definition.
`samples_in`(bbox, time)	Return the native samples inside `bbox` that match `time`.

Attributes

`ADDITIONAL_PRODUCT_VARIABLES`
`INSTRUMENT`
`MAX_TIME_OFFSET`
`PASS_WINDOW`
`PRODUCT_STACKS`
`READER_KEY`
`RESOLUTION_KM`
`TIMESTAMP_SOURCE`
`TIME_MATCH`
`VARIABLES`
`file_paths`	The staged granule paths passed at construction, in the order given.
`n_variables`	Number of read variables (the first axis of every `values` array).
`single_file_path`	The one granule of a reader that reads exactly one per period.

ADDITIONAL_PRODUCT_VARIABLES: tuple[VariableSpec, ...] = ()#

INSTRUMENT: str = 'NOAA20'#

MAX_TIME_OFFSET: timedelta64 | None = None#

PASS_WINDOW: timedelta64 | None = None#

PRODUCT_STACKS: tuple[ProductStack, ...] = ()#

READER_KEY: str = 'VIIRS_BRDF'#

RESOLUTION_KM: float = 5.6#

TIMESTAMP_SOURCE: str | None = None#

TIME_MATCH: TimeMatch = 'daily'#

VARIABLES: tuple[VariableSpec, ...] = (VariableSpec(name='Shortwave_Fiso', dtype='float32', aggregation='weighted_mean', modes=frozenset({<OperationalMode.IMAGER: 'FMATCH-IMAGER'>, <OperationalMode.IMAGER_CAMTIME: 'FMATCH-IMAGER-CAMTIME'>}), n_categories=None), VariableSpec(name='Shortwave_Fvol', dtype='float32', aggregation='weighted_mean', modes=frozenset({<OperationalMode.IMAGER: 'FMATCH-IMAGER'>, <OperationalMode.IMAGER_CAMTIME: 'FMATCH-IMAGER-CAMTIME'>}), n_categories=None), VariableSpec(name='Shortwave_Fgeo', dtype='float32', aggregation='weighted_mean', modes=frozenset({<OperationalMode.IMAGER: 'FMATCH-IMAGER'>, <OperationalMode.IMAGER_CAMTIME: 'FMATCH-IMAGER-CAMTIME'>}), n_categories=None), VariableSpec(name='VIS_Fiso', dtype='float32', aggregation='weighted_mean', modes=frozenset({<OperationalMode.IMAGER: 'FMATCH-IMAGER'>, <OperationalMode.IMAGER_CAMTIME: 'FMATCH-IMAGER-CAMTIME'>}), n_categories=None), VariableSpec(name='VIS_Fvol', dtype='float32', aggregation='weighted_mean', modes=frozenset({<OperationalMode.IMAGER: 'FMATCH-IMAGER'>, <OperationalMode.IMAGER_CAMTIME: 'FMATCH-IMAGER-CAMTIME'>}), n_categories=None), VariableSpec(name='VIS_Fgeo', dtype='float32', aggregation='weighted_mean', modes=frozenset({<OperationalMode.IMAGER: 'FMATCH-IMAGER'>, <OperationalMode.IMAGER_CAMTIME: 'FMATCH-IMAGER-CAMTIME'>}), n_categories=None), VariableSpec(name='NIR_Fiso', dtype='float32', aggregation='weighted_mean', modes=frozenset({<OperationalMode.IMAGER: 'FMATCH-IMAGER'>, <OperationalMode.IMAGER_CAMTIME: 'FMATCH-IMAGER-CAMTIME'>}), n_categories=None), VariableSpec(name='NIR_Fvol', dtype='float32', aggregation='weighted_mean', modes=frozenset({<OperationalMode.IMAGER: 'FMATCH-IMAGER'>, <OperationalMode.IMAGER_CAMTIME: 'FMATCH-IMAGER-CAMTIME'>}), n_categories=None), VariableSpec(name='NIR_Fgeo', dtype='float32', aggregation='weighted_mean', modes=frozenset({<OperationalMode.IMAGER: 'FMATCH-IMAGER'>, <OperationalMode.IMAGER_CAMTIME: 'FMATCH-IMAGER-CAMTIME'>}), n_categories=None))#

_box_samples(native: GridSamples | _PointIndex | _BoxNative, bbox: BoundingBox) → tuple[ndarray, ndarray, ndarray, ndarray | None]#

Return (lats, lons, values, times) of the native samples inside one plain box.

Raises:: TypeError – If native is none of the three native forms.

classmethod _check_product_stacks() → None#

Reject a PRODUCT_STACKS declaration the product could not be built from.

Raises:: TypeError – If a stack names a spec the reader does not produce, a spec is stacked twice, a stack takes the name of a product spec, a stack’s members have standard-deviation companions for some members but not others, or a stack’s members do not all have the same modes.

_index_points(native: PointSamples) → _PointIndex#: Validate a loaded point cloud against the reader’s contract and index it.

_load_native() → GridSamples#

Read all nine BRDF fields from the HDF5 file once.

Each field is int16 with its own _FillValue, valid_range, scale_factor and add_offset. Fill and out-of-range pixels become NaN, the scale and offset are applied, and the cell-edge lat/lon axes are checked against the StructMetadata.0 corners and served as cell centers, latitude ascending.

Returns:

data is float32 shape (9, n_lat, n_lon), axis 0 in VARIABLES order, fill and out-of-range as NaN; lat_axis and lon_axis are float64 cell centers, ascending.

Return type:

GridSamples

Raises:

KeyError – If the grid’s group, a field, the lat/lon datasets or StructMetadata.0 are not found in the file.
OSError – If the file cannot be opened (not found, corrupt HDF5 header, etc.).
ValueError – If a field lacks one of _FillValue, valid_range, scale_factor or add_offset; if StructMetadata.0 is not text or does not describe the grid as a geographic (HE5_GCTP_GEO) grid of the axes’ size; or if the axes are not uniformly spaced cell edges spanning its corners.

Notes

The fill and valid-range test runs on the raw int16 values, before scaling, and each field is scaled straight into the preallocated float32 array.

property _native: GridSamples | _PointIndex | _BoxNative#

The loaded, indexed native data (loaded on first access, then held).

Raises:

ValueError – If the loaded data carries a different number of variables than VARIABLES.
TypeError – If _load_native() returned none of the three native forms.

_native_for(time: datetime64) → tuple[GridSamples | _PointIndex | _BoxNative, ...]#

Return the native data a query at time draws from; empty for no match.

StaticReader returns its one indexed body of data regardless of time; SteppedGridReader the step nearest time, or nothing when that step is farther than MAX_TIME_OFFSET.

_nearest_pass_mask(times: ndarray, time: datetime64, inside: ndarray) → ndarray#

Boolean mask of the samples that belong to the pass nearest time.

The nearest pass is every sample, padding included, within PASS_WINDOW of the observation time of the sample nearest time among the samples inside the footprint’s own (unpadded) box, so a pass that only clips the padding, where the PSF weight is zero, cannot displace one that covers the footprint. The window is centred on that sample’s own time rather than on its offset, so two passes on either side of the footprint time are never served together. When no sample lies inside the box (a footprint smaller than one native cell), the nearest sample is taken from every served sample; on a tie, the first in array order. When its offset exceeds MAX_TIME_OFFSET no sample is kept.

_padded(bbox: BoundingBox, resolution_km: float) → BoundingBox#

Widen bbox by half a native cell (resolution_km) on every side.

A sample whose cell overlaps the footprint’s box belongs to the footprint even when its center lies just outside: without the padding a coarse grid (ERA5 at 0.25 degree) never serves the points whose cells cover the rim of the contour, and the weigher scores the rim as missing. The PSF weight of a sample outside the contour is zero, so padding changes no aggregated value, only the coverage.

_require_valid_time(time: datetime64) → None#

Raise when a footprint time a time-selecting query needs is NaT.

Raises:: ValueError – If time is NaT.

_resolution_km(natives: tuple[GridSamples | _PointIndex | _BoxNative, ...]) → float#

The native cell size the served samples stand for, km.

The largest spacing a loaded point cloud measured on itself, else the reader’s declared RESOLUTION_KM. A measured spacing wins because a swath subsampled on delivery (e.g. every eighth pixel) must be credited with the cell it really stands for, not the instrument’s nominal one.

_unknown_native_message(native: object) → str#

property file_paths: tuple[Path, ...]#: The staged granule paths passed at construction, in the order given.

property n_variables: int#: Number of read variables (the first axis of every values array).

preload() → None#

Load and index the whole source now, rather than on the first query.

Raises:

ValueError – If the loaded data carries a different number of variables than VARIABLES.
TypeError – If _load_native() returned none of the three native forms.

classmethod product_stacks() → tuple[ProductStack, ...]#

Return every stacked product variable this reader contributes.

Each stack in PRODUCT_STACKS, followed by a <name>_Standard_Deviation stack of its members’ companions when its members are continuous. The stacked members leave the product as separate variables; every other spec in product_variable_specs stays a 1-D product variable.

classmethod product_variable_specs() → tuple[VariableSpec, ...]#

Return every variable this reader contributes to the product definition.

This is the full set of output variables – what appears, prefixed with <READER_KEY>_, in the FMATCH product definition YAMLs and what the product/test cross-check is built against. It is distinct from VARIABLES (the smaller set actually read from the source file) and is composed of, in order:

each read variable in VARIABLES;
a <name>_Standard_Deviation companion for every continuous (mean-aggregated) read variable, inserted right after its parent by with_standard_deviation_companions();
any reader-specific derived outputs declared in ADDITIONAL_PRODUCT_VARIABLES (e.g. IGBP ranked scenes).

samples_in(bbox: BoundingBox, time: datetime64) → SampleSet#

Return the native samples inside bbox that match time.

Parameters:

bbox (BoundingBox) – The footprint’s geographic box, widened by half a native cell (RESOLUTION_KM) on every side so a sample whose cell overlaps the box is served. A box that wraps the antimeridian (given in the [0, 360) form) is split and both halves served.
time (np.datetime64) – The footprint’s observation time (radiometer or camera time). Ignored by a DAILY reader.

Returns:

The selected samples; zero samples when the box holds none, or when no sample lies within MAX_TIME_OFFSET of time.

Return type:

SampleSet

Raises:

ValueError – If time is NaT and this reader selects in time; if more than one granule is staged for a reader that reads one per period; if the loaded data carries a different number of variables than VARIABLES; if a stepped reader’s step times are empty, hold a NaT or are not strictly ascending; or if a NEAREST_PASS reader serves samples without per-sample times.
TypeError – If the reader’s native data is none of GridSamples, PointSamples or a box-queried native.

property single_file_path: Path#

The one granule of a reader that reads exactly one per period.

Raises:: ValueError – If more than one granule was staged for this reader.