libera_utils.io.umm_g.UMMGranule

class libera_utils.io.umm_g.UMMGranule(*, GranuleUR: ~typing.Annotated[str, ~annotated_types.MinLen(min_length=1), ~annotated_types.MaxLen(max_length=250)], ProviderDates: ~typing.Annotated[list[~libera_utils.io.umm_g.ProviderDateType], ~annotated_types.MinLen(min_length=1), ~annotated_types.MaxLen(max_length=4)], CollectionReference: ~libera_utils.io.umm_g.CollectionReferenceType, MetadataSpecification: ~libera_utils.io.umm_g.MetadataSpecificationType = <factory>, AccessConstraints: ~libera_utils.io.umm_g.AccessConstraintsType | None = None, DataGranule: ~libera_utils.io.umm_g.DataGranuleType | None = None, PGEVersionClass: ~libera_utils.io.umm_g.PGEVersionClassType | None = None, TemporalExtent: ~libera_utils.io.umm_g.TemporalExtentType | None = None, SpatialExtent: ~libera_utils.io.umm_g.SpatialExtentType | None = None, OrbitCalculatedSpatialDomains: ~types.Annotated[list[~libera_utils.io.umm_g.OrbitCalculatedSpatialDomainType] | None, ~annotated_types.MinLen(min_length=1)] = None, MeasuredParameters: ~types.Annotated[list[~libera_utils.io.umm_g.MeasuredParameterType] | None, ~annotated_types.MinLen(min_length=1)] = None, Platforms: ~types.Annotated[list[~libera_utils.io.umm_g.PlatformType] | None, ~annotated_types.MinLen(min_length=1)] = None, Projects: ~types.Annotated[list[~libera_utils.io.umm_g.ProjectType] | None, ~annotated_types.MinLen(min_length=1)] = None, AdditionalAttributes: ~types.Annotated[list[~libera_utils.io.umm_g.AdditionalAttributeType] | None, ~annotated_types.MinLen(min_length=1)] = None, InputGranules: ~types.Annotated[list[~typing.Annotated[str, FieldInfo(annotation=NoneType, required=True, metadata=[MinLen(min_length=1), MaxLen(max_length=500)])]] | None, ~annotated_types.MinLen(min_length=1)] = None, TilingIdentificationSystem: ~libera_utils.io.umm_g.TilingIdentificationSystemType | None = None, CloudCover: float | None = None, RelatedUrls: ~types.Annotated[list[~libera_utils.io.umm_g.RelatedUrlType] | None, ~annotated_types.MinLen(min_length=1)] = None, NativeProjectionNames: list[~libera_utils.io.umm_g.ProjectionNameEnum] | None = None, GridMappingNames: list[~typing.Annotated[str, FieldInfo(annotation=NoneType, required=True, metadata=[MinLen(min_length=1), MaxLen(max_length=1024)])]] | None = None)

Bases: BaseModel

The Unified Metadata Model (UMM) for Granule (UMM-G) defines the metadata structure for describing individual data granules in NASA’s Common Metadata Repository (CMR).

Attributes:

model_extra

Get extra fields set during validation.

model_fields_set

Returns the set of fields that have been explicitly set on this model instance.

Methods

copy(*[, include, exclude, update, deep])	Returns a copy of the model.
model_construct([_fields_set])	Creates a new instance of the Model class with validated data.
model_copy(*[, update, deep])	!!! abstract "Usage Documentation"
model_dump(*[, mode, include, exclude, ...])	!!! abstract "Usage Documentation"
model_dump_json(*[, indent, ensure_ascii, ...])	!!! abstract "Usage Documentation"
model_json_schema([by_alias, ref_template, ...])	Generates a JSON schema for a model class.
model_parametrized_name(params)	Compute the class name for parametrizations of generic classes.
model_post_init(context, /)	Override this method to perform additional initialization after __init__ and model_construct.
model_rebuild(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
model_validate(obj, *[, strict, extra, ...])	Validate a pydantic model instance.
model_validate_json(json_data, *[, strict, ...])	!!! abstract "Usage Documentation"
model_validate_strings(obj, *[, strict, ...])	Validate the given object with string data against the Pydantic model.

construct
dict
from_dataset
from_orm
json
parse_file
parse_obj
parse_raw
schema
schema_json
update_forward_refs
validate

__init__(**data: Any) → None

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Methods

from_dataset(input_dataset, **kwargs)

Attributes

model_computed_fields
model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
model_extra	Get extra fields set during validation.
model_fields
model_fields_set	Returns the set of fields that have been explicitly set on this model instance.
GranuleUR
ProviderDates
CollectionReference
MetadataSpecification
AccessConstraints
DataGranule
PGEVersionClass
TemporalExtent
SpatialExtent
OrbitCalculatedSpatialDomains
MeasuredParameters
Platforms
Projects
AdditionalAttributes
InputGranules
TilingIdentificationSystem
CloudCover
RelatedUrls
NativeProjectionNames
GridMappingNames

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.