hydromt.data_catalog.sources.DataSource

class hydromt.data_catalog.sources.DataSource(*, name: str, uri: str, data_adapter: ~hydromt.data_catalog.adapters.data_adapter_base.DataAdapterBase, driver: ~hydromt.data_catalog.drivers.base_driver.BaseDriver, uri_resolver: ~hydromt.data_catalog.uri_resolvers.uri_resolver.URIResolver = <factory>, root: str | None = None, version: str | int | float | None = None, provider: str | None = None, metadata: ~hydromt._typing.metadata.SourceMetadata = <factory>)

A DataSource is a parsed section of a DataCatalog.

The DataSource, specific for a data type within HydroMT, is responsible for validating the input from the DataCatalog, to ensure the workflow fails as early as possible. A DataSource has information on the driver that the data should be read with, and is responsible for initializing this driver.

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.

__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

__init__(**data)	Create a new model by parsing and validating input data from keyword arguments.
construct([_fields_set])
copy(*[, include, exclude, update, deep])	Returns a copy of the model.
dict(*[, include, exclude, by_alias, ...])
from_orm(obj)
json(*[, include, exclude, by_alias, ...])
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, include, ...])	!!! 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, /)	This function is meant to behave like a BaseModel method to initialise private attributes.
model_rebuild(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
model_validate(obj, *[, strict, ...])	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.
parse_file(path, *[, content_type, ...])
parse_obj(obj)
parse_raw(b, *[, content_type, encoding, ...])
schema([by_alias, ref_template])
schema_json(*[, by_alias, ref_template])
summary()	Return a summary of the DataSource.
update_forward_refs(**localns)
validate(value)

Attributes

full_uri	Join root with uri.
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.
name
uri
data_adapter
driver
uri_resolver
data_type
root
version
provider
metadata