RasterSourceConfig#
Note
All Configs are derived from rastervision.pipeline.config.Config
, which itself is a pydantic Model.
- pydantic model RasterSourceConfig[source]#
Configure a
RasterSource
.Show JSON schema
{ "title": "RasterSourceConfig", "description": "Configure a :class:`.RasterSource`.", "type": "object", "properties": { "channel_order": { "title": "Channel Order", "description": "The sequence of channel indices to use when reading imagery.", "type": "array", "items": { "type": "integer" } }, "transformers": { "title": "Transformers", "default": [], "type": "array", "items": { "$ref": "#/definitions/RasterTransformerConfig" } }, "bbox": { "title": "Bbox", "description": "User-specified bbox in pixel coords in the form (ymin, xmin, ymax, xmax). Useful for cropping the raster source so that only part of the raster is read from.", "type": "array", "minItems": 4, "maxItems": 4, "items": [ { "type": "integer" }, { "type": "integer" }, { "type": "integer" }, { "type": "integer" } ] }, "type_hint": { "title": "Type Hint", "default": "raster_source", "enum": [ "raster_source" ], "type": "string" } }, "additionalProperties": false, "definitions": { "RasterTransformerConfig": { "title": "RasterTransformerConfig", "description": "Configure a :class:`.RasterTransformer`.", "type": "object", "properties": { "type_hint": { "title": "Type Hint", "default": "raster_transformer", "enum": [ "raster_transformer" ], "type": "string" } }, "additionalProperties": false } } }
- Config
extra: str = forbid
validate_assignment: bool = True
- Fields
- field bbox: Optional[Tuple[int, int, int, int]] = None#
User-specified bbox in pixel coords in the form (ymin, xmin, ymax, xmax). Useful for cropping the raster source so that only part of the raster is read from.
- field channel_order: Optional[List[int]] = None#
The sequence of channel indices to use when reading imagery.
- field transformers: List[RasterTransformerConfig] = []#
- build(tmp_dir: Optional[str] = None, use_transformers: bool = True) RasterSource [source]#
Build an instance of the corresponding type of object using this config.
For example, BackendConfig will build a Backend object. The arguments to this method will vary depending on the type of Config.
- Parameters
- Return type
- classmethod from_file(uri: str) Config #
Deserialize a Config from a JSON file, upgrading if possible.
- recursive_validate_config()#
Recursively validate hierarchies of Configs.
This uses reflection to call validate_config on a hierarchy of Configs using a depth-first pre-order traversal.
- revalidate()#
Re-validate an instantiated Config.
Runs all Pydantic validators plus self.validate_config().
Adapted from: https://github.com/samuelcolvin/pydantic/issues/1864#issuecomment-679044432
- to_file(uri: str, with_rv_metadata: bool = True) None #
Save a Config to a JSON file, optionally with RV metadata.
- update(pipeline: Optional[RVPipelineConfig] = None, scene: Optional[SceneConfig] = None) None [source]#
Update any fields before validation.
Subclasses should override this to provide complex default behavior, for example, setting default values as a function of the values of other fields. The arguments to this method will vary depending on the type of Config.
- Parameters
pipeline (Optional[RVPipelineConfig]) –
scene (Optional[SceneConfig]) –
- Return type
None
- validate_config()#
Validate fields that should be checked after update is called.
This is to complement the builtin validation that Pydantic performs at the time of object construction.