StatsAnalyzerConfig#

Note

All Configs are derived from rastervision.pipeline.config.Config, which itself is a pydantic Model.

pydantic model StatsAnalyzerConfig[source]#

Configure a StatsAnalyzer.

A StatsAnalyzer computes imagery statistics of scenes which can be used to normalize chips read from them.

Show JSON schema

{
   "title": "StatsAnalyzerConfig",
   "description": "Configure a :class:`.StatsAnalyzer`.\n\nA :class:`.StatsAnalyzer` computes imagery statistics of scenes which can\nbe used to normalize chips read from them.",
   "type": "object",
   "properties": {
      "type_hint": {
         "const": "stats_analyzer",
         "default": "stats_analyzer",
         "enum": [
            "stats_analyzer"
         ],
         "title": "Type Hint",
         "type": "string"
      },
      "output_uri": {
         "anyOf": [
            {
               "type": "string"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "URI of directory where stats will be saved. Stats for a scene-group will be save in a JSON file at <output_uri>/<scene-group-name>/stats.json. If None, and this Config is part of an RVPipeline, this field will be auto-generated.",
         "title": "Output Uri"
      },
      "sample_prob": {
         "anyOf": [
            {
               "type": "number"
            },
            {
               "type": "null"
            }
         ],
         "default": 0.1,
         "description": "The probability of using a random window for computing statistics. If None, will use a sliding window.",
         "title": "Sample Prob"
      },
      "chip_sz": {
         "default": 300,
         "description": "Chip size to use when sampling chips to compute stats from.",
         "title": "Chip Sz",
         "type": "integer"
      },
      "nodata_value": {
         "anyOf": [
            {
               "type": "number"
            },
            {
               "type": "null"
            }
         ],
         "default": 0,
         "description": "NODATA value. If set, these pixels will be ignored when computing stats.",
         "title": "Nodata Value"
      }
   },
   "additionalProperties": false
}

Config:

extra: str = forbid
validate_assignment: bool = True

Fields:

chip_sz ()
nodata_value ()
output_uri ()
sample_prob ()
type_hint (Literal['stats_analyzer'])

field chip_sz: int = 300#: Chip size to use when sampling chips to compute stats from.

field nodata_value: float | None = 0#: NODATA value. If set, these pixels will be ignored when computing stats.

field output_uri: str | None = None#: URI of directory where stats will be saved. Stats for a scene-group will be save in a JSON file at <output_uri>/<scene-group-name>/stats.json. If None, and this Config is part of an RVPipeline, this field will be auto-generated.

field sample_prob: float | None = 0.1#: The probability of using a random window for computing statistics. If None, will use a sliding window.

field type_hint: Literal['stats_analyzer'] = 'stats_analyzer'#

build(scene_group: tuple[str, Iterable[str]] | None = None) → StatsAnalyzer[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:: scene_group (tuple[str, Iterable[str]] | None) –
Return type:: StatsAnalyzer

classmethod deserialize(inp: str | dict | Config) → Self#

Deserialize Config from a JSON file or dict, upgrading if possible.

If inp is already a Config, it is returned as is.

Parameters:: inp (str | dict | Config) – a URI to a JSON file or a dict.
Return type:: Self

classmethod from_dict(cfg_dict: dict) → Self#

Deserialize Config from a dict.

Parameters:: cfg_dict (dict) – Dict to deserialize.
Return type:: Self

classmethod from_file(uri: str) → Self#

Deserialize Config from a JSON file, upgrading if possible.

Parameters:: uri (str) – URI to load from.
Return type:: Self

get_bundle_filenames()[source]#

Returns the names of files that should be included in a model bundle.

The files are assumed to be in the analyze/ directory generated by the analyze command. Note that only the names, not the full paths should be returned.

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().

to_file(uri: str, with_rv_metadata: bool = True) → None#

Save a Config to a JSON file, optionally with RV metadata.

Parameters:

uri (str) – URI to save to.
with_rv_metadata (bool) – If True, inject Raster Vision metadata such as plugin_versions, so that the config can be upgraded when loaded.

Return type:

None

update(pipeline: RVPipelineConfig | None = 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 (RVPipelineConfig | None) –
Return type:: None

validate_config()[source]#

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.

validate_list(field: str, valid_options: list[str])#

Validate a list field.

Parameters:

field (str) – name of field to validate
valid_options (list[str]) – values that field is allowed to take

Raises:

ConfigError – if field is invalid