AutoTransformSchema (autotransform.schema.schema)

The heart of AutoTransform, AutoTransformSchemas represent all information required to fully execute a change.

class autotransform.schema.schema.AutoTransformSchema(*, input: Input, batcher: Batcher, transformer: Transformer, config: SchemaConfig, filters: List[Filter] = None, validators: List[Validator] = None, commands: List[Command] = None, repo: Repo | None = None)

Bases: ComponentModel

The heart of AutoTransform, pulls together all components required to execute a transformation.

input

The Input which gets Items.

Type:

Input

batcher

The Batcher which batches filtered Items in to logical groups.

Type:

Batcher

transformer

The Transformer which actually modifies files.

Type:

Transformer

config

Any configuration needed by the schema so that it can run.

Type:

SchemaConfig

filters

A list of Filters to apply to Items. Defaults to [].

Type:

List[Filter], optional

validators

A list of Validators to ensure the changes did not break anything. Defaults to [].

Type:

List[Validator], optional

commands

A list of Commands that run post-processing on the changes. Defaults to [].

Type:

List[Command], optional

repo

A Repo to control submission of changes to version control or code review systems. Defaults to None.

Type:

Optional[Repo], optional

batcher: Batcher

commands: List[Command]

config: SchemaConfig

execute_batch(batch: Batch, change: Change | None = None) → bool

Executes changes for a batch, including setting up the Repo, running the Transformer, checking all Validators, running Commands, submitting changes if present, and rewinding the Repo if changes are submitted. Note: this function is not thread safe.

Parameters:

• batch (Batch) – The Batch to execute.

• change (Optional[Change]) – An associated Change that is being updated.

Raises:

ValidationError – If one of the Schema’s Validators fails raises an exception.

Returns:

Whether the batch triggered a submission.

Return type:

bool

filters: List[Filter]

static from_console(prev_schema: AutoTransformSchema | None = None) → AutoTransformSchema

Gets a AutoTransformSchema using console inputs.

Parameters:

prev_schema (Optional[AutoTransformSchema], optional) – A previously input AutoTransformSchema. Defaults to None.

Returns:

The input AutoTransformSchema.

Return type:

AutoTransformSchema

static from_data(data: Dict[str, Any]) → AutoTransformSchema

Takes data from a source like JSON and produces the associated Schema.

Parameters:

data (Dict[str, Any]) – The data representing the Schema.

Returns:

The Schema represented by the data.

Return type:

AutoTransformSchema

get_batches(items: List[Item]) → List[Batch]

Runs the Input to get eligible Items, filters them, then batches them. Note: this function is not thread safe.

Parameters:

items (List[Item]) – The Items to batch.

Returns:

The Batches for the change

Return type:

List[Batch]

get_items() → List[Item]

Runs the Input to get eligible Items and filters them. Note: this function is not thread safe.

Returns:

The valid Items for the Schema.

Return type:

List[Item]

input: Input

model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}

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

model_fields: ClassVar[dict[str, FieldInfo]] = {'batcher': FieldInfo(annotation=Batcher, required=True), 'commands': FieldInfo(annotation=List[Command], required=False, default_factory=list), 'config': FieldInfo(annotation=SchemaConfig, required=True), 'filters': FieldInfo(annotation=List[Filter], required=False, default_factory=list), 'input': FieldInfo(annotation=Input, required=True), 'repo': FieldInfo(annotation=Union[Repo, NoneType], required=False), 'transformer': FieldInfo(annotation=Transformer, required=True), 'validators': FieldInfo(annotation=List[Validator], required=False, default_factory=list)}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

repo: Repo | None

run()

Fully run a given Schema including getting and executing all Batches. Note: this function is not thread safe.

transformer: Transformer

validators: List[Validator]