mypy-boto3-lookoutequipment

Warnings

• breaking Support for Python 3.8 was removed with `mypy-boto3-builder` version 8.12.0. Projects using Python 3.8 will need to upgrade their Python version or pin to an older `mypy-boto3-lookoutequipment` version.
  Fix: Upgrade Python environment to 3.9 or newer. Alternatively, pin `mypy-boto3-lookoutequipment` to a version compatible with Python 3.8 (e.g., `<1.42.3`).
• breaking The `mypy-boto3-builder` (and thus generated packages) migrated to PEP 561-compliant distribution. While this generally improves compatibility with `mypy`, some non-standard build setups or older `mypy` versions might require adjustments to properly discover the stubs.
  Fix: Ensure `mypy` is up-to-date. If issues persist, refer to the `mypy-boto3` documentation on PEP 561 compatibility and stub discovery.
• breaking TypeDef naming conventions changed in `mypy-boto3-builder` 8.9.0. TypeDefs for packed method arguments may use shorter names, and 'Extra' postfixes were moved. This can cause type-checking errors if you explicitly imported and used these TypeDefs in previous versions.
  Fix: Update your explicit TypeDef imports and usage in code to reflect the new naming conventions as suggested by your IDE or `mypy`.
• gotcha PyCharm users might experience slow performance with Literal overloads when using the full `boto3-stubs` package. `boto3-stubs-lite` is recommended as a workaround, though it requires more explicit type annotations.
  Fix: For PyCharm, consider installing `boto3-stubs-lite` (e.g., `pip install 'boto3-stubs-lite[lookoutequipment]'`) and explicitly annotating all types. Alternatively, disable PyCharm's internal type checker and rely solely on `mypy` or `pyright`.
• gotcha This package only provides type annotations. The actual `boto3` library must be installed and correctly configured with AWS credentials for the code to run successfully at runtime.
  Fix: Ensure `pip install boto3` is executed and AWS credentials (e.g., `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, `AWS_REGION_NAME` environment variables or `~/.aws/credentials` file) are correctly set up.

Install

• pip install mypy-boto3-lookoutequipment Standalone package
• pip install 'boto3-stubs[lookoutequipment]' As part of boto3-stubs (recommended)

Imports

• LookoutEquipmentClient

  from mypy_boto3_lookoutequipment.client import LookoutEquipmentClient

  Required for explicit type annotation of the boto3 client object.
• CreateDatasetResponseTypeDef

  from mypy_boto3_lookoutequipment.type_defs import CreateDatasetResponseTypeDef

  Required for explicit type annotation of method response dictionaries.
• boto3.client('lookoutequipment')

  import boto3
  client: LookoutEquipmentClient = boto3.client('lookoutequipment')

  While implicit typing often works for `mypy`, explicit type annotation (`client: LookoutEquipmentClient = ...`) is recommended for full IDE support (e.g., VSCode) and clearer code.

Quickstart

This quickstart demonstrates how to use `mypy-boto3-lookoutequipment` to get type hints for the `boto3` LookoutEquipment client and its responses. It explicitly imports `LookoutEquipmentClient` for client annotation and `ListDatasetsResponseTypeDef` for response typing, ensuring static analysis can fully leverage the provided stubs. The `TYPE_CHECKING` block prevents runtime dependency on the stub package.

import boto3
from typing import TYPE_CHECKING

if TYPE_CHECKING:
    from mypy_boto3_lookoutequipment.client import LookoutEquipmentClient
    from mypy_boto3_lookoutequipment.type_defs import ListDatasetsResponseTypeDef


def list_lookout_equipment_datasets() -> 'ListDatasetsResponseTypeDef':
    """Lists LookoutEquipment datasets and returns a typed response."""
    # boto3 client is dynamically typed, mypy-boto3 provides static types
    client: LookoutEquipmentClient = boto3.client("lookoutequipment")

    # The response is automatically typed by mypy-boto3
    response = client.list_datasets()
    print(f"Found {len(response['DatasetSummaries'])} datasets.")
    return response

if __name__ == "__main__":
    # Example usage (requires AWS credentials configured)
    try:
        datasets = list_lookout_equipment_datasets()
        for dataset in datasets.get('DatasetSummaries', []):
            print(f"- Dataset ARN: {dataset['DatasetArn']}, Name: {dataset['DatasetName']}")
    except Exception as e:
        print(f"Error listing datasets: {e}")
        print("Please ensure AWS credentials are configured (e.g., AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION_NAME environment variables).")