Pydantic and Parameters

Tsdat makes use of [Pydantic](https://pydantic-docs.helpmanual.io/) to support automatic validation of the yaml configuration files. This means that each customizable class type in Tsdat that can be specified in a yaml file extends a Pydantic base model object. To facilitate consistent format and validation checking of parameters, all Tsdat objects should include a Parameters inner class. This means that if you create a custom object, such as a custom DataReader, which needs parameters to be specified in the config file, then your class should include a Parameters object as shown in the following example:

import xarray as xr
from tsdat import DataReader
from pydantic import BaseModel, Extra


class CustomDataReader(DataReader):
    """---------------------------------------------------------------------------------
    Data reader that can read from *xyz* formatted-data files.
    ---------------------------------------------------------------------------------"""

    class Parameters(BaseModel, extra=Extra.forbid):
        """If your CustomDataReader should take any additional arguments from the
        retriever config configuration file, then those should be specified here.
        """
        custom_parameter: bool = True  # <- Use Python type hinting to validate the type when loaded from config

    parameters: Parameters = Parameters()

    def read(self, input_key: str) -> xr.Dataset:
        raise NotImplementedError
        return xr.Dataset()

The following object types all support custom parameters via the same mechanism shown above:

• DataReader

• DataWriter

• DataHandler

• DataConverter

• QualityChecker

• QualityHandler

• Storage

• Retriever