HermesDataSchema

class hermes_core.util.schema.HermesDataSchema(global_schema_layers: list[Path] | None = None, variable_schema_layers: list[Path] | None = None, use_defaults: bool | None = True)[source]

Bases: SWXSchema

Class representing a schema for data requirements and formatting, specific to the HERMES Mission.

There are two main components to the HERMES Data Schema, including both global and variable attribute information.

Global schema information is loaded from YAML (dict-like) files in the following format:

attribute_name:
    description: >
        Include a meaningful description of the attribute and context needed to understand
        its values.
    default: <string> # A default value for the attribute if needed/desired
    derived: <bool> # Whether or not the attribute's value can be derived using a python function
    derivation_fn: <string> # The name of a Python function to derive the value. Must be a function member of the schema class and match the signature below.
    required: <bool> # Whether the attribute is required
    validate: <bool> # Whether the attribute should be validated by the Validation module
    overwrite: <bool> # Whether an existing value for the attribute should be overwritten if a different value is derived.

The signature for all functions to derive global attributes should follow the format below. The function takes in a parameter data which is a HermesData object, or that of an extended data class, and returns a single attribute value for the given attribute to be derived.

def derivation_fn(self, data: HermesData):
    # ... do manipulations as needed from `data`
    return "attribute_value"

Variable schema information is loaded from YAML (dict-like) files in the following format:

attribute_key:
    attribute_name:
        description: >
            Include a meaningful description of the attribute and context needed to understand
            its values.
        derived: <bool> # Whether or not the attribute's value can be derived using a python function
        derivation_fn: <string> # The name of a Python function to derive the value. Must be a function member of the schema class and match the signature below.
        required: <bool> # Whether the attribute is required
        validate: <bool> # Whether the attribute should be validated by the Validation module
        overwrite: <bool> # Whether an existing value for the attribute should be overwritten if a different value is derived.
        valid_values: <list> # A list of valid values that the attribute can take. The value of the attribute is checked against the `valid_values` in the Validation module.
        alternate: <string> An additional attribute name that can be treated as an alternative of the given attribute.
data:
    - attribute_name
    - ...
support_data:
    - ...
metadata:
    - ...

The signature for all functions to derive variable attributes should follow the format below. The function takes in parameters var_name, var_data, and guess_type, where:

  • var_name is the variable name of the variable for which the attribute is being derived

  • var_data is the variable data of the variable for which the attribute is being derived

  • guess_type is the guessed CDF variable type of the data for which the attribute is being derived.

The function must return a single attribute value for the given attribute to be derived.

def derivation_fn(self, var_name: str, var_data: Union[Quantity, NDData, NDCube], guess_type: ctypes.c_long):
    # ... do manipulations as needed from data
    return "attribute_value"
Parameters:

  • global_schema_layers (Optional[list[Path]]) – Absolute file paths to global attribute schema files. These schema files are layered on top of one another in a latest-priority ordering. That is, the latest file that modifies a common schema attribute will take precedence over earlier values for a given attribute.

  • variable_schema_layers (Optional[list[Path]]) – Absolute file paths to variable attribute schema files. These schema files are layered on top of one another in a latest-priority ordering. That is, the latest file that modifies a common schema attribute will take precedence over earlier values for a given attribute.

  • use_defaults (Optional[bool]) – Whether or not to load the default global and variable attribute schema files. These default schema files contain only the requirements for CDF ISTP validation.