libera_utils.io.netcdf.VariableMetadata

class libera_utils.io.netcdf.VariableMetadata(*, long_name: str, dimensions: dict[str, LiberaDimension], valid_range: list, missing_value: int | float, units: str | None, dtype: str | None)

Bases: BaseModel

Pydantic model for variable-level metadata for NetCDF-4 files.

available_dimensions

A class variable listing all available dimensions that can be used in the variable metadata.

Type:

ClassVar[list[LiberaDimension]]

long_name

A long name for the variable, providing a human-readable description of what the variable represents.

Type:

str

dimensions

A list of dimensions that the variable’s data array will have. These should be instances of LiberaDimension.

Type:

list[LiberaDimension]

valid_range

A list specifying the valid range of values for the variable’s data, excluding missing values.

Type:

list

missing_value

The value used to represent missing data in the variable’s data array. This should be the same type as the data.

Type:

int | float

units

The units of the variable’s data, if applicable. This can be None if the variable does not have units.

Type:

str | None

dtype

The data type of the variable’s data, if applicable. This can be None if the variable does not have a specific data type.

Type:

str | None

Notes

These derive from the CF conventions. More information at: https://cfconventions.org/cf-conventions/cf-conventions.html

Attributes:

dimensions_name_list

A computed property tuple of the string names of the dimensions

dimensions_shape

A tuple of the shape of the dimensions for this variable

model_extra

Get extra fields set during validation.

model_fields_set

Returns the set of fields that have been explicitly set on this model instance.

Methods

copy(*[, include, exclude, update, deep])	Returns a copy of the model.
model_construct([_fields_set])	Creates a new instance of the Model class with validated data.
model_copy(*[, update, deep])	!!! abstract "Usage Documentation"
model_dump(*[, mode, include, exclude, ...])	!!! abstract "Usage Documentation"
model_dump_json(*[, indent, include, ...])	!!! abstract "Usage Documentation"
model_json_schema([by_alias, ref_template, ...])	Generates a JSON schema for a model class.
model_parametrized_name(params)	Compute the class name for parametrizations of generic classes.
model_post_init(context, /)	Override this method to perform additional initialization after __init__ and model_construct.
model_rebuild(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
model_validate(obj, *[, strict, ...])	Validate a pydantic model instance.
model_validate_json(json_data, *[, strict, ...])	!!! abstract "Usage Documentation"
model_validate_strings(obj, *[, strict, ...])	Validate the given object with string data against the Pydantic model.
set_dimensions(input_dimensions)	Class method validating the list of input dimensions when they are provided.
set_dynamic_dimension(selected_dimension, ...)	Internal method to set a dynamic dimension length

construct
dict
from_orm
json
parse_file
parse_obj
parse_raw
schema
schema_json
update_forward_refs
validate

__init__(**data: Any) → None

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Methods

set_dimensions(input_dimensions)	Class method validating the list of input dimensions when they are provided.
set_dynamic_dimension(selected_dimension, ...)	Internal method to set a dynamic dimension length

Attributes

available_dimensions
dimensions_name_list	A computed property tuple of the string names of the dimensions
dimensions_shape	A tuple of the shape of the dimensions for this variable
model_computed_fields
model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
model_extra	Get extra fields set during validation.
model_fields
model_fields_set	Returns the set of fields that have been explicitly set on this model instance.
long_name
dimensions
valid_range
missing_value
units
dtype

property dimensions_name_list: tuple

A computed property tuple of the string names of the dimensions

Returns:

A tuple of dimension names as strings.

Return type:

tuple

Notes

This property returns a tuple of the names of the dimensions in the order they are defined in the metadata.

property dimensions_shape: tuple

A tuple of the shape of the dimensions for this variable

Returns:

A tuple of dimension sizes, where dynamic dimensions are represented as “dynamic”.

Return type:

tuple

Notes

This property returns a tuple of the sizes of the dimensions in the order they are defined in the metadata.

model_config: ClassVar[ConfigDict] = {}

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

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod set_dimensions(input_dimensions: list[str] | list[LiberaDimension])

Class method validating the list of input dimensions when they are provided.

Parameters:

input_dimensions (list[str] | list[LiberaDimension]) – A list of dimension names as strings or LiberaDimension instances.

Returns:

A list of LiberaDimension instances corresponding to the input dimensions.

Return type:

list[LiberaDimension]

Raises:

TypeError – If the input dimensions are not a list or if the items in the list are not of type str or LiberaDimension.

Notes

This method ensures that the dimensions are either strings that match the available dimensions or instances of the LiberaDimension class. It converts string dimensions to LiberaDimension instances.

set_dynamic_dimension(selected_dimension: LiberaDimension, data_length: int)

Internal method to set a dynamic dimension length

Parameters:

• selected_dimension (LiberaDimension) – The dimension that has a dynamic dimension to be set.

• data_length (int) – The length of the dimension that will replace the dynamic size.

Raises:

ValueError – If the selected dimension is not dynamic or if the size is already set.

Notes

This method is used to set the size of a dimension that has been defined as dynamic in the metadata.