Body

class Body

Bases: CommonBaseModel

Represents the parameters of a body.

qty_cross_sections

Number of cross-sections.

Type:

Optional[int]

stations

Axial stations.

Type:

List[Optional[float]]

cross_sectional_areas

Cross-sectional areas at stations.

Type:

List[Optional[float]]

cross_sectional_perimeters

Perimeters at stations.

Type:

List[Optional[float]]

max_halfbredth

Maximum Halfbredth at stations.

Type:

List[Optional[float]]

crown_line

Crown line height at stations.

Type:

List[Optional[float]]

keel_line

Keel Line height at stations.

Type:

List[Optional[float]]

nose_type

Type of nose geometry.

Type:

Optional[BodyShape]

aftbody_type

Type of aftbody geometry.

Type:

Optional[BodyShape]

nose_length

Nose length.

Type:

Optional[float]

aftbody_length

Afterbody length.

Type:

Optional[float]

nose_bluntness

Diameter of nose bluntness.

Type:

Optional[float]

area_rule

Area Ruling: 1 - Straight Wing, None, 2 - Swept Wing, None, 3 - area ruled.

Type:

Optional[int]

Attributes Summary

model_config

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

Methods Summary

validate_list_length(values)	Validate that the length of lists matches the number of cross-sections.
validate_non_negative(v)	Validate that all values in the list are non-negative.

Attributes Documentation

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'allow', 'protected_namespaces': (), 'validate_assignment': True}

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

Methods Documentation

classmethod validate_list_length(values)

Validate that the length of lists matches the number of cross-sections.

Parameters:

values (dict) – The current values of the model.

Returns:

The validated values.

Return type:

dict

Raises:

ValueError – If the length of any list doesn’t match the number of cross-sections.

classmethod validate_non_negative(v)

Validate that all values in the list are non-negative.

Parameters:

v (List[Optional[float]]) – The list of values to validate.

Returns:

The validated list of values.

Return type:

List[Optional[float]]

Raises:

ValueError – If any value in the list is negative.

Methods:

validate_non_negative(v)	Validate that all values in the list are non-negative.
validate_list_length(values)	Validate that the length of lists matches the number of cross-sections.

Attributes:

__pydantic_setattr_handlers__

__setattr__ handlers.

classmethod validate_non_negative(v)

Validate that all values in the list are non-negative.

Parameters:

v (List[Optional[float]]) – The list of values to validate.

Returns:

The validated list of values.

Return type:

List[Optional[float]]

Raises:

ValueError – If any value in the list is negative.

classmethod validate_list_length(values)

Validate that the length of lists matches the number of cross-sections.

Parameters:

values (dict) – The current values of the model.

Returns:

The validated values.

Return type:

dict

Raises:

ValueError – If the length of any list doesn’t match the number of cross-sections.

__pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}

__setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__