Anto01
6c30224341
Config Layer: - spec_models.py: Pydantic models for AtomizerSpec v2.0 - spec_validator.py: Semantic validation with detailed error reporting Extractors: - custom_extractor_loader.py: Runtime custom extractor loading - spec_extractor_builder.py: Build extractors from spec definitions Tools: - migrate_to_spec_v2.py: CLI tool for batch migration Tests: - test_migrator.py: Migration tests - test_spec_manager.py: SpecManager service tests - test_spec_api.py: REST API tests - test_mcp_tools.py: MCP tool tests - test_e2e_unified_config.py: End-to-end config tests
675 lines
22 KiB
Python
675 lines
22 KiB
Python
"""
|
|
AtomizerSpec v2.0 Pydantic Models
|
|
|
|
These models match the JSON Schema at optimization_engine/schemas/atomizer_spec_v2.json
|
|
They provide validation and type safety for the unified configuration system.
|
|
"""
|
|
|
|
from datetime import datetime
|
|
from enum import Enum
|
|
from typing import Any, Dict, List, Literal, Optional, Union
|
|
from pydantic import BaseModel, Field, field_validator, model_validator
|
|
import re
|
|
|
|
|
|
# ============================================================================
|
|
# Enums
|
|
# ============================================================================
|
|
|
|
class SpecCreatedBy(str, Enum):
|
|
"""Who/what created the spec."""
|
|
CANVAS = "canvas"
|
|
CLAUDE = "claude"
|
|
API = "api"
|
|
MIGRATION = "migration"
|
|
MANUAL = "manual"
|
|
|
|
|
|
class SolverType(str, Enum):
|
|
"""Supported solver types."""
|
|
NASTRAN = "nastran"
|
|
NX_NASTRAN = "NX_Nastran"
|
|
ABAQUS = "abaqus"
|
|
|
|
|
|
class SubcaseType(str, Enum):
|
|
"""Subcase analysis types."""
|
|
STATIC = "static"
|
|
MODAL = "modal"
|
|
THERMAL = "thermal"
|
|
BUCKLING = "buckling"
|
|
|
|
|
|
class DesignVariableType(str, Enum):
|
|
"""Design variable types."""
|
|
CONTINUOUS = "continuous"
|
|
INTEGER = "integer"
|
|
CATEGORICAL = "categorical"
|
|
|
|
|
|
class ExtractorType(str, Enum):
|
|
"""Physics extractor types."""
|
|
DISPLACEMENT = "displacement"
|
|
FREQUENCY = "frequency"
|
|
STRESS = "stress"
|
|
MASS = "mass"
|
|
MASS_EXPRESSION = "mass_expression"
|
|
ZERNIKE_OPD = "zernike_opd"
|
|
ZERNIKE_CSV = "zernike_csv"
|
|
TEMPERATURE = "temperature"
|
|
CUSTOM_FUNCTION = "custom_function"
|
|
|
|
|
|
class OptimizationDirection(str, Enum):
|
|
"""Optimization direction."""
|
|
MINIMIZE = "minimize"
|
|
MAXIMIZE = "maximize"
|
|
|
|
|
|
class ConstraintType(str, Enum):
|
|
"""Constraint types."""
|
|
HARD = "hard"
|
|
SOFT = "soft"
|
|
|
|
|
|
class ConstraintOperator(str, Enum):
|
|
"""Constraint comparison operators."""
|
|
LE = "<="
|
|
GE = ">="
|
|
LT = "<"
|
|
GT = ">"
|
|
EQ = "=="
|
|
|
|
|
|
class PenaltyMethod(str, Enum):
|
|
"""Penalty methods for constraints."""
|
|
LINEAR = "linear"
|
|
QUADRATIC = "quadratic"
|
|
EXPONENTIAL = "exponential"
|
|
|
|
|
|
class AlgorithmType(str, Enum):
|
|
"""Optimization algorithm types."""
|
|
TPE = "TPE"
|
|
CMA_ES = "CMA-ES"
|
|
NSGA_II = "NSGA-II"
|
|
RANDOM_SEARCH = "RandomSearch"
|
|
SAT_V3 = "SAT_v3"
|
|
GP_BO = "GP-BO"
|
|
|
|
|
|
class SurrogateType(str, Enum):
|
|
"""Surrogate model types."""
|
|
MLP = "MLP"
|
|
GNN = "GNN"
|
|
ENSEMBLE = "ensemble"
|
|
|
|
|
|
# ============================================================================
|
|
# Position Model
|
|
# ============================================================================
|
|
|
|
class CanvasPosition(BaseModel):
|
|
"""Canvas position for nodes."""
|
|
x: float = 0
|
|
y: float = 0
|
|
|
|
|
|
# ============================================================================
|
|
# Meta Models
|
|
# ============================================================================
|
|
|
|
class SpecMeta(BaseModel):
|
|
"""Metadata about the spec."""
|
|
version: str = Field(
|
|
...,
|
|
pattern=r"^2\.\d+$",
|
|
description="Schema version (e.g., '2.0')"
|
|
)
|
|
created: Optional[datetime] = Field(
|
|
default=None,
|
|
description="When the spec was created"
|
|
)
|
|
modified: Optional[datetime] = Field(
|
|
default=None,
|
|
description="When the spec was last modified"
|
|
)
|
|
created_by: Optional[SpecCreatedBy] = Field(
|
|
default=None,
|
|
description="Who/what created the spec"
|
|
)
|
|
modified_by: Optional[str] = Field(
|
|
default=None,
|
|
description="Who/what last modified the spec"
|
|
)
|
|
study_name: str = Field(
|
|
...,
|
|
min_length=3,
|
|
max_length=100,
|
|
pattern=r"^[a-z0-9_]+$",
|
|
description="Unique study identifier (snake_case)"
|
|
)
|
|
description: Optional[str] = Field(
|
|
default=None,
|
|
max_length=1000,
|
|
description="Human-readable description"
|
|
)
|
|
tags: Optional[List[str]] = Field(
|
|
default=None,
|
|
description="Tags for categorization"
|
|
)
|
|
engineering_context: Optional[str] = Field(
|
|
default=None,
|
|
description="Real-world engineering context"
|
|
)
|
|
|
|
|
|
# ============================================================================
|
|
# Model Configuration Models
|
|
# ============================================================================
|
|
|
|
class NxPartConfig(BaseModel):
|
|
"""NX geometry part file configuration."""
|
|
path: Optional[str] = Field(default=None, description="Path to .prt file")
|
|
hash: Optional[str] = Field(default=None, description="File hash for change detection")
|
|
idealized_part: Optional[str] = Field(default=None, description="Idealized part filename (_i.prt)")
|
|
|
|
|
|
class FemConfig(BaseModel):
|
|
"""FEM mesh file configuration."""
|
|
path: Optional[str] = Field(default=None, description="Path to .fem file")
|
|
element_count: Optional[int] = Field(default=None, description="Number of elements")
|
|
node_count: Optional[int] = Field(default=None, description="Number of nodes")
|
|
|
|
|
|
class Subcase(BaseModel):
|
|
"""Simulation subcase definition."""
|
|
id: int
|
|
name: Optional[str] = None
|
|
type: Optional[SubcaseType] = None
|
|
|
|
|
|
class SimConfig(BaseModel):
|
|
"""Simulation file configuration."""
|
|
path: str = Field(..., description="Path to .sim file")
|
|
solver: SolverType = Field(..., description="Solver type")
|
|
solution_type: Optional[str] = Field(
|
|
default=None,
|
|
pattern=r"^SOL\d+$",
|
|
description="Solution type (e.g., SOL101)"
|
|
)
|
|
subcases: Optional[List[Subcase]] = Field(default=None, description="Defined subcases")
|
|
|
|
|
|
class NxSettings(BaseModel):
|
|
"""NX runtime settings."""
|
|
nx_install_path: Optional[str] = None
|
|
simulation_timeout_s: Optional[int] = Field(default=None, ge=60, le=7200)
|
|
auto_start_nx: Optional[bool] = None
|
|
|
|
|
|
class ModelConfig(BaseModel):
|
|
"""NX model files and configuration."""
|
|
nx_part: Optional[NxPartConfig] = None
|
|
fem: Optional[FemConfig] = None
|
|
sim: SimConfig
|
|
nx_settings: Optional[NxSettings] = None
|
|
|
|
|
|
# ============================================================================
|
|
# Design Variable Models
|
|
# ============================================================================
|
|
|
|
class DesignVariableBounds(BaseModel):
|
|
"""Design variable bounds."""
|
|
min: float
|
|
max: float
|
|
|
|
@model_validator(mode='after')
|
|
def validate_bounds(self) -> 'DesignVariableBounds':
|
|
if self.min >= self.max:
|
|
raise ValueError(f"min ({self.min}) must be less than max ({self.max})")
|
|
return self
|
|
|
|
|
|
class DesignVariable(BaseModel):
|
|
"""A design variable to optimize."""
|
|
id: str = Field(
|
|
...,
|
|
pattern=r"^dv_\d{3}$",
|
|
description="Unique identifier (pattern: dv_XXX)"
|
|
)
|
|
name: str = Field(..., description="Human-readable name")
|
|
expression_name: str = Field(
|
|
...,
|
|
pattern=r"^[a-zA-Z_][a-zA-Z0-9_]*$",
|
|
description="NX expression name (must match model)"
|
|
)
|
|
type: DesignVariableType = Field(..., description="Variable type")
|
|
bounds: DesignVariableBounds = Field(..., description="Value bounds")
|
|
baseline: Optional[float] = Field(default=None, description="Current/initial value")
|
|
units: Optional[str] = Field(default=None, description="Physical units (mm, deg, etc.)")
|
|
step: Optional[float] = Field(default=None, description="Step size for integer/discrete")
|
|
enabled: bool = Field(default=True, description="Whether to include in optimization")
|
|
description: Optional[str] = None
|
|
canvas_position: Optional[CanvasPosition] = None
|
|
|
|
|
|
# ============================================================================
|
|
# Extractor Models
|
|
# ============================================================================
|
|
|
|
class ExtractorConfig(BaseModel):
|
|
"""Type-specific extractor configuration."""
|
|
inner_radius_mm: Optional[float] = None
|
|
outer_radius_mm: Optional[float] = None
|
|
n_modes: Optional[int] = None
|
|
filter_low_orders: Optional[int] = None
|
|
displacement_unit: Optional[str] = None
|
|
reference_subcase: Optional[int] = None
|
|
expression_name: Optional[str] = None
|
|
mode_number: Optional[int] = None
|
|
element_type: Optional[str] = None
|
|
result_type: Optional[str] = None
|
|
metric: Optional[str] = None
|
|
|
|
class Config:
|
|
extra = "allow" # Allow additional fields for flexibility
|
|
|
|
|
|
class CustomFunction(BaseModel):
|
|
"""Custom function definition for custom_function extractors."""
|
|
name: Optional[str] = Field(default=None, description="Function name")
|
|
module: Optional[str] = Field(default=None, description="Python module path")
|
|
signature: Optional[str] = Field(default=None, description="Function signature")
|
|
source_code: Optional[str] = Field(default=None, description="Python source code")
|
|
|
|
|
|
class ExtractorOutput(BaseModel):
|
|
"""Output definition for an extractor."""
|
|
name: str = Field(..., description="Output name (used by objectives/constraints)")
|
|
metric: Optional[str] = Field(default=None, description="Specific metric (max, total, rms, etc.)")
|
|
subcase: Optional[int] = Field(default=None, description="Subcase ID for this output")
|
|
units: Optional[str] = None
|
|
|
|
|
|
class Extractor(BaseModel):
|
|
"""Physics extractor that computes outputs from FEA."""
|
|
id: str = Field(
|
|
...,
|
|
pattern=r"^ext_\d{3}$",
|
|
description="Unique identifier (pattern: ext_XXX)"
|
|
)
|
|
name: str = Field(..., description="Human-readable name")
|
|
type: ExtractorType = Field(..., description="Extractor type")
|
|
builtin: bool = Field(default=True, description="Whether this is a built-in extractor")
|
|
config: Optional[ExtractorConfig] = Field(default=None, description="Type-specific configuration")
|
|
function: Optional[CustomFunction] = Field(
|
|
default=None,
|
|
description="Custom function definition (for custom_function type)"
|
|
)
|
|
outputs: List[ExtractorOutput] = Field(..., min_length=1, description="Output values")
|
|
canvas_position: Optional[CanvasPosition] = None
|
|
|
|
@model_validator(mode='after')
|
|
def validate_custom_function(self) -> 'Extractor':
|
|
if self.type == ExtractorType.CUSTOM_FUNCTION and self.function is None:
|
|
raise ValueError("custom_function extractor requires function definition")
|
|
return self
|
|
|
|
|
|
# ============================================================================
|
|
# Objective Models
|
|
# ============================================================================
|
|
|
|
class ObjectiveSource(BaseModel):
|
|
"""Source reference for objective value."""
|
|
extractor_id: str = Field(..., description="Reference to extractor")
|
|
output_name: str = Field(..., description="Which output from the extractor")
|
|
|
|
|
|
class Objective(BaseModel):
|
|
"""Optimization objective."""
|
|
id: str = Field(
|
|
...,
|
|
pattern=r"^obj_\d{3}$",
|
|
description="Unique identifier (pattern: obj_XXX)"
|
|
)
|
|
name: str = Field(..., description="Human-readable name")
|
|
direction: OptimizationDirection = Field(..., description="Optimization direction")
|
|
weight: float = Field(default=1.0, ge=0, description="Weight for weighted sum")
|
|
source: ObjectiveSource = Field(..., description="Where the value comes from")
|
|
target: Optional[float] = Field(default=None, description="Target value (for goal programming)")
|
|
units: Optional[str] = None
|
|
description: Optional[str] = None
|
|
canvas_position: Optional[CanvasPosition] = None
|
|
|
|
|
|
# ============================================================================
|
|
# Constraint Models
|
|
# ============================================================================
|
|
|
|
class ConstraintSource(BaseModel):
|
|
"""Source reference for constraint value."""
|
|
extractor_id: str
|
|
output_name: str
|
|
|
|
|
|
class PenaltyConfig(BaseModel):
|
|
"""Penalty method configuration for constraints."""
|
|
method: Optional[PenaltyMethod] = None
|
|
weight: Optional[float] = None
|
|
margin: Optional[float] = Field(default=None, description="Soft margin before penalty kicks in")
|
|
|
|
|
|
class Constraint(BaseModel):
|
|
"""Hard or soft constraint."""
|
|
id: str = Field(
|
|
...,
|
|
pattern=r"^con_\d{3}$",
|
|
description="Unique identifier (pattern: con_XXX)"
|
|
)
|
|
name: str
|
|
type: ConstraintType = Field(..., description="Constraint type")
|
|
operator: ConstraintOperator = Field(..., description="Comparison operator")
|
|
threshold: float = Field(..., description="Constraint threshold value")
|
|
source: ConstraintSource = Field(..., description="Where the value comes from")
|
|
penalty_config: Optional[PenaltyConfig] = None
|
|
description: Optional[str] = None
|
|
canvas_position: Optional[CanvasPosition] = None
|
|
|
|
|
|
# ============================================================================
|
|
# Optimization Models
|
|
# ============================================================================
|
|
|
|
class AlgorithmConfig(BaseModel):
|
|
"""Algorithm-specific settings."""
|
|
population_size: Optional[int] = None
|
|
n_generations: Optional[int] = None
|
|
mutation_prob: Optional[float] = None
|
|
crossover_prob: Optional[float] = None
|
|
seed: Optional[int] = None
|
|
n_startup_trials: Optional[int] = None
|
|
sigma0: Optional[float] = None
|
|
|
|
class Config:
|
|
extra = "allow" # Allow additional algorithm-specific fields
|
|
|
|
|
|
class Algorithm(BaseModel):
|
|
"""Optimization algorithm configuration."""
|
|
type: AlgorithmType
|
|
config: Optional[AlgorithmConfig] = None
|
|
|
|
|
|
class OptimizationBudget(BaseModel):
|
|
"""Computational budget for optimization."""
|
|
max_trials: Optional[int] = Field(default=None, ge=1, le=10000)
|
|
max_time_hours: Optional[float] = None
|
|
convergence_patience: Optional[int] = Field(
|
|
default=None,
|
|
description="Stop if no improvement for N trials"
|
|
)
|
|
|
|
|
|
class SurrogateConfig(BaseModel):
|
|
"""Neural surrogate model configuration."""
|
|
n_models: Optional[int] = None
|
|
architecture: Optional[List[int]] = None
|
|
train_every_n_trials: Optional[int] = None
|
|
min_training_samples: Optional[int] = None
|
|
acquisition_candidates: Optional[int] = None
|
|
fea_validations_per_round: Optional[int] = None
|
|
|
|
|
|
class Surrogate(BaseModel):
|
|
"""Surrogate model settings."""
|
|
enabled: Optional[bool] = None
|
|
type: Optional[SurrogateType] = None
|
|
config: Optional[SurrogateConfig] = None
|
|
|
|
|
|
class OptimizationConfig(BaseModel):
|
|
"""Optimization algorithm configuration."""
|
|
algorithm: Algorithm
|
|
budget: OptimizationBudget
|
|
surrogate: Optional[Surrogate] = None
|
|
canvas_position: Optional[CanvasPosition] = None
|
|
|
|
|
|
# ============================================================================
|
|
# Workflow Models
|
|
# ============================================================================
|
|
|
|
class WorkflowStage(BaseModel):
|
|
"""A stage in a multi-stage optimization workflow."""
|
|
id: str
|
|
name: str
|
|
algorithm: Optional[str] = None
|
|
trials: Optional[int] = None
|
|
purpose: Optional[str] = None
|
|
|
|
|
|
class WorkflowTransition(BaseModel):
|
|
"""Transition between workflow stages."""
|
|
from_: str = Field(..., alias="from")
|
|
to: str
|
|
condition: Optional[str] = None
|
|
|
|
class Config:
|
|
populate_by_name = True
|
|
|
|
|
|
class Workflow(BaseModel):
|
|
"""Multi-stage optimization workflow."""
|
|
stages: Optional[List[WorkflowStage]] = None
|
|
transitions: Optional[List[WorkflowTransition]] = None
|
|
|
|
|
|
# ============================================================================
|
|
# Reporting Models
|
|
# ============================================================================
|
|
|
|
class InsightConfig(BaseModel):
|
|
"""Insight-specific configuration."""
|
|
include_html: Optional[bool] = None
|
|
show_pareto_evolution: Optional[bool] = None
|
|
|
|
class Config:
|
|
extra = "allow"
|
|
|
|
|
|
class Insight(BaseModel):
|
|
"""Reporting insight definition."""
|
|
type: Optional[str] = None
|
|
for_trials: Optional[str] = None
|
|
config: Optional[InsightConfig] = None
|
|
|
|
|
|
class ReportingConfig(BaseModel):
|
|
"""Reporting configuration."""
|
|
auto_report: Optional[bool] = None
|
|
report_triggers: Optional[List[str]] = None
|
|
insights: Optional[List[Insight]] = None
|
|
|
|
|
|
# ============================================================================
|
|
# Canvas Models
|
|
# ============================================================================
|
|
|
|
class CanvasViewport(BaseModel):
|
|
"""Canvas viewport settings."""
|
|
x: float = 0
|
|
y: float = 0
|
|
zoom: float = 1.0
|
|
|
|
|
|
class CanvasEdge(BaseModel):
|
|
"""Connection between canvas nodes."""
|
|
source: str
|
|
target: str
|
|
sourceHandle: Optional[str] = None
|
|
targetHandle: Optional[str] = None
|
|
|
|
|
|
class CanvasGroup(BaseModel):
|
|
"""Grouping of canvas nodes."""
|
|
id: str
|
|
name: str
|
|
node_ids: List[str]
|
|
|
|
|
|
class CanvasConfig(BaseModel):
|
|
"""Canvas UI state (persisted for reconstruction)."""
|
|
layout_version: Optional[str] = None
|
|
viewport: Optional[CanvasViewport] = None
|
|
edges: Optional[List[CanvasEdge]] = None
|
|
groups: Optional[List[CanvasGroup]] = None
|
|
|
|
|
|
# ============================================================================
|
|
# Main AtomizerSpec Model
|
|
# ============================================================================
|
|
|
|
class AtomizerSpec(BaseModel):
|
|
"""
|
|
AtomizerSpec v2.0 - The unified configuration schema for Atomizer optimization studies.
|
|
|
|
This is the single source of truth used by:
|
|
- Canvas UI (rendering and editing)
|
|
- Backend API (validation and storage)
|
|
- Claude Assistant (reading and modifying)
|
|
- Optimization Engine (execution)
|
|
"""
|
|
meta: SpecMeta = Field(..., description="Metadata about the spec")
|
|
model: ModelConfig = Field(..., description="NX model files and configuration")
|
|
design_variables: List[DesignVariable] = Field(
|
|
...,
|
|
min_length=1,
|
|
max_length=50,
|
|
description="Design variables to optimize"
|
|
)
|
|
extractors: List[Extractor] = Field(
|
|
...,
|
|
min_length=1,
|
|
description="Physics extractors"
|
|
)
|
|
objectives: List[Objective] = Field(
|
|
...,
|
|
min_length=1,
|
|
max_length=5,
|
|
description="Optimization objectives"
|
|
)
|
|
constraints: Optional[List[Constraint]] = Field(
|
|
default=None,
|
|
description="Hard and soft constraints"
|
|
)
|
|
optimization: OptimizationConfig = Field(..., description="Algorithm configuration")
|
|
workflow: Optional[Workflow] = Field(default=None, description="Multi-stage workflow")
|
|
reporting: Optional[ReportingConfig] = Field(default=None, description="Reporting config")
|
|
canvas: Optional[CanvasConfig] = Field(default=None, description="Canvas UI state")
|
|
|
|
@model_validator(mode='after')
|
|
def validate_references(self) -> 'AtomizerSpec':
|
|
"""Validate that all references are valid."""
|
|
# Collect valid extractor IDs and their outputs
|
|
extractor_outputs: Dict[str, set] = {}
|
|
for ext in self.extractors:
|
|
extractor_outputs[ext.id] = {o.name for o in ext.outputs}
|
|
|
|
# Validate objective sources
|
|
for obj in self.objectives:
|
|
if obj.source.extractor_id not in extractor_outputs:
|
|
raise ValueError(
|
|
f"Objective '{obj.name}' references unknown extractor: {obj.source.extractor_id}"
|
|
)
|
|
if obj.source.output_name not in extractor_outputs[obj.source.extractor_id]:
|
|
raise ValueError(
|
|
f"Objective '{obj.name}' references unknown output: {obj.source.output_name}"
|
|
)
|
|
|
|
# Validate constraint sources
|
|
if self.constraints:
|
|
for con in self.constraints:
|
|
if con.source.extractor_id not in extractor_outputs:
|
|
raise ValueError(
|
|
f"Constraint '{con.name}' references unknown extractor: {con.source.extractor_id}"
|
|
)
|
|
if con.source.output_name not in extractor_outputs[con.source.extractor_id]:
|
|
raise ValueError(
|
|
f"Constraint '{con.name}' references unknown output: {con.source.output_name}"
|
|
)
|
|
|
|
return self
|
|
|
|
def get_enabled_design_variables(self) -> List[DesignVariable]:
|
|
"""Return only enabled design variables."""
|
|
return [dv for dv in self.design_variables if dv.enabled]
|
|
|
|
def get_extractor_by_id(self, extractor_id: str) -> Optional[Extractor]:
|
|
"""Find an extractor by ID."""
|
|
for ext in self.extractors:
|
|
if ext.id == extractor_id:
|
|
return ext
|
|
return None
|
|
|
|
def get_objective_by_id(self, objective_id: str) -> Optional[Objective]:
|
|
"""Find an objective by ID."""
|
|
for obj in self.objectives:
|
|
if obj.id == objective_id:
|
|
return obj
|
|
return None
|
|
|
|
def get_constraint_by_id(self, constraint_id: str) -> Optional[Constraint]:
|
|
"""Find a constraint by ID."""
|
|
if not self.constraints:
|
|
return None
|
|
for con in self.constraints:
|
|
if con.id == constraint_id:
|
|
return con
|
|
return None
|
|
|
|
def has_custom_extractors(self) -> bool:
|
|
"""Check if spec has any custom function extractors."""
|
|
return any(ext.type == ExtractorType.CUSTOM_FUNCTION for ext in self.extractors)
|
|
|
|
def is_multi_objective(self) -> bool:
|
|
"""Check if this is a multi-objective optimization."""
|
|
return len(self.objectives) > 1
|
|
|
|
|
|
# ============================================================================
|
|
# Validation Response Models
|
|
# ============================================================================
|
|
|
|
class ValidationError(BaseModel):
|
|
"""A validation error."""
|
|
type: str # 'schema', 'semantic', 'reference'
|
|
path: List[str]
|
|
message: str
|
|
|
|
|
|
class ValidationWarning(BaseModel):
|
|
"""A validation warning."""
|
|
type: str
|
|
path: List[str]
|
|
message: str
|
|
|
|
|
|
class ValidationSummary(BaseModel):
|
|
"""Summary of spec contents."""
|
|
design_variables: int
|
|
extractors: int
|
|
objectives: int
|
|
constraints: int
|
|
custom_functions: int
|
|
|
|
|
|
class ValidationReport(BaseModel):
|
|
"""Full validation report."""
|
|
valid: bool
|
|
errors: List[ValidationError]
|
|
warnings: List[ValidationWarning]
|
|
summary: ValidationSummary
|