Anto01
a26914bbe8
Dashboard: - Add Studio page with drag-drop model upload and Claude chat - Add intake system for study creation workflow - Improve session manager and context builder - Add intake API routes and frontend components Optimization Engine: - Add CLI module for command-line operations - Add intake module for study preprocessing - Add validation module with gate checks - Improve Zernike extractor documentation - Update spec models with better validation - Enhance solve_simulation robustness Documentation: - Add ATOMIZER_STUDIO.md planning doc - Add ATOMIZER_UX_SYSTEM.md for UX patterns - Update extractor library docs - Add study-readme-generator skill Tools: - Add test scripts for extraction validation - Add Zernike recentering test Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
811 lines
26 KiB
Python
811 lines
26 KiB
Python
"""
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AtomizerSpec v2.0 Pydantic Models
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These models match the JSON Schema at optimization_engine/schemas/atomizer_spec_v2.json
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They provide validation and type safety for the unified configuration system.
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"""
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from datetime import datetime
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from enum import Enum
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from typing import Any, Dict, List, Literal, Optional, Tuple, Union
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from pydantic import BaseModel, Field, field_validator, model_validator
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import re
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# ============================================================================
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# Enums
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# ============================================================================
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class SpecCreatedBy(str, Enum):
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"""Who/what created the spec."""
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CANVAS = "canvas"
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CLAUDE = "claude"
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API = "api"
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MIGRATION = "migration"
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MANUAL = "manual"
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DASHBOARD_INTAKE = "dashboard_intake"
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class SpecStatus(str, Enum):
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"""Study lifecycle status."""
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DRAFT = "draft"
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INTROSPECTED = "introspected"
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CONFIGURED = "configured"
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VALIDATED = "validated"
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READY = "ready"
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RUNNING = "running"
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COMPLETED = "completed"
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FAILED = "failed"
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class SolverType(str, Enum):
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"""Supported solver types."""
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NASTRAN = "nastran"
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NX_NASTRAN = "NX_Nastran"
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ABAQUS = "abaqus"
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class SubcaseType(str, Enum):
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"""Subcase analysis types."""
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STATIC = "static"
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MODAL = "modal"
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THERMAL = "thermal"
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BUCKLING = "buckling"
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class DesignVariableType(str, Enum):
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"""Design variable types."""
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CONTINUOUS = "continuous"
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INTEGER = "integer"
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CATEGORICAL = "categorical"
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class ExtractorType(str, Enum):
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"""Physics extractor types."""
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DISPLACEMENT = "displacement"
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FREQUENCY = "frequency"
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STRESS = "stress"
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MASS = "mass"
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MASS_EXPRESSION = "mass_expression"
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ZERNIKE_OPD = "zernike_opd"
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ZERNIKE_CSV = "zernike_csv"
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TEMPERATURE = "temperature"
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CUSTOM_FUNCTION = "custom_function"
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class OptimizationDirection(str, Enum):
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"""Optimization direction."""
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MINIMIZE = "minimize"
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MAXIMIZE = "maximize"
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class ConstraintType(str, Enum):
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"""Constraint types."""
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HARD = "hard"
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SOFT = "soft"
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class ConstraintOperator(str, Enum):
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"""Constraint comparison operators."""
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LE = "<="
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GE = ">="
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LT = "<"
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GT = ">"
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EQ = "=="
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class PenaltyMethod(str, Enum):
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"""Penalty methods for constraints."""
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LINEAR = "linear"
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QUADRATIC = "quadratic"
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EXPONENTIAL = "exponential"
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class AlgorithmType(str, Enum):
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"""Optimization algorithm types."""
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TPE = "TPE"
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CMA_ES = "CMA-ES"
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NSGA_II = "NSGA-II"
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RANDOM_SEARCH = "RandomSearch"
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SAT_V3 = "SAT_v3"
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GP_BO = "GP-BO"
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class SurrogateType(str, Enum):
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"""Surrogate model types."""
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MLP = "MLP"
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GNN = "GNN"
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ENSEMBLE = "ensemble"
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# ============================================================================
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# Position Model
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# ============================================================================
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class CanvasPosition(BaseModel):
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"""Canvas position for nodes."""
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x: float = 0
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y: float = 0
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# ============================================================================
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# Introspection Models (for intake workflow)
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# ============================================================================
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class ExpressionInfo(BaseModel):
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"""Information about an NX expression from introspection."""
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name: str = Field(..., description="Expression name in NX")
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value: Optional[float] = Field(default=None, description="Current value")
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units: Optional[str] = Field(default=None, description="Physical units")
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formula: Optional[str] = Field(default=None, description="Expression formula if any")
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is_candidate: bool = Field(
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default=False, description="Whether this is a design variable candidate"
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)
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confidence: float = Field(
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default=0.0, ge=0.0, le=1.0, description="Confidence that this is a DV"
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)
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class BaselineData(BaseModel):
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"""Results from baseline FEA solve."""
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timestamp: datetime = Field(..., description="When baseline was run")
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solve_time_seconds: float = Field(..., description="How long the solve took")
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mass_kg: Optional[float] = Field(default=None, description="Computed mass from BDF/FEM")
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max_displacement_mm: Optional[float] = Field(
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default=None, description="Max displacement result"
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)
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max_stress_mpa: Optional[float] = Field(default=None, description="Max von Mises stress")
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success: bool = Field(default=True, description="Whether baseline solve succeeded")
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error: Optional[str] = Field(default=None, description="Error message if failed")
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class IntrospectionData(BaseModel):
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"""Model introspection results stored in the spec."""
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timestamp: datetime = Field(..., description="When introspection was run")
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solver_type: Optional[str] = Field(default=None, description="Detected solver type")
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mass_kg: Optional[float] = Field(
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default=None, description="Mass from expressions or properties"
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)
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volume_mm3: Optional[float] = Field(default=None, description="Volume from mass properties")
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expressions: List[ExpressionInfo] = Field(
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default_factory=list, description="Discovered expressions"
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)
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baseline: Optional[BaselineData] = Field(default=None, description="Baseline solve results")
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warnings: List[str] = Field(default_factory=list, description="Warnings from introspection")
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def get_design_candidates(self) -> List[ExpressionInfo]:
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"""Return expressions marked as design variable candidates."""
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return [e for e in self.expressions if e.is_candidate]
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# ============================================================================
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# Meta Models
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# ============================================================================
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class SpecMeta(BaseModel):
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"""Metadata about the spec."""
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version: str = Field(..., pattern=r"^2\.\d+$", description="Schema version (e.g., '2.0')")
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created: Optional[datetime] = Field(default=None, description="When the spec was created")
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modified: Optional[datetime] = Field(
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default=None, description="When the spec was last modified"
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)
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created_by: Optional[SpecCreatedBy] = Field(
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default=None, description="Who/what created the spec"
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)
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modified_by: Optional[str] = Field(default=None, description="Who/what last modified the spec")
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study_name: str = Field(
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...,
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min_length=3,
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max_length=100,
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pattern=r"^[a-z0-9_]+$",
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description="Unique study identifier (snake_case)",
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)
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description: Optional[str] = Field(
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default=None, max_length=1000, description="Human-readable description"
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)
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tags: Optional[List[str]] = Field(default=None, description="Tags for categorization")
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engineering_context: Optional[str] = Field(
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default=None, description="Real-world engineering context"
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)
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status: SpecStatus = Field(default=SpecStatus.DRAFT, description="Study lifecycle status")
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topic: Optional[str] = Field(
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default=None,
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pattern=r"^[A-Za-z0-9_]+$",
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description="Topic folder for grouping related studies",
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)
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# ============================================================================
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# Model Configuration Models
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# ============================================================================
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class NxPartConfig(BaseModel):
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"""NX geometry part file configuration."""
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path: Optional[str] = Field(default=None, description="Path to .prt file")
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hash: Optional[str] = Field(default=None, description="File hash for change detection")
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idealized_part: Optional[str] = Field(
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default=None, description="Idealized part filename (_i.prt)"
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)
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class FemConfig(BaseModel):
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"""FEM mesh file configuration."""
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path: Optional[str] = Field(default=None, description="Path to .fem file")
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element_count: Optional[int] = Field(default=None, description="Number of elements")
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node_count: Optional[int] = Field(default=None, description="Number of nodes")
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class Subcase(BaseModel):
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"""Simulation subcase definition."""
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id: int
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name: Optional[str] = None
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type: Optional[SubcaseType] = None
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class SimConfig(BaseModel):
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"""Simulation file configuration."""
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path: str = Field(..., description="Path to .sim file")
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solver: SolverType = Field(..., description="Solver type")
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solution_type: Optional[str] = Field(
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default=None, pattern=r"^SOL\d+$", description="Solution type (e.g., SOL101)"
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)
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subcases: Optional[List[Subcase]] = Field(default=None, description="Defined subcases")
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class NxSettings(BaseModel):
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"""NX runtime settings."""
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nx_install_path: Optional[str] = None
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simulation_timeout_s: Optional[int] = Field(default=None, ge=60, le=7200)
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auto_start_nx: Optional[bool] = None
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class ModelConfig(BaseModel):
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"""NX model files and configuration."""
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nx_part: Optional[NxPartConfig] = None
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fem: Optional[FemConfig] = None
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sim: Optional[SimConfig] = Field(
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default=None, description="Simulation file config (required for optimization)"
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)
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nx_settings: Optional[NxSettings] = None
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introspection: Optional[IntrospectionData] = Field(
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default=None, description="Model introspection results from intake"
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)
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# ============================================================================
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# Design Variable Models
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# ============================================================================
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class DesignVariableBounds(BaseModel):
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"""Design variable bounds."""
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min: float
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max: float
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@model_validator(mode="after")
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def validate_bounds(self) -> "DesignVariableBounds":
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if self.min >= self.max:
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raise ValueError(f"min ({self.min}) must be less than max ({self.max})")
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return self
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class DesignVariable(BaseModel):
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"""A design variable to optimize."""
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id: str = Field(..., pattern=r"^dv_\d{3}$", description="Unique identifier (pattern: dv_XXX)")
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name: str = Field(..., description="Human-readable name")
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expression_name: str = Field(
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...,
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pattern=r"^[a-zA-Z_][a-zA-Z0-9_]*$",
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description="NX expression name (must match model)",
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)
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type: DesignVariableType = Field(..., description="Variable type")
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bounds: DesignVariableBounds = Field(..., description="Value bounds")
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baseline: Optional[float] = Field(default=None, description="Current/initial value")
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units: Optional[str] = Field(default=None, description="Physical units (mm, deg, etc.)")
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step: Optional[float] = Field(default=None, description="Step size for integer/discrete")
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enabled: bool = Field(default=True, description="Whether to include in optimization")
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description: Optional[str] = None
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canvas_position: Optional[CanvasPosition] = None
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# ============================================================================
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# Extractor Models
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# ============================================================================
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class ExtractorConfig(BaseModel):
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"""Type-specific extractor configuration."""
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inner_radius_mm: Optional[float] = None
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outer_radius_mm: Optional[float] = None
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n_modes: Optional[int] = None
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filter_low_orders: Optional[int] = None
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displacement_unit: Optional[str] = None
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reference_subcase: Optional[int] = None
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expression_name: Optional[str] = None
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mode_number: Optional[int] = None
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element_type: Optional[str] = None
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result_type: Optional[str] = None
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metric: Optional[str] = None
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class Config:
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extra = "allow" # Allow additional fields for flexibility
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class CustomFunction(BaseModel):
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"""Custom function definition for custom_function extractors."""
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name: Optional[str] = Field(default=None, description="Function name")
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module: Optional[str] = Field(default=None, description="Python module path")
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signature: Optional[str] = Field(default=None, description="Function signature")
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source_code: Optional[str] = Field(default=None, description="Python source code")
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class ExtractorOutput(BaseModel):
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"""Output definition for an extractor."""
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name: str = Field(..., description="Output name (used by objectives/constraints)")
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metric: Optional[str] = Field(
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default=None, description="Specific metric (max, total, rms, etc.)"
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)
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subcase: Optional[int] = Field(default=None, description="Subcase ID for this output")
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units: Optional[str] = None
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class Extractor(BaseModel):
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"""Physics extractor that computes outputs from FEA."""
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id: str = Field(..., pattern=r"^ext_\d{3}$", description="Unique identifier (pattern: ext_XXX)")
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name: str = Field(..., description="Human-readable name")
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type: ExtractorType = Field(..., description="Extractor type")
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builtin: bool = Field(default=True, description="Whether this is a built-in extractor")
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config: Optional[ExtractorConfig] = Field(
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default=None, description="Type-specific configuration"
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)
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function: Optional[CustomFunction] = Field(
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default=None, description="Custom function definition (for custom_function type)"
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)
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outputs: List[ExtractorOutput] = Field(..., min_length=1, description="Output values")
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canvas_position: Optional[CanvasPosition] = None
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@model_validator(mode="after")
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def validate_custom_function(self) -> "Extractor":
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if self.type == ExtractorType.CUSTOM_FUNCTION and self.function is None:
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raise ValueError("custom_function extractor requires function definition")
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return self
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# ============================================================================
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# Objective Models
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# ============================================================================
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class ObjectiveSource(BaseModel):
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"""Source reference for objective value."""
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extractor_id: str = Field(..., description="Reference to extractor")
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output_name: str = Field(..., description="Which output from the extractor")
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class Objective(BaseModel):
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"""Optimization objective."""
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id: str = Field(..., pattern=r"^obj_\d{3}$", description="Unique identifier (pattern: obj_XXX)")
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name: str = Field(..., description="Human-readable name")
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direction: OptimizationDirection = Field(..., description="Optimization direction")
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weight: float = Field(default=1.0, ge=0, description="Weight for weighted sum")
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source: ObjectiveSource = Field(..., description="Where the value comes from")
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target: Optional[float] = Field(default=None, description="Target value (for goal programming)")
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units: Optional[str] = None
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description: Optional[str] = None
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canvas_position: Optional[CanvasPosition] = None
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# ============================================================================
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# Constraint Models
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# ============================================================================
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class ConstraintSource(BaseModel):
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"""Source reference for constraint value."""
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extractor_id: str
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output_name: str
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class PenaltyConfig(BaseModel):
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"""Penalty method configuration for constraints."""
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method: Optional[PenaltyMethod] = None
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weight: Optional[float] = None
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margin: Optional[float] = Field(default=None, description="Soft margin before penalty kicks in")
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class Constraint(BaseModel):
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"""Hard or soft constraint."""
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id: str = Field(..., pattern=r"^con_\d{3}$", description="Unique identifier (pattern: con_XXX)")
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name: str
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type: ConstraintType = Field(..., description="Constraint type")
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operator: ConstraintOperator = Field(..., description="Comparison operator")
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threshold: float = Field(..., description="Constraint threshold value")
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source: ConstraintSource = Field(..., description="Where the value comes from")
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penalty_config: Optional[PenaltyConfig] = None
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description: Optional[str] = None
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canvas_position: Optional[CanvasPosition] = None
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# ============================================================================
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# Optimization Models
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# ============================================================================
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class AlgorithmConfig(BaseModel):
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"""Algorithm-specific settings."""
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population_size: Optional[int] = None
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n_generations: Optional[int] = None
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mutation_prob: Optional[float] = None
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crossover_prob: Optional[float] = None
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seed: Optional[int] = None
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n_startup_trials: Optional[int] = None
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sigma0: Optional[float] = None
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class Config:
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extra = "allow" # Allow additional algorithm-specific fields
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class Algorithm(BaseModel):
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"""Optimization algorithm configuration."""
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type: AlgorithmType
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config: Optional[AlgorithmConfig] = None
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class OptimizationBudget(BaseModel):
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"""Computational budget for optimization."""
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max_trials: Optional[int] = Field(default=None, ge=1, le=10000)
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max_time_hours: Optional[float] = None
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convergence_patience: Optional[int] = Field(
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default=None, description="Stop if no improvement for N trials"
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)
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class SurrogateConfig(BaseModel):
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"""Neural surrogate model configuration."""
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n_models: Optional[int] = None
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architecture: Optional[List[int]] = None
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train_every_n_trials: Optional[int] = None
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min_training_samples: Optional[int] = None
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acquisition_candidates: Optional[int] = None
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fea_validations_per_round: Optional[int] = None
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class Surrogate(BaseModel):
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"""Surrogate model settings."""
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enabled: Optional[bool] = None
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type: Optional[SurrogateType] = None
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config: Optional[SurrogateConfig] = None
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class OptimizationConfig(BaseModel):
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"""Optimization algorithm configuration."""
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algorithm: Algorithm
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budget: OptimizationBudget
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surrogate: Optional[Surrogate] = None
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canvas_position: Optional[CanvasPosition] = None
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# ============================================================================
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# Workflow Models
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# ============================================================================
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class WorkflowStage(BaseModel):
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"""A stage in a multi-stage optimization workflow."""
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id: str
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name: str
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algorithm: Optional[str] = None
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|
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(
|
|
default_factory=list,
|
|
max_length=50,
|
|
description="Design variables to optimize (required for running)",
|
|
)
|
|
extractors: List[Extractor] = Field(
|
|
default_factory=list, description="Physics extractors (required for running)"
|
|
)
|
|
objectives: List[Objective] = Field(
|
|
default_factory=list,
|
|
max_length=5,
|
|
description="Optimization objectives (required for running)",
|
|
)
|
|
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
|
|
|
|
def is_ready_for_optimization(self) -> Tuple[bool, List[str]]:
|
|
"""
|
|
Check if spec is complete enough to run optimization.
|
|
|
|
Returns:
|
|
Tuple of (is_ready, list of missing requirements)
|
|
"""
|
|
missing = []
|
|
|
|
# Check required fields for optimization
|
|
if not self.model.sim:
|
|
missing.append("No simulation file (.sim) configured")
|
|
|
|
if not self.design_variables:
|
|
missing.append("No design variables defined")
|
|
|
|
if not self.extractors:
|
|
missing.append("No extractors defined")
|
|
|
|
if not self.objectives:
|
|
missing.append("No objectives defined")
|
|
|
|
# Check that enabled DVs have valid bounds
|
|
for dv in self.get_enabled_design_variables():
|
|
if dv.bounds.min >= dv.bounds.max:
|
|
missing.append(f"Design variable '{dv.name}' has invalid bounds")
|
|
|
|
return len(missing) == 0, missing
|
|
|
|
|
|
# ============================================================================
|
|
# 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
|