Service Layer and Infrastructure
This diagram shows the service layer components that provide infrastructure support for the core simulation and analysis.
classDiagram
%% Batch Processing Services
class BatchProcessor {
-config: BatchConfig
-executor: ParallelExecutor
-cache: SmartCache
-progress_monitor: ProgressMonitor
+process_scenarios(scenarios: List) BatchResults
+process_parameter_sweep(params: dict) SweepResults
+process_convergence_study() ConvergenceResults
+resume_from_checkpoint(checkpoint_id: str)
}
class ParallelExecutor {
-n_workers: int
-chunk_size: int
-backend: str
-memory_limit: float
+map(func: Callable, items: List) List
+map_reduce(map_func, reduce_func, items) Any
+scatter_gather(tasks: List) List
+get_worker_status() dict
}
class SmartCache {
-cache_dir: Path
-max_size_gb: float
-ttl_hours: int
-compression: bool
+get(key: str) Optional[Any]
+set(key: str, value: Any)
+invalidate(pattern: str)
+get_stats() CacheStats
}
%% Progress and Monitoring
class ProgressMonitor {
-total_tasks: int
-completed_tasks: int
-start_time: float
-update_interval: float
+update(progress: float, message: str)
+estimate_time_remaining() float
+get_throughput() float
+display_progress_bar()
}
class ConvergenceMonitor {
-metrics: List[float]
-threshold: float
-window_size: int
-patience: int
+add_metric(value: float)
+check_convergence() bool
+get_convergence_rate() float
+plot_convergence_history()
}
%% Trajectory Storage
class TrajectoryStorage {
-storage_backend: str
-compression_level: int
-chunk_size: int
+store_trajectory(sim_id: str, data: ndarray)
+load_trajectory(sim_id: str) ndarray
+store_batch(trajectories: dict)
+query_trajectories(filter: dict) List
+get_storage_stats() dict
}
%% Parameter Management
class ParameterSweep {
-base_params: dict
-sweep_params: dict
-sweep_type: str
-n_points: int
+generate_grid() List[dict]
+generate_latin_hypercube() List[dict]
+generate_sobol_sequence() List[dict]
+adaptive_sampling(results: List) List[dict]
}
class ScenarioManager {
-scenarios: Dict[str, Scenario]
-active_scenario: str
+add_scenario(name: str, params: dict)
+load_scenario(name: str) Scenario
+compare_scenarios(names: List[str]) ComparisonResults
+export_scenarios(path: str)
}
%% Performance Optimization
class PerformanceOptimizer {
-profiler: cProfile
-memory_profiler: MemoryProfiler
-optimization_level: int
+profile_function(func: Callable) ProfileResult
+optimize_memory_usage()
+enable_numba_jit()
+vectorize_operations()
+get_bottlenecks() List[Bottleneck]
}
class VectorizedOperations {
<<static>>
+calculate_returns(prices: ndarray) ndarray
+calculate_drawdowns(equity: ndarray) ndarray
+calculate_rolling_stats(data: ndarray, window: int) dict
+apply_vectorized_claim(claims: ndarray, limits: ndarray) ndarray
}
%% Benchmarking
class BenchmarkSuite {
-benchmarks: List[Benchmark]
-baseline_results: dict
-comparison_results: dict
+add_benchmark(benchmark: Benchmark)
+run_all_benchmarks() BenchmarkResults
+compare_implementations(impl1, impl2) ComparisonReport
+generate_performance_report() str
}
class Benchmark {
+name: str
+setup_func: Callable
+test_func: Callable
+teardown_func: Callable
+n_iterations: int
+run() BenchmarkResult
}
%% Validation Services
class AccuracyValidator {
-tolerance: float
-reference_impl: ReferenceImplementations
+validate_calculation(func, inputs, expected) bool
+validate_convergence(results: List) bool
+validate_edge_cases() ValidationReport
+cross_validate(method1, method2) float
}
class StrategyBacktester {
-historical_data: DataFrame
-strategy: InsuranceStrategy
-metrics: List[str]
+backtest(start_date, end_date) BacktestResults
+walk_forward_analysis(window_size: int) WalkForwardResults
+calculate_performance_metrics() dict
+generate_backtest_report() str
}
%% Reporting Services
class ExcelReporter {
-template_path: str
-output_path: str
-workbook: Workbook
+create_summary_sheet(results: dict)
+create_charts(data: DataFrame)
+add_sensitivity_tables(sensitivity: SensitivityResult)
+add_risk_metrics(metrics: RiskMetrics)
+format_report()
+save()
}
class ResultAggregator {
-results: List[SimulationResults]
-aggregation_funcs: dict
+add_results(results: SimulationResults)
+calculate_statistics() SummaryStatistics
+calculate_percentiles(percentiles: List) dict
+group_by(key: str) Dict[str, List]
+export_aggregated_data(format: str)
}
%% Visualization Services
class FigureFactory {
-style_manager: StyleManager
-default_size: tuple
-dpi: int
+create_line_plot(data: DataFrame) Figure
+create_distribution_plot(data: ndarray) Figure
+create_heatmap(data: ndarray) Figure
+create_dashboard(results: dict) Figure
+save_figure(fig: Figure, path: str)
}
class StyleManager {
-theme: Theme
-color_palette: List[str]
-font_settings: dict
+apply_theme(theme: Theme)
+get_color_cycle() List
+format_axis(ax: Axes)
+add_annotations(ax: Axes, annotations: List)
}
%% Relationships
BatchProcessor --> ParallelExecutor : uses
BatchProcessor --> SmartCache : caches with
BatchProcessor --> ProgressMonitor : monitors with
ParallelExecutor --> VectorizedOperations : optimizes with
ParameterSweep --> ScenarioManager : generates for
PerformanceOptimizer --> VectorizedOperations : creates
PerformanceOptimizer --> BenchmarkSuite : benchmarks with
BenchmarkSuite --> Benchmark : runs
AccuracyValidator --> StrategyBacktester : validates
ResultAggregator --> ExcelReporter : feeds
ResultAggregator --> FigureFactory : visualizes with
FigureFactory --> StyleManager : styled by
BatchProcessor --> TrajectoryStorage : stores in
ConvergenceMonitor --> ProgressMonitor : reports to
Service Interaction Flow
sequenceDiagram
participant Client
participant BP as BatchProcessor
participant PE as ParallelExecutor
participant SC as SmartCache
participant TS as TrajectoryStorage
participant PM as ProgressMonitor
participant RA as ResultAggregator
participant ER as ExcelReporter
Client->>BP: Submit batch job
BP->>SC: Check cache
alt Cache hit
SC-->>BP: Cached results
BP-->>Client: Return results
else Cache miss
BP->>PM: Initialize monitor
BP->>PE: Distribute work
loop Parallel execution
PE->>PE: Process chunk
PE->>PM: Update progress
PM-->>Client: Progress update
end
PE-->>BP: Raw results
BP->>TS: Store trajectories
BP->>SC: Cache results
BP->>RA: Aggregate results
RA->>ER: Generate report
ER-->>Client: Excel report
BP-->>Client: Complete results
end
Service Layer Patterns
1. Service Locator Pattern
Central registry for service discovery
Dynamic service binding at runtime
2. Repository Pattern
TrajectoryStorage abstracts data persistence
ScenarioManager provides scenario repository
3. Unit of Work Pattern
BatchProcessor coordinates complex operations
Ensures consistency across service calls
4. Pipeline Pattern
Data flows through processing pipeline
Each service transforms data for next stage
5. Decorator Pattern
ProgressMonitor decorates long-running operations
SmartCache decorates expensive computations
6. Adapter Pattern
ExcelReporter adapts results to Excel format
Different storage backends adapted by TrajectoryStorage