Monet Stats System Integration Documentation

Overview

This document provides a comprehensive overview of the Monet Stats system integration, detailing the architecture, components, interfaces, and validation status for the Earth system modeling statistics package.

System Architecture

Package Structure

monet-stats/
├── src/monet_stats/           # Core statistical modules
│   ├── __init__.py           # Package initialization and API exports
│   ├── error_metrics.py      # Error-based statistical metrics
│   ├── correlation_metrics.py # Correlation and statistical relationship metrics
│   ├── efficiency_metrics.py # Efficiency and performance metrics
│   ├── relative_metrics.py   # Relative difference metrics
│   ├── contingency_metrics.py # Contingency table and categorical metrics
│   ├── spatial_ensemble_metrics.py # Spatial and ensemble verification metrics
│   └── utils_stats.py        # Utility functions for statistical computations
├── tests/                     # Comprehensive test suite
│   ├── test_*.py             # Unit and integration tests
│   ├── conftest.py           # Test configuration
│   └── test_utils.py         # Test utilities
├── docs/                      # Documentation
├── .github/workflows/         # CI/CD pipeline configuration
├── pyproject.toml            # Project configuration and dependencies
└── README.md                 # Project overview

Component Dependencies

Core Dependencies

numpy: Numerical computing foundation
pandas: Data manipulation and analysis
scipy: Scientific computing and statistical functions
statsmodels: Statistical modeling and tests
xarray: Multi-dimensional array data structures (optional)

Development Dependencies

pytest: Testing framework
pytest-cov: Coverage reporting
black: Code formatting
isort: Import sorting
ruff: Linting (replaces flake8)
pycodestyle: Code style checking
mypy: Type checking
pre-commit: Git hooks

Statistical Modules Integration

1. Error Metrics Module (`error_metrics.py`)

Purpose: Measures of error and deviation between model and observations
Key Functions: MAE, RMSE, MB, NRMSE, STDO, STDP, MedAE, etc.
Integration Points: Used by all other modules for error analysis
Test Coverage: 41% (needs improvement)

2. Correlation Metrics Module (`correlation_metrics.py`)

Purpose: Statistical relationship measures
Key Functions: R2, pearsonr, spearmanr, kendalltau, IOA, KGE
Integration Points: Cross-references with error metrics
Test Coverage: 34% (needs significant improvement)

3. Efficiency Metrics Module (`efficiency_metrics.py`)

Purpose: Model performance and efficiency measures
Key Functions: NSE, MAPE, PC, NSEm
Integration Points: Uses error and correlation metrics
Test Coverage: 51% (moderate coverage)

4. Relative Metrics Module (`relative_metrics.py`)

Purpose: Normalized difference measures
Key Functions: NMB, MPE, FB, FE
Integration Points: Complementary to error metrics
Test Coverage: 51% (moderate coverage)

5. Contingency Metrics Module (`contingency_metrics.py`)

Purpose: Categorical verification and contingency analysis
Key Functions: POD, FAR, CSI, HSS, TSS, BSS
Integration Points: Specialized for categorical data
Test Coverage: 91% (excellent coverage)

6. Spatial Ensemble Metrics Module (`spatial_ensemble_metrics.py`)

Purpose: Spatial verification and ensemble analysis
Key Functions: FSS, EDS, CRPS, SAL, BSS
Integration Points: Uses spatial data structures
Test Coverage: 81% (good coverage)

7. Utility Functions Module (`utils_stats.py`)

Purpose: Common statistical utilities and helper functions
Key Functions: matchedcompressed, matchmasks, circlebias, rmse, mae, correlation
Integration Points: Used across all metric modules
Test Coverage: 41% (needs improvement)

Interface Compatibility Analysis

Cross-Module Compatibility

✅ Compatible Interfaces

Error ↔ Correlation: All functions can operate on the same input data types
Error ↔ Efficiency: Error metrics provide foundation for efficiency calculations
Utility Functions: Consistent interface across modules for data processing
Type Consistency: All functions return consistent numeric types

⚠️ Interface Issues Identified

Parameter Inconsistencies: Some functions require different parameter sets
Return Type Variations: Functions may return tuples vs. single values
Missing Default Parameters: Several functions lack sensible defaults
Xarray Integration: Partial support for xarray DataArray objects

❌ Critical Issues

Low Test Coverage: Only 48% overall coverage (target: 95%)
Import/Export Mismatches: Some functions in __all__ not properly imported
Mathematical Inconsistencies: Some metrics don't match expected values
Edge Case Handling: Poor handling of constant/zero arrays

Data Flow Architecture

Input Data (numpy arrays, xarray DataArrays)
    ↓
Utility Functions (data cleaning, mask handling)
    ↓
Error Metrics → Correlation Metrics → Efficiency Metrics
    ↓
Relative Metrics ← Contingency Metrics ← Spatial Metrics
    ↓
Integrated Analysis & Reporting

CI/CD Pipeline Integration

GitHub Actions Configuration

Test Matrix

Python Versions: 3.8, 3.9, 3.10, 3.11, 3.12
Test Coverage: Minimum 95% required
Code Quality:
Black formatting
Isort import sorting
Ruff linting (replaces flake8)
Pycodestyle style checking
MyPy type checking

Pipeline Stages

Testing: Unit tests, integration tests, coverage validation
Build Package: Create distribution artifacts
Documentation: Build and deploy documentation
Artifact Upload: Upload test results, coverage reports, and builds

Quality Gates

Coverage Requirements

Overall Coverage: 95% minimum
Critical Modules: Error and correlation metrics need improvement
Test Types: Unit, integration, performance, and edge case tests

Code Quality Standards

Line Length: 88 characters maximum
Naming Conventions: Consistent function and parameter naming
Documentation: Comprehensive docstrings with examples
Type Hints: Full type annotation coverage

Test Integration Status

Test Coverage Analysis

Module	Coverage	Status	Priority
contingency_metrics.py	91%	✅ Excellent	Low
spatial_ensemble_metrics.py	81%	✅ Good	Low
efficiency_metrics.py	51%	⚠️ Moderate	Medium
relative_metrics.py	51%	⚠️ Moderate	Medium
utils_stats.py	41%	⚠️ Needs Work	High
error_metrics.py	41%	⚠️ Needs Work	High
correlation_metrics.py	34%	❌ Critical	Critical
Total	48%	❌ Below Target	High

Integration Test Suite

Test Categories

Module Compatibility: Cross-module function interactions
Data Type Support: NumPy arrays, xarray DataArrays
Edge Cases: Zero arrays, constant values, NaN handling
Performance: Large dataset processing
API Completeness: All exported functions accessible

Test Results

Integration Tests: 9 passed, 3 failed
Critical Issues:
HSS metric returning NaN values
R2 metric failing on constant arrays
Low API function availability (only 2/10 functions working)

Known Issues and Limitations

1. Test Coverage Gaps

Error Metrics: 59% of code untested
Correlation Metrics: 66% of code untested
Missing Test Cases: Edge conditions, error handling

2. Mathematical Inconsistencies

HSS Calculation: Returns NaN in certain conditions
R2 Calculation: Fails on constant input arrays
MB Sign Convention: Inconsistent with expected meteorological conventions

3. Interface Issues

Parameter Variability: Inconsistent parameter naming and defaults
Return Type Ambiguity: Some functions return tuples, others single values
Xarray Support: Limited and inconsistent across modules

4. Performance Considerations

Large Arrays: Some functions may be slow on very large datasets
Memory Usage: Potential memory overhead with masked arrays
Vectorization: Some operations could be more efficient

Production Readiness Assessment

✅ Ready Components

Contingency Metrics: Well-tested, stable interface
Spatial Metrics: Good coverage, comprehensive testing
Package Structure: Clean, modular design
CI/CD Pipeline: Comprehensive automation

⚠️ Needs Work Components

Error Metrics: Requires additional test coverage
Correlation Metrics: Critical gaps in testing
Integration Tests: Several failing test cases
Documentation: Needs more detailed API documentation

❌ Not Ready Components

Overall System: 48% coverage below 95% requirement
API Completeness: Only 20% of exported functions tested
Performance: No comprehensive performance validation

Recommendations for Production Release

Immediate Actions (High Priority)

Increase Test Coverage: Focus on error and correlation metrics
Fix Mathematical Issues: Resolve HSS and R2 calculation problems
Standardize Interfaces: Consistent parameter naming and return types
API Validation: Ensure all exported functions work correctly

Medium Priority Actions

Performance Optimization: Profile and optimize critical functions
Enhanced Documentation: Add more examples and use cases
Edge Case Testing: Improve boundary condition handling
Xarray Integration: Full xarray support across all modules

Long-term Improvements

Type Hints: Complete type annotation coverage
Asynchronous Support: Consider async operations for large datasets
Plugin Architecture: Extensible metric system
Visualization Integration: Built-in plotting capabilities

Conclusion

The Monet Stats system demonstrates a solid foundation with well-structured code and comprehensive statistical modules. However, the current state shows significant gaps in test coverage and some mathematical inconsistencies that prevent immediate production readiness.

Key Strengths:

Modular, well-organized architecture
Comprehensive statistical coverage
Good CI/CD pipeline setup
Strong testing framework

Critical Areas for Improvement:

Test coverage (48% vs. 95% target)
Mathematical accuracy in key metrics
API consistency and completeness
Error handling and edge cases

With focused effort on the high-priority recommendations, the system can achieve production readiness within 2-3 development cycles. The modular design and existing infrastructure provide a strong foundation for future enhancements.

Integration Status: In Progress Last Updated: 2025-11-18 Target Release: v1.0.0