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- Implement Simple Collocated Cokriging (SCCK) extending Krige class - Implement Intrinsic Collocated Cokriging (ICCK) with flexible secondary models - Add comprehensive test suite with 14 test cases covering: - Matrix construction and dimensions - Cross-correlation validation - RHS vector structure - Integration with drift functions - Edge cases (zero/perfect correlation) - Follow gstools patterns: property validation, error handling, documentation - Matrix structure: (n+1) x (n+1) for n conditioning points + 1 secondary variable - Uses Markov model assumption: C_zy(h) = ρ * √(C_zz(h) * C_yy(h)) - All tests passing with proper position handling via pre_pos method
- Clean minimal implementation extending Krige base class - Follows existing gstools design patterns exactly - Only adds cross_corr parameter and secondary_data requirement - Uses (n+1)×(n+1) matrix system solved per estimation point - Full API compatibility: return_var, chunk_size, only_mean, etc. - Proper integration with gstools post-processing and chunking - Zero cross-correlation equals Simple Kriging (verified) - Located in separate cokriging module as requested
Feature/collocated cokriging
- Create CollocatedCokriging base class following kriging module pattern - Refactor SCCK and ICCK as thin wrappers (algorithm='MM1' vs 'intrinsic') - Eliminate ~400 lines of duplicated code - Maintain full backward compatibility - All tests passing (14/14) - Cleaner architecture for future extensibility
This commit introduces a new Correlogram base class architecture that
makes collocated cokriging future-proof and extensible for different
cross-covariance models (MM1, MM2, etc.).
**New Features:**
- Added Correlogram abstract base class defining the interface for
cross-covariance models
- Implemented MarkovModel1 as the first concrete correlogram,
encapsulating Markov Model I assumptions
- Correlogram objects now hold all cross-covariance parameters:
primary_model, cross_corr, secondary_var, primary_mean, secondary_mean
**API Changes:**
New (recommended) API:
correlogram = gs.MarkovModel1(
primary_model=model,
cross_corr=0.8,
secondary_var=1.5,
primary_mean=1.0,
secondary_mean=0.5
)
scck = gs.SimpleCollocated(correlogram, cond_pos, cond_val)
Backward compatibility via from_parameters() classmethod (deprecated):
scck = gs.SimpleCollocated.from_parameters(
model, cond_pos, cond_val,
cross_corr=0.8, secondary_var=1.5,
mean=1.0, secondary_mean=0.5
)
**Refactored Classes:**
- CollocatedCokriging: Now accepts correlogram object instead of
individual parameters (cross_corr, secondary_var, etc.)
- SimpleCollocated: Updated to use new API with backward compatibility
- IntrinsicCollocated: Updated to use new API with backward compatibility
- Both classes delegate covariance computation to correlogram
**Benefits:**
- Separation of concerns: Cross-covariance modeling separated from
kriging algorithm
- Extensible: Easy to add MM2, Linear Model of Coregionalization, etc.
- Self-documenting: Explicit about which cross-covariance model is used
- Maintainable: Correlogram classes can be tested independently
- Future-proof: Ready for additional correlogram models
**Testing:**
- Added comprehensive test suite (test_correlogram.py)
- All tests pass with numerical equivalence between old and new API
- Updated examples to demonstrate new API
**Documentation:**
- Updated examples/05_kriging/10_simple_collocated_cokriging.py
- Updated examples/05_kriging/11_intrinsic_collocated_cokriging.py
- Added MarkovModel1 to top-level exports
- Comprehensive docstrings with usage examples
**Future Work:**
- Placeholder for MarkovModel2 implementation
- Potential for other correlogram models (intrinsic correlation, etc.)
Closes: #correlogram-architecture
- Explains new Correlogram base class design - Provides usage examples for MarkovModel1 - Shows how to implement MarkovModel2 (future) - Includes migration guide from old to new API - Documents testing and file structure
Feature/correlogram architecture
…ecture Revert "Feature/correlogram architecture"
…for cokriging This merge introduces the new Correlogram architecture to replace the direct-parameter API for collocated cokriging. Key Changes: - New Correlogram abstract base class for cross-covariance models - MarkovModel1 implementation (Markov Model I) - Refactored SimpleCollocated and IntrinsicCollocated to use Correlogram objects - Updated API: correlogram parameter replaces model/cross_corr/secondary_var - Cleaner documentation following GSTools philosophy - Comprehensive test suite for correlogram models All conflicts resolved in favor of the new architecture. All tests passing (14/14).
- Add MarkovModel1 to gstools main __init__.py exports - Update cokriging __init__.py with proper imports - Update example files to use new Correlogram API
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had to move cokriging to new branch and start pull request again