[ENH] Add Greenstone example model and CG solver tests #1048
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Leguark
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Graphite Automations"Add gempy label" took an action on this PR • (09/17/25)1 label was added to this PR based on Miguel de la Varga's automation. |
…ing test Integrated a new `Greenstone` example model into the `generate_example_model` API. Added its binary file and implemented a test for its functionality.
Switched the `use_gpu` configuration to `True` in the CG solver test, ensuring the test runs with GPU acceleration. Removed unused imports for cleanup.
Introduced a `clear_cache` method in the `WeightCache` class for better memory management. Enhanced the numerical stability of the conjugate gradient solver with improved initialization and adaptive tolerances. Refactored interpolation logic to handle weights more efficiently, and adjusted benchmarking to test expanded solver configurations.
Extended CG solver tests to include configurations using both the PYTORCH and PYKEOPS backends. Configured tests with varying `use_gpu` and `dtype` options, introduced a new Greenstone model test, and added 3D plotting support in the test suite.
Upgraded `gempy_viewer` dependency to version 2025.1.4. Adjusted benchmarking configuration to use parallel fork triangulation with 6 levels and disabled benchmarking by default. Enabled debug plotting in `_compute_model_fn.py` for enhanced troubleshooting.
This was referenced Sep 25, 2025
Merged
…uration Deleted redundant `evaluation_chunk_size` parameter adjustments and added `validate_serialization=False` to the `compute_model` call for enhanced model processing flexibility.
Enabled gradient computation in the optimizer initialization by adding the `grads` parameter for enhanced flexibility in optimization workflows.
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Description
Added a new Greenstone example model to GemPy with supporting functionality. The implementation includes:
GREENSTONEenum toExampleModel_generate_greenstone_modelfunction in the examples generatorRelates to #weights-caching-and-cg-solver
Checklist
or 2. verifies that outputs are as expected for given inputs (e.g. unit tests).