krigekit 0.2.0

Python wrapper for a Fortran kriging and sequential Gaussian simulation engine

krigekit

A Python wrapper for a high-performance Fortran kriging engine parallelised with OpenMP.

Capability	Notes
Ordinary and simple kriging	Point and block support
Co-kriging	Linear Model of Coregionalisation
Universal kriging / KED	External drift variables
Sequential Gaussian Simulation	Reproducible paths, multi-realisation
Space-time kriging	Sum-metric and product-sum ST models
Multiple Indicator Kriging / SIS	Categorical variables, three cross-variogram strategies
Spatially Varying Anisotropy	Per-block variogram
Cross-validation	Leave-one-out
Kriging weight reuse	Store and replay weights

Full documentation →

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Installation

conda / mamba (recommended)

mamba env create -f environment.yml
mamba activate krigekit

pip

pip install -e ".[dev]"   # after compiling the Fortran library

Compile the Fortran library first:

python build_lib.py                    # Linux/macOS (gfortran)
python build_lib.py --compiler ifx     # Windows (Intel ifx)

See docs/installation.md for full details including debug builds, --no-openmp, and docs dependencies.

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Quick start

import numpy as np
from krigekit import Kriging

obs_coord  = np.array([[0,0],[1,0],[0,1],[1,1],[0.5,0.5]], dtype=float)
obs_value  = np.array([1.0, 2.0, 3.0, 4.0, 2.5])
grid_coord = np.mgrid[0:1.1:0.25, 0:1.1:0.25].reshape(2,-1).T

k = Kriging()
k.set_obs(ivar=1, coord=obs_coord, value=obs_value)
k.set_grid(coord=grid_coord)
k.set_vgm(ivar=1, jvar=1, vtype="sph", sill=1.0, a_major=1.0)
k.set_search()
k.solve()
df = k.get_result_df()
del k

For the full class API, co-kriging, SGSIM, space-time kriging, indicator simulation, and more, see the user guide and gallery examples.

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Repository structure

krigekit/
├── src/
│   ├── libkriging/      Fortran kriging engine
│   ├── sparks/          Pilot-point kriging/SGSIM CLI
│   └── krigekit/       Python ctypes wrapper
├── examples/            Sphinx-Gallery example scripts
├── tests/               pytest test suite
├── test_data/           CSV/image data used by tests and examples
├── docs/                Sphinx documentation source
├── build_lib.py         Fortran compile script
├── environment.yml      conda/mamba environment
└── pyproject.toml       pip package configuration

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Contributing

1. Fork the repository and create a feature branch.
2. Add tests for any new behaviour.
3. Run pytest to confirm all tests pass.
4. Open a pull request.

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License

MIT — see LICENSE.