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*Smith for ML models

Project description

GeoSmith

*Smith for geospatial ML models with strict 4-layer architecture.

PyPI version License: MIT Documentation

Features

  • 4-Layer Architecture: Clean separation between objects, primitives, tasks, and workflows
  • Optional Integrations: Works seamlessly with PlotSmith, AnomSmith, and TimeSmith
  • Geostatistics: Variogram analysis, kriging, IDW interpolation
  • Mining Tools: Block models, drillhole processing, ore grade estimation
  • I/O Support: GRDECL, LAS, vector/raster formats
  • No Hard Dependencies: Core works with just NumPy and Pandas

Installation

# Core installation
pip install geosmith

# With optional integrations
pip install geosmith[plotsmith,anomsmith,timesmith]

# Everything
pip install geosmith[all]

Quick Start

from geosmith import PointSet, OrdinaryKriging
from geosmith.primitives.variogram import fit_variogram_model

# Create sample points
points = PointSet(coordinates=sample_coords)

# Fit variogram and kriging
variogram = fit_variogram_model(lags, semi_vars)
kriging = OrdinaryKriging(variogram_model=variogram)
kriging.fit(points, values)

# Predict
result = kriging.predict(query_points)

*Smith Family Integration

GeoSmith works seamlessly with other *Smith libraries:

See SMITH_FAMILY_INTEGRATION.md for details.

Documentation

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