{"id":22635788,"url":"https://github.com/grongier/pyanodi","last_synced_at":"2025-10-08T04:42:49.766Z","repository":{"id":49613922,"uuid":"235806179","full_name":"grongier/pyanodi","owner":"grongier","description":"Python package to perform Tan et al. 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(2014)](https://doi.org/10.1007/s11004-013-9482-1) to compare geostatistical simulation algorithms.\n\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/grongier/pyanodi/master?filepath=examples)\n\n## Disclaimer\n\nPyanodi only contains the cluster-based histograms of patterns but not the multiple-point histograms.\n\n## Installation\n\nYou can directly install pyanodi from GitHub using pip:\n\n    pip install git+https://github.com/grongier/pyanodi.git\n\n## Use\n\nBasic use:\n\n```\nfrom pyanodi import ANODI\nfrom sklearn.manifold import MDS\n\ntraining_image = ... # nD array\nrealizations = ...   # (n_methods, n_realizations_per_method, nD) array\n\n# Set the parameters\nanodi = ANODI(pyramid=(1, 2, 3),\n              random_state=42,\n              n_jobs=4)\n\n# Perform the analysis\nanodi.fit_transform(training_image, realizations)\n\n# Get the MDS representation of the distances between the images\npyramid_level = 0\nmds = MDS(n_components=2, dissimilarity='precomputed', random_state=100)\nmds_points = mds.fit_transform(anodi.distances_[..., pyramid_level])\n\n# Get the rankings of the methods\nanodi.score()\n```\n\nFor a more complete example, see the Jupyter notebook [methods_comparison.ipynb](examples/methods_comparison.ipynb) or the Binder link above.\n\n## Citation\n\nIf you use pyanodi in your research, please cite the original article:\n\n\u003e Tan, X., Tahmasebi, P. \u0026 Caers, J. (2014). Comparing Training-Image Based Algorithms Using an Analysis of Distance. *Mathematical Geosciences*, 46(2), 149-169. doi:[10.1007/s11004-013-9482-1](https://doi.org/10.1007/s11004-013-9482-1)\n\nHere is the corresponding BibTex entry if you use LaTex:\n\n    @Article{Tan2014,\n        author=\"Tan, Xiaojin\n        and Tahmasebi, Pejman\n        and Caers, Jef\",\n        title=\"Comparing Training-Image Based Algorithms Using an Analysis of Distance\",\n        journal=\"Mathematical Geosciences\",\n        year=\"2014\",\n        volume=\"46\",\n        number=\"2\",\n        pages=\"149--169\",\n        issn=\"1874-8953\",\n        doi=\"10.1007/s11004-013-9482-1\",\n    }\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgrongier%2Fpyanodi","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgrongier%2Fpyanodi","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgrongier%2Fpyanodi/lists"}