{"id":13720336,"url":"https://github.com/mmaelicke/scikit-gstat","last_synced_at":"2025-04-14T08:53:45.376Z","repository":{"id":38050847,"uuid":"98853365","full_name":"mmaelicke/scikit-gstat","owner":"mmaelicke","description":"Geostatistical variogram estimation expansion in the scipy style","archived":false,"fork":false,"pushed_at":"2025-03-20T05:47:12.000Z","size":56022,"stargazers_count":236,"open_issues_count":30,"forks_count":60,"subscribers_count":14,"default_branch":"main","last_synced_at":"2025-04-07T03:04:47.178Z","etag":null,"topics":["geostatistics","scikit","scipy"],"latest_commit_sha":null,"homepage":"https://mmaelicke.github.io/scikit-gstat/","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mmaelicke.png","metadata":{"files":{"readme":"README.rst","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2017-07-31T05:59:40.000Z","updated_at":"2025-03-20T05:47:06.000Z","dependencies_parsed_at":"2024-01-12T21:19:10.938Z","dependency_job_id":"b01a53b1-58e5-43fe-88df-2dabd2588a3d","html_url":"https://github.com/mmaelicke/scikit-gstat","commit_stats":{"total_commits":847,"total_committers":15,"mean_commits":56.46666666666667,"dds":0.6812278630460449,"last_synced_commit":"21ef9233c6df8a9d94672e659053b272662736fe"},"previous_names":[],"tags_count":28,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mmaelicke%2Fscikit-gstat","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mmaelicke%2Fscikit-gstat/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mmaelicke%2Fscikit-gstat/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mmaelicke%2Fscikit-gstat/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mmaelicke","download_url":"https://codeload.github.com/mmaelicke/scikit-gstat/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248852108,"owners_count":21171839,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["geostatistics","scikit","scipy"],"created_at":"2024-08-03T01:01:02.655Z","updated_at":"2025-04-14T08:53:45.350Z","avatar_url":"https://github.com/mmaelicke.png","language":"Jupyter Notebook","funding_links":[],"categories":["Python","Software"],"sub_categories":["Geostatistics"],"readme":"SciKit-GStat\n============\n\n.. image:: https://img.shields.io/pypi/v/scikit-gstat?color=green\u0026logo=pypi\u0026logoColor=yellow\u0026style=flat-square   :alt: PyPI\n    :target: https://pypi.org/project/scikit-gstat\n\n.. image:: https://img.shields.io/github/v/release/mmaelicke/scikit-gstat?color=green\u0026logo=github\u0026style=flat-square   :alt: GitHub release (latest by date)\n    :target: https://github.com/mmaelicke/scikit-gstat\n\n.. image:: https://github.com/mmaelicke/scikit-gstat/workflows/Test%20and%20build%20docs/badge.svg\n    :target: https://github.com/mmaelicke/scikit-gstat/actions\n\n.. image:: https://codecov.io/gh/mmaelicke/scikit-gstat/branch/master/graph/badge.svg\n    :target: https://codecov.io/gh/mmaelicke/scikit-gstat\n    :alt: Codecov\n\n.. image:: https://zenodo.org/badge/98853365.svg\n   :target: https://zenodo.org/badge/latestdoi/98853365\n\nHow to cite\n-----------\n\nIn case you use SciKit-GStat in other software or scientific publications,\nplease reference this module. There is a `GMD \u003chttps://www.geoscientific-model-development.net\u003e`_  publication. Please cite it like:\n\n  Mälicke, M.: SciKit-GStat 1.0: a SciPy-flavored geostatistical variogram estimation toolbox written in Python, Geosci. Model Dev., 15, 2505–2532, https://doi.org/10.5194/gmd-15-2505-2022, 2022.\n\nThe code itself is published and has a DOI. It can be cited as:\n\n  Mirko Mälicke, Romain Hugonnet, Helge David Schneider, Sebastian Müller, Egil Möller, \u0026 Johan Van de Wauw. (2022). mmaelicke/scikit-gstat: Version 1.0 (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.5970098\n\n\nFull Documentation\n------------------\n\nThe full documentation can be found at: https://mmaelicke.github.io/scikit-gstat\n\nDescription\n-----------\n\nSciKit-Gstat is a scipy-styled analysis module for geostatistics. It includes\ntwo base classes ``Variogram`` and ``OrdinaryKriging``. Additionally, various\nvariogram classes inheriting from ``Variogram`` are available for solving\ndirectional or space-time related tasks.\nThe module makes use of a rich selection of semi-variance\nestimators and variogram model functions, while being extensible at the same\ntime.\nThe estimators include:\n\n- matheron\n- cressie\n- dowd\n- genton\n- entropy\n- two experimental ones: quantiles, minmax\n\nThe models include:\n\n- sperical\n- exponential\n- gaussian\n- cubic\n- stable\n- matérn\n\nwith all of them in a nugget and no-nugget variation. All the estimator are\nimplemented using numba's jit decorator. The usage of numba might be subject\nto change in future versions.\n\n\nInstallation\n~~~~~~~~~~~~\n\nPyPI\n^^^^\n.. code-block:: bash\n\n  pip install scikit-gstat\n\n**Note:** It can happen that the installation of numba or numpy is failing using pip. Especially on Windows systems.\nUsually, a missing Dll (see eg. `#31 \u003chttps://github.com/mmaelicke/scikit-gstat/issues/31\u003e`_) or visual c++ redistributable is the reason.\n\nGIT:\n^^^^\n\n.. code-block:: bash\n\n  git clone https://github.com/mmaelicke/scikit-gstat.git\n  cd scikit-gstat\n  pip install -r requirements.txt\n  pip install -e .\n\nConda-Forge:\n^^^^^^^^^^^^\n\nFrom Version `0.5.5` on `scikit-gstat` is also available on conda-forge.\nNote that for versions `\u003c 1.0` conda-forge will not always be up to date, but\nfrom `1.0` on, each minor release will be available.\n\n.. code-block:: bash\n\n  conda install -c conda-forge scikit-gstat\n\n\nQuickstart\n----------\n\nThe `Variogram` class needs at least a list of coordiantes and values.\nAll other attributes are set by default.\nYou can easily set up an example by using the `skgstat.data` sub-module,\nthat includes a growing list of sample data.\n\n.. code-block:: python\n\n  import skgstat as skg\n\n  # the data functions return a dict of 'sample' and 'description'\n  coordinates, values = skg.data.pancake(N=300).get('sample')\n\n  V = skg.Variogram(coordinates=coordinates, values=values)\n  print(V)\n\n.. code-block:: bash\n\n  spherical Variogram\n  -------------------\n  Estimator:         matheron\n  Effective Range:   353.64\n  Sill:              1512.24\n  Nugget:            0.00\n\nAll variogram parameters can be changed in place and the class will automatically\ninvalidate and update dependent results and parameters.\n\n.. code-block:: python\n\n  V.model = 'exponential'\n  V.n_lags = 15\n  V.maxlag = 500\n\n  # plot - matplotlib and plotly are available backends\n  fig = V.plot()\n\n.. image:: ./example.png\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmmaelicke%2Fscikit-gstat","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmmaelicke%2Fscikit-gstat","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmmaelicke%2Fscikit-gstat/lists"}