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        "author": "Lennart Schueler, Sebastian Mueller",
        "author_email": "info@geostat-framework.org",
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            "Development Status :: 5 - Production/Stable",
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            "License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)",
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        "description": "# Welcome to GSTools\n\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1313628.svg)](https://doi.org/10.5281/zenodo.1313628)\n[![PyPI version](https://badge.fury.io/py/gstools.svg)](https://badge.fury.io/py/gstools)\n[![Build Status](https://travis-ci.org/GeoStat-Framework/GSTools.svg?branch=master)](https://travis-ci.org/GeoStat-Framework/GSTools)\n[![Build status](https://ci.appveyor.com/api/projects/status/oik6h65n0xdy4h4j/branch/master?svg=true)](https://ci.appveyor.com/project/GeoStat-Framework/gstools/branch/master)\n[![Coverage Status](https://coveralls.io/repos/github/GeoStat-Framework/GSTools/badge.svg?branch=master)](https://coveralls.io/github/GeoStat-Framework/GSTools?branch=master)\n[![Documentation Status](https://readthedocs.org/projects/docs/badge/?version=latest)](https://geostat-framework.readthedocs.io/projects/gstools/en/latest/)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)\n\n<p align=\"center\">\n<img src=\"https://raw.githubusercontent.com/GeoStat-Framework/GSTools/master/docs/source/pics/gstools.png\" alt=\"GSTools-LOGO\" width=\"251px\"/>\n</p>\n\n\n## Purpose\n\n<img align=\"right\" width=\"450\" src=\"https://raw.githubusercontent.com/GeoStat-Framework/GSTools/master/docs/source/pics/demonstrator.png\" alt=\"\">\n\nGeoStatTools provides geostatistical tools for various purposes:\n- random field generation\n- conditioned field generation\n- incompressible random vector field generation\n- simple and ordinary kriging\n- variogram estimation and fitting\n- many readily provided and even user-defined covariance models\n- plotting and exporting routines\n\n\n## Installation\n\nThe package can be installed via [pip][pip_link] on Windows, Linux and Mac.\nOn Windows you can install [WinPython][winpy_link] to get\nPython and pip running. Also [conda provides pip support][conda_pip].\nInstall GSTools by typing the following into the command prompt:\n\n    pip install gstools\n\nTo get the latest development version you can install it directly from GitHub:\n\n    pip install https://github.com/GeoStat-Framework/GSTools/archive/master.zip\n\nTo enable the OpenMP support, you have to provide a C compiler, Cython and OpenMP.\nTo get all other dependencies, it is recommended to first install gstools once\nin the standard way just decribed.\nThen use the following command:\n\n    pip install --global-option=\"--openmp\" gstools\n\nOr for the development version:\n\n    pip install --global-option=\"--openmp\" https://github.com/GeoStat-Framework/GSTools/archive/master.zip\n\nIf something went wrong during installation, try the [``-I`` flag from pip][pipiflag].\n\n\n## Citation\n\nAt the moment you can cite the Zenodo code publication of GSTools:\n\n> *Sebastian M\u00fcller, & Lennart Sch\u00fcler. (2019, January 18). GeoStat-Framework/GSTools: Bouncy Blue (Version v1.0.1). Zenodo. http://doi.org/10.5281/zenodo.2543658*\n\nA publication for the GeoStat-Framework is in preperation.\n\n\n## Documentation for GSTools\n\nYou can find the documentation under [geostat-framework.readthedocs.io][doc_link].\n\n\n### Tutorials and Examples\n\nThe documentation also includes some [tutorials][tut_link], showing the most important use cases of GSTools, which are\n\n- [Random Field Generation][tut1_link]\n- [The Covariance Model][tut2_link]\n- [Variogram Estimation][tut3_link]\n- [Random Vector Field Generation][tut4_link]\n- [Kriging][tut5_link]\n- [Conditioned random field generation][tut6_link]\n- [Field transformations][tut7_link]\n\nSome more examples are provided in the examples folder.\n\n\n## Spatial Random Field Generation\n\nThe core of this library is the generation of spatial random fields. These fields are generated using the randomisation method, described by [He\u00dfe et al. 2014][rand_link].\n\n[rand_link]: https://doi.org/10.1016/j.envsoft.2014.01.013\n\n\n### Examples\n\n#### Gaussian Covariance Model\n\nThis is an example of how to generate a 2 dimensional spatial random field with a gaussian covariance model.\n\n```python\nfrom gstools import SRF, Gaussian\nimport matplotlib.pyplot as plt\n# structured field with a size 100x100 and a grid-size of 1x1\nx = y = range(100)\nmodel = Gaussian(dim=2, var=1, len_scale=10)\nsrf = SRF(model)\nsrf((x, y), mesh_type='structured')\nsrf.plot()\n```\n<p align=\"center\">\n<img src=\"https://raw.githubusercontent.com/GeoStat-Framework/GSTools/master/docs/source/pics/gau_field.png\" alt=\"Random field\" width=\"600px\"/>\n</p>\n\nA similar example but for a three dimensional field is exported to a [VTK](https://vtk.org/) file, which can be visualized with [ParaView](https://www.paraview.org/) or [PyVista](https://docs.pyvista.org) in Python:\n\n```python\nfrom gstools import SRF, Gaussian\nimport matplotlib.pyplot as pt\n# structured field with a size 100x100x100 and a grid-size of 1x1x1\nx = y = z = range(100)\nmodel = Gaussian(dim=3, var=0.6, len_scale=20)\nsrf = SRF(model)\nsrf((x, y, z), mesh_type='structured')\nsrf.vtk_export('3d_field') # Save to a VTK file for ParaView\n\nmesh = srf.to_pyvista() # Create a PyVista mesh for plotting in Python\nmesh.threshold_percent(0.5).plot()\n```\n\n<p align=\"center\">\n<img src=\"https://raw.githubusercontent.com/GeoStat-Framework/GSTools/master/docs/source/pics/3d_gau_field.png\" alt=\"3d Random field\" width=\"600px\"/>\n</p>\n\n\n## Estimating and Fitting Variograms\n\nThe spatial structure of a field can be analyzed with the variogram, which contains the same information as the covariance function.\n\nAll covariance models can be used to fit given variogram data by a simple interface.\n\n### Example\n\nThis is an example of how to estimate the variogram of a 2 dimensional unstructured field and estimate the parameters of the covariance\nmodel again.\n\n```python\nimport numpy as np\nfrom gstools import SRF, Exponential, Stable, vario_estimate_unstructured\n# generate a synthetic field with an exponential model\nx = np.random.RandomState(19970221).rand(1000) * 100.\ny = np.random.RandomState(20011012).rand(1000) * 100.\nmodel = Exponential(dim=2, var=2, len_scale=8)\nsrf = SRF(model, mean=0, seed=19970221)\nfield = srf((x, y))\n# estimate the variogram of the field with 40 bins\nbins = np.arange(40)\nbin_center, gamma = vario_estimate_unstructured((x, y), field, bins)\n# fit the variogram with a stable model. (no nugget fitted)\nfit_model = Stable(dim=2)\nfit_model.fit_variogram(bin_center, gamma, nugget=False)\n# output\nax = fit_model.plot(x_max=40)\nax.plot(bin_center, gamma)\nprint(fit_model)\n```\n\nWhich gives:\n\n```python\nStable(dim=2, var=1.92, len_scale=8.15, nugget=0.0, anis=[1.], angles=[0.], alpha=1.05)\n```\n\n<p align=\"center\">\n<img src=\"https://raw.githubusercontent.com/GeoStat-Framework/GSTools/master/docs/source/pics/exp_vario_fit.png\" alt=\"Variogram\" width=\"600px\"/>\n</p>\n\n\n## Kriging and Conditioned Random Fields\n\nAn important part of geostatistics is Kriging and conditioning spatial random\nfields to measurements. With conditioned random fields, an ensemble of field realizations with their variability depending on the proximity of the measurements can be generated.\n\n### Example\nFor better visualization, we will condition a 1d field to a few \"measurements\", generate 100 realizations and plot them:\n\n```python\nimport numpy as np\nfrom gstools import Gaussian, SRF\nimport matplotlib.pyplot as plt\n\n# conditions\ncond_pos = [0.3, 1.9, 1.1, 3.3, 4.7]\ncond_val = [0.47, 0.56, 0.74, 1.47, 1.74]\n\ngridx = np.linspace(0.0, 15.0, 151)\n\n# spatial random field class\nmodel = Gaussian(dim=1, var=0.5, len_scale=2)\nsrf = SRF(model)\nsrf.set_condition(cond_pos, cond_val, \"ordinary\")\n\n# generate the ensemble of field realizations\nfields = []\nfor i in range(100):\n    fields.append(srf(gridx, seed=i))\n    plt.plot(gridx, fields[i], color=\"k\", alpha=0.1)\nplt.scatter(cond_pos, cond_val, color=\"k\")\nplt.show()\n```\n\n<p align=\"center\">\n<img src=\"https://raw.githubusercontent.com/GeoStat-Framework/GSTools/master/docs/source/pics/cond_ens.png\" alt=\"Conditioned\" width=\"600px\"/>\n</p>\n\n## User Defined Covariance Models\n\nOne of the core-features of GSTools is the powerful\n[CovModel][cov_link]\nclass, which allows to easy define covariance models by the user.\n\n### Example\n\nHere we re-implement the Gaussian covariance model by defining just a\n[correlation][cor_link] function, which takes a non-dimensional distance ``h = r/l``:\n\n```python\nfrom gstools import CovModel\nimport numpy as np\n# use CovModel as the base-class\nclass Gau(CovModel):\n    def cor(self, h):\n        return np.exp(-h**2)\n```\n\nAnd that's it! With ``Gau`` you now have a fully working covariance model,\nwhich you could use for field generation or variogram fitting as shown above.\n\nHave a look at the [documentation ][doc_link] for further information on incorporating\noptional parameters and optimizations.\n\n\n## Incompressible Vector Field Generation\n\nUsing the original [Kraichnan method][kraichnan_link], incompressible random\nspatial vector fields can be generated.\n\n\n### Example\n\n```python\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom gstools import SRF, Gaussian\nx = np.arange(100)\ny = np.arange(100)\nmodel = Gaussian(dim=2, var=1, len_scale=10)\nsrf = SRF(model, generator='VectorField')\nsrf((x, y), mesh_type='structured', seed=19841203)\nsrf.plot()\n```\n\nyielding\n\n<p align=\"center\">\n<img src=\"https://raw.githubusercontent.com/GeoStat-Framework/GSTools/master/docs/source/pics/vec_srf_tut_gau.png\" alt=\"vector field\" width=\"600px\"/>\n</p>\n\n\n[kraichnan_link]: https://doi.org/10.1063/1.1692799\n\n\n## VTK/PyVista Export\n\nAfter you have created a field, you may want to save it to file, so we provide\na handy [VTK][vtk_link] export routine using the `.vtk_export()` or you could\ncreate a VTK/PyVista dataset for use in Python with to `.to_pyvista()` method:\n\n```python\nfrom gstools import SRF, Gaussian\nx = y = range(100)\nmodel = Gaussian(dim=2, var=1, len_scale=10)\nsrf = SRF(model)\nsrf((x, y), mesh_type='structured')\nsrf.vtk_export(\"field\") # Saves to a VTK file\nmesh = srf.to_pyvista() # Create a VTK/PyVista dataset in memory\nmesh.plot()\n```\n\nWhich gives a RectilinearGrid VTK file ``field.vtr`` or creates a PyVista mesh\nin memory for immediate 3D plotting in Python.\n\n\n## Requirements:\n\n- [NumPy >= 1.14.5](https://www.numpy.org)\n- [SciPy >= 1.1.0](https://www.scipy.org/scipylib)\n- [hankel >= 0.3.6](https://github.com/steven-murray/hankel)\n- [emcee >= 3.0.0](https://github.com/dfm/emcee)\n- [pyevtk](https://bitbucket.org/pauloh/pyevtk)\n- [six](https://github.com/benjaminp/six)\n\n### Optional\n\n- [matplotlib](https://matplotlib.org)\n- [pyvista](https://docs.pyvista.org/)\n\n\n## Contact\n\nYou can contact us via <info@geostat-framework.org>.\n\n\n## License\n\n[LGPLv3][license_link] \u00a9 2018-2019\n\n[pip_link]: https://pypi.org/project/gstools\n[conda_pip]: https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-pkgs.html#installing-non-conda-packages\n[pipiflag]: https://pip-python3.readthedocs.io/en/latest/reference/pip_install.html?highlight=i#cmdoption-i\n[winpy_link]: https://winpython.github.io/\n[license_link]: https://github.com/GeoStat-Framework/GSTools/blob/master/LICENSE\n[cov_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/latest/covmodel.base.html#gstools.covmodel.base.CovModel\n[stable_link]: https://en.wikipedia.org/wiki/Stable_distribution\n[doc_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/latest/\n[tut_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/latest/tutorials.html\n[tut1_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/latest/tutorial_01_srf.html\n[tut2_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/latest/tutorial_02_cov.html\n[tut3_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/latest/tutorial_03_vario.html\n[tut4_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/latest/tutorial_04_vec_field.html\n[tut5_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/latest/tutorial_05_kriging.html\n[tut6_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/latest/tutorial_06_conditioning.html\n[tut7_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/latest/tutorial_07_transformations.html\n[cor_link]: https://en.wikipedia.org/wiki/Autocovariance#Normalization\n[vtk_link]: https://www.vtk.org/\n\n\n",
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