Metadata-Version: 2.1
Name: gstools
Version: 1.1.1
Summary: GSTools: A geostatistical toolbox.
Home-page: https://github.com/GeoStat-Framework/GSTools
Author: Lennart Schueler, Sebastian Mueller
Author-email: info@geostat-framework.org
Maintainer: Lennart Schueler, Sebastian Mueller
Maintainer-email: info@geostat-framework.org
License: LGPLv3
Description: # Welcome to GSTools
        
        [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1313628.svg)](https://doi.org/10.5281/zenodo.1313628)
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        <p align="center">
        <img src="https://raw.githubusercontent.com/GeoStat-Framework/GSTools/master/docs/source/pics/gstools.png" alt="GSTools-LOGO" width="251px"/>
        </p>
        
        
        ## Purpose
        
        <img align="right" width="450" src="https://raw.githubusercontent.com/GeoStat-Framework/GSTools/master/docs/source/pics/demonstrator.png" alt="">
        
        GeoStatTools provides geostatistical tools for various purposes:
        - random field generation
        - conditioned field generation
        - incompressible random vector field generation
        - simple and ordinary kriging
        - variogram estimation and fitting
        - many readily provided and even user-defined covariance models
        - plotting and exporting routines
        
        
        ## Installation
        
        The package can be installed via [pip][pip_link] on Windows, Linux and Mac.
        On Windows you can install [WinPython][winpy_link] to get
        Python and pip running. Also [conda provides pip support][conda_pip].
        Install GSTools by typing the following into the command prompt:
        
            pip install gstools
        
        To get the latest development version you can install it directly from GitHub:
        
            pip install https://github.com/GeoStat-Framework/GSTools/archive/develop.zip
        
        To enable the OpenMP support, you have to provide a C compiler, Cython and OpenMP.
        To get all other dependencies, it is recommended to first install gstools once
        in the standard way just decribed.
        Then use the following command:
        
            pip install --global-option="--openmp" gstools
        
        Or for the development version:
        
            pip install --global-option="--openmp" https://github.com/GeoStat-Framework/GSTools/archive/develop.zip
        
        If something went wrong during installation, try the [``-I`` flag from pip][pipiflag].
        
        
        ## Citation
        
        At the moment you can cite the Zenodo code publication of GSTools:
        
        > Sebastian MÃ¼ller, & Lennart SchÃ¼ler. (2019, October 1). GeoStat-Framework/GSTools: Reverberating Red (Version v1.1.0). Zenodo. http://doi.org/10.5281/zenodo.3468230
        
        A publication for the GeoStat-Framework is in preperation.
        
        
        ## Documentation for GSTools
        
        You can find the documentation under [geostat-framework.readthedocs.io][doc_link].
        
        
        ### Tutorials and Examples
        
        The documentation also includes some [tutorials][tut_link], showing the most important use cases of GSTools, which are
        
        - [Random Field Generation][tut1_link]
        - [The Covariance Model][tut2_link]
        - [Variogram Estimation][tut3_link]
        - [Random Vector Field Generation][tut4_link]
        - [Kriging][tut5_link]
        - [Conditioned random field generation][tut6_link]
        - [Field transformations][tut7_link]
        
        Some more examples are provided in the examples folder.
        
        
        ## Spatial Random Field Generation
        
        The core of this library is the generation of spatial random fields. These fields are generated using the randomisation method, described by [HeÃŸe et al. 2014][rand_link].
        
        [rand_link]: https://doi.org/10.1016/j.envsoft.2014.01.013
        
        
        ### Examples
        
        #### Gaussian Covariance Model
        
        This is an example of how to generate a 2 dimensional spatial random field with a gaussian covariance model.
        
        ```python
        from gstools import SRF, Gaussian
        import matplotlib.pyplot as plt
        # structured field with a size 100x100 and a grid-size of 1x1
        x = y = range(100)
        model = Gaussian(dim=2, var=1, len_scale=10)
        srf = SRF(model)
        srf((x, y), mesh_type='structured')
        srf.plot()
        ```
        <p align="center">
        <img src="https://raw.githubusercontent.com/GeoStat-Framework/GSTools/master/docs/source/pics/gau_field.png" alt="Random field" width="600px"/>
        </p>
        
        A 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:
        
        ```python
        from gstools import SRF, Gaussian
        import matplotlib.pyplot as pt
        # structured field with a size 100x100x100 and a grid-size of 1x1x1
        x = y = z = range(100)
        model = Gaussian(dim=3, var=0.6, len_scale=20)
        srf = SRF(model)
        srf((x, y, z), mesh_type='structured')
        srf.vtk_export('3d_field') # Save to a VTK file for ParaView
        
        mesh = srf.to_pyvista() # Create a PyVista mesh for plotting in Python
        mesh.threshold_percent(0.5).plot()
        ```
        
        <p align="center">
        <img src="https://raw.githubusercontent.com/GeoStat-Framework/GSTools/master/docs/source/pics/3d_gau_field.png" alt="3d Random field" width="600px"/>
        </p>
        
        
        ## Estimating and Fitting Variograms
        
        The spatial structure of a field can be analyzed with the variogram, which contains the same information as the covariance function.
        
        All covariance models can be used to fit given variogram data by a simple interface.
        
        ### Example
        
        This is an example of how to estimate the variogram of a 2 dimensional unstructured field and estimate the parameters of the covariance
        model again.
        
        ```python
        import numpy as np
        from gstools import SRF, Exponential, Stable, vario_estimate_unstructured
        # generate a synthetic field with an exponential model
        x = np.random.RandomState(19970221).rand(1000) * 100.
        y = np.random.RandomState(20011012).rand(1000) * 100.
        model = Exponential(dim=2, var=2, len_scale=8)
        srf = SRF(model, mean=0, seed=19970221)
        field = srf((x, y))
        # estimate the variogram of the field with 40 bins
        bins = np.arange(40)
        bin_center, gamma = vario_estimate_unstructured((x, y), field, bins)
        # fit the variogram with a stable model. (no nugget fitted)
        fit_model = Stable(dim=2)
        fit_model.fit_variogram(bin_center, gamma, nugget=False)
        # output
        ax = fit_model.plot(x_max=40)
        ax.plot(bin_center, gamma)
        print(fit_model)
        ```
        
        Which gives:
        
        ```python
        Stable(dim=2, var=1.92, len_scale=8.15, nugget=0.0, anis=[1.], angles=[0.], alpha=1.05)
        ```
        
        <p align="center">
        <img src="https://raw.githubusercontent.com/GeoStat-Framework/GSTools/master/docs/source/pics/exp_vario_fit.png" alt="Variogram" width="600px"/>
        </p>
        
        
        ## Kriging and Conditioned Random Fields
        
        An important part of geostatistics is Kriging and conditioning spatial random
        fields to measurements. With conditioned random fields, an ensemble of field realizations with their variability depending on the proximity of the measurements can be generated.
        
        ### Example
        For better visualization, we will condition a 1d field to a few "measurements", generate 100 realizations and plot them:
        
        ```python
        import numpy as np
        from gstools import Gaussian, SRF
        import matplotlib.pyplot as plt
        
        # conditions
        cond_pos = [0.3, 1.9, 1.1, 3.3, 4.7]
        cond_val = [0.47, 0.56, 0.74, 1.47, 1.74]
        
        gridx = np.linspace(0.0, 15.0, 151)
        
        # spatial random field class
        model = Gaussian(dim=1, var=0.5, len_scale=2)
        srf = SRF(model)
        srf.set_condition(cond_pos, cond_val, "ordinary")
        
        # generate the ensemble of field realizations
        fields = []
        for i in range(100):
            fields.append(srf(gridx, seed=i))
            plt.plot(gridx, fields[i], color="k", alpha=0.1)
        plt.scatter(cond_pos, cond_val, color="k")
        plt.show()
        ```
        
        <p align="center">
        <img src="https://raw.githubusercontent.com/GeoStat-Framework/GSTools/master/docs/source/pics/cond_ens.png" alt="Conditioned" width="600px"/>
        </p>
        
        ## User Defined Covariance Models
        
        One of the core-features of GSTools is the powerful
        [CovModel][cov_link]
        class, which allows to easy define covariance models by the user.
        
        ### Example
        
        Here we re-implement the Gaussian covariance model by defining just a
        [correlation][cor_link] function, which takes a non-dimensional distance ``h = r/l``:
        
        ```python
        from gstools import CovModel
        import numpy as np
        # use CovModel as the base-class
        class Gau(CovModel):
            def cor(self, h):
                return np.exp(-h**2)
        ```
        
        And that's it! With ``Gau`` you now have a fully working covariance model,
        which you could use for field generation or variogram fitting as shown above.
        
        Have a look at the [documentation ][doc_link] for further information on incorporating
        optional parameters and optimizations.
        
        
        ## Incompressible Vector Field Generation
        
        Using the original [Kraichnan method][kraichnan_link], incompressible random
        spatial vector fields can be generated.
        
        
        ### Example
        
        ```python
        import numpy as np
        import matplotlib.pyplot as plt
        from gstools import SRF, Gaussian
        x = np.arange(100)
        y = np.arange(100)
        model = Gaussian(dim=2, var=1, len_scale=10)
        srf = SRF(model, generator='VectorField')
        srf((x, y), mesh_type='structured', seed=19841203)
        srf.plot()
        ```
        
        yielding
        
        <p align="center">
        <img src="https://raw.githubusercontent.com/GeoStat-Framework/GSTools/master/docs/source/pics/vec_srf_tut_gau.png" alt="vector field" width="600px"/>
        </p>
        
        
        [kraichnan_link]: https://doi.org/10.1063/1.1692799
        
        
        ## VTK/PyVista Export
        
        After you have created a field, you may want to save it to file, so we provide
        a handy [VTK][vtk_link] export routine using the `.vtk_export()` or you could
        create a VTK/PyVista dataset for use in Python with to `.to_pyvista()` method:
        
        ```python
        from gstools import SRF, Gaussian
        x = y = range(100)
        model = Gaussian(dim=2, var=1, len_scale=10)
        srf = SRF(model)
        srf((x, y), mesh_type='structured')
        srf.vtk_export("field") # Saves to a VTK file
        mesh = srf.to_pyvista() # Create a VTK/PyVista dataset in memory
        mesh.plot()
        ```
        
        Which gives a RectilinearGrid VTK file ``field.vtr`` or creates a PyVista mesh
        in memory for immediate 3D plotting in Python.
        
        
        ## Requirements:
        
        - [NumPy >= 1.14.5](https://www.numpy.org)
        - [SciPy >= 1.1.0](https://www.scipy.org/scipylib)
        - [hankel >= 0.3.6](https://github.com/steven-murray/hankel)
        - [emcee >= 3.0.0](https://github.com/dfm/emcee)
        - [pyevtk](https://bitbucket.org/pauloh/pyevtk)
        - [six](https://github.com/benjaminp/six)
        
        ### Optional
        
        - [matplotlib](https://matplotlib.org)
        - [pyvista](https://docs.pyvista.org/)
        
        
        ## Contact
        
        You can contact us via <info@geostat-framework.org>.
        
        
        ## License
        
        [LGPLv3][license_link] Â© 2018-2019
        
        [pip_link]: https://pypi.org/project/gstools
        [conda_pip]: https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-pkgs.html#installing-non-conda-packages
        [pipiflag]: https://pip-python3.readthedocs.io/en/latest/reference/pip_install.html?highlight=i#cmdoption-i
        [winpy_link]: https://winpython.github.io/
        [license_link]: https://github.com/GeoStat-Framework/GSTools/blob/master/LICENSE
        [cov_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/latest/covmodel.base.html#gstools.covmodel.base.CovModel
        [stable_link]: https://en.wikipedia.org/wiki/Stable_distribution
        [doc_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/latest/
        [tut_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/latest/tutorials.html
        [tut1_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/latest/tutorial_01_srf.html
        [tut2_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/latest/tutorial_02_cov.html
        [tut3_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/latest/tutorial_03_vario.html
        [tut4_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/latest/tutorial_04_vec_field.html
        [tut5_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/latest/tutorial_05_kriging.html
        [tut6_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/latest/tutorial_06_conditioning.html
        [tut7_link]: https://geostat-framework.readthedocs.io/projects/gstools/en/latest/tutorial_07_transformations.html
        [cor_link]: https://en.wikipedia.org/wiki/Autocovariance#Normalization
        [vtk_link]: https://www.vtk.org/
        
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