Metadata-Version: 2.1
Name: PyKrige
Version: 1.6.0
Summary: Kriging Toolkit for Python.
Home-page: https://github.com/GeoStat-Framework/PyKrige
Author: Benjamin S. Murphy
Author-email: bscott.murphy@gmail.com
Maintainer: Sebastian Mueller, Roman Yurchak
Maintainer-email: info@geostat-framework.org
License: BSD (3 clause)
Description: # PyKrige
        
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        <p align="center">
        <img src="https://github.com/GeoStat-Framework/GeoStat-Framework.github.io/raw/master/docs/source/pics/PyKrige_250.png" alt="PyKrige-LOGO" width="251px"/>
        </p>
        
        Kriging Toolkit for Python.
        
        ## Purpose
        
        The code supports 2D and 3D ordinary and universal kriging. Standard
        variogram models (linear, power, spherical, gaussian, exponential) are
        built in, but custom variogram models can also be used. The 2D universal
        kriging code currently supports regional-linear, point-logarithmic, and
        external drift terms, while the 3D universal kriging code supports a
        regional-linear drift term in all three spatial dimensions. Both
        universal kriging classes also support generic 'specified' and
        'functional' drift capabilities. With the 'specified' drift capability,
        the user may manually specify the values of the drift(s) at each data
        point and all grid points. With the 'functional' drift capability, the
        user may provide callable function(s) of the spatial coordinates that
        define the drift(s). The package includes a module that contains
        functions that should be useful in working with ASCII grid files (`\*.asc`).
        
        See the documentation at <http://pykrige.readthedocs.io/> for more
        details and examples.
        
        ## Installation
        
        PyKrige requires Python 3.5+ as well as numpy, scipy. It can be
        installed from PyPi with,
        
        ``` bash
        pip install pykrige
        ```
        
        scikit-learn is an optional dependency needed for parameter tuning and
        regression kriging. matplotlib is an optional dependency needed for
        plotting.
        
        If you use conda, PyKrige can be installed from the <span
        class="title-ref">conda-forge</span> channel with,
        
        ``` bash
        conda install -c conda-forge pykrige
        ```
        
        ## Features
        
        ### Kriging algorithms
        
        -   `OrdinaryKriging`: 2D ordinary kriging with estimated mean
        -   `UniversalKriging`: 2D universal kriging providing drift terms
        -   `OrdinaryKriging3D`: 3D ordinary kriging
        -   `UniversalKriging3D`: 3D universal kriging
        -   `RegressionKriging`: An implementation of Regression-Kriging
        -   `ClassificationKriging`: An implementation of Simplicial Indicator
            Kriging
        
        ### Wrappers
        
        -   `rk.Krige`: A scikit-learn wrapper class for Ordinary and Universal
            Kriging
        
        ### Tools
        
        -   `kriging_tools.write_asc_grid`: Writes gridded data to ASCII grid
            file (`\*.asc`)
        -   `kriging_tools.read_asc_grid`: Reads ASCII grid file (`\*.asc`)
        
        ### Kriging Parameters Tuning
        
        A scikit-learn compatible API for parameter tuning by cross-validation
        is exposed in
        [sklearn.model\_selection.GridSearchCV](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html).
        See the [Krige
        CV](http://pykrige.readthedocs.io/en/latest/examples/08_krige_cv.html#sphx-glr-examples-08-krige-cv-py)
        example for a more practical illustration.
        
        ### Regression Kriging
        
        [Regression kriging](https://en.wikipedia.org/wiki/Regression-Kriging)
        can be performed with
        [pykrige.rk.RegressionKriging](http://pykrige.readthedocs.io/en/latest/examples/07_regression_kriging2d.html).
        This class takes as parameters a scikit-learn regression model, and
        details of either either the `OrdinaryKriging` or the `UniversalKriging`
        class, and performs a correction steps on the ML regression prediction.
        
        A demonstration of the regression kriging is provided in the
        [corresponding
        example](http://pykrige.readthedocs.io/en/latest/examples/07_regression_kriging2d.html#sphx-glr-examples-07-regression-kriging2d-py).
        
        ### Classification Kriging
        
        [Simplifical Indicator
        kriging](https://www.sciencedirect.com/science/article/abs/pii/S1002070508600254)
        can be performed with
        [pykrige.rk.ClassificationKriging](http://pykrige.readthedocs.io/en/latest/examples/10_classification_kriging2d.html).
        This class takes as parameters a scikit-learn classification model, and
        details of either the `OrdinaryKriging` or the `UniversalKriging` class,
        and performs a correction steps on the ML classification prediction.
        
        A demonstration of the classification kriging is provided in the
        [corresponding
        example](http://pykrige.readthedocs.io/en/latest/examples/10_classification_kriging2d.html#sphx-glr-examples-10-classification-kriging2d-py).
        
        ## License
        
        PyKrige uses the BSD 3-Clause License.
        
Platform: Windows
Platform: Linux
Platform: Solaris
Platform: Mac OS-X
Platform: Unix
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: GIS
Classifier: Topic :: Utilities
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: plot
Provides-Extra: sklearn
Provides-Extra: doc
Provides-Extra: test
Provides-Extra: dev
