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"description": "PyKrige\n=======\n\nKriging Toolkit for Python\n\n.. image:: https://img.shields.io/pypi/v/pykrige.svg\n :target: https://pypi.python.org/pypi/pykrige\n\n.. image:: https://anaconda.org/conda-forge/pykrige/badges/version.svg\n :target: https://github.com/conda-forge/pykrige-feedstock\n\n.. image:: https://readthedocs.org/projects/pykrige/badge/?version=latest\n :target: http://pykrige.readthedocs.io/en/latest/?badge=latest\n :alt: Documentation Status\n\n.. image:: https://travis-ci.org/bsmurphy/PyKrige.svg?branch=master\n :target: https://travis-ci.org/bsmurphy/PyKrige\n\n.. image:: https://ci.appveyor.com/api/projects/status/github/bsmurphy/PyKrige?branch=master&svg=true\n :target: https://ci.appveyor.com/project/bsmurphy/pykrige\n\n\n\nThe code supports 2D and 3D ordinary and universal kriging. Standard variogram models\n(linear, power, spherical, gaussian, exponential) are built in, but custom variogram models can also be used.\nThe 2D universal kriging code currently supports regional-linear, point-logarithmic, and external drift terms,\nwhile the 3D universal kriging code supports a regional-linear drift term in all three spatial dimensions.\nBoth universal kriging classes also support generic 'specified' and 'functional' drift capabilities.\nWith the 'specified' drift capability, the user may manually specify the values of the drift(s) at each data\npoint and all grid points. With the 'functional' drift capability, the user may provide callable function(s)\nof the spatial coordinates that define the drift(s). The package includes a module that contains functions\nthat should be useful in working with ASCII grid files (`*.asc`).\n\nSee the documentation at `http://pykrige.readthedocs.io/ `_ for more details.\n\nInstallation\n^^^^^^^^^^^^\n\nPyKrige requires Python 2.7 or 3.5+ as well as numpy, scipy and matplotlib. It can be installed from PyPi with,\n\n.. code:: bash\n\n pip install pykrige\n\nscikit-learn is an optional dependency needed for parameter tuning and regression kriging.\n\n\nIf you use conda, PyKrige can be installed from the `conda-forge` channel with,\n\n.. code:: bash\n\n conda install -c conda-forge pykrige\n\n\nOrdinary Kriging Example\n^^^^^^^^^^^^^^^^^^^^^^^^\n\nFirst we will create a 2D dataset together with the associated x, y grids,\n\n.. code:: python\n\n import numpy as np\n import pykrige.kriging_tools as kt\n from pykrige.ok import OrdinaryKriging\n \n data = np.array([[0.3, 1.2, 0.47],\n [1.9, 0.6, 0.56],\n [1.1, 3.2, 0.74],\n [3.3, 4.4, 1.47],\n [4.7, 3.8, 1.74]])\n \n gridx = np.arange(0.0, 5.5, 0.5)\n gridy = np.arange(0.0, 5.5, 0.5)\n \n # Create the ordinary kriging object. Required inputs are the X-coordinates of\n # the data points, the Y-coordinates of the data points, and the Z-values of the\n # data points. If no variogram model is specified, defaults to a linear variogram\n # model. If no variogram model parameters are specified, then the code automatically\n # calculates the parameters by fitting the variogram model to the binned \n # experimental semivariogram. The verbose kwarg controls code talk-back, and\n # the enable_plotting kwarg controls the display of the semivariogram.\n OK = OrdinaryKriging(data[:, 0], data[:, 1], data[:, 2], variogram_model='linear',\n verbose=False, enable_plotting=False)\n \t\t\t\t\t \n # Creates the kriged grid and the variance grid. Allows for kriging on a rectangular\n # grid of points, on a masked rectangular grid of points, or with arbitrary points.\n # (See OrdinaryKriging.__doc__ for more information.)\n z, ss = OK.execute('grid', gridx, gridy)\n \n # Writes the kriged grid to an ASCII grid file.\n kt.write_asc_grid(gridx, gridy, z, filename=\"output.asc\")\n\nUniversal Kriging Example\n^^^^^^^^^^^^^^^^^^^^^^^^^\n\n.. code:: python\n\n from pykrige.uk import UniversalKriging\n import numpy as np\n\n data = np.array([[0.3, 1.2, 0.47],\n [1.9, 0.6, 0.56],\n [1.1, 3.2, 0.74],\n [3.3, 4.4, 1.47],\n [4.7, 3.8, 1.74]])\n\n gridx = np.arange(0.0, 5.5, 0.5)\n gridy = np.arange(0.0, 5.5, 0.5)\n\n # Create the ordinary kriging object. Required inputs are the X-coordinates of\n # the data points, the Y-coordinates of the data points, and the Z-values of the\n # data points. Variogram is handled as in the ordinary kriging case.\n # drift_terms is a list of the drift terms to include; currently supported terms\n # are 'regional_linear', 'point_log', and 'external_Z'. Refer to \n # UniversalKriging.__doc__ for more information.\n UK = UniversalKriging(data[:, 0], data[:, 1], data[:, 2], variogram_model='linear',\n drift_terms=['regional_linear'])\n \n # Creates the kriged grid and the variance grid. Allows for kriging on a rectangular\n # grid of points, on a masked rectangular grid of points, or with arbitrary points.\n # (See UniversalKriging.__doc__ for more information.)\n z, ss = UK.execute('grid', gridx, gridy)\n\nThree-Dimensional Kriging Example\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n.. code:: python\n\n from pykrige.ok3d import OrdinaryKriging3D\n from pykrige.uk3d import UniversalKriging3D\n import numpy as np\n\n data = np.array([[0.1, 0.1, 0.3, 0.9],\n [0.2, 0.1, 0.4, 0.8],\n [0.1, 0.3, 0.1, 0.9],\n [0.5, 0.4, 0.4, 0.5],\n [0.3, 0.3, 0.2, 0.7]])\n\n gridx = np.arange(0.0, 0.6, 0.05)\n gridy = np.arange(0.0, 0.6, 0.01)\n gridz = np.arange(0.0, 0.6, 0.1)\n\n # Create the 3D ordinary kriging object and solves for the three-dimension kriged \n # volume and variance. Refer to OrdinaryKriging3D.__doc__ for more information.\n ok3d = OrdinaryKriging3D(data[:, 0], data[:, 1], data[:, 2], data[:, 3],\n variogram_model='linear')\n k3d, ss3d = ok3d.execute('grid', gridx, gridy, gridz)\n\n # Create the 3D universal kriging object and solves for the three-dimension kriged \n # volume and variance. Refer to UniversalKriging3D.__doc__ for more information.\n uk3d = UniversalKriging3D(data[:, 0], data[:, 1], data[:, 2], data[:, 3], \n variogram_model='linear', drift_terms=['regional_linear'])\n k3d, ss3d = uk3d.execute('grid', gridx, gridy, gridz)\n\n # To use the generic 'specified' drift term, the user must provide the drift values \n # at each data point and at every grid point. The following example is equivalent to \n # using a linear drift in all three spatial dimensions. Refer to\n # UniversalKriging3D.__doc__ for more information.\n zg, yg, xg = np.meshgrid(gridz, gridy, gridx, indexing='ij')\n uk3d = UniversalKriging3D(data[:, 0], data[:, 1], data[:, 2], data[:, 3], \n variogram_model='linear', drift_terms=['specified'],\n specified_drift=[data[:, 0], data[:, 1]])\n k3d, ss3d = uk3d.execute('grid', gridx, gridy, gridz, specified_drift_arrays=[xg, yg, zg])\n\n # To use the generic 'functional' drift term, the user must provide a callable \n # function that takes only the spatial dimensions as arguments. The following example \n # is equivalent to using a linear drift only in the x-direction. Refer to \n # UniversalKriging3D.__doc__ for more information.\n func = lambda x, y, z: x\n uk3d = UniversalKriging3D(data[:, 0], data[:, 1], data[:, 2], data[:, 3], \n variogram_model='linear', drift_terms=['functional'],\n functional_drift=[func])\n k3d, ss3d = uk3d.execute('grid', gridx, gridy, gridz)\n\n # Note that the use of the 'specified' and 'functional' generic drift capabilities is \n # essentially identical in the two-dimensional universal kriging class (except for a \n # difference in the number of spatial coordinates for the passed drift functions). \n # See UniversalKriging.__doc__ for more information.\n\n\nKriging Parameters Tuning\n^^^^^^^^^^^^^^^^^^^^^^^^^\n\nA scikit-learn compatible API for parameter tuning by cross-validation is exposed in\n`sklearn.model_selection.GridSearchCV `_.\nSee the `Krige CV `_\nexample for a more practical illustration.\n\n\nRegression Kriging\n^^^^^^^^^^^^^^^^^^\n\n`Regression kriging `_ can be performed\nwith `pykrige.rk.RegressionKriging `_.\nThis class takes as parameters a scikit-learn regression model, and details of either either\nthe ``OrdinaryKriging`` or the ``UniversalKriging`` class, and performs a correction steps on the ML regression prediction.\n \nA demonstration of the regression kriging is provided in the \n`corresponding example `_.\n\nLicense\n^^^^^^^\n\nPyKrige uses the BSD 3-Clause License.",
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