PyKrige 1.7.2

Description:

PyKrige 1.7.2

PyKrige










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,
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 conda-forge channel with,
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_tools.write_zmap_grid: Writes gridded data to zmap file (\*.zmap)
kriging_tools.read_zmap_grid: Reads zmap file (\*.zmap)

Kriging Parameters Tuning
A scikit-learn compatible API for parameter tuning by cross-validation
is exposed in
sklearn.model_selection.GridSearchCV.
See the Krige
CV
example for a more practical illustration.
Regression Kriging
Regression kriging
can be performed with
pykrige.rk.RegressionKriging.
This class takes as parameters a scikit-learn regression model, and
details of either the OrdinaryKriging or the UniversalKriging
class, and performs a correction step on the ML regression prediction.
A demonstration of the regression kriging is provided in the
corresponding
example.
Classification Kriging
Simplifical Indicator
kriging
can be performed with
pykrige.ck.ClassificationKriging.
This class takes as parameters a scikit-learn classification model, and
details of either the OrdinaryKriging or the UniversalKriging class,
and performs a correction step on the ML classification prediction.
A demonstration of the classification kriging is provided in the
corresponding
example.
License
PyKrige uses the BSD 3-Clause License.