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"""
This is a module for Kriging Interpolation
"""
import numpy as np
from ..base import Base
from pykrige.ok import OrdinaryKriging
from pykrige.uk import UniversalKriging
class Kriging(Base):
"""A class that is declared for performing Kriging interpolation.
Kriging interpolation (usually) works on the principle of finding the
best unbiased predictor. Ordinary Kriging, for an example, involves finding out the
best unbaised linear predictor.
Parameters
----------
type : str, optional
This parameter defines the type of Kriging under consideration. This
implementation uses PyKrige package (https://github.com/bsmurphy/PyKrige).
The user needs to choose between "Ordinary" and "Universal".
plotting: boolean, optional
This parameter plots the fit semivariogram. We use PyKrige's inbuilt plotter for the same.s
variogram_model : str, optional
Specifies which variogram model to use; may be one of the following:
linear, power, gaussian, spherical, exponential, hole-effect.
Default is linear variogram model. To utilize a custom variogram model,
specify 'custom'; you must also provide variogram_parameters and
variogram_function. Note that the hole-effect model is only technically
correct for one-dimensional problems.
require_variance : Boolean, optional
This variable returns the uncertainity in the interpolated values using Kriging
interpolation. If this is True, kindly call the attribute return_variance, of this class
to retreive the computed variances. False is the default value.d
nlags: int, optional
Number of lags to be considered for semivariogram. As in PyKrige, we set default to be 6.
"""
def __init__(
self,
type="Ordinary",
plotting=False,
variogram_model="linear",
require_variance=False,
resolution="standard",
coordinate_type="Eucledian",
nlags=6,
):
super().__init__(resolution, coordinate_type)
self.variogram_model = variogram_model
self.ok = None
self.uk = None
self.type = type
self.plotting = plotting
self.coordinate_type = None
self.require_variance = require_variance
self.variance = None
if coordinate_type == "Eucledian":
self.coordinate_type = "euclidean"
else:
self.coordinate_type = "geographic"
self.nlags = nlags
def _fit(self, X, y):
"""This method of the Kriging Class is used to fit Kriging interpolation model to
the train data. This function shouldn't be called directly."""
if self.type == "Ordinary":
self.ok = OrdinaryKriging(
X[:, 0],
X[:, 1],
y,
variogram_model=self.variogram_model,
enable_plotting=self.plotting,
coordinates_type=self.coordinate_type,
nlags=self.nlags,
)
elif self.type == "Universal":
self.uk = UniversalKriging(
X[:, 0],
X[:, 1],
y,
variogram_model=self.variogram_model,
enable_plotting=self.plotting,
)
else:
raise ValueError(
"Choose either Universal or Ordinary - Given argument is neither"
)
return self
def _predict_grid(self, x1lim, x2lim):
"""The function that is called to return the interpolated data in Kriging Interpolation
in a grid. This method shouldn't be called directly"""
lims = (*x1lim, *x2lim)
x1min, x1max, x2min, x2max = lims
x1 = np.linspace(x1min, x1max, self.resolution)
x2 = np.linspace(x2min, x2max, self.resolution)
if self.ok is not None:
predictions, self.variance = self.ok.execute(
style="grid", xpoints=x1, ypoints=x2
)
else:
predictions, self.variance = self.uk.execute(
style="grid", xpoints=x1, ypoints=x2
)
return predictions
def _predict(self, X):
"""This function should be called to return the interpolated data in kriging
in a pointwise manner. This method shouldn't be called directly."""
if self.ok is not None:
predictions, self.variance = self.ok.execute(
style="points", xpoints=X[:, 0], ypoints=X[:, 1]
)
else:
predictions, self.variance = self.uk.execute(
style="points", xpoints=X[:, 0], ypoints=X[:, 1]
)
return predictions
def return_variance(self):
"""This method of the Kriging class returns the variance at the interpolated
points if the user chooses to use this option at the beginning of the interpolation
"""
if self.require_variance:
return self.variance
else:
print(
"Variance not asked for, while instantiating the object. Returning None"
)
return None
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