| """ | |
| 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 | |