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""" Standard Utility Script for Gridding Data
1. Contains all the common functions that
will be employed across various different interpolators
"""
import numpy as np
from scipy import spatial
def make_grid(self, x, y, res, offset=0.2):
"""This function returns the grid to perform interpolation on.
This function is used inside the fit() attribute of the idw class.
Parameters
----------
x: array-like, shape(n_samples,)
The first coordinate values of all points where
ground truth is available
y: array-like, shape(n_samples,)
The second coordinate values of all points where
ground truth is available
res: int
The resolution value
offset: float, optional
A value between 0 and 0.5 that specifies the extra interpolation to be done
Default is 0.2
Returns
-------
xx : {array-like, 2D}, shape (n_samples, n_samples)
yy : {array-like, 2D}, shape (n_samples, n_samples)
"""
y_min = y.min() - offset
y_max = y.max() + offset
x_min = x.min() - offset
x_max = x.max() + offset
x_arr = np.linspace(x_min, x_max, res)
y_arr = np.linspace(y_min, y_max, res)
xx, yy = np.meshgrid(x_arr, y_arr)
return xx, yy
def find_closest(grid, X, l=2):
"""Function used to find the indices of the grid points closest
to the passed points in X.
Parameters
----------
grid: {list of 2 arrays}, (shape(res, res), shape(res, res))
This is generated by meshgrid.
X: {array-like, 2D matrix}, shape(n_samples, 2)
The set of points to which we need to provide closest points
on the grid.
l: str, optional
To decide the `l`th norm to use. `Default = 2`.
Returns
-------
ix: array, shape(X.shape[0],)
The index of the point closest to points in X.
ref - https://stackoverflow.com/questions/10818546/finding-index-of-nearest-point-in-numpy-arrays-of-x-and-y-coordinates
"""
points = np.asarray(
[grid[0].ravel(), grid[1].ravel()]
).T # ravel is inplace
kdtree = spatial.KDTree(points)
ixs = [] # for containing the indices of closest points found on grid
for point_ix in range(X.shape[0]):
point = X[point_ix, :]
_, ix = kdtree.query(point)
ixs.append(ix)
return ixs
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