Source code for verde.neighbors

# Copyright (c) 2017 The Verde Developers.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
#
# This code is part of the Fatiando a Terra project (https://www.fatiando.org)
#
"""
Nearest neighbor interpolation
"""
import warnings

import numpy as np
from sklearn.utils.validation import check_is_fitted

from .base import BaseGridder, check_fit_input, n_1d_arrays
from .coordinates import get_region
from .utils import kdtree


[docs] class KNeighbors(BaseGridder): """ Nearest neighbor interpolation. This gridder assumes Cartesian coordinates. Interpolation based on the values of the *k* nearest neighbors of each interpolated point. The number of neighbors *k* can be controlled and mostly influences the spatial smoothness of the interpolated values. The data values of the *k* nearest neighbors are combined into a single value by a reduction function, which defaults to the mean. This can also be configured. .. note:: If installed, package ``pykdtree`` will be used for the nearest neighbors look-up instead of :class:`scipy.spatial.cKDTree` for better performance. Parameters ---------- k : int The number of neighbors to use for each interpolated point. Default is 1. reduction : function Function used to combine the values of the *k* neighbors into a single value. Can be any function that takes a 1D numpy array as input and outputs a single value. Default is :func:`numpy.mean`. Attributes ---------- tree_ : K-D tree An instance of the K-D tree data structure for the data points that is used to query for nearest neighbors. data_ : 1D array A copy of the input data as a 1D array. Used to look up values for interpolation/prediction. region_ : tuple The boundaries (``[W, E, S, N]``) of the data used to fit the interpolator. Used as the default region for the :meth:`~verde.KNeighbors.grid`` method. """ def __init__(self, k=1, reduction=np.mean): super().__init__() self.k = k self.reduction = reduction
[docs] def fit(self, coordinates, data, weights=None): """ Fit the interpolator to the given data. The data region is captured and used as default for the :meth:`~verde.KNeighbors.grid` method. Parameters ---------- coordinates : tuple of arrays Arrays with the coordinates of each data point. Should be in the following order: (easting, northing, vertical, ...). Only easting and northing will be used, all subsequent coordinates will be ignored. data : array The data values that will be interpolated. weights : None or array Data weights are **not supported** by this interpolator and will be ignored. Only present for compatibility with other gridders. Returns ------- self Returns this gridder instance for chaining operations. """ if weights is not None: warnings.warn( "{} does not support weights and they will be ignored.".format( self.__class__.__name__ ), stacklevel=2, ) coordinates, data, weights = check_fit_input(coordinates, data, weights) self.region_ = get_region(coordinates[:2]) self.tree_ = kdtree(coordinates[:2]) # Make sure this is an array and not a subclass of array (pandas, # xarray, etc) so that we can index it later during predict. self.data_ = np.asarray(data).ravel().copy() return self
[docs] def predict(self, coordinates): """ Interpolate data on the given set of points. Requires a fitted gridder (see :meth:`~verde.KNeighbors.fit`). Parameters ---------- coordinates : tuple of arrays Arrays with the coordinates of each data point. Should be in the following order: (easting, northing, vertical, ...). Only easting and northing will be used, all subsequent coordinates will be ignored. Returns ------- data : array The data values interpolated on the given points. """ check_is_fitted(self, ["tree_"]) distances, indices = self.tree_.query( np.transpose(n_1d_arrays(coordinates, 2)), k=self.k ) if indices.ndim == 1: indices = np.atleast_2d(indices).T neighbor_values = np.reshape(self.data_[indices.ravel()], indices.shape) data = self.reduction(neighbor_values, axis=1) shape = np.broadcast(*coordinates[:2]).shape return data.reshape(shape)