# verde.KNeighbors#

class verde.KNeighbors(k=1, reduction=<function mean>)[source]#

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 `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 `numpy.mean`.

Variables
• 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 `grid`` method.

Methods Summary

 `KNeighbors.filter`(coordinates, data[, weights]) Filter the data through the gridder and produce residuals. `KNeighbors.fit`(coordinates, data[, weights]) Fit the interpolator to the given data. `KNeighbors.get_params`([deep]) Get parameters for this estimator. `KNeighbors.grid`([region, shape, spacing, ...]) Interpolate the data onto a regular grid. `KNeighbors.predict`(coordinates) Interpolate data on the given set of points. `KNeighbors.profile`(point1, point2, size[, ...]) Interpolate data along a profile between two points. `KNeighbors.scatter`([region, size, ...]) Interpolate values onto a random scatter of points. `KNeighbors.score`(coordinates, data[, weights]) Score the gridder predictions against the given data. `KNeighbors.set_params`(**params) Set the parameters of this estimator.

KNeighbors.filter(coordinates, data, weights=None)#

Filter the data through the gridder and produce residuals.

Calls `fit` on the data, evaluates the residuals (data - predicted data), and returns the coordinates, residuals, and weights.

Not very useful by itself but this interface makes gridders compatible with other processing operations and is used by `verde.Chain` to join them together (for example, so you can fit a spline on the residuals of a trend).

Parameters
• coordinates (tuple of arrays) – Arrays with the coordinates of each data point. Should be in the following order: (easting, northing, vertical, …). For the specific definition of coordinate systems and what these names mean, see the class docstring.

• data (array or tuple of arrays) – The data values of each data point. If the data has more than one component, data must be a tuple of arrays (one for each component).

• weights (None or array or tuple of arrays) – If not None, then the weights assigned to each data point. If more than one data component is provided, you must provide a weights array for each data component (if not None).

Returns

coordinates, residuals, weights – The coordinates and weights are same as the input. Residuals are the input data minus the predicted data.

KNeighbors.fit(coordinates, data, weights=None)[source]#

Fit the interpolator to the given data.

The data region is captured and used as default for the `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.

KNeighbors.get_params(deep=True)#

Get parameters for this estimator.

Parameters

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params (dict) – Parameter names mapped to their values.

KNeighbors.grid(region=None, shape=None, spacing=None, dims=None, data_names=None, projection=None, coordinates=None, **kwargs)#

Interpolate the data onto a regular grid.

The grid can be specified by two methods:

Use the dims and data_names arguments to set custom names for the dimensions and the data field(s) in the output `xarray.Dataset`. Default names will be provided if none are given.

Parameters
• region (list = [W, E, S, N]) – The west, east, south, and north boundaries of a given region. Use only if `coordinates` is None.

• shape (tuple = (n_north, n_east) or None) – The number of points in the South-North and West-East directions, respectively. Use only if `coordinates` is None.

• spacing (tuple = (s_north, s_east) or None) – The grid spacing in the South-North and West-East directions, respectively. Use only if `coordinates` is None.

• dims (list or None) – The names of the northing and easting data dimensions, respectively, in the output grid. Default is determined from the `dims` attribute of the class. Must be defined in the following order: northing dimension, easting dimension. NOTE: This is an exception to the “easting” then “northing” pattern but is required for compatibility with xarray.

• data_names (str, list or None) – The name(s) of the data variables in the output grid. Defaults to `'scalars'` for scalar data, `['east_component', 'north_component']` for 2D vector data, and `['east_component', 'north_component', 'vertical_component']` for 3D vector data.

• projection (callable or None) – If not None, then should be a callable object `projection(easting, northing) -> (proj_easting, proj_northing)` that takes in easting and northing coordinate arrays and returns projected northing and easting coordinate arrays. This function will be used to project the generated grid coordinates before passing them into `predict`. For example, you can use this to generate a geographic grid from a Cartesian gridder.

• coordinates (tuple of arrays) – Tuple of arrays containing the coordinates of the grid in the following order: (easting, northing, vertical, …). The easting and northing arrays could be 1d or 2d arrays, if they are 2d they must be part of a meshgrid. If coordinates are passed, `region`, `shape`, and `spacing` are ignored.

Returns

grid (xarray.Dataset) – The interpolated grid. Metadata about the interpolator is written to the `attrs` attribute.

`verde.grid_coordinates`

Generate the coordinate values for the grid.

KNeighbors.predict(coordinates)[source]#

Interpolate data on the given set of points.

Requires a fitted gridder (see `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.

KNeighbors.profile(point1, point2, size, dims=None, data_names=None, projection=None, **kwargs)#

Interpolate data along a profile between two points.

Generates the profile along a straight line assuming Cartesian distances. Point coordinates are generated by `verde.profile_coordinates`. Other arguments for this function can be passed as extra keyword arguments (`kwargs`) to this method.

Use the dims and data_names arguments to set custom names for the dimensions and the data field(s) in the output `pandas.DataFrame`. Default names are provided.

Includes the calculated Cartesian distance from point1 for each data point in the profile.

To specify point1 and point2 in a coordinate system that would require projection to Cartesian (geographic longitude and latitude, for example), use the `projection` argument. With this option, the input points will be projected using the given projection function prior to computations. The generated Cartesian profile coordinates will be projected back to the original coordinate system. Note that the profile points are evenly spaced in projected coordinates, not the original system (e.g., geographic).

Warning

The profile calculation method with a projection has changed in Verde 1.4.0. Previous versions generated coordinates (assuming they were Cartesian) and projected them afterwards. This led to “distances” being incorrectly handled and returned in unprojected coordinates. For example, if `projection` is from geographic to Mercator, the distances would be “angles” (incorrectly calculated as if they were Cartesian). After 1.4.0, point1 and point2 are projected prior to generating coordinates for the profile, guaranteeing that distances are properly handled in a Cartesian system. With this change, the profile points are now evenly spaced in projected coordinates and the distances are returned in projected coordinates as well.

Parameters
• point1 (tuple) – The easting and northing coordinates, respectively, of the first point.

• point2 (tuple) – The easting and northing coordinates, respectively, of the second point.

• size (int) – The number of points to generate.

• dims (list or None) – The names of the northing and easting data dimensions, respectively, in the output dataframe. Default is determined from the `dims` attribute of the class. Must be defined in the following order: northing dimension, easting dimension. NOTE: This is an exception to the “easting” then “northing” pattern but is required for compatibility with xarray.

• data_names (str, list or None) – The name(s) of the data variables in the output dataframe. Defaults to `'scalars'` for scalar data, `['east_component', 'north_component']` for 2D vector data, and `['east_component', 'north_component', 'vertical_component']` for 3D vector data.

• projection (callable or None) – If not None, then should be a callable object ```projection(easting, northing, inverse=False) -> (proj_easting, proj_northing)``` that takes in easting and northing coordinate arrays and returns projected northing and easting coordinate arrays. Should also take an optional keyword argument `inverse` (default to False) that if True will calculate the inverse transform instead. This function will be used to project the profile end points before generating coordinates and passing them into `predict`. It will also be used to undo the projection of the coordinates before returning the results.

Returns

table (pandas.DataFrame) – The interpolated values along the profile.

KNeighbors.scatter(region=None, size=300, random_state=0, dims=None, data_names=None, projection=None, **kwargs)#

Interpolate values onto a random scatter of points.

Point coordinates are generated by `verde.scatter_points`. Other arguments for this function can be passed as extra keyword arguments (`kwargs`) to this method.

If the interpolator collected the input data region, then it will be used if `region=None`. Otherwise, you must specify the grid region.

Use the dims and data_names arguments to set custom names for the dimensions and the data field(s) in the output `pandas.DataFrame`. Default names are provided.

Warning

The `scatter` method is deprecated and will be removed in Verde 2.0.0. Use `verde.scatter_points` and the `predict` method instead.

Parameters
• region (list = [W, E, S, N]) – The west, east, south, and north boundaries of a given region.

• size (int) – The number of points to generate.

• random_state (numpy.random.RandomState or an int seed) – A random number generator used to define the state of the random permutations. Use a fixed seed to make sure computations are reproducible. Use `None` to choose a seed automatically (resulting in different numbers with each run).

• dims (list or None) – The names of the northing and easting data dimensions, respectively, in the output dataframe. Default is determined from the `dims` attribute of the class. Must be defined in the following order: northing dimension, easting dimension. NOTE: This is an exception to the “easting” then “northing” pattern but is required for compatibility with xarray.

• data_names (str, list or None) – The name(s) of the data variables in the output dataframe. Defaults to `'scalars'` for scalar data, `['east_component', 'north_component']` for 2D vector data, and `['east_component', 'north_component', 'vertical_component']` for 3D vector data.

• projection (callable or None) – If not None, then should be a callable object `projection(easting, northing) -> (proj_easting, proj_northing)` that takes in easting and northing coordinate arrays and returns projected northing and easting coordinate arrays. This function will be used to project the generated scatter coordinates before passing them into `predict`. For example, you can use this to generate a geographic scatter from a Cartesian gridder.

Returns

table (pandas.DataFrame) – The interpolated values on a random set of points.

KNeighbors.score(coordinates, data, weights=None)#

Score the gridder predictions against the given data.

Calculates the R^2 coefficient of determination of between the predicted values and the given data values. A maximum score of 1 means a perfect fit. The score can be negative.

Warning

The default scoring will change from R² to negative root mean squared error (RMSE) in Verde 2.0.0. This may change model selection results slightly. The negative version will be used to maintain the behaviour of larger scores being better, which is more compatible with current model selection code.

If the data has more than 1 component, the scores of each component will be averaged.

Parameters
• coordinates (tuple of arrays) – Arrays with the coordinates of each data point. Should be in the following order: (easting, northing, vertical, …). For the specific definition of coordinate systems and what these names mean, see the class docstring.

• data (array or tuple of arrays) – The data values of each data point. If the data has more than one component, data must be a tuple of arrays (one for each component).

• weights (None or array or tuple of arrays) – If not None, then the weights assigned to each data point. If more than one data component is provided, you must provide a weights array for each data component (if not None).

Returns

score (float) – The R^2 score

KNeighbors.set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as `Pipeline`). The latter have parameters of the form `<component>__<parameter>` so that it’s possible to update each component of a nested object.

Parameters

**params (dict) – Estimator parameters.

Returns

self (estimator instance) – Estimator instance.