# API Reference¶

## Interpolators¶

 Spline([mindist, damping, force_coords, engine]) Biharmonic spline interpolation using Green’s functions. SplineCV([mindists, dampings, force_coords, …]) Cross-validated biharmonic spline interpolation. VectorSpline2D([poisson, mindist, damping, …]) Elastically coupled interpolation of 2-component vector data. ScipyGridder([method, extra_args]) A scipy.interpolate based gridder for scalar Cartesian data.

## Data Processing¶

 BlockReduce(reduction[, spacing, region, …]) Apply a reduction/aggregation operation to the data in blocks/windows. BlockMean([spacing, region, adjust, …]) Apply a (weighted) mean to the data in blocks/windows. Trend(degree) Fit a 2D polynomial trend to spatial data.

## Composite Estimators¶

 Chain(steps) Chain filtering operations to fit on each subsequent output. Vector(components) Fit an estimator to each component of multi-component vector data.

## Model Selection¶

 train_test_split(coordinates, data[, …]) Split a dataset into a training and a testing set for cross-validation. cross_val_score(estimator, coordinates, data) Score an estimator/gridder using cross-validation. BlockShuffleSplit([spacing, shape, …]) Random permutation of spatial blocks cross-validator. BlockKFold([spacing, shape, n_splits, …]) K-Folds over spatial blocks cross-validator.

## Coordinate Manipulation¶

 grid_coordinates(region[, shape, spacing, …]) Generate the coordinates for each point on a regular grid. scatter_points(region, size[, random_state, …]) Generate the coordinates for a random scatter of points. profile_coordinates(point1, point2, size[, …]) Coordinates for a profile along a straight line between two points. get_region(coordinates) Get the bounding region of the given coordinates. pad_region(region, pad) Extend the borders of a region by the given amount. inside(coordinates, region) Determine which points fall inside a given region. block_split(coordinates[, spacing, adjust, …]) Split a region into blocks and label points according to where they fall. rolling_window(coordinates, size[, spacing, …]) Select points on a rolling (moving) window. expanding_window(coordinates, center, sizes) Select points on windows of changing size around a center point.

## Projection¶

 project_region(region, projection) Calculate the bounding box of a region in projected coordinates. project_grid(grid, projection[, method, …]) Apply the given map projection to a grid and re-sample it.

 distance_mask(data_coordinates, maxdist[, …]) Mask grid points that are too far from the given data points. convexhull_mask(data_coordinates[, …]) Mask grid points that are outside the convex hull of the given data points.

## Utilities¶

 test([doctest, verbose, coverage, figures]) Run the test suite. maxabs(*args[, nan]) Calculate the maximum absolute value of the given array(s). variance_to_weights(variance[, tol, dtype]) Converts data variances to weights for gridding. grid_to_table(grid) Convert a grid to a table with the values and coordinates of each point. median_distance(coordinates[, k_nearest, …]) Median distance between the k nearest neighbors of each point.

## Input/Output¶

 load_surfer(fname[, dtype]) Read data from a Surfer ASCII grid file.

## Datasets¶

 The absolute path to the sample data storage location on disk. datasets.CheckerBoard([amplitude, region, …]) Generate synthetic data in a checkerboard pattern. Fetch sample bathymetry data from Baja California. Setup a Cartopy map for the Baja California bathymetry dataset. Fetch sample GPS velocity data from California (the U.S. Setup a Cartopy map for the California GPS velocity dataset. Fetch sample wind speed and air temperature data for Texas, USA. datasets.setup_texas_wind_map(ax[, region, …]) Setup a Cartopy map for the Texas wind speed and air temperature dataset. Fetch total-field magnetic anomaly data from Rio de Janeiro, Brazil. datasets.setup_rio_magnetic_map(ax[, region]) Setup a Cartopy map for the Rio de Janeiro magnetic anomaly dataset.

## Base Classes and Functions¶

 base.BaseGridder Base class for gridders. base.BaseBlockCrossValidator([spacing, …]) Base class for spatially blocked cross-validators. base.n_1d_arrays(arrays, n) Get the first n elements from a tuple/list, convert to arrays, and ravel. base.check_fit_input(coordinates, data, weights) Validate the inputs to the fit method of gridders. base.least_squares(jacobian, data, weights) Solve a weighted least-squares problem with optional damping regularization