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.

Masking

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

datasets.locate()	The absolute path to the sample data storage location on disk.
datasets.CheckerBoard([amplitude, region, …])	Generate synthetic data in a checkerboard pattern.
datasets.fetch_baja_bathymetry()	Fetch sample bathymetry data from Baja California.
datasets.setup_baja_bathymetry_map(ax[, …])	Setup a Cartopy map for the Baja California bathymetry dataset.
datasets.fetch_california_gps()	Fetch sample GPS velocity data from California (the U.S.
datasets.setup_california_gps_map(ax[, …])	Setup a Cartopy map for the California GPS velocity dataset.
datasets.fetch_texas_wind()	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.
datasets.fetch_rio_magnetic()	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