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[, weights]) |
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. |
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. |
project_region(region, projection) |
Calculate the bounding box of a region in projected coordinates. |
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. |
Utilities¶
test([doctest, verbose, coverage, figures]) |
Run the test suite. |
maxabs(\*args[, nan]) |
Calculate the maximum absolute value of the given array(s). |
distance_mask(data_coordinates, maxdist[, …]) |
Mask grid points that are too far from the given data points. |
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.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 the state of 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 sample 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.n_1d_arrays(arrays, n) |
Get the first n elements from a tuple/list, make sure they are 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. |