API Reference¶
Interpolators¶
Spline([mindist, damping, force_coords, engine]) |
Biharmonic spline interpolation using Green’s functions. |
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 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[, out, tmp]) |
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¶
distance_mask(data_coordinates, maxdist[, …]) |
Mask grid points that are too far from the given data points. |
maxabs(*args) |
Calculate the maximum absolute value of the given array(s). |
variance_to_weights(variance[, tol, dtype]) |
Converts data variances to weights for gridding. |
test([doctest, verbose, coverage, figures]) |
Run the test suite. |
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¶
base.BaseGridder |
Base class for gridders. |