# that was not in the input dataset.
score = chain.score(*test)
print("\nScore: {:.3f}".format(score))
# Create a grid of the vertical velocity and mask it to only show points close to the
# actual data.
region = vd.get_region(coordinates)
grid_full = chain.grid(
region=region,
spacing=spacing,
projection=projection,
dims=["latitude", "longitude"],
data_names=["velocity"],
)
grid = vd.convexhull_mask(
(data.longitude, data.latitude), grid=grid_full, projection=projection
)
fig, axes = plt.subplots(
1, 2, figsize=(9, 7), subplot_kw=dict(projection=ccrs.Mercator())
)
crs = ccrs.PlateCarree()
# Plot the data uncertainties
ax = axes[0]
ax.set_title("Data uncertainty")
# Plot the uncertainties in mm/yr and using a power law for the color scale to highlight
# the smaller values
pc = ax.scatter(
*coordinates,
c=data.std_up * 1000,
s=20,
# we'll need to use some damping regularization or not use the data locations for the
# point forces. Here, we'll apply a bit of damping.
spline = vd.Chain([
# Convert the spacing to meters because Spline is a Cartesian gridder
("mean", vd.BlockMean(spacing=spacing * 111e3, uncertainty=True)),
("spline", vd.Spline(damping=1e-10)),
]).fit(proj_coords, data.velocity_up, data.weights)
grid = spline.grid(
region=region,
spacing=spacing,
projection=projection,
dims=["latitude", "longitude"],
data_names=["velocity"],
)
# Avoid showing interpolation outside of the convex hull of the data points.
grid = vd.convexhull_mask(coordinates, grid=grid, projection=projection)
########################################################################################
# Calculate an unweighted spline as well for comparison.
spline_unweighted = vd.Chain([
("mean", vd.BlockReduce(np.mean, spacing=spacing * 111e3)),
("spline", vd.Spline()),
]).fit(proj_coords, data.velocity_up)
grid_unweighted = spline_unweighted.grid(
region=region,
spacing=spacing,
projection=projection,
dims=["latitude", "longitude"],
data_names=["velocity"],
)
grid_unweighted = vd.convexhull_mask(coordinates,
# The Baja California bathymetry dataset has big gaps on land. We want to mask
# these gaps on a dummy grid that we'll generate over the region just to show
# what that looks like.
data = vd.datasets.fetch_baja_bathymetry()
region = vd.get_region((data.longitude, data.latitude))
# Generate the coordinates for a regular grid mask
spacing = 10 / 60
coordinates = vd.grid_coordinates(region, spacing=spacing)
# Generate a mask for points. The mask is True for points that are within the
# convex hull. We can provide a projection function to convert the coordinates
# before the convex hull is calculated (Mercator in this case).
mask = vd.convexhull_mask(
data_coordinates=(data.longitude, data.latitude),
coordinates=coordinates,
projection=pyproj.Proj(proj="merc", lat_ts=data.latitude.mean()),
)
print(mask)
# Create a dummy grid with ones that we can mask to show the results. Turn
# points that are outside of the convex hull into NaNs so they won't show up in
# our plot.
dummy_data = np.ones_like(coordinates[0])
dummy_data[~mask] = np.nan
# Make a plot of the masked data and the data locations.
crs = ccrs.PlateCarree()
plt.figure(figsize=(7, 6))
ax = plt.axes(projection=ccrs.Mercator())
ax.set_title("Only keep grid points that inside of the convex hull")
print(chain)
# Fit on the training data
chain.fit(*train)
# And score on the testing data. The best possible score is 1, meaning a perfect
# prediction of the test data.
score = chain.score(*test)
print("Cross-validation R^2 score: {:.2f}".format(score))
# Interpolate the wind speed onto a regular geographic grid and mask the data that are
# outside of the convex hull of the data points.
grid_full = chain.grid(region,
spacing=spacing,
projection=projection,
dims=["latitude", "longitude"])
grid = vd.convexhull_mask(coordinates, grid=grid_full, projection=projection)
# Make maps of the original and gridded wind speed
plt.figure(figsize=(6, 6))
ax = plt.axes(projection=ccrs.Mercator())
ax.set_title("Uncoupled spline gridding of wind speed")
tmp = ax.quiver(
grid.longitude.values,
grid.latitude.values,
grid.east_component.values,
grid.north_component.values,
width=0.0015,
scale=100,
color="tab:blue",
transform=ccrs.PlateCarree(),
label="Interpolated",
