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Vector Data

Note

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Vector Data#

Some datasets have multiple vector components measured for each location, like the East and West components of wind speed or GPS velocities. For example, let’s look at our sample GPS velocity data from the U.S. West coast.

import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import pyproj

import verde as vd

data = vd.datasets.fetch_california_gps()

# We'll need to project the geographic coordinates to work with our Cartesian
# classes/functions
projection = pyproj.Proj(proj="merc", lat_ts=data.latitude.mean())
proj_coords = projection(data.longitude.values, data.latitude.values)
# This will be our desired grid spacing in degrees
spacing = 12 / 60

plt.figure(figsize=(6, 8))
ax = plt.axes(projection=ccrs.Mercator())
crs = ccrs.PlateCarree()
tmp = ax.quiver(
    data.longitude.values,
    data.latitude.values,
    data.velocity_east.values,
    data.velocity_north.values,
    scale=0.3,
    transform=crs,
    width=0.002,
)
ax.quiverkey(tmp, 0.2, 0.15, 0.05, label="0.05 m/yr", coordinates="figure")
ax.set_title("GPS horizontal velocities")
vd.datasets.setup_california_gps_map(ax)
plt.show()
GPS horizontal velocities

Verde classes and functions are equipped to deal with vector data natively or through the use of the verde.Vector class. Function and classes that can take vector data as input will accept tuples as the data and weights arguments. Each element of the tuple must be an array with the data values for a component of the vector data. As with coordinates, the order of components must be (east_component, north_component, up_component).

Blocked reductions#

Operations with verde.BlockReduce and verde.BlockMean can handle multi-component data automatically. The reduction operation is applied to each data component separately. The blocked data and weights will be returned in tuples as well following the same ordering as the inputs. This will work for an arbitrary number of components.

# Use a blocked mean with uncertainty type weights
reducer = vd.BlockMean(spacing=spacing * 111e3, uncertainty=True)
block_coords, block_data, block_weights = reducer.filter(
    coordinates=proj_coords,
    data=(data.velocity_east, data.velocity_north),
    weights=(1 / data.std_east**2, 1 / data.std_north**2),
)
print(len(block_data), len(block_weights))

/usr/share/miniconda/envs/test/lib/python3.12/site-packages/verde/base/utils.py:254: FutureWarning: Series.ravel is deprecated. The underlying array is already 1D, so ravel is not necessary.  Use `to_numpy()` for conversion to a numpy array instead.
  weights = tuple(i.ravel() for i in weights)
/usr/share/miniconda/envs/test/lib/python3.12/site-packages/verde/blockreduce.py:417: FutureWarning: Series.ravel is deprecated. The underlying array is already 1D, so ravel is not necessary.  Use `to_numpy()` for conversion to a numpy array instead.
  columns = {"data{}".format(i): comp.ravel() for i, comp in enumerate(data)}
2 2

We can convert the blocked coordinates back to longitude and latitude to plot with Cartopy.

block_lon, block_lat = projection(*block_coords, inverse=True)

plt.figure(figsize=(6, 8))
ax = plt.axes(projection=ccrs.Mercator())
crs = ccrs.PlateCarree()
tmp = ax.quiver(
    block_lon,
    block_lat,
    block_data[0],
    block_data[1],
    scale=0.3,
    transform=crs,
    width=0.002,
)
ax.quiverkey(tmp, 0.2, 0.15, 0.05, label="0.05 m/yr", coordinates="figure")
ax.set_title("Block mean velocities")
vd.datasets.setup_california_gps_map(ax)
plt.show()
Block mean velocities

Gridding#

You can use verde.Vector to create multi-component gridders out of verde.Spline the same way as we did for trends. In this case, each component is treated separately.

We can start by splitting the data into training and testing sets (see Model Selection). Notice that verde.train_test_split work for multicomponent data automatically.

train, test = vd.train_test_split(
    coordinates=proj_coords,
    data=(data.velocity_east, data.velocity_north),
    weights=(1 / data.std_east**2, 1 / data.std_north**2),
    random_state=1,
)

/usr/share/miniconda/envs/test/lib/python3.12/site-packages/verde/base/utils.py:254: FutureWarning: Series.ravel is deprecated. The underlying array is already 1D, so ravel is not necessary.  Use `to_numpy()` for conversion to a numpy array instead.
  weights = tuple(i.ravel() for i in weights)
/usr/share/miniconda/envs/test/lib/python3.12/site-packages/verde/model_selection.py:820: FutureWarning: Series.ravel is deprecated. The underlying array is already 1D, so ravel is not necessary.  Use `to_numpy()` for conversion to a numpy array instead.
  return tuple(i.ravel()[index] for i in arrays)

Now we can make a 2-component spline. Since verde.Vector implements fit, predict, and filter, we can use it in a verde.Chain to build a pipeline.

We need to use a bit of damping so that the weights can be taken into account. Splines without damping provide a perfect fit to the data and ignore the weights as a consequence.

chain = vd.Chain(
    [
        ("mean", vd.BlockMean(spacing=spacing * 111e3, uncertainty=True)),
        ("trend", vd.Vector([vd.Trend(1), vd.Trend(1)])),
        ("spline", vd.Vector([vd.Spline(damping=1e-10), vd.Spline(damping=1e-10)])),
    ]
)
print(chain)

Chain(steps=[('mean', BlockMean(spacing=22200.0, uncertainty=True)),
             ('trend', Vector(components=[Trend(degree=1), Trend(degree=1)])),
             ('spline',
              Vector(components=[Spline(damping=1e-10, mindist=0),
                                 Spline(damping=1e-10, mindist=0)]))])

Warning

Never generate the component gridders with [vd.Spline()]*2. This will result in each component being a represented by the same Spline object, causing problems when trying to fit it to different components.

Fitting the spline and gridding is exactly the same as what we’ve done before.

chain.fit(*train)

# Score on the test data
print(chain.score(*test))

grid = chain.grid(
    region=region,
    spacing=spacing,
    projection=projection,
    dims=["latitude", "longitude"],
)
print(grid)

/home/runner/work/verde/verde/doc/tutorials_src/vectors.py:252: FutureWarning: The default scoring will change from R² to negative root mean squared error (RMSE) in Verde 2.0.0. This may change model selection results slightly.
  print(chain.score(*test))
0.9926763528286442
<xarray.Dataset> Size: 38kB
Dimensions:          (latitude: 49, longitude: 47)
Coordinates:
  * longitude        (longitude) float64 376B 235.7 235.9 236.1 ... 244.8 245.0
  * latitude         (latitude) float64 392B 32.29 32.49 32.69 ... 41.7 41.9
Data variables:
    east_component   (latitude, longitude) float64 18kB -0.0466 ... -0.0006166
    north_component  (latitude, longitude) float64 18kB 0.08795 ... -0.0006054
Attributes:
    metadata:  Generated by Chain(steps=[('mean', BlockMean(spacing=22200.0, ...

Mask out the points too far from data and plot the gridded vectors.

grid = vd.distance_mask(
    (data.longitude, data.latitude),
    maxdist=spacing * 2 * 111e3,
    grid=grid,
    projection=projection,
)

plt.figure(figsize=(6, 8))
ax = plt.axes(projection=ccrs.Mercator())
tmp = ax.quiver(
    grid.longitude.values,
    grid.latitude.values,
    grid.east_component.values,
    grid.north_component.values,
    scale=0.3,
    transform=crs,
    width=0.002,
)
ax.quiverkey(tmp, 0.2, 0.15, 0.05, label="0.05 m/yr", coordinates="figure")
ax.set_title("Gridded velocities")
vd.datasets.setup_california_gps_map(ax)
plt.show()
Gridded velocities

GPS/GNSS data#

For some types of vector data, like GPS/GNSS displacements, the vector components are coupled through elasticity. In these cases, elastic Green’s functions can be used to achieve better interpolation results. The verde.VectorSpline2D implements these Green’s functions.

Total running time of the script: (0 minutes 1.270 seconds)

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