Mask grid points by distance

Mask grid points by distance#

Sometimes, data points are unevenly distributed. In such cases, we might not want to have interpolated grid points that are too far from any data point. Function verde.distance_mask allows us to set such points to NaN or some other value.

Only keep grid points that are close to data
[[ True  True  True ...  True  True  True]
 [ True  True  True ...  True  True  True]
 [ True  True  True ...  True  True  True]
 ...
 [False False False ... False False False]
 [False False False ... False False False]
 [False False False ... False False False]]
/home/runner/work/verde/verde/doc/gallery_src/distance_mask.py:58: UserWarning: All kwargs are being ignored. They are accepted to guarantee backward compatibility.
  vd.datasets.setup_baja_bathymetry_map(ax, land=None)

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

import verde as vd

# 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.
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 that are more than 2 grid spacings away from any
# data point. The mask is True for points that are within the maximum distance.
# Distance calculations in the mask are Cartesian only. We can provide a
# projection function to convert the coordinates before distances are
# calculated (Mercator in this case). In this case, the maximum distance is
# also Cartesian and must be converted from degrees to meters.
mask = vd.distance_mask(
    (data.longitude, data.latitude),
    maxdist=spacing * 2 * 111e3,
    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 too far 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 are close to data")
ax.plot(data.longitude, data.latitude, ".y", markersize=0.5, transform=crs)
ax.pcolormesh(*coordinates, dummy_data, transform=crs)
vd.datasets.setup_baja_bathymetry_map(ax, land=None)
plt.show()

Total running time of the script: (0 minutes 2.795 seconds)

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