text: 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.
  Out:
  [[ 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]] /usr/share/miniconda3/envs/test/lib/python3.9/site-packages/verde/datasets/sample_data.py:152: UserWarning: All kwargs are being ignored. They are accepted to guarantee backward compatibility. warnings.warn(
  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()
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