How to use the verde.datasets.setup_baja_bathymetry_map function in verde

)
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.tight_layout()
plt.show()

(data.longitude, data.latitude), data.bathymetry_m
)
lon, lat = coordinates

print("Original data size:", data.bathymetry_m.size)
print("Decimated data size:", bathymetry.size)

# Make a plot of the decimated data using Cartopy
plt.figure(figsize=(7, 6))
ax = plt.axes(projection=ccrs.Mercator())
ax.set_title("10' Block Median Bathymetry")
# Plot the bathymetry as colored circles.
plt.scatter(lon, lat, c=bathymetry, s=5, transform=ccrs.PlateCarree())
plt.colorbar().set_label("meters")
# Use a utility function to setup the tick labels and land feature
vd.datasets.setup_baja_bathymetry_map(ax)
plt.tight_layout()
plt.show()

########################################################################################
# For example, our sample shipborne bathymetry data has a higher sampling frequency
# along the tracks than between tracks:

# Load the data as a pandas.DataFrame
data = vd.datasets.fetch_baja_bathymetry()

# Plot it using matplotlib and Cartopy
crs = ccrs.PlateCarree()
plt.figure(figsize=(7, 7))
ax = plt.axes(projection=ccrs.Mercator())
ax.set_title("Locations of bathymetry measurements from Baja California")
# Plot the bathymetry data locations as black dots
plt.plot(data.longitude, data.latitude, ".k", markersize=1, transform=crs)
vd.datasets.setup_baja_bathymetry_map(ax)
plt.tight_layout()
plt.show()

########################################################################################
# Class :class:`verde.BlockReduce` can be used to apply a reduction/aggregation
# operation (mean, median, standard deviation, etc) to the data in regular blocks. All
# data inside each block will be replaced by their aggregated value.
# :class:`~verde.BlockReduce` takes an aggregation function as input. It can be any
# function that receives a numpy array as input and returns a single scalar value. The
# :func:`numpy.mean` or :func:`numpy.median` functions are usually what we want.
import numpy as np

########################################################################################
# Blocked means and medians are good ways to decimate data for interpolation. Let's use
# a blocked median on our data to decimate it to our desired grid interval of 5
# arc-minutes. The reason for using a median over a mean is because bathymetry data can

# geographic data.
crs = ccrs.PlateCarree()

plt.figure(figsize=(7, 6))
# Make a Mercator map of our gridded bathymetry
ax = plt.axes(projection=ccrs.Mercator())
ax.set_title("Gridded Bathymetry Using Scipy")
# Plot the gridded bathymetry
pc = grid.bathymetry_m.plot.pcolormesh(
    ax=ax, transform=crs, vmax=0, zorder=-1, add_colorbar=False
)
plt.colorbar(pc).set_label("meters")
# Plot the locations of the decimated data
ax.plot(*coordinates, ".k", markersize=0.5, transform=crs)
# Use an utility function to setup the tick labels and the land feature
vd.datasets.setup_baja_bathymetry_map(ax)
plt.tight_layout()
plt.show()

grid_geo = vd.distance_mask(
    (data.longitude, data.latitude), maxdist=30e3, grid=grid_geo, projection=projection
)

########################################################################################
# Now we can use the Cartopy library to plot our geographic grid.

plt.figure(figsize=(7, 6))
ax = plt.axes(projection=ccrs.Mercator())
ax.set_title("Geographic grid of bathymetry")
pc = grid_geo.bathymetry.plot.pcolormesh(
    ax=ax, transform=ccrs.PlateCarree(), vmax=0, zorder=-1, add_colorbar=False
)
plt.colorbar(pc).set_label("meters")
vd.datasets.setup_baja_bathymetry_map(ax, land=None)
plt.tight_layout()
plt.show()

)
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.tight_layout()
plt.show()

# Make a Mercator map of the data using Cartopy
plt.figure(figsize=(7, 6))
ax = plt.axes(projection=ccrs.Mercator())
ax.set_title("Bathymetry from Baja California")
# Plot the bathymetry as colored circles. Cartopy requires setting the projection of the
# original data through the transform argument. Use PlateCarree for geographic data.
plt.scatter(
    data.longitude,
    data.latitude,
    c=data.bathymetry_m,
    s=0.1,
    transform=ccrs.PlateCarree(),
)
plt.colorbar().set_label("meters")
# Use an utility function to add tick labels and land and ocean features to the map.
vd.datasets.setup_baja_bathymetry_map(ax)
plt.tight_layout()
plt.show()

(data.longitude, data.latitude), data.bathymetry_m
)
lon, lat = coordinates

print("Original data size:", data.bathymetry_m.size)
print("Decimated data size:", bathymetry.size)

# Make a plot of the decimated data using Cartopy
plt.figure(figsize=(7, 6))
ax = plt.axes(projection=ccrs.Mercator())
ax.set_title("10' Block Median Bathymetry")
# Plot the bathymetry as colored circles.
plt.scatter(lon, lat, c=bathymetry, s=5, transform=ccrs.PlateCarree())
plt.colorbar().set_label("meters")
# Use a utility function to setup the tick labels and land feature
vd.datasets.setup_baja_bathymetry_map(ax)
plt.tight_layout()
plt.show()