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

data = vd.datasets.fetch_texas_wind()
coordinates = (data.longitude, data.latitude)

plt.figure(figsize=(8, 6))
ax = plt.axes(projection=ccrs.Mercator())
plt.scatter(
    data.longitude,
    data.latitude,
    c=data.air_temperature_c,
    s=100,
    cmap="plasma",
    transform=ccrs.PlateCarree(),
)
plt.colorbar().set_label("Air temperature (C)")
vd.datasets.setup_texas_wind_map(ax)
plt.show()

########################################################################################
# We can estimate the polynomial coefficients for this trend:

trend = vd.Trend(degree=1).fit(coordinates, data.air_temperature_c)
print(trend.coef_)

########################################################################################
# More importantly, we can predict the trend values and remove them from our data:

trend_values = trend.predict(coordinates)
residuals = data.air_temperature_c - trend_values

fig, axes = plt.subplots(
    1, 2, figsize=(10, 6), subplot_kw=dict(projection=ccrs.Mercator())

ax = plt.subplot(1, 2, i + 1, projection=ccrs.Mercator())
    ax.set_title(title)
    pc = grd.temperature.plot.pcolormesh(
        ax=ax,
        cmap="plasma",
        transform=ccrs.PlateCarree(),
        vmin=data.air_temperature_c.min(),
        vmax=data.air_temperature_c.max(),
        add_colorbar=False,
        add_labels=False,
    )
    plt.colorbar(pc, orientation="horizontal", aspect=50, pad=0.05).set_label("C")
    ax.plot(
        data.longitude, data.latitude, ".k", markersize=1, transform=ccrs.PlateCarree()
    )
    vd.datasets.setup_texas_wind_map(ax)
plt.tight_layout()
plt.show()

c=data.air_temperature_c,
    s=100,
    cmap="plasma",
    transform=ccrs.PlateCarree(),
)
plt.colorbar().set_label("Air temperature (C)")
ax.quiver(
    data.longitude.values,
    data.latitude.values,
    data.wind_speed_east_knots.values,
    data.wind_speed_north_knots.values,
    width=0.003,
    transform=ccrs.PlateCarree(),
)
# Use an utility function to add tick labels and land and ocean features to the map.
vd.datasets.setup_texas_wind_map(ax)
plt.tight_layout()
plt.show()

)
grid = vd.distance_mask(
    coordinates, maxdist=3 * spacing * 111e3, grid=grid_full, projection=projection
)

# Plot the grid and the original data points
plt.figure(figsize=(8, 6))
ax = plt.axes(projection=ccrs.Mercator())
ax.set_title("Air temperature gridded with biharmonic spline")
ax.plot(*coordinates, ".k", markersize=1, transform=ccrs.PlateCarree())
tmp = grid.temperature.plot.pcolormesh(
    ax=ax, cmap="plasma", transform=ccrs.PlateCarree(), add_colorbar=False
)
plt.colorbar(tmp).set_label("Air temperature (C)")
# Use an utility function to add tick labels and land and ocean features to the map.
vd.datasets.setup_texas_wind_map(ax, region=region)
plt.tight_layout()
plt.show()

ax = plt.subplot(2, 2, i, projection=ccrs.Mercator())
    ax.set_title(title)
    # Set vmin and vmax to the extremes of the original data
    maxabs = vd.maxabs(data.air_temperature_c)
    mappable = ax.scatter(
        data.longitude,
        data.latitude,
        c=data[column],
        s=50,
        cmap="seismic",
        vmin=-maxabs,
        vmax=maxabs,
        transform=crs,
    )
    # Set the proper ticks for a Cartopy map
    vd.datasets.setup_texas_wind_map(ax)
    return mappable

ax = plt.subplot(1, 2, i + 1, projection=ccrs.Mercator())
    ax.set_title(title)
    pc = grd.temperature.plot.pcolormesh(
        ax=ax,
        cmap="plasma",
        transform=ccrs.PlateCarree(),
        vmin=data.air_temperature_c.min(),
        vmax=data.air_temperature_c.max(),
        add_colorbar=False,
        add_labels=False,
    )
    plt.colorbar(pc, orientation="horizontal", aspect=50, pad=0.05).set_label("C")
    ax.plot(
        data.longitude, data.latitude, ".k", markersize=1, transform=ccrs.PlateCarree()
    )
    vd.datasets.setup_texas_wind_map(ax)
plt.tight_layout()
plt.show()

)
grid = grid.where(mask)

plt.figure(figsize=(8, 6))
ax = plt.axes(projection=ccrs.Mercator())
ax.set_title("Gridded temperature")
pc = grid.temperature.plot.pcolormesh(
    ax=ax,
    cmap="plasma",
    transform=ccrs.PlateCarree(),
    add_colorbar=False,
    add_labels=False,
)
plt.colorbar(pc).set_label("C")
ax.plot(data.longitude, data.latitude, ".k", markersize=1, transform=ccrs.PlateCarree())
vd.datasets.setup_texas_wind_map(ax)
plt.tight_layout()
plt.show()

########################################################################################
# Scoring
# --------
#
# Gridders in Verde implement the :meth:`~verde.base.BaseGridder.score` method that
# calculates the `R² coefficient of determination
# `__
# for a given comparison dataset (``test`` in our case). The R² score is at most 1,
# meaning a perfect prediction, but has no lower bound.

score = spline.score(*test)
print("R² score:", score)