How to use the verde.maxabs function in verde

# the smaller values
pc = ax.scatter(
    *coordinates,
    c=data.std_up * 1000,
    s=20,
    cmap="magma",
    transform=crs,
    norm=PowerNorm(gamma=1 / 2)
)
cb = plt.colorbar(pc, ax=ax, orientation="horizontal", pad=0.05)
cb.set_label("uncertainty [mm/yr]")
vd.datasets.setup_california_gps_map(ax, region=region)
# Plot the gridded velocities
ax = axes[1]
ax.set_title("Weighted spline interpolated velocity")
maxabs = vd.maxabs(data.velocity_up) * 1000
pc = (grid.velocity * 1000).plot.pcolormesh(
    ax=ax,
    cmap="seismic",
    vmin=-maxabs,
    vmax=maxabs,
    transform=crs,
    add_colorbar=False,
    add_labels=False,
)
cb = plt.colorbar(pc, ax=ax, orientation="horizontal", pad=0.05)
cb.set_label("vertical velocity [mm/yr]")
ax.scatter(*coordinates, c="black", s=0.5, alpha=0.1, transform=crs)
vd.datasets.setup_california_gps_map(ax, region=region)
ax.coastlines()
plt.tight_layout()
plt.show()

# Plot the horizontal velocity vectors
ax = axes[0]
ax.set_title("GPS horizontal velocities")
ax.quiver(
    data.longitude.values,
    data.latitude.values,
    data.velocity_east.values,
    data.velocity_north.values,
    scale=0.3,
    transform=crs,
)
vd.datasets.setup_california_gps_map(ax)
# Plot the vertical velocity
ax = axes[1]
ax.set_title("Vertical velocity")
maxabs = vd.maxabs(data.velocity_up)
tmp = ax.scatter(
    data.longitude,
    data.latitude,
    c=data.velocity_up,
    s=10,
    vmin=-maxabs / 3,
    vmax=maxabs / 3,
    cmap="seismic",
    transform=crs,
)
plt.colorbar(tmp, ax=ax).set_label("meters/year")
vd.datasets.setup_california_gps_map(ax)
plt.tight_layout(w_pad=0)
plt.show()

# Now we can block average the points with and without weights to compare the outputs.
reducer = vd.BlockReduce(reduction=np.average, spacing=30 / 60, center_coordinates=True)
coordinates, no_weights = reducer.filter(
    (data.longitude, data.latitude), data.velocity_up
)
__, with_weights = reducer.filter(
    (data.longitude, data.latitude), data.velocity_up, weights
)

# Now we can plot the data sets side by side on Mercator maps
fig, axes = plt.subplots(
    1, 2, figsize=(9, 7), subplot_kw=dict(projection=ccrs.Mercator())
)
titles = ["No Weights", "Weights"]
crs = ccrs.PlateCarree()
maxabs = vd.maxabs(data.velocity_up)
for ax, title, velocity in zip(axes, titles, (no_weights, with_weights)):
    ax.set_title(title)
    # Plot the locations of the outliers
    ax.plot(
        data.longitude[outliers],
        data.latitude[outliers],
        "xk",
        transform=crs,
        label="Outliers",
    )
    # Plot the block means and saturate the colorbar a bit to better show the
    # differences in the data.
    pc = ax.scatter(
        *coordinates,
        c=velocity,
        s=70,

print("\nGridded 2-component trend:")
print(grid)


# Now we can map both trends along with the original data for comparison
fig, axes = plt.subplots(
    1, 2, figsize=(9, 7), subplot_kw=dict(projection=ccrs.Mercator())
)
crs = ccrs.PlateCarree()
# Plot the two trend components
titles = ["East component trend", "North component trend"]
components = [grid.east_component, grid.north_component]
for ax, component, title in zip(axes, components, titles):
    ax.set_title(title)
    # Plot the trend in pseudo color
    maxabs = vd.maxabs(component)
    tmp = component.plot.pcolormesh(
        ax=ax,
        vmin=-maxabs,
        vmax=maxabs,
        cmap="seismic",
        transform=crs,
        add_colorbar=False,
        add_labels=False,
    )
    cb = plt.colorbar(tmp, ax=ax, orientation="horizontal", pad=0.05)
    cb.set_label("meters/year")
    # Plot the original data
    ax.quiver(
        data.longitude.values,
        data.latitude.values,
        data.velocity_east.values,

def plot_data(coordinates, velocity, weights, title_data, title_weights):
    "Make two maps of our data, one with the data and one with the weights/uncertainty"
    fig, axes = plt.subplots(
        1, 2, figsize=(9.5, 7), subplot_kw=dict(projection=ccrs.Mercator())
    )
    crs = ccrs.PlateCarree()
    ax = axes[0]
    ax.set_title(title_data)
    maxabs = vd.maxabs(velocity)
    pc = ax.scatter(
        *coordinates,
        c=velocity,
        s=30,
        cmap="seismic",
        vmin=-maxabs,
        vmax=maxabs,
        transform=crs,
    )
    plt.colorbar(pc, ax=ax, orientation="horizontal", pad=0.05).set_label("m/yr")
    vd.datasets.setup_california_gps_map(ax)
    ax = axes[1]
    ax.set_title(title_weights)
    pc = ax.scatter(
        *coordinates, c=weights, s=30, cmap="magma", transform=crs, norm=LogNorm()
    )

ax = axes[0]
ax.set_title("Trend")
tmp = ax.scatter(
    data.longitude,
    data.latitude,
    c=trend_values,
    s=60,
    cmap="plasma",
    transform=ccrs.PlateCarree(),
)
plt.colorbar(tmp, ax=ax, orientation="horizontal", pad=0.06)
vd.datasets.setup_texas_wind_map(ax)

ax = axes[1]
ax.set_title("Residuals")
maxabs = vd.maxabs(residuals)
tmp = ax.scatter(
    data.longitude,
    data.latitude,
    c=residuals,
    s=60,
    cmap="bwr",
    vmin=-maxabs,
    vmax=maxabs,
    transform=ccrs.PlateCarree(),
)
plt.colorbar(tmp, ax=ax, orientation="horizontal", pad=0.08)
vd.datasets.setup_texas_wind_map(ax)
plt.tight_layout()
plt.show()

########################################################################################

print("\nGridded 2-component trend:")
print(grid)


# Now we can map both trends along with the original data for comparison
fig, axes = plt.subplots(
    1, 2, figsize=(9, 7), subplot_kw=dict(projection=ccrs.Mercator())
)
crs = ccrs.PlateCarree()
# Plot the two trend components
titles = ["East component trend", "North component trend"]
components = [grid.east_component, grid.north_component]
for ax, component, title in zip(axes, components, titles):
    ax.set_title(title)
    # Plot the trend in pseudo color
    maxabs = vd.maxabs(component)
    tmp = component.plot.pcolormesh(
        ax=ax,
        vmin=-maxabs,
        vmax=maxabs,
        cmap="seismic",
        transform=crs,
        add_colorbar=False,
        add_labels=False,
    )
    cb = plt.colorbar(tmp, ax=ax, orientation="horizontal", pad=0.05)
    cb.set_label("meters/year")
    # Plot the original data
    ax.quiver(
        data.longitude.values,
        data.latitude.values,
        data.velocity_east.values,

def plot_data(column, i, title):
    "Plot the column from the DataFrame in the ith subplot"
    crs = ccrs.PlateCarree()
    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.total_field_anomaly_nt)
    mappable = ax.scatter(
        data.longitude,
        data.latitude,
        c=data[column],
        s=1,
        cmap="seismic",
        vmin=-maxabs,
        vmax=maxabs,
        transform=crs,
    )
    # Set the proper ticks for a Cartopy map
    vd.datasets.setup_rio_magnetic_map(ax)
    return mappable