[DEPRECATED] GPS velocities from California
Note
Click here to download the full example code
[DEPRECATED] GPS velocities from CaliforniaΒΆ
Warning
All sample datasets in Verde are deprecated and will be removed in Verde v2.0.0. The tutorials/examples will transition to using Ensaio instead.
We provide sample 3-component GPS velocity data from the West coast of the U.S. The data were cut from EarthScope Plate Boundary Observatory data provided by UNAVCO. The velocities are in the North American tectonic plate reference system (NAM08). The velocities and their associated standard deviations are in meters/year.
Out:
latitude longitude height ... std_north std_east std_up
0 34.116409 242.906804 762.11978 ... 0.0002 0.00037 0.00053
1 34.116409 242.906804 762.10883 ... 0.0002 0.00037 0.00053
2 34.116409 242.906805 762.09364 ... 0.0002 0.00037 0.00053
3 34.116409 242.906805 762.09073 ... 0.0002 0.00037 0.00053
4 34.116409 242.906805 762.07699 ... 0.0002 0.00037 0.00053
[5 rows x 9 columns]
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import verde as vd
# The data are in a pandas.DataFrame
data = vd.datasets.fetch_california_gps()
print(data.head())
# Make a plot of the data using Cartopy to handle projections and coastlines
crs = ccrs.PlateCarree()
fig, axes = plt.subplots(
1, 2, figsize=(8, 4), subplot_kw=dict(projection=ccrs.Mercator())
)
# 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.show()
Total running time of the script: ( 0 minutes 0.295 seconds)