%matplotlib widget
import matplotlib.pyplot as plt
plt.ioff()
# turn of warnings
import warnings
warnings.filterwarnings('ignore')In this example we'll create a time series of a 2D synthetic precipitation field.
Very similar to the previous tutorial, we'll start off by creating a Gaussian random field with an exponential variogram, which seems to reproduce the spatial correlations of precipitation fields quite well. We'll create a daily timeseries over a two dimensional domain of 50km x 40km. This workflow is suited for sub daily precipitation time series.
import matplotlib.animation as animation
import numpy as np
import gstools as gs
# fix the seed for reproducibility
seed = 20170521
# 1st spatial axis of 50km with a resolution of 1km
x = np.arange(0, 50, 1.0)
# 2nd spatial axis of 40km with a resolution of 1km
y = np.arange(0, 40, 1.0)
# half daily timesteps over three months
t = np.arange(0.0, 90.0, 0.5)
# total spatio-temporal dimension
st_dim = 2 + 1
# space-time anisotropy ratio given in units d / km
st_anis = 0.4
# an exponential variogram with a corr. lengths of 5km, 5km, and 2d
model = gs.Exponential(dim=st_dim, var=1.0, len_scale=5.0, anis=st_anis)
# create a spatial random field instance
srf = gs.SRF(model, seed=seed)
pos, time = [x, y], [t]
# the Gaussian random field
srf.structured(pos + time)
# account for the skewness and the dry periods
cutoff = 0.55
gs.transform.boxcox(srf, lmbda=0.5, shift=-1.0 / cutoff)
# adjust the amount of precipitation
amount = 4.0
srf.field *= amountplot the 2d precipitation field over time as an animation.
def _update_ani(time_step):
im.set_array(srf.field[:, :, time_step].T)
return (im,)
fig, ax = plt.subplots()
im = ax.imshow(
srf.field[:, :, 0].T,
cmap="Blues",
interpolation="bicubic",
origin="lower",
)
cbar = fig.colorbar(im)
cbar.ax.set_ylabel(r"Precipitation $P$ / mm")
ax.set_xlabel(r"$x$ / km")
ax.set_ylabel(r"$y$ / km")
ani = animation.FuncAnimation(
fig, _update_ani, len(t), interval=100, blit=True
)
fig.show()Loading...