.1 the relevance of the Generalized Extreme Value distribution to extreme events, this distribution to observation and model data, and the fit. [t3,t4,t5,t6,t7]
.2 howthe moments and parameters of the Generalized Extreme Value distribution vary with time. [t1,t3,t5,t7]
.3 extreme event probabilities (return periods/levels). [t1,t2,t4,t6,t7,t8]
.4 extreme events (e.g. precipitation, sea level height, and heat) by these probabilities and prescribed thresholds. [t2,t5,t7,t8]
cnt = 0
import time
n_sample = 100
t_sample = np.zeros(n_sample)
for j in range(n_sample):
t = time.process_time()
for i in range(250):
law_ns.fit(data.ssh.values, c_loc=np.arange(data.index.size))
if law_ns.coef_[0] != 0:
cnt+=1
nonzero = law_ns.coef_
break
elapsed_time = time.process_time() - t
t_sample[j] = elapsed_time
print(f'This took: {elapsed_time:.2f} seconds.')
print(cnt)
print(nonzero)
sns.histplot(t_sample)