def test_est_theis(self):
campaign = wtp.load_campaign("Cmp_UFZ-campaign.cmp")
estimation = wtp.estimate.Theis("est_theis", campaign, generate=True)
estimation.run()
res = estimation.estimated_para
estimation.sensitivity()
self.assertAlmostEqual(np.exp(res["mu"]), self.transmissivity, 2)
self.assertAlmostEqual(np.exp(res["lnS"]), self.storage, 2)
sens = estimation.sens
for s_typ in self.s_types:
self.assertTrue(sens[s_typ]["mu"] > sens[s_typ]["lnS"])
def test_est_thiem(self):
campaign = wtp.load_campaign("Cmp_UFZ-campaign.cmp")
estimation = wtp.estimate.Thiem("est_thiem", campaign, generate=True)
estimation.run()
res = estimation.estimated_para
# since we only have one parameter,
# we need a dummy parameter to estimate sensitivity
estimation.gen_setup(dummy=True)
estimation.sensitivity()
self.assertAlmostEqual(np.exp(res["mu"]), self.transmissivity, 2)
sens = estimation.sens
for s_typ in self.s_types:
self.assertTrue(sens[s_typ]["mu"] > sens[s_typ]["dummy"])
def test_est_ext_thiem2D(self):
campaign = wtp.load_campaign("Cmp_UFZ-campaign.cmp")
estimation = wtp.estimate.ExtThiem2D("est_ext_thiem2D",
campaign,
generate=True)
estimation.run()
res = estimation.estimated_para
estimation.sensitivity()
self.assertAlmostEqual(np.exp(res["mu"]), self.transmissivity, 2)
self.assertAlmostEqual(res["var"], 0.0, 0)
sens = estimation.sens
for s_typ in self.s_types:
self.assertTrue(sens[s_typ]["mu"] > sens[s_typ]["var"])
self.assertTrue(sens[s_typ]["var"] > sens[s_typ]["len_scale"])
"""
Estimate steady heterogeneous parameters
----------------------------------------
Here we demonstrate how to estimate parameters of heterogeneity, namely
mean, variance and correlation length of log-transmissivity,
with the aid the the extended Thiem solution in 2D.
"""
import welltestpy as wtp
campaign = wtp.load_campaign("Cmp_UFZ-campaign.cmp")
estimation = wtp.estimate.ExtThiem2D("Est_steady_het", campaign, generate=True)
estimation.run()
estimation.sensitivity()