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()