def test_transform(self):
self.cov_model.dim = 2
srf = SRF(self.cov_model, mean=self.mean, mode_no=self.mode_no)
srf((self.x_grid, self.y_grid), seed=self.seed, mesh_type="structured")
tf.normal_force_moments(srf) # force ergodicity of the given field
self.assertAlmostEqual(srf.field.mean(), srf.mean)
self.assertAlmostEqual(srf.field.var(), srf.model.var)
tf.zinnharvey(srf) # make high values mostly connected
tf.normal_force_moments(srf) # force ergodicity of the given field
tf.normal_to_lognormal(srf) # log-normal
srf((self.x_grid, self.y_grid), seed=self.seed, mesh_type="structured")
tf.normal_to_arcsin(srf)
srf((self.x_grid, self.y_grid), seed=self.seed, mesh_type="structured")
tf.normal_to_uquad(srf)
srf((self.x_grid, self.y_grid), seed=self.seed, mesh_type="structured")
tf.normal_to_uniform(srf)
def test_transform(self):
self.cov_model.dim = 2
srf = gs.SRF(self.cov_model, mean=self.mean, mode_no=self.mode_no)
srf((self.x_grid, self.y_grid), seed=self.seed, mesh_type="structured")
tf.normal_force_moments(srf) # force ergodicity of the given field
self.assertAlmostEqual(srf.field.mean(), srf.mean)
self.assertAlmostEqual(srf.field.var(), srf.model.var)
tf.zinnharvey(srf) # make high values mostly connected
tf.normal_force_moments(srf) # force ergodicity of the given field
tf.normal_to_lognormal(srf) # log-normal
srf((self.x_grid, self.y_grid), seed=self.seed, mesh_type="structured")
tf.normal_to_arcsin(srf)
srf((self.x_grid, self.y_grid), seed=self.seed, mesh_type="structured")
tf.normal_to_uquad(srf)
srf((self.x_grid, self.y_grid), seed=self.seed, mesh_type="structured")
tf.normal_to_uniform(srf)
srf((self.x_grid, self.y_grid), seed=self.seed, mesh_type="structured")
tf.binary(srf)
srf((self.x_grid, self.y_grid), seed=self.seed, mesh_type="structured")
tf.boxcox(srf)
srf((self.x_grid, self.y_grid), seed=self.seed, mesh_type="structured")
values = np.linspace(np.min(srf.field), np.max(srf.field), 3)
tf.discrete(srf, values)
srf((self.x_grid, self.y_grid), seed=self.seed, mesh_type="structured")
values = [-1, 0, 1]
thresholds = [-0.9, 0.1]
tf.discrete(srf, values, thresholds)
np.testing.assert_array_equal(np.unique(srf.field), [-1, 0, 1])
srf((self.x_grid, self.y_grid), seed=self.seed, mesh_type="structured")
values = [-1, 0, 1]
tf.discrete(srf, values, thresholds="arithmetic")
np.testing.assert_array_equal(np.unique(srf.field), [-1.0, 0.0, 1.0])
srf((self.x_grid, self.y_grid), seed=self.seed, mesh_type="structured")
values = [-1, 0, 0.5, 1]
tf.discrete(srf, values, thresholds="equal")
np.testing.assert_array_equal(np.unique(srf.field), values)
# set storativity
model.mmp.update_block(STORAGE=[1, para[0]])
model.mmp.write_file()
# init cov model (truncated power law with gaussian modes)
cov = gs.TPLGaussian(dim=2, var=para[2], len_scale=para[3], hurst=para[4])
# init spatial random field class
srf = gs.SRF(cov, mean=np.log(para[1]), upscaling="coarse_graining")
# run the ensemble
for i in range(ens_size):
# parallel running the right jobs on each core
if (para_no * ens_size + i) % size != rank:
continue
# generate new transmissivity field
srf.mesh(model.msh, seed=seed(), point_volumes=model.msh.volumes_flat)
# transfrom to log-normal field
tf.normal_to_lognormal(srf)
# add the transmissivity to the ogs project
model.mpd.update_block(DATA=by_id(srf.field))
# write the new mpd file
model.mpd.write_file()
# set the new output-directory
model.output_dir = os.path.join(
task_root, "para{:04}".format(para_no), "seed{:04}".format(i)
)
print(" run model {:04}".format(i), end=" ")
success = model.run_model(print_log=False, save_log=keep_output)
print(" ...success") if success else print(" ...error!")
if not success:
FAIL.append(str(para_no) + "_" + str(i))
# calculate angular means
angles_mean(time, rad, angles, model.output_dir)
