def test_gen_data_collector(self):
config = self.createTestPath("local/mini_ert/mini_config")
with ErtTestContext(
"python/enkf/export/gen_data_observation_collector", config
) as context:
ert = context.getErt()
obs_key = GenDataObservationCollector.getObservationKeyForDataKey(
ert, "PERLIN", 1
)
self.assertEqual(obs_key, "GEN_PERLIN_1")
obs_key = GenDataObservationCollector.getObservationKeyForDataKey(
ert, "PERLIN", 2
)
self.assertEqual(obs_key, "GEN_PERLIN_2")
obs_key = GenDataObservationCollector.getObservationKeyForDataKey(
ert, "PERLIN", 3
)
self.assertEqual(obs_key, "GEN_PERLIN_3")
obs_key = GenDataObservationCollector.getObservationKeyForDataKey(
ert, "PERLIN", 4
)
self.assertIsNone(obs_key)
obs_key = GenDataObservationCollector.getObservationKeyForDataKey(
ert, "PERLINk", 1
)
self.assertIsNone(obs_key)
def test_gen_data_report_steps():
with open("config_file.ert", "w") as fout:
# Write a minimal config file
fout.write(
dedent("""
NUM_REALIZATIONS 1
OBS_CONFIG observations
TIME_MAP time_map
GEN_DATA RESPONSE RESULT_FILE:result_%d.out REPORT_STEPS:0,1 INPUT_FORMAT:ASCII
"""))
with open("obs_data_0.txt", "w") as fout:
fout.write("1.0 0.1")
with open("obs_data_1.txt", "w") as fout:
fout.write("2.0 0.1")
with open("time_map", "w") as fout:
fout.write("2014-09-10\n2017-02-05")
with open("observations", "w") as fout:
fout.write(
dedent("""
GENERAL_OBSERVATION OBS_0 {
DATA = RESPONSE;
INDEX_LIST = 0;
RESTART = 0;
OBS_FILE = obs_data_0.txt;
};
GENERAL_OBSERVATION OBS_1 {
DATA = RESPONSE;
INDEX_LIST = 0;
RESTART = 1;
OBS_FILE = obs_data_1.txt;
};
"""))
res_config = ResConfig("config_file.ert")
ert = EnKFMain(res_config)
obs_key = GenDataObservationCollector.getObservationKeyForDataKey(
ert, "RESPONSE", 0)
assert obs_key == "OBS_0"
obs_key = GenDataObservationCollector.getObservationKeyForDataKey(
ert, "RESPONSE", 1)
assert obs_key == "OBS_1"
obs_key = GenDataObservationCollector.getObservationKeyForDataKey(
ert, "RESPONSE", 2)
assert obs_key is None
obs_key = GenDataObservationCollector.getObservationKeyForDataKey(
ert, "NOT_A_KEY", 0)
assert obs_key is None
def gatherGenDataObservationData(ert, case, key_with_report_step):
""" :rtype: pandas.DataFrame """
key, report_step = key_with_report_step.split("@", 1)
report_step = int(report_step)
obs_key = GenDataObservationCollector.getObservationKeyForDataKey(ert, key, report_step)
if obs_key is not None:
obs_data = GenDataObservationCollector.loadGenDataObservations(ert, case, obs_key)
columns = {obs_key: key_with_report_step, "STD_%s" % obs_key: "STD_%s" % key_with_report_step}
obs_data = obs_data.rename(columns=columns)
else:
obs_data = DataFrame()
return obs_data.dropna()
def observation_keys(self, key):
if self._enkf_main.getKeyManager().isGenDataKey(key):
key_parts = key.split("@")
key = key_parts[0]
if len(key_parts) > 1:
report_step = int(key_parts[1])
else:
report_step = 0
obs_key = GenDataObservationCollector.getObservationKeyForDataKey(
self._enkf_main, key, report_step
)
if obs_key is not None:
return [obs_key]
else:
return []
elif self._enkf_main.getKeyManager().isSummaryKey(key):
return [
str(k)
for k in self._enkf_main.ensembleConfig()
.getNode(key)
.getObservationKeys()
]
else:
return []
def test_gen_data_collector(self):
config = self.createTestPath("local/mini_ert/mini_config")
with ErtTestContext(
"python/enkf/export/gen_data_observation_collector", config
) as context:
ert = context.getErt()
obs_key = GenDataObservationCollector.getObservationKeyForDataKey(
ert, "PERLIN", 1
)
self.assertEqual(obs_key, "GEN_PERLIN_1")
obs_key = GenDataObservationCollector.getObservationKeyForDataKey(
ert, "PERLIN", 2
)
self.assertEqual(obs_key, "GEN_PERLIN_2")
obs_key = GenDataObservationCollector.getObservationKeyForDataKey(
ert, "PERLIN", 3
)
self.assertEqual(obs_key, "GEN_PERLIN_3")
obs_key = GenDataObservationCollector.getObservationKeyForDataKey(
ert, "PERLIN", 4
)
self.assertIsNone(obs_key)
obs_key = GenDataObservationCollector.getObservationKeyForDataKey(
ert, "PERLINk", 1
)
self.assertIsNone(obs_key)
data = GenDataObservationCollector.loadGenDataObservations(
ert, "default", "GEN_PERLIN_1"
)
self.assertFloatEqual(data["GEN_PERLIN_1"][0], -0.616789)
self.assertFloatEqual(data["STD_GEN_PERLIN_1"][0], 0.2)
with self.assertRaises(KeyError):
GenDataObservationCollector.loadGenDataObservations(
ert, "default", "GEN_PERLIN_4"
)
def test_ahmanalysis_run_field(test_data_root, grid_prop):
"""test data_set with scalar and Field parameters"""
test_data_dir = os.path.join(test_data_root, "snake_oil")
shutil.copytree(test_data_dir, "test_data")
os.chdir(os.path.join("test_data"))
os.makedirs("fields")
grid = EclGridGenerator.createRectangular((10, 12, 5), (1, 1, 1))
for iens in range(10):
grid_prop("PERMX", 10, grid.getGlobalSize(), "fields/permx%d.grdecl" % iens)
grid_prop("PORO", 0.2, grid.getGlobalSize(), "fields/poro%d.grdecl" % iens)
res_config = ResConfig("snake_oil_field.ert")
res_config.convertToCReference(None)
ert = EnKFMain(res_config)
ahmanalysis.AhmAnalysisJob(ert).run(prior_name="default")
# assert that this returns/generates the delta field parameter
gen_obs_list = GenDataObservationCollector.getAllObservationKeys(ert)
summary_obs_list = SummaryObservationCollector.getAllObservationKeys(ert)
obs_keys = gen_obs_list + summary_obs_list
output_deltafield = os.path.join(
"storage",
"snake_oil_field",
"reports",
"default",
"AhmAnalysisJob",
"delta_fieldPERMX.csv",
)
assert os.path.isfile(output_deltafield)
delta_df = pd.read_csv(output_deltafield, index_col=0)
assert len(delta_df.columns) == 8 + (len(obs_keys) * 2) + 1
# check field parameter is present and not empty in the final KS matrix
output_ks = output_deltafield.replace("delta_fieldPERMX.csv", "ks.csv")
ks_df = pd.read_csv(output_ks, index_col=0)
assert not ks_df.empty
assert "FIELD_PERMX" in ks_df.index.tolist()
check_empty = ks_df.loc[["FIELD_PERMX"], :].isnull().all(axis=1)
assert not check_empty["FIELD_PERMX"]
key, report_step = key.split("@", 1)
report_step = int(report_step)
try:
data = GenDataCollector.loadGenData(ert, case, key, report_step)
except ValueError:
data = DataFrame()
return data.dropna() # removes all rows that has a NaN
@staticmethod
def gatherGenDataObservationData(ert, case, key_with_report_step):
""" :rtype: pandas.DataFrame """
key, report_step = key_with_report_step.split("@", 1)
report_step = int(report_step)
obs_key = GenDataObservationCollector.getObservationKeyForDataKey(
ert, key, report_step)
if obs_key is not None:
obs_data = GenDataObservationCollector.loadGenDataObservations(
ert, case, obs_key)
columns = {
obs_key: key_with_report_step,
"STD_%s" % obs_key: "STD_%s" % key_with_report_step
}
obs_data = obs_data.rename(columns=columns)
else:
obs_data = DataFrame()
return obs_data.dropna()
@staticmethod
