def test_resample_schedule_dates() -> None:
"""
This function checks if the observation files (complete, training, and test) in ERT and YAML version are equal.
Returns:
Nothing
"""
# Load production
headers = [
"date",
"WOPR",
"WGPR",
"WWPR",
"WOPT",
"WGPT",
"WWPT",
"WBHP",
"WTHP",
"WGIR",
"WWIR",
"WGIT",
"WWIT",
"WSTAT",
"WELL_NAME",
"PHASE",
"TYPE",
"date",
]
df_production_data: pd.DataFrame = pd.read_csv(_PRODUCTION_DATA_FILE_NAME,
usecols=headers)
df_production_data["date"] = pd.to_datetime(df_production_data["date"])
start_date = date(2005, 10, 1)
schedule = _create_schedule_from_data(df_production_data, start_date)
days_original = [
(d - start_date).days
for d in resample_schedule_dates(schedule, resampling=None)
]
days_monthly = [(d - start_date).days
for d in resample_schedule_dates(schedule, resampling="M")]
days_quarterly = [
(d - start_date).days
for d in resample_schedule_dates(schedule, resampling="Q")
]
days_yearly = [(d - start_date).days
for d in resample_schedule_dates(schedule, resampling="A")]
assert (np.allclose(days_original[0:3], [31, 61, 62])
and np.allclose(days_monthly[0:3], [31, 61, 92])
and np.allclose(days_quarterly[0:3], [92, 182, 273])
and np.allclose(days_yearly[0:3], [92]))
def test_check_obsfiles_ert_yaml() -> None:
"""
This function checks if the observation files (complete, training, and test) in ERT and YAML version are equal.
Returns:
Nothing
"""
# pylint: disable-msg=too-many-locals
# pylint: disable-msg=too-many-statements
# pylint: disable=maybe-no-member
config = collections.namedtuple("configuration", "flownet")
config.flownet = collections.namedtuple("flownet", "data_source")
config.flownet.data_source = collections.namedtuple(
"data_source", "simulation")
config.flownet.data_source.simulation = collections.namedtuple(
"simulation", "vectors")
config.flownet.data_source.simulation.vectors = collections.namedtuple(
"vectors", "WTHP")
config.flownet.data_source.simulation.vectors.WOPR = collections.namedtuple(
"WOPR", "min_error")
config.flownet.data_source.simulation.vectors.WOPR.min_error = _MIN_ERROR
config.flownet.data_source.simulation.vectors.WOPR.rel_error = _REL_ERROR
config.flownet.data_source.simulation.vectors.WGPR = collections.namedtuple(
"WGPR", "min_error")
config.flownet.data_source.simulation.vectors.WGPR.min_error = _MIN_ERROR
config.flownet.data_source.simulation.vectors.WGPR.rel_error = _REL_ERROR
config.flownet.data_source.simulation.vectors.WWPR = collections.namedtuple(
"WWPR", "min_error")
config.flownet.data_source.simulation.vectors.WWPR.min_error = _MIN_ERROR
config.flownet.data_source.simulation.vectors.WWPR.rel_error = _REL_ERROR
config.flownet.data_source.simulation.vectors.WOPT = collections.namedtuple(
"WOPT", "min_error")
config.flownet.data_source.simulation.vectors.WOPT.min_error = _MIN_ERROR
config.flownet.data_source.simulation.vectors.WOPT.rel_error = _REL_ERROR
config.flownet.data_source.simulation.vectors.WGPT = collections.namedtuple(
"WGPT", "min_error")
config.flownet.data_source.simulation.vectors.WGPT.min_error = _MIN_ERROR
config.flownet.data_source.simulation.vectors.WGPT.rel_error = _REL_ERROR
config.flownet.data_source.simulation.vectors.WWPT = collections.namedtuple(
"WWPT", "min_error")
config.flownet.data_source.simulation.vectors.WWPT.min_error = _MIN_ERROR
config.flownet.data_source.simulation.vectors.WWPT.rel_error = _REL_ERROR
config.flownet.data_source.simulation.vectors.WBHP = collections.namedtuple(
"WBHP", "min_error")
config.flownet.data_source.simulation.vectors.WBHP.min_error = _MIN_ERROR
config.flownet.data_source.simulation.vectors.WBHP.rel_error = _REL_ERROR
config.flownet.data_source.simulation.vectors.WTHP = collections.namedtuple(
"WTHP", "min_error")
config.flownet.data_source.simulation.vectors.WTHP.min_error = _MIN_ERROR
config.flownet.data_source.simulation.vectors.WTHP.rel_error = _REL_ERROR
config.flownet.data_source.simulation.vectors.WGIR = collections.namedtuple(
"WGIR", "min_error")
config.flownet.data_source.simulation.vectors.WGIR.min_error = _MIN_ERROR
config.flownet.data_source.simulation.vectors.WGIR.rel_error = _REL_ERROR
config.flownet.data_source.simulation.vectors.WWIR = collections.namedtuple(
"WWIR", "min_error")
config.flownet.data_source.simulation.vectors.WWIR.min_error = _MIN_ERROR
config.flownet.data_source.simulation.vectors.WWIR.rel_error = _REL_ERROR
config.flownet.data_source.simulation.vectors.WGIT = collections.namedtuple(
"WGIT", "min_error")
config.flownet.data_source.simulation.vectors.WGIT.min_error = _MIN_ERROR
config.flownet.data_source.simulation.vectors.WGIT.rel_error = _REL_ERROR
config.flownet.data_source.simulation.vectors.WWIT = collections.namedtuple(
"WWIT", "min_error")
config.flownet.data_source.simulation.vectors.WWIT.min_error = _MIN_ERROR
config.flownet.data_source.simulation.vectors.WWIT.rel_error = _REL_ERROR
config.flownet.data_source.simulation.vectors.WSPR = collections.namedtuple(
"WSPR", "min_error")
config.flownet.data_source.simulation.vectors.WSPR.min_error = _MIN_ERROR
config.flownet.data_source.simulation.vectors.WSPR.rel_error = _REL_ERROR
config.flownet.data_source.simulation.vectors.WSPT = collections.namedtuple(
"WSPT", "min_error")
config.flownet.data_source.simulation.vectors.WSPT.min_error = _MIN_ERROR
config.flownet.data_source.simulation.vectors.WSPT.rel_error = _REL_ERROR
config.flownet.data_source.simulation.vectors.WSIR = collections.namedtuple(
"WSIR", "min_error")
config.flownet.data_source.simulation.vectors.WSIR.min_error = _MIN_ERROR
config.flownet.data_source.simulation.vectors.WSIR.rel_error = _REL_ERROR
config.flownet.data_source.simulation.vectors.WSIT = collections.namedtuple(
"WSIT", "min_error")
config.flownet.data_source.simulation.vectors.WSIT.min_error = _MIN_ERROR
config.flownet.data_source.simulation.vectors.WSIT.rel_error = _REL_ERROR
config.flownet.data_source.resampling = _RESAMPLING
# Load production
headers = [
"date",
"WOPR",
"WGPR",
"WWPR",
"WOPT",
"WGPT",
"WWPT",
"WBHP",
"WTHP",
"WGIR",
"WWIR",
"WGIT",
"WWIT",
"WSTAT",
"WELL_NAME",
"PHASE",
"TYPE",
"date",
]
df_production_data: pd.DataFrame = pd.read_csv(_PRODUCTION_DATA_FILE_NAME,
usecols=headers)
df_production_data["date"] = pd.to_datetime(df_production_data["date"])
start_date = date(2005, 10, 1)
schedule = _create_schedule_from_data(df_production_data, start_date)
# Testing with resampling
create_observation_file(
schedule,
_OBSERVATION_FILES / "observations.ertobs",
config,
_TRAINING_SET_FRACTION,
)
create_observation_file(
schedule,
_OBSERVATION_FILES / "observations.yamlobs",
config,
_TRAINING_SET_FRACTION,
yaml=True,
)
dates_resampled = resample_schedule_dates(
schedule, config.flownet.data_source.resampling)
num_dates = len(dates_resampled)
num_training_dates = round(num_dates * _TRAINING_SET_FRACTION)
export_settings = [
["_complete", 0, num_dates],
["_training", 0, num_training_dates],
["_test", num_training_dates + 1, num_dates],
]
file_root = pathlib.Path(_OBSERVATION_FILES / "observations")
for setting in export_settings:
ert_obs_file_name = f"{file_root}{setting[0]}.ertobs"
yaml_obs_file_name = f"{file_root}{setting[0]}.yamlobs"
# Comparing the complete observation data
# Reading ERT file
ert_obs = _read_ert_obs(ert_obs_file_name)
# Reading YAML file
parsed_yaml_file = _read_yaml_obs(yaml_obs_file_name)
# Comparing observation data
compare(ert_obs, parsed_yaml_file)
# Testing without resampling
config.flownet.data_source.resampling = None
create_observation_file(
schedule,
_OBSERVATION_FILES / "observations.ertobs",
config,
_TRAINING_SET_FRACTION,
)
create_observation_file(
schedule,
_OBSERVATION_FILES / "observations.yamlobs",
config,
_TRAINING_SET_FRACTION,
yaml=True,
)
dates_original = resample_schedule_dates(
schedule, config.flownet.data_source.resampling)
num_dates = len(dates_original)
num_training_dates = round(num_dates * _TRAINING_SET_FRACTION)
export_settings = [
["_complete", 0, num_dates],
["_training", 0, num_training_dates],
["_test", num_training_dates + 1, num_dates],
]
file_root = pathlib.Path(_OBSERVATION_FILES / "observations")
for setting in export_settings:
ert_obs_file_name = f"{file_root}{setting[0]}.ertobs"
yaml_obs_file_name = f"{file_root}{setting[0]}.yamlobs"
# Comparing the complete observation data
# Reading ERT file
ert_obs = _read_ert_obs(ert_obs_file_name)
# Reading YAML file
parsed_yaml_file = _read_yaml_obs(yaml_obs_file_name)
print(ert_obs)
print(parsed_yaml_file)
# Comparing observation data
compare(ert_obs, parsed_yaml_file)