def add_webvizstore(self) -> List[Tuple[Callable, List[Dict[str, Any]]]]:
functions: List[Tuple[Callable, List[Dict[str, Any]]]] = [
(
read_csv,
[
{"csv_file": path}
for path in [
self.faultlines,
self.formations,
self.obsdata,
self.csvfile_rft,
self.csvfile_rft_ert,
]
if path is not None
],
)
]
if self.csvfile_rft_ert is None:
functions.append(
(
load_csv,
[
{
"ensemble_paths": self.ens_paths,
"csv_file": "share/results/tables/rft_ert.csv",
},
],
)
)
try:
load_csv(self.ens_paths, "share/results/tables/rft.csv")
functions.append(
(
load_csv,
[
{
"ensemble_paths": self.ens_paths,
"csv_file": "share/results/tables/rft.csv",
},
],
)
)
except KeyError:
pass
return functions
def __init__(
self,
app,
csvfile_rft: Path = None,
csvfile_rft_ert: Path = None,
ensembles: list = None,
formations: Path = None,
obsdata: Path = None,
faultlines: Path = None,
):
super().__init__()
self.formations = formations
self.faultlines = faultlines
self.obsdata = obsdata
self.csvfile_rft = csvfile_rft
self.csvfile_rft_ert = csvfile_rft_ert
self.simdf = read_csv(
self.csvfile_rft) if csvfile_rft is not None else None
self.formationdf = read_csv(
self.formations) if self.formations else None
self.faultlinesdf = read_csv(
self.faultlines) if self.faultlines else None
self.obsdatadf = read_csv(self.obsdata) if self.obsdata else None
if csvfile_rft_ert and ensembles:
raise ValueError(
'Incorrent arguments. Either provide a "csvfile_rft_ert" or "ensembles"'
)
if csvfile_rft_ert is not None:
self.ertdatadf = read_csv(self.csvfile_rft_ert)
if ensembles:
self.ens_paths = ({
ens: app.webviz_settings["shared_settings"]
["scratch_ensembles"][ens]
for ens in ensembles
} if ensembles is not None else None)
try:
self.simdf = load_csv(self.ens_paths,
"share/results/tables/rft.csv")
except (KeyError, OSError):
self.simdf = None
try:
self.ertdatadf = load_csv(self.ens_paths,
"share/results/tables/rft_ert.csv")
except KeyError as exc:
raise KeyError(
"CSV file for ERT RFT observations/simulations "
"(share/results/tables/rft_ert.csv) not found!") from exc
self.ertdatadf = self.ertdatadf.rename(
columns={
"time": "DATE",
"is_active": "ACTIVE",
"isactive": "ACTIVE",
"well": "WELL",
"zone": "ZONE",
"pressure": "SIMULATED",
"true_vertical_depth": "TVD",
"measured_depth": "MD",
"observed": "OBSERVED",
"obs": "OBSERVED",
"error": "OBSERVED_ERR",
"utm_x": "EAST",
"utm_y": "NORTH",
})
self.ertdatadf["DIFF"] = (self.ertdatadf["SIMULATED"] -
self.ertdatadf["OBSERVED"])
self.ertdatadf["ABSDIFF"] = abs(self.ertdatadf["SIMULATED"] -
self.ertdatadf["OBSERVED"])
self.ertdatadf["YEAR"] = pd.to_datetime(self.ertdatadf["DATE"]).dt.year
self.ertdatadf["DATE_IDX"] = self.ertdatadf["DATE"].apply(
lambda x: list(self.ertdatadf["DATE"].unique()).index(x))
self.date_marks = self.set_date_marks()
self.ertdatadf = filter_frame(
self.ertdatadf,
{
"ACTIVE": 1,
},
)
self.ertdatadf["STDDEV"] = self.ertdatadf.groupby(
["WELL", "DATE", "ZONE", "ENSEMBLE",
"TVD"])["SIMULATED"].transform("std")
self.set_callbacks(app)
def __init__(
self,
app,
webviz_settings: WebvizSettings,
parameter_csv: Path = None,
response_csv: Path = None,
ensembles: list = None,
response_file: str = None,
response_filters: dict = None,
response_ignore: list = None,
response_include: list = None,
column_keys: list = None,
sampling: str = "monthly",
aggregation: str = "sum",
corr_method: str = "pearson",
):
super().__init__()
self.parameter_csv = parameter_csv if parameter_csv else None
self.response_csv = response_csv if response_csv else None
self.response_file = response_file if response_file else None
self.response_filters = response_filters if response_filters else {}
self.column_keys = column_keys
self.time_index = sampling
self.corr_method = corr_method
self.aggregation = aggregation
if response_ignore and response_include:
raise ValueError(
'Incorrent argument. either provide "response_include", '
'"response_ignore" or neither')
if parameter_csv and response_csv:
if ensembles or response_file:
raise ValueError(
'Incorrect arguments. Either provide "csv files" or '
'"ensembles and response_file".')
self.parameterdf = read_csv(self.parameter_csv)
self.responsedf = read_csv(self.response_csv)
elif ensembles:
self.ens_paths = {
ens: webviz_settings.shared_settings["scratch_ensembles"][ens]
for ens in ensembles
}
self.parameterdf = load_parameters(ensemble_paths=self.ens_paths,
ensemble_set_name="EnsembleSet")
if self.response_file:
self.responsedf = load_csv(
ensemble_paths=self.ens_paths,
csv_file=response_file,
ensemble_set_name="EnsembleSet",
)
else:
self.emodel: EnsembleSetModel = (
caching_ensemble_set_model_factory.get_or_create_model(
ensemble_paths=self.ens_paths,
column_keys=self.column_keys,
time_index=self.time_index,
))
self.responsedf = self.emodel.get_or_load_smry_cached()
self.response_filters["DATE"] = "single"
else:
raise ValueError(
'Incorrect arguments. Either provide "csv files" or "ensembles and response_file".'
)
parresp.check_runs(self.parameterdf, self.responsedf)
parresp.check_response_filters(self.responsedf, self.response_filters)
# Only select numerical responses
self.response_columns = parresp.filter_numerical_columns(
df=self.responsedf,
column_ignore=response_ignore,
column_include=response_include,
filter_columns=self.response_filters.keys(),
)
# Only select numerical parameters
self.parameter_columns = parresp.filter_numerical_columns(
df=self.parameterdf)
self.theme = webviz_settings.theme
self.set_callbacks(app)
def __init__(
self,
app,
webviz_settings: WebvizSettings,
parameter_csv: Path = None,
response_csv: Path = None,
ensembles: list = None,
rel_file_pattern: str = "share/results/unsmry/*.arrow",
response_file: str = None,
response_filters: dict = None,
response_ignore: list = None,
response_include: list = None,
column_keys: list = None,
sampling: str = "monthly",
aggregation: str = "sum",
corr_method: str = "pearson",
):
super().__init__()
self.parameter_csv = parameter_csv if parameter_csv else None
self.response_csv = response_csv if response_csv else None
self.response_file = response_file if response_file else None
self.response_filters = response_filters if response_filters else {}
self.column_keys = column_keys
self._sampling = Frequency(sampling)
self.corr_method = corr_method
self.aggregation = aggregation
if response_ignore and response_include:
raise ValueError(
'Incorrent argument. either provide "response_include", '
'"response_ignore" or neither')
if parameter_csv and response_csv:
if ensembles or response_file:
raise ValueError(
'Incorrect arguments. Either provide "csv files" or '
'"ensembles and response_file".')
parameterdf = read_csv(self.parameter_csv)
self.responsedf = read_csv(self.response_csv)
elif ensembles:
self.ens_paths = {
ens: webviz_settings.shared_settings["scratch_ensembles"][ens]
for ens in ensembles
}
table_provider_factory = EnsembleTableProviderFactory.instance()
parameterdf = create_df_from_table_provider(
table_provider_factory.
create_provider_set_from_per_realization_parameter_file(
self.ens_paths))
if self.response_file:
self.responsedf = load_csv(
ensemble_paths=self.ens_paths,
csv_file=response_file,
ensemble_set_name="EnsembleSet",
)
else:
smry_provider_factory = EnsembleSummaryProviderFactory.instance(
)
provider_set = {
ens_name:
smry_provider_factory.create_from_arrow_unsmry_presampled(
ens_path, rel_file_pattern, self._sampling)
for ens_name, ens_path in self.ens_paths.items()
}
self.response_filters["DATE"] = "single"
self.responsedf = create_df_from_summary_provider(
provider_set,
self.column_keys,
)
else:
raise ValueError(
'Incorrect arguments. Either provide "csv files" or "ensembles and response_file".'
)
pmodel = ParametersModel(dataframe=parameterdf,
keep_numeric_only=True,
drop_constants=True)
self.parameterdf = pmodel.dataframe
self.parameter_columns = pmodel.parameters
parresp.check_runs(self.parameterdf, self.responsedf)
parresp.check_response_filters(self.responsedf, self.response_filters)
# Only select numerical responses
self.response_columns = parresp.filter_numerical_columns(
df=self.responsedf,
column_ignore=response_ignore,
column_include=response_include,
filter_columns=self.response_filters.keys(),
)
self.theme = webviz_settings.theme
self.set_callbacks(app)