def __init__(
self,
app,
webviz_settings: WebvizSettings,
ensembles: list = None,
parameter_csv: Path = None,
response_csv: Path = None,
response_file: str = None,
response_filters: dict = None,
response_ignore: list = None,
response_include: list = None,
parameter_ignore: list = None,
column_keys: list = None,
sampling: str = "monthly",
aggregation: str = "sum",
no_responses=False,
):
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.aggregation = aggregation
self.no_responses = no_responses
self.response_columns = []
if response_ignore and response_include:
raise ValueError(
'Incorrent argument. Either provide "response_include", '
'"response_ignore" or neither')
if parameter_csv:
if ensembles or response_file:
raise ValueError(
'Incorrect arguments. Either provide "parameter_csv" or '
'"ensembles and/or response_file".')
if not self.no_responses:
if self.response_csv:
self.responsedf = read_csv(self.response_csv)
else:
raise ValueError(
"Incorrect arguments. Missing response_csv.")
self.parameterdf = read_csv(self.parameter_csv)
elif ensembles:
if self.response_csv:
raise ValueError(
'Incorrect arguments. Either provide "response_csv" or '
'"ensembles and/or response_file".')
self.emodel: EnsembleSetModel = (
caching_ensemble_set_model_factory.get_or_create_model(
ensemble_paths={
ens:
webviz_settings.shared_settings["scratch_ensembles"]
[ens]
for ens in ensembles
},
time_index=self.time_index,
column_keys=self.column_keys,
))
self.parameterdf = self.emodel.load_parameters()
if not self.no_responses:
if self.response_file:
self.responsedf = self.emodel.load_csv(
csv_file=response_file)
else:
self.responsedf = self.emodel.get_or_load_smry_cached()
self.response_filters["DATE"] = "single"
else:
raise ValueError(
"Incorrect arguments."
'You have to define at least "ensembles" or "parameter_csv".')
if not self.no_responses:
parresp.check_runs(parameterdf=self.parameterdf,
responsedf=self.responsedf)
parresp.check_response_filters(
responsedf=self.responsedf,
response_filters=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, column_ignore=parameter_ignore)
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,
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)