def __init__(
self,
app,
ensembles: Optional[list] = None,
csvfile_parameters: pathlib.Path = None,
csvfile_smry: pathlib.Path = None,
time_index: str = "monthly",
column_keys: Optional[list] = None,
drop_constants: bool = True,
):
super().__init__()
WEBVIZ_ASSETS.add(
pathlib.Path(webviz_subsurface.__file__).parent / "_assets" /
"css" / "container.css")
self.theme = app.webviz_settings["theme"]
self.time_index = time_index
self.column_keys = column_keys
self.ensembles = ensembles
self.csvfile_parameters = csvfile_parameters
self.csvfile_smry = csvfile_smry
if ensembles is not None:
self.emodel = EnsembleSetModel(
ensemble_paths={
ens: app.webviz_settings["shared_settings"]
["scratch_ensembles"][ens]
for ens in ensembles
})
self.pmodel = ParametersModel(
dataframe=self.emodel.load_parameters(),
theme=self.theme,
drop_constants=drop_constants,
)
self.vmodel = SimulationTimeSeriesModel(
dataframe=self.emodel.load_smry(time_index=self.time_index,
column_keys=self.column_keys),
theme=self.theme,
)
elif self.csvfile_parameters is None:
raise ValueError(
"Either ensembles or csvfile_parameters must be specified")
else:
self.pmodel = ParametersModel(
dataframe=read_csv(csvfile_parameters),
theme=self.theme,
drop_constants=drop_constants,
)
if self.csvfile_smry is not None:
self.vmodel = SimulationTimeSeriesModel(
dataframe=read_csv(csvfile_smry),
theme=self.theme.plotly_theme)
else:
self.vmodel = None
self.set_callbacks(app)
def __init__(
self,
app: dash.Dash,
ensembles: Optional[list] = None,
statistics_file: str = "share/results/tables/gridpropstatistics.csv",
csvfile_statistics: pathlib.Path = None,
csvfile_smry: pathlib.Path = None,
surface_renaming: Optional[dict] = None,
time_index: str = "monthly",
column_keys: Optional[list] = None,
):
super().__init__()
WEBVIZ_ASSETS.add(
pathlib.Path(webviz_subsurface.__file__).parent / "_assets" /
"css" / "container.css")
# TODO(Sigurd) fix this once we get a separate webviz_settings parameter
self.theme: WebvizConfigTheme = app.webviz_settings["theme"]
self.time_index = time_index
self.column_keys = column_keys
self.statistics_file = statistics_file
self.ensembles = ensembles
self.csvfile_statistics = csvfile_statistics
self.csvfile_smry = csvfile_smry
self.surface_folders: Union[dict, None]
if ensembles is not None:
self.emodel = EnsembleSetModel(
ensemble_paths={
ens: app.webviz_settings["shared_settings"]
["scratch_ensembles"][ens]
for ens in ensembles
})
self.pmodel = PropertyStatisticsModel(
dataframe=self.emodel.load_csv(
csv_file=pathlib.Path(self.statistics_file)),
theme=self.theme,
)
self.vmodel = SimulationTimeSeriesModel(
dataframe=self.emodel.load_smry(time_index=self.time_index,
column_keys=self.column_keys),
theme=self.theme,
)
self.surface_folders = {
ens: folder / "share" / "results" / "maps" / ens
for ens, folder in self.emodel.ens_folders.items()
}
else:
self.pmodel = PropertyStatisticsModel(
dataframe=read_csv(csvfile_statistics), theme=self.theme)
self.vmodel = SimulationTimeSeriesModel(
dataframe=read_csv(csvfile_smry), theme=self.theme)
self.surface_folders = None
self.surface_renaming = surface_renaming if surface_renaming else {}
self.set_callbacks(app)
def __init__(
self,
app: dash.Dash,
ensembles: list,
wells: Optional[List[str]] = None,
):
super().__init__()
if wells is None:
self.column_keys = ["WBHP:*"]
else:
self.column_keys = [f"WBHP:{well}" for well in wells]
self.emodel = EnsembleSetModel(
ensemble_paths={
ens: app.webviz_settings["shared_settings"]
["scratch_ensembles"][ens]
for ens in ensembles
})
self.smry = self.emodel.load_smry(time_index="raw",
column_keys=self.column_keys)
self.theme = app.webviz_settings["theme"]
self.set_callbacks(app)
def __init__(
self,
app: dash.Dash,
csvfile_smry: Path = None,
csvfile_parameters: Path = None,
ensembles: list = None,
column_keys: list = None,
initial_vector: str = None,
sampling: str = "monthly",
line_shape_fallback: str = "linear",
) -> None:
super().__init__()
self.time_index = sampling
self.column_keys = column_keys
self.csvfile_smry = csvfile_smry
self.csvfile_parameters = csvfile_parameters
if csvfile_smry and ensembles:
raise ValueError(
'Incorrent arguments. Either provide a "csvfile_smry" and "csvfile_parameters" or '
'"ensembles"')
if csvfile_smry and csvfile_parameters:
smry = read_csv(csvfile_smry)
parameters = read_csv(csvfile_parameters)
parameters["SENSTYPE"] = parameters.apply(
lambda row: find_sens_type(row.SENSCASE), axis=1)
self.smry_meta = None
elif ensembles:
self.ens_paths = {
ensemble: app.webviz_settings["shared_settings"]
["scratch_ensembles"][ensemble]
for ensemble in ensembles
}
self.emodel = EnsembleSetModel(ensemble_paths=self.ens_paths)
smry = self.emodel.load_smry(time_index=self.time_index,
column_keys=self.column_keys)
self.smry_meta = self.emodel.load_smry_meta(
column_keys=self.column_keys, )
# Extract realizations and sensitivity information
parameters = get_realizations(ensemble_paths=self.ens_paths,
ensemble_set_name="EnsembleSet")
else:
raise ValueError(
'Incorrent arguments. Either provide a "csvfile_smry" and "csvfile_parameters" or '
'"ensembles"')
self.data = pd.merge(smry, parameters, on=["ENSEMBLE", "REAL"])
self.smry_cols = [
c for c in self.data.columns
if c not in ReservoirSimulationTimeSeriesOneByOne.ENSEMBLE_COLUMNS
and not historical_vector(c, self.smry_meta,
False) in self.data.columns
]
self.initial_vector = (initial_vector if initial_vector
and initial_vector in self.smry_cols else
self.smry_cols[0])
self.line_shape_fallback = set_simulation_line_shape_fallback(
line_shape_fallback)
self.tornadoplot = TornadoPlot(app, parameters, allow_click=True)
self.uid = uuid4()
self.theme = app.webviz_settings["theme"]
self.set_callbacks(app)
def __init__(
self,
app,
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.response_ignore = response_ignore if response_ignore else None
self.parameter_ignore = parameter_ignore if parameter_ignore else None
self.column_keys = column_keys
self.time_index = sampling
self.aggregation = aggregation
self.no_responses = no_responses
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(
ensemble_paths={
ens: app.webviz_settings["shared_settings"]["scratch_ensembles"][
ens
]
for ens in ensembles
}
)
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.load_smry(
time_index=self.time_index, column_keys=self.column_keys
)
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:
self.check_runs()
self.check_response_filters()
if response_ignore:
self.responsedf.drop(
response_ignore, errors="ignore", axis=1, inplace=True
)
if response_include:
self.responsedf.drop(
self.responsedf.columns.difference(
[
"REAL",
"ENSEMBLE",
*response_include,
*list(response_filters.keys()),
]
),
errors="ignore",
axis=1,
inplace=True,
)
if parameter_ignore:
self.parameterdf.drop(parameter_ignore, axis=1, inplace=True)
# Integer value for each ensemble to be used for ensemble colormap
# self.uuid("COLOR") used to mitigate risk of already having a column named "COLOR" in the
# DataFrame.
self.parameterdf[self.uuid("COLOR")] = self.parameterdf.apply(
lambda row: self.ensembles.index(row["ENSEMBLE"]), axis=1
)
self.theme = app.webviz_settings["theme"]
self.set_callbacks(app)
def __init__(
self,
app,
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.response_ignore = response_ignore if response_ignore else None
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: app.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(ensemble_paths=self.ens_paths)
self.responsedf = self.emodel.load_smry(
column_keys=self.column_keys,
time_index=self.time_index,
)
self.response_filters["DATE"] = "single"
else:
raise ValueError(
'Incorrect arguments. Either provide "csv files" or "ensembles and response_file".'
)
self.check_runs()
self.check_response_filters()
if response_ignore:
self.responsedf.drop(response_ignore,
errors="ignore",
axis=1,
inplace=True)
if response_include:
self.responsedf.drop(
self.responsedf.columns.difference([
"REAL",
"ENSEMBLE",
*response_include,
*list(response_filters.keys()),
]),
errors="ignore",
axis=1,
inplace=True,
)
self.plotly_theme = app.webviz_settings["theme"].plotly_theme
self.uid = uuid4()
self.set_callbacks(app)
def __init__(
self,
app: dash.Dash,
csvfile: Path = None,
ensembles: list = None,
obsfile: Path = None,
column_keys: list = None,
sampling: str = "monthly",
options: dict = None,
line_shape_fallback: str = "linear",
):
super().__init__()
self.csvfile = csvfile
self.obsfile = obsfile
self.time_index = sampling
self.column_keys = column_keys
if csvfile and ensembles:
raise ValueError(
'Incorrent arguments. Either provide a "csvfile" or "ensembles"'
)
self.observations = {}
if obsfile:
self.observations = check_and_format_observations(
get_path(self.obsfile))
self.smry: pd.DataFrame
self.smry_meta: Union[pd.DataFrame, None]
if csvfile:
self.smry = read_csv(csvfile)
self.smry_meta = None
# Check of time_index for data to use in resampling. Quite naive as it only checks for
# unique values of the DATE column, and not per realization.
#
# Currently not necessary as we don't allow resampling for average rates and intervals
# unless we have metadata, which csvfile input currently doesn't support.
# See: https://github.com/equinor/webviz-subsurface/issues/402
self.time_index = pd.infer_freq(
sorted(pd.to_datetime(self.smry["DATE"]).unique()))
elif ensembles:
self.emodel = EnsembleSetModel(
ensemble_paths={
ens: app.webviz_settings["shared_settings"]
["scratch_ensembles"][ens]
for ens in ensembles
})
self.smry = self.emodel.load_smry(time_index=self.time_index,
column_keys=self.column_keys)
self.smry_meta = self.emodel.load_smry_meta(
column_keys=self.column_keys, )
else:
raise ValueError(
'Incorrent arguments. Either provide a "csvfile" or "ensembles"'
)
if any(
[col.startswith(("AVG_", "INTVL_")) for col in self.smry.columns]):
raise ValueError(
"Your data set includes time series vectors which have names starting with"
"'AVG_' and/or 'INTVL_'. These prefixes are not allowed, as they are used"
"internally in the plugin.")
self.smry_cols = [
c for c in self.smry.columns
if c not in ReservoirSimulationTimeSeries.ENSEMBLE_COLUMNS
and not historical_vector(c, self.smry_meta,
False) in self.smry.columns
]
self.dropdown_options = []
for vec in self.smry_cols:
self.dropdown_options.append({
"label": f"{simulation_vector_description(vec)} ({vec})",
"value": vec
})
if (self.smry_meta is not None and self.smry_meta.is_total[vec]
and self.time_index is not None):
# Get the likely name for equivalent rate vector and make dropdown options.
# Requires that the time_index was either defined or possible to infer.
avgrate_vec = rename_vec_from_cum(vector=vec, as_rate=True)
interval_vec = rename_vec_from_cum(vector=vec, as_rate=False)
self.dropdown_options.append({
"label":
f"{simulation_vector_description(avgrate_vec)} ({avgrate_vec})",
"value": avgrate_vec,
})
self.dropdown_options.append({
"label":
f"{simulation_vector_description(interval_vec)} ({interval_vec})",
"value": interval_vec,
})
self.ensembles = list(self.smry["ENSEMBLE"].unique())
self.theme = app.webviz_settings["theme"]
self.plot_options = options if options else {}
self.plot_options["date"] = (str(self.plot_options.get("date")) if
self.plot_options.get("date") else None)
self.line_shape_fallback = set_simulation_line_shape_fallback(
line_shape_fallback)
# Check if initially plotted vectors exist in data, raise ValueError if not.
missing_vectors = [
value for key, value in self.plot_options.items()
if key in ["vector1", "vector2", "vector3"]
and value not in self.smry_cols
]
if missing_vectors:
raise ValueError(
f"Cannot find: {', '.join(missing_vectors)} to plot initially in "
"ReservoirSimulationTimeSeries. Check that the vectors exist in your data, and "
"that they are not missing in a non-default column_keys list in the yaml config "
"file.")
self.allow_delta = len(self.ensembles) > 1
self.set_callbacks(app)