def __init__(self, provider_set: dict, column_keys: Optional[list] = None) -> None:
self._provider_set = provider_set
self.line_shape_fallback = set_simulation_line_shape_fallback("linear")
all_vector_names = self._create_union_of_vector_names_from_providers(
list(provider_set.values())
)
self._vector_names = (
self.filter_vectorlist_on_column_keys(column_keys, all_vector_names)
if column_keys is not None
else all_vector_names
)
if not self._vector_names:
raise ValueError("No vectors match the selected 'column_keys' criteria")
self._dates = self.all_dates()
# add vectors to vector selector
self.vector_selector_data: list = []
for vector in self.get_non_historical_vector_names():
add_vector_to_vector_selector_data(self.vector_selector_data, vector)
def __init__(
self,
app: dash.Dash,
webviz_settings: WebvizSettings,
ensembles: Optional[list] = None,
rel_file_pattern: str = "share/results/unsmry/*.arrow",
perform_presampling: bool = False,
obsfile: Path = None,
options: dict = None,
sampling: str = Frequency.MONTHLY.value,
predefined_expressions: str = None,
line_shape_fallback: str = "linear",
) -> None:
super().__init__()
# NOTE: Temporary css, pending on new wcc modal component.
# See: https://github.com/equinor/webviz-core-components/issues/163
WEBVIZ_ASSETS.add(
Path(webviz_subsurface.__file__).parent / "_assets" / "css" /
"modal.css")
self._webviz_settings = webviz_settings
self._obsfile = obsfile
self._line_shape_fallback = set_simulation_line_shape_fallback(
line_shape_fallback)
# Must define valid freqency!
if Frequency.from_string_value(sampling) is None:
raise ValueError(
'Sampling frequency conversion is "None", i.e. Raw sampling, and '
"is not supported by plugin yet!")
self._sampling = Frequency(sampling)
self._presampled_frequency = None
# TODO: Update functionality when allowing raw data and csv file input
# NOTE: If csv is implemented-> handle/disable statistics, INTVL_, AVG_, delta
# ensemble, etc.
if ensembles is not None:
ensemble_paths: Dict[str, Path] = {
ensemble_name:
webviz_settings.shared_settings["scratch_ensembles"]
[ensemble_name]
for ensemble_name in ensembles
}
if perform_presampling:
self._presampled_frequency = self._sampling
self._input_provider_set = create_presampled_provider_set_from_paths(
ensemble_paths, rel_file_pattern,
self._presampled_frequency)
else:
self._input_provider_set = create_lazy_provider_set_from_paths(
ensemble_paths, rel_file_pattern)
else:
raise ValueError('Incorrect argument, must provide "ensembles"')
if not self._input_provider_set:
raise ValueError(
"Initial provider set is undefined, and ensemble summary providers"
" are not instanciated for plugin")
self._theme = webviz_settings.theme
self._observations = {}
if self._obsfile:
self._observations = check_and_format_observations(
get_path(self._obsfile))
# NOTE: Initially keep set of all vector names - can make dynamic if wanted?
vector_names = self._input_provider_set.all_vector_names()
non_historical_vector_names = [
vector for vector in vector_names
if historical_vector(vector, None, False) not in vector_names
]
# NOTE: Initially: With set of vector names, the vector selector data is static
# Can be made dynamic based on selected ensembles - i.e. vectors present among
# selected providers?
self._vector_selector_base_data: list = []
self._vector_calculator_data: list = []
for vector in non_historical_vector_names:
split = vector.split(":")
add_vector_to_vector_selector_data(
self._vector_selector_base_data,
vector,
simulation_vector_description(split[0]),
)
add_vector_to_vector_selector_data(
self._vector_calculator_data,
vector,
simulation_vector_description(split[0]),
)
metadata = (self._input_provider_set.vector_metadata(vector)
if self._input_provider_set else None)
if metadata and metadata.is_total:
# 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_vector_from_cumulative(vector=vector,
as_rate=True)
interval_vec = rename_vector_from_cumulative(vector=vector,
as_rate=False)
avgrate_split = avgrate_vec.split(":")
interval_split = interval_vec.split(":")
add_vector_to_vector_selector_data(
self._vector_selector_base_data,
avgrate_vec,
f"{simulation_vector_description(avgrate_split[0])} ({avgrate_vec})",
)
add_vector_to_vector_selector_data(
self._vector_selector_base_data,
interval_vec,
f"{simulation_vector_description(interval_split[0])} ({interval_vec})",
)
# Retreive predefined expressions from configuration and validate
self._predefined_expressions_path = (
None if predefined_expressions is None else webviz_settings.
shared_settings["predefined_expressions"][predefined_expressions])
self._predefined_expressions = expressions_from_config(
get_path(self._predefined_expressions_path) if self.
_predefined_expressions_path else None)
for expression in self._predefined_expressions:
valid, message = validate_predefined_expression(
expression, self._vector_selector_base_data)
if not valid:
warnings.warn(message)
expression["isValid"] = valid
# Create initial vector selector data with predefined expressions
self._initial_vector_selector_data = copy.deepcopy(
self._vector_selector_base_data)
add_expressions_to_vector_selector_data(
self._initial_vector_selector_data, self._predefined_expressions)
plot_options = options if options else {}
self._initial_visualization_selection = VisualizationOptions(
plot_options.get("visualization", "statistics"))
self._initial_vectors: List[str] = []
if "vectors" not in plot_options:
self._initial_vectors = []
for vector in [
vector for vector in ["vector1", "vector2", "vector3"]
if vector in plot_options
]:
self._initial_vectors.append(plot_options[vector])
self._initial_vectors = self._initial_vectors[:3]
# Set callbacks
self.set_callbacks(app)
