def __init__(
self,
app: Dash,
webviz_settings: WebvizSettings,
realizations: pd.DataFrame,
reference: str = "rms_seed",
allow_click: bool = False,
):
self.realizations = realizations
self.sensnames = list(self.realizations["SENSNAME"].unique())
if self.sensnames == [None]:
raise KeyError(
"No sensitivity information found in ensemble. "
"Containers utilizing tornadoplot can only be used for ensembles with "
"one by one design matrix setup "
"(SENSNAME and SENSCASE must be present in parameter file)."
)
self.initial_reference = (
reference if reference in self.sensnames else self.sensnames[0]
)
self.allow_click = allow_click
self.uid = uuid4()
self.plotly_theme = webviz_settings.theme.plotly_theme
WEBVIZ_ASSETS.add(
Path(webviz_subsurface.__file__).parent
/ "_assets"
/ "js"
/ "clientside_functions.js"
)
self.set_callbacks(app)
def __init__(
self,
app: dash.Dash,
webviz_settings: WebvizSettings,
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")
self.theme: WebvizConfigTheme = 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 = (
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.pmodel = PropertyStatisticsModel(
dataframe=self.emodel.load_csv(
csv_file=pathlib.Path(self.statistics_file)),
theme=self.theme,
)
self.vmodel = SimulationTimeSeriesModel(
dataframe=self.emodel.get_or_load_smry_cached(),
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,
webviz_settings: WebvizSettings,
csvfile_vol: Path = None,
csvfile_parameters: Path = None,
ensembles: list = None,
volfiles: dict = None,
volfolder: str = "share/results/volumes",
non_net_facies: Optional[List[str]] = None,
):
super().__init__()
WEBVIZ_ASSETS.add(
Path(webviz_subsurface.__file__).parent / "_assets" / "css" /
"container.css")
WEBVIZ_ASSETS.add(
Path(webviz_subsurface.__file__).parent / "_assets" / "css" /
"inplace_volumes.css")
self.csvfile_vol = csvfile_vol
self.csvfile_parameters = csvfile_parameters
self.volfiles = volfiles
self.volfolder = volfolder
if csvfile_vol and ensembles:
raise ValueError(
'Incorrent arguments. Either provide a "csvfile" or "ensembles" and "volfiles"'
)
if csvfile_vol:
volumes_table = read_csv(csvfile_vol)
parameters: Optional[pd.DataFrame] = (
read_csv(csvfile_parameters) if csvfile_parameters else None)
elif ensembles and volfiles:
ensemble_paths = {
ens: webviz_settings.shared_settings["scratch_ensembles"][ens]
for ens in ensembles
}
self.emodel: EnsembleSetModel = (
caching_ensemble_set_model_factory.get_or_create_model(
ensemble_paths=ensemble_paths, ))
parameters = self.emodel.load_parameters()
volumes_table = extract_volumes(self.emodel, volfolder, volfiles)
else:
raise ValueError(
'Incorrent arguments. Either provide a "csvfile" or "ensembles" and "volfiles"'
)
self.volmodel = InplaceVolumesModel(
volumes_table=volumes_table,
parameter_table=parameters,
non_net_facies=non_net_facies,
)
self.theme = webviz_settings.theme
self.set_callbacks(app)
def __init__(
self,
app,
webviz_settings: WebvizSettings,
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 = 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 = (
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.pmodel = ParametersModel(
dataframe=self.emodel.load_parameters(),
theme=self.theme,
drop_constants=drop_constants,
)
self.vmodel = SimulationTimeSeriesModel(
dataframe=self.emodel.get_or_load_smry_cached())
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))
else:
self.vmodel = None
self.set_callbacks(app)
def __init__(self, app, csv_file):
super().__init__()
self.graph_id = f"graph-{uuid4()}"
self.controls_dropdown_id = f"dropdown-{uuid4()}"
self.csv_file = csv_file
self.set_callbacks(app)
ASSETS_DIR = Path(pkg_resources.resource_filename("everviz", "assets"))
WEBVIZ_ASSETS.add(ASSETS_DIR / "axis_customization.css")
def __init__(
self,
app,
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")
self.theme = 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
if ensembles is not None:
self.emodel = EnsembleSetModel(
ensemble_paths={
ens: app.webviz_settings["shared_settings"]
["scratch_ensembles"][ens]
for ens in ensembles
} if ensembles is not None else None)
self.pmodel = PropertyStatisticsModel(
dataframe=self.emodel.load_csv(csv_file=self.statistics_file),
theme=self.theme,
)
self.vmodel = SimulationTimeSeriesModel(
dataframe=self.emodel.load_smry(time_index=self.time_index, ),
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.plotly_theme)
self.surface_folders = None
self.surface_renaming = surface_renaming if surface_renaming else {}
self.set_callbacks(app)
def __init__(self, app, values_file, statistics_file):
super().__init__()
self.graph_id = f"graph-{uuid4()}"
self.radio_id = f"dropdown-{uuid4()}"
self.function_dropdown_id = f"dropdown-{uuid4()}"
self.values_file = values_file
self.statistics_file = statistics_file
self.set_callbacks(app)
ASSETS_DIR = Path(pkg_resources.resource_filename("everviz", "assets"))
WEBVIZ_ASSETS.add(ASSETS_DIR / "axis_customization.css")
def __init__(self, app, csv_file):
super().__init__()
self.graph_id = f"graph-{uuid4()}"
self.radio_id = f"dropdown-{uuid4()}"
self.radio_id_mode = f"dropdown-{uuid4()}"
self.function_dropdown_id = f"dropdown-{uuid4()}"
self.realization_filter_check_id = f"check-{uuid4()}"
self.realization_filter_input_id = f"input-{uuid4()}"
self.csv_file = csv_file
self.set_callbacks(app)
ASSETS_DIR = Path(pkg_resources.resource_filename("everviz", "assets"))
WEBVIZ_ASSETS.add(ASSETS_DIR / "axis_customization.css")
def __init__(
self,
app: Dash,
webviz_settings: WebvizSettings,
ensembles: Optional[List[str]] = None,
rel_file_pattern: str = "share/results/unsmry/*.arrow",
gruptree_file: str = "share/results/tables/gruptree.csv",
well_attributes_file: str = "rms/output/wells/well_attributes.json",
time_index: str = Frequency.YEARLY.value,
filter_out_startswith: Optional[str] = None,
) -> None:
super().__init__()
# This is used to format the buttons in the well overview tab
WEBVIZ_ASSETS.add(
Path(webviz_subsurface.__file__).parent / "_assets" / "css" /
"inplace_volumes.css")
self._ensembles = ensembles
self._theme = webviz_settings.theme
if ensembles is None:
raise ValueError('Incorrect argument, must provide "ensembles"')
self._ensemble_paths: Dict[str, Path] = {
ensemble_name:
webviz_settings.shared_settings["scratch_ensembles"][ensemble_name]
for ensemble_name in ensembles
}
provider_factory = EnsembleSummaryProviderFactory.instance()
self._data_models: Dict[str, EnsembleWellAnalysisData] = {}
sampling = Frequency(time_index)
for ens_name, ens_path in self._ensemble_paths.items():
provider: EnsembleSummaryProvider = (
provider_factory.create_from_arrow_unsmry_presampled(
str(ens_path), rel_file_pattern, sampling))
self._data_models[ens_name] = EnsembleWellAnalysisData(
ens_name,
provider,
GruptreeModel(ens_name, ens_path, gruptree_file),
WellAttributesModel(ens_name, ens_path, well_attributes_file),
filter_out_startswith=filter_out_startswith,
)
self.set_callbacks(app)
def __init__(self, app, values_file, statistics_file, xaxis="date"):
super().__init__()
self.graph_id = f"graph-{uuid4()}"
self.key_dropdown_id = f"dropdown-{uuid4()}"
self.xaxis_dropdown_id = f"dropdown-{uuid4()}"
self.radio_id = f"radio-{uuid4()}"
self.values_file = values_file
self.statistics_file = statistics_file
self.xaxis = xaxis
self.set_callbacks(app)
ASSETS_DIR = pkg_resources.resource_filename("everviz", "assets")
WEBVIZ_ASSETS.add(Path(ASSETS_DIR) / "axis_customization.css")
def __init__(self, app, csv_file, xaxis="date"):
super().__init__()
self.graph_id = f"graph-{uuid4()}"
self.key_dropdown_id = f"dropdown-{uuid4()}"
self.xaxis_dropdown_id = f"dropdown-{uuid4()}"
self.radio_id = f"radio-{uuid4()}"
self.realization_filter_check_id = f"check-{uuid4()}"
self.realization_filter_input_id = f"input-{uuid4()}"
self.csv_file = csv_file
self.xaxis = xaxis
self.set_callbacks(app)
ASSETS_DIR = pkg_resources.resource_filename("everviz", "assets")
WEBVIZ_ASSETS.add(Path(ASSETS_DIR) / "axis_customization.css")
def __init__(self, app, data_path, title="Indexed Crossplot"):
super().__init__()
self.title = title
self.graph_id = f"graph-{uuid4()}"
self.dropdown_x_id = f"dropdown-{uuid4()}"
self.dropdown_y_id = f"dropdown-{uuid4()}"
self.axis_type_x_id = f"radio-{uuid4()}"
self.axis_type_y_id = f"radio-{uuid4()}"
self.axis_options_x_id = f"radio-{uuid4()}"
self.axis_options_y_id = f"radio-{uuid4()}"
self.dropdown_realization_id = f"dropdown-{uuid4()}"
self.interpolation_id = f"interpolation-{uuid4()}"
self.data_path = data_path
self.set_callbacks(app)
ASSETS_DIR = pkg_resources.resource_filename("everviz",
os.path.join("assets"))
WEBVIZ_ASSETS.add(Path(ASSETS_DIR) / "axis_customization.css")
def __init__(
self,
app: Dash,
webviz_settings: WebvizSettings,
basedir: Path,
planned_wells_dir: Path = None,
):
super().__init__()
self.plotly_theme = webviz_settings.theme.plotly_theme
self.uid = uuid4()
WEBVIZ_ASSETS.add(
Path(webviz_subsurface.__file__).parent / "_assets" / "css" /
"modal.css")
self.set_callbacks(app)
self.basedir = basedir
self.planned_wells_dir = planned_wells_dir
self.modelfile_path = basedir / "model_file.xml"
self.modelfile = get_path(self.modelfile_path)
self.surfaces = load_surfaces(basedir, self.modelfile_path)
self.planned_wellfiles = (json.load(
find_files(planned_wells_dir, "*.txt"))
if planned_wells_dir else None)
self.wellfiles = json.load(
find_files(basedir / "input" / "welldata", "*.txt"))
self.wellfiles = [str(get_path(Path(w))) for w in self.wellfiles]
self.allfiles = json.load(find_files(basedir))
self.allfiles.append(self.modelfile_path)
self.allfiles += self.planned_wellfiles
self.planned_wellfiles = [
str(get_path(Path(w))) for w in self.planned_wellfiles
]
self.surface_attributes = {}
for i, surface in enumerate(self.surfaces):
self.surface_attributes[surface["name"]] = {
"color": get_color(i),
"order": i,
"name": surface["name"],
"topofzone": surface["topofzone"],
"surface": surface["d_"],
"surface_de": surface["de_"],
"surface_dt": surface["dt_"],
"surface_dr": surface["dr_"],
"surface_dte": surface["dte_"],
}
self.surfacenames = [surface["name"] for surface in self.surfaces]
# Log files
zonation_status_file = get_zonation_status(basedir)
well_points_file = get_well_points(basedir)
zonelog_name = get_zonelog_name(self.modelfile)
self.xsec = HuvXsection(
self.surface_attributes,
zonation_status_file,
well_points_file,
zonelog_name,
)
target_points_file = get_target_points(basedir)
self.df_well_target_points = FilterTable(target_points_file,
well_points_file)
# Wellfiles and planned wells
self.planned_wells = {}
if planned_wells_dir is not None:
self.planned_wells = {
wf: xtgeo.well_from_file(wfile=wf)
for wf in self.planned_wellfiles
}
self.wells = {
wf: xtgeo.well_from_file(wfile=wf)
for wf in self.wellfiles
}
# Store current layers
self.state = {"switch": False}
self.layers_state = []
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)
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,
user_defined_vector_definitions: 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
# Retrieve user defined vector descriptions from configuration and validate
self._user_defined_vector_descriptions_path = (
None if user_defined_vector_definitions is None else
webviz_settings.shared_settings["user_defined_vector_definitions"]
[user_defined_vector_definitions])
self._user_defined_vector_definitions: Dict[
str, wsc.
VectorDefinition] = create_user_defined_vector_descriptions_from_config(
get_path(self._user_defined_vector_descriptions_path) if self.
_user_defined_vector_descriptions_path else None)
self._custom_vector_definitions = copy.deepcopy(
self._user_defined_vector_definitions)
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, PER_INTVL_, PER_DAY_, 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:
add_vector_to_vector_selector_data(
self._vector_selector_base_data,
vector,
)
# Only vectors from providers are provided to vector calculator
add_vector_to_vector_selector_data(
self._vector_calculator_data,
vector,
)
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.
per_day_vec = create_per_day_vector_name(vector)
per_intvl_vec = create_per_interval_vector_name(vector)
add_vector_to_vector_selector_data(
self._vector_selector_base_data,
per_day_vec,
)
add_vector_to_vector_selector_data(
self._vector_selector_base_data,
per_intvl_vec,
)
# Add vector base to custom vector definition if not existing
vector_base = vector.split(":")[0]
_definition = wsc.VectorDefinitions.get(vector_base, None)
_type = _definition["type"] if _definition else "others"
per_day_vec_base = per_day_vec.split(":")[0]
per_intvl_vec_base = per_intvl_vec.split(":")[0]
if per_day_vec_base not in self._custom_vector_definitions:
self._custom_vector_definitions[
per_day_vec_base] = wsc.VectorDefinition(
type=_type,
description=simulation_vector_description(
per_day_vec_base,
self._user_defined_vector_definitions),
)
if per_intvl_vec_base not in self._custom_vector_definitions:
self._custom_vector_definitions[
per_intvl_vec_base] = wsc.VectorDefinition(
type=_type,
description=simulation_vector_description(
per_intvl_vec_base,
self._user_defined_vector_definitions),
)
# 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
# Add expressions to custom vector definitions
self._custom_vector_definitions_base = copy.deepcopy(
self._custom_vector_definitions)
_custom_vector_definitions_from_expressions = (
get_vector_definitions_from_expressions(
self._predefined_expressions))
for key, value in _custom_vector_definitions_from_expressions.items():
if key not in self._custom_vector_definitions:
self._custom_vector_definitions[key] = value
# 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"))
# Initial selected vectors - NB: {vector1, vector2, vector3} is deprecated!
initial_vectors: List[str] = plot_options.get("vectors", [])
# TODO: Remove when depretaced code is not utilized anymore
if "vectors" in plot_options and any(
elm in plot_options
for elm in ["vector1", "vector2", "vector3"]):
warnings.warn(
'Providing new user input option "vectors" and deprecated user input options '
'"vector1", "vector2" and "vector3" simultaneously. Initially selected vectors '
'for plugin are set equal to new user input option "vectors".')
if not initial_vectors:
initial_vectors = [
plot_options[elm] for elm in ["vector1", "vector2", "vector3"]
if elm in plot_options
][:3]
# Check if initially selected vectors exist in data, raise ValueError if not
missing_vectors = [
elm for elm in initial_vectors
if not is_vector_name_in_vector_selector_data(
elm, self._initial_vector_selector_data)
]
if missing_vectors:
raise ValueError(
f"Cannot find: {', '.join(missing_vectors)} to plot initially in "
"SimulationTimeSeries. Check that the vector(s) exist in your data."
)
if len(initial_vectors) > 3:
warnings.warn(
'User input option "vectors" contains more than 3 vectors. Only the first 3 listed '
"vectors are kept for initially selected vectors - the remaining are neglected."
)
self._initial_vectors = initial_vectors[:3]
# Set callbacks
self.set_callbacks(app)
def __init__(
self,
app,
webviz_settings: WebvizSettings,
ensembles: list,
relpermfile: str = None,
scalfile: Path = None,
sheet_name: Optional[Union[str, int, list]] = None,
):
super().__init__()
WEBVIZ_ASSETS.add(
Path(webviz_subsurface.__file__).parent / "_assets" / "css" /
"block_options.css")
self.ens_paths = {
ens: webviz_settings.shared_settings["scratch_ensembles"][ens]
for ens in ensembles
}
self.plotly_theme = webviz_settings.theme.plotly_theme
self.relpermfile = relpermfile
if self.relpermfile is not None:
self.satfunc = load_csv(ensemble_paths=self.ens_paths,
csv_file=relpermfile)
self.satfunc = self.satfunc.rename(
str.upper, axis="columns").rename(columns={"TYPE": "KEYWORD"})
if "KEYWORD" not in self.satfunc.columns:
raise ValueError(
"There has to be a KEYWORD or TYPE column with corresponding Eclipse keyword: "
"e.g SWOF, SGOF and etc.")
# pylint: disable=literal-comparison
valid_columns = (["ENSEMBLE", "REAL", "KEYWORD", "SATNUM"] +
RelativePermeability.SATURATIONS + [
key
for key in RelativePermeability.SCAL_COLORMAP
if key != "Missing"
])
self.satfunc = self.satfunc[[
col for col in self.satfunc.columns if col in valid_columns
]]
else:
self.satfunc = load_satfunc(self.ens_paths)
if any(keyword in RelativePermeability.RELPERM_FAMILIES[1]
for keyword in self.satfunc["KEYWORD"].unique()):
self.family = 1
if any(keyword in RelativePermeability.RELPERM_FAMILIES[2]
for keyword in self.satfunc["KEYWORD"].unique()):
warnings.warn((
"Mix of keyword family 1 and 2, currently only support one family at the "
"time. Dropping all data of family 2 ('SWFN', 'SGFN', 'SGWFN', 'SOF2', "
"'SOF3', 'SOF32D') and continues with family 1 ('SWOF', 'SGOF', 'SLGOF')."
), )
self.satfunc = self.satfunc[self.satfunc["KEYWORD"].isin(
RelativePermeability.RELPERM_FAMILIES["fam1"])]
if "SGOF" in self.satfunc["KEYWORD"].unique():
if "SLGOF" in self.satfunc["KEYWORD"].unique():
warnings.warn((
"Mix of 'SGOF' and 'SLGOF' in ensembles, resulting in non-unique "
"horizontal axis ('SG' and 'SL') for 'KRG', 'KROG' and 'PCOG'. "
"Dropping all data with 'SLGOF'."), )
self.satfunc = self.satfunc[
self.satfunc["KEYWORD"] != "SLGOF"]
self.sat_axes_maps = {
"SW": ["KRW", "KROW", "PCOW"],
"SG": ["KRG", "KROG", "PCOG"],
}
else:
self.sat_axes_maps = {
"SW": ["KRW", "KROW", "PCOW"],
"SL": ["KRG", "KROG", "PCOG"],
}
elif not all(keyword in RelativePermeability.RELPERM_FAMILIES[2]
for keyword in self.satfunc["KEYWORD"].unique()):
raise ValueError(
"Unrecognized saturation table keyword in data. This should not occur unless "
"there has been changes to ecl2df. Update of this plugin might be required."
)
else:
self.family = 2
self.sat_axes_maps = {
"SW": ["KRW", "PCOW"],
"SG": ["KRG", "PCOG"],
"SO": ["KROW", "KROG"],
}
self.scalfile = scalfile
self.sheet_name = sheet_name
self.scal = (load_scal_recommendation(self.scalfile, self.sheet_name)
if self.scalfile is not None else None)
self.set_callbacks(app)
def __init__(
self,
app: Dash,
webviz_settings: WebvizSettings,
csvfile: str = None,
ensembles: list = None,
aggregated_csvfile: Path = None,
aggregated_parameterfile: Path = None,
observation_file: Path = None,
observation_group: str = "general",
remap_observation_keys: Dict[str, str] = None,
remap_observation_values: Dict[str, str] = None,
colors: Dict = None,
initial_data: Dict = None,
initial_layout: Dict = None,
):
super().__init__()
provider = EnsembleTableProviderFactory.instance()
self._initial_data = initial_data if initial_data else {}
self._initial_layout = initial_layout if initial_layout else {}
if ensembles is not None and csvfile is not None:
ensembles_dict: Dict[str, str] = {
ens_name:
webviz_settings.shared_settings["scratch_ensembles"][ens_name]
for ens_name in ensembles
}
self._parameterproviderset = (
provider.
create_provider_set_from_per_realization_parameter_file(
ensembles_dict))
self._tableproviderset = (
provider.create_provider_set_from_per_realization_csv_file(
ensembles_dict, csvfile))
self._ensemble_names = ensembles
elif aggregated_csvfile and aggregated_parameterfile is not None:
self._tableproviderset = (
provider.create_provider_set_from_aggregated_csv_file(
aggregated_csvfile))
self._parameterproviderset = (
provider.create_provider_set_from_aggregated_csv_file(
aggregated_parameterfile))
self._ensemble_names = self._tableproviderset.ensemble_names()
else:
raise ValueError(
"Specify either ensembles and csvfile or aggregated_csvfile "
"and aggregated_parameterfile")
all_parameters: list = [
self._parameterproviderset.ensemble_provider(ens).column_names()
for ens in self._ensemble_names
]
self._parameter_names: list = list(set().union(*all_parameters))
all_data_columns: list = [
self._tableproviderset.ensemble_provider(ens).column_names()
for ens in self._ensemble_names
]
self._data_column_names: list = list(set().union(*all_data_columns))
dfs = []
for ens in self._ensemble_names:
df = self._parameterproviderset.ensemble_provider(
ens).get_column_data(column_names=self._parameterproviderset.
ensemble_provider(ens).column_names())
df["ENSEMBLE"] = ens
dfs.append(df)
parameterdf = pd.concat(dfs)
self._realizations = sorted(list(parameterdf["REAL"].unique()))
self._parameter_filter = ParameterFilter(self.uuid("parameter-filter"),
parameterdf)
self._observationfile = observation_file
self._observationmodel = (ObservationModel(
get_path(self._observationfile),
observation_group,
remap_observation_keys,
remap_observation_values,
) if self._observationfile else None)
WEBVIZ_ASSETS.add(
Path(webviz_subsurface.__file__).parent / "_assets" / "js" /
"clientside_functions.js")
self._colors: Dict = unique_colors(self._ensemble_names,
webviz_settings.theme)
if colors is not None:
self._colors.update(colors)
self.set_callbacks(app)
def __init__(self):
super().__init__()
self._screenshot_filename = f"{self.plugin_name()}.png"
ASSETS_DIR = Path(pkg_resources.resource_filename("everviz", "assets"))
WEBVIZ_ASSETS.add(ASSETS_DIR / "axis_customization.css")
import pkg_resources
import json
from pathlib import Path
from webviz_config.webviz_assets import WEBVIZ_ASSETS
ASSETS_DIR = Path(pkg_resources.resource_filename("webviz_ert", "assets"))
WEBVIZ_ASSETS.add(ASSETS_DIR / "bootstrap-grid.css")
WEBVIZ_ASSETS.add(ASSETS_DIR / "ert-style.css")
with open(ASSETS_DIR / "ert-style.json") as f:
ERTSTYLE = json.load(f)
WEBVIZ_CONFIG = (
Path(pkg_resources.resource_filename("webviz_ert", "assets")) /
"webviz-config.yml")
import pkg_resources
import json
from pathlib import Path
from webviz_config.webviz_assets import WEBVIZ_ASSETS
ASSETS_DIR = Path(pkg_resources.resource_filename("ertviz", "assets"))
WEBVIZ_ASSETS.add(ASSETS_DIR / "ert-style.css")
with open(ASSETS_DIR / "ert-style.json") as f:
ERTSTYLE = json.load(f)
WEBVIZ_CONFIG = (Path(pkg_resources.resource_filename("ertviz", "assets")) /
"webviz-config.yml")
def __init__(
self,
app: Dash,
webviz_settings: WebvizSettings,
ensembles: list,
surface_attributes: list,
surface_name_filter: List[str] = None,
wellfolder: Path = None,
wellsuffix: str = ".w",
zonelog: str = None,
mdlog: str = None,
well_tvdmin: Union[int, float] = None,
well_tvdmax: Union[int, float] = None,
well_downsample_interval: int = None,
calculate_percentiles: bool = False,
initial_settings: Dict = None,
):
super().__init__()
self._initial_settings = initial_settings if initial_settings else {}
WEBVIZ_ASSETS.add(
Path(webviz_subsurface.__file__).parent / "_assets" / "css" /
"structural_uncertainty.css")
WEBVIZ_ASSETS.add(
Path(webviz_subsurface.__file__).parent / "_assets" / "js" /
"clientside_functions.js")
self._calculate_percentiles = calculate_percentiles
self._wellfolder = wellfolder
self._wellsuffix = wellsuffix
self._wellfiles: List = []
if wellfolder is not None:
self._wellfiles = json.load(find_files(wellfolder, wellsuffix))
self._well_set_model = WellSetModel(
self._wellfiles,
zonelog=zonelog,
mdlog=mdlog,
tvdmin=well_tvdmin,
tvdmax=well_tvdmax,
downsample_interval=well_downsample_interval,
)
self._use_wells = bool(self._wellfiles)
if (self._initial_settings.get("intersection_data", {}).get("well")
and not self._use_wells):
raise KeyError(
"Well is specified in initial settings but no well data is found!"
)
self._surf_attrs = surface_attributes
self._ensemble_paths = {
ens: webviz_settings.shared_settings["scratch_ensembles"][ens]
for ens in ensembles
}
# Create a table of surface files
surface_table = find_surfaces(self._ensemble_paths)
# Filter on provided surface attributes
surface_table = surface_table[surface_table["attribute"].isin(
self._surf_attrs)]
# Filter on provided surface names
self._surfacenames = (list(surface_table["name"].unique())
if surface_name_filter is None else
surface_name_filter)
surface_table = surface_table[surface_table["name"].isin(
surface_name_filter)]
if surface_table.empty:
raise ValueError("No surfaces found with the given attributes")
self.ensembles = list(surface_table["ENSEMBLE"].unique())
for _, attr_df in surface_table.groupby("attribute"):
if set(attr_df["name"].unique()) != set(self._surfacenames):
raise ValueError(
"Surface attributes has different surfaces. This is not supported!"
)
self._surface_ensemble_set_model = {
ens: SurfaceSetModel(surf_ens_df)
for ens, surf_ens_df in surface_table.groupby("ENSEMBLE")
}
self._realizations = sorted(list(surface_table["REAL"].unique()))
self._zonelog = zonelog
colors = [
"#1f77b4", # muted blue
"#ff7f0e", # safety orange
"#2ca02c", # cooked asparagus green
"#d62728", # brick red
"#9467bd", # muted purple
"#8c564b", # chestnut brown
"#e377c2", # raspberry yogurt pink
"#7f7f7f", # middle gray
"#bcbd22", # curry yellow-green
"#17becf", # blue-teal
]
self._surfacecolors = [{
"surfacename":
surfacename,
"ensemble":
ens,
"COLOR":
self._initial_settings.get("colors", {}).get(surfacename, {}).get(
ens, colors[idx % len(colors)]),
} for idx, surfacename in enumerate(self._surfacenames)
for ens in self.ensembles]
self._color_picker = ColorPicker(
app=app,
uuid=self.uuid("colorpicker"),
dframe=pd.DataFrame(self._surfacecolors),
)
self.first_surface_geometry = self._surface_ensemble_set_model[
self.ensembles[0]].first_surface_geometry
self.set_callbacks(app)
