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inputs_pack

inputs_pack

Input packing utilities for batch scenario operations.

Classes

InputsPack

Bases: Packable

InputsPack handles the import, export, and management of scenario input values, including support for scenario short names, flexible scenario resolution, and comprehensive data validation.

Features
  • Optional inclusion of default values and min/max ranges
  • Multi-level column headers for organized data export
  • Proper handling of list/tuple values in Excel export
  • Comprehensive error handling with detailed logging
Functions
add_to_workbook 
add_to_workbook(workbook, sheet_name='SLIDER_SETTINGS', **kwargs)

Add inputs sheet with proper field handling. Optionally override sheet name.

Source code in src/pyetm/models/packables/inputs_pack.py 
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def add_to_workbook(
    self,
    workbook: Any,
    sheet_name: Optional[str] = "SLIDER_SETTINGS",
    **kwargs: Any,
) -> None:
    """Add inputs sheet with proper field handling. Optionally override sheet name."""
    # Extract our specific parameters from kwargs
    include_defaults = kwargs.get("include_defaults", False)
    include_min_max = kwargs.get("include_min_max", False)

    name = sheet_name or "SLIDER_SETTINGS"
    df = self.to_dataframe(
        include_defaults=include_defaults, include_min_max=include_min_max
    )
    if df is not None and not df.empty:
        df = df.map(
            lambda v: (
                ", ".join(map(str, v)) if isinstance(v, (list, tuple, set)) else v
            )
        )
        self._add_dataframe_to_workbook(workbook, name, df)
from_dataframe 
from_dataframe(df, update_set=None)

Import input values from DataFrame.

Source code in src/pyetm/models/packables/inputs_pack.py 
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def from_dataframe(self, df: Any, update_set: Optional[set[str]] = None) -> None:
    """Import input values from DataFrame."""
    if df is None or getattr(df, "empty", False):
        return

    skip_upload = not self._should_include_upload(update_set or set())

    try:
        # Extract grid data using base class helper
        data_df = self._extract_grid_data(df)
        if data_df is None:
            return

        # Prepare grid data using base class helper
        data_df, input_keys, scenario_columns = self._prepare_grid_data(data_df)

        for column_name in scenario_columns:
            # Resolve scenario with automatic warning
            scenario = self._resolve_scenario_with_warning(
                column_name, "SLIDER_SETTINGS sheet"
            )
            if scenario is None:
                continue

            raw_updates = data_df[column_name].dropna().to_dict()
            if not raw_updates:
                continue

            try:
                scenario.update_user_values(raw_updates, skip_upload=skip_upload)
            except Exception as e:
                logger.warning(
                    "Failed updating inputs for scenario '%s' from column '%s': %s",
                    scenario.identifier(),
                    column_name,
                    e,
                )
            finally:
                self.log_scenario_warnings(scenario, "_inputs", "Inputs")

    except Exception as e:
        logger.warning("Failed to parse SLIDER_SETTINGS sheet: %s", e)