nodes.analysis_core

nodes.analysis_core

Analysis Hamilton Nodes

Advanced analysis nodes for scenarios, sensitivity, uncertainty, and risk analysis.

All functions are Hamilton nodes tagged with layer=‘analysis’ and appropriate domain.

Functions

Name	Description
analysis_results	Combine all analysis results into a dictionary.
carbon_price_sensitivity_results	Run carbon price sensitivity analysis across multiple scenarios.
risk_adjusted_scores	Calculate risk-adjusted benefit scores incorporating uncertainty.
scenario_results	Run scenario analysis for different decarbonization pathways.
tail_risk_results	Run tail risk analysis to identify extreme emissions scenarios.
uncertainty_intervals	Run bootstrap uncertainty quantification via Monte Carlo resampling.
weight_sensitivity_results	Run weight sensitivity analysis to test scoring robustness.

analysis_results

nodes.analysis_core.analysis_results(
    comparison_analysis: pd.DataFrame,
    carbon_price_sensitivity_results: pd.DataFrame,
    weight_sensitivity_results: pd.DataFrame,
    scenario_results: pd.DataFrame,
    uncertainty_intervals: pd.DataFrame | None,
    risk_adjusted_scores: pd.DataFrame,
    tail_risk_results: pd.DataFrame,
    baseline_rankings: pd.DataFrame,
    key_differences: pd.DataFrame,
)

Combine all analysis results into a dictionary.

carbon_price_sensitivity_results

nodes.analysis_core.carbon_price_sensitivity_results(
    operator_petrinex_emissions: pd.DataFrame,
)

Run carbon price sensitivity analysis across multiple scenarios.

risk_adjusted_scores

nodes.analysis_core.risk_adjusted_scores(
    operator_petrinex_emissions: pd.DataFrame,
    uncertainty_intervals: pd.DataFrame | None,
)

Calculate risk-adjusted benefit scores incorporating uncertainty.

scenario_results

nodes.analysis_core.scenario_results(operator_petrinex_emissions: pd.DataFrame)

Run scenario analysis for different decarbonization pathways.

tail_risk_results

nodes.analysis_core.tail_risk_results(operator_petrinex_emissions: pd.DataFrame)

Run tail risk analysis to identify extreme emissions scenarios.

uncertainty_intervals

nodes.analysis_core.uncertainty_intervals(
    facilities_with_emissions: pd.DataFrame,
)

Run bootstrap uncertainty quantification via Monte Carlo resampling.

Returns: Uncertainty intervals with confidence bounds, or None if analysis fails

weight_sensitivity_results

nodes.analysis_core.weight_sensitivity_results(
    operator_petrinex_emissions: pd.DataFrame,
)

Run weight sensitivity analysis to test scoring robustness.