# IO Simulation v2.4 (Continuous State) - 1D Run 15 Analysis Protocol (Awaiting Actual Data) This node defines the protocol for analyzing the actual simulation data from Run 15 [[releases/archive/Information Ontology 1/0127_IO_Simulation_Run15]] once it is available. This protocol is designed to ensure data integrity, reproducibility, and a rigorous analysis process. **Crucially, this node does not present any results or interpretations, as the actual data is not yet loaded. It only outlines the steps to be taken when the data is available.** This protocol builds upon the general data loading protocol defined in [[releases/archive/Information Ontology 1/0134_IO_Data_Loading]] and assumes the simulation was performed using the code from [[releases/archive/Information Ontology 1/0116_IO_Simulation_v2.2_Code]] and that the quantitative metrics to be used are implemented in [[releases/archive/Information Ontology 1/0129_IO_Metrics_Implementation]]. ## 1. Data Loading (Following [[releases/archive/Information Ontology 1/0134_IO_Data_Loading]]) 1. **Specify Data Source:** Identify the exact filenames and paths to the data files containing: * `phi_history` (φ state evolution) * `avg_theta_history` (average stability) * Simulation parameters 2. **Load Parameters:** Load the simulation parameters from their storage location (e.g., a NumPy file or JSON file) into a dictionary. 3. **Load Data Arrays:** Load the `phi_history` and `theta_history` arrays using NumPy's `np.load()` or Pandas' data loading functions, as appropriate for the chosen storage format. 4. **Verify Data Integrity:** Perform the following checks: * Print the shape and data type of the loaded arrays (`phi_history.shape`, `phi_history.dtype`, etc.). * Print a few sample values from the arrays (e.g., the first 5 rows of `phi_history`, the final value of `avg_theta_history`). * Verify that the loaded parameters match the expected values for Run 15 (as documented in [[releases/archive/Information Ontology 1/0127_IO_Simulation_Run15]]). 5. **Create Results Dictionary:** Construct a `results` dictionary containing the loaded parameters and data arrays, following the structure used in the simulation code (e.g., `results = {"parameters": params, "phi_history": phi_history, "theta_history": theta_history, ...}`). ## 2. Metric Calculation (Using [[releases/archive/Information Ontology 1/0129_IO_Metrics_Implementation]]) 1. **Import Metric Functions:** Ensure that the metric calculation functions defined in [[releases/archive/Information Ontology 1/0129_IO_Metrics_Implementation]] are accessible (e.g., by importing them from a separate Python file). 2. **Set Analysis Parameters:** Based on a visual inspection of the `phi_history` data (once loaded), tune the analysis parameters for the metrics (e.g., `domain_delta`, `grad_threshold`, `amplitude_threshold`) to effectively capture the relevant features of the emergent structures. 3. **Calculate Metrics:** Apply the metric functions to the loaded data and store the results. ## 3. Data Interpretation 1. **Analyze Summary Statistics:** Examine the calculated average domain length, boundary density, boundary velocity, dominant frequencies, and compactness. 2. **Relate to Visual Patterns:** Connect the quantitative metrics to the visual patterns observed in the `phi_history` spacetime plot. Do the metrics accurately reflect the perceived domain structure, boundary dynamics, and oscillatory behavior? 3. **Compare to Run 13/14:** Compare the metrics to those obtained in previous runs (Run 13, Run 14) to understand how the intermediate coupling regime influences the emergent dynamics. 4. **Draw Conclusions:** Based on the quantitative analysis and visual interpretation, draw conclusions about the nature of the emergent structures and dynamics in Run 15. Does this regime show promise for generating more complex, particle-like entities or other targeted behaviors? ## 4. Next Steps (Based on Results) The next steps will depend on the outcome of the analysis: * **If Clear Structures Emerge:** * Explore the parameter space around the Run 15 settings to optimize the emergence of these structures. * Develop new metrics to characterize the structures more fully (e.g., topological measures, stability measures). * **If No Clear Structures Emerge:** * Revisit the parameter choices. Are the analysis parameters (e.g., thresholds) appropriate? * Consider modifying the simulation parameters (e.g., increasing noise, changing the coupling strengths). * If repeated attempts fail, re-evaluate the underlying model and potentially pivot to a different approach. ## 5. Conclusion: Awaiting Data for Meaningful Analysis This node defines the protocol for analyzing the actual Run 15 data. The key principle is to ensure data integrity and a rigorous, objective analysis process. No results or interpretations are presented here, as the actual data has not yet been loaded. The next step is to execute this protocol once the data is available, replacing the placeholder with a substantive analysis of the emergent dynamics.