ciq visual atlas v2 Python real data Simulator

Here is the precise technical analysis of the data structure and functionality defined in the script: 1. Directory Configuration Base Path: /content/drive/MyDrive/1 grundstruktur Scan Depth: The script performs a recursive walk through the base directory, limited to a maximum depth of 2 levels. 2. Specific Input Files (CSV) The system expects the following specific filenames in the base directory for MCMC and observational analysis: MCMC Samples: ciq_mcmc_samples_5000.csv, mcmc_samples_standard.csv, mcmc_samples_adjusted.csv, mcmc_v31_standard.csv, mcmc_v31_adjusted.csv, and mcmc_v32_refined_fs8.csv. Reference Data: bao_fs8_data.csv, correlation_matrix_v32.csv, and ciq_isomorphie_correlation_matrix.csv. 3. Image Classification Logic Images (.png, .jpg, .jpeg) are automatically categorized into virtual galleries based on keywords in their filenames: Statistics: mcmc, corner, trace, posterior. Cosmology: hubble, h0, tension, bao, cosmolog. Dark Matter: dark, halo, rotation, sdm, archive. Physics & Models: gravit, pde, stabil, entropy, spin, v3_. AI & Isomorphy: isomorphie, ai, ki, agent, omni. 4. Forensic Key Figures (Reference Values) If no files are found on Drive, the script utilizes hard-coded "real data": Hubble Constant (H0): Median 73.01 (+1.04 / -1.05). Matter Density (Omega_m): Median 0.315 (+0.030 / -0.030). Information Loss (A_CIQ): Median 0.0958 (Theoretical Sweet Spot: 0.0965). Stability: Final Q-factor of 15,686.9. 5. Automated Outputs Dashboard: Generates the file ciq_forensic_dashboard_v2.png directly in the base folder. Scorecard: Conducts 11 statistical tests (including MCMC convergence, Chi2 comparison, and causal correlation) visualized within the dashboard.