Enhance benchmarking, diagnostics, and observation prior handling - #11
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…andling - Updated the ground truth export button in `speculate_inference.py` and `speculate_quick_fit.py` to ensure proper callback registration by binding them to unique public cell names. - Introduced handling for observation priors, including inclination and distance, to dynamically adjust parameter bounds and defaults based on the selected observation file. - Enhanced the UI for parameter configuration to reflect whether parameters use Normal or Uniform priors, improving clarity for users. - Adjusted the handling of distance priors to ensure they are correctly represented in the UI and calculations.
…optimization/MCMC - Introduced a checkbox to enable fixed local covariance kernels during MLE fitting. - Integrated functions to find and optimize residual peaks for local covariance. - Enhanced MLE process to handle local covariance in two passes: pre-fit and final fit. - Updated the return values and UI elements to reflect the addition of local covariance. - Improved error handling and messaging for local covariance placement. - Added diagnostics for local covariance in the output.
…ed for latin hypercube samples
- Added additional MCMC diagnostic metrics (n_retained_draws, autocorr_time, effective_sample_size, mcse_mean, acceptance_fraction, mcmc_diagnostic_reasons) to the benchmark viewer. - Updated status messages to reflect diagnostic flags instead of convergence status. - Improved coverage reporting by introducing a new function to format coverage metrics for different credible levels (68%, 95%, 99.7%). - Refactored posterior predictive plot generation to separate all runs and converged runs into distinct tabs for better clarity. - Adjusted inclination selection for Tier 2 to ensure valid inclinations are assigned to each run, implementing a round-robin assignment strategy. - Enhanced error handling and messaging for incomplete or corrupted runs.
…ewer for these benchmark tests
- Added state management for Sirocco best-fit plots and spectra in `speculate_inference.py`. - Implemented a separate cell to render the posterior-mean plot independently of the MCMC run. - Introduced Sirocco CPU slider and run button in `speculate_quick_fit.py` for local execution. - Updated export functions to handle Sirocco outputs and maintain clean directory structures. - Improved error handling and user feedback for Sirocco runtime checks and model runs.
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Improvements include enhanced MCMC diagnostics, support for local covariance kernels, and refined observation prior handling for better emulator compatibility. The UI for parameter configuration has been updated for clarity, and several fixes ensure accurate calculations for luminosity and temperature. Additionally, the integration with Sirocco has been enhanced, improving user experience and error handling.