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analysis: n_cores: 1 # The number of cores a parallelized sum of Analysis classes uses by default. hpc: hpc_mode: false # If True, use HPC mode, which disables GUI visualization, logging to screen and other settings which are not suited to running on a super computer. iterations_per_update: 5000 # The number of iterations between every update (visualization, results output, etc) in HPC mode. inversion: check_reconstruction: true # If True, the inversion's reconstruction is checked to ensure the solution of a meshs's mapper is not an invalid solution where the values are all the same. reconstruction_vmax_factor: 0.5 # Plots of an Inversion's reconstruction use the reconstructed data's bright value multiplied by this factor. model: ignore_prior_limits: false # If ``True`` the limits applied to priors will be ignored, where limits set upper / lower limits. This stops PriorLimitException's from being raised. output: force_pickle_overwrite: false # force_pickle_overwrite: false # If True, pickle files output by a search (e.g. samples.pickle) are recreated when a new model-fit is performed. force_visualize_overwrite: false # If True, visualization images output by a search (e.g. subplots of the fit) are recreated when a new model-fit is performed. info_whitespace_length: 80 # Length of whitespace between the parameter names and values in the model.info / result.info log_level: INFO # The level of information output by logging. log_to_file: false # If True, outputs the non-linear search log to a file (and not printed to screen). log_file: output.log # The name of the file the logged output is written to (in the non-linear search output folder) model_results_decimal_places: 3 # Number of decimal places estimated parameter values / errors are output in model.results. remove_files: false # If True, all output files of a non-linear search (e.g. samples, visualization, etc.) are deleted once the model-fit has completed, such that only the .zip file remains. samples_to_csv: true # If True, non-linear search samples are written to a .csv file. unconverged_sample_size : 100 # If outputting results of an unconverged search, the number of samples used to estimate the median PDF values and errors. parallel: warn_environment_variables: true # If True, a warning is displayed when the search's number of CPU > 1 and enviromment variables related to threading are also > 1. prior_passer: sigma: 3.0 # For non-linear search chaining and model prior passing, the sigma value of the inferred model parameter used as the sigma of the passed Gaussian prior. use_errors: true # If True, the errors of the previous model's results are used when passing priors. use_widths: true # If True the width of the model parameters defined in the priors config file are used. profiling: parallel_profile: false # If True, the parallelization of the fit is profiled outputting a cPython graph. should_profile: false # If True, the ``profile_log_likelihood_function()`` function of an analysis class is called throughout a model-fit, profiling run times. repeats: 1 # The number of repeat function calls used to measure run-times when profiling. test: check_preloads: false exception_override: false lh_timeout_seconds: # If a float is input, the log_likelihood_function call is timed out after this many seconds, to diagnose infinite loops. Default is None, meaning no timeout. preloads_check_threshold: 1.0 # If the figure of merit of a fit with and without preloads is greater than this threshold, the check preload test fails and an exception raised for a model-fit. parallel_profile: false