PyAutoNervesBoard

1fits:2  flip_for_ds9: false3hpc:4  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.5  iterations_per_update: 5000       # The number of iterations between every update (visualization, results output, etc) in HPC mode.6inversion:7  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.8  reconstruction_vmax_factor: 0.5   # Plots of an Inversion's reconstruction use the reconstructed data's bright value multiplied by this factor.9model:10  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.11output:12  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.13  force_visualize_overwrite: true   # If True, visualization images output by a search (e.g. subplots of the fit) are recreated when a new model-fit is performed.14  info_whitespace_length: 80        # Length of whitespace between the parameter names and values in the model.info / result.info15  log_level: INFO                   # The level of information output by logging.16  log_to_file: false                # If True, outputs the non-linear search log to a file (and not printed to screen).17  log_file: output.log              # The name of the file the logged output is written to (in the non-linear search output folder)18  model_results_decimal_places: 3   # Number of decimal places estimated parameter values / errors are output in model.results.19  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.20  samples_to_csv: true              # If True, non-linear search samples are written to a .csv file.21  unconverged_sample_size : 100     # If outputting results of an unconverged search, the number of samples used to estimate the median PDF values and errors.22parallel:23  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 > 1.2425profiling:26  parallel_profile: false           # If True, the parallelization of the fit is profiled outputting a cPython graph.27  repeats: 1                        # The number of repeat function calls used to measure run-times when profiling.28structures:29  native_binned_only: false           # If True, data structures are only stored in their native and binned format. This is used to reduce memory usage in autocti.30test:31  check_likelihood_function: true   # if True, when a search is resumed the likelihood of a previous sample is recalculated to ensure it is consistent with the previous run.32  exception_override: false33  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.

1version: 12disable_existing_loggers: false34handlers:5  console:6    class: logging.StreamHandler7    level: INFO8    stream: ext://sys.stdout9    formatter: formatter1011root:12  level: INFO13  handlers: [ console ]1415formatters:16  formatter:17    format: '%(asctime)s - %(name)s - %(levelname)s - %(message)s'

1# The notation configs define the labels of every model parameter and its derived quantities, which are used when2# visualizing results (for example labeling the axis of the PDF triangle plots output by a non-linear search).345# label: The label given to the each parameter, for plots like PDF corner plots.67# For example, if `centre=x`, the plot axis will be labeled 'x'.8910# superscript: the superscript used on certain plots that show the results of different model-components.1112# For example, if `TrapInstantCapture=s`, plots where the parameters of the TrapInstantCapture model-component have superscript `s`.1314label:15  label:16    density: \rho17    full_well_depth: h18    gamma: \gamma19    ka: ka20    kv: kv21    omega: \omega22    release_timescale: \tau23    release_timescale_sigma: \sigma24    scale_factor: \omega25    well_fill_alpha: \alpha26    well_fill_gamma: \gamma27    well_fill_power: \beta28    well_notch_depth: d29  superscript:30    CCDComplex: ccd31    CCDPhase: ccd32    HyperCINoiseScalar: H33    PixelBounce: pb34    TrapInstantCapture: s35    TrapInstantCaptureContinuum: sc3637# label_format: The format certain parameters are output as in output files like the `model.results` file.3839# For example, if  `density={:.2f}`, the format of the centre parameter in results files will use this Python format.4041label_format:42  format:43    density: '{:.2f}'44    full_well_depth: '{:.2f}'45    gamma: '{:.2f}'46    ka: '{:.2f}'47    kv: '{:.2f}'48    omega: '{:.2f}'49    release_timescale: '{:.2f}'50    release_timescale_sigma: '{:.2f}'51    scale_factor: '{:.2f}'52    well_fill_alpha: '{:.2f}'53    well_fill_gamma: '{:.2f}'54    well_fill_power: '{:.2f}'55    well_notch_depth: '{:.2f}'

1# Per-script environment variable configuration for automated runs2# (smoke tests, pre-release checks, CI). Same schema as the *_workspace_test3# repos — see autocti_workspace_test/config/build/profile_smoke.yaml.4#5# PYAUTO_TEST_MODE=2 bypasses sampling entirely, so a script's search returns a6# deterministic assertion-valid point instead of fitting. That is what makes the7# modeling/start_here.py-class scripts smokeable at all: they build ordered trap8# models whose identical priors tie at the prior medians, which used to make the9# bypass hard-fail until PyAutoFit#1520 (438f56fac).10defaults:11  PYAUTO_TEST_MODE: "2"12  PYAUTO_SKIP_WORKSPACE_VERSION_CHECK: "1"13  # Drop every figure before it is rasterised or saved, as the autolens /14  # autogalaxy smoke profiles do. The bypassed fit still exercises every plot15  # call (the data/model extraction, the zoom, the subplot layout) — only the16  # matplotlib draw + PNG write and the mask-edge overlay are skipped. Measured17  # locally on imaging_ci/modeling/start_here.py (PyAutoBrain /ci_speedup,18  # 2026-09-08): ~17s of image rasterisation + ~20-40s of per-figure mask-edge19  # derivation on the 2000x100 frame, across ~100 on-the-fly figures per run.20  PYAUTO_FAST_PLOTS: "1"21  MPLBACKEND: "Agg"22  NUMBA_CACHE_DIR: "/tmp/numba_cache"23  MPLCONFIGDIR: "/tmp/matplotlib"24overrides: {}

1# The settings of a parallelized grid search of non-linear searches.23parallel:4  number_of_cores: 3 # The number of cores the search is parallelized over by default, using Python multiprocessing.5  step_size: 0.1     # The default step size of each grid search parameter, in terms of unit values of the priors.

1general:2  backend: default                  # The matploblib backend used for visualization. `default` uses the system default, can specifiy specific backend (e.g. TKAgg, Qt5Agg, WXAgg).3  imshow_origin: upper              # The `origin` input of `imshow`, determining if pixel values are ascending or descending on the y-axis.4  zoom_around_mask: true            # If True, plots of data structures with a mask automatically zoom in the masked region.5  symmetric_cmap_value: 100.0       # The vmin and vmax of all pre-cti data residual-maps.6  subplot_ascending_fpr: true       # If True, subplots showing FPR / EPER trails of many datasets are in ascending order of FPR value.

1subplot_format: [png]                     # Output format of all plots, can be png, pdf or both (e.g. [png, pdf]).2combined_only: false                      # If True, only the combined subplots of multi-dataset analyses are output (no per-dataset visualization).3dataset:4  subplot_dataset: true                   # Plot the subplot of all dataset quantities (2D for charge injection imaging, 1D for Dataset1D)?5  subplot_dataset_regions: true           # Plot per-region binned 1D subplots (e.g. the parallel/serial FPR and EPER)?6  data: true                              # Plot single 1D figures of the data extracted and binned over each region?7  data_logy: true                         # Plot single 1D figures of the data over each region with a log10 y-axis?8  data_binned: true                       # Plot the data binned over rows / columns with and without the FPR (charge injection only)?9  fpr_non_uniformity: false               # Include the fpr_non_uniformity region in the per-region plots (charge injection only)?10fit:11  subplot_fit: true                       # Plot the subplot of all fit quantities (e.g. model data, residual-map, chi-squared map)?12  subplot_fit_regions: true               # Plot per-region binned 1D fit subplots (e.g. the parallel/serial FPR and EPER)?13  data: true                              # Plot single 1D figures of the fit data (with model overlay) over each region?14  data_logy: true                         # Plot single 1D figures of the fit data over each region with a log10 y-axis?15  residual_map: true                      # Plot single 1D figures of the residual map over each region?16  residual_map_logy: true                 # Plot single 1D figures of the residual map over each region with a log10 y-axis?17  fits_fit: true                          # Output a fit.fits file containing the model data, residual map, normalized residual map and chi-squared map?

1nest:2  corner_anesthetic: true   # Output corner figure (using anestetic) during a non-linear search fit?3mcmc:4  corner_cornerpy: true     # Output corner figure (using corner.py) during a non-linear search fit?