PyAutoNervesBoard

1fits: unused2  flip_for_ds9: false unused3hpc:4  hpc_mode: false5  iterations_per_update: 5000 unused6model: unused7  ignore_prior_limits: false unused8output:9  force_pickle_overwrite: false10  info_whitespace_length: 8011  log_file: output.log unused12  log_level: INFO unused13  log_to_file: false unused14  model_results_decimal_places: 315  remove_files: false16  samples_to_csv: false17structures:18  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.

1label:2  label:3    density: \rho4    full_well_depth: h5    gamma: \gamma6    ka: ka7    kv: kv8    omega: \omega9    release_timescale: \tau10    release_timescale_sigma: \sigma11    scale_factor: \omega12    well_fill_alpha: \alpha13    well_fill_gamma: \gamma14    well_fill_power: \beta15    well_notch_depth: d16  superscript:17    CCDComplex: CCD18    CCDPhase: CCD19    HyperCINoiseScalar: H20    PixelBounce: pb21    TrapInstantCapture: s22    TrapInstantCaptureContinuum: sc23label_format:24  format:25    density: '{:.2f}'26    full_well_depth: '{:.2f}'27    gamma: '{:.2f}'28    ka: '{:.2f}'29    kv: '{:.2f}'30    omega: '{:.2f}'31    release_timescale: '{:.2f}'32    release_timescale_sigma: '{:.2f}'33    scale_factor: '{:.2f}'34    well_fill_alpha: '{:.2f}'35    well_fill_gamma: '{:.2f}'36    well_fill_power: '{:.2f}'37    well_notch_depth: '{:.2f}'

1general:2  general:3    backend: default                  # The matploblib backend used for visualization. `default` uses the system default, can specifiy specific backend (e.g. TKAgg, Qt5Agg, WXAgg).4    imshow_origin: upper              # The `origin` input of `imshow`, determining if pixel values are ascending or descending on the y-axis.5    zoom_around_mask: true            # If True, plots of data structures with a mask automatically zoom in the masked region.6    symmetric_cmap_value: 100.0       # The vmin and vmax of all pre-cti data residual-maps.7    subplot_ascending_fpr: true       # If True, subplots showing FPR / EPER trails of many datasets are in ascending order of FPR value.8plots:9  subplot_format: [png]                     # Output format of all plots, can be png, pdf or both (e.g. [png, pdf]).10  combined_only: false                      # If True, only the combined subplots of multi-dataset analyses are output (no per-dataset visualization).11  dataset:12    subplot_dataset: true                   # Plot the subplot of all dataset quantities (2D for charge injection imaging, 1D for Dataset1D)?13    subplot_dataset_regions: true           # Plot per-region binned 1D subplots (e.g. the parallel/serial FPR and EPER)?14    data: true                              # Plot single 1D figures of the data extracted and binned over each region?15    data_logy: true                         # Plot single 1D figures of the data over each region with a log10 y-axis?16    data_binned: true                       # Plot the data binned over rows / columns with and without the FPR (charge injection only)?17    fpr_non_uniformity: false               # Include the fpr_non_uniformity region in the per-region plots (charge injection only)?18  fit:19    subplot_fit: true                       # Plot the subplot of all fit quantities (e.g. model data, residual-map, chi-squared map)?20    subplot_fit_regions: true               # Plot per-region binned 1D fit subplots (e.g. the parallel/serial FPR and EPER)?21    data: true                              # Plot single 1D figures of the fit data (with model overlay) over each region?22    data_logy: true                         # Plot single 1D figures of the fit data over each region with a log10 y-axis?23    residual_map: true                      # Plot single 1D figures of the residual map over each region?24    residual_map_logy: true                 # Plot single 1D figures of the residual map over each region with a log10 y-axis?25    fits_fit: true                          # Output a fit.fits file containing the model data, residual map, normalized residual map and chi-squared map?