{"env_vars":[{"comment":"Return True if the JAX backend is disabled for this process.","line":260,"modules":["autonerves/test_mode.py"],"name":"PYAUTO_DISABLE_JAX"},{"comment":"Return the ``PYAUTO_LATENT_NAN_INJECT`` spec string, or ``None``.","line":93,"modules":["autonerves/test_mode.py"],"name":"PYAUTO_LATENT_NAN_INJECT"},{"comment":"Return True if validation checks should be skipped.","line":75,"modules":["autonerves/test_mode.py"],"name":"PYAUTO_SKIP_CHECKS"},{"comment":"Return True if fit I/O should be skipped.","line":55,"modules":["autonerves/test_mode.py"],"name":"PYAUTO_SKIP_FIT_OUTPUT"},{"comment":"Return True if latent variable computation should be skipped.","line":84,"modules":["autonerves/test_mode.py"],"name":"PYAUTO_SKIP_LATENTS"},{"comment":"Return True if fit visualization should be skipped.","line":65,"modules":["autonerves/test_mode.py"],"name":"PYAUTO_SKIP_VISUALIZATION"},{"comment":"Verify that the installed library is new enough for the workspace at ``workspace_root``.","line":13,"modules":["autonerves/workspace.py"],"name":"PYAUTO_SKIP_WORKSPACE_VERSION_CHECK"},{"comment":"Return True if the small-datasets regime is active.","line":223,"modules":["autonerves/test_mode.py"],"name":"PYAUTO_SMALL_DATASETS"},{"comment":"Return the current test mode level.","line":14,"modules":["autonerves/test_mode.py"],"name":"PYAUTO_TEST_MODE"},{"comment":"Return the number of fake samples the test-mode sampler bypass writes.","line":28,"modules":["autonerves/test_mode.py"],"name":"PYAUTO_TEST_MODE_SAMPLES"}],"errors":[],"files":[{"error":null,"keys":[{"c":"","k":"updates","line":1,"use":"used"},{"c":"Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit.","k":"updates.iterations_per_quick_update","line":2,"use":"used"},{"c":"Non-linear search iterations between every full update, which outputs all visuals and result fits (e.g. model.result, search.summary), this exits the search and can be slow.","k":"updates.iterations_per_full_update","line":3,"use":"used"},{"c":"If True, quick-update visualization runs on a background thread so sampling is not blocked.","k":"updates.quick_update_background","line":4,"use":"used"},{"c":"If True, quick-update visuals are pushed to a live surface (a Jupyter cell when in a kernel, a matplotlib viewer subprocess otherwise) in addition to being written to disk.","k":"updates.live_visual_update","line":5,"use":"used"},{"c":"","k":"hpc","line":6,"use":"used"},{"c":"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.","k":"hpc.hpc_mode","line":7,"use":"used"},{"c":"Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit.","k":"hpc.iterations_per_quick_update","line":8,"use":"used"},{"c":"Non-linear search iterations between every full update, which outputs all visuals and result fits (e.g. model.result, search.summary), this exits the search and can be slow.","k":"hpc.iterations_per_full_update","line":9,"use":"used"},{"c":"","k":"inversion","line":10,"use":"used"},{"c":"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.","k":"inversion.check_reconstruction","line":11,"use":"section-read"},{"c":"Plots of an Inversion's reconstruction use the reconstructed data's bright value multiplied by this factor.","k":"inversion.reconstruction_vmax_factor","line":12,"use":"section-read"},{"c":"","k":"output","line":13,"use":"used"},{"c":"If True pickle files output by a search (e.g. samples.pickle) are recreated when a new model-fit is performed.","k":"output.force_pickle_overwrite","line":14,"use":"used"},{"c":"If True, visualization images output by a search (e.g. subplots of the fit) are recreated when a new model-fit is performed.","k":"output.force_visualize_overwrite","line":15,"use":"used"},{"c":"Length of whitespace between the parameter names and values in the model.info / result.info","k":"output.info_whitespace_length","line":16,"use":"used"},{"c":"The level of information output by logging.","k":"output.log_level","line":17,"use":"unused"},{"c":"If True, outputs the non-linear search log to a file (and not printed to screen).","k":"output.log_to_file","line":18,"use":"unused"},{"c":"The name of the file the logged output is written to (in the non-linear search output folder)","k":"output.log_file","line":19,"use":"unused"},{"c":"Number of decimal places estimated parameter values / errors are output in model.results.","k":"output.model_results_decimal_places","line":20,"use":"used"},{"c":"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.","k":"output.remove_files","line":21,"use":"used"},{"c":"If True, the search internal folder which contains a saved state of the non-linear search is delete, saving on hard-disk space.","k":"output.search_internal","line":22,"use":"unused"},{"c":"If True, non-linear search samples are written to a .csv file.","k":"output.samples_to_csv","line":23,"use":"used"},{"c":"If outputting results of an unconverged search, the number of samples used to estimate the median PDF values and errors.","k":"output.unconverged_sample_size","line":24,"use":"used"},{"c":"Expectation propagation only. If True, every EP factor search draws its own per-search visuals (e.g. corner plots, model-fit images) and redraws its before-fit visuals. If False, only the first searc\u2026","k":"output.visualize_ep_factor_searches","line":25,"use":"used"},{"c":"","k":"parallel","line":26,"use":"used"},{"c":"If True, a warning is displayed when the search's number of CPU > 1 and enviromment variables related to threading are also > 1.","k":"parallel.warn_environment_variables","line":27,"use":"used"},{"c":"","k":"profiling","line":28,"use":"used"},{"c":"If True, the parallelization of the fit is profiled outputting a cPython graph.","k":"profiling.parallel_profile","line":29,"use":"used"},{"c":"The number of repeat function calls used to measure run-times when profiling.","k":"profiling.repeats","line":30,"use":"unused"},{"c":"","k":"test","line":31,"use":"used"},{"c":"if True, when a search is resumed the likelihood of a previous sample is recalculated to ensure it is consistent with the previous run.","k":"test.check_likelihood_function","line":32,"use":"used"},{"c":"","k":"test.exception_override","line":33,"use":"used"},{"c":"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.","k":"test.lh_timeout_seconds","line":34,"use":"used"},{"c":"If a float is input, log likelihoods whose magnitude exceeds it are treated as numerical garbage and replaced by the resample figure of merit. Blank (null) or inf disables the guard, which is the def\u2026","k":"test.log_likelihood_ceiling","line":35,"use":"used"},{"c":"","k":"test.parallel_profile","line":36,"use":"used"}],"lines":36,"path":"general.yaml","prior":false,"priors":[],"repo":"PyAutoFit","text":"updates:\n  iterations_per_quick_update: 1e99 # Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit.\n  iterations_per_full_update: 1e99  # Non-linear search iterations between every full update, which outputs all visuals and result fits (e.g. model.result, search.summary), this exits the search and can be slow.\n  quick_update_background: false    # If True, quick-update visualization runs on a background thread so sampling is not blocked.\n  live_visual_update: false         # If True, quick-update visuals are pushed to a live surface (a Jupyter cell when in a kernel, a matplotlib viewer subprocess otherwise) in addition to being written to disk.\nhpc:\n  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.\n  iterations_per_quick_update: 1e99 #  Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit.\n  iterations_per_full_update: 1e99  # Non-linear search iterations between every full update, which outputs all visuals and result fits (e.g. model.result, search.summary), this exits the search and can be slow.\ninversion:\n  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.\n  reconstruction_vmax_factor: 0.5   # Plots of an Inversion's reconstruction use the reconstructed data's bright value multiplied by this factor.\noutput:\n  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.\n  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.\n  info_whitespace_length: 80        # Length of whitespace between the parameter names and values in the model.info / result.info\n  log_level: INFO                   # The level of information output by logging.\n  log_to_file: false                # If True, outputs the non-linear search log to a file (and not printed to screen).\n  log_file: output.log              # The name of the file the logged output is written to (in the non-linear search output folder)\n  model_results_decimal_places: 3   # Number of decimal places estimated parameter values / errors are output in model.results.\n  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.\n  search_internal : false    # If True, the search internal folder which contains a saved state of the non-linear search is delete, saving on hard-disk space.\n  samples_to_csv: true              # If True, non-linear search samples are written to a .csv file.\n  unconverged_sample_size : 100     # If outputting results of an unconverged search, the number of samples used to estimate the median PDF values and errors.\n  visualize_ep_factor_searches: false  # Expectation propagation only. If True, every EP factor search draws its own per-search visuals (e.g. corner plots, model-fit images) and redraws its before-fit visuals. If False, only the first search of each factor draws before-fit visuals and no factor search draws per-search visuals (samples and summaries are still written; the EP optimiser's own graph.png / ep_history output is unaffected).\nparallel:\n  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.\nprofiling:\n  parallel_profile: false           # If True, the parallelization of the fit is profiled outputting a cPython graph.\n  repeats: 1                        # The number of repeat function calls used to measure run-times when profiling.\ntest:\n  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.\n  exception_override: false\n  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.\n  log_likelihood_ceiling:           # If a float is input, log likelihoods whose magnitude exceeds it are treated as numerical garbage and replaced by the resample figure of merit. Blank (null) or inf disables the guard, which is the default: a log likelihood scales with the noise-map units, so a fixed magnitude can reject a legitimate fit on a badly-scaled dataset. Opt in where the units are known (e.g. autolens_profiling sets 1.0e+20).\n  parallel_profile: false\n","tooling":false,"top_keys":["updates","hpc","inversion","output","parallel","profiling","test"],"use_counts":{"section-read":2,"unused":5,"used":29}},{"error":null,"keys":[{"c":"","k":"version","line":1,"use":"section-read"},{"c":"","k":"disable_existing_loggers","line":2,"use":"section-read"},{"c":"","k":"handlers","line":4,"use":"section-read"},{"c":"","k":"handlers.console","line":5,"use":"section-read"},{"c":"","k":"handlers.console.class","line":6,"use":"section-read"},{"c":"","k":"handlers.console.level","line":7,"use":"section-read"},{"c":"","k":"handlers.console.stream","line":8,"use":"section-read"},{"c":"","k":"handlers.console.formatter","line":9,"use":"section-read"},{"c":"","k":"root","line":11,"use":"section-read"},{"c":"","k":"root.level","line":12,"use":"section-read"},{"c":"","k":"root.handlers","line":13,"use":"section-read"},{"c":"","k":"formatters","line":15,"use":"section-read"},{"c":"","k":"formatters.formatter","line":16,"use":"section-read"},{"c":"","k":"formatters.formatter.format","line":17,"use":"section-read"},{"c":"Whether to output info on the total number of open files in the system, used for debugging parallelism issues.","k":"total_files_open","line":19,"use":"used"}],"lines":19,"path":"logging.yaml","prior":false,"priors":[],"repo":"PyAutoFit","text":"version: 1\ndisable_existing_loggers: false\n\nhandlers:\n  console:\n    class: logging.StreamHandler\n    level: INFO\n    stream: ext://sys.stdout\n    formatter: formatter\n\nroot:\n  level: INFO\n  handlers: [ console ]\n\nformatters:\n  formatter:\n    format: '%(asctime)s - %(name)s - %(levelname)s - %(message)s'\n\ntotal_files_open : false # Whether to output info on the total number of open files in the system, used for debugging parallelism issues.","tooling":false,"top_keys":["version","disable_existing_loggers","handlers","root","formatters","total_files_open"],"use_counts":{"section-read":14,"unused":0,"used":1}},{"error":null,"keys":[{"c":"","k":"parallel","line":3,"use":"unused"},{"c":"The number of cores the search is parallelized over by default, using Python multiprocessing.","k":"parallel.number_of_cores","line":4,"use":"unused"},{"c":"The default step size of each grid search parameter, in terms of unit values of the priors.","k":"parallel.step_size","line":5,"use":"unused"}],"lines":5,"path":"non_linear/GridSearch.yaml","prior":false,"priors":[],"repo":"PyAutoFit","text":"# The settings of a parallelized grid search of non-linear searches.\n\nparallel:\n  number_of_cores: 3 # The number of cores the search is parallelized over by default, using Python multiprocessing.\n  step_size: 0.1     # The default step size of each grid search parameter, in terms of unit values of the priors.","tooling":false,"top_keys":["parallel"],"use_counts":{"section-read":0,"unused":3,"used":0}},{"error":null,"keys":[{"c":"","k":"label","line":14,"use":"used"},{"c":"","k":"label.label","line":15,"use":"used"},{"c":"","k":"label.label.centre","line":16,"use":"section-read"},{"c":"","k":"label.label.normalization","line":17,"use":"section-read"},{"c":"","k":"label.label.parameter0","line":18,"use":"section-read"},{"c":"","k":"label.label.parameter1","line":19,"use":"section-read"},{"c":"","k":"label.label.parameter2","line":20,"use":"section-read"},{"c":"","k":"label.label.rate","line":21,"use":"section-read"},{"c":"","k":"label.label.sigma","line":22,"use":"section-read"},{"c":"","k":"label.superscript","line":23,"use":"used"},{"c":"","k":"label.superscript.Exponential","line":24,"use":"section-read"},{"c":"","k":"label.superscript.Gaussian","line":25,"use":"section-read"},{"c":"","k":"label.superscript.ModelComponent0","line":26,"use":"section-read"},{"c":"","k":"label.superscript.ModelComponent1","line":27,"use":"section-read"},{"c":"","k":"label_format","line":33,"use":"used"},{"c":"","k":"label_format.format","line":34,"use":"used"},{"c":"","k":"label_format.format.centre","line":35,"use":"section-read"},{"c":"","k":"label_format.format.normalization","line":36,"use":"section-read"},{"c":"","k":"label_format.format.parameter0","line":37,"use":"section-read"},{"c":"","k":"label_format.format.parameter1","line":38,"use":"section-read"},{"c":"","k":"label_format.format.parameter2","line":39,"use":"section-read"},{"c":"","k":"label_format.format.rate","line":40,"use":"section-read"},{"c":"","k":"label_format.format.sigma","line":41,"use":"section-read"}],"lines":41,"path":"notation.yaml","prior":false,"priors":[],"repo":"PyAutoFit","text":"# The notation configs define the labels of every model parameter which are used when\n# visualizing results (for example labeling the axis of the PDF triangle plots output by a non-linear search).\n\n\n# label: The label given to the each parameter, for plots like PDF corner plots.\n\n# For example, if `centre=x`, the plot axis will be labeled 'x'.\n\n\n# superscript: the superscript used on certain plots that show the results of different model-components.\n\n# For example, if `Gaussian=g`, plots where the parameters of the Gaussian model-component have superscript `g`.\n\nlabel:\n  label:\n    centre: x\n    normalization: norm\n    parameter0: a\n    parameter1: b\n    parameter2: c\n    rate: \\lambda\n    sigma: \\sigma\n  superscript:\n    Exponential: e\n    Gaussian: g\n    ModelComponent0: M0\n    ModelComponent1: M1\n\n# label_format: The format certain parameters are output as in output files like the `model.results` file.\n\n# For example, if `centre={:.2d}`, the format of the centre parameter in results files will use this Python format.\n\nlabel_format:\n  format:\n    centre: '{:.2f}'\n    normalization: '{:.2f}'\n    parameter0: '{:.2f}'\n    parameter1: '{:.2f}'\n    parameter2: '{:.2f}'\n    rate: '{:.2f}'\n    sigma: '{:.2f}'\n","tooling":false,"top_keys":["label","label_format"],"use_counts":{"section-read":18,"unused":0,"used":5}},{"error":null,"keys":[{"c":"If true then files which are not explicitly listed here are output anyway. If false then they are not.","k":"default","line":4,"use":"used"},{"c":"","k":"samples","line":17,"use":"section-read"},{"c":"","k":"samples_weight_threshold","line":34,"use":"used"},{"c":"","k":"search_internal","line":56,"use":"used"},{"c":"","k":"start_point","line":65,"use":"used"},{"c":"Whether to output the `latent.csv`, `latent.results` and `latent_summary.json` files during the fit when it performs on-the-fly output.","k":"latent_during_fit","line":89,"use":"used"},{"c":"If `latent_during_fit` is False, whether to output the `latent.csv`, `latent.results` and `latent_summary.json` files after the fit is complete.","k":"latent_after_fit","line":90,"use":"used"},{"c":"Whether to draw latent variable values via the PDF of every sample, which uses fewer samples to estimate latent variable errors. If False, latent variable values are drawn from every sample.","k":"latent_draw_via_pdf","line":91,"use":"used"},{"c":"The number of samples drawn to estimate latent variable errors if `latent_draw_via_pdf` is True.","k":"latent_draw_via_pdf_size","line":92,"use":"used"},{"c":"Whether to ouptut the `latent.csv` file.","k":"latent_csv","line":93,"use":"section-read"},{"c":"Whether to output the `latent.results` file.","k":"latent_results","line":94,"use":"section-read"},{"c":"`covariance.csv`: The [free parameters x free parameters] covariance matrix.","k":"covariance","line":98,"use":"used"},{"c":"`data.json`: The value of every data point in the data.","k":"data","line":99,"use":"section-read"},{"c":"`noise_map.json`: The value of every RMS noise map value.","k":"noise_map","line":100,"use":"section-read"},{"c":"`search.log`: logging produced whilst running the fit method","k":"search_log","line":102,"use":"used"},{"c":"`model.graph`: graphical-model factor graph summary. Only meaningful for graphical models (autofit.graphical); empty for ordinary PriorModel/Collection fits, so disabled by default.","k":"model_graph","line":104,"use":"used"},{"c":"","k":"model_figure","line":110,"use":"used"}],"lines":110,"path":"output.yaml","prior":false,"priors":[],"repo":"PyAutoFit","text":"# Determines whether files saved by the search are output to the hard-disk. This is true both when saving to the\n# directory structure and when saving to database.\n\ndefault: true # If true then files which are not explicitly listed here are output anyway. If false then they are not.\n\n### Samples ###\n\n# The `samples.csv`file contains every sampled value of every free parameter with its log likelihood and weight.\n\n# This file is often large, therefore disabling it can significantly reduce hard-disk space use.\n\n# `samples.csv` is used to perform marginalization, infer model parameter errors and do other analysis of the search\n# chains. Even if output of `samples.csv` is disabled, these tasks are still performed by the fit and output to\n# the `samples_summary.json` file. However, without a `samples.csv` file these types of tasks cannot be performed\n# after the fit is complete, for example via the database.\n\nsamples: true\n\n# The `samples.csv` file contains every accepted sampled value of every free parameter with its log likelihood and\n# weight. For certain searches, the majority of samples have a very low weight and have no numerical impact on the\n# results of the model-fit. However, these samples are still output to the `samples.csv` file, taking up hard-disk\n# space and slowing down analysis of the samples (e.g. via the database).\n\n# The `samples_weight_threshold` below specifies the threshold value of the weight such that samples with a weight\n# below this value are not output to the `samples.csv` file. This can be used to reduce the size of the `samples.csv`\n# file and speed up analysis of the samples.\n\n# For many searches (e.g. MCMC) all samples have an equal weight of 1.0, and this threshold therefore has no impact.\n# For these searches, there is no simple way to save hard-disk space. This input is more suited to nested sampling,\n# where the majority of samples have a very low weight..\n\n# Set value to empty (e.g. delete 1.0e-10 below) to disable this feature.\n\nsamples_weight_threshold: 1.0e-10\n\n### Search Internal ###\n\n# The search internal folder which contains a saved state of the non-linear search in its internal reprsenetation,\n# as a .pickle or .dill file.\n\n# For example, for the nested sampling dynesty, this .dill file is the `DynestySampler` object which is used to\n# perform sampling, and it therefore contains all internal dynesty representations of the results, samples, weights, etc.\n\n# If the entry below is false, the folder is still output during the model-fit, as it is required to resume the fit\n# from where it left off. Therefore, settings `false` below does not impact model-fitting checkpointing and resumption.\n# Instead, the search internal folder is deleted once the fit is completed.\n\n# The search internal folder file is often large, therefore deleting it after a fit is complete can significantly\n# reduce hard-disk space use.\n\n# The search internal representation that can be loaded from the .dill file has many additional quantities specific to\n# the non-linear search that the standardized autofit forms do not. For example, for emcee, it contains information on\n# every walker. This information is required to do certain analyes and make certain plots, therefore deleting the\n# folder means this information is list.\n\nsearch_internal: false\n\n### Start Point ###\n\n# If an Initalizer is used to provide a start point for the non-linear search, visualization of that start point can be\n# output to hard-disk to show the user the initial model-fit that is used to start the search. This visualization is\n# the visualizer wrapped in the Analysis class, and therefore should show things like the quality of the fit\n# to the data and the residuals at the start point.\n\nstart_point: true\n\n### Latent Variables ###\n\n# A latent variable is not a model parameter but can be derived from the model. Its value and errors may be of interest\n# and aid in the interpretation of a model-fit.\n\n# For example, for the simple 1D Gaussian example, it could be the full-width half maximum (FWHM) of the Gaussian. This\n# is not included in the model but can be easily derived from the Gaussian's sigma value.\n\n# By overwriting an Analysis class's `compute_latent_variables` method we can manually specify latent variables that\n# are calculated and output to a `latent.csv` file, which mirrors the `samples.csv` file. The `latent.csv` file has\n# the same weight resampling performed on the `samples.csv` file, controlled via the `samples_weight_threshold` above.\n\n# There may also be a `latent.results` and `latent_summary.json` files output, which the inputs below control whether\n# they are output and how often.\n\n# Outputting latent variables manually after a fit is complete is simple, just call\n# the `analysis.compute_latent_variables()` function.\n\n# For many use cases, the best set up may be to disable autofit latent variable output during the fit and perform it\n# manually after completing a successful model-fit. This will save computational run time by not computing latent\n# variables during a any model-fit which is unsuccessful.\n\nlatent_during_fit: false # Whether to output the `latent.csv`, `latent.results` and `latent_summary.json` files during the fit when it performs on-the-fly output.\nlatent_after_fit: true # If `latent_during_fit` is False, whether to output the `latent.csv`, `latent.results` and `latent_summary.json` files after the fit is complete.\nlatent_draw_via_pdf : true # Whether to draw latent variable values via the PDF of every sample, which uses fewer samples to estimate latent variable errors. If False, latent variable values are drawn from every sample.\nlatent_draw_via_pdf_size : 100 # The number of samples drawn to estimate latent variable errors if `latent_draw_via_pdf` is True.\nlatent_csv: true # Whether to ouptut the `latent.csv` file.\nlatent_results: true # Whether to output the `latent.results` file.\n\n# Other Files:\n\ncovariance: true # `covariance.csv`: The [free parameters x free parameters] covariance matrix.\ndata: true # `data.json`: The value of every data point in the data.\nnoise_map: true # `noise_map.json`: The value of every RMS noise map value.\n\nsearch_log: true # `search.log`: logging produced whilst running the fit method\n\nmodel_graph: false # `model.graph`: graphical-model factor graph summary. Only meaningful for graphical models (autofit.graphical); empty for ordinary PriorModel/Collection fits, so disabled by default.\n\n# `model.png`: the model figure drawn beside `model.info` (autofit.model_figure / af.ModelPlotter).\n# Opt-in until the lens acceptance renders pass (model-figures epic phase 3). Unlike other keys this\n# one does NOT fall back to `default` when absent -- an absent key means off.\n\nmodel_figure: 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upper_limit: 25.0\n  width_modifier:\n    type: Relative\n    value: 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lower_limit: 0.0\n  type: Uniform\n  upper_limit: 100.0\n  width_modifier:\n    type: Absolute\n    value: 20.0\nnormalization:\n  limits:\n    lower: 0.0\n    upper: inf\n  lower_limit: 1.0e-06\n  type: LogUniform\n  upper_limit: 1000000.0\n  width_modifier:\n    type: Relative\n    value: 0.5\nsigma:\n  limits:\n    lower: 0.0\n    upper: inf\n  lower_limit: 0.0\n  type: Uniform\n  upper_limit: 25.0\n  width_modifier:\n    type: Relative\n    value: 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lower_limit: 0.0\n  type: Uniform\n  upper_limit: 100.0\n  width_modifier:\n    type: Absolute\n    value: 20.0\nnormalization:\n  limits:\n    lower: 0.0\n    upper: inf\n  lower_limit: 1.0e-06\n  type: LogUniform\n  upper_limit: 1000000.0\n  width_modifier:\n    type: Relative\n    value: 0.5\nsigma:\n  limits:\n    lower: 0.0\n    upper: inf\n  lower_limit: 0.0\n  type: Uniform\n  upper_limit: 25.0\n  width_modifier:\n    type: Relative\n    value: 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0.0\n    type: Uniform\n    upper_limit: 100.0\n    width_modifier:\n      type: Absolute\n      value: 20.0\n  normalization:\n    limits:\n      lower: 0.0\n      upper: inf\n    lower_limit: 1.0e-06\n    type: LogUniform\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n  rate:\n    limits:\n      lower: 0.0\n      upper: inf\n    lower_limit: 0.0\n    type: Uniform\n    upper_limit: 10.0\n    width_modifier:\n      type: Relative\n      value: 0.5\nGaussian:\n  centre:\n    limits:\n      lower: -inf\n      upper: inf\n    lower_limit: 0.0\n    type: Uniform\n    upper_limit: 100.0\n    width_modifier:\n      type: Absolute\n      value: 20.0\n  normalization:\n    limits:\n      lower: 0.0\n      upper: inf\n    lower_limit: 1.0e-06\n    type: LogUniform\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n  sigma:\n    limits:\n      lower: 0.0\n      upper: inf\n    lower_limit: 0.0\n    type: Uniform\n    upper_limit: 25.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n","tooling":false,"top_keys":["Exponential","Gaussian"]},{"error":null,"keys":[{"c":"","k":"gaussian.GaussianPrior","line":1},{"c":"","k":"gaussian.GaussianPrior.lower_limit","line":2},{"c":"","k":"gaussian.GaussianPrior.lower_limit.type","line":3},{"c":"","k":"gaussian.GaussianPrior.lower_limit.value","line":4},{"c":"","k":"gaussian.GaussianPrior.upper_limit","line":5},{"c":"","k":"gaussian.GaussianPrior.upper_limit.type","line":6},{"c":"","k":"gaussian.GaussianPrior.upper_limit.value","line":7}],"lines":7,"path":"priors/prior.yaml","prior":true,"priors":[{"a":"value -inf","b":"","cls":"gaussian.GaussianPrior","limits":"","line":2,"param":"lower_limit","type":"Constant","width":""},{"a":"value inf","b":"","cls":"gaussian.GaussianPrior","limits":"","line":5,"param":"upper_limit","type":"Constant","width":""}],"repo":"PyAutoFit","text":"gaussian.GaussianPrior:\n  lower_limit:\n    type: Constant\n    value: -inf\n  upper_limit:\n    type: Constant\n    value: inf\n","tooling":false,"top_keys":["gaussian.GaussianPrior"]},{"error":null,"keys":[{"c":"","k":"Exponential","line":1},{"c":"","k":"Exponential.centre","line":2},{"c":"","k":"Exponential.centre.limits","line":3},{"c":"","k":"Exponential.centre.limits.lower","line":4},{"c":"","k":"Exponential.centre.limits.upper","line":5},{"c":"","k":"Exponential.centre.lower_limit","line":6},{"c":"","k":"Exponential.centre.type","line":7},{"c":"","k":"Exponential.centre.upper_limit","line":8},{"c":"","k":"Exponential.centre.width_modifier","line":9},{"c":"","k":"Exponential.centre.width_modifier.type","line":10},{"c":"","k":"Exponential.centre.width_modifier.value","line":11},{"c":"","k":"Exponential.normalization","line":12},{"c":"","k":"Exponential.normalization.limits","line":13},{"c":"","k":"Exponential.normalization.limits.lower","line":14},{"c":"","k":"Exponential.normalization.limits.upper","line":15},{"c":"","k":"Exponential.normalization.lower_limit","line":16},{"c":"","k":"Exponential.normalization.type","line":17},{"c":"","k":"Exponential.normalization.upper_limit","line":18},{"c":"","k":"Exponential.normalization.width_modifier","line":19},{"c":"","k":"Exponential.normalization.width_modifier.type","line":20},{"c":"","k":"Exponential.normalization.width_modifier.value","line":21},{"c":"","k":"Exponential.rate","line":22},{"c":"","k":"Exponential.rate.limits","line":23},{"c":"","k":"Exponential.rate.limits.lower","line":24},{"c":"","k":"Exponential.rate.limits.upper","line":25},{"c":"","k":"Exponential.rate.lower_limit","line":26},{"c":"","k":"Exponential.rate.type","line":27},{"c":"","k":"Exponential.rate.upper_limit","line":28},{"c":"","k":"Exponential.rate.width_modifier","line":29},{"c":"","k":"Exponential.rate.width_modifier.type","line":30},{"c":"","k":"Exponential.rate.width_modifier.value","line":31},{"c":"","k":"Gaussian","line":32},{"c":"","k":"Gaussian.centre","line":33},{"c":"","k":"Gaussian.centre.limits","line":34},{"c":"","k":"Gaussian.centre.limits.lower","line":35},{"c":"","k":"Gaussian.centre.limits.upper","line":36},{"c":"","k":"Gaussian.centre.lower_limit","line":37},{"c":"","k":"Gaussian.centre.type","line":38},{"c":"","k":"Gaussian.centre.upper_limit","line":39},{"c":"","k":"Gaussian.centre.width_modifier","line":40},{"c":"","k":"Gaussian.centre.width_modifier.type","line":41},{"c":"","k":"Gaussian.centre.width_modifier.value","line":42},{"c":"","k":"Gaussian.normalization","line":43},{"c":"","k":"Gaussian.normalization.limits","line":44},{"c":"","k":"Gaussian.normalization.limits.lower","line":45},{"c":"","k":"Gaussian.normalization.limits.upper","line":46},{"c":"","k":"Gaussian.normalization.lower_limit","line":47},{"c":"","k":"Gaussian.normalization.type","line":48},{"c":"","k":"Gaussian.normalization.upper_limit","line":49},{"c":"","k":"Gaussian.normalization.width_modifier","line":50},{"c":"","k":"Gaussian.normalization.width_modifier.type","line":51},{"c":"","k":"Gaussian.normalization.width_modifier.value","line":52},{"c":"","k":"Gaussian.sigma","line":53},{"c":"","k":"Gaussian.sigma.limits","line":54},{"c":"","k":"Gaussian.sigma.limits.lower","line":55},{"c":"","k":"Gaussian.sigma.limits.upper","line":56},{"c":"","k":"Gaussian.sigma.lower_limit","line":57},{"c":"","k":"Gaussian.sigma.type","line":58},{"c":"","k":"Gaussian.sigma.upper_limit","line":59},{"c":"","k":"Gaussian.sigma.width_modifier","line":60},{"c":"","k":"Gaussian.sigma.width_modifier.type","line":61},{"c":"","k":"Gaussian.sigma.width_modifier.value","line":62}],"lines":62,"path":"priors/profiles.yaml","prior":true,"priors":[{"a":"lower 0.0","b":"upper 100.0","cls":"Exponential","limits":"[-inf, inf]","line":2,"param":"centre","type":"Uniform","width":"Absolute 20.0"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"Exponential","limits":"[0.0, inf]","line":12,"param":"normalization","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 10.0","cls":"Exponential","limits":"[0.0, inf]","line":22,"param":"rate","type":"Uniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 100.0","cls":"Gaussian","limits":"[-inf, inf]","line":33,"param":"centre","type":"Uniform","width":"Absolute 20.0"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"Gaussian","limits":"[0.0, inf]","line":43,"param":"normalization","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 25.0","cls":"Gaussian","limits":"[0.0, inf]","line":53,"param":"sigma","type":"Uniform","width":"Relative 0.5"}],"repo":"PyAutoFit","text":"Exponential:\n  centre:\n    limits:\n      lower: -inf\n      upper: inf\n    lower_limit: 0.0\n    type: Uniform\n    upper_limit: 100.0\n    width_modifier:\n      type: Absolute\n      value: 20.0\n  normalization:\n    limits:\n      lower: 0.0\n      upper: inf\n    lower_limit: 1.0e-06\n    type: LogUniform\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n  rate:\n    limits:\n      lower: 0.0\n      upper: inf\n    lower_limit: 0.0\n    type: Uniform\n    upper_limit: 10.0\n    width_modifier:\n      type: Relative\n      value: 0.5\nGaussian:\n  centre:\n    limits:\n      lower: -inf\n      upper: inf\n    lower_limit: 0.0\n    type: Uniform\n    upper_limit: 100.0\n    width_modifier:\n      type: Absolute\n      value: 20.0\n  normalization:\n    limits:\n      lower: 0.0\n      upper: inf\n    lower_limit: 1.0e-06\n    type: LogUniform\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n  sigma:\n    limits:\n      lower: 0.0\n      upper: inf\n    lower_limit: 0.0\n    type: Uniform\n    upper_limit: 25.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n","tooling":false,"top_keys":["Exponential","Gaussian"]},{"error":null,"keys":[{"c":"","k":"ModelComponent0","line":1},{"c":"","k":"ModelComponent0.parameter0","line":2},{"c":"","k":"ModelComponent0.parameter0.lower_limit","line":3},{"c":"","k":"ModelComponent0.parameter0.type","line":4},{"c":"","k":"ModelComponent0.parameter0.upper_limit","line":5},{"c":"","k":"ModelComponent0.parameter0.width_modifier","line":6},{"c":"","k":"ModelComponent0.parameter0.width_modifier.type","line":7},{"c":"","k":"ModelComponent0.parameter0.width_modifier.value","line":8},{"c":"","k":"ModelComponent0.parameter1","line":9},{"c":"","k":"ModelComponent0.parameter1.lower_limit","line":10},{"c":"","k":"ModelComponent0.parameter1.type","line":11},{"c":"","k":"ModelComponent0.parameter1.upper_limit","line":12},{"c":"","k":"ModelComponent0.parameter1.width_modifier","line":13},{"c":"","k":"ModelComponent0.parameter1.width_modifier.type","line":14},{"c":"","k":"ModelComponent0.parameter1.width_modifier.value","line":15},{"c":"","k":"ModelComponent0.parameter2","line":16},{"c":"","k":"ModelComponent0.parameter2.lower_limit","line":17},{"c":"","k":"ModelComponent0.parameter2.type","line":18},{"c":"","k":"ModelComponent0.parameter2.upper_limit","line":19},{"c":"","k":"ModelComponent0.parameter2.width_modifier","line":20},{"c":"","k":"ModelComponent0.parameter2.width_modifier.type","line":21},{"c":"","k":"ModelComponent0.parameter2.width_modifier.value","line":22},{"c":"","k":"ModelComponent1","line":23},{"c":"","k":"ModelComponent1.parameter0","line":24},{"c":"","k":"ModelComponent1.parameter0.limits","line":25},{"c":"","k":"ModelComponent1.parameter0.limits.lower","line":26},{"c":"","k":"ModelComponent1.parameter0.limits.upper","line":27},{"c":"","k":"ModelComponent1.parameter0.lower_limit","line":28},{"c":"","k":"ModelComponent1.parameter0.type","line":29},{"c":"","k":"ModelComponent1.parameter0.upper_limit","line":30},{"c":"","k":"ModelComponent1.parameter0.width_modifier","line":31},{"c":"","k":"ModelComponent1.parameter0.width_modifier.type","line":32},{"c":"","k":"ModelComponent1.parameter0.width_modifier.value","line":33},{"c":"","k":"ModelComponent1.parameter1","line":34},{"c":"","k":"ModelComponent1.parameter1.limits","line":35},{"c":"","k":"ModelComponent1.parameter1.limits.lower","line":36},{"c":"","k":"ModelComponent1.parameter1.limits.upper","line":37},{"c":"","k":"ModelComponent1.parameter1.lower_limit","line":38},{"c":"","k":"ModelComponent1.parameter1.type","line":39},{"c":"","k":"ModelComponent1.parameter1.upper_limit","line":40},{"c":"","k":"ModelComponent1.parameter1.width_modifier","line":41},{"c":"","k":"ModelComponent1.parameter1.width_modifier.type","line":42},{"c":"","k":"ModelComponent1.parameter1.width_modifier.value","line":43},{"c":"","k":"ModelComponent1.parameter2","line":44},{"c":"","k":"ModelComponent1.parameter2.limits","line":45},{"c":"","k":"ModelComponent1.parameter2.limits.lower","line":46},{"c":"","k":"ModelComponent1.parameter2.limits.upper","line":47},{"c":"","k":"ModelComponent1.parameter2.lower_limit","line":48},{"c":"","k":"ModelComponent1.parameter2.type","line":49},{"c":"","k":"ModelComponent1.parameter2.upper_limit","line":50},{"c":"","k":"ModelComponent1.parameter2.width_modifier","line":51},{"c":"","k":"ModelComponent1.parameter2.width_modifier.type","line":52},{"c":"","k":"ModelComponent1.parameter2.width_modifier.value","line":53}],"lines":53,"path":"priors/template.yaml","prior":true,"priors":[{"a":"lower 0.0","b":"upper 1.0","cls":"ModelComponent0","limits":"","line":2,"param":"parameter0","type":"Uniform","width":"Absolute 20.0"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"ModelComponent0","limits":"","line":9,"param":"parameter1","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 25.0","cls":"ModelComponent0","limits":"","line":16,"param":"parameter2","type":"Uniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 1.0","cls":"ModelComponent1","limits":"[-inf, inf]","line":24,"param":"parameter0","type":"Uniform","width":"Absolute 20.0"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"ModelComponent1","limits":"[0.0, inf]","line":34,"param":"parameter1","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 1.0","cls":"ModelComponent1","limits":"[0.0, inf]","line":44,"param":"parameter2","type":"Uniform","width":"Relative 0.5"}],"repo":"PyAutoFit","text":"ModelComponent0:\n  parameter0:\n    lower_limit: 0.0\n    type: Uniform\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 20.0\n  parameter1:\n    lower_limit: 1.0e-06\n    type: LogUniform\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n  parameter2:\n    lower_limit: 0.0\n    type: Uniform\n    upper_limit: 25.0\n    width_modifier:\n      type: Relative\n      value: 0.5\nModelComponent1:\n  parameter0:\n    limits:\n      lower: -inf\n      upper: inf\n    lower_limit: 0.0\n    type: Uniform\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 20.0\n  parameter1:\n    limits:\n      lower: 0.0\n      upper: inf\n    lower_limit: 1.0e-06\n    type: LogUniform\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n  parameter2:\n    limits:\n      lower: 0.0\n      upper: inf\n    lower_limit: 0.0\n    type: Uniform\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n","tooling":false,"top_keys":["ModelComponent0","ModelComponent1"]},{"error":null,"keys":[{"c":"","k":"general","line":1,"use":"used"},{"c":"The matploblib backend used for visualization. `default` uses the system default, can specifiy specific backend (e.g. TKAgg, Qt5Agg, WXAgg).","k":"general.backend","line":2,"use":"section-read"}],"lines":2,"path":"visualize/general.yaml","prior":false,"priors":[],"repo":"PyAutoFit","text":"general:\n  backend: default         # The matploblib backend used for visualization. `default` uses the system default, can specifiy specific backend (e.g. TKAgg, Qt5Agg, WXAgg).","tooling":false,"top_keys":["general"],"use_counts":{"section-read":1,"unused":0,"used":1}},{"error":null,"keys":[{"c":"","k":"nest","line":1,"use":"used"},{"c":"Output corner figure (using anestetic) during a non-linear search fit?","k":"nest.corner_anesthetic","line":2,"use":"section-read"},{"c":"","k":"mcmc","line":3,"use":"used"},{"c":"Output corner figure (using corner.py) during a non-linear search fit?","k":"mcmc.corner_cornerpy","line":4,"use":"section-read"},{"c":"","k":"mle","line":5,"use":"used"},{"c":"Output a subplot of the best-fit parameters of the model?","k":"mle.subplot_parameters","line":6,"use":"section-read"},{"c":"Output a plot of the log likelihood versus iteration number?","k":"mle.log_likelihood_vs_iteration","line":7,"use":"section-read"},{"c":"Output the global-best figure-of-merit trace (auto-convergence gradient searches)?","k":"mle.figure_of_merit_vs_iteration","line":8,"use":"section-read"}],"lines":8,"path":"visualize/plots_search.yaml","prior":false,"priors":[],"repo":"PyAutoFit","text":"nest:\n  corner_anesthetic: true   # Output corner figure (using anestetic) during a non-linear search fit?\nmcmc:\n  corner_cornerpy: true     # Output corner figure (using corner.py) during a non-linear search fit?\nmle:\n  subplot_parameters: true   # Output a subplot of the best-fit parameters of the model?\n  log_likelihood_vs_iteration: true  # Output a plot of the log likelihood versus iteration number?\n  figure_of_merit_vs_iteration: true  # Output the global-best figure-of-merit trace (auto-convergence gradient searches)?","tooling":false,"top_keys":["nest","mcmc","mle"],"use_counts":{"section-read":5,"unused":0,"used":3}},{"error":null,"keys":[{"c":"","k":"corner_anesthetic","line":1,"use":"used"},{"c":"The figsize of the matplotlib figure is given as the number of parameters times this value, for exmaple with 3 free parameters and a value 4 the figsize will be 3*4 = 12.","k":"corner_anesthetic.figsize_per_parammeter","line":2,"use":"used"},{"c":"The size of the font of the tick values and parameter labels on the x and y axis of the corner plot.","k":"corner_anesthetic.fontsize","line":3,"use":"used"},{"c":"The facecolor of the corner plot.","k":"corner_anesthetic.facecolor","line":4,"use":"used"},{"c":"The alpha value of the corner plot.","k":"corner_anesthetic.alpha","line":5,"use":"used"},{"c":"","k":"corner_cornerpy","line":6,"use":"used"},{"c":"The size of the font of the tick values and parameter labels on the x and y axis of the corner plot.","k":"corner_cornerpy.fontsize","line":7,"use":"used"}],"lines":7,"path":"visualize/plots_settings.yaml","prior":false,"priors":[],"repo":"PyAutoFit","text":"corner_anesthetic:\n  figsize_per_parammeter: 4   # The figsize of the matplotlib figure is given as the number of parameters times this value, for exmaple with 3 free parameters and a value 4 the figsize will be 3*4 = 12.\n  fontsize: 20               # The size of the font of the tick values and parameter labels on the x and y axis of the corner plot.\n  facecolor: white           # The facecolor of the corner plot.\n  alpha: 0.9                 # The alpha value of the corner plot.\ncorner_cornerpy:\n  fontsize: 14               # The size of the font of the tick values and parameter labels on the x and y axis of the corner plot.","tooling":false,"top_keys":["corner_anesthetic","corner_cornerpy"],"use_counts":{"section-read":0,"unused":0,"used":7}},{"error":null,"keys":[{"c":"","k":"psf","line":1,"use":"used"},{"c":"If True, PSFs are convolved using FFTs by default, which is faster and uses less memory in all cases except for very small PSFs, False uses direct convolution.","k":"psf.use_fft_default","line":2,"use":"used"},{"c":"","k":"inversion","line":3,"use":"used"},{"c":"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.","k":"inversion.check_reconstruction","line":4,"use":"section-read"},{"c":"If True, inversion's use a positive-only linear algebra solver by default, which is slower but prevents unphysical negative values in the reconstructed solutuion.","k":"inversion.use_positive_only_solver","line":5,"use":"used"},{"c":"If True, the edge pixels of a pixelization are set to zero, which prevents unphysical values in the reconstructed solution at the edge of the pixelization. NOTE: this is applied ONLY when use_positiv\u2026","k":"inversion.use_edge_zeroed_pixels","line":6,"use":"used"},{"c":"The default value added to the curvature matrix's diagonal when regularization is not applied to a linear object, which prevents inversion's failing due to the matrix being singular.","k":"inversion.no_regularization_add_to_curvature_diag_value","line":7,"use":"used"},{"c":"If True, by default a pixelization's border is used to relocate all pixels outside its border to the border.","k":"inversion.use_border_relocator","line":8,"use":"used"},{"c":"If True (default), the curvature matrix passed to jaxnnls.solve_nnls_primal is Jacobi-preconditioned (D Q D y = D q, x = D y). Fixes NaN backward-pass gradients on ill-conditioned Q and roughly halve\u2026","k":"inversion.nnls_jacobi_preconditioning","line":9,"use":"used"},{"c":"Central-path relaxation parameter passed to jaxnnls.solve_nnls_primal. Larger values smooth the relaxed-KKT backward pass and prevent NaN gradients on ill-conditioned Q; smaller values tighten the pr\u2026","k":"inversion.nnls_target_kappa","line":10,"use":"used"},{"c":"How the JAX positive-only PDIP solve scales inversions with NO mapper (linear light profiles / MGE only). \"raw\" (default) runs the forward solve on the un-preconditioned system with a data-scaled KKT\u2026","k":"inversion.nnls_preconditioning_no_mapper","line":11,"use":"section-read"},{"c":"If True (default), the NumPy/numba positive-only (fnnls) solve warm-starts its active set from the previous likelihood evaluation's passive set, cutting active-set iterations on successive sampler ev\u2026","k":"inversion.nnls_warm_start_memo","line":12,"use":"used"},{"c":"Relative quality guard on a warm-start memo seed. Each memo entry remembers the error fraction of the most recent dense-sign-started solve for that key; a seeded solve whose own error fraction exceed\u2026","k":"inversion.nnls_warm_start_error_tolerance","line":13,"use":"used"},{"c":"Which solver the JAX (xp=jnp) positive-only reconstruction uses. \"pdip\" (default) is the jaxnnls interior-point solve; \"certified\" is the certified active-set solve (budgeted masked-Cholesky passes t\u2026","k":"inversion.positive_only_solver","line":14,"use":"section-read"},{"c":"Maximum restricted active-set passes of the \"certified\" solver. Measured passes to certification: rectangular <= 11, Delaunay <= 7; the loop exits at certification so unused budget is free.","k":"inversion.certified_pass_budget","line":15,"use":"section-read"},{"c":"What an uncertified (budget-exhausted) \"certified\" solve returns: \"pdip\" runs the PDIP solve instead (via lax.cond -- under vmap both solvers then run for every lane), \"none\" returns the last uncerti\u2026","k":"inversion.certified_fallback","line":16,"use":"section-read"},{"c":"Relative KKT tolerance of the \"certified\" solver: primal violation x < -tau*max|x|, dual violation g < -tau*max|q|.","k":"inversion.certified_tau_rel","line":17,"use":"section-read"},{"c":"Plots of an Inversion's reconstruction use the reconstructed data's bright value multiplied by this factor.","k":"inversion.reconstruction_vmax_factor","line":18,"use":"section-read"},{"c":"How the Bayesian-evidence log-determinant terms are computed. \"cholesky\" (default) is the historical 2*sum(log(diag(cholesky(M)))); \"slogdet\" uses logabsdet of slogdet(M), which is identical where M \u2026","k":"inversion.log_det_method","line":19,"use":"used"},{"c":"How the Bayesian-evidence regularization term s^T H s is computed. \"matmul\" (default) is the historical s @ (H @ s) against the explicitly formed regularization matrix; \"cho_solve\" evaluates coeffici\u2026","k":"inversion.regularization_term_method","line":20,"use":"used"},{"c":"Geometry gate for the numba `direct_conv` interferometer curvature path, in mean non-zeros per source column (mapper.pix_sizes_for_sub_slim_index.sum() / mapper.params). At or below this the factory \u2026","k":"inversion.interferometer_numba_nnz_per_source_max","line":21,"use":"used"},{"c":"","k":"numba","line":22,"use":"used"},{"c":"","k":"numba.use_numba","line":23,"use":"unused"},{"c":"","k":"numba.cache","line":24,"use":"used"},{"c":"","k":"numba.nopython","line":25,"use":"used"},{"c":"","k":"numba.parallel","line":26,"use":"used"},{"c":"","k":"structures","line":27,"use":"used"},{"c":"If True, data structures are only stored in their native and binned format. This is used to reduce memory usage in autocti.","k":"structures.native_binned_only","line":28,"use":"used"}],"lines":28,"path":"general.yaml","prior":false,"priors":[],"repo":"PyAutoArray","text":"psf:\n  use_fft_default: true              # If True, PSFs are convolved using FFTs by default, which is faster and uses less memory in all cases except for very small PSFs, False uses direct convolution.\ninversion:\n  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.\n  use_positive_only_solver: true      # If True, inversion's use a positive-only linear algebra solver by default, which is slower but prevents unphysical negative values in the reconstructed solutuion.\n  use_edge_zeroed_pixels : true       # If True, the edge pixels of a pixelization are set to zero, which prevents unphysical values in the reconstructed solution at the edge of the pixelization. NOTE: this is applied ONLY when use_positive_only_solver is True -- with the positive-negative solver it has no effect. That scoping is deliberate, not an oversight.\n  no_regularization_add_to_curvature_diag_value : 1.0e-3 # The default value added to the curvature matrix's diagonal when regularization is not applied to a linear object, which prevents inversion's failing due to the matrix being singular.\n  use_border_relocator: false          # If True, by default a pixelization's border is used to relocate all pixels outside its border to the border.\n  nnls_jacobi_preconditioning: true   # If True (default), the curvature matrix passed to jaxnnls.solve_nnls_primal is Jacobi-preconditioned (D Q D y = D q, x = D y). Fixes NaN backward-pass gradients on ill-conditioned Q and roughly halves forward solve time. Set False to restore the raw unpreconditioned solve.\n  nnls_target_kappa: 1.0e-11          # Central-path relaxation parameter passed to jaxnnls.solve_nnls_primal. Larger values smooth the relaxed-KKT backward pass and prevent NaN gradients on ill-conditioned Q; smaller values tighten the primal solve. Verified finite gradients across all MGE/rectangular/delaunay pipelines (imaging + interferometer) with scale invariance over 5 orders of magnitude in noise. jaxnnls's own default (1e-3) is too aggressive for the backward pass. The relaxed solve must start from an iterate whose complementarity s*z is not far above this value; the \"raw\" no-mapper mode polishes its forward iterate to ensure that (PyAutoArray#573).\n  nnls_preconditioning_no_mapper: raw # How the JAX positive-only PDIP solve scales inversions with NO mapper (linear light profiles / MGE only). \"raw\" (default) runs the forward solve on the un-preconditioned system with a data-scaled KKT tolerance (1e-2 * n * eps * max(1, max|data_vector|)) and keeps the Jacobi-space relaxed-KKT gradient, whose relaxed solve starts from the forward iterate polished by <= 10 tight warm-started PDIP iterations on the Jacobi system (without the polish the loose forward tolerance leaves s*z far above nnls_target_kappa and the relaxed solve diverged to NaN gradients on 4/16 jax_grad/mge.py points, PyAutoArray#573); \"jacobi\" uses the Jacobi-preconditioned solve. Jacobi scaling of signal-free MGE columns (diagonal = the no-regularization floor) made the PDIP dual diverge on 14/48 SLaM source_lp[1] points (PyAutoArray#571). Inversions with a mapper always use jacobi; the NumPy path is unaffected.\n  nnls_warm_start_memo: true         # If True (default), the NumPy/numba positive-only (fnnls) solve warm-starts its active set from the previous likelihood evaluation's passive set, cutting active-set iterations on successive sampler evaluations. The NNLS optimum is unique so the reconstruction is unchanged. On by default as of PyAutoArray#498, measured on the euclid+hst Delaunay-1250 fiducial (9.9x / 4.0x fewer active-set iterations on successive evaluations, reconstruction unchanged). Set false, or AUTOARRAY_NNLS_WARM_START=0, to disable. JAX path unaffected.\n  nnls_warm_start_error_tolerance: 1.5 # Relative quality guard on a warm-start memo seed. Each memo entry remembers the error fraction of the most recent dense-sign-started solve for that key; a seeded solve whose own error fraction exceeds this multiple of that reference is dropped, so the next solve restarts from the dense-sign start and refreshes the reference. Default 1.5 sits above the worst seed/dense error-fraction ratio seen in the PyAutoArray#498 32-cell robustness matrix (1.42), so it is protective against unmeasured regimes rather than flapping. Any non-finite or non-positive value (e.g. .inf) disables the guard. NumPy/numba fnnls path only.\n  positive_only_solver: pdip          # Which solver the JAX (xp=jnp) positive-only reconstruction uses. \"pdip\" (default) is the jaxnnls interior-point solve; \"certified\" is the certified active-set solve (budgeted masked-Cholesky passes that stop once the KKT conditions certify, exact implicit gradient, PDIP fallback), measured 1.2-2.6x faster on source-only inversions (PyAutoArray#566). Applied only to mapper-only JAX inversions (MGE / linear light profiles keep PDIP); the NumPy path always uses fnnls. Opt-in until the batched (vmap) policy is measured.\n  certified_pass_budget: 16           # Maximum restricted active-set passes of the \"certified\" solver. Measured passes to certification: rectangular <= 11, Delaunay <= 7; the loop exits at certification so unused budget is free.\n  certified_fallback: pdip            # What an uncertified (budget-exhausted) \"certified\" solve returns: \"pdip\" runs the PDIP solve instead (via lax.cond -- under vmap both solvers then run for every lane), \"none\" returns the last uncertified iterate.\n  certified_tau_rel: 1.0e-9           # Relative KKT tolerance of the \"certified\" solver: primal violation x < -tau*max|x|, dual violation g < -tau*max|q|.\n  reconstruction_vmax_factor: 0.5     # Plots of an Inversion's reconstruction use the reconstructed data's bright value multiplied by this factor.\n  log_det_method: cholesky            # How the Bayesian-evidence log-determinant terms are computed. \"cholesky\" (default) is the historical 2*sum(log(diag(cholesky(M)))); \"slogdet\" uses logabsdet of slogdet(M), which is identical where M is positive-definite but finite (not NaN) where the Cholesky fails, for gradient-based searches (opt-in, non-default; does not change the default evidence). Under \"slogdet\" the kernel regularization schemes (Matern/Gaussian/Exponential) also compute the regularization log-det analytically from a Cholesky of their covariance instead of factorizing the formed inverse. See PyAutoArray#391.\n  regularization_term_method: matmul  # How the Bayesian-evidence regularization term s^T H s is computed. \"matmul\" (default) is the historical s @ (H @ s) against the explicitly formed regularization matrix; \"cho_solve\" evaluates coefficient * s^T C^-1 s for the kernel schemes (Matern/Gaussian/Exponential/MaternAdapt) via one Cholesky solve of their covariance C, avoiding the explicit inverse whose round-off is amplified by cond(C) (~1e9 on clustered traced mesh vertices). Opt-in, non-default; does not change the default evidence. Schemes with no such factorization fall back to the formed matrix.\n  interferometer_numba_nnz_per_source_max: 60.0 # Geometry gate for the numba `direct_conv` interferometer curvature path, in mean non-zeros per source column (mapper.pix_sizes_for_sub_slim_index.sum() / mapper.params). At or below this the factory routes a NumPy (xp=np) single-mapper interferometer inversion to InversionInterferometerSparseNumba, which is 2-7x faster than the JAX/FFT route while the mapping operator stays sparse; above it the FFT route wins and is used. Measured crossovers are ~60 (Delaunay) and ~77 (rectangular) on the reference CPU (autolens_profiling#226 verdict section 2) -- this is a machine-dependent constant, so re-measure before tuning. Set 0 to disable the numba path entirely.\nnumba:\n  use_numba: true\n  cache: true\n  nopython: true\n  parallel: false\nstructures:\n  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.\n","tooling":false,"top_keys":["psf","inversion","numba","structures"],"use_counts":{"section-read":7,"unused":1,"used":20}},{"error":null,"keys":[{"c":"","k":"version","line":1,"use":"section-read"},{"c":"","k":"disable_existing_loggers","line":2,"use":"section-read"},{"c":"","k":"handlers","line":4,"use":"section-read"},{"c":"","k":"handlers.console","line":5,"use":"section-read"},{"c":"","k":"handlers.console.class","line":6,"use":"section-read"},{"c":"","k":"handlers.console.level","line":7,"use":"section-read"},{"c":"","k":"handlers.console.stream","line":8,"use":"section-read"},{"c":"","k":"handlers.console.formatter","line":9,"use":"section-read"},{"c":"","k":"root","line":11,"use":"section-read"},{"c":"","k":"root.level","line":12,"use":"section-read"},{"c":"","k":"root.handlers","line":13,"use":"section-read"},{"c":"","k":"formatters","line":15,"use":"section-read"},{"c":"","k":"formatters.formatter","line":16,"use":"section-read"},{"c":"","k":"formatters.formatter.format","line":17,"use":"section-read"}],"lines":17,"path":"logging.yaml","prior":false,"priors":[],"repo":"PyAutoArray","text":"version: 1\ndisable_existing_loggers: false\n\nhandlers:\n  console:\n    class: logging.StreamHandler\n    level: INFO\n    stream: ext://sys.stdout\n    formatter: formatter\n\nroot:\n  level: INFO\n  handlers: [ console ]\n\nformatters:\n  formatter:\n    format: '%(asctime)s - %(name)s - %(levelname)s - %(message)s'\n","tooling":false,"top_keys":["version","disable_existing_loggers","handlers","root","formatters"],"use_counts":{"section-read":14,"unused":0,"used":0}},{"error":null,"keys":[{"c":"","k":"general","line":1,"use":"used"},{"c":"The matplotlib backend used for visualization. `default` uses the system default, can specify specific backend (e.g. TKAgg, Qt5Agg, WXAgg).","k":"general.backend","line":2,"use":"section-read"},{"c":"Resolution in dots per inch used when saving figures. Lower values reduce file size (e.g. 150 gives ~50% smaller files than 300 with negligible quality loss for diagnostic subplots).","k":"general.dpi","line":3,"use":"used"},{"c":"The `origin` input of `imshow`, determining if pixel values are ascending or descending on the y-axis.","k":"general.imshow_origin","line":4,"use":"used"},{"c":"If negative values are being plotted on a log10 scale, values below this value are rounded up to it (e.g. to remove negative values).","k":"general.log10_min_value","line":5,"use":"used"},{"c":"If positive values are being plotted on a log10 scale, values above this value are rounded down to it.","k":"general.log10_max_value","line":6,"use":"section-read"},{"c":"If True, plots of data structures with a mask automatically zoom in the masked region.","k":"general.zoom_around_mask","line":7,"use":"used"},{"c":"Default output format: \"show\" displays the figure interactively via plt.show(), \"png\"/\"pdf\"/etc. saves to file.","k":"general.output_format","line":8,"use":"used"},{"c":"","k":"inversion","line":9,"use":"used"},{"c":"","k":"inversion.reconstruction_vmax_factor","line":10,"use":"section-read"},{"c":"The maximum number of source clumps drawn by subplot_mappings.","k":"inversion.total_mappings","line":11,"use":"section-read"},{"c":"A source pixel joins a clump if its reconstructed value exceeds this fraction of the reconstruction's maximum.","k":"inversion.mappings_threshold","line":12,"use":"section-read"},{"c":"Connected groups of source pixels smaller than this are not drawn as a clump.","k":"inversion.mappings_min_pixels","line":13,"use":"section-read"},{"c":"","k":"zoom","line":14,"use":"used"},{"c":"","k":"zoom.plane_percent","line":15,"use":"used"},{"c":"Plots of an Inversion's reconstruction use the reconstructed data's bright value multiplied by this factor.","k":"zoom.inversion_percent","line":16,"use":"used"},{"c":"","k":"units","line":17,"use":"used"},{"c":"Whether to plot spatial coordinates in scaled units computed via the pixel_scale (e.g. arc-seconds) or pixel units by default.","k":"units.use_scaled","line":18,"use":"section-read"},{"c":"The string or latex unit label used for the colorbar of the image, for example electrons per second.","k":"units.cb_unit","line":19,"use":"used"},{"c":"The symbol used when plotting spatial coordinates computed via the pixel_scale (e.g. for Astronomy data this is arc-seconds).","k":"units.scaled_symbol","line":20,"use":"section-read"},{"c":"The symbol used when plotting spatial coordinates in unscaled pixel units.","k":"units.unscaled_symbol","line":21,"use":"section-read"},{"c":"Default colormap for 2D plots. Use any matplotlib name to override (e.g. jet, viridis).","k":"colormap","line":22,"use":"used"},{"c":"","k":"ticks","line":23,"use":"used"},{"c":"Fraction of half-extent used for 2D tick positions (< 1.0 pulls ticks inward from edges).","k":"ticks.extent_factor_2d","line":24,"use":"section-read"},{"c":"Number of ticks on each spatial axis of 2D plots.","k":"ticks.number_of_ticks_2d","line":25,"use":"section-read"},{"c":"If true, place the arcsec double-prime symbol over the decimal point, e.g. 3.\u20338, instead of after the value, e.g. 3.8\".","k":"ticks.symbol_over_decimal","line":26,"use":"section-read"},{"c":"If true, render negative tick labels with the Unicode/math minus sign (U+2212) instead of an ASCII hyphen (U+002D), e.g. \u22120.\u203305 instead of -0.\u203305.","k":"ticks.minus_in_math","line":27,"use":"section-read"},{"c":"","k":"contour","line":28,"use":"used"},{"c":"Number of contour levels drawn over log10 (and explicit linear) plots.","k":"contour.total_contours","line":29,"use":"used"},{"c":"Whether to label each contour line with its value.","k":"contour.include_values","line":30,"use":"used"},{"c":"","k":"colorbar","line":31,"use":"used"},{"c":"Fraction of original axes to use for the colorbar.","k":"colorbar.fraction","line":32,"use":"section-read"},{"c":"Padding between colorbar and axes.","k":"colorbar.pad","line":33,"use":"section-read"},{"c":"Rotation of colorbar tick labels in degrees.","k":"colorbar.labelrotation","line":34,"use":"section-read"},{"c":"Font size of colorbar tick labels for single-panel figures.","k":"colorbar.labelsize","line":35,"use":"section-read"},{"c":"Font size of colorbar tick labels for subplot panels.","k":"colorbar.labelsize_subplot","line":36,"use":"section-read"},{"c":"","k":"mat_plot","line":37,"use":"used"},{"c":"","k":"mat_plot.figure","line":38,"use":"used"},{"c":"Default figure size. Override via aplt.Figure(figsize=(...)).","k":"mat_plot.figure.figsize","line":39,"use":"used"},{"c":"Per-panel size factor for subplots. figsize = (cols*fx, rows*fy).","k":"mat_plot.figure.subplot_shape_to_figsize_factor","line":40,"use":"used"},{"c":"","k":"mat_plot.title","line":41,"use":"section-read"},{"c":"Default title font size for single-panel figures.","k":"mat_plot.title.fontsize","line":42,"use":"section-read"},{"c":"","k":"mat_plot.title_subplot","line":43,"use":"section-read"},{"c":"Default title font size for subplot panels.","k":"mat_plot.title_subplot.fontsize","line":44,"use":"section-read"},{"c":"","k":"mat_plot.yticks","line":45,"use":"section-read"},{"c":"Default y-tick font size. Override via aplt.YTicks(fontsize=...).","k":"mat_plot.yticks.fontsize","line":46,"use":"section-read"},{"c":"","k":"mat_plot.yticks_subplot","line":47,"use":"section-read"},{"c":"Default y-tick font size for subplot panels.","k":"mat_plot.yticks_subplot.fontsize","line":48,"use":"section-read"},{"c":"","k":"mat_plot.xticks","line":49,"use":"section-read"},{"c":"Default x-tick font size. Override via aplt.XTicks(fontsize=...).","k":"mat_plot.xticks.fontsize","line":50,"use":"section-read"},{"c":"","k":"mat_plot.xticks_subplot","line":51,"use":"section-read"},{"c":"Default x-tick font size for subplot panels.","k":"mat_plot.xticks_subplot.fontsize","line":52,"use":"section-read"},{"c":"","k":"mat_plot.ylabel","line":53,"use":"section-read"},{"c":"Default y-label font size. Override via aplt.YLabel(fontsize=...).","k":"mat_plot.ylabel.fontsize","line":54,"use":"section-read"},{"c":"","k":"mat_plot.xlabel","line":55,"use":"section-read"},{"c":"Default x-label font size. Override via aplt.XLabel(fontsize=...).","k":"mat_plot.xlabel.fontsize","line":56,"use":"section-read"}],"lines":56,"path":"visualize/general.yaml","prior":false,"priors":[],"repo":"PyAutoArray","text":"general:\n  backend: default                      # The matplotlib backend used for visualization. `default` uses the system default, can specify specific backend (e.g. TKAgg, Qt5Agg, WXAgg).\n  dpi: 150                              # Resolution in dots per inch used when saving figures. Lower values reduce file size (e.g. 150 gives ~50% smaller files than 300 with negligible quality loss for diagnostic subplots).\n  imshow_origin: upper                  # The `origin` input of `imshow`, determining if pixel values are ascending or descending on the y-axis.\n  log10_min_value: 1.0e-4               # If negative values are being plotted on a log10 scale, values below this value are rounded up to it (e.g. to remove negative values).\n  log10_max_value: 1.0e99               # If positive values are being plotted on a log10 scale, values above this value are rounded down to it.\n  zoom_around_mask: true                # If True, plots of data structures with a mask automatically zoom in the masked region.\n  output_format: show                   # Default output format: \"show\" displays the figure interactively via plt.show(), \"png\"/\"pdf\"/etc. saves to file.\ninversion:\n  reconstruction_vmax_factor: 0.5\n  total_mappings: 5                     # The maximum number of source clumps drawn by subplot_mappings.\n  mappings_threshold: 0.5               # A source pixel joins a clump if its reconstructed value exceeds this fraction of the reconstruction's maximum.\n  mappings_min_pixels: 3                # Connected groups of source pixels smaller than this are not drawn as a clump.\nzoom:\n  plane_percent: 0.01\n  inversion_percent: 0.01               # Plots of an Inversion's reconstruction use the reconstructed data's bright value multiplied by this factor.\nunits:\n  use_scaled: true                      # Whether to plot spatial coordinates in scaled units computed via the pixel_scale (e.g. arc-seconds) or pixel units by default.\n  cb_unit: $\\,\\,\\mathrm{e^{-}}\\,\\mathrm{s^{-1}}$ # The string or latex unit label used for the colorbar of the image, for example electrons per second.\n  scaled_symbol: '\"'                    # The symbol used when plotting spatial coordinates computed via the pixel_scale (e.g. for Astronomy data this is arc-seconds).\n  unscaled_symbol: pix                  # The symbol used when plotting spatial coordinates in unscaled pixel units.\ncolormap: autoarray               # Default colormap for 2D plots. Use any matplotlib name to override (e.g. jet, viridis).\nticks:\n  extent_factor_2d: 0.75  # Fraction of half-extent used for 2D tick positions (< 1.0 pulls ticks inward from edges).\n  number_of_ticks_2d: 3   # Number of ticks on each spatial axis of 2D plots.\n  symbol_over_decimal: false  # If true, place the arcsec double-prime symbol over the decimal point, e.g. 3.\u20338, instead of after the value, e.g. 3.8\".\n  minus_in_math: false  # If true, render negative tick labels with the Unicode/math minus sign (U+2212) instead of an ASCII hyphen (U+002D), e.g. \u22120.\u203305 instead of -0.\u203305.\ncontour:\n  total_contours: 10       # Number of contour levels drawn over log10 (and explicit linear) plots.\n  include_values: true     # Whether to label each contour line with its value.\ncolorbar:\n  fraction: 0.047          # Fraction of original axes to use for the colorbar.\n  pad: 0.01                # Padding between colorbar and axes.\n  labelrotation: 90        # Rotation of colorbar tick labels in degrees.\n  labelsize: 16            # Font size of colorbar tick labels for single-panel figures.\n  labelsize_subplot: 16    # Font size of colorbar tick labels for subplot panels.\nmat_plot:\n  figure:\n    figsize: (7, 7)                     # Default figure size. Override via aplt.Figure(figsize=(...)).\n    subplot_shape_to_figsize_factor: (6, 6)  # Per-panel size factor for subplots. figsize = (cols*fx, rows*fy).\n  title:\n    fontsize: 24                        # Default title font size for single-panel figures.\n  title_subplot:\n    fontsize: 20                        # Default title font size for subplot panels.\n  yticks:\n    fontsize: 22                        # Default y-tick font size. Override via aplt.YTicks(fontsize=...).\n  yticks_subplot:\n    fontsize: 22                        # Default y-tick font size for subplot panels.\n  xticks:\n    fontsize: 22                        # Default x-tick font size. Override via aplt.XTicks(fontsize=...).\n  xticks_subplot:\n    fontsize: 22                        # Default x-tick font size for subplot panels.\n  ylabel:\n    fontsize: 16                        # Default y-label font size. Override via aplt.YLabel(fontsize=...).\n  xlabel:\n    fontsize: 16                        # Default x-label font size. Override via aplt.XLabel(fontsize=...).\n","tooling":false,"top_keys":["general","inversion","zoom","units","colormap","ticks","contour","colorbar","mat_plot"],"use_counts":{"section-read":34,"unused":0,"used":22}},{"error":null,"keys":[{"c":"Settings for plots of all datasets (e.g. Imaging, Interferometer).","k":"dataset","line":6,"use":"used"},{"c":"Plot subplot containing all dataset quantities (e.g. the data, noise-map, etc.)?","k":"dataset.subplot_dataset","line":7,"use":"section-read"},{"c":"Settings for plots of imaging datasets (e.g. Imaging)","k":"imaging","line":8,"use":"section-read"},{"c":"","k":"imaging.psf","line":9,"use":"section-read"},{"c":"Settings for plots of all fits (e.g. FitImaging, FitInterferometer).","k":"fit","line":10,"use":"section-read"},{"c":"Plot subplot of all fit quantities for any dataset (e.g. the model data, residual-map, etc.)?","k":"fit.subplot_fit","line":11,"use":"section-read"},{"c":"Plot subplot of all fit quantities for any dataset using log10 color maps (e.g. the model data, residual-map, etc.)?","k":"fit.subplot_fit_log10","line":12,"use":"section-read"},{"c":"Plot individual plots of the data?","k":"fit.data","line":13,"use":"section-read"},{"c":"Plot individual plots of the noise-map?","k":"fit.noise_map","line":14,"use":"section-read"},{"c":"Plot individual plots of the signal-to-noise-map?","k":"fit.signal_to_noise_map","line":15,"use":"section-read"},{"c":"Plot individual plots of the model-data?","k":"fit.model_data","line":16,"use":"section-read"},{"c":"Plot individual plots of the residual-map?","k":"fit.residual_map","line":17,"use":"section-read"},{"c":"Plot individual plots of the normalized-residual-map?","k":"fit.normalized_residual_map","line":18,"use":"section-read"},{"c":"Plot individual plots of the chi-squared-map?","k":"fit.chi_squared_map","line":19,"use":"section-read"},{"c":"Plot individual plots of the residual_flux_fraction?","k":"fit.residual_flux_fraction","line":20,"use":"section-read"},{"c":"Settings for plots of fits to imaging datasets (e.g. FitImaging).","k":"fit_imaging","line":21,"use":"section-read"},{"c":"Settings for plots of inversions (e.g. Inversion).","k":"inversion","line":22,"use":"section-read"},{"c":"Plot subplot of all quantities in each inversion (e.g. reconstrucuted image, reconstruction)?","k":"inversion.subplot_inversion","line":23,"use":"section-read"},{"c":"Plot subplot of the image-to-source pixels mappings of each pixelization?","k":"inversion.subplot_mappings","line":24,"use":"section-read"},{"c":"Plot individual plots of the data with the other inversion linear objects subtracted?","k":"inversion.data_subtracted","line":25,"use":"section-read"},{"c":"Plot image of the noise of every mesh-pixel reconstructed value?","k":"inversion.reconstruction_noise_map","line":26,"use":"section-read"},{"c":"Plot the number of sub pixels per masked data pixels?","k":"inversion.sub_pixels_per_image_pixels","line":27,"use":"section-read"},{"c":"Plot the number of image-plane mesh pixels per masked data pixels?","k":"inversion.mesh_pixels_per_image_pixels","line":28,"use":"section-read"},{"c":"Plot the number of image pixels in each pixel of the mesh?","k":"inversion.image_pixels_per_mesh_pixels","line":29,"use":"section-read"},{"c":"Plot image of the reconstructed data (e.g. in the image-plane)?","k":"inversion.reconstructed_operated_data","line":30,"use":"section-read"},{"c":"Plot the reconstructed inversion (e.g. the pixelization's mesh in the source-plane)?","k":"inversion.reconstruction","line":31,"use":"section-read"},{"c":"Plot the effective regularization weight of every inversion mesh pixel?","k":"inversion.regularization_weights","line":32,"use":"section-read"},{"c":"Settings for plots of fits to interferometer datasets (e.g. FitInterferometer).","k":"fit_interferometer","line":33,"use":"section-read"},{"c":"Plot subplot of the dirty-images of all interferometer datasets?","k":"fit_interferometer.subplot_fit_dirty_images","line":34,"use":"section-read"},{"c":"Plot subplot of the real-space images of all interferometer datasets?","k":"fit_interferometer.subplot_fit_real_space","line":35,"use":"section-read"}],"lines":35,"path":"visualize/plots.yaml","prior":false,"priors":[],"repo":"PyAutoArray","text":"# The `plots` section customizes every image that is output to hard-disk during a model-fit.\n\n# For example, if `plots: fit: subplot_fit=True``, the ``fit_dataset.png`` subplot file will \n# be plotted every time visualization is performed.\n\ndataset:                                   # Settings for plots of all datasets (e.g. Imaging, Interferometer).\n  subplot_dataset: true                    # Plot subplot containing all dataset quantities (e.g. the data, noise-map, etc.)?\nimaging:                                   # Settings for plots of imaging datasets (e.g. Imaging)\n   psf: false\nfit:                                       # Settings for plots of all fits (e.g. FitImaging, FitInterferometer).\n  subplot_fit: true                        # Plot subplot of all fit quantities for any dataset (e.g. the model data, residual-map, etc.)?\n  subplot_fit_log10: false                  # Plot subplot of all fit quantities for any dataset using log10 color maps (e.g. the model data, residual-map, etc.)?\n  data: false                              # Plot individual plots of the data?\n  noise_map: false                         # Plot individual plots of the noise-map?\n  signal_to_noise_map: false               # Plot individual plots of the signal-to-noise-map?\n  model_data: false                        # Plot individual plots of the model-data?\n  residual_map: false                      # Plot individual plots of the residual-map?\n  normalized_residual_map: false           # Plot individual plots of the normalized-residual-map?\n  chi_squared_map: false                   # Plot individual plots of the chi-squared-map?\n  residual_flux_fraction: false            # Plot individual plots of the residual_flux_fraction?\nfit_imaging: {}                            # Settings for plots of fits to imaging datasets (e.g. FitImaging).\ninversion:                                 # Settings for plots of inversions (e.g. Inversion).\n  subplot_inversion: true                  # Plot subplot of all quantities in each inversion (e.g. reconstrucuted image, reconstruction)?\n  subplot_mappings: false                  # Plot subplot of the image-to-source pixels mappings of each pixelization?\n  data_subtracted: false                   # Plot individual plots of the data with the other inversion linear objects subtracted?\n  reconstruction_noise_map: false          # Plot image of the noise of every mesh-pixel reconstructed value?\n  sub_pixels_per_image_pixels: false       # Plot the number of sub pixels per masked data pixels?\n  mesh_pixels_per_image_pixels: false      # Plot the number of image-plane mesh pixels per masked data pixels?\n  image_pixels_per_mesh_pixels: false      # Plot the number of image pixels in each pixel of the mesh?\n  reconstructed_operated_data: false               # Plot image of the reconstructed data (e.g. in the image-plane)?\n  reconstruction: false                    # Plot the reconstructed inversion (e.g. the pixelization's mesh in the source-plane)?\n  regularization_weights: false            # Plot the effective regularization weight of every inversion mesh pixel?\nfit_interferometer:                        # Settings for plots of fits to interferometer datasets (e.g. FitInterferometer).\n  subplot_fit_dirty_images: false          # Plot subplot of the dirty-images of all interferometer datasets?\n  subplot_fit_real_space: false            # Plot subplot of the real-space images of all interferometer datasets?","tooling":false,"top_keys":["dataset","imaging","fit","fit_imaging","inversion","fit_interferometer"],"use_counts":{"section-read":29,"unused":0,"used":1}},{"error":null,"keys":[{"c":"","k":"psf","line":1,"use":"used"},{"c":"If True, PSFs are convolved using FFTs by default, which is faster and uses less memory in all cases except for very small PSFs, False uses direct convolution.","k":"psf.use_fft_default","line":2,"use":"used"},{"c":"","k":"updates","line":3,"use":"used"},{"c":"Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit.","k":"updates.iterations_per_quick_update","line":4,"use":"used"},{"c":"Non-linear search iterations between every full update, which outputs all visuals and result fits (e.g. model.result, search.summary), this exits the search and can be slow.","k":"updates.iterations_per_full_update","line":5,"use":"used"},{"c":"","k":"hpc","line":6,"use":"used"},{"c":"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.","k":"hpc.hpc_mode","line":7,"use":"used"},{"c":"Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit.","k":"hpc.iterations_per_quick_update","line":8,"use":"used"},{"c":"Non-linear search iterations between every full update, which outputs all visuals and result fits (e.g. model.result, search.summary), this exits the search and can be slow.","k":"hpc.iterations_per_full_update","line":9,"use":"used"},{"c":"","k":"grid","line":10,"use":"used"},{"c":"An evaluation grid whose shape is adaptive chosen is used to compute quantities like critical curves, this integer is the max size of the grid ensuring faster run times.","k":"grid.max_evaluation_grid_size","line":11,"use":"used"},{"c":"","k":"adapt","line":12,"use":"used"},{"c":"","k":"adapt.adapt_minimum_percent","line":13,"use":"used"},{"c":"","k":"adapt.adapt_noise_limit","line":14,"use":"unused"},{"c":"","k":"inversion","line":15,"use":"used"},{"c":"If True, by default a pixelization's border is used to relocate all pixels outside its border to the border.","k":"inversion.use_border_relocator","line":16,"use":"used"},{"c":"","k":"test","line":17,"use":"used"},{"c":"if True, when a search is resumed the likelihood of a previous sample is recalculated to ensure it is consistent with the previous run.","k":"test.check_likelihood_function","line":18,"use":"used"},{"c":"","k":"test.exception_override","line":19,"use":"used"}],"lines":19,"path":"general.yaml","prior":false,"priors":[],"repo":"PyAutoGalaxy","text":"psf:\n  use_fft_default: true              # If True, PSFs are convolved using FFTs by default, which is faster and uses less memory in all cases except for very small PSFs, False uses direct convolution.\nupdates:\n  iterations_per_quick_update: 1e99 # Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit.\n  iterations_per_full_update: 1e99  # Non-linear search iterations between every full update, which outputs all visuals and result fits (e.g. model.result, search.summary), this exits the search and can be slow.\nhpc:\n  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.\n  iterations_per_quick_update: 1e99 #  Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit.\n  iterations_per_full_update: 1e99  # Non-linear search iterations between every full update, which outputs all visuals and result fits (e.g. model.result, search.summary), this exits the search and can be slow.\ngrid:\n  max_evaluation_grid_size: 1000   # An evaluation grid whose shape is adaptive chosen is used to compute quantities like critical curves, this integer is the max size of the grid ensuring faster run times.\nadapt:\n  adapt_minimum_percent: 0.01\n  adapt_noise_limit: 100000000.0\ninversion:\n  use_border_relocator: true          # If True, by default a pixelization's border is used to relocate all pixels outside its border to the border.\ntest:\n  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.\n  exception_override: false","tooling":false,"top_keys":["psf","updates","hpc","grid","adapt","inversion","test"],"use_counts":{"section-read":0,"unused":1,"used":18}},{"error":null,"keys":[{"c":"Requires no instrument inputs \u2014 default `true`. See the workspace flux guide (`scripts/guides/units/flux.py`) for how to convert this to a microjansky flux using a user-supplied `magzero`.","k":"total_galaxy_0_flux","line":18,"use":"section-read"},{"c":"warning per process \u2014 it does not raise. Workspaces with a known zero-point (e.g. the Euclid pipeline) override this to `true` and pass `magzero` explicitly.","k":"total_galaxy_0_flux_mujy","line":29,"use":"section-read"}],"lines":29,"path":"latent.yaml","prior":false,"priors":[],"repo":"PyAutoGalaxy","text":"# Toggles for the catalogue of latent variables computed by `AnalysisImaging`.\n#\n# Each entry maps a registered latent name (see\n# `autogalaxy/imaging/model/latent.py::LATENT_FUNCTIONS`) to a bool. Setting\n# `false` excludes that latent from `LATENT_KEYS` so it is neither computed\n# nor written to `latent/samples.csv` / `latent/latent_summary.json`.\n#\n# Workspaces should mirror this file in their own `config/latent.yaml` to\n# override defaults locally (workspace values shadow library values).\n\n# `total_galaxy_0_flux` \u2014 total integrated flux of the first galaxy\n# (`fit.galaxies[0]`) in the raw image units the fit was performed in.\n# Returns NaN when galaxy 0 has no light profile.\n#\n# Requires no instrument inputs \u2014 default `true`. See the workspace flux\n# guide (`scripts/guides/units/flux.py`) for how to convert this to a\n# microjansky flux using a user-supplied `magzero`.\ntotal_galaxy_0_flux: true\n\n# `total_galaxy_0_flux_mujy` \u2014 the same total flux converted to\n# microjanskies via `magzero` passed through Analysis kwargs.\n#\n# Default `false` because the conversion needs a per-instrument zero-point\n# that the user must supply (`AnalysisImaging(..., magzero=<value>)`). When\n# enabled without a `magzero`, the latent returns NaN and emits a single\n# warning per process \u2014 it does not raise. Workspaces with a known\n# zero-point (e.g. the Euclid pipeline) override this to `true` and pass\n# `magzero` explicitly.\ntotal_galaxy_0_flux_mujy: false\n","tooling":false,"top_keys":["total_galaxy_0_flux","total_galaxy_0_flux_mujy"],"use_counts":{"section-read":2,"unused":0,"used":0}},{"error":null,"keys":[{"c":"","k":"label","line":1,"use":"used"},{"c":"","k":"label.label","line":2,"use":"used"},{"c":"","k":"label.label.sigma","line":3,"use":"section-read"},{"c":"","k":"label.label.alpha","line":4,"use":"section-read"},{"c":"","k":"label.label.angle_binary","line":5,"use":"section-read"},{"c":"","k":"label.label.beta","line":6,"use":"section-read"},{"c":"","k":"label.label.break_radius","line":7,"use":"section-read"},{"c":"","k":"label.label.centre_0","line":8,"use":"section-read"},{"c":"","k":"label.label.centre_1","line":9,"use":"section-read"},{"c":"","k":"label.label.coefficient","line":10,"use":"section-read"},{"c":"","k":"label.label.c_2","line":11,"use":"section-read"},{"c":"","k":"label.label.concentration","line":12,"use":"section-read"},{"c":"","k":"label.label.core_radius","line":13,"use":"section-read"},{"c":"","k":"label.label.core_radius_0","line":14,"use":"section-read"},{"c":"","k":"label.label.core_radius_1","line":15,"use":"section-read"},{"c":"","k":"label.label.effective_radius","line":16,"use":"section-read"},{"c":"","k":"label.label.einstein_radius","line":17,"use":"section-read"},{"c":"","k":"label.label.ell_comps_0","line":18,"use":"section-read"},{"c":"","k":"label.label.ell_comps_1","line":19,"use":"section-read"},{"c":"","k":"label.label.multipole_comps_0","line":20,"use":"section-read"},{"c":"","k":"label.label.multipole_comps_1","line":21,"use":"section-read"},{"c":"","k":"label.label.scaled_multipole_comps_0","line":22,"use":"section-read"},{"c":"","k":"label.label.scaled_multipole_comps_1","line":23,"use":"section-read"},{"c":"","k":"label.label.flux","line":24,"use":"section-read"},{"c":"","k":"label.label.gamma","line":25,"use":"section-read"},{"c":"","k":"label.label.gamma_1","line":26,"use":"section-read"},{"c":"","k":"label.label.gamma_2","line":27,"use":"section-read"},{"c":"","k":"label.label.inner_coefficient","line":28,"use":"section-read"},{"c":"","k":"label.label.inner_slope","line":29,"use":"section-read"},{"c":"","k":"label.label.intensity","line":30,"use":"section-read"},{"c":"","k":"label.label.kappa","line":31,"use":"section-read"},{"c":"","k":"label.label.kappa_s","line":32,"use":"section-read"},{"c":"","k":"label.label.log10m_vir","line":33,"use":"section-read"},{"c":"","k":"label.label.m","line":34,"use":"section-read"},{"c":"","k":"label.label.mass","line":35,"use":"section-read"},{"c":"","k":"label.label.mass_at_200","line":36,"use":"section-read"},{"c":"","k":"label.label.mass_ratio","line":37,"use":"section-read"},{"c":"","k":"label.label.mass_to_light_gradient","line":38,"use":"section-read"},{"c":"","k":"label.label.mass_to_light_ratio","line":39,"use":"section-read"},{"c":"","k":"label.label.mass_to_light_ratio_base","line":40,"use":"section-read"},{"c":"","k":"label.label.mass_to_light_radius","line":41,"use":"section-read"},{"c":"","k":"label.label.noise_factor","line":42,"use":"section-read"},{"c":"","k":"label.label.noise_power","line":43,"use":"section-read"},{"c":"","k":"label.label.noise_scale","line":44,"use":"section-read"},{"c":"","k":"label.label.normalization_scale","line":45,"use":"section-read"},{"c":"","k":"label.label.outer_coefficient","line":46,"use":"section-read"},{"c":"","k":"label.label.outer_slope","line":47,"use":"section-read"},{"c":"","k":"label.label.overdens","line":48,"use":"section-read"},{"c":"","k":"label.label.pixels","line":49,"use":"section-read"},{"c":"","k":"label.label.ra","line":50,"use":"section-read"},{"c":"","k":"label.label.radius_break","line":51,"use":"section-read"},{"c":"","k":"label.label.redshift","line":52,"use":"section-read"},{"c":"","k":"label.label.redshift_object","line":53,"use":"section-read"},{"c":"","k":"label.label.redshift_source","line":54,"use":"section-read"},{"c":"","k":"label.label.rs","line":55,"use":"section-read"},{"c":"","k":"label.label.scale_radius","line":56,"use":"section-read"},{"c":"","k":"label.label.scatter","line":57,"use":"section-read"},{"c":"","k":"label.label.separation","line":58,"use":"section-read"},{"c":"","k":"label.label.sersic_index","line":59,"use":"section-read"},{"c":"","k":"label.label.shape_0","line":60,"use":"section-read"},{"c":"","k":"label.label.shape_1","line":61,"use":"section-read"},{"c":"","k":"label.label.signal_scale","line":62,"use":"section-read"},{"c":"","k":"label.label.sky_scale","line":63,"use":"section-read"},{"c":"","k":"label.label.slope","line":64,"use":"section-read"},{"c":"","k":"label.label.truncation_radius","line":65,"use":"section-read"},{"c":"","k":"label.label.virial_mass","line":66,"use":"section-read"},{"c":"","k":"label.label.virial_overdens","line":67,"use":"section-read"},{"c":"","k":"label.label.weight_floor","line":68,"use":"section-read"},{"c":"","k":"label.label.weight_power","line":69,"use":"section-read"},{"c":"","k":"label.label.zeroth_coefficient","line":70,"u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 label:\n    sigma: \\sigma\n    alpha: \\alpha\n    angle_binary: \\theta\n    beta: \\beta\n    break_radius: \\theta_{\\rm B}\n    centre_0: y\n    centre_1: x\n    coefficient: \\lambda\n    c_2: c_{\\rm 2}\n    concentration: conc\n    core_radius: C_{\\rm r}\n    core_radius_0: C_{rm r0}\n    core_radius_1: C_{\\rm r1}\n    effective_radius: R_{\\rm eff}\n    einstein_radius: \\theta_{\\rm Ein}\n    ell_comps_0: \\epsilon_{\\rm 1}\n    ell_comps_1: \\epsilon_{\\rm 2}\n    multipole_comps_0: M_{\\rm 1}\n    multipole_comps_1: M_{\\rm 2}\n    scaled_multipole_comps_0: M_{\\rm 1}\n    scaled_multipole_comps_1: M_{\\rm 2}\n    flux: F\n    gamma: \\gamma\n    gamma_1: \\gamma\n    gamma_2: \\gamma\n    inner_coefficient: \\lambda_{\\rm 1}\n    inner_slope: t_{\\rm 1}\n    intensity: I_{\\rm b}\n    kappa: \\kappa\n    kappa_s: \\kappa_{\\rm s}\n    log10m_vir: log_{\\rm 10}(m_{vir})\n    m: m\n    mass: M\n    mass_at_200: M_{\\rm 200}\n    mass_ratio: M_{\\rm ratio}\n    mass_to_light_gradient: \\Gamma\n    mass_to_light_ratio: \\Psi\n    mass_to_light_ratio_base: \\Psi_{\\rm base}\n    mass_to_light_radius: R_{\\rm ref}\n    noise_factor: \\omega_{\\rm 1}\n    noise_power: \\omega{\\rm 2}\n    noise_scale: \\sigma_{\\rm 1}\n    normalization_scale: n\n    outer_coefficient: \\lambda_{\\rm 2}\n    outer_slope: t_{\\rm 2}\n    overdens: \\Delta_{\\rm vir}\n    pixels: N_{\\rm pix}\n    ra: r_{\\rm a}\n    radius_break: R_{\\rm b}\n    redshift: z\n    redshift_object: z_{\\rm obj}\n    redshift_source: z_{\\rm src}\n    rs: r_{\\rm s}\n    scale_radius: R_{\\rm s}\n    scatter: \\sigma\n    separation: s\n    sersic_index: n\n    shape_0: y_{\\rm pix}\n    shape_1: x_{\\rm pix}\n    signal_scale: V\n    sky_scale: \\sigma_{\\rm 0}\n    slope: \\gamma\n    truncation_radius: R_{\\rm t}\n    virial_mass: M_{\\rm vir}\n    virial_overdens: \\Delta_{\\rm vir}\n    weight_floor: W_{\\rm f}\n    weight_power: W_{\\rm p}\n    zeroth_coefficient: \\lambda_{\\rm 0}\n    zeroth_signal_scale: V\n  superscript:\n    ExternalShear: ext\n    GaussianRandomField: grf\n    InputDeflections: input\n    InputPotential: input\n    Mesh: mesh\n    Point: point\n    SMBH: smbh\n    Redshift: z\n    Regularization: reg\nlabel_format:\n  format:\n    sigma: '{:.4f}'\n    alpha: '{:.4f}'\n    angle_binary: '{:.4f}'\n    angular_diameter_distance_to_earth: '{:.4f}'\n    beta: '{:.4f}'\n    c_2: '{:.4f}'\n    centre_0: '{:.4f}'\n    centre_1: '{:.4f}'\n    coefficient: '{:.4f}'\n    concentration: '{:.4f}'\n    core_radius: '{:.4f}'\n    core_radius_0: '{:.4f}'\n    core_radius_1: '{:.4f}'\n    effective_radius: '{:.4f}'\n    einstein_mass: '{:.4e}'\n    einstein_radius: '{:.4f}'\n    ell_comps_0: '{:.4f}'\n    ell_comps_1: '{:.4f}'\n    multipole_comps_0: '{:.4f}'\n    multipole_comps_1: '{:.4f}'\n    input_multipole_comps_0: '{:.4f}'\n    input_multipole_comps_1: '{:.4f}'\n    flux: '{:.4e}'\n    gamma: '{:.4f}'\n    inner_coefficient: '{:.4f}'\n    inner_slope: '{:.4f}'\n    intensity: '{:.4f}'\n    kappa: '{:.4f}'\n    kappa_s: '{:.4f}'\n    kpc_per_arcsec: '{:.4f}'\n    log10m_vir: '{:.4f}'\n    luminosity: '{:.4e}'\n    m: '{:.1f}'\n    mass: '{:.4e}'\n    mass_at_200: '{:.4e}'\n    mass_at_truncation_radius: '{:.4e}'\n    mass_ratio: '{:.4f}'\n    mass_to_light_gradient: '{:.4f}'\n    mass_to_light_ratio: '{:.4f}'\n    n_x: '{:.1d}'\n    n_y: '{:.1d}'\n    noise_factor: '{:.3f}'\n    noise_power: '{:.3f}'\n    noise_scale: '{:.3f}'\n    normalization_scale: '{:.4f}'\n    outer_coefficient: '{:.4f}'\n    outer_slope: '{:.4f}'\n    overdens: '{:.4f}'\n    pixels: '{:.4f}'\n    ra: '{:.4f}'\n    radius: '{:.4f}'\n    radius_break: '{:.4f}'\n    redshift: '{:.4f}'\n    redshift_object: '{:.4f}'\n    redshift_source: '{:.4f}'\n    rho: '{:.4f}'\n    rs: '{:.4f}'\n    scale_radius: '{:.4f}'\n    separation: '{:.4f}'\n    sersic_index: '{:.4f}'\n    shape_0: '{:.4f}'\n    shape_1: '{:.4f}'\n    signal_scale: '{:.4f}'\n    sky_scale: '{:.4f}'\n    slope: '{:.4f}'\n    truncation_radius: '{:.4f}'\n    virial_mass: '{:.4f}'\n    virial_overdens: '{:.4f}'\n    weight_floor: '{:.4f}'\n    weight_power: '{:.4f}'","tooling":false,"top_keys":["label","label_format"],"use_counts":{"section-read":148,"unused":0,"used":5}},{"error":null,"keys":[{"c":"If true then files which are not explicitly listed here are output anyway. If false then they are not.","k":"default","line":4,"use":"used"},{"c":"","k":"samples","line":17,"use":"section-read"},{"c":"","k":"samples_weight_threshold","line":34,"use":"used"},{"c":"","k":"search_internal","line":56,"use":"used"},{"c":"","k":"start_point","line":65,"use":"used"},{"c":"Whether to output the `latent.csv`, `latent.results` and `latent_summary.json` files during the fit when it performs on-the-fly output.","k":"latent_during_fit","line":89,"use":"used"},{"c":"If `latent_during_fit` is False, whether to output the `latent.csv`, `latent.results` and `latent_summary.json` files after the fit is complete.","k":"latent_after_fit","line":90,"use":"used"},{"c":"Whether to draw latent variable values via the PDF of every sample, which uses fewer samples to estimate latent variable errors. If False, latent variable values are drawn from every sample.","k":"latent_draw_via_pdf","line":91,"use":"used"},{"c":"The number of samples drawn to estimate latent variable errors if `latent_draw_via_pdf` is True.","k":"latent_draw_via_pdf_size","line":92,"use":"used"},{"c":"Whether to ouptut the `latent.csv` file.","k":"latent_csv","line":93,"use":"section-read"},{"c":"Whether to output the `latent.results` file.","k":"latent_results","line":94,"use":"section-read"},{"c":"`search.log`: logging produced whilst running the fit method","k":"search_log","line":98,"use":"used"}],"lines":98,"path":"output.yaml","prior":false,"priors":[],"repo":"PyAutoGalaxy","text":"# Determines whether files saved by the search are output to the hard-disk. This is true both when saving to the\n# directory structure and when saving to database.\n\ndefault: true # If true then files which are not explicitly listed here are output anyway. If false then they are not.\n\n### Samples ###\n\n# The `samples.csv`file contains every sampled value of every free parameter with its log likelihood and weight.\n\n# This file is often large, therefore disabling it can significantly reduce hard-disk space use.\n\n# `samples.csv` is used to perform marginalization, infer model parameter errors and do other analysis of the search\n# chains. Even if output of `samples.csv` is disabled, these tasks are still performed by the fit and output to\n# the `samples_summary.json` file. However, without a `samples.csv` file these types of tasks cannot be performed\n# after the fit is complete, for example via the database.\n\nsamples: true\n\n# The `samples.csv` file contains every accepted sampled value of every free parameter with its log likelihood and\n# weight. For certain searches, the majority of samples have a very low weight and have no numerical impact on the\n# results of the model-fit. However, these samples are still output to the `samples.csv` file, taking up hard-disk\n# space and slowing down analysis of the samples (e.g. via the database).\n\n# The `samples_weight_threshold` below specifies the threshold value of the weight such that samples with a weight\n# below this value are not output to the `samples.csv` file. This can be used to reduce the size of the `samples.csv`\n# file and speed up analysis of the samples.\n\n# For many searches (e.g. MCMC) all samples have an equal weight of 1.0, and this threshold therefore has no impact.\n# For these searches, there is no simple way to save hard-disk space. This input is more suited to nested sampling,\n# where the majority of samples have a very low weight..\n\n# Set value to empty (e.g. delete 1.0e-10 below) to disable this feature.\n\nsamples_weight_threshold: 1.0e-10\n\n### Search Internal ###\n\n# The search internal folder which contains a saved state of the non-linear search in its internal reprsenetation,\n# as a .pickle or .dill file.\n\n# For example, for the nested sampling dynesty, this .dill file is the `DynestySampler` object which is used to\n# perform sampling, and it therefore contains all internal dynesty representations of the results, samples, weights, etc.\n\n# If the entry below is false, the folder is still output during the model-fit, as it is required to resume the fit\n# from where it left off. Therefore, settings `false` below does not impact model-fitting checkpointing and resumption.\n# Instead, the search internal folder is deleted once the fit is completed.\n\n# The search internal folder file is often large, therefore deleting it after a fit is complete can significantly\n# reduce hard-disk space use.\n\n# The search internal representation that can be loaded from the .dill file has many additional quantities specific to\n# the non-linear search that the standardized autofit forms do not. For example, for emcee, it contains information on\n# every walker. This information is required to do certain analyes and make certain plots, therefore deleting the\n# folder means this information is list.\n\nsearch_internal: false\n\n### Start Point ###\n\n# If an Initalizer is used to provide a start point for the non-linear search, visualization of that start point can be\n# output to hard-disk to show the user the initial model-fit that is used to start the search. This visualization is\n# the visualizer wrapped in the Analysis class, and therefore should show things like the quality of the fit\n# to the data and the residuals at the start point.\n\nstart_point: true\n\n### Latent Variables ###\n\n# A latent variable is not a model parameter but can be derived from the model. Its value and errors may be of interest\n# and aid in the interpretation of a model-fit.\n\n# For example, for the simple 1D Gaussian example, it could be the full-width half maximum (FWHM) of the Gaussian. This\n# is not included in the model but can be easily derived from the Gaussian's sigma value.\n\n# By overwriting an Analysis class's `compute_latent_variables` method we can manually specify latent variables that\n# are calculated and output to a `latent.csv` file, which mirrors the `samples.csv` file. The `latent.csv` file has\n# the same weight resampling performed on the `samples.csv` file, controlled via the `samples_weight_threshold` above.\n\n# There may also be a `latent.results` and `latent_summary.json` files output, which the inputs below control whether\n# they are output and how often.\n\n# Outputting latent variables manually after a fit is complete is simple, just call\n# the `analysis.compute_latent_variables()` function.\n\n# For many use cases, the best set up may be to disable autofit latent variable output during the fit and perform it\n# manually after completing a successful model-fit. This will save computational run time by not computing latent\n# variables during a any model-fit which is unsuccessful.\n\nlatent_during_fit: false # Whether to output the `latent.csv`, `latent.results` and `latent_summary.json` files during the fit when it performs on-the-fly output.\nlatent_after_fit: true # If `latent_during_fit` is False, whether to output the `latent.csv`, `latent.results` and `latent_summary.json` files after the fit is complete.\nlatent_draw_via_pdf : true # Whether to draw latent variable values via the PDF of every sample, which uses fewer samples to estimate latent variable errors. If False, latent variable values are drawn from every sample.\nlatent_draw_via_pdf_size : 100 # The number of samples drawn to estimate latent variable errors if `latent_draw_via_pdf` is True.\nlatent_csv: true # Whether to ouptut the `latent.csv` file.\nlatent_results: true # Whether to output the `latent.results` file.\n\n# Other Files:\n\nsearch_log: true # `search.log`: logging produced whilst running the fit method","tooling":false,"top_keys":["default","samples","samples_weight_threshold","search_internal","start_point","latent_during_fit","latent_after_fit","latent_draw_via_pdf","latent_draw_via_pdf_size","latent_csv","latent_results","search_log"],"use_counts":{"section-read":3,"unused":0,"used":9}},{"error":null,"keys":[{"c":"","k":"Basis","line":1}],"lines":1,"path":"priors/basis.yaml","prior":true,"priors":[],"repo":"PyAutoGalaxy","text":"Basis: {}\n","tooling":false,"top_keys":["Basis"]},{"error":null,"keys":[{"c":"","k":"model.FlatLambdaCDM","line":1},{"c":"","k":"model.FlatLambdaCDM.H0","line":2},{"c":"","k":"model.FlatLambdaCDM.H0.type","line":3},{"c":"","k":"model.FlatLambdaCDM.H0.value","line":4},{"c":"","k":"model.FlatLambdaCDM.Om0","line":5},{"c":"","k":"model.FlatLambdaCDM.Om0.type","line":6},{"c":"","k":"model.FlatLambdaCDM.Om0.value","line":7},{"c":"","k":"model.FlatLambdaCDM.Tcmb0","line":8},{"c":"","k":"model.FlatLambdaCDM.Tcmb0.type","line":9},{"c":"","k":"model.FlatLambdaCDM.Tcmb0.value","line":10},{"c":"","k":"model.FlatLambdaCDM.Neff","line":11},{"c":"","k":"model.FlatLambdaCDM.Neff.type","line":12},{"c":"","k":"model.FlatLambdaCDM.Neff.value","line":13},{"c":"","k":"model.FlatLambdaCDM.m_nu","line":14},{"c":"","k":"model.FlatLambdaCDM.m_nu.type","line":15},{"c":"","k":"model.FlatLambdaCDM.m_nu.value","line":16},{"c":"","k":"model.FlatLambdaCDM.Ob0","line":17},{"c":"","k":"model.FlatLambdaCDM.Ob0.type","line":18},{"c":"","k":"model.FlatLambdaCDM.Ob0.value","line":19}],"lines":19,"path":"priors/cosmology.yaml","prior":true,"priors":[{"a":"value 67.66","b":"","cls":"model.FlatLambdaCDM","limits":"","line":2,"param":"H0","type":"Constant","width":""},{"a":"value 0.30966","b":"","cls":"model.FlatLambdaCDM","limits":"","line":5,"param":"Om0","type":"Constant","width":""},{"a":"value 2.7255","b":"","cls":"model.FlatLambdaCDM","limits":"","line":8,"param":"Tcmb0","type":"Constant","width":""},{"a":"value 3.046","b":"","cls":"model.FlatLambdaCDM","limits":"","line":11,"param":"Neff","type":"Constant","width":""},{"a":"value 0.06","b":"","cls":"model.FlatLambdaCDM","limits":"","line":14,"param":"m_nu","type":"Constant","width":""},{"a":"value 0.04897","b":"","cls":"model.FlatLambdaCDM","limits":"","line":17,"param":"Ob0","type":"Constant","width":""}],"repo":"PyAutoGalaxy","text":"model.FlatLambdaCDM:\n  H0:\n    type: Constant\n    value: 67.66\n  Om0:\n    type: Constant\n    value: 0.30966\n  Tcmb0:\n    type: Constant\n    value: 2.7255\n  Neff:\n    type: Constant\n    value: 3.046\n  m_nu:\n    type: Constant\n    value: 0.06\n  Ob0:\n    type: Constant\n    value: 0.04897","tooling":false,"top_keys":["model.FlatLambdaCDM"]},{"error":null,"keys":[{"c":"","k":"DatasetModel","line":1},{"c":"","k":"DatasetModel.background_sky_level","line":2},{"c":"","k":"DatasetModel.background_sky_level.type","line":3},{"c":"","k":"DatasetModel.background_sky_level.value","line":4},{"c":"","k":"DatasetModel.grid_offset_0","line":5},{"c":"","k":"DatasetModel.grid_offset_0.type","line":6},{"c":"","k":"DatasetModel.grid_offset_0.value","line":7},{"c":"","k":"DatasetModel.grid_offset_1","line":8},{"c":"","k":"DatasetModel.grid_offset_1.type","line":9},{"c":"","k":"DatasetModel.grid_offset_1.value","line":10},{"c":"","k":"DatasetModel.grid_rotation_angle","line":11},{"c":"","k":"DatasetModel.grid_rotation_angle.type","line":12},{"c":"","k":"DatasetModel.grid_rotation_angle.value","line":13}],"lines":13,"path":"priors/dataset_model.yaml","prior":true,"priors":[{"a":"value 0.0","b":"","cls":"DatasetModel","limits":"","line":2,"param":"background_sky_level","type":"Constant","width":""},{"a":"value 0.0","b":"","cls":"DatasetModel","limits":"","line":5,"param":"grid_offset_0","type":"Constant","width":""},{"a":"value 0.0","b":"","cls":"DatasetModel","limits":"","line":8,"param":"grid_offset_1","type":"Constant","width":""},{"a":"value 0.0","b":"","cls":"DatasetModel","limits":"","line":11,"param":"grid_rotation_angle","type":"Constant","width":""}],"repo":"PyAutoGalaxy","text":"DatasetModel:\n  background_sky_level:\n    type: Constant\n    value: 0.0\n  grid_offset_0:\n    type: Constant\n    value: 0.0\n  grid_offset_1:\n    type: Constant\n    value: 0.0\n  grid_rotation_angle:\n    type: Constant\n    value: 0.0","tooling":false,"top_keys":["DatasetModel"]},{"error":null,"keys":[{"c":"","k":"Ellipse","line":1},{"c":"","k":"Ellipse.centre_0","line":2},{"c":"","k":"Ellipse.centre_0.type","line":3},{"c":"","k":"Ellipse.centre_0.mean","line":4},{"c":"","k":"Ellipse.centre_0.sigma","line":5},{"c":"","k":"Ellipse.centre_0.width_modifier","line":6},{"c":"","k":"Ellipse.centre_0.width_modifier.type","line":7},{"c":"","k":"Ellipse.centre_0.width_modifier.value","line":8},{"c":"","k":"Ellipse.centre_0.limits","line":9},{"c":"","k":"Ellipse.centre_0.limits.lower","line":10},{"c":"","k":"Ellipse.centre_0.limits.upper","line":11},{"c":"","k":"Ellipse.centre_1","line":12},{"c":"","k":"Ellipse.centre_1.type","line":13},{"c":"","k":"Ellipse.centre_1.mean","line":14},{"c":"","k":"Ellipse.centre_1.sigma","line":15},{"c":"","k":"Ellipse.centre_1.width_modifier","line":16},{"c":"","k":"Ellipse.centre_1.width_modifier.type","line":17},{"c":"","k":"Ellipse.centre_1.width_modifier.value","line":18},{"c":"","k":"Ellipse.centre_1.limits","line":19},{"c":"","k":"Ellipse.centre_1.limits.lower","line":20},{"c":"","k":"Ellipse.centre_1.limits.upper","line":21},{"c":"","k":"Ellipse.ell_comps_0","line":22},{"c":"","k":"Ellipse.ell_comps_0.type","line":23},{"c":"","k":"Ellipse.ell_comps_0.mean","line":24},{"c":"","k":"Ellipse.ell_comps_0.sigma","line":25},{"c":"","k":"Ellipse.ell_comps_0.width_modifier","line":26},{"c":"","k":"Ellipse.ell_comps_0.width_modifier.type","line":27},{"c":"","k":"Ellipse.ell_comps_0.width_modifier.value","line":28},{"c":"","k":"Ellipse.ell_comps_0.limits","line":29},{"c":"","k":"Ellipse.ell_comps_0.limits.lower","line":30},{"c":"","k":"Ellipse.ell_comps_0.limits.upper","line":31},{"c":"","k":"Ellipse.ell_comps_1","line":32},{"c":"","k":"Ellipse.ell_comps_1.type","line":33},{"c":"","k":"Ellipse.ell_comps_1.mean","line":34},{"c":"","k":"Ellipse.ell_comps_1.sigma","line":35},{"c":"","k":"Ellipse.ell_comps_1.width_modifier","line":36},{"c":"","k":"Ellipse.ell_comps_1.width_modifier.type","line":37},{"c":"","k":"Ellipse.ell_comps_1.width_modifier.value","line":38},{"c":"","k":"Ellipse.ell_comps_1.limits","line":39},{"c":"","k":"Ellipse.ell_comps_1.limits.lower","line":40},{"c":"","k":"Ellipse.ell_comps_1.limits.upper","line":41}],"lines":41,"path":"priors/ellipse/ellipse.yaml","prior":true,"priors":[{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Ellipse","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Ellipse","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Ellipse","limits":"[-1.0, 1.0]","line":22,"param":"ell_comps_0","type":"Gaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Ellipse","limits":"[-1.0, 1.0]","line":32,"param":"ell_comps_1","type":"Gaussian","width":"Absolute 0.2"}],"repo":"PyAutoGalaxy","text":"Ellipse:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  ell_comps_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n","tooling":false,"top_keys":["Ellipse"]},{"error":null,"keys":[{"c":"","k":"EllipseMultipole","line":1},{"c":"","k":"EllipseMultipole.multipole_comps_0","line":2},{"c":"","k":"EllipseMultipole.multipole_comps_0.type","line":3},{"c":"","k":"EllipseMultipole.multipole_comps_0.lower_limit","line":4},{"c":"","k":"EllipseMultipole.multipole_comps_0.upper_limit","line":5},{"c":"","k":"EllipseMultipole.multipole_comps_0.width_modifier","line":6},{"c":"","k":"EllipseMultipole.multipole_comps_0.width_modifier.type","line":7},{"c":"","k":"EllipseMultipole.multipole_comps_0.width_modifier.value","line":8},{"c":"","k":"EllipseMultipole.multipole_comps_0.limits","line":9},{"c":"","k":"EllipseMultipole.multipole_comps_0.limits.lower","line":10},{"c":"","k":"EllipseMultipole.multipole_comps_0.limits.upper","line":11},{"c":"","k":"EllipseMultipole.multipole_comps_1","line":12},{"c":"","k":"EllipseMultipole.multipole_comps_1.type","line":13},{"c":"","k":"EllipseMultipole.multipole_comps_1.lower_limit","line":14},{"c":"","k":"EllipseMultipole.multipole_comps_1.upper_limit","line":15},{"c":"","k":"EllipseMultipole.multipole_comps_1.width_modifier","line":16},{"c":"","k":"EllipseMultipole.multipole_comps_1.width_modifier.type","line":17},{"c":"","k":"EllipseMultipole.multipole_comps_1.width_modifier.value","line":18},{"c":"","k":"EllipseMultipole.multipole_comps_1.limits","line":19},{"c":"","k":"EllipseMultipole.multipole_comps_1.limits.lower","line":20},{"c":"","k":"EllipseMultipole.multipole_comps_1.limits.upper","line":21},{"c":"","k":"EllipseMultipoleScaled","line":23},{"c":"","k":"EllipseMultipoleScaled.scaled_multipole_comps_0","line":24},{"c":"","k":"EllipseMultipoleScaled.scaled_multipole_comps_0.type","line":25},{"c":"","k":"EllipseMultipoleScaled.scaled_multipole_comps_0.lower_limit","line":26},{"c":"","k":"EllipseMultipoleScaled.scaled_multipole_comps_0.upper_limit","line":27},{"c":"","k":"EllipseMultipoleScaled.scaled_multipole_comps_0.width_modifier","line":28},{"c":"","k":"EllipseMultipoleScaled.scaled_multipole_comps_0.width_modifier.type","line":29},{"c":"","k":"EllipseMultipoleScaled.scaled_multipole_comps_0.width_modifier.value","line":30},{"c":"","k":"EllipseMultipoleScaled.scaled_multipole_comps_0.limits","line":31},{"c":"","k":"EllipseMultipoleScaled.scaled_multipole_comps_0.limits.lower","line":32},{"c":"","k":"EllipseMultipoleScaled.scaled_multipole_comps_0.limits.upper","line":33},{"c":"","k":"EllipseMultipoleScaled.scaled_multipole_comps_1","line":34},{"c":"","k":"EllipseMultipoleScaled.scaled_multipole_comps_1.type","line":35},{"c":"","k":"EllipseMultipoleScaled.scaled_multipole_comps_1.lower_limit","line":36},{"c":"","k":"EllipseMultipoleScaled.scaled_multipole_comps_1.upper_limit","line":37},{"c":"","k":"EllipseMultipoleScaled.scaled_multipole_comps_1.width_modifier","line":38},{"c":"","k":"EllipseMultipoleScaled.scaled_multipole_comps_1.width_modifier.type","line":39},{"c":"","k":"EllipseMultipoleScaled.scaled_multipole_comps_1.width_modifier.value","line":40},{"c":"","k":"EllipseMultipoleScaled.scaled_multipole_comps_1.limits","line":41},{"c":"","k":"EllipseMultipoleScaled.scaled_multipole_comps_1.limits.lower","line":42},{"c":"","k":"EllipseMultipoleScaled.scaled_multipole_comps_1.limits.upper","line":43}],"lines":43,"path":"priors/ellipse/ellipse_multipole.yaml","prior":true,"priors":[{"a":"lower 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width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n\nEllipseMultipoleScaled:\n  scaled_multipole_comps_0:\n    type: Uniform\n    lower_limit: -0.1\n    upper_limit: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  scaled_multipole_comps_1:\n    type: Uniform\n    lower_limit: -0.1\n    upper_limit: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n","tooling":false,"top_keys":["EllipseMultipole","EllipseMultipoleScaled"]},{"error":null,"keys":[{"c":"","k":"Redshift","line":1},{"c":"","k":"Redshift.redshift","line":2},{"c":"","k":"Redshift.redshift.type","line":3},{"c":"","k":"Redshift.redshift.lower_limit","line":4},{"c":"","k":"Redshift.redshift.upper_limit","line":5},{"c":"","k":"Redshift.redshift.width_modifier","line":6},{"c":"","k":"Redshift.redshift.width_modifier.type","line":7},{"c":"","k":"Redshift.redshift.width_modifier.value","line":8},{"c":"","k":"Redshift.redshift.limits","line":9},{"c":"","k":"Redshift.redshift.limits.lower","line":10},{"c":"","k":"Redshift.redshift.limits.upper","line":11}],"lines":11,"path":"priors/galaxy/redshift.yaml","prior":true,"priors":[{"a":"lower 0.0","b":"upper 3.0","cls":"Redshift","limits":"[0.0, inf]","line":2,"param":"redshift","type":"Uniform","width":"Absolute 1.0"}],"repo":"PyAutoGalaxy","text":"Redshift:\n  redshift:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 3.0\n    width_modifier:\n      type: Absolute\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: 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Sph.effective_radius.upper_limit","line":80},{"c":"","k":"DevVaucouleursSph.effective_radius.width_modifier","line":81},{"c":"","k":"DevVaucouleursSph.effective_radius.width_modifier.type","line":82},{"c":"","k":"DevVaucouleursSph.effective_radius.width_modifier.value","line":83},{"c":"","k":"DevVaucouleursSph.effective_radius.limits","line":84},{"c":"","k":"DevVaucouleursSph.effective_radius.limits.lower","line":85},{"c":"","k":"DevVaucouleursSph.effective_radius.limits.upper","line":86}],"lines":86,"path":"priors/light/linear/dev_vaucouleurs.yaml","prior":true,"priors":[{"a":"mean 0.0","b":"\u03c3 0.3","cls":"DevVaucouleurs","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"DevVaucouleurs","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 30.0","cls":"DevVaucouleurs","limits":"[0.0, inf]","line":22,"param":"effective_radius","type":"Uniform","width":"Relative 1.0"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"DevVaucouleurs","limits":"[-1.0, 1.0]","line":32,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"DevVaucouleurs","limits":"[-1.0, 1.0]","line":44,"param":"ell_comps_1","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"DevVaucouleursSph","limits":"[-inf, inf]","line":57,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"DevVaucouleursSph","limits":"[-inf, inf]","line":67,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 30.0","cls":"DevVaucouleursSph","limits":"[0.0, inf]","line":77,"param":"effective_radius","type":"Uniform","width":"Relative 1.0"}],"repo":"PyAutoGalaxy","text":"DevVaucouleurs:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\nDevVaucouleursSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["DevVaucouleurs","DevVaucouleursSph"]},{"error":null,"keys":[{"c":"","k":"Exponential","line":1},{"c":"","k":"Exponential.centre_0","line":2},{"c":"","k":"Exponential.centre_0.type","line":3},{"c":"","k":"Exponential.centre_0.mean","line":4},{"c":"","k":"Exponential.centre_0.sigma","line":5},{"c":"","k":"Exponential.centre_0.width_modifier","line":6},{"c":"","k":"Exponential.centre_0.width_modifier.type","line":7},{"c":"","k":"Exponential.centre_0.width_modifier.value","line":8},{"c":"","k":"Exponential.centre_0.limits","line":9},{"c":"","k":"Exponential.centre_0.limits.lower","line":10},{"c":"","k":"Exponential.centre_0.limits.upper","line":11},{"c":"","k":"Exponential.centre_1","line":12},{"c":"","k":"Exponential.centre_1.type","line":13},{"c":"","k":"Exponential.centre_1.mean","line":14},{"c":"","k":"Exponential.centre_1.sigma","line":15},{"c":"","k":"Exponential.centre_1.width_modifier","line":16},{"c":"","k":"Exponential.centre_1.width_modifier.type","line":17},{"c":"","k":"Exponential.centre_1.width_modifier.value","line":18},{"c":"","k":"Exponential.centre_1.limits","line":19},{"c":"","k":"Exponential.centre_1.limits.lower","line":20},{"c":"","k":"Exponential.centre_1.limits.upper","line":21},{"c":"","k":"Exponential.effective_radius","line":22},{"c":"","k":"Exponential.effective_radius.type","line":23},{"c":"","k":"Exponential.effective_radius.lower_limit","line":24},{"c":"","k":"Exponential.effective_radius.upper_limit","line":25},{"c":"","k":"Exponential.effective_radius.width_modifier","line":26},{"c":"","k":"Exponential.effective_radius.width_modifier.type","line":27},{"c":"","k":"Exponential.effective_radius.width_modifier.value","line":28},{"c":"","k":"Exponential.effective_radius.limits","line":29},{"c":"","k":"Exponential.effective_radius.limits.lower","line":30},{"c":"","k":"Exponential.effective_radius.limits.upper","line":31},{"c":"","k":"Exponential.ell_comps_0","line":32},{"c":"","k":"Exponential.ell_comps_0.type","line":33},{"c":"","k":"Exponential.ell_comps_0.mean","line":34},{"c":"","k":"Exponential.ell_comps_0.sigma","line":35},{"c":"","k":"Exponential.ell_comps_0.lower_limit","line":36},{"c":"","k":"Exponential.ell_comps_0.upper_limit","line":37},{"c":"","k":"Exponential.ell_comps_0.width_modifier","line":38},{"c":"","k":"Exponential.ell_comps_0.width_modifier.type","line":39},{"c":"","k":"Exponential.ell_comps_0.width_modifier.value","line":40},{"c":"","k":"Exponential.ell_comps_0.limits","line":41},{"c":"","k":"Exponential.ell_comps_0.limits.lower","line":42},{"c":"","k":"Exponential.ell_comps_0.limits.upper","line":43},{"c":"","k":"Exponential.ell_comps_1","line":44},{"c":"","k":"Exponential.ell_comps_1.type","line":45},{"c":"","k":"Exponential.ell_comps_1.mean","line":46},{"c":"","k":"Exponential.ell_comps_1.sigma","line":47},{"c":"","k":"Exponential.ell_comps_1.lower_limit","line":48},{"c":"","k":"Exponential.ell_comps_1.upper_limit","line":49},{"c":"","k":"Exponential.ell_comps_1.width_modifier","line":50},{"c":"","k":"Exponential.ell_comps_1.width_modifier.type","line":51},{"c":"","k":"Exponential.ell_comps_1.width_modifier.value","line":52},{"c":"","k":"Exponential.ell_comps_1.limits","line":53},{"c":"","k":"Exponential.ell_comps_1.limits.lower","line":54},{"c":"","k":"Exponential.ell_comps_1.limits.upper","line":55},{"c":"","k":"ExponentialSph","line":56},{"c":"","k":"ExponentialSph.centre_0","line":57},{"c":"","k":"ExponentialSph.centre_0.type","line":58},{"c":"","k":"ExponentialSph.centre_0.mean","line":59},{"c":"","k":"ExponentialSph.centre_0.sigma","line":60},{"c":"","k":"ExponentialSph.centre_0.width_modifier","line":61},{"c":"","k":"ExponentialSph.centre_0.width_modifier.type","line":62},{"c":"","k":"ExponentialSph.centre_0.width_modifier.value","line":63},{"c":"","k":"ExponentialSph.centre_0.limits","line":64},{"c":"","k":"ExponentialSph.centre_0.limits.lower","line":65},{"c":"","k":"ExponentialSph.centre_0.limits.upper","line":66},{"c":"","k":"ExponentialSph.centre_1","line":67},{"c":"","k":"ExponentialSph.centre_1.type","line":68},{"c":"","k":"ExponentialSph.centre_1.mean","line":69},{"c":"","k":"ExponentialSph.centre_1.sigma","line":70},{"c":"","k":"ExponentialSph.centre_1.width_modifier","line":71},{"c":"","k":"ExponentialSph.centre_1.width_modifier.type","line":72},{"c":"","k":"ExponentialSph.centre_1.width_modifier.value","line":73},{"c":"","k":"ExponentialSph.centre_1.limits","line":74},{"c":"","k":"ExponentialSph.centre_1.limits.lower","line":75},{"c":"","k":"ExponentialSph.centre_1.limits.upper","line":76},{"c":"","k":"ExponentialSph.effective_radius","line":77},{"c":"","k":"ExponentialSph.effective_radius.type","line":78},{"c":"","k":"ExponentialSph.effective_radius.lower_limit","line":79},{"c":"","k":"ExponentialSph.effective_radius.upper_limit","line":80},{"c":"","k":"ExponentialSph.effective_radius.width_modifier","line":81},{"c":"","k":"ExponentialSph.effective_radius.width_modifier.type","line":82},{"c":"","k":"ExponentialSph.effective_radius.width_modifier.value","line":83},{"c":"","k":"ExponentialSph.effective_radius.limits","line":84},{"c":"","k":"ExponentialSph.effective_radius.limits.lower","line":85},{"c":"","k":"ExponentialSph.effective_radius.limits.upper","line":86}],"lines":86,"path":"priors/light/linear/exponential.yaml","prior":true,"priors":[{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Exponential","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Exponential","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 30.0","cls":"Exponential","limits":"[0.0, inf]","line":22,"param":"effective_radius","type":"Uniform","width":"Relative 1.0"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Exponential","limits":"[-1.0, 1.0]","line":32,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Exponential","limits":"[-1.0, 1.0]","line":44,"param":"ell_comps_1","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ExponentialSph","limits":"[-inf, inf]","line":57,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ExponentialSph","limits":"[-inf, 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value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\nExponentialSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["Exponential","ExponentialSph"]},{"error":null,"keys":[{"c":"","k":"ExponentialCore","line":1},{"c":"","k":"ExponentialCore.centre_0","line":2},{"c":"","k":"ExponentialCore.centre_0.type","line":3},{"c":"","k":"ExponentialCore.centre_0.mean","line":4},{"c":"","k":"ExponentialCore.centre_0.sigma","line":5},{"c":"","k":"ExponentialCore.centre_0.width_modifier","line":6},{"c":"","k":"ExponentialCore.centre_0.width_modifier.type","line":7},{"c":"","k":"ExponentialCore.centre_0.width_modifier.value","line":8},{"c":"","k":"ExponentialCore.centre_0.limits","line":9},{"c":"","k":"ExponentialCore.centre_0.limits.lower","line":10},{"c":"","k":"ExponentialCore.centre_0.limits.upper","line":11},{"c":"","k":"ExponentialCore.centre_1","line":12},{"c":"","k":"ExponentialCore.centre_1.type","line":13},{"c":"","k":"ExponentialCore.centre_1.mean","line":14},{"c":"","k":"ExponentialCore.centre_1.sigma","line":15},{"c":"","k":"ExponentialCore.centre_1.width_modifier","line":16},{"c":"","k":"ExponentialCore.centre_1.width_modifier.type","line":17},{"c":"","k":"ExponentialCore.centre_1.width_modifier.value","line":18},{"c":"","k":"ExponentialCore.centre_1.limits","line":19},{"c":"","k":"ExponentialCore.centre_1.limits.lower","line":20},{"c":"","k":"ExponentialCore.centre_1.limits.upper","line":21},{"c":"","k":"ExponentialCore.effective_radius","line":22},{"c":"","k":"ExponentialCore.effective_radius.type","line":23},{"c":"","k":"ExponentialCore.effective_radius.lower_limit","line":24},{"c":"","k":"ExponentialCore.effective_radius.upper_limit","line":25},{"c":"","k":"ExponentialCore.effective_radius.width_modifier","line":26},{"c":"","k":"ExponentialCore.effective_radius.width_modifier.type","line":27},{"c":"","k":"ExponentialCore.effective_radius.width_modifier.value","line":28},{"c":"","k":"ExponentialCore.effective_radius.limits","line":29},{"c":"","k":"ExponentialCore.effective_radius.limits.lower","line":30},{"c":"","k":"ExponentialCore.effective_radius.limits.upper","line":31},{"c":"","k":"ExponentialCore.ell_comps_0","line":32},{"c":"","k":"ExponentialCore.ell_comps_0.type","line":33},{"c":"","k":"ExponentialCore.ell_comps_0.mean","line":34},{"c":"","k":"ExponentialCore.ell_comps_0.sigma","line":35},{"c":"","k":"ExponentialCore.ell_comps_0.lower_limit","line":36},{"c":"","k":"ExponentialCore.ell_comps_0.upper_limit","line":37},{"c":"","k":"ExponentialCore.ell_comps_0.width_modifier","line":38},{"c":"","k":"ExponentialCore.ell_comps_0.width_modifier.type","line":39},{"c":"","k":"ExponentialCore.ell_comps_0.width_modifier.value","line":40},{"c":"","k":"ExponentialCore.ell_comps_0.limits","line":41},{"c":"","k":"ExponentialCore.ell_comps_0.limits.lower","line":42},{"c":"","k":"ExponentialCore.ell_comps_0.limits.upper","line":43},{"c":"","k":"ExponentialCore.ell_comps_1","line":44},{"c":"","k":"ExponentialCore.ell_comps_1.type","line":45},{"c":"","k":"ExponentialCore.ell_comps_1.mean","line":46},{"c":"","k":"ExponentialCore.ell_comps_1.sigma","line":47},{"c":"","k":"ExponentialCore.ell_comps_1.lower_limit","line":48},{"c":"","k":"ExponentialCore.ell_comps_1.upper_limit","line":49},{"c":"","k":"ExponentialCore.ell_comps_1.width_modifier","line":50},{"c":"","k":"ExponentialCore.ell_comps_1.width_modifier.type","line":51},{"c":"","k":"ExponentialCore.ell_comps_1.width_modifier.value","line":52},{"c":"","k":"ExponentialCore.ell_comps_1.limits","line":53},{"c":"","k":"ExponentialCore.ell_comps_1.limits.lower","line":54},{"c":"","k":"ExponentialCore.ell_comps_1.limits.upper","line":55},{"c":"","k":"ExponentialCore.alpha","line":56},{"c":"","k":"ExponentialCore.alpha.type","line":57},{"c":"","k":"ExponentialCore.alpha.value","line":58},{"c":"","k":"ExponentialCore.gamma","line":59},{"c":"","k":"ExponentialCore.gamma.type","line":60},{"c":"","k":"ExponentialCore.gamma.value","line":61},{"c":"","k":"ExponentialCore.radius_break","line":62},{"c":"","k":"ExponentialCore.radius_break.type","line":63},{"c":"","k":"ExponentialCore.radius_break.value","line":64},{"c":"","k":"ExponentialCoreSph","line":65},{"c":"","k":"ExponentialCoreSph.alpha","line":66},{"c":"","k":"ExponentialCoreSph.alpha.type","line":67},{"c":"","k":"ExponentialCoreSph.alpha.value","line":68},{"c":"","k":"ExponentialCoreSph.centre_0","line":69},{"c":"","k":"ExponentialCoreSph.centre_0.type","line":70},{"c":"","k":"ExponentialCoreSph.centre_0.mean","line":71},{"c":"","k":"ExponentialCoreSph.centre_0.sigma","line":72},{"c":"","k":"ExponentialCoreSph.centre_0.width_modifier","line":73},{"c":"","k":"ExponentialCoreSph.centre_0.width_modifier.type","line":74},{"c":"","k":"ExponentialCoreSph.centre_0.width_modifier.value","line":75},{"c":"","k":"ExponentialCoreSph.centre_0.limits","line":76},{"c":"","k":"ExponentialCoreSph.centre_0.limits.lower","line":77},{"c":"","k":"ExponentialCoreSph.centre_0.limits.upper","line":78},{"c":"","k":"ExponentialCoreSph.centre_1","line":79},{"c":"","k":"ExponentialCoreSph.centre_1.type","line":80},{"c":"","k":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0.0","b":"\u03c3 0.3","cls":"ExponentialCore","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ExponentialCore","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 30.0","cls":"ExponentialCore","limits":"[0.0, inf]","line":22,"param":"effective_radius","type":"Uniform","width":"Relative 1.0"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ExponentialCore","limits":"[-1.0, 1.0]","line":32,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ExponentialCore","limits":"[-1.0, 1.0]","line":44,"param":"ell_comps_1","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"value 3.0","b":"","cls":"ExponentialCore","limits":"","line":56,"param":"alpha","type":"Constant","width":""},{"a":"value 0.25","b":"","cls":"ExponentialCore","limits":"","line":59,"param":"gamma","type":"Constant","width":""},{"a":"value 0.025","b":"","cls":"ExponentialCore","limits":"","line":62,"param":"radius_break","type":"Constant","width":""},{"a":"value 3.0","b":"","cls":"ExponentialCoreSph","limits":"","line":66,"param":"alpha","type":"Constant","width":""},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ExponentialCoreSph","limits":"[-inf, inf]","line":69,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ExponentialCoreSph","limits":"[-inf, inf]","line":79,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 30.0","cls":"ExponentialCoreSph","limits":"[0.0, inf]","line":89,"param":"effective_radius","type":"Uniform","width":"Relative 1.0"},{"a":"value 0.25","b":"","cls":"ExponentialCoreSph","limits":"","line":99,"param":"gamma","type":"Constant","width":""},{"a":"value 0.025","b":"","cls":"ExponentialCoreSph","limits":"","line":102,"param":"radius_break","type":"Constant","width":""}],"repo":"PyAutoGalaxy","text":"ExponentialCore:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  alpha:\n    type: Constant\n    value: 3.0      \n  gamma:\n    type: Constant\n    value: 0.25\n  radius_break:\n    type: Constant\n    value: 0.025\nExponentialCoreSph:\n  alpha:\n    type: Constant\n    value: 3.0\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  gamma:\n    type: Constant\n    value: 0.25\n  radius_break:\n    type: Constant\n    value: 0.025\n","tooling":false,"top_keys":["ExponentialCore","ExponentialCoreSph"]},{"error":null,"keys":[{"c":"","k":"Gaussian","line":1},{"c":"","k":"Gaussian.sigma","line":2},{"c":"","k":"Gaussian.sigma.type","line":3},{"c":"","k":"Gaussian.sigma.lower_limit","line":4},{"c":"","k":"Gaussian.sigma.upper_limit","line":5},{"c":"","k":"Gaussian.sigma.width_modifier","line":6},{"c":"","k":"Gaussian.sigma.width_modifier.type","line":7},{"c":"","k":"Gaussian.sigma.width_modifier.value","line":8},{"c":"","k":"Gaussian.sigma.limits","line":9},{"c":"","k":"Gaussian.sigma.limits.lower","line":10},{"c":"","k":"Gaussian.sigma.limits.upper","line":11},{"c":"","k":"Gaussian.centre_0","line":12},{"c":"","k":"Gaussian.centre_0.type","line":13},{"c":"","k":"Gaussian.centre_0.mean","line":14},{"c":"","k":"Gaussian.centre_0.sigma","line":15},{"c":"","k":"Gaussian.centre_0.width_modifier","line":16},{"c":"","k":"Gaussian.centre_0.width_modifier.type","line":17},{"c":"","k":"Gaussian.centre_0.width_modifier.value","line":18},{"c":"","k":"Gaussian.centre_0.limits","line":19},{"c":"","k":"Gaussian.centre_0.limits.lower","line":20},{"c":"","k":"Gaussian.centre_0.limits.upper","line":21},{"c":"","k":"Gaussian.centre_1","line":22},{"c":"","k":"Gaussian.centre_1.type","line":23},{"c":"","k":"Gaussian.centre_1.mean","line":24},{"c":"","k":"Gaussian.centre_1.sigma","line":25},{"c":"","k":"Gaussian.centre_1.width_modifier","line":26},{"c":"","k":"Gaussian.centre_1.width_modifier.type","line":27},{"c":"","k":"Gaussian.centre_1.width_modifier.value","line":28},{"c":"","k":"Gaussian.centre_1.limits","line":29},{"c":"","k":"Gaussian.centre_1.limits.lower","line":30},{"c":"","k":"Gaussian.centre_1.limits.upper","line":31},{"c":"","k":"Gaussian.ell_comps_0","line":32},{"c":"","k":"Gaussian.ell_comps_0.type","line":33},{"c":"","k":"Gaussian.ell_comps_0.mean","line":34},{"c":"","k":"Gaussian.ell_comps_0.sigma","line":35},{"c":"","k":"Gaussian.ell_comps_0.lower_limit","line":36},{"c":"","k":"Gaussian.ell_comps_0.upper_limit","line":37},{"c":"","k":"Gaussian.ell_comps_0.width_modifier","line":38},{"c":"","k":"Gaussian.ell_comps_0.width_modifier.type","line":39},{"c":"","k":"Gaussian.ell_comps_0.width_modifier.value","line":40},{"c":"","k":"Gaussian.ell_comps_0.limits","line":41},{"c":"","k":"Gaussian.ell_comps_0.limits.lower","line":42},{"c":"","k":"Gaussian.ell_comps_0.limits.upper","line":43},{"c":"","k":"Gaussian.ell_comps_1","line":44},{"c":"","k":"Gaussian.ell_comps_1.type","line":45},{"c":"","k":"Gaussian.ell_comps_1.mean","line":46},{"c":"","k":"Gaussian.ell_comps_1.sigma","line":47},{"c":"","k":"Gaussian.ell_comps_1.lower_limit","line":48},{"c":"","k":"Gaussian.ell_comps_1.upper_limit","line":49},{"c":"","k":"Gaussian.ell_comps_1.width_modifier","line":50},{"c":"","k":"Gaussian.ell_comps_1.width_modifier.type","line":51},{"c":"","k":"Gaussian.ell_comps_1.width_modifier.value","line":52},{"c":"","k":"Gaussian.ell_comps_1.limits","line":53},{"c":"","k":"Gaussian.ell_comps_1.limits.lower","line":54},{"c":"","k":"Gaussian.ell_comps_1.limits.upper","line":55},{"c":"","k":"GaussianSph","line":56},{"c":"","k":"GaussianSph.sigma","line":57},{"c":"","k":"GaussianSph.sigma.type","line":58},{"c":"","k":"GaussianSph.sigma.lower_limit","line":59},{"c":"","k":"GaussianSph.sigma.upper_limit","line":60},{"c":"","k":"GaussianSph.sigma.width_modifier","line":61},{"c":"","k":"GaussianSph.sigma.width_modifier.type","line":62},{"c":"","k":"GaussianSph.sigma.width_modifier.value","line":63},{"c":"","k":"GaussianSph.sigma.limits","line":64},{"c":"","k":"GaussianSph.sigma.limits.lower","line":65},{"c":"","k":"GaussianSph.sigma.limits.upper","line":66},{"c":"","k":"GaussianSph.centre_0","line":67},{"c":"","k":"GaussianSph.centre_0.type","line":68},{"c":"","k":"GaussianSph.centre_0.mean","line":69},{"c":"","k":"GaussianSph.centre_0.sigma","line":70},{"c":"","k":"GaussianSph.centre_0.width_modifier","line":71},{"c":"","k":"GaussianSph.centre_0.width_modifier.type","line":72},{"c":"","k":"GaussianSph.centre_0.width_modifier.value","line":73},{"c":"","k":"GaussianSph.centre_0.limits","line":74},{"c":"","k":"GaussianSph.centre_0.limits.lower","line":75},{"c":"","k":"GaussianSph.centre_0.limits.upper","line":76},{"c":"","k":"GaussianSph.centre_1","line":77},{"c":"","k":"GaussianSph.centre_1.type","line":78},{"c":"","k":"GaussianSph.centre_1.mean","line":79},{"c":"","k":"GaussianSph.centre_1.sigma","line":80},{"c":"","k":"GaussianSph.centre_1.width_modifier","line":81},{"c":"","k":"GaussianSph.centre_1.width_modifier.type","line":82},{"c":"","k":"GaussianSph.centre_1.width_modifier.value","line":83},{"c":"","k":"GaussianSph.centre_1.limits","line":84},{"c":"","k":"GaussianSph.centre_1.limits.lower","line":85},{"c":"","k":"GaussianSph.centre_1.limits.upper","line":86}],"lines":86,"path":"priors/light/linear/gaussian.yaml","prior":true,"priors":[{"a":"lower 0.0","b":"upper 25.0","cls":"Gaussian","limits":"[0.0, inf]","line":2,"param":"sigma","type":"Uniform","width":"Relative 0.5"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Gaussian","limits":"[-inf, inf]","line":12,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Gaussian","limits":"[-inf, inf]","line":22,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Gaussian","limits":"[-1.0, 1.0]","line":32,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Gaussian","limits":"[-1.0, 1.0]","line":44,"param":"ell_comps_1","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"lower 0.0","b":"upper 25.0","cls":"GaussianSph","limits":"[0.0, inf]","line":57,"param":"sigma","type":"Uniform","width":"Relative 0.5"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"GaussianSph","limits":"[-inf, inf]","line":67,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"GaussianSph","limits":"[-inf, inf]","line":77,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"}],"repo":"PyAutoGalaxy","text":"Gaussian:\n  sigma:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 25.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\nGaussianSph:\n  sigma:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 25.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: 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   lower: 0.0\n      upper: inf\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: 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   width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  sersic_index:\n    type: Uniform\n    lower_limit: 0.8\n    upper_limit: 5.0\n    width_modifier:\n      type: Absolute\n      value: 1.5\n    limits:\n      lower: 0.8\n      upper: 5.0\nSersicSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  sersic_index:\n    type: Uniform\n    lower_limit: 0.8\n    upper_limit: 5.0\n    width_modifier:\n      type: Absolute\n      value: 1.5\n    limits:\n      lower: 0.8\n      upper: 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3.0","b":"","cls":"SersicCore","limits":"","line":66,"param":"alpha","type":"Constant","width":""},{"a":"value 0.25","b":"","cls":"SersicCore","limits":"","line":69,"param":"gamma","type":"Constant","width":""},{"a":"value 0.025","b":"","cls":"SersicCore","limits":"","line":72,"param":"radius_break","type":"Constant","width":""},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"SersicCoreSph","limits":"[-inf, inf]","line":76,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"SersicCoreSph","limits":"[-inf, inf]","line":86,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 30.0","cls":"SersicCoreSph","limits":"[0.0, inf]","line":96,"param":"effective_radius","type":"Uniform","width":"Relative 1.0"},{"a":"lower 0.8","b":"upper 5.0","cls":"SersicCoreSph","limits":"[0.8, 5.0]","line":106,"param":"sersic_index","type":"Uniform","width":"Absolute 1.5"},{"a":"value 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sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  sersic_index:\n    type: Uniform\n    lower_limit: 0.8\n    upper_limit: 5.0\n    width_modifier:\n      type: Absolute\n      value: 1.5\n    limits:\n      lower: 0.8\n      upper: 5.0\n  alpha:\n    type: Constant\n    value: 3.0\n  gamma:\n    type: Constant\n    value: 0.25\n  radius_break:\n    type: Constant\n    value: 0.025\nSersicCoreSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  sersic_index:\n    type: Uniform\n    lower_limit: 0.8\n    upper_limit: 5.0\n    width_modifier:\n      type: Absolute\n      value: 1.5\n    limits:\n      lower: 0.8\n      upper: 5.0\n  alpha:\n    type: Constant\n    value: 3.0\n  gamma:\n    type: Constant\n    value: 0.25\n  radius_break:\n    type: Constant\n    value: 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0.0","b":"\u03c3 0.3","cls":"ShapeletCartesianSph","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletCartesianSph","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 30.0","cls":"ShapeletCartesianSph","limits":"[0.0, inf]","line":22,"param":"beta","type":"Uniform","width":"Relative 0.5"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletCartesian","limits":"[-inf, inf]","line":33,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletCartesian","limits":"[-inf, inf]","line":43,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletCartesian","limits":"[-1.0, 1.0]","line":53,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 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Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  beta:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["ShapeletCartesianSph","ShapeletCartesian"]},{"error":null,"keys":[{"c":"","k":"ShapeletExponentialSph","line":1},{"c":"","k":"ShapeletExponentialSph.centre_0","line":2},{"c":"","k":"ShapeletExponentialSph.centre_0.type","line":3},{"c":"","k":"ShapeletExponentialSph.centre_0.mean","line":4},{"c":"","k":"ShapeletExponentialSph.centre_0.sigma","line":5},{"c":"","k":"ShapeletExponentialSph.centre_0.width_modifier","line":6},{"c":"","k":"ShapeletExponentialSph.centre_0.width_modifier.type","line":7},{"c":"","k":"ShapeletExponentialSph.centre_0.width_modifier.value","line":8},{"c":"","k":"ShapeletExponentialSph.centre_0.limits","line":9},{"c":"","k":"ShapeletExponentialSph.centre_0.limits.lower","line":10},{"c":"","k":"ShapeletExponentialSph.centre_0.limits.upper","line":11},{"c":"","k":"ShapeletExponentialSph.centre_1","line":12},{"c":"","k":"ShapeletExponentialSph.centre_1.type","line":13},{"c":"","k":"ShapeletExponentialSph.centre_1.mean","line":14},{"c":"","k":"ShapeletExponentialSph.centre_1.sigma","line":15},{"c":"","k":"ShapeletExponentialSph.centre_1.width_modifier","line":16},{"c":"","k":"ShapeletExponentialSph.centre_1.width_modifier.type","line":17},{"c":"","k":"ShapeletExponentialSph.centre_1.width_modifier.value","line":18},{"c":"","k":"ShapeletExponentialSph.centre_1.limits","line":19},{"c":"","k":"ShapeletExponentialSph.centre_1.limits.lower","line":20},{"c":"","k":"ShapeletExponentialSph.centre_1.limits.upper","line":21},{"c":"","k":"ShapeletExponentialSph.beta","line":22},{"c":"","k":"ShapeletExponentialSph.beta.type","line":23},{"c":"","k":"ShapeletExponentialSph.beta.lower_limit","line":24},{"c":"","k":"ShapeletExponentialSph.beta.upper_limit","line":25},{"c":"","k":"ShapeletExponentialSph.beta.width_modifier","line":26},{"c":"","k":"ShapeletExponentialSph.beta.width_modifier.type","line":27},{"c":"","k":"ShapeletExponentialSph.beta.width_modifier.value","line":28},{"c":"","k":"ShapeletExponentialSph.beta.limits","line":29},{"c":"","k":"ShapeletExponentialSph.beta.limits.lower","line":30},{"c":"","k":"ShapeletExponentialSph.beta.limits.upper","line":31},{"c":"","k":"ShapeletExponential","line":32},{"c":"","k":"ShapeletExponential.centre_0","line":33},{"c":"","k":"ShapeletExponential.centre_0.type","line":34},{"c":"","k":"ShapeletExponential.centre_0.mean","line":35},{"c":"","k":"ShapeletExponential.centre_0.sigma","line":36},{"c":"","k":"ShapeletExponential.centre_0.width_modifier","line":37},{"c":"","k":"ShapeletExponential.centre_0.width_modifier.type","line":38},{"c":"","k":"ShapeletExponential.centre_0.width_modifier.value","line":39},{"c":"","k":"ShapeletExponential.centre_0.limits","line":40},{"c":"","k":"ShapeletExponential.centre_0.limits.lower","line":41},{"c":"","k":"ShapeletExponential.centre_0.limits.upper","line":42},{"c":"","k":"ShapeletExponential.centre_1","line":43},{"c":"","k":"ShapeletExponential.centre_1.type","line":44},{"c":"","k":"ShapeletExponential.centre_1.mean","line":45},{"c":"","k":"ShapeletExponential.centre_1.sigma","line":46},{"c":"","k":"ShapeletExponential.centre_1.width_modifier","line":47},{"c":"","k":"ShapeletExponential.centre_1.width_modifier.type","line":48},{"c":"","k":"ShapeletExponential.centre_1.width_modifier.value","line":49},{"c":"","k":"ShapeletExponential.centre_1.limits","line":50},{"c":"","k":"ShapeletExponential.centre_1.limits.lower","line":51},{"c":"","k":"ShapeletExponential.centre_1.limits.upper","line":52},{"c":"","k":"ShapeletExponential.ell_comps_0","line":53},{"c":"","k":"ShapeletExponential.ell_comps_0.type","line":54},{"c":"","k":"ShapeletExponential.ell_comps_0.mean","line":55},{"c":"","k":"ShapeletExponential.ell_comps_0.sigma","line":56},{"c":"","k":"ShapeletExponential.ell_comps_0.lower_limit","line":57},{"c":"","k":"ShapeletExponential.ell_comps_0.upper_limit","line":58},{"c":"","k":"ShapeletExponential.ell_comps_0.width_modifier","line":59},{"c":"","k":"ShapeletExponential.ell_comps_0.width_modifier.type","line":60},{"c":"","k":"ShapeletExponential.ell_comps_0.width_modifier.value","line":61},{"c":"","k":"ShapeletExponential.ell_comps_0.limits","line":62},{"c":"","k":"ShapeletExponential.ell_comps_0.limits.lower","line":63},{"c":"","k":"ShapeletExponential.ell_comps_0.limits.upper","line":64},{"c":"","k":"ShapeletExponential.ell_comps_1","line":65},{"c":"","k":"ShapeletExponential.ell_comps_1.type","line":66},{"c":"","k":"ShapeletExponential.ell_comps_1.mean","line":67},{"c":"","k":"ShapeletExponential.ell_comps_1.sigma","line":68},{"c":"","k":"ShapeletExponential.ell_comps_1.lower_limit","line":69},{"c":"","k":"ShapeletExponential.ell_comps_1.upper_limit","line":70},{"c":"","k":"ShapeletExponential.ell_comps_1.width_modifier","line":71},{"c":"","k":"ShapeletExponential.ell_comps_1.width_modifier.type","line":72},{"c":"","k":"ShapeletExponential.ell_comps_1.width_modifier.value","line":73},{"c":"","k":"ShapeletExponential.ell_comps_1.limits","line":74},{"c":"","k":"ShapeletExponential.ell_com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0.0","b":"\u03c3 0.3","cls":"ShapeletExponentialSph","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletExponentialSph","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 30.0","cls":"ShapeletExponentialSph","limits":"[0.0, inf]","line":22,"param":"beta","type":"Uniform","width":"Relative 0.5"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletExponential","limits":"[-inf, inf]","line":33,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletExponential","limits":"[-inf, inf]","line":43,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletExponential","limits":"[-1.0, 1.0]","line":53,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 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Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  beta:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["ShapeletExponentialSph","ShapeletExponential"]},{"error":null,"keys":[{"c":"","k":"ShapeletPolarSph","line":1},{"c":"","k":"ShapeletPolarSph.centre_0","line":2},{"c":"","k":"ShapeletPolarSph.centre_0.type","line":3},{"c":"","k":"ShapeletPolarSph.centre_0.mean","line":4},{"c":"","k":"ShapeletPolarSph.centre_0.sigma","line":5},{"c":"","k":"ShapeletPolarSph.centre_0.width_modifier","line":6},{"c":"","k":"ShapeletPolarSph.centre_0.width_modifier.type","line":7},{"c":"","k":"ShapeletPolarSph.centre_0.width_modifier.value","line":8},{"c":"","k":"ShapeletPolarSph.centre_0.limits","line":9},{"c":"","k":"ShapeletPolarSph.centre_0.limits.lower","line":10},{"c":"","k":"ShapeletPolarSph.centre_0.limits.upper","line":11},{"c":"","k":"ShapeletPolarSph.centre_1","line":12},{"c":"","k":"ShapeletPolarSph.centre_1.type","line":13},{"c":"","k":"ShapeletPolarSph.centre_1.mean","line":14},{"c":"","k":"ShapeletPolarSph.centre_1.sigma","line":15},{"c":"","k":"ShapeletPolarSph.centre_1.width_modifier","line":16},{"c":"","k":"ShapeletPolarSph.centre_1.width_modifier.type","line":17},{"c":"","k":"ShapeletPolarSph.centre_1.width_modifier.value","line":18},{"c":"","k":"ShapeletPolarSph.centre_1.limits","line":19},{"c":"","k":"ShapeletPolarSph.centre_1.limits.lower","line":20},{"c":"","k":"ShapeletPolarSph.centre_1.limits.upper","line":21},{"c":"","k":"ShapeletPolarSph.beta","line":22},{"c":"","k":"ShapeletPolarSph.beta.type","line":23},{"c":"","k":"ShapeletPolarSph.beta.lower_limit","line":24},{"c":"","k":"ShapeletPolarSph.beta.upper_limit","line":25},{"c":"","k":"ShapeletPolarSph.beta.width_modifier","line":26},{"c":"","k":"ShapeletPolarSph.beta.width_modifier.type","line":27},{"c":"","k":"ShapeletPolarSph.beta.width_modifier.value","line":28},{"c":"","k":"ShapeletPolarSph.beta.limits","line":29},{"c":"","k":"ShapeletPolarSph.beta.limits.lower","line":30},{"c":"","k":"ShapeletPolarSph.beta.limits.upper","line":31},{"c":"","k":"ShapeletPolar","line":32},{"c":"","k":"ShapeletPolar.centre_0","line":33},{"c":"","k":"ShapeletPolar.centre_0.type","line":34},{"c":"","k":"ShapeletPolar.centre_0.mean","line":35},{"c":"","k":"ShapeletPolar.centre_0.sigma","line":36},{"c":"","k":"ShapeletPolar.centre_0.width_modifier","line":37},{"c":"","k":"ShapeletPolar.centre_0.width_modifier.type","line":38},{"c":"","k":"ShapeletPolar.centre_0.width_modifier.value","line":39},{"c":"","k":"ShapeletPolar.centre_0.limits","line":40},{"c":"","k":"ShapeletPolar.centre_0.limits.lower","line":41},{"c":"","k":"ShapeletPolar.centre_0.limits.upper","line":42},{"c":"","k":"ShapeletPolar.centre_1","line":43},{"c":"","k":"ShapeletPolar.centre_1.type","line":44},{"c":"","k":"ShapeletPolar.centre_1.mean","line":45},{"c":"","k":"ShapeletPolar.centre_1.sigma","line":46},{"c":"","k":"ShapeletPolar.centre_1.width_modifier","line":47},{"c":"","k":"ShapeletPolar.centre_1.width_modifier.type","line":48},{"c":"","k":"ShapeletPolar.centre_1.width_modifier.value","line":49},{"c":"","k":"ShapeletPolar.centre_1.limits","line":50},{"c":"","k":"ShapeletPolar.centre_1.limits.lower","line":51},{"c":"","k":"ShapeletPolar.centre_1.limits.upper","line":52},{"c":"","k":"ShapeletPolar.ell_comps_0","line":53},{"c":"","k":"ShapeletPolar.ell_comps_0.type","line":54},{"c":"","k":"ShapeletPolar.ell_comps_0.mean","line":55},{"c":"","k":"ShapeletPolar.ell_comps_0.sigma","line":56},{"c":"","k":"ShapeletPolar.ell_comps_0.lower_limit","line":57},{"c":"","k":"ShapeletPolar.ell_comps_0.upper_limit","line":58},{"c":"","k":"ShapeletPolar.ell_comps_0.width_modifier","line":59},{"c":"","k":"ShapeletPolar.ell_comps_0.width_modifier.type","line":60},{"c":"","k":"ShapeletPolar.ell_comps_0.width_modifier.value","line":61},{"c":"","k":"ShapeletPolar.ell_comps_0.limits","line":62},{"c":"","k":"ShapeletPolar.ell_comps_0.limits.lower","line":63},{"c":"","k":"ShapeletPolar.ell_comps_0.limits.upper","line":64},{"c":"","k":"ShapeletPolar.ell_comps_1","line":65},{"c":"","k":"ShapeletPolar.ell_comps_1.type","line":66},{"c":"","k":"ShapeletPolar.ell_comps_1.mean","line":67},{"c":"","k":"ShapeletPolar.ell_comps_1.sigma","line":68},{"c":"","k":"ShapeletPolar.ell_comps_1.lower_limit","line":69},{"c":"","k":"ShapeletPolar.ell_comps_1.upper_limit","line":70},{"c":"","k":"ShapeletPolar.ell_comps_1.width_modifier","line":71},{"c":"","k":"ShapeletPolar.ell_comps_1.width_modifier.type","line":72},{"c":"","k":"ShapeletPolar.ell_comps_1.width_modifier.value","line":73},{"c":"","k":"ShapeletPolar.ell_comps_1.limits","line":74},{"c":"","k":"ShapeletPolar.ell_comps_1.limits.lower","line":75},{"c":"","k":"ShapeletPolar.ell_comps_1.limits.upper","line":76},{"c":"","k":"ShapeletPolar.beta","line":77},{"c":"","k":"ShapeletPolar.beta.type","line":78},{"c":"","k":"ShapeletPolar.beta.lower_limit","line":79},{"c":"","k":"ShapeletPolar.beta.upper_limit","line":80},{"c":"","k":"ShapeletPolar.beta.width_modifier","line":81},{"c":"","k":"ShapeletPolar.beta.width_modifier.type","line":82},{"c":"","k":"ShapeletPol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0.0","b":"\u03c3 0.3","cls":"ShapeletPolarSph","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletPolarSph","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 30.0","cls":"ShapeletPolarSph","limits":"[0.0, inf]","line":22,"param":"beta","type":"Uniform","width":"Relative 0.5"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletPolar","limits":"[-inf, inf]","line":33,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletPolar","limits":"[-inf, inf]","line":43,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletPolar","limits":"[-1.0, 1.0]","line":53,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletPolar","limits":"[-1.0, 1.0]","line":65,"param":"ell_comps_1","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"lower 0.0","b":"upper 30.0","cls":"ShapeletPolar","limits":"[0.0, inf]","line":77,"param":"beta","type":"Uniform","width":"Relative 0.5"}],"repo":"PyAutoGalaxy","text":"ShapeletPolarSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  beta:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\nShapeletPolar:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  beta:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["ShapeletPolarSph","ShapeletPolar"]},{"error":null,"keys":[{"c":"","k":"Gaussian","line":1},{"c":"","k":"Gaussian.sigma","line":2},{"c":"","k":"Gaussian.sigma.type","line":3},{"c":"","k":"Gaussian.sigma.lower_limit","line":4},{"c":"","k":"Gaussian.sigma.upper_limit","line":5},{"c":"","k":"Gaussian.sigma.width_modifier","line":6},{"c":"","k":"Gaussian.sigma.width_modifier.type","line":7},{"c":"","k":"Gaussian.sigma.width_modifier.value","line":8},{"c":"","k":"Gaussian.sigma.limits","line":9},{"c":"","k":"Gaussian.sigma.limits.lower","line":10},{"c":"","k":"Gaussian.sigma.limits.upper","line":11},{"c":"","k":"Gaussian.centre_0","line":12},{"c":"","k":"Gaussian.centre_0.type","line":13},{"c":"","k":"Gaussian.centre_0.mean","line":14},{"c":"","k":"Gaussian.centre_0.sigma","line":15},{"c":"","k":"Gaussian.centre_0.width_modifier","line":16},{"c":"","k":"Gaussian.centre_0.width_modifier.type","line":17},{"c":"","k":"Gaussian.centre_0.width_modifier.value","line":18},{"c":"","k":"Gaussian.centre_0.limits","line":19},{"c":"","k":"Gaussian.centre_0.limits.lower","line":20},{"c":"","k":"Gaussian.centre_0.limits.upper","line":21},{"c":"","k":"Gaussian.centre_1","line":22},{"c":"","k":"Gaussian.centre_1.type","line":23},{"c":"","k":"Gaussian.centre_1.mean","line":24},{"c":"","k":"Gaussian.centre_1.sigma","line":25},{"c":"","k":"Gaussian.centre_1.width_modifier","line":26},{"c":"","k":"Gaussian.centre_1.width_modifier.type","line":27},{"c":"","k":"Gaussian.centre_1.width_modifier.value","line":28},{"c":"","k":"Gaussian.centre_1.limits","line":29},{"c":"","k":"Gaussian.centre_1.limits.lower","line":30},{"c":"","k":"Gaussian.centre_1.limits.upper","line":31},{"c":"","k":"Gaussian.ell_comps_0","line":32},{"c":"","k":"Gaussian.ell_comps_0.type","line":33},{"c":"","k":"Gaussian.ell_comps_0.mean","line":34},{"c":"","k":"Gaussian.ell_comps_0.sigma","line":35},{"c":"","k":"Gaussian.ell_comps_0.lower_limit","line":36},{"c":"","k":"Gaussian.ell_comps_0.upper_limit","line":37},{"c":"","k":"Gaussian.ell_comps_0.width_modifier","line":38},{"c":"","k":"Gaussian.ell_comps_0.width_modifier.type","line":39},{"c":"","k":"Gaussian.ell_comps_0.width_modifier.value","line":40},{"c":"","k":"Gaussian.ell_comps_0.limits","line":41},{"c":"","k":"Gaussian.ell_comps_0.limits.lower","line":42},{"c":"","k":"Gaussian.ell_comps_0.limits.upper","line":43},{"c":"","k":"Gaussian.ell_comps_1","line":44},{"c":"","k":"Gaussian.ell_comps_1.type","line":45},{"c":"","k":"Gaussian.ell_comps_1.mean","line":46},{"c":"","k":"Gaussian.ell_comps_1.sigma","line":47},{"c":"","k":"Gaussian.ell_comps_1.lower_limit","line":48},{"c":"","k":"Gaussian.ell_comps_1.upper_limit","line":49},{"c":"","k":"Gaussian.ell_comps_1.width_modifier","line":50},{"c":"","k":"Gaussian.ell_comps_1.width_modifier.type","line":51},{"c":"","k":"Gaussian.ell_comps_1.width_modifier.value","line":52},{"c":"","k":"Gaussian.ell_comps_1.limits","line":53},{"c":"","k":"Gaussian.ell_comps_1.limits.lower","line":54},{"c":"","k":"Gaussian.ell_comps_1.limits.upper","line":55}],"lines":55,"path":"priors/light/linear_operated/gaussian.yaml","prior":true,"priors":[{"a":"lower 0.0","b":"upper 5.0","cls":"Gaussian","limits":"[0.0, inf]","line":2,"param":"sigma","type":"Uniform","width":"Relative 0.5"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Gaussian","limits":"[-inf, inf]","line":12,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Gaussian","limits":"[-inf, inf]","line":22,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Gaussian","limits":"[-1.0, 1.0]","line":32,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Gaussian","limits":"[-1.0, 1.0]","line":44,"param":"ell_comps_1","type":"TruncatedGaussian","width":"Absolute 0.2"}],"repo":"PyAutoGalaxy","text":"Gaussian:\n  sigma:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 5.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n","tooling":false,"top_keys":["Gaussian"]},{"error":null,"keys":[{"c":"","k":"Moffat","line":1},{"c":"","k":"Moffat.alpha","line":2},{"c":"","k":"Moffat.alpha.type","line":3},{"c":"","k":"Moffat.alpha.lower_limit","line":4},{"c":"","k":"Moffat.alpha.upper_limit","line":5},{"c":"","k":"Moffat.alpha.width_modifier","line":6},{"c":"","k":"Moffat.alpha.width_modifier.type","line":7},{"c":"","k":"Moffat.alpha.width_modifier.value","line":8},{"c":"","k":"Moffat.alpha.limits","line":9},{"c":"","k":"Moffat.alpha.limits.lower","line":10},{"c":"","k":"Moffat.alpha.limits.upper","line":11},{"c":"","k":"Moffat.beta","line":12},{"c":"","k":"Moffat.beta.type","line":13},{"c":"","k":"Moffat.beta.lower_limit","line":14},{"c":"","k":"Moffat.beta.upper_limit","line":15},{"c":"","k":"Moffat.beta.width_modifier","line":16},{"c":"","k":"Moffat.beta.width_modifier.type","line":17},{"c":"","k":"Moffat.beta.width_modifier.value","line":18},{"c":"","k":"Moffat.beta.limits","line":19},{"c":"","k":"Moffat.beta.limits.lower","line":20},{"c":"","k":"Moffat.beta.limits.upper","line":21},{"c":"","k":"Moffat.centre_0","line":22},{"c":"","k":"Moffat.centre_0.type","line":23},{"c":"","k":"Moffat.centre_0.mean","line":24},{"c":"","k":"Moffat.centre_0.sigma","line":25},{"c":"","k":"Moffat.centre_0.width_modifier","line":26},{"c":"","k":"Moffat.centre_0.width_modifier.type","line":27},{"c":"","k":"Moffat.centre_0.width_modifier.value","line":28},{"c":"","k":"Moffat.centre_0.limits","line":29},{"c":"","k":"Moffat.centre_0.limits.lower","line":30},{"c":"","k":"Moffat.centre_0.limits.upper","line":31},{"c":"","k":"Moffat.centre_1","line":32},{"c":"","k":"Moffat.centre_1.type","line":33},{"c":"","k":"Moffat.centre_1.mean","line":34},{"c":"","k":"Moffat.centre_1.sigma","line":35},{"c":"","k":"Moffat.centre_1.width_modifier","line":36},{"c":"","k":"Moffat.centre_1.width_modifier.type","line":37},{"c":"","k":"Moffat.centre_1.width_modifier.value","line":38},{"c":"","k":"Moffat.centre_1.limits","line":39},{"c":"","k":"Moffat.centre_1.limits.lower","line":40},{"c":"","k":"Moffat.centre_1.limits.upper","line":41},{"c":"","k":"Moffat.ell_comps_0","line":42},{"c":"","k":"Moffat.ell_comps_0.type","line":43},{"c":"","k":"Moffat.ell_comps_0.mean","line":44},{"c":"","k":"Moffat.ell_comps_0.sigma","line":45},{"c":"","k":"Moffat.ell_comps_0.lower_limit","line":46},{"c":"","k":"Moffat.ell_comps_0.upper_limit","line":47},{"c":"","k":"Moffat.ell_comps_0.width_modifier","line":48},{"c":"","k":"Moffat.ell_comps_0.width_modifier.type","line":49},{"c":"","k":"Moffat.ell_comps_0.width_modifier.value","line":50},{"c":"","k":"Moffat.ell_comps_0.limits","line":51},{"c":"","k":"Moffat.ell_comps_0.limits.lower","line":52},{"c":"","k":"Moffat.ell_comps_0.limits.upper","line":53},{"c":"","k":"Moffat.ell_comps_1","line":54},{"c":"","k":"Moffat.ell_comps_1.type","line":55},{"c":"","k":"Moffat.ell_comps_1.mean","line":56},{"c":"","k":"Moffat.ell_comps_1.sigma","line":57},{"c":"","k":"Moffat.ell_comps_1.lower_limit","line":58},{"c":"","k":"Moffat.ell_comps_1.upper_limit","line":59},{"c":"","k":"Moffat.ell_comps_1.width_modifier","line":60},{"c":"","k":"Moffat.ell_comps_1.width_modifier.type","line":61},{"c":"","k":"Moffat.ell_comps_1.width_modifier.value","line":62},{"c":"","k":"Moffat.ell_comps_1.limits","line":63},{"c":"","k":"Moffat.ell_comps_1.limits.lower","line":64},{"c":"","k":"Moffat.ell_comps_1.limits.upper","line":65}],"lines":65,"path":"priors/light/linear_operated/moffat.yaml","prior":true,"priors":[{"a":"lower 0.0","b":"upper 1.0","cls":"Moffat","limits":"[0.0, inf]","line":2,"param":"alpha","type":"Uniform","width":"Relative 0.5"},{"a":"lower 1.0","b":"upper 5.0","cls":"Moffat","limits":"[0.0, inf]","line":12,"param":"beta","type":"Uniform","width":"Relative 0.5"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Moffat","limits":"[-inf, inf]","line":22,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Moffat","limits":"[-inf, inf]","line":32,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Moffat","limits":"[-1.0, 1.0]","line":42,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Moffat","limits":"[-1.0, 1.0]","line":54,"param":"ell_comps_1","type":"TruncatedGaussian","width":"Absolute 0.2"}],"repo":"PyAutoGalaxy","text":"Moffat:\n  alpha:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: 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Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 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width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: 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0.5"}],"repo":"PyAutoGalaxy","text":"Moffat:\n  alpha:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  beta:\n    type: Uniform\n    lower_limit: 1.0\n    upper_limit: 5.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: 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 upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  sersic_index:\n    type: Uniform\n    lower_limit: 0.8\n    upper_limit: 5.0\n    width_modifier:\n      type: Absolute\n      value: 1.5\n    limits:\n      lower: 0.8\n      upper: 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  type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\nChameleonSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  core_radius_0:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Absolute\n      value: 0.3\n    limits:\n      lower: 0.0\n      upper: inf\n  core_radius_1:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Absolute\n      value: 0.3\n    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width_modifier:\n      type: Absolute\n      value: 1.5\n    limits:\n      lower: 0.0\n      upper: 5.0\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\nElsonFreeFallSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  eta:\n    type: Uniform\n    lower_limit: 0.5\n    upper_limit: 3.0\n    width_modifier:\n      type: Absolute\n      value: 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lower_limit: 1.0\n    upper_limit: 5.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\nMoffatSph:\n  alpha:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  beta:\n    type: Uniform\n    lower_limit: 1.0\n    upper_limit: 5.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["Moffat","MoffatSph"]},{"error":null,"keys":[{"c":"","k":"PointSource","line":1},{"c":"","k":"PointSource.centre_0","line":2},{"c":"","k":"PointSource.centre_0.type","line":3},{"c":"","k":"PointSource.centre_0.mean","line":4},{"c":"","k":"PointSource.centre_0.sigma","line":5},{"c":"","k":"PointSource.centre_0.width_modifier","line":6},{"c":"","k":"PointSource.centre_0.width_modifier.type","line":7},{"c":"","k":"PointSource.centre_0.width_modifier.value","line":8},{"c":"","k":"PointSource.centre_0.limits","line":9},{"c":"","k":"PointSource.centre_0.limits.lower","line":10},{"c":"","k":"PointSource.centre_0.limits.upper","line":11},{"c":"","k":"PointSource.centre_1","line":12},{"c":"","k":"PointSource.centre_1.type","line":13},{"c":"","k":"PointSource.centre_1.mean","line":14},{"c":"","k":"PointSource.centre_1.sigma","line":15},{"c":"","k":"PointSource.centre_1.width_modifier","line":16},{"c":"","k":"PointSource.centre_1.width_modifier.type","line":17},{"c":"","k":"PointSource.centre_1.width_modifier.value","line":18},{"c":"","k":"PointSource.centre_1.limits","line":19},{"c":"","k":"PointSource.centre_1.limits.lower","line":20},{"c":"","k":"PointSource.centre_1.limits.upper","line":21},{"c":"","k":"PointSource.intensity","line":22},{"c":"","k":"PointSource.intensity.type","line":23},{"c":"","k":"PointSource.intensity.lower_limit","line":24},{"c":"","k":"PointSource.intensity.upper_limit","line":25},{"c":"","k":"PointSource.intensity.width_modifier","line":26},{"c":"","k":"PointSource.intensity.width_modifier.type","line":27},{"c":"","k":"PointSource.intensity.width_modifier.value","line":28},{"c":"","k":"PointSource.intensity.limits","line":29},{"c":"","k":"PointSource.intensity.limits.lower","line":30},{"c":"","k":"PointSource.intensity.limits.upper","line":31}],"lines":31,"path":"priors/light/standard/point_source.yaml","prior":true,"priors":[{"a":"mean 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limits:\n      lower: 0.0\n      upper: 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limits:\n      lower: 0.8\n      upper: 5.0      \n  alpha:\n    type: Constant\n    value: 3.0      \n  gamma:\n    type: Constant\n    value: 0.25\n  radius_break:\n    type: Constant\n    value: 0.025\nSersicCoreSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-05\n    upper_limit: 1000.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  sersic_index:\n    type: 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lower_limit: 0.0\n    upper_limit: 15.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\ncNFWSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  kappa_s:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  scale_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  core_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 15.0\n    width_modifier:\n      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width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  mass_at_200:\n    type: LogUniform\n    lower_limit: 100000000.0\n    upper_limit: 1000000000000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  f_c:\n    type: Uniform\n    lower_limit: 0.0001\n    upper_limit: 0.5\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0001\n      upper: inf\n  redshift_object:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  redshift_source:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\ncNFWMCRLudlowSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n  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 limits:\n      lower: -inf\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  mass_at_200:\n    type: LogUniform\n    lower_limit: 100000000.0\n    upper_limit: 1000000000000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  f_c:\n    type: Uniform\n    lower_limit: 0.0001\n    upper_limit: 0.5\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0001\n      upper: inf\n  redshift_object:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    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lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\ngNFWSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  inner_slope:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 2.0\n    width_modifier:\n      type: Absolute\n      value: 0.3\n    limits:\n      lower: -1.0\n      upper: 3.0\n  kappa_s:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  scale_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      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width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  mass_at_200:\n    type: LogUniform\n    lower_limit: 100000000.0\n    upper_limit: 1000000000000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  redshift_object:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  redshift_source:\n 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upper: inf\n  mass:\n    type: LogUniform\n    lower_limit: 1000000.0\n    upper_limit: 10000000000000.0\n    width_modifier:\n      type: Relative\n      value: 0.25\n    limits:\n      lower: 0.0\n      upper: inf\n  mass_ratio:\n    type: Uniform\n    lower_limit: 1.0\n    upper_limit: 10.0\n    width_modifier:\n      type: Relative\n      value: 0.25\n    limits:\n      lower: 0.0\n      upper: inf\n  redshift_object:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  redshift_source:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: 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upper: inf\n  tau_1:\n    type: Uniform\n    lower_limit: -0.3\n    upper_limit: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  tau_2:\n    type: Uniform\n    lower_limit: -0.3\n    upper_limit: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  delta_1:\n    type: Uniform\n    lower_limit: -0.3\n    upper_limit: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  delta_2:\n    type: Uniform\n    lower_limit: -0.3\n    upper_limit: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: 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lower: 0.0\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  mass_to_light_ratio:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.3\n    limits:\n      lower: 0.0\n      upper: inf\nChameleonSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    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width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  intensity:\n    type: LogUniform\n    lower_limit: 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TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 10.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  mass_to_light_ratio:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.3\n    limits:\n      lower: 0.0\n      upper: inf\n  sersic_index:\n    type: Uniform\n    lower_limit: 0.8\n    upper_limit: 5.0\n    width_modifier:\n      type: Absolute\n      value: 1.5\n    limits:\n      lower: 0.8\n      upper: 5.0\nSersicSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  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upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  gamma:\n    type: Constant\n    value: 0.25\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-05\n    upper_limit: 1000.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  radius_break:\n    type: Constant\n    value: 0.025\n  sersic_index:\n    type: Uniform\n    lower_limit: 0.8\n    upper_limit: 5.0\n    width_modifier:\n      type: Absolute\n      value: 1.5\n    limits:\n      lower: 0.8\n      upper: 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 lower_limit: 0.0\n    upper_limit: 10.0\n    width_modifier:\n      type: Relative\n      value: 0.25\n    limits:\n      lower: 0.0\n      upper: 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0.0","b":"\u03c3 0.1","cls":"IsothermalCore","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"IsothermalCore","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 0.2","cls":"IsothermalCore","limits":"[0.0, inf]","line":22,"param":"core_radius","type":"Uniform","width":"Absolute 0.1"},{"a":"lower 0.0","b":"upper 8.0","cls":"IsothermalCore","limits":"[0.0, inf]","line":32,"param":"einstein_radius","type":"Uniform","width":"Relative 0.25"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"IsothermalCore","limits":"[-1.0, 1.0]","line":42,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"IsothermalCore","limits":"[-1.0, 1.0]","line":54,"param":"ell_comps_1","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"IsothermalCoreSph","limits":"[-inf, inf]","line":67,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"IsothermalCoreSph","limits":"[-inf, inf]","line":77,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 0.2","cls":"IsothermalCoreSph","limits":"[0.0, inf]","line":87,"param":"core_radius","type":"Uniform","width":"Absolute 0.1"},{"a":"lower 0.0","b":"upper 8.0","cls":"IsothermalCoreSph","limits":"[0.0, inf]","line":97,"param":"einstein_radius","type":"Uniform","width":"Relative 0.25"}],"repo":"PyAutoGalaxy","text":"IsothermalCore:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  core_radius:\n    type: Uniform\n    lower_limit: 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th_modifier.value","line":83},{"c":"","k":"PowerLawCoreSph.centre_0.limits","line":84},{"c":"","k":"PowerLawCoreSph.centre_0.limits.lower","line":85},{"c":"","k":"PowerLawCoreSph.centre_0.limits.upper","line":86},{"c":"","k":"PowerLawCoreSph.centre_1","line":87},{"c":"","k":"PowerLawCoreSph.centre_1.type","line":88},{"c":"","k":"PowerLawCoreSph.centre_1.mean","line":89},{"c":"","k":"PowerLawCoreSph.centre_1.sigma","line":90},{"c":"","k":"PowerLawCoreSph.centre_1.width_modifier","line":91},{"c":"","k":"PowerLawCoreSph.centre_1.width_modifier.type","line":92},{"c":"","k":"PowerLawCoreSph.centre_1.width_modifier.value","line":93},{"c":"","k":"PowerLawCoreSph.centre_1.limits","line":94},{"c":"","k":"PowerLawCoreSph.centre_1.limits.lower","line":95},{"c":"","k":"PowerLawCoreSph.centre_1.limits.upper","line":96},{"c":"","k":"PowerLawCoreSph.core_radius","line":97},{"c":"","k":"PowerLawCoreSph.core_radius.type","line":98},{"c":"","k":"PowerLawCoreSph.core_radius.lower_limit","line":99},{"c":"","k":"PowerLawCoreSph.core_radius.upper_limit","line":100},{"c":"","k":"PowerLawCoreSph.core_radius.width_modifier","line":101},{"c":"","k":"PowerLawCoreSph.core_radius.width_modifier.type","line":102},{"c":"","k":"PowerLawCoreSph.core_radius.width_modifier.value","line":103},{"c":"","k":"PowerLawCoreSph.core_radius.limits","line":104},{"c":"","k":"PowerLawCoreSph.core_radius.limits.lower","line":105},{"c":"","k":"PowerLawCoreSph.core_radius.limits.upper","line":106},{"c":"","k":"PowerLawCoreSph.einstein_radius","line":107},{"c":"","k":"PowerLawCoreSph.einstein_radius.type","line":108},{"c":"","k":"PowerLawCoreSph.einstein_radius.lower_limit","line":109},{"c":"","k":"PowerLawCoreSph.einstein_radius.upper_limit","line":110},{"c":"","k":"PowerLawCoreSph.einstein_radius.width_modifier","line":111},{"c":"","k":"PowerLawCoreSph.einstein_radius.width_modifier.type","line":112},{"c":"","k":"PowerLawCoreSph.einstein_radius.width_modifier.value","line":113},{"c":"","k":"PowerLawCoreSph.einstein_radius.limits","line":114},{"c":"","k":"PowerLawCoreSph.einstein_radius.limits.lower","line":115},{"c":"","k":"PowerLawCoreSph.einstein_radius.limits.upper","line":116},{"c":"","k":"PowerLawCoreSph.slope","line":117},{"c":"","k":"PowerLawCoreSph.slope.type","line":118},{"c":"","k":"PowerLawCoreSph.slope.lower_limit","line":119},{"c":"","k":"PowerLawCoreSph.slope.upper_limit","line":120},{"c":"","k":"PowerLawCoreSph.slope.width_modifier","line":121},{"c":"","k":"PowerLawCoreSph.slope.width_modifier.type","line":122},{"c":"","k":"PowerLawCoreSph.slope.width_modifier.value","line":123},{"c":"","k":"PowerLawCoreSph.slope.limits","line":124},{"c":"","k":"PowerLawCoreSph.slope.limits.lower","line":125},{"c":"","k":"PowerLawCoreSph.slope.limits.upper","line":126}],"lines":126,"path":"priors/mass/total/power_law_core.yaml","prior":true,"priors":[{"a":"mean 0.0","b":"\u03c3 0.1","cls":"PowerLawCore","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"PowerLawCore","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 0.2","cls":"PowerLawCore","limits":"[0.0, inf]","line":22,"param":"core_radius","type":"Uniform","width":"Absolute 0.1"},{"a":"lower 0.0","b":"upper 8.0","cls":"PowerLawCore","limits":"[0.0, inf]","line":32,"param":"einstein_radius","type":"Uniform","width":"Relative 0.25"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"PowerLawCore","limits":"[-1.0, 1.0]","line":42,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"PowerLawCore","limits":"[-1.0, 1.0]","line":54,"param":"ell_comps_1","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"lower 1.5","b":"upper 3.0","cls":"PowerLawCore","limits":"[1.0, 3.0]","line":66,"param":"slope","type":"Uniform","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"PowerLawCoreSph","limits":"[-inf, inf]","line":77,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"PowerLawCoreSph","limits":"[-inf, inf]","line":87,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 0.2","cls":"PowerLawCoreSph","limits":"[0.0, inf]","line":97,"param":"core_radius","type":"Uniform","width":"Absolute 0.1"},{"a":"lower 0.0","b":"upper 8.0","cls":"PowerLawCoreSph","limits":"[0.0, inf]","line":107,"param":"einstein_radius","type":"Uniform","width":"Relative 0.25"},{"a":"lower 1.5","b":"upper 3.0","cls":"PowerLawCoreSph","limits":"[1.0, 3.0]","line":117,"param":"slope","type":"Uniform","width":"Absolute 0.2"}],"repo":"PyAutoGalaxy","text":"PowerLawCore:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  core_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 0.2\n    width_modifier:\n      type: Absolute\n      value: 0.1\n    limits:\n      lower: 0.0\n      upper: inf\n  einstein_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 8.0\n    width_modifier:\n      type: Relative\n      value: 0.25\n    limits:\n      lower: 0.0\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  slope:\n    type: Uniform\n    lower_limit: 1.5\n    upper_limit: 3.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: 1.0\n      upper: 3.0\nPowerLawCoreSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  core_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 0.2\n    width_modifier:\n      type: Absolute\n      value: 0.1\n    limits:\n      lower: 0.0\n      upper: inf\n  einstein_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 8.0\n    width_modifier:\n      type: Relative\n      value: 0.25\n    limits:\n      lower: 0.0\n      upper: inf\n  slope:\n    type: Uniform\n    lower_limit: 1.5\n    upper_limit: 3.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: 1.0\n      upper: 3.0\n","tooling":false,"top_keys":["PowerLawCore","PowerLawCoreSph"]},{"error":null,"keys":[{"c":"","k":"PowerLawMultipole","line":1},{"c":"","k":"PowerLawMultipole.m","line":2},{"c":"","k":"PowerLawMultipole.m.type","line":3},{"c":"","k":"PowerLawMultipole.m.value","line":4},{"c":"","k":"PowerLawMultipole.centre_0","line":5},{"c":"","k":"PowerLawMultipole.centre_0.type","line":6},{"c":"","k":"PowerLawMultipole.centre_0.mean","line":7},{"c":"","k":"PowerLawMultipole.centre_0.sigma","line":8},{"c":"","k":"PowerLawMultipole.centre_0.width_modifier","line":9},{"c":"","k":"PowerLawMultipole.centre_0.width_modifier.type","line":10},{"c":"","k":"PowerLawMultipole.centre_0.width_modifier.value","line":11},{"c":"","k":"PowerLawMultipole.centre_0.limits","line":12},{"c":"","k":"PowerLawMultipole.centre_0.limits.lower","line":13},{"c":"","k":"PowerLawMultipole.centre_0.limits.upper","line":14},{"c":"","k":"PowerLawMultipole.centre_1","line":15},{"c":"","k":"PowerLawMultipole.centre_1.type","line":16},{"c":"","k":"PowerLawMultipole.centre_1.mean","line":17},{"c":"","k":"PowerLawMultipole.centre_1.sigma","line":18},{"c":"","k":"PowerLawMultipole.centre_1.width_modifier","line":19},{"c":"","k":"PowerLawMultipole.centre_1.width_modifier.type","line":20},{"c":"","k":"PowerLawMultipole.centre_1.width_modifier.value","line":21},{"c":"","k":"PowerLawMultipole.centre_1.limits","line":22},{"c":"","k":"PowerLawMultipole.centre_1.limits.lower","line":23},{"c":"","k":"PowerLawMultipole.centre_1.limits.upper","line":24},{"c":"","k":"PowerLawMultipole.einstein_radius","line":25},{"c":"","k":"PowerLawMultipole.einstein_radius.type","line":26},{"c":"","k":"PowerLawMultipole.einstein_radius.lower_limit","line":27},{"c":"","k":"PowerLawMultipole.einstein_radius.upper_limit","line":28},{"c":"","k":"PowerLawMultipole.einstein_radius.width_modifier","line":29},{"c":"","k":"PowerLawMultipole.einstein_radius.width_modifier.type","line":30},{"c":"","k":"PowerLawMultipole.einstein_radius.width_modifier.value","line":31},{"c":"","k":"PowerLawMultipole.einstein_radius.limits","line":32},{"c":"","k":"PowerLawMultipole.einstein_radius.limits.lower","line":33},{"c":"","k":"PowerLawMultipole.einstein_radius.limits.upper","line":34},{"c":"","k":"PowerLawMultipole.slope","line":35},{"c":"","k":"PowerLawMultipole.slope.type","line":36},{"c":"","k":"PowerLawMultipole.slope.lower_limit","line":37},{"c":"","k":"PowerLawMultipole.slope.upper_limit","line":38},{"c":"","k":"PowerLawMultipole.slope.width_modifier","line":39},{"c":"","k":"PowerLawMultipole.slope.width_modifier.type","line":40},{"c":"","k":"PowerLawMultipole.slope.width_modifier.value","line":41},{"c":"","k":"PowerLawMultipole.slope.limits","line":42},{"c":"","k":"PowerLawMultipole.slope.limits.lower","line":43},{"c":"","k":"PowerLawMultipole.slope.limits.upper","line":44},{"c":"","k":"PowerLawMultipole.multipole_comps_0","line":45},{"c":"","k":"PowerLawMultipole.multipole_comps_0.type","line":46},{"c":"","k":"PowerLawMultipole.multipole_comps_0.lower_limit","line":47},{"c":"","k":"PowerLawMultipole.multipole_comps_0.upper_limit","line":48},{"c":"","k":"PowerLawMultipole.multipole_comps_0.width_modifier","line":49},{"c":"","k":"PowerLawMultipole.multipole_comps_0.width_modifier.type","line":50},{"c":"","k":"PowerLawMultipole.multipole_comps_0.width_modifier.value","line":51},{"c":"","k":"PowerLawMultipole.multipole_comps_0.limits","line":52},{"c":"","k":"PowerLawMultipole.multipole_comps_0.limits.lower","line":53},{"c":"","k":"PowerLawMultipole.multipole_comps_0.limits.upper","line":54},{"c":"","k":"PowerLawMultipole.multipole_comps_1","line":55},{"c":"","k":"PowerLawMultipole.multipole_comps_1.type","line":56},{"c":"","k":"PowerLawMultipole.multipole_comps_1.lower_limit","line":57},{"c":"","k":"PowerLawMultipole.multipole_comps_1.upper_limit","line":58},{"c":"","k":"PowerLawMultipole.multipole_comps_1.width_modifier","line":59},{"c":"","k":"PowerLawMultipole.multipole_comps_1.width_modifier.type","line":60},{"c":"","k":"PowerLawMultipole.multipole_comps_1.width_modifier.value","line":61},{"c":"","k":"PowerLawMultipole.multipole_comps_1.limits","line":62},{"c":"","k":"PowerLawMultipole.multipole_comps_1.limits.lower","line":63},{"c":"","k":"PowerLawMultipole.multipole_comps_1.limits.upper","line":64}],"lines":64,"path":"priors/mass/total/power_law_multipole.yaml","prior":true,"priors":[{"a":"value 4","b":"","cls":"PowerLawMultipole","limits":"","line":2,"param":"m","type":"Constant","width":""},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"PowerLawMultipole","limits":"[-inf, inf]","line":5,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"PowerLawMultipole","limits":"[-inf, inf]","line":15,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 8.0","cls":"PowerLawMultipole","limits":"[0.0, inf]","line":25,"param":"einstein_radius","type":"Uniform","width":"Relative 0.25"},{"a":"lower 1.5","b":"upper 3.0","cls":"PowerLawMultipole","limits":"[1.0, 3.0]","line":35,"param":"slope","type":"Uniform","width":"Absolute 0.2"},{"a":"lower -0.1","b":"upper 0.1","cls":"PowerLawMultipole","limits":"[-inf, inf]","line":45,"param":"multipole_comps_0","type":"Uniform","width":"Absolute 0.05"},{"a":"lower -0.1","b":"upper 0.1","cls":"PowerLawMultipole","limits":"[-inf, inf]","line":55,"param":"multipole_comps_1","type":"Uniform","width":"Absolute 0.05"}],"repo":"PyAutoGalaxy","text":"PowerLawMultipole:\n  m:\n    type: Constant\n    value: 4\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  einstein_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 8.0\n    width_modifier:\n      type: Relative\n      value: 0.25\n    limits:\n      lower: 0.0\n      upper: inf\n  slope:\n    type: Uniform\n    lower_limit: 1.5\n    upper_limit: 3.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: 1.0\n      upper: 3.0\n  multipole_comps_0:\n    type: Uniform\n    lower_limit: -0.1\n    upper_limit: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  multipole_comps_1:\n    type: Uniform\n    lower_limit: -0.1\n    upper_limit: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf","tooling":false,"top_keys":["PowerLawMultipole"]},{"error":null,"keys":[{"c":"","k":"Delaunay","line":1},{"c":"","k":"Delaunay.areas_factor","line":2},{"c":"","k":"Delaunay.areas_factor.type","line":3},{"c":"","k":"Delaunay.areas_factor.value","line":4}],"lines":4,"path":"priors/mesh/delaunay.yaml","prior":true,"priors":[{"a":"value 0.5","b":"","cls":"Delaunay","limits":"","line":2,"param":"areas_factor","type":"Constant","width":""}],"repo":"PyAutoGalaxy","text":"Delaunay:\n  areas_factor:\n    type: Constant\n    value: 0.5\n","tooling":false,"top_keys":["Delaunay"]},{"error":null,"keys":[{"c":"","k":"RectangularBilinearAdaptDensity","line":1},{"c":"","k":"RectangularBilinearAdaptDensity.shape_0","line":2},{"c":"","k":"RectangularBilinearAdaptDensity.shape_0.type","line":3},{"c":"","k":"RectangularBilinearAdaptDensity.shape_0.lower_limit","line":4},{"c":"","k":"RectangularBilinearAdaptDensity.shape_0.upper_limit","line":5},{"c":"","k":"RectangularBilinearAdaptDensity.shape_0.width_modifier","line":6},{"c":"","k":"RectangularBilinearAdaptDensity.shape_0.width_modifier.type","line":7},{"c":"","k":"RectangularBilinearAdaptDensity.shape_0.width_modifier.value","line":8},{"c":"","k":"RectangularBilinearAdaptDensity.shape_0.limits","line":9},{"c":"","k":"RectangularBilinearAdaptDensity.shape_0.limits.lower","line":10},{"c":"","k":"RectangularBilinearAdaptDensity.shape_0.limits.upper","line":11},{"c":"","k":"RectangularBilinearAdaptDensity.shape_1","line":12},{"c":"","k":"RectangularBilinearAdaptDensity.shape_1.type","line":13},{"c":"","k":"RectangularBilinearAdaptDensity.shape_1.lower_limit","line":14},{"c":"","k":"RectangularBilinearAdaptDensity.shape_1.upper_limit","line":15},{"c":"","k":"RectangularBilinearAdaptDensity.shape_1.width_modifier","line":16},{"c":"","k":"RectangularBilinearAdaptDensity.shape_1.width_modifier.type","line":17},{"c":"","k":"RectangularBilinearAdaptDensity.shape_1.width_modifier.value","line":18},{"c":"","k":"RectangularBilinearAdaptDensity.shape_1.limits","line":19},{"c":"","k":"RectangularBilinearAdaptDensity.shape_1.limits.lower","line":20},{"c":"","k":"RectangularBilinearAdaptDensity.shape_1.limits.upper","line":21}],"lines":21,"path":"priors/mesh/rectangular_bilinear_adapt_density.yaml","prior":true,"priors":[{"a":"lower 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width_modifier:\n      type: Absolute\n      value: 8.0\n    limits:\n      lower: 3.0\n      upper: inf\n  weight_power:\n    type : Uniform\n    lower_limit: 0.0\n    upper_limit: 10.0\n    width_modifier:\n      type: Absolute\n      value: 2.0\n    limits:\n      lower: -100.0\n      upper: 100.0\n  weight_floor:\n    type: LogUniform\n    lower_limit: 0.00001\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n    limits:\n      lower: 0.0\n      upper: 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Absolute\n      value: 8.0\n    limits:\n      lower: 3.0\n      upper: inf\n  weight_power:\n    type : Uniform\n    lower_limit: 0.0\n    upper_limit: 10.0\n    width_modifier:\n      type: Absolute\n      value: 2.0\n    limits:\n      lower: -100.0\n      upper: 100.0\n  weight_floor:\n    type: LogUniform\n    lower_limit: 0.00001\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n    limits:\n      lower: 0.0\n      upper: inf","tooling":false,"top_keys":["RectangularRTUAdaptImage"]},{"error":null,"keys":[{"c":"","k":"RectangularUniform","line":1},{"c":"","k":"RectangularUniform.shape_0","line":2},{"c":"","k":"RectangularUniform.shape_0.type","line":3},{"c":"","k":"RectangularUniform.shape_0.lower_limit","line":4},{"c":"","k":"RectangularUniform.shape_0.upper_limit","line":5},{"c":"","k":"RectangularUniform.shape_0.width_modifier","line":6},{"c":"","k":"RectangularUniform.shape_0.width_modifier.type","line":7},{"c":"","k":"RectangularUniform.shape_0.width_modifier.value","line":8},{"c":"","k":"RectangularUniform.shape_0.limits","line":9},{"c":"","k":"RectangularUniform.shape_0.limits.lower","line":10},{"c":"","k":"RectangularUniform.shape_0.limits.upper","line":11},{"c":"","k":"RectangularUniform.shape_1","line":12},{"c":"","k":"RectangularUniform.shape_1.type","line":13},{"c":"","k":"RectangularUniform.shape_1.lower_limit","line":14},{"c":"","k":"RectangularUniform.shape_1.upper_limit","line":15},{"c":"","k":"RectangularUniform.shape_1.width_modifier","line":16},{"c":"","k":"RectangularUniform.shape_1.width_modifier.type","line":17},{"c":"","k":"RectangularUniform.shape_1.width_modifier.value","line":18},{"c":"","k":"RectangularUniform.shape_1.limits","line":19},{"c":"","k":"RectangularUniform.shape_1.limits.lower","line":20},{"c":"","k":"RectangularUniform.shape_1.limits.upper","line":21}],"lines":21,"path":"priors/mesh/rectangular_uniform.yaml","prior":true,"priors":[{"a":"lower 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width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\nPointFlux:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  flux:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["Point","PointFlux"]},{"error":null,"keys":[{"c":"","k":"Adapt","line":1},{"c":"","k":"Adapt.inner_coefficient","line":2},{"c":"","k":"Adapt.inner_coefficient.type","line":3},{"c":"","k":"Adapt.inner_coefficient.lower_limit","line":4},{"c":"","k":"Adapt.inner_coefficient.upper_limit","line":5},{"c":"","k":"Adapt.inner_coefficient.width_modifier","line":6},{"c":"","k":"Adapt.inner_coefficient.width_modifier.type","line":7},{"c":"","k":"Adapt.inner_coefficient.width_modifier.value","line":8},{"c":"","k":"Adapt.inner_coefficient.limits","line":9},{"c":"","k":"Adapt.inner_coefficient.limits.lower","line":10},{"c":"","k":"Adapt.inner_coefficient.limits.upper","line":11},{"c":"","k":"Adapt.outer_coefficient","line":12},{"c":"","k":"Adapt.outer_coefficient.type","line":13},{"c":"","k":"Adapt.outer_coefficient.lower_limit","line":14},{"c":"","k":"Adapt.outer_coefficient.upper_limit","line":15},{"c":"","k":"Adapt.outer_coefficient.width_modifier","line":16},{"c":"","k":"Adapt.outer_coefficient.width_modifier.type","line":17},{"c":"","k":"Adapt.outer_coefficient.width_modifier.value","line":18},{"c":"","k":"Adapt.outer_coefficient.limits","line":19},{"c":"","k":"Adapt.outer_coefficient.limits.lower","line":20},{"c":"","k":"Adapt.outer_coefficient.limits.upper","line":21},{"c":"","k":"Adapt.signal_scale","line":22},{"c":"","k":"Adapt.signal_scale.type","line":23},{"c":"","k":"Adapt.signal_scale.lower_limit","line":24},{"c":"","k":"Adapt.signal_scale.upper_limit","line":25},{"c":"","k":"Adapt.signal_scale.width_modifier","line":26},{"c":"","k":"Adapt.signal_scale.width_modifier.type","line":27},{"c":"","k":"Adapt.signal_scale.width_modifier.value","line":28},{"c":"","k":"Adapt.signal_scale.limits","line":29},{"c":"","k":"Adapt.signal_scale.limits.lower","line":30},{"c":"","k":"Adapt.signal_scale.limits.upper","line":31}],"lines":31,"path":"priors/regularization/adapt.yaml","prior":true,"priors":[{"a":"lower 1e-06","b":"upper 1000000.0","cls":"Adapt","limits":"[0.0, inf]","line":2,"param":"inner_coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"Adapt","limits":"[0.0, inf]","line":12,"param":"outer_coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 1.0","cls":"Adapt","limits":"[0.0, inf]","line":22,"param":"signal_scale","type":"Uniform","width":"Relative 0.2"}],"repo":"PyAutoGalaxy","text":"Adapt:\n  inner_coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  outer_coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  signal_scale:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf","tooling":false,"top_keys":["Adapt"]},{"error":null,"keys":[{"c":"","k":"AdaptPower","line":1},{"c":"","k":"AdaptPower.inner_coefficient","line":2},{"c":"","k":"AdaptPower.inner_coefficient.type","line":3},{"c":"","k":"AdaptPower.inner_coefficient.lower_limit","line":4},{"c":"","k":"AdaptPower.inner_coefficient.upper_limit","line":5},{"c":"","k":"AdaptPower.inner_coefficient.width_modifier","line":6},{"c":"","k":"AdaptPower.inner_coefficient.width_modifier.type","line":7},{"c":"","k":"AdaptPower.inner_coefficient.width_modifier.value","line":8},{"c":"","k":"AdaptPower.inner_coefficient.limits","line":9},{"c":"","k":"AdaptPower.inner_coefficient.limits.lower","line":10},{"c":"","k":"AdaptPower.inner_coefficient.limits.upper","line":11},{"c":"","k":"AdaptPower.outer_coefficient","line":12},{"c":"","k":"AdaptPower.outer_coefficient.type","line":13},{"c":"","k":"AdaptPower.outer_coefficient.lower_limit","line":14},{"c":"","k":"AdaptPower.outer_coefficient.upper_limit","line":15},{"c":"","k":"AdaptPower.outer_coefficient.width_modifier","line":16},{"c":"","k":"AdaptPower.outer_coefficient.width_modifier.type","line":17},{"c":"","k":"AdaptPower.outer_coefficient.width_modifier.value","line":18},{"c":"","k":"AdaptPower.outer_coefficient.limits","line":19},{"c":"","k":"AdaptPower.outer_coefficient.limits.lower","line":20},{"c":"","k":"AdaptPower.outer_coefficient.limits.upper","line":21},{"c":"","k":"AdaptPower.signal_scale","line":22},{"c":"","k":"AdaptPower.signal_scale.type","line":23},{"c":"","k":"AdaptPower.signal_scale.lower_limit","line":24},{"c":"","k":"AdaptPower.signal_scale.upper_limit","line":25},{"c":"","k":"AdaptPower.signal_scale.width_modifier","line":26},{"c":"","k":"AdaptPower.signal_scale.width_modifier.type","line":27},{"c":"","k":"AdaptPower.signal_scale.width_modifier.value","line":28},{"c":"","k":"AdaptPower.signal_scale.limits","line":29},{"c":"","k":"AdaptPower.signal_scale.limits.lower","line":30},{"c":"","k":"AdaptPower.signal_scale.limits.upper","line":31},{"c":"","k":"AdaptPower.power","line":32},{"c":"","k":"AdaptPower.power.type","line":33},{"c":"","k":"AdaptPower.power.value","line":34}],"lines":34,"path":"priors/regularization/adapt_power.yaml","prior":true,"priors":[{"a":"lower 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   lower: 0.0\n      upper: inf\n  signal_scale:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  power:\n    type: Constant\n    value: 1.0\n","tooling":false,"top_keys":["AdaptPower"]},{"error":null,"keys":[{"c":"","k":"AdaptSplit","line":1},{"c":"","k":"AdaptSplit.inner_coefficient","line":2},{"c":"","k":"AdaptSplit.inner_coefficient.type","line":3},{"c":"","k":"AdaptSplit.inner_coefficient.lower_limit","line":4},{"c":"","k":"AdaptSplit.inner_coefficient.upper_limit","line":5},{"c":"","k":"AdaptSplit.inner_coefficient.width_modifier","line":6},{"c":"","k":"AdaptSplit.inner_coefficient.width_modifier.type","line":7},{"c":"","k":"AdaptSplit.inner_coefficient.width_modifier.value","line":8},{"c":"","k":"AdaptSplit.inner_coefficient.limits","line":9},{"c":"","k":"AdaptSplit.inner_coefficient.limits.lower","line":10},{"c":"","k":"AdaptSplit.inner_coefficient.limits.upper","line":11},{"c":"","k":"AdaptSplit.outer_coefficient","line":12},{"c":"","k":"AdaptSplit.outer_coefficient.type","line":13},{"c":"","k":"AdaptSplit.outer_coefficient.lower_limit","line":14},{"c":"","k":"AdaptSplit.outer_coefficient.upper_limit","line":15},{"c":"","k":"AdaptSplit.outer_coefficient.width_modifier","line":16},{"c":"","k":"AdaptSplit.outer_coefficient.width_modifier.type","line":17},{"c":"","k":"AdaptSplit.outer_coefficient.width_modifier.value","line":18},{"c":"","k":"AdaptSplit.outer_coefficient.limits","line":19},{"c":"","k":"AdaptSplit.outer_coefficient.limits.lower","line":20},{"c":"","k":"AdaptSplit.outer_coefficient.limits.upper","line":21},{"c":"","k":"AdaptSplit.signal_scale","line":22},{"c":"","k":"AdaptSplit.signal_scale.type","line":23},{"c":"","k":"AdaptSplit.signal_scale.lower_limit","line":24},{"c":"","k":"AdaptSplit.signal_scale.upper_limit","line":25},{"c":"","k":"AdaptSplit.signal_scale.width_modifier","line":26},{"c":"","k":"AdaptSplit.signal_scale.width_modifier.type","line":27},{"c":"","k":"AdaptSplit.signal_scale.width_modifier.value","line":28},{"c":"","k":"AdaptSplit.signal_scale.limits","line":29},{"c":"","k":"AdaptSplit.signal_scale.limits.lower","line":30},{"c":"","k":"AdaptSplit.signal_scale.limits.upper","line":31}],"lines":31,"path":"priors/regularization/adapt_split.yaml","prior":true,"priors":[{"a":"lower 1e-06","b":"upper 1000000.0","cls":"AdaptSplit","limits":"[0.0, inf]","line":2,"param":"inner_coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"AdaptSplit","limits":"[0.0, inf]","line":12,"param":"outer_coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 1.0","cls":"AdaptSplit","limits":"[0.0, inf]","line":22,"param":"signal_scale","type":"Uniform","width":"Relative 0.2"}],"repo":"PyAutoGalaxy","text":"AdaptSplit:\n  inner_coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  outer_coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  signal_scale:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["AdaptSplit"]},{"error":null,"keys":[{"c":"","k":"AdaptSplitPower","line":1},{"c":"","k":"AdaptSplitPower.inner_coefficient","line":2},{"c":"","k":"AdaptSplitPower.inner_coefficient.type","line":3},{"c":"","k":"AdaptSplitPower.inner_coefficient.lower_limit","line":4},{"c":"","k":"AdaptSplitPower.inner_coefficient.upper_limit","line":5},{"c":"","k":"AdaptSplitPower.inner_coefficient.width_modifier","line":6},{"c":"","k":"AdaptSplitPower.inner_coefficient.width_modifier.type","line":7},{"c":"","k":"AdaptSplitPower.inner_coefficient.width_modifier.value","line":8},{"c":"","k":"AdaptSplitPower.inner_coefficient.limits","line":9},{"c":"","k":"AdaptSplitPower.inner_coefficient.limits.lower","line":10},{"c":"","k":"AdaptSplitPower.inner_coefficient.limits.upper","line":11},{"c":"","k":"AdaptSplitPower.outer_coefficient","line":12},{"c":"","k":"AdaptSplitPower.outer_coefficient.type","line":13},{"c":"","k":"AdaptSplitPower.outer_coefficient.lower_limit","line":14},{"c":"","k":"AdaptSplitPower.outer_coefficient.upper_limit","line":15},{"c":"","k":"AdaptSplitPower.outer_coefficient.width_modifier","line":16},{"c":"","k":"AdaptSplitPower.outer_coefficient.width_modifier.type","line":17},{"c":"","k":"AdaptSplitPower.outer_coefficient.width_modifier.value","line":18},{"c":"","k":"AdaptSplitPower.outer_coefficient.limits","line":19},{"c":"","k":"AdaptSplitPower.outer_coefficient.limits.lower","line":20},{"c":"","k":"AdaptSplitPower.outer_coefficient.limits.upper","line":21},{"c":"","k":"AdaptSplitPower.signal_scale","line":22},{"c":"","k":"AdaptSplitPower.signal_scale.type","line":23},{"c":"","k":"AdaptSplitPower.signal_scale.lower_limit","line":24},{"c":"","k":"AdaptSplitPower.signal_scale.upper_limit","line":25},{"c":"","k":"AdaptSplitPower.signal_scale.width_modifier","line":26},{"c":"","k":"AdaptSplitPower.signal_scale.width_modifier.type","line":27},{"c":"","k":"AdaptSplitPower.signal_scale.width_modifier.value","line":28},{"c":"","k":"AdaptSplitPower.signal_scale.limits","line":29},{"c":"","k":"AdaptSplitPower.signal_scale.limits.lower","line":30},{"c":"","k":"AdaptSplitPower.signal_scale.limits.upper","line":31},{"c":"","k":"AdaptSplitPower.power","line":32},{"c":"","k":"AdaptSplitPower.power.type","line":33},{"c":"","k":"AdaptSplitPower.power.value","line":34}],"lines":34,"path":"priors/regularization/adapt_split_power.yaml","prior":true,"priors":[{"a":"lower 1e-06","b":"upper 1000000.0","cls":"AdaptSplitPower","limits":"[0.0, inf]","line":2,"param":"inner_coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"AdaptSplitPower","limits":"[0.0, inf]","line":12,"param":"outer_coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 1.0","cls":"AdaptSplitPower","limits":"[0.0, inf]","line":22,"param":"signal_scale","type":"Uniform","width":"Relative 0.2"},{"a":"value 1.0","b":"","cls":"AdaptSplitPower","limits":"","line":32,"param":"power","type":"Constant","width":""}],"repo":"PyAutoGalaxy","text":"AdaptSplitPower:\n  inner_coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  outer_coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  signal_scale:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  power:\n    type: Constant\n    value: 1.0\n","tooling":false,"top_keys":["AdaptSplitPower"]},{"error":null,"keys":[{"c":"","k":"AdaptSplitZeroth","line":1},{"c":"","k":"AdaptSplitZeroth.zeroth_coefficient","line":2},{"c":"","k":"AdaptSplitZeroth.zeroth_coefficient.type","line":3},{"c":"","k":"AdaptSplitZeroth.zeroth_coefficient.lower_limit","line":4},{"c":"","k":"AdaptSplitZeroth.zeroth_coefficient.upper_limit","line":5},{"c":"","k":"AdaptSplitZeroth.zeroth_coefficient.width_modifier","line":6},{"c":"","k":"AdaptSplitZeroth.zeroth_coefficient.width_modifier.type","line":7},{"c":"","k":"AdaptSplitZeroth.zeroth_coefficient.width_modifier.value","line":8},{"c":"","k":"AdaptSplitZeroth.zeroth_coefficient.limits","line":9},{"c":"","k":"AdaptSplitZeroth.zeroth_coefficient.limits.lower","line":10},{"c":"","k":"AdaptSplitZeroth.zeroth_coefficient.limits.upper","line":11},{"c":"","k":"AdaptSplitZeroth.zeroth_signal_scale","line":12},{"c":"","k":"AdaptSplitZeroth.zeroth_signal_scale.type","line":13},{"c":"","k":"AdaptSplitZeroth.zeroth_signal_scale.lower_limit","line":14},{"c":"","k":"AdaptSplitZeroth.zeroth_signal_scale.upper_limit","line":15},{"c":"","k":"AdaptSplitZeroth.zeroth_signal_scale.width_modifier","line":16},{"c":"","k":"AdaptSplitZeroth.zeroth_signal_scale.width_modifier.type","line":17},{"c":"","k":"AdaptSplitZeroth.zeroth_signal_scale.width_modifier.value","line":18},{"c":"","k":"AdaptSplitZeroth.zeroth_signal_scale.limits","line":19},{"c":"","k":"AdaptSplitZeroth.zeroth_signal_scale.limits.lower","line":20},{"c":"","k":"AdaptSplitZeroth.zeroth_signal_scale.limits.upper","line":21},{"c":"","k":"AdaptSplitZeroth.inner_coefficient","line":22},{"c":"","k":"AdaptSplitZeroth.inner_coefficient.type","line":23},{"c":"","k":"AdaptSplitZeroth.inner_coefficient.lower_limit","line":24},{"c":"","k":"AdaptSplitZeroth.inner_coefficient.upper_limit","line":25},{"c":"","k":"AdaptSplitZeroth.inner_coefficient.width_modifier","line":26},{"c":"","k":"AdaptSplitZeroth.inner_coefficient.width_modifier.type","line":27},{"c":"","k":"AdaptSplitZeroth.inner_coefficient.width_modifier.value","line":28},{"c":"","k":"AdaptSplitZeroth.inner_coefficient.limits","line":29},{"c":"","k":"AdaptSplitZeroth.inner_coefficient.limits.lower","line":30},{"c":"","k":"AdaptSplitZeroth.inner_coefficient.limits.upper","line":31},{"c":"","k":"AdaptSplitZeroth.outer_coefficient","line":32},{"c":"","k":"AdaptSplitZeroth.outer_coefficient.type","line":33},{"c":"","k":"AdaptSplitZeroth.outer_coefficient.lower_limit","line":34},{"c":"","k":"AdaptSplitZeroth.outer_coefficient.upper_limit","line":35},{"c":"","k":"AdaptSplitZeroth.outer_coefficient.width_modifier","line":36},{"c":"","k":"AdaptSplitZeroth.outer_coefficient.width_modifier.type","line":37},{"c":"","k":"AdaptSplitZeroth.outer_coefficient.width_modifier.value","line":38},{"c":"","k":"AdaptSplitZeroth.outer_coefficient.limits","line":39},{"c":"","k":"AdaptSplitZeroth.outer_coefficient.limits.lower","line":40},{"c":"","k":"AdaptSplitZeroth.outer_coefficient.limits.upper","line":41},{"c":"","k":"AdaptSplitZeroth.signal_scale","line":42},{"c":"","k":"AdaptSplitZeroth.signal_scale.type","line":43},{"c":"","k":"AdaptSplitZeroth.signal_scale.lower_limit","line":44},{"c":"","k":"AdaptSplitZeroth.signal_scale.upper_limit","line":45},{"c":"","k":"AdaptSplitZeroth.signal_scale.width_modifier","line":46},{"c":"","k":"AdaptSplitZeroth.signal_scale.width_modifier.type","line":47},{"c":"","k":"AdaptSplitZeroth.signal_scale.width_modifier.value","line":48},{"c":"","k":"AdaptSplitZeroth.signal_scale.limits","line":49},{"c":"","k":"AdaptSplitZeroth.signal_scale.limits.lower","line":50},{"c":"","k":"AdaptSplitZeroth.signal_scale.limits.upper","line":51}],"lines":51,"path":"priors/regularization/adapt_split_zeroth.yaml","prior":true,"priors":[{"a":"lower 1e-06","b":"upper 1000000.0","cls":"AdaptSplitZeroth","limits":"[0.0, inf]","line":2,"param":"zeroth_coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 1.0","cls":"AdaptSplitZeroth","limits":"[0.0, inf]","line":12,"param":"zeroth_signal_scale","type":"Uniform","width":"Relative 0.2"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"AdaptSplitZeroth","limits":"[0.0, inf]","line":22,"param":"inner_coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"AdaptSplitZeroth","limits":"[0.0, inf]","line":32,"param":"outer_coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 1.0","cls":"AdaptSplitZeroth","limits":"[0.0, inf]","line":42,"param":"signal_scale","type":"Uniform","width":"Relative 0.2"}],"repo":"PyAutoGalaxy","text":"AdaptSplitZeroth:\n  zeroth_coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  zeroth_signal_scale:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  inner_coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  outer_coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  signal_scale:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf","tooling":false,"top_keys":["AdaptSplitZeroth"]},{"error":null,"keys":[{"c":"","k":"AdaptSplitZerothPower","line":1},{"c":"","k":"AdaptSplitZerothPower.zeroth_coefficient","line":2},{"c":"","k":"AdaptSplitZerothPower.zeroth_coefficient.type","line":3},{"c":"","k":"AdaptSplitZerothPower.zeroth_coefficient.lower_limit","line":4},{"c":"","k":"AdaptSplitZerothPower.zeroth_coefficient.upper_limit","line":5},{"c":"","k":"AdaptSplitZerothPower.zeroth_coefficient.width_modifier","line":6},{"c":"","k":"AdaptSplitZerothPower.zeroth_coefficient.width_modifier.type","line":7},{"c":"","k":"AdaptSplitZerothPower.zeroth_coefficient.width_modifier.value","line":8},{"c":"","k":"AdaptSplitZerothPower.zeroth_coefficient.limits","line":9},{"c":"","k":"AdaptSplitZerothPower.zeroth_coefficient.limits.lower","line":10},{"c":"","k":"AdaptSplitZerothPower.zeroth_coefficient.limits.upper","line":11},{"c":"","k":"AdaptSplitZerothPower.zeroth_signal_scale","line":12},{"c":"","k":"AdaptSplitZerothPower.zeroth_signal_scale.type","line":13},{"c":"","k":"AdaptSplitZerothPower.zeroth_signal_scale.lower_limit","line":14},{"c":"","k":"AdaptSplitZerothPower.zeroth_signal_scale.upper_limit","line":15},{"c":"","k":"AdaptSplitZerothPower.zeroth_signal_scale.width_modifier","line":16},{"c":"","k":"AdaptSplitZerothPower.zeroth_signal_scale.width_modifier.type","line":17},{"c":"","k":"AdaptSplitZerothPower.zeroth_signal_scale.width_modifier.value","line":18},{"c":"","k":"AdaptSplitZerothPower.zeroth_signal_scale.limits","line":19},{"c":"","k":"AdaptSplitZerothPower.zeroth_signal_scale.limits.lower","line":20},{"c":"","k":"AdaptSplitZerothPower.zeroth_signal_scale.limits.upper","line":21},{"c":"","k":"AdaptSplitZerothPower.inner_coefficient","line":22},{"c":"","k":"AdaptSplitZerothPower.inner_coefficient.type","line":23},{"c":"","k":"AdaptSplitZerothPower.inner_coefficient.lower_limit","line":24},{"c":"","k":"AdaptSplitZerothPower.inner_coefficient.upper_limit","line":25},{"c":"","k":"AdaptSplitZerothPower.inner_coefficient.width_modifier","line":26},{"c":"","k":"AdaptSplitZerothPower.inner_coefficient.width_modifier.type","line":27},{"c":"","k":"AdaptSplitZerothPower.inner_coefficient.width_modifier.value","line":28},{"c":"","k":"AdaptSplitZerothPower.inner_coefficient.limits","line":29},{"c":"","k":"AdaptSplitZerothPower.inner_coefficient.limits.lower","line":30},{"c":"","k":"AdaptSplitZerothPower.inner_coefficient.limits.upper","line":31},{"c":"","k":"AdaptSplitZerothPower.outer_coefficient","line":32},{"c":"","k":"AdaptSplitZerothPower.outer_coefficient.type","line":33},{"c":"","k":"AdaptSplitZerothPower.outer_coefficient.lower_limit","line":34},{"c":"","k":"AdaptSplitZerothPower.outer_coefficient.upper_limit","line":35},{"c":"","k":"AdaptSplitZerothPower.outer_coefficient.width_modifier","line":36},{"c":"","k":"AdaptSplitZerothPower.outer_coefficient.width_modifier.type","line":37},{"c":"","k":"AdaptSplitZerothPower.outer_coefficient.width_modifier.value","line":38},{"c":"","k":"AdaptSplitZerothPower.outer_coefficient.limits","line":39},{"c":"","k":"AdaptSplitZerothPower.outer_coefficient.limits.lower","line":40},{"c":"","k":"AdaptSplitZerothPower.outer_coefficient.limits.upper","line":41},{"c":"","k":"AdaptSplitZerothPower.signal_scale","line":42},{"c":"","k":"AdaptSplitZerothPower.signal_scale.type","line":43},{"c":"","k":"AdaptSplitZerothPower.signal_scale.lower_limit","line":44},{"c":"","k":"AdaptSplitZerothPower.signal_scale.upper_limit","line":45},{"c":"","k":"AdaptSplitZerothPower.signal_scale.width_modifier","line":46},{"c":"","k":"AdaptSplitZerothPower.signal_scale.width_modifier.type","line":47},{"c":"","k":"AdaptSplitZerothPower.signal_scale.width_modifier.value","line":48},{"c":"","k":"AdaptSplitZerothPower.signal_scale.limits","line":49},{"c":"","k":"AdaptSplitZerothPower.signal_scale.limits.lower","line":50},{"c":"","k":"AdaptSplitZerothPower.signal_scale.limits.upper","line":51},{"c":"","k":"AdaptSplitZerothPower.power","line":52},{"c":"","k":"AdaptSplitZerothPower.power.type","line":53},{"c":"","k":"AdaptSplitZerothPower.power.value","line":54}],"lines":54,"path":"priors/regularization/adapt_split_zeroth_power.yaml","prior":true,"priors":[{"a":"lower 1e-06","b":"upper 1000000.0","cls":"AdaptSplitZerothPower","limits":"[0.0, inf]","line":2,"param":"zeroth_coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 1.0","cls":"AdaptSplitZerothPower","limits":"[0.0, inf]","line":12,"param":"zeroth_signal_scale","type":"Uniform","width":"Relative 0.2"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"AdaptSplitZerothPower","limits":"[0.0, inf]","line":22,"param":"inner_coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"AdaptSplitZerothPower","limits":"[0.0, inf]","line":32,"param":"outer_coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 1.0","cls":"AdaptSplitZerothPower","limits":"[0.0, inf]","line":42,"param":"signal_scale","type":"Uniform","width":"Relative 0.2"},{"a":"value 1.0","b":"","cls":"AdaptSplitZerothPower","limits":"","line":52,"param":"power","type":"Constant","width":""}],"repo":"PyAutoGalaxy","text":"AdaptSplitZerothPower:\n  zeroth_coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  zeroth_signal_scale:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  inner_coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  outer_coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  signal_scale:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  power:\n    type: Constant\n    value: 1.0\n","tooling":false,"top_keys":["AdaptSplitZerothPower"]},{"error":null,"keys":[{"c":"","k":"Constant","line":1},{"c":"","k":"Constant.coefficient","line":2},{"c":"","k":"Constant.coefficient.type","line":3},{"c":"","k":"Constant.coefficient.lower_limit","line":4},{"c":"","k":"Constant.coefficient.upper_limit","line":5},{"c":"","k":"Constant.coefficient.width_modifier","line":6},{"c":"","k":"Constant.coefficient.width_modifier.type","line":7},{"c":"","k":"Constant.coefficient.width_modifier.value","line":8},{"c":"","k":"Constant.coefficient.limits","line":9},{"c":"","k":"Constant.coefficient.limits.lower","line":10},{"c":"","k":"Constant.coefficient.limits.upper","line":11}],"lines":11,"path":"priors/regularization/constant.yaml","prior":true,"priors":[{"a":"lower 1e-06","b":"upper 1000000.0","cls":"Constant","limits":"[0.0, inf]","line":2,"param":"coefficient","type":"LogUniform","width":"Relative 0.5"}],"repo":"PyAutoGalaxy","text":"Constant:\n  coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["Constant"]},{"error":null,"keys":[{"c":"","k":"ConstantSplit","line":1},{"c":"","k":"ConstantSplit.coefficient","line":2},{"c":"","k":"ConstantSplit.coefficient.type","line":3},{"c":"","k":"ConstantSplit.coefficient.lower_limit","line":4},{"c":"","k":"ConstantSplit.coefficient.upper_limit","line":5},{"c":"","k":"ConstantSplit.coefficient.width_modifier","line":6},{"c":"","k":"ConstantSplit.coefficient.width_modifier.type","line":7},{"c":"","k":"ConstantSplit.coefficient.width_modifier.value","line":8},{"c":"","k":"ConstantSplit.coefficient.limits","line":9},{"c":"","k":"ConstantSplit.coefficient.limits.lower","line":10},{"c":"","k":"ConstantSplit.coefficient.limits.upper","line":11}],"lines":11,"path":"priors/regularization/constant_split.yaml","prior":true,"priors":[{"a":"lower 1e-06","b":"upper 1000000.0","cls":"ConstantSplit","limits":"[0.0, inf]","line":2,"param":"coefficient","type":"LogUniform","width":"Relative 0.5"}],"repo":"PyAutoGalaxy","text":"ConstantSplit:\n  coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["ConstantSplit"]},{"error":null,"keys":[{"c":"","k":"ConstantZeroth","line":1},{"c":"","k":"ConstantZeroth.coefficient_neighbor","line":2},{"c":"","k":"ConstantZeroth.coefficient_neighbor.type","line":3},{"c":"","k":"ConstantZeroth.coefficient_neighbor.lower_limit","line":4},{"c":"","k":"ConstantZeroth.coefficient_neighbor.upper_limit","line":5},{"c":"","k":"ConstantZeroth.coefficient_neighbor.width_modifier","line":6},{"c":"","k":"ConstantZeroth.coefficient_neighbor.width_modifier.type","line":7},{"c":"","k":"ConstantZeroth.coefficient_neighbor.width_modifier.value","line":8},{"c":"","k":"ConstantZeroth.coefficient_neighbor.limits","line":9},{"c":"","k":"ConstantZeroth.coefficient_neighbor.limits.lower","line":10},{"c":"","k":"ConstantZeroth.coefficient_neighbor.limits.upper","line":11},{"c":"","k":"ConstantZeroth.coefficient_zeroth","line":12},{"c":"","k":"ConstantZeroth.coefficient_zeroth.type","line":13},{"c":"","k":"ConstantZeroth.coefficient_zeroth.lower_limit","line":14},{"c":"","k":"ConstantZeroth.coefficient_zeroth.upper_limit","line":15},{"c":"","k":"ConstantZeroth.coefficient_zeroth.width_modifier","line":16},{"c":"","k":"ConstantZeroth.coefficient_zeroth.width_modifier.type","line":17},{"c":"","k":"ConstantZeroth.coefficient_zeroth.width_modifier.value","line":18},{"c":"","k":"ConstantZeroth.coefficient_zeroth.limits","line":19},{"c":"","k":"ConstantZeroth.coefficient_zeroth.limits.lower","line":20},{"c":"","k":"ConstantZeroth.coefficient_zeroth.limits.upper","line":21}],"lines":21,"path":"priors/regularization/constant_zeroth.yaml","prior":true,"priors":[{"a":"lower 1e-06","b":"upper 1000000.0","cls":"ConstantZeroth","limits":"[0.0, inf]","line":2,"param":"coefficient_neighbor","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"ConstantZeroth","limits":"[0.0, inf]","line":12,"param":"coefficient_zeroth","type":"LogUniform","width":"Relative 0.5"}],"repo":"PyAutoGalaxy","text":"ConstantZeroth:\n  coefficient_neighbor:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  coefficient_zeroth:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["ConstantZeroth"]},{"error":null,"keys":[{"c":"","k":"ExponentialKernel","line":1},{"c":"","k":"ExponentialKernel.coefficient","line":2},{"c":"","k":"ExponentialKernel.coefficient.type","line":3},{"c":"","k":"ExponentialKernel.coefficient.lower_limit","line":4},{"c":"","k":"ExponentialKernel.coefficient.upper_limit","line":5},{"c":"","k":"ExponentialKernel.coefficient.width_modifier","line":6},{"c":"","k":"ExponentialKernel.coefficient.width_modifier.type","line":7},{"c":"","k":"ExponentialKernel.coefficient.width_modifier.value","line":8},{"c":"","k":"ExponentialKernel.coefficient.limits","line":9},{"c":"","k":"ExponentialKernel.coefficient.limits.lower","line":10},{"c":"","k":"ExponentialKernel.coefficient.limits.upper","line":11},{"c":"","k":"ExponentialKernel.scale","line":12},{"c":"","k":"ExponentialKernel.scale.type","line":13},{"c":"","k":"ExponentialKernel.scale.lower_limit","line":14},{"c":"","k":"ExponentialKernel.scale.upper_limit","line":15},{"c":"","k":"ExponentialKernel.scale.width_modifier","line":16},{"c":"","k":"ExponentialKernel.scale.width_modifier.type","line":17},{"c":"","k":"ExponentialKernel.scale.width_modifier.value","line":18},{"c":"","k":"ExponentialKernel.scale.limits","line":19},{"c":"","k":"ExponentialKernel.scale.limits.lower","line":20},{"c":"","k":"ExponentialKernel.scale.limits.upper","line":21}],"lines":21,"path":"priors/regularization/exponential_kernel.yaml","prior":true,"priors":[{"a":"lower 1e-06","b":"upper 1000000.0","cls":"ExponentialKernel","limits":"[0.0, inf]","line":2,"param":"coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"ExponentialKernel","limits":"[0.0, inf]","line":12,"param":"scale","type":"LogUniform","width":"Relative 0.2"}],"repo":"PyAutoGalaxy","text":"ExponentialKernel:\n  coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  scale:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["ExponentialKernel"]},{"error":null,"keys":[{"c":"","k":"GaussianKernel","line":1},{"c":"","k":"GaussianKernel.coefficient","line":2},{"c":"","k":"GaussianKernel.coefficient.type","line":3},{"c":"","k":"GaussianKernel.coefficient.lower_limit","line":4},{"c":"","k":"GaussianKernel.coefficient.upper_limit","line":5},{"c":"","k":"GaussianKernel.coefficient.width_modifier","line":6},{"c":"","k":"GaussianKernel.coefficient.width_modifier.type","line":7},{"c":"","k":"GaussianKernel.coefficient.width_modifier.value","line":8},{"c":"","k":"GaussianKernel.coefficient.limits","line":9},{"c":"","k":"GaussianKernel.coefficient.limits.lower","line":10},{"c":"","k":"GaussianKernel.coefficient.limits.upper","line":11},{"c":"","k":"GaussianKernel.scale","line":12},{"c":"","k":"GaussianKernel.scale.type","line":13},{"c":"","k":"GaussianKernel.scale.lower_limit","line":14},{"c":"","k":"GaussianKernel.scale.upper_limit","line":15},{"c":"","k":"GaussianKernel.scale.width_modifier","line":16},{"c":"","k":"GaussianKernel.scale.width_modifier.type","line":17},{"c":"","k":"GaussianKernel.scale.width_modifier.value","line":18},{"c":"","k":"GaussianKernel.scale.limits","line":19},{"c":"","k":"GaussianKernel.scale.limits.lower","line":20},{"c":"","k":"GaussianKernel.scale.limits.upper","line":21}],"lines":21,"path":"priors/regularization/gaussian_kernel.yaml","prior":true,"priors":[{"a":"lower 1e-06","b":"upper 1000000.0","cls":"GaussianKernel","limits":"[0.0, inf]","line":2,"param":"coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"GaussianKernel","limits":"[0.0, inf]","line":12,"param":"scale","type":"LogUniform","width":"Relative 0.2"}],"repo":"PyAutoGalaxy","text":"GaussianKernel:\n  coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  scale:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: 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1e-06","b":"upper 1000000.0","cls":"MaternAdaptKernel","limits":"[0.0, inf]","line":2,"param":"scale","type":"LogUniform","width":"Relative 0.2"},{"a":"lower 0.5","b":"upper 5.5","cls":"MaternAdaptKernel","limits":"[0.0, inf]","line":12,"param":"nu","type":"Uniform","width":"Relative 0.2"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"MaternAdaptKernel","limits":"[0.0, inf]","line":22,"param":"inner_coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"MaternAdaptKernel","limits":"[0.0, inf]","line":32,"param":"outer_coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 1.0","cls":"MaternAdaptKernel","limits":"[0.0, inf]","line":42,"param":"signal_scale","type":"Uniform","width":"Relative 0.2"}],"repo":"PyAutoGalaxy","text":"MaternAdaptKernel:\n  scale:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  nu:\n    type: Uniform\n    lower_limit: 0.5\n    upper_limit: 5.5\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  inner_coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  outer_coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  signal_scale:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf","tooling":false,"top_keys":["MaternAdaptKernel"]},{"error":null,"keys":[{"c":"","k":"MaternAdaptPowerKernel","line":1},{"c":"","k":"MaternAdaptPowerKernel.scale","line":2},{"c":"","k":"MaternAdaptPowerKernel.scale.type","line":3},{"c":"","k":"MaternAdaptPowerKernel.scale.lower_limit","line":4},{"c":"","k":"MaternAdaptPowerKernel.scale.upper_limit","line":5},{"c":"","k":"MaternAdaptPowerKernel.scale.width_modifier","line":6},{"c":"","k":"MaternAdaptPowerKernel.scale.width_modifier.type","line":7},{"c":"","k":"MaternAdaptPowerKernel.scale.width_modifier.value","line":8},{"c":"","k":"MaternAdaptPowerKernel.scale.limits","line":9},{"c":"","k":"MaternAdaptPowerKernel.scale.limits.lower","line":10},{"c":"","k":"MaternAdaptPowerKernel.scale.limits.upper","line":11},{"c":"","k":"MaternAdaptPowerKernel.nu","line":12},{"c":"","k":"MaternAdaptPowerKernel.nu.type","line":13},{"c":"","k":"MaternAdaptPowerKernel.nu.lower_limit","line":14},{"c":"","k":"MaternAdaptPowerKernel.nu.upper_limit","line":15},{"c":"","k":"MaternAdaptPowerKernel.nu.width_modifier","line":16},{"c":"","k":"MaternAdaptPowerKernel.nu.width_modifier.type","line":17},{"c":"","k":"MaternAdaptPowerKernel.nu.width_modifier.value","line":18},{"c":"","k":"MaternAdaptPowerKernel.nu.limits","line":19},{"c":"","k":"MaternAdaptPowerKernel.nu.limits.lower","line":20},{"c":"","k":"MaternAdaptPowerKernel.nu.limits.upper","line":21},{"c":"","k":"MaternAdaptPowerKernel.inner_coefficient","line":22},{"c":"","k":"MaternAdaptPowerKernel.inner_coefficient.type","line":23},{"c":"","k":"MaternAdaptPowerKernel.inner_coefficient.lower_limit","line":24},{"c":"","k":"MaternAdaptPowerKernel.inner_coefficient.upper_limit","line":25},{"c":"","k":"MaternAdaptPowerKernel.inner_coefficient.width_modifier","line":26},{"c":"","k":"MaternAdaptPowerKernel.inner_coefficient.width_modifier.type","line":27},{"c":"","k":"MaternAdaptPowerKernel.inner_coefficient.width_modifier.value","line":28},{"c":"","k":"MaternAdaptPowerKernel.inner_coefficient.limits","line":29},{"c":"","k":"MaternAdaptPowerKernel.inner_coefficient.limits.lower","line":30},{"c":"","k":"MaternAdaptPowerKernel.inner_coefficient.limits.upper","line":31},{"c":"","k":"MaternAdaptPowerKernel.outer_coefficient","line":32},{"c":"","k":"MaternAdaptPowerKernel.outer_coefficient.type","line":33},{"c":"","k":"MaternAdaptPowerKernel.outer_coefficient.lower_limit","line":34},{"c":"","k":"MaternAdaptPowerKernel.outer_coefficient.upper_limit","line":35},{"c":"","k":"MaternAdaptPowerKernel.outer_coefficient.width_modifier","line":36},{"c":"","k":"MaternAdaptPowerKernel.outer_coefficient.width_modifier.type","line":37},{"c":"","k":"MaternAdaptPowerKernel.outer_coefficient.width_modifier.value","line":38},{"c":"","k":"MaternAdaptPowerKernel.outer_coefficient.limits","line":39},{"c":"","k":"MaternAdaptPowerKernel.outer_coefficient.limits.lower","line":40},{"c":"","k":"MaternAdaptPowerKernel.outer_coefficient.limits.upper","line":41},{"c":"","k":"MaternAdaptPowerKernel.signal_scale","line":42},{"c":"","k":"MaternAdaptPowerKernel.signal_scale.type","line":43},{"c":"","k":"MaternAdaptPowerKernel.signal_scale.lower_limit","line":44},{"c":"","k":"MaternAdaptPowerKernel.signal_scale.upper_limit","line":45},{"c":"","k":"MaternAdaptPowerKernel.signal_scale.width_modifier","line":46},{"c":"","k":"MaternAdaptPowerKernel.signal_scale.width_modifier.type","line":47},{"c":"","k":"MaternAdaptPowerKernel.signal_scale.width_modifier.value","line":48},{"c":"","k":"MaternAdaptPowerKernel.signal_scale.limits","line":49},{"c":"","k":"MaternAdaptPowerKernel.signal_scale.limits.lower","line":50},{"c":"","k":"MaternAdaptPowerKernel.signal_scale.limits.upper","line":51},{"c":"","k":"MaternAdaptPowerKernel.power","line":52},{"c":"","k":"MaternAdaptPowerKernel.power.type","line":53},{"c":"","k":"MaternAdaptPowerKernel.power.value","line":54}],"lines":54,"path":"priors/regularization/matern_adapt_power_kernel.yaml","prior":true,"priors":[{"a":"lower 1e-06","b":"upper 1000000.0","cls":"MaternAdaptPowerKernel","limits":"[0.0, inf]","line":2,"param":"scale","type":"LogUniform","width":"Relative 0.2"},{"a":"lower 0.5","b":"upper 5.5","cls":"MaternAdaptPowerKernel","limits":"[0.0, inf]","line":12,"param":"nu","type":"Uniform","width":"Relative 0.2"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"MaternAdaptPowerKernel","limits":"[0.0, inf]","line":22,"param":"inner_coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"MaternAdaptPowerKernel","limits":"[0.0, inf]","line":32,"param":"outer_coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 1.0","cls":"MaternAdaptPowerKernel","limits":"[0.0, inf]","line":42,"param":"signal_scale","type":"Uniform","width":"Relative 0.2"},{"a":"value 1.0","b":"","cls":"MaternAdaptPowerKernel","limits":"","line":52,"param":"power","type":"Constant","width":""}],"repo":"PyAutoGalaxy","text":"MaternAdaptPowerKernel:\n  scale:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  nu:\n    type: Uniform\n    lower_limit: 0.5\n    upper_limit: 5.5\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  inner_coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  outer_coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  signal_scale:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  power:\n    type: Constant\n    value: 1.0\n","tooling":false,"top_keys":["MaternAdaptPowerKernel"]},{"error":null,"keys":[{"c":"","k":"MaternKernel","line":1},{"c":"","k":"MaternKernel.coefficient","line":2},{"c":"","k":"MaternKernel.coefficient.type","line":3},{"c":"","k":"MaternKernel.coefficient.lower_limit","line":4},{"c":"","k":"MaternKernel.coefficient.upper_limit","line":5},{"c":"","k":"MaternKernel.coefficient.width_modifier","line":6},{"c":"","k":"MaternKernel.coefficient.width_modifier.type","line":7},{"c":"","k":"MaternKernel.coefficient.width_modifier.value","line":8},{"c":"","k":"MaternKernel.coefficient.limits","line":9},{"c":"","k":"MaternKernel.coefficient.limits.lower","line":10},{"c":"","k":"MaternKernel.coefficient.limits.upper","line":11},{"c":"","k":"MaternKernel.scale","line":12},{"c":"","k":"MaternKernel.scale.type","line":13},{"c":"","k":"MaternKernel.scale.lower_limit","line":14},{"c":"","k":"MaternKernel.scale.upper_limit","line":15},{"c":"","k":"MaternKernel.scale.width_modifier","line":16},{"c":"","k":"MaternKernel.scale.width_modifier.type","line":17},{"c":"","k":"MaternKernel.scale.width_modifier.value","line":18},{"c":"","k":"MaternKernel.scale.limits","line":19},{"c":"","k":"MaternKernel.scale.limits.lower","line":20},{"c":"","k":"MaternKernel.scale.limits.upper","line":21},{"c":"","k":"MaternKernel.nu","line":22},{"c":"","k":"MaternKernel.nu.type","line":23},{"c":"","k":"MaternKernel.nu.lower_limit","line":24},{"c":"","k":"MaternKernel.nu.upper_limit","line":25},{"c":"","k":"MaternKernel.nu.width_modifier","line":26},{"c":"","k":"MaternKernel.nu.width_modifier.type","line":27},{"c":"","k":"MaternKernel.nu.width_modifier.value","line":28},{"c":"","k":"MaternKernel.nu.limits","line":29},{"c":"","k":"MaternKernel.nu.limits.lower","line":30},{"c":"","k":"MaternKernel.nu.limits.upper","line":31}],"lines":31,"path":"priors/regularization/matern_kernel.yaml","prior":true,"priors":[{"a":"lower 1e-06","b":"upper 1000000.0","cls":"MaternKernel","limits":"[0.0, inf]","line":2,"param":"coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"MaternKernel","limits":"[0.0, inf]","line":12,"param":"scale","type":"LogUniform","width":"Relative 0.2"},{"a":"lower 0.5","b":"upper 5.5","cls":"MaternKernel","limits":"[0.0, inf]","line":22,"param":"nu","type":"Uniform","width":"Relative 0.2"}],"repo":"PyAutoGalaxy","text":"MaternKernel:\n  coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  scale:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  nu:\n    type: Uniform\n    lower_limit: 0.5\n    upper_limit: 5.5\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["MaternKernel"]},{"error":null,"keys":[{"c":"","k":"Zeroth","line":1},{"c":"","k":"Zeroth.coefficient","line":2},{"c":"","k":"Zeroth.coefficient.type","line":3},{"c":"","k":"Zeroth.coefficient.lower_limit","line":4},{"c":"","k":"Zeroth.coefficient.upper_limit","line":5},{"c":"","k":"Zeroth.coefficient.width_modifier","line":6},{"c":"","k":"Zeroth.coefficient.width_modifier.type","line":7},{"c":"","k":"Zeroth.coefficient.width_modifier.value","line":8},{"c":"","k":"Zeroth.coefficient.limits","line":9},{"c":"","k":"Zeroth.coefficient.limits.lower","line":10},{"c":"","k":"Zeroth.coefficient.limits.upper","line":11}],"lines":11,"path":"priors/regularization/zeroth.yaml","prior":true,"priors":[{"a":"lower 1e-06","b":"upper 1000000.0","cls":"Zeroth","limits":"[0.0, inf]","line":2,"param":"coefficient","type":"LogUniform","width":"Relative 0.5"}],"repo":"PyAutoGalaxy","text":"Zeroth:\n  coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["Zeroth"]},{"error":null,"keys":[{"c":"","k":"general","line":1,"use":"used"},{"c":"","k":"general.backend","line":2,"use":"section-read"},{"c":"","k":"general.dpi","line":3,"use":"used"},{"c":"","k":"general.imshow_origin","line":4,"use":"used"},{"c":"","k":"general.log10_min_value","line":5,"use":"used"},{"c":"","k":"general.log10_max_value","line":6,"use":"section-read"},{"c":"","k":"general.zoom_around_mask","line":7,"use":"used"},{"c":"","k":"general.critical_curves_method","line":8,"use":"used"},{"c":"","k":"inversion","line":9,"use":"used"},{"c":"","k":"inversion.reconstruction_vmax_factor","line":10,"use":"section-read"},{"c":"","k":"inversion.total_mappings_pixels","line":11,"use":"section-read"},{"c":"","k":"zoom","line":12,"use":"used"},{"c":"","k":"zoom.plane_percent","line":13,"use":"used"},{"c":"","k":"zoom.inversion_percent","line":14,"use":"used"},{"c":"","k":"colormap","line":15,"use":"used"},{"c":"","k":"ticks","line":16,"use":"used"},{"c":"","k":"ticks.extent_factor_2d","line":17,"use":"section-read"},{"c":"","k":"ticks.number_of_ticks_2d","line":18,"use":"section-read"},{"c":"","k":"contour","line":19,"use":"used"},{"c":"","k":"contour.total_contours","line":20,"use":"used"},{"c":"","k":"contour.include_values","line":21,"use":"used"},{"c":"","k":"colorbar","line":22,"use":"used"},{"c":"","k":"colorbar.fraction","line":23,"use":"section-read"},{"c":"","k":"colorbar.pad","line":24,"use":"section-read"},{"c":"","k":"colorbar.labelrotation","line":25,"use":"section-read"},{"c":"","k":"colorbar.labelsize","line":26,"use":"section-read"},{"c":"","k":"colorbar.labelsize_subplot","line":27,"use":"section-read"}],"lines":27,"path":"visualize/general.yaml","prior":false,"priors":[],"repo":"PyAutoGalaxy","text":"general:\n  backend: default\n  dpi: 150\n  imshow_origin: upper\n  log10_min_value: 1.0e-4\n  log10_max_value: 1.0e99\n  zoom_around_mask: true\n  critical_curves_method: marching_squares\ninversion:\n  reconstruction_vmax_factor: 0.5\n  total_mappings_pixels: 8\nzoom:\n  plane_percent: 0.01\n  inversion_percent: 0.01\ncolormap: autoarray\nticks:\n  extent_factor_2d: 0.75\n  number_of_ticks_2d: 3\ncontour:\n  total_contours: 10\n  include_values: true\ncolorbar:\n  fraction: 0.047\n  pad: 0.01\n  labelrotation: 90\n  labelsize: 16\n  labelsize_subplot: 16","tooling":false,"top_keys":["general","inversion","zoom","colormap","ticks","contour","colorbar"],"use_counts":{"section-read":11,"unused":0,"used":16}},{"error":null,"keys":[{"c":"Output format of all subplots, can be png, pdf or both (e.g. [png, pdf])","k":"subplot_format","line":15,"use":"used"},{"c":"If true, output .fits files are zoomed in on the center of the unmasked region image, saving hard-disk space.","k":"fits_are_zoomed","line":16,"use":"used"},{"c":"Settings for plots of all datasets (e.g. Imaging, Interferometer).","k":"dataset","line":18,"use":"used"},{"c":"Plot subplot containing all dataset quantities (e.g. the data, noise-map, etc.)?","k":"dataset.subplot_dataset","line":19,"use":"section-read"},{"c":"Settings for plots of all fits (e.g. FitImaging, FitInterferometer).","k":"fit","line":21,"use":"section-read"},{"c":"Plot subplot of all fit quantities for any dataset (e.g. the model data, residual-map, etc.)?","k":"fit.subplot_fit","line":22,"use":"section-read"},{"c":"Plot subplot of all fit quantities for any dataset using log10 color maps (e.g. the model data, residual-map, etc.)?","k":"fit.subplot_fit_log10","line":23,"use":"section-read"},{"c":"Plot subplot of the model-image, subtracted image and other quantities of each galaxy?","k":"fit.subplot_of_galaxies","line":24,"use":"section-read"},{"c":"Plot subplot of the image of each galaxy in the model?","k":"fit.subplot_galaxy_images","line":25,"use":"section-read"},{"c":"Output a .fits file containing the fit model data, residual map, normalized residual map and chi-squared?","k":"fit.fits_fit","line":26,"use":"section-read"},{"c":"Output a .fits file containing the images (e.g. without PSF convolution) of every galaxy?","k":"fit.fits_galaxy_images","line":27,"use":"section-read"},{"c":"Output a .fits file containing the model images (e.g. with PSF convolution) of every galaxy?","k":"fit.fits_model_galaxy_images","line":28,"use":"section-read"},{"c":"Settings for plots of fits to imaging datasets (e.g. FitImaging).","k":"fit_imaging","line":30,"use":"section-read"},{"c":"Settings for plots of galaxies (e.g. Galaxies).","k":"galaxies","line":32,"use":"section-read"},{"c":"Plot subplot of all quantities in each galaxies group (e.g. images, convergence)?","k":"galaxies.subplot_galaxies","line":33,"use":"section-read"},{"c":"Plot subplot of the image of each galaxy in the model?","k":"galaxies.subplot_galaxy_images","line":34,"use":"section-read"},{"c":"Output a .fits file containing images of every galaxy?","k":"galaxies.fits_galaxy_images","line":35,"use":"section-read"},{"c":"Settings for plots of inversions (e.g. InversionPlotter).","k":"inversion","line":37,"use":"section-read"},{"c":"Plot subplot of all quantities in each inversion (e.g. reconstrucuted image, reconstruction)?","k":"inversion.subplot_inversion","line":38,"use":"section-read"},{"c":"Plot subplot of the image-to-source pixels mappings of each pixelization?","k":"inversion.subplot_mappings","line":39,"use":"section-read"},{"c":"output source_plane_reconstruction_0.csv containing the source-plane mesh y, x, reconstruction and noise map values.","k":"inversion.csv_reconstruction","line":40,"use":"section-read"},{"c":"Settings for plots of adapt images used by adaptive pixelizations.","k":"adapt","line":42,"use":"section-read"},{"c":"Plot subplot showing each adapt image used for adaptive pixelization?","k":"adapt.subplot_adapt_images","line":43,"use":"section-read"},{"c":"Output a .fits file containing the adapt images used for adaptive pixelization?","k":"adapt.fits_adapt_images","line":44,"use":"section-read"},{"c":"Settings for plots of fits to interferometer datasets (e.g. FitInterferometer).","k":"fit_interferometer","line":46,"use":"section-read"},{"c":"Plot subplot of the dirty-images of all interferometer datasets?","k":"fit_interferometer.subplot_fit_dirty_images","line":47,"use":"section-read"},{"c":"Plot subplot of the real-space images of all interferometer datasets?","k":"fit_interferometer.subplot_fit_real_space","line":48,"use":"section-read"},{"c":"output dirty_images.fits showing the dirty image, noise-map, model-data, resiual-map, normalized residual map and chi-squared map?","k":"fit_interferometer.fits_dirty_images","line":49,"use":"section-read"},{"c":"Settings for plots of ellipse fitting fits (e.g. FitEllipse)","k":"fit_ellipse","line":51,"use":"section-read"},{"c":"Plot subplot of all fit quantities for ellipse fits (e.g. the model data, residual-map, etc.)?","k":"fit_ellipse.subplot_fit_ellipse","line":52,"use":"section-read"},{"c":"Plot the data of the ellipse fit?","k":"fit_ellipse.data","line":53,"use":"section-read"},{"c":"Plot the data without the black data ellipses, which obscure noisy data?","k":"fit_ellipse.data_no_ellipse","line":54,"use":"section-read"},{"c":"Plot the residuals of the ellipse fit?","k":"fit_ellipse.ellipse_residuals","line":55,"use":"section-read"}],"lines":55,"path":"visualize/plots.yaml","prior":false,"priors":[],"repo":"PyAutoGalaxy","text":"# The `plots` section customizes every image that is output to hard-disk during a model-fit.\n\n# For example, if `plots: fit: subplot_fit=True``, the ``subplot_fit.png`` subplot file will\n# be plotted every time visualization is performed.\n\n# One setting is important for inspecting results via the dataset after a fit is complete:\n\n# -`fits_adapt_images`, This outputs `adapt_images.fits` which the database functionality may use to reperform fits.\n\n# It can be disabled to save on hard-disk space but will lead to certain database functionality being disabled.\n\n# The dataset itself is always output as `dataset.fits` to the `image` folder of every fit, and is not controlled\n# by any setting here.\n\nsubplot_format: [png]                      # Output format of all subplots, can be png, pdf or both (e.g. [png, pdf])\nfits_are_zoomed: false                     # If true, output .fits files are zoomed in on the center of the unmasked region image, saving hard-disk space.\n\ndataset:                                   # Settings for plots of all datasets (e.g. Imaging, Interferometer).\n  subplot_dataset: true                    # Plot subplot containing all dataset quantities (e.g. the data, noise-map, etc.)?\n\nfit:                                       # Settings for plots of all fits (e.g. FitImaging, FitInterferometer).\n  subplot_fit: true                        # Plot subplot of all fit quantities for any dataset (e.g. the model data, residual-map, etc.)?\n  subplot_fit_log10: false                  # Plot subplot of all fit quantities for any dataset using log10 color maps (e.g. the model data, residual-map, etc.)?\n  subplot_of_galaxies: false               # Plot subplot of the model-image, subtracted image and other quantities of each galaxy?\n  subplot_galaxy_images: false             # Plot subplot of the image of each galaxy in the model?\n  fits_fit: true                           # Output a .fits file containing the fit model data, residual map, normalized residual map and chi-squared?\n  fits_galaxy_images : true                # Output a .fits file containing the images (e.g. without PSF convolution) of every galaxy?\n  fits_model_galaxy_images : true          # Output a .fits file containing the model images (e.g. with PSF convolution) of every galaxy?\n\nfit_imaging: {}                            # Settings for plots of fits to imaging datasets (e.g. FitImaging).\n\ngalaxies:                                  # Settings for plots of galaxies (e.g. Galaxies).\n  subplot_galaxies: true                   # Plot subplot of all quantities in each galaxies group (e.g. images, convergence)?\n  subplot_galaxy_images: false             # Plot subplot of the image of each galaxy in the model?\n  fits_galaxy_images: false                # Output a .fits file containing images of every galaxy?\n\ninversion:                                 # Settings for plots of inversions (e.g. InversionPlotter).\n  subplot_inversion: true                  # Plot subplot of all quantities in each inversion (e.g. reconstrucuted image, reconstruction)?\n  subplot_mappings: false                  # Plot subplot of the image-to-source pixels mappings of each pixelization?\n  csv_reconstruction: true                 # output source_plane_reconstruction_0.csv containing the source-plane mesh y, x, reconstruction and noise map values.\n\nadapt:                                     # Settings for plots of adapt images used by adaptive pixelizations.\n  subplot_adapt_images: true               # Plot subplot showing each adapt image used for adaptive pixelization?\n  fits_adapt_images: true                  # Output a .fits file containing the adapt images used for adaptive pixelization?\n\nfit_interferometer:                        # Settings for plots of fits to interferometer datasets (e.g. FitInterferometer).\n  subplot_fit_dirty_images: false          # Plot subplot of the dirty-images of all interferometer datasets?\n  subplot_fit_real_space: false            # Plot subplot of the real-space images of all interferometer datasets?\n  fits_dirty_images: true                  # output dirty_images.fits showing the dirty image, noise-map, model-data, resiual-map, normalized residual map and chi-squared map?\n\nfit_ellipse:                               # Settings for plots of ellipse fitting fits (e.g. FitEllipse)\n  subplot_fit_ellipse : true               # Plot subplot of all fit quantities for ellipse fits (e.g. the model data, residual-map, etc.)?\n  data : true                              # Plot the data of the ellipse fit?\n  data_no_ellipse: true                    # Plot the data without the black data ellipses, which obscure noisy data?\n  ellipse_residuals: true                  # Plot the residuals of the ellipse fit?","tooling":false,"top_keys":["subplot_format","fits_are_zoomed","dataset","fit","fit_imaging","galaxies","inversion","adapt","fit_interferometer","fit_ellipse"],"use_counts":{"section-read":30,"unused":0,"used":3}},{"error":null,"keys":[{"c":"","k":"output","line":1,"use":"used"},{"c":"","k":"output.fit_dill","line":2,"use":"unused"},{"c":"","k":"test","line":3,"use":"used"},{"c":"","k":"test.disable_positions_lh_inversion_check","line":4,"use":"used"}],"lines":4,"path":"general.yaml","prior":false,"priors":[],"repo":"PyAutoLens","text":"output:\n  fit_dill: false\ntest:\n  disable_positions_lh_inversion_check: false\n","tooling":false,"top_keys":["output","test"],"use_counts":{"section-read":0,"unused":1,"used":3}},{"error":null,"keys":[{"c":"`total_lens_flux` \u2014 total integrated flux of the lens galaxy (`fit.tracer.galaxies[0]`), in the raw image units the fit was performed in. No instrument inputs required.","k":"total_lens_flux","line":25,"use":"section-read"},{"c":"`total_lensed_source_flux` \u2014 image-plane integrated flux of the source galaxy after lensing (`fit.galaxy_image_dict[tracer.galaxies[-1]]`), in raw image units.","k":"total_lensed_source_flux","line":30,"use":"section-read"},{"c":"in raw image units. Reads from `tracer_linear_light_profiles_to_light_profiles` so linear-profile fits get the correct (inversion-solved) flux.","k":"total_source_flux","line":36,"use":"section-read"},{"c":"`total_lens_flux_mujy` \u2014 total integrated flux of the lens galaxy (`fit.tracer.galaxies[0]`) in microjanskies. Requires `magzero` via Analysis kwargs. Returns NaN + one warning if `magzero` is missin\u2026","k":"total_lens_flux_mujy","line":41,"use":"section-read"},{"c":"`total_lensed_source_flux_mujy` \u2014 image-plane integrated flux of the source galaxy after lensing (`fit.galaxy_image_dict[tracer.galaxies[-1]]`) in microjanskies. Requires `magzero`.","k":"total_lensed_source_flux_mujy","line":46,"use":"section-read"},{"c":"`total_source_flux_mujy` \u2014 source-plane intrinsic flux of the source galaxy (computed via the source's light profile on `fit.dataset.grids.lp`) in microjanskies. Requires `magzero`.","k":"total_source_flux_mujy","line":51,"use":"section-read"},{"c":"`total_lensed_source_flux_mujy` and `total_source_flux_mujy` and supply a `magzero`. A follow-up could rewire `magnification` to the raw-flux latents so it's universally enable-able.","k":"magnification","line":60,"use":"section-read"},{"c":"from the tangential critical curve via `LensCalc.einstein_radius_jit_from` (JAX) or `einstein_radius_from` (numpy). Does NOT require `magzero`.","k":"effective_einstein_radius","line":66,"use":"section-read"}],"lines":66,"path":"latent.yaml","prior":false,"priors":[],"repo":"PyAutoLens","text":"# Toggles for the catalogue of latent variables computed by `AnalysisImaging`\n# (and, when wired in a follow-up, `AnalysisInterferometer`).\n#\n# Each entry maps a registered latent name (see\n# `autolens/analysis/latent.py::LATENT_FUNCTIONS`) to a bool. Setting `false`\n# excludes that latent from `LATENT_KEYS` so it is neither computed nor\n# written to `latent/samples.csv` / `latent/latent_summary.json`.\n#\n# Workspaces should mirror this file in their own `config/latent.yaml` to\n# override defaults locally (workspace values shadow library values).\n#\n# Raw-flux keys (`total_lens_flux`, `total_lensed_source_flux`,\n# `total_source_flux`) require no instrument inputs and default `true`.\n# The `_mujy` variants require `magzero` on the Analysis; they default\n# `false` and return NaN + one warning per process if enabled without\n# `magzero` (rather than raising, which would discard a converged search).\n#\n# autonerves lowercases yaml keys at read time, so the registry/yaml names\n# must be snake_case-lowercase (this leaks into the `latent.csv` column\n# header \u2014 e.g. `total_lens_flux_mujy`, not `_muJy`).\n\n# `total_lens_flux` \u2014 total integrated flux of the lens galaxy\n# (`fit.tracer.galaxies[0]`), in the raw image units the fit was performed\n# in. No instrument inputs required.\ntotal_lens_flux: true\n\n# `total_lensed_source_flux` \u2014 image-plane integrated flux of the source\n# galaxy after lensing (`fit.galaxy_image_dict[tracer.galaxies[-1]]`), in\n# raw image units.\ntotal_lensed_source_flux: true\n\n# `total_source_flux` \u2014 source-plane intrinsic flux of the source galaxy,\n# in raw image units. Reads from\n# `tracer_linear_light_profiles_to_light_profiles` so linear-profile fits\n# get the correct (inversion-solved) flux.\ntotal_source_flux: true\n\n# `total_lens_flux_mujy` \u2014 total integrated flux of the lens galaxy\n# (`fit.tracer.galaxies[0]`) in microjanskies. Requires `magzero` via\n# Analysis kwargs. Returns NaN + one warning if `magzero` is missing.\ntotal_lens_flux_mujy: false\n\n# `total_lensed_source_flux_mujy` \u2014 image-plane integrated flux of the\n# source galaxy after lensing (`fit.galaxy_image_dict[tracer.galaxies[-1]]`)\n# in microjanskies. Requires `magzero`.\ntotal_lensed_source_flux_mujy: false\n\n# `total_source_flux_mujy` \u2014 source-plane intrinsic flux of the source\n# galaxy (computed via the source's light profile on `fit.dataset.grids.lp`)\n# in microjanskies. Requires `magzero`.\ntotal_source_flux_mujy: false\n\n# `magnification` \u2014 ratio of image-plane lensed source flux to source-plane\n# intrinsic source flux. Dimensionless; `magzero` is accepted but unused.\n# Default `false` because it routes through the `_mujy` latents internally\n# (the \u00b5Jy conversions cancel in the ratio) \u2014 to flip on, also flip on\n# `total_lensed_source_flux_mujy` and `total_source_flux_mujy` and supply\n# a `magzero`. A follow-up could rewire `magnification` to the raw-flux\n# latents so it's universally enable-able.\nmagnification: false\n\n# `effective_einstein_radius` \u2014 effective Einstein radius in arcseconds,\n# from the tangential critical curve via\n# `LensCalc.einstein_radius_jit_from` (JAX) or `einstein_radius_from`\n# (numpy). Does NOT require `magzero`.\neffective_einstein_radius: false\n","tooling":false,"top_keys":["total_lens_flux","total_lensed_source_flux","total_source_flux","total_lens_flux_mujy","total_lensed_source_flux_mujy","total_source_flux_mujy","magnification","effective_einstein_radius"],"use_counts":{"section-read":8,"unused":0,"used":0}},{"error":null,"keys":[{"c":"","k":"nest","line":1,"use":"unused"},{"c":"","k":"nest.DynestyDynamic","line":2,"use":"unused"},{"c":"","k":"nest.DynestyDynamic.initialize","line":3,"use":"unused"},{"c":"","k":"nest.DynestyDynamic.initialize.method","line":4,"use":"unused"},{"c":"","k":"nest.DynestyDynamic.parallel","line":5,"use":"unused"},{"c":"","k":"nest.DynestyDynamic.parallel.force_x1_cpu","line":6,"use":"unused"},{"c":"","k":"nest.DynestyDynamic.parallel.number_of_cores","line":7,"use":"unused"},{"c":"","k":"nest.DynestyDynamic.printing","line":8,"use":"unused"},{"c":"","k":"nest.DynestyDynamic.printing.silence","line":9,"use":"unused"},{"c":"","k":"nest.DynestyDynamic.run","line":10,"use":"unused"},{"c":"","k":"nest.DynestyDynamic.run.dlogz_init","line":11,"use":"unused"},{"c":"","k":"nest.DynestyDynamic.run.logl_max_init","line":12,"use":"unused"},{"c":"","k":"nest.DynestyDynamic.run.maxcall","line":13,"use":"unused"},{"c":"","k":"nest.DynestyDynamic.run.maxcall_init","line":14,"use":"unused"},{"c":"","k":"nest.DynestyDynamic.run.maxiter","line":15,"use":"unused"},{"c":"","k":"nest.DynestyDynamic.run.maxiter_init","line":16,"use":"unused"},{"c":"","k":"nest.DynestyDynamic.run.n_effective","line":17,"use":"unused"},{"c":"","k":"nest.DynestyDynamic.run.n_effective_init","line":18,"use":"unused"},{"c":"","k":"nest.DynestyDynamic.run.nlive_init","line":19,"use":"unused"},{"c":"","k":"nest.DynestyDynamic.search","line":20,"use":"unused"},{"c":"","k":"nest.DynestyDynamic.search.bootstrap","line":21,"use":"unused"},{"c":"","k":"nest.DynestyDynamic.search.bound","line":22,"use":"unused"},{"c":"","k":"nest.DynestyDynamic.search.enlarge","line":23,"use":"unused"},{"c":"","k":"nest.DynestyDynamic.search.facc","line":24,"use":"unused"},{"c":"","k":"nest.DynestyDynamic.search.first_update","line":25,"use":"unused"},{"c":"","k":"nest.DynestyDynamic.search.fmove","line":26,"use":"unused"},{"c":"","k":"nest.DynestyDynamic.search.max_move","line":27,"use":"unused"},{"c":"","k":"nest.DynestyDynamic.search.sample","line":28,"use":"unused"},{"c":"","k":"nest.DynestyDynamic.search.slices","line":29,"use":"unused"},{"c":"","k":"nest.DynestyDynamic.search.update_interval","line":30,"use":"unused"},{"c":"","k":"nest.DynestyDynamic.search.walks","line":31,"use":"unused"},{"c":"","k":"nest.DynestyStatic","line":32,"use":"unused"},{"c":"","k":"nest.DynestyStatic.initialize","line":33,"use":"unused"},{"c":"","k":"nest.DynestyStatic.initialize.method","line":34,"use":"unused"},{"c":"","k":"nest.DynestyStatic.parallel","line":35,"use":"unused"},{"c":"","k":"nest.DynestyStatic.parallel.number_of_cores","line":36,"use":"unused"},{"c":"","k":"nest.DynestyStatic.printing","line":37,"use":"unused"},{"c":"","k":"nest.DynestyStatic.printing.silence","line":38,"use":"unused"},{"c":"","k":"nest.DynestyStatic.run","line":39,"use":"unused"},{"c":"","k":"nest.DynestyStatic.run.dlogz","line":40,"use":"unused"},{"c":"","k":"nest.DynestyStatic.run.logl_max","line":41,"use":"unused"},{"c":"","k":"nest.DynestyStatic.run.maxcall","line":42,"use":"unused"},{"c":"","k":"nest.DynestyStatic.run.maxiter","line":43,"use":"unused"},{"c":"","k":"nest.DynestyStatic.run.n_effective","line":44,"use":"unused"},{"c":"","k":"nest.DynestyStatic.search","line":45,"use":"unused"},{"c":"","k":"nest.DynestyStatic.search.bootstrap","line":46,"use":"unused"},{"c":"","k":"nest.DynestyStatic.search.bound","line":47,"use":"unused"},{"c":"","k":"nest.DynestyStatic.search.enlarge","line":48,"use":"unused"},{"c":"","k":"nest.DynestyStatic.search.facc","line":49,"use":"unused"},{"c":"","k":"nest.DynestyStatic.search.first_update","line":50,"use":"unused"},{"c":"","k":"nest.DynestyStatic.search.fmove","line":51,"use":"unused"},{"c":"","k":"nest.DynestyStatic.search.max_move","line":52,"use":"unused"},{"c":"","k":"nest.DynestyStatic.search.nlive","line":53,"use":"unused"},{"c":"","k":"nest.DynestyStatic.search.sample","line":54,"use":"unused"},{"c":"","k":"nest.DynestyStatic.search.slices","line":55,"use":"unused"},{"c":"","k":"nest.DynestyStatic.search.update_interval","line":56,"use":"unused"},{"c":"","k":"nest.DynestyStatic.search.walks","line":57,"use":"unused"}],"lines":57,"path":"non_linear.yaml","prior":false,"priors":[],"repo":"PyAutoLens","text":"nest:\n  DynestyDynamic:\n    initialize:\n      method: prior\n    parallel:\n      force_x1_cpu: false\n      number_of_cores: 1\n    printing:\n      silence: false\n    run:\n      dlogz_init: 0.01\n      logl_max_init: .inf\n      maxcall: null\n      maxcall_init: null\n      maxiter: null\n      maxiter_init: null\n      n_effective: .inf\n      n_effective_init: .inf\n      nlive_init: 500\n    search:\n      bootstrap: null\n      bound: multi\n      enlarge: null\n      facc: 0.2\n      first_update: null\n      fmove: 0.9\n      max_move: 100\n      sample: rwalk\n      slices: 5\n      update_interval: null\n      walks: 5\n  DynestyStatic:\n    initialize:\n      method: prior\n    parallel:\n      number_of_cores: 1\n    printing:\n      silence: false\n    run:\n      dlogz: null\n      logl_max: .inf\n      maxcall: null\n      maxiter: null\n      n_effective: null\n    search:\n      bootstrap: null\n      bound: multi\n      enlarge: null\n      facc: 0.2\n      first_update: null\n      fmove: 0.9\n      max_move: 100\n      nlive: 50\n      sample: rwalk\n      slices: 5\n      update_interval: null\n      walks: 5\n","tooling":false,"top_keys":["nest"],"use_counts":{"section-read":0,"unused":57,"used":0}},{"error":null,"keys":[{"c":"If true then files which are not explicitly listed here are output anyway. If false then they are not.","k":"default","line":4,"use":"used"},{"c":"","k":"samples","line":17,"use":"section-read"},{"c":"","k":"samples_weight_threshold","line":34,"use":"used"},{"c":"","k":"search_internal","line":56,"use":"used"},{"c":"","k":"start_point","line":65,"use":"used"},{"c":"Whether to output the `latent.csv`, `latent.results` and `latent_summary.json` files during the fit when it performs on-the-fly output.","k":"latent_during_fit","line":89,"use":"used"},{"c":"If `latent_during_fit` is False, whether to output the `latent.csv`, `latent.results` and `latent_summary.json` files after the fit is complete.","k":"latent_after_fit","line":90,"use":"used"},{"c":"Whether to draw latent variable values via the PDF of every sample, which uses fewer samples to estimate latent variable errors. If False, latent variable values are drawn from every sample.","k":"latent_draw_via_pdf","line":91,"use":"used"},{"c":"The number of samples drawn to estimate latent variable errors if `latent_draw_via_pdf` is True.","k":"latent_draw_via_pdf_size","line":92,"use":"used"},{"c":"Whether to ouptut the `latent.csv` file.","k":"latent_csv","line":93,"use":"section-read"},{"c":"Whether to output the `latent.results` file.","k":"latent_results","line":94,"use":"section-read"},{"c":"`search.log`: logging produced whilst running the fit method","k":"search_log","line":98,"use":"used"}],"lines":98,"path":"output.yaml","prior":false,"priors":[],"repo":"PyAutoLens","text":"# Determines whether files saved by the search are output to the hard-disk. This is true both when saving to the\n# directory structure and when saving to database.\n\ndefault: true # If true then files which are not explicitly listed here are output anyway. If false then they are not.\n\n### Samples ###\n\n# The `samples.csv`file contains every sampled value of every free parameter with its log likelihood and weight.\n\n# This file is often large, therefore disabling it can significantly reduce hard-disk space use.\n\n# `samples.csv` is used to perform marginalization, infer model parameter errors and do other analysis of the search\n# chains. Even if output of `samples.csv` is disabled, these tasks are still performed by the fit and output to\n# the `samples_summary.json` file. However, without a `samples.csv` file these types of tasks cannot be performed\n# after the fit is complete, for example via the database.\n\nsamples: true\n\n# The `samples.csv` file contains every accepted sampled value of every free parameter with its log likelihood and\n# weight. For certain searches, the majority of samples have a very low weight and have no numerical impact on the\n# results of the model-fit. However, these samples are still output to the `samples.csv` file, taking up hard-disk\n# space and slowing down analysis of the samples (e.g. via the database).\n\n# The `samples_weight_threshold` below specifies the threshold value of the weight such that samples with a weight\n# below this value are not output to the `samples.csv` file. This can be used to reduce the size of the `samples.csv`\n# file and speed up analysis of the samples.\n\n# For many searches (e.g. MCMC) all samples have an equal weight of 1.0, and this threshold therefore has no impact.\n# For these searches, there is no simple way to save hard-disk space. This input is more suited to nested sampling,\n# where the majority of samples have a very low weight..\n\n# Set value to empty (e.g. delete 1.0e-10 below) to disable this feature.\n\nsamples_weight_threshold: 1.0e-10\n\n### Search Internal ###\n\n# The search internal folder which contains a saved state of the non-linear search in its internal reprsenetation,\n# as a .pickle or .dill file.\n\n# For example, for the nested sampling dynesty, this .dill file is the `DynestySampler` object which is used to\n# perform sampling, and it therefore contains all internal dynesty representations of the results, samples, weights, etc.\n\n# If the entry below is false, the folder is still output during the model-fit, as it is required to resume the fit\n# from where it left off. Therefore, settings `false` below does not impact model-fitting checkpointing and resumption.\n# Instead, the search internal folder is deleted once the fit is completed.\n\n# The search internal folder file is often large, therefore deleting it after a fit is complete can significantly\n# reduce hard-disk space use.\n\n# The search internal representation that can be loaded from the .dill file has many additional quantities specific to\n# the non-linear search that the standardized autofit forms do not. For example, for emcee, it contains information on\n# every walker. This information is required to do certain analyes and make certain plots, therefore deleting the\n# folder means this information is list.\n\nsearch_internal: false\n\n### Start Point ###\n\n# If an Initalizer is used to provide a start point for the non-linear search, visualization of that start point can be\n# output to hard-disk to show the user the initial model-fit that is used to start the search. This visualization is\n# the visualizer wrapped in the Analysis class, and therefore should show things like the quality of the fit\n# to the data and the residuals at the start point.\n\nstart_point: true\n\n### Latent Variables ###\n\n# A latent variable is not a model parameter but can be derived from the model. Its value and errors may be of interest\n# and aid in the interpretation of a model-fit.\n\n# For example, for the simple 1D Gaussian example, it could be the full-width half maximum (FWHM) of the Gaussian. This\n# is not included in the model but can be easily derived from the Gaussian's sigma value.\n\n# By overwriting an Analysis class's `compute_latent_variables` method we can manually specify latent variables that\n# are calculated and output to a `latent.csv` file, which mirrors the `samples.csv` file. The `latent.csv` file has\n# the same weight resampling performed on the `samples.csv` file, controlled via the `samples_weight_threshold` above.\n\n# There may also be a `latent.results` and `latent_summary.json` files output, which the inputs below control whether\n# they are output and how often.\n\n# Outputting latent variables manually after a fit is complete is simple, just call\n# the `analysis.compute_latent_variables()` function.\n\n# For many use cases, the best set up may be to disable autofit latent variable output during the fit and perform it\n# manually after completing a successful model-fit. This will save computational run time by not computing latent\n# variables during a any model-fit which is unsuccessful.\n\nlatent_during_fit: false # Whether to output the `latent.csv`, `latent.results` and `latent_summary.json` files during the fit when it performs on-the-fly output.\nlatent_after_fit: true # If `latent_during_fit` is False, whether to output the `latent.csv`, `latent.results` and `latent_summary.json` files after the fit is complete.\nlatent_draw_via_pdf : true # Whether to draw latent variable values via the PDF of every sample, which uses fewer samples to estimate latent variable errors. If False, latent variable values are drawn from every sample.\nlatent_draw_via_pdf_size : 100 # The number of samples drawn to estimate latent variable errors if `latent_draw_via_pdf` is True.\nlatent_csv: true # Whether to ouptut the `latent.csv` file.\nlatent_results: true # Whether to output the `latent.results` file.\n\n# Other Files:\n\nsearch_log: true # `search.log`: logging produced whilst running the fit method","tooling":false,"top_keys":["default","samples","samples_weight_threshold","search_internal","start_point","latent_during_fit","latent_after_fit","latent_draw_via_pdf","latent_draw_via_pdf_size","latent_csv","latent_results","search_log"],"use_counts":{"section-read":3,"unused":0,"used":9}},{"error":null,"keys":[{"c":"Output format of all subplots, can be png, pdf or both (e.g. [png, pdf])","k":"subplot_format","line":15,"use":"used"},{"c":"If true, output .fits files are zoomed in on the center of the unmasked region image, saving hard-disk space.","k":"fits_are_zoomed","line":16,"use":"used"},{"c":"Settings for plots of all datasets (e.g. Imaging, Interferometer).","k":"dataset","line":18,"use":"used"},{"c":"Plot subplot containing all dataset quantities (e.g. the data, noise-map, etc.)?","k":"dataset.subplot_dataset","line":19,"use":"section-read"},{"c":"Settings for plots with resampling image-positions on (e.g. the image).","k":"positions","line":21,"use":"section-read"},{"c":"","k":"positions.image_with_positions","line":22,"use":"section-read"},{"c":"Settings for plots of all fits (e.g. FitImaging, FitInterferometer).","k":"fit","line":24,"use":"section-read"},{"c":"Plot subplot of all fit quantities for any dataset (e.g. the model data, residual-map, etc.)?","k":"fit.subplot_fit","line":25,"use":"section-read"},{"c":"Plot subplot of all fit quantities for any dataset using log10 color maps (e.g. the model data, residual-map, etc.)?","k":"fit.subplot_fit_log10","line":26,"use":"section-read"},{"c":"Plot subplot of the model-image, subtracted image and other quantities of each plane?","k":"fit.subplot_of_planes","line":27,"use":"section-read"},{"c":"Plot subplot of the image of each plane in the model?","k":"fit.subplot_galaxies_images","line":28,"use":"section-read"},{"c":"Output a .fits file containing the fit model data, residual map, normalized residual map and chi-squared?","k":"fit.fits_fit","line":29,"use":"section-read"},{"c":"Output a .fits file containing the images (e.g. without PSF convolution) of every galaxy?","k":"fit.fits_galaxy_images","line":30,"use":"section-read"},{"c":"Output a .fits file containing the model images (e.g. with PSF convolution) of every galaxy?","k":"fit.fits_model_galaxy_images","line":31,"use":"section-read"},{"c":"Settings for plots of fits to imaging datasets (e.g. FitImaging).","k":"fit_imaging","line":33,"use":"section-read"},{"c":"Settings for plots of tracers (e.g. Tracer).","k":"tracer","line":35,"use":"used"},{"c":"Plot subplot of all quantities in each tracer (e.g. images, convergence)?","k":"tracer.subplot_tracer","line":36,"use":"section-read"},{"c":"Plot subplot of the image of each plane in the tracer?","k":"tracer.subplot_galaxies_images","line":37,"use":"section-read"},{"c":"Output tracer.fits file of tracer's convergence, potential, deflections_y and deflections_x?","k":"tracer.fits_tracer","line":38,"use":"section-read"},{"c":"Output source_plane_images.fits file of the source-plane image (light profiles only) of each galaxy in the tracer?","k":"tracer.fits_source_plane_images","line":39,"use":"section-read"},{"c":"The shape of the source-plane image output in the fits_source_plane_images.fits file.","k":"tracer.fits_source_plane_shape","line":40,"use":"used"},{"c":"Settings for plots of inversions (e.g. InversionPlotter).","k":"inversion","line":42,"use":"section-read"},{"c":"Plot subplot of all quantities in each inversion (e.g. reconstrucuted image, reconstruction)?","k":"inversion.subplot_inversion","line":43,"use":"section-read"},{"c":"Plot subplot of how the brightest source regions map to their multiple images in the image-plane (works for pixelized and parametric sources)?","k":"inversion.subplot_mappings","line":44,"use":"section-read"},{"c":"output source_plane_reconstruction_0.csv containing the source-plane mesh y, x, reconstruction and noise map values.","k":"inversion.csv_reconstruction","line":45,"use":"section-read"},{"c":"Settings for plots of adapt images used by adaptive pixelizations.","k":"adapt","line":47,"use":"section-read"},{"c":"Plot subplot showing each adapt image used for adaptive pixelization?","k":"adapt.subplot_adapt_images","line":48,"use":"section-read"},{"c":"Output a .fits file containing the adapt images used for adaptive pixelization?","k":"adapt.fits_adapt_images","line":49,"use":"section-read"},{"c":"Settings for plots of fits to interferometer datasets (e.g. FitInterferometer).","k":"fit_interferometer","line":51,"use":"section-read"},{"c":"Plot subplot of the dirty-images of all interferometer datasets?","k":"fit_interferometer.subplot_fit_dirty_images","line":52,"use":"section-read"},{"c":"Plot subplot of the real-space images of all interferometer datasets?","k":"fit_interferometer.subplot_fit_real_space","line":53,"use":"section-read"},{"c":"output dirty_images.fits showing the dirty image, noise-map, model-data, resiual-map, normalized residual map and chi-squared map?","k":"fit_interferometer.fits_dirty_images","line":54,"use":"section-read"},{"c":"Settings for plots of point source datasets (e.g. PointDatasetPlotter).","k":"point_dataset","line":56,"use":"section-read"},{"c":"Plot subplot containing all dataset quantities (e.g. the data, noise-map, etc.)?","k":"point_dataset.subplot_dataset","line":57,"use":"section-read"},{"c":"Settings for plots of fits to point source datasets (e.g. FitPointDatasetPlotter).","k":"fit_point_dataset","line":59,"use":"section-read"},{"c":"Settings for plots of weak lensing shear catalogues (e.g. PlotterWeak).","k":"weak_dataset","line":61,"use":"section-read"},{"c":"Plot subplot containing all dataset quantities (e.g. the shear field, noise-map, etc.)?","k":"weak_dataset.subplot_dataset","line":62,"use":"section-read"},{"c":"Settings for plots of fits to weak lensing shear catalogues (e.g. PlotterWeak).","k":"fit_weak","line":64,"use":"section-read"},{"c":"Settings for plots of ellipse fitting fits (e.g. FitEllipse)","k":"fit_ellipse","line":66,"use":"section-read"},{"c":"Plot the data of the ellipse fit?","k":"fit_ellipse.data","line":67,"use":"section-read"},{"c":"Plot the data without the black data ellipses, which obscure noisy data?","k":"fit_ellipse.data_no_ellipse","line":68,"use":"section-read"},{"c":"Settings for plots of galaxies (e.g. Galaxies).","k":"galaxies","line":70,"use":"section-read"},{"c":"Plot subplot of all quantities in each galaxies group (e.g. images, convergence)?","k":"galaxies.subplot_galaxies","line":71,"use":"section-read"},{"c":"Plot subplot of the image of each galaxy in the model?","k":"galaxies.subplot_galaxy_images","line":72,"use":"section-read"}],"lines":72,"path":"visualize/plots.yaml","prior":false,"priors":[],"repo":"PyAutoLens","text":"# The `plots` section customizes every image that is output to hard-disk during a model-fit.\n\n# For example, if `plots: fit: subplot_fit=True``, the ``subplot_fit.png`` subplot file will\n# be plotted every time visualization is performed.\n\n# One setting is important for inspecting results via the dataset after a fit is complete:\n\n# -`fits_adapt_images`, This outputs `adapt_images.fits` which the database functionality may use to reperform fits.\n\n# It can be disabled to save on hard-disk space but will lead to certain database functionality being disabled.\n\n# The dataset itself is always output as `dataset.fits` to the `image` folder of every fit, and is not controlled\n# by any setting here.\n\nsubplot_format: [png]                      # Output format of all subplots, can be png, pdf or both (e.g. [png, pdf])\nfits_are_zoomed: false                     # If true, output .fits files are zoomed in on the center of the unmasked region image, saving hard-disk space.\n\ndataset:                                   # Settings for plots of all datasets (e.g. Imaging, Interferometer).\n  subplot_dataset: true                    # Plot subplot containing all dataset quantities (e.g. the data, noise-map, etc.)?\n\npositions:                                 # Settings for plots with resampling image-positions on (e.g. the image).\n  image_with_positions: true\n\nfit:                                       # Settings for plots of all fits (e.g. FitImaging, FitInterferometer).\n  subplot_fit: true                        # Plot subplot of all fit quantities for any dataset (e.g. the model data, residual-map, etc.)?\n  subplot_fit_log10: false                  # Plot subplot of all fit quantities for any dataset using log10 color maps (e.g. the model data, residual-map, etc.)?\n  subplot_of_planes: false                 # Plot subplot of the model-image, subtracted image and other quantities of each plane?\n  subplot_galaxies_images: false              # Plot subplot of the image of each plane in the model?\n  fits_fit: true                           # Output a .fits file containing the fit model data, residual map, normalized residual map and chi-squared?\n  fits_galaxy_images : true                # Output a .fits file containing the images (e.g. without PSF convolution) of every galaxy?\n  fits_model_galaxy_images : true          # Output a .fits file containing the model images (e.g. with PSF convolution) of every galaxy?\n\nfit_imaging: {}                            # Settings for plots of fits to imaging datasets (e.g. FitImaging).\n\ntracer:                                    # Settings for plots of tracers (e.g. Tracer).\n  subplot_tracer: true                     # Plot subplot of all quantities in each tracer (e.g. images, convergence)?\n  subplot_galaxies_images: false           # Plot subplot of the image of each plane in the tracer?\n  fits_tracer: true                        # Output tracer.fits file of tracer's convergence, potential, deflections_y and deflections_x?\n  fits_source_plane_images: true           # Output source_plane_images.fits file of the source-plane image (light profiles only) of each galaxy in the tracer?\n  fits_source_plane_shape: (100, 100)      # The shape of the source-plane image output in the fits_source_plane_images.fits file.\n\ninversion:                                 # Settings for plots of inversions (e.g. InversionPlotter).\n  subplot_inversion: true                  # Plot subplot of all quantities in each inversion (e.g. reconstrucuted image, reconstruction)?\n  subplot_mappings: false                  # Plot subplot of how the brightest source regions map to their multiple images in the image-plane (works for pixelized and parametric sources)?\n  csv_reconstruction: true               # output source_plane_reconstruction_0.csv containing the source-plane mesh y, x, reconstruction and noise map values.\n\nadapt:                                     # Settings for plots of adapt images used by adaptive pixelizations.\n  subplot_adapt_images: true               # Plot subplot showing each adapt image used for adaptive pixelization?\n  fits_adapt_images: true                  # Output a .fits file containing the adapt images used for adaptive pixelization?\n\nfit_interferometer:                        # Settings for plots of fits to interferometer datasets (e.g. FitInterferometer).\n  subplot_fit_dirty_images: false          # Plot subplot of the dirty-images of all interferometer datasets?\n  subplot_fit_real_space: false            # Plot subplot of the real-space images of all interferometer datasets?\n  fits_dirty_images: true                  # output dirty_images.fits showing the dirty image, noise-map, model-data, resiual-map, normalized residual map and chi-squared map?\n\npoint_dataset:                             # Settings for plots of point source datasets (e.g. PointDatasetPlotter).\n  subplot_dataset: true                    # Plot subplot containing all dataset quantities (e.g. the data, noise-map, etc.)?\n\nfit_point_dataset: {}                      # Settings for plots of fits to point source datasets (e.g. FitPointDatasetPlotter).\n\nweak_dataset:                              # Settings for plots of weak lensing shear catalogues (e.g. PlotterWeak).\n  subplot_dataset: true                    # Plot subplot containing all dataset quantities (e.g. the shear field, noise-map, etc.)?\n\nfit_weak: {}                               # Settings for plots of fits to weak lensing shear catalogues (e.g. PlotterWeak).\n\nfit_ellipse:                               # Settings for plots of ellipse fitting fits (e.g. FitEllipse)\n  data : true                              # Plot the data of the ellipse fit?\n  data_no_ellipse: true                    # Plot the data without the black data ellipses, which obscure noisy data?\n\ngalaxies:                                  # Settings for plots of galaxies (e.g. Galaxies).\n  subplot_galaxies: true                   # Plot subplot of all quantities in each galaxies group (e.g. images, convergence)?\n  subplot_galaxy_images: false             # Plot subplot of the image of each galaxy in the model?\n","tooling":false,"top_keys":["subplot_format","fits_are_zoomed","dataset","positions","fit","fit_imaging","tracer","inversion","adapt","fit_interferometer","point_dataset","fit_point_dataset","weak_dataset","fit_weak","fit_ellipse","galaxies"],"use_counts":{"section-read":39,"unused":0,"used":5}},{"error":null,"keys":[{"c":"","k":"fits","line":1,"use":"unused"},{"c":"","k":"fits.flip_for_ds9","line":2,"use":"unused"},{"c":"","k":"hpc","line":3,"use":"used"},{"c":"","k":"hpc.hpc_mode","line":4,"use":"used"},{"c":"","k":"hpc.iterations_per_update","line":5,"use":"unused"},{"c":"","k":"model","line":6,"use":"unused"},{"c":"","k":"model.ignore_prior_limits","line":7,"use":"unused"},{"c":"","k":"output","line":8,"use":"used"},{"c":"","k":"output.force_pickle_overwrite","line":9,"use":"used"},{"c":"","k":"output.info_whitespace_length","line":10,"use":"used"},{"c":"","k":"output.log_file","line":11,"use":"unused"},{"c":"","k":"output.log_level","line":12,"use":"unused"},{"c":"","k":"output.log_to_file","line":13,"use":"unused"},{"c":"","k":"output.model_results_decimal_places","line":14,"use":"used"},{"c":"","k":"output.remove_files","line":15,"use":"used"},{"c":"","k":"output.samples_to_csv","line":16,"use":"used"},{"c":"","k":"structures","line":17,"use":"used"},{"c":"If True, data structures are only stored in their native and binned format. This is used to reduce memory usage in autocti.","k":"structures.native_binned_only","line":18,"use":"used"}],"lines":18,"path":"general.yaml","prior":false,"priors":[],"repo":"PyAutoCTI","text":"fits:\n  flip_for_ds9: false\nhpc:\n  hpc_mode: false\n  iterations_per_update: 5000\nmodel:\n  ignore_prior_limits: false\noutput:\n  force_pickle_overwrite: false\n  info_whitespace_length: 80\n  log_file: output.log\n  log_level: INFO\n  log_to_file: false\n  model_results_decimal_places: 3\n  remove_files: false\n  samples_to_csv: false\nstructures:\n  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.","tooling":false,"top_keys":["fits","hpc","model","output","structures"],"use_counts":{"section-read":0,"unused":8,"used":10}},{"error":null,"keys":[{"c":"","k":"label","line":1,"use":"used"},{"c":"","k":"label.label","line":2,"use":"used"},{"c":"","k":"label.label.density","line":3,"use":"section-read"},{"c":"","k":"label.label.full_well_depth","line":4,"use":"section-read"},{"c":"","k":"label.label.gamma","line":5,"use":"section-read"},{"c":"","k":"label.label.ka","line":6,"use":"section-read"},{"c":"","k":"label.label.kv","line":7,"use":"section-read"},{"c":"","k":"label.label.omega","line":8,"use":"section-read"},{"c":"","k":"label.label.release_timescale","line":9,"use":"section-read"},{"c":"","k":"label.label.release_timescale_sigma","line":10,"use":"section-read"},{"c":"","k":"label.label.scale_factor","line":11,"use":"section-read"},{"c":"","k":"label.label.well_fill_alpha","line":12,"use":"section-read"},{"c":"","k":"label.label.well_fill_gamma","line":13,"use":"section-read"},{"c":"","k":"label.label.well_fill_power","line":14,"use":"section-read"},{"c":"","k":"label.label.well_notch_depth","line":15,"use":"section-read"},{"c":"","k":"label.superscript","line":16,"use":"used"},{"c":"","k":"label.superscript.CCDComplex","line":17,"use":"section-read"},{"c":"","k":"label.superscript.CCDPhase","line":18,"use":"section-read"},{"c":"","k":"label.superscript.HyperCINoiseScalar","line":19,"use":"section-read"},{"c":"","k":"label.superscript.PixelBounce","line":20,"use":"section-read"},{"c":"","k":"label.superscript.TrapInstantCapture","line":21,"use":"section-read"},{"c":"","k":"label.superscript.TrapInstantCaptureContinuum","line":22,"use":"section-read"},{"c":"","k":"label_format","line":23,"use":"used"},{"c":"","k":"label_format.format","line":24,"use":"used"},{"c":"","k":"label_format.format.density","line":25,"use":"section-read"},{"c":"","k":"label_format.format.full_well_depth","line":26,"use":"section-read"},{"c":"","k":"label_format.format.gamma","line":27,"use":"section-read"},{"c":"","k":"label_format.format.ka","line":28,"use":"section-read"},{"c":"","k":"label_format.format.kv","line":29,"use":"section-read"},{"c":"","k":"label_format.format.omega","line":30,"use":"section-read"},{"c":"","k":"label_format.format.release_timescale","line":31,"use":"section-read"},{"c":"","k":"label_format.format.release_timescale_sigma","line":32,"use":"section-read"},{"c":"","k":"label_format.format.scale_factor","line":33,"use":"section-read"},{"c":"","k":"label_format.format.well_fill_alpha","line":34,"use":"section-read"},{"c":"","k":"label_format.format.well_fill_gamma","line":35,"use":"section-read"},{"c":"","k":"label_format.format.well_fill_power","line":36,"use":"section-read"},{"c":"","k":"label_format.format.well_notch_depth","line":37,"use":"section-read"}],"lines":37,"path":"notation.yaml","prior":false,"priors":[],"repo":"PyAutoCTI","text":"label:\n  label:\n    density: \\rho\n    full_well_depth: h\n    gamma: \\gamma\n    ka: ka\n    kv: kv\n    omega: \\omega\n    release_timescale: \\tau\n    release_timescale_sigma: \\sigma\n    scale_factor: \\omega\n    well_fill_alpha: \\alpha\n    well_fill_gamma: \\gamma\n    well_fill_power: \\beta\n    well_notch_depth: d\n  superscript:\n    CCDComplex: CCD\n    CCDPhase: CCD\n    HyperCINoiseScalar: H\n    PixelBounce: pb\n    TrapInstantCapture: s\n    TrapInstantCaptureContinuum: sc\nlabel_format:\n  format:\n    density: '{:.2f}'\n    full_well_depth: '{:.2f}'\n    gamma: '{:.2f}'\n    ka: '{:.2f}'\n    kv: '{:.2f}'\n    omega: '{:.2f}'\n    release_timescale: '{:.2f}'\n    release_timescale_sigma: '{:.2f}'\n    scale_factor: '{:.2f}'\n    well_fill_alpha: '{:.2f}'\n    well_fill_gamma: '{:.2f}'\n    well_fill_power: '{:.2f}'\n    well_notch_depth: '{:.2f}'\n","tooling":false,"top_keys":["label","label_format"],"use_counts":{"section-read":32,"unused":0,"used":5}},{"error":null,"keys":[{"c":"","k":"CCDPhase","line":1},{"c":"","k":"CCDPhase.full_well_depth","line":2},{"c":"","k":"CCDPhase.full_well_depth.type","line":3},{"c":"","k":"CCDPhase.full_well_depth.lower_limit","line":4},{"c":"","k":"CCDPhase.full_well_depth.upper_limit","line":5},{"c":"","k":"CCDPhase.full_well_depth.width_modifier","line":6},{"c":"","k":"CCDPhase.full_well_depth.width_modifier.type","line":7},{"c":"","k":"CCDPhase.full_well_depth.width_modifier.value","line":8},{"c":"","k":"CCDPhase.full_well_depth.limits","line":9},{"c":"","k":"CCDPhase.full_well_depth.limits.lower","line":10},{"c":"","k":"CCDPhase.full_well_depth.limits.upper","line":11},{"c":"","k":"CCDPhase.well_fill_power","line":12},{"c":"","k":"CCDPhase.well_fill_power.type","line":13},{"c":"","k":"CCDPhase.well_fill_power.lower_limit","line":14},{"c":"","k":"CCDPhase.well_fill_power.upper_limit","line":15},{"c":"","k":"CCDPhase.well_fill_power.width_modifier","line":16},{"c":"","k":"CCDPhase.well_fill_power.width_modifier.type","line":17},{"c":"","k":"CCDPhase.well_fill_power.width_modifier.value","line":18},{"c":"","k":"CCDPhase.well_fill_power.limits","line":19},{"c":"","k":"CCDPhase.well_fill_power.limits.lower","line":20},{"c":"","k":"CCDPhase.well_fill_power.limits.upper","line":21},{"c":"","k":"CCDPhase.well_notch_depth","line":22},{"c":"","k":"CCDPhase.well_notch_depth.type","line":23},{"c":"","k":"CCDPhase.well_notch_depth.lower_limit","line":24},{"c":"","k":"CCDPhase.well_notch_depth.upper_limit","line":25},{"c":"","k":"CCDPhase.well_notch_depth.width_modifier","line":26},{"c":"","k":"CCDPhase.well_notch_depth.width_modifier.type","line":27},{"c":"","k":"CCDPhase.well_notch_depth.width_modifier.value","line":28},{"c":"","k":"CCDPhase.well_notch_depth.limits","line":29},{"c":"","k":"CCDPhase.well_notch_depth.limits.lower","line":30},{"c":"","k":"CCDPhase.well_notch_depth.limits.upper","line":31},{"c":"","k":"CCDPhase.first_electron_fill","line":32},{"c":"","k":"CCDPhase.first_electron_fill.type","line":33},{"c":"","k":"CCDPhase.first_electron_fill.value","line":34}],"lines":34,"path":"priors/ccd.yaml","prior":true,"priors":[{"a":"lower 0.0","b":"upper 200000.0","cls":"CCDPhase","limits":"[0.0, 1.0]","line":2,"param":"full_well_depth","type":"Uniform","width":"Absolute 0.2"},{"a":"lower 0.0","b":"upper 1.0","cls":"CCDPhase","limits":"[0.0, 1.0]","line":12,"param":"well_fill_power","type":"Uniform","width":"Absolute 0.2"},{"a":"lower 0.0","b":"upper 1.0","cls":"CCDPhase","limits":"[0.0, 1.0]","line":22,"param":"well_notch_depth","type":"Uniform","width":"Absolute 0.2"},{"a":"value 0.0","b":"","cls":"CCDPhase","limits":"","line":32,"param":"first_electron_fill","type":"Constant","width":""}],"repo":"PyAutoCTI","text":"CCDPhase:\n  full_well_depth:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 200000.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: 1.0\n  well_fill_power:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: 1.0\n  well_notch_depth:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: 1.0\n  first_electron_fill:\n    type: Constant\n    value: 0.0","tooling":false,"top_keys":["CCDPhase"]},{"error":null,"keys":[{"c":"","k":"HyperCINoiseScalar","line":1},{"c":"","k":"HyperCINoiseScalar.scale_factor","line":2},{"c":"","k":"HyperCINoiseScalar.scale_factor.type","line":3},{"c":"","k":"HyperCINoiseScalar.scale_factor.lower_limit","line":4},{"c":"","k":"HyperCINoiseScalar.scale_factor.upper_limit","line":5},{"c":"","k":"HyperCINoiseScalar.scale_factor.width_modifier","line":6},{"c":"","k":"HyperCINoiseScalar.scale_factor.width_modifier.type","line":7},{"c":"","k":"HyperCINoiseScalar.scale_factor.width_modifier.value","line":8},{"c":"","k":"HyperCINoiseScalar.scale_factor.limits","line":9},{"c":"","k":"HyperCINoiseScalar.scale_factor.limits.lower","line":10},{"c":"","k":"HyperCINoiseScalar.scale_factor.limits.upper","line":11}],"lines":11,"path":"priors/hyper.yaml","prior":true,"priors":[{"a":"lower 0.0","b":"upper 10.0","cls":"HyperCINoiseScalar","limits":"[0.0, inf]","line":2,"param":"scale_factor","type":"Uniform","width":"Relative 0.5"}],"repo":"PyAutoCTI","text":"HyperCINoiseScalar:\n  scale_factor:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 10.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["HyperCINoiseScalar"]},{"error":null,"keys":[{"c":"","k":"TrapInstantCapture","line":1},{"c":"","k":"TrapInstantCapture.density","line":2},{"c":"","k":"TrapInstantCapture.density.type","line":3},{"c":"","k":"TrapInstantCapture.density.lower_limit","line":4},{"c":"","k":"TrapInstantCapture.density.upper_limit","line":5},{"c":"","k":"TrapInstantCapture.density.width_modifier","line":6},{"c":"","k":"TrapInstantCapture.density.width_modifier.type","line":7},{"c":"","k":"TrapInstantCapture.density.width_modifier.value","line":8},{"c":"","k":"TrapInstantCapture.density.limits","line":9},{"c":"","k":"TrapInstantCapture.density.limits.lower","line":10},{"c":"","k":"TrapInstantCapture.density.limits.upper","line":11},{"c":"","k":"TrapInstantCapture.release_timescale","line":12},{"c":"","k":"TrapInstantCapture.release_timescale.type","line":13},{"c":"","k":"TrapInstantCapture.release_timescale.lower_limit","line":14},{"c":"","k":"TrapInstantCapture.release_timescale.upper_limit","line":15},{"c":"","k":"TrapInstantCapture.release_timescale.width_modifier","line":16},{"c":"","k":"TrapInstantCapture.release_timescale.width_modifier.type","line":17},{"c":"","k":"TrapInstantCapture.release_timescale.width_modifier.value","line":18},{"c":"","k":"TrapInstantCapture.release_timescale.limits","line":19},{"c":"","k":"TrapInstantCapture.release_timescale.limits.lower","line":20},{"c":"","k":"TrapInstantCapture.release_timescale.limits.upper","line":21},{"c":"","k":"TrapInstantCapture.fractional_volume_full_exposed","line":22},{"c":"","k":"TrapInstantCapture.fractional_volume_full_exposed.type","line":23},{"c":"","k":"TrapInstantCapture.fractional_volume_full_exposed.value","line":24},{"c":"","k":"TrapInstantCapture.fractional_volume_none_exposed","line":25},{"c":"","k":"TrapInstantCapture.fractional_volume_none_exposed.type","line":26},{"c":"","k":"TrapInstantCapture.fractional_volume_none_exposed.value","line":27},{"c":"","k":"TrapInstantCaptureContinuum","line":28},{"c":"","k":"TrapInstantCaptureContinuum.density","line":29},{"c":"","k":"TrapInstantCaptureContinuum.density.type","line":30},{"c":"","k":"TrapInstantCaptureContinuum.density.lower_limit","line":31},{"c":"","k":"TrapInstantCaptureContinuum.density.upper_limit","line":32},{"c":"","k":"TrapInstantCaptureContinuum.density.width_modifier","line":33},{"c":"","k":"TrapInstantCaptureContinuum.density.width_modifier.type","line":34},{"c":"","k":"TrapInstantCaptureContinuum.density.width_modifier.value","line":35},{"c":"","k":"TrapInstantCaptureContinuum.density.limits","line":36},{"c":"","k":"TrapInstantCaptureContinuum.density.limits.lower","line":37},{"c":"","k":"TrapInstantCaptureContinuum.density.limits.upper","line":38},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale","line":39},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale.type","line":40},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale.lower_limit","line":41},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale.upper_limit","line":42},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale.width_modifier","line":43},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale.width_modifier.type","line":44},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale.width_modifier.value","line":45},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale.limits","line":46},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale.limits.lower","line":47},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale.limits.upper","line":48},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale_sigma","line":49},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale_sigma.type","line":50},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale_sigma.lower_limit","line":51},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale_sigma.upper_limit","line":52},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale_sigma.width_modifier","line":53},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale_sigma.width_modifier.type","line":54},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale_sigma.width_modifier.value","line":55},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale_sigma.limits","line":56},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale_sigma.limits.lower","line":57},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale_sigma.limits.upper","line":58}],"lines":58,"path":"priors/traps.yaml","prior":true,"priors":[{"a":"lower 0.0","b":"upper 10.0","cls":"TrapInstantCapture","limits":"[0.0, inf]","line":2,"param":"density","type":"Uniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 50.0","cls":"TrapInstantCapture","limits":"[0.0, inf]","line":12,"param":"release_timescale","type":"Uniform","width":"Relative 0.5"},{"a":"value 0.0","b":"","cls":"TrapInstantCapture","limits":"","line":22,"param":"fractional_volume_full_exposed","type":"Constant","width":""},{"a":"value 0.0","b":"","cls":"TrapInstantCapture","limits":"","line":25,"param":"fractional_volume_none_exposed","type":"Constant","width":""},{"a":"lower 0.0","b":"upper 10.0","cls":"TrapInstantCaptureContinuum","limits":"[0.0, inf]","line":29,"param":"density","type":"Uniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 50.0","cls":"TrapInstantCaptureContinuum","limits":"[0.0, inf]","line":39,"param":"release_timescale","type":"Uniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 1.0","cls":"TrapInstantCaptureContinuum","limits":"[0.0, inf]","line":49,"param":"release_timescale_sigma","type":"Uniform","width":"Relative 0.5"}],"repo":"PyAutoCTI","text":"TrapInstantCapture:\n  density:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 10.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  release_timescale:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 50.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  fractional_volume_full_exposed:\n    type: Constant\n    value: 0.0\n  fractional_volume_none_exposed:\n    type: Constant\n    value: 0.0\nTrapInstantCaptureContinuum:\n  density:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 10.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  release_timescale:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 50.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  release_timescale_sigma:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["TrapInstantCapture","TrapInstantCaptureContinuum"]},{"error":null,"keys":[{"c":"","k":"general","line":1,"use":"used"},{"c":"","k":"general.general","line":2,"use":"used"},{"c":"The matploblib backend used for visualization. `default` uses the system default, can specifiy specific backend (e.g. TKAgg, Qt5Agg, WXAgg).","k":"general.general.backend","line":3,"use":"section-read"},{"c":"The `origin` input of `imshow`, determining if pixel values are ascending or descending on the y-axis.","k":"general.general.imshow_origin","line":4,"use":"used"},{"c":"If True, plots of data structures with a mask automatically zoom in the masked region.","k":"general.general.zoom_around_mask","line":5,"use":"used"},{"c":"The vmin and vmax of all pre-cti data residual-maps.","k":"general.general.symmetric_cmap_value","line":6,"use":"used"},{"c":"If True, subplots showing FPR / EPER trails of many datasets are in ascending order of FPR value.","k":"general.general.subplot_ascending_fpr","line":7,"use":"used"},{"c":"","k":"plots","line":8,"use":"used"},{"c":"Output format of all plots, can be png, pdf or both (e.g. [png, pdf]).","k":"plots.subplot_format","line":9,"use":"used"},{"c":"If True, only the combined subplots of multi-dataset analyses are output (no per-dataset visualization).","k":"plots.combined_only","line":10,"use":"used"},{"c":"","k":"plots.dataset","line":11,"use":"used"},{"c":"Plot the subplot of all dataset quantities (2D for charge injection imaging, 1D for Dataset1D)?","k":"plots.dataset.subplot_dataset","line":12,"use":"section-read"},{"c":"Plot per-region binned 1D subplots (e.g. the parallel/serial FPR and EPER)?","k":"plots.dataset.subplot_dataset_regions","line":13,"use":"section-read"},{"c":"Plot single 1D figures of the data extracted and binned over each region?","k":"plots.dataset.data","line":14,"use":"section-read"},{"c":"Plot single 1D figures of the data over each region with a log10 y-axis?","k":"plots.dataset.data_logy","line":15,"use":"section-read"},{"c":"Plot the data binned over rows / columns with and without the FPR (charge injection only)?","k":"plots.dataset.data_binned","line":16,"use":"section-read"},{"c":"Include the fpr_non_uniformity region in the per-region plots (charge injection only)?","k":"plots.dataset.fpr_non_uniformity","line":17,"use":"used"},{"c":"","k":"plots.fit","line":18,"use":"section-read"},{"c":"Plot the subplot of all fit quantities (e.g. model data, residual-map, chi-squared map)?","k":"plots.fit.subplot_fit","line":19,"use":"section-read"},{"c":"Plot per-region binned 1D fit subplots (e.g. the parallel/serial FPR and EPER)?","k":"plots.fit.subplot_fit_regions","line":20,"use":"section-read"},{"c":"Plot single 1D figures of the fit data (with model overlay) over each region?","k":"plots.fit.data","line":21,"use":"section-read"},{"c":"Plot single 1D figures of the fit data over each region with a log10 y-axis?","k":"plots.fit.data_logy","line":22,"use":"section-read"},{"c":"Plot single 1D figures of the residual map over each region?","k":"plots.fit.residual_map","line":23,"use":"section-read"},{"c":"Plot single 1D figures of the residual map over each region with a log10 y-axis?","k":"plots.fit.residual_map_logy","line":24,"use":"section-read"},{"c":"Output a fit.fits file containing the model data, residual map, normalized residual map and chi-squared map?","k":"plots.fit.fits_fit","line":25,"use":"section-read"}],"lines":25,"path":"visualize.yaml","prior":false,"priors":[],"repo":"PyAutoCTI","text":"general:\n  general:\n    backend: default                  # The matploblib backend used for visualization. `default` uses the system default, can specifiy specific backend (e.g. TKAgg, Qt5Agg, WXAgg).\n    imshow_origin: upper              # The `origin` input of `imshow`, determining if pixel values are ascending or descending on the y-axis.\n    zoom_around_mask: true            # If True, plots of data structures with a mask automatically zoom in the masked region.\n    symmetric_cmap_value: 100.0       # The vmin and vmax of all pre-cti data residual-maps.\n    subplot_ascending_fpr: true       # If True, subplots showing FPR / EPER trails of many datasets are in ascending order of FPR value.\nplots:\n  subplot_format: [png]                     # Output format of all plots, can be png, pdf or both (e.g. [png, pdf]).\n  combined_only: false                      # If True, only the combined subplots of multi-dataset analyses are output (no per-dataset visualization).\n  dataset:\n    subplot_dataset: true                   # Plot the subplot of all dataset quantities (2D for charge injection imaging, 1D for Dataset1D)?\n    subplot_dataset_regions: true           # Plot per-region binned 1D subplots (e.g. the parallel/serial FPR and EPER)?\n    data: true                              # Plot single 1D figures of the data extracted and binned over each region?\n    data_logy: true                         # Plot single 1D figures of the data over each region with a log10 y-axis?\n    data_binned: true                       # Plot the data binned over rows / columns with and without the FPR (charge injection only)?\n    fpr_non_uniformity: false               # Include the fpr_non_uniformity region in the per-region plots (charge injection only)?\n  fit:\n    subplot_fit: true                       # Plot the subplot of all fit quantities (e.g. model data, residual-map, chi-squared map)?\n    subplot_fit_regions: true               # Plot per-region binned 1D fit subplots (e.g. the parallel/serial FPR and EPER)?\n    data: true                              # Plot single 1D figures of the fit data (with model overlay) over each region?\n    data_logy: true                         # Plot single 1D figures of the fit data over each region with a log10 y-axis?\n    residual_map: true                      # Plot single 1D figures of the residual map over each region?\n    residual_map_logy: true                 # Plot single 1D figures of the residual map over each region with a log10 y-axis?\n    fits_fit: true                          # Output a fit.fits file containing the model data, residual map, normalized residual map and chi-squared map?\n","tooling":false,"top_keys":["general","plots"],"use_counts":{"section-read":14,"unused":0,"used":11}},{"error":null,"keys":[{"c":"execution order and index order). autofit has no root start_here.py; the overview scripts are the flagships (1D-Gaussian fits \u2014 seconds each).","k":"script","line":8},{"c":"","k":"max_minutes","line":9},{"c":"","k":"script","line":10},{"c":"","k":"max_minutes","line":11},{"c":"","k":"script","line":12},{"c":"","k":"max_minutes","line":13}],"lines":13,"path":"build/markdown_examples.yaml","prior":false,"priors":[],"repo":"autofit_workspace","text":"# Curated examples rendered to executed markdown pages (markdown/) with their\n# real output images, so they can be read on GitHub. Built manually by\n# PyAutoHands's generate_markdown.py \u2014 see that module's docstring for the\n# rules (never TEST_MODE; features/ scripts never rendered; list order is\n# execution order and index order).\n# autofit has no root start_here.py; the overview scripts are the flagships\n# (1D-Gaussian fits \u2014 seconds each).\n- script: scripts/overview/overview_1_the_basics.py\n  max_minutes: 30\n- script: scripts/overview/overview_2_scientific_workflow.py\n  max_minutes: 45\n- script: scripts/overview/overview_3_statistical_methods.py\n  max_minutes: 45\n","tooling":true,"top_keys":["script","max_minutes","script","max_minutes","script","max_minutes"]},{"error":null,"keys":[],"lines":26,"path":"build/no_run.yaml","prior":false,"priors":[],"repo":"autofit_workspace","text":"# Scripts to skip during automated runs (smoke tests, pre-release checks, CI).\n# Each entry is matched against script paths:\n#   - Entries with '/' do a substring match against the file path\n#   - Entries without '/' match the file stem exactly\n# Add an inline # comment to document the reason for skipping.\n#\n# SLOW-skip convention:\n#   Entries tagged `# SLOW <YYYY-MM-DD> - <reason>` mark scripts that are\n#   skipped because they exceed the per-script timeout cap (300s by\n#   default; 1800s for mode=release runs). These are\n#   NOT permanent skips \u2014 every mega-run surfaces them with a loud warning\n#   banner. Fix the performance issue and remove the SLOW marker.\n#\n# NEEDS_FIX convention:\n#   Entries tagged `# NEEDS_FIX <YYYY-MM-DD> - <reason>` mark scripts that\n#   are broken and parked as a to-do list. Like SLOW-skips, these are NOT\n#   permanent skips \u2014 every mega-run surfaces them with a loud warning\n#   banner. Investigate the failure, fix the underlying bug, and remove\n#   the NEEDS_FIX marker.\n\n- get_dist # Cant get it to install, even in optional requirements.\n- mcmc # Zeus section in merged mcmc.py fails Test Model Initialization.\n- zeus_plotter # Test Model Iniitalization no good.\n- dynesty_plotter # Test Model Iniitalization no good.\n- start_point # bug https://github.com/rhayes777/PyAutoFit/issues/1017\n- features/expectation_propagation.py # NEEDS_FIX 2026-08-03 - EP parked as not release-ready. EP message projection is unstable: a truncated per-factor search projects an ESS=1 posterior with zero weighted variance, which EP feeds back as a delta-function prior. See PyAutoFit #1332 F10 and autofit_workspace_test graphical/ep.py.\n","tooling":true,"top_keys":[]},{"error":null,"keys":[{"c":"","k":"defaults","line":28},{"c":"reduced iterations (real sampler), not bypassed","k":"defaults.PYAUTO_TEST_MODE","line":29},{"c":"real fit output (release fidelity, not smoke)","k":"defaults.PYAUTO_SKIP_FIT_OUTPUT","line":30},{"c":"real visualization (release fidelity, not smoke)","k":"defaults.PYAUTO_SKIP_VISUALIZATION","line":31},{"c":"real checks (release fidelity, not smoke)","k":"defaults.PYAUTO_SKIP_CHECKS","line":32},{"c":"JAX enabled (release fidelity, not smoke)","k":"defaults.PYAUTO_DISABLE_JAX","line":33},{"c":"TestPyPI dev version won't match the workspace pin","k":"defaults.PYAUTO_SKIP_WORKSPACE_VERSION_CHECK","line":34},{"c":"enable 64-bit precision in JAX","k":"defaults.JAX_ENABLE_X64","line":35},{"c":"writable cache dir for numba","k":"defaults.NUMBA_CACHE_DIR","line":36},{"c":"writable config dir for matplotlib","k":"defaults.MPLCONFIGDIR","line":37},{"c":"real (if reduced) sampler and PYAUTO_SKIP_FIT_OUTPUT=\"0\" already writes real output. No script in this workspace needs to deviate from the defaults above under the release profile.","k":"overrides","line":45}],"lines":45,"path":"build/profile_release.yaml","prior":false,"priors":[],"repo":"autofit_workspace","text":"# Per-script environment variable configuration for the RELEASE-FIDELITY\n# validation run (Heart's workspace-validation.yml, mode=release \u2014 the M3\n# wheel-based release-fidelity path). Distinct from profile_smoke.yaml, which is the\n# `smoke` profile used by the per-PR CI gate.\n#\n# The `release` profile trades speed for fidelity: it is run once per release\n# rehearsal against the TestPyPI wheels, not on every PR, so it can afford a\n# reduced (not bypassed) sampler and real fit output/visualization/checks.\n# Spec + acceptance table: PyAutoHeart/docs/release_validation.md.\n#\n# \"defaults\" are applied to every script on top of the inherited environment.\n# Every var this profile cares about is given an EXPLICIT value in \"defaults\"\n# (not left absent) \u2014 the runner only ever *sets* keys it's given, it never\n# clears unrelated inherited env vars, so an absent key silently falls through\n# to whatever the calling process already had (a leftover smoke-mode \"1\" from\n# an earlier step, a developer's local shell, ...). Pinning everything here\n# makes the profile self-contained regardless of the caller's environment.\n#\n# \"overrides\" then only ever `set:` a var to flip it away from this profile's\n# own default for specific scripts \u2014 never `unset:` a var that \"defaults\"\n# already pins, since unsetting just re-exposes the same inherited-env gap\n# `defaults` exists to close.\n#\n# Pattern convention (same as no_run.yaml):\n#   - Patterns containing '/' do a substring match against the file path\n#   - Patterns without '/' match the file stem exactly\n\ndefaults:\n  PYAUTO_TEST_MODE: \"1\"                     # reduced iterations (real sampler), not bypassed\n  PYAUTO_SKIP_FIT_OUTPUT: \"0\"               # real fit output (release fidelity, not smoke)\n  PYAUTO_SKIP_VISUALIZATION: \"0\"            # real visualization (release fidelity, not smoke)\n  PYAUTO_SKIP_CHECKS: \"0\"                   # real checks (release fidelity, not smoke)\n  PYAUTO_DISABLE_JAX: \"0\"                   # JAX enabled (release fidelity, not smoke)\n  PYAUTO_SKIP_WORKSPACE_VERSION_CHECK: \"1\"  # TestPyPI dev version won't match the workspace pin\n  JAX_ENABLE_X64: \"True\"                    # enable 64-bit precision in JAX\n  NUMBA_CACHE_DIR: \"/tmp/numba_cache\"       # writable cache dir for numba\n  MPLCONFIGDIR: \"/tmp/matplotlib\"           # writable config dir for matplotlib\n\n# plot/emcee_plotter + plot/zeus_plotter (which needed an override under the\n# smoke profile to force a real search so `result.search_internal` is\n# populated) need no override here: PYAUTO_TEST_MODE=\"1\" above already runs a\n# real (if reduced) sampler and PYAUTO_SKIP_FIT_OUTPUT=\"0\" already writes\n# real output. No script in this workspace needs to deviate from the\n# defaults above under the release profile.\noverrides: []\n","tooling":true,"top_keys":["defaults","overrides"]},{"error":null,"keys":[{"c":"","k":"defaults","line":11},{"c":"0=normal, 1=reduced iterations, 2=skip sampler (fastest)","k":"defaults.PYAUTO_TEST_MODE","line":12},{"c":"Skip pre/post-fit I/O, VRAM profiling, result text","k":"defaults.PYAUTO_SKIP_FIT_OUTPUT","line":13},{"c":"Skip fit visualization and plotting","k":"defaults.PYAUTO_SKIP_VISUALIZATION","line":14},{"c":"Skip mesh validation, position checks, weight thresholds","k":"defaults.PYAUTO_SKIP_CHECKS","line":15},{"c":"Force use_jax=False, avoid JIT compilation overhead","k":"defaults.PYAUTO_DISABLE_JAX","line":16},{"c":"Enable 64-bit precision in JAX","k":"defaults.JAX_ENABLE_X64","line":17},{"c":"Writable cache dir for numba","k":"defaults.NUMBA_CACHE_DIR","line":18},{"c":"Writable config dir for matplotlib","k":"defaults.MPLCONFIGDIR","line":19},{"c":"","k":"overrides","line":21},{"c":"Plotter scripts need the real search to run so `result.search_internal` is populated \u2014 bypass mode skips the sampler and returns None.","k":"overrides.pattern","line":24},{"c":"","k":"overrides.unset","line":25},{"c":"","k":"overrides.pattern","line":26},{"c":"","k":"overrides.unset","line":27}],"lines":27,"path":"build/profile_smoke.yaml","prior":false,"priors":[],"repo":"autofit_workspace","text":"# Per-script environment variable configuration for automated runs\n# (smoke tests, pre-release checks, CI).\n#\n# \"defaults\" are applied to every script on top of the inherited environment.\n# \"overrides\" selectively unset or replace vars for matching path patterns.\n#\n# Pattern convention (same as no_run.yaml):\n#   - Patterns containing '/' do a substring match against the file path\n#   - Patterns without '/' match the file stem exactly\n\ndefaults:\n  PYAUTO_TEST_MODE: \"2\"                     # 0=normal, 1=reduced iterations, 2=skip sampler (fastest)\n  PYAUTO_SKIP_FIT_OUTPUT: \"1\"               # Skip pre/post-fit I/O, VRAM profiling, result text\n  PYAUTO_SKIP_VISUALIZATION: \"1\"            # Skip fit visualization and plotting\n  PYAUTO_SKIP_CHECKS: \"1\"                   # Skip mesh validation, position checks, weight thresholds\n  PYAUTO_DISABLE_JAX: \"1\"                   # Force use_jax=False, avoid JIT compilation overhead\n  JAX_ENABLE_X64: \"True\"                    # Enable 64-bit precision in JAX\n  NUMBA_CACHE_DIR: \"/tmp/numba_cache\"       # Writable cache dir for numba\n  MPLCONFIGDIR: \"/tmp/matplotlib\"           # Writable config dir for matplotlib\n\noverrides:\n  # Plotter scripts need the real search to run so `result.search_internal` is\n  # populated \u2014 bypass mode skips the sampler and returns None.\n  - pattern: \"plot/emcee_plotter\"\n    unset: [PYAUTO_TEST_MODE, PYAUTO_SKIP_FIT_OUTPUT]\n  - pattern: \"plot/zeus_plotter\"\n    unset: [PYAUTO_TEST_MODE, PYAUTO_SKIP_FIT_OUTPUT]\n","tooling":true,"top_keys":["defaults","overrides"]},{"error":null,"keys":[],"lines":10,"path":"build/visualise_notebooks.yaml","prior":false,"priors":[],"repo":"autofit_workspace","text":"# Notebook stems that should run when PyAutoHands's generate / run pipeline\n# is invoked with --visualise. Used to refresh notebook output cells in main.\n#\n# Format: flat list of notebook stems (no extension, no path).\n# An empty list means no notebooks need re-visualisation in this workspace.\n#\n# This file overrides PyAutoHands/autohands/config/visualise_notebooks.yaml\n# for this workspace. Add or remove entries here, not there.\n\n[]\n","tooling":true,"top_keys":[]},{"error":null,"keys":[{"c":"","k":"updates","line":5},{"c":"Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit.","k":"updates.iterations_per_quick_update","line":6},{"c":"Non-linear search iterations between every full update, which outputs all visuals and result fits (e.g. model.result, search.summary), this exits the search and can be slow.","k":"updates.iterations_per_full_update","line":7},{"c":"If True, perform_quick_update runs on a background daemon thread so the sampler is never blocked while visuals render. Requires the Analysis subclass to set `supports_background_update = True`.","k":"updates.quick_update_background","line":8},{"c":"If True, quick-update visuals are pushed to a live surface (a Jupyter cell when in a kernel, a matplotlib viewer subprocess otherwise) in addition to being written to disk.","k":"updates.live_visual_update","line":9},{"c":"","k":"hpc","line":10},{"c":"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.","k":"hpc.hpc_mode","line":11},{"c":"Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit.","k":"hpc.iterations_per_quick_update","line":12},{"c":"Non-linear search iterations between every full update, which outputs all visuals and result fits (e.g. model.result, search.summary), this exits the search and can be slow.","k":"hpc.iterations_per_full_update","line":13},{"c":"","k":"inversion","line":14},{"c":"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.","k":"inversion.check_reconstruction","line":15},{"c":"Plots of an Inversion's reconstruction use the reconstructed data's bright value multiplied by this factor.","k":"inversion.reconstruction_vmax_factor","line":16},{"c":"","k":"output","line":17},{"c":"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.","k":"output.force_pickle_overwrite","line":18},{"c":"If True, visualization images output by a search (e.g. subplots of the fit) are recreated when a new model-fit is performed.","k":"output.force_visualize_overwrite","line":19},{"c":"Length of whitespace between the parameter names and values in the model.info / result.info","k":"output.info_whitespace_length","line":20},{"c":"The level of information output by logging.","k":"output.log_level","line":21},{"c":"If True, outputs the non-linear search log to a file (and not printed to screen).","k":"output.log_to_file","line":22},{"c":"The name of the file the logged output is written to (in the non-linear search output folder)","k":"output.log_file","line":23},{"c":"Number of decimal places estimated parameter values / errors are output in model.results.","k":"output.model_results_decimal_places","line":24},{"c":"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.","k":"output.remove_files","line":25},{"c":"If True, non-linear search samples are written to a .csv file.","k":"output.samples_to_csv","line":26},{"c":"If outputting results of an unconverged search, the number of samples used to estimate the median PDF values and errors.","k":"output.unconverged_sample_size","line":27},{"c":"","k":"parallel","line":28},{"c":"If True, a warning is displayed when the search's number of CPU > 1 and enviromment variables related to threading are also > 1.","k":"parallel.warn_environment_variables","line":29},{"c":"","k":"profiling","line":30},{"c":"If True, the parallelization of the fit is profiled outputting a cPython graph.","k":"profiling.parallel_profile","line":31},{"c":"The number of repeat function calls used to measure run-times when profiling.","k":"profiling.repeats","line":32},{"c":"","k":"test","line":33},{"c":"","k":"test.exception_override","line":34},{"c":"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.","k":"test.lh_timeout_seconds","line":35},{"c":"","k":"test.parallel_profile","line":36},{"c":"","k":"version","line":37},{"c":"(autonerves/workspace.py). Bump DELIBERATELY \u2014 only when a script starts needing new API \u2014 never per release. Must always name an INSTALLABLE (non-yanked) release.","k":"version.minimum_library_version","line":43},{"c":"If False, bypass the workspace/library version check. Set to False on `main`-branch clones \u2014 `main` updates faster than releases, so mismatches are expected and not actionable.","k":"version.workspace_version_check","line":44}],"lines":44,"path":"general.yaml","prior":false,"priors":[],"repo":"autofit_workspace","text":"# version:\n#   python_version_check: False  # uncomment to suppress the Python version warning\n#                                # if running on a non-recommended Python (anything other than 3.12 / 3.13).\n\nupdates:\n  iterations_per_quick_update: 1e99 # Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit.\n  iterations_per_full_update: 1e99  # Non-linear search iterations between every full update, which outputs all visuals and result fits (e.g. model.result, search.summary), this exits the search and can be slow.\n  quick_update_background: false    # If True, perform_quick_update runs on a background daemon thread so the sampler is never blocked while visuals render. Requires the Analysis subclass to set `supports_background_update = True`.\n  live_visual_update: false         # If True, quick-update visuals are pushed to a live surface (a Jupyter cell when in a kernel, a matplotlib viewer subprocess otherwise) in addition to being written to disk.\nhpc:\n  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.\n  iterations_per_quick_update: 10000 #  Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit.\n  iterations_per_full_update: 1e99  # Non-linear search iterations between every full update, which outputs all visuals and result fits (e.g. model.result, search.summary), this exits the search and can be slow.\ninversion:\n  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.\n  reconstruction_vmax_factor: 0.5   # Plots of an Inversion's reconstruction use the reconstructed data's bright value multiplied by this factor.  \noutput:\n  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.\n  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.\n  info_whitespace_length: 80        # Length of whitespace between the parameter names and values in the model.info / result.info\n  log_level: INFO                   # The level of information output by logging.\n  log_to_file: false                # If True, outputs the non-linear search log to a file (and not printed to screen).\n  log_file: output.log              # The name of the file the logged output is written to (in the non-linear search output folder)\n  model_results_decimal_places: 3   # Number of decimal places estimated parameter values / errors are output in model.results.\n  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.\n  samples_to_csv: true              # If True, non-linear search samples are written to a .csv file.\n  unconverged_sample_size : 100     # If outputting results of an unconverged search, the number of samples used to estimate the median PDF values and errors.\nparallel:\n  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.\nprofiling:\n  parallel_profile: false           # If True, the parallelization of the fit is profiled outputting a cPython graph.\n  repeats: 1                        # The number of repeat function calls used to measure run-times when profiling.\ntest:\n  exception_override: false\n  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.\n  parallel_profile: false\nversion:\n  # The compatibility FLOOR: the oldest library release whose API this\n  # workspace's scripts require. Preferred over workspace_version\n  # (autonerves/workspace.py). Bump DELIBERATELY \u2014 only when a script\n  # starts needing new API \u2014 never per release. Must always name an\n  # INSTALLABLE (non-yanked) release.\n  minimum_library_version: 2026.7.9.1\n  workspace_version_check: True     # If False, bypass the workspace/library version check. Set to False on `main`-branch clones \u2014 `main` updates faster than releases, so mismatches are expected and not actionable.\n","tooling":false,"top_keys":["updates","hpc","inversion","output","parallel","profiling","test","version"]},{"error":null,"keys":[{"c":"","k":"version","line":1},{"c":"","k":"disable_existing_loggers","line":2},{"c":"","k":"handlers","line":4},{"c":"","k":"handlers.console","line":5},{"c":"","k":"handlers.console.class","line":6},{"c":"","k":"handlers.console.level","line":7},{"c":"","k":"handlers.console.stream","line":8},{"c":"","k":"handlers.console.formatter","line":9},{"c":"","k":"root","line":11},{"c":"","k":"root.level","line":12},{"c":"","k":"root.handlers","line":13},{"c":"","k":"formatters","line":15},{"c":"","k":"formatters.formatter","line":16},{"c":"","k":"formatters.formatter.format","line":17}],"lines":17,"path":"logging.yaml","prior":false,"priors":[],"repo":"autofit_workspace","text":"version: 1\ndisable_existing_loggers: false\n\nhandlers:\n  console:\n    class: logging.StreamHandler\n    level: INFO\n    stream: ext://sys.stdout\n    formatter: formatter\n\nroot:\n  level: INFO\n  handlers: [ console ]\n\nformatters:\n  formatter:\n    format: '%(asctime)s - %(name)s - %(levelname)s - %(message)s'\n","tooling":false,"top_keys":["version","disable_existing_loggers","handlers","root","formatters"]},{"error":null,"keys":[{"c":"","k":"parallel","line":3},{"c":"The number of cores the search is parallelized over by default, using Python multiprocessing.","k":"parallel.number_of_cores","line":4},{"c":"The default step size of each grid search parameter, in terms of unit values of the priors.","k":"parallel.step_size","line":5}],"lines":5,"path":"non_linear/GridSearch.yaml","prior":false,"priors":[],"repo":"autofit_workspace","text":"# The settings of a parallelized grid search of non-linear searches.\n\nparallel:\n  number_of_cores: 3 # The number of cores the search is parallelized over by default, using Python multiprocessing.\n  step_size: 0.1     # The default step size of each grid search parameter, in terms of unit values of the priors.","tooling":false,"top_keys":["parallel"]},{"error":null,"keys":[{"c":"","k":"label","line":14},{"c":"","k":"label.label","line":15},{"c":"","k":"label.label.sigma","line":16},{"c":"","k":"label.label.centre","line":17},{"c":"","k":"label.label.normalization","line":18},{"c":"","k":"label.label.parameter0","line":19},{"c":"","k":"label.label.parameter1","line":20},{"c":"","k":"label.label.parameter2","line":21},{"c":"","k":"label.label.rate","line":22},{"c":"","k":"label.superscript","line":23},{"c":"","k":"label.superscript.Exponential","line":24},{"c":"","k":"label.superscript.Gaussian","line":25},{"c":"","k":"label.superscript.ModelComponent0","line":26},{"c":"","k":"label.superscript.ModelComponent1","line":27},{"c":"","k":"label_format","line":33},{"c":"","k":"label_format.format","line":34},{"c":"","k":"label_format.format.sigma","line":35},{"c":"","k":"label_format.format.centre","line":36},{"c":"","k":"label_format.format.normalization","line":37},{"c":"","k":"label_format.format.parameter0","line":38},{"c":"","k":"label_format.format.parameter1","line":39},{"c":"","k":"label_format.format.parameter2","line":40},{"c":"","k":"label_format.format.rate","line":41}],"lines":41,"path":"notation.yaml","prior":false,"priors":[],"repo":"autofit_workspace","text":"# The notation configs define the labels of every model parameter and its derived quantities, which are used when\n# visualizing results (for example labeling the axis of the PDF triangle plots output by a non-linear search).\n\n\n# label: The label given to the each parameter, for plots like PDF corner plots.\n\n# For example, if `centre=x`, the plot axis will be labeled 'x'.\n\n\n# superscript: the superscript used on certain plots that show the results of different model-components.\n\n# For example, if `Gaussian=g`, plots where the parameters of the Gaussian model-component have superscript `g`.\n\nlabel:\n  label:\n    sigma: \\sigma\n    centre: x\n    normalization: norm\n    parameter0: a\n    parameter1: b\n    parameter2: c\n    rate: \\lambda\n  superscript:\n    Exponential: e\n    Gaussian: g\n    ModelComponent0: M0\n    ModelComponent1: M1\n\n# label_format: The format certain parameters are output as in output files like the `model.results` file.\n\n# For example, if `centre={:.2d}`, the format of the centre parameter in results files will use this Python format.\n\nlabel_format:\n  format:\n    sigma: '{:.2f}'\n    centre: '{:.2f}'\n    normalization: '{:.2f}'\n    parameter0: '{:.2f}'\n    parameter1: '{:.2f}'\n    parameter2: '{:.2f}'\n    rate: '{:.2f}'\n","tooling":false,"top_keys":["label","label_format"]},{"error":null,"keys":[{"c":"If true then files which are not explicitly listed here are output anyway. If false then they are not.","k":"default","line":4},{"c":"","k":"samples","line":17},{"c":"","k":"samples_weight_threshold","line":34},{"c":"","k":"search_internal","line":56},{"c":"","k":"start_point","line":65},{"c":"Whether to output the `latent.csv`, `latent.results` and `latent_summary.json` files during the fit when it performs on-the-fly output.","k":"latent_during_fit","line":89},{"c":"If `latent_during_fit` is False, whether to output the `latent.csv`, `latent.results` and `latent_summary.json` files after the fit is complete.","k":"latent_after_fit","line":90},{"c":"Whether to draw latent variable values via the PDF of every sample, which uses fewer samples to estimate latent variable errors. If False, latent variable values are drawn from every sample.","k":"latent_draw_via_pdf","line":91},{"c":"The number of samples drawn to estimate latent variable errors if `latent_draw_via_pdf` is True.","k":"latent_draw_via_pdf_size","line":92},{"c":"Whether to ouptut the `latent.csv` file.","k":"latent_csv","line":93},{"c":"Whether to output the `latent.results` file.","k":"latent_results","line":94},{"c":"`covariance.csv`: The [free parameters x free parameters] covariance matrix.","k":"covariance","line":98},{"c":"`data.json`: The value of every data point in the data.","k":"data","line":99},{"c":"`noise_map.json`: The value of every RMS noise map value.","k":"noise_map","line":100},{"c":"`search.log`: logging produced whilst running the fit method","k":"search_log","line":102},{"c":"`model.graph`: graphical-model factor graph summary. Only meaningful for graphical models (autofit.graphical); empty for ordinary PriorModel/Collection fits, so disabled by default.","k":"model_graph","line":104},{"c":"`model.png`: the model figure drawn beside `model.info` (af.ModelPlotter). Opt-in until the model-figures epic's phase-3 acceptance renders pass. Unlike other keys an absent key means off.","k":"model_figure","line":108}],"lines":108,"path":"output.yaml","prior":false,"priors":[],"repo":"autofit_workspace","text":"# Determines whether files saved by the search are output to the hard-disk. This is true both when saving to the\n# directory structure and when saving to database.\n\ndefault: true # If true then files which are not explicitly listed here are output anyway. If false then they are not.\n\n### Samples ###\n\n# The `samples.csv`file contains every sampled value of every free parameter with its log likelihood and weight.\n\n# This file is often large, therefore disabling it can significantly reduce hard-disk space use.\n\n# `samples.csv` is used to perform marginalization, infer model parameter errors and do other analysis of the search\n# chains. Even if output of `samples.csv` is disabled, these tasks are still performed by the fit and output to\n# the `samples_summary.json` file. However, without a `samples.csv` file these types of tasks cannot be performed\n# after the fit is complete, for example via the database.\n\nsamples: true\n\n# The `samples.csv` file contains every accepted sampled value of every free parameter with its log likelihood and\n# weight. For certain searches, the majority of samples have a very low weight and have no numerical impact on the\n# results of the model-fit. However, these samples are still output to the `samples.csv` file, taking up hard-disk\n# space and slowing down analysis of the samples (e.g. via the database).\n\n# The `samples_weight_threshold` below specifies the threshold value of the weight such that samples with a weight\n# below this value are not output to the `samples.csv` file. This can be used to reduce the size of the `samples.csv`\n# file and speed up analysis of the samples.\n\n# For many searches (e.g. MCMC) all samples have an equal weight of 1.0, and this threshold therefore has no impact.\n# For these searches, there is no simple way to save hard-disk space. This input is more suited to nested sampling,\n# where the majority of samples have a very low weight..\n\n# Set value to empty (e.g. delete 1.0e-10 below) to disable this feature.\n\nsamples_weight_threshold: 1.0e-10\n\n### Search Internal ###\n\n# The search internal folder which contains a saved state of the non-linear search in its internal reprsenetation,\n# as a .pickle or .dill file.\n\n# For example, for the nested sampling dynesty, this .dill file is the `DynestySampler` object which is used to\n# perform sampling, and it therefore contains all internal dynesty representations of the results, samples, weights, etc.\n\n# If the entry below is false, the folder is still output during the model-fit, as it is required to resume the fit\n# from where it left off. Therefore, settings `false` below does not impact model-fitting checkpointing and resumption.\n# Instead, the search internal folder is deleted once the fit is completed.\n\n# The search internal folder file is often large, therefore deleting it after a fit is complete can significantly\n# reduce hard-disk space use.\n\n# The search internal representation that can be loaded from the .dill file has many additional quantities specific to\n# the non-linear search that the standardized autofit forms do not. For example, for emcee, it contains information on\n# every walker. This information is required to do certain analyes and make certain plots, therefore deleting the\n# folder means this information is list.\n\nsearch_internal: false\n\n### Start Point ###\n\n# If an Initalizer is used to provide a start point for the non-linear search, visualization of that start point can be\n# output to hard-disk to show the user the initial model-fit that is used to start the search. This visualization is\n# the visualizer wrapped in the Analysis class, and therefore should show things like the quality of the fit\n# to the data and the residuals at the start point.\n\nstart_point: true\n\n### Latent Variables ###\n\n# A latent variable is not a model parameter but can be derived from the model. Its value and errors may be of interest\n# and aid in the interpretation of a model-fit.\n\n# For example, for the simple 1D Gaussian example, it could be the full-width half maximum (FWHM) of the Gaussian. This\n# is not included in the model but can be easily derived from the Gaussian's sigma value.\n\n# By overwriting an Analysis class's `compute_latent_variables` method we can manually specify latent variables that\n# are calculated and output to a `latent.csv` file, which mirrors the `samples.csv` file. The `latent.csv` file has\n# the same weight resampling performed on the `samples.csv` file, controlled via the `samples_weight_threshold` above.\n\n# There may also be a `latent.results` and `latent_summary.json` files output, which the inputs below control whether\n# they are output and how often.\n\n# Outputting latent variables manually after a fit is complete is simple, just call\n# the `analysis.compute_latent_variables()` function.\n\n# For many use cases, the best set up may be to disable autofit latent variable output during the fit and perform it\n# manually after completing a successful model-fit. This will save computational run time by not computing latent\n# variables during a any model-fit which is unsuccessful.\n\nlatent_during_fit: false # Whether to output the `latent.csv`, `latent.results` and `latent_summary.json` files during the fit when it performs on-the-fly output.\nlatent_after_fit: true # If `latent_during_fit` is False, whether to output the `latent.csv`, `latent.results` and `latent_summary.json` files after the fit is complete.\nlatent_draw_via_pdf : true # Whether to draw latent variable values via the PDF of every sample, which uses fewer samples to estimate latent variable errors. If False, latent variable values are drawn from every sample.\nlatent_draw_via_pdf_size : 100 # The number of samples drawn to estimate latent variable errors if `latent_draw_via_pdf` is True.\nlatent_csv: true # Whether to ouptut the `latent.csv` file.\nlatent_results: true # Whether to output the `latent.results` file.\n\n# Other Files:\n\ncovariance: true # `covariance.csv`: The [free parameters x free parameters] covariance matrix.\ndata: true # `data.json`: The value of every data point in the data.\nnoise_map: true # `noise_map.json`: The value of every RMS noise map value.\n\nsearch_log: true # `search.log`: logging produced whilst running the fit method\n\nmodel_graph: false # `model.graph`: graphical-model factor graph summary. Only meaningful for graphical models (autofit.graphical); empty for ordinary PriorModel/Collection fits, so disabled by default.\n\n# `model.png`: the model figure drawn beside `model.info` (af.ModelPlotter). Opt-in until the\n# model-figures epic's phase-3 acceptance renders pass. Unlike other keys an absent key means off.\nmodel_figure: 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1.0\n  limits:\n    lower: 0.0\n    upper: inf\nangle:\n  type: Uniform\n  lower_limit: 0.0\n  upper_limit: 180.0\n  width_modifier:\n    type: Relative\n    value: 1.0\n  limits:\n    lower: 0.0\n    upper: inf\neffective_radius:\n  type: Uniform\n  lower_limit: 0.0\n  upper_limit: 30.0\n  width_modifier:\n    type: Relative\n    value: 1.0\n  limits:\n    lower: 0.0\n    upper: inf\nintensity:\n  type: LogUniform\n  lower_limit: 1.0e-02\n  upper_limit: 1000.0\n  width_modifier:\n    type: Relative\n    value: 0.5\n  limits:\n    lower: 0.0\n    upper: 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1.0\n  limits:\n    lower: 0.0\n    upper: inf\nangle:\n  type: Uniform\n  lower_limit: 0.0\n  upper_limit: 180.0\n  width_modifier:\n    type: Relative\n    value: 1.0\n  limits:\n    lower: 0.0\n    upper: inf\neffective_radius:\n  type: Uniform\n  lower_limit: 0.0\n  upper_limit: 30.0\n  width_modifier:\n    type: Relative\n    value: 1.0\n  limits:\n    lower: 0.0\n    upper: inf\nintensity:\n  type: LogUniform\n  lower_limit: 1.0e-02\n  upper_limit: 1000.0\n  width_modifier:\n    type: Relative\n    value: 0.5\n  limits:\n    lower: 0.0\n    upper: inf\n","tooling":false,"top_keys":["centre_0","centre_1","axis_ratio","angle","effective_radius","intensity"]},{"error":null,"keys":[{"c":"","k":"parameter0","line":1},{"c":"","k":"parameter0.type","line":2},{"c":"","k":"parameter0.lower_limit","line":3},{"c":"","k":"parameter0.upper_limit","line":4},{"c":"","k":"parameter1","line":5},{"c":"","k":"parameter1.type","line":6},{"c":"","k":"parameter1.mean","line":7},{"c":"","k":"parameter1.sigma","line":8},{"c":"","k":"parameter1.lower_limit","line":9},{"c":"","k":"parameter1.upper_limit","line":10},{"c":"","k":"parameter2","line":11},{"c":"","k":"parameter2.type","line":12},{"c":"","k":"parameter2.lower_limit","line":13},{"c":"","k":"parameter2.upper_limit","line":14}],"lines":14,"path":"priors/TemplateObject.yaml","prior":true,"priors":[],"repo":"autofit_workspace","text":"parameter0:\n  type: Uniform\n  lower_limit: 0.0\n  upper_limit: 1.0\nparameter1:\n  type: TruncatedGaussian\n  mean: 0.0\n  sigma: 0.1\n  lower_limit: 0.0\n  upper_limit: inf\nparameter2:\n  type: Uniform\n  lower_limit: 0.0\n  upper_limit: 10.0","tooling":false,"top_keys":["parameter0","parameter1","parameter2"]},{"error":null,"keys":[{"c":"","k":"Gaussian","line":1},{"c":"","k":"Gaussian.sigma","line":2},{"c":"","k":"Gaussian.sigma.type","line":3},{"c":"","k":"Gaussian.sigma.lower_limit","line":4},{"c":"","k":"Gaussian.sigma.upper_limit","line":5},{"c":"","k":"Gaussian.sigma.width_modifier","line":6},{"c":"","k":"Gaussian.sigma.width_modifier.type","line":7},{"c":"","k":"Gaussian.sigma.width_modifier.value","line":8},{"c":"","k":"Gaussian.sigma.limits","line":9},{"c":"","k":"Gaussian.sigma.limits.lower","line":10},{"c":"","k":"Gaussian.sigma.limits.upper","line":11},{"c":"","k":"Gaussian.centre","line":12},{"c":"","k":"Gaussian.centre.type","line":13},{"c":"","k":"Gaussian.centre.lower_limit","line":14},{"c":"","k":"Gaussian.centre.upper_limit","line":15},{"c":"","k":"Gaussian.centre.width_modifier","line":16},{"c":"","k":"Gaussian.centre.width_modifier.type","line":17},{"c":"","k":"Gaussian.centre.width_modifier.value","line":18},{"c":"","k":"Gaussian.centre.limits","line":19},{"c":"","k":"Gaussian.centre.limits.lower","line":20},{"c":"","k":"Gaussian.centre.limits.upper","line":21},{"c":"","k":"Gaussian.normalization","line":22},{"c":"","k":"Gaussian.normalization.type","line":23},{"c":"","k":"Gaussian.normalization.lower_limit","line":24},{"c":"","k":"Gaussian.normalization.upper_limit","line":25},{"c":"","k":"Gaussian.normalization.width_modifier","line":26},{"c":"","k":"Gaussian.normalization.width_modifier.type","line":27},{"c":"","k":"Gaussian.normalization.width_modifier.value","line":28},{"c":"","k":"Gaussian.normalization.limits","line":29},{"c":"","k":"Gaussian.normalization.limits.lower","line":30},{"c":"","k":"Gaussian.normalization.limits.upper","line":31}],"lines":31,"path":"priors/gaussian.yaml","prior":true,"priors":[{"a":"lower 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100.0\n    width_modifier:\n      type: Absolute\n      value: 20.0\n    limits:\n      lower: -inf\n      upper: inf\n  normalization:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  rate:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 10.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\nGaussian:\n  sigma:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 25.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  centre:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 100.0\n    width_modifier:\n      type: Absolute\n      value: 20.0\n    limits:\n      lower: -inf\n      upper: inf\n  normalization:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["Exponential","Gaussian"]},{"error":null,"keys":[{"c":"","k":"Exponential","line":1},{"c":"","k":"Exponential.centre","line":2},{"c":"","k":"Exponential.centre.type","line":3},{"c":"","k":"Exponential.centre.lower_limit","line":4},{"c":"","k":"Exponential.centre.upper_limit","line":5},{"c":"","k":"Exponential.centre.width_modifier","line":6},{"c":"","k":"Exponential.centre.width_modifier.type","line":7},{"c":"","k":"Exponential.centre.width_modifier.value","line":8},{"c":"","k":"Exponential.centre.limits","line":9},{"c":"","k":"Exponential.centre.limits.lower","line":10},{"c":"","k":"Exponential.centre.limits.upper","line":11},{"c":"","k":"Exponential.normalization","line":12},{"c":"","k":"Exponential.normalization.type","line":13},{"c":"","k":"Exponential.normalization.lower_limit","line":14},{"c":"","k":"Exponential.normalization.upper_limit","line":15},{"c":"","k":"Exponential.normalization.width_modifier","line":16},{"c":"","k":"Exponential.normalization.width_modifier.type","line":17},{"c":"","k":"Exponential.normalization.width_modifier.value","line":18},{"c":"","k":"Exponential.normalization.limits","line":19},{"c":"","k":"Exponential.normalization.limits.lower","line":20},{"c":"","k":"Exponential.normalization.limits.upper","line":21},{"c":"","k":"Exponential.rate","line":22},{"c":"","k":"Exponential.rate.type","line":23},{"c":"","k":"Exponential.rate.lower_limit","line":24},{"c":"","k":"Exponential.rate.upper_limit","line":25},{"c":"","k":"Exponential.rate.width_modifier","line":26},{"c":"","k":"Exponential.rate.width_modifier.type","line":27},{"c":"","k":"Exponential.rate.width_modifier.value","line":28},{"c":"","k":"Exponential.rate.limits","line":29},{"c":"","k":"Exponential.rate.limits.lower","line":30},{"c":"","k":"Exponential.rate.limits.upper","line":31},{"c":"","k":"Gaussian","line":32},{"c":"","k":"Gaussian.sigma","line":33},{"c":"","k":"Gaussian.sigma.type","line":34},{"c":"","k":"Gaussian.sigma.lower_limit","line":35},{"c":"","k":"Gaussian.sigma.upper_limit","line":36},{"c":"","k":"Gaussian.sigma.width_modifier","line":37},{"c":"","k":"Gaussian.sigma.width_modifier.type","line":38},{"c":"","k":"Gaussian.sigma.width_modifier.value","line":39},{"c":"","k":"Gaussian.sigma.limits","line":40},{"c":"","k":"Gaussian.sigma.limits.lower","line":41},{"c":"","k":"Gaussian.sigma.limits.upper","line":42},{"c":"","k":"Gaussian.centre","line":43},{"c":"","k":"Gaussian.centre.type","line":44},{"c":"","k":"Gaussian.centre.lower_limit","line":45},{"c":"","k":"Gaussian.centre.upper_limit","line":46},{"c":"","k":"Gaussian.centre.width_modifier","line":47},{"c":"","k":"Gaussian.centre.width_modifier.type","line":48},{"c":"","k":"Gaussian.centre.width_modifier.value","line":49},{"c":"","k":"Gaussian.centre.limits","line":50},{"c":"","k":"Gaussian.centre.limits.lower","line":51},{"c":"","k":"Gaussian.centre.limits.upper","line":52},{"c":"","k":"Gaussian.normalization","line":53},{"c":"","k":"Gaussian.normalization.type","line":54},{"c":"","k":"Gaussian.normalization.lower_limit","line":55},{"c":"","k":"Gaussian.normalization.upper_limit","line":56},{"c":"","k":"Gaussian.normalization.width_modifier","line":57},{"c":"","k":"Gaussian.normalization.width_modifier.type","line":58},{"c":"","k":"Gaussian.normalization.width_modifier.value","line":59},{"c":"","k":"Gaussian.normalization.limits","line":60},{"c":"","k":"Gaussian.normalization.limits.lower","line":61},{"c":"","k":"Gaussian.normalization.limits.upper","line":62}],"lines":62,"path":"priors/profiles.yaml","prior":true,"priors":[{"a":"lower 0.0","b":"upper 100.0","cls":"Exponential","limits":"[-inf, inf]","line":2,"param":"centre","type":"Uniform","width":"Absolute 20.0"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"Exponential","limits":"[0.0, inf]","line":12,"param":"normalization","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 10.0","cls":"Exponential","limits":"[0.0, inf]","line":22,"param":"rate","type":"Uniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 25.0","cls":"Gaussian","limits":"[0.0, inf]","line":33,"param":"sigma","type":"Uniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 100.0","cls":"Gaussian","limits":"[-inf, inf]","line":43,"param":"centre","type":"Uniform","width":"Absolute 20.0"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"Gaussian","limits":"[0.0, inf]","line":53,"param":"normalization","type":"LogUniform","width":"Relative 0.5"}],"repo":"autofit_workspace","text":"Exponential:\n  centre:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 100.0\n    width_modifier:\n      type: Absolute\n      value: 20.0\n    limits:\n      lower: -inf\n      upper: inf\n  normalization:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  rate:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 10.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\nGaussian:\n  sigma:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 25.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  centre:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 100.0\n    width_modifier:\n      type: Absolute\n      value: 20.0\n    limits:\n      lower: -inf\n      upper: inf\n  normalization:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["Exponential","Gaussian"]},{"error":null,"keys":[{"c":"","k":"ModelComponent0","line":1},{"c":"","k":"ModelComponent0.parameter0","line":2},{"c":"","k":"ModelComponent0.parameter0.type","line":3},{"c":"","k":"ModelComponent0.parameter0.lower_limit","line":4},{"c":"","k":"ModelComponent0.parameter0.upper_limit","line":5},{"c":"","k":"ModelComponent0.parameter1","line":6},{"c":"","k":"ModelComponent0.parameter1.type","line":7},{"c":"","k":"ModelComponent0.parameter1.lower_limit","line":8},{"c":"","k":"ModelComponent0.parameter1.upper_limit","line":9},{"c":"","k":"ModelComponent0.parameter2","line":10},{"c":"","k":"ModelComponent0.parameter2.type","line":11},{"c":"","k":"ModelComponent0.parameter2.lower_limit","line":12},{"c":"","k":"ModelComponent0.parameter2.upper_limit","line":13},{"c":"","k":"ModelComponent1","line":14},{"c":"","k":"ModelComponent1.parameter0","line":15},{"c":"","k":"ModelComponent1.parameter0.type","line":16},{"c":"","k":"ModelComponent1.parameter0.lower_limit","line":17},{"c":"","k":"ModelComponent1.parameter0.upper_limit","line":18},{"c":"","k":"ModelComponent1.parameter1","line":19},{"c":"","k":"ModelComponent1.parameter1.type","line":20},{"c":"","k":"ModelComponent1.parameter1.lower_limit","line":21},{"c":"","k":"ModelComponent1.parameter1.upper_limit","line":22},{"c":"","k":"ModelComponent1.parameter2","line":23},{"c":"","k":"ModelComponent1.parameter2.type","line":24},{"c":"","k":"ModelComponent1.parameter2.lower_limit","line":25},{"c":"","k":"ModelComponent1.parameter2.upper_limit","line":26}],"lines":26,"path":"priors/template_module.yaml","prior":true,"priors":[{"a":"lower 0.0","b":"upper 1.0","cls":"ModelComponent0","limits":"","line":2,"param":"parameter0","type":"Uniform","width":""},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"ModelComponent0","limits":"","line":6,"param":"parameter1","type":"LogUniform","width":""},{"a":"lower 0.0","b":"upper 25.0","cls":"ModelComponent0","limits":"","line":10,"param":"parameter2","type":"Uniform","width":""},{"a":"lower 0.0","b":"upper 1.0","cls":"ModelComponent1","limits":"","line":15,"param":"parameter0","type":"Uniform","width":""},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"ModelComponent1","limits":"","line":19,"param":"parameter1","type":"LogUniform","width":""},{"a":"lower 0.0","b":"upper 1.0","cls":"ModelComponent1","limits":"","line":23,"param":"parameter2","type":"Uniform","width":""}],"repo":"autofit_workspace","text":"ModelComponent0:\n  parameter0:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n  parameter1:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n  parameter2:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 25.0\nModelComponent1:\n  parameter0:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n  parameter1:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n  parameter2:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n","tooling":false,"top_keys":["ModelComponent0","ModelComponent1"]},{"error":null,"keys":[{"c":"","k":"general","line":1},{"c":"The matploblib backend used for visualization. `default` uses the system default, can specifiy specific backend (e.g. TKAgg, Qt5Agg, WXAgg).","k":"general.backend","line":2}],"lines":2,"path":"visualize/general.yaml","prior":false,"priors":[],"repo":"autofit_workspace","text":"general:\n  backend: default         # The matploblib backend used for visualization. `default` uses the system default, can specifiy specific backend (e.g. TKAgg, Qt5Agg, WXAgg).","tooling":false,"top_keys":["general"]},{"error":null,"keys":[{"c":"","k":"nest","line":1},{"c":"Output corner figure (using anestetic) during a non-linear search fit?","k":"nest.corner_anesthetic","line":2},{"c":"","k":"mcmc","line":3},{"c":"Output corner figure (using corner.py) during a non-linear search fit?","k":"mcmc.corner_cornerpy","line":4}],"lines":4,"path":"visualize/plots_search.yaml","prior":false,"priors":[],"repo":"autofit_workspace","text":"nest:\n  corner_anesthetic: true   # Output corner figure (using anestetic) during a non-linear search fit?\nmcmc:\n  corner_cornerpy: true     # Output corner figure (using corner.py) during a non-linear search fit?\n","tooling":false,"top_keys":["nest","mcmc"]},{"error":null,"keys":[{"c":"PyAutoHands's generate_markdown.py \u2014 see that module's docstring for the rules (never TEST_MODE; features/ scripts never rendered; list order is execution order and index order).","k":"script","line":6},{"c":"","k":"max_minutes","line":7},{"c":"","k":"script","line":8},{"c":"","k":"max_minutes","line":9},{"c":"","k":"script","line":10},{"c":"","k":"max_minutes","line":11},{"c":"","k":"script","line":12},{"c":"","k":"max_minutes","line":13},{"c":"","k":"script","line":14},{"c":"","k":"max_minutes","line":15},{"c":"","k":"script","line":16},{"c":"","k":"max_minutes","line":17},{"c":"","k":"script","line":18},{"c":"","k":"max_minutes","line":19},{"c":"","k":"script","line":20},{"c":"","k":"max_minutes","line":21},{"c":"","k":"script","line":22},{"c":"","k":"max_minutes","line":23},{"c":"","k":"script","line":24},{"c":"","k":"max_minutes","line":25},{"c":"","k":"script","line":26},{"c":"","k":"max_minutes","line":27},{"c":"","k":"script","line":28},{"c":"","k":"max_minutes","line":29},{"c":"","k":"script","line":30},{"c":"","k":"max_minutes","line":31},{"c":"","k":"script","line":32},{"c":"","k":"max_minutes","line":33},{"c":"","k":"script","line":34},{"c":"","k":"max_minutes","line":35},{"c":"","k":"script","line":36},{"c":"","k":"max_minutes","line":37},{"c":"worst cell is ~6 min and the whole page ~13 min. An earlier attempt was abandoned on a 7200s CellTimeoutError and recorded as \"genuinely multi-hour\" \u2014 that diagnosis does not reproduce; 120 is ~20x h\u2026","k":"script","line":44},{"c":"","k":"max_minutes","line":45},{"c":"","k":"script","line":46},{"c":"","k":"max_minutes","line":47},{"c":"","k":"script","line":48},{"c":"","k":"max_minutes","line":49}],"lines":49,"path":"build/markdown_examples.yaml","prior":false,"priors":[],"repo":"autogalaxy_workspace","text":"# Curated examples rendered to executed markdown pages (markdown/) with their\n# real output images, so they can be read on GitHub. Built manually by\n# PyAutoHands's generate_markdown.py \u2014 see that module's docstring for the\n# rules (never TEST_MODE; features/ scripts never rendered; list order is\n# execution order and index order).\n- script: start_here.py\n  max_minutes: 120\n- script: scripts/imaging/start_here.py\n  max_minutes: 240\n- script: scripts/imaging/simulator.py\n  max_minutes: 45\n- script: scripts/imaging/likelihood_function.py\n  max_minutes: 45\n- script: scripts/imaging/fit.py\n  max_minutes: 45\n- script: scripts/imaging/modeling.py\n  max_minutes: 240\n- script: scripts/interferometer/start_here.py\n  max_minutes: 240\n- script: scripts/interferometer/simulator.py\n  max_minutes: 45\n- script: scripts/interferometer/likelihood_function.py\n  max_minutes: 45\n- script: scripts/interferometer/fit.py\n  max_minutes: 45\n- script: scripts/interferometer/modeling.py\n  max_minutes: 300\n- script: scripts/multi_dataset/start_here.py\n  max_minutes: 120\n- script: scripts/multi_dataset/simulator.py\n  max_minutes: 45\n- script: scripts/multi_dataset/modeling.py\n  max_minutes: 240\n- script: scripts/ellipse/simulator.py\n  max_minutes: 45\n- script: scripts/ellipse/fit.py\n  max_minutes: 45\n# The \"Multiple Ellipses\" + \"Masking\" sections are the two heaviest cells here:\n# 10 and 11 sequential DynestyStatic/Drawer fits respectively. Measured 2026-08-20\n# on 1 core: ~32s per fit, flat in major_axis (0.3\" and 3.6\" both 32s), so the\n# worst cell is ~6 min and the whole page ~13 min. An earlier attempt was\n# abandoned on a 7200s CellTimeoutError and recorded as \"genuinely multi-hour\" \u2014\n# that diagnosis does not reproduce; 120 is ~20x headroom over the measured cell.\n- script: scripts/ellipse/modeling.py\n  max_minutes: 120\n- script: scripts/guides/galaxies.py\n  max_minutes: 45\n- script: scripts/guides/data_structures.py\n  max_minutes: 45\n","tooling":true,"top_keys":["script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes"]},{"error":null,"keys":[],"lines":28,"path":"build/no_run.yaml","prior":false,"priors":[],"repo":"autogalaxy_workspace","text":"# Scripts to skip during automated runs (smoke tests, pre-release checks, CI).\n# Each entry is matched against script paths:\n#   - Entries with '/' do a substring match against the file path\n#   - Entries without '/' match the file stem exactly\n# Add an inline # comment to document the reason for skipping.\n#\n# SLOW-skip convention:\n#   Entries tagged `# SLOW <YYYY-MM-DD> - <reason>` mark scripts that are\n#   skipped because they exceed the per-script timeout cap (300s by\n#   default; 1800s for mode=release runs). These are\n#   NOT permanent skips \u2014 every mega-run surfaces them with a loud warning\n#   banner. Fix the performance issue and remove the SLOW marker.\n#\n# NEEDS_FIX convention:\n#   Entries tagged `# NEEDS_FIX <YYYY-MM-DD> - <reason>` mark scripts that\n#   are broken and parked as a to-do list. Like SLOW-skips, these are NOT\n#   permanent skips \u2014 every mega-run surfaces them with a loud warning\n#   banner. Investigate the failure, fix the underlying bug, and remove\n#   the NEEDS_FIX marker.\n\n- gui/light_centre # GUI scripts cannot be run\n- gui/mask_extra_galaxies # GUI scripts cannot be run\n- gui/mask # GUI scripts cannot be run\n- gui/extra_galaxies_centres # GUI scripts cannot be run\n- fits_make # Test mode does not output .fits images.\n- png_make # Test mode does not output .png images.\n- guides/plot/searches # Test mode breaks search visualization.\n- imaging/features/shapelets/modeling # SLOW 2026-07-14 - real-search JAX shapelet fit exceeds the 1800s mode=release cap (>30min); speedup tracked by the Profiling Agent (PyAutoHeart#72). Not a bug.\n","tooling":true,"top_keys":[]},{"error":null,"keys":[{"c":"","k":"defaults","line":27},{"c":"reduced iterations (real sampler), not bypassed","k":"defaults.PYAUTO_TEST_MODE","line":28},{"c":"real fit output (release fidelity, not smoke)","k":"defaults.PYAUTO_SKIP_FIT_OUTPUT","line":29},{"c":"real visualization (release fidelity, not smoke)","k":"defaults.PYAUTO_SKIP_VISUALIZATION","line":30},{"c":"real checks (release fidelity, not smoke)","k":"defaults.PYAUTO_SKIP_CHECKS","line":31},{"c":"cap grids/masks to 15x15, reduce MGE gaussians","k":"defaults.PYAUTO_SMALL_DATASETS","line":32},{"c":"JAX enabled (release fidelity, not smoke)","k":"defaults.PYAUTO_DISABLE_JAX","line":33},{"c":"skip tight_layout() + critical curve/caustic overlays","k":"defaults.PYAUTO_FAST_PLOTS","line":34},{"c":"TestPyPI dev version won't match the workspace pin","k":"defaults.PYAUTO_SKIP_WORKSPACE_VERSION_CHECK","line":35},{"c":"enable 64-bit precision in JAX","k":"defaults.JAX_ENABLE_X64","line":36},{"c":"writable cache dir for numba","k":"defaults.NUMBA_CACHE_DIR","line":37},{"c":"writable config dir for matplotlib","k":"defaults.MPLCONFIGDIR","line":38},{"c":"","k":"overrides","line":40},{"c":"Non-simulator scripts that load committed FITS data need full-size datasets to avoid shape mismatch with pre-existing 100x100 data.","k":"overrides.pattern","line":43},{"c":"","k":"overrides.set","line":44},{"c":"need PYAUTO_FAST_PLOTS forced off (it would otherwise close every figure without saving via the subplot_save / save_figure short-circuit in autoarray/plot/utils.py). PYAUTO_SKIP_VISUALIZATION is alre\u2026","k":"overrides.pattern","line":53},{"c":"","k":"overrides.set","line":54},{"c":"","k":"overrides.pattern","line":55},{"c":"","k":"overrides.set","line":56}],"lines":56,"path":"build/profile_release.yaml","prior":false,"priors":[],"repo":"autogalaxy_workspace","text":"# Per-script environment variable configuration for the RELEASE-FIDELITY\n# validation run (Heart's workspace-validation.yml, mode=release \u2014 the M3\n# wheel-based release-fidelity path). Distinct from profile_smoke.yaml, which is the\n# `smoke` profile used by the per-PR CI gate.\n#\n# The `release` profile trades speed for fidelity: it is run once per release\n# rehearsal against the TestPyPI wheels, not on every PR, so it can afford a\n# reduced (not bypassed) sampler and real fit output/visualization/checks.\n# Spec + acceptance table: PyAutoHeart/docs/release_validation.md.\n#\n# \"defaults\" are applied to every script on top of the inherited environment.\n# Every var this profile cares about is given an EXPLICIT value in \"defaults\"\n# (not left absent) \u2014 the runner only ever *sets* keys it's given, it never\n# clears unrelated inherited env vars, so an absent key silently falls through\n# to whatever the calling process already had (a leftover smoke-mode \"1\" from\n# an earlier step, a developer's local shell, ...). Pinning everything here\n# makes the profile self-contained regardless of the caller's environment.\n#\n# \"overrides\" should normally `set:` a var away from this profile's own default.\n# Use `unset:` only when the script genuinely needs the variable absent, such as\n# guides that must not run under PyAuto test mode at all.\n#\n# Pattern convention (same as no_run.yaml):\n#   - Patterns containing '/' do a substring match against the file path\n#   - Patterns without '/' match the file stem exactly\n\ndefaults:\n  PYAUTO_TEST_MODE: \"1\"                     # reduced iterations (real sampler), not bypassed\n  PYAUTO_SKIP_FIT_OUTPUT: \"0\"               # real fit output (release fidelity, not smoke)\n  PYAUTO_SKIP_VISUALIZATION: \"0\"            # real visualization (release fidelity, not smoke)\n  PYAUTO_SKIP_CHECKS: \"0\"                   # real checks (release fidelity, not smoke)\n  PYAUTO_SMALL_DATASETS: \"1\"                # cap grids/masks to 15x15, reduce MGE gaussians\n  PYAUTO_DISABLE_JAX: \"0\"                   # JAX enabled (release fidelity, not smoke)\n  PYAUTO_FAST_PLOTS: \"1\"                    # skip tight_layout() + critical curve/caustic overlays\n  PYAUTO_SKIP_WORKSPACE_VERSION_CHECK: \"1\"  # TestPyPI dev version won't match the workspace pin\n  JAX_ENABLE_X64: \"True\"                    # enable 64-bit precision in JAX\n  NUMBA_CACHE_DIR: \"/tmp/numba_cache\"       # writable cache dir for numba\n  MPLCONFIGDIR: \"/tmp/matplotlib\"           # writable config dir for matplotlib\n\noverrides:\n  # Non-simulator scripts that load committed FITS data need full-size\n  # datasets to avoid shape mismatch with pre-existing 100x100 data.\n  - pattern: \"guides/\"\n    set: { PYAUTO_SMALL_DATASETS: \"0\" }\n  # (guides/results/ formerly unset PYAUTO_TEST_MODE here; that intent now\n  # lives in-file as '# ENV: real_search' declarations on the scripts, which\n  # apply in every profile \u2014 the override became redundant.)\n  #\n  # fits_make / png_make produce .fits / .png outputs from real fits, so they\n  # need PYAUTO_FAST_PLOTS forced off (it would otherwise close every figure\n  # without saving via the subplot_save / save_figure short-circuit in\n  # autoarray/plot/utils.py). PYAUTO_SKIP_VISUALIZATION is already \"0\" above.\n  - pattern: fits_make\n    set: { PYAUTO_FAST_PLOTS: \"0\" }\n  - pattern: png_make\n    set: { PYAUTO_FAST_PLOTS: \"0\" }\n","tooling":true,"top_keys":["defaults","overrides"]},{"error":null,"keys":[{"c":"","k":"defaults","line":11},{"c":"0=normal, 1=reduced iterations, 2=skip sampler (fastest)","k":"defaults.PYAUTO_TEST_MODE","line":12},{"c":"Skip pre/post-fit I/O, VRAM profiling, result text","k":"defaults.PYAUTO_SKIP_FIT_OUTPUT","line":13},{"c":"Skip fit visualization and plotting","k":"defaults.PYAUTO_SKIP_VISUALIZATION","line":14},{"c":"Skip mesh validation, position checks, weight thresholds","k":"defaults.PYAUTO_SKIP_CHECKS","line":15},{"c":"Cap grids/masks to 15x15, reduce MGE gaussians","k":"defaults.PYAUTO_SMALL_DATASETS","line":16},{"c":"Force use_jax=False, avoid JIT compilation overhead","k":"defaults.PYAUTO_DISABLE_JAX","line":17},{"c":"Skip tight_layout() + critical curve/caustic overlays","k":"defaults.PYAUTO_FAST_PLOTS","line":18},{"c":"Enable 64-bit precision in JAX","k":"defaults.JAX_ENABLE_X64","line":19},{"c":"Writable cache dir for numba","k":"defaults.NUMBA_CACHE_DIR","line":20},{"c":"Writable config dir for matplotlib","k":"defaults.MPLCONFIGDIR","line":21},{"c":"","k":"overrides","line":23},{"c":"(data_fitting, queries, models, samples_via_aggregator) find a non-empty aggregator. Covers all scripts under guides/results/. (PYAUTO_TEST_MODE moved to `# ENV: real_search`.)","k":"overrides.pattern","line":33},{"c":"","k":"overrides.unset","line":34},{"c":"fits_make / png_make produce .fits / .png outputs from real fits, so they need visualization turned on. (PYAUTO_FAST_PLOTS moved to `# ENV: real_plots`.)","k":"overrides.pattern","line":37},{"c":"","k":"overrides.unset","line":38},{"c":"","k":"overrides.pattern","line":39},{"c":"","k":"overrides.unset","line":40}],"lines":40,"path":"build/profile_smoke.yaml","prior":false,"priors":[],"repo":"autogalaxy_workspace","text":"# Per-script environment variable configuration for automated runs\n# (smoke tests, pre-release checks, CI).\n#\n# \"defaults\" are applied to every script on top of the inherited environment.\n# \"overrides\" selectively unset or replace vars for matching path patterns.\n#\n# Pattern convention (same as no_run.yaml):\n#   - Patterns containing '/' do a substring match against the file path\n#   - Patterns without '/' match the file stem exactly\n\ndefaults:\n  PYAUTO_TEST_MODE: \"2\"                     # 0=normal, 1=reduced iterations, 2=skip sampler (fastest)\n  PYAUTO_SKIP_FIT_OUTPUT: \"1\"               # Skip pre/post-fit I/O, VRAM profiling, result text\n  PYAUTO_SKIP_VISUALIZATION: \"1\"            # Skip fit visualization and plotting\n  PYAUTO_SKIP_CHECKS: \"1\"                   # Skip mesh validation, position checks, weight thresholds\n  PYAUTO_SMALL_DATASETS: \"1\"                # Cap grids/masks to 15x15, reduce MGE gaussians\n  PYAUTO_DISABLE_JAX: \"1\"                   # Force use_jax=False, avoid JIT compilation overhead\n  PYAUTO_FAST_PLOTS: \"1\"                    # Skip tight_layout() + critical curve/caustic overlays\n  JAX_ENABLE_X64: \"True\"                    # Enable 64-bit precision in JAX\n  NUMBA_CACHE_DIR: \"/tmp/numba_cache\"       # Writable cache dir for numba\n  MPLCONFIGDIR: \"/tmp/matplotlib\"           # Writable config dir for matplotlib\n\noverrides:\n  # The `guides/` PYAUTO_SMALL_DATASETS unset migrated to in-file\n  # `# ENV: full_datasets` declarations on each guides/ script (#187 Stage 2).\n  # PYAUTO_TEST_MODE / PYAUTO_FAST_PLOTS below likewise migrated to `# ENV:`\n  # declarations; the non-declarable PYAUTO_SKIP_* vars remain here.\n  # guides/results/start_here.py must produce real samples so the example\n  # scripts that read from `output/results_folder` afterwards\n  # (data_fitting, queries, models, samples_via_aggregator) find a\n  # non-empty aggregator. Covers all scripts under guides/results/.\n  # (PYAUTO_TEST_MODE moved to `# ENV: real_search`.)\n  - pattern: \"guides/results/\"\n    unset: [PYAUTO_SKIP_FIT_OUTPUT]\n  # fits_make / png_make produce .fits / .png outputs from real fits, so they need\n  # visualization turned on. (PYAUTO_FAST_PLOTS moved to `# ENV: real_plots`.)\n  - pattern: fits_make\n    unset: [PYAUTO_SKIP_VISUALIZATION]\n  - pattern: png_make\n    unset: [PYAUTO_SKIP_VISUALIZATION]\n","tooling":true,"top_keys":["defaults","overrides"]},{"error":null,"keys":[],"lines":10,"path":"build/visualise_notebooks.yaml","prior":false,"priors":[],"repo":"autogalaxy_workspace","text":"# Notebook stems that should run when PyAutoHands's generate / run pipeline\n# is invoked with --visualise. Used to refresh notebook output cells in main.\n#\n# Format: flat list of notebook stems (no extension, no path).\n# An empty list means no notebooks need re-visualisation in this workspace.\n#\n# This file overrides PyAutoHands/autohands/config/visualise_notebooks.yaml\n# for this workspace. Add or remove entries here, not there.\n\n[]\n","tooling":true,"top_keys":[]},{"error":null,"keys":[{"c":"","k":"updates","line":6},{"c":"Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit.","k":"updates.iterations_per_quick_update","line":7},{"c":"Non-linear search iterations between every full update, which outputs all visuals and result fits (e.g. model.result, search.summary), this exits the search and can be slow.","k":"updates.iterations_per_full_update","line":8},{"c":"If True, perform_quick_update runs on a background daemon thread so the sampler is never blocked while visuals render. Requires the Analysis subclass to set `supports_background_update = True`.","k":"updates.quick_update_background","line":9},{"c":"If True, quick-update visuals are pushed to a live surface (a Jupyter cell when in a kernel, a matplotlib viewer subprocess otherwise) in addition to being written to disk.","k":"updates.live_visual_update","line":10},{"c":"","k":"psf","line":11},{"c":"If True, PSFs are convolved using FFTs by default, which is faster and uses less memory in all cases except for very small PSFs, False uses direct convolution.","k":"psf.use_fft_default","line":12},{"c":"","k":"grid","line":13},{"c":"An evaluation grid whose shape is adaptive chosen is used to compute quantities like critical curves, this integer is the max size of the grid ensuring faster run times.","k":"grid.max_evaluation_grid_size","line":14},{"c":"","k":"inversion","line":15},{"c":"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.","k":"inversion.check_reconstruction","line":16},{"c":"If True, inversion's use a positive-only linear algebra solver by default, which is slower but prevents unphysical negative values in the reconstructed solutuion.","k":"inversion.use_positive_only_solver","line":17},{"c":"If True, the edge pixels of a pixelization are set to zero, which prevents unphysical values in the reconstructed solution at the edge of the pixelization.","k":"inversion.use_edge_zeroed_pixels","line":18},{"c":"The default value added to the curvature matrix's diagonal when regularization is not applied to a linear object, which prevents inversion's failing due to the matrix being singular.","k":"inversion.no_regularization_add_to_curvature_diag_value","line":19},{"c":"If True, by default a pixelization's border is used to relocate all pixels outside its border to the border.","k":"inversion.use_border_relocator","line":20},{"c":"Plots of an Inversion's reconstruction use the reconstructed data's bright value multiplied by this factor.","k":"inversion.reconstruction_vmax_factor","line":21},{"c":"","k":"hpc","line":22},{"c":"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.","k":"hpc.hpc_mode","line":23},{"c":"Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit.","k":"hpc.iterations_per_quick_update","line":24},{"c":"Non-linear search iterations between every full update, which outputs all visuals and result fits (e.g. model.result, search.summary), this exits the search and can be slow.","k":"hpc.iterations_per_full_update","line":25},{"c":"","k":"adapt","line":26},{"c":"","k":"adapt.adapt_minimum_percent","line":27},{"c":"","k":"adapt.adapt_noise_limit","line":28},{"c":"","k":"numba","line":29},{"c":"","k":"numba.use_numba","line":30},{"c":"","k":"numba.cache","line":31},{"c":"","k":"numba.nopython","line":32},{"c":"","k":"numba.parallel","line":33},{"c":"","k":"output","line":34},{"c":"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.","k":"output.force_pickle_overwrite","line":35},{"c":"If True, visualization images output by a search (e.g. subplots of the fit) are recreated when a new model-fit is performed.","k":"output.force_visualize_overwrite","line":36},{"c":"Length of whitespace between the parameter names and values in the model.info / result.info","k":"output.info_whitespace_length","line":37},{"c":"The level of information output by logging.","k":"output.log_level","line":38},{"c":"If True, outputs the non-linear search log to a file (and not printed to screen).","k":"output.log_to_file","line":39},{"c":"The name of the file the logged output is written to (in the non-linear search output folder)","k":"output.log_file","line":40},{"c":"Number of decimal places estimated parameter values / errors are output in model.results.","k":"output.model_results_decimal_places","line":41},{"c":"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.","k":"output.remove_files","line":42},{"c":"If True, non-linear search samples are written to a .csv file.","k":"output.samples_to_csv","line":43},{"c":"If outputting results of an unconverged search, the number of samples used to estimate the median PDF values and errors.","k":"output.unconverged_sample_size","line":44},{"c":"","k":"parallel","line":45},{"c":"If True, a warning is displayed when the search's number of CPU > 1 and enviromment variables related to threading are also > 1.","k":"parallel.warn_environment_variables","line":46},{"c":"","k":"profiling","line":48},{"c":"If True, the parallelization of the fit is profiled outputting a cPython graph.","k":"profiling.parallel_profile","line":49},{"c":"The number of repeat function calls used to measure run-times when profiling.","k":"profiling.repeats","line":50},{"c":"","k":"structures","line":51},{"c":"If True, data structures are only stored in their native and binned format. This is used to reduce memory usage in autocti.","k":"structures.native_binned_only","line":52},{"c":"","k":"test","line":53},{"c":"if True, when a search is resumed the likelihood of a previous sample is recalculated to ensure it is consistent with the previous run.","k":"test.check_likelihood_function","line":54},{"c":"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.","k":"test.lh_timeout_seconds","line":55},{"c":"","k":"version","line":56},{"c":"(autonerves/workspace.py). Bump DELIBERATELY \u2014 only when a script starts needing new API \u2014 never per release. Must always name an INSTALLABLE (non-yanked) release.","k":"version.minimum_library_version","line":62},{"c":"If False, bypass the workspace/library version check. Set to False on `main`-branch clones \u2014 `main` updates faster than releases, so mismatches are expected and not actionable.","k":"version.workspace_version_check","line":63}],"lines":63,"path":"general.yaml","prior":false,"priors":[],"repo":"autogalaxy_workspace","text":"\n# version:\n#   python_version_check: False  # uncomment to suppress the Python version warning\n#                                # if running on a non-recommended Python (anything other than 3.12 / 3.13).\n\nupdates:\n  iterations_per_quick_update: 1e99 # Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit.\n  iterations_per_full_update: 1e99  # Non-linear search iterations between every full update, which outputs all visuals and result fits (e.g. model.result, search.summary), this exits the search and can be slow.\n  quick_update_background: false    # If True, perform_quick_update runs on a background daemon thread so the sampler is never blocked while visuals render. Requires the Analysis subclass to set `supports_background_update = True`.\n  live_visual_update: false         # If True, quick-update visuals are pushed to a live surface (a Jupyter cell when in a kernel, a matplotlib viewer subprocess otherwise) in addition to being written to disk.\npsf:\n  use_fft_default: true              # If True, PSFs are convolved using FFTs by default, which is faster and uses less memory in all cases except for very small PSFs, False uses direct convolution.\ngrid:\n  max_evaluation_grid_size: 1000   # An evaluation grid whose shape is adaptive chosen is used to compute quantities like critical curves, this integer is the max size of the grid ensuring faster run times.\ninversion:\n  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.\n  use_positive_only_solver: true      # If True, inversion's use a positive-only linear algebra solver by default, which is slower but prevents unphysical negative values in the reconstructed solutuion.\n  use_edge_zeroed_pixels : true       # If True, the edge pixels of a pixelization are set to zero, which prevents unphysical values in the reconstructed solution at the edge of the pixelization.\n  no_regularization_add_to_curvature_diag_value : 1.0e-3 # The default value added to the curvature matrix's diagonal when regularization is not applied to a linear object, which prevents inversion's failing due to the matrix being singular.\n  use_border_relocator: false       # If True, by default a pixelization's border is used to relocate all pixels outside its border to the border.\n  reconstruction_vmax_factor: 0.5   # Plots of an Inversion's reconstruction use the reconstructed data's bright value multiplied by this factor.\nhpc:\n  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.\n  iterations_per_quick_update: 10000 #  Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit.\n  iterations_per_full_update: 1e99  # Non-linear search iterations between every full update, which outputs all visuals and result fits (e.g. model.result, search.summary), this exits the search and can be slow.\nadapt:\n  adapt_minimum_percent: 0.01\n  adapt_noise_limit: 100000000.0\nnumba:\n  use_numba: true\n  cache: true\n  nopython: true\n  parallel: false\noutput:\n  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.\n  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.\n  info_whitespace_length: 80        # Length of whitespace between the parameter names and values in the model.info / result.info\n  log_level: INFO                   # The level of information output by logging.\n  log_to_file: false                # If True, outputs the non-linear search log to a file (and not printed to screen).\n  log_file: output.log              # The name of the file the logged output is written to (in the non-linear search output folder)\n  model_results_decimal_places: 3   # Number of decimal places estimated parameter values / errors are output in model.results.\n  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.\n  samples_to_csv: true              # If True, non-linear search samples are written to a .csv file.\n  unconverged_sample_size : 100     # If outputting results of an unconverged search, the number of samples used to estimate the median PDF values and errors.\nparallel:\n  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.\n\nprofiling:\n  parallel_profile: false           # If True, the parallelization of the fit is profiled outputting a cPython graph.\n  repeats: 1                        # The number of repeat function calls used to measure run-times when profiling.\nstructures:\n  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.\ntest:\n  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.\n  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.\nversion:\n  # The compatibility FLOOR: the oldest library release whose API this\n  # workspace's scripts require. Preferred over workspace_version\n  # (autonerves/workspace.py). Bump DELIBERATELY \u2014 only when a script\n  # starts needing new API \u2014 never per release. Must always name an\n  # INSTALLABLE (non-yanked) release.\n  minimum_library_version: 2026.7.9.1\n  workspace_version_check: True     # If False, bypass the workspace/library version check. Set to False on `main`-branch clones \u2014 `main` updates faster than releases, so mismatches are expected and not actionable.\n","tooling":false,"top_keys":["updates","psf","grid","inversion","hpc","adapt","numba","output","parallel","profiling","structures","test","version"]},{"error":null,"keys":[{"c":"`total_galaxy_0_flux` \u2014 total integrated flux of `fit.galaxies[0]` in the fit's raw image units. No instrument inputs required.","k":"total_galaxy_0_flux","line":14},{"c":"`total_galaxy_0_flux_mujy` \u2014 same flux converted to microjanskies via magzero. Requires `magzero` via Analysis kwargs; returns NaN + one warning per process if missing.","k":"total_galaxy_0_flux_mujy","line":19}],"lines":19,"path":"latent.yaml","prior":false,"priors":[],"repo":"autogalaxy_workspace","text":"# Workspace overrides for the library latent toggles. The PyAutoGalaxy\n# library defaults `total_galaxy_0_flux` to `true` (no instrument inputs\n# needed) and `total_galaxy_0_flux_mujy` to `false`. Enabling the \u00b5Jy\n# variant here means a workspace fit produces microjansky output as long\n# as the user also passes `magzero` to `ag.AnalysisImaging(...)` \u2014 without\n# `magzero` it returns NaN + one warning per process.\n#\n# Run `scripts/guides/results/latent_variables.py` for a tutorial on\n# what each key means and `scripts/guides/units/flux.py` for how to\n# convert a raw-flux latent to microjanskies in post.\n\n# `total_galaxy_0_flux` \u2014 total integrated flux of `fit.galaxies[0]` in\n# the fit's raw image units. No instrument inputs required.\ntotal_galaxy_0_flux: true\n\n# `total_galaxy_0_flux_mujy` \u2014 same flux converted to microjanskies via\n# magzero. Requires `magzero` via Analysis kwargs; returns NaN + one\n# warning per process if missing.\ntotal_galaxy_0_flux_mujy: true\n","tooling":false,"top_keys":["total_galaxy_0_flux","total_galaxy_0_flux_mujy"]},{"error":null,"keys":[{"c":"","k":"version","line":1},{"c":"","k":"disable_existing_loggers","line":2},{"c":"","k":"handlers","line":4},{"c":"","k":"handlers.console","line":5},{"c":"","k":"handlers.console.class","line":6},{"c":"","k":"handlers.console.level","line":7},{"c":"","k":"handlers.console.stream","line":8},{"c":"","k":"handlers.console.formatter","line":9},{"c":"","k":"root","line":11},{"c":"","k":"root.level","line":12},{"c":"","k":"root.handlers","line":13},{"c":"","k":"formatters","line":15},{"c":"","k":"formatters.formatter","line":16},{"c":"","k":"formatters.formatter.format","line":17}],"lines":17,"path":"logging.yaml","prior":false,"priors":[],"repo":"autogalaxy_workspace","text":"version: 1\ndisable_existing_loggers: false\n\nhandlers:\n  console:\n    class: logging.StreamHandler\n    level: INFO\n    stream: ext://sys.stdout\n    formatter: formatter\n\nroot:\n  level: INFO\n  handlers: [ console ]\n\nformatters:\n  formatter:\n    format: '%(asctime)s - %(name)s - %(levelname)s - %(message)s'\n","tooling":false,"top_keys":["version","disable_existing_loggers","handlers","root","formatters"]},{"error":null,"keys":[{"c":"","k":"parallel","line":3},{"c":"The number of cores the search is parallelized over by default, using Python multiprocessing.","k":"parallel.number_of_cores","line":4},{"c":"The default step size of each grid search parameter, in terms of unit values of the priors.","k":"parallel.step_size","line":5}],"lines":5,"path":"non_linear/GridSearch.yaml","prior":false,"priors":[],"repo":"autogalaxy_workspace","text":"# The settings of a parallelized grid search of non-linear searches.\n\nparallel:\n  number_of_cores: 3 # The number of cores the search is parallelized over by default, using Python multiprocessing.\n  step_size: 0.1     # The default step size of each grid search parameter, in terms of unit values of the 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The notation configs define the labels of every model parameter and its derived quantities, which are used when\n# visualizing results (for example labeling the axis of the PDF triangle plots output by a non-linear search).\n\n\n# label: The label given to the each parameter, for plots like PDF corner plots.\n\n# For example, if `centre=x`, the plot axis will be labeled 'x'.\n\n\n# superscript: the superscript used on certain plots that show the results of different model-components.\n\n# For example, if `Gaussian=g`, plots where the parameters of the Gaussian model-component have superscript `g`.\n\nlabel:\n  label:\n    sigma: \\sigma\n    alpha: \\alpha\n    angle_binary: \\theta\n    beta: \\beta\n    break_radius: \\theta_{\\rm B}\n    centre_0: y\n    centre_1: x\n    coefficient: \\lambda\n    core_radius: C_{\\rm r}\n    core_radius_0: C_{rm r0}\n    core_radius_1: C_{\\rm r1}\n    effective_radius: R_{\\rm eff}\n    einstein_radius: \\theta_{\\rm Ein}\n    ell_comps_0: \\epsilon_{\\rm 1}\n    ell_comps_1: \\epsilon_{\\rm 2}\n    multipole_comps_0: M_{\\rm 1}\n    multipole_comps_1: M_{\\rm 2}\n    flux: F\n    gamma: \\gamma\n    gamma_1: \\gamma\n    gamma_2: \\gamma\n    inner_coefficient: \\lambda_{\\rm 1}\n    inner_slope: t_{\\rm 1}\n    intensity: I_{\\rm b}\n    kappa: \\kappa\n    kappa_s: \\kappa_{\\rm s}\n    log10m_vir: log_{\\rm 10}(m_{vir})\n    m: m\n    mass: M\n    mass_at_200: M_{\\rm 200}\n    mass_ratio: M_{\\rm ratio}\n    mass_to_light_gradient: \\Gamma\n    mass_to_light_ratio: \\Psi\n    mass_to_light_ratio_base: \\Psi_{\\rm base}\n    mass_to_light_radius: R_{\\rm ref}\n    noise_factor: \\omega_{\\rm 1}\n    noise_power: \\omega{\\rm 2}\n    noise_scale: \\sigma_{\\rm 1}\n    normalization_scale: n\n    outer_coefficient: \\lambda_{\\rm 2}\n    outer_slope: t_{\\rm 2}\n    overdens: \\Delta_{\\rm vir}\n    pixels: N_{\\rm pix}\n    radius_break: R_{\\rm b}\n    redshift: z\n    redshift_object: z_{\\rm obj}\n    redshift_source: z_{\\rm src}\n    scale_radius: R_{\\rm s}\n    scatter: \\sigma\n    separation: s\n    sersic_index: n\n    shape_0: y_{\\rm pix}\n    shape_1: x_{\\rm pix}\n    signal_scale: V\n    sky_scale: \\sigma_{\\rm 0}\n    slope: \\gamma\n    truncation_radius: R_{\\rm t}\n    weight_floor: W_{\\rm f}\n    weight_power: W_{\\rm p}\n  superscript:\n    ExternalShear: ext\n    Mesh: mesh\n    Point: point\n    SMBH: smbh\n    Redshift: z\n    Regularization: reg\n    InputDeflections: defl\n\n# label_format: The format certain parameters are output as in output files like the `model.results` file.\n\n# For example, if `centre={:.2d}`, the format of the centre parameter in results files will use this Python format.\n\nlabel_format:\n  format:\n    sigma: '{:.4f}'\n    alpha: '{:.4f}'\n    angle_binary: '{:.4f}'\n    angular_diameter_distance_to_earth: '{:.4f}'\n    beta: '{:.4f}'\n    c_2: '{:.4f}'\n    centre_0: '{:.4f}'\n    centre_1: '{:.4f}'\n    coefficient: '{:.4f}'\n    concentration: '{:.4f}'\n    core_radius: '{:.4f}'\n    core_radius_0: '{:.4f}'\n    core_radius_1: '{:.4f}'\n    effective_radius: '{:.4f}'\n    einstein_mass: '{:.4e}'\n    einstein_radius: '{:.4f}'\n    ell_comps_0: '{:.4f}'\n    ell_comps_1: '{:.4f}'\n    multipole_comps_0: '{:.4f}'\n    multipole_comps_1: '{:.4f}'\n    flux: '{:.4e}'\n    gamma: '{:.4f}'\n    inner_coefficient: '{:.4f}'\n    inner_slope: '{:.4f}'\n    intensity: '{:.4f}'\n    kappa: '{:.4f}'\n    kappa_s: '{:.4f}'\n    kpc_per_arcsec: '{:.4f}'\n    log10m_vir: '{:.4f}'\n    luminosity: '{:.4e}'\n    m: '{:.1f}'\n    mass: '{:.4e}'\n    mass_at_200: '{:.4e}'\n    mass_at_truncation_radius: '{:.4e}'\n    mass_ratio: '{:.4f}'\n    mass_to_light_gradient: '{:.4f}'\n    mass_to_light_ratio: '{:.4f}'\n    n_x: '{:.1d}'\n    n_y: '{:.1d}'\n    noise_factor: '{:.3f}'\n    noise_power: '{:.3f}'\n    noise_scale: '{:.3f}'\n    normalization_scale: '{:.4f}'\n    outer_coefficient: '{:.4f}'\n    outer_slope: '{:.4f}'\n    overdens: '{:.4f}'\n    pixels: '{:.4f}'\n    radius: '{:.4f}'\n    radius_break: '{:.4f}'\n    redshift: '{:.4f}'\n    redshift_object: '{:.4f}'\n    redshift_source: '{:.4f}'\n    rho: '{:.4f}'\n    scale_radius: '{:.4f}'\n    separation: '{:.4f}'\n    sersic_index: '{:.4f}'\n    shape_0: '{:.4f}'\n    shape_1: '{:.4f}'\n    signal_scale: '{:.4f}'\n    sky_scale: '{:.4f}'\n    slope: '{:.4f}'\n    truncation_radius: '{:.4f}'\n    weight_floor: '{:.4f}'\n    weight_power: '{:.4f}'\n","tooling":false,"top_keys":["label","label_format"]},{"error":null,"keys":[{"c":"If true then files which are not explicitly listed here are output anyway. If false then they are not.","k":"default","line":4},{"c":"","k":"samples","line":17},{"c":"","k":"samples_weight_threshold","line":34},{"c":"","k":"search_internal","line":56},{"c":"","k":"start_point","line":65},{"c":"Whether to output the `latent.csv`, `latent.results` and `latent_summary.json` files during the fit when it performs on-the-fly output.","k":"latent_during_fit","line":89},{"c":"If `latent_during_fit` is False, whether to output the `latent.csv`, `latent.results` and `latent_summary.json` files after the fit is complete.","k":"latent_after_fit","line":90},{"c":"Whether to draw latent variable values via the PDF of every sample, which uses fewer samples to estimate latent variable errors. If False, latent variable values are drawn from every sample.","k":"latent_draw_via_pdf","line":91},{"c":"The number of samples drawn to estimate latent variable errors if `latent_draw_via_pdf` is True.","k":"latent_draw_via_pdf_size","line":92},{"c":"Whether to ouptut the `latent.csv` file.","k":"latent_csv","line":93},{"c":"Whether to output the `latent.results` file.","k":"latent_results","line":94},{"c":"`search.log`: logging produced whilst running the fit method","k":"search_log","line":98},{"c":"`model.graph`: graphical-model factor graph summary. Only meaningful for graphical models (autofit.graphical); empty for ordinary PriorModel/Collection fits, so disabled by default.","k":"model_graph","line":100},{"c":"`model.png`: the model figure drawn beside `model.info` (af.ModelPlotter). Opt-in until the model-figures epic's phase-3 acceptance renders pass. Unlike other keys an absent key means off.","k":"model_figure","line":104}],"lines":104,"path":"output.yaml","prior":false,"priors":[],"repo":"autogalaxy_workspace","text":"# Determines whether files saved by the search are output to the hard-disk. This is true both when saving to the\n# directory structure and when saving to database.\n\ndefault: true # If true then files which are not explicitly listed here are output anyway. If false then they are not.\n\n### Samples ###\n\n# The `samples.csv`file contains every sampled value of every free parameter with its log likelihood and weight.\n\n# This file is often large, therefore disabling it can significantly reduce hard-disk space use.\n\n# `samples.csv` is used to perform marginalization, infer model parameter errors and do other analysis of the search\n# chains. Even if output of `samples.csv` is disabled, these tasks are still performed by the fit and output to\n# the `samples_summary.json` file. However, without a `samples.csv` file these types of tasks cannot be performed\n# after the fit is complete, for example via the database.\n\nsamples: true\n\n# The `samples.csv` file contains every accepted sampled value of every free parameter with its log likelihood and\n# weight. For certain searches, the majority of samples have a very low weight and have no numerical impact on the\n# results of the model-fit. However, these samples are still output to the `samples.csv` file, taking up hard-disk\n# space and slowing down analysis of the samples (e.g. via the database).\n\n# The `samples_weight_threshold` below specifies the threshold value of the weight such that samples with a weight\n# below this value are not output to the `samples.csv` file. This can be used to reduce the size of the `samples.csv`\n# file and speed up analysis of the samples.\n\n# For many searches (e.g. MCMC) all samples have an equal weight of 1.0, and this threshold therefore has no impact.\n# For these searches, there is no simple way to save hard-disk space. This input is more suited to nested sampling,\n# where the majority of samples have a very low weight..\n\n# Set value to empty (e.g. delete 1.0e-10 below) to disable this feature.\n\nsamples_weight_threshold: 1.0e-10\n\n### Search Internal ###\n\n# The search internal folder which contains a saved state of the non-linear search in its internal reprsenetation,\n# as a .pickle or .dill file.\n\n# For example, for the nested sampling dynesty, this .dill file is the `DynestySampler` object which is used to\n# perform sampling, and it therefore contains all internal dynesty representations of the results, samples, weights, etc.\n\n# If the entry below is false, the folder is still output during the model-fit, as it is required to resume the fit\n# from where it left off. Therefore, settings `false` below does not impact model-fitting checkpointing and resumption.\n# Instead, the search internal folder is deleted once the fit is completed.\n\n# The search internal folder file is often large, therefore deleting it after a fit is complete can significantly\n# reduce hard-disk space use.\n\n# The search internal representation that can be loaded from the .dill file has many additional quantities specific to\n# the non-linear search that the standardized autofit forms do not. For example, for emcee, it contains information on\n# every walker. This information is required to do certain analyes and make certain plots, therefore deleting the\n# folder means this information is list.\n\nsearch_internal: false\n\n### Start Point ###\n\n# If an Initalizer is used to provide a start point for the non-linear search, visualization of that start point can be\n# output to hard-disk to show the user the initial model-fit that is used to start the search. This visualization is\n# the visualizer wrapped in the Analysis class, and therefore should show things like the quality of the fit\n# to the data and the residuals at the start point.\n\nstart_point: true\n\n### Latent Variables ###\n\n# A latent variable is not a model parameter but can be derived from the model. Its value and errors may be of interest\n# and aid in the interpretation of a model-fit.\n\n# For example, for the simple 1D Gaussian example, it could be the full-width half maximum (FWHM) of the Gaussian. This\n# is not included in the model but can be easily derived from the Gaussian's sigma value.\n\n# By overwriting an Analysis class's `compute_latent_variables` method we can manually specify latent variables that\n# are calculated and output to a `latent.csv` file, which mirrors the `samples.csv` file. The `latent.csv` file has\n# the same weight resampling performed on the `samples.csv` file, controlled via the `samples_weight_threshold` above.\n\n# There may also be a `latent.results` and `latent_summary.json` files output, which the inputs below control whether\n# they are output and how often.\n\n# Outputting latent variables manually after a fit is complete is simple, just call\n# the `analysis.compute_latent_variables()` function.\n\n# For many use cases, the best set up may be to disable autofit latent variable output during the fit and perform it\n# manually after completing a successful model-fit. This will save computational run time by not computing latent\n# variables during a any model-fit which is unsuccessful.\n\nlatent_during_fit: false # Whether to output the `latent.csv`, `latent.results` and `latent_summary.json` files during the fit when it performs on-the-fly output.\nlatent_after_fit: true # If `latent_during_fit` is False, whether to output the `latent.csv`, `latent.results` and `latent_summary.json` files after the fit is complete.\nlatent_draw_via_pdf : true # Whether to draw latent variable values via the PDF of every sample, which uses fewer samples to estimate latent variable errors. If False, latent variable values are drawn from every sample.\nlatent_draw_via_pdf_size : 100 # The number of samples drawn to estimate latent variable errors if `latent_draw_via_pdf` is True.\nlatent_csv: true # Whether to ouptut the `latent.csv` file.\nlatent_results: true # Whether to output the `latent.results` file.\n\n# Other Files:\n\nsearch_log: true # `search.log`: logging produced whilst running the fit method\n\nmodel_graph: false # `model.graph`: graphical-model factor graph summary. Only meaningful for graphical models (autofit.graphical); empty for ordinary PriorModel/Collection fits, so disabled by default.\n\n# `model.png`: the model figure drawn beside `model.info` (af.ModelPlotter). Opt-in until the\n# model-figures epic's phase-3 acceptance renders pass. Unlike other keys an absent key means off.\nmodel_figure: false\n","tooling":false,"top_keys":["default","samples","samples_weight_threshold","search_internal","start_point","latent_during_fit","latent_after_fit","latent_draw_via_pdf","latent_draw_via_pdf_size","latent_csv","latent_results","search_log","model_graph","model_figure"]},{"error":null,"keys":[{"c":"","k":"Basis","line":1}],"lines":1,"path":"priors/basis.yaml","prior":true,"priors":[],"repo":"autogalaxy_workspace","text":"Basis: {}\n","tooling":false,"top_keys":["Basis"]},{"error":null,"keys":[{"c":"","k":"model.FlatLambdaCDM","line":1},{"c":"","k":"model.FlatLambdaCDM.H0","line":2},{"c":"","k":"model.FlatLambdaCDM.H0.type","line":3},{"c":"","k":"model.FlatLambdaCDM.H0.value","line":4},{"c":"","k":"model.FlatLambdaCDM.Om0","line":5},{"c":"","k":"model.FlatLambdaCDM.Om0.type","line":6},{"c":"","k":"model.FlatLambdaCDM.Om0.value","line":7},{"c":"","k":"model.FlatLambdaCDM.Tcmb0","line":8},{"c":"","k":"model.FlatLambdaCDM.Tcmb0.type","line":9},{"c":"","k":"model.FlatLambdaCDM.Tcmb0.value","line":10},{"c":"","k":"model.FlatLambdaCDM.Neff","line":11},{"c":"","k":"model.FlatLambdaCDM.Neff.type","line":12},{"c":"","k":"model.FlatLambdaCDM.Neff.value","line":13},{"c":"","k":"model.FlatLambdaCDM.m_nu","line":14},{"c":"","k":"model.FlatLambdaCDM.m_nu.type","line":15},{"c":"","k":"model.FlatLambdaCDM.m_nu.value","line":16},{"c":"","k":"model.FlatLambdaCDM.Ob0","line":17},{"c":"","k":"model.FlatLambdaCDM.Ob0.type","line":18},{"c":"","k":"model.FlatLambdaCDM.Ob0.value","line":19}],"lines":19,"path":"priors/cosmology.yaml","prior":true,"priors":[{"a":"value 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value: 0.06\n  Ob0:\n    type: Constant\n    value: 0.04897","tooling":false,"top_keys":["model.FlatLambdaCDM"]},{"error":null,"keys":[{"c":"","k":"DatasetModel","line":1},{"c":"","k":"DatasetModel.background_sky_level","line":2},{"c":"","k":"DatasetModel.background_sky_level.type","line":3},{"c":"","k":"DatasetModel.background_sky_level.value","line":4},{"c":"","k":"DatasetModel.grid_offset_0","line":5},{"c":"","k":"DatasetModel.grid_offset_0.type","line":6},{"c":"","k":"DatasetModel.grid_offset_0.value","line":7},{"c":"","k":"DatasetModel.grid_offset_1","line":8},{"c":"","k":"DatasetModel.grid_offset_1.type","line":9},{"c":"","k":"DatasetModel.grid_offset_1.value","line":10}],"lines":10,"path":"priors/dataset_model.yaml","prior":true,"priors":[{"a":"value 0.0","b":"","cls":"DatasetModel","limits":"","line":2,"param":"background_sky_level","type":"Constant","width":""},{"a":"value 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ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n","tooling":false,"top_keys":["Ellipse"]},{"error":null,"keys":[{"c":"","k":"EllipseMultipole","line":1},{"c":"","k":"EllipseMultipole.multipole_comps_0","line":2},{"c":"","k":"EllipseMultipole.multipole_comps_0.type","line":3},{"c":"","k":"EllipseMultipole.multipole_comps_0.lower_limit","line":4},{"c":"","k":"EllipseMultipole.multipole_comps_0.upper_limit","line":5},{"c":"","k":"EllipseMultipole.multipole_comps_0.width_modifier","line":6},{"c":"","k":"EllipseMultipole.multipole_comps_0.width_modifier.type","line":7},{"c":"","k":"EllipseMultipole.multipole_comps_0.width_modifier.value","line":8},{"c":"","k":"EllipseMultipole.multipole_comps_0.limits","line":9},{"c":"","k":"EllipseMultipole.multipole_comps_0.limits.lower","line":10},{"c":"","k":"EllipseMultipole.multipole_comps_0.limits.upper","line":11},{"c":"","k":"EllipseMultipole.multipole_comps_1","line":12},{"c":"","k":"EllipseMultipole.multipole_comps_1.type","line":13},{"c":"","k":"EllipseMultipole.multipole_comps_1.lower_limit","line":14},{"c":"","k":"EllipseMultipole.multipole_comps_1.upper_limit","line":15},{"c":"","k":"EllipseMultipole.multipole_comps_1.width_modifier","line":16},{"c":"","k":"EllipseMultipole.multipole_comps_1.width_modifier.type","line":17},{"c":"","k":"EllipseMultipole.multipole_comps_1.width_modifier.value","line":18},{"c":"","k":"EllipseMultipole.multipole_comps_1.limits","line":19},{"c":"","k":"EllipseMultipole.multipole_comps_1.limits.lower","line":20},{"c":"","k":"EllipseMultipole.multipole_comps_1.limits.upper","line":21}],"lines":21,"path":"priors/ellipse/ellipse_multipole.yaml","prior":true,"priors":[{"a":"lower 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inf\n","tooling":false,"top_keys":["EllipseMultipole"]},{"error":null,"keys":[{"c":"","k":"Redshift","line":1},{"c":"","k":"Redshift.redshift","line":2},{"c":"","k":"Redshift.redshift.type","line":3},{"c":"","k":"Redshift.redshift.lower_limit","line":4},{"c":"","k":"Redshift.redshift.upper_limit","line":5},{"c":"","k":"Redshift.redshift.width_modifier","line":6},{"c":"","k":"Redshift.redshift.width_modifier.type","line":7},{"c":"","k":"Redshift.redshift.width_modifier.value","line":8},{"c":"","k":"Redshift.redshift.limits","line":9},{"c":"","k":"Redshift.redshift.limits.lower","line":10},{"c":"","k":"Redshift.redshift.limits.upper","line":11}],"lines":11,"path":"priors/galaxy/redshift.yaml","prior":true,"priors":[{"a":"lower 0.0","b":"upper 3.0","cls":"Redshift","limits":"[0.0, inf]","line":2,"param":"redshift","type":"Uniform","width":"Absolute 1.0"}],"repo":"autogalaxy_workspace","text":"Redshift:\n  redshift:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 3.0\n    width_modifier:\n      type: Absolute\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: 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Sph.effective_radius.upper_limit","line":80},{"c":"","k":"DevVaucouleursSph.effective_radius.width_modifier","line":81},{"c":"","k":"DevVaucouleursSph.effective_radius.width_modifier.type","line":82},{"c":"","k":"DevVaucouleursSph.effective_radius.width_modifier.value","line":83},{"c":"","k":"DevVaucouleursSph.effective_radius.limits","line":84},{"c":"","k":"DevVaucouleursSph.effective_radius.limits.lower","line":85},{"c":"","k":"DevVaucouleursSph.effective_radius.limits.upper","line":86}],"lines":86,"path":"priors/light/linear/dev_vaucouleurs.yaml","prior":true,"priors":[{"a":"mean 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Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\nDevVaucouleursSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["DevVaucouleurs","DevVaucouleursSph"]},{"error":null,"keys":[{"c":"","k":"Exponential","line":1},{"c":"","k":"Exponential.centre_0","line":2},{"c":"","k":"Exponential.centre_0.type","line":3},{"c":"","k":"Exponential.centre_0.mean","line":4},{"c":"","k":"Exponential.centre_0.sigma","line":5},{"c":"","k":"Exponential.centre_0.width_modifier","line":6},{"c":"","k":"Exponential.centre_0.width_modifier.type","line":7},{"c":"","k":"Exponential.centre_0.width_modifier.value","line":8},{"c":"","k":"Exponential.centre_0.limits","line":9},{"c":"","k":"Exponential.centre_0.limits.lower","line":10},{"c":"","k":"Exponential.centre_0.limits.upper","line":11},{"c":"","k":"Exponential.centre_1","line":12},{"c":"","k":"Exponential.centre_1.type","line":13},{"c":"","k":"Exponential.centre_1.mean","line":14},{"c":"","k":"Exponential.centre_1.sigma","line":15},{"c":"","k":"Exponential.centre_1.width_modifier","line":16},{"c":"","k":"Exponential.centre_1.width_modifier.type","line":17},{"c":"","k":"Exponential.centre_1.width_modifier.value","line":18},{"c":"","k":"Exponential.centre_1.limits","line":19},{"c":"","k":"Exponential.centre_1.limits.lower","line":20},{"c":"","k":"Exponential.centre_1.limits.upper","line":21},{"c":"","k":"Exponential.effective_radius","line":22},{"c":"","k":"Exponential.effective_radius.type","line":23},{"c":"","k":"Exponential.effective_radius.lower_limit","line":24},{"c":"","k":"Exponential.effective_radius.upper_limit","line":25},{"c":"","k":"Exponential.effective_radius.width_modifier","line":26},{"c":"","k":"Exponential.effective_radius.width_modifier.type","line":27},{"c":"","k":"Exponential.effective_radius.width_modifier.value","line":28},{"c":"","k":"Exponential.effective_radius.limits","line":29},{"c":"","k":"Exponential.effective_radius.limits.lower","line":30},{"c":"","k":"Exponential.effective_radius.limits.upper","line":31},{"c":"","k":"Exponential.ell_comps_0","line":32},{"c":"","k":"Exponential.ell_comps_0.type","line":33},{"c":"","k":"Exponential.ell_comps_0.mean","line":34},{"c":"","k":"Exponential.ell_comps_0.sigma","line":35},{"c":"","k":"Exponential.ell_comps_0.lower_limit","line":36},{"c":"","k":"Exponential.ell_comps_0.upper_limit","line":37},{"c":"","k":"Exponential.ell_comps_0.width_modifier","line":38},{"c":"","k":"Exponential.ell_comps_0.width_modifier.type","line":39},{"c":"","k":"Exponential.ell_comps_0.width_modifier.value","line":40},{"c":"","k":"Exponential.ell_comps_0.limits","line":41},{"c":"","k":"Exponential.ell_comps_0.limits.lower","line":42},{"c":"","k":"Exponential.ell_comps_0.limits.upper","line":43},{"c":"","k":"Exponential.ell_comps_1","line":44},{"c":"","k":"Exponential.ell_comps_1.type","line":45},{"c":"","k":"Exponential.ell_comps_1.mean","line":46},{"c":"","k":"Exponential.ell_comps_1.sigma","line":47},{"c":"","k":"Exponential.ell_comps_1.lower_limit","line":48},{"c":"","k":"Exponential.ell_comps_1.upper_limit","line":49},{"c":"","k":"Exponential.ell_comps_1.width_modifier","line":50},{"c":"","k":"Exponential.ell_comps_1.width_modifier.type","line":51},{"c":"","k":"Exponential.ell_comps_1.width_modifier.value","line":52},{"c":"","k":"Exponential.ell_comps_1.limits","line":53},{"c":"","k":"Exponential.ell_comps_1.limits.lower","line":54},{"c":"","k":"Exponential.ell_comps_1.limits.upper","line":55},{"c":"","k":"ExponentialSph","line":56},{"c":"","k":"ExponentialSph.centre_0","line":57},{"c":"","k":"ExponentialSph.centre_0.type","line":58},{"c":"","k":"ExponentialSph.centre_0.mean","line":59},{"c":"","k":"ExponentialSph.centre_0.sigma","line":60},{"c":"","k":"ExponentialSph.centre_0.width_modifier","line":61},{"c":"","k":"ExponentialSph.centre_0.width_modifier.type","line":62},{"c":"","k":"ExponentialSph.centre_0.width_modifier.value","line":63},{"c":"","k":"ExponentialSph.centre_0.limits","line":64},{"c":"","k":"ExponentialSph.centre_0.limits.lower","line":65},{"c":"","k":"ExponentialSph.centre_0.limits.upper","line":66},{"c":"","k":"ExponentialSph.centre_1","line":67},{"c":"","k":"ExponentialSph.centre_1.type","line":68},{"c":"","k":"ExponentialSph.centre_1.mean","line":69},{"c":"","k":"ExponentialSph.centre_1.sigma","line":70},{"c":"","k":"ExponentialSph.centre_1.width_modifier","line":71},{"c":"","k":"ExponentialSph.centre_1.width_modifier.type","line":72},{"c":"","k":"ExponentialSph.centre_1.width_modifier.value","line":73},{"c":"","k":"ExponentialSph.centre_1.limits","line":74},{"c":"","k":"ExponentialSph.centre_1.limits.lower","line":75},{"c":"","k":"ExponentialSph.centre_1.limits.upper","line":76},{"c":"","k":"ExponentialSph.effective_radius","line":77},{"c":"","k":"ExponentialSph.effective_radius.type","line":78},{"c":"","k":"ExponentialSph.effective_radius.lower_limit","line":79},{"c":"","k":"ExponentialSph.effective_radius.upper_limit","line":80},{"c":"","k":"ExponentialSph.effective_radius.width_modifier","line":81},{"c":"","k":"ExponentialSph.effective_radius.width_modifier.type","line":82},{"c":"","k":"Expon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0.0","b":"\u03c3 0.3","cls":"Exponential","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Exponential","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 30.0","cls":"Exponential","limits":"[0.0, inf]","line":22,"param":"effective_radius","type":"Uniform","width":"Relative 1.0"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Exponential","limits":"[-1.0, 1.0]","line":32,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Exponential","limits":"[-1.0, 1.0]","line":44,"param":"ell_comps_1","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ExponentialSph","limits":"[-inf, inf]","line":57,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ExponentialSph","limits":"[-inf, inf]","line":67,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 30.0","cls":"ExponentialSph","limits":"[0.0, inf]","line":77,"param":"effective_radius","type":"Uniform","width":"Relative 1.0"}],"repo":"autogalaxy_workspace","text":"Exponential:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\nExponentialSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["Exponential","ExponentialSph"]},{"error":null,"keys":[{"c":"","k":"ExponentialCore","line":1},{"c":"","k":"ExponentialCore.centre_0","line":2},{"c":"","k":"ExponentialCore.centre_0.type","line":3},{"c":"","k":"ExponentialCore.centre_0.mean","line":4},{"c":"","k":"ExponentialCore.centre_0.sigma","line":5},{"c":"","k":"ExponentialCore.centre_0.width_modifier","line":6},{"c":"","k":"ExponentialCore.centre_0.width_modifier.type","line":7},{"c":"","k":"ExponentialCore.centre_0.width_modifier.value","line":8},{"c":"","k":"ExponentialCore.centre_0.limits","line":9},{"c":"","k":"ExponentialCore.centre_0.limits.lower","line":10},{"c":"","k":"ExponentialCore.centre_0.limits.upper","line":11},{"c":"","k":"ExponentialCore.centre_1","line":12},{"c":"","k":"ExponentialCore.centre_1.type","line":13},{"c":"","k":"ExponentialCore.centre_1.mean","line":14},{"c":"","k":"ExponentialCore.centre_1.sigma","line":15},{"c":"","k":"ExponentialCore.centre_1.width_modifier","line":16},{"c":"","k":"ExponentialCore.centre_1.width_modifier.type","line":17},{"c":"","k":"ExponentialCore.centre_1.width_modifier.value","line":18},{"c":"","k":"ExponentialCore.centre_1.limits","line":19},{"c":"","k":"ExponentialCore.centre_1.limits.lower","line":20},{"c":"","k":"ExponentialCore.centre_1.limits.upper","line":21},{"c":"","k":"ExponentialCore.effective_radius","line":22},{"c":"","k":"ExponentialCore.effective_radius.type","line":23},{"c":"","k":"ExponentialCore.effective_radius.lower_limit","line":24},{"c":"","k":"ExponentialCore.effective_radius.upper_limit","line":25},{"c":"","k":"ExponentialCore.effective_radius.width_modifier","line":26},{"c":"","k":"ExponentialCore.effective_radius.width_modifier.type","line":27},{"c":"","k":"ExponentialCore.effective_radius.width_modifier.value","line":28},{"c":"","k":"ExponentialCore.effective_radius.limits","line":29},{"c":"","k":"ExponentialCore.effective_radius.limits.lower","line":30},{"c":"","k":"ExponentialCore.effective_radius.limits.upper","line":31},{"c":"","k":"ExponentialCore.ell_comps_0","line":32},{"c":"","k":"ExponentialCore.ell_comps_0.type","line":33},{"c":"","k":"ExponentialCore.ell_comps_0.mean","line":34},{"c":"","k":"ExponentialCore.ell_comps_0.sigma","line":35},{"c":"","k":"ExponentialCore.ell_comps_0.lower_limit","line":36},{"c":"","k":"ExponentialCore.ell_comps_0.upper_limit","line":37},{"c":"","k":"ExponentialCore.ell_comps_0.width_modifier","line":38},{"c":"","k":"ExponentialCore.ell_comps_0.width_modifier.type","line":39},{"c":"","k":"ExponentialCore.ell_comps_0.width_modifier.value","line":40},{"c":"","k":"ExponentialCore.ell_comps_0.limits","line":41},{"c":"","k":"ExponentialCore.ell_comps_0.limits.lower","line":42},{"c":"","k":"ExponentialCore.ell_comps_0.limits.upper","line":43},{"c":"","k":"ExponentialCore.ell_comps_1","line":44},{"c":"","k":"ExponentialCore.ell_comps_1.type","line":45},{"c":"","k":"ExponentialCore.ell_comps_1.mean","line":46},{"c":"","k":"ExponentialCore.ell_comps_1.sigma","line":47},{"c":"","k":"ExponentialCore.ell_comps_1.lower_limit","line":48},{"c":"","k":"ExponentialCore.ell_comps_1.upper_limit","line":49},{"c":"","k":"ExponentialCore.ell_comps_1.width_modifier","line":50},{"c":"","k":"ExponentialCore.ell_comps_1.width_modifier.type","line":51},{"c":"","k":"ExponentialCore.ell_comps_1.width_modifier.value","line":52},{"c":"","k":"ExponentialCore.ell_comps_1.limits","line":53},{"c":"","k":"ExponentialCore.ell_comps_1.limits.lower","line":54},{"c":"","k":"ExponentialCore.ell_comps_1.limits.upper","line":55},{"c":"","k":"ExponentialCore.alpha","line":56},{"c":"","k":"ExponentialCore.alpha.type","line":57},{"c":"","k":"ExponentialCore.alpha.value","line":58},{"c":"","k":"ExponentialCore.gamma","line":59},{"c":"","k":"ExponentialCore.gamma.type","line":60},{"c":"","k":"ExponentialCore.gamma.value","line":61},{"c":"","k":"ExponentialCore.radius_break","line":62},{"c":"","k":"ExponentialCore.radius_break.type","line":63},{"c":"","k":"ExponentialCore.radius_break.value","line":64},{"c":"","k":"ExponentialCoreSph","line":65},{"c":"","k":"ExponentialCoreSph.alpha","line":66},{"c":"","k":"ExponentialCoreSph.alpha.type","line":67},{"c":"","k":"ExponentialCoreSph.alpha.value","line":68},{"c":"","k":"ExponentialCoreSph.centre_0","line":69},{"c":"","k":"ExponentialCoreSph.centre_0.type","line":70},{"c":"","k":"ExponentialCoreSph.centre_0.mean","line":71},{"c":"","k":"ExponentialCoreSph.centre_0.sigma","line":72},{"c":"","k":"ExponentialCoreSph.centre_0.width_modifier","line":73},{"c":"","k":"ExponentialCoreSph.centre_0.width_modifier.type","line":74},{"c":"","k":"ExponentialCoreSph.centre_0.width_modifier.value","line":75},{"c":"","k":"ExponentialCoreSph.centre_0.limits","line":76},{"c":"","k":"ExponentialCoreSph.centre_0.limits.lower","line":77},{"c":"","k":"ExponentialCoreSph.centre_0.limits.upper","line":78},{"c":"","k":"ExponentialCoreSph.centre_1","line":79},{"c":"","k":"ExponentialCoreSph.centre_1.type","line":80},{"c":"","k":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0.0","b":"\u03c3 0.3","cls":"ExponentialCore","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ExponentialCore","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 30.0","cls":"ExponentialCore","limits":"[0.0, inf]","line":22,"param":"effective_radius","type":"Uniform","width":"Relative 1.0"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ExponentialCore","limits":"[-1.0, 1.0]","line":32,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ExponentialCore","limits":"[-1.0, 1.0]","line":44,"param":"ell_comps_1","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"value 3.0","b":"","cls":"ExponentialCore","limits":"","line":56,"param":"alpha","type":"Constant","width":""},{"a":"value 0.25","b":"","cls":"ExponentialCore","limits":"","line":59,"param":"gamma","type":"Constant","width":""},{"a":"value 0.025","b":"","cls":"ExponentialCore","limits":"","line":62,"param":"radius_break","type":"Constant","width":""},{"a":"value 3.0","b":"","cls":"ExponentialCoreSph","limits":"","line":66,"param":"alpha","type":"Constant","width":""},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ExponentialCoreSph","limits":"[-inf, inf]","line":69,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ExponentialCoreSph","limits":"[-inf, inf]","line":79,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 30.0","cls":"ExponentialCoreSph","limits":"[0.0, inf]","line":89,"param":"effective_radius","type":"Uniform","width":"Relative 1.0"},{"a":"value 0.25","b":"","cls":"ExponentialCoreSph","limits":"","line":99,"param":"gamma","type":"Constant","width":""},{"a":"value 0.025","b":"","cls":"ExponentialCoreSph","limits":"","line":102,"param":"radius_break","type":"Constant","width":""}],"repo":"autogalaxy_workspace","text":"ExponentialCore:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  alpha:\n    type: Constant\n    value: 3.0      \n  gamma:\n    type: Constant\n    value: 0.25\n  radius_break:\n    type: Constant\n    value: 0.025\nExponentialCoreSph:\n  alpha:\n    type: Constant\n    value: 3.0\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  gamma:\n    type: Constant\n    value: 0.25\n  radius_break:\n    type: Constant\n    value: 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0.0","b":"upper 25.0","cls":"Gaussian","limits":"[0.0, inf]","line":2,"param":"sigma","type":"Uniform","width":"Relative 0.5"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Gaussian","limits":"[-inf, inf]","line":12,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Gaussian","limits":"[-inf, inf]","line":22,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Gaussian","limits":"[-1.0, 1.0]","line":32,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Gaussian","limits":"[-1.0, 1.0]","line":44,"param":"ell_comps_1","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"lower 0.0","b":"upper 25.0","cls":"GaussianSph","limits":"[0.0, inf]","line":57,"param":"sigma","type":"Uniform","width":"Relative 0.5"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"GaussianSph","limits":"[-inf, inf]","line":67,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"GaussianSph","limits":"[-inf, inf]","line":77,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"}],"repo":"autogalaxy_workspace","text":"Gaussian:\n  sigma:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 25.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\nGaussianSph:\n  sigma:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 25.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: 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0.0","b":"upper 1.0","cls":"Moffat","limits":"[0.0, inf]","line":2,"param":"alpha","type":"Uniform","width":"Relative 0.5"},{"a":"lower 1.0","b":"upper 5.0","cls":"Moffat","limits":"[0.0, inf]","line":12,"param":"beta","type":"Uniform","width":"Relative 0.5"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Moffat","limits":"[-inf, inf]","line":22,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Moffat","limits":"[-inf, inf]","line":32,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Moffat","limits":"[-1.0, 1.0]","line":42,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Moffat","limits":"[-1.0, 1.0]","line":54,"param":"ell_comps_1","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"lower 0.0","b":"upper 1.0","cls":"MoffatSph","limits":"[0.0, inf]","line":67,"param":"alpha","type":"Uniform","width":"Relative 0.5"},{"a":"lower 1.0","b":"upper 5.0","cls":"MoffatSph","limits":"[0.0, inf]","line":77,"param":"beta","type":"Uniform","width":"Relative 0.5"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"MoffatSph","limits":"[-inf, inf]","line":87,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"MoffatSph","limits":"[-inf, inf]","line":97,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"}],"repo":"autogalaxy_workspace","text":"Moffat:\n  alpha:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  beta:\n    type: Uniform\n    lower_limit: 1.0\n    upper_limit: 5.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      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0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  sersic_index:\n    type: Uniform\n    lower_limit: 0.8\n    upper_limit: 5.0\n    width_modifier:\n      type: Absolute\n      value: 1.5\n    limits:\n      lower: 0.8\n      upper: 5.0      \n  alpha:\n    type: Constant\n    value: 3.0      \n  gamma:\n    type: Constant\n    value: 0.25\n  radius_break:\n    type: Constant\n    value: 0.025\nSersicCoreSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  sersic_index:\n    type: Uniform\n    lower_limit: 0.8\n    upper_limit: 5.0\n    width_modifier:\n      type: Absolute\n      value: 1.5\n    limits:\n      lower: 0.8\n      upper: 5.0\n  alpha:\n    type: Constant\n    value: 3.0      \n  gamma:\n    type: Constant\n    value: 0.25\n  radius_break:\n    type: Constant\n    value: 0.025\n","tooling":false,"top_keys":["SersicCore","SersicCoreSph"]},{"error":null,"keys":[{"c":"","k":"Gaussian","line":1},{"c":"","k":"Gaussian.sigma","line":2},{"c":"","k":"Gaussian.sigma.type","line":3},{"c":"","k":"Gaussian.sigma.lower_limit","line":4},{"c":"","k":"Gaussian.sigma.upper_limit","line":5},{"c":"","k":"Gaussian.sigma.width_modifier","line":6},{"c":"","k":"Gaussian.sigma.width_modifier.type","line":7},{"c":"","k":"Gaussian.sigma.width_modifier.value","line":8},{"c":"","k":"Gaussian.sigma.limits","line":9},{"c":"","k":"Gaussian.sigma.limits.lower","line":10},{"c":"","k":"Gaussian.sigma.limits.upper","line":11},{"c":"","k":"Gaussian.centre_0","line":12},{"c":"","k":"Gaussian.centre_0.type","line":13},{"c":"","k":"Gaussian.centre_0.mean","line":14},{"c":"","k":"Gaussian.centre_0.sigma","line":15},{"c":"","k":"Gaussian.centre_0.width_modifier","line":16},{"c":"","k":"Gaussian.centre_0.width_modifier.type","line":17},{"c":"","k":"Gaussian.centre_0.width_modifier.value","line":18},{"c":"","k":"Gaussian.centre_0.limits","line":19},{"c":"","k":"Gaussian.centre_0.limits.lower","line":20},{"c":"","k":"Gaussian.centre_0.limits.upper","line":21},{"c":"","k":"Gaussian.centre_1","line":22},{"c":"","k":"Gaussian.centre_1.type","line":23},{"c":"","k":"Gaussian.centre_1.mean","line":24},{"c":"","k":"Gaussian.centre_1.sigma","line":25},{"c":"","k":"Gaussian.centre_1.width_modifier","line":26},{"c":"","k":"Gaussian.centre_1.width_modifier.type","line":27},{"c":"","k":"Gaussian.centre_1.width_modifier.value","line":28},{"c":"","k":"Gaussian.centre_1.limits","line":29},{"c":"","k":"Gaussian.centre_1.limits.lower","line":30},{"c":"","k":"Gaussian.centre_1.limits.upper","line":31},{"c":"","k":"Gaussian.ell_comps_0","line":32},{"c":"","k":"Gaussian.ell_comps_0.type","line":33},{"c":"","k":"Gaussian.ell_comps_0.mean","line":34},{"c":"","k":"Gaussian.ell_comps_0.sigma","line":35},{"c":"","k":"Gaussian.ell_comps_0.lower_limit","line":36},{"c":"","k":"Gaussian.ell_comps_0.upper_limit","line":37},{"c":"","k":"Gaussian.ell_comps_0.width_modifier","line":38},{"c":"","k":"Gaussian.ell_comps_0.width_modifier.type","line":39},{"c":"","k":"Gaussian.ell_comps_0.width_modifier.value","line":40},{"c":"","k":"Gaussian.ell_comps_0.limits","line":41},{"c":"","k":"Gaussian.ell_comps_0.limits.lower","line":42},{"c":"","k":"Gaussian.ell_comps_0.limits.upper","line":43},{"c":"","k":"Gaussian.ell_comps_1","line":44},{"c":"","k":"Gaussian.ell_comps_1.type","line":45},{"c":"","k":"Gaussian.ell_comps_1.mean","line":46},{"c":"","k":"Gaussian.ell_comps_1.sigma","line":47},{"c":"","k":"Gaussian.ell_comps_1.lower_limit","line":48},{"c":"","k":"Gaussian.ell_comps_1.upper_limit","line":49},{"c":"","k":"Gaussian.ell_comps_1.width_modifier","line":50},{"c":"","k":"Gaussian.ell_comps_1.width_modifier.type","line":51},{"c":"","k":"Gaussian.ell_comps_1.width_modifier.value","line":52},{"c":"","k":"Gaussian.ell_comps_1.limits","line":53},{"c":"","k":"Gaussian.ell_comps_1.limits.lower","line":54},{"c":"","k":"Gaussian.ell_comps_1.limits.upper","line":55}],"lines":55,"path":"priors/light/linear_operated/gaussian.yaml","prior":true,"priors":[{"a":"lower 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mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n","tooling":false,"top_keys":["Gaussian"]},{"error":null,"keys":[{"c":"","k":"Moffat","line":1},{"c":"","k":"Moffat.alpha","line":2},{"c":"","k":"Moffat.alpha.type","line":3},{"c":"","k":"Moffat.alpha.lower_limit","line":4},{"c":"","k":"Moffat.alpha.upper_limit","line":5},{"c":"","k":"Moffat.alpha.width_modifier","line":6},{"c":"","k":"Moffat.alpha.width_modifier.type","line":7},{"c":"","k":"Moffat.alpha.width_modifier.value","line":8},{"c":"","k":"Moffat.alpha.limits","line":9},{"c":"","k":"Moffat.alpha.limits.lower","line":10},{"c":"","k":"Moffat.alpha.limits.upper","line":11},{"c":"","k":"Moffat.beta","line":12},{"c":"","k":"Moffat.beta.type","line":13},{"c":"","k":"Moffat.beta.lower_limit","line":14},{"c":"","k":"Moffat.beta.upper_limit","line":15},{"c":"","k":"Moffat.beta.width_modifier","line":16},{"c":"","k":"Moffat.beta.width_modifier.type","line":17},{"c":"","k":"Moffat.beta.width_modifier.value","line":18},{"c":"","k":"Moffat.beta.limits","line":19},{"c":"","k":"Moffat.beta.limits.lower","line":20},{"c":"","k":"Moffat.beta.limits.upper","line":21},{"c":"","k":"Moffat.centre_0","line":22},{"c":"","k":"Moffat.centre_0.type","line":23},{"c":"","k":"Moffat.centre_0.mean","line":24},{"c":"","k":"Moffat.centre_0.sigma","line":25},{"c":"","k":"Moffat.centre_0.width_modifier","line":26},{"c":"","k":"Moffat.centre_0.width_modifier.type","line":27},{"c":"","k":"Moffat.centre_0.width_modifier.value","line":28},{"c":"","k":"Moffat.centre_0.limits","line":29},{"c":"","k":"Moffat.centre_0.limits.lower","line":30},{"c":"","k":"Moffat.centre_0.limits.upper","line":31},{"c":"","k":"Moffat.centre_1","line":32},{"c":"","k":"Moffat.centre_1.type","line":33},{"c":"","k":"Moffat.centre_1.mean","line":34},{"c":"","k":"Moffat.centre_1.sigma","line":35},{"c":"","k":"Moffat.centre_1.width_modifier","line":36},{"c":"","k":"Moffat.centre_1.width_modifier.type","line":37},{"c":"","k":"Moffat.centre_1.width_modifier.value","line":38},{"c":"","k":"Moffat.centre_1.limits","line":39},{"c":"","k":"Moffat.centre_1.limits.lower","line":40},{"c":"","k":"Moffat.centre_1.limits.upper","line":41},{"c":"","k":"Moffat.ell_comps_0","line":42},{"c":"","k":"Moffat.ell_comps_0.type","line":43},{"c":"","k":"Moffat.ell_comps_0.mean","line":44},{"c":"","k":"Moffat.ell_comps_0.sigma","line":45},{"c":"","k":"Moffat.ell_comps_0.lower_limit","line":46},{"c":"","k":"Moffat.ell_comps_0.upper_limit","line":47},{"c":"","k":"Moffat.ell_comps_0.width_modifier","line":48},{"c":"","k":"Moffat.ell_comps_0.width_modifier.type","line":49},{"c":"","k":"Moffat.ell_comps_0.width_modifier.value","line":50},{"c":"","k":"Moffat.ell_comps_0.limits","line":51},{"c":"","k":"Moffat.ell_comps_0.limits.lower","line":52},{"c":"","k":"Moffat.ell_comps_0.limits.upper","line":53},{"c":"","k":"Moffat.ell_comps_1","line":54},{"c":"","k":"Moffat.ell_comps_1.type","line":55},{"c":"","k":"Moffat.ell_comps_1.mean","line":56},{"c":"","k":"Moffat.ell_comps_1.sigma","line":57},{"c":"","k":"Moffat.ell_comps_1.lower_limit","line":58},{"c":"","k":"Moffat.ell_comps_1.upper_limit","line":59},{"c":"","k":"Moffat.ell_comps_1.width_modifier","line":60},{"c":"","k":"Moffat.ell_comps_1.width_modifier.type","line":61},{"c":"","k":"Moffat.ell_comps_1.width_modifier.value","line":62},{"c":"","k":"Moffat.ell_comps_1.limits","line":63},{"c":"","k":"Moffat.ell_comps_1.limits.lower","line":64},{"c":"","k":"Moffat.ell_comps_1.limits.upper","line":65}],"lines":65,"path":"priors/light/linear_operated/moffat.yaml","prior":true,"priors":[{"a":"lower 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 type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  beta:\n    type: Uniform\n    lower_limit: 1.0\n    upper_limit: 5.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 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upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: 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0.5"}],"repo":"autogalaxy_workspace","text":"Moffat:\n  alpha:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  beta:\n    type: Uniform\n    lower_limit: 1.0\n    upper_limit: 5.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: 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-1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  sersic_index:\n    type: Uniform\n    lower_limit: 0.8\n    upper_limit: 5.0\n    width_modifier:\n      type: Absolute\n      value: 1.5\n    limits:\n      lower: 0.8\n      upper: 5.0\n","tooling":false,"top_keys":["Sersic"]},{"error":null,"keys":[{"c":"","k":"ShapeletCartesianSph","line":1},{"c":"","k":"ShapeletCartesianSph.centre_0","line":2},{"c":"","k":"ShapeletCartesianSph.centre_0.type","line":3},{"c":"","k":"ShapeletCartesianSph.centre_0.mean","line":4},{"c":"","k":"ShapeletCartesianSph.centre_0.sigma","line":5},{"c":"","k":"ShapeletCartesianSph.centre_0.width_modifier","line":6},{"c":"","k":"ShapeletCartesianSph.centre_0.width_modifier.type","line":7},{"c":"","k":"ShapeletCartesianSph.centre_0.width_modifier.value","line":8},{"c":"","k":"ShapeletCartesianSph.centre_0.limits","line":9},{"c":"","k":"ShapeletCartesianSph.centre_0.limits.lower","line":10},{"c":"","k":"ShapeletCartesianSph.centre_0.limits.upper","line":11},{"c":"","k":"ShapeletCartesianSph.centre_1","line":12},{"c":"","k":"ShapeletCartesianSph.centre_1.type","line":13},{"c":"","k":"ShapeletCartesianSph.centre_1.mean","line":14},{"c":"","k":"ShapeletCartesianSph.centre_1.sigma","line":15},{"c":"","k":"ShapeletCartesianSph.centre_1.width_modifier","line":16},{"c":"","k":"ShapeletCartesianSph.centre_1.width_modifier.type","line":17},{"c":"","k":"ShapeletCartesianSph.centre_1.width_modifier.value","line":18},{"c":"","k":"ShapeletCartesianSph.centre_1.limits","line":19},{"c":"","k":"ShapeletCartesianSph.centre_1.limits.lower","line":20},{"c":"","k":"ShapeletCartesianSph.centre_1.limits.upper","line":21},{"c":"","k":"ShapeletCartesianSph.beta","line":22},{"c":"","k":"ShapeletCartesianSph.beta.type","line":23},{"c":"","k":"ShapeletCartesianSph.beta.lower_limit","line":24},{"c":"","k":"ShapeletCartesianSph.beta.upper_limit","line":25},{"c":"","k":"ShapeletCartesianSph.beta.width_modifier","line":26},{"c":"","k":"ShapeletCartesianSph.beta.width_modifier.type","line":27},{"c":"","k":"ShapeletCartesianSph.beta.width_modifier.value","line":28},{"c":"","k":"ShapeletCartesianSph.beta.limits","line":29},{"c":"","k":"ShapeletCartesianSph.beta.limits.lower","line":30},{"c":"","k":"ShapeletCartesianSph.beta.limits.upper","line":31},{"c":"","k":"ShapeletCartesian","line":32},{"c":"","k":"ShapeletCartesian.centre_0","line":33},{"c":"","k":"ShapeletCartesian.centre_0.type","line":34},{"c":"","k":"ShapeletCartesian.centre_0.mean","line":35},{"c":"","k":"ShapeletCartesian.centre_0.sigma","line":36},{"c":"","k":"ShapeletCartesian.centre_0.width_modifier","line":37},{"c":"","k":"ShapeletCartesian.centre_0.width_modifier.type","line":38},{"c":"","k":"ShapeletCartesian.centre_0.width_modifier.value","line":39},{"c":"","k":"ShapeletCartesian.centre_0.limits","line":40},{"c":"","k":"ShapeletCartesian.centre_0.limits.lower","line":41},{"c":"","k":"ShapeletCartesian.centre_0.limits.upper","line":42},{"c":"","k":"ShapeletCartesian.centre_1","line":43},{"c":"","k":"ShapeletCartesian.centre_1.type","line":44},{"c":"","k":"ShapeletCartesian.centre_1.mean","line":45},{"c":"","k":"ShapeletCartesian.centre_1.sigma","line":46},{"c":"","k":"ShapeletCartesian.centre_1.width_modifier","line":47},{"c":"","k":"ShapeletCartesian.centre_1.width_modifier.type","line":48},{"c":"","k":"ShapeletCartesian.centre_1.width_modifier.value","line":49},{"c":"","k":"ShapeletCartesian.centre_1.limits","line":50},{"c":"","k":"ShapeletCartesian.centre_1.limits.lower","line":51},{"c":"","k":"ShapeletCartesian.centre_1.limits.upper","line":52},{"c":"","k":"ShapeletCartesian.ell_comps_0","line":53},{"c":"","k":"ShapeletCartesian.ell_comps_0.type","line":54},{"c":"","k":"ShapeletCartesian.ell_comps_0.mean","line":55},{"c":"","k":"ShapeletCartesian.ell_comps_0.sigma","line":56},{"c":"","k":"ShapeletCartesian.ell_comps_0.lower_limit","line":57},{"c":"","k":"ShapeletCartesian.ell_comps_0.upper_limit","line":58},{"c":"","k":"ShapeletCartesian.ell_comps_0.width_modifier","line":59},{"c":"","k":"ShapeletCartesian.ell_comps_0.width_modifier.type","line":60},{"c":"","k":"ShapeletCartesian.ell_comps_0.width_modifier.value","line":61},{"c":"","k":"ShapeletCartesian.ell_comps_0.limits","line":62},{"c":"","k":"ShapeletCartesian.ell_comps_0.limits.lower","line":63},{"c":"","k":"ShapeletCartesian.ell_comps_0.limits.upper","line":64},{"c":"","k":"ShapeletCartesian.ell_comps_1","line":65},{"c":"","k":"ShapeletCartesian.ell_comps_1.type","line":66},{"c":"","k":"ShapeletCartesian.ell_comps_1.mean","line":67},{"c":"","k":"ShapeletCartesian.ell_comps_1.sigma","line":68},{"c":"","k":"ShapeletCartesian.ell_comps_1.lower_limit","line":69},{"c":"","k":"ShapeletCartesian.ell_comps_1.upper_limit","line":70},{"c":"","k":"ShapeletCartesian.ell_comps_1.width_modifier","line":71},{"c":"","k":"ShapeletCartesian.ell_comps_1.width_modifier.type","line":72},{"c":"","k":"ShapeletCartesian.ell_comps_1.width_modifier.value","line":73},{"c":"","k":"ShapeletCartesian.ell_comps_1.limits","line":74},{"c":"","k":"ShapeletCartesian.ell_comps_1.limits.lower","line":75},{"c":"","k":"ShapeletCartesian.ell_comps_1.limits.upper","line":76},{"c":"","k":"ShapeletCartesian.beta","line":77},{"c":"","k":"ShapeletCartesian.beta.ty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0.0","b":"\u03c3 0.3","cls":"ShapeletCartesianSph","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletCartesianSph","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 30.0","cls":"ShapeletCartesianSph","limits":"[0.0, inf]","line":22,"param":"beta","type":"Uniform","width":"Relative 0.5"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletCartesian","limits":"[-inf, inf]","line":33,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletCartesian","limits":"[-inf, inf]","line":43,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletCartesian","limits":"[-1.0, 1.0]","line":53,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletCartesian","limits":"[-1.0, 1.0]","line":65,"param":"ell_comps_1","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"lower 0.0","b":"upper 30.0","cls":"ShapeletCartesian","limits":"[0.0, inf]","line":77,"param":"beta","type":"Uniform","width":"Relative 0.5"}],"repo":"autogalaxy_workspace","text":"ShapeletCartesianSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  beta:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\nShapeletCartesian:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  beta:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["ShapeletCartesianSph","ShapeletCartesian"]},{"error":null,"keys":[{"c":"","k":"ShapeletExponentialSph","line":1},{"c":"","k":"ShapeletExponentialSph.centre_0","line":2},{"c":"","k":"ShapeletExponentialSph.centre_0.type","line":3},{"c":"","k":"ShapeletExponentialSph.centre_0.mean","line":4},{"c":"","k":"ShapeletExponentialSph.centre_0.sigma","line":5},{"c":"","k":"ShapeletExponentialSph.centre_0.width_modifier","line":6},{"c":"","k":"ShapeletExponentialSph.centre_0.width_modifier.type","line":7},{"c":"","k":"ShapeletExponentialSph.centre_0.width_modifier.value","line":8},{"c":"","k":"ShapeletExponentialSph.centre_0.limits","line":9},{"c":"","k":"ShapeletExponentialSph.centre_0.limits.lower","line":10},{"c":"","k":"ShapeletExponentialSph.centre_0.limits.upper","line":11},{"c":"","k":"ShapeletExponentialSph.centre_1","line":12},{"c":"","k":"ShapeletExponentialSph.centre_1.type","line":13},{"c":"","k":"ShapeletExponentialSph.centre_1.mean","line":14},{"c":"","k":"ShapeletExponentialSph.centre_1.sigma","line":15},{"c":"","k":"ShapeletExponentialSph.centre_1.width_modifier","line":16},{"c":"","k":"ShapeletExponentialSph.centre_1.width_modifier.type","line":17},{"c":"","k":"ShapeletExponentialSph.centre_1.width_modifier.value","line":18},{"c":"","k":"ShapeletExponentialSph.centre_1.limits","line":19},{"c":"","k":"ShapeletExponentialSph.centre_1.limits.lower","line":20},{"c":"","k":"ShapeletExponentialSph.centre_1.limits.upper","line":21},{"c":"","k":"ShapeletExponentialSph.beta","line":22},{"c":"","k":"ShapeletExponentialSph.beta.type","line":23},{"c":"","k":"ShapeletExponentialSph.beta.lower_limit","line":24},{"c":"","k":"ShapeletExponentialSph.beta.upper_limit","line":25},{"c":"","k":"ShapeletExponentialSph.beta.width_modifier","line":26},{"c":"","k":"ShapeletExponentialSph.beta.width_modifier.type","line":27},{"c":"","k":"ShapeletExponentialSph.beta.width_modifier.value","line":28},{"c":"","k":"ShapeletExponentialSph.beta.limits","line":29},{"c":"","k":"ShapeletExponentialSph.beta.limits.lower","line":30},{"c":"","k":"ShapeletExponentialSph.beta.limits.upper","line":31},{"c":"","k":"ShapeletExponential","line":32},{"c":"","k":"ShapeletExponential.centre_0","line":33},{"c":"","k":"ShapeletExponential.centre_0.type","line":34},{"c":"","k":"ShapeletExponential.centre_0.mean","line":35},{"c":"","k":"ShapeletExponential.centre_0.sigma","line":36},{"c":"","k":"ShapeletExponential.centre_0.width_modifier","line":37},{"c":"","k":"ShapeletExponential.centre_0.width_modifier.type","line":38},{"c":"","k":"ShapeletExponential.centre_0.width_modifier.value","line":39},{"c":"","k":"ShapeletExponential.centre_0.limits","line":40},{"c":"","k":"ShapeletExponential.centre_0.limits.lower","line":41},{"c":"","k":"ShapeletExponential.centre_0.limits.upper","line":42},{"c":"","k":"ShapeletExponential.centre_1","line":43},{"c":"","k":"ShapeletExponential.centre_1.type","line":44},{"c":"","k":"ShapeletExponential.centre_1.mean","line":45},{"c":"","k":"ShapeletExponential.centre_1.sigma","line":46},{"c":"","k":"ShapeletExponential.centre_1.width_modifier","line":47},{"c":"","k":"ShapeletExponential.centre_1.width_modifier.type","line":48},{"c":"","k":"ShapeletExponential.centre_1.width_modifier.value","line":49},{"c":"","k":"ShapeletExponential.centre_1.limits","line":50},{"c":"","k":"ShapeletExponential.centre_1.limits.lower","line":51},{"c":"","k":"ShapeletExponential.centre_1.limits.upper","line":52},{"c":"","k":"ShapeletExponential.ell_comps_0","line":53},{"c":"","k":"ShapeletExponential.ell_comps_0.type","line":54},{"c":"","k":"ShapeletExponential.ell_comps_0.mean","line":55},{"c":"","k":"ShapeletExponential.ell_comps_0.sigma","line":56},{"c":"","k":"ShapeletExponential.ell_comps_0.lower_limit","line":57},{"c":"","k":"ShapeletExponential.ell_comps_0.upper_limit","line":58},{"c":"","k":"ShapeletExponential.ell_comps_0.width_modifier","line":59},{"c":"","k":"ShapeletExponential.ell_comps_0.width_modifier.type","line":60},{"c":"","k":"ShapeletExponential.ell_comps_0.width_modifier.value","line":61},{"c":"","k":"ShapeletExponential.ell_comps_0.limits","line":62},{"c":"","k":"ShapeletExponential.ell_comps_0.limits.lower","line":63},{"c":"","k":"ShapeletExponential.ell_comps_0.limits.upper","line":64},{"c":"","k":"ShapeletExponential.ell_comps_1","line":65},{"c":"","k":"ShapeletExponential.ell_comps_1.type","line":66},{"c":"","k":"ShapeletExponential.ell_comps_1.mean","line":67},{"c":"","k":"ShapeletExponential.ell_comps_1.sigma","line":68},{"c":"","k":"ShapeletExponential.ell_comps_1.lower_limit","line":69},{"c":"","k":"ShapeletExponential.ell_comps_1.upper_limit","line":70},{"c":"","k":"ShapeletExponential.ell_comps_1.width_modifier","line":71},{"c":"","k":"ShapeletExponential.ell_comps_1.width_modifier.type","line":72},{"c":"","k":"ShapeletExponential.ell_comps_1.width_modifier.value","line":73},{"c":"","k":"ShapeletExponential.ell_comps_1.limits","line":74},{"c":"","k":"ShapeletExponential.ell_com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0.0","b":"\u03c3 0.3","cls":"ShapeletExponentialSph","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletExponentialSph","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 30.0","cls":"ShapeletExponentialSph","limits":"[0.0, inf]","line":22,"param":"beta","type":"Uniform","width":"Relative 0.5"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletExponential","limits":"[-inf, inf]","line":33,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletExponential","limits":"[-inf, inf]","line":43,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletExponential","limits":"[-1.0, 1.0]","line":53,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 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type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  beta:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: 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0.0","b":"\u03c3 0.3","cls":"ShapeletPolarSph","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletPolarSph","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 30.0","cls":"ShapeletPolarSph","limits":"[0.0, inf]","line":22,"param":"beta","type":"Uniform","width":"Relative 0.5"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletPolar","limits":"[-inf, inf]","line":33,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletPolar","limits":"[-inf, inf]","line":43,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletPolar","limits":"[-1.0, 1.0]","line":53,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletPolar","limits":"[-1.0, 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     upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  beta:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: 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width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\nChameleonSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  core_radius_0:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Absolute\n      value: 0.3\n    limits:\n      lower: 0.0\n      upper: inf\n  core_radius_1:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Absolute\n      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lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\nExponentialSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: 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TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-05\n    upper_limit: 1000.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  alpha:\n    type: Constant\n    value: 3.0      \n  gamma:\n    type: Constant\n    value: 0.25\n  radius_break:\n    type: Constant\n    value: 0.025\nExponentialCoreSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-05\n    upper_limit: 1000.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  alpha:\n    type: Constant\n    value: 3.0      \n  gamma:\n    type: Constant\n    value: 0.25      \n  radius_break:\n    type: Constant\n    value: 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width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  sersic_index:\n    type: Uniform\n    lower_limit: 0.8\n    upper_limit: 5.0\n    width_modifier:\n      type: Absolute\n      value: 1.5\n    limits:\n      lower: 0.8\n      upper: 5.0\nSersicSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 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limits:\n      lower: 0.8\n      upper: 5.0      \n  alpha:\n    type: Constant\n    value: 3.0      \n  gamma:\n    type: Constant\n    value: 0.25\n  radius_break:\n    type: Constant\n    value: 0.025\nSersicCoreSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-05\n    upper_limit: 1000.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  sersic_index:\n    type: Uniform\n    lower_limit: 0.8\n    upper_limit: 5.0\n    width_modifier:\n      type: Absolute\n      value: 1.5\n    limits:\n      lower: 0.8\n      upper: 5.0\n  alpha:\n    type: Constant\n    value: 3.0      \n  gamma:\n    type: Constant\n    value: 0.25      \n  radius_break:\n    type: Constant\n    value: 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0.0","b":"\u03c3 0.1","cls":"NFW","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"NFW","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"NFW","limits":"[-1.0, 1.0]","line":22,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"NFW","limits":"[-1.0, 1.0]","line":34,"param":"ell_comps_1","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"lower 0.0","b":"upper 1.0","cls":"NFW","limits":"[0.0, inf]","line":46,"param":"kappa_s","type":"Uniform","width":"Relative 0.2"},{"a":"lower 0.0","b":"upper 30.0","cls":"NFW","limits":"[0.0, inf]","line":56,"param":"scale_radius","type":"Uniform","width":"Relative 0.2"},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"NFWSph","limits":"[-inf, inf]","line":67,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"NFWSph","limits":"[-inf, inf]","line":77,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 1.0","cls":"NFWSph","limits":"[0.0, inf]","line":87,"param":"kappa_s","type":"Uniform","width":"Relative 0.2"},{"a":"lower 0.0","b":"upper 30.0","cls":"NFWSph","limits":"[0.0, inf]","line":97,"param":"scale_radius","type":"Uniform","width":"Relative 0.2"}],"repo":"autogalaxy_workspace","text":"NFW:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  kappa_s:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  scale_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\nNFWSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      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 width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  scatter_sigma:\n    type: Gaussian\n    mean: 0.0\n    sigma: 3.0\n    width_modifier:\n      type: Absolute\n      value: 1.0\n    limits:\n      lower: -inf\n      upper: inf\n","tooling":false,"top_keys":["NFWTruncatedMCRScatterLudlowSph"]},{"error":null,"keys":[{"c":"","k":"PointMass","line":1},{"c":"","k":"PointMass.centre_0","line":2},{"c":"","k":"PointMass.centre_0.type","line":3},{"c":"","k":"PointMass.centre_0.mean","line":4},{"c":"","k":"PointMass.centre_0.sigma","line":5},{"c":"","k":"PointMass.centre_0.width_modifier","line":6},{"c":"","k":"PointMass.centre_0.width_modifier.type","line":7},{"c":"","k":"PointMass.centre_0.width_modifier.value","line":8},{"c":"","k":"PointMass.centre_0.limits","line":9},{"c":"","k":"PointMass.centre_0.limits.lower","line":10},{"c":"","k":"PointMass.centre_0.limits.upper","line":11},{"c":"","k":"PointMass.centre_1","line":12},{"c":"","k":"PointMass.centre_1.type","line":13},{"c":"","k":"PointMass.centre_1.mean","line":14},{"c":"","k":"PointMass.centre_1.sigma","line":15},{"c":"","k":"PointMass.centre_1.width_modifier","line":16},{"c":"","k":"PointMass.centre_1.width_modifier.type","line":17},{"c":"","k":"PointMass.centre_1.width_modifier.value","line":18},{"c":"","k":"PointMass.centre_1.limits","line":19},{"c":"","k":"PointMass.centre_1.limits.lower","line":20},{"c":"","k":"PointMass.centre_1.limits.upper","line":21},{"c":"","k":"PointMass.einstein_radius","line":22},{"c":"","k":"PointMass.einstein_radius.type","line":23},{"c":"","k":"PointMass.einstein_radius.lower_limit","line":24},{"c":"","k":"PointMass.einstein_radius.upper_limit","line":25},{"c":"","k":"PointMass.einstein_radius.width_modifier","line":26},{"c":"","k":"PointMass.einstein_radius.width_modifier.type","line":27},{"c":"","k":"PointMass.einstein_radius.width_modifier.value","line":28},{"c":"","k":"PointMass.einstein_radius.limits","line":29},{"c":"","k":"PointMass.einstein_radius.limits.lower","line":30},{"c":"","k":"PointMass.einstein_radius.limits.upper","line":31}],"lines":31,"path":"priors/mass/point/point.yaml","prior":true,"priors":[{"a":"mean 0.0","b":"\u03c3 0.1","cls":"PointMass","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"PointMass","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 8.0","cls":"PointMass","limits":"[0.0, inf]","line":22,"param":"einstein_radius","type":"Uniform","width":"Relative 0.25"}],"repo":"autogalaxy_workspace","text":"PointMass:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  einstein_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 8.0\n    width_modifier:\n      type: Relative\n      value: 0.25\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["PointMass"]},{"error":null,"keys":[{"c":"","k":"SMBH","line":1},{"c":"","k":"SMBH.centre_0","line":2},{"c":"","k":"SMBH.centre_0.type","line":3},{"c":"","k":"SMBH.centre_0.mean","line":4},{"c":"","k":"SMBH.centre_0.sigma","line":5},{"c":"","k":"SMBH.centre_0.width_modifier","line":6},{"c":"","k":"SMBH.centre_0.width_modifier.type","line":7},{"c":"","k":"SMBH.centre_0.width_modifier.value","line":8},{"c":"","k":"SMBH.centre_0.limits","line":9},{"c":"","k":"SMBH.centre_0.limits.lower","line":10},{"c":"","k":"SMBH.centre_0.limits.upper","line":11},{"c":"","k":"SMBH.centre_1","line":12},{"c":"","k":"SMBH.centre_1.type","line":13},{"c":"","k":"SMBH.centre_1.mean","line":14},{"c":"","k":"SMBH.centre_1.sigma","line":15},{"c":"","k":"SMBH.centre_1.width_modifier","line":16},{"c":"","k":"SMBH.centre_1.width_modifier.type","line":17},{"c":"","k":"SMBH.centre_1.width_modifier.value","line":18},{"c":"","k":"SMBH.centre_1.limits","line":19},{"c":"","k":"SMBH.centre_1.limits.lower","line":20},{"c":"","k":"SMBH.centre_1.limits.upper","line":21},{"c":"","k":"SMBH.mass","line":22},{"c":"","k":"SMBH.mass.type","line":23},{"c":"","k":"SMBH.mass.lower_limit","line":24},{"c":"","k":"SMBH.mass.upper_limit","line":25},{"c":"","k":"SMBH.mass.width_modifier","line":26},{"c":"","k":"SMBH.mass.width_modifier.type","line":27},{"c":"","k":"SMBH.mass.width_modifier.value","line":28},{"c":"","k":"SMBH.mass.limits","line":29},{"c":"","k":"SMBH.mass.limits.lower","line":30},{"c":"","k":"SMBH.mass.limits.upper","line":31},{"c":"","k":"SMBH.redshift_object","line":32},{"c":"","k":"SMBH.redshift_object.type","line":33},{"c":"","k":"SMBH.redshift_object.lower_limit","line":34},{"c":"","k":"SMBH.redshift_object.upper_limit","line":35},{"c":"","k":"SMBH.redshift_object.width_modifier","line":36},{"c":"","k":"SMBH.redshift_object.width_modifier.type","line":37},{"c":"","k":"SMBH.redshift_object.width_modifier.value","line":38},{"c":"","k":"SMBH.redshift_object.limits","line":39},{"c":"","k":"SMBH.redshift_object.limits.lower","line":40},{"c":"","k":"SMBH.redshift_object.limits.upper","line":41},{"c":"","k":"SMBH.redshift_source","line":42},{"c":"","k":"SMBH.redshift_source.type","line":43},{"c":"","k":"SMBH.redshift_source.lower_limit","line":44},{"c":"","k":"SMBH.redshift_source.upper_limit","line":45},{"c":"","k":"SMBH.redshift_source.width_modifier","line":46},{"c":"","k":"SMBH.redshift_source.width_modifier.type","line":47},{"c":"","k":"SMBH.redshift_source.width_modifier.value","line":48},{"c":"","k":"SMBH.redshift_source.limits","line":49},{"c":"","k":"SMBH.redshift_source.limits.lower","line":50},{"c":"","k":"SMBH.redshift_source.limits.upper","line":51}],"lines":51,"path":"priors/mass/point/smbh.yaml","prior":true,"priors":[{"a":"mean 0.0","b":"\u03c3 0.1","cls":"SMBH","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"SMBH","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 1000000.0","b":"upper 10000000000000.0","cls":"SMBH","limits":"[0.0, inf]","line":22,"param":"mass","type":"LogUniform","width":"Relative 0.25"},{"a":"lower 0.0","b":"upper 1.0","cls":"SMBH","limits":"[0.0, inf]","line":32,"param":"redshift_object","type":"Uniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 1.0","cls":"SMBH","limits":"[0.0, inf]","line":42,"param":"redshift_source","type":"Uniform","width":"Relative 0.5"}],"repo":"autogalaxy_workspace","text":"SMBH:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  mass:\n    type: LogUniform\n    lower_limit: 1000000.0\n    upper_limit: 10000000000000.0\n    width_modifier:\n      type: Relative\n      value: 0.25\n    limits:\n      lower: 0.0\n      upper: inf\n  redshift_object:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  redshift_source:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf","tooling":false,"top_keys":["SMBH"]},{"error":null,"keys":[{"c":"","k":"ExternalShear","line":1},{"c":"","k":"ExternalShear.gamma_1","line":2},{"c":"","k":"ExternalShear.gamma_1.type","line":3},{"c":"","k":"ExternalShear.gamma_1.lower_limit","line":4},{"c":"","k":"ExternalShear.gamma_1.upper_limit","line":5},{"c":"","k":"ExternalShear.gamma_1.width_modifier","line":6},{"c":"","k":"ExternalShear.gamma_1.width_modifier.type","line":7},{"c":"","k":"ExternalShear.gamma_1.width_modifier.value","line":8},{"c":"","k":"ExternalShear.gamma_1.limits","line":9},{"c":"","k":"ExternalShear.gamma_1.limits.lower","line":10},{"c":"","k":"ExternalShear.gamma_1.limits.upper","line":11},{"c":"","k":"ExternalShear.gamma_2","line":12},{"c":"","k":"ExternalShear.gamma_2.type","line":13},{"c":"","k":"ExternalShear.gamma_2.lower_limit","line":14},{"c":"","k":"ExternalShear.gamma_2.upper_limit","line":15},{"c":"","k":"ExternalShear.gamma_2.width_modifier","line":16},{"c":"","k":"ExternalShear.gamma_2.width_modifier.type","line":17},{"c":"","k":"ExternalShear.gamma_2.width_modifier.value","line":18},{"c":"","k":"ExternalShear.gamma_2.limits","line":19},{"c":"","k":"ExternalShear.gamma_2.limits.lower","line":20},{"c":"","k":"ExternalShear.gamma_2.limits.upper","line":21}],"lines":21,"path":"priors/mass/sheets/external_shear.yaml","prior":true,"priors":[{"a":"lower -0.3","b":"upper 0.3","cls":"ExternalShear","limits":"[-inf, inf]","line":2,"param":"gamma_1","type":"Uniform","width":"Absolute 0.05"},{"a":"lower -0.3","b":"upper 0.3","cls":"ExternalShear","limits":"[-inf, inf]","line":12,"param":"gamma_2","type":"Uniform","width":"Absolute 0.05"}],"repo":"autogalaxy_workspace","text":"ExternalShear:\n  gamma_1:\n    type: Uniform\n    lower_limit: -0.3\n    upper_limit: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  gamma_2:\n    type: Uniform\n    lower_limit: -0.3\n    upper_limit: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n","tooling":false,"top_keys":["ExternalShear"]},{"error":null,"keys":[{"c":"","k":"MassSheet","line":1},{"c":"","k":"MassSheet.centre_0","line":2},{"c":"","k":"MassSheet.centre_0.type","line":3},{"c":"","k":"MassSheet.centre_0.mean","line":4},{"c":"","k":"MassSheet.centre_0.sigma","line":5},{"c":"","k":"MassSheet.centre_0.width_modifier","line":6},{"c":"","k":"MassSheet.centre_0.width_modifier.type","line":7},{"c":"","k":"MassSheet.centre_0.width_modifier.value","line":8},{"c":"","k":"MassSheet.centre_0.limits","line":9},{"c":"","k":"MassSheet.centre_0.limits.lower","line":10},{"c":"","k":"MassSheet.centre_0.limits.upper","line":11},{"c":"","k":"MassSheet.centre_1","line":12},{"c":"","k":"MassSheet.centre_1.type","line":13},{"c":"","k":"MassSheet.centre_1.mean","line":14},{"c":"","k":"MassSheet.centre_1.sigma","line":15},{"c":"","k":"MassSheet.centre_1.width_modifier","line":16},{"c":"","k":"MassSheet.centre_1.width_modifier.type","line":17},{"c":"","k":"MassSheet.centre_1.width_modifier.value","line":18},{"c":"","k":"MassSheet.centre_1.limits","line":19},{"c":"","k":"MassSheet.centre_1.limits.lower","line":20},{"c":"","k":"MassSheet.centre_1.limits.upper","line":21},{"c":"","k":"MassSheet.kappa","line":22},{"c":"","k":"MassSheet.kappa.type","line":23},{"c":"","k":"MassSheet.kappa.lower_limit","line":24},{"c":"","k":"MassSheet.kappa.upper_limit","line":25},{"c":"","k":"MassSheet.kappa.width_modifier","line":26},{"c":"","k":"MassSheet.kappa.width_modifier.type","line":27},{"c":"","k":"MassSheet.kappa.width_modifier.value","line":28},{"c":"","k":"MassSheet.kappa.limits","line":29},{"c":"","k":"MassSheet.kappa.limits.lower","line":30},{"c":"","k":"MassSheet.kappa.limits.upper","line":31}],"lines":31,"path":"priors/mass/sheets/mass_sheet.yaml","prior":true,"priors":[{"a":"mean 0.0","b":"\u03c3 0.1","cls":"MassSheet","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"MassSheet","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower -1.0","b":"upper 1.0","cls":"MassSheet","limits":"[-inf, inf]","line":22,"param":"kappa","type":"Uniform","width":"Absolute 0.05"}],"repo":"autogalaxy_workspace","text":"MassSheet:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  kappa:\n    type: Uniform\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      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width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  intensity:\n    type: LogUniform\n    lower_limit: 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upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-05\n    upper_limit: 1000.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    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effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 10.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  mass_to_light_gradient:\n    type: Uniform\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 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0.0","b":"\u03c3 0.1","cls":"Isothermal","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"Isothermal","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 8.0","cls":"Isothermal","limits":"[0.0, inf]","line":22,"param":"einstein_radius","type":"Uniform","width":"Relative 0.25"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Isothermal","limits":"[-1.0, 1.0]","line":32,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Isothermal","limits":"[-1.0, 1.0]","line":44,"param":"ell_comps_1","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"IsothermalSph","limits":"[-inf, inf]","line":57,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"IsothermalSph","limits":"[-inf, inf]","line":67,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 8.0","cls":"IsothermalSph","limits":"[0.0, inf]","line":77,"param":"einstein_radius","type":"Uniform","width":"Relative 0.25"}],"repo":"autogalaxy_workspace","text":"Isothermal:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  einstein_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 8.0\n    width_modifier:\n      type: Relative\n      value: 0.25\n    limits:\n      lower: 0.0\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\nIsothermalSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  einstein_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 8.0\n    width_modifier:\n      type: Relative\n      value: 0.25\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["Isothermal","IsothermalSph"]},{"error":null,"keys":[{"c":"","k":"IsothermalCore","line":1},{"c":"","k":"IsothermalCore.centre_0","line":2},{"c":"","k":"IsothermalCore.centre_0.type","line":3},{"c":"","k":"IsothermalCore.centre_0.mean","line":4},{"c":"","k":"IsothermalCore.centre_0.sigma","line":5},{"c":"","k":"IsothermalCore.centre_0.width_modifier","line":6},{"c":"","k":"IsothermalCore.centre_0.width_modifier.type","line":7},{"c":"","k":"IsothermalCore.centre_0.width_modifier.value","line":8},{"c":"","k":"IsothermalCore.centre_0.limits","line":9},{"c":"","k":"IsothermalCore.centre_0.limits.lower","line":10},{"c":"","k":"IsothermalCore.centre_0.limits.upper","line":11},{"c":"","k":"IsothermalCore.centre_1","line":12},{"c":"","k":"IsothermalCore.centre_1.type","line":13},{"c":"","k":"IsothermalCore.centre_1.mean","line":14},{"c":"","k":"IsothermalCore.centre_1.sigma","line":15},{"c":"","k":"IsothermalCore.centre_1.width_modifier","line":16},{"c":"","k":"IsothermalCore.centre_1.width_modifier.type","line":17},{"c":"","k":"IsothermalCore.centre_1.width_modifier.value","line":18},{"c":"","k":"IsothermalCore.centre_1.limits","line":19},{"c":"","k":"IsothermalCore.centre_1.limits.lower","line":20},{"c":"","k":"IsothermalCore.centre_1.limits.upper","line":21},{"c":"","k":"IsothermalCore.core_radius","line":22},{"c":"","k":"IsothermalCore.core_radius.type","line":23},{"c":"","k":"IsothermalCore.core_radius.lower_limit","line":24},{"c":"","k":"IsothermalCore.core_radius.upper_limit","line":25},{"c":"","k":"IsothermalCore.core_radius.width_modifier","line":26},{"c":"","k":"IsothermalCore.core_radius.width_modifier.type","line":27},{"c":"","k":"IsothermalCore.core_radius.width_modifier.value","line":28},{"c":"","k":"IsothermalCore.core_radius.limits","line":29},{"c":"","k":"IsothermalCore.core_radius.limits.lower","line":30},{"c":"","k":"IsothermalCore.core_radius.limits.upper","line":31},{"c":"","k":"IsothermalCore.einstein_radius","line":32},{"c":"","k":"IsothermalCore.einstein_radius.type","line":33},{"c":"","k":"IsothermalCore.einstein_radius.lower_limit","line":34},{"c":"","k":"IsothermalCore.einstein_radius.upper_limit","line":35},{"c":"","k":"IsothermalCore.einstein_radius.width_modifier","line":36},{"c":"","k":"IsothermalCore.einstein_radius.width_modifier.type","line":37},{"c":"","k":"IsothermalCore.einstein_radius.width_modifier.value","line":38},{"c":"","k":"IsothermalCore.einstein_radius.limits","line":39},{"c":"","k":"IsothermalCore.einstein_radius.limits.lower","line":40},{"c":"","k":"IsothermalCore.einstein_radius.limits.upper","line":41},{"c":"","k":"IsothermalCore.ell_comps_0","line":42},{"c":"","k":"IsothermalCore.ell_comps_0.type","line":43},{"c":"","k":"IsothermalCore.ell_comps_0.mean","line":44},{"c":"","k":"IsothermalCore.ell_comps_0.sigma","line":45},{"c":"","k":"IsothermalCore.ell_comps_0.lower_limit","line":46},{"c":"","k":"IsothermalCore.ell_comps_0.upper_limit","line":47},{"c":"","k":"IsothermalCore.ell_comps_0.width_modifier","line":48},{"c":"","k":"IsothermalCore.ell_comps_0.width_modifier.type","line":49},{"c":"","k":"IsothermalCore.ell_comps_0.width_modifier.value","line":50},{"c":"","k":"IsothermalCore.ell_comps_0.limits","line":51},{"c":"","k":"IsothermalCore.ell_comps_0.limits.lower","line":52},{"c":"","k":"IsothermalCore.ell_comps_0.limits.upper","line":53},{"c":"","k":"IsothermalCore.ell_comps_1","line":54},{"c":"","k":"IsothermalCore.ell_comps_1.type","line":55},{"c":"","k":"IsothermalCore.ell_comps_1.mean","line":56},{"c":"","k":"IsothermalCore.ell_comps_1.sigma","line":57},{"c":"","k":"IsothermalCore.ell_comps_1.lower_limit","line":58},{"c":"","k":"IsothermalCore.ell_comps_1.upper_limit","line":59},{"c":"","k":"IsothermalCore.ell_comps_1.width_modifier","line":60},{"c":"","k":"IsothermalCore.ell_comps_1.width_modifier.type","line":61},{"c":"","k":"IsothermalCore.ell_comps_1.width_modifier.value","line":62},{"c":"","k":"IsothermalCore.ell_comps_1.limits","line":63},{"c":"","k":"IsothermalCore.ell_comps_1.limits.lower","line":64},{"c":"","k":"IsothermalCore.ell_comps_1.limits.upper","line":65},{"c":"","k":"IsothermalCoreSph","line":66},{"c":"","k":"IsothermalCoreSph.centre_0","line":67},{"c":"","k":"IsothermalCoreSph.centre_0.type","line":68},{"c":"","k":"IsothermalCoreSph.centre_0.mean","line":69},{"c":"","k":"IsothermalCoreSph.centre_0.sigma","line":70},{"c":"","k":"IsothermalCoreSph.centre_0.width_modifier","line":71},{"c":"","k":"IsothermalCoreSph.centre_0.width_modifier.type","line":72},{"c":"","k":"IsothermalCoreSph.centre_0.width_modifier.value","line":73},{"c":"","k":"IsothermalCoreSph.centre_0.limits","line":74},{"c":"","k":"IsothermalCoreSph.centre_0.limits.lower","line":75},{"c":"","k":"IsothermalCoreSph.centre_0.limits.upper","line":76},{"c":"","k":"IsothermalCoreSph.centre_1","line":77},{"c":"","k":"IsothermalCoreSph.centre_1.type","line":78},{"c":"","k":"IsothermalCoreSph.centre_1.mean","line":79},{"c":"","k":"IsothermalCoreSph.cent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0.0","b":"\u03c3 0.1","cls":"IsothermalCore","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"IsothermalCore","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 0.2","cls":"IsothermalCore","limits":"[0.0, inf]","line":22,"param":"core_radius","type":"Uniform","width":"Absolute 0.1"},{"a":"lower 0.0","b":"upper 8.0","cls":"IsothermalCore","limits":"[0.0, inf]","line":32,"param":"einstein_radius","type":"Uniform","width":"Relative 0.25"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"IsothermalCore","limits":"[-1.0, 1.0]","line":42,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"IsothermalCore","limits":"[-1.0, 1.0]","line":54,"param":"ell_comps_1","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"IsothermalCoreSph","limits":"[-inf, inf]","line":67,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"IsothermalCoreSph","limits":"[-inf, inf]","line":77,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 0.2","cls":"IsothermalCoreSph","limits":"[0.0, inf]","line":87,"param":"core_radius","type":"Uniform","width":"Absolute 0.1"},{"a":"lower 0.0","b":"upper 8.0","cls":"IsothermalCoreSph","limits":"[0.0, inf]","line":97,"param":"einstein_radius","type":"Uniform","width":"Relative 0.25"}],"repo":"autogalaxy_workspace","text":"IsothermalCore:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  core_radius:\n    type: Uniform\n    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value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  core_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 0.2\n    width_modifier:\n      type: Absolute\n      value: 0.1\n    limits:\n      lower: 0.0\n      upper: inf\n  einstein_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 8.0\n    width_modifier:\n      type: Relative\n      value: 0.25\n    limits:\n      lower: 0.0\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  slope:\n    type: Uniform\n    lower_limit: 1.5\n    upper_limit: 3.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: 1.0\n      upper: 3.0\nPowerLawCoreSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  core_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 0.2\n    width_modifier:\n      type: Absolute\n      value: 0.1\n    limits:\n      lower: 0.0\n      upper: inf\n  einstein_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 8.0\n    width_modifier:\n      type: Relative\n      value: 0.25\n    limits:\n      lower: 0.0\n      upper: inf\n  slope:\n    type: Uniform\n    lower_limit: 1.5\n    upper_limit: 3.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: 1.0\n      upper: 3.0\n","tooling":false,"top_keys":["PowerLawCore","PowerLawCoreSph"]},{"error":null,"keys":[{"c":"","k":"PowerLawMultipole","line":1},{"c":"","k":"PowerLawMultipole.m","line":2},{"c":"","k":"PowerLawMultipole.m.type","line":3},{"c":"","k":"PowerLawMultipole.m.value","line":4},{"c":"","k":"PowerLawMultipole.centre_0","line":5},{"c":"","k":"PowerLawMultipole.centre_0.type","line":6},{"c":"","k":"PowerLawMultipole.centre_0.mean","line":7},{"c":"","k":"PowerLawMultipole.centre_0.sigma","line":8},{"c":"","k":"PowerLawMultipole.centre_0.width_modifier","line":9},{"c":"","k":"PowerLawMultipole.centre_0.width_modifier.type","line":10},{"c":"","k":"PowerLawMultipole.centre_0.width_modifier.value","line":11},{"c":"","k":"PowerLawMultipole.centre_0.limits","line":12},{"c":"","k":"PowerLawMultipole.centre_0.limits.lower","line":13},{"c":"","k":"PowerLawMultipole.centre_0.limits.upper","line":14},{"c":"","k":"PowerLawMultipole.centre_1","line":15},{"c":"","k":"PowerLawMultipole.centre_1.type","line":16},{"c":"","k":"PowerLawMultipole.centre_1.mean","line":17},{"c":"","k":"PowerLawMultipole.centre_1.sigma","line":18},{"c":"","k":"PowerLawMultipole.centre_1.width_modifier","line":19},{"c":"","k":"PowerLawMultipole.centre_1.width_modifier.type","line":20},{"c":"","k":"PowerLawMultipole.centre_1.width_modifier.value","line":21},{"c":"","k":"PowerLawMultipole.centre_1.limits","line":22},{"c":"","k":"PowerLawMultipole.centre_1.limits.lower","line":23},{"c":"","k":"PowerLawMultipole.centre_1.limits.upper","line":24},{"c":"","k":"PowerLawMultipole.einstein_radius","line":25},{"c":"","k":"PowerLawMultipole.einstein_radius.type","line":26},{"c":"","k":"PowerLawMultipole.einstein_radius.lower_limit","line":27},{"c":"","k":"PowerLawMultipole.einstein_radius.upper_limit","line":28},{"c":"","k":"PowerLawMultipole.einstein_radius.width_modifier","line":29},{"c":"","k":"PowerLawMultipole.einstein_radius.width_modifier.type","line":30},{"c":"","k":"PowerLawMultipole.einstein_radius.width_modifier.value","line":31},{"c":"","k":"PowerLawMultipole.einstein_radius.limits","line":32},{"c":"","k":"PowerLawMultipole.einstein_radius.limits.lower","line":33},{"c":"","k":"PowerLawMultipole.einstein_radius.limits.upper","line":34},{"c":"","k":"PowerLawMultipole.slope","line":35},{"c":"","k":"PowerLawMultipole.slope.type","line":36},{"c":"","k":"PowerLawMultipole.slope.lower_limit","line":37},{"c":"","k":"PowerLawMultipole.slope.upper_limit","line":38},{"c":"","k":"PowerLawMultipole.slope.width_modifier","line":39},{"c":"","k":"PowerLawMultipole.slope.width_modifier.type","line":40},{"c":"","k":"PowerLawMultipole.slope.width_modifier.value","line":41},{"c":"","k":"PowerLawMultipole.slope.limits","line":42},{"c":"","k":"PowerLawMultipole.slope.limits.lower","line":43},{"c":"","k":"PowerLawMultipole.slope.limits.upper","line":44},{"c":"","k":"PowerLawMultipole.multipole_comps_0","line":45},{"c":"","k":"PowerLawMultipole.multipole_comps_0.type","line":46},{"c":"","k":"PowerLawMultipole.multipole_comps_0.lower_limit","line":47},{"c":"","k":"PowerLawMultipole.multipole_comps_0.upper_limit","line":48},{"c":"","k":"PowerLawMultipole.multipole_comps_0.width_modifier","line":49},{"c":"","k":"PowerLawMultipole.multipole_comps_0.width_modifier.type","line":50},{"c":"","k":"PowerLawMultipole.multipole_comps_0.width_modifier.value","line":51},{"c":"","k":"PowerLawMultipole.multipole_comps_0.limits","line":52},{"c":"","k":"PowerLawMultipole.multipole_comps_0.limits.lower","line":53},{"c":"","k":"PowerLawMultipole.multipole_comps_0.limits.upper","line":54},{"c":"","k":"PowerLawMultipole.multipole_comps_1","line":55},{"c":"","k":"PowerLawMultipole.multipole_comps_1.type","line":56},{"c":"","k":"PowerLawMultipole.multipole_comps_1.lower_limit","line":57},{"c":"","k":"PowerLawMultipole.multipole_comps_1.upper_limit","line":58},{"c":"","k":"PowerLawMultipole.multipole_comps_1.width_modifier","line":59},{"c":"","k":"PowerLawMultipole.multipole_comps_1.width_modifier.type","line":60},{"c":"","k":"PowerLawMultipole.multipole_comps_1.width_modifier.value","line":61},{"c":"","k":"PowerLawMultipole.multipole_comps_1.limits","line":62},{"c":"","k":"PowerLawMultipole.multipole_comps_1.limits.lower","line":63},{"c":"","k":"PowerLawMultipole.multipole_comps_1.limits.upper","line":64}],"lines":64,"path":"priors/mass/total/power_law_multipole.yaml","prior":true,"priors":[{"a":"value 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inf]","line":55,"param":"multipole_comps_1","type":"Uniform","width":"Absolute 0.05"}],"repo":"autogalaxy_workspace","text":"PowerLawMultipole:\n  m:\n    type: Constant\n    value: 4\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  einstein_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 8.0\n    width_modifier:\n      type: Relative\n      value: 0.25\n    limits:\n      lower: 0.0\n      upper: inf\n  slope:\n    type: Uniform\n    lower_limit: 1.5\n    upper_limit: 3.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: 1.0\n      upper: 3.0\n  multipole_comps_0:\n    type: Uniform\n    lower_limit: -0.1\n    upper_limit: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  multipole_comps_1:\n    type: Uniform\n    lower_limit: -0.1\n    upper_limit: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: 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0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\nPointFlux:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  flux:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["Point","PointFlux"]},{"error":null,"keys":[{"c":"","k":"Adapt","line":1},{"c":"","k":"Adapt.inner_coefficient","line":2},{"c":"","k":"Adapt.inner_coefficient.type","line":3},{"c":"","k":"Adapt.inner_coefficient.lower_limit","line":4},{"c":"","k":"Adapt.inner_coefficient.upper_limit","line":5},{"c":"","k":"Adapt.inner_coefficient.width_modifier","line":6},{"c":"","k":"Adapt.inner_coefficient.width_modifier.type","line":7},{"c":"","k":"Adapt.inner_coefficient.width_modifier.value","line":8},{"c":"","k":"Adapt.inner_coefficient.limits","line":9},{"c":"","k":"Adapt.inner_coefficient.limits.lower","line":10},{"c":"","k":"Adapt.inner_coefficient.limits.upper","line":11},{"c":"","k":"Adapt.outer_coefficient","line":12},{"c":"","k":"Adapt.outer_coefficient.type","line":13},{"c":"","k":"Adapt.outer_coefficient.lower_limit","line":14},{"c":"","k":"Adapt.outer_coefficient.upper_limit","line":15},{"c":"","k":"Adapt.outer_coefficient.width_modifier","line":16},{"c":"","k":"Adapt.outer_coefficient.width_modifier.type","line":17},{"c":"","k":"Adapt.outer_coefficient.width_modifier.value","line":18},{"c":"","k":"Adapt.outer_coefficient.limits","line":19},{"c":"","k":"Adapt.outer_coefficient.limits.lower","line":20},{"c":"","k":"Adapt.outer_coefficient.limits.upper","line":21},{"c":"","k":"Adapt.signal_scale","line":22},{"c":"","k":"Adapt.signal_scale.type","line":23},{"c":"","k":"Adapt.signal_scale.lower_limit","line":24},{"c":"","k":"Adapt.signal_scale.upper_limit","line":25},{"c":"","k":"Adapt.signal_scale.width_modifier","line":26},{"c":"","k":"Adapt.signal_scale.width_modifier.type","line":27},{"c":"","k":"Adapt.signal_scale.width_modifier.value","line":28},{"c":"","k":"Adapt.signal_scale.limits","line":29},{"c":"","k":"Adapt.signal_scale.limits.lower","line":30},{"c":"","k":"Adapt.signal_scale.limits.upper","line":31}],"lines":31,"path":"priors/regularization/adapt.yaml","prior":true,"priors":[{"a":"lower 1e-06","b":"upper 1000000.0","cls":"Adapt","limits":"[0.0, inf]","line":2,"param":"inner_coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"Adapt","limits":"[0.0, inf]","line":12,"param":"outer_coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 1.0","cls":"Adapt","limits":"[0.0, inf]","line":22,"param":"signal_scale","type":"Uniform","width":"Relative 0.2"}],"repo":"autogalaxy_workspace","text":"Adapt:\n  inner_coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  outer_coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  signal_scale:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf","tooling":false,"top_keys":["Adapt"]},{"error":null,"keys":[{"c":"","k":"AdaptSplit","line":1},{"c":"","k":"AdaptSplit.inner_coefficient","line":2},{"c":"","k":"AdaptSplit.inner_coefficient.type","line":3},{"c":"","k":"AdaptSplit.inner_coefficient.lower_limit","line":4},{"c":"","k":"AdaptSplit.inner_coefficient.upper_limit","line":5},{"c":"","k":"AdaptSplit.inner_coefficient.width_modifier","line":6},{"c":"","k":"AdaptSplit.inner_coefficient.width_modifier.type","line":7},{"c":"","k":"AdaptSplit.inner_coefficient.width_modifier.value","line":8},{"c":"","k":"AdaptSplit.inner_coefficient.limits","line":9},{"c":"","k":"AdaptSplit.inner_coefficient.limits.lower","line":10},{"c":"","k":"AdaptSplit.inner_coefficient.limits.upper","line":11},{"c":"","k":"AdaptSplit.outer_coefficient","line":12},{"c":"","k":"AdaptSplit.outer_coefficient.type","line":13},{"c":"","k":"AdaptSplit.outer_coefficient.lower_limit","line":14},{"c":"","k":"AdaptSplit.outer_coefficient.upper_limit","line":15},{"c":"","k":"AdaptSplit.outer_coefficient.width_modifier","line":16},{"c":"","k":"AdaptSplit.outer_coefficient.width_modifier.type","line":17},{"c":"","k":"AdaptSplit.outer_coefficient.width_modifier.value","line":18},{"c":"","k":"AdaptSplit.outer_coefficient.limits","line":19},{"c":"","k":"AdaptSplit.outer_coefficient.limits.lower","line":20},{"c":"","k":"AdaptSplit.outer_coefficient.limits.upper","line":21},{"c":"","k":"AdaptSplit.signal_scale","line":22},{"c":"","k":"AdaptSplit.signal_scale.type","line":23},{"c":"","k":"AdaptSplit.signal_scale.lower_limit","line":24},{"c":"","k":"AdaptSplit.signal_scale.upper_limit","line":25},{"c":"","k":"AdaptSplit.signal_scale.width_modifier","line":26},{"c":"","k":"AdaptSplit.signal_scale.width_modifier.type","line":27},{"c":"","k":"AdaptSplit.signal_scale.width_modifier.value","line":28},{"c":"","k":"AdaptSplit.signal_scale.limits","line":29},{"c":"","k":"AdaptSplit.signal_scale.limits.lower","line":30},{"c":"","k":"AdaptSplit.signal_scale.limits.upper","line":31}],"lines":31,"path":"priors/regularization/adapt_split.yaml","prior":true,"priors":[{"a":"lower 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upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["AdaptSplit"]},{"error":null,"keys":[{"c":"","k":"Constant","line":1},{"c":"","k":"Constant.coefficient","line":2},{"c":"","k":"Constant.coefficient.type","line":3},{"c":"","k":"Constant.coefficient.lower_limit","line":4},{"c":"","k":"Constant.coefficient.upper_limit","line":5},{"c":"","k":"Constant.coefficient.width_modifier","line":6},{"c":"","k":"Constant.coefficient.width_modifier.type","line":7},{"c":"","k":"Constant.coefficient.width_modifier.value","line":8},{"c":"","k":"Constant.coefficient.limits","line":9},{"c":"","k":"Constant.coefficient.limits.lower","line":10},{"c":"","k":"Constant.coefficient.limits.upper","line":11}],"lines":11,"path":"priors/regularization/constant.yaml","prior":true,"priors":[{"a":"lower 1e-06","b":"upper 1000000.0","cls":"Constant","limits":"[0.0, inf]","line":2,"param":"coefficient","type":"LogUniform","width":"Relative 0.5"}],"repo":"autogalaxy_workspace","text":"Constant:\n  coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["Constant"]},{"error":null,"keys":[{"c":"","k":"ConstantSplit","line":1},{"c":"","k":"ConstantSplit.coefficient","line":2},{"c":"","k":"ConstantSplit.coefficient.type","line":3},{"c":"","k":"ConstantSplit.coefficient.lower_limit","line":4},{"c":"","k":"ConstantSplit.coefficient.upper_limit","line":5},{"c":"","k":"ConstantSplit.coefficient.width_modifier","line":6},{"c":"","k":"ConstantSplit.coefficient.width_modifier.type","line":7},{"c":"","k":"ConstantSplit.coefficient.width_modifier.value","line":8},{"c":"","k":"ConstantSplit.coefficient.limits","line":9},{"c":"","k":"ConstantSplit.coefficient.limits.lower","line":10},{"c":"","k":"ConstantSplit.coefficient.limits.upper","line":11}],"lines":11,"path":"priors/regularization/constant_split.yaml","prior":true,"priors":[{"a":"lower 1e-06","b":"upper 1000000.0","cls":"ConstantSplit","limits":"[0.0, inf]","line":2,"param":"coefficient","type":"LogUniform","width":"Relative 0.5"}],"repo":"autogalaxy_workspace","text":"ConstantSplit:\n  coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["ConstantSplit"]},{"error":null,"keys":[{"c":"","k":"MaternAdaptKernel","line":1},{"c":"","k":"MaternAdaptKernel.scale","line":2},{"c":"","k":"MaternAdaptKernel.scale.type","line":3},{"c":"","k":"MaternAdaptKernel.scale.lower_limit","line":4},{"c":"","k":"MaternAdaptKernel.scale.upper_limit","line":5},{"c":"","k":"MaternAdaptKernel.scale.width_modifier","line":6},{"c":"","k":"MaternAdaptKernel.scale.width_modifier.type","line":7},{"c":"","k":"MaternAdaptKernel.scale.width_modifier.value","line":8},{"c":"","k":"MaternAdaptKernel.scale.limits","line":9},{"c":"","k":"MaternAdaptKernel.scale.limits.lower","line":10},{"c":"","k":"MaternAdaptKernel.scale.limits.upper","line":11},{"c":"","k":"MaternAdaptKernel.nu","line":12},{"c":"","k":"MaternAdaptKernel.nu.type","line":13},{"c":"","k":"MaternAdaptKernel.nu.lower_limit","line":14},{"c":"","k":"MaternAdaptKernel.nu.upper_limit","line":15},{"c":"","k":"MaternAdaptKernel.nu.width_modifier","line":16},{"c":"","k":"MaternAdaptKernel.nu.width_modifier.type","line":17},{"c":"","k":"MaternAdaptKernel.nu.width_modifier.value","line":18},{"c":"","k":"MaternAdaptKernel.nu.limits","line":19},{"c":"","k":"MaternAdaptKernel.nu.limits.lower","line":20},{"c":"","k":"MaternAdaptKernel.nu.limits.upper","line":21},{"c":"","k":"MaternAdaptKernel.inner_coefficient","line":22},{"c":"","k":"MaternAdaptKernel.inner_coefficient.type","line":23},{"c":"","k":"MaternAdaptKernel.inner_coefficient.lower_limit","line":24},{"c":"","k":"MaternAdaptKernel.inner_coefficient.upper_limit","line":25},{"c":"","k":"MaternAdaptKernel.inner_coefficient.width_modifier","line":26},{"c":"","k":"MaternAdaptKernel.inner_coefficient.width_modifier.type","line":27},{"c":"","k":"MaternAdaptKernel.inner_coefficient.width_modifier.value","line":28},{"c":"","k":"MaternAdaptKernel.inner_coefficient.limits","line":29},{"c":"","k":"MaternAdaptKernel.inner_coefficient.limits.lower","line":30},{"c":"","k":"MaternAdaptKernel.inner_coefficient.limits.upper","line":31},{"c":"","k":"MaternAdaptKernel.outer_coefficient","line":32},{"c":"","k":"MaternAdaptKernel.outer_coefficient.type","line":33},{"c":"","k":"MaternAdaptKernel.outer_coefficient.lower_limit","line":34},{"c":"","k":"MaternAdaptKernel.outer_coefficient.upper_limit","line":35},{"c":"","k":"MaternAdaptKernel.outer_coefficient.width_modifier","line":36},{"c":"","k":"MaternAdaptKernel.outer_coefficient.width_modifier.type","line":37},{"c":"","k":"MaternAdaptKernel.outer_coefficient.width_modifier.value","line":38},{"c":"","k":"MaternAdaptKernel.outer_coefficient.limits","line":39},{"c":"","k":"MaternAdaptKernel.outer_coefficient.limits.lower","line":40},{"c":"","k":"MaternAdaptKernel.outer_coefficient.limits.upper","line":41},{"c":"","k":"MaternAdaptKernel.signal_scale","line":42},{"c":"","k":"MaternAdaptKernel.signal_scale.type","line":43},{"c":"","k":"MaternAdaptKernel.signal_scale.lower_limit","line":44},{"c":"","k":"MaternAdaptKernel.signal_scale.upper_limit","line":45},{"c":"","k":"MaternAdaptKernel.signal_scale.width_modifier","line":46},{"c":"","k":"MaternAdaptKernel.signal_scale.width_modifier.type","line":47},{"c":"","k":"MaternAdaptKernel.signal_scale.width_modifier.value","line":48},{"c":"","k":"MaternAdaptKernel.signal_scale.limits","line":49},{"c":"","k":"MaternAdaptKernel.signal_scale.limits.lower","line":50},{"c":"","k":"MaternAdaptKernel.signal_scale.limits.upper","line":51}],"lines":51,"path":"priors/regularization/matern_adapt_kernel.yaml","prior":true,"priors":[{"a":"lower 1e-06","b":"upper 1000000.0","cls":"MaternAdaptKernel","limits":"[0.0, inf]","line":2,"param":"scale","type":"LogUniform","width":"Relative 0.2"},{"a":"lower 0.5","b":"upper 5.5","cls":"MaternAdaptKernel","limits":"[0.0, inf]","line":12,"param":"nu","type":"Uniform","width":"Relative 0.2"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"MaternAdaptKernel","limits":"[0.0, inf]","line":22,"param":"inner_coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"MaternAdaptKernel","limits":"[0.0, inf]","line":32,"param":"outer_coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 1.0","cls":"MaternAdaptKernel","limits":"[0.0, inf]","line":42,"param":"signal_scale","type":"Uniform","width":"Relative 0.2"}],"repo":"autogalaxy_workspace","text":"MaternAdaptKernel:\n  scale:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  nu:\n    type: Uniform\n    lower_limit: 0.5\n    upper_limit: 5.5\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  inner_coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  outer_coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  signal_scale:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf","tooling":false,"top_keys":["MaternAdaptKernel"]},{"error":null,"keys":[{"c":"","k":"MaternAdaptKernel","line":1},{"c":"","k":"MaternAdaptKernel.scale","line":2},{"c":"","k":"MaternAdaptKernel.scale.type","line":3},{"c":"","k":"MaternAdaptKernel.scale.lower_limit","line":4},{"c":"","k":"MaternAdaptKernel.scale.upper_limit","line":5},{"c":"","k":"MaternAdaptKernel.scale.width_modifier","line":6},{"c":"","k":"MaternAdaptKernel.scale.width_modifier.type","line":7},{"c":"","k":"MaternAdaptKernel.scale.width_modifier.value","line":8},{"c":"","k":"MaternAdaptKernel.scale.limits","line":9},{"c":"","k":"MaternAdaptKernel.scale.limits.lower","line":10},{"c":"","k":"MaternAdaptKernel.scale.limits.upper","line":11},{"c":"","k":"MaternAdaptKernel.nu","line":12},{"c":"","k":"MaternAdaptKernel.nu.type","line":13},{"c":"","k":"MaternAdaptKernel.nu.lower_limit","line":14},{"c":"","k":"MaternAdaptKernel.nu.upper_limit","line":15},{"c":"","k":"MaternAdaptKernel.nu.width_modifier","line":16},{"c":"","k":"MaternAdaptKernel.nu.width_modifier.type","line":17},{"c":"","k":"MaternAdaptKernel.nu.width_modifier.value","line":18},{"c":"","k":"MaternAdaptKernel.nu.limits","line":19},{"c":"","k":"MaternAdaptKernel.nu.limits.lower","line":20},{"c":"","k":"MaternAdaptKernel.nu.limits.upper","line":21},{"c":"","k":"MaternAdaptKernel.rho","line":22},{"c":"","k":"MaternAdaptKernel.rho.type","line":23},{"c":"","k":"MaternAdaptKernel.rho.lower_limit","line":24},{"c":"","k":"MaternAdaptKernel.rho.upper_limit","line":25},{"c":"","k":"MaternAdaptKernel.rho.width_modifier","line":26},{"c":"","k":"MaternAdaptKernel.rho.width_modifier.type","line":27},{"c":"","k":"MaternAdaptKernel.rho.width_modifier.value","line":28},{"c":"","k":"MaternAdaptKernel.rho.limits","line":29},{"c":"","k":"MaternAdaptKernel.rho.limits.lower","line":30},{"c":"","k":"MaternAdaptKernel.rho.limits.upper","line":31}],"lines":31,"path":"priors/regularization/matern_adapt_kernel_rho.yaml","prior":true,"priors":[{"a":"lower 1e-06","b":"upper 1000000.0","cls":"MaternAdaptKernel","limits":"[0.0, inf]","line":2,"param":"scale","type":"LogUniform","width":"Relative 0.2"},{"a":"lower 0.5","b":"upper 5.5","cls":"MaternAdaptKernel","limits":"[0.0, inf]","line":12,"param":"nu","type":"Uniform","width":"Relative 0.2"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"MaternAdaptKernel","limits":"[0.0, inf]","line":22,"param":"rho","type":"Uniform","width":"Relative 0.2"}],"repo":"autogalaxy_workspace","text":"MaternAdaptKernel:\n  scale:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  nu:\n    type: Uniform\n    lower_limit: 0.5\n    upper_limit: 5.5\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  rho:\n    type: Uniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf","tooling":false,"top_keys":["MaternAdaptKernel"]},{"error":null,"keys":[{"c":"","k":"MaternKernel","line":1},{"c":"","k":"MaternKernel.coefficient","line":2},{"c":"","k":"MaternKernel.coefficient.type","line":3},{"c":"","k":"MaternKernel.coefficient.lower_limit","line":4},{"c":"","k":"MaternKernel.coefficient.upper_limit","line":5},{"c":"","k":"MaternKernel.coefficient.width_modifier","line":6},{"c":"","k":"MaternKernel.coefficient.width_modifier.type","line":7},{"c":"","k":"MaternKernel.coefficient.width_modifier.value","line":8},{"c":"","k":"MaternKernel.coefficient.limits","line":9},{"c":"","k":"MaternKernel.coefficient.limits.lower","line":10},{"c":"","k":"MaternKernel.coefficient.limits.upper","line":11},{"c":"","k":"MaternKernel.scale","line":12},{"c":"","k":"MaternKernel.scale.type","line":13},{"c":"","k":"MaternKernel.scale.lower_limit","line":14},{"c":"","k":"MaternKernel.scale.upper_limit","line":15},{"c":"","k":"MaternKernel.scale.width_modifier","line":16},{"c":"","k":"MaternKernel.scale.width_modifier.type","line":17},{"c":"","k":"MaternKernel.scale.width_modifier.value","line":18},{"c":"","k":"MaternKernel.scale.limits","line":19},{"c":"","k":"MaternKernel.scale.limits.lower","line":20},{"c":"","k":"MaternKernel.scale.limits.upper","line":21},{"c":"","k":"MaternKernel.nu","line":22},{"c":"","k":"MaternKernel.nu.type","line":23},{"c":"","k":"MaternKernel.nu.lower_limit","line":24},{"c":"","k":"MaternKernel.nu.upper_limit","line":25},{"c":"","k":"MaternKernel.nu.width_modifier","line":26},{"c":"","k":"MaternKernel.nu.width_modifier.type","line":27},{"c":"","k":"MaternKernel.nu.width_modifier.value","line":28},{"c":"","k":"MaternKernel.nu.limits","line":29},{"c":"","k":"MaternKernel.nu.limits.lower","line":30},{"c":"","k":"MaternKernel.nu.limits.upper","line":31}],"lines":31,"path":"priors/regularization/matern_kernel.yaml","prior":true,"priors":[{"a":"lower 1e-06","b":"upper 1000000.0","cls":"MaternKernel","limits":"[0.0, inf]","line":2,"param":"coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"MaternKernel","limits":"[0.0, inf]","line":12,"param":"scale","type":"LogUniform","width":"Relative 0.2"},{"a":"lower 0.5","b":"upper 5.5","cls":"MaternKernel","limits":"[0.0, inf]","line":22,"param":"nu","type":"Uniform","width":"Relative 0.2"}],"repo":"autogalaxy_workspace","text":"MaternKernel:\n  coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  scale:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  nu:\n    type: Uniform\n    lower_limit: 0.5\n    upper_limit: 5.5\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["MaternKernel"]},{"error":null,"keys":[{"c":"","k":"Zeroth","line":1},{"c":"","k":"Zeroth.coefficient","line":2},{"c":"","k":"Zeroth.coefficient.type","line":3},{"c":"","k":"Zeroth.coefficient.lower_limit","line":4},{"c":"","k":"Zeroth.coefficient.upper_limit","line":5},{"c":"","k":"Zeroth.coefficient.width_modifier","line":6},{"c":"","k":"Zeroth.coefficient.width_modifier.type","line":7},{"c":"","k":"Zeroth.coefficient.width_modifier.value","line":8},{"c":"","k":"Zeroth.coefficient.limits","line":9},{"c":"","k":"Zeroth.coefficient.limits.lower","line":10},{"c":"","k":"Zeroth.coefficient.limits.upper","line":11}],"lines":11,"path":"priors/regularization/zeroth.yaml","prior":true,"priors":[{"a":"lower 1e-06","b":"upper 1000000.0","cls":"Zeroth","limits":"[0.0, inf]","line":2,"param":"coefficient","type":"LogUniform","width":"Relative 0.5"}],"repo":"autogalaxy_workspace","text":"Zeroth:\n  coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["Zeroth"]},{"error":null,"keys":[{"c":"","k":"general","line":1},{"c":"The matploblib backend used for visualization. `default` uses the system default, can specifiy specific backend (e.g. TKAgg, Qt5Agg, WXAgg).","k":"general.backend","line":2},{"c":"The `origin` input of `imshow`, determining if pixel values are ascending or descending on the y-axis.","k":"general.imshow_origin","line":3},{"c":"If negative values are being plotted on a log10 scale, values below this value are rounded up to it (e.g. to remove negative values).","k":"general.log10_min_value","line":4},{"c":"If positive values are being plotted on a log10 scale, values above this value are rounded down to it (e.g. to prevent white blobs).","k":"general.log10_max_value","line":5},{"c":"If True, plots of data structures with a mask automatically zoom in the masked region.","k":"general.zoom_around_mask","line":6},{"c":"Default output format: \"show\" displays the figure interactively via plt.show(), \"png\"/\"pdf\"/etc. saves to file.","k":"general.output_format","line":7},{"c":"","k":"inversion","line":8},{"c":"Plots of an Inversion's reconstruction use the reconstructed data's bright value multiplied by this factor.","k":"inversion.reconstruction_vmax_factor","line":9},{"c":"","k":"zoom","line":10},{"c":"When the plane-image of a parametric source is plotted, the percentage of its maximum flux that is used to zoom in the plot on its brightest galaxy regions.","k":"zoom.plane_percent","line":11},{"c":"When the plane-image of an inversion is plotted, the percentage of its maximum flux that is used to zoom in the plot on its brightest galaxy regions.","k":"zoom.inversion_percent","line":12},{"c":"","k":"units","line":13},{"c":"Whether to plot spatial coordinates in scaled units computed via the pixel_scale (e.g. arc-seconds) or pixel units by default.","k":"units.use_scaled","line":14},{"c":"The string or latex unit label used for the colorbar of the image, for example electrons per second.","k":"units.cb_unit","line":15},{"c":"The symbol used when plotting spatial coordinates computed via the pixel_scale (e.g. for Astronomy data this is arc-seconds).","k":"units.scaled_symbol","line":16},{"c":"The symbol used when plotting spatial coordinates in unscaled pixel units.","k":"units.unscaled_symbol","line":17},{"c":"Default colormap for 2D plots. Use any matplotlib name to override (e.g. jet, viridis).","k":"colormap","line":18},{"c":"","k":"ticks","line":19},{"c":"Fraction of half-extent used for 2D tick positions (< 1.0 pulls ticks inward from edges).","k":"ticks.extent_factor_2d","line":20},{"c":"Number of ticks on each spatial axis of 2D plots.","k":"ticks.number_of_ticks_2d","line":21},{"c":"","k":"contour","line":22},{"c":"Number of contour levels drawn over log10 (and explicit linear) plots.","k":"contour.total_contours","line":23},{"c":"Whether to label each contour line with its value.","k":"contour.include_values","line":24},{"c":"","k":"colorbar","line":25},{"c":"Fraction of original axes to use for the colorbar.","k":"colorbar.fraction","line":26},{"c":"Padding between colorbar and axes.","k":"colorbar.pad","line":27},{"c":"Rotation of colorbar tick labels in degrees.","k":"colorbar.labelrotation","line":28},{"c":"Font size of colorbar tick labels for single-panel figures.","k":"colorbar.labelsize","line":29},{"c":"Font size of colorbar tick labels for subplot panels.","k":"colorbar.labelsize_subplot","line":30}],"lines":30,"path":"visualize/general.yaml","prior":false,"priors":[],"repo":"autogalaxy_workspace","text":"general:\n  backend: default                  # The matploblib backend used for visualization. `default` uses the system default, can specifiy specific backend (e.g. TKAgg, Qt5Agg, WXAgg).\n  imshow_origin: upper                  # The `origin` input of `imshow`, determining if pixel values are ascending or descending on the y-axis.\n  log10_min_value: 1.0e-4               # If negative values are being plotted on a log10 scale, values below this value are rounded up to it (e.g. to remove negative values).\n  log10_max_value: 1.0e99               # If positive values are being plotted on a log10 scale, values above this value are rounded down to it (e.g. to prevent white blobs).\n  zoom_around_mask: true                # If True, plots of data structures with a mask automatically zoom in the masked region.\n  output_format: show                   # Default output format: \"show\" displays the figure interactively via plt.show(), \"png\"/\"pdf\"/etc. saves to file.\ninversion:\n  reconstruction_vmax_factor: 0.5   # Plots of an Inversion's reconstruction use the reconstructed data's bright value multiplied by this factor.\nzoom:\n  plane_percent: 0.01               # When the plane-image of a parametric source is plotted, the percentage of its maximum flux that is used to zoom in the plot on its brightest galaxy regions.\n  inversion_percent: 0.05           # When the plane-image of an inversion is plotted, the percentage of its maximum flux that is used to zoom in the plot on its brightest galaxy regions.\nunits:\n  use_scaled: true                  # Whether to plot spatial coordinates in scaled units computed via the pixel_scale (e.g. arc-seconds) or pixel units by default.\n  cb_unit: $\\,\\,\\mathrm{e^{-}}\\,\\mathrm{s^{-1}}$ # The string or latex unit label used for the colorbar of the image, for example electrons per second.\n  scaled_symbol: '\"'                    # The symbol used when plotting spatial coordinates computed via the pixel_scale (e.g. for Astronomy data this is arc-seconds).\n  unscaled_symbol: pix                  # The symbol used when plotting spatial coordinates in unscaled pixel units.\ncolormap: autoarray               # Default colormap for 2D plots. Use any matplotlib name to override (e.g. jet, viridis).\nticks:\n  extent_factor_2d: 0.75          # Fraction of half-extent used for 2D tick positions (< 1.0 pulls ticks inward from edges).\n  number_of_ticks_2d: 3           # Number of ticks on each spatial axis of 2D plots.\ncontour:\n  total_contours: 10              # Number of contour levels drawn over log10 (and explicit linear) plots.\n  include_values: false           # Whether to label each contour line with its value.\ncolorbar:\n  fraction: 0.047                 # Fraction of original axes to use for the colorbar.\n  pad: 0.01                       # Padding between colorbar and axes.\n  labelrotation: 90               # Rotation of colorbar tick labels in degrees.\n  labelsize: 16                   # Font size of colorbar tick labels for single-panel figures.\n  labelsize_subplot: 16           # Font size of colorbar tick labels for subplot panels.","tooling":false,"top_keys":["general","inversion","zoom","units","colormap","ticks","contour","colorbar"]},{"error":null,"keys":[{"c":"Output format of all subplots, can be png, pdf or both (e.g. [png, pdf])","k":"subplot_format","line":15},{"c":"If true, output .fits files are zoomed in on the center of the unmasked region image, saving hard-disk space.","k":"fits_are_zoomed","line":16},{"c":"Settings for plots of all datasets (e.g. ImagingPlotter, InterferometerPlotter).","k":"dataset","line":18},{"c":"Plot subplot containing all dataset quantities (e.g. the data, noise-map, etc.)?","k":"dataset.subplot_dataset","line":19},{"c":"Settings for plots of all fits (e.g. FitImagingPlotter, FitInterferometerPlotter).","k":"fit","line":21},{"c":"Plot subplot of all fit quantities for any dataset (e.g. the model data, residual-map, etc.)?","k":"fit.subplot_fit","line":22},{"c":"Plot subplot of all fit quantities for any dataset using log10 color maps (e.g. the model data, residual-map, etc.)?","k":"fit.subplot_fit_log10","line":23},{"c":"Plot subplot of the model-image, subtracted image and other quantities of each galaxy?","k":"fit.subplot_of_galaxies","line":24},{"c":"Plot subplot of the image of each galaxy in the model?","k":"fit.subplot_galaxy_images","line":25},{"c":"Output a .fits file containing the fit model data, residual map, normalized residual map and chi-squared?","k":"fit.fits_fit","line":26},{"c":"Output a .fits file containing the images (e.g. without PSF convolution) of every galaxy?","k":"fit.fits_galaxy_images","line":27},{"c":"Output a .fits file containing the model images (e.g. with PSF convolution) of every galaxy?","k":"fit.fits_model_galaxy_images","line":28},{"c":"Settings for plots of fits to imaging datasets (e.g. FitImagingPlotter).","k":"fit_imaging","line":30},{"c":"Settings for plots of galaxies (e.g. GalaxiesPlotter).","k":"galaxies","line":32},{"c":"Plot subplot of all quantities in each galaxies group (e.g. images, convergence)?","k":"galaxies.subplot_galaxies","line":33},{"c":"Plot subplot of the image of each galaxy in the model?","k":"galaxies.subplot_galaxy_images","line":34},{"c":"Output a .fits file containing images of every galaxy?","k":"galaxies.fits_galaxy_images","line":35},{"c":"Settings for plots of inversions (e.g. InversionPlotter).","k":"inversion","line":37},{"c":"Plot subplot of all quantities in each inversion (e.g. reconstrucuted image, reconstruction)?","k":"inversion.subplot_inversion","line":38},{"c":"Plot subplot of the image-to-source pixels mappings of each pixelization?","k":"inversion.subplot_mappings","line":39},{"c":"output source_plane_reconstruction_0.csv containing the source-plane mesh y, x, reconstruction and noise map values.","k":"inversion.csv_reconstruction","line":40},{"c":"Settings for plots of adapt images used by adaptive pixelizations.","k":"adapt","line":42},{"c":"Plot subplot showing each adapt image used for adaptive pixelization?","k":"adapt.subplot_adapt_images","line":43},{"c":"Output a .fits file containing the adapt images used for adaptive pixelization?","k":"adapt.fits_adapt_images","line":44},{"c":"Settings for plots of fits to interferometer datasets (e.g. FitInterferometerPlotter).","k":"fit_interferometer","line":46},{"c":"Plot subplot of the dirty-images of all interferometer datasets?","k":"fit_interferometer.subplot_fit_dirty_images","line":47},{"c":"Plot subplot of the real-space images of all interferometer datasets?","k":"fit_interferometer.subplot_fit_real_space","line":48},{"c":"output dirty_images.fits showing the dirty image, noise-map, model-data, resiual-map, normalized residual map and chi-squared map?","k":"fit_interferometer.fits_dirty_images","line":49},{"c":"Settings for plots of ellipse fitting fits (e.g. FitEllipse)","k":"fit_ellipse","line":51},{"c":"Plot the data of the ellipse fit?","k":"fit_ellipse.data","line":52},{"c":"Plot the data without the black data ellipses, which obscure noisy data?","k":"fit_ellipse.data_no_ellipse","line":53}],"lines":53,"path":"visualize/plots.yaml","prior":false,"priors":[],"repo":"autogalaxy_workspace","text":"# The `plots` section customizes every image that is output to hard-disk during a model-fit.\n\n# For example, if `plots: fit: subplot_fit=True``, the ``subplot_fit.png`` subplot file will\n# be plotted every time visualization is performed.\n\n# One setting is important for inspecting results via the dataset after a fit is complete:\n\n# -`fits_adapt_images`, This outputs `adapt_images.fits` which the database functionality may use to reperform fits.\n\n# It can be disabled to save on hard-disk space but will lead to certain database functionality being disabled.\n\n# The dataset itself is always output as `dataset.fits` to the `image` folder of every fit, and is not controlled\n# by any setting here.\n\nsubplot_format: [png]                      # Output format of all subplots, can be png, pdf or both (e.g. [png, pdf])\nfits_are_zoomed: false                     # If true, output .fits files are zoomed in on the center of the unmasked region image, saving hard-disk space.\n\ndataset:                                   # Settings for plots of all datasets (e.g. ImagingPlotter, InterferometerPlotter).\n  subplot_dataset: true                    # Plot subplot containing all dataset quantities (e.g. the data, noise-map, etc.)?\n\nfit:                                       # Settings for plots of all fits (e.g. FitImagingPlotter, FitInterferometerPlotter).\n  subplot_fit: true                        # Plot subplot of all fit quantities for any dataset (e.g. the model data, residual-map, etc.)?\n  subplot_fit_log10: false                  # Plot subplot of all fit quantities for any dataset using log10 color maps (e.g. the model data, residual-map, etc.)?\n  subplot_of_galaxies: false               # Plot subplot of the model-image, subtracted image and other quantities of each galaxy?\n  subplot_galaxy_images: false             # Plot subplot of the image of each galaxy in the model?\n  fits_fit: true                           # Output a .fits file containing the fit model data, residual map, normalized residual map and chi-squared?\n  fits_galaxy_images : true                # Output a .fits file containing the images (e.g. without PSF convolution) of every galaxy?\n  fits_model_galaxy_images : true          # Output a .fits file containing the model images (e.g. with PSF convolution) of every galaxy?\n\nfit_imaging: {}                            # Settings for plots of fits to imaging datasets (e.g. FitImagingPlotter).\n\ngalaxies:                                  # Settings for plots of galaxies (e.g. GalaxiesPlotter).\n  subplot_galaxies: false                  # Plot subplot of all quantities in each galaxies group (e.g. images, convergence)?\n  subplot_galaxy_images: false             # Plot subplot of the image of each galaxy in the model?\n  fits_galaxy_images: true                 # Output a .fits file containing images of every galaxy?\n\ninversion:                                 # Settings for plots of inversions (e.g. InversionPlotter).\n  subplot_inversion: true                  # Plot subplot of all quantities in each inversion (e.g. reconstrucuted image, reconstruction)?\n  subplot_mappings: false                  # Plot subplot of the image-to-source pixels mappings of each pixelization?\n  csv_reconstruction: true                 # output source_plane_reconstruction_0.csv containing the source-plane mesh y, x, reconstruction and noise map values.\n\nadapt:                                     # Settings for plots of adapt images used by adaptive pixelizations.\n  subplot_adapt_images: true               # Plot subplot showing each adapt image used for adaptive pixelization?\n  fits_adapt_images: true                  # Output a .fits file containing the adapt images used for adaptive pixelization?\n\nfit_interferometer:                        # Settings for plots of fits to interferometer datasets (e.g. FitInterferometerPlotter).\n  subplot_fit_dirty_images: false          # Plot subplot of the dirty-images of all interferometer datasets?\n  subplot_fit_real_space: false            # Plot subplot of the real-space images of all interferometer datasets?\n  fits_dirty_images: true                  # output dirty_images.fits showing the dirty image, noise-map, model-data, resiual-map, normalized residual map and chi-squared map?\n\nfit_ellipse:                               # Settings for plots of ellipse fitting fits (e.g. FitEllipse)\n  data : true                              # Plot the data of the ellipse fit?\n  data_no_ellipse: true                    # Plot the data without the black data ellipses, which obscure noisy data?\n","tooling":false,"top_keys":["subplot_format","fits_are_zoomed","dataset","fit","fit_imaging","galaxies","inversion","adapt","fit_interferometer","fit_ellipse"]},{"error":null,"keys":[{"c":"","k":"nest","line":1},{"c":"Output corner figure (using anestetic) during a non-linear search fit?","k":"nest.corner_anesthetic","line":2},{"c":"","k":"mcmc","line":3},{"c":"Output corner figure (using corner.py) during a non-linear search fit?","k":"mcmc.corner_cornerpy","line":4},{"c":"","k":"mle","line":5},{"c":"Output a subplot of the best-fit parameters of the model?","k":"mle.subplot_parameters","line":6},{"c":"Output a plot of the log likelihood versus iteration number?","k":"mle.log_likelihood_vs_iteration","line":7},{"c":"Output the global-best figure-of-merit trace (auto-convergence gradient searches)?","k":"mle.figure_of_merit_vs_iteration","line":8}],"lines":8,"path":"visualize/plots_search.yaml","prior":false,"priors":[],"repo":"autogalaxy_workspace","text":"nest:\n  corner_anesthetic: true   # Output corner figure (using anestetic) during a non-linear search fit?\nmcmc:\n  corner_cornerpy: true     # Output corner figure (using corner.py) during a non-linear search fit?\nmle:\n  subplot_parameters: true   # Output a subplot of the best-fit parameters of the model?\n  log_likelihood_vs_iteration: true  # Output a plot of the log likelihood versus iteration number?\n  figure_of_merit_vs_iteration: true  # Output the global-best figure-of-merit trace (auto-convergence gradient searches)?","tooling":false,"top_keys":["nest","mcmc","mle"]},{"error":null,"keys":[{"c":"PyAutoHands's generate_markdown.py \u2014 see that module's docstring for the rules (never TEST_MODE; features/ scripts never rendered; list order is execution order and index order).","k":"script","line":6},{"c":"","k":"max_minutes","line":7},{"c":"","k":"script","line":8},{"c":"","k":"max_minutes","line":9},{"c":"","k":"script","line":10},{"c":"","k":"max_minutes","line":11},{"c":"","k":"script","line":12},{"c":"","k":"max_minutes","line":13},{"c":"","k":"script","line":14},{"c":"","k":"max_minutes","line":15},{"c":"","k":"script","line":16},{"c":"","k":"max_minutes","line":17},{"c":"","k":"script","line":18},{"c":"","k":"max_minutes","line":19},{"c":"","k":"script","line":20},{"c":"","k":"max_minutes","line":21},{"c":"","k":"script","line":22},{"c":"","k":"max_minutes","line":23},{"c":"--- batch 2a: remaining dataset types (cluster excluded pending runtime call) ---","k":"script","line":25},{"c":"","k":"max_minutes","line":26},{"c":"","k":"script","line":27},{"c":"","k":"max_minutes","line":28},{"c":"","k":"script","line":29},{"c":"","k":"max_minutes","line":30},{"c":"","k":"script","line":31},{"c":"","k":"max_minutes","line":32},{"c":"","k":"script","line":33},{"c":"","k":"max_minutes","line":34},{"c":"","k":"script","line":35},{"c":"","k":"max_minutes","line":36},{"c":"","k":"script","line":37},{"c":"","k":"max_minutes","line":38},{"c":"","k":"script","line":39},{"c":"","k":"max_minutes","line":40},{"c":"","k":"script","line":41},{"c":"","k":"max_minutes","line":42},{"c":"","k":"script","line":43},{"c":"","k":"max_minutes","line":44},{"c":"","k":"script","line":45},{"c":"","k":"max_minutes","line":46},{"c":"","k":"script","line":47},{"c":"","k":"max_minutes","line":48},{"c":"","k":"script","line":49},{"c":"","k":"max_minutes","line":50},{"c":"","k":"script","line":51},{"c":"","k":"max_minutes","line":52},{"c":"","k":"script","line":53},{"c":"","k":"max_minutes","line":54},{"c":"","k":"script","line":55},{"c":"","k":"max_minutes","line":56},{"c":"","k":"script","line":57},{"c":"","k":"max_minutes","line":58},{"c":"","k":"script","line":59},{"c":"","k":"max_minutes","line":60},{"c":"","k":"script","line":61},{"c":"","k":"max_minutes","line":62},{"c":"","k":"script","line":63},{"c":"","k":"max_minutes","line":64},{"c":"","k":"script","line":65},{"c":"","k":"max_minutes","line":66}],"lines":66,"path":"build/markdown_examples.yaml","prior":false,"priors":[],"repo":"autolens_workspace","text":"# Curated examples rendered to executed markdown pages (markdown/) with their\n# real output images, so they can be read on GitHub. Built manually by\n# PyAutoHands's generate_markdown.py \u2014 see that module's docstring for the\n# rules (never TEST_MODE; features/ scripts never rendered; list order is\n# execution order and index order).\n- script: start_here.py\n  max_minutes: 45\n- script: scripts/imaging/start_here.py\n  max_minutes: 240\n- script: scripts/imaging/simulator.py\n  max_minutes: 45\n- script: scripts/imaging/fit.py\n  max_minutes: 45\n- script: scripts/imaging/likelihood_function.py\n  max_minutes: 45\n- script: scripts/imaging/modeling.py\n  max_minutes: 240\n- script: scripts/guides/tracer.py\n  max_minutes: 45\n- script: scripts/guides/galaxies.py\n  max_minutes: 45\n- script: scripts/guides/lens_calc.py\n  max_minutes: 45\n# --- batch 2a: remaining dataset types (cluster excluded pending runtime call) ---\n- script: scripts/interferometer/start_here.py\n  max_minutes: 240\n- script: scripts/interferometer/simulator.py\n  max_minutes: 45\n- script: scripts/interferometer/likelihood_function.py\n  max_minutes: 45\n- script: scripts/interferometer/fit.py\n  max_minutes: 45\n- script: scripts/interferometer/modeling.py\n  max_minutes: 300\n- script: scripts/point_source/start_here.py\n  max_minutes: 120\n- script: scripts/point_source/simulator.py\n  max_minutes: 45\n- script: scripts/point_source/fit.py\n  max_minutes: 45\n- script: scripts/point_source/modeling.py\n  max_minutes: 120\n- script: scripts/multi_dataset/start_here.py\n  max_minutes: 120\n- script: scripts/multi_dataset/simulator.py\n  max_minutes: 45\n- script: scripts/multi_dataset/modeling.py\n  max_minutes: 240\n- script: scripts/group/start_here.py\n  max_minutes: 120\n- script: scripts/group/simulator.py\n  max_minutes: 45\n- script: scripts/group/likelihood_function.py\n  max_minutes: 45\n- script: scripts/group/fit.py\n  max_minutes: 45\n- script: scripts/group/modeling.py\n  max_minutes: 300\n- script: scripts/weak/simulator.py\n  max_minutes: 45\n- script: scripts/weak/likelihood_function.py\n  max_minutes: 45\n- script: scripts/weak/fit.py\n  max_minutes: 45\n- script: scripts/weak/modeling.py\n  max_minutes: 240\n","tooling":true,"top_keys":["script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes","script","max_minutes"]},{"error":null,"keys":[],"lines":47,"path":"build/no_run.yaml","prior":false,"priors":[],"repo":"autolens_workspace","text":"# Scripts to skip during automated runs (smoke tests, pre-release checks, CI).\n# Each entry is matched against script paths:\n#   - Entries with '/' do a substring match against the file path\n#   - Entries without '/' match the file stem exactly\n# Add an inline # comment to document the reason for skipping.\n#\n# THIS LIST IS REPO-LOCAL. Do not copy entries in from HowToLens (or any other\n# repo), and do not copy entries out. Tutorial stems such as `tutorial_searches`\n# belong to HowToLens and can never match a file here; a pattern that matches\n# nothing is silently inert, not a skip. Every entry below must match a file in\n# THIS repo \u2014 verify with `should_skip` in PyAutoHands/autohands/build_util.py\n# before adding one, and re-check the pattern whenever a script is moved.\n#\n# SLOW-skip convention:\n#   Entries tagged `# SLOW <YYYY-MM-DD> - <reason>` mark scripts that are\n#   skipped because they exceed the per-script timeout cap (300s by\n#   default; 1800s for mode=release runs). These are\n#   NOT permanent skips \u2014 every mega-run surfaces them with a loud warning\n#   banner. Fix the performance issue and remove the SLOW marker.\n#\n# NEEDS_FIX convention:\n#   Entries tagged `# NEEDS_FIX <YYYY-MM-DD> - <reason>` mark scripts that\n#   are broken and parked as a to-do list. Like SLOW-skips, these are NOT\n#   permanent skips \u2014 every mega-run surfaces them with a loud warning\n#   banner. Investigate the failure, fix the underlying bug, and remove\n#   the NEEDS_FIX marker.\n\n- gui/extra_galaxies_centres # GUI scripts cannot be run\n- gui/mask_extra_galaxies # GUI scripts cannot be run\n- gui/lens_light_centre # GUI scripts cannot be run\n- gui/mask # GUI scripts cannot be run\n- gui/positions # GUI scripts cannot be run\n- fits_make # Test mode does not output .fits images.\n- png_make # Test mode does not output .png images.\n- time_delays # Test mode does not support cosmology ift\n- guides/plot/searches # Test mode breaks search visualization.\n- mass_stellar_dark/modeling # Requires CSE to be JAX enabled.\n- mass_stellar_dark/slam # Requires CSE to be JAX enabled.\n- detect/database # Unsure but not a feature actively used currently.\n- imaging/features/advanced/subhalo/sensitivity/ # All sensitivity scripts need updating when visualization refactored.\n- point_source/features/multiple_sources/simulator # Blocked by PyAutoLens #480: solver finds 0 positions for intermediate-plane source\n- point_source/features/multiple_sources/modeling # Blocked by PyAutoLens #480: same root cause as simulator above\n- interferometer/casa_reduction # Requires CASA MeasurementSet output, not runnable standalone\n- cluster/start_here # SLOW 2026-07-22 - hits the full 1800s mode=release cap in workspace-validation (PyAutoHeart run 29912642195). Script-specific, not a shard-wide problem: every other cluster script passes, the next-slowest being lenstool/modeling.py at 137.8s. Consistent with cluster scripts being known un-smoke-able (>500s even in TEST_MODE). Not yet profiled \u2014 SLOW-skipped to unblock the release; remove once the cost is found and fixed (autolens_workspace#314).\n- multi_galaxy/start_here # SLOW 2026-09-18 - blocked two consecutive release runs after autolens_workspace#554 (2026-09-17) swapped it off the simulated `simple` dataset onto the real SDSS J1011+0143 ACS/WFC F814W frame. On the profile defaults it hit the 1800s mode=release cap (PyAutoHeart run 35319361459, \"1 timeout\"); with a per-script BUILD_SCRIPT_TIMEOUT of 3600s AND PYAUTO_SMALL_DATASETS lifted (#563, reverted here) the runner died at ~1478s on a shutdown signal (exit 143, PyAutoHeart run 35372831809) before either cap could fire \u2014 consistent with the uncapped real frame exhausting runner memory, though that is inferred, not measured. Not yet profiled - SLOW-skipped to unblock the release; remove once the cost is found and fixed (PyAutoMind draft/bug/autolens_workspace/multi_galaxy_start_here_release_cost.md).\n- weak/features/strong_lensing/a2744 # SLOW 2026-07-22 - hits the full 1800s mode=release cap in workspace-validation (PyAutoHeart run 29912642195). Script-specific: its sibling weak/real_data/a2744.py passes in 8.9s and the next-slowest weak script is modeling.py at 23.6s, so the cost is in the strong_lensing feature path rather than the A2744 dataset itself. Not yet profiled \u2014 SLOW-skipped to unblock the release (autolens_workspace#314).\n- guides/modeling/advanced/expectation_propagation.py # NEEDS_FIX 2026-08-03 - EP parked as not release-ready. EP message projection is unstable: a truncated per-factor search projects an ESS=1 posterior with zero weighted variance, which EP feeds back as a delta-function prior. See PyAutoFit #1332 F10 and autofit_workspace_test graphical/ep.py.\n","tooling":true,"top_keys":[]},{"error":null,"keys":[{"c":"","k":"defaults","line":27},{"c":"reduced iterations (real sampler), not bypassed","k":"defaults.PYAUTO_TEST_MODE","line":28},{"c":"real fit output (release fidelity, not smoke)","k":"defaults.PYAUTO_SKIP_FIT_OUTPUT","line":29},{"c":"real visualization (release fidelity, not smoke)","k":"defaults.PYAUTO_SKIP_VISUALIZATION","line":30},{"c":"real mesh/position/weight checks (release fidelity)","k":"defaults.PYAUTO_SKIP_CHECKS","line":31},{"c":"cap grids/masks to 15x15, reduce MGE gaussians","k":"defaults.PYAUTO_SMALL_DATASETS","line":32},{"c":"JAX enabled (release fidelity, not smoke)","k":"defaults.PYAUTO_DISABLE_JAX","line":33},{"c":"skip tight_layout() + critical curve/caustic overlays","k":"defaults.PYAUTO_FAST_PLOTS","line":34},{"c":"TestPyPI dev version won't match the workspace pin","k":"defaults.PYAUTO_SKIP_WORKSPACE_VERSION_CHECK","line":35},{"c":"enable 64-bit precision in JAX","k":"defaults.JAX_ENABLE_X64","line":36},{"c":"writable cache dir for numba","k":"defaults.NUMBA_CACHE_DIR","line":37},{"c":"writable config dir for matplotlib","k":"defaults.MPLCONFIGDIR","line":38},{"c":"","k":"overrides","line":40},{"c":"The real fix - an explicit batch_size or a uniform over-sample map so the compile is cheap - is tracked separately; retire this entry once that lands.","k":"overrides.pattern","line":62},{"c":"","k":"overrides.set","line":63},{"c":"","k":"overrides.set.PYAUTO_SMALL_DATASETS","line":64},{"c":"","k":"overrides.set.BUILD_SCRIPT_TIMEOUT","line":65},{"c":"","k":"overrides.pattern","line":66},{"c":"","k":"overrides.set","line":67},{"c":"","k":"overrides.pattern","line":68},{"c":"","k":"overrides.set","line":69},{"c":"","k":"overrides.pattern","line":70},{"c":"","k":"overrides.set","line":71},{"c":"paths are {interferometer,imaging}/features/advanced/potential_correction/..., which that substring misses. Uncapped they are cheap: start_here.py runs in ~58s against the 1800s cap.","k":"overrides.pattern","line":80},{"c":"","k":"overrides.set","line":81},{"c":"","k":"overrides.pattern","line":82},{"c":"","k":"overrides.set","line":83},{"c":"Simulators run first and regenerate data at the capped size, but scripts using the old `if not dataset_path.exists()` pattern skip re-simulation when data already exists.","k":"overrides.pattern","line":89},{"c":"","k":"overrides.set","line":90},{"c":"need PYAUTO_FAST_PLOTS forced off (it would otherwise close every figure without saving via the subplot_save / save_figure short-circuit in autoarray/plot/utils.py). PYAUTO_SKIP_VISUALIZATION is alre\u2026","k":"overrides.pattern","line":99},{"c":"","k":"overrides.set","line":100},{"c":"","k":"overrides.pattern","line":101},{"c":"","k":"overrides.set","line":102},{"c":"it still validates the full Delaunay pipeline (compose + one likelihood eval) within budget; the reduced-sampler datacube path stays covered by the rectangular sibling.","k":"overrides.pattern","line":111},{"c":"","k":"overrides.set","line":112}],"lines":112,"path":"build/profile_release.yaml","prior":false,"priors":[],"repo":"autolens_workspace","text":"# Per-script environment variable configuration for the RELEASE-FIDELITY\n# validation run (Heart's workspace-validation.yml, mode=release \u2014 the M3\n# wheel-based release-fidelity path). Distinct from profile_smoke.yaml, which is the\n# `smoke` profile used by the per-PR CI gate.\n#\n# The `release` profile trades speed for fidelity: it is run once per release\n# rehearsal against the TestPyPI wheels, not on every PR, so it can afford a\n# reduced (not bypassed) sampler and real fit output/visualization/checks.\n# Spec + acceptance table: PyAutoHeart/docs/release_validation.md.\n#\n# \"defaults\" are applied to every script on top of the inherited environment.\n# Every var this profile cares about is given an EXPLICIT value in \"defaults\"\n# (not left absent) \u2014 the runner only ever *sets* keys it's given, it never\n# clears unrelated inherited env vars, so an absent key silently falls through\n# to whatever the calling process already had (a leftover smoke-mode \"1\" from\n# an earlier step, a developer's local shell, ...). Pinning everything here\n# makes the profile self-contained regardless of the caller's environment.\n#\n# \"overrides\" should normally `set:` a var away from this profile's own default.\n# Use `unset:` only when the script genuinely needs the variable absent, such as\n# guides that must not run under PyAuto test mode at all.\n#\n# Pattern convention (same as no_run.yaml):\n#   - Patterns containing '/' do a substring match against the file path\n#   - Patterns without '/' match the file stem exactly\n\ndefaults:\n  PYAUTO_TEST_MODE: \"1\"                     # reduced iterations (real sampler), not bypassed\n  PYAUTO_SKIP_FIT_OUTPUT: \"0\"               # real fit output (release fidelity, not smoke)\n  PYAUTO_SKIP_VISUALIZATION: \"0\"            # real visualization (release fidelity, not smoke)\n  PYAUTO_SKIP_CHECKS: \"0\"                   # real mesh/position/weight checks (release fidelity)\n  PYAUTO_SMALL_DATASETS: \"1\"                # cap grids/masks to 15x15, reduce MGE gaussians\n  PYAUTO_DISABLE_JAX: \"0\"                   # JAX enabled (release fidelity, not smoke)\n  PYAUTO_FAST_PLOTS: \"1\"                    # skip tight_layout() + critical curve/caustic overlays\n  PYAUTO_SKIP_WORKSPACE_VERSION_CHECK: \"1\"  # TestPyPI dev version won't match the workspace pin\n  JAX_ENABLE_X64: \"True\"                    # enable 64-bit precision in JAX\n  NUMBA_CACHE_DIR: \"/tmp/numba_cache\"       # writable cache dir for numba\n  MPLCONFIGDIR: \"/tmp/matplotlib\"           # writable config dir for matplotlib\n\noverrides:\n  # start_here scripts load real FITS data that breaks with small datasets\n  #\n  # imaging/start_here.py additionally gets its own per-script BUILD_SCRIPT_TIMEOUT\n  # (autolens_workspace#547). It was killed at the release leg's run-wide 1800s cap\n  # in 3 of the last 8 Release Integrate runs (2026-09-09, 09-12, 09-14 run\n  # 34898325503) at 1805s, while passing runs of the same script span 586-1695s and\n  # every sibling in the leg is stable. Each kill lands inside one ~26 min XLA CPU\n  # compile of MultiStartProdigy (n_starts=48, batch_size=None) under this script's\n  # non-uniform over-sample map, BEFORE the first gradient step - a compile-time\n  # flake, not a regression. 3600s buys the compile room; worst case it adds +30 min\n  # to the single slowest leg of a ~72 min job, and only when the flake actually\n  # fires. The run-wide 1800s cap is deliberately left alone: it is the only guard on\n  # the other 84 entries in this profile.\n  #\n  # Spelled with `set:` because validate_env_profiles.ALLOWED_OVERRIDE_KEYS is\n  # {pattern, set, unset} - a `timeout:` key would be rejected. build_util.timeout_for\n  # reads it parent-side and it WINS over the workflow global (PyAutoHands#227);\n  # precedent: autolens_workspace_test/config/build/profile_smoke.yaml \"jax_grad/\".\n  #\n  # The real fix - an explicit batch_size or a uniform over-sample map so the compile\n  # is cheap - is tracked separately; retire this entry once that lands.\n  - pattern: \"imaging/start_here\"\n    set:\n      PYAUTO_SMALL_DATASETS: \"0\"\n      BUILD_SCRIPT_TIMEOUT: \"3600\"\n  - pattern: \"interferometer/start_here\"\n    set: { PYAUTO_SMALL_DATASETS: \"0\" }\n  - pattern: \"group/start_here\"\n    set: { PYAUTO_SMALL_DATASETS: \"0\" }\n  - pattern: \"multi_dataset/start_here\"\n    set: { PYAUTO_SMALL_DATASETS: \"0\" }\n  # Potential correction builds a dpsi mesh by subsampling the image grid, so the\n  # 15x15 small-datasets cap starves it: al.pc.PairRegularDpsiMesh(dpsi_factor=2)\n  # raises \"The dpsi grid is too sparse\" from mesh.py's get_itp_box_ctr. Both the\n  # interferometer and imaging siblings under features/advanced/potential_correction/ need\n  # the cap lifted; the \"*/start_here\" patterns above do NOT cover them \u2014 their\n  # paths are {interferometer,imaging}/features/advanced/potential_correction/..., which\n  # that substring misses. Uncapped they are cheap: start_here.py runs in ~58s\n  # against the 1800s cap.\n  - pattern: \"interferometer/features/advanced/potential_correction/\"\n    set: { PYAUTO_SMALL_DATASETS: \"0\" }\n  - pattern: \"imaging/features/advanced/potential_correction/\"\n    set: { PYAUTO_SMALL_DATASETS: \"0\" }\n  # Non-simulator scripts that load committed FITS data need full-size\n  # datasets to avoid shape mismatch with pre-existing 100x100 data.\n  # Simulators run first and regenerate data at the capped size, but\n  # scripts using the old `if not dataset_path.exists()` pattern skip\n  # re-simulation when data already exists.\n  - pattern: \"guides/\"\n    set: { PYAUTO_SMALL_DATASETS: \"0\" }\n  # (guides/results/ formerly unset PYAUTO_TEST_MODE here; that intent now\n  # lives in-file as '# ENV: real_search' declarations on the scripts, which\n  # apply in every profile \u2014 the override became redundant.)\n  #\n  # fits_make / png_make produce .fits / .png outputs from real fits, so they\n  # need PYAUTO_FAST_PLOTS forced off (it would otherwise close every figure\n  # without saving via the subplot_save / save_figure short-circuit in\n  # autoarray/plot/utils.py). PYAUTO_SKIP_VISUALIZATION is already \"0\" above.\n  - pattern: fits_make\n    set: { PYAUTO_FAST_PLOTS: \"0\" }\n  - pattern: png_make\n    set: { PYAUTO_FAST_PLOTS: \"0\" }\n  # The datacube Delaunay fit runs a reduced-but-real sampler over a 4-channel\n  # Delaunay + per-channel NUFFT FactorGraph. Its per-likelihood cost is far\n  # higher than the rectangular sibling (interferometer/features/datacube/\n  # modeling.py, which passes under the reduced sampler), so the reduced-sampler\n  # run overruns the 1800s per-script cap on CI CPU. Bypass the sampler here so\n  # it still validates the full Delaunay pipeline (compose + one likelihood\n  # eval) within budget; the reduced-sampler datacube path stays covered by the\n  # rectangular sibling.\n  - pattern: \"interferometer/features/datacube/delaunay\"\n    set: { PYAUTO_TEST_MODE: \"2\" }\n","tooling":true,"top_keys":["defaults","overrides"]},{"error":null,"keys":[{"c":"","k":"defaults","line":11},{"c":"0=normal, 1=reduced iterations, 2=skip sampler (fastest)","k":"defaults.PYAUTO_TEST_MODE","line":12},{"c":"Skip pre/post-fit I/O, VRAM profiling, result text","k":"defaults.PYAUTO_SKIP_FIT_OUTPUT","line":13},{"c":"Skip fit visualization and plotting","k":"defaults.PYAUTO_SKIP_VISUALIZATION","line":14},{"c":"Skip mesh validation, position checks, weight thresholds","k":"defaults.PYAUTO_SKIP_CHECKS","line":15},{"c":"Cap grids/masks to 15x15, reduce MGE gaussians","k":"defaults.PYAUTO_SMALL_DATASETS","line":16},{"c":"Force use_jax=False, avoid JIT compilation overhead","k":"defaults.PYAUTO_DISABLE_JAX","line":17},{"c":"Skip tight_layout() + critical curve/caustic overlays","k":"defaults.PYAUTO_FAST_PLOTS","line":18},{"c":"Enable 64-bit precision in JAX","k":"defaults.JAX_ENABLE_X64","line":19},{"c":"Writable cache dir for numba","k":"defaults.NUMBA_CACHE_DIR","line":20},{"c":"Writable config dir for matplotlib","k":"defaults.MPLCONFIGDIR","line":21},{"c":"","k":"overrides","line":23},{"c":"(data_fitting, queries, models, samples_via_aggregator) find a non-empty aggregator. Covers all scripts under guides/results/. (PYAUTO_TEST_MODE moved to `ENV: real_search`.)","k":"overrides.pattern","line":36},{"c":"","k":"overrides.unset","line":37},{"c":"fits_make / png_make produce .fits / .png outputs from real fits, so they need visualization turned on. (PYAUTO_FAST_PLOTS moved to `ENV: real_plots`.)","k":"overrides.pattern","line":40},{"c":"","k":"overrides.unset","line":41},{"c":"","k":"overrides.pattern","line":42},{"c":"","k":"overrides.unset","line":43}],"lines":43,"path":"build/profile_smoke.yaml","prior":false,"priors":[],"repo":"autolens_workspace","text":"# Per-script environment variable configuration for automated runs\n# (smoke tests, pre-release checks, CI).\n#\n# \"defaults\" are applied to every script on top of the inherited environment.\n# \"overrides\" selectively unset or replace vars for matching path patterns.\n#\n# Pattern convention (same as no_run.yaml):\n#   - Patterns containing '/' do a substring match against the file path\n#   - Patterns without '/' match the file stem exactly\n\ndefaults:\n  PYAUTO_TEST_MODE: \"2\"                     # 0=normal, 1=reduced iterations, 2=skip sampler (fastest)\n  PYAUTO_SKIP_FIT_OUTPUT: \"1\"               # Skip pre/post-fit I/O, VRAM profiling, result text\n  PYAUTO_SKIP_VISUALIZATION: \"1\"            # Skip fit visualization and plotting\n  PYAUTO_SKIP_CHECKS: \"1\"                   # Skip mesh validation, position checks, weight thresholds\n  PYAUTO_SMALL_DATASETS: \"1\"                # Cap grids/masks to 15x15, reduce MGE gaussians\n  PYAUTO_DISABLE_JAX: \"1\"                   # Force use_jax=False, avoid JIT compilation overhead\n  PYAUTO_FAST_PLOTS: \"1\"                    # Skip tight_layout() + critical curve/caustic overlays\n  JAX_ENABLE_X64: \"True\"                    # Enable 64-bit precision in JAX\n  NUMBA_CACHE_DIR: \"/tmp/numba_cache\"       # Writable cache dir for numba\n  MPLCONFIGDIR: \"/tmp/matplotlib\"           # Writable config dir for matplotlib\n\noverrides:\n  # The */start_here and guides/ PYAUTO_SMALL_DATASETS unsets migrated to\n  # in-file `ENV: full_datasets` declarations on each matched script, and\n  # the PYAUTO_TEST_MODE / PYAUTO_FAST_PLOTS unsets below to `ENV:\n  # real_search` / `ENV: real_plots` \u2014 #187 Stage 2. A declaration is an\n  # `__Env__` docstring SECTION holding a bare `ENV: <tokens>` line; the older\n  # `# ENV:` comment form was removed and now RAISES (PyAutoHands#189/#190).\n  # The non-declarable PYAUTO_SKIP_* vars remain here.\n  # guides/results/start_here.py must produce real samples so the example\n  # scripts that read from `output/results_folder` afterwards\n  # (data_fitting, queries, models, samples_via_aggregator) find a\n  # non-empty aggregator. Covers all scripts under guides/results/.\n  # (PYAUTO_TEST_MODE moved to `ENV: real_search`.)\n  - pattern: \"guides/results/\"\n    unset: [PYAUTO_SKIP_FIT_OUTPUT]\n  # fits_make / png_make produce .fits / .png outputs from real fits, so they need\n  # visualization turned on. (PYAUTO_FAST_PLOTS moved to `ENV: real_plots`.)\n  - pattern: fits_make\n    unset: [PYAUTO_SKIP_VISUALIZATION]\n  - pattern: png_make\n    unset: [PYAUTO_SKIP_VISUALIZATION]\n","tooling":true,"top_keys":["defaults","overrides"]},{"error":null,"keys":[],"lines":10,"path":"build/visualise_notebooks.yaml","prior":false,"priors":[],"repo":"autolens_workspace","text":"# Notebook stems that should run when PyAutoHands's generate / run pipeline\n# is invoked with --visualise. Used to refresh notebook output cells in main.\n#\n# Format: flat list of notebook stems (no extension, no path).\n# An empty list means no notebooks need re-visualisation in this workspace.\n#\n# This file overrides PyAutoHands/autohands/config/visualise_notebooks.yaml\n# for this workspace. Add or remove entries here, not there.\n\n- start_here\n","tooling":true,"top_keys":[]},{"error":null,"keys":[{"c":"","k":"updates","line":5},{"c":"Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit.","k":"updates.iterations_per_quick_update","line":6},{"c":"Non-linear search iterations between every full update, which outputs all visuals and result fits (e.g. model.result, search.summary), this exits the search and can be slow.","k":"updates.iterations_per_full_update","line":7},{"c":"If True, perform_quick_update runs on a background daemon thread so the sampler is never blocked while visuals render. Requires the Analysis subclass to set `supports_background_update = True`.","k":"updates.quick_update_background","line":8},{"c":"If True, quick-update visuals are pushed to a live surface (a Jupyter cell when in a kernel, a matplotlib viewer subprocess otherwise) in addition to being written to disk.","k":"updates.live_visual_update","line":9},{"c":"","k":"psf","line":10},{"c":"If True, PSFs are convolved using FFTs by default, which is faster and uses less memory in all cases except for very small PSFs, False uses direct convolution.","k":"psf.use_fft_default","line":11},{"c":"","k":"grid","line":12},{"c":"An evaluation grid whose shape is adaptive chosen is used to compute quantities like critical curves, this integer is the max size of the grid ensuring faster run times.","k":"grid.max_evaluation_grid_size","line":13},{"c":"","k":"inversion","line":14},{"c":"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.","k":"inversion.check_reconstruction","line":15},{"c":"If True, inversion's use a positive-only linear algebra solver by default, which is slower but prevents unphysical negative values in the reconstructed solutuion.","k":"inversion.use_positive_only_solver","line":16},{"c":"If True, the edge pixels of a pixelization are set to zero, which prevents unphysical values in the reconstructed solution at the edge of the pixelization.","k":"inversion.use_edge_zeroed_pixels","line":17},{"c":"The default value added to the curvature matrix's diagonal when regularization is not applied to a linear object, which prevents inversion's failing due to the matrix being singular.","k":"inversion.no_regularization_add_to_curvature_diag_value","line":18},{"c":"If True, by default a pixelization's border is used to relocate all pixels outside its border to the border.","k":"inversion.use_border_relocator","line":19},{"c":"Plots of an Inversion's reconstruction use the reconstructed data's bright value multiplied by this factor.","k":"inversion.reconstruction_vmax_factor","line":20},{"c":"","k":"hpc","line":21},{"c":"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.","k":"hpc.hpc_mode","line":22},{"c":"Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit.","k":"hpc.iterations_per_quick_update","line":23},{"c":"Non-linear search iterations between every full update, which outputs all visuals and result fits (e.g. model.result, search.summary), this exits the search and can be slow.","k":"hpc.iterations_per_full_update","line":24},{"c":"Keep off on HPC: background rendering pulls matplotlib into the sampler process even when no display is attached.","k":"hpc.quick_update_background","line":25},{"c":"Keep off on HPC: nodes are headless with no GUI and no notebook kernel, so the live display surface has nothing to attach to.","k":"hpc.live_visual_update","line":26},{"c":"","k":"adapt","line":27},{"c":"","k":"adapt.adapt_minimum_percent","line":28},{"c":"","k":"adapt.adapt_noise_limit","line":29},{"c":"","k":"numba","line":30},{"c":"","k":"numba.use_numba","line":31},{"c":"","k":"numba.cache","line":32},{"c":"","k":"numba.nopython","line":33},{"c":"","k":"numba.parallel","line":34},{"c":"","k":"output","line":35},{"c":"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.","k":"output.force_pickle_overwrite","line":36},{"c":"If True, visualization images output by a search (e.g. subplots of the fit) are recreated when a new model-fit is performed.","k":"output.force_visualize_overwrite","line":37},{"c":"Length of whitespace between the parameter names and values in the model.info / result.info","k":"output.info_whitespace_length","line":38},{"c":"The level of information output by logging.","k":"output.log_level","line":39},{"c":"If True, outputs the non-linear search log to a file (and not printed to screen).","k":"output.log_to_file","line":40},{"c":"The name of the file the logged output is written to (in the non-linear search output folder)","k":"output.log_file","line":41},{"c":"Number of decimal places estimated parameter values / errors are output in model.results.","k":"output.model_results_decimal_places","line":42},{"c":"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.","k":"output.remove_files","line":43},{"c":"If True, non-linear search samples are written to a .csv file.","k":"output.samples_to_csv","line":44},{"c":"If outputting results of an unconverged search, the number of samples used to estimate the median PDF values and errors.","k":"output.unconverged_sample_size","line":45},{"c":"","k":"parallel","line":46},{"c":"If True, a warning is displayed when the search's number of CPU > 1 and enviromment variables related to threading are also > 1.","k":"parallel.warn_environment_variables","line":47},{"c":"","k":"profiling","line":48},{"c":"If True, the parallelization of the fit is profiled outputting a cPython graph.","k":"profiling.parallel_profile","line":49},{"c":"The number of repeat function calls used to measure run-times when profiling.","k":"profiling.repeats","line":50},{"c":"","k":"structures","line":51},{"c":"If True, data structures are only stored in their native and binned format. This is used to reduce memory usage in autocti.","k":"structures.native_binned_only","line":52},{"c":"","k":"test","line":53},{"c":"if True, when a search is resumed the likelihood of a previous sample is recalculated to ensure it is consistent with the previous run.","k":"test.check_likelihood_function","line":54},{"c":"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.","k":"test.lh_timeout_seconds","line":55},{"c":"","k":"test.disable_positions_lh_inversion_check","line":56},{"c":"","k":"version","line":57},{"c":"If False, bypass the PyAutoNerves Python 3.12+ recommendation (3.9/3.10/3.11 technically work but are not officially supported).","k":"version.python_version_check","line":58},{"c":"(autonerves/workspace.py). Bump DELIBERATELY \u2014 only when a script starts needing new API \u2014 never per release. Must always name an INSTALLABLE (non-yanked) release.","k":"version.minimum_library_version","line":64},{"c":"If False, bypass the workspace/library version check. Set to False on `main`-branch clones \u2014 `main` updates faster than releases, so mismatches are expected and not actionable.","k":"version.workspace_version_check","line":65}],"lines":65,"path":"general.yaml","prior":false,"priors":[],"repo":"autolens_workspace","text":"# version:\n#   python_version_check: False  # uncomment to suppress the Python version warning\n#                                # if running on a non-recommended Python (anything other than 3.12 / 3.13).\n\nupdates:\n  iterations_per_quick_update: 1e99 # Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit.\n  iterations_per_full_update: 1e99  # Non-linear search iterations between every full update, which outputs all visuals and result fits (e.g. model.result, search.summary), this exits the search and can be slow.\n  quick_update_background: false    # If True, perform_quick_update runs on a background daemon thread so the sampler is never blocked while visuals render. Requires the Analysis subclass to set `supports_background_update = True`.\n  live_visual_update: false         # If True, quick-update visuals are pushed to a live surface (a Jupyter cell when in a kernel, a matplotlib viewer subprocess otherwise) in addition to being written to disk.\npsf:\n  use_fft_default: true              # If True, PSFs are convolved using FFTs by default, which is faster and uses less memory in all cases except for very small PSFs, False uses direct convolution.\ngrid:\n  max_evaluation_grid_size: 1000   # An evaluation grid whose shape is adaptive chosen is used to compute quantities like critical curves, this integer is the max size of the grid ensuring faster run times.\ninversion:\n  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.\n  use_positive_only_solver: true      # If True, inversion's use a positive-only linear algebra solver by default, which is slower but prevents unphysical negative values in the reconstructed solutuion.\n  use_edge_zeroed_pixels : true       # If True, the edge pixels of a pixelization are set to zero, which prevents unphysical values in the reconstructed solution at the edge of the pixelization.\n  no_regularization_add_to_curvature_diag_value : 1.0e-3 # The default value added to the curvature matrix's diagonal when regularization is not applied to a linear object, which prevents inversion's failing due to the matrix being singular.\n  use_border_relocator: true          # If True, by default a pixelization's border is used to relocate all pixels outside its border to the border.\n  reconstruction_vmax_factor: 0.5   # Plots of an Inversion's reconstruction use the reconstructed data's bright value multiplied by this factor.\nhpc:\n  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.\n  iterations_per_quick_update: 250000 #  Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit.\n  iterations_per_full_update: 1e99  # Non-linear search iterations between every full update, which outputs all visuals and result fits (e.g. model.result, search.summary), this exits the search and can be slow.\n  quick_update_background: false    # Keep off on HPC: background rendering pulls matplotlib into the sampler process even when no display is attached.\n  live_visual_update: false         # Keep off on HPC: nodes are headless with no GUI and no notebook kernel, so the live display surface has nothing to attach to.\nadapt:\n  adapt_minimum_percent: 0.01\n  adapt_noise_limit: 100000000.0\nnumba:\n  use_numba: true\n  cache: true\n  nopython: true\n  parallel: false\noutput:\n  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.\n  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.\n  info_whitespace_length: 80        # Length of whitespace between the parameter names and values in the model.info / result.info\n  log_level: INFO                   # The level of information output by logging.\n  log_to_file: false                # If True, outputs the non-linear search log to a file (and not printed to screen).\n  log_file: output.log              # The name of the file the logged output is written to (in the non-linear search output folder)\n  model_results_decimal_places: 3   # Number of decimal places estimated parameter values / errors are output in model.results.\n  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.\n  samples_to_csv: true              # If True, non-linear search samples are written to a .csv file.\n  unconverged_sample_size : 100     # If outputting results of an unconverged search, the number of samples used to estimate the median PDF values and errors.\nparallel:\n  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.\nprofiling:\n  parallel_profile: false           # If True, the parallelization of the fit is profiled outputting a cPython graph.\n  repeats: 1                        # The number of repeat function calls used to measure run-times when profiling.\nstructures:\n  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.\ntest:\n  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.\n  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.\n  disable_positions_lh_inversion_check: true\nversion:\n  python_version_check: True        # If False, bypass the PyAutoNerves Python 3.12+ recommendation (3.9/3.10/3.11 technically work but are not officially supported).\n  # The compatibility FLOOR: the oldest library release whose API this\n  # workspace's scripts require. Preferred over workspace_version\n  # (autonerves/workspace.py). Bump DELIBERATELY \u2014 only when a script\n  # starts needing new API \u2014 never per release. Must always name an\n  # INSTALLABLE (non-yanked) release.\n  minimum_library_version: 2026.7.9.1\n  workspace_version_check: True     # If False, bypass the workspace/library version check. Set to False on `main`-branch clones \u2014 `main` updates faster than releases, so mismatches are expected and not actionable.\n","tooling":false,"top_keys":["updates","psf","grid","inversion","hpc","adapt","numba","output","parallel","profiling","structures","test","version"]},{"error":null,"keys":[{"c":"total_lens_flux \u2014 integrated lens-galaxy flux in the fit's raw image units. No instrument inputs required. NaN when lens has no light profile.","k":"total_lens_flux","line":15},{"c":"total_lensed_source_flux \u2014 image-plane source flux after lensing, in raw image units. No instrument inputs required.","k":"total_lensed_source_flux","line":19},{"c":"total_source_flux \u2014 source-plane intrinsic source flux in raw image units. Uses fit.tracer_linear_light_profiles_to_light_profiles so MGE / linear light profiles work correctly. No instrument inputs \u2026","k":"total_source_flux","line":24},{"c":"total_lens_flux_mujy \u2014 same as total_lens_flux but converted to microjanskies. Requires magzero on AnalysisImaging; NaN + one warning per process if missing.","k":"total_lens_flux_mujy","line":29},{"c":"total_lensed_source_flux_mujy \u2014 image-plane source flux after lensing, in microjanskies. Requires magzero.","k":"total_lensed_source_flux_mujy","line":33},{"c":"total_source_flux_mujy \u2014 source-plane intrinsic source flux in microjanskies. Uses fit.tracer_linear_light_profiles_to_light_profiles so MGE / linear light profiles work correctly. Requires magzero.","k":"total_source_flux_mujy","line":38},{"c":"magnification \u2014 dimensionless ratio of image-plane to source-plane flux. magzero is not required.","k":"magnification","line":42},{"c":"effective_einstein_radius \u2014 Einstein radius in arcseconds via the zero-contour of the tangential eigenvalue field. magzero is not required.","k":"effective_einstein_radius","line":47},{"c":"autogalaxy library default (loaded by autonerves into the same `latent` conf node) \u2014 explicitly disabled here to silence the cross-library \"unknown latent\" warning that would otherwise fire on every \u2026","k":"total_galaxy_0_flux","line":52}],"lines":52,"path":"latent.yaml","prior":false,"priors":[],"repo":"autolens_workspace","text":"# Workspace overrides for the library lensing latent toggles. The\n# PyAutoLens library defaults the three raw-flux keys to `true` (they\n# need no instrument inputs) and the \u00b5Jy / dimensionless variants to\n# `false`. Enabling the \u00b5Jy ones here means a workspace fit produces\n# real microjansky output as long as the user also passes `magzero` to\n# `al.AnalysisImaging(...)` \u2014 without `magzero` they return NaN and emit\n# one warning per process.\n#\n# Run `scripts/guides/results/latent_variables.py` for a tutorial on\n# what each key means and `scripts/guides/units/flux.py` for how to\n# convert a raw-flux latent to microjanskies in post.\n\n# total_lens_flux \u2014 integrated lens-galaxy flux in the fit's raw image\n# units. No instrument inputs required. NaN when lens has no light profile.\ntotal_lens_flux: true\n\n# total_lensed_source_flux \u2014 image-plane source flux after lensing, in\n# raw image units. No instrument inputs required.\ntotal_lensed_source_flux: true\n\n# total_source_flux \u2014 source-plane intrinsic source flux in raw image\n# units. Uses fit.tracer_linear_light_profiles_to_light_profiles so MGE /\n# linear light profiles work correctly. No instrument inputs required.\ntotal_source_flux: true\n\n# total_lens_flux_mujy \u2014 same as total_lens_flux but converted to\n# microjanskies. Requires magzero on AnalysisImaging; NaN + one warning\n# per process if missing.\ntotal_lens_flux_mujy: true\n\n# total_lensed_source_flux_mujy \u2014 image-plane source flux after lensing,\n# in microjanskies. Requires magzero.\ntotal_lensed_source_flux_mujy: true\n\n# total_source_flux_mujy \u2014 source-plane intrinsic source flux in microjanskies.\n# Uses fit.tracer_linear_light_profiles_to_light_profiles so MGE / linear\n# light profiles work correctly. Requires magzero.\ntotal_source_flux_mujy: true\n\n# magnification \u2014 dimensionless ratio of image-plane to source-plane flux.\n# magzero is not required.\nmagnification: true\n\n# effective_einstein_radius \u2014 Einstein radius in arcseconds via the\n# zero-contour of the tangential eigenvalue field.\n# magzero is not required.\neffective_einstein_radius: true\n\n# autogalaxy library default (loaded by autonerves into the same `latent`\n# conf node) \u2014 explicitly disabled here to silence the cross-library\n# \"unknown latent\" warning that would otherwise fire on every fit.\ntotal_galaxy_0_flux: false\n","tooling":false,"top_keys":["total_lens_flux","total_lensed_source_flux","total_source_flux","total_lens_flux_mujy","total_lensed_source_flux_mujy","total_source_flux_mujy","magnification","effective_einstein_radius","total_galaxy_0_flux"]},{"error":null,"keys":[{"c":"","k":"version","line":1},{"c":"","k":"disable_existing_loggers","line":2},{"c":"","k":"handlers","line":4},{"c":"","k":"handlers.console","line":5},{"c":"","k":"handlers.console.class","line":6},{"c":"","k":"handlers.console.level","line":7},{"c":"","k":"handlers.console.stream","line":8},{"c":"","k":"handlers.console.formatter","line":9},{"c":"","k":"root","line":11},{"c":"","k":"root.level","line":12},{"c":"","k":"root.handlers","line":13},{"c":"","k":"formatters","line":15},{"c":"","k":"formatters.formatter","line":16},{"c":"","k":"formatters.formatter.format","line":17}],"lines":17,"path":"logging.yaml","prior":false,"priors":[],"repo":"autolens_workspace","text":"version: 1\ndisable_existing_loggers: false\n\nhandlers:\n  console:\n    class: logging.StreamHandler\n    level: INFO\n    stream: ext://sys.stdout\n    formatter: formatter\n\nroot:\n  level: INFO\n  handlers: [ console ]\n\nformatters:\n  formatter:\n    format: '%(asctime)s - %(name)s - %(levelname)s - %(message)s'\n","tooling":false,"top_keys":["version","disable_existing_loggers","handlers","root","formatters"]},{"error":null,"keys":[{"c":"","k":"parallel","line":3},{"c":"The number of cores the search is parallelized over by default, using Python multiprocessing.","k":"parallel.number_of_cores","line":4},{"c":"The default step size of each grid search parameter, in terms of unit values of the priors.","k":"parallel.step_size","line":5}],"lines":5,"path":"non_linear/GridSearch.yaml","prior":false,"priors":[],"repo":"autolens_workspace","text":"# The settings of a parallelized grid search of non-linear searches.\n\nparallel:\n  number_of_cores: 3 # The number of cores the search is parallelized over by default, using Python multiprocessing.\n  step_size: 0.1     # The default step size of each grid search parameter, in terms of unit values of the priors.","tooling":false,"top_keys":["parallel"]},{"error":null,"keys":[{"c":"","k":"label","line":14},{"c":"","k":"label.label","line":15},{"c":"","k":"label.label.sigma","line":16},{"c":"","k":"label.label.alpha","line":17},{"c":"","k":"label.label.angle_binary","line":18},{"c":"","k":"label.label.beta","line":19},{"c":"","k":"label.label.break_radius","line":20},{"c":"","k":"label.label.centre_0","line":21},{"c":"","k":"label.label.centre_1","line":22},{"c":"","k":"label.label.coefficient","line":23},{"c":"","k":"label.label.contribution_factor","line":24},{"c":"","k":"label.label.core_radius","line":25},{"c":"","k":"label.label.core_radius_0","line":26},{"c":"","k":"label.label.core_radius_1","line":27},{"c":"","k":"label.label.effective_radius","line":28},{"c":"","k":"label.label.einstein_radius","line":29},{"c":"","k":"label.label.ell_comps_0","line":30},{"c":"","k":"label.label.ell_comps_1","line":31},{"c":"","k":"label.label.multipole_comps_0","line":32},{"c":"","k":"label.label.multipole_comps_1","line":33},{"c":"","k":"label.label.flux","line":34},{"c":"","k":"label.label.gamma","line":35},{"c":"","k":"label.label.gamma_1","line":36},{"c":"","k":"label.label.gamma_2","line":37},{"c":"","k":"label.label.inner_coefficient","line":38},{"c":"","k":"label.label.inner_slope","line":39},{"c":"","k":"label.label.intensity","line":40},{"c":"","k":"label.label.kappa","line":41},{"c":"","k":"label.label.kappa_s","line":42},{"c":"","k":"label.label.log10m_vir","line":43},{"c":"","k":"label.label.m","line":44},{"c":"","k":"label.label.mass","line":45},{"c":"","k":"label.label.mass_at_200","line":46},{"c":"","k":"label.label.mass_ratio","line":47},{"c":"","k":"label.label.mass_to_light_gradient","line":48},{"c":"","k":"label.label.mass_to_light_ratio","line":49},{"c":"","k":"label.label.mass_to_light_ratio_base","line":50},{"c":"","k":"label.label.mass_to_light_radius","line":51},{"c":"","k":"label.label.noise_factor","line":52},{"c":"","k":"label.label.noise_power","line":53},{"c":"","k":"label.label.noise_scale","line":54},{"c":"","k":"label.label.normalization_scale","line":55},{"c":"","k":"label.label.outer_coefficient","line":56},{"c":"","k":"label.label.outer_slope","line":57},{"c":"","k":"label.label.overdens","line":58},{"c":"","k":"label.label.pixels","line":59},{"c":"","k":"label.label.radius_break","line":60},{"c":"","k":"label.label.redshift","line":61},{"c":"","k":"label.label.redshift_object","line":62},{"c":"","k":"label.label.redshift_source","line":63},{"c":"","k":"label.label.rs","line":64},{"c":"","k":"label.label.ra","line":65},{"c":"","k":"label.label.scale_radius","line":66},{"c":"","k":"label.label.scatter","line":67},{"c":"","k":"label.label.sigma_scale","line":68},{"c":"","k":"label.label.separation","line":69},{"c":"","k":"label.label.sersic_index","line":70},{"c":"","k":"label.label.shape_0","line":71},{"c":"","k":"label.label.shape_1","line":72},{"c":"","k":"label.label.signal_scale","line":73},{"c":"","k":"label.label.sky_scale","line":74},{"c":"","k":"label.label.slope","line":75},{"c":"","k":"label.label.truncation_radius","line":76},{"c":"","k":"label.label.weight_floor","line":77},{"c":"","k":"label.label.weight_power","line":78},{"c":"","k":"label.superscript","line":79},{"c":"","k":"label.superscript.ExternalShear","line":80},{"c":"","k":"label.superscript.Mesh","line":81},{"c":"","k":"label.superscript.Point","line":82},{"c":"","k":"label.superscript.SMBH","line":83},{"c":"","k":"label.superscript.Redshift","line":84},{"c":"","k":"label.superscript.Regularization","line":85},{"c":"","k":"label.superscript.HyperBackgroundNoise","line":86},{"c":"","k":"label.superscript.HyperGalaxy","line":87},{"c":"","k":"label.superscript.HyperImageSky","line":88},{"c":"","k":"label.superscript.InputDeflections","line":89},{"c":"","k":"label_format","line":95},{"c":"","k":"label_format.format","line":96},{"c":"","k":"label_format.format.sigma","line":97},{"c":"","k":"label_format.format.alpha","line":98},{"c":"","k":"label_format.format.angle_binary","line":99},{"c":"","k":"label_format.format.angular_diameter_distance_to_earth","line":100},{"c":"","k":"label_format.format.beta","line":101},{"c":"","k":"label_format.format.c_2","line":102},{"c":"","k":"label_format.format.centre_0","line":103},{"c":"","k":"label_format.format.centre_1","line":104},{"c":"","k":"label_format.format.coefficient","line":105},{"c":"","k":"label_format.format.concentration","line":106},{"c":"","k":"label_format.format.contribution_factor","line":107},{"c":"","k":"label_format.format.core_radius","line":108},{"c":"","k":"label_format.format.core_radius_0","line":109},{"c":"","k":"label_format.format.core_radius_1","line":110},{"c":"","k":"label_format.format.effective_radius","line":111},{"c":"","k":"label_format.format.einstein_mass","line":112},{"c":"","k":"label_format.format.einstein_radius","line":113},{"c":"","k":"label_format.format.ell_comps_0","line":114},{"c":"","k":"label_format.format.ell_comps_1","line":115},{"c":"","k":"label_format.format.multipole_comps_0","line":116},{"c":"","k":"label_format.format.multipole_comps_1","line":117},{"c":"","k":"label_format.format.flux","line":118},{"c":"","k":"label_format.format.gamma","line":119},{"c":"","k":"label_format.format.inner_coefficient","line":120},{"c":"","k":"label_format.format.inner_slope","line":121},{"c":"","k":"label_format.format.intensity","line":122},{"c":"","k":"label_format.format.kappa","line":123},{"c":"","k":"label_format.format.kappa_s","line":124},{"c":"","k":"label_format.format.kpc_per_arcsec","line":125},{"c":"","k":"label_format.format.log10m_vir","line":126},{"c":"","k":"label_format.format.luminosity","line":127},{"c":"","k":"label_format.format.m","line":128},{"c":"","k":"label_format.format.mass","line":129},{"c":"","k":"label_format.format.mass_at_200","line":130},{"c":"","k":"label_format.format.mass_at_truncation_radius","line":131},{"c":"","k":"label_format.format.mass_ratio","line":132},{"c":"","k":"label_format.format.mass_to_light_gradient","line":133},{"c":"","k":"label_format.format.mass_to_light_ratio","line":134},{"c":"","k":"label_format.format.n_x","line":135},{"c":"","k":"label_format.format.n_y","line":136},{"c":"","k":"label_format.format.noise_factor","line":137},{"c":"","k":"label_format.format.noise_power","line":138},{"c":"","k":"label_format.format.noise_scale","line":139},{"c":"","k":"label_format.format.normalization_scale","line":140},{"c":"","k":"label_format.format.outer_coefficient","line":141},{"c":"","k":"label_format.format.outer_slope","line":142},{"c":"","k":"label_format.format.overdens","line":143},{"c":"","k":"label_format.format.pixels","line":144},{"c":"","k":"label_format.format.ra","line":145},{"c":"","k":"label_format.format.radius","line":146},{"c":"","k":"label_format.format.radius_break","line":147},{"c":"","k":"label_format.format.redshift","line":148},{"c":"","k":"label_format.format.redshift_object","line":149},{"c":"","k":"label_format.format.redshift_source","line":150},{"c":"","k":"label_format.format.rho","line":151},{"c":"","k":"label_format.format.rs","line":152},{"c":"","k":"label_format.format.scale_radius","line":153},{"c":"","k":"label_format.format.separation","line":154},{"c":"","k":"label_format.format.sersic_index","line":155},{"c":"","k":"label_format.format.shape_0","line":156},{"c":"","k":"label_format.format.shape_1","line":157},{"c":"","k":"label_format.format.signal_scale","line":158},{"c":"","k":"label_format.format.sky_scale","line":159},{"c":"","k":"label_format.format.slope","line":160},{"c":"","k":"label_format.format.truncation_radius","line":161},{"c":"","k":"label_format.format.weight_floor","line":162},{"c":"","k":"label_format.format.weight_power","line":163}],"lines":163,"path":"notation.yaml","prior":false,"priors":[],"repo":"autolens_workspace","text":"# The notation configs define the labels of every model parameter and its derived quantities, which are used when\n# visualizing results (for example labeling the axis of the PDF triangle plots output by a non-linear search).\n\n\n# label: The label given to the each parameter, for plots like PDF corner plots.\n\n# For example, if `centre=x`, the plot axis will be labeled 'x'.\n\n\n# superscript: the superscript used on certain plots that show the results of different model-components.\n\n# For example, if `Gaussian=g`, plots where the parameters of the Gaussian model-component have superscript `g`.\n\nlabel:\n  label:\n    sigma: \\sigma\n    alpha: \\alpha\n    angle_binary: \\theta\n    beta: \\beta\n    break_radius: \\theta_{\\rm B}\n    centre_0: y\n    centre_1: x\n    coefficient: \\lambda\n    contribution_factor: \\omega_{\\rm 0}\n    core_radius: C_{\\rm r}\n    core_radius_0: C_{rm r0}\n    core_radius_1: C_{\\rm r1}\n    effective_radius: R_{\\rm eff}\n    einstein_radius: \\theta_{\\rm Ein}\n    ell_comps_0: \\epsilon_{\\rm 1}\n    ell_comps_1: \\epsilon_{\\rm 2}\n    multipole_comps_0: M_{\\rm 1}\n    multipole_comps_1: M_{\\rm 2}\n    flux: F\n    gamma: \\gamma\n    gamma_1: \\gamma\n    gamma_2: \\gamma\n    inner_coefficient: \\lambda_{\\rm 1}\n    inner_slope: t_{\\rm 1}\n    intensity: I_{\\rm b}\n    kappa: \\kappa\n    kappa_s: \\kappa_{\\rm s}\n    log10m_vir: log_{\\rm 10}(m_{vir})\n    m: m\n    mass: M\n    mass_at_200: M_{\\rm 200}\n    mass_ratio: M_{\\rm ratio}\n    mass_to_light_gradient: \\Gamma\n    mass_to_light_ratio: \\Psi\n    mass_to_light_ratio_base: \\Psi_{\\rm base}\n    mass_to_light_radius: R_{\\rm ref}\n    noise_factor: \\omega_{\\rm 1}\n    noise_power: \\omega{\\rm 2}\n    noise_scale: \\sigma_{\\rm 1}\n    normalization_scale: n\n    outer_coefficient: \\lambda_{\\rm 2}\n    outer_slope: t_{\\rm 2}\n    overdens: \\Delta_{\\rm vir}\n    pixels: N_{\\rm pix}\n    radius_break: R_{\\rm b}\n    redshift: z\n    redshift_object: z_{\\rm obj}\n    redshift_source: z_{\\rm src}\n    rs: r_{\\rm s}\n    ra: r_{\\rm a}\n    scale_radius: R_{\\rm s}\n    scatter: \\sigma\n    sigma_scale: \\sigma\n    separation: s\n    sersic_index: n\n    shape_0: y_{\\rm pix}\n    shape_1: x_{\\rm pix}\n    signal_scale: V\n    sky_scale: \\sigma_{\\rm 0}\n    slope: \\gamma\n    truncation_radius: R_{\\rm t}\n    weight_floor: W_{\\rm f}\n    weight_power: W_{\\rm p}\n  superscript:\n    ExternalShear: ext\n    Mesh: mesh\n    Point: point\n    SMBH: smbh\n    Redshift: z\n    Regularization: reg\n    HyperBackgroundNoise: hyper\n    HyperGalaxy: hyper\n    HyperImageSky: hyper\n    InputDeflections: defl\n\n# label_format: The format certain parameters are output as in output files like the `model.results` file.\n\n# For example, if `centre={:.2d}`, the format of the centre parameter in results files will use this Python format.\n\nlabel_format:\n  format:\n    sigma: '{:.4f}'\n    alpha: '{:.4f}'\n    angle_binary: '{:.4f}'\n    angular_diameter_distance_to_earth: '{:.4f}'\n    beta: '{:.4f}'\n    c_2: '{:.4f}'\n    centre_0: '{:.4f}'\n    centre_1: '{:.4f}'\n    coefficient: '{:.4f}'\n    concentration: '{:.4f}'\n    contribution_factor: '{:.4f}'\n    core_radius: '{:.4f}'\n    core_radius_0: '{:.4f}'\n    core_radius_1: '{:.4f}'\n    effective_radius: '{:.4f}'\n    einstein_mass: '{:.4e}'\n    einstein_radius: '{:.4f}'\n    ell_comps_0: '{:.4f}'\n    ell_comps_1: '{:.4f}'\n    multipole_comps_0: '{:.4f}'\n    multipole_comps_1: '{:.4f}'\n    flux: '{:.4e}'\n    gamma: '{:.4f}'\n    inner_coefficient: '{:.4f}'\n    inner_slope: '{:.4f}'\n    intensity: '{:.4f}'\n    kappa: '{:.4f}'\n    kappa_s: '{:.4f}'\n    kpc_per_arcsec: '{:.4f}'\n    log10m_vir: '{:.4f}'\n    luminosity: '{:.4e}'\n    m: '{:.1f}'\n    mass: '{:.4e}'\n    mass_at_200: '{:.4e}'\n    mass_at_truncation_radius: '{:.4e}'\n    mass_ratio: '{:.4f}'\n    mass_to_light_gradient: '{:.4f}'\n    mass_to_light_ratio: '{:.4f}'\n    n_x: '{:.1d}'\n    n_y: '{:.1d}'\n    noise_factor: '{:.3f}'\n    noise_power: '{:.3f}'\n    noise_scale: '{:.3f}'\n    normalization_scale: '{:.4f}'\n    outer_coefficient: '{:.4f}'\n    outer_slope: '{:.4f}'\n    overdens: '{:.4f}'\n    pixels: '{:.4f}'\n    ra : '{:.4f}'\n    radius: '{:.4f}'\n    radius_break: '{:.4f}'\n    redshift: '{:.4f}'\n    redshift_object: '{:.4f}'\n    redshift_source: '{:.4f}'\n    rho: '{:.4f}'\n    rs: '{:.4f}'\n    scale_radius: '{:.4f}'\n    separation: '{:.4f}'\n    sersic_index: '{:.4f}'\n    shape_0: '{:.4f}'\n    shape_1: '{:.4f}'\n    signal_scale: '{:.4f}'\n    sky_scale: '{:.4f}'\n    slope: '{:.4f}'\n    truncation_radius: '{:.4f}'\n    weight_floor: '{:.4f}'\n    weight_power: '{:.4f}'\n","tooling":false,"top_keys":["label","label_format"]},{"error":null,"keys":[{"c":"If true then files which are not explicitly listed here are output anyway. If false then they are not.","k":"default","line":4},{"c":"","k":"samples","line":17},{"c":"","k":"samples_weight_threshold","line":34},{"c":"","k":"search_internal","line":56},{"c":"","k":"start_point","line":65},{"c":"Whether to output the `latent.csv`, `latent.results` and `latent_summary.json` files during the fit when it performs on-the-fly output.","k":"latent_during_fit","line":89},{"c":"If `latent_during_fit` is False, whether to output the `latent.csv`, `latent.results` and `latent_summary.json` files after the fit is complete.","k":"latent_after_fit","line":90},{"c":"Whether to draw latent variable values via the PDF of every sample, which uses fewer samples to estimate latent variable errors. If False, latent variable values are drawn from every sample.","k":"latent_draw_via_pdf","line":91},{"c":"The number of samples drawn to estimate latent variable errors if `latent_draw_via_pdf` is True.","k":"latent_draw_via_pdf_size","line":92},{"c":"Whether to ouptut the `latent.csv` file.","k":"latent_csv","line":93},{"c":"Whether to output the `latent.results` file.","k":"latent_results","line":94},{"c":"`search.log`: logging produced whilst running the fit method","k":"search_log","line":98},{"c":"`model.graph`: graphical-model factor graph summary. Only meaningful for graphical models (autofit.graphical); empty for ordinary PriorModel/Collection fits, so disabled by default.","k":"model_graph","line":100},{"c":"`model.png`: the model figure drawn beside `model.info` (af.ModelPlotter). Opt-in until the model-figures epic's phase-3 acceptance renders pass. Unlike other keys an absent key means off.","k":"model_figure","line":104}],"lines":104,"path":"output.yaml","prior":false,"priors":[],"repo":"autolens_workspace","text":"# Determines whether files saved by the search are output to the hard-disk. This is true both when saving to the\n# directory structure and when saving to database.\n\ndefault: true # If true then files which are not explicitly listed here are output anyway. If false then they are not.\n\n### Samples ###\n\n# The `samples.csv`file contains every sampled value of every free parameter with its log likelihood and weight.\n\n# This file is often large, therefore disabling it can significantly reduce hard-disk space use.\n\n# `samples.csv` is used to perform marginalization, infer model parameter errors and do other analysis of the search\n# chains. Even if output of `samples.csv` is disabled, these tasks are still performed by the fit and output to\n# the `samples_summary.json` file. However, without a `samples.csv` file these types of tasks cannot be performed\n# after the fit is complete, for example via the database.\n\nsamples: true\n\n# The `samples.csv` file contains every accepted sampled value of every free parameter with its log likelihood and\n# weight. For certain searches, the majority of samples have a very low weight and have no numerical impact on the\n# results of the model-fit. However, these samples are still output to the `samples.csv` file, taking up hard-disk\n# space and slowing down analysis of the samples (e.g. via the database).\n\n# The `samples_weight_threshold` below specifies the threshold value of the weight such that samples with a weight\n# below this value are not output to the `samples.csv` file. This can be used to reduce the size of the `samples.csv`\n# file and speed up analysis of the samples.\n\n# For many searches (e.g. MCMC) all samples have an equal weight of 1.0, and this threshold therefore has no impact.\n# For these searches, there is no simple way to save hard-disk space. This input is more suited to nested sampling,\n# where the majority of samples have a very low weight..\n\n# Set value to empty (e.g. delete 1.0e-10 below) to disable this feature.\n\nsamples_weight_threshold: 1.0e-10\n\n### Search Internal ###\n\n# The search internal folder which contains a saved state of the non-linear search in its internal reprsenetation,\n# as a .pickle or .dill file.\n\n# For example, for the nested sampling dynesty, this .dill file is the `DynestySampler` object which is used to\n# perform sampling, and it therefore contains all internal dynesty representations of the results, samples, weights, etc.\n\n# If the entry below is false, the folder is still output during the model-fit, as it is required to resume the fit\n# from where it left off. Therefore, settings `false` below does not impact model-fitting checkpointing and resumption.\n# Instead, the search internal folder is deleted once the fit is completed.\n\n# The search internal folder file is often large, therefore deleting it after a fit is complete can significantly\n# reduce hard-disk space use.\n\n# The search internal representation that can be loaded from the .dill file has many additional quantities specific to\n# the non-linear search that the standardized autofit forms do not. For example, for emcee, it contains information on\n# every walker. This information is required to do certain analyes and make certain plots, therefore deleting the\n# folder means this information is list.\n\nsearch_internal: false\n\n### Start Point ###\n\n# If an Initalizer is used to provide a start point for the non-linear search, visualization of that start point can be\n# output to hard-disk to show the user the initial model-fit that is used to start the search. This visualization is\n# the visualizer wrapped in the Analysis class, and therefore should show things like the quality of the fit\n# to the data and the residuals at the start point.\n\nstart_point: true\n\n### Latent Variables ###\n\n# A latent variable is not a model parameter but can be derived from the model. Its value and errors may be of interest\n# and aid in the interpretation of a model-fit.\n\n# For example, for the simple 1D Gaussian example, it could be the full-width half maximum (FWHM) of the Gaussian. This\n# is not included in the model but can be easily derived from the Gaussian's sigma value.\n\n# By overwriting an Analysis class's `compute_latent_variables` method we can manually specify latent variables that\n# are calculated and output to a `latent.csv` file, which mirrors the `samples.csv` file. The `latent.csv` file has\n# the same weight resampling performed on the `samples.csv` file, controlled via the `samples_weight_threshold` above.\n\n# There may also be a `latent.results` and `latent_summary.json` files output, which the inputs below control whether\n# they are output and how often.\n\n# Outputting latent variables manually after a fit is complete is simple, just call\n# the `analysis.compute_latent_variables()` function.\n\n# For many use cases, the best set up may be to disable autofit latent variable output during the fit and perform it\n# manually after completing a successful model-fit. This will save computational run time by not computing latent\n# variables during a any model-fit which is unsuccessful.\n\nlatent_during_fit: false # Whether to output the `latent.csv`, `latent.results` and `latent_summary.json` files during the fit when it performs on-the-fly output.\nlatent_after_fit: true # If `latent_during_fit` is False, whether to output the `latent.csv`, `latent.results` and `latent_summary.json` files after the fit is complete.\nlatent_draw_via_pdf : true # Whether to draw latent variable values via the PDF of every sample, which uses fewer samples to estimate latent variable errors. If False, latent variable values are drawn from every sample.\nlatent_draw_via_pdf_size : 100 # The number of samples drawn to estimate latent variable errors if `latent_draw_via_pdf` is True.\nlatent_csv: true # Whether to ouptut the `latent.csv` file.\nlatent_results: true # Whether to output the `latent.results` file.\n\n# Other Files:\n\nsearch_log: true # `search.log`: logging produced whilst running the fit method\n\nmodel_graph: false # `model.graph`: graphical-model factor graph summary. Only meaningful for graphical models (autofit.graphical); empty for ordinary PriorModel/Collection fits, so disabled by default.\n\n# `model.png`: the model figure drawn beside `model.info` (af.ModelPlotter). Opt-in until the\n# model-figures epic's phase-3 acceptance renders pass. Unlike other keys an absent key means off.\nmodel_figure: false\n","tooling":false,"top_keys":["default","samples","samples_weight_threshold","search_internal","start_point","latent_during_fit","latent_after_fit","latent_draw_via_pdf","latent_draw_via_pdf_size","latent_csv","latent_results","search_log","model_graph","model_figure"]},{"error":null,"keys":[{"c":"","k":"Basis","line":1}],"lines":1,"path":"priors/basis.yaml","prior":true,"priors":[],"repo":"autolens_workspace","text":"Basis: {}\n","tooling":false,"top_keys":["Basis"]},{"error":null,"keys":[{"c":"","k":"model.FlatLambdaCDM","line":1},{"c":"","k":"model.FlatLambdaCDM.H0","line":2},{"c":"","k":"model.FlatLambdaCDM.H0.type","line":3},{"c":"","k":"model.FlatLambdaCDM.H0.value","line":4},{"c":"","k":"model.FlatLambdaCDM.Om0","line":5},{"c":"","k":"model.FlatLambdaCDM.Om0.type","line":6},{"c":"","k":"model.FlatLambdaCDM.Om0.value","line":7},{"c":"","k":"model.FlatLambdaCDM.Tcmb0","line":8},{"c":"","k":"model.FlatLambdaCDM.Tcmb0.type","line":9},{"c":"","k":"model.FlatLambdaCDM.Tcmb0.value","line":10},{"c":"","k":"model.FlatLambdaCDM.Neff","line":11},{"c":"","k":"model.FlatLambdaCDM.Neff.type","line":12},{"c":"","k":"model.FlatLambdaCDM.Neff.value","line":13},{"c":"","k":"model.FlatLambdaCDM.m_nu","line":14},{"c":"","k":"model.FlatLambdaCDM.m_nu.type","line":15},{"c":"","k":"model.FlatLambdaCDM.m_nu.value","line":16},{"c":"","k":"model.FlatLambdaCDM.Ob0","line":17},{"c":"","k":"model.FlatLambdaCDM.Ob0.type","line":18},{"c":"","k":"model.FlatLambdaCDM.Ob0.value","line":19}],"lines":19,"path":"priors/cosmology.yaml","prior":true,"priors":[{"a":"value 67.66","b":"","cls":"model.FlatLambdaCDM","limits":"","line":2,"param":"H0","type":"Constant","width":""},{"a":"value 0.30966","b":"","cls":"model.FlatLambdaCDM","limits":"","line":5,"param":"Om0","type":"Constant","width":""},{"a":"value 2.7255","b":"","cls":"model.FlatLambdaCDM","limits":"","line":8,"param":"Tcmb0","type":"Constant","width":""},{"a":"value 3.046","b":"","cls":"model.FlatLambdaCDM","limits":"","line":11,"param":"Neff","type":"Constant","width":""},{"a":"value 0.06","b":"","cls":"model.FlatLambdaCDM","limits":"","line":14,"param":"m_nu","type":"Constant","width":""},{"a":"value 0.04897","b":"","cls":"model.FlatLambdaCDM","limits":"","line":17,"param":"Ob0","type":"Constant","width":""}],"repo":"autolens_workspace","text":"model.FlatLambdaCDM:\n  H0:\n    type: Constant\n    value: 67.66\n  Om0:\n    type: Constant\n    value: 0.30966\n  Tcmb0:\n    type: Constant\n    value: 2.7255\n  Neff:\n    type: Constant\n    value: 3.046\n  m_nu:\n    type: Constant\n    value: 0.06\n  Ob0:\n    type: Constant\n    value: 0.04897","tooling":false,"top_keys":["model.FlatLambdaCDM"]},{"error":null,"keys":[{"c":"","k":"DatasetModel","line":1},{"c":"","k":"DatasetModel.background_sky_level","line":2},{"c":"","k":"DatasetModel.background_sky_level.type","line":3},{"c":"","k":"DatasetModel.background_sky_level.value","line":4},{"c":"","k":"DatasetModel.grid_offset_0","line":5},{"c":"","k":"DatasetModel.grid_offset_0.type","line":6},{"c":"","k":"DatasetModel.grid_offset_0.value","line":7},{"c":"","k":"DatasetModel.grid_offset_1","line":8},{"c":"","k":"DatasetModel.grid_offset_1.type","line":9},{"c":"","k":"DatasetModel.grid_offset_1.value","line":10}],"lines":10,"path":"priors/dataset_model.yaml","prior":true,"priors":[{"a":"value 0.0","b":"","cls":"DatasetModel","limits":"","line":2,"param":"background_sky_level","type":"Constant","width":""},{"a":"value 0.0","b":"","cls":"DatasetModel","limits":"","line":5,"param":"grid_offset_0","type":"Constant","width":""},{"a":"value 0.0","b":"","cls":"DatasetModel","limits":"","line":8,"param":"grid_offset_1","type":"Constant","width":""}],"repo":"autolens_workspace","text":"DatasetModel:\n  background_sky_level:\n    type: Constant\n    value: 0.0\n  grid_offset_0:\n    type: Constant\n    value: 0.0\n  grid_offset_1:\n    type: Constant\n    value: 0.0","tooling":false,"top_keys":["DatasetModel"]},{"error":null,"keys":[{"c":"","k":"Redshift","line":1},{"c":"","k":"Redshift.redshift","line":2},{"c":"","k":"Redshift.redshift.type","line":3},{"c":"","k":"Redshift.redshift.lower_limit","line":4},{"c":"","k":"Redshift.redshift.upper_limit","line":5},{"c":"","k":"Redshift.redshift.width_modifier","line":6},{"c":"","k":"Redshift.redshift.width_modifier.type","line":7},{"c":"","k":"Redshift.redshift.width_modifier.value","line":8},{"c":"","k":"Redshift.redshift.limits","line":9},{"c":"","k":"Redshift.redshift.limits.lower","line":10},{"c":"","k":"Redshift.redshift.limits.upper","line":11}],"lines":11,"path":"priors/galaxy/redshift.yaml","prior":true,"priors":[{"a":"lower 0.0","b":"upper 3.0","cls":"Redshift","limits":"[0.0, inf]","line":2,"param":"redshift","type":"Uniform","width":"Absolute 1.0"}],"repo":"autolens_workspace","text":"Redshift:\n  redshift:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 3.0\n    width_modifier:\n      type: Absolute\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: 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  value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\nExponentialSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["Exponential","ExponentialSph"]},{"error":null,"keys":[{"c":"","k":"ExponentialCore","line":1},{"c":"","k":"ExponentialCore.centre_0","line":2},{"c":"","k":"ExponentialCore.centre_0.type","line":3},{"c":"","k":"ExponentialCore.centre_0.mean","line":4},{"c":"","k":"ExponentialCore.centre_0.sigma","line":5},{"c":"","k":"ExponentialCore.centre_0.width_modifier","line":6},{"c":"","k":"ExponentialCore.centre_0.width_modifier.type","line":7},{"c":"","k":"ExponentialCore.centre_0.width_modifier.value","line":8},{"c":"","k":"ExponentialCore.centre_0.limits","line":9},{"c":"","k":"ExponentialCore.centre_0.limits.lower","line":10},{"c":"","k":"ExponentialCore.centre_0.limits.upper","line":11},{"c":"","k":"ExponentialCore.centre_1","line":12},{"c":"","k":"ExponentialCore.centre_1.type","line":13},{"c":"","k":"ExponentialCore.centre_1.mean","line":14},{"c":"","k":"ExponentialCore.centre_1.sigma","line":15},{"c":"","k":"ExponentialCore.centre_1.width_modifier","line":16},{"c":"","k":"ExponentialCore.centre_1.width_modifier.type","line":17},{"c":"","k":"ExponentialCore.centre_1.width_modifier.value","line":18},{"c":"","k":"ExponentialCore.centre_1.limits","line":19},{"c":"","k":"ExponentialCore.centre_1.limits.lower","line":20},{"c":"","k":"ExponentialCore.centre_1.limits.upper","line":21},{"c":"","k":"ExponentialCore.effective_radius","line":22},{"c":"","k":"ExponentialCore.effective_radius.type","line":23},{"c":"","k":"ExponentialCore.effective_radius.lower_limit","line":24},{"c":"","k":"ExponentialCore.effective_radius.upper_limit","line":25},{"c":"","k":"ExponentialCore.effective_radius.width_modifier","line":26},{"c":"","k":"ExponentialCore.effective_radius.width_modifier.type","line":27},{"c":"","k":"ExponentialCore.effective_radius.width_modifier.value","line":28},{"c":"","k":"ExponentialCore.effective_radius.limits","line":29},{"c":"","k":"ExponentialCore.effective_radius.limits.lower","line":30},{"c":"","k":"ExponentialCore.effective_radius.limits.upper","line":31},{"c":"","k":"ExponentialCore.ell_comps_0","line":32},{"c":"","k":"ExponentialCore.ell_comps_0.type","line":33},{"c":"","k":"ExponentialCore.ell_comps_0.mean","line":34},{"c":"","k":"ExponentialCore.ell_comps_0.sigma","line":35},{"c":"","k":"ExponentialCore.ell_comps_0.lower_limit","line":36},{"c":"","k":"ExponentialCore.ell_comps_0.upper_limit","line":37},{"c":"","k":"ExponentialCore.ell_comps_0.width_modifier","line":38},{"c":"","k":"ExponentialCore.ell_comps_0.width_modifier.type","line":39},{"c":"","k":"ExponentialCore.ell_comps_0.width_modifier.value","line":40},{"c":"","k":"ExponentialCore.ell_comps_0.limits","line":41},{"c":"","k":"ExponentialCore.ell_comps_0.limits.lower","line":42},{"c":"","k":"ExponentialCore.ell_comps_0.limits.upper","line":43},{"c":"","k":"ExponentialCore.ell_comps_1","line":44},{"c":"","k":"ExponentialCore.ell_comps_1.type","line":45},{"c":"","k":"ExponentialCore.ell_comps_1.mean","line":46},{"c":"","k":"ExponentialCore.ell_comps_1.sigma","line":47},{"c":"","k":"ExponentialCore.ell_comps_1.lower_limit","line":48},{"c":"","k":"ExponentialCore.ell_comps_1.upper_limit","line":49},{"c":"","k":"ExponentialCore.ell_comps_1.width_modifier","line":50},{"c":"","k":"ExponentialCore.ell_comps_1.width_modifier.type","line":51},{"c":"","k":"ExponentialCore.ell_comps_1.width_modifier.value","line":52},{"c":"","k":"ExponentialCore.ell_comps_1.limits","line":53},{"c":"","k":"ExponentialCore.ell_comps_1.limits.lower","line":54},{"c":"","k":"ExponentialCore.ell_comps_1.limits.upper","line":55},{"c":"","k":"ExponentialCore.alpha","line":56},{"c":"","k":"ExponentialCore.alpha.type","line":57},{"c":"","k":"ExponentialCore.alpha.value","line":58},{"c":"","k":"ExponentialCore.gamma","line":59},{"c":"","k":"ExponentialCore.gamma.type","line":60},{"c":"","k":"ExponentialCore.gamma.value","line":61},{"c":"","k":"ExponentialCore.radius_break","line":62},{"c":"","k":"ExponentialCore.radius_break.type","line":63},{"c":"","k":"ExponentialCore.radius_break.value","line":64},{"c":"","k":"ExponentialCoreSph","line":65},{"c":"","k":"ExponentialCoreSph.centre_0","line":66},{"c":"","k":"ExponentialCoreSph.centre_0.type","line":67},{"c":"","k":"ExponentialCoreSph.centre_0.mean","line":68},{"c":"","k":"ExponentialCoreSph.centre_0.sigma","line":69},{"c":"","k":"ExponentialCoreSph.centre_0.width_modifier","line":70},{"c":"","k":"ExponentialCoreSph.centre_0.width_modifier.type","line":71},{"c":"","k":"ExponentialCoreSph.centre_0.width_modifier.value","line":72},{"c":"","k":"ExponentialCoreSph.centre_0.limits","line":73},{"c":"","k":"ExponentialCoreSph.centre_0.limits.lower","line":74},{"c":"","k":"ExponentialCoreSph.centre_0.limits.upper","line":75},{"c":"","k":"ExponentialCoreSph.centre_1","line":76},{"c":"","k":"ExponentialCoreSph.centre_1.type","line":77},{"c":"","k":"ExponentialCoreSph.centre_1.mean","line":78},{"c":"","k":"ExponentialCoreSph.centre_1.sigma","line":79},{"c":"","k":"ExponentialCoreSph.centre_1.width_modifier"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0.0","b":"\u03c3 0.3","cls":"ExponentialCore","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ExponentialCore","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 30.0","cls":"ExponentialCore","limits":"[0.0, inf]","line":22,"param":"effective_radius","type":"Uniform","width":"Relative 1.0"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ExponentialCore","limits":"[-1.0, 1.0]","line":32,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ExponentialCore","limits":"[-1.0, 1.0]","line":44,"param":"ell_comps_1","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"value 3.0","b":"","cls":"ExponentialCore","limits":"","line":56,"param":"alpha","type":"Constant","width":""},{"a":"value 0.25","b":"","cls":"ExponentialCore","limits":"","line":59,"param":"gamma","type":"Constant","width":""},{"a":"value 0.025","b":"","cls":"ExponentialCore","limits":"","line":62,"param":"radius_break","type":"Constant","width":""},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ExponentialCoreSph","limits":"[-inf, inf]","line":66,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ExponentialCoreSph","limits":"[-inf, inf]","line":76,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 30.0","cls":"ExponentialCoreSph","limits":"[0.0, inf]","line":86,"param":"effective_radius","type":"Uniform","width":"Relative 1.0"},{"a":"value 3.0","b":"","cls":"ExponentialCoreSph","limits":"","line":96,"param":"alpha","type":"Constant","width":""},{"a":"value 0.25","b":"","cls":"ExponentialCoreSph","limits":"","line":99,"param":"gamma","type":"Constant","width":""},{"a":"value 0.025","b":"","cls":"ExponentialCoreSph","limits":"","line":102,"param":"radius_break","type":"Constant","width":""}],"repo":"autolens_workspace","text":"ExponentialCore:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  alpha:\n    type: Constant\n    value: 3.0\n  gamma:\n    type: Constant\n    value: 0.25\n  radius_break:\n    type: Constant\n    value: 0.025\nExponentialCoreSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  alpha:\n    type: Constant\n    value: 3.0\n  gamma:\n    type: Constant\n    value: 0.25\n  radius_break:\n    type: Constant\n    value: 0.025\n","tooling":false,"top_keys":["ExponentialCore","ExponentialCoreSph"]},{"error":null,"keys":[{"c":"","k":"Gaussian","line":1},{"c":"","k":"Gaussian.sigma","line":2},{"c":"","k":"Gaussian.sigma.type","line":3},{"c":"","k":"Gaussian.sigma.lower_limit","line":4},{"c":"","k":"Gaussian.sigma.upper_limit","line":5},{"c":"","k":"Gaussian.sigma.width_modifier","line":6},{"c":"","k":"Gaussian.sigma.width_modifier.type","line":7},{"c":"","k":"Gaussian.sigma.width_modifier.value","line":8},{"c":"","k":"Gaussian.sigma.limits","line":9},{"c":"","k":"Gaussian.sigma.limits.lower","line":10},{"c":"","k":"Gaussian.sigma.limits.upper","line":11},{"c":"","k":"Gaussian.centre_0","line":12},{"c":"","k":"Gaussian.centre_0.type","line":13},{"c":"","k":"Gaussian.centre_0.mean","line":14},{"c":"","k":"Gaussian.centre_0.sigma","line":15},{"c":"","k":"Gaussian.centre_0.width_modifier","line":16},{"c":"","k":"Gaussian.centre_0.width_modifier.type","line":17},{"c":"","k":"Gaussian.centre_0.width_modifier.value","line":18},{"c":"","k":"Gaussian.centre_0.limits","line":19},{"c":"","k":"Gaussian.centre_0.limits.lower","line":20},{"c":"","k":"Gaussian.centre_0.limits.upper","line":21},{"c":"","k":"Gaussian.centre_1","line":22},{"c":"","k":"Gaussian.centre_1.type","line":23},{"c":"","k":"Gaussian.centre_1.mean","line":24},{"c":"","k":"Gaussian.centre_1.sigma","line":25},{"c":"","k":"Gaussian.centre_1.width_modifier","line":26},{"c":"","k":"Gaussian.centre_1.width_modifier.type","line":27},{"c":"","k":"Gaussian.centre_1.width_modifier.value","line":28},{"c":"","k":"Gaussian.centre_1.limits","line":29},{"c":"","k":"Gaussian.centre_1.limits.lower","line":30},{"c":"","k":"Gaussian.centre_1.limits.upper","line":31},{"c":"","k":"Gaussian.ell_comps_0","line":32},{"c":"","k":"Gaussian.ell_comps_0.type","line":33},{"c":"","k":"Gaussian.ell_comps_0.mean","line":34},{"c":"","k":"Gaussian.ell_comps_0.sigma","line":35},{"c":"","k":"Gaussian.ell_comps_0.lower_limit","line":36},{"c":"","k":"Gaussian.ell_comps_0.upper_limit","line":37},{"c":"","k":"Gaussian.ell_comps_0.width_modifier","line":38},{"c":"","k":"Gaussian.ell_comps_0.width_modifier.type","line":39},{"c":"","k":"Gaussian.ell_comps_0.width_modifier.value","line":40},{"c":"","k":"Gaussian.ell_comps_0.limits","line":41},{"c":"","k":"Gaussian.ell_comps_0.limits.lower","line":42},{"c":"","k":"Gaussian.ell_comps_0.limits.upper","line":43},{"c":"","k":"Gaussian.ell_comps_1","line":44},{"c":"","k":"Gaussian.ell_comps_1.type","line":45},{"c":"","k":"Gaussian.ell_comps_1.mean","line":46},{"c":"","k":"Gaussian.ell_comps_1.sigma","line":47},{"c":"","k":"Gaussian.ell_comps_1.lower_limit","line":48},{"c":"","k":"Gaussian.ell_comps_1.upper_limit","line":49},{"c":"","k":"Gaussian.ell_comps_1.width_modifier","line":50},{"c":"","k":"Gaussian.ell_comps_1.width_modifier.type","line":51},{"c":"","k":"Gaussian.ell_comps_1.width_modifier.value","line":52},{"c":"","k":"Gaussian.ell_comps_1.limits","line":53},{"c":"","k":"Gaussian.ell_comps_1.limits.lower","line":54},{"c":"","k":"Gaussian.ell_comps_1.limits.upper","line":55},{"c":"","k":"GaussianSph","line":56},{"c":"","k":"GaussianSph.sigma","line":57},{"c":"","k":"GaussianSph.sigma.type","line":58},{"c":"","k":"GaussianSph.sigma.lower_limit","line":59},{"c":"","k":"GaussianSph.sigma.upper_limit","line":60},{"c":"","k":"GaussianSph.sigma.width_modifier","line":61},{"c":"","k":"GaussianSph.sigma.width_modifier.type","line":62},{"c":"","k":"GaussianSph.sigma.width_modifier.value","line":63},{"c":"","k":"GaussianSph.sigma.limits","line":64},{"c":"","k":"GaussianSph.sigma.limits.lower","line":65},{"c":"","k":"GaussianSph.sigma.limits.upper","line":66},{"c":"","k":"GaussianSph.centre_0","line":67},{"c":"","k":"GaussianSph.centre_0.type","line":68},{"c":"","k":"GaussianSph.centre_0.mean","line":69},{"c":"","k":"GaussianSph.centre_0.sigma","line":70},{"c":"","k":"GaussianSph.centre_0.width_modifier","line":71},{"c":"","k":"GaussianSph.centre_0.width_modifier.type","line":72},{"c":"","k":"GaussianSph.centre_0.width_modifier.value","line":73},{"c":"","k":"GaussianSph.centre_0.limits","line":74},{"c":"","k":"GaussianSph.centre_0.limits.lower","line":75},{"c":"","k":"GaussianSph.centre_0.limits.upper","line":76},{"c":"","k":"GaussianSph.centre_1","line":77},{"c":"","k":"GaussianSph.centre_1.type","line":78},{"c":"","k":"GaussianSph.centre_1.mean","line":79},{"c":"","k":"GaussianSph.centre_1.sigma","line":80},{"c":"","k":"GaussianSph.centre_1.width_modifier","line":81},{"c":"","k":"GaussianSph.centre_1.width_modifier.type","line":82},{"c":"","k":"GaussianSph.centre_1.width_modifier.value","line":83},{"c":"","k":"GaussianSph.centre_1.limits","line":84},{"c":"","k":"GaussianSph.centre_1.limits.lower","line":85},{"c":"","k":"GaussianSph.centre_1.limits.upper","line":86}],"lines":86,"path":"priors/light/linear/gaussian.yaml","prior":true,"priors":[{"a":"lower 0.0","b":"upper 25.0","cls":"Gaussian","limits":"[0.0, inf]","line":2,"param":"sigma","type":"Uniform","width":"Relative 0.5"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Gaussian","limits":"[-inf, inf]","line":12,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Gaussian","limits":"[-inf, inf]","line":22,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Gaussian","limits":"[-1.0, 1.0]","line":32,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Gaussian","limits":"[-1.0, 1.0]","line":44,"param":"ell_comps_1","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"lower 0.0","b":"upper 25.0","cls":"GaussianSph","limits":"[0.0, inf]","line":57,"param":"sigma","type":"Uniform","width":"Relative 0.5"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"GaussianSph","limits":"[-inf, inf]","line":67,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"GaussianSph","limits":"[-inf, inf]","line":77,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"}],"repo":"autolens_workspace","text":"Gaussian:\n  sigma:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 25.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\nGaussianSph:\n  sigma:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 25.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: 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0.0","b":"upper 1.0","cls":"Moffat","limits":"[0.0, inf]","line":2,"param":"alpha","type":"Uniform","width":"Relative 0.5"},{"a":"lower 1.0","b":"upper 5.0","cls":"Moffat","limits":"[0.0, inf]","line":12,"param":"beta","type":"Uniform","width":"Relative 0.5"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Moffat","limits":"[-inf, inf]","line":22,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Moffat","limits":"[-inf, inf]","line":32,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Moffat","limits":"[-1.0, 1.0]","line":42,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Moffat","limits":"[-1.0, 1.0]","line":54,"param":"ell_comps_1","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"lower 0.0","b":"upper 1.0","cls":"MoffatSph","limits":"[0.0, inf]","line":67,"param":"alpha","type":"Uniform","width":"Relative 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-inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\nMoffatSph:\n  alpha:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  beta:\n    type: Uniform\n    lower_limit: 1.0\n    upper_limit: 5.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: 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0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  sersic_index:\n    type: Uniform\n    lower_limit: 0.8\n    upper_limit: 5.0\n    width_modifier:\n      type: Absolute\n      value: 1.5\n    limits:\n      lower: 0.8\n      upper: 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   sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  sersic_index:\n    type: Uniform\n    lower_limit: 0.8\n    upper_limit: 5.0\n    width_modifier:\n      type: Absolute\n      value: 1.5\n    limits:\n      lower: 0.8\n      upper: 5.0\n  radius_break:\n    type: Constant\n    value: 0.025\n  gamma:\n    type: Constant\n    value: 0.25\n  alpha:\n    type: Constant\n    value: 3.0\nSersicCoreSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    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0.0","b":"\u03c3 0.3","cls":"ShapeletCartesianSph","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletCartesianSph","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 30.0","cls":"ShapeletCartesianSph","limits":"[0.0, inf]","line":22,"param":"beta","type":"Uniform","width":"Relative 0.5"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletCartesian","limits":"[-inf, inf]","line":33,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletCartesian","limits":"[-inf, inf]","line":43,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletCartesian","limits":"[-1.0, 1.0]","line":53,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 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Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  beta:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["ShapeletCartesianSph","ShapeletCartesian"]},{"error":null,"keys":[{"c":"","k":"ShapeletExponentialSph","line":1},{"c":"","k":"ShapeletExponentialSph.centre_0","line":2},{"c":"","k":"ShapeletExponentialSph.centre_0.type","line":3},{"c":"","k":"ShapeletExponentialSph.centre_0.mean","line":4},{"c":"","k":"ShapeletExponentialSph.centre_0.sigma","line":5},{"c":"","k":"ShapeletExponentialSph.centre_0.width_modifier","line":6},{"c":"","k":"ShapeletExponentialSph.centre_0.width_modifier.type","line":7},{"c":"","k":"ShapeletExponentialSph.centre_0.width_modifier.value","line":8},{"c":"","k":"ShapeletExponentialSph.centre_0.limits","line":9},{"c":"","k":"ShapeletExponentialSph.centre_0.limits.lower","line":10},{"c":"","k":"ShapeletExponentialSph.centre_0.limits.upper","line":11},{"c":"","k":"ShapeletExponentialSph.centre_1","line":12},{"c":"","k":"ShapeletExponentialSph.centre_1.type","line":13},{"c":"","k":"ShapeletExponentialSph.centre_1.mean","line":14},{"c":"","k":"ShapeletExponentialSph.centre_1.sigma","line":15},{"c":"","k":"ShapeletExponentialSph.centre_1.width_modifier","line":16},{"c":"","k":"ShapeletExponentialSph.centre_1.width_modifier.type","line":17},{"c":"","k":"ShapeletExponentialSph.centre_1.width_modifier.value","line":18},{"c":"","k":"ShapeletExponentialSph.centre_1.limits","line":19},{"c":"","k":"ShapeletExponentialSph.centre_1.limits.lower","line":20},{"c":"","k":"ShapeletExponentialSph.centre_1.limits.upper","line":21},{"c":"","k":"ShapeletExponentialSph.beta","line":22},{"c":"","k":"ShapeletExponentialSph.beta.type","line":23},{"c":"","k":"ShapeletExponentialSph.beta.lower_limit","line":24},{"c":"","k":"ShapeletExponentialSph.beta.upper_limit","line":25},{"c":"","k":"ShapeletExponentialSph.beta.width_modifier","line":26},{"c":"","k":"ShapeletExponentialSph.beta.width_modifier.type","line":27},{"c":"","k":"ShapeletExponentialSph.beta.width_modifier.value","line":28},{"c":"","k":"ShapeletExponentialSph.beta.limits","line":29},{"c":"","k":"ShapeletExponentialSph.beta.limits.lower","line":30},{"c":"","k":"ShapeletExponentialSph.beta.limits.upper","line":31},{"c":"","k":"ShapeletExponential","line":32},{"c":"","k":"ShapeletExponential.centre_0","line":33},{"c":"","k":"ShapeletExponential.centre_0.type","line":34},{"c":"","k":"ShapeletExponential.centre_0.mean","line":35},{"c":"","k":"ShapeletExponential.centre_0.sigma","line":36},{"c":"","k":"ShapeletExponential.centre_0.width_modifier","line":37},{"c":"","k":"ShapeletExponential.centre_0.width_modifier.type","line":38},{"c":"","k":"ShapeletExponential.centre_0.width_modifier.value","line":39},{"c":"","k":"ShapeletExponential.centre_0.limits","line":40},{"c":"","k":"ShapeletExponential.centre_0.limits.lower","line":41},{"c":"","k":"ShapeletExponential.centre_0.limits.upper","line":42},{"c":"","k":"ShapeletExponential.centre_1","line":43},{"c":"","k":"ShapeletExponential.centre_1.type","line":44},{"c":"","k":"ShapeletExponential.centre_1.mean","line":45},{"c":"","k":"ShapeletExponential.centre_1.sigma","line":46},{"c":"","k":"ShapeletExponential.centre_1.width_modifier","line":47},{"c":"","k":"ShapeletExponential.centre_1.width_modifier.type","line":48},{"c":"","k":"ShapeletExponential.centre_1.width_modifier.value","line":49},{"c":"","k":"ShapeletExponential.centre_1.limits","line":50},{"c":"","k":"ShapeletExponential.centre_1.limits.lower","line":51},{"c":"","k":"ShapeletExponential.centre_1.limits.upper","line":52},{"c":"","k":"ShapeletExponential.ell_comps_0","line":53},{"c":"","k":"ShapeletExponential.ell_comps_0.type","line":54},{"c":"","k":"ShapeletExponential.ell_comps_0.mean","line":55},{"c":"","k":"ShapeletExponential.ell_comps_0.sigma","line":56},{"c":"","k":"ShapeletExponential.ell_comps_0.lower_limit","line":57},{"c":"","k":"ShapeletExponential.ell_comps_0.upper_limit","line":58},{"c":"","k":"ShapeletExponential.ell_comps_0.width_modifier","line":59},{"c":"","k":"ShapeletExponential.ell_comps_0.width_modifier.type","line":60},{"c":"","k":"ShapeletExponential.ell_comps_0.width_modifier.value","line":61},{"c":"","k":"ShapeletExponential.ell_comps_0.limits","line":62},{"c":"","k":"ShapeletExponential.ell_comps_0.limits.lower","line":63},{"c":"","k":"ShapeletExponential.ell_comps_0.limits.upper","line":64},{"c":"","k":"ShapeletExponential.ell_comps_1","line":65},{"c":"","k":"ShapeletExponential.ell_comps_1.type","line":66},{"c":"","k":"ShapeletExponential.ell_comps_1.mean","line":67},{"c":"","k":"ShapeletExponential.ell_comps_1.sigma","line":68},{"c":"","k":"ShapeletExponential.ell_comps_1.lower_limit","line":69},{"c":"","k":"ShapeletExponential.ell_comps_1.upper_limit","line":70},{"c":"","k":"ShapeletExponential.ell_comps_1.width_modifier","line":71},{"c":"","k":"ShapeletExponential.ell_comps_1.width_modifier.type","line":72},{"c":"","k":"ShapeletExponential.ell_comps_1.width_modifier.value","line":73},{"c":"","k":"ShapeletExponential.ell_comps_1.limits","line":74},{"c":"","k":"ShapeletExponential.ell_com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0.0","b":"\u03c3 0.3","cls":"ShapeletExponentialSph","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletExponentialSph","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 30.0","cls":"ShapeletExponentialSph","limits":"[0.0, inf]","line":22,"param":"beta","type":"Uniform","width":"Relative 0.5"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletExponential","limits":"[-inf, inf]","line":33,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletExponential","limits":"[-inf, inf]","line":43,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletExponential","limits":"[-1.0, 1.0]","line":53,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 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type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  beta:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["ShapeletExponentialSph","ShapeletExponential"]},{"error":null,"keys":[{"c":"","k":"ShapeletPolarSph","line":1},{"c":"","k":"ShapeletPolarSph.centre_0","line":2},{"c":"","k":"ShapeletPolarSph.centre_0.type","line":3},{"c":"","k":"ShapeletPolarSph.centre_0.mean","line":4},{"c":"","k":"ShapeletPolarSph.centre_0.sigma","line":5},{"c":"","k":"ShapeletPolarSph.centre_0.width_modifier","line":6},{"c":"","k":"ShapeletPolarSph.centre_0.width_modifier.type","line":7},{"c":"","k":"ShapeletPolarSph.centre_0.width_modifier.value","line":8},{"c":"","k":"ShapeletPolarSph.centre_0.limits","line":9},{"c":"","k":"ShapeletPolarSph.centre_0.limits.lower","line":10},{"c":"","k":"ShapeletPolarSph.centre_0.limits.upper","line":11},{"c":"","k":"ShapeletPolarSph.centre_1","line":12},{"c":"","k":"ShapeletPolarSph.centre_1.type","line":13},{"c":"","k":"ShapeletPolarSph.centre_1.mean","line":14},{"c":"","k":"ShapeletPolarSph.centre_1.sigma","line":15},{"c":"","k":"ShapeletPolarSph.centre_1.width_modifier","line":16},{"c":"","k":"ShapeletPolarSph.centre_1.width_modifier.type","line":17},{"c":"","k":"ShapeletPolarSph.centre_1.width_modifier.value","line":18},{"c":"","k":"ShapeletPolarSph.centre_1.limits","line":19},{"c":"","k":"ShapeletPolarSph.centre_1.limits.lower","line":20},{"c":"","k":"ShapeletPolarSph.centre_1.limits.upper","line":21},{"c":"","k":"ShapeletPolarSph.beta","line":22},{"c":"","k":"ShapeletPolarSph.beta.type","line":23},{"c":"","k":"ShapeletPolarSph.beta.lower_limit","line":24},{"c":"","k":"ShapeletPolarSph.beta.upper_limit","line":25},{"c":"","k":"ShapeletPolarSph.beta.width_modifier","line":26},{"c":"","k":"ShapeletPolarSph.beta.width_modifier.type","line":27},{"c":"","k":"ShapeletPolarSph.beta.width_modifier.value","line":28},{"c":"","k":"ShapeletPolarSph.beta.limits","line":29},{"c":"","k":"ShapeletPolarSph.beta.limits.lower","line":30},{"c":"","k":"ShapeletPolarSph.beta.limits.upper","line":31},{"c":"","k":"ShapeletPolar","line":32},{"c":"","k":"ShapeletPolar.centre_0","line":33},{"c":"","k":"ShapeletPolar.centre_0.type","line":34},{"c":"","k":"ShapeletPolar.centre_0.mean","line":35},{"c":"","k":"ShapeletPolar.centre_0.sigma","line":36},{"c":"","k":"ShapeletPolar.centre_0.width_modifier","line":37},{"c":"","k":"ShapeletPolar.centre_0.width_modifier.type","line":38},{"c":"","k":"ShapeletPolar.centre_0.width_modifier.value","line":39},{"c":"","k":"ShapeletPolar.centre_0.limits","line":40},{"c":"","k":"ShapeletPolar.centre_0.limits.lower","line":41},{"c":"","k":"ShapeletPolar.centre_0.limits.upper","line":42},{"c":"","k":"ShapeletPolar.centre_1","line":43},{"c":"","k":"ShapeletPolar.centre_1.type","line":44},{"c":"","k":"ShapeletPolar.centre_1.mean","line":45},{"c":"","k":"ShapeletPolar.centre_1.sigma","line":46},{"c":"","k":"ShapeletPolar.centre_1.width_modifier","line":47},{"c":"","k":"ShapeletPolar.centre_1.width_modifier.type","line":48},{"c":"","k":"ShapeletPolar.centre_1.width_modifier.value","line":49},{"c":"","k":"ShapeletPolar.centre_1.limits","line":50},{"c":"","k":"ShapeletPolar.centre_1.limits.lower","line":51},{"c":"","k":"ShapeletPolar.centre_1.limits.upper","line":52},{"c":"","k":"ShapeletPolar.ell_comps_0","line":53},{"c":"","k":"ShapeletPolar.ell_comps_0.type","line":54},{"c":"","k":"ShapeletPolar.ell_comps_0.mean","line":55},{"c":"","k":"ShapeletPolar.ell_comps_0.sigma","line":56},{"c":"","k":"ShapeletPolar.ell_comps_0.lower_limit","line":57},{"c":"","k":"ShapeletPolar.ell_comps_0.upper_limit","line":58},{"c":"","k":"ShapeletPolar.ell_comps_0.width_modifier","line":59},{"c":"","k":"ShapeletPolar.ell_comps_0.width_modifier.type","line":60},{"c":"","k":"ShapeletPolar.ell_comps_0.width_modifier.value","line":61},{"c":"","k":"ShapeletPolar.ell_comps_0.limits","line":62},{"c":"","k":"ShapeletPolar.ell_comps_0.limits.lower","line":63},{"c":"","k":"ShapeletPolar.ell_comps_0.limits.upper","line":64},{"c":"","k":"ShapeletPolar.ell_comps_1","line":65},{"c":"","k":"ShapeletPolar.ell_comps_1.type","line":66},{"c":"","k":"ShapeletPolar.ell_comps_1.mean","line":67},{"c":"","k":"ShapeletPolar.ell_comps_1.sigma","line":68},{"c":"","k":"ShapeletPolar.ell_comps_1.lower_limit","line":69},{"c":"","k":"ShapeletPolar.ell_comps_1.upper_limit","line":70},{"c":"","k":"ShapeletPolar.ell_comps_1.width_modifier","line":71},{"c":"","k":"ShapeletPolar.ell_comps_1.width_modifier.type","line":72},{"c":"","k":"ShapeletPolar.ell_comps_1.width_modifier.value","line":73},{"c":"","k":"ShapeletPolar.ell_comps_1.limits","line":74},{"c":"","k":"ShapeletPolar.ell_comps_1.limits.lower","line":75},{"c":"","k":"ShapeletPolar.ell_comps_1.limits.upper","line":76},{"c":"","k":"ShapeletPolar.beta","line":77},{"c":"","k":"ShapeletPolar.beta.type","line":78},{"c":"","k":"ShapeletPolar.beta.lower_limit","line":79},{"c":"","k":"ShapeletPolar.beta.upper_limit","line":80},{"c":"","k":"ShapeletPolar.beta.width_modifier","line":81},{"c":"","k":"ShapeletPolar.beta.width_modifier.type","line":82},{"c":"","k":"ShapeletPolar.beta.width_modifier.value","line":83},{"c":"","k":"ShapeletPolar.beta.limits","line":84},{"c":"","k":"ShapeletPolar.beta.limits.lower","line":85},{"c":"","k":"ShapeletPolar.beta.limits.upper","line":86}],"lines":86,"path":"priors/light/linear/shapelets/polar.yaml","prior":true,"priors":[{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletPolarSph","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletPolarSph","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 30.0","cls":"ShapeletPolarSph","limits":"[0.0, inf]","line":22,"param":"beta","type":"Uniform","width":"Relative 0.5"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletPolar","limits":"[-inf, inf]","line":33,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletPolar","limits":"[-inf, inf]","line":43,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletPolar","limits":"[-1.0, 1.0]","line":53,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ShapeletPolar","limits":"[-1.0, 1.0]","line":65,"param":"ell_comps_1","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"lower 0.0","b":"upper 30.0","cls":"ShapeletPolar","limits":"[0.0, inf]","line":77,"param":"beta","type":"Uniform","width":"Relative 0.5"}],"repo":"autolens_workspace","text":"ShapeletPolarSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  beta:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\nShapeletPolar:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  beta:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["ShapeletPolarSph","ShapeletPolar"]},{"error":null,"keys":[{"c":"","k":"Gaussian","line":1},{"c":"","k":"Gaussian.sigma","line":2},{"c":"","k":"Gaussian.sigma.type","line":3},{"c":"","k":"Gaussian.sigma.lower_limit","line":4},{"c":"","k":"Gaussian.sigma.upper_limit","line":5},{"c":"","k":"Gaussian.sigma.width_modifier","line":6},{"c":"","k":"Gaussian.sigma.width_modifier.type","line":7},{"c":"","k":"Gaussian.sigma.width_modifier.value","line":8},{"c":"","k":"Gaussian.sigma.limits","line":9},{"c":"","k":"Gaussian.sigma.limits.lower","line":10},{"c":"","k":"Gaussian.sigma.limits.upper","line":11},{"c":"","k":"Gaussian.centre_0","line":12},{"c":"","k":"Gaussian.centre_0.type","line":13},{"c":"","k":"Gaussian.centre_0.mean","line":14},{"c":"","k":"Gaussian.centre_0.sigma","line":15},{"c":"","k":"Gaussian.centre_0.width_modifier","line":16},{"c":"","k":"Gaussian.centre_0.width_modifier.type","line":17},{"c":"","k":"Gaussian.centre_0.width_modifier.value","line":18},{"c":"","k":"Gaussian.centre_0.limits","line":19},{"c":"","k":"Gaussian.centre_0.limits.lower","line":20},{"c":"","k":"Gaussian.centre_0.limits.upper","line":21},{"c":"","k":"Gaussian.centre_1","line":22},{"c":"","k":"Gaussian.centre_1.type","line":23},{"c":"","k":"Gaussian.centre_1.mean","line":24},{"c":"","k":"Gaussian.centre_1.sigma","line":25},{"c":"","k":"Gaussian.centre_1.width_modifier","line":26},{"c":"","k":"Gaussian.centre_1.width_modifier.type","line":27},{"c":"","k":"Gaussian.centre_1.width_modifier.value","line":28},{"c":"","k":"Gaussian.centre_1.limits","line":29},{"c":"","k":"Gaussian.centre_1.limits.lower","line":30},{"c":"","k":"Gaussian.centre_1.limits.upper","line":31},{"c":"","k":"Gaussian.ell_comps_0","line":32},{"c":"","k":"Gaussian.ell_comps_0.type","line":33},{"c":"","k":"Gaussian.ell_comps_0.mean","line":34},{"c":"","k":"Gaussian.ell_comps_0.sigma","line":35},{"c":"","k":"Gaussian.ell_comps_0.lower_limit","line":36},{"c":"","k":"Gaussian.ell_comps_0.upper_limit","line":37},{"c":"","k":"Gaussian.ell_comps_0.width_modifier","line":38},{"c":"","k":"Gaussian.ell_comps_0.width_modifier.type","line":39},{"c":"","k":"Gaussian.ell_comps_0.width_modifier.value","line":40},{"c":"","k":"Gaussian.ell_comps_0.limits","line":41},{"c":"","k":"Gaussian.ell_comps_0.limits.lower","line":42},{"c":"","k":"Gaussian.ell_comps_0.limits.upper","line":43},{"c":"","k":"Gaussian.ell_comps_1","line":44},{"c":"","k":"Gaussian.ell_comps_1.type","line":45},{"c":"","k":"Gaussian.ell_comps_1.mean","line":46},{"c":"","k":"Gaussian.ell_comps_1.sigma","line":47},{"c":"","k":"Gaussian.ell_comps_1.lower_limit","line":48},{"c":"","k":"Gaussian.ell_comps_1.upper_limit","line":49},{"c":"","k":"Gaussian.ell_comps_1.width_modifier","line":50},{"c":"","k":"Gaussian.ell_comps_1.width_modifier.type","line":51},{"c":"","k":"Gaussian.ell_comps_1.width_modifier.value","line":52},{"c":"","k":"Gaussian.ell_comps_1.limits","line":53},{"c":"","k":"Gaussian.ell_comps_1.limits.lower","line":54},{"c":"","k":"Gaussian.ell_comps_1.limits.upper","line":55}],"lines":55,"path":"priors/light/linear_operated/gaussian.yaml","prior":true,"priors":[{"a":"lower 0.0","b":"upper 5.0","cls":"Gaussian","limits":"[0.0, inf]","line":2,"param":"sigma","type":"Uniform","width":"Relative 0.5"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Gaussian","limits":"[-inf, inf]","line":12,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Gaussian","limits":"[-inf, inf]","line":22,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Gaussian","limits":"[-1.0, 1.0]","line":32,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Gaussian","limits":"[-1.0, 1.0]","line":44,"param":"ell_comps_1","type":"TruncatedGaussian","width":"Absolute 0.2"}],"repo":"autolens_workspace","text":"Gaussian:\n  sigma:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 5.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n","tooling":false,"top_keys":["Gaussian"]},{"error":null,"keys":[{"c":"","k":"Moffat","line":1},{"c":"","k":"Moffat.alpha","line":2},{"c":"","k":"Moffat.alpha.type","line":3},{"c":"","k":"Moffat.alpha.lower_limit","line":4},{"c":"","k":"Moffat.alpha.upper_limit","line":5},{"c":"","k":"Moffat.alpha.width_modifier","line":6},{"c":"","k":"Moffat.alpha.width_modifier.type","line":7},{"c":"","k":"Moffat.alpha.width_modifier.value","line":8},{"c":"","k":"Moffat.alpha.limits","line":9},{"c":"","k":"Moffat.alpha.limits.lower","line":10},{"c":"","k":"Moffat.alpha.limits.upper","line":11},{"c":"","k":"Moffat.beta","line":12},{"c":"","k":"Moffat.beta.type","line":13},{"c":"","k":"Moffat.beta.lower_limit","line":14},{"c":"","k":"Moffat.beta.upper_limit","line":15},{"c":"","k":"Moffat.beta.width_modifier","line":16},{"c":"","k":"Moffat.beta.width_modifier.type","line":17},{"c":"","k":"Moffat.beta.width_modifier.value","line":18},{"c":"","k":"Moffat.beta.limits","line":19},{"c":"","k":"Moffat.beta.limits.lower","line":20},{"c":"","k":"Moffat.beta.limits.upper","line":21},{"c":"","k":"Moffat.centre_0","line":22},{"c":"","k":"Moffat.centre_0.type","line":23},{"c":"","k":"Moffat.centre_0.mean","line":24},{"c":"","k":"Moffat.centre_0.sigma","line":25},{"c":"","k":"Moffat.centre_0.width_modifier","line":26},{"c":"","k":"Moffat.centre_0.width_modifier.type","line":27},{"c":"","k":"Moffat.centre_0.width_modifier.value","line":28},{"c":"","k":"Moffat.centre_0.limits","line":29},{"c":"","k":"Moffat.centre_0.limits.lower","line":30},{"c":"","k":"Moffat.centre_0.limits.upper","line":31},{"c":"","k":"Moffat.centre_1","line":32},{"c":"","k":"Moffat.centre_1.type","line":33},{"c":"","k":"Moffat.centre_1.mean","line":34},{"c":"","k":"Moffat.centre_1.sigma","line":35},{"c":"","k":"Moffat.centre_1.width_modifier","line":36},{"c":"","k":"Moffat.centre_1.width_modifier.type","line":37},{"c":"","k":"Moffat.centre_1.width_modifier.value","line":38},{"c":"","k":"Moffat.centre_1.limits","line":39},{"c":"","k":"Moffat.centre_1.limits.lower","line":40},{"c":"","k":"Moffat.centre_1.limits.upper","line":41},{"c":"","k":"Moffat.ell_comps_0","line":42},{"c":"","k":"Moffat.ell_comps_0.type","line":43},{"c":"","k":"Moffat.ell_comps_0.mean","line":44},{"c":"","k":"Moffat.ell_comps_0.sigma","line":45},{"c":"","k":"Moffat.ell_comps_0.lower_limit","line":46},{"c":"","k":"Moffat.ell_comps_0.upper_limit","line":47},{"c":"","k":"Moffat.ell_comps_0.width_modifier","line":48},{"c":"","k":"Moffat.ell_comps_0.width_modifier.type","line":49},{"c":"","k":"Moffat.ell_comps_0.width_modifier.value","line":50},{"c":"","k":"Moffat.ell_comps_0.limits","line":51},{"c":"","k":"Moffat.ell_comps_0.limits.lower","line":52},{"c":"","k":"Moffat.ell_comps_0.limits.upper","line":53},{"c":"","k":"Moffat.ell_comps_1","line":54},{"c":"","k":"Moffat.ell_comps_1.type","line":55},{"c":"","k":"Moffat.ell_comps_1.mean","line":56},{"c":"","k":"Moffat.ell_comps_1.sigma","line":57},{"c":"","k":"Moffat.ell_comps_1.lower_limit","line":58},{"c":"","k":"Moffat.ell_comps_1.upper_limit","line":59},{"c":"","k":"Moffat.ell_comps_1.width_modifier","line":60},{"c":"","k":"Moffat.ell_comps_1.width_modifier.type","line":61},{"c":"","k":"Moffat.ell_comps_1.width_modifier.value","line":62},{"c":"","k":"Moffat.ell_comps_1.limits","line":63},{"c":"","k":"Moffat.ell_comps_1.limits.lower","line":64},{"c":"","k":"Moffat.ell_comps_1.limits.upper","line":65}],"lines":65,"path":"priors/light/linear_operated/moffat.yaml","prior":true,"priors":[{"a":"lower 0.0","b":"upper 1.0","cls":"Moffat","limits":"[0.0, inf]","line":2,"param":"alpha","type":"Uniform","width":"Relative 0.5"},{"a":"lower 1.0","b":"upper 5.0","cls":"Moffat","limits":"[0.0, inf]","line":12,"param":"beta","type":"Uniform","width":"Relative 0.5"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Moffat","limits":"[-inf, inf]","line":22,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Moffat","limits":"[-inf, inf]","line":32,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Moffat","limits":"[-1.0, 1.0]","line":42,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Moffat","limits":"[-1.0, 1.0]","line":54,"param":"ell_comps_1","type":"TruncatedGaussian","width":"Absolute 0.2"}],"repo":"autolens_workspace","text":"Moffat:\n  alpha:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  beta:\n    type: Uniform\n    lower_limit: 1.0\n    upper_limit: 5.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 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5.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: 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0.5"}],"repo":"autolens_workspace","text":"Moffat:\n  alpha:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  beta:\n    type: Uniform\n    lower_limit: 1.0\n    upper_limit: 5.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["Moffat"]},{"error":null,"keys":[{"c":"","k":"Sersic","line":1},{"c":"","k":"Sersic.centre_0","line":2},{"c":"","k":"Sersic.centre_0.type","line":3},{"c":"","k":"Sersic.centre_0.mean","line":4},{"c":"","k":"Sersic.centre_0.sigma","line":5},{"c":"","k":"Sersic.centre_0.width_modifier","line":6},{"c":"","k":"Sersic.centre_0.width_modifier.type","line":7},{"c":"","k":"Sersic.centre_0.width_modifier.value","line":8},{"c":"","k":"Sersic.centre_0.limits","line":9},{"c":"","k":"Sersic.centre_0.limits.lower","line":10},{"c":"","k":"Sersic.centre_0.limits.upper","line":11},{"c":"","k":"Sersic.centre_1","line":12},{"c":"","k":"Sersic.centre_1.type","line":13},{"c":"","k":"Sersic.centre_1.mean","line":14},{"c":"","k":"Sersic.centre_1.sigma","line":15},{"c":"","k":"Sersic.centre_1.width_modifier","line":16},{"c":"","k":"Sersic.centre_1.width_modifier.type","line":17},{"c":"","k":"Sersic.centre_1.width_modifier.value","line":18},{"c":"","k":"Sersic.centre_1.limits","line":19},{"c":"","k":"Sersic.centre_1.limits.lower","line":20},{"c":"","k":"Sersic.centre_1.limits.upper","line":21},{"c":"","k":"Sersic.effective_radius","line":22},{"c":"","k":"Sersic.effective_radius.type","line":23},{"c":"","k":"Sersic.effective_radius.lower_limit","line":24},{"c":"","k":"Sersic.effective_radius.upper_limit","line":25},{"c":"","k":"Sersic.effective_radius.width_modifier","line":26},{"c":"","k":"Sersic.effective_radius.width_modifier.type","line":27},{"c":"","k":"Sersic.effective_radius.width_modifier.value","line":28},{"c":"","k":"Sersic.effective_radius.limits","line":29},{"c":"","k":"Sersic.effective_radius.limits.lower","line":30},{"c":"","k":"Sersic.effective_radius.limits.upper","line":31},{"c":"","k":"Sersic.ell_comps_0","line":32},{"c":"","k":"Sersic.ell_comps_0.type","line":33},{"c":"","k":"Sersic.ell_comps_0.mean","line":34},{"c":"","k":"Sersic.ell_comps_0.sigma","line":35},{"c":"","k":"Sersic.ell_comps_0.lower_limit","line":36},{"c":"","k":"Sersic.ell_comps_0.upper_limit","line":37},{"c":"","k":"Sersic.ell_comps_0.width_modifier","line":38},{"c":"","k":"Sersic.ell_comps_0.width_modifier.type","line":39},{"c":"","k":"Sersic.ell_comps_0.width_modifier.value","line":40},{"c":"","k":"Sersic.ell_comps_0.limits","line":41},{"c":"","k":"Sersic.ell_comps_0.limits.lower","line":42},{"c":"","k":"Sersic.ell_comps_0.limits.upper","line":43},{"c":"","k":"Sersic.ell_comps_1","line":44},{"c":"","k":"Sersic.ell_comps_1.type","line":45},{"c":"","k":"Sersic.ell_comps_1.mean","line":46},{"c":"","k":"Sersic.ell_comps_1.sigma","line":47},{"c":"","k":"Sersic.ell_comps_1.lower_limit","line":48},{"c":"","k":"Sersic.ell_comps_1.upper_limit","line":49},{"c":"","k":"Sersic.ell_comps_1.width_modifier","line":50},{"c":"","k":"Sersic.ell_comps_1.width_modifier.type","line":51},{"c":"","k":"Sersic.ell_comps_1.width_modifier.value","line":52},{"c":"","k":"Sersic.ell_comps_1.limits","line":53},{"c":"","k":"Sersic.ell_comps_1.limits.lower","line":54},{"c":"","k":"Sersic.ell_comps_1.limits.upper","line":55},{"c":"","k":"Sersic.intensity","line":56},{"c":"","k":"Sersic.intensity.type","line":57},{"c":"","k":"Sersic.intensity.lower_limit","line":58},{"c":"","k":"Sersic.intensity.upper_limit","line":59},{"c":"","k":"Sersic.intensity.width_modifier","line":60},{"c":"","k":"Sersic.intensity.width_modifier.type","line":61},{"c":"","k":"Sersic.intensity.width_modifier.value","line":62},{"c":"","k":"Sersic.intensity.limits","line":63},{"c":"","k":"Sersic.intensity.limits.lower","line":64},{"c":"","k":"Sersic.intensity.limits.upper","line":65},{"c":"","k":"Sersic.sersic_index","line":66},{"c":"","k":"Sersic.sersic_index.type","line":67},{"c":"","k":"Sersic.sersic_index.lower_limit","line":68},{"c":"","k":"Sersic.sersic_index.upper_limit","line":69},{"c":"","k":"Sersic.sersic_index.width_modifier","line":70},{"c":"","k":"Sersic.sersic_index.width_modifier.type","line":71},{"c":"","k":"Sersic.sersic_index.width_modifier.value","line":72},{"c":"","k":"Sersic.sersic_index.limits","line":73},{"c":"","k":"Sersic.sersic_index.limits.lower","line":74},{"c":"","k":"Sersic.sersic_index.limits.upper","line":75}],"lines":75,"path":"priors/light/operated/sersic.yaml","prior":true,"priors":[{"a":"mean 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-1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  sersic_index:\n    type: Uniform\n    lower_limit: 0.8\n    upper_limit: 5.0\n    width_modifier:\n      type: Absolute\n      value: 1.5\n    limits:\n      lower: 0.8\n      upper: 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   value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  core_radius_0:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Absolute\n      value: 0.3\n    limits:\n      lower: 0.0\n      upper: inf\n  core_radius_1:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Absolute\n      value: 0.3\n    limits:\n      lower: 0.0\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\nChameleonSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  core_radius_0:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Absolute\n      value: 0.3\n    limits:\n      lower: 0.0\n      upper: inf\n  core_radius_1:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Absolute\n      value: 0.3\n    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width_modifier:\n      type: Absolute\n      value: 1.5\n    limits:\n      lower: 0.0\n      upper: 5.0\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\nElsonFreeFallSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  eta:\n    type: Uniform\n    lower_limit: 0.5\n    upper_limit: 3.0\n    width_modifier:\n      type: Absolute\n      value: 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0.0","b":"\u03c3 0.3","cls":"Exponential","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Exponential","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 30.0","cls":"Exponential","limits":"[0.0, inf]","line":22,"param":"effective_radius","type":"Uniform","width":"Relative 1.0"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Exponential","limits":"[-1.0, 1.0]","line":32,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Exponential","limits":"[-1.0, 1.0]","line":44,"param":"ell_comps_1","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"Exponential","limits":"[0.0, inf]","line":56,"param":"intensity","type":"LogUniform","width":"Relative 0.5"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"ExponentialSph","limits":"[-inf, 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lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\nExponentialSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      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type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-05\n    upper_limit: 1000.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  radius_break:\n    type: Constant\n    value: 0.025\n  gamma:\n    type: Constant\n    value: 0.25\n  alpha:\n    type: Constant\n    value: 3.0\n","tooling":false,"top_keys":["ExponentialCore","ExponentialCoreSph"]},{"error":null,"keys":[{"c":"","k":"Gaussian","line":1},{"c":"","k":"Gaussian.sigma","line":2},{"c":"","k":"Gaussian.sigma.type","line":3},{"c":"","k":"Gaussian.sigma.lower_limit","line":4},{"c":"","k":"Gaussian.sigma.upper_limit","line":5},{"c":"","k":"Gaussian.sigma.width_modifier","line":6},{"c":"","k":"Gaussian.sigma.width_modifier.type","line":7},{"c":"","k":"Gaussian.sigma.width_modifier.value","line":8},{"c":"","k":"Gaussian.sigma.limits","line":9},{"c":"","k":"Gaussian.sigma.limits.lower","line":10},{"c":"","k":"Gaussian.sigma.limits.upper","line":11},{"c":"","k":"Gaussian.centre_0","line":12},{"c":"","k":"Gaussian.centre_0.type","line":13},{"c":"","k":"Gaussian.centre_0.mean","line":14},{"c":"","k":"Gaussian.centre_0.sigma","line":15},{"c":"","k":"Gaussian.centre_0.width_modifier","line":16},{"c":"","k":"Gaussian.centre_0.width_modifier.type","line":17},{"c":"","k":"Gaussian.centre_0.width_modifier.value","line":18},{"c":"","k":"Gaussian.centre_0.limits","line":19},{"c":"","k":"Gaussian.centre_0.limits.lower","line":20},{"c":"","k":"Gaussian.centre_0.limits.upper","line":21},{"c":"","k":"Gaussian.centre_1","line":22},{"c":"","k":"Gaussian.centre_1.type","line":23},{"c":"","k":"Gaussian.centre_1.mean","line":24},{"c":"","k":"Gaussian.centre_1.sigma","line":25},{"c":"","k":"Gaussian.centre_1.width_modifier","line":26},{"c":"","k":"Gaussian.centre_1.width_modifier.type","line":27},{"c":"","k":"Gaussian.centre_1.width_modifier.value","line":28},{"c":"","k":"Gaussian.centre_1.limits","line":29},{"c":"","k":"Gaussian.centre_1.limits.lower","line":30},{"c":"","k":"Gaussian.centre_1.limits.upper","line":31},{"c":"","k":"Gaussian.ell_comps_0","line":32},{"c":"","k":"Gaussian.ell_comps_0.type","line":33},{"c":"","k":"Gaussian.ell_comps_0.mean","line":34},{"c":"","k":"Gaussian.ell_comps_0.sigma","line":35},{"c":"","k":"Gaussian.ell_comps_0.lower_limit","line":36},{"c":"","k":"Gaussian.ell_comps_0.upper_limit","line":37},{"c":"","k":"Gaussian.ell_comps_0.width_modifier","line":38},{"c":"","k":"Gaussian.ell_comps_0.width_modifier.type","line":39},{"c":"","k":"Gaussian.ell_comps_0.width_modifier.value","line":40},{"c":"","k":"Gaussian.ell_comps_0.limits","line":41},{"c":"","k":"Gaussian.ell_comps_0.limits.lower","line":42},{"c":"","k":"Gaussian.ell_comps_0.limits.upper","line":43},{"c":"","k":"Gaussian.ell_comps_1","line":44},{"c":"","k":"Gaussian.ell_comps_1.type","line":45},{"c":"","k":"Gaussian.ell_comps_1.mean","line":46},{"c":"","k":"Gaussian.ell_comps_1.sigma","line":47},{"c":"","k":"Gaussian.ell_comps_1.lower_limit","line":48},{"c":"","k":"Gaussian.ell_comps_1.upper_limit","line":49},{"c":"","k":"Gaussian.ell_comps_1.width_modifier","line":50},{"c":"","k":"Gaussian.ell_comps_1.width_modifier.type","line":51},{"c":"","k":"Gaussian.ell_comps_1.width_modifier.value","line":52},{"c":"","k":"Gaussian.ell_comps_1.limits","line":53},{"c":"","k":"Gaussian.ell_comps_1.limits.lower","line":54},{"c":"","k":"Gaussian.ell_comps_1.limits.upper","line":55},{"c":"","k":"Gaussian.intensity","line":56},{"c":"","k":"Gaussian.intensity.type","line":57},{"c":"","k":"Gaussian.intensity.lower_limit","line":58},{"c":"","k":"Gaussian.intensity.upper_limit","line":59},{"c":"","k":"Gaussian.intensity.width_modifier","line":60},{"c":"","k":"Gaussian.intensity.width_modifier.type","line":61},{"c":"","k":"Gaussian.intensity.width_modifier.value","line":62},{"c":"","k":"Gaussian.intensity.limits","line":63},{"c":"","k":"Gaussian.intensity.limits.lower","line":64},{"c":"","k":"Gaussian.intensity.limits.upper","line":65},{"c":"","k":"GaussianSph","line":66},{"c":"","k":"GaussianSph.sigma","line":67},{"c":"","k":"GaussianSph.sigma.type","line":68},{"c":"","k":"GaussianSph.sigma.lower_limit","line":69},{"c":"","k":"GaussianSph.sigma.upper_limit","line":70},{"c":"","k":"GaussianSph.sigma.width_modifier","line":71},{"c":"","k":"GaussianSph.sigma.width_modifier.type","line":72},{"c":"","k":"GaussianSph.sigma.width_modifier.value","line":73},{"c":"","k":"GaussianSph.sigma.limits","line":74},{"c":"","k":"GaussianSph.sigma.limits.lower","line":75},{"c":"","k":"GaussianSph.sigma.limits.upper","line":76},{"c":"","k":"GaussianSph.centre_0","line":77},{"c":"","k":"GaussianSph.centre_0.type","line":78},{"c":"","k":"GaussianSph.centre_0.mean","line":79},{"c":"","k":"GaussianSph.centre_0.sigma","line":80},{"c":"","k":"GaussianSph.centre_0.width_modifier","line":81},{"c":"","k":"GaussianSph.centre_0.width_modifier.type","line":82},{"c":"","k":"GaussianSph.centre_0.width_modifier.value","line":83},{"c":"","k":"GaussianSph.centre_0.limits","line":84},{"c":"","k":"GaussianSph.centre_0.limits.lower","line":85},{"c":"","k":"GaussianSph.centre_0.limits.upper","line":86},{"c":"","k":"GaussianSph.centre_1","line":87},{"c":"","k":"GaussianSph.centre_1.type","line":88},{"c":"","k":"GaussianSph.centre_1.mean","line":89},{"c":"","k":"GaussianSph.centre_1.sigma","line":90},{"c":"","k":"Gaussi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 width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\nGaussianSph:\n  sigma:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 25.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  centre_0:\n    type: 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width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: 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limits:\n      lower: 0.8\n      upper: 5.0\n  radius_break:\n    type: Constant\n    value: 0.025\n  gamma:\n    type: Constant\n    value: 0.25\n  alpha:\n    type: Constant\n    value: 3.0\nSersicCoreSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-05\n    upper_limit: 1000.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  sersic_index:\n    type: Uniform\n    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lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  inner_slope:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 2.0\n    width_modifier:\n      type: Absolute\n      value: 0.3\n    limits:\n      lower: -1.0\n      upper: 3.0\n  mass_at_200:\n    type: LogUniform\n    lower_limit: 100000000.0\n    upper_limit: 1000000000000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  redshift_object:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  redshift_source:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: 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inf]","line":52,"param":"scatter_sigma","type":"Gaussian","width":"Absolute 1.0"}],"repo":"autolens_workspace","text":"NFWTruncatedMCRScatterLudlowSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  mass_at_200:\n    type: LogUniform\n    lower_limit: 100000000.0\n    upper_limit: 1000000000000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  redshift_object:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  redshift_source:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  scatter_sigma:\n    type: Gaussian\n    mean: 0.0\n    sigma: 3.0\n    width_modifier:\n      type: Absolute\n      value: 1.0\n    limits:\n      lower: -inf\n      upper: inf\n","tooling":false,"top_keys":["NFWTruncatedMCRScatterLudlowSph"]},{"error":null,"keys":[{"c":"","k":"PointMass","line":1},{"c":"","k":"PointMass.centre_0","line":2},{"c":"","k":"PointMass.centre_0.type","line":3},{"c":"","k":"PointMass.centre_0.mean","line":4},{"c":"","k":"PointMass.centre_0.sigma","line":5},{"c":"","k":"PointMass.centre_0.width_modifier","line":6},{"c":"","k":"PointMass.centre_0.width_modifier.type","line":7},{"c":"","k":"PointMass.centre_0.width_modifier.value","line":8},{"c":"","k":"PointMass.centre_0.limits","line":9},{"c":"","k":"PointMass.centre_0.limits.lower","line":10},{"c":"","k":"PointMass.centre_0.limits.upper","line":11},{"c":"","k":"PointMass.centre_1","line":12},{"c":"","k":"PointMass.centre_1.type","line":13},{"c":"","k":"PointMass.centre_1.mean","line":14},{"c":"","k":"PointMass.centre_1.sigma","line":15},{"c":"","k":"PointMass.centre_1.width_modifier","line":16},{"c":"","k":"PointMass.centre_1.width_modifier.type","line":17},{"c":"","k":"PointMass.centre_1.width_modifier.value","line":18},{"c":"","k":"PointMass.centre_1.limits","line":19},{"c":"","k":"PointMass.centre_1.limits.lower","line":20},{"c":"","k":"PointMass.centre_1.limits.upper","line":21},{"c":"","k":"PointMass.einstein_radius","line":22},{"c":"","k":"PointMass.einstein_radius.type","line":23},{"c":"","k":"PointMass.einstein_radius.lower_limit","line":24},{"c":"","k":"PointMass.einstein_radius.upper_limit","line":25},{"c":"","k":"PointMass.einstein_radius.width_modifier","line":26},{"c":"","k":"PointMass.einstein_radius.width_modifier.type","line":27},{"c":"","k":"PointMass.einstein_radius.width_modifier.value","line":28},{"c":"","k":"PointMass.einstein_radius.limits","line":29},{"c":"","k":"PointMass.einstein_radius.limits.lower","line":30},{"c":"","k":"PointMass.einstein_radius.limits.upper","line":31}],"lines":31,"path":"priors/mass/point/point.yaml","prior":true,"priors":[{"a":"mean 0.0","b":"\u03c3 0.1","cls":"PointMass","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"PointMass","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 8.0","cls":"PointMass","limits":"[0.0, inf]","line":22,"param":"einstein_radius","type":"Uniform","width":"Relative 0.25"}],"repo":"autolens_workspace","text":"PointMass:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  einstein_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 8.0\n    width_modifier:\n      type: Relative\n      value: 0.25\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["PointMass"]},{"error":null,"keys":[{"c":"","k":"SMBH","line":1},{"c":"","k":"SMBH.centre_0","line":2},{"c":"","k":"SMBH.centre_0.type","line":3},{"c":"","k":"SMBH.centre_0.mean","line":4},{"c":"","k":"SMBH.centre_0.sigma","line":5},{"c":"","k":"SMBH.centre_0.width_modifier","line":6},{"c":"","k":"SMBH.centre_0.width_modifier.type","line":7},{"c":"","k":"SMBH.centre_0.width_modifier.value","line":8},{"c":"","k":"SMBH.centre_0.limits","line":9},{"c":"","k":"SMBH.centre_0.limits.lower","line":10},{"c":"","k":"SMBH.centre_0.limits.upper","line":11},{"c":"","k":"SMBH.centre_1","line":12},{"c":"","k":"SMBH.centre_1.type","line":13},{"c":"","k":"SMBH.centre_1.mean","line":14},{"c":"","k":"SMBH.centre_1.sigma","line":15},{"c":"","k":"SMBH.centre_1.width_modifier","line":16},{"c":"","k":"SMBH.centre_1.width_modifier.type","line":17},{"c":"","k":"SMBH.centre_1.width_modifier.value","line":18},{"c":"","k":"SMBH.centre_1.limits","line":19},{"c":"","k":"SMBH.centre_1.limits.lower","line":20},{"c":"","k":"SMBH.centre_1.limits.upper","line":21},{"c":"","k":"SMBH.mass","line":22},{"c":"","k":"SMBH.mass.type","line":23},{"c":"","k":"SMBH.mass.lower_limit","line":24},{"c":"","k":"SMBH.mass.upper_limit","line":25},{"c":"","k":"SMBH.mass.width_modifier","line":26},{"c":"","k":"SMBH.mass.width_modifier.type","line":27},{"c":"","k":"SMBH.mass.width_modifier.value","line":28},{"c":"","k":"SMBH.mass.limits","line":29},{"c":"","k":"SMBH.mass.limits.lower","line":30},{"c":"","k":"SMBH.mass.limits.upper","line":31},{"c":"","k":"SMBH.redshift_object","line":32},{"c":"","k":"SMBH.redshift_object.type","line":33},{"c":"","k":"SMBH.redshift_object.lower_limit","line":34},{"c":"","k":"SMBH.redshift_object.upper_limit","line":35},{"c":"","k":"SMBH.redshift_object.width_modifier","line":36},{"c":"","k":"SMBH.redshift_object.width_modifier.type","line":37},{"c":"","k":"SMBH.redshift_object.width_modifier.value","line":38},{"c":"","k":"SMBH.redshift_object.limits","line":39},{"c":"","k":"SMBH.redshift_object.limits.lower","line":40},{"c":"","k":"SMBH.redshift_object.limits.upper","line":41},{"c":"","k":"SMBH.redshift_source","line":42},{"c":"","k":"SMBH.redshift_source.type","line":43},{"c":"","k":"SMBH.redshift_source.lower_limit","line":44},{"c":"","k":"SMBH.redshift_source.upper_limit","line":45},{"c":"","k":"SMBH.redshift_source.width_modifier","line":46},{"c":"","k":"SMBH.redshift_source.width_modifier.type","line":47},{"c":"","k":"SMBH.redshift_source.width_modifier.value","line":48},{"c":"","k":"SMBH.redshift_source.limits","line":49},{"c":"","k":"SMBH.redshift_source.limits.lower","line":50},{"c":"","k":"SMBH.redshift_source.limits.upper","line":51}],"lines":51,"path":"priors/mass/point/smbh.yaml","prior":true,"priors":[{"a":"mean 0.0","b":"\u03c3 0.1","cls":"SMBH","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"SMBH","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 1000000.0","b":"upper 10000000000000.0","cls":"SMBH","limits":"[0.0, inf]","line":22,"param":"mass","type":"LogUniform","width":"Relative 0.25"},{"a":"lower 0.0","b":"upper 1.0","cls":"SMBH","limits":"[0.0, inf]","line":32,"param":"redshift_object","type":"Uniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 1.0","cls":"SMBH","limits":"[0.0, inf]","line":42,"param":"redshift_source","type":"Uniform","width":"Relative 0.5"}],"repo":"autolens_workspace","text":"SMBH:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  mass:\n    type: LogUniform\n    lower_limit: 1000000.0\n    upper_limit: 10000000000000.0\n    width_modifier:\n      type: Relative\n      value: 0.25\n    limits:\n      lower: 0.0\n      upper: inf\n  redshift_object:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  redshift_source:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf","tooling":false,"top_keys":["SMBH"]},{"error":null,"keys":[{"c":"","k":"ExternalShear","line":1},{"c":"","k":"ExternalShear.gamma_1","line":2},{"c":"","k":"ExternalShear.gamma_1.type","line":3},{"c":"","k":"ExternalShear.gamma_1.lower_limit","line":4},{"c":"","k":"ExternalShear.gamma_1.upper_limit","line":5},{"c":"","k":"ExternalShear.gamma_1.width_modifier","line":6},{"c":"","k":"ExternalShear.gamma_1.width_modifier.type","line":7},{"c":"","k":"ExternalShear.gamma_1.width_modifier.value","line":8},{"c":"","k":"ExternalShear.gamma_1.limits","line":9},{"c":"","k":"ExternalShear.gamma_1.limits.lower","line":10},{"c":"","k":"ExternalShear.gamma_1.limits.upper","line":11},{"c":"","k":"ExternalShear.gamma_2","line":12},{"c":"","k":"ExternalShear.gamma_2.type","line":13},{"c":"","k":"ExternalShear.gamma_2.lower_limit","line":14},{"c":"","k":"ExternalShear.gamma_2.upper_limit","line":15},{"c":"","k":"ExternalShear.gamma_2.width_modifier","line":16},{"c":"","k":"ExternalShear.gamma_2.width_modifier.type","line":17},{"c":"","k":"ExternalShear.gamma_2.width_modifier.value","line":18},{"c":"","k":"ExternalShear.gamma_2.limits","line":19},{"c":"","k":"ExternalShear.gamma_2.limits.lower","line":20},{"c":"","k":"ExternalShear.gamma_2.limits.upper","line":21}],"lines":21,"path":"priors/mass/sheets/external_shear.yaml","prior":true,"priors":[{"a":"lower -0.3","b":"upper 0.3","cls":"ExternalShear","limits":"[-inf, inf]","line":2,"param":"gamma_1","type":"Uniform","width":"Absolute 0.05"},{"a":"lower -0.3","b":"upper 0.3","cls":"ExternalShear","limits":"[-inf, inf]","line":12,"param":"gamma_2","type":"Uniform","width":"Absolute 0.05"}],"repo":"autolens_workspace","text":"ExternalShear:\n  gamma_1:\n    type: Uniform\n    lower_limit: -0.3\n    upper_limit: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  gamma_2:\n    type: Uniform\n    lower_limit: -0.3\n    upper_limit: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n","tooling":false,"top_keys":["ExternalShear"]},{"error":null,"keys":[{"c":"","k":"MassSheet","line":1},{"c":"","k":"MassSheet.centre_0","line":2},{"c":"","k":"MassSheet.centre_0.type","line":3},{"c":"","k":"MassSheet.centre_0.mean","line":4},{"c":"","k":"MassSheet.centre_0.sigma","line":5},{"c":"","k":"MassSheet.centre_0.width_modifier","line":6},{"c":"","k":"MassSheet.centre_0.width_modifier.type","line":7},{"c":"","k":"MassSheet.centre_0.width_modifier.value","line":8},{"c":"","k":"MassSheet.centre_0.limits","line":9},{"c":"","k":"MassSheet.centre_0.limits.lower","line":10},{"c":"","k":"MassSheet.centre_0.limits.upper","line":11},{"c":"","k":"MassSheet.centre_1","line":12},{"c":"","k":"MassSheet.centre_1.type","line":13},{"c":"","k":"MassSheet.centre_1.mean","line":14},{"c":"","k":"MassSheet.centre_1.sigma","line":15},{"c":"","k":"MassSheet.centre_1.width_modifier","line":16},{"c":"","k":"MassSheet.centre_1.width_modifier.type","line":17},{"c":"","k":"MassSheet.centre_1.width_modifier.value","line":18},{"c":"","k":"MassSheet.centre_1.limits","line":19},{"c":"","k":"MassSheet.centre_1.limits.lower","line":20},{"c":"","k":"MassSheet.centre_1.limits.upper","line":21},{"c":"","k":"MassSheet.kappa","line":22},{"c":"","k":"MassSheet.kappa.type","line":23},{"c":"","k":"MassSheet.kappa.lower_limit","line":24},{"c":"","k":"MassSheet.kappa.upper_limit","line":25},{"c":"","k":"MassSheet.kappa.width_modifier","line":26},{"c":"","k":"MassSheet.kappa.width_modifier.type","line":27},{"c":"","k":"MassSheet.kappa.width_modifier.value","line":28},{"c":"","k":"MassSheet.kappa.limits","line":29},{"c":"","k":"MassSheet.kappa.limits.lower","line":30},{"c":"","k":"MassSheet.kappa.limits.upper","line":31}],"lines":31,"path":"priors/mass/sheets/mass_sheet.yaml","prior":true,"priors":[{"a":"mean 0.0","b":"\u03c3 0.1","cls":"MassSheet","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"MassSheet","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower -1.0","b":"upper 1.0","cls":"MassSheet","limits":"[-inf, inf]","line":22,"param":"kappa","type":"Uniform","width":"Absolute 0.05"}],"repo":"autolens_workspace","text":"MassSheet:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  kappa:\n    type: Uniform\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      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width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 10.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  mass_to_light_ratio:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.3\n    limits:\n      lower: 0.0\n      upper: inf\nExponentialSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 10.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  mass_to_light_ratio:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.3\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["Exponential","ExponentialSph"]},{"error":null,"keys":[{"c":"","k":"Gaussian","line":1},{"c":"","k":"Gaussian.sigma","line":2},{"c":"","k":"Gaussian.sigma.type","line":3},{"c":"","k":"Gaussian.sigma.lower_limit","line":4},{"c":"","k":"Gaussian.sigma.upper_limit","line":5},{"c":"","k":"Gaussian.sigma.width_modifier","line":6},{"c":"","k":"Gaussian.sigma.width_modifier.type","line":7},{"c":"","k":"Gaussian.sigma.width_modifier.value","line":8},{"c":"","k":"Gaussian.sigma.limits","line":9},{"c":"","k":"Gaussian.sigma.limits.lower","line":10},{"c":"","k":"Gaussian.sigma.limits.upper","line":11},{"c":"","k":"Gaussian.centre_0","line":12},{"c":"","k":"Gaussian.centre_0.type","line":13},{"c":"","k":"Gaussian.centre_0.mean","line":14},{"c":"","k":"Gaussian.centre_0.sigma","line":15},{"c":"","k":"Gaussian.centre_0.width_modifier","line":16},{"c":"","k":"Gaussian.centre_0.width_modifier.type","line":17},{"c":"","k":"Gaussian.centre_0.width_modifier.value","line":18},{"c":"","k":"Gaussian.centre_0.limits","line":19},{"c":"","k":"Gaussian.centre_0.limits.lower","line":20},{"c":"","k":"Gaussian.centre_0.limits.upper","line":21},{"c":"","k":"Gaussian.centre_1","line":22},{"c":"","k":"Gaussian.centre_1.type","line":23},{"c":"","k":"Gaussian.centre_1.mean","line":24},{"c":"","k":"Gaussian.centre_1.sigma","line":25},{"c":"","k":"Gaussian.centre_1.width_modifier","line":26},{"c":"","k":"Gaussian.centre_1.width_modifier.type","line":27},{"c":"","k":"Gaussian.centre_1.width_modifier.value","line":28},{"c":"","k":"Gaussian.centre_1.limits","line":29},{"c":"","k":"Gaussian.centre_1.limits.lower","line":30},{"c":"","k":"Gaussian.centre_1.limits.upper","line":31},{"c":"","k":"Gaussian.ell_comps_0","line":32},{"c":"","k":"Gaussian.ell_comps_0.type","line":33},{"c":"","k":"Gaussian.ell_comps_0.mean","line":34},{"c":"","k":"Gaussian.ell_comps_0.sigma","line":35},{"c":"","k":"Gaussian.ell_comps_0.lower_limit","line":36},{"c":"","k":"Gaussian.ell_comps_0.upper_limit","line":37},{"c":"","k":"Gaussian.ell_comps_0.width_modifier","line":38},{"c":"","k":"Gaussian.ell_comps_0.width_modifier.type","line":39},{"c":"","k":"Gaussian.ell_comps_0.width_modifier.value","line":40},{"c":"","k":"Gaussian.ell_comps_0.limits","line":41},{"c":"","k":"Gaussian.ell_comps_0.limits.lower","line":42},{"c":"","k":"Gaussian.ell_comps_0.limits.upper","line":43},{"c":"","k":"Gaussian.ell_comps_1","line":44},{"c":"","k":"Gaussian.ell_comps_1.type","line":45},{"c":"","k":"Gaussian.ell_comps_1.mean","line":46},{"c":"","k":"Gaussian.ell_comps_1.sigma","line":47},{"c":"","k":"Gaussian.ell_comps_1.lower_limit","line":48},{"c":"","k":"Gaussian.ell_comps_1.upper_limit","line":49},{"c":"","k":"Gaussian.ell_comps_1.width_modifier","line":50},{"c":"","k":"Gaussian.ell_comps_1.width_modifier.type","line":51},{"c":"","k":"Gaussian.ell_comps_1.width_modifier.value","line":52},{"c":"","k":"Gaussian.ell_comps_1.limits","line":53},{"c":"","k":"Gaussian.ell_comps_1.limits.lower","line":54},{"c":"","k":"Gaussian.ell_comps_1.limits.upper","line":55},{"c":"","k":"Gaussian.intensity","line":56},{"c":"","k":"Gaussian.intensity.type","line":57},{"c":"","k":"Gaussian.intensity.lower_limit","line":58},{"c":"","k":"Gaussian.intensity.upper_limit","line":59},{"c":"","k":"Gaussian.intensity.width_modifier","line":60},{"c":"","k":"Gaussian.intensity.width_modifier.type","line":61},{"c":"","k":"Gaussian.intensity.width_modifier.value","line":62},{"c":"","k":"Gaussian.intensity.limits","line":63},{"c":"","k":"Gaussian.intensity.limits.lower","line":64},{"c":"","k":"Gaussian.intensity.limits.upper","line":65},{"c":"","k":"Gaussian.mass_to_light_ratio","line":66},{"c":"","k":"Gaussian.mass_to_light_ratio.type","line":67},{"c":"","k":"Gaussian.mass_to_light_ratio.lower_limit","line":68},{"c":"","k":"Gaussian.mass_to_light_ratio.upper_limit","line":69},{"c":"","k":"Gaussian.mass_to_light_ratio.width_modifier","line":70},{"c":"","k":"Gaussian.mass_to_light_ratio.width_modifier.type","line":71},{"c":"","k":"Gaussian.mass_to_light_ratio.width_modifier.value","line":72},{"c":"","k":"Gaussian.mass_to_light_ratio.limits","line":73},{"c":"","k":"Gaussian.mass_to_light_ratio.limits.lower","line":74},{"c":"","k":"Gaussian.mass_to_light_ratio.limits.upper","line":75}],"lines":75,"path":"priors/mass/stellar/gaussian.yaml","prior":true,"priors":[{"a":"lower 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upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-05\n    upper_limit: 1000.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    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upper: inf\n  effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  gamma:\n    type: Constant\n    value: 0.25\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-05\n    upper_limit: 1000.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  radius_break:\n    type: Constant\n    value: 0.025\n  sersic_index:\n    type: Uniform\n    lower_limit: 0.8\n    upper_limit: 5.0\n    width_modifier:\n      type: Absolute\n      value: 1.5\n    limits:\n      lower: 0.8\n      upper: 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effective_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 30.0\n    width_modifier:\n      type: Relative\n      value: 1.0\n    limits:\n      lower: 0.0\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  intensity:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 10.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  mass_to_light_gradient:\n    type: Uniform\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 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0.0","b":"\u03c3 0.1","cls":"Isothermal","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"Isothermal","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 8.0","cls":"Isothermal","limits":"[0.0, inf]","line":22,"param":"einstein_radius","type":"Uniform","width":"Relative 0.25"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Isothermal","limits":"[-1.0, 1.0]","line":32,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"Isothermal","limits":"[-1.0, 1.0]","line":44,"param":"ell_comps_1","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"IsothermalSph","limits":"[-inf, inf]","line":57,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"IsothermalSph","limits":"[-inf, 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value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\nIsothermalSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  einstein_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 8.0\n    width_modifier:\n      type: Relative\n      value: 0.25\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["Isothermal","IsothermalSph"]},{"error":null,"keys":[{"c":"","k":"IsothermalCore","line":1},{"c":"","k":"IsothermalCore.centre_0","line":2},{"c":"","k":"IsothermalCore.centre_0.type","line":3},{"c":"","k":"IsothermalCore.centre_0.mean","line":4},{"c":"","k":"IsothermalCore.centre_0.sigma","line":5},{"c":"","k":"IsothermalCore.centre_0.width_modifier","line":6},{"c":"","k":"IsothermalCore.centre_0.width_modifier.type","line":7},{"c":"","k":"IsothermalCore.centre_0.width_modifier.value","line":8},{"c":"","k":"IsothermalCore.centre_0.limits","line":9},{"c":"","k":"IsothermalCore.centre_0.limits.lower","line":10},{"c":"","k":"IsothermalCore.centre_0.limits.upper","line":11},{"c":"","k":"IsothermalCore.centre_1","line":12},{"c":"","k":"IsothermalCore.centre_1.type","line":13},{"c":"","k":"IsothermalCore.centre_1.mean","line":14},{"c":"","k":"IsothermalCore.centre_1.sigma","line":15},{"c":"","k":"IsothermalCore.centre_1.width_modifier","line":16},{"c":"","k":"IsothermalCore.centre_1.width_modifier.type","line":17},{"c":"","k":"IsothermalCore.centre_1.width_modifier.value","line":18},{"c":"","k":"IsothermalCore.centre_1.limits","line":19},{"c":"","k":"IsothermalCore.centre_1.limits.lower","line":20},{"c":"","k":"IsothermalCore.centre_1.limits.upper","line":21},{"c":"","k":"IsothermalCore.core_radius","line":22},{"c":"","k":"IsothermalCore.core_radius.type","line":23},{"c":"","k":"IsothermalCore.core_radius.lower_limit","line":24},{"c":"","k":"IsothermalCore.core_radius.upper_limit","line":25},{"c":"","k":"IsothermalCore.core_radius.width_modifier","line":26},{"c":"","k":"IsothermalCore.core_radius.width_modifier.type","line":27},{"c":"","k":"IsothermalCore.core_radius.width_modifier.value","line":28},{"c":"","k":"IsothermalCore.core_radius.limits","line":29},{"c":"","k":"IsothermalCore.core_radius.limits.lower","line":30},{"c":"","k":"IsothermalCore.core_radius.limits.upper","line":31},{"c":"","k":"IsothermalCore.einstein_radius","line":32},{"c":"","k":"IsothermalCore.einstein_radius.type","line":33},{"c":"","k":"IsothermalCore.einstein_radius.lower_limit","line":34},{"c":"","k":"IsothermalCore.einstein_radius.upper_limit","line":35},{"c":"","k":"IsothermalCore.einstein_radius.width_modifier","line":36},{"c":"","k":"IsothermalCore.einstein_radius.width_modifier.type","line":37},{"c":"","k":"IsothermalCore.einstein_radius.width_modifier.value","line":38},{"c":"","k":"IsothermalCore.einstein_radius.limits","line":39},{"c":"","k":"IsothermalCore.einstein_radius.limits.lower","line":40},{"c":"","k":"IsothermalCore.einstein_radius.limits.upper","line":41},{"c":"","k":"IsothermalCore.ell_comps_0","line":42},{"c":"","k":"IsothermalCore.ell_comps_0.type","line":43},{"c":"","k":"IsothermalCore.ell_comps_0.mean","line":44},{"c":"","k":"IsothermalCore.ell_comps_0.sigma","line":45},{"c":"","k":"IsothermalCore.ell_comps_0.lower_limit","line":46},{"c":"","k":"IsothermalCore.ell_comps_0.upper_limit","line":47},{"c":"","k":"IsothermalCore.ell_comps_0.width_modifier","line":48},{"c":"","k":"IsothermalCore.ell_comps_0.width_modifier.type","line":49},{"c":"","k":"IsothermalCore.ell_comps_0.width_modifier.value","line":50},{"c":"","k":"IsothermalCore.ell_comps_0.limits","line":51},{"c":"","k":"IsothermalCore.ell_comps_0.limits.lower","line":52},{"c":"","k":"IsothermalCore.ell_comps_0.limits.upper","line":53},{"c":"","k":"IsothermalCore.ell_comps_1","line":54},{"c":"","k":"IsothermalCore.ell_comps_1.type","line":55},{"c":"","k":"IsothermalCore.ell_comps_1.mean","line":56},{"c":"","k":"IsothermalCore.ell_comps_1.sigma","line":57},{"c":"","k":"IsothermalCore.ell_comps_1.lower_limit","line":58},{"c":"","k":"IsothermalCore.ell_comps_1.upper_limit","line":59},{"c":"","k":"IsothermalCore.ell_comps_1.width_modifier","line":60},{"c":"","k":"IsothermalCore.ell_comps_1.width_modifier.type","line":61},{"c":"","k":"IsothermalCore.ell_comps_1.width_modifier.value","line":62},{"c":"","k":"IsothermalCore.ell_comps_1.limits","line":63},{"c":"","k":"IsothermalCore.ell_comps_1.limits.lower","line":64},{"c":"","k":"IsothermalCore.ell_comps_1.limits.upper","line":65},{"c":"","k":"IsothermalCoreSph","line":66},{"c":"","k":"IsothermalCoreSph.centre_0","line":67},{"c":"","k":"IsothermalCoreSph.centre_0.type","line":68},{"c":"","k":"IsothermalCoreSph.centre_0.mean","line":69},{"c":"","k":"IsothermalCoreSph.centre_0.sigma","line":70},{"c":"","k":"IsothermalCoreSph.centre_0.width_modifier","line":71},{"c":"","k":"IsothermalCoreSph.centre_0.width_modifier.type","line":72},{"c":"","k":"IsothermalCoreSph.centre_0.width_modifier.value","line":73},{"c":"","k":"IsothermalCoreSph.centre_0.limits","line":74},{"c":"","k":"IsothermalCoreSph.centre_0.limits.lower","line":75},{"c":"","k":"IsothermalCoreSph.centre_0.limits.upper","line":76},{"c":"","k":"IsothermalCoreSph.centre_1","line":77},{"c":"","k":"IsothermalCoreSph.centre_1.type","line":78},{"c":"","k":"IsothermalCoreSph.centre_1.mean","line":79},{"c":"","k":"IsothermalCoreSph.cent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0.0","b":"\u03c3 0.1","cls":"IsothermalCore","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"IsothermalCore","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 0.2","cls":"IsothermalCore","limits":"[0.0, inf]","line":22,"param":"core_radius","type":"Uniform","width":"Absolute 0.1"},{"a":"lower 0.0","b":"upper 8.0","cls":"IsothermalCore","limits":"[0.0, inf]","line":32,"param":"einstein_radius","type":"Uniform","width":"Relative 0.25"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"IsothermalCore","limits":"[-1.0, 1.0]","line":42,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"IsothermalCore","limits":"[-1.0, 1.0]","line":54,"param":"ell_comps_1","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"IsothermalCoreSph","limits":"[-inf, inf]","line":67,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"IsothermalCoreSph","limits":"[-inf, inf]","line":77,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 0.2","cls":"IsothermalCoreSph","limits":"[0.0, inf]","line":87,"param":"core_radius","type":"Uniform","width":"Absolute 0.1"},{"a":"lower 0.0","b":"upper 8.0","cls":"IsothermalCoreSph","limits":"[0.0, inf]","line":97,"param":"einstein_radius","type":"Uniform","width":"Relative 0.25"}],"repo":"autolens_workspace","text":"IsothermalCore:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  core_radius:\n    type: Uniform\n    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0.0","b":"\u03c3 0.1","cls":"PowerLawCore","limits":"[-inf, inf]","line":2,"param":"centre_0","type":"Gaussian","width":"Absolute 0.05"},{"a":"mean 0.0","b":"\u03c3 0.1","cls":"PowerLawCore","limits":"[-inf, inf]","line":12,"param":"centre_1","type":"Gaussian","width":"Absolute 0.05"},{"a":"lower 0.0","b":"upper 0.2","cls":"PowerLawCore","limits":"[0.0, inf]","line":22,"param":"core_radius","type":"Uniform","width":"Absolute 0.1"},{"a":"lower 0.0","b":"upper 8.0","cls":"PowerLawCore","limits":"[0.0, inf]","line":32,"param":"einstein_radius","type":"Uniform","width":"Relative 0.25"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"PowerLawCore","limits":"[-1.0, 1.0]","line":42,"param":"ell_comps_0","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"mean 0.0","b":"\u03c3 0.3","cls":"PowerLawCore","limits":"[-1.0, 1.0]","line":54,"param":"ell_comps_1","type":"TruncatedGaussian","width":"Absolute 0.2"},{"a":"lower 1.5","b":"upper 3.0","cls":"PowerLawCore","limits":"[1.0, 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value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  core_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 0.2\n    width_modifier:\n      type: Absolute\n      value: 0.1\n    limits:\n      lower: 0.0\n      upper: inf\n  einstein_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 8.0\n    width_modifier:\n      type: Relative\n      value: 0.25\n    limits:\n      lower: 0.0\n      upper: inf\n  ell_comps_0:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  ell_comps_1:\n    type: TruncatedGaussian\n    mean: 0.0\n    sigma: 0.3\n    lower_limit: -1.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: -1.0\n      upper: 1.0\n  slope:\n    type: Uniform\n    lower_limit: 1.5\n    upper_limit: 3.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: 1.0\n      upper: 3.0\nPowerLawCoreSph:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  core_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 0.2\n    width_modifier:\n      type: Absolute\n      value: 0.1\n    limits:\n      lower: 0.0\n      upper: inf\n  einstein_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 8.0\n    width_modifier:\n      type: Relative\n      value: 0.25\n    limits:\n      lower: 0.0\n      upper: inf\n  slope:\n    type: Uniform\n    lower_limit: 1.5\n    upper_limit: 3.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: 1.0\n      upper: 3.0\n","tooling":false,"top_keys":["PowerLawCore","PowerLawCoreSph"]},{"error":null,"keys":[{"c":"","k":"PowerLawMultipole","line":1},{"c":"","k":"PowerLawMultipole.m","line":2},{"c":"","k":"PowerLawMultipole.m.type","line":3},{"c":"","k":"PowerLawMultipole.m.value","line":4},{"c":"","k":"PowerLawMultipole.centre_0","line":5},{"c":"","k":"PowerLawMultipole.centre_0.type","line":6},{"c":"","k":"PowerLawMultipole.centre_0.mean","line":7},{"c":"","k":"PowerLawMultipole.centre_0.sigma","line":8},{"c":"","k":"PowerLawMultipole.centre_0.width_modifier","line":9},{"c":"","k":"PowerLawMultipole.centre_0.width_modifier.type","line":10},{"c":"","k":"PowerLawMultipole.centre_0.width_modifier.value","line":11},{"c":"","k":"PowerLawMultipole.centre_0.limits","line":12},{"c":"","k":"PowerLawMultipole.centre_0.limits.lower","line":13},{"c":"","k":"PowerLawMultipole.centre_0.limits.upper","line":14},{"c":"","k":"PowerLawMultipole.centre_1","line":15},{"c":"","k":"PowerLawMultipole.centre_1.type","line":16},{"c":"","k":"PowerLawMultipole.centre_1.mean","line":17},{"c":"","k":"PowerLawMultipole.centre_1.sigma","line":18},{"c":"","k":"PowerLawMultipole.centre_1.width_modifier","line":19},{"c":"","k":"PowerLawMultipole.centre_1.width_modifier.type","line":20},{"c":"","k":"PowerLawMultipole.centre_1.width_modifier.value","line":21},{"c":"","k":"PowerLawMultipole.centre_1.limits","line":22},{"c":"","k":"PowerLawMultipole.centre_1.limits.lower","line":23},{"c":"","k":"PowerLawMultipole.centre_1.limits.upper","line":24},{"c":"","k":"PowerLawMultipole.einstein_radius","line":25},{"c":"","k":"PowerLawMultipole.einstein_radius.type","line":26},{"c":"","k":"PowerLawMultipole.einstein_radius.lower_limit","line":27},{"c":"","k":"PowerLawMultipole.einstein_radius.upper_limit","line":28},{"c":"","k":"PowerLawMultipole.einstein_radius.width_modifier","line":29},{"c":"","k":"PowerLawMultipole.einstein_radius.width_modifier.type","line":30},{"c":"","k":"PowerLawMultipole.einstein_radius.width_modifier.value","line":31},{"c":"","k":"PowerLawMultipole.einstein_radius.limits","line":32},{"c":"","k":"PowerLawMultipole.einstein_radius.limits.lower","line":33},{"c":"","k":"PowerLawMultipole.einstein_radius.limits.upper","line":34},{"c":"","k":"PowerLawMultipole.slope","line":35},{"c":"","k":"PowerLawMultipole.slope.type","line":36},{"c":"","k":"PowerLawMultipole.slope.lower_limit","line":37},{"c":"","k":"PowerLawMultipole.slope.upper_limit","line":38},{"c":"","k":"PowerLawMultipole.slope.width_modifier","line":39},{"c":"","k":"PowerLawMultipole.slope.width_modifier.type","line":40},{"c":"","k":"PowerLawMultipole.slope.width_modifier.value","line":41},{"c":"","k":"PowerLawMultipole.slope.limits","line":42},{"c":"","k":"PowerLawMultipole.slope.limits.lower","line":43},{"c":"","k":"PowerLawMultipole.slope.limits.upper","line":44},{"c":"","k":"PowerLawMultipole.multipole_comps_0","line":45},{"c":"","k":"PowerLawMultipole.multipole_comps_0.type","line":46},{"c":"","k":"PowerLawMultipole.multipole_comps_0.lower_limit","line":47},{"c":"","k":"PowerLawMultipole.multipole_comps_0.upper_limit","line":48},{"c":"","k":"PowerLawMultipole.multipole_comps_0.width_modifier","line":49},{"c":"","k":"PowerLawMultipole.multipole_comps_0.width_modifier.type","line":50},{"c":"","k":"PowerLawMultipole.multipole_comps_0.width_modifier.value","line":51},{"c":"","k":"PowerLawMultipole.multipole_comps_0.limits","line":52},{"c":"","k":"PowerLawMultipole.multipole_comps_0.limits.lower","line":53},{"c":"","k":"PowerLawMultipole.multipole_comps_0.limits.upper","line":54},{"c":"","k":"PowerLawMultipole.multipole_comps_1","line":55},{"c":"","k":"PowerLawMultipole.multipole_comps_1.type","line":56},{"c":"","k":"PowerLawMultipole.multipole_comps_1.lower_limit","line":57},{"c":"","k":"PowerLawMultipole.multipole_comps_1.upper_limit","line":58},{"c":"","k":"PowerLawMultipole.multipole_comps_1.width_modifier","line":59},{"c":"","k":"PowerLawMultipole.multipole_comps_1.width_modifier.type","line":60},{"c":"","k":"PowerLawMultipole.multipole_comps_1.width_modifier.value","line":61},{"c":"","k":"PowerLawMultipole.multipole_comps_1.limits","line":62},{"c":"","k":"PowerLawMultipole.multipole_comps_1.limits.lower","line":63},{"c":"","k":"PowerLawMultipole.multipole_comps_1.limits.upper","line":64}],"lines":64,"path":"priors/mass/total/power_law_multipole.yaml","prior":true,"priors":[{"a":"value 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inf]","line":55,"param":"multipole_comps_1","type":"Uniform","width":"Absolute 0.05"}],"repo":"autolens_workspace","text":"PowerLawMultipole:\n  m:\n    type: Constant\n    value: 4\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  einstein_radius:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 8.0\n    width_modifier:\n      type: Relative\n      value: 0.25\n    limits:\n      lower: 0.0\n      upper: inf\n  slope:\n    type: Uniform\n    lower_limit: 1.5\n    upper_limit: 3.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: 1.0\n      upper: 3.0\n  multipole_comps_0:\n    type: Uniform\n    lower_limit: -0.1\n    upper_limit: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  multipole_comps_1:\n    type: Uniform\n    lower_limit: -0.1\n    upper_limit: 0.1\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf","tooling":false,"top_keys":["PowerLawMultipole"]},{"error":null,"keys":[{"c":"","k":"Delaunay","line":1},{"c":"","k":"Delaunay.areas_factor","line":2},{"c":"","k":"Delaunay.areas_factor.type","line":3},{"c":"","k":"Delaunay.areas_factor.value","line":4}],"lines":4,"path":"priors/mesh/delaunay.yaml","prior":true,"priors":[{"a":"value 0.5","b":"","cls":"Delaunay","limits":"","line":2,"param":"areas_factor","type":"Constant","width":""}],"repo":"autolens_workspace","text":"Delaunay:\n  areas_factor:\n    type: Constant\n    value: 0.5\n","tooling":false,"top_keys":["Delaunay"]},{"error":null,"keys":[{"c":"","k":"RectangularBilinearAdaptDensity","line":1},{"c":"","k":"RectangularBilinearAdaptDensity.shape_0","line":2},{"c":"","k":"RectangularBilinearAdaptDensity.shape_0.type","line":3},{"c":"","k":"RectangularBilinearAdaptDensity.shape_0.lower_limit","line":4},{"c":"","k":"RectangularBilinearAdaptDensity.shape_0.upper_limit","line":5},{"c":"","k":"RectangularBilinearAdaptDensity.shape_0.width_modifier","line":6},{"c":"","k":"RectangularBilinearAdaptDensity.shape_0.width_modifier.type","line":7},{"c":"","k":"RectangularBilinearAdaptDensity.shape_0.width_modifier.value","line":8},{"c":"","k":"RectangularBilinearAdaptDensity.shape_0.limits","line":9},{"c":"","k":"RectangularBilinearAdaptDensity.shape_0.limits.lower","line":10},{"c":"","k":"RectangularBilinearAdaptDensity.shape_0.limits.upper","line":11},{"c":"","k":"RectangularBilinearAdaptDensity.shape_1","line":12},{"c":"","k":"RectangularBilinearAdaptDensity.shape_1.type","line":13},{"c":"","k":"RectangularBilinearAdaptDensity.shape_1.lower_limit","line":14},{"c":"","k":"RectangularBilinearAdaptDensity.shape_1.upper_limit","line":15},{"c":"","k":"RectangularBilinearAdaptDensity.shape_1.width_modifier","line":16},{"c":"","k":"RectangularBilinearAdaptDensity.shape_1.width_modifier.type","line":17},{"c":"","k":"RectangularBilinearAdaptDensity.shape_1.width_modifier.value","line":18},{"c":"","k":"RectangularBilinearAdaptDensity.shape_1.limits","line":19},{"c":"","k":"RectangularBilinearAdaptDensity.shape_1.limits.lower","line":20},{"c":"","k":"RectangularBilinearAdaptDensity.shape_1.limits.upper","line":21}],"lines":21,"path":"priors/mesh/rectangular_bilinear_adapt_density.yaml","prior":true,"priors":[{"a":"lower 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type: Absolute\n      value: 8.0\n    limits:\n      lower: 3.0\n      upper: inf\n  weight_power:\n    type : Uniform\n    lower_limit: 0.0\n    upper_limit: 10.0\n    width_modifier:\n      type: Absolute\n      value: 2.0\n    limits:\n      lower: -100.0\n      upper: 100.0\n  weight_floor:\n    type: LogUniform\n    lower_limit: 0.00001\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n    limits:\n      lower: 0.0\n      upper: inf","tooling":false,"top_keys":["RectangularRTUAdaptImage"]},{"error":null,"keys":[{"c":"","k":"RectangularUniform","line":1},{"c":"","k":"RectangularUniform.shape_0","line":2},{"c":"","k":"RectangularUniform.shape_0.type","line":3},{"c":"","k":"RectangularUniform.shape_0.lower_limit","line":4},{"c":"","k":"RectangularUniform.shape_0.upper_limit","line":5},{"c":"","k":"RectangularUniform.shape_0.width_modifier","line":6},{"c":"","k":"RectangularUniform.shape_0.width_modifier.type","line":7},{"c":"","k":"RectangularUniform.shape_0.width_modifier.value","line":8},{"c":"","k":"RectangularUniform.shape_0.limits","line":9},{"c":"","k":"RectangularUniform.shape_0.limits.lower","line":10},{"c":"","k":"RectangularUniform.shape_0.limits.upper","line":11},{"c":"","k":"RectangularUniform.shape_1","line":12},{"c":"","k":"RectangularUniform.shape_1.type","line":13},{"c":"","k":"RectangularUniform.shape_1.lower_limit","line":14},{"c":"","k":"RectangularUniform.shape_1.upper_limit","line":15},{"c":"","k":"RectangularUniform.shape_1.width_modifier","line":16},{"c":"","k":"RectangularUniform.shape_1.width_modifier.type","line":17},{"c":"","k":"RectangularUniform.shape_1.width_modifier.value","line":18},{"c":"","k":"RectangularUniform.shape_1.limits","line":19},{"c":"","k":"RectangularUniform.shape_1.limits.lower","line":20},{"c":"","k":"RectangularUniform.shape_1.limits.upper","line":21}],"lines":21,"path":"priors/mesh/rectangular_uniform.yaml","prior":true,"priors":[{"a":"lower 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  width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\nPointFlux:\n  centre_0:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  centre_1:\n    type: Gaussian\n    mean: 0.0\n    sigma: 0.3\n    width_modifier:\n      type: Absolute\n      value: 0.05\n    limits:\n      lower: -inf\n      upper: inf\n  flux:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["Point","PointFlux"]},{"error":null,"keys":[{"c":"","k":"Adapt","line":1},{"c":"","k":"Adapt.inner_coefficient","line":2},{"c":"","k":"Adapt.inner_coefficient.type","line":3},{"c":"","k":"Adapt.inner_coefficient.lower_limit","line":4},{"c":"","k":"Adapt.inner_coefficient.upper_limit","line":5},{"c":"","k":"Adapt.inner_coefficient.width_modifier","line":6},{"c":"","k":"Adapt.inner_coefficient.width_modifier.type","line":7},{"c":"","k":"Adapt.inner_coefficient.width_modifier.value","line":8},{"c":"","k":"Adapt.inner_coefficient.limits","line":9},{"c":"","k":"Adapt.inner_coefficient.limits.lower","line":10},{"c":"","k":"Adapt.inner_coefficient.limits.upper","line":11},{"c":"","k":"Adapt.outer_coefficient","line":12},{"c":"","k":"Adapt.outer_coefficient.type","line":13},{"c":"","k":"Adapt.outer_coefficient.lower_limit","line":14},{"c":"","k":"Adapt.outer_coefficient.upper_limit","line":15},{"c":"","k":"Adapt.outer_coefficient.width_modifier","line":16},{"c":"","k":"Adapt.outer_coefficient.width_modifier.type","line":17},{"c":"","k":"Adapt.outer_coefficient.width_modifier.value","line":18},{"c":"","k":"Adapt.outer_coefficient.limits","line":19},{"c":"","k":"Adapt.outer_coefficient.limits.lower","line":20},{"c":"","k":"Adapt.outer_coefficient.limits.upper","line":21},{"c":"","k":"Adapt.signal_scale","line":22},{"c":"","k":"Adapt.signal_scale.type","line":23},{"c":"","k":"Adapt.signal_scale.lower_limit","line":24},{"c":"","k":"Adapt.signal_scale.upper_limit","line":25},{"c":"","k":"Adapt.signal_scale.width_modifier","line":26},{"c":"","k":"Adapt.signal_scale.width_modifier.type","line":27},{"c":"","k":"Adapt.signal_scale.width_modifier.value","line":28},{"c":"","k":"Adapt.signal_scale.limits","line":29},{"c":"","k":"Adapt.signal_scale.limits.lower","line":30},{"c":"","k":"Adapt.signal_scale.limits.upper","line":31}],"lines":31,"path":"priors/regularization/adapt.yaml","prior":true,"priors":[{"a":"lower 1e-06","b":"upper 1000000.0","cls":"Adapt","limits":"[0.0, inf]","line":2,"param":"inner_coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"Adapt","limits":"[0.0, inf]","line":12,"param":"outer_coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 1.0","cls":"Adapt","limits":"[0.0, inf]","line":22,"param":"signal_scale","type":"Uniform","width":"Relative 0.2"}],"repo":"autolens_workspace","text":"Adapt:\n  inner_coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  outer_coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  signal_scale:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf","tooling":false,"top_keys":["Adapt"]},{"error":null,"keys":[{"c":"","k":"AdaptSplit","line":1},{"c":"","k":"AdaptSplit.inner_coefficient","line":2},{"c":"","k":"AdaptSplit.inner_coefficient.type","line":3},{"c":"","k":"AdaptSplit.inner_coefficient.lower_limit","line":4},{"c":"","k":"AdaptSplit.inner_coefficient.upper_limit","line":5},{"c":"","k":"AdaptSplit.inner_coefficient.width_modifier","line":6},{"c":"","k":"AdaptSplit.inner_coefficient.width_modifier.type","line":7},{"c":"","k":"AdaptSplit.inner_coefficient.width_modifier.value","line":8},{"c":"","k":"AdaptSplit.inner_coefficient.limits","line":9},{"c":"","k":"AdaptSplit.inner_coefficient.limits.lower","line":10},{"c":"","k":"AdaptSplit.inner_coefficient.limits.upper","line":11},{"c":"","k":"AdaptSplit.outer_coefficient","line":12},{"c":"","k":"AdaptSplit.outer_coefficient.type","line":13},{"c":"","k":"AdaptSplit.outer_coefficient.lower_limit","line":14},{"c":"","k":"AdaptSplit.outer_coefficient.upper_limit","line":15},{"c":"","k":"AdaptSplit.outer_coefficient.width_modifier","line":16},{"c":"","k":"AdaptSplit.outer_coefficient.width_modifier.type","line":17},{"c":"","k":"AdaptSplit.outer_coefficient.width_modifier.value","line":18},{"c":"","k":"AdaptSplit.outer_coefficient.limits","line":19},{"c":"","k":"AdaptSplit.outer_coefficient.limits.lower","line":20},{"c":"","k":"AdaptSplit.outer_coefficient.limits.upper","line":21},{"c":"","k":"AdaptSplit.signal_scale","line":22},{"c":"","k":"AdaptSplit.signal_scale.type","line":23},{"c":"","k":"AdaptSplit.signal_scale.lower_limit","line":24},{"c":"","k":"AdaptSplit.signal_scale.upper_limit","line":25},{"c":"","k":"AdaptSplit.signal_scale.width_modifier","line":26},{"c":"","k":"AdaptSplit.signal_scale.width_modifier.type","line":27},{"c":"","k":"AdaptSplit.signal_scale.width_modifier.value","line":28},{"c":"","k":"AdaptSplit.signal_scale.limits","line":29},{"c":"","k":"AdaptSplit.signal_scale.limits.lower","line":30},{"c":"","k":"AdaptSplit.signal_scale.limits.upper","line":31}],"lines":31,"path":"priors/regularization/adapt_split.yaml","prior":true,"priors":[{"a":"lower 1e-06","b":"upper 1000000.0","cls":"AdaptSplit","limits":"[0.0, inf]","line":2,"param":"inner_coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"AdaptSplit","limits":"[0.0, inf]","line":12,"param":"outer_coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 1.0","cls":"AdaptSplit","limits":"[0.0, inf]","line":22,"param":"signal_scale","type":"Uniform","width":"Relative 0.2"}],"repo":"autolens_workspace","text":"AdaptSplit:\n  inner_coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  outer_coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  signal_scale:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["AdaptSplit"]},{"error":null,"keys":[{"c":"","k":"Constant","line":1},{"c":"","k":"Constant.coefficient","line":2},{"c":"","k":"Constant.coefficient.type","line":3},{"c":"","k":"Constant.coefficient.lower_limit","line":4},{"c":"","k":"Constant.coefficient.upper_limit","line":5},{"c":"","k":"Constant.coefficient.width_modifier","line":6},{"c":"","k":"Constant.coefficient.width_modifier.type","line":7},{"c":"","k":"Constant.coefficient.width_modifier.value","line":8},{"c":"","k":"Constant.coefficient.limits","line":9},{"c":"","k":"Constant.coefficient.limits.lower","line":10},{"c":"","k":"Constant.coefficient.limits.upper","line":11}],"lines":11,"path":"priors/regularization/constant.yaml","prior":true,"priors":[{"a":"lower 1e-06","b":"upper 1000000.0","cls":"Constant","limits":"[0.0, inf]","line":2,"param":"coefficient","type":"LogUniform","width":"Relative 0.5"}],"repo":"autolens_workspace","text":"Constant:\n  coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["Constant"]},{"error":null,"keys":[{"c":"","k":"ConstantSplit","line":1},{"c":"","k":"ConstantSplit.coefficient","line":2},{"c":"","k":"ConstantSplit.coefficient.type","line":3},{"c":"","k":"ConstantSplit.coefficient.lower_limit","line":4},{"c":"","k":"ConstantSplit.coefficient.upper_limit","line":5},{"c":"","k":"ConstantSplit.coefficient.width_modifier","line":6},{"c":"","k":"ConstantSplit.coefficient.width_modifier.type","line":7},{"c":"","k":"ConstantSplit.coefficient.width_modifier.value","line":8},{"c":"","k":"ConstantSplit.coefficient.limits","line":9},{"c":"","k":"ConstantSplit.coefficient.limits.lower","line":10},{"c":"","k":"ConstantSplit.coefficient.limits.upper","line":11}],"lines":11,"path":"priors/regularization/constant_split.yaml","prior":true,"priors":[{"a":"lower 1e-06","b":"upper 1000000.0","cls":"ConstantSplit","limits":"[0.0, inf]","line":2,"param":"coefficient","type":"LogUniform","width":"Relative 0.5"}],"repo":"autolens_workspace","text":"ConstantSplit:\n  coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["ConstantSplit"]},{"error":null,"keys":[{"c":"","k":"MaternAdaptKernel","line":1},{"c":"","k":"MaternAdaptKernel.scale","line":2},{"c":"","k":"MaternAdaptKernel.scale.type","line":3},{"c":"","k":"MaternAdaptKernel.scale.lower_limit","line":4},{"c":"","k":"MaternAdaptKernel.scale.upper_limit","line":5},{"c":"","k":"MaternAdaptKernel.scale.width_modifier","line":6},{"c":"","k":"MaternAdaptKernel.scale.width_modifier.type","line":7},{"c":"","k":"MaternAdaptKernel.scale.width_modifier.value","line":8},{"c":"","k":"MaternAdaptKernel.scale.limits","line":9},{"c":"","k":"MaternAdaptKernel.scale.limits.lower","line":10},{"c":"","k":"MaternAdaptKernel.scale.limits.upper","line":11},{"c":"","k":"MaternAdaptKernel.nu","line":12},{"c":"","k":"MaternAdaptKernel.nu.type","line":13},{"c":"","k":"MaternAdaptKernel.nu.lower_limit","line":14},{"c":"","k":"MaternAdaptKernel.nu.upper_limit","line":15},{"c":"","k":"MaternAdaptKernel.nu.width_modifier","line":16},{"c":"","k":"MaternAdaptKernel.nu.width_modifier.type","line":17},{"c":"","k":"MaternAdaptKernel.nu.width_modifier.value","line":18},{"c":"","k":"MaternAdaptKernel.nu.limits","line":19},{"c":"","k":"MaternAdaptKernel.nu.limits.lower","line":20},{"c":"","k":"MaternAdaptKernel.nu.limits.upper","line":21},{"c":"","k":"MaternAdaptKernel.inner_coefficient","line":22},{"c":"","k":"MaternAdaptKernel.inner_coefficient.type","line":23},{"c":"","k":"MaternAdaptKernel.inner_coefficient.lower_limit","line":24},{"c":"","k":"MaternAdaptKernel.inner_coefficient.upper_limit","line":25},{"c":"","k":"MaternAdaptKernel.inner_coefficient.width_modifier","line":26},{"c":"","k":"MaternAdaptKernel.inner_coefficient.width_modifier.type","line":27},{"c":"","k":"MaternAdaptKernel.inner_coefficient.width_modifier.value","line":28},{"c":"","k":"MaternAdaptKernel.inner_coefficient.limits","line":29},{"c":"","k":"MaternAdaptKernel.inner_coefficient.limits.lower","line":30},{"c":"","k":"MaternAdaptKernel.inner_coefficient.limits.upper","line":31},{"c":"","k":"MaternAdaptKernel.outer_coefficient","line":32},{"c":"","k":"MaternAdaptKernel.outer_coefficient.type","line":33},{"c":"","k":"MaternAdaptKernel.outer_coefficient.lower_limit","line":34},{"c":"","k":"MaternAdaptKernel.outer_coefficient.upper_limit","line":35},{"c":"","k":"MaternAdaptKernel.outer_coefficient.width_modifier","line":36},{"c":"","k":"MaternAdaptKernel.outer_coefficient.width_modifier.type","line":37},{"c":"","k":"MaternAdaptKernel.outer_coefficient.width_modifier.value","line":38},{"c":"","k":"MaternAdaptKernel.outer_coefficient.limits","line":39},{"c":"","k":"MaternAdaptKernel.outer_coefficient.limits.lower","line":40},{"c":"","k":"MaternAdaptKernel.outer_coefficient.limits.upper","line":41},{"c":"","k":"MaternAdaptKernel.signal_scale","line":42},{"c":"","k":"MaternAdaptKernel.signal_scale.type","line":43},{"c":"","k":"MaternAdaptKernel.signal_scale.lower_limit","line":44},{"c":"","k":"MaternAdaptKernel.signal_scale.upper_limit","line":45},{"c":"","k":"MaternAdaptKernel.signal_scale.width_modifier","line":46},{"c":"","k":"MaternAdaptKernel.signal_scale.width_modifier.type","line":47},{"c":"","k":"MaternAdaptKernel.signal_scale.width_modifier.value","line":48},{"c":"","k":"MaternAdaptKernel.signal_scale.limits","line":49},{"c":"","k":"MaternAdaptKernel.signal_scale.limits.lower","line":50},{"c":"","k":"MaternAdaptKernel.signal_scale.limits.upper","line":51}],"lines":51,"path":"priors/regularization/matern_adapt_kernel.yaml","prior":true,"priors":[{"a":"lower 1e-06","b":"upper 1000000.0","cls":"MaternAdaptKernel","limits":"[0.0, inf]","line":2,"param":"scale","type":"LogUniform","width":"Relative 0.2"},{"a":"lower 0.5","b":"upper 5.5","cls":"MaternAdaptKernel","limits":"[0.0, inf]","line":12,"param":"nu","type":"Uniform","width":"Relative 0.2"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"MaternAdaptKernel","limits":"[0.0, inf]","line":22,"param":"inner_coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"MaternAdaptKernel","limits":"[0.0, inf]","line":32,"param":"outer_coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 1.0","cls":"MaternAdaptKernel","limits":"[0.0, inf]","line":42,"param":"signal_scale","type":"Uniform","width":"Relative 0.2"}],"repo":"autolens_workspace","text":"MaternAdaptKernel:\n  scale:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  nu:\n    type: Uniform\n    lower_limit: 0.5\n    upper_limit: 5.5\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  inner_coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  outer_coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  signal_scale:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf","tooling":false,"top_keys":["MaternAdaptKernel"]},{"error":null,"keys":[{"c":"","k":"MaternAdaptKernel","line":1},{"c":"","k":"MaternAdaptKernel.scale","line":2},{"c":"","k":"MaternAdaptKernel.scale.type","line":3},{"c":"","k":"MaternAdaptKernel.scale.lower_limit","line":4},{"c":"","k":"MaternAdaptKernel.scale.upper_limit","line":5},{"c":"","k":"MaternAdaptKernel.scale.width_modifier","line":6},{"c":"","k":"MaternAdaptKernel.scale.width_modifier.type","line":7},{"c":"","k":"MaternAdaptKernel.scale.width_modifier.value","line":8},{"c":"","k":"MaternAdaptKernel.scale.limits","line":9},{"c":"","k":"MaternAdaptKernel.scale.limits.lower","line":10},{"c":"","k":"MaternAdaptKernel.scale.limits.upper","line":11},{"c":"","k":"MaternAdaptKernel.nu","line":12},{"c":"","k":"MaternAdaptKernel.nu.type","line":13},{"c":"","k":"MaternAdaptKernel.nu.lower_limit","line":14},{"c":"","k":"MaternAdaptKernel.nu.upper_limit","line":15},{"c":"","k":"MaternAdaptKernel.nu.width_modifier","line":16},{"c":"","k":"MaternAdaptKernel.nu.width_modifier.type","line":17},{"c":"","k":"MaternAdaptKernel.nu.width_modifier.value","line":18},{"c":"","k":"MaternAdaptKernel.nu.limits","line":19},{"c":"","k":"MaternAdaptKernel.nu.limits.lower","line":20},{"c":"","k":"MaternAdaptKernel.nu.limits.upper","line":21},{"c":"","k":"MaternAdaptKernel.rho","line":22},{"c":"","k":"MaternAdaptKernel.rho.type","line":23},{"c":"","k":"MaternAdaptKernel.rho.lower_limit","line":24},{"c":"","k":"MaternAdaptKernel.rho.upper_limit","line":25},{"c":"","k":"MaternAdaptKernel.rho.width_modifier","line":26},{"c":"","k":"MaternAdaptKernel.rho.width_modifier.type","line":27},{"c":"","k":"MaternAdaptKernel.rho.width_modifier.value","line":28},{"c":"","k":"MaternAdaptKernel.rho.limits","line":29},{"c":"","k":"MaternAdaptKernel.rho.limits.lower","line":30},{"c":"","k":"MaternAdaptKernel.rho.limits.upper","line":31}],"lines":31,"path":"priors/regularization/matern_adapt_kernel_rho.yaml","prior":true,"priors":[{"a":"lower 1e-06","b":"upper 1000000.0","cls":"MaternAdaptKernel","limits":"[0.0, inf]","line":2,"param":"scale","type":"LogUniform","width":"Relative 0.2"},{"a":"lower 0.5","b":"upper 5.5","cls":"MaternAdaptKernel","limits":"[0.0, inf]","line":12,"param":"nu","type":"Uniform","width":"Relative 0.2"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"MaternAdaptKernel","limits":"[0.0, inf]","line":22,"param":"rho","type":"Uniform","width":"Relative 0.2"}],"repo":"autolens_workspace","text":"MaternAdaptKernel:\n  scale:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  nu:\n    type: Uniform\n    lower_limit: 0.5\n    upper_limit: 5.5\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  rho:\n    type: Uniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf","tooling":false,"top_keys":["MaternAdaptKernel"]},{"error":null,"keys":[{"c":"","k":"MaternKernel","line":1},{"c":"","k":"MaternKernel.coefficient","line":2},{"c":"","k":"MaternKernel.coefficient.type","line":3},{"c":"","k":"MaternKernel.coefficient.lower_limit","line":4},{"c":"","k":"MaternKernel.coefficient.upper_limit","line":5},{"c":"","k":"MaternKernel.coefficient.width_modifier","line":6},{"c":"","k":"MaternKernel.coefficient.width_modifier.type","line":7},{"c":"","k":"MaternKernel.coefficient.width_modifier.value","line":8},{"c":"","k":"MaternKernel.coefficient.limits","line":9},{"c":"","k":"MaternKernel.coefficient.limits.lower","line":10},{"c":"","k":"MaternKernel.coefficient.limits.upper","line":11},{"c":"","k":"MaternKernel.scale","line":12},{"c":"","k":"MaternKernel.scale.type","line":13},{"c":"","k":"MaternKernel.scale.lower_limit","line":14},{"c":"","k":"MaternKernel.scale.upper_limit","line":15},{"c":"","k":"MaternKernel.scale.width_modifier","line":16},{"c":"","k":"MaternKernel.scale.width_modifier.type","line":17},{"c":"","k":"MaternKernel.scale.width_modifier.value","line":18},{"c":"","k":"MaternKernel.scale.limits","line":19},{"c":"","k":"MaternKernel.scale.limits.lower","line":20},{"c":"","k":"MaternKernel.scale.limits.upper","line":21},{"c":"","k":"MaternKernel.nu","line":22},{"c":"","k":"MaternKernel.nu.type","line":23},{"c":"","k":"MaternKernel.nu.lower_limit","line":24},{"c":"","k":"MaternKernel.nu.upper_limit","line":25},{"c":"","k":"MaternKernel.nu.width_modifier","line":26},{"c":"","k":"MaternKernel.nu.width_modifier.type","line":27},{"c":"","k":"MaternKernel.nu.width_modifier.value","line":28},{"c":"","k":"MaternKernel.nu.limits","line":29},{"c":"","k":"MaternKernel.nu.limits.lower","line":30},{"c":"","k":"MaternKernel.nu.limits.upper","line":31}],"lines":31,"path":"priors/regularization/matern_kernel.yaml","prior":true,"priors":[{"a":"lower 1e-06","b":"upper 1000000.0","cls":"MaternKernel","limits":"[0.0, inf]","line":2,"param":"coefficient","type":"LogUniform","width":"Relative 0.5"},{"a":"lower 1e-06","b":"upper 1000000.0","cls":"MaternKernel","limits":"[0.0, inf]","line":12,"param":"scale","type":"LogUniform","width":"Relative 0.2"},{"a":"lower 0.5","b":"upper 5.5","cls":"MaternKernel","limits":"[0.0, inf]","line":22,"param":"nu","type":"Uniform","width":"Relative 0.2"}],"repo":"autolens_workspace","text":"MaternKernel:\n  coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  scale:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n  nu:\n    type: Uniform\n    lower_limit: 0.5\n    upper_limit: 5.5\n    width_modifier:\n      type: Relative\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["MaternKernel"]},{"error":null,"keys":[{"c":"","k":"Zeroth","line":1},{"c":"","k":"Zeroth.coefficient","line":2},{"c":"","k":"Zeroth.coefficient.type","line":3},{"c":"","k":"Zeroth.coefficient.lower_limit","line":4},{"c":"","k":"Zeroth.coefficient.upper_limit","line":5},{"c":"","k":"Zeroth.coefficient.width_modifier","line":6},{"c":"","k":"Zeroth.coefficient.width_modifier.type","line":7},{"c":"","k":"Zeroth.coefficient.width_modifier.value","line":8},{"c":"","k":"Zeroth.coefficient.limits","line":9},{"c":"","k":"Zeroth.coefficient.limits.lower","line":10},{"c":"","k":"Zeroth.coefficient.limits.upper","line":11}],"lines":11,"path":"priors/regularization/zeroth.yaml","prior":true,"priors":[{"a":"lower 1e-06","b":"upper 1000000.0","cls":"Zeroth","limits":"[0.0, inf]","line":2,"param":"coefficient","type":"LogUniform","width":"Relative 0.5"}],"repo":"autolens_workspace","text":"Zeroth:\n  coefficient:\n    type: LogUniform\n    lower_limit: 1.0e-06\n    upper_limit: 1000000.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["Zeroth"]},{"error":null,"keys":[{"c":"","k":"general","line":1},{"c":"The matploblib backend used for visualization. `default` uses the system default, can specifiy specific backend (e.g. TKAgg, Qt5Agg, WXAgg).","k":"general.backend","line":2},{"c":"The `origin` input of `imshow`, determining if pixel values are ascending or descending on the y-axis.","k":"general.imshow_origin","line":3},{"c":"If negative values are being plotted on a log10 scale, values below this value are rounded up to it (e.g. to remove negative values).","k":"general.log10_min_value","line":4},{"c":"If positive values are being plotted on a log10 scale, values above this value are rounded down to it (e.g. to prevent white blobs).","k":"general.log10_max_value","line":5},{"c":"If True, plots of data structures with a mask automatically zoom in the masked region.","k":"general.zoom_around_mask","line":6},{"c":"Default output format: \"show\" displays the figure interactively via plt.show(), \"png\"/\"pdf\"/etc. saves to file.","k":"general.output_format","line":7},{"c":"","k":"inversion","line":8},{"c":"Plots of an Inversion's reconstruction use the reconstructed data's bright value multiplied by this factor.","k":"inversion.reconstruction_vmax_factor","line":9},{"c":"","k":"zoom","line":10},{"c":"When the plane-image of a parametric source is plotted, the percentage of its maximum flux that is used to zoom in the plot on its brightest galaxy regions.","k":"zoom.plane_percent","line":11},{"c":"When the plane-image of an inversion is plotted, the percentage of its maximum flux that is used to zoom in the plot on its brightest galaxy regions.","k":"zoom.inversion_percent","line":12},{"c":"The shape of a subplots for figures with an input number of subplots (e.g. for a figure with 4 subplots, the shape is (2, 2)).","k":"subplot_shape","line":13},{"c":"The shape of subplots for a figure with 1 subplot.","k":"subplot_shape.1","line":14},{"c":"The shape of subplots for a figure with 2 subplots.","k":"subplot_shape.2","line":15},{"c":"The shape of subplots for a figure with 4 (or less than the above value) of subplots.","k":"subplot_shape.4","line":16},{"c":"The shape of subplots for a figure with 6 (or less than the above value) of subplots.","k":"subplot_shape.6","line":17},{"c":"The shape of subplots for a figure with 9 (or less than the above value) of subplots.","k":"subplot_shape.9","line":18},{"c":"The shape of subplots for a figure with 12 (or less than the above value) of subplots.","k":"subplot_shape.12","line":19},{"c":"The shape of subplots for a figure with 16 (or less than the above value) of subplots.","k":"subplot_shape.16","line":20},{"c":"The shape of subplots for a figure with 20 (or less than the above value) of subplots.","k":"subplot_shape.20","line":21},{"c":"The shape of subplots for a figure with 36 (or less than the above value) of subplots.","k":"subplot_shape.36","line":22},{"c":"The shape of subplots for a figure with 49 (or less than the above value) of subplots.","k":"subplot_shape.49","line":23},{"c":"The shape of subplots for a figure with 64 (or less than the above value) of subplots.","k":"subplot_shape.64","line":24},{"c":"The shape of subplots for a figure with 81 (or less than the above value) of subplots.","k":"subplot_shape.81","line":25},{"c":"The shape of subplots for a figure with 100 (or less than the above value) of subplots.","k":"subplot_shape.100","line":26},{"c":"The factors by which the subplot_shape is multiplied to determine the figsize of a subplot (e.g. if the subplot_shape is (2,2), the figsize will be (2*6, 2*6).","k":"subplot_shape_to_figsize_factor","line":27},{"c":"","k":"units","line":28},{"c":"Whether to plot spatial coordinates in scaled units computed via the pixel_scale (e.g. arc-seconds) or pixel units by default.","k":"units.use_scaled","line":29},{"c":"The string or latex unit label used for the colorbar of the image, for example electrons per second.","k":"units.cb_unit","line":30},{"c":"The symbol used when plotting spatial coordinates computed via the pixel_scale (e.g. for Astronomy data this is arc-seconds).","k":"units.scaled_symbol","line":31},{"c":"The symbol used when plotting spatial coordinates in unscaled pixel units.","k":"units.unscaled_symbol","line":32},{"c":"Default colormap for 2D plots. Use any matplotlib name to override (e.g. jet, viridis).","k":"colormap","line":33},{"c":"","k":"ticks","line":34},{"c":"Fraction of half-extent used for 2D tick positions (< 1.0 pulls ticks inward from edges).","k":"ticks.extent_factor_2d","line":35},{"c":"Number of ticks on each spatial axis of 2D plots.","k":"ticks.number_of_ticks_2d","line":36},{"c":"","k":"contour","line":37},{"c":"Number of contour levels drawn over log10 (and explicit linear) plots.","k":"contour.total_contours","line":38},{"c":"Whether to label each contour line with its value.","k":"contour.include_values","line":39},{"c":"","k":"colorbar","line":40},{"c":"Fraction of original axes to use for the colorbar.","k":"colorbar.fraction","line":41},{"c":"Padding between colorbar and axes.","k":"colorbar.pad","line":42},{"c":"Rotation of colorbar tick labels in degrees.","k":"colorbar.labelrotation","line":43},{"c":"Font size of colorbar tick labels for single-panel figures.","k":"colorbar.labelsize","line":44},{"c":"Font size of colorbar tick labels for subplot panels.","k":"colorbar.labelsize_subplot","line":45}],"lines":45,"path":"visualize/general.yaml","prior":false,"priors":[],"repo":"autolens_workspace","text":"general:\n  backend: default                  # The matploblib backend used for visualization. `default` uses the system default, can specifiy specific backend (e.g. TKAgg, Qt5Agg, WXAgg).\n  imshow_origin: upper                  # The `origin` input of `imshow`, determining if pixel values are ascending or descending on the y-axis.\n  log10_min_value: 1.0e-4               # If negative values are being plotted on a log10 scale, values below this value are rounded up to it (e.g. to remove negative values).\n  log10_max_value: 1.0e99               # If positive values are being plotted on a log10 scale, values above this value are rounded down to it (e.g. to prevent white blobs).\n  zoom_around_mask: true                # If True, plots of data structures with a mask automatically zoom in the masked region.\n  output_format: show                   # Default output format: \"show\" displays the figure interactively via plt.show(), \"png\"/\"pdf\"/etc. saves to file.\ninversion:\n  reconstruction_vmax_factor: 0.5   # Plots of an Inversion's reconstruction use the reconstructed data's bright value multiplied by this factor.\nzoom:\n  plane_percent: 0.01               # When the plane-image of a parametric source is plotted, the percentage of its maximum flux that is used to zoom in the plot on its brightest galaxy regions.\n  inversion_percent: 0.05           # When the plane-image of an inversion is plotted, the percentage of its maximum flux that is used to zoom in the plot on its brightest galaxy regions.\nsubplot_shape:                      # The shape of a subplots for figures with an input number of subplots (e.g. for a figure with 4 subplots, the shape is (2, 2)).\n  1: (1, 1)                         # The shape of subplots for a figure with 1 subplot.\n  2: (1, 2)                         # The shape of subplots for a figure with 2 subplots.\n  4: (2, 2)                         # The shape of subplots for a figure with 4 (or less than the above value) of subplots.\n  6: (2, 3)                         # The shape of subplots for a figure with 6 (or less than the above value) of subplots.\n  9: (3, 3)                         # The shape of subplots for a figure with 9 (or less than the above value) of subplots.\n  12: (3, 4)                        # The shape of subplots for a figure with 12 (or less than the above value) of subplots.\n  16: (4, 4)                        # The shape of subplots for a figure with 16 (or less than the above value) of subplots.\n  20: (4, 5)                        # The shape of subplots for a figure with 20 (or less than the above value) of subplots.\n  36: (6, 6)                        # The shape of subplots for a figure with 36 (or less than the above value) of subplots.\n  49: (7, 7)                        # The shape of subplots for a figure with 49 (or less than the above value) of subplots.\n  64: (8, 8)                        # The shape of subplots for a figure with 64 (or less than the above value) of subplots.\n  81: (9, 9)                        # The shape of subplots for a figure with 81 (or less than the above value) of subplots.\n  100: (10, 10)                     # The shape of subplots for a figure with 100 (or less than the above value) of subplots.\nsubplot_shape_to_figsize_factor: (6, 6) # The factors by which the subplot_shape is multiplied to determine the figsize of a subplot (e.g. if the subplot_shape is (2,2), the figsize will be (2*6, 2*6).\nunits:\n  use_scaled: true                  # Whether to plot spatial coordinates in scaled units computed via the pixel_scale (e.g. arc-seconds) or pixel units by default.\n  cb_unit: $\\,\\,\\mathrm{e^{-}}\\,\\mathrm{s^{-1}}$ # The string or latex unit label used for the colorbar of the image, for example electrons per second.\n  scaled_symbol: '\"'                    # The symbol used when plotting spatial coordinates computed via the pixel_scale (e.g. for Astronomy data this is arc-seconds).\n  unscaled_symbol: pix                  # The symbol used when plotting spatial coordinates in unscaled pixel units.\ncolormap: autoarray               # Default colormap for 2D plots. Use any matplotlib name to override (e.g. jet, viridis).\nticks:\n  extent_factor_2d: 0.75          # Fraction of half-extent used for 2D tick positions (< 1.0 pulls ticks inward from edges).\n  number_of_ticks_2d: 3           # Number of ticks on each spatial axis of 2D plots.\ncontour:\n  total_contours: 10              # Number of contour levels drawn over log10 (and explicit linear) plots.\n  include_values: false           # Whether to label each contour line with its value.\ncolorbar:\n  fraction: 0.047                 # Fraction of original axes to use for the colorbar.\n  pad: 0.01                       # Padding between colorbar and axes.\n  labelrotation: 90               # Rotation of colorbar tick labels in degrees.\n  labelsize: 16                   # Font size of colorbar tick labels for single-panel figures.\n  labelsize_subplot: 16           # Font size of colorbar tick labels for subplot panels.","tooling":false,"top_keys":["general","inversion","zoom","subplot_shape","subplot_shape_to_figsize_factor","units","colormap","ticks","contour","colorbar"]},{"error":null,"keys":[{"c":"Output format of all subplots, can be png, pdf or both (e.g. [png, pdf])","k":"subplot_format","line":15},{"c":"If true, output .fits files are zoomed in on the center of the unmasked region image, saving hard-disk space.","k":"fits_are_zoomed","line":16},{"c":"Settings for plots of all datasets (e.g. ImagingPlotter, InterferometerPlotter).","k":"dataset","line":18},{"c":"Plot subplot containing all dataset quantities (e.g. the data, noise-map, etc.)?","k":"dataset.subplot_dataset","line":19},{"c":"Settings for plots with resampling image-positions on (e.g. the image).","k":"positions","line":21},{"c":"","k":"positions.image_with_positions","line":22},{"c":"Settings for plots of all fits (e.g. FitImagingPlotter, FitInterferometerPlotter).","k":"fit","line":24},{"c":"Plot subplot of all fit quantities for any dataset (e.g. the model data, residual-map, etc.)?","k":"fit.subplot_fit","line":25},{"c":"Plot subplot of all fit quantities for any dataset using log10 color maps (e.g. the model data, residual-map, etc.)?","k":"fit.subplot_fit_log10","line":26},{"c":"Plot subplot of the model-image, subtracted image and other quantities of each plane?","k":"fit.subplot_of_planes","line":27},{"c":"Plot subplot of the image of each plane in the model?","k":"fit.subplot_galaxies_images","line":28},{"c":"Output a .fits file containing the fit model data, residual map, normalized residual map and chi-squared?","k":"fit.fits_fit","line":29},{"c":"Output a .fits file containing the images (e.g. without PSF convolution) of every galaxy?","k":"fit.fits_galaxy_images","line":30},{"c":"Output a .fits file containing the model images (e.g. with PSF convolution) of every galaxy?","k":"fit.fits_model_galaxy_images","line":31},{"c":"Settings for plots of fits to imaging datasets (e.g. FitImagingPlotter).","k":"fit_imaging","line":33},{"c":"Settings for plots of tracers (e.g. TracerPlotter).","k":"tracer","line":35},{"c":"Plot subplot of all quantities in each tracer (e.g. images, convergence)?","k":"tracer.subplot_tracer","line":36},{"c":"Plot subplot of the image of each plane in the tracer?","k":"tracer.subplot_galaxies_images","line":37},{"c":"Output tracer.fits file of tracer's convergence, potential, deflections_y and deflections_x?","k":"tracer.fits_tracer","line":38},{"c":"Output source_plane_images.fits file of the source-plane image (light profiles only) of each galaxy in the tracer?","k":"tracer.fits_source_plane_images","line":39},{"c":"The shape of the source-plane image output in the fits_source_plane_images.fits file.","k":"tracer.fits_source_plane_shape","line":40},{"c":"Settings for plots of inversions (e.g. InversionPlotter). # Settings for plots of inversions (e.g. InversionPlotter).","k":"inversion","line":42},{"c":"Plot subplot of all quantities in each inversion (e.g. reconstrucuted image, reconstruction)?","k":"inversion.subplot_inversion","line":43},{"c":"Plot subplot of the image-to-source pixels mappings of each pixelization?","k":"inversion.subplot_mappings","line":44},{"c":"output source_plane_reconstruction_0.csv containing the source-plane mesh y, x, reconstruction and noise map values.","k":"inversion.csv_reconstruction","line":45},{"c":"Settings for plots of adapt images used by adaptive pixelizations.","k":"adapt","line":47},{"c":"Plot subplot showing each adapt image used for adaptive pixelization?","k":"adapt.subplot_adapt_images","line":48},{"c":"Output a .fits file containing the adapt images used for adaptive pixelization?","k":"adapt.fits_adapt_images","line":49},{"c":"Settings for plots of fits to interferometer datasets (e.g. FitInterferometerPlotter).","k":"fit_interferometer","line":51},{"c":"Plot subplot of the dirty-images of all interferometer datasets?","k":"fit_interferometer.subplot_fit_dirty_images","line":52},{"c":"Plot subplot of the real-space images of all interferometer datasets?","k":"fit_interferometer.subplot_fit_real_space","line":53},{"c":"output dirty_images.fits showing the dirty image, noise-map, model-data, resiual-map, normalized residual map and chi-squared map?","k":"fit_interferometer.fits_dirty_images","line":54},{"c":"Settings for plots of point source datasets (e.g. PointDatasetPlotter).","k":"point_dataset","line":56},{"c":"Plot subplot containing all dataset quantities (e.g. the data, noise-map, etc.)?","k":"point_dataset.subplot_dataset","line":57},{"c":"Settings for plots of fits to point source datasets (e.g. FitPointDatasetPlotter).","k":"fit_point_dataset","line":59},{"c":"Settings for plots of weak lensing shear catalogues (e.g. PlotterWeak).","k":"weak_dataset","line":61},{"c":"Plot subplot containing all dataset quantities (e.g. the shear field, noise-map, etc.)?","k":"weak_dataset.subplot_dataset","line":62},{"c":"Settings for plots of fits to weak lensing shear catalogues (e.g. PlotterWeak).","k":"fit_weak","line":64},{"c":"Settings for plots of ellipse fitting fits (e.g. FitEllipse)","k":"fit_ellipse","line":66},{"c":"Plot the data of the ellipse fit?","k":"fit_ellipse.data","line":67},{"c":"Plot the data without the black data ellipses, which obscure noisy data?","k":"fit_ellipse.data_no_ellipse","line":68},{"c":"Settings for plots of galaxies (e.g. GalaxiesPlotter).","k":"galaxies","line":70},{"c":"Plot subplot of all quantities in each galaxies group (e.g. images, convergence)?","k":"galaxies.subplot_galaxies","line":71},{"c":"Plot subplot of the image of each galaxy in the model?","k":"galaxies.subplot_galaxy_images","line":72}],"lines":72,"path":"visualize/plots.yaml","prior":false,"priors":[],"repo":"autolens_workspace","text":"# The `plots` section customizes every image that is output to hard-disk during a model-fit.\n\n# For example, if `plots: fit: subplot_fit=True``, the ``subplot_fit.png`` subplot file will\n# be plotted every time visualization is performed.\n\n# One setting is important for inspecting results via the dataset after a fit is complete:\n\n# -`fits_adapt_images`, This outputs `adapt_images.fits` which the database functionality may use to reperform fits.\n\n# It can be disabled to save on hard-disk space but will lead to certain database functionality being disabled.\n\n# The dataset itself is always output as `dataset.fits` to the `image` folder of every fit, and is not controlled\n# by any setting here.\n\nsubplot_format: [png]                      # Output format of all subplots, can be png, pdf or both (e.g. [png, pdf])\nfits_are_zoomed: false                     # If true, output .fits files are zoomed in on the center of the unmasked region image, saving hard-disk space.\n\ndataset:                                   # Settings for plots of all datasets (e.g. ImagingPlotter, InterferometerPlotter).\n  subplot_dataset: true                    # Plot subplot containing all dataset quantities (e.g. the data, noise-map, etc.)?\n\npositions:                                 # Settings for plots with resampling image-positions on (e.g. the image).\n  image_with_positions: true\n\nfit:                                       # Settings for plots of all fits (e.g. FitImagingPlotter, FitInterferometerPlotter).\n  subplot_fit: true                        # Plot subplot of all fit quantities for any dataset (e.g. the model data, residual-map, etc.)?\n  subplot_fit_log10: false                  # Plot subplot of all fit quantities for any dataset using log10 color maps (e.g. the model data, residual-map, etc.)?\n  subplot_of_planes: false                 # Plot subplot of the model-image, subtracted image and other quantities of each plane?\n  subplot_galaxies_images: false           # Plot subplot of the image of each plane in the model?\n  fits_fit: true                           # Output a .fits file containing the fit model data, residual map, normalized residual map and chi-squared?\n  fits_galaxy_images : true                # Output a .fits file containing the images (e.g. without PSF convolution) of every galaxy?\n  fits_model_galaxy_images : true          # Output a .fits file containing the model images (e.g. with PSF convolution) of every galaxy?\n\nfit_imaging: {}                            # Settings for plots of fits to imaging datasets (e.g. FitImagingPlotter).\n\ntracer:                                    # Settings for plots of tracers (e.g. TracerPlotter).\n  subplot_tracer: true                     # Plot subplot of all quantities in each tracer (e.g. images, convergence)?\n  subplot_galaxies_images: false           # Plot subplot of the image of each plane in the tracer?\n  fits_tracer: true                        # Output tracer.fits file of tracer's convergence, potential, deflections_y and deflections_x?\n  fits_source_plane_images: true           # Output source_plane_images.fits file of the source-plane image (light profiles only) of each galaxy in the tracer?\n  fits_source_plane_shape: (100, 100)      # The shape of the source-plane image output in the fits_source_plane_images.fits file.\n\ninversion:                                 # Settings for plots of inversions (e.g. InversionPlotter).                          # Settings for plots of inversions (e.g. InversionPlotter).\n  subplot_inversion: true                  # Plot subplot of all quantities in each inversion (e.g. reconstrucuted image, reconstruction)?\n  subplot_mappings: false                  # Plot subplot of the image-to-source pixels mappings of each pixelization?\n  csv_reconstruction: true                 # output source_plane_reconstruction_0.csv containing the source-plane mesh y, x, reconstruction and noise map values.\n\nadapt:                                     # Settings for plots of adapt images used by adaptive pixelizations.\n  subplot_adapt_images: true               # Plot subplot showing each adapt image used for adaptive pixelization?\n  fits_adapt_images: true                  # Output a .fits file containing the adapt images used for adaptive pixelization?\n\nfit_interferometer:                        # Settings for plots of fits to interferometer datasets (e.g. FitInterferometerPlotter).\n  subplot_fit_dirty_images: false          # Plot subplot of the dirty-images of all interferometer datasets?\n  subplot_fit_real_space: false            # Plot subplot of the real-space images of all interferometer datasets?\n  fits_dirty_images: true                  # output dirty_images.fits showing the dirty image, noise-map, model-data, resiual-map, normalized residual map and chi-squared map?\n\npoint_dataset:                             # Settings for plots of point source datasets (e.g. PointDatasetPlotter).\n  subplot_dataset: true                    # Plot subplot containing all dataset quantities (e.g. the data, noise-map, etc.)?\n\nfit_point_dataset: {}                      # Settings for plots of fits to point source datasets (e.g. FitPointDatasetPlotter).\n\nweak_dataset:                              # Settings for plots of weak lensing shear catalogues (e.g. PlotterWeak).\n  subplot_dataset: true                    # Plot subplot containing all dataset quantities (e.g. the shear field, noise-map, etc.)?\n\nfit_weak: {}                               # Settings for plots of fits to weak lensing shear catalogues (e.g. PlotterWeak).\n\nfit_ellipse:                               # Settings for plots of ellipse fitting fits (e.g. FitEllipse)\n  data : true                              # Plot the data of the ellipse fit?\n  data_no_ellipse: true                    # Plot the data without the black data ellipses, which obscure noisy data?\n\ngalaxies:                                  # Settings for plots of galaxies (e.g. GalaxiesPlotter).\n  subplot_galaxies: false                  # Plot subplot of all quantities in each galaxies group (e.g. images, convergence)?\n  subplot_galaxy_images: false             # Plot subplot of the image of each galaxy in the model?\n","tooling":false,"top_keys":["subplot_format","fits_are_zoomed","dataset","positions","fit","fit_imaging","tracer","inversion","adapt","fit_interferometer","point_dataset","fit_point_dataset","weak_dataset","fit_weak","fit_ellipse","galaxies"]},{"error":null,"keys":[{"c":"","k":"nest","line":1},{"c":"Output corner figure (using anestetic) during a non-linear search fit?","k":"nest.corner_anesthetic","line":2},{"c":"","k":"mcmc","line":3},{"c":"Output corner figure (using corner.py) during a non-linear search fit?","k":"mcmc.corner_cornerpy","line":4},{"c":"","k":"mle","line":5},{"c":"Output a subplot of the best-fit parameters of the model?","k":"mle.subplot_parameters","line":6},{"c":"Output a plot of the log likelihood versus iteration number?","k":"mle.log_likelihood_vs_iteration","line":7},{"c":"Output the global-best figure-of-merit trace (auto-convergence gradient searches)?","k":"mle.figure_of_merit_vs_iteration","line":8}],"lines":8,"path":"visualize/plots_search.yaml","prior":false,"priors":[],"repo":"autolens_workspace","text":"nest:\n  corner_anesthetic: true   # Output corner figure (using anestetic) during a non-linear search fit?\nmcmc:\n  corner_cornerpy: true     # Output corner figure (using corner.py) during a non-linear search fit?\nmle:\n  subplot_parameters: true   # Output a subplot of the best-fit parameters of the model?\n  log_likelihood_vs_iteration: true  # Output a plot of the log likelihood versus iteration number?\n  figure_of_merit_vs_iteration: true  # Output the global-best figure-of-merit trace (auto-convergence gradient searches)?","tooling":false,"top_keys":["nest","mcmc","mle"]},{"error":null,"keys":[{"c":"modeling/start_here.py-class scripts smokeable at all: they build ordered trap models whose identical priors tie at the prior medians, which used to make the bypass hard-fail until PyAutoFit#1520 (43\u2026","k":"defaults","line":10},{"c":"","k":"defaults.PYAUTO_TEST_MODE","line":11},{"c":"","k":"defaults.PYAUTO_SKIP_WORKSPACE_VERSION_CHECK","line":12},{"c":"locally on imaging_ci/modeling/start_here.py (PyAutoBrain /ci_speedup, 2026-09-08): ~17s of image rasterisation + ~20-40s of per-figure mask-edge derivation on the 2000x100 frame, across ~100 on-the-\u2026","k":"defaults.PYAUTO_FAST_PLOTS","line":20},{"c":"","k":"defaults.MPLBACKEND","line":21},{"c":"","k":"defaults.NUMBA_CACHE_DIR","line":22},{"c":"","k":"defaults.MPLCONFIGDIR","line":23},{"c":"","k":"overrides","line":24}],"lines":24,"path":"build/profile_smoke.yaml","prior":false,"priors":[],"repo":"autocti_workspace","text":"# Per-script environment variable configuration for automated runs\n# (smoke tests, pre-release checks, CI). Same schema as the *_workspace_test\n# repos \u2014 see autocti_workspace_test/config/build/profile_smoke.yaml.\n#\n# PYAUTO_TEST_MODE=2 bypasses sampling entirely, so a script's search returns a\n# deterministic assertion-valid point instead of fitting. That is what makes the\n# modeling/start_here.py-class scripts smokeable at all: they build ordered trap\n# models whose identical priors tie at the prior medians, which used to make the\n# bypass hard-fail until PyAutoFit#1520 (438f56fac).\ndefaults:\n  PYAUTO_TEST_MODE: \"2\"\n  PYAUTO_SKIP_WORKSPACE_VERSION_CHECK: \"1\"\n  # Drop every figure before it is rasterised or saved, as the autolens /\n  # autogalaxy smoke profiles do. The bypassed fit still exercises every plot\n  # call (the data/model extraction, the zoom, the subplot layout) \u2014 only the\n  # matplotlib draw + PNG write and the mask-edge overlay are skipped. Measured\n  # locally on imaging_ci/modeling/start_here.py (PyAutoBrain /ci_speedup,\n  # 2026-09-08): ~17s of image rasterisation + ~20-40s of per-figure mask-edge\n  # derivation on the 2000x100 frame, across ~100 on-the-fly figures per run.\n  PYAUTO_FAST_PLOTS: \"1\"\n  MPLBACKEND: \"Agg\"\n  NUMBA_CACHE_DIR: \"/tmp/numba_cache\"\n  MPLCONFIGDIR: \"/tmp/matplotlib\"\noverrides: {}\n","tooling":true,"top_keys":["defaults","overrides"]},{"error":null,"keys":[{"c":"","k":"fits","line":1},{"c":"","k":"fits.flip_for_ds9","line":2},{"c":"","k":"hpc","line":3},{"c":"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.","k":"hpc.hpc_mode","line":4},{"c":"The number of iterations between every update (visualization, results output, etc) in HPC mode.","k":"hpc.iterations_per_update","line":5},{"c":"","k":"inversion","line":6},{"c":"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.","k":"inversion.check_reconstruction","line":7},{"c":"Plots of an Inversion's reconstruction use the reconstructed data's bright value multiplied by this factor.","k":"inversion.reconstruction_vmax_factor","line":8},{"c":"","k":"model","line":9},{"c":"If ``True`` the limits applied to priors will be ignored, where limits set upper / lower limits. This stops PriorLimitException's from being raised.","k":"model.ignore_prior_limits","line":10},{"c":"","k":"output","line":11},{"c":"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.","k":"output.force_pickle_overwrite","line":12},{"c":"If True, visualization images output by a search (e.g. subplots of the fit) are recreated when a new model-fit is performed.","k":"output.force_visualize_overwrite","line":13},{"c":"Length of whitespace between the parameter names and values in the model.info / result.info","k":"output.info_whitespace_length","line":14},{"c":"The level of information output by logging.","k":"output.log_level","line":15},{"c":"If True, outputs the non-linear search log to a file (and not printed to screen).","k":"output.log_to_file","line":16},{"c":"The name of the file the logged output is written to (in the non-linear search output folder)","k":"output.log_file","line":17},{"c":"Number of decimal places estimated parameter values / errors are output in model.results.","k":"output.model_results_decimal_places","line":18},{"c":"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.","k":"output.remove_files","line":19},{"c":"If True, non-linear search samples are written to a .csv file.","k":"output.samples_to_csv","line":20},{"c":"If outputting results of an unconverged search, the number of samples used to estimate the median PDF values and errors.","k":"output.unconverged_sample_size","line":21},{"c":"","k":"parallel","line":22},{"c":"If True, a warning is displayed when the search's number of CPU > 1 and enviromment variables related to threading are also > 1.","k":"parallel.warn_environment_variables","line":23},{"c":"","k":"profiling","line":25},{"c":"If True, the parallelization of the fit is profiled outputting a cPython graph.","k":"profiling.parallel_profile","line":26},{"c":"The number of repeat function calls used to measure run-times when profiling.","k":"profiling.repeats","line":27},{"c":"","k":"structures","line":28},{"c":"If True, data structures are only stored in their native and binned format. This is used to reduce memory usage in autocti.","k":"structures.native_binned_only","line":29},{"c":"","k":"test","line":30},{"c":"if True, when a search is resumed the likelihood of a previous sample is recalculated to ensure it is consistent with the previous run.","k":"test.check_likelihood_function","line":31},{"c":"","k":"test.exception_override","line":32},{"c":"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.","k":"test.lh_timeout_seconds","line":33}],"lines":33,"path":"general.yaml","prior":false,"priors":[],"repo":"autocti_workspace","text":"fits:\n  flip_for_ds9: false\nhpc:\n  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.\n  iterations_per_update: 5000       # The number of iterations between every update (visualization, results output, etc) in HPC mode.\ninversion:\n  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.\n  reconstruction_vmax_factor: 0.5   # Plots of an Inversion's reconstruction use the reconstructed data's bright value multiplied by this factor.\nmodel:\n  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.\noutput:\n  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.\n  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.\n  info_whitespace_length: 80        # Length of whitespace between the parameter names and values in the model.info / result.info\n  log_level: INFO                   # The level of information output by logging.\n  log_to_file: false                # If True, outputs the non-linear search log to a file (and not printed to screen).\n  log_file: output.log              # The name of the file the logged output is written to (in the non-linear search output folder)\n  model_results_decimal_places: 3   # Number of decimal places estimated parameter values / errors are output in model.results.\n  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.\n  samples_to_csv: true              # If True, non-linear search samples are written to a .csv file.\n  unconverged_sample_size : 100     # If outputting results of an unconverged search, the number of samples used to estimate the median PDF values and errors.\nparallel:\n  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.\n\nprofiling:\n  parallel_profile: false           # If True, the parallelization of the fit is profiled outputting a cPython graph.\n  repeats: 1                        # The number of repeat function calls used to measure run-times when profiling.\nstructures:\n  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.\ntest:\n  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.\n  exception_override: false\n  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.\n","tooling":false,"top_keys":["fits","hpc","inversion","model","output","parallel","profiling","structures","test"]},{"error":null,"keys":[{"c":"","k":"version","line":1},{"c":"","k":"disable_existing_loggers","line":2},{"c":"","k":"handlers","line":4},{"c":"","k":"handlers.console","line":5},{"c":"","k":"handlers.console.class","line":6},{"c":"","k":"handlers.console.level","line":7},{"c":"","k":"handlers.console.stream","line":8},{"c":"","k":"handlers.console.formatter","line":9},{"c":"","k":"root","line":11},{"c":"","k":"root.level","line":12},{"c":"","k":"root.handlers","line":13},{"c":"","k":"formatters","line":15},{"c":"","k":"formatters.formatter","line":16},{"c":"","k":"formatters.formatter.format","line":17}],"lines":17,"path":"logging.yaml","prior":false,"priors":[],"repo":"autocti_workspace","text":"version: 1\ndisable_existing_loggers: false\n\nhandlers:\n  console:\n    class: logging.StreamHandler\n    level: INFO\n    stream: ext://sys.stdout\n    formatter: formatter\n\nroot:\n  level: INFO\n  handlers: [ console ]\n\nformatters:\n  formatter:\n    format: '%(asctime)s - %(name)s - %(levelname)s - %(message)s'\n","tooling":false,"top_keys":["version","disable_existing_loggers","handlers","root","formatters"]},{"error":null,"keys":[{"c":"","k":"parallel","line":3},{"c":"The number of cores the search is parallelized over by default, using Python multiprocessing.","k":"parallel.number_of_cores","line":4},{"c":"The default step size of each grid search parameter, in terms of unit values of the priors.","k":"parallel.step_size","line":5}],"lines":5,"path":"non_linear/GridSearch.yaml","prior":false,"priors":[],"repo":"autocti_workspace","text":"# The settings of a parallelized grid search of non-linear searches.\n\nparallel:\n  number_of_cores: 3 # The number of cores the search is parallelized over by default, using Python multiprocessing.\n  step_size: 0.1     # The default step size of each grid search parameter, in terms of unit values of the priors.","tooling":false,"top_keys":["parallel"]},{"error":null,"keys":[{"c":"","k":"label","line":14},{"c":"","k":"label.label","line":15},{"c":"","k":"label.label.density","line":16},{"c":"","k":"label.label.full_well_depth","line":17},{"c":"","k":"label.label.gamma","line":18},{"c":"","k":"label.label.ka","line":19},{"c":"","k":"label.label.kv","line":20},{"c":"","k":"label.label.omega","line":21},{"c":"","k":"label.label.release_timescale","line":22},{"c":"","k":"label.label.release_timescale_sigma","line":23},{"c":"","k":"label.label.scale_factor","line":24},{"c":"","k":"label.label.well_fill_alpha","line":25},{"c":"","k":"label.label.well_fill_gamma","line":26},{"c":"","k":"label.label.well_fill_power","line":27},{"c":"","k":"label.label.well_notch_depth","line":28},{"c":"","k":"label.superscript","line":29},{"c":"","k":"label.superscript.CCDComplex","line":30},{"c":"","k":"label.superscript.CCDPhase","line":31},{"c":"","k":"label.superscript.HyperCINoiseScalar","line":32},{"c":"","k":"label.superscript.PixelBounce","line":33},{"c":"","k":"label.superscript.TrapInstantCapture","line":34},{"c":"","k":"label.superscript.TrapInstantCaptureContinuum","line":35},{"c":"","k":"label_format","line":41},{"c":"","k":"label_format.format","line":42},{"c":"","k":"label_format.format.density","line":43},{"c":"","k":"label_format.format.full_well_depth","line":44},{"c":"","k":"label_format.format.gamma","line":45},{"c":"","k":"label_format.format.ka","line":46},{"c":"","k":"label_format.format.kv","line":47},{"c":"","k":"label_format.format.omega","line":48},{"c":"","k":"label_format.format.release_timescale","line":49},{"c":"","k":"label_format.format.release_timescale_sigma","line":50},{"c":"","k":"label_format.format.scale_factor","line":51},{"c":"","k":"label_format.format.well_fill_alpha","line":52},{"c":"","k":"label_format.format.well_fill_gamma","line":53},{"c":"","k":"label_format.format.well_fill_power","line":54},{"c":"","k":"label_format.format.well_notch_depth","line":55}],"lines":55,"path":"notation.yaml","prior":false,"priors":[],"repo":"autocti_workspace","text":"# The notation configs define the labels of every model parameter and its derived quantities, which are used when\n# visualizing results (for example labeling the axis of the PDF triangle plots output by a non-linear search).\n\n\n# label: The label given to the each parameter, for plots like PDF corner plots.\n\n# For example, if `centre=x`, the plot axis will be labeled 'x'.\n\n\n# superscript: the superscript used on certain plots that show the results of different model-components.\n\n# For example, if `TrapInstantCapture=s`, plots where the parameters of the TrapInstantCapture model-component have superscript `s`.\n\nlabel:\n  label:\n    density: \\rho\n    full_well_depth: h\n    gamma: \\gamma\n    ka: ka\n    kv: kv\n    omega: \\omega\n    release_timescale: \\tau\n    release_timescale_sigma: \\sigma\n    scale_factor: \\omega\n    well_fill_alpha: \\alpha\n    well_fill_gamma: \\gamma\n    well_fill_power: \\beta\n    well_notch_depth: d\n  superscript:\n    CCDComplex: ccd\n    CCDPhase: ccd\n    HyperCINoiseScalar: H\n    PixelBounce: pb\n    TrapInstantCapture: s\n    TrapInstantCaptureContinuum: sc\n\n# label_format: The format certain parameters are output as in output files like the `model.results` file.\n\n# For example, if  `density={:.2f}`, the format of the centre parameter in results files will use this Python format.\n\nlabel_format:\n  format:\n    density: '{:.2f}'\n    full_well_depth: '{:.2f}'\n    gamma: '{:.2f}'\n    ka: '{:.2f}'\n    kv: '{:.2f}'\n    omega: '{:.2f}'\n    release_timescale: '{:.2f}'\n    release_timescale_sigma: '{:.2f}'\n    scale_factor: '{:.2f}'\n    well_fill_alpha: '{:.2f}'\n    well_fill_gamma: '{:.2f}'\n    well_fill_power: '{:.2f}'\n    well_notch_depth: '{:.2f}'\n","tooling":false,"top_keys":["label","label_format"]},{"error":null,"keys":[{"c":"","k":"CCDPhase","line":1},{"c":"","k":"CCDPhase.well_fill_power","line":2},{"c":"","k":"CCDPhase.well_fill_power.type","line":3},{"c":"","k":"CCDPhase.well_fill_power.lower_limit","line":4},{"c":"","k":"CCDPhase.well_fill_power.upper_limit","line":5},{"c":"","k":"CCDPhase.well_fill_power.width_modifier","line":6},{"c":"","k":"CCDPhase.well_fill_power.width_modifier.type","line":7},{"c":"","k":"CCDPhase.well_fill_power.width_modifier.value","line":8},{"c":"","k":"CCDPhase.well_fill_power.limits","line":9},{"c":"","k":"CCDPhase.well_fill_power.limits.lower","line":10},{"c":"","k":"CCDPhase.well_fill_power.limits.upper","line":11},{"c":"","k":"CCDPhase.well_notch_depth","line":12},{"c":"","k":"CCDPhase.well_notch_depth.type","line":13},{"c":"","k":"CCDPhase.well_notch_depth.lower_limit","line":14},{"c":"","k":"CCDPhase.well_notch_depth.upper_limit","line":15},{"c":"","k":"CCDPhase.well_notch_depth.width_modifier","line":16},{"c":"","k":"CCDPhase.well_notch_depth.width_modifier.type","line":17},{"c":"","k":"CCDPhase.well_notch_depth.width_modifier.value","line":18},{"c":"","k":"CCDPhase.well_notch_depth.limits","line":19},{"c":"","k":"CCDPhase.well_notch_depth.limits.lower","line":20},{"c":"","k":"CCDPhase.well_notch_depth.limits.upper","line":21},{"c":"","k":"CCDPhase.full_well_depth","line":22},{"c":"","k":"CCDPhase.full_well_depth.type","line":23},{"c":"","k":"CCDPhase.full_well_depth.lower_limit","line":24},{"c":"","k":"CCDPhase.full_well_depth.upper_limit","line":25},{"c":"","k":"CCDPhase.full_well_depth.width_modifier","line":26},{"c":"","k":"CCDPhase.full_well_depth.width_modifier.type","line":27},{"c":"","k":"CCDPhase.full_well_depth.width_modifier.value","line":28},{"c":"","k":"CCDPhase.full_well_depth.limits","line":29},{"c":"","k":"CCDPhase.full_well_depth.limits.lower","line":30},{"c":"","k":"CCDPhase.full_well_depth.limits.upper","line":31},{"c":"","k":"CCDPhase.first_electron_fill","line":32},{"c":"","k":"CCDPhase.first_electron_fill.type","line":33},{"c":"","k":"CCDPhase.first_electron_fill.value","line":34}],"lines":34,"path":"priors/ccd.yaml","prior":true,"priors":[{"a":"lower 0.0","b":"upper 1.0","cls":"CCDPhase","limits":"[0.0, 1.0]","line":2,"param":"well_fill_power","type":"Uniform","width":"Absolute 0.2"},{"a":"lower 0.0","b":"upper 1.0","cls":"CCDPhase","limits":"[0.0, 1.0]","line":12,"param":"well_notch_depth","type":"Uniform","width":"Absolute 0.2"},{"a":"lower 0.0","b":"upper 200000.0","cls":"CCDPhase","limits":"[0.0, 1.0]","line":22,"param":"full_well_depth","type":"Uniform","width":"Absolute 0.2"},{"a":"value 0.0","b":"","cls":"CCDPhase","limits":"","line":32,"param":"first_electron_fill","type":"Constant","width":""}],"repo":"autocti_workspace","text":"CCDPhase:\n  well_fill_power:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: 1.0\n  well_notch_depth:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: 1.0\n  full_well_depth:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 200000.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: 1.0\n  first_electron_fill:\n    type: Constant\n    value: 0.0","tooling":false,"top_keys":["CCDPhase"]},{"error":null,"keys":[{"c":"","k":"HyperCINoiseScalar","line":1},{"c":"","k":"HyperCINoiseScalar.scale_factor","line":2},{"c":"","k":"HyperCINoiseScalar.scale_factor.type","line":3},{"c":"","k":"HyperCINoiseScalar.scale_factor.lower_limit","line":4},{"c":"","k":"HyperCINoiseScalar.scale_factor.upper_limit","line":5},{"c":"","k":"HyperCINoiseScalar.scale_factor.width_modifier","line":6},{"c":"","k":"HyperCINoiseScalar.scale_factor.width_modifier.type","line":7},{"c":"","k":"HyperCINoiseScalar.scale_factor.width_modifier.value","line":8},{"c":"","k":"HyperCINoiseScalar.scale_factor.limits","line":9},{"c":"","k":"HyperCINoiseScalar.scale_factor.limits.lower","line":10},{"c":"","k":"HyperCINoiseScalar.scale_factor.limits.upper","line":11}],"lines":11,"path":"priors/hyper.yaml","prior":true,"priors":[{"a":"lower 0.0","b":"upper 10.0","cls":"HyperCINoiseScalar","limits":"[0.0, inf]","line":2,"param":"scale_factor","type":"Uniform","width":"Relative 0.5"}],"repo":"autocti_workspace","text":"HyperCINoiseScalar:\n  scale_factor:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 10.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["HyperCINoiseScalar"]},{"error":null,"keys":[{"c":"","k":"CCDPhase","line":1},{"c":"","k":"CCDPhase.well_fill_power","line":2},{"c":"","k":"CCDPhase.well_fill_power.type","line":3},{"c":"","k":"CCDPhase.well_fill_power.lower_limit","line":4},{"c":"","k":"CCDPhase.well_fill_power.upper_limit","line":5},{"c":"","k":"CCDPhase.well_fill_power.width_modifier","line":6},{"c":"","k":"CCDPhase.well_fill_power.width_modifier.type","line":7},{"c":"","k":"CCDPhase.well_fill_power.width_modifier.value","line":8},{"c":"","k":"CCDPhase.well_fill_power.limits","line":9},{"c":"","k":"CCDPhase.well_fill_power.limits.lower","line":10},{"c":"","k":"CCDPhase.well_fill_power.limits.upper","line":11},{"c":"","k":"CCDPhase.well_notch_depth","line":12},{"c":"","k":"CCDPhase.well_notch_depth.type","line":13},{"c":"","k":"CCDPhase.well_notch_depth.lower_limit","line":14},{"c":"","k":"CCDPhase.well_notch_depth.upper_limit","line":15},{"c":"","k":"CCDPhase.well_notch_depth.width_modifier","line":16},{"c":"","k":"CCDPhase.well_notch_depth.width_modifier.type","line":17},{"c":"","k":"CCDPhase.well_notch_depth.width_modifier.value","line":18},{"c":"","k":"CCDPhase.well_notch_depth.limits","line":19},{"c":"","k":"CCDPhase.well_notch_depth.limits.lower","line":20},{"c":"","k":"CCDPhase.well_notch_depth.limits.upper","line":21},{"c":"","k":"CCDPhase.full_well_depth","line":22},{"c":"","k":"CCDPhase.full_well_depth.type","line":23},{"c":"","k":"CCDPhase.full_well_depth.lower_limit","line":24},{"c":"","k":"CCDPhase.full_well_depth.upper_limit","line":25},{"c":"","k":"CCDPhase.full_well_depth.width_modifier","line":26},{"c":"","k":"CCDPhase.full_well_depth.width_modifier.type","line":27},{"c":"","k":"CCDPhase.full_well_depth.width_modifier.value","line":28},{"c":"","k":"CCDPhase.full_well_depth.limits","line":29},{"c":"","k":"CCDPhase.full_well_depth.limits.lower","line":30},{"c":"","k":"CCDPhase.full_well_depth.limits.upper","line":31},{"c":"","k":"CCDPhase.first_electron_fill","line":32},{"c":"","k":"CCDPhase.first_electron_fill.type","line":33},{"c":"","k":"CCDPhase.first_electron_fill.value","line":34}],"lines":34,"path":"priors/pixel_bounce.yaml","prior":true,"priors":[{"a":"lower 0.0","b":"upper 1.0","cls":"CCDPhase","limits":"[0.0, 1.0]","line":2,"param":"well_fill_power","type":"Uniform","width":"Absolute 0.2"},{"a":"lower 0.0","b":"upper 1.0","cls":"CCDPhase","limits":"[0.0, 1.0]","line":12,"param":"well_notch_depth","type":"Uniform","width":"Absolute 0.2"},{"a":"lower 0.0","b":"upper 200000.0","cls":"CCDPhase","limits":"[0.0, 1.0]","line":22,"param":"full_well_depth","type":"Uniform","width":"Absolute 0.2"},{"a":"value 0.0","b":"","cls":"CCDPhase","limits":"","line":32,"param":"first_electron_fill","type":"Constant","width":""}],"repo":"autocti_workspace","text":"CCDPhase:\n  well_fill_power:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: 1.0\n  well_notch_depth:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: 1.0\n  full_well_depth:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 200000.0\n    width_modifier:\n      type: Absolute\n      value: 0.2\n    limits:\n      lower: 0.0\n      upper: 1.0\n  first_electron_fill:\n    type: Constant\n    value: 0.0","tooling":false,"top_keys":["CCDPhase"]},{"error":null,"keys":[{"c":"","k":"TrapInstantCapture","line":1},{"c":"","k":"TrapInstantCapture.density","line":2},{"c":"","k":"TrapInstantCapture.density.type","line":3},{"c":"","k":"TrapInstantCapture.density.lower_limit","line":4},{"c":"","k":"TrapInstantCapture.density.upper_limit","line":5},{"c":"","k":"TrapInstantCapture.density.width_modifier","line":6},{"c":"","k":"TrapInstantCapture.density.width_modifier.type","line":7},{"c":"","k":"TrapInstantCapture.density.width_modifier.value","line":8},{"c":"","k":"TrapInstantCapture.density.limits","line":9},{"c":"","k":"TrapInstantCapture.density.limits.lower","line":10},{"c":"","k":"TrapInstantCapture.density.limits.upper","line":11},{"c":"","k":"TrapInstantCapture.release_timescale","line":12},{"c":"","k":"TrapInstantCapture.release_timescale.type","line":13},{"c":"","k":"TrapInstantCapture.release_timescale.lower_limit","line":14},{"c":"","k":"TrapInstantCapture.release_timescale.upper_limit","line":15},{"c":"","k":"TrapInstantCapture.release_timescale.width_modifier","line":16},{"c":"","k":"TrapInstantCapture.release_timescale.width_modifier.type","line":17},{"c":"","k":"TrapInstantCapture.release_timescale.width_modifier.value","line":18},{"c":"","k":"TrapInstantCapture.release_timescale.limits","line":19},{"c":"","k":"TrapInstantCapture.release_timescale.limits.lower","line":20},{"c":"","k":"TrapInstantCapture.release_timescale.limits.upper","line":21},{"c":"","k":"TrapInstantCapture.fractional_volume_none_exposed","line":22},{"c":"","k":"TrapInstantCapture.fractional_volume_none_exposed.type","line":23},{"c":"","k":"TrapInstantCapture.fractional_volume_none_exposed.value","line":24},{"c":"","k":"TrapInstantCapture.fractional_volume_full_exposed","line":25},{"c":"","k":"TrapInstantCapture.fractional_volume_full_exposed.type","line":26},{"c":"","k":"TrapInstantCapture.fractional_volume_full_exposed.value","line":27},{"c":"","k":"TrapInstantCaptureContinuum","line":28},{"c":"","k":"TrapInstantCaptureContinuum.density","line":29},{"c":"","k":"TrapInstantCaptureContinuum.density.type","line":30},{"c":"","k":"TrapInstantCaptureContinuum.density.lower_limit","line":31},{"c":"","k":"TrapInstantCaptureContinuum.density.upper_limit","line":32},{"c":"","k":"TrapInstantCaptureContinuum.density.width_modifier","line":33},{"c":"","k":"TrapInstantCaptureContinuum.density.width_modifier.type","line":34},{"c":"","k":"TrapInstantCaptureContinuum.density.width_modifier.value","line":35},{"c":"","k":"TrapInstantCaptureContinuum.density.limits","line":36},{"c":"","k":"TrapInstantCaptureContinuum.density.limits.lower","line":37},{"c":"","k":"TrapInstantCaptureContinuum.density.limits.upper","line":38},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale","line":39},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale.type","line":40},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale.lower_limit","line":41},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale.upper_limit","line":42},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale.width_modifier","line":43},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale.width_modifier.type","line":44},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale.width_modifier.value","line":45},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale.limits","line":46},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale.limits.lower","line":47},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale.limits.upper","line":48},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale_sigma","line":49},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale_sigma.type","line":50},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale_sigma.lower_limit","line":51},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale_sigma.upper_limit","line":52},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale_sigma.width_modifier","line":53},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale_sigma.width_modifier.type","line":54},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale_sigma.width_modifier.value","line":55},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale_sigma.limits","line":56},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale_sigma.limits.lower","line":57},{"c":"","k":"TrapInstantCaptureContinuum.release_timescale_sigma.limits.upper","line":58}],"lines":58,"path":"priors/traps.yaml","prior":true,"priors":[{"a":"lower 0.0","b":"upper 10.0","cls":"TrapInstantCapture","limits":"[0.0, inf]","line":2,"param":"density","type":"Uniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 50.0","cls":"TrapInstantCapture","limits":"[0.0, inf]","line":12,"param":"release_timescale","type":"Uniform","width":"Relative 0.5"},{"a":"value 0.0","b":"","cls":"TrapInstantCapture","limits":"","line":22,"param":"fractional_volume_none_exposed","type":"Constant","width":""},{"a":"value 0.0","b":"","cls":"TrapInstantCapture","limits":"","line":25,"param":"fractional_volume_full_exposed","type":"Constant","width":""},{"a":"lower 0.0","b":"upper 10.0","cls":"TrapInstantCaptureContinuum","limits":"[0.0, inf]","line":29,"param":"density","type":"Uniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 50.0","cls":"TrapInstantCaptureContinuum","limits":"[0.0, inf]","line":39,"param":"release_timescale","type":"Uniform","width":"Relative 0.5"},{"a":"lower 0.0","b":"upper 1.0","cls":"TrapInstantCaptureContinuum","limits":"[0.0, inf]","line":49,"param":"release_timescale_sigma","type":"Uniform","width":"Relative 0.5"}],"repo":"autocti_workspace","text":"TrapInstantCapture:\n  density:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 10.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  release_timescale:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 50.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  fractional_volume_none_exposed:\n    type: Constant\n    value: 0.0\n  fractional_volume_full_exposed:\n    type: Constant\n    value: 0.0\nTrapInstantCaptureContinuum:\n  density:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 10.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  release_timescale:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 50.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n  release_timescale_sigma:\n    type: Uniform\n    lower_limit: 0.0\n    upper_limit: 1.0\n    width_modifier:\n      type: Relative\n      value: 0.5\n    limits:\n      lower: 0.0\n      upper: inf\n","tooling":false,"top_keys":["TrapInstantCapture","TrapInstantCaptureContinuum"]},{"error":null,"keys":[{"c":"","k":"general","line":1},{"c":"The matploblib backend used for visualization. `default` uses the system default, can specifiy specific backend (e.g. TKAgg, Qt5Agg, WXAgg).","k":"general.backend","line":2},{"c":"The `origin` input of `imshow`, determining if pixel values are ascending or descending on the y-axis.","k":"general.imshow_origin","line":3},{"c":"If True, plots of data structures with a mask automatically zoom in the masked region.","k":"general.zoom_around_mask","line":4},{"c":"The vmin and vmax of all pre-cti data residual-maps.","k":"general.symmetric_cmap_value","line":5},{"c":"If True, subplots showing FPR / EPER trails of many datasets are in ascending order of FPR value.","k":"general.subplot_ascending_fpr","line":6}],"lines":6,"path":"visualize/general.yaml","prior":false,"priors":[],"repo":"autocti_workspace","text":"general:\n  backend: default                  # The matploblib backend used for visualization. `default` uses the system default, can specifiy specific backend (e.g. TKAgg, Qt5Agg, WXAgg).\n  imshow_origin: upper              # The `origin` input of `imshow`, determining if pixel values are ascending or descending on the y-axis.\n  zoom_around_mask: true            # If True, plots of data structures with a mask automatically zoom in the masked region.\n  symmetric_cmap_value: 100.0       # The vmin and vmax of all pre-cti data residual-maps.\n  subplot_ascending_fpr: true       # If True, subplots showing FPR / EPER trails of many datasets are in ascending order of FPR value.\n","tooling":false,"top_keys":["general"]},{"error":null,"keys":[{"c":"Output format of all plots, can be png, pdf or both (e.g. [png, pdf]).","k":"subplot_format","line":1},{"c":"If True, only the combined subplots of multi-dataset analyses are output (no per-dataset visualization).","k":"combined_only","line":2},{"c":"","k":"dataset","line":3},{"c":"Plot the subplot of all dataset quantities (2D for charge injection imaging, 1D for Dataset1D)?","k":"dataset.subplot_dataset","line":4},{"c":"Plot per-region binned 1D subplots (e.g. the parallel/serial FPR and EPER)?","k":"dataset.subplot_dataset_regions","line":5},{"c":"Plot single 1D figures of the data extracted and binned over each region?","k":"dataset.data","line":6},{"c":"Plot single 1D figures of the data over each region with a log10 y-axis?","k":"dataset.data_logy","line":7},{"c":"Plot the data binned over rows / columns with and without the FPR (charge injection only)?","k":"dataset.data_binned","line":8},{"c":"Include the fpr_non_uniformity region in the per-region plots (charge injection only)?","k":"dataset.fpr_non_uniformity","line":9},{"c":"","k":"fit","line":10},{"c":"Plot the subplot of all fit quantities (e.g. model data, residual-map, chi-squared map)?","k":"fit.subplot_fit","line":11},{"c":"Plot per-region binned 1D fit subplots (e.g. the parallel/serial FPR and EPER)?","k":"fit.subplot_fit_regions","line":12},{"c":"Plot single 1D figures of the fit data (with model overlay) over each region?","k":"fit.data","line":13},{"c":"Plot single 1D figures of the fit data over each region with a log10 y-axis?","k":"fit.data_logy","line":14},{"c":"Plot single 1D figures of the residual map over each region?","k":"fit.residual_map","line":15},{"c":"Plot single 1D figures of the residual map over each region with a log10 y-axis?","k":"fit.residual_map_logy","line":16},{"c":"Output a fit.fits file containing the model data, residual map, normalized residual map and chi-squared map?","k":"fit.fits_fit","line":17}],"lines":17,"path":"visualize/plots.yaml","prior":false,"priors":[],"repo":"autocti_workspace","text":"subplot_format: [png]                     # Output format of all plots, can be png, pdf or both (e.g. [png, pdf]).\ncombined_only: false                      # If True, only the combined subplots of multi-dataset analyses are output (no per-dataset visualization).\ndataset:\n  subplot_dataset: true                   # Plot the subplot of all dataset quantities (2D for charge injection imaging, 1D for Dataset1D)?\n  subplot_dataset_regions: true           # Plot per-region binned 1D subplots (e.g. the parallel/serial FPR and EPER)?\n  data: true                              # Plot single 1D figures of the data extracted and binned over each region?\n  data_logy: true                         # Plot single 1D figures of the data over each region with a log10 y-axis?\n  data_binned: true                       # Plot the data binned over rows / columns with and without the FPR (charge injection only)?\n  fpr_non_uniformity: false               # Include the fpr_non_uniformity region in the per-region plots (charge injection only)?\nfit:\n  subplot_fit: true                       # Plot the subplot of all fit quantities (e.g. model data, residual-map, chi-squared map)?\n  subplot_fit_regions: true               # Plot per-region binned 1D fit subplots (e.g. the parallel/serial FPR and EPER)?\n  data: true                              # Plot single 1D figures of the fit data (with model overlay) over each region?\n  data_logy: true                         # Plot single 1D figures of the fit data over each region with a log10 y-axis?\n  residual_map: true                      # Plot single 1D figures of the residual map over each region?\n  residual_map_logy: true                 # Plot single 1D figures of the residual map over each region with a log10 y-axis?\n  fits_fit: true                          # Output a fit.fits file 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