PyAutoNervesBoard

1psf:2  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.3updates:4  iterations_per_quick_update: 1e99 # Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit.5  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.6hpc:7  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.8  iterations_per_quick_update: 1e99 #  Non-linear search iterations between every quick update, which just displays the maximum likelihood model fit.9  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.10grid:11  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.12adapt:13  adapt_minimum_percent: 0.0114  adapt_noise_limit: 100000000.0 unused15inversion:16  use_border_relocator: true          # If True, by default a pixelization's border is used to relocate all pixels outside its border to the border.17test:18  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.19  exception_override: false

1# Toggles for the catalogue of latent variables computed by `AnalysisImaging`.2#3# Each entry maps a registered latent name (see4# `autogalaxy/imaging/model/latent.py::LATENT_FUNCTIONS`) to a bool. Setting5# `false` excludes that latent from `LATENT_KEYS` so it is neither computed6# nor written to `latent/samples.csv` / `latent/latent_summary.json`.7#8# Workspaces should mirror this file in their own `config/latent.yaml` to9# override defaults locally (workspace values shadow library values).1011# `total_galaxy_0_flux` — total integrated flux of the first galaxy12# (`fit.galaxies[0]`) in the raw image units the fit was performed in.13# Returns NaN when galaxy 0 has no light profile.14#15# Requires no instrument inputs — default `true`. See the workspace flux16# guide (`scripts/guides/units/flux.py`) for how to convert this to a17# microjansky flux using a user-supplied `magzero`.18total_galaxy_0_flux: true1920# `total_galaxy_0_flux_mujy` — the same total flux converted to21# microjanskies via `magzero` passed through Analysis kwargs.22#23# Default `false` because the conversion needs a per-instrument zero-point24# that the user must supply (`AnalysisImaging(..., magzero=<value>)`). When25# enabled without a `magzero`, the latent returns NaN and emits a single26# warning per process — it does not raise. Workspaces with a known27# zero-point (e.g. the Euclid pipeline) override this to `true` and pass28# `magzero` explicitly.29total_galaxy_0_flux_mujy: false

1label:2  label:3    sigma: \sigma4    alpha: \alpha5    angle_binary: \theta6    beta: \beta7    break_radius: \theta_{\rm B}8    centre_0: y9    centre_1: x10    coefficient: \lambda11    c_2: c_{\rm 2}12    concentration: conc13    core_radius: C_{\rm r}14    core_radius_0: C_{rm r0}15    core_radius_1: C_{\rm r1}16    effective_radius: R_{\rm eff}17    einstein_radius: \theta_{\rm Ein}18    ell_comps_0: \epsilon_{\rm 1}19    ell_comps_1: \epsilon_{\rm 2}20    multipole_comps_0: M_{\rm 1}21    multipole_comps_1: M_{\rm 2}22    scaled_multipole_comps_0: M_{\rm 1}23    scaled_multipole_comps_1: M_{\rm 2}24    flux: F25    gamma: \gamma26    gamma_1: \gamma27    gamma_2: \gamma28    inner_coefficient: \lambda_{\rm 1}29    inner_slope: t_{\rm 1}30    intensity: I_{\rm b}31    kappa: \kappa32    kappa_s: \kappa_{\rm s}33    log10m_vir: log_{\rm 10}(m_{vir})34    m: m35    mass: M36    mass_at_200: M_{\rm 200}37    mass_ratio: M_{\rm ratio}38    mass_to_light_gradient: \Gamma39    mass_to_light_ratio: \Psi40    mass_to_light_ratio_base: \Psi_{\rm base}41    mass_to_light_radius: R_{\rm ref}42    noise_factor: \omega_{\rm 1}43    noise_power: \omega{\rm 2}44    noise_scale: \sigma_{\rm 1}45    normalization_scale: n46    outer_coefficient: \lambda_{\rm 2}47    outer_slope: t_{\rm 2}48    overdens: \Delta_{\rm vir}49    pixels: N_{\rm pix}50    ra: r_{\rm a}51    radius_break: R_{\rm b}52    redshift: z53    redshift_object: z_{\rm obj}54    redshift_source: z_{\rm src}55    rs: r_{\rm s}56    scale_radius: R_{\rm s}57    scatter: \sigma58    separation: s59    sersic_index: n60    shape_0: y_{\rm pix}61    shape_1: x_{\rm pix}62    signal_scale: V63    sky_scale: \sigma_{\rm 0}64    slope: \gamma65    truncation_radius: R_{\rm t}66    virial_mass: M_{\rm vir}67    virial_overdens: \Delta_{\rm vir}68    weight_floor: W_{\rm f}69    weight_power: W_{\rm p}70    zeroth_coefficient: \lambda_{\rm 0}71    zeroth_signal_scale: V72  superscript:73    ExternalShear: ext74    GaussianRandomField: grf75    InputDeflections: input76    InputPotential: input77    Mesh: mesh78    Point: point79    SMBH: smbh80    Redshift: z81    Regularization: reg82label_format:83  format:84    sigma: '{:.4f}'85    alpha: '{:.4f}'86    angle_binary: '{:.4f}'87    angular_diameter_distance_to_earth: '{:.4f}'88    beta: '{:.4f}'89    c_2: '{:.4f}'90    centre_0: '{:.4f}'91    centre_1: '{:.4f}'92    coefficient: '{:.4f}'93    concentration: '{:.4f}'94    core_radius: '{:.4f}'95    core_radius_0: '{:.4f}'96    core_radius_1: '{:.4f}'97    effective_radius: '{:.4f}'98    einstein_mass: '{:.4e}'99    einstein_radius: '{:.4f}'100    ell_comps_0: '{:.4f}'101    ell_comps_1: '{:.4f}'102    multipole_comps_0: '{:.4f}'103    multipole_comps_1: '{:.4f}'104    input_multipole_comps_0: '{:.4f}'105    input_multipole_comps_1: '{:.4f}'106    flux: '{:.4e}'107    gamma: '{:.4f}'108    inner_coefficient: '{:.4f}'109    inner_slope: '{:.4f}'110    intensity: '{:.4f}'111    kappa: '{:.4f}'112    kappa_s: '{:.4f}'113    kpc_per_arcsec: '{:.4f}'114    log10m_vir: '{:.4f}'115    luminosity: '{:.4e}'116    m: '{:.1f}'117    mass: '{:.4e}'118    mass_at_200: '{:.4e}'119    mass_at_truncation_radius: '{:.4e}'120    mass_ratio: '{:.4f}'121    mass_to_light_gradient: '{:.4f}'122    mass_to_light_ratio: '{:.4f}'123    n_x: '{:.1d}'124    n_y: '{:.1d}'125    noise_factor: '{:.3f}'126    noise_power: '{:.3f}'127    noise_scale: '{:.3f}'128    normalization_scale: '{:.4f}'129    outer_coefficient: '{:.4f}'130    outer_slope: '{:.4f}'131    overdens: '{:.4f}'132    pixels: '{:.4f}'133    ra: '{:.4f}'134    radius: '{:.4f}'135    radius_break: '{:.4f}'136    redshift: '{:.4f}'137    redshift_object: '{:.4f}'138    redshift_source: '{:.4f}'139    rho: '{:.4f}'140    rs: '{:.4f}'141    scale_radius: '{:.4f}'142    separation: '{:.4f}'143    sersic_index: '{:.4f}'144    shape_0: '{:.4f}'145    shape_1: '{:.4f}'146    signal_scale: '{:.4f}'147    sky_scale: '{:.4f}'148    slope: '{:.4f}'149    truncation_radius: '{:.4f}'150    virial_mass: '{:.4f}'151    virial_overdens: '{:.4f}'152    weight_floor: '{:.4f}'153    weight_power: '{:.4f}'

1# Determines whether files saved by the search are output to the hard-disk. This is true both when saving to the2# directory structure and when saving to database.34default: true # If true then files which are not explicitly listed here are output anyway. If false then they are not.56### Samples ###78# The `samples.csv`file contains every sampled value of every free parameter with its log likelihood and weight.910# This file is often large, therefore disabling it can significantly reduce hard-disk space use.1112# `samples.csv` is used to perform marginalization, infer model parameter errors and do other analysis of the search13# chains. Even if output of `samples.csv` is disabled, these tasks are still performed by the fit and output to14# the `samples_summary.json` file. However, without a `samples.csv` file these types of tasks cannot be performed15# after the fit is complete, for example via the database.1617samples: true1819# The `samples.csv` file contains every accepted sampled value of every free parameter with its log likelihood and20# weight. For certain searches, the majority of samples have a very low weight and have no numerical impact on the21# results of the model-fit. However, these samples are still output to the `samples.csv` file, taking up hard-disk22# space and slowing down analysis of the samples (e.g. via the database).2324# The `samples_weight_threshold` below specifies the threshold value of the weight such that samples with a weight25# below this value are not output to the `samples.csv` file. This can be used to reduce the size of the `samples.csv`26# file and speed up analysis of the samples.2728# For many searches (e.g. MCMC) all samples have an equal weight of 1.0, and this threshold therefore has no impact.29# For these searches, there is no simple way to save hard-disk space. This input is more suited to nested sampling,30# where the majority of samples have a very low weight..3132# Set value to empty (e.g. delete 1.0e-10 below) to disable this feature.3334samples_weight_threshold: 1.0e-103536### Search Internal ###3738# The search internal folder which contains a saved state of the non-linear search in its internal reprsenetation,39# as a .pickle or .dill file.4041# For example, for the nested sampling dynesty, this .dill file is the `DynestySampler` object which is used to42# perform sampling, and it therefore contains all internal dynesty representations of the results, samples, weights, etc.4344# If the entry below is false, the folder is still output during the model-fit, as it is required to resume the fit45# from where it left off. Therefore, settings `false` below does not impact model-fitting checkpointing and resumption.46# Instead, the search internal folder is deleted once the fit is completed.4748# The search internal folder file is often large, therefore deleting it after a fit is complete can significantly49# reduce hard-disk space use.5051# The search internal representation that can be loaded from the .dill file has many additional quantities specific to52# the non-linear search that the standardized autofit forms do not. For example, for emcee, it contains information on53# every walker. This information is required to do certain analyes and make certain plots, therefore deleting the54# folder means this information is list.5556search_internal: false5758### Start Point ###5960# If an Initalizer is used to provide a start point for the non-linear search, visualization of that start point can be61# output to hard-disk to show the user the initial model-fit that is used to start the search. This visualization is62# the visualizer wrapped in the Analysis class, and therefore should show things like the quality of the fit63# to the data and the residuals at the start point.6465start_point: true6667### Latent Variables ###6869# A latent variable is not a model parameter but can be derived from the model. Its value and errors may be of interest70# and aid in the interpretation of a model-fit.7172# For example, for the simple 1D Gaussian example, it could be the full-width half maximum (FWHM) of the Gaussian. This73# is not included in the model but can be easily derived from the Gaussian's sigma value.7475# By overwriting an Analysis class's `compute_latent_variables` method we can manually specify latent variables that76# are calculated and output to a `latent.csv` file, which mirrors the `samples.csv` file. The `latent.csv` file has77# the same weight resampling performed on the `samples.csv` file, controlled via the `samples_weight_threshold` above.7879# There may also be a `latent.results` and `latent_summary.json` files output, which the inputs below control whether80# they are output and how often.8182# Outputting latent variables manually after a fit is complete is simple, just call83# the `analysis.compute_latent_variables()` function.8485# For many use cases, the best set up may be to disable autofit latent variable output during the fit and perform it86# manually after completing a successful model-fit. This will save computational run time by not computing latent87# variables during a any model-fit which is unsuccessful.8889latent_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.90latent_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.91latent_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.92latent_draw_via_pdf_size : 100 # The number of samples drawn to estimate latent variable errors if `latent_draw_via_pdf` is True.93latent_csv: true # Whether to ouptut the `latent.csv` file.94latent_results: true # Whether to output the `latent.results` file.9596# Other Files:9798search_log: true # `search.log`: logging produced whilst running the fit method

1Basis: {}